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fix for flash attn w mistral w/o sammple packing #648

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Sep 28, 2023
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fix for flash attn w mistral w/o sammple packing #648

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236 changes: 188 additions & 48 deletions src/axolotl/monkeypatch/mistral_attn_hijack_flash.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,13 +2,17 @@
# pylint: disable=duplicate-code

import logging
import math
from typing import List, Optional, Tuple, Union

import torch
import transformers
from einops import rearrange
from torch import nn
from flash_attn.bert_padding import pad_input, unpad_input
from flash_attn.flash_attn_interface import ( # pylint: disable=ungrouped-imports
flash_attn_kvpacked_func,
flash_attn_varlen_kvpacked_func,
flash_attn_varlen_qkvpacked_func,
)
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.models.mistral.modeling_mistral import (
MistralDecoderLayer as OriginalMistralDecoderLayer,
Expand All @@ -17,16 +21,6 @@

from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids

try:
from flash_attn.flash_attn_interface import ( # pylint: disable=ungrouped-imports
flash_attn_varlen_qkvpacked_func,
)
except ImportError:
from flash_attn.flash_attn_interface import (
flash_attn_unpadded_qkvpacked_func as flash_attn_varlen_qkvpacked_func,
)


LOG = logging.getLogger("axolotl.monkeypatch.mistral")


Expand Down Expand Up @@ -108,6 +102,15 @@ def flashattn_forward(
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)

if self.training:
# during training q,k,v always have same seqlen
assert key_states.shape == query_states.shape
is_causal = True
else:
# turn off FA causal mask after first inference autoregressive iteration
# only on first autoregressive step q,k,v have same seqlen
is_causal = key_states.shape == query_states.shape

if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
# special handling using sample packing
qkv = torch.stack(
Expand All @@ -120,46 +123,84 @@ def flashattn_forward(
qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=None, causal=True
)
output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
attn_output = output
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 = rearrange(attn_output, "b s h d -> b s (h d)")
attn_weights = None
elif query_states.shape == key_states.shape:
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
qkv_unpad, cu_seqlens_q, max_seqlen_q, _, output_pad_fn = generate_qkv(
query_states,
key_states,
value_states,
qkvpacked=True,
# We have disabled _prepare_decoder_attention_mask in LlamaModel
# the attention_mask should be the same as the key_padding_mask
key_padding_mask=attention_mask,
query_padding_mask=attention_mask[:, -query_states.size(1) :]
if attention_mask is not None
else None,
)
output_unpad = flash_attn_varlen_qkvpacked_func(
qkv_unpad,
cu_seqlens_q,
max_seqlen_q,
0.0,
softmax_scale=None,
causal=is_causal,
)
output = output_pad_fn(output_unpad)
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()}"
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
if attention_mask is None or attention_mask.all().item():
output = flash_attn_kvpacked_func(
query_states,
torch.stack([key_states, value_states], 2),
causal=is_causal,
)

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

# 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()}"
else:
( # pylint: disable=unbalanced-tuple-unpacking
q_unpad,
kv_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
_,
_,
output_pad_fn,
) = generate_qkv(
query_states,
key_states,
value_states,
kvpacked=True,
key_padding_mask=attention_mask,
query_padding_mask=attention_mask[:, -query_states.size(1) :]
if attention_mask is not None
else None,
)
if q_unpad.dtype != kv_unpad.dtype:
kv_unpad = kv_unpad.to(q_unpad.dtype)
output_unpad = flash_attn_varlen_kvpacked_func(
q_unpad,
kv_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
0.0,
softmax_scale=None,
causal=is_causal,
)
output = output_pad_fn(output_unpad)

attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = output
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 = rearrange(attn_output, "b s h d -> b s (h d)")

attn_output = self.o_proj(attn_output)

Expand All @@ -169,6 +210,105 @@ def flashattn_forward(
return attn_output, attn_weights, past_key_value


# based on https://github.com/Dao-AILab/flash-attention/blob/364a5b/tests/test_flash_attn.py#L38
def generate_qkv(
q,
k,
v,
query_padding_mask=None,
key_padding_mask=None,
kvpacked=False,
qkvpacked=False,
): # pylint: disable=invalid-name,unnecessary-lambda-assignment
"""
Arguments:
q: (batch_size, seqlen_q, nheads, d)
k: (batch_size, seqlen_k, nheads_k, d)
v: (batch_size, seqlen_k, nheads_k, d)
query_padding_mask: (batch_size, seqlen), bool
key_padding_mask: (batch_size, seqlen), bool
"""
assert not (kvpacked and qkvpacked)
batch_size, seqlen_q, nheads, d = q.shape
_, seqlen_k, nheads_k, _ = k.shape
assert k.shape == (batch_size, seqlen_k, nheads_k, d)
assert v.shape == (batch_size, seqlen_k, nheads_k, d)

if query_padding_mask is not None:
q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
q, query_padding_mask
)

output_pad_fn = lambda output_unpad: pad_input( # noqa: E731
output_unpad, indices_q, batch_size, seqlen_q
)

else:
q_unpad = rearrange(q, "b s h d -> (b s) h d")
cu_seqlens_q = torch.arange(
0,
(batch_size + 1) * seqlen_q,
step=seqlen_q,
dtype=torch.int32,
device=q_unpad.device,
)
max_seqlen_q = seqlen_q

output_pad_fn = lambda output_unpad: rearrange( # noqa: E731
output_unpad, "(b s) h d -> b s h d", b=batch_size
)

if key_padding_mask is not None:
k_unpad, _, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
else:
k_unpad = rearrange(k, "b s h d -> (b s) h d")
v_unpad = rearrange(v, "b s h d -> (b s) h d")
cu_seqlens_k = torch.arange(
0,
(batch_size + 1) * seqlen_k,
step=seqlen_k,
dtype=torch.int32,
device=k_unpad.device,
)
max_seqlen_k = seqlen_k

if qkvpacked:
assert nheads == nheads_k
qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
qkv = torch.stack([q, k, v], dim=2)
return (qkv_unpad, cu_seqlens_q, max_seqlen_q, qkv, output_pad_fn)

if kvpacked:
kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
kv = torch.stack([k, v], dim=2)
return (
q_unpad,
kv_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
q,
kv,
output_pad_fn,
)

return (
q_unpad,
k_unpad,
v_unpad,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q,
max_seqlen_k,
q,
k,
v,
output_pad_fn,
)


def mistral_model_forward(
self,
input_ids: torch.LongTensor = None,
Expand Down