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[Reland] Add py-3.10 config (#82329)

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This is a re-land of #81372 and #81233 with the exception that it does not force the range-checks on older Python runtime versions and as such should not affect the internal workloads, which were the reason for revert, see https://github.com/pytorch/pytorch/pull/81372#issuecomment-1187516464

- [Py3.10] Allow floats to be imported as Long (#81372)
- [CI] Move CUDA-11.6 to Python-3.10 configuration (#81233)
- Don't do anything about range checks for pre-py3.10
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82329
Approved by: https://github.com/kit1980
This commit is contained in:
Nikita Shulga
2022-07-27 20:22:47 +00:00
committed by PyTorch MergeBot
parent 3b6b27e9d7
commit d80fe49de0
9 changed files with 49 additions and 19 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
.circleci/docker/build.sh
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@ -141,7 +141,7 @@ case "$image" in
pytorch-linux-bionic-cuda11.6-cudnn8-py3-gcc7)
CUDA_VERSION=11.6.2
CUDNN_VERSION=8
ANACONDA_PYTHON_VERSION=3.7
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=7
PROTOBUF=yes
DB=yes

5
.circleci/docker/common/install_conda.sh
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@ -76,7 +76,10 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
# DO NOT install cmake here as it would install a version newer than 3.13, but
# we want to pin to version 3.13.
CONDA_COMMON_DEPS="astunparse pyyaml mkl=2022.0.1 mkl-include=2022.0.1 setuptools cffi future six"
if [ "$ANACONDA_PYTHON_VERSION" = "3.9" ]; then
if [ "$ANACONDA_PYTHON_VERSION" = "3.10" ]; then
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
conda_install numpy=1.21.2 ${CONDA_COMMON_DEPS} llvmdev=8.0.0
elif [ "$ANACONDA_PYTHON_VERSION" = "3.9" ]; then
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
conda_install numpy=1.19.2 ${CONDA_COMMON_DEPS} llvmdev=8.0.0
elif [ "$ANACONDA_PYTHON_VERSION" = "3.8" ]; then

4
.circleci/docker/requirements-ci.txt
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@ -100,6 +100,7 @@ networkx==2.6.3
numba==0.49.0 ; python_version < "3.9"
numba==0.54.1 ; python_version == "3.9"
numba==0.55.2 ; python_version == "3.10"
#Description: Just-In-Time Compiler for Numerical Functions
#Pinned versions: 0.54.1, 0.49.0, <=0.49.1
#test that import: test_numba_integration.py
@ -188,7 +189,8 @@ scikit-image
#Pinned versions: 0.20.3
#test that import:
scipy==1.6.3
scipy==1.6.3 ; python_version < "3.10"
scipy==1.8.1 ; python_version == "3.10"
# Pin SciPy because of failing distribution tests (see #60347)
#Description: scientific python
#Pinned versions: 1.6.3

16
.github/workflows/pull.yml vendored
View File

@ -149,20 +149,20 @@ jobs:
{ config: "default", shard: 1, num_shards: 1, runner: "linux.2xlarge" },
]}
linux-bionic-cuda11_6-py3_7-gcc7-build:
name: linux-bionic-cuda11.6-py3.7-gcc7
linux-bionic-cuda11_6-py3_10-gcc7-build:
name: linux-bionic-cuda11.6-py3.10-gcc7
uses: ./.github/workflows/_linux-build.yml
with:
build-environment: linux-bionic-cuda11.6-py3.7-gcc7
build-environment: linux-bionic-cuda11.6-py3.10-gcc7
docker-image-name: pytorch-linux-bionic-cuda11.6-cudnn8-py3-gcc7
linux-bionic-cuda11_6-py3_7-gcc7-test:
name: linux-bionic-cuda11.6-py3.7-gcc7
linux-bionic-cuda11_6-py3_10-gcc7-test:
name: linux-bionic-cuda11.6-py3.10-gcc7
uses: ./.github/workflows/_linux-test.yml
needs: linux-bionic-cuda11_6-py3_7-gcc7-build
needs: linux-bionic-cuda11_6-py3_10-gcc7-build
with:
build-environment: linux-bionic-cuda11.6-py3.7-gcc7
docker-image: ${{ needs.linux-bionic-cuda11_6-py3_7-gcc7-build.outputs.docker-image }}
build-environment: linux-bionic-cuda11.6-py3.10-gcc7
docker-image: ${{ needs.linux-bionic-cuda11_6-py3_10-gcc7-build.outputs.docker-image }}
test-matrix: |
{ include: [
{ config: "default", shard: 1, num_shards: 4, runner: "linux.4xlarge.nvidia.gpu" },

