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Author SHA1 Message Date
Nikita Vedeneev
dfc7fa03e5 lu_backward: more numerically stable and with complex support. (#53994) 
Summary:
As per title.

Numerical stability increased by replacing inverses with solutions to systems of linear triangular equations.

Unblocks computing `torch.det` for FULL-rank inputs of complex dtypes via the LU decomposition once https://github.com/pytorch/pytorch/pull/48125/files is merged:
```
LU, pivots = input.lu()
P, L, U = torch.lu_unpack(LU, pivots)
det_input = P.det() * torch.prod(U.diagonal(0, -1, -2), dim=-1)  # P is not differentiable, so we are fine even if it is complex.
```

Unfortunately, since `lu_backward` is implemented as `autograd.Function`, we cannot support both autograd and scripting at the moment.
The solution would be to move all the lu-related methods to ATen, see https://github.com/pytorch/pytorch/issues/53364.

Resolves https://github.com/pytorch/pytorch/issues/52891
TODOs:
* extend lu_backward for tall/wide matrices of full rank.
* move lu-related functionality to ATen and make it differentiable.
* handle rank-deficient inputs.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/53994

Reviewed By: pbelevich

Differential Revision: D27188529

Pulled By: anjali411

fbshipit-source-id: 8e053b240413dbf074904dce01cd564583d1f064
 2021-03-25 13:33:58 -07:00
Yanan Cao
f48a9712b7 Rewrite functional.tensordot to be TorchScript-able (#53672) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/53487

Pull Request resolved: https://github.com/pytorch/pytorch/pull/53672

Reviewed By: VitalyFedyunin

Differential Revision: D26934392

Pulled By: gmagogsfm

fbshipit-source-id: f842af340e4be723bf90b903793b0221af158ca7
 2021-03-20 23:03:30 -07:00
Philip Meier
b0afe945a7 Fix pylint error torch.tensor is not callable (#53424) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53424

Fixes https://github.com/pytorch/pytorch/issues/24807 and supersedes the stale https://github.com/pytorch/pytorch/issues/25093 (Cc Microsheep). If you now run the reproduction

```python
import torch

if __name__ == "__main__":
    t = torch.tensor([1, 2, 3], dtype=torch.float64)
```

with `pylint==2.6.0`, you get the following output

```
test_pylint.py:1:0: C0114: Missing module docstring (missing-module-docstring)
test_pylint.py:4:8: E1101: Module 'torch' has no 'tensor' member; maybe 'Tensor'? (no-
member)
test_pylint.py:4:38: E1101: Module 'torch' has no 'float64' member (no-member)
```

Now `pylint` doesn't recognize `torch.tensor` at all, but it is promoted in the stub. Given that it also doesn't recognize `torch.float64`, I think fixing this is out of scope of this PR.

 ---

## TL;DR

This BC-breaking only for users that rely on unintended behavior. Since `torch/__init__.py` loaded `torch/tensor.py` it was populated in `sys.modules`. `torch/__init__.py` then overwrote `torch.tensor` with the actual function. With this `import torch.tensor as tensor` does not fail, but returns the function rather than the module. Users that rely on this import need to change it to `from torch import tensor`.

Reviewed By: zou3519

Differential Revision: D26223815

Pulled By: bdhirsh

fbshipit-source-id: 125b9ff3d276e84a645cd7521e8d6160b1ca1c21
 2021-03-09 11:32:53 -08:00
momohatt
3403babd94 [doc] Fix documentations of torch functions (#52982) 
Summary:
This PR includes multiple small fixes of docstrings.

* Fix documentation for [`torch.atleast_2d`](https://pytorch.org/docs/master/generated/torch.atleast_2d.html) and [`torch.atleast_3d`](https://pytorch.org/docs/master/generated/torch.atleast_3d.html) by adding a new line before `Args::`.
* Fix indentation for [`torch.isfinite`](https://pytorch.org/docs/master/generated/torch.isfinite.html) and [`torch.isinf`](https://pytorch.org/docs/master/generated/torch.isinf.html). The "Arguments", "Parameters" and "Examples" sections need to be at the same level as the first description.
* Insert a new line after `Example::` where it is missing. This makes difference in the way the documentations are rendered: see [this](https://pytorch.org/docs/master/generated/torch.gt.html) (with a new line) and [this](https://pytorch.org/docs/master/generated/torch.triu_indices.html) (without). As the majority of the docs seems to follow the former style, this PR amends the latter cases.
* Fix the "Returns" section of [`torch.block_diag`](https://pytorch.org/docs/master/generated/torch.block_diag.html) and [`torch.cartesian_prod`](https://pytorch.org/docs/master/generated/torch.cartesian_prod.html). The second and the subsequent lines shouldn't be indented, as can be seen in the docstring of [`torch.vander`](https://pytorch.org/docs/master/generated/torch.vander.html).
* Fix variable names in the example of `torch.fft.(i)fftn`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/52982

Reviewed By: mruberry

Differential Revision: D26724408

Pulled By: H-Huang

fbshipit-source-id: c65aa0621f7858b05fd16f497caacf6ea8eb33c9
 2021-03-01 09:59:57 -08:00
Helene
47557b95ef Removed typographical error from tech docs (#51286) 
Summary:
Dublications removed from tech docs.

![Screenshot](https://user-images.githubusercontent.com/71665475/106158807-6e5b8100-6184-11eb-9036-bccdf2086c31.png)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/51286

Reviewed By: albanD

Differential Revision: D26227627

Pulled By: ailzhang

fbshipit-source-id: efa0cd90face458673b8530388378d5a7eb0f1cf
 2021-02-03 14:09:36 -08:00
Sam Estep
c147aa306c Use doctest directly to get docstring examples (#50596) 
Summary:
This PR addresses [a two-year-old TODO in `test/test_type_hints.py`](12942ea52b/test/test_type_hints.py (L21-L22)) by replacing most of the body of our custom `get_examples_from_docstring` function with [a function from Python's built-in `doctest.DocTestParser` class](https://docs.python.org/3/library/doctest.html#doctest.DocTestParser.get_examples). This mostly made the parser more strict, catching a few errors in existing doctests:

- missing `...` in multiline statements
- missing space after `>>>`
- unmatched closing parenthesis

Also, as shown by [the resulting diff of the untracked `test/generated_type_hints_smoketest.py` file](https://pastebin.com/vC5Wz6M0) (also linked from the test plan below), this introduces a few incidental changes as well:

- standalone comments are no longer preserved
- indentation is now visually correct
- [`example_torch_promote_types`](4da9ceb743/torch/_torch_docs.py (L6753-L6772)) is now present
- an example called `example_torch_tensor___array_priority__` is added, although I can't tell where it comes from
- the last nine lines of code from [`example_torch_tensor_align_as`](5d45140d68/torch/_tensor_docs.py (L386-L431)) are now present
- the previously-misformatted third line from [`example_torch_tensor_stride`](5d45140d68/torch/_tensor_docs.py (L3508-L3532)) is now present

Pull Request resolved: https://github.com/pytorch/pytorch/pull/50596

Test Plan:
Checkout the base commit, typecheck the doctests, and save the generated file:
```
$ python test/test_type_hints.py TestTypeHints.test_doc_examples
$ cp test/generated_type_hints_smoketest.py /tmp
```
Then checkout this PR, do the same thing, and compare:
```
$ python test/test_type_hints.py TestTypeHints.test_doc_examples
$ git diff --no-index {/tmp,test}/generated_type_hints_smoketest.py
```
The test should succeed, and the diff should match [this paste](https://pastebin.com/vC5Wz6M0).

Reviewed By: walterddr

Differential Revision: D25926245

Pulled By: samestep

fbshipit-source-id: 23bc379ff438420e556263c19582dba06d8e42ec
 2021-01-20 15:55:36 -08:00
Taylor Robie
6a3fc0c21c Treat has_torch_function and object_has_torch_function as static False when scripting (#48966) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48966

This PR lets us skip the `if not torch.jit.is_scripting():` guards on `functional` and `nn.functional` by directly registering `has_torch_function` and `object_has_torch_function` to the JIT as statically False.

**Benchmarks**

The benchmark script is kind of long. The reason is that it's testing all four PRs in the stack, plus threading and subprocessing so that the benchmark can utilize multiple cores while still collecting good numbers. Both wall times and instruction counts were collected. This stack changes dozens of operators / functions, but very mechanically such that there are only a handful of codepath changes. Each row is a slightly different code path (e.g. testing in Python, testing in the arg parser, different input types, etc.)

