PyTorch Deployment via “torch.compile”

The torch.compile feature enables you to use OpenVINO for PyTorch-native applications. It speeds up PyTorch code by JIT-compiling it into optimized kernels. By default, Torch code runs in eager-mode, but with the use of torch.compile it goes through the following steps:

  1. Graph acquisition - the model is rewritten as blocks of subgraphs that are either:

    • compiled by TorchDynamo and “flattened”,

    • falling back to the eager-mode, due to unsupported Python constructs (like control-flow code).

  2. Graph lowering - all PyTorch operations are decomposed into their constituent kernels specific to the chosen backend.

  3. Graph compilation - the kernels call their corresponding low-level device-specific operations.

How to Use

To use torch.compile, you need to add an import statement and define one of the two available backends:

openvino
With this backend, Torch FX subgraphs are directly converted to OpenVINO representation without any additional PyTorch based tracing/scripting.
openvino_ts
With this backend, Torch FX subgraphs are first traced/scripted with PyTorch Torchscript, and then converted to OpenVINO representation.
import openvino.torch
...
model = torch.compile(model, backend='openvino')

Execution diagram:

../_images/torch_compile_backend_openvino.svg
import openvino.torch
...
model = torch.compile(model, backend='openvino_ts')

Execution diagram:

../_images/torch_compile_backend_openvino_ts.svg

Options

It is possible to use additional arguments for torch.compile to set the backend device, enable model caching, set the cache directory etc. You can use a dictionary of the available options:

  • device - enables selecting a specific hardware device to run the application. By default, the OpenVINO backend for torch.compile runs PyTorch applications on CPU. If you set this variable to GPU.0, for example, the application will use the integrated graphics processor instead.

  • model_caching - enables saving the optimized model files to a hard drive, after the first application run. This makes them available for the following application executions, reducing the first-inference latency. By default, this variable is set to False. Set it to True to enable caching.

  • cache_dir - enables defining a custom directory for the model files (if model_caching is set to True). By default, the OpenVINO IR is saved in the cache sub-directory, created in the application’s root directory.

  • config - enables passing any OpenVINO configuration option as a dictionary to this variable. For details on the various options, refer to the OpenVINO Advanced Features.

See the example below for details:

model = torch.compile(model, backend="openvino", options = {"device" : "CPU", "model_caching" : True, "cache_dir": "./model_cache"})

You can also set OpenVINO specific configuration options by adding them as a dictionary under config key in options:

opts = {"device" : "CPU", "config" : {"PERFORMANCE_HINT" : "LATENCY"}}
model = torch.compile(model, backend="openvino", options=opts)

Important

The environment variables used in the previous release are still available but are not recommended. They will be removed fully in future releases.

Click to view the deprecated options.
  • OPENVINO_TORCH_BACKEND_DEVICE - enables selecting a specific hardware device to run the application. By default, the OpenVINO backend for torch.compile runs PyTorch applications using the CPU. Setting this variable to GPU.0, for example, will make the application use the integrated graphics processor instead.

  • OPENVINO_TORCH_MODEL_CACHING- enables saving the optimized model files to a hard drive, after the first application run. This makes them available for the following application executions, reducing the first-inference latency. By default, this variable is set to False. Setting it to True enables caching.

  • OPENVINO_TORCH_CACHE_DIR- enables defining a custom directory for the model files (if model_caching is set to True). By default, the OpenVINO IR is saved in the cache sub-directory, created in the application’s root directory.

Windows support

Currently, PyTorch does not support torch.compile feature on Windows officially. However, it can be accessed by running the below instructions:

  1. Install the PyTorch nightly wheel file - 2.1.0.dev20230713 ,

  2. Update the file at <python_env_root>/Lib/site-packages/torch/_dynamo/eval_frames.py

  3. Find the function called check_if_dynamo_supported():

    def check_if_dynamo_supported():
        if sys.platform == "win32":
            raise RuntimeError("Windows not yet supported for torch.compile")
        if sys.version_info >= (3, 11):
            raise RuntimeError("Python 3.11+ not yet supported for torch.compile")
    
  4. Put in comments the first two lines in this function, so it looks like this:

    def check_if_dynamo_supported():
     #if sys.platform == "win32":
     #    raise RuntimeError("Windows not yet supported for torch.compile")
     if sys.version_info >= (3, 11):
         `raise RuntimeError("Python 3.11+ not yet supported for torch.compile")
    

Support for Automatic1111 Stable Diffusion WebUI

Automatic1111 Stable Diffusion WebUI is an open-source repository that hosts a browser-based interface for the Stable Diffusion based image generation. It allows users to create realistic and creative images from text prompts. Stable Diffusion WebUI is supported on Intel CPUs, Intel integrated GPUs, and Intel discrete GPUs by leveraging OpenVINO torch.compile capability. Detailed instructions are available in Stable Diffusion WebUI repository.

Architecture

The torch.compile feature is part of PyTorch 2.0, and is based on:

  • TorchDynamo - a Python-level JIT that hooks into the frame evaluation API in CPython, (PEP 523) to dynamically modify Python bytecode right before it is executed (PyTorch operators that cannot be extracted to FX graph are executed in the native Python environment). It maintains the eager-mode capabilities using Guards to ensure the generated graphs are valid.

  • AOTAutograd - generates the backward graph corresponding to the forward graph captured by TorchDynamo.

  • PrimTorch - decomposes complicated PyTorch operations into simpler and more elementary ops.

  • TorchInductor - a deep learning compiler that generates fast code for multiple accelerators and backends.

When the PyTorch module is wrapped with torch.compile, TorchDynamo traces the module and rewrites Python bytecode to extract sequences of PyTorch operations into an FX Graph, which can be optimized by the OpenVINO backend. The Torch FX graphs are first converted to inlined FX graphs and the graph partitioning module traverses inlined FX graph to identify operators supported by OpenVINO.

All the supported operators are clustered into OpenVINO submodules, converted to the OpenVINO graph using OpenVINO’s PyTorch decoder, and executed in an optimized manner using OpenVINO runtime. All unsupported operators fall back to the native PyTorch runtime on CPU. If the subgraph fails during OpenVINO conversion, the subgraph falls back to PyTorch’s default inductor backend.

Additional Resources