From ee50ee1eadff58777ae746827b04de7ba0befc55 Mon Sep 17 00:00:00 2001 From: yael-works <106673277+yael-works@users.noreply.github.com> Date: Thu, 16 Oct 2025 07:21:28 +0300 Subject: [PATCH 01/77] SYCL: Add GGML_OP_MEAN operator support (#16009) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * SYCL: Add GGML_OP_MEAN operator support * SYCL: Fix formatting for GGML_OP_MEAN case * Update ggml/src/ggml-sycl/ggml-sycl.cpp Co-authored-by: Sigbjørn Skjæret --------- Co-authored-by: Sigbjørn Skjæret --- ggml/src/ggml-sycl/ggml-sycl.cpp | 34 ++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 45b8c216c94d2..f3407a813d731 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -2151,6 +2151,30 @@ inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, ggml_tensor * sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream); } +inline void ggml_sycl_op_mean(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + dpct::queue_ptr main_stream = ctx.stream(); + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + + const float * src0_dd = static_cast(dst->src[0]->data); + float * dst_dd = static_cast(dst->data); + + const int64_t ncols = dst->src[0]->ne[0]; + const int64_t nrows = ggml_nrows(dst->src[0]); + + sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream); + + main_stream->parallel_for( + sycl::range<1>(nrows), + [=](sycl::id<1> row) { + dst_dd[row] /= ncols; + } + ); +} + + inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_I32); @@ -3535,6 +3559,12 @@ static void ggml_sycl_sum_rows(ggml_backend_sycl_context & ctx, ggml_tensor * ds ggml_sycl_op_sum_rows(ctx, dst); } +static void ggml_sycl_mean(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + GGML_ASSERT(ggml_is_contiguous(dst->src[0])); + ggml_sycl_op_mean(ctx, dst); +} + static void ggml_sycl_argsort(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); GGML_ASSERT(ggml_is_contiguous(dst->src[0])); @@ -3784,6 +3814,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_SUM_ROWS: ggml_sycl_sum_rows(ctx, dst); break; + case GGML_OP_MEAN: + ggml_sycl_mean(ctx, dst); + break; case GGML_OP_ARGSORT: ggml_sycl_argsort(ctx, dst); break; @@ -4431,6 +4464,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST; case GGML_OP_SUM: case GGML_OP_SUM_ROWS: + case GGML_OP_MEAN: case GGML_OP_ARGSORT: return ggml_is_contiguous(op->src[0]); case GGML_OP_POOL_2D: From adc9b60f190c1016a09f439862fa1cbb302262ac Mon Sep 17 00:00:00 2001 From: takuya kodama Date: Thu, 16 Oct 2025 13:10:32 +0800 Subject: [PATCH 02/77] ggml-cpu: replace putenv with setenv for const-correctness (#16573) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Why it failed When compiling with strict compiler flags (-Wwrite-strings -Werror=discarded-qualifiers), the build fails with the following error: ``` cmake \ -S . \ -B ../llama.cpp.build \ --preset=x64-linux-gcc-debug \ -DCMAKE_INSTALL_PREFIX=/tmp/local \ -DCMAKE_C_FLAGS="-Wwrite-strings -Werror=discarded-qualifiers" && \ cmake --build ../llama.cpp.build/ ... /home/otegami/work/cpp/llama.cpp/ggml/src/ggml-cpu/ggml-cpu.c: In function ‘ggml_cpu_init’: /home/otegami/work/cpp/llama.cpp/ggml/src/ggml-cpu/ggml-cpu.c:3572:24: error: passing argument 1 of ‘putenv’ discards ‘const’ qualifier from pointer target type [-Werror=discarded-qualifiers] 3572 | putenv("KMP_BLOCKTIME=200"); // 200ms | ^~~~~~~~~~~~~~~~~~~ In file included from /home/otegami/work/cpp/llama.cpp/ggml/src/./ggml-impl.h:10, from /home/otegami/work/cpp/llama.cpp/ggml/src/ggml-cpu/ggml-cpu-impl.h:6, from /home/otegami/work/cpp/llama.cpp/ggml/src/ggml-cpu/traits.h:3, from /home/otegami/work/cpp/llama.cpp/ggml/src/ggml-cpu/ggml-cpu.c:6: /usr/include/stdlib.h:786:26: note: expected ‘char *’ but argument is of type ‘const char *’ 786 | extern int putenv (char *__string) __THROW __nonnull ((1)); | ~~~~~~^~~~~~~~ cc1: some warnings being treated as errors ninja: build stopped: subcommand failed. ``` The issue is that putenv() expects a non-const char * but receives a string literal (const char *). ## How to fix This PR replaces putenv("KMP_BLOCKTIME=200") with setenv("KMP_BLOCKTIME", "200", 0). Benefits of setenv(): - Accepts const char * parameters (no qualifier warnings) - Makes copies of the strings (safer memory handling) - The third parameter (0) ensures we don't overwrite if already set --- ggml/src/ggml-cpu/ggml-cpu.c | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index 29c870600ba93..9ec485cfa2ff7 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -3567,13 +3567,17 @@ void ggml_cpu_init(void) { #ifdef GGML_USE_OPENMP //if (!getenv("OMP_WAIT_POLICY")) { // // set the wait policy to active, so that OpenMP threads don't sleep - // putenv("OMP_WAIT_POLICY=active"); + // setenv("OMP_WAIT_POLICY", "active", 0) //} if (!getenv("KMP_BLOCKTIME")) { // set the time to wait before sleeping a thread // this is less aggressive than setting the wait policy to active, but should achieve similar results in most cases - putenv("KMP_BLOCKTIME=200"); // 200ms +#ifdef _WIN32 + _putenv_s("KMP_BLOCKTIME", "200"); // 200ms +#else + setenv("KMP_BLOCKTIME", "200", 0); // 200ms +#endif } #endif } From 6f5d924637a15abedb111cbbffd7da5f31c81855 Mon Sep 17 00:00:00 2001 From: takasurazeem Date: Thu, 16 Oct 2025 01:11:33 -0400 Subject: [PATCH 03/77] common : Update the docs on -t --threads (#16236) * Update the docs on -t --threads * Revert "Update the docs on -t --threads" This reverts commit eba97345e2c88d8ca510abec87d00bf6b9b0e0c2. * docs: clarify -t/--threads parameter uses CPU threads and defaults to all available cores * Update arg.cpp --- common/arg.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/arg.cpp b/common/arg.cpp index c0b718071127d..33ed7ae857269 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1760,7 +1760,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP})); add_opt(common_arg( {"-t", "--threads"}, "N", - string_format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads), + string_format("number of CPU threads to use during generation (default: %d)", params.cpuparams.n_threads), [](common_params & params, int value) { params.cpuparams.n_threads = value; if (params.cpuparams.n_threads <= 0) { From 7a50cf388a530127cbbdd2a507ef81a451c9d819 Mon Sep 17 00:00:00 2001 From: Chenguang Li <757486878@qq.com> Date: Thu, 16 Oct 2025 16:41:11 +0800 Subject: [PATCH 04/77] CANN: format code using .clang-format (#15863) This commit applies .clang-format rules to all source files under the ggml-cann directory to ensure consistent coding style and readability. The .clang-format option `SortIncludes: false` has been set to disable automatic reordering of include directives. No functional changes are introduced. Co-authored-by: hipudding --- ggml/src/ggml-cann/acl_tensor.cpp | 89 +- ggml/src/ggml-cann/acl_tensor.h | 97 +- ggml/src/ggml-cann/aclnn_ops.cpp | 2542 ++++++++++++++--------------- ggml/src/ggml-cann/aclnn_ops.h | 407 +++-- ggml/src/ggml-cann/common.h | 191 ++- ggml/src/ggml-cann/ggml-cann.cpp | 1107 ++++++------- 6 files changed, 2082 insertions(+), 2351 deletions(-) mode change 100755 => 100644 ggml/src/ggml-cann/acl_tensor.cpp mode change 100755 => 100644 ggml/src/ggml-cann/acl_tensor.h mode change 100755 => 100644 ggml/src/ggml-cann/aclnn_ops.cpp mode change 100755 => 100644 ggml/src/ggml-cann/aclnn_ops.h mode change 100755 => 100644 ggml/src/ggml-cann/common.h mode change 100755 => 100644 ggml/src/ggml-cann/ggml-cann.cpp diff --git a/ggml/src/ggml-cann/acl_tensor.cpp b/ggml/src/ggml-cann/acl_tensor.cpp old mode 100755 new mode 100644 index 8ffac31dd661a..8958ebcd78704 --- a/ggml/src/ggml-cann/acl_tensor.cpp +++ b/ggml/src/ggml-cann/acl_tensor.cpp @@ -51,28 +51,31 @@ aclDataType ggml_cann_type_mapping(ggml_type type) { return ACL_DT_UNDEFINED; } -aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne, - size_t* nb, int64_t dims, aclFormat format, - size_t offset) { +aclTensor * ggml_cann_create_tensor(const ggml_tensor * tensor, + int64_t * ne, + size_t * nb, + int64_t dims, + aclFormat format, + size_t offset) { // If tensor is bcasted, Up to GGML_MAX_DIMS additional dimensions will be // added. int64_t acl_ne[GGML_MAX_DIMS * 2], acl_stride[GGML_MAX_DIMS * 2]; if (ne == nullptr) { for (int i = 0; i < GGML_MAX_DIMS; i++) { - acl_ne[i] = tensor->ne[i]; + acl_ne[i] = tensor->ne[i]; // The step size of acl is in elements. acl_stride[i] = tensor->nb[i] / ggml_element_size(tensor); } } else { // With bcast for (int i = 0; i < dims; i++) { - acl_ne[i] = ne[i]; + acl_ne[i] = ne[i]; acl_stride[i] = nb[i] / ggml_element_size(tensor); } } - int64_t final_dims = (dims == 0 ? GGML_MAX_DIMS : dims); + int64_t final_dims = (dims == 0 ? GGML_MAX_DIMS : dims); int64_t acl_storage_len = 1; for (int i = 0; i < final_dims; i++) { acl_storage_len += (acl_ne[i] - 1) * acl_stride[i]; @@ -84,15 +87,13 @@ aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne, std::reverse(acl_ne, acl_ne + final_dims); std::reverse(acl_stride, acl_stride + final_dims); - aclTensor* acl_tensor = aclCreateTensor( - acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride, - elem_offset, format, &acl_storage_len, 1, - tensor->data); + aclTensor * acl_tensor = aclCreateTensor(acl_ne, final_dims, ggml_cann_type_mapping(tensor->type), acl_stride, + elem_offset, format, &acl_storage_len, 1, tensor->data); return acl_tensor; } -bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) { +bool ggml_cann_need_bcast(const ggml_tensor * t0, const ggml_tensor * t1) { for (int i = 0; i < GGML_MAX_DIMS; i++) { if (t1->ne[i] != t0->ne[i] && t1->ne[i] != 1) { return true; @@ -101,15 +102,16 @@ bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1) { return false; } -int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, - const ggml_tensor* src1, - int64_t* bcast_src0_ne, - int64_t* bcast_src1_ne, size_t* bcast_src0_nb, - size_t* bcast_src1_nb) { +int64_t ggml_cann_get_bcast_shape(const ggml_tensor * src0, + const ggml_tensor * src1, + int64_t * bcast_src0_ne, + int64_t * bcast_src1_ne, + size_t * bcast_src0_nb, + size_t * bcast_src1_nb) { GGML_ASSERT(ggml_can_repeat(src1, src0)); int bcast_dim_cnt = 0; for (int i = 0; i < GGML_MAX_DIMS; i++) { - int64_t nr = src0->ne[i] / src1->ne[i]; + int64_t nr = src0->ne[i] / src1->ne[i]; bcast_src0_ne[bcast_dim_cnt] = src0->ne[i] / nr; bcast_src1_ne[bcast_dim_cnt] = src1->ne[i]; bcast_src0_nb[bcast_dim_cnt] = src0->nb[i]; @@ -119,21 +121,26 @@ int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, // Need to add an extra dim. bcast_src0_ne[bcast_dim_cnt] = nr; bcast_src1_ne[bcast_dim_cnt] = 1; - bcast_src0_nb[bcast_dim_cnt] = bcast_src0_nb[bcast_dim_cnt - 1] * - bcast_src0_ne[bcast_dim_cnt - 1]; - bcast_src1_nb[bcast_dim_cnt] = bcast_src1_nb[bcast_dim_cnt - 1] * - bcast_src1_ne[bcast_dim_cnt - 1]; + bcast_src0_nb[bcast_dim_cnt] = bcast_src0_nb[bcast_dim_cnt - 1] * bcast_src0_ne[bcast_dim_cnt - 1]; + bcast_src1_nb[bcast_dim_cnt] = bcast_src1_nb[bcast_dim_cnt - 1] * bcast_src1_ne[bcast_dim_cnt - 1]; bcast_dim_cnt++; } } return bcast_dim_cnt; } -int64_t ggml_cann_get_mulmat_bcast_shape( - const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne, - const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb, - int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne, - size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb) { +int64_t ggml_cann_get_mulmat_bcast_shape(const int64_t * input_ne, + const int64_t * weight_ne, + const int64_t * dst_ne, + const size_t * input_nb, + const size_t * weight_nb, + const size_t * dst_nb, + int64_t * bcast_input_ne, + int64_t * bcast_weight_ne, + int64_t * bcast_dst_ne, + size_t * bcast_input_nb, + size_t * bcast_weight_nb, + size_t * bcast_dst_nb) { // input and dst shoule in same shape, except first two dims. GGML_ASSERT(input_ne[2] == dst_ne[2]); GGML_ASSERT(input_ne[3] == dst_ne[3]); @@ -148,34 +155,30 @@ int64_t ggml_cann_get_mulmat_bcast_shape( // Do not use bcast in the first two dimensions because we only support // the bcast batch dimension. Just copy them. if (i < 2 || nr == 1) { - bcast_input_ne[bcast_dim_cnt] = input_ne[i]; + bcast_input_ne[bcast_dim_cnt] = input_ne[i]; bcast_weight_ne[bcast_dim_cnt] = weight_ne[i]; - bcast_dst_ne[bcast_dim_cnt] = dst_ne[i]; + bcast_dst_ne[bcast_dim_cnt] = dst_ne[i]; - bcast_input_nb[bcast_dim_cnt] = input_nb[i]; + bcast_input_nb[bcast_dim_cnt] = input_nb[i]; bcast_weight_nb[bcast_dim_cnt] = weight_nb[i]; - bcast_dst_nb[bcast_dim_cnt] = dst_nb[i]; + bcast_dst_nb[bcast_dim_cnt] = dst_nb[i]; bcast_dim_cnt++; } else { // Need to add an extra dim. - bcast_input_ne[bcast_dim_cnt] = nr; - bcast_dst_ne[bcast_dim_cnt] = nr; + bcast_input_ne[bcast_dim_cnt] = nr; + bcast_dst_ne[bcast_dim_cnt] = nr; bcast_weight_ne[bcast_dim_cnt] = 1; - bcast_input_nb[bcast_dim_cnt] = input_nb[i]; - bcast_dst_nb[bcast_dim_cnt] = dst_nb[i]; + bcast_input_nb[bcast_dim_cnt] = input_nb[i]; + bcast_dst_nb[bcast_dim_cnt] = dst_nb[i]; bcast_weight_nb[bcast_dim_cnt] = weight_nb[i]; bcast_dim_cnt++; - bcast_input_ne[bcast_dim_cnt] = input_ne[i] / nr; - bcast_dst_ne[bcast_dim_cnt] = dst_ne[i] / nr; + bcast_input_ne[bcast_dim_cnt] = input_ne[i] / nr; + bcast_dst_ne[bcast_dim_cnt] = dst_ne[i] / nr; bcast_weight_ne[bcast_dim_cnt] = weight_ne[i]; - bcast_input_nb[bcast_dim_cnt] = bcast_input_nb[bcast_dim_cnt - 1] * - bcast_input_ne[bcast_dim_cnt - 1]; - bcast_dst_nb[bcast_dim_cnt] = bcast_dst_nb[bcast_dim_cnt - 1] * - bcast_dst_ne[bcast_dim_cnt - 1]; - bcast_weight_nb[bcast_dim_cnt] = - bcast_weight_nb[bcast_dim_cnt - 1] * - bcast_weight_ne[bcast_dim_cnt - 1]; + bcast_input_nb[bcast_dim_cnt] = bcast_input_nb[bcast_dim_cnt - 1] * bcast_input_ne[bcast_dim_cnt - 1]; + bcast_dst_nb[bcast_dim_cnt] = bcast_dst_nb[bcast_dim_cnt - 1] * bcast_dst_ne[bcast_dim_cnt - 1]; + bcast_weight_nb[bcast_dim_cnt] = bcast_weight_nb[bcast_dim_cnt - 1] * bcast_weight_ne[bcast_dim_cnt - 1]; bcast_dim_cnt++; } } diff --git a/ggml/src/ggml-cann/acl_tensor.h b/ggml/src/ggml-cann/acl_tensor.h old mode 100755 new mode 100644 index 93f09937efb31..cb17ebcc1bbe2 --- a/ggml/src/ggml-cann/acl_tensor.h +++ b/ggml/src/ggml-cann/acl_tensor.h @@ -62,10 +62,12 @@ aclDataType ggml_cann_type_mapping(ggml_type type); * @param offset Offset in bytes for the ACL tensor data. Defaults to 0. * @return Pointer to the created ACL tensor. */ -aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne = nullptr, - size_t* nb = nullptr, int64_t dims = 0, - aclFormat format = ACL_FORMAT_ND, - size_t offset = 0); +aclTensor * ggml_cann_create_tensor(const ggml_tensor * tensor, + int64_t * ne = nullptr, + size_t * nb = nullptr, + int64_t dims = 0, + aclFormat format = ACL_FORMAT_ND, + size_t offset = 0); /** * @brief Template for creating an ACL tensor from provided parameters. typename TYPE @@ -87,12 +89,15 @@ aclTensor* ggml_cann_create_tensor(const ggml_tensor* tensor, int64_t* ne = null * @param offset Offset in bytes for the ACL tensor data. Defaults to 0. * @return Pointer to the created ACL tensor. */ -template -aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype, - TYPE type_size, int64_t* ne, TYPE* nb, - int64_t dims, - aclFormat format = ACL_FORMAT_ND, - size_t offset = 0) { +template +aclTensor * ggml_cann_create_tensor(void * data_ptr, + aclDataType dtype, + TYPE type_size, + int64_t * ne, + TYPE * nb, + int64_t dims, + aclFormat format = ACL_FORMAT_ND, + size_t offset = 0) { int64_t tmp_ne[GGML_MAX_DIMS * 2]; int64_t tmp_stride[GGML_MAX_DIMS * 2]; @@ -109,9 +114,8 @@ aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype, std::reverse(tmp_ne, tmp_ne + dims); std::reverse(tmp_stride, tmp_stride + dims); - aclTensor* acl_tensor = - aclCreateTensor(tmp_ne, dims, dtype, tmp_stride, offset / type_size, - format, &acl_storage_len, 1, data_ptr); + aclTensor * acl_tensor = + aclCreateTensor(tmp_ne, dims, dtype, tmp_stride, offset / type_size, format, &acl_storage_len, 1, data_ptr); return acl_tensor; } @@ -132,7 +136,7 @@ aclTensor* ggml_cann_create_tensor(void* data_ptr, aclDataType dtype, * to 1. If such a dimension is found, broadcasting is required to align t1 * with t0 for element-wise operations. */ -bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1); +bool ggml_cann_need_bcast(const ggml_tensor * t0, const ggml_tensor * t1); /** * @brief Computes broadcast shapes and strides for two ggml_tensors. @@ -187,19 +191,21 @@ bool ggml_cann_need_bcast(const ggml_tensor* t0, const ggml_tensor* t1); * dim1 in a inserted dim, should add nb for dim1, * and all other nb moves to next in order. */ -int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* src1, - int64_t* bcast_ne_src0, int64_t* bcast_ne_src1, - size_t* bcast_nb_src0, size_t* bcast_nb_src1); +int64_t ggml_cann_get_bcast_shape(const ggml_tensor * src0, + const ggml_tensor * src1, + int64_t * bcast_ne_src0, + int64_t * bcast_ne_src1, + size_t * bcast_nb_src0, + size_t * bcast_nb_src1); // Bcast macro to avoid duplicate code. -#define BCAST_SHAPE(src0, src1) \ - int64_t bcast_##src0##_ne[GGML_MAX_DIMS * 2]; \ - int64_t bcast_##src1##_ne[GGML_MAX_DIMS * 2]; \ - size_t bcast_##src0##_nb[GGML_MAX_DIMS * 2]; \ - size_t bcast_##src1##_nb[GGML_MAX_DIMS * 2]; \ - int64_t bcast_dims = ggml_cann_get_bcast_shape( \ - src0, src1, bcast_##src0##_ne, bcast_##src1##_ne, bcast_##src0##_nb, \ - bcast_##src1##_nb); +#define BCAST_SHAPE(src0, src1) \ + int64_t bcast_##src0##_ne[GGML_MAX_DIMS * 2]; \ + int64_t bcast_##src1##_ne[GGML_MAX_DIMS * 2]; \ + size_t bcast_##src0##_nb[GGML_MAX_DIMS * 2]; \ + size_t bcast_##src1##_nb[GGML_MAX_DIMS * 2]; \ + int64_t bcast_dims = ggml_cann_get_bcast_shape(src0, src1, bcast_##src0##_ne, bcast_##src1##_ne, \ + bcast_##src0##_nb, bcast_##src1##_nb); #define BCAST_PARAM(tensor) bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims @@ -233,26 +239,31 @@ int64_t ggml_cann_get_bcast_shape(const ggml_tensor* src0, const ggml_tensor* sr * before cast dim. * @sa ggml_cann_get_bcast_shape */ -int64_t ggml_cann_get_mulmat_bcast_shape( - const int64_t* input_ne, const int64_t* weight_ne, const int64_t* dst_ne, - const size_t* input_nb, const size_t* weight_nb, const size_t* dst_nb, - int64_t* bcast_input_ne, int64_t* bcast_weight_ne, int64_t* bcast_dst_ne, - size_t* bcast_input_nb, size_t* bcast_weight_nb, size_t* bcast_dst_nb); +int64_t ggml_cann_get_mulmat_bcast_shape(const int64_t * input_ne, + const int64_t * weight_ne, + const int64_t * dst_ne, + const size_t * input_nb, + const size_t * weight_nb, + const size_t * dst_nb, + int64_t * bcast_input_ne, + int64_t * bcast_weight_ne, + int64_t * bcast_dst_ne, + size_t * bcast_input_nb, + size_t * bcast_weight_nb, + size_t * bcast_dst_nb); // Bcast macro to avoid duplicate code. -#define BCAST_MUL_MAT_SHAPE(input, weight, dst) \ - int64_t bcast_##input##_ne[GGML_MAX_DIMS * 2]; \ - int64_t bcast_##weight##_ne[GGML_MAX_DIMS * 2]; \ - int64_t bcast_##dst##_ne[GGML_MAX_DIMS * 2]; \ - size_t bcast_##input##_nb[GGML_MAX_DIMS * 2]; \ - size_t bcast_##weight##_nb[GGML_MAX_DIMS * 2]; \ - size_t bcast_##dst##_nb[GGML_MAX_DIMS * 2]; \ - int64_t