GPT-OSS Fails: Debugging `num_stages` Assertion Error In Triton Kernels

by Marco 72 views

Hey everyone! We've got an interesting issue to dive into today regarding the GPT-OSS model and a pesky assertion error that pops up in Triton kernels. Specifically, the error occurs when the length of the initial prompt exceeds 519 tokens, triggering an AssertionError: assert num_stages >= 1 within the opt_flags.py file. Let's break down the bug, explore a temporary fix, and see how to reproduce the issue. This deep dive will help you understand the error, its potential impact, and how you can get around it for now. So, let's get started!

The Bug: AssertionError in Triton Kernels

This bug manifests as an AssertionError within the Triton kernels, specifically in the opt_flags.py file. The assertion assert num_stages >= 1 fails when the variable num_stages is found to be 0. This issue seems to be directly correlated with the length of the input prompt provided to the GPT-OSS model. If the input prompt contains 520 tokens or more, the assertion error is triggered, leading to a program crash. However, if the prompt length is 519 tokens or less, the model runs successfully without any issues. This behavior suggests that there is a threshold in the input length that causes a specific condition within the Triton kernels to fail.

Digging Deeper into the Code

The problematic code snippet resides within the triton_kernels/matmul_ogs_details/opt_flags.py file. Here's the relevant chunk:

 num_stages = -1
 for ep in subtiles_to_check:
 ns = opt_flags_nvidia.compute_num_stages(*compute_num_stages_args, ep, epilogue_effective_itemsize)
 if ns > num_stages:
 epilogue_subtile, num_stages = ep, ns
 assert num_stages >= 1
 if constraints.get("num_stages", None):
 num_stages = constraints["num_stages"]

In this code block, the variable num_stages is initialized to -1 and is expected to be updated within the loop based on the computation of opt_flags_nvidia.compute_num_stages. The assertion assert num_stages >= 1 is intended to ensure that num_stages is a positive integer. However, in cases where the input prompt exceeds the 519-token threshold, num_stages remains at 0, causing the assertion to fail. The subsequent conditional block attempts to override num_stages based on constraints, but this is not reached because the assertion fails first.

The variables ep, subtiles_to_check, and epilogue_effective_itemsize play a role in the loop's logic. The loop iterates through subtiles_to_check, calculating num_stages for each ep. The computed num_stages is compared with the current maximum, and if it's greater, the epilogue_subtile and num_stages are updated. Understanding the exact computations within opt_flags_nvidia.compute_num_stages would provide more insights into why num_stages ends up being 0 for longer input prompts.

Potential Causes and Implications

One potential cause could be an interaction between the input prompt length and the memory allocation or computational requirements within the Triton kernel. When the input prompt is longer, it might trigger a different execution path or resource allocation strategy that leads to an invalid num_stages value. This could be related to memory limitations, thread management, or other low-level optimization parameters within the kernel.

The implications of this bug are significant for anyone using GPT-OSS with longer prompts. It effectively limits the input context length, which can negatively impact the model's ability to understand and generate coherent responses. In applications where longer contexts are necessary, this issue becomes a major roadblock.

Temporary Fix: Forcing num_stages to 1

If you're running into this issue and need a quick workaround, there's a temporary fix you can implement. This involves manually modifying the opt_flags.py file within the Triton kernels cache. Here's how you can do it:

  1. Locate the opt_flags.py file:
    • The file is typically located in your .cache directory under the huggingface/hub path. The exact path will depend on your environment and how you've set up your Hugging Face cache. A common location is ~/.cache/huggingface/hub/models--kernels-community--triton_kernels/snapshots/<commit_hash>/build/torch-universal/triton_kernels/matmul_ogs_details/opt_flags.py.
    • The <commit_hash> is a unique identifier for the specific version of the Triton kernels you're using. You might need to explore the directories within the snapshots directory to find the correct one.
  2. Edit the opt_flags.py file:
    • Open the opt_flags.py file in a text editor.
    • Navigate to line ~220 (the exact line number might vary slightly depending on the version).
    • Find the line assert num_stages >= 1.
    • Replace this line with num_stages, epilogue_subtile = 1, 1.
  3. Save the file:
    • Make sure to save the changes you've made to the opt_flags.py file.

By replacing the assertion with this assignment, you're effectively forcing num_stages and epilogue_subtile to be 1, bypassing the failing assertion. While this fix allows the code to run, it's essential to understand that it might impact the performance or accuracy of the model. This is a temporary solution to unblock your development, and a proper fix should be implemented in the Triton kernels.

