[Model] Systematic support for fp32 head, pooling models part (#23810)

Signed-off-by: wang.yuqi <noooop@126.com>

tests/models/language/pooling/mteb_utils.py

@@ -9,6 +9,7 @@ import mteb
 import numpy as np
 import pytest
 import requests
+import torch
 
 from tests.models.utils import (EmbedModelInfo, RerankModelInfo,
                                 check_embeddings_close)
@@ -165,16 +166,19 @@ def mteb_test_embed_models(hf_runner,
                            vllm_extra_kwargs=None,
                            hf_model_callback=None,
                            atol=MTEB_EMBED_TOL):
-    if not model_info.enable_test:
     # A model family has many models with the same architecture,
     # and we don't need to test each one.
+    if not model_info.enable_test:
         pytest.skip("Skipping test.")
 
-    example_prompts = ["The chef prepared a delicious meal."]
+    # Test embed_dims, isnan and whether to use normalize
+    example_prompts = ["The chef prepared a delicious meal." * 1000]
 
+    # Allow vllm to test using the given dtype, such as float32
     vllm_extra_kwargs = vllm_extra_kwargs or {}
     vllm_extra_kwargs["dtype"] = model_info.dtype
 
+    # Allow vllm to test using hf_overrides
     if model_info.hf_overrides is not None:
         vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
 
@@ -186,21 +190,32 @@ def mteb_test_embed_models(hf_runner,
 
         model_config = vllm_model.llm.llm_engine.model_config
 
+        # Confirm whether vllm is using the correct architecture
         if model_info.architecture:
             assert model_info.architecture in model_config.architectures
+
+        # Confirm whether vllm uses the correct default_pooling_type, which
+        # relates to whether chunked prefill and prefix caching are enabled
         assert (model_config._model_info.default_pooling_type ==
                 model_info.default_pooling_type)
 
         vllm_main_score = run_mteb_embed_task(VllmMtebEncoder(vllm_model),
                                               MTEB_EMBED_TASKS)
         vllm_dtype = vllm_model.llm.llm_engine.model_config.dtype
-        vllm_outputs = vllm_model.embed(example_prompts)
 
+        # Test embed_dims, isnan and whether to use normalize
+        vllm_outputs = vllm_model.embed(example_prompts,
+                                        truncate_prompt_tokens=-1)
+        assert not torch.any(torch.isnan(torch.tensor(vllm_outputs)))
+
+    # Accelerate mteb test by setting
+    # SentenceTransformers mteb score to a constant
     if model_info.mteb_score is None:
         with hf_runner(model_info.name,
                        is_sentence_transformer=True,
                        dtype="float32") as hf_model:
 
+            # e.g. setting default parameters for the encode method of hf_runner
             if hf_model_callback is not None:
                 hf_model_callback(hf_model)
 
@@ -299,14 +314,16 @@ def mteb_test_rerank_models(hf_runner,
                             hf_model_callback=None,
                             vllm_mteb_encoder=VllmMtebEncoder,
                             atol=MTEB_RERANK_TOL):
-    if not model_info.enable_test:
     # A model family has many models with the same architecture,
     # and we don't need to test each one.
+    if not model_info.enable_test:
         pytest.skip("Skipping test.")
 
+    # Allow vllm to test using the given dtype, such as float32
     vllm_extra_kwargs = vllm_extra_kwargs or {}
     vllm_extra_kwargs["dtype"] = model_info.dtype
 
+    # Allow vllm to test using hf_overrides
     if model_info.hf_overrides is not None:
         vllm_extra_kwargs["hf_overrides"] = model_info.hf_overrides
 
@@ -319,9 +336,15 @@ def mteb_test_rerank_models(hf_runner,
 
         model_config = vllm_model.llm.llm_engine.model_config
 
+        # Confirm whether vllm is using the correct architecture
         if model_info.architecture:
             assert (model_info.architecture in model_config.architectures)
+
+        # Score API is only enabled for num_labels == 1
         assert model_config.hf_config.num_labels == 1
+
+        # Confirm whether vllm uses the correct default_pooling_type, which
+        # relates to whether chunked prefill and prefix caching are enabled
         assert (model_config._model_info.default_pooling_type ==
                 model_info.default_pooling_type)
 
