206403950e
* add offload opt util (#10607) * fix offload optimizer (#10608) * fix sharding reshard bug (#10613) * reset ema (#10790) * Move fused_quanted_ops and token_dispatcher_utils to FleetY (#10803) * move setup_fp8.py (#10820) * Add tokens_zip_unique_add_subbatch and merge_subbatch_cast ops (#10822) * add tokens_zip_unique_add_subbatch and merge_subbatch_cast ops * enhance ut * enhance ut again * remove duplicate codes * enhance ut again * Sharding reshard supports mismatch parameter name (#10479) * Sharding Reshard Supports N -> 1 (#10532) * cherry-pick https://github.com/PaddlePaddle/PaddleNLP/pull/10531 * support tpdp-ep sharding reshard (#10568) * fix typo * revert sharding_first
37 lines
1.3 KiB
Python
37 lines
1.3 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 numpy as np
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import paddle
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import TokenDispatcherUtils as TDU
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subbatch_rows = 30
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remainder_row = 0
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left_shape = [100, 200]
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for num in range(33):
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for dtype in [paddle.bfloat16, paddle.float32]:
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x = []
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for i in range(num - 1):
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x.append(paddle.randn([subbatch_rows] + left_shape, dtype=paddle.float32))
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x.append(
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paddle.randn([subbatch_rows if remainder_row == 0 else remainder_row] + left_shape, dtype=paddle.float32)
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)
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y1 = paddle.concat(x, axis=0).astype(dtype)
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y2 = TDU.merge_subbatch_cast(x, dtype)
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diff = np.abs(y1.numpy() - y2.numpy()).max()
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assert diff == 0, diff
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