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Update llama.py

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This commit is contained in:
Daniel Han 2025-02-02 02:23:53 -08:00
commit c77471114b
1 changed files with 7 additions and 3 deletions
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unsloth/models/llama.py
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@ -90,6 +90,8 @@ pass
from math import sqrt as math_sqrt
KV_CACHE_INCREMENT = 256 # KV Cache update size
torch_nn_functional_softmax = torch.nn.functional.softmax
# SDPA has GQA internally
SDPA_HAS_GQA = "enable_gqa" in scaled_dot_product_attention.__doc__
# Fix new HF's inference code
def _fast_prepare_inputs_for_generation(self, input_ids, **kwargs,):
@ -244,7 +246,7 @@ def LlamaAttention_fast_forward_inference(
# Grouped query attention
_, _, cached_len, _ = Knn.shape
if n_groups != 1:
if 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)
@ -263,8 +265,10 @@ def LlamaAttention_fast_forward_inference(
A[:] = torch_nn_functional_softmax(A, dim = -1, dtype = torch.float32)#.to(A.dtype)
A = torch_matmul(A, Vnn, out = Qn)
else:
print(attention_mask)
A = scaled_dot_product_attention(Qn, Knn, Vnn, attn_mask = attention_mask, is_causal = False)
if SDPA_HAS_GQA:
A = scaled_dot_product_attention(Qn, Knn, Vnn, attn_mask = attention_mask, is_causal = False, enable_gqa = True)
else:
A = scaled_dot_product_attention(Qn, Knn, Vnn, attn_mask = attention_mask, is_causal = False)
pass
A = A.transpose(1, 2)
A = A.reshape(bsz, 1, attention_size)