@@ -330,6 +353,8 @@ def mteb_test_rerank_models(hf_runner,
                                           languages=MTEB_RERANK_LANGS)
         vllm_dtype = model_config.dtype
 
+    # Accelerate mteb test by setting
+    # SentenceTransformers mteb score to a constant
     if model_info.mteb_score is None:
         st_main_score, st_dtype = mteb_test_rerank_models_hf(
             hf_runner, model_info.name, hf_model_callback)

tests/models/language/pooling/test_bge_reranker_v2_gemma.py

@@ -14,6 +14,7 @@ from .mteb_utils import VllmMtebEncoder, mteb_test_rerank_models
 RERANK_MODELS = [
     LASTPoolingRerankModelInfo("BAAI/bge-reranker-v2-gemma",
                                architecture="GemmaForSequenceClassification",
+                               mteb_score=0.33757,
                                hf_overrides={
                                    "architectures":
                                    ["GemmaForSequenceClassification"],

vllm/config/__init__.py

@@ -745,7 +745,7 @@ class ModelConfig:
 
         self.pooler_config = self._init_pooler_config()
 
-        self.dtype = _get_and_verify_dtype(
+        self.dtype: torch.dtype = _get_and_verify_dtype(
             self.model,
             self.hf_config,
             self.dtype,
@@ -1751,6 +1751,32 @@ class ModelConfig:
         # `llm as reranker` models defaults to not using pad_token.
         return getattr(self.hf_config, "use_pad_token", True)
 
+    @property
+    def head_dtype(self) -> torch.dtype:
+        """
+        "head" refers to the last Linear layer(s) of an LLM,
+        such as the lm_head in a generation model,
+        or the score or classifier in a classification model.
+
+        The default head_dtype based on runner_type.\n
+        - The pooling model defaults to using fp32 head,
+        you can use --hf-overrides '{"head_dtype": "model"}' to disable it.\n
+        - The generate model defaults to not using fp32 head,
+        you can use --hf-overrides '{"head_dtype": "float32"}' to enable it.
+        """
+        head_dtype = _get_head_dtype(config=self.hf_config,
+                                     dtype=self.dtype,
+                                     runner_type=self.runner_type)
+
+        if head_dtype not in current_platform.supported_dtypes:
+            logger.warning_once(
+                "The current platform does not support [%s] head dtype, "
+                "fallback to model dtype [%s].", head_dtype, self.dtype)
+            return self.dtype
+
+        logger.debug_once("head dtype: %s", head_dtype)
+        return head_dtype
+
     def get_and_verify_max_len(self, max_model_len: int):
         # Consider max_model_len in tokenizer_config only when
         # pooling models use absolute position_embedding.
@@ -2893,6 +2919,31 @@ def _get_and_verify_dtype(
     return torch_dtype
 
 
+def _get_head_dtype(config: PretrainedConfig, dtype: torch.dtype,
+                    runner_type: str) -> torch.dtype:
+    head_dtype: Optional[Union[str,
+                               torch.dtype]] = getattr(config, "head_dtype",
+                                                       None)
+
+    if head_dtype == "model":
+        return dtype
+    elif isinstance(head_dtype, str):
+        head_dtype = head_dtype.lower()
+        if head_dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
+            raise ValueError(f"Unknown dtype: {head_dtype!r}")
+        return _STR_DTYPE_TO_TORCH_DTYPE[head_dtype]
+    elif isinstance(head_dtype, torch.dtype):
+        return head_dtype
+    elif head_dtype is None:
+        if torch.float32 not in current_platform.supported_dtypes:
+            return dtype
+        if runner_type == "pooling":
+            return torch.float32
+        return dtype
+    else:
+        raise ValueError(f"Unknown dtype: {head_dtype}")
+
+
 def _get_and_verify_max_len(
     hf_config: PretrainedConfig,
     tokenizer_config: Optional[dict],

