XLA Kernel Fusion / Selection Expectations

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Introduction

XLA (Accelerated Linear Algebra) is a compiler for machine learning and linear algebra operations. It is designed to optimize the performance of machine learning models by fusing and selecting the best kernels for execution. In this article, we will explore the expectations of XLA kernel selection and fusion, particularly in the context of XLA<>GPU and XLA<>TPU.

XLA Kernel Selection and Fusion

XLA kernel selection and fusion are critical components of the XLA compiler. The goal of kernel selection is to choose the best kernel for a given operation, taking into account factors such as performance, memory usage, and accuracy. The goal of kernel fusion is to combine multiple operations into a single kernel, reducing the overhead of function calls and improving performance.

XLA Kernel Selection Mechanism

The XLA kernel selection mechanism is based on a combination of heuristics and machine learning algorithms. The compiler analyzes the input data and the model architecture to determine the best kernel for each operation. The selection process takes into account factors such as:

  • Operation type: The type of operation being performed, such as matrix multiplication or convolution.
  • Data type: The data type of the input and output tensors, such as float32 or float16.
  • Memory usage: The amount of memory required to perform the operation.
  • Performance: The expected performance of the kernel, taking into account factors such as clock speed and memory bandwidth.

XLA Kernel Fusion Mechanism

The XLA kernel fusion mechanism is based on a combination of graph analysis and machine learning algorithms. The compiler analyzes the graph of operations to identify opportunities for fusion. The fusion process takes into account factors such as:

  • Operation dependencies: The dependencies between operations, such as data flow and control flow.
  • Operation types: The types of operations being fused, such as matrix multiplication and convolution.
  • Data types: The data types of the input and output tensors, such as float32 or float16.

XLA<>GPU Kernel Selection and Fusion

In the context of XLA<>GPU, the kernel selection and fusion mechanisms are designed to take advantage of the unique characteristics of GPU architectures. The compiler analyzes the input data and the model architecture to determine the best kernel for each operation, taking into account factors such as:

  • GPU architecture: The specific GPU architecture being used, such as NVIDIA Tesla V100 or AMD Radeon Instinct MI8.
  • Memory hierarchy: The memory hierarchy of the GPU, including the L1 cache, L2 cache, and global memory.
  • Clock speed: The clock speed of the GPU, which affects the performance of the kernel.

XLA<>TPU Kernel Selection and Fusion

In the context of XLA<>TPU, the kernel selection and fusion mechanisms are designed to take advantage of the unique characteristics of TPU architectures. The compiler analyzes the input data and the model architecture to determine the best kernel for each operation, taking into account factors such as:

  • TPU architecture: The specific TPU architecture being used, such as TPU v2 or TPU v3.
  • Memory hierarchy: The memory hierarchy of the TPU, including the L1 cache, L2 cache, and global memory.
  • Clock speed: The clock speed of the TPU, which affects the performance of the kernel.

Modifying the Behavior of XLA Kernel Selection and Fusion

To modify the behavior of XLA kernel selection and fusion, you can use a combination of techniques, including:

  • Custom kernel selection: You can write custom kernel selection functions to override the default selection mechanism.
  • Custom kernel fusion: You can write custom kernel fusion functions to override the default fusion mechanism.
  • Model optimization: You can optimize the model architecture to take advantage of the unique characteristics of the target hardware.

Documentations Describing the Fusion / Kernel Selection Logics in XLA

There are several documentations that describe the fusion and kernel selection logics in XLA, including:

  • XLA Compiler Documentation: The official XLA compiler documentation provides a detailed description of the kernel selection and fusion mechanisms.
  • XLA GitHub Repository: The XLA GitHub repository provides access to the source code of the XLA compiler, which can be used to understand the kernel selection and fusion mechanisms in detail.
  • XLA Research Papers: There are several research papers that describe the kernel selection and fusion mechanisms in XLA, including papers on custom kernel selection and fusion.

