* Refactor CUDA 2D transpose implementation to support multiple kernel types and improve parameter handling
- Introduced a `conv2d_transpose_params` struct for better parameter management.
- Updated `conv2d_transpose_kernel` to be templated for different kernel types (float and half).
- Modified `ggml_cuda_conv_2d_transpose_p0` to handle both F16 and F32 kernel types.
- Enhanced test cases to validate functionality for both kernel types.
* Refactor test cases for 2D convolution transpose to support dynamic kernel types
- Updated `test_conv_transpose_2d` structure to improve parameter handling by reordering constructor arguments.
- Enhanced test case generation to iterate over kernel types, allowing for flexible testing of different configurations.
- Removed hardcoded kernel type instances in favor of a loop for better maintainability and scalability.
* Refactor ggml_compute_forward_conv_transpose_2d to support both F16 and F32 tensor types.
* Refactor conv2d transpose kernel to use a template for kernel type, enhancing flexibility for different data types.
Update test cases to include both F16 and F32 tensor types for comprehensive coverage.
* Update ggml/src/ggml-cuda/conv2d-transpose.cu
Co-authored-by: Aman Gupta <redacted>
* Update ggml/src/ggml-cpu/ggml-cpu.c
Co-authored-by: Aman Gupta <redacted>
* Refactor conv2d transpose implementation by removing the conv2d_transpose_params struct and dispatching with direct kernel launch.
* Enhance cpu conv2d transpose implementation by introducing a templated kernel type for improved flexibility with F16 and F32 data types.
---------
Co-authored-by: Aman Gupta <redacted>
const int64_t ne11 = node->src[1]->ne[1]; // H
const int64_t ne12 = node->src[1]->ne[2]; // Channels In
- cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03;
- cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12;
+ GGML_ASSERT(node->src[0]->type == GGML_TYPE_F16 || node->src[0]->type == GGML_TYPE_F32);
+ GGML_ASSERT(node->src[1]->type == GGML_TYPE_F32);
+
+ cur += ggml_type_size(node->src[0]->type) * ne00 * ne01 * ne02 * ne03;
+ cur += ggml_type_size(node->src[0]->type) * ne10 * ne11 * ne12;
+
} break;
case GGML_OP_TOP_K:
{
ggml_compute_forward_conv_3d_impl(params, src0, src1, dst, src0->type);
}
-// ggml_compute_forward_conv_transpose_2d
-
-void ggml_compute_forward_conv_transpose_2d(
- const ggml_compute_params * params,
- ggml_tensor * dst) {
+template <typename kernel_t>
+static void ggml_compute_forward_conv_transpose_2d_impl(
+ const ggml_compute_params * params,
+ ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
- GGML_ASSERT(src0->type == GGML_TYPE_F16);
+ GGML_ASSERT(src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
const int nk = ne00*ne01*ne02*ne03;
- GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
+ GGML_ASSERT(nb00 == ggml_type_size(src0->type));
GGML_ASSERT(nb10 == sizeof(float));
if (ith == 0) {
// permute kernel data (src0) from (Kw x Kh x Cout x Cin) to (Cin x Kw x Kh x Cout)
{
- ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
+ kernel_t * const wdata = (kernel_t *) params->wdata + 0;
for (int64_t i03 = 0; i03 < ne03; i03++) {
for (int64_t i02 = 0; i02 < ne02; i02++) {
- const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i03*nb03 + i02*nb02);
- ggml_fp16_t * dst_data = wdata + i02*ne01*ne00*ne03;
+ const kernel_t * const src = (kernel_t *)((char *) src0->data + i03*nb03 + i02*nb02);
+ kernel_t * dst_data = wdata + i02*ne01*ne00*ne03;
for (int64_t i01 = 0; i01 < ne01; i01++) {
for (int64_t i00 = 0; i00 < ne00; i00++) {
dst_data[i01*ne00*ne03 + i00*ne03 + i03] = src[i01 * ne00 + i00];
// permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh)
{
- ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + nk;
+ kernel_t * const wdata = (kernel_t *) params->wdata + nk;
for (int i12 = 0; i12 < ne12; i12++) {
for (int i11 = 0; i11 < ne11; i11++) {
const float * const src = (float *)((char *) src1->data + i12*nb12 + i11*nb11);
- ggml_fp16_t * dst_data = wdata + i11*ne10*ne12;
+ kernel_t * dst_data = wdata + i11*ne10*ne12;
for (int i10 = 0; i10 < ne10; i10++) {
- dst_data[i10*ne12 + i12] = GGML_CPU_FP32_TO_FP16(src[i10]);
+ if constexpr (std::is_same_v<kernel_t, ggml_fp16_t>) {
