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ggml : generalize quantize_fns for simpler FP16 handling (#1237)
* Generalize quantize_fns for simpler FP16 handling * Remove call to ggml_cuda_mul_mat_get_wsize * ci : disable FMA for mac os actions --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Stephan Walter
GitHub
parent
8567c76b53
commit
1b107b8550
9 changed files with 172 additions and 548 deletions
3
.github/workflows/build.yml
vendored
3
.github/workflows/build.yml
vendored
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@ -137,9 +137,10 @@ jobs:
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- name: Build
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id: cmake_build
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run: |
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sysctl -a
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mkdir build
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cd build
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cmake -DLLAMA_AVX2=OFF ..
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cmake -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF ..
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cmake --build . --config Release
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- name: Test
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@ -147,7 +147,7 @@ void test_roundtrip_on_chunk(
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const ggml_tensor * layer,
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int64_t offset,
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int64_t chunk_size,
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const quantize_fns_t & qfns,
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const ggml_type_traits_t & qfns,
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bool use_reference,
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float * input_scratch,
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char * quantized_scratch,
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@ -163,11 +163,11 @@ void test_roundtrip_on_chunk(
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}
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if (use_reference) {
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qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
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qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size);
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} else {
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qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
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qfns.from_float(input_scratch, quantized_scratch, chunk_size);
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}
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qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
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qfns.to_float(quantized_scratch, output_scratch, chunk_size);
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update_error_stats(chunk_size, input_scratch, output_scratch, stats);
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}
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@ -177,7 +177,7 @@ void test_roundtrip_on_chunk(
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void test_roundtrip_on_layer(
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std::string & name,
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bool print_layer_stats,
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const quantize_fns_t & qfns,
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const ggml_type_traits_t & qfns,
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bool use_reference,
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const ggml_tensor * layer,
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std::vector<float> & input_scratch,
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@ -388,8 +388,8 @@ int main(int argc, char ** argv) {
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if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
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continue;
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}
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quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
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if (qfns.quantize_row_q && qfns.dequantize_row_q) {
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ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
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if (qfns.from_float && qfns.to_float) {
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if (params.verbose) {
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printf("testing %s ...\n", ggml_type_name(type));
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}
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568
ggml.c
568
ggml.c
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@ -481,14 +481,14 @@ ggml_fp16_t ggml_fp32_to_fp16(float x) {
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return GGML_FP32_TO_FP16(x);
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}
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void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n) {
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for (size_t i = 0; i < n; i++) {
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void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n) {
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for (int i = 0; i < n; i++) {
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y[i] = GGML_FP16_TO_FP32(x[i]);
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}
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}
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void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n) {
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size_t i = 0;
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void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) {
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int i = 0;
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#if defined(__F16C__)
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for (; i + 7 < n; i += 8) {
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__m256 x_vec = _mm256_loadu_ps(x + i);
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@ -1627,109 +1627,112 @@ static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, in
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}
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}
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static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y);
