[Cherry-Pick][HybridParallel]Fix pipeline in dygraph (#33097)

* [HybridParallel]Fix pipeline in dygraph (#33007)

* fix pipeline

* fix mp pp dp

* fix utest of hybrid parallel

* add utest for tuple

* fix utest (#33108)

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

@@ -253,3 +253,8 @@ class HybridCommunicateGroup(object):
     # check parallel group
     def get_check_parallel_group(self):
         return self._check_comm_group
+
+    def get_rank_from_stage(self, stage_id):
+        coord = self._topo.get_coord(self.global_rank)
+        tf = coord._replace(pipe=stage_id)._asdict()
+        return self._topo.get_rank(**tf)

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

@@ -89,12 +89,14 @@ class HybridParallelOptimizer:
         self._inner_opt = optimizer
         self._strategy = strategy
         self._hcg = hcg
-        self._is_mp = (
-            self._hcg.get_parallel_mode() == ParallelMode.TENSOR_PARALLEL)
+
+        self._use_dp_mode = (
+            self._hcg.get_parallel_mode() == ParallelMode.DATA_PARALLEL)
+
         self._need_dp = (self._hcg.get_data_parallel_world_size() > 1)
 
         if isinstance(self._inner_opt._grad_clip,
-                      ClipGradByGlobalNorm) and self._is_mp:
+                      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(
@@ -103,7 +105,7 @@ class HybridParallelOptimizer:
     @imperative_base.no_grad
     @framework.dygraph_only
     def step(self):
-        if self._is_mp and self._need_dp:
+        if not self._use_dp_mode and self._need_dp:
             fused_allreduce_gradients(
                 list(self._inner_opt._parameter_list), self._hcg)
         self._inner_opt.step()
@@ -119,7 +121,7 @@ class HybridParallelOptimizer:
         parameter_list = parameters if parameters \
             else self._parameter_list
 
-        if self._is_mp and self._need_dp:
+        if not self._use_dp_mode and self._need_dp:
             fused_allreduce_gradients(list(parameter_list), self._hcg)
 
         return self._inner_opt.minimize(loss, startup_program, parameters,

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

@@ -11,39 +11,29 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 
-import time
-import copy
-import os
-
 from types import MethodType
 
-from numpy import prod
-
 import paddle
 import paddle.fluid as fluid
 from .meta_parallel_base import MetaParallelBase
-from .pp_utils.utils import get_tensor_bytes, is_float_tensor
+from .pp_utils.utils import is_float_tensor, get_tensor_dtype, paddle_2_number, number_2_dtype
 from .pp_utils import utils
 from .parallel_layers.pp_layers import PipelineLayer
 
 from ..utils.hybrid_parallel_util import broadcast_mp_parameters
 from ..utils.hybrid_parallel_util import broadcast_dp_parameters
-from ..utils.hybrid_parallel_util import fused_allreduce_gradients
 from ..utils.log_util import logger
+from ..meta_optimizers.dygraph_optimizer import HybridParallelOptimizer
 
 __all__ = []
 
-FLOAT_TYPES = [
-    paddle.float16,
-    paddle.float32,
-    paddle.float64,
-]
-
 
 class PipelineParallel(MetaParallelBase):
     def __init__(self, layers, hcg, strategy):
+        if not isinstance(layers, PipelineLayer):
+            raise TypeError(
+                "The Layer should be a derived class of PipelineLayer.")
         super(PipelineParallel, self).__init__(layers, hcg, strategy)
-
         self.use_pipe_parallel = self._hcg.get_pipe_parallel_world_size() > 1
         self.use_data_parallel = self._hcg.get_data_parallel_world_size() > 1
         self.use_model_parallel = self._hcg.get_model_parallel_world_size() > 1
@@ -63,8 +53,6 @@ class PipelineParallel(MetaParallelBase):
         self.current_loss = paddle.to_tensor(0.0)
         self.total_loss = None
 
-        self.use_amp = self._strategy.amp
-        self.init_loss_scaling = self._strategy.amp_configs['init_loss_scaling']
         self.micro_batch_size = self._strategy.pipeline_configs[
             'micro_batch_size']
         self.accumulate_steps = self._strategy.pipeline_configs[
@@ -75,6 +63,11 @@ class PipelineParallel(MetaParallelBase):
         self.prev_stage_id = self.stage_id - 1
         self.next_stage_id = self.stage_id + 1
         self.pp_group = self._hcg.get_pipe_parallel_group()
+
+        self.is_first_stage = self.stage_id == 0
+        self.is_last_stage = (self.stage_id == (self.num_stages - 1))
+        self.global_rank = self._hcg.get_global_rank()
+
         logger.info("Pipeline Info -- num_stages: {}, stage_id: {}".format(
             self.num_stages, self.stage_id))
 
@@ -83,51 +76,72 @@ class PipelineParallel(MetaParallelBase):
             broadcast_mp_parameters(self._layers, self._hcg)
 
         if self.use_data_parallel:
-            logger.info("start broadcast mp parameters")
+            logger.info("start broadcast dp parameters")
             broadcast_dp_parameters(self._layers, self._hcg)
 
-    def _allocate_caches(self, num_caches):
+    def _init_caches(self, num_caches):
         if self.num_caches >= num_caches:
             return
-
-        num = num_caches - self.num_caches
-        self.num_caches = num_caches
+        self.num_caches = num_caches - self.num_caches
         for key in self.caches:
-            self.caches[key].extend([None] * num)
+            self.caches[key].extend([None] * self.num_caches)
+
+    def _reduce_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.clone() / self.accumulate_steps
+            paddle.distributed.broadcast(
+                loss,
+                src=self.global_rank,
+                use_calc_stream=True,
+                group=self.pp_group)
+        else:
+            loss = paddle.to_tensor(0.0)
+            paddle.distributed.broadcast(
+                loss,
+                src=self._hcg.get_rank_from_stage(self.num_stages - 1),
+                use_calc_stream=True,
+                group=self.pp_group)
+        return loss
 
-    def train_batch(self, data, optimizer):
+    def train_batch(self, data, optimizer, lr_scheduler=None):
+        assert isinstance(optimizer, HybridParallelOptimizer), (
+            'optimizer should be HybridParallelOptimizer subclass.')
         self.optimizer = optimizer
+        self.lr_scheduler = lr_scheduler
         assert fluid.framework._dygraph_tracer()._has_grad, (
             'Please enable the generation of gradients.')
 
