add testing PT 1.12 (#13386)
* add testing PT 1.12 * Fix quantization tests * Fix another set of tests * Fix check since https://github.com/pytorch/pytorch/pull/80139 is only going to be available for 1.13 * Skip this test for now for 1.12 Co-authored-by: SeanNaren <sean@grid.ai>
This commit is contained in:
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cdb493ec42
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@ -15,6 +15,7 @@ from urllib import request
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from urllib.request import Request, urlopen
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import fire
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import pkg_resources
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REQUIREMENT_FILES = {
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"pytorch": (
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@ -78,17 +79,12 @@ class AssistantCLI:
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@staticmethod
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def _prune_packages(req_file: str, packages: Sequence[str]) -> None:
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"""Remove some packages from given requirement files."""
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with open(req_file) as fp:
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lines = fp.readlines()
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if isinstance(packages, str):
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packages = [packages]
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for pkg in packages:
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lines = [ln for ln in lines if not ln.startswith(pkg)]
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pprint(lines)
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with open(req_file, "w") as fp:
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fp.writelines(lines)
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path = Path(req_file)
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assert path.exists()
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text = path.read_text()
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final = [str(req) for req in pkg_resources.parse_requirements(text) if req.name not in packages]
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pprint(final)
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path.write_text("\n".join(final))
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@staticmethod
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def _replace_min(fname: str) -> None:
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@ -22,11 +22,12 @@ jobs:
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.8"] # previous to last Python version as that one is already used in test-full
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pytorch-version: ["1.9", "1.10"]
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# nightly: add when there's a release candidate
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include:
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- {python-version: "3.8", pytorch-version: "1.9"}
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- {python-version: "3.8", pytorch-version: "1.10"}
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- {python-version: "3.9", pytorch-version: "1.11"}
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- {python-version: "3.9", pytorch-version: "1.12"}
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timeout-minutes: 30
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@ -66,6 +67,10 @@ jobs:
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conda list
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pip install -e .[test]
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- name: Freeze PIL (hotfix)
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# import of PILLOW_VERSION which they recently removed in v9.0 in favor of __version__
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run: pip install "Pillow<9.0" # It messes with torchvision
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- name: DocTests
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if: ${{ (steps.skip.outputs.continue == '1') }}
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working-directory: ./src
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@ -79,10 +84,11 @@ jobs:
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HOROVOD_WITHOUT_TENSORFLOW: 1
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run: |
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set -e
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pip list
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# adjust versions according installed Torch version
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python ./requirements/pytorch/adjust-versions.py requirements/pytorch/extra.txt
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python ./requirements/pytorch/adjust-versions.py requirements/pytorch/examples.txt
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pip install -r requirements/pytorch/devel.txt --find-links https://download.pytorch.org/whl/cpu/torch_stable.html
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pip install -r requirements/pytorch/devel.txt --find-links https://download.pytorch.org/whl/torch_stable.html
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pip install -r requirements/pytorch/strategies.txt
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# set a per-test timeout of 2.5 minutes to fail sooner; this aids with hanging tests
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pip install pytest-timeout
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@ -127,7 +127,7 @@ jobs:
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run: |
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# adjust versions according installed Torch version
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python ./requirements/pytorch/adjust-versions.py requirements/pytorch/extra.txt
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pip install --requirement ./requirements/pytorch/extra.txt --find-links https://download.pytorch.org/whl/cpu/torch_stable.html --upgrade
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pip install -r ./requirements/pytorch/extra.txt --find-links https://download.pytorch.org/whl/cpu/torch_stable.html --upgrade
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pip list
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shell: bash
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@ -71,8 +71,11 @@ ENV \
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COPY environment.yml environment.yml
