[Bugfix] Fix the method of importing environment variables in DeepSee… (#817)

### What this PR does / why we need it?
Fix the method of importing environment variables in DeepSeek model to
support successful compilation via aclgraph.

Signed-off-by: rjg-lyh <[email protected]>

Author: rjg-lyh

vllm_ascend/envs.py

@@ -34,6 +34,10 @@
     lambda: os.getenv("CMAKE_BUILD_TYPE"),
     "COMPILE_CUSTOM_KERNELS":
     lambda: bool(int(os.getenv("COMPILE_CUSTOM_KERNELS", "1"))),
+    "VLLM_ENABLE_MC2":
+    lambda: bool(int(os.getenv("VLLM_ENABLE_MC2", '0'))),
+    "USING_LCCL_COM":
+    lambda: bool(int(os.getenv("USING_LCCL_COM", '0'))),
     "SOC_VERSION":
     lambda: os.getenv("SOC_VERSION", "ASCEND910B1"),
     # If set, vllm-ascend will print verbose logs during compilation

vllm_ascend/models/deepseek_v2.py

@@ -25,7 +25,6 @@
 # # vllm-project/vllm/vllm/model_executor/models/deepseek_v2.py
 # """Inference-only DeepseekV2/DeepseekV3 model."""
 
-import os
 from typing import Any, Dict, List, Optional, Union
 
 import torch
@@ -66,9 +65,12 @@
     maybe_prefix)
 from vllm.sequence import IntermediateTensors
 
+import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ops.fused_moe import AscendFusedMoE
 from vllm_ascend.quantization.w8a8_dynamic import AscendW8A8DynamicLinearMethod
 
+VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
+
 
 class CustomDeepseekV2MLP(nn.Module):
 
@@ -206,7 +208,6 @@ def __init__(
         vllm_config = get_current_vllm_config()
         self.dp_size = get_dp_group().world_size
         batch_size = vllm_config.scheduler_config.max_num_seqs
-        self.enable_mc2 = int(os.environ.get("VLLM_ENABLE_MC2", '0')) == 1
 
         params_dtype = torch.get_default_dtype()
         self.final_hidden_states = torch.zeros(
@@ -223,7 +224,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         num_tokens, hidden_dim = hidden_states.shape
         hidden_states = hidden_states.view(-1, hidden_dim)
 
-        if (self.tp_size > 1 and self.enable_mc2 and not is_prefill):
+        if (self.tp_size > 1 and VLLM_ENABLE_MC2 and not is_prefill):
             chunks = torch.chunk(hidden_states,
                                  get_tp_group().world_size,
                                  dim=0)
@@ -239,7 +240,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             top_k=CustomDeepseekV2MoE.top_k) * self.routed_scaling_factor
 
         if self.tp_size > 1:
-            if self.enable_mc2 and not is_prefill:
+            if VLLM_ENABLE_MC2 and not is_prefill:
                 dist.all_gather_into_tensor(self.final_hidden_states,
                                             final_hidden_states, self.tp_group)
                 final_hidden_states = self.final_hidden_states

vllm_ascend/ops/fused_moe.py

@@ -15,7 +15,6 @@
 # This file is a part of the vllm-ascend project.
 # Adapted from vllm/tests/kernels/test_moe.py
 
-import os
 from typing import Callable, Optional
 
 import torch
@@ -29,8 +28,12 @@
 from vllm.model_executor.layers.quantization.base_config import \
     QuantizeMethodBase
 
+import vllm_ascend.envs as envs_ascend
 from vllm_ascend.distributed.parallel_state import get_ep_group, get_etp_group
 
+VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2
+USING_LCCL_COM: bool = envs_ascend.USING_LCCL_COM
+
 
 def fused_experts_with_mc2(
     hidden_states: torch.Tensor,
@@ -493,7 +496,7 @@ def apply(
                 e_score_correction_bias=e_score_correction_bias,
             )
 
-        if os.environ.get("VLLM_ENABLE_MC2", '0') == "1" and not is_prefill:
+        if VLLM_ENABLE_MC2 and not is_prefill:
             return fused_experts_with_mc2(
                 hidden_states=x,
                 w1=layer.w13_weight,
@@ -624,11 +627,9 @@ def forward(self,
             real_top_k = self.top_k
 
         if self.dp_size > 1:
-            if int(os.environ.get("VLLM_ENABLE_MC2", '0')  # type: ignore
-                   ) == 1 and not is_prefill:
+            if VLLM_ENABLE_MC2 and not is_prefill:
                 ...
-            elif int(os.environ.get("USING_LCCL_COM",
-                                    '0')) == 1:  # type: ignore
+            elif USING_LCCL_COM:  # type: ignore
                 hidden_states = get_dp_group().all_gather(
                     hidden_states, 0, False)
                 router_logits = get_dp_group().all_gather(
@@ -655,8 +656,7 @@ def forward(self,
             is_prefill=is_prefill)
 
         if self.dp_size > 1:
-            if int(os.environ.get("VLLM_ENABLE_MC2", '0')  # type: ignore
-                   ) == 1 and not is_prefill:
+            if VLLM_ENABLE_MC2 and not is_prefill:
                 ...
             else:
                 final_hidden_states = dist._functional_collectives.reduce_scatter_tensor(