-        if self.stage_id == 0 or self.stage_id == self.num_stages - 1:
-            assert data, (
+        if self.is_first_stage or self.is_last_stage:
+            assert data is not None, (
                 "For the first and the last stage, the data_iter must be set.")
         else:
-            assert data is None, (
-                "For pipe stages other than the first and the last one, "
-                "the data_iter must be None.")
+            data = None
+
         self.data = data
         self._layers.train()
-        self.total_loss = None
-
-        minibatch_cmds = utils.TrainGenerator(self.accumulate_steps,
-                                              self.num_stages, self.stage_id)
-        self._train(minibatch_cmds)
-        return self.total_loss
 
-    def _train(self, minibatch_cmds):
-        self._allocate_caches(self.accumulate_steps)
-        for micro_cmds in minibatch_cmds:
-            for cmd in micro_cmds:
-                assert type(cmd) in self._COMMAND_MAP, "unknow cmd: {}".format(
-                    type(cmd))
-                self._apply_cmd = MethodType(self._COMMAND_MAP[type(cmd)], self)
-                self._apply_cmd(**cmd.kwargs)
-
-    def _allreduce_grads(self):
-        if not self.use_data_parallel: return
-        fused_allreduce_gradients(list(self._layers.parameters()), self._hcg)
+        # store total loss of entire batch
+        self.total_loss = None
+        self._init_caches(self.accumulate_steps)
+        startup_steps = self.num_stages - self.stage_id - 1
+        forward_steps = 0
+        backward_steps = 0
+
+        # forward
+        while (forward_steps < self.accumulate_steps):
+            self._forward(cache_id=forward_steps)
+            forward_steps += 1
+
+        # backward
+        while (backward_steps < self.accumulate_steps):
+            self._backward(cache_id=backward_steps)
+            backward_steps += 1
+
+        # optimizer
+        self._step()
+        self.train_loss = self._reduce_final_loss()
+        return self.train_loss
 
     def _forward(self, cache_id):
         # load data
@@ -140,16 +154,17 @@ class PipelineParallel(MetaParallelBase):
         else:
             inputs = self.caches['inputs'][cache_id]
 
-        self._clear_grads(inputs)
         outputs = self._layers.forward(inputs)
+        self._clear_grads(inputs)
+
         self.caches['outputs'][cache_id] = outputs
 
-        if self.stage_id == self.num_stages - 1:
+        if self.is_last_stage:
             if self._layers._loss_fn is not None:
                 labels = self.caches['labels'][cache_id]
                 outputs = self._layers._loss_fn(outputs, labels)
 
-        if self.stage_id == self.num_stages - 1:
+        if self.is_last_stage:
             self.current_loss = outputs
             if isinstance(self.current_loss, paddle.Tensor):
                 if self.total_loss is None:
@@ -162,18 +177,17 @@ class PipelineParallel(MetaParallelBase):
                     ]
                 for idx, v in enumerate(self.current_loss):
                     self.total_loss[idx] += v.detach()
-            if self.use_data_parallel:
-                self.current_loss = self.current_loss / self._hcg.get_data_parallel_world_size(
-                )
+
             if self.accumulate_steps > 1:
                 self.current_loss = self.current_loss / self.accumulate_steps
+
             self.caches['outputs'][cache_id] = self.current_loss.clone()
+
         else:
             self._send_activations(cache_id)
 
     def _backward(self, cache_id):
-        assert self.optimizer is not None
-        if self.stage_id == self.num_stages - 1:
+        if self.is_last_stage:
             paddle.autograd.backward(self.caches['outputs'][cache_id])
             self._send_gradients(cache_id)
             return
@@ -194,92 +208,89 @@ class PipelineParallel(MetaParallelBase):
         grad_tensors = None
         if self.stage_id != 0: self._send_gradients(cache_id)
         self.caches['outputs'][cache_id] = None
-        #self.caches['backward_tensors'][cache_id] = None
 
-    def _get_data(self):
-        if self.use_model_parallel:
-            mp_rank = self._hcg.get_model_parallel_rank()
-        else:
-            mp_rank = 0
-
-        # mp rank 0 loads the data and broadcat it to others.
-        data = self.data
-        if self.use_model_parallel and (self.stage_id == 0 or
-                                        self.stage_id == self.num_stages - 1):
-            assert isinstance(data, (tuple, paddle.Tensor))
+    def _broadcast_data(self, data):
         if isinstance(data, paddle.Tensor):
             paddle.distributed.broadcast(
                 data,
                 src=self._hcg.get_model_parallel_group_src_rank(),
                 group=self._hcg.get_model_parallel_group())
         else:
-                data = []
-                for d in self.data:
+            for d in data:
                 assert isinstance(d, paddle.Tensor)
                 paddle.distributed.broadcast(
                     d,
                     src=self._hcg.get_model_parallel_group_src_rank(),
                     group=self._hcg.get_model_parallel_group())
-                    data.append(d)
-            data = tuple(data)
         return data
 
     def _load_micro_batch(self, cache_id):
-        inputs = self._get_data()
+        inputs = self.data
+        begin = cache_id * self.micro_batch_size
+        end = begin + self.micro_batch_size
 
