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"""
Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-generation-webui/main/modules/llama_attn_hijack.py and made some adjustments
"""
import logging
import math
from typing import Optional, Tuple
import torch
import torch.nn.functional as F
import transformers.models.llama.modeling_llama
from torch import nn
try:
import xformers.ops
except ImportError:
logging.error("xformers not found! Please install it before trying to use it.")
def hijack_llama_attention():
transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
def hijack_llama_sdp_attention():
transformers.models.llama.modeling_llama.LlamaAttention.forward = (
sdp_attention_forward
)
def xformers_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,
) -> 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)
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,
) = transformers.models.llama.modeling_llama.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 = transformers.models.llama.modeling_llama.repeat_kv(
key_states, self.num_key_value_groups
)
value_states = transformers.models.llama.modeling_llama.repeat_kv(
value_states, self.num_key_value_groups
)
# We only apply xformers optimizations if we don't need to output the whole attention matrix
if not output_attentions:
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
# This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros.
# We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros.
if attention_mask is None or attention_mask[0, 0, 0, 1] == 0:
# input and output should be of form (bsz, q_len, num_heads, head_dim)
attn_output = xformers.ops.memory_efficient_attention(
query_states, key_states, value_states, attn_bias=None
)
else:
# input and output should be of form (bsz, q_len, num_heads, head_dim)
attn_output = xformers.ops.memory_efficient_attention(
query_states,
key_states,
value_states,
attn_bias=xformers.ops.LowerTriangularMask(),
)
attn_weights = None
else:
attn_weights = torch.matmul(
query_states, key_states.transpose(2, 3)
) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
attn_weights = torch.max(
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
)
# upcast attention to fp32
attn_weights = nn.functional.softmax(
attn_weights, dim=-1, dtype=torch.float32
).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
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).contiguous()
# end x-formers vs. not x-formers if-else block
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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, attn_weights, past_key_value
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,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# pylint: disable=duplicate-code
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,
) = transformers.models.llama.modeling_llama.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
# We only apply sdp attention if we don't need to output the whole attention matrix
if not output_attentions:
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,
)
attn_weights = None
else:
attn_weights = torch.matmul(
query_states, key_states.transpose(2, 3)
) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
attn_weights = torch.max(
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
)
# upcast attention to fp32
attn_weights = nn.functional.softmax(
attn_weights, dim=-1, dtype=torch.float32
).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
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)
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights, past_key_value
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