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Support Metal Performance Shaders #125

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Support Metal Performance Shaders #125

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f164e36
add mps choice
Tps-F Feb 17, 2024
6699b70
support mps event
Tps-F Feb 17, 2024
1b66259
timerevent in benchmark
Tps-F Feb 17, 2024
16baeb5
using getattr to write simple
Tps-F Feb 17, 2024
22c5c59
fix device
Tps-F Feb 17, 2024
70c523e
skip acceleration on mps
Tps-F Feb 17, 2024
d579e64
Merge branch 'cumulo-autumn:main' into main
Tps-F Mar 4, 2024
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7 changes: 4 additions & 3 deletions examples/benchmark/multi.py
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Original file line number Diff line number Diff line change
Expand Up @@ -127,15 +127,16 @@ def run(

results = []

start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
timer_event = getattr(torch, "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
start = timer_event.Event(enable_timing=True)
end = timer_event.Event(enable_timing=True)
for _ in tqdm(range(iterations)):
start.record()
out_tensor = stream.stream(image_tensor).cpu()
queue.put(out_tensor)
end.record()

torch.cuda.synchronize()
timer_event.synchronize()
results.append(start.elapsed_time(end))

print(f"Average time: {sum(results) / len(results)}ms")
Expand Down
7 changes: 4 additions & 3 deletions examples/benchmark/single.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,16 +112,17 @@ def run(

results = []

start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
timer_event = getattr(torch, "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
start = timer_event.Event(enable_timing=True)
end = timer_event.Event(enable_timing=True)

for _ in tqdm(range(iterations)):
start.record()
image_tensor = stream.preprocess_image(downloaded_image)
stream(image=image_tensor)
end.record()

torch.cuda.synchronize()
timer_event.synchronize()
results.append(start.elapsed_time(end))

print(f"Average time: {sum(results) / len(results)}ms")
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9 changes: 6 additions & 3 deletions src/streamdiffusion/pipeline.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import time

from typing import List, Optional, Union, Any, Dict, Tuple, Literal

import numpy as np
Expand Down Expand Up @@ -30,6 +31,8 @@ def __init__(
self.dtype = torch_dtype
self.generator = None

self.timer_event = getattr(torch, str(self.device).split(':', 1)[0])

self.height = height
self.width = width

Expand Down Expand Up @@ -440,8 +443,8 @@ def predict_x0_batch(self, x_t_latent: torch.Tensor) -> torch.Tensor:
def __call__(
self, x: Union[torch.Tensor, PIL.Image.Image, np.ndarray] = None
) -> torch.Tensor:
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start = self.timer_event.Event(enable_timing=True)
end = self.timer_event.Event(enable_timing=True)
start.record()
if x is not None:
x = self.image_processor.preprocess(x, self.height, self.width).to(
Expand All @@ -463,7 +466,7 @@ def __call__(

self.prev_image_result = x_output
end.record()
torch.cuda.synchronize()
self.timer_event.synchronize()
inference_time = start.elapsed_time(end) / 1000
self.inference_time_ema = 0.9 * self.inference_time_ema + 0.1 * inference_time
return x_output
Expand Down
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