text-generation-inference/server/text_generation_server/quant/fused_attn.py

import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.cuda.amp import custom_bwd, custom_fwd
from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb
from .quant_linear import *


class QuantLlamaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        hidden_size,
        num_heads,
        qkv_proj,
        o_proj,
        rotary_emb,
    ):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.head_dim = hidden_size // num_heads

        if (self.head_dim * num_heads) != self.hidden_size:
            raise ValueError(f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                             f" and `num_heads`: {num_heads}).")
        self.qkv_proj = qkv_proj
        self.o_proj = o_proj
        self.rotary_emb = rotary_emb

    def _shape(self, tensor, seq_len, bsz):
        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()

    def forward(self, hidden_states, past_key_value=None, attention_mask=None, position_ids=None, output_attentions=False, use_cache=False):
        """Input shape: Batch x Time x Channel"""

        bsz, q_len, _ = hidden_states.size()

        qkv_states = self.qkv_proj(hidden_states)
        query_states, key_states, value_states = torch.split(qkv_states, self.hidden_size, dim=2)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
        # [bsz, nh, t, hd]

        is_causal = past_key_value is None
        if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

        if use_cache:
            # Since qkv_proj is fused, query_states etc will hold a reference to the original qkv_states tensor
            # which can cause excessive memory usage by the cache. `contiguous` is a convenient way to workaround this.
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()


        past_key_value = (key_states, value_states) if use_cache else None

        with torch.backends.cuda.sdp_kernel(enable_math=False):
            attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, is_causal=is_causal)

        attn_output = attn_output.transpose(1, 2)
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


def make_quant_attn(model):
    """
    Replace all LlamaAttention modules with QuantLlamaAttention modules, fusing the q, k, v projections.
    """
    for name, m in model.named_modules():
        if not isinstance(m, LlamaAttention):
            continue

        q_proj = m.q_proj
        k_proj = m.k_proj
        v_proj = m.v_proj

        qweights = torch.cat([q_proj.qweight, k_proj.qweight, v_proj.qweight], dim=1)
        qzeros = torch.cat([q_proj.qzeros, k_proj.qzeros, v_proj.qzeros], dim=1)
        scales = torch.cat([q_proj.scales, k_proj.scales, v_proj.scales], dim=1)
        g_idx = torch.cat([q_proj.g_idx, k_proj.g_idx, v_proj.g_idx], dim=0)
        bias = torch.cat([q_proj.bias, k_proj.bias, v_proj.bias], dim=0) if q_proj.bias is not None else None

        qkv_layer = QuantLinear(q_proj.bits, q_proj.groupsize, q_proj.infeatures, q_proj.outfeatures + k_proj.outfeatures + v_proj.outfeatures, True if q_proj.bias is not None else False)
        qkv_layer.qweight = qweights
        qkv_layer.qzeros = qzeros
        qkv_layer.scales = scales
        qkv_layer.g_idx = g_idx
        qkv_layer.bias = bias

        attn = QuantLlamaAttention(m.hidden_size, m.num_heads, qkv_layer, m.o_proj, m.rotary_emb)

        if '.' in name:
            parent_name = name.rsplit('.', 1)[0]
            child_name = name[len(parent_name) + 1:]
            parent = model.get_submodule(parent_name)
        else:
            parent_name = ''
            parent = model
            child_name = name

        #print(f"Replacing {name} with quant_attn; parent: {parent_name}, child's name: {child_name}")

        setattr(parent, child_name, attn)