#13776: Add hypot_bw_nonzero and div_bw_nonzero sweeps

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

@@ -279,7 +279,6 @@ on:
           - eltwise.binary.ne.ne_scalar_pytorch2
           - eltwise.binary.ne.ne_forge
           - eltwise.binary.hypot.hypot
-          - eltwise.binary.hypot.hypot_nonzero
           - eltwise.binary.xlogy.xlogy
           - eltwise.binary_backward.ldexp_bw.ldexp_bw
           - eltwise.binary_backward.logaddexp_bw
@@ -288,10 +287,12 @@ on:
           - eltwise.binary_backward.subalpha_bw.subalpha_bw
           - eltwise.binary_backward.xlogy_bw.xlogy_bw
           - eltwise.binary_backward.hypot_bw.hypot_bw
+          - eltwise.binary_backward.hypot_bw.hypot_bw_nonzero
           - eltwise.binary_backward.add_bw.add_bw
           - eltwise.binary_backward.sub_bw.sub_bw
           - eltwise.binary_backward.mul_bw.mul_bw
           - eltwise.binary_backward.div_bw.div_bw
+          - eltwise.binary_backward.div_bw.div_bw_nonzero
           - eltwise.binary_backward.fmod_bw.fmod_bw
           - eltwise.binary_backward.remainder_bw.remainder_bw
           - eltwise.binary_backward.rsub_bw.rsub_bw

tests/sweep_framework/sweeps/eltwise/binary_backward/div_bw/div_bw_nonzero.py

@@ -0,0 +1,145 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+import random
+
+import torch
+import ttnn
+
+from tests.sweep_framework.sweep_utils.utils import gen_shapes, gen_rand_exclude_range, sanitize_shape_rm
+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
+
+
+# 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" and "suite_2") 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)
+        + gen_shapes([1, 1], [256, 256], [1, 1], 8),
+        "exclude_range": [[-1, 1]],
+        "round_mode": [None, "floor", "trunc"],
+        "grad_dtype": [ttnn.bfloat8_b],
+        "input_a_dtype": [ttnn.bfloat8_b],
+        "input_b_dtype": [ttnn.bfloat8_b],
+        "input_layout": [ttnn.TILE_LAYOUT],
+        "grad_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_b_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]]:
+    if test_vector["input_layout"] == ttnn.ROW_MAJOR_LAYOUT:
+        return True, "Unary operation requires tensor to be in Tile layout when working with non-sharded input tensor"
+    if test_vector["input_layout"] == ttnn.ROW_MAJOR_LAYOUT and (
+        test_vector["grad_dtype"] == ttnn.bfloat8_b
+        or test_vector["input_a_dtype"] == ttnn.bfloat8_b
+        or test_vector["input_b_dtype"] == ttnn.bfloat8_b
+    ):
+        return True, "bfloat8_b is only supported on tiled 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 mesh_device_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,
+    exclude_range,
+    round_mode,
+    grad_dtype,
+    input_a_dtype,
+    input_b_dtype,
+    input_layout,
+    grad_memory_config,
+    input_a_memory_config,
+    input_b_memory_config,
+    output_memory_config,
+    *,
+    device,
+) -> list:
+    torch.manual_seed(0)
+
+    if input_layout == ttnn.ROW_MAJOR_LAYOUT:
+        input_shape = sanitize_shape_rm(input_shape)
+
+    torch_grad_tensor = gen_func_with_cast_tt(
+        partial(gen_rand_exclude_range, excluderange=exclude_range, low=-100, high=100), grad_dtype
+    )(input_shape)
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(gen_rand_exclude_range, excluderange=exclude_range, low=-100, high=100), input_a_dtype
+    )(input_shape)
+    torch_input_tensor_b = gen_func_with_cast_tt(
+        partial(gen_rand_exclude_range, excluderange=exclude_range, low=-100, high=100), input_b_dtype
+    )(input_shape)
+    torch_input_tensor_a.requires_grad = True
+    torch_input_tensor_b.requires_grad = True
+
+    assert not torch.any(torch_grad_tensor == 0.0)
+    assert not torch.any(torch_input_tensor_a == 0.0)
+    assert not torch.any(torch_input_tensor_b == 0.0)
+
+    golden_function = ttnn.get_golden_function(ttnn.div_bw)
+    torch_output_tensors = golden_function(
+        torch_grad_tensor, torch_input_tensor_a, torch_input_tensor_b, round_mode if round_mode != "None" else None
+    )
+
+    grad_tensor = ttnn.from_torch(
+        torch_grad_tensor,
+        dtype=grad_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=grad_memory_config,
+    )
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=input_a_memory_config,
+    )
+    input_tensor_b = ttnn.from_torch(
+        torch_input_tensor_b,
+        dtype=input_b_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=input_b_memory_config,
+    )
+
+    start_time = start_measuring_time()
+    output_tensors = ttnn.div_bw(
+        grad_tensor, input_tensor_a, input_tensor_b, round_mode=round_mode, memory_config=output_memory_config
+    )
+    e2e_perf = stop_measuring_time(start_time)
+
+    passed = []
+    output_string = ""
+    for i in range(len(torch_output_tensors)):
+        output_tensor = ttnn.to_torch(output_tensors[i])
+        passed_, output_string_ = check_with_pcc(torch_output_tensors[i], output_tensor, 0.999)
+        passed.append(passed_)
+        output_string += output_string_ + ", "
+
+    if all(passed):
+        passed = True
+    else:
+        passed = False
+
+    output_string = output_string[:-2]
+
+    return [(passed, output_string), e2e_perf]