[CI] Speed up slow tests (#708)

* [CI] Localize the HF cache

* pip cache

* de-env

* refactor matrix

* fix fast cache

* less onnx steps

* revert

* revert pip cache

* revert pip cache

* remove debugging trigger
This commit is contained in:
Anton Lozhkov 2022-10-03 22:16:23 +02:00 committed by GitHub
parent b35bac4d3b
commit 1070e1a38a
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 14 additions and 22 deletions

View File

@ -21,7 +21,7 @@ jobs:
runs-on: [ self-hosted, docker-gpu ]
container:
image: python:3.7
options: --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
options: --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache/
steps:
- name: Checkout diffusers

View File

@ -15,14 +15,10 @@ env:
jobs:
run_tests_single_gpu:
name: Diffusers tests
strategy:
fail-fast: false
matrix:
machine_type: [ single-gpu ]
runs-on: [ self-hosted, docker-gpu, '${{ matrix.machine_type }}' ]
runs-on: [ self-hosted, docker-gpu, single-gpu ]
container:
image: nvcr.io/nvidia/pytorch:22.07-py3
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache
steps:
- name: Checkout diffusers
@ -66,14 +62,10 @@ jobs:
run_examples_single_gpu:
name: Examples tests
strategy:
fail-fast: false
matrix:
machine_type: [ single-gpu ]
runs-on: [ self-hosted, docker-gpu, '${{ matrix.machine_type }}' ]
runs-on: [ self-hosted, docker-gpu, single-gpu ]
container:
image: nvcr.io/nvidia/pytorch:22.07-py3
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/hf_cache:/mnt/cache
steps:
- name: Checkout diffusers

View File

@ -92,7 +92,7 @@ _deps = [
"jaxlib>=0.1.65,<=0.3.6",
"modelcards>=0.1.4",
"numpy",
"onnxruntime-gpu",
"onnxruntime",
"pytest",
"pytest-timeout",
"pytest-xdist",
@ -178,7 +178,7 @@ extras["docs"] = deps_list("hf-doc-builder")
extras["training"] = deps_list("accelerate", "datasets", "tensorboard", "modelcards")
extras["test"] = deps_list(
"datasets",
"onnxruntime-gpu",
"onnxruntime",
"pytest",
"pytest-timeout",
"pytest-xdist",

View File

@ -17,7 +17,7 @@ deps = {
"jaxlib": "jaxlib>=0.1.65,<=0.3.6",
"modelcards": "modelcards>=0.1.4",
"numpy": "numpy",
"onnxruntime-gpu": "onnxruntime-gpu",
"onnxruntime": "onnxruntime",
"pytest": "pytest",
"pytest-timeout": "pytest-timeout",
"pytest-xdist": "pytest-xdist",

View File

@ -1422,18 +1422,18 @@ class PipelineTesterMixin(unittest.TestCase):
@slow
def test_stable_diffusion_onnx(self):
sd_pipe = StableDiffusionOnnxPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CUDAExecutionProvider", use_auth_token=True
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider", use_auth_token=True
)
prompt = "A painting of a squirrel eating a burger"
np.random.seed(0)
output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=20, output_type="np")
output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=5, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.0385, 0.0252, 0.0234, 0.0287, 0.0358, 0.0287, 0.0276, 0.0235, 0.0010])
expected_slice = np.array([0.3602, 0.3688, 0.3652, 0.3895, 0.3782, 0.3747, 0.3927, 0.4241, 0.4327])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@slow
@ -1592,7 +1592,7 @@ class PipelineTesterMixin(unittest.TestCase):
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.6254, -0.2742, -1.0710, 0.2296, -1.1683, 0.6913, -2.0605, -0.0682, 0.9700]
[-0.5950, -0.3039, -1.1672, 0.1594, -1.1572, 0.6719, -1.9712, -0.0403, 0.9592]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
@ -1606,6 +1606,6 @@ class PipelineTesterMixin(unittest.TestCase):
prompt = "Andromeda galaxy in a bottle"
np.random.seed(0)
pipe(prompt=prompt, num_inference_steps=50, guidance_scale=7.5, callback=test_callback_fn, callback_steps=1)
pipe(prompt=prompt, num_inference_steps=5, guidance_scale=7.5, callback=test_callback_fn, callback_steps=1)
assert test_callback_fn.has_been_called
assert number_of_steps == 51
assert number_of_steps == 6