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bugfix for npu not support float64 (#10123)

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* bugfix for npu not support float64

* is_mps is_npu

---------

Co-authored-by: 白超 <baichao19@huawei.com>
Co-authored-by: hlky <hlky@hlky.ac>
This commit is contained in:
baymax591
2025-01-21 03:35:24 +08:00
committed by GitHub
parent 4842f5d8de
commit 75a636da48
21 changed files with 63 additions and 42 deletions
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5
examples/community/fresco_v2v.py
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@@ -404,10 +404,11 @@ def my_forward(
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
examples/community/matryoshka.py
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@@ -2806,10 +2806,11 @@ class MatryoshkaUNet2DConditionModel(
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
examples/research_projects/pixart/pipeline_pixart_alpha_controlnet.py
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@@ -1031,10 +1031,11 @@ class PixArtAlphaControlnetPipeline(DiffusionPipeline):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
is_npu = latent_model_input.device.type == "npu"
if isinstance(current_timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
elif len(current_timestep.shape) == 0:
current_timestep = current_timestep[None].to(latent_model_input.device)

5
examples/research_projects/promptdiffusion/promptdiffusioncontrolnet.py
View File

@@ -258,10 +258,11 @@ class PromptDiffusionControlNetModel(ControlNetModel):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/models/controlnets/controlnet.py
View File

@@ -740,10 +740,11 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/models/controlnets/controlnet_sparsectrl.py
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@@ -671,10 +671,11 @@ class SparseControlNetModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/models/controlnets/controlnet_union.py
View File

@@ -681,10 +681,11 @@ class ControlNetUnionModel(ModelMixin, ConfigMixin, FromOriginalModelMixin):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/models/controlnets/controlnet_xs.py
View File

@@ -1088,10 +1088,11 @@ class UNetControlNetXSModel(ModelMixin, ConfigMixin):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/models/unets/unet_2d_condition.py
View File

@@ -915,10 +915,11 @@ class UNet2DConditionModel(
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/models/unets/unet_3d_condition.py
View File

@@ -624,10 +624,11 @@ class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/models/unets/unet_i2vgen_xl.py
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@@ -575,10 +575,11 @@ class I2VGenXLUNet(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
# TODO: this requires sync between CPU and GPU. So try to pass `timesteps` as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timesteps, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/models/unets/unet_motion_model.py
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@@ -2114,10 +2114,11 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, Peft
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/models/unets/unet_spatio_temporal_condition.py
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@@ -402,10 +402,11 @@ class UNetSpatioTemporalConditionModel(ModelMixin, ConfigMixin, UNet2DConditionL
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/pipelines/audioldm2/modeling_audioldm2.py
View File

@@ -768,10 +768,11 @@ class AudioLDM2UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoad
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py
View File

@@ -1163,10 +1163,11 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
is_npu = sample.device.type == "npu"
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)

5
src/diffusers/pipelines/dit/pipeline_dit.py
View File

@@ -187,10 +187,11 @@ class DiTPipeline(DiffusionPipeline):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
is_npu = latent_model_input.device.type == "npu"
if isinstance(timesteps, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=latent_model_input.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(latent_model_input.device)

5
src/diffusers/pipelines/latte/pipeline_latte.py
View File

@@ -798,10 +798,11 @@ class LattePipeline(DiffusionPipeline):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
is_npu = latent_model_input.device.type == "npu"
if isinstance(current_timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
elif len(current_timestep.shape) == 0:
current_timestep = current_timestep[None].to(latent_model_input.device)

5
src/diffusers/pipelines/lumina/pipeline_lumina.py
View File

@@ -806,10 +806,11 @@ class LuminaText2ImgPipeline(DiffusionPipeline):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
is_npu = latent_model_input.device.type == "npu"
if isinstance(current_timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
current_timestep = torch.tensor(
[current_timestep],
dtype=dtype,

5
src/diffusers/pipelines/pag/pipeline_pag_pixart_sigma.py
View File

@@ -807,10 +807,11 @@ class PixArtSigmaPAGPipeline(DiffusionPipeline, PAGMixin):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
is_npu = latent_model_input.device.type == "npu"
if isinstance(current_timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
elif len(current_timestep.shape) == 0:
current_timestep = current_timestep[None].to(latent_model_input.device)

5
src/diffusers/pipelines/pixart_alpha/pipeline_pixart_alpha.py
View File

@@ -907,10 +907,11 @@ class PixArtAlphaPipeline(DiffusionPipeline):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
is_npu = latent_model_input.device.type == "npu"
if isinstance(current_timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
elif len(current_timestep.shape) == 0:
current_timestep = current_timestep[None].to(latent_model_input.device)

5
src/diffusers/pipelines/pixart_alpha/pipeline_pixart_sigma.py
View File

@@ -822,10 +822,11 @@ class PixArtSigmaPipeline(DiffusionPipeline):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
is_npu = latent_model_input.device.type == "npu"
if isinstance(current_timestep, float):
dtype = torch.float32 if is_mps else torch.float64
dtype = torch.float32 if (is_mps or is_npu) else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
dtype = torch.int32 if (is_mps or is_npu) else torch.int64
current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device)
elif len(current_timestep.shape) == 0:
current_timestep = current_timestep[None].to(latent_model_input.device)