marlin情况下eager+rmsquant输出正常

vllm/model_executor/layers/fused_moe/layer.py

@@ -1670,7 +1670,7 @@ class FusedMoE(CustomOp):
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
         i_q: Optional[torch.Tensor] = None,
-        i_s: Optional[torch.Tensor] = None, **_
+        i_s: Optional[torch.Tensor] = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         og_hidden_states = hidden_states.shape[-1]
         if self.hidden_size != og_hidden_states:
@@ -1742,7 +1742,7 @@ class FusedMoE(CustomOp):
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
         i_q: Optional[torch.Tensor] = None,
-        i_s: Optional[torch.Tensor] = None, **_
+        i_s: Optional[torch.Tensor] = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         return self.forward_native(hidden_states, router_logits, i_q=i_q, i_s=i_s)
 
@@ -1912,7 +1912,6 @@ class FusedMoE(CustomOp):
         #        parallel execution of shared experts with the FusedMoE via
         #        separate cuda stream)
         if self.gate is not None:
-            print("YYYY: unsupported using gate in FusedMOE.forward_impl") # true
             router_logits, _ = self.gate(hidden_states)
 
         if use_chunked_impl:
@@ -2188,12 +2187,6 @@ def moe_forward_shared(
     i_q: Optional[torch.Tensor] = None,
     i_s: Optional[torch.Tensor] = None
 ) -> tuple[torch.Tensor, torch.Tensor]:
-    import traceback
-    import torch.distributed as dist
-    print("=======")
-    # if dist.get_rank() == 0:
-        # traceback.print_stack()
-    print("=======")
     self = get_layer_from_name(layer_name)
     assert self.shared_experts is not None
     if envs.USE_FUSED_RMS_QUANT:

vllm/model_executor/layers/fused_moe/shared_fused_moe.py

@@ -62,8 +62,6 @@ class SharedFusedMoE(FusedMoE):
         router_logits: torch.Tensor,
         *, iqis: tuple[torch.Tensor, torch.Tensor] | None = None
     ) -> tuple[torch.Tensor, torch.Tensor]:
-        if self._shared_experts is not None: print("YYYY: 处理下游专家输入, self.use_overlapped is :", self.use_overlapped)
-
         if not self.use_overlapped:
             if self._shared_experts is not None:
                 shared_out = self._shared_experts(hidden_states,
@@ -81,8 +79,6 @@ class SharedFusedMoE(FusedMoE):
             else:
                 shared_out = None
 
-            print("YYYY:  i suppose not this branch!!!")
-
             fused_out = super().forward(
                 hidden_states=hidden_states,
                 router_logits=router_logits,

vllm/model_executor/layers/layernorm.py

@@ -21,7 +21,6 @@ from vllm import envs
 from lightop.op import rms_norm_dynamic_per_token_quant as ligtop_rms_norm_dynamic_per_token_quant
 
 
-
 def rms_norm(
     x: torch.Tensor, weight: torch.Tensor, variance_epsilon: float
 ) -> torch.Tensor:
@@ -395,7 +394,7 @@ def fused_rmsquant(input: torch.Tensor,
     quant_dtype: torch.dtype,
     residual: Optional[torch.Tensor] = None,
     update_input: Optional[bool] = True):
-    from lmslim.quantize.quant_ops import lm_faster_rmsquant
+    from lmslim.quantize.quant_ops import lm_faster_rmsquant # TODO:wjl
     i_q, _scales = lm_faster_rmsquant(input=input,
                                       rms_weight=rms_weight,
                                       epsilon=epsilon,

vllm/model_executor/layers/linear.py

@@ -717,7 +717,6 @@ class MergedColumnParallelLinear(ColumnParallelLinear):
         # Matrix multiply.
         assert self.quant_method is not None
         if envs.USE_FUSED_RMS_QUANT and iqis is not None:
-            print("YYYYY: mlp.gate_up self.quant_method.apply: ", self.quant_method.apply)
             output_parallel = self.quant_method.apply(self, input_, bias, input_quant_args=iqis)
         else:
             output_parallel = self.quant_method.apply(self, input_, bias)

