mirror of https://github.com/ggerganov/llama.cpp
Add OpenCL add kernel (#5151)
* Add OpenCL add kernel * Put add kernel into different string to stay within MSVC string length limit, disable float16 support due to bad results
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0cc4m
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@ -714,7 +714,6 @@ __kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx,
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dst[row] = tmp[0];
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}
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}
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);
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@ -784,6 +783,7 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float
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dst[row] = tmp[0];
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}
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}
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);
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@ -799,6 +799,18 @@ __kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y
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}
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);
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std::string add_template = MULTILINE_QUOTE(
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__kernel void add_f32(__global float * x, const int x_offset, __global float * y, const int y_offset, __global float * dst, const int dst_offset, const int ky) {
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const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);
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if (i >= get_global_size(0)) {
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return;
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}
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dst[dst_offset + i] = x[x_offset + i] + y[y_offset + i%ky];
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}
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);
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#define CL_CHECK(err) \
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do { \
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cl_int err_ = (err); \
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@ -878,6 +890,7 @@ static std::string generate_kernels() {
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}
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src << mul_kernel << '\n';
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}
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src << add_template << '\n';
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return src.str();
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}
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@ -893,6 +906,7 @@ static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl,
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static cl_kernel dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl;
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static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl;
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static cl_kernel mul_f32_cl;
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static cl_kernel add_f32_cl;
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static bool fp16_support;
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static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) {
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@ -1100,9 +1114,10 @@ void ggml_cl_init(void) {
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char *ext_buffer = (char *)alloca(ext_str_size + 1);
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clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL);
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ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated
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// Disabled due to faulty outputs
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// Check if ext_buffer contains cl_khr_fp16
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fp16_support = strstr(ext_buffer, "cl_khr_fp16") != NULL;
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fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false");
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fp16_support = false; // strstr(ext_buffer, "cl_khr_fp16") != NULL;
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// fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false");
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cl_context_properties properties[] = {
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(intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0
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@ -1150,6 +1165,8 @@ void ggml_cl_init(void) {
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// mul kernel
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CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err));
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CL_CHECK((add_f32_cl = clCreateKernel(program, "add_f32", &err), err));
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}
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static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
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@ -1458,6 +1475,70 @@ void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src
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ggml_cl_mul_f32(src0, src1, dst);
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}
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static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
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const int64_t ne00 = src0->ne[0];
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const int64_t ne01 = src0->ne[1];
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const int64_t ne02 = src0->ne[2];
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const int64_t ne03 = src0->ne[3];
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const int64_t ne10 = src1->ne[0];
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const int64_t ne11 = src1->ne[1];
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const int64_t ne12 = src1->ne[2];
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const int64_t ne13 = src1->ne[3];
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const int nb2 = dst->nb[2];
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const int nb3 = dst->nb[3];
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size_t x_size;
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size_t d_size;
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cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0
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cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted.
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cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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for (int64_t i02 = 0; i02 < ne02; i02++) {
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cl_event ev;
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// copy src0 to device
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CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev));
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const int64_t i13 = i03%ne13;
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const int64_t i12 = i02%ne12;
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const int i1 = i13*ne12*ne11 + i12*ne11;
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cl_int x_offset = 0;
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cl_int y_offset = i1*ne10;
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cl_int d_offset = 0;
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size_t global = ne00 * ne01;
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cl_int ky = ne10 * ne11;
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CL_CHECK(clSetKernelArg(add_f32_cl, 0, sizeof(cl_mem), &d_X));
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CL_CHECK(clSetKernelArg(add_f32_cl, 1, sizeof(cl_int), &x_offset));
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CL_CHECK(clSetKernelArg(add_f32_cl, 2, sizeof(cl_mem), &d_Y));
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CL_CHECK(clSetKernelArg(add_f32_cl, 3, sizeof(cl_int), &y_offset));
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CL_CHECK(clSetKernelArg(add_f32_cl, 4, sizeof(cl_mem), &d_D));
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CL_CHECK(clSetKernelArg(add_f32_cl, 5, sizeof(cl_int), &d_offset));
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CL_CHECK(clSetKernelArg(add_f32_cl, 6, sizeof(cl_int), &ky));
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CL_CHECK(clEnqueueNDRangeKernel(queue, add_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
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CL_CHECK(clReleaseEvent(ev));
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CL_CHECK(clFinish(queue));
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// copy dst to host
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float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
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CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL));
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}
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}
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ggml_cl_pool_free(d_X, x_size);
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ggml_cl_pool_free(d_D, d_size);
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}
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void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
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GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
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ggml_cl_add_f32(src0, src1, dst);
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}
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static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
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const int64_t ne00 = src0->ne[0];
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const int64_t ne01 = src0->ne[1];
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@ -10,6 +10,7 @@ extern "C" {
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GGML_API void ggml_cl_init(void);
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GGML_API void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
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GGML_API void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
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GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst);
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GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
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GGML_API void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
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11
ggml.c
11
ggml.c
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@ -7207,6 +7207,17 @@ static void ggml_compute_forward_add_f32(
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const int ith = params->ith;
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const int nth = params->nth;
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#ifdef GGML_USE_CLBLAST
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if (src1->backend == GGML_BACKEND_GPU) {
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// TODO: OpenCL kernel support full broadcast
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GGML_ASSERT(ggml_can_repeat_rows(src1, src0));
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if (ith == 0) {
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ggml_cl_add(src0, src1, dst);
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}
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return;
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}
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#endif
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const int nr = ggml_nrows(src0);
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GGML_TENSOR_BINARY_OP_LOCALS
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