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https://github.com/ggerganov/whisper.cpp.git
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add basic tensor data validation function (llama/6884)
* add basic tensor data validation function * add --check-tensors command line argument tensor validation is disabled by default and can be enabled by adding `--check-tensors` to the command line arguments. quantize always validates tensors.
This commit is contained in:
slaren
Georgi Gerganov
parent
ecfac1e240
commit
9d4c8b8aa5
284
ggml-quants.c
284
ggml-quants.c
@ -12389,3 +12389,287 @@ void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k)
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block_iq2_s * restrict y = vy;
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quantize_row_iq2_s_reference(x, y, k);
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}
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static bool validate_float(float f, size_t i) {
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if (isinf(f)) {
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fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
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return false;
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}
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if (isnan(f)) {
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fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
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return false;
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}
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return true;
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}
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static bool isinf_fp16(ggml_fp16_t f) {
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return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) == 0;
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}
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static bool isnan_fp16(ggml_fp16_t f) {
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return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) != 0;
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}
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static bool validate_fp16(ggml_fp16_t f, size_t i) {
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if (isinf_fp16(f)) {
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fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
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return false;
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}
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if (isnan_fp16(f)) {
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fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
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return false;
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}
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return true;
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}
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#define VALIDATE_ROW_DATA_D_F16_IMPL(type, data, nb) \
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const type * q = (const type *) (data); \
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for (size_t i = 0; i < (nb); ++i) { \
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if (!validate_fp16(q[i].d, i)) { \
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return false; \
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} \
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}
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#define VALIDATE_ROW_DATA_DM_F16_IMPL(type, data, nb, d, m) \
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const type * q = (const type *) (data); \
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for (size_t i = 0; i < (nb); ++i) { \
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if (!validate_fp16(q[i].d, i) || !validate_fp16(q[i].m, i)) { \
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return false; \
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} \
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}
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bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes) {
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if (type < 0 || type >= GGML_TYPE_COUNT) {
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fprintf(stderr, "%s: invalid type %d\n", __func__, type);
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return false;
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}
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if (nbytes % ggml_type_size(type) != 0) {
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fprintf(stderr, "%s: invalid size %zu for type %d\n", __func__, nbytes, type);
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return false;
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}
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const size_t nb = nbytes/ggml_type_size(type);
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switch (type) {
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case GGML_TYPE_F16:
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{
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const ggml_fp16_t * f = (const ggml_fp16_t *) data;
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size_t i = 0;
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#if defined(__AVX2__)
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for (; i + 15 < nb; i += 16) {
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__m256i v = _mm256_loadu_si256((const __m256i *)(f + i));
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__m256i vexp = _mm256_and_si256(v, _mm256_set1_epi16(0x7c00));
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__m256i cmp = _mm256_cmpeq_epi16(vexp, _mm256_set1_epi16(0x7c00));
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int mask = _mm256_movemask_epi8(cmp);
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if (mask) {
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for (size_t j = 0; j < 16; ++j) {
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if (!validate_fp16(f[i + j], i + j)) {
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return false;
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}
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}
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GGML_UNREACHABLE();
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}
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}
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#elif defined(__ARM_NEON)
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for (; i + 7 < nb; i += 8) {
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uint16x8_t v = vld1q_u16(f + i);
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uint16x8_t vexp = vandq_u16(v, vdupq_n_u16(0x7c00));
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uint16x8_t cmp = vceqq_u16(vexp, vdupq_n_u16(0x7c00));
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uint64_t mask = vget_lane_u64(vreinterpret_u64_u8(vshrn_n_u16(cmp, 4)), 0);
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if (mask) {
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for (size_t j = 0; j < 8; ++j) {
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if (!validate_fp16(f[i + j], i + j)) {
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return false;
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}
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}
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GGML_UNREACHABLE();
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}
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}
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#endif
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for (; i < nb; ++i) {
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if (!validate_fp16(f[i], i)) {
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return false;
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}
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}
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} break;
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case GGML_TYPE_F32:
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{
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const float * f = (const float *) data;
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size_t i = 0;
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#if defined(__AVX2__)
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for (; i + 7 < nb; i += 8) {
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__m256i v = _mm256_loadu_si256((const __m256i *)(f + i));
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__m256i vexp = _mm256_and_si256(v, _mm256_set1_epi32(0x7f800000));
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__m256i cmp = _mm256_cmpeq_epi32(vexp, _mm256_set1_epi32(0x7f800000));
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int mask = _mm256_movemask_epi8(cmp);
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if (mask) {
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for (size_t j = 0; j < 8; ++j) {
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if (!validate_float(f[i + j], i + j)) {
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return false;
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}
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}
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GGML_UNREACHABLE();
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}
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}
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#elif defined(__ARM_NEON)
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for (; i + 3 < nb; i += 4) {
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uint32x4_t v = vld1q_u32((const uint32_t *)f + i);
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uint32x4_t vexp = vandq_u32(v, vdupq_n_u32(0x7f800000));
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uint32x4_t cmp = vceqq_u32(vexp, vdupq_n_u32(0x7f800000));
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uint64_t mask = vget_lane_u64(vreinterpret_u64_u16(vshrn_n_u32(cmp, 8)), 0);