-        if self.stage_id == 0:
-            data = None
-            #if isinstance(inputs[0], paddle.Tensor):
-            if len(inputs) == 1:
-                assert isinstance(inputs[0], paddle.Tensor)
-                data = inputs[0].clone().detach()
-                #data.stop_gradient = not is_float_tensor(data)
-                data.stop_gradient = True
+        if self.is_first_stage:
+            assert len(inputs) == 2, "length of input should be 2"
+            if self.use_model_parallel:
+                inputs[0] = self._broadcast_data(inputs[0])
+            if isinstance(inputs[0], tuple):
+                batch_size = inputs[0][0].shape[0]
+                assert self.micro_batch_size * self.accumulate_steps == batch_size, (
+                    "batch_size needs to be divisible by micro_batch_size. Currently, "
+                    "batch_size = %d, micro_batch_size = %d, accumulate_steps = %d."
+                    %
+                    (batch_size, self.micro_batch_size, self.accumulate_steps))
+                data = [
+                    input[begin:end, :].clone().detach() for input in inputs[0]
+                ]
+                self.caches['inputs'][cache_id] = tuple(data)
             else:
-                assert isinstance(inputs, tuple)
-                data = []
-                for d in inputs:
-                    assert isinstance(d, paddle.Tensor)
-                    i = d.clone().detach()
-                    #i.stop_gradient = not is_float_tensor(i)
-                    i.stop_gradient = True
-                    data.append(i)
-                data = tuple(data)
-            self.caches['inputs'][cache_id] = data
-
-        if self.stage_id == self.num_stages - 1:
-            labels = None
-            #if isinstance(inputs[1], paddle.Tensor):
-            if len(inputs) == 1:
-                assert isinstance(inputs[0], paddle.Tensor)
-                labels = inputs[0]
-            elif isinstance(inputs, tuple):
-                labels = []
-                for label in inputs:
-                    assert isinstance(label, paddle.Tensor)
-                    label = label.detach()
-                    labels.append(label)
-                labels = tuple(labels)
-            self.caches['labels'][cache_id] = labels
+                batch_size = inputs[0].shape[0]
+                assert self.micro_batch_size * self.accumulate_steps == batch_size
+                self.caches['inputs'][cache_id] = inputs[0][begin:end, :].clone(
+                ).detach()
+        elif self.is_last_stage:
+            assert len(inputs) == 2, "length of input should be 2"
+            if self.use_model_parallel:
+                inputs[1] = self._broadcast_data(inputs[1])
+            if isinstance(inputs[1], tuple):
+                batch_size = inputs[1][0].shape[0]
+                assert self.micro_batch_size * self.accumulate_steps == batch_size
+                data = [
+                    input[begin:end, :].clone().detach() for input in inputs[1]
+                ]
+                self.caches['labels'][cache_id] = tuple(data)
+            else:
+                batch_size = inputs[1].shape[0]
+                assert self.micro_batch_size * self.accumulate_steps == batch_size
+                self.caches['labels'][cache_id] = inputs[1][begin:end, :].clone(
+                ).detach()
+        else:
+            # No data input is required for other stages
+            inputs = None
 
     def _send_meta(self, data, peer):
-        """
-        % type (0: tensor, 1: tuple)
-        % num_tensors if type=tuple
-        foreach tensor:
-          % ndims
-          % shape
-        """
         if isinstance(data, paddle.Tensor):
             tensor_type = paddle.to_tensor([0])
+            # send tensor type
             paddle.distributed.send(
                 tensor_type, peer, use_calc_stream=True, group=self.pp_group)
+
+            # send len(shape)
             dims = paddle.to_tensor(len(data.shape))
             paddle.distributed.send(
                 dims, peer, use_calc_stream=True, group=self.pp_group)
+
+            # send shape
             shape = paddle.to_tensor(data.shape)
             paddle.distributed.send(
                 shape, peer, use_calc_stream=True, group=self.pp_group)
+
+            # send dtype
+            dtype = paddle.to_tensor(paddle_2_number(data.dtype))
+            paddle.distributed.send(
+                dtype, peer, use_calc_stream=True, group=self.pp_group)
+
         elif isinstance(data, tuple):
             tensor_type = paddle.to_tensor([1])
             paddle.distributed.send(
@@ -289,48 +300,73 @@ class PipelineParallel(MetaParallelBase):
                 nums, peer, use_calc_stream=True, group=self.pp_group)
             for idx, d in enumerate(data):
                 assert isinstance(d, paddle.Tensor)
+                # send len(shape)
                 dims = paddle.to_tensor(len(d.shape))
                 paddle.distributed.send(
                     dims, peer, use_calc_stream=True, group=self.pp_group)
+
+                # send shape
                 shape = paddle.to_tensor(d.shape)
                 paddle.distributed.send(
                     shape, peer, use_calc_stream=True, group=self.pp_group)
 
+                # send dtype
+                dtype = paddle.to_tensor(paddle_2_number(d.dtype))
+                paddle.distributed.send(
+                    dtype, peer, use_calc_stream=True, group=self.pp_group)
+
     def _recv_meta(self, peer):
         tensor_type = paddle.to_tensor([0])
         paddle.distributed.recv(
             tensor_type, peer, use_calc_stream=True, group=self.pp_group)
-        tensor_type = tensor_type.numpy()[0]
+        tensor_type = tensor_type.item()
 
         if tensor_type == 0:
+            # recv len(shape)
             dims = paddle.to_tensor([0])
             paddle.distributed.recv(
                 dims, peer, use_calc_stream=True, group=self.pp_group)
-            dims = dims.numpy()[0]
+            dims = dims.item()
+
+            # recv shape
             shape = paddle.to_tensor([0] * dims)
             paddle.distributed.recv(
                 shape, peer, use_calc_stream=True, group=self.pp_group)
             shape = shape.numpy().tolist()
-            return self._allocate_buffer(
-                shape, dtype="float32", num_caches=1)[0]
+
+            # recv dtype
+            dtype = paddle.to_tensor([0])
+            paddle.distributed.recv(
+                dtype, peer, use_calc_stream=True, group=self.pp_group)
+            return self._allocate_cache(
+                shape, dtype=number_2_dtype(dtype.item()), num_caches=1)[0]
         elif tensor_type == 1:
             num = paddle.to_tensor([0])
             paddle.distributed.recv(
                 num, peer, use_calc_stream=True, group=self.pp_group)
-            num = num.numpy()[0]
+            num = num.item()
             shapes = []
+            dtypes = []
             for i in range(num):
+                # recv len(shape)
                 dims = paddle.to_tensor([0])
                 paddle.distributed.recv(
                     dims, peer, use_calc_stream=True, group=self.pp_group)
-                dims = dims.numpy()[0]
+
+                # recv shape
+                dims = dims.item()
                 shape = paddle.to_tensor([0] * dims)
                 paddle.distributed.recv(
                     shape, peer, use_calc_stream=True, group=self.pp_group)
                 shapes.append(shape.numpy().tolist())
 
