diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index fde939b4ad2a..e1f47e5ab094 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -7299,6 +7299,13 @@ struct ggml_conv_2d_dw_params { int dilation_y; }; +static inline float ggml_conv_2d_dw_knl_f32(const char * data, int64_t i, ggml_type type) { + if (type == GGML_TYPE_F16) { + return GGML_FP16_TO_FP32(((const ggml_fp16_t *)data)[i]); + } + return ((const float *)data)[i]; +} + static void ggml_compute_forward_conv_2d_dw_cwhn( const ggml_compute_params * params, const ggml_tensor * src, @@ -7307,7 +7314,8 @@ static void ggml_compute_forward_conv_2d_dw_cwhn( const ggml_conv_2d_dw_params & p) { const int64_t c = p.channels; - const float * knl_data = (const float *)kernel->data; + const char * knl_data = (const char *)kernel->data; + const ggml_type knl_type = kernel->type; const int64_t rows_total = p.dst_h * p.batch; const int64_t rows_per_thread = (rows_total + params->nth - 1) / params->nth; @@ -7315,13 +7323,15 @@ static void ggml_compute_forward_conv_2d_dw_cwhn( const int64_t row_end = MIN(row_start + rows_per_thread, rows_total); #ifdef GGML_SIMD + int64_t c_pkg_end = 0; + if (knl_type == GGML_TYPE_F32) { #if defined(__ARM_FEATURE_SVE) const int64_t pkg_size = svcntw(); #else const int64_t pkg_size = GGML_F32_EPR; #endif - const int64_t pkg_count = c / pkg_size; - const int64_t c_pkg_end = pkg_count * pkg_size; + c_pkg_end = (c / pkg_size) * pkg_size; + } #else const int64_t c_pkg_end = 0; #endif @@ -7335,8 +7345,7 @@ static void ggml_compute_forward_conv_2d_dw_cwhn( const int64_t src_x_base = dst_x * p.stride_x - p.pad_x; #ifdef GGML_SIMD - // Vectorized loop - for (int64_t c_i = 0; c_i < c_pkg_end; c_i += pkg_size) { + for (int64_t c_i = 0; c_i < c_pkg_end; c_i += GGML_F32_EPR) { GGML_F32_VEC sum = GGML_F32_VEC_ZERO; for (int64_t knl_y = 0; knl_y < p.knl_h; ++knl_y) { const int64_t src_y = src_y_base + knl_y * p.dilation_y; @@ -7348,7 +7357,8 @@ static void ggml_compute_forward_conv_2d_dw_cwhn( if (src_x < 0 || src_x >= p.src_w) { continue; } - GGML_F32_VEC k = GGML_F32_VEC_LOAD(knl_data + (knl_y * p.knl_w + knl_x) * c + c_i); + const float * kp = (const float *)knl_data + (knl_y * p.knl_w + knl_x) * c + c_i; + GGML_F32_VEC k = GGML_F32_VEC_LOAD(kp); GGML_F32_VEC s = GGML_F32_VEC_LOAD(src_data + (src_y * p.src_w + src_x) * c + c_i); sum = GGML_F32_VEC_FMA(sum, k, s); } @@ -7356,7 +7366,6 @@ static void ggml_compute_forward_conv_2d_dw_cwhn( GGML_F32_VEC_STORE(dst_data + c_i, sum); } #endif - // Scalar loop for (int64_t c_i = c_pkg_end; c_i < c; ++c_i) { float sum = 0.0f; for (int64_t knl_y = 0; knl_y < p.knl_h; ++knl_y) { @@ -7369,7 +7378,7 @@ static void ggml_compute_forward_conv_2d_dw_cwhn( if (src_x < 0 || src_x >= p.src_w) { continue; } - sum += knl_data[(knl_y * p.knl_w + knl_x) * c + c_i] + sum += ggml_conv_2d_dw_knl_f32(knl_data, (knl_y * p.knl_w + knl_x) * c + c_i, knl_type) * src_data[(src_y * p.src_w + src_x) * c + c_i]; } } @@ -7390,9 +7399,11 @@ static void ggml_compute_forward_conv_2d_dw_whcn( const int64_t per_thread = (n + params->nth - 1) / params->nth; const int64_t start = params->ith * per_thread; const int64_t end = MIN(start + per_thread, n); + const char * knl_base = (const char *)kernel->data; + const ggml_type knl_type = kernel->type; for (int64_t i = start; i < end; ++i) { - const float * knl_data = (const float *)kernel->data + (i % p.channels) * p.knl_w * p.knl_h; + const int64_t knl_offset = (i % p.channels) * p.knl_w * p.knl_h; const float * src_data = (const float *)src->data + i * p.src_w * p.src_h; float * dst_data = (float *)dst->data + i * p.dst_w * p.dst_h; @@ -7410,7 +7421,7 @@ static void ggml_compute_forward_conv_2d_dw_whcn( if (src_x < 0 || src_x >= p.src_w) { continue; } - sum += knl_data[knl_y * p.knl_w + knl_x] + sum += ggml_conv_2d_dw_knl_f32(knl_base, knl_offset + knl_y * p.knl_w + knl_x, knl_type) * src_data[src_y * p.src_w + src_x]; } } @@ -7442,13 +7453,13 @@ void ggml_compute_forward_conv_2d_dw( p.dilation_x = dst->op_params[4]; p.dilation_y = dst->op_params[5]; + GGML_ASSERT(kernel->type == GGML_TYPE_F32 || kernel->type == GGML_TYPE_F16); GGML_ASSERT(kernel->ne[3] == p.channels); GGML_ASSERT(dst->ne[3] == p.batch); if (ggml_is_contiguous(src)) { ggml_compute_forward_conv_2d_dw_whcn(params, src, kernel, dst, p); } else if (ggml_is_contiguous_channels(src)) { - // kernel should also have channels most contiguous in memory GGML_ASSERT(kernel->nb[0] >= kernel->nb[2] && kernel->nb[1] >= kernel->nb[0]); ggml_compute_forward_conv_2d_dw_cwhn(params, src, kernel, dst, p); } else { diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index e7ac21a2be29..15290c3d1091 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -1869,6 +1869,29 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_2d(ggml_met return res; } +ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_2d_dw(ggml_metal_library_t lib, const ggml_tensor * op, bool tiled) { + assert(op->op == GGML_OP_CONV_2D_DW); + + GGML_ASSERT(op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32); + GGML_ASSERT(op->type == GGML_TYPE_F32); + + char base[256]; + char name[256]; + + snprintf(base, 256, "kernel_conv_2d_dw%s_%s_%s", + tiled ? "_tiled" : "", + ggml_type_name(op->src[0]->type), ggml_type_name(op->src[1]->type)); + snprintf(name, 256, "%s", base); + + ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name); + if (!res.pipeline) { + res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + } + + return res; +} + ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_3d(ggml_metal_library_t lib, const ggml_tensor * op) { assert(op->op == GGML_OP_CONV_3D); diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index dc75a34b2b77..9d4aca121595 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -152,6 +152,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_tran struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_transpose_2d (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_col2im_1d (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_2d (ggml_metal_library_t lib, const struct ggml_tensor * op); +struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_2d_dw (ggml_metal_library_t lib, const struct ggml_tensor * op, bool tiled); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_conv_3d (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_upscale (ggml_metal_library_t lib, const struct ggml_tensor * op); struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_pad (ggml_metal_library_t lib, const struct ggml_tensor * op); diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index 962a001f39f8..5d29250f654b 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -1198,6 +1198,10 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 && (op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32); + case GGML_OP_CONV_2D_DW: + return op->src[1]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32 && + (op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32); case GGML_OP_UPSCALE: return op->src[0]->type == GGML_TYPE_F32; case GGML_OP_POOL_1D: diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index 6bf61423ce73..d6761023b76c 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -656,6 +656,34 @@ typedef struct { int32_t d1; } ggml_metal_kargs_conv_2d; +typedef struct { + uint64_t nb00; // kernel strides + uint64_t nb01; + uint64_t nb02; + uint64_t nb10; // input strides + uint64_t nb11; + uint64_t nb12; + uint64_t nb13; + uint64_t nb0; // output strides + uint64_t nb1; + uint64_t nb2; + uint64_t nb3; + int32_t IW; // input width + int32_t IH; // input height + int32_t KW; // kernel width + int32_t KH; // kernel height + int32_t C; // channels (IC == OC for depthwise) + int32_t OW; // output width + int32_t OH; // output height + int32_t N; // batch size + int32_t s0; // stride x + int32_t s1; // stride y + int32_t p0; // padding x + int32_t p1; // padding y + int32_t d0; // dilation x + int32_t d1; // dilation y +} ggml_metal_kargs_conv_2d_dw; + typedef struct { uint64_t ofs0; uint64_t ofs1; diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index d196bae4faf2..45909c4777b5 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -387,6 +387,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) { { n_fuse = ggml_metal_op_conv_2d(ctx, idx); } break; + case GGML_OP_CONV_2D_DW: + { + n_fuse = ggml_metal_op_conv_2d_dw(ctx, idx); + } break; case GGML_OP_CONV_TRANSPOSE_1D: { n_fuse = ggml_metal_op_conv_transpose_1d(ctx, idx); @@ -3742,6 +3746,86 @@ int ggml_metal_op_conv_2d(ggml_metal_op_t ctx, int idx) { return 1; } +int ggml_metal_op_conv_2d_dw(ggml_metal_op_t ctx, int idx) { + ggml_tensor * op = ctx->node(idx); + + ggml_metal_library_t lib = ctx->lib; + ggml_metal_encoder_t enc = ctx->enc; + + GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne); + GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb); + GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne); + GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb); + GGML_TENSOR_LOCALS( int32_t, ne, op, ne); + GGML_TENSOR_LOCALS(uint64_t, nb, op, nb); + + GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32); + GGML_ASSERT(op->type == GGML_TYPE_F32); + GGML_ASSERT(op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32); + + const int32_t s0 = ((const int32_t *) op->op_params)[0]; + const int32_t s1 = ((const int32_t *) op->op_params)[1]; + const int32_t p0 = ((const int32_t *) op->op_params)[2]; + const int32_t p1 = ((const int32_t *) op->op_params)[3]; + const int32_t d0 = ((const int32_t *) op->op_params)[4]; + const int32_t d1 = ((const int32_t *) op->op_params)[5]; + + ggml_metal_kargs_conv_2d_dw args = { + /*.nb00 =*/ nb00, + /*.nb01 =*/ nb01, + /*.nb02 =*/ nb03, + /*.nb10 =*/ nb10, + /*.nb11 =*/ nb11, + /*.nb12 =*/ nb12, + /*.nb13 =*/ nb13, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + /*.nb3 =*/ nb3, + /*.IW =*/ ne10, + /*.IH =*/ ne11, + /*.KW =*/ ne00, + /*.KH =*/ ne01, + /*.C =*/ ne12, + /*.OW =*/ ne0, + /*.OH =*/ ne1, + /*.N =*/ ne13, + /*.s0 =*/ s0, + /*.s1 =*/ s1, + /*.p0 =*/ p0, + /*.p1 =*/ p1, + /*.d0 =*/ d0, + /*.d1 =*/ d1, + }; + + const bool use_tiled = (nb12 < nb10); + + auto pipeline = ggml_metal_library_get_pipeline_conv_2d_dw(lib, op, use_tiled); + + int nth = ggml_metal_pipeline_max_theads_per_threadgroup(pipeline); + nth = std::min(nth, 256); + nth = std::max(nth, 1); + + const int32_t OW = ne0; + const int32_t OH = ne1; + const int32_t C = ne12; + const int32_t N = ne13; + + const int tg_x = use_tiled ? (C + nth - 1) / nth : (OW + nth - 1) / nth; + const int tg_y = OH; + const int tg_z = use_tiled ? OW * N : C * N; + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), 2); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 3); + + ggml_metal_encoder_dispatch_threadgroups(enc, tg_x, tg_y, tg_z, nth, 1, 1); + + return 1; +} + int ggml_metal_op_conv_3d(ggml_metal_op_t ctx, int idx) { ggml_tensor * op = ctx->node(idx); diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h index 13f5274c7953..0bebd836a185 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.h +++ b/ggml/src/ggml-metal/ggml-metal-ops.h @@ -75,6 +75,7 @@ int ggml_metal_op_norm (ggml_metal_op_t ctx, int idx); int ggml_metal_op_rope (ggml_metal_op_t ctx, int idx); int ggml_metal_op_im2col (ggml_metal_op_t ctx, int idx); int ggml_metal_op_conv_2d (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_conv_2d_dw (ggml_metal_op_t ctx, int idx); int ggml_metal_op_conv_3d (ggml_metal_op_t ctx, int idx); int ggml_metal_op_conv_transpose_1d (ggml_metal_op_t ctx, int idx); int ggml_metal_op_conv_transpose_2d (ggml_metal_op_t ctx, int idx); diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index dcb6803f5f6a..6b6f9fd870c2 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -4908,6 +4908,202 @@ kernel void kernel_conv_2d( uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]); +// grid: x = C tile, y = OH, z = OW * N (for channel-contiguous layouts) +template +kernel void kernel_conv_2d_dw_tiled( + constant ggml_metal_kargs_conv_2d_dw & args, + device const char * weights, + device const char * src, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + const int32_t c = (int32_t)(tgpig.x * ntg.x + tpitg.x); + if (c >= args.C) { + return; + } + + const int32_t oh = tgpig.y; + const int32_t own = tgpig.z; + const int32_t ow = own % args.OW; + const int32_t n = own / args.OW; + + const int32_t base_y = oh*args.s1 - args.p1; + + int32_t ky_start = 0; + if (base_y < 0) { + ky_start = (-base_y + args.d1 - 1)/args.d1; + } + int32_t ky_end = args.KH; + const int32_t y_max = args.IH - 1 - base_y; + if (y_max < 0) { + ky_end = ky_start; + } else if (base_y + (args.KH - 1)*args.d1 >= args.IH) { + ky_end = min(ky_end, y_max/args.d1 + 1); + } + + const int32_t base_x = ow*args.s0 - args.p0; + + int32_t kx_start = 0; + if (base_x < 0) { + kx_start = (-base_x + args.d0 - 1)/args.d0; + } + int32_t kx_end = args.KW; + const int32_t x_max = args.IW - 1 - base_x; + if (x_max < 0) { + kx_end = kx_start; + } else if (base_x + (args.KW - 1)*args.d0 >= args.IW) { + kx_end = min(kx_end, x_max/args.d0 + 1); + } + + float acc = 0.0f; + + if (ky_start < ky_end && kx_start < kx_end) { + const uint64_t w_base = (uint64_t) c * args.nb02; + const uint64_t src_base = (uint64_t) n * args.nb13 + (uint64_t) c * args.nb12; + + for (int32_t ky = ky_start; ky < ky_end; ++ky) { + const int32_t iy = base_y + ky*args.d1; + const uint64_t src_row = src_base + (uint64_t) iy * args.nb11; + const uint64_t w_row = w_base + (uint64_t) ky * args.nb01; + + for (int32_t kx = kx_start; kx < kx_end; ++kx) { + const int32_t ix = base_x + kx*args.d0; + const float x = *(device const float *)(src + src_row + (uint64_t) ix * args.nb10); + const float w = (float)(*(device const TK *)(weights + w_row + (uint64_t) kx * args.nb00)); + acc += x * w; + } + } + } + + const uint64_t dst_offs = + (uint64_t) n * args.nb3 + + (uint64_t) c * args.nb2 + + (uint64_t) oh * args.nb1 + + (uint64_t) ow * args.nb0; + + *(device float *)(dst + dst_offs) = acc; +} + +// grid: x = OW tile, y = OH, z = C * N (for spatially-contiguous layouts) +template +kernel void kernel_conv_2d_dw( + constant