feat(ml): introduce support of onnxruntime-rocm for AMD GPU #11063

Zelnes · 2024-07-12T23:29:35Z

I'd like to propose this feature, which introduces support for machine learning on AMD GPU.

⚠️ Not stable

It's relying on an opened PR which disable some caching features, in order to be able to run in parallel. (IMHO parallelizing without cache is still faster than caching in single threaded mode).

Important note

I just tried to make something work for me, and I'm not pretending to propose something completely working for anyone anywhere.
I'm proposing this here, so advanced users/developers, can provide help, add some tests, and to make this available for others.

I hope I'll have some feedback 👍

Notes

Docker size

Second note: the downside of all this new docker AMD capable, is the 28GB size of final image. I hope someone can help reduce this size.

Links

Please, see this discussion where I exchanged with @mertalev on this, and I posted more explanations, which led me here.

machine-learning/rocm-patch-onnxruntime.patch

mertalev

Needs some polishing, but looks very promising!

machine-learning/Dockerfile

machine-learning/app/sessions/ort.py

bo0tzz · 2024-07-13T11:03:05Z

machine-learning/Dockerfile

+# I ran into a compilation error when parallelizing the build
+# I used 12 threads to build onnxruntime, but it needs more than 16GB of RAM, and that's the amount of RAM I have on my machine
+# I lowered the number of threads to 8, and it worked
+# Even with 12 threads, the compilation took more than 1,5 hours to fail
+RUN ./build.sh --allow_running_as_root --config Release --build_wheel --update --build --parallel 9 --cmake_extra_defines\
+        ONNXRUNTIME_VERSION=1.18.1 --use_rocm --rocm_home=/opt/rocm


Should we run this on Mich @mertalev?

Absolutely yes.

Zelnes · 2024-07-15T10:22:30Z

I did some tests on the latest version.
I've launched the 4 ML related jobs at the same time, using this concurrency settings

"job": {
  "backgroundTask": {
    "concurrency": 5
  },
  "smartSearch": {
    "concurrency": 5
  },
  "metadataExtraction": {
    "concurrency": 5
  },
  "faceDetection": {
    "concurrency": 8
  },
  "search": {
    "concurrency": 5
  },
  "sidecar": {
    "concurrency": 5
  },
  "library": {
    "concurrency": 5
  },
  "migration": {
    "concurrency": 5
  },
  "thumbnailGeneration": {
    "concurrency": 3
  },
  "videoConversion": {
    "concurrency": 1
  },
  "notifications": {
    "concurrency": 5
  }
}

Which crashes, because of memory allocation failure I think, see logs bellow.

Error logs 'Failed to allocate memory'

2024-07-15 10:04:14.356608464 [E:onnxruntime:, sequential_executor.cc:516 ExecuteKernel] Non-zero status code returned while running PRelu node. Name:'PRelu_1' Status Message: /code/onnxruntime/onnxruntime/core/framework/bfc_arena.cc:376 void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool, onnxruntime::Stream*, bool, onnxruntime::WaitNotificationFn) Failed to allocate memory for requested buffer of size 9633792

