wehub-resource-sync
275 lines
8.2 KiB
Python
275 lines
8.2 KiB
Python
# Copyright (c) 2021 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 random
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import unittest
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import numpy as np
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os.environ['FLAGS_profile_optimizer_details_steps'] = "1"
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import paddle
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import paddle.distributed as dist
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from paddle import nn
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from paddle.distributed import fleet
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from paddle.distributed.fleet.meta_parallel import (
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LayerDesc,
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PipelineLayer,
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SharedLayerDesc,
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)
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from paddle.nn import Layer
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def print_hook_fn(grad):
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print(grad)
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def set_random_seed(seed, dp_id, rank_id):
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"""Set random seed for reproducibility."""
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random.seed(seed)
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np.random.seed(seed + dp_id)
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paddle.seed(seed + dp_id)
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batch_size = 8
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micro_batch_size = 2
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vocab_size = 128
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hidden_size = 16
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class SimpleNet(Layer):
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def __init__(self):
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super().__init__()
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self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
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self.softmax_weight = self.create_parameter(
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shape=[hidden_size, vocab_size]
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)
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self.softmax_bias = self.create_parameter(
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shape=[vocab_size], is_bias=False
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)
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def forward(self, x1, x2, y1):
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x_emb = self.word_embeddings(x1)
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fc = paddle.matmul(x_emb, self.softmax_weight)
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fc = paddle.add(fc, self.softmax_bias)
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projection = paddle.reshape(fc, shape=[-1, vocab_size])
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projection = paddle.matmul(projection, self.word_embeddings.weight)
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loss = paddle.nn.functional.softmax_with_cross_entropy(
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logits=projection, label=y1, soft_label=False
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)
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return loss.mean()
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class EmbeddingPipe(Layer):
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def __init__(self):
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super().__init__()
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self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
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@property
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def embedding_weight(self):
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return self.word_embeddings.weight
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def forward(self, args):
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x1, x2 = args
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x_emb = self.word_embeddings(x1)
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return x_emb, x2
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class MatmulNet(Layer):
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def __init__(self):
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super().__init__()
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self.softmax_weight = self.create_parameter(
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shape=[hidden_size, vocab_size]
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)
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def forward(self, args):
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x1, x2 = args
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fc = paddle.matmul(x1, self.softmax_weight)
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return fc, x2
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class BiasNet(Layer):
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def __init__(self):
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super().__init__()
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self.softmax_bias = self.create_parameter(shape=[vocab_size])
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def forward(self, args):
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fc, x2 = args
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fc = paddle.add(fc, self.softmax_bias)
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projection = paddle.reshape(fc, shape=[-1, vocab_size])
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return projection, x2
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class LossNet(Layer):
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def __init__(self):
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super().__init__()
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def forward(self, args, y1):
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projection = args
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loss = paddle.nn.functional.softmax_with_cross_entropy(
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logits=projection, label=y1[0], soft_label=False
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)
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return loss.mean()
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class SimpleNetPipe(PipelineLayer):
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def __init__(self, **kwargs):
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self.descs = []
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self.descs.append(
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SharedLayerDesc(
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'embed', EmbeddingPipe, shared_weight_attr='embedding_weight'
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)
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)
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self.descs.append(LayerDesc(MatmulNet))
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self.descs.append(LayerDesc(BiasNet))
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def _logits_helper(embedding, output):
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return paddle.matmul(output[0], embedding.embedding_weight)
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self.descs.append(
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SharedLayerDesc(
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'embed',
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EmbeddingPipe,
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forward_func=_logits_helper,
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shared_weight_attr='embedding_weight',
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)
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)
