#13795: Add requested nonzero sweeps for ops realted to ttnn.reciprocal

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

@@ -167,6 +167,7 @@ on:
           - eltwise.unary_backward.sin_bw.sin_bw
           - eltwise.unary_backward.square_bw.square_bw
           - eltwise.unary_backward.rdiv_bw.rdiv_bw
+          - eltwise.unary_backward.rdiv_bw.rdiv_bw_nonzero
           - eltwise.unary_backward.bias_gelu_bw.bias_gelu_bw
           - eltwise.unary_backward.pow_bw.pow_bw
           - eltwise.unary_backward.exp_bw.exp_bw
@@ -278,6 +279,7 @@ 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

tests/sweep_framework/sweeps/eltwise/binary/hypot/hypot_nonzero.py

@@ -0,0 +1,99 @@
+# 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
+
+
+# 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], 16)
+        + gen_shapes([1, 1, 1], [12, 256, 256], [1, 1, 1], 16)
+        + gen_shapes([1, 1], [256, 256], [1, 1], 16),
+        "input_a_dtype": [ttnn.bfloat8_b],
+        "input_b_dtype": [ttnn.bfloat8_b],
+        "input_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_b_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 or test_vector["input_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_b_dtype,
+    input_layout,
+    input_a_memory_config,
+    input_b_memory_config,
+    *,
+    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)
+    torch_input_tensor_b = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_b_dtype
+    )(input_shape)
+
+    while torch.any(torch_input_tensor_a == 0.0):
+        torch_input_tensor_a = torch.where(torch_input_tensor_a == 0.0, random.uniform(-100, 100), torch_input_tensor_a)
+    while torch.any(torch_input_tensor_b == 0.0):
+        torch_input_tensor_b = torch.where(torch_input_tensor_b == 0.0, random.uniform(-100, 100), torch_input_tensor_b)
+
+    assert not torch.any(torch_input_tensor_a == 0)
+    assert not torch.any(torch_input_tensor_b == 0)
+
+    golden_function = ttnn.get_golden_function(ttnn.hypot)
+    torch_output_tensor = golden_function(torch_input_tensor_a, torch_input_tensor_b)
+
+    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()
+    result = ttnn.hypot(input_tensor_a, input_tensor_b)
+    e2e_perf = stop_measuring_time(start_time)
+
+    output_tensor = ttnn.to_torch(result)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]

tests/sweep_framework/sweeps/eltwise/unary_backward/rdiv_bw/rdiv_bw_nonzero.py

@@ -0,0 +1,116 @@
+# 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),
+        "grad_dtype": [ttnn.bfloat8_b],
+        "input_a_dtype": [ttnn.bfloat8_b],
+        "input_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT],
+        "grad_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "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]]:
+    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
+    ):
+        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,
+    grad_dtype,
+    input_a_dtype,
+    input_layout,
+    grad_memory_config,
+    input_a_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(torch_random, low=-100, high=100, dtype=torch.float32), grad_dtype
+    )(input_shape)
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+    torch_input_tensor_a.requires_grad = True
+
+    factor = torch.tensor(1, dtype=torch.bfloat16).uniform_(-100, 100).item()
+
+    while torch.any(torch_grad_tensor == 0.0):
+        torch_grad_tensor = torch.where(torch_grad_tensor == 0.0, random.uniform(-100, 100), torch_grad_tensor)
+    while torch.any(torch_input_tensor_a == 0.0):
+        torch_input_tensor_a = torch.where(torch_input_tensor_a == 0.0, random.uniform(-100, 100), torch_input_tensor_a)
+    while factor == 0.0:
+        factor = torch.tensor(1, dtype=torch.bfloat16).uniform_(-100, 100).item()
+
+    assert not torch.any(torch_grad_tensor == 0)
+    assert not torch.any(torch_input_tensor_a == 0)
+    assert not factor == 0.0
+
+    golden_function = ttnn.get_golden_function(ttnn.rdiv_bw)
+    torch_output_tensor = golden_function(torch_grad_tensor, torch_input_tensor_a, factor)[0]
+
+    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,
+    )
+
+    start_time = start_measuring_time()
+    output_tensor = ttnn.rdiv_bw(grad_tensor, input_tensor_a, scalar=factor, memory_config=output_memory_config)[0]
+    output_tensor = ttnn.to_torch(output_tensor)
+    e2e_perf = stop_measuring_time(start_time)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]