[CORE] Quantized lm-head Framework (#4442)

Co-authored-by: Robert Shaw <rshaw@neuralmagic.com>
Co-authored-by: ZX <zx@lbx.dev>

tests/lora/test_layers.py

@@ -475,10 +475,10 @@ def test_lm_head_logits_processor(dist_init, num_loras, device,
 
         lora_result = lora_logits_processor._get_logits(
             hidden_states=torch.cat(inputs),
-            embedding=linear.weight,
+            lm_head=linear,
             embedding_bias=None)
 
-        original_weight = linear.weight.clone()
+        original_lm_head = deepcopy(linear)
 
         linear.weight[logits_processor.
                       org_vocab_size:logits_processor.org_vocab_size +
@@ -490,7 +490,7 @@ def test_lm_head_logits_processor(dist_init, num_loras, device,
         for input_, lora_id in zip(inputs, prompt_mapping):
             lora = lora_dict[lora_id]
             result = logits_processor._get_logits(hidden_states=input_,
-                                                  embedding=linear.weight,
+                                                  lm_head=linear,
                                                   embedding_bias=None)
             result[:, vocab_size + embeddings_tensor_len:] = float("-inf")
             result += input_ @ lora.lora_a @ lora.lora_b * lora.scaling
@@ -519,11 +519,11 @@ def test_lm_head_logits_processor(dist_init, num_loras, device,
 
         lora_result = lora_logits_processor._get_logits(
             hidden_states=torch.cat(inputs),
-            embedding=original_weight,
+            lm_head=original_lm_head,
             embedding_bias=None)[:, :vocab_size]
         expected_result = logits_processor._get_logits(
             hidden_states=torch.cat(inputs),
-            embedding=original_weight,
+            lm_head=original_lm_head,
             embedding_bias=None)
 
         rtol, atol = TOLERANCES[lora_result.dtype]

tests/quantization/test_lm_head.py

+"""Tests whether gptq models with quantized lm_head can be loaded.
+
+Run `pytest tests/quantization/test_quant_lm_head_true.py --forked`.
+"""
+from typing import Tuple
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.linear import UnquantizedLinearMethod
+from vllm.model_executor.layers.quantization.gptq import GPTQLinearMethod
+from vllm.model_executor.layers.quantization.gptq_marlin import (
+    GPTQMarlinLinearMethod)
+from vllm.model_executor.layers.quantization.marlin import MarlinLinearMethod
+
+PROMPT = "On the surface of Mars, we found"
+
+MODELS_QUANT = [(
+    "LnL-AI/TinyLlama-1.1B-intermediate-step-1341k-3T-autoround-lm_head-symFalse",
+    True), ("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", False),
+                ("neuralmagic/Meta-Llama-3-8B-Instruct-FP8", False)]
+
+
+@pytest.mark.parametrize("model_lm_head_quant", MODELS_QUANT)
+def test_lm_head(
+    vllm_runner,
+    model_lm_head_quant: Tuple[str, bool],
+) -> None:
+    model, lm_head_quantized = model_lm_head_quant
+    vllm_model = vllm_runner(model, dtype=torch.float16, max_model_len=2048)
+
+    lm_head_layer = (vllm_model.model.llm_engine.model_executor.driver_worker.
+                     model_runner.model.lm_head)
+
+    if lm_head_quantized:
+        assert isinstance(
+            lm_head_layer.linear_method,
+            (GPTQLinearMethod, GPTQMarlinLinearMethod, MarlinLinearMethod))
+    else:
+        assert isinstance(lm_head_layer.linear_method, UnquantizedLinearMethod)
+
+    print(
+        vllm_model.generate_greedy(prompts=["Hello my name is"],
+                                   max_tokens=10)[0][1])
+    del vllm_model

tests/spec_decode/e2e/test_mlp_correctness.py

@@ -34,7 +34,7 @@ SPEC_MODEL = "ibm-granite/granite-3b-code-instruct-accelerator"
 MAX_SPEC_TOKENS = 5
 
