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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 | |