vllm/model_executor/layers/pooler.py

@@ -5,7 +5,7 @@ from collections.abc import Mapping, Set
 from dataclasses import dataclass
 from enum import IntEnum
 from itertools import groupby
-from typing import Callable, Optional, TypeVar, Union, cast
+from typing import Callable, Optional, TypeVar, Union
 
 import torch
 import torch.nn as nn
@@ -362,14 +362,13 @@ class PoolerIdentity(PoolerActivation):
 class PoolerNormalize(PoolerActivation):
 
     def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
-        x = F.normalize(pooled_data.float(), p=2, dim=-1)
-        return x.to(pooled_data.dtype)
+        return F.normalize(pooled_data, p=2, dim=-1)
 
 
 class PoolerMultiLabelClassify(PoolerActivation):
 
     def forward_chunk(self, pooled_data: torch.Tensor) -> torch.Tensor:
-        return F.sigmoid(pooled_data.float()).to(pooled_data.dtype)
+        return F.sigmoid(pooled_data)
 
 
 class PoolerClassify(PoolerActivation):
@@ -394,9 +393,9 @@ class PoolerClassify(PoolerActivation):
                       pooled_data.shape[-1])
 
         if num_labels < 2:
-            return F.sigmoid(pooled_data.float()).to(pooled_data.dtype)
+            return F.sigmoid(pooled_data)
 
-        return F.softmax(pooled_data.float(), dim=-1).to(pooled_data.dtype)
+        return F.softmax(pooled_data, dim=-1)
 
 
 class LambdaPoolerActivation(PoolerActivation):
@@ -432,8 +431,9 @@ class EmbeddingPoolerHead(PoolerHead):
         from vllm.model_executor.models.adapters import _load_st_projector
 
         vllm_config = get_current_vllm_config()
-        self.projector = _load_st_projector(
+        self.projector: Optional[nn.Module] = _load_st_projector(
             vllm_config.model_config) if vllm_config else None
+        self.head_dtype = vllm_config.model_config.head_dtype
 
     def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor],
                 pooling_metadata: PoolingMetadata):
@@ -442,16 +442,11 @@ class EmbeddingPoolerHead(PoolerHead):
             pooled_data = torch.stack(pooled_data)
         # pooled_data shape: [batchsize, hidden_dimension]
 
+        pooled_data = pooled_data.to(self.head_dtype)
+
         # Apply ST projector
         if self.projector is not None:
-            projector = cast(nn.Module, self.projector)
-
-            def _proj(x: torch.Tensor) -> torch.Tensor:
-                orig_dtype = x.dtype
-                y = projector(x.to(torch.float32))
-                return y.to(orig_dtype)
-
-            pooled_data = _proj(pooled_data)
+            pooled_data = self.projector(pooled_data)
         # pooled_data shape: [batchsize, embedding_dimension]
 
         pooling_params = get_pooling_params(pooling_metadata)
@@ -494,8 +489,18 @@ class RewardPoolerHead(PoolerHead):
     def __init__(self) -> None:
         super().__init__(activation=PoolerClassify(static_num_labels=False))
 
+        from vllm.config import get_current_vllm_config
+        vllm_config = get_current_vllm_config()
+        self.head_dtype = vllm_config.model_config.head_dtype
+
     def forward(self, pooled_data: Union[list[torch.Tensor], torch.Tensor],
                 pooling_metadata: PoolingMetadata):
+
+        if isinstance(pooled_data, list):
+            pooled_data = [p.to(self.head_dtype) for p in pooled_data]
+        else:
+            pooled_data = pooled_data.to(self.head_dtype)
+
         pooling_params = get_pooling_params(pooling_metadata)
 
