mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-07-20 19:12:24 +00:00
Update torch to 1.13.1 in CI and packaging pipelines for ort training (#14055)
This commit is contained in:
Baiju Meswani
GitHub
parent
b29a1c7348
commit
0ff61f7b97
17 changed files with 32 additions and 300 deletions
|
|
@ -54,26 +54,3 @@ class NeuralNetCustomClassOutput(torch.nn.Module):
|
|||
out2 = self.fc2_2(self.relu2(self.fc2_1(input2)))
|
||||
out3 = self.fc3_2(self.relu3(self.fc3_1(input3)))
|
||||
return NeuralNetCustomClassOutput.CustomClass(out1, out2, out3)
|
||||
|
||||
|
||||
class MyCustomFunctionReluModel(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
class MyReLU(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, input):
|
||||
ctx.save_for_backward(input)
|
||||
return input.clamp(min=0)
|
||||
|
||||
@staticmethod
|
||||
def backward(ctx, grad_output):
|
||||
(input,) = ctx.saved_tensors
|
||||
grad_input = grad_output.clone()
|
||||
grad_input[input < 0] = 0
|
||||
return grad_input
|
||||
|
||||
self.relu = MyReLU.apply
|
||||
|
||||
def forward(self, input):
|
||||
return self.relu(input)
|
||||
|
|
|
|||
|
|
@ -142,6 +142,14 @@ def run_data_sampler_tests(cwd, log):
|
|||
run_subprocess(command, cwd=cwd, log=log).check_returncode()
|
||||
|
||||
|
||||
def run_pytorch_export_contrib_ops_tests(cwd, log):
|
||||
log.debug("Running: PyTorch Export Contrib Ops Tests")
|
||||
|
||||
command = [sys.executable, "-m", "pytest", "-sv", "test_pytorch_export_contrib_ops.py"]
|
||||
|
||||
run_subprocess(command, cwd=cwd, log=log).check_returncode()
|
||||
|
||||
|
||||
def main():
|
||||
args = parse_arguments()
|
||||
cwd = args.cwd
|
||||
|
|
@ -178,6 +186,9 @@ def main():
|
|||
|
||||
run_experimental_gradient_graph_tests(cwd, log)
|
||||
|
||||
# TODO(bmeswani): Enable this test once it can run with latest pytorch
|
||||
# run_pytorch_export_contrib_ops_tests(cwd, log)
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4820,9 +4820,14 @@ def test_ortmodule_attribute_name_collision_warning():
|
|||
with pytest.warns(UserWarning) as warning_record:
|
||||
ort_model = ORTModule(pt_model)
|
||||
|
||||
assert len(warning_record) == 2
|
||||
assert "_torch_module collides with ORTModule's attribute name." in warning_record[0].message.args[0]
|
||||
assert "load_state_dict collides with ORTModule's attribute name." in warning_record[1].message.args[0]
|
||||
# FutureWarning('The first argument to symbolic functions is deprecated in 1.13 and will be removed in the future.
|
||||
# Please annotate treat the first argument (g) as GraphContext and use context information from the object
|
||||
# instead.')
|
||||
# TODO(bmeswani): Check with the exporter team as to what this might mean for ortmodule.
|
||||
assert len(warning_record) == 3
|
||||
|
||||
assert "_torch_module collides with ORTModule's attribute name." in warning_record[1].message.args[0]
|
||||
assert "load_state_dict collides with ORTModule's attribute name." in warning_record[2].message.args[0]
|
||||
|
||||
|
||||
def test_ortmodule_ortmodule_method_attribute_copy():
|
||||
|
|
|
|||
|
|
@ -14,7 +14,6 @@ import pytest
|
|||
import torch
|
||||
from _orttraining_ortmodule_models import (
|
||||
MyCustomClassInputNet,
|
||||
MyCustomFunctionReluModel,
|
||||
NeuralNetCustomClassOutput,
|
||||
NeuralNetSinglePositionalArgument,
|
||||
)
|
||||
|
|
@ -482,75 +481,6 @@ def test_ortmodule_fallback_init__missing_cpp_extensions(
|
|||
assert "ORTModule's extensions were not detected" in str(ex_info.value)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
|
||||
)
|
||||
def test_ortmodule_fallback_onnx_model__custom_autograd(
|
||||
is_training, fallback_enabled, matching_policy, persist_fallback
|
||||
):
|
||||
from onnxruntime.training.ortmodule._custom_autograd_function import (
|
||||
custom_autograd_function_enabler,
|
||||
enable_custom_autograd_support,
|
||||
)
