#8157: Update doc and move sweep test for leaky_relu

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

@@ -66,6 +66,7 @@ on:
           - eltwise.unary.hardswish.hardswish
           - eltwise.unary.hardsigmoid.hardsigmoid
           - eltwise.unary.hardshrink.hardshrink
+          - eltwise.unary.leaky_relu.leaky_relu
           - eltwise.binary.subtract.subtract
           - eltwise.binary.multiply.multiply
           - eltwise.binary.div.div

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

@@ -0,0 +1,76 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+
+import torch
+import random
+import ttnn
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+
+# 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": {
+        "batch_sizes": [(1,)],
+        "height": [384, 1024],
+        "width": [1024, 4096],
+        "dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_memory_config": [ttnn.DRAM_MEMORY_CONFIG],
+        "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG],
+        "layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
+        "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["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(
+    batch_sizes,
+    height,
+    width,
+    dtype,
+    input_memory_config,
+    output_memory_config,
+    layout,
+    negative_slope,
+    *,
+    device,
+) -> list:
+    input_shape = (*batch_sizes, height, width)
+
+    torch_input_tensor = torch.randn(input_shape, dtype=torch.float32)
+    torch_output_tensor = torch.nn.functional.leaky_relu(torch_input_tensor, negative_slope)
+
+    input_tensor = ttnn.from_torch(
+        torch_input_tensor, dtype=dtype, device=device, memory_config=input_memory_config, layout=layout
+    )
+
+    start_time = start_measuring_time()
+    result = ttnn.leaky_relu(input_tensor, 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

@@ -252,7 +252,8 @@ void bind_unary_operation_with_float_parameter(
     const unary_operation_t& operation,
     const std::string& parameter_name,
     const std::string& parameter_doc,
-    const std::string& info_doc) {
+    const std::string& info_doc,
+    const std::string& note=" ") {
     auto doc = fmt::format(
         R"doc(
         Applies {0} to :attr:`input_tensor` element-wise.
@@ -275,7 +276,7 @@ void bind_unary_operation_with_float_parameter(
             ttnn.Tensor: the output tensor.
 
         Note:
-            {4}
+            {5}
 
         Example:
             >>> tensor = ttnn.from_torch(torch.tensor((1, 2), dtype=torch.bfloat16), device=device)
@@ -285,7 +286,8 @@ void bind_unary_operation_with_float_parameter(
         operation.python_fully_qualified_name(),
         parameter_name,
         parameter_doc,
-        info_doc);
+        info_doc,
+        note);
 
     bind_registered_operation(
         module,
@@ -1410,18 +1412,28 @@ void py_module(py::module& module) {
     detail::bind_unary_operation_with_fast_and_approximate_mode(module, ttnn::rsqrt);
 
     // Unaries with float parameter
-    detail::bind_unary_operation_with_float_parameter(module, ttnn::elu, "alpha", "The alpha parameter for the ELU function",
-    R"doc(Supported dtypes, layouts, and ranks:
+    detail::bind_unary_operation_with_float_parameter(module, ttnn::elu, "alpha", "The alpha parameter for the ELU function", "",
+        R"doc(Supported dtypes, layouts, and ranks:
 
-        +----------------------------+---------------------------------+-------------------+
-        |     Dtypes                 |         Layouts                 |     Ranks         |
-        +----------------------------+---------------------------------+-------------------+
-        |    BFLOAT16, BFLOAT8_B     |          TILE                   |      2, 3, 4      |
-        +----------------------------+---------------------------------+-------------------+)doc");
+           +----------------------------+---------------------------------+-------------------+
+           |     Dtypes                 |         Layouts                 |     Ranks         |
+           +----------------------------+---------------------------------+-------------------+
+           |    BFLOAT16, BFLOAT8_B     |          TILE                   |      2, 3, 4      |
+           +----------------------------+---------------------------------+-------------------+
+        )doc");
 
     detail::bind_unary_operation_with_float_parameter(module, ttnn::rsub, "value", "subtrahent value which is actually calculated as minuend", "Returns tensor with respective elements of the input tensor subtracted from the value.");
     detail::bind_unary_operation_with_float_parameter(module, ttnn::heaviside, "value", "The value parameter for the Heaviside function", "");
-    detail::bind_unary_operation_with_float_parameter(module, ttnn::leaky_relu, "slope", "The slope parameter for the Leaky ReLU function", "");
+    detail::bind_unary_operation_with_float_parameter(module, ttnn::leaky_relu, "slope", "The slope parameter for the Leaky ReLU 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::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");
     detail::bind_unary_operation_with_float_parameter(module, ttnn::relu_min, "lower_limit", "The min value for ReLU function", "This will carry out ReLU operation at min value instead of the standard 0");