[cherry-pick]Support FP16 in HybridParallel and Fix bugs in HybridOptimizer (#36707)

* fix bugs in HybridParallelClipGrad of hybrid_parallel_optimizer (#36237)

* fix bugs in HybridParallelClipGrad of hybrid_parallel_optimizer

* update

* update

* fix bugs in mp_layers、pp_layers and HybridParallelClipGrad (#36144)

* fix calling bug of HybridParallelClipGrad

* fix bugs of HybridParallelClipGrad

* add unittest of pp with HybridParallelClipGrad

* fix bugs in mp_layers.py

* update

* fix bugs in pp_layers.py

* update

* [HybridParallel]Rebuild code for pipeline (#36396)

* add no_sync for parameters sync

* add pipeline for moe

* [HybridParallel]Support fp16 in dygraph hybrid parallel (#36420)

* [HybridParallel]Support fp16 in dygraph hybrid parallel

* update

* update

* update for recompute

* add unittest of pp+fp16

* add unittest of recompute+fp16

* update

* modify ut

* modify ut of cond (#36475)

* fix bugs of ClipGradByGlobalNorm in HybridParallel (#36555)

* fix bugs of ClipGradByGlobalNorm

* add unittests

* add unittests

* [HybridParallel]fix bug of check_inf in fleet_base.py (#36651)

* fix bug of check_inf

* fix allreduce

* support ClipGradByGlobalNorm in sharding (#36012)

* support ClipGradByGlobalNorm in sharding

* support ClipGradByGlobalNorm in sharding

* test=allcase

* Update test_linalg_cond.py

* Update hybrid_parallel_util.py

* Update hybrid_parallel_util.py
Co-authored-by: NShenLiang <1422485404@qq.com>
Co-authored-by: Nzhaoyingli <86812880+zhaoyinglia@users.noreply.github.com>

python/paddle/distributed/fleet/base/fleet_base.py

@@ -35,6 +35,8 @@ from ..meta_parallel import TensorParallel, model_parallel_random_seed
 from ..meta_parallel import PipelineParallel, ShardingParallel
 from ..meta_optimizers import HybridParallelOptimizer
 from paddle import _C_ops
+from paddle.fluid import core
+from paddle.fluid.dygraph import to_variable
 
 __all__ = []
 
@@ -1547,26 +1549,52 @@ class Fleet(object):
             if getattr(optimizer, '_param_groups', None) and isinstance(
                     optimizer._param_groups[0], dict):
                 param_grads = []
+                param_grads_fp16 = []
+                param_grads_fp32 = []
                 for group in optimizer._param_groups:
                     for param in group['params']:
                         if param._grad_ivar() is not None:
                             param_grads.append(param._grad_ivar())
+                            if param._grad_ivar(
+                            ).dtype == core.VarDesc.VarType.FP16:
+                                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._parameter_list
                     if param._grad_ivar() is not None
                 ]
-            _C_ops.check_finite_and_unscale(param_grads, self._scale,
-                                            param_grads, self._found_inf)
-
-            self._found_inf = paddle.cast(self._found_inf, dtype="int32")
+                param_grads_fp16 = [
+                    param._grad_ivar() for param in optimizer._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._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_fp32 = to_variable(np.array([0]).astype(np.bool))
+            if len(param_grads_fp16):
+                _C_ops.check_finite_and_unscale(param_grads_fp16, self._scale,
+                                                param_grads_fp16,
+                                                temp_found_inf_fp16)
+            if len(param_grads_fp32):
+                _C_ops.check_finite_and_unscale(param_grads_fp32, self._scale,
+                                                param_grads_fp32,
+                                                temp_found_inf_fp32)
+
+            self._found_inf = 1 if temp_found_inf_fp16 or temp_found_inf_fp32 else 0
+            is_found_inf = paddle.to_tensor([self._found_inf], dtype="int32")
 
             # TODO(shenliang03) Since dp allreduce in the optimizer is 
             # after the gradscaler, check_finite needs to synchronize global 
             # information. In the future, we should use check_group to speed.
             paddle.distributed.all_reduce(
-                self._found_inf, op=paddle.distributed.ReduceOp.MAX, group=None)
-            self._found_inf = paddle.cast(self._found_inf, dtype="bool")
+                is_found_inf, op=paddle.distributed.ReduceOp.MAX, group=None)
+            self._found_inf = is_found_inf.numpy()[0]
 
         # Only tensor_parallel and pipeline_parallel need to modify scaler
         if self._hcg.get_parallel_mode() in (ParallelMode.TENSOR_PARALLEL,

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

@@ -12,5 +12,6 @@
 # See the License for the specific language governing permissions and
 from .hybrid_parallel_optimizer import HybridParallelOptimizer
 from .hybrid_parallel_gradscaler import HybridParallelGradScaler
+from .dygraph_sharding_optimizer import DygraphShardingOptimizer
 
