let allgather and alltoall execute in parallel when both attention and MOE used TP

Signed-off-by: taozhiwei <[email protected]>

Author: taozhiwei

deepspeed/moe/sharded_moe.py

@@ -26,7 +26,6 @@
 import torch.nn.functional as F
 from deepspeed.utils import groups
 from .mappings import drop_tokens, gather_tokens
-
 if TYPE_CHECKING:
     Base = Module[Tensor]
 else:
@@ -96,16 +95,19 @@ def gumbel_rsample(shape: Tuple, device: torch.device) -> Tensor:
 class _AllToAll(torch.autograd.Function):
 
     @staticmethod
-    def forward(ctx: Any, group: dist.ProcessGroup, input: Tensor) -> Tensor:  # type: ignore
+    def forward(ctx: Any, group: dist.ProcessGroup, input: Tensor, async_op=False) -> Tensor:  # type: ignore
         ctx.group = group
         input = input.contiguous()
         output = torch.empty_like(input)
-        dist.all_to_all_single(output, input, group=group)
-        return output
+        work = dist.all_to_all_single(output, input, group=group, async_op=async_op)
+        if async_op:
+            return output, work
+        else:
+            return output
 
     @staticmethod
     def backward(ctx: Any, *grad_output: Tensor) -> Tuple[None, Tensor]:
-        return (None, _AllToAll.apply(ctx.group, *grad_output))
+        return (None, _AllToAll.apply(ctx.group, *grad_output), None)
 
 
 # einsum rewrites are on par or more performant
@@ -550,6 +552,7 @@ class MOELayer(Base):
         expert (torch.nn.Module):
             expert network
     """
+    d2d_stream = torch.cuda.Stream()
 
     def __init__(self,
                  gate: Module,
@@ -572,6 +575,8 @@ def __init__(self,
         self.wall_clock_breakdown = False
 
         self.use_tutel = use_tutel and TUTEL_INSTALLED and gate.k == 1
+        self.enable_pipelie = True
+        self.shard_num = 4
 
         if self.use_tutel:
             logger.info('Using Tutel optimizations.')
@@ -586,8 +591,54 @@ def _set_ep_group(self, ep_group):
         self.ep_group = ep_group
         self.gate._set_ep_group(ep_group)
 
-    def forward(self, *input: Tensor, **kwargs: Any) -> Tensor:
+    # During multi machine MOE training, alltoall is the communication between machines,
+    # allgather is the communication within machines. They use different communication links,
+    # so they can be executed in parallel
+    # input shape (E,C,M)，Shard input in C dim, first execute alltoall on the shard,
+    # So the allgather of this shard and the alltoall of the next shard are executed in parallel
+    # A        E        I       M
+    # A1       E1       I1      M1
+    #   A2       E2       I2      M2
+    #     A3       E3       I3      M3
+    #       A4       E4       I4      M4
+    def pipeline_alltoall_with_allgather(self, input, shard_dim=1) -> Tensor:
+        if not self.enable_pipelie:
+            input = _AllToAll.apply(self.ep_group, input)
+            input = gather_tokens(input, dim=shard_dim)
+            return input
+
+        assert self.shard_num > 0, f"shard_num must be a positive number,but get is {self.shard_num}"
+        input_chunks = list(input.chunk(self.shard_num, dim=shard_dim))
+        world_size = bwc_tensor_model_parallel_world_size(groups.mpu)
+        dims = list(input.size())
+        dims[shard_dim] = dims[shard_dim] * world_size
+        output = torch.empty(dims, device=input.device)
+        input_gather_dim_len = input.shape[shard_dim]
+        have_gather_len = 0
+        works = []
+        for i in range(len(input_chunks)):
+            input_chunks[i], work = _AllToAll.apply(self.ep_group, input_chunks[i], True)
+            works.append(work)
+
+        current_stream = torch.cuda.current_stream()
+        for i in range(len(input_chunks)):
+            works[i].wait()
+            # we use dim 0 do allgather and chunk, so we can avoid unnecessary cat in gather_tokens
+            gather_out = gather_tokens(input_chunks[i], dim=0)
+            gather_list = gather_out.chunk(world_size, dim=0)
+            dim_len = gather_list[0].shape[shard_dim]
+            MOELayer.d2d_stream.wait_stream(current_stream)
+
+            for j in range(len(gather_list)):
+                start = input_gather_dim_len * j + have_gather_len
+                with torch.cuda.stream(MOELayer.d2d_stream):
+                    torch.narrow(output, shard_dim, start, dim_len).copy_(gather_list[j])
+            have_gather_len += dim_len
+
+        current_stream.wait_stream(MOELayer.d2d_stream)
+        return output
 