|
||||
|
||||
# Disable the autograd support to test the fallback.
|
||||
old_state = custom_autograd_function_enabler.state
|
||||
enable_custom_autograd_support(False)
|
||||
|
||||
# is_training: True for torch.nn.Module training model, eval mode otherwise
|
||||
# fallback_enabled: True PyTorch executes the forward graph instead of ORT backend
|
||||
# matching_policy: True matches FALLBACK_UNSUPPORTED_ONNX_MODEL policy to ORTModuleDeviceException exception.
|
||||
# Otherwise, an incorrect policy (FALLBACK_UNSUPPORTED_DEVICE) is used to verify that the fallback does not happen
|
||||
|
||||
if fallback_enabled:
|
||||
if matching_policy:
|
||||
policy = "FALLBACK_UNSUPPORTED_ONNX_MODEL"
|
||||
else:
|
||||
policy = "FALLBACK_UNSUPPORTED_DEVICE"
|
||||
else:
|
||||
policy = "FALLBACK_DISABLE"
|
||||
os.environ["ORTMODULE_FALLBACK_POLICY"] = policy
|
||||
os.environ["ORTMODULE_FALLBACK_RETRY"] = str(not persist_fallback)
|
||||
|
||||
dtype = torch.float
|
||||
device = torch.device("cuda")
|
||||
N, D_in, H, D_out = 64, 1000, 100, 10
|
||||
|
||||
x = torch.randn(N, D_in, device=device, dtype=dtype)
|
||||
y = torch.randn(N, D_out, device=device, dtype=dtype)
|
||||
w1 = torch.randn(D_in, H, device=device, dtype=dtype, requires_grad=True)
|
||||
w2 = torch.randn(H, D_out, device=device, dtype=dtype, requires_grad=True)
|
||||
|
||||
pt_model = MyCustomFunctionReluModel()
|
||||
ort_model = ORTModule(copy.deepcopy(pt_model))
|
||||
ort_model.train(is_training)
|
||||
pt_model.train(is_training)
|
||||
|
||||
for i in range(3):
|
||||
if fallback_enabled:
|
||||
if matching_policy:
|
||||
if i > 0 and persist_fallback:
|
||||
assert (
|
||||
ort_model._torch_module._execution_manager(is_training=is_training)._fallback_manager._exception
|
||||
is not None
|
||||
)
|
||||
pt_out = pt_model(x.mm(w1)).mm(w2)
|
||||
ort_out = ort_model(x.mm(w1)).mm(w2)
|
||||
_test_helpers.assert_values_are_close(ort_out, pt_out, rtol=1e-03, atol=1e-04)
|
||||
else:
|
||||
with pytest.raises(_fallback.ORTModuleONNXModelException) as ex_info:
|
||||
_ = ort_model(x.mm(w1)).mm(w2)
|
||||
assert "There was an error while exporting the PyTorch model to ONNX" in str(ex_info.value)
|
||||
else:
|
||||
with pytest.raises(_fallback.ORTModuleONNXModelException) as ex_info:
|
||||
# Initialize with fallback policy because Exception will happen during __init__
|
||||
_ = ort_model(x.mm(w1)).mm(w2)
|
||||
assert "There was an error while exporting the PyTorch model to ONNX" in str(ex_info.value)
