[HybridParallel]Support fp16 in dygraph hybrid parallel

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

@@ -35,6 +35,8 @@
 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__ = []
 
@@ -1548,26 +1550,52 @@ def unscale_method(self, optimizer):
             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
 
             # 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")
+                paddle.to_tensor(
+                    [self._found_inf], dtype="int32"),
+                op=paddle.distributed.ReduceOp.MAX,
+                group=None)
 
         # 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_parallel/pipeline_parallel.py

@@ -145,9 +145,8 @@ def forward_backward_pipeline(self, data, scaler=None):
             p2p.send_backward(input_tensor_grad)
 
         self._layers.allreduce_shared_weight_gradients()
-
-        train_loss = self._broadcast_final_loss()
-
+        with paddle.amp.auto_cast(enable=False):
+            train_loss = self._broadcast_final_loss()
         return train_loss
 
     def train_batch(self, data, optimizer, lr_scheduler=None, scaler=None):
@@ -172,7 +171,8 @@ def train_batch(self, data, optimizer, lr_scheduler=None, scaler=None):
         train_loss = self.forward_backward_pipeline(data, scaler)
 
         # optimizer
-        self._optimizer_step()
+        with paddle.amp.auto_cast(enable=False):
+            self._optimizer_step()
 
         return train_loss
 
@@ -242,12 +242,13 @@ def _forward_step(self, input_tensor):
                     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
@@ -321,13 +322,29 @@ def _broadcast_final_loss(self):
         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 @@ def forward(ctx, run_function, all_outputs, *args):
 
         # 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 @@ def backward(ctx, *args):
                         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 @@ def forward(ctx, run_function, preserve_rng_state, *args):
 
         # 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():
@@ -133,15 +136,15 @@ def backward(ctx, *args):
                             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:
                 with paddle.amp.auto_cast(
                         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/framework.py

@@ -6085,7 +6085,7 @@ def __init__(self, shape, dtype, **kwargs):
 
         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_pp_fp16.py

@@ -0,0 +1,138 @@
+# 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)
+
+        #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())
+
+        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,
+                                           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()