<details>

<summary> Test script </summary>

```
import argparse
import multiprocessing
import multiprocessing.dummy
import os
import pickle
import queue
import random
import sys
import subprocess
import tempfile
import time

import torch
from torch.utils.benchmark import Timer, Compare, Measurement

NUM_CORES = multiprocessing.cpu_count()
ENVS = {
    "ref": "HEAD (current)",
    "torch_fn_overhead_stack_0": "#48963",
    "torch_fn_overhead_stack_1": "#48964",
    "torch_fn_overhead_stack_2": "#48965",
    "torch_fn_overhead_stack_3": "#48966",
}

CALLGRIND_ENVS = tuple(ENVS.keys())

MIN_RUN_TIME = 3
REPLICATES = {
    "longer": 1_000,
    "long": 300,
    "short": 50,
}

CALLGRIND_NUMBER = {
    "overnight": 500_000,
    "long": 250_000,
    "short": 10_000,
}

CALLGRIND_TIMEOUT = {
    "overnight": 800,
    "long": 400,
    "short": 100,
}

SETUP = """
    x = torch.ones((1, 1))
    y = torch.ones((1, 1))
    w_tensor = torch.ones((1, 1), requires_grad=True)
    linear = torch.nn.Linear(1, 1, bias=False)
    linear_w = linear.weight
"""

TASKS = {
    "C++: unary                 `.t()`": "w_tensor.t()",
    "C++: unary  (Parameter)    `.t()`": "linear_w.t()",
    "C++: binary (Parameter)    `mul` ": "x + linear_w",
    "tensor.py: _wrap_type_error_to_not_implemented `__floordiv__`": "x // y",
    "tensor.py: method          `__hash__`": "hash(x)",
    "Python scalar              `__rsub__`": "1 - x",
    "functional.py: (unary)     `unique`": "torch.functional.unique(x)",
    "functional.py: (args)      `atleast_1d`": "torch.functional.atleast_1d((x, y))",
    "nn/functional.py: (unary)  `relu`": "torch.nn.functional.relu(x)",
    "nn/functional.py: (args)   `linear`": "torch.nn.functional.linear(x, w_tensor)",
    "nn/functional.py: (args)   `linear (Parameter)`": "torch.nn.functional.linear(x, linear_w)",
    "Linear(..., bias=False)": "linear(x)",
}

def _worker_main(argv, fn):
    parser = argparse.ArgumentParser()
    parser.add_argument("--output_file", type=str)
    parser.add_argument("--single_task", type=int, default=None)
    parser.add_argument("--length", type=str)
    args = parser.parse_args(argv)
    single_task = args.single_task

    conda_prefix = os.getenv("CONDA_PREFIX")
    assert torch.__file__.startswith(conda_prefix)

    env = os.path.split(conda_prefix)[1]
    assert env in ENVS

    results = []
    for i, (k, stmt) in enumerate(TASKS.items()):
        if single_task is not None and single_task != i:
            continue

        timer = Timer(
            stmt=stmt,
            setup=SETUP,
            sub_label=k,
            description=ENVS[env],
        )
        results.append(fn(timer, args.length))

    with open(args.output_file, "wb") as f:
        pickle.dump(results, f)

def worker_main(argv):
    _worker_main(
        argv,
        lambda timer, _: timer.blocked_autorange(min_run_time=MIN_RUN_TIME)
    )

def callgrind_worker_main(argv):
    _worker_main(
        argv,
        lambda timer, length: timer.collect_callgrind(number=CALLGRIND_NUMBER[length], collect_baseline=False))

def main(argv):
    parser = argparse.ArgumentParser()
    parser.add_argument("--long", action="store_true")
    parser.add_argument("--longer", action="store_true")
    args = parser.parse_args(argv)

    if args.longer:
        length = "longer"
    elif args.long:
        length = "long"
    else:
        length = "short"
    replicates = REPLICATES[length]

    num_workers = int(NUM_CORES // 2)
    tasks = list(ENVS.keys()) * replicates
    random.shuffle(tasks)
    task_queue = queue.Queue()
    for _ in range(replicates):
        envs = list(ENVS.keys())
        random.shuffle(envs)
        for e in envs:
            task_queue.put((e, None))

    callgrind_task_queue = queue.Queue()
    for e in CALLGRIND_ENVS:
        for i, _ in enumerate(TASKS):
            callgrind_task_queue.put((e, i))

    results = []
    callgrind_results = []

    def map_fn(worker_id):
        # Adjacent cores often share cache and maxing out a machine can distort
        # timings so we space them out.
        callgrind_cores = f"{worker_id * 2}-{worker_id * 2 + 1}"
        time_cores = str(worker_id * 2)
        _, output_file = tempfile.mkstemp(suffix=".pkl")
        try:
            loop_tasks = (
                # Callgrind is long running, and then the workers can help with
                # timing after they finish collecting counts.
                (callgrind_task_queue, callgrind_results, "callgrind_worker", callgrind_cores, CALLGRIND_TIMEOUT[length]),
                (task_queue, results, "worker", time_cores, None))

            for queue_i, results_i, mode_i, cores, timeout in loop_tasks:
                while True:
                    try:
                        env, task_i = queue_i.get_nowait()
                    except queue.Empty:
                        break

                    remaining_attempts = 3
                    while True:
                        try:
                            subprocess.run(
                                " ".join([
                                    "source", "activate", env, "&&",
                                    "taskset", "--cpu-list", cores,
                                    "python", os.path.abspath(__file__),
                                    "--mode", mode_i,
                                    "--length", length,
                                    "--output_file", output_file
                                ] + ([] if task_i is None else ["--single_task", str(task_i)])),
                                shell=True,
                                check=True,
                                timeout=timeout,
                            )
                            break

                        except subprocess.TimeoutExpired:
                            # Sometimes Valgrind will hang if there are too many
                            # concurrent runs.
                            remaining_attempts -= 1
                            if not remaining_attempts:
                                print("Too many failed attempts.")
                                raise
                            print(f"Timeout after {timeout} sec. Retrying.")

                    # We don't need a lock, as the GIL is enough.
                    with open(output_file, "rb") as f:
                        results_i.extend(pickle.load(f))

        finally:
            os.remove(output_file)

    with multiprocessing.dummy.Pool(num_workers) as pool:
        st, st_estimate, eta, n_total = time.time(), None, "", len(tasks) * len(TASKS)
        map_job = pool.map_async(map_fn, range(num_workers))
        while not map_job.ready():
            n_complete = len(results)
            if n_complete and len(callgrind_results):
                if st_estimate is None:
                    st_estimate = time.time()
                else:
                    sec_per_element = (time.time() - st_estimate) / n_complete
                    n_remaining = n_total - n_complete
                    eta = f"ETA: {n_remaining * sec_per_element:.0f} sec"

            print(
                f"\r{n_complete} / {n_total}  "
                f"({len(callgrind_results)} / {len(CALLGRIND_ENVS) * len(TASKS)})   "
                f"{eta}".ljust(40), end="")
            sys.stdout.flush()
            time.sleep(2)
    total_time = int(time.time() - st)
    print(f"\nTotal time: {int(total_time // 60)} min, {total_time % 60} sec")

    desc_to_ind = {k: i for i, k in enumerate(ENVS.values())}
    results.sort(key=lambda r: desc_to_ind[r.description])

    # TODO: Compare should be richer and more modular.
    compare = Compare(results)
    compare.trim_significant_figures()
    compare.colorize(rowwise=True)

    # Manually add master vs. overall relative delta t.
    merged_results = {
        (r.description, r.sub_label): r
        for r in Measurement.merge(results)
    }

    cmp_lines = str(compare).splitlines(False)
    print(cmp_lines[0][:-1] + "-" * 15 + "]")
    print(f"{cmp_lines[1]} |{'':>10}\u0394t")
    print(cmp_lines[2] + "-" * 15)
    for l, t in zip(cmp_lines[3:3 + len(TASKS)], TASKS.keys()):
        assert l.strip().startswith(t)
        t0 = merged_results[(ENVS["ref"], t)].median
        t1 = merged_results[(ENVS["torch_fn_overhead_stack_3"], t)].median
        print(f"{l} |{'':>5}{(t1 / t0 - 1) * 100:>6.1f}%")
    print("\n".join(cmp_lines[3 + len(TASKS):]))

    counts_dict = {
        (r.task_spec.description, r.task_spec.sub_label): r.counts(denoise=True)
        for r in callgrind_results
    }

    def rel_diff(x, x0):
        return f"{(x / x0 - 1) * 100:>6.1f}%"

    task_pad = max(len(t) for t in TASKS)
    print(f"\n\nInstruction % change (relative to `{CALLGRIND_ENVS[0]}`)")
    print(" " * (task_pad + 8)  + (" " * 7).join([ENVS[env] for env in CALLGRIND_ENVS[1:]]))
    for t in TASKS:
        values = [counts_dict[(ENVS[env], t)] for env in CALLGRIND_ENVS]

        print(t.ljust(task_pad + 3) + "  ".join([
            rel_diff(v, values[0]).rjust(len(ENVS[env]) + 5)
            for v, env in zip(values[1:], CALLGRIND_ENVS[1:])]))

        print("\033[4m" + "    Instructions per invocation".ljust(task_pad + 3) + "  ".join([
            f"{v // CALLGRIND_NUMBER[length]:.0f}".rjust(len(ENVS[env]) + 5)
            for v, env in zip(values[1:], CALLGRIND_ENVS[1:])]) + "\033[0m")
        print()

    import pdb
    pdb.set_trace()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--mode", type=str, choices=("main", "worker", "callgrind_worker"), default="main")
    args, remaining = parser.parse_known_args()

    if args.mode == "main":
        main(remaining)

    elif args.mode == "callgrind_worker":
        callgrind_worker_main(remaining)

    else:
        worker_main(remaining)

```

</details>

**Wall time**
<img width="1178" alt="Screen Shot 2020-12-12 at 12 28 13 PM" src="https://user-images.githubusercontent.com/13089297/101994419-284f6a00-3c77-11eb-8dc8-4f69a890302e.png">

<details>

<summary> Longer run (`python test.py --long`) is basically identical. </summary>

<img width="1184" alt="Screen Shot 2020-12-12 at 5 02 47 PM" src="https://user-images.githubusercontent.com/13089297/102000425-2350e180-3c9c-11eb-999e-a95b37e9ef54.png">

</details>

**Callgrind**
<img width="936" alt="Screen Shot 2020-12-12 at 12 28 54 PM" src="https://user-images.githubusercontent.com/13089297/101994421-2e454b00-3c77-11eb-9cd3-8cde550f536e.png">

Test Plan: existing unit tests.

Reviewed By: ezyang

Differential Revision: D25590731

Pulled By: robieta

fbshipit-source-id: fe05305ff22b0e34ced44b60f2e9f07907a099dd
 2021-01-10 19:23:38 -08:00
Taylor Robie
d31a760be4 move has_torch_function to C++, and make a special case object_has_torch_function (#48965) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48965

This PR pulls `__torch_function__` checking entirely into C++, and adds a special `object_has_torch_function` method for ops which only have one arg as this lets us skip tuple construction and unpacking. We can now also do away with the Python side fast bailout for `Tensor` (e.g. `if any(type(t) is not Tensor for t in tensors) and has_torch_function(tensors)`) because they're actually slower than checking with the Python C API.

