Lumpiasty/llama.cpp
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ggml : fix conv_transpose_2d for multiple batches (#26132)

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* ggml : fix conv_transpose_2d for multiple batches

ggml_compute_forward_conv_transpose_2d_impl only computed the first
batch (ne[3] of the destination); every batch after the first was left
as zero. Both the src1 permutation and the main compute loop now iterate
over the batch dimension, and the work buffer size in ggml_graph_plan is
scaled by the src1 batch count so the extra permuted batches fit. A
multi-batch test case is added to test-backend-ops.

Fixes ggml-org/ggml#1448

* metal : fix conv_transpose_2d for multiple batches

The kernel only computed batch 0 of the input (src1->ne[3]); every
output batch after the first was left as zero, so multi-batch
conv_transpose_2d results diverged from the CPU reference.

The grid now covers all batches (OW x OH x OC x N), the kernel decodes
the batch from the grid z coordinate and offsets both the input and
destination indices accordingly. nb3 is passed in the kernel args.

Assisted-by: pi:llama.cpp/Qwen3.8-27B

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Tekin Ertekin
2026-08-28 20:09:08 +03:00
committed by GitHub
co-authored by Georgi Gerganov
parent 90c26fcd4b
commit a43c3986b4
6 changed files with 43 additions and 31 deletions
Download Patch File Download Diff File Expand all files Collapse all files
ggml/src/ggml-cpu/ggml-cpu.c
+2 -1
View File
@@ -2936,12 +2936,13 @@ struct ggml_cplan ggml_graph_plan(
const int64_t ne10 = node->src[1]->ne[0]; // W
const int64_t ne11 = node->src[1]->ne[1]; // H
const int64_t ne12 = node->src[1]->ne[2]; // Channels In
const int64_t ne13 = node->src[1]->ne[3]; // Batch
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;
cur += ggml_type_size(node->src[0]->type) * ne10 * ne11 * ne12 * ne13;
} break;
case GGML_OP_TOP_K:
ggml/src/ggml-cpu/ops.cpp
+32 -26
View File
@@ -7267,18 +7267,21 @@ static void ggml_compute_forward_conv_transpose_2d_impl(
}
}
// permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh)
// permute source data (src1) from (Sw x Sh x Cin) to (Cin x Sw x Sh), for all batches
{
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);
kernel_t * dst_data = wdata + i11*ne10*ne12;
for (int i10 = 0; i10 < ne10; 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];
for (int i13 = 0; i13 < ne13; i13++) {
kernel_t * const wdata_b = wdata + i13*ne10*ne11*ne12;
for (int i12 = 0; i12 < ne12; i12++) {
for (int i11 = 0; i11 < ne11; i11++) {
const float * const src = (float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
kernel_t * dst_data = wdata_b + i11*ne10*ne12;
for (int i10 = 0; i10 < ne10; 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];
}
}
}
}
@@ -7305,24 +7308,27 @@ static void ggml_compute_forward_conv_transpose_2d_impl(
kernel_t * const wdata_src = wdata + nk;
for (int i2 = ip0; i2 < ip1; i2++) { // Cout
float * dst_data = (float *)((char *) dst->data + i2*nb2);
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;
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);
for (int i3 = 0; i3 < ne3; i3++) { // batch
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2);
kernel_t * wdata_src_b = wdata_src + i3*ne10*ne11*ne12;
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;
if constexpr (std::is_same_v<kernel_t, ggml_fp16_t>) {
ggml_vec_dot_f16(ne03, &v, 0,
wdata_src_b + i1n, 0,
wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
} else {
ggml_vec_dot_f32(ne03, &v, 0,
wdata_src_b + i1n, 0,
wdata_kernel + i01*ne00*ne03 + i00*ne03, 0, 1);
}
dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v;
}
dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v;
}
}
}