[gpt-oss] Add gpt-oss bf16 support
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362
vllm/model_executor/models/qwen.py
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362
vllm/model_executor/models/qwen.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://huggingface.co/Qwen/Qwen-7B/blob/main/modeling_qwen.py
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# Copyright (c) Alibaba Cloud.
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# LICENSE: https://huggingface.co/Qwen/Qwen-7B/blob/main/LICENSE
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"""Inference-only QWen model compatible with HuggingFace weights."""
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import json
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from collections.abc import Iterable
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from typing import Any, Optional, Union
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention import Attention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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class QWenMLP(nn.Module):
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"""MLP for the language component of the Qwen model, which contains a
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MergedColumnParallelLinear merging 2 outputs via silu activation."""
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str = "silu",
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quant_config: Optional[QuantizationConfig] = None,
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):
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size, [intermediate_size] * 2,
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bias=False,
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quant_config=quant_config)
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self.c_proj = RowParallelLinear(intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config)
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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = SiluAndMul()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.c_proj(x)
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return x
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class QWenAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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max_position_embeddings: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[dict[str, Any]] = None,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = hidden_size
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
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)
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self.total_num_heads = num_heads
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assert self.total_num_heads % tensor_model_parallel_world_size == 0
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self.num_heads = (self.total_num_heads //
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tensor_model_parallel_world_size)
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self.head_dim = hidden_size // self.total_num_heads
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self.c_attn = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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bias=True,
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quant_config=quant_config,
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)
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self.c_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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self.scaling = self.head_dim**-0.5
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn")
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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qkv, _ = self.c_attn(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v)
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output, _ = self.c_proj(attn_output)
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return output
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class QWenBlock(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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):
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super().__init__()
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self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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self.attn = QWenAttention(config.hidden_size,
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config.num_attention_heads,
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config.max_position_embeddings,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn")
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self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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self.mlp = QWenMLP(config.hidden_size,
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config.intermediate_size // 2,
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quant_config=quant_config)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: Optional[torch.Tensor],
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) -> tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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if residual is None:
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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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else:
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hidden_states, residual = self.ln_1(hidden_states, residual)
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hidden_states = self.attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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# Fully Connected
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hidden_states, residual = self.ln_2(hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_torch_compile
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class QWenModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
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quant_config = vllm_config.quant_config
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self.config = config
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self.vocab_size = config.vocab_size
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self.wte = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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)
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self.start_layer, self.end_layer, self.h = make_layers(
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config.num_hidden_layers,
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lambda prefix: QWenBlock(
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config, cache_config, quant_config, prefix=prefix),
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prefix=f"{prefix}.h")
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self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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self.make_empty_intermediate_tensors = (
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make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"], config.hidden_size))
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.wte(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors],
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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for layer in self.h[self.start_layer:self.end_layer]:
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hidden_states, residual = layer(
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positions,
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hidden_states,
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residual,
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors({
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"hidden_states": hidden_states,
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"residual": residual
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})
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hidden_states, _ = self.ln_f(hidden_states, residual)
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return hidden_states
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class QWenBaseModel(nn.Module):
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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prefix: str = "",
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transformer_type: type[QWenModel] = QWenModel,
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) -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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multimodal_config = vllm_config.model_config.multimodal_config
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self.config = config
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self.multimodal_config = multimodal_config
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self.quant_config = quant_config
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self.transformer = transformer_type(vllm_config=vllm_config,
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prefix=maybe_prefix(
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prefix, "transformer"))
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self.lm_head = ParallelLMHead(config.vocab_size,
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config.hidden_size,
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quant_config=quant_config)
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.transformer.wte.weight
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.make_empty_intermediate_tensors = (
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self.transformer.make_empty_intermediate_tensors)
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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return logits
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("gate_up_proj", "w2", 0),
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("gate_up_proj", "w1", 1),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class QWenLMHeadModel(QWenBaseModel, SupportsPP, SupportsLoRA):
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packed_modules_mapping = {
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"c_attn": ["c_attn"],
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"gate_up_proj": [
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"w2",
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"w1",
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],
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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config = vllm_config.model_config.hf_config
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if hasattr(config, "visual"):
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hf_overrides = {
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"architectures": ["QwenVLForConditionalGeneration"]
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}
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raise RuntimeError(
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"The configuration of this model indicates that it supports "
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"vision inputs, but you instantiated the text-only version "
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"of this model. Please use the vision model by setting "
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f"`--hf-overrides '{json.dumps(hf_overrides)}'`")
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super().__init__(vllm_config=vllm_config, prefix=prefix)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, IntermediateTensors]:
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hidden_states = self.transformer(input_ids, positions,
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intermediate_tensors, inputs_embeds)
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return hidden_states
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