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* 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>
192 lines
7.3 KiB
C++
192 lines
7.3 KiB
C++
// Unit tests for quantization specific functions - quantize, dequantize and dot product
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#include "ggml.h"
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#include "ggml-cpu.h"
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#undef NDEBUG
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#include <assert.h>
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#include <math.h>
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#include <stdio.h>
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#include <string>
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#include <vector>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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constexpr float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f;
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constexpr float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f;
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constexpr float MAX_QUANTIZATION_TOTAL_ERROR_TERNARY = 0.01f;
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constexpr float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f;
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constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f;
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constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS = 0.0050f;
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constexpr float MAX_QUANTIZATION_TOTAL_ERROR_FP4 = 0.0030f;
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constexpr float MAX_DOT_PRODUCT_ERROR = 0.02f;
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constexpr float MAX_DOT_PRODUCT_ERROR_LOWBIT = 0.04f;
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constexpr float MAX_DOT_PRODUCT_ERROR_FP4 = 0.03f;
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constexpr float MAX_DOT_PRODUCT_ERROR_TERNARY = 0.15f;
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static const char* RESULT_STR[] = {"ok", "FAILED"};
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// Generate synthetic data
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static void generate_data(float offset, size_t n, float * dst) {
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for (size_t i = 0; i < n; i++) {
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dst[i] = 0.1 + 2*cosf(i + offset);
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}
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}
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// Calculate RMSE between two float arrays
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static float array_rmse(const float * a1, const float * a2, size_t n) {
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double sum = 0;
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for (size_t i = 0; i < n; i++) {
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double diff = a1[i] - a2[i];
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sum += diff * diff;
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}
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return sqrtf(sum) / n;
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}
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// Total quantization error on test data
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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) {
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std::vector<uint8_t> tmp_q(2*test_size);
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std::vector<float> tmp_out(test_size);
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qfns_cpu->from_float(test_data, tmp_q.data(), test_size);
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qfns->to_float(tmp_q.data(), tmp_out.data(), test_size);
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return array_rmse(test_data, tmp_out.data(), test_size);
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}
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// Total quantization error on test data
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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) {
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std::vector<uint8_t> tmp_q(2*test_size);
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std::vector<float> tmp_out(test_size);
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std::vector<float> tmp_out_ref(test_size);
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// FIXME: why is done twice?
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qfns_cpu->from_float(test_data, tmp_q.data(), test_size);
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qfns->to_float(tmp_q.data(), tmp_out.data(), test_size);
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qfns->from_float_ref(test_data, tmp_q.data(), test_size);
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qfns->to_float(tmp_q.data(), tmp_out_ref.data(), test_size);
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return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size);
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}
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static float dot_product(const float * a1, const float * a2, size_t test_size) {
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double sum = 0;
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for (size_t i = 0; i < test_size; i++) {
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sum += a1[i] * a2[i];
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}
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return sum;
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}
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// Total dot product error
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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) {
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GGML_UNUSED(qfns);
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std::vector<uint8_t> tmp_q1(2*test_size);
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std::vector<uint8_t> tmp_q2(2*test_size);
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const auto * vdot = ggml_get_type_traits_cpu(qfns_cpu->vec_dot_type);
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qfns_cpu->from_float(test_data1, tmp_q1.data(), test_size);
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vdot->from_float(test_data2, tmp_q2.data(), test_size);
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float result = INFINITY;
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qfns_cpu->vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1);
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const float dot_ref = dot_product(test_data1, test_data2, test_size);
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return fabsf(result - dot_ref) / test_size;
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}
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int main(int argc, char * argv[]) {
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bool verbose = false;
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const size_t test_size = 32 * 128;
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std::string arg;
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for (int i = 1; i < argc; i++) {
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arg = argv[i];
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if (arg == "-v") {
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verbose = true;
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} else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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return 1;
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}
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}
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std::vector<float> test_data(test_size);
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std::vector<float> test_data2(test_size);
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generate_data(0.0, test_data.size(), test_data.data());
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generate_data(1.0, test_data2.size(), test_data2.data());
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ggml_cpu_init();
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int num_failed = 0;
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bool failed = false;
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for (int i = 0; i < GGML_TYPE_COUNT; i++) {
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ggml_type type = (ggml_type) i;
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const auto * qfns = ggml_get_type_traits(type);
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const auto * qfns_cpu = ggml_get_type_traits_cpu(type);
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// deprecated - skip
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if (qfns->blck_size == 0) {
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continue;
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}
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const ggml_type ei = (ggml_type)i;
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printf("Testing %s\n", ggml_type_name((ggml_type) i));
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ggml_quantize_init(ei);
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if (qfns_cpu->from_float && qfns->to_float) {
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const float total_error = total_quantization_error(qfns, qfns_cpu, test_size, test_data.data());
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const float max_quantization_error =
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type == GGML_TYPE_TQ1_0 ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY :
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type == GGML_TYPE_TQ2_0 ? MAX_QUANTIZATION_TOTAL_ERROR_TERNARY :
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type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
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type == GGML_TYPE_IQ2_S ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
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type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
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type == GGML_TYPE_IQ3_S ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
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type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS :
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type == GGML_TYPE_NVFP4 ? MAX_QUANTIZATION_TOTAL_ERROR_FP4 : MAX_QUANTIZATION_TOTAL_ERROR;
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failed = !(total_error < max_quantization_error);
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num_failed += failed;
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if (failed || verbose) {
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printf("%5s absolute quantization error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], total_error);
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}
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const float reference_error = reference_quantization_error(qfns, qfns_cpu, test_size, test_data.data());
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failed = !(reference_error < MAX_QUANTIZATION_REFERENCE_ERROR);
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num_failed += failed;
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if (failed || verbose) {
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printf("%5s reference implementation error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], reference_error);
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}
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const float vec_dot_error = dot_product_error(qfns, qfns_cpu, test_size, test_data.data(), test_data2.data());
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const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS ||
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type == GGML_TYPE_IQ3_XXS || type == GGML_TYPE_IQ3_S || type == GGML_TYPE_IQ2_S
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? MAX_DOT_PRODUCT_ERROR_LOWBIT
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: type == GGML_TYPE_TQ1_0 || type == GGML_TYPE_TQ2_0
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? MAX_DOT_PRODUCT_ERROR_TERNARY
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: type == GGML_TYPE_NVFP4
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? MAX_DOT_PRODUCT_ERROR_FP4
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: MAX_DOT_PRODUCT_ERROR;
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failed = !(vec_dot_error < max_allowed_error);
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num_failed += failed;
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if (failed || verbose) {
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printf("%5s dot product error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], vec_dot_error);
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}
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}
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}
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if (num_failed || verbose) {
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printf("%d tests failed\n", num_failed);
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}
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return num_failed > 0;
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}
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