Previously specialization error messages would render sources that were pretty far from source-code names. E.g., given args named `x, y, zs`, the source for `y.size()[0]` would be rendered as `args[0][1].size()[0]`.
This is because we created artificial local names following `(args, kwargs)` structure instead of reusing signatures. This PR fixes that situation.
Basically we map prefixes of key paths that correspond to original arg names to root sources corresponding to those names; the rest of the key paths hang from these root sources.
Differential Revision: [D76461391](https://our.internmc.facebook.com/intern/diff/D76461391/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155738
Approved by: https://github.com/bobrenjc93
Previously when processing `sym_and(a, b, c)`, symbolic shapes wouldn't individually process a, b, and c and store their implications. This would lead us to data-dependent error on individual checks, e.g. we stored `u0 >= 0 & u0 <= 10`, but then couldn't figure out `u0 <= 10`.
This handles that, and also makes `sym_and/or` user-code friendly, for testing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154737
Approved by: https://github.com/laithsakka
Example new error message
```
torch.fx.experimental.symbolic_shapes.ConstraintViolationError: Constraints violated (L['x'].size()[0])! For more information, run with TORCH_LOGS="+dynamic".
- You marked L['x'].size()[0] as dynamic but your code specialized it to be a constant (5). Either remove the mark_dynamic or use a less strict API such as maybe_mark_dynamic or Dim.AUTO.
Framework stack:
File "??", line 0, in _start
File "", line 0, in __libc_start_main_alias_2
File "??", line 0, in __libc_start_call_main
File "/usr/local/src/conda/python-3.10.16/Modules/main.c", line 1094, in Py_BytesMain
File "/usr/local/src/conda/python-3.10.16/Modules/main.c", line 357, in pymain_run_file_obj
File "/usr/local/src/conda/python-3.10.16/Python/pythonrun.c", line 90, in _PyRun_AnyFileObject
File "/usr/local/src/conda/python-3.10.16/Python/pythonrun.c", line 456, in _PyRun_SimpleFileObject
File "/usr/local/src/conda/python-3.10.16/Python/pythonrun.c", line 1208, in pyrun_file
File "/usr/local/src/conda/python-3.10.16/Python/pythonrun.c", line 1312, in run_mod
File "/usr/local/src/conda/python-3.10.16/Python/pythonrun.c", line 1291, in run_eval_code_obj
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 1134, in PyEval_EvalCode
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/scratch/repro.py", line 9, in <module>
foo(x)
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 5945, in do_call_core
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/eval_frame.py", line 699, in compile_wrapper
return fn(*args, **kwargs)
File "offloadstuff.c", line 0, in dynamo__custom_eval_frame
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 305, in _PyObject_Call
File "/usr/local/src/conda/python-3.10.16/Objects/typeobject.c", line 7494, in slot_tp_call
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 431, in _PyObject_Call_Prepend
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/convert_frame.py", line 1469, in __call__
return self._torchdynamo_orig_callable(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 112, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 215, in _PyObject_MakeTpCall
File "/usr/local/src/conda/python-3.10.16/Objects/typeobject.c", line 7494, in slot_tp_call
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 431, in _PyObject_Call_Prepend
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 153, in _PyObject_FastCallDictTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/convert_frame.py", line 1248, in __call__
result = self._inner_convert(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 112, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 215, in _PyObject_MakeTpCall
File "/usr/local/src/conda/python-3.10.16/Objects/typeobject.c", line 7494, in slot_tp_call
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 431, in _PyObject_Call_Prepend
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 153, in _PyObject_FastCallDictTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/convert_frame.py", line 625, in __call__
return _compile(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/convert_frame.py", line 1092, in _compile
guarded_code = compile_inner(code, one_graph, hooks, transform)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_utils_internal.py", line 97, in wrapper_function
return function(*args, **kwargs)
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 5945, in do_call_core
