Kimi

这两个C文件（passthrough.h.txt 和 passthrough.c.txt）构成了一个名为 cudawrap 的库，它的作用是提供一个封装层，用于加载和访问 CUDA 相关的库（如 CUDA Runtime、cuDNN 和 cuBLAS）中的函数。这个封装层允许程序在运行时动态地加载这些库，并提供了一种机制来调用这些库中的函数。以下是对这两个文件的详细分析：

passthrough.h 文件

这个头文件定义了 cudawrap 库的数据结构和宏，用于封装对 CUDA 相关库的调用。

1. 数据结构定义：

   • entry_t：一个结构体，包含一个函数指针 fn_ptr 和一个函数名 name，用于存储库中函数的地址和名称。

2. 宏定义：

   • ENTER 和 LEAVE：用于调试目的，记录函数的进入和离开时间，以及计算函数执行时间。
   • TIME_GEMM_START 和 TIME_GEMM_STOP：用于性能分析，记录和计算 GEMM（General Matrix Multiply）操作的开始和结束时间。
   • CUDART_ENTRY_ENUM、CUDNN_ENTRY_ENUM 和 CUBLAS_ENTRY_ENUM：宏，用于生成枚举值，代表不同的库函数。
   • CUDART_FIND_ENTRY、CUDNN_FIND_ENTRY 和 CUBLAS_FIND_ENTRY：宏，用于查找特定函数的入口。
   • REAL_CUDA_RT_API_CALL、REAL_CUDNN_CALL 和 REAL_CUBLAS_CALL：宏，用于实际调用库中的函数。

3. 枚举定义：

   • cudart_entry_enum_t、cudnn_entry_enum_t 和 cublas_entry_enum_t：枚举类型，列出了所有需要封装的 CUDA Runtime、cuDNN 和 cuBLAS 函数。

passthrough.c 文件

这个源文件实现了 passthrough.h 中定义的数据结构和函数。

1. 全局变量初始化：

   • _cudart_inited_、_cudnn_inited_ 和 _cublas_inited_：用于标记对应的库是否已经初始化。

2. 库函数入口数组：

   • cudart_lib_entry、cudnn_lib_entry 和 cublas_lib_entry：数组，存储了对应库的所有函数入口。

3. 加载库函数：

   • load_cudart_lib、load_cudnn_lib 和 load_cublas_lib：函数，用于动态加载 CUDA Runtime、cuDNN 和 cuBLAS 库，并查找所需的函数地址。

4. 初始化检查函数：

   • cudart_lib_inited、cudnn_lib_inited 和 cublas_lib_inited：函数，用于检查对应的库是否已经初始化。

总结

cudawrap 库的主要作用是提供一个中间层，使得应用程序可以更加灵活地使用 CUDA 相关的库。通过动态加载和查找函数，它允许应用程序在不同的环境（如不同的 CUDA 版本或配置）下运行，而无需重新编译。此外，它还提供了性能分析和调试支持，帮助开发者优化和调试 CUDA 相关的代码。

使用方法

要使用 cudawrap 库，你需要按照以下步骤操作：

1. 包含头文件：在你的 C 或 C++ 源文件中包含 passthrough.h 头文件。

2. 链接库文件：在编译你的程序时，确保链接了 cudawrap 库以及它依赖的 CUDA 相关库（如 libcudart.so, libcudnn.so, libcublas.so）。

3. 初始化库：在程序中调用 load_cudart_lib(), load_cudnn_lib(), 和 load_cublas_lib() 函数来加载对应的 CUDA 库，并初始化 cudawrap 库。

4. 调用封装函数：使用 cudawrap 提供的宏来调用 CUDA 库中的函数。

下面是一个简单的例子，展示了如何使用 cudawrap 库来调用 CUDA Runtime API 中的 cudaMalloc 和 cudaFree 函数：

#include "passthrough.h"

int main() {
    // 初始化 CUDA 运行时库
    if (load_cudart_lib() != 0) {
        fprintf(stderr, "Failed to load CUDA runtime library.\n");
        return -1;
    }

    // 分配 CUDA 设备内存
    void *devPtr;
    size_t size = 1024 * 1024; // 分配 1MB 内存
    REAL_CUDA_RT_API_CALL(cudart_lib_entry, , cudaMalloc, (void**)&devPtr, size);

    if (devPtr == NULL) {
        fprintf(stderr, "Failed to allocate device memory.\n");
        // 这里应该进行清理工作
        return -1;
    }

    // ... 在这里进行其他 CUDA 操作 ...

