diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin128_cout256.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin128_cout256.json index 40865862d11db74306659b58162e2b27c6a34baf..34f9c225e0537c44f68b83fd6c396dc5015b7d7f 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin128_cout256.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin128_cout256.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 256;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 256;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin256_cout512.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin256_cout512.json index c955f4869e215cbb3e8c7d3ae515fbb8ed9c86df..fce6e2ea8f39a80d2752314b6275398d6cff1e66 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin256_cout512.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin256_cout512.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 512;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 512;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin512_cout1024.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin512_cout1024.json index a446820d56d23570346f713d5388f12d2df0b3a4..cbb0d56256a954c1fec81c96657371a011ad15e0 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin512_cout1024.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin512_cout1024.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 1024;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 1024;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin64_cout128.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin64_cout128.json index aa72537253755674bff691f8e8e3022c1469a1f8..e51342dfffde73e1c9da120574cd5f61a4fc6897 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin64_cout128.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin64_cout128.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 128;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 128;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin128_cout256.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin128_cout256.json index 9dbe02ded9ad506cc380d74af57fe4fee7c05ee4..1d3070eb511645e92800760057f8a6f31184f8a1 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin128_cout256.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin128_cout256.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 256;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 256;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin256_cout512.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin256_cout512.json index d09225186853d7b77928ebd4e1c69fd66f6beb96..b2ca93ed785eed0b89b67a8ad7f85f2ab9c09092 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin256_cout512.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin256_cout512.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 512;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 512;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.json index 50b11dd9422644dec73ddeef260e694e94b81c17..8a434d4d5738480de2df97f581e566440ffebcdb 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 1024;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 1024;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin64_cout128.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin64_cout128.json index 241c2479ae155acd136452e333f728bdc9deed9a..8f1ce646f6399713b07b2201ce67d96c32d703f4 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin64_cout128.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin64_cout128.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 128;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 128;\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin128_cout256.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin128_cout256.json index 884a048800c7d28e6751c00dcef9cd527309f8b2..040e4a344886d0892453a181e4261770bd61b028 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin128_cout256.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin128_cout256.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 256;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 256;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin256_cout512.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin256_cout512.json index 8f4dbe68401f10357502151d04a852d2755b8b02..80b7c2047e9d8df4f025992708abaa84f1641748 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin256_cout512.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin256_cout512.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 512;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 512;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin512_cout1024.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin512_cout1024.json index 6dc986ec35febaa5ce733127a83f539edabf9671..f43c3c8c9af12d042c757d90baa5cc83f98d69e1 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin512_cout1024.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin512_cout1024.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 1024;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 1024;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin64_cout128.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin64_cout128.json index 5eea6a2b1060f776f658f93561cfda3e9a86254c..c0525e4af02e4cdaf36fab8c57c95eccb628dabe 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin64_cout128.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin64_cout128.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 128;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 128;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin128_cout256.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin128_cout256.json index a5a5c94a981429a15119860fd9ba4d814d60606d..6a8c6389cafb51031daa330de16f508c27b153a3 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin128_cout256.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin128_cout256.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 256;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 256;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin32_cout64.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin32_cout64.json index 21028e04b57d241c8538929c636e5f88249e072d..d4713e2dafbd4207b4989147ee0f98cc2a83bf82 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin32_cout64.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin32_cout64.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 64;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 64;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin512_cout512.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin512_cout512.json index c0aa01745aa6148cee45d30d0022a85226c53c53..add6fb40c5e81f533d5feea0c71e278c298dd2bb 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin512_cout512.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin512_cout512.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 512;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 512;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.json index 8ae4e18379168ba734380fbad378215a0c5322ae..8f0bdf622b3cfb7d99ad38cf5bb84ea7cb93d427 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d Solution;\n// identical across all deconv2d Definitions (const params arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d.h by delegating to\n// ncnn::Deconvolution_arm. The shim armbench_entry_deconv2d (binding.cpp)\n// allocates the full transposed-convolution output\n// (out_w = (in_w-1)*stride_w + dilation_w*(kernel_w-1)+1, likewise out_h) and\n// does NOT crop, so we disable Deconvolution_arm's internal cut_padding\n// (pad_* / output_pad_* / output_w/h all zero). Const params come from the\n// Definition's const axes, so this kernel.cpp is identical across all deconv2d\n// Definitions.\n\n#include \"deconv2d.h\"\n#include \"deconvolution_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolution_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n Deconvolution_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of\n // Deconvolution_arm's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int out_c = 128;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d via ctypes with ONLY the Mat/Option pointers\n// (no scalar args). The body computes transposed-convolution output dims,\n// allocates the output Mat, then dispatches to the Solution's\n// deconvolution_kernel (defined in kernel.cpp).\n//\n// Output formula (from ref_conv.h::ref_deconv2d):\n// ke_h = dilation_h * (kernel_h - 1) + 1\n// ke_w = dilation_w * (kernel_w - 1) + 1\n// out_h = (in_h - 1) * stride_h + ke_h\n// out_w = (in_w - 1) * stride_w + ke_w\n\n#include \"deconv2d.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int out_c = 128;\nconstexpr int kernel_w = 4;\nconstexpr int kernel_h = 4;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (in_c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = out_c*in_c*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_deconv2d in ref_conv.h)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n\n // 2. Allocate top\n top.create(out_w, out_h, out_c, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to the Solution-supplied kernel\n return ncnn::deconvolution_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c1024.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c1024.json index 6b468315e503978aff1fbf1e0460b1d9c53ac357..65e4495e59eb5168d1f44476889401fcc404b248 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c1024.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c1024.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c128.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c128.json index af23f134b93c387f3fd576788744d4e52bbfdd05..1dfda16388db9d98ac604bf123cc0bede245489d 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c128.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c128.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c256.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c256.json index 798e27287deab2d04027f9568ceb1376968bc375..1bcb70736d0b58d1b21ccfe38dc3b6d41abc425a 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c256.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c256.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c32.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c32.json index 04488ec5a6a3646b4973061eaad0b62d253bac07..317efd761e869c4914a275c849b391a938c26a5c 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c32.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c32.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c512.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c512.json index 67626aabee2c08833ef8b97c080085ca7c3c63b5..5b5f7e7ed487257292e5effe8801781abcd99344 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c512.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c512.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c64.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c64.json index 6b67feeded33a2430494281fac39748fa19cc1b1..73c977ea549a6c0be53da0a30d7f2cd664261bad 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c64.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c64.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 2;\nconstexpr int kernel_h = 2;\nconstexpr int stride_w = 2;\nconstexpr int stride_h = 2;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c128.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c128.json index b02da1521ce4ec136f0b1b2e7fda1f9464af53d4..f62731ccfd8c332d27fca36b5304cad7d8e4ca2e 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c128.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c128.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c2048.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c2048.json index e2b46d0e60a94d12e3d6985027c15a3ae67aa122..7a9c6e3312bd7c1e707cc83e4d00e4dbba342c4a 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c2048.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c2048.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c32.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c32.json index b77b1e32b5e4bb2446d73001db03a81617c19294..a56527446ecbdb8373e551544044ff8b923959cb 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c32.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c32.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c512.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c512.json index e410b5d0025f3d6d4d5525b6a790ac00990ec673..7d7f470806ee60b9137ec0a5d4501f10a6704af7 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c512.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c512.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c64.json b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c64.json index 717b12440d3c220a02c121dbe91a4ed2367b4972..5ebd3e463904d2c2ee409060056fd97fc63af819 100644 --- a/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c64.json +++ b/solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c64.json @@ -29,7 +29,7 @@ "sources": [ { "path": "kernel.cpp", - "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n Mat local_top;\n int ret = deconv.forward(bottom_blob, local_top, opt);\n if (ret != 0) return ret;\n if (local_top.empty()) return -1;\n\n // Copy into the caller-allocated top_blob.