RX 9070 Slow Convolution Performance: A ROCm Deep Dive

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RX 9070 Slow Convolution Performance: A ROCm Deep Dive

Hey guys! Today, we're diving deep into a fascinating issue regarding the performance of convolution operations on AMD GPUs, specifically the Radeon RX 9070. A user reported a significant performance gap between the RX 9070 and the RX 7800XT when running image processing tasks using PyTorch and ROCm. Let's break down the problem, analyze the benchmark results, and explore potential solutions.

Problem Description: The Performance Gap

Our user encountered a substantial performance difference while working on a video super-resolution (VSR) model. When processing a 180-frame video at 256x256 resolution, the RX 9070 took approximately 8 seconds, whereas the RX 7800XT completed the same task in a blazing-fast 2 seconds. That's a 4x difference! This is a major concern, especially considering that these GPUs should have comparable performance. The VSR model in question utilizes several Conv2D layers with 7x7 kernels. Initial testing suggests that the RX 9070 (gfx120x) and RX 7800XT (gfx110x) are employing different convolution algorithms internally. The user is unsure how to achieve comparable speeds between these GPUs or whether it's a matter of waiting for full algorithm support for the RX 9070. This is a critical issue for anyone relying on these GPUs for deep learning and image processing tasks. Understanding the root cause and finding a solution is paramount for maximizing the efficiency of these powerful cards. We need to figure out why the RX 9070 is lagging behind and what steps can be taken to rectify this. It's not just about raw speed; it's about ensuring that the hardware is being utilized effectively and that users are getting the performance they expect.

The Benchmark: Unveiling the Bottleneck

To investigate further, the user implemented a benchmark inspired by a previous issue concerning NHWC-to-NCHW conversion. However, in this case, the input format was directly NCHW. Let's dissect the benchmark code:

import torch
import time

# Convolution settings
batch_size = 32
height = 256
width = 256
in_channels = 64
out_channels = 64
kernel_size = (3, 3)
stride = (1, 1)
padding = 1

# Number of warmup and measured runs
warmup = 2
runs = 10

# Dtypes to test
dtypes = {
    "float32": torch.float32,
}

def benchmark(dtype_name, dtype):
    print(f"\nTesting {dtype_name}...")

    x = torch.randn((batch_size, in_channels, height, width), dtype=dtype, device="cuda")  # NCHW
    w = torch.randn((out_channels, in_channels, *kernel_size), dtype=dtype, device="cuda") # OIHW

    conv = torch.nn.functional.conv2d

    # Warmup
    for _ in range(warmup):
        y = conv(x, w, stride=stride, padding=padding)
        torch.cuda.synchronize()

    # Timed run
    start = time.time()
    for _ in range(runs):
        y = conv(x, w, stride=stride, padding=padding)
        torch.cuda.synchronize()
    end = time.time()

    total_time = end - start
    avg_time = total_time / runs

    # Approx FLOPs for Conv2D: 2 * H * W * Cout * (Cin * Kx * Ky) * B
    flops = 2 * height * width * out_channels * (in_channels * kernel_size[0] * kernel_size[1]) * batch_size
    tflops = flops / avg_time / 1e12

    print(f"Avg time: {avg_time:.6f} s, Throughput: {tflops:.2f} TFLOP/s")


for name, dtype in dtypes.items():
    try:
        benchmark(name, dtype)
    except Exception as e:
        print(f"Skipped {name} due to error: {e}")

This Python script uses PyTorch to benchmark the conv2d operation. It sets up a convolution layer with specific parameters (batch size, height, width, channels, kernel size, stride, and padding). It then performs a series of warmup runs followed by timed runs to measure the average execution time. The script calculates the theoretical FLOPs (floating-point operations) for the convolution and uses this to estimate the throughput in TFLOP/s. The use of warmup runs is crucial for accurate benchmarking, as it allows the GPU to reach a stable state before timing begins. The torch.cuda.synchronize() calls ensure that all CUDA operations are completed before the timer starts and stops, preventing asynchronous execution from skewing the results. This benchmark provides a controlled environment to isolate and measure the performance of the convolution operation, helping to identify any bottlenecks or inefficiencies.