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# conda init
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RUN conda update -n base -c defaults conda && \
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conda create -y --name $CONDA_ENV python=${PYTHON_VERSION} pytorch=${PYTORCH_VERSION} torchvision torchtext cudatoolkit=${CUDA_VERSION} -c nvidia -c pytorch -c pytorch-test -c pytorch-nightly && \
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RUN \
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conda update -n base -c defaults conda && \
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conda create -y --name $CONDA_ENV \
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python=${PYTHON_VERSION} pytorch=${PYTORCH_VERSION} torchvision torchtext cudatoolkit=${CUDA_VERSION} \
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-c nvidia -c pytorch -c pytorch-test -c pytorch-nightly && \
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conda init bash && \
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# NOTE: this requires that the channel is presented in the yaml before packages \
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printf "import re;\nfname = 'environment.yml';\nreq = open(fname).read();\nfor n in ['python', 'pytorch', 'torchtext', 'torchvision']:\n req = re.sub(rf'- {n}[>=]+', f'# - {n}=', req);\nopen(fname, 'w').write(req)" > prune.py && \
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@ -80,6 +83,7 @@ RUN conda update -n base -c defaults conda && \
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rm prune.py && \
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cat environment.yml && \
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conda env update --name $CONDA_ENV --file environment.yml && \
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conda install "Pillow<9.0" && \
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conda clean -ya && \
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rm environment.yml
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@ -94,12 +98,11 @@ RUN \
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pip list | grep torch && \
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python -c "import torch; print(torch.__version__)" && \
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pip install -q fire && \
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python requirements/pytorch/adjust-versions.py requirements/pytorch/extra.txt && \
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python requirements/pytorch/adjust-versions.py requirements/pytorch/examples.txt && \
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python assistant.py requirements_prune_pkgs torch,torchvision,torchtext && \
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# Install remaining requirements
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pip install -r requirements/pytorch/base.txt --no-cache-dir --find-links https://download.pytorch.org/whl/test/torch_test.html && \
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pip install -r requirements/pytorch/extra.txt --no-cache-dir --find-links https://download.pytorch.org/whl/test/torch_test.html && \
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pip install -r requirements/pytorch/examples.txt --no-cache-dir --find-links https://download.pytorch.org/whl/test/torch_test.html && \
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pip install --no-cache-dir -r requirements/pytorch/base.txt \
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-r requirements/pytorch/extra.txt \
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-r requirements/pytorch/examples.txt && \
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rm assistant.py
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ENV \
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@ -5,9 +5,9 @@ from typing import Dict, Optional
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# IMPORTANT: this list needs to be sorted in reverse
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VERSIONS = [
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dict(torch="1.12.0", torchvision="0.12.*", torchtext=""), # nightly
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dict(torch="1.11.0", torchvision="0.12.0", torchtext="0.12.0"), # pre-release
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dict(torch="1.10.2", torchvision="0.11.3", torchtext="0.11.2"), # stable
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dict(torch="1.12.0", torchvision="0.13.0", torchtext="0.13.0"), # stable
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dict(torch="1.11.0", torchvision="0.12.0", torchtext="0.12.0"),
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dict(torch="1.10.2", torchvision="0.11.3", torchtext="0.11.2"),
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dict(torch="1.10.1", torchvision="0.11.2", torchtext="0.11.1"),
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dict(torch="1.10.0", torchvision="0.11.1", torchtext="0.11.0"),
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dict(torch="1.9.1", torchvision="0.10.1", torchtext="0.10.1"),
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return req
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def test():
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def test_check():
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requirements = """
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torch>=1.2.*
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torch==1.2.3
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@ -74,7 +74,7 @@ def test():
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if __name__ == "__main__":
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test() # sanity check
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test_check() # sanity check
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if len(sys.argv) == 3:
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requirements_path, torch_version = sys.argv[1:]
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@ -1,5 +1,5 @@
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numpy>=1.17.2, <1.23.1
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torch>=1.9.*, <=1.11.0 # strict
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torch>=1.9.*, <=1.12.0
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tqdm>=4.57.0, <=4.63.0
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PyYAML>=5.4, <=6.0
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fsspec[http]>=2021.05.0, !=2021.06.0, <2022.6.0
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@ -26,7 +26,7 @@ from torch.quantization import FakeQuantizeBase