 __all__ = []

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

@@ -50,7 +50,12 @@ class HybridParallelClipGrad:
     @imperative_base.no_grad
     def _dygraph_clip(self, params_grads):
         params_and_grads = []
-        sum_square_list = []
+
+        sum_square_dist_fp16 = []
+        sum_square_dist_fp32 = []
+        sum_square_not_dist_fp16 = []
+        sum_square_not_dist_fp32 = []
+
         for p, g in params_grads:
             if g is None:
                 continue
@@ -62,32 +67,98 @@ class HybridParallelClipGrad:
                 merge_grad = layers.get_tensor_from_selected_rows(merge_grad)
             square = layers.square(merge_grad)
             sum_square = layers.reduce_sum(square)
-            sum_square_list.append(sum_square)
 
-        # all parameters have been filterd out
-        if len(sum_square_list) == 0:
-            return params_grads
-
-        global_norm_var = layers.concat(sum_square_list)
-        global_norm_var = layers.reduce_sum(global_norm_var)
-        # add all reduce to get global norm in world size
-        paddle.distributed.all_reduce(global_norm_var,
-                                      self._hcg.get_check_parallel_group())
-        global_norm_var = layers.sqrt(global_norm_var)
+            not_shared_enable = (not hasattr(p, 'is_firstly_shared')) or (
+                hasattr(p, 'is_firstly_shared') and
+                getattr(p, 'is_firstly_shared', True))
+
+            if not_shared_enable:
+                if p.is_distributed:
+                    if p.dtype == paddle.float16:
+                        sum_square_dist_fp16.append(sum_square)
+                    elif p.dtype == paddle.float32:
+                        sum_square_dist_fp32.append(sum_square)
+                else:
+                    if p.dtype == paddle.float16:
+                        sum_square_not_dist_fp16.append(sum_square)
+                    elif p.dtype == paddle.float32:
+                        sum_square_not_dist_fp32.append(sum_square)
+
+        # global norm of distributed FP16 params_and_grads
+        if len(sum_square_dist_fp16) == 0:
+            global_norm_dist_fp16 = paddle.to_tensor([0.], dtype=paddle.float32)
+        else:
+            global_norm_dist_fp16 = layers.concat(sum_square_dist_fp16)
+            global_norm_dist_fp16 = layers.reduce_sum(global_norm_dist_fp16)
+            global_norm_dist_fp16 = paddle.cast(
+                global_norm_dist_fp16, dtype=paddle.float32)
+
+        # global norm of non-distributed FP16 params_and_grads
+        if len(sum_square_not_dist_fp16) == 0:
+            global_norm_not_dist_fp16 = paddle.to_tensor(
+                [0.], dtype=paddle.float32)
+        else:
+            global_norm_not_dist_fp16 = layers.concat(sum_square_not_dist_fp16)
+            global_norm_not_dist_fp16 = layers.reduce_sum(
+                global_norm_not_dist_fp16)
+            global_norm_not_dist_fp16 = paddle.cast(
+                global_norm_not_dist_fp16, dtype=paddle.float32)
+
+        # global norm of distributed FP32 params_and_grads
+        global_norm_dist_fp32 = layers.concat(sum_square_dist_fp32) if len(
+            sum_square_dist_fp32) != 0 else paddle.to_tensor(
+                [0.], dtype=paddle.float32)
+        global_norm_dist_fp32 = layers.reduce_sum(global_norm_dist_fp32)
+
+        # global norm of non-distributed FP32 params_and_grads
+        global_norm_not_dist_fp32 = layers.concat(
+            sum_square_not_dist_fp32) if len(
+                sum_square_not_dist_fp32) != 0 else paddle.to_tensor(
+                    [0.], dtype=paddle.float32)
+        global_norm_not_dist_fp32 = layers.reduce_sum(global_norm_not_dist_fp32)
+
+        global_norm_var_dist = global_norm_dist_fp16 + global_norm_dist_fp32
+        global_norm_var_not_dist = global_norm_not_dist_fp16 + global_norm_not_dist_fp32
+
+        # add all reduce to get global norm of distributed params_and_grads
+        if self._hcg.get_model_parallel_world_size() > 1:
+            paddle.distributed.all_reduce(
+                global_norm_var_dist,
+                group=self._hcg.get_check_parallel_group())
+
+        # add all reduce to get global norm of non-distributed params_and_grads in groups of pp
+        if self._hcg.get_pipe_parallel_world_size() > 1:
+            paddle.distributed.all_reduce(
+                global_norm_var_not_dist,
+                group=self._hcg.get_pipe_parallel_group())
+
+        # In Sharding mode, param and grad is mapping different rank in optimizer.
+        # ClipGradByGlobalNorm need allreduce to get globol norm
+        if self._hcg.get_sharding_parallel_world_size() > 1:
+            paddle.distributed.all_reduce(
+                global_norm_var_not_dist,
+                group=self._hcg.get_sharding_parallel_group())
+
+        global_norm_var_fp32 = layers.sqrt(global_norm_var_dist +
+                                           global_norm_var_not_dist)
 
