qwen.py

# coding=utf-8
# Adapted from
# https://huggingface.co/Qwen/Qwen-7B/blob/main/modeling_qwen.py
# Copyright (c) Alibaba Cloud.
# LICENSE: https://huggingface.co/Qwen/Qwen-7B/blob/main/LICENSE
"""Inference-only QWen model compatible with HuggingFace weights."""
from typing import Any, Dict, Iterable, List, Optional, Tuple

import torch
from torch import nn
from transformers import PretrainedConfig

import os
import re

from vllm.attention import Attention, AttentionMetadata
from vllm.config import CacheConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig)
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import Sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import SamplerOutput

from vllm import _custom_ops as ops
from vllm.model_executor.utils import pad_weight, gemm_bank_conf


class QWenMLP(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str = "silu",
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size, [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config)
        self.c_proj = RowParallelLinear(intermediate_size,
                                        hidden_size,
                                        bias=False,
                                        quant_config=quant_config)
        if hidden_act != "silu":
            raise ValueError(f"Unsupported activation: {hidden_act}. "
                             "Only silu is supported for now.")
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.c_proj(x)
        return x


class QWenAttention(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        max_position_embeddings: int,
        rope_theta: float = 10000,
        rope_scaling: Optional[Dict[str, Any]] = None,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.hidden_size = hidden_size
        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
        )
        self.total_num_heads = num_heads
        assert self.total_num_heads % tensor_model_parallel_world_size == 0
        self.num_heads = (self.total_num_heads //
                          tensor_model_parallel_world_size)
        self.head_dim = hidden_size // self.total_num_heads
        self.c_attn = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            bias=True,
            quant_config=quant_config,
        )
        self.c_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
            quant_config=quant_config,
        )
        self.scaling = self.head_dim**-0.5

        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
        )
        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              cache_config=cache_config,
                              quant_config=quant_config)
        
        self.quant_method = None
        if quant_config is not None:
            self.quant_method=quant_config.get_name()
            self.quant_config=quant_config

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        qkv, _ = self.c_attn(hidden_states)
        if os.environ.get('FA_PAD') == '1' and self.quant_method is None:
            qkv = qkv[...,:-32]
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
        output, _ = self.c_proj(attn_output)
        return output


class QWenBlock(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)

        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
        self.attn = QWenAttention(config.hidden_size,
                                  config.num_attention_heads,
                                  config.max_position_embeddings,
                                  rope_theta=rope_theta,
                                  rope_scaling=rope_scaling,
                                  cache_config=cache_config,
                                  quant_config=quant_config)

        self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)

        self.mlp = QWenMLP(config.hidden_size,
                           config.intermediate_size // 2,
                           quant_config=quant_config)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        # Self Attention
        if residual is None:
            residual = hidden_states
            hidden_states = self.ln_1(hidden_states)
        else:
            hidden_states, residual = self.ln_1(hidden_states, residual)
        hidden_states = self.attn(
            positions=positions,
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )

        # Fully Connected
        hidden_states, residual = self.ln_2(hidden_states, residual)
        hidden_states = self.mlp(hidden_states)
        return hidden_states, residual


class QWenModel(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config
        self.vocab_size = config.vocab_size

        self.wte = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
        self.h = nn.ModuleList([
            QWenBlock(config, cache_config, quant_config)
            for _ in range(config.num_hidden_layers)
        ])
        self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        hidden_states = self.wte(input_ids)
        residual = None
        for i in range(len(self.h)):
            layer = self.h[i]
            hidden_states, residual = layer(
                positions,
                hidden_states,
                kv_caches[i],
                attn_metadata,
                residual,
            )
        hidden_states, _ = self.ln_f(hidden_states, residual)
        return hidden_states


class QWenLMHeadModel(nn.Module):

    def __init__(
        self,
        config: PretrainedConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config
        self.quant_config = quant_config
        self.transformer = QWenModel(config, cache_config, quant_config)
        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.sampler = Sampler()
        
        self.quant_method = None
        if quant_config is not None:
            self.quant_method=quant_config.get_name()
            self.quant_config=quant_config
              
