wehub-resource-sync
90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import numpy as np
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import paddle
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import TokenDispatcherUtils as TDU
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def tokens_zip_unique_add_with_subbatch(zipped, unzipped, index_unzipped, zipped_rows, subbatch_rows=None):
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if subbatch_rows is None or subbatch_rows <= 0 or zipped_rows <= 0:
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return TDU.tokens_zip_unique_add(zipped, unzipped, index_unzipped, zipped_rows)
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else:
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if isinstance(zipped, paddle.Tensor):
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num_split = (zipped_rows + subbatch_rows - 1) // subbatch_rows
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remainder = zipped_rows % subbatch_rows
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if remainder == 0:
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rows = [subbatch_rows] * num_split
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else:
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rows = [subbatch_rows] * (num_split - 1) + [remainder]
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if zipped.shape[0] == 0:
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dtype = zipped.dtype
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hidden_size = zipped.shape[1]
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zipped = [paddle.zeros([r, hidden_size], dtype=dtype) for r in rows]
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else:
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zipped = paddle.split(zipped, rows, axis=0)
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return TDU.tokens_zip_unique_add_subbatch(zipped, unzipped, index_unzipped, zipped_rows, subbatch_rows)
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def generate_index_unzipped(zipped_rows, unzipped_rows):
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index = random.sample(range(zipped_rows), unzipped_rows)
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assert len(index) == len(set(index))
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return paddle.to_tensor(index, dtype=paddle.int64)
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def main():
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seed = 2048
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hidden_size = 7168
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zipped_rows = 5800
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unzipped_rows = 4788
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subbatch_rows = 380
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dtype = paddle.bfloat16
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paddle.seed(seed)
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np.random.seed(seed)
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random.seed(seed)
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zipped_origin = paddle.randn([zipped_rows, hidden_size], dtype=paddle.float32)
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unzipped = paddle.randn([unzipped_rows, hidden_size], dtype=dtype)
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index_unzipped = generate_index_unzipped(zipped_rows, unzipped_rows)
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md5sum = None
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for use_subbatch in [False, True]:
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random.seed(seed + 100)
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args = [zipped_origin.clone(), unzipped, index_unzipped, zipped_rows]
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if use_subbatch and hasattr(TDU, "tokens_zip_unique_add_subbatch"):
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args.append(subbatch_rows)
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for _ in range(4):
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output = tokens_zip_unique_add_with_subbatch(*args)
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args[0] = output
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args[2] = generate_index_unzipped(zipped_rows, unzipped_rows)
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if isinstance(output, (list, tuple)):
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output = paddle.concat(output, axis=0)
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cur_md5sum = output._md5sum()
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if md5sum is None:
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md5sum = output._md5sum()
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print(f"MD5SUM: {md5sum}")
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else:
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assert md5sum == cur_md5sum, f"{md5sum} vs {cur_md5sum}"
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if __name__ == "__main__":
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main()
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