[MPS][Perf] Dispatch to SDP-math-mps for non-contig Tensors #139791
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As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks.
Test plan: Run
```python
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
import torch
import time
vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
subfolder='vae',
torch_dtype=torch.bfloat16,
force_upcast=False).to('mps')
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
torch_dtype=torch.bfloat16, variant="fp16").to('mps')
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
start_time = time.time()
start_mps_mem = torch.mps.driver_allocated_memory()
image = pipe(prompt="Spherical cow in vacuum",
num_inference_steps=10,
guidance_scale=8,
generator=torch.Generator("mps").manual_seed(42),
).images[0]
end_mps_mem = torch.mps.driver_allocated_memory()
run_time = time.time() - start_time
print(f"run time in {run_time:.2f} sec, end_mps_mem {end_mps_mem/1024.0**2:.2f} Mb mem increase {(end_mps_mem-start_time)/1024.0**2:.2f} Mb")
image.save(f'bfloat16.png')
```
Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro
Fixes #139389
[ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/139791
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 0094374 with merge base 4d5cc1b ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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Nov 5, 2024
| if (query_device_type == DeviceType::MPS && dropout_p == 0.0 | ||
| && !(GradMode::is_enabled() && any_inputs_require_grad) | ||
| && all_contiguous | ||
| && (all_contiguous || mps::is_macos_13_or_newer(mps::MacOSVersion::MACOS_VER_15_0_PLUS)) |
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goes back to my last comment, that this codepath is kinda an anti pattern with how we dispatch to different backends
but that is somewhat beyond the scope of this PR
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@malfet there is a specific pattern in the SDPA op which is not optimized and we don't recognize the pattern for it and it causes the slow down. I don't think we need to disable it for all cases. cc @jhavukainen
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@kulinseth this simply enables call to SDPA op for-non contiguous tensors on MacOS15, as well are using strided API
drisspg
approved these changes
Nov 5, 2024
Skylion007
approved these changes
Nov 5, 2024
As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks.
Test plan: Run
```python
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
import torch
import time
vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
subfolder='vae',
torch_dtype=torch.bfloat16,
force_upcast=False).to('mps')
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
torch_dtype=torch.bfloat16, variant="fp16").to('mps')
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
start_time = time.time()
start_mps_mem = torch.mps.driver_allocated_memory()
image = pipe(prompt="Spherical cow in vacuum",
num_inference_steps=10,
guidance_scale=8,
generator=torch.Generator("mps").manual_seed(42),
).images[0]
end_mps_mem = torch.mps.driver_allocated_memory()
run_time = time.time() - start_time
print(f"run time in {run_time:.2f} sec, end_mps_mem {end_mps_mem/1024.0**2:.2f} Mb mem increase {(end_mps_mem-start_time)/1024.0**2:.2f} Mb")
image.save(f'bfloat16.png')
```
Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro, though generated image remains the same:
Fixes #139389
[ghstack-poisoned]
As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks.
Test plan: Run
```python
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
import torch
import time
vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
subfolder='vae',
torch_dtype=torch.bfloat16,
force_upcast=False).to('mps')
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
torch_dtype=torch.bfloat16, variant="fp16").to('mps')
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
start_time = time.time()
start_mps_mem = torch.mps.driver_allocated_memory()
image = pipe(prompt="Spherical cow in vacuum",
num_inference_steps=10,
guidance_scale=8,
generator=torch.Generator("mps").manual_seed(42),
).images[0]
end_mps_mem = torch.mps.driver_allocated_memory()
run_time = time.time() - start_time
print(f"run time in {run_time:.2f} sec, end_mps_mem {end_mps_mem/1024.0**2:.2f} Mb mem increase {(end_mps_mem-start_time)/1024.0**2:.2f} Mb")
image.save(f'bfloat16.png')
```
Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro, though generated image remains the same:
Fixes #139389
[ghstack-poisoned]
As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks.
Test plan: Run
```python
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
import torch
import time
vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
subfolder='vae',
torch_dtype=torch.bfloat16,
force_upcast=False).to('mps')
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
torch_dtype=torch.bfloat16, variant="fp16").to('mps')
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
start_time = time.time()
start_mps_mem = torch.mps.driver_allocated_memory()
image = pipe(prompt="Spherical cow in vacuum",
num_inference_steps=10,
guidance_scale=8,
generator=torch.Generator("mps").manual_seed(42),
).images[0]
end_mps_mem = torch.mps.driver_allocated_memory()
run_time = time.time() - start_time
print(f"run time in {run_time:.2f} sec, end_mps_mem {end_mps_mem/1024.0**2:.2f} Mb mem increase {(end_mps_mem-start_time)/1024.0**2:.2f} Mb")
image.save(f'bfloat16.png')
```
Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro, though generated image remains the same:
Fixes #139389
[ghstack-poisoned]
malfet
added a commit
that referenced
this pull request
Nov 5, 2024
As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks.
