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Sequential cpu offload: require accelerate 0.14.0 (#2517)

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* Sequential cpu offload: require accelerate 0.14.0.

* Import is_accelerate_version

* Missing copy.
This commit is contained in:
Pedro Cuenca
2023-02-28 20:34:36 +01:00
committed by GitHub
parent 477aaa96d0
commit a75ac3fa8d
15 changed files with 44 additions and 35 deletions
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4
src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py
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@@ -191,10 +191,10 @@ class AltDiffusionPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

4
src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
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@@ -213,10 +213,10 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

4
src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py
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@@ -230,10 +230,10 @@ class CycleDiffusionPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

4
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
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@@ -194,10 +194,10 @@ class StableDiffusionPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

6
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py
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@@ -24,7 +24,7 @@ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.cross_attention import CrossAttention
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import is_accelerate_available, logging, randn_tensor, replace_example_docstring
from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
@@ -256,10 +256,10 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

4
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
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@@ -218,10 +218,10 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

4
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
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@@ -265,10 +265,10 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

4
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py
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@@ -209,10 +209,10 @@ class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

4
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py
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@@ -398,10 +398,10 @@ class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

4
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py
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@@ -130,10 +130,10 @@ class StableDiffusionKDiffusionPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

6
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py
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@@ -19,7 +19,7 @@ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import DDIMScheduler, PNDMScheduler
from ...utils import is_accelerate_available, logging, randn_tensor, replace_example_docstring
from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
@@ -151,10 +151,10 @@ class StableDiffusionPanoramaPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

4
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py
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@@ -365,10 +365,10 @@ class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

6
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py
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@@ -22,7 +22,7 @@ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import is_accelerate_available, logging, randn_tensor, replace_example_docstring
from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring
from ..pipeline_utils import DiffusionPipeline
from . import StableDiffusionPipelineOutput
from .safety_checker import StableDiffusionSafetyChecker
@@ -169,10 +169,10 @@ class StableDiffusionSAGPipeline(DiffusionPipeline):
Note that offloading happens on a submodule basis. Memory savings are higher than with
`enable_model_cpu_offload`, but performance is lower.
"""
if is_accelerate_available():
if is_accelerate_available() and is_accelerate_version(">=", "0.14.0"):
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`")
raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher")
device = torch.device(f"cuda:{gpu_id}")

15
tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py
View File

@@ -39,7 +39,16 @@ from diffusers import (
StableDiffusionDepth2ImgPipeline,
UNet2DConditionModel,
)
from diffusers.utils import floats_tensor, is_accelerate_available, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils import (
floats_tensor,
is_accelerate_available,
is_accelerate_version,
load_image,
load_numpy,
nightly,
slow,
torch_device,
)
from diffusers.utils.testing_utils import require_torch_gpu, skip_mps
from ...test_pipelines_common import PipelineTesterMixin
@@ -227,8 +236,8 @@ class StableDiffusionDepth2ImgPipelineFastTests(PipelineTesterMixin, unittest.Te
self.assertLess(max_diff, 1.3e-2, "The outputs of the fp16 and fp32 pipelines are too different.")
@unittest.skipIf(
torch_device != "cuda" or not is_accelerate_available(),
reason="CPU offload is only available with CUDA and `accelerate` installed",
torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.14.0"),
reason="CPU offload is only available with CUDA and `accelerate v0.14.0` or higher",
)
def test_cpu_offload_forward_pass(self):
components = self.get_dummy_components()

6
tests/test_pipelines_common.py
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@@ -13,7 +13,7 @@ import torch
import diffusers
from diffusers import DiffusionPipeline
from diffusers.utils import logging
from diffusers.utils.import_utils import is_accelerate_available, is_xformers_available
from diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version, is_xformers_available
from diffusers.utils.testing_utils import require_torch, torch_device
@@ -417,8 +417,8 @@ class PipelineTesterMixin:
assert_mean_pixel_difference(output_with_slicing[0], output_without_slicing[0])
@unittest.skipIf(
torch_device != "cuda" or not is_accelerate_available(),
reason="CPU offload is only available with CUDA and `accelerate` installed",
torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.14.0"),
reason="CPU offload is only available with CUDA and `accelerate v0.14.0` or higher",
)
def test_cpu_offload_forward_pass(self):
if not self.test_cpu_offload: