diff --git a/.github/workflows/ttnn-run-sweeps.yaml b/.github/workflows/ttnn-run-sweeps.yaml index f789e3e678fd..a7062de178d3 100644 --- a/.github/workflows/ttnn-run-sweeps.yaml +++ b/.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 diff --git a/tests/sweep_framework/sweeps/eltwise/binary/hypot/hypot_nonzero.py b/tests/sweep_framework/sweeps/eltwise/binary/hypot/hypot_nonzero.py new file mode 100644 index 000000000000..0c1715e6b70a --- /dev/null +++ b/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] diff --git a/tests/sweep_framework/sweeps/eltwise/unary_backward/rdiv_bw/rdiv_bw_nonzero.py b/tests/sweep_framework/sweeps/eltwise/unary_backward/rdiv_bw/rdiv_bw_nonzero.py new file mode 100644 index 000000000000..06d5448a809a --- /dev/null +++ b/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]