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Jimmy9507 avatar Jimmy9507 commented on August 17, 2024

After diving deep in the code, look like this line

torch::Tensor decompress_residuals_cuda(
const torch::Tensor binary_residuals, const torch::Tensor bucket_weights,
const torch::Tensor reversed_bit_map,
const torch::Tensor bucket_weight_combinations, const torch::Tensor codes,
const torch::Tensor centroids, const int dim, const int nbits) {
auto options = torch::TensorOptions()
.dtype(torch::kFloat16)
.device(torch::kCUDA, 0)
.requires_grad(false);

restrict cpp method decompress_residuals_cuda on GPU device 0 only. decompress_residuals_cuda will crash when running on other GPUs.

After update it to .device(torch::kCUDA, residuals.device().index()). The crash problem is resolved.

Should we update to .device(torch::kCUDA, residuals.device().index())? This should also significantly increase the model inferencing efficiency by enabling model inference on multiple GPUs.

Wondering if this is a bug or designed intentionally.

from colbert.

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