[moe] implement transit between non moe tp and ep

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -1068,7 +1068,7 @@ class HybridParallelPlugin(PipelinePluginBase):
             self.pp_axis, self.dp_axis, self.tp_axis, self.sp_axis = 0, 1, 2, 3
             self.pg_mesh = ProcessGroupMesh(self.pp_size, self.dp_size, self.tp_size, self.sp_size)
 
-        self.logger.info(f"{type(self).__name__}: {self.pp_size=} {self.dp_size=} {self.tp_size=} {self.sp_size=}")
+        self.logger.info(f"{type(self).__name__}: {self.pp_size=} {self.dp_size=} {self.tp_size=} {self.sp_size=}", ranks=[0])
         
         self.stage_manager = None
         self.schedule = None

colossalai/booster/plugin/moe_hybrid_parallel_plugin.py

@@ -30,8 +30,8 @@ class MoeHybridParallelZeroOptimizer(LowLevelZeroOptimizer):
         optimizer: Optimizer,
         model: Module,
         use_pipeline: bool,
-        dp_process_group: ProcessGroup,  # the dp pg for comm
-        moe_dp_group: ProcessGroup,  # the moe dp pg for gomm
+        dp_process_group: ProcessGroup,  # dp pg for comm
+        moe_dp_group: ProcessGroup,  # moe dp pg for comm
         param_info: OrderedDict,
         initial_scale: int = 2**16,  # grad scaler config
         min_scale: int = 1,
@@ -44,7 +44,7 @@ class MoeHybridParallelZeroOptimizer(LowLevelZeroOptimizer):
         verbose: bool = False,
         reduce_bucket_size: int = 1024 * 1024,  # communication
         communication_dtype: Optional[torch.dtype] = None,
-        overlap_communication: bool = True,
+        overlap_communication: bool = False,
         partition_grad: bool = False,  # stage 2 flag
         cpu_offload: bool = False,  # cpu offload
         forced_dtype: Optional[torch.dtype] = None,
@@ -88,7 +88,7 @@ class MoeHybridParallelPlugin(HybridParallelPlugin):
     TODO: add docstring
     """
 
-    def __init__(self, ep_size: int, ep_tp_size: int = 1, *args, **kwargs) -> None:
+    def __init__(self, ep_size: int, moe_tp_size: int = 1, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
 
         self.use_ddp = self.dp_size > 1 and self.pp_size == 1 and self.zero_stage == 0
@@ -98,14 +98,14 @@ class MoeHybridParallelPlugin(HybridParallelPlugin):
             )
             self.ddp_config["find_unused_parameters"] = True
 
-        if ep_tp_size != 1:
+        if moe_tp_size != 1:
             raise NotImplementedError
 
         world_size = dist.get_world_size()
 
-        self.moe_dp_size = world_size // (ep_size * ep_tp_size)
+        self.moe_dp_size = world_size // (ep_size * moe_tp_size)
         self.ep_size = ep_size
-        self.moe_tp_size = ep_tp_size
+        self.moe_tp_size = moe_tp_size
 
         self.moe_pg_mesh = ProcessGroupMesh(self.moe_dp_size, self.ep_size, self.moe_tp_size)
         self.moe_dp_axis, self.ep_axis, self.moe_tp_axis = 0, 1, 2
@@ -114,7 +114,7 @@ class MoeHybridParallelPlugin(HybridParallelPlugin):
         self.ep_group = self.moe_pg_mesh.get_group_along_axis(self.ep_axis)
         self.moe_tp_group = self.moe_pg_mesh.get_group_along_axis(self.moe_tp_axis)
 
-        self.logger.info(f"{type(self).__name__}: {self.ep_size=} {self.moe_dp_size=} {self.moe_tp_size=}")
+        self.logger.info(f"{type(self).__name__}: {self.ep_size=} {self.moe_dp_size=} {self.moe_tp_size=}", ranks=[0])
 
