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https://github.com/ggerganov/llama.cpp.git
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ggml : fix quantized cpy op (#12310)
* ggml : fix quantized cpy op ggml-ci * tests : add cpy tests for all types ggml-ci * tests : add BF16 copy tests ggml-ci * tests : fix loop for same-type copy ggml-ci * tests : add option to permute the dst tensor ggml-ci
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@ -3110,17 +3110,17 @@ static void ggml_compute_forward_dup_same_cont(
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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 elements
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const int ne = ggml_nelements(dst);
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const int dr = (ne + nth - 1) / nth;
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const int ie0 = dr * ith;
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const int ie1 = MIN(ie0 + dr, ne);
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// parallelize by blocks
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const int nk = ggml_nelements(src0)/ggml_blck_size(src0->type);
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const int dr = (nk + nth - 1) / nth;
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const int k0 = dr * ith;
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const int k1 = MIN(k0 + dr, nk);
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if (ie0 < ie1) {
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if (k0 < k1) {
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memcpy(
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((char *) dst->data + ie0*nb0),
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((char *) src0->data + ie0*nb0),
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(ie1 - ie0) * nb0);
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((char *) dst->data + k0*nb0),
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((char *) src0->data + k0*nb0),
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(k1 - k0) * nb0);
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}
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}
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@ -4055,7 +4055,6 @@ static void ggml_compute_forward_dup_f32(
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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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struct ggml_tensor * dst) {
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const struct ggml_tensor * src0 = dst->src[0];
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GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
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@ -4069,10 +4068,10 @@ static void ggml_compute_forward_dup_bytes(
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}
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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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@ -4082,10 +4081,10 @@ static void ggml_compute_forward_dup_bytes(
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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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ggml_are_same_shape(src0, dst) &&
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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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const size_t rs = ggml_row_size(src0->type, ne00);
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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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@ -4140,17 +4139,20 @@ static void ggml_compute_forward_dup_bytes(
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}
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// dst counters
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int64_t i10 = 0;
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int64_t k10 = 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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// number of blocks in a row
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const int64_t nk00 = ne00 / ggml_blck_size(src0->type);
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const int64_t nk0 = ne0 / ggml_blck_size(dst->type);
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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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k10 += nk00 * ir0;
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while (k10 >= nk0) {
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k10 -= nk0;
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if (++i11 == ne1) {
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i11 = 0;
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if (++i12 == ne2) {
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@ -4162,14 +4164,14 @@ static void ggml_compute_forward_dup_bytes(
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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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for (int64_t k00 = 0; k00 < nk00; k00++) {
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const char * src0_ptr = ((char *) src0->data + k00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
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char * dst_ptr = ((char *) dst->data + k10*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 (++k10 == nk0) {
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k10 = 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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@ -4182,9 +4184,9 @@ static void ggml_compute_forward_dup_bytes(
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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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k10 += nk00 * (ne01 - ir1);
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while (k10 >= nk0) {
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k10 -= nk0;
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if (++i11 == ne1) {
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i11 = 0;
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if (++i12 == ne2) {
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@ -14308,7 +14310,9 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
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}
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// extra_buffer op?
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if (ggml_cpu_extra_compute_forward(params, tensor)) return;
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if (ggml_cpu_extra_compute_forward(params, tensor)) {
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return;
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}
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switch (tensor->op) {
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case GGML_OP_DUP:
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@ -1463,11 +1463,13 @@ struct test_cpy : public test_case {
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const ggml_type type_src;
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const ggml_type type_dst;
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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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const std::array<int64_t, 4> permute_src;
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const std::array<int64_t, 4> permute_dst;
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bool _src_use_permute;
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bool _dst_use_permute;
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std::string vars() override {
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return VARS_TO_STR4(type_src, type_dst, ne, permute);
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return VARS_TO_STR5(type_src, type_dst, ne, permute_src, permute_dst);
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}
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double max_nmse_err() override {
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@ -1480,9 +1482,11 @@ struct test_cpy : public test_case {
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test_cpy(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
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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_src(type_src), type_dst(type_dst), ne(ne), permute(permute),
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_src_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
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std::array<int64_t, 4> permute_src = {0, 0, 0, 0},
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std::array<int64_t, 4> permute_dst = {0, 0, 0, 0})
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: type_src(type_src), type_dst(type_dst), ne(ne), permute_src(permute_src), permute_dst(permute_dst),
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_src_use_permute(permute_src[0] + permute_src[1] + permute_src[2] + permute_src[3] > 0),
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_dst_use_permute(permute_dst[0] + permute_dst[1] + permute_dst[2] + permute_dst[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_src, 4, ne.data());
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@ -1490,13 +1494,18 @@ struct test_cpy : public test_case {
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ggml_set_name(src, "src");
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if (_src_use_permute) {
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src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
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src = ggml_permute(ctx, src, permute_src[0], permute_src[1], permute_src[2], permute_src[3]);
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ggml_set_name(src, "src_permuted");
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}
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ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, src->ne);
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ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, src->ne);
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ggml_set_name(dst, "dst");
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if (_dst_use_permute) {
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dst = ggml_permute(ctx, dst, permute_dst[0], permute_dst[1], permute_dst[2], permute_dst[3]);
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ggml_set_name(dst, "dst_permuted");
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}
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ggml_tensor * out = ggml_cpy(ctx, src, dst);
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ggml_set_name(out, "out");
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@ -4004,14 +4013,25 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
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test_cases.emplace_back(new test_set(GGML_TYPE_I32, GGML_TYPE_I32, {6, 5, 4, 3}, dim));
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}
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for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
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// same-type copy
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for (ggml_type type : all_types) {
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const auto nk = ggml_blck_size(type);
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for (int k = 1; k < 4; ++k) {
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test_cases.emplace_back(new test_cpy(type, type, {k*nk, 2, 3, 4}));
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test_cases.emplace_back(new test_cpy(type, type, {k*nk, 2, 3, 4}, {0, 2, 1, 3}));
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test_cases.emplace_back(new test_cpy(type, type, {k*nk, 2, 3, 4}, {0, 3, 1, 2}, {0, 2, 1, 3}));
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}
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}
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for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_F32}) {
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for (ggml_type type_dst : all_types) {
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test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
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test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
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}
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}
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for (ggml_type type_dst : {GGML_TYPE_F32}) {
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for (ggml_type type_src : all_types) {
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for (ggml_type type_src : all_types) {
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for (ggml_type type_dst : {GGML_TYPE_F32}) {
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test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
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test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
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}
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