Fix _aten_native_group_norm_onnx dtype | fix(torchlib) (#1171) #493
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failed
Nov 21, 2023 in 0s
55 fail, 2 796 skipped, 8 343 pass in 1h 6m 27s
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All 3 runs failed: test_output_match_opinfo__nn_functional_scaled_dot_product_attention_bool_mask_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[4,3,8] input_0, float16[4,6,8] input_1, float16[4,6,8] input_2) => (float16[4,3,8] _val_5)
<float16 _val_3, float[3,6] _val_4>
{
_val_3 = pkg.onnxscript.torch_lib._attention_scale (input_0)
_val_4 = pkg.onnxscript.torch_lib._causal_attention_mask (input_0, input_1)
_val_5 = pkg.onnxscript.torch_lib._aten_scaled_dot_product_attention_float_mask_onnx <dropout_p: float = 0> (input_0, input_1, input_2, _val_4, _val_3)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_attention_scale (query) => (scale)
{
tmp = Shape (query)
int64_m1 = Constant <value: tensor = int64 int64_m1 {-1}> ()
tmp_subscripted = Gather <axis: int = 0> (tmp, int64_m1)
embedding_size = CastLike (tmp_subscripted, query)
const = Constant <value: tensor = float const {1}> ()
tmp_0 = Sqrt (embedding_size)
const_cast = CastLike (const, tmp_0)
scale = Div (const_cast, tmp_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_causal_attention_mask (query, key) => (attn_mask_10)
{
tmp = Shape (query)
int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
target_length = Slice (tmp, int64_m2_1d, int64_m1_1d, int64_0_1d, int64_1_1d)
tmp_0 = Shape (key)
int64_0_1d_1 = Constant <value: tensor = int64[1] int64_0_1d_1 {0}> ()
int64_1_1d_2 = Constant <value: tensor = int64[1] int64_1_1d_2 {1}> ()
int64_m2_1d_3 = Constant <value: tensor = int64[1] int64_m2_1d_3 {-2}> ()
int64_m1_1d_4 = Constant <value: tensor = int64[1] int64_m1_1d_4 {-1}> ()
source_length = Slice (tmp_0, int64_m2_1d_3, int64_m1_1d_4, int64_0_1d_1, int64_1_1d_2)
size = Concat <axis: int = 0> (target_length, source_length)
const = Constant <value: tensor = float const {1}> ()
attn_mask = Expand (const, size)
attn_mask_5 = Trilu <upper: int = 0> (attn_mask)
const_6 = Constant <value: tensor = float const_6 {0}> ()
const_6_cast = CastLike (const_6, attn_mask_5)
tmp_7 = Equal (attn_mask_5, const_6_cast)
tmp_8 = Constant <value_float: float = -inf> ()
const_9 = Constant <value: tensor = float const_9 {0}> ()
const_9_cast = CastLike (const_9, tmp_8)
attn_mask_10 = Where (tmp_7, tmp_8, const_9_cast)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_scaled_dot_product_attention_float_mask_onnx <dropout_p>(query, key, value, attn_mask, scale) => (return_val)
{
key_shape = Shape (key)
int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
int64_9223372036854775807_1d = Constant <value: tensor = int64[1] int64_9223372036854775807_1d {9223372036854775807}> ()
key_last_dim = Slice (key_shape, int64_m1_1d, int64_9223372036854775807_1d, int64_0_1d, int64_1_1d)
int64_0_1d_0 = Constant <value: tensor = int64[1] int64_0_1d_0 {0}> ()
int64_1_1d_1 = Constant <value: tensor = int64[1] int64_1_1d_1 {1}> ()
int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
int64_m1_1d_2 = Constant <value: tensor = int64[1] int64_m1_1d_2 {-1}> ()
key_second_last_dim = Slice (key_shape, int64_m2_1d, int64_m1_1d_2, int64_0_1d_0, int64_1_1d_1)
int64_0_1d_3 = Constant <value: tensor = int64[1] int64_0_1d_3 {0}> ()
int64_1_1d_4 = Constant <value: tensor = int64[1] int64_1_1d_4 {1}> ()
int64_m2_1d_5 = Constant <value: tensor = int64[1] int64_m2_1d_5 {-2}> ()
key_first_dims = Slice (key_shape, int64_0_1d_3, int64_m2_1d_5, int64_0_1d_3, int64_1_1d_4)
tmp = Constant <value_ints: ints = [-1]> ()
key_squeezed_shape = Concat <axis: int = 0> (tmp, key_second_last_dim, key_last_dim)
key_squeezed = Reshape (key, key_squeezed_shape)
key_squeezed_transposed = Transpose <perm: ints = [0, 2, 1]> (key_squeezed)
key_transposed_shape = Concat <axis: int = 0> (key_first_dims, key_last_dim, key_second_last_dim)
key_transposed = Reshape (key_squeezed_transposed, key_transposed_shape)
tmp_6 = Sqrt (scale)
query_scaled = Mul (query, tmp_6)
tmp_7 = Sqrt (scale)
key_transposed_scaled = Mul (key_transposed, tmp_7)
tmp_8 = MatMul (query_scaled, key_transposed_scaled)
tmp_9 = Add (tmp_8, attn_mask)
attn_weight = Softmax <axis: int = -1> (tmp_9)
dropout_p = Constant <value_float: float = @dropout_p> ()
attn_weight_10, _ = Dropout (attn_weight, dropout_p)
return_val = MatMul (attn_weight_10, value)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([[[ 2.04 , 2.672 , 4.613 , 1.248 , 3.639 , 4.5 ,
3.27 , 4.965 ],
[ 7.777 , 2.268 , -3.242 , -4.867 , 8.37 , -0.4746 ,
0.0967 , 2.89 ],
[-2.54 , -2.117 , -0.501 , -5.863 , -0.4658 , 3.578 ,
-3.137 , -5.73 ]],
[[ 0.06152, 1.063 , 3.05 , -3.066 , -8.22 , 5.688 ,
-2.875 , 2.77 ],
[ 1.556 , -4.26 , -2.574 , -1.371 , -8.65 , -2.504 ,
6.18 , 8.1 ],
[-4.598 , -0.58 , 1.266 , 1.758 , -0.8438 , 8.89 ,
6.355 , -4.957 ]],
[[-2.574 , -2.398 , -0.879 , -1.02 , 4.895 , 4.797 ,
2.215 , -5.582 ],
[ 5.21 , -1.248 , -6.758 , -2.777 , 7.156 , 8.086 ,
-5.055 , -8.92 ],
[-1.767 , -1.995 , 2.117 , 2.197 , -1.301 , 0.03516,
1.037 , -0.0791 ]],
[[ 7.03 , -5.723 , 0.5625 , -7.727 , -7.04 , 2.092 ,
-7.453 , 6.836 ],
[-1.512 , 2.469 , -8.45 , 1.898 , 7.496 , -1.74 ,
-2.021 , -2.953 ],
[ 4.043 , 8.16 , 5.35 , -8.086 , 0.8613 , -4.516 ,
-5.625 , -5.45 ]]], dtype=float16),
'input_1': array([[[ 8.85 , -1.775 , -4.457 , -4.824 , 8.58 , -2.777 ,
7.58 , 5.66 ],
[ 7.637 , -2.232 , 3.832 , 0.1934 , -0.2461 , 4.957 ,
-3.059 , -2.734 ],
[ 4.016 , -8.28 , 1.266 , 0.7383 , 0.677 , -4.992 ,
7.707 , -9. ],
[-8.56 , -2.988 , -2.707 , 6.777 , 3.91 , -5.062 ,
-1.266 , -4.72 ],
[ 7.023 , -8.71 , 3.05 , -8.17 , 0.624 , 4.836 ,
-7.656 , -6.812 ],
[-3.086 , -5.16 , -7.973 , -2.232 , 7.82 , 2.68 ,
-6.652 , 8.44 ]],
[[ 0.4658 , -6.934 , -5.59 , -0.3076 , 6.44 , -2.303 ,
7.242 , -5.484 ],
[-3.523 , -2.268 , 2.654 , -0.9316 , 1.811 , 2.004 ,
-1.512 , 7.99 ],
[-3.93 , -8.35 , -5.188 , -8.1 , 3.7 , 6.18 ,
-2.293 , -2.523 ],
[-1.925 , 2.68 , -8.15 , 7.46 , -1.995 , 2.936 ,
-1.459 , -5.188 ],
[-5.08 , 8.73 , 2.7 , -6.82 , -7.55 , 4.22 ,
-0.3604 , 2.936 ],
[-0.04395, -4.246 , -2.338 , 0.923 , 4.938 , -8.3 ,
-7.84 , -2.004 ]],
[[-1.099 , -7.797 , -7.39 , 3.516 , 2.89 , -2.11 ,
4.457 , 7.48 ],
[-0.3604 , -8.41 , -4.21 , 6.793 , -8.55 , 3.945 ,
-7.207 , -7.902 ],
[ 6.555 , -8.63 , 6.6 , 8.52 , 7.75 , -8.03 ,
-2.32 , 5.82 ],
[ 1.6 , -1.556 , -8.17 , 8.52 , 3.277 , 8.01 ,
4.562 , -1.099 ],
[-5.844 , -1.099 , 6.11 , -6.54 , 1.705 , 7.586 ,
1.705 , -3.146 ],
[-8.19 , -3.102 , 8.305 , -8.47 , -3.438 , 0.4395 ,
3.533 , 6.926 ]],
[[ 0.03516, 4.086 , -3.7 , -3.016 , 7.277 , -4.316 ,
3.55 , -1.644 ],
[ 4.5 , -3.34 , -6.96 , -4.402 , -5.97 , 0.3955 ,
-4.21 , 8.3 ],
[ 0.677 , 6.406 , 7.137 , 8.1 , 0.633 , -2.031 ,
-6.82 , -8.59 ],
[ 1.055 , -7.13 , -6.906 , 0.4834 , -5.934 , -8.07 ,
-1.705 , -8.586 ],
[ 5.027 , -6.047 , 0.2197 , -1.942 , 2.25 , -8.94 ,
-3.516 , 7.61 ],
[ 2.215 , 6.074 , -2.69 , -6.344 , -3.393 , -8.516 ,
-2.629 , -4.387 ]]], dtype=float16),
'input_2': array([[[-4.844 , -8.766 , 8.63 , -8.32 , 1.89 , 3.383 ,
-5.8 , -3.156 ],
[-4.387 , -2.865 , 2.734 , -1.248 , 0.05273, 0.01758,
5.47 , -0.9316 ],
[-2.418 , -5.82 , 6.594 , 4.457 , 8.83 , 2.398 ,
4.438 , -1.925 ],
[-2.514 , 7.75 , 0.12305, 1.679 , 8.65 , 5.54 ,
-4.746 , -8.766 ],
[-2.734 , 0.334 , 8.37 , 2.39 , 2.021 , -8.25 ,
4.156 , -7.902 ],
[-1.872 , -4.29 , -7.734 , 4.605 , 1.8545 , -8.79 ,
5.09 , 3.453 ]],
[[-8.42 , -6.96 , -8.05 , 1.274 , -8.03 , -7.004 ,
-8.03 , 4.12 ],
[-8.71 , -3.533 , 6.812 , 8.22 , 3.234 , -2.434 ,
-3.78 , 4.86 ],
[-5.273 , -3.621 , 4.543 , -2.926 , 2.469 , 2.805 ,
6.477 , 3.885 ],
[ 8.36 , -6.242 , -1.301 , 8.484 , 6.504 , 3.305 ,
2.531 , 3.832 ],
[-3.191 , -6.574 , 6.23 , 5.105 , 4.414 , -3.523 ,
-4.473 , 3.066 ],
[-1.6 , 3.91 , 7.285 , -5.934 , 5.33 , 5.83 ,
-1.775 , 1.195 ]],
[[-2.338 , -1.107 , -6.875 , -4.234 , 0.3428 , -6.996 ,
-4.19 , -0.923 ],
[ 1.951 , -8.95 , 2.82 , -4.895 , 6.426 , -8.35 ,
-8.98 , 7.438 ],
[-3.332 , -7.973 , -1.266 , 5.316 , -4.58 , 8.766 ,
-0.6855 , -3.965 ],
[ 3.867 , -7.305 , -1.564 , -2.725 , 3.438 , 0.2197 ,
3.814 , -7.49 ],
[ 2.629 , 5.66 , -6.145 , 3.594 , 1.028 , -1.661 ,
6.906 , -2.645 ],
[-7.03 , -4.332 , -8.016 , -7.777 , -4.094 , -9. ,
-8.22 , -3.262 ]],
[[-6.195 , 4.824 , 7.066 , 7.848 , -7.79 , 4.484 ,
7.62 , -1.582 ],
[ 8.805 , -6.734 , 5.906 , -1.081 , -2.945 , 8.92 ,
-8.92 , -6.715 ],
[ 8.07 , 5.703 , -7.496 , -1.116 , -8.89 , 1.468 ,
5.633 , 5.23 ],
[-6.188 , 2.795 , 5.38 , -6.117 , -2.11 , -2.18 ,
8.52 , -1.002 ],
[ 8.08 , 4.824 , 8.914 , 3.674 , 5.316 , 0.826 ,
7.17 , 5.098 ],
[-6.215 , 8.39 , -6.934 , 8.305 , -6.074 , 7.77 ,
-5.703 , 4.023 ]]], dtype=float16)}
Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[4,3,8] input_0, float16[4,6,8] input_1, float16[4,6,8] input_2) => (float16[4,3,8] _val_4)
<float16 _val_3>
{
_val_3 = pkg.onnxscript.torch_lib._attention_scale (input_0)
_val_4 = pkg.onnxscript.torch_lib._aten_scaled_dot_product_attention_no_mask_onnx <dropout_p: float = 0> (input_0, input_1, input_2, _val_3)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_attention_scale (query) => (scale)
{
tmp = Shape (query)
int64_m1 = Constant <value: tensor = int64 int64_m1 {-1}> ()
tmp_subscripted = Gather <axis: int = 0> (tmp, int64_m1)
embedding_size = CastLike (tmp_subscripted, query)
const = Constant <value: tensor = float const {1}> ()
tmp_0 = Sqrt (embedding_size)
const_cast = CastLike (const, tmp_0)
scale = Div (const_cast, tmp_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_scaled_dot_product_attention_no_mask_onnx <dropout_p>(query, key, value, scale) => (return_val)
{
key_shape = Shape (key)
int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
int64_9223372036854775807_1d = Constant <value: tensor = int64[1] int64_9223372036854775807_1d {9223372036854775807}> ()
key_last_dim = Slice (key_shape, int64_m1_1d, int64_9223372036854775807_1d, int64_0_1d, int64_1_1d)
int64_0_1d_0 = Constant <value: tensor = int64[1] int64_0_1d_0 {0}> ()
int64_1_1d_1 = Constant <value: tensor = int64[1] int64_1_1d_1 {1}> ()
int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
int64_m1_1d_2 = Constant <value: tensor = int64[1] int64_m1_1d_2 {-1}> ()
key_second_last_dim = Slice (key_shape, int64_m2_1d, int64_m1_1d_2, int64_0_1d_0, int64_1_1d_1)
int64_0_1d_3 = Constant <value: tensor = int64[1] int64_0_1d_3 {0}> ()
int64_1_1d_4 = Constant <value: tensor = int64[1] int64_1_1d_4 {1}> ()
int64_m2_1d_5 = Constant <value: tensor = int64[1] int64_m2_1d_5 {-2}> ()
key_first_dims = Slice (key_shape, int64_0_1d_3, int64_m2_1d_5, int64_0_1d_3, int64_1_1d_4)
tmp = Constant <value_ints: ints = [-1]> ()
key_squeezed_shape = Concat <axis: int = 0> (tmp, key_second_last_dim, key_last_dim)
key_squeezed = Reshape (key, key_squeezed_shape)
key_squeezed_transposed = Transpose <perm: ints = [0, 2, 1]> (key_squeezed)
key_transposed_shape = Concat <axis: int = 0> (key_first_dims, key_last_dim, key_second_last_dim)
key_transposed = Reshape (key_squeezed_transposed, key_transposed_shape)
tmp_6 = Sqrt (scale)
query_scaled = Mul (query, tmp_6)
tmp_7 = Sqrt (scale)
key_transposed_scaled = Mul (key_transposed, tmp_7)
tmp_8 = MatMul (query_scaled, key_transposed_scaled)
attn_weight = Softmax <axis: int = -1> (tmp_8)
dropout_p = Constant <value_float: float = @dropout_p> ()
attn_weight_9, _ = Dropout (attn_weight, dropout_p)
return_val = MatMul (attn_weight_9, value)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[4,4,3,8] input_0, float16[4,4,6,8] input_1, float16[4,4,6,8] input_2) => (float16[4,4,3,8] _val_5)
<float16 _val_3, float[3,6] _val_4>
{
_val_3 = pkg.onnxscript.torch_lib._attention_scale (input_0)
_val_4 = pkg.onnxscript.torch_lib._causal_attention_mask (input_0, input_1)
_val_5 = pkg.onnxscript.torch_lib._aten_scaled_dot_product_attention_float_mask_onnx <dropout_p: float = 0> (input_0, input_1, input_2, _val_4, _val_3)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_attention_scale (query) => (scale)
{
tmp = Shape (query)
int64_m1 = Constant <value: tensor = int64 int64_m1 {-1}> ()
tmp_subscripted = Gather <axis: int = 0> (tmp, int64_m1)
embedding_size = CastLike (tmp_subscripted, query)
const = Constant <value: tensor = float const {1}> ()
tmp_0 = Sqrt (embedding_size)
const_cast = CastLike (const, tmp_0)
scale = Div (const_cast, tmp_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_causal_attention_mask (query, key) => (attn_mask_10)
{
tmp = Shape (query)
int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
target_length = Slice (tmp, int64_m2_1d, int64_m1_1d, int64_0_1d, int64_1_1d)
tmp_0 = Shape (key)
int64_0_1d_1 = Constant <value: tensor = int64[1] int64_0_1d_1 {0}> ()
int64_1_1d_2 = Constant <value: tensor = int64[1] int64_1_1d_2 {1}> ()
int64_m2_1d_3 = Constant <value: tensor = int64[1] int64_m2_1d_3 {-2}> ()
int64_m1_1d_4 = Constant <value: tensor = int64[1] int64_m1_1d_4 {-1}> ()
source_length = Slice (tmp_0, int64_m2_1d_3, int64_m1_1d_4, int64_0_1d_1, int64_1_1d_2)
size = Concat <axis: int = 0> (target_length, source_length)
const = Constant <value: tensor = float const {1}> ()
attn_mask = Expand (const, size)
attn_mask_5 = Trilu <upper: int = 0> (attn_mask)
const_6 = Constant <value: tensor = float const_6 {0}> ()
const_6_cast = CastLike (const_6, attn_mask_5)
tmp_7 = Equal (attn_mask_5, const_6_cast)
tmp_8 = Constant <value_float: float = -inf> ()
const_9 = Constant <value: tensor = float const_9 {0}> ()
const_9_cast = CastLike (const_9, tmp_8)
attn_mask_10 = Where (tmp_7, tmp_8, const_9_cast)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_scaled_dot_product_attention_float_mask_onnx <dropout_p>(query, key, value, attn_mask, scale) => (return_val)
{
key_shape = Shape (key)
int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
int64_9223372036854775807_1d = Constant <value: tensor = int64[1] int64_9223372036854775807_1d {9223372036854775807}> ()
key_last_dim = Slice (key_shape, int64_m1_1d, int64_9223372036854775807_1d, int64_0_1d, int64_1_1d)
int64_0_1d_0 = Constant <value: tensor = int64[1] int64_0_1d_0 {0}> ()
int64_1_1d_1 = Constant <value: tensor = int64[1] int64_1_1d_1 {1}> ()
int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
int64_m1_1d_2 = Constant <value: tensor = int64[1] int64_m1_1d_2 {-1}> ()