-            dtypes = ["float32"] * len(shapes)
-            caches = self._allocate_buffers(shapes, dtypes, num_caches=1)[0]
+                # recv dtype
+                dtype = paddle.to_tensor([0])
+                paddle.distributed.recv(
+                    dtype, peer, use_calc_stream=True, group=self.pp_group)
+                dtypes.append(number_2_dtype(dtype.item()))
+
+            caches = self._allocate_caches(shapes, dtypes, num_caches=1)[0]
             caches = tuple(caches)
             return caches
 
@@ -357,7 +393,6 @@ class PipelineParallel(MetaParallelBase):
 
     def _send_gradients(self, cache_id):
         inputs = self.caches['inputs'][cache_id]
-
         if isinstance(inputs, paddle.Tensor):
             assert inputs.grad is not None
             paddle.distributed.send(
@@ -371,7 +406,6 @@ class PipelineParallel(MetaParallelBase):
                 if not is_float_tensor(d):
                     assert d.grad is None
                     continue
-                assert d.grad is not None
                 paddle.distributed.send(
                     d.grad,
                     self.prev_stage_id,
@@ -381,8 +415,6 @@ class PipelineParallel(MetaParallelBase):
 
     def _recv_activations(self, cache_id):
         inputs = None
-
-        # Allocate the buffer if necessary
         if self.recv_cache is None:
             self.recv_cache = self._recv_meta(self.prev_stage_id)
 
@@ -419,14 +451,16 @@ class PipelineParallel(MetaParallelBase):
         if self.grad_tensors is None:
             if isinstance(outputs, paddle.Tensor):
                 s = list(outputs.shape)
-                dtype = 'float16' if self.use_amp else "float32"
-                self.grad_tensors = self._allocate_buffer(
-                    s, dtype, num_buffers=1)[0]
+                dtype = get_tensor_dtype(outputs.dtype)
+                self.grad_tensors = self._allocate_cache(
+                    s, dtype, num_caches=1)[0]
             else:
                 sizes = [list(d.shape) for d in outputs if is_float_tensor(d)]
-                dtypes = ['float16'] * len(
-                    sizes) if self.use_amp else ['float32'] * len(sizes)
-                self.grad_tensors = self._allocate_buffers(
+                dtypes = [
+                    get_tensor_dtype(d.dtype) for d in outputs
+                    if is_float_tensor(d)
+                ]
+                self.grad_tensors = self._allocate_caches(
                     sizes, dtypes, num_caches=1)[0]
 
         if isinstance(self.grad_tensors, paddle.Tensor):
@@ -445,9 +479,10 @@ class PipelineParallel(MetaParallelBase):
                     group=self.pp_group)
 
     def _step(self):
-        self._allreduce_grads()
         self.optimizer.step()
-        self.optimizer.clear_gradients()
+        self.optimizer.clear_grad()
+        if self.lr_scheduler:
+            self.lr_scheduler.step()
 
     def _clear_grads(self, inputs):
         if isinstance(inputs, paddle.Tensor):
@@ -461,7 +496,7 @@ class PipelineParallel(MetaParallelBase):
     def _allocate_zeros(self, shape, dtype):
         return paddle.zeros(shape, dtype)
 
-    def _allocate_buffer(self, shape, dtype, num_caches=-1):
+    def _allocate_cache(self, shape, dtype, num_caches=-1):
         caches = []
         if num_caches == -1:
             num_caches = self.num_caches
@@ -469,7 +504,7 @@ class PipelineParallel(MetaParallelBase):
             caches.append(self._allocate_zeros(shape, dtype))
         return caches
 
-    def _allocate_buffers(self, shapes, dtypes, num_caches=-1):
+    def _allocate_caches(self, shapes, dtypes, num_caches=-1):
         caches = []
         if num_caches == -1:
             num_caches = self.num_caches
@@ -488,11 +523,5 @@ class PipelineParallel(MetaParallelBase):
         state_dict = paddle.load(self.model_path)
         self._layers.set_state_dict(state_dict)
 
-    _COMMAND_MAP = {
-        utils.Optimize: _step,
-        utils.Forward: _forward,
-        utils.Backward: _backward,
-    }
-
     def forward(self, *inputs, **kwargs):
         raise RuntimeError("Call train_batch for pipeline instead of forward.")

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

@@ -14,20 +14,51 @@
 
 import abc
 import paddle
-from ...utils import hybrid_parallel_util as hp_util
+from ...utils import log_util as hp_util
 
 __all__ = []
 
-FLOAT_TYPES = [
-    paddle.float16,
-    paddle.float32,
-    paddle.float64,
-]
+FLOAT_TYPE_DICT = {
+    paddle.float16: "float16",
+    paddle.float32: "float32",
+    paddle.float64: "float64",
+}
+
+PADDLE_TO_NUMBER = {
+    paddle.float16: 0,
+    paddle.float32: 1,
+    paddle.float64: 2,
+    paddle.int32: 3,
+    paddle.int64: 4
+}
+
+NUMBER_TO_DTYPE = {
+    0: "float16",
+    1: "float32",
+    2: "float64",
+    3: "int32",
+    4: "int64"
+}
 
 
 def is_float_tensor(tensor):
     """Is a float tensor"""
-    return tensor.dtype in FLOAT_TYPES
+    return tensor.dtype in FLOAT_TYPE_DICT.keys()
+
+
+def get_tensor_dtype(dtype):
+    assert dtype in FLOAT_TYPE_DICT.keys()
+    return FLOAT_TYPE_DICT[dtype]
+
+
+def paddle_2_number(dtype):
+    assert dtype in PADDLE_TO_NUMBER.keys()
+    return PADDLE_TO_NUMBER[dtype]
+
+
+def number_2_dtype(number):
+    assert number in NUMBER_TO_DTYPE.keys()
+    return NUMBER_TO_DTYPE[number]
 
