File size: 3,329 Bytes
61b850a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
#include "conv-transpose-1d.cuh"
static __global__ void conv_transpose_1d_kernel(
const int s0, const int p0, const int d0, const int output_size,
const int src0_ne0, const int src0_ne1, const int src0_ne2, const int src0_ne3,
const int src1_ne0, const int src1_ne1, const int src1_ne2, const int src1_ne3,
const int dst_ne0, const int dst_ne1, const int dst_ne2, const int dst_ne3,
const float * src0, const float * src1, float * dst) {
int global_index = threadIdx.x + blockIdx.x * blockDim.x;
if (global_index >= output_size) {
return;
}
int out_index = global_index / dst_ne0;
float accumulator = 0;
for (int c = 0; c < src0_ne2; c++) {
int idx = global_index % dst_ne0;
int kernel_offset = (src0_ne0 * src0_ne1 * c) + (out_index * src0_ne0);
int input_offset = src1_ne0 * c;
for (int i = 0; i < src1_ne0; i++) {
if (!(idx >= i*s0 && idx < i*s0 + src0_ne0)) {
continue;
}
int weight_idx = idx - i*s0;
float kernel_weight = src0[kernel_offset + weight_idx];
float input_value = src1[input_offset+i];
accumulator += kernel_weight * input_value;
}
}
dst[global_index] = accumulator;
}
static void conv_transpose_1d_f32_f32_cuda(
const int s0, const int p0, const int d0, const int output_size,
const int src0_ne0, const int src0_ne1, const int src0_ne2, const int src0_ne3,
const int src1_ne0, const int src1_ne1, const int src1_ne2, const int src1_ne3,
const int dst_ne0, const int dst_ne1, const int dst_ne2, const int dst_ne3,
const float * src0, const float * src1, float * dst,
cudaStream_t stream) {
const int num_blocks = (output_size + CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE - 1) / CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE;
conv_transpose_1d_kernel<<<num_blocks,CUDA_CONV_TRANPOSE_1D_BLOCK_SIZE, 0, stream>>>(
s0,p0,d0,output_size,
src0_ne0, src0_ne1, src0_ne2, src0_ne3,
src1_ne0, src1_ne1, src1_ne2, src1_ne3,
dst_ne0, dst_ne1, dst_ne2, dst_ne3,
src0,src1, dst);
}
void ggml_cuda_op_conv_transpose_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *)src0->data;
const ggml_tensor * src1 = dst->src[1];
const float * src1_d = (const float *)src1->data;
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
const int32_t * opts = (const int32_t *)dst->op_params;
const int s0 = opts[0];
const int p0 = 0;//opts[3];
const int d0 = 1;//opts[4];
const int64_t kernel_size = ggml_nelements(src0);
const int64_t input_size = ggml_nelements(src1);
const int64_t output_size = ggml_nelements(dst);
conv_transpose_1d_f32_f32_cuda(s0, p0, d0, output_size,
src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
src0_d, src1_d, dst_d, stream);
}
|