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Update llama.py

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Daniel Han 2025-02-02 14:54:58 -08:00
commit b6851a3760
1 changed files with 1 additions and 2 deletions
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unsloth/models/llama.py
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@ -246,7 +246,7 @@ def LlamaAttention_fast_forward_inference(
# Grouped query attention
_, _, cached_len, _ = Knn.shape
if not SDPA_HAS_GQA and n_groups != 1:
if bsz == 1 or not SDPA_HAS_GQA and n_groups != 1:
Knn = Knn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)
Vnn = Vnn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)
Knn = Knn.reshape(bsz, n_heads, cached_len, head_dim)
@ -260,7 +260,6 @@ def LlamaAttention_fast_forward_inference(
if bsz == 1:
Qn *= self.scalar # See https://github.com/ggerganov/llama.cpp/issues/7805#issuecomment-2153349963
# It seems like doing (Q * scalar) @ K is better than (Q @ K) * scalar to stop overflows
print(Qn.shape, Knn.transpose(2, 3).shape, self.attention[:,:,:,:cached_len].shape, self.attention.shape, cached_len)
A = torch_matmul(Qn, Knn.transpose(2, 3), out = self.attention[:,:,:,:cached_len])
# if attention_mask is not None: A += attention_mask # Must add attention_mask for batched
A[:] = torch_nn_functional_softmax(A, dim = -1, dtype = torch.float32)#.to(A.dtype)