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llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp

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#ifndef GGML_WEBGPU_SHADER_LIB_HPP
#define GGML_WEBGPU_SHADER_LIB_HPP
#include "ggml-wgsl-shaders.hpp"
#include "ggml.h"
#include "pre_wgsl.hpp"
#include <webgpu/webgpu_cpp.h>
#include <algorithm>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
#define GGML_WEBGPU_F16_SIZE_BYTES 2
#define GGML_WEBGPU_F32_SIZE_BYTES 4
#define GGML_WEBGPU_I32_SIZE_BYTES 4
#define GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES 8u
#define GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE 128u
// Matches GGML_PAD(..., 256) in src/llama-context.cpp for KV cache sizing.
#define GGML_WEBGPU_KV_SEQ_PAD 256u
#define GGML_WEBGPU_ARGSORT_MERGE_MAX_WG_SIZE 512u
// Matrix multiplication parameters
// Register tiling parameters
#define WEBGPU_MUL_MAT_TILE_M 8
#define WEBGPU_MUL_MAT_TILE_N 8
#define WEBGPU_MUL_MAT_WG_SIZE_M 8
#define WEBGPU_MUL_MAT_WG_SIZE_N 8
#define WEBGPU_MUL_MAT_TILE_K 32
// Subgroup matrix parameters
// The number of subgroups in the M dimension
#define WEBGPU_MUL_MAT_SUBGROUP_M 2
// The number of subgroups in the N dimension
#define WEBGPU_MUL_MAT_SUBGROUP_N 2
// The number of subgroup matrices each subgroup accumulates over
#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M 4
#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N 2
// Matrix-vector multiplication parameters
#define WEBGPU_MUL_MAT_VEC_WG_SIZE 256
// Must be multiple of 4 to work with vectorized paths, and must divide
// mul_mat_vec wg size
#define WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG 64
#define WEBGPU_MUL_MAT_VEC_TILE_K 256
// default size for legacy matrix multiplication
#define WEBGPU_MUL_MAT_WG_SIZE 256
// Same hash combine function as in boost
template <typename T> inline void ggml_webgpu_hash_combine(size_t & seed, const T & value) {
seed ^= std::hash<T>{}(value) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
}
struct ggml_webgpu_shader_lib_context {
ggml_tensor * src0;
ggml_tensor * src1;
ggml_tensor * src2;
ggml_tensor * src3;
ggml_tensor * src4;
ggml_tensor * dst;
uint32_t max_wg_size;
size_t wg_mem_limit_bytes = 0;
bool inplace = false;
bool overlap = false;
bool src_overlap = false;
bool supports_subgroup_matrix = false;
uint32_t sg_mat_m = 0;
uint32_t sg_mat_n = 0;
uint32_t sg_mat_k = 0;
uint32_t max_subgroup_size = 0;
};
struct webgpu_pipeline {
wgpu::ComputePipeline pipeline;
std::string name;
std::shared_ptr<void> context = nullptr;
};
struct ggml_webgpu_generic_shader_decisions {
uint32_t wg_size = 0;
};
/** Argsort **/
struct ggml_webgpu_argsort_shader_lib_context {
uint32_t max_wg_size;
size_t wg_mem_limit_bytes;
int32_t order;
};
/** Set Rows **/
struct ggml_webgpu_set_rows_pipeline_key {
int dst_type;
int vec4;
int i64_idx;
bool operator==(const ggml_webgpu_set_rows_pipeline_key & other) const {
return dst_type == other.dst_type && vec4 == other.vec4 && i64_idx == other.i64_idx;
}
};
struct ggml_webgpu_set_rows_pipeline_key_hash {
size_t operator()(const ggml_webgpu_set_rows_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.dst_type);
ggml_webgpu_hash_combine(seed, key.vec4);
ggml_webgpu_hash_combine(seed, key.i64_idx);
return seed;
}
};
struct ggml_webgpu_set_rows_shader_decisions {
bool vec4;
bool i64_idx;
uint32_t wg_size;
};
/** Get Rows **/
struct ggml_webgpu_get_rows_pipeline_key {
ggml_type src_type;
int vectorized;
bool operator==(const ggml_webgpu_get_rows_pipeline_key & other) const {
return src_type == other.src_type && vectorized == other.vectorized;
}
};
struct ggml_webgpu_get_rows_pipeline_key_hash {
size_t operator()(const ggml_webgpu_get_rows_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.src_type);
ggml_webgpu_hash_combine(seed, key.vectorized);
return seed;
}
};
/** Pad **/
struct ggml_webgpu_pad_pipeline_key {
bool circular;
bool operator==(const ggml_webgpu_pad_pipeline_key & other) const { return circular == other.circular; }
};
struct ggml_webgpu_pad_pipeline_key_hash {
size_t operator()(const ggml_webgpu_pad_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.circular);
return seed;
}
};
/** Scale **/
struct ggml_webgpu_scale_pipeline_key {
int inplace;
bool operator==(const ggml_webgpu_scale_pipeline_key & other) const { return inplace == other.inplace; }
};
struct ggml_webgpu_scale_pipeline_key_hash {
size_t operator()(const ggml_webgpu_scale_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.inplace);
return seed;
}
};
/** Binary **/
struct ggml_webgpu_binary_pipeline_key {
int type;
int op;
bool inplace;
bool overlap;
bool src_overlap;
