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ggml: fix typo in ggml.c #14531

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116 changes: 58 additions & 58 deletions ggml/src/ggml.c
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Original file line number Diff line number Diff line change
Expand Up @@ -330,7 +330,7 @@ void * ggml_aligned_malloc(size_t size) {
#endif
if (result != 0) {
// Handle allocation failure
const char *error_desc = "unknown allocation error";
const char * error_desc = "unknown allocation error";
switch (result) {
case EINVAL:
error_desc = "invalid alignment value";
Expand Down Expand Up @@ -1174,13 +1174,13 @@ void ggml_print_objects(const struct ggml_context * ctx) {
int64_t ggml_nelements(const struct ggml_tensor * tensor) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
return tensor->ne[0] * tensor->ne[1] * tensor->ne[2] * tensor->ne[3];
}

int64_t ggml_nrows(const struct ggml_tensor * tensor) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

return tensor->ne[1]*tensor->ne[2]*tensor->ne[3];
return tensor->ne[1] * tensor->ne[2] * tensor->ne[3];
}

size_t ggml_nbytes(const struct ggml_tensor * tensor) {
Expand All @@ -1194,14 +1194,14 @@ size_t ggml_nbytes(const struct ggml_tensor * tensor) {
const size_t blck_size = ggml_blck_size(tensor->type);
if (blck_size == 1) {
nbytes = ggml_type_size(tensor->type);
for (int i = 0; i < GGML_MAX_DIMS; ++i) {
nbytes += (tensor->ne[i] - 1)*tensor->nb[i];
for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
nbytes += (tensor->ne[i] - 1) * tensor->nb[i];
}
}
else {
nbytes = tensor->ne[0]*tensor->nb[0]/blck_size;
for (int i = 1; i < GGML_MAX_DIMS; ++i) {
nbytes += (tensor->ne[i] - 1)*tensor->nb[i];
nbytes = tensor->ne[0] * tensor->nb[0] / blck_size;
for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
nbytes += (tensor->ne[i] - 1) * tensor->nb[i];
}
}

Expand All @@ -1222,11 +1222,11 @@ size_t ggml_type_size(enum ggml_type type) {

size_t ggml_row_size(enum ggml_type type, int64_t ne) {
assert(ne % ggml_blck_size(type) == 0);
return ggml_type_size(type)*ne/ggml_blck_size(type);
return ggml_type_size(type) * ne / ggml_blck_size(type);
}

double ggml_type_sizef(enum ggml_type type) {
return ((double)(type_traits[type].type_size))/type_traits[type].blck_size;
return ((double)(type_traits[type].type_size)) / type_traits[type].blck_size;
}

const char * ggml_type_name(enum ggml_type type) {
Expand Down Expand Up @@ -1304,19 +1304,19 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
enum ggml_type wtype = GGML_TYPE_COUNT;

switch (ftype) {
case GGML_FTYPE_ALL_F32: wtype = GGML_TYPE_F32; break;
case GGML_FTYPE_MOSTLY_F16: wtype = GGML_TYPE_F16; break;
case GGML_FTYPE_MOSTLY_BF16: wtype = GGML_TYPE_BF16; break;
case GGML_FTYPE_MOSTLY_Q4_0: wtype = GGML_TYPE_Q4_0; break;
case GGML_FTYPE_MOSTLY_Q4_1: wtype = GGML_TYPE_Q4_1; break;
case GGML_FTYPE_MOSTLY_Q5_0: wtype = GGML_TYPE_Q5_0; break;
case GGML_FTYPE_MOSTLY_Q5_1: wtype = GGML_TYPE_Q5_1; break;
case GGML_FTYPE_MOSTLY_Q8_0: wtype = GGML_TYPE_Q8_0; break;
case GGML_FTYPE_MOSTLY_Q2_K: wtype = GGML_TYPE_Q2_K; break;
case GGML_FTYPE_MOSTLY_Q3_K: wtype = GGML_TYPE_Q3_K; break;
case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break;
case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break;
case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break;
case GGML_FTYPE_ALL_F32: wtype = GGML_TYPE_F32; break;
case GGML_FTYPE_MOSTLY_F16: wtype = GGML_TYPE_F16; break;
case GGML_FTYPE_MOSTLY_BF16: wtype = GGML_TYPE_BF16; break;
case GGML_FTYPE_MOSTLY_Q4_0: wtype = GGML_TYPE_Q4_0; break;
case GGML_FTYPE_MOSTLY_Q4_1: wtype = GGML_TYPE_Q4_1; break;
case GGML_FTYPE_MOSTLY_Q5_0: wtype = GGML_TYPE_Q5_0; break;
case GGML_FTYPE_MOSTLY_Q5_1: wtype = GGML_TYPE_Q5_1; break;
case GGML_FTYPE_MOSTLY_Q8_0: wtype = GGML_TYPE_Q8_0; break;
case GGML_FTYPE_MOSTLY_Q2_K: wtype = GGML_TYPE_Q2_K; break;
case GGML_FTYPE_MOSTLY_Q3_K: wtype = GGML_TYPE_Q3_K; break;
case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break;
case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break;
case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break;
case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break;
case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break;
Expand All @@ -1326,8 +1326,8 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break;
case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break;
case GGML_FTYPE_MOSTLY_IQ2_S: wtype = GGML_TYPE_IQ2_S; break;
case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break;
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break;
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break;
}

