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d0a9629435
When we compute contiguity for a tensor with dynamic shapes we first: 1) Try to compute it without guarding. 2) If all shapes hinted, compute it with potentially adding guards. 3) if any input is not hinted, compute it symbolically. sym_is_contiguous return a SymBool that is then either evaluated or guard_or_false can be called on it to avoid data dependent errors. ex: bool is_contiguous = input.sym_is_contiguous().guard_or_false(__FILE__, __LINE__); is_contiguous_or_false is a helper function that does that. In this PR I only handle default contiguity, will follow up with changes for other formats like channel_last . We use this patter in this PR for several locations to avoid DDEs. Differential Revision: [D77183032](https://our.internmc.facebook.com/intern/diff/D77183032) Pull Request resolved: https://github.com/pytorch/pytorch/pull/155590 Approved by: https://github.com/ezyang
52 lines
1.5 KiB
C++
52 lines
1.5 KiB
C++
#include <c10/core/UndefinedTensorImpl.h>
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#include <c10/util/Exception.h>
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namespace c10 {
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// should this use the globalContext? Can it get a context passed in somehow?
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UndefinedTensorImpl::UndefinedTensorImpl()
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: TensorImpl(DispatchKey::Undefined, caffe2::TypeMeta(), std::nullopt) {
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set_storage_access_should_throw();
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// TODO: accessing the sizes on an undefined tensor is not meaningful
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// and should error too, but empirically it does not!
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set_custom_sizes_strides(SizesStridesPolicy::CustomStrides);
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}
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c10::SymBool UndefinedTensorImpl::sym_is_contiguous_custom(
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MemoryFormat format) const {
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return is_contiguous_default(format);
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}
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IntArrayRef UndefinedTensorImpl::strides_custom() const {
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TORCH_CHECK(false, "strides() called on an undefined Tensor");
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}
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SymIntArrayRef UndefinedTensorImpl::sym_strides_custom() const {
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TORCH_CHECK(false, "sym_strides() called on an undefined Tensor");
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}
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#ifdef DEBUG
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bool UndefinedTensorImpl::has_storage() const {
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TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
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!storage_, "UndefinedTensorImpl assumes that storage_ is never set");
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return false;
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}
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#endif
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void UndefinedTensorImpl::set_storage_offset(int64_t) {
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TORCH_CHECK(false, "set_storage_offset() called on an undefined Tensor");
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}
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const char* UndefinedTensorImpl::tensorimpl_type_name() const {
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return "UndefinedTensorImpl";
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}
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#ifdef _WIN32
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UndefinedTensorImpl& UndefinedTensorImpl::getInstance() {
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static UndefinedTensorImpl instance;
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return instance;
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}
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#else
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UndefinedTensorImpl UndefinedTensorImpl::_singleton;
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#endif
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} // namespace c10
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