EraseInverseOps Pass: Random Op Order Issue In TT-MLIR

by Alex Johnson 55 views

Have you ever encountered a situation where your operations in MLIR seem to be shuffled unexpectedly? This article delves into a peculiar issue with the EraseInverseOps pass in the Tenstorrent (TT-MLIR) ecosystem, where the order of operations gets randomized, potentially causing headaches during codegen.

Understanding the Issue: Randomization of Operation Order

The EraseInverseOps (EIO) pass is designed to optimize MLIR code by identifying and canceling pairs of inverse operations. This can lead to significant performance improvements by simplifying the computational graph. However, a side effect of the current implementation is that if the pass fails to cancel operations, it may leave the moved operations in a different order than they were originally. This randomization of the operation order can create problems during the codegen phase, where the original structure of the module is crucial for correct reconstruction.

To illustrate this, consider the analogy of assembling a puzzle. Imagine you have a set of puzzle pieces, each representing an operation in your MLIR code. The original order of these pieces is essential for forming the complete picture. The EIO pass, in its attempt to simplify the puzzle, might move some pieces around. If it doesn't succeed in fitting the pieces together, it leaves them in a jumbled state, making it difficult to restore the original image. This is precisely what happens with the operation order in MLIR after the EIO pass, which makes correct code generation a challenge.

Real-World Impact on Codegen

The consequences of this randomized operation order become particularly apparent during codegen. Codegen relies on the predictable structure of the MLIR module to generate efficient machine code. When the order of operations is shuffled, the codegen process can become significantly more complex, potentially leading to incorrect or suboptimal code. In some cases, it may even prevent the codegen from completing successfully.

This issue highlights the importance of maintaining a consistent and predictable operation order throughout the MLIR compilation pipeline. While the EIO pass aims to optimize the code, its current behavior introduces an undesirable side effect that needs to be addressed.

Analyzing the IRs: A Deep Dive into the Problem

To better understand this issue, let's examine the provided Intermediate Representations (IRs). These IRs capture the state of the code at different stages of the compilation process, allowing us to pinpoint the exact changes introduced by the EIO pass. The provided IRs are:

  • ttir.mlir.txt: This file likely represents the initial MLIR code before any optimizations have been applied.
  • ttir-before-erase-inverse-ops.mlir.txt: This IR shows the code just before the EIO pass is executed.
  • ttir-after-erase-inverse-ops.mlir.txt: This IR captures the state of the code after the EIO pass has been applied.

By comparing these IRs, we can observe the specific transformations performed by the EIO pass and identify the source of the operation order randomization.

Tracing Operation Movement with DEBUG Locations

One crucial aspect of these IRs is the inclusion of location data inlined within the code. Each operation is tagged with a DEBUG|op_index|... string, where op_index indicates the original position of the operation when the IR was created. This debugging information provides valuable insight into how the EIO pass moves operations around. Analyzing these indices before and after the EIO pass allows us to track the changes in operation order.

Consider the following example snippet from the provided IRs:

%55 = "ttir.batch_norm_inference"(%50, %53, %54, %51, %52) <{dimension = 1 : i32, epsilon = 9.99999974E-6 : f32}> : (tensor<1x64x56x56xf32>, tensor<1x64x1x1xf32>, tensor<1x64x1x1xf32>, tensor<1x64x1x1xf32>, tensor<1x64x1x1xf32>) -> tensor<1x64x56x56xf32> loc("DEBUG|15|...")
%56 = "ttir.permute"(%55) <{permutation = array<i64: 0, 2, 3, 1>}> : (tensor<1x64x56x56xf32>) -> tensor<1x56x56x64xf32> loc("DEBUG|23|...")
%57 = "ttir.reshape"(%56) <{shape = [1 : i32, 1 : i32, 3136 : i32, 64 : i32]}> : (tensor<1x56x56x64xf32>) -> tensor<1x1x3136x64xf32> loc("DEBUG|18|...")

