ggml: adds CONV_2D op and direct GEMM Vulkan implementation (ggml-org#14316)

* ggml/ggml-vulkan/test-backend-ops: adds CONV_2D for Vulkan

* ggml-vulkan: adds f32 scalar shader to compute 2D convolution directly
with gemm (no need for im2col),

* test-backend-ops: adds test_case_ref to check the validity/performance of ops
against reference implementations having different graphs, adds tests

* * Performance fixes: minimized branch divergence, uses collectives to
  eliminate redundant calculation, macros removed.

* Kernel shared memory size check

* Updates test-backend-ops to support graphs for performance
  measurement.

* * Apple/Win32 compile errors fixed

* Subgroup size used to determine tile size -> fixes llvmpipe errors.

* Collectives disabled by default.

* Intel support is disabled as the performance is poor.

* Conv2d enabled for Intel with disabled collectives, disabled for Apple

* test-backend-ops modifications are reverted

* Trailing spaces and missing override fixed.

* Triggering pipeline relaunch.

* Code formatted with .clang-format.

Author: etasnadi

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -483,6 +483,7 @@ 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;
 
@@ -876,6 +877,38 @@ 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;
@@ -975,18 +1008,45 @@ class vk_memory_logger {
 #endif // GGML_VULKAN_MEMORY_DEBUG
 
 class vk_perf_logger {
-public:
+  public:
     void print_timings() {
+        if (timings.empty()) {
+            return;
+        }
+        uint64_t total_all_op_times = 0;
         std::cerr << "----------------\nVulkan Timings:" << std::endl;
-        for (const auto& t : timings) {
-            uint64_t total = 0;
-            for (const auto& time : t.second) {
-                total += time;
+        for (const auto & t : timings) {
+            uint64_t total_op_times = 0;
+            for (const auto & time : t.second) {
+                total_op_times += time;
+            }
+            std::cerr << t.first << ": " << t.second.size() << " x " << (total_op_times / 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_op_flops = 0;
+                for (const auto & elem : it->second) {
+                    total_op_flops += elem;
+                }
+                std::cerr << " ("
+                          << (double(total_op_flops) / (1000.0 * 1000.0 * 1000.0)) /
+                                 (double(total_op_times) / (1000.0 * 1000.0 * 1000.0))
+                          << " GFLOPS/s)";
             }
-            std::cerr << t.first << ": " << t.second.size() << " x " << (total / t.second.size() / 1000.0) << " us" << std::endl;
+
+            total_all_op_times += total_op_times;
+
+            std::cerr << std::endl;
+        }
+
+        if (timings.size() > 0) {
+            std::cerr << "Total time: " << total_all_op_times / 1000.0 << " us." << std::endl;
         }
 
         timings.clear();
+        flops.clear();
     }
 
     void log_timing(const ggml_tensor * node, uint64_t time) {
@@ -995,22 +1055,45 @@ class vk_perf_logger {
             return;
         }
         if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
-            const uint64_t m = node->src[0]->ne[1];
-            const uint64_t n = node->src[1]->ne[1];
-            const uint64_t k = node->src[1]->ne[0];
-            std::string name = ggml_op_name(node->op);
+            const uint64_t m    = node->src[0]->ne[1];
+            const uint64_t n    = node->src[1]->ne[1];
+            const uint64_t k    = node->src[1]->ne[0];
+            std::string    name = ggml_op_name(node->op);
             if (n == 1) {
                 name += "_VEC m=" + std::to_string(m) + " k=" + std::to_string(k);
             } else {
                 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));
+            name += " M=Cout=" + std::to_string(size_M) + ", K=Cin*KW*KH=" + std::to_string(size_K) +
+                    ", N=N*OW*OH=" + std::to_string(size_N);
+            flops[name].push_back(n_flops);
+            timings[name].push_back(time);
             return;
         }
         timings[ggml_op_name(node->op)].push_back(time);
     }
-private:
+  private:
     std::map<std::string, std::vector<uint64_t>> timings;
+    std::map<std::string, std::vector<uint64_t>> flops;
 };
 
 struct ggml_backend_vk_context {
@@ -2113,6 +2196,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
             }
             compile_count++;
         }
+
         compiles.push_back(std::async(ggml_vk_create_pipeline_func, std::ref(device), std::ref(pipeline), spv_size, spv_data, entrypoint,
                                       parameter_count, wg_denoms, specialization_constants, disable_robustness, require_full_subgroups, required_subgroup_size));
     };
@@ -2962,6 +3046,42 @@ 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);
 
