MLX `prefill_step_size`: Understanding Unexpected Output Shifts

Hey everyone! So, you're diving deep into the world of MLX, getting your models running efficiently, and you stumble upon something pretty peculiar: changing your prefill_step_size suddenly changes your model's output. Wait, what?! That's a real head-scratcher, right? Ideally, a parameter designed for performance optimization shouldn't mess with the deterministic output of your large language model (LLM). But as the example from the MLX community shows, sometimes reality throws us a curveball. Let's dig into why this might be happening, even if it's not the expected behavior, and what we can do about it. This isn't just a minor tweak; it's about understanding the deep mechanics of how our models process information and how subtle numerical differences can have big impacts.

Demystifying prefill_step_size: What It Is and Why Consistency Matters

Let's start by breaking down what prefill_step_size actually refers to in the context of MLX-LM. When you feed a long prompt to an LLM, the model doesn't immediately start generating new tokens. Instead, it first needs to ingest or process the entire input prompt. This initial processing phase is called prefill. During prefill, the model computes the Key and Value (KV) states for each token in your prompt, which are then stored in a KV cache. This cache is absolutely crucial because it allows the model to efficiently generate subsequent tokens without re-processing the entire prompt every single time. Think of it like a memory bank for the prompt's context.

The prefill_step_size parameter comes into play here by dictating how this initial prompt processing is chunked. If you have a prompt that's, say, 5000 tokens long, and your prefill_step_size is set to 2048, the MLX framework will process that 5000-token prompt in multiple chunks (e.g., first 2048 tokens, then the next 2048, and finally the remaining 904 tokens). The primary goal of this chunking is efficiency and memory management. For incredibly long prompts, processing everything in one massive go might hit GPU memory limits or be less optimal for parallelization on certain hardware architectures, especially on unified memory systems like Apple Silicon. By breaking it into smaller, manageable steps, the system can better manage resources and potentially improve throughput.

Now, here's the kicker and why this issue is so perplexing: LLMs, at their core, are designed to be deterministic. Given the exact same input prompt, the exact same model weights, and the exact same inference parameters (like a sampling temperature of 0 for greedy decoding), a well-implemented inference engine should produce the exact same sequence of tokens every single time. This determinism is foundational for everything we do with LLMs – from debugging and testing to ensuring consistent user experiences in production applications. Whether you sum a list of numbers as (1+2)+3 or 1+(2+3), the result should be the same. Similarly, whether you process a prompt in one big gulp or several smaller sips, the final KV cache state after prefill should be identical. This identical state should, in turn, lead to identical subsequent token generation. The fact that prefill_step_size seems to challenge this core assumption points to a deeper, more subtle issue that warrants our attention and investigation. It's not just a minor numerical difference; it suggests the internal state after prefill diverges significantly enough to alter the very first token generated by the model's chat template, which is a big deal for consistent LLM behavior.

The Curious Case of Changing Outputs: A Deeper Look

Alright, folks, let's get into the nitty-gritty of the user's observation. The provided script and logs clearly demonstrate the problem: the exact same prompt (a whopping 1300 repetitions of "Write a story about Einstein", leading to 6567 tokens) fed into the exact same openai/gpt-oss-20b model with only one parameter changed – the prefill_step_size – resulted in notably different outputs. When prefill_step_size was 2048, the model's initial internal analysis message was one thing. But when it was set to 2048 * 4 (which is 8192), that initial analysis message shifted. This isn't just a slight variation in numerical precision; it implies a semantic difference right at the very beginning of the model's thought process, which then cascades into completely different generated text.

Think about it: the prefill_step_size primarily affects how the initial prompt is processed to build the KV cache. It shouldn't, in a perfectly deterministic system, influence the content of the generated tokens themselves, especially the very first ones derived from the prefill state. Even if we weren't explicitly setting temperature=0 (which is often the default for generate without specifying a temperature), the divergence occurring during the prefill phase itself is the critical point here. Sampling randomness, which typically happens after prefill when new tokens are predicted, isn't the primary culprit. The issue surfaces even before any true