key_second_last_dim = Slice (key_shape, int64_m2_1d, int64_m1_1d_2, int64_0_1d_0, int64_1_1d_1)
int64_0_1d_3 = Constant <value: tensor = int64[1] int64_0_1d_3 {0}> ()
int64_1_1d_4 = Constant <value: tensor = int64[1] int64_1_1d_4 {1}> ()
int64_m2_1d_5 = Constant <value: tensor = int64[1] int64_m2_1d_5 {-2}> ()
key_first_dims = Slice (key_shape, int64_0_1d_3, int64_m2_1d_5, int64_0_1d_3, int64_1_1d_4)
tmp = Constant <value_ints: ints = [-1]> ()
key_squeezed_shape = Concat <axis: int = 0> (tmp, key_second_last_dim, key_last_dim)
key_squeezed = Reshape (key, key_squeezed_shape)
key_squeezed_transposed = Transpose <perm: ints = [0, 2, 1]> (key_squeezed)
key_transposed_shape = Concat <axis: int = 0> (key_first_dims, key_last_dim, key_second_last_dim)
key_transposed = Reshape (key_squeezed_transposed, key_transposed_shape)
tmp_6 = Sqrt (scale)
query_scaled = Mul (query, tmp_6)
tmp_7 = Sqrt (scale)
key_transposed_scaled = Mul (key_transposed, tmp_7)
tmp_8 = MatMul (query_scaled, key_transposed_scaled)
tmp_9 = Add (tmp_8, attn_mask)
attn_weight = Softmax <axis: int = -1> (tmp_9)
dropout_p = Constant <value_float: float = @dropout_p> ()
attn_weight_10, _ = Dropout (attn_weight, dropout_p)
return_val = MatMul (attn_weight_10, value)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([[[[ 1.283 , -8.02 , -3.604 , 4.895 , -0.8877 , 4.332 ,
4.43 , -4.836 ],
[-1.661 , 7.812 , -6.625 , 8.734 , -5.31 , 7.875 ,
-2.795 , 3.217 ],
[ 2.453 , 2.479 , -8.66 , 8.42 , -7.348 , 1.433 ,
-0.2725 , 8.69 ]],
[[ 4.703 , -4.297 , 8.8 , 0.02637, 7.33 , -6.46 ,
8.37 , -5.203 ],
[-4.867 , 1.582 , 2.172 , -3.488 , -6.555 , -1.301 ,
-3.902 , -1.424 ],
[-6.777 , -7.418 , 7.285 , -3.023 , -1.758 , 4.465 ,
6.68 , -2.855 ]],
[[-4.035 , 2.855 , 3.129 , 7.242 , 5.703 , -3.031 ,
-5.57 , 5.4 ],
[ 8.2 , 0.7383 , 2.777 , -7.145 , 4.516 , -5.633 ,
6.062 , -6.004 ],
[ 7.156 , 8.46 , 8.67 , -1.591 , 0.3252 , 8.875 ,
4.484 , -5.316 ]],
[[-2.408 , 1.477 , 8.47 , 7.98 , 3.006 , 7.25 ,
-6.32 , 4.754 ],
[-2.84 , -1.371 , -4.29 , 0.9756 , 3.604 , 8.31 ,
-7.2 , 1.617 ],
[ 3.348 , -4.65 , 3.322 , 0.4043 , 7.777 , 6.496 ,
4.836 , 4.633 ]]],
[[[-4.58 , 0.1846 , -7.137 , -6.285 , -3.2 , 8.5 ,
-2.39 , -6.617 ],
[ 5.008 , 8.914 , 7.016 , -0.7646 , 1.767 , -8.73 ,
-5.117 , -7.586 ],
[-2.268 , -6.777 , -4.43 , -0.4219 , 5.71 , 4.21 ,
-8.92 , -2.629 ]],
[[-4.57 , -2.11 , 7.34 , 4.914 , -5.176 , 0.967 ,
-7.664 , 5.57 ],
[-0.949 , -1.371 , 0.8877 , -2.39 , 7.312 , 1.67 ,
6.4 , -4.062 ],
[ 7.06 , 0.703 , -4.71 , -1.143 , 0.7646 , 1.696 ,
-8.09 , 6.875 ]],
[[ 0.835 , 3.709 , 7.82 , 1.731 , 0. , -0.1582 ,
-5.43 , 3.312 ],
[-7.54 , -4.535 , 5.598 , -6.258 , 5.203 , 3.664 ,
-2.303 , -3.023 ],
[-4.816 , -8.37 , 1.23 , -3.895 , -6.707 , 2.98 ,
2.7 , -6.855 ]],
[[ 4.516 , 2.953 , 5.047 , -4.08 , 0.659 , 7.727 ,
-7.47 , -8.05 ],
[-7.4 , -7.32 , -8.44 , 7.453 , -0.545 , 4.156 ,
6.875 , 0.87 ],
[ 4.773 , -1.415 , 1.116 , 3.965 , 8.02 , -5.766 ,
-1.529 , -8.63 ]]],
[[[ 7.76 , 3.674 , -4.72 , -8.32 , -1.749 , 7.03 ,
2.363 , -3.086 ],
[-2.848 , -2.338 , 7.902 , -2.61 , 2.76 , -0.879 ,
7.47 , -1.081 ],
[-1.195 , -0.2812 , 8.3 , 1.468 , -6.03 , 8.89 ,
-7.312 , 3.973 ]],
[[-6.523 , 2.945 , -5.582 , -0.2197 , -4.395 , 2.102 ,
7.305 , 8.414 ],
[ 4.234 , -3.217 , -2.715 , 5.195 , -1.178 , 4.867 ,
4.016 , 0.7734 ],
[ 5.793 , 0.826 , -5.035 , -5.246 , 1.318 , 4.508 ,
3.297 , 0.1846 ]],
[[-1.248 , -6.258 , 8.75 , -1.626 , -4.445 , 1.802 ,
8.36 , -5.71 ],
[-5.047 , 5.492 , 6.883 , -0.0879 , -3.78 , 1.564 ,
1.837 , -4.613 ],
[-4.93 , 7.375 , 1.081 , 8.72 , -8.016 , 0.0967 ,
1.099 , 4.957 ]],
[[-6.1 , 7.91 , 2.479 , -7.777 , 3.516 , -1.081 ,
0.8438 , -4.465 ],
[-4.008 , 8.11 , -1.573 , 0.5977 , -7.973 , -1.204 ,
0.51 , 0.2812 ],
[ 2.785 , -8.57 , 7.727 , 4.29 , -8.84 , -2.629 ,
-7.277 , 7.82 ]]],
[[[ 3.945 , 8.695 , -4.094 , 5.96 , -5.035 , -6.47 ,
1.23 , 0.7295 ],
[ 6.09 , -5.57 , 5.188 , -7.117 , 4.613 , -7.117 ,
3.533 , -7.883 ],
[ 7.215 , 4.184 , -2.328 , -5.457 , 0.2461 , 6.953 ,
-6.04 , -1.705 ]],
[[-2.734 , -5.836 , -4.008 , 3.438 , -7.094 , 5.035 ,
5.87 , -7.234 ],
[-8.86 , -6.18 , -4.457 , 5. , 2.848 , 3.613 ,
2.785 , -3.023 ],
[ 8.47 , 0.712 , 4.156 , 4.105 , -5.273 , 8.3 ,
6.414 , 6.047 ]],
[[ 1.784 , 5.117 , -0.05273, -5.61 , -2.172 , -8.15 ,
3.023 , 7.047 ],
[-7.18 , 4.508 , 5.582 , 6.953 , -3.86 , -7.55 ,
-8.81 , -7.656 ],
[ 8.24 , 3.85 , 2.584 , -7.086 , -3.129 , 4.344 ,
-6.99 , -8.836 ]],
[[ 8.664 , -4.15 , -0.659 , -7.707 , 0.9404 , -5.47 ,
-3.77 , 4.234 ],
[-5.78 , 7.32 , 3.629 , 2.707 , -1.96 , -0.9404 ,
7.33 , 1.169 ],
[ 6.312 , 2.479 , 6.83 , -8.37 , -4.78 , 3.086 ,
-4.086 , 2.855 ]]]], dtype=float16),
'input_1': array([[[[-2.4609e+00, 3.8848e+00, -8.1328e+00, 5.0977e-01,
-4.5430e+00, -6.7422e+00, -5.3789e+00, 3.9648e+00],
[ 5.6250e-01, -3.7793e+00, 1.3447e+00, 8.6484e+00,
-6.6719e+00, -1.7930e+00, 6.8555e-01, 2.7598e+00],
[-3.1914e+00, -6.8555e-01, -4.0859e+00, -9.4922e-01,
-1.1777e+00, 2.1719e+00, 6.9336e+00, -1.3799e+00],
[-3.6484e+00, -5.3711e+00, -8.7891e+00, 8.8281e+00,
-6.5117e+00, 3.9375e+00, -1.2656e+00, -6.3633e+00],
[ 5.8887e-01, 5.2734e-02, -1.8281e+00, 1.1953e+00,
1.4326e+00, -8.2812e+00, 7.8750e+00, 5.7031e+00],
[ 3.6836e+00, 6.3281e-01, 2.0742e+00, -8.6016e+00,
-3.5781e+00, -8.5254e-01, 7.0234e+00, -6.7070e+00]],
[[-7.4609e+00, 1.4502e+00, -3.2344e+00, -1.6084e+00,
-5.7578e+00, 6.9766e+00, -8.5312e+00, -2.9453e+00],
[ 3.6738e+00, 6.6367e+00, 4.4453e+00, 2.9883e+00,
1.1074e+00, 3.5859e+00, 8.1094e+00, -5.7812e+00],
[ 2.3730e+00, -3.3477e+00, 5.3086e+00, -5.6797e+00,
-8.3672e+00, 8.1016e+00, 8.0938e+00, -9.6680e-01],
[ 2.1719e+00, -1.4502e+00, 6.8906e+00, -8.6328e+00,
-5.6953e+00, 3.5156e-01, -7.5156e+00, 8.3047e+00],
[-7.1445e+00, 4.3242e+00, 5.9688e+00, -8.6641e+00,
-5.7656e+00, -2.3555e+00, -7.6797e+00, 9.6680e-01],
[ 4.2109e+00, 3.8242e+00, 4.0430e-01, -8.7891e-03,
5.0469e+00, -2.5312e+00, 8.9297e+00, 3.2070e+00]],
[[-8.4062e+00, -6.3828e+00, -7.1191e-01, -1.9600e+00,
-6.4062e+00, -7.2266e+00, -8.4688e+00, -6.9434e-01],
[-5.4492e-01, 7.2852e+00, 2.7500e+00, 1.4062e+00,
-6.1016e+00, 4.6328e+00, -6.0391e+00, 5.3164e+00],
[ 2.6641e+00, 4.4141e+00, 5.7031e+00, 2.9609e+00,
4.8242e+00, -3.3926e+00, -6.5938e+00, 1.4326e+00],
[ 3.8848e+00, -6.2031e+00, -3.8320e+00, -4.5781e+00,
6.0195e+00, -5.6094e+00, 8.5156e+00, -1.3623e+00],
[-1.6875e+00, -8.7891e-02, 7.7969e+00, -3.7090e+00,
6.5820e+00, 6.8125e+00, -2.9355e+00, -4.6680e+00],
[-4.7031e+00, -2.6719e+00, 6.3281e-01, -4.5352e+00,
-5.5820e+00, 5.4297e+00, 7.5234e+00, -6.4141e+00]],
[[ 3.2344e+00, 8.3906e+00, -2.0117e+00, -1.4062e+00,
6.0898e+00, -4.1836e+00, -3.3047e+00, 7.4609e+00],
[-4.0156e+00, 1.6348e+00, -1.5117e+00, -2.2422e+00,
-5.0977e+00, 5.0000e+00, -5.8203e+00, -7.9297e+00],
[ 1.6436e+00, -2.4883e+00, 8.9375e+00, -3.0312e+00,
-5.3164e+00, 5.4922e+00, 2.2070e+00, 5.4297e+00],
[ 4.8164e+00, 2.9355e+00, -8.2031e+00, 4.6484e+00,
7.6016e+00, -8.4531e+00, 5.8086e+00, -4.5078e+00],
[ 1.2129e+00, 6.1250e+00, 1.6172e+00, 1.7930e+00,
-2.2227e+00, 1.8721e+00, -6.7578e+00, -7.8203e+00],
[ 5.0273e+00, -1.3184e+00, -1.7842e+00, -8.2344e+00,
7.3398e+00, -3.2520e-01, 1.8105e+00, 1.1250e+00]]],
[[[-1.7754e+00, 1.7578e-02, -4.4824e-01, -7.9980e-01,
6.6172e+00, -7.9453e+00, 3.3750e+00, 5.3867e+00],
[ 3.2520e-01, 2.9883e+00, -1.4941e-01, -8.7891e-01,
-3.3398e+00, 8.8594e+00, -2.7344e+00, -1.4502e+00],
[-8.8281e+00, -6.8828e+00, -5.0078e+00, -5.4492e-01,
-3.5508e+00, -6.3438e+00, 5.2305e+00, 5.2188e+00],
[-1.9512e+00, -5.3613e-01, -8.4062e+00, 1.1250e+00,
-7.2852e+00, -7.7773e+00, 3.8945e+00, -6.5234e+00],
[-6.2656e+00, -2.4531e+00, 4.6953e+00, 5.8359e+00,
-6.5742e+00, -8.4688e+00, 2.7949e+00, 7.8125e+00],
[-8.5547e+00, 7.5156e+00, -7.6641e+00, 6.5742e+00,
2.7246e+00, 7.1445e+00, -4.2266e+00, -4.0234e+00]],
[[ 7.5859e+00, 3.4102e+00, 4.8086e+00, -3.5430e+00,
8.7969e+00, 1.5469e+00, 3.1719e+00, -6.2500e+00],
[-1.9160e+00, -1.5557e+00, -8.1562e+00, -1.2656e+00,
1.8633e+00, -1.7227e+00, -7.8047e+00, 4.3945e-02],
[-1.2305e-01, -8.0469e+00, -4.2031e+00, 3.3398e-01,
-3.1914e+00, -5.2734e+00, -3.4727e+00, -6.7148e+00],
[ 3.7344e+00, -8.2422e+00, -4.9219e+00, -8.3438e+00,
-4.8672e+00, 8.5703e+00, 8.4531e+00, -5.3711e+00],
[-1.7578e+00, -1.1426e+00, -2.6289e+00, -4.9922e+00,
2.4961e+00, 2.0918e+00, 5.9414e+00, 5.2578e+00],
[-3.2695e+00, -3.1016e+00, 4.8945e+00, 3.2617e+00,
-4.6562e+00, 5.4766e+00, 8.0703e+00, 3.8672e-01]],
[[ 3.5156e+00, 2.1621e+00, 5.4062e+00, -3.7617e+00,
-5.0547e+00, -4.7461e+00, -3.3320e+00, -3.5586e+00],
[ 1.4941e-01, 5.3164e+00, -3.1914e+00, -2.3477e+00,
-6.4688e+00, 3.6289e+00, -2.6719e+00, -3.4023e+00],
[-6.9453e+00, -5.3516e+00, -7.6465e-01, -4.6250e+00,
-4.4824e-01, -3.4375e+00, 5.7031e+00, 8.8438e+00],
[-4.3945e-02, -8.0000e+00, -8.1738e-01, -3.0859e+00,
6.2578e+00, 2.0469e+00, 4.7383e+00, 8.7891e-02],
[-7.1992e+00, 5.9609e+00, -6.8359e+00, 4.9062e+00,
-2.3477e+00, 1.1074e+00, -7.7188e+00, -7.9727e+00],
[ 8.0000e+00, 3.3477e+00, -5.3867e+00, 5.8281e+00,
5.1250e+00, -5.8203e+00, 4.4648e+00, -7.8047e+00]],
[[ 1.5293e+00, 2.5312e+00, -5.8887e-01, 4.2188e+00,
-8.8438e+00, -7.2969e+00, -6.1094e+00, 8.2188e+00],
[-7.4688e+00, -2.0215e-01, -5.0625e+00, 8.1250e+00,
-6.9434e-01, 3.1016e+00, 4.3750e+00, -7.7695e+00],
[-2.3477e+00, 7.8750e+00, -8.3496e-01, -8.1875e+00,
8.2891e+00, -6.9062e+00, -7.2070e+00, -4.0859e+00],
[ 3.7969e+00, -6.8555e+00, -1.6963e+00, 2.7773e+00,
-8.7891e-01, -6.4141e+00, -3.3828e+00, -1.4854e+00],
[ 2.4688e+00, -8.8594e+00, -8.3828e+00, 2.3555e+00,
2.8477e+00, 6.9688e+00, -6.3281e+00, 3.9551e+00],
[ 5.2734e-02, 2.7500e+00, -1.8281e+00, 8.9648e-01,
-1.7930e+00, -7.3125e+00, 6.6094e+00, 5.0352e+00]]],
[[[-4.3594e+00, -7.5078e+00, 4.7109e+00, -6.8047e+00,
6.7148e+00, 3.3926e+00, 1.8105e+00, -4.2539e+00],
[ 5.5195e+00, 8.8047e+00, -5.3516e+00, -2.0312e+00,
3.3320e+00, 1.2568e+00, -8.7891e+00, 7.2148e+00],
[-3.1367e+00, 6.3281e-01, -3.4531e+00, 7.2344e+00,
7.3125e+00, 8.0859e-01, -4.5703e+00, 5.2461e+00],
[-6.0547e+00, -5.2734e+00, -4.6250e+00, 3.1914e+00,
-6.8555e-01, -3.4727e+00, 6.5391e+00, 1.7402e+00],
[-2.6992e+00, 3.6211e+00, -6.0312e+00, -3.2168e+00,
-5.5371e-01, 8.2266e+00, -5.2383e+00, 6.8750e+00],
[-1.1074e+00, -7.7…4_1_1d_1 = Constant <value: tensor = int64[1] int64_1_1d_1 {1}> ()
E int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
E int64_m1_1d_2 = Constant <value: tensor = int64[1] int64_m1_1d_2 {-1}> ()
E key_second_last_dim = Slice (key_shape, int64_m2_1d, int64_m1_1d_2, int64_0_1d_0, int64_1_1d_1)
E int64_0_1d_3 = Constant <value: tensor = int64[1] int64_0_1d_3 {0}> ()
E int64_1_1d_4 = Constant <value: tensor = int64[1] int64_1_1d_4 {1}> ()
E int64_m2_1d_5 = Constant <value: tensor = int64[1] int64_m2_1d_5 {-2}> ()
E key_first_dims = Slice (key_shape, int64_0_1d_3, int64_m2_1d_5, int64_0_1d_3, int64_1_1d_4)
E tmp = Constant <value_ints: ints = [-1]> ()
E key_squeezed_shape = Concat <axis: int = 0> (tmp, key_second_last_dim, key_last_dim)
E key_squeezed = Reshape (key, key_squeezed_shape)
E key_squeezed_transposed = Transpose <perm: ints = [0, 2, 1]> (key_squeezed)
E key_transposed_shape = Concat <axis: int = 0> (key_first_dims, key_last_dim, key_second_last_dim)
E key_transposed = Reshape (key_squeezed_transposed, key_transposed_shape)
E tmp_6 = Sqrt (scale)
E query_scaled = Mul (query, tmp_6)
E tmp_7 = Sqrt (scale)
E key_transposed_scaled = Mul (key_transposed, tmp_7)
E tmp_8 = MatMul (query_scaled, key_transposed_scaled)
E tmp_9 = Add (tmp_8, attn_mask)
E attn_weight = Softmax <axis: int = -1> (tmp_9)
E dropout_p = Constant <value_float: float = @dropout_p> ()
E attn_weight_10, _ = Dropout (attn_weight, dropout_p)
E return_val = MatMul (attn_weight_10, value)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:534: in _capture_graph_and_evaluate_torch_script_evaluator
return _safe_ort_session_run(onnx_model.SerializeToString(), ort_inputs)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:349: in _safe_ort_session_run
raise return_dict["error"]
E onnxruntime.capi.onnxruntime_pybind11_state.InvalidGraph: [ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model. In Node, ("", ReduceMax, "", -1) : ("_inline__aten_scaled_dot_product_attention_no_mask_onnxtmp_8": tensor(float16),) -> ("_inline_SoftmaxX_ReduceMax",) , Error Unrecognized attribute: axes for operator ReduceMax
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:550: in _capture_graph_and_evaluate_torch_script_evaluator
raise RuntimeError(
E RuntimeError: ONNX Runtime failed to evaluate:
E Inputs:
E {'input_0': array([[[-2.89 , 5.555 , -1.591 , 4.395 , -3.7 , -3.895 ,
E 8.945 , 8.04 ],
E [-7.84 , -2.057 , -3.305 , 2.75 , 6.133 , -7.066 ,
E -0.4307 , 3.84 ],
E [ 0.3955 , 6.934 , 1.679 , 4.562 , 3.322 , -2.082 ,
E -2.936 , 2.926 ]],
E
E [[-8.664 , 2.566 , -2.945 , -7.586 , 2.9 , 7.777 ,
E 0.9053 , -7.594 ],
E [ 7.016 , 5.57 , 0.4658 , 8.25 , 0.29 , -6.883 ,
E -0.51 , -1.187 ],
E [-8.016 , -0.0967 , -6.215 , -8.805 , 4.895 , -1.767 ,
E 5.68 , 1.028 ]],
E
E [[ 8.65 , -2.848 , 5.64 , -0.05273, 8.2 , 1.8545 ,
E 3.664 , 0.9844 ],
E [ 0.378 , -7.883 , -1.31 , -7.62 , 6.89 , -1.74 ,
E 3.453 , 7.61 ],
E [-1.5205 , -4.508 , 8.02 , -8.61 , 0.9404 , -7.086 ,
E -5.117 , 7.285 ]],
E
E [[-6.242 , -7.453 , -7.004 , 6.17 , 8.63 , 5.9 ,
E 7.953 , -6.664 ],
E [-1.362 , -1.644 , 3.322 , -4.71 , -2.84 , -2.97 ,
E 2.54 , -0.255 ],
E [-5.87 , 5.766 , 6.53 , -1.345 , 8.34 , -7.215 ,
E 1.863 , -4.156 ]]], dtype=float16),
E 'input_1': array([[[[ 6.906 , -6.17 , -3.79 , -7.418 , -4.625 , 2.805 ,
E -8.28 , -6.344 ],
E [-6.707 , -2.117 , 1.749 , 2.102 , -0.9756 , -2.926 ,
E -0.712 , -3.006 ],
E [ 2.584 , 4.492 , -2.953 , 7.734 , -5.527 , 6.574 ,
E 1.204 , -1.635 ],
E [-1.292 , -1.775 , 1.125 , -8.58 , 5.934 , -2.074 ,
E -6.434 , 5.51 ],
E [-4.297 , -6.273 , -4.29 , 7.93 , -3.393 , -6.953 ,
E 1.828 , -3.691 ],
E [-8.18 , -6.133 , -7.03 , -0.51 , -1.187 , 6.953 ,
E -8.914 , 2.883 ]],
E
E [[ 0.0967 , -8.7 , -5.08 , 3.965 , 6.496 , 1.925 ,
E -5.133 , -3.27 ],
E [ 4.695 , -3.78 , 8.8 , -6.996 , 8.21 , -5.984 ,
E 0.6943 , 3.094 ],
E [ 3.473 , 2.54 , 3.99 , 7.4 , 5.89 , -0.3164 ,
E 1.116 , 6.16 ],
E [-1.02 , 4.766 , 7.883 , 3.146 , -4.375 , 2.742 ,
E -8.35 , 4.79 ],
E [ 7.65 , -7.285 , 7.4 , -8.75 , -2.805 , -0.03516,
E -8.87 , 3.709 ],
E [ 5.168 , -1.934 , 5.203 , 3.55 , -0.747 , 5.582 ,
