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[ExecuTorch] Some updated to kv cache #5615

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[ExecuTorch] Some updated to kv cache #5615

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19 changes: 13 additions & 6 deletions examples/models/llama2/llama_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,6 +151,7 @@ def __init__(
):
super().__init__()
self.max_seq_length = max_seq_length
self.is_tranposed = transpose_cache
if transpose_cache:
cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
else:
Expand All @@ -173,28 +174,34 @@ def update(
) -> Tuple[torch.Tensor, torch.Tensor]:
# input_pos: [S], k_val: [B, H, S, D] or [B, S, H, D] depending on transpose_cache
if self.enable_dynamic_shape:
start_pos = input_pos[-1].item()
start_pos = input_pos[0].item()
torch._check_is_size(start_pos)
torch._check(start_pos < self.max_seq_length)
seq_length = k_val.size(2)
dim_to_slice = 2 if self.transpose_cache else 1
seq_length = k_val.size(dim_to_slice)
# Replace the entry in the cache for this token
# The following lines are equivalent to:
# cache_k[:bsz, start_pos : start_pos + seqlen] = xk
# cache_v[:bsz, start_pos : start_pos + seqlen] = xv
# when dim_to_slice is 1
# We use .narrow() here to make the compiler happy
# pyre-ignore: Incompatible parameter type [6]
narrowed_k = self.k_cache.narrow(2, start_pos, seq_length)
narrowed_k = self.k_cache.narrow(dim_to_slice, start_pos, seq_length)
# pyre-ignore: Incompatible parameter type [6]
narrowed_v = self.v_cache.narrow(2, start_pos, seq_length)
narrowed_v = self.v_cache.narrow(dim_to_slice, start_pos, seq_length)

narrowed_k.copy_(k_val)
narrowed_v.copy_(v_val)
return self.k_cache, self.v_cache
else:
k_out = self.k_cache
v_out = self.v_cache
k_out[:, :, input_pos] = k_val
v_out[:, :, input_pos] = v_val
if self.transpose_cache:
k_out[:, :, input_pos] = k_val
v_out[:, :, input_pos] = v_val
else:
k_out[:, input_pos] = k_val
v_out[:, input_pos] = v_val

return k_out, v_out

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