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/convert_frame.py", line 779, in compile_inner
return _compile_inner(code, one_graph, hooks, transform)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/convert_frame.py", line 818, in _compile_inner
out_code = transform_code_object(code, transform)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/bytecode_transformation.py", line 1424, in transform_code_object
transformations(instructions, code_options)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/convert_frame.py", line 265, in _fn
return fn(*args, **kwargs)
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 5945, in do_call_core
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/convert_frame.py", line 743, in transform
tracer.run()
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/symbolic_convert.py", line 3531, in run
super().run()
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/symbolic_convert.py", line 1359, in run
while self.step():
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/symbolic_convert.py", line 1263, in step
self.dispatch_table[inst.opcode](self, inst)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/symbolic_convert.py", line 422, in impl
self.push(fn_var.call_function(self, self.popn(nargs), {}))
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/variables/builtin.py", line 1160, in call_function
return handler(tx, args, kwargs)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/variables/builtin.py", line 792, in <lambda>
return lambda tx, args, kwargs: obj.call_function(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/variables/builtin.py", line 1160, in call_function
return handler(tx, args, kwargs)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/variables/builtin.py", line 1120, in _handle_insert_op_in_graph
return wrap_fx_proxy(tx, proxy)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/variables/builder.py", line 2500, in wrap_fx_proxy
return wrap_fx_proxy_cls(target_cls=TensorVariable, **kwargs)
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 5945, in do_call_core
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 267, in PyVectorcall_Call
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/variables/builder.py", line 2566, in wrap_fx_proxy_cls
return _wrap_fx_proxy(
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 5945, in do_call_core
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/variables/builder.py", line 2664, in _wrap_fx_proxy
example_value = get_fake_value(proxy.node, tx, allow_non_graph_fake=True)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/utils.py", line 3205, in get_fake_value
ret_val = wrap_fake_exception(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/utils.py", line 2705, in wrap_fake_exception
return fn()
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/utils.py", line 3206, in <lambda>
lambda: run_node(tx.output, node, args, kwargs, nnmodule)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_dynamo/utils.py", line 3373, in run_node
return node.target(*args, **kwargs)
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 5917, in do_call_core
File "/usr/local/src/conda/python-3.10.16/Objects/methodobject.c", line 430, in cfunction_vectorcall_FASTCALL
File "/usr/local/src/conda/python-3.10.16/Objects/abstract.c", line 891, in binary_op1
File "/usr/local/src/conda/python-3.10.16/Objects/typeobject.c", line 7284, in slot_nb_multiply
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Objects/descrobject.c", line 344, in method_vectorcall_VARARGS_KEYWORDS
File "python_variable_methods.cpp", line 0, in _object* torch::autograd::TypeError_to_NotImplemented_<&torch::autograd::THPVariable_mul>(_object*, _object*, _object*)
File "python_variable_methods.cpp", line 0, in torch::autograd::THPVariable_mul(_object*, _object*, _object*)
File "??", line 0, in at::_ops::mul_Tensor::call(at::Tensor const&, at::Tensor const&)
File "offloadstuff.c", line 0, in c10::impl::BoxedKernelWrapper<at::Tensor (at::Tensor const&, at::Tensor const&), void>::call(c10::BoxedKernel const&, c10::OperatorHandle const&, c10::DispatchKeySet, at::Tensor const&, at::Tensor const&)
File "PyInterpreter.cpp", line 0, in torch::detail::(anonymous namespace)::ConcretePyInterpreterVTable::python_dispatcher(c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const
File "offloadstuff.c", line 0, in c10::OperatorHandle::callBoxedForDispatchKey(c10::DispatchKey, std::vector<c10::IValue, std::allocator<c10::IValue> >&) const