         max_global_norm = layers.fill_constant(
-            shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm)
+            shape=[1], dtype=global_norm_var_fp32.dtype, value=self.clip_norm)
         clip_var = layers.elementwise_div(
             x=max_global_norm,
             y=layers.elementwise_max(
-                x=global_norm_var, y=max_global_norm))
+                x=global_norm_var_fp32, y=max_global_norm))
+        clip_var_fp16 = paddle.cast(clip_var, paddle.float16)
         for p, g in params_grads:
             if g is None:
                 continue
             if getattr(p, 'need_clip', True) is False:
                 params_and_grads.append((p, g))
                 continue
-            new_grad = layers.elementwise_mul(x=g, y=clip_var)
+            if p.dtype == paddle.float16:
+                new_grad = layers.elementwise_mul(x=g, y=clip_var_fp16)
+            else:
+                new_grad = layers.elementwise_mul(x=g, y=clip_var)
             params_and_grads.append((p, new_grad))
 
         return params_and_grads
@@ -96,7 +167,7 @@ class HybridParallelClipGrad:
         return getattr(self._clip, item)
 
     def __call__(self, params_grads):
-        return self._clip(params_grads)
+        return self._dygraph_clip(params_grads)
 
 
 class HybridParallelOptimizer:
@@ -112,7 +183,7 @@ class HybridParallelOptimizer:
         self._need_dp = (self._hcg.get_data_parallel_world_size() > 1)
 
         # NOTE(shenliang03): Because of the pure DataParallel mode, the gradient synchronization 
-        # is achieved through reducer, so there is no need to call fuse_allreduce in oprimizer. 
+        # is achieved through reducer, so there is no need to call fuse_allreduce in optimizer. 
         self._dp_enable = not self._use_dp_mode and self._need_dp
 
         self._sharding_enable = (
@@ -120,11 +191,16 @@ class HybridParallelOptimizer:
 
         if isinstance(self._inner_opt._grad_clip,
                       ClipGradByGlobalNorm) and not self._use_dp_mode:
-            logger.warning("using ClipGradByGlobalNorm in TensorParallel, the origin " \
-                  "optmizer'grad clip will be changed.")
-
-            self._inner_opt._grad_clip = HybridParallelClipGrad(
-                self._inner_opt._grad_clip, hcg)
+            logger.warning("While using ClipGradByGlobalNorm in TensorParallel, PipelineParallel " \
+                           "or Sharding, the grad clip of original optimizer will be changed.")
+
+            if self._sharding_enable:
+                # change sharding inner_optimizer's _grad_clip
+                self._inner_opt._inner_optimizer._grad_clip = HybridParallelClipGrad(
+                    self._inner_opt._grad_clip, hcg)
+            else:
+                self._inner_opt._grad_clip = HybridParallelClipGrad(
+                    self._inner_opt._grad_clip, hcg)
 
     @imperative_base.no_grad
     @framework.dygraph_only

python/paddle/distributed/fleet/meta_parallel/parallel_layers/mp_layers.py

@@ -70,7 +70,7 @@ class VocabParallelEmbedding(Layer):
                 dtype=self._dtype,
                 is_bias=False)
 
-        self.weight.is_distributed = True
+        self.weight.is_distributed = True if self.is_mp else False
 
     def forward(self, x):
         if self.is_mp:
@@ -135,7 +135,7 @@ class ColumnParallelLinear(Layer):
                 dtype=self._dtype,
                 is_bias=False)
 
-        self.weight.is_distributed = True
+        self.weight.is_distributed = True if self.is_mp else False
 
         if has_bias:
             # initialize bias to zero like Megatron
@@ -144,7 +144,7 @@ class ColumnParallelLinear(Layer):
                 attr=paddle.nn.initializer.Constant(value=0.0),
                 dtype=self._dtype,
                 is_bias=True)
-            self.bias.is_distributed = True
+            self.bias.is_distributed = True if self.is_mp else False
         else:
             self.bias = None
 
@@ -212,7 +212,7 @@ class RowParallelLinear(Layer):
                 dtype=self._dtype,
                 is_bias=False)
 
-        self.weight.is_distributed = True
+        self.weight.is_distributed = True if self.is_mp else False
 
         if has_bias:
             self.bias = self.create_parameter(

python/paddle/distributed/fleet/meta_parallel/parallel_layers/pp_layers.py

@@ -261,6 +261,10 @@ class PipelineLayer(Layer):
                     src=min(comm['ranks']),
                     group=comm['group'])
 
+            for param in comm['layer'].parameters():
+                if self.global_rank != min(comm['ranks']):
+                    setattr(param, 'is_firstly_shared', False)
+
     def allreduce_shared_weight_gradients(self):
         for key, comm in self.shared_comm.items():
             param = getattr(self.shared_layers[key], comm['weight_attr'])
@@ -316,6 +320,9 @@ class PipelineLayer(Layer):
                     self.shared_layers[layer.layer_name] = layer.build_layer()
                     self.shared_weight_attrs[
                         layer.layer_name] = layer.shared_weight_attr
+                    for param in self.shared_layers[
+                            layer.layer_name].parameters():
+                        setattr(param, "is_firstly_shared", True)
 
                 if layer.forward_func is None:
                     self.run_function.append(self.shared_layers[

python/paddle/distributed/fleet/meta_parallel/pipeline_parallel.py

@@ -77,26 +77,15 @@ class PipelineParallel(MetaParallelBase):
             logger.info("start broadcast dp parameters")
             broadcast_dp_parameters(self._layers, self._hcg)
 
