mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 19:34:35 +00:00
9bc6db28d0
* ggml-quants : 1.625 bpw ternary packing for BitNet 1.58b * ggml-quants : faster 1.625 bpw AVX2 vec_dot Not using a lookup table anymore makes it match q4_0 speed. * gguf-py : fix formatting * llama : remove spaces on empty line * ggml-quants : subtract 1 when back in epi8 This makes the 1.625 bpw type go faster than q4_0. Still not the fastest. * ggml-quants : Q2_2 now faster than Q4_K on with AVX2 * ggml-quants : cleanup Q1_3 code formatting * ggml-quants : ARM NEON vec_dot for q2_2 and q1_3 * ggml-quants : use ceiling division when quantizing q1_3 * convert-hf : simplify BitNet pre-quantization This still results in the exact same tensor weights and scales, but it reveals some weirdness in the current algorithm. * convert-hf : allow converting the weird BitNet 1.3B Its FFN size is 5460 which is not convenient. The offending tensors are kept in F16, which makes the final model 5.01 bpw. * bitnet : replace 1.58b with b1.58, as in the paper * ggml-quants : fix build failure on Windows * ggml-quants : attempt to fix Arm 32-bit support * ggml : add some informative comments in q1_3 vec_dot * ggml : add TQ1_0 and TQ2_0 ternary quantization types * ggml : even faster TQ2_0 * ggml : also faster TQ1_0 Same optimization as for TQ2_0 by offsetting the sum instead of the weights. This makes TQ1_0 almost as fast as Q8_0 on AVX2. * ggml : fix build issues in certain environments * ggml : add NEON vec_dot implementation for TQ1_0 and TQ2_0 * ggml : avoid directly using vmlal_high_s8, for 32-bit ARM compat The compiler seems smart enough to use the same instruction even when using vget_high_s8 instead. * ggml : remove q1_3 and q2_2 No more 1.625 bpw and 2.000 bpw, now instead using 1.6875 bpw and 2.0625 bpw with TQ1_0 and TQ2_0, respectively. * llama : remove the separate scale tensors of BitNet b1.58 They won't be needed, since the remaining ternary quant types have built-in scales. * ggml-quants : rename fields of TQ1_0 and TQ2_0 structs for consistency * ggml-quants : allow using vdotq_s32 in TQ2_0 vec_dot Not yet tested on hardware which supports it, might not work or might not even compile. But also it might. It should make the performance better on recent ARM CPUs. * ggml-quants : remove comment about possible format change of TQ2_0 Making it slightly more convenient for AVX512 but less convenient for everything else is not worth the trouble. * gguf-py : Numpy (de)quantization for TQ1_0 and TQ2_0 * ggml-quants : use roundf instead of nearest_int for TQ1_0 and TQ2_0 This does not change anything for ternary models, since their values should never end up being in halfway cases anyway. * convert : allow direct conversion to TQ1_0 and TQ2_0 The token embeddings and output tensors are kept in F16 to allow quantizing them to Q4_K and Q6_K with llama-quantize. * llama : handle fallback for TQ1_0 and TQ2_0 with Q4_0 Q4_0 is not completely symmetric (so not lossless for ternary models), but it should be good enough. * ggml-quants : allow using ARM dot product instructions for TQ1_0 * ggml-quants : deduplicate TQ1_0 and TQ2_0 __ARM_FEATURE_DOTPROD support * ggml : remove unused ggml_mul special case It would otherwise conflict with the more general optimization coming with Mamba-2. * ggml : handle TQ1_0 and TQ2_0 in dequantization-based operators * test-backend-ops : add TQ1_0 and TQ2_0 comments for later Not yet adding uncommented, because some backends like SYCL and Metal do not properly handle unknown types in supports_op for GGML_OP_MUL_MAT. (and Metal also doesn't handle it with GGML_OP_GET_ROWS) Support for TQ1_0 and TQ2_0 for other backends than CPU will be added in follow-up pull requests.
192 lines
7.0 KiB
C++
192 lines
7.0 KiB
C++
// Unit tests for quantization specific functions - quantize, dequantize and dot product
|
|
|
|
#include "ggml.h"
|
|
|
|
#undef NDEBUG
|
|
#include <assert.h>
|
|
#include <math.h>
|
|
#include <stdio.h>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
#if defined(_MSC_VER)
|
|
#pragma warning(disable: 4244 4267) // possible loss of data
|
|
#endif
|
|
|
|
constexpr float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f;
|
|
constexpr float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f;
|
|
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_TERNARY = 0.01f;
|
|
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f;
|
|
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f;
|
|
constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS = 0.0050f;
|
|
constexpr float MAX_DOT_PRODUCT_ERROR = 0.02f;
|
|
constexpr float MAX_DOT_PRODUCT_ERROR_LOWBIT = 0.04f;
|
|
constexpr float MAX_DOT_PRODUCT_ERROR_TERNARY = 0.15f;
|
|
|
|
static const char* RESULT_STR[] = {"ok", "FAILED"};
|
|
|
|
|
|
// Generate synthetic data
|
|
static void generate_data(float offset, size_t n, float * dst) {
|
|
for (size_t i = 0; i < n; i++) {
|
|
dst[i] = 0.1 + 2*cosf(i + offset);
|
|
}
|
|
}
|
|
|
|
// Calculate RMSE between two float arrays
|
|
static float array_rmse(const float * a1, const float * a2, size_t n) {
|
|
double sum = 0;
|
|
for (size_t i = 0; i < n; i++) {
|
|
double diff = a1[i] - a2[i];
|
|
sum += diff * diff;