vllm/model_executor/layers/mla.py

@@ -131,7 +131,6 @@ class MultiHeadLatentAttentionWrapper(PluggableLayer):
             assert self.q_b_proj is not None, (
                 "q_b_proj is required when q_lora_rank is not None"
             )
-            # print("YYYY: self.fused_qkv_a_proj is:", self.fused_qkv_a_proj) # MergedColumnParallelLinear(in_features=7168, output_features=2112, bias=False, tp_size=1, gather_output=False)
             if envs.USE_FUSED_RMS_QUANT and iqis is not None:
                 qkv_lora = self.fused_qkv_a_proj(hidden_states, iqis=iqis)[0]
             else:
@@ -143,7 +142,6 @@ class MultiHeadLatentAttentionWrapper(PluggableLayer):
             q_c = self.q_a_layernorm(q_c)
             q = self.q_b_proj(q_c)[0]
         else:
-            # print("YYYY: self.q_lora_rank is None. unsupported for now!!!")
             assert self.kv_a_proj_with_mqa is not None, (
                 "kv_a_proj_with_mqa is required when q_lora_rank is None"
             )

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -1255,6 +1255,8 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
         use_nn_moe: bool | None = False,
+        i_q: torch.Tensor | None = None,
+        i_s: torch.Tensor | None = None,
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         from vllm.model_executor.layers.fused_moe import fused_experts
 
@@ -1271,6 +1273,8 @@ class CompressedTensorsW8A8Int8MoEMethod(CompressedTensorsMoEMethod):
             expert_map=layer.expert_map,
             quant_config=self.moe_quant_config,
             use_nn_moe=use_nn_moe,
+            i_q=i_q,
+            i_s=i_s
         )
 
 

vllm/model_executor/layers/quantization/utils/w8a8_utils.py

@@ -7,6 +7,7 @@ import torch
 from vllm import _custom_ops as ops
 from vllm.platforms import current_platform
 from vllm.utils import W8a8GetCacheJSON
+from vllm import envs
 
 try:
     from lmslim.layers.gemm.int8_utils import per_token_quant_int8
@@ -167,6 +168,15 @@ def apply_int8_linear(
     # ops.scaled_int8_quant supports both dynamic and static quant.
     # * dynamic, layer.input_scale is None and x_scale computed from x.
     # * static, layer.input_scale is scalar and x_scale is input_scale.
+    if envs.USE_FUSED_RMS_QUANT and input_quant_args is not None:
+        assert len(input_quant_args) == 2
+        x_zp =None
+        x_q, x_scale = input_quant_args
+    elif envs.USE_FUSED_RMS_QUANT and silu_quant_args is not None:
+        assert len(silu_quant_args) == 2
+        x_zp =None
+        x_q, x_scale = silu_quant_args
+    else:
         symmetric = azp_adj is None
         if input_scale is None and input_zero_point is None and symmetric is True:
             x_q, x_scale=per_token_quant_int8(input)

vllm/model_executor/models/deepseek_v2.py

@@ -413,15 +413,11 @@ class DeepseekV2MoE(nn.Module):
             hidden_states = sequence_parallel_chunk(hidden_states)
 