Why This Fix Works (and Its Limitations)

This fix works because it bypasses the assertion that's causing the program to crash. By forcing num_stages to be 1, the condition that triggered the assertion is no longer met, and the code can continue execution. However, it's crucial to understand that this is a hack, not a proper solution.

The variables num_stages and epilogue_subtile are likely related to optimization parameters within the Triton kernel. They might influence how the matrix multiplication is performed, affecting both performance and potentially numerical accuracy. By forcing these values to 1, you're overriding the intended optimization logic, which could lead to suboptimal performance or even slightly different results.

Therefore, while this fix allows you to use longer prompts with GPT-OSS, it's crucial to test your application thoroughly to ensure that the results are still accurate and that the performance is acceptable. This fix should be considered a temporary measure until a proper solution is available.

How to Reproduce the Bug: A Step-by-Step Guide

To help the developers address this issue, it's crucial to provide a clear and reproducible test case. Here's a step-by-step guide on how you can reproduce the bug:

1. Set up the Environment

First, you'll need to create a Conda environment with the necessary dependencies. This ensures that you have a consistent environment where the bug can be reliably reproduced.

Create the environment.llm.yaml file:

Create a file named environment.llm.yaml with the following content:

name: asr_llm
channels:
 - pytorch
 - nvidia
 - conda-forge
 - defaults
dependencies:
 - python==3.12
 - pip
 - pytorch-gpu
 - pytorch-cuda
 - pip:
 --extra-index-url https://download.pytorch.org/whl/cu128
 - torch
 - transformers==4.55.2
 - accelerate==1.10.0
 - triton>=3.4.0
 - kernels==0.9.0
 - python-multipart==0.0.20
 - bitsandbytes==0.46.1
 - git+https://github.com/triton-lang/triton.git@main#subdirectory=python/triton_kernels
 - transformers==4.55.2

This YAML file specifies the Conda environment's name, channels, and dependencies. It includes Python 3.12, PyTorch with GPU support, Transformers, Accelerate, Triton, and other necessary packages. The --extra-index-url is used to ensure that PyTorch is installed with CUDA 12.8 support.

Create the Conda environment:

Open your terminal and run the following commands:

conda env create --name asr_llm --file=environment.llm.yaml
conda activate asr_llm

The first command creates the Conda environment named asr_llm based on the environment.llm.yaml file. The second command activates the newly created environment.

Warning: This step will require downloading and installing approximately 30GB of data, so ensure you have sufficient network bandwidth and disk space.

2. Create the main.py file

Next, you'll need to create a Python script that uses the Transformers library and the GPT-OSS model to reproduce the bug. Create a file named main.py with the following content:

text = "Hello " * 4000
MAX_TOKENS = 520


import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "openai/gpt-oss-20b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
 model_id,
 device_map="auto",
 torch_dtype="auto",
)

inputs = tokenizer(text, return_tensors="pt").to("cuda")
print("Original shape:", inputs["input_ids"].shape)
inputs["input_ids"] = inputs["input_ids"][:, :MAX_TOKENS]

model.generate(**inputs, max_new_tokens=10)

This script initializes a long text string, sets MAX_TOKENS to 520 (the threshold for triggering the bug), loads the GPT-OSS model and tokenizer, and then generates text using the model. The key part is the line inputs["input_ids"] = inputs["input_ids"][:, :MAX_TOKENS], which truncates the input sequence to the specified MAX_TOKENS length. If MAX_TOKENS is set to 520 or greater, the bug will be triggered.

3. Run the Script

Now, run the Python script from your terminal using the following command:

python main.py

If MAX_TOKENS is set to 520 or larger, you should encounter the AssertionError. If MAX_TOKENS is set to a value less than 520, the script should run without errors.

Expected Outcome

When MAX_TOKENS is 520 or greater, the script will fail with the following traceback:

