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Merged
merged 1 commit into from
Dec 14, 2023
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ggml : remove n_dims from ggml_tensor #4469

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18 changes: 10 additions & 8 deletions common/train.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -71,7 +71,7 @@ void free_random_uniform_distribution(struct random_uniform_distribution * rnd)

struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) {
float scale = 1.0f; // xavier
switch (tensor->n_dims) {
switch (ggml_n_dims(tensor)) {
case 1:
scale /= sqrtf((float) tensor->ne[0]);
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
Expand Down Expand Up @@ -119,7 +119,7 @@ struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct
}

struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struct random_uniform_distribution * rnd) {
switch (tensor->n_dims) {
switch (ggml_n_dims(tensor)) {
case 1:
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float * dst = (float *) ((char *) tensor->data + i0*tensor->nb[0]);
Expand Down Expand Up @@ -183,25 +183,27 @@ float fclamp(const float v, const float min, const float max) {
}

void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) {
GGML_ASSERT(tensor->n_dims == 1);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == 1);
GGML_ASSERT(tensor->ne[2] == 1);
GGML_ASSERT(tensor->ne[3] == 1);
}

void assert_shape_2d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1) {
GGML_ASSERT(tensor->n_dims == 2);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
GGML_ASSERT(tensor->ne[2] == 1);
GGML_ASSERT(tensor->ne[3] == 1);
}

void assert_shape_3d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2) {
GGML_ASSERT(tensor->n_dims == 3);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
GGML_ASSERT(tensor->ne[2] == ne2);
GGML_ASSERT(tensor->ne[3] == 1);
}

void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
GGML_ASSERT(tensor->n_dims == 4);
GGML_ASSERT(tensor->ne[0] == ne0);
GGML_ASSERT(tensor->ne[1] == ne1);
GGML_ASSERT(tensor->ne[2] == ne2);
Expand All @@ -225,8 +227,8 @@ int64_t get_example_targets_batch(
bool sample_random_offsets
) {
GGML_ASSERT(samples_count > 0);
GGML_ASSERT(tokens_input->n_dims == 2);
GGML_ASSERT(target_probs->n_dims == 3);
GGML_ASSERT(ggml_is_matrix(tokens_input));
GGML_ASSERT(ggml_is_3d(target_probs));
int64_t n_vocab = target_probs->ne[0];
int64_t n_tokens = tokens_input->ne[0];
int64_t n_batch = tokens_input->ne[1];
Expand Down
18 changes: 9 additions & 9 deletions examples/baby-llama/baby-llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1258,9 +1258,9 @@ static struct ggml_tensor * forward_lora(
}

static void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) {
assert(logits->n_dims == 2);
assert(probs->n_dims == 2);
assert(best_samples->n_dims == 1);
assert(ggml_is_matrix(logits));
assert(ggml_is_matrix(probs));
assert(ggml_is_vector(best_samples));
assert(logits->ne[1] == best_samples->ne[0]);
assert(logits->ne[0] == probs->ne[0]);
assert(logits->ne[1] == probs->ne[1]);
Expand Down Expand Up @@ -1292,9 +1292,9 @@ static void sample_softmax_batch(
struct ggml_context * ctx, struct ggml_tensor * logits, struct ggml_tensor * probs,
struct ggml_tensor * best_samples
) {
GGML_ASSERT(best_samples->n_dims == 2);
GGML_ASSERT(logits->n_dims == 3);
GGML_ASSERT(probs->n_dims == 3);
GGML_ASSERT(ggml_is_matrix(best_samples));
GGML_ASSERT(ggml_is_3d(logits));
GGML_ASSERT(ggml_is_3d(probs));
int n_tokens = best_samples->ne[0];
int n_batch = best_samples->ne[1];
int n_vocab = logits->ne[0];
Expand Down Expand Up @@ -1334,7 +1334,7 @@ static void print_row(struct ggml_tensor * probs, int i) {
}

static void print_matrix(struct ggml_tensor * probs) {
assert(probs->n_dims == 2);
assert(ggml_is_matrix(probs));
for (int i = 0; i < probs->ne[1]; ++i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);
Expand Down Expand Up @@ -1386,8 +1386,8 @@ static void get_example_targets(int example_id, struct ggml_tensor * tokens_inpu
static void get_example_targets_batch(
struct ggml_context * ctx, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets
) {
GGML_ASSERT(tokens_input->n_dims == 2);
GGML_ASSERT( targets->n_dims == 3);
GGML_ASSERT(ggml_is_matrix(tokens_input));
GGML_ASSERT(ggml_is_3d(targets));
int n_tokens = tokens_input->ne[0];
int n_batch = tokens_input->ne[1];
GGML_ASSERT(n_tokens == targets->ne[1]);
Expand Down
4 changes: 2 additions & 2 deletions examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
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Expand Up @@ -427,7 +427,7 @@ static void print_row(struct ggml_tensor * probs, int i) {
}

static void print_matrix(struct ggml_tensor * probs) {
assert(probs->n_dims == 2);
assert(ggml_is_matrix(probs));
for (int i = 0; i < probs->ne[1]; ++i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = get_f32_2d(probs, k, i);
Expand Down Expand Up @@ -639,7 +639,7 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab

static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) {
int ct;
switch (gg_weights->n_dims){
switch (ggml_n_dims(gg_weights)) {
case 1:
ct = 0;
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){
Expand Down
2 changes: 1 addition & 1 deletion examples/finetune/finetune.cpp
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Expand Up @@ -1110,7 +1110,7 @@ static void write_tensor(struct llama_file * file, struct ggml_tensor * tensor,
name = ggml_get_name(tensor);
}
uint32_t name_len = strlen(name);
uint32_t nd = tensor->n_dims;
uint32_t nd = ggml_n_dims(tensor);
uint32_t ne[4] = { (uint32_t)tensor->ne[0],
(uint32_t)tensor->ne[1],
(uint32_t)tensor->ne[2],
Expand Down
2 changes: 1 addition & 1 deletion examples/gguf/gguf.cpp
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Expand Up @@ -195,7 +195,7 @@ static bool gguf_ex_read_1(const std::string & fname) {

struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);

printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, cur->n_dims, cur->name, cur->data);
printf("%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, ggml_n_dims(cur), cur->name, cur->data);

// print first 10 elements
const float * data = (const float *) cur->data;
Expand Down
6 changes: 3 additions & 3 deletions examples/llava/clip.cpp
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Expand Up @@ -514,7 +514,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
ctx_size += padded_size;
if (verbosity >= 3) {
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, padded_size=%zu, offset=%zu\n", __func__, i,
cur->n_dims, cur->name, tensor_size, padded_size, offset);
ggml_n_dims(cur), cur->name, tensor_size, padded_size, offset);
}
}
}
Expand Down Expand Up @@ -962,7 +962,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
}

// quantize only 2D tensors
quantize &= (cur->n_dims == 2);
quantize &= (ggml_n_dims(cur) == 2);

if (quantize) {
new_type = type;
Expand Down Expand Up @@ -1035,7 +1035,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
fout.put(0);
}

printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), cur->n_dims, quantize,
printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
}

Expand Down
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