File "PythonFallbackKernel.cpp", line 0, in void c10::BoxedKernel::make_boxed_function<&(anonymous namespace)::pythonTLSSnapshotFallback>(c10::OperatorKernel*, c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*)
File "PyInterpreter.cpp", line 0, in torch::detail::(anonymous namespace)::ConcretePyInterpreterVTable::python_dispatcher(c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const
File "offloadstuff.c", line 0, in c10::OperatorHandle::callBoxedForDispatchKey(c10::DispatchKey, std::vector<c10::IValue, std::allocator<c10::IValue> >&) const
File "VariableType_0.cpp", line 0, in c10::impl::make_boxed_from_unboxed_functor<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor (c10::DispatchKeySet, at::Tensor const&, at::Tensor const&), &torch::autograd::VariableType::(anonymous namespace)::mul_Tensor>, at::Tensor, c10::guts::typelist::typelist<c10::DispatchKeySet, at::Tensor const&, at::Tensor const&> >, false>::call(c10::OperatorKernel*, c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*)
File "VariableType_0.cpp", line 0, in torch::autograd::VariableType::(anonymous namespace)::mul_Tensor(c10::DispatchKeySet, at::Tensor const&, at::Tensor const&)
File "??", line 0, in at::_ops::mul_Tensor::redispatch(c10::DispatchKeySet, at::Tensor const&, at::Tensor const&)
File "offloadstuff.c", line 0, in c10::impl::BoxedKernelWrapper<at::Tensor (at::Tensor const&, at::Tensor const&), void>::call(c10::BoxedKernel const&, c10::OperatorHandle const&, c10::DispatchKeySet, at::Tensor const&, at::Tensor const&)
File "PyInterpreter.cpp", line 0, in torch::detail::(anonymous namespace)::ConcretePyInterpreterVTable::python_dispatcher(c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const
File "offloadstuff.c", line 0, in c10::OperatorHandle::callBoxedForDispatchKey(c10::DispatchKey, std::vector<c10::IValue, std::allocator<c10::IValue> >&) const
File "PythonFallbackKernel.cpp", line 0, in (anonymous namespace)::pythonFallback(c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*)
File "PyInterpreter.cpp", line 0, in torch::detail::(anonymous namespace)::ConcretePyInterpreterVTable::dispatch(c10::OperatorHandle const&, std::vector<c10::IValue, std::allocator<c10::IValue> >*) const
File "??", line 0, in torch::handle_torch_function_no_python_arg_parser(c10::ArrayRef<_object*>, _object*, _object*, char const*, _object*, char const*, torch::TorchFunctionName)
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 577, in PyObject_CallMethod
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/utils/_stats.py", line 27, in wrapper
return fn(*args, **kwargs)
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 5945, in do_call_core
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_subclasses/fake_tensor.py", line 1346, in __torch_dispatch__
return self.dispatch(func, types, args, kwargs)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_subclasses/fake_tensor.py", line 2029, in dispatch
return self._cached_dispatch_impl(func, types, args, kwargs)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_subclasses/fake_tensor.py", line 1442, in _cached_dispatch_impl
return self._dispatch_impl(func, types, args, kwargs)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_subclasses/fake_tensor.py", line 2552, in _dispatch_impl
return maybe_propagate_real_tensors(fast_impl(self, *args, **kwargs))
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 5945, in do_call_core
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_subclasses/fake_impls.py", line 956, in fast_binary_impl
final_shape = infer_size(final_shape, shape)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/_subclasses/fake_impls.py", line 916, in infer_size
torch._check(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/__init__.py", line 1669, in _check
_check_with(RuntimeError, cond, message)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/__init__.py", line 1632, in _check_with
if expect_true(cond):
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/symbolic_shapes.py", line 1686, in expect_true
return a.node.expect_true(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/sym_node.py", line 552, in expect_true
return self.guard_bool(file, line)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/sym_node.py", line 536, in guard_bool
r = self.evaluate()
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/sym_node.py", line 510, in evaluate
return self.shape_env.evaluate_sym_node(self, size_oblivious)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/symbolic_shapes.py", line 7113, in evaluate_sym_node
