Add embedding_renorm code | feat(torchlib) (#1098)

onnxscript/function_libs/torch_lib/ops/core.py

@@ -3000,6 +3000,57 @@ def aten_embedding_dense_backward(
     raise NotImplementedError()
 
 
+@torch_op("aten::embedding_renorm", trace_only=True)
+def aten_embedding_renorm(
+    weight: TFloat, indices: INT64, max_norm: float, norm_type: float = 2.0
+) -> TFloat:
+    """embedding_renorm(Tensor weight, Tensor indices, float max_norm, float norm_type) -> Tensor"""
+
+    unique_indices = op.Unique(indices)
+    unique_indices_Y = op.SequenceAt(unique_indices, 0)
+    # using _onnx private function because op.SrquenceAt(unique_indices, 0) cannot pass module checker
+    # The error message is:
+    # onnx.onnx_cpp2py_export.shape_inference.InferenceError:
+    # [ShapeInferenceError] Shape inference error(s): (op_type:aten_embedding_renorm,
+    # node name: aten_embedding_renorm_0): [ShapeInferenceError] (op_type:SequenceAt,
+    # node name: n2): input_sequence typestr: S, has unsupported type: tensor(int64)
+    return aten_embedding_renorm_onnx(weight, unique_indices_Y, max_norm, norm_type)
+
+
+@torch_op("aten::embedding_renorm", private=True)
+def aten_embedding_renorm_onnx(
+    weight: TFloat, indices: INT64, max_norm: float, norm_type: float = 2.0
+) -> TFloat:
+    partial_weight = op.Gather(weight, indices)
+    # partial_weight_norm = sum(|w|^p)^(1/p)
+    if norm_type == 1.0:
+        # This is not necessary, but op.ReduceL1 is faster than function list in 'else'
+        partial_weight_norm = op.ReduceL1(partial_weight, axes=[1], keepdims=True)
+    elif norm_type == 2.0:
+        # This is not necessary, but op.ReduceL2 is faster than function list in 'else'
+        partial_weight_norm = op.ReduceL2(partial_weight, axes=[1], keepdims=True)
+    else:
+        # Abs -> Pow -> ReduceSum -> Pow -> Pow
+        partial_weight_abs = op.Abs(partial_weight)
+        partial_weight_pow = op.Pow(partial_weight_abs, op.Constant(value_float=norm_type))
+        partial_weight_norm = op.ReduceSum(partial_weight_pow, axes=[1], keepdims=True)
+        pow_value = op.CastLike(1.0 / norm_type, weight)
+        partial_weight_norm = op.Pow(partial_weight_norm, pow_value)
+
+    max_norm = op.CastLike(op.Constant(value_float=max_norm), weight)
+    # This is to avoid weight is zero
+    err = op.CastLike(op.Constant(value_float=1e-7), weight)
+    partial_weight_norm_ = op.Add(partial_weight_norm, err)
+    scales = op.Div(max_norm, partial_weight_norm_)
+    partial_weight_renorm = op.Mul(partial_weight, scales)
+    # Set values to renormed values where weight_norm > max_norm, but keep the original values where weight_norm <= max_norm
+    partial_weight_renorm = op.Where(
+        op.Greater(partial_weight_norm, max_norm), partial_weight_renorm, partial_weight
+    )
+    value = op.ScatterND(weight, op.Unsqueeze(indices, [1]), partial_weight_renorm)
+    return value
+
+
 def aten_embedding_sparse_backward(
     grad: TensorType,
     indices: TensorType,

onnxscript/tests/function_libs/torch_lib/extra_opinfo.py

@@ -818,6 +818,36 @@ def sample_inputs_bernoulli_p_deterministic(op_info, device, dtype, requires_gra
             yield opinfo_core.SampleInput(t, kwargs={"p": p})
 
 
+def sample_inputs_embedding_renorm(op_info, device, dtype, requires_grad, **kwargs):
+    del op_info
+    del kwargs
+
+    def make_input(shape):
+        return common_methods_invocations.make_tensor(
+            shape, device=device, dtype=dtype, requires_grad=requires_grad
+        )
+
+    def make_long_input(shape, *, low, high, noncontiguous=False):
+        return common_methods_invocations.make_tensor(
+            shape,
+            device=device,
+            dtype=torch.long,
+            low=low,
+            high=high,
+            noncontiguous=noncontiguous,
+        )
+
+    for max_norm in (0.5, 1.0, 5.0):
+        for norm_type in (0.8, 1.0, 2.0, 2.5):
+            idx = make_long_input((6,), low=0, high=S)
+            weights = make_input((S, S)) * 2
+            yield common_methods_invocations.SampleInput(
+                weights,
+                args=(idx,),
+                kwargs={"max_norm": max_norm, "norm_type": norm_type},
+            )
+
+
 def sample_inputs_embedding_bag(op_info, device, dtype, requires_grad, **kwargs):
     del op_info
     del kwargs
@@ -1240,6 +1270,13 @@ def sample_inputs_scaled_dot_product_flash_attention(
         sample_inputs_func=sample_inputs_embedding_bag_padding_idx,
         supports_out=False,
     ),
+    opinfo_core.OpInfo(
+        "ops.aten.embedding_renorm",
+        aten_name="embedding_renorm",
+        dtypes=common_dtype.floating_types_and_half(),
+        sample_inputs_func=sample_inputs_embedding_renorm,
+        supports_out=False,
+    ),
     opinfo_core.OpInfo(
         "nn.functional.conv3d",
         aten_name="conv3d",

onnxscript/tests/function_libs/torch_lib/ops_test_data.py

@@ -1044,6 +1044,13 @@ def _where_input_wrangler(
         tolerance={torch.float16: (1e-2, 1e-2)},
         compare_shape_only_for_output=(1, 2, 3),
     ),
+    TorchLibOpInfo(
+        "ops.aten.embedding_renorm",
+        core_ops.aten_embedding_renorm,
+        tolerance={torch.float16: (1e-2, 1e-2)},
+        trace_only=True,
+        compare_shape_only_for_output=(1, 2, 3),
+    ),
     TorchLibOpInfo(
         "nn.functional.embedding",
         core_ops.aten_embedding,