Comments (3)
I wonder if this issue will be dealt or not. Since I'd like to use different backends fo different modules in a single model, (e.g., using cudnn for LSTM but not using for ConvNet) it would be great if this issue is resolved.
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This is still a valid issue! Primary blocker for it is coming up with a good API for specifying what algorithm you want.
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@soumith Is this issue related to #18182, where we want a way to specify different initialization for layers based on different pytorch versions. Or something new.
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Related Issues (20)
- How to use system cuda/cudnn HOT 1
- DISABLED test_inplace_on_view_weak_grad_fn (__main__.TestAutogradWithCompiledAutograd) HOT 3
- torch.utils.cpp_extension.load recompiling every time HOT 2
- CudaHostAlloc takes a lot of time during training HOT 3
- Understand the oneDNN graph fusion with torch script
- DISABLED test_aot_dispatch_incorrect_backward (__main__.TestAOTDispatch) HOT 3
- DISABLED test_isolated_node (__main__.TestAutogradWithCompiledAutograd) HOT 3
- Support constant value in torch.jit.script
- Find a bug from beta-released "scaled_dot_product_attention" HOT 2
- Matmul with broadcast makes copy because of reshape to bmm, and it is slow
- Race in ProcessGroupNCCL shutdown HOT 2
- DISABLED test_mark_non_differentiable (__main__.TestAutogradWithCompiledAutograd) HOT 3
- Dataloader codeowner
- DISABLED test_creation_with_zeros_cuda_float8_e5m2 (__main__.TestFloat8DtypeCUDA) HOT 1
- DISABLED test_mark_non_differentiable_none (__main__.TestAutogradWithCompiledAutograd) HOT 2
- DISABLED test_aot_module_simplified (__main__.TestAOTModuleSimplified) HOT 2
- Stable Diffusion Model Error: torch._dynamo.exc.InternalTorchDynamoError: raw
- Fused Linear and Cross-Entropy Loss `torch.nn.functional.linear_cross_entropy` HOT 4
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