         # set ep_group after super init
         # TODO do it in a better way

colossalai/moe/_operation.py

@@ -397,3 +397,106 @@ def all_to_all_uneven(
         inputs.requires_grad
     ), "Input must require grad to assure that backward is executed, otherwise it might hang the program."
     return AllToAllUneven.apply(inputs, input_split_sizes, output_split_sizes, group, overlap)
+
+
+# ===========================================================
+# This code section was modified from 
+# https://github.com/microsoft/DeepSpeed/blob/3d347276ce80e1a29e777c839d1d7fabe8e5f034/deepspeed/moe/mappings.py
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# The file has been adapted from the following Megatron-LM file:
+# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/mpu/mappings.py
+# Git commit hash: 9dc3c42a84aa656f583703cf8b6b4f79f712b796
+# We retain the following copyright from the original files:
+
+# Copyright (c) 2020, NVIDIA CORPORATION.  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.
+
+
+def _gather_tokens(input_, dim: int, tp_group: ProcessGroup):
+    """Gather tensors and concatenate them along a dimension"""
+
+    input_ = input_.contiguous()
+    # Size and dimension.
+    rank = tp_group.rank()
+
+    tensor_list = [torch.empty_like(input_) for _ in range(tp_group.size())]
+    tensor_list[rank] = input_
+    dist.all_gather(tensor_list, input_, group=tp_group)
+
+    # Note: torch.cat already creates a contiguous tensor.
+    output = torch.cat(tensor_list, dim=dim).contiguous()
+
+    return output
+
+
+def _drop_tokens(input_, dim: int, tp_group: ProcessGroup):
+    """Divide a tensor among the tensor parallel ranks"""
+
+    total_chunks = tp_group.size()
+    this_chunk = tp_group.rank()
+    assert input_.shape[
+        dim] % total_chunks == 0, f"input dimension {dim} ({input_.shape[dim]}) is not divisible by tensor parallel world size ({total_chunks})"
+    chunk_size = input_.shape[dim] // total_chunks
+
+    return torch.narrow(input_, dim, this_chunk * chunk_size, chunk_size)
+
+
+class _GatherTokens(torch.autograd.Function):
+    """All gather tokens among the tensor parallel ranks"""
+
+    @staticmethod
+    def forward(ctx, input_: torch.Tensor, dim: int, tp_group: ProcessGroup) -> torch.Tensor:
+        ctx.dim = dim
+        ctx.tp_group = tp_group
+        return _gather_tokens(input_, dim, tp_group)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _drop_tokens(grad_output, ctx.dim, ctx.tp_group), None, None
+
+
+class _DropTokens(torch.autograd.Function):
+    "Divide tokens equally among the tensor parallel ranks"
+
+    @staticmethod
+    def forward(ctx, input_: torch.Tensor, dim: int, tp_group: ProcessGroup) -> torch.Tensor:
+        ctx.dim = dim
+        ctx.tp_group = tp_group
+        return _drop_tokens(input_, dim, tp_group)
+
+    @staticmethod
+    def backward(ctx, input_: torch.Tensor) -> Tuple[torch.Tensor, None]:
+        return _gather_tokens(input_, ctx.dim, ctx.tp_group), None, None
+
+
+def gather_tokens(input_, dim: int, tp_group: ProcessGroup):
+    if tp_group.size() == 1:
+        # no tensor parallelism for non-experts
+        return input_
+    assert input_.requires_grad, "Input must require grad to assure that backward is executed, otherwise it might hang the program."
+    return _GatherTokens.apply(input_, dim)
+
+
+def drop_tokens(input_, dim: int, tp_group: ProcessGroup):
+    if tp_group.size() == 1:
+        # no tensor parallelism for non-experts
+        return input_
+    assert input_.requires_grad, "Input must require grad to assure that backward is executed, otherwise it might hang the program."
+    return _DropTokens.apply(input_, dim, tp_group)
+
+# ===========================================================

colossalai/shardformer/modeling/mixtral.py

@@ -14,21 +14,21 @@ from transformers.models.mixtral.modeling_mixtral import (
 from transformers.utils import is_flash_attn_2_available, logging
 
 from colossalai.lazy import LazyInitContext
-from colossalai.moe._operation import MoeInGradScaler, MoeOutGradScaler, all_to_all_uneven
+from colossalai.moe._operation import MoeInGradScaler, MoeOutGradScaler, all_to_all_uneven, drop_tokens, gather_tokens
 from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.shardformer.shard import ShardConfig
 from colossalai.shardformer.shard.utils import set_tensors_to_none
 
 
 class EPMixtralSparseMoeBlock(MixtralSparseMoeBlock):
-    def __init__(self, config, ep_group):
+    def __init__(self, config, ep_group: ProcessGroup, tp_group: Optional[ProcessGroup]=None, moe_tp_group: Optional[ProcessGroup]=None):
         super().__init__(config)
-        self.setup_ep(ep_group)
+        self.setup_process_groups(ep_group, tp_group, moe_tp_group)
 