bcast_dims = ggml_cann_get_mulmat_bcast_shape( \ - input->ne, weight->ne, dst->ne, input->nb, weight->nb, dst->nb, \ - bcast_##input##_ne, bcast_##weight##_ne, bcast_##dst##_ne, \ - bcast_##input##_nb, bcast_##weight##_nb, bcast_##dst##_nb); +#define BCAST_MUL_MAT_SHAPE(input, weight, dst) \ + int64_t bcast_##input##_ne[GGML_MAX_DIMS * 2]; \ + int64_t bcast_##weight##_ne[GGML_MAX_DIMS * 2]; \ + int64_t bcast_##dst##_ne[GGML_MAX_DIMS * 2]; \ + size_t bcast_##input##_nb[GGML_MAX_DIMS * 2]; \ + size_t bcast_##weight##_nb[GGML_MAX_DIMS * 2]; \ + size_t bcast_##dst##_nb[GGML_MAX_DIMS * 2]; \ + int64_t bcast_dims = ggml_cann_get_mulmat_bcast_shape( \ + input->ne, weight->ne, dst->ne, input->nb, weight->nb, dst->nb, bcast_##input##_ne, bcast_##weight##_ne, \ + bcast_##dst##_ne, bcast_##input##_nb, bcast_##weight##_nb, bcast_##dst##_nb); -#define BCAST_MUL_MAT_PARAM(tensor) \ - bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims +#define BCAST_MUL_MAT_PARAM(tensor) bcast_##tensor##_ne, bcast_##tensor##_nb, bcast_dims #endif // CANN_ACL_TENSOR_H diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp old mode 100755 new mode 100644 index 2857e080b4c16..f030ea0136a95 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -86,9 +86,12 @@ #include "../ggml-common.h" - -void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, aclTensor ** acl_src0, - aclTensor ** acl_src1, aclTensor ** acl_dst) { +void bcast_shape(ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst, + aclTensor ** acl_src0, + aclTensor ** acl_src1, + aclTensor ** acl_dst) { GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_can_repeat(src1, src0)); // Need bcast if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) { @@ -103,40 +106,40 @@ void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, aclT } } -void ggml_cann_op_unary( - std::function unary_op, - ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_op_unary(std::function unary_op, + ggml_backend_cann_context & ctx, + ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); unary_op(ctx, acl_src, acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst); } -void ggml_cann_op_unary_gated( - std::function unary_op, - ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; +void ggml_cann_op_unary_gated(std::function unary_op, + ggml_backend_cann_context & ctx, + ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; GGML_ASSERT(ggml_is_contiguous_1(src0)); GGML_ASSERT(ggml_is_contiguous_1(dst)); const int32_t swapped = ggml_get_op_params_i32(dst, 1); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - aclTensor *acl_src0 = nullptr, *acl_src1 = nullptr; - if(src1) { + aclTensor * acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src0 = nullptr, *acl_src1 = nullptr; + if (src1) { GGML_ASSERT(ggml_is_contiguous_1(src1)); GGML_ASSERT(src0->type == src1->type); acl_src0 = ggml_cann_create_tensor(src0); acl_src1 = ggml_cann_create_tensor(src1); } else { - int64_t ne[] = {src0->ne[0] / 2, src0->ne[1], src0->ne[2], src0->ne[3]}; - size_t nb[] = {src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]}; - acl_src0 = ggml_cann_create_tensor(src0, ne, nb, GGML_MAX_DIMS, ACL_FORMAT_ND, 0); + int64_t ne[] = { src0->ne[0] / 2, src0->ne[1], src0->ne[2], src0->ne[3] }; + size_t nb[] = { src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3] }; + acl_src0 = ggml_cann_create_tensor(src0, ne, nb, GGML_MAX_DIMS, ACL_FORMAT_ND, 0); acl_src1 = ggml_cann_create_tensor(src0, ne, nb, GGML_MAX_DIMS, ACL_FORMAT_ND, ne[0] * ggml_element_size(src0)); if (swapped) { std::swap(acl_src0, acl_src1); @@ -159,10 +162,12 @@ void ggml_cann_op_unary_gated( * @param repeat_array The array specifying the number of repetitions along each * dimension. */ -static void aclnn_repeat(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, int64_t* repeat_array) { +static void aclnn_repeat(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + int64_t * repeat_array) { // repeat tensor along each dim with repeat_array - aclIntArray* repeats = aclCreateIntArray(repeat_array, GGML_MAX_DIMS); + aclIntArray * repeats = aclCreateIntArray(repeat_array, GGML_MAX_DIMS); GGML_CANN_CALL_ACLNN_OP(ctx, Repeat, acl_src, repeats, acl_dst); ggml_cann_release_resources(ctx, repeats); @@ -181,61 +186,63 @@ static void aclnn_repeat(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param cast_data_type The target data type to which the source tensor will be * casted. */ -static void aclnn_cast(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, aclDataType cast_data_type) { +static void aclnn_cast(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + aclDataType cast_data_type) { GGML_CANN_CALL_ACLNN_OP(ctx, Cast, acl_src, cast_data_type, acl_dst); } -void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_repeat(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; GGML_ASSERT(ggml_can_repeat(src, dst)); - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - int64_t repeatsArray[] = {dst->ne[3] / src->ne[3], dst->ne[2] / src->ne[2], - dst->ne[1] / src->ne[1], dst->ne[0] / src->ne[0]}; + int64_t repeatsArray[] = { dst->ne[3] / src->ne[3], dst->ne[2] / src->ne[2], dst->ne[1] / src->ne[1], + dst->ne[0] / src->ne[0] }; aclnn_repeat(ctx, acl_src, acl_dst, repeatsArray); ggml_cann_release_resources(ctx, acl_src, acl_dst); } -void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0, - aclTensor* acl_src1, aclTensor* acl_dst) { - float alphaValue = 1.0f; - aclScalar* alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); - if (acl_dst != nullptr) +void aclnn_add(ggml_backend_cann_context & ctx, aclTensor * acl_src0, aclTensor * acl_src1, aclTensor * acl_dst) { + float alphaValue = 1.0f; + aclScalar * alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + if (acl_dst != nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, Add, acl_src0, acl_src1, alpha, acl_dst); - else + } else { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdd, acl_src0, acl_src1, alpha); + } ggml_cann_release_resources(ctx, alpha); } -void aclnn_sub(ggml_backend_cann_context& ctx, aclTensor* acl_src0, - aclTensor* acl_src1, aclTensor* acl_dst) { - float alphaValue = 1.0f; - aclScalar* alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); - if (acl_dst != nullptr) +void aclnn_sub(ggml_backend_cann_context & ctx, aclTensor * acl_src0, aclTensor * acl_src1, aclTensor * acl_dst) { + float alphaValue = 1.0f; + aclScalar * alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + if (acl_dst != nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, Sub, acl_src0, acl_src1, alpha, acl_dst); - else + } else { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSub, acl_src0, acl_src1, alpha); + } ggml_cann_release_resources(ctx, alpha); } -void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_other, aclTensor* acl_dst) { - if (acl_dst != nullptr) +void aclnn_mul(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_other, aclTensor * acl_dst) { + if (acl_dst != nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, Mul, acl_src, acl_other, acl_dst); - else + } else { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, acl_src, acl_other); + } } -void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_other, aclTensor* acl_dst) { - if (acl_dst != nullptr) +void aclnn_div(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_other, aclTensor * acl_dst) { + if (acl_dst != nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, Div, acl_src, acl_other, acl_dst); - else + } else { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceDiv, acl_src, acl_other); + } } /** @@ -260,9 +267,12 @@ void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param inplace Flag indicating whether to perform the operation in-place on * `acl_src`. */ -static void aclnn_muls(ggml_backend_cann_context& ctx, aclTensor* acl_src, - float scale, aclTensor* acl_dst, bool inplace) { - aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT); +static void aclnn_muls(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + float scale, + aclTensor * acl_dst, + bool inplace) { + aclScalar * acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT); if (inplace) { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_src, acl_scale); } else { @@ -271,19 +281,18 @@ static void aclnn_muls(ggml_backend_cann_context& ctx, aclTensor* acl_src, ggml_cann_release_resources(ctx, acl_scale); } -void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_leaky_relu(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); float negative_slope; memcpy(&negative_slope, dst->op_params, sizeof(float)); - aclScalar* acl_negative_slope = - aclCreateScalar(&negative_slope, aclDataType::ACL_FLOAT); + aclScalar * acl_negative_slope = aclCreateScalar(&negative_slope, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, LeakyRelu, acl_src, acl_negative_slope, acl_dst); ggml_cann_release_resources(ctx, acl_negative_slope, acl_src, acl_dst); @@ -299,26 +308,27 @@ void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * stored. * @param concat_dim The dimension along which the tensors will be concatenated. */ -static void aclnn_concat(ggml_backend_cann_context& ctx, - aclTensorList* tensorList, aclTensor* acl_dst, - int64_t concat_dim) { +static void aclnn_concat(ggml_backend_cann_context & ctx, + aclTensorList * tensorList, + aclTensor * acl_dst, + int64_t concat_dim) { GGML_CANN_CALL_ACLNN_OP(ctx, Cat, tensorList, concat_dim, acl_dst); } -void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; - aclTensor* acl_src0 = ggml_cann_create_tensor(src0); - aclTensor* acl_src1 = ggml_cann_create_tensor(src1); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); +void ggml_cann_concat(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; + aclTensor * acl_src0 = ggml_cann_create_tensor(src0); + aclTensor * acl_src1 = ggml_cann_create_tensor(src1); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); const int32_t dim = ggml_get_op_params_i32(dst, 0); GGML_ASSERT(dim >= 0 && dim < 4); int32_t acl_dim = 3 - dim; - aclTensor* tensors[] = {acl_src0, acl_src1}; - aclTensorList* tensor_list = aclCreateTensorList(tensors, 2); + aclTensor * tensors[] = { acl_src0, acl_src1 }; + aclTensorList * tensor_list = aclCreateTensorList(tensors, 2); aclnn_concat(ctx, tensor_list, acl_dst, acl_dim); ggml_cann_release_resources(ctx, tensor_list, acl_dst); @@ -341,162 +351,157 @@ void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param step The step size between consecutive values. * @param n_elements The number of elements in the destination tensor. */ -static void aclnn_arange(ggml_backend_cann_context& ctx, aclTensor* acl_dst, - float start, float stop, float step, - int64_t n_elements) { - int64_t steps = (int64_t)std::ceil((stop - start) / step); +static void aclnn_arange(ggml_backend_cann_context & ctx, + aclTensor * acl_dst, + float start, + float stop, + float step, + int64_t n_elements) { + int64_t steps = (int64_t) std::ceil((stop - start) / step); GGML_ASSERT(n_elements == steps); - aclScalar* acl_start = aclCreateScalar(&start, aclDataType::ACL_FLOAT); - aclScalar* acl_end = aclCreateScalar(&stop, aclDataType::ACL_FLOAT); - aclScalar* acl_step = aclCreateScalar(&step, aclDataType::ACL_FLOAT); + aclScalar * acl_start = aclCreateScalar(&start, aclDataType::ACL_FLOAT); + aclScalar * acl_end = aclCreateScalar(&stop, aclDataType::ACL_FLOAT); + aclScalar * acl_step = aclCreateScalar(&step, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, Arange, acl_start, acl_end, acl_step, acl_dst); ggml_cann_release_resources(ctx, acl_start, acl_end, acl_step); } -void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +void ggml_cann_arange(ggml_backend_cann_context & ctx, ggml_tensor * dst) { GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); int64_t n_elements = ggml_nelements(dst); - float start; - float stop; - float step; - memcpy(&start, (float*)dst->op_params + 0, sizeof(float)); - memcpy(&stop, (float*)dst->op_params + 1, sizeof(float)); - memcpy(&step, (float*)dst->op_params + 2, sizeof(float)); + float start; + float stop; + float step; + memcpy(&start, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&stop, (float *) dst->op_params + 1, sizeof(float)); + memcpy(&step, (float *) dst->op_params + 2, sizeof(float)); aclnn_arange(ctx, acl_dst, start, stop, step, n_elements); ggml_cann_release_resources(ctx, acl_dst); } -void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_clamp(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; float min; float max; memcpy(&min, dst->op_params, sizeof(float)); - memcpy(&max, (float*)dst->op_params + 1, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - aclScalar* acl_min = aclCreateScalar(&min, aclDataType::ACL_FLOAT); - aclScalar* acl_max = aclCreateScalar(&max, aclDataType::ACL_FLOAT); + aclScalar * acl_min = aclCreateScalar(&min, aclDataType::ACL_FLOAT); + aclScalar * acl_max = aclCreateScalar(&max, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, Clamp, acl_src, acl_min, acl_max, acl_dst); ggml_cann_release_resources(ctx, acl_min, acl_max, acl_src, acl_dst); } -void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_scale(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; // scale factor float v; memcpy(&v, dst->op_params, sizeof(float)); - aclScalar* scale = aclCreateScalar(&v, aclDataType::ACL_FLOAT); - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclScalar * scale = aclCreateScalar(&v, aclDataType::ACL_FLOAT); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); GGML_CANN_CALL_ACLNN_OP(ctx, Muls, acl_src, scale, acl_dst); ggml_cann_release_resources(ctx, scale, acl_src, acl_dst); } -void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; - enum ggml_sort_order order = (enum ggml_sort_order)dst->op_params[0]; - - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - ggml_cann_pool_alloc temp_buffer_allocator( - ctx.pool(), ggml_nelements(dst) * sizeof(int64_t)); - void* buffer = temp_buffer_allocator.get(); - aclTensor* tmp_tensor = - ggml_cann_create_tensor(buffer, ACL_INT64, ggml_type_size(dst->type), - dst->ne, dst->nb, GGML_MAX_DIMS); - GGML_CANN_CALL_ACLNN_OP(ctx, Argsort, acl_src, -1, (order == GGML_SORT_ORDER_DESC ? true : false), - tmp_tensor); +void ggml_cann_argsort(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; + enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; + + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); + ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), ggml_nelements(dst) * sizeof(int64_t)); + void * buffer = temp_buffer_allocator.get(); + aclTensor * tmp_tensor = + ggml_cann_create_tensor(buffer, ACL_INT64, ggml_type_size(dst->type), dst->ne, dst->nb, GGML_MAX_DIMS); + GGML_CANN_CALL_ACLNN_OP(ctx, Argsort, acl_src, -1, (order == GGML_SORT_ORDER_DESC ? true : false), tmp_tensor); GGML_CANN_CALL_ACLNN_OP(ctx, Cast, tmp_tensor, ggml_cann_type_mapping(dst->type), acl_dst); ggml_cann_release_resources(ctx, acl_src, tmp_tensor, acl_dst); } -void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); float eps; memcpy(&eps, dst->op_params, sizeof(float)); - std::vector normData = {dst->ne[0]}; - aclIntArray* norm = aclCreateIntArray(normData.data(), normData.size()); - GGML_CANN_CALL_ACLNN_OP(ctx, LayerNorm, acl_src, norm, nullptr, nullptr, - eps, acl_dst, nullptr, nullptr); + std::vector normData = { dst->ne[0] }; + aclIntArray * norm = aclCreateIntArray(normData.data(), normData.size()); + GGML_CANN_CALL_ACLNN_OP(ctx, LayerNorm, acl_src, norm, nullptr, nullptr, eps, acl_dst, nullptr, nullptr); ggml_cann_release_resources(ctx, norm, acl_src, acl_dst); } -void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_group_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); int n_groups = dst->op_params[0]; float eps; memcpy(&eps, dst->op_params + 1, sizeof(float)); - int64_t N = src->ne[3]; - int64_t C = src->ne[2]; + int64_t N = src->ne[3]; + int64_t C = src->ne[2]; int64_t HxW = src->ne[1] * src->ne[0]; - size_t type_size = ggml_type_size(src->type); - int64_t ne[] = {n_groups, N}; - size_t nb[] = {type_size, type_size * n_groups}; - size_t n_bytes = N * n_groups; + size_t type_size = ggml_type_size(src->type); + int64_t ne[] = { n_groups, N }; + size_t nb[] = { type_size, type_size * n_groups }; + size_t n_bytes = N * n_groups; ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), n_bytes * 2); - void* buffer = temp_buffer_allocator.get(); - aclTensor* acl_mean_out = ggml_cann_create_tensor( - buffer, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); - aclTensor* acl_rstd_out = ggml_cann_create_tensor( - (char*)buffer + n_bytes, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); - - GGML_CANN_CALL_ACLNN_OP(ctx, GroupNorm, acl_src, nullptr, nullptr, N, C, HxW, n_groups, eps, - acl_dst, acl_mean_out, acl_rstd_out); + void * buffer = temp_buffer_allocator.get(); + aclTensor * acl_mean_out = ggml_cann_create_tensor(buffer, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); + aclTensor * acl_rstd_out = + ggml_cann_create_tensor((char *) buffer + n_bytes, ACL_FLOAT, type_size, ne, nb, ACL_FORMAT_ND); + + GGML_CANN_CALL_ACLNN_OP(ctx, GroupNorm, acl_src, nullptr, nullptr, N, C, HxW, n_groups, eps, acl_dst, acl_mean_out, + acl_rstd_out); ggml_cann_release_resources(ctx, acl_src, acl_dst, acl_mean_out, acl_rstd_out); } -void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; +void ggml_cann_acc(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; - size_t nb1 = ((int32_t*)dst->op_params)[0]; - size_t nb2 = ((int32_t*)dst->op_params)[1]; - size_t nb3 = ((int32_t*)dst->op_params)[2]; - size_t offset = ((int32_t*)dst->op_params)[3]; - bool inplace = (bool)((int32_t*)dst->op_params)[4]; + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; - size_t param_nb[] = {ggml_element_size(src0), nb1, nb2, nb3}; + size_t param_nb[] = { ggml_element_size(src0), nb1, nb2, nb3 }; - aclTensor* acl_dst = ggml_cann_create_tensor( - dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); - aclTensor* acl_src1 = ggml_cann_create_tensor(src1); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); + aclTensor * acl_src1 = ggml_cann_create_tensor(src1); - aclScalar* alpha = nullptr; - float alphaValue = 1.0f; - alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + aclScalar * alpha = nullptr; + float alphaValue = 1.0f; + alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); if (!inplace) { size_t cpy_size = ggml_nbytes(dst); - ggml_cann_async_memcpy(ctx, dst->data, src0->data, cpy_size, - ACL_MEMCPY_DEVICE_TO_DEVICE); - aclTensor* acl_src0 = ggml_cann_create_tensor( - src0, src1->ne, src0->nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); + ggml_cann_async_memcpy(ctx, dst->data, src0->data, cpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE); + aclTensor * acl_src0 = ggml_cann_create_tensor(src0, src1->ne, src0->nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset); GGML_CANN_CALL_ACLNN_OP(ctx, Add, acl_src0, acl_src1, alpha, acl_dst); ggml_cann_release_resources(ctx, acl_src0); @@ -516,39 +521,34 @@ void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param dim An array of dimension indices. * @param dim_size The number of dimensions. */ -static void aclnn_reduce_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst, - int64_t* dim, size_t dim_size) { +static void aclnn_reduce_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst, int64_t * dim, size_t dim_size) { GGML_ASSERT(dst->ne[0] == 1); - ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - aclIntArray* reduce_dims = aclCreateIntArray(dim, dim_size); + ggml_tensor * src = dst->src[0]; + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); + aclIntArray * reduce_dims = aclCreateIntArray(dim, dim_size); - GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_src, reduce_dims, true, - ggml_cann_type_mapping(dst->type), acl_dst); + GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_src, reduce_dims, true, ggml_cann_type_mapping(dst->type), acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst, reduce_dims); } -void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - int64_t reduce_dims[] = {3}; +void ggml_cann_sum_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + int64_t reduce_dims[] = { 3 }; aclnn_reduce_sum(ctx, dst, reduce_dims, 1); } -void ggml_cann_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - int64_t reduce_dims[] = {0, 1, 2, 3}; +void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + int64_t reduce_dims[] = { 0, 1, 2, 3 }; aclnn_reduce_sum(ctx, dst, reduce_dims, 4); } -void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, - ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = - ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - aclTensor* acl_dst = - ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); +void ggml_cann_upsample_nearest2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; + aclTensor * acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - std::vector output_size{dst->ne[1], dst->ne[0]}; - auto output_size_array = aclCreateIntArray(output_size.data(), 2); + std::vector output_size{ dst->ne[1], dst->ne[0] }; + auto output_size_array = aclCreateIntArray(output_size.data(), 2); GGML_CANN_CALL_ACLNN_OP(ctx, UpsampleNearest2d, acl_src, output_size_array, acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst, output_size_array); @@ -568,20 +568,22 @@ void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, * The size of the array should be twice the number of dimensions of the tensor. * @param value The value to be used for padding. The default value is 0.0. */ -static void aclnn_pad(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, int64_t* paddings, - float value = 0.0f) { - aclIntArray* acl_pad = aclCreateIntArray(paddings, GGML_MAX_DIMS * 2); - aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); +static void aclnn_pad(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + int64_t * paddings, + float value = 0.0f) { + aclIntArray * acl_pad = aclCreateIntArray(paddings, GGML_MAX_DIMS * 2); + aclScalar * acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, ConstantPadNd, acl_src, acl_pad, acl_value, acl_dst); ggml_cann_release_resources(ctx, acl_pad, acl_value); } -void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); +void ggml_cann_pad(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); // padding: value in the array means how much distance will be padding. // the position of elements in the array means which dirction to padding, @@ -596,7 +598,7 @@ void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) { const int32_t lp3 = ggml_get_op_params_i32(dst, 6); const int32_t rp3 = ggml_get_op_params_i32(dst, 7); - int64_t paddings[] = {lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3}; + int64_t paddings[] = { lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3 }; aclnn_pad(ctx, acl_src, acl_dst, paddings); ggml_cann_release_resources(ctx, acl_src, acl_dst); } @@ -613,46 +615,41 @@ void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param dst The destination tensor where the result will be stored. The source * tensor is referenced by `dst->src[0]`. */ -static void ggml_cann_avg_pool2d(ggml_backend_cann_context& ctx, - ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +static void ggml_cann_avg_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_src = - ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - aclTensor* acl_dst = - ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - - const int32_t* opts = (const int32_t*)dst->op_params; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; - - std::vector kernel_dims = {k1, k0}; - std::vector stride_dims = {s1, s0}; - std::vector padding_avg_dims = {p1, p0}; // (padH, padW) - - auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2); - auto* strides = aclCreateIntArray(stride_dims.data(), 2); - auto* paddings_avg = aclCreateIntArray(padding_avg_dims.data(), 2); - - bool ceil_mode = false; - bool count_include_pad = true; - int64_t divisor_override = 0; - int8_t cube_math_type = 0; + aclTensor * acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + + const int32_t * opts = (const int32_t *) dst->op_params; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + + std::vector kernel_dims = { k1, k0 }; + std::vector stride_dims = { s1, s0 }; + std::vector padding_avg_dims = { p1, p0 }; // (padH, padW) + + auto * kernel_size = aclCreateIntArray(kernel_dims.data(), 2); + auto * strides = aclCreateIntArray(stride_dims.data(), 2); + auto * paddings_avg = aclCreateIntArray(padding_avg_dims.data(), 2); + + bool ceil_mode = false; + bool count_include_pad = true; + int64_t divisor_override = 0; + int8_t cube_math_type = 0; #ifdef ASCEND_310P cube_math_type = 1; #endif - GGML_CANN_CALL_ACLNN_OP(ctx, AvgPool2d, acl_src, kernel_size, strides, paddings_avg, - ceil_mode, count_include_pad, divisor_override, - cube_math_type, acl_dst); - ggml_cann_release_resources(ctx, acl_src, acl_dst, kernel_size, strides, - paddings_avg); + GGML_CANN_CALL_ACLNN_OP(ctx, AvgPool2d, acl_src, kernel_size, strides, paddings_avg, ceil_mode, count_include_pad, + divisor_override, cube_math_type, acl_dst); + ggml_cann_release_resources(ctx, acl_src, acl_dst, kernel_size, strides, paddings_avg); } /** @@ -667,68 +664,61 @@ static void ggml_cann_avg_pool2d(ggml_backend_cann_context& ctx, * @param dst The destination tensor where the result will be stored. The source * tensor is referenced by `dst->src[0]`. */ -static void ggml_cann_max_pool2d(ggml_backend_cann_context& ctx, - ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +static void ggml_cann_max_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - aclTensor* acl_src = - ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - aclTensor* acl_dst = - ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + aclTensor * acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, nullptr, nullptr, 0, ACL_FORMAT_NCHW); - const int32_t* opts = (const int32_t*)dst->op_params; - const int k0 = opts[1]; - const int k1 = opts[2]; - const int s0 = opts[3]; - const int s1 = opts[4]; - const int p0 = opts[5]; - const int p1 = opts[6]; + const int32_t * opts = (const int32_t *) dst->op_params; + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; - int64_t temp_ne[] = {src->ne[0] + p0 * 2, src->ne[1] + p1 * 2, src->ne[2], - src->ne[3]}; - size_t temp_nb[GGML_MAX_DIMS]; + int64_t temp_ne[] = { src->ne[0] + p0 * 2, src->ne[1] + p1 * 2, src->ne[2], src->ne[3] }; + size_t temp_nb[GGML_MAX_DIMS]; temp_nb[0] = ggml_element_size(src); for (int i = 1; i < GGML_MAX_DIMS; i++) { temp_nb[i] = temp_nb[i - 1] * temp_ne[i - 1]; } - ggml_cann_pool_alloc temp_buffer_allocator( - ctx.pool(), ggml_nbytes(src) + p0 * 2 + p1 * 2 * src->nb[1]); - void* buffer = temp_buffer_allocator.get(); - aclTensor* tmp_tensor = ggml_cann_create_tensor( - buffer, ACL_FLOAT, ggml_element_size(src), temp_ne, temp_nb, - GGML_MAX_DIMS, ACL_FORMAT_NCHW); + ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), ggml_nbytes(src) + p0 * 2 + p1 * 2 * src->nb[1]); + void * buffer = temp_buffer_allocator.get(); + aclTensor * tmp_tensor = ggml_cann_create_tensor(buffer, ACL_FLOAT, ggml_element_size(src), temp_ne, temp_nb, + GGML_MAX_DIMS, ACL_FORMAT_NCHW); // pad: see padding in ggml_cann_pad() - int64_t paddings[] = {p0, p0, p1, p1, 0, 0, 0, 0}; - float value = -FLT_MAX; + int64_t paddings[] = { p0, p0, p1, p1, 0, 0, 0, 0 }; + float value = -FLT_MAX; aclnn_pad(ctx, acl_src, tmp_tensor, paddings, value); // max_pool - std::vector kernel_dims = {k1, k0}; - std::vector stride_dims = {s1, s0}; + std::vector kernel_dims = { k1, k0 }; + std::vector stride_dims = { s1, s0 }; // padding_max_dims: [dim0_start, dim0_end, dim1_start, dim1_end] - std::vector padding_max_dims = {0, 0, 0, 0}; - std::vector dilation_size = {1, 1}; - auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2); - auto* strides = aclCreateIntArray(stride_dims.data(), 2); - auto* paddings_max = aclCreateIntArray(padding_max_dims.data(), 4); - auto* dilations = aclCreateIntArray(dilation_size.data(), 2); - - bool ceil_mode = false; + std::vector padding_max_dims = { 0, 0, 0, 0 }; + std::vector dilation_size = { 1, 1 }; + auto * kernel_size = aclCreateIntArray(kernel_dims.data(), 2); + auto * strides = aclCreateIntArray(stride_dims.data(), 2); + auto * paddings_max = aclCreateIntArray(padding_max_dims.data(), 4); + auto * dilations = aclCreateIntArray(dilation_size.data(), 2); + + bool