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sync xformers patch to follow shared format and be diffable

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This commit is contained in:
Aman Karmani
2023-08-13 15:41:06 +00:00
committed by Wing Lian
parent 5d0b27e5a1
commit 985dcbc051
1 changed files with 45 additions and 69 deletions
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114
src/axolotl/monkeypatch/llama_attn_hijack_xformers.py
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@@ -3,13 +3,13 @@ Directly copied the code from https://raw.githubusercontent.com/oobabooga/text-g
"""
import logging
import math
import warnings
from typing import Optional, Tuple
import torch
import torch.nn.functional as F
import transformers.models.llama.modeling_llama
from torch import nn
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
try:
import xformers.ops
@@ -75,15 +75,15 @@ def xformers_forward(
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,
) = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(
query_states, key_states = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
# [bsz, nh, t, hd]
@@ -96,74 +96,50 @@ def xformers_forward(
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
)
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = 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)
if output_attentions:
warnings.warn(
"Output attentions is not supported for patched `LlamaAttention`, returning `None` instead."
)
# 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=attention_mask,
attn_bias=xformers.ops.LowerTriangularMask(),
)
attn_weights = None
#
# xformers-attn start
#
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:
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
# 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=attention_mask,
attn_bias=xformers.ops.LowerTriangularMask(),
)
if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
#
# xformers-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(
@@ -176,4 +152,4 @@ def xformers_forward(
else:
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights, past_key_value
return attn_output, None, past_key_value