修复residual FP16 overflow，解决mtp采样率和数据集精度的冲突

vllm/model_executor/models/deepseek_v2.py

@@ -66,7 +66,7 @@ from .utils import (PPMissingLayer, is_pp_missing_parameter,
 from vllm import _custom_ops as ops
 from vllm.utils import W8a8GetCacheJSON
 
-os.environ['DPSK_FP16_QUICK'] = os.environ.get('DPSK_FP16_QUICK', '1')
+os.environ['DPSK_FP16_QUICK'] = os.environ.get('DPSK_FP16_QUICK', '0')
 class DeepseekV2MLP(nn.Module):
 
     def __init__(
@@ -621,9 +621,13 @@ class DeepseekV2DecoderLayer(nn.Module):
         residual: Optional[torch.Tensor],
     ) -> torch.Tensor:
         # Self Attention
+        
+        # Fix residual FP16 overflow
+        residual_fix_overflow = False
         if residual is None:
             residual = hidden_states
             hidden_states = self.input_layernorm(hidden_states)
+            residual_fix_overflow = True
         else:
             hidden_states, residual = self.input_layernorm(
                 hidden_states, residual)
@@ -637,7 +641,7 @@ class DeepseekV2DecoderLayer(nn.Module):
             # We scale both hidden_states and residual before
             # rmsnorm, and rmsnorm result would not affect by scale.
             hidden_states *= 1. / self.routed_scaling_factor
-            if self.layer_idx == 0:
+            if self.layer_idx == 0 or residual_fix_overflow:
                 # The residual is shared by all layers, we only scale it on
                 # first layer.
                 residual *= 1. / self.routed_scaling_factor