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ggml: adds CONV_2D op and direct GEMM Vulkan implementation #14316

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ggml: adds CONV_2D op and direct GEMM Vulkan implementation #14316

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2 changes: 2 additions & 0 deletions ggml/include/ggml-backend.h
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Expand Up @@ -340,6 +340,8 @@ extern "C" {

// Compare the output of two backends
GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
// Compare the output of two backends, graphs can be different and only the selected nodes will be compared
GGML_API bool ggml_backend_compare_graph_backend_node(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph1, struct ggml_cgraph * graph2, ggml_backend_eval_callback callback, void * user_data, char* op_name_out_1, char* op_name_out_2);

// Tensor initialization
GGML_API enum ggml_status ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
Expand Down
12 changes: 12 additions & 0 deletions ggml/include/ggml.h
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Expand Up @@ -481,6 +481,7 @@ extern "C" {
GGML_OP_CONV_TRANSPOSE_1D,
GGML_OP_IM2COL,
GGML_OP_IM2COL_BACK,
GGML_OP_CONV_2D,
GGML_OP_CONV_2D_DW,
GGML_OP_CONV_TRANSPOSE_2D,
GGML_OP_POOL_1D,
Expand Down Expand Up @@ -1663,6 +1664,17 @@ extern "C" {
int d0, // dilation dimension 0
int d1); // dilation dimension 1

GGML_API struct ggml_tensor * ggml_conv_2d_direct(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
struct ggml_tensor * b, // data
int stride0, // stride dimension 0
int stride1, // stride dimension 1
int padding0, // padding dimension 0
int padding1, // padding dimension 1
int dilation0, // dilation dimension 0
int dilation1); // dilation dimension 1

// kernel size is a->ne[0] x a->ne[1]
// stride is equal to kernel size
// padding is zero
Expand Down
49 changes: 49 additions & 0 deletions ggml/src/ggml-backend.cpp
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Expand Up @@ -1864,6 +1864,55 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t
return true;
}

bool ggml_backend_compare_graph_backend_node(
ggml_backend_t backend1,
ggml_backend_t backend2,
struct ggml_cgraph * graph1,
struct ggml_cgraph * graph2,
ggml_backend_eval_callback callback, void * user_data, char* op_name_out_1, char* op_name_out_2) {

ggml_tensor * out1 = NULL;
ggml_tensor * out2 = NULL;

struct ggml_cgraph * g1 = graph1;
struct ggml_cgraph * g2 = graph2;

for (int i = 0; i < g1->n_nodes; i++) {
struct ggml_tensor * t1 = g1->nodes[i];
struct ggml_cgraph g1v = ggml_graph_view(g1, i, i + 1);
ggml_backend_graph_compute(backend1, &g1v);
if (ggml_is_view_op(t1->op)) {
continue;
}
if(strcmp(t1 -> name, op_name_out_1) == 0){
out1 = t1;
}
}

for (int i = 0; i < g2->n_nodes; i++) {
struct ggml_tensor * t2 = g2->nodes[i];
struct ggml_cgraph g2v = ggml_graph_view(g2, i, i + 1);
ggml_backend_graph_compute(backend2, &g2v);
if (ggml_is_view_op(t2->op)) {
continue;
}
if(strcmp(t2 -> name, op_name_out_2) == 0){
out2 = t2;
}
}

assert(out1 != NULL);
assert(out2 != NULL);
assert(ggml_are_same_layout(out1, out2));

// compare results, calculate rms etc
if (!callback(0, out1, out2, user_data)) {
return false;
}

return true;
}

// CPU backend - buffer

static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
Expand Down
4 changes: 4 additions & 0 deletions ggml/src/ggml-cpu/ggml-cpu.c
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Expand Up @@ -1858,6 +1858,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_im2col_back_f32(params, tensor);
} break;
case GGML_OP_CONV_2D:
{
GGML_ABORT("Op not supported on CPU yet.");
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I think a CPU implementation is generally required.