+    def forward(self, *input: Tensor, **kwargs: Any) -> Tensor:
         if self.wall_clock_breakdown:
             self.timers(MOE_TIMER).start()
 
@@ -611,9 +662,6 @@ def forward(self, *input: Tensor, **kwargs: Any) -> Tensor:
             self.l_aux, combine_weights, dispatch_mask, self.exp_counts = self.gate(reshaped_input, input[1])
             dispatched_input = einsum("sec,sm->ecm", dispatch_mask.type_as(input[0]), reshaped_input)
 
-        if self.wall_clock_breakdown:
-            self.timers(FIRST_ALLTOALL_TIMER).start()
-
         tensor_model_world_size = bwc_tensor_model_parallel_world_size(groups.mpu)
         if tensor_model_world_size > 1:
             # If the non-expert is tensor-parallel,
@@ -628,18 +676,17 @@ def forward(self, *input: Tensor, **kwargs: Any) -> Tensor:
             # an allgather to ensure correctness,
             dispatched_input = drop_tokens(dispatched_input, dim=1)
 
-        dispatched_input = _AllToAll.apply(self.ep_group, dispatched_input)
-
         if self.wall_clock_breakdown:
-            self.timers(FIRST_ALLTOALL_TIMER).stop()
-            self.time_falltoall = self.timers(FIRST_ALLTOALL_TIMER).elapsed(reset=False)
+            self.timers(FIRST_ALLTOALL_TIMER).start()
 
         if tensor_model_world_size > 1 and groups._get_expert_model_parallel_world_size() > 1:
-            # if both expert and non-expert are tensor-parallel
-            # the dropped duplicate tokens need to be gathered on each
-            # tensor parallel rank again to ensure correctness
-            dispatched_input = gather_tokens(dispatched_input, dim=1)
+            dispatched_input = self.pipeline_alltoall_with_allgather(dispatched_input)
+        else:
+            dispatched_input = _AllToAll.apply(self.ep_group, dispatched_input)
 
+        if self.wall_clock_breakdown:
+            self.timers(FIRST_ALLTOALL_TIMER).stop()
+            self.time_falltoall = self.timers(FIRST_ALLTOALL_TIMER).elapsed(reset=False)
         # Re-shape after all-to-all: ecm -> gecm
         dispatched_input = dispatched_input.reshape(self.ep_size, self.num_local_experts, -1, d_model)
         expert_output = self.experts(dispatched_input)
@@ -654,18 +701,12 @@ def forward(self, *input: Tensor, **kwargs: Any) -> Tensor:
         if self.wall_clock_breakdown:
             self.timers(SECOND_ALLTOALL_TIMER).start()
 
-        expert_output = _AllToAll.apply(self.ep_group, expert_output)
+        expert_output = self.pipeline_alltoall_with_allgather(expert_output)
 
         if self.wall_clock_breakdown:
             self.timers(SECOND_ALLTOALL_TIMER).stop()
             self.time_salltoall = self.timers(SECOND_ALLTOALL_TIMER).elapsed(reset=False)
 
