return pool(device);
}
};
+
+struct ggml_cuda_mm_fusion_args_host {
+ const ggml_tensor * x_bias = nullptr;
+ const ggml_tensor * gate = nullptr;
+ const ggml_tensor * gate_bias = nullptr;
+ ggml_glu_op glu_op;
+};
+struct ggml_cuda_mm_fusion_args_device {
+ const void * x_bias = nullptr;
+ const void * gate = nullptr;
+ const void * gate_bias = nullptr;
+ ggml_glu_op glu_op;
+};
+#pragma once
#include "common.cuh"
#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
}
}
+static bool ggml_cuda_should_fuse_mul_mat(const ggml_tensor * ffn_up,
+ const ggml_tensor * ffn_gate,
+ const ggml_tensor * glu,
+ const ggml_tensor * ffn_up_bias = nullptr,
+ const ggml_tensor * ffn_gate_bias = nullptr) {
+ const bool has_bias = ffn_up_bias != nullptr || ffn_gate_bias != nullptr;
+
+ if (has_bias && (!ffn_up_bias || !ffn_gate_bias)) {
+ return false;
+ }
+
+ const bool is_mul_mat = ffn_up->op == GGML_OP_MUL_MAT && ffn_gate->op == GGML_OP_MUL_MAT && glu->op == GGML_OP_GLU;
+ const bool is_mul_mat_id = ffn_up->op == GGML_OP_MUL_MAT_ID && ffn_gate->op == GGML_OP_MUL_MAT_ID && glu->op == GGML_OP_GLU;
+
+ GGML_ASSERT(ffn_up && ffn_gate && glu);
+
+ if (!is_mul_mat && !is_mul_mat_id) {
+ return false;
+ }
+
+ const ggml_op expected_bias_op = is_mul_mat ? GGML_OP_ADD : GGML_OP_ADD_ID;
+
+ if (has_bias) {
+ if (ffn_up_bias->op != expected_bias_op || ffn_gate_bias->op != expected_bias_op) {
+ return false;
+ }
+
+ if (glu->src[0] != ffn_gate_bias || glu->src[1] != ffn_up_bias) {
+ return false;
+ }
+
+ if (expected_bias_op == GGML_OP_ADD) {
+ const bool up_has_mul = ffn_up_bias->src[0] == ffn_up || ffn_up_bias->src[1] == ffn_up;
+ const bool gate_has_mul = ffn_gate_bias->src[0] == ffn_gate || ffn_gate_bias->src[1] == ffn_gate;
+ if (!up_has_mul || !gate_has_mul) {
+ return false;
+ }
+ } else { // GGML_OP_ADD_ID
+ if (ffn_up_bias->src[0] != ffn_up || ffn_gate_bias->src[0] != ffn_gate) {
+ return false;
+ }
+ if (ffn_up_bias->src[2] != ffn_up->src[2] || ffn_gate_bias->src[2] != ffn_gate->src[2]) {
+ return false;
+ }
+ }
+ } else {
+ if (glu->src[0] != ffn_gate && glu->src[1] != ffn_up) {
+ return false;
+ }
+ }
+
+ if (ffn_up->src[0]->type != ffn_gate->src[0]->type || !ggml_are_same_shape(ffn_up->src[0], ffn_gate->src[0]) ||
+ !ggml_are_same_stride(ffn_up->src[0], ffn_gate->src[0])) {
+ return false;
+ }
+
+ if (ffn_up->src[1] != ffn_gate->src[1]) {
+ return false;
+ }
+
+ if (ffn_up->src[2] && (ffn_up->src[2] != ffn_gate->src[2])) {
+ return false;
+ }
+
+ static constexpr std::array<ggml_glu_op, 3> valid_glu_ops = { GGML_GLU_OP_SWIGLU, GGML_GLU_OP_GEGLU, GGML_GLU_OP_SWIGLU_OAI };
+
+ if (std::find(valid_glu_ops.begin(), valid_glu_ops.end(), ggml_get_glu_op(glu)) == valid_glu_ops.end()) {
+ return false;
+ }
+
+ if (const bool swapped = ggml_get_op_params_i32(glu, 1); swapped) {
+ return false;
+ }
+
+ const bool split = ggml_backend_buft_is_cuda_split(ffn_up->src[0]->buffer->buft) ||
+ ggml_backend_buft_is_cuda_split(ffn_gate->src[0]->buffer->buft);
+
+ //TODO: add support for fusion for split buffers
+ if (split) {
+ return false;
+ }
+
+ return true;
+}
+
+static bool ggml_cuda_should_fuse_mul_mat_vec_f(const ggml_tensor * tensor) {
+ ggml_tensor * src0 = tensor->src[0];
+ ggml_tensor * src1 = tensor->src[1];
+ const ggml_tensor * dst = tensor;
+
+ const bool is_mul_mat_id = tensor->op == GGML_OP_MUL_MAT_ID;
+
+ bool use_mul_mat_vec_f =
+ (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16) &&
+ src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
+
+ const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+ use_mul_mat_vec_f = use_mul_mat_vec_f && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, is_mul_mat_id ? src1->ne[2] : src1->ne[1]);
+
+ //we only support fusion for ncols_dst = 1
+ if (tensor->op == GGML_OP_MUL_MAT && dst->ne[1] != 1) {
+ return false;
+ }
+
+ if (tensor->op == GGML_OP_MUL_MAT_ID && dst->ne[2] != 1) {
+ return false;
+ }
+
+
+ return use_mul_mat_vec_f;
+}
+
+static bool ggml_cuda_should_fuse_mul_mat_vec_q(const ggml_tensor * tensor) {
+ ggml_tensor * src0 = tensor->src[0];
+ ggml_tensor * src1 = tensor->src[1];
+ const ggml_tensor * dst = tensor;
+
+ const bool bad_padding_clear = ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE &&
+ ggml_nbytes(src0) != ggml_backend_buffer_get_alloc_size(src0->buffer, src0) &&
+ src0->view_src;
+
+ bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) && !bad_padding_clear && src1->type == GGML_TYPE_F32 &&
+ dst->type == GGML_TYPE_F32 && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE;
+
+ // fusion is not universally faster on Pascal
+ const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+ if (cc <= GGML_CUDA_CC_PASCAL) {
+ return false;
+ }
+ //we only support fusion for ncols_dst = 1
+ if (tensor->op == GGML_OP_MUL_MAT && dst->ne[1] != 1) {
+ return false;
+ }
+
+ if (tensor->op == GGML_OP_MUL_MAT_ID && dst->ne[2] != 1) {
+ return false;
+ }
+
+ return use_mul_mat_vec_q;
+}
+
static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft);
}
}
- if (node->op == GGML_OP_SCALE &&
+ if ((node->op == GGML_OP_SCALE || node->op == GGML_OP_GLU) &&
memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
return false;
}
}
}
+ std::initializer_list<enum ggml_op> mul_mat_bias_glu_ops = { GGML_OP_MUL_MAT, GGML_OP_ADD, GGML_OP_MUL_MAT, GGML_OP_ADD, GGML_OP_GLU };
+ std::initializer_list<enum ggml_op> mul_mat_id_bias_glu_ops = { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID, GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID, GGML_OP_GLU };
+
+ std::initializer_list<enum ggml_op> mul_mat_id_glu_ops = { GGML_OP_MUL_MAT_ID, GGML_OP_MUL_MAT_ID, GGML_OP_GLU };
+ std::initializer_list<enum ggml_op> mul_mat_glu_ops = { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT, GGML_OP_GLU };
+
+ if (ops.size() == 5 && (ggml_can_fuse_subgraph(cgraph, node_idx, ops, {node_idx + 4}) ||
+ ggml_can_fuse_subgraph(cgraph, node_idx, ops, {node_idx + 4}))) {
+
+ const ggml_tensor * ffn_gate = cgraph->nodes[node_idx];
+ const ggml_tensor * ffn_gate_bias = cgraph->nodes[node_idx + 1];
+ const ggml_tensor * ffn_up = cgraph->nodes[node_idx + 2];
+ const ggml_tensor * ffn_up_bias = cgraph->nodes[node_idx + 3];
+ const ggml_tensor * glu = cgraph->nodes[node_idx + 4];
+
+ if (ggml_cuda_should_fuse_mul_mat(ffn_up, ffn_gate, glu, ffn_up_bias, ffn_gate_bias)) {
+ return true;
+ }
+ }
+
+ if (ops.size() == 3 && (ggml_can_fuse_subgraph(cgraph, node_idx, ops, {node_idx + 2}) ||
+ ggml_can_fuse_subgraph(cgraph, node_idx, ops, {node_idx + 2}))) {
+
+ const ggml_tensor * ffn_gate = cgraph->nodes[node_idx];
+ const ggml_tensor * ffn_up = cgraph->nodes[node_idx + 1];
+ const ggml_tensor * glu = cgraph->nodes[node_idx + 2];
+
+ if (ggml_cuda_should_fuse_mul_mat(ffn_up, ffn_gate, glu)) {
+ return true;
+ }
+ }
+
if (!ggml_can_fuse(cgraph, node_idx, ops)) {
return false;
}
}
}
+ bool fused_mul_mat_vec = false;
