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Feature: Circular Padding #638

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Feature: Circular Padding #638

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6e3e76e
Implement Circular Padding and Test Validation
gartia Dec 6, 2023
9395e55
Small fix in test function
gartia Dec 6, 2023
6fddbed
Rename circularpadding->pad-circular & Test
gartia Dec 7, 2023
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3 changes: 3 additions & 0 deletions build.zig
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@ const builtin = @import("builtin");
// zig build run_test-mul-mat2
// zig build run_test-opt
// zig build run_test-vec1
// zig build run_test-pad-circular
// zig build run_test0
// zig build run_test1
// zig build run_test2
Expand Down Expand Up @@ -95,6 +96,7 @@ pub fn build(b: *std.build.Builder) void {
"test1",
"test2",
"test3",
"test-pad-circular",
} else .{
// "test-blas0",
// "test-grad0",
Expand All @@ -110,6 +112,7 @@ pub fn build(b: *std.build.Builder) void {
"test1",
"test2",
"test3",
"test-pad-circular",
};
inline for (tests) |name| {
const exe = b.addExecutable(.{
Expand Down
9 changes: 9 additions & 0 deletions include/ggml/ggml.h
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Original file line number Diff line number Diff line change
Expand Up @@ -450,6 +450,8 @@ extern "C" {
GGML_OP_CROSS_ENTROPY_LOSS,
GGML_OP_CROSS_ENTROPY_LOSS_BACK,

GGML_OP_PAD_CIRCULAR,

GGML_OP_COUNT,
};

Expand Down Expand Up @@ -1778,6 +1780,13 @@ extern "C" {
struct ggml_tensor * b,
struct ggml_tensor * c);

// Circular Padding

GGML_API struct ggml_tensor * ggml_pad_circular(
struct ggml_context * ctx,
struct ggml_tensor * a,
int padding);

//
// automatic differentiation
//
Expand Down
205 changes: 203 additions & 2 deletions src/ggml.c
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Original file line number Diff line number Diff line change
Expand Up @@ -1648,9 +1648,11 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {

"CROSS_ENTROPY_LOSS",
"CROSS_ENTROPY_LOSS_BACK",

"PAD_CIRCULAR"
};

static_assert(GGML_OP_COUNT == 70, "GGML_OP_COUNT != 70");
static_assert(GGML_OP_COUNT == 71, "GGML_OP_COUNT != 71");

static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
Expand Down Expand Up @@ -1732,9 +1734,11 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {

"cross_entropy_loss(x,y)",
"cross_entropy_loss_back(x,y)",

"pad_circular(x)"
};

static_assert(GGML_OP_COUNT == 70, "GGML_OP_COUNT != 70");
static_assert(GGML_OP_COUNT == 71, "GGML_OP_COUNT != 71");

static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");

Expand Down Expand Up @@ -6303,6 +6307,44 @@ struct ggml_tensor * ggml_cross_entropy_loss_back(
return result;
}

//GGML_PAD_CIRCULAR

static struct ggml_tensor * ggml_pad_circular_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
int padding) {
bool is_node = false;

if (a->grad) {
GGML_ASSERT(false);
is_node = true;
}

int new_height = a->ne[0] + 2 * padding;
int new_width = a->ne[1] + 2 * padding;

struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type,
new_height,
new_width,
a->ne[2], a->ne[3]);

result->op = GGML_OP_PAD_CIRCULAR;
result->op_params[0] = padding;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
result->src[1] = NULL;

return result;
}

struct ggml_tensor * ggml_pad_circular(
struct ggml_context * ctx,
struct ggml_tensor * a,
int padding) {
return ggml_pad_circular_impl(ctx, a, padding);
}


////////////////////////////////////////////////////////////////////////////////

void ggml_set_param(
Expand Down Expand Up @@ -13834,6 +13876,152 @@ static void ggml_compute_forward_cross_entropy_loss_back(
}
}

// ggml_compute_pad_circular

static void ggml_compute_forward_pad_circular_f16(
const struct ggml_compute_params * params,
const struct ggml_tensor * src,
struct ggml_tensor * dst) {

if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int padding = dst->op_params[0];
const int ith = params->ith;
const int nth = params->nth;

ggml_fp16_t * src_data = (ggml_fp16_t *) src->data;
ggml_fp16_t * dst_data = (ggml_fp16_t *) dst->data;

const int64_t orig_height = src->ne[0];
const int64_t orig_width = src->ne[1];
const int64_t new_height = orig_height + 2 * padding;
const int64_t new_width = orig_width + 2 * padding;

int64_t total_elements = new_height * new_width;
int64_t start_index = ith * (total_elements / nth);
int64_t end_index = (ith + 1) == nth ? total_elements : (ith + 1) * (total_elements / nth);