E 4.656 , -0.51 ]],
E
E [[ 5.387 , 7.938 , 0.668 , -6.996 , -0.5537 , 2.602 ,
E 4.473 , -2.725 ],
E [-0.3604 , 8.484 , -3.973 , 4.72 , 5.117 , 8.93 ,
E -3.604 , 8.59 ],
E [-6.145 , 3.182 , 5.87 , -1.318 , -5.85 , 6.195 ,
E 6.906 , -6.16 ],
E [ 6.125 , 2.855 , 5.484 , 7.242 , -7.56 , 5.027 ,
E 5.3 , -1.995 ],
E [-5.008 , 8.51 , 1.336 , 6.47 , 1.107 , -7.312 ,
E -3.498 , 6.793 ],
E [-4.35 , 0.0791 , -8.2 , -7.79 , -4.78 , -5.062 ,
E 2.918 , -1.371 ]],
E
E [[-6.02 , 8.44 , 0.4482 , 6.203 , -4.914 , 8.79 ,
E -2.7 , -1.986 ],
E [-4.836 , -5.484 , 3.85 , 7.707 , -3.691 , -0.8174 ,
E -0.2461 , -0.9053 ],
E [-1.793 , -3.234 , -3.754 , 4.465 , 1.336 , -4.492 ,
E 4.414 , 7.496 ],
E [ 7.91 , 6.777 , 5.934 , 0.04395, -4.613 , 5.8 ,
E -2.707 , 1.028 ],
E [ 4.254 , 8.914 , -3.727 , -3.156 , 8.35 , 5.96 ,
E -4.895 , -7.594 ],
E [-6.637 , 0.4834 , -7.004 , -1.547 , -7.7 , 0.3955 ,
E 1.257 , 6.582 ]]],
E
E
E [[[ 6.883 , -3.453 , 2.98 , -5.38 , 6.426 , -4.438 ,
E 3.016 , -5.61 ],
E [ 4.15 , -6.594 , -5.906 , 0.5713 , 0.6855 , -3.754 ,
E 5.582 , 1.767 ],
E [ 8.586 , 5.81 , -4.105 , -8.016 , 0.791 , -7.77 ,
E 2.531 , 7.91 ],
E [-2.594 , -3.832 , -5.4 , -5.695 , 3.367 , 7.426 ,
E 1.327 , 7.33 ],
E [-2.408 , -3.498 , 2.795 , 3.234 , 5.316 , -0.2637 ,
E 4.914 , -2.83 ],
E [-4.95 , -8.91 , 1.362 , 6.004 , -4.816 , -0.756 ,
E -5.484 , -1.907 ]],
E
E [[-7.453 , 2.988 , -8.51 , 7.508 , 7.48 , -5.07 ,
E -7.227 , 6.117 ],
E [ 5.555 , -2.523 , -7.234 , 2.89 , -5.82 , -1.169 ,
E -5.02 , -5.88 ],
E [-7.11 , -0.4658 , -4.133 , -1.213 , -8.805 , -7.65 ,
E 1.626 , -6.82 ],
E [-2.426 , -1.934 , -7.832 , -6.188 , -5.582 , -5.195 ,
E 0.1846 , -1.336 ],
E [ 5.266 , 5.836 , 1.09 , 0.6855 , -1.468 , 8.5 ,
E -8.56 , 6.39 ],
E [-7.46 , -1.362 , -6.785 , -0.5186 , 2.152 , -2.514 ,
E 0.923 , 1.6875 ]],
E
E [[ 2.77 , -6.355 , -4.19 , 8.375 , -2.715 , 8.2 ,
E -2.162 , -6.652 ],
E [ 1.485 , -0.1318 , -4.164 , 3.99 , 5.71 , 2.559 ,
E 4.95 , 7.016 ],
E [ 7.03 , 1.23 , 1.274 , 3.727 , -6.25 , -7.39 ,
E 5.83 , -2.742 ],
E [-7.883 , 2.514 , 7.227 , 2.215 , 8.79 , 6.83 ,
E -2.855 , -5.617 ],
E [ 2.012 , -6.625 , 6.766 , -1.881 , -5.36 , -7.27 ,
E 2.83 , 0.4834 ],
E [-1.705 , 1.775 , 6.875 , 5.45 , -3.488 , -3.7 ,
E 1.204 , 3.7 ]],
E
E [[ 7.156 , 0.4922 , 4.93 , -0.1318 , -8.38 , -1.002 ,
E 8.02 , -3.79 ],
E [ 3.262 , 5.414 , -3.734 , -4.492 , 7.656 , -8.27 ,
E -0.2725 , 0.3691 ],
E [ 5.844 , -1.635 , 8.2 , -3.684 , -1.89 , -7.84 ,
E 3.463 , 0.5625 ],
E [-6.46 , -5.703 , -2.25 , -4.906 , 7.883 , 1.863 ,
E 1.292 , 5.027 ],
E [-4.867 , -7.7 , -8.234 , 2.84 , 7.86 , -5.527 ,
E -2.066 , 3.543 ],
E [ 0.712 , 5.625 , 2.504 , 3.348 , -1.406 , 6.953 ,
E 2.363 , 2.559 ]]],
E
E
E [[[ 3.973 , 6.273 , -8.8 , -0.6064 , 7.58 , -6.273 ,
E 4.043 , 1.819 ],
E [ 6.953 , 6.293 , -3.172 , -5.16 , 4.72 , -2.805 ,
E 2.945 , -5.3 ],
E [ 6.582 , -6.188 , 4.36 , -3.85 , -0.6855 , 1.424 ,
E 7.117 , 4.746 ],
E [ 1.441 , -1.547 , 6.125 , 4.965 , 2.584 , -8.64 ,
E 8.11 , 2.496 ],
E [-0.756 , -5.61 , 8.88 , 1.204 , 1.635 , 8.8 ,
E -1.011 , -2.715 ],
E [-5.22 , -4.14 , 7.566 , 3.648 , -1.978 , -5.266 ,
E 3.648 , -8.78 ]],
E
E [[ 3.252 , -5.625 , -8.516 , 2.215 , -3.438 , -8.08 ,
E 1.134 , -1.547 ],
E [-3.121 , -5.703 , -2.479 , -0.51 , -6.3 , -8.21 ,
E 3.445 , 4.684 ],
E [-1.96 , -8.04 , 4.92 , 5.625 , -3.867 , -6.02 ,
E 4.957 , -0.4043 ],
E [-5.88 , 7.832 , -7.53 , -4.113 , 3.965 , -1.055 ,
E -8.086 , 8.99 ],
E [-7.69 , -7.727 , -2.715 , -1.327 , -6.383 , 1.459 ,
E 6.258 , -3.332 ],
E [-2.654 , 0.413 , -3.402 , -6.906 , 0.05273, -4.93 ,
E -5.027 , -2.117 ]],
E
E [[-1.125 , -5.75 , 4.816 , -4.727 , 1.846 , 8.91 ,
E -7.56 , 1.09 ],
E [ 2.953 , -7.32 , -2.594 , -7.13 , -3.965 , 2.875 ,
E -0.5977 , -5.66 ],
E [-3.094 , 5.07 , -1.995 , -6.906 , 4.668 , -2.855 ,
E 0.712 , 3.182 ],
E [-4.57 , -7.234 , -0.923 , 4.42 , 5.652 , -8.69 ,
E -3.348 , 5.168 ],
E [ 5.273 , 8.55 , 3.523 , -2.285 , 8.945 , 7.516 ,
E -2.805 , 1.538 ],
E [-4.22 , 5. , 4.094 , 1.758 , -4.26 , 4.797 ,
E -0.0703 , 8.77 ]],
E
E [[ 5.59 , -5.547 , -1.6875 , 0.3604 , -3.762 , -5.555 ,
E -2.855 , 5.47 ],
E [ 6.875 , 8.734 , -5.133 , 7.348 , 5.414 , 4.438 ,
E -0.9404 , -6.195 ],
E [-4.15 , 1.881 , 0.615 , -4.016 , -0.9316 , -6.934 ,
E 2.46 , -0.334 ],
E [ 5.414 , 7.918 , 7.79 , 1.415 , 1.67 , 0.0703 ,
E -8.484 , -1.16 ],
E [ 7.17 , -7.03 , 7.188 , -2.855 , -3.895 , 7.066 ,
E 2.514 , -0.2725 ],
E [-7.523 , 0.536 , 3.059 , 3.875 , -8.88 , -7.566 ,
E 3.684 , -2.355 ]]],
E
E
E [[[ 7.438 , 4.562 , 5.844 , 4.81 , 4.914 , 5.24 ,
E 0.2373 , 6.215 ],
E [-5.09 , 1.819 , 4.12 , -2.715 , -0.4307 , -1.811 ,
E -8.09 , -2.488 ],
E [-5.266 , 0.879 , 4.746 , -3.393 , -1.538 , 4.58 ,
E 0.923 , 5.96 ],
E [ 8.586 , -2.25 , 7.086 , -6.574 , 0.5625 , 6.875 ,
E -1.099 , -7.355 ],
E [ 2.777 , 8.04 , 1.204 , 3.006 , -8.086 , -4.21 ,
E -5.668 , -8.04 ],
E [ 8.29 , -4.22 , -5.9 , -2.479 , -6.223 , 2.328 ,
E 6.09 , 7.48 ]],
E
E [[ 0.5625 , -6.082 , 4.5 , 1.16 , -3.805 , -2.127 ,
E 6.61 , -4.26 ],
E [-2.549 , 4.695 , 4.35 , -3.55 , -3.568 , 5.766 ,
E 3.234 , 7.6 ],
E [-1.02 , 7.99 , 5.71 , 6.99 , 7.11 , -0.29 ,
E -8.14 , -6.223 ],
E [-7.93 , 2.805 , -1.986 , 5.203 , 4.344 , -4.992 ,
E -4.42 , -4.438 ],
E [-0.949 , -2.707 , -6.242 , 8.305 , -2.777 , -2.77 ,
E 4.754 , 4.203 ],
E [ 8.016 , 8.52 , 0.167 , -4.086 , -0.4922 , 1.134 ,
E 2.566 , 7.875 ]],
E
E [[ 1.151 , -5.88 , 0.4658 , -1.187 , -2.04 , 3.393 ,
E -6.68 , -0.378 ],
E [ 1.881 , 4.035 , 4.016 , -1.96 , -8.63 , 5.14 ,
E -1.433 , 2.434 ],
E [ 7.355 , 6.617 , 1.134 , -5.934 , 6.195 , 4.773 ,
E 0.923 , -0.993 ],
E [ 6.285 , -9. , 6.645 , -3.875 , -5.21 , 2.68 ,
E 7.98 , 4.625 ],
E [-8.55 , -0.747 , -4.438 , 2.89 , 4.895 , 2.848 ,
E 0.993 , -7.72 ],
E [ 8.84 , 1.45 , 3.322 , 1.213 , -3.48 , -6.383 ,
E 2.418 , 8.04 ]],
E
E [[-2.074 , -1.538 , 7.242 , -7.445 , -1.608 , 3.102 ,
E 1.345 , 8.13 ],
E [ 1.758 , -1.292 , 1.002 , 5.89 , 4.86 , -2.338 ,
E 0.589 , 3.902 ],
E [-5.098 , 2.855 , 7.426 , -2.785 , -3.719 , 5.47 ,
E 7.727 , 3.146 ],
E [-7.734 , -6.594 , -4.535 , 1.564 , -2.39 , 7.355 ,
E 7.297 , -4.816 ],
E [ 7.4 , 8.57 , 3.629 , 1.336 , 0.9053 , 0.6504 ,
E 1.116 , 6.242 ],
E [-3.156 , -4.016 , 6.934 , 3.023 , -4.344 , -7.61 ,
E 4.72 , 3.734 ]]]], dtype=float16),
E 'input_2': array([[[[-3.0234e+00, 8.7422e+00, -8.8047e+00, 4.0430e-01,
E -4.3945e+00, -2.6016e+00, 7.3203e+00, 6.1953e+00],
E [-7.7422e+00, 5.1328e+00, -3.1367e+00, 5.1680e+00,
E 3.6738e+00, -3.0234e+00, -4.5703e-01, -1.9863e+00],
E [-8.2656e+00, -5.3516e+00, -2.9102e+00, -4.7734e+00,
E -5.0000e+00, -1.7402e+00, -6.3984e+00, 9.7559e-01],
E [ 8.4453e+00, 2.0117e+00, 8.3496e-01, -6.5918e-01,
E -3.0312e+00, -5.6875e+00, 1.3623e+00, -5.3086e+00],
E [-8.0859e+00, 6.1875e+00, 8.7891e+00, -6.1094e+00,
E -5.1484e+00, 1.1514e+00, 7.6094e+00, -5.6523e+00],
E [-7.7969e+00, -7.2070e-01, -1.5996e+00, -6.6367e+00,
E 4.8242e+00, -5.5273e+00, 4.8867e+00, -9.8438e-01]],
E
E [[-4.9297e+00, 5.9219e+00, -5.0547e+00, 2.9004e+00,
E 2.2148e+00, 5.9766e+00, 8.4531e+00, 2.4961e+00],
E [ 2.3984e+00, -8.8359e+00, -6.9062e+00, 2.3633e+00,
E -5.3359e+00, 8.2969e+00, 2.7148e+00, 2.6367e-01],
E [ 7.2500e+00, 9.0527e-01, 5.9844e+00, -7.3750e+00,
E 1.1162e+00, 1.8369e+00, 3.6289e+00, 2.4609e+00],
E [ 8.4219e+00, -6.7852e+00, -8.8438e+00, -2.7500e+00,
E 6.4258e+00, 1.1865e+00, -7.0469e+00, -8.9844e+00],
E [ 8.4531e+00, 2.0664e+00, 8.9844e+00, -1.5029e+00,
E 4.0781e+00, -7.0039e+00, -9.8438e-01, 5.7383e+00],
E [-4.6250e+00, -4.4297e+00, -2.8398e+00, 7.5664e+00,
E -9.4922e-01, 8.5312e+00, 5.9414e+00, 8.7109e+00]],
E
E [[ 1.2129e+00, 2.4180e+00, -6.4531e+00, 4.5703e-01,
E 6.0312e+00, 9.5801e-01, -2.7246e+00, -6.7676e-01],
E [-3.3125e+00, 2.6367e-02, 5.8516e+00, -2.9961e+00,
E 8.2188e+00, -1.3623e+00, 4.0430e-01, -8.1797e+00],
E [-7.4707e-01, -4.0859e+00, 6.6016e+00, 7.5391e+00,
E -7.1367e+00, -5.1758e+00, -1.5205e+00, -4.5430e+00],
E [-1.6611e+00, -3.2871e+00, -7.2070e+00, -5.6602e+00,
E 6.3281e-01, 4.5273e+00, 6.5918e-01, 7.3828e+00],
E [-8.3281e+00, -1.8369e+00, -2.2578e+00, -5.4062e+00,
E 5.3867e+00, -8.0781e+00, 7.4180e+00, -4.1641e+00],
E [ 4.1758e+00, -1.7490e+00, 2.4082e+00, -8.2344e+00,
E 6.3438e+00, -1.2656e+00, 6.6797e-01, 6.6797e-01]],
E
E [[ 3.8750e+00, -4.2617e+00, -6.8555e+00, -3.9297e+00,
E 6.8281e+00, 3.3477e+00, -3.7793e-01, -2.7070e+00],
E [ 5.0000e+00, 6.2578e+00, 6.4688e+00, -7.7617e+00,
E -1.1602e+00, 3.2266e+00, 7.2949e-01, 1.2480e+00],
E [-5.9141e+00, -4.2188e+00, -9.8438e-01, -7.6719e+00,
E 1.3799e+00, 3.9102e+00, -3.2695e+00, -5.8887e-01],
E [-2.3027e+00, 2.6543e+00, -3.5234e+00, -3.2422e+00,
E -5.0547e+00, -2.6719e+00, 1.9600e+00, -8.6328e+00],
E [-6.6875e+00, 6.6367e+00, 1.7578e-02, 5.5898e+00,
E 2.3477e+00, -3.7695e+00, 1.3184e-01, 2.9258e+00],
E [ 4.0703e+00, 5.0977e-01, -1.0547e-01, -7.4531e+00,
E -8.4531e+00, -5.8887e-01, -1.9336e-01, -4.0781e+00]]],
E
E
E [[[ 2.6895e+00, 2.1523e+00, 2.5312e+00, 7.0039e+00,
E -6.1523e-01, 2.9102e+00, 4.6250e+00, -1.5381e+00],
E [ 2.2324e+00, -3.6992e+00, 4.4727e+00, 6.9453e+00,
E 6.3008e+00, -3.6836e+00, 6.4688e+00, 7.1367e+00],
E [-1.7139e+00, 4.0625e+00, 1.7402e+00, 2.3730e+00,
E 8.8828e+00, -2.4531e+00, -4.1055e+00, -8.4453e+00],
E [ 2.0117e+00, -7.2949e-01, 8.3594e+00, -6.8203e+00,
E 2.1523e+00, -7.4707e-01, 5.1484e+00, -7.6289e+00],
E [-2.5488e+00, 6.1602e+00, -3.5156e-01, 4.9492e+00,
E 8.3438e+00, 7.6465e-01, 2.7422e+00, 7.4688e+00],
E [ 5.7129e-01, -3.1914e+00, 1.6699e-01, -8.7188e+00,
E 7.3750e+00, 6.9609e+00, -4.1055e+00, -8.3672e+00]],
E
E [[ 7.6016e+00, -1.7578e+00, 3.0059e+00, 1.1602e+00,
E 4.7461e-01, -8.4688e+00, 7.5234e+00, 4.5625e+00],
E [-6.6172e+00, -6.8477e+00, 1.5381e+00, 8.8281e+00,
E -3.4883e+00, 2.7773e+00, 6.8750e+00, -2.8125e+00],
E [-3.1914e+00, 1.1426e+00, 8.8770e-01, -6.2852e+00,
E 6.8633e+00, 2.5586e+00, 6.3359e+00, -4.6562e+00],
E [ 3.1641e+00, -6.0391e+00, -3.9199e+00, -8.9219e+00,
E -8.5781e+00, 4.2734e+00, -3.5332e+00, 2.8125e+00],
E [-2.5840e+00, 4.9922e+00, -2.0215e+00, -4.7461e-01,
E 5.4844e+00, -7.8398e+00, 5.1172e+00, -8.2812e+00],
E [-8.5078e+00, -5.7031e+00, -5.9414e+00, -6.6250e+00,
E 4.2344e+00, -8.0312e+00, -1.3711e+00, -1.2129e+00]],
E
E [[-8.3438e+00, 1.3799e+00, 4.6680e+00, 8.1875e+00,
E 5.6406e+00, -6.1172e+00, -7.9375e+00, -8.6484e+00],
E [ 1.4590e+00, -9.0527e-01, -3.3672e+00, 1.9336e-01,
E -6.5547e+00, -2.3027e+00, -4.8867e+00, 3.5078e+00],
E [-8.9297e+00, -8.8906e+00, -8.8770e-01, -8.9648e-01,
E 7.1875e+00, 7.3047e+00, 4.4824e-01, 1.8281e+00],
E [ 4.8242e+00, 5.3711e+00, 6.1875e+00, 3.5234e+00,
E 1.6699e+00, 4.3945e-01, 3.4453e+00, -3.2695e+00],
E [ 5.9688e+00, 7.2266e+00, -3.5508e+00, 6.3125e+00,
E 8.2422e+00, 2.6289e+00, 5.4570e+00, -7.2852e+00],
E [-5.3438e+00, -3.1719e+00, 3.6836e+00, 6.6250e+00,
E 5.6172e+00, 5.4141e+00, 4.5781e+00, 3.1211e+00]],
E
E [[-7.7070e+00, 8.9766e+00, -2.1094e-01, 3.1992e+00,
E -5.8086e+00, 8.5234e+00, 5.7227e+00, 6.1602e+00],
E [-5.4766e+00, -3.6836e+00, 4.3164e+00, 8.8516e+00,
E -6.9961e+00, 5.0469e+00, 7.4805e+00, -4.2891e+00],
E [-6.6523e+00, 3.5508e+00, 6.9609e+00, 8.7891e-02,
E 2.8047e+00, 6.2578e+00, -7.9023e+00, -6.3203e+00],
E [ 6.1602e+00, 4.4844e+00, 7.7344e-01, 3.1113e+00,
E -3.4883e+00, 3.0859e+00, -2.9531e+00, 1.1426e-01],
E [ 2.2500e+00, 1.8281e+00, -6.5391e+00, -5.1172e+00,
E 7.3750e+00, -1.6523e+00, -5.5898e+00, -3.5859e+00],
E [ 4.2344e+00, -7.7188e+00, 2.4434e+00, -4.4297e+00,
E -3.4727e+00, 5.3164e+00, -5.1953e+00, -7.7422e+00]]],
E
E
E [[[-6.4688e+00, 3.3828e+00, -8.8828e+00, 8.8359e+00,
E -6.7500e+00, 3.0312e+00, 6.1250e+00, 3.0234e+00],
E [ 3.5078e+00, 1.8105e+00, -1.4678e+00, -7.2070e-01,
E 1.5029e+00, -4.9297e+00, -6.6367e+00, 1.9336e+00],
E [ 1.3271e+00, -3.5332e+00, 1.5996e+00, 3.4980e+00,
E 7.4688e+00, -8.3750e+00, 3.6641e+00, 7.8828e+00],
E [ 2.4785e+00, -8.3594e+00, -5.8906e+00, 6.3828e+00,
E -4.6406e+00, 2.8750e+00, -3.2520e-01, 4.6953e+00],
E [-3.9102e+00, -1.0107e+00, 3.9199e+00, -5.7305e+00,
E -5.8281e+00, -8.1875e+00, 2.8125e+00, -6.1602e+00],
E [-5.6328e+00, -3.9551e+00, -2.5391e+00, -2.5312e+00,
E 7.7344e+00, -5.9688e+00, 7.4531e+00, 8.9648e-01]],
E
E [[ 1.8809e+00, 1.6787e+00, 5.8984e+00, 7.7617e+00,
E -2.6895e+00, -2.1445e+00, -4.3945e-02, 1.6963e+00],
E [ 3.3320e+00, 3.5332e+00, -3.1719e+00, 6.5742e+00,
E -3.7344e+00, -8.9141e+00, -7.3672e+00, -2.6289e+00],
E [ 1.2305e-01, -2.1719e+00, 2.6016e+00, -5.3984e+00,
E -6.0645e-01, -3.8672e+00, 4.4824e-01, 5.6172e+00],
E [ 2.3730e-01, -3.9375e+00, 1.4062e-01, 3.0762e+00,
E 3.7539e+00, 5.7031e+00, 7.2969e+00, 2.0039e+00],
E [ 6.7227e+00, -3.1641e-01, 1.8369e+00, 3.7793e+00,
E 2.5742e+00, -2.4785e+00, 6.6094e+00, 5.1055e+00],
E [ 9.0527e-01, -7.2148e+00, 4.5703e-01, -4.4824e-01,
E -7.4707e-01, 3.2871e+00, 7.3906e+00, 4.6582e-01]],
E
E [[ 2.4180e+00, 2.9355e+00, 2.6367e-01, 1.1777e+00,
E 5.0098e-01, -8.0859e+00, 2.3281e+00, 4.5781e+00],
E [ 4.3516e+00, -6.7852e+00, -6.4141e+00, -3.1367e+00,
E 1.3799e+00, -4.1836e+00, -3.4629e+00, 5.8008e-01],
E [ 6.6172e+00, -6.3828e+00, 4.0625e+00, 6.3633e+00,
E 7.1992e+00, -8.1250e+00, 4.8340e-01, -6.0898e+00],
E [-1.6611e+00, 6.5938e+00, -5.5547e+00, 6.5234e+00,
E -1.3271e+00, -5.6602e+00, -8.2031e+00, 6.9453e+00],
E [-5.0781e+00, -8.0469e+00, -6.2227e+00, 1.4678e+00,
E 7.1289e+00, 8.6250e+00, 8.0781e+00, 3.8145e+00],
E [-5.1250e+00, -7.0938e+00, 1.9424e+00, -3.0938e+00,
E 4.6328e+00, -6.5664e+00, -1.4062e-01, -4.0078e+00]],
E
E [[ 5.0352e+00, -1.3184e-01, -1.3184e+00, -4.9141e+00,
E -1.6348e+00, -2.3828e+00, 5.2812e+00, 5.1250e+00],
E [-1.6436e+00, -8.7422e+00, -5.8516e+00, 8.0703e+00,
E 2.5234e+00, -8.2500e+00, 2.0469e+00, -2.4434e+00],
E [-6.2578e+00, -5.8008e+00, 6.8203e+00, 7.0156e+00,
E 7.3672e+00, -1.0547e+00, 2.3555e+00, -2.8750e+00],
E [-7.8594e+00, -1.9512e+00, 8.7188e+00, -2.0742e+00,
E 7.3047e+00, -5.9219e+00, -7.4258e+00, 7.0391e+00],
E [ 4.2109e+00, 7.8047e+00, 1.4238e+00, 6.8750e+00,
E 7.7969e+00, 8.3496e-01, -2.2070e+00, -4.5273e+00],
E [-5.8008e+00, -3.8672e-01, -5.9062e+00, -1.9775e+00,
E 3.8242e+00, 7.2070e-01, 1.9688e+00, -5.3867e+00]]],
E
E
E [[[-2.5488e+00, -3.8672e-01, -7.9727e+00, -4.7734e+00,
E -7.3203e+00, -3.8145e+00, -3.8242e+00, -3.2168e+00],
E [-8.0703e+00, -5.6523e+00, -2.1270e+00, -5.5820e+00,
E -6.8633e+00, -3.8672e+00, -1.3711e+00, -4.8945e+00],
E [-1.3975e+00, -7.9980e-01, 4.7188e+00, 1.8369e+00,
E -3.6992e+00, 5.5898e+00, -8.7891e-02, 8.3125e+00],