} break;
case GGML_OP_CONV_2D_DW:
{
ggml_compute_forward_conv_2d_dw(params, tensor);
Expand Down
181 changes: 180 additions & 1 deletion ggml/src/ggml-vulkan/ggml-vulkan.cpp
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Expand Up @@ -457,6 +457,7 @@ struct vk_device_struct {
vk_pipeline pipeline_rwkv_wkv6_f32;
vk_pipeline pipeline_rwkv_wkv7_f32;
vk_pipeline pipeline_opt_step_adamw_f32;
vk_pipeline pipeline_conv2d_f32;
vk_pipeline pipeline_conv2d_dw_whcn_f32;
vk_pipeline pipeline_conv2d_dw_cwhn_f32;

Expand Down Expand Up @@ -816,6 +817,38 @@ struct vk_op_rwkv_wkv7_push_constants {
uint32_t H;
};

struct vk_op_conv2d_push_constants {
uint32_t Cout;
uint32_t Cin;
uint32_t N;

uint32_t KW;
uint32_t KH;
uint32_t W;
uint32_t H;
uint32_t OW;
uint32_t OH;

uint32_t s0;
uint32_t s1;
uint32_t p0;
uint32_t p1;
uint32_t d0;
uint32_t d1;

uint32_t nb01;
uint32_t nb02;
uint32_t nb03;

uint32_t nb11;
uint32_t nb12;
uint32_t nb13;

uint32_t nb1;
uint32_t nb2;
uint32_t nb3;
};

struct vk_op_conv2d_dw_push_constants {
uint32_t ne;
uint32_t batches;
Expand Down Expand Up @@ -916,16 +949,33 @@ class vk_memory_logger {
class vk_perf_logger {
public:
void print_timings() {
if(timings.empty()){
return;
}
std::cerr << "----------------\nVulkan Timings:" << std::endl;
for (const auto& t : timings) {
uint64_t total = 0;
for (const auto& time : t.second) {
total += time;
}
std::cerr << t.first << ": " << t.second.size() << " x " << (total / t.second.size() / 1000.0) << " us" << std::endl;
std::cerr << t.first << ": " << t.second.size() << " x " << (total / t.second.size() / 1000.0) << " us";

// If we have as many flops entries as timing entries for the op, then compute and log the flops/S.
auto it = flops.find(t.first);
if(it != flops.end() && (it->second).size() == t.second.size()){
uint64_t total_nflops = 0;
for(const auto& elem : it->second){
total_nflops += elem;
}
std::cout << " (" << (double(total_nflops)/(1000.0*1000.0*1000.0)) / (double(total)/(1000.0*1000.0*1000.0)) << " GFLOPS/s)";
}


std::cerr << std::endl;
}

timings.clear();
flops.clear();
}

void log_timing(const ggml_tensor * node, uint64_t time) {
Expand All @@ -944,12 +994,33 @@ class vk_perf_logger {
name += " m=" + std::to_string(m) + " n=" + std::to_string(n) + " k=" + std::to_string(k);
}
timings[name].push_back(time);
flops[name].push_back( m*n*(k+(k-1)) );
return;
}
if(node->op == GGML_OP_CONV_2D){
std::string name = ggml_op_name(node->op);
ggml_tensor * knl = node->src[0];
uint64_t OW = node->ne[0];
uint64_t OH = node->ne[1];
uint64_t N = node->ne[3];
uint64_t Cout = node->ne[2];
uint64_t KW = knl->ne[0];
uint64_t KH = knl->ne[1];
uint64_t Cin = knl->ne[2];
// KxCRS @ CRSxNPQ = KxNPQ -> M=K, K=CRS, N=NPQ
uint64_t size_M = Cout;
uint64_t size_K = Cin*KW*KH;
uint64_t size_N = N*OW*OH;
uint64_t n_flops = size_M*size_N*(size_K+(size_K-1));
flops[name].push_back(n_flops);
timings[name].push_back(time);
return;
}
timings[ggml_op_name(node->op)].push_back(time);
}
private:
std::map<std::string, std::vector<uint64_t>> timings;
std::map<std::string, std::vector<uint64_t>> flops;
};