+ int fused_node_count = 0;
+
+ for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
+ const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
+
+ if (ggml_cuda_can_fuse(cgraph, i, { op, bias_op, op, bias_op, GGML_OP_GLU }, {})) {
+ ggml_tensor * glu = cgraph->nodes[i + 4];
+ ggml_tensor * gate_bias_n = glu->src[0];
+ ggml_tensor * up_bias_n = glu->src[1];
+
+ //we don't assume the order for {gate, up}. Instead infer it from the bias tensor
+ ggml_tensor * gate_n = nullptr;
+ ggml_tensor * up_n = nullptr;
+
+ if (gate_bias_n->src[0] == cgraph->nodes[i] || gate_bias_n->src[1] == cgraph->nodes[i]) {
+ gate_n = cgraph->nodes[i];
+ up_n = cgraph->nodes[i + 2];
+ } else if (gate_bias_n->src[0] == cgraph->nodes[i + 2] || gate_bias_n->src[1] == cgraph->nodes[i + 2]) {
+ gate_n = cgraph->nodes[i + 2];
+ up_n = cgraph->nodes[i];
+ } else {
+ continue;
+ }
+
+ auto get_bias_tensor = [](const ggml_tensor * bias_node, const ggml_tensor * mul_node, ggml_op op_bias) {
+ if (op_bias == GGML_OP_ADD) {
+ if (bias_node->src[0] == mul_node) {
+ return bias_node->src[1];
+ }
+ if (bias_node->src[1] == mul_node) {
+ return bias_node->src[0];
+ }
+ return (ggml_tensor *) nullptr;
+ }
+ GGML_ASSERT(op_bias == GGML_OP_ADD_ID);
+ GGML_ASSERT(bias_node->src[0] == mul_node);
+ return bias_node->src[1];
+ };
+
+ ggml_tensor * up_bias_tensor = get_bias_tensor(up_bias_n, up_n, bias_op);
+ ggml_tensor * gate_bias_tensor = get_bias_tensor(gate_bias_n, gate_n, bias_op);
+
+ if (!up_bias_tensor || !gate_bias_tensor) {
+ continue;
+ }
+
+ const ggml_tensor * src0 = up_n->src[0];
+ const ggml_tensor * src1 = up_n->src[1];
+ const ggml_tensor * ids = up_n->src[2];
+
+ if (ggml_cuda_should_fuse_mul_mat_vec_f(up_n)) {
+ ggml_cuda_mm_fusion_args_host fusion_data{};
+ fusion_data.gate = gate_n->src[0];
+ fusion_data.x_bias = up_bias_tensor;
+ fusion_data.gate_bias = gate_bias_tensor;
+ fusion_data.glu_op = ggml_get_glu_op(glu);
+
+ ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+ fused_mul_mat_vec = true;
+ fused_node_count = 5;
+ break;
+ }
+
+ if (ggml_cuda_should_fuse_mul_mat_vec_q(up_n)) {
+ ggml_cuda_mm_fusion_args_host fusion_data{};
+ fusion_data.gate = gate_n->src[0];
+ fusion_data.x_bias = up_bias_tensor;
+ fusion_data.gate_bias = gate_bias_tensor;
+ fusion_data.glu_op = ggml_get_glu_op(glu);
+
+ ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+ fused_mul_mat_vec = true;
+ fused_node_count = 5;
+ break;
+ }
+ } else if (ggml_cuda_can_fuse(cgraph, i, { op, op, GGML_OP_GLU }, {})) {
+ ggml_tensor * glu = cgraph->nodes[i + 2];
+ ggml_tensor * gate = glu->src[0];
+ ggml_tensor * up = glu->src[1];
+
+ bool ok = (gate == cgraph->nodes[i] && up == cgraph->nodes[i + 1])
+ || (gate == cgraph->nodes[i + 1] && up == cgraph->nodes[i]);
+
+ if (!ok) continue;
+
+ const ggml_tensor * src0 = up->src[0];
+ const ggml_tensor * src1 = up->src[1];
+ const ggml_tensor * ids = up->src[2];
+
+ if (ggml_cuda_should_fuse_mul_mat_vec_f(up)) {
+ ggml_cuda_mm_fusion_args_host fusion_data{};
+ fusion_data.gate = gate->src[0];
+ fusion_data.glu_op = ggml_get_glu_op(glu);
+
+ ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+ fused_mul_mat_vec = true;
+ fused_node_count = 3;
+ break;
+ }
+
+ if (ggml_cuda_should_fuse_mul_mat_vec_q(up)) {
+ ggml_cuda_mm_fusion_args_host fusion_data{};
+ fusion_data.gate = gate->src[0];
+ fusion_data.glu_op = ggml_get_glu_op(glu);
+
+ ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, glu, &fusion_data);
+ fused_mul_mat_vec = true;
+ fused_node_count = 3;
+ break;
+ }
+ }
+ }
+
+ if (fused_mul_mat_vec) {
+ i += fused_node_count - 1;
+ continue;
+ }
+
+ fused_mul_mat_vec = false;
+ fused_node_count = 0;
+
+ for (ggml_op op : { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT_ID }) {
+ const ggml_op bias_op = op == GGML_OP_MUL_MAT ? GGML_OP_ADD : GGML_OP_ADD_ID;
+
+ if (!ggml_can_fuse(cgraph, i, { op, bias_op })) {
+ continue;
+ }
+
+ ggml_tensor * mm_node = cgraph->nodes[i];
+ ggml_tensor * bias_node = cgraph->nodes[i + 1];
+
+ ggml_tensor * bias_tensor = nullptr;
+ if (bias_op == GGML_OP_ADD) {
+ if (bias_node->src[0] == mm_node) {
+ bias_tensor = bias_node->src[1];
+ } else if (bias_node->src[1] == mm_node) {
+ bias_tensor = bias_node->src[0];
+ } else {
+ continue;
+ }
+ } else {
+ if (bias_node->src[0] != mm_node) {
+ continue;
+ }
+ bias_tensor = bias_node->src[1];
+ }
+
+ const ggml_tensor * src0 = mm_node->src[0];
+ const ggml_tensor * src1 = mm_node->src[1];
+ const ggml_tensor * ids = mm_node->src[2];
+
+ if (bias_op == GGML_OP_ADD_ID && bias_node->src[2] != ids) {
+ continue;
+ }
+
+ ggml_cuda_mm_fusion_args_host fusion_data{};
+ fusion_data.x_bias = bias_tensor;
+
+ if (ggml_cuda_should_fuse_mul_mat_vec_f(mm_node)) {
+ ggml_cuda_mul_mat_vec_f(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
+ fused_mul_mat_vec = true;
+ fused_node_count = 2;
+ break;
+ }
+
+ if (ggml_cuda_should_fuse_mul_mat_vec_q(mm_node)) {
+ ggml_cuda_mul_mat_vec_q(*cuda_ctx, src0, src1, ids, bias_node, &fusion_data);
+ fused_mul_mat_vec = true;
+ fused_node_count = 2;
+ break;
+ }
+ }
+
+ if (fused_mul_mat_vec) {
+ i += fused_node_count - 1;
+ continue;
+ }
if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ADD}, {})) {
ggml_cuda_op_rms_norm_fused_add(*cuda_ctx, node, cgraph->nodes[i+1], cgraph->nodes[i+2]);
#include "ggml.h"
#include "common.cuh"
-#include "convert.cuh"
+#include "unary.cuh"
#include "mmvf.cuh"
+#include "convert.cuh"
-template <typename T, typename type_acc, int ncols_dst, int block_size>
+template <typename T, typename type_acc, int ncols_dst, int block_size, bool has_fusion = false>
static __global__ void mul_mat_vec_f(
- const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst,
+ const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, const ggml_cuda_mm_fusion_args_device fusion, float * __restrict__ dst,
const int ncols2, const int nchannels_y, const int stride_row, const int stride_col_y2, const int stride_col_dst,
const uint3 channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
const uint3 sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) {
y += int64_t(sample_y) *stride_sample_y + channel_y *stride_channel_y;
dst += int64_t(sample_dst)*stride_sample_dst + channel_dst*stride_channel_dst;
+ bool use_gate = false;
+ bool use_bias = false;
+ bool use_gate_bias = false;
+ ggml_glu_op glu_op = ggml_glu_op::GGML_GLU_OP_SWIGLU;
+ const T * gate_x = nullptr;
+ const float * x_bias = nullptr;
+ const float * gate_bias = nullptr;
+
+ if constexpr (has_fusion) {
+ use_gate = fusion.gate != nullptr;
+ use_bias = fusion.x_bias != nullptr;
+ use_gate_bias = fusion.gate_bias != nullptr;
+ glu_op = fusion.glu_op;
+
+ if (use_gate) {
+ gate_x = static_cast<const T *>(fusion.gate);
+ }
+ if (use_bias) {
+ x_bias = static_cast<const float *>(fusion.x_bias);
+ }
+ if (use_gate_bias) {
+ gate_bias = static_cast<const float *>(fusion.gate_bias);
