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from typing import Optional, Tuple | |
import warnings | |
import torch | |
import transformers | |
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv | |
try: | |
from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func | |
except ImportError: | |
from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as 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]]]: | |
if output_attentions: | |
warnings.warn( | |
"Output attentions is not supported for patched `LlamaAttention`, returning `None` instead." | |
) | |
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_key_value_heads, self.head_dim) | |
.transpose(1, 2) | |
) | |
value_states = ( | |
self.v_proj(hidden_states) | |
.view(bsz, q_len, self.num_key_value_heads, self.head_dim) | |
.transpose(1, 2) | |
) # shape: (b, num_heads, s, head_dim) | |
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 | |
) | |
if past_key_value is not None: | |
# reuse k, v | |
key_states = torch.cat([past_key_value[0], key_states], dim=2) | |
value_states = torch.cat([past_key_value[1], value_states], dim=2) | |
past_key_value = (key_states, value_states) if use_cache else None | |
# repeat k/v heads if n_kv_heads < n_heads | |
key_states = repeat_kv(key_states, self.num_key_value_groups) | |
value_states = repeat_kv(value_states, self.num_key_value_groups) | |
# Transform the data into the format required by flash attention | |
qkv = torch.stack([query_states, key_states, value_states], dim=2) | |
qkv = qkv.transpose(1, 3) # shape: [b, s, 3, num_heads, head_dim] | |
key_padding_mask = attention_mask | |
if key_padding_mask is None: | |
qkv = qkv.reshape(-1, 3, self.num_heads, self.head_dim) | |
cu_q_lens = torch.arange( | |
0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device | |
) | |
max_s = q_len | |
output = flash_attn_unpadded_qkvpacked_func( | |
qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True | |
) | |
output = output.view(bsz, q_len, -1) | |
else: | |
qkv = qkv.reshape(bsz, q_len, -1) | |
qkv, indices, cu_q_lens, max_s = unpad_input(qkv, key_padding_mask) | |
qkv = qkv.view(-1, 3, self.num_heads, self.head_dim) | |
output_unpad = flash_attn_unpadded_qkvpacked_func( | |
qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True | |
) | |
output_unpad = output_unpad.reshape(-1, self.num_heads * self.head_dim) | |
output = pad_input(output_unpad, indices, bsz, q_len) | |
return self.o_proj(output), None, past_key_value | |
# 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(): | |
cuda_major, cuda_minor = torch.cuda.get_device_capability() | |
if cuda_major < 8: | |
warnings.warn( | |
"Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward." | |
"ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593" | |
) | |
transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = ( | |
_prepare_decoder_attention_mask | |
) | |
transformers.models.llama.modeling_llama.LlamaAttention.forward = forward | |