This PR removes copycast of reduced precision types to float before printing, that was added in https://github.com/pytorch/pytorch/pull/14418 to probably unblock printing when many operations, like `is_nan` and `max` were not supported on CPUs
(Reusing old test plan) Before the PR:
```python
In [1]: import torch; a = torch.rand(1, 1700, 34, 50, dtype=torch.float16)
In [2]: %timeit str(a)
621 μs ± 5.06 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
```
after the PR
```python
In [1]: import torch; a = torch.rand(1, 1700, 34, 50, dtype=torch.float16)
In [2]: %timeit str(a)
449 μs ± 2.34 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
```
Also, this allows one printing 15Gb Metal tensors on 32GB Mac machine:
```
% python3 -c "import torch;print(torch.empty(72250,72250, device='mps', dtype=torch.float16))"
tensor([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]], device='mps:0', dtype=torch.float16)
```
Before this change it failed with non-descriptive
```
% python3 -c "import torch;print(torch.empty(72250,72250, device='mps', dtype=torch.float16))"
Traceback (most recent call last):
File "<string>", line 1, in <module>
import torch;print(torch.empty(72250,72250, device='mps', dtype=torch.float16))
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/malfet/git/pytorch/pytorch/torch/_tensor.py", line 568, in __repr__
return torch._tensor_str._str(self, tensor_contents=tensor_contents)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/malfet/git/pytorch/pytorch/torch/_tensor_str.py", line 708, in _str
return _str_intern(self, tensor_contents=tensor_contents)
File "/Users/malfet/git/pytorch/pytorch/torch/_tensor_str.py", line 625, in _str_intern
tensor_str = _tensor_str(self, indent)
File "/Users/malfet/git/pytorch/pytorch/torch/_tensor_str.py", line 339, in _tensor_str
self = self.float()
RuntimeError: Invalid buffer size: 19.45 GB
```
Convert fp8 dtypes to float16, as float range is an overkill
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141927
Approved by: https://github.com/ezyang
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Co-authored-by: Catherine Lee <csl@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Fixes https://github.com/pytorch/pytorch/issues/118129
Suppressions automatically added with
```
import re
with open("error_file.txt", "r") as f:
errors = f.readlines()
error_lines = {}
for error in errors:
match = re.match(r"(.*):(\d+):\d+: error:.*\[(.*)\]", error)
if match:
file_path, line_number, error_type = match.groups()
if file_path not in error_lines:
error_lines[file_path] = {}
error_lines[file_path][int(line_number)] = error_type
for file_path, lines in error_lines.items():
with open(file_path, "r") as f:
code = f.readlines()
for line_number, error_type in sorted(lines.items(), key=lambda x: x[0], reverse=True):
code[line_number - 1] = code[line_number - 1].rstrip() + f" # type: ignore[{error_type}]\n"
with open(file_path, "w") as f:
f.writelines(code)
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118533
Approved by: https://github.com/Skylion007, https://github.com/zou3519
Proposal of two float8 variants - e5m2 and e4m3 - based on https://arxiv.org/pdf/2209.05433.pdf
Hide all Float8 operator implementations behind `#if !defined(C10_MOBILE)` guard to keep Android build size almost unchanged
TODO:
- Refactor duplicated code
- Cleanup unbalanced pragma pop in dtype utils
- Add native implementation on the CUDA size
Co-authored-by: Nikita Shulga <nshulga@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104242
Approved by: https://github.com/albanD
Proposal of two float8 variants - e5m2 and e4m3 - based on https://arxiv.org/pdf/2209.05433.pdf
Hide all Float8 operator implementations behind `#if !defined(C10_MOBILE)` guard to keep Android build size almost unchanged
TODO:
- Refactor duplicated code
- Cleanup unbalanced pragma pop in dtype utils
- Add native implementation on the CUDA size
Co-authored-by: Nikita Shulga <nshulga@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104242
Approved by: https://github.com/albanD
- Add get_printoptions and printoptions context manager
- Improve edgeitems handling when it is zero
- Add render_call which can be used to conveniently print command
line arguments of a function call, while suppressing actual
tensor data
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102623
Approved by: https://github.com/albanD
I got too confused by the FakeTensor printing, so this PR fixes it to
print normally.