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if (mask) {
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for (size_t j = 0; j < 4; ++j) {
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if (!validate_float(f[i + j], i + j)) {
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return false;
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}
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}
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GGML_UNREACHABLE();
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}
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}
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#endif
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for (; i < nb; ++i) {
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if (!validate_float(f[i], i)) {
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return false;
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}
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}
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} break;
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case GGML_TYPE_F64:
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{
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const double * f = (const double *) data;
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for (size_t i = 0; i < nb; ++i) {
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if (!validate_float(f[i], i)) {
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return false;
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}
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}
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} break;
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case GGML_TYPE_Q4_0:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_q4_0, data, nb);
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} break;
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case GGML_TYPE_Q4_1:
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{
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VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_1, data, nb, d, m);
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} break;
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case GGML_TYPE_Q5_0:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_0, data, nb);
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} break;
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case GGML_TYPE_Q5_1:
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{
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VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_1, data, nb, d, m);
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} break;
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case GGML_TYPE_Q8_0:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_q8_0, data, nb);
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} break;
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case GGML_TYPE_Q2_K:
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{
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VALIDATE_ROW_DATA_DM_F16_IMPL(block_q2_K, data, nb, d, dmin);
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} break;
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case GGML_TYPE_Q3_K:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_q3_K, data, nb);
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} break;
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case GGML_TYPE_Q4_K:
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{
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#ifdef GGML_QKK_64
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VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]);
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#else
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VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin);
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#endif
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} break;
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case GGML_TYPE_Q5_K:
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{
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#ifdef GGML_QKK_64
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VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb);
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#else
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VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin);
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#endif
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} break;
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case GGML_TYPE_Q6_K:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_q6_K, data, nb);
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} break;
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case GGML_TYPE_Q8_K:
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{
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const block_q8_K * q = (const block_q8_K *) data;
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for (size_t i = 0; i < nb; ++i) {
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if (!validate_float(q[i].d, i)) {
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return false;
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}
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}
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} break;
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case GGML_TYPE_IQ1_S:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_iq1_s, data, nb);
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} break;
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case GGML_TYPE_IQ1_M:
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{
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const block_iq1_m * q = (const block_iq1_m *) data;
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for (size_t i = 0; i < nb; ++i) {
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#if QK_K == 64
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if (!validate_fp16(q[i].d, i)) {
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return false;
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}
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#else
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iq1m_scale_t scale;
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const uint16_t * sc = (const uint16_t *)q[i].scales;
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scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
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if (!validate_fp16(scale.f16, i)) {
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return false;
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}
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#endif
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}
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} break;
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case GGML_TYPE_IQ2_XXS:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xxs, data, nb);
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} break;
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case GGML_TYPE_IQ2_XS:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xs, data, nb);
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} break;
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case GGML_TYPE_IQ2_S:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_s, data, nb);
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} break;
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case GGML_TYPE_IQ3_XXS:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_xxs, data, nb);
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} break;
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case GGML_TYPE_IQ3_S:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb);
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} break;
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case GGML_TYPE_IQ4_XS:
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#if QK_K != 64
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb);
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} break;
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#endif
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// with QK_K == 64, iq4_xs is iq4_nl
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case GGML_TYPE_IQ4_NL:
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{
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VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb);
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} break;
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case GGML_TYPE_I8:
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case GGML_TYPE_I16:
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case GGML_TYPE_I32:
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case GGML_TYPE_I64:
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// nothing to validate
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break;
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default:
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{
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fprintf(stderr, "%s: invalid type %d\n", __func__, type);
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return false;
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}
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}
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return true;
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}
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2
ggml.h
2
ggml.h
@ -763,6 +763,8 @@ extern "C" {
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// use this to compute the memory overhead of a tensor
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GGML_API size_t ggml_tensor_overhead(void);
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GGML_API bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes);
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// main
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GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);
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