[Dygraph]optimizer sharding paramters

python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/sharding_optimizer_stage2.py

@@ -65,9 +65,9 @@ def __init__(self,
                  params,
                  optim,
                  group=None,
-                 broadcast_fp16=False,
                  offload=False,
                  device="gpu",
+                 pertrain_sync_models=True,
                  **kw):
 
         super().__init__(optim._learning_rate, params, kw)
@@ -98,8 +98,12 @@ def __init__(self,
 
         self.world_size = self.group.nranks
         self.rank = self.group.rank
+        self._global_root_rank = 0
+
+        # Synchronous all ranks models
+        if pertrain_sync_models:
+            self._sync_params_and_buffers()
 
-        self.broadcast_fp16 = broadcast_fp16
         self.param_storages = {}  # {dtype: {rank: InternalStorage}}
 
         if isinstance(self._optim._grad_clip, ClipGradByGlobalNorm):
@@ -132,6 +136,22 @@ def __init__(self,
         # Update optimizer parameters and adjust parameter storage and use according to rank.
         self._update_opt_status()
 
+    @paddle.no_grad()
+    def _sync_params_and_buffers(self):
+        """
+        Sync all model states for all ranks
+        """
+
+        for p in self._local_params:
+            dist.broadcast(
+                p,
+                src=self._global_root_rank,
+                group=self.group,
+                use_calc_stream=True)
+
+        # Multi stream operation will be supported later
+        dist.wait(tensor=p, group=self.group, use_calc_stream=True)
+
     def _generate_master_params(self, trainable_params):
         if self.offload:
             for param in trainable_params:

python/paddle/distributed/fleet/meta_parallel/sharding/sharding_stage2.py

@@ -61,12 +61,10 @@ def __init__(
             sharding_optimizer,
             group=None,
             sync_buffers=False,
-            pertrain_sync_models=True,
             buffer_max_size=2**23,  #8MB
             auto_refresh_trainable=True,
             device="gpu",
-            use_grad_storage=True,
-            accumulate_grads=False):
+            use_grad_storage=True):
         super().__init__()
 
         # training options
@@ -81,9 +79,6 @@ def __init__(
         self._sync_buffers = sync_buffers
         self._auto_refresh_trainable = auto_refresh_trainable
 
-        # Gradient accumulation, Gradient flip
-        self._accumulate_grads = accumulate_grads
-
         # Communication related attributes
         self._group = dist.new_group(_get_global_group()
                                      .ranks) if group is None else group
@@ -128,16 +123,11 @@ def __init__(
         # Set backward pass hooks
         self._bw_hooks = []
 
-        # Synchronous all ranks models
-        if pertrain_sync_models:
-            self._sync_params_and_buffers()
-
         # Set tasks flow
         self._tasks_flow = deque()
 
         # Define optimizer step and clear_grad
-        if self._accumulate_grads:
-            self._redefine_opt_step()
+        self._redefine_opt_step()
         self._redefine_opt_clear()
 
     def forward(self, *inputs, **kwargs):
@@ -313,9 +303,6 @@ def reduce(*_):
 
                     # Change reduce information
                     self._grad_reduced[index] = False
-                    if not self._accumulate_grads:
-                        param.grad.scale_(scale=self._world_size_scaling)
-                        param._reset_grad_inplace_version(True)
 
                     # Clear the gradient that does not belong to the current rank through the callback function
                     def cleanup():
@@ -362,11 +349,6 @@ def reduce(*_):
                     if grad_storage.all_checked_in:
                         assert grad_storage.buffer is not None
 
-                        # Normalize all ranks grad_storage
-                        if not self._accumulate_grads:
-                            grad_storage.buffer.scale_(
-                                scale=self._world_size_scaling)
-
                         # Clearing up the grad_storage buffer
                         def cleanup():
                             if dst_rank != self._rank:
@@ -432,22 +414,6 @@ def _setup_backward_hooks(self):
             self._bw_hooks.append(
                 param._register_backward_hook(reduce_function))
 
