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https://github.com/pytorch/pytorch.git
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18d8c548f4
Based on @ezyang's suggestion, mode stack now has "one true mode" which is the _only_ mode that can ever be active at the C++ level. That mode's torch dispatch is just to take the top mode in the stack, reenable itself (if we aren't at the end of the mode stack), and run the top mode's torch_{dispatch|function}
This maintains that in the middle of a mode's torch dispatch, the mode itself will not be active. It changes the function the user has to call to see what the current mode is (no longer queries the C++, it's python only) but allows the user to also see the entire mode stack easily
Removes `enable_torch_dispatch_mode` and `.restore()` since neither makes sense in this new setup
### Background
Why do we want this? Well, a pretty common pattern that was coming up was that users had to do something like
```python
## PRE-PR UX
def f(mode):
with mode.restore(): # user needs to understand this restore thing?
...
with Mode() as m:
pass
f(m)
```
Many users were getting error from forgetting to call `.restore` or from forgetting to add the (tbh weird) "mode instantiation" step where they use the mode as a context manager with an empty body. Really, they wanted to treat modes like context managers and just write
```python
## FROM FEEDBACK, USER DESIRED CODE. POSSIBLE POST-PR
def f(mode):
with mode:
...
f(Mode())
```
** Technical Details **
With the old mode stack, we basically had a linked list so the mode itself could only be used once and had a fixed parent. In this new design, the mode stack is just a python list that we're pushing to and popping from. There's only one mode that's ever active at the C++ level and it runs the next mode in the Python list. The modes don't have state on them anymore
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84774
Approved by: https://github.com/ezyang, https://github.com/zou3519
1655 lines
64 KiB
Python
1655 lines
64 KiB
Python
# Owner(s): ["module: __torch_dispatch__"]
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import tempfile
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import torch
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from copy import deepcopy
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from torch.library import Library
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from torch.cuda.jiterator import _create_jit_fn
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import unittest
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from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_ROCM, IS_WINDOWS
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from torch.utils._mode_utils import no_dispatch, all_same_mode
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from torch.testing._internal.logging_tensor import LoggingTensor, LoggingTensorReentrant, LoggingTensorMode, \
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log_input, capture_logs, capture_logs_with_logging_tensor_mode
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from torch.utils._pytree import tree_map, tree_map_only
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from torch.utils._python_dispatch import TorchDispatchMode, _get_current_dispatch_mode, _get_current_dispatch_mode_stack
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import logging
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class TestPythonRegistration(TestCase):
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def test_override_aten_ops_with_multiple_libraries(self) -> None:
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x = torch.tensor([1, 2])
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my_lib1 = Library("aten", "IMPL")
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my_lib2 = Library("aten", "IMPL")
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# Example 1
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def my_neg(*args, **kwargs):
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return args[0]._neg_view()
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# Now we are secretly making the operator a view op so autograd needs to know how
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# to handle it
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my_lib1.impl('neg', my_neg, "AutogradCPU")
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self.assertTrue(torch.neg(x).is_neg())
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# RuntimeError: impl("aten::neg", ...):
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# Explicitly provided namespace (aten) in operator name does not match ...
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with self.assertRaisesRegex(RuntimeError, "operator name does not match namespace"):
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my_lib3 = Library("foo", "DEF")
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my_lib3.define("neg(Tensor self) -> Tensor")
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my_lib3.impl(torch.ops.aten.neg.default, my_neg, "AutogradCPU")
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del my_lib3
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# Example 2
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def my_mul(*args, **kwargs):
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return torch.zeros_like(args[0])
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# torch.ops.aten.mul.Tensor
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my_lib2.impl("aten::mul.Tensor", my_mul, "ZeroTensor")
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y = torch._efficientzerotensor(2)
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self.assertFalse(torch.mul(x, y)._is_zerotensor())
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# Assert that a user can't override the behavior of a (ns, op, dispatch_key)
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# combination if someone overrided the behavior for the same before them
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with self.assertRaisesRegex(RuntimeError, 'already a kernel registered from python'):
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my_lib2.impl(torch.ops.aten.mul.Tensor, my_mul, "ZeroTensor")
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del my_lib1
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# Validate that lib2 is not affected by removing lib1
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self.assertFalse(torch.mul(x, y)._is_zerotensor())
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del my_lib2
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# Validate that the old behavior is restored for neg and mul
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self.assertFalse(torch.neg(x).is_neg())
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self.assertTrue(torch.mul(x, y)._is_zerotensor())
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def test_error_if_fn_not_callable(self):
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with self.assertRaisesRegex(TypeError, "Input function is required to be a callable"):
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my_lib = Library("aten", "IMPL")
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my_lib.impl(torch.ops.aten.neg.default, [], "AutogradCPU")
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def test_override_cpu_sum(self) -> None:
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# Example 1
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run = [False]
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def my_sum(*args, **kwargs):
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run[0] = True
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return args[0]
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my_lib1 = Library("aten", "IMPL")
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my_lib1.impl('aten::sum', my_sum, "CPU")
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x = torch.tensor([1, 2])
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self.assertEqual(torch.sum(x), x)
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self.assertTrue(run[0])
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del my_lib1
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# Validate that the old behavior is restored for sum
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self.assertEqual(torch.sum(x), torch.tensor(3))
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def test_override_cuda_with_jiterator(self) -> None:
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def override_where_cuda() -> None:
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# Example 1: Invert the behavior of where's condition input
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not_where_code_string = '''
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template <typename T> T inverted_where(bool cond, T a, T b){
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return !cond ? a : b;
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}
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'''
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jitted_where = _create_jit_fn(not_where_code_string)
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CALLED = [False]
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def inverted_where(*args, **kwargs):
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CALLED[0] = True
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return jitted_where(*args, **kwargs)
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# overriding where's cuda kernel with Jiterator generated kernel
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my_lib = Library("aten", "IMPL")
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my_lib.impl('aten::where.self', inverted_where, "CUDA")
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device = 'cuda'
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cond = torch.tensor([True, True, False], device=device, dtype=torch.bool)
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x = torch.tensor([1, 2, 3], device=device)
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y = torch.tensor([-1, -2, -3], device=device)
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self.assertEqual(torch.where(cond, x, y), torch.tensor([-1, -2, 3]))
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self.assertTrue(CALLED[0])
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del my_lib
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# behavior restored after deregistration
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self.assertEqual(torch.where(cond, x, y), torch.tensor([1, 2, -3]))
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def override_gelu_cuda() -> None:
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# Example 2: Use relu to approximate gelu for faster compute
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fastest_gelu_code_string = '''
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template <typename T> T fast_gelu(T a){
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return a > 0 ? a : 0;
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}
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'''
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jitted_gelu = _create_jit_fn(fastest_gelu_code_string)
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CALLED = [False]
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def fast_gelu(*args, **kwargs):
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CALLED[0] = True
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return jitted_gelu(*args, **kwargs)
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# overriding gelu's cuda kernel with Jiterator generated relu kernel
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my_lib = Library("aten", "IMPL")
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my_lib.impl('aten::gelu', fast_gelu, "CUDA")
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x = torch.rand([3, 3], device='cuda', dtype=torch.float)
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self.assertEqual(torch.nn.functional.gelu(x), torch.nn.functional.relu(x))
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self.assertTrue(CALLED[0])
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del my_lib
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# behavior restored after deregistration
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self.assertNotEqual(torch.nn.functional.gelu(x), torch.nn.functional.relu(x))
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def override_exp_cuda() -> None:
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# Example 3: Preventing exp from exploding for float16
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clipped_exp_code_string = '''
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template <typename T> T clipped_exp(T a){
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return a > T(10.0) ? T(22026.4657948) : exp(a);
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}