ceil_mode = false; int64_t auto_pads = 0; - GGML_CANN_CALL_ACLNN_OP(ctx, MaxPool, tmp_tensor, kernel_size, strides, auto_pads, - paddings_max, dilations, ceil_mode, acl_dst); - ggml_cann_release_resources(ctx, acl_src, acl_dst, tmp_tensor, kernel_size, - strides, paddings_max, dilations); + GGML_CANN_CALL_ACLNN_OP(ctx, MaxPool, tmp_tensor, kernel_size, strides, auto_pads, paddings_max, dilations, + ceil_mode, acl_dst); + ggml_cann_release_resources(ctx, acl_src, acl_dst, tmp_tensor, kernel_size, strides, paddings_max, dilations); } -void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - const int32_t* opts = (const int32_t*)dst->op_params; - enum ggml_op_pool op = static_cast(opts[0]); +void ggml_cann_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + const int32_t * opts = (const int32_t *) dst->op_params; + enum ggml_op_pool op = static_cast(opts[0]); switch (op) { case GGML_OP_POOL_AVG: ggml_cann_avg_pool2d(ctx, dst); @@ -752,17 +742,16 @@ void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param acl_src The source tensor from which data will be copied. * @param acl_dst The destination tensor where the data will be copied to. */ -static void cann_copy(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst) { +static void cann_copy(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_dst, acl_src); } -void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; +void ggml_cann_dup(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; if (ggml_are_same_shape(src0, dst)) { - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); if (dst->type == src0->type) { cann_copy(ctx, acl_src, acl_dst); } else { @@ -770,22 +759,20 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { } ggml_cann_release_resources(ctx, acl_src, acl_dst); } else { - void* src_trans_buffer = src0->data; + void * src_trans_buffer = src0->data; ggml_cann_pool_alloc src_buffer_allocator; if (!ggml_is_contiguous(src0)) { - aclTensor* acl_src = ggml_cann_create_tensor(src0); - src_buffer_allocator.alloc(ctx.pool(), - ggml_nelements(src0) * ggml_type_size(src0->type)); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + src_buffer_allocator.alloc(ctx.pool(), ggml_nelements(src0) * ggml_type_size(src0->type)); src_trans_buffer = src_buffer_allocator.get(); size_t src_trans_nb[GGML_MAX_DIMS]; src_trans_nb[0] = ggml_type_size(src0->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; } - aclTensor* src_trans_tensor = ggml_cann_create_tensor( - src_trans_buffer, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), src0->ne, src_trans_nb, - GGML_MAX_DIMS); + aclTensor * src_trans_tensor = + ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), src0->ne, src_trans_nb, GGML_MAX_DIMS); cann_copy(ctx, acl_src, src_trans_tensor); ggml_cann_release_resources(ctx, acl_src, src_trans_tensor); } @@ -796,10 +783,10 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { src_reshape_nb[i] = src_reshape_nb[i - 1] * dst->ne[i - 1]; } - aclTensor* trans_acl_src = ggml_cann_create_tensor(src_trans_buffer, - ggml_cann_type_mapping(src0->type),ggml_type_size(src0->type), - dst->ne, src_reshape_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * trans_acl_src = + ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), + dst->ne, src_reshape_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); if (dst->type == src0->type) { cann_copy(ctx, trans_acl_src, acl_dst); @@ -827,17 +814,20 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param type_size The size of each element in the tensor data type. * @return An ACL tensor initialized with zeros. */ -static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer, - size_t n_bytes, int64_t* ne, int64_t dims, - aclDataType type, size_t type_size) { +static aclTensor * aclnn_zero(ggml_backend_cann_context & ctx, + void * buffer, + size_t n_bytes, + int64_t * ne, + int64_t dims, + aclDataType type, + size_t type_size) { size_t nb[GGML_MAX_DIMS]; nb[0] = type_size; for (int i = 1; i < dims; i++) { nb[i] = nb[i - 1] * ne[i - 1]; } - aclTensor* zero = - ggml_cann_create_tensor(buffer, type, type_size, ne, nb, dims); + aclTensor * zero = ggml_cann_create_tensor(buffer, type, type_size, ne, nb, dims); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceZero, zero); return zero; GGML_UNUSED(n_bytes); @@ -861,15 +851,18 @@ static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer, * is 1.0). * @return An ACL tensor initialized with value. */ -static aclTensor* aclnn_values(ggml_backend_cann_context& ctx, void* buffer, - size_t n_bytes, int64_t* ne, int64_t dims, - aclDataType type, size_t type_size, - float value = 1.0f) { - aclTensor* acl_tensor = - aclnn_zero(ctx, buffer, n_bytes, ne, dims, type, type_size); - float alpha_host = 1.0f; - aclScalar* alpha = aclCreateScalar(&alpha_host, aclDataType::ACL_FLOAT); - aclScalar* other = aclCreateScalar(&value, aclDataType::ACL_FLOAT); +static aclTensor * aclnn_values(ggml_backend_cann_context & ctx, + void * buffer, + size_t n_bytes, + int64_t * ne, + int64_t dims, + aclDataType type, + size_t type_size, + float value = 1.0f) { + aclTensor * acl_tensor = aclnn_zero(ctx, buffer, n_bytes, ne, dims, type, type_size); + float alpha_host = 1.0f; + aclScalar * alpha = aclCreateScalar(&alpha_host, aclDataType::ACL_FLOAT); + aclScalar * other = aclCreateScalar(&value, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdds, acl_tensor, other, alpha); return acl_tensor; } @@ -884,8 +877,7 @@ static aclTensor* aclnn_values(ggml_backend_cann_context& ctx, void* buffer, * @param scalar The scalar value used to fill the tensor. * @param acl_dst The destination tensor to be filled with the scalar value. */ -static void aclnn_fill_scalar(ggml_backend_cann_context& ctx, float scalar, - aclTensor* acl_dst) { +static void aclnn_fill_scalar(ggml_backend_cann_context & ctx, float scalar, aclTensor * acl_dst) { auto acl_scalar = aclCreateScalar(&scalar, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst, acl_scalar); ggml_cann_release_resources(ctx, acl_scalar); @@ -913,15 +905,14 @@ static void aclnn_fill_scalar(ggml_backend_cann_context& ctx, float scalar, * initialization via memset or arbitrary values via fill_scalar). * @return An aclTensor pointer created from the cached buffer. */ -static aclTensor* get_cache_acl_tensor( - ggml_backend_cann_context& ctx, - void** buffer, - int64_t &cache_element, - int64_t* ne, - size_t* nb, - ggml_type dtype, - int64_t dims, - float value) { +static aclTensor * get_cache_acl_tensor(ggml_backend_cann_context & ctx, + void ** buffer, + int64_t & cache_element, + int64_t * ne, + size_t * nb, + ggml_type dtype, + int64_t dims, + float value) { // Calculate total number of elements int64_t n_element = 1; for (int i = 0; i < dims; i++) { @@ -940,24 +931,22 @@ static aclTensor* get_cache_acl_tensor( cache_element = n_element; // Initialize cache - int64_t pool_ne[1] = { n_element }; - size_t pool_nb[1] = { ggml_type_size(dtype) }; - aclTensor* acl_value = ggml_cann_create_tensor( - *buffer, ggml_cann_type_mapping(dtype), ggml_type_size(dtype), - pool_ne, pool_nb, 1); + int64_t pool_ne[1] = { n_element }; + size_t pool_nb[1] = { ggml_type_size(dtype) }; + aclTensor * acl_value = + ggml_cann_create_tensor(*buffer, ggml_cann_type_mapping(dtype), ggml_type_size(dtype), pool_ne, pool_nb, 1); aclnn_fill_scalar(ctx, value, acl_value); ggml_cann_release_resources(ctx, acl_value); } - return ggml_cann_create_tensor(*buffer, ggml_cann_type_mapping(dtype), - ggml_type_size(dtype), ne, nb, dims); + return ggml_cann_create_tensor(*buffer, ggml_cann_type_mapping(dtype), ggml_type_size(dtype), ne, nb, dims); } -void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_rms_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); float eps; memcpy(&eps, dst->op_params, sizeof(float)); @@ -969,61 +958,50 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { for (int i = 1; i < GGML_MAX_DIMS; i++) { acl_gamma_nb[i] = acl_gamma_nb[i - 1] * src->ne[i - 1]; } - aclTensor* acl_gamma = get_cache_acl_tensor( - ctx, - &ctx.rms_norm_one_tensor_cache.cache, - ctx.rms_norm_one_tensor_cache.size, - src->ne, - acl_gamma_nb, - dst->type, - 1, // dims - 1.0f // value + aclTensor * acl_gamma = get_cache_acl_tensor(ctx, &ctx.rms_norm_one_tensor_cache.cache, + ctx.rms_norm_one_tensor_cache.size, src->ne, acl_gamma_nb, dst->type, + 1, // dims + 1.0f // value ); // build rstd. - int64_t acl_rstd_ne[] = {src->ne[1], src->ne[2], src->ne[3]}; - size_t acl_rstd_nb[GGML_MAX_DIMS - 1]; + int64_t acl_rstd_ne[] = { src->ne[1], src->ne[2], src->ne[3] }; + size_t acl_rstd_nb[GGML_MAX_DIMS - 1]; // rstd will always be F32. acl_rstd_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS - 1; i++) { acl_rstd_nb[i] = acl_rstd_nb[i - 1] * acl_rstd_ne[i - 1]; } - aclTensor* acl_rstd = get_cache_acl_tensor( - ctx, - &ctx.rms_norm_zero_tensor_cache.cache, - ctx.rms_norm_zero_tensor_cache.size, - acl_rstd_ne, - acl_rstd_nb, - GGML_TYPE_F32, - GGML_MAX_DIMS - 1, - 0.0f // value - ); + aclTensor * acl_rstd = + get_cache_acl_tensor(ctx, &ctx.rms_norm_zero_tensor_cache.cache, ctx.rms_norm_zero_tensor_cache.size, + acl_rstd_ne, acl_rstd_nb, GGML_TYPE_F32, GGML_MAX_DIMS - 1, + 0.0f // value + ); GGML_CANN_CALL_ACLNN_OP(ctx, RmsNorm, acl_src, acl_gamma, eps, acl_dst, acl_rstd); ggml_cann_release_resources(ctx, acl_src, acl_dst, acl_gamma, acl_rstd); } // TODO: performace is low. -void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, - float value) { - ggml_tensor* src = dst->src[0]; +void