\n if (local_top.c != top_blob.c || local_top.h != top_blob.h || local_top.w != top_blob.w)\n return -1;\n for (int cc = 0; cc < local_top.c; ++cc) {\n std::memcpy(top_blob.channel(cc), local_top.channel(cc),\n local_top.h * local_top.w * sizeof(float));\n }\n return 0;\n}\n\n} // namespace ncnn\n" + "content": "// solutions/ncnn/baseline-ncnn-arm/deconv2d_depthwise//kernel.cpp\n//\n// Embedded verbatim as kernel.cpp in every baseline-ncnn-arm deconv2d_depthwise\n// Solution; identical across all deconv2d_depthwise Definitions (const params\n// arrive as runtime args).\n//\n// Implements the harness contract declared in deconv2d_depthwise.h by\n// delegating to ncnn::DeconvolutionDepthWise_arm. The shim\n// armbench_entry_deconv2d_depthwise (binding.cpp) allocates the full\n// transposed-convolution output (out_c == in_c) and does NOT crop, so we\n// disable the layer's internal cut_padding. group == channels (depthwise).\n// Const params come from the Definition's const axes, so this kernel.cpp is\n// identical across all deconv2d_depthwise Definitions.\n\n#include \"deconv2d_depthwise.h\"\n#include \"deconvolutiondepthwise_arm.h\"\n\n#include \n\nnamespace ncnn {\n\nint deconvolutiondepthwise_kernel(const Mat& bottom_blob, Mat& top_blob,\n const Mat& weight_data, const Mat& bias_data,\n int kernel_w, int kernel_h,\n int stride_w, int stride_h,\n int dilation_w, int dilation_h,\n int group,\n int activation_type, const Mat& activation_params,\n const Option& opt)\n{\n DeconvolutionDepthWise_arm deconv;\n deconv.num_output = top_blob.c;\n deconv.kernel_w = kernel_w; deconv.kernel_h = kernel_h;\n deconv.stride_w = stride_w; deconv.stride_h = stride_h;\n deconv.dilation_w = dilation_w; deconv.dilation_h = dilation_h;\n // Harness allocated the full (un-cropped) output, so disable all of the\n // layer's output padding / cropping.\n deconv.pad_left = 0; deconv.pad_right = 0;\n deconv.pad_top = 0; deconv.pad_bottom = 0;\n deconv.output_pad_right = 0; deconv.output_pad_bottom = 0;\n deconv.output_w = 0; deconv.output_h = 0;\n deconv.bias_term = bias_data.empty() ? 0 : 1;\n deconv.weight_data_size = weight_data.w;\n deconv.group = group;\n deconv.activation_type = activation_type;\n deconv.activation_params = const_cast(activation_params);\n deconv.dynamic_weight = 0;\n deconv.weight_data = const_cast(weight_data);\n if (!bias_data.empty()) deconv.bias_data = const_cast(bias_data);\n\n if (deconv.create_pipeline(opt) != 0) return -1;\n\n // forward() may produce pack4 output when use_packing_layout=true;\n // the Python harness unwraps pack4->pack1 in unwrap_output.\n return deconv.forward(bottom_blob, top_blob, opt);\n}\n\n} // namespace ncnn\n" }, { "path": "deconv2d_depthwise.h", @@ -37,7 +37,7 @@ }, { "path": "binding.cpp", - "content": "// ncnn::Mat shim for deconv2d_depthwise \u2014 shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// \u2500\u2500 Per-Definition constants (baked from the Definition's const axes) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nnamespace {\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* \u2014 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* \u2014 empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* \u2014 flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* \u2014 empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c; // depthwise: out_c == in_c\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" + "content": "// ncnn::Mat shim for deconv2d_depthwise — shipped in this Solution's own sources.\n//\n// All per-Definition scalar params are baked as constexpr below, so the runner\n// calls armbench_entry_deconv2d_depthwise via ctypes with ONLY the Mat/Option\n// pointers (no scalar args). The body computes transposed-convolution output\n// dims, allocates the output Mat, then dispatches to the Solution's\n// deconvolutiondepthwise_kernel (defined in kernel.cpp).\n//\n// Depthwise: in_c == out_c == channels; group == channels (derived from input).\n\n#include \"deconv2d_depthwise.h\"\n\n// ── Per-Definition constants (baked from the Definition's const axes) ────────\nnamespace {\nconstexpr int kernel_w = 3;\nconstexpr int kernel_h = 3;\nconstexpr int stride_w = 1;\nconstexpr int stride_h = 1;\nconstexpr int dilation_w = 1;\nconstexpr int dilation_h = 1;\nconstexpr int activation_type = 0;\n} // namespace\n\nextern \"C\" {\n\nint armbench_entry_deconv2d_depthwise(\n // tensors (opaque ncnn::Mat*)\n void* bottom_mat_v, // const ncnn::Mat* — 3D input (c, h, w)\n void* top_mat_v, // ncnn::Mat* — empty Mat; we create() output here\n void* weight_mat_v, // const ncnn::Mat* — flat 1D Mat, size = C*kh*kw\n void* bias_mat_v, // const ncnn::Mat* — flat 1D Mat or empty\n void* activation_params_v, // const ncnn::Mat* — empty for type=0/1\n void* opt_v) // const ncnn::Option*\n{\n const auto& bottom = *reinterpret_cast(bottom_mat_v);\n auto& top = *reinterpret_cast(top_mat_v);\n const auto& weight = *reinterpret_cast(weight_mat_v);\n const auto& bias = *reinterpret_cast(bias_mat_v);\n const auto& act_par = *reinterpret_cast(activation_params_v);\n const auto& opt = *reinterpret_cast(opt_v);\n\n if (bottom.empty()) return -100;\n\n // 1. Compute transposed-convolution output dims (matches ref_depthwise_deconv2d)\n const int ke_h = dilation_h * (kernel_h - 1) + 1;\n const int ke_w = dilation_w * (kernel_w - 1) + 1;\n const int out_w = (bottom.w - 1) * stride_w + ke_w;\n const int out_h = (bottom.h - 1) * stride_h + ke_h;\n const int channels = bottom.c * bottom.elempack; // depthwise: out_c == in_c; elempack>1 when pack4\n\n // 2. Allocate top\n top.create(out_w, out_h, channels, (size_t)4u, opt.blob_allocator);\n if (top.empty()) return -100;\n\n // 3. Dispatch to Solution kernel with group == channels (depthwise)\n return ncnn::deconvolutiondepthwise_kernel(\n bottom, top, weight, bias,\n kernel_w, kernel_h,\n stride_w, stride_h,\n dilation_w, dilation_h,\n channels,\n activation_type, act_par,\n opt);\n}\n\n} // extern \"C\"\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin1024_cout1024.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin1024_cout1024.json new file mode 100644 index 0000000000000000000000000000000000000000..7f3f915511baa43f8f2edcca73bed8bc17d3d119 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin1024_cout1024.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw1_sw1_dw1_p0_cin1024_cout1024", + "definition": "conv1d_kw1_sw1_dw1_p0_cin1024_cout1024", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw1_sw1_dw1_p0_cin1024_cout1024. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 1024;\nconstexpr int Cout = 1024;\nconstexpr int Kw = 1;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 0;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin128_cout512.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin128_cout512.json new file mode 100644 index 0000000000000000000000000000000000000000..26e0a232c8297af1fd4759ff7d7d296c215cfaf1 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin128_cout512.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw1_sw1_dw1_p0_cin128_cout512", + "definition": "conv1d_kw1_sw1_dw1_p0_cin128_cout512", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw1_sw1_dw1_p0_cin128_cout512. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 512;\nconstexpr int Kw = 1;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 0;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin256_cout1024.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin256_cout1024.json new file mode 100644 index 0000000000000000000000000000000000000000..0ca1200e70b34834482da1b3323ee192dd2de9ca --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin256_cout1024.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw1_sw1_dw1_p0_cin256_cout1024", + "definition": "conv1d_kw1_sw1_dw1_p0_cin256_cout1024", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw1_sw1_dw1_p0_cin256_cout1024. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 256;\nconstexpr int Cout = 1024;\nconstexpr int Kw = 1;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 0;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin512_cout2048.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin512_cout2048.json new file mode 100644 index 0000000000000000000000000000000000000000..7760413cd03e62281ea4a392c4f453212b56bc03 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin512_cout2048.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw1_sw1_dw1_p0_cin512_cout2048", + "definition": "conv1d_kw1_sw1_dw1_p0_cin512_cout2048", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw1_sw1_dw1_p0_cin512_cout2048. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 2048;\nconstexpr int Kw = 1;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 0;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin64_cout256.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin64_cout256.json new file mode 100644 index 0000000000000000000000000000000000000000..c68f47582e51aceb5bdb998b035bcc10a11bdc6a --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin64_cout256.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw1_sw1_dw1_p0_cin64_cout256", + "definition": "conv1d_kw1_sw1_dw1_p0_cin64_cout256", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw1_sw1_dw1_p0_cin64_cout256. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 256;\nconstexpr int Kw = 1;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 0;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin64_cout64.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin64_cout64.json new file mode 100644 index 0000000000000000000000000000000000000000..03d51e2f3df54aad23e86e4e1f5fc4d0cdc1ea77 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw1_sw1_dw1_p0_cin64_cout64.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw1_sw1_dw1_p0_cin64_cout64", + "definition": "conv1d_kw1_sw1_dw1_p0_cin64_cout64", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw1_sw1_dw1_p0_cin64_cout64. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 64;\nconstexpr int Kw = 1;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 0;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin1024_cout1024.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin1024_cout1024.json new file mode 100644 index 0000000000000000000000000000000000000000..6813654d349e511a3b93bf469859248a550db41d --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin1024_cout1024.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw3_sw1_dw1_p1_cin1024_cout1024", + "definition": "conv1d_kw3_sw1_dw1_p1_cin1024_cout1024", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw3_sw1_dw1_p1_cin1024_cout1024. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 1024;\nconstexpr int Cout = 1024;\nconstexpr int Kw = 3;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin128_cout256.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin128_cout256.json new file mode 100644 index 0000000000000000000000000000000000000000..7cf74d386f0a54060b46284fced8ec55a03ed0dd --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin128_cout256.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw3_sw1_dw1_p1_cin128_cout256", + "definition": "conv1d_kw3_sw1_dw1_p1_cin128_cout256", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw3_sw1_dw1_p1_cin128_cout256. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 256;\nconstexpr int Kw = 3;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin256_cout512.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin256_cout512.json new file mode 100644 index 0000000000000000000000000000000000000000..b214b413b12145f41ec70ac9a45a1bfc257e05c3 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin256_cout512.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw3_sw1_dw1_p1_cin256_cout512", + "definition": "conv1d_kw3_sw1_dw1_p1_cin256_cout512", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw3_sw1_dw1_p1_cin256_cout512. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 256;\nconstexpr int Cout = 512;\nconstexpr int Kw = 3;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin3_cout64.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin3_cout64.json new file mode 100644 index 0000000000000000000000000000000000000000..94ed7d7ef7f68876005a2bd2acad895e9db4bb57 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin3_cout64.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw3_sw1_dw1_p1_cin3_cout64", + "definition": "conv1d_kw3_sw1_dw1_p1_cin3_cout64", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw3_sw1_dw1_p1_cin3_cout64. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 3;\nconstexpr int Cout = 64;\nconstexpr int Kw = 3;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin512_cout1024.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin512_cout1024.json new file mode 100644 index 0000000000000000000000000000000000000000..c6cc172618cbe829e7138d38f0e84f10080947bd --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin512_cout1024.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw3_sw1_dw1_p1_cin512_cout1024", + "definition": "conv1d_kw3_sw1_dw1_p1_cin512_cout1024", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw3_sw1_dw1_p1_cin512_cout1024. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 1024;\nconstexpr int Kw = 3;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin64_cout128.json b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin64_cout128.json new file mode 100644 index 0000000000000000000000000000000000000000..3449f5f9cbeae0fe615051afa81c6689259edcc0 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv1d/conv1d_kw3_sw1_dw1_p1_cin64_cout128.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv1d_kw3_sw1_dw1_p1_cin64_cout128", + "definition": "conv1d_kw3_sw1_dw1_p1_cin64_cout128", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv1d for conv1d_kw3_sw1_dw1_p1_cin64_cout128. Constexpr-baked dims; armbench_entry_conv1d calls inner_conv1d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv1d.cpp::armbench_entry_conv1d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv1d.h", + "content": "#pragma once\n\n// Per-definition constants for this conv1d specialisation.\nnamespace conv1d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 128;\nconstexpr int Kw = 3;\nconstexpr int Sw = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv1d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// W and W_out are var dims; pre-computed by the binding harness.\n// Input layout: (Cin, W), output layout: (Cout, W_out).\nvoid inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv1d.cpp", + "content": "// Binding harness: computes W_out from constexpr params, calls inner_conv1d.\n// ABI: armbench_entry_conv1d(input*, output*, weight*, bias*, W)\n// Input layout: (Cin, W); output layout: (Cout, W_out).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" int armbench_entry_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W)\n{\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv1d(input, output, weight, bias, W, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar conv1d.\n// LLM target: replace this file with an optimised inner_conv1d.\n// All per-definition constants live in conv1d_def:: (conv1d.h).\n#include \"conv1d.h\"\nusing namespace conv1d_def;\n\nextern \"C\" void inner_conv1d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int W, int W_out)\n{\n for (int oc = 0; oc < Cout; ++oc) {\n float* out_c = output + (long)oc * W_out;\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[oc];\n for (int ic = 0; ic < Cin; ++ic) {\n const float* in_c = input + (long)ic * W;\n for (int kw = 0; kw < Kw; ++kw) {\n int iw = ow * Sw - pad + kw * Dw;\n if (iw >= 0 && iw < W)\n sum += in_c[iw] * weight[((long)oc * Cin + ic) * Kw + kw];\n }\n }\n out_c[ow] = sum;\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c1024_c1024.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c1024_c1024.json index 46a8bc55deae2280eb231d9c509e2c66061013a3..40e4448745d18dbf1658e65e02f118dd1f978484 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c1024_c1024.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c1024_c1024.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 1024;\nconstexpr int Cout = 1024;\nconstexpr int Kh = 1;\nconstexpr int Kw = 1;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 0;\nconstexpr int pad_left = 0;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c128_c512.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c128_c512.json index 3ea84d5c2e3aa5e00e25a7881f2be8f35b7a711c..fb62a316d33482ca09838a392fca30946f83ef5f 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c128_c512.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c128_c512.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 512;\nconstexpr int Kh = 1;\nconstexpr int Kw = 1;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 0;\nconstexpr int pad_left = 0;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c256_c1024.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c256_c1024.json index 0d1de02adbab3332b2c25b183f9b4aada2de88b8..ff2f3fff396cb030efc72520dc09cf8b4ea79914 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c256_c1024.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c256_c1024.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 256;\nconstexpr int Cout = 1024;\nconstexpr int Kh = 1;\nconstexpr int Kw = 1;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 0;\nconstexpr int pad_left = 0;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c512_c2048.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c512_c2048.json index 603eedea74f71ddbcd05bb05a470998a6ad7d3ab..4a344fb81bc8aee01f5031aaddb5d9c0182a66e6 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c512_c2048.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c512_c2048.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 2048;\nconstexpr int Kh = 1;\nconstexpr int Kw = 1;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 0;\nconstexpr int pad_left = 0;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c64_c256.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c64_c256.json index b75ab0700b6111eb36cc95ea37ce2860f434f981..20991a39fa84c944bd513b7727c7d8ad19714ecd 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c64_c256.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c64_c256.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 256;\nconstexpr int Kh = 1;\nconstexpr int Kw = 1;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 0;\nconstexpr int pad_left = 0;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c64_c64.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c64_c64.json index 21b5028d8f9b16ce19f4b13e6c40e8ae3f89817e..3af72d28497fdc83941a4d8cc8fddebdb11c031d 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c64_c64.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c64_c64.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 64;\nconstexpr int Kh = 1;\nconstexpr int Kw = 1;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 0;\nconstexpr int pad_left = 0;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c128_c256.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c128_c256.json index 70c5cb79d899debb6c5477682977be58639e968c..f6dbfe8b3b4fd92f6def09f5eba714d72df92d14 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c128_c256.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c128_c256.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 256;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 1;\nconstexpr int pad_left = 1;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c256_c512.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c256_c512.json index e028c22f7cae34e55a1ac5931670000655bc65c2..9b9db8eadd128c68d140772d90daec9622e1b71c 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c256_c512.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c256_c512.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 256;\nconstexpr int Cout = 512;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 1;\nconstexpr int pad_left = 1;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c3_c64.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c3_c64.json index e285a37804ad912b320ce387fcaac020b48d7a24..e92e265cb6b170ddf4f2cb88ae94a86475d56b15 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c3_c64.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c3_c64.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 3;\nconstexpr int Cout = 64;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 1;\nconstexpr int pad_left = 1;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c512_c1024.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c512_c1024.json index 6a3a3a243e7fb334c66070a61596c3d6fd5c75d4..5dca5908c93bcfeca4c6531468e7e7d5dde3a81f 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c512_c1024.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c512_c1024.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 1024;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 1;\nconstexpr int pad_left = 1;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c64_c128.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c64_c128.json index 8bbdb6e61b33e55c352115310d532845d56cadf6..bd92b416407815881311b400ba2db03e6c3ac8cc 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c64_c128.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c64_c128.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 128;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 1;\nconstexpr int pad_left = 1;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c128_c256.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c128_c256.json index bd28674dcf5593f2c069d4dcd84b468157ef6df7..c603c681019955f07881304d0f6be453cbe87d7a 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c128_c256.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c128_c256.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 256;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 2;\nconstexpr int Dw = 2;\nconstexpr int pad_top = 2;\nconstexpr int pad_left = 2;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c256_c256.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c256_c256.json index d4def6f3797fb9391dfcc30196ab6faaa52f5d18..7f2e64ac22590af466aca19c4b8098c2217943fc 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c256_c256.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c256_c256.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 256;\nconstexpr int Cout = 256;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 2;\nconstexpr int Dw = 2;\nconstexpr int pad_top = 2;\nconstexpr int pad_left = 2;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c512_c512.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c512_c512.json index 8d8c96574f5a0aff5f397efa55bafe8b3883ea86..56bab478dbe3e331f60721c688daa198b9a60f90 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c512_c512.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c512_c512.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 512;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 2;\nconstexpr int Dw = 2;\nconstexpr int pad_top = 2;\nconstexpr int pad_left = 2;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c64_c128.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c64_c128.json index 98f55933847b281d0d0091182960e78be4070b93..849d1830908eb0b7783163da02ed87c48aaaa7ed 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c64_c128.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c64_c128.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 128;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 2;\nconstexpr int Dw = 2;\nconstexpr int pad_top = 2;\nconstexpr int pad_left = 2;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c128_c256.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c128_c256.json index 2235c392f5c8e9b679388713a37c317d80918a30..ad5c239573b94123ae16119fbe880df1d180b6af 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c128_c256.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c128_c256.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 256;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 1;\nconstexpr int pad_left = 1;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c256_c512.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c256_c512.json index 4ff4b48e039d325e17b0b832b4864c360f742cc6..95bfff900cd902ab21b8323cb178cfba2e5189a3 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c256_c512.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c256_c512.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 256;\nconstexpr int Cout = 512;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 1;\nconstexpr int pad_left = 1;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c512_c1024.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c512_c1024.json index 4de3b2f830591e041f180e298cdf7a0bb76edfb4..d258833776988aefafa9b7e358a22890b6f063e7 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c512_c1024.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c512_c1024.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 1024;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 1;\nconstexpr int pad_left = 1;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c64_c128.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c64_c128.json index ea5be3cba6850db9df05cf2a41ba6da51907df79..1462addb6376cb321270f5cc68ee23ef5dca033b 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c64_c128.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c64_c128.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 128;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 1;\nconstexpr int pad_left = 1;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c128_c128.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c128_c128.json index d1e04b8874306231ebec8b07b3d87a634506bf10..a23f9069754dae60a843866d8ad273ed17ee0c8e 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c128_c128.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c128_c128.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 128;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 2;\nconstexpr int pad_left = 2;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c256_c256.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c256_c256.json index f8d2a1f786b3fd847ce170818efdc2a50562ab05..ff2f8644cead8357dc6015dd570f73ea4dc5d030 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c256_c256.