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opencl: add multi and vision rope, gelu_quick and im2col (#12600)

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* opencl: add `im2col`

* opencl: add `gelu_quick`

* opencl: add mrope

* opencl: add vision rope
This commit is contained in:
lhez
2025-03-27 08:08:08 -07:00
committed by GitHub
parent f125b8dccf
commit 5dec47dcd4
4 changed files with 774 additions and 14 deletions
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1
ggml/src/ggml-opencl/CMakeLists.txt
View File

@@ -63,6 +63,7 @@ set(GGML_OPENCL_KERNELS
ggml-opencl_transpose_16
ggml-opencl_transpose_32
ggml-opencl_transpose_32_16
ggml-opencl_im2col
)
foreach (K ${GGML_OPENCL_KERNELS})

252
ggml/src/ggml-opencl/ggml-opencl.cpp
View File

@@ -224,12 +224,14 @@ struct ggml_backend_opencl_context {
cl_program program;
cl_program program_1;
cl_program program_2;
cl_program program_im2col;
cl_kernel kernel_add, kernel_add_row;
cl_kernel kernel_mul, kernel_mul_row;
cl_kernel kernel_scale;
cl_kernel kernel_silu, kernel_silu_4;
cl_kernel kernel_gelu, kernel_gelu_4;
cl_kernel kernel_gelu_quick, kernel_gelu_quick_4;
cl_kernel kernel_relu;
cl_kernel kernel_clamp;
cl_kernel kernel_norm;
@@ -239,6 +241,7 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16;
cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0;
cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16;
cl_kernel kernel_rope_multi_f32, kernel_rope_multi_f16, kernel_rope_vision_f32, kernel_rope_vision_f16;
cl_kernel kernel_cpy_f16_f16, kernel_cpy_f16_f32, kernel_cpy_f32_f16, kernel_cpy_f32_f32;
cl_kernel kernel_mul_mat_f32_f32;
cl_kernel kernel_mul_mat_f16_f16;
@@ -252,6 +255,7 @@ struct ggml_backend_opencl_context {
kernel_mul_mat_q4_0_f32_flat_img_v0;
cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
cl_kernel kernel_mul_mv_q6_K_f32;
cl_kernel kernel_im2col_f32, kernel_im2col_f16;
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
// Transpose kernels
@@ -708,6 +712,8 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
CL_CHECK((backend_ctx->kernel_silu_4 = clCreateKernel(backend_ctx->program, "kernel_silu_4", &err), err));
CL_CHECK((backend_ctx->kernel_gelu = clCreateKernel(backend_ctx->program, "kernel_gelu", &err), err));
CL_CHECK((backend_ctx->kernel_gelu_4 = clCreateKernel(backend_ctx->program, "kernel_gelu_4", &err), err));
CL_CHECK((backend_ctx->kernel_gelu_quick = clCreateKernel(backend_ctx->program, "kernel_gelu_quick", &err), err));
CL_CHECK((backend_ctx->kernel_gelu_quick_4 = clCreateKernel(backend_ctx->program, "kernel_gelu_quick_4", &err), err));
CL_CHECK((backend_ctx->kernel_relu = clCreateKernel(backend_ctx->program, "kernel_relu", &err), err));
CL_CHECK((backend_ctx->kernel_clamp = clCreateKernel(backend_ctx->program, "kernel_clamp", &err), err));
CL_CHECK((backend_ctx->kernel_norm = clCreateKernel(backend_ctx->program, "kernel_norm", &err), err));
@@ -722,6 +728,10 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
CL_CHECK((backend_ctx->kernel_rope_norm_f16 = clCreateKernel(backend_ctx->program, "kernel_rope_norm_f16", &err), err));
CL_CHECK((backend_ctx->kernel_rope_neox_f32 = clCreateKernel(backend_ctx->program, "kernel_rope_neox_f32", &err), err));
CL_CHECK((backend_ctx->kernel_rope_neox_f16 = clCreateKernel(backend_ctx->program, "kernel_rope_neox_f16", &err), err));
CL_CHECK((backend_ctx->kernel_rope_multi_f32 = clCreateKernel(backend_ctx->program, "kernel_rope_multi_f32", &err), err));
CL_CHECK((backend_ctx->kernel_rope_multi_f16 = clCreateKernel(backend_ctx->program, "kernel_rope_multi_f16", &err), err));
CL_CHECK((backend_ctx->kernel_rope_vision_f32 = clCreateKernel(backend_ctx->program, "kernel_rope_vision_f32", &err), err));
CL_CHECK((backend_ctx->kernel_rope_vision_f16 = clCreateKernel(backend_ctx->program, "kernel_rope_vision_f16", &err), err));
CL_CHECK((backend_ctx->kernel_cpy_f16_f16 = clCreateKernel(backend_ctx->program, "kernel_cpy_f16_f16", &err), err));