[07/15/24 10:04:14] ERROR    Exception in ASGI application

                             ╭─────── Traceback (most recent call last) ───────╮
                             │ /usr/src/app/main.py:151 in predict             │
                             │                                                 │
                             │   148 │   │   inputs = text                     │
                             │   149 │   else:                                 │
                             │   150 │   │   raise HTTPException(400, "Either  │
                             │ ❱ 151 │   response = await run_inference(inputs │
                             │   152 │   return ORJSONResponse(response)       │
                             │   153                                           │
                             │   154                                           │
                             │                                                 │
                             │ /usr/src/app/main.py:176 in run_inference       │
                             │                                                 │
                             │   173 │   without_deps, with_deps = entries     │
                             │   174 │   await asyncio.gather(*[_run_inference │
                             │   175 │   if with_deps:                         │
                             │ ❱ 176 │   │   await asyncio.gather(*[_run_infer │
                             │   177 │   if isinstance(payload, Image):        │
                             │   178 │   │   response["imageHeight"], response │
                             │   179                                           │
                             │                                                 │
                             │ /usr/src/app/main.py:169 in _run_inference      │
                             │                                                 │
                             │   166 │   │   │   │   message = f"Task {entry[' │
                             │       output of {dep}"                          │
                             │   167 │   │   │   │   raise HTTPException(400,  │
                             │   168 │   │   model = await load(model)         │
                             │ ❱ 169 │   │   output = await run(model.predict, │
                             │   170 │   │   outputs[model.identity] = output  │
                             │   171 │   │   response[entry["task"]] = output  │
                             │   172                                           │
                             │                                                 │
                             │ /usr/src/app/main.py:187 in run                 │
                             │                                                 │
                             │   184 │   if thread_pool is None:               │
                             │   185 │   │   return func(*args, **kwargs)      │
                             │   186 │   partial_func = partial(func, *args, * │
                             │ ❱ 187 │   return await asyncio.get_running_loop │
                             │   188                                           │
                             │   189                                           │
                             │   190 async def load(model: InferenceModel) ->  │
                             │                                                 │
                             │ /usr/lib/python3.10/concurrent/futures/thread.p │
                             │ y:58 in run                                     │
                             │                                                 │
                             │ /usr/src/app/models/base.py:60 in predict       │
                             │                                                 │
                             │    57 │   │   self.load()                       │
                             │    58 │   │   if model_kwargs:                  │
                             │    59 │   │   │   self.configure(**model_kwargs │
                             │ ❱  60 │   │   return self._predict(*inputs, **m │
                             │    61 │                                         │
                             │    62 │   @abstractmethod                       │
                             │    63 │   def _predict(self, *inputs: Any, **mo │
                             │                                                 │
                             │ /usr/src/app/models/facial_recognition/recognit │
                             │ ion.py:52 in _predict                           │
                             │                                                 │
                             │   49 │   │   │   return []                      │
                             │   50 │   │   inputs = decode_cv2(inputs)        │
                             │   51 │   │   cropped_faces = self._crop(inputs, │
                             │ ❱ 52 │   │   embeddings = self._predict_batch(c │
                             │      self._predict_single(cropped_faces)        │
                             │   53 │   │   return self.postprocess(faces, emb │
                             │   54 │                                          │
                             │   55 │   def _predict_batch(self, cropped_faces │
                             │      NDArray[np.float32]:                       │
                             │                                                 │
                             │ /usr/src/app/models/facial_recognition/recognit │
                             │ ion.py:56 in _predict_batch                     │
                             │                                                 │
                             │   53 │   │   return self.postprocess(faces, emb │
                             │   54 │                                          │
                             │   55 │   def _predict_batch(self, cropped_faces │
                             │      NDArray[np.float32]:                       │
                             │ ❱ 56 │   │   embeddings: NDArray[np.float32] =  │
                             │   57 │   │   return embeddings                  │
                             │   58 │                                          │
                             │   59 │   def _predict_single(self, cropped_face │
                             │      NDArray[np.float32]:                       │
                             │                                                 │
                             │ /opt/venv/lib/python3.10/site-packages/insightf │
                             │ ace/model_zoo/arcface_onnx.py:84 in get_feat    │
                             │                                                 │
                             │   81 │   │                                      │
                             │   82 │   │   blob = cv2.dnn.blobFromImages(imgs │
                             │   83 │   │   │   │   │   │   │   │   │     (sel │
                             │      self.input_mean), swapRB=True)             │
                             │ ❱ 84 │   │   net_out = self.session.run(self.ou │
                             │   85 │   │   return net_out                     │
                             │   86 │                                          │
                             │   87 │   def forward(self, batch_data):         │
                             │                                                 │
                             │ /usr/src/app/sessions/ort.py:49 in run          │
                             │                                                 │
                             │    46 │   │   input_feed: dict[str, NDArray[np. │
                             │    47 │   │   run_options: Any = None,          │
                             │    48 │   ) -> list[NDArray[np.float32]]:       │
                             │ ❱  49 │   │   outputs: list[NDArray[np.float32] │
                             │       run_options)                              │
                             │    50 │   │   return outputs                    │
                             │    51 │                                         │
                             │    52 │   @property                             │
                             │                                                 │
                             │ /opt/venv/lib/python3.10/site-packages/onnxrunt │
                             │ ime/capi/onnxruntime_inference_collection.py:22 │
                             │ 0 in run                                        │
                             │                                                 │
                             │    217 │   │   if not output_names:             │
                             │    218 │   │   │   output_names = [output.name  │
                             │    219 │   │   try:                             │
                             │ ❱  220 │   │   │   return self._sess.run(output │
                             │    221 │   │   except C.EPFail as err:          │
                             │    222 │   │   │   if self._enable_fallback:    │
                             │    223 │   │   │   │   print(f"EP Error: {err!s │
                             ╰─────────────────────────────────────────────────╯
                             RuntimeException: [ONNXRuntimeError] : 6 :
                             RUNTIME_EXCEPTION : Non-zero status code returned
                             while running PRelu node. Name:'PRelu_1' Status
                             Message:
                             /code/onnxruntime/onnxruntime/core/framework/bfc_ar
                             ena.cc:376 void*
                             onnxruntime::BFCArena::AllocateRawInternal(size_t,
                             bool, onnxruntime::Stream*, bool,
                             onnxruntime::WaitNotificationFn) Failed to allocate
                             memory for requested buffer of size 9633792

