W8A8 Inference Memory Bug: More NPU Needed Than Bfloat16

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W8A8 Inference Memory Bug: More NPU Needed than bfloat16

Hey guys! Today, we're diving deep into a tricky bug that some of you might have encountered while working with vLLM and Ascend. Specifically, we're looking at an issue where using W8A8 quantization for inference seems to demand more NPU memory compared to bfloat16. Let's break down what this means, why it's happening, and how it impacts your projects. This article aims to provide an in-depth understanding of the memory-related challenges in W8A8 inference compared to bfloat16, especially within the vLLM and Ascend ecosystems. We'll explore the configurations, error messages, and potential solutions to help you optimize your model deployment.

Understanding the Issue

The core problem? When you're running inference with a model quantized to W8A8 (which means weights and activations are represented in 8-bit integers), it unexpectedly gobbles up more NPU (Neural Processing Unit) memory than when you use bfloat16 (Brain Floating Point 16-bit format). This is kinda counterintuitive because W8A8 is supposed to be a memory-saving technique! We will explore the nuances of the W8A8 quantization method, shedding light on its memory behavior and comparing it to bfloat16. The goal is to clarify why W8A8, which is designed for memory efficiency, sometimes exhibits higher memory usage in practice. Let's explore this further.

What's the Deal with W8A8 and bfloat16?

Before we get any further, let's clarify these two terms. W8A8 quantization is a method that reduces the memory footprint of a model by representing both weights and activations using 8 bits. It’s a popular strategy for deploying large models on hardware with limited memory resources. Think of it like compressing a large file – you make it smaller so it’s easier to handle.

On the flip side, bfloat16 is a 16-bit floating-point format designed specifically for machine learning. It offers a good balance between precision and memory usage, making it a favorite for training and inference. It’s like using a slightly smaller piece of paper to write on, still giving you enough space without being cumbersome.

The expectation is that W8A8 should use less memory due to its lower bit representation. However, as the bug report indicates, this isn't always the case. This unexpected behavior can lead to Out-of-Memory (OOM) errors, which, as you might guess, are super annoying because they stop your inference in its tracks.

Diving into the Technical Details

Let's look at a specific scenario to understand the problem better. Imagine you're trying to serve a large model, like Qwen3-235B, using vLLM on Ascend NPUs. You've configured your system to handle inputs of 32K tokens and generate outputs of 4K tokens. You set up your configuration like this:

# `bfloat16`
# model: Qwen3-235B-A22B
# `w8a8`
# model: Qwen3-235B-A22B-W8A8
# quantization: ascend
served-model-name: qwen3_moe
tensor-parallel-size: 8
data-parallel-size: 2
data-parallel-size-local: 2
data-parallel-rpc-port: 4567
enable-expert-parallel: true
trust-remote-code: true
enforce-eager: false
no-enable-prefix-caching: true
async-scheduling: false
max_num_seqs: 8
max-num-batched-tokens: 16384
max-model-len: 40960
gpu-memory-utilization: 0.95
host: 127.0.0.1
port: 38713
rope-scaling: '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}'
additional-config: '{"ascend_scheduler_config":{"enabled":false}}'
compilation-config: '{"cudagraph_mode":"FULL_DECODE_ONLY","cudagraph_capture_sizes":[1,2,4,8]}'

In this setup, you're using tensor parallelism (tensor-parallel-size: 8) and data parallelism (data-parallel-size: 2) to distribute the model across multiple NPUs. You've also enabled expert parallelism (enable-expert-parallel: true) to further optimize the distribution of the model's layers.