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static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y);
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static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
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static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
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static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
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static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
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static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy);
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static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
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static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
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[GGML_TYPE_F32] = {
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.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32,
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.vec_dot_type = GGML_TYPE_F32,
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},
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[GGML_TYPE_F16] = {
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.to_float = (ggml_to_float_t) ggml_fp16_to_fp32_row,
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.from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row,
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.from_float_reference = (ggml_from_float_t) ggml_fp32_to_fp16_row,
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.vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16,
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.vec_dot_type = GGML_TYPE_F16,
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},
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[GGML_TYPE_Q4_0] = {
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.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q4_0,
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.quantize_row_q = quantize_row_q4_0,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_0_reference,
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.quantize_row_q_dot = quantize_row_q8_0,
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.vec_dot_q = ggml_vec_dot_q4_0_q8_0,
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.to_float = (ggml_to_float_t) dequantize_row_q4_0,
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.from_float = quantize_row_q4_0,
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.from_float_reference = (ggml_from_float_t) quantize_row_q4_0_reference,
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.vec_dot = ggml_vec_dot_q4_0_q8_0,
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.vec_dot_type = GGML_TYPE_Q8_0,
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},
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[GGML_TYPE_Q4_1] = {
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.dequantize_row_q = (dequantize_row_q_t)dequantize_row_q4_1,
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.quantize_row_q = quantize_row_q4_1,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_1_reference,
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.quantize_row_q_dot = quantize_row_q8_1,
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.vec_dot_q = ggml_vec_dot_q4_1_q8_1,
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.to_float = (ggml_to_float_t) dequantize_row_q4_1,
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.from_float = quantize_row_q4_1,
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.from_float_reference = (ggml_from_float_t) quantize_row_q4_1_reference,
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.vec_dot = ggml_vec_dot_q4_1_q8_1,
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.vec_dot_type = GGML_TYPE_Q8_1,
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},
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[GGML_TYPE_Q5_0] = {
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.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_0,
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.quantize_row_q = quantize_row_q5_0,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_0_reference,
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.quantize_row_q_dot = quantize_row_q8_0,
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.vec_dot_q = ggml_vec_dot_q5_0_q8_0,
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.to_float = (ggml_to_float_t) dequantize_row_q5_0,
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.from_float = quantize_row_q5_0,
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.from_float_reference = (ggml_from_float_t) quantize_row_q5_0_reference,
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.vec_dot = ggml_vec_dot_q5_0_q8_0,
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.vec_dot_type = GGML_TYPE_Q8_0,
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},
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[GGML_TYPE_Q5_1] = {
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.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_1,
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.quantize_row_q = quantize_row_q5_1,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_1_reference,
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.quantize_row_q_dot = quantize_row_q8_1,
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.vec_dot_q = ggml_vec_dot_q5_1_q8_1,
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.to_float = (ggml_to_float_t) dequantize_row_q5_1,
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.from_float = quantize_row_q5_1,
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.from_float_reference = (ggml_from_float_t) quantize_row_q5_1_reference,
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.vec_dot = ggml_vec_dot_q5_1_q8_1,
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.vec_dot_type = GGML_TYPE_Q8_1,
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},
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[GGML_TYPE_Q8_0] = {
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.dequantize_row_q = dequantize_row_q8_0,
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.quantize_row_q = quantize_row_q8_0,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_0_reference,
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.quantize_row_q_dot = quantize_row_q8_0,
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.vec_dot_q = ggml_vec_dot_q8_0_q8_0,
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.to_float = dequantize_row_q8_0,
