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ggml-cpu : "align corners" for bilinear upscale/downscale (ggml/1285)

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* add "align corners" mode for bilinear upscale, and allow downscaling
* add ggml_interpolate, deprecate ggml_upscale_ext, pass in align-corners as bit-flag
* test-backend-ops: replace ggml_upscale_ext with ggml_interpolate, add test cases for downscale and align-corners
This commit is contained in:
Acly
2025-07-01 09:11:00 +02:00
committed by Georgi Gerganov
parent 497be7c01d
commit 431b2c24f3
4 changed files with 65 additions and 31 deletions
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22
ggml/include/ggml.h
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@ -1867,6 +1867,12 @@ extern "C" {
enum ggml_scale_mode {
GGML_SCALE_MODE_NEAREST = 0,
GGML_SCALE_MODE_BILINEAR = 1,
GGML_SCALE_MODE_COUNT
};
enum ggml_scale_flag {
GGML_SCALE_FLAG_ALIGN_CORNERS = (1 << 8)
};
// interpolate
@ -1879,14 +1885,26 @@ extern "C" {
// interpolate
// interpolate scale to specified dimensions
GGML_API struct ggml_tensor * ggml_upscale_ext(
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_upscale_ext(
struct ggml_context * ctx,
struct ggml_tensor * a,
int ne0,
int ne1,
int ne2,
int ne3,
enum ggml_scale_mode mode);
enum ggml_scale_mode mode),
"use ggml_interpolate instead");
// Up- or downsamples the input to the specified size.
// 2D scale modes (eg. bilinear) are applied to the first two dimensions.
GGML_API struct ggml_tensor * ggml_interpolate(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3,
uint32_t mode); // ggml_scale_mode [ | ggml_scale_flag...]
// pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0]
GGML_API struct ggml_tensor * ggml_pad(

19
ggml/src/ggml-cpu/ops.cpp
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@ -7276,12 +7276,13 @@ static void ggml_compute_forward_upscale_f32(
GGML_TENSOR_UNARY_OP_LOCALS
const float sf0 = (float)ne0/src0->ne[0];
const float sf1 = (float)ne1/src0->ne[1];
const float sf2 = (float)ne2/src0->ne[2];
const float sf3 = (float)ne3/src0->ne[3];
float sf0 = (float)ne0/src0->ne[0];
float sf1 = (float)ne1/src0->ne[1];
float sf2 = (float)ne2/src0->ne[2];
float sf3 = (float)ne3/src0->ne[3];
const ggml_scale_mode mode = (ggml_scale_mode) ggml_get_op_params_i32(dst, 0);
const int32_t mode_flags = ggml_get_op_params_i32(dst, 0);
const ggml_scale_mode mode = (ggml_scale_mode) (mode_flags & 0xFF);
if (mode == GGML_SCALE_MODE_NEAREST) {
for (int64_t i3 = 0; i3 < ne3; i3++) {
@ -7302,8 +7303,12 @@ static void ggml_compute_forward_upscale_f32(
}
}
} else if (mode == GGML_SCALE_MODE_BILINEAR) {
// setting a pixel offset of 0 would replicate the behavior of pytorch interpolate with align_corners=True
const float pixel_offset = 0.5f;
float pixel_offset = 0.5f;
if (mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) {
pixel_offset = 0.0f;
sf0 = (float)(ne0 - 1) / (src0->ne[0] - 1);
sf1 = (float)(ne1 - 1) / (src0->ne[1] - 1);
}
for (int64_t i3 = 0; i3 < ne3; i3++) {
const int64_t i03 = i3 / sf3;