+ use_gate_bias = use_gate;
+ } else {
+ use_gate_bias = false;
+ }
+ }
+
+ if (use_gate) {
+ gate_x += int64_t(sample_x) *stride_sample_x + channel_x *stride_channel_x + row*stride_row;
+ }
+ if constexpr (has_fusion) {
+ const int channel_bias = ids ? channel_x : channel_dst;
+ if (use_bias) {
+ x_bias += int64_t(sample_dst)*stride_sample_dst + channel_bias*stride_channel_dst;
+ }
+ if (use_gate_bias) {
+ gate_bias += int64_t(sample_dst)*stride_sample_dst + channel_bias*stride_channel_dst;
+ }
+ }
+
const float2 * y2 = (const float2 *) y;
extern __shared__ char data_mmv[];
float * buf_iw = (float *) data_mmv;
+ float * buf_iw_gate = nullptr;
+ if constexpr (has_fusion) {
+ buf_iw_gate = (float *) (data_mmv + warp_size*sizeof(float));
+ }
if (block_size > warp_size) {
if (tid < warp_size) {
buf_iw[tid] = 0.0f;
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ buf_iw_gate[tid] = 0.0f;
+ }
+ }
}
__syncthreads();
}
float sumf[ncols_dst] = {0.0f};
+ float sumf_gate[ncols_dst];
+ if constexpr (has_fusion) {
+#pragma unroll
+ for (int j = 0; j < ncols_dst; ++j) {
+ sumf_gate[j] = 0.0f;
+ }
+ }
if constexpr (std::is_same_v<T, float>) {
const float2 * x2 = (const float2 *) x;
+ const float2 * gate_x2 = nullptr;
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ gate_x2 = (const float2 *) gate_x;
+ }
+ }
for (int col2 = tid; col2 < ncols2; col2 += block_size) {
const float2 tmpx = x2[col2];
+ float2 tmpx_gate = make_float2(0.0f, 0.0f);
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ tmpx_gate = gate_x2[col2];
+ }
+ }
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
const float2 tmpy = y2[j*stride_col_y2 + col2];
ggml_cuda_mad(sumf[j], tmpx.x, tmpy.x);
ggml_cuda_mad(sumf[j], tmpx.y, tmpy.y);
+
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ ggml_cuda_mad(sumf_gate[j], tmpx_gate.x, tmpy.x);
+ ggml_cuda_mad(sumf_gate[j], tmpx_gate.y, tmpy.y);
+ }
+ }
}
}
} else if constexpr (std::is_same_v<T, half>) {
const half2 * x2 = (const half2 *) x;
+ const half2 * gate_x2 = nullptr;
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ gate_x2 = (const half2 *) gate_x;
+ }
+ }
if (std::is_same_v<type_acc, float>) {
for (int col2 = tid; col2 < ncols2; col2 += block_size) {
const float2 tmpx = __half22float2(x2[col2]);
-
+ float2 tmpx_gate = make_float2(0.0f, 0.0f);
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ tmpx_gate = __half22float2(gate_x2[col2]);
+ }
+ }
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
const float2 tmpy = y2[j*stride_col_y2 + col2];
ggml_cuda_mad(sumf[j], tmpx.x, tmpy.x);
ggml_cuda_mad(sumf[j], tmpx.y, tmpy.y);
+
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ ggml_cuda_mad(sumf_gate[j], tmpx_gate.x, tmpy.x);
+ ggml_cuda_mad(sumf_gate[j], tmpx_gate.y, tmpy.y);
+ }
+ }
}
}
} else {
#ifdef FP16_AVAILABLE
half2 sumh2[ncols_dst] = {{0.0f, 0.0f}};
+ half2 sumh2_gate[ncols_dst] = {{0.0f, 0.0f}};
for (int col2 = tid; col2 < ncols2; col2 += block_size) {
const half2 tmpx = x2[col2];
-
+ half2 tmpx_gate = make_half2(0.0f, 0.0f);
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ tmpx_gate = gate_x2[col2];
+ }
+ }
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
const float2 tmpy = y2[j*stride_col_y2 + col2];
sumh2[j] += tmpx * make_half2(tmpy.x, tmpy.y);
+
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ sumh2_gate[j] += tmpx_gate * make_half2(tmpy.x, tmpy.y);
+ }
+ }
}
}
for (int j = 0; j < ncols_dst; ++j) {
sumf[j] = __low2float(sumh2[j]) + __high2float(sumh2[j]);
}
+
+ if constexpr (has_fusion) {
+ if (use_gate) {
+#pragma unroll
+ for (int j = 0; j < ncols_dst; ++j) {
+ sumf_gate[j] = __low2float(sumh2_gate[j]) + __high2float(sumh2_gate[j]);
+ }
+ }
+ }
#else
NO_DEVICE_CODE;
#endif // FP16_AVAILABLE
//TODO: add support for ggml_cuda_mad for hip_bfloat162
#if defined(GGML_USE_HIP)
const int * x2 = (const int *) x;
+ const int * gate_x2 = nullptr;
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ gate_x2 = (const int *) gate_x;
+ }
+ }
for (int col2 = tid; col2 < ncols2; col2 += block_size) {
const int tmpx = x2[col2];
+ int tmpx_gate = 0;
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ tmpx_gate = gate_x2[col2];
+ }
+ }
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
const float2 tmpy = y2[j*stride_col_y2 + col2];
const float tmpx1 = ggml_cuda_cast<float>(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[1]);
ggml_cuda_mad(sumf[j], tmpx0, tmpy.x);
ggml_cuda_mad(sumf[j], tmpx1, tmpy.y);
+
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ const float tmpx0_gate = ggml_cuda_cast<float>(reinterpret_cast<const nv_bfloat16 *>(&tmpx_gate)[0]);
+ const float tmpx1_gate = ggml_cuda_cast<float>(reinterpret_cast<const nv_bfloat16 *>(&tmpx_gate)[1]);
+ ggml_cuda_mad(sumf_gate[j], tmpx0_gate, tmpy.x);
+ ggml_cuda_mad(sumf_gate[j], tmpx1_gate, tmpy.y);
+ }
+ }
}
}
#else
const nv_bfloat162 * x2 = (const nv_bfloat162 *) x;
+ const nv_bfloat162 * gate_x2 = nullptr;
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ gate_x2 = (const nv_bfloat162 *) gate_x;
+ }
+ }
for (int col2 = tid; col2 < ncols2; col2 += block_size) {
const nv_bfloat162 tmpx = x2[col2];
+ nv_bfloat162 tmpx_gate;
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ tmpx_gate = gate_x2[col2];
+ }
+ }
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
const float2 tmpy = y2[j*stride_col_y2 + col2];
ggml_cuda_mad(sumf[j], tmpx.x, tmpy.x);
ggml_cuda_mad(sumf[j], tmpx.y, tmpy.y);
+
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ ggml_cuda_mad(sumf_gate[j], tmpx_gate.x, tmpy.x);
+ ggml_cuda_mad(sumf_gate[j], tmpx_gate.y, tmpy.y);
+ }
+ }
}
}
#endif
for (int j = 0; j < ncols_dst; ++j) {
sumf[j] = warp_reduce_sum<warp_size>(sumf[j]);
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ sumf_gate[j] = warp_reduce_sum<warp_size>(sumf_gate[j]);
+ }
+ }
+
if (block_size > warp_size) {
buf_iw[tid/warp_size] = sumf[j];
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ buf_iw_gate[tid/warp_size] = sumf_gate[j];
+ }
+ }
__syncthreads();
if (tid < warp_size) {
sumf[j] = buf_iw[tid];
sumf[j] = warp_reduce_sum<warp_size>(sumf[j]);
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ sumf_gate[j] = buf_iw_gate[tid];
+ sumf_gate[j] = warp_reduce_sum<warp_size>(sumf_gate[j]);
+ }
+ }
}
+
if (j < ncols_dst) {
__syncthreads();
}
return;
}
- dst[tid*stride_col_dst + row] = sumf[tid];
+ float value = sumf[tid];
+
+ if constexpr (has_fusion) {
+ if (use_bias) {
+ value += x_bias[tid*stride_col_dst + row];
+ }
+
+ if (use_gate) {
+ float gate_value = sumf_gate[tid];
+ if (use_gate_bias) {
+ gate_value += gate_bias[tid*stride_col_dst + row];
+ }
+ switch (glu_op) {
+ case GGML_GLU_OP_SWIGLU:
+ value *= ggml_cuda_op_silu_single(gate_value);
+ break;
+ case GGML_GLU_OP_GEGLU:
+ value *= ggml_cuda_op_gelu_single(gate_value);
+ break;
+ case GGML_GLU_OP_SWIGLU_OAI: {
+ value = ggml_cuda_op_swiglu_oai_single(gate_value, value);
+ break;
+ }
+ default:
+ break;
+ }
+ }
+ }
+
+ dst[tid*stride_col_dst + row] = value;
+}
+
+template<typename T, typename type_acc, int ncols_dst, int block_size>
+static void mul_mat_vec_f_switch_fusion(
+ const T * x, const float * y, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
+ const int64_t ncols, const int64_t nrows,
+ const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
+ const uint3 channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
+ const uint3 sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
+ const dim3 & block_dims, const dim3 & block_nums, const int nbytes_shared, const cudaStream_t stream) {
+
+ const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
+ if constexpr (ncols_dst == 1) {
+ if (has_fusion) {
+ mul_mat_vec_f<T, type_acc, ncols_dst, block_size, true><<<block_nums, block_dims, nbytes_shared, stream>>>
+ (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+ channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+ sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+ return;
+ }
+ }
+
+ GGML_ASSERT(!has_fusion && "fusion only supported for ncols_dst=1");
+
+ mul_mat_vec_f<T, type_acc, ncols_dst, block_size><<<block_nums, block_dims, nbytes_shared, stream>>>
+ (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+ channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+ sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+
}
template <typename T, typename type_acc, int ncols_dst>
-static void launch_mul_mat_vec_f_cuda(
- const T * x, const float * y, const int32_t * ids, float * dst,
+void launch_mul_mat_vec_f_cuda(
+ const T * x, const float * y, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
const int64_t ncols, const int64_t nrows,
const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
}
}
- const int nbytes_shared = warp_size*sizeof(float);
+ const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
+
+ const int nbytes_shared = warp_size*sizeof(float) + (has_fusion ? warp_size*sizeof(float) : 0);
const dim3 block_nums(nrows, nchannels_dst, nsamples_dst);
const dim3 block_dims(block_size_best, 1, 1);
switch (block_size_best) {
case 32: {
- mul_mat_vec_f<T, type_acc, ncols_dst, 32><<<block_nums, block_dims, nbytes_shared, stream>>>
- (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+ mul_mat_vec_f_switch_fusion<T, type_acc, ncols_dst, 32>
+ (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
} break;
case 64: {
- mul_mat_vec_f<T, type_acc, ncols_dst, 64><<<block_nums, block_dims, nbytes_shared, stream>>>
- (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+ mul_mat_vec_f_switch_fusion<T, type_acc, ncols_dst, 64>
+ (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
} break;
case 96: {
- mul_mat_vec_f<T, type_acc, ncols_dst, 96><<<block_nums, block_dims, nbytes_shared, stream>>>
- (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+ mul_mat_vec_f_switch_fusion<T, type_acc, ncols_dst, 96>
+ (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
} break;
case 128: {
- mul_mat_vec_f<T, type_acc, ncols_dst, 128><<<block_nums, block_dims, nbytes_shared, stream>>>
- (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+ mul_mat_vec_f_switch_fusion<T, type_acc, ncols_dst, 128>
+ (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
} break;
case 160: {
- mul_mat_vec_f<T, type_acc, ncols_dst, 160><<<block_nums, block_dims, nbytes_shared, stream>>>
- (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+ mul_mat_vec_f_switch_fusion<T, type_acc, ncols_dst, 160>
+ (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
} break;
case 192: {
- mul_mat_vec_f<T, type_acc, ncols_dst, 192><<<block_nums, block_dims, nbytes_shared, stream>>>
- (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+ mul_mat_vec_f_switch_fusion<T, type_acc, ncols_dst, 192>
+ (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
} break;
case 224: {
- mul_mat_vec_f<T, type_acc, ncols_dst, 224><<<block_nums, block_dims, nbytes_shared, stream>>>
- (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+ mul_mat_vec_f_switch_fusion<T, type_acc, ncols_dst, 224>
+ (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
} break;
case 256: {
- mul_mat_vec_f<T, type_acc, ncols_dst, 256><<<block_nums, block_dims, nbytes_shared, stream>>>
- (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
+ mul_mat_vec_f_switch_fusion<T, type_acc, ncols_dst, 256>
+ (x, y, ids, fusion, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst, block_dims, block_nums, nbytes_shared, stream);
} break;
default: {
GGML_ABORT("fatal error");
template <typename T, typename type_acc>
static void mul_mat_vec_f_cuda_switch_ncols_dst(
- const T * x, const float * y, const int32_t * ids, float * dst,
+ const T * x, const float * y, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
const int64_t ncols, const int64_t nrows, const int64_t ncols_dst,
const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
switch (ncols_dst) {
case 1:
launch_mul_mat_vec_f_cuda<T, type_acc, 1>
- (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+ (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 2:
launch_mul_mat_vec_f_cuda<T, type_acc, 2>
- (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+ (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 3:
launch_mul_mat_vec_f_cuda<T, type_acc, 3>
- (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+ (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 4:
launch_mul_mat_vec_f_cuda<T, type_acc, 4>
- (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+ (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 5:
launch_mul_mat_vec_f_cuda<T, type_acc, 5>
- (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+ (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 6:
launch_mul_mat_vec_f_cuda<T, type_acc, 6>
- (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+ (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 7:
launch_mul_mat_vec_f_cuda<T, type_acc, 7>
- (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+ (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case 8:
launch_mul_mat_vec_f_cuda<T, type_acc, 8>
- (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
+ (x, y, ids, fusion, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
template<typename T>
static void mul_mat_vec_f_cuda(
- const T * x, const float * y, const int32_t * ids, float * dst,
+ const T * x, const float * y, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
const int64_t ncols, const int64_t nrows, const int64_t ncols_dst,
const int64_t stride_row, const int64_t stride_col_y, const int stride_col_dst,
const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
enum ggml_prec prec, cudaStream_t stream) {
+
if constexpr(std::is_same_v<T, half>) {
if (prec == GGML_PREC_DEFAULT) {
mul_mat_vec_f_cuda_switch_ncols_dst<T, half>
- (x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
- nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
- stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
+ (x, y, ids, fusion, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
+ nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
+ stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
return;
}
}
mul_mat_vec_f_cuda_switch_ncols_dst<T, float>
- (x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
- nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
- stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
+ (x, y, ids, fusion, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
+ nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
+ stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
}
-void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
+void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst,
+ const ggml_cuda_mm_fusion_args_host * fusion) {
GGML_ASSERT( src1->type == GGML_TYPE_F32);
GGML_ASSERT(!ids || ids->type == GGML_TYPE_I32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
const int32_t * ids_d = ids ? (const int32_t *) ids->data : nullptr;
float * dst_d = (float *) dst->data;
+ ggml_cuda_mm_fusion_args_device fusion_local{};
+
+ if (fusion) {
+ GGML_ASSERT( !ids || dst->ne[2] == 1);
+ GGML_ASSERT( ids || dst->ne[1] == 1);
+ if (fusion->x_bias) {
+ GGML_ASSERT(fusion->x_bias->type == GGML_TYPE_F32);
+ GGML_ASSERT(fusion->x_bias->ne[0] == dst->ne[0]);
+ GGML_ASSERT(!ids || fusion->x_bias->ne[1] == src0->ne[2]);
+ fusion_local.x_bias = fusion->x_bias->data;
+ }
+ if (fusion->gate) {
+ GGML_ASSERT(fusion->gate->type == src0->type && ggml_are_same_stride(fusion->gate, src0));
+ fusion_local.gate = fusion->gate->data;
+ }
+ if (fusion->gate_bias) {
+ GGML_ASSERT(fusion->gate_bias->type == GGML_TYPE_F32);
+ GGML_ASSERT(fusion->gate_bias->ne[0] == dst->ne[0]);
+ GGML_ASSERT(!ids || fusion->gate_bias->ne[1] == src0->ne[2]);
+ fusion_local.gate_bias = fusion->gate_bias->data;
+ }
+ fusion_local.glu_op = fusion->glu_op;
+ }
+
const int64_t s01 = src0->nb[1] / ts_src0;
const int64_t s11 = src1->nb[1] / ts_src1;
const int64_t s1 = dst->nb[1] / ts_dst;
switch (src0->type) {
case GGML_TYPE_F32: {
const float * src0_d = (const float *) src0->data;
- mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+ mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, fusion_local, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
ne03, ne3, s03, s13, s3, prec, ctx.stream());
} break;
case GGML_TYPE_F16: {
const half * src0_d = (const half *) src0->data;
- mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+ mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, fusion_local, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
ne03, ne3, s03, s13, s3, prec, ctx.stream());
} break;
case GGML_TYPE_BF16: {
const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data;
- mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+ mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, fusion_local, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
ne03, ne3, s03, s13, s3, prec, ctx.stream());
} break;
const int cc = ggml_cuda_info().devices[id].cc;
const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32;
-
// ggml_cuda_op provides single, contiguous matrices
const int64_t stride_row = ne00;
const int64_t stride_col_y = ne10;
const int64_t stride_sample_y = 0;
const int64_t stride_sample_dst = 0;
+ ggml_cuda_mm_fusion_args_device empty{};
switch (src0->type) {
case GGML_TYPE_F32: {
const float * src0_d = (const float *) src0_dd_i;
- mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+ mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, empty, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
} break;
case GGML_TYPE_F16: {
const half * src0_d = (const half *) src0_dd_i;
- mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+ mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, empty, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
} break;
case GGML_TYPE_BF16: {
const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i;
- mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+ mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, empty, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, prec, stream);
} break;
#include "common.cuh"
-void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
+void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst,
+ const ggml_cuda_mm_fusion_args_host * fusion = nullptr);
void ggml_cuda_op_mul_mat_vec_f(
ggml_backend_cuda_context & ctx,
#include "mmvq.cuh"
#include "quantize.cuh"
+#include "unary.cuh"
#include "vecdotq.cuh"
#include <cstdint>
return MMVQ_PARAMETERS_GENERIC;
}
-static constexpr __host__ __device__ int calc_nwarps(int ncols_dst, mmvq_parameter_table_id table_id) {
+static constexpr __host__ __device__ int calc_nwarps(int ncols_dst, mmvq_parameter_table_id table_id) {
if (table_id == MMVQ_PARAMETERS_GENERIC) {
switch (ncols_dst) {
case 1:
return 1;
}
-template <ggml_type type, int ncols_dst>
// tell the compiler to use as many registers as it wants, see nwarps definition below
+template <ggml_type type, int ncols_dst, bool has_fusion>
__launch_bounds__(calc_nwarps(ncols_dst, get_device_table_id())*ggml_cuda_get_physical_warp_size(), 1)
static __global__ void mul_mat_vec_q(
- const void * __restrict__ vx, const void * __restrict__ vy, const int32_t * __restrict__ ids, float * __restrict__ dst,
+ const void * __restrict__ vx, const void * __restrict__ vy, const int32_t * __restrict__ ids, const ggml_cuda_mm_fusion_args_device fusion, float * __restrict__ dst,
const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t stride_row_x, const uint32_t stride_col_y,
const uint32_t stride_col_dst, const uint3 channel_ratio, const uint32_t stride_channel_x,
const uint32_t stride_channel_y, const uint32_t stride_channel_dst, const uint3 sample_ratio,
const uint32_t sample_x = fastdiv(sample_dst, sample_ratio);
const uint32_t sample_y = sample_dst;
+ bool use_gate = false;
+ bool use_bias = false;
+ bool use_gate_bias = false;
+ const void * vgate = nullptr;
+ const float * x_bias = nullptr;
+ const float * gate_bias = nullptr;
+ ggml_glu_op active_glu;
+
+ if constexpr (has_fusion) {
+ use_gate = fusion.gate != nullptr;
+ use_bias = fusion.x_bias != nullptr;
+ use_gate_bias = fusion.gate_bias != nullptr && use_gate;
+ vgate = fusion.gate;
+ x_bias = (const float *) fusion.x_bias;
+ gate_bias = (const float *) fusion.gate_bias;
+ active_glu = fusion.glu_op;
+ }
+
+ const uint32_t channel_bias = ids ? channel_x : channel_dst;
+
+ if constexpr (has_fusion) {
+ if (use_bias) {
+ x_bias = x_bias + sample_dst*stride_sample_dst + channel_bias*stride_channel_dst + row0;
+ }
+ if (use_gate_bias) {
+ gate_bias = gate_bias + sample_dst*stride_sample_dst + channel_bias*stride_channel_dst + row0;
+ }
+ }
+
// partial sum for each thread
float tmp[ncols_dst][rows_per_cuda_block] = {{0.0f}};
