Error: Non-zero Status Code Returned While Running Reshape Node: A Shape Tensor Must Be A Vector Tensor.

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Understanding the Reshape Node Error in ONNX Runtime

When working with ONNX Runtime, encountering errors can be a common challenge, especially when dealing with complex models. One such error is the "Non-zero status code returned while running Reshape node: A shape tensor must be a vector tensor." This error typically arises during the execution of a Reshape operation within the ONNX graph, indicating an issue with the shape tensor provided to the Reshape node. To effectively address this error, it's crucial to understand the underlying causes and how to troubleshoot them.

Root Cause Analysis of Reshape Node Errors

The Reshape node in ONNX is responsible for altering the dimensions of a tensor, making it a fundamental operation in many neural networks. The node requires two inputs: the data tensor to be reshaped and a shape tensor that specifies the new dimensions. The shape tensor must be a one-dimensional vector, where each element represents the size of the corresponding dimension in the output tensor. When the shape tensor does not conform to this requirement, ONNX Runtime throws the aforementioned error.

In the provided bug report, the error messages from both the CPU and WebGPU execution providers clearly indicate the problem: the shape tensor is not a vector tensor. The CPU EP message, "shapeTensor->Shape().NumDimensions() == 1 was false. A shape tensor must be a vector tensor," and the WebGPU EP message, "A shape tensor must be a vector tensor, got 2 dimensions," both point to the same issue. This means that the shape tensor being fed into the Reshape node has more than one dimension, which is not allowed.

To illustrate, consider a scenario where you want to reshape a tensor of shape (1, 24) into a tensor of shape (2, 12). The correct shape tensor should be a 1D tensor like [2, 12]. If, instead, the shape tensor is something like [[2, 12]], which is a 2D tensor, the Reshape node will fail and raise the error.

Steps to Reproduce the Error

To reproduce the error, the following steps were performed, highlighting a common workflow for encountering such issues:

  1. Model Acquisition: The process begins by downloading the model files. In this case, the model in question is the Qwen3-4B model, which was converted to the ONNX format. The specific files were obtained from the Hugging Face model repository, a common source for pre-trained models and related resources. The link provided, https://huggingface.co/onnx-community/Qwen3-4B-ONNX/tree/refs%2Fpr%2F1/onnx, directs to a specific branch containing the ONNX version of the Qwen3-4B model.
  2. Input Preparation: To execute the model, it is essential to provide valid inputs that conform to the model's expected input schema. The inputs include input_ids, attention_mask, position_ids, and a series of past_key_values tensors. Each input has a specific data type, dimensions, and data location (CPU in this case). For instance, input_ids is of type int64 with dimensions [1, 24], while the past_key_values tensors are of type float16 with dimensions [1, 8, 0, 128]. These inputs are crucial for the model to perform its computations correctly.
  3. Single Forward Pass: The core step in reproducing the error involves performing a single forward pass through the model using the prepared inputs. This is where the ONNX Runtime engine processes the input tensors and executes the operations defined in the ONNX graph. During this process, if there is a mismatch in the shape or dimensions of the tensors, such as the Reshape node receiving an invalid shape tensor, the error will be triggered.

The provided input data includes tensors like input_ids, attention_mask, and position_ids, all of which are 1D tensors of type int64 with a dimension of [1, 24]. Additionally, there are numerous past_key_values tensors, which are of type float16 and have dimensions of [1, 8, 0, 128]. These past_key_values tensors are likely used for handling the model's memory or state across multiple forward passes, which is a common technique in sequence-to-sequence models.

By following these steps, the error "Non-zero status code returned while running Reshape node: A shape tensor must be a vector tensor" can be reliably reproduced, allowing for targeted debugging and resolution efforts.

Analyzing the Input Data

To understand why the error occurs, let's examine the provided input data more closely. The input data includes several tensors, such as input_ids, attention_mask, position_ids, and a series of past_key_values. The error message specifically mentions the Reshape node named /model/layers.0/attn/k_norm/Reshape_1. This suggests that the issue lies within the first layer's attention mechanism, specifically in the key normalization step.

The past_key_values tensors are particularly relevant here. These tensors are used for caching the keys and values from previous attention computations, which is a common optimization technique in transformer models. The dimensions of these tensors are [1, 8, 0, 128], where:

  • 1 represents the batch size.
  • 8 represents the number of attention heads.
  • 0 represents the sequence length (initially 0 for the first forward pass).
  • 128 represents the embedding dimension.

