[feature] support ep for deepseek v3

colossalai/booster/plugin/moe_hybrid_parallel_plugin.py

@@ -19,7 +19,6 @@
     HybridParallelPlugin,
     HybridParallelZeroOptimizer,
     get_param_info,
-    reinitialize_optimizer,
 )
 from colossalai.checkpoint_io import MoECheckpointIO
 from colossalai.cluster.process_group_mesh import ProcessGroupMesh
@@ -468,18 +467,13 @@ def configure(
                 use_fp8=self.use_fp8,
             )
         if optimizer is not None and not isinstance(optimizer, OptimizerWrapper):
-            if self.ep_size > 1:
-                # if ep is enabled, the num of (moe) paramaters changed since they are sharded among ep groups
-                # but the optimizer is not aware of ep, so we need to update the optimizer
-                reinitialize_optimizer(optimizer, model)
-
             if self.zero_stage == 0:
                 is_zero = False
                 if self.precision in ["fp16", "bf16"]:
                     optimizer = HybridParallelAMPOptimizer(
                         optimizer,
                         model,
-                        use_pipeline=self.enable_pipeline_parallelism,
+                        use_pipeline=self.enable_pipeline_parallelism or self.ep_size > 1,
                         param_info=param_info,
                         precision=self.precision,
                         max_norm=self.max_norm,
@@ -489,7 +483,7 @@ def configure(
                     optimizer = HybridParallelNaiveOptimizer(
                         optimizer,
                         model,
-                        use_pipeline=self.enable_pipeline_parallelism,
+                        use_pipeline=self.enable_pipeline_parallelism or self.ep_size > 1,
                         param_info=param_info,
                         max_norm=self.max_norm,
                         pp_process_group=self.pp_group,
@@ -507,7 +501,7 @@ def configure(
                 optimizer = MoeHybridParallelZeroOptimizer(
                     optimizer,
                     model,
-                    use_pipeline=self.enable_pipeline_parallelism,
+                    use_pipeline=self.enable_pipeline_parallelism or self.ep_size > 1,
                     param_info=param_info,
                     dp_process_group=self.mixed_dp_group,
                     tp_process_group=self.tp_group,

colossalai/cluster/process_group_mesh.py

@@ -64,7 +64,10 @@ def destroy_mesh_process_groups(self):
             system resources.
         """
         for group in self._ranks_to_group.values():
-            dist.destroy_process_group(group)
+            try:
+                dist.destroy_process_group(group)
+            except ValueError:
+                pass
 
         # Manually clear all process groups to save memory
         gc.collect()

colossalai/lazy/lazy_init.py

@@ -104,7 +104,7 @@ def _data_tolist(tensor: torch.Tensor) -> list:
     return tensor.data.tolist()
 
 
-def _convert_cls(tensor: "LazyTensor", target: torch.Tensor) -> torch.Tensor:
+def _convert_cls(tensor: "LazyTensor", target: torch.Tensor, requires_grad=None) -> torch.Tensor:
     """Convert a lazy tensor's class to target's class, with target's data.
 
     The reason why we change the class of a lazy tensor in-place is that this can easily handle shared modules/parameters, which is common in huggingface models.
@@ -117,13 +117,14 @@ def _convert_cls(tensor: "LazyTensor", target: torch.Tensor) -> torch.Tensor:
     Returns:
         torch.Tensor: the converted tensor
     """
+    requires_grad = target.requires_grad if requires_grad is None else requires_grad
     cls_to_become = Parameter if isinstance(tensor, Parameter) else torch.Tensor
     tensor.__class__ = cls_to_become
     if cls_to_become is Parameter:
         # to fit UninitializedParameter
         delattr(tensor, "_is_param")
     tensor.data = target
-    tensor.requires_grad = target.requires_grad
+    tensor.requires_grad = requires_grad
     # subclass of torch.Tensor does not have tolist() method
     # overwrite this method after materialization or distribution
     tensor.tolist = MethodType(_data_tolist, tensor)
@@ -212,9 +213,10 @@ def materialize(self) -> torch.Tensor:
         Returns:
             torch.Tensor: The materialized tensor (self).
         """
+        requires_grad = self.requires_grad
         target = self._materialize_data()
         self.clean()
-        return _convert_cls(self, target)
+        return _convert_cls(self, target, requires_grad=requires_grad)
 
     def clean(self) -> None:
         """Clean all stored operations, meta data and materialized data, which prevents memory leaking. This should be called after all tensors are materialized."""

