RWKV-7 World 2.9B: Wkv_state Always Zero

Have you ever been deep into a project, tweaking a large language model like RWKV-7 World 2.9B, only to hit a snag that makes you scratch your head? That's exactly what happened to a user who reported a peculiar issue: the wkv_state variable consistently showing as 0, even when it seemed like it should be updated. This isn't just a minor glitch; it can significantly impact how the model processes and remembers information across sequences. Let's dive into this problem, explore its potential causes, and discuss how we might tackle it.

Understanding the WKV State in RWKV Models

Before we can unravel the mystery of the zero wkv_state, it's crucial to understand what this state actually represents. In the context of Recurrent Neural Networks (RNNs) and models like RWKV (which stands for Receptance Weighted Key Value), the 'state' is essentially the model's memory. It's a collection of information that the model carries over from one step of processing to the next. Think of it like your own short-term memory; as you read this sentence, you're holding onto the words you just read to understand the current one. The wkv_state in RWKV models serves a similar purpose, capturing the essence of the sequence processed so far. This memory is vital for tasks that require understanding context, such as generating coherent text, answering questions based on a passage, or translating languages. Without a properly functioning wkv_state, the model would essentially be starting from scratch with every new token it processes, leading to a severe loss of context and a significant degradation in performance. The wkv_state is particularly important in RWKV architecture, which aims to achieve the parallelizability of Transformers while retaining the efficiency of RNNs. This state is updated iteratively, and each update should ideally incorporate the information from the new input token and the previous state. If this state is not being correctly saved, loaded, or updated, the model's ability to maintain context will be compromised, leading to nonsensical or repetitive outputs.

The problem reported involves the rwkv7-world-2.9B model, a specific variant of the RWKV architecture. The user observed that after implementing callbacks to monitor wkv_state in the src/models/rwkv7-base.cpp file, the state variable, despite appearing to be updated during a call to ggml_rwkv_wkv7 and seemingly saved to mctx_cur->get_s_l(il), was consistently retrieved as empty (zero) when passed back into ggml_rwkv_wkv7. This suggests a potential disconnect or error in the state management pipeline. The user even experimented with different prompt lengths, from a very short one to a prompt of about 100 tokens, but the wkv_state remained zero. This persistence of the issue across different input lengths hints that the problem might not be directly related to prompt length itself, but rather to a fundamental aspect of how the state is being handled within the model's execution flow.

Deconstructing the Debugging Process

The user's debugging efforts provide valuable clues. By inserting callbacks and observing the wkv_state at different points, they pinpointed that the state is being updated within the ggml_rwkv_wkv7 function call. This is a critical piece of information because it suggests that the core computation for updating the state is likely functioning correctly. The problem seems to lie in the subsequent steps: how this updated state is stored and then retrieved for the next processing step. The observation that the state is seemingly saved to mctx_cur->get_s_l(il) but then appears empty when fetched back implies a potential issue with either the storage mechanism, the retrieval mechanism, or perhaps a misunderstanding of how these components interact. The mctx_cur likely refers to the current context or memory for the model, and get_s_l(il) might be a function to access a specific layer's state. If the data written to this location is not what's expected, or if the read operation is faulty, it would explain why the wkv_state appears as zero.

The provided log output further reinforces this. The ggml_debug messages show wkv_state_in-0 and wkv_state_in-1 both originating from cache_s_l0 and cache_s_l1 respectively. Crucially, the values displayed are all zeros, with a sum of 0.000000. This indicates that when the ggml_rwkv_wkv7 function is called (or at least when these debug logs are generated), the input state it receives is indeed zero. The reshaping and view operations mentioned in the logs (reshaped{163840, 1, 1, 1}, (view){1, 1, 1, 1}) suggest complex memory management within GGML, the underlying tensor library. If there's an error in how these tensors are being managed, allocated, or accessed, it could lead to stale or zeroed data being presented as the state. The fact that the user is using the llama-eval-callback tool with the rwkv7-world-2.9B model, and specifically disabling CUDA (CUDA_VISIBLE_DEVICES="") while using the CPU backend, might also be relevant. While the user's hardware includes a powerful NVIDIA GPU, forcing CPU execution could expose different bugs or behaviors compared to GPU execution.

Potential Causes and Areas for Investigation

Given the symptoms, several areas warrant closer inspection. First, the mechanism for saving and loading the wkv_state between processing steps needs thorough verification. This could involve debugging the mctx_cur->get_s_l(il) calls to ensure that the data being written is actually persisting correctly and that the subsequent read operation retrieves the intended values. It's possible there's a subtle bug in the memory management of the ggml context, especially concerning how these recurrent states are handled.

Second, the interaction between the ggml_rwkv_wkv7 function and the GGML context (mctx_cur) is a prime suspect. The log output shows that the function is receiving zeroed states. This could mean that either the state is being reset unintentionally before being passed to the function, or the mechanism responsible for providing the state to the function is malfunctioning. We need to trace the data flow from where the state is updated to where it's used in the next iteration.

Third, the specific implementation details of the RWKV-7 model within the GGML framework might be introducing issues. While the core RWKV logic might be sound, its integration with GGML's tensor operations and memory management could be where the problem lies. Different model architectures and specific operations within them can interact with GGML's backend in unique ways, sometimes exposing edge cases or bugs.

Fourth, although the user ruled out prompt length as the primary cause, it's worth considering if extremely short or unusually structured prompts could trigger an uninitialized state path in the code. However, since the issue persisted with a 100-token prompt, this is less likely to be the sole cause. Nevertheless, examining the code paths for initializing and handling the very first state of a sequence is always a good practice.

Finally, the environment and build configuration could play a role. The user is running on Linux with a specific version of ggml-org/llama.cpp (version 7232, commit ae9771d1) and is explicitly disabling CUDA. While the problem might persist on GPU, debugging on the CPU first can sometimes be simpler. It's worth checking if this issue is reproducible with different versions of llama.cpp or ggml, or if specific build flags might be involved. The