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llama.cpp/tests/test-model-random.cpp
Francis Couture-Harpin 9d873d7543 test-model-random : shuffle across sequences but not within 
There isn't really a use-case for fully-shuffled batches

* test-model-random : use F32 as the KV cache type

Temporary until F16 is fixed on ARM when using FP16_VECTOR_ARITHMETIC
2025-06-18 15:07:24 -04:00

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#include <common.h>
#include <llama.h>
#include <string.h>
#include <cmath>
#include <algorithm>
#include <cstdint>
#include <cstdio>
#include <queue>
#include <random>
#include <utility>
// NOTE: the llm_arch enum is in the private API
#include "../src/llama-model.h"
#include "ggml.h"
#include "gguf.h"
// For gguf_type_size
#include "../ggml/src/ggml-impl.h"
struct random_tensor {
const std::string name;
const std::vector<int64_t> shape;
const ggml_type type = GGML_TYPE_F32; // TODO: maybe make this configurable?
const std::function<float(std::mt19937 &, int64_t i)> distribution;
random_tensor(std::string name, std::vector<int64_t> shape,
const std::function<float(std::mt19937 &)> & distribution = std::normal_distribution<float>()) :
name(std::move(name)),
shape(std::move(shape)),
distribution([distribution](std::mt19937 & rng, int64_t i) {
GGML_UNUSED(i);
return distribution(rng);
}) {}
random_tensor(std::string name, std::vector<int64_t> shape, const std::function<float(int64_t)> & distribution) :
name(std::move(name)),
shape(std::move(shape)),
distribution([distribution](std::mt19937 & rng, int64_t i) {
GGML_UNUSED(rng);
return distribution(i);
}) {}
random_tensor(std::string name, std::vector<int64_t> shape, const std::function<float()> & distribution) :
name(std::move(name)),
shape(std::move(shape)),
distribution([distribution](std::mt19937 & rng, int64_t i) {
GGML_UNUSED(rng);
GGML_UNUSED(i);
return distribution();
}) {}
random_tensor(std::string name, std::vector<int64_t> shape,
std::function<float(std::mt19937 &, int64_t i)> distribution) :
name(std::move(name)),
shape(std::move(shape)),
distribution(std::move(distribution)) {}
random_tensor(const random_tensor & other) :
name(other.name),
shape(other.shape),
type(other.type),
distribution(other.distribution) {}
size_t n_bytes() const {
int64_t prod = 1;
for (int64_t d : shape) {
prod *= d;
}
GGML_ASSERT(prod != 0);
return ggml_row_size(type, prod);
}
ggml_tensor * to_ggml_tensor(ggml_context * ctx, std::mt19937 & rng) const {
ggml_tensor * tensor = ggml_new_tensor(ctx, type, shape.size(), shape.data());
GGML_ASSERT(tensor->data != nullptr);
int64_t n_elems = 1;
for (size_t i = 0; i < shape.size(); ++i) {
n_elems *= shape[i];
}
for (int64_t i = 0; i < n_elems; ++i) {
((float *) tensor->data)[i] = distribution(rng, i);
}
return tensor;
}
};
// TODO: move this to the gguf library?
struct gguf_value {
const gguf_type type;
union {
uint8_t uint8;
int8_t int8;
uint16_t uint16;
int16_t int16;
uint32_t uint32;
int32_t int32;
float float32;
bool boolean;
std::string * string;
std::vector<gguf_value> * array;
uint64_t uint64;
int64_t int64;
double float64;
} value;
~gguf_value() {
switch (type) {
case GGUF_TYPE_STRING:
delete value.string;
break;
case GGUF_TYPE_ARRAY:
delete value.array;
break;
default:
break;
}
}
gguf_value(const gguf_value & other) : type(other.type) {
switch (type) {
case GGUF_TYPE_UINT8:
case GGUF_TYPE_INT8:
case GGUF_TYPE_UINT16:
case GGUF_TYPE_INT16:
case GGUF_TYPE_UINT32:
case GGUF_TYPE_INT32:
case GGUF_TYPE_FLOAT32:
case GGUF_TYPE_BOOL:
case GGUF_TYPE_UINT64:
case GGUF_TYPE_INT64:
case GGUF_TYPE_FLOAT64:
case GGUF_TYPE_COUNT:
value = other.value;
break;
case GGUF_TYPE_STRING:
value.string = new std::string(*other.value.string);
break;
case GGUF_TYPE_ARRAY:
value.array = new std::vector(*other.value.array);
break;
}
}
gguf_value(int8_t val) : type(GGUF_TYPE_INT8) { value.int8 = val; }
gguf_value(uint8_t val) : type(GGUF_TYPE_UINT8) { value.uint8 = val; }
gguf_value(int16_t val) : type(GGUF_TYPE_INT16) { value.int16 = val; }
gguf_value(uint16_t val) : type(GGUF_TYPE_UINT16) { value.uint16 = val; }
gguf_value(int32_t val) : type(GGUF_TYPE_INT32) { value.int32 = val; }
gguf_value(uint32_t val) : type(GGUF_TYPE_UINT32) { value.uint32 = val; }
