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OpenCL: add initial FA support (#14987)

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* add F16/F16 fa support

* fix kernel init

* use mad instead of fma

* use inline function

* mark FA with sinks as unsupported for now

* add pragma unroll to loops
This commit is contained in:
rmatif
2025-08-16 10:05:55 +02:00
committed by GitHub
parent 5e6229a840
commit 912ff8c119
5 changed files with 1283 additions and 0 deletions
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3
ggml/src/ggml-opencl/CMakeLists.txt
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@@ -112,6 +112,9 @@ set(GGML_OPENCL_KERNELS
mul_mat_f16_f32 mul_mat_f16_f32
conv2d conv2d
conv2d_f16_f32 conv2d_f16_f32
flash_attn_f32_f16
flash_attn_f16
flash_attn_f32
) )
foreach (K ${GGML_OPENCL_KERNELS}) foreach (K ${GGML_OPENCL_KERNELS})

248
ggml/src/ggml-opencl/ggml-opencl.cpp
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@@ -25,6 +25,7 @@
#include <vector> #include <vector>
#include <string> #include <string>
#include <cmath> #include <cmath>
#include <map>
#include <memory> #include <memory>
#include <charconv> #include <charconv>
#include <mutex> #include <mutex>
@@ -424,6 +425,14 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_diag_mask_inf, kernel_diag_mask_inf_8; cl_kernel kernel_diag_mask_inf, kernel_diag_mask_inf_8;
cl_kernel kernel_soft_max, kernel_soft_max_4; cl_kernel kernel_soft_max, kernel_soft_max_4;
cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16; cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16;
std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f16;
std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f16_q1;
std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32;
std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32_q1;
std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32_f16;
std::map<std::pair<int, int>, cl_kernel> kernels_flash_attn_f32_f16_q1;
std::map<std::pair<int, int>, int> kernels_flash_attn_bm;
std::map<std::pair<int, int>, int> kernels_flash_attn_bn;
cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0; cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0;
cl_kernel kernel_set_rows_f32, kernel_set_rows_f16; cl_kernel kernel_set_rows_f32, kernel_set_rows_f16;
cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16; cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16;
@@ -1308,6 +1317,73 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT("."); GGML_LOG_CONT(".");
} }
// flash_attn
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src_f16 {
#include "flash_attn_f16.cl.h"
};
const std::string kernel_src_f32 {
#include "flash_attn_f32.cl.h"
};
const std::string kernel_src_f32_f16 {
#include "flash_attn_f32_f16.cl.h"
};
#else
const std::string kernel_src_f16 = read_file("flash_attn_f16.cl");
const std::string kernel_src_f32 = read_file("flash_attn_f32.cl");
const std::string kernel_src_f32_f16 = read_file("flash_attn_f32_f16.cl");
#endif
if (!kernel_src_f16.empty() && !kernel_src_f32.empty() && !kernel_src_f32_f16.empty()) {
const struct { int dk; int dv; int bm; int bn; } fa_dims[] = {
{ 64, 64, 64, 64}, { 80, 80, 64, 32}, { 96, 96, 64, 32},
{112, 112, 32, 32}, {128, 128, 32, 32}, {192, 128, 16, 16},
{192, 192, 16, 16}, {256, 256, 16, 16},
};
for (size_t i = 0; i < sizeof(fa_dims)/sizeof(fa_dims[0]); ++i) {
const int dk = fa_dims[i].dk;
const int dv = fa_dims[i].dv;
const int bm = fa_dims[i].bm;
const int bn = fa_dims[i].bn;
std::string OPTS = compile_opts +
" -D DK=" + std::to_string(dk) +
" -D DV=" + std::to_string(dv) +
" -D BLOCK_M=" + std::to_string(bm) +
" -D BLOCK_N=" + std::to_string(bn);
cl_program prog_f16 = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f16.c_str(), OPTS);
cl_kernel k_f16, k_f16_q1;
CL_CHECK((k_f16 = clCreateKernel(prog_f16, "flash_attn_f16", &err), err));
CL_CHECK((k_f16_q1 = clCreateKernel(prog_f16, "flash_attn_f16_q1", &err), err));
backend_ctx->kernels_flash_attn_f16[{dk, dv}] = k_f16;
backend_ctx->kernels_flash_attn_f16_q1[{dk, dv}] = k_f16_q1;
CL_CHECK(clReleaseProgram(prog_f16));
cl_program prog_f32 = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f32.c_str(), OPTS);
cl_kernel k_f32, k_f32_q1;
CL_CHECK((k_f32 = clCreateKernel(prog_f32, "flash_attn_f32", &err), err));
CL_CHECK((k_f32_q1 = clCreateKernel(prog_f32, "flash_attn_f32_q1", &err), err));
backend_ctx->kernels_flash_attn_f32[{dk, dv}] = k_f32;
backend_ctx->kernels_flash_attn_f32_q1[{dk, dv}] = k_f32_q1;
CL_CHECK(clReleaseProgram(prog_f32));
cl_program prog_f32_f16 = build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src_f32_f16.c_str(), OPTS);
cl_kernel k_f32_f16, k_f32_f16_q1;
CL_CHECK((k_f32_f16 = clCreateKernel(prog_f32_f16, "flash_attn_f32_f16", &err), err));
CL_CHECK((k_f32_f16_q1 = clCreateKernel(prog_f32_f16, "flash_attn_f32_f16_q1", &err), err));
backend_ctx->kernels_flash_attn_f32_f16[{dk, dv}] = k_f32_f16;
backend_ctx->kernels_flash_attn_f32_f16_q1[{dk, dv}] = k_f32_f16_q1;
CL_CHECK(clReleaseProgram(prog_f32_f16));
backend_ctx->kernels_flash_attn_bm[{dk, dv}] = bm;
backend_ctx->kernels_flash_attn_bn[{dk, dv}] = bn;
}
GGML_LOG_CONT(".");
}
}
// argsort // argsort
{ {
#ifdef GGML_OPENCL_EMBED_KERNELS #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -2636,6 +2712,45 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