E [ 6.3281e-01, 8.7109e+00, 5.5703e+00, 2.7070e+00,
E 6.1094e+00, 6.1328e+00, 6.2305e+00, 5.1758e+00],
E [-1.4502e+00, 1.6260e+00, 7.5156e+00, 2.2148e+00,
E -8.8906e+00, -6.3828e+00, 6.5918e-01, 4.3945e-01],
E [-2.0918e+00, -7.6992e+00, 6.4141e+00, -7.9375e+00,
E 1.8457e+00, 2.4531e+00, -4.2812e+00, 6.0117e+00]],
E
E [[ 1.3623e+00, 2.0820e+00, -5.7031e+00, -8.2891e+00,
E -4.6836e+00, -5.8789e+00, 8.7969e+00, -8.7891e-01],
E [ 2.3906e+00, 5.0898e+00, 3.7793e-01, -1.7578e-01,
E 5.0273e+00, -7.9297e+00, 3.0586e+00, -2.7852e+00],
E [-6.1094e+00, 3.5859e+00, -2.9883e-01, -6.1719e+00,
E 9.6680e-01, -2.2422e+00, 8.9297e+00, 7.7344e-01],
E [-5.2031e+00, -2.7695e+00, -2.0312e+00, -2.7500e+00,
E 8.6133e-01, -4.2734e+00, -3.7266e+00, 1.2744e+00],
E [ 6.0898e+00, -6.7500e+00, 1.6523e+00, 2.1016e+00,
E -1.3535e+00, -1.1074e+00, 4.4141e+00, 6.2148e+00],
E [-1.8984e+00, -3.0586e+00, 7.1875e+00, -6.6250e+00,
E 4.5625e+00, -4.1758e+00, 9.9316e-01, 2.9707e+00]],
E
E [[-4.0156e+00, 8.2812e+00, -3.4277e-01, -5.8516e+00,
E 5.7031e+00, 5.3867e+00, 2.9258e+00, 6.0469e+00],
E [ 1.4766e+00, 2.9707e+00, -1.9951e+00, 2.2852e+00,
E -1.2129e+00, 6.4531e+00, 1.7402e+00, -7.1562e+00],
E [-6.4844e+00, 6.0742e+00, 6.6875e+00, -2.4785e+00,
E 4.1406e+00, 4.2266e+00, 8.9297e+00, 1.9336e-01],
E [ 4.9141e+00, 9.4922e-01, -6.4062e+00, -8.9531e+00,
E -4.1758e+00, -3.0234e+00, 7.6211e+00, -2.0215e+00],
E [-7.2266e+00, -6.1523e+00, -1.7578e+00, 6.8906e+00,
E -6.8633e+00, -6.1328e+00, -3.3398e-01, 4.0430e-01],
E [-2.3203e+00, 5.1875e+00, 5.4570e+00, 1.0547e-01,
E -1.0371e+00, 3.3320e+00, -5.8008e+00, 5.2812e+00]],
E
E [[ 2.9961e+00, 7.5508e+00, 6.9531e+00, 1.9336e+00,
E -8.3672e+00, 6.5469e+00, 6.7773e+00, 5.4844e+00],
E [ 3.9551e-01, 4.4219e+00, -6.6016e+00, 4.8672e+00,
E -7.1797e+00, 8.1562e+00, 7.5586e+00, 5.3984e+00],
E [-7.9180e+00, 7.9102e-02, 2.8828e+00, 1.6348e+00,
E -4.2344e+00, 5.8633e+00, -8.4844e+00, 6.6797e+00],
E [-1.1074e+00, -1.2217e+00, 8.3516e+00, -2.1367e+00,
E -5.1250e+00, 1.8984e+00, -1.0020e+00, 8.7891e-03],
E [ 4.7031e+00, 6.3008e+00, -1.6963e+00, -1.4941e-01,
E 6.2422e+00, 3.1211e+00, -3.5078e+00, 6.9531e+00],
E [ 5.8516e+00, 1.4414e+00, -2.3730e-01, -2.5586e+00,
E -8.2109e+00, -7.5391e+00, -5.8008e-01, -7.1875e+00]]]],
E dtype=float16)}
E Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[4,3,8] input_0, float16[4,4,6,8] input_1, float16[4,4,6,8] input_2) => (float16[4,4,3,8] _val_4)
E <float16 _val_3>
E {
E _val_3 = pkg.onnxscript.torch_lib._attention_scale (input_0)
E _val_4 = pkg.onnxscript.torch_lib._aten_scaled_dot_product_attention_no_mask_onnx <dropout_p: float = 0> (input_0, input_1, input_2, _val_3)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E _attention_scale (query) => (scale)
E {
E tmp = Shape (query)
E int64_m1 = Constant <value: tensor = int64 int64_m1 {-1}> ()
E tmp_subscripted = Gather <axis: int = 0> (tmp, int64_m1)
E embedding_size = CastLike (tmp_subscripted, query)
E const = Constant <value: tensor = float const {1}> ()
E tmp_0 = Sqrt (embedding_size)
E const_cast = CastLike (const, tmp_0)
E scale = Div (const_cast, tmp_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E _aten_scaled_dot_product_attention_no_mask_onnx <dropout_p>(query, key, value, scale) => (return_val)
E {
E key_shape = Shape (key)
E int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
E int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
E int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
E int64_9223372036854775807_1d = Constant <value: tensor = int64[1] int64_9223372036854775807_1d {9223372036854775807}> ()
E key_last_dim = Slice (key_shape, int64_m1_1d, int64_9223372036854775807_1d, int64_0_1d, int64_1_1d)
E int64_0_1d_0 = Constant <value: tensor = int64[1] int64_0_1d_0 {0}> ()
E int64_1_1d_1 = Constant <value: tensor = int64[1] int64_1_1d_1 {1}> ()
E int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
E int64_m1_1d_2 = Constant <value: tensor = int64[1] int64_m1_1d_2 {-1}> ()
E key_second_last_dim = Slice (key_shape, int64_m2_1d, int64_m1_1d_2, int64_0_1d_0, int64_1_1d_1)
E int64_0_1d_3 = Constant <value: tensor = int64[1] int64_0_1d_3 {0}> ()
E int64_1_1d_4 = Constant <value: tensor = int64[1] int64_1_1d_4 {1}> ()
E int64_m2_1d_5 = Constant <value: tensor = int64[1] int64_m2_1d_5 {-2}> ()
E key_first_dims = Slice (key_shape, int64_0_1d_3, int64_m2_1d_5, int64_0_1d_3, int64_1_1d_4)
E tmp = Constant <value_ints: ints = [-1]> ()
E key_squeezed_shape = Concat <axis: int = 0> (tmp, key_second_last_dim, key_last_dim)
E key_squeezed = Reshape (key, key_squeezed_shape)
E key_squeezed_transposed = Transpose <perm: ints = [0, 2, 1]> (key_squeezed)
E key_transposed_shape = Concat <axis: int = 0> (key_first_dims, key_last_dim, key_second_last_dim)
E key_transposed = Reshape (key_squeezed_transposed, key_transposed_shape)
E tmp_6 = Sqrt (scale)
E query_scaled = Mul (query, tmp_6)
E tmp_7 = Sqrt (scale)
E key_transposed_scaled = Mul (key_transposed, tmp_7)
E tmp_8 = MatMul (query_scaled, key_transposed_scaled)
E attn_weight = Softmax <axis: int = -1> (tmp_8)
E dropout_p = Constant <value_float: float = @dropout_p> ()
E attn_weight_9, _ = Dropout (attn_weight, dropout_p)
E return_val = MatMul (attn_weight_9, value)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__logaddexp2_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,5] input_0, float16[5,5] input_1) => (float16[5,5] _val_2) {
_val_2 = pkg.onnxscript.torch_lib.aten_logaddexp2 (input_0, input_1)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
aten_logaddexp2 (self, other) => (return_val)
{
const = Constant <value: tensor = float const {2}> ()
tmp = Pow (const, self)
const_0 = Constant <value: tensor = float const_0 {2}> ()
tmp_1 = Pow (const_0, other)
summation = Add (tmp, tmp_1)
tmp_2 = Log (summation)
const_3 = Constant <value: tensor = float const_3 {2}> ()
tmp_4 = Log (const_3)
tmp_5 = CastLike (tmp_4, self)
return_val = Div (tmp_2, tmp_5)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_logaddexp2, node name: aten_logaddexp2_0): [ShapeInferenceError] (op_type:Div, node name: n9): B has inconsistent type tensor(float16)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,5] input_0, float16[5,5] input_1) => (float16[5,5] _val_2) {
E _val_2 = pkg.onnxscript.torch_lib.aten_logaddexp2 (input_0, input_1)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E aten_logaddexp2 (self, other) => (return_val)
E {
E const = Constant <value: tensor = float const {2}> ()
E tmp = Pow (const, self)
E const_0 = Constant <value: tensor = float const_0 {2}> ()
E tmp_1 = Pow (const_0, other)
E summation = Add (tmp, tmp_1)
E tmp_2 = Log (summation)
E const_3 = Constant <value: tensor = float const_3 {2}> ()
E tmp_4 = Log (const_3)
E tmp_5 = CastLike (tmp_4, self)
E return_val = Div (tmp_2, tmp_5)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32 input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32 input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32 input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32 input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int32[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__linspace_tensor_overload_cpu_int64 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 9s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 2s]
Raw output
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 43 / 50 (86.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: 1.0 at index (7,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 43 / 50 (86.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: 1.0 at index (7,)
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16 input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16 input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__logit_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 16 / 125 (12.8%)
Greatest absolute difference: 0.000732421875 at index (1, 2, 2) (up to 1e-05 allowed)
Greatest relative difference: 0.10821533203125 at index (3, 3, 3) (up to 0.001 allowed)
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,5,5] input_0) => (float16[5,5,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib._aten_logit_clamp_onnx <eps: float = 0.2> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_logit_clamp_onnx <eps>(self) => (return_val)
{
const = Constant <value: tensor = float const {1}> ()
eps = Constant <value_float: float = @eps> ()
tmp = Sub (const, eps)
tmp_0 = LessOrEqual (self, tmp)
const_1 = Constant <value: tensor = float const_1 {1}> ()
eps_2 = Constant <value_float: float = @eps> ()
tmp_3 = Sub (const_1, eps_2)
temporary_self = Where (tmp_0, self, tmp_3)
eps_4 = Constant <value_float: float = @eps> ()
eps_4_cast = CastLike (eps_4, temporary_self)
tmp_5 = Less (temporary_self, eps_4_cast)
eps_6 = Constant <value_float: float = @eps> ()
eps_6_cast = CastLike (eps_6, temporary_self)
z = Where (tmp_5, eps_6_cast, temporary_self)
const_7 = Constant <value: tensor = float const_7 {1}> ()
const_7_cast = CastLike (const_7, z)
tmp_8 = Sub (const_7_cast, z)
tmp_9 = Div (z, tmp_8)
return_val = Log (tmp_9)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16 input_0) => (float16 _val_1) {
_val_1 = pkg.onnxscript.torch_lib._aten_logit_clamp_onnx <eps: float = 0.2> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_logit_clamp_onnx <eps>(self) => (return_val)
{
const = Constant <value: tensor = float const {1}> ()
eps = Constant <value_float: float = @eps> ()
tmp = Sub (const, eps)
tmp_0 = LessOrEqual (self, tmp)
const_1 = Constant <value: tensor = float const_1 {1}> ()
eps_2 = Constant <value_float: float = @eps> ()
tmp_3 = Sub (const_1, eps_2)
temporary_self = Where (tmp_0, self, tmp_3)
eps_4 = Constant <value_float: float = @eps> ()
eps_4_cast = CastLike (eps_4, temporary_self)
tmp_5 = Less (temporary_self, eps_4_cast)
eps_6 = Constant <value_float: float = @eps> ()
eps_6_cast = CastLike (eps_6, temporary_self)
z = Where (tmp_5, eps_6_cast, temporary_self)
const_7 = Constant <value: tensor = float const_7 {1}> ()
const_7_cast = CastLike (const_7, z)
tmp_8 = Sub (const_7_cast, z)
tmp_9 = Div (z, tmp_8)
return_val = Log (tmp_9)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 16 / 125 (12.8%)
E Greatest absolute difference: 0.000732421875 at index (1, 2, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.10821533203125 at index (3, 3, 3) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:_aten_logit_clamp_onnx, node name: _aten_logit_clamp_onnx_0): [ShapeInferenceError] (op_type:Where, node name: n7): Y has inconsistent type tensor(float)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,5,5] input_0) => (float16[5,5,5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib._aten_logit_clamp_onnx <eps: float = 0.2> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E _aten_logit_clamp_onnx <eps>(self) => (return_val)
E {
E const = Constant <value: tensor = float const {1}> ()
E eps = Constant <value_float: float = @eps> ()
E tmp = Sub (const, eps)
E tmp_0 = LessOrEqual (self, tmp)
E const_1 = Constant <value: tensor = float const_1 {1}> ()
E eps_2 = Constant <value_float: float = @eps> ()
E tmp_3 = Sub (const_1, eps_2)
E temporary_self = Where (tmp_0, self, tmp_3)
E eps_4 = Constant <value_float: float = @eps> ()
E eps_4_cast = CastLike (eps_4, temporary_self)
E tmp_5 = Less (temporary_self, eps_4_cast)
E eps_6 = Constant <value_float: float = @eps> ()
E eps_6_cast = CastLike (eps_6, temporary_self)
E z = Where (tmp_5, eps_6_cast, temporary_self)
E const_7 = Constant <value: tensor = float const_7 {1}> ()
E const_7_cast = CastLike (const_7, z)
E tmp_8 = Sub (const_7_cast, z)
E tmp_9 = Div (z, tmp_8)
E return_val = Log (tmp_9)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:_aten_logit_clamp_onnx, node name: _aten_logit_clamp_onnx_0): [ShapeInferenceError] (op_type:Where, node name: n7): Y has inconsistent type tensor(float)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16 input_0) => (float16 _val_1) {
E _val_1 = pkg.onnxscript.torch_lib._aten_logit_clamp_onnx <eps: float = 0.2> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E _aten_logit_clamp_onnx <eps>(self) => (return_val)
E {
E const = Constant <value: tensor = float const {1}> ()
E eps = Constant <value_float: float = @eps> ()
E tmp = Sub (const, eps)
E tmp_0 = LessOrEqual (self, tmp)
E const_1 = Constant <value: tensor = float const_1 {1}> ()
E eps_2 = Constant <value_float: float = @eps> ()
E tmp_3 = Sub (const_1, eps_2)
E temporary_self = Where (tmp_0, self, tmp_3)
E eps_4 = Constant <value_float: float = @eps> ()
E eps_4_cast = CastLike (eps_4, temporary_self)
E tmp_5 = Less (temporary_self, eps_4_cast)
E eps_6 = Constant <value_float: float = @eps> ()
E eps_6_cast = CastLike (eps_6, temporary_self)
E z = Where (tmp_5, eps_6_cast, temporary_self)
E const_7 = Constant <value: tensor = float const_7 {1}> ()
E const_7_cast = CastLike (const_7, z)
E tmp_8 = Sub (const_7_cast, z)
E tmp_9 = Div (z, tmp_8)
E return_val = Log (tmp_9)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_bool (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__any_dim_cpu_float32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_any_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (any_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_any_dim, node name: aten_any_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_any_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_any_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E any_true = ReduceMax <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (any_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__addbmm_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 9 / 50 (18.0%)
Greatest absolute difference: 0.09375 at index (2, 3) (up to 1e-05 allowed)
Greatest relative difference: 0.0338134765625 at index (4, 1) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 5 / 50 (10.0%)
Greatest absolute difference: 0.0625 at index (2, 7) (up to 1e-05 allowed)
Greatest relative difference: 0.01500701904296875 at index (1, 2) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 6 / 50 (12.0%)
Greatest absolute difference: 0.0078125 at index (3, 7) (up to 1e-05 allowed)
Greatest relative difference: 0.00559234619140625 at index (0, 8) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 5 / 50 (10.0%)
