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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,
modality_indicators: 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,
padding_mask: bool = None,
) -> 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, modality_indicators)
.view(bsz, q_len, self.num_key_value_heads, self.head_dim)
.transpose(1, 2)
)
value_states = (
self.v_proj(hidden_states, modality_indicators)
.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 |