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cudaErrorInvalidValue (1)cudaErrorInvalidValue (error code 1) occurs when you pass an invalid argument to a CUDA runtime function. This includes null pointers, negative sizes, invalid flags, or parameters that violate API constraints. This error is common when working with cudaMalloc, cudaMemcpy, kernel launch configurations, or when values overflow or become negative due to integer arithmetic errors. This guide covers the most common causes and provides practical solutions for each scenario.
CUDA error: invalid argument cudaErrorInvalidValue: invalid argument invalid configuration argument cudaMemcpy: invalid argument invalid pitch value
Check for zero, negative, or overflowed size values.
// BAD: Can overflow or be zero
size_t size = width * height * sizeof(float);
// GOOD: Validate before allocation
size_t validateSize(size_t width, size_t height, size_t elemSize) {
if (width == 0 || height == 0 || elemSize == 0) {
fprintf(stderr, "Invalid dimensions\n");
return 0;
}
// Check for overflow
if (width > SIZE_MAX / height) {
fprintf(stderr, "Size overflow\n");
return 0;
}
size_t pixels = width * height;
if (pixels > SIZE_MAX / elemSize) {
fprintf(stderr, "Size overflow\n");
return 0;
}
return pixels * elemSize;
}
size_t size = validateSize(width, height, sizeof(float));
if (size > 0) {
cudaMalloc(&d_ptr, size);
}Use the correct cudaMemcpyKind for your transfer.
// cudaMemcpy(destination, source, size, direction)
// Host to Device
cudaMemcpy(d_array, h_array, size, cudaMemcpyHostToDevice);
// Device to Host
cudaMemcpy(h_array, d_array, size, cudaMemcpyDeviceToHost);
// Device to Device
cudaMemcpy(d_dest, d_src, size, cudaMemcpyDeviceToDevice);
// WRONG: Direction reversed - causes invalid argument
cudaMemcpy(h_array, d_array, size, cudaMemcpyHostToDevice); // ERROR!
// Use cudaMemcpyDefault to auto-detect (requires unified memory pointers)
cudaMemcpy(dest, src, size, cudaMemcpyDefault);Ensure block and grid dimensions are valid.
// Maximum limits (typical):
// - Threads per block: 1024
// - Block dimensions: (1024, 1024, 64)
// - Grid dimensions: (2^31-1, 65535, 65535)
// BAD: Exceeds limits
kernel<<<1, 2048>>>(); // Error: >1024 threads
// GOOD: Validate before launch
bool validateLaunchConfig(dim3 grid, dim3 block) {
if (grid.x == 0 || grid.y == 0 || grid.z == 0) return false;
if (block.x == 0 || block.y == 0 || block.z == 0) return false;
size_t threadsPerBlock = block.x * block.y * block.z;
if (threadsPerBlock > 1024) return false;
if (block.x > 1024 || block.y > 1024 || block.z > 64) return false;
return true;
}
dim3 block(256);
dim3 grid((n + 255) / 256);
if (validateLaunchConfig(grid, block)) {
kernel<<<grid, block>>>(data, n);
}Check pointers before passing to CUDA functions.
// Validate device pointers
cudaError_t safeCudaMemcpy(void* dst, const void* src, size_t size, cudaMemcpyKind kind) {
if (dst == nullptr || src == nullptr) {
return cudaErrorInvalidValue;
}
if (size == 0) {
return cudaSuccess; // Nothing to copy
}
return cudaMemcpy(dst, src, size, kind);
}
// Check if pointer is on device
cudaPointerAttributes attrs;
cudaError_t err = cudaPointerGetAttributes(&attrs, ptr);
if (err == cudaSuccess) {
if (attrs.type == cudaMemoryTypeDevice) {
printf("Pointer is on device\n");
} else if (attrs.type == cudaMemoryTypeHost) {
printf("Pointer is on host\n");
}
}Common framework-specific causes of invalid value errors.
# PyTorch: Ensure contiguous memory
tensor = tensor.contiguous() # Fix non-contiguous tensor
# Check tensor properties before CUDA operations
def validate_tensor(t, name="tensor"):
assert t.is_cuda, f"{name} must be on CUDA"
assert t.is_contiguous(), f"{name} must be contiguous"
assert t.numel() > 0, f"{name} must not be empty"
print(f"{name}: shape={t.shape}, dtype={t.dtype}, device={t.device}")
# Empty tensor can cause issues
if tensor.numel() == 0:
print("Warning: empty tensor")
return
# TensorFlow: Check for None values
if tf_tensor is None:
raise ValueError("Tensor is None")No validation of n before use. If n is 0 or negative, cudaMalloc and kernel launch fail.
int n = getInputSize(); // Could be 0 or negative
float* d_array;
cudaMalloc(&d_array, n * sizeof(float)); // Invalid if n <= 0
kernel<<<n, 256>>>(d_array); // Invalid if n == 0Validates input, checks for overflow, verifies allocation success, and uses safe grid calculation.
int n = getInputSize();
// Validate input
if (n <= 0) {
fprintf(stderr, "Invalid size: %d\n", n);
return cudaErrorInvalidValue;
}
// Check for overflow
size_t bytes = (size_t)n * sizeof(float);
if (bytes / sizeof(float) != (size_t)n) {
fprintf(stderr, "Size overflow\n");
return cudaErrorInvalidValue;
}
float* d_array = nullptr;
cudaError_t err = cudaMalloc(&d_array, bytes);
if (err != cudaSuccess || d_array == nullptr) {
fprintf(stderr, "cudaMalloc failed: %s\n", cudaGetErrorString(err));
return err;
}
// Valid launch configuration
int threadsPerBlock = 256;
int blocksPerGrid = (n + threadsPerBlock - 1) / threadsPerBlock;
kernel<<<blocksPerGrid, threadsPerBlock>>>(d_array, n);cudaMemcpy validates both source and destination pointers, direction flag, and size. A common cause is passing host pointer where device pointer is expected, or vice versa. Check your cudaMemcpyKind matches the actual pointer types.
Print all arguments before the CUDA call. Check for null pointers, zero sizes, and valid enum values. Use cuda-gdb or Nsight for more detailed debugging. Sometimes the error comes from a previous async operation.
Yes! If you calculate size as width * height * sizeof(type) and the multiplication overflows, the size becomes a large garbage value or wraps to a small number. Always use size_t and check for overflow.
Likely a grid/block configuration issue. With small N, you might launch 0 blocks (when N < threadsPerBlock and you use integer division). Always use ceiling division: (N + threadsPerBlock - 1) / threadsPerBlock.
Invalid device index is a specific case
Valid size but not enough memory
Invalid kernel arguments cause launch failure
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