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Update Caching logic to only trigger on the first inference sample #1369

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merged 3 commits into from
Nov 13, 2024
Merged

Update Caching logic to only trigger on the first inference sample #1369

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116c5c2
Only set up during the first sample
Jack-Khuu Nov 13, 2024
0163f61
Cleaner
Jack-Khuu Nov 13, 2024
7047d79
Merge branch 'main' into skip-setup
Jack-Khuu Nov 13, 2024
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48 changes: 26 additions & 22 deletions torchchat/generate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -591,6 +591,7 @@ def generate(
Dict[str, Any]
] = None, # List of Image prompt tensors for multimodal models
start_pos: int = 0,
skip_cache_setup: bool = False,
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@Gasoonjia Gasoonjia Nov 13, 2024 •
edited
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I'm ok for now, but introducing new inputs into generate function might trigger my nightmare 😣, making it farther away from our target.
I would like to delegate it to model side to suppress the warning mgs.

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I agree, it's not great. Luckily it's light so we can abstract it easily later on

draft_model: Model,
speculate_k: Optional[int] = 8,
sequential_prefill=True,
Expand All @@ -614,26 +615,27 @@ def generate(
max_new_tokens = min(max_new_tokens, max_seq_length - start_pos - prompt_length)
# set up caches only if first inference
if start_pos == 0:
model = model.to(device=device)
with torch.device(device):
if (
self.is_torchtune_model
or self.model.config.model_type == ModelType.Flamingo
):
# 6404 is one-gpu affordable max_seq_length for single image input
model.setup_caches(
batch_size=1,
dtype=self.dtype,
encoder_max_seq_len=6404,
decoder_max_seq_len=max_seq_length,
)
else:
model.setup_caches(max_batch_size=1, max_seq_length=max_seq_length)
if is_speculative and draft_model is not model:
draft_model.setup_caches(
max_batch_size=1,
max_seq_length=max_seq_length,
)
if not skip_cache_setup:
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Only change in this block: rest is whitespace

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Is there any way to directly telling the cache status from model, instead of forwarding a new attribute from outside?

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Not off the top of my head, but definitely worth baking into our model abstraction in the future

model = model.to(device=device)
with torch.device(device):
if (
self.is_torchtune_model
or self.model.config.model_type == ModelType.Flamingo
):
# 6404 is one-gpu affordable max_seq_length for single image input
model.setup_caches(
batch_size=1,
dtype=self.dtype,
encoder_max_seq_len=6404,
decoder_max_seq_len=max_seq_length,
)
else:
model.setup_caches(max_batch_size=1, max_seq_length=max_seq_length)
if is_speculative and draft_model is not model:
draft_model.setup_caches(
max_batch_size=1,
max_seq_length=max_seq_length,
)
if model.config.model_type == ModelType.Flamingo:
model.reset_caches()

Expand Down Expand Up @@ -1013,6 +1015,7 @@ def chat(
)
for i in range(num_samples):
device_sync(device=self.builder_args.device)
is_first_sample: bool = i == 0
if generator_args.chat_mode:
prompt = input("User: ")
if prompt == "/bye":
Expand All @@ -1038,7 +1041,7 @@ def chat(
]
)
self.system_prompt = None
elif i == 0:
elif is_first_sample:
encoded = self.chat_formatter.encode_dialog_prompt(
[{"role": "user", "content": prompt}]
)
Expand Down Expand Up @@ -1107,6 +1110,7 @@ def callback(x, *, done_generating=False):
top_k=generator_args.top_k,
sequential_prefill=generator_args.sequential_prefill,
start_pos=start_pos,
skip_cache_setup=not is_first_sample,
max_seq_length=max_seq_length,
)
for token_tensor, metrics in generator_func:
Expand All @@ -1116,7 +1120,7 @@ def callback(x, *, done_generating=False):
if metrics is not None:
aggregate_metrics.update(metrics)
yield token_tensor, metrics
jit_compile = (i == 0) and (
jit_compile = is_first_sample and (
generator_args.compile or generator_args.compile_prefill
)
compilation_time = time.perf_counter() - t0
Expand Down
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