Call PyArray_Check only if NumPy is available

[malfet]

Fixes #66353

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💊 CI failures summary and remediations

As of commit d7145f4 (more details on the Dr. CI page):

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✅ None of the CI failures appear to be your fault 💚

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• 1/1 tentatively recognized as flaky ❄️
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❄️ 1 failure tentatively classified as flaky

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linux-xenial-cuda11.3-py3.6-gcc7 / test (default, 2, 2, linux.8xlarge.nvidia.gpu) (1/1)

Step: "Install nvidia driver, nvidia-docker runtime, set GPU_FLAG" (full log | diagnosis details | 🔁 rerun) ❄️

2021-10-11T19:55:11.2404340Z WARNING: infoROM is corrupted at gpu 0000:00:1E.0

2021-10-11T19:55:11.2120230Z |                               |                      |                  N/A |
2021-10-11T19:55:11.2121426Z +-------------------------------+----------------------+----------------------+
2021-10-11T19:55:11.2122026Z                                                                                
2021-10-11T19:55:11.2122816Z +-----------------------------------------------------------------------------+
2021-10-11T19:55:11.2123475Z | Processes:                                                                  |
2021-10-11T19:55:11.2124082Z |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
2021-10-11T19:55:11.2124700Z |        ID   ID                                                   Usage      |
2021-10-11T19:55:11.2125170Z |=============================================================================|
2021-10-11T19:55:11.2125687Z |  No running processes found                                                 |
2021-10-11T19:55:11.2126554Z +-----------------------------------------------------------------------------+
2021-10-11T19:55:11.2404340Z WARNING: infoROM is corrupted at gpu 0000:00:1E.0
2021-10-11T19:55:11.7252637Z ##[error]Process completed with exit code 1.
2021-10-11T19:55:11.7341372Z ##[group]Run # Ensure the working directory gets chowned back to the current user
2021-10-11T19:55:11.7342723Z �[36;1m# Ensure the working directory gets chowned back to the current user�[0m
2021-10-11T19:55:11.7344040Z �[36;1mdocker run --rm -v "$(pwd)":/v -w /v "${ALPINE_IMAGE}" chown -R "$(id -u):$(id -g)" .�[0m
2021-10-11T19:55:11.7359575Z shell: /usr/bin/bash -e {0}
2021-10-11T19:55:11.7360089Z env:
2021-10-11T19:55:11.7360816Z   BUILD_ENVIRONMENT: linux-xenial-cuda11.3-py3.6-gcc7
2021-10-11T19:55:11.7362429Z   DOCKER_IMAGE_BASE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda11.3-cudnn8-py3-gcc7
2021-10-11T19:55:11.7364100Z   SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
2021-10-11T19:55:11.7365361Z   XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla

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[ptrblck]

I was looking into the same lines of code, but couldn't narrow it down to a missing numpy check, since a source build wasn't failing there. Do you know, why the binaries were hitting the issue, while a source build (without numpy) was working fine?

[seemethere]

I was looking into the same lines of code, but couldn't narrow it down to a missing numpy check, since a source build wasn't failing there. Do you know, why the binaries were hitting the issue, while a source build (without numpy) was working fine?

I guess the expectation with binary builds is that they always build with USE_NUMPY=1 while if you're building from source without numpy the expectation is that you would set it to USE_NUMPY=0