|
||||
|
||||
# Restore the autograd support state.
|
||||
enable_custom_autograd_support(old_state)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"is_training,fallback_enabled,matching_policy,persist_fallback", list(itertools.product([True, False], repeat=4))
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1667,168 +1667,6 @@ def run_ios_tests(args, source_dir, config, cwd):
|
|||
)
|
||||
|
||||
|
||||
def run_orttraining_test_orttrainer_frontend_separately(cwd):
|
||||
class TestNameCollecterPlugin:
|
||||
def __init__(self):
|
||||
self.collected = set()
|
||||
|
||||
def pytest_collection_modifyitems(self, items):
|
||||
for item in items:
|
||||
print("item.name: ", item.name)
|
||||
test_name = item.name
|
||||
start = test_name.find("[")
|
||||
if start > 0:
|
||||
test_name = test_name[:start]
|
||||
self.collected.add(test_name)
|
||||
|
||||
import pytest
|
||||
|
||||
plugin = TestNameCollecterPlugin()
|
||||
test_script_filename = os.path.join(cwd, "orttraining_test_orttrainer_frontend.py")
|
||||
pytest.main(["--collect-only", test_script_filename], plugins=[plugin])
|
||||
|
||||
for test_name in plugin.collected:
|
||||
run_subprocess(
|
||||
[sys.executable, "-m", "pytest", "orttraining_test_orttrainer_frontend.py", "-v", "-k", test_name], cwd=cwd
|
||||
)
|
||||
|
||||
|
||||
def run_training_python_frontend_tests(cwd):
|
||||
# have to disable due to (with torchvision==0.9.1+cu102 which is required by ortmodule):
|
||||
# Downloading http://yann.lecun.com/exdb/mnist/
|
||||
# https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz
|
||||
# Failed to download (trying next):
|
||||
# HTTP Error 404: Not Found
|
||||
# run_subprocess([sys.executable, 'onnxruntime_test_ort_trainer.py'], cwd=cwd)
|
||||
|
||||
run_subprocess([sys.executable, "onnxruntime_test_training_unit_tests.py"], cwd=cwd)
|
||||
run_subprocess(
|
||||
[
|
||||
sys.executable,
|
||||
"orttraining_test_transformers.py",
|
||||
"BertModelTest.test_for_pretraining_full_precision_list_input",
|
||||
],
|
||||
cwd=cwd,
|
||||
)
|
||||
run_subprocess(
|
||||
[
|
||||
sys.executable,
|
||||
"orttraining_test_transformers.py",
|
||||
"BertModelTest.test_for_pretraining_full_precision_dict_input",
|
||||
],
|
||||
cwd=cwd,
|
||||
)
|
||||
run_subprocess(
|
||||
[
|
||||
sys.executable,
|
||||
"orttraining_test_transformers.py",
|
||||
"BertModelTest.test_for_pretraining_full_precision_list_and_dict_input",
|
||||
],
|
||||
cwd=cwd,
|
||||
)
|
||||
|
||||
# TODO: use run_orttraining_test_orttrainer_frontend_separately to work around a sporadic segfault.
|
||||
# shall revert to run_subprocess call once the segfault issue is resolved.
|
||||
run_orttraining_test_orttrainer_frontend_separately(cwd)
|
||||
# run_subprocess([sys.executable, '-m', 'pytest', '-sv', 'orttraining_test_orttrainer_frontend.py'], cwd=cwd)
|
||||
|
||||
run_subprocess([sys.executable, "-m", "pytest", "-sv", "orttraining_test_orttrainer_bert_toy_onnx.py"], cwd=cwd)
|
||||
|
||||
run_subprocess([sys.executable, "-m", "pytest", "-sv", "orttraining_test_checkpoint_storage.py"], cwd=cwd)
|
||||
|
||||
run_subprocess(
|
||||
[sys.executable, "-m", "pytest", "-sv", "orttraining_test_orttrainer_checkpoint_functions.py"], cwd=cwd
|
||||
)
|
||||
# Not technically training related, but it needs torch to be installed.
|
||||
run_subprocess([sys.executable, "-m", "pytest", "-sv", "test_pytorch_export_contrib_ops.py"], cwd=cwd)
|
||||
|
||||
|
||||
def run_training_python_frontend_e2e_tests(cwd):
|
||||
# frontend tests are to be added here:
|
||||
log.info("Running python frontend e2e tests.")
