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https://github.com/ggerganov/llama.cpp.git
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ggml : extend ggml_get_rows, ggml_repeat, ggml_concat (ggml/639)
* add more int ops * ggml_compute_forward_dup_bytes * add tests * PR comments * tests : minor indentations --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Guillaume Wenzek
Georgi Gerganov
parent
f2eb19bd8b
commit
5f66ebca9c
166
ggml.c
166
ggml.c
@ -4766,8 +4766,11 @@ struct ggml_tensor * ggml_get_rows(
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}
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// TODO: implement non F32 return
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//struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]);
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struct ggml_tensor * result = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, a->ne[0], b->ne[0], b->ne[1], b->ne[2]);
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enum ggml_type type = GGML_TYPE_F32;
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if (a->type == GGML_TYPE_I32) {
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type = a->type;
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}
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struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, a->ne[0], b->ne[0], b->ne[1], b->ne[2]);
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result->op = GGML_OP_GET_ROWS;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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@ -6938,14 +6941,165 @@ static void ggml_compute_forward_dup_f32(
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}
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}
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// A simplified version of ggml_compute_forward_dup that doesn't do float upcasting, and just plain old memcpy.
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static void ggml_compute_forward_dup_bytes(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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struct ggml_tensor * dst) {
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GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
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GGML_ASSERT(src0->type == dst->type);
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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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if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
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ggml_compute_forward_dup_same_cont(params, src0, dst);
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return;
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}
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GGML_TENSOR_UNARY_OP_LOCALS;
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const size_t type_size = ggml_type_size(src0->type);
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const int ith = params->ith; // thread index
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const int nth = params->nth; // number of threads
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// parallelize by rows
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const int nr = ne01;
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// number of 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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if (src0->type == dst->type &&
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ne00 == ne0 &&
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nb00 == type_size && nb0 == type_size) {
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// copy by rows
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const size_t rs = ne00 * type_size;
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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for (int64_t i02 = 0; i02 < ne02; i02++) {
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for (int64_t i01 = ir0; i01 < ir1; i01++) {
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memcpy(
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((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3),
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((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03),
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rs);
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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 (ggml_is_contiguous(dst)) {
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size_t id = 0;
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char * dst_ptr = (char *) dst->data;
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const size_t rs = ne00 * type_size;
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if (nb00 == type_size) {
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// src0 is contigous on first dimension, copy by rows
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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for (int64_t i02 = 0; i02 < ne02; i02++) {
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id += rs * ir0;
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for (int64_t i01 = ir0; i01 < ir1; i01++) {
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const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03;
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memcpy(dst_ptr + id, src0_ptr, rs);
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id += rs;
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}
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id += rs * (ne01 - ir1);
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}
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}
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} else {
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//printf("%s: this is not optimal - fix me\n", __func__);
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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for (int64_t i02 = 0; i02 < ne02; i02++) {
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id += rs * ir0;
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for (int64_t i01 = ir0; i01 < ir1; i01++) {
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for (int64_t i00 = 0; i00 < ne00; i00++) {
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const char * src0_ptr = (char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03;
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memcpy(dst_ptr + id, src0_ptr, type_size);
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id += type_size;
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}
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}
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id += rs * (ne01 - ir1);
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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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// dst counters
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int64_t i10 = 0;
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int64_t i11 = 0;
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int64_t i12 = 0;
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int64_t i13 = 0;
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for (int64_t i03 = 0; i03 < ne03; i03++) {
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for (int64_t i02 = 0; i02 < ne02; i02++) {
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i10 += ne00 * ir0;
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while (i10 >= ne0) {
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i10 -= ne0;
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if (++i11 == ne1) {
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i11 = 0;
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if (++i12 == ne2) {
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i12 = 0;
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if (++i13 == ne3) {
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i13 = 0;
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}
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}
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}
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}
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for (int64_t i01 = ir0; i01 < ir1; i01++) {
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for (int64_t i00 = 0; i00 < ne00; i00++) {
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const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
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char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3);
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memcpy(dst_ptr, src0_ptr, type_size);
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if (++i10 == ne0) {
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i10 = 0;
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if (++i11 == ne1) {
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i11 = 0;
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if (++i12 == ne2) {
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i12 = 0;
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if (++i13 == ne3) {
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i13 = 0;
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}
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}
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}
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}
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}
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}
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i10 += ne00 * (ne01 - ir1);
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while (i10 >= ne0) {
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i10 -= ne0;
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if (++i11 == ne1) {
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i11 = 0;
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if (++i12 == ne2) {
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i12 = 0;
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if (++i13 == ne3) {
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i13 = 0;
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}
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}
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}
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}
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}
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}
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}
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static void ggml_compute_forward_dup(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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struct ggml_tensor * dst) {
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if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) {
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ggml_compute_forward_dup_same_cont(params, src0, dst);
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if (src0->type == dst->type) {
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ggml_compute_forward_dup_bytes(params, src0, dst);
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return;
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}
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switch (src0->type) {
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case GGML_TYPE_F16:
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{
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@ -8404,10 +8558,12 @@ static void ggml_compute_forward_repeat(
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struct ggml_tensor * dst) {
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switch (src0->type) {
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case GGML_TYPE_F16:
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case GGML_TYPE_I16:
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{
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ggml_compute_forward_repeat_f16(params, src0, dst);
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} break;
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case GGML_TYPE_F32:
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case GGML_TYPE_I32:
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{
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ggml_compute_forward_repeat_f32(params, src0, dst);
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} break;
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@ -8550,6 +8706,7 @@ static void ggml_compute_forward_concat(
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struct ggml_tensor* dst) {
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switch (src0->type) {
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case GGML_TYPE_F32:
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case GGML_TYPE_I32:
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{
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ggml_compute_forward_concat_f32(params, src0, src1, dst);
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} break;
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@ -10674,6 +10831,7 @@ static void ggml_compute_forward_get_rows(
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ggml_compute_forward_get_rows_f16(params, src0, src1, dst);
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} break;
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case GGML_TYPE_F32:
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case GGML_TYPE_I32:
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{
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ggml_compute_forward_get_rows_f32(params, src0, src1, dst);
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} break;
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@ -58,6 +58,9 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
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int64_t hist[16];
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ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size, hist);
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ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
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} else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
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// This is going to create some weird integers though.