Greatest absolute difference: 0.015625 at index (4, 2) (up to 1e-05 allowed)
Greatest relative difference: 0.01526641845703125 at index (1, 2) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 12 / 50 (24.0%)
Greatest absolute difference: 0.0625 at index (3, 2) (up to 1e-05 allowed)
Greatest relative difference: 0.08935546875 at index (4, 7) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 7 / 50 (14.0%)
Greatest absolute difference: 0.013671875 at index (4, 5) (up to 1e-05 allowed)
Greatest relative difference: 0.00673675537109375 at index (2, 6) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 9 / 50 (18.0%)
E Greatest absolute difference: 0.09375 at index (2, 3) (up to 1e-05 allowed)
E Greatest relative difference: 0.0338134765625 at index (4, 1) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 5 / 50 (10.0%)
E Greatest absolute difference: 0.0625 at index (2, 7) (up to 1e-05 allowed)
E Greatest relative difference: 0.01500701904296875 at index (1, 2) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 6 / 50 (12.0%)
E Greatest absolute difference: 0.0078125 at index (3, 7) (up to 1e-05 allowed)
E Greatest relative difference: 0.00559234619140625 at index (0, 8) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 5 / 50 (10.0%)
E Greatest absolute difference: 0.015625 at index (4, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.01526641845703125 at index (1, 2) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 12 / 50 (24.0%)
E Greatest absolute difference: 0.0625 at index (3, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.08935546875 at index (4, 7) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 7 / 50 (14.0%)
E Greatest absolute difference: 0.013671875 at index (4, 5) (up to 1e-05 allowed)
E Greatest relative difference: 0.00673675537109375 at index (2, 6) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__log_softmax_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5] input_0) => (float16[5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 0, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_special_log_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = LogSoftmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 0, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_special_log_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = LogSoftmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 1, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_special_log_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = LogSoftmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = -1, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_special_log_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = LogSoftmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,10,5] input_0) => (float16[5,10,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 2, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_special_log_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = LogSoftmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,0,0] input_0) => (float16[5,0,0] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = -1, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_special_log_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = LogSoftmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16 input_0) => (float16 _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 0, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_special_log_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = LogSoftmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_special_log_softmax, node name: aten_special_log_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5] input_0) => (float16[5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 0, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_special_log_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = LogSoftmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_special_log_softmax, node name: aten_special_log_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 0, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_special_log_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = LogSoftmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_special_log_softmax, node name: aten_special_log_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 1, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_special_log_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = LogSoftmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_special_log_softmax, node name: aten_special_log_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = -1, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_special_log_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = LogSoftmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_special_log_softmax, node name: aten_special_log_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,10,5] input_0) => (float16[5,10,5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 2, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_special_log_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = LogSoftmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_special_log_softmax, node name: aten_special_log_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,0,0] input_0) => (float16[5,0,0] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = -1, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_special_log_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = LogSoftmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_special_log_softmax, node name: aten_special_log_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16 input_0) => (float16 _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_special_log_softmax <dim: int = 0, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_special_log_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = LogSoftmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__cross_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 1 / 15 (6.7%)
Greatest absolute difference: 0.00390625 at index (3, 0) (up to 1e-05 allowed)
Greatest relative difference: 0.0014905929565429688 at index (3, 0) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 5 / 75 (6.7%)
Greatest absolute difference: 0.015625 at index (1, 0, 4) (up to 1e-05 allowed)
Greatest relative difference: 0.00612640380859375 at index (3, 2, 1) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 2 / 15 (13.3%)
Greatest absolute difference: 0.0013427734375 at index (1, 0) (up to 1e-05 allowed)
Greatest relative difference: 0.006610870361328125 at index (1, 0) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 15 (6.7%)
E Greatest absolute difference: 0.00390625 at index (3, 0) (up to 1e-05 allowed)
E Greatest relative difference: 0.0014905929565429688 at index (3, 0) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 5 / 75 (6.7%)
E Greatest absolute difference: 0.015625 at index (1, 0, 4) (up to 1e-05 allowed)
E Greatest relative difference: 0.00612640380859375 at index (3, 2, 1) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 2 / 15 (13.3%)
E Greatest absolute difference: 0.0013427734375 at index (1, 0) (up to 1e-05 allowed)
E Greatest relative difference: 0.006610870361328125 at index (1, 0) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__matmul_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 1 / 5 (20.0%)
Greatest absolute difference: 0.01171875 at index (0,) (up to 1e-05 allowed)
Greatest relative difference: 0.002384185791015625 at index (0,) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 2 / 25 (8.0%)
Greatest absolute difference: 0.03125 at index (2, 3) (up to 1e-05 allowed)
Greatest relative difference: 0.004360198974609375 at index (2, 3) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 38 / 250 (15.2%)
Greatest absolute difference: 0.0625 at index (0, 3, 9) (up to 1e-05 allowed)
Greatest relative difference: 0.0582275390625 at index (3, 4, 6) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 5 (20.0%)
E Greatest absolute difference: 0.01171875 at index (0,) (up to 1e-05 allowed)
E Greatest relative difference: 0.002384185791015625 at index (0,) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 2 / 25 (8.0%)
E Greatest absolute difference: 0.03125 at index (2, 3) (up to 1e-05 allowed)
E Greatest relative difference: 0.004360198974609375 at index (2, 3) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 38 / 250 (15.2%)
E Greatest absolute difference: 0.0625 at index (0, 3, 9) (up to 1e-05 allowed)
E Greatest relative difference: 0.0582275390625 at index (3, 4, 6) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__nn_functional_scaled_dot_product_attention_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[4,3,8] input_0, float16[4,6,8] input_1, float16[4,6,8] input_2) => (float16[4,3,8] _val_5)
<float16 _val_3, float[3,6] _val_4>
{
_val_3 = pkg.onnxscript.torch_lib._attention_scale (input_0)
_val_4 = pkg.onnxscript.torch_lib._causal_attention_mask (input_0, input_1)
_val_5 = pkg.onnxscript.torch_lib._aten_scaled_dot_product_attention_float_mask_onnx <dropout_p: float = 0> (input_0, input_1, input_2, _val_4, _val_3)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_attention_scale (query) => (scale)
{
tmp = Shape (query)
int64_m1 = Constant <value: tensor = int64 int64_m1 {-1}> ()
tmp_subscripted = Gather <axis: int = 0> (tmp, int64_m1)
embedding_size = CastLike (tmp_subscripted, query)
const = Constant <value: tensor = float const {1}> ()
tmp_0 = Sqrt (embedding_size)
const_cast = CastLike (const, tmp_0)
scale = Div (const_cast, tmp_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_causal_attention_mask (query, key) => (attn_mask_10)
{
tmp = Shape (query)
int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
target_length = Slice (tmp, int64_m2_1d, int64_m1_1d, int64_0_1d, int64_1_1d)
tmp_0 = Shape (key)
int64_0_1d_1 = Constant <value: tensor = int64[1] int64_0_1d_1 {0}> ()
int64_1_1d_2 = Constant <value: tensor = int64[1] int64_1_1d_2 {1}> ()
int64_m2_1d_3 = Constant <value: tensor = int64[1] int64_m2_1d_3 {-2}> ()
int64_m1_1d_4 = Constant <value: tensor = int64[1] int64_m1_1d_4 {-1}> ()
source_length = Slice (tmp_0, int64_m2_1d_3, int64_m1_1d_4, int64_0_1d_1, int64_1_1d_2)
size = Concat <axis: int = 0> (target_length, source_length)
const = Constant <value: tensor = float const {1}> ()
attn_mask = Expand (const, size)
attn_mask_5 = Trilu <upper: int = 0> (attn_mask)
const_6 = Constant <value: tensor = float const_6 {0}> ()
const_6_cast = CastLike (const_6, attn_mask_5)
tmp_7 = Equal (attn_mask_5, const_6_cast)
tmp_8 = Constant <value_float: float = -inf> ()
const_9 = Constant <value: tensor = float const_9 {0}> ()
const_9_cast = CastLike (const_9, tmp_8)
attn_mask_10 = Where (tmp_7, tmp_8, const_9_cast)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_scaled_dot_product_attention_float_mask_onnx <dropout_p>(query, key, value, attn_mask, scale) => (return_val)
{
key_shape = Shape (key)
int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
int64_9223372036854775807_1d = Constant <value: tensor = int64[1] int64_9223372036854775807_1d {9223372036854775807}> ()
key_last_dim = Slice (key_shape, int64_m1_1d, int64_9223372036854775807_1d, int64_0_1d, int64_1_1d)
int64_0_1d_0 = Constant <value: tensor = int64[1] int64_0_1d_0 {0}> ()
int64_1_1d_1 = Constant <value: tensor = int64[1] int64_1_1d_1 {1}> ()
int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
int64_m1_1d_2 = Constant <value: tensor = int64[1] int64_m1_1d_2 {-1}> ()
key_second_last_dim = Slice (key_shape, int64_m2_1d, int64_m1_1d_2, int64_0_1d_0, int64_1_1d_1)
int64_0_1d_3 = Constant <value: tensor = int64[1] int64_0_1d_3 {0}> ()
int64_1_1d_4 = Constant <value: tensor = int64[1] int64_1_1d_4 {1}> ()
int64_m2_1d_5 = Constant <value: tensor = int64[1] int64_m2_1d_5 {-2}> ()
key_first_dims = Slice (key_shape, int64_0_1d_3, int64_m2_1d_5, int64_0_1d_3, int64_1_1d_4)
tmp = Constant <value_ints: ints = [-1]> ()
key_squeezed_shape = Concat <axis: int = 0> (tmp, key_second_last_dim, key_last_dim)
key_squeezed = Reshape (key, key_squeezed_shape)
key_squeezed_transposed = Transpose <perm: ints = [0, 2, 1]> (key_squeezed)
key_transposed_shape = Concat <axis: int = 0> (key_first_dims, key_last_dim, key_second_last_dim)
key_transposed = Reshape (key_squeezed_transposed, key_transposed_shape)
tmp_6 = Sqrt (scale)
query_scaled = Mul (query, tmp_6)
tmp_7 = Sqrt (scale)
key_transposed_scaled = Mul (key_transposed, tmp_7)
tmp_8 = MatMul (query_scaled, key_transposed_scaled)
tmp_9 = Add (tmp_8, attn_mask)
attn_weight = Softmax <axis: int = -1> (tmp_9)
dropout_p = Constant <value_float: float = @dropout_p> ()
attn_weight_10, _ = Dropout (attn_weight, dropout_p)
return_val = MatMul (attn_weight_10, value)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([[[ 2.04 , 2.672 , 4.613 , 1.248 , 3.639 , 4.5 ,
3.27 , 4.965 ],
[ 7.777 , 2.268 , -3.242 , -4.867 , 8.37 , -0.4746 ,
0.0967 , 2.89 ],
[-2.54 , -2.117 , -0.501 , -5.863 , -0.4658 , 3.578 ,
-3.137 , -5.73 ]],
[[ 0.06152, 1.063 , 3.05 , -3.066 , -8.22 , 5.688 ,
-2.875 , 2.77 ],
[ 1.556 , -4.26 , -2.574 , -1.371 , -8.65 , -2.504 ,
6.18 , 8.1 ],
[-4.598 , -0.58 , 1.266 , 1.758 , -0.8438 , 8.89 ,
6.355 , -4.957 ]],
[[-2.574 , -2.398 , -0.879 , -1.02 , 4.895 , 4.797 ,
2.215 , -5.582 ],
[ 5.21 , -1.248 , -6.758 , -2.777 , 7.156 , 8.086 ,
-5.055 , -8.92 ],
[-1.767 , -1.995 , 2.117 , 2.197 , -1.301 , 0.03516,
1.037 , -0.0791 ]],
[[ 7.03 , -5.723 , 0.5625 , -7.727 , -7.04 , 2.092 ,
-7.453 , 6.836 ],
[-1.512 , 2.469 , -8.45 , 1.898 , 7.496 , -1.74 ,
-2.021 , -2.953 ],
[ 4.043 , 8.16 , 5.35 , -8.086 , 0.8613 , -4.516 ,
-5.625 , -5.45 ]]], dtype=float16),
'input_1': array([[[ 8.85 , -1.775 , -4.457 , -4.824 , 8.58 , -2.777 ,
7.58 , 5.66 ],
[ 7.637 , -2.232 , 3.832 , 0.1934 , -0.2461 , 4.957 ,
-3.059 , -2.734 ],
[ 4.016 , -8.28 , 1.266 , 0.7383 , 0.677 , -4.992 ,
7.707 , -9. ],
[-8.56 , -2.988 , -2.707 , 6.777 , 3.91 , -5.062 ,
-1.266 , -4.72 ],
[ 7.023 , -8.71 , 3.05 , -8.17 , 0.624 , 4.836 ,
-7.656 , -6.812 ],
[-3.086 , -5.16 , -7.973 , -2.232 , 7.82 , 2.68 ,
-6.652 , 8.44 ]],
[[ 0.4658 , -6.934 , -5.59 , -0.3076 , 6.44 , -2.303 ,
7.242 , -5.484 ],
[-3.523 , -2.268 , 2.654 , -0.9316 , 1.811 , 2.004 ,