return op->src[0]->type == GGML_TYPE_F32; return op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_SUM_ROWS: case GGML_OP_SUM_ROWS:
return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]); return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]);
case GGML_OP_FLASH_ATTN_EXT:
{
if (op->src[4]) {
return false;
}
const ggml_tensor * q = op->src[0];
const ggml_tensor * k = op->src[1];
const ggml_tensor * v = op->src[2];
const int dk = q->ne[0];
const int dv = v->ne[0];
const struct { int dk; int dv; } supported_dims[] = {
{ 64, 64}, { 80, 80}, { 96, 96},
{112, 112}, {128, 128}, {192, 128},
{192, 192}, {256, 256},
};
bool dims_supported = false;
for (size_t i = 0; i < sizeof(supported_dims)/sizeof(supported_dims[0]); ++i) {
if (supported_dims[i].dk == dk && supported_dims[i].dv == dv) {
dims_supported = true;
break;
}
}
if (!dims_supported) {
return false;
}
const bool is_f32_f32 = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F32 &&
v->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
const bool is_f16_f16 = q->type == GGML_TYPE_F16 && k->type == GGML_TYPE_F16 &&
v->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16;
const bool is_f32_f16 = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F16 &&
v->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F32;
return is_f32_f32 || is_f16_f16 || is_f32_f16;
}
default: default:
return false; return false;
} }
@@ -5451,6 +5566,133 @@ static void ggml_cl_timestep_embedding(ggml_backend_t backend, const ggml_tensor
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, NULL, dst); backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, NULL, dst);
} }
static void ggml_cl_flash_attn(ggml_backend_t backend, const ggml_tensor * q, const ggml_tensor * k, ggml_tensor * dst) {
const ggml_tensor * v = dst->src[2];
const ggml_tensor * mask = dst->src[3];
GGML_ASSERT(q->extra);
GGML_ASSERT(k->extra);
GGML_ASSERT(v->extra);
GGML_ASSERT(dst->extra);
if (mask) {
GGML_ASSERT(mask->extra);
}
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
const int n_q = q->ne[1];
const int n_kv = k->ne[1];
const int d_head_q = q->ne[0];
const int d_head_v = v->ne[0];
const int n_head = q->ne[2];
const int n_head_kv = k->ne[2];
const int n_batch = q->ne[3];
cl_kernel kernel = NULL;
const bool is_f16 = q->type == GGML_TYPE_F16;
const bool is_mixed = q->type == GGML_TYPE_F32 && k->type == GGML_TYPE_F16;
const std::pair<int, int> dk_dv = {d_head_q, d_head_v};
if (n_q == 1) {
if (is_mixed) {
kernel = backend_ctx->kernels_flash_attn_f32_f16_q1.at(dk_dv);
} else if (is_f16) {
kernel = backend_ctx->kernels_flash_attn_f16_q1.at(dk_dv);
} else {
kernel = backend_ctx->kernels_flash_attn_f32_q1.at(dk_dv);
}
} else {
if (is_mixed) {
kernel = backend_ctx->kernels_flash_attn_f32_f16.at(dk_dv);
} else if (is_f16) {
kernel = backend_ctx->kernels_flash_attn_f16.at(dk_dv);
} else {
kernel = backend_ctx->kernels_flash_attn_f32.at(dk_dv);
}
}
GGML_ASSERT(kernel != NULL);
ggml_tensor_extra_cl * extra_q = (ggml_tensor_extra_cl *)q->extra;
ggml_tensor_extra_cl * extra_k = (ggml_tensor_extra_cl *)k->extra;
ggml_tensor_extra_cl * extra_v = (ggml_tensor_extra_cl *)v->extra;
ggml_tensor_extra_cl * extra_o = (ggml_tensor_extra_cl *)dst->extra;
ggml_tensor_extra_cl * extra_mask = mask ? (ggml_tensor_extra_cl *)mask->extra : NULL;
cl_ulong offset_q = extra_q->offset + q->view_offs;
cl_ulong offset_k = extra_k->offset + k->view_offs;
cl_ulong offset_v = extra_v->offset + v->view_offs;
cl_ulong offset_o = extra_o->offset + dst->view_offs;
cl_mem mask_buffer = extra_mask ? extra_mask->data_device : NULL;
cl_ulong offset_mask = extra_mask ? extra_mask->offset + mask->view_offs : 0;
const cl_ulong q_nb1 = q->nb[1], q_nb2 = q->nb[2], q_nb3 = q->nb[3];
const cl_ulong k_nb1 = k->nb[1], k_nb2 = k->nb[2], k_nb3 = k->nb[3];
const cl_ulong v_nb1 = v->nb[1], v_nb2 = v->nb[2], v_nb3 = v->nb[3];
const cl_ulong o_nb1 = dst->nb[1], o_nb2 = dst->nb[2], o_nb3 = dst->nb[3];
const cl_ulong mask_nb1 = mask ? mask->nb[1] : 0;
const cl_ulong mask_nb2 = mask ? mask->nb[2] : 0;
const cl_ulong mask_nb3 = mask ? mask->nb[3] : 0;
const int mask_ne2 = mask ? mask->ne[2] : 0;
const int mask_ne3 = mask ? mask->ne[3] : 0;
float scale, max_bias, logit_softcap;
const float * params = (const float *)dst->op_params;
scale = params[0];
max_bias = params[1];
logit_softcap = params[2];
const int is_causal = (mask == NULL && n_q > 1 && n_q == n_kv);
const int n_head_log2_val = n_head > 0 ? 1u << (int)floorf(log2f((float)n_head)) : 0;
const float n_head_log2_f = n_head_log2_val > 0 ? (float)n_head_log2_val : 1.0f;
const float m0 = powf(2.0f, -(max_bias) / n_head_log2_f);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2_f);