gguf_value(float val) : type(GGUF_TYPE_FLOAT32) { value.float32 = val; }
gguf_value(bool val) : type(GGUF_TYPE_BOOL) { value.boolean = val; }
gguf_value(uint64_t val) : type(GGUF_TYPE_UINT64) { value.uint64 = val; }
gguf_value(int64_t val) : type(GGUF_TYPE_INT64) { value.int64 = val; }
gguf_value(double val) : type(GGUF_TYPE_FLOAT64) { value.float64 = val; }
gguf_value(std::string val) : type(GGUF_TYPE_STRING) { value.string = new std::string(std::move(val)); }
gguf_value(const char * val) : type(GGUF_TYPE_STRING) { value.string = new std::string(val); }
gguf_value(std::vector<gguf_value> val) : type(GGUF_TYPE_ARRAY) { value.array = new std::vector(std::move(val)); }
gguf_value(const std::vector<gguf_value> & val) : type(GGUF_TYPE_ARRAY) { value.array = new std::vector(val); }
template <typename T> gguf_value(const std::vector<T> & val) : type(GGUF_TYPE_ARRAY) {
value.array = new std::vector<gguf_value>();
for (const auto & v : val) {
value.array->push_back(gguf_value(v));
}
}
void set(gguf_context * ctx, const std::string & key) const {
const char * k = key.c_str();
switch (type) {
case GGUF_TYPE_UINT8:
gguf_set_val_u8(ctx, k, value.uint8);
break;
case GGUF_TYPE_INT8:
gguf_set_val_i8(ctx, k, value.int8);
break;
case GGUF_TYPE_UINT16:
gguf_set_val_u16(ctx, k, value.uint16);
break;
case GGUF_TYPE_INT16:
gguf_set_val_i16(ctx, k, value.int16);
break;
case GGUF_TYPE_UINT32:
gguf_set_val_u32(ctx, k, value.uint32);
break;
case GGUF_TYPE_INT32:
gguf_set_val_i32(ctx, k, value.int32);
break;
case GGUF_TYPE_FLOAT32:
gguf_set_val_f32(ctx, k, value.float32);
break;
case GGUF_TYPE_BOOL:
gguf_set_val_bool(ctx, k, value.boolean);
break;
case GGUF_TYPE_STRING:
gguf_set_val_str(ctx, k, value.string->c_str());
break;
case GGUF_TYPE_ARRAY:
{
const size_t arr_size = value.array->size();
if (arr_size > 0) {
const gguf_type arr_type = (*value.array)[0].type;
if (arr_type == GGUF_TYPE_STRING) {
std::vector<const char *> strings(arr_size);
for (size_t i = 0; i < arr_size; ++i) {
strings[i] = (*value.array)[i].value.string->c_str();
}
gguf_set_arr_str(ctx, k, strings.data(), strings.size());
} else {
const size_t type_size = gguf_type_size(arr_type);
std::vector<uint8_t> data(type_size * arr_size);
for (size_t i = 0; i < arr_size; ++i) {
memcpy(data.data() + type_size * i, &(*value.array)[i].value, type_size);
}
gguf_set_arr_data(ctx, k, arr_type, data.data(), data.size() / type_size);
}
// TODO: handle nested arrays
}
break;
}
case GGUF_TYPE_UINT64:
gguf_set_val_u64(ctx, k, value.uint64);
break;
case GGUF_TYPE_INT64:
gguf_set_val_i64(ctx, k, value.int64);
break;
case GGUF_TYPE_FLOAT64:
gguf_set_val_f64(ctx, k, value.float64);
break;
case GGUF_TYPE_COUNT:
break;
}
}
};
struct model_variant {
llm_arch arch;
std::string name;
std::vector<random_tensor> tensors;
std::vector<std::pair<llm_kv, gguf_value>> metadata;
model_variant(llm_arch arch, const std::string & name) : arch(arch), name(name) {
add_kv(LLM_KV_GENERAL_TYPE, "model");
add_kv(LLM_KV_GENERAL_ARCHITECTURE, llm_arch_name(arch));
add_kv(LLM_KV_GENERAL_NAME, name);
}
model_variant(const model_variant & other) :
arch(other.arch),
name(other.name),
tensors(other.tensors),
metadata(other.metadata) {}
void add_tensor(const std::string & name, const std::vector<int64_t> & shape, float gain = 1.0f) {
// ref: https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/nn/init.py#L515-L516
const auto init_kaiming_uniform = [gain](uint32_t fan_in) {
const float std = gain * std::sqrt(fan_in);
const float bound = std::sqrt(3.0f) * std;
return std::uniform_real_distribution<float>(-bound, bound);
};
tensors.push_back(random_tensor(name, shape, init_kaiming_uniform(shape[0])));
}
void add_tensor(const std::string & name, const std::vector<int64_t> & shape,
const std::function<float(std::mt19937 &)> & distribution) {
tensors.push_back(random_tensor(name, shape, distribution));
}
void add_tensor(const std::string & name, const std::vector<int64_t> & shape,
const std::function<float(std::mt19937 &, int64_t)> & distribution) {
tensors.push_back(random_tensor(name, shape, distribution));
}
void add_tensor(const std::string & name, const std::vector<int64_t> & shape,
const std::function<float(int64_t)> & distribution) {
