未验证 提交 4c77a908 编写于 作者: B Baibaifan 提交者: GitHub

Add dygraph sharding stage3 (#38052)

上级 556d5097
...@@ -435,6 +435,10 @@ inline T* DenseTensor::mutable_data(const paddle::platform::Place& place, ...@@ -435,6 +435,10 @@ inline T* DenseTensor::mutable_data(const paddle::platform::Place& place,
} }
void DenseTensor::ShareBufferWith(const DenseTensor& tensor) { void DenseTensor::ShareBufferWith(const DenseTensor& tensor) {
if (storage_ == nullptr) {
storage_ = make_intrusive<paddle::experimental::SharedStorage>(
paddle::platform::CPUPlace());
}
if (storage_ != nullptr && tensor.storage_ != nullptr) { if (storage_ != nullptr && tensor.storage_ != nullptr) {
storage_->set_data_shared(tensor.storage_->data_shared()); storage_->set_data_shared(tensor.storage_->data_shared());
} }
......
...@@ -152,6 +152,9 @@ def ShardingScaler(scaler): ...@@ -152,6 +152,9 @@ def ShardingScaler(scaler):
param_grads = [] param_grads = []
param_grads_fp16 = [] param_grads_fp16 = []
param_grads_fp32 = [] param_grads_fp32 = []
if hasattr(optimizer, "update_slice"):
optimizer.update_slice()
optimizer.update_scaler = True
if getattr(optimizer._optim, '_param_groups', None) and isinstance( if getattr(optimizer._optim, '_param_groups', None) and isinstance(
optimizer._optim._param_groups[0], dict): optimizer._optim._param_groups[0], dict):
...@@ -161,27 +164,21 @@ def ShardingScaler(scaler): ...@@ -161,27 +164,21 @@ def ShardingScaler(scaler):
if param._grad_ivar() is not None: if param._grad_ivar() is not None:
param_grads.append(param._grad_ivar()) param_grads.append(param._grad_ivar())
if param._grad_ivar( if param._grad_ivar(
).dtype == core.VarDesc.VarType.FP16: ).dtype in [core.VarDesc.VarType.FP16, paddle.float16]:
param_grads_fp16.append(param._grad_ivar()) param_grads_fp16.append(param._grad_ivar())
else: else:
param_grads_fp32.append(param._grad_ivar()) param_grads_fp32.append(param._grad_ivar())
else: else:
param_grads = [ for param in optimizer._optim._parameter_list:
param._grad_ivar() for param in optimizer._optim._parameter_list if param.grad is not None:
if param._grad_ivar() is not None param_grads.append(param.grad)
] if param.grad.dtype in [
param_grads_fp16 = [ core.VarDesc.VarType.FP16, paddle.float16
param._grad_ivar() for param in optimizer._optim._parameter_list ]:
if (param._grad_ivar() is not None param_grads_fp16.append(param.grad)
) and (param._grad_ivar().dtype == core.VarDesc.VarType.FP16 else:
) param_grads_fp32.append(param.grad)
]
param_grads_fp32 = [
param._grad_ivar() for param in optimizer._optim._parameter_list
if (param._grad_ivar() is not None
) and (param._grad_ivar().dtype == core.VarDesc.VarType.FP32
)
]
temp_found_inf_fp16 = to_variable(np.array([0]).astype(np.bool)) temp_found_inf_fp16 = to_variable(np.array([0]).astype(np.bool))
temp_found_inf_fp32 = to_variable(np.array([0]).astype(np.bool)) temp_found_inf_fp32 = to_variable(np.array([0]).astype(np.bool))
......