Now, when you run this configuration with the W8A8 quantized model, you might hit an OOM error. The error message might look something like this:

[1;36m(Worker_DP0_TP0_EP0 pid=1355611) [0;0m ERROR 10-30 16:22:05 [multiproc_executor.py:703]     global_input_tokens_local_experts_indices = torch.repeat_interleave(
[1;36m(Worker_DP0_TP0_EP0 pid=1355611) [0;0m ERROR 10-30 16:22:05 [multiproc_executor.py:703]                                                 ^^^^^^^^^^^^^^^^^^^^^^^^
[1;36m(Worker_DP0_TP0_EP0 pid=1355611) [0;0m ERROR 10-30 16:22:05 [multiproc_executor.py:703] RuntimeError: The Inner error is reported as above. The process exits for this inner error, and the current working operator name is SelfAttentionOperation.
[1;36m(Worker_DP0_TP0_EP0 pid=1355611) [0;0m ERROR 10-30 16:22:05 [multiproc_executor.py:703] Since the operator is called asynchronously, the stacktrace may be inaccurate. If you want to get the accurate stacktrace, please set the environment variable ASCEND_LAUNCH_BLOCKING=1.
[1;36m(Worker_DP0_TP0_EP0 pid=1355611) [0;0m ERROR 10-30 16:22:05 [multiproc_executor.py:703] Note: ASCEND_LAUNCH_BLOCKING=1 will force ops to run in synchronous mode, resulting in performance degradation. Please unset ASCEND_LAUNCH_BLOCKING in time after debugging.
[1;36m(Worker_DP0_TP0_EP0 pid=1355611) [0;0m ERROR 10-30 16:22:05 [multiproc_executor.py:703] [ERROR] 2025-10-30-16:22:05 (PID:1355611, Device:0, RankID:-1) ERR00100 PTA call acl api failed.
[1;36m(Worker_DP0_TP0_EP0 pid=1355611) [0;0m ERROR 10-30 16:22:05 [multiproc_executor.py:703] [PID: 1355611] 2025-10-30-16:22:05.620.760 Memory_Allocation_Failure(EL0004): Failed to allocate memory.
[1;36m(Worker_DP0_TP0_EP0 pid=1355611) [0;0m ERROR 10-30 16:22:05 [multiproc_executor.py:703]         Possible Cause: Available memory is insufficient.
[1;36m(Worker_DP0_TP0_EP0 pid=1355611) [0;0m ERROR 10-30 16:22:05 [multiproc_executor.py:703]         Solution: Close applications not in use.

This error clearly indicates a memory allocation failure. The stack trace points to the SelfAttentionOperation, suggesting that the attention mechanism within the model is a key area of memory consumption.

Why Does This Happen?

So, why is W8A8 using more memory than expected? There are several potential reasons:

  1. Intermediate Tensors: Quantization reduces the size of the model's weights, but the intermediate tensors (the data generated during computations) might still be in higher precision formats (like bfloat16 or float32). These larger intermediate tensors can eat up memory, negating the memory savings from weight quantization.
  2. Inefficient Kernel Implementations: The Ascend NPU might not have highly optimized kernels for all operations in W8A8. This can lead to the system falling back to more memory-intensive operations.
  3. Attention Mechanism: The self-attention mechanism, especially in large models, is notoriously memory-intensive. The operations involved, such as calculating attention scores and applying them to the value vectors, require significant memory.
  4. Parallelism Overhead: While tensor and data parallelism help distribute the workload, they also introduce communication overhead and might require duplicating some data across devices, increasing memory usage.

Investigating the Root Cause

To really nail down why this is happening, we need to do some digging. Here’s a breakdown of the steps you can take:

  1. Profiling: Use profiling tools to monitor memory usage during inference. This will help you identify which operations are the biggest memory hogs. Ascend provides tools like the CANN Profiler that can give you detailed insights into memory allocation and operator execution.
  2. Operator-Level Analysis: Focus on the SelfAttentionOperation since the error message points to it. Check the memory usage of each step within this operation. Are the attention scores or the intermediate tensors consuming a lot of memory?
  3. Kernel Optimization: Investigate whether the kernels used for the attention operations are optimized for W8A8 on Ascend. If not, there might be opportunities to improve performance by using more efficient kernels or custom implementations.
  4. Configuration Tweaks: Experiment with different configurations. For instance, try reducing the max-num-batched-tokens or max-model-len to see if it alleviates the memory pressure. You might also explore different parallelism strategies or adjust the gpu-memory-utilization setting.
  5. Reproducing with Simpler Cases: Try to reproduce the issue with smaller models or shorter sequences. This can help isolate the problem and make debugging easier. If a smaller model runs without issues, the problem might be specific to the scale of the Qwen3-235B model.