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra_q->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset_q));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra_k->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset_k));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra_v->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offset_v));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem), &extra_o->data_device));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &offset_o));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(float), &scale));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &n_q));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &n_kv));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &is_causal));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &n_head));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &q_nb1)); CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &q_nb2)); CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &q_nb3));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &k_nb1)); CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &k_nb2)); CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &k_nb3));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &v_nb1)); CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &v_nb2)); CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &v_nb3));
CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &o_nb1)); CL_CHECK(clSetKernelArg(kernel, 23, sizeof(cl_ulong), &o_nb2)); CL_CHECK(clSetKernelArg(kernel, 24, sizeof(cl_ulong), &o_nb3));
CL_CHECK(clSetKernelArg(kernel, 25, sizeof(float), &max_bias));
CL_CHECK(clSetKernelArg(kernel, 26, sizeof(float), &m0));
CL_CHECK(clSetKernelArg(kernel, 27, sizeof(float), &m1));
CL_CHECK(clSetKernelArg(kernel, 28, sizeof(int), &n_head_log2_val));
CL_CHECK(clSetKernelArg(kernel, 29, sizeof(float), &logit_softcap));
CL_CHECK(clSetKernelArg(kernel, 30, sizeof(int), &n_head_kv));
CL_CHECK(clSetKernelArg(kernel, 31, sizeof(cl_mem), &mask_buffer));
CL_CHECK(clSetKernelArg(kernel, 32, sizeof(cl_ulong), &offset_mask));
CL_CHECK(clSetKernelArg(kernel, 33, sizeof(cl_ulong), &mask_nb1));
CL_CHECK(clSetKernelArg(kernel, 34, sizeof(cl_ulong), &mask_nb2));
CL_CHECK(clSetKernelArg(kernel, 35, sizeof(cl_ulong), &mask_nb3));
CL_CHECK(clSetKernelArg(kernel, 36, sizeof(int), &mask_ne2));
CL_CHECK(clSetKernelArg(kernel, 37, sizeof(int), &mask_ne3));
if (n_q == 1) {
const size_t wg_size = 64;
size_t local_work_size[] = { wg_size, 1 };
size_t global_work_size[] = { wg_size, (size_t)(n_head * n_batch) };
backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
} else {
const int block_m = backend_ctx->kernels_flash_attn_bm.at(dk_dv);
const size_t wg_size = block_m;
size_t local_work_size[] = { wg_size, 1 };
size_t global_work_size[] = { (size_t)((n_q + block_m - 1) / block_m) * wg_size, (size_t)(n_head * n_batch) };
backend_ctx->enqueue_ndrange_kernel(kernel, 2, global_work_size, local_work_size, dst);
}
}
static void ggml_cl_mul_mat_f16_f32_tiled(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { static void ggml_cl_mul_mat_f16_f32_tiled(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context; ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
@@ -7607,6 +7849,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
} }
func = ggml_cl_sum_rows; func = ggml_cl_sum_rows;
break; break;
case GGML_OP_FLASH_ATTN_EXT:
if (!any_on_device) {
return false;
}
ggml_cl_flash_attn(backend, tensor->src[0], tensor->src[1], tensor);
return true;
default: default:
return false; return false;
} }

343
ggml/src/ggml-opencl/kernels/flash_attn_f16.cl Normal file
View File

@@ -0,0 +1,343 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define ACC_TYPE float
#define ACC_TYPE4 float4
#define DATA_TYPE half
#define DATA_TYPE4 half4
#define CONVERT_ACC4(x) convert_float4(x)
#define CONVERT_DATA4(x) convert_half4(x)
#define DK_VEC (DK/4)
#define DV_VEC (DV/4)
#define WG_SIZE (BLOCK_M)
#define Q1_WG_SIZE 64
inline float get_alibi_slope(
const float max_bias, const uint h, const uint n_head_log2, const float m0, const float m1
) {
if (max_bias <= 0.0f) {
return 1.0f;
}
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
return pow(base, exph);
}
__kernel void flash_attn_f16(
const global void * q_void, ulong q_offset,
const global void * k_void, ulong k_offset,
const global void * v_void, ulong v_offset,
global void * o_void, ulong o_offset,
const float scale,
const int n_q,
const int n_kv,
const int is_causal,
const int n_head,
const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
const float max_bias,
const float m0,
const float m1,
const int n_head_log2,
const float logit_softcap,
const int n_head_kv,
const global void* mask_void,
const ulong mask_offset,
const ulong mask_nb1,
const ulong mask_nb2,
const ulong mask_nb3,
const int mask_ne2,
const int mask_ne3
) {
const int tid = get_local_id(0);
const int block_q_idx = get_group_id(0);
const int head_batch_idx = get_global_id(1);
const int my_query_row = block_q_idx * BLOCK_M + tid;
const int batch_idx = head_batch_idx / n_head;
const int head_idx = head_batch_idx % n_head;
const int gqa_ratio = n_head / n_head_kv;
const int head_kv_idx = head_idx / gqa_ratio;
const global char* q_base = (const global char*)q_void + q_offset;
const global char* k_base = (const global char*)k_void + k_offset;
const global char* v_base = (const global char*)v_void + v_offset;
global char* o_base = (global char*)o_void + o_offset;
const global char* mask_base = NULL;
if (mask_void != NULL) {
const int mask_head_idx = head_idx % mask_ne2;
const int mask_batch_idx = batch_idx % mask_ne3;
mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
}
ACC_TYPE4 q_priv[DK_VEC];
if (my_query_row < n_q) {
const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