CL_CHECK((backend_ctx->kernel_cpy_f16_f32 = clCreateKernel(backend_ctx->program, "kernel_cpy_f16_f32", &err), err));
CL_CHECK((backend_ctx->kernel_cpy_f32_f16 = clCreateKernel(backend_ctx->program, "kernel_cpy_f32_f16", &err), err));
@@ -769,6 +779,19 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
CL_CHECK((backend_ctx->kernel_convert_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_2, "kernel_convert_block_q4_0_noshuffle", &err), err));
// im2col kernels
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src_im2col {
#include "ggml-opencl_im2col.cl.h"
};
#else
const std::string kernel_src_im2col = read_file("ggml-opencl_im2col.cl");
#endif
backend_ctx->program_im2col = build_program_from_source(context, device, kernel_src_im2col.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_im2col_f32 = clCreateKernel(backend_ctx->program_im2col, "kernel_im2col_f32", &err), err));
CL_CHECK((backend_ctx->kernel_im2col_f16 = clCreateKernel(backend_ctx->program_im2col, "kernel_im2col_f16", &err), err));
// Kernels for Adreno
#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
#ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1187,6 +1210,7 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
case GGML_UNARY_OP_GELU:
case GGML_UNARY_OP_SILU:
case GGML_UNARY_OP_RELU:
case GGML_UNARY_OP_GELU_QUICK:
return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
default:
return false;
@@ -1216,14 +1240,26 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
return op->ne[3] == 1;
case GGML_OP_ROPE: {
const int mode = ((const int32_t *) op->op_params)[2];
if (mode & GGML_ROPE_TYPE_MROPE) {
const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE;
const bool is_vision = mode == GGML_ROPE_TYPE_VISION;
if (is_mrope && !is_vision) {
if (op->src[0]->type == GGML_TYPE_F32 ||
op->src[0]->type == GGML_TYPE_F16) {
return true;
}
return false;
}
if (mode & GGML_ROPE_TYPE_VISION) {
if (is_vision) {
if (op->src[0]->type == GGML_TYPE_F32 ||
op->src[0]->type == GGML_TYPE_F16) {
return true;
}
return false;
}
return true;
}
case GGML_OP_IM2COL:
return true;
default:
return false;
}
@@ -2582,6 +2618,53 @@ static void ggml_cl_gelu(ggml_backend_t backend, const ggml_tensor * src0, const
#endif
}
static void ggml_cl_gelu_quick(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);
UNUSED(src1);
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
cl_command_queue queue = backend_ctx->queue;
ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
cl_ulong offset0 = extra0->offset + src0->view_offs;
cl_ulong offsetd = extrad->offset + dst->view_offs;
cl_kernel kernel;
int n = ggml_nelements(dst);
if (n % 4 == 0) {
kernel = backend_ctx->kernel_gelu_quick_4;
n /= 4;
} else {
kernel = backend_ctx->kernel_gelu_quick;
}
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
size_t global_work_size[] = {(size_t)n, 1, 1};
size_t local_work_size[] = {64, 1, 1};
#ifdef GGML_OPENCL_PROFILING
cl_event evt;
clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt);
g_profiling_info.emplace_back();
populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL);
#endif
}
static void ggml_cl_silu(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src0->extra);
@@ -3980,6 +4063,7 @@ static void ggml_cl_rope(ggml_backend_t backend, const ggml_tensor * src0, const
float attn_factor;
float beta_fast;
float beta_slow;
int32_t sections[4];
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
@@ -3987,23 +4071,23 @@ static void ggml_cl_rope(ggml_backend_t backend, const ggml_tensor * src0, const
memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
memcpy(&sections, (int32_t *) dst->op_params + 11, sizeof(int32_t)*4);
const bool is_neox = mode & 2;
const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE;
const bool is_vision = mode == GGML_ROPE_TYPE_VISION;
if (is_mrope) {
GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0);