Conclusion

In conclusion, XLA kernel selection and fusion are critical components of the XLA compiler. The kernel selection mechanism is based on a combination of heuristics and machine learning algorithms, while the kernel fusion mechanism is based on graph analysis and machine learning algorithms. By understanding the kernel selection and fusion mechanisms in XLA, you can optimize the performance of your machine learning models and take advantage of the unique characteristics of the target hardware.

References

Code

The code provided in this article is a simple example of how to use the XLA compiler to export a PyTorch model to stableHLO. The code includes a custom kernel selection function that overrides the default selection mechanism.

import torch
import torch.nn as nn
import torch.nn.functional as F
import os
from torch.export import export
from torch_xla.stablehlo import exported_program_to_stablehlo
from torch_xla.stablehlo import VariableType

xla_flags = os.environ.get("XLA_FLAGS", "")

def torch2hlo(model: torch.nn.Module, sample_input: Any, output_dir: str,
              input_names: List[str], output_names: List[str]):
    """Export a torch model to stableHLO.

    Args:
        model: The torch model to export.
        sample_input: A sample input to the model.
        output_dir: The directory to save the HLO files to.
        inputs: The names of the input tensors.
        outputs: The names of the output tensors.

    Returns:
        The stableHLO program. Useful for validation.
    """
    exported = export(model, sample_input)

    # freeze the weights / attribute params as tensors and save them to disk
    params_to_freeze = {}
    input_specs = exported.graph_signature.input_specs
    state_dict = exported.state_dict
    for idx, input_spec in enumerate(input_specs):
        # TODO(yixzhou): I believe these are all the types that corresponsd to weights
        # but we should be able to easily support other types if needed.
        if input_spec.kind in INPUTKINDS_TO_FREEZE:
            params_to_freeze[input_spec.target] = state_dict[
                input_spec.target].detach().cpu().numpy()

    output_data_dir = os.path.join(output_dir, constants.MLIR_DATA_DIR)
    os.makedirs(output_data_dir, exist_ok=True)
    for k, v in params_to_freeze.items():
        tensor = TensorProto(
            tensor_info=TensorInfoProto(
                name=k,
                dtype=numpy_dtype_to_enum_dtype[v.dtype.type],
                shape=v.shape,
            ),
            data=v.tobytes(),
        )
        tensor_path = os.path.join(output_data_dir, f"{k}.npy")
        np.save(tensor_path, v)
        glog.info(f"Saved tensor to {tensor_path}")

    stablehlo_program = exported_program_to_stablehlo(exported)

    # save the mapping from position arguments to the names of the arguments
    func = stablehlo_program._name_to_stablehlo[
        constants.MLIR_DEFAULT_FUNCTION_NAME]
    meta = func.meta
    arg_position_to_name_mapping = {}
    for idx, loc in enumerate(meta.input_locations):
        if loc.type_ in POSITIONAL_PARAM_TYPES_TO_FREEZE:
            arg_position_to_name_mapping[idx] = loc.name

    json_file = os.path.join(output_data_dir,
                     constants.MLIR_POSITION_TO_ARG_NAME_MAP_FILENAME)
    json_str = json.dumps(arg_position_to_name_mapping)
    json_file.write_text(json_str)
    glog.info(f"Saved arg position to name mapping to {json_file}")

    mlir_binary_path = os.path.join(output_dir, constants.MLIR_BINARY_FILENAME)
    with open(mlir_binary_path, "wb+") as f:
        f.write(stablehlo_program.get_stablehlo_bytecode('forward'))
    glog.info(f"Saved MLIR binary to {mlir_binary_path}")
    mlir_text_path = os.path.join(output_dir, constants.MLIR_TEXT_FILENAME)
    with open(mlir_text_path, "w+") as f:
        f.write(stablehlo_program.get_stablehlo_text('forward'))
    glog.info(f"Saved MLIR debug text to {mlir_text_path}")
    return stablehlo_program

Q: What is XLA kernel fusion and selection?