        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
        self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
        self.use_fa_pad = os.environ.get('FA_PAD') == '1'

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        hidden_states = self.transformer(input_ids, positions, kv_caches,
                                         attn_metadata)
        return hidden_states

    def compute_logits(self, hidden_states: torch.Tensor,
                       sampling_metadata: SamplingMetadata) -> torch.Tensor:
        logits = self.logits_processor(self.lm_head.weight, hidden_states,
                                       sampling_metadata)
        return logits

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("gate_up_proj", "w2", 0),
            ("gate_up_proj", "w1", 1),
        ]
        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                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)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
        if self.use_llama_nn and self.quant_method is None:
            lay_key_words = [
                "attn.c_attn.weight",
                "attn.c_proj.weight",
                "mlp.gate_up_proj.weight",
                "mlp.c_proj.weight"
            ]
            combined_words = "|".join(lay_key_words)
            
            lay_qkv_words = ["attn.c_attn.weight"]   
            qkv_words = "|".join(lay_qkv_words)  
            
            lay_qkv_bias_words = ["attn.c_attn.bias"]   
            qkv_bias_words = "|".join(lay_qkv_bias_words) 
                      
            for layername, weight in params_dict.items():
                if self.use_fa_pad and (re.findall(qkv_bias_words, layername)):
                    weight.data = pad_weight(weight.data, 32)
                
                matches = re.findall(combined_words, layername)
                if matches:         
                    if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
                        weight.data = pad_weight(weight.data, 32)  
                        
                    if self.use_fa_pad and (re.findall(qkv_words, layername)):
                        if not gemm_bank_conf(weight.data.shape[0]):
                            weight.data = pad_weight(weight.data, 32)
                        
                    _weight = torch.zeros_like(weight.data)
                    ori_shape =_weight.shape
                    
                    ops.trans_w16_gemm(_weight, weight.data, _weight.shape[0], _weight.shape[1])
                    weight.data.copy_(_weight)
                    
                    weight.data=weight.data.reshape(ori_shape[1],-1)
                    
        if self.quant_method == "awq":
            lay_key_words = [
                "attn.c_attn.qweight",
                "attn.c_proj.qweight",
                "mlp.gate_up_proj.qweight",
                "mlp.c_proj.qweight"
            ]
            combined_words = "|".join(lay_key_words)
            
            for layername, weight in params_dict.items():
                
                matches = re.findall(combined_words, layername)
                if matches:
                    qweight =params_dict[layername]
                    qzeros=params_dict[layername.replace("qweight", "qzeros")]
                    scales=params_dict[layername.replace("qweight", "scales")]
                    zeros_and_scalse =params_dict[layername.replace("qweight", "zeros_and_scales")]
                    
                    group_size= self.quant_config.group_size 
                   
                    dim_n = scales.data.shape[1]
                    dim_k = qweight.data.shape[0]
                    pad_group=2              
                    
                    _qw, _sz=ops.convert_s4(qweight,qzeros,scales,int(group_size)) 
                    
                    sz = ops.sz_permute(_sz).reshape(-1,dim_n)       
                    
                    zeros_and_scalse.data.copy_(sz)
                    qweight.data.copy_(_qw)
                    
                    #reshape
                    zeros_and_scalse.data=zeros_and_scalse.reshape(dim_n,-1)    #[k/greop_size,n]------>[n,k/group_size]
                    qweight.data=qweight.data.reshape(dim_n,-1)                      #[k,n/8]---->[n,k/8]  
                
                    if dim_k % 4096==0:
                        zeros_and_scalse_pad= torch.zeros(dim_n,pad_group,dtype=torch.int32).cuda()
                        zeros_and_scalse.data=torch.cat((zeros_and_scalse.data,zeros_and_scalse_pad),dim=1).contiguous()
                        qweight_pad= torch.zeros(dim_n,int(group_size//4),dtype=torch.int32).cuda()
                        qweight.data=torch.cat((qweight.data,qweight_pad),dim=1).contiguous()