 
 def get_tensor_bytes(tensor):
@@ -48,78 +79,3 @@ def get_tensor_bytes(tensor):
     else:
         raise ValueError("unknown data type: {}".format(tensor.dtype))
     return tensor.numel() * elem_size
-
-
-class Generator():
-    def __init__(self, micro_batches, stages, stage_id):
-        __metaclass__ = abc.ABCMeta
-
-        self.micro_batches = micro_batches
-        self.stages = stages
-        self.stage_id = stage_id
-        self.prev_stage = self.stage_id - 1
-        self.next_stage = self.stage_id + 1
-
-    @abc.abstractmethod
-    def generate(self):
-        pass
-
-    def __iter__(self):
-        self.iter = None
-        return self
-
-    def __next__(self):
-        if self.iter is None:
-            self.iter = self.generate()
-        return next(self.iter)
-
-
-class TrainGenerator(Generator):
-    def generate(self):
-        startup_steps = self.stages - self.stage_id - 1
-        cmds = []
-        forward_steps = 0
-        backward_steps = 0
-        #while (forward_steps < startup_steps):
-        #    cmds.append(Forward(cache_id=forward_steps))
-        #    forward_steps += 1
-        #while (forward_steps < self.micro_batches):
-        #    cmds.append(Forward(cache_id=forward_steps))
-        #    forward_steps += 1
-        #    cmds.append(Backward(cache_id=backward_steps))
-        #    backward_steps += 1
-        #while (backward_steps < self.micro_batches):
-        #    cmds.append(Backward(cache_id=backward_steps))
-        #    backward_steps += 1
-        #cmds.append(Optimize())
-        while (forward_steps < self.micro_batches):
-            cmds.append(Forward(cache_id=forward_steps))
-            forward_steps += 1
-        while (backward_steps < self.micro_batches):
-            cmds.append(Backward(cache_id=backward_steps))
-            backward_steps += 1
-        cmds.append(Optimize())
-        yield cmds
-
-
-class Command:
-    def __init__(self, **kwargs):
-        self.name = self.__class__.__name__
-        self.kwargs = kwargs
-        for key, val in kwargs.items():
-            setattr(self, key, val)
-
-    def __repr__(self):
-        return hp_util.call_to_str(self.name, **self.kwargs)
-
-
-class Optimize(Command):
-    pass
-
-
-class Forward(Command):
-    pass
-
-
-class Backward(Command):
-    pass

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

@@ -22,7 +22,8 @@ list(APPEND DIST_TEST_OPS test_gen_nccl_id_op)
 list(APPEND DIST_TEST_OPS test_parallel_dygraph_unused_variables)
 list(APPEND DIST_TEST_OPS test_parallel_dygraph_control_flow)
 list(APPEND DIST_TEST_OPS test_parallel_dygraph_dataparallel)
-list(APPEND DIST_TEST_OPS test_parallel_dygraph_pipeline_layer)
+list(APPEND DIST_TEST_OPS test_parallel_dygraph_pipeline_parallel)
+list(APPEND DIST_TEST_OPS test_parallel_dygraph_tensor_parallel)
 list(APPEND DIST_TEST_OPS test_parallel_dygraph_mp_layers)
 set(MIXED_DIST_TEST_OPS ${DIST_TEST_OPS})
 #remove distribute unittests.
@@ -176,7 +177,8 @@ if ((NOT WITH_GPU) AND (NOT WITH_ROCM))
     LIST(REMOVE_ITEM TEST_OPS test_parallel_dygraph_sync_batch_norm)
     list(REMOVE_ITEM TEST_OPS test_parallel_dygraph_control_flow)
     list(REMOVE_ITEM TEST_OPS test_parallel_dygraph_dataparallel)
-    list(REMOVE_ITEM TEST_OPS test_parallel_dygraph_pipeline_layer)
+    list(REMOVE_ITEM TEST_OPS test_parallel_dygraph_pipeline_parallel)
+    list(REMOVE_ITEM TEST_OPS test_parallel_dygraph_tensor_parallel)
     list(REMOVE_ITEM TEST_OPS test_parallel_dygraph_mp_layers)
     LIST(REMOVE_ITEM TEST_OPS test_imperative_auto_mixed_precision)
     LIST(REMOVE_ITEM TEST_OPS test_fleet_base_single)
@@ -555,7 +557,7 @@ if(WITH_DISTRIBUTE)
         set(dist_ut_port 20001)
         foreach(TEST_OP ${DIST_TEST_OPS})
             bash_test_modules(${TEST_OP} START_BASH dist_test.sh SERIAL LABELS "RUN_TYPE=EXCLUSIVE" ENVS "PADDLE_DIST_UT_PORT=${dist_ut_port}")
-            MATH(EXPR dist_ut_port "${dist_ut_port}+40")
+            MATH(EXPR dist_ut_port "${dist_ut_port}+35")
             if(dist_ut_port GREATER_EQUAL 22998)
                 message(FATAL_ERROR "available ports have been exhausted:${dist_ut_port}")
             endif()
@@ -863,7 +865,8 @@ if(WITH_DISTRIBUTE AND WITH_GPU AND WITH_NCCL)
     set_tests_properties(test_parallel_dygraph_dataparallel PROPERTIES TIMEOUT 120)
     set_tests_properties(test_parallel_dygraph_unused_variables PROPERTIES TIMEOUT 120)
     set_tests_properties(test_parallel_dygraph_control_flow PROPERTIES TIMEOUT 120)
-    set_tests_properties(test_parallel_dygraph_pipeline_layer PROPERTIES TIMEOUT 120)
+    set_tests_properties(test_parallel_dygraph_pipeline_parallel PROPERTIES TIMEOUT 120)
+    set_tests_properties(test_parallel_dygraph_tensor_parallel PROPERTIES TIMEOUT 200)
     set_tests_properties(test_parallel_dygraph_mp_layers PROPERTIES TIMEOUT 120)
     if(${NCCL_VERSION} VERSION_GREATER_EQUAL 2212)
         set_tests_properties(test_parallel_dygraph_sparse_embedding PROPERTIES TIMEOUT 120)

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

@@ -37,6 +37,7 @@ hidden_size = 10
 inner_size = 8
 output_size = 2
 seq_length = 2
+batch_size = 4
 
 
 class SimpleMPNet(fluid.dygraph.Layer):
@@ -130,18 +131,6 @@ class SimpleDPNet(fluid.dygraph.Layer):
         return x
 
