#include "ggml-cpu.h"
#include "ggml-impl.h"
#include "binary-ops.h"
+#include "ggml.h"
#include "unary-ops.h"
#include "vec.h"
}
}
+static void ggml_call_mul_mat(ggml_type type, const ggml_compute_params * params, int64_t m, int64_t n, int64_t k,
+ void * a, void * b, float * c) {
+ const ggml_type_traits * traits = ggml_get_type_traits(type);
+ struct ggml_tensor src1 = {};
+ src1.type = type;
+ src1.ne[0] = k;
+ src1.ne[1] = m;
+ src1.ne[2] = 1;
+ src1.ne[3] = 1;
+ src1.nb[0] = traits->type_size;
+ src1.nb[1] = k * traits->type_size;
+ src1.nb[2] = src1.nb[1];
+ src1.nb[3] = src1.nb[2];
+ src1.data = a;
+
+ struct ggml_tensor src0 = {};
+ src0.type = type;
+ src0.ne[0] = k;
+ src0.ne[1] = n;
+ src0.ne[2] = 1;
+ src0.ne[3] = 1;
+ src0.nb[0] = traits->type_size;
+ src0.nb[1] = k * traits->type_size;
+ src0.nb[2] = src0.nb[1];
+ src0.nb[3] = src0.nb[2];
+ src0.data = b;
+
+ struct ggml_tensor dst = {};
+ dst.ne[0] = n;
+ dst.ne[1] = m;
+ dst.ne[2] = 1;
+ dst.ne[3] = 1;
+ dst.nb[0] = sizeof(float);
+ dst.nb[1] = n * sizeof(float);
+ dst.nb[2] = dst.nb[1];
+ dst.nb[3] = dst.nb[2];
+ dst.data = c;
+ dst.src[0] = &src0;
+ dst.src[1] = &src1;
+
+ ggml_compute_forward_mul_mat(params, &dst);
+}
+
+// ggml_compute_forward_conv_2d
+
+static void ggml_compute_forward_conv_2d_impl(const ggml_compute_params * params,
+ const ggml_tensor * kernel, // [KW, KH, IC, OC]
+ const ggml_tensor * src, // [W, H, C, N]
+ ggml_tensor * dst, // [OW, OH, OC, N]
+ ggml_type kernel_type) {
+
+ GGML_ASSERT(ggml_is_contiguous(kernel));
+ GGML_ASSERT(kernel_type == GGML_TYPE_F16 || kernel_type == GGML_TYPE_F32);
+ GGML_ASSERT(kernel->type == kernel_type);
+
+ const ggml_type_traits * traits = ggml_get_type_traits(kernel_type);
+
+ const int32_t stride_x = dst->op_params[0];
+ const int32_t stride_y = dst->op_params[1];
+ const int32_t pad_x = dst->op_params[2];
+ const int32_t pad_y = dst->op_params[3];
+ const int32_t dilation_x = dst->op_params[4];
+ const int32_t dilation_y = dst->op_params[5];
+
+ const int64_t c_in = src->ne[2];
+ const int64_t c_out = kernel->ne[3];
+ GGML_ASSERT(c_in == kernel->ne[2]);
+
+ const int64_t src_w = src->ne[0];
+ const int64_t src_h = src->ne[1];
+ const int64_t knl_w = kernel->ne[0];
+ const int64_t knl_h = kernel->ne[1];
+ const int64_t dst_w = dst->ne[0];
+ const int64_t dst_h = dst->ne[1];
+
+ const float * src_data = (float *) src->data;
+ void * knl_data = kernel->data;
+ float * dst_data = (float *) dst->data;
+
+ const int64_t knl_n = knl_w * knl_h * c_in;
+ const int64_t patch_total = dst->ne[3] * dst_w * dst_h;
+
+ const int64_t space_per_patch = knl_n * traits->type_size + c_out * sizeof(float);
+ const int64_t batch_size = params->wsize / space_per_patch;
+ const int64_t patches_per_batch = batch_size > 8 ? (batch_size / 8) * 8 : batch_size;
+ const int64_t batch_n = (patch_total + patches_per_batch - 1) / patches_per_batch;
+
+ GGML_ASSERT(patches_per_batch > 0 && batch_size >= 1);
+
+ void * tmp = params->wdata;
+
+ for (int64_t batch_i = 0; batch_i < batch_n; ++batch_i) {
+
+ const int64_t patch_start_batch = batch_i * patches_per_batch;
+ const int64_t patch_end_batch = std::min(patch_start_batch + patches_per_batch,
+ patch_total);
+ const int64_t patch_n = patch_end_batch - patch_start_batch;
+
+ const int64_t patch_per_thread = (patch_n + params->nth - 1) / params->nth;
+ const int64_t patch_start = patch_start_batch + params->ith * patch_per_thread;
+ const int64_t patch_end = std::min(patch_start + patch_per_thread, patch_end_batch);
+
+ //im2col for a patch
+ for (int64_t p = patch_start; p < patch_end; ++p) {
+ const int64_t batch_n = p / (dst_w * dst_h);
+ const int64_t src_x = (p / dst_w) % dst_h;
+ const int64_t src_y = p % dst_w;
+
+ const float * src_base = (const float *)((const char *)src_data + batch_n * src->nb[3]);
+ char * dst_row = (char *) tmp + (p % patches_per_batch) * knl_n * traits->type_size;
+
+ for (int64_t ic = 0; ic < c_in; ++ic) {
+ for (int64_t ky = 0; ky < knl_h; ++ky) {
+ for (int64_t kx = 0; kx < knl_w; ++kx) {
+ const int64_t sy = src_x * stride_y + ky * dilation_y - pad_y;