-1.512 , 7.99 ],
[-3.93 , -8.35 , -5.188 , -8.1 , 3.7 , 6.18 ,
-2.293 , -2.523 ],
[-1.925 , 2.68 , -8.15 , 7.46 , -1.995 , 2.936 ,
-1.459 , -5.188 ],
[-5.08 , 8.73 , 2.7 , -6.82 , -7.55 , 4.22 ,
-0.3604 , 2.936 ],
[-0.04395, -4.246 , -2.338 , 0.923 , 4.938 , -8.3 ,
-7.84 , -2.004 ]],
[[-1.099 , -7.797 , -7.39 , 3.516 , 2.89 , -2.11 ,
4.457 , 7.48 ],
[-0.3604 , -8.41 , -4.21 , 6.793 , -8.55 , 3.945 ,
-7.207 , -7.902 ],
[ 6.555 , -8.63 , 6.6 , 8.52 , 7.75 , -8.03 ,
-2.32 , 5.82 ],
[ 1.6 , -1.556 , -8.17 , 8.52 , 3.277 , 8.01 ,
4.562 , -1.099 ],
[-5.844 , -1.099 , 6.11 , -6.54 , 1.705 , 7.586 ,
1.705 , -3.146 ],
[-8.19 , -3.102 , 8.305 , -8.47 , -3.438 , 0.4395 ,
3.533 , 6.926 ]],
[[ 0.03516, 4.086 , -3.7 , -3.016 , 7.277 , -4.316 ,
3.55 , -1.644 ],
[ 4.5 , -3.34 , -6.96 , -4.402 , -5.97 , 0.3955 ,
-4.21 , 8.3 ],
[ 0.677 , 6.406 , 7.137 , 8.1 , 0.633 , -2.031 ,
-6.82 , -8.59 ],
[ 1.055 , -7.13 , -6.906 , 0.4834 , -5.934 , -8.07 ,
-1.705 , -8.586 ],
[ 5.027 , -6.047 , 0.2197 , -1.942 , 2.25 , -8.94 ,
-3.516 , 7.61 ],
[ 2.215 , 6.074 , -2.69 , -6.344 , -3.393 , -8.516 ,
-2.629 , -4.387 ]]], dtype=float16),
'input_2': array([[[-4.844 , -8.766 , 8.63 , -8.32 , 1.89 , 3.383 ,
-5.8 , -3.156 ],
[-4.387 , -2.865 , 2.734 , -1.248 , 0.05273, 0.01758,
5.47 , -0.9316 ],
[-2.418 , -5.82 , 6.594 , 4.457 , 8.83 , 2.398 ,
4.438 , -1.925 ],
[-2.514 , 7.75 , 0.12305, 1.679 , 8.65 , 5.54 ,
-4.746 , -8.766 ],
[-2.734 , 0.334 , 8.37 , 2.39 , 2.021 , -8.25 ,
4.156 , -7.902 ],
[-1.872 , -4.29 , -7.734 , 4.605 , 1.8545 , -8.79 ,
5.09 , 3.453 ]],
[[-8.42 , -6.96 , -8.05 , 1.274 , -8.03 , -7.004 ,
-8.03 , 4.12 ],
[-8.71 , -3.533 , 6.812 , 8.22 , 3.234 , -2.434 ,
-3.78 , 4.86 ],
[-5.273 , -3.621 , 4.543 , -2.926 , 2.469 , 2.805 ,
6.477 , 3.885 ],
[ 8.36 , -6.242 , -1.301 , 8.484 , 6.504 , 3.305 ,
2.531 , 3.832 ],
[-3.191 , -6.574 , 6.23 , 5.105 , 4.414 , -3.523 ,
-4.473 , 3.066 ],
[-1.6 , 3.91 , 7.285 , -5.934 , 5.33 , 5.83 ,
-1.775 , 1.195 ]],
[[-2.338 , -1.107 , -6.875 , -4.234 , 0.3428 , -6.996 ,
-4.19 , -0.923 ],
[ 1.951 , -8.95 , 2.82 , -4.895 , 6.426 , -8.35 ,
-8.98 , 7.438 ],
[-3.332 , -7.973 , -1.266 , 5.316 , -4.58 , 8.766 ,
-0.6855 , -3.965 ],
[ 3.867 , -7.305 , -1.564 , -2.725 , 3.438 , 0.2197 ,
3.814 , -7.49 ],
[ 2.629 , 5.66 , -6.145 , 3.594 , 1.028 , -1.661 ,
6.906 , -2.645 ],
[-7.03 , -4.332 , -8.016 , -7.777 , -4.094 , -9. ,
-8.22 , -3.262 ]],
[[-6.195 , 4.824 , 7.066 , 7.848 , -7.79 , 4.484 ,
7.62 , -1.582 ],
[ 8.805 , -6.734 , 5.906 , -1.081 , -2.945 , 8.92 ,
-8.92 , -6.715 ],
[ 8.07 , 5.703 , -7.496 , -1.116 , -8.89 , 1.468 ,
5.633 , 5.23 ],
[-6.188 , 2.795 , 5.38 , -6.117 , -2.11 , -2.18 ,
8.52 , -1.002 ],
[ 8.08 , 4.824 , 8.914 , 3.674 , 5.316 , 0.826 ,
7.17 , 5.098 ],
[-6.215 , 8.39 , -6.934 , 8.305 , -6.074 , 7.77 ,
-5.703 , 4.023 ]]], dtype=float16)}
Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[4,3,8] input_0, float16[4,6,8] input_1, float16[4,6,8] input_2) => (float16[4,3,8] _val_4)
<float16 _val_3>
{
_val_3 = pkg.onnxscript.torch_lib._attention_scale (input_0)
_val_4 = pkg.onnxscript.torch_lib._aten_scaled_dot_product_attention_no_mask_onnx <dropout_p: float = 0> (input_0, input_1, input_2, _val_3)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_attention_scale (query) => (scale)
{
tmp = Shape (query)
int64_m1 = Constant <value: tensor = int64 int64_m1 {-1}> ()
tmp_subscripted = Gather <axis: int = 0> (tmp, int64_m1)
embedding_size = CastLike (tmp_subscripted, query)
const = Constant <value: tensor = float const {1}> ()
tmp_0 = Sqrt (embedding_size)
const_cast = CastLike (const, tmp_0)
scale = Div (const_cast, tmp_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_scaled_dot_product_attention_no_mask_onnx <dropout_p>(query, key, value, scale) => (return_val)
{
key_shape = Shape (key)
int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
int64_9223372036854775807_1d = Constant <value: tensor = int64[1] int64_9223372036854775807_1d {9223372036854775807}> ()
key_last_dim = Slice (key_shape, int64_m1_1d, int64_9223372036854775807_1d, int64_0_1d, int64_1_1d)
int64_0_1d_0 = Constant <value: tensor = int64[1] int64_0_1d_0 {0}> ()
int64_1_1d_1 = Constant <value: tensor = int64[1] int64_1_1d_1 {1}> ()
int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
int64_m1_1d_2 = Constant <value: tensor = int64[1] int64_m1_1d_2 {-1}> ()
key_second_last_dim = Slice (key_shape, int64_m2_1d, int64_m1_1d_2, int64_0_1d_0, int64_1_1d_1)
int64_0_1d_3 = Constant <value: tensor = int64[1] int64_0_1d_3 {0}> ()
int64_1_1d_4 = Constant <value: tensor = int64[1] int64_1_1d_4 {1}> ()
int64_m2_1d_5 = Constant <value: tensor = int64[1] int64_m2_1d_5 {-2}> ()
key_first_dims = Slice (key_shape, int64_0_1d_3, int64_m2_1d_5, int64_0_1d_3, int64_1_1d_4)
tmp = Constant <value_ints: ints = [-1]> ()
key_squeezed_shape = Concat <axis: int = 0> (tmp, key_second_last_dim, key_last_dim)
key_squeezed = Reshape (key, key_squeezed_shape)
key_squeezed_transposed = Transpose <perm: ints = [0, 2, 1]> (key_squeezed)
key_transposed_shape = Concat <axis: int = 0> (key_first_dims, key_last_dim, key_second_last_dim)
key_transposed = Reshape (key_squeezed_transposed, key_transposed_shape)
tmp_6 = Sqrt (scale)
query_scaled = Mul (query, tmp_6)
tmp_7 = Sqrt (scale)
key_transposed_scaled = Mul (key_transposed, tmp_7)
tmp_8 = MatMul (query_scaled, key_transposed_scaled)
attn_weight = Softmax <axis: int = -1> (tmp_8)
dropout_p = Constant <value_float: float = @dropout_p> ()
attn_weight_9, _ = Dropout (attn_weight, dropout_p)
return_val = MatMul (attn_weight_9, value)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[4,4,3,8] input_0, float16[4,4,6,8] input_1, float16[4,4,6,8] input_2) => (float16[4,4,3,8] _val_5)
<float16 _val_3, float[3,6] _val_4>
{
_val_3 = pkg.onnxscript.torch_lib._attention_scale (input_0)
_val_4 = pkg.onnxscript.torch_lib._causal_attention_mask (input_0, input_1)
_val_5 = pkg.onnxscript.torch_lib._aten_scaled_dot_product_attention_float_mask_onnx <dropout_p: float = 0> (input_0, input_1, input_2, _val_4, _val_3)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_attention_scale (query) => (scale)
{
tmp = Shape (query)
int64_m1 = Constant <value: tensor = int64 int64_m1 {-1}> ()
tmp_subscripted = Gather <axis: int = 0> (tmp, int64_m1)
embedding_size = CastLike (tmp_subscripted, query)
const = Constant <value: tensor = float const {1}> ()
tmp_0 = Sqrt (embedding_size)
const_cast = CastLike (const, tmp_0)
scale = Div (const_cast, tmp_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_causal_attention_mask (query, key) => (attn_mask_10)
{
tmp = Shape (query)
int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
target_length = Slice (tmp, int64_m2_1d, int64_m1_1d, int64_0_1d, int64_1_1d)
tmp_0 = Shape (key)
int64_0_1d_1 = Constant <value: tensor = int64[1] int64_0_1d_1 {0}> ()
int64_1_1d_2 = Constant <value: tensor = int64[1] int64_1_1d_2 {1}> ()
int64_m2_1d_3 = Constant <value: tensor = int64[1] int64_m2_1d_3 {-2}> ()
int64_m1_1d_4 = Constant <value: tensor = int64[1] int64_m1_1d_4 {-1}> ()
source_length = Slice (tmp_0, int64_m2_1d_3, int64_m1_1d_4, int64_0_1d_1, int64_1_1d_2)
size = Concat <axis: int = 0> (target_length, source_length)
const = Constant <value: tensor = float const {1}> ()
attn_mask = Expand (const, size)
attn_mask_5 = Trilu <upper: int = 0> (attn_mask)
const_6 = Constant <value: tensor = float const_6 {0}> ()
const_6_cast = CastLike (const_6, attn_mask_5)
tmp_7 = Equal (attn_mask_5, const_6_cast)
tmp_8 = Constant <value_float: float = -inf> ()
const_9 = Constant <value: tensor = float const_9 {0}> ()
const_9_cast = CastLike (const_9, tmp_8)
attn_mask_10 = Where (tmp_7, tmp_8, const_9_cast)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["" : 18]
>
_aten_scaled_dot_product_attention_float_mask_onnx <dropout_p>(query, key, value, attn_mask, scale) => (return_val)
{
key_shape = Shape (key)
int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
int64_9223372036854775807_1d = Constant <value: tensor = int64[1] int64_9223372036854775807_1d {9223372036854775807}> ()
key_last_dim = Slice (key_shape, int64_m1_1d, int64_9223372036854775807_1d, int64_0_1d, int64_1_1d)
int64_0_1d_0 = Constant <value: tensor = int64[1] int64_0_1d_0 {0}> ()
int64_1_1d_1 = Constant <value: tensor = int64[1] int64_1_1d_1 {1}> ()
int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
int64_m1_1d_2 = Constant <value: tensor = int64[1] int64_m1_1d_2 {-1}> ()
key_second_last_dim = Slice (key_shape, int64_m2_1d, int64_m1_1d_2, int64_0_1d_0, int64_1_1d_1)
int64_0_1d_3 = Constant <value: tensor = int64[1] int64_0_1d_3 {0}> ()
int64_1_1d_4 = Constant <value: tensor = int64[1] int64_1_1d_4 {1}> ()
int64_m2_1d_5 = Constant <value: tensor = int64[1] int64_m2_1d_5 {-2}> ()
key_first_dims = Slice (key_shape, int64_0_1d_3, int64_m2_1d_5, int64_0_1d_3, int64_1_1d_4)
tmp = Constant <value_ints: ints = [-1]> ()
key_squeezed_shape = Concat <axis: int = 0> (tmp, key_second_last_dim, key_last_dim)
key_squeezed = Reshape (key, key_squeezed_shape)
key_squeezed_transposed = Transpose <perm: ints = [0, 2, 1]> (key_squeezed)
key_transposed_shape = Concat <axis: int = 0> (key_first_dims, key_last_dim, key_second_last_dim)
key_transposed = Reshape (key_squeezed_transposed, key_transposed_shape)
tmp_6 = Sqrt (scale)
query_scaled = Mul (query, tmp_6)
tmp_7 = Sqrt (scale)
key_transposed_scaled = Mul (key_transposed, tmp_7)
tmp_8 = MatMul (query_scaled, key_transposed_scaled)
tmp_9 = Add (tmp_8, attn_mask)
attn_weight = Softmax <axis: int = -1> (tmp_9)
dropout_p = Constant <value_float: float = @dropout_p> ()
attn_weight_10, _ = Dropout (attn_weight, dropout_p)
return_val = MatMul (attn_weight_10, value)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
RuntimeError: ONNX Runtime failed to evaluate:
Inputs:
{'input_0': array([[[[ 1.283 , -8.02 , -3.604 , 4.895 , -0.8877 , 4.332 ,
4.43 , -4.836 ],
[-1.661 , 7.812 , -6.625 , 8.734 , -5.31 , 7.875 ,
-2.795 , 3.217 ],
[ 2.453 , 2.479 , -8.66 , 8.42 , -7.348 , 1.433 ,
-0.2725 , 8.69 ]],
[[ 4.703 , -4.297 , 8.8 , 0.02637, 7.33 , -6.46 ,
8.37 , -5.203 ],
[-4.867 , 1.582 , 2.172 , -3.488 , -6.555 , -1.301 ,
-3.902 , -1.424 ],
[-6.777 , -7.418 , 7.285 , -3.023 , -1.758 , 4.465 ,
6.68 , -2.855 ]],
[[-4.035 , 2.855 , 3.129 , 7.242 , 5.703 , -3.031 ,
-5.57 , 5.4 ],
[ 8.2 , 0.7383 , 2.777 , -7.145 , 4.516 , -5.633 ,
6.062 , -6.004 ],
[ 7.156 , 8.46 , 8.67 , -1.591 , 0.3252 , 8.875 ,
4.484 , -5.316 ]],
[[-2.408 , 1.477 , 8.47 , 7.98 , 3.006 , 7.25 ,
-6.32 , 4.754 ],
[-2.84 , -1.371 , -4.29 , 0.9756 , 3.604 , 8.31 ,
-7.2 , 1.617 ],
[ 3.348 , -4.65 , 3.322 , 0.4043 , 7.777 , 6.496 ,
4.836 , 4.633 ]]],
[[[-4.58 , 0.1846 , -7.137 , -6.285 , -3.2 , 8.5 ,
-2.39 , -6.617 ],
[ 5.008 , 8.914 , 7.016 , -0.7646 , 1.767 , -8.73 ,
-5.117 , -7.586 ],
[-2.268 , -6.777 , -4.43 , -0.4219 , 5.71 , 4.21 ,
-8.92 , -2.629 ]],
[[-4.57 , -2.11 , 7.34 , 4.914 , -5.176 , 0.967 ,
-7.664 , 5.57 ],
[-0.949 , -1.371 , 0.8877 , -2.39 , 7.312 , 1.67 ,
6.4 , -4.062 ],
[ 7.06 , 0.703 , -4.71 , -1.143 , 0.7646 , 1.696 ,
-8.09 , 6.875 ]],
[[ 0.835 , 3.709 , 7.82 , 1.731 , 0. , -0.1582 ,
-5.43 , 3.312 ],
[-7.54 , -4.535 , 5.598 , -6.258 , 5.203 , 3.664 ,
-2.303 , -3.023 ],
[-4.816 , -8.37 , 1.23 , -3.895 , -6.707 , 2.98 ,
2.7 , -6.855 ]],
[[ 4.516 , 2.953 , 5.047 , -4.08 , 0.659 , 7.727 ,
-7.47 , -8.05 ],
[-7.4 , -7.32 , -8.44 , 7.453 , -0.545 , 4.156 ,
6.875 , 0.87 ],
[ 4.773 , -1.415 , 1.116 , 3.965 , 8.02 , -5.766 ,
-1.529 , -8.63 ]]],
[[[ 7.76 , 3.674 , -4.72 , -8.32 , -1.749 , 7.03 ,
2.363 , -3.086 ],
[-2.848 , -2.338 , 7.902 , -2.61 , 2.76 , -0.879 ,
7.47 , -1.081 ],
[-1.195 , -0.2812 , 8.3 , 1.468 , -6.03 , 8.89 ,
-7.312 , 3.973 ]],
[[-6.523 , 2.945 , -5.582 , -0.2197 , -4.395 , 2.102 ,
7.305 , 8.414 ],
[ 4.234 , -3.217 , -2.715 , 5.195 , -1.178 , 4.867 ,
4.016 , 0.7734 ],
[ 5.793 , 0.826 , -5.035 , -5.246 , 1.318 , 4.508 ,
3.297 , 0.1846 ]],
[[-1.248 , -6.258 , 8.75 , -1.626 , -4.445 , 1.802 ,
8.36 , -5.71 ],
[-5.047 , 5.492 , 6.883 , -0.0879 , -3.78 , 1.564 ,
1.837 , -4.613 ],
[-4.93 , 7.375 , 1.081 , 8.72 , -8.016 , 0.0967 ,
1.099 , 4.957 ]],
[[-6.1 , 7.91 , 2.479 , -7.777 , 3.516 , -1.081 ,
0.8438 , -4.465 ],
[-4.008 , 8.11 , -1.573 , 0.5977 , -7.973 , -1.204 ,
0.51 , 0.2812 ],
[ 2.785 , -8.57 , 7.727 , 4.29 , -8.84 , -2.629 ,
-7.277 , 7.82 ]]],
[[[ 3.945 , 8.695 , -4.094 , 5.96 , -5.035 , -6.47 ,
1.23 , 0.7295 ],
[ 6.09 , -5.57 , 5.188 , -7.117 , 4.613 , -7.117 ,
3.533 , -7.883 ],
[ 7.215 , 4.184 , -2.328 , -5.457 , 0.2461 , 6.953 ,
-6.04 , -1.705 ]],
[[-2.734 , -5.836 , -4.008 , 3.438 , -7.094 , 5.035 ,
5.87 , -7.234 ],
[-8.86 , -6.18 , -4.457 , 5. , 2.848 , 3.613 ,
2.785 , -3.023 ],
[ 8.47 , 0.712 , 4.156 , 4.105 , -5.273 , 8.3 ,
6.414 , 6.047 ]],
[[ 1.784 , 5.117 , -0.05273, -5.61 , -2.172 , -8.15 ,
3.023 , 7.047 ],
[-7.18 , 4.508 , 5.582 , 6.953 , -3.86 , -7.55 ,
-8.81 , -7.656 ],
[ 8.24 , 3.85 , 2.584 , -7.086 , -3.129 , 4.344 ,
-6.99 , -8.836 ]],
[[ 8.664 , -4.15 , -0.659 , -7.707 , 0.9404 , -5.47 ,
-3.77 , 4.234 ],
[-5.78 , 7.32 , 3.629 , 2.707 , -1.96 , -0.9404 ,
7.33 , 1.169 ],
[ 6.312 , 2.479 , 6.83 , -8.37 , -4.78 , 3.086 ,
-4.086 , 2.855 ]]]], dtype=float16),
'input_1': array([[[[-2.4609e+00, 3.8848e+00, -8.1328e+00, 5.0977e-01,
-4.5430e+00, -6.7422e+00, -5.3789e+00, 3.9648e+00],
[ 5.6250e-01, -3.7793e+00, 1.3447e+00, 8.6484e+00,
-6.6719e+00, -1.7930e+00, 6.8555e-01, 2.7598e+00],
[-3.1914e+00, -6.8555e-01, -4.0859e+00, -9.4922e-01,
-1.1777e+00, 2.1719e+00, 6.9336e+00, -1.3799e+00],
[-3.6484e+00, -5.3711e+00, -8.7891e+00, 8.8281e+00,
-6.5117e+00, 3.9375e+00, -1.2656e+00, -6.3633e+00],
[ 5.8887e-01, 5.2734e-02, -1.8281e+00, 1.1953e+00,
1.4326e+00, -8.2812e+00, 7.8750e+00, 5.7031e+00],
[ 3.6836e+00, 6.3281e-01, 2.0742e+00, -8.6016e+00,
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E 0.1934 , -1.582 ]],
E
E [[-4.035 , 7.207 , -8.58 , -4.95 , -3.824 , -6.055 ,
E 8.3 , 3.137 ],
E [ 1.063 , 3.895 , -5.85 , 5.28 , -5.97 , -7.312 ,
E 3.648 , 3.305 ],
E [ 4.062 , -2.363 , 5.8 , 1.274 , -4.81 , -1.731 ,
E -1.187 , -3.27 ],
E [-5.42 , -6.496 , 7.883 , -1.529 , -8.03 , -3.559 ,
E 7.72 , -2.04 ],
E [-8.57 , 0.11426, 1.169 , -3.84 , -3.77 , 3.006 ,
E -3.121 , 2.504 ],
E [ 8.2 , -6.25 , -3.684 , -6.4 , 5.316 , -4.008 ,
E 8.94 , 1.433 ]],
E
E [[-1.696 , 4.36 , -3.648 , -8.87 , 3.016 , 0.677 ,