The Reshape operation in this context likely aims to prepare these cached key and value tensors for the attention computation. If the shape tensor provided to the Reshape node is incorrect (i.e., not a 1D vector), the error will occur.

Resolving the Reshape Node Error

To resolve the "Non-zero status code returned while running Reshape node: A shape tensor must be a vector tensor" error, several strategies can be employed. The primary focus should be on ensuring that the shape tensor passed to the Reshape node is a 1D vector with the correct dimensions. Here are some key steps to consider:

  1. Inspect the ONNX Graph: The first step is to examine the ONNX graph to identify the Reshape node in question and its inputs. Tools like Netron can be invaluable for visualizing the graph structure and inspecting the properties of each node. By visualizing the graph, you can trace the origin of the shape tensor and understand how it is computed.
  2. Validate the Shape Tensor: Once the Reshape node is identified, the next step is to validate the shape tensor that is being fed into it. This involves checking the tensor's dimensions and ensuring it is a 1D vector. If the shape tensor is computed dynamically, it is crucial to inspect the computation logic and identify any potential issues.
  3. Correct the Shape Tensor: If the shape tensor is found to be incorrect, it needs to be corrected. This might involve modifying the model itself (if you have control over the model definition) or preprocessing the inputs to ensure the shape tensor is in the correct format. For example, if the shape tensor is a 2D tensor like [[2, 12]], it should be reshaped into a 1D tensor like [2, 12].
  4. Debugging Techniques: Debugging ONNX models can be challenging, but several techniques can help:
    • Print Intermediate Tensors: Inserting print statements to display the values and shapes of intermediate tensors can help pinpoint where the shape tensor is being computed incorrectly.
    • Simplify the Model: If the model is complex, try simplifying it by removing parts of the graph to isolate the issue. This can help narrow down the source of the error.
    • Use ONNX Runtime's Debug Mode: ONNX Runtime provides a debug mode that can provide more detailed information about the execution of the graph. This can be helpful for identifying issues with specific nodes.
  5. Check Input Data: Ensure that the input data types and dimensions match the model's expectations. Mismatched input data can sometimes lead to unexpected behavior in Reshape nodes.

Specific Solution for the Qwen3-4B Model

In the context of the Qwen3-4B model, the error occurs in the attention mechanism's key normalization step. This suggests that the shape tensor used to reshape the key tensor might be the source of the problem. Given the dimensions of the past_key_values tensors (1, 8, 0, 128), the Reshape operation likely aims to combine or rearrange these dimensions for the attention computation.

To address this specific issue, the following steps can be taken:

  1. Inspect the Reshape Node: Use Netron or a similar tool to inspect the /model/layers.0/attn/k_norm/Reshape_1 node in the ONNX graph. Identify the inputs to the node, particularly the shape tensor.
  2. Trace the Shape Tensor: Trace the origin of the shape tensor. Determine how it is computed and what inputs it depends on. This might involve examining other nodes in the graph that feed into the Reshape node.
  3. Validate the Shape Computation: Check the logic that computes the shape tensor. Ensure that it produces a 1D vector with the correct dimensions. The dimensions should be compatible with the expected output shape of the Reshape operation.
  4. Correct the Computation: If the shape tensor computation is incorrect, modify it to produce the correct 1D vector. This might involve adjusting the operations used to compute the shape or providing different inputs.

By systematically analyzing the ONNX graph and the shape tensor computation, the root cause of the error can be identified and corrected. This will ensure that the Qwen3-4B model runs as expected in ONNX Runtime.

Expected Behavior and Outcome

The expected behavior is that the model should run without any errors. After applying the necessary corrections to the shape tensor, the Reshape node should execute successfully, and the model should produce the desired output. This involves ensuring that the shape tensor is a 1D vector with the correct dimensions, allowing the Reshape operation to transform the tensor into the intended shape.

Conclusion

The "Non-zero status code returned while running Reshape node: A shape tensor must be a vector tensor" error in ONNX Runtime can be a common hurdle when working with complex models. However, by understanding the root cause of the error and following a systematic approach to debugging, it can be effectively resolved. Inspecting the ONNX graph, validating the shape tensor, and correcting the shape tensor computation are crucial steps in addressing this issue. For the Qwen3-4B model, focusing on the attention mechanism's key normalization step and ensuring the shape tensor is correctly computed will lead to a successful resolution. By addressing this error, the model should run as expected, producing the desired outcomes without any interruptions.

By carefully analyzing the model's structure and the data flow, developers and researchers can ensure that their ONNX models run smoothly and efficiently, unlocking the full potential of ONNX Runtime for various applications.