|
||||
|
||||
run_subprocess(
|
||||
[sys.executable, "orttraining_run_frontend_batch_size_test.py", "-v"],
|
||||
cwd=cwd,
|
||||
env={"CUDA_VISIBLE_DEVICES": "0"},
|
||||
)
|
||||
|
||||
import torch
|
||||
|
||||
ngpus = torch.cuda.device_count()
|
||||
if ngpus > 1:
|
||||
bert_pretrain_script = "orttraining_run_bert_pretrain.py"
|
||||
# TODO: this test will be replaced with convergence test ported from backend
|
||||
log.debug(
|
||||
"RUN: mpirun -n {} "
|
||||
"-x"
|
||||
"NCCL_DEBUG=INFO"
|
||||
" {} {} {}".format(
|
||||
ngpus, sys.executable, bert_pretrain_script, "ORTBertPretrainTest.test_pretrain_convergence"
|
||||
)
|
||||
)
|
||||
run_subprocess(
|
||||
[
|
||||
"mpirun",
|
||||
"-n",
|
||||
str(ngpus),
|
||||
"-x",
|
||||
"NCCL_DEBUG=INFO",
|
||||
sys.executable,
|
||||
bert_pretrain_script,
|
||||
"ORTBertPretrainTest.test_pretrain_convergence",
|
||||
],
|
||||
cwd=cwd,
|
||||
)
|
||||
|
||||
log.debug("RUN: mpirun -n {} {} orttraining_run_glue.py".format(ngpus, sys.executable))
|
||||
run_subprocess(
|
||||
["mpirun", "-n", str(ngpus), "-x", "NCCL_DEBUG=INFO", sys.executable, "orttraining_run_glue.py"], cwd=cwd
|
||||
)
|
||||
|
||||
# with orttraining_run_glue.py.
|
||||
# 1. we like to force to use single GPU (with CUDA_VISIBLE_DEVICES)
|
||||
# for fine-tune tests.
|
||||
# 2. need to run test separately (not to mix between fp16
|
||||
# and full precision runs. this need to be investigated).
|
||||
run_subprocess(
|
||||
[sys.executable, "orttraining_run_glue.py", "ORTGlueTest.test_bert_with_mrpc", "-v"],
|
||||
cwd=cwd,
|
||||
env={"CUDA_VISIBLE_DEVICES": "0"},
|
||||
)
|
||||
|
||||
run_subprocess(
|
||||
[sys.executable, "orttraining_run_glue.py", "ORTGlueTest.test_bert_fp16_with_mrpc", "-v"],
|
||||
cwd=cwd,
|
||||
env={"CUDA_VISIBLE_DEVICES": "0"},
|
||||
)
|
||||
|
||||
run_subprocess(
|
||||
[sys.executable, "orttraining_run_glue.py", "ORTGlueTest.test_roberta_with_mrpc", "-v"],
|
||||
cwd=cwd,
|
||||
env={"CUDA_VISIBLE_DEVICES": "0"},
|
||||
)
|
||||
|
||||
run_subprocess(
|
||||
[sys.executable, "orttraining_run_glue.py", "ORTGlueTest.test_roberta_fp16_with_mrpc", "-v"],
|
||||
cwd=cwd,
|
||||
env={"CUDA_VISIBLE_DEVICES": "0"},
|
||||
)
|
||||
|
||||
run_subprocess(
|
||||
[sys.executable, "orttraining_run_multiple_choice.py", "ORTMultipleChoiceTest.test_bert_fp16_with_swag", "-v"],
|
||||
cwd=cwd,
|
||||
env={"CUDA_VISIBLE_DEVICES": "0"},
|
||||
)
|
||||
|
||||
run_subprocess([sys.executable, "onnxruntime_test_ort_trainer_with_mixed_precision.py"], cwd=cwd)
|
||||
|
||||
run_subprocess(
|
||||
[sys.executable, "orttraining_test_transformers.py", "BertModelTest.test_for_pretraining_mixed_precision"],
|
||||
cwd=cwd,
|
||||
)
|
||||
|
||||
|
||||
def run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs):
|
||||
for config in configs:
|
||||
log.info("Running tests for %s configuration", config)
|
||||
|
|
@ -1947,11 +1785,6 @@ def run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs):
|
|||
if not args.disable_ml_ops and not args.use_tensorrt:
|
||||
run_subprocess([sys.executable, "onnxruntime_test_python_mlops.py"], cwd=cwd, dll_path=dll_path)
|
||||
|
||||
# The following test has multiple failures on Windows
|