+ const int64_t sx = src_y * stride_x + kx * dilation_x - pad_x;
+
+ int64_t dst_idx = ic * (knl_h * knl_w) + ky * knl_w + kx;
+
+ float src_val;
+ if (sy < 0 || sy >= src_h || sx < 0 || sx >= src_w) {
+ src_val = 0.0f;
+ } else {
+ const float * src_ptr = (const float *)((const char *)src_base + sx * src->nb[0] + sy * src->nb[1] + ic * src->nb[2]);
+ src_val = *src_ptr;
+ }
+
+ char * element_ptr = dst_row + dst_idx * traits->type_size;
+ if (kernel_type == GGML_TYPE_F32) {
+ *(float *) element_ptr = src_val;
+ } else if (kernel_type == GGML_TYPE_F16) {
+ *(ggml_fp16_t *) element_ptr = GGML_CPU_FP32_TO_FP16(src_val);
+ }
+ }
+ }
+ }
+ } // patches handled by this thread
+
+ ggml_barrier(params->threadpool);
+
+ float * gemm_output = (float *) ((char *) tmp + patches_per_batch * knl_n * traits->type_size);
+
+ GGML_ASSERT(gemm_output + patch_n * c_out <= (float*)tmp + params->wsize);
+
+ // GEMM: patches[patch_n, knl_n] × kernel[knl_n, c_out] = output[patch_n, c_out]
+ ggml_call_mul_mat(kernel_type, params, patch_n, c_out, knl_n, tmp, knl_data, gemm_output);
+
+ ggml_barrier(params->threadpool);
+
+
+ //permute back [OC, N, OH, OW] to [N, OC, OH, OW]
+ const int64_t permute_per_thread = (patch_n + params->nth - 1) / params->nth;
+ const int64_t permute_start = params->ith * permute_per_thread;
+ const int64_t permute_end = std::min(permute_start + permute_per_thread, patch_n);
+
+ for (int64_t i = permute_start; i < permute_end; ++i) {
+ const int64_t p = patch_start_batch + i;
+ const int64_t batch_n = p / (dst_w * dst_h);
+ const int64_t dst_y = (p / dst_w) % dst_h;
+ const int64_t dst_x = p % dst_w;
+
+ for (int64_t oc = 0; oc < c_out; ++oc) {
+ const float value = gemm_output[i * c_out + oc];
+ float * dst_ptr = (float *)((char *)dst_data + dst_x * dst->nb[0] + dst_y * dst->nb[1] + oc * dst->nb[2] + batch_n * dst->nb[3]);
+ *dst_ptr = value;
+ }
+ }
+ }
+}
+
+void ggml_compute_forward_conv_2d(
+ const ggml_compute_params * params,
+ ggml_tensor * dst) {
+
+ const ggml_tensor * src0 = dst->src[0];
+ const ggml_tensor * src1 = dst->src[1];
+
+ ggml_compute_forward_conv_2d_impl(params, src0, src1, dst, src0->type);
+}
+
// ggml_compute_forward_conv_transpose_2d
void ggml_compute_forward_conv_transpose_2d(
"CONV_TRANSPOSE_1D",
"IM2COL",
"IM2COL_BACK",
+ "CONV_2D",
"CONV_2D_DW",
"CONV_TRANSPOSE_2D",
"POOL_1D",
"GLU",
};
-static_assert(GGML_OP_COUNT == 85, "GGML_OP_COUNT != 85");
+static_assert(GGML_OP_COUNT == 86, "GGML_OP_COUNT != 86");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
"conv_transpose_1d(x)",
"im2col(x)",
"im2col_back(x)",
+ "conv_2d(x)",
"conv_2d_dw(x)",
"conv_transpose_2d(x)",
"pool_1d(x)",
"glu(x)",
};
-static_assert(GGML_OP_COUNT == 85, "GGML_OP_COUNT != 85");
+static_assert(GGML_OP_COUNT == 86, "GGML_OP_COUNT != 86");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
return result;
}
+// ggml_conv_2d_direct
+
+struct ggml_tensor * ggml_conv_2d_direct(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a, // convolution kernel [KW, KH, IC, OC]
+ struct ggml_tensor * b, // input data [W, H, C, N]
+ int s0, // stride dimension 0
+ int s1, // stride dimension 1
+ int p0, // padding dimension 0
+ int p1, // padding dimension 1
+ int d0, // dilation dimension 0
+ int d1) {// dilation dimension 1
+
+ GGML_ASSERT(a->ne[2] == b->ne[2]);
+ //GGML_ASSERT(a->type == b->type);
+
+ int64_t ne[4];
+ ne[0] = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0);
+ ne[1] = ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1);
+ ne[2] = a->ne[3];
+ ne[3] = b->ne[3];
+
+ struct ggml_tensor * result = ggml_new_tensor(ctx, b->type, 4, ne);
+
+ ggml_set_op_params_i32(result, 0, s0);
+ ggml_set_op_params_i32(result, 1, s1);
+ ggml_set_op_params_i32(result, 2, p0);
+ ggml_set_op_params_i32(result, 3, p1);
+ ggml_set_op_params_i32(result, 4, d0);
+ ggml_set_op_params_i32(result, 5, d1);
+
+ result->op = GGML_OP_CONV_2D;
+ result->src[0] = a;
+ result->src[1] = b;
+
+ return result;
+}
+
// ggml_conv_transpose_2d_p0
static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) {
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