Test Plan: Existing unit tests. Benchmarks are in #48966

Reviewed By: ezyang

Differential Revision: D25590732

Pulled By: robieta

fbshipit-source-id: 6bd74788f06cdd673f3a2db898143d18c577eb42
 2021-01-10 19:23:35 -08:00
Samuel Marks
e6779d4357 [*.py] Rename "Arguments:" to "Args:" (#49736) 
Summary:
I've written custom parsers and emitters for everything from docstrings to classes and functions. However, I recently came across an issue when I was parsing/generating from the TensorFlow codebase: inconsistent use of `Args:` and `Arguments:` in its docstrings.

```sh
(pytorch#c348fae)$ for name in 'Args:' 'Arguments:'; do
    printf '%-10s %04d\n' "$name" "$(rg -IFtpy --count-matches "$name" | paste -s -d+ -- | bc)"; done
Args:      1095
Arguments: 0336
```

It is easy enough to extend my parsers to support both variants, however it looks like `Arguments:` is wrong anyway, as per:

  - https://google.github.io/styleguide/pyguide.html#doc-function-args @ [`ddccc0f`](https://github.com/google/styleguide/blob/ddccc0f/pyguide.md)

  - https://chromium.googlesource.com/chromiumos/docs/+/master/styleguide/python.md#describing-arguments-in-docstrings @ [`9fc0fc0`](https://chromium.googlesource.com/chromiumos/docs/+/9fc0fc0/styleguide/python.md)

  - https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html @ [`c0ae8e3`](https://github.com/sphinx-contrib/napoleon/blob/c0ae8e3/docs/source/example_google.rst)

Therefore, only `Args:` is valid. This PR replaces them throughout the codebase.

PS: For related PRs, see tensorflow/tensorflow/pull/45420

PPS: The trackbacks automatically appearing below are sending the same changes to other repositories in the [PyTorch](https://github.com/pytorch) organisation.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/49736

Reviewed By: albanD

Differential Revision: D25710534

Pulled By: soumith

fbshipit-source-id: 61e8ff01abb433e9f78185c2d1d0cbd7c22c1619
 2020-12-28 09:34:47 -08:00
Peter Bell
5c25f8faf3 stft: Change require_complex warning to an error (#49022) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49022

**BC-breaking note**:

Previously torch.stft took an optional `return_complex` parameter that indicated whether the output would be a floating point tensor or a complex tensor. By default `return_complex` was False to be consistent with the previous behavior of torch.stft. This PR changes this behavior so `return_complex` is a required argument.

**PR Summary**:

* **#49022 stft: Change require_complex warning to an error**

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D25658906

Pulled By: mruberry

fbshipit-source-id: 11932d1102e93f8c7bd3d2d0b2a607fd5036ec5e
 2020-12-20 14:48:25 -08:00
Mike Ruberry
47c65f8223 Revert D25569586: stft: Change require_complex warning to an error 
Test Plan: revert-hammer

Differential Revision:
D25569586 (5874925b46)

Original commit changeset: 09608088f540

fbshipit-source-id: 6a5953b327a4a2465b046e29bb007a0c5f4cf14a
 2020-12-16 16:21:52 -08:00
Peter Bell
5874925b46 stft: Change require_complex warning to an error (#49022) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49022

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D25569586

Pulled By: mruberry

fbshipit-source-id: 09608088f540c2c3fc70465f6a23f2aec5f24f85
 2020-12-16 12:47:56 -08:00
Heitor Schueroff
45b33c83f1 Revert "Revert D24923679: Fixed einsum compatibility/performance issues (#46398)" (#49189) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49189

This reverts commit d307601365 and fixes the bug with diagonals and ellipsis combined.

Test Plan: Imported from OSS

Reviewed By: glaringlee

Differential Revision: D25540722

Pulled By: heitorschueroff

fbshipit-source-id: 86d0c9a7dcfda600b546457dad102af2ff33e353
 2020-12-16 10:38:07 -08:00
Peter Bell
524adfbffd Use new FFT operators in stft (#47601) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47601

Fixes https://github.com/pytorch/pytorch/issues/42175#issuecomment-719933913

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D25457217

Pulled By: mruberry

fbshipit-source-id: 455d216edd0b962eb7967ecb47cccc8d6865975b
 2020-12-10 10:31:50 -08:00
Kurt Mohler
54f0556ee4 Add missing complex support for torch.norm and torch.linalg.norm (#48284) 
Summary:
**BC-breaking note:**

Previously, when given a complex input, `torch.linalg.norm` and `torch.norm` would return a complex output. `torch.linalg.cond` would sometimes return a complex output and sometimes return a real output when given a complex input, depending on its `p` argument. This PR changes this behavior to match `numpy.linalg.norm` and `numpy.linalg.cond`, so that a complex input will result in the downgraded real number type, consistent with NumPy.

**PR Summary:**

The following cases were previously unsupported for complex inputs, and this commit adds support:

- Frobenius norm
- Norm order 2 (vector and matrix)
- CUDA vector norm

Part of https://github.com/pytorch/pytorch/issues/47833

Pull Request resolved: https://github.com/pytorch/pytorch/pull/48284

Reviewed By: H-Huang

Differential Revision: D25420880

Pulled By: mruberry

fbshipit-source-id: 11f6a2f3cad57d66476d30921c3f6ab8f3cd4017
 2020-12-10 10:23:45 -08:00
Heitor Schueroff
d307601365 Revert D24923679: Fixed einsum compatibility/performance issues (#46398) 
Test Plan: revert-hammer

Differential Revision:
D24923679 (ea2a568cca)

Original commit changeset: 47e48822cd67

fbshipit-source-id: 52f17b66a4aa075d0159bdf1c98616e6098091b8
 2020-12-07 11:48:36 -08:00
Heitor Schueroff
ea2a568cca Fixed einsum compatibility/performance issues (#46398) (#47860) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47860

This PR makes torch.einsum compatible with numpy.einsum except for the sublist input option as requested here https://github.com/pytorch/pytorch/issues/21412. It also fixed 2 performance issues linked below and adds a check for reducing to torch.dot instead of torch.bmm which is faster in some cases.

fixes #45854, #37628, #30194, #15671

fixes #41467 with benchmark below
```python
import torch
from torch.utils.benchmark import Timer

a = torch.randn(10000, 100, 101, device='cuda')
b = torch.randn(10000, 101, 3, device='cuda')

c = torch.randn(10000, 100, 1, device='cuda')
d = torch.randn(10000, 100, 1, 3, device='cuda')

print(Timer(
    stmt='torch.einsum("bij,bjf->bif", a, b)',
    globals={'a': a, 'b': b}
).blocked_autorange())

print()

print(Timer(
    stmt='torch.einsum("bic,bicf->bif", c, d)',
    globals={'c': c, 'd': d}
).blocked_autorange())
```
```
<torch.utils.benchmark.utils.common.Measurement object at 0x7fa37c413850>
torch.einsum("bij,bjf->bif", a, b)
  Median: 4.53 ms
  IQR:    0.00 ms (4.53 to 4.53)
  45 measurements, 1 runs per measurement, 1 thread

<torch.utils.benchmark.utils.common.Measurement object at 0x7fa37c413700>
torch.einsum("bic,bicf->bif", c, d)
  Median: 63.86 us
  IQR:    1.52 us (63.22 to 64.73)
  4 measurements, 1000 runs per measurement, 1 thread
```

fixes #32591 with benchmark below
```python
import torch
from torch.utils.benchmark import Timer

a = torch.rand(1, 1, 16, 2, 16, 2, 16, 2, 2, 2, 2, device="cuda")
b = torch.rand(729, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, device="cuda")

print(Timer(
    stmt='(a * b).sum(dim = (-3, -2, -1))',
    globals={'a': a, 'b': b}
).blocked_autorange())

print()

print(Timer(
    stmt='torch.einsum("...ijk, ...ijk -> ...", a, b)',
    globals={'a': a, 'b': b}
).blocked_autorange())
```
```
<torch.utils.benchmark.utils.common.Measurement object at 0x7efe0de28850>
(a * b).sum(dim = (-3, -2, -1))
  Median: 17.86 ms
  2 measurements, 10 runs per measurement, 1 thread

<torch.utils.benchmark.utils.common.Measurement object at 0x7efe0de286a0>
torch.einsum("...ijk, ...ijk -> ...", a, b)
  Median: 296.11 us
  IQR:    1.38 us (295.42 to 296.81)
  662 measurements, 1 runs per measurement, 1 thread
```

TODO

- [x] add support for ellipsis broadcasting
- [x] fix corner case issues with sumproduct_pair
- [x] update docs and add more comments
- [x] add tests for error cases

Test Plan: Imported from OSS

Reviewed By: mruberry

Differential Revision: D24923679

Pulled By: heitorschueroff

fbshipit-source-id: 47e48822cd67bbcdadbdfc5ffa25ee8ba4c9620a
 2020-12-06 08:02:37 -08:00
Fritz Obermeyer
313e77fc06 Add broadcast_shapes() function and use it in MultivariateNormal (#43935) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/43837

This adds a `torch.broadcast_shapes()` function similar to Pyro's [broadcast_shape()](7c2c22c10d/pyro/distributions/util.py (L151)) and JAX's [lax.broadcast_shapes()](https://jax.readthedocs.io/en/test-docs/_modules/jax/lax/lax.html). This helper is useful e.g. in multivariate distributions that are parameterized by multiple tensors and we want to `torch.broadcast_tensors()` but the parameter tensors have different "event shape" (e.g. mean vectors and covariance matrices). This helper is already heavily used in Pyro's distribution codebase, and we would like to start using it in `torch.distributions`.

