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ggml : add bilinear upscale support (ggml/1185)

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Diego Devesa 2025-04-09 12:32:13 +02:00 committed by Georgi Gerganov
parent 459895c326
commit fe92821ea9
9 changed files with 119 additions and 40 deletions
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19
ggml/include/ggml.h
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@ -1717,24 +1717,29 @@ extern "C" {
float p0,
float p1);
// nearest interpolate
enum ggml_scale_mode {
GGML_SCALE_MODE_NEAREST = 0,
GGML_SCALE_MODE_BILINEAR = 1,
};
// interpolate
// multiplies ne0 and ne1 by scale factor
// used in stable-diffusion
GGML_API struct ggml_tensor * ggml_upscale(
struct ggml_context * ctx,
struct ggml_tensor * a,
int scale_factor);
int scale_factor,
enum ggml_scale_mode mode);
// nearest interpolate
// nearest interpolate to specified dimensions
// used in tortoise.cpp
// interpolate
// interpolate scale to specified dimensions
GGML_API struct ggml_tensor * ggml_upscale_ext(
struct ggml_context * ctx,
struct ggml_tensor * a,
int ne0,
int ne1,
int ne2,
int ne3);
int ne3,
enum ggml_scale_mode mode);
// pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0]
GGML_API struct ggml_tensor * ggml_pad(

3
ggml/src/ggml-cann/ggml-cann.cpp
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@ -1824,6 +1824,9 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
if (op->src[0]->ne[2] * op->ne[3] != op->src[0]->ne[3] * op->ne[2]) {
return false;
}
if (op->op_params[0] != GGML_SCALE_MODE_NEAREST) {
return false;
}
return true;
}
case GGML_OP_POOL_2D: {

72
ggml/src/ggml-cpu/ops.cpp
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@ -6351,24 +6351,72 @@ static void ggml_compute_forward_upscale_f32(
const float sf2 = (float)ne2/src0->ne[2];
const float sf3 = (float)ne3/src0->ne[3];
// TODO: optimize
const ggml_scale_mode mode = (ggml_scale_mode) ggml_get_op_params_i32(dst, 0);
for (int64_t i3 = 0; i3 < ne3; i3++) {
const int64_t i03 = i3 / sf3;
for (int64_t i2 = ith; i2 < ne2; i2 += nth) {
const int64_t i02 = i2 / sf2;
for (int64_t i1 = 0; i1 < ne1; i1++) {
const int64_t i01 = i1 / sf1;
for (int64_t i0 = 0; i0 < ne0; i0++) {
const int64_t i00 = i0 / sf0;
if (mode == GGML_SCALE_MODE_NEAREST) {
for (int64_t i3 = 0; i3 < ne3; i3++) {
const int64_t i03 = i3 / sf3;
for (int64_t i2 = ith; i2 < ne2; i2 += nth) {
const int64_t i02 = i2 / sf2;
for (int64_t i1 = 0; i1 < ne1; i1++) {
const int64_t i01 = i1 / sf1;
for (int64_t i0 = 0; i0 < ne0; i0++) {
const int64_t i00 = i0 / sf0;
const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
*y = *x;
*y = *x;
}
}
}
}
} 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;
for (int64_t i3 = 0; i3 < ne3; i3++) {
const int64_t i03 = i3 / sf3;
for (int64_t i2 = ith; i2 < ne2; i2 += nth) {
const int64_t i02 = i2 / sf2;
for (int64_t i1 = 0; i1 < ne1; i1++) {
const float y = ((float)i1 + pixel_offset) / sf1 - pixel_offset;
int64_t y0 = (int64_t)floorf(y);
int64_t y1 = y0 + 1;
y0 = std::max(int64_t(0), std::min(y0, ne01 - 1));
y1 = std::max(int64_t(0), std::min(y1, ne01 - 1));
float dy = y - (float)y0;
dy = std::max(0.0f, std::min(dy, 1.0f));
for (int64_t i0 = 0; i0 < ne0; i0++) {
const float x = ((float)i0 + pixel_offset) / sf0 - pixel_offset;
int64_t x0 = (int64_t)floorf(x);
int64_t x1 = x0 + 1;
x0 = std::max(int64_t(0), std::min(x0, ne00 - 1));
x1 = std::max(int64_t(0), std::min(x1, ne00 - 1));
float dx = x - (float)x0;
dx = std::max(0.0f, std::min(dx, 1.0f));
// fetch the four surrounding pixel values and interpolate
const float a = *(const float *)((const char *)src0->data + x0*nb00 + y0*nb01 + i02*nb02 + i03*nb03);
const float b = *(const float *)((const char *)src0->data + x1*nb00 + y0*nb01 + i02*nb02 + i03*nb03);
const float c = *(const float *)((const char *)src0->data + x0*nb00 + y1*nb01 + i02*nb02 + i03*nb03);
const float d = *(const float *)((const char *)src0->data + x1*nb00 + y1*nb01 + i02*nb02 + i03*nb03);
const float val = a*(1 - dx)*(1 - dy) + b*dx*(1 - dy) + c*(1 - dx)*dy + d*dx*dy;
float * y_dst = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
*y_dst = val;
}
}
}
}
} else {
GGML_ABORT("unsupported upscale mode");
}
}