tensors.push_back(random_tensor(name, shape, distribution));
}
void add_tensor(const std::string & name, const std::vector<int64_t> & shape,
const std::function<float()> & distribution) {
tensors.push_back(random_tensor(name, shape, distribution));
}
void add_kv(llm_kv kv, const gguf_value & value) { metadata.push_back(std::pair(kv, value)); }
bool write_to_file(const char * fname, std::mt19937 & rng) const {
gguf_context * ctx_gguf = gguf_init_empty();
auto kv = LLM_KV(arch);
for (const auto & m : metadata) {
m.second.set(ctx_gguf, kv(m.first));
}
size_t total_size = 0;
for (const auto & t : tensors) {
total_size += GGML_PAD(t.n_bytes() + ggml_tensor_overhead(), GGML_MEM_ALIGN);
}
ggml_init_params init_params = {
total_size,
nullptr, // allocate internally
false, // do allocate memory when creating tensors
};
ggml_context * ctx = ggml_init(init_params);
// initialize the tensors and add to GGUF
for (const auto & t : tensors) {
ggml_tensor * tensor = t.to_ggml_tensor(ctx, rng);
ggml_set_name(tensor, t.name.c_str());
gguf_add_tensor(ctx_gguf, tensor);
}
bool status = gguf_write_to_file(ctx_gguf, fname, false);
ggml_free(ctx);
gguf_free(ctx_gguf);
return status;
}
static void insert_from_arch(std::vector<model_variant> & variants, llm_arch arch) {
uint32_t n_vocab = 256;
uint32_t n_embd = 32;
uint32_t n_ff = 3 * n_embd;
uint32_t n_layer = 2;
auto tn = LLM_TN(arch);
// random vocab
const auto add_tokenizer = [](model_variant & m, uint32_t n_vocab) {
std::vector<std::string> vocab_tokens(n_vocab);
std::vector<float> vocab_scores(n_vocab);
std::vector<int32_t> vocab_types(n_vocab);
char buf[32];
for (size_t i = 0; i < n_vocab; ++i) {
snprintf(buf, sizeof(buf), "<%zu>", i);
vocab_tokens[i] = std::string(buf);
vocab_scores[i] = -1000.0f;
vocab_types[i] = 4; // USER_DEFINED type
}
m.add_kv(LLM_KV_TOKENIZER_MODEL, "llama");
m.add_kv(LLM_KV_TOKENIZER_PRE, "default");
m.add_kv(LLM_KV_TOKENIZER_LIST, vocab_tokens);
m.add_kv(LLM_KV_TOKENIZER_SCORES, vocab_scores);
m.add_kv(LLM_KV_TOKENIZER_TOKEN_TYPE, vocab_types);
};
// don't actually use bias
const auto init_bias = []() {
return 0.0f;
};
// TODO: fill the variants
// TODO: how to make the variants more modular?
switch (arch) {
case LLM_ARCH_LLAMA:
{
variants.push_back(model_variant(arch, "Llama2"));
model_variant & cur = variants.back();
n_embd = 16;
const uint32_t n_head = 4;
const uint32_t n_head_kv = n_head / 2;
const uint32_t n_embd_head_k = n_embd / n_head;
const uint32_t n_embd_k_gqa = n_embd_head_k * n_head_kv;
const uint32_t n_embd_v_gqa = n_embd_k_gqa;
cur.add_kv(LLM_KV_BLOCK_COUNT, n_layer);
cur.add_kv(LLM_KV_CONTEXT_LENGTH, (uint32_t) 4096);
cur.add_kv(LLM_KV_EMBEDDING_LENGTH, n_embd);
cur.add_kv(LLM_KV_FEED_FORWARD_LENGTH, n_ff);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head_kv);
cur.add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
cur.add_kv(LLM_KV_ROPE_DIMENSION_COUNT, n_embd / n_head);
add_tokenizer(cur, n_vocab);
cur.add_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
cur.add_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
// omitting the actual output tensor to leave it use tok_embd
for (uint32_t i = 0; i < n_layer; ++i) {
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
cur.add_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
cur.add_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_LLAMA4: // has chunked interleaved sliding-window
{
variants.push_back(model_variant(arch, "Llama4"));
model_variant & cur = variants.back();
n_layer = 4; // for the swa pattern
n_embd = 16;
const uint32_t n_head = 4;
const uint32_t n_head_kv = n_head / 2;
const uint32_t n_embd_head_k = n_embd / n_head;
const uint32_t n_embd_k_gqa = n_embd_head_k * n_head_kv;
const uint32_t n_embd_v_gqa = n_embd_k_gqa;
const uint32_t n_moe_layer_step = 2;
const uint32_t n_ff_exp = n_embd * 2;
const uint32_t n_expert = 4;
cur.add_kv(LLM_KV_BLOCK_COUNT, n_layer);
cur.add_kv(LLM_KV_CONTEXT_LENGTH, (uint32_t) 4096);
cur.add_kv(LLM_KV_EMBEDDING_LENGTH, n_embd);
cur.add_kv(LLM_KV_FEED_FORWARD_LENGTH, n_ff);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head_kv);
cur.add_kv(LLM_KV_EXPERT_COUNT, n_expert);
cur.add_kv(LLM_KV_EXPERT_USED_COUNT, (uint32_t) 2);