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c256_c256.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 256;\nconstexpr int Cout = 256;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 2;\nconstexpr int pad_left = 2;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c32_c64.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c32_c64.json index 87fc730e6c747c531b5e72cbc02b242f8f245a4d..659bea27b23420347dc7d047f9faf811308b22cd 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c32_c64.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c32_c64.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 32;\nconstexpr int Cout = 64;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 2;\nconstexpr int pad_left = 2;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c3_c32.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c3_c32.json index 0c9d2e68bf91028d8f6a38d5755fb32ee099ea6b..7fb1c4582364e99116bf48b3fa77d171f1e751bd 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c3_c32.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c3_c32.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 3;\nconstexpr int Cout = 32;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 2;\nconstexpr int pad_left = 2;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c512_c512.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c512_c512.json index 6fc548e39b6d8bc61f6ba5644b26baa76e978c9e..26af50c1f6cda041925611efb8a6676cfe16532c 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c512_c512.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c512_c512.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 512;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 2;\nconstexpr int pad_left = 2;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c64_c128.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c64_c128.json index f4ef616f532414e7244fae65abf863beaa253ddb..bb274e321bbe4911a4fdeb062c392816b2ea6a71 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c64_c128.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c64_c128.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 128;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 2;\nconstexpr int pad_left = 2;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh7_kw7_sh2_sw2_dh1_dw1_c3_c64.json b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh7_kw7_sh2_sw2_dh1_dw1_c3_c64.json index 786d369c07d1d08178ee9a86caecde4748c9c204..1a7337c572e6238257f9d57a9f89424520bf12ca 100644 --- a/solutions/ncnn/reference-scalar/conv2d/conv2d_kh7_kw7_sh2_sw2_dh1_dw1_c3_c64.json +++ b/solutions/ncnn/reference-scalar/conv2d/conv2d_kh7_kw7_sh2_sw2_dh1_dw1_c3_c64.json @@ -10,7 +10,7 @@ "graviton3", "aarch64-sve" ], - "entry_point": "kernel.cpp::armbench_entry_conv2d", + "entry_point": "conv2d.cpp::armbench_entry_conv2d", "dependencies": [], "isa_features": [], "compile_flags": [ @@ -20,9 +20,17 @@ "link_flags": [] }, "sources": [ + { + "path": "conv2d.h", + "content": "// Auto-generated by scripts/gen_candidate_bindings.py \u2014 do not hand-edit.\n#pragma once\n\n// Per-definition constants for this conv2d specialisation.\nnamespace conv2d_def {\nconstexpr int Cin = 3;\nconstexpr int Cout = 64;\nconstexpr int Kh = 7;\nconstexpr int Kw = 7;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad_top = 3;\nconstexpr int pad_left = 3;\nconstexpr int activation_type = 0;\n} // namespace conv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\nvoid inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d.cpp", + "content": "// Binding harness: computes output dims from constexpr params, calls inner_conv2d.\n// The ctypes mirror of armbench_entry_conv2d is bench/datasets/raw.py\n// SIGNATURES[\"conv2d\"] \u2014 edit one, edit the other.\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n inner_conv2d(input, output, weight, N, H, W, H_out, W_out);\n return 0;\n}" + }, { "path": "kernel.cpp", - "content": "// reference-scalar conv2d \u2014 raw float* port of ref_conv.h::ref_conv2d.\n// No ncnn dependency; implements the CandidateBuilder C-ABI directly.\n\n#include \"conv2d.h\"\n\nextern \"C\" int armbench_entry_conv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int Cin, int H, int W,\n int Cout, int Kh, int Kw, int Sh, int Sw, int Dh, int Dw,\n int pad_top, int pad_left,\n int activation_type, const float* act_params, int n_act)\n{\n (void)act_params; (void)n_act;\n const int ext_kh = Dh * (Kh - 1) + 1;\n const int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad_top - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad_left - ext_kw) / Sw + 1;\n\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias ? bias[oc] : 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n return 0;\n}\n" + "content": "// Reference-scalar conv2d \u2014 port of ref_conv.h::ref_conv2d.\n// LLM target: replace this file with an optimised inner_conv2d.\n// All per-definition constants live in conv2d_def:: (conv2d.h).\n#include \"conv2d.h\"\nusing namespace conv2d_def;\n\nextern \"C\" void inner_conv2d(\n const float* input, float* output, const float* weight,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int oc = 0; oc < Cout; ++oc) {\n float* outc = out_n + (long)oc * H_out * W_out;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = 0.0f;\n for (int ic = 0; ic < Cin; ++ic) {\n const float* inc = in_n + (long)ic * H * W;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad_top + kh * Dh;\n int iw = ow * Sw - pad_left + kw * Dw;\n float px = (ih >= 0 && ih < H && iw >= 0 && iw < W)\n ? inc[ih * W + iw] : 0.0f;\n int widx = ((oc * Cin + ic) * Kh + kh) * Kw + kw;\n sum += px * weight[widx];\n }\n }\n }\n if (activation_type == 1 && sum < 0.0f) sum = 0.0f;\n outc[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" } ] } diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c1024.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c1024.json new file mode 100644 index 0000000000000000000000000000000000000000..0f22f5d374a050983e69a13ebd016927229c7b5c --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c1024.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c1024", + "definition": "conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c1024", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c1024. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 1024;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c128.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c128.json new file mode 100644 index 0000000000000000000000000000000000000000..3b28de5b02c3b4f6a08c8a244797239bd15f3776 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c128.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c128", + "definition": "conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c128", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c128. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 128;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c2048.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c2048.json new file mode 100644 index 0000000000000000000000000000000000000000..e3de5b0ce0dada0cea8c295b800cec695e5d4e9a --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c2048.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c2048", + "definition": "conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c2048", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c2048. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 2048;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c256.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c256.json new file mode 100644 index 0000000000000000000000000000000000000000..8d647e1d762901898fa534e334b87758a8b865d3 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c256.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c256", + "definition": "conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c256", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c256. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 256;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c32.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c32.json new file mode 100644 index 0000000000000000000000000000000000000000..d076df669a88a3d185934ad69a2c3aa2700e6242 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c32.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c32", + "definition": "conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c32", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c32. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 32;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c512.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c512.json new file mode 100644 index 0000000000000000000000000000000000000000..b0f8ba25bdc32bf388529ebe9863dce93d53414d --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c512.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c512", + "definition": "conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c512", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c512. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 512;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c64.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c64.json new file mode 100644 index 0000000000000000000000000000000000000000..48f591e9efd8662e22ad34197c612c66972e6f84 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c64.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c64", + "definition": "conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c64", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c64. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 64;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 1;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c1024.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c1024.json new file mode 100644 index 0000000000000000000000000000000000000000..703d894729110172ed735d3d282170cfcdeae91a --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c1024.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c1024", + "definition": "conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c1024", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c1024. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 1024;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 2;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c128.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c128.json new file mode 100644 index 0000000000000000000000000000000000000000..598bbe83f063077c4f12c6086dbdafea9fe00c0f --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c128.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c128", + "definition": "conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c128", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c128. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 128;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 2;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c256.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c256.json new file mode 100644 index 0000000000000000000000000000000000000000..5ceb041d21b881ce7f0435ad145765def4f135f9 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c256.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c256", + "definition": "conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c256", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c256. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 256;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 2;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c32.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c32.json new file mode 100644 index 0000000000000000000000000000000000000000..d9cc0da88535d2466fee3ee2599b6e0ab1253655 --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c32.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c32", + "definition": "conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c32", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c32. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 32;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 2;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c512.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c512.json new file mode 100644 index 0000000000000000000000000000000000000000..682b0c75bcff82eed0d25735b308f020dcd0858c --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c512.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c512", + "definition": "conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c512", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c512. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 512;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 2;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c64.json b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c64.json new file mode 100644 index 0000000000000000000000000000000000000000..4581f58024fc3c5b203cd08f0aaef930cb4abfbf --- /dev/null +++ b/solutions/ncnn/reference-scalar/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c64.