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'''
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jitted_exp = _create_jit_fn(clipped_exp_code_string)
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CALLED = [False]
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def clipped_exp(*args, **kwargs):
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CALLED[0] = True
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return jitted_exp(*args, **kwargs)
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# overriding exp's cuda kernel with clipped_exp kernel
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my_lib = Library("aten", "IMPL")
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my_lib.impl('aten::exp', clipped_exp, "CUDA")
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x = torch.tensor([0.0, 100.0], device='cuda', dtype=torch.float16)
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self.assertEqual(torch.exp(x), torch.tensor([1.0, 22026.4657948], dtype=torch.float16))
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self.assertTrue(CALLED[0])
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del my_lib
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# behavior restored after deregistration
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self.assertEqual(torch.exp(x), torch.tensor([1.0, torch.inf], dtype=torch.float16))
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def override_add_cuda() -> None:
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# Example 4: simulate a hardware bug, where the adder is always off by 1
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buggy_add_code_string = '''
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template <typename T> T buggy_add(T a, T b){
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return a + b + T(1);
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}
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'''
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jitted_add = _create_jit_fn(buggy_add_code_string)
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CALLED = [False]
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def buggy_add(*args, **kwargs):
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CALLED[0] = True
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return jitted_add(*args, **kwargs)
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my_lib = Library("aten", "IMPL")
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my_lib.impl('aten::add.Tensor', buggy_add, "CUDA")
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x_cpu = torch.rand([3, 3], device='cpu')
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y_cpu = torch.rand([3], device='cpu')
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x_cuda = x_cpu.cuda()
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y_cuda = y_cpu.cuda()
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self.assertEqual(x_cuda + y_cuda, x_cpu + y_cpu + 1)
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self.assertTrue(CALLED[0])
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del my_lib
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# behavior restored after deregistration
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self.assertEqual(x_cuda + y_cuda, x_cpu + y_cpu)
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if torch.cuda.is_available() and not TEST_WITH_ROCM:
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override_where_cuda()
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override_gelu_cuda()
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override_exp_cuda()
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override_add_cuda()
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def test_extend_library_with_dispatch_key_arg(self):
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def my_sum(*args, **kwargs):
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return args[0]
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my_lib1 = Library("aten", "IMPL", dispatch_key="CPU")
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# RuntimeError: Explicitly provided dispatch key (Conjugate) is
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# inconsistent with the dispatch key of the enclosing TORCH_LIBRARY_IMPL block
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with self.assertRaisesRegex(RuntimeError, "inconsistent with the dispatch key"):
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my_lib1.impl('sum', my_sum, "Conjugate")
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my_lib1.impl('aten::sum', my_sum)
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x = torch.tensor([1, 2])
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self.assertEqual(torch.sum(x), x)
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del my_lib1
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def test_create_new_library(self) -> None:
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my_lib1 = Library("foo", "DEF")
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my_lib1.define("sum(Tensor self) -> Tensor")
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# Example 1
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@torch.library.impl(my_lib1, "sum", "CPU")
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def my_sum(*args, **kwargs):
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return args[0]
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x = torch.tensor([1, 2])
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self.assertEqual(torch.ops.foo.sum(x), x)
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my_lib2 = Library("foo", "IMPL")
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# Example 2
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@torch.library.impl(my_lib2, torch.ops.foo.sum.default, "ZeroTensor")
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def my_sum_zt(*args, **kwargs):
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if args[0]._is_zerotensor():
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return torch._efficientzerotensor(args[0].shape)
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else:
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return args[0]
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y = torch._efficientzerotensor(3)
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self.assertTrue(torch.ops.foo.sum(y)._is_zerotensor())
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self.assertEqual(torch.ops.foo.sum(x), x)
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del my_lib2
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del my_lib1
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@unittest.skipIf(IS_WINDOWS, "Skipped under Windows")
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def test_alias_analysis(self):
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def test_helper(alias_analysis=""):
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my_lib1 = Library("foo", "DEF")
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called = [0]
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@torch.library.define(my_lib1, "_op() -> None", alias_analysis=alias_analysis)
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def _op(*args, **kwargs):
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called[0] += 1
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@torch.jit.script
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def _test():
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torch.ops.foo._op()
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assert "foo::_op" in str(_test.graph)
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with self.assertRaises(AssertionError):
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test_helper("") # alias_analysis="FROM_SCHEMA"
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test_helper("CONSERVATIVE")
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def test_error_for_unsupported_ns_or_kind(self) -> None:
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with self.assertRaisesRegex(ValueError, "Unsupported kind"):
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my_lib1 = Library("myns", "BLA")
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with self.assertRaisesRegex(ValueError, "reserved namespace"):
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my_lib1 = Library("prim", "DEF")
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class TestPythonDispatch(TestCase):
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def test_basic(self) -> None:
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with capture_logs() as logs:
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x = LoggingTensor(torch.tensor([3.0]), requires_grad=True)
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log_input("x", x)
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y = x * x
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saved_x = y.grad_fn._saved_self
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grad_y = LoggingTensor(torch.tensor([1.0]))
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log_input("grad_y", grad_y)
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g, = torch.autograd.grad((y,), (x,), (grad_y,))
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self.assertEqual(g.elem, torch.tensor([6.0]))
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with torch.no_grad():
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self.assertEqual(saved_x, x)
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self.assertEqual(saved_x._version, x._version)
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x.add_(2)
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self.assertEqual(saved_x, x)
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# TODO: figure out why broken
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# self.assertEqual(saved_x._version, x._version)
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self.assertExpectedInline('\n'.join(logs), '''\
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$0 = input('x')
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$1 = torch._ops.aten.mul.Tensor($0, $0)
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$2 = input('grad_y')
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True = torch._ops.aten.is_same_size.default($1, $2)
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$3 = torch._ops.aten.mul.Tensor($2, $0)
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$4 = torch._ops.aten.mul.Tensor($2, $0)
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$5 = torch._ops.aten.add.Tensor($4, $3)''')
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def test_out(self) -> None:
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with capture_logs() as logs:
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x = LoggingTensor(torch.ones(1))
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y = LoggingTensor(torch.zeros(1))
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log_input("x", x)
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log_input("y", y)
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torch.abs(x, out=y)
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self.assertEqual(y.elem, torch.ones(1))
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# TODO: arguably this shouldn't pass and we should complain
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# that out isn't a kwarg
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self.assertExpectedInline('\n'.join(logs), '''\
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$0 = input('x')
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$1 = input('y')
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$2 = torch._ops.aten.abs.out($0, out=$1)''')
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def test_kwarg_only(self) -> None:
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with capture_logs() as logs:
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x = LoggingTensor(torch.ones(1))
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y = LoggingTensor(torch.ones(1, 1))
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z = LoggingTensor(torch.ones(1))
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log_input("x", x)
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log_input("y", y)
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log_input("z", z)
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torch.addmv(x, y, z)
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torch.addmv(x, y, z, beta=1)
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torch.addmv(x, y, z, beta=2)
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torch.addmv(x, y, z, alpha=2)
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torch.addmv(x, y, z, beta=2, alpha=2)
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# The expectation is that beta/alpha don't show up when they're
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# defaulted. This is even if the user explicitly specified it.