-    def train_batch(self, data, optimizer, lr_scheduler=None, scaler=None):
-        assert isinstance(optimizer, HybridParallelOptimizer), (
-            'optimizer should be HybridParallelOptimizer subclass.')
-
-        assert fluid.framework._dygraph_tracer()._has_grad, (
-            'Please enable the generation of gradients.')
-
-        if self.is_first_stage or self.is_last_stage:
-            assert data is not None, (
-                "For the first and the last stage, the data must be set.")
-        else:
-            data = None
+    def forward_backward_pipeline(self, data, scaler=None):
+        # use the 1f1b scheduling strategy.
+        # this strategy is inspired by:
+        # https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/schedules.py
 
-        self.optimizer = optimizer
-        self.lr_scheduler = lr_scheduler
         self.scaler = scaler
-        self.data = data
-        self._compute_loss = True
 
-        self._layers.train()
+        # store data for train
+        self.data = data
 
         # store total loss of entire batch
         self.total_loss = None
@@ -104,10 +93,6 @@ class PipelineParallel(MetaParallelBase):
         # store data id for micro_batch
         self.micro_batch_id = 0
 
-        # Next, use the 1f1b scheduling strategy.
-        # this strategy is inspired by:
-        # https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/schedules.py
-
         startup_steps = (self.num_stages - self.stage_id - 1)
         startup_steps = min(startup_steps, self.accumulate_steps)
         steady_steps = self.accumulate_steps - startup_steps
@@ -160,12 +145,36 @@ class PipelineParallel(MetaParallelBase):
             p2p.send_backward(input_tensor_grad)
 
         self._layers.allreduce_shared_weight_gradients()
+        with paddle.amp.auto_cast(enable=False):
+            train_loss = self._broadcast_final_loss()
+        return train_loss
 
-        self.train_loss = self._broadcast_final_loss()
+    def train_batch(self, data, optimizer, lr_scheduler=None, scaler=None):
+        assert isinstance(optimizer, HybridParallelOptimizer), (
+            'optimizer should be HybridParallelOptimizer subclass.')
+
+        assert fluid.framework._dygraph_tracer()._has_grad, (
+            'Please enable the generation of gradients.')
+
+        if self.is_first_stage or self.is_last_stage:
+            assert data is not None, (
+                "For the first and the last stage, the data must be set.")
+        else:
+            data = None
+
+        self.optimizer = optimizer
+        self.lr_scheduler = lr_scheduler
+
+        self._layers.train()
+
+        # 1f1b for pipeline
+        train_loss = self.forward_backward_pipeline(data, scaler)
 
         # optimizer
-        self._optimizer_step()
-        return self.train_loss
+        with paddle.amp.auto_cast(enable=False):
+            self._optimizer_step()
+
+        return train_loss
 
     def eval_batch(self, data, compute_loss=False):
         self._layers.eval()
@@ -233,12 +242,13 @@ class PipelineParallel(MetaParallelBase):
                     output_tensor, paddle.Tensor
                 ), "Currently, loss_fn should obtain Paddle.Tensor dtype"
 
-                if self.accumulate_steps > 1:
-                    output_tensor = output_tensor / self.accumulate_steps
+                with paddle.amp.auto_cast(enable=False):
+                    if self.accumulate_steps > 1:
+                        output_tensor = output_tensor / self.accumulate_steps
 
-                if self.total_loss is None:
-                    self.total_loss = paddle.zeros_like(output_tensor)
-                self.total_loss += output_tensor.detach()
+                    if self.total_loss is None:
+                        self.total_loss = paddle.zeros_like(output_tensor)
+                    self.total_loss += output_tensor.detach()
 
         self.micro_batch_id += 1
         return output_tensor
@@ -312,13 +322,29 @@ class PipelineParallel(MetaParallelBase):
         if self.is_last_stage:
             assert self.total_loss is not None, "train_batch() in last stage should obtain vaild loss"
             loss = self.total_loss.detach()
+            is_fp32 = paddle.to_tensor(
+                1) if loss.dtype == paddle.float32 else paddle.to_tensor(0)
+            paddle.distributed.broadcast(
+                is_fp32,
+                src=self.global_rank,
+                use_calc_stream=True,
+                group=self.pp_group)
             paddle.distributed.broadcast(
                 loss,
                 src=self.global_rank,
                 use_calc_stream=True,
                 group=self.pp_group)
         else:
-            loss = paddle.zeros(shape=[1], dtype="float32")
+            is_fp32 = paddle.to_tensor(1)
+            paddle.distributed.broadcast(
+                is_fp32,
+                src=self._hcg.get_rank_from_stage(self.num_stages - 1),
+                use_calc_stream=True,
+                group=self.pp_group)
+            loss = paddle.zeros(
+                shape=[1],
+                dtype="float32") if is_fp32.numpy()[0] else paddle.zeros(
+                    shape=[1], dtype="float16")
             paddle.distributed.broadcast(
                 loss,
                 src=self._hcg.get_rank_from_stage(self.num_stages - 1),

python/paddle/distributed/fleet/meta_parallel/pp_utils/utils.py

@@ -198,11 +198,14 @@ class _HPRecomputeFunction(PyLayer):
 