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c64", + "definition": "conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c64", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* conv2d_depthwise for conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c64. Constexpr-baked dims; armbench_entry_conv2d_depthwise calls inner_conv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "conv2d_depthwise.cpp::armbench_entry_conv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "conv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this conv2d_depthwise specialisation.\nnamespace conv2d_depthwise_def {\nconstexpr int C = 64;\nconstexpr int Kh = 5;\nconstexpr int Kw = 5;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\nconstexpr int pad = 2;\n} // namespace conv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\nvoid inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "conv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_conv2d_depthwise.\n// ABI: armbench_entry_conv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n constexpr int ext_kh = Dh * (Kh - 1) + 1;\n constexpr int ext_kw = Dw * (Kw - 1) + 1;\n const int H_out = (H + 2 * pad - ext_kh) / Sh + 1;\n const int W_out = (W + 2 * pad - ext_kw) / Sw + 1;\n inner_conv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise conv2d.\n// LLM target: replace this file with an optimised inner_conv2d_depthwise.\n// All per-definition constants live in conv2d_depthwise_def:: (conv2d_depthwise.h).\n#include \"conv2d_depthwise.h\"\nusing namespace conv2d_depthwise_def;\n\nextern \"C\" void inner_conv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int oh = 0; oh < H_out; ++oh) {\n for (int ow = 0; ow < W_out; ++ow) {\n float sum = bias[c];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int ih = oh * Sh - pad + kh * Dh;\n int iw = ow * Sw - pad + kw * Dw;\n if (ih >= 0 && ih < H && iw >= 0 && iw < W)\n sum += in_c[ih * W + iw] * w_c[kh * Kw + kw];\n }\n }\n out_c[oh * W_out + ow] = sum;\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin128_cout256.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin128_cout256.json new file mode 100644 index 0000000000000000000000000000000000000000..43f1622c345b24e78162a5b5b4bf445cf75e0ad7 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin128_cout256.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh3_kw3_sh1_sw1_cin128_cout256", + "definition": "deconv2d_kh3_kw3_sh1_sw1_cin128_cout256", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh3_kw3_sh1_sw1_cin128_cout256. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 256;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin256_cout512.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin256_cout512.json new file mode 100644 index 0000000000000000000000000000000000000000..4e0561e245d5446b3199ac9d4331a9e34dcaa23b --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin256_cout512.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh3_kw3_sh1_sw1_cin256_cout512", + "definition": "deconv2d_kh3_kw3_sh1_sw1_cin256_cout512", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh3_kw3_sh1_sw1_cin256_cout512. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 256;\nconstexpr int Cout = 512;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin512_cout1024.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin512_cout1024.json new file mode 100644 index 0000000000000000000000000000000000000000..79d06f618a23aab50a64212f3a5ff8f1e3ff98ae --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin512_cout1024.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh3_kw3_sh1_sw1_cin512_cout1024", + "definition": "deconv2d_kh3_kw3_sh1_sw1_cin512_cout1024", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh3_kw3_sh1_sw1_cin512_cout1024. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 1024;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin64_cout128.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin64_cout128.json new file mode 100644 index 0000000000000000000000000000000000000000..21042dbad6f4560ba97d3f5213b5bbbe5aa36985 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh1_sw1_cin64_cout128.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh3_kw3_sh1_sw1_cin64_cout128", + "definition": "deconv2d_kh3_kw3_sh1_sw1_cin64_cout128", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh3_kw3_sh1_sw1_cin64_cout128. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 128;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin128_cout256.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin128_cout256.json new file mode 100644 index 0000000000000000000000000000000000000000..8e42d413dfff4dbd31219758589da0225475659c --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin128_cout256.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh3_kw3_sh2_sw2_cin128_cout256", + "definition": "deconv2d_kh3_kw3_sh2_sw2_cin128_cout256", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh3_kw3_sh2_sw2_cin128_cout256. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 256;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin256_cout512.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin256_cout512.json new file mode 100644 index 0000000000000000000000000000000000000000..6f643a308536bcae8e367627c3ca7f573a9ae103 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin256_cout512.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh3_kw3_sh2_sw2_cin256_cout512", + "definition": "deconv2d_kh3_kw3_sh2_sw2_cin256_cout512", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh3_kw3_sh2_sw2_cin256_cout512. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 256;\nconstexpr int Cout = 512;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.json new file mode 100644 index 0000000000000000000000000000000000000000..cae063eadc0e0d8c12b3c1576bc5ebe3c7ca66cb --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024", + "definition": "deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 1024;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin64_cout128.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin64_cout128.json new file mode 100644 index 0000000000000000000000000000000000000000..7a002fadb3e0e70156766ce489beb1e1b6d8dc4f --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin64_cout128.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh3_kw3_sh2_sw2_cin64_cout128", + "definition": "deconv2d_kh3_kw3_sh2_sw2_cin64_cout128", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh3_kw3_sh2_sw2_cin64_cout128. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 128;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin128_cout256.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin128_cout256.json new file mode 100644 index 0000000000000000000000000000000000000000..0eaec68c01c8ac554841fc852179daa2b4a78f5a --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin128_cout256.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh4_kw4_sh1_sw1_cin128_cout256", + "definition": "deconv2d_kh4_kw4_sh1_sw1_cin128_cout256", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh4_kw4_sh1_sw1_cin128_cout256. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 256;\nconstexpr int Kh = 4;\nconstexpr int Kw = 4;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin256_cout512.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin256_cout512.json new file mode 100644 index 0000000000000000000000000000000000000000..ca8469dab3eced45e97371d78d271b06e77692d2 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin256_cout512.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh4_kw4_sh1_sw1_cin256_cout512", + "definition": "deconv2d_kh4_kw4_sh1_sw1_cin256_cout512", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh4_kw4_sh1_sw1_cin256_cout512. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 256;\nconstexpr int Cout = 512;\nconstexpr int Kh = 4;\nconstexpr int Kw = 4;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin512_cout1024.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin512_cout1024.json new file mode 100644 index 0000000000000000000000000000000000000000..e9f17b7847df72b45b0a3b24c3a93d659cf738ff --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin512_cout1024.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh4_kw4_sh1_sw1_cin512_cout1024", + "definition": "deconv2d_kh4_kw4_sh1_sw1_cin512_cout1024", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh4_kw4_sh1_sw1_cin512_cout1024. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 1024;\nconstexpr int Kh = 4;\nconstexpr int Kw = 4;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin64_cout128.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin64_cout128.json new file mode 100644 index 0000000000000000000000000000000000000000..9e9ccba35045a615f29d76357d50ce1a3abf94b3 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh1_sw1_cin64_cout128.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh4_kw4_sh1_sw1_cin64_cout128", + "definition": "deconv2d_kh4_kw4_sh1_sw1_cin64_cout128", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh4_kw4_sh1_sw1_cin64_cout128. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 128;\nconstexpr int Kh = 4;\nconstexpr int Kw = 4;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin128_cout256.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin128_cout256.json new file mode 100644 index 0000000000000000000000000000000000000000..606b39d38c8d17b7d417f92a607cf5b43c23fdc8 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin128_cout256.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh4_kw4_sh2_sw2_cin128_cout256", + "definition": "deconv2d_kh4_kw4_sh2_sw2_cin128_cout256", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh4_kw4_sh2_sw2_cin128_cout256. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 128;\nconstexpr int Cout = 256;\nconstexpr int Kh = 4;\nconstexpr int Kw = 4;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin32_cout64.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin32_cout64.json new file mode 100644 index 0000000000000000000000000000000000000000..72f061ab5955fea518add627da2084914441a30e --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin32_cout64.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh4_kw4_sh2_sw2_cin32_cout64", + "definition": "deconv2d_kh4_kw4_sh2_sw2_cin32_cout64", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh4_kw4_sh2_sw2_cin32_cout64. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 32;\nconstexpr int Cout = 64;\nconstexpr int Kh = 4;\nconstexpr int Kw = 4;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin512_cout512.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin512_cout512.json new file mode 100644 index 0000000000000000000000000000000000000000..fecd33a72b103faabdc24750c04973c83dbaa228 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin512_cout512.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh4_kw4_sh2_sw2_cin512_cout512", + "definition": "deconv2d_kh4_kw4_sh2_sw2_cin512_cout512", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh4_kw4_sh2_sw2_cin512_cout512. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 512;\nconstexpr int Cout = 512;\nconstexpr int Kh = 4;\nconstexpr int Kw = 4;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.json b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.json new file mode 100644 index 0000000000000000000000000000000000000000..aa20e3597bbd656431109260b36103dabc847792 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_kh4_kw4_sh2_sw2_cin64_cout128", + "definition": "deconv2d_kh4_kw4_sh2_sw2_cin64_cout128", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d for deconv2d_kh4_kw4_sh2_sw2_cin64_cout128. Constexpr-baked dims; armbench_entry_deconv2d calls inner_deconv2d. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d.cpp::armbench_entry_deconv2d", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d specialisation.\nnamespace deconv2d_def {\nconstexpr int Cin = 64;\nconstexpr int Cout = 128;\nconstexpr int Kh = 4;\nconstexpr int Kw = 4;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, Cin, H, W), output layout: (N, Cout, H_out, W_out).\n// Weight layout: (Cout, Cin, Kh, Kw).\nvoid inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d.\n// ABI: armbench_entry_deconv2d(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, Cin, H, W); output layout: (N, Cout, H_out, W_out).\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" int armbench_entry_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar transposed conv2d (deconv2d).\n// LLM target: replace this file with an optimised inner_deconv2d.\n// All per-definition constants live in deconv2d_def:: (deconv2d.h).\n// Weight layout: (Cout, Cin, Kh, Kw); no padding applied.\n#include \"deconv2d.h\"\nusing namespace deconv2d_def;\n\nextern \"C\" void inner_deconv2d(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_co[i] = bias[co];\n }\n }\n\n // Scatter-accumulate from each input element\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * Cin * H * W;\n float* out_n = output + (long)n * Cout * H_out * W_out;\n for (int ci = 0; ci < Cin; ++ci) {\n const float* in_ci = in_n + (long)ci * H * W;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_ci[ih * W + iw];\n for (int co = 0; co < Cout; ++co) {\n float* out_co = out_n + (long)co * H_out * W_out;\n const float* w_co_ci = weight +\n ((long)co * Cin + ci) * Kh * Kw;\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_co[oh * W_out + ow] +=\n in_val * w_co_ci[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c1024.