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self.assertExpectedInline('\n'.join(logs), '''\
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$0 = input('x')
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$1 = input('y')
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$2 = input('z')
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$3 = torch._ops.aten.addmv.default($0, $1, $2)
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$4 = torch._ops.aten.addmv.default($0, $1, $2)
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$5 = torch._ops.aten.addmv.default($0, $1, $2, beta=2)
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$6 = torch._ops.aten.addmv.default($0, $1, $2, alpha=2)
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$7 = torch._ops.aten.addmv.default($0, $1, $2, beta=2, alpha=2)''')
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def test_kwarg_only_and_positional_default(self) -> None:
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with capture_logs() as logs:
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x = LoggingTensor(torch.ones(1))
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log_input("x", x)
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torch.ops.aten._foobar(x)
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torch.ops.aten._foobar(x, False)
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torch.ops.aten._foobar(x, arg3=False)
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torch.ops.aten._foobar(x, False, arg3=False)
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# What we are testing here is that we omit arg2
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# if it is defaulted, even if a kwarg is set
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self.assertExpectedInline('\n'.join(logs), '''\
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$0 = input('x')
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$1 = torch._ops.aten._foobar.default($0)
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$2 = torch._ops.aten._foobar.default($0, False)
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$3 = torch._ops.aten._foobar.default($0, arg3=False)
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$4 = torch._ops.aten._foobar.default($0, False, arg3=False)''')
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def test_produce_real_type(self) -> None:
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with capture_logs() as logs:
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x = LoggingTensor(torch.ones(2, 2))
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log_input("x", x)
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x.to(dtype=torch.double) # non-optional dtype
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torch.cumprod(x, 0, dtype=torch.double) # optional dtype
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x[:, 1].contiguous(memory_format=torch.contiguous_format) # optional memory format
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# There doesn't appear to be any layout signatures which are
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# triggerable using tensor subclasses (need to use a mode)
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self.assertExpectedInline('\n'.join(logs), '''\
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$0 = input('x')
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$1 = torch._ops.aten._to_copy.default($0, dtype=torch.float64)
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$2 = torch._ops.aten.cumprod.default($0, 0, dtype=torch.float64)
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$3 = torch._ops.aten.slice.Tensor($0, 0, 0, 9223372036854775807)
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$4 = torch._ops.aten.select.int($3, 1, 1)
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$5 = torch._ops.aten.clone.default($4, memory_format=torch.contiguous_format)''')
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def test_list_ret(self) -> None:
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# test all sequence types are permissible returns
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for list_type in (list, tuple):
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class A(torch._C._TensorBase):
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@staticmethod
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def __new__(cls, elem):
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return torch.Tensor._make_subclass(cls, elem, elem.requires_grad)
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@classmethod
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def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
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if func.overloadpacket == torch.ops.aten.split:
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with no_dispatch():
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return list_type(torch.split(*args))
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else:
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raise AssertionError(f"unrecognized func: {func}")
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self.assertEqual(
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torch.split(A(torch.tensor([0, 1])), 2),
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torch.split(torch.tensor([0, 1]), 2)
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)
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def test_invalid_ret(self) -> None:
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# test invalid return gets reasonable error message
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class A(torch._C._TensorBase):
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@staticmethod
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def __new__(cls, elem):
|
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return torch.Tensor._make_subclass(cls, elem, elem.requires_grad)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
return "arf"
|
|
|
|
# Wobbles depending on NDEBUG mode of pybind11
|
|
self.assertRaisesRegex(
|
|
RuntimeError, "Unable to cast", lambda: A(torch.zeros(1)).neg(),
|
|
)
|
|
self.assertRaisesRegexp(
|
|
RuntimeError, "Unable to cast", lambda: A(torch.zeros(1)).detach(),
|
|
)
|
|
|
|
def test_detach_appears_twice_when_called_once(self) -> None:
|
|
with capture_logs() as logs:
|
|
x = LoggingTensor(torch.tensor([3.0]), requires_grad=True)
|
|
log_input("x", x)
|
|
x.detach()
|
|
# FIXME: We actually want this to emit a single detach. However,
|
|
# it currently emits two, for reasons unclear to us. Leaving
|
|
# this test here to make sure we don't regress even further (it
|
|
# would be bad if calling .detach() once emits 3+ detaches).
|
|
self.assertExpectedInline('\n'.join(logs), '''\
|
|
$0 = input('x')
|
|
$1 = torch._ops.aten.detach.default($0)
|
|
$2 = torch._ops.aten.detach.default($1)''')
|
|
|
|
def test_storage(self) -> None:
|
|
# For now, just make sure it doesn't crash. Ideally, we should
|
|
# return some virtual storage that is safe to work with
|
|
x = LoggingTensor(torch.ones(1))
|
|
self.assertRaises(RuntimeError, lambda: x.storage())
|
|
|
|
def test_make_wrapper_subclass_noalloc(self) -> None:
|
|
# This is ludicrously big (8TB) and this should pass because wrapper
|
|
# subclasses don't allocate
|
|
torch.Tensor._make_wrapper_subclass(LoggingTensor, (1000000000000,))
|
|
|
|
def test_version(self) -> None:
|
|
x = LoggingTensor(torch.ones(1))
|
|
prev_vc = x._version
|
|
x.detach().add_(2)
|
|
cur_vc = x._version
|
|
self.assertNotEqual(prev_vc, cur_vc)
|
|
x.data.add_(2)
|
|
self.assertEqual(cur_vc, x._version)
|
|
|
|
def test_subclass_priority(self) -> None:
|
|
class ErrorA(RuntimeError):
|
|
pass
|
|
|
|
class ErrorB(RuntimeError):
|
|
pass
|
|
|
|
# The big tests for code coverage are test_precedence_semantics in
|
|
# test_overrides.py; this is just to make sure it is wired up at all
|
|
# correctly for __torch_dispatch__
|
|
class A(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, elem):
|
|
return torch.Tensor._make_subclass(cls, elem, elem.requires_grad)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
raise ErrorA
|
|
|
|
class B(A):
|
|
@staticmethod
|
|
def __new__(cls, elem):
|
|
return torch.Tensor._make_subclass(cls, elem, elem.requires_grad)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
raise ErrorB
|
|
|
|
self.assertRaises(ErrorA, lambda: torch.add(A(torch.empty(1)), A(torch.empty(1))))
|
|
self.assertRaises(ErrorB, lambda: torch.add(A(torch.empty(1)), B(torch.empty(1))))
|
|
self.assertRaises(ErrorB, lambda: torch.add(B(torch.empty(1)), A(torch.empty(1))))
|
|
self.assertRaises(ErrorB, lambda: torch.add(B(torch.empty(1)), B(torch.empty(1))))
|
|
|
|
def test_format(self) -> None:
|
|
x = LoggingTensor(torch.ones(1))
|
|
s1 = str(x)
|
|
s2 = repr(x)
|
|
s3 = f"{x}"
|
|
self.assertExpectedInline(s1, """LoggingTensor(tensor([1.]))""")
|
|
self.assertEqual(s1, s2)
|
|
self.assertEqual(s1, s3)
|
|
|
|
def test_custom_autograd(self) -> None:
|
|
escape = [None]
|
|
|
|
class Square(torch.autograd.Function):
|
|
@staticmethod
|
|
def forward(ctx, x):
|
|
y = x ** 2
|
|
ctx.save_for_backward(x)
|
|
return y
|
|
|
|
@staticmethod
|
|
def backward(ctx, grad_output):
|
|
assert isinstance(grad_output, LoggingTensor)
|
|
x, = ctx.saved_tensors
|
|
assert isinstance(x, LoggingTensor)
|
|
escape[0] = x
|
|
return grad_output * 2 * x
|
|
|
|
with capture_logs() as logs:
|
|
x = LoggingTensor(torch.ones(1), requires_grad=True)
|
|
log_input("x", x)
|
|
x.grad = LoggingTensor(torch.zeros(1))
|
|
log_input("x.grad", x.grad)
|
|
y = Square.apply(x)
|
|
grad_output = LoggingTensor(torch.ones(1))
|
|
log_input("grad_output", grad_output)
|
|
y.backward(grad_output)
|
|
|
|
with torch.no_grad():
|
|
self.assertEqual(escape[0], x)
|
|
self.assertEqual(escape[0]._version, x._version)
|
|
# TODO: figure out why x.requires_grad = False doesn't
|
|
# trigger an error for LoggingTensor
|
|
x.add_(2)
|
|
self.assertEqual(escape[0], x)
|
|
# TODO: figure out why this is broken
|
|
# self.assertEqual(escape[0]._version, x._version)
|
|
|
|
self.assertExpectedInline('\n'.join(logs), '''\
|
|
$0 = input('x')
|
|
$1 = input('x.grad')
|
|
$2 = torch._ops.aten.pow.Tensor_Scalar($0, 2)
|
|
$3 = input('grad_output')
|
|
True = torch._ops.aten.is_same_size.default($2, $3)
|
|
$4 = torch._ops.aten.mul.Tensor($3, 2)
|
|
$5 = torch._ops.aten.mul.Tensor($4, $0)
|
|
$6 = torch._ops.aten.add_.Tensor($1, $5)''')
|
|
|
|
def test_subclass_creation(self):