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.from_float = quantize_row_q8_0,
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.from_float_reference = (ggml_from_float_t) quantize_row_q8_0_reference,
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.vec_dot = ggml_vec_dot_q8_0_q8_0,
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.vec_dot_type = GGML_TYPE_Q8_0,
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},
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[GGML_TYPE_Q8_1] = {
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.dequantize_row_q = NULL, // TODO
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.quantize_row_q = quantize_row_q8_1,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_1_reference,
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.quantize_row_q_dot = quantize_row_q8_1,
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.vec_dot_q = NULL, // TODO
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.from_float = quantize_row_q8_1,
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.from_float_reference = (ggml_from_float_t) quantize_row_q8_1_reference,
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.vec_dot_type = GGML_TYPE_Q8_1,
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},
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#ifdef GGML_USE_K_QUANTS
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[GGML_TYPE_Q2_K] = {
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.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q2_K,
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.quantize_row_q = quantize_row_q2_K,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q2_K_reference,
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.quantize_row_q_dot = quantize_row_q8_K,
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.vec_dot_q = ggml_vec_dot_q2_K_q8_K,
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.to_float = (ggml_to_float_t) dequantize_row_q2_K,
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.from_float = quantize_row_q2_K,
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.from_float_reference = (ggml_from_float_t) quantize_row_q2_K_reference,
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.vec_dot = ggml_vec_dot_q2_K_q8_K,
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.vec_dot_type = GGML_TYPE_Q8_K,
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},
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[GGML_TYPE_Q3_K] = {
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.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q3_K,
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.quantize_row_q = quantize_row_q3_K,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q3_K_reference,
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.quantize_row_q_dot = quantize_row_q8_K,
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.vec_dot_q = ggml_vec_dot_q3_K_q8_K,
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.to_float = (ggml_to_float_t) dequantize_row_q3_K,
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.from_float = quantize_row_q3_K,
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.from_float_reference = (ggml_from_float_t) quantize_row_q3_K_reference,
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.vec_dot = ggml_vec_dot_q3_K_q8_K,
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.vec_dot_type = GGML_TYPE_Q8_K,
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},
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[GGML_TYPE_Q4_K] = {
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.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q4_K,
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.quantize_row_q = quantize_row_q4_K,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_K_reference,
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.quantize_row_q_dot = quantize_row_q8_K,
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.vec_dot_q = ggml_vec_dot_q4_K_q8_K,
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.to_float = (ggml_to_float_t) dequantize_row_q4_K,
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.from_float = quantize_row_q4_K,
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.from_float_reference = (ggml_from_float_t) quantize_row_q4_K_reference,
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.vec_dot = ggml_vec_dot_q4_K_q8_K,
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.vec_dot_type = GGML_TYPE_Q8_K,
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},
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[GGML_TYPE_Q5_K] = {
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.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_K,
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.quantize_row_q = quantize_row_q5_K,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_K_reference,
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.quantize_row_q_dot = quantize_row_q8_K,
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.vec_dot_q = ggml_vec_dot_q5_K_q8_K,
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.to_float = (ggml_to_float_t) dequantize_row_q5_K,
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.from_float = quantize_row_q5_K,
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.from_float_reference = (ggml_from_float_t) quantize_row_q5_K_reference,
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.vec_dot = ggml_vec_dot_q5_K_q8_K,
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.vec_dot_type = GGML_TYPE_Q8_K,
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},
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[GGML_TYPE_Q6_K] = {
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.dequantize_row_q = (dequantize_row_q_t) dequantize_row_q6_K,
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.quantize_row_q = quantize_row_q6_K,
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.quantize_row_q_reference = (quantize_row_q_t) quantize_row_q6_K_reference,
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.quantize_row_q_dot = quantize_row_q8_K,
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.vec_dot_q = ggml_vec_dot_q6_K_q8_K,
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.to_float = (ggml_to_float_t) dequantize_row_q6_K,
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.from_float = quantize_row_q6_K,
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.from_float_reference = (ggml_from_float_t) quantize_row_q6_K_reference,
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.vec_dot = ggml_vec_dot_q6_K_q8_K,
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.vec_dot_type = GGML_TYPE_Q8_K,
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},