bool operator==(const ggml_webgpu_binary_pipeline_key & other) const {
return type == other.type && op == other.op && inplace == other.inplace && overlap == other.overlap && src_overlap == other.src_overlap;
}
};
struct ggml_webgpu_binary_pipeline_key_hash {
size_t operator()(const ggml_webgpu_binary_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.type);
ggml_webgpu_hash_combine(seed, key.op);
ggml_webgpu_hash_combine(seed, key.inplace);
ggml_webgpu_hash_combine(seed, key.overlap);
ggml_webgpu_hash_combine(seed, key.src_overlap);
return seed;
}
};
/** Unary **/
struct ggml_webgpu_unary_pipeline_key {
int type;
int op;
bool is_unary; // many unary operators fall under the GGML_OP_UNARY umbrella
bool inplace;
bool operator==(const ggml_webgpu_unary_pipeline_key & other) const {
return type == other.type && op == other.op && is_unary == other.is_unary && inplace == other.inplace;
}
};
struct ggml_webgpu_unary_pipeline_key_hash {
size_t operator()(const ggml_webgpu_unary_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.type);
ggml_webgpu_hash_combine(seed, key.op);
ggml_webgpu_hash_combine(seed, key.is_unary);
ggml_webgpu_hash_combine(seed, key.inplace);
return seed;
}
};
/** FlashAttention */
struct ggml_webgpu_flash_attn_pipeline_key {
ggml_type kv_type;
uint32_t head_dim_qk;
uint32_t head_dim_v;
bool kv_direct;
bool has_mask;
bool has_sinks;
bool uses_logit_softcap;
bool operator==(const ggml_webgpu_flash_attn_pipeline_key & other) const {
return kv_type == other.kv_type && head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v &&
kv_direct == other.kv_direct && has_mask == other.has_mask && has_sinks == other.has_sinks &&
uses_logit_softcap == other.uses_logit_softcap;
}
};
struct ggml_webgpu_flash_attn_pipeline_key_hash {
size_t operator()(const ggml_webgpu_flash_attn_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.kv_type);
ggml_webgpu_hash_combine(seed, key.head_dim_qk);
ggml_webgpu_hash_combine(seed, key.head_dim_v);
ggml_webgpu_hash_combine(seed, key.kv_direct);
ggml_webgpu_hash_combine(seed, key.has_mask);
ggml_webgpu_hash_combine(seed, key.has_sinks);
ggml_webgpu_hash_combine(seed, key.uses_logit_softcap);
return seed;
}
};
struct ggml_webgpu_flash_attn_shader_lib_context {
ggml_webgpu_flash_attn_pipeline_key key;
uint32_t sg_mat_m;
uint32_t sg_mat_n;
uint32_t sg_mat_k;
size_t wg_mem_limit_bytes;
uint32_t max_subgroup_size;
};
struct ggml_webgpu_flash_attn_shader_decisions {
uint32_t q_tile = 0;
uint32_t kv_tile = 0;
uint32_t wg_size = 0;
};
// This is exposed because it's necessary in supports_op
inline size_t ggml_webgpu_flash_attn_wg_mem_bytes(uint32_t q_tile,
uint32_t kv_tile,
uint32_t head_dim_qk,
uint32_t head_dim_v,
bool has_mask,
bool kv_direct) {
const uint32_t max_head_dim = std::max(head_dim_qk, head_dim_v);
size_t f16_elems = 0;
size_t f32_elems = 0;
f16_elems += q_tile * head_dim_qk; // q_shmem
if (!kv_direct) {
f16_elems += kv_tile * max_head_dim; // kv_shmem
}
f16_elems += q_tile * head_dim_v; // o_shmem
if (has_mask) {
f16_elems += q_tile * kv_tile; // mask_shmem
}
f16_elems += q_tile * kv_tile; // inter_shmem
f32_elems += q_tile; // row_max_shmem
f32_elems += q_tile; // exp_sum_shmem
return f16_elems * GGML_WEBGPU_F16_SIZE_BYTES + f32_elems * GGML_WEBGPU_F32_SIZE_BYTES;
}
/** Matrix Multiplication **/
struct ggml_webgpu_legacy_mul_mat_pipeline_key {
ggml_type src0_type;
ggml_type src1_type;
bool operator==(const ggml_webgpu_legacy_mul_mat_pipeline_key & other) const {
return src0_type == other.src0_type && src1_type == other.src1_type;
}
};
struct ggml_webgpu_legacy_mul_mat_pipeline_key_hash {
size_t operator()(const ggml_webgpu_legacy_mul_mat_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.src0_type);
ggml_webgpu_hash_combine(seed, key.src1_type);
return seed;
}
};
struct ggml_webgpu_mul_mat_vec_pipeline_key {
ggml_type src0_type;
ggml_type src1_type;
int vectorized;
bool operator==(const ggml_webgpu_mul_mat_vec_pipeline_key & other) const {
return src0_type == other.src0_type && src1_type == other.src1_type && vectorized == other.vectorized;
}
};
struct ggml_webgpu_mul_mat_vec_pipeline_key_hash {
size_t operator()(const ggml_webgpu_mul_mat_vec_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.src0_type);
ggml_webgpu_hash_combine(seed, key.src1_type);
ggml_webgpu_hash_combine(seed, key.vectorized);
return seed;
}
};
struct ggml_webgpu_mul_mat_vec_shader_decisions {
uint32_t wg_size;
uint32_t tile_k;
uint32_t outputs_per_wg;
uint32_t vec_size;
};
struct ggml_webgpu_mul_mat_pipeline_key {
ggml_type src0_type;
ggml_type src1_type;
int vectorized;
int use_subgroup_matrix;
bool operator==(const ggml_webgpu_mul_mat_pipeline_key & other) const {