|
|
# Make sure these statements runs without error
|
|
# In particular checking that when internal detach returns
|
|
# subclasses, these are cleanly overwritten.
|
|
class Foo(torch.Tensor):
|
|
pass
|
|
|
|
err_msg = "subclass Foo but.*already associated to a python object of type LoggingTensor"
|
|
with self.assertRaisesRegex(RuntimeError, err_msg):
|
|
a = torch.Tensor._make_subclass(Foo, LoggingTensor(torch.rand(2)))
|
|
with self.assertRaisesRegex(RuntimeError, err_msg):
|
|
b = LoggingTensor(torch.rand(2)).as_subclass(Foo)
|
|
with self.assertRaisesRegex(RuntimeError, err_msg):
|
|
Foo(LoggingTensor(torch.rand(2)))
|
|
|
|
with self.assertRaisesRegex(TypeError, "Foo must define __torch_dispatch__"):
|
|
torch.Tensor._make_wrapper_subclass(Foo, (2, 2))
|
|
|
|
def test_new_ones(self) -> None:
|
|
class MyTensor(torch.Tensor):
|
|
__torch_function__ = torch._C._disabled_torch_function_impl
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
return MyTensor(3)
|
|
|
|
self.assertEqual(type(MyTensor(2).new_ones(3)), MyTensor)
|
|
|
|
def test_like(self) -> None:
|
|
class MyTensor(torch.Tensor):
|
|
__torch_function__ = torch._C._disabled_torch_function_impl
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
return MyTensor(3)
|
|
|
|
for f in ["empty", "ones", "rand", "randn", "zeros"]:
|
|
f_name = f + "_like"
|
|
self.assertEqual(type(getattr(torch, f_name)(MyTensor(2))), MyTensor)
|
|
|
|
self.assertEqual(type(torch.full_like(MyTensor(2), 1.)), MyTensor)
|
|
self.assertEqual(type(torch.randint_like(MyTensor(2), high=3)), MyTensor)
|
|
|
|
def test_make_wrapper_subclass_propagates_metadata(self) -> None:
|
|
class WrapperTensor(torch.Tensor):
|
|
elem: torch.Tensor
|
|
|
|
__slots__ = ['elem']
|
|
|
|
@staticmethod
|
|
def __new__(cls, elem, *args, **kwargs):
|
|
r = torch.Tensor._make_wrapper_subclass( # type: ignore[attr-defined]
|
|
cls, elem.size(),
|
|
dtype=elem.dtype, layout=elem.layout,
|
|
device=elem.device, requires_grad=elem.requires_grad,
|
|
strides=elem.stride(), storage_offset=elem.storage_offset())
|
|
r.elem = elem
|
|
return r
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
raise RuntimeError("NYI")
|
|
|
|
# non-contiguous strides, non-zero storage offset
|
|
x = torch.randn(4, 6).t().diagonal(offset=2)
|
|
y = WrapperTensor(x)
|
|
self.assertEqual(y.size(), x.size())
|
|
self.assertEqual(y.stride(), x.stride())
|
|
self.assertEqual(y.storage_offset(), x.storage_offset())
|
|
|
|
def test_wrapper_subclass_serializes(self) -> None:
|
|
with tempfile.TemporaryFile() as f:
|
|
x = LoggingTensor(torch.randn(3))
|
|
torch.save(x, f)
|
|
f.seek(0)
|
|
x_loaded = torch.load(f)
|
|
self.assertTrue(type(x_loaded) is type(x))
|
|
self.assertEqual(x.elem, x_loaded.elem)
|
|
self.assertFalse(x is x_loaded)
|
|
|
|
def test_deepcopy_wrapper_subclass(self) -> None:
|
|
x = LoggingTensor(torch.randn(3))
|
|
x_copy = deepcopy(x)
|
|
self.assertTrue(type(x_copy) is type(x))
|
|
self.assertEqual(x.elem, x_copy.elem)
|
|
self.assertFalse(x is x_copy)
|
|
|
|
def test_deepcopy_wrapper_subclass_with_clone_returning_different_type(self) -> None:
|
|
|
|
class MyWrapperTensor(torch.Tensor):
|
|
elem: torch.Tensor
|
|
|
|
__slots__ = ['elem']
|
|
|
|
@staticmethod
|
|
def __new__(cls, elem, *args, **kwargs):
|
|
r = torch.Tensor._make_wrapper_subclass( # type: ignore[attr-defined]
|
|
cls, elem.size(),
|
|
dtype=elem.dtype, layout=elem.layout,
|
|
device=elem.device, requires_grad=elem.requires_grad,
|
|
strides=elem.stride(), storage_offset=elem.storage_offset())
|
|
r.elem = elem
|
|
return r
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
if func.overloadpacket.__name__ == "clone":
|
|
# Return a plain tensor from clone().
|
|
return args[0].elem.clone()
|
|
raise RuntimeError("NYI")
|
|
|
|
# NB: The default Tensor.__torch_function__ implementation called for deepcopy
|
|
# disables __torch_function__ by the time we get to clone(), so there is no need to
|
|
# explicitly disable __torch_function__ for this subclass.
|
|
|
|
x = MyWrapperTensor(torch.randn(3))
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
"for which cloning returns another instance of the same subclass"):
|
|
x_copy = deepcopy(x)
|
|
|
|
def test_deepcopy_non_wrapper_subclass(self) -> None:
|
|
|
|
# Ensure correct error is thrown for common error cases.
|
|
class SubTensorError1(torch.Tensor):
|
|
# Default implementation of new_empty() returns a plain tensor.
|
|
pass
|
|
|
|
class SubTensorError2(torch.Tensor):
|
|
# new_empty() incorrectly returns a different type (i.e. a plain tensor).
|
|
def new_empty(self, shape):
|
|
return torch.Tensor(shape)
|
|
|
|
for error_cls in [SubTensorError1, SubTensorError2]:
|
|
x = error_cls(3)
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
"for which that function returns another instance of the same subclass"):
|
|
x_copy = deepcopy(x)