return self.evaluate_expr(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 112, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 215, in _PyObject_MakeTpCall
File "/usr/local/src/conda/python-3.10.16/Modules/_functoolsmodule.c", line 1020, in bounded_lru_cache_wrapper
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 267, in PyVectorcall_Call
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/recording.py", line 272, in wrapper
return retlog(fn(*args, **kwargs))
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 5945, in do_call_core
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 267, in PyVectorcall_Call
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/symbolic_shapes.py", line 7215, in evaluate_expr
return self._inner_evaluate_expr(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 112, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 215, in _PyObject_MakeTpCall
File "/usr/local/src/conda/python-3.10.16/Modules/_functoolsmodule.c", line 1020, in bounded_lru_cache_wrapper
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/recording.py", line 272, in wrapper
return retlog(fn(*args, **kwargs))
File "/usr/local/src/conda/python-3.10.16/Python/ceval.c", line 5945, in do_call_core
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/symbolic_shapes.py", line 7238, in _inner_evaluate_expr
return self._evaluate_expr(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/symbolic_shapes.py", line 7505, in _evaluate_expr
self._maybe_guard_rel(g)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 112, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 215, in _PyObject_MakeTpCall
File "/usr/local/src/conda/python-3.10.16/Modules/_functoolsmodule.c", line 1020, in bounded_lru_cache_wrapper
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/symbolic_shapes.py", line 6758, in _maybe_guard_rel
self._refine_ranges(expr)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/symbolic_shapes.py", line 7709, in _refine_ranges
self._set_replacement(
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/fx/experimental/symbolic_shapes.py", line 6667, in _set_replacement
self.framework_specialization_stacks[source] = CapturedTraceback.extract(cpp=True)
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 114, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Include/internal/pycore_ceval.h", line 46, in _PyEval_EvalFrame
File "/home/bobren/local/a/pytorch/torch/utils/_traceback.py", line 207, in extract
torch._C._profiler.gather_traceback(python=True, script=script, cpp=cpp),
File "/usr/local/src/conda/python-3.10.16/Include/cpython/abstract.h", line 112, in _PyObject_VectorcallTstate
File "/usr/local/src/conda/python-3.10.16/Objects/call.c", line 215, in _PyObject_MakeTpCall
File "/usr/local/src/conda/python-3.10.16/Objects/methodobject.c", line 543, in cfunction_call
File "offloadstuff.c", line 0, in pybind11::cpp_function::dispatcher(_object*, _object*, _object*)
File "offloadstuff.c", line 0, in pybind11::cpp_function::initialize<std::shared_ptr<torch::CapturedTraceback> (*&)(bool, bool, bool), std::shared_ptr<torch::CapturedTraceback>, bool, bool, bool, pybind11::name, pybind11::scope, pybind11::sibling, pybind11::arg_v, pybind11::arg_v, pybind11::arg_v>(std::shared_ptr<torch::CapturedTraceback> (*&)(bool, bool, bool), std::shared_ptr<torch::CapturedTraceback> (*)(bool, bool, bool), pybind11::name const&, pybind11::scope const&, pybind11::sibling const&, pybind11::arg_v const&, pybind11::arg_v const&, pybind11::arg_v const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call&)
File "??", line 0, in torch::CapturedTraceback::gather(bool, bool, bool)
File "??", line 0, in torch::unwind::unwind()
User stack:
File "/home/bobren/local/a/pytorch/scratch/repro.py", line 5, in foo
return torch.randn(5) * x
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155603
Approved by: https://github.com/zou3519, https://github.com/cyyever
ghstack dependencies: #155133
Summary:
This backs out D60320595 which itself turned off FakeTensor caching when a SymInt was present.
There has been a lot of dynamic shape fixes done this year and tests pass so I'm assuming some of that work fixed what was breaking previously.
Test Plan: Reran the tests listed in T196779132 and they pass.
## Perf
### Instruction Counter Benchmark:
- 26% win on add_loop_eager_dynamic
- 13% win on add_loop_inductor_dynamic_gpu
### Perf Dashboard
Compilation Latency wins across the board but especially strong on the dynamic tests (like cudagraphs_dynamic) - for example MobileBertForMaskedLM went from 66s -> 50s.
