I was trying to use TORCH.NN.FUNCTIONAL.LINEAR on my model. However, I got an error message saying that “mat1 and mat2 must have the same dtype”. It is just a linear function, I don’t get why the matrices have to be in the same dtype. Thank you for any reply, it will help me to gain a better understanding.
The internal calls expect to get data in the same
dtype. I guess you might be manually casing the input to this linear layer to another
dtype than its parameters, so cast it back or could you explain what’s your use case and why you expect the
dtype mismatch to work?
Thanks for your reply. So what is the expected dtype requires? I only have one input X (a tensor of floats) to the linear function.
Could you tell me what is mat1 and mat2 is? I suppose it is x and transpose of A in the linear equation?
mat2 refer most likely to the input tensor and the weight matrix of the linear layer.
Here is a small example showing one way to run into this error which is caused by the
dtype mismatch between the input tensor and the layer’s parameters:
# initialzie linear layer linear = nn.Linear(10, 1, bias=False) # by default float32 is used as the dtype print(linear.weight.dtype) # torch.float32 # create input tensor x = torch.randn(10, 10) # by default float32 is also used print(x.dtype) # torch.float32 # linear layer works and output dtype is also float32 out = linear(x) print(out.dtype) # torch.float32 # transform to float64 x = x.to(torch.float64) print(x.dtype) # torch.float64 # create dtype mismatch out = linear(x) # RuntimeError: expected scalar type Double but found Float # same for an explicit matmul out = torch.matmul(x, linear.weight.T) # RuntimeError: expected scalar type Double but found Float
I think I got it now. Apprecite your help!
Also, in case you are trying to use mixed-precision training, use the util. functions from
torch.amp as e.g. the
autocast context will cast the tensors to the appropriate types for you.