Introduction

TensorFlow's public C API lets you create tensors, load graphs, and run sessions, but it does not expose a special primitive for copying data directly into CUDA constant memory. If you need CUDA constant memory, the usual design is to implement it inside a custom GPU op or CUDA kernel, while the C API continues to pass ordinary tensors into the TensorFlow runtime.

What the C API Actually Gives You

The TensorFlow C API is intentionally narrow. It is designed around graph execution, tensors, sessions, and status objects. From that API, you can feed tensors into a graph and fetch outputs, but you do not get direct control over low-level CUDA memory spaces such as constant memory, shared memory, or manual kernel launches.

That means the practical answer is:

• use the C API to feed normal tensors
• handle CUDA constant memory inside custom op implementation code
• do not expect a public TF_... function that copies bytes straight into a CUDA __constant__ symbol

Feed a Normal Tensor Through the C API

The C API side of the problem still looks like normal tensor feeding.

1#include <stdio.h>
2#include <stdlib.h>
3#include <string.h>
4#include <tensorflow/c/c_api.h>
5
6static void Deallocator(void* data, size_t len, void* arg) {
7    free(data);
8}
9
10int main(void) {
11    float* values = (float*)malloc(4 * sizeof(float));
12    values[0] = 1.0f;
13    values[1] = 2.0f;
14    values[2] = 3.0f;
15    values[3] = 4.0f;
16
17    int64_t dims[1] = {4};
18    TF_Tensor* tensor = TF_NewTensor(
19        TF_FLOAT,
20        dims,
21        1,
22        values,
23        4 * sizeof(float),
24        Deallocator,
25        NULL
26    );
27
28    printf("Tensor created: %lld elements\n", (long long)dims[0]);
29    TF_DeleteTensor(tensor);
30    return 0;
31}

This is the level at which the C API operates. It deals with TensorFlow tensors, not with explicit CUDA constant-memory symbols.

Where Constant Memory Belongs

CUDA constant memory is a GPU-kernel detail. If you really need it, the transfer normally happens inside custom GPU code such as a TensorFlow custom op or a kernel launcher used by that op.

A simplified CUDA example looks like this:

1#include <cuda_runtime.h>
2
3__constant__ float kWeights[4];
4
5void CopyWeightsToConstantMemory(const float* host_values) {
6    cudaMemcpyToSymbol(kWeights, host_values, 4 * sizeof(float));
7}

That code belongs in the GPU implementation layer, not in the public TensorFlow C API layer.

Typical Architecture

The usual architecture is:

1. C API creates or feeds a TensorFlow tensor.
2. TensorFlow executes a graph that includes a custom GPU op.
3. The custom op's CUDA implementation decides whether to stage some small read-only values in constant memory.

In other words, the C API hands data to TensorFlow. The kernel implementation decides how device memory is organized internally.

When Constant Memory Is Actually Worth It

CUDA constant memory is small and read-only. It is most useful when:

• the data is tiny
• many threads read the same values
• the values do not change often

If the data is larger, changes frequently, or is not broadcast-friendly, normal tensor access patterns may be the better choice anyway.

That is important because sometimes the right answer is not "find a constant-memory transfer call." Sometimes the right answer is "do not use constant memory here."

Common Pitfalls

The biggest mistake is assuming the TensorFlow C API exposes every CUDA-level memory control surface. It does not.

Another issue is trying to design constant memory as if TensorFlow tensors and CUDA constant symbols were the same abstraction. They are not.

A third problem is choosing constant memory without first checking whether the access pattern and data size actually justify it.

Summary

• The public TensorFlow C API does not provide a direct constant-memory transfer primitive.
• Use the C API to feed normal tensors into TensorFlow.
• Handle CUDA constant memory inside a custom GPU op or CUDA kernel implementation.
• Constant memory is a kernel optimization detail, not a TensorFlow C API feature.
• Verify that your data size and access pattern actually fit constant-memory usage well.