E 3.031 , -8.375 ],
E [-6.637 , -6.484 , -8.43 , -1.274 , -8.414 , 1.6875 ,
E 0.4746 , -0.3955 ],
E [ 3.322 , -0.4482 , 1.468 , 3.586 , -4.543 , 4.71 ,
E 5.64 , -1.652 ],
E [ 0.8877 , -1.222 , -2.031 , 7.094 , 7.074 , -5.3 ,
E 8.46 , -2.197 ],
E [-4.81 , -0.87 , -7.953 , 8.03 , 6.523 , 2.549 ,
E -4.72 , 1.406 ],
E [ 5.906 , -8.32 , 0.3252 , 3.453 , -5.133 , 4.74 ,
E -4.95 , -3.629 ]]]], dtype=float16)}
E Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[4,4,3,8] input_0, float16[4,4,6,8] input_1, float16[4,4,6,8] input_2, float16[3,6] attn_mask) => (float16[4,4,3,8] _val_5)
E <float16 _val_4>
E {
E _val_4 = pkg.onnxscript.torch_lib._attention_scale (input_0)
E _val_5 = pkg.onnxscript.torch_lib._aten_scaled_dot_product_attention_float_mask_onnx <dropout_p: float = 0> (input_0, input_1, input_2, attn_mask, _val_4)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E _attention_scale (query) => (scale)
E {
E tmp = Shape (query)
E int64_m1 = Constant <value: tensor = int64 int64_m1 {-1}> ()
E tmp_subscripted = Gather <axis: int = 0> (tmp, int64_m1)
E embedding_size = CastLike (tmp_subscripted, query)
E const = Constant <value: tensor = float const {1}> ()
E tmp_0 = Sqrt (embedding_size)
E const_cast = CastLike (const, tmp_0)
E scale = Div (const_cast, tmp_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["" : 18]
E >
E _aten_scaled_dot_product_attention_float_mask_onnx <dropout_p>(query, key, value, attn_mask, scale) => (return_val)
E {
E key_shape = Shape (key)
E int64_0_1d = Constant <value: tensor = int64[1] int64_0_1d {0}> ()
E int64_1_1d = Constant <value: tensor = int64[1] int64_1_1d {1}> ()
E int64_m1_1d = Constant <value: tensor = int64[1] int64_m1_1d {-1}> ()
E int64_9223372036854775807_1d = Constant <value: tensor = int64[1] int64_9223372036854775807_1d {9223372036854775807}> ()
E key_last_dim = Slice (key_shape, int64_m1_1d, int64_9223372036854775807_1d, int64_0_1d, int64_1_1d)
E int64_0_1d_0 = Constant <value: tensor = int64[1] int64_0_1d_0 {0}> ()
E int64_1_1d_1 = Constant <value: tensor = int64[1] int64_1_1d_1 {1}> ()
E int64_m2_1d = Constant <value: tensor = int64[1] int64_m2_1d {-2}> ()
E int64_m1_1d_2 = Constant <value: tensor = int64[1] int64_m1_1d_2 {-1}> ()
E key_second_last_dim = Slice (key_shape, int64_m2_1d, int64_m1_1d_2, int64_0_1d_0, int64_1_1d_1)
E int64_0_1d_3 = Constant <value: tensor = int64[1] int64_0_1d_3 {0}> ()
E int64_1_1d_4 = Constant <value: tensor = int64[1] int64_1_1d_4 {1}> ()
E int64_m2_1d_5 = Constant <value: tensor = int64[1] int64_m2_1d_5 {-2}> ()
E key_first_dims = Slice (key_shape, int64_0_1d_3, int64_m2_1d_5, int64_0_1d_3, int64_1_1d_4)
E tmp = Constant <value_ints: ints = [-1]> ()
E key_squeezed_shape = Concat <axis: int = 0> (tmp, key_second_last_dim, key_last_dim)
E key_squeezed = Reshape (key, key_squeezed_shape)
E key_squeezed_transposed = Transpose <perm: ints = [0, 2, 1]> (key_squeezed)
E key_transposed_shape = Concat <axis: int = 0> (key_first_dims, key_last_dim, key_second_last_dim)
E key_transposed = Reshape (key_squeezed_transposed, key_transposed_shape)
E tmp_6 = Sqrt (scale)
E query_scaled = Mul (query, tmp_6)
E tmp_7 = Sqrt (scale)
E key_transposed_scaled = Mul (key_transposed, tmp_7)
E tmp_8 = MatMul (query_scaled, key_transposed_scaled)
E tmp_9 = Add (tmp_8, attn_mask)
E attn_weight = Softmax <axis: int = -1> (tmp_9)
E dropout_p = Constant <value_float: float = @dropout_p> ()
E attn_weight_10, _ = Dropout (attn_weight, dropout_p)
E return_val = MatMul (attn_weight_10, value)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__native_batch_norm_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Output 0 mismatch
AssertionError: Output 0 mismatch
AssertionError: Output 0 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 10 / 125 (8.0%)
E Greatest absolute difference: 0.002197265625 at index (2, 1, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.01470947265625 at index (1, 0, 0) (up to 0.001 allowed)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
raise AssertionError(f"Output {j} mismatch") from e
E AssertionError: Output 0 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 3 (33.3%)
E Greatest absolute difference: 0.000732421875 at index (1, 0) (up to 1e-05 allowed)
E Greatest relative difference: 0.0014848709106445312 at index (1, 0) (up to 0.001 allowed)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
raise AssertionError(f"Output {j} mismatch") from e
E AssertionError: Output 0 mismatch
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 2 / 72 (2.8%)
E Greatest absolute difference: 0.000732421875 at index (0, 0, 0, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.0090484619140625 at index (1, 0, 0, 0) (up to 0.001 allowed)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:280: in run_test_output_match
raise AssertionError(f"Output {j} mismatch") from e
E AssertionError: Output 0 mismatch
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__softmax_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5] input_0) => (float16[5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = 0, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = Softmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = 0, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = Softmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = 1, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = Softmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = -1, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = Softmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,10,5] input_0) => (float16[5,10,5] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = 2, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = Softmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16[5,0,0] input_0) => (float16[5,0,0] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = -1, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = Softmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float16 input_0) => (float16 _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = 0, dtype: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_softmax <dim>(self) => (result_6)
{
self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
tmp = Constant <value_ints: ints = [0]> ()
self_0 = Unsqueeze (self, tmp)
}, else_branch: graph = elseGraph_8 () => ( self_1) {
self_1 = Identity (self)
}>
result = Softmax <axis: int = @dim> (self_2)
result_3 = Cast <to: int = @dtype> (result)
result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
result_4 = Squeeze (result_3)
}, else_branch: graph = elseGraph_12 () => ( result_5) {
result_5 = Identity (result_3)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_softmax, node name: aten_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5] input_0) => (float16[5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = 0, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = Softmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_softmax, node name: aten_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = 0, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = Softmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_softmax, node name: aten_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = 1, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = Softmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_softmax, node name: aten_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,5] input_0) => (float16[5,5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = -1, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = Softmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_softmax, node name: aten_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,10,5] input_0) => (float16[5,10,5] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = 2, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = Softmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_softmax, node name: aten_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16[5,0,0] input_0) => (float16[5,0,0] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = -1, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = Softmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_softmax, node name: aten_softmax_0): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (float16 input_0) => (float16 _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_softmax <dim: int = 0, dtype: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_softmax <dim>(self) => (result_6)
E {
E self_is_scalar = pkg.onnxscript.torch_lib.common.IsScalar (self)
E self_2 = If (self_is_scalar) <then_branch: graph = thenGraph_8 () => ( self_0) {
E tmp = Constant <value_ints: ints = [0]> ()
E self_0 = Unsqueeze (self, tmp)
E }, else_branch: graph = elseGraph_8 () => ( self_1) {
E self_1 = Identity (self)
E }>
E result = Softmax <axis: int = @dim> (self_2)
E result_3 = Cast <to: int = @dtype> (result)
E result_6 = If (self_is_scalar) <then_branch: graph = thenGraph_12 () => ( result_4) {
E result_4 = Squeeze (result_3)
E }, else_branch: graph = elseGraph_12 () => ( result_5) {
E result_5 = Identity (result_3)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__all_dim_cpu_bool (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 1s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 1s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
_val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
}
<
domain: "pkg.onnxscript.torch_lib",
opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
>
aten_all_dim <dim>(self) => (result_1)
{
cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
result = Cast <to: int = 9> (self)
}, else_branch: graph = elseGraph_5 () => ( result_0) {
self_bool = Cast <to: int = 9> (self)
self_int = Cast <to: int = 7> (self_bool)
dim = Constant <value_int: int = @dim> ()
tmp = Constant <value_ints: ints = [-1]> ()
dims = Reshape (dim, tmp)
all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
result_0 = Cast <to: int = 9> (all_true)
}>
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,5] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,5] input_0) => (bool[1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, 1, 2, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool[1,2,1,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [0, -1], keepdim: int = 1> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:aten_all_dim, node name: aten_all_dim_0): [ShapeInferenceError] Inference error(s): (op_type:If, node name: n1): [ShapeInferenceError] Inference error(s): (op_type:Constant, node name: n2): [ShapeInferenceError] Attribute 'value_int' expect an integer.
E (op_type:Reshape, node name: n4): [TypeInferenceError] Input 0 expected to have type but instead is null
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["pkg.onnxscript.torch_lib" : 1, "" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (bool[3,2,1,2] input_0) => (bool[3,1] _val_1) {
E _val_1 = pkg.onnxscript.torch_lib.aten_all_dim <dim: ints = [1, 3], keepdim: int = 0> (input_0)
E }
E <
E domain: "pkg.onnxscript.torch_lib",
E opset_import: ["pkg.onnxscript.torch_lib.common" : 1,"" : 18]
E >
E aten_all_dim <dim>(self) => (result_1)
E {
E cond = pkg.onnxscript.torch_lib.common.IsScalar (self)
E result_1 = If (cond) <then_branch: graph = thenGraph_5 () => ( result) {
E result = Cast <to: int = 9> (self)
E }, else_branch: graph = elseGraph_5 () => ( result_0) {
E self_bool = Cast <to: int = 9> (self)
E self_int = Cast <to: int = 7> (self_bool)
E dim = Constant <value_int: int = @dim> ()
E tmp = Constant <value_ints: ints = [-1]> ()
E dims = Reshape (dim, tmp)
E all_true = ReduceMin <keepdims: int = @keepdim> (self_int, dims)
E result_0 = Cast <to: int = 9> (all_true)
E }>
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
3 out of 9 runs failed: test_output_match_opinfo__ops_aten_native_group_norm_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
Failed: Unexpected success
Unexpected success
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__nn_functional_scaled_dot_product_attention_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 4s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 3s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 2s]
Raw output
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
<
ir_version: 7,
opset_import: ["" : 14]
>
node_graph (float16[4,3,6] input0, float[3,6] input1) => (float16[4,3,6] output0) {
output0 = Add (input0, input1)
}
AssertionError: Tensor-likes are not close!
Mismatched elements: 18 / 96 (18.8%)
Greatest absolute difference: 0.0390625 at index (1, 2, 0) (up to 1e-05 allowed)
Greatest relative difference: 0.052001953125 at index (1, 2, 2) (up to 0.001 allowed)
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
<
ir_version: 7,
opset_import: ["" : 14]
>
node_graph (float16[4,4,3,6] input0, float[3,6] input1) => (float16[4,4,3,6] output0) {
output0 = Add (input0, input1)
}
AssertionError: Tensor-likes are not close!
Mismatched elements: 56 / 384 (14.6%)
Greatest absolute difference: 0.03515625 at index (1, 0, 1, 5) (up to 1e-05 allowed)
Greatest relative difference: 0.11883544921875 at index (2, 0, 0, 0) (up to 0.001 allowed)
onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
<
ir_version: 7,
opset_import: ["" : 14]
>
node_graph (float16[4,4,3,6] input0, float[3,6] input1) => (float16[4,4,3,6] output0) {
output0 = Add (input0, input1)
}
AssertionError: Tensor-likes are not close!
Mismatched elements: 51 / 384 (13.3%)
Greatest absolute difference: 0.102294921875 at index (3, 2, 0, 2) (up to 1e-05 allowed)
Greatest relative difference: 4.1796875 at index (0, 0, 0, 2) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 48 / 384 (12.5%)
Greatest absolute difference: 0.12890625 at index (3, 2, 1, 0) (up to 1e-05 allowed)
Greatest relative difference: 0.1820068359375 at index (0, 2, 0, 3) (up to 0.001 allowed)
onnxscript/evaluator.py:476: in _call_ort
session = ort.InferenceSession(
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:419: in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:454: in _create_inference_session
sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Type Error: Type parameter (T) of Optype (Add) bound to different types (tensor(float16) and tensor(float) in node ().