colossalai/shardformer/modeling/deepseek_v3.py

@@ -0,0 +1,277 @@
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.distributed as dist
+from torch.distributed import ProcessGroup
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.modeling_outputs import BaseModelOutputWithPast
+
+from colossalai.lazy import LazyInitContext
+from colossalai.moe._operation import (
+    DPGradScalerIn,
+    DPGradScalerOut,
+    EPGradScalerIn,
+    EPGradScalerOut,
+    all_to_all_uneven,
+)
+from colossalai.shardformer.layer.linear import ParallelModule
+from colossalai.shardformer.shard.utils import set_tensors_to_none
+from colossalai.tensor.moe_tensor.api import set_moe_tensor_ep_group
+
+
+class EpDeepseekV3MoE(ParallelModule):
+    """
+    A mixed expert module containing shared experts.
+    """
+
+    def __init__(self, config):
+        raise RuntimeError(f"Please use `from_native_module` to create an instance of {self.__class__.__name__}")
+
+    def setup_process_groups(
+        self,
+        moe_dp_group: ProcessGroup,
+        ep_group: ProcessGroup,
+    ):
+        assert moe_dp_group is not None
+        assert ep_group is not None
+
+        self.ep_size = dist.get_world_size(ep_group)
+        self.ep_rank = dist.get_rank(ep_group)
+        self.num_experts = self.config.n_routed_experts
+        assert self.num_experts % self.ep_size == 0
+
+        self.ep_group = ep_group
+        self.num_experts_per_ep = self.num_experts // self.ep_size
+        self.experts_per_rank = self.num_experts_per_ep
+        self.expert_start_idx = self.ep_rank * self.num_experts_per_ep
+        held_experts = self.experts[self.expert_start_idx : self.expert_start_idx + self.num_experts_per_ep]
+
+        set_tensors_to_none(self.experts, exclude=set(held_experts))
+
+        # setup moe_dp group
+        self.moe_dp_group = moe_dp_group
+        self.moe_dp_size = dist.get_world_size(moe_dp_group)
+
+        for p in self.experts.parameters():
+            set_moe_tensor_ep_group(p, ep_group)
+
+    @staticmethod
+    def from_native_module(
+        module,
+        moe_dp_group: ProcessGroup,
+        ep_group: ProcessGroup,
+        *args,
+        **kwargs,
+    ) -> "EpDeepseekV3MoE":
+        if module.__class__.__name__ != "DeepseekV3MLP":
+            module.__class__ = EpDeepseekV3MoE
+            module.setup_process_groups(moe_dp_group, ep_group)
+        LazyInitContext.materialize(module)
+        return module
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        identity = hidden_states
+        orig_shape = hidden_states.shape
+        topk_idx, topk_weight = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        y = self.moe_forward(hidden_states, topk_idx, topk_weight).view(*orig_shape)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        return y
+
+    def moe_forward(self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor) -> torch.Tensor:
+        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
+        cnts.scatter_(1, topk_ids, 1)
+        tokens_per_expert = cnts.sum(dim=0)
+        idxs = topk_ids.view(-1).argsort()
+        sorted_tokens = x[idxs // topk_ids.shape[1]]
+        if self.ep_size > 1:
+            tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1)
+            tokens_per_expert_group = tokens_per_expert.new_empty(tokens_per_expert.shape[0])
+            dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert, group=self.ep_group)
+
+            output_splits = tokens_per_expert_group.view(self.ep_size, -1).sum(1).tolist()
+            input_split_sizes = tokens_per_ep_rank.tolist()
+
+            gathered_tokens, _ = all_to_all_uneven(sorted_tokens, input_split_sizes, output_splits, self.ep_group)
+            tokens_per_expert_post_gather = tokens_per_expert_group.view(self.ep_size, self.experts_per_rank).sum(dim=0)
+            gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32)
+            s = 0
+            for i, k in enumerate(tokens_per_expert_group.cpu().numpy()):
+                gatherd_idxs[s : s + k] = i % self.experts_per_rank
+                s += k
+            gatherd_idxs = gatherd_idxs.argsort()
+            sorted_tokens = gathered_tokens[gatherd_idxs]
+            tokens_per_expert = tokens_per_expert_post_gather
+
+            # moe-dp related code
+            activate_experts = tokens_per_expert_post_gather > 0
+            activate_experts = activate_experts.int()
+            dist.all_reduce(activate_experts, group=self.moe_dp_group)
+
+            # ep related code
+            sorted_tokens = EPGradScalerIn.apply(sorted_tokens, self.ep_size)
+
+        tokens_per_expert = tokens_per_expert.cpu().numpy()
+
+        outputs = []
+        start_idx = 0
+        for i, num_tokens in enumerate(tokens_per_expert):
+            end_idx = start_idx + num_tokens
+            if num_tokens == 0:
+                continue
+            expert = self.experts[i + self.ep_rank * self.experts_per_rank]
+            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
+            # moe-dp related code
+            tokens_for_this_expert = DPGradScalerIn.apply(tokens_for_this_expert, self.moe_dp_size, activate_experts[i])