Differential Revision: [D75467694](https://our.internmc.facebook.com/intern/diff/D75467694)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152662
Approved by: https://github.com/anijain2305
The goal of this multigraph work is to enable a compiled region that has a single dynamo trace but multiple backend specializations. This work was inspired by vLLM which does this in a somewhat hacky way where they use a custom backend to capture a dynamo graph and then manually invoke compile_fx multiple times to get specialized graphs.
There's really two parts of this work:
**The frontend changes:**
1) we introduce an optional kwarg `specialize_on` to mark_{dynamic,unbacked} that takes in a list of specializations. I debated other methods including specifying specializations via decorators, but ultimately decided this approach was more harmonious. The big issue with decorators is the difficulty of composing well with the rest of the torch.compile ecosystem including graph breaks, lazy initialization of variable trackers and symbolic variables, etc.
**The backend changes (this PR):**
1) We capture the backend_specialization specified in the mark_{dynamic,unbacked} API into a SymbolicContext. See changes in `/_dynamo/variables/builder.py`
2) After we are done dynamo tracing, we will lazily (more on this later) invoke `call_user_compiler` up to N + 1 times for N specializations and 1 generic graph. Under the hood this will call compile_fx, which composes nicely with both Async Compile and AOTAutogradCache. We do this by using a context manager to patch in specialization specific axioms into the ShapeEnv before invoking the user compiler.
3) When we have specializations, we install a lazy specialized dispatch function that checks each specialization and dispatches to the first one that matches. Instead of doing all of the specialization compiles up front, we do the compiles lazily. The first time a specialization is invoked, we will do the compilation and save it in a cache so subsequent invocations are fast. If none of the specializations match, we dispatch to the generic graph. I decided to do this over returning N different GuardedCodes since 1) it doesn't pollute the dynamo cache (eg. if you have 8 specializations, you would hit the cache limit) 2) it naturally incorporates the hierarchical lattice structure of the guards since the specializations are always necessarily stricter than the generic region's guards.
I benchmarked this PR stack with #152596 and found around a 50% reduction when dispatching to the specialized regions:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153449
Approved by: https://github.com/zou3519
ghstack dependencies: #153433
Lets explore firs a couple of problem related to replacements and runtime assertions.
#### example problem 1
if we have a runtime assertions that u0==s0, u0 is an input coming from mark_unbacked. A replacement u0=s0 will be added, the function f(u0, s0) will become f(s0, s0), this leads to the assert not being inserted during insert_deferred_runtime_asserts.
The reason is that insert_deferred_runtime_asserts logic insert each assertion once all its inputs are seen, but u0 will never be seen. Same thing can happen when we defer assertion on backed i.e: s0==s2 ..etc.
#### example problem 2
Consider u0==s0, where u0 is coming from a call to .item() Imagine later on that a specialization happens to s0 to become 2. In that case s0 as input wont be seen during insert_deferred_runtime_asserts and the assertion won't be inserted in the graph. Worse, Inductor will generate some code that refers to s0 in the cpp wrapper while it does not exist, causing a failure.
internal xref: https://fb.workplace.com/groups/1075192433118967/permalink/1669766396994898/
## The solution :
Runtime assertions insertion loops depend on detecting that the symbols that are used in the runtime assertions are seen, note that those symbols are either graph inputs or generated in the graph from data dependent ops like .item().
The issues above happen when symbols are graph inputs, in order to force the symbols to exist in the graph and to be seen by the runtime assertions we do not do replacements on placeholders expressions during codegen and during runtime assertions insertion.
This should not have performance overhead, since we already optimized the graph with replacements, the only effect is not mistakenly dropping graph inputs that are used in runtime assertions.
I added extended testing. A solo unrelated follow up that I noticed, is that we might want to rename unbacked symbols in runtime assertions when we do unbacked renaming, but that's a different issue.
Other approaches that did not work :
#### ban replacements on unbacked.
1. does not work when we defer runtime assertions on backed ex: s0==s1. we could also ban such replacements
but problem 2 becomes more problematic.
