291 lines
10 KiB
Python
291 lines
10 KiB
Python
# coding=utf-8
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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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from typing import Any, Dict, List, Optional, Tuple
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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, AttentionMetadata
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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 (LinearMethodBase,
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding, ParallelLMHead)
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from vllm.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_world_size)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.model_executor.weight_utils import (default_weight_loader,
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hf_model_weights_iterator)
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from vllm.sequence import SamplerOutput
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class QWenMLP(nn.Module):
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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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linear_method: Optional[LinearMethodBase] = 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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linear_method=linear_method)
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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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linear_method=linear_method)
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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):
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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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linear_method: Optional[LinearMethodBase] = None,
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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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linear_method=linear_method,
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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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linear_method=linear_method,
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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, self.head_dim, self.scaling)
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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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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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, kv_cache, attn_metadata)
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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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linear_method: Optional[LinearMethodBase] = None,
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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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linear_method=linear_method)
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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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linear_method=linear_method)
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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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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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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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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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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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class QWenModel(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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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.h = nn.ModuleList([
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QWenBlock(config, linear_method)
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for _ in range(config.num_hidden_layers)
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])
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self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
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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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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hidden_states = self.wte(input_ids)
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residual = None
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for i in range(len(self.h)):
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layer = self.h[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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kv_caches[i],
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attn_metadata,
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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 QWenLMHeadModel(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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linear_method: Optional[LinearMethodBase] = None,
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):
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super().__init__()
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self.config = config
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self.linear_method = linear_method
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self.transformer = QWenModel(config, linear_method)
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self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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self.sampler = Sampler()
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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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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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hidden_states = self.transformer(input_ids, positions, kv_caches,
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attn_metadata)
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return hidden_states
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head.weight, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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load_format: str = "auto",
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revision: Optional[str] = None):
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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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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, load_format, revision):
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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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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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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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