}
if (is_vision) {
GGML_ASSERT(n_dims == ne00/2);
}
cl_kernel kernel;
if (!is_neox) {
switch (src0->type) {
case GGML_TYPE_F32:
kernel = backend_ctx->kernel_rope_norm_f32;
break;
case GGML_TYPE_F16:
kernel = backend_ctx->kernel_rope_norm_f16;
break;
default:
GGML_ASSERT(false);
};
} else {
if (is_neox) {
switch (src0->type) {
case GGML_TYPE_F32:
kernel = backend_ctx->kernel_rope_neox_f32;
@@ -4014,6 +4098,39 @@ static void ggml_cl_rope(ggml_backend_t backend, const ggml_tensor * src0, const
default:
GGML_ASSERT(false);
};
} else if (is_mrope && !is_vision) {
switch (src0->type) {
case GGML_TYPE_F32:
kernel = backend_ctx->kernel_rope_multi_f32;
break;
case GGML_TYPE_F16:
kernel = backend_ctx->kernel_rope_multi_f16;
break;
default:
GGML_ASSERT(false);
};
} else if (is_vision) {
switch (src0->type) {
case GGML_TYPE_F32:
kernel = backend_ctx->kernel_rope_vision_f32;
break;
case GGML_TYPE_F16:
kernel = backend_ctx->kernel_rope_vision_f16;
break;
default:
GGML_ASSERT(false);
}
} else {
switch (src0->type) {
case GGML_TYPE_F32:
kernel = backend_ctx->kernel_rope_norm_f32;
break;
case GGML_TYPE_F16:
kernel = backend_ctx->kernel_rope_norm_f16;
break;
default:
GGML_ASSERT(false);
};
}
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
@@ -4049,6 +4166,9 @@ static void ggml_cl_rope(ggml_backend_t backend, const ggml_tensor * src0, const
CL_CHECK(clSetKernelArg(kernel, 30, sizeof(float), &attn_factor));
CL_CHECK(clSetKernelArg(kernel, 31, sizeof(float), &beta_fast));
CL_CHECK(clSetKernelArg(kernel, 32, sizeof(float), &beta_slow));
if (is_mrope || is_vision) {
CL_CHECK(clSetKernelArg(kernel, 33, sizeof(int32_t)*4, &sections));
}
size_t global_work_size[] = {(size_t)ne01*nth, (size_t)ne02, (size_t)ne03};
size_t local_work_size[] = {(size_t)nth, 1, 1};
@@ -4064,6 +4184,98 @@ static void ggml_cl_rope(ggml_backend_t backend, const ggml_tensor * src0, const
#endif
}
static void ggml_cl_im2col(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_ASSERT(src0);
GGML_ASSERT(src1);
GGML_ASSERT(src1->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);
// src0 - filter, src1 - input
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F16 || dst->type == GGML_TYPE_F32);
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
cl_command_queue queue = backend_ctx->queue;
ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
cl_ulong offset1 = extra1->offset + src1->view_offs;
cl_ulong offsetd = extrad->offset + dst->view_offs;
const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
const cl_long IC = src1->ne[is_2D ? 2 : 1];
const cl_long IH = is_2D ? src1->ne[1] : 1;
const cl_long IW = src1->ne[0];
const cl_long KH = is_2D ? src0->ne[1] : 1;
const cl_long KW = src0->ne[0];
const cl_long OH = is_2D ? dst->ne[2] : 1;
const cl_long OW = dst->ne[1];
// nb is byte offset, src is type float32
const cl_ulong delta_offset = src1->nb[is_2D ? 2 : 1]/4;
const cl_long batch = src1->ne[is_2D ? 3 : 2];
const cl_ulong batch_offset = src1->nb[is_2D ? 3 : 2]/4;
const cl_long pelements = OW*KW*KH;
const cl_long CHW = IC*KH*KW;
cl_kernel kernel;
if(dst->type == GGML_TYPE_F16) {
kernel = backend_ctx->kernel_im2col_f16;
} else {
kernel = backend_ctx->kernel_im2col_f32;
}
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_ulong), &batch_offset));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &delta_offset));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_long), &IW));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_long), &IH));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_long), &IC));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_long), &OW));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_long), &OH));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_long), &KW));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_long), &KH));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_long), &pelements));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_long), &CHW));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &s0));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &s1));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &p0));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &p1));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int), &d0));
CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int), &d1));
const int num_blocks = (pelements + 256 - 1) / 256;
size_t global_work_size[] = {(size_t)num_blocks*256, (size_t)OH, (size_t)batch*IC};
size_t local_work_size[] = {256, 1, 1};
#ifdef GGML_OPENCL_PROFILING
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
g_profiling_info.emplace_back();
populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
#else
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
#endif
}
//------------------------------------------------------------------------------
// Op offloading
//------------------------------------------------------------------------------
@@ -4122,6 +4334,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
}
func = ggml_cl_gelu;
break;
case GGML_UNARY_OP_GELU_QUICK:
if (!any_on_device) {
return false;
}
func = ggml_cl_gelu_quick;
break;
case GGML_UNARY_OP_SILU:
if (!any_on_device) {
return false;
@@ -4194,6 +4412,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
}
func = ggml_cl_rope;
break;
case GGML_OP_IM2COL:
if (!any_on_device) {
return false;
}
func = ggml_cl_im2col;
break;
default:
return false;
}

389
ggml/src/ggml-opencl/kernels/ggml-opencl.cl
View File

@@ -404,6 +404,7 @@ kernel void kernel_scale(
// gelu
//------------------------------------------------------------------------------
#define GELU_COEF_A 0.044715f
#define GELU_QUICK_COEF -1.702f
#define SQRT_2_OVER_PI 0.79788456080286535587989211986876f
kernel void kernel_gelu(
@@ -434,6 +435,32 @@ kernel void kernel_gelu_4(
dst[get_global_id(0)] = 0.5f*x*(1.0f + tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
}
kernel void kernel_gelu_quick(
global float * src0,
ulong offset0,
global float * dst,
ulong offsetd
) {
src0 = (global float*)((global char*)src0 + offset0);
dst = (global float*)((global char*)dst + offsetd);
float x = src0[get_global_id(0)];
dst[get_global_id(0)] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
}
kernel void kernel_gelu_quick_4(
global float4 * src0,
ulong offset0,
global float4 * dst,
ulong offsetd
) {
src0 = (global float4*)((global char*)src0 + offset0);
dst = (global float4*)((global char*)dst + offsetd);
float4 x = src0[get_global_id(0)];
dst[get_global_id(0)] = x*(1.0f/(1.0f+exp(GELU_QUICK_COEF*x)));
}
//------------------------------------------------------------------------------
// silu
//------------------------------------------------------------------------------
@@ -1325,6 +1352,368 @@ kernel void kernel_rope_neox_f16(
}
}
kernel void kernel_rope_multi_f32(
global void * src0,
ulong offset0,
global int * src1,
ulong offset1,
global float * src2,
ulong offset2,
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne03,
ulong nb00,
ulong nb01,
ulong nb02,
ulong nb03,
int ne0,
int ne1,
int ne2,
int ne3,
ulong nb0,
ulong nb1,
ulong nb2,
ulong nb3,
int n_past,
int n_dims,
int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
float attn_factor,
float beta_fast,
float beta_slow,
int4 sections
) {
src0 = (global void*)((global char*)src0 + offset0);
src1 = (global int*)((global char*)src1 + offset1);
src2 = (global float*)((global char*)src2 + offset2);
dst = (global float*)((global char*)dst + offsetd);
int i3 = get_group_id(2);
int i2 = get_group_id(1);
int i1 = get_group_id(0);
float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
global int * pos = src1;
const int sect_dims = sections.s0 + sections.s1 + sections.s2 + sections.s3;
const int sec_w = sections.s1 + sections.s0;
float inv_ndims = -1.f/n_dims;
for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