||||
if args.enable_training and args.use_cuda and not is_windows():
|
||||
# run basic frontend tests
|
||||
run_training_python_frontend_tests(cwd=cwd)
|
||||
|
||||
if args.build_eager_mode:
|
||||
# run eager mode test
|
||||
args_list = [sys.executable, os.path.join(cwd, "eager_test")]
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ jobs:
|
|||
RunDockerBuildArgs: >
|
||||
-o ubuntu20.04 -d gpu
|
||||
-t onnxruntime_orttraining_ortmodule_tests_image
|
||||
-u
|
||||
-e
|
||||
-x "
|
||||
--enable_training
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ stages:
|
|||
- template: templates/py-packaging-training-cuda-stage.yml
|
||||
parameters:
|
||||
build_py_parameters: --enable_training --update --build --enable_training_apis
|
||||
torch_version: '1.11.0'
|
||||
torch_version: '1.13.1'
|
||||
opset_version: '15'
|
||||
cuda_version: '11.6'
|
||||
gcc_version: 11
|
||||
|
|
@ -25,7 +25,7 @@ stages:
|
|||
- template: templates/py-packaging-training-cuda-stage.yml
|
||||
parameters:
|
||||
build_py_parameters: --enable_training --update --build --enable_training_apis
|
||||
torch_version: '1.11.0'
|
||||
torch_version: '1.13.1'
|
||||
opset_version: '15'
|
||||
cuda_version: '11.6'
|
||||
gcc_version: 11
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ steps:
|
|||
--volume /bert_data:/bert_data \
|
||||
--volume /hf_models_cache:/hf_models_cache \
|
||||
${{ parameters.DockerImageTag }} \
|
||||
bash -c "python3 -m pip uninstall -y -r /onnxruntime_src/tools/ci_build/github/linux/docker/scripts/training/requirements.txt && python3 -m pip install -r /onnxruntime_src/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage1/requirements_torch1.12.1_cu11.6/requirements.txt && python3 -m pip install -r /onnxruntime_src/tools/ci_build/github/linux/docker/scripts/training/ortmodule/stage2/requirements.txt && rm -rf /build/onnxruntime/ && python3 -m pip install /build/dist/onnxruntime*.whl && python3 -m onnxruntime.training.ortmodule.torch_cpp_extensions.install && /build/launch_test.py --cmd_line_with_args 'python orttraining_ortmodule_tests.py --mnist /mnist --bert_data /bert_data/hf_data/glue_data/CoLA/original/raw --transformers_cache /hf_models_cache/huggingface/transformers' --cwd /build" \
|
||||
bash -c "rm -rf /build/onnxruntime/ && python3 -m pip install /build/dist/onnxruntime*.whl && python3 -m onnxruntime.training.ortmodule.torch_cpp_extensions.install && /build/launch_test.py --cmd_line_with_args 'python orttraining_ortmodule_tests.py --mnist /mnist --bert_data /bert_data/hf_data/glue_data/CoLA/original/raw --transformers_cache /hf_models_cache/huggingface/transformers' --cwd /build" \
|
||||
displayName: 'Run orttraining_ortmodule_tests.py'
|
||||
condition: succeededOrFailed()
|
||||
timeoutInMinutes: 60
|
||||
|
|
|
|||
|
|
@ -127,14 +127,6 @@ jobs:
|
|||
workingDirectory: '$(Build.BinariesDirectory)'
|
||||
arguments: -cpu_arch ${{ parameters.buildArch }} -install_prefix $(Build.BinariesDirectory)\${{ parameters.BuildConfig }}\installed -build_config ${{ parameters.BuildConfig }}
|
||||
|
||||
|
||||
#Build.py disables running training python frontend tests on Windows, so here we don't install pytorch.
