RuntimeError: mixed dtype (CPU): expect input to have scalar type of BFloat16

3 min read 05-10-2024

RuntimeError: mixed dtype (CPU): expect input to have scalar type of BFloat16 - Understanding and Troubleshooting

Deep learning models often leverage specialized data types like BFloat16 (Brain Floating Point 16) to enhance performance and memory efficiency. However, inconsistencies in data types can lead to runtime errors like "RuntimeError: mixed dtype (CPU): expect input to have scalar type of BFloat16." This article will delve into the root of this error, provide clear explanations, and offer practical solutions to overcome it.

Scenario and Original Code:

Imagine you're training a deep learning model using PyTorch, and you've decided to use BFloat16 for your model's weights and activations. The code snippet below illustrates a potential scenario where this error might arise:

import torch
import torch.nn as nn

# Define model
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 5, dtype=torch.bfloat16)

    def forward(self, x):
        return self.linear(x)

# Create model and data
model = MyModel()
data = torch.randn(10, dtype=torch.float32)

# Pass data through the model
output = model(data)

This code defines a simple linear model and then attempts to feed floating-point (torch.float32) data through it. The model, however, expects BFloat16 data, leading to the "RuntimeError: mixed dtype (CPU): expect input to have scalar type of BFloat16" error.

Understanding the Error:

This error signals that your model is expecting data in BFloat16 format, but you're providing it with a different data type, in this case, torch.float32. BFloat16 is a specialized floating-point format designed for faster computation on hardware like TPUs and GPUs. It uses a reduced number of bits compared to torch.float32 to represent numbers, resulting in memory savings and potential speed improvements.

Causes and Solutions:

Mismatched Data Types: The most common reason is feeding data of a different type than what your model expects. The solution here is to ensure data consistency. Convert your input data to BFloat16 before feeding it to the model:
```
data = torch.randn(10, dtype=torch.float32) 
data = data.to(torch.bfloat16) # Convert to BFloat16
output = model(data)
```

Incorrectly Defined Layers: The error can also occur if you define layers within your model using different data types than BFloat16. Ensure all layers utilize BFloat16, either explicitly during layer creation or by setting the model's default data type.

# Set the model's default data type to BFloat16
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 5) # No explicit dtype, using default
        self.linear = self.linear.to(torch.bfloat16) # Cast to BFloat16

# ... rest of the code

Unintentional Type Conversion: Sometimes, implicit conversions during data manipulation can lead to type inconsistencies. Carefully inspect your code for any operations that might inadvertently change the data type of your input before it reaches the model.
Hardware Compatibility: BFloat16 support is not universal across all hardware. Ensure that your CPU or GPU supports BFloat16 operations to avoid compatibility issues.

Additional Tips:

Inspect Data Types: Use data.dtype to check the data type of your input and ensure it matches the model's expectations.
Utilize dtype Parameter: When defining layers or operations, explicitly specify dtype=torch.bfloat16 to avoid potential issues with default behavior.
Consider Hardware: If you encounter this error while using hardware that doesn't natively support BFloat16, consider using a different data type like torch.float32 or torch.float16.
Check Documentation: Consult the documentation of your deep learning framework (PyTorch, TensorFlow, etc.) for specific guidance on using BFloat16.

Conclusion:

The "RuntimeError: mixed dtype (CPU): expect input to have scalar type of BFloat16" error is a common issue when working with BFloat16 data in deep learning. By understanding the root causes and applying the solutions outlined in this article, you can effectively troubleshoot this error and ensure your models function correctly.

RuntimeError: mixed dtype (CPU): expect input to have scalar type of BFloat16