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Update llama.py

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Daniel Han-Chen 2024-02-24 03:03:00 +11:00
commit 73a8616f99
1 changed files with 4 additions and 3 deletions
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unsloth/models/llama.py
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@ -119,6 +119,7 @@ def LlamaAttention_fast_forward_inference(
n_groups = self.num_key_value_groups
n_kv_heads = self.num_key_value_heads
head_dim = self.head_dim
attention_size = n_heads*head_dim
# assert(n_kv_heads * n_groups == n_heads)
seq_len = K1.shape[-2]
kv_seq_len = seq_len + 1
@ -131,7 +132,7 @@ def LlamaAttention_fast_forward_inference(
self.paged_attention_V = self.paged_attention[:,1]
self.paged_attention_K[:seq_len] = K1.permute(2, 0, 1, 3)
self.paged_attention_V[:seq_len] = V1.permute(2, 0, 1, 3)
self.temp_QA = torch.empty((2, bsz, 1, hd), dtype = dtype, device = "cuda")
self.temp_QA = torch.empty((2, bsz, 1, attention_size), dtype = dtype, device = "cuda")
self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = "cuda")
self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = "cuda")
self.attention = torch.empty((bsz, n_heads, 1, KV_CACHE_INCREMENT+seq_len), dtype = dtype, device = "cuda")
@ -201,8 +202,8 @@ 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)
A = A.transpose(1, 2)
A = A.reshape(bsz, 1, self.hidden_size)
A = fast_linear_forward(self.o_proj, A, out = self.temp_QA[1])
A = A.reshape(bsz, 1, attention_size)
A = fast_linear_forward(self.o_proj, A, out = self.temp_QA[1][:,:,self.hidden_size])
return A, (Kn, Vn)
pass