2. Problem two, it affects the quality of reasoning ! in a bad way.
#### Apply specialization on runtime assertions before codegen .
1. Can fix some issues, but may lead also to runtime assertions becoming NOPs.
2. Does not fix the issue if not inserting runtime assertions during insert_deferred_runtime_asserts due to input not being detected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153661
Approved by: https://github.com/jansel
Summary:
This backs out D60320595 which itself turned off FakeTensor caching when a SymInt was present.
There has been a lot of dynamic shape fixes done this year and tests pass so I'm assuming some of that work fixed what was breaking previously.
Test Plan: Reran the tests listed in T196779132 and they pass.
## Perf
### Instruction Counter Benchmark:
- 26% win on add_loop_eager_dynamic
- 13% win on add_loop_inductor_dynamic_gpu
### Perf Dashboard
Compilation Latency wins across the board but especially strong on the dynamic tests (like cudagraphs_dynamic) - for example MobileBertForMaskedLM went from 66s -> 50s.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152662
Approved by: https://github.com/anijain2305
PR time benchmarks has been showing regressions as we move to guard_or_false, reason is that prev implementation do not cache.
This new approach will propagate the fallback value to eval and return it. allowing eval to cache and reducing scamming logs and complexity.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153674
Approved by: https://github.com/bobrenjc93
#### change 1: if compute_strides stride fail for reshape just clone.
Lets consider the most general case, if torch compile is asked to reshape [u0, u1][u3, u4] -> [u5, u6] what shall it do?
The shape is general enough to represent both contiguous and non contiguous tensors, tensors where a clone free reshape can happen and other where a clone free cant happen. The current algorithm will fail due to data dependent errors.
The general idea is if its impossible to tell if the reshape can happen in place, (because for some concrete inputs
it will and other not) then its ok to take the general path and clone, instead of failing or asking the user to give hints.
**Because the user want a single graph (single compilations)** and this is the only way it can be done.
Had this been a view? then the user is explicitly asking for a copy-free reshape, we would fail asking for more
information (hints in torch.checks form).
with this change reshape works as the following:
1. if we know the input is contiguous we will convert the reshape to view.
2. if compute_strides succeed we will use view. (compute_strides was changed to not fail when when unbacked presented instead it will just return nullptr if it cant compute the strides meaning we shall use a clone).
3. if neither 1, 2 works clone and use a view.
Side note: having a view does not mean that inductor will not clone, for inductor there is a pass that converts all views back to reshapes and inductor has its logic dealing with those.
#### change 2 : skip _reshape_view_helper and fall back to simpler logic if it fail.
We trace _reshape_view_helper when doing fake tensor tracing , but not during proxy tracing. hence such tracing wont effect the graph (only compute output shapes of several operations). We should not fail there, because it should always be possible for us to pass it in case of reshape.
i.e. when reshape_symint was called we would have either cloned, or compute_strides succeeded so the view should pass. What I did is the following: we run _reshape_view_helper, if we fail due to unbacked we call _view_simple which will succeed always for reshapes, (might fail for views when its impossible to do the view, in such case we throw the dde that was thrown by the original algorithm).
Ideally I would want to register _view_simple as the meta for view and avoid calling _reshape_view_helper completely but I am running some issues with the dispatcher with subclasses and I do not have time to debug it. Namely one test
would end up calling some c++ view function that does not support symints during meta dispatch when i register a
python meta decompositions
```python test/dynamo/test_subclasses.py SubclassTests.test_subclass_views_dynamic_True ```
https://github.com/pytorch/pytorch/issues/153303.I will follow up with that change in a separate PR. cc @H-Huang @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @bdhirsh
Two other alternatives for registering _view_simple as meta and the try catch approach in this PR is:
1. call _view_simple if any input is dynamic see #153521
2. if we make is_compiling works for framework code tracing (does not work rn) we can call _view_simple
is if is_compiling.