- [x] refactor `MultivariateNormal`'s expansion logic to use `torch.broadcast_shapes()`
- [x] add unit tests for `torch.broadcast_shapes()`
- [x] add docs

cc neerajprad

Pull Request resolved: https://github.com/pytorch/pytorch/pull/43935

Reviewed By: bdhirsh

Differential Revision: D25275213

Pulled By: neerajprad

fbshipit-source-id: 1011fdd597d0a7a4ef744ebc359bbb3c3be2aadc
 2020-12-03 02:42:04 -08:00
Jeff Yang
84fafbe49c [docs] docstring for no type checked meshgrid (#48471) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/48395

I am not sure this is a correct way for fixing tho

cc mruberry

Locally built preview:
![Screen Shot 2020-11-26 at 14 57 49](https://user-images.githubusercontent.com/32727188/100326034-d14f6100-2ff7-11eb-8abb-53317b9f518e.png)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/48471

Reviewed By: mrshenli

Differential Revision: D25191033

Pulled By: mruberry

fbshipit-source-id: e5d9cb2748f7cb81923a1d4f204ffb330f6da1ee
 2020-11-26 17:28:41 -08:00
Gregory Chanan
b6cb2caa68 Revert "Fixed einsum compatibility/performance issues (#46398)" (#47821) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47821

This reverts commit a5c65b86ce.

 Conflicts:
	test/test_linalg.py

Test Plan: Imported from OSS

Reviewed By: mruberry

Differential Revision: D24909923

Pulled By: gchanan

fbshipit-source-id: 9dcf98e7c4a3c7e5aaffe475867fa086f3bb6ff2
 2020-11-12 08:11:40 -08:00
Heitor Schueroff
a5c65b86ce Fixed einsum compatibility/performance issues (#46398) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46398

This PR makes torch.einsum compatible with numpy.einsum except for the sublist input option as requested here https://github.com/pytorch/pytorch/issues/21412. It also fixed 2 performance issues linked below and adds a check for reducing to torch.dot instead of torch.bmm which is faster in some cases.

fixes #45854, #37628, #30194, #15671

fixes #41467 with benchmark below
```python
import torch
from torch.utils.benchmark import Timer

a = torch.randn(10000, 100, 101, device='cuda')
b = torch.randn(10000, 101, 3, device='cuda')

c = torch.randn(10000, 100, 1, device='cuda')
d = torch.randn(10000, 100, 1, 3, device='cuda')

print(Timer(
    stmt='torch.einsum("bij,bjf->bif", a, b)',
    globals={'a': a, 'b': b}
).blocked_autorange())

print()

print(Timer(
    stmt='torch.einsum("bic,bicf->bif", c, d)',
    globals={'c': c, 'd': d}
).blocked_autorange())
```
```
<torch.utils.benchmark.utils.common.Measurement object at 0x7fa37c413850>
torch.einsum("bij,bjf->bif", a, b)
  Median: 4.53 ms
  IQR:    0.00 ms (4.53 to 4.53)
  45 measurements, 1 runs per measurement, 1 thread

<torch.utils.benchmark.utils.common.Measurement object at 0x7fa37c413700>
torch.einsum("bic,bicf->bif", c, d)
  Median: 63.86 us
  IQR:    1.52 us (63.22 to 64.73)
  4 measurements, 1000 runs per measurement, 1 thread
```

fixes #32591 with benchmark below
```python
import torch
from torch.utils.benchmark import Timer

a = torch.rand(1, 1, 16, 2, 16, 2, 16, 2, 2, 2, 2, device="cuda")
b = torch.rand(729, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, device="cuda")

print(Timer(
    stmt='(a * b).sum(dim = (-3, -2, -1))',
    globals={'a': a, 'b': b}
).blocked_autorange())

print()

print(Timer(
    stmt='torch.einsum("...ijk, ...ijk -> ...", a, b)',
    globals={'a': a, 'b': b}
).blocked_autorange())
```
```
<torch.utils.benchmark.utils.common.Measurement object at 0x7efe0de28850>
(a * b).sum(dim = (-3, -2, -1))
  Median: 17.86 ms
  2 measurements, 10 runs per measurement, 1 thread

<torch.utils.benchmark.utils.common.Measurement object at 0x7efe0de286a0>
torch.einsum("...ijk, ...ijk -> ...", a, b)
  Median: 296.11 us
  IQR:    1.38 us (295.42 to 296.81)
  662 measurements, 1 runs per measurement, 1 thread
```

TODO

- [x] add support for ellipsis broadcasting
- [x] fix corner case issues with sumproduct_pair
- [x] update docs and add more comments
- [x] add tests for error cases

Test Plan: Imported from OSS

Reviewed By: malfet

Differential Revision: D24860367

Pulled By: heitorschueroff

fbshipit-source-id: 31110ee598fd598a43acccf07929b67daee160f9
 2020-11-10 19:38:43 -08:00
Elias Ellison
f3ad7b2919 [JIT][Reland] add list() support (#42382) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/40869

Resubmit of https://github.com/pytorch/pytorch/pull/33818.

Adds support for `list()` by desugaring  it to a list comprehension.

Last time I landed this it made one of the tests slow, and got unlanded. I think that's bc the previous PR changed the emission of `list()` on a list input or a str input to a list comprehension, which is the more general way of emitting `list()`, but also a little bit slower. I updated this version to emit to the builtin operators for these two case. Hopefully it can land without being reverted this time...

Pull Request resolved: https://github.com/pytorch/pytorch/pull/42382

Reviewed By: navahgar

Differential Revision: D24767674

Pulled By: eellison

fbshipit-source-id: a1aa3d104499226b28f47c3698386d365809c23c
 2020-11-06 01:28:54 -08:00
Howard Huang
a8ef4d3f0b Provide 'out' parameter for 'tensordot' (#47278) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/42102

Added an optional out parameter to the tensordot operation to allow using buffers.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/47278

Test Plan: pytest test/test_torch.py -k tensordot -v

Reviewed By: agolynski

Differential Revision: D24706258

Pulled By: H-Huang

fbshipit-source-id: eb4bcd114795f67de3a670291034107d2826ea69
 2020-11-03 15:56:00 -08:00
Nikita Vedeneev
cd26d027b3 [doc] Fix info on the shape of pivots in torch.lu + more info on what and how they encode permutations. (#46844) 
Summary:
As per title.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/46844

Reviewed By: VitalyFedyunin

Differential Revision: D24595538

Pulled By: ezyang

fbshipit-source-id: 1bb9c0310170124c3b6e33bd26ce38c22b36e926
 2020-10-28 14:56:31 -07:00
Nikita Vedeneev
c31ced4246 make torch.lu differentiable. (#46284) 
Summary:
As per title. Limitations: only for batches of squared full-rank matrices.

CC albanD

Pull Request resolved: https://github.com/pytorch/pytorch/pull/46284

Reviewed By: zou3519

Differential Revision: D24448266

Pulled By: albanD

fbshipit-source-id: d98215166268553a648af6bdec5a32ad601b7814
 2020-10-23 10:13:46 -07:00
Xu Zhao
146721f1df Fix typing errors in the torch.distributions module (#45689) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/42979.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/45689

Reviewed By: agolynski

Differential Revision: D24229870

Pulled By: xuzhao9

fbshipit-source-id: 5fc87cc428170139962ab65b71cacba494d46130
 2020-10-12 10:29:45 -07:00
Kurt Mohler
a0a8bc8870 Fix mistakes and increase clarity of norm documentation (#42696) 
Summary:
* Removes incorrect statement that "the vector norm will be applied to the last dimension".
* More clearly describe each different combination of `p`, `ord`, and input size.
* Moves norm tests from `test/test_torch.py` to `test/test_linalg.py`
* Adds test ensuring that `p='fro'` and `p=2` give same results for mutually valid inputs

Fixes https://github.com/pytorch/pytorch/issues/41388

Pull Request resolved: https://github.com/pytorch/pytorch/pull/42696

Reviewed By: bwasti

Differential Revision: D23876862

Pulled By: mruberry

fbshipit-source-id: 36f33ccb6706d5fe13f6acf3de8ae14d7fbdff85
 2020-10-10 14:12:43 -07:00
Meghan Lele
78f055272c [docs] Add 3D reduction example to tensordot docs (#45697) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45697

**Summary**
This commit adds an example of a reduction over three dimensions with
`torch.tensordot`. It is unclear from existing docs whether `dims`
should be a list of pairs or a pair of lists.

**Test Plan**
Built the docs locally.

*Before*
<img width="864" alt="Captura de Pantalla 2020-10-01 a la(s) 1 35 46 p  m" src="https://user-images.githubusercontent.com/4392003/94866838-f0b17f80-03f4-11eb-8692-8f50fe3b9863.png">

*After*
<img width="831" alt="Captura de Pantalla 2020-10-05 a la(s) 12 06 28 p  m" src="https://user-images.githubusercontent.com/4392003/95121092-670af600-0703-11eb-959f-73c7797a76ee.png">

**Fixes**
This commit closes #22748.

Test Plan: Imported from OSS

Reviewed By: ansley

Differential Revision: D24118186

Pulled By: SplitInfinity

fbshipit-source-id: c19b0b7e001f8cd099dc4c2e0e8ec39310510b46
 2020-10-05 15:36:59 -07:00
Mike Ruberry
bb19a55429 Improves fft doc consistency and makes deprecation warnings more prominent (#45409) 
Summary:
This PR makes the deprecation warnings for existing fft functions more prominent and makes the torch.stft deprecation warning consistent with our current deprecation planning.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/45409

Reviewed By: ngimel

Differential Revision: D23974975

Pulled By: mruberry

fbshipit-source-id: b90d8276095122ac3542ab625cb49b991379c1f8
 2020-09-29 09:07:49 -07:00
Mike Ruberry
6417a70465 Updates linalg warning + docs (#45415) 
Summary:
Changes the deprecation of norm to a docs deprecation, since PyTorch components still rely on norm and some behavior, like automatically flattening tensors, may need to be ported to torch.linalg.norm. The documentation is also updated to clarify that torch.norm and torch.linalg.norm are distinct.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/45415

Reviewed By: ngimel

Differential Revision: D23958252

Pulled By: mruberry

fbshipit-source-id: fd54e807c59a2655453a6bcd9f4073cb2c12e8ac
 2020-09-28 05:28:42 -07:00
Peter Bell
caea1adc35 Complex support for stft and istft (#43886) 
Summary:
Ref https://github.com/pytorch/pytorch/issues/42175, fixes https://github.com/pytorch/pytorch/issues/34797

This adds complex support to `torch.stft` and `torch.istft`. Note that there are really two issues with complex here: complex signals, and returning complex tensors.