 # precision
-PRECISION = "float16"
+PRECISION = "float32"
 
 
 @pytest.mark.parametrize(

tests/test_logits_processor.py

@@ -83,7 +83,7 @@ def test_logits_processors(seed: int, device: str):
         device=device,
         pin_memory=is_pin_memory_available())
     logits_processor_output = logits_processor(
-        embedding=None,
+        lm_head=None,
         hidden_states=input_tensor,
         sampling_metadata=sampling_metadata)
 

vllm/lora/layers.py

@@ -1172,11 +1172,11 @@ class LogitsProcessorWithLoRA(BaseLayerWithLoRA):
     def _get_logits(
         self,
         hidden_states: torch.Tensor,
-        embedding: torch.Tensor,
+        lm_head: VocabParallelEmbedding,
         embedding_bias: Optional[torch.Tensor] = None,
     ) -> Optional[torch.Tensor]:
         # Get the logits for the next tokens.
-        logits = torch.matmul(hidden_states, embedding.t())
+        logits = lm_head.linear_method.apply(lm_head, hidden_states)
         if embedding_bias is not None:
             logits += embedding_bias
         logits = tensor_model_parallel_gather(logits)

vllm/model_executor/layers/logits_processor.py

@@ -6,6 +6,8 @@ import torch
 import torch.nn as nn
 
 from vllm.distributed import tensor_model_parallel_gather
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    VocabParallelEmbedding)
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 
 
@@ -40,7 +42,7 @@ class LogitsProcessor(nn.Module):
 
     def forward(
         self,
-        embedding: torch.Tensor,
+        lm_head: VocabParallelEmbedding,
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
         embedding_bias: Optional[torch.Tensor] = None,
@@ -52,8 +54,7 @@ class LogitsProcessor(nn.Module):
                                                  sampling_metadata)
 
             # Get the logits for the next tokens.
-            logits = self._get_logits(hidden_states, embedding, embedding_bias)
-
+            logits = self._get_logits(hidden_states, lm_head, embedding_bias)
         if logits is not None:
             if self.soft_cap is not None:
                 logits = logits / self.soft_cap
@@ -68,12 +69,13 @@ class LogitsProcessor(nn.Module):
 
         return logits
 
-    def _get_logits(self, hidden_states: torch.Tensor, embedding: torch.Tensor,
+    def _get_logits(self, hidden_states: torch.Tensor,
+                    lm_head: VocabParallelEmbedding,
                     embedding_bias: Optional[torch.Tensor]) -> torch.Tensor:
         # Get the logits for the next tokens.
-        logits = torch.matmul(hidden_states, embedding.t())
-        if embedding_bias is not None:
-            logits += embedding_bias
+        logits = lm_head.linear_method.apply(lm_head,
+                                             hidden_states,
+                                             bias=embedding_bias)
         logits = tensor_model_parallel_gather(logits)
         # Remove paddings in vocab (if any).
         if logits is not None:

vllm/model_executor/layers/quantization/base_config.py

@@ -87,6 +87,15 @@ class QuantizationConfig(ABC):
         raise ValueError(f"Cannot find any of {keys} in the model's "
                          "quantization config.")
 
+    @staticmethod
+    def get_from_keys_or(config: Dict[str, Any], keys: List[str],
+                         default: Any) -> Any:
+        """Get a optional value from the model's quantization config."""
+        try:
+            return QuantizationConfig.get_from_keys(config, keys)
+        except ValueError:
+            return default
+
     @abstractmethod
     def get_quant_method(
             self, layer: torch.nn.Module) -> Optional[QuantizeMethodBase]:

vllm/model_executor/layers/quantization/gptq.py

@@ -10,6 +10,7 @@ from vllm import _custom_ops as ops
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.utils import set_weight_attrs
 
 
@@ -24,10 +25,12 @@ class GPTQConfig(QuantizationConfig):
         weight_bits: int,
         group_size: int,
         desc_act: bool,
+        lm_head_quantized: bool,
     ) -> None:
         self.weight_bits = weight_bits
         self.group_size = group_size
         self.desc_act = desc_act
+        self.lm_head_quantized = lm_head_quantized
         self.pack_factor = Fraction(32, self.weight_bits)
         if self.weight_bits not in [2, 3, 4, 8]:
             raise ValueError(
@@ -37,7 +40,8 @@ class GPTQConfig(QuantizationConfig):
     def __repr__(self) -> str:
         return (f"GPTQConfig(weight_bits={self.weight_bits}, "
                 f"group_size={self.group_size}, "
-                f"desc_act={self.desc_act})")
+                f"desc_act={self.desc_act}),"
+                f"lm_head_quantized={self.lm_head_quantized}")
 