But, with

 "job": {
   "backgroundTask": {
     "concurrency": 5
   },
   "smartSearch": {
-    "concurrency": 5
+    "concurrency": 4
   },
   "metadataExtraction": {
     "concurrency": 5
   },
   "faceDetection": {
-    "concurrency": 8
+    "concurrency": 4
   },
   "search": {
     "concurrency": 5
   },
   "sidecar": {
     "concurrency": 5
   },
   "library": {
     "concurrency": 5
   },
   "migration": {
     "concurrency": 5
   },
   "thumbnailGeneration": {
     "concurrency": 3
   },
   "videoConversion": {
     "concurrency": 1
   },
   "notifications": {
     "concurrency": 5
   }
 }

It works well.

For info, my server runs on 12 cores and 16Gb RAM, with an AMD Rx 6400

machine-learning/Dockerfile

mertalev

Great job updating the documentation!

machine-learning/Dockerfile

mertalev · 2024-07-15T14:28:07Z

machine-learning/Dockerfile

+# Even with 12 threads, the compilation took more than 1,5 hours to fail
+RUN ./build.sh --allow_running_as_root --config Release --build_wheel --update --build --parallel 9 --cmake_extra_defines\
+        ONNXRUNTIME_VERSION=1.18.1 --use_rocm --rocm_home=/opt/rocm
+RUN mv /code/onnxruntime/build/Linux/Release/dist/*.whl /opt/


What are the different .whl files? We should only need one.

There's only one, and it's name is known after compilation. I copied this from this Dockerfile, but if you prefer, I can replace with the full name.

It'd be nice for clarity that there's only one wheel involved here.

mertalev · 2024-07-15T14:32:25Z

machine-learning/Dockerfile

+    if [ "${DEVICE}" != "rocm" ]; then \
+        extra=libmimalloc2.0; \
+    fi && \


I assume you did this to not duplicate the apt command. You can change the apt command to be multi-line and make this if condition provide libmimalloc2.0 or nothing.

I'm sorry but I'm not sure to understand what you mean. Could you provide a suggestion ?

machine-learning/Dockerfile

mertalev · 2024-07-15T14:51:37Z

machine-learning/pyproject.toml

+[tool.poetry.group.rocm]
+optional = true
+
+[tool.poetry.group.rocm.dependencies]


Just thinking if we can put anything here.

We could list onnxruntime-rocm with a file path, but that's basically just for our build, not usable by others. It's very easy to do though and at least points to the relevant package.