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[GGML_TYPE_Q8_K] = {
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.from_float = quantize_row_q8_K,
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}
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#endif
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};
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// For internal test use
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quantize_fns_t ggml_internal_get_quantize_fn(size_t i) {
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ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i) {
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GGML_ASSERT(i < GGML_TYPE_COUNT);
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return quantize_fns[i];
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return type_traits[i];
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}
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@ -2275,7 +2278,7 @@ inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x)
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inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; }
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inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; }
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inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
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static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) {
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#ifdef GGML_SIMD
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float sumf = 0.0f;
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const int np = (n & ~(GGML_F32_STEP - 1));
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@ -2312,7 +2315,7 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float
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*s = sumf;
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}
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inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
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static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
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ggml_float sumf = 0.0;
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#if defined(GGML_SIMD)
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@ -7825,8 +7828,8 @@ static void ggml_compute_forward_dup_f16(
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id += ne00 * (ne01 - ir1);
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}
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}
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} else if (ggml_is_quantized(dst->type)) {
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quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q;
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} else if (type_traits[dst->type].from_float) {
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ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
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float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith;
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size_t id = 0;
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@ -8078,26 +8081,8 @@ static void ggml_compute_forward_dup_f32(
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id += rs * (ne01 - ir1);
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}
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}
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} else if (dst->type == GGML_TYPE_F16) {
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size_t id = 0;
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ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data;
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for (int i03 = 0; i03 < ne03; i03++) {
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for (int i02 = 0; i02 < ne02; i02++) {
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id += ne00 * ir0;
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for (int i01 = ir0; i01 < ir1; i01++) {
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for (int i00 = 0; i00 < ne00; i00++) {
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const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
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dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr);
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id++;
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}
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}
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id += ne00 * (ne01 - ir1);
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}
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}
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} else if (ggml_is_quantized(dst->type)) {
|
||||
quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q;
|
||||
} else if (type_traits[dst->type].from_float) {
|
||||
ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float;
|
||||
|
||||
size_t id = 0;
|
||||
size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]);
|
||||
|
|
@ -8503,8 +8488,8 @@ static void ggml_compute_forward_add_q_f32(
|
|||
const int nth = params->nth;
|
||||
|
||||
const enum ggml_type type = src0->type;
|
||||
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
|
||||
quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q;
|
||||
ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
|
||||
ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
|
||||
|
||||
// we don't support permuted src0 or src1
|
||||
GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
|
||||
|
|
@ -8777,8 +8762,8 @@ static void ggml_compute_forward_add1_q_f32(
|
|||
GGML_TENSOR_UNARY_OP_LOCALS;
|
||||
|
||||
const enum ggml_type type = src0->type;
|
||||
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
|
||||
quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q;
|
||||
ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
|
||||
ggml_from_float_t const quantize_row_q = type_traits[type].from_float;
|
||||
|
||||
// we don't support permuted src0
|
||||
GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
|
||||
|
|
@ -10578,317 +10563,7 @@ static bool ggml_compute_forward_mul_mat_use_blas(
|
|||
}
|
||||
#endif
|
||||
|
||||
static void ggml_compute_forward_mul_mat_f32(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * src0,
|
||||
const struct ggml_tensor * src1,
|
||||
struct ggml_tensor * dst) {
|
||||
int64_t t0 = ggml_perf_time_us();
|
||||
UNUSED(t0);
|
||||
|
||||
GGML_TENSOR_BINARY_OP_LOCALS;
|
||||
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
assert(ne02 == ne12);
|
||||
assert(ne03 == ne13);