ggml_cann_diag_mask(ggml_backend_cann_context & ctx, ggml_tensor * dst, float value) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - const int n_past = ((int32_t*)dst->op_params)[0]; + const int n_past = ((int32_t *) dst->op_params)[0]; ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), ggml_nbytes(src)); - void* buffer = one_tensor_allocator.get(); + void * buffer = one_tensor_allocator.get(); - aclTensor* mask_tensor = ggml_cann_create_tensor(buffer, ggml_cann_type_mapping(src->type), - ggml_type_size(src->type), src->ne, src->nb, GGML_MAX_DIMS); + aclTensor * mask_tensor = ggml_cann_create_tensor(buffer, ggml_cann_type_mapping(src->type), + ggml_type_size(src->type), src->ne, src->nb, GGML_MAX_DIMS); aclnn_fill_scalar(ctx, value, mask_tensor); - aclScalar* alpha = nullptr; - float alphaValue = 1.0f; - alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + aclScalar * alpha = nullptr; + float alphaValue = 1.0f; + alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceTriu, mask_tensor, n_past + 1); GGML_CANN_CALL_ACLNN_OP(ctx, Tril, acl_src, n_past + 1, acl_dst); @@ -1046,25 +1024,27 @@ void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, * tensor. * @param dims The number of dimensions in the tensor. */ -static void aclnn_permute(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, int64_t* new_dim, uint64_t dims) { - aclIntArray* acl_dims = aclCreateIntArray(new_dim, dims); +static void aclnn_permute(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + int64_t * new_dim, + uint64_t dims) { + aclIntArray * acl_dims = aclCreateIntArray(new_dim, dims); GGML_CANN_CALL_ACLNN_OP(ctx, Permute, acl_src, acl_dims, acl_dst); ggml_cann_release_resources(ctx, acl_dims); } -static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context& ctx, - ggml_tensor* dst, - ggml_tensor* src1, - aclTensor* tmp_cast_tensor, - aclTensor* tmp_im2col_tensor) { +static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context & ctx, + ggml_tensor * dst, + ggml_tensor * src1, + aclTensor * tmp_cast_tensor, + aclTensor * tmp_im2col_tensor) { // Permute: [N, IC * KH * KW, OW * OH] -> [N, OW * OH, IC * KH * KW] - int64_t dst_ne[] = {dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3]}; - size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[3]}; - aclTensor* acl_dst = - ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1); + int64_t dst_ne[] = { dst->ne[0], dst->ne[1] * dst->ne[2], dst->ne[3] }; + size_t dst_nb[] = { dst->nb[0], dst->nb[1], dst->nb[3] }; + aclTensor * acl_dst = ggml_cann_create_tensor(dst, dst_ne, dst_nb, GGML_MAX_DIMS - 1); - int64_t permute_dim[] = {0, 2, 1}; + int64_t permute_dim[] = { 0, 2, 1 }; if (src1->type != dst->type) { aclnn_permute(ctx, tmp_cast_tensor, acl_dst, permute_dim, 3); } else { @@ -1074,101 +1054,95 @@ static void ggml_cann_im2col_2d_post_process(ggml_backend_cann_context& ctx, ggml_cann_release_resources(ctx, acl_dst); } -static void ggml_cann_im2col_1d_post_process( - ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_tensor* src1, - aclTensor* tmp_cast_tensor, aclTensor* tmp_im2col_tensor, - const std::vector& im2col_op_params) { +static void ggml_cann_im2col_1d_post_process(ggml_backend_cann_context & ctx, + ggml_tensor * dst, + ggml_tensor * src1, + aclTensor * tmp_cast_tensor, + aclTensor * tmp_im2col_tensor, + const std::vector & im2col_op_params) { // get params - const int64_t KH = im2col_op_params[0]; - const int64_t KW = im2col_op_params[1]; - const int64_t IW = im2col_op_params[2]; - const int64_t IC = im2col_op_params[3]; - const int64_t N = im2col_op_params[4]; - const int64_t OH = im2col_op_params[5]; - const int64_t OW = im2col_op_params[6]; - const int64_t s0 = im2col_op_params[7]; - const int64_t p0 = im2col_op_params[8]; - const int64_t d0 = im2col_op_params[9]; + const int64_t KH = im2col_op_params[0]; + const int64_t KW = im2col_op_params[1]; + const int64_t IW = im2col_op_params[2]; + const int64_t IC = im2col_op_params[3]; + const int64_t N = im2col_op_params[4]; + const int64_t OH = im2col_op_params[5]; + const int64_t OW = im2col_op_params[6]; + const int64_t s0 = im2col_op_params[7]; + const int64_t p0 = im2col_op_params[8]; + const int64_t d0 = im2col_op_params[9]; const int64_t n_bytes_factor = im2col_op_params[10]; // Permute: [N, IC * KH * KW, OW * OH] -> // [N, OW * OH * n_bytes_factor, IC * KH * KW] ggml_cann_pool_alloc tmp_permute_allocator(ctx.pool()); tmp_permute_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor); - void* tmp_permute_buffer = tmp_permute_allocator.get(); + void * tmp_permute_buffer = tmp_permute_allocator.get(); - int64_t tmp_permute_ne[] = {IC * KH * KW, OW * OH * n_bytes_factor, N}; - size_t tmp_permute_nb[GGML_MAX_DIMS - 1]; + int64_t tmp_permute_ne[] = { IC * KH * KW, OW * OH * n_bytes_factor, N }; + size_t tmp_permute_nb[GGML_MAX_DIMS - 1]; tmp_permute_nb[0] = ggml_type_size(dst->type); for (int i = 1; i < GGML_MAX_DIMS - 1; i++) { tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1]; } - aclTensor* tmp_permute_tensor = ggml_cann_create_tensor( - tmp_permute_buffer, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), tmp_permute_ne, tmp_permute_nb, - GGML_MAX_DIMS - 1, ACL_FORMAT_ND); + aclTensor * tmp_permute_tensor = + ggml_cann_create_tensor(tmp_permute_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_permute_ne, tmp_permute_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND); - int64_t permute_dim[] = {0, 2, 1}; + int64_t permute_dim[] = { 0, 2, 1 }; if (src1->type != dst->type) { aclnn_permute(ctx, tmp_cast_tensor, tmp_permute_tensor, permute_dim, 3); } else { - aclnn_permute(ctx, tmp_im2col_tensor, tmp_permute_tensor, permute_dim, - 3); + aclnn_permute(ctx, tmp_im2col_tensor, tmp_permute_tensor, permute_dim, 3); } // number of times the kernel moves in W dimension const int n_step_w = (IW + 2 * p0 - d0 * (KW - 1) - 1) / s0 + 1; - size_t offset; - void *cur_dst_buffer = dst->data, *cur_permute_buffer = tmp_permute_buffer; + size_t offset; + void * cur_dst_buffer = dst->data, *cur_permute_buffer = tmp_permute_buffer; // memory copy with offset to restore 1D im2col from 2d if (IC > 1) { - offset = IC * KH * KW * n_step_w * ggml_type_size(dst->type); + offset = IC * KH * KW * n_step_w * ggml_type_size(dst->type); size_t size_cpy = KH * KW * ggml_type_size(dst->type); for (int c = 0; c < IC; c++) { - cur_permute_buffer = (char*)tmp_permute_buffer + offset + - KH * KW * c * ggml_type_size(dst->type); - cur_dst_buffer = (char*)dst->data + - c * KH * KW * n_step_w * ggml_type_size(dst->type); + cur_permute_buffer = (char *) tmp_permute_buffer + offset + KH * KW * c * ggml_type_size(dst->type); + cur_dst_buffer = (char *) dst->data + c * KH * KW * n_step_w * ggml_type_size(dst->type); for (int i = 0; i < n_step_w; i++) { - ggml_cann_async_memcpy(ctx, cur_dst_buffer, cur_permute_buffer, size_cpy, - ACL_MEMCPY_DEVICE_TO_DEVICE); - cur_dst_buffer = - (char*)cur_dst_buffer + KH * KW * ggml_type_size(dst->type); - cur_permute_buffer = (char*)cur_permute_buffer + - KH * KW * IC * ggml_type_size(dst->type); + ggml_cann_async_memcpy(ctx, cur_dst_buffer, cur_permute_buffer, size_cpy, ACL_MEMCPY_DEVICE_TO_DEVICE); + cur_dst_buffer = (char *) cur_dst_buffer + KH * KW * ggml_type_size(dst->type); + cur_permute_buffer = (char *) cur_permute_buffer + KH * KW * IC * ggml_type_size(dst->type); } } } else { - offset = KH * KW * n_step_w * - ggml_type_size(dst->type); // equal to ggml_nbytes(dst) - ggml_cann_async_memcpy(ctx, dst->data, (char*)tmp_permute_buffer + offset, offset, - ACL_MEMCPY_DEVICE_TO_DEVICE); + offset = KH * KW * n_step_w * ggml_type_size(dst->type); // equal to ggml_nbytes(dst) + ggml_cann_async_memcpy(ctx, dst->data, (char *) tmp_permute_buffer + offset, offset, + ACL_MEMCPY_DEVICE_TO_DEVICE); } ggml_cann_release_resources(ctx, tmp_permute_tensor); } -void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; // kernel - ggml_tensor* src1 = dst->src[1]; // input +void ggml_cann_im2col(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; // kernel + ggml_tensor * src1 = dst->src[1]; // input GGML_TENSOR_BINARY_OP_LOCALS; // aclnnIm2col only works on 2D. set s1, p1, d1 to 1 to perform 2D // im2col and do post-processing to restore it to 1D. - const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1; - const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t s1 = is_2D ? ((const int32_t*)(dst->op_params))[1] : 1; - const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; - const int32_t p1 = is_2D ? ((const int32_t*)(dst->op_params))[3] : 1; - const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; - const int32_t d1 = is_2D ? ((const int32_t*)(dst->op_params))[5] : 1; - - const int64_t N = ne13; + const bool is_2D = ((const int32_t *) (dst->op_params))[6] == 1; + const int32_t s0 = ((const int32_t *) (dst->op_params))[0]; + const int32_t s1 = is_2D ? ((const int32_t *) (dst->op_params))[1] : 1; + const int32_t p0 = ((const int32_t *) (dst->op_params))[2]; + const int32_t p1 = is_2D ? ((const int32_t *) (dst->op_params))[3] : 1; + const int32_t d0 = ((const int32_t *) (dst->op_params))[4]; + const int32_t d1 = is_2D ? ((const int32_t *) (dst->op_params))[5] : 1; + + const int64_t N = ne13; const int64_t IC = ne12; const int64_t KH = ne01; const int64_t KW = ne00; @@ -1181,9 +1155,9 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { const int64_t n_bytes_factor = is_2D ? 