         # for softmax
@@ -641,6 +646,7 @@ class ClassifierPooler(Pooler):
         self.act_fn = act_fn or PoolerClassify()
         self.logit_bias: Optional[
             float] = vllm_config.model_config.pooler_config.logit_bias
+        self.head_dtype = vllm_config.model_config.head_dtype
 
     def get_supported_tasks(self) -> Set[PoolingTask]:
         return {"classify", "score"}
@@ -655,6 +661,8 @@ class ClassifierPooler(Pooler):
             pooled_data = torch.stack(pooled_data)
         # pooled_data shape: [batchsize, hidden_size]
 
+        pooled_data = pooled_data.to(self.head_dtype)
+
         if self.classifier is not None:
             pooled_data = self.classifier(pooled_data)
         # pooled_data shape: [batchsize, num_labels]

vllm/model_executor/models/adapters.py

@@ -62,7 +62,7 @@ def _load_st_projector(model_config: "ModelConfig") -> Optional[nn.Module]:
             linear = nn.Linear(layer_config.get("in_features", 768),
                                layer_config.get("out_features", 768),
                                bias=layer_config.get("bias", True),
-                               dtype=torch.float32)
+                               dtype=model_config.head_dtype)
 
             if not _load_dense_weights(linear, folder, model_config):
                 continue
@@ -70,7 +70,7 @@ def _load_st_projector(model_config: "ModelConfig") -> Optional[nn.Module]:
             layers.append(linear)
             if act_name := layer_config.get("activation_function"):
                 layers.append(get_act_fn(act_name))
-        return nn.Sequential(*layers).to(dtype=torch.float32)
+        return nn.Sequential(*layers).to(dtype=model_config.head_dtype)
     except Exception:
         logger.exception("ST projector loading failed")
 
@@ -105,15 +105,13 @@ def _load_dense_weights(linear: nn.Linear, folder: str,
                 if weight_key in state_dict:
                     weight_loader = getattr(linear.weight, "weight_loader",
                                             default_weight_loader)
-                    weight_loader(linear.weight,
-                                  state_dict[weight_key].to(torch.float32))
+                    weight_loader(linear.weight, state_dict[weight_key])
 
                     bias_key = weight_key.replace("weight", "bias")
                     if linear.bias is not None and bias_key in state_dict:
                         bias_loader = getattr(linear.bias, "weight_loader",
                                               default_weight_loader)
-                        bias_loader(linear.bias,
-                                    state_dict[bias_key].to(torch.float32))
+                        bias_loader(linear.bias, state_dict[bias_key])
                     return True
         except Exception:
             logger.exception("Failed to load %s", filename)

vllm/model_executor/models/bert.py

@@ -562,7 +562,9 @@ class BertForSequenceClassification(nn.Module, SupportsCrossEncoding,
         self.bert = BertPoolingModel(vllm_config=vllm_config,
                                      prefix=maybe_prefix(prefix, "bert"),
                                      embedding_class=BertEmbedding)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+        self.classifier = nn.Linear(config.hidden_size,
+                                    config.num_labels,
+                                    dtype=vllm_config.model_config.head_dtype)
 
         pooler_config = vllm_config.model_config.pooler_config
         assert pooler_config is not None

vllm/model_executor/models/bert_with_rope.py

@@ -637,13 +637,13 @@ class GteNewForSequenceClassification(nn.Module, SupportsCrossEncoding):
         self.new = GteNewModel(vllm_config=vllm_config,
                                prefix=prefix,
                                add_pooling_layer=True)
-        self.classifier = RowParallelLinear(config.hidden_size,
+        self.classifier = ReplicatedLinear(
+            config.hidden_size,
             config.num_labels,
-                                            input_is_parallel=False,
             bias=True,
             quant_config=quant_config,
-                                            prefix=maybe_prefix(
-                                                prefix, "classifier"),
+            params_dtype=vllm_config.model_config.head_dtype,
+            prefix=maybe_prefix(prefix, "classifier"),
             return_bias=False)
 