...@@ -34,6 +34,7 @@ list(APPEND DIST_TEST_OPS test_parallel_dygraph_tensor_parallel) ...@@ -34,6 +34,7 @@ list(APPEND DIST_TEST_OPS test_parallel_dygraph_tensor_parallel)
list(APPEND DIST_TEST_OPS test_parallel_dygraph_sharding_parallel) list(APPEND DIST_TEST_OPS test_parallel_dygraph_sharding_parallel)
list(APPEND DIST_TEST_OPS test_dygraph_sharding_optimizer_stage2) list(APPEND DIST_TEST_OPS test_dygraph_sharding_optimizer_stage2)
list(APPEND DIST_TEST_OPS test_dygraph_sharding_stage2) list(APPEND DIST_TEST_OPS test_dygraph_sharding_stage2)
list(APPEND DIST_TEST_OPS test_dygraph_sharding_stage3)
list(APPEND DIST_TEST_OPS test_auto_parallel_parallelizer) list(APPEND DIST_TEST_OPS test_auto_parallel_parallelizer)
list(APPEND DIST_TEST_OPS test_parallel_dygraph_mp_layers) list(APPEND DIST_TEST_OPS test_parallel_dygraph_mp_layers)
list(APPEND DIST_TEST_OPS test_hybrid_parallel_inference_helper) list(APPEND DIST_TEST_OPS test_hybrid_parallel_inference_helper)
...@@ -250,6 +251,7 @@ if ((NOT WITH_GPU) AND (NOT WITH_ROCM)) ...@@ -250,6 +251,7 @@ if ((NOT WITH_GPU) AND (NOT WITH_ROCM))
list(REMOVE_ITEM TEST_OPS test_parallel_dygraph_sharding_parallel) list(REMOVE_ITEM TEST_OPS test_parallel_dygraph_sharding_parallel)
list(REMOVE_ITEM TEST_OPS test_dygraph_sharding_optimizer_stage2) list(REMOVE_ITEM TEST_OPS test_dygraph_sharding_optimizer_stage2)
list(REMOVE_ITEM TEST_OPS test_dygraph_sharding_stage2) list(REMOVE_ITEM TEST_OPS test_dygraph_sharding_stage2)
list(REMOVE_ITEM TEST_OPS test_dygraph_sharding_stage3)
list(REMOVE_ITEM TEST_OPS test_auto_parallel_parallelizer) list(REMOVE_ITEM TEST_OPS test_auto_parallel_parallelizer)
list(REMOVE_ITEM TEST_OPS test_parallel_dygraph_mp_layers) list(REMOVE_ITEM TEST_OPS test_parallel_dygraph_mp_layers)
LIST(REMOVE_ITEM TEST_OPS test_imperative_auto_mixed_precision) LIST(REMOVE_ITEM TEST_OPS test_imperative_auto_mixed_precision)
...@@ -1058,6 +1060,7 @@ if(WITH_DISTRIBUTE AND WITH_GPU AND WITH_NCCL) ...@@ -1058,6 +1060,7 @@ if(WITH_DISTRIBUTE AND WITH_GPU AND WITH_NCCL)
set_tests_properties(test_parallel_dygraph_sharding_parallel PROPERTIES TIMEOUT 120) set_tests_properties(test_parallel_dygraph_sharding_parallel PROPERTIES TIMEOUT 120)
set_tests_properties(test_dygraph_sharding_optimizer_stage2 PROPERTIES TIMEOUT 120) set_tests_properties(test_dygraph_sharding_optimizer_stage2 PROPERTIES TIMEOUT 120)
set_tests_properties(test_dygraph_sharding_stage2 PROPERTIES TIMEOUT 120) set_tests_properties(test_dygraph_sharding_stage2 PROPERTIES TIMEOUT 120)
set_tests_properties(test_dygraph_sharding_stage3 PROPERTIES TIMEOUT 120)
set_tests_properties(test_auto_parallel_parallelizer PROPERTIES TIMEOUT 120) set_tests_properties(test_auto_parallel_parallelizer PROPERTIES TIMEOUT 120)
set_tests_properties(test_parallel_dygraph_mp_layers PROPERTIES TIMEOUT 120) set_tests_properties(test_parallel_dygraph_mp_layers PROPERTIES TIMEOUT 120)
set_tests_properties(test_hybrid_parallel_inference_helper PROPERTIES TIMEOUT 120) set_tests_properties(test_hybrid_parallel_inference_helper PROPERTIES TIMEOUT 120)
......