GGML_ASSERT(wtype != GGML_TYPE_COUNT);
Expand All @@ -1348,7 +1348,7 @@ static bool ggml_is_contiguous_n(const struct ggml_tensor * tensor, int n) {
if (tensor->ne[0] != ggml_blck_size(tensor->type) && tensor->nb[0] != next_nb) {
return false;
}
next_nb *= tensor->ne[0]/ggml_blck_size(tensor->type);
next_nb *= tensor->ne[0] / ggml_blck_size(tensor->type);
for (int i = 1; i < GGML_MAX_DIMS; i++) {
if (tensor->ne[i] != 1) {
if (i > n) {
Expand All @@ -1358,7 +1358,7 @@ static bool ggml_is_contiguous_n(const struct ggml_tensor * tensor, int n) {
next_nb *= tensor->ne[i];
} else {
// this dimension does not need to be contiguous
next_nb = tensor->ne[i]*tensor->nb[i];
next_nb = tensor->ne[i] * tensor->nb[i];
}
}
}
Expand All @@ -1382,7 +1382,7 @@ bool ggml_is_contiguous_2(const struct ggml_tensor * tensor) {
}

bool ggml_is_contiguously_allocated(const struct ggml_tensor * tensor) {
return ggml_nbytes(tensor) == ggml_nelements(tensor) * ggml_type_size(tensor->type)/ggml_blck_size(tensor->type);
return ggml_nbytes(tensor) == ggml_nelements(tensor) * ggml_type_size(tensor->type) / ggml_blck_size(tensor->type);
}

bool ggml_is_permuted(const struct ggml_tensor * tensor) {
Expand All @@ -1409,8 +1409,8 @@ static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) {

return
tensor->nb[0] == ggml_type_size(tensor->type) &&
tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
tensor->nb[2] == tensor->nb[1] * tensor->ne[1] &&
tensor->nb[3] == tensor->nb[2] * tensor->ne[2];
}

bool ggml_is_empty(const struct ggml_tensor * tensor) {
Expand Down Expand Up @@ -1675,9 +1675,9 @@ static struct ggml_tensor * ggml_new_tensor_impl(
}

result->nb[0] = ggml_type_size(type);
result->nb[1] = result->nb[0]*(result->ne[0]/ggml_blck_size(type));
for (int i = 2; i < GGML_MAX_DIMS; i++) {
result->nb[i] = result->nb[i - 1]*result->ne[i - 1];
result->nb[1] = result->nb[0] * (result->ne[0] / ggml_blck_size(type));
for (size_t i = 2; i < GGML_MAX_DIMS; i++) {
result->nb[i] = result->nb[i - 1] * result->ne[i - 1];
}

ctx->n_objects++;
Expand Down Expand Up @@ -1745,22 +1745,22 @@ void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t *
const int64_t ne1 = tensor->ne[1];
const int64_t ne0 = tensor->ne[0];

const int64_t i3_ = (i/(ne2*ne1*ne0));
const int64_t i2_ = (i - i3_*ne2*ne1*ne0)/(ne1*ne0);
const int64_t i1_ = (i - i3_*ne2*ne1*ne0 - i2_*ne1*ne0)/ne0;
const int64_t i0_ = (i - i3_*ne2*ne1*ne0 - i2_*ne1*ne0 - i1_*ne0);
const int64_t i3_ = (i / (ne2 * ne1 * ne0));
const int64_t i2_ = (i - i3_ * ne2 * ne1 * ne0) / (ne1 * ne0);
const int64_t i1_ = (i - i3_ * ne2 * ne1 * ne0 - i2_ * ne1 * ne0) / ne0;
const int64_t i0_ = (i - i3_ * ne2 * ne1 * ne0 - i2_ * ne1 * ne0 - i1_ * ne0);

if (i0) {
* i0 = i0_;
*i0 = i0_;
}
if (i1) {
* i1 = i1_;
*i1 = i1_;
}
if (i2) {
* i2 = i2_;
*i2 = i2_;
}
if (i3) {
* i3 = i3_;
*i3 = i3_;
}
}

Expand Down Expand Up @@ -1810,7 +1810,7 @@ struct ggml_tensor * ggml_view_tensor(
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, GGML_MAX_DIMS, src->ne, src, 0);
ggml_format_name(result, "%s (view)", src->name);

for (int i = 0; i < GGML_MAX_DIMS; i++) {
for (size_t i = 0; i < GGML_MAX_DIMS; i++) {
result->nb[i] = src->nb[i];
}