A: XLA kernel fusion and selection are critical components of the XLA compiler. The kernel fusion mechanism combines multiple operations into a single kernel, reducing the overhead of function calls and improving performance. The kernel selection mechanism chooses the best kernel for a given operation, taking into account factors such as performance, memory usage, and accuracy.

Q: How does XLA kernel fusion work?

A: XLA kernel fusion works by analyzing the graph of operations to identify opportunities for fusion. The compiler uses graph analysis and machine learning algorithms to determine the best kernel for each operation, taking into account factors such as operation dependencies, operation types, and data types.

Q: How does XLA kernel selection work?

A: XLA kernel selection works by analyzing the input data and the model architecture to determine the best kernel for each operation. The compiler uses a combination of heuristics and machine learning algorithms to choose the best kernel, taking into account factors such as operation type, data type, memory usage, and performance.

Q: What are the benefits of XLA kernel fusion and selection?

A: The benefits of XLA kernel fusion and selection include:

  • Improved performance: By combining multiple operations into a single kernel, XLA kernel fusion can improve performance by reducing the overhead of function calls.
  • Reduced memory usage: By choosing the best kernel for each operation, XLA kernel selection can reduce memory usage by minimizing the amount of memory required to perform the operation.
  • Improved accuracy: By taking into account factors such as operation dependencies and data types, XLA kernel fusion and selection can improve accuracy by ensuring that the best kernel is chosen for each operation.

Q: How can I modify the behavior of XLA kernel fusion and selection?

A: You can modify the behavior of XLA kernel fusion and selection by using a combination of techniques, including:

  • Custom kernel selection: You can write custom kernel selection functions to override the default selection mechanism.
  • Custom kernel fusion: You can write custom kernel fusion functions to override the default fusion mechanism.
  • Model optimization: You can optimize the model architecture to take advantage of the unique characteristics of the target hardware.

Q: What are the limitations of XLA kernel fusion and selection?

A: The limitations of XLA kernel fusion and selection include:

  • Complexity: XLA kernel fusion and selection can be complex and difficult to understand, especially for large and complex models.
  • Performance overhead: XLA kernel fusion and selection can introduce performance overhead due to the overhead of function calls and the complexity of the kernel selection and fusion mechanisms.
  • Memory usage: XLA kernel fusion and selection can require significant memory usage due to the need to store the kernel selection and fusion graphs.

Q: How can I troubleshoot XLA kernel fusion and selection issues?

A: You can troubleshoot XLA kernel fusion and selection issues by using a combination of techniques, including:

  • Logging: You can use logging to track the kernel selection and fusion process and identify any issues.
  • Debugging: You can use debugging tools to step through the kernel selection and fusion process and identify any issues.
  • Model optimization: You can optimize the model architecture to take advantage of the unique characteristics of the target hardware.

Q: What are the best practices for using XLA kernel fusion and selection?

A: The best practices for using XLA kernel fusion and selection include:

  • Model optimization: You should optimize the model architecture to take advantage of the unique characteristics of the target hardware.
  • Kernel selection and fusion customization: You should customize the kernel selection and fusion mechanisms to take advantage of the unique characteristics of the target hardware.
  • Logging and debugging: You should use logging and debugging tools to track the kernel selection and fusion process and identify any issues.

Q: What are the future directions for XLA kernel fusion and selection?

A: The future directions for XLA kernel fusion and selection include:

  • Improved performance: XLA kernel fusion and selection can be improved to provide better performance by reducing the overhead of function calls and improving the accuracy of the kernel selection and fusion mechanisms.
  • Reduced memory usage: XLA kernel fusion and selection can be improved to reduce memory usage by minimizing the amount of memory required to perform the operation.
  • Improved accuracy: XLA kernel fusion and selection can be improved to provide better accuracy by taking into account factors such as operation dependencies and data types.