Files
llama.cpp/tests/test-quantize-fns.cpp
Richard Davison 5eae9cb1d9 ggml : add NVFP4 quantization type support (#19769)
* WIP: add NVFP4 quantization support

* tests

* improve NVFP4 dot product implementation performance and fix bad super call

* typo

* Use nvfp4 kvalues

* vulkan : fix NVFP4 shader compilation by including kvalues_mxfp4 lookup table

* vulcal and perf fixes

* wip

* Fix metal

* fix vulcan

* Rename threshold & fix wrong scale

* Fix MOE

* Shelf backend implementations (CUDA, Metal, Vulkan, arch-specific SIMD)

Remove NVFP4 support from GPU backends and architecture-specific
optimized dot products. These should be added in separate PRs so
backend specialists can review them independently.

Reverted files:
- ggml-cuda: common.cuh, convert.cu, mmq.cu/cuh, mmvq.cu, vecdotq.cuh,
  quantize.cu/cuh, mma.cuh, ggml-cuda.cu, fattn-tile.cuh
- ggml-metal: ggml-metal.metal, ggml-metal-device.cpp, ggml-metal-impl.h,
  ggml-metal-ops.cpp
- ggml-vulkan: ggml-vulkan.cpp, all vulkan-shaders/*
- ggml-cpu arch: arm/quants.c, x86/quants.c, powerpc/quants.c, s390/quants.c

Core NVFP4 support (type definition, CPU fallback dot product,
quantization, dequantization, conversion) is retained.

* Fix arch-fallback.h: add NVFP4 generic fallback for all platforms

After shelving backend-specific SIMD implementations, the generic
CPU dot product needs to be aliased on ARM, x86, PowerPC, and s390
platforms that previously relied on arch-specific versions.

* quantize: add NVFP4 as a quantization type option

* Fix ggml_fp32_to_ue4m3: handle subnormal values

Previously, values with ue4m3_exp <= 0 were clamped to 0, causing
all small scales to underflow. This made NVFP4 quantization via
llama-quantize produce garbage (PPL = 5.8M) since typical transformer
weights have amax/6.0 in the range 0.001-0.01, which falls in the
UE4M3 subnormal range.

Now subnormals are properly encoded as man * 2^-9 (exp=0, man=1..7),
matching the decode path in ggml_ue4m3_to_fp32.

Result: NVFP4 requantization now produces PPL = 15.25 (vs F16 = 14.33),
comparable to Q4_1 (PPL = 15.81) at slightly lower BPW (4.70 vs 5.15).

* Restore ARM NEON NVFP4 dot product implementation

Restores the optimized ggml_vec_dot_nvfp4_q8_0 for ARM NEON using
vqtbl1q_s8 lookup and ggml_vdotq_s32 dot products.

tg128 performance: 4.37 t/s (generic) -> 13.66 t/s (NEON) = 3.1x speedup

* Optimize ARM NEON NVFP4 dot product: LUT + vpaddq + vfmaq

- Add ue4m3_scale_lut[128] to ggml-common.h replacing branch-heavy
  ggml_ue4m3_to_fp32() in the hot loop
- Use vpaddq_s32 for pairwise int32 reduction instead of vaddvq_s32
- Accumulate with vfmaq_f32 into float32x4_t vector accumulators

tg128: 8.1 -> 31.0 t/s (3.8x speedup, 77% of Q4_1 speed)

* ARM NEON NVFP4: rearrange q8 to match nibble layout

Alternative approach: rearrange q8 data to match the NVFP4 lo/hi
nibble layout instead of rearranging the looked-up NVFP4 values.
Eliminates vcombine_s8(vget_low, vget_low) shuffles.

Performance is equivalent (~18.5 t/s) - the bottleneck is the 2x
block overhead from QK=16 vs QK=32, not the shuffle instructions.

* CPU only backend 64 super-block layout

* cleanup

* Remove unused LUT

* int

* exclude NVFP4 from unsupported ops in metal build

* remove quantization for now

* store scales as native UE4M3, preserve original model bits when possible

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* correct comment

* format

* reduce duplication and cleanup

* Address comments

* move detection to prepare_tensors

* Use math instead of const

* Move

* fix comment

* Shelf quantize tests

* Rebase and move check

* cleanup

* lint

* Update gguf-py/gguf/scripts/gguf_convert_endian.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Use fallback quant config

* Simplify

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* organize

* Refactor

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* add quantize_nvfp4 (required for test_quants.py)

* add quantize_nvfp4 (required for test_quants.py)

* add quantize_nvfp4 (required for test_quants.py)

* fix return type

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
2026-03-11 21:02:54 +01:00

192 lines
7.3 KiB
C++

// Unit tests for quantization specific functions - quantize, dequantize and dot product
#include "ggml.h"
#include "ggml-cpu.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_QUANTIZATION_TOTAL_ERROR_FP4 = 0.0030f;
constexpr float MAX_DOT_PRODUCT_ERROR = 0.02f;
constexpr float MAX_DOT_PRODUCT_ERROR_LOWBIT = 0.04f;
constexpr float MAX_DOT_PRODUCT_ERROR_FP4 = 0.03f;
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(const ggml_type_traits * qfns, const ggml_type_traits_cpu * qfns_cpu, 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_cpu->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(const ggml_type_traits * qfns, const ggml_type_traits_cpu * qfns_cpu, 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);
// FIXME: why is done twice?
qfns_cpu->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(const ggml_type_traits * qfns, const ggml_type_traits_cpu * qfns_cpu, size_t test_size, const float * test_data1, const float * test_data2) {
GGML_UNUSED(qfns);
std::vector<uint8_t> tmp_q1(2*test_size);
std::vector<uint8_t> tmp_q2(2*test_size);
const auto * vdot = ggml_get_type_traits_cpu(qfns_cpu->vec_dot_type);
qfns_cpu->from_float(test_data1, tmp_q1.data(), test_size);
vdot->from_float(test_data2, tmp_q2.data(), test_size);
float result = INFINITY;
qfns_cpu->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());
ggml_cpu_init();
int num_failed = 0;
bool failed = false;
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
ggml_type type = (ggml_type) i;
const auto * qfns = ggml_get_type_traits(type);
const auto * qfns_cpu = ggml_get_type_traits_cpu(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_cpu->from_float && qfns->to_float) {
const float total_error = total_quantization_error(qfns, qfns_cpu, 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 :
type == GGML_TYPE_NVFP4 ? MAX_QUANTIZATION_TOTAL_ERROR_FP4 : 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, qfns_cpu, 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, qfns_cpu, 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
: type == GGML_TYPE_NVFP4
? MAX_DOT_PRODUCT_ERROR_FP4
: 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);
}
return num_failed > 0;
}