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Cache: Static cache as a standalone object #30476

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merged 12 commits into from
Apr 30, 2024
Merged

Cache: Static cache as a standalone object #30476

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fa0b05e
working :D
gante Apr 25, 2024
56cf73b
tmp commit
gante Apr 26, 2024
97aa785
working on generate too; make fixup
gante Apr 26, 2024
a6853a1
dbrx
gante Apr 26, 2024
326a1c7
missing in dbrx
gante Apr 26, 2024
9de9556
more dbrx details; gemma
gante Apr 26, 2024
90771df
tmp
gante Apr 26, 2024
cd6aecd
finalized mvp
gante Apr 26, 2024
b8caa5e
test for different cuda compute capabilities
gante Apr 29, 2024
16e01f2
missing doc entry
gante Apr 29, 2024
2d1eab8
tmp commit
gante Apr 30, 2024
bb159d2
final nits
gante Apr 30, 2024
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tmp commit
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@gante
gante committed Apr 29, 2024
commit 56cf73b2230c146d5257ae2771aff6ff9584e0e8
7 changes: 4 additions & 3 deletions src/transformers/cache_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
from .configuration_utils import PretrainedConfig
from .utils import logging


logger = logging.get_logger(__name__)


Expand Down Expand Up @@ -370,11 +371,11 @@ def __init__(self, config: PretrainedConfig, max_batch_size: int, max_cache_len:
# Note: `mark_static_address` is used to tag the cache as an fixed data pointer, preventing cuda graph
# breaks when updating the cache.
new_layer_key_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
self.key_cache.append(new_layer_key_cache)
torch._dynamo.mark_static_address(new_layer_key_cache)
new_layer_value_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
self.value_cache.append(new_layer_value_cache)
torch._dynamo.mark_static_address(new_layer_key_cache)
torch._dynamo.mark_static_address(new_layer_value_cache)
self.key_cache.append(new_layer_key_cache)
self.value_cache.append(new_layer_value_cache)

def update(
self,
Expand Down
22 changes: 13 additions & 9 deletions src/transformers/models/llama/modeling_llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -428,7 +428,6 @@ def forward(
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:

if isinstance(past_key_value, StaticCache):
raise ValueError(
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
Expand Down Expand Up @@ -930,7 +929,7 @@ def forward(
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache | List[torch.FloatTensor]] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
Expand Down Expand Up @@ -959,7 +958,7 @@ def forward(
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)

if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs)
if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs)
past_key_values = DynamicCache.from_legacy_cache(past_key_values)

if cache_position is None:
Expand Down Expand Up @@ -1023,7 +1022,9 @@ def forward(
next_cache = None
if use_cache:
next_cache = (
next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, DynamicCache) else next_decoder_cache
next_decoder_cache.to_legacy_cache()
if isinstance(next_decoder_cache, DynamicCache)
else next_decoder_cache
)
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)
Expand Down Expand Up @@ -1089,6 +1090,10 @@ def _update_causal_mask(
# backwards compatibility: we allow passing a 4D attention mask shorter than the input length with
# cache. In that case, the 4D attention mask attends to the newest tokens only.
if attention_mask.shape[-2] < cache_position[0] + sequence_length:
logger.warning_once(
"Passing a 4d mask shorter than the input length is deprecated and will be removed in "
"transformers v4.42.0"
)
offset = cache_position[0]
else:
offset = 0
Expand Down Expand Up @@ -1148,7 +1153,7 @@ def forward(
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache | List[torch.FloatTensor]] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
Expand Down Expand Up @@ -1263,8 +1268,7 @@ def prepare_inputs_for_generation(

# Keep only the unprocessed tokens:
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
# input)
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as input)
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
Expand Down Expand Up @@ -1362,7 +1366,7 @@ def forward(
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache | List[torch.FloatTensor]] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
Expand Down Expand Up @@ -1479,7 +1483,7 @@ def forward(
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache | List[torch.FloatTensor]] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
Expand Down
8 changes: 4 additions & 4 deletions tests/models/llama/test_modeling_llama.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,9 +20,8 @@
import pytest
from parameterized import parameterized

from transformers import LlamaConfig, StaticCache, is_torch_available, logging, set_seed
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import (
CaptureLogger,
require_bitsandbytes,
require_flash_attn,
require_read_token,
Expand Down Expand Up @@ -699,7 +698,7 @@ def test_compile_static_cache(self):

prompts = [
"Simply put, the theory of relativity states that ",
"My favorite all time favorite condiment is ketchup.",
"My favorite all time favorite condiment is ketchup.",
]
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf", pad_token="</s>", padding_side="right")
model = LlamaForCausalLM.from_pretrained(
Expand Down Expand Up @@ -744,7 +743,7 @@ def test_compile_repeated_calls(self):

prompts = [
"Simply put, the theory of relativity states that ",
"My favorite all time favorite condiment is ketchup.",
"My favorite all time favorite condiment is ketchup.",
]
tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf", pad_token="</s>", padding_side="right")
model = LlamaForCausalLM.from_pretrained(
Expand Down Expand Up @@ -772,6 +771,7 @@ def test_compile_repeated_calls(self):
static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text)


@require_torch
class CodeLlamaIntegrationTest(unittest.TestCase):
PROMPTS = [
Expand Down