first commit

vllm_br/model_executor/models/supa_module/moe.py

@@ -0,0 +1,116 @@
+################################################################################
+# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. 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 typing import Optional
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.distributed import (get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.models.deepseek_v2 import (DeepseekV2MLP,
+                                                    ParallelConfig)
+from vllm_br import envs
+from vllm_br.utils import get_grandparent_pid
+
+
+class DeepseekV2MoE(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        parallel_config: ParallelConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.n_shared_experts = config.n_shared_experts
+        self.static_moe_decoder_max_len = 512
+
+        self.is_sequence_parallel = parallel_config.use_sequence_parallel_moe
+
+        if config.hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {config.hidden_act}. "
+                             "Only silu is supported for now.")
+
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     config.n_routed_experts,
+                                     bias=False,
+                                     quant_config=None,
+                                     prefix=f"{prefix}.gate")
+        if config.topk_method == "noaux_tc":
+            self.gate.e_score_correction_bias = nn.Parameter(
+                torch.empty(config.n_routed_experts, device="cpu"))
+        else:
+            self.gate.e_score_correction_bias = None
+
+        self.experts = FusedMoE(
+            num_experts=config.n_routed_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            use_grouped_topk=True,
+            num_expert_group=config.n_group,
+            topk_group=config.topk_group,
+            prefix=f"{prefix}.experts",
+            scoring_func=config.scoring_func,
+            e_score_correction_bias=self.gate.e_score_correction_bias)
+
+        if config.n_shared_experts is not None:
+            intermediate_size = (config.moe_intermediate_size *
+                                 config.n_shared_experts)
+            self.shared_experts = DeepseekV2MLP(
+                hidden_size=config.hidden_size,
+                intermediate_size=intermediate_size,
+                hidden_act=config.hidden_act,
+                quant_config=quant_config,
+                reduce_results=False,
+                prefix=f"{prefix}.shared_experts",
+            )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if envs.VLLM_BR_USE_CPU_ALL_REDUCE != 0 and not hasattr(
+                self, "grandparent_pid"):
+            self.grandparent_pid = get_grandparent_pid()
+        orig_shape = hidden_states.shape
+        assert self.n_shared_experts is not None, 'n_shared_experts must be set'
+        # NOTE: gate has been fused with shared_experts, no more single gate call
+        # and we packed router weights, shared_experts weights and down weights in a tuple
+        tuple_router_shared_expert_weight = (
+            self.gate.weight, self.shared_experts.gate_up_proj.weight,
+            self.shared_experts.down_proj.weight)
+        hidden_states = hidden_states.view(-1, orig_shape[-1])
+
+        final_hidden_states = self.experts(
+            hidden_states=hidden_states,
+            router_logits=tuple_router_shared_expert_weight)
+
+        if hasattr(final_hidden_states, 'all_reduced'):
+            # NOTE: this flag indicates that the final_hidden_states has been reduced in fused_moe
+            delattr(final_hidden_states, 'all_reduced')
+        elif self.tp_size > 1:
+            final_hidden_states = tensor_model_parallel_all_reduce(
+                final_hidden_states)
+        return final_hidden_states.view(orig_shape)