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c1024.json new file mode 100644 index 0000000000000000000000000000000000000000..3db838e79349a42731953c2521185c31988adbd7 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c1024.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh2_kw2_sh2_sw2_c1024", + "definition": "deconv2d_depthwise_kh2_kw2_sh2_sw2_c1024", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh2_kw2_sh2_sw2_c1024. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 1024;\nconstexpr int Kh = 2;\nconstexpr int Kw = 2;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c128.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c128.json new file mode 100644 index 0000000000000000000000000000000000000000..506916c5603b1da500653936bcef681f563144af --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c128.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh2_kw2_sh2_sw2_c128", + "definition": "deconv2d_depthwise_kh2_kw2_sh2_sw2_c128", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh2_kw2_sh2_sw2_c128. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 128;\nconstexpr int Kh = 2;\nconstexpr int Kw = 2;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c256.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c256.json new file mode 100644 index 0000000000000000000000000000000000000000..0e3ed8f6b2300e9b54f41e7f030c2e6b994baf8b --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c256.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh2_kw2_sh2_sw2_c256", + "definition": "deconv2d_depthwise_kh2_kw2_sh2_sw2_c256", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh2_kw2_sh2_sw2_c256. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 256;\nconstexpr int Kh = 2;\nconstexpr int Kw = 2;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c32.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c32.json new file mode 100644 index 0000000000000000000000000000000000000000..2fc33b93038519a1c38b27c990d90471ce0cf13d --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c32.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh2_kw2_sh2_sw2_c32", + "definition": "deconv2d_depthwise_kh2_kw2_sh2_sw2_c32", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh2_kw2_sh2_sw2_c32. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 32;\nconstexpr int Kh = 2;\nconstexpr int Kw = 2;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c512.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c512.json new file mode 100644 index 0000000000000000000000000000000000000000..85814f37922c44852fe4156ffd4c67b4a6aad053 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c512.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh2_kw2_sh2_sw2_c512", + "definition": "deconv2d_depthwise_kh2_kw2_sh2_sw2_c512", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh2_kw2_sh2_sw2_c512. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 512;\nconstexpr int Kh = 2;\nconstexpr int Kw = 2;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c64.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c64.json new file mode 100644 index 0000000000000000000000000000000000000000..f55e9ad56257ab7f043f1261b2c6045b676cc204 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh2_kw2_sh2_sw2_c64.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh2_kw2_sh2_sw2_c64", + "definition": "deconv2d_depthwise_kh2_kw2_sh2_sw2_c64", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh2_kw2_sh2_sw2_c64. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 64;\nconstexpr int Kh = 2;\nconstexpr int Kw = 2;\nconstexpr int Sh = 2;\nconstexpr int Sw = 2;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c128.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c128.json new file mode 100644 index 0000000000000000000000000000000000000000..426df15255f4ae20c057439199f3edd1b92ab00f --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c128.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh3_kw3_sh1_sw1_c128", + "definition": "deconv2d_depthwise_kh3_kw3_sh1_sw1_c128", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh3_kw3_sh1_sw1_c128. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 128;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c2048.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c2048.json new file mode 100644 index 0000000000000000000000000000000000000000..15ddb15e193b8a1d9d11e6daae3fb4e974e698a9 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c2048.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh3_kw3_sh1_sw1_c2048", + "definition": "deconv2d_depthwise_kh3_kw3_sh1_sw1_c2048", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh3_kw3_sh1_sw1_c2048. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 2048;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c32.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c32.json new file mode 100644 index 0000000000000000000000000000000000000000..e1e0f10b36836b9ade80042f1e558f1f35086118 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c32.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh3_kw3_sh1_sw1_c32", + "definition": "deconv2d_depthwise_kh3_kw3_sh1_sw1_c32", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh3_kw3_sh1_sw1_c32. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 32;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c512.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c512.json new file mode 100644 index 0000000000000000000000000000000000000000..c6858e0406bd606aecdac9edf35d194a4a2a7199 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c512.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh3_kw3_sh1_sw1_c512", + "definition": "deconv2d_depthwise_kh3_kw3_sh1_sw1_c512", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh3_kw3_sh1_sw1_c512. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 512;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c64.json b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c64.json new file mode 100644 index 0000000000000000000000000000000000000000..ecc8f36aa7316bf634c089a007aa3fe1a614b6e6 --- /dev/null +++ b/solutions/ncnn/reference-scalar/deconv2d_depthwise/deconv2d_depthwise_kh3_kw3_sh1_sw1_c64.json @@ -0,0 +1,36 @@ +{ + "name": "reference-scalar_deconv2d_depthwise_kh3_kw3_sh1_sw1_c64", + "definition": "deconv2d_depthwise_kh3_kw3_sh1_sw1_c64", + "dataset": "ncnn", + "author": "reference-scalar", + "description": "Scalar raw-float* deconv2d_depthwise for deconv2d_depthwise_kh3_kw3_sh1_sw1_c64. Constexpr-baked dims; armbench_entry_deconv2d_depthwise calls inner_deconv2d_depthwise. Ground-truth correctness baseline.", + "spec": { + "language": "cpp", + "target_hardware": [ + "graviton3", + "aarch64-sve" + ], + "entry_point": "deconv2d_depthwise.cpp::armbench_entry_deconv2d_depthwise", + "dependencies": [], + "isa_features": [], + "compile_flags": [ + "-O2", + "-std=c++14" + ], + "link_flags": [] + }, + "sources": [ + { + "path": "deconv2d_depthwise.h", + "content": "#pragma once\n\n// Per-definition constants for this deconv2d_depthwise specialisation.\nnamespace deconv2d_depthwise_def {\nconstexpr int C = 64;\nconstexpr int Kh = 3;\nconstexpr int Kw = 3;\nconstexpr int Sh = 1;\nconstexpr int Sw = 1;\nconstexpr int Dh = 1;\nconstexpr int Dw = 1;\n} // namespace deconv2d_depthwise_def\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n// LLM target: implement this in kernel.cpp.\n// N, H, W are var dims; H_out/W_out are pre-computed by the binding harness.\n// Input layout: (N, C, H, W), output layout: (N, C, H_out, W_out).\n// Weight layout: (C, Kh, Kw) \u2014 one filter per channel.\nvoid inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out);\n#ifdef __cplusplus\n}\n#endif\n" + }, + { + "path": "deconv2d_depthwise.cpp", + "content": "// Binding harness: computes H_out/W_out from constexpr params, calls inner_deconv2d_depthwise.\n// ABI: armbench_entry_deconv2d_depthwise(input*, output*, weight*, bias*, N, H, W)\n// H_out = (H - 1) * Sh + Kh (no-padding formula)\n// Input layout: (N, C, H, W); output layout: (N, C, H_out, W_out).\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" int armbench_entry_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W)\n{\n const int H_out = (H - 1) * Sh + Kh;\n const int W_out = (W - 1) * Sw + Kw;\n inner_deconv2d_depthwise(input, output, weight, bias, N, H, W, H_out, W_out);\n return 0;\n}\n" + }, + { + "path": "kernel.cpp", + "content": "// Reference-scalar depthwise transposed conv2d (deconv2d_depthwise).\n// LLM target: replace this file with an optimised inner_deconv2d_depthwise.\n// All per-definition constants live in deconv2d_depthwise_def:: (deconv2d_depthwise.h).\n// Weight layout: (C, Kh, Kw); no padding applied.\n#include \"deconv2d_depthwise.h\"\nusing namespace deconv2d_depthwise_def;\n\nextern \"C\" void inner_deconv2d_depthwise(\n const float* input, float* output,\n const float* weight, const float* bias,\n int N, int H, int W, int H_out, int W_out)\n{\n // Zero-fill + bias initialisation\n for (int n = 0; n < N; ++n) {\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n float* out_c = out_n + (long)c * H_out * W_out;\n for (int i = 0; i < H_out * W_out; ++i)\n out_c[i] = bias[c];\n }\n }\n\n // Scatter-accumulate from each input element (depthwise: channels independent)\n for (int n = 0; n < N; ++n) {\n const float* in_n = input + (long)n * C * H * W;\n float* out_n = output + (long)n * C * H_out * W_out;\n for (int c = 0; c < C; ++c) {\n const float* in_c = in_n + (long)c * H * W;\n float* out_c = out_n + (long)c * H_out * W_out;\n const float* w_c = weight + (long)c * Kh * Kw;\n for (int ih = 0; ih < H; ++ih) {\n for (int iw = 0; iw < W; ++iw) {\n float in_val = in_c[ih * W + iw];\n for (int kh = 0; kh < Kh; ++kh) {\n for (int kw = 0; kw < Kw; ++kw) {\n int oh = ih * Sh + kh;\n int ow = iw * Sw + kw;\n out_c[oh * W_out + ow] += in_val * w_c[kh * Kw + kw];\n }\n }\n }\n }\n }\n }\n}\n" + } + ] +} diff --git a/traces/conv1d/conv1d_kw1_sw1_dw1_p0_cin1024_cout1024.jsonl b/traces/conv1d/conv1d_kw1_sw1_dw1_p0_cin1024_cout1024.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8ccc8471792099c22659aca660aa353afbc0329f --- /dev/null +++ b/traces/conv1d/conv1d_kw1_sw1_dw1_p0_cin1024_cout1024.jsonl @@ -0,0 +1,6 @@ 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-0,0 +1,6 @@ +{"definition":"loop_033","workload":{"axes":{"N":1},"inputs":{"a":{"type":"random"},"b":{"type":"random"}},"uuid":"8557c066b9385ad697afbd6e23979f61","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_033","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:55.636554+00:00","log":"","correctness":{"max_relative_error":0.0,"max_absolute_error":0.0,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":752,"p5_ns":760,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":3623,"instructions":9534,"ipc":2.6313,"cache_misses":34.78,"reference_cycles":null}}} +{"definition":"loop_033","workload":{"axes":{"N":3},"inputs":{"a":{"type":"random"},"b":{"type":"random"}},"uuid":"c31980a0567a5c27b55f9e671e3e04c4","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_033","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:55.636554+00:00","log":"","correctness":{"max_relative_error":1.5550425631919803e-16,"max_absolute_error":5.551115123125783e-17,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":761,"p5_ns":764,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":3457,"instructions":9546,"ipc":2.7617,"cache_misses":30.5,"reference_cycles":null}}} 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+{"definition":"loop_033","workload":{"axes":{"N":3001},"inputs":{"a":{"type":"random"},"b":{"type":"random"}},"uuid":"ecdf3e7918315138a0952bfc1c8bf290","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_033","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:55.636554+00:00","log":"","correctness":{"max_relative_error":4.2439590093939196e-15,"max_absolute_error":8.881784197001252e-14,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":3119,"p5_ns":3120,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":9634,"instructions":20040,"ipc":2.0802,"cache_misses":205.74,"reference_cycles":null}}} +{"definition":"loop_033","workload":{"axes":{"N":8000000},"inputs":{"a":{"type":"random"},"b":{"type":"random"}},"uuid":"60dfe8bb5b0857f4861ff055601fbb91","tags":{"source":"perf"},"description":null},"solution":"reference-scalar_loop_033","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:55.636554+00:00","log":"","correctness":{"max_relative_error":6.074471642568212e-14,"max_absolute_error":2.3590018827235326e-11,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":6262367,"p5_ns":6280260,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":16467684,"instructions":28057214,"ipc":1.7038,"cache_misses":21128.0,"reference_cycles":null}}} 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b/traces/loop_035/loop_035.jsonl @@ -0,0 +1,6 @@ +{"definition":"loop_035","workload":{"axes":{"N":1},"inputs":{"a":{"type":"random"},"b":{"type":"random"}},"uuid":"edf3063d2543527c9d92cb158f02d1ba","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_035","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.004696+00:00","log":"","correctness":{"max_relative_error":0.0,"max_absolute_error":0.0,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":757,"p5_ns":760,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":3492,"instructions":9551,"ipc":2.7352,"cache_misses":33.94,"reference_cycles":null}}} +{"definition":"loop_035","workload":{"axes":{"N":7},"inputs":{"a":{"type":"random"},"b":{"type":"random"}},"uuid":"ec3ccb1d36d85386b98f88d8d28424fb","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_035","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.004696+00:00","log":"","correctness":{"max_relative_error":0.0,"max_absolute_error":0.0,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":764,"p5_ns":768,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":3335,"instructions":9596,"ipc":2.8769,"cache_misses":35.1,"reference_cycles":null}}} +{"definition":"loop_035","workload":{"axes":{"N":9999},"inputs":{"a":{"type":"random"},"b":{"type":"random"}},"uuid":"c07f949b2d1655429cfacf134144ed9a","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_035","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.004696+00:00","log":"","correctness":{"max_relative_error":0.0,"max_absolute_error":0.0,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":2109,"p5_ns":2130,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":7211,"instructions":22105,"ipc":3.0656,"cache_misses":385.32,"reference_cycles":null}}} +{"definition":"loop_035","workload":{"axes":{"N":10001},"inputs":{"a":{"type":"random"},"b":{"type":"random"}},"uuid":"c000abcb0b9f55d3a474f5c5d1a57b9f","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_035","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.004696+00:00","log":"","correctness":{"max_relative_error":0.0,"max_absolute_error":0.0,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":2087,"p5_ns":2121,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":7098,"instructions":22069,"ipc":3.1093,"cache_misses":398.9,"reference_cycles":null}}} +{"definition":"loop_035","workload":{"axes":{"N":4000000},"inputs":{"a":{"type":"random"},"b":{"type":"random"}},"uuid":"47a35c4ec9b05981a2642f052146f91a","tags":{"source":"perf"},"description":null},"solution":"reference-scalar_loop_035","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.004696+00:00","log":"","correctness":{"max_relative_error":0.0,"max_absolute_error":0.0,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":788705,"p5_ns":796948,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":2654923,"instructions":5017316,"ipc":1.8898,"cache_misses":350710.12,"reference_cycles":null}}} +{"definition":"loop_035","workload":{"axes":{"N":16000000},"inputs":{"a":{"type":"random"},"b":{"type":"random"}},"uuid":"0e670d83c32952f399747f68a3287d7d","tags":{"source":"perf"},"description":null},"solution":"reference-scalar_loop_035","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.004696+00:00","log":"","correctness":{"max_relative_error":0.0,"max_absolute_error":0.0,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":4037648,"p5_ns":4063984,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":11742101,"instructions":20042001,"ipc":1.7068,"cache_misses":1483901.32,"reference_cycles":null}}} diff --git a/traces/loop_108/loop_108.jsonl b/traces/loop_108/loop_108.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b2c4f5e36796e95dd10f034606a30504cc735ba6 --- /dev/null +++ b/traces/loop_108/loop_108.jsonl @@ -0,0 +1,6 @@ +{"definition":"loop_108","workload":{"axes":{"N":1},"inputs":{"rgba":{"type":"random"}},"uuid":"9e355256598e5974ada0e65a89d513ba","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_108","evaluation":{"status":"RUNTIME_ERROR","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.821290+00:00","log":"gen_inputs failed: dtype DType.UINT32 not yet supported by inputs.py\nTraceback (most recent call last):\n File \"/home/ubuntu/arm-bench/bench/evaluators/default.py\", line 51, in build_baseline\n np_inputs = gen_inputs_for_workload(definition, workload)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 160, in gen_inputs_for_workload\n out[tname] = _gen_random_tensor(shape, _dtype_to_np(tspec.dtype), rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 124, in _dtype_to_np\n raise NotImplementedError(f\"dtype {dt} not yet supported by inputs.py\")\nNotImplementedError: dtype DType.UINT32 not yet supported by inputs.py\n","correctness":null,"performance":null}} +{"definition":"loop_108","workload":{"axes":{"N":7},"inputs":{"rgba":{"type":"random"}},"uuid":"5426013671ee59d1a0599ea232ba73e8","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_108","evaluation":{"status":"RUNTIME_ERROR","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.821290+00:00","log":"gen_inputs failed: dtype DType.UINT32 not yet supported by inputs.py\nTraceback (most recent call last):\n File \"/home/ubuntu/arm-bench/bench/evaluators/default.py\", line 51, in build_baseline\n np_inputs = gen_inputs_for_workload(definition, workload)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 160, in gen_inputs_for_workload\n out[tname] = _gen_random_tensor(shape, _dtype_to_np(tspec.dtype), rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 124, in _dtype_to_np\n raise NotImplementedError(f\"dtype {dt} not yet supported by inputs.py\")\nNotImplementedError: dtype DType.UINT32 not yet supported by inputs.py\n","correctness":null,"performance":null}} +{"definition":"loop_108","workload":{"axes":{"N":9999},"inputs":{"rgba":{"type":"random"}},"uuid":"ac8ed2981cad59b1aeb36b2c70786bb0","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_108","evaluation":{"status":"RUNTIME_ERROR","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.821290+00:00","log":"gen_inputs failed: dtype DType.UINT32 not yet supported by inputs.py\nTraceback (most recent call last):\n File \"/home/ubuntu/arm-bench/bench/evaluators/default.py\", line 51, in build_baseline\n np_inputs = gen_inputs_for_workload(definition, workload)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 160, in gen_inputs_for_workload\n out[tname] = _gen_random_tensor(shape, _dtype_to_np(tspec.dtype), rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 124, in _dtype_to_np\n raise NotImplementedError(f\"dtype {dt} not yet supported by inputs.py\")\nNotImplementedError: dtype DType.UINT32 not yet supported by inputs.py\n","correctness":null,"performance":null}} +{"definition":"loop_108","workload":{"axes":{"N":10001},"inputs":{"rgba":{"type":"random"}},"uuid":"34466b56eb3b5952acc996b0a9667d3d","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_108","evaluation":{"status":"RUNTIME_ERROR","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.821290+00:00","log":"gen_inputs failed: dtype DType.UINT32 not yet supported by inputs.py\nTraceback (most recent call last):\n File \"/home/ubuntu/arm-bench/bench/evaluators/default.py\", line 51, in build_baseline\n np_inputs = gen_inputs_for_workload(definition, workload)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 160, in gen_inputs_for_workload\n out[tname] = _gen_random_tensor(shape, _dtype_to_np(tspec.dtype), rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 124, in _dtype_to_np\n raise NotImplementedError(f\"dtype {dt} not yet supported by inputs.py\")\nNotImplementedError: dtype DType.UINT32 not yet supported by inputs.py\n","correctness":null,"performance":null}} +{"definition":"loop_108","workload":{"axes":{"N":8000000},"inputs":{"rgba":{"type":"random"}},"uuid":"1829cbb347a6523d862a7242bd9240e5","tags":{"source":"perf"},"description":null},"solution":"reference-scalar_loop_108","evaluation":{"status":"RUNTIME_ERROR","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.821290+00:00","log":"gen_inputs failed: dtype DType.UINT32 not yet supported by inputs.py\nTraceback (most recent call last):\n File \"/home/ubuntu/arm-bench/bench/evaluators/default.py\", line 51, in build_baseline\n np_inputs = gen_inputs_for_workload(definition, workload)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 160, in gen_inputs_for_workload\n out[tname] = _gen_random_tensor(shape, _dtype_to_np(tspec.dtype), rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 124, in _dtype_to_np\n raise NotImplementedError(f\"dtype {dt} not yet supported by inputs.py\")\nNotImplementedError: dtype DType.UINT32 not yet supported by inputs.py\n","correctness":null,"performance":null}} +{"definition":"loop_108","workload":{"axes":{"N":32000000},"inputs":{"rgba":{"type":"random"}},"uuid":"67e882c66af45a2dba1efd9e2bba27b5","tags":{"source":"perf"},"description":null},"solution":"reference-scalar_loop_108","evaluation":{"status":"RUNTIME_ERROR","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.821290+00:00","log":"gen_inputs failed: dtype DType.UINT32 not yet supported by inputs.py\nTraceback (most recent call last):\n File \"/home/ubuntu/arm-bench/bench/evaluators/default.py\", line 51, in build_baseline\n np_inputs = gen_inputs_for_workload(definition, workload)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 160, in gen_inputs_for_workload\n out[tname] = _gen_random_tensor(shape, _dtype_to_np(tspec.dtype), rng)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/arm-bench/bench/runtime/inputs.py\", line 124, in _dtype_to_np\n raise NotImplementedError(f\"dtype {dt} not yet supported by inputs.py\")\nNotImplementedError: dtype DType.UINT32 not yet supported by inputs.py\n","correctness":null,"performance":null}} diff --git a/traces/loop_113/loop_113.jsonl b/traces/loop_113/loop_113.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0d4621244c6d3d66d6a34092a60cdc967e9ded1e --- /dev/null +++ b/traces/loop_113/loop_113.jsonl @@ -0,0 +1,6 @@ +{"definition":"loop_113","workload":{"axes":{"N":1},"inputs":{"a0":{"type":"random"},"b0":{"type":"random"}},"uuid":"f023767efc6e58d0b307b0d4630c5fd3","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_113","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.958843+00:00","log":"","correctness":{"max_relative_error":0.0,"max_absolute_error":0.0,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":764,"p5_ns":767,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":3860,"instructions":9533,"ipc":2.4696,"cache_misses":19.28,"reference_cycles":null}}} +{"definition":"loop_113","workload":{"axes":{"N":7},"inputs":{"a0":{"type":"random"},"b0":{"type":"random"}},"uuid":"842e841aafe655bd842d5ab3949ddd48","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_113","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.958843+00:00","log":"","correctness":{"max_relative_error":0.0,"max_absolute_error":0.0,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":756,"p5_ns":758,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":3735,"instructions":9565,"ipc":2.5609,"cache_misses":19.44,"reference_cycles":null}}} +{"definition":"loop_113","workload":{"axes":{"N":9999},"inputs":{"a0":{"type":"random"},"b0":{"type":"random"}},"uuid":"89d51faf81475863a71deda9bc13e1ed","tags":{"source":"edge"},"description":null},"solution":"reference-scalar_loop_113","evaluation":{"status":"PASSED","environment":{"hardware":"aarch64-ip-172-31-59-130","cpu_pinned":0,"libs":{"python":"3.12.3","numpy":"2.4.6"}},"timestamp":"2026-06-15T03:15:58.958843+00:00","log":"","correctness":{"max_relative_error":0.0,"max_absolute_error":0.0,"matched_ratio":1.0,"extra":null},"performance":{"min_ns":2597,"p5_ns":2601,"reference_min_ns":null,"speedup":null,"repeat":50,"warmup":5,"cycles":8392,"instructions":18302,"ipc":2.1809,"cache_misses":355.66,"reference_cycles":null}}} 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--git a/workloads/conv/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c512_c2048.jsonl b/workloads/conv/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c512_c2048.jsonl index fbf0ed31d13fe34585b1a9322bfaf00e17bdb9b6..683299be768121460d29fe98cb952ed310f7e755 100644 --- a/workloads/conv/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c512_c2048.jsonl +++ b/workloads/conv/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c512_c2048.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 7, "W": 7}, "scalar_inputs": {"pad_top": 0, "pad_left": 0, "activation_type": 0, "with_bias": 0}, "uuid": "H7_W7_padh0_padw0_b0", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 7, "W": 7}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 0}, "pad_left": {"type": "scalar", "value": 0}, "activation_type": {"type": "scalar", "value": 0}, "with_bias": {"type": "scalar", "value": 0}}, "uuid": "2ad67410f0d0552f8f6ae305f25cefe6", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": 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a/workloads/conv/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c64_c256.jsonl +++ b/workloads/conv/conv2d_kh1_kw1_sh1_sw1_dh1_dw1_c64_c256.