## Complex signals and windows
`stft` currently assumes all signals are real and uses `rfft` with `onesided=True` by default. Similarly, `istft` always takes a complex fourier series and uses `irfft` to return real signals.

For `stft`, I now allow complex inputs and windows by calling the full `fft` if either are complex. If the user gives `onesided=True` and the signal is complex, then this doesn't work and raises an error instead. For `istft`, there's no way to automatically know what to do when `onesided=False` because that could either be a redundant representation of a real signal or a complex signal. So there, the user needs to pass the argument `return_complex=True` in order to use `ifft` and get a complex result back.

## stft returning complex tensors
The other issue is that `stft` returns a complex result, represented as a `(... X 2)` real tensor. I think ideally we want this to return proper complex tensors but to preserver BC I've had to add a `return_complex` argument to manage this transition. `return_complex` defaults to false for real inputs to preserve BC but defaults to True for complex inputs where there is no BC to consider.

In order to `return_complex` by default everywhere without a sudden BC-breaking change, a simple transition plan could be:
1. introduce `return_complex`, defaulted to false when BC is an issue but giving a warning. (this PR)
2. raise an error in cases where `return_complex` defaults to false, making it a required argument.
3. change `return_complex` default to true in all cases.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/43886

Reviewed By: glaringlee

Differential Revision: D23760174

Pulled By: mruberry

fbshipit-source-id: 2fec4404f5d980ddd6bdd941a63852a555eb9147
 2020-09-18 01:39:47 -07:00
Kurt Mohler
28a23fce4c Deprecate torch.norm and torch.functional.norm (#44321) 
Summary:
Part of https://github.com/pytorch/pytorch/issues/24802

Pull Request resolved: https://github.com/pytorch/pytorch/pull/44321

Reviewed By: mrshenli

Differential Revision: D23617273

Pulled By: mruberry

fbshipit-source-id: 6f88b5cb097fd0acb9cf0e415172c5a86f94e9f2
 2020-09-10 01:16:41 -07:00
Nikita Shulga
0c01f136f3 [BE] Use f-string in various Python functions (#44161) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/44161

Reviewed By: seemethere

Differential Revision: D23515874

Pulled By: malfet

fbshipit-source-id: 868cf65aedd58fce943c08f8e079e84e0a36df1f
 2020-09-04 07:38:25 -07:00
Ralf Gommers
573940f8d7 Fix type annotation errors in torch.functional (#43446) 
Summary:
Closes gh-42968

Pull Request resolved: https://github.com/pytorch/pytorch/pull/43446

Reviewed By: albanD

Differential Revision: D23280962

Pulled By: malfet

fbshipit-source-id: de5386a95a20ecc814c39cbec3e4252112340b3a
 2020-08-26 08:27:59 -07:00
Ralf Gommers
b430347a60 Address JIT/Mypy issue with torch._VF (#43454) 
Summary:
- `torch._VF` is a hack to work around the lack of support for `torch.functional` in the JIT
- that hack hides `torch._VF` functions from Mypy
- could be worked around by re-introducing a stub file for `torch.functional`, but that's undesirable
- so instead try to make both happy at the same time: the type ignore comments are needed for Mypy, and don't seem to affect the JIT after excluding them from the `get_type_line()` logic

Encountered this issue while trying to make `mypy` run on `torch/functional.py` in gh-43446.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/43454

Reviewed By: glaringlee

Differential Revision: D23305579

Pulled By: malfet

fbshipit-source-id: 50e490693c1e53054927b57fd9acc7dca57e88ca
 2020-08-25 09:23:54 -07:00
Hameer Abbasi
3d46e02ea1 Add __torch_function__ for methods (#37091) 
Summary:
According to pytorch/rfcs#3

From the goals in the RFC:

1. Support subclassing `torch.Tensor` in Python (done here)
2. Preserve `torch.Tensor` subclasses when calling `torch` functions on them (done here)
3. Use the PyTorch API with `torch.Tensor`-like objects that are _not_ `torch.Tensor`
   subclasses (done in https://github.com/pytorch/pytorch/issues/30730)
4. Preserve `torch.Tensor` subclasses when calling `torch.Tensor` methods. (done here)
5. Propagating subclass instances correctly also with operators, using
   views/slices/indexing/etc. (done here)
6. Preserve subclass attributes when using methods or views/slices/indexing. (done here)
7. A way to insert code that operates on both functions and methods uniformly
   (so we can write a single function that overrides all operators). (done here)
8. The ability to give external libraries a way to also define
   functions/methods that follow the `__torch_function__` protocol. (will be addressed in a separate PR)

This PR makes the following changes:

1. Adds the `self` argument to the arg parser.
2. Dispatches on `self` as well if `self` is not `nullptr`.
3. Adds a `torch._C.DisableTorchFunction` context manager to disable `__torch_function__`.
4. Adds a `torch::torch_function_enabled()` and `torch._C._torch_function_enabled()` to check the state of `__torch_function__`.
5. Dispatches all `torch._C.TensorBase` and `torch.Tensor` methods via `__torch_function__`.

TODO:

- [x] Sequence Methods
- [x] Docs
- [x] Tests

Closes https://github.com/pytorch/pytorch/issues/28361

Benchmarks in https://github.com/pytorch/pytorch/pull/37091#issuecomment-633657778

Pull Request resolved: https://github.com/pytorch/pytorch/pull/37091

Reviewed By: ngimel

Differential Revision: D22765678

Pulled By: ezyang

fbshipit-source-id: 53f8aa17ddb8b1108c0997f6a7aa13cb5be73de0
 2020-08-05 20:44:13 -07:00
Kurt Mohler
206db5c127 Improve torch.norm functionality, errors, and tests (#41956) 
Summary:
**BC-Breaking Note:**
BC breaking changes in the case where keepdim=True. Before this change, when calling `torch.norm` with keepdim=True and p='fro' or p=number, leaving all other optional arguments as their default values, the keepdim argument would be ignored. Also, any time `torch.norm` was called with p='nuc', the result would have one fewer dimension than the input, and the dimensions could be out of order depending on which dimensions were being reduced. After the change, for each of these cases, the result has the same number and order of dimensions as the input.

**PR Summary:**

* Fix keepdim behavior
* Throw descriptive errors for unsupported sparse norm args
* Increase unit test coverage for these cases and for complex inputs

These changes were taken from part of PR https://github.com/pytorch/pytorch/issues/40924. That PR is not going to be merged because it overrides `torch.norm`'s interface, which we want to avoid. But these improvements are still useful.

Issue https://github.com/pytorch/pytorch/issues/24802

Pull Request resolved: https://github.com/pytorch/pytorch/pull/41956

Reviewed By: albanD

Differential Revision: D22837455

Pulled By: mruberry

fbshipit-source-id: 509ecabfa63b93737996f48a58c7188b005b7217
 2020-08-01 01:55:12 -07:00
mattip
8c653e05ff DOC: fail to build if there are warnings (#41335) 
Summary:
Merge after gh-41334 and gh-41321 (EDIT: both are merged).
Closes gh-38011

This is the last in a series of PRs to build documentation without warnings. It adds `-WT --keepgoing` to the shpinx build which will [fail the build if there are warnings](https://www.sphinx-doc.org/en/master/man/sphinx-build.html#cmdoption-sphinx-build-W), print a [trackeback on error](https://www.sphinx-doc.org/en/master/man/sphinx-build.html#cmdoption-sphinx-build-T) and [finish the build](https://www.sphinx-doc.org/en/master/man/sphinx-build.html#cmdoption-sphinx-build-keep-going) even when there are warnings.

It should fail now, but pass once the PRs mentioned at the top are merged.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/41335

Reviewed By: pbelevich

Differential Revision: D22794425

Pulled By: mruberry

fbshipit-source-id: eb2903e50759d1d4f66346ee2ceebeecfac7b094
 2020-07-28 22:33:44 -07:00
kshitij12345
71fdf748e5 Add torch.atleast_{1d/2d/3d} (#41317) 
Summary:
https://github.com/pytorch/pytorch/issues/38349

TODO:
 * [x] Docs
 * [x] Tests

Pull Request resolved: https://github.com/pytorch/pytorch/pull/41317

Reviewed By: ngimel

Differential Revision: D22575456

Pulled By: mruberry

fbshipit-source-id: cc79f4cd2ca4164108ed731c33cf140a4d1c9dd8
 2020-07-17 10:10:41 -07:00
KushajveerSingh
88fe05e106 [Docs] Update torch.(squeeze, split, set_printoptions, save) docs. (#39303) 
Summary:
I added the following to the docs:
1. `torch.save`.
    1. Added doc for `_use_new_zipfile_serialization` argument.
    2. Added a note telling that extension does not matter while saving.
    3. Added an example showing the use of above argument along with `pickle_protocol=5`.

2. `torch.split`
    1. Added an example showing the use of the function.

3. `torch.squeeze`
   1. Added a warning for batch_size=1 case.