#pragma unroll
for (int i = 0; i < DK_VEC; ++i) {
q_priv[i] = CONVERT_ACC4(q_ptr[i]);
}
}
ACC_TYPE4 o_acc[DV_VEC];
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_acc[i] = (ACC_TYPE4)(0.0f);
}
ACC_TYPE m_i = -INFINITY;
ACC_TYPE l_i = 0.0f;
float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
__local DATA_TYPE4 l_k[BLOCK_N][DK_VEC];
__local DATA_TYPE4 l_v[BLOCK_N][DV_VEC];
for (int k_start = 0; k_start < n_kv; k_start += BLOCK_N) {
for (int i = tid; i < BLOCK_N * DK_VEC; i += WG_SIZE) {
const int row = i / DK_VEC;
const int col = i % DK_VEC;
const int k_row_idx = k_start + row;
if (k_row_idx < n_kv) {
const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
l_k[row][col] = ((__global DATA_TYPE4*)(k_base + k_row_offset))[col];
}
}
for (int i = tid; i < BLOCK_N * DV_VEC; i += WG_SIZE) {
const int row = i / DV_VEC;
const int col = i % DV_VEC;
const int v_row_idx = k_start + row;
if (v_row_idx < n_kv) {
const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + v_row_idx * v_nb1;
l_v[row][col] = ((__global DATA_TYPE4*)(v_base + v_row_offset))[col];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (my_query_row >= n_q) {
continue;
}
for (int j = 0; j < BLOCK_N; j += 2) {
const int k_row0 = k_start + j;
const int k_row1 = k_start + j + 1;
ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
#pragma unroll
for (int k = 0; k < DK_VEC; k++) {
dot_acc0 = mad(q_priv[k], CONVERT_ACC4(l_k[j][k]), dot_acc0);
dot_acc1 = mad(q_priv[k], CONVERT_ACC4(l_k[j+1][k]), dot_acc1);
}
ACC_TYPE score0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
ACC_TYPE score1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
if (is_causal) {
if (k_row0 > (n_kv - n_q + my_query_row)) score0 = -INFINITY;
if (k_row1 > (n_kv - n_q + my_query_row)) score1 = -INFINITY;
}
if (k_row0 >= n_kv) score0 = -INFINITY;
if (k_row1 >= n_kv) score1 = -INFINITY;
if (mask_base != NULL) {
const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0];
if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1];
}
if (logit_softcap > 0.0f) {
score0 = logit_softcap * tanh(score0 / logit_softcap);
score1 = logit_softcap * tanh(score1 / logit_softcap);
}
const ACC_TYPE m_new = max(m_i, max(score0, score1));
const ACC_TYPE p0 = exp(score0 - m_new);
const ACC_TYPE p1 = exp(score1 - m_new);
const ACC_TYPE scale_prev = exp(m_i - m_new);
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_acc[i] = o_acc[i] * scale_prev + p0 * CONVERT_ACC4(l_v[j][i]) + p1 * CONVERT_ACC4(l_v[j+1][i]);
}
l_i = l_i * scale_prev + p0 + p1;
m_i = m_new;
}
}
if (my_query_row < n_q) {
const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
if (l_i > 0.0f) {
const ACC_TYPE l_inv = 1.0f / l_i;
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_row[i] = CONVERT_DATA4(o_acc[i] * l_inv);
}
} else {
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_row[i] = (DATA_TYPE4)(0.0f);
}
}
}
}
__kernel void flash_attn_f16_q1(
const global void * q_void, ulong q_offset,
const global void * k_void, ulong k_offset,
const global void * v_void, ulong v_offset,
global void * o_void, ulong o_offset,
const float scale,
const int n_q,
const int n_kv,
const int is_causal,
const int n_head,
const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
const float max_bias,
const float m0,
const float m1,
const int n_head_log2,
const float logit_softcap,
const int n_head_kv,
const global void* mask_void,
const ulong mask_offset,
const ulong mask_nb1,
const ulong mask_nb2,
const ulong mask_nb3,
const int mask_ne2,
const int mask_ne3
) {
const int tid = get_local_id(0);
const int head_batch_idx = get_global_id(1);
const int batch_idx = head_batch_idx / n_head;
const int head_idx = head_batch_idx % n_head;
const int gqa_ratio = n_head / n_head_kv;
const int head_kv_idx = head_idx / gqa_ratio;
const global char* q_base = (const global char*)q_void + q_offset;
const global char* k_base = (const global char*)k_void + k_offset;
const global char* v_base = (const global char*)v_void + v_offset;
global char* o_base = (global char*)o_void + o_offset;
const global char* mask_base = NULL;
if (mask_void != NULL) {
const int mask_head_idx = head_idx % mask_ne2;
const int mask_batch_idx = batch_idx % mask_ne3;
mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
}
ACC_TYPE4 q_priv[DK_VEC];
const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
#pragma unroll
for (int i = 0; i < DK_VEC; ++i) {
q_priv[i] = CONVERT_ACC4(q_ptr[i]);
}
float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
ACC_TYPE m_i = -INFINITY;
for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
#pragma unroll
for (int k = 0; k < DK_VEC; k++) {
dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
}
ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
if (mask_base != NULL) {
const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base);
score += slope * (ACC_TYPE)mask_ptr[k_idx];
}
if (logit_softcap > 0.0f) {
score = logit_softcap * tanh(score / logit_softcap);
}
m_i = max(m_i, score);
}
__local ACC_TYPE local_m[Q1_WG_SIZE];
local_m[tid] = m_i;
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
barrier(CLK_LOCAL_MEM_FENCE);
}
const ACC_TYPE m_final = local_m[0];
ACC_TYPE4 o_acc[DV_VEC];
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
ACC_TYPE l_i = 0.0f;
