update for untrainable params for stage3.

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage3.py

@@ -346,7 +346,7 @@ def _add_manage_info(trainable_param):
 
         current_params = list()
         for p in current_layer_params:
-            if p.trainable and p._numel() > self._segment_size:
+            if p._numel() > self._segment_size:
                 current_params.append(_add_manage_info(p))
             elif p.trainable:
                 self._unslice_params.add(_UnsliceParam(p))
@@ -430,7 +430,11 @@ def _param_storage(self, param, buffer_size):
         param.status = "part"
 
         # Updata optimizer master weights
-        if param.dtype == Type.fp16.value and not self._offload:
+        if (
+            param.trainable
+            and param.dtype == Type.fp16.value
+            and not self._offload
+        ):
             master_tensor = paddle.cast(param.fw_storage, Type.fp32.value)
             master_tensor.name = param.name
             self._optim._master_weights[param.fw_storage.name] = master_tensor
@@ -599,6 +603,9 @@ def _register_backward_hooks(self):
     def _get_allreduce_fn(self, param):
         @paddle.autograd.no_grad()
         def allreduce_(*_):
+            assert (
+                param.trainable
+            ), "the param must be trainable for grad allreduced"
             if param.name in self._task_flow.full_grad.keys():
                 full_grad = self._task_flow.full_grad[param.name]
                 # Only support sync allreduce current rank's layer now
@@ -962,6 +969,8 @@ def _allgather_buffer(
 @paddle.autograd.no_grad()
 def _create_params_grad(trainable_params, param2buffer_size, task_flow):
     for param in trainable_params:
+        if not param.trainable:
+            continue
         if param.name in task_flow.full_grad.keys():
             continue
         assert isinstance(param2buffer_size[param.name], int)

python/paddle/distributed/sharding/group_sharded.py

@@ -140,7 +140,9 @@ def check_dtype(param):
 
     params_fp16 = list(filter(check_dtype, model.parameters()))
     if scaler is None and len(params_fp16) > 0:
-        raise ValueError("Please enter the correct scaler.")
+        logger_.warning(
+            "the input of scaler is None, please ensure the logic of your scaler outside is same as GroupShardedScaler."
+        )
     # convert model/optimizer/scaler
     if level in ['os', 'os_g']:
         logger_.info("*" * 30)

python/paddle/fluid/tests/unittests/collective/fleet/dygraph_group_sharded_stage3_eager.py

