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"""
Patched LlamaAttention to use torch.nn.functional.scaled_dot_product_attention
"""
import warnings
from typing import Optional, Tuple
import torch
import torch.nn.functional as F
import transformers.models.llama.modeling_llama
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
def hijack_llama_sdp_attention():
transformers.models.llama.modeling_llama.LlamaAttention.forward = (
sdp_attention_forward
)
def sdp_attention_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
padding_mask: Optional[torch.LongTensor] = None, # pylint: disable=unused-argument
**kwargs, # pylint: disable=unused-argument
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# pylint: disable=duplicate-code
bsz, q_len, _ = hidden_states.size()
if not hasattr(self, "pretraining_tp"):
self.pretraining_tp = 1
if self.pretraining_tp > 1:
key_value_slicing = (
self.num_key_value_heads * self.head_dim
) // self.pretraining_tp
query_slices = self.q_proj.weight.split(
(self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
)
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
query_states = [
F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)
]
query_states = torch.cat(query_states, dim=-1)
key_states = [
F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)
]
key_states = torch.cat(key_states, dim=-1)
value_states = [
F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)
]
value_states = torch.cat(value_states, dim=-1)
else:
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(
bsz, q_len, self.num_heads, self.head_dim
).transpose(1, 2)
key_states = key_states.view(
bsz, q_len, self.num_key_value_heads, self.head_dim
).transpose(1, 2)
value_states = value_states.view(
bsz, q_len, self.num_key_value_heads, self.head_dim
).transpose(1, 2)
# [bsz, q_len, nh, hd]
# [bsz, nh, q_len, hd]
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
)
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
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)
if output_attentions:
warnings.warn(
"Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
)
#
# sdp-attn start
#
with torch.backends.cuda.sdp_kernel():
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
is_causal=False,
)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
#
# sdp-attn end
#
if self.pretraining_tp > 1:
attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
o_proj_slices = self.o_proj.weight.split(
self.hidden_size // self.pretraining_tp, dim=1
)
attn_output = sum(
F.linear(attn_output[i], o_proj_slices[i])
for i in range(self.pretraining_tp)
)
else:
attn_output = self.o_proj(attn_output)
return attn_output, None, past_key_value
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