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ggml : remove ggml_exp and ggml_soft_plus

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They did not exist anyway outside of this branch,
and since ggml_ssm_scan fused operations together, they are unused.
It's always possible to bring them back if needed.
This commit is contained in:
Francis Couture-Harpin 2024-02-04 17:08:54 -05:00
parent cd0f33f281
commit de92f15634
2 changed files with 2 additions and 208 deletions
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191
ggml.c
View file

@ -1577,7 +1577,6 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); }
inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; }
inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); }
inline static void ggml_vec_exp_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = expf(x[i]); }
inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); } inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = logf(x[i]); }
inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); }
inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); }
@ -1779,7 +1778,6 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"DIV", "DIV",
"SQR", "SQR",
"SQRT", "SQRT",
"EXP",
"LOG", "LOG",
"SUM", "SUM",
"SUM_ROWS", "SUM_ROWS",
@ -1813,7 +1811,6 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"DIAG_MASK_ZERO", "DIAG_MASK_ZERO",
"SOFT_MAX", "SOFT_MAX",
"SOFT_MAX_BACK", "SOFT_MAX_BACK",
"SOFT_PLUS",
"ROPE", "ROPE",
"ROPE_BACK", "ROPE_BACK",
"ALIBI", "ALIBI",
@ -1854,7 +1851,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CROSS_ENTROPY_LOSS_BACK", "CROSS_ENTROPY_LOSS_BACK",
}; };
static_assert(GGML_OP_COUNT == 75, "GGML_OP_COUNT != 75"); static_assert(GGML_OP_COUNT == 73, "GGML_OP_COUNT != 73");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none", "none",
@ -1868,7 +1865,6 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"x/y", "x/y",
"x^2", "x^2",
"√x", "√x",
"e^x", // or should this be "exp(x)"?
"log(x)", "log(x)",
"Σx", "Σx",
"Σx_k", "Σx_k",
@ -1902,7 +1898,6 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"diag_mask_zero(x)", "diag_mask_zero(x)",
"soft_max(x)", "soft_max(x)",
"soft_max_back(x)", "soft_max_back(x)",
"soft_plus(x)",
"rope(x)", "rope(x)",
"rope_back(x)", "rope_back(x)",
"alibi(x)", "alibi(x)",
@ -1943,7 +1938,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"cross_entropy_loss_back(x,y)", "cross_entropy_loss_back(x,y)",
}; };
static_assert(GGML_OP_COUNT == 75, "GGML_OP_COUNT != 75"); static_assert(GGML_OP_COUNT == 73, "GGML_OP_COUNT != 73");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@ -3803,39 +3798,6 @@ struct ggml_tensor * ggml_sqrt_inplace(
return ggml_sqrt_impl(ctx, a, true); return ggml_sqrt_impl(ctx, a, true);
} }
// ggml_exp
static struct ggml_tensor * ggml_exp_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
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);
result->op = GGML_OP_EXP;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
return result;
}
struct ggml_tensor * ggml_exp(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_exp_impl(ctx, a, false);
}
struct ggml_tensor * ggml_exp_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_exp_impl(ctx, a, true);
}
// ggml_log // ggml_log
static struct ggml_tensor * ggml_log_impl( static struct ggml_tensor * ggml_log_impl(
@ -5331,40 +5293,6 @@ struct ggml_tensor * ggml_soft_max_back_inplace(
return ggml_soft_max_back_impl(ctx, a, b, true); return ggml_soft_max_back_impl(ctx, a, b, true);
} }
// ggml_soft_plus
static struct ggml_tensor * ggml_soft_plus_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
bool inplace) {
bool is_node = false;
if (a->grad) {
is_node = true; // TODO : implement backward pass
}
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
result->op = GGML_OP_SOFT_PLUS;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
return result;
}
struct ggml_tensor * ggml_soft_plus(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_soft_plus_impl(ctx, a, false);
}
struct ggml_tensor * ggml_soft_plus_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_soft_plus_impl(ctx, a, true);
}
// ggml_rope // ggml_rope
static struct ggml_tensor * ggml_rope_impl( static struct ggml_tensor * ggml_rope_impl(
@ -8715,57 +8643,6 @@ static void ggml_compute_forward_sqrt(
} }
} }
// ggml_compute_forward_exp
static void ggml_compute_forward_exp_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
GGML_ASSERT(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
}
const int ith = params->ith;
const int nth = params->nth;
const int nc = src0->ne[0];
const int nr = ggml_nrows(src0);
// rows per thread
const int dr = (nr + nth - 1)/nth;
// row range for this thread
const int ir0 = dr*ith;
const int ir1 = MIN(ir0 + dr, nr);
for (int i1 = ir0; i1 < ir1; i1++) {
ggml_vec_exp_f32(nc,
(float *) ((char *) dst->data + i1*( dst->nb[1])),
(float *) ((char *) src0->data + i1*(src0->nb[1])));
};
}
static void ggml_compute_forward_exp(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_exp_f32(params, src0, dst);
} break;
case GGML_TYPE_F16: // TODO: use ggml_table_exp_f16
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_log // ggml_compute_forward_log
static void ggml_compute_forward_log_f32( static void ggml_compute_forward_log_f32(
@ -12225,48 +12102,6 @@ static void ggml_compute_forward_soft_max_back(
} }
} }
static void ggml_compute_forward_soft_plus_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
GGML_ASSERT(params->ith == 0);
GGML_ASSERT(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
}
const int nc = src0->ne[0];
const int nr = ggml_nrows(src0);
GGML_ASSERT( dst->nb[0] == sizeof(float));
GGML_ASSERT(src0->nb[0] == sizeof(float));
for (int i = 0; i < nr; ++i) {
float * x = (float *) ((char *) dst->data + i*( dst->nb[1]));
float * y = (float *) ((char *) src0->data + i*(src0->nb[1]));
for (int j = 0; j < nc; ++j) {
x[j] = logf(1.0f + expf(y[j]));
}
}
}
static void ggml_compute_forward_soft_plus(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_soft_plus_f32(params, src0, dst);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_alibi // ggml_compute_forward_alibi
static void ggml_compute_forward_alibi_f32( static void ggml_compute_forward_alibi_f32(
@ -15764,10 +15599,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{ {
ggml_compute_forward_sqrt(params, tensor); ggml_compute_forward_sqrt(params, tensor);
} break; } break;
case GGML_OP_EXP:
{
ggml_compute_forward_exp(params, tensor->src[0], tensor);
} break;
case GGML_OP_LOG: case GGML_OP_LOG:
{ {
ggml_compute_forward_log(params, tensor); ggml_compute_forward_log(params, tensor);
@ -15892,10 +15723,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{ {
ggml_compute_forward_soft_max_back(params, tensor); ggml_compute_forward_soft_max_back(params, tensor);
} break; } break;
case GGML_OP_SOFT_PLUS:
{
ggml_compute_forward_soft_plus(params, tensor->src[0], tensor);
} break;
case GGML_OP_ROPE: case GGML_OP_ROPE:
{ {
ggml_compute_forward_rope(params, tensor); ggml_compute_forward_rope(params, tensor);
@ -16452,10 +16279,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
zero_table); zero_table);
} }
} break; } break;
case GGML_OP_EXP:
{
GGML_ASSERT(false); // TODO: implement
} break;
case GGML_OP_LOG: case GGML_OP_LOG:
{ {
if (src0->grad) { if (src0->grad) {
@ -16834,10 +16657,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{ {
GGML_ASSERT(false); // TODO: not implemented GGML_ASSERT(false); // TODO: not implemented
} break; } break;
case GGML_OP_SOFT_PLUS:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_ROPE: case GGML_OP_ROPE:
{ {
// necessary for llama // necessary for llama
@ -17578,7 +17397,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
case GGML_OP_ADD: case GGML_OP_ADD:
case GGML_OP_ADD1: case GGML_OP_ADD1:
case GGML_OP_ACC: case GGML_OP_ACC:
case GGML_OP_EXP:
{ {
n_tasks = n_threads; n_tasks = n_threads;
} break; } break;
@ -17685,10 +17503,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
{ {
n_tasks = MIN(n_threads, ggml_nrows(node->src[0])); n_tasks = MIN(n_threads, ggml_nrows(node->src[0]));
} break; } break;
case GGML_OP_SOFT_PLUS:
{
n_tasks = 1; //TODO
} break;
case GGML_OP_CONV_TRANSPOSE_1D: case GGML_OP_CONV_TRANSPOSE_1D:
{ {
n_tasks = n_threads; n_tasks = n_threads;
@ -18065,7 +17879,6 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
} }
} break; } break;
case GGML_OP_SOFT_MAX: case GGML_OP_SOFT_MAX:
case GGML_OP_SOFT_PLUS:
case GGML_OP_ROPE: case GGML_OP_ROPE:
{ {
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;