|
|
|
|
# Ensure a correctly implemented new_empty() causes deepcopy() to work.
|
|
class SubTensorSuccess(torch.Tensor):
|
|
def new_empty(self, shape):
|
|
return type(self)(shape)
|
|
|
|
x = SubTensorSuccess(3)
|
|
x_copy = deepcopy(x)
|
|
self.assertIs(type(x_copy), type(x))
|
|
|
|
def test_index_put_where_only_index_is_subclass(self) -> None:
|
|
called_funcs = []
|
|
|
|
class MyTensor(torch.Tensor):
|
|
__torch_function__ = torch._C._disabled_torch_function_impl
|
|
elem: torch.Tensor
|
|
__slots__ = ['elem']
|
|
|
|
@staticmethod
|
|
def __new__(cls, elem, *args, **kwargs):
|
|
r = torch.Tensor._make_wrapper_subclass(
|
|
cls, elem.size(),
|
|
dtype=elem.dtype, layout=elem.layout,
|
|
device=elem.device, requires_grad=elem.requires_grad
|
|
)
|
|
r.elem = elem
|
|
return r
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
called_funcs.append(func)
|
|
return MyTensor(torch.tensor(3))
|
|
|
|
x = torch.randn(3, 3)
|
|
idxs = (MyTensor(torch.tensor(0)),)
|
|
v = torch.randn(1)
|
|
res = x.index_put_(idxs, v)
|
|
self.assertEqual(called_funcs, [torch.ops.aten.index_put_.default])
|
|
|
|
def test_torch_dispatch_mode_basic(self) -> None:
|
|
with capture_logs(is_mode=True) as logs:
|
|
with LoggingTensorMode():
|
|
torch.empty([])
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = torch._ops.aten.empty.memory_format([], device=device(type='cpu'), pin_memory=False)""")
|
|
|
|
def test_torch_dispatch_mode_unrelated_tensors(self) -> None:
|
|
x = torch.randn([])
|
|
y = torch.randn([])
|
|
with capture_logs(is_mode=True) as logs:
|
|
with LoggingTensorMode():
|
|
x + y
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$2 = torch._ops.aten.add.Tensor($0, $1)""")
|
|
|
|
def test_nested_push_logging_tensor_mode(self):
|
|
x = torch.randn([])
|
|
y = torch.randn([])
|
|
with capture_logs(is_mode=True) as logs:
|
|
with LoggingTensorMode():
|
|
with LoggingTensorMode():
|
|
torch.empty([])
|
|
x + y
|
|
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = torch._ops.aten.empty.memory_format([], device=device(type='cpu'), pin_memory=False)
|
|
$0 = torch._ops.aten.empty.memory_format([], device=device(type='cpu'), pin_memory=False)
|
|
$3 = torch._ops.aten.add.Tensor($1, $2)
|
|
$3 = torch._ops.aten.add.Tensor($1, $2)""")
|
|
|
|
def test_capture_logs_with_torch_dispatch_mode(self):
|
|
x = torch.randn([])
|
|
y = torch.randn([])
|
|
with capture_logs_with_logging_tensor_mode() as logs:
|
|
torch.empty([])
|
|
x + y
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = torch._ops.aten.empty.memory_format([], device=device(type='cpu'), pin_memory=False)
|
|
$3 = torch._ops.aten.add.Tensor($1, $2)""")
|
|
|
|
x = torch.randn([])
|
|
y = torch.randn([])
|
|
|
|
with capture_logs_with_logging_tensor_mode() as logs1:
|
|
with capture_logs_with_logging_tensor_mode() as logs2:
|
|
torch.empty([])
|
|
x + y
|
|
|
|
self.assertExpectedInline('\n'.join(logs2), """\
|
|
$0 = torch._ops.aten.empty.memory_format([], device=device(type='cpu'), pin_memory=False)
|
|
$0 = torch._ops.aten.empty.memory_format([], device=device(type='cpu'), pin_memory=False)
|
|
$3 = torch._ops.aten.add.Tensor($1, $2)
|
|
$3 = torch._ops.aten.add.Tensor($1, $2)""")
|
|
|
|
self.assertEqual(logs1, logs2)
|
|
|
|
def test_torch_dispatch_mode_subclass_priority(self) -> None:
|
|
class ErrorA(RuntimeError):
|
|
pass
|
|
|
|
class ErrorB(RuntimeError):
|
|
pass
|
|
|
|
class A(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, elem):
|
|
return torch.Tensor._make_subclass(cls, elem, elem.requires_grad)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
with AMode():
|
|
raise ErrorA
|
|
|
|
class B(A):
|
|
@staticmethod
|
|
def __new__(cls, elem):
|
|
return torch.Tensor._make_subclass(cls, elem, elem.requires_grad)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
with BMode():
|
|
func(*args, **kwargs)
|
|
|
|
class AMode(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
raise ErrorA
|
|
|
|
class BMode(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
raise ErrorB
|
|
|
|
a = A(torch.empty(1))
|
|
b = B(torch.empty(1))
|
|
with self.assertRaises(ErrorA):
|
|
a + a
|
|
with self.assertRaises(ErrorB):
|
|
a + b
|
|
|
|
# B has precedence over A due to the subclass relationship yet
|
|
# modes take precedence over arguments
|
|
with self.assertRaises(ErrorA):
|
|
with AMode():
|
|
b + b
|
|
with self.assertRaises(ErrorB):
|
|
with BMode():
|
|
a + a
|
|
with self.assertRaises(ErrorB):
|
|
with BMode():
|
|
a + b
|
|
|
|
def test_mode_with_make_subclass(self):
|
|
class SubTensor(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, elem):
|
|
return torch.Tensor._make_subclass(cls, elem, elem.requires_grad)
|
|
|
|
class BasicMode(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
return func(*args, **kwargs)
|
|
|
|
x = torch.randn(3)
|
|
with BasicMode():
|
|
y = SubTensor(x)
|
|
self.assertIsInstance(y, SubTensor)
|
|
|
|
def test_torch_dispatch_mode_respects_no_dispatch(self) -> None:
|
|
with capture_logs(is_mode=True) as logs1:
|
|
with LoggingTensorMode():
|
|
torch.ones([2, 3])
|
|
with no_dispatch():
|
|
torch.ones([2, 3])
|
|
with capture_logs(is_mode=True) as logs2:
|
|
with LoggingTensorMode():
|
|
torch.ones([2, 3])
|
|
self.assertEqual(logs1, logs2)
|
|
|
|
def test_shallow_copy_and_detach(self) -> None:
|
|
seen = set()
|
|
test_case = self
|
|
|
|
class TestMode(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
tree_map_only(torch.Tensor, lambda t: test_case.assertIn(t, seen), (args, kwargs))
|
|
if kwargs is None:
|
|
kwargs = {}
|
|
r = func(*args, **kwargs)
|
|
tree_map_only(torch.Tensor, lambda t: seen.add(t), r)
|
|
return r
|
|
|
|
with TestMode():
|
|
x = torch.randn(3, requires_grad=True)
|
|
loss = (x * x).sum()
|
|
loss.backward()
|
|
|
|
def test_exception_handling(self):
|
|
class A(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, elem):
|
|
return torch.Tensor._make_subclass(cls, elem, elem.requires_grad)
|
|
|
|
class AMode(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
if func.__name__ == 'randn.default':
|
|
raise RuntimeError()
|
|
return A(torch.zeros(()))
|
|
|
|
with AMode():
|
|
try:
|
|
torch.randn(())
|
|
except RuntimeError:
|
|
pass
|
|
self.assertTrue(isinstance(torch.zeros(()), A))
|
|
|
|
def test_with_mode_created_separately(self):
|
|
class ErrorA(RuntimeError):
|
|
pass
|
|
|
|
class A(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
raise ErrorA()
|
|
|
|
x = A()
|
|
with self.assertRaises(ErrorA):
|
|
with x:
|
|
torch.empty([])
|
|
|
|
def test_with_nested_modes(self):
|
|
class ErrorA(RuntimeError):
|
|
def __init__(self, msg):
|
|
return super().__init__(msg)
|
|
|
|
class A(TorchDispatchMode):
|
|
def __init__(self, msg):
|
|
self.msg = msg
|
|
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
raise ErrorA(self.msg)
|
|
|
|
with self.assertRaisesRegex(ErrorA, "layer2"):
|
|
with A("layer1"):
|
|
with A("layer2"):
|
|
torch.empty([])
|
|
|
|
def test_make_subclass_with_modes(self):
|
|
class ModeTensor(torch.Tensor):
|
|
def __new__(cls, elem, mode):
|
|
r = torch.Tensor._make_subclass(cls, elem, elem.requires_grad)
|
|
r.elem = elem
|
|
r.mode = mode
|
|
return r
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
raise NotImplementedError("Shouldn't be here")
|
|
|
|
class Mode(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
def unwrap(e):
|
|
if isinstance(e, ModeTensor):
|
|
return e.elem
|
|
else:
|
|
return e
|
|
|
|
def wrap(t):
|
|
if isinstance(t, torch.Tensor):
|
|
return ModeTensor(t, self)
|
|
else:
|
|
return t
|
|
|
|
return wrap(func(*tuple(unwrap(a) for a in args), **kwargs))
|
|
|
|
class BasicMode(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
return func(*args, **kwargs)
|
|
|
|
x = torch.tensor(4.)
