#11512: Add sweep test for ttnn.transformers.attention_softmax (#14655)

.github/workflows/ttnn-run-sweeps.yaml

@@ -312,12 +312,16 @@ on:
           - conv2d.full.conv2d_sharding
           - conv2d.full.conv2d_sliding_window
           - conv2d.short.conv2d_short_sweep
+          - pooling.global_avg_pool2d
+          - pooling.max_pool2d
           - max_pool2d.short.max_pool2d_short_sweep
           - max_pool2d.full.max_pool2d_params
           - max_pool2d.full.max_pool2d_large_dims
           - transformer.concatenate_heads.concatenate_heads
           - transformer.split_query_key_value_and_split_heads.split_query_key_value_and_split_heads
           - transformer.split_query_key_value_and_split_heads.split_query_key_value_and_split_heads_kv_input
+          - transformer.attention_softmax.attention_softmax
+          - transformer.attention_softmax.attention_softmax_
           - data_movement.stack.stack_pytorch2
           - data_movement.repeat.repeat_pytorch2
           - data_movement.split.split_pytorch2

tests/sweep_framework/sweeps/pooling/global_avg_pool2d.py

@@ -0,0 +1,135 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import torch
+import random
+import ttnn
+from tests.sweep_framework.sweep_utils.utils import gen_shapes
+from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+# Override the default timeout in seconds for hang detection.
+TIMEOUT = 30
+
+random.seed(0)
+
+# Parameters provided to the test vector generator are defined here.
+# They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
+# Each suite has a key name (in this case "suite_1") which will associate the test vectors to this specific suite of inputs.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "nightly": {
+        "input_shape": gen_shapes([1, 1, 1, 1], [6, 12, 256, 256], [1, 1, 1, 1], 8)
+        + gen_shapes([1, 1, 1], [12, 256, 256], [1, 1, 1], 8),
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_a_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+    },
+}
+
+
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    # input_shape = test_vector["input_shape"]
+
+    if test_vector["input_a_layout"] == ttnn.ROW_MAJOR_LAYOUT and test_vector["input_a_dtype"] == ttnn.bfloat8_b:
+        return True, "bfloat8_b/bfloat4_b requires TILE_LAYOUT!"
+
+    return False, None
+
+
+# This is the run instructions for the test, defined by the developer.
+# The run function must take the above-defined parameters as inputs.
+# The runner will call this run function with each test vector, and the returned results from this function will be stored.
+# If you defined a device_mesh_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
+def run(
+    input_shape,
+    input_a_dtype,
+    input_a_layout,
+    input_a_memory_config,
+    output_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    if input_a_layout == ttnn.ROW_MAJOR_LAYOUT and input_shape[-3] % 2 == 1:
+        input_shape[-3] += 1
+
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+
+    # print(f"input_shape {input_shape} input_a_dtype {input_a_dtype} input_a_layout {input_a_layout}")
+
+    torch_output_tensor = torch.nn.functional.adaptive_avg_pool2d(torch_input_tensor_a, (1, 1))
+
+    # ttnn operates on channels-last tensors
+    if len(input_shape) == 4:
+        torch_input_tensor_a = torch.permute(torch_input_tensor_a, (0, 2, 3, 1))
+    elif len(input_shape) == 3:
+        torch_input_tensor_a = torch.permute(torch_input_tensor_a, (1, 2, 0))
+    elif len(input_shape) == 2:
+        torch_input_tensor_a = torch.permute(torch_input_tensor_a, (1, 0))
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_a_layout,
+        device=device,
+        memory_config=input_a_memory_config,
+    )
+
+    start_time = start_measuring_time()
+    result = ttnn.global_avg_pool2d(input_tensor_a, memory_config=output_memory_config)
+    result = ttnn.to_torch(result)
+    e2e_perf = stop_measuring_time(start_time)
+
+    # ttnn operates on channels-last tensors
+    if len(input_shape) == 4:
+        output_tensor = torch.permute(result, (0, 3, 1, 2))
+    elif len(input_shape) == 3:
+        output_tensor = torch.permute(result, (2, 0, 1))
+    elif len(input_shape) == 2:
+        output_tensor = torch.permute(result, (1, 0))
+
+    pcc = check_with_pcc(torch_output_tensor, output_tensor, 0.99)
+    # print(f"pcc {pcc}")
+    return [pcc, e2e_perf]
+
+
+# Run sweeps locally
+# from tests.sweep_framework.framework.permutations import *
+
+# start_time = start_measuring_time()
+# for suite in parameters.keys():
+#     device_id = 0
+#     device = ttnn.open_device(device_id=device_id)
+#     suite_vectors = list(permutations(parameters[suite]))
+#     print(len(suite_vectors))
+#     for vector in suite_vectors:
+#         invalidate_res = invalidate_vector(vector)
+#         if invalidate_res[0]:
+#             print(f"Invalidated: {invalidate_res[1]}")
+#             continue
+#         try:
+#             passed, _ = run(**vector, device=device)
+#             if passed[0] != True:
+#                 print(passed)
+#         except Exception as e:
+#             print(e)
+
+#     ttnn.close_device(device)
+
+# e2e_perf = stop_measuring_time(start_time)
+# print(f"time {e2e_perf / 1000000000}s")