19
ggml.h
View file

@ -410,7 +410,6 @@ extern "C" {
GGML_OP_DIV, GGML_OP_DIV,
GGML_OP_SQR, GGML_OP_SQR,
GGML_OP_SQRT, GGML_OP_SQRT,
GGML_OP_EXP,
GGML_OP_LOG, GGML_OP_LOG,
GGML_OP_SUM, GGML_OP_SUM,
GGML_OP_SUM_ROWS, GGML_OP_SUM_ROWS,
@ -444,7 +443,6 @@ extern "C" {
GGML_OP_DIAG_MASK_ZERO, GGML_OP_DIAG_MASK_ZERO,
GGML_OP_SOFT_MAX, GGML_OP_SOFT_MAX,
GGML_OP_SOFT_MAX_BACK, GGML_OP_SOFT_MAX_BACK,
GGML_OP_SOFT_PLUS,
GGML_OP_ROPE, GGML_OP_ROPE,
GGML_OP_ROPE_BACK, GGML_OP_ROPE_BACK,
GGML_OP_ALIBI, GGML_OP_ALIBI,
@ -935,14 +933,6 @@ extern "C" {
struct ggml_context * ctx, struct ggml_context * ctx,
struct ggml_tensor * a); struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_exp(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_exp_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_log( GGML_API struct ggml_tensor * ggml_log(
struct ggml_context * ctx, struct ggml_context * ctx,
struct ggml_tensor * a); struct ggml_tensor * a);
@ -1431,15 +1421,6 @@ extern "C" {
struct ggml_tensor * a, struct ggml_tensor * a,
struct ggml_tensor * b); struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_soft_plus(
struct ggml_context * ctx,
struct ggml_tensor * a);
// in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_soft_plus_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
// rotary position embedding // rotary position embedding
// if mode & 1 == 1, skip n_past elements (DEPRECATED) // if mode & 1 == 1, skip n_past elements (DEPRECATED)
// if mode & 2 == 1, GPT-NeoX style // if mode & 2 == 1, GPT-NeoX style