Expand Down Expand Up @@ -2290,7 +2290,7 @@ struct ggml_tensor * ggml_sum_rows(
struct ggml_context * ctx,
struct ggml_tensor * a) {
int64_t ne[GGML_MAX_DIMS] = { 1 };
for (int i = 1; i < GGML_MAX_DIMS; ++i) {
for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
ne[i] = a->ne[i];
}

Expand Down Expand Up @@ -2678,7 +2678,7 @@ static struct ggml_tensor * ggml_glu_impl(
GGML_ASSERT(a->type == b->type);
}

int64_t ne[GGML_MAX_DIMS] = { a->ne[0] / 2 }; for (int i = 1; i < GGML_MAX_DIMS; i++) ne[i] = a->ne[i];
int64_t ne[GGML_MAX_DIMS] = { a->ne[0] / 2 }; for (size_t i = 1; i < GGML_MAX_DIMS; i++) ne[i] = a->ne[i];
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, GGML_MAX_DIMS, b ? a->ne : ne, NULL, 0);

ggml_set_op_params_i32(result, 0, (int32_t) op);
Expand Down Expand Up @@ -2963,9 +2963,9 @@ struct ggml_tensor * ggml_l2_norm_inplace(
static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

return (t0->ne[0] == t1->ne[0]) &&
(t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable
(t1->ne[3]%t0->ne[3] == 0);
return (t0->ne[0] == t1->ne[0]) &&
(t1->ne[2] % t0->ne[2] == 0) && // verify t0 is broadcastable
(t1->ne[3] % t0->ne[3] == 0);
}

struct ggml_tensor * ggml_mul_mat(
Expand Down Expand Up @@ -3040,9 +3040,9 @@ struct ggml_tensor * ggml_mul_mat_id(
static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct ggml_tensor * t1) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");

return (t0->ne[1] == t1->ne[1]) &&
(t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable
(t1->ne[3]%t0->ne[3] == 0);
return (t0->ne[1] == t1->ne[1]) &&
(t1->ne[2] % t0->ne[2] == 0) && // verify t0 is broadcastable
(t1->ne[3] % t0->ne[3] == 0);
}

struct ggml_tensor * ggml_out_prod(
Expand Down Expand Up @@ -3720,8 +3720,8 @@ static struct ggml_tensor * ggml_soft_max_impl(
GGML_ASSERT(ggml_is_contiguous(mask));
GGML_ASSERT(mask->ne[0] == a->ne[0]);
GGML_ASSERT(mask->ne[1] >= a->ne[1]);
GGML_ASSERT(a->ne[2]%mask->ne[2] == 0);
GGML_ASSERT(a->ne[3]%mask->ne[3] == 0);
GGML_ASSERT(a->ne[2] % mask->ne[2] == 0);
GGML_ASSERT(a->ne[3] % mask->ne[3] == 0);
}

if (max_bias > 0.0f) {
Expand Down Expand Up @@ -4418,7 +4418,7 @@ struct ggml_tensor * ggml_conv_transpose_2d_p0(
a->ne[2], b->ne[3],
};

struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);

ggml_set_op_params_i32(result, 0, stride);

Expand Down Expand Up @@ -5043,8 +5043,8 @@ static struct ggml_tensor * ggml_add_rel_pos_impl(
GGML_ASSERT(ph->type == GGML_TYPE_F32);
GGML_ASSERT(pw->type == GGML_TYPE_F32);
GGML_ASSERT(pw->ne[3] == a->ne[2]);
GGML_ASSERT(pw->ne[0]*pw->ne[0] == a->ne[0]);
GGML_ASSERT(pw->ne[1]*pw->ne[2] == a->ne[1]);
GGML_ASSERT(pw->ne[0] * pw->ne[0] == a->ne[0]);
GGML_ASSERT(pw->ne[1] * pw->ne[2] == a->ne[1]);

struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params_i32(result, 0, inplace ? 1 : 0);
Expand Down Expand Up @@ -5544,7 +5544,7 @@ size_t ggml_hash_size(size_t min_sz) {
size_t l = 0;
size_t r = n_primes;
while (l < r) {
size_t m = (l + r)/2;
size_t m = (l + r) / 2;
if (primes[m] < min_sz) {
l = m + 1;
} else {
Expand Down Expand Up @@ -6138,7 +6138,7 @@ static size_t ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor
return node_hash_pos;
}

for (int i = 0; i < GGML_MAX_SRC; ++i) {
for (size_t i = 0; i < GGML_MAX_SRC; ++i) {
const int k =
(cgraph->order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? i :
(cgraph->order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? (GGML_MAX_SRC-1-i) :
Expand Down Expand Up @@ -6260,7 +6260,7 @@ void ggml_build_backward_expand(
default:
break;
}
for (int j = 0; j < GGML_MAX_SRC; ++j) {
for (size_t j = 0; j < GGML_MAX_SRC; ++j) {
if (!node->src[j] || ignore_src[j] || !grads_needed[ggml_hash_find(&cgraph->visited_hash_set, node->src[j])]) {
continue;
}
Expand Down
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