return src0_type == other.src0_type && src1_type == other.src1_type && vectorized == other.vectorized &&
use_subgroup_matrix == other.use_subgroup_matrix;
}
};
struct ggml_webgpu_mul_mat_pipeline_key_hash {
size_t operator()(const ggml_webgpu_mul_mat_pipeline_key & key) const {
size_t seed = 0;
ggml_webgpu_hash_combine(seed, key.src0_type);
ggml_webgpu_hash_combine(seed, key.src1_type);
ggml_webgpu_hash_combine(seed, key.vectorized);
ggml_webgpu_hash_combine(seed, key.use_subgroup_matrix);
return seed;
}
};
struct ggml_webgpu_mul_mat_shader_decisions {
uint32_t tile_k;
uint32_t wg_size_m;
uint32_t wg_size_n;
uint32_t wg_size;
uint32_t outputs_per_wg;
int use_subgroup_matrix;
uint32_t tile_m;
uint32_t tile_n;
// Subgroup matrix parameters
uint32_t subgroup_m;
uint32_t subgroup_n;
uint32_t subgroup_matrix_m;
uint32_t subgroup_matrix_n;
uint32_t mul_mat_wg_size;
};
class ggml_webgpu_shader_lib {
wgpu::Device device;
pre_wgsl::Preprocessor preprocessor;
std::unordered_map<int, webgpu_pipeline> sum_rows_pipelines; // key is fixed, no variants yet
std::unordered_map<int, webgpu_pipeline> argmax_pipelines; // key is vec4
std::unordered_map<int, webgpu_pipeline> argsort_pipelines; // key is order
std::unordered_map<int, webgpu_pipeline> argsort_merge_pipelines; // key is order
std::unordered_map<int, webgpu_pipeline> cumsum_pipelines; // key is fixed, no variants yet
std::unordered_map<ggml_webgpu_get_rows_pipeline_key, webgpu_pipeline, ggml_webgpu_get_rows_pipeline_key_hash>
get_rows_pipelines; // src_type, vectorized
std::unordered_map<ggml_webgpu_unary_pipeline_key, webgpu_pipeline, ggml_webgpu_unary_pipeline_key_hash>
unary_pipelines; // type/op/inplace
std::unordered_map<ggml_webgpu_scale_pipeline_key, webgpu_pipeline, ggml_webgpu_scale_pipeline_key_hash>
scale_pipelines; // inplace
std::unordered_map<ggml_webgpu_pad_pipeline_key, webgpu_pipeline, ggml_webgpu_pad_pipeline_key_hash>
pad_pipelines; // circular/non-circular
std::unordered_map<ggml_webgpu_binary_pipeline_key, webgpu_pipeline, ggml_webgpu_binary_pipeline_key_hash>
binary_pipelines; // type/op/inplace/overlap
std::unordered_map<ggml_webgpu_flash_attn_pipeline_key, webgpu_pipeline, ggml_webgpu_flash_attn_pipeline_key_hash>
flash_attn_pipelines;
std::unordered_map<ggml_webgpu_legacy_mul_mat_pipeline_key,
webgpu_pipeline,
ggml_webgpu_legacy_mul_mat_pipeline_key_hash>
mul_mat_legacy_pipelines; // legacy mul_mat (non-subgroup/non-regtile/non-vec)
std::unordered_map<ggml_webgpu_mul_mat_vec_pipeline_key, webgpu_pipeline, ggml_webgpu_mul_mat_vec_pipeline_key_hash>
mul_mat_vec_pipelines; // fast mat-vec (n==1)
std::unordered_map<ggml_webgpu_mul_mat_pipeline_key, webgpu_pipeline, ggml_webgpu_mul_mat_pipeline_key_hash>
mul_mat_fast_pipelines; // fast mat-mat (reg-tile or subgroup)
std::unordered_map<ggml_webgpu_set_rows_pipeline_key, webgpu_pipeline, ggml_webgpu_set_rows_pipeline_key_hash>
set_rows_pipelines;
public:
ggml_webgpu_shader_lib(wgpu::Device device) { this->device = device; }
webgpu_pipeline get_sum_rows_pipeline(const ggml_webgpu_shader_lib_context & context) {
auto it = sum_rows_pipelines.find(1);
if (it != sum_rows_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_sum_rows, defines);
sum_rows_pipelines[1] = ggml_webgpu_create_pipeline(device, processed, "sum_rows");
return sum_rows_pipelines[1];
}
webgpu_pipeline get_argmax_pipeline(const ggml_webgpu_shader_lib_context & context) {
bool vec4 = context.src0->ne[0] % 4 == 0;
auto it = argmax_pipelines.find(vec4);
if (it != argmax_pipelines.end()) {
return it->second;
}
std::string variant = "argmax";
std::vector<std::string> defines;
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
if (vec4) {
defines.push_back("VEC4");
variant += "_vec4";
}
auto processed = preprocessor.preprocess(wgsl_argmax, defines);
argmax_pipelines[vec4] = ggml_webgpu_create_pipeline(device, processed, variant);
return argmax_pipelines.at(vec4);
}
webgpu_pipeline get_set_rows_pipeline(const ggml_webgpu_shader_lib_context & context) {
ggml_webgpu_set_rows_pipeline_key key = { .dst_type = context.dst->type,
.vec4 = context.src0->ne[0] % 4 == 0,
.i64_idx = context.src1->type == GGML_TYPE_I64 };
auto it = set_rows_pipelines.find(key);
if (it != set_rows_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant = "set_rows";
switch (context.dst->type) {
case GGML_TYPE_F32:
defines.push_back("DST_F32");
variant += "_dstf32";
break;
case GGML_TYPE_F16:
defines.push_back("DST_F16");
variant += "_dstf16";
break;
default:
GGML_ABORT("Unsupported dst type for set_rows shader");
}
if (key.vec4) {
defines.push_back("VEC4");
variant += "_vec4";
}
if (key.i64_idx) {
defines.push_back("I64_IDX");
variant += "_i64idx";