# -*- coding: UTF-8 -*-
# Copyright (c) 2021 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.
import numpy as np
import argparse
import ast
import time
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.nn import Linear
from paddle.distributed import fleet
from paddle.fluid.dygraph import nn
from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer.sharding_optimizer_stage2 import ShardingOptimizerStage2
from paddle.distributed.fleet.meta_parallel.sharding.sharding_stage2 import ShardingStage2
from paddle.distributed.fleet.meta_parallel.sharding.sharding_stage3 import ShardingStage3
from paddle.distributed.fleet.meta_parallel.sharding.sharding_utils import ShardingScaler
epoch = 10
batch_size = 32
paddle.seed(2021)
np.random.seed(2021)
base_lr = 0.1
momentum_rate = 0.9
l2_decay = 1e-4
fleet.init(is_collective=True)
class MLP(fluid.Layer):
def __init__(self, linear_size=1000, param_attr=None, bias_attr=None):
super(MLP, self).__init__()
self._linear1 = Linear(linear_size, linear_size)
self._linear2 = Linear(linear_size, linear_size)
self._linear3 = Linear(linear_size, 10)
def forward(self, inputs):
y = self._linear1(inputs)
y = self._linear2(y)
y = self._linear3(y)
return y
def reader_decorator(linear_size=1000):
def __reader__():
for _ in range(100):
img = np.random.rand(linear_size).astype('float32')
label = np.ones(1).astype('int64')
yield img, label
return __reader__
def optimizer_setting(model, use_pure_fp16, opt_group=False):
clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0)
optimizer = paddle.optimizer.AdamW(
parameters=[{
"params": model.parameters()
}] if opt_group else model.parameters(),
learning_rate=0.001,
weight_decay=0.00001,
grad_clip=clip,
multi_precision=use_pure_fp16)
return optimizer
def train_mlp(model,
sharding_stage,
use_pure_fp16=False,
accumulate_grad=False,
opt_group=False,
recompute=False):
group = paddle.distributed.new_group([0, 1])
if opt_group:
optimizer = optimizer_setting(
model=model, use_pure_fp16=use_pure_fp16, opt_group=opt_group)
else:
optimizer = optimizer_setting(model=model, use_pure_fp16=use_pure_fp16)
if use_pure_fp16:
model = paddle.amp.decorate(
models=model, level='O2', save_dtype='float32')
scaler = paddle.amp.GradScaler(init_loss_scaling=32768)
scaler = ShardingScaler(scaler)
if sharding_stage == 2:
optimizer = ShardingOptimizerStage2(
params=model.parameters(), optim=optimizer, group=group)
model = ShardingStage2(
model,
optimizer,
group=group,
buffer_max_size=2**21,
accumulate_grads=accumulate_grad)
elif sharding_stage == 3:
model = ShardingStage3(
model, optimizer=optimizer, group=group, sync_comm=recompute)
train_reader = paddle.batch(
reader_decorator(), batch_size=batch_size, drop_last=True)
train_loader = paddle.io.DataLoader.from_generator(
capacity=32,
use_double_buffer=True,
iterable=True,
return_list=True,
use_multiprocess=True)
train_loader.set_sample_list_generator(train_reader)
for eop in range(epoch):
model.train()
for batch_id, data in enumerate(train_loader()):
img, label = data
label.stop_gradient = True
img.stop_gradient = True
with paddle.amp.auto_cast(True, level='O2'):
out = model(img)
loss = paddle.nn.functional.cross_entropy(
input=out, label=label)