cur.add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
cur.add_kv(LLM_KV_ROPE_DIMENSION_COUNT, n_embd / n_head);
cur.add_kv(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, n_moe_layer_step);
cur.add_kv(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, n_ff_exp);
// FIXME: this isn't used because the default is 8192
cur.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW, (uint32_t) 389); // prime number
add_tokenizer(cur, n_vocab);
cur.add_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
cur.add_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
// omitting the actual output tensor to leave it use tok_embd
for (uint32_t i = 0; i < n_layer; ++i) {
bool is_moe_layer = (i + 1) % n_moe_layer_step == 0;
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
cur.add_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
if (is_moe_layer) {
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert});
cur.add_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert});
cur.add_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert});
// Shared expert
const int64_t n_ff_shexp = n_ff_exp;
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp});
cur.add_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd });
cur.add_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp});
} else {
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
cur.add_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
}
} break;
case LLM_ARCH_DECI:
case LLM_ARCH_FALCON:
case LLM_ARCH_BAICHUAN:
case LLM_ARCH_GROK:
case LLM_ARCH_GPT2:
case LLM_ARCH_GPTJ:
case LLM_ARCH_GPTNEOX:
case LLM_ARCH_MPT:
case LLM_ARCH_STARCODER:
case LLM_ARCH_REFACT:
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_JINA_BERT_V2:
case LLM_ARCH_BLOOM:
case LLM_ARCH_STABLELM:
case LLM_ARCH_QWEN:
case LLM_ARCH_QWEN2:
case LLM_ARCH_QWEN2MOE:
case LLM_ARCH_QWEN2VL:
case LLM_ARCH_QWEN3:
case LLM_ARCH_QWEN3MOE:
case LLM_ARCH_PHI2:
case LLM_ARCH_PHI3:
case LLM_ARCH_PHIMOE:
case LLM_ARCH_PLAMO:
case LLM_ARCH_CODESHELL:
case LLM_ARCH_ORION:
case LLM_ARCH_INTERNLM2:
case LLM_ARCH_MINICPM:
case LLM_ARCH_MINICPM3:
case LLM_ARCH_GEMMA:
break;
case LLM_ARCH_GEMMA2: // has standard interleaved sliding-window
{
variants.push_back(model_variant(arch, "Gemma2"));
model_variant & cur = variants.back();
n_layer = 2; // minimum for the swa pattern
n_embd = 16;
const uint32_t n_head = 4;
const uint32_t n_head_kv = n_head / 2;
const uint32_t n_embd_head_k = n_embd / n_head;
const uint32_t n_embd_k_gqa = n_embd_head_k * n_head_kv;
const uint32_t n_embd_v_gqa = n_embd_k_gqa;
cur.add_kv(LLM_KV_CONTEXT_LENGTH, (uint32_t) 4096);
cur.add_kv(LLM_KV_EMBEDDING_LENGTH, n_embd);
cur.add_kv(LLM_KV_BLOCK_COUNT, n_layer);
cur.add_kv(LLM_KV_FEED_FORWARD_LENGTH, n_ff);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, n_head);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT_KV, n_head_kv);
cur.add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
cur.add_kv(LLM_KV_ATTN_LOGIT_SOFTCAPPING, 50.0f);
cur.add_kv(LLM_KV_FINAL_LOGIT_SOFTCAPPING, 30.0f);
cur.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW, (uint32_t) 389); // prime number
add_tokenizer(cur, n_vocab);
cur.add_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// output
cur.add_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
for (uint32_t i = 0; i < n_layer; ++i) {
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
cur.add_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
cur.add_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
cur.add_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
cur.add_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
cur.add_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
cur.add_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd});
}
} break;
case LLM_ARCH_GEMMA3:
case LLM_ARCH_STARCODER2:
break;
case LLM_ARCH_MAMBA:
{
variants.push_back(model_variant(arch, "Mamba"));
model_variant & cur = variants.back();
const uint32_t d_inner = 2 * n_embd;
const uint32_t d_conv = 4;
const uint32_t d_state = 16;
const uint32_t dt_rank = (n_embd + 15) / 16;
const auto init_A_S4D = [](int64_t i) {
return -((i % d_state) + 1);
};
cur.add_kv(LLM_KV_CONTEXT_LENGTH, (uint32_t) 1024 * 1024);