5
test/jit/test_hash.py
View File

@ -75,7 +75,10 @@ class TestHash(JitTestCase):
self.checkScript(fn, (1.2345, float("inf")))
self.checkScript(fn, (float("inf"), float("inf")))
self.checkScript(fn, (1.2345, float('nan')))
self.checkScript(fn, (float("nan"), float("nan")))
if sys.version_info < (3, 10):
# Hash of two nans are not guaranteed to be equal. From https://docs.python.org/3/whatsnew/3.10.html :
# Hashes of NaN values of both float type and decimal.Decimal type now depend on object identity.
self.checkScript(fn, (float("nan"), float("nan")))
self.checkScript(fn, (float("nan"), float("inf")))
def test_hash_int(self):

3
test/test_fx.py
View File

@ -3283,6 +3283,7 @@ class TestFX(JitTestCase):
.run(scripted.code)
@unittest.skipIf(IS_WINDOWS, "Python Windows bug? https://bugs.python.org/issue45108")
@unittest.skipIf(sys.version_info >= (3, 10), "Does not work on Python-3.10")
def test_assert(self):
def f(x):
assert x > 1
@ -4020,7 +4021,7 @@ class TestFunctionalTracing(JitTestCase):
def functional_test(self):
if func_name in self.UNTRACEABLE_FUNCTIONALS_PY38 and \
sys.version_info >= (3, 8) and sys.version_info < (3, 10):
sys.version_info >= (3, 8) and sys.version_info < (3, 11):
exc, err = self.UNTRACEABLE_FUNCTIONALS_PY38[func_name]
with self.assertRaisesRegex(exc, err):
symbolic_trace(fn)

3
test/test_jit.py
View File

@ -7442,6 +7442,9 @@ a")
# tensor from empty list is type float in python and annotated type in torchscript
if "annotate" in li and "dtype" not in option:
continue
# Skip unsigned tensor initializaton for signed values on 3.10
if sys.version_info[:2] >= (3, 10) and "torch.uint8" in option and "-" in li:
continue
code = tensor_template.format(list_create=li, tensor_op=op, options=option)
scope = {}
exec(code, globals(), scope)

5
test/test_sparse_csr.py
View File

@ -207,11 +207,14 @@ class TestSparseCompressed(TestCase):
# replaced with a N-list where N==len(densesize) and the
# shape corresponds to densesize.
max_val = torch.iinfo(dtype).max if dtype in [torch.int16, torch.int8, torch.uint8] else None
def list_add(lst, value):
# recursively add a value to lst items
if isinstance(lst, list):
return [list_add(item, value) for item in lst]
return lst + value
rc = lst + value
return rc if max_val is None else (rc % max_val)
def stretch_values(value, bdim, values_item_shape):
# replace a value with a new value that extends the

25
torch/csrc/utils/python_scalars.h
View File

@ -1,6 +1,7 @@
#pragma once
#include <ATen/ATen.h>
#include <c10/util/TypeCast.h>
#include <torch/csrc/python_headers.h>
#include <torch/csrc/Exceptions.h>
@ -9,22 +10,36 @@
namespace torch {
namespace utils {
template <typename T>
inline T unpackIntegral(PyObject* obj, const char* type) {
#if PY_VERSION_HEX >= 0x030a00f0
// In Python-3.10 floats can no longer be silently converted to integers
// Keep backward compatible behavior for now
if (PyFloat_Check(obj)) {
return c10::checked_convert<T>(THPUtils_unpackDouble(obj), type);
}
return c10::checked_convert<T>(THPUtils_unpackLong(obj), type);
#else
return static_cast<T>(THPUtils_unpackLong(obj));
#endif
}
inline void store_scalar(void* data, at::ScalarType scalarType, PyObject* obj) {
switch (scalarType) {
case at::kByte:
*(uint8_t*)data = (uint8_t)THPUtils_unpackLong(obj);
*(uint8_t*)data = unpackIntegral<uint8_t>(obj, "uint8");
break;
case at::kChar:
*(int8_t*)data = (int8_t)THPUtils_unpackLong(obj);
*(int8_t*)data = unpackIntegral<int8_t>(obj, "int8");
break;
case at::kShort:
*(int16_t*)data = (int16_t)THPUtils_unpackLong(obj);
*(int16_t*)data = unpackIntegral<int16_t>(obj, "int16");
break;
case at::kInt:
*(int32_t*)data = (int32_t)THPUtils_unpackLong(obj);
*(int32_t*)data = unpackIntegral<int32_t>(obj, "int32");
break;
case at::kLong:
*(int64_t*)data = THPUtils_unpackLong(obj);
*(int64_t*)data = unpackIntegral<int64_t>(obj, "int64");
break;
case at::kHalf:
*(at::Half*)data =