    // 释放 CUDA 设备内存
    REAL_CUDA_RT_API_CALL(cudart_lib_entry, , cudaFree, (devPtr));

    // 清理工作，关闭程序
    return 0;
}

在这个例子中，我们首先调用 load_cudart_lib() 来加载 CUDA 运行时库并初始化 cudawrap 库。然后，我们使用 REAL_CUDA_RT_API_CALL 宏来封装对 cudaMalloc 和 cudaFree 函数的调用。这个宏会查找对应的函数入口，并执行函数调用。

请注意，这个例子假设 cudawrap 库已经被正确编译并链接到你的程序中。在实际使用中，你可能需要根据你的环境和编译器来调整编译和链接选项。此外，错误处理和资源管理（如内存泄漏）也是实际编程中需要考虑的重要方面。

Hijack CUBLAS

hijack_cublas 是一个库，它提供了对 cuBLAS 库函数的封装和可能的替代实现。这个库的目的是在不同的环境下，如模拟 CUDA 环境或在不支持 CUDA 的系统上，提供 cuBLAS 函数的接口。以下是对 hijack_cublas 的进一步分析：

hijack_cublas.h 文件

这个头文件定义了 hijack_cublas 库的主要结构和枚举类型，以及函数原型。

1. 枚举类型：

   • cublasStatus_t：定义了 cuBLAS API 可能返回的状态码。
   • cublasOperation_t：定义了矩阵操作类型，如 CUBLAS_OP_N（无转置）、CUBLAS_OP_T（转置）和 CUBLAS_OP_C（共轭转置）。

2. 结构体定义：

   • cublasContext：定义了一个结构体，包含一个初始化标志和一个 CUDA 流。
   • cublasHandle_t：cublasContext 结构体的指针类型，用于作为 cuBLAS 库函数的上下文句柄。

3. 外部变量声明：

   • cublas_entry[]：声明了一个 entry_t 类型的数组，用于存储 cuBLAS 库函数的入口。

4. 函数类型定义：

   • cublas_sym_t：定义了一个函数指针类型，指向一个返回 cublasStatus_t 的函数。

hijack_cublas.c 文件

这个源文件实现了 hijack_cublas.h 中声明的函数，提供了 cuBLAS 函数的具体实现。

1. cuBLAS 函数实现：

   • cublasCreate_v2：创建一个 cuBLAS 上下文（句柄）。
   • cublasDestroy_v2：销毁一个 cuBLAS 上下文（句柄）。
   • cublasSetStream_v2：设置 cuBLAS 上下文的 CUDA 流。

2. 异步 GEMM 实现：

   • cublasSgemm_async_fn：一个异步执行 SGEMM 操作的函数，它使用 OpenMP 进行多线程计算。

3. SGEMM 函数实现：

   • cublasSgemm_v2：执行矩阵乘法操作（SGEMM）。这个函数根据编译时定义的条件（如 USE_MocCUDA 和 USE_GCD）来决定是调用真实的 cuBLAS 函数还是模拟的实现。

4. 条件编译：

   • 根据定义的宏（如 USE_MocCUDA、USE_GCD 等），hijack_cublas 可以选择使用不同的实现策略。例如，如果定义了 USE_MocCUDA，则会使用模拟的 cuBLAS 实现，而不是调用真实的 CUDA 函数。

5. 调试和性能分析：

   • 文件中包含了 ENTER 和 LEAVE 宏，用于调试和性能分析，记录函数的进入和离开时间。

总结

hijack_cublas 库的作用是在不具备完整 CUDA 支持的环境下，提供 cuBLAS 库的接口和可能的模拟实现。它允许开发者在编写代码时，不需要关心后端是真实的 CUDA 环境还是模拟环境，从而提高了代码的可移植性。此外，它还提供了异步执行和多线程支持，以优化性能。通过条件编译，hijack_cublas 可以灵活地适应不同的使用场景和需求。

ChatGPT

The provided C and header files implement a dynamic loader for CUDA libraries (libcudart, cudnn, and cublas) and a wrapper for their functionality. Here's a detailed breakdown:

Purpose

The main role of these files is to dynamically load CUDA libraries at runtime instead of linking them statically. This approach:

1. Enables the use of CUDA functionalities without requiring the application to depend on specific versions of CUDA libraries during compilation.
2. Provides a mechanism for runtime flexibility, allowing switching between different library versions.
3. Facilitates debugging, logging, and performance tracking of library function calls.

passthrough.c Analysis

Key Features

1. Dynamic Library Loading:

   • Functions like dlopen() and dlsym() dynamically load the CUDA libraries and resolve symbols (function pointers).
   • If the library or a specific function is not found, it emits errors and gracefully exits.