if (i0 < n_dims) {
int ic = i0/2;
const int sector = (i0 / 2) % sect_dims;
float theta_base = 0.0f;
if (sector < sections.s0) {
theta_base = pos[i2];
}
else if (sector >= sections.s0 && sector < sec_w) {
theta_base = pos[i2 + ne2 * 1];
}
else if (sector >= sec_w && sector < sec_w + sections.s2) {
theta_base = pos[i2 + ne2 * 2];
}
else if (sector >= sec_w + sections.s2) {
theta_base = pos[i2 + ne2 * 3];
}
const float theta = theta_base * pow(freq_base, inv_ndims*i0);
const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
global float * src = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
global float * dst_data = (global float *)((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
const float x0 = src[0];
const float x1 = src[n_dims/2];
dst_data[0] = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
dst_data[n_dims/2] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
} else {
global float * const src = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
global float * dst_data = (global float *)((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}
kernel void kernel_rope_multi_f16(
global void * src0,
ulong offset0,
global int * src1,
ulong offset1,
global float * src2,
ulong offset2,
global half * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne03,
ulong nb00,
ulong nb01,
ulong nb02,
ulong nb03,
int ne0,
int ne1,
int ne2,
int ne3,
ulong nb0,
ulong nb1,
ulong nb2,
ulong nb3,
int n_past,
int n_dims,
int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
float attn_factor,
float beta_fast,
float beta_slow,
int4 sections
) {
src0 = (global void*)((global char*)src0 + offset0);
src1 = (global int*)((global char*)src1 + offset1);
src2 = (global float*)((global char*)src2 + offset2);
dst = (global float*)((global char*)dst + offsetd);
int i3 = get_group_id(2);
int i2 = get_group_id(1);
int i1 = get_group_id(0);
float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
global int * pos = src1;
const int sect_dims = sections.s0 + sections.s1 + sections.s2 + sections.s3;
const int sec_w = sections.s1 + sections.s0;
float inv_ndims = -1.f/n_dims;
for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
if (i0 < n_dims) {
int ic = i0/2;
const int sector = (i0 / 2) % sect_dims;
float theta_base = 0.0f;
if (sector < sections.s0) {
theta_base = pos[i2];
}
else if (sector >= sections.s0 && sector < sec_w) {
theta_base = pos[i2 + ne2 * 1];
}
else if (sector >= sec_w && sector < sec_w + sections.s2) {
theta_base = pos[i2 + ne2 * 2];
}
else if (sector >= sec_w + sections.s2) {
theta_base = pos[i2 + ne2 * 3];
}
const float theta = theta_base * pow(freq_base, inv_ndims*i0);
const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
float2 cos_sin_theta = rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
global half * src = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
global half * dst_data = (global half *)((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
const float x0 = src[0];
const float x1 = src[n_dims/2];
dst_data[0] = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
dst_data[n_dims/2] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
} else {
global half * const src = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
global half * dst_data = (global half *)((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
dst_data[0] = src[0];
dst_data[1] = src[1];
}
}
}
kernel void kernel_rope_vision_f32(
global void * src0,
ulong offset0,
global int * src1,
ulong offset1,
global float * src2,
ulong offset2,
global float * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne03,
ulong nb00,
ulong nb01,
ulong nb02,
ulong nb03,
int ne0,
int ne1,
int ne2,
int ne3,
ulong nb0,
ulong nb1,
ulong nb2,
ulong nb3,
int n_past,
int n_dims,
int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
float attn_factor,
float beta_fast,
float beta_slow,
int4 sections
) {