         pooler_config = vllm_config.model_config.pooler_config

vllm/model_executor/models/gpt2.py

@@ -339,7 +339,10 @@ class GPT2ForSequenceClassification(nn.Module):
         config = vllm_config.model_config.hf_config
         self.transformer = GPT2Model(vllm_config=vllm_config,
                                      prefix=maybe_prefix(prefix, "gpt2"))
-        self.score = nn.Linear(config.n_embd, config.num_labels, bias=False)
+        self.score = nn.Linear(config.n_embd,
+                               config.num_labels,
+                               bias=False,
+                               dtype=vllm_config.model_config.head_dtype)
 
         pooler_config = vllm_config.model_config.pooler_config
         assert pooler_config is not None
@@ -348,7 +351,7 @@ class GPT2ForSequenceClassification(nn.Module):
             "encode":
             Pooler.for_encode(pooler_config),
             "classify":
-            Pooler.for_classify(pooler_config, classifier=None),
+            Pooler.for_classify(pooler_config, classifier=self.score),
         })
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
@@ -367,8 +370,7 @@ class GPT2ForSequenceClassification(nn.Module):
             position_ids=positions,
             inputs_embeds=inputs_embeds,
             intermediate_tensors=intermediate_tensors)
-        logits = self.score(hidden_states)
-        return logits
+        return hidden_states
 
 
 def _add_transformer_prefix(

vllm/model_executor/models/internlm2.py

@@ -423,13 +423,15 @@ class InternLM2ForRewardModel(InternLM2ForCausalLM):
             delattr(self, attr)
 
         config = vllm_config.model_config.hf_config
-        self.v_head = RowParallelLinear(
-            config.hidden_size,
+        self.head_dtype = vllm_config.model_config.head_dtype
+
+        self.v_head = RowParallelLinear(config.hidden_size,
                                         1,
                                         bias=False,
                                         input_is_parallel=False,
+                                        params_dtype=self.head_dtype,
                                         prefix=maybe_prefix(prefix, "v_head"),
-        )
+                                        return_bias=False)
 
         pooler_config = vllm_config.model_config.pooler_config
         assert pooler_config is not None
@@ -446,5 +448,6 @@ class InternLM2ForRewardModel(InternLM2ForCausalLM):
     ) -> Union[torch.Tensor, IntermediateTensors]:
         hidden_states = self.model(input_ids, positions, intermediate_tensors,
                                    inputs_embeds)
-        logits, _ = self.v_head(hidden_states)
+        hidden_states = hidden_states.to(self.head_dtype)
+        logits = self.v_head(hidden_states)
         return logits

vllm/model_executor/models/jamba.py

@@ -613,7 +613,7 @@ class JambaForSequenceClassification(JambaForCausalLM):
             config.hidden_size,
             num_labels,
             bias=score_bias,
-            dtype=torch.float32,
+            dtype=vllm_config.model_config.head_dtype,
         )
 
         pooler_config = vllm_config.model_config.pooler_config

vllm/model_executor/models/jina_vl.py

@@ -5,9 +5,9 @@ from typing import Optional
 
 import torch
 import torch.nn as nn
-from transformers import BatchFeature, PretrainedConfig
+from transformers import BatchFeature
 
-from vllm.config import VllmConfig
+from vllm.config import ModelConfig, VllmConfig
 from vllm.inputs import TokensPrompt
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -28,13 +28,17 @@ logger = init_logger(__name__)
 
 class JinaVLScorer(nn.Module):
 
-    def __init__(self, config: PretrainedConfig):
+    def __init__(self, model_config: "ModelConfig"):
         super().__init__()
+        config = model_config.hf_config
+        head_dtype = model_config.head_dtype
         self.dense = ColumnParallelLinear(config.hidden_size,
                                           config.hidden_size,
+                                          params_dtype=head_dtype,
                                           bias=True)
         self.out_proj = RowParallelLinear(config.hidden_size,
                                           config.num_labels,
+                                          params_dtype=head_dtype,
                                           bias=True)
 
     def forward(self, x, **kwargs):
@@ -88,11 +92,10 @@ class JinaVLForSequenceClassification(Qwen2VLForConditionalGeneration,
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__(vllm_config=vllm_config,
                          prefix=maybe_prefix(prefix, "qwen2_vl"))
-        config = vllm_config.model_config.hf_config
         pooler_config = vllm_config.model_config.pooler_config
         assert pooler_config is not None
 
