mirror of
https://github.com/ggerganov/llama.cpp.git
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ggml : refactor quantized processing functions (#509)
* Refactor quantized processing functions * ggml : minor --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Stephan Walter
GitHub
parent
692ce3164e
commit
99c5b27654
307
ggml.c
307
ggml.c
@ -1540,7 +1540,7 @@ inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t
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*s = sumf;
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}
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inline static void ggml_vec_dot_q4_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_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
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const int nb = n / QK;
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assert(n % QK == 0);
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@ -1824,7 +1824,7 @@ inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void
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*s = sumf;
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}
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inline static void ggml_vec_dot_q4_1(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(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) {
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const int nb = n / QK;
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const block_q4_1 * restrict x = vx;
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@ -6106,7 +6106,30 @@ static void ggml_compute_forward_mul_mat_f16_f32(
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//}
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}
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static void ggml_compute_forward_mul_mat_q4_0_f32(
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typedef void (*dequantize_row_q_t)(const void * restrict x, float * restrict y, int k);
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typedef void (*quantize_row_q_t)(const float * restrict x, void * restrict y, int k);
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typedef void (*vec_dot_q_t)(const int n, float * restrict s, const void * restrict x, const void * restrict y);
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typedef struct {
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dequantize_row_q_t dequantize_row_q;
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quantize_row_q_t quantize_row_q;
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vec_dot_q_t vec_dot_q;
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} quantize_fns_t;
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static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
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[GGML_TYPE_Q4_0] = {
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.dequantize_row_q = dequantize_row_q4_0,
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.quantize_row_q = quantize_row_q4_0,
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.vec_dot_q = ggml_vec_dot_q4_0,
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},
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[GGML_TYPE_Q4_1] = {
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.dequantize_row_q = dequantize_row_q4_1,
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.quantize_row_q = quantize_row_q4_1,
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.vec_dot_q = ggml_vec_dot_q4_1,
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},
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};
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static void ggml_compute_forward_mul_mat_q_f32(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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@ -6152,8 +6175,12 @@ static void ggml_compute_forward_mul_mat_q4_0_f32(
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GGML_ASSERT(ne2 == ne12);
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GGML_ASSERT(ne3 == ne13);
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const enum ggml_type type = src0->type;
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quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q;
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vec_dot_q_t const vec_dot_q = quantize_fns[type].vec_dot_q;
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// we don't support permuted src0 or src1
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GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[GGML_TYPE_Q4_0]);
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GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[type]);
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GGML_ASSERT(nb10 == sizeof(float));
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// dst cannot be transposed or permuted
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@ -6185,194 +6212,14 @@ static void ggml_compute_forward_mul_mat_q4_0_f32(
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}
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float * const wdata = params->wdata;
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dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
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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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{
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size_t id = 0;
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for (int i01 = 0; i01 < ne01; ++i01) {
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dequantize_row_q4_0((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
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id += ne00;
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}
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}
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const float * x = wdata;
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const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13);
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float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
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// zT = y * xT
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cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
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ne11, ne01, ne10,
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1.0f, y, ne10,
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x, ne10,
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0.0f, d, ne01);
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}
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}
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/*printf("CBLAS Q4_0 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/
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return;
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}
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#endif
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if (params->type == GGML_TASK_INIT) {
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char * wdata = params->wdata;
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for (int i13 = 0; i13 < ne13; ++i13) {
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for (int i12 = 0; i12 < ne12; ++i12) {
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for (int i11 = 0; i11 < ne11; ++i11) {
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quantize_row_q4_0((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
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wdata += (ne10*GGML_TYPE_SIZE[GGML_TYPE_Q4_0])/GGML_BLCK_SIZE[GGML_TYPE_Q4_0];
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}
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}
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}
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return;
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}
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if (params->type == GGML_TASK_FINALIZE) {
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return;
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}
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// parallelize by src0 rows using ggml_vec_dot_q4_0
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// total rows in src0
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const int nr = ne01*ne02*ne03;
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// rows per thread
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const int dr = (nr + nth - 1)/nth;
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// row range for this thread
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const int ir0 = dr*ith;
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const int ir1 = MIN(ir0 + dr, nr);
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void * wdata = params->wdata;
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for (int ir = ir0; ir < ir1; ++ir) {
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// src0 indices