src0 = (global void*)((global char*)src0 + offset0);
src1 = (global int*)((global char*)src1 + offset1);
src2 = (global float*)((global char*)src2 + offset2);
dst = (global float*)((global char*)dst + offsetd);
int i3 = get_group_id(2);
int i2 = get_group_id(1);
int i1 = get_group_id(0);
float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
global int * pos = src1;
const int sect_dims = sections.s0 + sections.s1;
const int sec_w = sections.s1 + sections.s0;
float inv_ndims = -1.f/n_dims;
for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
int ic = i0/2;
const int sector = (i0/2) % sect_dims;
float theta_base = 0.0f;
if (sector < sections.s0) {
const int p = sector;
theta_base = pos[i2] * pow(freq_base, inv_ndims*2.0f*p);
} else if (sector >= sections.s0 && sector < sec_w) {
const int p = sector - sections.s0;
theta_base = pos[i2 + ne2] * pow(freq_base, inv_ndims*2.0f*p);
}
const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
float2 cos_sin_theta = rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
global float * src = (global float *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
global float * dst_data = (global float *)((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
const float x0 = src[0];
const float x1 = src[n_dims];
dst_data[0] = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
dst_data[n_dims] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
}
}
kernel void kernel_rope_vision_f16(
global void * src0,
ulong offset0,
global int * src1,
ulong offset1,
global float * src2,
ulong offset2,
global half * dst,
ulong offsetd,
int ne00,
int ne01,
int ne02,
int ne03,
ulong nb00,
ulong nb01,
ulong nb02,
ulong nb03,
int ne0,
int ne1,
int ne2,
int ne3,
ulong nb0,
ulong nb1,
ulong nb2,
ulong nb3,
int n_past,
int n_dims,
int n_ctx_orig,
float freq_base,
float freq_scale,
float ext_factor,
float attn_factor,
float beta_fast,
float beta_slow,
int4 sections
) {
src0 = (global void*)((global char*)src0 + offset0);
src1 = (global int*)((global char*)src1 + offset1);
src2 = (global float*)((global char*)src2 + offset2);
dst = (global float*)((global char*)dst + offsetd);
int i3 = get_group_id(2);
int i2 = get_group_id(1);
int i1 = get_group_id(0);
float2 corr_dims = rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow);
global int * pos = src1;
const int sect_dims = sections.s0 + sections.s1;
const int sec_w = sections.s1 + sections.s0;
float inv_ndims = -1.f/n_dims;
for (int i0 = 2*get_local_id(0); i0 < ne0; i0 += 2*get_local_size(0)) {
int ic = i0/2;
const int sector = (i0/2) % sect_dims;
float theta_base = 0.0f;
if (sector < sections.s0) {
const int p = sector;
theta_base = pos[i2] * pow(freq_base, inv_ndims*2.0f*p);
} else if (sector >= sections.s0 && sector < sec_w) {
const int p = sector - sections.s0;
theta_base = pos[i2 + ne2] * pow(freq_base, inv_ndims*2.0f*p);
}
const float freq_factor = src2 != src0 ? src2[ic] : 1.0f;
float2 cos_sin_theta = rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor);
global half * src = (global half *)((global char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00);
global half * dst_data = (global half *)((global char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0);
const float x0 = src[0];
const float x1 = src[n_dims];
dst_data[0] = x0*cos_sin_theta.s0 - x1*cos_sin_theta.s1;
dst_data[n_dims] = x0*cos_sin_theta.s1 + x1*cos_sin_theta.s0;
}
}
//------------------------------------------------------------------------------
// cpy
//------------------------------------------------------------------------------

146
ggml/src/ggml-opencl/kernels/ggml-opencl_im2col.cl Normal file
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#ifdef cl_khr_fp16
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#elif defined(cl_amd_fp16)
#pragma OPENCL EXTENSION cl_amd_fp16 : enable
#else
#error "Half precision floating point not supportedby OpenCL implementation on your device."