     @classmethod
     def get_name(cls) -> str:
@@ -61,11 +65,14 @@ class GPTQConfig(QuantizationConfig):
         weight_bits = cls.get_from_keys(config, ["bits"])
         group_size = cls.get_from_keys(config, ["group_size"])
         desc_act = cls.get_from_keys(config, ["desc_act"])
-        return cls(weight_bits, group_size, desc_act)
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        return cls(weight_bits, group_size, desc_act, lm_head_quantized)
 
     def get_quant_method(
             self, layer: torch.nn.Module) -> Optional["GPTQLinearMethod"]:
-        if isinstance(layer, LinearBase):
+        if (isinstance(layer, LinearBase) or
+            (isinstance(layer, ParallelLMHead) and self.lm_head_quantized)):
             return GPTQLinearMethod(self)
         return None
 

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -11,6 +11,7 @@ from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                set_weight_attrs)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.utils import get_device_capability_stateless
 
 logger = init_logger(__name__)
@@ -59,7 +60,7 @@ class GPTQMarlinConfig(QuantizationConfig):
     """Config class for GPTQ Marlin"""
 
     def __init__(self, weight_bits: int, group_size: int, desc_act: bool,
-                 is_sym: bool) -> None:
+                 is_sym: bool, lm_head_quantized: bool) -> None:
         if desc_act and group_size == -1:
             # In this case, act_order == True is the same as act_order == False
             # (since we have only one group per output channel)
@@ -69,6 +70,7 @@ class GPTQMarlinConfig(QuantizationConfig):
         self.group_size = group_size
         self.desc_act = desc_act
         self.is_sym = is_sym
+        self.lm_head_quantized = lm_head_quantized
 
         # Verify
         if self.weight_bits not in GPTQ_MARLIN_SUPPORTED_NUM_BITS:
@@ -96,7 +98,8 @@ class GPTQMarlinConfig(QuantizationConfig):
     def __repr__(self) -> str:
         return (f"GPTQMarlinConfig(weight_bits={self.weight_bits}, "
                 f"group_size={self.group_size}, "
-                f"desc_act={self.desc_act})")
+                f"desc_act={self.desc_act}, "
+                f"lm_head_quantized={self.lm_head_quantized})")
 
     @classmethod
     def get_name(cls) -> str:
@@ -120,7 +123,10 @@ class GPTQMarlinConfig(QuantizationConfig):
         group_size = cls.get_from_keys(config, ["group_size"])
         desc_act = cls.get_from_keys(config, ["desc_act"])
         is_sym = cls.get_from_keys(config, ["sym"])
-        return cls(weight_bits, group_size, desc_act, is_sym)
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        return cls(weight_bits, group_size, desc_act, is_sym,
+                   lm_head_quantized)
 
     @classmethod
     def override_quantization_method(cls, hf_quant_cfg,
@@ -145,7 +151,8 @@ class GPTQMarlinConfig(QuantizationConfig):
     def get_quant_method(
             self,
             layer: torch.nn.Module) -> Optional["GPTQMarlinLinearMethod"]:
-        if isinstance(layer, LinearBase):
+        if (isinstance(layer, LinearBase) or
+            (isinstance(layer, ParallelLMHead) and self.lm_head_quantized)):
             return GPTQMarlinLinearMethod(self)
         return None
 

vllm/model_executor/layers/quantization/marlin.py

@@ -8,6 +8,7 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
 from vllm.model_executor.utils import set_weight_attrs
 
 logger = init_logger(__name__)
@@ -22,9 +23,11 @@ class MarlinConfig(QuantizationConfig):
     def __init__(
         self,
         group_size: int,
+        lm_head_quantized: bool,
     ) -> None:
         # Group size for the quantization.
         self.group_size = group_size
+        self.lm_head_quantized = lm_head_quantized
         if self.group_size != 128 and self.group_size != -1:
             raise ValueError(
                 "Currently, only group size 128 and -1 (channelwise) "
@@ -51,7 +54,8 @@ class MarlinConfig(QuantizationConfig):
         self.perm_len = 1024
 
     def __repr__(self) -> str:
-        return f"MarlinConfig(group_size={self.group_size})"
+        return (f"MarlinConfig(group_size={self.group_size}, "
+                f"lm_head_quantized={self.lm_head_quantized})")
 