for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
const global DATA_TYPE4* v_ptr = (const global DATA_TYPE4*)(v_base + v_row_offset);
ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
#pragma unroll
for (int k = 0; k < DK_VEC; k++) {
dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
}
ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
if (mask_base != NULL) {
const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base);
score += slope * (ACC_TYPE)mask_ptr[k_idx];
}
if (logit_softcap > 0.0f) {
score = logit_softcap * tanh(score / logit_softcap);
}
const ACC_TYPE p = exp(score - m_final);
l_i += p;
#pragma unroll
for (int i = 0; i < DV_VEC; i++) {
o_acc[i] = mad(p, CONVERT_ACC4(v_ptr[i]), o_acc[i]);
}
}
__local ACC_TYPE local_l[Q1_WG_SIZE];
__local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
local_l[tid] = l_i;
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) local_l[tid] += local_l[tid + s];
barrier(CLK_LOCAL_MEM_FENCE);
}
const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
const ACC_TYPE l_final = local_l[0];
if (l_final > 0.0f) {
const ACC_TYPE l_inv = 1.0f / l_final;
for (int i = 0; i < DV_VEC; i++) {
local_o_comp[tid] = o_acc[i];
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0) {
o_row[i] = CONVERT_DATA4(local_o_comp[0] * l_inv);
}
}
} else if (tid == 0) {
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) o_row[i] = (DATA_TYPE4)(0.0f);
}
}

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ggml/src/ggml-opencl/kernels/flash_attn_f32.cl Normal file
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#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define ACC_TYPE float
#define ACC_TYPE4 float4
#define DATA_TYPE float
#define DATA_TYPE4 float4
#define CONVERT_ACC4(x) (x)
#define CONVERT_DATA4(x) (x)
#define DK_VEC (DK/4)
#define DV_VEC (DV/4)
#define WG_SIZE (BLOCK_M)
#define Q1_WG_SIZE 64
inline float get_alibi_slope(
const float max_bias, const uint h, const uint n_head_log2, const float m0, const float m1
) {
if (max_bias <= 0.0f) {
return 1.0f;
}
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
return pow(base, exph);
}
__kernel void flash_attn_f32(
const global void * q_void, ulong q_offset,
const global void * k_void, ulong k_offset,
const global void * v_void, ulong v_offset,
global void * o_void, ulong o_offset,
const float scale,
const int n_q,
const int n_kv,
const int is_causal,
const int n_head,
const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
const float max_bias,
const float m0,
const float m1,
const int n_head_log2,
const float logit_softcap,
const int n_head_kv,
const global void* mask_void,
const ulong mask_offset,
const ulong mask_nb1,
const ulong mask_nb2,
const ulong mask_nb3,
const int mask_ne2,
const int mask_ne3
) {
const int tid = get_local_id(0);
const int block_q_idx = get_group_id(0);
const int head_batch_idx = get_global_id(1);
const int my_query_row = block_q_idx * BLOCK_M + tid;
const int batch_idx = head_batch_idx / n_head;
const int head_idx = head_batch_idx % n_head;
const int gqa_ratio = n_head / n_head_kv;
const int head_kv_idx = head_idx / gqa_ratio;
const global char* q_base = (const global char*)q_void + q_offset;
const global char* k_base = (const global char*)k_void + k_offset;
const global char* v_base = (const global char*)v_void + v_offset;
global char* o_base = (global char*)o_void + o_offset;
const global char* mask_base = NULL;
if (mask_void != NULL) {
const int mask_head_idx = head_idx % mask_ne2;
const int mask_batch_idx = batch_idx % mask_ne3;
mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
}
ACC_TYPE4 q_priv[DK_VEC];
if (my_query_row < n_q) {
const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
#pragma unroll
for (int i = 0; i < DK_VEC; ++i) {
q_priv[i] = CONVERT_ACC4(q_ptr[i]);
}
}
ACC_TYPE4 o_acc[DV_VEC];
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_acc[i] = (ACC_TYPE4)(0.0f);
}
ACC_TYPE m_i = -INFINITY;
ACC_TYPE l_i = 0.0f;
float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
__local DATA_TYPE4 l_k[BLOCK_N][DK_VEC];
__local DATA_TYPE4 l_v[BLOCK_N][DV_VEC];
for (int k_start = 0; k_start < n_kv; k_start += BLOCK_N) {
for (int i = tid; i < BLOCK_N * DK_VEC; i += WG_SIZE) {
const int row = i / DK_VEC;
const int col = i % DK_VEC;
const int k_row_idx = k_start + row;
if (k_row_idx < n_kv) {
const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
l_k[row][col] = ((__global DATA_TYPE4*)(k_base + k_row_offset))[col];
}
}
for (int i = tid; i < BLOCK_N * DV_VEC; i += WG_SIZE) {
const int row = i / DV_VEC;
const int col = i % DV_VEC;
const int v_row_idx = k_start + row;
if (v_row_idx < n_kv) {
const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + v_row_idx * v_nb1;
l_v[row][col] = ((__global DATA_TYPE4*)(v_base + v_row_offset))[col];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (my_query_row >= n_q) {
continue;
}
for (int j = 0; j < BLOCK_N; j += 2) {
const int k_row0 = k_start + j;
const int k_row1 = k_start + j + 1;
ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
#pragma unroll
for (int k = 0; k < DK_VEC; k++) {
dot_acc0 = mad(q_priv[k], CONVERT_ACC4(l_k[j][k]), dot_acc0);
dot_acc1 = mad(q_priv[k], CONVERT_ACC4(l_k[j+1][k]), dot_acc1);
}
ACC_TYPE score0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
