2023-04-22 11:10:39 +02:00
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// Unit tests for quantization specific functions - quantize, dequantize and dot product
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#include "ggml.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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2023-06-16 20:23:53 +02:00
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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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2023-09-15 21:38:27 +02:00
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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_2BITS = 0.0075f;
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constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f;
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2024-01-30 14:14:12 +01:00
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constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS = 0.0050f;
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2023-09-15 21:38:27 +02:00
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constexpr float MAX_DOT_PRODUCT_ERROR = 0.02f;
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2024-01-30 14:14:12 +01:00
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constexpr float MAX_DOT_PRODUCT_ERROR_LOWBIT = 0.04f;
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2023-04-22 11:10:39 +02:00
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2023-09-15 21:38:27 +02:00
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static const char* RESULT_STR[] = {"ok", "FAILED"};
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2023-04-22 11:10:39 +02:00
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// Generate synthetic data
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2023-09-15 21:38:27 +02:00
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static void generate_data(float offset, size_t n, float * dst) {
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2023-04-22 11:10:39 +02:00
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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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2023-09-15 21:38:27 +02:00
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static float array_rmse(const float * a1, const float * a2, size_t n) {
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2023-04-22 11:10:39 +02:00
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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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2023-09-15 21:38:27 +02:00
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static float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
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2023-04-25 22:40:51 +02:00
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std::vector<uint8_t> tmp_q(2*test_size);
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2023-04-22 11:10:39 +02:00
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std::vector<float> tmp_out(test_size);
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2023-07-05 18:13:06 +02:00
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qfns.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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2023-04-22 11:10:39 +02:00
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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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2023-09-15 21:38:27 +02:00
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static float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
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2023-04-25 22:40:51 +02:00
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std::vector<uint8_t> tmp_q(2*test_size);
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2023-04-22 11:10:39 +02:00
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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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2023-07-05 18:13:06 +02:00
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qfns.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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2023-04-22 11:10:39 +02:00
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2023-07-05 18:13:06 +02:00
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qfns.from_float_reference(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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2023-04-22 11:10:39 +02:00
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return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size);
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}
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2023-09-15 21:38:27 +02:00
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static float dot_product(const float * a1, const float * a2, size_t test_size) {
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2023-04-22 11:10:39 +02:00
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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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2023-09-15 21:38:27 +02:00
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static float dot_product_error(
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ggml_type_traits_t & qfns, size_t test_size, const float * test_data1, const float *test_data2
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) {
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2023-04-25 22:40:51 +02:00
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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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2023-04-22 11:10:39 +02:00
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2023-07-05 18:13:06 +02:00
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auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
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qfns.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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2023-04-22 11:10:39 +02:00
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float result = INFINITY;
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2024-02-11 14:22:33 +01:00
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qfns.vec_dot(test_size, &result, 0, tmp_q1.data(), 0, tmp_q2.data(), 0, 1);
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2023-04-22 11:10:39 +02:00
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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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// Initialize GGML, ensures float conversion tables are initialized
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struct ggml_init_params ggml_params = {
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/* .mem_size = */ 1*1024,
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/* .mem_buffer = */ NULL,
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/* .no_alloc = */ true,
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};
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struct ggml_context * ctx = ggml_init(ggml_params);
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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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2023-07-05 18:13:06 +02:00
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ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
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2023-04-22 11:10:39 +02:00
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2023-10-30 18:19:15 +01:00
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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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2024-01-11 20:39:39 +01:00
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const ggml_type ei = (ggml_type)i;
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2024-01-30 14:14:12 +01:00
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2023-10-30 18:19:15 +01:00
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printf("Testing %s\n", ggml_type_name((ggml_type) i));
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2024-01-30 14:14:12 +01:00
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ggml_quantize_init(ei);
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2023-10-30 18:19:15 +01:00
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2023-07-05 18:13:06 +02:00
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if (qfns.from_float && qfns.to_float) {
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2023-04-22 11:10:39 +02:00
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const float total_error = total_quantization_error(qfns, test_size, test_data.data());
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ggml : add SOTA 2,3,4,5,6 bit k-quantizations (#1684)
* Starting to add k-quantization to ggml
I think it is better to have quantization separate from
ggml. For now just adding the k-quants there, but it would be
better to also factor out the existing ggml quantizations.
* Adding Q3_K and Q8_K (de)-quantization
* Q3_K now working on CUDA and AVX2/scalar
CUDA is not ideal - ~50% slower than Q4_0 for
single token prediction, about the same in batch
mode (perplexity). CPU single token is ~55 ms
(on Ryzen 7950X).
* Some improvement for Q3_K on CUDA
It is now ~22.5 ms/token on my GPU, so ~30% slower than Q4_0.
* Some more CUDA optimizations for Q3_K
Single token is now 20.5 ms/token (~20% slower than Q4_0).