}
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_set_rows, defines);
auto decisions = std::make_shared<ggml_webgpu_set_rows_shader_decisions>();
decisions->vec4 = key.vec4;
decisions->i64_idx = key.i64_idx;
decisions->wg_size = context.max_wg_size;
set_rows_pipelines[key] = ggml_webgpu_create_pipeline(device, processed, variant);
set_rows_pipelines[key].context = decisions;
return set_rows_pipelines[key];
}
webgpu_pipeline get_cumsum_pipeline(const ggml_webgpu_shader_lib_context & context) {
auto it = cumsum_pipelines.find(1);
if (it != cumsum_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_cumsum, defines);
cumsum_pipelines[1] = ggml_webgpu_create_pipeline(device, processed, "cumsum");
return cumsum_pipelines[1];
}
webgpu_pipeline get_argsort_pipeline(const ggml_webgpu_shader_lib_context & context) {
bool is_top_k = context.dst->op == GGML_OP_TOP_K;
// ascending order is 0, descending order is 1
const int32_t order =
is_top_k ? (int32_t) GGML_SORT_ORDER_DESC : (int32_t) ggml_get_op_params_i32(context.dst, 0);
auto it = argsort_pipelines.find(order);
if (it != argsort_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant = "argsort";
defines.push_back(std::string("ORDER=") + std::to_string(order));
variant += std::string("_order") + std::to_string(order);
uint32_t wg_size = 1;
while (wg_size * 2 <= context.max_wg_size &&
wg_size * GGML_WEBGPU_I32_SIZE_BYTES <= context.wg_mem_limit_bytes / 2) {
wg_size *= 2;
}
defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
auto processed = preprocessor.preprocess(wgsl_argsort, defines);
auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
decisions->wg_size = wg_size;
argsort_pipelines[order] = ggml_webgpu_create_pipeline(device, processed, variant);
argsort_pipelines[order].context = decisions;
return argsort_pipelines[order];
}
webgpu_pipeline get_argsort_merge_pipeline(const ggml_webgpu_shader_lib_context & context) {
bool is_top_k = context.dst->op == GGML_OP_TOP_K;
// ascending order is 0, descending order is 1
const int32_t order =
is_top_k ? (int32_t) GGML_SORT_ORDER_DESC : (int32_t) ggml_get_op_params_i32(context.dst, 0);
auto it = argsort_merge_pipelines.find(order);
if (it != argsort_merge_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant = "argsort_merge";
defines.push_back(std::string("ORDER=") + std::to_string(order));
variant += std::string("_order") + std::to_string(order);
uint32_t wg_size = std::min(GGML_WEBGPU_ARGSORT_MERGE_MAX_WG_SIZE, context.max_wg_size);
defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
auto processed = preprocessor.preprocess(wgsl_argsort_merge, defines);
argsort_merge_pipelines[order] = ggml_webgpu_create_pipeline(device, processed, variant);
return argsort_merge_pipelines[order];
}
webgpu_pipeline get_get_rows_pipeline(const ggml_webgpu_shader_lib_context & context) {
const bool vectorized = context.src0->type == GGML_TYPE_F32 && context.dst->ne[0] % 4 == 0;
ggml_webgpu_get_rows_pipeline_key key = {
.src_type = context.src0->type,
.vectorized = (int) vectorized,
};
auto it = get_rows_pipelines.find(key);
if (it != get_rows_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant = "get_rows";
const struct ggml_type_traits * type_traits = ggml_get_type_traits(key.src_type);
const char * type_str = type_traits->type_name;
switch (key.src_type) {
case GGML_TYPE_F32:
if (key.vectorized) {
defines.push_back("F32_VEC");
defines.push_back("SRC_TYPE=vec4<f32>");
defines.push_back("DST_TYPE=vec4<f32>");
defines.push_back("BLOCK_SIZE=4u");
} else {
defines.push_back("F32");
defines.push_back("SRC_TYPE=f32");
defines.push_back("DST_TYPE=f32");
defines.push_back("BLOCK_SIZE=1u");
}
variant += "_f32";
break;
case GGML_TYPE_F16:
defines.push_back("F16");
defines.push_back("SRC_TYPE=f16");
defines.push_back("DST_TYPE=f32");
defines.push_back("BLOCK_SIZE=1u");
variant += "_f16";
break;
case GGML_TYPE_I32:
defines.push_back("I32");
defines.push_back("SRC_TYPE=i32");
defines.push_back("DST_TYPE=i32");
defines.push_back("BLOCK_SIZE=1u");
variant += "_i32";
break;
default:
{
std::string type_upper = type_str;
std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper);
defines.push_back("BYTE_HELPERS");
defines.push_back(type_upper + "_T");
defines.push_back(type_upper);
defines.push_back(type_upper + "_SCALE_MIN");
defines.push_back(type_upper + "_TABLES");
defines.push_back(type_upper + "_GRID");
variant += "_";
variant += type_str;
defines.push_back(std::string("SRC_TYPE=") + type_str);
defines.push_back("DST_TYPE=f32");
if ((key.src_type >= GGML_TYPE_Q4_0 && key.src_type <= GGML_TYPE_Q8_1) ||
key.src_type == GGML_TYPE_IQ4_NL) {
defines.push_back("BLOCK_SIZE=32u");
