Update fast_lora.py

This commit is contained in:
Daniel Han-Chen 2024-01-25 23:14:37 +11:00
commit 2d25facc7b

View file

@ -18,27 +18,28 @@ from .swiglu import swiglu_fg_kernel, swiglu_DWf_DW_dfg_kernel
def matmul_lora(X, W, W_quant, A, B, s, out = None):
dtype = X.dtype
W = fast_dequantize(W.t(), W_quant)
with torch.cuda.amp.autocast(dtype = torch.float16):
dtype = X.dtype
W = fast_dequantize(W.t(), W_quant)
if X.dim() == 3:
batch, seq_len, d = X.shape
X = X.view(-1, X.shape[-1])
reshape = True
else:
reshape = False
pass
if X.dim() == 3:
batch, seq_len, d = X.shape
X = X.view(-1, X.shape[-1])
reshape = True
else:
reshape = False
pass
out = torch.matmul(X, W, out = out)
if W_quant is not None: del W
out = torch.matmul(X, W, out = out)
if W_quant is not None: del W
if A is not None:
# LoRA is enabled
A, B = A.t(), B.t()
out += (X @ A.to(dtype)) @ (s * B.to(dtype))
pass
return out.view(batch, seq_len, -1) if reshape else out
if A is not None:
# LoRA is enabled
A, B = A.t(), B.t()
out += (X @ A.to(dtype)) @ (s * B.to(dtype))
pass
return out.view(batch, seq_len, -1) if reshape else out
pass