fix: CUDA error 710 bugfix #1424
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gs-olive
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Oct 28, 2022
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@bowang007 Make sure to review this |
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From my perspective see nothing wrong with sampling between |
- Issue arising when compiling BERT models with 3+ inputs - Added temporary fix by decreasing the range of allowed values to the random number generator for creating input tensors to [0,2), instead of [0,5) - Used random float inputs in the range [0, 2) instead of int, then casted to desired type. The ultimate effect of this change with regard to bug pytorch#1418, is random floats are selected in the range [0, 2), then casted to Int, effectively making the range of allowed ints {0, 1}, as required by the model - More robust fix to follow
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Nov 3, 2022
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| // Make the value range for input tensor a uniform (float) distribution | ||
| // over [LoValIncl, HiValExcl), then cast to the desired dtype | ||
| auto in = ((HiValExcl - LoValIncl) * at::rand(shape, {at::kCUDA}) + LoValIncl).to(type); |
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Used float inputs in the range
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seems like a little bit hard-coded for this model only, but will be resolved once the input range is open to users by this RFC #1425.
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Description
Resolves a CUDA 710 error Issue arising when compiling BERT models with 3+ inputs. The issue arises due to the role of the third tensor in inference computations. Specifically, as specified in the BERT model code linked here, the third argument,$[0,1]$ . This means that when values outside of this set are used, the input is invalid.
token_type_idsis of typetorch.LongTensor, but can only take indices inThis becomes problematic when the inputs are, for example, indices in a dictionary or embedding - which seems to be the case here. Specifically,$[0,4]$ .
aten::embeddingis used with Tensors which are the product oftoken_type_ids. The issue traces to one line in theshape_analysiscode previewed below, which initializes a random tensor with values in the rangeThis tensor is run through the
forwardfunction of the module to determine the shapes of outputs and causes the model compilation-time error, as featured here in the shape analysis code.I have added a temporary fix by decreasing the range of allowed values to the random number generator for creating input tensors to 0-1, instead of 0-4, and am working on a more robust fix.
Fixes #1418
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