[gpt-oss] Add gpt-oss bf16 support

2025-08-13 21:25:57 +08:00
parent 5d2e7edf78
commit 17ea2ec6aa
1232 changed files with 777 additions and 36 deletions
--- a/vllm/model_executor/models/clip.py
+++ b/vllm/model_executor/models/clip.py
@@ -0,0 +1,407 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Minimal implementation of CLIPVisionModel intended to be only used
+within a vision language model."""
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+import torch.nn as nn
+from transformers import CLIPVisionConfig
+
+from vllm.attention.layer import MultiHeadAttention
+from vllm.distributed import divide, get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               QKVParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.interfaces import SupportsQuant
+
+from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
+
+
+class CLIPEncoderInfo(VisionEncoderInfo[CLIPVisionConfig]):
+
+    def get_num_image_tokens(
+        self,
+        *,
+        image_width: int,
+        image_height: int,
+    ) -> int:
+        return self.get_patch_grid_length()**2 + 1
+
+    def get_image_size(self) -> int:
+        return self.vision_config.image_size
+
+    def get_patch_size(self) -> int:
+        return self.vision_config.patch_size
+
+    def get_patch_grid_length(self) -> int:
+        image_size, patch_size = self.get_image_size(), self.get_patch_size()
+        assert image_size % patch_size == 0
+        return image_size // patch_size
+
+
+# Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py#L164 # noqa
+class CLIPVisionEmbeddings(nn.Module):
+
+    def __init__(self, config: CLIPVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+        assert self.image_size % self.patch_size == 0
+
+        self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            bias=False,
+        )
+
+        self.num_patches = (self.image_size // self.patch_size)**2
+        self.num_positions = self.num_patches + 1
+        self.position_embedding = nn.Embedding(self.num_positions,
+                                               self.embed_dim)
+        self.register_buffer("position_ids",
+                             torch.arange(self.num_positions).expand((1, -1)),
+                             persistent=False)
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        batch_size = pixel_values.shape[0]
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values.to(
+            dtype=target_dtype))  # shape = [*, width, grid, grid]
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        embeddings = embeddings + self.position_embedding(self.position_ids)
+
+        return embeddings
+
+
+class CLIPAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                "embed_dim must be divisible by num_heads "
+                f"(got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_heads}).")
+        self.scale = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.embed_dim,
+            head_size=self.head_dim,
+            total_num_heads=self.num_heads,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+
+        self.out_proj = RowParallelLinear(
+            input_size=self.embed_dim,
+            output_size=self.embed_dim,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+        )
+
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.num_heads_per_partition = divide(self.num_heads, self.tp_size)
+
+        self.attn = MultiHeadAttention(self.num_heads_per_partition,
+                                       self.head_dim, self.scale)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+    ):
+        """Input shape: Batch x Time x Channel"""
+
+        qkv_states, _ = self.qkv_proj(hidden_states)
+        query_states, key_states, value_states = qkv_states.chunk(3, dim=-1)
+        out = self.attn(query_states, key_states, value_states)
+        attn_output, _ = self.out_proj(out)
+
+        return attn_output, None
+
+
+class CLIPMLP(nn.Module):
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.activation_fn = get_act_fn(config.hidden_act)
+        self.fc1 = ColumnParallelLinear(config.hidden_size,
+                                        config.intermediate_size,
+                                        bias=True,
+                                        quant_config=quant_config,
+                                        prefix=f"{prefix}.fc1")
+        self.fc2 = RowParallelLinear(config.intermediate_size,
+                                     config.hidden_size,
+                                     bias=True,
+                                     quant_config=quant_config,
+                                     prefix=f"{prefix}.fc2")
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states, _ = self.fc2(hidden_states)
+
+        return hidden_states
+
+
+class CLIPEncoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.self_attn = CLIPAttention(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.layer_norm1 = nn.LayerNorm(config.hidden_size,
+                                        eps=config.layer_norm_eps)
+        self.mlp = CLIPMLP(config,
+                           quant_config=quant_config,
+                           prefix=f"{prefix}.mlp")
+        self.layer_norm2 = nn.LayerNorm(config.hidden_size,
+                                        eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, _ = self.self_attn(hidden_states=hidden_states)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class CLIPEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self
+    attention layers. Each layer is a [`CLIPEncoderLayer`].