+ float tmp_gate[ncols_dst][rows_per_cuda_block] = {{0.0f}};
const block_q8_1 * y = ((const block_q8_1 *) vy) + sample_y*stride_sample_y + channel_y*stride_channel_y;
const int kbx_offset = sample_x*stride_sample_x + channel_x*stride_channel_x + row0*stride_row_x;
for (int i = 0; i < rows_per_cuda_block; ++i) {
tmp[j][i] += vec_dot_q_cuda(
vx, &y[j*stride_col_y + kby], kbx_offset + i*stride_row_x + kbx, kqs);
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ tmp_gate[j][i] += vec_dot_q_cuda(
+ vgate, &y[j*stride_col_y + kby], kbx_offset + i*stride_row_x + kbx, kqs);
+ }
+ }
}
}
}
__shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_dst][rows_per_cuda_block][warp_size];
+ __shared__ float tmp_shared_gate[(has_fusion && (nwarps-1 > 0)) ? nwarps-1 : 1][ncols_dst][rows_per_cuda_block][warp_size];
+ if constexpr (!has_fusion) {
+ (void) tmp_shared_gate;
+ } else if (!use_gate) {
+ (void) tmp_shared_gate;
+ }
+
if (threadIdx.y > 0) {
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
#pragma unroll
for (int i = 0; i < rows_per_cuda_block; ++i) {
tmp_shared[threadIdx.y-1][j][i][threadIdx.x] = tmp[j][i];
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ tmp_shared_gate[threadIdx.y-1][j][i][threadIdx.x] = tmp_gate[j][i];
+ }
+ }
}
}
}
#pragma unroll
for (int l = 0; l < nwarps-1; ++l) {
tmp[j][i] += tmp_shared[l][j][i][threadIdx.x];
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ tmp_gate[j][i] += tmp_shared_gate[l][j][i][threadIdx.x];
+ }
+ }
}
tmp[j][i] = warp_reduce_sum<warp_size>(tmp[j][i]);
+ if constexpr (has_fusion) {
+ if (use_gate) {
+ tmp_gate[j][i] = warp_reduce_sum<warp_size>(tmp_gate[j][i]);
+ }
+ }
}
if (threadIdx.x < rows_per_cuda_block && (rows_per_cuda_block == 1 || uint32_t(row0 + threadIdx.x) < stride_col_dst)) {
- dst[j*stride_col_dst + threadIdx.x] = tmp[j][threadIdx.x];
+ float result = tmp[j][threadIdx.x];
+ if constexpr (has_fusion) {
+ if (use_bias) {
+ result += x_bias[j*stride_col_dst + threadIdx.x];
+ }
+ if (use_gate) {
+ float gate_value = tmp_gate[j][threadIdx.x];
+ if (use_gate_bias) {
+ gate_value += gate_bias[j*stride_col_dst + threadIdx.x];
+ }
+ switch (active_glu) {
+ case GGML_GLU_OP_SWIGLU:
+ result *= ggml_cuda_op_silu_single(gate_value);
+ break;
+ case GGML_GLU_OP_GEGLU:
+ result *= ggml_cuda_op_gelu_single(gate_value);
+ break;
+ case GGML_GLU_OP_SWIGLU_OAI: {
+ result = ggml_cuda_op_swiglu_oai_single(gate_value, result);
+ break;
+ }
+ default:
+ result = result * gate_value;
+ break;
+ }
+ }
+ }
+ dst[j*stride_col_dst + threadIdx.x] = result;
}
}
}
return {block_nums, block_dims};
}
+template<ggml_type type, int c_ncols_dst>
+static void mul_mat_vec_q_switch_fusion(
+ const void * vx, const void * vy, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
+ const uint32_t ncols_x, const uint3 nchannels_y, const uint32_t stride_row_x, const uint32_t stride_col_y,
+ const uint32_t stride_col_dst, const uint3 channel_ratio, const uint32_t stride_channel_x,
+ const uint32_t stride_channel_y, const uint32_t stride_channel_dst, const uint3 sample_ratio,
+ const uint32_t stride_sample_x, const uint32_t stride_sample_y, const uint32_t stride_sample_dst,
+ const dim3 & block_nums, const dim3 & block_dims, const int nbytes_shared, cudaStream_t stream) {
+
+ const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
+ if constexpr (c_ncols_dst == 1) {
+ if (has_fusion) {
+ mul_mat_vec_q<type, c_ncols_dst, true><<<block_nums, block_dims, nbytes_shared, stream>>>
+ (vx, vy, ids, fusion, dst, ncols_x, nchannels_y, stride_row_x, stride_col_y, stride_col_dst,
+ channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+ sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+ return;
+ }
+ }
+
+ GGML_ASSERT(!has_fusion && "fusion only supported for ncols_dst=1");
+
+ mul_mat_vec_q<type, c_ncols_dst, false><<<block_nums, block_dims, nbytes_shared, stream>>>
+ (vx, vy, ids, fusion, dst, ncols_x, nchannels_y, stride_row_x, stride_col_y, stride_col_dst,
+ channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
+ sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
+}
+
template <ggml_type type>
static void mul_mat_vec_q_switch_ncols_dst(
- const void * vx, const void * vy, const int32_t * ids, float * dst,
+ const void * vx, const void * vy, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
const int ncols_x, const int nrows_x, const int ncols_dst,
const int stride_row_x, const int stride_col_y, const int stride_col_dst,
const int nchannels_x, const int nchannels_y, const int nchannels_dst,
const int warp_size = ggml_cuda_info().devices[device].warp_size;
const mmvq_parameter_table_id table_id = get_device_table_id(ggml_cuda_info().devices[device].cc);
+ const bool has_fusion = fusion.gate != nullptr || fusion.x_bias != nullptr || fusion.gate_bias != nullptr;
+
GGML_ASSERT(!ids || ncols_dst == 1);
switch (ncols_dst) {
case 1: {
constexpr int c_ncols_dst = 1;
std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id);
- mul_mat_vec_q<type, c_ncols_dst><<<dims.first, dims.second, 0, stream>>>
- (vx, vy, ids, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
+ mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
+ dims.first, dims.second, 0, stream);
} break;
case 2: {
constexpr int c_ncols_dst = 2;
std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id);
- mul_mat_vec_q<type, c_ncols_dst><<<dims.first, dims.second, 0, stream>>>
- (vx, vy, ids, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
+ mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
+ dims.first, dims.second, 0, stream);
} break;
case 3: {
constexpr int c_ncols_dst = 3;
std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id);
- mul_mat_vec_q<type, c_ncols_dst><<<dims.first, dims.second, 0, stream>>>
- (vx, vy, ids, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
+ mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
+ dims.first, dims.second, 0, stream);
} break;
case 4: {
constexpr int c_ncols_dst = 4;
std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id);
- mul_mat_vec_q<type, c_ncols_dst><<<dims.first, dims.second, 0, stream>>>
- (vx, vy, ids, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
+ mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
+ dims.first, dims.second, 0, stream);
} break;
case 5: {
constexpr int c_ncols_dst = 5;
std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id);
- mul_mat_vec_q<type, c_ncols_dst><<<dims.first, dims.second, 0, stream>>>
- (vx, vy, ids, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
+ mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
+ dims.first, dims.second, 0, stream);
} break;
case 6: {
constexpr int c_ncols_dst = 6;
std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id);
- mul_mat_vec_q<type, c_ncols_dst><<<dims.first, dims.second, 0, stream>>>
- (vx, vy, ids, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
+ mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