+ dst_data[i10*ne12 + i12] = GGML_CPU_FP32_TO_FP16(src[i10]);
+ } else {
+ dst_data[i10*ne12 + i12] = src[i10];
+ }
}
}
}
const int ip0 = dp*ith;
const int ip1 = MIN(ip0 + dp, np);
- ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0;
- ggml_fp16_t * const wdata_src = wdata + nk;
+ kernel_t * const wdata = (kernel_t *) params->wdata + 0;
+ kernel_t * const wdata_src = wdata + nk;
for (int i2 = ip0; i2 < ip1; i2++) { // Cout
float * dst_data = (float *)((char *) dst->data + i2*nb2);
- ggml_fp16_t * wdata_kernel = wdata + i2*ne01*ne00*ne03;
+ kernel_t * wdata_kernel = wdata + i2*ne01*ne00*ne03;
for (int i11 = 0; i11 < ne11; i11++) {
for (int i10 = 0; i10 < ne10; i10++) {
const int i1n = i11*ne10*ne12 + i10*ne12;
for (int i01 = 0; i01 < ne01; i01++) {
for (int i00 = 0; i00 < ne00; i00++) {
float v = 0;
- ggml_vec_dot_f16(ne03, &v, 0,
- wdata_src + i1n, 0,
- wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
+ if constexpr (std::is_same_v<kernel_t, ggml_fp16_t>) {
+ ggml_vec_dot_f16(ne03, &v, 0,
+ wdata_src + i1n, 0,
+ wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
+ } else {
+ ggml_vec_dot_f32(ne03, &v, 0,
+ wdata_src + i1n, 0,
+ wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
+ }
dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v;
}
}
}
}
+void ggml_compute_forward_conv_transpose_2d(
+ const ggml_compute_params * params,
+ ggml_tensor * dst) {
+
+ const ggml_tensor * src0 = dst->src[0];
+
+ switch (src0->type) {
+ case GGML_TYPE_F16:
+ {
+ ggml_compute_forward_conv_transpose_2d_impl<ggml_fp16_t>(params, dst);
+ } break;
+ case GGML_TYPE_F32:
+ {
+ ggml_compute_forward_conv_transpose_2d_impl<float>(params, dst);
+ } break;
+ default:
+ {
+ GGML_ABORT("fatal error");
+ }
+ }
+}
+
// ggml_compute_forward_conv_2d_dw
struct ggml_conv_2d_dw_params {
-#include <algorithm>
-
#include "conv2d-transpose.cuh"
-#include "ggml.h"
-
-__global__ void conv2d_transpose_kernel(const float * __restrict__ input, const half * __restrict__ kernel,
- float * __restrict__ output, const int in_w, const int in_h, const int out_w,
- const int out_h, const int kernel_w, const int kernel_h, const int stride,
- const int c_in, const int c_out, const int batches) {
+#include "convert.cuh"
+
+template <typename kernel_t>
+static __global__ void conv2d_transpose_kernel(const float * __restrict__ input,
+ const kernel_t * __restrict__ kernel,
+ float * __restrict__ output,
+ const int in_w,
+ const int in_h,
+ const int out_w,
+ const int out_h,
+ const int kernel_w,
+ const int kernel_h,
+ const int stride,
+ const int c_in,
+ const int c_out,
+ const int batches) {
const int global_idx = blockIdx.x * blockDim.x + threadIdx.x;
const int total_elements = out_w * out_h * c_out * batches;
for (int c_in_idx = 0; c_in_idx < c_in; c_in_idx++) {
for (int kh = 0; kh < kernel_h; ++kh) {
int in_y = out_y_idx - kh;
- if (in_y < 0 || in_y % stride) continue;
+ if (in_y < 0 || in_y % stride) {
+ continue;
+ }
in_y /= stride;
- if (in_y >= in_h) continue;
+ if (in_y >= in_h) {
+ continue;
+ }
for (int kw = 0; kw < kernel_w; ++kw) {
int in_x = out_x_idx - kw;
- if (in_x < 0 || in_x % stride) continue;
+ if (in_x < 0 || in_x % stride) {
+ continue;
+ }
in_x /= stride;
- if (in_x >= in_w) continue;
+ if (in_x >= in_w) {
+ continue;
+ }
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;
const int kernel_idx =
(kernel_h * kernel_w * c_out) * c_in_idx + (kernel_h * kernel_w) * c_idx + (kernel_w) *kh + kw;
- float input_val = input[input_idx];
- half kern_val = kernel[kernel_idx];
+ float input_val = input[input_idx];
+ kernel_t kern_val = kernel[kernel_idx];
- accumulator += input_val * (float) kern_val;
+ accumulator += input_val * ggml_cuda_cast<float>(kern_val);
}
}
}
const ggml_tensor * kernel = dst->src[0];
const ggml_tensor * input = dst->src[1];
- GGML_ASSERT(kernel->type == GGML_TYPE_F16 && input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+ GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);
+ GGML_ASSERT(input->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
const float * input_data = (const float *) input->data;
float * output_data = (float *) dst->data;