         if self.experts.is_internal_router:
-            # print("YYYY: self.experts.is_internal_router is True.") # True
             # In this case, the gate/router runs inside the FusedMoE class
             fused_moe_out = self.experts(
                 hidden_states=hidden_states, router_logits=hidden_states, iqis=iqis
             )
-        else: # NO
-            print("YYYY: self.experts.is_internal_router is False.")
-            # router_logits: (num_tokens, n_experts)
-            print("YYYY: RQ MOE.gate quant:  self.gate.quant_method.apply: ", self.gate.quant_method.apply)
+        else:
             router_logits, _ = self.gate(hidden_states)
             fused_moe_out = self.experts(
                 hidden_states=hidden_states, router_logits=router_logits, iqis=iqis
@@ -1093,7 +1089,6 @@ class DeepseekV2DecoderLayer(nn.Module):
     ) -> torch.Tensor:
         # Self Attention
         # Fix residual FP16 overflow
-        # print("YYYYY: forward_RQ is called")
         residual_fix_overflow = False
         assert self.input_layernorm.has_weight is True
         if residual is None:
@@ -1101,40 +1096,20 @@ class DeepseekV2DecoderLayer(nn.Module):
             i_q, i_s, _ = self.input_layernorm(x=hidden_states, 
                                                residual=None, 
                                                quant_dtype=torch.int8,
-                                            #    update_input=False # wjl, del
-                                               update_input=True
-                                               )
-            # print("YYYY: i_q:", i_q.flatten()[:5], i_q.shape)
-            # print("YYYY: i_s:", i_s.flatten()[:5], i_s.shape)
-            # i_q, i_s = lm_faster_rmsquant(input=hidden_states,
-            #                     rms_weight = self.input_layernorm.weight.data,
-            #                     epsilon=self._eps,
-            #                     quant_dtype=torch.int8,
-            #                     residual=None,
-            #                     update_input=False)
+                                               update_input=False
+                                               )
             residual_fix_overflow = True
         else:
-            # hidden_states, residual = self.input_layernorm(hidden_states, residual)
-            # i_q, i_s = lm_faster_rmsquant(input=hidden_states,
-            #                              rms_weight = self.input_layernorm.weight.data,
-            #                              epsilon=self._eps,
-            #                              quant_dtype=torch.int8,
-            #                              residual=residual, 
-            #                              update_input=False)
-            # print("YYYY: input rms residual bf", residual.flatten()[:5])
             i_q, i_s, residual = self.input_layernorm(x=hidden_states, 
                                                       residual=residual, 
                                                       quant_dtype=torch.int8,
-                                                    #   update_input=False, # wjl, del
-                                                      update_input=True,
+                                                      update_input=False
                                                       )
-            # print("YYYY:input rms  residual af", residual.flatten()[:5])
-            
 
         attn_kwargs = {
             "positions": positions,
             "hidden_states": hidden_states,
-            # "iqis": (i_q, i_s)  # wjl, del
+            "iqis": (i_q, i_s)
         }
         if not self.use_mha:
             attn_kwargs["llama_4_scaling"] = llama_4_scaling
@@ -1144,7 +1119,6 @@ class DeepseekV2DecoderLayer(nn.Module):
             not isinstance(self.self_attn, DeepseekAttention)
             and hidden_states.dtype == torch.float16
         ):
-            print("YYYYY: FP16 overflow fix is applied")
             # Fix FP16 overflow
             # We scale both hidden_states and residual before
             # rmsnorm, and rmsnorm result would not affect by scale.
@@ -1155,25 +1129,17 @@ class DeepseekV2DecoderLayer(nn.Module):
                 residual *= 1.0 / self.routed_scaling_factor
 
         # Fully Connected
-        # hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
         update_hs = True if isinstance(self.mlp, DeepseekV2MoE) else False
-        # _i_q, _i_s = lm_faster_rmsquant(input=hidden_states,
-        #                                 rms_weight=self.post_attention_layernorm.weight.data,
-        #                                 epsilon=self._eps,
-        #                                 quant_dtype=torch.int8,
-        #                                 residual=residual,
-        #                                 update_input=update_hs)
         assert self.post_attention_layernorm.has_weight is True
         _i_q, _i_s, residual = self.post_attention_layernorm(x=hidden_states, 
                                                              residual=residual, 
                                                              quant_dtype=torch.int8,
-                                                             update_input=update_hs, # wjl, del
-                                                            #  update_input=True,
+                                                             update_input=update_hs
                                                              )
 
         new_resi = residual
         hidden_states = self.mlp(hidden_states,
-                                #  iqis=(_i_q, _i_s)   # wjl, del 
+                                #  iqis=(_i_q, _i_s) # TODO:wjl
                                  )
 
         if isinstance(self.mlp, DeepseekV2MLP) and hidden_states.dtype == torch.float16:

vllm/model_executor/models/glm4_moe.py

@@ -75,9 +75,6 @@ from .utils import (
 )
 from vllm.utils.torch_utils import direct_register_custom_op
 
-if envs.USE_FUSED_RMS_QUANT:
-    from lightop import rms_norm_dynamic_per_token_quant
-
 logger = init_logger(__name__)