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ggml : add unary and binary map operations (#874)

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* GGML map ops proof of concept.

* Various cleanups.

Add handling for task setting.

Add handling for ggml_compute_backward.

Rename functions to ggml_map_unary_f32 and ggml_map_binary_f32

Fix compiler warnings related to casting function pointers and `void *`

Reorder functions and definitions based on the GGML op number.

Use typedefs for map op function pointer types.

* Fix position of map ops cases in ggml_compute_forward
pull/970/head master-c9a59b7
Kerfuffle 1 year ago committed by GitHub
parent a32f7acc9f
commit c9a59b70a5
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2 changed files with 237 additions and 2 deletions
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221
ggml.c
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@ -2712,9 +2712,12 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
"FLASH_ATTN",
"FLASH_FF",
"MAP_UNARY",
"MAP_BINARY",
};
static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -2757,9 +2760,12 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"flash_attn(x)",
"flash_ff(x)",
"f(x)",
"f(x,y)",
};
static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36");
static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38");
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");
@ -4907,6 +4913,90 @@ struct ggml_tensor * ggml_flash_ff(
return result;
}
// ggml_map_unary
struct ggml_tensor * ggml_map_unary_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_unary_op_f32_t fun,
bool inplace) {
bool is_node = false;
if (!inplace && a->grad) {
is_node = true;
}
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);
result->op = GGML_OP_MAP_UNARY;
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_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_unary_op_f32_t fun) {
return ggml_map_unary_impl_f32(ctx, a, fun, false);
}
struct ggml_tensor * ggml_map_unary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_unary_op_f32_t fun) {
return ggml_map_unary_impl_f32(ctx, a, fun, true);
}
// ggml_map_binary
struct ggml_tensor * ggml_map_binary_impl_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_binary_op_f32_t fun,
bool inplace) {
GGML_ASSERT(ggml_are_same_shape(a, b));
bool is_node = false;
if (!inplace && (a->grad || b->grad)) {
is_node = true;
}
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);
result->op = GGML_OP_MAP_BINARY;
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_binary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_binary_op_f32_t fun) {
return ggml_map_binary_impl_f32(ctx, a, b, fun, false);
}
struct ggml_tensor * ggml_map_binary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_binary_op_f32_t fun) {
return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
}
////////////////////////////////////////////////////////////////////////////////
void ggml_set_param(
@ -8875,6 +8965,111 @@ static void ggml_compute_forward_flash_ff(
}
}
// ggml_compute_forward_map_unary
static void ggml_compute_forward_map_unary_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst,
const ggml_unary_op_f32_t fun) {
GGML_ASSERT(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
assert( dst->nb[0] == sizeof(float));
assert(src0->nb[0] == sizeof(float));
for (int i = 0; i < n; i++) {
fun(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
(float *) ((char *) src0->data + i*(src0->nb[1])));
}
}
static void ggml_compute_forward_map_unary(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst,
const ggml_unary_op_f32_t fun) {
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_map_unary_f32(params, src0, dst, fun);
} break;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_map_binary
static void ggml_compute_forward_map_binary_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst,
const ggml_binary_op_f32_t fun) {
assert(params->ith == 0);
assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
assert( dst->nb[0] == sizeof(float));
assert(src0->nb[0] == sizeof(float));
assert(src1->nb[0] == sizeof(float));
for (int i = 0; i < n; i++) {
fun(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
(float *) ((char *) src0->data + i*(src0->nb[1])),
(float *) ((char *) src1->data + i*(src1->nb[1])));
}
}
static void ggml_compute_forward_map_binary(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst,
const ggml_binary_op_f32_t fun) {
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun);
} break;
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_F16:
case GGML_TYPE_COUNT:
{
GGML_ASSERT(false);
} break;
}
}
/////////////////////////////////
static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
@ -9024,6 +9219,18 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_flash_ff(params, tensor->src0, tensor->src1, tensor->opt[0], tensor->opt[1], tensor->opt[2], tensor);
} break;
case GGML_OP_MAP_UNARY:
{
const ggml_unary_op_f32_t fun = *((ggml_unary_op_f32_t *)tensor->opt[0]->data);
ggml_compute_forward_map_unary(params, tensor->src0, tensor, fun);
}
break;
case GGML_OP_MAP_BINARY:
{
const ggml_binary_op_f32_t fun = *((ggml_binary_op_f32_t *)tensor->opt[0]->data);
ggml_compute_forward_map_binary(params, tensor->src0, tensor->src1, tensor, fun);
}
break;
case GGML_OP_NONE:
{
// nop
@ -9283,6 +9490,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ASSERT(false); // not supported
} break;
case GGML_OP_MAP_UNARY:
case GGML_OP_MAP_BINARY:
{
GGML_ASSERT(false); // not supported
} break;
case GGML_OP_NONE:
{
// nop
@ -9775,6 +9987,11 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
work_size = MAX(work_size, cur);
} break;
case GGML_OP_MAP_UNARY:
case GGML_OP_MAP_BINARY:
{
node->n_tasks = 1;
} break;
case GGML_OP_NONE:
{
node->n_tasks = 1;

18
ggml.h
Unescape Escape View File

@ -253,6 +253,9 @@ enum ggml_op {
GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF,
GGML_OP_MAP_UNARY,
GGML_OP_MAP_BINARY,
GGML_OP_COUNT,
};
@ -652,6 +655,21 @@ struct ggml_tensor * ggml_flash_ff(
struct ggml_tensor * c0,
struct ggml_tensor * c1);
// Mapping operations
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 *);
struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
const ggml_unary_op_f32_t fun);
struct ggml_tensor * ggml_map_binary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const ggml_binary_op_f32_t fun);
//
// automatic differentiation
//

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