wehub-resource-sync
139 lines
4.7 KiB
Python
139 lines
4.7 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 os
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import numpy as np
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import paddle
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import paddle.distributed as dist
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class Layer(paddle.nn.Layer):
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def __init__(self, vocab_size, hidden_size):
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super().__init__()
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self.embedding = paddle.nn.Embedding(vocab_size, hidden_size)
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def forward(self, x):
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return self.embedding(x)
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class TestEmbeddingSubgraphSemiAutoParallel:
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def __init__(self):
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self._dtype = os.getenv("dtype")
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self._backend = os.getenv("backend")
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self._seed = eval(os.getenv("seed"))
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self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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self._batch_size = 17
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self._seq_length = 23
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self._vocab_size = 48
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self._hidden_size = 16
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def test_dp(self):
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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self._input = np.random.randint(
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0, self._vocab_size, size=(self._batch_size, self._seq_length)
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)
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x = paddle.to_tensor(self._input)
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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layer = Layer(self._vocab_size, self._hidden_size)
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desired_out = layer(x)
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desired_out.backward()
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desired_grad = layer.embedding.weight.grad
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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dist_x = dist.shard_tensor(x, self._mesh, placements=(dist.Shard(0),))
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layer = Layer(self._vocab_size, self._hidden_size)
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actual_out = layer(x)
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actual_out.backward()
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actual_grad = layer.embedding.weight.grad
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np.testing.assert_allclose(actual_out, desired_out, rtol=1e-6, atol=0)
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np.testing.assert_allclose(actual_grad, desired_grad, rtol=1e-6, atol=0)
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# The threshold setting refers to Megatron-LM
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assert (
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np.max(np.abs(actual_out.numpy() - desired_out.numpy())) < 1.0e-12
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), (
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f'embedding dp forward error. actual: {actual_out}, desired: {desired_out}'
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)
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assert (
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np.max(np.abs(actual_grad.numpy() - desired_grad.numpy())) < 1.0e-12
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), (
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f'embedding dp backward error. actual: {actual_out}, desired: {desired_out}'
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)
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def test_mp(self):
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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self._input = np.random.randint(
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0, self._vocab_size, size=(self._batch_size, self._seq_length)
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)
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x = paddle.to_tensor(self._input)
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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layer = Layer(self._vocab_size, self._hidden_size)
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desired_out = layer(x)
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desired_out.backward()
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desired_grad = layer.embedding.weight.grad
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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dist_x = dist.shard_tensor(
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x, self._mesh, placements=(dist.Replicate(),)
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)
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def shard_fn(layer_name, layer, process_mesh):
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if 'embedding' in layer_name:
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layer.weight = dist.shard_tensor(
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layer.weight, process_mesh, (dist.Shard(1),)
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)
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layer = dist.shard_layer(
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Layer(self._vocab_size, self._hidden_size), self._mesh, shard_fn
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)
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actual_out = layer(x)
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actual_out.backward()
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actual_grad = layer.embedding.weight.grad
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# The threshold setting refers to Megatron-LM
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assert (
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np.max(np.abs(actual_out.numpy() - desired_out.numpy())) < 1.0e-12
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), (
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f'embedding mp forward error. actual: {actual_out}, desired: {desired_out}'
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)
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assert (
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np.max(np.abs(actual_grad.numpy() - desired_grad.numpy())) < 1.0e-12
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), (
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f'embedding mp backward error. actual: {actual_out}, desired: {desired_out}'
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)
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def run_test_case(self):
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if self._backend == "cpu":
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paddle.set_device("cpu")
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elif self._backend == "gpu":
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paddle.set_device("gpu:" + str(dist.get_rank()))
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else:
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raise ValueError("Only support cpu or gpu backend.")
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self.test_dp()
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self.test_mp()
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if __name__ == '__main__':
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TestEmbeddingSubgraphSemiAutoParallel().run_test_case()
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