2. Function Resolution:

   • Arrays (cudart_lib_entry, cudnn_lib_entry, and cublas_lib_entry) store the function names and their resolved function pointers (fn_ptr).
   • Functions like load_cudart_lib(), load_cudnn_lib(), and load_cublas_lib() populate these arrays.

3. Initialization Flags:

   • Static variables like _cudart_inited_, _cudnn_inited_, and _cublas_inited_ prevent redundant re-initialization.

4. Error Handling:

   • Robust error checking for missing libraries and functions.

CUDA Libraries

• libcudart: Basic CUDA Runtime functionalities (e.g., cudaMalloc, cudaMemcpy).
• cudnn: Deep Neural Network primitives.
• cublas: Basic Linear Algebra operations.

passthrough.h Analysis

Structure Definitions

• entry_t: A structure for maintaining a function pointer (fn_ptr) and the corresponding function name (name).

Wrapper Macros

1. Function Wrapping:

   • REAL_CUDA_RT_API_CALL, REAL_CUDNN_CALL, and REAL_CUBLAS_CALL resolve symbols and execute them.
   • Example: A call to cudaMalloc() gets resolved dynamically using the corresponding function pointer.

2. Logging and Debugging:

   • ENTER and LEAVE macros log function entry and exit with execution timings, but only if compiled with DEBUG or FUNC_TIMINGS flags.
   • TIME_GEMM_START and TIME_GEMM_STOP specifically measure performance of GEMM (General Matrix Multiplication) operations, crucial for BLAS and deep learning workloads.

Enumerations

• Enumerates all supported library functions (CUDART_ENTRY_ENUM, CUDNN_ENTRY_ENUM, and CUBLAS_ENTRY_ENUM) for easy indexing in the arrays.

Initialization Functions

• load_cudart_lib(), load_cudnn_lib(), and load_cublas_lib() are declared for library loading at runtime.

Typical Workflow

1. Initialization:

   • A CUDA library (e.g., libcudart) is dynamically loaded when a function is called for the first time.
   • Initialization flags ensure one-time setup.

2. Function Execution:

   • For example, a wrapped call to cudaMalloc() would:
     • Resolve the function pointer via dlsym().
     • Execute the function through the resolved pointer.

3. Debugging and Logging (Optional):

   • Execution times and other statistics are logged if debugging macros are enabled.

Role in CUDAWRAP

These files act as the core implementation for a dynamic wrapper around CUDA libraries, suitable for environments where:

1. Runtime Flexibility: The application can dynamically switch between different versions of CUDA libraries by setting paths via environment variables (SWITCHCUDART, SWITCHCUDNN, SWITCHCUBLAS).
2. Debugging Support: Tracks performance and function calls without recompiling the CUDA libraries.
3. Ease of Integration: Reduces compilation dependencies, making applications portable across systems with varying CUDA versions.

Would you like to explore specific sections or use cases in more detail?

How to use

To use the CUDAWRAP library defined in the provided files, follow these steps. Here's a detailed explanation with an example of how to integrate it into a project and use its features.

1. Setting Up the Environment

The CUDAWRAP library uses environment variables to dynamically load CUDA libraries. You need to set these variables to point to the correct library paths. For example:

export SWITCHCUDART=/usr/local/cuda/lib64/libcudart.so
export SWITCHCUDNN=/usr/local/cuda/lib64/libcudnn.so
export SWITCHCUBLAS=/usr/local/cuda/lib64/libcublas.so

If the environment variables are not set, the library defaults to paths like PATHTOCUDA_ROOT/lib64/libcudart.so.

2. Compilation

You need to compile the passthrough.c and link it into your project. Use the following steps:

1. Compile the passthrough.c:

   gcc -fPIC -c passthrough.c -o passthrough.o -ldl
   

   • -fPIC: Generates position-independent code (necessary for shared libraries).
   • -ldl: Links the dynamic loader library (dlopen, dlsym).

2. Link the compiled object into your application:

   gcc main.c passthrough.o -o myapp -ldl
   

3. Example Code

main.c – Using CUDAWRAP to call CUDA functions.