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super().__init__(layers=self.descs, loss_fn=LossNet(), **kwargs)
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class TestDistEmbeddingTraining(unittest.TestCase):
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def setUp(self):
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strategy = fleet.DistributedStrategy()
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self.model_parallel_size = 1
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self.data_parallel_size = 1
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self.pipeline_parallel_size = 2
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strategy.hybrid_configs = {
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"dp_degree": self.data_parallel_size,
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"mp_degree": self.model_parallel_size,
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"pp_degree": self.pipeline_parallel_size,
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}
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strategy.pipeline_configs = {
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"accumulate_steps": batch_size // micro_batch_size,
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"micro_batch_size": micro_batch_size,
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}
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strategy.hybrid_configs["pp_configs"].clear_every_step_cache = True
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fleet.init(is_collective=True, strategy=strategy)
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def test_pp_model(self):
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hcg = fleet.get_hybrid_communicate_group()
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word_size = hcg.get_model_parallel_world_size()
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dp_id = hcg.get_data_parallel_rank()
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pp_id = hcg.get_stage_id()
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rank_id = dist.get_rank()
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set_random_seed(1024, dp_id, rank_id)
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# construct model a
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model_a = SimpleNet()
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scheduler_a = paddle.optimizer.lr.PiecewiseDecay(
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boundaries=[2, 3, 4], values=[0.01, 0.02, 0.03, 0.04], verbose=True
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)
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optimizer_a = paddle.optimizer.SGD(
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learning_rate=scheduler_a, parameters=model_a.parameters()
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)
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model_b = SimpleNetPipe(topology=hcg.topology())
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scheduler_b = paddle.optimizer.lr.PiecewiseDecay(
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boundaries=[2, 3, 4], values=[0.01, 0.02, 0.03, 0.04], verbose=True
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)
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optimizer_b = paddle.optimizer.SGD(
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learning_rate=scheduler_b, parameters=model_b.parameters()
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)
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model_b = fleet.distributed_model(model_b)
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optimizer_b = fleet.distributed_optimizer(optimizer_b)
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param_len = len(model_a.parameters())
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parameters = []
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for param in model_a.parameters():
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parameters.append(param.numpy())
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model_b_params = model_b.parameters()
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if pp_id == 0:
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model_b_params[0].set_value(parameters[2])
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model_b_params[1].set_value(parameters[0])
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else:
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model_b_params[0].set_value(parameters[2])
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model_b_params[1].set_value(parameters[1])
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for step in range(5):
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x1_data = np.random.randint(0, vocab_size, size=[batch_size, 1])
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x2_data = np.random.randint(0, vocab_size, size=[batch_size, 1])
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y1_data = np.random.randint(0, hidden_size, size=[batch_size, 1])
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x1 = paddle.to_tensor(x1_data)
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x2 = paddle.to_tensor(x2_data)
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y1 = paddle.to_tensor(y1_data)
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x1.stop_gradient = True
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x2.stop_gradient = True
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y1.stop_gradient = True
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loss_a = model_a(x1, x2, y1)
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loss_a.backward()
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optimizer_a.step()
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optimizer_a.clear_grad()
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scheduler_a.step()
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loss_b = model_b.train_batch(
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[(x1, x2), (y1,)], optimizer_b, scheduler_b
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)
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print("loss", loss_a.numpy(), loss_b.numpy())
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np.testing.assert_allclose(loss_a.numpy(), loss_b.numpy())
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class TestDistEmbeddingTrainingWithSync(TestDistEmbeddingTraining):
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def setUp(self):
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strategy = fleet.DistributedStrategy()
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self.model_parallel_size = 1
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self.data_parallel_size = 1
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self.pipeline_parallel_size = 2
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strategy.hybrid_configs = {
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"dp_degree": self.data_parallel_size,
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"mp_degree": self.model_parallel_size,
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"pp_degree": self.pipeline_parallel_size,
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}
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strategy.pipeline_configs = {
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"accumulate_steps": batch_size // micro_batch_size,
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"micro_batch_size": micro_batch_size,
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}
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strategy.hybrid_configs["pp_configs"].clear_every_step_cache = True
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strategy.hybrid_configs["pp_configs"].sync_moment = True
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strategy.hybrid_configs["pp_configs"].sync_param = True
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fleet.init(is_collective=True, strategy=strategy)
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def test_pp_model(self):
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super().test_pp_model()
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if __name__ == "__main__":
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unittest.main()
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