struct ggml_backend_vk_context {
Expand Down Expand Up @@ -2806,6 +2877,8 @@ static void ggml_vk_load_shaders(vk_device& device) {

ggml_vk_create_pipeline(device, device->pipeline_opt_step_adamw_f32, "opt_step_adamw_f32", opt_step_adamw_f32_len, opt_step_adamw_f32_data, "main", 5, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);

ggml_vk_create_pipeline(device, device->pipeline_conv2d_f32, "conv2d_f32", conv2d_f32_len, conv2d_f32_data, "main", 3, sizeof(vk_op_conv2d_push_constants), {128 /* equal to BS_K in the shader */, 128 /* equal to BS_NPQ in the shader */, 1}, {}, 1);

ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f32, "conv2d_dw_whcn_f32", conv2d_dw_whcn_f32_len, conv2d_dw_whcn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f32, "conv2d_dw_cwhn_f32", conv2d_dw_cwhn_f32_len, conv2d_dw_cwhn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);

Expand Down Expand Up @@ -6578,6 +6651,16 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
return ctx->device->pipeline_leaky_relu_f32;
}
return nullptr;
case GGML_OP_CONV_2D:
if (src0->type == GGML_TYPE_F32 &&
src1->type == GGML_TYPE_F32 &&
dst->type == GGML_TYPE_F32 &&
ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
ggml_is_contiguous(dst)) {
return ctx->device->pipeline_conv2d_f32;
}
return nullptr;
case GGML_OP_CONV_2D_DW:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
if (ggml_is_contiguous(src1)) {
Expand Down Expand Up @@ -6899,6 +6982,30 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
const uint32_t OW = dst->ne[0];
elements = { N * OC * OH * OW, 1, 1};
} break;
case GGML_OP_CONV_2D:
{
// src0 - kernel: [KW, KH, Cin, Cout]
// src1 - input: [W, H, Cin, N]
// dst - result: [OW, OH, Cout, N]

// Copied from ggml.c: int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d)
auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t {
return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
};
// parallelize in {OW/BS_K, OH/BS_NPQ, 1}
int64_t W = src1->ne[0];
int64_t H = src1->ne[1];
int64_t KW = src0->ne[0];
int64_t KH = src0->ne[1];
int64_t Cout = src0->ne[3];
int64_t N = src1->ne[3];
int64_t OH = calc_conv_output_size(H, KH, dst->op_params[1], dst->op_params[3], dst->op_params[5]);
int64_t OW = calc_conv_output_size(W, KW, dst->op_params[0], dst->op_params[2], dst->op_params[4]);
int64_t NPQ = N*OW*OH;

// Tile output matrix to (K/NB_K, NPQ/NB_NPQ, 1) workgroups
elements = {static_cast<uint32_t>(Cout), static_cast<uint32_t>(NPQ), 1};
} break;
case GGML_OP_ADD:
case GGML_OP_SUB:
case GGML_OP_DIV:
Expand Down Expand Up @@ -7753,6 +7860,55 @@ static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, c
}, dryrun);
}

static void ggml_vk_conv_2d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);

GGML_TENSOR_BINARY_OP_LOCALS

GGML_ASSERT(nb00 == sizeof(float));
GGML_ASSERT(nb10 == sizeof(float));
GGML_ASSERT(nb0 == sizeof(float));

vk_op_conv2d_push_constants p{};
p.Cout = static_cast<uint32_t>(ne03);
p.Cin = static_cast<uint32_t>(ne02);
p.N = static_cast<uint32_t>(ne13);

p.KW = static_cast<uint32_t>(ne00);
p.KH = static_cast<uint32_t>(ne01);
p.W = static_cast<uint32_t>(ne10);
p.H = static_cast<uint32_t>(ne11);
p.OW = static_cast<uint32_t>(ne0);
p.OH = static_cast<uint32_t>(ne1);

p.s0 = static_cast<uint32_t>(dst->op_params[0]);
p.s1 = static_cast<uint32_t>(dst->op_params[1]);
p.p0 = static_cast<uint32_t>(dst->op_params[2]);
p.p1 = static_cast<uint32_t>(dst->op_params[3]);
p.d0 = static_cast<uint32_t>(dst->op_params[4]);
p.d1 = static_cast<uint32_t>(dst->op_params[5]);

