ggml_compute_forward_conv_2d_impl(params, src0, src1, dst, src0->type);
}
+// ggml_compute_forward_conv_3d
+
+static void ggml_compute_forward_conv_3d_impl(const ggml_compute_params * params,
+ const ggml_tensor * kernel,
+ const ggml_tensor * src,
+ ggml_tensor * dst,
+ 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 s0 = dst->op_params[0];
+ const int32_t s1 = dst->op_params[1];
+ const int32_t s2 = dst->op_params[2];
+ const int32_t p0 = dst->op_params[3];
+ const int32_t p1 = dst->op_params[4];
+ const int32_t p2 = dst->op_params[5];
+ const int32_t d0 = dst->op_params[6];
+ const int32_t d1 = dst->op_params[7];
+ const int32_t d2 = dst->op_params[8];
+ const int32_t c = dst->op_params[9];
+ const int32_t n = dst->op_params[10];
+ const int32_t oc = dst->op_params[11];
+
+ const int64_t src_w = src->ne[0];
+ const int64_t src_h = src->ne[1];
+ const int64_t src_d = src->ne[2];
+ const int64_t knl_w = kernel->ne[0];
+ const int64_t knl_h = kernel->ne[1];
+ const int64_t knl_d = kernel->ne[2];
+ const int64_t dst_w = dst->ne[0];
+ const int64_t dst_h = dst->ne[1];
+ const int64_t dst_d = dst->ne[2];
+
+ const float * src_data = (float *) src->data;
+ void * knl_data = kernel->data;
+ float * dst_data = (float *) dst->data;
+
+ const int64_t knl_n_per_channel = knl_w * knl_h * knl_d;
+ const int64_t knl_n_total = knl_n_per_channel * c;
+ const int64_t patch_total = n * dst_w * dst_h * dst_d;
+
+ const int64_t space_per_patch = knl_n_total * traits->type_size + oc * 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_in_batch = patch_end_batch - patch_start_batch;
+
+ const int64_t patch_per_thread = (patch_n_in_batch + 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);
+
+ for (int64_t p = patch_start; p < patch_end; ++p) {
+ const int64_t p_in_batch = p % (dst_w * dst_h * dst_d);
+ const int64_t p_in_depth = p_in_batch % (dst_w * dst_h);
+ const int64_t batch_idx = p / (dst_w * dst_h * dst_d);
+ const int64_t dst_z = p_in_batch / (dst_w * dst_h);
+ const int64_t dst_y = p_in_depth / dst_w;
+ const int64_t dst_x = p_in_depth % dst_w;
+
+ char * dst_row = (char *) tmp + (p % patches_per_batch) * knl_n_total * traits->type_size;
+
+ for (int64_t ic = 0; ic < c; ++ic) {
+ for (int64_t kz = 0; kz < knl_d; ++kz) {
+ for (int64_t ky = 0; ky < knl_h; ++ky) {
+ for (int64_t kx = 0; kx < knl_w; ++kx) {
+ const int64_t sz = dst_z * s2 + kz * d2 - p2;
+ const int64_t sy = dst_y * s1 + ky * d1 - p1;
+ const int64_t sx = dst_x * s0 + kx * d0 - p0;
+
+ int64_t dst_idx = ic * knl_n_per_channel + kz * (knl_h * knl_w) + ky * knl_w + kx;
+
+ float src_val;
+ if (sz < 0 || sz >= src_d || sy < 0 || sy >= src_h || sx < 0 || sx >= src_w) {
+ src_val = 0.0f;
+ } else {
+ const int64_t cn_idx = batch_idx * c + ic;
+ const float * src_ptr = (const float *)((const char *)src_data + sx*src->nb[0] + sy*src->nb[1] + sz*src->nb[2] + cn_idx*src->nb[3]);
+ 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);
+ }
+ }
+ }
+ }
+ }
+ }
+
+ ggml_barrier(params->threadpool);
+