} else if (key.src_type >= GGML_TYPE_Q2_K) {
defines.push_back("BLOCK_SIZE=256u");
} else {
defines.push_back("BLOCK_SIZE=1u");
}
break;
}
}
if (key.vectorized) {
variant += "_vec";
}
defines.push_back("WG_SIZE=" + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_get_rows, defines);
auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
decisions->wg_size = context.max_wg_size;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
get_rows_pipelines[key] = pipeline;
return get_rows_pipelines[key];
}
webgpu_pipeline get_scale_pipeline(const ggml_webgpu_shader_lib_context & context) {
ggml_webgpu_scale_pipeline_key key = { .inplace = context.inplace };
auto it = scale_pipelines.find(key);
if (it != scale_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant = "scale";
if (key.inplace) {
defines.push_back("INPLACE");
variant += "_inplace";
}
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_scale, defines);
auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
decisions->wg_size = context.max_wg_size;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
scale_pipelines[key] = pipeline;
return scale_pipelines[key];
}
webgpu_pipeline get_pad_pipeline(const ggml_webgpu_shader_lib_context & context) {
ggml_webgpu_pad_pipeline_key key = { .circular = ggml_get_op_params_i32(context.dst, 8) != 0 };
auto it = pad_pipelines.find(key);
if (it != pad_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant = "pad";
if (key.circular) {
defines.push_back("CIRCULAR");
variant += "_circular";
}
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_pad, defines);
auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
decisions->wg_size = context.max_wg_size;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
pad_pipelines[key] = pipeline;
return pad_pipelines[key];
}
webgpu_pipeline get_mul_mat_vec_pipeline(const ggml_webgpu_shader_lib_context & context) {
ggml_webgpu_mul_mat_vec_pipeline_key key = {
.src0_type = context.src0->type,
.src1_type = context.src1->type,
// Quantized mat-vec path currently runs scalar; only allow vectorization when both inputs are float
.vectorized = (context.src0->ne[0] % 4 == 0 && context.dst->ne[0] % 4 == 0 &&
(context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ?
1 :
0,
};
auto it = mul_mat_vec_pipelines.find(key);
if (it != mul_mat_vec_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant = "mul_mat_vec";
// src1 type (vector)
switch (context.src1->type) {
case GGML_TYPE_F32:
defines.push_back("SRC1_INNER_TYPE=f32");
variant += "_f32";
break;
case GGML_TYPE_F16:
defines.push_back("SRC1_INNER_TYPE=f16");
variant += "_f16";
break;
default:
GGML_ABORT("Unsupported src1 type for mul_mat_vec shader");
}
// src0 type (matrix row)
switch (context.src0->type) {
case GGML_TYPE_F32:
defines.push_back("SRC0_INNER_TYPE=f32");
defines.push_back("MUL_ACC_FLOAT");
break;
case GGML_TYPE_F16:
defines.push_back("SRC0_INNER_TYPE=f16");
defines.push_back("MUL_ACC_FLOAT");
break;
default:
{
// Quantized types: use helpers but accumulate in f16
const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type);
std::string src0_name = src0_traits->type_name;
std::string type_upper = src0_name;
std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper);
defines.push_back("BYTE_HELPERS");
defines.push_back("MUL_ACC_" + type_upper);
// For fast path we always dequantize from f16 inside the shader
defines.push_back("SRC0_INNER_TYPE=f16");
break;
}
}
// VEC/SCALAR controls
defines.push_back(key.vectorized ? "VEC" : "SCALAR");
uint32_t wg_size = WEBGPU_MUL_MAT_VEC_WG_SIZE;
uint32_t tile_k = WEBGPU_MUL_MAT_VEC_TILE_K;
uint32_t outputs_per_wg = WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG;
defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
defines.push_back(std::string("TILE_K=") + std::to_string(tile_k));
defines.push_back(std::string("OUTPUTS_PER_WG=") + std::to_string(outputs_per_wg));
auto processed = preprocessor.preprocess(wgsl_mul_mat_vec, defines);
auto decisions = std::make_shared<ggml_webgpu_mul_mat_vec_shader_decisions>();
decisions->wg_size = wg_size;
decisions->tile_k = tile_k;
decisions->outputs_per_wg = outputs_per_wg;
decisions->vec_size = key.vectorized ? 4 : 1;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
mul_mat_vec_pipelines[key] = pipeline;
return mul_mat_vec_pipelines[key];
}
webgpu_pipeline get_mul_mat_fast_pipeline(const ggml_webgpu_shader_lib_context & context) {
ggml_webgpu_mul_mat_pipeline_key key = {
.src0_type = context.src0->type,
.src1_type = context.src1->type,
.vectorized = (context.src0->ne[0] % 4 == 0 && context.dst->ne[0] % 4 == 0 && context.dst->ne[1] % 4 == 0 &&
(context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ?
