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Fixed FP4 import and data type conversion in backward.

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This commit is contained in:
Tim Dettmers 2023-02-14 13:31:39 -08:00
parent 7f0773aede
commit c93a90d075
2 changed files with 2 additions and 6 deletions
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6
bitsandbytes/autograd/_functions.py
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@ -525,13 +525,9 @@ class MatMulFP4(torch.autograd.Function):
# compute grad_bias first before changing grad_output dtype # compute grad_bias first before changing grad_output dtype
grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias) grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
# Cast grad_output to fp16
if len(grad_output.shape) == 3:
grad_output = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
# not supported by PyTorch. TODO: create work-around # not supported by PyTorch. TODO: create work-around
#if req_gradB: grad_B = torch.matmul(grad_output.t(), A) #if req_gradB: grad_B = torch.matmul(grad_output.t(), A)
if req_gradA: grad_A = torch.matmul(grad_output, F.dequantize_fp4(B, ctx.state).to(ctx.dtype_A).t()) if req_gradA: grad_A = torch.matmul(grad_output, F.dequantize_fp4(B, ctx.state).to(grad_output.dtype).t())
return grad_A, grad_B, None, grad_bias, None return grad_A, grad_B, None, grad_bias, None

2
bitsandbytes/nn/__init__.py
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@ -2,4 +2,4 @@
# #
# This source code is licensed under the MIT license found in the # This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree. # LICENSE file in the root directory of this source tree.
from .modules import Int8Params, Linear8bitLt, StableEmbedding, LinearFP4 from .modules import Int8Params, Linear8bitLt, StableEmbedding, LinearFP4, FP4Params