+ dims.first, dims.second, 0, stream);
} break;
case 7: {
constexpr int c_ncols_dst = 7;
std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id);
- mul_mat_vec_q<type, c_ncols_dst><<<dims.first, dims.second, 0, stream>>>
- (vx, vy, ids, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
+ mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
+ dims.first, dims.second, 0, stream);
} break;
case 8: {
constexpr int c_ncols_dst = 8;
std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_dst, nrows_x, nchannels_dst, nsamples_dst, warp_size, table_id);
- mul_mat_vec_q<type, c_ncols_dst><<<dims.first, dims.second, 0, stream>>>
- (vx, vy, ids, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
+ mul_mat_vec_q_switch_fusion<type, c_ncols_dst>(vx, vy, ids, fusion, dst, ncols_x, nchannels_y_fd, stride_row_x, stride_col_y, stride_col_dst,
channel_ratio_fd, stride_channel_x, stride_channel_y, stride_channel_dst,
- sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst);
+ sample_ratio_fd, stride_sample_x, stride_sample_y, stride_sample_dst,
+ dims.first, dims.second, 0, stream);
} break;
default:
GGML_ABORT("fatal error");
break;
}
-}
+ GGML_UNUSED(has_fusion);
+}
static void mul_mat_vec_q_switch_type(
- const void * vx, const ggml_type type_x, const void * vy, const int32_t * ids, float * dst,
+ const void * vx, const ggml_type type_x, const void * vy, const int32_t * ids, const ggml_cuda_mm_fusion_args_device fusion, float * dst,
const int ncols_x, const int nrows_x, const int ncols_dst,
const int stride_row_x, const int stride_col_y, const int stride_col_dst,
const int nchannels_x, const int nchannels_y, const int nchannels_dst,
switch (type_x) {
case GGML_TYPE_Q4_0:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q4_0>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_Q4_1:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q4_1>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_Q5_0:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q5_0>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_Q5_1:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q5_1>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_Q8_0:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q8_0>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_MXFP4:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_MXFP4>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_Q2_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q2_K>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_Q3_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q3_K>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_Q4_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q4_K>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_Q5_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q5_K>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_Q6_K:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q6_K>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_IQ2_XXS:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ2_XXS>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_IQ2_XS:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ2_XS>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_IQ2_S:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ2_S>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_IQ3_XXS:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ3_XXS>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_IQ1_S:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ1_S>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_IQ1_M:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ1_M>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_IQ4_NL:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ4_NL>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_IQ4_XS:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ4_XS>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
case GGML_TYPE_IQ3_S:
mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_IQ3_S>
- (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+ (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
- nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
- stream);
+ nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
break;
default:
GGML_ABORT("fatal error");
}
void ggml_cuda_mul_mat_vec_q(
- ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
+ ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst,
+ const ggml_cuda_mm_fusion_args_host * fusion) {
GGML_ASSERT( src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_ASSERT(!ids || ids->type == GGML_TYPE_I32); // Optional, used for batched GGML_MUL_MAT_ID.
const int32_t * ids_d = ids ? (const int32_t *) ids->data : nullptr;
float * dst_d = (float *) dst->data;
+ ggml_cuda_mm_fusion_args_device fusion_local{};
+
+ if (fusion) {
+ GGML_ASSERT( !ids || dst->ne[2] == 1);
+ GGML_ASSERT( ids || dst->ne[1] == 1);
+
+ if (fusion->x_bias) {
+ GGML_ASSERT(fusion->x_bias->type == GGML_TYPE_F32);
+ GGML_ASSERT(fusion->x_bias->ne[0] == dst->ne[0]);
+ GGML_ASSERT(!ids || fusion->x_bias->ne[1] == src0->ne[2]);
+ fusion_local.x_bias = fusion->x_bias->data;
+ }
+ if (fusion->gate) {
+ GGML_ASSERT(fusion->gate->type == src0->type && ggml_are_same_stride(fusion->gate, src0));
+ fusion_local.gate = fusion->gate->data;
+ }
+ if (fusion->gate_bias) {
+ GGML_ASSERT(fusion->gate_bias->type == GGML_TYPE_F32);
+ GGML_ASSERT(fusion->gate_bias->ne[0] == dst->ne[0]);
+ GGML_ASSERT(!ids || fusion->gate_bias->ne[1] == src0->ne[2]);
+ fusion_local.gate_bias = fusion->gate_bias->data;
+ }
+ fusion_local.glu_op = fusion->glu_op;
+ }
+
// If src0 is a temporary compute buffer, clear any potential padding.
if (ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) {
const size_t size_data = ggml_nbytes(src0);
const int64_t stride_channel_y = ids ? s11 : s12;
mul_mat_vec_q_switch_type(
- src0->data, src0->type, src1_q8_1.get(), ids_d, dst_d, ne00,
+ src0->data, src0->type, src1_q8_1.get(), ids_d, fusion_local, dst_d, ne00,
ne01, ncols_dst, s01, stride_col_y, stride_col_dst,
ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
- ne03, ne3, s03, s13, s3, stream);
+ ne03, ne3, s03, s13, s3, stream);
}
void ggml_cuda_op_mul_mat_vec_q(
const int stride_row_x = ne00 / ggml_blck_size(src0->type);
const int stride_col_y = src1_padded_row_size / QK8_1;
+ ggml_cuda_mm_fusion_args_device fusion_local{};
mul_mat_vec_q_switch_type(
- src0_dd_i, src0->type, src1_ddq_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row_x, stride_col_y, nrows_dst,
+ src0_dd_i, src0->type, src1_ddq_i, nullptr, fusion_local, dst_dd_i, ne00, row_diff, src1_ncols, stride_row_x, stride_col_y, nrows_dst,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, stream);
GGML_UNUSED_VARS(src1, dst, src1_ddf_i, src1_ncols, src1_padded_row_size);
#define MMVQ_MAX_BATCH_SIZE 8 // Max. batch size for which to use MMVQ kernels.
