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This refactor was prompted by challenges handling mixed int/float operations in C++. A previous version of this patch added overloads for each permutation of int/float and was unwieldy https://github.com/pytorch/pytorch/pull/87722/ This PR takes a different approach. The general outline of the patch is to combine the C++ types SymIntNode and SymFloatNode into a single type, SymNode. This is type erased; we no longer know statically at C++ if we have an int/float and have to test it with the is_int()/is_float() virtual methods. This has a number of knock on effects. - We no longer have C++ classes to bind to Python. Instead, we take an entirely new approach to our Python API, where we have a SymInt/SymFloat class defined entirely in Python, which hold a SymNode (which corresponds to the C++ SymNode). However, SymNode is not pybind11-bound; instead, it lives as-is in Python, and is wrapped into C++ SymNode using PythonSymNode when it goes into C++. This implies a userland rename. In principle, it is also possible for the canonical implementation of SymNode to be written in C++, and then bound to Python with pybind11 (we have this code, although it is commented out.) However, I did not implement this as we currently have no C++ implementations of SymNode. Because we do return SymInt/SymFloat from C++ bindings, the C++ binding code needs to know how to find these classes. Currently, this is done just by manually importing torch and getting the attributes. - Because SymInt/SymFloat are easy Python wrappers, __sym_dispatch__ now takes SymInt/SymFloat, rather than SymNode, bringing it in line with how __torch_dispatch__ works. Some miscellaneous improvements: - SymInt now has a constructor that takes SymNode. Note that this constructor is ambiguous if you pass in a subclass of SymNode, so an explicit downcast is necessary. This means toSymFloat/toSymInt are no more. This is a mild optimization as it means rvalue reference works automatically. - We uniformly use the caster for c10::SymInt/SymFloat, rather than going the long way via the SymIntNode/SymFloatNode. - Removed some unnecessary toSymInt/toSymFloat calls in normalize_* functions, pretty sure this doesn't do anything. - guard_int is now a free function, since to guard on an int you cannot assume the method exists. A function can handle both int and SymInt inputs. - We clean up the magic method definition code for SymInt/SymFloat/SymNode. ONLY the user classes (SymInt/SymFloat) get magic methods; SymNode gets plain methods; this is to help avoid confusion between the two types. Signed-off-by: Edward Z. Yang <ezyang@fb.com> cc @jansel @mlazos @soumith @voznesenskym @yanboliang @penguinwu @anijain2305 Pull Request resolved: https://github.com/pytorch/pytorch/pull/87817 Approved by: https://github.com/albanD, https://github.com/anjali411 |
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| _static | ||
| _templates | ||
| community | ||
| elastic | ||
| notes | ||
| rpc | ||
| scripts | ||
| amp.rst | ||
| autograd.rst | ||
| backends.rst | ||
| benchmark_utils.rst | ||
| bottleneck.rst | ||
| checkpoint.rst | ||
| complex_numbers.rst | ||
| conf.py | ||
| config_mod.rst | ||
| cpp_extension.rst | ||
| cpp_index.rst | ||
| cuda._sanitizer.rst | ||
| cuda.rst | ||
| cudnn_persistent_rnn.rst | ||
| cudnn_rnn_determinism.rst | ||
| data.rst | ||
| ddp_comm_hooks.rst | ||
| deploy.rst | ||
| distributed.algorithms.join.rst | ||
| distributed.elastic.rst | ||
| distributed.optim.rst | ||
| distributed.rst | ||
| distributions.rst | ||
| dlpack.rst | ||
| docutils.conf | ||
| fft.rst | ||
| fsdp.rst | ||
| futures.rst | ||
| fx.rst | ||
| hub.rst | ||
| index.rst | ||
| jit_builtin_functions.rst | ||
| jit_language_reference_v2.rst | ||
| jit_language_reference.rst | ||
| jit_python_reference.rst | ||
| jit_unsupported.rst | ||
| jit_utils.rst | ||
| jit.rst | ||
| library.rst | ||
| linalg.rst | ||
| masked.rst | ||
| math-quantizer-equation.png | ||
| mobile_optimizer.rst | ||
| model_zoo.rst | ||
| monitor.rst | ||
| multiprocessing.rst | ||
| name_inference.rst | ||
| named_tensor.rst | ||
| nested.rst | ||
| nn.functional.rst | ||
| nn.init.rst | ||
| nn.rst | ||
| onnx_supported_aten_ops.rst | ||
| onnx.rst | ||
| optim.rst | ||
| package.rst | ||
| pipeline.rst | ||
| profiler.rst | ||
| quantization-accuracy-debugging.rst | ||
| quantization-backend-configuration.rst | ||
| quantization-support.rst | ||
| quantization.rst | ||
| random.rst | ||
| rpc.rst | ||
| signal.rst | ||
| sparse.rst | ||
| special.rst | ||
| storage.rst | ||
| tensor_attributes.rst | ||
| tensor_view.rst | ||
| tensorboard.rst | ||
| tensors.rst | ||
| testing.rst | ||
| torch.ao.ns._numeric_suite_fx.rst | ||
| torch.ao.ns._numeric_suite.rst | ||
| torch.overrides.rst | ||
| torch.rst | ||
| type_info.rst | ||
