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Mistral flash attn packing (#646)

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* add mistral monkeypatch

* add arg for decoder attention masl

* fix lint for duplicate code

* make sure to update transformers too

* tweak install for e2e

* move mistral patch to conditional
This commit is contained in:
Wing Lian
2023-09-27 18:41:00 -04:00
committed by GitHub
parent 85b0be2ba7
commit b6ab8aad62
4 changed files with 412 additions and 4 deletions
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5
.github/workflows/tests.yml vendored
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@@ -44,7 +44,7 @@ jobs:
- name: Install dependencies
run: |
pip3 install -e .
pip3 install -U -e .
pip3 install -r requirements-tests.txt
- name: Run tests
@@ -69,8 +69,7 @@ jobs:
- name: Install dependencies
run: |
pip3 install -e .
pip3 install flash-attn
pip3 install -U -e .[flash-attn]
pip3 install -r requirements-tests.txt
- name: Run e2e tests

2
requirements.txt
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@@ -4,7 +4,7 @@ torch==2.0.1
auto-gptq
packaging
peft @ git+https://github.com/huggingface/peft.git
transformers @ git+https://github.com/huggingface/transformers.git@0ac3875011d32dc85e0e83970507e3afe8f0febb
transformers @ git+https://github.com/huggingface/transformers.git@78dd120
bitsandbytes>=0.41.1
accelerate @ git+https://github.com/huggingface/accelerate@80da9cfb09bb3cc9f1b385cb55d6b90d025a5fd9
deepspeed

401
src/axolotl/monkeypatch/mistral_attn_hijack_flash.py Normal file
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@@ -0,0 +1,401 @@
"""Flash attention monkey patch for mistral model"""
# 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 transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.models.mistral.modeling_mistral import (
MistralDecoderLayer as OriginalMistralDecoderLayer,
)
from transformers.models.mistral.modeling_mistral import apply_rotary_pos_emb, repeat_kv
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")
def replace_mistral_attn_with_flash_attn(
packed: Optional[bool] = False,
):
transformers.models.mistral.modeling_mistral.MistralModel._prepare_decoder_attention_mask = ( # pylint: disable=protected-access
_prepare_decoder_attention_mask
)
transformers.models.mistral.modeling_mistral.MistralAttention.forward = (
flashattn_forward
)
if packed:
transformers.models.mistral.modeling_mistral.MistralDecoderLayer = (
MistralDecoderLayer
)
transformers.models.mistral.modeling_mistral.MistralModel.forward = (
mistral_model_forward
)
# 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,
sliding_window,
): # pylint: disable=unused-argument
# [bsz, seq_len]
return attention_mask
def flashattn_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,
cu_seqlens: Optional[torch.Tensor] = None,
max_seqlen: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
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 = apply_rotary_pos_emb(
query_states, key_states, cos, sin, position_ids
)
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 cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
# special handling using sample packing
qkv = torch.stack(
[query_states, key_states, value_states], dim=2
) # [bsz, nh, 3, q_len, hd]
qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
qkv = rearrange(qkv, "b s ... -> (b s) ...")
output = flash_attn_varlen_qkvpacked_func(
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
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
# 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()
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
def mistral_model_forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = (
output_attentions
if output_attentions is not None
else self.config.output_attentions
)
output_hidden_states = (
output_hidden_states
if output_hidden_states is not None
else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
)
if input_ids is not None:
batch_size, seq_length = input_ids.shape
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape
else:
raise ValueError(
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
)
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
cu_seqlens = None
max_seqlen = None
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
cu_seqlens = cu_seqlens.squeeze()
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past),
dtype=torch.bool,
device=inputs_embeds.device,
)
attention_mask = (
self._prepare_decoder_attention_mask( # pylint: disable=protected-access
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
sliding_window=self.config.sliding_window,
)
)
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
transformers.logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = past_key_values[idx] if past_key_values is not None else None
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
# None for past_key_value
return module(*inputs)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(decoder_layer),
hidden_states,
attention_mask,
position_ids,
past_key_value,
output_attentions,
None,
cu_seqlens,
max_seqlen,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
if v is not None
)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
class MistralDecoderLayer(OriginalMistralDecoderLayer):
"""
patched version of MistralDecoderLayer to pass through the precalculated cu_seqlens
"""
def 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: Optional[bool] = False,
use_cache: Optional[bool] = False,
cu_seqlens: Optional[torch.Tensor] = None,
max_seqlen: Optional[torch.Tensor] = None,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
cu_seqlens (`torch.Tensor`, *optional*) cumulative sequence len when packing
"""
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cu_seqlens=cu_seqlens,
max_seqlen=max_seqlen,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs

8
src/axolotl/utils/models.py
View File

@@ -150,6 +150,14 @@ def load_model(
# Note: This might overwrite previous additional_special_tokens
tokenizer.add_special_tokens({"additional_special_tokens": [MEM_TOKEN]})
if cfg.is_mistral_derived_model and cfg.flash_attention:
from axolotl.monkeypatch.mistral_attn_hijack_flash import (
replace_mistral_attn_with_flash_attn,
)
LOG.info("patching with flash attention")
replace_mistral_attn_with_flash_attn(packed=cfg.sample_packing)
if cfg.is_llama_derived_model and cfg.xpos_rope:
from axolotl.monkeypatch.xpos_rope_llama_monkey_patch import (
replace_llama_rope_with_xpos_rope,