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ggml : sync latest ggml (custom operators)

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This commit is contained in:
Georgi Gerganov
2023-06-25 14:25:08 +03:00
parent c2a08f87b8
commit bd34cdde38
2 changed files with 397 additions and 32 deletions
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369
ggml.c
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@@ -1,5 +1,5 @@
// Defines CLOCK_MONOTONIC on Linux
#define _GNU_SOURCE
#define _GNU_SOURCE // Defines CLOCK_MONOTONIC on Linux
#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
#include "ggml.h"
@@ -131,6 +131,34 @@ typedef void* thread_ret_t;
#define GGML_MEM_ALIGN 16
#endif
//
// logging
//
#if (GGML_DEBUG >= 1)
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG(...)
#endif
#if (GGML_DEBUG >= 5)
#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_5(...)
#endif
#if (GGML_DEBUG >= 10)
#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_10(...)
#endif
#define GGML_PRINT(...) printf(__VA_ARGS__)
//
// end of logging block
//
#if defined(_MSC_VER) || defined(__MINGW32__)
#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN)
#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr)
@@ -144,6 +172,17 @@ inline static void* ggml_aligned_malloc(size_t size) {
#endif
if (result != 0) {
// Handle allocation failure
const char *error_desc = "unknown allocation error";
switch (result) {
case EINVAL:
error_desc = "invalid alignment value";
break;
case ENOMEM:
error_desc = "insufficient memory";
break;
}
GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n",
__func__, error_desc, size/(1024.0*1024.0));
return NULL;
}
return aligned_memory;
@@ -3530,30 +3569,6 @@ inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x
*s = 1.f/(*s);
}
//
// logging
//
#if (GGML_DEBUG >= 1)
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG(...)
#endif
#if (GGML_DEBUG >= 5)
#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_5(...)
#endif
#if (GGML_DEBUG >= 10)
#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
#else
#define GGML_PRINT_DEBUG_10(...)
#endif
#define GGML_PRINT(...) printf(__VA_ARGS__)
//
// data types
//
@@ -3713,11 +3728,15 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"MAP_UNARY",
"MAP_BINARY",
"MAP_CUSTOM1",
"MAP_CUSTOM2",
"MAP_CUSTOM3",
"CROSS_ENTROPY_LOSS",
"CROSS_ENTROPY_LOSS_BACK",
};
static_assert(GGML_OP_COUNT == 61, "GGML_OP_COUNT != 61");
static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@@ -3785,11 +3804,15 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"f(x)",
"f(x,y)",
"custom(x)",
"custom(x,y)",
"custom(x,y,z)",
"cross_entropy_loss(x,y)",
"cross_entropy_loss_back(x,y)",
};
static_assert(GGML_OP_COUNT == 61, "GGML_OP_COUNT != 61");
static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64");
static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@@ -7094,9 +7117,14 @@ struct ggml_tensor * ggml_map_unary_impl_f32(
is_node = true;
}
struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_scratch_save(ctx);
struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
*((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_scratch_load(ctx);
result->op = GGML_OP_MAP_UNARY;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -7136,9 +7164,14 @@ struct ggml_tensor * ggml_map_binary_impl_f32(
is_node = true;
}
struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_scratch_save(ctx);
struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
*((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_scratch_load(ctx);
result->op = GGML_OP_MAP_BINARY;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -7165,6 +7198,150 @@ struct ggml_tensor * ggml_map_binary_inplace_f32(
return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
}
// ggml_map_custom1
struct ggml_tensor * ggml_map_custom1_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_f32_t fun,
bool inplace) {
bool is_node = false;
if (!inplace && a->grad) {
is_node = true;
}
struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_scratch_save(ctx);
struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
*((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
ggml_scratch_load(ctx);
result->op = GGML_OP_MAP_CUSTOM1;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->opt[0] = addr_tensor;
return result;
}
struct ggml_tensor * ggml_map_custom1_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_f32_t fun) {
return ggml_map_custom1_impl_f32(ctx, a, fun, false);
}
struct ggml_tensor * ggml_map_custom1_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_custom1_op_f32_t fun) {
return ggml_map_custom1_impl_f32(ctx, a, fun, true);
}
// ggml_map_custom2
struct ggml_tensor * ggml_map_custom2_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_custom2_op_f32_t fun,
bool inplace) {
bool is_node = false;
if (!inplace && (a->grad || b->grad)) {
is_node = true;
}
struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_scratch_save(ctx);
struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
*((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
ggml_scratch_load(ctx);
result->op = GGML_OP_MAP_CUSTOM2;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
result->opt[0] = addr_tensor;
return result;
}
struct ggml_tensor * ggml_map_custom2_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_custom2_op_f32_t fun) {
return ggml_map_custom2_impl_f32(ctx, a, b, fun, false);
}
struct ggml_tensor * ggml_map_custom2_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_custom2_op_f32_t fun) {