         # TODO support AMP
         tracer = framework._dygraph_tracer()
-        if tracer._amp_level == core.AmpLevel.O0:
-            ctx.is_fw_autocast = False
+        ctx.is_fw_autocast = False if tracer._amp_level == core.AmpLevel.O0 else True
+        if tracer._amp_level == core.AmpLevel.O2:
+            ctx.amp_level = 'O2'
+        elif tracer._amp_level in (core.AmpLevel.O1, core.AmpLevel.O0):
+            ctx.amp_level = 'O1'
         else:
-            ctx.is_fw_autocast = True
-        ctx.amp_mode = 'O1'
+            raise ValueError("unsupported amp level: {}".format(
+                tracer._amp_level))
         ctx.amp_white_list, ctx.amp_black_list = tracer._get_amp_op_list()
 
         with paddle.no_grad():
@@ -263,7 +266,7 @@ class _HPRecomputeFunction(PyLayer):
                         enable=ctx.is_fw_autocast,
                         custom_white_list=ctx.amp_white_list,
                         custom_black_list=ctx.amp_black_list,
-                        level=ctx.amp_mode):
+                        level=ctx.amp_level):
                     detached_inputs = detach_variable(tuple(inputs))
                     outputs = ctx.run_function(*detached_inputs)
 

python/paddle/distributed/fleet/utils/recompute.py

@@ -98,11 +98,14 @@ class RecomputeFunction(PyLayer):
 
         # TODO support AMP
         tracer = framework._dygraph_tracer()
-        if tracer._amp_level == core.AmpLevel.O0:
-            ctx.is_fw_autocast = False
+        ctx.is_fw_autocast = False if tracer._amp_level == core.AmpLevel.O0 else True
+        if tracer._amp_level == core.AmpLevel.O2:
+            ctx.amp_level = 'O2'
+        elif tracer._amp_level in (core.AmpLevel.O1, core.AmpLevel.O0):
+            ctx.amp_level = 'O1'
         else:
-            ctx.is_fw_autocast = True
-        ctx.amp_mode = 'O1'
+            raise ValueError("unsupported amp level: {}".format(
+                tracer._amp_level))
         ctx.amp_white_list, ctx.amp_black_list = tracer._get_amp_op_list()
 
         with paddle.no_grad():
@@ -133,7 +136,7 @@ class RecomputeFunction(PyLayer):
                             enable=ctx.is_fw_autocast,
                             custom_white_list=ctx.amp_white_list,
                             custom_black_list=ctx.amp_black_list,
-                            level=ctx.amp_mode):
+                            level=ctx.amp_level):
                         detached_inputs = detach_variable(tuple(inputs))
                         outputs = ctx.run_function(*detached_inputs)
             else:
@@ -141,7 +144,7 @@ class RecomputeFunction(PyLayer):
                         enable=ctx.is_fw_autocast,
                         custom_white_list=ctx.amp_white_list,
                         custom_black_list=ctx.amp_black_list,
-                        level=ctx.amp_mode):
+                        level=ctx.amp_level):
                     detached_inputs = detach_variable(tuple(inputs))
                     outputs = ctx.run_function(*detached_inputs)
 

python/paddle/fluid/dygraph/parallel.py

@@ -354,9 +354,15 @@ def sync_params_buffers(model,
         if not isinstance(param, core.VarBase):
             raise TypeError("The data type of '%s' must be Varbase" %
                             param.name)
+
         # is_distributed param not need to sync when in mp mode
-        if is_model_parallel and isinstance(param, ParamBase):
-            if param.is_distributed:
+        if isinstance(param, ParamBase):
+            if is_model_parallel and param.is_distributed:
+                continue
+
+            # NOTE(shenliang03): Support situations that do not require synchronization parameters, 
+            # such as moe's expert parameters
+            if getattr(param, "no_sync", False):
                 continue
 
         model_vars.append(param.detach())

python/paddle/fluid/framework.py

@@ -5995,7 +5995,7 @@ class ParamBase(core.VarBase):
 
         self.need_clip = kwargs.get('need_clip', True)
 
-        self.is_distributed = False
+        self.is_distributed = kwargs.get('is_distributed', False)
         # self.block = default_main_program().global_block()
 
     @property

python/paddle/fluid/tests/unittests/hybrid_parallel_mp_fp16.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 division
+from __future__ import print_function
+
+import paddle
+import numpy as np
+from hybrid_parallel_mp_model import TestDistMPTraning
+import paddle.distributed.fleet as fleet
+import unittest
+
+
+class TestMPFP16(TestDistMPTraning):
+    def build_optimizer(self, model):
+        grad_clip = paddle.nn.ClipGradByGlobalNorm(1.0)
+        scheduler = paddle.optimizer.lr.ExponentialDecay(
+            learning_rate=0.001, gamma=0.999, verbose=True)
+        optimizer = paddle.optimizer.SGD(scheduler,
+                                         grad_clip=grad_clip,
+                                         parameters=model.parameters())
+
+        model, optimizer = paddle.amp.decorate(
+            models=model,
+            optimizers=optimizer,
+            level='O2',
+            save_dtype='float32')
+
+        return optimizer
+
+    def train_batch(self, batch, model, optimizer, is_mp):
+        scaler = paddle.amp.GradScaler(init_loss_scaling=5160)
+        if is_mp:
+            scaler = fleet.distributed_scaler(scaler)
+        with paddle.amp.auto_cast(enable=True, level="O2"):
+            output = model(batch)
+            loss = output.mean()
+
+        scaled = scaler.scale(loss)
+        scaled.backward()
+        scaler.step(optimizer)
+        scaler.update()
+        optimizer.clear_grad()
+        return scaled
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -53,6 +53,13 @@ class TestDistPPTraning(unittest.TestCase):
         }
         fleet.init(is_collective=True, strategy=strategy)
 
