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https://github.com/zebrajr/pytorch.git
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fd68b0931f
### Description This PR makes `numel` symint-aware similar to `sym_sizes()` and `sym_strides()`. Similar to https://github.com/pytorch/pytorch/pull/81300 . This PR is the part of a bigger project to support dynamic_shapes. Pull Request resolved: https://github.com/pytorch/pytorch/pull/82374 Approved by: https://github.com/ezyang
102 lines
3.1 KiB
C++
102 lines
3.1 KiB
C++
#include <c10/core/SymIntArrayRef.h>
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#include <c10/core/TensorImpl.h>
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#include <c10/core/impl/PyInterpreter.h>
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namespace c10 {
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namespace impl {
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static std::string noop_name_fn(const PyInterpreter*) {
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return "<unloaded interpreter>";
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}
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static void noop_decref_fn(const PyInterpreter*, PyObject*, bool) {
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// no-op
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}
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static c10::intrusive_ptr<TensorImpl> noop_detach_fn(
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const PyInterpreter*,
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const TensorImpl*) {
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TORCH_INTERNAL_ASSERT(
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0,
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"attempted to detach (shallow_copy_and_detach) Tensor with nontrivial PyObject after corresponding interpreter died");
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}
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static void noop_dispatch_fn(
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const PyInterpreter*,
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const c10::OperatorHandle& op,
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torch::jit::Stack* stack) {
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TORCH_INTERNAL_ASSERT(
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0,
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"attempted to dispatch (__torch_dispatch__) an operator on Tensor with nontrivial PyObject after corresponding interpreter died");
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}
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static bool noop_is_contiguous_fn(const PyInterpreter*, const TensorImpl*) {
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TORCH_INTERNAL_ASSERT(
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0,
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"attempted to call `is_contiguous` on Tensor with nontrivial PyObject after corresponding interpreter died");
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}
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static c10::Device noop_device_fn(const PyInterpreter*, const TensorImpl*) {
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TORCH_INTERNAL_ASSERT(
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0,
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"attempted to call `device` on Tensor with nontrivial PyObject after corresponding interpreter died");
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}
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static int64_t noop_dim_fn(const PyInterpreter*, const TensorImpl*) {
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TORCH_INTERNAL_ASSERT(
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0,
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"attempted to call `dim` on Tensor with nontrivial PyObject after corresponding interpreter died");
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}
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static c10::IntArrayRef noop_strides_fn(
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const PyInterpreter*,
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const TensorImpl*) {
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TORCH_INTERNAL_ASSERT(
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0,
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"attempted to call `strides` on Tensor with nontrivial PyObject after corresponding interpreter died");
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}
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static c10::IntArrayRef noop_sizes_fn(const PyInterpreter*, const TensorImpl*) {
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TORCH_INTERNAL_ASSERT(
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0,
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"attempted to call `sizes` on Tensor with nontrivial PyObject after corresponding interpreter died");
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}
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static c10::SymIntArrayRef noop_sym_sizes_fn(
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const PyInterpreter*,
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const TensorImpl*) {
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TORCH_INTERNAL_ASSERT(
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0,
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"attempted to call `sym_sizes` on Tensor with nontrivial PyObject after corresponding interpreter died");
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}
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static c10::Layout noop_layout_fn(const PyInterpreter*, const TensorImpl*) {
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TORCH_INTERNAL_ASSERT(
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0,
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"attempted to call `layout` on Tensor with nontrivial PyObject after corresponding interpreter died");
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}
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static c10::SymInt noop_sym_numel_fn(const PyInterpreter*, const TensorImpl*) {
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TORCH_INTERNAL_ASSERT(
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0,
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"attempted to call `sym_numel` on Tensor with nontrivial PyObject after corresponding interpreter died");
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}
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void PyInterpreter::disarm() noexcept {
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name_fn_ = &noop_name_fn;
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decref_fn_ = &noop_decref_fn;
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detach_fn_ = &noop_detach_fn;
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dispatch_fn_ = &noop_dispatch_fn;
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is_contiguous_fn_ = &noop_is_contiguous_fn;
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device_fn_ = &noop_device_fn;
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dim_fn_ = &noop_dim_fn;
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strides_fn_ = &noop_strides_fn;
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sizes_fn_ = &noop_sizes_fn;
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sym_sizes_fn_ = &noop_sym_sizes_fn;
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layout_fn_ = &noop_layout_fn;
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sym_numel_fn_ = &noop_sym_numel_fn;
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}
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} // namespace impl
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} // namespace c10
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