|
|
with Mode():
|
|
y = x + x
|
|
z = y + y
|
|
self.assertIsInstance(y, ModeTensor)
|
|
self.assertIsInstance(z, ModeTensor)
|
|
|
|
with Mode():
|
|
with BasicMode(): # we can't nest two modes that call make_subclass because it only accepts vanilla tensors
|
|
y = x + x
|
|
z = y + y
|
|
self.assertIsInstance(y, ModeTensor)
|
|
self.assertIsInstance(z, ModeTensor)
|
|
|
|
assert self.assertRaisesRegex(RuntimeError, "subclass Mode but.* associated to a python object of type Mode")
|
|
|
|
def test_notimplemented_mode(self):
|
|
sub_count = 0
|
|
|
|
class PoliteMode(TorchDispatchMode):
|
|
def __init__(self):
|
|
self.pre_count = 0
|
|
self.post_count = 0
|
|
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
self.pre_count += 1
|
|
if any(t is not torch.Tensor for t in types):
|
|
return NotImplemented
|
|
self.post_count += 1
|
|
return func(*args, **kwargs)
|
|
|
|
class SubTensor(torch.Tensor):
|
|
def __new__(cls, elem):
|
|
r = torch.Tensor._make_wrapper_subclass(cls, elem.shape)
|
|
r.elem = elem
|
|
return r
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
nonlocal sub_count
|
|
sub_count += 1
|
|
|
|
def unwrap(t):
|
|
if isinstance(t, SubTensor):
|
|
return t.elem
|
|
else:
|
|
return t
|
|
|
|
return func(*tree_map(unwrap, args), **tree_map(unwrap, kwargs))
|
|
|
|
__torch_function__ = torch._C._disabled_torch_function_impl
|
|
|
|
a = SubTensor(torch.randn(2))
|
|
with PoliteMode() as mode:
|
|
a.abs()
|
|
|
|
self.assertEqual(mode.pre_count, 2)
|
|
self.assertEqual(mode.post_count, 1)
|
|
self.assertEqual(sub_count, 1)
|
|
|
|
# make sure this doesn't error
|
|
with PoliteMode():
|
|
with PoliteMode():
|
|
a.abs()
|
|
|
|
def test_nesting_same_mode(self):
|
|
# If the pushed mode is the same instance as the current mode, we allow pushing an already active mode.
|
|
|
|
with capture_logs(is_mode=True) as logs:
|
|
with LoggingTensorMode() as reenabled:
|
|
with reenabled:
|
|
torch.empty([])
|
|
self.assertExpectedInline('\n'.join(logs), """\
|
|
$0 = torch._ops.aten.empty.memory_format([], device=device(type='cpu'), pin_memory=False)
|
|
$0 = torch._ops.aten.empty.memory_format([], device=device(type='cpu'), pin_memory=False)""")
|
|
|
|
|
|
def test_error_using_class_method_on_mode(self):
|
|
class A(TorchDispatchMode):
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
return func(args, kwargs)
|
|
|
|
x = torch.tensor(5.)
|
|
with self.assertRaisesRegex(RuntimeError, "should be a normal method not a class method"):
|
|
with A():
|
|
x + x
|
|
|
|
def test_get_cur_mode(self):
|
|
class A(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
pass
|
|
|
|
self.assertEqual(_get_current_dispatch_mode(), None)
|
|
|
|
with A() as mode1:
|
|
self.assertEqual(_get_current_dispatch_mode(), mode1)
|
|
|
|
with mode1:
|
|
with A() as mode2:
|
|
self.assertEqual(_get_current_dispatch_mode(), mode2)
|
|
|
|
def test_get_mode_stack(self):
|
|
class A(TorchDispatchMode):
|
|
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
|
|
pass
|
|
|
|
self.assertEqual(_get_current_dispatch_mode_stack(), [])
|
|
|
|
with A() as mode1:
|
|
self.assertEqual(_get_current_dispatch_mode_stack(), [mode1])
|
|
|
|
with mode1:
|
|
with A() as mode2:
|
|
self.assertEqual(_get_current_dispatch_mode_stack(), [mode1, mode2])
|
|
|
|
def test_all_same_mode(self):
|
|
x = LoggingTensorMode()
|
|
y = LoggingTensorMode()
|
|
self.assertTrue(all_same_mode([x, x, x]))
|
|
self.assertFalse(all_same_mode([x, None]))
|
|
self.assertFalse(all_same_mode([x, y]))
|
|
|
|
def test_tolist_numpy_with_torch_dispatch_mode(self) -> None:
|
|
x = LoggingTensor(torch.tensor([2.0, 3.0]))
|
|
with self.assertRaisesRegex(RuntimeError, "is not supported for tensor subclasses."):
|
|
x.tolist()
|
|
with self.assertRaisesRegex(RuntimeError, "is not supported for tensor subclasses."):
|
|
x.numpy()
|
|
with self.assertRaises(AssertionError):
|
|
self.assertEqual(x, None)
|
|
|
|
def test_subclass_autograd_device_check(self) -> None:
|
|
class NonWrapperSubclass(torch.Tensor):
|
|
elem: torch.Tensor
|
|
|
|
__slots__ = ['elem']
|
|
|
|
@staticmethod
|
|
def __new__(cls, elem, *args, **kwargs):
|
|
# Wrong device here!
|
|
r = torch.Tensor._make_subclass(cls, elem.to("meta"), elem.requires_grad)
|
|
# ...the real tensor is held as an element on the tensor.
|
|
r.elem = elem
|
|
return r
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
def unwrap(e):
|
|
return e.elem if isinstance(e, NonWrapperSubclass) else e
|
|
|
|
def wrap(e):
|
|
return NonWrapperSubclass(e) if isinstance(e, torch.Tensor) else e
|
|
|
|
rs = tree_map(wrap, func(*tree_map(unwrap, args), **tree_map(unwrap, kwargs)))
|
|
logging.getLogger("NonWrapperSubclass").info(f"{func.__module__}.{func.__name__}", args, kwargs, rs)
|
|
return rs
|
|
|
|
x = NonWrapperSubclass(torch.tensor([3.0, 4.0], requires_grad=True))
|
|
y = torch.randn(2, requires_grad=True)
|
|
z = x * y
|
|
self.assertIsInstance(z, NonWrapperSubclass)
|
|
z.sum().backward(torch.tensor(1))
|
|
self.assertEqual(x.grad, y)
|
|
self.assertEqual(y.grad, x)
|
|
|
|
def test_none_wrapping(self):
|
|
# A Tensor subclass that returns None when doing add
|
|
# See LoggingTensor above for more details on the subclass
|
|
class SubclassWithNone(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, elem, *args, **kwargs):
|
|
r = torch.Tensor._make_wrapper_subclass(
|
|
cls, elem.size(),
|
|
dtype=elem.dtype, layout=elem.layout,
|
|
device=elem.device, requires_grad=elem.requires_grad
|
|
)
|
|
r.elem = elem
|
|
return r
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
def unwrap(e):
|
|
return e.elem if isinstance(e, SubclassWithNone) else e
|
|
|
|
def wrap(e):
|
|
return SubclassWithNone(e) if isinstance(e, torch.Tensor) else e
|
|
|
|
rs = tree_map(wrap, func(*tree_map(unwrap, args), **tree_map(unwrap, kwargs)))
|
|
if func.overloadpacket.__name__ == "add":
|
|
return None
|
|
else:
|
|
return rs
|
|
|
|
x = SubclassWithNone(torch.rand(2))
|
|
# Make sure both run without error
|
|
self.assertIsInstance(x * 2, SubclassWithNone)
|
|
self.assertIsNone(x + 2)
|
|
|
|
x.requires_grad_()
|
|
out = x.acos().sum()
|
|
|
|
# The backward of acos does add then rsqrt so here we make sure that the
|
|
# undefined Tensor generated by the user code is nicely handled.
|
|
# If acos formula changes in the future, this can be replaced by any other
|
|
# function that does add then something in the backward in a composite way
|
|
with self.assertRaisesRegex(RuntimeError, "but got None"):
|
|
out.backward()
|
|
|
|
def test_storage_can_be_converted_to_python_object(self):
|
|
s = torch.Storage()
|
|
z = LoggingTensor(torch.empty([]))
|
|
z.set_(s)
|
|
|
|
def test_autograd_in_attr(self):
