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Compute flash data layout info once and for all when possible #4

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48 changes: 36 additions & 12 deletions src/transformers/models/llama/modeling_llama.py
Original file line number Diff line number Diff line change
@@ -19,7 +19,7 @@
# limitations under the License.
""" PyTorch LLaMA model."""
import math
from typing import List, Optional, Tuple, Union
from typing import List, Optional, Tuple, Union, Dict

import torch
import torch.nn.functional as F
@@ -323,6 +323,7 @@ def forward(
output_attentions: bool = False,
use_cache: bool = False,
padding_mask: Optional[torch.LongTensor] = None,
flash_kwargs: None = None
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()

@@ -478,6 +479,7 @@ def forward(
output_attentions: bool = False,
use_cache: bool = False,
padding_mask: Optional[torch.LongTensor] = None,
flash_kwargs: Optional[Dict] = None,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
# LlamaFlashAttention attention does not support output_attentions
output_attentions = False
@@ -519,9 +521,12 @@ def forward(
# when training.
dropout_rate = 0.0 # if not self.training else self.attn_dropout

# contains at least one padding token
if padding_mask is not None:
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(padding_mask)
# contains at least one masked token
if flash_kwargs["masking"]:
indices_k = flash_kwargs["indices_k"]
cu_seqlens_k = flash_kwargs["cu_seqlens_k"]
max_seqlen_in_batch_k = flash_kwargs["max_seqlen_in_batch_k"]

key_states = index_first_axis(rearrange(key_states, "b s ... -> (b s) ..."), indices_k)
value_states = index_first_axis(rearrange(value_states, "b s ... -> (b s) ..."), indices_k)

@@ -533,11 +538,9 @@ def forward(
indices_q = indices_k
elif q_len == 1:
max_seqlen_in_batch_q = 1
cu_seqlens_q = torch.arange(
bsz + 1, dtype=torch.int32, device=query_states.device
) # There is a memcpy here, that is very bad.
indices_q = cu_seqlens_q[:-1]
query_states = query_states.squeeze(1)
cu_seqlens_q = flash_kwargs["cu_seqlens_q"]
indices_q = flash_kwargs["indices_q"]
query_states = query_states.squeeze(1) # [batch_size, 1, num_heads, head_dim] -> [batch_size, num_heads, head_dim]
else:
# The -q_len: slice assumes left padding.
padding_mask = padding_mask[:, -q_len:]
@@ -591,6 +594,7 @@ def forward(
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
padding_mask: Optional[torch.LongTensor] = None,
flash_kwargs: Optional[Dict] = None,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
"""
Args:
@@ -619,6 +623,7 @@ def forward(
output_attentions=output_attentions,
use_cache=use_cache,
padding_mask=padding_mask,
flash_kwargs=flash_kwargs,
)
hidden_states = residual + hidden_states

@@ -770,6 +775,7 @@ def __init__(self, config: LlamaConfig):
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

self.gradient_checkpointing = False
self._flash = getattr(config, "_flash_attn_2_enabled", False)
# Initialize weights and apply final processing
self.post_init()

@@ -864,9 +870,26 @@ def forward(
else:
padding_mask = None

attention_mask = self._prepare_decoder_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)

flash_kwargs = None
if not self._flash:
attention_mask = self._prepare_decoder_attention_mask(
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
)
else:
flash_kwargs = {}
flash_kwargs["masking"] = padding_mask is not None

if padding_mask is not None:
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(padding_mask)
flash_kwargs["indices_k"] = indices_k
flash_kwargs["cu_seqlens_k"] = cu_seqlens_k
flash_kwargs["max_seqlen_in_batch_k"] = max_seqlen_in_batch_k
if seq_length == 1:
flash_kwargs["cu_seqlens_q"] = torch.arange(
batch_size + 1, dtype=torch.int32, device=input_ids.device
) # There is a memcpy here, that is very bad. At least happening only once.
flash_kwargs["indices_q"] = flash_kwargs["cu_seqlens_q"][:-1]

hidden_states = inputs_embeds

@@ -909,6 +932,7 @@ def custom_forward(*inputs):
output_attentions=output_attentions,
use_cache=use_cache,
padding_mask=padding_mask,
flash_kwargs=flash_kwargs,
)

hidden_states = layer_outputs[0]