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This commit is contained in:
Daniel Han-Chen 2024-03-10 17:42:41 +11:00
commit 1e8922af2b
3 changed files with 9 additions and 9 deletions
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2
unsloth/kernels/rms_layernorm.py
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@ -41,7 +41,7 @@ def _rms_layernorm_forward(
r += row_idx * r_row_stride
X_row = tl.load(X + col_offsets, mask = mask, other = 0).to(tl.float32)
W_row = tl.load(W + col_offsets, mask = mask, other = 0).to(tl.float32)
W_row = tl.load(W + col_offsets, mask = mask, other = 0)#.to(tl.float32)
row_var = tl.sum(X_row * X_row, axis = 0) / n_cols
inv_var = tl.math.rsqrt(row_var + eps)

6
unsloth/kernels/swiglu.py
View file

@ -25,7 +25,7 @@ def _fg_kernel(e, g, h, n_elements, BLOCK_SIZE : tl.constexpr,):
mask = offsets < n_elements
e_row = tl.load(e + offsets, mask = mask, other = 0).to(tl.float32)
g_row = tl.load(g + offsets, mask = mask, other = 0).to(tl.float32)
g_row = tl.load(g + offsets, mask = mask, other = 0)#.to(tl.float32)
# f = e * sigmoid(e)
f_row = e_row * tl.sigmoid(e_row) # e_row / (1 + tl.exp(-e_row))
@ -63,9 +63,9 @@ def _DWf_DW_dfg_kernel(DW, e, g, n_elements, BLOCK_SIZE : tl.constexpr,):
offsets = block_idx*BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
DW_row = tl.load(DW + offsets, mask = mask, other = 0).to(tl.float32)
DW_row = tl.load(DW + offsets, mask = mask, other = 0)#.to(tl.float32)
e_row = tl.load(e + offsets, mask = mask, other = 0).to(tl.float32)
g_row = tl.load(g + offsets, mask = mask, other = 0).to(tl.float32)
g_row = tl.load(g + offsets, mask = mask, other = 0)#.to(tl.float32)
# e = e.float()
# se = 1.0 / (1.0 + torch.exp(-e))

10
unsloth/save.py
View file

@ -90,13 +90,13 @@ def _merge_lora(layer, name):
W = fast_dequantize(W, quant_state)
else:
dtype = W.dtype
# W = W.to(torch.float32).t()
W = W.t()
W = W.to(torch.float32).t()
# W = W.t()
if A is not None:
sAB = (A.t().to(W.dtype) @ (s * B.t().to(W.dtype)))
W += sAB
# W.addmm_(A.t().to(torch.float32), B.t().to(torch.float32), alpha = s)
# sAB = (A.t().to(torch.float32) @ (s * B.t().to(torch.float32)))
# W += sAB
W.addmm_(A.t().to(torch.float32), B.t().to(torch.float32), alpha = s)
# W.addmm_(A.t().to(W.dtype), B.t().to(W.dtype), alpha = s)
# if not torch.isfinite(W).all():
maximum_element = torch.max(W.min().abs(), W.max())