feat: added mps support for inference#81
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I haven't reviewed yet, but I would like to comment that this would be a nice feature to have if we can review it carefully and make sure it doesn't break existing code. It's a small change that would allow people running the code on more hardware platforms. CC @russellhowes |
mmuckley
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After reading through this, I think it is mostly good, except there is one function whose default signature is changed. Can you update it so that it has something like device="cuda:0" in the function signature?
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| def forward_vjepa_video(model_hf, model_pt, hf_transform, pt_transform): | ||
| def forward_vjepa_video(model_hf, model_pt, hf_transform, pt_transform, device): |
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This changes the default function signature. Can you modify it so that the default device is cuda, as was the case with the original function signature?
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Hi @Skylerwiernik, this looks pretty good but I noticed one case where cuda still isn't the default. I also think the notebook diff looks too big - is there any way to just change the lines relevant to mps?
| if torch.backends.mps.is_available(): | ||
| device = "mps" | ||
| else: | ||
| device = "cuda:0" |
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Default should be cuda here, not "mps".
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, |
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The noteobook diff is too big - any way to just change the mps lines?
| def forward_vjepa_video(model_hf, model_pt, hf_transform, pt_transform, device="cuda"): | ||
| # Run a sample inference with VJEPA | ||
| with torch.inference_mode(): | ||
| # Read and pre-process the image | ||
| video = get_video() # T x H x W x C | ||
| video = torch.from_numpy(video).permute(0, 3, 1, 2) # T x C x H x W | ||
| x_pt = pt_transform(video).cuda().unsqueeze(0) | ||
| x_hf = hf_transform(video, return_tensors="pt")["pixel_values_videos"].to("cuda") | ||
| x_pt = pt_transform(video).to(device).unsqueeze(0) | ||
| x_hf = hf_transform(video, return_tensors="pt")["pixel_values_videos"].to(device) |
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Fixes #80