4. `torch.set_printoptions`
    1. Changed the docs of `sci_mode` argument from
        ```
        sci_mode: Enable (True) or disable (False) scientific notation. If
                 None (default) is specified, the value is defined by `_Formatter`
        ```
        to
        ```
        sci_mode: Enable (True) or disable (False) scientific notation. If
                 None (default=False) is specified, the value is defined by
                `torch._tensor_str._Formatter`.
        ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39303

Differential Revision: D21904504

Pulled By: zou3519

fbshipit-source-id: 92a324257d09d6bcfa0b410d4578859782b94488
 2020-06-05 12:57:53 -07:00
Xiang Gao
ebd4125e7e [JIT] Make torch.unique_consecutive compatible (#39339) 
Summary:
A `unique_consecutive` version of https://github.com/pytorch/pytorch/pull/38156
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39339

Differential Revision: D21823997

Pulled By: eellison

fbshipit-source-id: d14596a36ba36497e296da5a344e0376cef56f1b
 2020-06-02 14:54:29 -07:00
Ralf Gommers
d363cf4639 Fix incorrect __torch_function__ handling in einsum (#38741) 
Summary:
Closes gh-38479
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38741

Differential Revision: D21662512

Pulled By: ezyang

fbshipit-source-id: 247e3b50b8f2dd842c03be8d6ebe71910b619bc6
 2020-05-21 06:59:25 -07:00
Elias Ellison
eb3e9872c9 [JIT] make torch.unique compilable (#38156) 
Summary:
Fix for https://github.com/pytorch/pytorch/issues/37986

Follows the stack in https://github.com/pytorch/pytorch/pull/33783 stack to make functions in `torch/functional.py` resolve to their python implementations. Because the return type of `torch.unique` depends on `return_inverse` and `return_counts` I had to refactor the implementation to use our boolean_dispatch mechanism.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38156

Differential Revision: D21504449

Pulled By: eellison

fbshipit-source-id: 7efb1dff3b5c00655da10168403ac4817286ff59
 2020-05-12 18:37:53 -07:00
moto
5a27ec09b8 Add Inverse Short Time Fourier Transform in ATen native (#35569) 
Summary:
Ported `torchaudio`'s implementation (test, and documentation as well) to ATen.

Note
 - Batch packing/unpacking is performed in Python. ATen implementation expects 4D input tensor.
 - The way `hop_length` is initialized in the same way as `stft` implementation. [The Torchaudio's version tried to mimic the same behavior but slightly different](7da61a4bee/torchaudio/functional.py (L152-L157)).

Closes https://github.com/pytorch/pytorch/issues/34827
Relates https://github.com/pytorch/pytorch/issues/3775
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35569

Differential Revision: D21178090

Pulled By: mthrok

fbshipit-source-id: 2701a8b241a36a6fb1b740c2fb2b07cb938185d4
 2020-04-24 12:14:55 -07:00
Alban Desmaison
3799d1d74a Fix many doc issues (#37099) 
Summary:
Fix https://github.com/pytorch/pytorch/issues/35643 https://github.com/pytorch/pytorch/issues/37063 https://github.com/pytorch/pytorch/issues/36307 https://github.com/pytorch/pytorch/issues/35861 https://github.com/pytorch/pytorch/issues/35299 https://github.com/pytorch/pytorch/issues/23108 https://github.com/pytorch/pytorch/issues/4661

Just a bunch of small updates on the doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37099

Differential Revision: D21185713

Pulled By: albanD

fbshipit-source-id: 4ac06d6709dc0da6109a6ad3daae75667ee5863e
 2020-04-23 10:01:03 -07:00
Ralf Gommers
78d5707041 Fix type annotations and make MyPy run on torch/ (#36584) 
Summary:
This PR fixes a couple of syntax errors in `torch/` that prevent MyPy from running, fixes simple type annotation errors (e.g. missing `from typing import List, Tuple, Optional`), and adds granular ignores for errors in particular modules as well as for missing typing in third party packages.

As a result, running `mypy` in the root dir of the repo now runs on:
- `torch/`
- `aten/src/ATen/function_wrapper.py` (the only file already covered in CI)

In CI this runs on GitHub Actions, job Lint, sub-job "quick-checks", task "MyPy typecheck". It should give (right now): `Success: no issues found in 329 source files`.

Here are the details of the original 855 errors when running `mypy torch` on current master (after fixing the couple of syntax errors that prevent `mypy` from running through):

