Fix cudaErrorAssert: Device-Side Assert Triggered

Overview

cudaErrorAssert (error code 710) occurs when an assert() statement in device code evaluates to false. This is actually a useful debugging tool - you can add assertions to kernels to catch programming errors during development. Unlike host-side asserts that crash immediately, device-side asserts are reported asynchronously. The kernel continues running other threads, but the error propagates to the host on the next synchronization. This guide covers how to use asserts effectively for debugging and how to resolve assertion failures.

Error Messages

CUDA error: device-side assert triggered
cudaErrorAssert: device-side assert triggered
block: [X,Y,Z], thread: [X,Y,Z] Assertion failed

Common Causes

• •Explicit assert() statement in kernel code failed
• •Index out of expected range
• •Invalid parameter values passed to kernel
• •Math operations producing NaN or Inf when not expected
• •Invariant violations in algorithm
• •Debug checks catching bugs as intended
• •Assertions left in production code
• •PyTorch/TensorFlow internal assertions (shape mismatches, etc.)

Solutions

# Compile with debug symbols
nvcc -G -g your_kernel.cu -o program

# For Python frameworks, set debug environment
export CUDA_LAUNCH_BLOCKING=1

# PyTorch: Enable anomaly detection
import torch
torch.autograd.set_detect_anomaly(True)

__global__ void kernel(float* data, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    
    // Print before assert for debugging
    if (idx >= n) {
        printf("Assert fail: idx=%d, n=%d, block=%d\n", 
               idx, n, blockIdx.x);
    }
    assert(idx < n);
    
    // Or use conditional return instead
    if (!(idx < n)) return;
}

# PyTorch: Common assert causes
# - Shape mismatch in operations
# - Index out of bounds in gather/scatter
# - NaN in loss causing backward issues

# Debug PyTorch CUDA errors
import torch
torch.cuda.synchronize()  # Force sync to get real error location

# Check for NaN
if torch.isnan(loss):
    print("NaN detected in loss!")
    # Don't call backward() with NaN

// Debug-only assertions
#ifdef DEBUG
    #define CUDA_ASSERT(cond) assert(cond)
#else
    #define CUDA_ASSERT(cond) ((void)0)
#endif

__global__ void kernel(float* data, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    CUDA_ASSERT(idx < n);  // Only active in debug
    if (idx < n) {
        data[idx] = 1.0f;
    }
}

Prevention Tips

• ✓Use asserts intentionally to catch bugs during development
• ✓Add printf before assert to log context
• ✓Remove or conditionally compile asserts for production
• ✓Check for NaN/Inf in intermediate values
• ✓Validate tensor shapes before CUDA operations
• ✓Use CUDA_LAUNCH_BLOCKING=1 when debugging asserts
• ✓Consider bounds checks instead of asserts for recoverable errors
• ✓Document what conditions each assert is checking

Code Examples

Before (Problematic)

Assert gives no information about which thread or what value failed.

__global__ void kernel(int* indices, float* data, int n, int max_idx) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) {
        // Assert with no context - hard to debug
        assert(indices[i] < max_idx);
        data[indices[i]] += 1.0f;
    }
}

After (Fixed)

Validates with informative logging, gracefully handles bad data instead of crashing.

__global__ void kernel(int* indices, float* data, int n, int max_idx) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) {
        int idx = indices[i];
        if (idx < 0 || idx >= max_idx) {
            printf("Bad index: thread=%d, idx=%d, max=%d\n", i, idx, max_idx);
            return;  // Graceful failure
        }
        data[idx] += 1.0f;
    }
}

Frequently Asked Questions

Related Errors

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