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[llm] add kv cache to eval #3162

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[llm] add kv cache to eval #3162

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30 changes: 25 additions & 5 deletions examples/models/llama2/eval_llama_lib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,13 +41,15 @@ def __init__(
model: nn.Module,
tokenizer: Union[SentencePieceTokenizer, Tiktoken],
max_seq_length: Optional[int] = None,
use_kv_cache: bool = False,
):
device = "cuda" if torch.cuda.is_available() else "cpu"
super().__init__(device=device)
self._model = model
self._tokenizer = tokenizer
self._device = torch.device(device)
self._max_seq_length = 2048 if max_seq_length is None else max_seq_length
self._use_kv_cache = use_kv_cache

@property
def eot_token_id(self):
Expand Down Expand Up @@ -83,7 +85,15 @@ def tok_decode(self, tokens):
return decoded

def _model_call(self, inps):
return self._model(inps)
if self._use_kv_cache:
result_logits = []
for pos in range(self._max_seq_length):
pos_tensor = torch.tensor([pos], dtype=torch.int64)
logits = self._model(inps[:, pos : pos + 1], pos_tensor)
result_logits.append(logits)
return torch.cat(result_logits, dim=1)
else:
return self._model(inps)

def _model_generate(self, context, max_length, eos_token_id):
raise Exception("unimplemented")
Expand All @@ -107,13 +117,22 @@ def __init__(
from executorch.extension.pybindings.portable_lib import _load_for_executorch

self._et_model = _load_for_executorch(self._model)
self._use_kv_cache = self._et_model.run_method("use_kv_cache")[0]

def _model_call(self, inps):
# Given inps (tokens), return the logits from a single forward call
# inps: Tensor of shape (1, max_seq_len - 1)
# logits: Tensor of shape (1, max_seq_len - 1, 32000)
result = self._et_model.forward((inps,))
return result[0]
# logits: Tensor of shape (1, max_seq_len - 1, vocab_size)
if self._use_kv_cache:
result_logits = []
for pos in range(self._max_seq_length):
pos_tensor = torch.tensor([pos], dtype=torch.int64)
logits = self._et_model.forward((inps[:, pos : pos + 1], pos_tensor))
result_logits.append(logits[0])
return torch.cat(result_logits, dim=1)
else:
result = self._et_model.forward((inps,))
return result[0]


class ETRunnerEvalWrapper(GPTFastEvalWrapper):
Expand All @@ -139,7 +158,7 @@ def _model_call(self, inps):

# Example:
# inps: Tensor of shape (1, N)
# logits: Tensor of shape (1, N, 32000)
# logits: Tensor of shape (1, N, vocab_size)
pass


Expand Down Expand Up @@ -225,6 +244,7 @@ def gen_eval_wrapper(
model=model,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
use_kv_cache=args.use_kv_cache,
)


Expand Down