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from typing import List, Optional, Tuple
import torch
from torch import nn
import transformers
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
from einops import rearrange
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
from flash_attn.bert_padding import unpad_input, pad_input
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel
attention_mask: [bsz, q_len]
"""
bsz, q_len, _ = hidden_states.size()
query_states = (
self.q_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
key_states = (
self.k_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
value_states = (
self.v_proj(hidden_states)
.view(bsz, q_len, self.num_heads, self.head_dim)
.transpose(1, 2)
)
# [bsz, q_len, nh, hd]
# [bsz, nh, q_len, hd]
kv_seq_len = key_states.shape[-2]
assert past_key_value is None, "past_key_value is not supported"
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]
assert not output_attentions, "output_attentions is not supported"
assert not use_cache, "use_cache is not supported"
# Flash attention codes from
# https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
# transform the data into the format required by flash attention
qkv = torch.stack(
[query_states, key_states, value_states], dim=2
) # [bsz, nh, 3, q_len, hd]
qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
# We have disabled _prepare_decoder_attention_mask in LlamaModel
# the attention_mask should be the same as the key_padding_mask
key_padding_mask = attention_mask
if key_padding_mask is None:
qkv = rearrange(qkv, "b s ... -> (b s) ...")
max_s = q_len
cu_q_lens = torch.arange(
0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device
)
output = flash_attn_unpadded_qkvpacked_func(
qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
)
output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
else:
nheads = qkv.shape[-2]
x = rearrange(qkv, "b s three h d -> b s (three h d)")
x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
x_unpad = rearrange(
x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads
)
output_unpad = flash_attn_unpadded_qkvpacked_func(
x_unpad, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
)
output = rearrange(
pad_input(
rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, bsz, q_len
),
"b s (h d) -> b s h d",
h=nheads,
)
return self.o_proj(rearrange(output, "b s h d -> b s (h d)")), None, None
# Disable the transformation of the attention mask in LlamaModel as the flash attention
# requires the attention mask to be the same as the key_padding_mask
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# [bsz, seq_len]
return attention_mask
def replace_llama_attn_with_flash_attn():
transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (
_prepare_decoder_attention_mask
)
transformers.models.llama.modeling_llama.LlamaAttention.forward = forward
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