Add dygraph sharding stage3 (#38052)

paddle/pten/core/dense_tensor.cc

@@ -435,6 +435,10 @@ inline T* DenseTensor::mutable_data(const paddle::platform::Place& place,
 }
 
 void DenseTensor::ShareBufferWith(const DenseTensor& tensor) {
+  if (storage_ == nullptr) {
+    storage_ = make_intrusive<paddle::experimental::SharedStorage>(
+        paddle::platform::CPUPlace());
+  }
   if (storage_ != nullptr && tensor.storage_ != nullptr) {
     storage_->set_data_shared(tensor.storage_->data_shared());
   }

python/paddle/distributed/fleet/meta_parallel/sharding/sharding_utils.py

@@ -152,6 +152,9 @@ def ShardingScaler(scaler):
         param_grads = []
         param_grads_fp16 = []
         param_grads_fp32 = []
+        if hasattr(optimizer, "update_slice"):
+            optimizer.update_slice()
+            optimizer.update_scaler = True
 
         if getattr(optimizer._optim, '_param_groups', None) and isinstance(
                 optimizer._optim._param_groups[0], dict):
@@ -161,27 +164,21 @@ def ShardingScaler(scaler):
                     if param._grad_ivar() is not None:
                         param_grads.append(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())
                         else:
                             param_grads_fp32.append(param._grad_ivar())
         else:
-            param_grads = [
-                param._grad_ivar() for param in optimizer._optim._parameter_list
-                if param._grad_ivar() is not None
-            ]
-            param_grads_fp16 = [
-                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.FP16
-                           )
-            ]
-            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
-                           )
-            ]
+            for param in optimizer._optim._parameter_list:
+                if param.grad is not None:
+                    param_grads.append(param.grad)
+                    if param.grad.dtype in [
+                            core.VarDesc.VarType.FP16, paddle.float16
+                    ]:
+                        param_grads_fp16.append(param.grad)
+                    else:
+                        param_grads_fp32.append(param.grad)
+
         temp_found_inf_fp16 = to_variable(np.array([0]).astype(np.bool))
         temp_found_inf_fp32 = to_variable(np.array([0]).astype(np.bool))
 

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -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_dygraph_sharding_optimizer_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_parallel_dygraph_mp_layers)
 list(APPEND DIST_TEST_OPS test_hybrid_parallel_inference_helper)
@@ -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_dygraph_sharding_optimizer_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_parallel_dygraph_mp_layers)
     LIST(REMOVE_ITEM TEST_OPS test_imperative_auto_mixed_precision)
@@ -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_dygraph_sharding_optimizer_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_parallel_dygraph_mp_layers PROPERTIES TIMEOUT 120)
     set_tests_properties(test_hybrid_parallel_inference_helper PROPERTIES TIMEOUT 120)

python/paddle/fluid/tests/unittests/dygraph_sharding_stage3.py

+# -*- 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()

python/paddle/fluid/tests/unittests/test_dygraph_sharding_stage3.py

+# 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()