attn.py

import math
import torch
import torch.nn as nn
import awq_inference_engine
from torch.nn import functional as F


class QuantLlamaRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (
            self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
        )
        self.register_buffer("inv_freq", inv_freq)
        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings,
            device=self.inv_freq.device,
            dtype=torch.get_default_dtype(),
        )

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(
            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
        )

        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)

        cos = freqs.cos()
        sin = freqs.sin()
        cache = torch.cat((cos, sin), dim=-1)

        # self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
        # self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
        self.register_buffer("cos_sin_cache", cache.half(), persistent=False)

    def forward(
        self,
        query: torch.Tensor,
        key: torch.Tensor,
        positions: torch.Tensor,
    ):
        # Apply rotary embedding to the query and key before passing them
        # to the attention op.
        # print(positions.shape, query.shape, key.shape, self.cos_sin_cache.shape)
        query = query.contiguous()
        key = key.contiguous()
        awq_inference_engine.rotary_embedding_neox(
            positions,
            query,
            key,
            self.dim,
            self.cos_sin_cache
        )
        return query, key


class QuantLlamaAttentionFused(nn.Module):
    def __init__(self, hidden_size, num_heads, qkv_layer, o_proj, dev, max_position_embeddings):
        super().__init__()
        self.hidden_size = hidden_size
        self.n_local_heads = num_heads
        self.head_dim = self.hidden_size // num_heads
        self.qkv_proj = qkv_layer
        self.o_proj = o_proj
        self.start_pos = 0

        # following fastertransformer definition
        self.cache_v = (
            torch.zeros(
                ( 1, self.n_local_heads, max_position_embeddings, self.head_dim, )
            ).to(dev).half()
        )
        
        # 8: pack 8 fp16 in FT, if fp32 then use 4
        self.cache_k = (
            torch.zeros(
                ( 1, self.n_local_heads, self.head_dim // 8, max_position_embeddings, 8, )
            ).to(dev).half()
        )

        self.rotary_emb = QuantLlamaRotaryEmbedding(
            dim=hidden_size // num_heads,
            max_position_embeddings=max_position_embeddings,
            device=dev
        )
        
    def forward(
        self,
        hidden_states, past_key_value=None, attention_mask=None, position_ids=None, output_attentions=False, use_cache=False
    ):
        bsz, seqlen, _ = hidden_states.shape
        xqkv = self.qkv_proj(hidden_states)
        xqkv = xqkv.view(bsz, seqlen, -1, self.n_local_heads, self.head_dim)
        xq = xqkv[:, :, 0]
        xk = xqkv[:, :, 1]
        xv = xqkv[:, :, 2]

        if seqlen > 1:
            xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
            xk = xk.view(bsz, seqlen, self.n_local_heads, self.head_dim)
            xv = xv.view(bsz, seqlen, self.n_local_heads, self.head_dim)

            xq, xk = self.rotary_emb(xq, xk, position_ids)

            self.cache_k = self.cache_k.to(xq)
            self.cache_v = self.cache_v.to(xq)

            values_store = xv.transpose(2, 1)
            keys_store = (
                xk.reshape(bsz, seqlen, self.n_local_heads, self.head_dim // 8, 8)
                .permute(0, 2, 3, 1, 4)
                .contiguous()
            )

            self.cache_v[:bsz, :, self.start_pos : self.start_pos + seqlen, :] = values_store
            self.cache_k[:bsz, :, :, self.start_pos : self.start_pos + seqlen, :] = keys_store

            keys = xk
            values = xv
            past_key_value = (xk, xv) if use_cache else None

            xq = xq.transpose(1, 2)
            keys = keys.transpose(1, 2)
            values = values.transpose(1, 2)
            scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
            if attention_mask is not None:
                scores = scores + attention_mask  # (bs, n_local_heads, slen, cache_len + slen)
            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
            output = torch.matmul(scores, values)  # (bs, n_local_heads, slen, head_dim)
            output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
        else:
            xq = xq[:, 0, :, :]
            xk = xk[:, 0, :, :]
            xv = xv[:, 0, :, :]
            past_key_value = (xk, xv) if use_cache else None
            output = awq_inference_engine.single_query_attention(
                xq,
                xk,
                xv,
                self.cache_k,
                self.cache_v,
                None,
                None,
                self.start_pos,
                self.head_dim,
                10000,
                True,
            )
            output = output.reshape(bsz, 1, -1)
        
        attn_output = self.o_proj(output)
        
        if use_cache:
            self.start_pos += seqlen
        else:
            self.start_pos = 0

        return attn_output, None, past_key_value