BF16 + TP1 working

examples/offline_inference.py

@@ -11,12 +11,17 @@
 sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
 
 # Create an LLM.
-llm = LLM(model="facebook/opt-125m")
+# llm = LLM(model="facebook/opt-125m")
+llm = LLM(model="/data/xmo/vllm/deepseekv3-lite-base-latest",
+          tokenizer="/data/xmo/whales",
+          trust_remote_code=True,
+          enforce_eager=True,
+          max_model_len=1024)
 # Generate texts from the prompts. The output is a list of RequestOutput objects
 # that contain the prompt, generated text, and other information.
 outputs = llm.generate(prompts, sampling_params)
 # Print the outputs.
 for output in outputs:
     prompt = output.prompt
     generated_text = output.outputs[0].text
-    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

vllm/config.py

@@ -160,7 +160,7 @@ class ModelConfig:
             override default pooling config for the pooling model.
         logits_processor_pattern: Optional regex pattern specifying valid
             logits processor qualified names that can be passed with the
-            `logits_processors` extra completion argument. Defaults to None, 
+            `logits_processors` extra completion argument. Defaults to None,
             which allows no processors.
         generation_config: Configuration parameter file for generation.
     """
@@ -363,7 +363,7 @@ def __init__(self,
     def maybe_pull_model_tokenizer_for_s3(self, model: str,
                                           tokenizer: str) -> None:
         """
-        Pull the model config or tokenizer to a temporary 
+        Pull the model config or tokenizer to a temporary
         directory in case of S3.
 
         Args:
@@ -721,7 +721,7 @@ def get_hidden_size(self) -> int:
     def get_head_size(self) -> int:
         # TODO remove hard code
         if hasattr(self.hf_text_config, "model_type"
-                   ) and self.hf_text_config.model_type == 'deepseek_v2':
+                   ) and (self.hf_text_config.model_type in ('deepseek_v2', 'deepseek_v3')):
             # FlashAttention supports only head_size 32, 64, 128, 256,
             # we need to pad head_size 192 to 256
             return 256
@@ -874,14 +874,14 @@ def try_get_generation_config(self) -> Dict[str, Any]:
 
     def get_diff_sampling_param(self) -> Dict[str, Any]:
         """
-        This method returns a dictionary containing the parameters 
-        that differ from the default sampling parameters, but only 
-        if `generation_config` is set. If `generation_config` is not 
+        This method returns a dictionary containing the parameters
+        that differ from the default sampling parameters, but only
+        if `generation_config` is set. If `generation_config` is not
         set, an empty dictionary is returned.
 
         Returns:
-            Dict[str, Any]: A dictionary with the differing sampling 
-            parameters if `generation_config` is set, otherwise an 
+            Dict[str, Any]: A dictionary with the differing sampling
+            parameters if `generation_config` is set, otherwise an
             empty dictionary.
         """
         if self.generation_config is None:

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -421,14 +421,15 @@ def fused_topk(
     return topk_weights, topk_ids
 
 
-# This is used by the Deepseek-V2 model
+# This is used by the Deepseek-V2 and Deepseek-V3 model
 def grouped_topk(hidden_states: torch.Tensor,
                  gating_output: torch.Tensor,
                  topk: int,
                  renormalize: bool,
                  num_expert_group: int = 0,
                  topk_group: int = 0,
-                 scoring_func: str = "softmax"):
+                 scoring_func: str = "softmax",
+                 e_score_correction_bias: Optional[torch.Tensor] = None):
 
     assert hidden_states.shape[0] == gating_output.shape[0], (
         "Number of tokens mismatch")
@@ -440,6 +441,9 @@ def grouped_topk(hidden_states: torch.Tensor,
     else:
         raise ValueError(f"Unsupported scoring function: {scoring_func}")
 
+    if e_score_correction_bias is not None:
+        scores.add_(e_score_correction_bias.unsqueeze(0))
+
     num_token = scores.shape[0]
     group_scores = scores.view(num_token, num_expert_group,
                                -1).max(dim=-1).values  # [n, n_group]

vllm/model_executor/layers/fused_moe/layer.py

@@ -81,7 +81,9 @@ def apply(
             use_grouped_topk: bool,
             topk_group: Optional[int] = None,
             num_expert_group: Optional[int] = None,
-            custom_routing_function: Optional[Callable] = None
+            custom_routing_function: Optional[Callable] = None,
+            scoring_func: str = "softmax",
+            e_score_correction_bias: Optional[torch.Tensor] = None
     ) -> torch.Tensor:
         return self.forward(x=x,
                             layer=layer,
@@ -91,7 +93,9 @@ def apply(
                             use_grouped_topk=use_grouped_topk,
                             topk_group=topk_group,
                             num_expert_group=num_expert_group,
-                            custom_routing_function=custom_routing_function)
+                            custom_routing_function=custom_routing_function,
+                            scoring_func=scoring_func,
+                            e_score_correction_bias=e_score_correction_bias)
 
     def forward_cuda(
             self,
@@ -103,7 +107,9 @@ def forward_cuda(
             renormalize: bool,
             topk_group: Optional[int] = None,
             num_expert_group: Optional[int] = None,
-            custom_routing_function: Optional[Callable] = None
+            custom_routing_function: Optional[Callable] = None,
+            scoring_func: str = "softmax",
+            e_score_correction_bias: Optional[torch.Tensor] = None
     ) -> torch.Tensor:
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
@@ -113,7 +119,9 @@ def forward_cuda(
             renormalize=renormalize,
             topk_group=topk_group,
             num_expert_group=num_expert_group,
-            custom_routing_function=custom_routing_function)
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias)
 