#endif
#ifdef cl_khr_subgroups
#pragma OPENCL EXTENSION cl_khr_subgroups : enable
#elif defined(cl_intel_subgroups)
#pragma OPENCL EXTENSION cl_intel_subgroups : enable
#else
#error "Subgroup not supported on your device."
#endif
#ifdef cl_intel_required_subgroup_size
// Always use subgroup size of 32 on Intel.
#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
#define INTEL_GPU 1
#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
#elif defined(cl_qcom_reqd_sub_group_size)
// Always use subgroups size of 64 on Adreno.
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#else
// TODO: do not know how to choose subgroup size on other GPUs.
#error "Selecting subgroup size is not supported on your device."
#endif
kernel void kernel_im2col_f32(
global float * src1,
ulong offset1,
global float * dst,
ulong offsetd,
ulong batch_offset,
ulong delta_offset,
long IW,
long IH,
long IC,
long OW,
long OH,
long KW,
long KH,
long pelements,
long CHW,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1
) {
// threadIdx.x + blockIdx.x * blockDim.x
long i = get_global_id(0);
if (i >= pelements) {
return;
}
src1 = (global float*)((global char*)src1 + offset1);
dst = (global float*)((global char*)dst + offsetd);
long ksize = OW * (KH > 1 ? KW : 1);
long kx = i / ksize;
long kd = kx * ksize;
long ky = (i - kd) / OW;
long ix = i % OW;
long oh = get_group_id(1);
long batch = get_group_id(2) / IC;
long ic = get_group_id(2) % IC;
long iiw = ix * s0 + kx * d0 - p0;
long iih = oh * s1 + ky * d1 - p1;
long offset_dst =
((batch * OH + oh) * OW + ix) * CHW +
(ic * (KW * KH) + ky * KW + kx);
if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
dst[offset_dst] = 0.0f;
} else {
long offset_src = ic * delta_offset + batch * batch_offset;
dst[offset_dst] = src1[offset_src + iih * IW + iiw];
}
}
kernel void kernel_im2col_f16(
global float * src1,
ulong offset1,
global half * dst,
ulong offsetd,
ulong batch_offset,
ulong delta_offset,
long IW,
long IH,
long IC,
long OW,
long OH,
long KW,
long KH,
long pelements,
long CHW,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1
) {
long i = get_global_id(0);
if (i >= pelements) {
return;
}
src1 = (global float*)((global char*)src1 + offset1);
dst = (global half*)((global char*)dst + offsetd);
long ksize = OW * (KH > 1 ? KW : 1);
long kx = i / ksize;
long kd = kx * ksize;
long ky = (i - kd) / OW;
long ix = i % OW;
long oh = get_group_id(1);
long batch = get_group_id(2) / IC;
long ic = get_group_id(2) % IC;
long iiw = ix * s0 + kx * d0 - p0;
long iih = oh * s1 + ky * d1 - p1;
long offset_dst =
((batch * OH + oh) * OW + ix) * CHW +
(ic * (KW * KH) + ky * KW + kx);
if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
dst[offset_dst] = 0.0f;
} else {
long offset_src = ic * delta_offset + batch * batch_offset;
dst[offset_dst] = src1[offset_src + iih * IW + iiw];
}
}