1 :
0,
.use_subgroup_matrix = context.supports_subgroup_matrix
};
auto it = mul_mat_fast_pipelines.find(key);
if (it != mul_mat_fast_pipelines.end()) {
return it->second;
}
const char * shader_src = key.use_subgroup_matrix ? wgsl_mul_mat_subgroup_matrix : wgsl_mul_mat_reg_tile;
std::vector<std::string> defines;
std::string variant = key.use_subgroup_matrix ? "mul_mat_subgroup_matrix" : "mul_mat_reg_tile";
// src1 type
switch (context.src1->type) {
case GGML_TYPE_F32:
defines.push_back("SRC1_INNER_TYPE=f32");
break;
case GGML_TYPE_F16:
defines.push_back("SRC1_INNER_TYPE=f16");
break;
default:
GGML_ABORT("Unsupported src1 type for mul_mat fast shader");
}
// src0 type
const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type);
const char * src0_name = src0_traits->type_name;
switch (context.src0->type) {
case GGML_TYPE_F32:
defines.push_back("SRC0_INNER_TYPE=f32");
defines.push_back("FLOAT");
defines.push_back("MUL_ACC_FLOAT");
defines.push_back("INIT_SRC0_SHMEM_FLOAT");
defines.push_back("INIT_SRC1_SHMEM_FLOAT");
variant += "_f32";
break;
case GGML_TYPE_F16:
defines.push_back("SRC0_INNER_TYPE=f16");
defines.push_back("FLOAT");
defines.push_back("MUL_ACC_FLOAT");
defines.push_back("INIT_SRC0_SHMEM_FLOAT");
defines.push_back("INIT_SRC1_SHMEM_FLOAT");
variant += "_f16";
break;
default:
{
std::string type_upper = src0_name;
std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper);
defines.push_back("BYTE_HELPERS");
defines.push_back("MUL_ACC_" + type_upper);
defines.push_back("INIT_SRC0_SHMEM_" + type_upper);
defines.push_back("INIT_SRC1_SHMEM_FLOAT");
// Use f16 inside the shader for quantized types
defines.push_back("SRC0_INNER_TYPE=f16");
variant += std::string("_") + src0_name;
break;
}
}
// VEC/SCALAR controls
defines.push_back(key.vectorized ? "VEC" : "SCALAR");
// Tiles
defines.push_back("TILE_M=" + std::to_string(WEBGPU_MUL_MAT_TILE_M) + "u");
defines.push_back("TILE_N=" + std::to_string(WEBGPU_MUL_MAT_TILE_N) + "u");
defines.push_back("TILE_K=" + std::to_string(WEBGPU_MUL_MAT_TILE_K) + "u");
// Subgroup matrix specifics
if (key.use_subgroup_matrix) {
defines.push_back("MAX_SUBGROUP_SIZE=" + std::to_string(context.max_subgroup_size) + "u");
defines.push_back("SUBGROUP_M=" + std::to_string(WEBGPU_MUL_MAT_SUBGROUP_M) + "u");
defines.push_back("SUBGROUP_N=" + std::to_string(WEBGPU_MUL_MAT_SUBGROUP_N) + "u");
defines.push_back("SUBGROUP_MATRIX_M=" + std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M) + "u");
defines.push_back("SUBGROUP_MATRIX_N=" + std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N) + "u");
defines.push_back("SUBGROUP_MATRIX_M_SIZE=" + std::to_string(context.sg_mat_m) + "u");
defines.push_back("SUBGROUP_MATRIX_N_SIZE=" + std::to_string(context.sg_mat_n) + "u");
defines.push_back("SUBGROUP_MATRIX_K_SIZE=" + std::to_string(context.sg_mat_k) + "u");
}
// variant suffix for src1 type
variant += std::string("_") + (context.src1->type == GGML_TYPE_F32 ? "f32" : "f16");
if (key.vectorized) {
variant += "_vectorized";
}
if (!key.use_subgroup_matrix) {
defines.push_back("WORKGROUP_SIZE_M=" + std::to_string(WEBGPU_MUL_MAT_WG_SIZE_M) + "u");
defines.push_back("WORKGROUP_SIZE_N=" + std::to_string(WEBGPU_MUL_MAT_WG_SIZE_N) + "u");
}
auto processed = preprocessor.preprocess(shader_src, defines);
auto decisions = std::make_shared<ggml_webgpu_mul_mat_shader_decisions>();
decisions->tile_k = WEBGPU_MUL_MAT_TILE_K;
decisions->tile_m = WEBGPU_MUL_MAT_TILE_M;
decisions->tile_n = WEBGPU_MUL_MAT_TILE_N;
decisions->use_subgroup_matrix = key.use_subgroup_matrix;
if (key.use_subgroup_matrix) {
decisions->subgroup_m = WEBGPU_MUL_MAT_SUBGROUP_M;
decisions->subgroup_n = WEBGPU_MUL_MAT_SUBGROUP_N;
decisions->subgroup_matrix_m = WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M;
decisions->subgroup_matrix_n = WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N;
decisions->wg_size = context.max_subgroup_size;
} else {
decisions->wg_size_m = WEBGPU_MUL_MAT_WG_SIZE_M;
decisions->wg_size_n = WEBGPU_MUL_MAT_WG_SIZE_N;
decisions->wg_size = WEBGPU_MUL_MAT_WG_SIZE_M * WEBGPU_MUL_MAT_WG_SIZE_N;
decisions->mul_mat_wg_size = WEBGPU_MUL_MAT_WG_SIZE;
}
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
mul_mat_fast_pipelines[key] = pipeline;
return mul_mat_fast_pipelines[key];
}
webgpu_pipeline get_mul_mat_legacy_pipeline(const ggml_webgpu_shader_lib_context & context) {
ggml_webgpu_legacy_mul_mat_pipeline_key key = { .src0_type = context.src0->type,