Shocking Results: RX 9070 vs. RX 7800XT

The benchmark results revealed a stark contrast in performance:

RX 9070:

Testing float32…
Avg time: 0.088085 s, Throughput: 1.76 TFLOP/s

RX 7800XT:

Testing float32...
Avg time: 0.004719 s, Throughput: 32.77 TFLOP/s

The RX 7800XT demonstrated a whopping 18x higher throughput than the RX 9070! This significant disparity strongly suggests that the RX 9070 is not utilizing its hardware resources as effectively as the RX 7800XT for convolution operations. The massive difference in TFLOP/s indicates that the RX 9070 is severely underperforming, potentially due to suboptimal algorithm selection or missing optimizations in the ROCm stack for this specific GPU architecture. This discrepancy highlights a critical issue that needs immediate attention, as it impacts the usability of the RX 9070 for deep learning and other compute-intensive tasks. The benchmark clearly points to a bottleneck in the convolution performance on the RX 9070, and further investigation is necessary to pinpoint the exact cause and implement the necessary fixes. It's crucial to understand why this performance gap exists and to ensure that future ROCm releases address this issue to unlock the full potential of the RX 9070.

System Configuration: The Details Matter

To further understand the context, here’s the system configuration used for the tests:

  • Operating System: Ubuntu 24.04
  • CPU: AMD R5-5700X
  • GPU: AMD RX 9070
  • ROCm Version: ROCm 7.1.0
  • ROCm Component: MIOpen
  • Python Version: 3.13

The user also provided the steps to reproduce the issue:

  1. Install Ubuntu 24.04
  2. Install ROCm 7.1
  3. Install Python 3.13
  4. Install PyTorch
  5. Run the benchmark script

This detailed information is invaluable for anyone attempting to replicate the issue and contribute to finding a solution. Knowing the exact operating system, ROCm version, and software stack helps to narrow down potential causes and ensures that any proposed fixes are tested in a consistent environment. The inclusion of the reproduction steps is particularly helpful, as it allows others to quickly verify the problem and experiment with different configurations or patches. This collaborative approach is essential for effectively addressing performance issues in complex software ecosystems like ROCm. The more information we have about the system and the steps to reproduce the problem, the better equipped we are to diagnose and resolve it.

Potential Causes and Solutions: Let's Brainstorm!

So, what could be causing this massive performance discrepancy? Here are a few potential reasons and some ideas for solutions:

  1. Suboptimal Convolution Algorithm Selection: The RX 9070 (gfx120x) and RX 7800XT (gfx110x) might be using different convolution algorithms internally. MIOpen, the ROCm library responsible for convolution operations, might be selecting a less efficient algorithm for the RX 9070. This could be due to missing optimizations or incorrect heuristics for this specific GPU architecture. Solution: Investigate MIOpen's algorithm selection process and ensure that the optimal algorithm is chosen for the RX 9070. This might involve adding new heuristics or fine-tuning existing ones. It's crucial to analyze the performance characteristics of different convolution algorithms on the RX 9070 and identify the most efficient option. The MIOpen team needs to delve into the algorithm selection logic and make sure it's making the right choices for this particular GPU.

  2. Missing Optimizations for gfx120x: The ROCm stack might not be fully optimized for the gfx120x architecture of the RX 9070. This could result in inefficient code generation or suboptimal memory access patterns. Solution: Work on optimizing the ROCm compiler and runtime for gfx120x. This might involve adding new code generation passes, improving memory allocation strategies, or fine-tuning kernel launch parameters. It's essential to profile the convolution kernels running on the RX 9070 and identify any performance bottlenecks. Targeted optimizations can then be implemented to address these bottlenecks and unlock the full potential of the GPU.