#### Note:
Reshape can still fail when is_contiguous is called, Next PR will handle that by calling is_known_contiguous.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153198
Approved by: https://github.com/etaf, https://github.com/bobrenjc93
so two things other than cleanups and refactoring
1) do not use propagate_real_tensors to resolve eval under guard_or_true/guard_or_false .
2) do not guard for dimensions of type DimDynamic.OBLIVIOUS_SIZE under guard_or_true/guard_or_false .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152657
Approved by: https://github.com/pianpwk
so two things other than cleanups and refactoring
1) do not use propagate_real_tensors to resolve eval under guard_or_true/guard_or_false .
2) do not guard for dimensions of type DimDynamic.OBLIVIOUS_SIZE under guard_or_true/guard_or_false .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152657
Approved by: https://github.com/pianpwk
Basically adds native _IntWrapper support to dynamo. Here's my process of trying to make symint input support work on dynamo, and how I ended up with this approach [(doc)](https://docs.google.com/document/d/1GvNRQd8BnxlMay_hrEVgEta6VUeUW_hcFeRuB7q1nDY/edit?tab=t.0).
What I did was, before passing inputs to dynamo.export, I first wrap them with a class, `_IntWrapper`. When processing dynamic shapes, I will then add the corresponding dynamic shape specification to the `dynamism` field stored on the `_IntWrapper`. If there is no dynamism specified, then this will get unwrapped back to an integer. When dynamo tracing, when we encounter an `_IntWrapper`, we will convert this to a symint if the dynamism was specified as `Dim.DYNAMIC/AUTO`. Dynamo will then trace a graph that contains symint inputs, which will get passed to AOTAutograd and so on.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152677
Approved by: https://github.com/pianpwk
Summary:
Some new errors have been showing up on the PT2 dashboard with
```
Invalid type for lengths: Expected BlobReference or torch.Tensor, got: Tensor(shape: torch.Size([10]), stride: (1,), storage_offset: 0)
```
This is caused by [this piece of code](https://fburl.com/code/5nbi9on7) which maps over a set of nodes (in this case type `IDListFeatureListField`) and turns the results into strings to be displayed later. However during pytree.tree_map we call pytree.tree_unflatten which will call the class's init function, which calls `assert_blob` (https://fburl.com/code/h3ainrn9). Because we've mapped over the values and converted them to strings, the assert_blob fails.
I initially thought to disable the assert_blob while tracing (D74684309) but then I think we should actually flatten the list first. Because tlparse will expect just a string out outputs instead of the actual structure.
Test Plan: `buck2 run mode/opt sigmoid/inference/ts_migration:pt2i_readiness_main -- --test_suite ads_all --mode test_full_model --model_id 542947220` fails with something else 😅
Differential Revision: D74744326
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153627
Approved by: https://github.com/yiming0416
Fixes#152918
Before:
```
File "/data/users/bobren/a/pytorch/torch/fx/experimental/symbolic_shapes.py", line 5588, in produce_guards_verbose
raise ConstraintViolationError(
torch.fx.experimental.symbolic_shapes.ConstraintViolationError: Constraints violated (L['x'].size()[0])! For more information, run with TORCH_LOGS="+dynamic".
- You marked L['x'].size()[0] as dynamic but your code specialized it to be a constant (5). Either remove the mark_dynamic or use a less strict API such as maybe_mark_dynamic or Dim.AUTO.
```
After:
```
File "/data/users/bobren/a/pytorch/torch/fx/experimental/symbolic_shapes.py", line 5588, in produce_guards_verbose
raise ConstraintViolationError(
torch.fx.experimental.symbolic_shapes.ConstraintViolationError: Constraints violated (L['x'].size()[0])! For more information, run with TORCH_LOGS="+dynamic".
- You marked L['x'].size()[0] as dynamic but your code specialized it to be a constant (5). Either remove the mark_dynamic or use a less strict API such as maybe_mark_dynamic or Dim.AUTO.
User stack:
File "/home/bobren/local/a/pytorch/error.py", line 5, in foo
return torch.randn(5) * x
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152924
Approved by: https://github.com/pianpwk