-    def setup_ep(self, ep_group: ProcessGroup):
-        ep_group = ep_group
-        self.ep_size = dist.get_world_size(ep_group) if ep_group is not None else 1
-        self.ep_rank = dist.get_rank(ep_group) if ep_group is not None else 0
+    def setup_process_groups(self, ep_group: ProcessGroup, tp_group: Optional[ProcessGroup]=None, moe_tp_group: Optional[ProcessGroup]=None):
+        # setup ep group
+        self.ep_size = dist.get_world_size(ep_group)
+        self.ep_rank = dist.get_rank(ep_group)
         self.ep_group = ep_group
 
         if self.num_experts % self.ep_size != 0:
@@ -42,13 +42,19 @@ class EPMixtralSparseMoeBlock(MixtralSparseMoeBlock):
         for p in self.experts.parameters():
             p.ep_group = ep_group
 
+        # setup global tp group
+        self.tp_group = tp_group
+
+        # setup moe tp group
+        self.moe_tp_group = moe_tp_group
+
     @staticmethod
     def from_native_module(
-        module: MixtralSparseMoeBlock, ep_group: ProcessGroup, *args, **kwargs
+        module: MixtralSparseMoeBlock, ep_group: ProcessGroup, tp_group: Optional[ProcessGroup]=None, moe_tp_group: Optional[ProcessGroup]=None, *args, **kwargs
     ) -> "EPMixtralSparseMoeBlock":
         LazyInitContext.materialize(module)
         module.__class__ = EPMixtralSparseMoeBlock
-        module.setup_ep(ep_group)
+        module.setup_process_groups(ep_group)
         return module
 
     def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
@@ -72,6 +78,10 @@ class EPMixtralSparseMoeBlock(MixtralSparseMoeBlock):
 
         input_split_list = input_split_sizes.view(self.ep_size, self.num_experts_per_ep).sum(dim=-1).tolist()
         output_split_list = output_split_sizes.view(self.ep_size, self.num_experts_per_ep).sum(dim=-1).tolist()
+
+        if self.tp_group is not None and self.tp_group.size() > 1:
+            dispatch_states = drop_tokens(dispatch_states, -1, self.tp_group)
+
         output_states, _ = all_to_all_uneven(dispatch_states, input_split_list, output_split_list, self.ep_group)
         # compute expert output
         output_states = MoeInGradScaler.apply(output_states, self.ep_size)
@@ -94,6 +104,10 @@ class EPMixtralSparseMoeBlock(MixtralSparseMoeBlock):
                 output_states = torch.cat(output_states_list)
         output_states = MoeOutGradScaler.apply(output_states, self.ep_size)
         dispatch_states, _ = all_to_all_uneven(output_states, output_split_list, input_split_list, self.ep_group)
+
+        if self.tp_group is not None and self.tp_group.size() > 1:
+            dispatch_states = gather_tokens(dispatch_states, -1, self.tp_group)
+
         recover_experts_idx = torch.empty_like(selected_experts_idx)
         recover_experts_idx[selected_experts_idx] = torch.arange(
             selected_experts_idx.size(0), device=selected_experts_idx.device

colossalai/shardformer/policies/mixtral.py

@@ -8,6 +8,7 @@ from torch.nn import Module
 from transformers.models.mixtral.modeling_mixtral import MixtralDecoderLayer, MixtralForCausalLM, MixtralModel
 
 from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col
+from colossalai.shardformer.layer.linear import Linear1D_Row
 from colossalai.shardformer.modeling.mixtral import EPMixtralSparseMoeBlock, MixtralPipelineForwards
 from colossalai.shardformer.policies.base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
 
@@ -20,15 +21,15 @@ class MixtralPolicy(Policy):
 
     def preprocess(self):
         if self.shard_config.enable_tensor_parallelism:
-            raise NotImplementedError
-    
-            # # Resize embedding
-            # vocab_size = self.model.config.vocab_size
-            # world_size = self.shard_config.tensor_parallel_size
+            # non-moe params tensor parallelism
+
+            # Resize embedding
+            vocab_size = self.model.config.vocab_size
+            world_size = self.shard_config.tensor_parallel_size
 