#include <stdio.h>
#include "passthrough.h" // Include the header

int main() {
    // Step 1: Load the CUDA Runtime Library (libcudart)
    if (load_cudart_lib() != 0) {
        fprintf(stderr, "Failed to load libcudart library!\n");
        return 1;
    }

    // Step 2: Initialize CUDA
    int deviceCount = 0;

    // Dynamically call the cudaGetDeviceCount function
    typedef cudaError_t (*cudaGetDeviceCount_t)(int *);
    cudaGetDeviceCount_t cudaGetDeviceCount =
        (cudaGetDeviceCount_t)CUDART_FIND_ENTRY(cudart_lib_entry, cudaGetDeviceCount);

    if (cudaGetDeviceCount == NULL) {
        fprintf(stderr, "Function cudaGetDeviceCount not found!\n");
        return 1;
    }

    cudaError_t err = cudaGetDeviceCount(&deviceCount);
    if (err != cudaSuccess) {
        fprintf(stderr, "CUDA Error: %s\n", cudaGetErrorString(err));
        return 1;
    }

    printf("Number of CUDA devices: %d\n", deviceCount);

    // Step 3: Use another CUDA function
    void *devPtr;
    size_t size = 1024;

    typedef cudaError_t (*cudaMalloc_t)(void **, size_t);
    cudaMalloc_t cudaMalloc =
        (cudaMalloc_t)CUDART_FIND_ENTRY(cudart_lib_entry, cudaMalloc);

    if (cudaMalloc == NULL) {
        fprintf(stderr, "Function cudaMalloc not found!\n");
        return 1;
    }

    err = cudaMalloc(&devPtr, size);
    if (err != cudaSuccess) {
        fprintf(stderr, "cudaMalloc failed: %s\n", cudaGetErrorString(err));
        return 1;
    }

    printf("Successfully allocated %zu bytes on the device\n", size);

    // Cleanup (if necessary)
    typedef cudaError_t (*cudaFree_t)(void *);
    cudaFree_t cudaFree =
        (cudaFree_t)CUDART_FIND_ENTRY(cudart_lib_entry, cudaFree);

    if (cudaFree != NULL) {
        cudaFree(devPtr);
    }

    return 0;
}

4. Explanation of Example

1. Loading the Library: The load_cudart_lib() function dynamically loads the CUDA runtime library and populates cudart_lib_entry with function pointers.

2. Finding a Function: Use CUDART_FIND_ENTRY macro to retrieve the pointer for the desired function (e.g., cudaGetDeviceCount, cudaMalloc).

3. Calling the Function: Cast the function pointer to the appropriate type and call it like a regular function.

4. Handling Errors: Check for NULL function pointers and handle errors using CUDA's error functions (cudaGetErrorString).

5. Logging and Debugging

Enable logging and performance tracking by compiling with the DEBUG or FUNC_TIMINGS flags. For example:

gcc -DDEBUG main.c passthrough.o -o myapp -ldl

This will log entry and exit times for CUDA functions, which is helpful for debugging and profiling.

6. Benefits of Using CUDAWRAP

• Runtime Flexibility: Swap between CUDA library versions without recompilation.
• Debugging: Trace function calls and measure performance.
• Compatibility: Avoid hard-coding library paths, making your application portable across systems with different CUDA installations.

Would you like further clarification on any part or help setting up a specific CUDA example?

Hijack CUBLAS

The hijack_cublas.h file builds upon the passthrough dynamic loading mechanism to provide wrappers for the cuBLAS library. Here’s how you can understand and use this extension effectively.

Purpose of hijack_cublas.h

This header file:

1. Extends the CUDAWRAP framework to specifically work with the cuBLAS library.
2. Introduces cuBLAS-specific types and enumerations (e.g., cublasStatus_t, cublasOperation_t).
3. Implements mechanisms to hijack cuBLAS calls for logging, debugging, or dynamically replacing the cuBLAS library.

Key Components

1. Enums for cuBLAS Status and Operations

• cublasStatus_t: Represents return statuses for cuBLAS functions (e.g., success, allocation failure, or execution errors).

• cublasOperation_t: Represents matrix operations (CUBLAS_OP_N for normal, CUBLAS_OP_T for transpose, etc.).

2. cublasContext

• Placeholder structure that mimics a cuBLAS handle (cublasHandle_t).
• Contains:
  • __inited__: Indicates if the handle has been initialized.
  • stream: CUDA stream associated with the handle.

Note: This is a stub and may require proper initialization in a real-world scenario.

3. Function Wrapping

• cublas_sym_t: A generic type for function pointers to cuBLAS functions.