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 56, "W": 56}, "scalar_inputs": {"pad_top": 0, "pad_left": 0, "activation_type": 0, "with_bias": 0}, "uuid": "H56_W56_padh0_padw0_b0", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 56, "W": 56}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 0}, "pad_left": {"type": "scalar", "value": 0}, "activation_type": {"type": "scalar", "value": 0}, "with_bias": {"type": "scalar", "value": 0}}, "uuid": "d604a2a8c81a52d1a6e45f2c3543d265", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 28, "W": 28}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 0}, "pad_left": {"type": "scalar", "value": 0}, "activation_type": {"type": "scalar", "value": 0}, 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a/workloads/conv/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c256_c512.jsonl b/workloads/conv/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c256_c512.jsonl index dffef8089492110fdfe12f12d3360f59f81ac8c3..78d615aecbf9986ce7166e123d11bd4abc2d3594 100644 --- a/workloads/conv/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c256_c512.jsonl +++ b/workloads/conv/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c256_c512.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 14, "W": 14}, "scalar_inputs": {"pad_top": 1, "pad_left": 1, "activation_type": 0, "with_bias": 1}, "uuid": "H14_W14_padh1_padw1_b1", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 14, "W": 14}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 1}, "pad_left": {"type": "scalar", "value": 1}, "activation_type": {"type": "scalar", "value": 0}, "with_bias": {"type": "scalar", "value": 1}}, "uuid": "9016e988b6fe5ebb83894c247f5f6ce6", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 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a/workloads/conv/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c3_c64.jsonl +++ b/workloads/conv/conv2d_kh3_kw3_sh1_sw1_dh1_dw1_c3_c64.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 224, "W": 224}, "scalar_inputs": {"pad_top": 1, "pad_left": 1, "activation_type": 0, "with_bias": 1}, "uuid": "H224_W224_padh1_padw1_b1", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 224, "W": 224}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 1}, "pad_left": {"type": "scalar", "value": 1}, "activation_type": {"type": "scalar", "value": 0}, "with_bias": {"type": "scalar", "value": 1}}, "uuid": "4932ca9d15cd5ab58f30158b896aa9bb", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 300, "W": 300}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 1}, "pad_left": {"type": "scalar", "value": 1}, "activation_type": {"type": "scalar", "value": 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a/workloads/conv/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c128_c256.jsonl b/workloads/conv/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c128_c256.jsonl index aa69b44a1b361aeee643064eb71bf7fa992f52ab..f022c3a64f41ae0853e49cf8be8e604f928ffc37 100644 --- a/workloads/conv/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c128_c256.jsonl +++ b/workloads/conv/conv2d_kh3_kw3_sh1_sw1_dh2_dw2_c128_c256.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 28, "W": 28}, "scalar_inputs": {"pad_top": 2, "pad_left": 2, "activation_type": 0, "with_bias": 0}, "uuid": "H28_W28_padh2_padw2_b0", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 28, "W": 28}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 2}, "pad_left": {"type": "scalar", "value": 2}, "activation_type": {"type": "scalar", "value": 0}, "with_bias": {"type": "scalar", "value": 0}}, "uuid": "43c5652744f65cc983f879b4edd5d7a2", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 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a/workloads/conv/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c128_c256.jsonl b/workloads/conv/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c128_c256.jsonl index 8c3b64889d697784da57ac20bb81afa3a76c584b..0b59af9e067b7625f49d10c2d9b392f4d6206b0b 100644 --- a/workloads/conv/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c128_c256.jsonl +++ b/workloads/conv/conv2d_kh3_kw3_sh2_sw2_dh1_dw1_c128_c256.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 56, "W": 56}, "scalar_inputs": {"pad_top": 1, "pad_left": 1, "activation_type": 0, "with_bias": 0}, "uuid": "H56_W56_padh1_padw1_b0", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 56, "W": 56}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 1}, "pad_left": {"type": "scalar", "value": 1}, "activation_type": {"type": "scalar", "value": 0}, "with_bias": {"type": "scalar", "value": 0}}, "uuid": "3967ac3bf8c75474915b83340931a6d1", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 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a/workloads/conv/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c128_c128.jsonl b/workloads/conv/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c128_c128.jsonl index 1be2bd9a24c997dd0636485f20d2320ebea6f6e7..8258224983271b740e8705fc5db99a3285715e5e 100644 --- a/workloads/conv/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c128_c128.jsonl +++ b/workloads/conv/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c128_c128.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 14, "W": 14}, "scalar_inputs": {"pad_top": 2, "pad_left": 2, "activation_type": 0, "with_bias": 0}, "uuid": "H14_W14_padh2_padw2_b0", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 14, "W": 14}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 2}, "pad_left": {"type": "scalar", "value": 2}, "activation_type": {"type": "scalar", "value": 0}, "with_bias": {"type": "scalar", "value": 0}}, "uuid": "e0fc0a5d014f5201ac5d7f0fdde3eb82", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 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a/workloads/conv/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c512_c512.jsonl b/workloads/conv/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c512_c512.jsonl index a66426e2c4c0748d358fd843cc1a7264335c8d9b..a59eb9192b1a5120d751a39ab5f8cfa3ee8c34b2 100644 --- a/workloads/conv/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c512_c512.jsonl +++ b/workloads/conv/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c512_c512.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 7, "W": 7}, "scalar_inputs": {"pad_top": 2, "pad_left": 2, "activation_type": 0, "with_bias": 0}, "uuid": "H7_W7_padh2_padw2_b0", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 7, "W": 7}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 2}, "pad_left": {"type": "scalar", "value": 2}, "activation_type": {"type": "scalar", "value": 0}, "with_bias": {"type": "scalar", "value": 0}}, "uuid": "dde30152afe35a56b216aad5fe01c0c0", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, 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a/workloads/conv/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c64_c128.jsonl +++ b/workloads/conv/conv2d_kh5_kw5_sh1_sw1_dh1_dw1_c64_c128.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 28, "W": 28}, "scalar_inputs": {"pad_top": 2, "pad_left": 2, "activation_type": 0, "with_bias": 0}, "uuid": "H28_W28_padh2_padw2_b0", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 28, "W": 28}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 2}, "pad_left": {"type": "scalar", "value": 2}, "activation_type": {"type": "scalar", "value": 0}, "with_bias": {"type": "scalar", "value": 0}}, "uuid": "29a4240dc9a5526990b53de998985581", "tags": {"from": "tests/ncnn/candidate/convolution.cpp"}} +{"axes": {"N": 1, "H": 56, "W": 56}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "pad_top": {"type": "scalar", "value": 2}, "pad_left": {"type": "scalar", "value": 2}, "activation_type": {"type": "scalar", "value": 0}, 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a/workloads/conv1d/conv1d_kw1_sw1_dw1_p0_cin64_cout256.jsonl b/workloads/conv1d/conv1d_kw1_sw1_dw1_p0_cin64_cout256.jsonl index 14b30a1b0cceec537cd317e7a75ff57b8fa0e342..ed50c991a9905d86cda370029294440f97856a2a 100644 --- a/workloads/conv1d/conv1d_kw1_sw1_dw1_p0_cin64_cout256.jsonl +++ b/workloads/conv1d/conv1d_kw1_sw1_dw1_p0_cin64_cout256.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "W": 112}, "scalar_inputs": {"pad_left": 0, "activation_type": 0}, "uuid": "27531c2a792b46e3941bd052d6d1fb73", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "W": 112}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, "uuid": "a3b0a501961750c98c7eb28acf8b9e22", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "W": 128}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, "uuid": "7ff60359cfce5a08902dad6b049c87b4", "tags": {"from": "gen_workload"}} +{"axes": {"N": 1, "W": 512}, "inputs": {"input": 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a/workloads/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c512.jsonl b/workloads/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c512.jsonl index 76c5f2e7d9dacc75b02a68f1c932f28bec917ef0..ce89833c9a3b37d78f93976bec8d2329a25516e9 100644 --- a/workloads/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c512.jsonl +++ b/workloads/conv2d_depthwise/conv2d_depthwise_kh3_kw3_sh1_sw1_dh1_dw1_p1_c512.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 14, "W": 14}, "scalar_inputs": {"pad_left": 1, "pad_top": 1, "activation_type": 0}, "uuid": "0d60f5f634484b6a901b7a2415a01304", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "H": 14, "W": 14}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, "uuid": "0dab312563a150818b5583d92d5bd3b3", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "H": 56, "W": 56}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, 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a/workloads/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c128.jsonl b/workloads/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c128.jsonl index d0e8d2f46da39eb74d18a201bf5de798160fbac5..66ca8b3e5b3d0d15ebaa1c8e610bc2aa0de90f2d 100644 --- a/workloads/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c128.jsonl +++ b/workloads/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c128.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 28, "W": 28}, "scalar_inputs": {"pad_left": 2, "pad_top": 2, "activation_type": 0}, "uuid": "d78b729a7a5a4fbb9b18a8d8cb1ea3fc", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "H": 28, "W": 28}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, "uuid": "388655f1308e585190af07c49b18a223", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "H": 14, "W": 14}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, 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a/workloads/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c512.jsonl b/workloads/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c512.jsonl index a409ff50cdbb6dd7d0f4dec810f47249d3242466..00c70a042aaf3f0c6fef00e60bf7a7979b039413 100644 --- a/workloads/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c512.jsonl +++ b/workloads/conv2d_depthwise/conv2d_depthwise_kh5_kw5_sh1_sw1_dh1_dw1_p2_c512.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 7, "W": 7}, "scalar_inputs": {"pad_left": 2, "pad_top": 2, "activation_type": 0}, "uuid": "585c5d72709c4b889859562d2cfcf929", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "H": 7, "W": 7}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, "uuid": "4ff49086ff80584a86ff0f316e900fa2", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "H": 28, "W": 28}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, 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a/workloads/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.jsonl b/workloads/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.jsonl index da9c0d687198d32d1cc5855cd45bc1c8f142e3ad..f7bcbf568d548c01c38404efcb61e1ead503d866 100644 --- a/workloads/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.jsonl +++ b/workloads/deconv2d/deconv2d_kh3_kw3_sh2_sw2_cin512_cout1024.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 7, "W": 7}, "scalar_inputs": {"activation_type": 0}, "uuid": "06314eee6b4b464997d8fba525e5f5f9", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "H": 7, "W": 7}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, "uuid": "c793189925d45c92b5df3139c412f90f", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "H": 14, "W": 14}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, "uuid": "89d19f1d5cfb5e14b73cd4020af05f5b", "tags": {"from": "gen_workload"}} +{"axes": {"N": 1, 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a/workloads/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.jsonl b/workloads/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.jsonl index d431af576c9941204d73ef0f78dee0e0ff6e6246..e5a557a0aada0d809f7bcbda0c10b75e165252e1 100644 --- a/workloads/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.jsonl +++ b/workloads/deconv2d/deconv2d_kh4_kw4_sh2_sw2_cin64_cout128.jsonl @@ -1 +1,3 @@ -{"axes": {"N": 1, "H": 28, "W": 28}, "scalar_inputs": {"activation_type": 0}, "uuid": "2985663624fb478e8b612d1365aa29bd", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "H": 28, "W": 28}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, "uuid": "081d1b988b495ddcb2d502a7ede5b7ce", "tags": {"from": "gen_definitions"}} +{"axes": {"N": 1, "H": 14, "W": 14}, "inputs": {"input": {"type": "random"}, "weight": {"type": "random"}, "bias": {"type": "random"}}, "uuid": "9df9693d73905250bae823389d6dbf40", "tags": {"from": "gen_workload"}} +{"axes": {"N": 1, "H": 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