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const int i03 = ir/(ne02*ne01);
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const int i02 = (ir - i03*ne02*ne01)/ne01;
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const int i01 = (ir - i03*ne02*ne01 - i02*ne01);
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const int i13 = i03;
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const int i12 = i02;
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const int i0 = i01;
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const int i2 = i02;
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const int i3 = i03;
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void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
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char * src1_col = ((char *) wdata + ( (0 + i12*ne11 + i13*ne12*ne11)*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_0])/GGML_BLCK_SIZE[GGML_TYPE_Q4_0]);
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float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
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assert(ne00 % 32 == 0);
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for (int ic = 0; ic < ne11; ++ic) {
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ggml_vec_dot_q4_0(ne00, &dst_col[ic*ne0], src0_row, ((void *) (src1_col + (ic*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_0])/GGML_BLCK_SIZE[GGML_TYPE_Q4_0])));
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}
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}
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//int64_t t1 = ggml_time_us();
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//static int64_t acc = 0;
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//acc += t1 - t0;
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//if (t1 - t0 > 10) {
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// printf("\n");
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// printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03);
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// printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03);
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// printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13);
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// printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc);
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//}
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}
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static void ggml_compute_forward_mul_mat_q4_1_f32(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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int64_t t0 = ggml_perf_time_us();
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UNUSED(t0);
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const int ne00 = src0->ne[0];
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const int ne01 = src0->ne[1];
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const int ne02 = src0->ne[2];
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const int ne03 = src0->ne[3];
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const int ne10 = src1->ne[0];
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const int ne11 = src1->ne[1];
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const int ne12 = src1->ne[2];
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const int ne13 = src1->ne[3];
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const int ne0 = dst->ne[0];
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const int ne1 = dst->ne[1];
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const int ne2 = dst->ne[2];
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const int ne3 = dst->ne[3];
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const int nb00 = src0->nb[0];
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const int nb01 = src0->nb[1];
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const int nb02 = src0->nb[2];
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const int nb03 = src0->nb[3];
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const int nb10 = src1->nb[0];
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const int nb11 = src1->nb[1];
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const int nb12 = src1->nb[2];
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const int nb13 = src1->nb[3];
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const int nb0 = dst->nb[0];
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const int nb1 = dst->nb[1];
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const int nb2 = dst->nb[2];
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const int nb3 = dst->nb[3];
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const int ith = params->ith;
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const int nth = params->nth;
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GGML_ASSERT(ne02 == ne12);
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GGML_ASSERT(ne03 == ne13);
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GGML_ASSERT(ne2 == ne12);
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GGML_ASSERT(ne3 == ne13);
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// we don't support permuted src0 or src1
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GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[GGML_TYPE_Q4_1]);
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GGML_ASSERT(nb10 == sizeof(float));
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// dst cannot be transposed or permuted
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GGML_ASSERT(nb0 == sizeof(float));
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GGML_ASSERT(nb0 <= nb1);
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GGML_ASSERT(nb1 <= nb2);
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GGML_ASSERT(nb2 <= nb3);
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GGML_ASSERT(ne0 == ne01);
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GGML_ASSERT(ne1 == ne11);
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GGML_ASSERT(ne2 == ne02);
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GGML_ASSERT(ne3 == ne03);
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// nb01 >= nb00 - src0 is not transposed
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// compute by src0 rows
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#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
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if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) {
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if (params->ith != 0) {
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return;
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}
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if (params->type == GGML_TASK_INIT) {
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return;
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}
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if (params->type == GGML_TASK_FINALIZE) {
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return;
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}
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float * const wdata = params->wdata;
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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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{
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size_t id = 0;
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for (int i01 = 0; i01 < ne01; ++i01) {
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dequantize_row_q4_1((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
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dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00);
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id += ne00;
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}
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}
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@ -6399,15 +6246,13 @@ static void ggml_compute_forward_mul_mat_q4_1_f32(
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if (params->type == GGML_TASK_INIT) {
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char * wdata = params->wdata;
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const size_t row_size = ne10*GGML_TYPE_SIZE[type]/GGML_BLCK_SIZE[type];
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for (int i13 = 0; i13 < ne13; ++i13) {
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for (int i12 = 0; i12 < ne12; ++i12) {
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for (int i11 = 0; i11 < ne11; ++i11) {
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//for (int i10 = 0; i10 < ne10; ++i10) {
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// wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10));
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//}
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quantize_row_q4_1((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
