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Enable flash attention for gemma #1454

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Nov 15, 2024
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Enable flash attention for gemma #1454

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62 changes: 35 additions & 27 deletions optimum/habana/transformers/models/gemma/modeling_gemma.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
"""PyTorch Gemma model."""

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

import torch
Expand Down Expand Up @@ -214,7 +215,7 @@ def pre_attn_forward(
use_flash_attention: Optional[bool] = False,
flash_attention_recompute: Optional[bool] = False,
flash_attention_causal_mask: Optional[bool] = False,
cache_idx: int = None,
cache_idx: Optional[int] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
"""
Expand Down Expand Up @@ -289,7 +290,8 @@ def pre_attn_forward(

if q_len == 1:
# next token
with ht.sdp_kernel(enable_recompute=False):
use_recompute = True if os.getenv("QUANT_CONFIG", "") else False
with ht.sdp_kernel(enable_recompute=use_recompute):
attn_output = FusedSDPA.apply(
query_states, key_states, value_states, attention_mask, 0.0, False, None
)
Expand Down Expand Up @@ -407,23 +409,23 @@ def pre_attn(
use_flash_attention: Optional[bool] = False,
flash_attention_recompute: Optional[bool] = False,
flash_attention_causal_mask: Optional[bool] = False,
cache_idx: int = None,
cache_idx: Optional[int] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
hidden_states = self.input_layernorm(hidden_states)
hidden_states, attn_weights, present_key_value = self.self_attn.pre_attn_forward(
hidden_states,
attention_mask,
position_ids,
past_key_value,
output_attentions,
use_cache,
cache_position,
token_idx,
attn_softmax_bf16,
reuse_cache,
use_flash_attention,
flash_attention_recompute,
flash_attention_causal_mask,
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,
cache_position=cache_position,
token_idx=token_idx,
attn_softmax_bf16=attn_softmax_bf16,
reuse_cache=reuse_cache,
use_flash_attention=use_flash_attention,
flash_attention_recompute=flash_attention_recompute,
flash_attention_causal_mask=flash_attention_causal_mask,
cache_idx=cache_idx,
)
return hidden_states, attn_weights, present_key_value
Expand All @@ -443,7 +445,7 @@ def forward(
use_flash_attention: Optional[bool] = False,
flash_attention_recompute: Optional[bool] = False,
flash_attention_causal_mask: Optional[bool] = False,
cache_idx: int = None,
cache_idx: Optional[int] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Copied from GemmaDecoderLayer.forward: https://github.com/huggingface/transformers/blob/v4.38.1/src/transformers/models/gemma/modeling_gemma.py
Expand All @@ -453,16 +455,16 @@ def forward(
residual = hidden_states

hidden_states, self_attn_weights, present_key_value = self.pre_attn(
hidden_states,
attention_mask,
position_ids,
past_key_value,
output_attentions,
use_cache,
cache_position,
token_idx,
attn_softmax_bf16,
reuse_cache,
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,
cache_position=cache_position,
token_idx=token_idx,
attn_softmax_bf16=attn_softmax_bf16,
reuse_cache=reuse_cache,
use_flash_attention=use_flash_attention,
flash_attention_recompute=flash_attention_recompute,
flash_attention_causal_mask=flash_attention_causal_mask,
Expand Down Expand Up @@ -717,6 +719,7 @@ def forward(
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
reuse_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
Expand Down Expand Up @@ -746,6 +749,7 @@ def forward(
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
reuse_cache=reuse_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
Expand Down Expand Up @@ -859,9 +863,13 @@ def prepare_inputs_for_generation(
"cache_position": cache_position,
"past_key_values": past_key_values,
"use_cache": use_cache,
"reuse_cache": kwargs.get("reuse_cache"),
"attention_mask": attention_mask,
"num_logits_to_keep": num_logits_to_keep,
"token_idx": token_idx,
"use_flash_attention": kwargs.get("use_flash_attention"),
"flash_attention_recompute": kwargs.get("flash_attention_recompute"),
"flash_attention_causal_mask": kwargs.get("flash_attention_causal_mask"),
}
)
return model_inputs
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