+    def build_optimizer(self, model):
+        scheduler = paddle.optimizer.lr.PiecewiseDecay(
+            boundaries=[2], values=[0.001, 0.002], verbose=True)
+        optimizer = paddle.optimizer.SGD(learning_rate=scheduler,
+                                         parameters=model.parameters())
+        return scheduler, optimizer
+
     def test_pp_model(self):
         hcg = fleet.get_hybrid_communicate_group()
         word_size = hcg.get_model_parallel_world_size()
@@ -63,10 +70,7 @@ class TestDistPPTraning(unittest.TestCase):
 
         #construct model a
         model_a = AlexNet(10)
-        scheduler_a = paddle.optimizer.lr.PiecewiseDecay(
-            boundaries=[2], values=[0.001, 0.002], verbose=True)
-        optimizer_a = paddle.optimizer.SGD(learning_rate=scheduler_a,
-                                           parameters=model_a.parameters())
+        scheduler_a, optimizer_a = self.build_optimizer(model_a)
 
         param_len = len(model_a.parameters())
 
@@ -76,10 +80,7 @@ class TestDistPPTraning(unittest.TestCase):
 
         # construct model b
         model_b = AlexNetPipeDesc(num_stages=self.pipeline_parallel_size)
-        scheduler_b = paddle.optimizer.lr.PiecewiseDecay(
-            boundaries=[2], values=[0.001, 0.002], verbose=True)
-        optimizer_b = paddle.optimizer.SGD(learning_rate=scheduler_b,
-                                           parameters=model_b.parameters())
+        scheduler_b, optimizer_b = self.build_optimizer(model_b)
         model_b = fleet.distributed_model(model_b)
         optimizer_b = fleet.distributed_optimizer(optimizer_b)
 

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

@@ -61,11 +61,14 @@ class TestDistPPTraning(unittest.TestCase):
         rank_id = dist.get_rank()
         set_random_seed(1024, dp_id, rank_id)
 
+        grad_clip = paddle.nn.ClipGradByGlobalNorm(1.0)
+
         #construct model a
         model_a = AlexNet(10)
         scheduler_a = paddle.optimizer.lr.PiecewiseDecay(
             boundaries=[2], values=[0.001, 0.002], verbose=True)
         optimizer_a = paddle.optimizer.SGD(learning_rate=scheduler_a,
+                                           grad_clip=grad_clip,
                                            parameters=model_a.parameters())
 
         scaler_a = paddle.amp.GradScaler(init_loss_scaling=2**5)
@@ -80,6 +83,7 @@ class TestDistPPTraning(unittest.TestCase):
         scheduler_b = paddle.optimizer.lr.PiecewiseDecay(
             boundaries=[2], values=[0.001, 0.002], verbose=True)
         optimizer_b = paddle.optimizer.SGD(learning_rate=scheduler_b,
+                                           grad_clip=grad_clip,
                                            parameters=model_b.parameters())
         model_b = fleet.distributed_model(model_b)
         optimizer_b = fleet.distributed_optimizer(optimizer_b)