39
ggml/src/ggml.c
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@ -4447,24 +4447,21 @@ struct ggml_tensor * ggml_pool_2d_back(
return result;
}
// ggml_upscale
// ggml_upscale / ggml_interpolate
static struct ggml_tensor * ggml_upscale_impl(
static struct ggml_tensor * ggml_interpolate_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
int ne0,
int ne1,
int ne2,
int ne3,
enum ggml_scale_mode mode) {
GGML_ASSERT(a->ne[0] <= ne0);
GGML_ASSERT(a->ne[1] <= ne1);
GGML_ASSERT(a->ne[2] <= ne2);
GGML_ASSERT(a->ne[3] <= ne3);
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3,
uint32_t mode) {
GGML_ASSERT((mode & 0xFF) < GGML_SCALE_MODE_COUNT);
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
ggml_set_op_params_i32(result, 0, mode);
ggml_set_op_params_i32(result, 0, (int32_t)mode);
result->op = GGML_OP_UPSCALE;
result->src[0] = a;
@ -4477,7 +4474,8 @@ struct ggml_tensor * ggml_upscale(
struct ggml_tensor * a,
int scale_factor,
enum ggml_scale_mode mode) {
return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3], mode);
GGML_ASSERT(scale_factor > 1);
return ggml_interpolate_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3], mode);
}
struct ggml_tensor * ggml_upscale_ext(
@ -4488,7 +4486,18 @@ struct ggml_tensor * ggml_upscale_ext(
int ne2,
int ne3,
enum ggml_scale_mode mode) {
return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3, mode);
return ggml_interpolate_impl(ctx, a, ne0, ne1, ne2, ne3, mode);
}
struct ggml_tensor * ggml_interpolate(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3,
uint32_t mode) {
return ggml_interpolate_impl(ctx, a, ne0, ne1, ne2, ne3, mode);
}
// ggml_pad

16
tests/test-backend-ops.cpp
View File

@ -3296,28 +3296,28 @@ struct test_upscale : public test_case {
}
};
// GGML_OP_UPSCALE (ext)
struct test_upscale_ext : public test_case {
// GGML_OP_UPSCALE (via ggml_interpolate)
struct test_interpolate : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
const std::array<int64_t, 4> ne_tgt;
const ggml_scale_mode mode = GGML_SCALE_MODE_NEAREST;
const uint32_t mode = GGML_SCALE_MODE_NEAREST;
std::string vars() override {
return VARS_TO_STR4(type, ne, ne_tgt, mode);
}
test_upscale_ext(ggml_type type = GGML_TYPE_F32,
test_interpolate(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {2, 5, 7, 11},
std::array<int64_t, 4> ne_tgt = {5, 7, 11, 13},
ggml_scale_mode mode = GGML_SCALE_MODE_NEAREST)
uint32_t mode = GGML_SCALE_MODE_NEAREST)
: type(type), ne(ne), ne_tgt(ne_tgt), mode(mode) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
ggml_set_name(a, "a");
ggml_tensor * out = ggml_upscale_ext(ctx, a, ne_tgt[0], ne_tgt[1],ne_tgt[2], ne_tgt[3], mode);
ggml_tensor * out = ggml_interpolate(ctx, a, ne_tgt[0], ne_tgt[1],ne_tgt[2], ne_tgt[3], mode);
ggml_set_name(out, "out");
return out;
@ -4799,8 +4799,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
for (ggml_scale_mode mode : {GGML_SCALE_MODE_NEAREST, GGML_SCALE_MODE_BILINEAR}) {
test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, {512, 512, 3, 2}, 2, mode));
test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, {512, 512, 3, 2}, 2, mode, true));
test_cases.emplace_back(new test_upscale_ext(GGML_TYPE_F32, {2, 5, 7, 11}, {5, 7, 11, 13}, mode));
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {2, 5, 7, 11}, {5, 7, 11, 13}, mode));
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {5, 7, 11, 13}, {2, 5, 7, 11}, mode));
}
test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {2, 5, 7, 11}, {5, 7, 11, 13}, GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ALIGN_CORNERS));
test_cases.emplace_back(new test_sum());
test_cases.emplace_back(new test_sum_rows());