|
||||
- ${{ if eq(parameters.isTraining, true) }}:
|
||||
- script: |
|
||||
python -m pip install -r $(Build.SourcesDirectory)\tools\ci_build\github\linux\docker\scripts\training\requirements.txt
|
||||
workingDirectory: '$(Build.BinariesDirectory)'
|
||||
displayName: 'Install python modules'
|
||||
|
||||
- task: NuGetToolInstaller@0
|
||||
displayName: Use Nuget 5.7.0
|
||||
inputs:
|
||||
|
|
|
|||
|
|
@ -165,7 +165,7 @@ CMD ["/bin/bash"]
|
|||
|
||||
#Build manylinux2014 docker image end
|
||||
ARG PYTHON_VERSION=3.9
|
||||
ARG TORCH_VERSION=1.12.1
|
||||
ARG TORCH_VERSION=1.13.1
|
||||
ARG OPSET_VERSION=15
|
||||
ARG INSTALL_DEPS_EXTRA_ARGS
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ INSTALL_DEPS_DISTRIBUTED_SETUP=false
|
|||
ORTMODULE_BUILD=false
|
||||
TARGET_ROCM=false
|
||||
CU_VER="11.6"
|
||||
TORCH_VERSION='1.12.1'
|
||||
TORCH_VERSION='1.13.1'
|
||||
USE_CONDA=false
|
||||
|
||||
while getopts p:h:d:v:tmurc parameter_Option
|
||||
|
|
|
|||
|
|
@ -96,7 +96,7 @@ do
|
|||
if ! [[ ${PYTHON_EXE} = "/opt/python/cp310-cp310/bin/python3.10" ]]; then
|
||||
${PYTHON_EXE} -m pip install -r ${0/%install_deps_aten\.sh/..\/training\/ortmodule\/stage1\/requirements_torch_cpu\/requirements.txt}
|
||||
else
|
||||
${PYTHON_EXE} -m pip install torch==1.12.1
|
||||
${PYTHON_EXE} -m pip install torch==1.13.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
|
||||
fi
|
||||
done
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +0,0 @@
|
|||
--pre
|
||||
-f https://download.pytorch.org/whl/torch_stable.html
|
||||
torch==1.12.1+cu116
|
||||
torchvision==0.13.1+cu116
|
||||
torchtext==0.13.1
|
||||
setuptools>=41.4.0
|
||||
|
|
@ -1,8 +1,8 @@
|
|||
--pre
|
||||
-f https://download.pytorch.org/whl/torch_stable.html
|
||||
torch==1.12.1+cu116
|
||||
torchvision==0.13.1+cu116
|
||||
torchtext==0.13.1
|
||||
torch==1.13.1+cu116
|
||||
torchvision==0.14.1+cu116
|
||||
torchtext==0.14.1
|
||||
# TODO(bmeswani): packaging 22.0 removes support for LegacyVersion leading to errors because transformers 4.4.2 uses LegacyVersion
|
||||
packaging==21.3
|
||||
setuptools>=41.4.0
|
||||
|
|
@ -1,2 +1,3 @@
|
|||
torch==1.12
|
||||
-f https://download.pytorch.org/whl/torch_stable.html
|
||||
torch==1.13.1+cpu
|
||||
setuptools>=41.4.0
|
||||
|
|
|
|||
|
|
@ -8,3 +8,4 @@ wget
|
|||
pytorch-lightning==1.6.0
|
||||
deepspeed==0.3.15
|
||||
fairscale==0.4.6
|
||||
parameterized>=0.8.1
|
||||
|
|
|
|||
|
|
@ -1,13 +0,0 @@
|
|||
--pre
|
||||
-f https://download.pytorch.org/whl/torch_stable.html
|
||||
# transformers requires scikit-learn
|
||||
scikit-learn
|
||||
numpy==1.21.6
|
||||
transformers==v2.10.0
|
||||
torch==1.10.0+cu113
|
||||
torchvision==0.11.1+cu113
|
||||
torchtext==0.11.0
|
||||
tensorboard>=2.2.0,<2.5.0
|
||||
h5py
|
||||
setuptools<=59.5.0
|
||||
parameterized>=0.8.1
|
||||
Loading…
Reference in a new issue