Ideally, we could build onnxruntime in the base-images repo, have the wheel be a GH artifact and reference the URL for it here. This would be nice but not something you need to work on (unless you want to).

I don't have any poetry knowledge at all, but from what I understood, we'd need to specify the dependency here (for instance onnxruntime), as a local file, but if we do so, we must provide the path to it.
But, the wheel is compiled during the build process, so the file can't exist on the host.
And if we update the poetry.lock, the file must exist. Actually, I tested some stuff, but the only way I found was to declare an empty group, and provide the installation during the build.

This is a problem for someone who wants to develop on his host without Docker. But I think this is linked to the compilation of onnxruntime, not the was poetry is configured.

Hmm that's a fair point. We can leave this as-is for this PR and work on providing it through a link later.

mertalev · 2024-07-15T14:58:21Z

machine-learning/rocm-PR19567.patch

@@ -0,0 +1,176 @@
+From a598a88db258f82a6e4bca75810921bd6bcee7e0 Mon Sep 17 00:00:00 2001


We need to add a license for this code since it isn't ours. It can go in its own folder with a LICENSE file inside (ONNX Runtime's license). This should also end up in the final image when the wheel is copied (unless the wheel already has the license embedded in it, not sure if it does).

I don't have a clue how this is done, can you help me here ?

I mean that you can make a patches folder that includes this patch and ONNX Runtime's [license](https://github.com/microsoft/onnxruntime/blob/7ec51f0a13d5d8cbd796de33276bf04210ce6176/LICENSE. For the wheel part, first use pip-licenses to see if the installed onnxruntime has a license in it. If it does, you don't need to do anything. If not, you would copy this LICENSE file in the Dockerfile so it's in the final image.

mghesh-yseop

I won't have the time to work on this before september, so if anyone wants to complete the PR, or reopen a new one, feel free.

machine-learning/Dockerfile

mghesh-yseop · 2024-07-16T06:14:05Z

machine-learning/Dockerfile

+# Even with 12 threads, the compilation took more than 1,5 hours to fail
+RUN ./build.sh --allow_running_as_root --config Release --build_wheel --update --build --parallel 9 --cmake_extra_defines\
+        ONNXRUNTIME_VERSION=1.18.1 --use_rocm --rocm_home=/opt/rocm
+RUN mv /code/onnxruntime/build/Linux/Release/dist/*.whl /opt/


There's only one, and it's name is known after compilation. I copied this from this Dockerfile, but if you prefer, I can replace with the full name.

mghesh-yseop · 2024-07-16T06:17:47Z

machine-learning/Dockerfile

+    if [ "${DEVICE}" != "rocm" ]; then \
+        extra=libmimalloc2.0; \
+    fi && \


I'm sorry but I'm not sure to understand what you mean. Could you provide a suggestion ?

mghesh-yseop · 2024-07-16T06:24:16Z

machine-learning/pyproject.toml

+[tool.poetry.group.rocm]
+optional = true
+
+[tool.poetry.group.rocm.dependencies]


I don't have any poetry knowledge at all, but from what I understood, we'd need to specify the dependency here (for instance onnxruntime), as a local file, but if we do so, we must provide the path to it.
But, the wheel is compiled during the build process, so the file can't exist on the host.
And if we update the poetry.lock, the file must exist. Actually, I tested some stuff, but the only way I found was to declare an empty group, and provide the installation during the build.

This is a problem for someone who wants to develop on his host without Docker. But I think this is linked to the compilation of onnxruntime, not the was poetry is configured.

mghesh-yseop · 2024-07-16T06:35:58Z

machine-learning/rocm-PR19567.patch

@@ -0,0 +1,176 @@
+From a598a88db258f82a6e4bca75810921bd6bcee7e0 Mon Sep 17 00:00:00 2001


I don't have a clue how this is done, can you help me here ?

machine-learning/Dockerfile

mertalev · 2024-07-21T20:22:06Z

machine-learning/Dockerfile

+    if [ "${DEVICE}" != "rocm" ]; then \
+        extra=libmimalloc2.0; \
+    fi && \
+    apt-get install -y --no-install-recommends tini "${extra}" && \


Suggested change

if [ "${DEVICE}" != "rocm" ]; then \

extra=libmimalloc2.0; \

fi && \

apt-get install -y --no-install-recommends tini "${extra}" && \

apt-get install -y --no-install-recommends tini $(if [ "${DEVICE}" != "rocm" ]; then echo "libmimalloc2.0"; fi) && \

NicholasFlamy · 2024-10-03T15:20:50Z

Second note: the downside of all this new docker AMD capable, is the 28GB size of final image. I hope someone can help reduce this size.

It may be necessary to do something similar to what Frigate did (before they removed this feature due to yolov8 licensing issues, nothing to do with AMD, just that the code was specific to running yolov8 ONNX models) by splitting up the containers per GPU chipset:
blakeblackshear/frigate#9762

zackpollard · 2024-11-13T01:31:03Z

I've converted this to a draft as it seems very much still a WIP.

@Zelnes can you update if this is still something you're trying to pursue getting merged into Immich?
@mertalev can you give some info on whether this is something we want to merge and what steps would still be left to get to that point?

mertalev · 2024-11-13T01:50:08Z

Definitely want to see this merged. I think the remaining work can be broken down into:

Updating and rebasing the PR to use more recent dependencies
Shrinking the image size
Testing correctness
(optional) Moving the build step to be a separate release artifact to be installed in normal builds

Zelnes · 2024-11-13T13:20:21Z

I've converted this to a draft as it seems very much still a WIP.

Thanks, it makes sense indeed.

@Zelnes can you update if this is still something you're trying to pursue getting merged into Immich?

I don't have the time unfortunately, and I don't have the hardware to pursue the work and testing.

Sorry if I didn't keep you updated before, I hope this can be merged one day.

zackpollard · 2024-11-13T18:38:40Z

@mertalev given the above comment, is this something you would want to pursue yourself? If not I am not sure if it's worth keeping this open.

mertalev · 2024-11-13T18:56:47Z

Yes, except for (3).

NicholasFlamy · 2024-11-13T19:02:52Z

Yes, except for (3).

(If this means testing, I can help, it's just that I've been very busy recently. I think I am at the point where I'm getting more free time though.)

zackpollard · 2024-11-13T23:49:34Z

Yes, except for (3).

Alright cool, will leave it with you then! 😄

NicholasFlamy · 2024-11-17T22:41:23Z

I got it running and working correctly (note about something funky later) on my PC:
AMD Ryzen 5 5600X CPU with 32 GB of RAM
AMD Radeon RX 6700 XT GPU
Debian 12 with KDE Plasma on a 240 GB SSD with LVM and split root and home.

Funky thing: https://discord.com/channels/979116623879368755/1291425089539018907/1307529638985203732

This was after I reran smart search and face detection. My guess is I had too much stuff going on and when the GPU got slammed with the AI stuff the display part of the driver crapped out. Even then, immich kept working and therefore the rest of the system (I accessed immich from my phone).

Edit: I theorize this is just funky AMD (because unstable drivers were normal with their 5000 series) and/or Linux behavior.

github-actions · 2024-12-20T01:40:23Z

📖 Documentation deployed to pr-11063.preview.immich.app

Zelnes requested review from mertalev and bo0tzz as code owners July 12, 2024 23:29

github-actions bot added documentation Improvements or additions to documentation 🧠machine-learning labels Jul 12, 2024

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feat(ml): introduce support of onnxruntime-rocm for AMD GPU

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		@@ -0,0 +1,176 @@
		From a598a88db258f82a6e4bca75810921bd6bcee7e0 Mon Sep 17 00:00:00 2001

feat(ml): introduce support of onnxruntime-rocm for AMD GPU #11063

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feat(ml): introduce support of onnxruntime-rocm for AMD GPU #11063

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Zelnes commented Jul 12, 2024 • edited Loading

⚠️ Not stable

Important note

Notes

Docker size

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