Perplexity is on par with Q4_0.
* Adding Q4_K - scalar, AVX2, CUDA
Performance is the same or perhaps very slightly better than Q4_0 on the CPU.
On the GPU, single token prediction is ~10% better than Q4_0,
batch mode (perplexity is about the same).
* Adding Q6_K - scalar, AVX2, CUDA
Performance is ~40% lower compared to Q4_K on the CPU.
This is to be expected, considering that we are memory bound
on the CPU and the 6-bit model is ~44% larger than the 4-bit.
On the GPU, single token prediction is ~6% lower than Q4_0,
batch mode (perplexity) is even closer (but still slower).
* Adding Q5_K - scalar, AVX2, CUDA
Performance is ~20% lower compared to Q4_K on the CPU.
This is to be expected, considering that we are memory bound
on the CPU and the 5-bit model is ~22% larger than the 4-bit.
On the GPU, single token prediction is about the same as Q4_0
for both, single token and batch prediction.
* Per convention, all QX_K quantizations use Q5_K for output.weight
* Adding quantization mixes
* Quantization mixes: didn't quite get what I wanted in the last commit
* Q4_K dot product for ARM_NEON
* Q6_K dot product for ARM_NEON
* Q5_K dot product for ARM_NEON
* Adding Q3_K dot for ARM_NEON
It is 22% slower than Q4_K, despite the smaller model size.
On x86_64, where we are memory bound, the Q3_K model is
quite a bit faster than Q4_K.
* A very slightly faster ARM_NEON Q3_K dot
* Adding Q2_K - just CUDA for now
Token prediction is pretty good - about 15.5 ms on a RTX 4080.
Perplexity is about the same as Q4_K.
* Adding scalar and AVX2 Q2_K dot
* Adding ARM_NEON Q2_K dot
About the same performance as Q4_K.
* A slightly faster ARM_NEON Q2_K dot
Single token prediction is now ~36 ms on M2 Max.
The code is much simpler too.
* Fixed bug in Q2_K CUDA dot product kernel
Stranegly enough, for the few prompts I tried with the 7B model
the responses looked perfectly reasonable. Only realized something
is not quite right when I tried the larger models and started getting
nonse back.
In any case, Q2_K single token evaluation time on an RTX 4080 in a Ryzen7950X
box iusing CUDA and model fully loaded on the GPU are
~15.5 ms for 7B, ~25.4 ms for 13B, and ~55.8 ms for 30B.
The max number of layers that fit in VRAM for The 65B is 32.
With that, we get ~330 ms per token, which is not that much faster
than just running on the CPU (~470 ms per token).
* Don't print zeros/NaNs when no count histogram has been collected
* A 10% faster CUDA vector dot kernel for Q3_K
Q3_K is now running at ~18.5 ms / token on CUDA,
so the gap to Q4_0 is only 10%.
It seems memory acccess pattern is more important for
performance than the amount of computation the kernel
does.
* A slightly daster Q4_K AVX2 dot product
For perplexity, where we are less memory bound, time per
pass drops by ~5%. Barely measurable difference for single
token prediction.
* A slightly faster ARM_NEON A4_K dot product
* Minor
* Fix quantization error test
We cannot possibly be expecting rmse < 0.002 for 2- and 3-bit
quantization variants.
* Fix docker build
I have been sloppy with vector reinterpret casts on ARM_NEON.
It seems clang is very forgiving in that regard.
* Added forgotten ggml.o dependence on k_quants.h to the Makefile
* Had unintentionally committed the Makefile with -Ofast enabled
* ggml : rename k_quants -> ggml-quants-k, use lowercase in code
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-05 21:56:18 +02:00
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const float max_quantization_error =
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2024-01-30 14:14:12 +01:00
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type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
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2024-02-26 17:28:38 +01:00
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type == GGML_TYPE_IQ2_S ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
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2024-01-30 14:14:12 +01:00
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type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
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2024-02-24 15:23:52 +01:00
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type == GGML_TYPE_IQ3_S ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS :
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2024-01-30 14:14:12 +01:00
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type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : MAX_QUANTIZATION_TOTAL_ERROR;
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ggml : add SOTA 2,3,4,5,6 bit k-quantizations (#1684)
* Starting to add k-quantization to ggml
I think it is better to have quantization separate from
ggml. For now just adding the k-quants there, but it would be
better to also factor out the existing ggml quantizations.
* Adding Q3_K and Q8_K (de)-quantization
* Q3_K now working on CUDA and AVX2/scalar
CUDA is not ideal - ~50% slower than Q4_0 for
single token prediction, about the same in batch
mode (perplexity). CPU single token is ~55 ms
(on Ryzen 7950X).
* Some improvement for Q3_K on CUDA
It is now ~22.5 ms/token on my GPU, so ~30% slower than Q4_0.