ggml_metal_kargs_conv_2d_dw & args, + device const char * weights, + device const char * src, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + const int32_t oh = tgpig.y; + const int32_t cn = tgpig.z; + const int32_t c = cn % args.C; + const int32_t n = cn / args.C; + + const int32_t base_y = oh*args.s1 - args.p1; + + int32_t ky_start = 0; + if (base_y < 0) { + ky_start = (-base_y + args.d1 - 1)/args.d1; + } + int32_t ky_end = args.KH; + const int32_t y_max = args.IH - 1 - base_y; + if (y_max < 0) { + ky_end = ky_start; + } else if (base_y + (args.KH - 1)*args.d1 >= args.IH) { + ky_end = min(ky_end, y_max/args.d1 + 1); + } + + const uint64_t w_base = (uint64_t) c * args.nb02; + const uint64_t src_base = (uint64_t) n * args.nb13 + (uint64_t) c * args.nb12; + + const int32_t ow = (int32_t)(tgpig.x * ntg.x + tpitg.x); + if (ow >= args.OW) { + return; + } + + float acc = 0.0f; + + const int32_t base_x = ow*args.s0 - args.p0; + + int32_t kx_start = 0; + if (base_x < 0) { + kx_start = (-base_x + args.d0 - 1)/args.d0; + } + int32_t kx_end = args.KW; + const int32_t x_max = args.IW - 1 - base_x; + if (x_max < 0) { + kx_end = kx_start; + } else if (base_x + (args.KW - 1)*args.d0 >= args.IW) { + kx_end = min(kx_end, x_max/args.d0 + 1); + } + + if (ky_start < ky_end && kx_start < kx_end) { + for (int32_t ky = ky_start; ky < ky_end; ++ky) { + const int32_t iy = base_y + ky*args.d1; + const uint64_t src_row = src_base + (uint64_t) iy * args.nb11; + const uint64_t w_row = w_base + (uint64_t) ky * args.nb01; + + for (int32_t kx = kx_start; kx < kx_end; ++kx) { + const int32_t ix = base_x + kx*args.d0; + const float x = *(device const float *)(src + src_row + (uint64_t) ix * args.nb10); + const float w = (float)(*(device const TK *)(weights + w_row + (uint64_t) kx * args.nb00)); + acc += x * w; + } + } + } + + const uint64_t dst_offs = + (uint64_t) n * args.nb3 + + (uint64_t) c * args.nb2 + + (uint64_t) oh * args.nb1 + + (uint64_t) ow * args.nb0; + + *(device float *)(dst + dst_offs) = acc; +} + +template [[host_name("kernel_conv_2d_dw_f32_f32")]] +kernel void kernel_conv_2d_dw( + constant ggml_metal_kargs_conv_2d_dw & args, + device const char * weights, + device const char * src, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + +template [[host_name("kernel_conv_2d_dw_f16_f32")]] +kernel void kernel_conv_2d_dw( + constant ggml_metal_kargs_conv_2d_dw & args, + device const char * weights, + device const char * src, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + +template [[host_name("kernel_conv_2d_dw_tiled_f32_f32")]] +kernel void kernel_conv_2d_dw_tiled( + constant ggml_metal_kargs_conv_2d_dw & args, + device const char * weights, + device const char * src, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + +template [[host_name("kernel_conv_2d_dw_tiled_f16_f32")]] +kernel void kernel_conv_2d_dw_tiled( + constant ggml_metal_kargs_conv_2d_dw & args, + device const char * weights, + device const char * src, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + typedef void (conv_transpose_1d_t)( constant ggml_metal_kargs_conv_transpose_1d & args, device const float * src0, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 0c0aac2bba76..09dc4a3e6d18 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -5451,25 +5451,28 @@ struct test_conv_2d : public test_case { struct test_conv_2d_dw : public test_case { const std::array ne_input; const std::array ne_kernel; + const ggml_type type_kernel; const int stride; const int padding; const int dilation; const bool