Before:
```
with FakeTensorMode():
x = torch.empty(2, 2, device="cpu")
print(x)
# FakeTensor(FakeTensor(..., device='meta', shape=(2, 2)), cpu)
```
After (Tensor printing doesn't print the default device):
```
FakeTensor(..., shape=(2, 2))
```
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99205
Approved by: https://github.com/eellison
Fixes https://github.com/pytorch/functorch/issues/1026
We need to disable functorch's stack-based dispatching mechanism inside
the tensor printing. Otherwise, all operations that clean up the data of
the Tensor for printing dispatch through the entire functorch stack and
causes problems.
Disabling stack-based dispatching and printing a functorch wrapped
tensor is not a problem; we're still able to get the attributes on the
wrapped tensor that we want.
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85556
Approved by: https://github.com/samdow
By upstreaming functorch's tensor printing logic into PyTorch. There's
no way of creating a custom print function for a TensorImpl subclass (as
opposed to a torch_dispatch or torch_function tensor subclass, which can
just override repr()) right now, so we need to directly interpose inside
regular Tensor printing in PyTorch.
Monkey patching is bad; users do not expect `import blah` to change
something about another library.
Fixes https://github.com/pytorch/functorch/issues/900
Test Plan:
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85430
Approved by: https://github.com/ezyang
Add support for sparse fake tensors.
- The testing strategy is to run a fake tensor cross ref test on `test_sparse.py`. This is necessary because OpInfo sparse coverage is completely nonexistent. We could have tried to turn on cross ref testing globally for all files, but that would be very time consuming and the tests I'm interested in are mostly in this file. There are some exclusions in testing for things that don't work.
- I make fake tensor converter raise a UnsupportedFakeTensorException if the meta converter fails to do a conversion (which can happen in a relatively large number of situations).
- I relax fake tensor invariants so that you can make a fake tensor from a meta tensor. This is useful because in the cross ref test sometimes we operate on meta tensors.
- Fake tensor wrapping is improved to handle the case when a function doesn't return any tensors
- Meta converter is taught how to convert sparse tensors to meta
There's still a little more cleanup that needs to be done, but this is good for review.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82172
Approved by: https://github.com/eellison
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74000
Now that we're in-core, we can just customize this.
ghstack-source-id: 151540966
Test Plan: Existing test_nestedtensor seems to pass
Reviewed By: ezyang
Differential Revision: D34665270
fbshipit-source-id: 5097944a4dc4fe80cea2b8576f0123466dbeab43
(cherry picked from commit d0315f46f9906c904639f43f218e439407f5b2a7)
Summary:
Fixes multiple compilation on xla tensor print. Please check the conversation here: https://github.com/pytorch/xla/pull/3253
This is done to avoid compilations during tensor printing. Torch performs some tensor operations like slicing to make the tensor readable. These operations result in compilations. Hence to avoid the compilations, copying the tensor to cpu before printing.
example:
```
dev = xm.xla_device()
def test_linear(input_shape=(8, 1024)):
import pdb
pdb.set_trace()
linear = torch.nn.Linear(in_features=1024, out_features=4096, bias=True).to(dev)
inp = torch.randn(*input_shape).to(dev)
output = linear(inp)
xm.mark_step()
return output
```
Returning from this function would have resulted in 63 compiles, since PDB prints the value of the return output. In this case it is a xla tensor.
Now with the current change, there is no compilation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71147
Reviewed By: shunting314
Differential Revision: D33795177
Pulled By: wconstab
fbshipit-source-id: 74b53d9a1cb7ef67f9d8b0a32064f3896be449b5
(cherry picked from commit a9e0687fc5c9981fb55ea4dc406c283c80fa20c9)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69327
Original commit changeset: d44096d88265
Original Phabricator Diff: D32144240 (668574af4a)
Test Plan:
CI
original diff failed 175 builds in CI
Reviewed By: airboyang, anjali411
Differential Revision: D32809407
fbshipit-source-id: c7c8e69bcee0274992e2d5da901f035332e60071