|
||||
assert(ne2 == ne12);
|
||||
assert(ne3 == ne13);
|
||||
|
||||
// we don't support permuted src0 or src1
|
||||
assert(nb00 == sizeof(float));
|
||||
assert(nb10 == sizeof(float));
|
||||
|
||||
// dst cannot be transposed or permuted
|
||||
assert(nb0 == sizeof(float));
|
||||
assert(nb0 <= nb1);
|
||||
assert(nb1 <= nb2);
|
||||
assert(nb2 <= nb3);
|
||||
|
||||
assert(ne0 == ne01);
|
||||
assert(ne1 == ne11);
|
||||
assert(ne2 == ne02);
|
||||
assert(ne3 == ne03);
|
||||
|
||||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
||||
if (params->ith != 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (params->type == GGML_TASK_INIT) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (params->type == GGML_TASK_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03);
|
||||
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
|
||||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
|
||||
ne11, ne01, ne10,
|
||||
1.0f, y, ne10,
|
||||
x, ne00,
|
||||
0.0f, d, ne01);
|
||||
}
|
||||
}
|
||||
//printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);
|
||||
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
if (params->type == GGML_TASK_INIT) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (params->type == GGML_TASK_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
// parallelize by src0 rows using ggml_vec_dot_f32
|
||||
|
||||
// total rows in src0
|
||||
const int nr = ne01*ne02*ne03;
|
||||
|
||||
// rows per thread
|
||||
const int dr = (nr + nth - 1)/nth;
|
||||
|
||||
// row range for this thread
|
||||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
for (int ir = ir0; ir < ir1; ++ir) {
|
||||
// src0 indices
|
||||
const int i03 = ir/(ne02*ne01);
|
||||
const int i02 = (ir - i03*ne02*ne01)/ne01;
|
||||
const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
|
||||
|
||||
for (int64_t ic = 0; ic < ne11; ++ic) {
|
||||
// src1 indices
|
||||
const int i13 = i03;
|
||||
const int i12 = i02;
|
||||
const int i11 = ic;
|
||||
|
||||
// dst indices
|
||||
const int i0 = i01;
|
||||
const int i1 = i11;
|
||||
const int i2 = i02;
|
||||
const int i3 = i03;
|
||||
|
||||
ggml_vec_dot_f32(ne00,
|
||||
(float *) ((char *) dst->data + (i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3)),
|
||||
(float *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)),
|
||||
(float *) ((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)));
|
||||
}
|
||||
}
|
||||
|
||||
//int64_t t1 = ggml_perf_time_us();
|
||||
//static int64_t acc = 0;
|
||||
//acc += t1 - t0;
|
||||
//if (t1 - t0 > 10) {
|
||||
// printf("\n");
|
||||
// printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
|
||||
// printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
|
||||
// printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
|
||||
// printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13);
|
||||
|
||||
// printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
|
||||
//}
|
||||
}
|
||||
|
||||
static void ggml_compute_forward_mul_mat_f16_f32(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * src0,
|
||||
const struct ggml_tensor * src1,
|
||||
struct ggml_tensor * dst) {
|
||||
int64_t t0 = ggml_perf_time_us();
|
||||
UNUSED(t0);
|
||||
|
||||
GGML_TENSOR_BINARY_OP_LOCALS;
|
||||
|
||||
//const int64_t ne = ne0*ne1*ne2*ne3;
|
||||
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
GGML_ASSERT(ne02 == ne12);
|
||||
GGML_ASSERT(ne03 == ne13);
|
||||
GGML_ASSERT(ne2 == ne12);
|
||||
GGML_ASSERT(ne3 == ne13);
|
||||
|
||||
// TODO: we don't support permuted src0
|
||||
GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
|
||||
|
||||
// dst cannot be transposed or permuted
|
||||
GGML_ASSERT(nb0 == sizeof(float));
|
||||
GGML_ASSERT(nb0 <= nb1);
|
||||
GGML_ASSERT(nb1 <= nb2);
|
||||
GGML_ASSERT(nb2 <= nb3);
|
||||
|
||||
GGML_ASSERT(ne0 == ne01);
|
||||
GGML_ASSERT(ne1 == ne11);
|
||||
GGML_ASSERT(ne2 == ne02);
|
||||
GGML_ASSERT(ne3 == ne03);
|
||||
|
||||
// nb01 >= nb00 - src0 is not transposed
|
||||
// compute by src0 rows
|
||||
|
||||
#if defined(GGML_USE_CLBLAST)
|
||||
if (ggml_cl_can_mul_mat(src0, src1, dst)) {
|
||||
if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) {
|
||||
ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize);
|
||||
}
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
|
||||
GGML_ASSERT(nb10 == sizeof(float));
|
||||
|
||||
if (params->ith != 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (params->type == GGML_TASK_INIT) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (params->type == GGML_TASK_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
float * const wdata = params->wdata;
|
||||
{
|
||||
size_t id = 0;
|
||||
for (int64_t i01 = 0; i01 < ne01; ++i01) {
|
||||
for (int64_t i00 = 0; i00 < ne00; ++i00) {
|
||||
wdata[id++] = GGML_FP16_TO_FP32(*(ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00));
|
||||
}
|
||||
}
|
||||
|
||||
assert(id*sizeof(float) <= params->wsize);
|
||||
}
|
||||
|
||||
const float * x = wdata;
|
||||
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
|
||||
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
|
||||
// zT = y * xT
|
||||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
|
||||
ne11, ne01, ne10,
|
||||
1.0f, y, ne10,
|
||||
x, ne00,
|
||||
0.0f, d, ne01);
|
||||
}
|
||||
}
|
||||
|
||||
/*printf("CBLAS F16 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/
|
||||
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
|
||||
if (params->type == GGML_TASK_INIT) {
|
||||
ggml_fp16_t * const wdata = params->wdata;
|
||||
|
||||
size_t id = 0;
|
||||
for (int64_t i13 = 0; i13 < ne13; ++i13) {
|
||||
for (int64_t i12 = 0; i12 < ne12; ++i12) {
|
||||
for (int64_t i11 = 0; i11 < ne11; ++i11) {
|
||||
for (int64_t i10 = 0; i10 < ne10; ++i10) {
|
||||
wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
GGML_ASSERT(id*sizeof(ggml_fp16_t) <= params->wsize);
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
if (params->type == GGML_TASK_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
// fp16 -> half the size, so divide by 2
|
||||
// TODO: do not support transposed src1
|
||||
assert(nb10/2 == sizeof(ggml_fp16_t));
|
||||
|
||||
// parallelize by src0 rows using ggml_vec_dot_f16
|
||||
|
||||
// total rows in src0
|
||||
const int nr = ne01*ne02*ne03;
|
||||
|
||||
// rows per thread
|
||||
const int dr = (nr + nth - 1)/nth;
|
||||
|
||||
// row range for this thread
|
||||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
ggml_fp16_t * wdata = params->wdata;
|
||||
|
||||
for (int ir = ir0; ir < ir1; ++ir) {
|
||||
// src0 indices
|
||||
const int i03 = ir/(ne02*ne01);
|
||||
const int i02 = (ir - i03*ne02*ne01)/ne01;
|
||||
const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
|
||||
|
||||
const int i13 = i03;