-        self.score = JinaVLScorer(config)
+        self.score = JinaVLScorer(vllm_config.model_config)
         self.pooler = DispatchPooler({
             "encode":
             Pooler.for_encode(pooler_config),

vllm/model_executor/models/modernbert.py

@@ -306,7 +306,9 @@ class ModernBertForSequenceClassification(nn.Module, SupportsCrossEncoding):
         self.config = config
         self.model = ModernBertModel(vllm_config=vllm_config,
                                      prefix=maybe_prefix(prefix, "modernbert"))
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+        self.classifier = nn.Linear(config.hidden_size,
+                                    config.num_labels,
+                                    dtype=vllm_config.model_config.head_dtype)
         self.pooling = ModernBertPooler(config)
 
         pooler_config = vllm_config.model_config.pooler_config

vllm/model_executor/models/qwen2_rm.py

@@ -53,15 +53,18 @@ class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP):
         self.quant_config = quant_config
         self.model = Qwen2Model(vllm_config=vllm_config,
                                 prefix=maybe_prefix(prefix, "model"))
+        self.head_dtype = vllm_config.model_config.head_dtype
 
         self.score = nn.Sequential(
             ColumnParallelLinear(config.hidden_size,
                                  config.hidden_size,
                                  quant_config=quant_config,
+                                 params_dtype=self.head_dtype,
                                  return_bias=False),
             nn.ReLU(),
             RowParallelLinear(config.hidden_size,
                               config.num_labels,
+                              params_dtype=self.head_dtype,
                               quant_config=quant_config,
                               return_bias=False),
         )
@@ -80,6 +83,7 @@ class Qwen2RewardBaseModel(nn.Module, SupportsLoRA, SupportsPP):
     ) -> Union[torch.Tensor, IntermediateTensors]:
         hidden_states = self.model(input_ids, positions, intermediate_tensors,
                                    inputs_embeds)
+        hidden_states = hidden_states.to(self.head_dtype)
         logits = self.score(hidden_states)
         return logits
 

vllm/model_executor/models/roberta.py

@@ -8,7 +8,7 @@ import torch
 from torch import nn
 from transformers import RobertaConfig
 
-from vllm.config import VllmConfig
+from vllm.config import ModelConfig, VllmConfig
 from vllm.model_executor.layers.pooler import (ClassifierPooler, CLSPool,
                                                DispatchPooler, Pooler)
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -73,10 +73,16 @@ class RobertaEmbedding(nn.Module):
 class RobertaClassificationHead(nn.Module):
     """Head for sentence-level classification tasks."""
 
-    def __init__(self, config: RobertaConfig):
+    def __init__(self, model_config: "ModelConfig"):
         super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
+        config = model_config.hf_config
+        head_dtype = model_config.head_dtype
+        self.dense = nn.Linear(config.hidden_size,
+                               config.hidden_size,
+                               dtype=head_dtype)
+        self.out_proj = nn.Linear(config.hidden_size,
+                                  config.num_labels,
+                                  dtype=head_dtype)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         # CLSPool has already been applied in `pooling`
@@ -184,7 +190,7 @@ class RobertaForSequenceClassification(nn.Module, SupportsCrossEncoding):
         self.roberta = BertModel(vllm_config=vllm_config,
                                  prefix=maybe_prefix(prefix, "bert"),
                                  embedding_class=RobertaEmbedding)
-        self.classifier = RobertaClassificationHead(config)
+        self.classifier = RobertaClassificationHead(vllm_config.model_config)
 
         pooler_config = vllm_config.model_config.pooler_config
         assert pooler_config is not None