.src1_type = context.src1->type };
auto it = mul_mat_legacy_pipelines.find(key);
if (it != mul_mat_legacy_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant = "mul_mat";
switch (context.src1->type) {
case GGML_TYPE_F32:
defines.push_back("SRC1_TYPE=f32");
variant += "_f32";
break;
case GGML_TYPE_F16:
defines.push_back("SRC1_TYPE=f16");
variant += "_f16";
break;
default:
GGML_ABORT("Unsupported src1 type for mul_mat legacy shader");
}
const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type);
const char * src0_name = src0_traits->type_name;
switch (context.src0->type) {
case GGML_TYPE_F32:
defines.push_back("SRC0_TYPE=f32");
defines.push_back("FLOAT");
variant += "_f32";
break;
case GGML_TYPE_F16:
defines.push_back("SRC0_TYPE=f16");
defines.push_back("FLOAT");
variant += "_f16";
break;
default:
{
// quantized types
std::string type_upper = src0_name;
std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper);
defines.push_back(std::string("SRC0_TYPE=") + src0_name);
defines.push_back("BYTE_HELPERS");
defines.push_back(type_upper + "_T");
defines.push_back(type_upper);
defines.push_back(type_upper + "_SCALE_MIN");
defines.push_back(type_upper + "_TABLES");
defines.push_back(type_upper + "_GRID");
variant += std::string("_") + src0_name;
break;
}
}
auto processed = preprocessor.preprocess(wgsl_mul_mat, defines);
auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
decisions->wg_size = WEBGPU_MUL_MAT_WG_SIZE;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
mul_mat_legacy_pipelines[key] = pipeline;
return mul_mat_legacy_pipelines[key];
}
webgpu_pipeline get_unary_pipeline(const ggml_webgpu_shader_lib_context & context) {
const bool is_unary = context.dst->op == GGML_OP_UNARY;
const int op = is_unary ? (int) ggml_get_unary_op(context.dst) : context.dst->op;
ggml_webgpu_unary_pipeline_key key = {
.type = context.dst->type,
.op = op,
.is_unary = is_unary,
.inplace = context.inplace,
};
auto it = unary_pipelines.find(key);
if (it != unary_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant =
key.is_unary ? ggml_unary_op_name((ggml_unary_op) key.op) : ggml_op_name((ggml_op) key.op);
defines.push_back(variant);
switch (key.type) {
case GGML_TYPE_F32:
defines.push_back("TYPE_F32");
variant += "_f32";
break;
case GGML_TYPE_F16:
defines.push_back("TYPE_F16");
variant += "_f16";
break;
default:
GGML_ABORT("Unsupported type for unary shader");
}
if (key.inplace) {
defines.push_back("INPLACE");
variant += "_inplace";
}
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_unary, defines);
auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
decisions->wg_size = context.max_wg_size;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
unary_pipelines[key] = pipeline;
return unary_pipelines[key];
}
webgpu_pipeline get_binary_pipeline(const ggml_webgpu_shader_lib_context & context) {
ggml_webgpu_binary_pipeline_key key = {
.type = context.dst->type,
.op = context.dst->op,
.inplace = context.inplace,
.overlap = context.overlap,
.src_overlap = context.src_overlap,
};
auto it = binary_pipelines.find(key);
if (it != binary_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string op_name = ggml_op_name((ggml_op) key.op);
std::string variant = op_name;
defines.push_back(std::string("OP_") + op_name);
switch (key.type) {
case GGML_TYPE_F32:
defines.push_back("TYPE_F32");
variant += "_f32";
break;
case GGML_TYPE_F16:
defines.push_back("TYPE_F16");
variant += "_f16";
break;
default:
GGML_ABORT("Unsupported type for binary shader");
}
if (key.inplace) {
defines.push_back("INPLACE");
variant += "_inplace";
} else if (key.overlap) {
defines.push_back("OVERLAP");
variant += "_overlap";
} else if (key.src_overlap) {
defines.push_back("SRC_OVERLAP");
variant += "_src_overlap";
}
defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size));
auto processed = preprocessor.preprocess(wgsl_binary, defines);
auto decisions = std::make_shared<ggml_webgpu_generic_shader_decisions>();
decisions->wg_size = context.max_wg_size;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
binary_pipelines[key] = pipeline;
return binary_pipelines[key];
}
webgpu_pipeline get_flash_attn_pipeline(const ggml_webgpu_shader_lib_context & context) {
const bool has_mask = context.src3 != nullptr;
const bool has_sinks = context.src4 != nullptr;
bool kv_direct = (context.src1->type == GGML_TYPE_F16) && (context.src0->ne[0] % context.sg_mat_k == 0) &&