-    @paddle.no_grad()
-    def _sync_params_and_buffers(self):
-        """
-        Sync all model states for all ranks
-        """
-
-        for t in self._layer.parameters():
-            dist.broadcast(
-                t,
-                src=self._global_root_rank,
-                group=self._group,
-                use_calc_stream=True)
-
-        # Multi stream operation will be supported later
-        dist.wait(tensor=t, group=self._group, use_calc_stream=True)
-
     def _setup_use_grad_storage(self):
         """
         Integrate the parameters gradient into a continuous memory according to rank, and support the update of training parameters.
@@ -555,8 +521,6 @@ def _rank_buffer_size(self, buffer_max_size, model_size):
         return rank_buffer_size
 
     def _redefine_opt_step(self):
-        if not self._accumulate_grads:
-            return
         grad_func = self._grad_scale
         for opt in self._sharding_optimizers:
             opt_step = opt.step

python/paddle/distributed/fleet/meta_parallel/sharding/sharding_stage3.py

@@ -72,7 +72,6 @@ def __init__(self,
                  device="gpu",
                  segment_size=2**15,
                  pertrain_sync_models=True,
-                 accumulate_grads=False,
                  offload=False,
                  sync_comm=False):
         super().__init__()
@@ -82,7 +81,6 @@ def __init__(self,
         self._layer = layer
         self._default_device = device
         self.__sync_buffers = sync_buffers
-        self._accumulate_grads = accumulate_grads
         self._offload = offload
         self._sync_comm = sync_comm
         # segmentation size
@@ -190,6 +188,7 @@ def _clear_gradients(self):
             param.fw_storage.clear_gradient(False)
             param.fw_storage._gradient_set_empty(False)
             param.bw_storage._clear()
+            param.bw_storage = None
         # 2.Handle unslice param
         if not self._offload:
             for grad_storage in self._grad_storages.values():
@@ -446,13 +445,12 @@ def _update_params(self):
                 param,
                 "fw_storage"), "Find {} don't have fw_storage attribute".format(
                     param.name)
-
-            if self._accumulate_grads:
-                if self._offload:
-                    with device_guard(device="cpu"):
-                        param.bw_storage.scale_(scale=self._world_size_scaling)
-                else:
+            # Gradient average
+            if self._offload:
+                with device_guard(device="cpu"):
                     param.bw_storage.scale_(scale=self._world_size_scaling)
+            else:
+                param.bw_storage.scale_(scale=self._world_size_scaling)
             param.fw_storage = _VarBaseWrapper(param)
             assert param.fw_storage.grad is None
             param.fw_storage._copy_gradient_from(param.bw_storage)
@@ -526,17 +524,14 @@ def _get_allreduce_fn(self, param):
         def reduce(*_):
             if param.name in self._task_flow.full_grad.keys():
                 full_grad = self._task_flow.full_grad[param.name]
-                if not self._accumulate_grads:
-                    full_grad.scale_(scale=self._world_size_scaling)
                 # Only support sync allreduce current rank's layer now
                 dist.all_reduce(
                     tensor=full_grad, group=self._group, use_calc_stream=True)
                 dist.wait(
                     tensor=full_grad, group=self._group, use_calc_stream=True)
 
                 start, end = self._param2buffer[param.name][self._rank]
-                if not self._accumulate_grads or param.bw_storage is None or not param.bw_storage.value(
-                ).get_tensor()._is_initialized():
+                if param.bw_storage is None:
                     param.bw_storage = core.VarBase(
                         full_grad._slice(start, end)).detach().clone()
                     if self._offload:

python/paddle/fluid/tests/unittests/dygraph_sharding_stage2.py

@@ -27,7 +27,7 @@
 from paddle.distributed.fleet.meta_optimizers.dygraph_optimizer.sharding_optimizer_stage2 import ShardingOptimizerStage2
 from paddle.distributed.fleet.meta_parallel.sharding.sharding_stage2 import ShardingStage2
 
