|
from typing import Optional, Tuple |
|
import warnings |
|
|
|
import torch |
|
from torch import nn |
|
import transformers |
|
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb |
|
|
|
from flash_attn.flash_attn_interface import flash_attn_varlen_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_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) |
|
) |
|
|
|
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: |
|
|
|
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 |
|
|
|
|
|
qkv = torch.stack([query_states, key_states, value_states], dim=2) |
|
qkv = qkv.transpose(1, 3) |
|
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_varlen_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_varlen_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 |
|
|
|
|
|
|
|
|
|
def _prepare_decoder_attention_mask( |
|
self, attention_mask, input_shape, inputs_embeds, past_key_values_length |
|
): |
|
|
|
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 |
|
|
