Comments (1)
I installed the LTC prototype as well as an XLA backend and ran all of our tests on CPU using XLA.
Only a few tests failed:
- test_per_sample_grads_embeddingnet_cpu
- test_resnet18_per_sample_grads_cpu
- test_maml_omniglot_cpu
The per_sample_grad tests failed with incorrect values, which means that something fishy might be going on (or a batching rule may be implemented incorrectly)
The test_maml_omniglot_cpu
test failed with an internal assert in XLA
E RuntimeError: Internal: From /job:localservice/replica:0/task:0:
E 2 root error(s) found.
E (0) Internal: RET_CHECK failure (tensorflow/compiler/xla/service/cpu/ir_emitter.cc:3211) ShapeUtil::SameElementType(operands[0]->shape(), operand->sh
ape())
E [[{{node XRTCompile}}]]
E (1) Internal: RET_CHECK failure (tensorflow/compiler/xla/service/cpu/ir_emitter.cc:3211) ShapeUtil::SameElementType(operands[0]->shape(), operand->sh
ape())
E [[{{node XRTCompile}}]]
E [[XRTCompile_G6]]
E 0 successful operations.
E 0 derived errors ignored.
E Recent warning and error logs:
E Internal: RET_CHECK failure (tensorflow/compiler/xla/service/cpu/ir_emitter.cc:3211) ShapeUtil::SameElementType(operands[0]->shape(), operand->shape(
))
E *** Begin stack trace ***
E tensorflow::CurrentStackTrace[abi:cxx11]()
E
E xla::status_macros::MakeErrorStream::Impl::GetStatus()
E xla::cpu::IrEmitter::ElementTypesSameAndSupported(xla::HloInstruction const&, absl::lts_2020_02_25::Span<xla::HloInstruction const* const>, abs
l::lts_2020_02_25::Span<xla::PrimitiveType const>)
E xla::cpu::IrEmitter::HandleDot(xla:
E OP_REQUIRES failed at xrt_compile_ops.cc:215 : Internal: RET_CHECK failure (tensorflow/compiler/xla/service/cpu/ir_emitter.cc:3211) ShapeUtil::SameEl
ementType(operands[0]->shape(), operand->shape())
/raid/rzou/pt/ltc/torch/nn/functional.py:1847: RuntimeError
from functorch.
Related Issues (20)
- batching over model parameters HOT 2
- Add pytorch 1.13.1 compatibility HOT 3
- Unit Test Error When Testing vmap With Missing Module "autograd_function_db" HOT 7
- Will pmap be supported in functorh? HOT 2
- How to get only the last few layers' gradident? HOT 2
- [Question] Packaging policy for `functorch` and `torch.func` HOT 5
- INTERNAL_ASSERT failed HOT 4
- RuntimeError: Batching rule not implemented for aten::is_same_size. We could not generate a fallback.
- Vmap and backward hook problem HOT 1
- item() support for vmap HOT 2
- Performance drop because of not yet implemented batching rule for bincount
- Use functional models inside usual nn.Module HOT 1
- Error about using a grad transform with in-place operation is inconsistent with and without DDP HOT 1
- How to get the jacobian matrix in GCNs?
- Per-sample-gradient: Get gradient 0 when using grad(params_tograd, params) with respect to part of model's parameters HOT 1
- Can I call torch.utils.data.WeightedRandomSampler inside vmap? HOT 1
- vmap fails if your model includes full_backward_hook in pytorch2.0 HOT 1
- wrapper->level().value() <= current_level INTERNAL ASSERT FAILED at "../aten/src/ATen/functorch/ADInterpreters.cpp":39 HOT 1
- Swapping 2 columns in a 2d tensor
- vmap does not support Tensor.clone()
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from functorch.