|
|
}
|
|
return sqrtf(sum) / n;
|
|
}
|
|
|
|
// Total quantization error on test data
|
|
static float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
|
|
std::vector<uint8_t> tmp_q(2*test_size);
|
|
std::vector<float> tmp_out(test_size);
|
|
|
|
qfns.from_float(test_data, tmp_q.data(), test_size);
|
|
qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
|
|
return array_rmse(test_data, tmp_out.data(), test_size);
|
|
}
|
|
|
|
// Total quantization error on test data
|
|
static float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
|
|
std::vector<uint8_t> tmp_q(2*test_size);
|
|
std::vector<float> tmp_out(test_size);
|
|
std::vector<float> tmp_out_ref(test_size);
|
|
|
|
qfns.from_float(test_data, tmp_q.data(), test_size);
|
|
qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
|
|
|
|
qfns.from_float_ref(test_data, tmp_q.data(), test_size);
|
|
qfns.to_float(tmp_q.data(), tmp_out_ref.data(), test_size);
|
|
|
|
return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size);
|
|
}
|
|
|
|
static float dot_product(const float * a1, const float * a2, size_t test_size) {
|
|
double sum = 0;
|
|
for (size_t i = 0; i < test_size; i++) {
|
|
sum += a1[i] * a2[i];
|
|
}
|
|
return sum;
|
|
}
|
|
|
|
// Total dot product error
|
|
static float dot_product_error(
|
|
ggml_type_traits_t & qfns, size_t test_size, const float * test_data1, const float *test_data2
|
|
) {
|
|
std::vector<uint8_t> tmp_q1(2*test_size);
|
|
std::vector<uint8_t> tmp_q2(2*test_size);
|
|
|
|
auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
|
|
|
|
qfns.from_float(test_data1, tmp_q1.data(), test_size);
|
|
vdot.from_float(test_data2, tmp_q2.data(), test_size);
|
|
|
|
float result = INFINITY;
|
|
qfns.vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1);
|
|
|
|
const float dot_ref = dot_product(test_data1, test_data2, test_size);
|
|
|
|
return fabsf(result - dot_ref) / test_size;
|
|
}
|
|
|
|
int main(int argc, char * argv[]) {
|
|
bool verbose = false;
|
|
const size_t test_size = 32 * 128;
|
|
|
|
std::string arg;
|
|
for (int i = 1; i < argc; i++) {
|
|
arg = argv[i];
|
|
|
|
if (arg == "-v") {
|
|
verbose = true;
|
|
} else {
|
|
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
std::vector<float> test_data(test_size);
|
|
std::vector<float> test_data2(test_size);
|
|
|
|
generate_data(0.0, test_data.size(), test_data.data());
|
|
generate_data(1.0, test_data2.size(), test_data2.data());
|
|
|
|
// Initialize GGML, ensures float conversion tables are initialized
|
|
struct ggml_init_params ggml_params = {
|
|
/* .mem_size = */ 1*1024,
|
|
/* .mem_buffer = */ NULL,
|
|
/* .no_alloc = */ true,
|
|
};
|
|
struct ggml_context * ctx = ggml_init(ggml_params);
|
|
|
|
int num_failed = 0;
|
|
bool failed = false;
|
|
|
|
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
|
|
ggml_type type = (ggml_type) i;
|
|
ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
|
|
|
|
// deprecated - skip
|
|
if (qfns.blck_size == 0) {
|
|
continue;
|
|
}
|
|
|
|
const ggml_type ei = (ggml_type)i;
|
|
|
|
printf("Testing %s\n", ggml_type_name((ggml_type) i));
|
|
ggml_quantize_init(ei);
|
|
|
|
if (qfns.from_float && qfns.to_float) {
|
|
const float total_error = total_quantization_error(qfns, test_size, test_data.data());
|
|
const float max_quantization_error =
|
|
type == GGML_TYPE_TQ1_0 ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY :
|
|
type == GGML_TYPE_TQ2_0 ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY :
|
|
type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
|
|
type == GGML_TYPE_IQ2_S ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
|
|
type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
|
|
type == GGML_TYPE_IQ3_S ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
|
|
type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : MAX_QUANTIZATION_TOTAL_ERROR;
|
|
failed = !(total_error < max_quantization_error);
|
|
num_failed += failed;
|
|
if (failed || verbose) {
|
|
printf("%5s absolute quantization error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], total_error);
|
|
}
|
|
|
|
const float reference_error = reference_quantization_error(qfns, test_size, test_data.data());
|
|
failed = !(reference_error < MAX_QUANTIZATION_REFERENCE_ERROR);
|
|
num_failed += failed;
|
|
if (failed || verbose) {
|
|
printf("%5s reference implementation error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], reference_error);
|
|
}
|
|
|
|
const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data());
|
|
const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS ||
|
|
type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S || type == GGML_TYPE_IQ2_S
|
|
? MAX_DOT_PRODUCT_ERROR_LOWBIT
|
|
: type == GGML_TYPE_TQ1_0 || type == GGML_TYPE_TQ2_0
|
|
? MAX_DOT_PRODUCT_ERROR_TERNARY
|
|
: MAX_DOT_PRODUCT_ERROR;
|
|
failed = !(vec_dot_error < max_allowed_error);
|
|
num_failed += failed;
|
|
if (failed || verbose) {
|
|
printf("%5s dot product error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], vec_dot_error);
|
|
}
|
|
}
|
|
}
|
|
|
|
if (num_failed || verbose) {
|
|
printf("%d tests failed\n", num_failed);
|
|
}
|
|
|
|
ggml_free(ctx);
|
|
|
|
return num_failed > 0;
|
|
}
|