     @classmethod
     def get_name(cls) -> str:
@@ -73,7 +77,9 @@ class MarlinConfig(QuantizationConfig):
     @classmethod
     def from_config(cls, config: Dict[str, Any]) -> "MarlinConfig":
         group_size = cls.get_from_keys(config, ["group_size"])
-        return cls(group_size)
+        lm_head_quantized = cls.get_from_keys_or(config, ["lm_head"],
+                                                 default=False)
+        return cls(group_size, lm_head_quantized)
 
     @classmethod
     def override_quantization_method(cls, hf_quant_cfg,
@@ -96,7 +102,8 @@ class MarlinConfig(QuantizationConfig):
 
     def get_quant_method(
             self, layer: torch.nn.Module) -> Optional["MarlinLinearMethod"]:
-        if isinstance(layer, LinearBase):
+        if (isinstance(layer, LinearBase) or
+            (isinstance(layer, ParallelLMHead) and self.lm_head_quantized)):
             return MarlinLinearMethod(self)
         return None
 

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -8,6 +8,9 @@ from torch.nn.parameter import Parameter
 from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
                               tensor_model_parallel_all_reduce)
+from vllm.model_executor.layers.linear import UnquantizedLinearMethod
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.utils import set_weight_attrs
 
 DEFAULT_VOCAB_PADDING_SIZE = 64
@@ -157,6 +160,7 @@ class VocabParallelEmbedding(torch.nn.Module):
         params_dtype: type of the parameters.
         org_num_embeddings: original vocabulary size (without LoRA).
         padding_size: padding size for the vocabulary.
+        quant_config: quant config for the layer
     """  # noqa: E501
 
     def __init__(self,
@@ -164,7 +168,8 @@ class VocabParallelEmbedding(torch.nn.Module):
                  embedding_dim: int,
                  params_dtype: Optional[torch.dtype] = None,
                  org_num_embeddings: Optional[int] = None,
-                 padding_size: int = DEFAULT_VOCAB_PADDING_SIZE):
+                 padding_size: int = DEFAULT_VOCAB_PADDING_SIZE,
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__()
 
         # Keep the input dimensions.
@@ -187,6 +192,14 @@ class VocabParallelEmbedding(torch.nn.Module):
                                                self.org_vocab_size, tp_rank,
                                                self.tp_size)
         self.embedding_dim = embedding_dim
+
+        linear_method = None
+        if quant_config is not None:
+            linear_method = quant_config.get_quant_method(self)
+        if linear_method is None:
+            linear_method = UnquantizedLinearMethod()
+        self.linear_method: QuantizeMethodBase = linear_method
+
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
         # Divide the weight matrix along the vocaburaly dimension.
@@ -201,14 +214,14 @@ class VocabParallelEmbedding(torch.nn.Module):
         self.num_added_embeddings_per_partition = (
             self.shard_indices.added_vocab_end_index -
             self.shard_indices.added_vocab_start_index)
-        self.weight = Parameter(
-            torch.empty(self.num_embeddings_per_partition,
-                        self.embedding_dim,
-                        dtype=params_dtype))
-        set_weight_attrs(self.weight, {
-            "parallel_dim": 0,
-            "weight_loader": self.weight_loader
-        })
+
+        self.linear_method.create_weights(self,
+                                          self.embedding_dim,
+                                          [self.num_embeddings_per_partition],
+                                          self.embedding_dim,
+                                          self.num_embeddings_padded,
+                                          params_dtype=params_dtype,
+                                          weight_loader=self.weight_loader)
 