-        if tensor_model_world_size > 1:
-            # the dropped duplicate tokens need to be gathered on each
-            # tensor parallel rank again for the tensor-parallel
-            # non-expert of the next layer.
-            expert_output = gather_tokens(expert_output, dim=1)
-
         if self.use_tutel:
             combined_output = self._tutel_dispatcher.decode(expert_output.view(E * C, M))
         else:

tests/unit/moe/test_pipeline.py

@@ -0,0 +1,89 @@
+import torch
+import deepspeed
+import pytest
+from unit.common import DistributedTest
+from deepspeed import get_accelerator
+from deepspeed.moe.sharded_moe import _AllToAll
+from deepspeed.moe.mappings import gather_tokens
+from deepspeed.moe.layer import MoE
+
+
+class MPU():
+
+    def __init__(self, tp_world_size):
+        self.rank = deepspeed.comm.get_rank()
+        self.world_size = deepspeed.comm.get_world_size()
+        self.tp_world_size = tp_world_size
+
+        for i in range(0, self.world_size, tp_world_size):
+            ranks = range(i, i + tp_world_size)
+            group = deepspeed.comm.new_group(ranks)
+            if self.rank in ranks:
+                self.tp_group = group
+
+        for i in range(0, tp_world_size):
+            ranks = range(i, self.world_size, tp_world_size)
+            group = deepspeed.comm.new_group(ranks)
+            if self.rank in ranks:
+                self.dp_group = group
+
+    def get_model_parallel_rank(self):
+        return self.rank % self.tp_world_size
+
+    def get_model_parallel_world_size(self):
+        return self.tp_world_size
+
+    def get_data_parallel_rank(self):
+        return self.rank // self.tp_world_size
+
+    def get_data_parallel_world_size(self):
+        return self.world_size // self.tp_world_size
+
+    def get_data_parallel_group(self):
+        return self.dp_group
+
+    def get_model_parallel_group(self):
+        return self.tp_group
+
+
+@pytest.mark.parametrize("shard_num", [6, 10])
+@pytest.mark.parametrize("C, M, scale", [(92, 32, 1),(209, 128, 5)])
+class TestPipelineCommunication(DistributedTest):
+    world_size = 8
+
+    def test(self, shard_num, C, M, scale):
+        tp_size = 2
+        world_size = deepspeed.comm.get_world_size()
+        E = world_size
+        ep_size = 4
+        config_dict = {"train_batch_size": 8, "steps_per_print": 1, "fp16": {"enabled": True}}
+        hidden_dim = M
+        device = get_accelerator().current_device_name()
+        tensor_parallel_expert = torch.nn.Sequential(torch.nn.Linear(hidden_dim, 4 * hidden_dim // tp_size),
+                                                     torch.nn.ReLU(),
+                                                     torch.nn.Linear(4 * hidden_dim // tp_size, hidden_dim))
+
+        model = MoE(
+            hidden_size=hidden_dim,
+            expert=tensor_parallel_expert,
+            num_experts=world_size * scale,
+            ep_size=ep_size,
+            use_residual=True,
+            enable_expert_tensor_parallelism=True,
+        )
+        optimizer = torch.optim.AdamW(params=model.parameters())
+        model, _, _, _ = deepspeed.initialize(config=config_dict,
+                                              model=model,
+                                              optimizer=optimizer,
+                                              dist_init_required=False,
+                                              mpu=MPU(tp_size))
+        model.deepspeed_moe.shard_num = shard_num
+        input = torch.rand(E, C, M, device=device)
+
+        # pipeline alltoall with allgather
+        pipeline_output = model.deepspeed_moe.pipeline_alltoall_with_allgather(input)
+
+        # first alltoall, then allgather
+        alltoall_output = _AllToAll.apply(model.deepspeed_moe.ep_group, input)
+        gather_output = gather_tokens(alltoall_output, dim=1)
+        assert torch.allclose(pipeline_output, gather_output, atol=1e-07), f"pipeline_output {pipeline_output} is not equal to gather_output {gather_output}"