+    // conv2d
+    uint32_t conv2d_WG_SIZE  = 256;
+    uint32_t conv2d_BS_K     = 128;
+    uint32_t conv2d_BS_CRS   = 16;
+    uint32_t use_collectives = 0;  // Enables subgroup ops for preventing the re-calculation of indices.
+    if (device->subgroup_shuffle &&
+        device->vendor_id != VK_VENDOR_ID_INTEL) {  // Do not enable collectives on Intel, see PR 14316
+        use_collectives = 1;
+        conv2d_BS_CRS   = std::min(
+            device->subgroup_size,
+            conv2d_BS_CRS);  // CRS block size should be capped at sugroup size for correctness when shuffle is used.
+    }
+    uint32_t conv2d_BS_NPQ = 128;
+    uint32_t conv2d_TS_K   = 8;
+    uint32_t conv2d_shmem_req =
+        (conv2d_BS_K * (conv2d_BS_CRS + 1) + conv2d_BS_CRS * (conv2d_BS_NPQ + 1)) * sizeof(float);
+    if (device->properties.limits.maxComputeSharedMemorySize < conv2d_shmem_req) {
+        conv2d_BS_CRS = 8;
+        if (use_collectives) {
+            conv2d_BS_CRS = std::min(device->subgroup_size, conv2d_BS_CRS);
+        }
+    }
+
+    if (use_collectives) {
+        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), { conv2d_BS_K, conv2d_BS_NPQ, 1 },
+            { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives }, 1, true, true);
+    } else {
+        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), { conv2d_BS_K, conv2d_BS_NPQ, 1 },
+            { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives }, 1, true,
+            false);
+    }
+
     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);
 
@@ -6837,6 +6957,12 @@ 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)) {
@@ -7159,6 +7285,31 @@ 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:
@@ -8025,6 +8176,55 @@ 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);
@@ -9087,6 +9287,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     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:
@@ -9154,6 +9355,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
         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:
             {
@@ -9360,6 +9562,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     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);
@@ -9490,6 +9696,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
     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:
@@ -10071,6 +10278,12 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         ggml_vk_build_graph(ctx, cgraph, i, nullptr, 0, true, false, false, false);
         if (cgraph->nodes[i]->op == GGML_OP_MUL_MAT || cgraph->nodes[i]->op == GGML_OP_MUL_MAT_ID) {
             total_mat_mul_bytes += ggml_nbytes(cgraph->nodes[i]->src[0]);
+        } else if (cgraph->nodes[i]->op == GGML_OP_CONV_2D) {
+            // Return CRSxNPQxsizeof(*) to account as many bytes as mul_mat has in im2col->mul_mat mode.
+            auto CRS_size =
+                cgraph->nodes[i]->src[0]->ne[0] * cgraph->nodes[i]->src[0]->ne[1] * cgraph->nodes[i]->src[0]->ne[2];
+            auto NPQ_size = cgraph->nodes[i]->ne[0] * cgraph->nodes[i]->ne[1] * cgraph->nodes[i]->ne[3];
+            total_mat_mul_bytes += NPQ_size * CRS_size * ggml_type_size(cgraph->nodes[i]->type);
         }
         i += ctx->num_additional_fused_ops;
         ctx->num_additional_fused_ops = 0;
@@ -10647,6 +10860,20 @@ 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:
+            {
+                // Op is disabled for Apple because it segfaults at pipeline create time on MoltenVK
+                ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
+                const vk_device& device = ggml_vk_get_device(ctx->device);
+                bool is_Apple = ggml_vk_get_device(ctx->device)->vendor_id == VK_VENDOR_ID_APPLE;
+                // 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)) && !is_Apple;
+            }
         default:
             return false;
     }
@@ -11205,6 +11432,14 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
         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);