 
-class TrainDataset(Dataset):
-    def __init__(self, length):
-        self.length = length
-
-    def __len__(self):
-        return self.length
-
-    def __getitem__(self, index):
-        np_input_data = np.random.randint(0, vocab_size, (seq_length, ))
-        return np_input_data
-
-
 class TestDistMPTraning(unittest.TestCase):
     def setUp(self):
         strategy = fleet.DistributedStrategy()
@@ -178,20 +167,6 @@ class TestDistMPTraning(unittest.TestCase):
         np_fc1 = np.random.random_sample((hidden_size, inner_size))
         np_fc2 = np.random.random_sample((inner_size, hidden_size))
 
-        train_data = TrainDataset(length=10000)
-
-        train_batch_sampler = paddle.io.DistributedBatchSampler(
-            train_data,
-            batch_size=4,
-            shuffle=False,
-            num_replicas=self.data_parallel_size,
-            rank=dp_id)
-        train_data_loader = DataLoader(
-            dataset=train_data,
-            batch_sampler=train_batch_sampler,
-            num_workers=0,
-            return_list=True)
-
         model_a = SimpleMPNet(vocab_size, hidden_size, inner_size, output_size,
                               np_fc1, np_fc2, mp_id)
         optimizer_a = self.build_optimizer(model_a)
@@ -202,16 +177,17 @@ class TestDistMPTraning(unittest.TestCase):
                               np_fc1, np_fc2)
         optimizer_b = self.build_optimizer(model_b)
 
-        return model_a, optimizer_a, model_b, optimizer_b, train_data_loader
+        return model_a, optimizer_a, model_b, optimizer_b
 
     def test_mp_model(self):
-        model_a, optimizer_a, model_b, optimizer_b, train_data_loader = self.build_model_optimizer(
+        model_a, optimizer_a, model_b, optimizer_b = self.build_model_optimizer(
         )
 
-        for step, batch in enumerate(train_data_loader):
-            if step > 5:
-                return
-
+        for _ in range(5):
+            np_data = np.random.randint(0, vocab_size, (
+                batch_size,
+                seq_length, ))
+            batch = paddle.to_tensor(np_data)
             loss_a = self.train_batch(batch, model_a, optimizer_a, True)
             loss_b = self.train_batch(batch, model_b, optimizer_b, False)
 

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

@@ -15,39 +15,25 @@
 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.fluid as fluid
 import paddle.distributed.fleet as fleet
-from paddle.io import DataLoader, Dataset
-import unittest
+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 + rank_id)
-
-
-HIDDEN_DIM = 32
-LAYERS = 8
+    paddle.seed(seed + dp_id)
 
 
-def sequential_model():
-    model = paddle.nn.Sequential(
-        paddle.nn.Linear(HIDDEN_DIM, HIDDEN_DIM),
-        paddle.nn.Linear(HIDDEN_DIM, HIDDEN_DIM),
-        paddle.nn.Linear(HIDDEN_DIM, HIDDEN_DIM),
-        paddle.nn.Linear(HIDDEN_DIM, HIDDEN_DIM),
-        paddle.nn.Linear(HIDDEN_DIM, HIDDEN_DIM),
-        paddle.nn.Linear(HIDDEN_DIM, HIDDEN_DIM),
-        paddle.nn.Linear(HIDDEN_DIM, HIDDEN_DIM),
-        paddle.nn.Linear(HIDDEN_DIM, HIDDEN_DIM),
-        paddle.nn.Linear(HIDDEN_DIM, 1), )
-    return model
+batch_size = 4
+micro_batch_size = 2
 
 
 class TestDistPPTraning(unittest.TestCase):
@@ -61,33 +47,74 @@ class TestDistPPTraning(unittest.TestCase):
             "mp_degree": self.model_parallel_size,
             "pp_degree": self.pipeline_parallel_size,
         }
-        strategy.pipeline_configs = {"accumulate_steps": 2}
-        paddle.distributed.init_parallel_env()
+        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_mp_model(self):
-        batch_input = paddle.randn(shape=(1, HIDDEN_DIM), dtype="float32")
-        pipe_model = sequential_model()
-        sgd = paddle.optimizer.SGD(learning_rate=0.0003, parameters=[])
-        pipe_model = paddle.distributed.fleet.distributed_model(pipe_model)
-
-        if pipe_model.stage_id == 0 or pipe_model.stage_id == 1:
-            pipe_input = batch_input.clone().detach()
-            pipe_input = paddle.cast(pipe_input, 'float32')
-
-            def data_gen():
-                gen = True
-                while gen:
-                    yield [pipe_input, 0]
-                    gen = False
-
-            loader = paddle.io.DataLoader.from_generator(capacity=5)
-            loader.set_batch_generator(data_gen)
-            data_iter = iter(loader)
-        else:
-            data_iter = None
+    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())
+
+        param_len = len(model_a.parameters())
+
+        parameters = []
+        for param in model_a.parameters():
+            parameters.append(param.numpy())
+
+        # 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())
+        model_b = fleet.distributed_model(model_b)
+        optimizer_b = fleet.distributed_optimizer(optimizer_b)
+
+        for idx, param in enumerate(model_b.parameters()):
+            param.set_value(parameters[idx + pp_id * (param_len // 2)])
+
+        # 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
 