+ float * gemm_output = (float *) ((char *) tmp + patches_per_batch * knl_n_total * traits->type_size);
+ ggml_call_mul_mat(kernel_type, params, patch_n_in_batch, oc, knl_n_total, tmp, knl_data, gemm_output);
+
+ ggml_barrier(params->threadpool);
+
+ const int64_t permute_per_thread = (patch_n_in_batch + 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_in_batch);
+
+ for (int64_t i = permute_start; i < permute_end; ++i) {
+ const int64_t p = patch_start_batch + i;
+ const int64_t p_in_batch = p % (dst_w * dst_h * dst_d);
+ const int64_t p_in_depth = p_in_batch % (dst_w * dst_h);
+ const int64_t batch_idx = p / (dst_w * dst_h * dst_d);
+ const int64_t dst_z = p_in_batch / (dst_w * dst_h);
+ const int64_t dst_y = p_in_depth / dst_w;
+ const int64_t dst_x = p_in_depth % dst_w;
+
+ for (int64_t ioc = 0; ioc < oc; ++ioc) {
+ const float value = gemm_output[i * oc + ioc];
+ const int64_t ocn_idx = batch_idx * oc + ioc;
+ float * dst_ptr = (float *)((char *)dst_data + dst_x*dst->nb[0] + dst_y*dst->nb[1] + dst_z*dst->nb[2] + ocn_idx*dst->nb[3]);
+ *dst_ptr = value;
+ }
+ }
+ }
+}
+
+void ggml_compute_forward_conv_3d(
+ 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_3d_impl(params, src0, src1, dst, src0->type);
+}
+
// ggml_compute_forward_conv_transpose_2d
void ggml_compute_forward_conv_transpose_2d(
"IM2COL",
"IM2COL_BACK",
"CONV_2D",
+ "CONV_3D",
"CONV_2D_DW",
"CONV_TRANSPOSE_2D",
"POOL_1D",
"GLU",
};
-static_assert(GGML_OP_COUNT == 88, "GGML_OP_COUNT != 88");
+static_assert(GGML_OP_COUNT == 89, "GGML_OP_COUNT != 89");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
"im2col(x)",
"im2col_back(x)",
"conv_2d(x)",
+ "conv_3d(x)",
"conv_2d_dw(x)",
"conv_transpose_2d(x)",
"pool_1d(x)",
"glu(x)",
};
-static_assert(GGML_OP_COUNT == 88, "GGML_OP_COUNT != 88");
+static_assert(GGML_OP_COUNT == 89, "GGML_OP_COUNT != 89");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
return result;
}
+// ggml_conv_3d
+
+struct ggml_tensor * ggml_conv_3d(
+ struct ggml_context * ctx,
+ struct ggml_tensor * a,
+ struct ggml_tensor * b,
+ int s0,
+ int s1,
+ int s2,
+ int p0,
+ int p1,
+ int p2,
+ int d0,
+ int d1,
+ int d2,
+ int c,
+ int n,
+ int oc) {
+
+ GGML_ASSERT(a->ne[3] == (int64_t) c * oc);
+ GGML_ASSERT(b->ne[3] == (int64_t) c * n);
+
+ 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] = ggml_calc_conv_output_size(b->ne[2], a->ne[2], s2, p2, d2);
+ ne[3] = (int64_t) oc * n;
+
+ struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 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, s2);
+ ggml_set_op_params_i32(result, 3, p0);
+ ggml_set_op_params_i32(result, 4, p1);
+ ggml_set_op_params_i32(result, 5, p2);
+ ggml_set_op_params_i32(result, 6, d0);
+ ggml_set_op_params_i32(result, 7, d1);
+ ggml_set_op_params_i32(result, 8, d2);
+ ggml_set_op_params_i32(result, 9, c);
+ ggml_set_op_params_i32(result, 10, n);
+ ggml_set_op_params_i32(result, 11, oc);
+
+ result->op = GGML_OP_CONV_3D;
+ 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) {
overview / pkg / ggml / sources / whisper.cpp / commitdiff