+            expert_out = expert(tokens_for_this_expert)
+            # moe-dp related code
+            expert_out = DPGradScalerOut.apply(expert_out, self.moe_dp_size, activate_experts[i])
+            outputs.append(expert_out)
+            start_idx = end_idx
+
+        if len(outputs) > 0:
+            outs = torch.cat(outputs, dim=0)
+        else:
+            assert sorted_tokens.numel() == 0, f"sorted_tokens: should be empty, but got {sorted_tokens.shape}"
+            outs = sorted_tokens
+
+        if self.ep_size > 1:
+            outs = EPGradScalerOut.apply(outs, self.ep_size)
+            new_x = torch.empty_like(outs)
+            new_x[gatherd_idxs] = outs
+            gathered_tokens, _ = all_to_all_uneven(new_x, output_splits, input_split_sizes, self.ep_group)
+            outs = gathered_tokens
+
+        new_x = torch.empty_like(outs)
+        new_x[idxs] = outs
+        final_out = (
+            (new_x.view(*topk_ids.shape, -1).type(topk_weight.dtype) * topk_weight.unsqueeze(dim=-1))
+            .sum(dim=1)
+            .type(new_x.dtype)
+        )
+
+        return final_out
+
+
+def deepseek_v3_model_forward(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, BaseModelOutputWithPast]:
+    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+    )
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+    # retrieve input_ids and inputs_embeds
+    if input_ids is not None and inputs_embeds is not None:
+        raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+    elif input_ids is not None:
+        batch_size, seq_length = input_ids.shape[:2]
+    elif inputs_embeds is not None:
+        batch_size, seq_length = inputs_embeds.shape[:2]
+    else:
+        raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+    past_key_values_length = 0
+    if use_cache:
+        use_legacy_cache = not isinstance(past_key_values, Cache)
+        if use_legacy_cache:
+            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+        past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+    if position_ids is None:
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        position_ids = torch.arange(
+            past_key_values_length,
+            seq_length + past_key_values_length,
+            dtype=torch.long,
+            device=device,
+        )
+        position_ids = position_ids.unsqueeze(0)
+
+    if inputs_embeds is None:
+        inputs_embeds = self.embed_tokens(input_ids)
+
+    if self._use_flash_attention_2:
+        # 2d mask is passed through the layers
+        attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+    else:
+        # 4d mask is passed through the layers
+        attention_mask = _prepare_4d_causal_attention_mask(
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+
+    # embed positions
+    hidden_states = inputs_embeds
+
+    # decoder layers
+    all_hidden_states = () if output_hidden_states else None
+    all_self_attns = () if output_attentions else None
+    next_decoder_cache = None
+
+    for i, decoder_layer in enumerate(self.layers):
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        if self.gradient_checkpointing and i > 0:
+            layer_outputs = self._gradient_checkpointing_func(
+                decoder_layer.__call__,
+                hidden_states,
+                attention_mask,
+                position_ids,
+                past_key_values,
+                output_attentions,
+                use_cache,
+            )
+        else:
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        hidden_states = layer_outputs[0]
+
+        if use_cache:
+            next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+        if output_attentions:
+            all_self_attns += (layer_outputs[1],)
+
+    hidden_states = self.norm(hidden_states)
+
+    # add hidden states from the last decoder layer
+    if output_hidden_states:
+        all_hidden_states += (hidden_states,)
+
+    next_cache = None
+    if use_cache:
+        next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+    if not return_dict:
+        return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+    return BaseModelOutputWithPast(
+        last_hidden_state=hidden_states,
+        past_key_values=next_cache,
+        hidden_states=all_hidden_states,
+        attentions=all_self_attns,
+    )

colossalai/shardformer/policies/auto_policy.py

@@ -167,6 +167,13 @@ class PolicyLocation:
     "transformers_modules.modeling_deepseek.DeepseekForCausalLM": PolicyLocation(
         file_name="deepseek", class_name="DeepseekForCausalLMPolicy"
     ),
+    # DeepseekV3
+    "transformers_modules.modeling_deepseek.DeepseekV3Model": PolicyLocation(
+        file_name="deepseek_v3", class_name="DeepseekV3ModelPolicy"
+    ),
+    "transformers_modules.modeling_deepseek.DeepseekV3ForCausalLM": PolicyLocation(
+        file_name="deepseek_v3", class_name="DeepseekV3ForCausalLMPolicy"
+    ),
     # Falcon
     "transformers.models.falcon.modeling_falcon.FalconModel": PolicyLocation(
         file_name="falcon", class_name="FalconModelPolicy"