+            loss_a = model_a(img, label)
+            loss_a.backward()
+            optimizer_a.step()
+            optimizer_a.clear_grad()
+            scheduler_a.step()
+
+            loss_b = model_b.train_batch([img, label], optimizer_b, scheduler_b)
+
+            print("loss: ", loss_a.numpy(), loss_b.numpy())
+            np.testing.assert_allclose(
+                loss_a.numpy(), loss_b.numpy(), rtol=5e-5)
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/hybrid_parallel_pp_embedding.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 paddle.fluid.dygraph.container import Sequential
+from paddle.distributed.fleet.meta_parallel import PipelineLayer
+from paddle.fluid.dygraph.layers import Layer
+import paddle.nn as nn
+import paddle.fluid as fluid
+
+
+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 = 16
+micro_batch_size = 4
+vocab_size = 128
+hidden_size = 8
+
+
+class SimpleNet(Layer):
+    def __init__(self):
+        super(SimpleNet, self).__init__()
+        self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
+
+        self.softmax_weight = self.create_parameter(
+            shape=[hidden_size, vocab_size])
+        self.softmax_bias = self.create_parameter(
+            shape=[vocab_size], is_bias=False)
+
+    def forward(self, x1, x2, y1):
+        x_emb = self.word_embeddings(x1)
+        fc = fluid.layers.matmul(x_emb, self.softmax_weight)
+        fc = fluid.layers.elementwise_add(fc, self.softmax_bias)
+        projection = fluid.layers.reshape(fc, shape=[-1, vocab_size])
+        loss = fluid.layers.softmax_with_cross_entropy(
+            logits=projection, label=y1, soft_label=False)
+        return loss.mean()
+
+
+class EmbeddingNet(Layer):
+    def __init__(self):
+        super(EmbeddingNet, self).__init__()
+        self.word_embeddings = nn.Embedding(vocab_size, hidden_size)
+
+    def forward(self, args):
+        x1, x2 = args
+        x_emb = self.word_embeddings(x1)
+        return x_emb, x2
+
+
+class MatmulNet(Layer):
+    def __init__(self):
+        super(MatmulNet, self).__init__()
+        self.softmax_weight = self.create_parameter(
+            shape=[hidden_size, vocab_size])
+
+    def forward(self, args):
+        x1, x2 = args
+        fc = fluid.layers.matmul(x1, self.softmax_weight)
+
+        return fc, x2
+
+
+class BiasNet(Layer):
+    def __init__(self):
+        super(BiasNet, self).__init__()
+        self.softmax_bias = self.create_parameter(shape=[vocab_size])
+
+    def forward(self, args):
+        fc, x2 = args
+        fc = fluid.layers.elementwise_add(fc, self.softmax_bias)
+        projection = fluid.layers.reshape(fc, shape=[-1, vocab_size])
+        return projection, x2
+
+
+class LossNet(Layer):
+    def __init__(self):
+        super(LossNet, self).__init__()
+
+    def forward(self, args, y1):
+        projection, x2 = args
+        loss = fluid.layers.softmax_with_cross_entropy(
+            logits=projection, label=y1[0], soft_label=False)
+        return loss.mean()
+
+
+class SimpleNetPipe(Layer):
+    def __init__(self):
+        super(SimpleNetPipe, self).__init__()
+        self.features = Sequential(EmbeddingNet(), MatmulNet(), BiasNet())
+
+    def to_layers(self):
+        feat = [self.features[i] for i in range(len(self.features))]
+        return feat
+
+
+class TestDistEmbeddingTraning(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 = SimpleNet()
+        scheduler_a = paddle.optimizer.lr.PiecewiseDecay(
+            boundaries=[2, 3, 4], values=[0.01, 0.02, 0.03, 0.04], verbose=True)
+        optimizer_a = paddle.optimizer.SGD(learning_rate=scheduler_a,
+                                           parameters=model_a.parameters())
+
+        init_net = SimpleNetPipe()
+        model_b = PipelineLayer(
+            layers=init_net.to_layers(),
+            num_stages=self.pipeline_parallel_size,
+            loss_fn=LossNet())
+
+        scheduler_b = paddle.optimizer.lr.PiecewiseDecay(
+            boundaries=[2, 3, 4], values=[0.01, 0.02, 0.03, 0.04], verbose=True)
+        optimizer_b = paddle.optimizer.SGD(learning_rate=scheduler_b,
+                                           parameters=model_b.parameters())
+        model_b = fleet.distributed_model(model_b)
+        optimizer_b = fleet.distributed_optimizer(optimizer_b)
+
+        param_len = len(model_a.parameters())
+
+        parameters = []
+        for param in model_a.parameters():
+            print(param.name, param.shape)
+            parameters.append(param.numpy())
+
+        model_b_params = model_b.parameters()
+        if pp_id == 0:
+            model_b_params[0].set_value(parameters[2])
+        else:
+            model_b_params[0].set_value(parameters[0])
+            model_b_params[1].set_value(parameters[1])
+
+        for step in range(5):
+            x1_data = np.random.randint(0, vocab_size, size=[batch_size, 1])
+            x2_data = np.random.randint(0, vocab_size, size=[batch_size, 1])
+            y1_data = np.random.randint(0, 10, size=[batch_size, 1])
+
+            x1 = paddle.to_tensor(x1_data)
+            x2 = paddle.to_tensor(x2_data)
+            y1 = paddle.to_tensor(y1_data)
+
+            x1.stop_gradient = True
+            x2.stop_gradient = True
+            y1.stop_gradient = True
+
+            loss_a = model_a(x1, x2, y1)
+            loss_a.backward()
+            optimizer_a.step()
+            optimizer_a.clear_grad()
+            scheduler_a.step()
+
+            loss_b = model_b.train_batch([(x1, x2), (y1, )], optimizer_b,
+                                         scheduler_b)
+
+            print("loss", loss_a.numpy(), loss_b.numpy())
+            np.testing.assert_allclose(loss_a.numpy(), loss_b.numpy())
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -12,17 +12,25 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import unittest
 import numpy as np
 import os
 import paddle
 from paddle.distributed import fleet
-import copy
 from paddle.fluid.dygraph.container import Sequential
 import paddle.nn as nn
 from paddle.fluid.dygraph.layers import Layer
 from paddle.distributed.fleet.meta_parallel import LayerDesc, PipelineLayer
 import paddle.nn.functional as F
-import unittest
+
+
+class ReshapeHelp(Layer):
+    def __init__(self, shape):
+        super(ReshapeHelp, self).__init__()
+        self.shape = shape
+
+    def forward(self, x):
+        return x.reshape(shape=self.shape)
 
 
 class AlexNet(Layer):
@@ -30,7 +38,7 @@ class AlexNet(Layer):
         super(AlexNet, self).__init__()
         self.features = Sequential(
             nn.Conv2D(
-                3, 64, kernel_size=11, stride=4, padding=5),
+                1, 64, kernel_size=11, stride=4, padding=5),
             nn.ReLU(),
             nn.MaxPool2D(
                 kernel_size=2, stride=2),
@@ -50,13 +58,14 @@ class AlexNet(Layer):
             nn.ReLU(),
             nn.MaxPool2D(
                 kernel_size=2, stride=2), )
+
+        self.reshape_layer = ReshapeHelp(shape=[-1, 256])
         self.classifier = nn.Linear(256, num_classes)
         self.loss_fn = nn.loss.CrossEntropyLoss()
 
     def forward(self, x, y):
         x = self.features(x)
-        x.flatten()
-
+        x = self.reshape_layer(x)
         x = self.classifier(x)
         return self.loss_fn(x, y)
 