• cublas_entry[]: Similar to cudart_lib_entry in passthrough.h, it stores cuBLAS function names and pointers, allowing dynamic resolution.

Integration into a Project

To use hijack_cublas.h for cuBLAS calls:

1. Include Headers

#include "passthrough.h"
#include "hijack_cublas.h"

2. Initialize cuBLAS Library

Call load_cublas_lib() to dynamically load the cuBLAS library and initialize the cublas_entry table:

if (load_cublas_lib() != 0) {
    fprintf(stderr, "Failed to load cuBLAS library!\n");
    return -1;
}

3. Use cuBLAS Functions Dynamically

Retrieve and execute cuBLAS functions via CUBLAS_FIND_ENTRY macro:

// Example: cublasCreate_v2
typedef cublasStatus_t (*cublasCreate_t)(cublasHandle_t *);
cublasCreate_t cublasCreate = (cublasCreate_t)CUBLAS_FIND_ENTRY(cublas_lib_entry, cublasCreate_v2);

cublasHandle_t handle;
cublasStatus_t status = cublasCreate(&handle);

if (status != CUBLAS_STATUS_SUCCESS) {
    fprintf(stderr, "cublasCreate failed: %d\n", status);
    return -1;
}

Example Application

Here’s a full example demonstrating how to use hijack_cublas.h with matrix multiplication (cublasSgemm_v2).

#include <stdio.h>
#include "hijack_cublas.h"

// Example: Matrix multiplication using cuBLAS
int main() {
    // Step 1: Load cuBLAS library
    if (load_cublas_lib() != 0) {
        fprintf(stderr, "Failed to load cuBLAS library!\n");
        return -1;
    }

    // Step 2: Create cuBLAS handle
    typedef cublasStatus_t (*cublasCreate_t)(cublasHandle_t *);
    cublasCreate_t cublasCreate = (cublasCreate_t)CUBLAS_FIND_ENTRY(cublas_lib_entry, cublasCreate_v2);

    cublasHandle_t handle;
    if (cublasCreate(&handle) != CUBLAS_STATUS_SUCCESS) {
        fprintf(stderr, "Failed to create cuBLAS handle!\n");
        return -1;
    }

    // Step 3: Perform SGEMM (Single-precision General Matrix Multiply)
    float alpha = 1.0f, beta = 0.0f;
    int m = 3, n = 3, k = 3;

    float A[m * k] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
    float B[k * n] = {9, 8, 7, 6, 5, 4, 3, 2, 1};
    float C[m * n] = {0};

    typedef cublasStatus_t (*cublasSgemm_t)(cublasHandle_t, cublasOperation_t, cublasOperation_t,
                                            int, int, int, const float *, const float *, int,
                                            const float *, const float *, int, float *);
    cublasSgemm_t cublasSgemm = (cublasSgemm_t)CUBLAS_FIND_ENTRY(cublas_lib_entry, cublasSgemm_v2);

    if (cublasSgemm == NULL) {
        fprintf(stderr, "Function cublasSgemm_v2 not found!\n");
        return -1;
    }

    cublasStatus_t status = cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N,
                                        m, n, k, &alpha, A, m, B, k, &beta, C, m);

    if (status != CUBLAS_STATUS_SUCCESS) {
        fprintf(stderr, "cublasSgemm failed: %d\n", status);
        return -1;
    }

    // Print result
    printf("Matrix C:\n");
    for (int i = 0; i < m; i++) {
        for (int j = 0; j < n; j++) {
            printf("%f ", C[i * n + j]);
        }
        printf("\n");
    }

    // Cleanup
    typedef cublasStatus_t (*cublasDestroy_t)(cublasHandle_t);
    cublasDestroy_t cublasDestroy = (cublasDestroy_t)CUBLAS_FIND_ENTRY(cublas_lib_entry, cublasDestroy_v2);

    if (cublasDestroy) cublasDestroy(handle);

    return 0;
}

Key Steps in Example

1. Loading: load_cublas_lib initializes the function pointer table.
2. Retrieving Function Pointers: Use CUBLAS_FIND_ENTRY to locate specific cuBLAS functions.
3. Calling Functions: Use resolved function pointers like normal cuBLAS functions.
4. Matrix Multiplication: Performs single-precision GEMM (cublasSgemm_v2).
5. Cleanup: Destroy cuBLAS handle using cublasDestroy_v2.

Would you like a deeper dive into any specific cuBLAS function or other areas of this framework?