cur.add_kv(LLM_KV_EMBEDDING_LENGTH, n_embd);
cur.add_kv(LLM_KV_FEED_FORWARD_LENGTH, (uint32_t) 0);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, (uint32_t) 0);
cur.add_kv(LLM_KV_BLOCK_COUNT, n_layer);
cur.add_kv(LLM_KV_SSM_CONV_KERNEL, d_conv);
cur.add_kv(LLM_KV_SSM_INNER_SIZE, d_inner);
cur.add_kv(LLM_KV_SSM_STATE_SIZE, d_state);
cur.add_kv(LLM_KV_SSM_TIME_STEP_RANK, dt_rank);
cur.add_kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
add_tokenizer(cur, n_vocab);
cur.add_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab });
cur.add_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd });
cur.add_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab });
for (uint32_t i = 0; i < n_layer; ++i) {
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd });
cur.add_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), { n_embd, 2 * d_inner });
cur.add_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), { d_conv, d_inner });
cur.add_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), { d_inner }, init_bias);
cur.add_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), { d_inner, dt_rank + 2 * d_state });
cur.add_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), { dt_rank, d_inner });
cur.add_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), { d_inner }, init_bias);
// no "weight" suffix for these
cur.add_tensor(tn(LLM_TENSOR_SSM_A, i), { d_state, d_inner }, init_A_S4D);
cur.add_tensor(tn(LLM_TENSOR_SSM_D, i), { d_inner }, []() { return 1.0f; });
// out_proj
cur.add_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), { d_inner, n_embd });
}
}
break;
case LLM_ARCH_XVERSE:
case LLM_ARCH_COMMAND_R:
case LLM_ARCH_COHERE2:
case LLM_ARCH_DBRX:
case LLM_ARCH_OLMO:
case LLM_ARCH_OLMO2:
case LLM_ARCH_OLMOE:
case LLM_ARCH_OPENELM:
case LLM_ARCH_ARCTIC:
case LLM_ARCH_DEEPSEEK:
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_CHATGLM:
case LLM_ARCH_GLM4:
case LLM_ARCH_BITNET:
case LLM_ARCH_T5:
case LLM_ARCH_T5ENCODER:
case LLM_ARCH_JAIS:
case LLM_ARCH_NEMOTRON:
case LLM_ARCH_EXAONE:
case LLM_ARCH_RWKV6:
case LLM_ARCH_RWKV6QWEN2:
break;
case LLM_ARCH_RWKV7:
break;
// TODO: proper initialization of the tensors
// ref: https://github.com/BlinkDL/RWKV-LM/blob/247ce631e372b743ff496908d3e29df710506661/RWKV-v7/train_temp/src/model.py
{
variants.push_back(model_variant(arch, "RWKV7"));
model_variant & cur = variants.back();
// TODO: use more realistic hyperparams
n_embd = 128; // TODO: why does this need to be bigger than head_size?
const uint32_t head_size = 64; // TODO: is this assumed by the ops?
const auto calc_lora_rank = [](uint32_t hidden_size, float exponent, float multiplier) {
return std::max((uint32_t) 1,
(uint32_t) std::round(std::pow(hidden_size, exponent) * multiplier / 32.0f)) *
(uint32_t) 32;
};
const uint32_t n_lora_decay = calc_lora_rank(n_embd, 0.5, 1.8);
const uint32_t n_lora_iclr = calc_lora_rank(n_embd, 0.5, 1.8);
const uint32_t n_lora_value_res_mix = calc_lora_rank(n_embd, 0.5, 1.3);
const uint32_t n_lora_gate = calc_lora_rank(n_embd, 0.8, 0.6);
const uint32_t attn_hidden_size = n_embd;
const uint32_t ffn_size = n_ff;
cur.add_kv(LLM_KV_CONTEXT_LENGTH, (uint32_t) 1024 * 1024);
cur.add_kv(LLM_KV_EMBEDDING_LENGTH, n_embd);
cur.add_kv(LLM_KV_BLOCK_COUNT, n_layer);
cur.add_kv(LLM_KV_ATTENTION_LAYERNORM_EPS, 1e-5f);
cur.add_kv(LLM_KV_WKV_HEAD_SIZE, head_size);
cur.add_kv(LLM_KV_ATTENTION_DECAY_LORA_RANK, n_lora_decay);
cur.add_kv(LLM_KV_ATTENTION_ICLR_LORA_RANK, n_lora_iclr);
cur.add_kv(LLM_KV_ATTENTION_VALUE_RESIDUAL_MIX_LORA_RANK, n_lora_value_res_mix);
cur.add_kv(LLM_KV_ATTENTION_GATE_LORA_RANK, n_lora_gate);
cur.add_kv(LLM_KV_FEED_FORWARD_LENGTH, n_ff);
cur.add_kv(LLM_KV_ATTENTION_HEAD_COUNT, (uint32_t) 0);
add_tokenizer(cur, n_vocab);
cur.add_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// Block 0, LN0
cur.add_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, init_bias);
// output
cur.add_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, init_bias);
cur.add_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
for (uint32_t i = 0; i < n_layer; ++i) {
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, init_bias);