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript/values.py:573: in __call__
return self.func(*args, **kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1689: in aten_scaled_dot_product_attention
return _aten_scaled_dot_product_attention_float_mask_onnx(
onnxscript/values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1913: in _aten_scaled_dot_product_attention_float_mask_onnx
op.Add(op.MatMul(query_scaled, key_transposed_scaled), attn_mask),
onnxscript/onnx_opset/_impl/opset14.py:82: in Add
return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:480: in _call_ort
raise EagerModeError(
E onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
E <
E ir_version: 7,
E opset_import: ["" : 14]
E >
E node_graph (float16[4,3,6] input0, float[3,6] input1) => (float16[4,3,6] output0) {
E output0 = Add (input0, input1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 18 / 96 (18.8%)
E Greatest absolute difference: 0.0390625 at index (1, 2, 0) (up to 1e-05 allowed)
E Greatest relative difference: 0.052001953125 at index (1, 2, 2) (up to 0.001 allowed)
onnxscript/evaluator.py:476: in _call_ort
session = ort.InferenceSession(
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:419: in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:454: in _create_inference_session
sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Type Error: Type parameter (T) of Optype (Add) bound to different types (tensor(float16) and tensor(float) in node ().
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript/values.py:573: in __call__
return self.func(*args, **kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1689: in aten_scaled_dot_product_attention
return _aten_scaled_dot_product_attention_float_mask_onnx(
onnxscript/values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1913: in _aten_scaled_dot_product_attention_float_mask_onnx
op.Add(op.MatMul(query_scaled, key_transposed_scaled), attn_mask),
onnxscript/onnx_opset/_impl/opset14.py:82: in Add
return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:480: in _call_ort
raise EagerModeError(
E onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
E <
E ir_version: 7,
E opset_import: ["" : 14]
E >
E node_graph (float16[4,4,3,6] input0, float[3,6] input1) => (float16[4,4,3,6] output0) {
E output0 = Add (input0, input1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 56 / 384 (14.6%)
E Greatest absolute difference: 0.03515625 at index (1, 0, 1, 5) (up to 1e-05 allowed)
E Greatest relative difference: 0.11883544921875 at index (2, 0, 0, 0) (up to 0.001 allowed)
onnxscript/evaluator.py:476: in _call_ort
session = ort.InferenceSession(
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:419: in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:454: in _create_inference_session
sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
E onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Type Error: Type parameter (T) of Optype (Add) bound to different types (tensor(float16) and tensor(float) in node ().
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:584: in executor
return function(*args, **kwargs)
onnxscript/values.py:573: in __call__
return self.func(*args, **kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1689: in aten_scaled_dot_product_attention
return _aten_scaled_dot_product_attention_float_mask_onnx(
onnxscript/values.py:525: in __call__
return evaluator.default().eval_function(self, args, kwargs)
onnxscript/evaluator.py:309: in eval_function
result = function.function(*adapted_args, **adapted_kwargs)
onnxscript/function_libs/torch_lib/ops/nn.py:1913: in _aten_scaled_dot_product_attention_float_mask_onnx
op.Add(op.MatMul(query_scaled, key_transposed_scaled), attn_mask),
onnxscript/onnx_opset/_impl/opset14.py:82: in Add
return op(*self._prepare_inputs(schema, A, B))
onnxscript/values.py:303: in __call__
return evaluator.default().eval(schema, args, kwargs)
onnxscript/evaluator.py:196: in eval
outputs = self._eval(schema, inputs, attributes, closure)
onnxscript/evaluator.py:510: in _eval
return _call_ort(schema, inputs, attributes, closure)
onnxscript/evaluator.py:480: in _call_ort
raise EagerModeError(
E onnxscript.evaluator.EagerModeError: Unable to create onnxruntime InferenceSession for executing .Add op with onnx model
E <
E ir_version: 7,
E opset_import: ["" : 14]
E >
E node_graph (float16[4,4,3,6] input0, float[3,6] input1) => (float16[4,4,3,6] output0) {
E output0 = Add (input0, input1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 51 / 384 (13.3%)
E Greatest absolute difference: 0.102294921875 at index (3, 2, 0, 2) (up to 1e-05 allowed)
E Greatest relative difference: 4.1796875 at index (0, 0, 0, 2) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 48 / 384 (12.5%)
E Greatest absolute difference: 0.12890625 at index (3, 2, 1, 0) (up to 1e-05 allowed)
E Greatest relative difference: 0.1820068359375 at index (0, 2, 0, 3) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__logaddexp_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 6 / 250 (2.4%)
Greatest absolute difference: 0.00048828125 at index (3, 1, 1) (up to 1e-05 allowed)
Greatest relative difference: 0.0264892578125 at index (0, 4, 3) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 5 / 250 (2.0%)
Greatest absolute difference: 0.00048828125 at index (2, 1, 2) (up to 1e-05 allowed)
Greatest relative difference: 0.0095367431640625 at index (0, 1, 0) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 4 / 500 (0.8%)
Greatest absolute difference: 0.00048828125 at index (1, 1, 0) (up to 1e-05 allowed)
Greatest relative difference: 1.0 at index (2, 1, 0) (up to 0.001 allowed)
AssertionError: Tensor-likes are not close!
Mismatched elements: 23 / 500 (4.6%)
Greatest absolute difference: 0.0009765625 at index (6, 6, 2) (up to 1e-05 allowed)
Greatest relative difference: 0.01522064208984375 at index (1, 2, 2) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 6 / 250 (2.4%)
E Greatest absolute difference: 0.00048828125 at index (3, 1, 1) (up to 1e-05 allowed)
E Greatest relative difference: 0.0264892578125 at index (0, 4, 3) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 5 / 250 (2.0%)
E Greatest absolute difference: 0.00048828125 at index (2, 1, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.0095367431640625 at index (0, 1, 0) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 4 / 500 (0.8%)
E Greatest absolute difference: 0.00048828125 at index (1, 1, 0) (up to 1e-05 allowed)
E Greatest relative difference: 1.0 at index (2, 1, 0) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 23 / 500 (4.6%)
E Greatest absolute difference: 0.0009765625 at index (6, 6, 2) (up to 1e-05 allowed)
E Greatest relative difference: 0.01522064208984375 at index (1, 2, 2) (up to 0.001 allowed)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__linspace_tensor_overload_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 5s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 11s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 6s]
Raw output
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16.
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__linspace_tensor_overload_cpu_int32 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyEagerCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 8s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 9s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 5s]
Raw output
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.3333333432674408 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 2 at index (25,)
Greatest relative difference: inf at index (25,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 6 at index (49,)
Greatest relative difference: inf at index (9,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 34 / 50 (68.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: inf at index (1,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 33 / 50 (66.0%)
Greatest absolute difference: 3 at index (49,)
Greatest relative difference: 1.0 at index (17,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 1.0 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 37 / 50 (74.0%)
Greatest absolute difference: 4 at index (49,)
Greatest relative difference: 1.0 at index (13,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 1 / 50 (2.0%)
Greatest absolute difference: 1 at index (49,)
Greatest relative difference: 0.019999999552965164 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: inf at index (22,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (37,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 3 at index (33,)
Greatest relative difference: 3.0 at index (33,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 48 / 50 (96.0%)
Greatest absolute difference: 46 at index (49,)
Greatest relative difference: 0.9200000166893005 at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 4 at index (37,)
Greatest relative difference: inf at index (46,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 1 at index (1,)
Greatest relative difference: inf at index (49,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 49 / 50 (98.0%)
Greatest absolute difference: 46 at index (48,)
Greatest relative difference: 11.5 at index (48,)
AssertionError: Tensor-likes are not equal!
Mismatched elements: 43 / 50 (86.0%)
Greatest absolute difference: 7 at index (49,)
Greatest relative difference: 1.0 at index (7,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.3333333432674408 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 2 at index (25,)
E Greatest relative difference: inf at index (25,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 6 at index (49,)
E Greatest relative difference: inf at index (9,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 34 / 50 (68.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: inf at index (1,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 33 / 50 (66.0%)
E Greatest absolute difference: 3 at index (49,)
E Greatest relative difference: 1.0 at index (17,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 1.0 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 37 / 50 (74.0%)
E Greatest absolute difference: 4 at index (49,)
E Greatest relative difference: 1.0 at index (13,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 1 / 50 (2.0%)
E Greatest absolute difference: 1 at index (49,)
E Greatest relative difference: 0.019999999552965164 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: inf at index (22,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (37,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 3 at index (33,)
E Greatest relative difference: 3.0 at index (33,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 48 / 50 (96.0%)
E Greatest absolute difference: 46 at index (49,)
E Greatest relative difference: 0.9200000166893005 at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 4 at index (37,)
E Greatest relative difference: inf at index (46,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 1 at index (1,)
E Greatest relative difference: inf at index (49,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 49 / 50 (98.0%)
E Greatest absolute difference: 46 at index (48,)
E Greatest relative difference: 11.5 at index (48,)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not equal!
E
E Mismatched elements: 43 / 50 (86.0%)
E Greatest absolute difference: 7 at index (49,)
E Greatest relative difference: 1.0 at index (7,)
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__linspace_tensor_overload_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 7s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 6s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 8s]
Raw output
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[0] _val_16)
<float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[0] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {-3}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[0] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[50] _val_16)
<float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[50] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {-3}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[50] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[0] _val_16)
<float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[0] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {0}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[0] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[50] _val_16)
<float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[50] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {0}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[50] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[0] _val_16)
<float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[0] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {1}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[0] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[50] _val_16)
<float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[50] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {1}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[50] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {50}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0, int64 input_1) => (float16[0] _val_16)
<float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_2 = Constant <value: tensor = float {0}> ()
_val_3 = Cast <to: int = 1> (_val_2)
_val_4 = Constant <value: tensor = float {1}> ()
_val_5 = Cast <to: int = 1> (_val_4)
_val_6 = Cast <to: int = 1> (input_0)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_3, _val_9, _val_5)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_5)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (float input_0) => (float16[0] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Cast <to: int = 1> (input_0)
_val_6 = Constant <value: tensor = int64 {4}> ()
_val_7 = Cast <to: int = 1> (_val_6)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_5, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
tmp = Shape (input)
tmp_0 = Size (tmp)
tmp_1 = Constant <value_int: int = 0> ()
return_val = Equal (tmp_0, tmp_1)
}
AssertionError: ONNX model is invalid. Model:
<
ir_version: 8,
opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
producer_name: "pytorch",
producer_version: "2.2.0"
>
main_graph (int64 input_1) => (float16[0] _val_16)
<float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
{
_val_1 = Constant <value: tensor = float {0}> ()
_val_2 = Cast <to: int = 1> (_val_1)
_val_3 = Constant <value: tensor = float {1}> ()
_val_4 = Cast <to: int = 1> (_val_3)
_val_5 = Constant <value: tensor = float {-2}> ()
_val_6 = Cast <to: int = 1> (_val_5)
_val_7 = Cast <to: int = 1> (input_1)
_val_8 = Constant <value: tensor = int64 {0}> ()
_val_9 = Cast <to: int = 1> (_val_8)
_val_10 = Range (_val_2, _val_9, _val_4)
_val_11 = CastLike (_val_6, _val_7)
_val_12 = Sub (_val_7, _val_11)
_val_13 = Sub (_val_9, _val_4)
_val_14 = Div (_val_12, _val_13)
_val_15 = Mul (_val_10, _val_14)
_val_16 = Add (_val_15, _val_11)
}
<
domain: "pkg.onnxscript.torch_lib.common",
opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
tmp = Shape (input)
return_val = Size (tmp)
}
<
domain:…
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_19): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (float16[0] _val_16)
E <float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Cast <to: int = 1> (input_0)
E _val_6 = Constant <value: tensor = int64 {4}> ()
E _val_7 = Cast <to: int = 1> (_val_6)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_5, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_19): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_1) => (float16[0] _val_16)
E <float _val_1, float _val_2, float _val_3, float _val_4, int64 _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Constant <value: tensor = int64 {50}> ()
E _val_6 = Cast <to: int = 1> (_val_5)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_17): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0, int64 input_1) => (float16[50] _val_16)
E <float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_2 = Constant <value: tensor = float {0}> ()
E _val_3 = Cast <to: int = 1> (_val_2)
E _val_4 = Constant <value: tensor = float {1}> ()
E _val_5 = Cast <to: int = 1> (_val_4)
E _val_6 = Cast <to: int = 1> (input_0)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_3, _val_9, _val_5)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_5)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_19): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (float16[50] _val_16)
E <float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Cast <to: int = 1> (input_0)
E _val_6 = Constant <value: tensor = int64 {4}> ()
E _val_7 = Cast <to: int = 1> (_val_6)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_5, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_19): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_1) => (float16[50] _val_16)
E <float _val_1, float _val_2, float _val_3, float _val_4, int64 _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Constant <value: tensor = int64 {50}> ()
E _val_6 = Cast <to: int = 1> (_val_5)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_17): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0, int64 input_1) => (float16[0] _val_16)
E <float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_2 = Constant <value: tensor = float {0}> ()
E _val_3 = Cast <to: int = 1> (_val_2)
E _val_4 = Constant <value: tensor = float {1}> ()
E _val_5 = Cast <to: int = 1> (_val_4)
E _val_6 = Cast <to: int = 1> (input_0)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_3, _val_9, _val_5)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_5)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_19): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (float16[0] _val_16)
E <float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Cast <to: int = 1> (input_0)
E _val_6 = Constant <value: tensor = int64 {50}> ()
E _val_7 = Cast <to: int = 1> (_val_6)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_5, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_19): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_1) => (float16[0] _val_16)
E <float _val_1, float _val_2, float _val_3, float _val_4, int64 _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Constant <value: tensor = int64 {50}> ()
E _val_6 = Cast <to: int = 1> (_val_5)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {0}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_17): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0, int64 input_1) => (float16[50] _val_16)
E <float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_2 = Constant <value: tensor = float {0}> ()
E _val_3 = Cast <to: int = 1> (_val_2)
E _val_4 = Constant <value: tensor = float {1}> ()
E _val_5 = Cast <to: int = 1> (_val_4)
E _val_6 = Cast <to: int = 1> (input_0)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_3, _val_9, _val_5)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_5)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_19): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0) => (float16[50] _val_16)
E <float _val_1, float _val_2, float _val_3, float _val_4, float _val_5, int64 _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Cast <to: int = 1> (input_0)
E _val_6 = Constant <value: tensor = int64 {50}> ()
E _val_7 = Cast <to: int = 1> (_val_6)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_5, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_19): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_1) => (float16[50] _val_16)
E <float _val_1, float _val_2, float _val_3, float _val_4, int64 _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_1 = Constant <value: tensor = float {0}> ()
E _val_2 = Cast <to: int = 1> (_val_1)
E _val_3 = Constant <value: tensor = float {1}> ()
E _val_4 = Cast <to: int = 1> (_val_3)
E _val_5 = Constant <value: tensor = int64 {50}> ()
E _val_6 = Cast <to: int = 1> (_val_5)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_2, _val_9, _val_4)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_4)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:522: in _capture_graph_and_evaluate_torch_script_evaluator
onnx.checker.check_model(onnx_model, full_check=True)
.nox/test_torch_nightly/lib/python3.10/site-packages/onnx/checker.py:157: in check_model
C.check_model(protobuf_string, full_check, skip_opset_compatibility_check)
E onnx.onnx_cpp2py_export.shape_inference.InferenceError: [ShapeInferenceError] Inference error(s): (op_type:Add, node name: Add_17): [TypeInferenceError] Inferred elem type differs from existing elem type: (1) vs (10)
The above exception was the direct cause of the following exception:
onnxscript/tests/function_libs/torch_lib/ops_test.py:229: in run_test_output_match
function_output = function_executor(test_name, reference_torch_outputs)(
onnxscript/tests/function_libs/torch_lib/ops_test_common.py:524: in _capture_graph_and_evaluate_torch_script_evaluator
raise AssertionError(
E AssertionError: ONNX model is invalid. Model:
E <
E ir_version: 8,
E opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
E producer_name: "pytorch",
E producer_version: "2.2.0"
E >
E main_graph (int64 input_0, int64 input_1) => (float16[50] _val_16)
E <float _val_2, float _val_3, float _val_4, float _val_5, float _val_6, float _val_7, int64 _val_8, float _val_9, float[unk__0] _val_10, float _val_11, float _val_12, float _val_13, float _val_14, float[unk__0] _val_15>
E {
E _val_2 = Constant <value: tensor = float {0}> ()
E _val_3 = Cast <to: int = 1> (_val_2)
E _val_4 = Constant <value: tensor = float {1}> ()
E _val_5 = Cast <to: int = 1> (_val_4)
E _val_6 = Cast <to: int = 1> (input_0)
E _val_7 = Cast <to: int = 1> (input_1)
E _val_8 = Constant <value: tensor = int64 {50}> ()
E _val_9 = Cast <to: int = 1> (_val_8)
E _val_10 = Range (_val_3, _val_9, _val_5)
E _val_11 = CastLike (_val_6, _val_7)
E _val_12 = Sub (_val_7, _val_11)
E _val_13 = Sub (_val_9, _val_5)
E _val_14 = Div (_val_12, _val_13)
E _val_15 = Mul (_val_10, _val_14)
E _val_16 = Add (_val_15, _val_11)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E Rank (input) => (return_val)
E {
E tmp = Shape (input)
E return_val = Size (tmp)
E }
E <
E domain: "pkg.onnxscript.torch_lib.common",
E opset_import: ["" : 18]
E >
E IsScalar (input) => (return_val)
E {
E tmp = Shape (input)
E tmp_0 = Size (tmp)
E tmp_1 = Constant <value_int: int = 0> ()
E return_val = Equal (tmp_0, tmp_1)
E }
github-actions / Test Results
All 3 runs failed: test_output_match_opinfo__addmv_cpu_float16 (onnxscript.tests.function_libs.torch_lib.ops_test.TestOutputConsistencyFullGraphCPU)
artifacts/Test Results (py310-torch-nightly-macos-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-ubuntu-latest)/pytest.xml [took 0s]
artifacts/Test Results (py310-torch-nightly-windows-latest)/pytest.xml [took 0s]
Raw output
AssertionError: Tensor-likes are not close!
Mismatched elements: 1 / 5 (20.0%)
Greatest absolute difference: 0.01171875 at index (1,) (up to 1e-05 allowed)
Greatest relative difference: 0.0018596649169921875 at index (1,) (up to 0.001 allowed)
onnxscript/tests/function_libs/torch_lib/ops_test.py:266: in run_test_output_match
torch.testing.assert_close(
E AssertionError: Tensor-likes are not close!
E
E Mismatched elements: 1 / 5 (20.0%)
E Greatest absolute difference: 0.01171875 at index (1,) (up to 1e-05 allowed)
E Greatest relative difference: 0.0018596649169921875 at index (1,) (up to 0.001 allowed)
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