vbatts/llama.cpp
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add basic tensor data validation function

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slaren 2024-04-24 21:48:16 +02:00
parent b4e4b8a935
commit 6aea16eb5a
3 changed files with 224 additions and 4 deletions
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203
ggml-quants.c
View file

@ -12389,3 +12389,206 @@ void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k)
block_iq2_s * restrict y = vy;
quantize_row_iq2_s_reference(x, y, k);
}
static bool validate_float(float f, size_t i) {
if (isinf(f)) {
fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
return false;
}
if (isnan(f)) {
fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
return false;
}
return true;
}
static bool validate_f16(ggml_fp16_t f, size_t i) {
return validate_float(GGML_FP16_TO_FP32(f), i);
}
#define VALIDATE_ROW_DATA_D_F16_IMPL(type, data, nb) \
const type * q = (const type *) (data); \
for (size_t i = 0; i < (nb); ++i) { \
if (!validate_f16(q[i].d, i)) { \
return false; \
} \
}
#define VALIDATE_ROW_DATA_DM_F16_IMPL(type, data, nb, d, m) \
const type * q = (const type *) (data); \
for (size_t i = 0; i < (nb); ++i) { \
if (!validate_f16(q[i].d, i) || !validate_f16(q[i].m, i)) { \
return false; \
} \
}
bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes) {
if (type < 0 || type >= GGML_TYPE_COUNT) {
fprintf(stderr, "%s: invalid type %d\n", __func__, type);
return false;
}
// size check
if (nbytes % ggml_type_size(type) != 0) {
fprintf(stderr, "%s: invalid size %zu for type %d\n", __func__, nbytes, type);
return false;
}
size_t nb = nbytes/ggml_type_size(type);
switch (type) {
case GGML_TYPE_F16:
{
const ggml_fp16_t * f = (const ggml_fp16_t *) data;
for (size_t i = 0; i < nb; ++i) {
if (!validate_f16(f[i], i)) {
return false;
}
}
} break;
case GGML_TYPE_F32:
{
const float * f = (const float *) data;
for (size_t i = 0; i < nb; ++i) {
if (!validate_float(f[i], i)) {
return false;
}
}
} break;
case GGML_TYPE_F64:
{
const double * f = (const double *) data;
for (size_t i = 0; i < nb; ++i) {
if (!validate_float(f[i], i)) {
return false;
}
}
} break;
case GGML_TYPE_Q4_0:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_q4_0, data, nb);
} break;
case GGML_TYPE_Q4_1:
{
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_1, data, nb, d, m);
} break;
case GGML_TYPE_Q5_0:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_0, data, nb);
} break;
case GGML_TYPE_Q5_1:
{
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_1, data, nb, d, m);
} break;
case GGML_TYPE_Q8_0:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_q8_0, data, nb);
} break;
case GGML_TYPE_Q2_K:
{
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q2_K, data, nb, d, dmin);
} break;
case GGML_TYPE_Q3_K:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_q3_K, data, nb);
} break;
case GGML_TYPE_Q4_K:
{
#ifdef GGML_QKK_64
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]);
#else
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin);
#endif
} break;
case GGML_TYPE_Q5_K:
{
#ifdef GGML_QKK_64
VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb);
#else
VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin);
#endif
} break;
case GGML_TYPE_Q6_K:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_q6_K, data, nb);
} break;
case GGML_TYPE_Q8_K:
{
const block_q8_K * q = (const block_q8_K *) data;
for (size_t i = 0; i < nb; ++i) {
if (!validate_float(q[i].d, i)) {
return false;
}
}
} break;
case GGML_TYPE_IQ1_S:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq1_s, data, nb);
} break;
case GGML_TYPE_IQ1_M:
{
const block_iq1_m * q = (const block_iq1_m *) data;
for (size_t i = 0; i < nb; ++i) {
#if QK_K == 64
if (!validate_f16(q[i].d, i)) {
return false;
}
#else
iq1m_scale_t scale;
const uint16_t * sc = (const uint16_t *)q[i].scales;
scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
if (!validate_f16(scale.f16, i)) {
return false;
}
#endif
}
} break;
case GGML_TYPE_IQ2_XXS:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xxs, data, nb);
} break;
case GGML_TYPE_IQ2_XS:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xs, data, nb);
} break;
case GGML_TYPE_IQ2_S:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_s, data, nb);
} break;
case GGML_TYPE_IQ3_XXS:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_xxs, data, nb);
} break;
case GGML_TYPE_IQ3_S:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb);
} break;
case GGML_TYPE_IQ4_XS:
#if QK_K != 64
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb);
} break;
#endif
// with QK_K == 64, iq4_xs is iq4_nl
case GGML_TYPE_IQ4_NL:
{
VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb);
} break;
case GGML_TYPE_I8:
case GGML_TYPE_I16:
case GGML_TYPE_I32:
case GGML_TYPE_I64:
// nothing to validate
break;
default:
{
fprintf(stderr, "%s: invalid type %d\n", __func__, type);
return false;
}
}
return true;
}

2
ggml.h
View file

@ -762,6 +762,8 @@ extern "C" {
// use this to compute the memory overhead of a tensor
GGML_API size_t ggml_tensor_overhead(void);
GGML_API bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes);
// main
GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);

23
llama.cpp
View file

@ -3472,6 +3472,10 @@ struct llama_model_loader {
file->seek(w.offs, SEEK_SET);
file->read_raw(cur->data, ggml_nbytes(cur));
}
if (!ggml_validate_row_data(cur->type, cur->data, ggml_nbytes(cur))) {
throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
}
}
size_t size_done = 0;
@ -3509,12 +3513,17 @@ struct llama_model_loader {
if (bufs_mmap.count(weight->idx)) {
buf_mmap = bufs_mmap.at(weight->idx);
}
if (!ggml_validate_row_data(cur->type, (uint8_t *) mapping->addr + weight->offs, n_size)) {
throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
}
GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated
if (buf_mmap && cur->data == nullptr) {
ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + weight->offs);
if (lmlocks) {
const auto & lmlock = lmlocks->at(weight->idx);
lmlock->grow_to(weight->offs + ggml_nbytes(cur));
lmlock->grow_to(weight->offs + n_size);
}
auto & mmap_used = mmaps_used[weight->idx];
@ -3528,12 +3537,18 @@ struct llama_model_loader {
const auto & file = files.at(weight->idx);
if (ggml_backend_buffer_is_host(cur->buffer)) {
file->seek(weight->offs, SEEK_SET);
file->read_raw(cur->data, ggml_nbytes(cur));
file->read_raw(cur->data, n_size);
if (!ggml_validate_row_data(cur->type, cur->data, n_size)) {
throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
}
} else {
read_buf.resize(ggml_nbytes(cur));
read_buf.resize(n_size);
file->seek(weight->offs, SEEK_SET);
file->read_raw(read_buf.data(), ggml_nbytes(cur));
file->read_raw(read_buf.data(), n_size);
ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
if (!ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
}
}
}