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, init_bias);
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_W0, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, n_lora_decay});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_lora_decay, n_embd});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_A0, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd});
if (i == 0) {
// actually not used
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd});
} else {
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd});
}
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 6});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_K_K, "weight", i), {attn_hidden_size});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_K_A, "weight", i), {attn_hidden_size});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_R_K, "weight", i), {attn_hidden_size});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd});
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, init_bias);
cur.add_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size});
cur.add_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1});
cur.add_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size});
cur.add_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd});
}
} break;
case LLM_ARCH_ARWKV7:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_CHAMELEON:
case LLM_ARCH_WAVTOKENIZER_DEC:
case LLM_ARCH_PLM:
case LLM_ARCH_BAILINGMOE:
case LLM_ARCH_DOTS1:
case LLM_ARCH_ARCEE:
case LLM_ARCH_UNKNOWN:
break;
}
}
};
struct reference_logits {
int32_t n_vocab;
int32_t prompt_len;
std::vector<llama_token> inputs;
std::vector<float> outputs;
reference_logits(llama_context * ctx, int32_t seq_len, std::mt19937 & rng) {
n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(llama_get_model(ctx)));
std::uniform_int_distribution<llama_token> rand_token(0, n_vocab - 1);
std::uniform_int_distribution<int32_t> rand_prompt_len(seq_len / 4, 3 * seq_len / 4);
llama_batch batch = llama_batch_init(seq_len, 0, 1);
outputs.reserve(n_vocab * seq_len);
prompt_len = rand_prompt_len(rng);
for (int32_t i = 0; i < prompt_len; ++i) {
const llama_token token = rand_token(rng);
inputs.push_back(token);
common_batch_add(batch, token, i, { 0 }, true);
}
const int status_prompt = llama_decode(ctx, batch);
GGML_ASSERT(status_prompt == 0);
const float * output_prompt = llama_get_logits(ctx);
GGML_ASSERT(output_prompt);
outputs.insert(outputs.end(), output_prompt, output_prompt + n_vocab * prompt_len);
for (int32_t i = prompt_len; i < seq_len; ++i) {
common_batch_clear(batch);
const llama_token token = rand_token(rng); // no real need to sample
inputs.push_back(token);
common_batch_add(batch, token, i, { 0 }, true);
const int status = llama_decode(ctx, batch);
GGML_ASSERT(status == 0);
const float * output = llama_get_logits_ith(ctx, -1);
GGML_ASSERT(output);
outputs.insert(outputs.end(), output, output + n_vocab);
}
llama_batch_free(batch);
}
// TODO: unlink pos from indice into inputs
// TODO: randomize which ouputs are enabled
void add_to_batch(llama_batch & batch, llama_pos initial_pos, int32_t seq_len, llama_seq_id seq_id) {
for (int32_t i = 0; i < seq_len; ++i) {
llama_pos pos = initial_pos + i;
common_batch_add(batch, inputs[pos], pos, { seq_id }, i == seq_len - 1);
}
}
// returns normalized squared error of the output for the seq_id
float validate_batch(llama_context * ctx, const llama_batch & batch, llama_seq_id seq_id) {
float sumr2 = 0.0f;
float sumo2 = 0.0f;
float sumerr2 = 0.0f;
llama_pos first_pos_error = -1;
for (int i = 0; i < batch.n_tokens; ++i) {
if (batch.seq_id[i][0] == seq_id && batch.logits[i]) {
const llama_pos pos = batch.pos[i];
const float * out = llama_get_logits_ith(ctx, i);
const float * reference_out = &outputs[pos * n_vocab];
GGML_ASSERT(out);
for (int j = 0; j < n_vocab; ++j) {
sumo2 += out[j] * out[j];
sumr2 += reference_out[j] * reference_out[j];
const float err = (out[j] - reference_out[j]);
if (err > 0.0f) {
if (first_pos_error < 0 || pos < first_pos_error) {
first_pos_error = pos;
}
}
sumerr2 += err * err;
}
}
}
if (first_pos_error >= 0) {
// fprintf(stderr, "Potential error in seq_id %i starting from pos %i\n", seq_id, first_pos_error);
}