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wdata += (ne10*GGML_TYPE_SIZE[GGML_TYPE_Q4_1])/GGML_BLCK_SIZE[GGML_TYPE_Q4_1];
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quantize_row_q((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10);
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wdata += row_size;
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}
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}
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}
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@ -6419,7 +6264,7 @@ static void ggml_compute_forward_mul_mat_q4_1_f32(
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return;
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}
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// parallelize by src0 rows using ggml_vec_dot_q4_1
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// parallelize by src0 rows using ggml_vec_dot_q
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// total rows in src0
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const int nr = ne01*ne02*ne03;
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@ -6432,6 +6277,7 @@ static void ggml_compute_forward_mul_mat_q4_1_f32(
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const int ir1 = MIN(ir0 + dr, nr);
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void * wdata = params->wdata;
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const size_t row_size = ne00*GGML_TYPE_SIZE[type]/GGML_BLCK_SIZE[type];
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for (int ir = ir0; ir < ir1; ++ir) {
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// src0 indices
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@ -6447,14 +6293,14 @@ static void ggml_compute_forward_mul_mat_q4_1_f32(
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const int i3 = i03;
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void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03));
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char * src1_col = ((char *) wdata + ( (0 + i12*ne11 + i13*ne12*ne11)*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_1])/GGML_BLCK_SIZE[GGML_TYPE_Q4_1]);
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char * src1_col = ((char *) wdata + ( (0 + i12*ne11 + i13*ne12*ne11)*row_size));
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float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3));
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assert(ne00 % 32 == 0);
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for (int ic = 0; ic < ne11; ++ic) {
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ggml_vec_dot_q4_1(ne00, &dst_col[ic*ne0], src0_row, ((void *) (src1_col + (ic*ne00*GGML_TYPE_SIZE[GGML_TYPE_Q4_1])/GGML_BLCK_SIZE[GGML_TYPE_Q4_1])));
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vec_dot_q(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size));
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}
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}
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@ -6478,12 +6324,9 @@ static void ggml_compute_forward_mul_mat(
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struct ggml_tensor * dst) {
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switch (src0->type) {
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case GGML_TYPE_Q4_0:
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{
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ggml_compute_forward_mul_mat_q4_0_f32(params, src0, src1, dst);
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} break;
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case GGML_TYPE_Q4_1:
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{
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ggml_compute_forward_mul_mat_q4_1_f32(params, src0, src1, dst);
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ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst);
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} break;
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case GGML_TYPE_F16:
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{
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@ -6644,7 +6487,7 @@ static void ggml_compute_forward_transpose(
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// ggml_compute_forward_get_rows
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static void ggml_compute_forward_get_rows_q4_0(
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static void ggml_compute_forward_get_rows_q(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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@ -6657,42 +6500,17 @@ static void ggml_compute_forward_get_rows_q4_0(
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const int nc = src0->ne[0];
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const int nr = ggml_nelements(src1);
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const enum ggml_type type = src0->type;
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dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q;
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assert( dst->ne[0] == nc);
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assert( dst->ne[1] == nr);
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assert(src0->nb[0] == GGML_TYPE_SIZE[GGML_TYPE_Q4_0]);
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assert(src0->nb[0] == GGML_TYPE_SIZE[type]);
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for (int i = 0; i < nr; ++i) {
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const int r = ((int32_t *) src1->data)[i];
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dequantize_row_q4_0(
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(const void *) ((char *) src0->data + r*src0->nb[1]),
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(float *) ((char *) dst->data + i*dst->nb[1]), nc);
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}
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}
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static void ggml_compute_forward_get_rows_q4_1(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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assert(params->ith == 0);
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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const int nc = src0->ne[0];
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const int nr = ggml_nelements(src1);
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assert( dst->ne[0] == nc);
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assert( dst->ne[1] == nr);
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assert(src0->nb[0] == GGML_TYPE_SIZE[GGML_TYPE_Q4_1]);
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for (int i = 0; i < nr; ++i) {
|
||||
const int r = ((int32_t *) src1->data)[i];
|
||||
|
||||
dequantize_row_q4_1(
|
||||
dequantize_row_q(
|
||||
(const void *) ((char *) src0->data + r*src0->nb[1]),
|
||||
(float *) ((char *) dst->data + i*dst->nb[1]), nc);
|
||||
}
|
||||
@ -6760,12 +6578,9 @@ static void ggml_compute_forward_get_rows(
|
||||
struct ggml_tensor * dst) {
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_Q4_0:
|
||||
{
|
||||
ggml_compute_forward_get_rows_q4_0(params, src0, src1, dst);
|
||||
} break;
|
||||
case GGML_TYPE_Q4_1:
|
||||
{
|
||||
ggml_compute_forward_get_rows_q4_1(params, src0, src1, dst);
|
||||
ggml_compute_forward_get_rows_q(params, src0, src1, dst);
|
||||
} break;
|
||||
case GGML_TYPE_F16:
|
||||
{
|
||||
@ -9098,8 +8913,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
||||
|
||||
size_t cur = 0;
|
||||
|
||||
if (node->src0->type == GGML_TYPE_F16 &&
|
||||
node->src1->type == GGML_TYPE_F32) {
|
||||
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
|
||||
@ -9114,33 +8928,18 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
|
||||
#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) {
|
||||
} else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) {
|
||||
cur = 0;
|
||||
} else if (node->src0->type == GGML_TYPE_Q4_0 &&
|
||||
node->src1->type == GGML_TYPE_F32) {
|
||||
} else if (quantize_fns[node->src0->type].vec_dot_q && 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 {
|
||||
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_0]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_0];
|
||||
}
|
||||
#else
|
||||
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_0]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_0];
|
||||
} else
|
||||
#endif
|
||||
} else if (node->src0->type == GGML_TYPE_Q4_1 &&
|
||||
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 {
|
||||
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_1]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_1];
|
||||
{
|
||||
cur = GGML_TYPE_SIZE[node->src0->type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[node->src0->type];
|
||||
}
|
||||
#else
|
||||
cur = (GGML_TYPE_SIZE[GGML_TYPE_Q4_1]*ggml_nelements(node->src1))/GGML_BLCK_SIZE[GGML_TYPE_Q4_1];
|
||||
#endif
|
||||
} else {
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user