return ggml_map_custom2_impl_f32(ctx, a, b, fun, true);
}
// ggml_map_custom3
struct ggml_tensor * ggml_map_custom3_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
const ggml_custom3_op_f32_t fun,
bool inplace) {
bool is_node = false;
if (!inplace && (a->grad || b->grad || c->grad)) {
is_node = true;
}
struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_scratch_save(ctx);
struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t));
*((void (**)(void))addr_tensor->data) = (void (*)(void))fun;
ggml_scratch_load(ctx);
result->op = GGML_OP_MAP_CUSTOM3;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src0 = a;
result->src1 = b;
result->opt[0] = addr_tensor;
result->opt[1] = c;
return result;
}
struct ggml_tensor * ggml_map_custom3_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
const ggml_custom3_op_f32_t fun) {
return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, false);
}
struct ggml_tensor * ggml_map_custom3_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
const ggml_custom3_op_f32_t fun) {
return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true);
}
// ggml_cross_entropy_loss
struct ggml_tensor * ggml_cross_entropy_loss(
@@ -14621,6 +14798,114 @@ static void ggml_compute_forward_map_binary(
}
}
// ggml_compute_forward_map_custom1
static void ggml_compute_forward_map_custom1_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
struct ggml_tensor * dst,
const ggml_custom1_op_f32_t fun) {
assert(params->ith == 0);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
fun(dst, a);
}
static void ggml_compute_forward_map_custom1(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
struct ggml_tensor * dst,
const ggml_custom1_op_f32_t fun) {
switch (a->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_map_custom1_f32(params, a, dst, fun);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_map_custom2
static void ggml_compute_forward_map_custom2_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
const struct ggml_tensor * b,
struct ggml_tensor * dst,
const ggml_custom2_op_f32_t fun) {
assert(params->ith == 0);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
fun(dst, a, b);
}
static void ggml_compute_forward_map_custom2(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
const struct ggml_tensor * b,
struct ggml_tensor * dst,
const ggml_custom2_op_f32_t fun) {
switch (a->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_map_custom2_f32(params, a, b, dst, fun);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_map_custom3
static void ggml_compute_forward_map_custom3_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
const struct ggml_tensor * b,
const struct ggml_tensor * c,
struct ggml_tensor * dst,
const ggml_custom3_op_f32_t fun) {
assert(params->ith == 0);
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
fun(dst, a, b, c);
}
static void ggml_compute_forward_map_custom3(
const struct ggml_compute_params * params,
const struct ggml_tensor * a,
const struct ggml_tensor * b,
const struct ggml_tensor * c,
struct ggml_tensor * dst,
const ggml_custom3_op_f32_t fun) {
switch (a->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_map_custom3_f32(params, a, b, c, dst, fun);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_cross_entropy_loss
static void ggml_compute_forward_cross_entropy_loss_f32(
@@ -15158,6 +15443,24 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
ggml_compute_forward_map_binary(params, tensor->src0, tensor->src1, tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM1:
{
const ggml_custom1_op_f32_t fun = *((ggml_custom1_op_f32_t *)tensor->opt[0]->data);
ggml_compute_forward_map_custom1(params, tensor->src0, tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM2:
{
const ggml_custom2_op_f32_t fun = *((ggml_custom2_op_f32_t *)tensor->opt[0]->data);
ggml_compute_forward_map_custom2(params, tensor->src0, tensor->src1, tensor, fun);
}
break;
case GGML_OP_MAP_CUSTOM3:
{
const ggml_custom3_op_f32_t fun = *((ggml_custom3_op_f32_t *)tensor->opt[0]->data);
ggml_compute_forward_map_custom3(params, tensor->src0, tensor->src1, tensor->opt[1], tensor, fun);
}
break;
case GGML_OP_CROSS_ENTROPY_LOSS:
{
ggml_compute_forward_cross_entropy_loss(params, tensor->src0, tensor->src1, tensor);
@@ -15964,6 +16267,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
case GGML_OP_WIN_UNPART:
case GGML_OP_MAP_UNARY:
case GGML_OP_MAP_BINARY:
case GGML_OP_MAP_CUSTOM1:
case GGML_OP_MAP_CUSTOM2:
case GGML_OP_MAP_CUSTOM3:
{
GGML_ASSERT(false); // not supported
} break;
@@ -16605,6 +16911,9 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
case GGML_OP_WIN_UNPART:
case GGML_OP_MAP_UNARY:
case GGML_OP_MAP_BINARY:
case GGML_OP_MAP_CUSTOM1:
case GGML_OP_MAP_CUSTOM2:
case GGML_OP_MAP_CUSTOM3:
{
node->n_tasks = 1;
} break;

60
ggml.h
View File

@@ -345,6 +345,10 @@ extern "C" {
GGML_OP_MAP_UNARY,
GGML_OP_MAP_BINARY,
GGML_OP_MAP_CUSTOM1,
GGML_OP_MAP_CUSTOM2,
GGML_OP_MAP_CUSTOM3,
GGML_OP_CROSS_ENTROPY_LOSS,
GGML_OP_CROSS_ENTROPY_LOSS_BACK,
@@ -1167,21 +1171,73 @@ extern "C" {
int h0,
int w);
// Mapping operations
typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *);
// custom operators
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
GGML_API struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_binary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom1_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom2_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom3_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_f32_t fun);
// loss function
GGML_API struct ggml_tensor * ggml_cross_entropy_loss(