-seed = 2021
+seed = 2022
 epoch = 2
 linear_size = 1000
 
@@ -105,11 +105,7 @@ def train_mlp(model,
             params=model.parameters(), optim=optimizer, group=group)
 
         model = ShardingStage2(
-            model,
-            optimizer,
-            group=group,
-            buffer_max_size=2**21,
-            accumulate_grads=batch_size == 20)
+            model, optimizer, group=group, buffer_max_size=2**21)
     else:
         optimizer = fleet.distributed_optimizer(optimizer)
         model = fleet.distributed_model(model)
@@ -140,6 +136,8 @@ def train_mlp(model,
             loss = paddle.nn.functional.cross_entropy(input=out, label=label)
 
             avg_loss = paddle.mean(x=loss.cast(dtype=paddle.float32))
+            if batch_size == 20:
+                avg_loss = avg_loss / 5
             avg_loss.backward()
 
             if not accumulate_grad:
@@ -166,6 +164,7 @@ def test_dp_stage2():
     mlp4.set_state_dict(state_dict)
     mlp5.set_state_dict(state_dict)
 
+    # DP VS stage2
     dp_params = train_mlp(
         mlp1, sharding_stage="dp", use_pure_fp16=False, opt_group=False)
     stage2_params = train_mlp(
@@ -174,7 +173,8 @@ def test_dp_stage2():
         np.testing.assert_allclose(
             dp_params[i].numpy(), stage2_params[i].numpy(), rtol=1e-6)
 
-    stage2_params = train_mlp(mlp3, sharding_stage=2)
+    # stage2 accumulate grad
+    stage2_params = train_mlp(mlp3, sharding_stage=2, accumulate_grad=True)
     stage2_accumulate_grad = train_mlp(
         mlp4, sharding_stage=2, batch_size=20, accumulate_grad=True)
     for i in range(len(stage2_params)):
@@ -184,6 +184,7 @@ def test_dp_stage2():
             rtol=1e-5,
             atol=1e-5)
 
+    # stage2 param list VS param group
     stage2_params = train_mlp(
         mlp2, sharding_stage=2, use_pure_fp16=False, opt_group=True)
     for i in range(len(dp_params)):

python/paddle/fluid/tests/unittests/dygraph_sharding_stage2_offload.py

@@ -43,13 +43,12 @@ def train_mlp(model, offload=False):
     optimizer = optimizer_setting(model=model, use_pure_fp16=True)
 
     model = paddle.amp.decorate(models=model, level='O2', save_dtype='float32')
-    scaler = paddle.amp.GradScaler(init_loss_scaling=32768)
+    scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
     scaler = ShardingScaler(scaler)
 
     optimizer = ShardingOptimizerStage2(
         params=model.parameters(), optim=optimizer, offload=offload)
-    model = ShardingStage2(
-        model, optimizer, buffer_max_size=2**21, accumulate_grads=False)
+    model = ShardingStage2(model, optimizer, buffer_max_size=2**21)
 
     train_reader = paddle.batch(
         reader_decorator(linear_size), batch_size=batch_size, drop_last=True)

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

@@ -101,18 +101,10 @@ def train_mlp(model,
         optimizer = ShardingOptimizerStage2(
             params=model.parameters(), optim=optimizer, group=group)
         model = ShardingStage2(
-            model,
-            optimizer,
-            group=group,
-            buffer_max_size=2**21,
-            accumulate_grads=batch_size == 20)
+            model, optimizer, group=group, buffer_max_size=2**21)
     elif sharding_stage == 3:
         model = ShardingStage3(
-            model,
-            optimizer=optimizer,
-            group=group,
-            accumulate_grads=batch_size == 20,
-            sync_comm=recompute)
+            model, optimizer=optimizer, group=group, sync_comm=recompute)
 
     # check optimizer.minimize() error
     if test_minimize:
@@ -231,7 +223,7 @@ def test_stage2_stage3():
             stage2_params[i].numpy(),
             stage3_params[i].numpy(),
             rtol=1e-4,
-            atol=1e-4)
+            atol=1e-3)
 
     # fp16 recompute
     stage3_params = train_mlp(

python/paddle/fluid/tests/unittests/dygraph_sharding_stage3_offload.py

@@ -91,11 +91,7 @@ def train_mlp(model,
         scaler = ShardingScaler(scaler)
 
     model = ShardingStage3(
-        model,
-        optimizer=optimizer,
-        group=group,
-        offload=offload,
-        accumulate_grads=accumulate_grad)
+        model, optimizer=optimizer, group=group, offload=offload)
 
     train_reader = paddle.batch(
         reader_decorator(), batch_size=batch_size, drop_last=True)