void ggml_cuda_mul_mat_vec_q(ggml_backend_cuda_context & ctx,
- const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
+ const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, const ggml_cuda_mm_fusion_args_host * fusion = nullptr);
void ggml_cuda_op_mul_mat_vec_q(
ggml_backend_cuda_context & ctx,
}
static __device__ __forceinline__ float op_gelu(float x) {
- const float GELU_COEF_A = 0.044715f;
- const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
-
- return 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+ return ggml_cuda_op_gelu_single(x);
}
static __device__ __forceinline__ float op_gelu_erf(float x) {
}
static __device__ __forceinline__ float op_silu(float x) {
- return x / (1.0f + expf(-x));
+ return ggml_cuda_op_silu_single(x);
}
static __device__ __forceinline__ float op_tanh(float x) {
float xi = x[j0];
float gi = g[j1];
- xi = fminf(xi, limit);
- gi = fmaxf(fminf(gi, limit), -limit);
-
- float out_glu = xi / (1.0f + expf(-xi * alpha));
- out_glu = out_glu * (1.0f + gi);
- dst[i] = out_glu;
+ dst[i] = ggml_cuda_op_swiglu_oai_single(xi, gi, alpha, limit);
}
template <typename T>
+#pragma once
#include "common.cuh"
#define CUDA_NEG_BLOCK_SIZE 256
void ggml_cuda_op_geglu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_xielu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+__device__ __forceinline__ float ggml_cuda_op_silu_single(float x) {
+ return x / (1.0f + expf(-x));
+}
+
+__device__ __forceinline__ float ggml_cuda_op_gelu_single(float x) {
+ const float GELU_COEF_A = 0.044715f;
+ const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+
+ return 0.5f * x * (1.0f + tanhf(SQRT_2_OVER_PI * x * (1.0f + GELU_COEF_A * x * x)));
+}
+
+__device__ __forceinline__ float ggml_cuda_op_swiglu_oai_single(float x, float g, float alpha = 1.702f, float limit = 7.0f) {
+ x = fminf(x, limit);
+ g = fmaxf(fminf(g, limit), -limit);
+
+ float out_glu = x / (1.0f + expf(-x * alpha));
+ out_glu = out_glu * (1.0f + g);
+ return out_glu;
+}
}
};
+struct test_mul_mat_vec_fusion : public test_case {
+ const ggml_type type;
+ const ggml_glu_op glu_op;
+ const int64_t m;
+ const int64_t n;
+ const int64_t k;
+ const bool use_id;
+ const int n_mats;
+ const int n_used;
+ const bool b; // broadcast b matrix (only for use_id)
+ const bool with_bias;
+ const bool with_gate;
+
+ test_mul_mat_vec_fusion(ggml_type type, ggml_glu_op op, int64_t m, int64_t n, int64_t k,
+ bool use_id = false, int n_mats = 1, int n_used = 1, bool b = false, bool with_bias = false, bool with_gate = true)
+ : type(type), glu_op(op), m(m), n(n), k(k), use_id(use_id), n_mats(n_mats), n_used(n_used), b(b), with_bias(with_bias), with_gate(with_gate) {
+ if (use_id) {
+ GGML_ASSERT(n_used <= n_mats);
+ }
+ }
+
+ std::string vars() override {
+ return VARS_TO_STR11(type, glu_op, m, n, k, use_id, n_mats, n_used, b, with_bias, with_gate);
+ }
+
+ std::string op_desc(ggml_tensor * t) override {
+ GGML_UNUSED(t);
+ return "MUL_MAT_VEC_FUSION";
+ }
+
+ bool run_whole_graph() override { return true; }
+
+ ggml_tensor * build_gate(ggml_context * ctx, ggml_tensor * ffn_gate, ggml_tensor * ffn_up) {
+ ggml_tensor * out = nullptr;
+ if (with_gate) {
+ if (glu_op == GGML_GLU_OP_SWIGLU_OAI) {
+ constexpr float alpha = 1.702f;
+ constexpr float limit = 7.0f;
+ out = ggml_swiglu_oai(ctx, ffn_gate, ffn_up, alpha, limit);
+ } else {
+ out = ggml_glu_split(ctx, ffn_gate, ffn_up, glu_op);
+ }
+ }
+ return out;
+ }
+
+ ggml_tensor * build_graph(ggml_context * ctx) override {
+ if (!use_id) {
+ std::array<int64_t, 4> ne = {k, m, 1, 1};
+ std::array<int64_t, 4> ne0 = {k, n, 1, 1};
+
+ ggml_tensor * cur = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne.data());
+ ggml_tensor * gate = with_gate ? ggml_new_tensor(ctx, type, 4, ne0.data()) : nullptr;
+ ggml_tensor * up = ggml_new_tensor(ctx, type, 4, ne0.data());
+
+ ggml_tensor * ffn_up = ggml_mul_mat(ctx, up, cur);
+ if (with_bias) {
+ std::array<int64_t, 4> bias_ne = {ffn_up->ne[0], 1, 1, 1};
+ ggml_tensor * up_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
+ ffn_up = ggml_add(ctx, ffn_up, up_bias);
+ }
+
+ ggml_tensor * ffn_gate = with_gate ? ggml_mul_mat(ctx, gate, cur) : nullptr;
+ if (with_bias && with_gate) {
+ std::array<int64_t, 4> bias_ne = {ffn_gate->ne[0], 1, 1, 1};
+ ggml_tensor * gate_bias = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, bias_ne.data());
+ ffn_gate = ggml_add(ctx, ffn_gate, gate_bias);
+ }
+
+ ggml_tensor * out = with_gate ? build_gate(ctx, ffn_gate, ffn_up) : ffn_up;
+ ggml_set_name(out, "out");
+ return out;
+ } else {
+ ggml_tensor * gates = ggml_new_tensor_3d(ctx, type, k, n, n_mats);
+ ggml_tensor * ups = ggml_new_tensor_3d(ctx, type, k, n, n_mats);
+ ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_mats, m);
+
+ if (n_used != n_mats) {
+ ids = ggml_view_2d(ctx, ids, n_used, m, ids->nb[1], 0);
+ }
+
+ ggml_tensor * cur = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, k, this->b ? 1 : n_used, m);
+ ggml_set_name(cur, "cur");
+
+ ggml_tensor * ffn_up = ggml_mul_mat_id(ctx, ups, cur, ids);
+ if (with_bias) {
+ ggml_tensor * up_bias_param = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ffn_up->ne[0], n_mats);
+ ffn_up = ggml_add_id(ctx, ffn_up, up_bias_param, ids);
+ }
+
+ ggml_tensor * ffn_gate = with_gate? ggml_mul_mat_id(ctx, gates, cur, ids) : nullptr;
+ if (with_bias && with_gate) {
+ ggml_tensor * gate_bias_param = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ffn_gate->ne[0], n_mats);
+ ffn_gate = ggml_add_id(ctx, ffn_gate, gate_bias_param, ids);
+ }
+
+ ggml_tensor * out = with_gate ? build_gate(ctx, ffn_gate, ffn_up) : ffn_up;
+ ggml_set_name(out, "out");
+ return out;
+ }
+ }
+
+ void initialize_tensors(ggml_context * ctx) override {
+ if (!use_id) {
+ for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+ init_tensor_uniform(t);
+ }
+ } else {
+ std::random_device rd;
+ std::default_random_engine rng(rd());
+ for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+ if (t->type == GGML_TYPE_I32) {
+ if (ggml_is_view_op(t->op)) { continue; }
+ // ids
+ for (int64_t r = 0; r < ggml_nrows(t); r++) {
+ std::vector<int32_t> data(t->ne[0]);
+ for (int i = 0; i < t->ne[0]; i++) {
+ data[i] = i % n_mats;
+ }
+ std::shuffle(data.begin(), data.end(), rng);
+ ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(int32_t));
+ }
+ } else {
+ init_tensor_uniform(t);
+ }
+ }
+ }
+ }
+
+ double max_nmse_err() override {
+ return 5e-3;
+ }
+};
+
// GGML_OP_SUM
struct test_sum : public test_case {
const ggml_type type;
test_cases.emplace_back(new test_opt_step_adamw(GGML_TYPE_F32, {10, 5, 4, 3}));
test_cases.emplace_back(new test_opt_step_sgd(GGML_TYPE_F32, {10, 5, 4, 3}));
+ for (ggml_type type : base_types) {
+ for (bool with_gate : {false, true}) {
+ for (bool use_id : {false, true}) {
+ for (bool b : {false, true}) {
+ if (!use_id && b) {
+ continue;
+ }
+ for (bool with_bias : {false, true}) {
+ if (!with_gate && !with_bias) {
+ continue;
+ }
+ for (ggml_glu_op glu_op : {GGML_GLU_OP_SWIGLU, GGML_GLU_OP_GEGLU}) {
+ if (!with_bias && glu_op == GGML_GLU_OP_SWIGLU_OAI) {
+ continue;
+ }
+ if (!with_gate && glu_op != GGML_GLU_OP_SWIGLU) {
+ continue;
+ }
+ test_cases.emplace_back(new test_mul_mat_vec_fusion(type, glu_op, 1, 32, 256,
+ use_id, 16, 8, b, with_bias, with_gate));
+ }
+ }
+ }
+ }
+ }
+ }
+
for (bool with_norm : {false, true}) {
test_cases.emplace_back(new test_topk_moe({8, 22, 1, 1}, 4, with_norm));
test_cases.emplace_back(new test_topk_moe({32, 22, 1, 1}, 8, with_norm));
overview / pkg / ggml / sources / ggml / commitdiff