for (int64_t idx = start_index; idx < end_index; ++idx) {
int64_t i = idx / new_width;
int64_t j = idx % new_width;

int64_t orig_i = (i - padding + orig_height) % orig_height;
int64_t orig_j = (j - padding + orig_width) % orig_width;

dst_data[i * new_width + j] = src_data[orig_i * orig_width + orig_j];
}
}

static void ggml_compute_forward_pad_circular_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src,
struct ggml_tensor * dst) {

if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}

const int padding = dst->op_params[0];
const int ith = params->ith;
const int nth = params->nth;

const int64_t orig_height = src->ne[0];
const int64_t orig_width = src->ne[1];
const int64_t new_height = orig_height + 2 * padding;
const int64_t new_width = orig_width + 2 * padding;

float * src_data = (float *) src->data;
float * dst_data = (float *) dst->data;

int64_t total_elements = new_height * new_width;
int64_t start_index = ith * (total_elements / nth);
int64_t end_index = (ith + 1) == nth ? total_elements : (ith + 1) * (total_elements / nth);

for (int64_t idx = start_index; idx < end_index; ++idx) {
int64_t i = idx / new_width;
int64_t j = idx % new_width;

int64_t orig_i = (i - padding + orig_height) % orig_height;
int64_t orig_j = (j - padding + orig_width) % orig_width;

dst_data[i * new_width + j] = src_data[orig_i * orig_width + orig_j];
}
}

// TODO: Make sure this works i am not familiar with quantization.
static void ggml_compute_forward_pad_circular_q_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src,
struct ggml_tensor * dst) {

if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}

const int padding = dst->op_params[0];
const int ith = params->ith;
const int nth = params->nth;

const int64_t orig_height = src->ne[0];
const int64_t orig_width = src->ne[1];
const int64_t new_height = orig_height + 2 * padding;
const int64_t new_width = orig_width + 2 * padding;

float * src_data = (float *) src->data;
float * dst_data = (float *) dst->data;

int64_t total_elements = new_height * new_width;
int64_t start_index = ith * (total_elements / nth);
int64_t end_index = (ith + 1) == nth ? total_elements : (ith + 1) * (total_elements / nth);

for (int64_t idx = start_index; idx < end_index; ++idx) {
int64_t i = idx / new_width;
int64_t j = idx % new_width;

int64_t orig_i = (i - padding + orig_height) % orig_height;
int64_t orig_j = (j - padding + orig_width) % orig_width;

dst_data[i * new_width + j] = src_data[orig_i * orig_width + orig_j];
}

}

static void ggml_compute_forward_pad_circular(
const struct ggml_compute_params * params,
const struct ggml_tensor * src,
struct ggml_tensor * dst) {

int padding = dst->op_params[0];

switch (src->type) {
case GGML_TYPE_F32:
ggml_compute_forward_pad_circular_f32(params, src, dst);
break;
case GGML_TYPE_F16:
ggml_compute_forward_pad_circular_f16(params, src, dst);
break;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
{
ggml_compute_forward_pad_circular_q_f32(params, src,dst);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}


/////////////////////////////////

static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
Expand Down Expand Up @@ -14155,6 +14343,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
ggml_compute_forward_cross_entropy_loss_back(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
}
break;
case GGML_OP_PAD_CIRCULAR:
{
ggml_compute_forward_pad_circular(params, tensor->src[0], tensor);
}
break;
case GGML_OP_NONE:
{
// nop
Expand Down Expand Up @@ -15220,6 +15413,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ASSERT(false); // not supported
} break;
case GGML_OP_PAD_CIRCULAR:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_NONE:
{
// nop
Expand Down Expand Up @@ -15848,6 +16045,10 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
{
n_tasks = n_threads;
} break;
case GGML_OP_PAD_CIRCULAR:
{
n_tasks=n_threads;
};
case GGML_OP_NONE:
{
n_tasks = 1;
Expand Down
9 changes: 9 additions & 0 deletions tests/CMakeLists.txt
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Expand Up @@ -404,3 +404,12 @@ add_executable(${TEST_TARGET} ${TEST_TARGET}.cpp)
target_link_libraries(${TEST_TARGET} PRIVATE ggml)
add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}>)
set_property(TEST ${TEST_TARGET} PROPERTY ENVIRONMENT "LLVM_PROFILE_FILE=${TEST_TARGET}.profraw")

#
# test-pad-circular

set(TEST_TARGET test-pad-circular)
add_executable(${TEST_TARGET} ${TEST_TARGET}.cpp)
target_link_libraries(${TEST_TARGET} PRIVATE ggml)
add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}>)
set_property(TEST ${TEST_TARGET} PROPERTY ENVIRONMENT "LLVM_PROFILE_FILE=${TEST_TARGET}.profraw")
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