|
|
# We want the wrapped Tensor to require gradients!
|
|
true_t = torch.rand(2, requires_grad=True)
|
|
t = LoggingTensorReentrant(true_t)
|
|
|
|
out = t + 2
|
|
|
|
self.assertFalse(out.requires_grad)
|
|
self.assertIsNone(out.grad_fn)
|
|
|
|
self.assertTrue(out.elem.requires_grad)
|
|
self.assertIsNotNone(out.elem.grad_fn)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "does not require grad"):
|
|
out.sum().backward()
|
|
|
|
out.elem.sum().backward()
|
|
|
|
self.assertIsNone(t.grad)
|
|
self.assertIsNotNone(t.elem.grad)
|
|
|
|
def test_dispatch_super_call(self):
|
|
called = []
|
|
|
|
class SubTensor(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, elem):
|
|
return torch.Tensor._make_subclass(cls, elem)
|
|
|
|
__torch_function__ = torch._C._disabled_torch_function_impl
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
called.append(func)
|
|
return super().__torch_dispatch__(func, types, args, kwargs)
|
|
|
|
x = torch.randn(2)
|
|
y = torch.randn(2)
|
|
self.assertEqual(SubTensor(x) + SubTensor(y), x + y)
|
|
self.assertEqual(called, [torch.ops.aten.add.Tensor])
|
|
|
|
def test_dispatch_super_call_list_arg(self):
|
|
called = []
|
|
|
|
class SubTensorWithListArg(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, elem):
|
|
return torch.Tensor._make_subclass(cls, elem)
|
|
|
|
__torch_function__ = torch._C._disabled_torch_function_impl
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
called.append(func)
|
|
return super().__torch_dispatch__(func, types, list(args), kwargs)
|
|
|
|
x = torch.randn(2)
|
|
self.assertEqual(SubTensorWithListArg(x).neg(), x.neg())
|
|
self.assertEqual(called, [torch.ops.aten.neg.default])
|
|
|
|
def test_dispatch_super_dont_autograd(self):
|
|
called = []
|
|
|
|
class SubTensor(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, elem):
|
|
return torch.Tensor._make_subclass(cls, elem, elem.requires_grad)
|
|
|
|
__torch_function__ = torch._C._disabled_torch_function_impl
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
called.append(func)
|
|
# This argument still requires grad because it was passed
|
|
# through directly...
|
|
self.assertTrue(args[0].requires_grad)
|
|
r = super().__torch_dispatch__(func, types, args, kwargs)
|
|
# But the output better not require grad, because that means
|
|
# you did autograd again in torch dispatch (oops)
|
|
self.assertFalse(r.requires_grad)
|
|
return r
|
|
|
|
x = SubTensor(torch.randn(2, requires_grad=True))
|
|
x.neg()
|
|
self.assertEqual(called, [torch.ops.aten.neg.default])
|
|
|
|
def test_set_data(self):
|
|
called = 0
|
|
|
|
class SubTensor(torch.Tensor):
|
|
__torch_function__ = torch._C._disabled_torch_function_impl
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
nonlocal called
|
|
called += 1
|
|
return super().__torch_dispatch__(func, types, args, kwargs)
|
|
|
|
x = SubTensor(torch.empty(2))
|
|
x.data
|
|
self.assertEqual(called, 1)
|
|
x.data = torch.empty(2)
|
|
self.assertEqual(called, 1)
|
|
x.data
|
|
self.assertEqual(called, 2)
|
|
self.assertIs(type(x), SubTensor)
|
|
x.set_(torch.empty(2))
|
|
self.assertEqual(called, 3)
|
|
x.data
|
|
self.assertEqual(called, 4)
|
|
self.assertIs(type(x), SubTensor)
|
|
|
|
def test_construct_int_tensor(self):
|
|
class SubTensor(torch.Tensor):
|
|
pass
|
|
# should not fail
|
|
SubTensor(torch.zeros(2, dtype=torch.int))
|
|
|
|
def test_multiple_ops_subclass(self):
|
|
# This is a Direct Subclass, don't do that!
|
|
class MySubclass(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, elem):
|
|
r = torch.Tensor._make_subclass(cls, elem)
|
|
return r
|
|
|
|
__torch_function__ = torch._C._disabled_torch_function_impl
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
|
|
with no_dispatch():
|
|
return func(*args, **kwargs)
|
|
|
|
x = MySubclass(torch.rand(2, 2, dtype=torch.complex64))
|
|
y = x.conj()