python/paddle/fluid/tests/unittests/hybrid_parallel_pp_clip_grad.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 division
+from __future__ import print_function
+
+import paddle
+import unittest
+from hybrid_parallel_pp_alexnet import TestDistPPTraning
+
+
+class TestPPClipGrad(TestDistPPTraning):
+    def build_optimizer(self, model):
+        grad_clip = paddle.nn.ClipGradByGlobalNorm(0.5)
+        scheduler = paddle.optimizer.lr.PiecewiseDecay(
+            boundaries=[2], values=[0.001, 0.002], verbose=True)
+        optimizer = paddle.optimizer.SGD(learning_rate=scheduler,
+                                         grad_clip=grad_clip,
+                                         parameters=model.parameters())
+        return scheduler, optimizer
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/hybrid_parallel_pp_fp16.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 division
+from __future__ import print_function
+
+import unittest
+import paddle
+import numpy as np
+import random
+import paddle
+import paddle.distributed as dist
+import paddle.distributed.fleet as fleet
+from hybrid_parallel_pp_layer import AlexNetPipeDesc, AlexNet
+
+
+def set_random_seed(seed, dp_id, rank_id):
+    """Set random seed for reproducability."""
+    random.seed(seed)
+    np.random.seed(seed + dp_id)
+    paddle.seed(seed + dp_id)
+
+
+batch_size = 4
+micro_batch_size = 2
+
+
+class TestDistPPTraning(unittest.TestCase):
+    def setUp(self):
+        strategy = fleet.DistributedStrategy()
+        self.model_parallel_size = 1
+        self.data_parallel_size = 1
+        self.pipeline_parallel_size = 2
+        strategy.hybrid_configs = {
+            "dp_degree": self.data_parallel_size,
+            "mp_degree": self.model_parallel_size,
+            "pp_degree": self.pipeline_parallel_size,
+        }
+        strategy.pipeline_configs = {
+            "accumulate_steps": batch_size // micro_batch_size,
+            "micro_batch_size": micro_batch_size
+        }
+        fleet.init(is_collective=True, strategy=strategy)
+
+    def test_pp_model(self):
+        hcg = fleet.get_hybrid_communicate_group()
+        word_size = hcg.get_model_parallel_world_size()
+        dp_id = hcg.get_data_parallel_rank()
+        pp_id = hcg.get_stage_id()
+        rank_id = dist.get_rank()
+        set_random_seed(1024, dp_id, rank_id)
+
+        grad_clip = paddle.nn.ClipGradByGlobalNorm(1.0)
+
+        #construct model a
+        model_a = AlexNet(10)
+        scheduler_a = paddle.optimizer.lr.PiecewiseDecay(
+            boundaries=[2], values=[0.001, 0.002], verbose=True)
+        optimizer_a = paddle.optimizer.SGD(learning_rate=scheduler_a,
+                                           grad_clip=grad_clip,
+                                           parameters=model_a.parameters())
+
+        scaler_a = paddle.amp.GradScaler(init_loss_scaling=2**5)
+
+        # construct model b
+        model_b = AlexNetPipeDesc(num_stages=self.pipeline_parallel_size)
+        scheduler_b = paddle.optimizer.lr.PiecewiseDecay(
+            boundaries=[2], values=[0.001, 0.002], verbose=True)
+        optimizer_b = paddle.optimizer.SGD(learning_rate=scheduler_b,
+                                           grad_clip=grad_clip,
+                                           parameters=model_b.parameters())
+
+        param_len = len(model_a.parameters())
+        parameters = []
+        for param in model_a.parameters():
+            parameters.append(param.numpy())
+
+        for idx, param in enumerate(model_b.parameters()):
+            param.set_value(parameters[idx + pp_id * (param_len // 2)])
+
+        model_a, optimizer_a = paddle.amp.decorate(
+            models=model_a,
+            optimizers=optimizer_a,
+            level='O2',
+            save_dtype='float32')
+        model_b, optimizer_b = paddle.amp.decorate(
+            models=model_b,
+            optimizers=optimizer_b,
+            level='O2',
+            save_dtype='float32')
+
+        model_b = fleet.distributed_model(model_b)
+        optimizer_b = fleet.distributed_optimizer(optimizer_b)
+        scaler_b = paddle.amp.GradScaler(init_loss_scaling=2**5)
+        scaler_b = fleet.distributed_scaler(scaler_b)
+
+        # construct reader
+        train_reader = paddle.batch(
+            paddle.dataset.mnist.train(), batch_size=batch_size, drop_last=True)
+
+        for step_id, data in enumerate(train_reader()):
+            x_data = np.array([x[0] for x in data]).astype('float32').reshape(
+                batch_size, 1, 28, 28)
+            y_data = np.array([x[1] for x in data]).astype('int64').reshape(
+                batch_size, 1)
+            img = paddle.to_tensor(x_data)
+            label = paddle.to_tensor(y_data)
+            img.stop_gradient = True
+            label.stop_gradient = True
+
+            if step_id >= 5:
+                return True
+
+            with paddle.amp.auto_cast(enable=True, level='O2'):
+                loss_a = model_a(img, label)
+                scaler_a.scale(loss_a).backward()
+                with paddle.amp.auto_cast(enable=False):
+                    scaler_a.minimize(optimizer_a, loss_a)
+                optimizer_a.clear_grad()
+                scheduler_a.step()
+
+                loss_b = model_b.train_batch(
+                    [img, label], optimizer_b, scheduler_b, scaler=scaler_b)
+
+            print("loss: ", loss_a.numpy(), loss_b.numpy())
+            np.testing.assert_allclose(
+                loss_a.numpy(), loss_b.numpy(), rtol=5e-3)
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -183,21 +183,23 @@ class TestDistMPTraning(unittest.TestCase):
                         strategy=None,
                         is_sharding=True,
                         Optimizer="adam"):
-
+        clip = paddle.nn.ClipGradByGlobalNorm(0.5)
         if Optimizer == "adam":
             if is_sharding:
                 optimizer = DygraphShardingOptimizer(
                     hcg=fleet.get_hybrid_communicate_group(),
                     user_defined_strategy=strategy,
                     params=model.parameters(),
-                    inner_optimizer_class=paddle.optimizer.Adam,
+                    inner_optimizer_class=paddle.optimizer.AdamW,
                     learning_rate=0.001,
-                    weight_decay=0.00001, )
+                    weight_decay=0.00001,
+                    grad_clip=clip)
             else:
-                optimizer = paddle.optimizer.Adam(
+                optimizer = paddle.optimizer.AdamW(
                     parameters=model.parameters(),
                     learning_rate=0.001,
-                    weight_decay=0.00001, )
+                    weight_decay=0.00001,
+                    grad_clip=clip)
         else:
             if is_sharding:
                 optimizer = DygraphShardingOptimizer(
@@ -205,10 +207,13 @@ class TestDistMPTraning(unittest.TestCase):
                     user_defined_strategy=strategy,
                     params=model.parameters(),
                     inner_optimizer_class=paddle.optimizer.Momentum,
-                    learning_rate=0.001, )
+                    learning_rate=0.001,
+                    grad_clip=clip)
             else:
                 optimizer = paddle.optimizer.Momentum(
-                    learning_rate=0.001, parameters=model.parameters())
+                    learning_rate=0.001,
+                    parameters=model.parameters(),
+                    grad_clip=clip)
         return optimizer
 