ACC_TYPE score1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
if (is_causal) {
if (k_row0 > (n_kv - n_q + my_query_row)) score0 = -INFINITY;
if (k_row1 > (n_kv - n_q + my_query_row)) score1 = -INFINITY;
}
if (k_row0 >= n_kv) score0 = -INFINITY;
if (k_row1 >= n_kv) score1 = -INFINITY;
if (mask_base != NULL) {
const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0];
if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1];
}
if (logit_softcap > 0.0f) {
score0 = logit_softcap * tanh(score0 / logit_softcap);
score1 = logit_softcap * tanh(score1 / logit_softcap);
}
const ACC_TYPE m_new = max(m_i, max(score0, score1));
const ACC_TYPE p0 = exp(score0 - m_new);
const ACC_TYPE p1 = exp(score1 - m_new);
const ACC_TYPE scale_prev = exp(m_i - m_new);
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_acc[i] = o_acc[i] * scale_prev + p0 * CONVERT_ACC4(l_v[j][i]) + p1 * CONVERT_ACC4(l_v[j+1][i]);
}
l_i = l_i * scale_prev + p0 + p1;
m_i = m_new;
}
}
if (my_query_row < n_q) {
const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
if (l_i > 0.0f) {
const ACC_TYPE l_inv = 1.0f / l_i;
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_row[i] = CONVERT_DATA4(o_acc[i] * l_inv);
}
} else {
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_row[i] = (DATA_TYPE4)(0.0f);
}
}
}
}
__kernel void flash_attn_f32_q1(
const global void * q_void, ulong q_offset,
const global void * k_void, ulong k_offset,
const global void * v_void, ulong v_offset,
global void * o_void, ulong o_offset,
const float scale,
const int n_q,
const int n_kv,
const int is_causal,
const int n_head,
const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
const float max_bias,
const float m0,
const float m1,
const int n_head_log2,
const float logit_softcap,
const int n_head_kv,
const global void* mask_void,
const ulong mask_offset,
const ulong mask_nb1,
const ulong mask_nb2,
const ulong mask_nb3,
const int mask_ne2,
const int mask_ne3
) {
const int tid = get_local_id(0);
const int head_batch_idx = get_global_id(1);
const int batch_idx = head_batch_idx / n_head;
const int head_idx = head_batch_idx % n_head;
const int gqa_ratio = n_head / n_head_kv;
const int head_kv_idx = head_idx / gqa_ratio;
const global char* q_base = (const global char*)q_void + q_offset;
const global char* k_base = (const global char*)k_void + k_offset;
const global char* v_base = (const global char*)v_void + v_offset;
global char* o_base = (global char*)o_void + o_offset;
const global char* mask_base = NULL;
if (mask_void != NULL) {
const int mask_head_idx = head_idx % mask_ne2;
const int mask_batch_idx = batch_idx % mask_ne3;
mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
}
ACC_TYPE4 q_priv[DK_VEC];
const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
const global DATA_TYPE4* q_ptr = (const global DATA_TYPE4*)(q_base + q_row_offset);
#pragma unroll
for (int i = 0; i < DK_VEC; ++i) {
q_priv[i] = CONVERT_ACC4(q_ptr[i]);
}
float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
ACC_TYPE m_i = -INFINITY;
for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
#pragma unroll
for (int k = 0; k < DK_VEC; k++) {
dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
}
ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
if (mask_base != NULL) {
const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base);
score += slope * (ACC_TYPE)mask_ptr[k_idx];
}
if (logit_softcap > 0.0f) {
score = logit_softcap * tanh(score / logit_softcap);
}
m_i = max(m_i, score);
}
__local ACC_TYPE local_m[Q1_WG_SIZE];
local_m[tid] = m_i;
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
barrier(CLK_LOCAL_MEM_FENCE);
}
const ACC_TYPE m_final = local_m[0];
ACC_TYPE4 o_acc[DV_VEC];
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
ACC_TYPE l_i = 0.0f;
for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
const global DATA_TYPE4* k_ptr = (const global DATA_TYPE4*)(k_base + k_row_offset);
const global DATA_TYPE4* v_ptr = (const global DATA_TYPE4*)(v_base + v_row_offset);
ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
#pragma unroll
for (int k = 0; k < DK_VEC; k++) {
dot_acc = mad(q_priv[k], CONVERT_ACC4(k_ptr[k]), dot_acc);
}
ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
if (mask_base != NULL) {
const global DATA_TYPE* mask_ptr = (const global DATA_TYPE*)(mask_base);
score += slope * (ACC_TYPE)mask_ptr[k_idx];
}
if (logit_softcap > 0.0f) {
score = logit_softcap * tanh(score / logit_softcap);
}
const ACC_TYPE p = exp(score - m_final);
l_i += p;
#pragma unroll
for (int i = 0; i < DV_VEC; i++) {
o_acc[i] = mad(p, CONVERT_ACC4(v_ptr[i]), o_acc[i]);
}
}
__local ACC_TYPE local_l[Q1_WG_SIZE];
__local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
local_l[tid] = l_i;
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) local_l[tid] += local_l[tid + s];
barrier(CLK_LOCAL_MEM_FENCE);
}
const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
global DATA_TYPE4 *o_row = (global DATA_TYPE4 *)(o_base + o_row_offset);
const ACC_TYPE l_final = local_l[0];
if (l_final > 0.0f) {
const ACC_TYPE l_inv = 1.0f / l_final;
for (int i = 0; i < DV_VEC; i++) {
local_o_comp[tid] = o_acc[i];
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0) {
o_row[i] = CONVERT_DATA4(local_o_comp[0] * l_inv);
}
}
} else if (tid == 0) {
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) o_row[i] = (DATA_TYPE4)(0.0f);
