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@@ -444,6 +444,7 @@ Normalization | |
| ttnn.group_norm | ||
| ttnn.layer_norm | ||
| ttnn.rms_norm | ||
| ttnn.batch_norm | ||
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| Moreh Operations | ||
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| Original file line number | Diff line number | Diff line change |
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| # SPDX-FileCopyrightText: © 2024 Tenstorrent Inc. | ||
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| # SPDX-License-Identifier: Apache-2.0 | ||
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| from typing import Optional, Tuple | ||
| from functools import partial | ||
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| import torch | ||
| 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 | ||
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| from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time | ||
| from models.utility_functions import torch_random | ||
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| # 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) | ||
| + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16) | ||
| + gen_shapes([32, 32], [256, 256], [32, 32], 32), | ||
| "input_dtype": [ttnn.bfloat16, ttnn.bfloat8_b], | ||
| "input_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT], | ||
| "input_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG], | ||
| } | ||
| } | ||
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| # 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_dtype"] == ttnn.bfloat8_b: | ||
| return True, "ROW_MAJOR_LAYOUT and ttnn.bfloat8_b are not supported" | ||
| return False, None | ||
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| # 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_dtype, | ||
| input_layout, | ||
| input_memory_config, | ||
| output_memory_config, | ||
| *, | ||
| device, | ||
| ) -> list: | ||
| torch.manual_seed(0) | ||
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| torch_input_tensor = gen_func_with_cast_tt(partial(torch_random, low=-1, high=1, dtype=torch.float32), input_dtype)( | ||
| input_shape | ||
| ) | ||
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| torch_output_tensor = torch.acos(torch_input_tensor) | ||
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| input_tensor = ttnn.from_torch( | ||
| torch_input_tensor, | ||
| dtype=input_dtype, | ||
| layout=input_layout, | ||
| device=device, | ||
| memory_config=input_memory_config, | ||
| ) | ||
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| start_time = start_measuring_time() | ||
| result = ttnn.acos(input_tensor, memory_config=output_memory_config) | ||
| output_tensor = ttnn.to_torch(result) | ||
| e2e_perf = stop_measuring_time(start_time) | ||
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| return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf] |
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