-            # if vocab_size % world_size != 0:
-            #     new_vocab_size = vocab_size + world_size - vocab_size % world_size
-            #     self.model.resize_token_embeddings(new_vocab_size)
+            if vocab_size % world_size != 0:
+                new_vocab_size = vocab_size + world_size - vocab_size % world_size
+                self.model.resize_token_embeddings(new_vocab_size)
 
         return self.model
 
@@ -42,74 +43,62 @@ class MixtralPolicy(Policy):
             )
 
         if self.shard_config.enable_tensor_parallelism:
-            raise NotImplementedError
-            # assert (
-            #     self.model.config.num_attention_heads % self.shard_config.tensor_parallel_size == 0
-            # ), f"The number of attention heads must be divisible by tensor parallel size."
-            # assert (
-            #     self.model.config.num_key_value_heads % self.shard_config.tensor_parallel_size == 0
-            # ), f"The number of key_value heads must be divisible by tensor parallel size."
-            # decoder_attribute_replacement = {
-            #     "self_attn.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
-            #     "self_attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
-            #     "self_attn.num_key_value_heads": self.model.config.num_key_value_heads
-            #     // self.shard_config.tensor_parallel_size,
-            # }
-
-            # policy[MixtralDecoderLayer] = ModulePolicyDescription(
-            #     attribute_replacement=decoder_attribute_replacement,
-            #     sub_module_replacement=[
-            #         SubModuleReplacementDescription(
-            #             suffix="self_attn.q_proj",
-            #             target_module=Linear1D_Col,
-            #             kwargs={
-            #                 'process_group': self.shard_config.tensor_parallel_process_group,
-            #             }
-            #         ),
-            #         SubModuleReplacementDescription(
-            #             suffix="self_attn.k_proj",
-            #             target_module=Linear1D_Col,
-            #             kwargs={
-            #                 'process_group': self.shard_config.tensor_parallel_process_group,
-            #             }
-            #         ),
-            #         SubModuleReplacementDescription(
-            #             suffix="self_attn.v_proj",
-            #             target_module=Linear1D_Col,
-            #             kwargs={
-            #                 'process_group': self.shard_config.tensor_parallel_process_group,
-            #             }
-            #         ),
-            #         SubModuleReplacementDescription(
-            #             suffix="self_attn.o_proj",
-            #             target_module=Linear1D_Row,
-            #             kwargs={
-            #                 'process_group': self.shard_config.tensor_parallel_process_group,
-            #             }
-            #         ),
-            #         # SubModuleReplacementDescription(
-            #         #     suffix="mlp.gate_proj",
-            #         #     target_module=Linear1D_Col,
-            #         # ),
-            #         # SubModuleReplacementDescription(
-            #         #     suffix="mlp.up_proj",
-            #         #     target_module=Linear1D_Col,
-            #         # ),
-            #         # SubModuleReplacementDescription(
-            #         #     suffix="mlp.down_proj",
-            #         #     target_module=Linear1D_Row,
-            #         # ),
-            #     ],
-            # )
-
-        if getattr(self.shard_config, "ep_group", None) is None:
+            # tensor parallelism for non-moe params
+            assert (
+                self.model.config.num_attention_heads % self.shard_config.tensor_parallel_size == 0
+            ), f"The number of attention heads must be divisible by tensor parallel size."
+            assert (
+                self.model.config.num_key_value_heads % self.shard_config.tensor_parallel_size == 0
+            ), f"The number of key_value heads must be divisible by tensor parallel size."
+            decoder_attribute_replacement = {
+                "self_attn.hidden_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
+                "self_attn.num_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
+                "self_attn.num_key_value_heads": self.model.config.num_key_value_heads
+                // self.shard_config.tensor_parallel_size,
+            }
+
+            policy[MixtralDecoderLayer] = ModulePolicyDescription(
+                attribute_replacement=decoder_attribute_replacement,
+                sub_module_replacement=[
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.q_proj",
+                        target_module=Linear1D_Col,
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.k_proj",
+                        target_module=Linear1D_Col,
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.v_proj",
+                        target_module=Linear1D_Col,
+                    ),
+                    SubModuleReplacementDescription(
+                        suffix="self_attn.o_proj",
+                        target_module=Linear1D_Row,
+                    ),
+                    # SubModuleReplacementDescription(  # TODO: enable moe tp parallel
+                    #     suffix="mlp.gate_proj",
+                    #     target_module=Linear1D_Col,
+                    # ),
+                    # SubModuleReplacementDescription(
+                    #     suffix="mlp.up_proj",
+                    #     target_module=Linear1D_Col,
+                    # ),
+                    # SubModuleReplacementDescription(
+                    #     suffix="mlp.down_proj",
+                    #     target_module=Linear1D_Row,
+                    # ),
+                ],
+            )
+
+        if self.shard_config.ep_group:
             # expert parallel
             self.append_or_create_submodule_replacement(
                 description=[
                     SubModuleReplacementDescription(
                         suffix="block_sparse_moe",
                         target_module=EPMixtralSparseMoeBlock,
-                        kwargs={"ep_group": self.shard_config.ep_group},
+                        kwargs={"ep_group": self.shard_config.ep_group, "tp_group": self.shard_config.tensor_parallel_process_group},
                     )
                 ],
                 policy=policy,