<details>

```
torch/utils/tensorboard/_proto_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2'
torch/utils/tensorboard/_proto_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2'
torch/utils/tensorboard/_proto_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2'
torch/utils/backcompat/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/for_onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch.for_onnx.onnx'
torch/cuda/nvtx.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/utils/show_pickle.py:59: error: Name 'pickle._Unpickler' is not defined
torch/utils/show_pickle.py:113: error: "Type[PrettyPrinter]" has no attribute "_dispatch"
torch/utils/tensorboard/_onnx_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.graph_pb2'
torch/utils/tensorboard/_onnx_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2'
torch/utils/tensorboard/_onnx_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.versions_pb2'
torch/utils/tensorboard/_onnx_graph.py:4: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2'
torch/utils/tensorboard/_onnx_graph.py:5: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2'
torch/utils/tensorboard/_onnx_graph.py:9: error: Cannot find implementation or library stub for module named 'onnx'
torch/contrib/_tensorboard_vis.py:10: error: Cannot find implementation or library stub for module named 'tensorflow.core.util'
torch/contrib/_tensorboard_vis.py:11: error: Cannot find implementation or library stub for module named 'tensorflow.core.framework'
torch/contrib/_tensorboard_vis.py:12: error: Cannot find implementation or library stub for module named 'tensorflow.python.summary.writer.writer'
torch/utils/hipify/hipify_python.py:43: error: Need type annotation for 'CAFFE2_TEMPLATE_MAP' (hint: "CAFFE2_TEMPLATE_MAP: Dict[<type>, <type>] = ...")
torch/utils/hipify/hipify_python.py:636: error: "object" has no attribute "items"
torch/nn/_reduction.py:27: error: Name 'Optional' is not defined
torch/nn/_reduction.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/_reduction.py:47: error: Name 'Optional' is not defined
torch/nn/_reduction.py:47: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib.pyplot': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends.backend_agg': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends': found module but no type hints or library stubs
torch/nn/modules/utils.py:27: error: Name 'List' is not defined
torch/nn/modules/utils.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
caffe2/proto/caffe2_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/caffe2_pb2.py:25: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:31: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:35: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:39: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:47: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:51: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:55: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:59: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:63: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:67: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:71: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:75: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:108: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:112: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:124: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:134: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:138: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:142: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:146: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:150: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:154: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:158: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:162: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:166: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:170: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:174: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:194: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:200: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:204: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:208: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:212: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:224: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:230: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:238: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:242: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:246: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:250: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:267: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:288: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:295: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:302: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:327: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:334: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:341: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:364: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:371: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:378: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:385: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:392: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:399: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:406: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:413: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:420: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:448: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:455: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:462: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:488: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:495: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:502: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:509: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:516: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:523: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:530: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:537: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:544: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:551: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:558: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:565: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:572: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:596: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:603: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:627: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:634: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:641: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:648: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:655: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:662: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:686: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:693: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:717: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:724: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:731: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:738: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:763: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:770: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:777: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:784: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:808: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:815: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:822: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:829: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:836: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:843: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:850: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:857: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:864: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:871: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:878: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:885: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:892: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:916: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:923: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:930: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:937: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:944: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:951: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:958: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:982: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:989: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:996: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1003: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1010: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1017: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1024: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1031: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1038: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1045: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1052: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1059: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1066: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1090: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1097: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1104: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1128: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1135: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1142: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1166: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1173: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1180: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1187: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1194: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1218: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1225: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1232: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1239: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1246: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1253: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1260: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1267: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1305: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1312: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1319: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1326: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1333: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1340: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1347: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1354: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1361: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1368: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1375: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1382: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1389: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1396: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1420: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1465: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1472: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1479: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1486: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1493: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1500: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1507: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1514: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1538: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1545: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1552: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1559: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1566: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1667: error: "GeneratedProtocolMessageType" has no attribute "Segment"
torch/multiprocessing/queue.py:4: error: No library stub file for standard library module 'multiprocessing.reduction'
caffe2/proto/torch_pb2.py:18: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/torch_pb2.py:27: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/torch_pb2.py:33: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/torch_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:81: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:109: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:116: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:123: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:137: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:144: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:151: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:175: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:189: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:196: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:220: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:227: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:241: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:265: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:272: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:279: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:286: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:293: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:300: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:307: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:314: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:321: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:328: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:335: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:342: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:366: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:373: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:397: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:404: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:411: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:418: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:425: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:432: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/metanet_pb2.py:29: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:36: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:64: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:126: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:133: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:140: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:164: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:171: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:202: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:209: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:216: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:240: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:247: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:261: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:268: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:275: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:282: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:289: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:296: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/__init__.py:13: error: Skipping analyzing 'caffe2.caffe2.fb.session.proto': found module but no type hints or library stubs
torch/multiprocessing/pool.py:3: error: No library stub file for standard library module 'multiprocessing.util'
torch/multiprocessing/pool.py:3: note: (Stub files are from https://github.com/python/typeshed)
caffe2/python/scope.py:10: error: Skipping analyzing 'past.builtins': found module but no type hints or library stubs
caffe2/python/__init__.py:7: error: Module has no attribute "CPU"
caffe2/python/__init__.py:8: error: Module has no attribute "CUDA"
caffe2/python/__init__.py:9: error: Module has no attribute "MKLDNN"
caffe2/python/__init__.py:10: error: Module has no attribute "OPENGL"
caffe2/python/__init__.py:11: error: Module has no attribute "OPENCL"
caffe2/python/__init__.py:12: error: Module has no attribute "IDEEP"
caffe2/python/__init__.py:13: error: Module has no attribute "HIP"
caffe2/python/__init__.py:14: error: Module has no attribute "COMPILE_TIME_MAX_DEVICE_TYPES"; maybe "PROTO_COMPILE_TIME_MAX_DEVICE_TYPES"?
caffe2/python/__init__.py:15: error: Module has no attribute "ONLY_FOR_TEST"; maybe "PROTO_ONLY_FOR_TEST"?
caffe2/python/__init__.py:34: error: Item "_Loader" of "Optional[_Loader]" has no attribute "exec_module"
caffe2/python/__init__.py:34: error: Item "None" of "Optional[_Loader]" has no attribute "exec_module"
caffe2/python/__init__.py:35: error: Module has no attribute "cuda"
caffe2/python/__init__.py:37: error: Module has no attribute "cuda"
caffe2/python/__init__.py:49: error: Module has no attribute "add_dll_directory"
torch/random.py:4: error: Cannot find implementation or library stub for module named 'torch._C'
torch/_classes.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/hub.py:21: error: Skipping analyzing 'tqdm.auto': found module but no type hints or library stubs
torch/hub.py:24: error: Skipping analyzing 'tqdm': found module but no type hints or library stubs
torch/hub.py:27: error: Name 'tqdm' already defined (possibly by an import)
torch/_tensor_str.py:164: error: Not all arguments converted during string formatting
torch/_ops.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/_linalg_utils.py:26: error: Name 'Optional' is not defined
torch/_linalg_utils.py:26: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:26: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:63: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:63: error: Name 'Optional' is not defined
torch/_linalg_utils.py:63: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:70: error: Name 'Optional' is not defined
torch/_linalg_utils.py:70: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:70: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:88: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:88: error: Name 'Optional' is not defined
torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:88: error: Name 'Tuple' is not defined
torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_jit_internal.py:17: error: Need type annotation for 'boolean_dispatched'
torch/_jit_internal.py:474: error: Need type annotation for '_overloaded_fns' (hint: "_overloaded_fns: Dict[<type>, <type>] = ...")
torch/_jit_internal.py:512: error: Need type annotation for '_overloaded_methods' (hint: "_overloaded_methods: Dict[<type>, <type>] = ...")
torch/_jit_internal.py:648: error: Incompatible types in assignment (expression has type "FinalCls", variable has type "_SpecialForm")
torch/sparse/__init__.py:11: error: Name 'Tensor' is not defined
torch/sparse/__init__.py:71: error: Name 'Tensor' is not defined
torch/sparse/__init__.py:71: error: Name 'Optional' is not defined
torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/sparse/__init__.py:71: error: Name 'Tuple' is not defined
torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/nn/init.py:109: error: Name 'Tensor' is not defined
torch/nn/init.py:126: error: Name 'Tensor' is not defined
torch/nn/init.py:142: error: Name 'Tensor' is not defined
torch/nn/init.py:165: error: Name 'Tensor' is not defined
torch/nn/init.py:180: error: Name 'Tensor' is not defined
torch/nn/init.py:194: error: Name 'Tensor' is not defined
torch/nn/init.py:287: error: Name 'Tensor' is not defined
torch/nn/init.py:315: error: Name 'Tensor' is not defined
torch/multiprocessing/reductions.py:8: error: No library stub file for standard library module 'multiprocessing.util'
torch/multiprocessing/reductions.py:9: error: No library stub file for standard library module 'multiprocessing.reduction'
torch/multiprocessing/reductions.py:17: error: No library stub file for standard library module 'multiprocessing.resource_sharer'
torch/jit/_builtins.py:72: error: Module has no attribute "_no_grad_embedding_renorm_"
torch/jit/_builtins.py:80: error: Module has no attribute "stft"
torch/jit/_builtins.py:81: error: Module has no attribute "cdist"
torch/jit/_builtins.py:82: error: Module has no attribute "norm"
torch/jit/_builtins.py:83: error: Module has no attribute "nuclear_norm"
torch/jit/_builtins.py:84: error: Module has no attribute "frobenius_norm"
