Understanding Padding Fusion Flags

Oct 13, 2025 by talynt.org 34 views

Introduction

In the world of compiler optimization, padding fusion flags play a crucial role in improving the performance of machine learning models. However, these flags can sometimes cause issues, especially when used in conjunction with other optimization techniques. In this article, we will explore the issues with padding fusion flags and how they can be resolved.

Understanding Padding Fusion Flags

Padding fusion flags are used to optimize the padding process in machine learning models. The two main flags used for padding fusion are:

--iree-dispatch-creation-enable-fuse-padding-into-linalg-consumer-ops
--iree-dispatch-creation-enable-fuse-padding-into-linalg-producer-ops

These flags are used to fuse the padding process with other operations, such as convolution, to improve performance.

Issue 1: No Effect with `--iree-dispatch-creation-enable-fuse-padding-into-linalg-consumer-ops`

When using the --iree-dispatch-creation-enable-fuse-padding-into-linalg-consumer-ops flag, we expect the padding process to be fused with the convolution operation. However, in some cases, this flag has no effect at all. This can be due to various reasons, such as:

The flag is not enabled correctly.
The padding process is not compatible with the convolution operation.
The flag is overridden by other optimization techniques.

Issue 2: Two Dispatches with `--iree-dispatch-creation-enable-fuse-padding-into-linalg-producer-ops`

When using the --iree-dispatch-creation-enable-fuse-padding-into-linalg-producer-ops flag, we expect the padding process to be fused with the convolution operation. However, in some cases, this flag causes two dispatches: one for padding and another for convolution. This can be due to various reasons, such as:

The flag is not enabled correctly.
The padding process is not compatible with the convolution operation.
The flag is overridden by other optimization techniques.

Issue 3: Passing Dispatch Failure with `--iree-dispatch-creation-enable-fuse-padding-into-linalg-producer-ops`

When using the --iree-dispatch-creation-enable-fuse-padding-into-linalg-producer-ops flag, we expect the padding process to be fused with the convolution operation. However, in some cases, this flag causes a passing dispatch failure with the following error:

error: LLVM Translation failed for operation: builtin.unrealized_conversion_cast

This error can be due to various reasons, such as:

The flag is not enabled correctly.
The padding process is not compatible with the convolution operation.
The flag is overridden by other optimization techniques.

Issue 4: Padding Fused with Conv with Both Flags

When using both flags at once, we expect the padding process to be fused with the convolution operation. However, in some cases, this flag causes the padding process to be fused with the convolution operation, which is not supported by the backend. This can be due to various reasons, such as:

The flag is not enabled correctly.
The padding process is not compatible with the convolution operation.
The flag is overridden by other optimization techniques.

Conclusion

Padding fusion flags are used to optimize the padding process in machine learning models. However, these flags can sometimes cause issues, especially when used in conjunction with other optimization techniques. In this article, we explored the issues with padding fusion flags and how they can be resolved. By understanding the issues and their causes, we can optimize the padding process and improve the performance of machine learning models.

Code Example

The following code example demonstrates the use of padding fusion flags:

!unpaddedA_T=tensor<1x8x128x128xf32>
!A_T=tensor<1x8x130x130xf32>
!B_T=tensor<16x8x3x3xf32>
!O_T=tensor<1x16x128x128xf32>

util.func public @conv_2d_nchw_fchw(%arg0: !unpaddedA_T, %arg1: !B_T) -> !O_T {
  %cst = arith.constant 0.000000e+00 : f32
  %offset = arith.constant 1 : index
  %padded_arg0 = tensor.pad %arg0 low[0, 0, %offset, %offset] high[0, 0, %offset, %offset] {
  ^bb0(%arg2: index, %arg3: index, %arg4: index, %arg5: index):
    tensor.yield %cst : f32
  } : !unpaddedA_T to !A_T
  %empty = tensor.empty() : !O_T
  %fill = linalg.fill ins(%cst : f32) outs(%empty : !O_T) -> !O_T
  %0 = linalg.conv_2d_nchw_fchw
    {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64> }
     ins(%padded_arg0, %arg1: !A_T, !B_T)
    outs(%fill: !O_T) -> !O_T
  util.return %0 : !O_T
}

This code example demonstrates the use of padding fusion flags to optimize the padding process in a machine learning model.

References

Q: What are padding fusion flags?

A: Padding fusion flags are used to optimize the padding process in machine learning models. They are used to fuse the padding process with other operations, such as convolution, to improve performance.

Q: What are the two main padding fusion flags?

A: The two main padding fusion flags are:

--iree-dispatch-creation-enable-fuse-padding-into-linalg-consumer-ops
--iree-dispatch-creation-enable-fuse-padding-into-linalg-producer-ops

Q: What is the purpose of the `--iree-dispatch-creation-enable-fuse-padding-into-linalg-consumer-ops` flag?

A: The --iree-dispatch-creation-enable-fuse-padding-into-linalg-consumer-ops flag is used to fuse the padding process with the convolution operation. It is used to optimize the padding process and improve performance.

Q: What is the purpose of the `--iree-dispatch-creation-enable-fuse-padding-into-linalg-producer-ops` flag?

A: The --iree-dispatch-creation-enable-fuse-padding-into-linalg-producer-ops flag is used to fuse the padding process with the convolution operation. It is used to optimize the padding process and improve performance.

Q: What are the issues with padding fusion flags?

A: The issues with padding fusion flags include:

No effect with --iree-dispatch-creation-enable-fuse-padding-into-linalg-consumer-ops
Two dispatches with --iree-dispatch-creation-enable-fuse-padding-into-linalg-producer-ops
Passing dispatch failure with --iree-dispatch-creation-enable-fuse-padding-into-linalg-producer-ops
Padding fused with conv with both flags

Q: How can I resolve the issues with padding fusion flags?

A: To resolve the issues with padding fusion flags, you can try the following:

Check the flag is enabled correctly
Check the padding process is compatible with the convolution operation
Check the flag is not overridden by other optimization techniques
Try using both flags at once

Q: What are the benefits of using padding fusion flags?

A: The benefits of using padding fusion flags include:

Improved performance
Optimized padding process
Reduced memory usage

Q: What are the limitations of padding fusion flags?

A: The limitations of padding fusion flags include:

May not work with all models
May not work with all backends
May require additional optimization techniques

Q: How can I get started with padding fusion flags?

A: To get started with padding fusion flags, you can try the following:

Read the documentation for the padding fusion flags
Experiment with different flags and settings
Check the output of the model to see if the padding fusion flags are working correctly

Q: What are some common mistakes to avoid when using padding fusion flags?

A: Some common mistakes to avoid when using padding fusion flags include:

Not enabling the flag correctly
Not checking the compatibility of the padding process with the convolution operation
Not checking if the flag is overridden by other optimization techniques

Q: What are some best practices for using padding fusion flags?

A: Some best practices for using padding fusion flags include:

Always check the documentation for the padding fusion flags
Always experiment with different flags and settings
Always check the output of the model to see if the padding fusion flags are working correctly

Q: What are some resources for learning more about padding fusion flags?

A: Some resources for learning more about padding fusion flags include:

Note: The Q&A article is for demonstration purposes only and may not be up-to-date or accurate.