  3. Driver Issues: There might be underlying driver issues that are affecting the performance of convolution operations on the RX 9070. Solution: Update to the latest ROCm drivers and ensure that they are properly installed. If the issue persists, consider reporting the problem to AMD's driver team. Driver updates often include performance improvements and bug fixes, so it's crucial to stay up-to-date. If a driver issue is suspected, providing detailed information and reproducible test cases to the driver team can help them quickly identify and resolve the problem.

  4. NHWC vs. NCHW Data Layout: Although the user explicitly used NCHW as the input format, there might be some implicit conversions or inefficiencies related to data layout. Solution: Double-check the data layout throughout the computation and ensure that it remains consistent. Experiment with different data layouts and see if it affects performance. While the user confirmed using NCHW, it's always a good idea to verify that the data layout is consistent throughout the entire pipeline. Subtle inconsistencies can sometimes lead to unexpected performance drops. Exploring different data layouts might reveal hidden performance bottlenecks.

  5. Kernel Size Specific Issues: The significant performance difference might be specific to the 7x7 kernels used in the VSR model. Solution: Benchmark convolution operations with different kernel sizes to see if the performance gap persists. This can help narrow down the issue and identify if it's related to a specific kernel size or a more general problem. Testing with a range of kernel sizes can provide valuable insights into the performance characteristics of the convolution implementation on the RX 9070. If the issue is specific to certain kernel sizes, it might indicate a problem with the algorithm selection or kernel implementation for those sizes.

Community Collaboration: Let's Fix This Together!

This is where you guys come in! If you have an RX 9070 or similar hardware, please try running the benchmark and share your results. Let's collaborate to identify the root cause and find a solution. The more data we gather, the better we can understand the issue and work towards a fix. Sharing your experiences and insights can be invaluable in this process. By working together, we can help improve the performance of ROCm on AMD GPUs and ensure that users get the performance they deserve. Don't hesitate to share your thoughts, ideas, and any potential solutions you might have. Collective effort is key to resolving complex issues like this.

Next Steps: What Needs to Happen?

  1. MIOpen Team Investigation: The MIOpen team needs to investigate the convolution algorithm selection and implementation for the RX 9070. They should profile the code and identify any bottlenecks. A deep dive into the MIOpen code is essential to understand how it's handling convolutions on the RX 9070. Profiling the code will help pinpoint the areas where performance is lacking and guide optimization efforts. The MIOpen team's expertise is crucial in addressing this issue effectively.

  2. ROCm Optimization: The ROCm developers should focus on optimizing the stack for the gfx120x architecture. This includes compiler optimizations, memory management improvements, and kernel launch tuning. Ensuring that ROCm is fully optimized for the RX 9070's architecture is key to unlocking its full potential. Continuous optimization efforts are necessary to keep pace with the evolving hardware landscape. The ROCm team should prioritize optimizations that address the specific performance bottlenecks identified in the convolution operations.

  3. Community Testing and Feedback: We need more community testing and feedback to validate any proposed solutions. Sharing your results and experiences is crucial for ensuring that the fix is effective and doesn't introduce any new issues. Community involvement is vital for the success of any open-source project. By working together, we can ensure that ROCm is a robust and performant platform for AMD GPUs. The more feedback we gather, the better equipped we are to make informed decisions and deliver a high-quality solution.

Conclusion: A Call to Action

The performance disparity between the RX 9070 and RX 7800XT in convolution operations is a significant issue that needs to be addressed. By working together, we can help the ROCm team identify the root cause and implement the necessary fixes. Let's make sure the RX 9070 can flex its muscles and deliver the performance we expect! This issue highlights the importance of continuous optimization and community collaboration in the open-source world. By actively participating in the process, we can contribute to the improvement of ROCm and ensure that it remains a competitive platform for GPU computing. Let's not let this performance gap hold back the potential of the RX 9070. Together, we can make a difference.