torch/backends/cudnn/__init__.py:8: error: Cannot find implementation or library stub for module named 'torch._C'
torch/backends/cudnn/__init__.py:86: error: Need type annotation for '_handles' (hint: "_handles: Dict[<type>, <type>] = ...")
torch/autograd/profiler.py:13: error: Name 'ContextDecorator' already defined (possibly by an import)
torch/autograd/function.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/autograd/function.py:2: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports
torch/autograd/function.py:109: error: Unsupported dynamic base class "with_metaclass"
torch/serialization.py:609: error: "Callable[[Any], Any]" has no attribute "cache"
torch/_lowrank.py:11: error: Name 'Tensor' is not defined
torch/_lowrank.py:13: error: Name 'Optional' is not defined
torch/_lowrank.py:13: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:14: error: Name 'Optional' is not defined
torch/_lowrank.py:14: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:14: error: Name 'Tensor' is not defined
torch/_lowrank.py:82: error: Name 'Tensor' is not defined
torch/_lowrank.py:82: error: Name 'Optional' is not defined
torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:82: error: Name 'Tuple' is not defined
torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_lowrank.py:130: error: Name 'Tensor' is not defined
torch/_lowrank.py:130: error: Name 'Optional' is not defined
torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:130: error: Name 'Tuple' is not defined
torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_lowrank.py:167: error: Name 'Tensor' is not defined
torch/_lowrank.py:167: error: Name 'Optional' is not defined
torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:167: error: Name 'Tuple' is not defined
torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:45: error: Variable "torch.quantization.observer.ABC" is not valid as a type
torch/quantization/observer.py:45: note: See https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases
torch/quantization/observer.py:45: error: Invalid base class "ABC"
torch/quantization/observer.py:127: error: Name 'Tensor' is not defined
torch/quantization/observer.py:127: error: Name 'Tuple' is not defined
torch/quantization/observer.py:127: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:172: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:172: error: Module has no attribute "per_channel_symmetric"
torch/quantization/observer.py:192: error: Name 'Tensor' is not defined
torch/quantization/observer.py:192: error: Name 'Tuple' is not defined
torch/quantization/observer.py:192: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:233: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:233: error: Module has no attribute "per_channel_symmetric"
torch/quantization/observer.py:534: error: Name 'Tensor' is not defined
torch/quantization/observer.py:885: error: Name 'Tensor' is not defined
torch/quantization/observer.py:885: error: Name 'Tuple' is not defined
torch/quantization/observer.py:885: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:894: error: Cannot determine type of 'max_val'
torch/quantization/observer.py:894: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:899: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:902: error: Name 'Tensor' is not defined
torch/quantization/observer.py:925: error: Name 'Tensor' is not defined
torch/quantization/observer.py:928: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:929: error: Cannot determine type of 'max_val'
torch/quantization/observer.py:946: error: Argument "min" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]"
torch/quantization/observer.py:946: error: Argument "max" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]"
torch/quantization/observer.py:1056: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:1058: error: Module has no attribute "per_channel_symmetric"
torch/nn/quantized/functional.py:76: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:76: error: Name 'BroadcastingList2' is not defined
torch/nn/quantized/functional.py:259: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:259: error: Name 'Optional' is not defined
torch/nn/quantized/functional.py:259: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/functional.py:289: error: Module has no attribute "ops"
torch/nn/quantized/functional.py:290: error: Module has no attribute "ops"
torch/nn/quantized/functional.py:308: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:326: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:356: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:371: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:400: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:400: error: Name 'Optional' is not defined
torch/nn/quantized/functional.py:400: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/functional.py:430: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:448: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/linear.py:26: error: Module has no attribute "ops"
torch/nn/quantized/modules/linear.py:28: error: Module has no attribute "ops"
torch/nn/quantized/modules/functional_modules.py:40: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:47: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:54: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:61: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:68: error: Name 'List' is not defined
torch/nn/quantized/modules/functional_modules.py:68: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/functional_modules.py:68: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:75: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:140: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:146: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:151: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:157: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:162: error: Name 'List' is not defined
torch/nn/quantized/modules/functional_modules.py:162: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/functional_modules.py:162: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:168: error: Name 'Tensor' is not defined
torch/multiprocessing/spawn.py:9: error: Module 'torch.multiprocessing' has no attribute '_prctl_pr_set_pdeathsig'
torch/multiprocessing/__init__.py:28: error: Module has no attribute "__all__"
torch/jit/frontend.py:9: error: Cannot find implementation or library stub for module named 'torch._C._jit_tree_views'
torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList2'; maybe "BroadcastingList1" or "BroadcastingListCls"?
torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList3'; maybe "BroadcastingList1" or "BroadcastingListCls"?
torch/jit/annotations.py:9: error: Cannot find implementation or library stub for module named 'torch._C'
torch/distributions/distribution.py:16: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/distribution.py:74: error: Name 'arg_constraints' already defined on line 16
torch/distributions/distribution.py:84: error: Name 'support' already defined on line 15
torch/functional.py:114: error: Name 'Tuple' is not defined
torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:114: error: Name 'Optional' is not defined
torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:189: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor")
torch/functional.py:200: error: Argument 1 to "_indices_product" has incompatible type "Tuple[int, ...]"; expected "List[int]"
torch/functional.py:204: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int"
torch/functional.py:204: note: Possible overload variants:
torch/functional.py:204: note:     def __setitem__(self, int, int) -> None
torch/functional.py:204: note:     def __setitem__(self, slice, Iterable[int]) -> None
torch/functional.py:204: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor"
torch/functional.py:204: note:     def __getitem__(self, int) -> int
torch/functional.py:204: note:     def __getitem__(self, slice) -> List[int]
torch/functional.py:207: error: "Tensor" has no attribute "copy_"
torch/functional.py:212: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int"
torch/functional.py:212: note: Possible overload variants:
torch/functional.py:212: note:     def __setitem__(self, int, int) -> None
torch/functional.py:212: note:     def __setitem__(self, slice, Iterable[int]) -> None
torch/functional.py:212: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor"
torch/functional.py:212: note:     def __getitem__(self, int) -> int
torch/functional.py:212: note:     def __getitem__(self, slice) -> List[int]
torch/functional.py:215: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor")
torch/functional.py:334: error: Name 'Optional' is not defined
torch/functional.py:334: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:429: error: Argument 2 to "pad" has incompatible type "Tuple[int, int]"; expected "List[int]"
torch/functional.py:431: error: Module has no attribute "stft"
torch/functional.py:766: error: Module has no attribute "cdist"
torch/functional.py:768: error: Module has no attribute "cdist"
torch/functional.py:770: error: Module has no attribute "cdist"
torch/functional.py:775: error: Name 'Optional' is not defined
torch/functional.py:775: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:780: error: Name 'Optional' is not defined
torch/functional.py:780: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:780: error: Name 'number' is not defined
torch/functional.py:780: error: Name 'norm' already defined on line 775
torch/functional.py:785: error: Name 'Optional' is not defined
torch/functional.py:785: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:785: error: Name 'number' is not defined
torch/functional.py:785: error: Name 'norm' already defined on line 775
torch/functional.py:790: error: Name 'Optional' is not defined
torch/functional.py:790: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:790: error: Name 'norm' already defined on line 775
torch/functional.py:795: error: Name 'norm' already defined on line 775
torch/functional.py:960: error: Name 'Any' is not defined
torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Any")
torch/functional.py:960: error: Name 'Tuple' is not defined
torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:1036: error: Argument 1 to "len" has incompatible type "int"; expected "Sized"
torch/functional.py:1041: error: Name 'Optional' is not defined
torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:1041: error: Name 'Tuple' is not defined
torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:1056: error: Name 'Optional' is not defined
torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:1056: error: Name 'Tuple' is not defined
torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/distributions/von_mises.py:87: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/negative_binomial.py:25: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/multivariate_normal.py:116: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/laplace.py:23: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/independent.py:34: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/cauchy.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/poisson.py:28: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/one_hot_categorical.py:32: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/normal.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/lowrank_multivariate_normal.py:79: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/gamma.py:30: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/exponential.py:23: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/fishersnedecor.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/dirichlet.py:44: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/nn/quantized/dynamic/modules/rnn.py:230: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:232: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:236: error: Incompatible return value type (got "Tuple[Any, Tensor, Any]", expected "Tuple[int, int, int]")
torch/nn/quantized/dynamic/modules/rnn.py:351: error: Incompatible types in assignment (expression has type "Type[LSTM]", base class "RNNBase" defined the type as "Type[RNNBase]")
torch/nn/quantized/dynamic/modules/rnn.py:381: error: Module has no attribute "quantized_lstm"
torch/nn/quantized/dynamic/modules/rnn.py:385: error: Module has no attribute "quantized_lstm"
torch/nn/quantized/dynamic/modules/rnn.py:414: error: Argument 1 to "forward_impl" of "LSTM" has incompatible type "PackedSequence"; expected "Tensor"
torch/nn/quantized/dynamic/modules/rnn.py:416: error: Incompatible types in assignment (expression has type "PackedSequence", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:418: error: Incompatible return value type (got "Tuple[Tensor, Tuple[Tensor, Tensor]]", expected "Tuple[PackedSequence, Tuple[Tensor, Tensor]]")
torch/nn/quantized/dynamic/modules/rnn.py:420: error: Argument 1 of "permute_hidden" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor"
torch/nn/quantized/dynamic/modules/rnn.py:420: error: Return type "Tuple[Tensor, Tensor]" of "permute_hidden" incompatible with return type "Tensor" in supertype "RNNBase"
torch/nn/quantized/dynamic/modules/rnn.py:426: error: Argument 2 of "check_forward_args" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor"
torch/nn/intrinsic/qat/modules/conv_fused.py:232: error: Incompatible types in assignment (expression has type "Type[ConvBnReLU2d]", base class "ConvBn2d" defined the type as "Type[ConvBn2d]")
torch/distributions/beta.py:27: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/geometric.py:31: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/continuous_bernoulli.py:38: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/bernoulli.py:30: error: Incompatible types in assignment (expression has type "_Boolean", base class "Distribution" defined the type as "None")
torch/quantization/fake_quantize.py:126: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/fake_quantize.py:132: error: Module has no attribute "per_channel_symmetric"
torch/distributions/transformed_distribution.py:41: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/jit/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/jit/__init__.py:15: error: Module 'torch.utils' has no attribute 'set_module'
torch/jit/__init__.py:70: error: Name 'Attribute' already defined on line 68
torch/jit/__init__.py:213: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/jit/__init__.py:215: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/jit/__init__.py:1524: error: Unsupported dynamic base class "with_metaclass"
torch/jit/__init__.py:1869: error: Name 'ScriptModule' already defined on line 1524
torch/jit/__init__.py:1998: error: Need type annotation for '_jit_caching_layer'
torch/jit/__init__.py:1999: error: Need type annotation for '_jit_function_overload_caching'
torch/distributions/relaxed_categorical.py:34: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_categorical.py:108: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_bernoulli.py:31: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_bernoulli.py:114: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/logistic_normal.py:31: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/log_normal.py:26: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/half_normal.py:27: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/half_cauchy.py:28: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/gumbel.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/nn/quantized/modules/conv.py:18: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights'
torch/nn/quantized/modules/conv.py:209: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:209: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:214: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:321: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:321: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:323: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:447: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:447: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:449: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:513: error: Name 'nn.modules.conv._ConvTransposeNd' is not defined
torch/nn/quantized/modules/conv.py:525: error: Name 'List' is not defined
torch/nn/quantized/modules/conv.py:525: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/conv.py:527: error: Name 'List' is not defined
torch/nn/quantized/modules/conv.py:527: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/intrinsic/quantized/modules/conv_relu.py:8: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights'
torch/nn/intrinsic/quantized/modules/conv_relu.py:21: error: Incompatible types in assignment (expression has type "Type[ConvReLU2d]", base class "Conv2d" defined the type as "Type[Conv2d]")
torch/nn/intrinsic/quantized/modules/conv_relu.py:62: error: Incompatible types in assignment (expression has type "Type[ConvReLU3d]", base class "Conv3d" defined the type as "Type[Conv3d]")
torch/distributions/weibull.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/kl.py:35: error: Need type annotation for '_KL_MEMOIZE' (hint: "_KL_MEMOIZE: Dict[<type>, <type>] = ...")
torch/distributions/studentT.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/mixture_same_family.py:48: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/__init__.py:158: error: Name 'transforms' is not defined
torch/onnx/utils.py:21: error: Cannot find implementation or library stub for module named 'torch._C'
torch/distributed/rendezvous.py:4: error: Cannot find implementation or library stub for module named 'urlparse'
torch/distributed/rendezvous.py:4: error: Name 'urlparse' already defined (possibly by an import)
torch/distributed/rendezvous.py:4: error: Name 'urlunparse' already defined (possibly by an import)
torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'FileStore'
torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'TCPStore'
torch/distributed/rendezvous.py:65: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceOptions'; maybe "ReduceOptions" or "AllreduceCoalescedOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceCoalescedOptions'; maybe "AllreduceOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllToAllOptions'
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'BroadcastOptions'
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'GatherOptions'; maybe "ScatterOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceOptions'; maybe "AllreduceOptions", "ReduceScatterOptions", or "ReduceOp"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceScatterOptions'; maybe "ScatterOptions" or "ReduceOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ScatterOptions'; maybe "ReduceScatterOptions" or
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36584

Reviewed By: seemethere, ailzhang

Differential Revision: D21155985

Pulled By: ezyang

fbshipit-source-id: f628d4293992576207167e7c417998fad15898d1
 2020-04-22 14:17:08 -07:00
David Reiss
e75fb4356b Remove (most) Python 2 support from Python code (#35615) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35615

Python 2 has reached end-of-life and is no longer supported by PyTorch.
Now we can clean up a lot of cruft that we put in place to support it.
These changes were all done manually, and I skipped anything that seemed
like it would take more than a few seconds, so I think it makes sense to
review it manually as well (though using side-by-side view and ignoring
whitespace change might be helpful).

Test Plan: CI

Differential Revision: D20842886

Pulled By: dreiss

fbshipit-source-id: 8cad4e87c45895e7ce3938a88e61157a79504aed
 2020-04-22 09:23:14 -07:00
Torsten Wörtwein
be52b7f0ea Documentation LU Decomposition: deriving L, U, and P (#36907) 
Summary:
Add note to LU decomposition to use `lu_unpack` to get `L`, `U`, and `P`.

Fixes https://github.com/pytorch/pytorch/issues/36752.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36907

Differential Revision: D21134545

Pulled By: albanD

fbshipit-source-id: 54d4872bb8c95dfb8048aedace9781f843ab8a30
 2020-04-21 07:40:21 -07:00
Kurt Mohler
2bc49a4b85 block_diag dense (#33449) 
Summary:
Add block_diag function for dense tensors, based on scipy.linalg.block_diag

Closes https://github.com/pytorch/pytorch/issues/31932
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33449

Differential Revision: D20943099

Pulled By: zou3519

fbshipit-source-id: 8b5c9476fb5af959aafa4169612c660396d9b717
 2020-04-13 10:04:55 -07:00
Edward Yang
83907ded1d Revert D20895316: [pytorch][PR] [JIT] List reland 
Test Plan: revert-hammer

Differential Revision:
D20895316

Original commit changeset: 9a2bc0e6bdcb

fbshipit-source-id: d135f0038cf240a0973ecfcd540121cbd4ecb5a7
 2020-04-08 14:40:10 -07:00