|
||||
const int i12 = i02;
|
||||
|
||||
const int i0 = i01;
|
||||
const int i2 = i02;
|
||||
const int i3 = i03;
|
||||
|
||||
ggml_fp16_t * src0_row = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
|
||||
ggml_fp16_t * src1_col = wdata + ( 0 + i12*ne11 + i13*ne12*ne11)*ne00;
|
||||
|
||||
float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
|
||||
|
||||
for (int64_t ic = 0; ic < ne11; ++ic) {
|
||||
ggml_vec_dot_f16(ne00, &dst_col[ic*ne0], src0_row, src1_col + ic*ne00);
|
||||
}
|
||||
}
|
||||
|
||||
//int64_t t1 = ggml_time_us();
|
||||
//static int64_t acc = 0;
|
||||
//acc += t1 - t0;
|
||||
//if (t1 - t0 > 10) {
|
||||
// printf("\n");
|
||||
// printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
|
||||
// printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
|
||||
// printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
|
||||
|
||||
// printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
|
||||
//}
|
||||
}
|
||||
|
||||
static void ggml_compute_forward_mul_mat_q_f32(
|
||||
static void ggml_compute_forward_mul_mat(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * src0,
|
||||
const struct ggml_tensor * src1,
|
||||
|
|
@ -10907,9 +10582,10 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
GGML_ASSERT(ne3 == ne13);
|
||||
|
||||
const enum ggml_type type = src0->type;
|
||||
quantize_row_q_t const quantize_row_q_dot = quantize_fns[type].quantize_row_q_dot;
|
||||
vec_dot_q_t const vec_dot_q = quantize_fns[type].vec_dot_q;
|
||||
enum ggml_type const vec_dot_type = quantize_fns[type].vec_dot_type;
|
||||
|
||||
ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot;
|
||||
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
|
||||
ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;
|
||||
|
||||
// we don't support permuted src0 or src1
|
||||
GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]);
|
||||
|
|
@ -10952,27 +10628,27 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
return;
|
||||
}
|
||||
|
||||
float * const wdata = params->wdata;
|
||||
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
|
||||
|
||||
for (int64_t i03 = 0; i03 < ne03; i03++) {
|
||||
for (int64_t i02 = 0; i02 < ne02; i02++) {
|
||||
const void * x = (char *) src0->data + i03*nb03 + i02*nb02;
|
||||
const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
|
||||
|
||||
float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
|
||||
|
||||
{
|
||||
if (type != GGML_TYPE_F32) {
|
||||
float * const wdata = params->wdata;
|
||||
ggml_to_float_t const to_float = type_traits[type].to_float;
|
||||
|
||||
size_t id = 0;
|
||||
for (int64_t i01 = 0; i01 < ne01; ++i01) {
|
||||
dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
|
||||
to_float((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
|
||||
id += ne00;
|
||||
}
|
||||
|
||||
assert(id*sizeof(float) <= params->wsize);
|
||||
x = wdata;
|
||||
}
|
||||
|
||||
const float * x = wdata;
|
||||
|
||||
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
|
||||
ne11, ne01, ne10,
|
||||
1.0f, y, ne10,
|
||||
|
|
@ -10988,17 +10664,19 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
#endif
|
||||
|
||||
if (params->type == GGML_TASK_INIT) {
|
||||
if (src1->type != vec_dot_type) {
|
||||
char * wdata = params->wdata;
|
||||
const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
|
||||
|
||||
for (int64_t i13 = 0; i13 < ne13; ++i13) {
|
||||
for (int64_t i12 = 0; i12 < ne12; ++i12) {
|
||||
for (int64_t i11 = 0; i11 < ne11; ++i11) {
|
||||
quantize_row_q_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
|
||||
from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
|
||||
wdata += row_size;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return;
|
||||
}
|
||||
|
|
@ -11019,7 +10697,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
void * wdata = params->wdata;
|
||||
void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
|
||||
const size_t row_size = ne00*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type];
|
||||
|
||||
for (int ir = ir0; ir < ir1; ++ir) {
|
||||
|
|
@ -11043,7 +10721,7 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
assert(ne00 % 32 == 0);
|
||||
|
||||
for (int64_t ic = 0; ic < ne11; ++ic) {
|
||||
vec_dot_q(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size));
|
||||
vec_dot(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -11060,40 +10738,6 @@ static void ggml_compute_forward_mul_mat_q_f32(
|
|||
//}
|
||||
}
|
||||
|
||||
static void ggml_compute_forward_mul_mat(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * src0,
|
||||
const struct ggml_tensor * src1,
|
||||
struct ggml_tensor * dst) {
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q5_1:
|
||||
case GGML_TYPE_Q8_0:
|
||||
case GGML_TYPE_Q8_1:
|
||||
case GGML_TYPE_Q2_K:
|
||||
case GGML_TYPE_Q3_K:
|
||||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
{
|
||||
ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
case GGML_TYPE_F16:
|
||||
{
|
||||
ggml_compute_forward_mul_mat_f16_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
case GGML_TYPE_F32:
|
||||
{
|
||||
ggml_compute_forward_mul_mat_f32(params, src0, src1, dst);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
// ggml_compute_forward_out_prod
|
||||
|
||||
|
|
@ -11483,7 +11127,7 @@ static void ggml_compute_forward_get_rows_q(
|
|||
const int nc = src0->ne[0];
|
||||
const int nr = ggml_nelements(src1);
|
||||
const enum ggml_type type = src0->type;
|
||||
dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
|
||||
ggml_to_float_t const dequantize_row_q = type_traits[type].to_float;
|
||||
|
||||
assert( dst->ne[0] == nc);
|
||||
assert( dst->ne[1] == nr);
|
||||
|
|
@ -16529,6 +16173,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
|||
//printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks = %d\n", nr0, nr1, nr0*nr1, node->n_tasks);
|
||||
|
||||
size_t cur = 0;
|
||||
const enum ggml_type vec_dot_type = type_traits[node->src0->type].vec_dot_type;
|
||||
|
||||
#if defined(GGML_USE_CUBLAS)
|
||||
if (ggml_cuda_can_mul_mat(node->src0, node->src1, node)) {
|
||||
|
|
@ -16544,37 +16189,18 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
|||
}
|
||||
else
|
||||
#endif
|
||||
if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) {
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1; // TODO: this actually is doing nothing
|
||||
// the threads are still spinning
|
||||
if (node->src0->type != GGML_TYPE_F32) {
|
||||
// here we need memory just for single 2D matrix from src0
|
||||
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
|
||||
} else {
|
||||
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
|
||||
}
|
||||
#else
|
||||
cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1);
|
||||
#endif
|
||||
} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
|
||||
cur = 0;
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1;
|
||||
}
|
||||
#endif
|
||||
} else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) {
|
||||
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
|
||||
if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) {
|
||||
node->n_tasks = 1;
|
||||
cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]);
|
||||
} else
|
||||
#endif
|
||||
{
|
||||
const enum ggml_type type_q = quantize_fns[node->src0->type].vec_dot_type;
|
||||
cur = GGML_TYPE_SIZE[type_q]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[type_q];
|
||||
}
|
||||
if (node->src1->type != vec_dot_type) {
|
||||
cur = GGML_TYPE_SIZE[vec_dot_type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[vec_dot_type];
|
||||
} else {
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
|
|
|||
29
ggml.h
29
ggml.h
|
|
@ -250,8 +250,8 @@ extern "C" {
|
|||
GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x);
|
||||
GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
|
||||
|
||||
GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n);
|
||||
GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n);
|
||||
GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n);
|
||||
GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n);
|
||||
|
||||
struct ggml_object;
|
||||
struct ggml_context;
|
||||
|
|
@ -1514,26 +1514,19 @@ extern "C" {
|
|||
// Internal types and functions exposed for tests and benchmarks
|
||||
//
|
||||
|
||||
#ifdef __cplusplus
|
||||
// restrict not standard in C++
|
||||
#define GGML_RESTRICT
|
||||
#else
|
||||
#define GGML_RESTRICT restrict
|
||||
#endif
|
||||
typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
|
||||
typedef void (*quantize_row_q_t) (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
|
||||
typedef void (*vec_dot_q_t) (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
|
||||
typedef void (*ggml_to_float_t)(const void * x, float * y, int k);
|
||||
typedef void (*ggml_from_float_t)(const float * x, void * y, int k);
|
||||
typedef void (*ggml_vec_dot_t)(const int n, float * s, const void * x, const void * y);
|
||||
|
||||
typedef struct {
|
||||
dequantize_row_q_t dequantize_row_q;
|
||||
quantize_row_q_t quantize_row_q;
|
||||
quantize_row_q_t quantize_row_q_reference;
|
||||
quantize_row_q_t quantize_row_q_dot;
|
||||
vec_dot_q_t vec_dot_q;
|
||||
ggml_to_float_t to_float;
|
||||
ggml_from_float_t from_float;
|
||||
ggml_from_float_t from_float_reference;
|
||||
ggml_vec_dot_t vec_dot;
|
||||
enum ggml_type vec_dot_type;
|
||||
} quantize_fns_t;
|
||||
} ggml_type_traits_t;
|
||||
|
||||
quantize_fns_t ggml_internal_get_quantize_fn(size_t i);
|
||||
ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
|
|
|||
10
llama.cpp
10
llama.cpp
|
|
@ -2257,10 +2257,10 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
|
|||
}
|
||||
float * f32_output = (float *) output.addr;
|
||||
|
||||
quantize_fns_t qtype;
|
||||
ggml_type_traits_t qtype;
|
||||
if (ggml_is_quantized(tensor.type)) {
|
||||
qtype = ggml_internal_get_quantize_fn(tensor.type);
|
||||
if (qtype.dequantize_row_q == NULL) {
|
||||
qtype = ggml_internal_get_type_traits(tensor.type);
|
||||
if (qtype.to_float == NULL) {
|
||||
throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor.type)));
|
||||
}
|
||||
} else if (tensor.type != GGML_TYPE_F16) {
|
||||
|
|
@ -2271,7 +2271,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
|
|||
if (tensor.type == GGML_TYPE_F16) {
|
||||
ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor.data, f32_output, nelements);
|
||||
} else if (ggml_is_quantized(tensor.type)) {
|
||||
qtype.dequantize_row_q(tensor.data, f32_output, nelements);
|
||||
qtype.to_float(tensor.data, f32_output, nelements);
|
||||
} else {
|
||||
LLAMA_ASSERT(false); // unreachable
|
||||
}
|
||||
|
|
@ -2296,7 +2296,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam
|
|||
if (typ == GGML_TYPE_F16) {
|
||||
ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
|
||||
} else {
|
||||
qtype.dequantize_row_q(inbuf, outbuf, nels);
|
||||
qtype.to_float(inbuf, outbuf, nels);
|
||||
}
|
||||
};
|
||||
workers.push_back(std::thread(compute, tensor.type, tensor.data + in_buff_offs, f32_output + out_buff_offs, thr_elems));
|
||||
|
|
|
|||
|
|
@ -136,7 +136,7 @@ int main(int argc, char** argv) {
|
|||
|
||||
auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1;
|
||||
|
||||
auto funcs = ggml_internal_get_quantize_fn(ggml_type);
|
||||
auto funcs = ggml_internal_get_type_traits(ggml_type);
|
||||
|
||||
Stat simple, ggml;
|
||||
|
||||
|
|
@ -156,8 +156,8 @@ int main(int argc, char** argv) {
|
|||
|
||||
t1 = std::chrono::high_resolution_clock::now();
|
||||
float fs;
|
||||
if (type == 0) funcs.vec_dot_q(kVecSize * QK4_1, &fs, x40.data(), y.data());
|
||||
else funcs.vec_dot_q(kVecSize * QK4_1, &fs, x41.data(), y.data());
|
||||
if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, x40.data(), y.data());
|
||||
else funcs.vec_dot(kVecSize * QK4_1, &fs, x41.data(), y.data());
|
||||
t2 = std::chrono::high_resolution_clock::now();
|
||||
t = 1e-3*std::chrono::duration_cast<std::chrono::nanoseconds>(t2-t1).count();
|
||||
if (iloop > 3) ggml.addResult(fs, t);
|
||||
|
|
|
|||
|
|
@ -235,7 +235,7 @@ int main(int argc, char** argv) {
|
|||
int n4 = useQ4_1 ? kVecSize / QK4_1 : kVecSize / QK4_0; n4 = 64*((n4 + 63)/64);
|
||||
int n8 = kVecSize / QK8_0; n8 = 64*((n8 + 63)/64);
|
||||
|
||||
auto funcs = useQ4_1 ? ggml_internal_get_quantize_fn(GGML_TYPE_Q4_1) : ggml_internal_get_quantize_fn(GGML_TYPE_Q4_0);
|
||||
auto funcs = useQ4_1 ? ggml_internal_get_type_traits(GGML_TYPE_Q4_1) : ggml_internal_get_type_traits(GGML_TYPE_Q4_0);
|
||||
|
||||
std::vector<block_q4_0> q40;
|
||||
std::vector<block_q4_1> q41;
|
||||
|
|
@ -261,9 +261,9 @@ int main(int argc, char** argv) {
|
|||
// Note, we do not include this in the timing as in practical application
|
||||
// we already have the quantized model weights.
|
||||
if (useQ4_1) {
|
||||
funcs.quantize_row_q(x1.data(), q41.data(), kVecSize);
|
||||
funcs.from_float(x1.data(), q41.data(), kVecSize);
|
||||
} else {
|
||||
funcs.quantize_row_q(x1.data(), q40.data(), kVecSize);
|
||||
funcs.from_float(x1.data(), q40.data(), kVecSize);
|
||||
}
|
||||
|
||||
// Now measure time the dot product needs using the "scalar" version above
|
||||
|
|
@ -282,9 +282,10 @@ int main(int argc, char** argv) {
|
|||
dot_q4_q8(kVecSize, &result, q40.data(), q8.data());
|
||||
}
|
||||
else {
|
||||
funcs.quantize_row_q_dot(y1.data(), q8.data(), kVecSize);
|
||||
if (useQ4_1) funcs.vec_dot_q(kVecSize, &result, q41.data(), q8.data());
|
||||
else funcs.vec_dot_q(kVecSize, &result, q40.data(), q8.data());
|
||||
auto vdot = ggml_internal_get_type_traits(funcs.vec_dot_type);