         return fused_experts(hidden_states=x,
                              w1=layer.w13_weight,
@@ -136,7 +144,8 @@ def forward_tpu(
             renormalize: bool,
             topk_group: Optional[int] = None,
             num_expert_group: Optional[int] = None,
-            custom_routing_function: Optional[Callable] = None
+            custom_routing_function: Optional[Callable] = None,
+            **kwargs,
     ) -> torch.Tensor:
         assert not use_grouped_topk
         assert num_expert_group is None
@@ -190,6 +199,7 @@ def __init__(
         prefix: str = "",
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
     ):
         super().__init__()
 
@@ -210,9 +220,12 @@ def __init__(
         self.topk_group = topk_group
         self.custom_routing_function = custom_routing_function
         self.scoring_func = scoring_func
+        self.e_score_correction_bias = e_score_correction_bias
 
         if self.scoring_func != "softmax" and not self.use_grouped_topk:
-            raise ValueError("Only softmax scoring function is supported for non-grouped topk.")
+            raise ValueError(
+                "Only softmax scoring function is supported for non-grouped topk."
+            )
 
         if quant_config is None:
             self.quant_method: Optional[QuantizeMethodBase] = (
@@ -447,7 +460,8 @@ def select_experts(hidden_states: torch.Tensor,
                        topk_group: Optional[int] = None,
                        num_expert_group: Optional[int] = None,
                        custom_routing_function: Optional[Callable] = None,
-                       scoring_func: str = "softmax"):
+                       scoring_func: str = "softmax",
+                       e_score_correction_bias: Optional[torch.Tensor] = None):
         from vllm.model_executor.layers.fused_moe.fused_moe import (
             fused_topk, grouped_topk)
 
@@ -462,7 +476,8 @@ def select_experts(hidden_states: torch.Tensor,
                 renormalize=renormalize,
                 num_expert_group=num_expert_group,
                 topk_group=topk_group,
-                scoring_func=scoring_func)
+                scoring_func=scoring_func,
+                e_score_correction_bias=e_score_correction_bias)
         elif custom_routing_function is None:
             topk_weights, topk_ids = fused_topk(hidden_states=hidden_states,
                                                 gating_output=router_logits,
@@ -491,7 +506,9 @@ def forward(self, hidden_states: torch.Tensor,
             use_grouped_topk=self.use_grouped_topk,
             topk_group=self.topk_group,
             num_expert_group=self.num_expert_group,
-            custom_routing_function=self.custom_routing_function)
+            custom_routing_function=self.custom_routing_function,
+            scoring_func=self.scoring_func,
+            e_score_correction_bias=self.e_score_correction_bias)
 
         if self.reduce_results and self.tp_size > 1:
             final_hidden_states = tensor_model_parallel_all_reduce(

vllm/model_executor/models/deepseek_v3.py

@@ -90,6 +90,28 @@ def forward(self, x):
         return x
 
 
+class MoEGate(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+    ):
+        super().__init__()
+        # TODO(simon): make this replicated linear
+        self.weight = nn.Parameter(
+            torch.empty(config.n_routed_experts, config.hidden_size))
+        if config.topk_method == "noaux_tc":
+            self.e_score_correction_bias = nn.Parameter(
+                torch.empty((config.n_routed_experts)))
+        else:
+            self.e_score_correction_bias = None
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return torch.nn.functional.linear(hidden_states,
+                                          self.weight,
+                                          bias=None)
+
+
 class DeepseekV3MoE(nn.Module):
 
     def __init__(
@@ -112,24 +134,22 @@ def __init__(
             raise ValueError(f"Unsupported activation: {config.hidden_act}. "
                              "Only silu is supported for now.")
 
-        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)
-
-        self.gate = ReplicatedLinear(config.hidden_size,
-                                     config.n_routed_experts,
-                                     bias=False,
-                                     quant_config=None,
-                                     prefix=f"{prefix}.gate")
+        self.gate = MoEGate(config)
+        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)
@@ -147,7 +167,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         if self.n_shared_experts is not None:
             shared_output = self.shared_experts(hidden_states)
         # router_logits: (num_tokens, n_experts)
-        router_logits, _ = self.gate(hidden_states)
+        router_logits = self.gate(hidden_states)
         final_hidden_states = self.experts(
             hidden_states=hidden_states,
             router_logits=router_logits) * self.routed_scaling_factor
@@ -244,8 +264,7 @@ def __init__(
                                         bias=False,
                                         quant_config=quant_config,
                                         prefix=f"{prefix}.o_proj")
-        if rope_scaling:
-            rope_scaling["rope_type"] = 'deepseek_yarn'
+        rope_scaling["rope_type"] = 'deepseek_yarn'
         self.rotary_emb = get_rope(qk_rope_head_dim,
                                    rotary_dim=qk_rope_head_dim,
                                    max_position=max_position_embeddings,
@@ -624,6 +643,9 @@ def load_weights(self, weights: Iterable[Tuple[str,
                     if is_pp_missing_parameter(name, self):
                         continue
 
+                    if name not in params_dict:
+                        for key in params_dict:
+                            print(key)
                     param = params_dict[name]
                     weight_loader = getattr(param, "weight_loader",
                                             default_weight_loader)