avg_loss = paddle.mean(x=loss.cast(dtype=paddle.float32))
if not accumulate_grad:
if not use_pure_fp16:
avg_loss.backward()
optimizer.step()
else:
scaler.scale(avg_loss).backward()
scaler.step(optimizer)
scaler.update()
optimizer.clear_grad()
if accumulate_grad:
if not use_pure_fp16:
avg_loss.backward()
optimizer.step()
else:
scaler.scale(avg_loss).backward()
scaler.step(optimizer)
scaler.update()
optimizer.clear_grad()
if sharding_stage == 3:
model.get_all_parameters()
return model.parameters()
def test_stage2_stage3():
mlp, mlp1, mlp2, mlp3, mlp4, mlp5, mlp6, mlp7, mlp8 = MLP(), MLP(), MLP(
), MLP(), MLP(), MLP(), MLP(), MLP(), MLP()
state_dict = mlp.state_dict()
mlp1.set_state_dict(state_dict)
mlp2.set_state_dict(state_dict)
mlp3.set_state_dict(state_dict)
mlp4.set_state_dict(state_dict)
mlp5.set_state_dict(state_dict)
mlp6.set_state_dict(state_dict)
mlp7.set_state_dict(state_dict)
mlp8.set_state_dict(state_dict)
# fp32
stage2_params = train_mlp(
mlp1, sharding_stage=2, use_pure_fp16=False, opt_group=True)
stage3_params = train_mlp(
mlp2, sharding_stage=3, use_pure_fp16=False, opt_group=True)
for i in range(len(stage2_params)):
for j in range(len(stage3_params)):
if stage2_params[i].name == stage3_params[j].name:
np.testing.assert_allclose(
stage2_params[i].numpy(),
stage3_params[j].numpy(),
rtol=1e-6)
# fp32 accumulate grad
stage2_params = train_mlp(
mlp3,
sharding_stage=2,
use_pure_fp16=False,
accumulate_grad=True,
opt_group=True)
stage3_params = train_mlp(
mlp4,
sharding_stage=3,
use_pure_fp16=False,
accumulate_grad=True,
opt_group=True)
for i in range(len(stage2_params)):
for j in range(len(stage3_params)):
if stage2_params[i].name == stage3_params[j].name:
np.testing.assert_allclose(
stage2_params[i].numpy(),
stage3_params[j].numpy(),
rtol=1e-6)
# fp16
stage2_params = train_mlp(
mlp5, sharding_stage=2, use_pure_fp16=True, opt_group=False)
stage3_params = train_mlp(
mlp6, sharding_stage=3, use_pure_fp16=True, opt_group=False)
for i in range(len(stage2_params)):
for j in range(len(stage3_params)):
if stage2_params[i].name == stage3_params[j].name:
np.testing.assert_allclose(
stage2_params[i].numpy(),
stage3_params[j].numpy(),
rtol=1e-6)
# fp16 recompute
stage3_params = train_mlp(
mlp7, sharding_stage=3, use_pure_fp16=True, opt_group=False)
stage3_params_re = train_mlp(
mlp8,
sharding_stage=3,
use_pure_fp16=True,
opt_group=False,
recompute=True)
for i in range(len(stage3_params)):
for j in range(len(stage3_params_re)):
if stage3_params[i].name == stage3_params_re[j].name:
np.testing.assert_allclose(
stage3_params[i].numpy(),
stage3_params_re[j].numpy(),
rtol=1e-6)
return
if __name__ == '__main__':
test_stage2_stage3()
# Copyright (c) 2021 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.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
from test_parallel_dygraph_dataparallel import TestMultipleGpus
class TestDygraphShardingStage3(TestMultipleGpus):
# check sharding logic as well as the accuracy with single mode
def test_dygraph_sharding_optimizer_stage3(self):
self.run_mnist_2gpu('dygraph_sharding_stage3.py')
if __name__ == "__main__":
unittest.main()
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