* Some more CUDA optimizations for Q3_K
Single token is now 20.5 ms/token (~20% slower than Q4_0).
Perplexity is on par with Q4_0.
* Adding Q4_K - scalar, AVX2, CUDA
Performance is the same or perhaps very slightly better than Q4_0 on the CPU.
On the GPU, single token prediction is ~10% better than Q4_0,
batch mode (perplexity is about the same).
* Adding Q6_K - scalar, AVX2, CUDA
Performance is ~40% lower compared to Q4_K on the CPU.
This is to be expected, considering that we are memory bound
on the CPU and the 6-bit model is ~44% larger than the 4-bit.
On the GPU, single token prediction is ~6% lower than Q4_0,
batch mode (perplexity) is even closer (but still slower).
* Adding Q5_K - scalar, AVX2, CUDA
Performance is ~20% lower compared to Q4_K on the CPU.
This is to be expected, considering that we are memory bound
on the CPU and the 5-bit model is ~22% larger than the 4-bit.
On the GPU, single token prediction is about the same as Q4_0
for both, single token and batch prediction.
* Per convention, all QX_K quantizations use Q5_K for output.weight
* Adding quantization mixes
* Quantization mixes: didn't quite get what I wanted in the last commit
* Q4_K dot product for ARM_NEON
* Q6_K dot product for ARM_NEON
* Q5_K dot product for ARM_NEON
* Adding Q3_K dot for ARM_NEON
It is 22% slower than Q4_K, despite the smaller model size.
On x86_64, where we are memory bound, the Q3_K model is
quite a bit faster than Q4_K.
* A very slightly faster ARM_NEON Q3_K dot
* Adding Q2_K - just CUDA for now
Token prediction is pretty good - about 15.5 ms on a RTX 4080.
Perplexity is about the same as Q4_K.
* Adding scalar and AVX2 Q2_K dot
* Adding ARM_NEON Q2_K dot
About the same performance as Q4_K.
* A slightly faster ARM_NEON Q2_K dot
Single token prediction is now ~36 ms on M2 Max.
The code is much simpler too.
* Fixed bug in Q2_K CUDA dot product kernel
Stranegly enough, for the few prompts I tried with the 7B model
the responses looked perfectly reasonable. Only realized something
is not quite right when I tried the larger models and started getting
nonse back.
In any case, Q2_K single token evaluation time on an RTX 4080 in a Ryzen7950X
box iusing CUDA and model fully loaded on the GPU are
~15.5 ms for 7B, ~25.4 ms for 13B, and ~55.8 ms for 30B.
The max number of layers that fit in VRAM for The 65B is 32.
With that, we get ~330 ms per token, which is not that much faster
than just running on the CPU (~470 ms per token).
* Don't print zeros/NaNs when no count histogram has been collected
* A 10% faster CUDA vector dot kernel for Q3_K
Q3_K is now running at ~18.5 ms / token on CUDA,
so the gap to Q4_0 is only 10%.
It seems memory acccess pattern is more important for
performance than the amount of computation the kernel
does.
* A slightly daster Q4_K AVX2 dot product
For perplexity, where we are less memory bound, time per
pass drops by ~5%. Barely measurable difference for single
token prediction.
* A slightly faster ARM_NEON A4_K dot product
* Minor
* Fix quantization error test
We cannot possibly be expecting rmse < 0.002 for 2- and 3-bit
quantization variants.
* Fix docker build
I have been sloppy with vector reinterpret casts on ARM_NEON.
It seems clang is very forgiving in that regard.
* Added forgotten ggml.o dependence on k_quants.h to the Makefile
* Had unintentionally committed the Makefile with -Ofast enabled
* ggml : rename k_quants -> ggml-quants-k, use lowercase in code
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-05 21:56:18 +02:00
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failed = !(total_error < max_quantization_error);
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2023-04-22 11:10:39 +02:00
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num_failed += failed;
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if (failed || verbose) {
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2023-04-25 22:40:51 +02:00
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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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2023-04-22 11:10:39 +02:00
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}
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const float reference_error = reference_quantization_error(qfns, 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, test_size, test_data.data(), test_data2.data());
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2024-01-30 14:14:12 +01:00
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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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2024-02-26 17:28:38 +01:00
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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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2024-02-24 15:23:52 +01:00
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: MAX_DOT_PRODUCT_ERROR;
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2024-01-30 14:14:12 +01:00
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failed = !(vec_dot_error < max_allowed_error);
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2023-04-22 11:10:39 +02:00
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num_failed += failed;
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if (failed || verbose) {
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2023-04-25 22:40:51 +02:00
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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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2023-04-22 11:10:39 +02:00
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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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ggml_free(ctx);
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return num_failed > 0;
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|
}
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