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks.callback import Callback
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from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_10, _TORCH_GREATER_EQUAL_1_11
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from pytorch_lightning.utilities import _TORCH_GREATER_EQUAL_1_10, _TORCH_GREATER_EQUAL_1_11, _TORCH_GREATER_EQUAL_1_12
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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if _TORCH_GREATER_EQUAL_1_10:
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@ -247,10 +247,14 @@ class QuantizationAwareTraining(Callback):
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if self._observer_type == "histogram":
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model.qconfig = torch.quantization.get_default_qconfig(self._qconfig)
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elif self._observer_type == "average":
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extra_kwargs = {}
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if _TORCH_GREATER_EQUAL_1_12:
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extra_kwargs["version"] = 0
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# version=None corresponds to using FakeQuantize rather than
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# FusedMovingAvgObsFakeQuantize which was introduced in PT1.10
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# details in https://github.com/pytorch/pytorch/issues/64564
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extra_kwargs = dict(version=None) if _TORCH_GREATER_EQUAL_1_10 else {}
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elif _TORCH_GREATER_EQUAL_1_10:
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extra_kwargs["version"] = None
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model.qconfig = torch.quantization.get_default_qat_qconfig(self._qconfig, **extra_kwargs)
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elif isinstance(self._qconfig, QConfig):
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@ -45,7 +45,7 @@ from pytorch_lightning.utilities.apply_func import apply_to_collection, convert_
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from pytorch_lightning.utilities.cloud_io import get_filesystem
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from pytorch_lightning.utilities.distributed import distributed_available, sync_ddp
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_11, _TORCH_GREATER_EQUAL_1_12
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from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_11, _TORCH_GREATER_EQUAL_1_13
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from pytorch_lightning.utilities.parsing import collect_init_args
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from pytorch_lightning.utilities.rank_zero import rank_zero_debug, rank_zero_deprecation, rank_zero_warn
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from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
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@ -1987,7 +1987,7 @@ class LightningModule(
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self._register_state_dict_hook(state_dict_hook)
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if _TORCH_GREATER_EQUAL_1_12:
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if _TORCH_GREATER_EQUAL_1_13:
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self._register_load_state_dict_pre_hook(pre_load_state_dict_hook, True)
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else:
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# We need to make sure the self inside the method is a weakref proxy
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@ -44,6 +44,7 @@ from pytorch_lightning.utilities.imports import ( # noqa: F401
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_POPTORCH_AVAILABLE,
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_TORCH_GREATER_EQUAL_1_10,
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_TORCH_GREATER_EQUAL_1_11,
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_TORCH_GREATER_EQUAL_1_12,
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_TORCH_QUANTIZE_AVAILABLE,
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_TORCHTEXT_AVAILABLE,
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_TORCHVISION_AVAILABLE,
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@ -128,7 +128,8 @@ _TORCH_GREATER_EQUAL_1_9_1 = _compare_version("torch", operator.ge, "1.9.1")
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_TORCH_GREATER_EQUAL_1_10 = _compare_version("torch", operator.ge, "1.10.0")
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_TORCH_LESSER_EQUAL_1_10_2 = _compare_version("torch", operator.le, "1.10.2")
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_TORCH_GREATER_EQUAL_1_11 = _compare_version("torch", operator.ge, "1.11.0")
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_TORCH_GREATER_EQUAL_1_12 = _compare_version("torch", operator.ge, "1.12.0", use_base_version=True)
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_TORCH_GREATER_EQUAL_1_12 = _compare_version("torch", operator.ge, "1.12.0")
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_TORCH_GREATER_EQUAL_1_13 = _compare_version("torch", operator.ge, "1.13.0", use_base_version=True)
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_APEX_AVAILABLE = _module_available("apex.amp")
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_DALI_AVAILABLE = _module_available("nvidia.dali")
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@ -22,7 +22,7 @@ from torch.utils.data import DataLoader
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from pytorch_lightning import LightningModule, seed_everything, Trainer
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from pytorch_lightning.callbacks import BackboneFinetuning, BaseFinetuning, ModelCheckpoint
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from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
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from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_11
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from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_11, _TORCH_GREATER_EQUAL_1_12
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class TestBackboneFinetuningCallback(BackboneFinetuning):
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@ -368,6 +368,8 @@ def test_callbacks_restore(tmpdir):
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}
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if _TORCH_GREATER_EQUAL_1_11:
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expected["maximize"] = False
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if _TORCH_GREATER_EQUAL_1_12:
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expected["foreach"] = None
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assert callback._internal_optimizer_metadata[0][0] == expected
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}
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if _TORCH_GREATER_EQUAL_1_11:
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expected["maximize"] = False
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if _TORCH_GREATER_EQUAL_1_12:
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expected["foreach"] = None
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assert callback._internal_optimizer_metadata[0][1] == expected
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trainer_kwargs["max_epochs"] = 3
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@ -30,10 +30,12 @@ from tests_pytorch.helpers.runif import RunIf
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from tests_pytorch.helpers.simple_models import RegressionModel
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# todo: [True-False-average] and [False-False-average] fail with 1.12
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# error: assert False (tensor(0.3262), tensor(0.8754), atol=0.45)
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@pytest.mark.parametrize("observe", ["average", "histogram"])
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@pytest.mark.parametrize("fuse", [True, False])
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@pytest.mark.parametrize("convert", [True, False])
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@RunIf(quantization=True)
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@RunIf(quantization=True, max_torch="1.11")
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def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
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"""Parity test for quant model."""
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cuda_available = GPUAccelerator.is_available()
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@ -12,6 +12,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from unittest.mock import patch
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import numpy as np
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import onnxruntime
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import tests_pytorch.helpers.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.demos.boring_classes import BoringModel
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from pytorch_lightning.utilities.imports import _TORCH_GREATER_EQUAL_1_12
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from tests_pytorch.helpers.runif import RunIf
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from tests_pytorch.utilities.test_model_summary import UnorderedModel
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"""Test that output is present when verbose parameter is set."""
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model = BoringModel()
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model.example_input_array = torch.randn(5, 32)
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file_path = os.path.join(tmpdir, "model.onnx")
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model.to_onnx(file_path, verbose=True)
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captured = capsys.readouterr()
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assert "graph(%" in captured.out
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if _TORCH_GREATER_EQUAL_1_12:
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with patch("torch.onnx.log", autospec=True) as test:
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model.to_onnx(file_path, verbose=True)
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args, kwargs = test.call_args
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prefix, graph = args
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assert prefix == "Exported graph: "
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else:
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model.to_onnx(file_path, verbose=True)
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captured = capsys.readouterr()
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assert "graph(%" in captured.out
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def test_error_if_no_input(tmpdir):
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@ -251,7 +251,8 @@ def _run_collab_training_fn(initial_peers, wait_seconds, barrier, recorded_proce
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recorded_process_steps.append(recorded_global_steps)
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@RunIf(hivemind=True)
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# TODO: check why it fails with PT 1.12
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@RunIf(hivemind=True, max_torch="1.12")
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@mock.patch.dict(os.environ, {"HIVEMIND_MEMORY_SHARING_STRATEGY": "file_descriptor"}, clear=True)
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@pytest.mark.parametrize(
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"num_processes, wait_seconds",
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@ -1501,7 +1501,7 @@ def test_cli_trainer_no_callbacks():
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def test_unresolvable_import_paths():
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class TestModel(BoringModel):
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def __init__(self, a_func: Callable = torch.softmax):
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def __init__(self, a_func: Callable = torch.nn.Softmax):
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super().__init__()
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self.a_func = a_func
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@ -1509,7 +1509,7 @@ def test_unresolvable_import_paths():
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with mock.patch("sys.argv", ["any.py", "--print_config"]), redirect_stdout(out), pytest.raises(SystemExit):
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LightningCLI(TestModel, run=False)
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assert "a_func: torch.softmax" in out.getvalue()
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assert "a_func: torch.nn.Softmax" in out.getvalue()
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def test_pytorch_profiler_init_args():
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