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150 changes: 150 additions & 0 deletions
150
tests/sweep_framework/sweeps/data_movement/repeat_interleave/repeat_interleave.py
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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 | ||
| from itertools import combinations | ||
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| import torch | ||
| import random | ||
| import ttnn | ||
| from functools import lru_cache | ||
| 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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| # Override the default timeout in seconds for hang detection. | ||
| TIMEOUT = 360 | ||
| random.seed(0) | ||
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| # Does not have memory_config parameter | ||
| parameters = { | ||
| "nightly": { | ||
| "input_shape": gen_shapes([1, 1, 1, 1], [6, 6, 256, 256], [1, 1, 1, 1], 8) | ||
| + gen_shapes([1, 1, 1], [6, 256, 256], [1, 1, 1], 8) | ||
| + gen_shapes([1, 1], [256, 256], [1, 1], 8), | ||
| "repeats": [1, 2, 4, 8], | ||
| "dim": [0, 1, 2, 3], | ||
| "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b], | ||
| "input_a_layout": [ttnn.TILE_LAYOUT, ttnn.ROW_MAJOR_LAYOUT], | ||
| "input_a_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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| def align_to_32(x): | ||
| if x % 32 == 0: | ||
| return x | ||
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| return ((x // 32) + 1) * 32 | ||
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| def max_volume(rehape_shape): | ||
| vol = align_to_32(rehape_shape[-1]) * align_to_32(rehape_shape[-2]) | ||
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| if len(rehape_shape) >= 3: | ||
| vol *= rehape_shape[-3] | ||
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| if len(rehape_shape) == 4: | ||
| vol *= rehape_shape[-4] | ||
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| return vol | ||
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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]]: | ||
| input_shape = test_vector["input_shape"] | ||
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| if test_vector["dim"] >= len(input_shape): | ||
| return True, "dim must be < len(input_shape)" | ||
|
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| if ( | ||
| test_vector["input_a_memory_config"] == ttnn.L1_MEMORY_CONFIG | ||
| or test_vector["output_memory_config"] == ttnn.L1_MEMORY_CONFIG | ||
| ): | ||
| if max_volume(input_shape) * test_vector["repeats"] > 1024 * 1024: | ||
| return True, "Too large output tensor size for L1 memory config" | ||
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| if test_vector["input_a_layout"] == ttnn.ROW_MAJOR_LAYOUT and test_vector["input_a_dtype"] == ttnn.bfloat8_b: | ||
| return True, "bfloat8_b/bfloat4_b requires TILE_LAYOUT!" | ||
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| return False, None | ||
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| def run( | ||
| input_shape, | ||
| repeats, | ||
| dim, | ||
| input_a_dtype, | ||
| input_a_layout, | ||
| input_a_memory_config, | ||
| output_memory_config, | ||
| *, | ||
| device, | ||
| ) -> list: | ||
| data_seed = random.randint(0, 20000000) | ||
| torch.manual_seed(data_seed) | ||
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| # Fix shape for row mayor | ||
| if input_a_layout == ttnn.ROW_MAJOR_LAYOUT and input_shape[-1] % 2 == 1: | ||
| input_shape[-1] += 1 | ||
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| torch_input_tensor_a = gen_func_with_cast_tt( | ||
| partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype | ||
| )(input_shape) | ||
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| # print(f"input_shape {input_shape} repeats {repeats} dim {dim} input_a_dtype {input_a_dtype} input_a_layout {input_a_layout}") | ||
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| golden_function = ttnn.get_golden_function(ttnn.repeat_interleave) | ||
| torch_output_tensor = golden_function(torch_input_tensor_a, repeats=repeats, dim=dim) | ||
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| input_tensor_a = ttnn.from_torch( | ||
| torch_input_tensor_a, | ||
| dtype=input_a_dtype, | ||
| layout=input_a_layout, | ||
| device=device, | ||
| memory_config=input_a_memory_config, | ||
| ) | ||
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| start_time = start_measuring_time() | ||
| result = ttnn.repeat_interleave(input_tensor_a, repeats=repeats, dim=dim, memory_config=output_memory_config) | ||
| output_tensor = ttnn.to_torch(result) | ||
| e2e_perf = stop_measuring_time(start_time) | ||
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| pcc = check_with_pcc(torch_output_tensor, output_tensor, 0.999) | ||
| # print(pcc) | ||
| return [pcc, e2e_perf] | ||
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| # # Run sweeps locally | ||
| # from tests.sweep_framework.framework.permutations import * | ||
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| # start_time = start_measuring_time() | ||
| # for suite in parameters.keys(): | ||
| # device_id = 0 | ||
| # device = ttnn.open_device(device_id=device_id) | ||
| # suite_vectors = list(permutations(parameters[suite])) | ||
| # print(len(suite_vectors)) | ||
| # for vector in suite_vectors: | ||
| # invalidate_res = invalidate_vector(vector) | ||
| # if invalidate_res[0]: | ||
| # print(f"Invalidated: {invalidate_res[1]}") | ||
| # continue | ||
| # try: | ||
| # passed, _ = run(**vector, device=device) | ||
| # if passed[0] != True: | ||
| # print(passed) | ||
| # except Exception as e: | ||
| # print(e) | ||
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| # ttnn.close_device(device) | ||
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| # e2e_perf = stop_measuring_time(start_time) | ||
| # print(f"time {e2e_perf / 1000000000}s") |
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