In this example, we see three operations with DEBUG locations indicating their original indices: 15, 23, and 18. Notice that the indices are not in ascending order after the EIO pass. This clearly demonstrates the randomization of operation order caused by the pass. The expectation is that these operations should appear in non-decreasing order, reflecting the original structure of the computation.

The Root Cause: Unsuccessful Operation Cancellation

The core of the problem lies in the EIO pass's behavior when it fails to cancel inverse operations. The pass attempts to move operations around to create opportunities for cancellation. However, if it cannot find suitable pairs to eliminate, it doesn't restore the operations to their original positions. This leaves the code in a state where the operation order is jumbled, leading to the issues described earlier. If the EIO pass had a mechanism to ensure the operations are restored to their proper place, the problem of randomized operation order would not exist.

A Puzzle with Missing Pieces

Think of it as trying to rearrange furniture in a room to create more space. The EIO pass is like someone who starts moving furniture but doesn't finish the job. Some pieces are moved, but not all, leaving the room in disarray. Similarly, the EIO pass moves operations, but if it doesn't achieve cancellation, it leaves the code in a disorganized state.

Expected Behavior: Maintaining Operation Order

The expected behavior is that the EIO pass should either successfully cancel inverse operations and simplify the code or, if it cannot, it should leave the operation order unchanged. This would ensure that the codegen process receives a consistent and predictable input, leading to correct and efficient code generation. The current behavior, where the randomized operation order remains after failed cancellation attempts, is a significant issue that needs to be addressed.

The Ideal Outcome: Non-Decreasing Operation Order

The desired outcome is that running the full TT-MLIR pipeline, including the EIO pass, should result in TTNN IR where all operations appear in non-decreasing order. This would guarantee that the code structure is preserved, making codegen more reliable and predictable. Maintaining the original order can be crucial for performance and correctness, especially in complex models where the sequence of operations is carefully designed.

Potential Solutions and Mitigation Strategies

Several strategies can be employed to address the issue of randomized operation order in the EIO pass.

  1. Restoring Original Order: The most straightforward solution is to modify the EIO pass to restore the original order of operations if it fails to cancel any inverse operations. This could involve storing the original positions of operations before moving them and then moving them back if no cancellation occurs.

  2. Selective Operation Movement: Another approach is to make the EIO pass more selective in its operation movement. Instead of blindly moving operations, it could analyze the code more carefully to ensure that only operations that are likely to be canceled are moved. This would reduce the risk of disrupting the operation order unnecessarily.

  3. Introducing a Reordering Pass: A separate pass could be introduced after the EIO pass to reorder operations back into their original sequence. This would add an extra step to the compilation pipeline but would guarantee that the operation order is consistent.

  4. Improving Inverse Operation Detection: Enhancing the EIO pass's ability to detect inverse operations could increase its success rate, reducing the likelihood of leaving operations in a randomized operation order. This might involve more sophisticated pattern matching or analysis techniques.

A Layered Approach to the Problem

It's likely that a combination of these strategies will be the most effective in addressing the issue. For example, the EIO pass could be modified to restore operation order in most cases, while a separate reordering pass could be used as a fallback to handle any remaining inconsistencies.

Conclusion: Preserving Order for Reliable Codegen

The randomization of operation order by the EraseInverseOps pass in TT-MLIR presents a significant challenge for codegen. By understanding the root cause of the issue and analyzing the IRs, we can develop effective solutions to mitigate this problem. Preserving the original operation order is crucial for ensuring the reliability and efficiency of the compilation pipeline, ultimately leading to better performance and more predictable behavior in the generated code. The techniques and solutions discussed here offer a pathway to addressing the randomized operation order, ensuring that the EIO pass remains a valuable optimization tool without introducing unwanted side effects.

To delve deeper into MLIR and its optimization techniques, visit the official MLIR documentation.