wehub-resource-sync
141 lines
4.6 KiB
Python
141 lines
4.6 KiB
Python
# Copyright (c) 2023 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 unittest
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import numpy as np
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from amp_base_models import AmpTestBase, _build_optimizer
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import paddle
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from paddle import nn
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from paddle.framework import in_dynamic_or_pir_mode
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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):
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def __init__(self):
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super().__init__()
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self.vocab_size = 64
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self.hidden_size = 64
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global _fixed_param
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self.param_attr = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Assign(_fixed_param)
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)
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self.embedding = nn.Embedding(
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self.vocab_size, self.hidden_size, weight_attr=self.param_attr
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)
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self.linear = nn.Linear(
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in_features=self.hidden_size,
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out_features=self.vocab_size,
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weight_attr=self.param_attr,
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)
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def forward(self, x):
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out = self.embedding(x)
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scale = paddle.full(shape=[1], fill_value=2, dtype="int64")
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out = paddle.multiply(out, scale.astype("float32"))
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out = self.linear(out)
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out = nn.functional.dropout(out, p=0.2)
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return out
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def build_unitted_embedding_model(
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use_amp,
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amp_dtype="float16",
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amp_level="O1",
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use_promote=False,
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):
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if in_dynamic_or_pir_mode():
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model = SimpleUnittedEmbeddingNet()
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optimizer = _build_optimizer(use_amp=False, model=model)
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if use_amp and amp_dtype == "float16":
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scaler = paddle.amp.GradScaler(init_loss_scaling=32768.0)
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else:
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scaler = None
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if use_amp and amp_level == "O2":
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model, optimizer = paddle.amp.decorate(
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models=model,
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optimizers=optimizer,
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level=amp_level,
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dtype=amp_dtype,
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)
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return model, optimizer, scaler
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else:
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raise ValueError("Only support pir mode")
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class TestUnittedEmbedding(AmpTestBase):
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def _generate_feed_x(self):
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seed = 0
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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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x = np.random.randint(1, 64, size=[1, 32]).astype("int64")
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return x
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def test_pir_compare_o1_and_o2_master_grad(self):
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def _run(data, level, use_promote=False):
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with paddle.pir_utils.IrGuard():
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startup = paddle.static.Program()
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main = paddle.static.Program()
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with paddle.static.program_guard(main, startup):
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model, optimizer, scaler = build_unitted_embedding_model(
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use_amp=True,
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amp_dtype="float16",
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amp_level=level,
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use_promote=use_promote,
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)
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model.train()
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with paddle.amp.auto_cast(
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enable=True,
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dtype='float16',
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level=level,
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use_promote=use_promote,
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):
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x = paddle.static.data('x', [None, 32], 'int64')
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out = model(x)
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loss = paddle.mean(out)
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scaled = scaler.scale(loss)
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scaler.minimize(optimizer, scaled)
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if paddle.is_compiled_with_cuda():
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place = paddle.CUDAPlace(0)
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elif paddle.device.is_compiled_with_xpu():
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place = paddle.device.XPUPlace(0)
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else:
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raise ValueError("Only support CUDA or XPU Place.")
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exe = paddle.static.Executor(place)
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exe.run(startup)
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exe.run(
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main,
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feed={
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'x': data,
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},
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fetch_list=[loss],
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)
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x = self._generate_feed_x()
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_run(x, 'O2', use_promote=False)
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if __name__ == "__main__":
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unittest.main()
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