1
ggml/src/ggml-cuda/ggml-cuda.cu
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@ -3216,6 +3216,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_GROUP_NORM:
return ggml_is_contiguous(op->src[0]);
case GGML_OP_UPSCALE:
return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
case GGML_OP_PAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:

3
ggml/src/ggml-metal/ggml-metal.m
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@ -1334,8 +1334,9 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
return op->src[0]->type == GGML_TYPE_F16;
case GGML_OP_POOL_1D:
return false;
case GGML_OP_POOL_2D:
case GGML_OP_UPSCALE:
return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
case GGML_OP_POOL_2D:
case GGML_OP_PAD:
case GGML_OP_PAD_REFLECT_1D:
case GGML_OP_TIMESTEP_EMBEDDING:

3
ggml/src/ggml-sycl/ggml-sycl.cpp
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@ -4055,12 +4055,13 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_IM2COL:
// TODO: add support for the new F32 operations
return op->src[0]->type == GGML_TYPE_F16;
case GGML_OP_UPSCALE:
return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
case GGML_OP_POOL_2D:
case GGML_OP_SUM:
case GGML_OP_SUM_ROWS:
case GGML_OP_ARGSORT:
case GGML_OP_ACC:
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_LEAKY_RELU:
case GGML_OP_TIMESTEP_EMBEDDING:

7
ggml/src/ggml-vulkan/ggml-vulkan.cpp
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@ -5749,7 +5749,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
}
return nullptr;
case GGML_OP_UPSCALE:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && dst->op_params[0] == GGML_SCALE_MODE_NEAREST) {
return ctx->device->pipeline_upscale_f32;
}
return nullptr;
@ -9404,9 +9404,10 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
case GGML_OP_COS:
case GGML_OP_CLAMP:
return op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_UPSCALE:
return op->op_params[0] == GGML_SCALE_MODE_NEAREST;
case GGML_OP_ACC:
case GGML_OP_CONCAT:
case GGML_OP_UPSCALE:
case GGML_OP_SCALE:
case GGML_OP_PAD:
case GGML_OP_DIAG_MASK_INF:
@ -9774,7 +9775,7 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
} else if (tensor->op == GGML_OP_CONCAT) {
tensor_clone = ggml_concat(ggml_ctx, src_clone[0], src_clone[1], *(int *)tensor->op_params);
} else if (tensor->op == GGML_OP_UPSCALE) {
tensor_clone = ggml_upscale_ext(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
tensor_clone = ggml_upscale_ext(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->op_params[0], tensor->op_params[1], (ggml_scale_mode) tensor->op_params[0]);
} else if (tensor->op == GGML_OP_SCALE) {
const float * params = (const float *)tensor->op_params;
tensor_clone = ggml_scale(ggml_ctx, src_clone[0], params[0]);