}
}

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ggml/src/ggml-opencl/kernels/flash_attn_f32_f16.cl Normal file
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#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#define ACC_TYPE float
#define ACC_TYPE4 float4
#define Q_DATA_TYPE4 float4
#define KV_DATA_TYPE4 half4
#define O_DATA_TYPE4 float4
#define MASK_DATA_TYPE half
#define CONVERT_Q_ACC4(x) (x)
#define CONVERT_KV_ACC4(x) convert_float4(x)
#define CONVERT_O_DATA4(x) (x)
#define DK_VEC (DK/4)
#define DV_VEC (DV/4)
#define WG_SIZE (BLOCK_M)
#define Q1_WG_SIZE 64
inline float get_alibi_slope(
const float max_bias, const uint h, const uint n_head_log2, const float m0, const float m1
) {
if (max_bias <= 0.0f) {
return 1.0f;
}
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
return pow(base, exph);
}
__kernel void flash_attn_f32_f16(
const global void * q_void, ulong q_offset,
const global void * k_void, ulong k_offset,
const global void * v_void, ulong v_offset,
global void * o_void, ulong o_offset,
const float scale,
const int n_q,
const int n_kv,
const int is_causal,
const int n_head,
const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
const float max_bias,
const float m0,
const float m1,
const int n_head_log2,
const float logit_softcap,
const int n_head_kv,
const global void* mask_void,
const ulong mask_offset,
const ulong mask_nb1,
const ulong mask_nb2,
const ulong mask_nb3,
const int mask_ne2,
const int mask_ne3
) {
const int tid = get_local_id(0);
const int block_q_idx = get_group_id(0);
const int head_batch_idx = get_global_id(1);
const int my_query_row = block_q_idx * BLOCK_M + tid;
const int batch_idx = head_batch_idx / n_head;
const int head_idx = head_batch_idx % n_head;
const int gqa_ratio = n_head / n_head_kv;
const int head_kv_idx = head_idx / gqa_ratio;
const global char* q_base = (const global char*)q_void + q_offset;
const global char* k_base = (const global char*)k_void + k_offset;
const global char* v_base = (const global char*)v_void + v_offset;
global char* o_base = (global char*)o_void + o_offset;
const global char* mask_base = NULL;
if (mask_void != NULL) {
const int mask_head_idx = head_idx % mask_ne2;
const int mask_batch_idx = batch_idx % mask_ne3;
mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
}
ACC_TYPE4 q_priv[DK_VEC];
if (my_query_row < n_q) {
const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2 + my_query_row * q_nb1;
const global Q_DATA_TYPE4* q_ptr = (const global Q_DATA_TYPE4*)(q_base + q_row_offset);
#pragma unroll
for (int i = 0; i < DK_VEC; ++i) {
q_priv[i] = CONVERT_Q_ACC4(q_ptr[i]);
}
}
ACC_TYPE4 o_acc[DV_VEC];
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_acc[i] = (ACC_TYPE4)(0.0f);
}
ACC_TYPE m_i = -INFINITY;
ACC_TYPE l_i = 0.0f;
float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
__local KV_DATA_TYPE4 l_k[BLOCK_N][DK_VEC];
__local KV_DATA_TYPE4 l_v[BLOCK_N][DV_VEC];
for (int k_start = 0; k_start < n_kv; k_start += BLOCK_N) {
for (int i = tid; i < BLOCK_N * DK_VEC; i += WG_SIZE) {
const int row = i / DK_VEC;
const int col = i % DK_VEC;
const int k_row_idx = k_start + row;
if (k_row_idx < n_kv) {
const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_row_idx * k_nb1;
l_k[row][col] = ((__global KV_DATA_TYPE4*)(k_base + k_row_offset))[col];
}
}
for (int i = tid; i < BLOCK_N * DV_VEC; i += WG_SIZE) {
const int row = i / DV_VEC;
const int col = i % DV_VEC;
const int v_row_idx = k_start + row;
if (v_row_idx < n_kv) {
const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + v_row_idx * v_nb1;
l_v[row][col] = ((__global KV_DATA_TYPE4*)(v_base + v_row_offset))[col];
}
}
barrier(CLK_LOCAL_MEM_FENCE);
if (my_query_row >= n_q) {
continue;
}
for (int j = 0; j < BLOCK_N; j += 2) {
const int k_row0 = k_start + j;
const int k_row1 = k_start + j + 1;
ACC_TYPE4 dot_acc0 = (ACC_TYPE4)(0.0f);
ACC_TYPE4 dot_acc1 = (ACC_TYPE4)(0.0f);
#pragma unroll
for (int k = 0; k < DK_VEC; k++) {
dot_acc0 = mad(q_priv[k], CONVERT_KV_ACC4(l_k[j][k]), dot_acc0);
dot_acc1 = mad(q_priv[k], CONVERT_KV_ACC4(l_k[j+1][k]), dot_acc1);
}
ACC_TYPE score0 = (dot_acc0.s0 + dot_acc0.s1 + dot_acc0.s2 + dot_acc0.s3) * scale;
ACC_TYPE score1 = (dot_acc1.s0 + dot_acc1.s1 + dot_acc1.s2 + dot_acc1.s3) * scale;
if (is_causal) {
if (k_row0 > (n_kv - n_q + my_query_row)) score0 = -INFINITY;
if (k_row1 > (n_kv - n_q + my_query_row)) score1 = -INFINITY;
}
if (k_row0 >= n_kv) score0 = -INFINITY;
if (k_row1 >= n_kv) score1 = -INFINITY;
if (mask_base != NULL) {
const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base + my_query_row * mask_nb1);
if (k_row0 < n_kv) score0 += slope * (ACC_TYPE)mask_ptr[k_row0];
if (k_row1 < n_kv) score1 += slope * (ACC_TYPE)mask_ptr[k_row1];
}
if (logit_softcap > 0.0f) {
score0 = logit_softcap * tanh(score0 / logit_softcap);
score1 = logit_softcap * tanh(score1 / logit_softcap);
}
const ACC_TYPE m_new = max(m_i, max(score0, score1));
const ACC_TYPE p0 = exp(score0 - m_new);
const ACC_TYPE p1 = exp(score1 - m_new);
const ACC_TYPE scale_prev = exp(m_i - m_new);
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_acc[i] = o_acc[i] * scale_prev + p0 * CONVERT_KV_ACC4(l_v[j][i]) + p1 * CONVERT_KV_ACC4(l_v[j+1][i]);
}
l_i = l_i * scale_prev + p0 + p1;
m_i = m_new;
}
}
if (my_query_row < n_q) {