(context.src1->ne[1] % context.sg_mat_n == 0);
ggml_webgpu_flash_attn_pipeline_key key = {
.kv_type = context.src1->type,
.head_dim_qk = (uint32_t) context.src0->ne[0],
.head_dim_v = (uint32_t) context.src2->ne[0],
.kv_direct = kv_direct,
.has_mask = has_mask,
.has_sinks = has_sinks,
.uses_logit_softcap = (*(float *) &context.dst->op_params[2]) != 0.0f,
};
auto it = flash_attn_pipelines.find(key);
if (it != flash_attn_pipelines.end()) {
return it->second;
}
std::vector<std::string> defines;
std::string variant = "flash_attn";
switch (key.kv_type) {
case GGML_TYPE_F32:
defines.push_back("KV_F32");
break;
case GGML_TYPE_F16:
defines.push_back("KV_F16");
break;
case GGML_TYPE_Q4_0:
defines.push_back("KV_Q4_0");
break;
case GGML_TYPE_Q8_0:
defines.push_back("KV_Q8_0");
break;
default:
GGML_ABORT("Unsupported KV type for flash attention shader");
}
variant += std::string("_") + ggml_type_name(key.kv_type);
if (key.has_mask) {
defines.push_back("MASK");
variant += "_mask";
}
if (key.has_sinks) {
defines.push_back("SINKS");
variant += "_sinks";
}
if (key.uses_logit_softcap) {
defines.push_back("LOGIT_SOFTCAP");
variant += "_lgsc";
}
if (key.kv_direct) {
defines.push_back("KV_DIRECT");
variant += "_kvdirect";
}
defines.push_back(std::string("HEAD_DIM_QK=") + std::to_string(key.head_dim_qk));
variant += std::string("_hsqk") + std::to_string(key.head_dim_qk);
defines.push_back(std::string("HEAD_DIM_V=") + std::to_string(key.head_dim_v));
variant += std::string("_hsv") + std::to_string(key.head_dim_v);
defines.push_back(std::string("SG_MAT_M=") + std::to_string(context.sg_mat_m));
defines.push_back(std::string("SG_MAT_N=") + std::to_string(context.sg_mat_n));
defines.push_back(std::string("SG_MAT_K=") + std::to_string(context.sg_mat_k));
uint32_t q_tile = context.sg_mat_m;
uint32_t kv_tile =
std::min(ggml_webgpu_flash_attn_max_kv_tile({ key, context.sg_mat_m, context.sg_mat_n, context.sg_mat_k,
context.wg_mem_limit_bytes, context.max_subgroup_size }),
context.sg_mat_n * GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES);
if (key.kv_direct) {
while (GGML_WEBGPU_KV_SEQ_PAD % kv_tile != 0) {
kv_tile -= context.sg_mat_n;
}
}
defines.push_back(std::string("Q_TILE=") + std::to_string(q_tile));
defines.push_back(std::string("KV_TILE=") + std::to_string(kv_tile));
uint32_t wg_size = std::max(context.max_subgroup_size, GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE);
defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
auto processed = preprocessor.preprocess(wgsl_flash_attn, defines);
auto decisions = std::make_shared<ggml_webgpu_flash_attn_shader_decisions>();
decisions->q_tile = q_tile;
decisions->kv_tile = kv_tile;
decisions->wg_size = wg_size;
webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
pipeline.context = decisions;
flash_attn_pipelines[key] = pipeline;
return flash_attn_pipelines[key];
}
private:
static webgpu_pipeline ggml_webgpu_create_pipeline(wgpu::Device & device,
std::string shader_code,
std::string label) {
wgpu::ShaderSourceWGSL shader_source;
shader_source.code = shader_code.c_str();
wgpu::ShaderModuleDescriptor shader_desc;
shader_desc.nextInChain = &shader_source;
wgpu::ShaderModule shader_module = device.CreateShaderModule(&shader_desc);
wgpu::ComputePipelineDescriptor pipeline_desc;
pipeline_desc.label = label.c_str();
pipeline_desc.compute.module = shader_module;
pipeline_desc.compute.entryPoint = "main"; // Entry point in the WGSL code
pipeline_desc.layout = nullptr; // nullptr means auto layout
return { device.CreateComputePipeline(&pipeline_desc), label };
}
static uint32_t ggml_webgpu_flash_attn_max_kv_tile(const ggml_webgpu_flash_attn_shader_lib_context & context) {
const size_t limit_bytes = context.wg_mem_limit_bytes;
const size_t q_tile = context.sg_mat_m;
const size_t base_q_bytes =
(context.key.head_dim_qk + context.key.head_dim_v) * q_tile * GGML_WEBGPU_F16_SIZE_BYTES +
2 * q_tile * GGML_WEBGPU_F32_SIZE_BYTES;
size_t bytes_per_kv = 0;
if (!context.key.kv_direct) {
bytes_per_kv += std::max(context.key.head_dim_qk, context.key.head_dim_v);
}
if (context.key.has_mask) {
bytes_per_kv += q_tile;
}
bytes_per_kv += q_tile;
bytes_per_kv *= GGML_WEBGPU_F16_SIZE_BYTES;
const uint32_t max_kv_tile = (limit_bytes - base_q_bytes) / bytes_per_kv;
return (max_kv_tile / context.sg_mat_n) * context.sg_mat_n;
}
};
#endif // GGML_WEBGPU_SHADER_LIB_HPP
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