const float denom = std::sqrt(sumr2) * std::sqrt(sumo2);
return sumerr2 / (denom > 0.0f ? denom : 1.0f);
}
};
struct reference_embd {
size_t n_embd;
std::vector<float> inputs;
std::vector<float> outputs;
// TODO: constructor
};
static void batch_add_embd(struct llama_batch & batch, const std::vector<float> & embd, llama_pos pos, llama_seq_id seq_id, bool logits) {
GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded");
memcpy(batch.embd + batch.n_tokens * embd.size(), embd.data(), sizeof(float) * embd.size());
batch.pos[batch.n_tokens] = pos;
batch.n_seq_id[batch.n_tokens] = 1;
batch.seq_id[batch.n_tokens][0] = seq_id;
batch.logits[batch.n_tokens] = logits;
batch.n_tokens++;
}
static void permute_from_ids(uint8_t * array, size_t elem_size, const std::vector<int32_t> & ids) {
std::vector<uint8_t> tmp(elem_size * ids.size(), 0);
for (size_t i = 0; i < ids.size(); ++i) {
memcpy(tmp.data() + i * elem_size, array + ids[i] * elem_size, elem_size);
}
memcpy(array, tmp.data(), ids.size() * elem_size);
}
static std::vector<int32_t> random_merge_ids(std::vector<std::queue<int32_t>> & ids_per_seq, std::mt19937 & rng) {
size_t total_size = 0;
for (const auto & v : ids_per_seq) {
total_size += v.size();
}
std::vector<int32_t> ids;
ids.reserve(total_size);
for (size_t i = 1; i <= total_size; ++i) {
// need weighted random selection
std::uniform_int_distribution<int32_t> rand(0, total_size - i);
int32_t rand_id = rand(rng);
// find out in which seq set this would belong
for (size_t j = 0; j < ids_per_seq.size(); ++j) {
if (rand_id < (int32_t) ids_per_seq[j].size()) {
ids.push_back(ids_per_seq[j].front());
ids_per_seq[j].pop();
break;
}
rand_id -= ids_per_seq[j].size();
}
}
return ids;
}
// shuffle across sequences but not within seqences
static void shuffle_batch(struct llama_batch & batch, std::mt19937 & rng) {
std::vector<std::set<llama_seq_id>> seq_sets;
std::vector<std::queue<int32_t>> ids_per_seq;
for (int32_t i = 0; i < batch.n_tokens; ++i) {
int32_t seq_set_id = -1;
for (size_t s = 0; s < seq_sets.size(); ++s) {
for (int j = 0; j < batch.n_seq_id[i]; ++j) {
if (seq_sets[s].find(batch.seq_id[i][j]) != seq_sets[s].end()) {
// any match, to avoid shuffling between dependent sets
seq_set_id = s;
break;
}
}
}
if (seq_set_id < 0) {
seq_sets.push_back({});
ids_per_seq.push_back({});
seq_set_id = seq_sets.size() - 1;
}
std::set<llama_seq_id> & seq_set = seq_sets[seq_set_id];
for (int j = 0; j < batch.n_seq_id[i]; ++j) {
// make sure the set contains all relevant seq_ids
seq_set.insert(batch.seq_id[i][j]);
}
ids_per_seq[seq_set_id].push(i);
}
std::vector<int32_t> ids = random_merge_ids(ids_per_seq, rng);
GGML_ASSERT(ids.size() == (size_t) batch.n_tokens);
if (batch.token) {
permute_from_ids((uint8_t *) batch.token, sizeof(*batch.token), ids);
}
if (batch.embd) {
permute_from_ids((uint8_t *) batch.embd, sizeof(*batch.embd), ids);
}
permute_from_ids((uint8_t *) batch.pos, sizeof(*batch.pos), ids);
permute_from_ids((uint8_t *) batch.n_seq_id, sizeof(*batch.n_seq_id), ids);
permute_from_ids((uint8_t *) batch.seq_id, sizeof(*batch.seq_id), ids);
permute_from_ids((uint8_t *) batch.logits, sizeof(*batch.logits), ids);
}
// TODO: use more args
int main(int argc, char ** argv) {
std::string tmp_fname = "test-model-random-tmp.gguf";
if (argc > 1) {
// TODO: ensure it ends with .gguf
std::string arg(argv[1]);
const std::string suffix = ".gguf";
if (suffix == arg.substr(arg.size() - suffix.size())) {
tmp_fname = std::move(arg);
} else {
// TODO: print usage
exit(1);
}
}
// only print errors
llama_log_set([](enum ggml_log_level level, const char * text, void * /* user_data */) {
if (level >= GGML_LOG_LEVEL_ERROR) {
fprintf(stderr, "%s", text);
}
}, nullptr);
// TODO: use specific backends
llama_backend_init();
// TODO: maybe use a faster rng algorithm
std::mt19937 rng(42);
// TODO: multiple sequences per token
const int32_t n_batch = 3 * 512;
const int32_t n_seq_len = 643; // prime number
llama_batch batch = llama_batch_init(n_batch, 0, 1);
// TODO: batch with embeddings
std::vector<model_variant> model_variants;
for (int i = 0; i < LLM_ARCH_UNKNOWN; ++i) {
llm_arch arch = (llm_arch) i;
model_variant::insert_from_arch(model_variants, arch);
}
// TODO: concurrent tests?