const ulong o_row_offset = batch_idx * o_nb3 + my_query_row * o_nb2 + head_idx * o_nb1;
global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
if (l_i > 0.0f) {
const ACC_TYPE l_inv = 1.0f / l_i;
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_row[i] = CONVERT_O_DATA4(o_acc[i] * l_inv);
}
} else {
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) {
o_row[i] = (O_DATA_TYPE4)(0.0f);
}
}
}
}
__kernel void flash_attn_f32_f16_q1(
const global void * q_void, ulong q_offset,
const global void * k_void, ulong k_offset,
const global void * v_void, ulong v_offset,
global void * o_void, ulong o_offset,
const float scale,
const int n_q,
const int n_kv,
const int is_causal,
const int n_head,
const ulong q_nb1, const ulong q_nb2, const ulong q_nb3,
const ulong k_nb1, const ulong k_nb2, const ulong k_nb3,
const ulong v_nb1, const ulong v_nb2, const ulong v_nb3,
const ulong o_nb1, const ulong o_nb2, const ulong o_nb3,
const float max_bias,
const float m0,
const float m1,
const int n_head_log2,
const float logit_softcap,
const int n_head_kv,
const global void* mask_void,
const ulong mask_offset,
const ulong mask_nb1,
const ulong mask_nb2,
const ulong mask_nb3,
const int mask_ne2,
const int mask_ne3
) {
const int tid = get_local_id(0);
const int head_batch_idx = get_global_id(1);
const int batch_idx = head_batch_idx / n_head;
const int head_idx = head_batch_idx % n_head;
const int gqa_ratio = n_head / n_head_kv;
const int head_kv_idx = head_idx / gqa_ratio;
const global char* q_base = (const global char*)q_void + q_offset;
const global char* k_base = (const global char*)k_void + k_offset;
const global char* v_base = (const global char*)v_void + v_offset;
global char* o_base = (global char*)o_void + o_offset;
const global char* mask_base = NULL;
if (mask_void != NULL) {
const int mask_head_idx = head_idx % mask_ne2;
const int mask_batch_idx = batch_idx % mask_ne3;
mask_base = (const global char*)mask_void + mask_offset + mask_batch_idx * mask_nb3 + mask_head_idx * mask_nb2;
}
ACC_TYPE4 q_priv[DK_VEC];
const ulong q_row_offset = batch_idx * q_nb3 + head_idx * q_nb2;
const global Q_DATA_TYPE4* q_ptr = (const global Q_DATA_TYPE4*)(q_base + q_row_offset);
#pragma unroll
for (int i = 0; i < DK_VEC; ++i) {
q_priv[i] = CONVERT_Q_ACC4(q_ptr[i]);
}
float slope = get_alibi_slope(max_bias, head_idx, n_head_log2, m0, m1);
ACC_TYPE m_i = -INFINITY;
for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
const global KV_DATA_TYPE4* k_ptr = (const global KV_DATA_TYPE4*)(k_base + k_row_offset);
ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
#pragma unroll
for (int k = 0; k < DK_VEC; k++) {
dot_acc = mad(q_priv[k], CONVERT_KV_ACC4(k_ptr[k]), dot_acc);
}
ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
if (mask_base != NULL) {
const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base);
score += slope * (ACC_TYPE)mask_ptr[k_idx];
}
if (logit_softcap > 0.0f) {
score = logit_softcap * tanh(score / logit_softcap);
}
m_i = max(m_i, score);
}
__local ACC_TYPE local_m[Q1_WG_SIZE];
local_m[tid] = m_i;
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) local_m[tid] = max(local_m[tid], local_m[tid + s]);
barrier(CLK_LOCAL_MEM_FENCE);
}
const ACC_TYPE m_final = local_m[0];
ACC_TYPE4 o_acc[DV_VEC];
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) o_acc[i] = (ACC_TYPE4)(0.0f);
ACC_TYPE l_i = 0.0f;
for (int k_idx = tid; k_idx < n_kv; k_idx += Q1_WG_SIZE) {
const ulong k_row_offset = batch_idx * k_nb3 + head_kv_idx * k_nb2 + k_idx * k_nb1;
const ulong v_row_offset = batch_idx * v_nb3 + head_kv_idx * v_nb2 + k_idx * v_nb1;
const global KV_DATA_TYPE4* k_ptr = (const global KV_DATA_TYPE4*)(k_base + k_row_offset);
const global KV_DATA_TYPE4* v_ptr = (const global KV_DATA_TYPE4*)(v_base + v_row_offset);
ACC_TYPE4 dot_acc = (ACC_TYPE4)(0.0f);
#pragma unroll
for (int k = 0; k < DK_VEC; k++) {
dot_acc = mad(q_priv[k], CONVERT_KV_ACC4(k_ptr[k]), dot_acc);
}
ACC_TYPE score = (dot_acc.s0 + dot_acc.s1 + dot_acc.s2 + dot_acc.s3) * scale;
if (mask_base != NULL) {
const global MASK_DATA_TYPE* mask_ptr = (const global MASK_DATA_TYPE*)(mask_base);
score += slope * (ACC_TYPE)mask_ptr[k_idx];
}
if (logit_softcap > 0.0f) {
score = logit_softcap * tanh(score / logit_softcap);
}
const ACC_TYPE p = exp(score - m_final);
l_i += p;
#pragma unroll
for (int i = 0; i < DV_VEC; i++) {
o_acc[i] = mad(p, CONVERT_KV_ACC4(v_ptr[i]), o_acc[i]);
}
}
__local ACC_TYPE local_l[Q1_WG_SIZE];
__local ACC_TYPE4 local_o_comp[Q1_WG_SIZE];
local_l[tid] = l_i;
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) local_l[tid] += local_l[tid + s];
barrier(CLK_LOCAL_MEM_FENCE);
}
const ulong o_row_offset = batch_idx * o_nb3 + head_idx * o_nb1;
global O_DATA_TYPE4 *o_row = (global O_DATA_TYPE4 *)(o_base + o_row_offset);
const ACC_TYPE l_final = local_l[0];
if (l_final > 0.0f) {
const ACC_TYPE l_inv = 1.0f / l_final;
for (int i = 0; i < DV_VEC; i++) {
local_o_comp[tid] = o_acc[i];
barrier(CLK_LOCAL_MEM_FENCE);
#pragma unroll
for (int s = Q1_WG_SIZE / 2; s > 0; s >>= 1) {
if (tid < s) local_o_comp[tid] += local_o_comp[tid + s];
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0) {
o_row[i] = CONVERT_O_DATA4(local_o_comp[0] * l_inv);
}
}
} else if (tid == 0) {
#pragma unroll
for (int i = 0; i < DV_VEC; ++i) o_row[i] = (O_DATA_TYPE4)(0.0f);
}
}