Debugging NaN Values In TensorRT FP16 Inference A Comprehensive Guide

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When deploying deep learning models for inference, achieving optimal performance often involves leveraging techniques like mixed-precision training and hardware acceleration. TensorRT, NVIDIA's high-performance inference optimizer and runtime, is a popular choice for accelerating deep learning inference on NVIDIA GPUs. However, converting models trained with Automatic Mixed Precision (AMP) to FP16 precision for TensorRT inference can sometimes lead to unexpected issues, such as the generation of NaN (Not a Number) values. This article delves into the common causes of NaN values in TensorRT FP16 inference and provides a comprehensive guide to debugging and resolving these issues, focusing on a specific case involving ONNX models, TensorRT versions, and Polygraphy error messages.

Understanding the Problem

The user encountered a problem when converting an AMP-trained model to FP16 for TensorRT inference. The model, converted to ONNX format using onnxruntime-gpu==1.19.2, produced NaN values during inference with both TensorRT v9.2 and v10.0.1.6. Polygraphy, a tool for debugging deep learning models, reported errors related to type mismatches and missing implementations for specific operations within the ONNX graph. Specifically, the error messages indicated issues with the ONNXTRT_Broadcast_106_output tensor and the ONNXTRT_unsqueezeTensor_12483 node.

Key Concepts

Before diving into the debugging process, let's clarify some key concepts:

  • Automatic Mixed Precision (AMP): A training technique that uses both FP16 (half-precision) and FP32 (single-precision) data types to accelerate training and reduce memory consumption. AMP can lead to numerical instability if not handled carefully.
  • FP16 (Half-Precision): A 16-bit floating-point format that offers reduced memory usage and faster computation compared to FP32. However, FP16 has a smaller dynamic range, which can lead to underflow or overflow issues.
  • TensorRT: NVIDIA's SDK for high-performance deep learning inference. It optimizes and deploys trained models for inference on NVIDIA GPUs.
  • ONNX (Open Neural Network Exchange): An open standard for representing machine learning models, enabling interoperability between different frameworks.
  • Polygraphy: A toolkit for debugging and optimizing deep learning models, particularly those deployed with TensorRT. It allows for comparing model outputs across different runtimes and identifying potential issues.
  • NaN (Not a Number): A special floating-point value representing an undefined or unrepresentable result, often caused by division by zero or other numerical errors.

Common Causes of NaN Values in TensorRT FP16 Inference

Several factors can contribute to the generation of NaN values during TensorRT FP16 inference. Understanding these potential causes is crucial for effective debugging:

  1. Numerical Instability: FP16's reduced dynamic range can lead to underflow or overflow issues, especially in models with large or small activations or gradients. Operations like division, exponentiation, and logarithm can be particularly susceptible to numerical instability.

  2. Type Mismatches: TensorRT requires strict type consistency within the model graph. Mismatches between expected and actual data types can lead to errors and NaN values. This includes mismatches between FP16 and FP32 tensors, or issues with integer and floating-point types.

  3. Unsupported Operations: TensorRT may not fully support all ONNX operations, or certain operations may have limited support in FP16. This can lead to errors or unexpected behavior, including NaN generation.

  4. Incorrect Quantization: If the model involves quantization (converting floating-point values to integers), errors in the quantization process can introduce inaccuracies and lead to NaN values.

  5. Bugs in Custom Layers or Plugins: If the model uses custom layers or TensorRT plugins, bugs in these components can cause numerical issues.

  6. Workspace Size: Insufficient workspace size allocated to TensorRT can sometimes lead to errors and NaN values, especially for complex models.

Debugging Steps and Solutions

Based on the user's description and the Polygraphy error messages, here's a structured approach to debugging the NaN value issue:

1. Analyze the Polygraphy Error Messages

The Polygraphy output provides valuable clues about the source of the problem. Let's examine the error messages:

  • E 9 Skipping tactic 0x00000 due to exception [shape.cpp:verify_output_type:1274] Mismatched type for tensor ONNXTRT_Broadcast_106_output fp1 vs expected type: f32: This error indicates a type mismatch for the tensor ONNXTRT_Broadcast_106_output. The tensor is inferred to be FP16 (fp1), but TensorRT expects FP32 (f32). This suggests a potential issue with how the broadcast operation is handled in FP16 or a mismatch in data types between connected layers.

  • E 10 [optimizer.cpp::computeCosts::40448] Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[(Unnamed Layer * 1183)[Cast]...ONNXTRT_unsqueezeTensor_12483]}): This error indicates that TensorRT couldn't find a suitable implementation for the ONNXTRT_unsqueezeTensor_12483 node. This might be due to the specific attributes or input shapes of the Unsqueeze operation, or a limitation in TensorRT's support for this operation in FP16.

2. Map Polygraphy Information to the ONNX Model

The next step is to map the Polygraphy error messages back to the corresponding nodes in the ONNX model. This will help pinpoint the exact location of the issue in the model graph.

  • Identify the Nodes: Use a tool like Netron (https://netron.app/) to visualize the ONNX model and locate the nodes mentioned in the error messages: ONNXTRT_Broadcast_106 and ONNXTRT_unsqueezeTensor_12483. These names are typically derived from the ONNX node names during the TensorRT conversion process.
  • Examine Node Attributes and Inputs/Outputs: Once you've located the nodes, carefully examine their attributes, input shapes, and output data types. Pay close attention to any potential type mismatches or unusual configurations.

3. Address the Type Mismatch Error

The type mismatch error for ONNXTRT_Broadcast_106_output suggests that the output of the broadcast operation is being inferred as FP16, while a subsequent layer expects FP32. Here are several approaches to address this:

  • Explicit Type Casting: Insert a Cast operator in the ONNX graph to explicitly convert the output of the broadcast operation to FP32. This can be done using the ONNX API or a tool like ONNX-GraphSurgeon (https://github.com/NVIDIA/TensorRT/tree/main/tools/onnx-graphsurgeon).
  • Investigate the Broadcast Operation: Examine the inputs and attributes of the broadcast operation to understand why it's producing an FP16 output. It's possible that one of the inputs is already in FP16, causing the output to be inferred as FP16. Ensure that the inputs to the broadcast operation have the correct data types.
  • Check TensorRT FP16 Mode: Verify that TensorRT's FP16 mode is configured correctly. Ensure that the input and output tensors of the engine are set to the appropriate data types.

4. Resolve the Unsupported Operation Error

The error related to ONNXTRT_unsqueezeTensor_12483 indicates that TensorRT couldn't find an implementation for the Unsqueeze operation. The user attempted to replace Unsqueeze with Reshape, but the error persisted. Here's a more detailed approach:

  • Verify TensorRT Support: Check the TensorRT documentation for the specific TensorRT version being used to confirm whether the Unsqueeze operation is fully supported in FP16. Some operations might have limitations or require specific attributes to be set.
  • Examine the Unsqueeze Attributes: Inspect the axes attribute of the Unsqueeze operation. This attribute specifies the dimensions to be inserted. Certain combinations of axes values might not be supported in FP16.
  • Alternative Implementations: If Unsqueeze is problematic, explore alternative ways to achieve the same functionality. While the user tried Reshape, it's important to ensure that the Reshape operation is correctly configured to produce the desired output shape. Double-check the shape tensor used in the Reshape operation.
  • Custom Plugin: As a last resort, consider implementing a custom TensorRT plugin for the Unsqueeze operation. This provides the most flexibility but requires more development effort.

