tenstorrent#8157: Update leaky_relu sweep config and doc (tenstorrent…

…#13481)

* tenstorrent#8157: Update doc and move sweep test for leaky_relu

* tenstorrent#8157: Add golden function

* tenstorrent#8157: Remove timeout parameter

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

@@ -154,6 +154,7 @@ on:
           - eltwise.unary.prelu.prelu
           - eltwise.unary.hardswish.hardswish_pytorch2
           - eltwise.unary.hardtanh.hardtanh_pytorch2
+          - eltwise.unary.leaky_relu.leaky_relu
           - eltwise.binary.subtract.subtract
           - eltwise.binary.subtract.subtract_tensor_pytorch2
           - eltwise.binary.multiply.multiply

tests/sweep_framework/sweeps/eltwise/unary/leaky_relu/leaky_relu.py

@@ -0,0 +1,78 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import torch
+import ttnn
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+from tests.sweep_framework.utils import gen_shapes
+from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
+from models.utility_functions import torch_random
+
+
+# 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, 32, 32], [6, 12, 256, 256], [1, 1, 32, 32], 16),
+        "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],
+        "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG],
+        "negative_slope": [-0.5, 0, 0.01, 0.5],
+    },
+}
+
+
+# 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]]:
+    if test_vector["input_a_layout"] == ttnn.ROW_MAJOR_LAYOUT:
+        return True, "Row Major layout is not supported"
+    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,
+    negative_slope,
+    *,
+    device,
+) -> list:
+    torch.manual_seed(0)
+
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+
+    golden_function = ttnn.get_golden_function(ttnn.leaky_relu)
+    torch_output_tensor = golden_function(torch_input_tensor_a, negative_slope=negative_slope)
+
+    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.leaky_relu(input_tensor_a, negative_slope, memory_config=output_memory_config)
+    output_tensor = ttnn.to_torch(result)
+    e2e_perf = stop_measuring_time(start_time)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]

ttnn/cpp/ttnn/operations/eltwise/unary/unary_pybind.hpp

@@ -1567,6 +1567,18 @@ void py_module(py::module& module) {
 
     detail::bind_unary_operation_with_float_parameter(module, ttnn::heaviside, "value", "The value parameter for the Heaviside function", "",
         R"doc(Supported dtypes, layouts, and ranks:
+
+           +----------------------------+---------------------------------+-------------------+
+           |     Dtypes                 |         Layouts                 |     Ranks         |
+           +----------------------------+---------------------------------+-------------------+
+           |    BFLOAT16, BFLOAT8_B     |          TILE                   |      2, 3, 4      |
+           +----------------------------+---------------------------------+-------------------+
+
+        )doc");
+
+    detail::bind_unary_operation_with_float_parameter(module, ttnn::leaky_relu, "negative_slope", "The slope parameter for the Leaky ReLU function", "",
+        R"doc(Supported dtypes, layouts, and ranks:
+
            +----------------------------+---------------------------------+-------------------+
            |     Dtypes                 |         Layouts                 |     Ranks         |
            +----------------------------+---------------------------------+-------------------+
@@ -1575,7 +1587,6 @@ void py_module(py::module& module) {
 
         )doc");
 
-    detail::bind_unary_operation_with_float_parameter(module, ttnn::leaky_relu, "negative_slope", "The slope parameter for the Leaky ReLU function", "");
     detail::bind_unary_operation_with_float_parameter(module, ttnn::fill, "fill_value", "The value to be filled in the output tensor", "This will create a tensor of same shape as input reference tensor with fill_value. Support provided for bfloat16, int, float32 dtypes in Wormhole_B0; Support provided for bfloat16 in Grayskull.");
     detail::bind_unary_operation_with_float_parameter(module, ttnn::relu_max, "upper_limit", "The max value for ReLU function", "This function caps off the input to a max value and a min value of 0",
         R"doc(Supported dtypes, layouts, and ranks: