PyTorch + IREE =

Caution - under development

We are still validating and fixing specific models. Between bug fixes in flight and releases running behind, we don't expect that you will be able to do a lot of advanced things without using nightly releases or working with us.

Stay tuned and join the discussion in our Discord server's #pytorch channel.

Overview

iree-turbine offers a tight integration between compatible versions of IREE, torch-mlir, and PyTorch.

• Seamless integration with standard PyTorch workflows
• Deployment support for running PyTorch models on cloud and edge devices
• General purpose model compilation and execution tools

Both just-in-time (JIT) and ahead-of-time (AOT) workflows are supported:

graph LR
  accTitle: PyTorch integration overview
  accDescr {
    PyTorch programs can be optimized within a Python session with
    iree-turbine's just-in-time tools.
    PyTorch programs can be exported out of Python to native binaries using
    iree-turbine's ahead-of-time export toolkit.
  }

  subgraph Python
    pytorch(PyTorch)
    subgraph turbine [iree-turbine]
      jit("Eager execution (JIT)")
      aot("Export toolkit (AOT)")
    end

    pytorch --> jit
    jit --> pytorch
    pytorch --> aot
  end

  subgraph Native
    binary(["binary (.vmfb)"])
  end

  aot -.-> binary

Prerequisites

Install a recent version of PyTorch (2.4.1, latest stable release as of September 2024):

python -m pip install \
  --index-url https://download.pytorch.org/whl/test/cpu torch==2.4.1

Install iree-turbine:

python -m pip install iree-turbine

Just-in-time (JIT) execution

Just-in-time integration allows for Python code using TorchDynamo to optimize PyTorch models/functions using IREE, all within an interactive Python session.

graph TD
  accTitle: PyTorch JIT workflow overview
  accDescr {
    Programs start as either PyTorch nn.Module objects or callable functions.
    Programs are compiled into optimized modules using torch.compile.
    Within torch.compile, Dynamo runs the program through Turbine and IREE.
  }

  subgraph Python
    input([nn.Module / function])

    subgraph compile ["torch.compile()"]
      direction LR
      dynamo{{TorchDynamo}}
      turbine{{iree-turbine}}
      iree{{IREE}}
      dynamo --> turbine --> iree
    end

    output([Optimized module])
    input --> compile --> output
  end

For deployment outside of Python, see the ahead-of-time sections below.

Quickstart

Turbine integrates into PyTorch as a custom backend for torch.compile.

Behind the scenes, PyTorch captures the structure of the input model into a computation graph and feeds that graph through to the selected backend compiler.

import torch

# Define the `nn.Module` or Python function to run.
class LinearModule(torch.nn.Module):
  def __init__(self, in_features, out_features):
    super().__init__()
    self.weight = torch.nn.Parameter(torch.randn(in_features, out_features))
    self.bias = torch.nn.Parameter(torch.randn(out_features))

  def forward(self, input):
    return (input @ self.weight) + self.bias

linear_module = LinearModule(4, 3)

# Compile the program using the turbine backend.(1)
opt_linear_module = torch.compile(linear_module, backend="turbine_cpu")

# Use the compiled program as you would the original program.
args = torch.randn(4)
turbine_output = opt_linear_module(args)

1. Initial integration only supports CPU, but support for many of IREE's other targets is coming soon.

Samples

Code samples
JIT compilation notebook
Simple MLP eager	core/examples/eager_mlp/mlp_eager_simple.py

Ahead-of-time (AOT) export

The ahead-of-time toolkit allows developers to define a program's structure in Python and then export deployment-ready artifacts that can be used in IREE's deployment configurations via the API bindings.

Simple API

For simple models, a one-shot export API is available.

graph LR
  accTitle: PyTorch simple AOT workflow overview
  accDescr {
    Programs start as PyTorch nn.Module objects.
    Modules are exported using the "aot" API.
    Exported outputs are then compiled to .vmfb files with executable binaries.
  }

  subgraph Python
    input([nn.Module])
    export(["ExportOutput (MLIR)"])
    input -- "aot.export()" --> export
  end

  subgraph Native
    binary(["binary (.vmfb)"])
  end

  export -. "compile()" .-> binary

import iree.runtime as ireert
import numpy as np
import iree.turbine.aot as aot
import torch

# Define the `nn.Module` to export.
class LinearModule(torch.nn.Module):
  def __init__(self, in_features, out_features):
    super().__init__()
    self.weight = torch.nn.Parameter(torch.randn(in_features, out_features))
    self.bias = torch.nn.Parameter(torch.randn(out_features))

  def forward(self, input):
    return (input @ self.weight) + self.bias

linear_module = LinearModule(4, 3)

# Export the program using the simple API.
example_arg = torch.randn(4)
export_output = aot.export(linear_module, example_arg)

# Compile to a deployable artifact.
binary = export_output.compile(save_to=None)

# Use the IREE runtime API to test the compiled program.
config = ireert.Config("local-task")
vm_module = ireert.load_vm_module(
    ireert.VmModule.copy_buffer(config.vm_instance, binary.map_memory()),
    config,
)
input = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
result = vm_module.main(input)
print(result.to_host())

Samples

Code samples
Simple AOT export notebook
Import Whisper from Hugging Face notebook
Simple MLP export	core/examples/aot_mlp/mlp_export_simple.py

Advanced API

For more complex models, an underlying advanced API is available that gives access to more features.

graph LR
  accTitle: PyTorch advanced AOT workflow overview
  accDescr {
    Programs are represented using the aot.CompiledModule class.
    CompiledModules can extend nn.Module objects, export globals, and set
    shapes and dtypes for each function.
    Modules are exported using the "aot" API.
    Exported outputs are then compiled to .vmfb files with executable binaries.
  }

  subgraph Python
    compiledmodule("aot.CompiledModule\n\n- extend nn.Module\n- export globals\n- set shapes/dtypes")
    export(["ExportOutput (MLIR)"])
    compiledmodule -- "aot.export()" --> export
  end

  subgraph Native
    binary(["binary (.vmfb)"])
  end

  export -. "compile()" .-> binary

Advanced export workflows can use the aot.CompiledModule class to define and constrain the structure of a program prior to compiling it.

import iree.turbine.aot as aot

# A minimal program, with no functions or variables.
class BasicModule(aot.CompiledModule):
  ...

# Create an instance of the program and convert it to MLIR.
from iree.compiler.ir import Context
instance = BasicModule(context=Context())
module_str = str(aot.CompiledModule.get_mlir_module(instance))

print(module_str)
# module @basic {
# }

Exporting functions

Exported functions are the API entry points into a compiled program.

Simple feed-forward neural networks used for inference may have a single exported function (typically called "forward"), while more complex programs can have multiple computation functions, initialization functions, "backward" methods for training, state management functions, debugging functions, etc.

Global variables

Global variables are used to represent persistent state within a program instance.

For example, they can be used to represent the weights and biases in a neural network, and exporting these as mutable variables can allow for setting their values independently at runtime.

• Individual globals can be exported using aot.export_global():

  state_example = torch.zeros([1], dtype=torch.int32)
  
  class SampleModule(aot.CompiledModule):
    value = aot.export_global(state_example, mutable=True)
  
    def get_value(self):
      return self.value
  
    def update_value(self, new_value=aot.abstractify(value)):
      self.value = new_value
  

Using external parameters

Model parameters can be stored in standalone files that can be efficiently stored and loaded separately from model compute graphs. See the Parameters guide for more general information about parameters in IREE.

When using iree-turbine, the aot.externalize_module_parameters() function separates parameters from program modules and encodes a symbolic relationship between them so they can be loaded at runtime.

We use Safetensors here to store the models parameters on disk, so that they can be loaded later during runtime.

import torch
from safetensors.torch import save_file
import numpy as np
import iree.turbine.aot as aot

class LinearModule(torch.nn.Module):
    def __init__(self, in_features, out_features):
        super().__init__()
        self.weight = torch.nn.Parameter(torch.randn(in_features, out_features))
        self.bias = torch.nn.Parameter(torch.randn(out_features))

    def forward(self, input):
        return (input @ self.weight) + self.bias

linear_module = LinearModule(4,3)

# Create a params dictionary. Note that the keys here match LinearModule's
# attributes. We will use the saved safetensor file for use from the command
# line.
wt = linear_module.weight.data.contiguous()
bias = linear_module.bias.data.contiguous()
params = { "weight": wt, "bias": bias }
save_file(params, "params.safetensors")

# Externalize the model parameters. This removes weight tensors from the IR
# module, allowing them to be loaded at runtime. Symbolic references to these
# parameters are still retained in the IR.
aot.externalize_module_parameters(linear_module)

input = torch.randn(4)
exported_module = aot.export(linear_module, input)

# Compile the exported module, to generate the binary. When `save_to` is
# not None, the binary will be stored at the path passed in to `save_to`.
# Here, we pass in None, so that the binary can stored in a variable.
binary = exported_module.compile(save_to=None)

# Save the input as an npy tensor, so that it can be passed in through the
# command line to `iree-run-module`.
input_np = input.numpy()
np.save("input.npy", input_np)

Python runtimeCommand line tools

Runtime invocation now requires loading the parameters as a separate module. To get the parameters as a module, iree.runtime provides a convenient method, called create_io_parameters_module().

import iree.runtime as ireert

# To load the parameters, we need to define ParameterIndex for each
# parameter class.
idx = ireert.ParameterIndex()
idx.add_buffer("weight", wt.detach().numpy().tobytes())
idx.add_buffer("bias", bias.detach().numpy().tobytes())


# Create the runtime instance, and load the runtime.
config = ireert.Config(driver_name="local-task")
instance = config.vm_instance

param_module = ireert.create_io_parameters_module(
    instance, idx.create_provider(scope="model"),
)

# Load the runtime. There are essentially two modules to load, one for the
# weights, and one for the main module. Ensure that the VMFB file is not
# already open or deleted before use.
vm_modules = ireert.load_vm_modules(
    param_module,
    ireert.create_hal_module(instance, config.device),
    ireert.VmModule.copy_buffer(instance, binary.map_memory()),
    config=config,
)

# vm_modules is a list of modules. The last module in the list is the one
# generated from the binary, so we use that to generate an output.
result = vm_modules[-1].main(input)
print(result.to_host())

It is also possible to save the VMFB binary to disk, then call iree-run-module through the command line to generate outputs.

# When save_to is not None, the binary is saved to the given path,
# and a None value is returned.
binary = exported_module.compile(save_to="compiled_module.vmfb")

The stored safetensors file, the input tensor, and the VMFB can now be passed in to IREE through the command line.

iree-run-module --module=compiled_module.vmfb --parameters=model=params.safetensors \
                --input=@input.npy

Note here that the --parameters flag has model= following it immediately. This simply specifies the scope of the parameters, and is reflected in the compiled module.

Samples

Code samples
Advanced AOT export notebook
PyTorch dynamic shapes notebook
AOT unit tests	tests/aot/
Dynamic MLP export	core/examples/aot_mlp/mlp_export_dynamic.py
stateless llama2	models/turbine_models/custom_models/stateless_llama.py