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317 lines
11 KiB
Python
317 lines
11 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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The input of the model is flattened to a 1D tensor of tokens. The model uses
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InputMetadata to extract the original 2D shape of the input.
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"""
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from typing import Dict, List, Optional, Tuple
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import torch
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from torch import nn
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from vllm.model_executor.input_metadata import InputMetadata
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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.attention import PagedAttentionWithRoPE
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.weight_utils import (
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hf_model_weights_iterator,
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load_tensor_parallel_weights,
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)
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from vllm.model_executor.parallel_utils.parallel_state import (
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from vllm.model_executor.parallel_utils.tensor_parallel import (
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VocabParallelEmbedding,
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ColumnParallelLinear,
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RowParallelLinear,
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)
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from vllm.sequence import SequenceOutputs
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from vllm.transformers_utils.configs.qwen import QWenConfig
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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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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):
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super().__init__()
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self.gate_up_proj = ColumnParallelLinear(
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hidden_size,
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2 * intermediate_size,
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bias=False,
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gather_output=False,
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perform_initialization=False,
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)
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self.c_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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input_is_parallel=True,
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perform_initialization=False,
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)
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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__(self, hidden_size: int, num_heads: int,
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max_position_embeddings: int):
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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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# pylint: disable=invalid-name
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self.c_attn = ColumnParallelLinear(
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hidden_size,
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3 * hidden_size,
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bias=True,
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gather_output=False,
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perform_initialization=False,
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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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input_is_parallel=True,
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perform_initialization=False,
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)
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self.scaling = self.head_dim**-0.5
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self.attn = PagedAttentionWithRoPE(
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self.num_heads,
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self.head_dim,
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self.scaling,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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)
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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: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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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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k_cache, v_cache = kv_cache
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attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
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input_metadata, cache_event)
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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__(self, config: QWenConfig):
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super().__init__()
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self.ln_1 = RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
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self.attn = QWenAttention(config.n_embd, config.num_attention_heads,
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config.max_position_embeddings)
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self.ln_2 = RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
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self.mlp = QWenMLP(config.n_embd, config.ffn_hidden_size // 2)
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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: KVCache,
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input_metadata: InputMetadata,
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cache_event: Optional[torch.cuda.Event],
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) -> torch.Tensor:
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# Self Attention
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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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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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input_metadata=input_metadata,
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cache_event=cache_event,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.ln_2(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class QWenModel(nn.Module):
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def __init__(self, config: QWenConfig):
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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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vocab_size = ((config.vocab_size + 63) // 64) * 64
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self.wte = VocabParallelEmbedding(vocab_size,
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config.n_embd,
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perform_initialization=False)
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self.h = nn.ModuleList(
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[QWenBlock(config) for _ in range(config.num_hidden_layers)])
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self.ln_f = RMSNorm(config.n_embd, 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[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> torch.Tensor:
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hidden_states = self.wte(input_ids)
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for i in range(len(self.h)):
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if cache_events is None:
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cache_event = None
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else:
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cache_event = cache_events[i]
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layer = self.h[i]
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hidden_states = layer(
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positions,
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hidden_states,
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kv_caches[i],
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input_metadata,
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cache_event,
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)
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hidden_states = self.ln_f(hidden_states)
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return hidden_states
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class QWenLMHeadModel(nn.Module):
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def __init__(self, config: QWenConfig):
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super().__init__()
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self.config = config
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self.transformer = QWenModel(config)
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vocab_size = ((config.vocab_size + 63) // 64) * 64
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self.lm_head = ColumnParallelLinear(
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config.n_embd,
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vocab_size,
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bias=False,
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gather_output=False,
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perform_initialization=False,
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)
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self.sampler = Sampler(config.vocab_size)
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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[KVCache],
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input_metadata: InputMetadata,
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cache_events: Optional[List[torch.cuda.Event]],
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) -> Dict[int, SequenceOutputs]:
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hidden_states = self.transformer(input_ids, positions, kv_caches,
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input_metadata, cache_events)
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next_tokens = self.sampler(self.lm_head.weight, hidden_states,
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input_metadata)
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return next_tokens
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_column_parallel_weights = ["wte.weight", "lm_head.weight"]
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_row_parallel_weights = ["c_proj.weight"]
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def load_weights(
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self,
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model_name_or_path: str,
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cache_dir: Optional[str] = None,
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use_np_cache: bool = False,
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):
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tp_world_size = get_tensor_model_parallel_world_size()
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tp_rank = get_tensor_model_parallel_rank()
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state_dict = self.state_dict()
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for name, loaded_weight in hf_model_weights_iterator(
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model_name_or_path, cache_dir, use_np_cache):
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if "rotary_emb.inv_freq" in name:
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continue
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if "wte" in name or "lm_head" in name:
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# Consider padding in the vocab size.
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param = state_dict[name]
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padded_vocab_size = param.shape[0] * tp_world_size
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num_extra_rows = padded_vocab_size - self.config.vocab_size
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extra_rows = torch.empty(num_extra_rows,
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loaded_weight.shape[1])
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extra_rows = extra_rows.to(loaded_weight)
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loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
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if "c_attn" in name:
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total_num_heads = self.config.num_attention_heads
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hidden_size = self.config.hidden_size
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head_size = hidden_size // total_num_heads
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num_heads = total_num_heads // tp_world_size
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head_start = tp_rank * num_heads
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head_end = (tp_rank + 1) * num_heads
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if "weight" in name:
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loaded_weight = loaded_weight.view(3, total_num_heads,
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head_size, hidden_size)
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loaded_weight = loaded_weight[:, head_start:head_end, :, :]
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loaded_weight = loaded_weight.reshape(-1, hidden_size)
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elif "bias" in name:
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loaded_weight = loaded_weight.view(3, total_num_heads,
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head_size)
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loaded_weight = loaded_weight[:, head_start:head_end, :]
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loaded_weight = loaded_weight.reshape(-1)
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is_gate_up_weight = False
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for stride_id, weight_name in enumerate(["w2", "w1"]):
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if weight_name not in name:
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continue
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param = state_dict[name.replace(weight_name, "gate_up_proj")]
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shard_size = param.shape[0] // 2
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loaded_weight = loaded_weight[shard_size * tp_rank:shard_size *
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(tp_rank + 1)]
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param_slice = param.data[shard_size * stride_id:shard_size *
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(stride_id + 1)]
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assert param_slice.shape == loaded_weight.shape
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param_slice.copy_(loaded_weight)
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is_gate_up_weight = True
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break
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if is_gate_up_weight:
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continue
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param = state_dict[name]
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load_tensor_parallel_weights(
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param,
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loaded_weight,
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name,
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self._column_parallel_weights,
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self._row_parallel_weights,
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tp_rank,
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)
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