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ggml_backend_tensor_set(tensor, data.data(), 0, ggml_nbytes(tensor));
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} else {
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GGML_ASSERT(false);
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}
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@ -87,8 +90,13 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) {
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tv.push_back(*(float *) &buf[i]);
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} else if (t->type == GGML_TYPE_I32) {
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tv.push_back((float)*(int32_t *) &buf[i]);
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} else if (t->type == GGML_TYPE_I16) {
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tv.push_back((float)*(int16_t *) &buf[i]);
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} else if (t->type == GGML_TYPE_I8) {
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tv.push_back((float)*(int8_t *) &buf[i]);
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} else if (quantized) {
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tt.to_float(&buf[i], vq.data(), bs);
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std::vector<float> vq(ggml_blck_size(t->type));
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tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type));
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tv.insert(tv.end(), vq.begin(), vq.end());
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} else {
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GGML_ASSERT(false);
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@ -661,17 +669,26 @@ struct test_repeat : public test_case {
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struct test_dup : public test_case {
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const ggml_type type;
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const std::array<int64_t, 4> ne;
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const std::array<int64_t, 4> permute;
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bool _use_permute;
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std::string vars() override {
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return VARS_TO_STR2(type, ne);
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std::string v = VARS_TO_STR2(type, ne);
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if (_use_permute) v += "," + VAR_TO_STR(permute);
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return v;
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}
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test_dup(ggml_type type = GGML_TYPE_F32,
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std::array<int64_t, 4> ne = {10, 10, 10, 1})
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: type(type), ne(ne) {}
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std::array<int64_t, 4> ne = {10, 10, 10, 1},
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std::array<int64_t, 4> permute = {0, 0, 0, 0})
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: type(type), ne(ne), permute(permute),
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_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
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ggml_tensor * build_graph(ggml_context * ctx) override {
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ggml_tensor * src = ggml_new_tensor(ctx, type, 4, ne.data());
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if (_use_permute) {
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src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
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}
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ggml_tensor * out = ggml_dup(ctx, src);
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return out;
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}
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@ -1450,14 +1467,26 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
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}
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}
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}
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for (int b : {1, 7}) {
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for (bool v : {false, true}) {
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test_cases.emplace_back(new test_get_rows(GGML_TYPE_I32, 256, 5, 4, b, v));
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}
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}
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test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1}));
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test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1}));
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test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1}));
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test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 2, 1}));
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test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 2}));
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test_cases.emplace_back(new test_repeat(GGML_TYPE_I32, {10, 10, 10, 10}, {2, 1, 1, 1}));
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test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 10, 10, 10}, {1, 1, 1, 2}));
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test_cases.emplace_back(new test_dup());
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test_cases.emplace_back(new test_dup(GGML_TYPE_F32));
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test_cases.emplace_back(new test_dup(GGML_TYPE_F16));
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test_cases.emplace_back(new test_dup(GGML_TYPE_I32));
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test_cases.emplace_back(new test_dup(GGML_TYPE_I16));
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test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3}));
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test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3}));
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for (ggml_type type : all_types) {
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test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, type, {256, 10, 10, 1}));
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@ -1565,7 +1594,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
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test_cases.emplace_back(new test_alibi());
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test_cases.emplace_back(new test_im2col());
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test_cases.emplace_back(new test_concat());
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test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
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test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
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for (ggml_sort_order order : {GGML_SORT_ASC, GGML_SORT_DESC}) {
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test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
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