Test plan: Run
```python
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
import torch
import time
vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
subfolder='vae',
torch_dtype=torch.bfloat16,
force_upcast=False).to('mps')
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
torch_dtype=torch.bfloat16, variant="fp16").to('mps')
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
start_time = time.time()
start_mps_mem = torch.mps.driver_allocated_memory()
image = pipe(prompt="Spherical cow in vacuum",
num_inference_steps=10,
guidance_scale=8,
generator=torch.Generator("mps").manual_seed(42),
).images[0]
end_mps_mem = torch.mps.driver_allocated_memory()
run_time = time.time() - start_time
print(f"run time in {run_time:.2f} sec, end_mps_mem {end_mps_mem/1024.0**2:.2f} Mb mem increase {(end_mps_mem-start_time)/1024.0**2:.2f} Mb")
image.save(f'bfloat16.png')
```
Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro
Fixes #139389
ghstack-source-id: a355a1a
Pull Request resolved: #139791
As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks.
Test plan: Run
```python
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
import torch
import time
vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
subfolder='vae',
torch_dtype=torch.bfloat16,
force_upcast=False).to('mps')
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
torch_dtype=torch.bfloat16, variant="fp16").to('mps')
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
start_time = time.time()
start_mps_mem = torch.mps.driver_allocated_memory()
image = pipe(prompt="Spherical cow in vacuum",
num_inference_steps=10,
guidance_scale=8,
generator=torch.Generator("mps").manual_seed(42),
).images[0]
end_mps_mem = torch.mps.driver_allocated_memory()
run_time = time.time() - start_time
print(f"run time in {run_time:.2f} sec, end_mps_mem {end_mps_mem/1024.0**2:.2f} Mb mem increase {(end_mps_mem-start_time)/1024.0**2:.2f} Mb")
image.save(f'bfloat16.png')
```
Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro, though generated image remains the same:
Fixes #139389
[ghstack-poisoned]
malfet
added a commit
that referenced
this pull request
Nov 6, 2024
As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks.
Test plan: Run
```python
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
import torch
import time
vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
subfolder='vae',
torch_dtype=torch.bfloat16,
force_upcast=False).to('mps')
pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae,
torch_dtype=torch.bfloat16, variant="fp16").to('mps')
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
start_time = time.time()
start_mps_mem = torch.mps.driver_allocated_memory()
image = pipe(prompt="Spherical cow in vacuum",
num_inference_steps=10,
guidance_scale=8,
generator=torch.Generator("mps").manual_seed(42),
).images[0]
end_mps_mem = torch.mps.driver_allocated_memory()
run_time = time.time() - start_time
print(f"run time in {run_time:.2f} sec, end_mps_mem {end_mps_mem/1024.0**2:.2f} Mb mem increase {(end_mps_mem-start_time)/1024.0**2:.2f} Mb")
image.save(f'bfloat16.png')
```
Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro
Fixes #139389
ghstack-source-id: ff4e3ba
Pull Request resolved: #139791
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pobin6
pushed a commit
to pobin6/pytorch
that referenced
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Dec 5, 2024
…139791) As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks. Test plan: Run ```python from diffusers import StableDiffusionXLPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler import torch import time vae = AutoencoderKL.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder='vae', torch_dtype=torch.bfloat16, force_upcast=False).to('mps') pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.bfloat16, variant="fp16").to('mps') pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) start_time = time.time() start_mps_mem = torch.mps.driver_allocated_memory() image = pipe(prompt="Spherical cow in vacuum", num_inference_steps=10, guidance_scale=8, generator=torch.Generator("mps").manual_seed(42), ).images[0] end_mps_mem = torch.mps.driver_allocated_memory() run_time = time.time() - start_time print(f"run time in {run_time:.2f} sec, end_mps_mem {end_mps_mem/1024.0**2:.2f} Mb mem increase {(end_mps_mem-start_time)/1024.0**2:.2f} Mb") image.save(f'bfloat16.png') ``` Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro, though generated image remains the same:  Fixes pytorch#139389 Pull Request resolved: pytorch#139791 Approved by: https://github.com/drisspg, https://github.com/Skylion007 ghstack dependencies: pytorch#139788, pytorch#139784, pytorch#139763
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Stack from ghstack (oldest at bottom):
isneginf#139763As MacOS-15 or newer supports those out of the box. This significantly reduces memory requirements and improves performance for some stable diffision networks.
Test plan: Run
Before the change total memory use were 16Gb and needed 65 sec to complete, after it drops down to 14Gb and takes 50 sec to finish on M2Pro, though generated image remains the same:
Fixes #139389