15
ggml/src/ggml.c
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@ -4174,7 +4174,8 @@ static struct ggml_tensor * ggml_upscale_impl(
int ne0,
int ne1,
int ne2,
int ne3) {
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);
@ -4182,6 +4183,8 @@ static struct ggml_tensor * ggml_upscale_impl(
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
ggml_set_op_params_i32(result, 0, mode);
result->op = GGML_OP_UPSCALE;
result->src[0] = a;
@ -4191,8 +4194,9 @@ static struct ggml_tensor * ggml_upscale_impl(
struct ggml_tensor * ggml_upscale(
struct ggml_context * ctx,
struct ggml_tensor * a,
int scale_factor) {
return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3]);
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);
}
struct ggml_tensor * ggml_upscale_ext(
@ -4201,8 +4205,9 @@ struct ggml_tensor * ggml_upscale_ext(
int ne0,
int ne1,
int ne2,
int ne3) {
return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3);
int ne3,
enum ggml_scale_mode mode) {
return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3, mode);
}
// ggml_pad

36
tests/test-backend-ops.cpp
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@ -271,6 +271,14 @@ static std::string var_to_str(ggml_op_pool pool) {
}
}
static std::string var_to_str(ggml_scale_mode mode) {
switch (mode) {
case GGML_SCALE_MODE_NEAREST: return "nearest";
case GGML_SCALE_MODE_BILINEAR: return "bilinear";
default: return std::to_string(mode);
}
}
#define VAR_TO_STR(x) (#x "=" + var_to_str(x))
#define VARS_TO_STR1(a) VAR_TO_STR(a)
@ -2948,15 +2956,16 @@ struct test_upscale : public test_case {
const std::array<int64_t, 4> ne;
const int32_t scale_factor;
const bool transpose;
const ggml_scale_mode mode;
std::string vars() override {
return VARS_TO_STR4(type, ne, scale_factor, transpose);
return VARS_TO_STR5(type, ne, scale_factor, mode, transpose);
}
test_upscale(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {512, 512, 3, 1},
int32_t scale_factor = 2, bool transpose = false)
: type(type), ne(ne), scale_factor(scale_factor), transpose(transpose) {}
int32_t scale_factor = 2, ggml_scale_mode mode = GGML_SCALE_MODE_NEAREST, bool transpose = false)
: type(type), ne(ne), scale_factor(scale_factor), mode(mode), transpose(transpose) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
@ -2967,7 +2976,7 @@ struct test_upscale : public test_case {
ggml_set_name(a, "a_transposed");
}
ggml_tensor * out = ggml_upscale(ctx, a, scale_factor);
ggml_tensor * out = ggml_upscale(ctx, a, scale_factor, mode);
ggml_set_name(out, "out");
return out;
@ -2979,21 +2988,23 @@ struct test_upscale_ext : 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;
std::string vars() override {
return VARS_TO_STR3(type, ne, ne_tgt);
return VARS_TO_STR4(type, ne, ne_tgt, mode);
}
test_upscale_ext(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})
: type(type), ne(ne), ne_tgt(ne_tgt) {}
std::array<int64_t, 4> ne_tgt = {5, 7, 11, 13},
ggml_scale_mode 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]);
ggml_tensor * out = ggml_upscale_ext(ctx, a, ne_tgt[0], ne_tgt[1],ne_tgt[2], ne_tgt[3], mode);
ggml_set_name(out, "out");
return out;
@ -4399,12 +4410,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {60, 10, 10, 10}, order)); // qwen
}
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_sum());
test_cases.emplace_back(new test_sum_rows());
test_cases.emplace_back(new test_mean());
test_cases.emplace_back(new test_upscale());
test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, { 512, 512, 3, 1 }, 2, true));
test_cases.emplace_back(new test_upscale_ext());
test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {64, 64, 320, 1}));
test_cases.emplace_back(new test_group_norm(GGML_TYPE_F32, {9, 9, 1280, 1}));
test_cases.emplace_back(new test_acc());