|
|
# Details of the bug that this tests for:
|
|
# Here, y dispatch keys are: {PythonTLSSnapshot, AutogradCPU, Conjugate, Python, CPU}
|
|
# There are a few calls to the dispatcher that are going to happen here:
|
|
# - call_exp: User calling exp on y
|
|
# - PythonTLSSnapshot: records the TLS on entry and redispatch
|
|
# - AutogradCPU: no input requires grad, so does nothing and redispatch
|
|
# - Conjugate: no special implementation for exp: use the fallback that
|
|
# first clone the Tensor (to materialize the conj) then redispatch
|
|
# - call_clone: conjugate fallback calling clone on y
|
|
# - PythonTLSSnapshot: records the TLS on entry and redispatch
|
|
# - (AutogradCPU: skipped as autograd added itself to the exclude set above)
|
|
# - Conjugate: special implementation for clone: just skip this key
|
|
# - Python: Reset the TLS based on the snapshot above and call the user implementation (this
|
|
# actually calls into the dispatcher again but since we disable both our keys
|
|
# before, not detailed here)
|
|
# - exit Python: restore the TLS and exit
|
|
# - exit Conjugate: nothing was inplace so just exit
|
|
# - exit PythonTLSSnapshot: done with this call, reset the saved TLS to empty
|
|
# - Python: Reset the TLS again based on the snapshot. <- this used to fail
|
|
# - More steps....
|
|
y.exp()
|
|
|
|
@staticmethod
|
|
def subclass_helper(cls, data, use_wrapper_subclass, **kwargs):
|
|
if use_wrapper_subclass:
|
|
kwargs["device"] = data.device
|
|
kwargs["dtype"] = data.dtype
|
|
kwargs["layout"] = data.layout
|
|
kwargs["requires_grad"] = True
|
|
return torch.Tensor._make_wrapper_subclass(cls, data.size(), **kwargs) # type: ignore[attr-defined]
|
|
else:
|
|
return torch.Tensor._make_subclass(cls, data, True, **kwargs)
|
|
|
|
def test_is_contiguous_slow_path(self):
|
|
data = torch.randn(3, 3)
|
|
contiguous_data = data.clone()
|
|
not_contiguous_data = torch.as_strided(data.clone(), (2, 2), (1, 2))
|
|
|
|
for use_wrapper_subclass in [True, False]:
|
|
class ExampleTensor1(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="strides")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
return NotImplemented
|
|
|
|
class ExampleTensor2(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="strides")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func.overloadpacket == torch.ops.aten.is_contiguous:
|
|
return contiguous_data.is_contiguous()
|
|
return NotImplemented
|
|
|
|
class ExampleTensor3(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="strides")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func.overloadpacket == torch.ops.aten.is_contiguous:
|
|
return not_contiguous_data.is_contiguous()
|
|
return NotImplemented
|
|
|
|
err_msg = "no implementation found for 'torch.ops.aten.is_contiguous'"
|
|
e = ExampleTensor1(torch.randn(3, 3), use_wrapper_subclass)
|
|
with self.assertRaisesRegex(TypeError, err_msg):
|
|
e.is_contiguous()
|
|
with self.assertRaisesRegex(TypeError, err_msg):
|
|
e.contiguous()
|
|
|
|
e = ExampleTensor2(torch.randn(3, 3), use_wrapper_subclass)
|
|
self.assertEqual(e.is_contiguous(), True)
|
|
e.contiguous() # this will just return the original TensorImpl since is_contiguous = True
|
|
|
|
err_msg = "no implementation found for"
|
|
e = ExampleTensor3(torch.randn(3, 3), use_wrapper_subclass)
|
|
self.assertEqual(e.is_contiguous(), False)
|
|
with self.assertRaisesRegex(TypeError, err_msg):
|
|
e.contiguous()
|
|
|
|
def test_device_slowpath(self):
|
|
for use_wrapper_subclass in [True]:
|
|
class ExampleTensor1(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_device=True)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
return NotImplemented
|
|
|
|
class ExampleTensor2(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_device=True)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func.overloadpacket == torch.ops.prim.device:
|
|
return torch.device('meta')
|
|
return NotImplemented
|
|
|
|
class ExampleTensor3(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_device=True)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func.overloadpacket == torch.ops.prim.device:
|
|
return torch.device('meta')
|
|
return NotImplemented
|
|
|
|
err_msg = "no implementation found for 'torch.ops.prim.device'"
|
|
with self.assertRaisesRegex(TypeError, err_msg):
|
|
e = ExampleTensor1(torch.randn(3, 3), use_wrapper_subclass)
|
|
e.device()
|
|
|
|
ten = torch.rand([1])
|
|
e = ExampleTensor2(torch.randn(3, 3, device='cpu'), use_wrapper_subclass)
|
|
self.assertEqual(e.device.type, 'meta')
|
|
self.assertEqual(ten.type_as(e).device.type, 'meta')
|
|
|
|
e = ExampleTensor3(torch.randn(3, 3, device='cpu'), use_wrapper_subclass)
|
|
self.assertEqual(e.device.type, 'meta')
|
|
self.assertEqual(ten.type_as(e).device.type, 'meta')
|
|
|
|
def test_dim_slowpath(self):
|
|
data = torch.randn(3, 3)
|
|
|
|
for use_wrapper_subclass in [True, False]:
|
|
class DimNotImplementedTensor(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="sizes")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
return NotImplemented
|
|
|
|
class DimImplementedTensor(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="sizes")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func.overloadpacket == torch.ops.aten.dim:
|
|
return data.dim()
|
|
return NotImplemented
|
|
|
|
err_msg = "no implementation found for 'torch.ops.aten.dim'"
|
|
e = DimNotImplementedTensor(torch.randn(3, 3), use_wrapper_subclass)
|
|
with self.assertRaisesRegex(TypeError, err_msg):
|
|
e.dim()
|
|
|
|
t = DimImplementedTensor(torch.randn(3, 3), use_wrapper_subclass)
|
|
self.assertEqual(t.dim(), 2)
|
|
|
|
def test_maybe_tuple_bug(self):
|
|
class T(torch.Tensor):
|
|
@classmethod
|
|
def __torch_function__(cls, *args, **kwargs):
|
|
pass
|
|
a = torch.rand(3)
|
|
|
|
a[[T(), T()]]
|
|
|
|
def test_standard_is_not_subclass(self):
|
|
# https://github.com/pytorch/pytorch/issues/79079
|
|
self.assertFalse(torch._C._dispatch_isTensorSubclassLike(torch.empty(0)))
|
|
|
|
def test_strides_slow_path(self):
|
|
for use_wrapper_subclass in [True, False]:
|
|
class StridesNotImplemented(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="strides")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
return NotImplemented
|
|
|
|
class StridesCustomReturn(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="strides")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func == torch.ops.aten.sym_stride.default:
|
|
return (4, 2)
|
|
return NotImplemented
|
|
|
|
class StridesDefaultReturn(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="strides")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func == torch.ops.aten.sym_stride.default:
|
|
return None
|
|
return NotImplemented
|
|
|
|
err_msg = "no implementation found for 'torch.ops.aten.sym_stride'"
|
|
e = StridesNotImplemented(torch.randn(3, 3), use_wrapper_subclass)
|
|
with self.assertRaisesRegex(RuntimeError, err_msg):
|
|
e.stride()
|
|
|
|
e = StridesCustomReturn(torch.randn(3, 3), use_wrapper_subclass)
|
|
self.assertEqual(e.stride(), (4, 2))
|
|
|
|
e = StridesDefaultReturn(torch.randn(6, 2), use_wrapper_subclass)
|
|
self.assertEqual(e.stride(), (2, 1))
|
|
|
|
def test_sizes_slow_path(self):
|
|
for use_wrapper_subclass in [True, False]:
|
|
data = torch.randn(6, 2)
|
|
|
|
class SizesNotImplemented(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="sizes")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func.overloadpacket == torch.ops.aten.dim:
|
|
return data.dim()
|
|
return NotImplemented
|
|
|
|
class SizesCustomReturn(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="sizes")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func.overloadpacket == torch.ops.aten.dim:
|
|
return data.dim()
|
|
if func.overloadpacket == torch.ops.aten.sym_size:
|
|
return (5, 3)
|
|
return NotImplemented
|
|
|
|
class SizesDefaultReturn(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_sizes_strides_policy="sizes")
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func.overloadpacket == torch.ops.aten.dim:
|
|
return data.dim()
|
|
if func.overloadpacket == torch.ops.aten.sym_size:
|
|
return None
|
|
return NotImplemented
|
|
|
|
err_msg = "no implementation found for 'torch.ops.aten.sym_size'"
|
|
e = SizesNotImplemented(torch.randn(3, 3), use_wrapper_subclass)
|
|
with self.assertRaisesRegex(RuntimeError, err_msg):
|
|
e.size()
|
|
|
|
e = SizesCustomReturn(torch.randn(3, 3), use_wrapper_subclass)
|
|
self.assertEqual(e.size(), (5, 3))
|
|
|
|
e = SizesDefaultReturn(torch.randn(4, 2), use_wrapper_subclass)
|
|
self.assertEqual(e.size(), (4, 2))
|
|
|
|
def test_layout_slow_path(self):
|
|
for use_wrapper_subclass in [True, False]:
|
|
data = torch.randn(6, 2)
|
|
|
|
class LayoutNotImplemented(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_layout=True)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
return NotImplemented
|
|
|
|
class LayoutCustomReturn(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_layout=True)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func.overloadpacket == torch.ops.prim.layout:
|
|
return torch.sparse_csr
|
|
return NotImplemented
|
|
|
|
class LayoutDefaultReturn(torch.Tensor):
|
|
@staticmethod
|
|
def __new__(cls, data, wrapper):
|
|
return TestPythonDispatch.subclass_helper(cls, data, wrapper, dispatch_layout=True)
|
|
|
|
@classmethod
|
|
def __torch_dispatch__(cls, func, types, args, kwargs):
|
|
if func.overloadpacket == torch.ops.prim.layout:
|
|
return data.layout
|
|
return NotImplemented
|
|
|
|
err_msg = "no implementation found for 'torch.ops.prim.layout'"
|
|
e = LayoutNotImplemented(torch.randn(3, 3), use_wrapper_subclass)
|
|
with self.assertRaisesRegex(TypeError, err_msg):
|
|
e.layout
|
|
|
|
e = LayoutCustomReturn(torch.randn(3, 3), use_wrapper_subclass)
|
|
self.assertEqual(e.layout, torch.sparse_csr)
|
|
|
|
e = LayoutDefaultReturn(torch.randn(4, 2), use_wrapper_subclass)
|
|
self.assertEqual(e.layout, torch.strided)
|
|
|
|
class TestPythonDispatcher(TestCase):
|
|
def test_basic(self):
|
|
x = torch.randn(2, requires_grad=True)
|
|
r = torch._C._EnablePythonDispatcher()
|
|
torch.add(x, x)
|
|
|
|
def test_lstsq(self):
|
|
a = torch.randn(4, 3)
|
|
b = torch.rand(4, 3)
|
|
expected_shape = torch.linalg.lstsq(a, b).solution.shape
|
|
r = torch._C._EnablePythonDispatcher()
|
|
python_disp_shape = torch.linalg.lstsq(a, b).solution.shape
|
|
self.assertEqual(expected_shape, python_disp_shape)
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|