@@ -64,7 +73,7 @@ class AlexNet(Layer):
 class AlexNetPipe(AlexNet):
     def to_layers(self):
         feat = [self.features[i] for i in range(len(self.features))]
-        loss_fn = [lambda x: x.flatten(), self.classifier]
+        loss_fn = [self.reshape_layer, self.classifier]
         feat.extend(loss_fn)
         return feat
 
@@ -74,7 +83,7 @@ class AlexNetPipeDesc(PipelineLayer):
         self.num_classes = num_classes
         decs = [
             LayerDesc(
-                nn.Conv2D, 3, 64, kernel_size=11, stride=4, padding=5),
+                nn.Conv2D, 1, 64, kernel_size=11, stride=4, padding=5),
             LayerDesc(nn.ReLU),
             LayerDesc(
                 nn.MaxPool2D, kernel_size=2, stride=2),
@@ -94,7 +103,8 @@ class AlexNetPipeDesc(PipelineLayer):
             F.relu,
             LayerDesc(
                 nn.MaxPool2D, kernel_size=2, stride=2),
-            lambda x: x.flatten(),
+            LayerDesc(
+                ReshapeHelp, shape=[-1, 256]),
             LayerDesc(nn.Linear, 256, self.num_classes),  # classifier
         ]
         super(AlexNetPipeDesc, self).__init__(
@@ -104,24 +114,24 @@ class AlexNetPipeDesc(PipelineLayer):
 class TestPipeLayerAPI(unittest.TestCase):
     def setUp(self):
         strategy = fleet.DistributedStrategy()
-        self.model_parallel_size = 2
+        self.pipeline_parallel_size = 2
         strategy.hybrid_configs = {
             "dp_degree": 1,
             "mp_degree": 1,
-            "pp_degree": self.model_parallel_size
+            "pp_degree": self.pipeline_parallel_size
         }
         fleet.init(is_collective=True, strategy=strategy)
         self.hcg = fleet.get_hybrid_communicate_group()
 
     def test_pipelayer_desc(self):
-        pipe_model = AlexNetPipeDesc(num_stages=self.model_parallel_size)
+        pipe_model = AlexNetPipeDesc(num_stages=self.pipeline_parallel_size)
         np.testing.assert_array_equal(len(pipe_model.parameters()), 6)
 
     def test_pipelayer_sequential(self):
         init_net = AlexNetPipe()
         pipe_model = PipelineLayer(
             layers=init_net.to_layers(),
-            num_stages=self.model_parallel_size,
+            num_stages=self.pipeline_parallel_size,
             loss_fn=nn.CrossEntropyLoss())
         stage_id = self.hcg.get_stage_id()
         init_parameters = init_net.parameters()

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

@@ -17,8 +17,11 @@ from __future__ import print_function
 import unittest
 import time
 import paddle.fluid as fluid
+import copy
+import os
+import subprocess
 
-from paddle.distributed.utils import find_free_ports, watch_local_trainers, get_cluster, start_local_trainers
+from paddle.distributed.utils import find_free_ports, watch_local_trainers, get_cluster, TrainerProc
 
 
 def get_cluster_from_args(selected_gpus):
@@ -46,6 +49,55 @@ def get_gpus(selected_gpus):
     return selected_gpus
 
 
+def start_local_trainers(cluster,
+                         pod,
+                         training_script,
+                         training_script_args,
+                         log_dir=None):
+    current_env = copy.copy(os.environ.copy())
+    #paddle broadcast ncclUniqueId use socket, and
+    #proxy maybe make trainers unreachable, so delete them.
+    #if we set them to "", grpc will log error message "bad uri"
+    #so just delete them.
+    current_env.pop("http_proxy", None)
+    current_env.pop("https_proxy", None)
+
+    procs = []
+    for t in pod.trainers:
+        proc_env = {
+            "FLAGS_selected_gpus": "%s" % ",".join([str(g) for g in t.gpus]),
+            "PADDLE_TRAINER_ID": "%d" % t.rank,
+            "PADDLE_CURRENT_ENDPOINT": "%s" % t.endpoint,
+            "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(),
+            "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints())
+        }
+
+        current_env.update(proc_env)
+
+        print("trainer proc env:{}".format(current_env))
+
+        if os.getenv('WITH_COVERAGE', 'OFF') == 'ON':
+            cmd = "python -m coverage run --branch -p " + training_script
+        else:
+            cmd = "python -u " + training_script
+
+        print("start trainer proc:{} env:{}".format(cmd, proc_env))
+
+        fn = None
+
+        proc = subprocess.Popen(cmd.split(" "), env=current_env)
+
+        tp = TrainerProc()
+        tp.proc = proc
+        tp.rank = t.rank
+        tp.log_fn = fn
+        tp.cmd = cmd
+
+        procs.append(tp)
+
+    return procs
+
+
 class TestMultipleGpus(unittest.TestCase):
     def run_mnist_2gpu(self, target_file_name):
         if not fluid.core.is_compiled_with_cuda(

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

@@ -24,6 +24,9 @@ class TestHybridPipeParallel(TestMultipleGpus):
     def test_hybrid_parallel_pp_layer(self):
         self.run_mnist_2gpu('hybrid_parallel_pp_layer.py')
 
+    def test_hybrid_parallel_pp_tuple_inputs(self):
+        self.run_mnist_2gpu('hybrid_parallel_pp_embedding.py')
+
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -22,7 +22,7 @@ from test_parallel_dygraph_dataparallel import TestMultipleGpus
 
 class TestPipelineParallel(TestMultipleGpus):
     def test_pipeline_parallel(self):
-        self.run_mnist_2gpu('hybrid_parallel_pp_model.py')
+        self.run_mnist_2gpu('hybrid_parallel_pp_alexnet.py')
 
 
 if __name__ == "__main__":