for (const model_variant & variant : model_variants) {
std::vector<std::string> splits = {};
variant.write_to_file(tmp_fname.c_str(), rng);
llama_model_params model_params = llama_model_default_params();
model_params.check_tensors = true;
llama_model * model = llama_model_load_from_file(tmp_fname.c_str(), model_params);
GGML_ASSERT(model);
// const auto n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
// const auto n_embd = llama_model_n_embd(model);
for (int32_t n_seq_max : { 1, 2, 5 } ) {
// TODO(later): context shift testing
for (int32_t n_ctx : { n_seq_len * n_seq_max }) {
std::vector<reference_logits> ref_outputs;
{
llama_context_params ref_params = llama_context_default_params();
ref_params.n_batch = n_seq_len;
ref_params.n_ubatch = 1;
ref_params.n_ctx = n_seq_len;
ref_params.n_seq_max = 1;
ref_params.type_k = GGML_TYPE_F32;
ref_params.type_v = GGML_TYPE_F32;
llama_context * ref_ctx = llama_init_from_model(model, ref_params);
llama_memory_t mem = llama_get_memory(ref_ctx);
for (llama_seq_id seq_id = 0; seq_id < n_seq_max; ++seq_id) {
llama_memory_clear(mem, true);
ref_outputs.push_back(reference_logits(ref_ctx, n_seq_len, rng));
}
llama_free(ref_ctx);
}
for (bool shuffle : { false, true }) {
// can't really shuffle a single sequence with itself
if (shuffle && n_seq_max == 1) {
continue;
}
for (int32_t n_ubatch : { 1, 2, 512 } ) {
std::vector<bool> valid(n_seq_max, true);
llama_context_params ctx_params = llama_context_default_params();
ctx_params.n_ctx = n_ctx;
ctx_params.n_seq_max = n_seq_max;
ctx_params.n_ubatch = n_ubatch;
ctx_params.n_batch = n_batch;
// TODO: remove once F16 is fixed on ARM
ctx_params.type_k = GGML_TYPE_F32;
ctx_params.type_v = GGML_TYPE_F32;
llama_context * ctx = llama_init_from_model(model, ctx_params);
common_batch_clear(batch);
std::set<llama_seq_id> seq_ids_in_batch;
std::vector<llama_pos> seq_id_n_past(n_seq_max, 0);
// start filling the batch with prompts
while (std::any_of(seq_id_n_past.begin(), seq_id_n_past.end(),
[](llama_pos p) { return p < n_seq_len; })) {
for (llama_seq_id seq_id = 0; seq_id < n_seq_max; ++seq_id) {
if (seq_id_n_past[seq_id] >= ref_outputs[seq_id].prompt_len) {
continue;
}
if (batch.n_tokens < n_batch) {
const int64_t seq_len =
std::min(n_batch - batch.n_tokens,
ref_outputs[seq_id].prompt_len - seq_id_n_past[seq_id]);
ref_outputs[seq_id].add_to_batch(batch, seq_id_n_past[seq_id], seq_len, seq_id);
seq_ids_in_batch.insert(seq_id);
seq_id_n_past[seq_id] += seq_len;
}
}
if (shuffle) {
shuffle_batch(batch, rng);
}
llama_decode(ctx, batch);
for (llama_seq_id seq_id = 0; seq_id < n_seq_max; ++seq_id) {
float err = ref_outputs[seq_id].validate_batch(ctx, batch, seq_id);
if (!isfinite(err) || err > 1.0f / 1024.0f) {
fprintf(stderr, "Error for seq_id %i is %f at n_past=%i\n", seq_id, err, seq_id_n_past[seq_id]);
valid[seq_id] = false;
}
}
common_batch_clear(batch);
GGML_ASSERT(n_seq_max <= n_batch); // not handling splitting this across batches here
// cont batching
for (llama_seq_id s : seq_ids_in_batch) {
llama_pos & pos = seq_id_n_past[s];
if (pos >= n_seq_len) {
continue;
}
ref_outputs[s].add_to_batch(batch, pos, 1, s);
pos += 1;
}
}
fprintf(stdout,
"Comparing output for '%s', with shuffle=%i, n_seq_max=%i, n_ctx=%i, n_ubatch=%i: ",
variant.name.c_str(), shuffle, n_seq_max, n_ctx, n_ubatch);
if (std::all_of(valid.begin(), valid.end(), [](bool v) { return v; })) {
fprintf(stdout, "\033[1;32mOK\033[0m\n");
} else {
fprintf(stdout, "(%zu%%) \033[1;31mFAILED\033[0m\n",
std::count_if(valid.begin(), valid.end(), [](bool v) { return v == false; }) * 100 / valid.size());
// cleanup and exit on first failure
llama_free(ctx);
llama_model_free(model);
llama_batch_free(batch);
exit(1);
}
// TODO: use seq_rm, seq_cp, etc. to test if they work properly
// TODO: test pooled embeddings
llama_free(ctx);
}
}
}
}
llama_model_free(model);
}
llama_batch_free(batch);
return 0;
}
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