p.nb01 = static_cast<uint32_t>(nb01/nb00);
p.nb02 = static_cast<uint32_t>(nb02/nb00);
p.nb03 = static_cast<uint32_t>(nb03/nb00);

p.nb11 = static_cast<uint32_t>(nb11/nb10);
p.nb12 = static_cast<uint32_t>(nb12/nb10);
p.nb13 = static_cast<uint32_t>(nb13/nb10);

p.nb1 = static_cast<uint32_t>(nb1 / nb0);
p.nb2 = static_cast<uint32_t>(nb2 / nb0);
p.nb3 = static_cast<uint32_t>(nb3 / nb0);

GGML_ASSERT(ne03 == ne2);
GGML_ASSERT(ne02 == ne12);

ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_CONV_2D, std::move(p), dryrun);

}

static void ggml_vk_conv_2d_dw(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
vk_op_conv2d_dw_push_constants p{};
p.ne = ggml_nelements(dst);
Expand Down Expand Up @@ -8799,6 +8955,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_CONV_TRANSPOSE_1D:
case GGML_OP_POOL_2D:
case GGML_OP_CONV_2D:
case GGML_OP_CONV_2D_DW:
case GGML_OP_RWKV_WKV6:
case GGML_OP_RWKV_WKV7:
Expand Down Expand Up @@ -8864,6 +9021,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_CONV_TRANSPOSE_1D:
case GGML_OP_POOL_2D:
case GGML_OP_CONV_2D:
case GGML_OP_CONV_2D_DW:
case GGML_OP_LEAKY_RELU:
{
Expand Down Expand Up @@ -9042,6 +9200,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_POOL_2D:
ggml_vk_pool_2d(ctx, compute_ctx, src0, node, dryrun);

break;
case GGML_OP_CONV_2D:
ggml_vk_conv_2d(ctx, compute_ctx, src0, src1, node, dryrun);

break;
case GGML_OP_CONV_2D_DW:
ggml_vk_conv_2d_dw(ctx, compute_ctx, src0, src1, node, dryrun);
Expand Down Expand Up @@ -9168,6 +9330,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_CONV_TRANSPOSE_1D:
case GGML_OP_POOL_2D:
case GGML_OP_CONV_2D:
case GGML_OP_CONV_2D_DW:
case GGML_OP_RWKV_WKV6:
case GGML_OP_RWKV_WKV7:
Expand Down Expand Up @@ -10242,6 +10405,14 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
return true;
case GGML_OP_CONV_TRANSPOSE_1D:
return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;
case GGML_OP_CONV_2D:
// Channel-contiguous format is not supported yet.
return (op->src[0]->type == GGML_TYPE_F32 &&
op->src[1]->type == GGML_TYPE_F32 &&
op->type == GGML_TYPE_F32 &&
ggml_is_contiguous(op->src[0]) &&
ggml_is_contiguous(op->src[1]) &&
ggml_is_contiguous(op));
default:
return false;
}
Expand Down Expand Up @@ -10765,6 +10936,14 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
const int32_t p1 = tensor->op_params[6];

tensor_clone = ggml_pool_2d(ggml_ctx, src_clone[0], op, k0, k1, s0, s1, p0, p1);
} else if (tensor->op == GGML_OP_CONV_2D) {
const int32_t s0 = tensor->op_params[0];
const int32_t s1 = tensor->op_params[1];
const int32_t p0 = tensor->op_params[2];
const int32_t p1 = tensor->op_params[3];
const int32_t d0 = tensor->op_params[4];
const int32_t d1 = tensor->op_params[5];
tensor_clone = ggml_conv_2d(ggml_ctx, src_clone[0], src_clone[1], s0, s1, p0, p1, d0, d1);
} else if (tensor->op == GGML_OP_LEAKY_RELU) {
const float * op_params = (const float *)tensor->op_params;
tensor_clone = ggml_leaky_relu(ggml_ctx, src_clone[0], op_params[0], false);
Expand Down
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