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[AMP]fix embedding model weight type mismatch error (#53770)
* fix embedding model weight type mismatch error * Update fp16_utils.py --------- Co-authored-by: Zhang Ting <zhangting_2017@163.com>
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| Original file line number | Diff line number | Diff line change |
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| # Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| import random | ||
| import unittest | ||
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| import numpy as np | ||
| from amp_base_models import AmpTestBase, _build_optimizer | ||
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| import paddle | ||
| from paddle import nn | ||
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| paddle.enable_static() | ||
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| _fixed_param = np.random.random(size=[64, 64]).astype("float32") | ||
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| class SimpleUnittedEmbeddingNet(nn.Layer): | ||
| def __init__(self): | ||
| super().__init__() | ||
| self.vocab_size = 64 | ||
| self.hidden_size = 64 | ||
| global _fixed_param | ||
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| self.param_attr = paddle.ParamAttr( | ||
| initializer=paddle.nn.initializer.Assign(_fixed_param) | ||
| ) | ||
| self.embedding = nn.Embedding( | ||
| self.vocab_size, self.hidden_size, weight_attr=self.param_attr | ||
| ) | ||
| self.linear = nn.Linear( | ||
| in_features=self.hidden_size, | ||
| out_features=self.vocab_size, | ||
| weight_attr=self.param_attr, | ||
| ) | ||
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| def forward(self, x): | ||
| out = self.embedding(x) | ||
| scale = paddle.full(shape=[1], fill_value=2, dtype="int64") | ||
| out = paddle.multiply(out, scale.astype("float32")) | ||
| out = self.linear(out) | ||
| out = nn.functional.dropout(out, p=0.2) | ||
| return out | ||
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| def build_unitted_embedding_model( | ||
| use_amp, | ||
| amp_dtype="float16", | ||
| amp_level="O1", | ||
| use_promote=False, | ||
| ): | ||
| main_program = paddle.static.Program() | ||
| startup_program = paddle.static.Program() | ||
| with paddle.utils.unique_name.guard(): | ||
| with paddle.static.program_guard(main_program, startup_program): | ||
| model = SimpleUnittedEmbeddingNet() | ||
| x = paddle.static.data(name='x', shape=[None, 32], dtype='int64') | ||
| out = model(x) | ||
| loss = paddle.mean(out) | ||
| if use_amp: | ||
| amp_lists = paddle.static.amp.AutoMixedPrecisionLists( | ||
| custom_white_list=["elementwise_mul"], | ||
| custom_black_list=["reduce_mean"], | ||
| dtype=amp_dtype, | ||
| ) | ||
| else: | ||
| amp_lists = None | ||
| optimizer = _build_optimizer( | ||
| use_amp, | ||
| amp_dtype, | ||
| amp_level, | ||
| amp_lists, | ||
| True, | ||
| use_promote=use_promote, | ||
| ) | ||
| optimizer.minimize(loss) | ||
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| feed_vars = [x] | ||
| fetch_vars = [loss] | ||
| return main_program, startup_program, optimizer, feed_vars, fetch_vars | ||
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| class TestUnittedEmbedding(AmpTestBase): | ||
| def _generate_feed_x(self): | ||
| seed = 0 | ||
| paddle.seed(seed) | ||
| np.random.seed(seed) | ||
| random.seed(seed) | ||
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| x = np.random.randint(1, 64, size=[1, 32]).astype("int64") | ||
| return x | ||
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| def test_compare_o1_and_o2_master_grad(self): | ||
| def _run(place, exe, x_np, max_iters, level): | ||
| ( | ||
| main_program, | ||
| startup_program, | ||
| optimizer, | ||
| feed_vars, | ||
| fetch_vars, | ||
| ) = build_unitted_embedding_model( | ||
| True, | ||
| "float16", | ||
| level, | ||
| ) | ||
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| seed = 0 | ||
| paddle.seed(seed) | ||
| np.random.seed(seed) | ||
| random.seed(seed) | ||
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| losses = self.run_program( | ||
| main_program, | ||
| startup_program, | ||
| optimizer, | ||
| feed_vars, | ||
| fetch_vars, | ||
| place, | ||
| exe, | ||
| x_np, | ||
| max_iters, | ||
| level, | ||
| ) | ||
| return losses | ||
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| max_iters = 5 | ||
| x = self._generate_feed_x() | ||
| place = paddle.CUDAPlace(0) | ||
| exe = paddle.static.Executor(place) | ||
| losses_o2 = _run(place, exe, x, max_iters, 'O2') | ||
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| if __name__ == "__main__": | ||
| unittest.main() |