5. Address Numerical Instability

Even if the type mismatch and unsupported operation errors are resolved, NaN values might still occur due to numerical instability in FP16. Here are some techniques to mitigate this:

  • Gradient Clipping: During AMP training, use gradient clipping to prevent gradients from becoming too large, which can lead to overflow in FP16.
  • Loss Scaling: AMP often employs loss scaling to prevent underflow during backpropagation. Ensure that loss scaling is properly configured and that the scale factor is appropriate.
  • Layer Normalization: Use layer normalization or batch normalization to keep activations within a reasonable range.
  • FP16 Safe Operations: Replace potentially unstable operations with FP16-safe alternatives. For example, use torch.nn.functional.logsigmoid instead of torch.log(torch.sigmoid(x)).
  • Mixed Precision Inference: If numerical instability persists, consider using a mixed-precision inference strategy, where some layers are run in FP32 while others are run in FP16. This can provide a balance between performance and accuracy.

6. Increase Workspace Size

Although the user mentioned trying to increase the workspace value, it's worth revisiting this aspect. An insufficient workspace size can sometimes lead to errors during TensorRT engine building. Try increasing the workspace size significantly to rule out this possibility.

7. Isolate the Problematic Layer

If the above steps don't resolve the issue, try to isolate the problematic layer by running the model with only a subset of layers enabled. This can help pinpoint the exact location where NaN values are being generated.

8. Reproduce the Issue with Minimal Example

Create a minimal, self-contained example that reproduces the NaN value issue. This will make it easier to share the problem with the TensorRT community and get targeted help.

9. Check TensorRT Versions and Compatibility

The user tried both TensorRT v9.2 and v10.0.1.6. It's essential to ensure that the TensorRT version is compatible with the CUDA version (CUDA 12.8 in this case) and the ONNX runtime version (onnxruntime-gpu==1.19.2). Refer to the TensorRT documentation for compatibility information.

10. Consult TensorRT Documentation and Forums

The TensorRT documentation (https://developer.nvidia.com/tensorrt) provides comprehensive information about TensorRT features, limitations, and best practices. The NVIDIA developer forums (https://forums.developer.nvidia.com/) are a valuable resource for asking questions and getting help from the TensorRT community.

Example: Adding a Cast Operator in ONNX using ONNX-GraphSurgeon

Here's an example of how to add a Cast operator in the ONNX graph to explicitly convert the output of the broadcast operation to FP32 using ONNX-GraphSurgeon:

import onnx
import onnx_graphsurgeon as gs
import numpy as np

# Load the ONNX model
model = onnx.load("model.onnx")
graph = gs.import_onnx(model)

# Find the ONNXTRT_Broadcast_106 node
broadcast_node = None
for node in graph.nodes:
    if node.name == "ONNXTRT_Broadcast_106":
        broadcast_node = node
        break

if broadcast_node is None:
    raise ValueError("Broadcast node not found")

# Create a Cast node to convert the output to FP32
cast_output = gs.Variable(name="broadcast_output_fp32", dtype=np.float32)
cast_node = gs.Node(
    op="Cast",
    name="Cast_Broadcast_To_FP32",
    attrs={"to": onnx.TensorProto.FLOAT},
    inputs=[broadcast_node.outputs[0]],
    outputs=[cast_output],
)

# Insert the Cast node into the graph
graph.nodes.append(cast_node)
broadcast_node.outputs.clear()
broadcast_node.outputs.append(cast_output)

# Cleanup the graph
graph.cleanup().toposort()

# Export the modified ONNX model
modified_model = gs.export_onnx(graph)
onnx.save(modified_model, "model_fp32.onnx")

print("Cast node added and model saved to model_fp32.onnx")

Conclusion

Debugging NaN values in TensorRT FP16 inference can be challenging, but by systematically investigating the potential causes and applying the techniques described in this article, you can effectively identify and resolve these issues. Remember to analyze Polygraphy error messages, map them to the ONNX model, address type mismatches and unsupported operations, mitigate numerical instability, and consult the TensorRT documentation and community for support. By following a structured approach, you can optimize your models for high-performance inference with TensorRT.