- const half * kernel_data = (const half *) kernel->data;
+ const void * kernel_data = kernel->data;
const int input_w = input->ne[0];
const int input_h = input->ne[1];
GGML_ASSERT(ggml_is_contiguous(kernel));
GGML_ASSERT(ggml_is_contiguous(dst));
- const int total = (output_w * output_h * channels_out * batches);
+ const int total = output_w * output_h * channels_out * batches;
const int blocks = (total + CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE - 1) / CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE;
- conv2d_transpose_kernel<<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
- input_data, kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w, kernel_h, stride,
- channels_in, channels_out, batches);
+ if (kernel->type == GGML_TYPE_F16) {
+ conv2d_transpose_kernel<half><<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
+ input_data, (const half *) kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w,
+ kernel_h, stride, channels_in, channels_out, batches);
+
+ } else {
+ conv2d_transpose_kernel<float><<<blocks, CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE, 0, st>>>(
+ input_data, (const float *) kernel_data, output_data, input_w, input_h, output_w, output_h, kernel_w,
+ kernel_h, stride, channels_in, channels_out, batches);
+ }
}
#include "common.cuh"
#define CUDA_CONV2D_TRANSPOSE_BLOCK_SIZE 256
+
void ggml_cuda_conv_2d_transpose_p0(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
// GGML_OP_CONV_TRANSPOSE_2D
struct test_conv_transpose_2d : public test_case {
+ // Dimensions
const std::array<int64_t, 4> ne_input;
const std::array<int64_t, 4> ne_kernel;
const int stride;
+ // Types
+ const ggml_type kernel_type;
std::string vars() override {
- return VARS_TO_STR3(ne_input, ne_kernel, stride);
+ return VARS_TO_STR4(kernel_type, ne_input, ne_kernel, stride);
}
double max_nmse_err() override {
return 5e-4; // The default 1e-7 is too small for Vulkan.
}
- test_conv_transpose_2d(std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
- std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
- int stride = 1)
- : ne_input(ne_input), ne_kernel(ne_kernel), stride(stride){}
+ test_conv_transpose_2d(
+ std::array<int64_t, 4> ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1]
+ std::array<int64_t, 4> ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1]
+ int stride = 1,
+ ggml_type kernel_type = GGML_TYPE_F16
+ ) : ne_input(ne_input), ne_kernel(ne_kernel), stride(stride), kernel_type(kernel_type) {}
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_F16, 4, ne_kernel.data());
+ ggml_tensor * kernel = ggml_new_tensor(ctx, kernel_type, 4, ne_kernel.data());
ggml_set_name(kernel, "kernel");
ggml_tensor * out = ggml_conv_transpose_2d_p0(ctx, kernel, input, stride);
test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1));
test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1));
- test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1));
- test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2));
- test_cases.emplace_back(new test_conv_transpose_2d({129, 63, 35, 1}, {3, 3, 48, 35}, 1));
+ for (ggml_type kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
+ test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1, kernel_type));
+ test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2, kernel_type));
+ test_cases.emplace_back(new test_conv_transpose_2d({129, 63, 35, 1}, {3, 3, 48, 35}, 1, kernel_type));
+ }
test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 500, 1, 1}));
test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 5000, 1, 1}));
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_transpose_2d({256, 256, 256, 1}, {3, 3, 16, 256}, 1));
- test_cases.emplace_back(new test_conv_transpose_2d({16, 16, 16, 1}, {3, 3, 8, 16}, 1));
- test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2));
+ 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));
+ test_cases.emplace_back(new test_conv_transpose_2d({16, 16, 16, 1}, {3, 3, 8, 16}, 1, kernel_type));
+ test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2, kernel_type));
+ }
test_cases.emplace_back(new test_mean(GGML_TYPE_F32, {256, 256, 3, 1}));
overview / pkg / ggml / sources / ggml / commitdiff