     @classmethod
     def _get_indices(cls, vocab_size_padded: int, org_vocab_size_padded: int,
@@ -288,10 +301,32 @@ class VocabParallelEmbedding(torch.nn.Module):
         return ret
 
     def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
-        parallel_dim = param.parallel_dim
-        assert loaded_weight.shape[parallel_dim] == self.org_vocab_size
-        loaded_weight = loaded_weight[self.shard_indices.org_vocab_start_index:
-                                      self.shard_indices.org_vocab_end_index]
+        output_dim = getattr(param, "output_dim", None)
+        packed_dim = getattr(param, "packed_dim", None)
+
+        # If parameter does not have output dim, then it should
+        # be copied onto all gpus (e.g. g_idx for act_order gptq).
+        if output_dim is None:
+            assert param.data.shape == loaded_weight.shape
+            param.data.copy_(loaded_weight)
+            return
+
+        # Shard indexes for loading the weight
+        start_idx = self.shard_indices.org_vocab_start_index
+        shard_size = self.shard_indices.org_vocab_end_index - start_idx
+
+        # If param packed on the same dim we are sharding on, then
+        # need to adjust offsets of loaded weight by pack_factor.
+        if packed_dim is not None and packed_dim == output_dim:
+            assert loaded_weight.shape[output_dim] == (self.org_vocab_size //
+                                                       param.pack_factor)
+            start_idx = start_idx // param.pack_factor
+            shard_size = shard_size // param.pack_factor
+        else:
+            assert loaded_weight.shape[output_dim] == self.org_vocab_size
+
+        # Copy the data.
+        loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size)
         param[:loaded_weight.shape[0]].data.copy_(loaded_weight)
         param[loaded_weight.shape[0]:].data.fill_(0)
 
@@ -346,16 +381,17 @@ class ParallelLMHead(VocabParallelEmbedding):
                  bias: bool = False,
                  params_dtype: Optional[torch.dtype] = None,
                  org_num_embeddings: Optional[int] = None,
-                 padding_size: int = DEFAULT_VOCAB_PADDING_SIZE):
+                 padding_size: int = DEFAULT_VOCAB_PADDING_SIZE,
+                 quant_config: Optional[QuantizationConfig] = None):
         super().__init__(num_embeddings, embedding_dim, params_dtype,
-                         org_num_embeddings, padding_size)
+                         org_num_embeddings, padding_size, quant_config)
         if bias:
             self.bias = Parameter(
                 torch.empty(self.num_embeddings_per_partition,
                             dtype=params_dtype))
             set_weight_attrs(self.bias, {
-                "parallel_dim": 0,
-                "weight_loader": self.weight_loader
+                "output_dim": 0,
+                "weight_loader": self.weight_loader,
             })
         else:
             self.register_parameter("bias", None)

vllm/model_executor/models/arctic.py

@@ -412,6 +412,7 @@ class ArcticForCausalLM(nn.Module):
         self.lm_head = ParallelLMHead(
             self.vocab_size,
             config.hidden_size,
+            quant_config=quant_config,
         )
         self.num_experts = config.num_local_experts
         self.num_experts_per_tok = config.num_experts_per_tok
@@ -434,7 +435,7 @@ class ArcticForCausalLM(nn.Module):
 
     def compute_logits(self, hidden_states: torch.Tensor,
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head.weight, hidden_states,
+        logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
         return logits
 

vllm/model_executor/models/baichuan.py

@@ -328,7 +328,9 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA):
         self.quant_config = quant_config
         self.model = BaiChuanModel(config, position_embedding, cache_config,
                                    quant_config)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
 
@@ -346,7 +348,7 @@ class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA):
 
     def compute_logits(self, hidden_states: torch.Tensor,
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head.weight, hidden_states,
+        logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
         return logits
 

vllm/model_executor/models/bloom.py

@@ -276,7 +276,7 @@ class BloomForCausalLM(nn.Module):
         self.config = config
         self.quant_config = quant_config
         self.transformer = BloomModel(config, cache_config, quant_config)
-        self.lm_head_weight = self.transformer.word_embeddings.weight
+        self.lm_head = self.transformer.word_embeddings
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
 
