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CUDA: add conv_2d_transpose
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‎ggml/src/ggml-cuda/conv2d-transpose.cu

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#include <cuda_fp16.h>
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#include <algorithm>
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#include "conv2d-transpose.cuh"
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#include "ggml.h"
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__global__ void conv2d_transpose_kernel(const float * __restrict__ input, const half * __restrict__ kernel,
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float * __restrict__ output, const int in_w, const int in_h, const int out_w,
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const int out_h, const int kernel_w, const int kernel_h, const int stride,
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const int c_in, const int c_out, const int batches) {
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const int global_idx = blockIdx.x * blockDim.x + threadIdx.x;
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const int total_elements = out_w * out_h * c_out * batches;
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if (global_idx >= total_elements) {
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return;
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}
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const int out_x_idx = global_idx % out_w;
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const int out_y_idx = global_idx / out_w % out_h;
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const int c_idx = global_idx / (out_w * out_h) % c_out;
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const int n_idx = global_idx / (out_w * out_h * c_out);
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float accumulator = 0;
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// For each output idx, find the inputs that contribute to it by checking stride alignment and bounds
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for (int c_in_idx = 0; c_in_idx < c_in; c_in_idx++) {
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for (int kh = 0; kh < kernel_h; ++kh) {
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int in_y = out_y_idx - kh;
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if (in_y < 0 || in_y % stride) continue;
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in_y /= stride;
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if (in_y >= in_h) continue;
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for (int kw = 0; kw < kernel_w; ++kw) {
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int in_x = out_x_idx - kw;
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if (in_x < 0 || in_x % stride) continue;
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in_x /= stride;
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if (in_x >= in_w) continue;
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const int input_idx = (in_w * in_h * c_in) * n_idx + (in_w * in_h) * c_in_idx + (in_w) *in_y + in_x;
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const int kernel_idx =
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(kernel_h * kernel_w * c_out) * c_in_idx + (kernel_h * kernel_w) * c_idx + (kernel_w) *kh + kw;
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float input_val = input[input_idx];
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half kern_val = kernel[kernel_idx];
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accumulator += input_val * (float) kern_val;
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}
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}
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}
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output[(out_w * out_h * c_out) * n_idx + (out_w * out_h) * c_idx + (out_w) *out_y_idx + out_x_idx] = accumulator;
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}
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//input is (W, H, C_in, N), Kernel is (W, H, C_out, C_in)
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void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * kernel = dst->src[0];
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const ggml_tensor * input = dst->src[1];
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GGML_ASSERT(kernel->type == GGML_TYPE_F16 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
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const float * input_data = (const float *) input->data;
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float * output_data = (float *) dst->data;
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const half * kernel_data = (const half *) kernel->data;
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const int input_w = input->ne[0];
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const int input_h = input->ne[1];
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const int output_w = dst->ne[0];
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const int output_h = dst->ne[1];
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const int channels_in = input->ne[2];
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const int channels_out = kernel->ne[2];
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const int kernel_w = kernel->ne[0];
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const int kernel_h = kernel->ne[1];
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const int stride = dst->op_params[0];
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const int batches = input->ne[3];
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GGML_ASSERT(channels_in == kernel->ne[3]);
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GGML_ASSERT(stride > 0);
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cudaStream_t st = ctx.stream();
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const int total = (output_w * output_h * channels_out * batches);
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const int blocks = (total + CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE - 1) / CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE;
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conv2d_transpose_kernel<<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
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input_data, kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w, kernel_h, stride,
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channels_in, channels_out, batches);
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}

‎ggml/src/ggml-cuda/conv2d-transpose.cuh

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#include "common.cuh"
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#define CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE 256
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void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

‎ggml/src/ggml-cuda/ggml-cuda.cu

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@@ -11,6 +11,7 @@
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#include "ggml-cuda/clamp.cuh"
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#include "ggml-cuda/concat.cuh"
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#include "ggml-cuda/conv-transpose-1d.cuh"
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#include "ggml-cuda/conv2d-transpose.cuh"
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#include "ggml-cuda/convert.cuh"
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#include "ggml-cuda/count-equal.cuh"
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#include "ggml-cuda/cpy.cuh"
@@ -2310,6 +2311,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
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case GGML_OP_IM2COL:
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ggml_cuda_op_im2col(ctx, dst);
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break;
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case GGML_OP_CONV_TRANSPOSE_2D:
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ggml_cuda_conv_2d_transpose_p0(ctx, dst);
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break;
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case GGML_OP_CONV_TRANSPOSE_1D:
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ggml_cuda_op_conv_transpose_1d(ctx,dst);
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break;
@@ -3209,6 +3213,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
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return op->src[0]->nb[0] == ggml_type_size(op->src[0]->type) && ggml_is_contiguous_2(op->src[0]);
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}
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case GGML_OP_IM2COL:
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case GGML_OP_CONV_TRANSPOSE_2D:
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case GGML_OP_POOL_2D:
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case GGML_OP_SUM:
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case GGML_OP_SUM_ROWS:

‎tests/test-backend-ops.cpp

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@@ -560,6 +560,7 @@ struct test_case {
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}
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double err = nmse(f1.data(), f2.data(), f1.size());
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if (err > ud->max_err) {
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printf("[%s] NMSE = %.9f > %.9f ", ggml_op_desc(t1), err, ud->max_err);
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//for (int i = 0; i < (int) f1.size(); i++) {
@@ -2725,6 +2726,37 @@ struct test_conv_transpose_1d : public test_case {
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}
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};
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// GGML_OP_CONV_TRANSPOSE_2D
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struct test_conv_transpose_2d : public test_case {
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const std::array<int64_t, 4> ne_input;
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const std::array<int64_t, 4> ne_kernel;
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const int stride;
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std::string vars() override {
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return VARS_TO_STR3(ne_input, ne_kernel, stride);
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}
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test_conv_transpose_2d(std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
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std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
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int stride = 1)
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: ne_input(ne_input), ne_kernel(ne_kernel), stride(stride){}
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ggml_tensor * build_graph(ggml_context * ctx) override {
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ggml_tensor * input = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne_input.data());
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ggml_set_param(input);
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ggml_set_name(input, "input");
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ggml_tensor * kernel = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne_kernel.data());
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ggml_set_param(kernel);
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ggml_set_name(kernel, "kernel");
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ggml_tensor * out = ggml_conv_transpose_2d_p0(ctx, kernel, input, stride);
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ggml_set_name(out, "out");
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return out;
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}
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};
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// GGML_OP_IM2COL
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struct test_im2col : public test_case {
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const ggml_type type_input;
@@ -4050,6 +4082,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
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test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1));
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test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1));
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test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1));
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test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2));
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test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 500, 1, 1}));
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test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 5000, 1, 1}));
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@@ -4618,6 +4653,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
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test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, false));
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test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, true));
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test_cases.emplace_back(new test_conv_transpose_2d({256, 256, 256, 1}, {3, 3, 16, 256}, 1));
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return test_cases;
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}
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