Traceback (most recent call last):
 File "/root/projects/CallCenterAutomation/llm/tmp2.py", line 20, in <module>
 model.generate(**inputs, max_new_tokens=10)
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
 return func(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/transformers/generation/utils.py", line 2617, in generate
 result = self._sample(
 ^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/transformers/generation/utils.py", line 3598, in _sample
 outputs = self(**model_inputs, return_dict=True)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
 return self._call_impl(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
 return forward_call(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/transformers/utils/generic.py", line 959, in wrapper
 output = func(self, *args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/transformers/models/gpt_oss/modeling_gpt_oss.py", line 658, in forward
 outputs: MoeModelOutputWithPast = self.model(
 ^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
 return self._call_impl(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
 return forward_call(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/transformers/utils/generic.py", line 1083, in wrapper
 outputs = func(self, *args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/transformers/models/gpt_oss/modeling_gpt_oss.py", line 491, in forward
 hidden_states = decoder_layer(
 ^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/transformers/modeling_layers.py", line 93, in __call__
 return super().__call__(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
 return self._call_impl(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
 return forward_call(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/transformers/models/gpt_oss/modeling_gpt_oss.py", line 370, in forward
 hidden_states, _ = self.mlp(hidden_states) # diff with llama: router scores
 ^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
 return self._call_impl(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
 return forward_call(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/transformers/integrations/mxfp4.py", line 300, in mlp_forward
 routed_out = self.experts(hidden_states, routing_data, gather_idx, scatter_idx)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
 return self._call_impl(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
 return forward_call(*args, **kwargs)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/miniconda3/envs/asr_llm/lib/python3.12/site-packages/transformers/integrations/mxfp4.py", line 190, in forward
 intermediate_cache1 = matmul_ogs(
 ^^^^^^^^^^^^^
 File "/root/.cache/huggingface/hub/models--kernels-community--triton_kernels/snapshots/22b535b359d6c144e0152060dc6fec78da07039e/build/torch-universal/triton_kernels/matmul_ogs.py", line 444, in matmul_ogs
 opt_flags = make_opt_flags(out_dtype, x.dtype, w.dtype, precision_config,
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/.cache/huggingface/hub/models--kernels-community--triton_kernels/snapshots/22b535b359d6c144e0152060dc6fec78da07039e/build/torch-universal/triton_kernels/matmul_ogs_details/opt_flags.py", line 299, in make_opt_flags
 return make_default_opt_flags_nvidia(*args)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/root/.cache/huggingface/hub/models--kernels-community--triton_kernels/snapshots/22b535b359d6c144e0152060dc6fec78da07039e/build/torch-universal/triton_kernels/matmul_ogs_details/opt_flags.py", line 216, in make_default_opt_flags_nvidia
 assert num_stages >= 1
AssertionError

This traceback clearly shows the AssertionError occurring in the opt_flags.py file, confirming that the bug has been successfully reproduced.

Stack Trace Analysis

Let's break down the stack trace to understand the sequence of calls that lead to the AssertionError:

  1. model.generate(**inputs, max_new_tokens=10): The generation process is initiated using the generate method of the GPT-OSS model.
  2. self._sample(...): The _sample method is called within the generate method to handle the sampling process.
  3. outputs = self(**model_inputs, return_dict=True): The model is called with the input tensors to produce the output.
  4. outputs: MoeModelOutputWithPast = self.model(...): The core model's forward pass is executed.
  5. hidden_states = decoder_layer(...): Each decoder layer is processed.
  6. hidden_states, _ = self.mlp(hidden_states): The Multi-Layer Perceptron (MLP) within the decoder layer is executed.
  7. routed_out = self.experts(hidden_states, ...): The experts within the MLP are processed.
  8. intermediate_cache1 = matmul_ogs(...): The matmul_ogs function, which performs optimized matrix multiplication, is called.
  9. opt_flags = make_opt_flags(...): The make_opt_flags function is called to determine optimization flags for the matrix multiplication.
  10. return make_default_opt_flags_nvidia(*args): The default optimization flags are created for NVIDIA GPUs.
  11. assert num_stages >= 1: The assertion fails because num_stages is 0.

This stack trace reveals that the error occurs during the optimization flag calculation for matrix multiplication within the Triton kernels. The make_default_opt_flags_nvidia function is responsible for determining the optimal number of stages for the matrix multiplication, and in this case, it's failing to produce a valid value.

Environment Details

Here are the environment details where the bug was observed:

  • Triton: 3.4.0
  • GPU: NVIDIA GeForce RTX 5090
  • CUDA: 12.8
  • OS: Ubuntu 24.04.2 LTS

These details are important for developers to understand the context in which the bug occurs. The combination of Triton version, GPU model, CUDA version, and operating system can influence the behavior of the code.

Conclusion

The AssertionError: assert num_stages >= 1 in Triton kernels when using GPT-OSS with prompts longer than 519 tokens is a significant issue that can hinder the use of the model in applications requiring longer contexts. The temporary fix of forcing num_stages to 1 allows the code to run but might impact performance and accuracy.

By providing a clear reproduction guide and a detailed stack trace analysis, this article aims to help developers understand and address the bug effectively. It's crucial to monitor the Triton kernels repository for updates and proper fixes to this issue. In the meantime, the temporary fix can be used with caution, ensuring thorough testing to validate the results.

Stay tuned for more updates, and let's hope for a permanent solution soon! If you guys have any other workarounds or insights, feel free to share them in the comments below. Let's keep the conversation going and help the community find the best solutions!