@@ -294,7 +294,7 @@ class BloomForCausalLM(nn.Module):
 
     def compute_logits(self, hidden_states: torch.Tensor,
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head_weight, hidden_states,
+        logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
         return logits
 

vllm/model_executor/models/chatglm.py

@@ -303,7 +303,8 @@ class ChatGLMModel(nn.Module):
         self.encoder = GLMTransformer(config, cache_config, quant_config)
 
         self.output_layer = ParallelLMHead(config.padded_vocab_size,
-                                           config.hidden_size)
+                                           config.hidden_size,
+                                           quant_config=quant_config)
 
     def forward(
         self,
@@ -355,7 +356,7 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA):
         self.max_position_embeddings = getattr(config, "max_sequence_length",
                                                8192)
         self.transformer = ChatGLMModel(config, cache_config, quant_config)
-        self.lm_head_weight = self.transformer.output_layer.weight
+        self.lm_head = self.transformer.output_layer
         self.logits_processor = LogitsProcessor(config.padded_vocab_size)
         self.sampler = Sampler()
 
@@ -373,7 +374,7 @@ class ChatGLMForCausalLM(nn.Module, SupportsLoRA):
 
     def compute_logits(self, hidden_states: torch.Tensor,
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head_weight, hidden_states,
+        logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
         return logits
 

vllm/model_executor/models/commandr.py

@@ -363,12 +363,12 @@ class CohereForCausalLM(nn.Module):
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
         is_not_lora = hasattr(self.model.embed_tokens, 'weight')
         if is_not_lora:
-            embedding_weights = self.model.embed_tokens.weight
+            logits = self.logits_processor(self.model.embed_tokens,
+                                           hidden_states, sampling_metadata)
         else:
-            embedding_weights = self.model.embed_tokens.base_layer.weight
+            logits = self.logits_processor(self.model.embed_tokens.base_layer,
+                                           hidden_states, sampling_metadata)
 
-        logits = self.logits_processor(embedding_weights, hidden_states,
-                                       sampling_metadata)
         return logits
 
     def sample(

vllm/model_executor/models/dbrx.py

@@ -370,6 +370,7 @@ class DbrxForCausalLM(nn.Module):
             config.d_model,
             org_num_embeddings=config.vocab_size,
             padding_size=DEFAULT_VOCAB_PADDING_SIZE,
+            quant_config=quant_config,
         )
         self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                 config.vocab_size)
@@ -389,7 +390,7 @@ class DbrxForCausalLM(nn.Module):
 
     def compute_logits(self, hidden_states: torch.Tensor,
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head.weight, hidden_states,
+        logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
         return logits
 

vllm/model_executor/models/deepseek.py

@@ -377,7 +377,9 @@ class DeepseekForCausalLM(nn.Module):
         self.config = config
         self.quant_config = quant_config
         self.model = DeepseekModel(config, cache_config, quant_config)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
 
@@ -395,7 +397,7 @@ class DeepseekForCausalLM(nn.Module):
 
     def compute_logits(self, hidden_states: torch.Tensor,
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head.weight, hidden_states,
+        logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
         return logits
 

vllm/model_executor/models/deepseek_v2.py

@@ -465,7 +465,9 @@ class DeepseekV2ForCausalLM(nn.Module):
         self.config = config
         self.quant_config = quant_config
         self.model = DeepseekV2Model(config, cache_config, quant_config)
-        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        self.lm_head = ParallelLMHead(config.vocab_size,
+                                      config.hidden_size,
+                                      quant_config=quant_config)
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
 
@@ -483,7 +485,7 @@ class DeepseekV2ForCausalLM(nn.Module):
 
     def compute_logits(self, hidden_states: torch.Tensor,
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head.weight, hidden_states,
+        logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
         return logits
 

vllm/model_executor/models/falcon.py

@@ -394,13 +394,13 @@ class FalconForCausalLM(nn.Module):
                                     if config.tie_word_embeddings is not None
                                     else True)
         if self.tie_word_embeddings:
-            self.lm_head_weight = self.transformer.word_embeddings.weight
+            self.lm_head = self.transformer.word_embeddings
         else:
             self.lm_head = ParallelLMHead(
                 config.vocab_size,
                 config.hidden_size,
+                quant_config=quant_config,
             )
-            self.lm_head_weight = self.lm_head.weight
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
 
@@ -422,7 +422,7 @@ class FalconForCausalLM(nn.Module):
 
     def compute_logits(self, hidden_states: torch.Tensor,
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
-        logits = self.logits_processor(self.lm_head_weight, hidden_states,
+        logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
         return logits