cwhn; std::string vars() override { - return VARS_TO_STR6(ne_input, ne_kernel, stride, padding, dilation, cwhn); + return VARS_TO_STR7(ne_input, ne_kernel, type_kernel, stride, padding, dilation, cwhn); } - test_conv_2d_dw(std::array ne_input = {64, 64, 16, 1}, + test_conv_2d_dw( + std::array ne_input = {64, 64, 16, 1}, std::array ne_kernel = {3, 3, 1, 16}, + ggml_type type_kernel = GGML_TYPE_F32, int stride = 1, int padding = 0, int dilation = 1, bool cwhn = false) - : ne_input(ne_input), ne_kernel(ne_kernel), stride(stride), padding(padding), dilation(dilation), cwhn(cwhn) {} + : ne_input(ne_input), ne_kernel(ne_kernel), type_kernel(type_kernel), stride(stride), padding(padding), dilation(dilation), cwhn(cwhn) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data()); ggml_set_name(input, "input"); - ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_kernel.data()); + ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data()); ggml_set_name(kernel, "kernel"); if (cwhn) { @@ -8114,10 +8117,15 @@ static std::vector> make_test_cases_eval() { // test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16, {1024, 1024, 256, 1}, {3, 3, 256, 1}, 1, 1, 1, 1, 1, 1, true)); // test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32, {1024, 1024, 256, 1}, {3, 3, 256, 1}, 1, 1, 1, 1, 1, 1, true)); - test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, 1, 0, 1, false)); - test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, 1, 0, 1, true)); - test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, 2, 1, 1, false)); - test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, 2, 1, 1, true)); + test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, GGML_TYPE_F32, 1, 0, 1, false)); + test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, GGML_TYPE_F32, 1, 0, 1, true)); + test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, GGML_TYPE_F32, 2, 1, 1, false)); + test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, GGML_TYPE_F32, 2, 1, 1, true)); + + test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, GGML_TYPE_F16, 1, 0, 1, false)); + test_cases.emplace_back(new test_conv_2d_dw({17, 34, 9, 1}, {3, 3, 1, 9}, GGML_TYPE_F16, 1, 0, 1, true)); + test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, GGML_TYPE_F16, 2, 1, 1, false)); + test_cases.emplace_back(new test_conv_2d_dw({32, 8, 64, 1}, {3, 3, 1, 64}, GGML_TYPE_F16, 2, 1, 1, true)); // CONV_3D auto calc_conv_output_size_3d = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t { @@ -9621,8 +9629,12 @@ static std::vector> make_test_cases_perf() { } } - test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, false)); - test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, true)); + test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, GGML_TYPE_F32, 1, 1, 1, false)); + test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, GGML_TYPE_F32, 1, 1, 1, true)); + test_cases.emplace_back(new test_conv_2d_dw({112, 112, 32, 1}, {3, 3, 1, 32}, GGML_TYPE_F32, 1, 1, 1, false)); + test_cases.emplace_back(new test_conv_2d_dw({112, 112, 32, 1}, {3, 3, 1, 32}, GGML_TYPE_F32, 1, 1, 1, true)); + test_cases.emplace_back(new test_conv_2d_dw({56, 56, 128, 1}, {5, 5, 1, 128}, GGML_TYPE_F32, 2, 2, 1, false)); + test_cases.emplace_back(new test_conv_2d_dw({56, 56, 128, 1}, {5, 5, 1, 128}, GGML_TYPE_F32, 2, 2, 1, true)); for (ggml_type kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) { test_cases.emplace_back(new test_conv_transpose_2d({256, 256, 256, 1}, {3, 3, 16, 256}, 1, kernel_type));