     def build_model_optimizer(self, Optimizer="adam"):

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

@@ -92,7 +92,10 @@ class Naive_fc_net(paddle.nn.Layer):
         return inputs
 
 
-def run_model(recompute_block=[], recompute_kwargs={}, enable_autocast=False):
+def run_model(recompute_block=[],
+              recompute_kwargs={},
+              enable_autocast=False,
+              pure_fp16=False):
     gen = paddle.seed(10)
     gen.manual_seed(10)
     np.random.seed(10)
@@ -118,7 +121,8 @@ def run_model(recompute_block=[], recompute_kwargs={}, enable_autocast=False):
         x_data = np.random.randn(batch_size, input_size).astype(np.float32)
         x = paddle.to_tensor(x_data)
         # x.stop_gradient = False
-        with paddle.amp.auto_cast(True):
+        level = 'O2' if pure_fp16 else 'O1'
+        with paddle.amp.auto_cast(True, level=level):
             y_pred = model(x)
             loss = y_pred.mean()
         if enable_autocast:
@@ -196,6 +200,36 @@ class TestPyLayer(unittest.TestCase):
             recompute_block=[1, 3], enable_autocast=True)
         check_identical(loss_ref, param_ref, grad_ref, loss, param, grad)
 
+    def test_fc_net_with_fp16(self):
+        def check_identical(loss_ref, param_ref, grad_ref, loss, param, grad):
+            self.assertEqual(loss_ref, loss)
+            self.assertEqual(param_ref, param)
+            self.assertEqual(grad_ref, grad)
+
+        # without recompute
+        loss_ref, param_ref, grad_ref = run_model(
+            recompute_block=[], enable_autocast=True, pure_fp16=True)
+
+        # recompute second block
+        loss, param, grad = run_model(
+            recompute_block=[1], enable_autocast=True, pure_fp16=True)
+        check_identical(loss_ref, param_ref, grad_ref, loss, param, grad)
+
+        # recompute fourth block
+        loss, param, grad = run_model(
+            recompute_block=[3], enable_autocast=True, pure_fp16=True)
+        check_identical(loss_ref, param_ref, grad_ref, loss, param, grad)
+
+        # recompute second to fourth block
+        loss, param, grad = run_model(
+            recompute_block=[1, 2, 3], enable_autocast=True, pure_fp16=True)
+        check_identical(loss_ref, param_ref, grad_ref, loss, param, grad)
+
+        # recompute second & fourth block
+        loss, param, grad = run_model(
+            recompute_block=[1, 3], enable_autocast=True, pure_fp16=True)
+        check_identical(loss_ref, param_ref, grad_ref, loss, param, grad)
+
     def test_recompute_kwargs(self):
         paddle.set_device("gpu")
         kwargs = {"is_test": False}

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

@@ -30,9 +30,12 @@ class TestHybridPipeParallel(TestMultipleGpus):
     def test_hybrid_parallel_shared_weight(self):
         self.run_mnist_2gpu('hybrid_parallel_shared_weight.py')
 
-    def test_pipeline_parallel(self):
+    def test_pipeline_parallel_amp(self):
         self.run_mnist_2gpu('hybrid_parallel_pp_amp.py')
 
+    def test_pipeline_parallel_fp16(self):
+        self.run_mnist_2gpu('hybrid_parallel_pp_fp16.py')
+
     def test_hybrid_parallel_transformer(self):
         self.run_mnist_2gpu('hybrid_parallel_pp_transformer.py')
 
@@ -42,6 +45,9 @@ class TestHybridPipeParallel(TestMultipleGpus):
     def test_hybrid_parallel_recompute(self):
         self.run_mnist_2gpu('hybrid_parallel_pp_recompute.py')
 
+    def test_hybrid_parallel_pp_clip_grad(self):
+        self.run_mnist_2gpu('hybrid_parallel_pp_clip_grad.py')
+
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -30,6 +30,9 @@ class TestHybridParallel(TestMultipleGpus):
     def test_hybrid_parallel_mp_amp(self):
         self.run_mnist_2gpu('hybrid_parallel_mp_amp.py')
 
+    def test_hybrid_parallel_mp_fp16(self):
+        self.run_mnist_2gpu('hybrid_parallel_mp_fp16.py')
+
     def test_hybrid_parallel_mp_clip_grad(self):
         self.run_mnist_2gpu('hybrid_parallel_mp_clip_grad.py')