CONVERT_SHLO_TO_SHARDY Constant Reuse Issue
Hey guys, let's dive into a tricky situation we've encountered with the CONVERT_SHLO_TO_SHARDY
feature. This post will break down the problem, why it's happening, and the impact it has on our MLIR passes and overall performance. Buckle up, it's gonna be a fun ride!
The Problem: Constant Reusability Gone Wild
So, here's the deal. When CONVERT_SHLO_TO_SHARDY
is activated, we've noticed that constants are being reused across different shapes. Now, on the surface, this might seem like a clever optimization, right? Sharing is caring, and reusing constants sounds efficient. However, it's causing some unexpected headaches in our MLIR (Multi-Level Intermediate Representation) passes and leading to performance regressions. Not cool!
To illustrate, let's consider the vovnet
model from tt-forge. Previously, for every unique shape of a tensor filled with zeros, we'd have a distinct constant operation. Think of it like this – each shape got its own dedicated zero-filled constant.
%4 = "ttir.constant"() <{value = dense<0.000000e+00> : tensor<8x1024x1x1xbf16>}> : () -> tensor<8x1024x1x1xbf16> loc(#loc)
%6 = "ttir.constant"() <{value = dense<0.000000e+00> : tensor<8x1024x7x7xbf16>}> : () -> tensor<8x1024x7x7xbf16> loc(#loc)
See those two separate ttir.constant
ops? Each one represents a tensor of zeros with a specific shape (8x1024x1x1xbf16 and 8x1024x7x7xbf16). This was the expected behavior before.
What's Happening Now? One Constant to Rule Them All!
But now, with CONVERT_SHLO_TO_SHARDY
enabled, things have changed. Instead of creating unique constant ops for each shape, we're seeing a single constant op being reused. This lone constant is then reshaped and broadcasted to fit the different tensor shapes needed throughout the computation. It's like one-size-fits-all, but for tensors. But one size doesn't fit all in ML!
Here's how it looks in the MLIR:
%4 = "ttir.constant"() <{value = dense<0.000000e+00> : tensor<bf16>}> : () -> tensor<bf16> loc(#loc)
...
%26 = "ttir.reshape"(%4, %25) <{shape = [1 : i32, 1 : i32, 1 : i32, 1 : i32]}> : (tensor<bf16>, tensor<1x1x1x1xbf16>) -> tensor<1x1x1x1xbf16> loc(#loc)
%27 = ttir.empty() : tensor<8x1024x1x1xbf16> loc(#loc)
%28 = "ttir.broadcast"(%26, %27) <{broadcast_dimensions = array<i64: 8, 1024, 1, 1>}> : (tensor<1x1x1x1xbf16>, tensor<8x1024x1x1xbf16>) -> tensor<8x1024x1x1xbf16> loc(#loc)
...
%34 = "ttir.reshape"(%4, %33) <{shape = [1 : i32, 1 : i32, 1 : i32, 1 : i32]}> : (tensor<bf16>, tensor<1x1x1x1xbf16>) -> tensor<1x1x1x1xbf16> loc(#loc)
%35 = ttir.empty() : tensor<8x1024x7x7xbf16> loc(#loc)
%36 = "ttir.broadcast"(%34, %35) <{broadcast_dimensions = array<i64: 8, 1024, 7, 7>}> : (tensor<1x1x1x1xbf16>, tensor<8x1024x7x7xbf16>) -> tensor<8x1024x7x7xbf16> loc(#loc)
Notice how %4
(our single constant op) is being reshaped and broadcasted multiple times to create tensors of different shapes. This wasn't the intention, and it's where our problems begin.
The Impact: MLIR Pass Issues and Performance Regression
This unexpected constant reuse is causing a couple of major issues:
1. MLIR Pass Problems
Our MLIR passes are designed with the assumption that constants with different shapes are distinct operations. When a single constant is being reshaped and broadcasted, it throws a wrench in the gears of these passes. They might not be able to correctly analyze or transform the code, leading to incorrect or suboptimal results. Essentially, it's messing with the internal workings of our compiler.
2. Performance Regression
Performance regression is just a fancy way of saying things are getting slower. And that's exactly what's happening here. The extra reshape and broadcast operations add overhead to the computation. Instead of directly using a constant of the desired shape, we're spending time and resources manipulating a single constant to fit the mold. This can lead to a noticeable slowdown in the execution of our models.
Imagine you're baking a cake. Previously, you had pre-portioned bags of flour for different recipes. Now, you only have one giant bag and have to measure out the flour for each recipe every time. It's doable, but it takes longer and is more prone to error.
Why is This Happening? Understanding CONVERT_SHLO_TO_SHARDY
To understand why this is happening, we need to delve a bit into what CONVERT_SHLO_TO_SHARDY
does. This feature is designed to convert operations from the SHLO (Shape Lowering) dialect to the Shardy dialect. The Shardy dialect is optimized for execution on our Tenstorrent hardware, which heavily utilizes sharded tensors. Sharding is a technique where a large tensor is divided into smaller pieces and distributed across multiple processing elements.
One of the optimizations that CONVERT_SHLO_TO_SHARDY
performs is to try and reuse constants whenever possible. The idea is that by reusing a single constant and then reshaping and broadcasting it, we can reduce the overall memory footprint and improve data locality. However, as we've seen, this optimization can backfire if not handled carefully. It's like trying to kill a fly with a sledgehammer – you might get the fly, but you'll also make a mess.
The Next Steps: Fixing the Constant Reuse Issue
So, where do we go from here? The first step is to thoroughly investigate the root cause of this issue. We need to understand why CONVERT_SHLO_TO_SHARDY
is behaving this way and identify the specific part of the transformation that's causing the problem. It's like being a detective and piecing together the clues.
Once we've pinpointed the culprit, we can start exploring potential solutions. Here are a few avenues we might consider:
- Refining the Constant Reuse Logic: We could tweak the logic within
CONVERT_SHLO_TO_SHARDY
to be more selective about when constants are reused. Perhaps we can introduce some heuristics to determine when reuse is beneficial and when it's detrimental. - Introducing Shape Awareness in MLIR Passes: We could modify our MLIR passes to be aware of the constant reuse behavior. This would allow them to correctly handle the reshaped and broadcasted constants, preventing errors and enabling further optimizations.
- Exploring Alternative Sharding Strategies: We might need to rethink our approach to sharding constants. Perhaps there are other ways to shard constants that don't involve excessive reshaping and broadcasting.
The ultimate goal is to find a solution that addresses the constant reuse issue without sacrificing the benefits of CONVERT_SHLO_TO_SHARDY
. We want to have our cake and eat it too – efficient sharding without performance regressions.
Conclusion: A Bump in the Road, Not the End of the Journey
The constant reuse issue with CONVERT_SHLO_TO_SHARDY
is definitely a challenge, but it's not an insurmountable one. It's a bump in the road on our journey to building high-performance machine learning systems. By understanding the problem, investigating the root cause, and exploring potential solutions, we're confident that we can overcome this obstacle.
Thanks for joining us on this deep dive! We'll keep you updated on our progress as we work towards a fix. Stay tuned for more adventures in the world of MLIR and sharded tensors!