+
+    Args:
+        config: CLIPConfig
+    """
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        num_hidden_layers_override: Optional[int] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+
+        if num_hidden_layers_override is None:
+            num_hidden_layers = config.num_hidden_layers
+        else:
+            num_hidden_layers = num_hidden_layers_override
+        self.layers = nn.ModuleList([
+            CLIPEncoderLayer(config=config,
+                             quant_config=quant_config,
+                             prefix=f"{prefix}.layers.{layer_idx}")
+            for layer_idx in range(num_hidden_layers)
+        ])
+
+    def forward(
+        self, inputs_embeds: torch.Tensor, return_all_hidden_states: bool
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        hidden_states_pool = [inputs_embeds]
+        hidden_states = inputs_embeds
+
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(hidden_states)
+            if return_all_hidden_states:
+                hidden_states_pool.append(hidden_states)
+        # If we have multiple feature sample layers, we return all hidden
+        # states in order and grab the ones we need by index.
+        if return_all_hidden_states:
+            return hidden_states_pool
+        return hidden_states
+
+
+class CLIPVisionTransformer(nn.Module):
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: Optional[bool] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        embed_dim = config.hidden_size
+
+        self.embeddings = CLIPVisionEmbeddings(config)
+
+        # NOTE: This typo of "layrnorm" is not fixed on purpose to match
+        # the original transformers code and name of the model weights.
+        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+
+        self.encoder = CLIPEncoder(
+            config=config,
+            quant_config=quant_config,
+            num_hidden_layers_override=num_hidden_layers_override,
+            prefix=f"{prefix}.encoder",
+        )
+
+        num_hidden_layers = config.num_hidden_layers
+        if len(self.encoder.layers) > config.num_hidden_layers:
+            raise ValueError(
+                f"The original encoder only has {num_hidden_layers} "
+                f"layers, but you requested {len(self.encoder.layers)} layers."
+            )
+
+        # If possible, skip post_layernorm to conserve memory
+        if require_post_norm is None:
+            require_post_norm = len(self.encoder.layers) == num_hidden_layers
+
+        if require_post_norm:
+            self.post_layernorm = nn.LayerNorm(embed_dim,
+                                               eps=config.layer_norm_eps)
+        else:
+            self.post_layernorm = None
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        feature_sample_layers: Optional[list[int]] = None,
+    ) -> torch.Tensor:
+
+        hidden_states = self.embeddings(pixel_values)
+        hidden_states = self.pre_layrnorm(hidden_states)
+
+        return_all_hidden_states = feature_sample_layers is not None
+
+        # Produces either the last layer output or all of the hidden states,
+        # depending on if we have feature_sample_layers or not
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            return_all_hidden_states=return_all_hidden_states)
+
+        # Handle post-norm (if applicable) and stacks feature layers if needed
+        encoder_outputs = resolve_visual_encoder_outputs(
+            encoder_outputs, feature_sample_layers, self.post_layernorm,
+            self.config.num_hidden_layers)
+
+        return encoder_outputs
+
+
+class CLIPVisionModel(nn.Module, SupportsQuant):
+    config_class = CLIPVisionConfig
+    main_input_name = "pixel_values"
+    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
+
+    def __init__(
+        self,
+        config: CLIPVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        *,
+        num_hidden_layers_override: Optional[int] = None,
+        require_post_norm: Optional[bool] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.vision_model = CLIPVisionTransformer(
+            config=config,
+            quant_config=quant_config,
+            num_hidden_layers_override=num_hidden_layers_override,
+            require_post_norm=require_post_norm,
+            prefix=f"{prefix}.vision_model")
+
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        feature_sample_layers: Optional[list[int]] = None,
+    ) -> torch.Tensor:
+        return self.vision_model(pixel_values, feature_sample_layers)
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    # (TODO) Add prefix argument for filtering out weights to be loaded
+    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        layer_count = len(self.vision_model.encoder.layers)
+
+        for name, loaded_weight in weights:
+            # post_layernorm is not needed in CLIPVisionModel
+            if (name.startswith("vision_model.post_layernorm")
+                    and self.vision_model.post_layernorm is None):
+                continue
+
+            # omit layers when num_hidden_layers_override is set
+            if name.startswith("vision_model.encoder.layers"):
+                layer_idx = int(name.split(".")[3])
+                if layer_idx >= layer_count:
+                    continue
+
+            for (param_name, weight_name, shard_id) in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params