|
||||
vdot.from_float(y1.data(), q8.data(), kVecSize);
|
||||
if (useQ4_1) funcs.vec_dot(kVecSize, &result, q41.data(), q8.data());
|
||||
else funcs.vec_dot(kVecSize, &result, q40.data(), q8.data());
|
||||
}
|
||||
sumq += result;
|
||||
t2 = std::chrono::high_resolution_clock::now();
|
||||
|
|
|
|||
|
|
@ -40,26 +40,26 @@ float array_rmse(const float * a1, const float * a2, size_t n) {
|
|||
}
|
||||
|
||||
// Total quantization error on test data
|
||||
float total_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) {
|
||||
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.quantize_row_q(test_data, tmp_q.data(), test_size);
|
||||
qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), 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
|
||||
float reference_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) {
|
||||
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.quantize_row_q(test_data, tmp_q.data(), test_size);
|
||||
qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size);
|
||||
qfns.from_float(test_data, tmp_q.data(), test_size);
|
||||
qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
|
||||
|
||||
qfns.quantize_row_q_reference(test_data, tmp_q.data(), test_size);
|
||||
qfns.dequantize_row_q(tmp_q.data(), tmp_out_ref.data(), test_size);
|
||||
qfns.from_float_reference(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);
|
||||
}
|
||||
|
|
@ -73,15 +73,17 @@ float dot_product(const float * a1, const float * a2, size_t test_size) {
|
|||
}
|
||||
|
||||
// Total dot product error
|
||||
float dot_product_error(quantize_fns_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) {
|
||||
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);
|
||||
|
||||
qfns.quantize_row_q (test_data1, tmp_q1.data(), test_size);
|
||||
qfns.quantize_row_q_dot(test_data2, tmp_q2.data(), 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_q(test_size, &result, tmp_q1.data(), tmp_q2.data());
|
||||
qfns.vec_dot(test_size, &result, tmp_q1.data(), tmp_q2.data());
|
||||
|
||||
const float dot_ref = dot_product(test_data1, test_data2, test_size);
|
||||
|
||||
|
|
@ -123,9 +125,9 @@ int main(int argc, char * argv[]) {
|
|||
|
||||
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
|
||||
ggml_type type = (ggml_type) i;
|
||||
quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
|
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ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
|
||||
|
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if (qfns.quantize_row_q && qfns.dequantize_row_q) {
|
||||
if (qfns.from_float && qfns.to_float) {
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const float total_error = total_quantization_error(qfns, test_size, test_data.data());
|
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const float max_quantization_error =
|
||||
type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :
|
||||
|
|
|
|||
|
|
@ -123,9 +123,9 @@ void usage(char * argv[]) {
|
|||
printf(" --type TYPE set test type as");
|
||||
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
|
||||
ggml_type type = (ggml_type) i;
|
||||
quantize_fns_t qfns = ggml_internal_get_quantize_fn(type);
|
||||
ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
|
||||
if (ggml_type_name(type) != NULL) {
|
||||
if (qfns.quantize_row_q && qfns.dequantize_row_q) {
|
||||
if (qfns.from_float && qfns.to_float) {
|
||||
printf(" %s", ggml_type_name(type));
|
||||
}
|
||||
}
|
||||
|
|
@ -271,12 +271,12 @@ int main(int argc, char * argv[]) {
|
|||
|
||||
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
|
||||
ggml_type type = (ggml_type) i;
|
||||
quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
|
||||
ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
|
||||
if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) {
|
||||
continue;
|
||||
}
|
||||
|
||||
if (qfns.quantize_row_q && qfns.dequantize_row_q) {
|
||||
if (qfns.from_float && qfns.to_float) {
|
||||
printf("%s\n", ggml_type_name(type));
|
||||
|
||||
if (params.op_quantize_row_q_reference) {
|
||||
|
|
@ -284,7 +284,7 @@ int main(int argc, char * argv[]) {
|
|||
for (size_t size : params.test_sizes) {
|
||||
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
|
||||
auto quantize_fn = [&](void ) {
|
||||
qfns.quantize_row_q_reference(test_data1, test_q1, size);
|
||||
qfns.from_float_reference(test_data1, test_q1, size);
|
||||
return test_q1[0];
|
||||
};
|
||||
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
|
||||
|
|
@ -298,7 +298,7 @@ int main(int argc, char * argv[]) {
|
|||
for (size_t size : params.test_sizes) {
|
||||
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
|
||||
auto quantize_fn = [&](void ) {
|
||||
qfns.quantize_row_q(test_data1, test_q1, size);
|
||||
qfns.from_float(test_data1, test_q1, size);
|
||||
return test_q1[0];
|
||||
};
|
||||
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
|
||||
|
|
@ -309,11 +309,11 @@ int main(int argc, char * argv[]) {
|
|||
|
||||
if (params.op_dequantize_row_q) {
|
||||
printf(" dequantize_row_q\n");
|
||||
qfns.quantize_row_q(test_data1, test_q1, largest);
|
||||
qfns.from_float(test_data1, test_q1, largest);
|
||||
for (size_t size : params.test_sizes) {
|
||||
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
|
||||
auto quantize_fn = [&](void ) {
|
||||
qfns.dequantize_row_q(test_q1, test_out, size);
|
||||
qfns.to_float(test_q1, test_out, size);
|
||||
return test_out[0];
|
||||
};
|
||||
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
|
||||
|
|
@ -327,7 +327,8 @@ int main(int argc, char * argv[]) {
|
|||
for (size_t size : params.test_sizes) {
|
||||
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
|
||||
auto quantize_fn = [&](void ) {
|
||||
qfns.quantize_row_q_dot(test_data1, test_q1, size);
|
||||
auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
|
||||
vdot.from_float(test_data1, test_q1, size);
|
||||
return test_q1[0];
|
||||
};
|
||||
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
|
||||
|
|
@ -338,13 +339,13 @@ int main(int argc, char * argv[]) {
|
|||
|
||||
if (params.op_vec_dot_q) {
|
||||
printf(" vec_dot_q\n");
|
||||
qfns.quantize_row_q(test_data1, test_q1, largest);
|
||||
qfns.quantize_row_q(test_data2, test_q2, largest);
|
||||
qfns.from_float(test_data1, test_q1, largest);
|
||||
qfns.from_float(test_data2, test_q2, largest);
|
||||
for (size_t size : params.test_sizes) {
|
||||
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
|
||||
auto quantize_fn = [&](void ) {
|
||||
float result;
|
||||
qfns.vec_dot_q(size, &result, test_q1, test_q2);
|
||||
qfns.vec_dot(size, &result, test_q1, test_q2);
|
||||
return result;
|
||||
};
|
||||
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
|
||||
|
|
|
|||
Loading…
Reference in a new issue