colossalai/shardformer/shard/shard_config.py

@@ -47,6 +47,8 @@ class ShardConfig:
     gradient_checkpoint_config: Optional[GradientCheckpointConfig] = None
     extra_kwargs: Dict[str, Any] = field(default_factory=dict)
     ep_group: Optional[ProcessGroup] = None
+    moe_tp_group: Optional[ProcessGroup] = None
+
     # pipeline_parallel_size: int
     # data_parallel_size: int
     # tensor_parallel_mode: Literal['1d', '2d', '2.5d', '3d']

tests/test_shardformer/test_model/test_shard_mixtral.py

@@ -113,40 +113,65 @@ def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn,
 @parameterize(
     "test_config",
     [
+        {
+            "tp_size": 2,
+            "pp_size": 1,
+            "ep_size": 1,
+            "zero_stage": 2,
+            "precision": "fp32",
+        },  # [dp(2) + tp(2)] + [moe_dp(4)]
+        {
+            "tp_size": 2,
+            "pp_size": 1,
+            "ep_size": 2,
+            "zero_stage": 2,
+            "precision": "fp32",
+        },  # [dp(2) + tp(2)] + [ep(2) + moe_dp(2)]
         {
             "tp_size": 1,
             "pp_size": 2,
             "num_microbatches": 2,
             "ep_size": 1,
-            "zero_stage": 0,
+            "zero_stage": 2,
             "precision": "fp32",
-        },  # pp + ep
+        },  # [dp(2) + pp(2)] + [moe_dp(4)]
         {
             "tp_size": 1,
             "pp_size": 2,
             "num_microbatches": 2,
             "ep_size": 1,
-            "zero_stage": 0,
+            "zero_stage": 2,
             "precision": "fp32",
-        },  # pp + ep
+        },  # [dp(2) + pp(2)] + [moe_dp(4)]
         {
             "tp_size": 1,
             "pp_size": 2,
             "num_microbatches": 2,
             "ep_size": 4,
-            "zero_stage": 0,
+            "zero_stage": 2,
             "precision": "fp32",
-        },  # pp + ep
-        {"tp_size": 1, "pp_size": 1, "ep_size": 1, "zero_stage": 1, "precision": "bf16"},  # full dp for moe and non-moe
-        {  # moe_dp = 2, non_moe_dp = 4
+        },  # [dp(2) + pp(2)] + [ep(4))]
+        {
             "tp_size": 1,
             "pp_size": 1,
             "ep_size": 2,
-            "zero_stage": 1,
+            "zero_stage": 2,
             "precision": "fp32",
-        },  # moe_dp = 1, non_moe_dp = 4
-        {"tp_size": 1, "pp_size": 1, "ep_size": 4, "zero_stage": 1, "precision": "fp32"},  # full dp for non-moe and full ep for moe
-        {"tp_size": 1, "pp_size": 1, "ep_size": 1, "zero_stage": 0, "precision": "fp32"},  # full dp for moe and non-moe
+        },  # [dp(4)] + [ep(2) + moe_tp(2)]
+        {
+            "tp_size": 1, 
+            "pp_size": 1, 
+            "ep_size": 4, 
+            "zero_stage": 2, 
+            "precision": "fp32"
+        },  # full dp for non-moe and full ep for moe
+        {
+            "tp_size": 1, 
+            "pp_size": 1, 
+            "ep_size": 1, 
+            "zero_stage": 2, 
+            "precision": "fp32"
+        },  # full dp for moe and non-moe
     ],
 )
 def run_mixtral_test(test_config):