Potential Solutions and Workarounds

Okay, so we know the problem and have some ideas about why it’s happening. Now, let’s talk solutions.

  1. Optimize Attention Mechanism: Given that the SelfAttentionOperation is a likely culprit, optimizing it can yield significant memory savings. Techniques like attention slicing, where the attention computation is broken down into smaller chunks, can reduce memory usage.
  2. Kernel Optimization: Work with Ascend’s documentation and community to identify and use the most efficient kernels for W8A8 operations. If necessary, consider writing custom kernels optimized for your specific hardware and model.
  3. Memory-Efficient Data Structures: Explore using memory-efficient data structures for intermediate tensors. For example, using sparse tensors can reduce memory usage if the tensors have many zero values.
  4. Reduce Batch Size: Lowering the max-num-batched-tokens can reduce the memory footprint, but it might also decrease throughput. It’s a trade-off you need to consider.
  5. Gradient Checkpointing: If you're fine-tuning the model, gradient checkpointing can reduce memory usage by recomputing activations during the backward pass instead of storing them. However, this increases computation time.
  6. Model Parallelism: Ensure that your model parallelism strategy is optimal. Sometimes, rebalancing the workload across devices can reduce memory pressure on individual NPUs.
  7. Hardware Upgrades: If all else fails, consider upgrading your hardware. More NPUs or NPUs with more memory can provide the headroom needed to run large models with W8A8 quantization.

Practical Steps to Take

Let's translate these solutions into actionable steps:

  1. Start Profiling: Use the CANN Profiler to get a detailed breakdown of memory usage during inference. Identify the operations consuming the most memory.
  2. Check Kernel Implementations: Consult Ascend’s documentation and community forums to understand the best practices for kernel selection and optimization. Look for any known issues or optimizations related to W8A8.
  3. Experiment with Batch Sizes: Try reducing max-num-batched-tokens to see if it resolves the OOM error. Monitor the impact on throughput.
  4. Implement Attention Slicing: If the attention mechanism is the bottleneck, implement attention slicing to reduce memory usage. This might involve modifying the vLLM code or using a custom attention implementation.
  5. Monitor Hardware Usage: Use npu-smi to monitor NPU memory usage in real-time. This can help you understand how close you are to the memory limits and identify potential bottlenecks.

Community and Support

Don't forget, you're not alone in this! Engaging with the vLLM and Ascend communities can provide valuable insights and support. Here’s how to get involved:

  • GitHub Issues: If you encounter a bug or have a specific question, open an issue on the vLLM or Ascend GitHub repository. Be sure to include detailed information about your setup, configuration, and the error messages you’re seeing.
  • Forums and Discussion Boards: Participate in community forums and discussion boards. Share your experiences, ask questions, and help others troubleshoot their issues.
  • Documentation: Refer to the official vLLM and Ascend documentation. They often contain valuable information about best practices, troubleshooting, and optimization techniques.

Final Thoughts

The issue of W8A8 inference using more NPU memory than bfloat16 is a tricky one, but with a systematic approach, you can diagnose and address it. By understanding the underlying causes, profiling your code, and experimenting with different solutions, you can optimize your model deployment and achieve the performance you need. This article has aimed to arm you with the knowledge and steps necessary to tackle this challenge. Remember, the key is to dive deep, analyze the bottlenecks, and leverage the community for support. Happy optimizing, guys!