tests/sweep_framework/sweeps/pooling/max_pool2d.py

@@ -0,0 +1,153 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import torch
+import random
+import ttnn
+from tests.sweep_framework.sweep_utils.utils import gen_shapes
+from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+# Override the default timeout in seconds for hang detection.
+TIMEOUT = 30
+
+random.seed(0)
+
+# Parameters provided to the test vector generator are defined here.
+# They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
+# Each suite has a key name (in this case "suite_1") which will associate the test vectors to this specific suite of inputs.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "xfail": {
+        "input_shape": [[4, 256, 40, 40]],  # gen_shapes([1, 1, 1, 1], [6, 12, 256, 256], [1, 1, 1, 1], 16),
+        "kH": [2],
+        "kW": [2],
+        "stride": [1],
+        "padding": [0],
+        "dilation": [1],
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_a_layout": [ttnn.TILE_LAYOUT],  # ttnn.ROW_MAJOR_LAYOUT
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG],  # ttnn.L1_MEMORY_CONFIG
+        "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG],
+    },
+}
+
+
+# Invalidate vector is called during the generation phase where each vector will be passed in.
+# If invalidated, the vector will still be stored but will be skipped.
+# Returns False, None if the vector is valid, and True, str with a reason for invalidation if it is invalid.
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    # input_shape = test_vector["input_shape"]
+
+    if test_vector["input_a_layout"] == ttnn.ROW_MAJOR_LAYOUT and test_vector["input_a_dtype"] == ttnn.bfloat8_b:
+        return True, "bfloat8_b/bfloat4_b requires TILE_LAYOUT!"
+
+    return False, None
+
+
+# This is the run instructions for the test, defined by the developer.
+# The run function must take the above-defined parameters as inputs.
+# The runner will call this run function with each test vector, and the returned results from this function will be stored.
+# If you defined a device_mesh_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
+def run(
+    input_shape,
+    kH,
+    kW,
+    stride,
+    padding,
+    dilation,
+    input_a_dtype,
+    input_a_layout,
+    input_a_memory_config,
+    output_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    if input_a_layout == ttnn.ROW_MAJOR_LAYOUT and input_shape[-3] % 2 == 1:
+        input_shape[-3] += 1
+
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+
+    # print(f"input_shape {input_shape} input_a_dtype {input_a_dtype} input_a_layout {input_a_layout}")
+
+    torch_output_tensor = torch.nn.functional.max_pool2d(
+        torch_input_tensor_a, (kH, kW), stride=(stride, stride), padding=padding, dilation=dilation
+    )
+
+    # The input tensor is expected to be in [NHW, C]
+    [N, C, H, W] = input_shape
+    torch_input_tensor_a = torch.permute(torch_input_tensor_a, (0, 2, 3, 1))
+    torch_input_tensor_a = torch.reshape(torch_input_tensor_a, [1, 1, N * H * W, C])
+
+    # print(f"bla {torch_input_tensor_a.shape}")
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_a_layout,
+        device=device,
+        memory_config=input_a_memory_config,
+    )
+
+    start_time = start_measuring_time()
+    result = ttnn.max_pool2d(
+        input_tensor=input_tensor_a,
+        batch_size=N,
+        input_h=H,
+        input_w=W,
+        channels=C,
+        kernel_size=[kH, kW],
+        stride=[stride, stride],
+        padding=[padding, padding],
+        dilation=[dilation, dilation],
+        memory_config=output_memory_config,
+        applied_shard_scheme=ttnn.TensorMemoryLayout.BLOCK_SHARDED,
+    )
+
+    result = ttnn.to_torch(result)
+    e2e_perf = stop_measuring_time(start_time)
+
+    # ttnn operates on channels-last tensors
+    output_tensor = torch.permute(result, (0, 3, 1, 2))
+
+    pcc = check_with_pcc(torch_output_tensor, output_tensor, 0.99)
+    print(f"pcc {pcc}")
+    return [pcc, e2e_perf]
+
+
+# Run sweeps locally
+# from tests.sweep_framework.framework.permutations import *
+
+# start_time = start_measuring_time()
+# for suite in parameters.keys():
+#     device_id = 0
+#     device = ttnn.open_device(device_id=device_id)
+#     suite_vectors = list(permutations(parameters[suite]))
+#     print(len(suite_vectors))
+#     for vector in suite_vectors:
+#         invalidate_res = invalidate_vector(vector)
+#         if invalidate_res[0]:
+#             print(f"Invalidated: {invalidate_res[1]}")
+#             continue
+#         try:
+#             passed, _ = run(**vector, device=device)
+#             if passed[0] != True:
+#                 print(passed)
+#         except Exception as e:
+#             print(e)
+
+#     ttnn.close_device(device)
+
+# e2e_perf = stop_measuring_time(start_time)
+# print(f"time {e2e_perf / 1000000000}s")