@@ -0,0 +1,178 @@
+# Copyright (c) 2022 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 paddle
+from paddle import nn
+from paddle.distributed.sharding import group_sharded_parallel
+from paddle.fluid.framework import _test_eager_guard
+
+paddle.seed(2022)
+np.random.seed(2022)
+
+
+class Model(nn.Layer):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.first_stage = nn.Linear(4096, 4096, bias_attr=False)
+        self.center_stage = nn.Linear(4096, 4096)
+        self.center_stage.weight.stop_gradient = True
+        self.center_stage.bias.stop_gradient = True
+        self.final_stage = nn.Linear(4096, 2, bias_attr=False)
+
+    def forward(self, x):
+        x = self.first_stage(x)
+        x = self.center_stage(x)
+        x = self.final_stage(x)
+        return x
+
+
+def optimizer_setting(model, use_multi_precision):
+    optimizer = paddle.optimizer.AdamW(
+        learning_rate=0.001,
+        parameters=model.parameters(),
+        multi_precision=use_multi_precision,
+    )
+    return optimizer
+
+
+def train_mlp(
+    model,
+    shard_level="p_g_os",
+    use_multi_precision=False,
+    output_dir="",
+    amp_level='O1',
+    sync_buffers=False,
+    use_sharding=True,
+    data=None,
+):
+    optimizer = optimizer_setting(
+        model=model, use_multi_precision=use_multi_precision
+    )
+    if use_multi_precision:
+        model = paddle.amp.decorate(models=model, level=amp_level)
+
+    scaler = paddle.amp.GradScaler(init_loss_scaling=32768)
+
+    if use_sharding:
+        model, optimizer, scaler = group_sharded_parallel(
+            model=model,
+            optimizer=optimizer,
+            level=shard_level,
+            scaler=scaler,
+            sync_buffers=sync_buffers,
+        )
+
+    res_loss = []
+    for i in range(20):
+        model.train()
+        img = data[i]
+        with paddle.amp.auto_cast(use_multi_precision, level=amp_level):
+            out = model(img)
+            avg_loss = out.mean()
+
+        res_loss.append(avg_loss.item())
+
+        if not use_multi_precision:
+            avg_loss.backward()
+            optimizer.step()
+        else:
+            scaler.scale(avg_loss).backward()
+            scaler.step(optimizer)
+            scaler.update()
+
+        optimizer.clear_grad()
+
+    return res_loss
+
+
+def test_sharding_api():
+    paddle.distributed.init_parallel_env()
+
+    # just test warning
+    model = Model()
+    model = paddle.amp.decorate(models=model, level="O2")
+    optimizer = optimizer_setting(model=model, use_multi_precision=True)
+    model, optimizer, scaler = group_sharded_parallel(
+        model=model,
+        optimizer=optimizer,
+        level="p_g_os",
+    )
+
+    data = [paddle.randn([8, 4096]) for i in range(20)]
+
+    model = Model()
+    sd3_model = Model()
+    sd3_model.set_state_dict(model.state_dict())
+
+    # dp fp32
+    dp_fp32_loss = train_mlp(
+        model, use_multi_precision=False, use_sharding=False, data=data
+    )
+
+    # stage3 fp32
+    sd3_fp32_loss = train_mlp(
+        sd3_model,
+        shard_level="p_g_os",
+        use_multi_precision=False,
+        use_sharding=True,
+        data=data,
+    )
+
+    print("dp_fp32_loss: ", dp_fp32_loss)
+    print("sd3_fp32_loss: ", sd3_fp32_loss)
+
+    for i in range(len(dp_fp32_loss)):
+        np.testing.assert_allclose(
+            np.array(dp_fp32_loss[i]),
+            np.array(sd3_fp32_loss[i]),
+            rtol=1e-8,
+            atol=1e-8,
+        )
+
+    model = Model()
+    sd3_model = Model()
+    sd3_model.set_state_dict(model.state_dict())
+
+    # dp fp16
+    dp_fp16_loss = train_mlp(
+        model, use_multi_precision=True, use_sharding=False, data=data
+    )
+
+    # stage3 fp16
+    sd3_fp16_loss = train_mlp(
+        sd3_model,
+        shard_level="p_g_os",
+        use_multi_precision=True,
+        use_sharding=True,
+        data=data,
+    )
+
+    print("dp_fp316_loss: ", dp_fp32_loss)
+    print("sd3_fp32_loss: ", sd3_fp32_loss)
+
+    for i in range(len(dp_fp16_loss)):
+        np.testing.assert_allclose(
+            np.array(dp_fp16_loss[i]),
+            np.array(sd3_fp16_loss[i]),
+            rtol=1e-5,
+            atol=1e-5,
+        )
+
+
+if __name__ == '__main__':
+    with _test_eager_guard():
+        test_sharding_api()

python/paddle/fluid/tests/unittests/collective/fleet/test_dygraph_group_sharded_api_for_eager.py

@@ -27,6 +27,10 @@ class TestDygraphGroupSharded(TestMultipleGpus):
     def test_dygraph_group_sharded(self):
         self.run_mnist_2gpu('dygraph_group_sharded_api_eager.py')
 
+    # check stage3 for some functions.
+    def test_dygraph_group_sharded(self):
+        self.run_mnist_2gpu('dygraph_group_sharded_stage3_eager.py')
+
 
 if __name__ == "__main__":
     unittest.main()