1 : 3; // im2col: [N,C,H,W] -> [N, IC * KH * KW, OW * OH * n_bytes_factor] - aclTensor* acl_src1 = ggml_cann_create_tensor(src1); - int64_t tmp_im2col_ne[] = {OW * OH * n_bytes_factor, IC * KH * KW, N}; - size_t tmp_im2col_nb[GGML_MAX_DIMS - 1]; + aclTensor * acl_src1 = ggml_cann_create_tensor(src1); + int64_t tmp_im2col_ne[] = { OW * OH * n_bytes_factor, IC * KH * KW, N }; + size_t tmp_im2col_nb[GGML_MAX_DIMS - 1]; tmp_im2col_nb[0] = ggml_type_size(src1->type); for (int i = 1; i < GGML_MAX_DIMS - 1; i++) { @@ -1193,31 +1167,27 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { // Calculate im2col. // If dst is f16, tmp_buffer is f32, we need alloc src.typesize * // dst.elemcount. - ggml_cann_pool_alloc im2col_allocator( - ctx.pool(), - ggml_nelements(dst) * ggml_element_size(src1) * n_bytes_factor); - void* tmp_im2col_buffer = im2col_allocator.get(); - - aclTensor* tmp_im2col_tensor = ggml_cann_create_tensor( - tmp_im2col_buffer, ggml_cann_type_mapping(src1->type), - ggml_type_size(src1->type), tmp_im2col_ne, tmp_im2col_nb, - GGML_MAX_DIMS - 1, ACL_FORMAT_ND); - - std::vector kernel_dims = {KH, KW}; - std::vector dilation_size = {d1, d0}; - std::vector padding_dims = {p1, p0}; - std::vector stride_dims = {s1, s0}; - auto* kernel_size = aclCreateIntArray(kernel_dims.data(), 2); - auto* dilations = aclCreateIntArray(dilation_size.data(), 2); - auto* paddings = aclCreateIntArray(padding_dims.data(), 2); - auto* strides = aclCreateIntArray(stride_dims.data(), 2); - GGML_CANN_CALL_ACLNN_OP(ctx, Im2col, acl_src1, kernel_size, dilations, - paddings, strides, tmp_im2col_tensor); + ggml_cann_pool_alloc im2col_allocator(ctx.pool(), ggml_nelements(dst) * ggml_element_size(src1) * n_bytes_factor); + void * tmp_im2col_buffer = im2col_allocator.get(); + + aclTensor * tmp_im2col_tensor = + ggml_cann_create_tensor(tmp_im2col_buffer, ggml_cann_type_mapping(src1->type), ggml_type_size(src1->type), + tmp_im2col_ne, tmp_im2col_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND); + + std::vector kernel_dims = { KH, KW }; + std::vector dilation_size = { d1, d0 }; + std::vector padding_dims = { p1, p0 }; + std::vector stride_dims = { s1, s0 }; + auto * kernel_size = aclCreateIntArray(kernel_dims.data(), 2); + auto * dilations = aclCreateIntArray(dilation_size.data(), 2); + auto * paddings = aclCreateIntArray(padding_dims.data(), 2); + auto * strides = aclCreateIntArray(stride_dims.data(), 2); + GGML_CANN_CALL_ACLNN_OP(ctx, Im2col, acl_src1, kernel_size, dilations, paddings, strides, tmp_im2col_tensor); // Cast if dst is f16. - aclTensor* tmp_cast_tensor = nullptr; + aclTensor * tmp_cast_tensor = nullptr; ggml_cann_pool_alloc tmp_cast_allocator(ctx.pool()); - void* tmp_cast_buffer = nullptr; + void * tmp_cast_buffer = nullptr; if (src1->type != dst->type) { tmp_cast_allocator.alloc(ggml_nbytes(dst) * n_bytes_factor); tmp_cast_buffer = tmp_cast_allocator.get(); @@ -1227,26 +1197,22 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { temp_cast_nb[i] = temp_cast_nb[i - 1] * tmp_im2col_ne[i - 1]; } - tmp_cast_tensor = ggml_cann_create_tensor( - tmp_cast_buffer, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), tmp_im2col_ne, temp_cast_nb, - GGML_MAX_DIMS - 1, ACL_FORMAT_ND); + tmp_cast_tensor = + ggml_cann_create_tensor(tmp_cast_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_im2col_ne, temp_cast_nb, GGML_MAX_DIMS - 1, ACL_FORMAT_ND); aclnn_cast(ctx, tmp_im2col_tensor, tmp_cast_tensor, ggml_cann_type_mapping(dst->type)); } // post-processing if (is_2D) { - ggml_cann_im2col_2d_post_process(ctx, dst, src1, tmp_cast_tensor, - tmp_im2col_tensor); + ggml_cann_im2col_2d_post_process(ctx, dst, src1, tmp_cast_tensor, tmp_im2col_tensor); } else { - std::vector im2col_op_params = { - KH, KW, IW, IC, N, OH, OW, s0, p0, d0, n_bytes_factor}; - ggml_cann_im2col_1d_post_process(ctx, dst, src1, tmp_cast_tensor, - tmp_im2col_tensor, im2col_op_params); + std::vector im2col_op_params = { KH, KW, IW, IC, N, OH, OW, s0, p0, d0, n_bytes_factor }; + ggml_cann_im2col_1d_post_process(ctx, dst, src1, tmp_cast_tensor, tmp_im2col_tensor, im2col_op_params); } - ggml_cann_release_resources(ctx, acl_src1, tmp_im2col_tensor, tmp_cast_tensor, - kernel_size, dilations, paddings, strides); + ggml_cann_release_resources(ctx, acl_src1, tmp_im2col_tensor, tmp_cast_tensor, kernel_size, dilations, paddings, + strides); } /** @@ -1262,136 +1228,123 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param ctx The context for the CANN backend operations. * @param acl_src The tensor on which the exponential function will be applied. */ -static void aclnn_exp(ggml_backend_cann_context& ctx, aclTensor* acl_src) { +static void aclnn_exp(ggml_backend_cann_context & ctx, aclTensor * acl_src) { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceExp, acl_src); } -void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst) { - if(acl_dst == nullptr) { +void aclnn_cos(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { + if (acl_dst == nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCos, acl_src); } else { GGML_CANN_CALL_ACLNN_OP(ctx, Cos, acl_src, acl_dst); } } -void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst) { - if(acl_dst == nullptr) { +void aclnn_sin(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { + if (acl_dst == nullptr) { GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSin, acl_src); } else { GGML_CANN_CALL_ACLNN_OP(ctx, Sin, acl_src, acl_dst); } } -void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, - ggml_tensor* dst) { - const ggml_tensor* src = dst->src[0]; +void ggml_cann_timestep_embedding(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src = dst->src[0]; GGML_ASSERT(src->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); - const int dim = dst->op_params[0]; + const int dim = dst->op_params[0]; const int max_period = dst->op_params[1]; - int half = dim / 2; + int half = dim / 2; - aclTensor* acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_src = ggml_cann_create_tensor(src); // arange: [0, ..., half) - float start = 0; - float stop = half; - float step = 1; + float start = 0; + float stop = half; + float step = 1; int64_t n_elements_arange = half; - int64_t tmp_arange_ne[] = {half}; - size_t tmp_arange_nb[] = {sizeof(dst->type)}; + int64_t tmp_arange_ne[] = { half }; + size_t tmp_arange_nb[] = { sizeof(dst->type) }; ggml_cann_pool_alloc arange_allocator(ctx.pool(), half * sizeof(dst->type)); - void* tmp_arange_buffer = arange_allocator.get(); - aclTensor* tmp_arange_tensor = ggml_cann_create_tensor( - tmp_arange_buffer, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), tmp_arange_ne, tmp_arange_nb, - GGML_MAX_DIMS - 3, ACL_FORMAT_ND); + void * tmp_arange_buffer = arange_allocator.get(); + aclTensor * tmp_arange_tensor = + ggml_cann_create_tensor(tmp_arange_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_arange_ne, tmp_arange_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND); aclnn_arange(ctx, tmp_arange_tensor, start, stop, step, n_elements_arange); // freq float freq_param = -logf(max_period) / half; - bool inplace = true; + bool inplace = true; aclnn_muls(ctx, tmp_arange_tensor, freq_param, nullptr, inplace); aclnn_exp(ctx, tmp_arange_tensor); // permute: src [0,1,2,3]->[0,1,3,2] - int64_t tmp_permute_ne[] = {src->ne[1], src->ne[0], src->ne[2], src->ne[3]}; - size_t tmp_permute_nb[GGML_MAX_DIMS]; + int64_t tmp_permute_ne[] = { src->ne[1], src->ne[0], src->ne[2], src->ne[3] }; + size_t tmp_permute_nb[GGML_MAX_DIMS]; tmp_permute_nb[0] = ggml_type_size(src->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { tmp_permute_nb[i] = tmp_permute_nb[i - 1] * tmp_permute_ne[i - 1]; } ggml_cann_pool_alloc permute_allocator(ctx.pool(), ggml_nbytes(src)); - void* tmp_permute_buffer = permute_allocator.get(); - aclTensor* tmp_permute_tensor = ggml_cann_create_tensor( - tmp_permute_buffer, ggml_cann_type_mapping(src->type), - ggml_type_size(src->type), tmp_permute_ne, tmp_permute_nb, - GGML_MAX_DIMS, ACL_FORMAT_ND); - int64_t permute_dim[] = {0, 1, 3, 2}; - int64_t num_dims = 4; + void * tmp_permute_buffer = permute_allocator.get(); + aclTensor * tmp_permute_tensor = + ggml_cann_create_tensor(tmp_permute_buffer, ggml_cann_type_mapping(src->type), ggml_type_size(src->type), + tmp_permute_ne, tmp_permute_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); + int64_t permute_dim[] = { 0, 1, 3, 2 }; + int64_t num_dims = 4; aclnn_permute(ctx, acl_src, tmp_permute_tensor, permute_dim, num_dims); // timestep * freq - int64_t tmp_mul_ne[] = {src->ne[1] * half, src->ne[0], src->ne[2], - src->ne[3]}; - size_t tmp_mul_nb[GGML_MAX_DIMS]; + int64_t tmp_mul_ne[] = { src->ne[1] * half, src->ne[0], src->ne[2], src->ne[3] }; + size_t tmp_mul_nb[GGML_MAX_DIMS]; tmp_mul_nb[0] = ggml_type_size(src->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { tmp_mul_nb[i] = tmp_mul_nb[i - 1] * tmp_mul_ne[i - 1]; } - int mul_nelements = - src->ne[1] * half * src->ne[0] * src->ne[2] * src->ne[3]; + int mul_nelements = src->ne[1] * half * src->ne[0] * src->ne[2] * src->ne[3]; - ggml_cann_pool_alloc mul_allocator( - ctx.pool(), mul_nelements * ggml_type_size(src->type)); - void* tmp_mul_buffer = mul_allocator.get(); - aclTensor* tmp_mul_tensor = ggml_cann_create_tensor( - tmp_mul_buffer, ggml_cann_type_mapping(src->type), - ggml_type_size(src->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, - ACL_FORMAT_ND); + ggml_cann_pool_alloc mul_allocator(ctx.pool(), mul_nelements * ggml_type_size(src->type)); + void * tmp_mul_buffer = mul_allocator.get(); + aclTensor * tmp_mul_tensor = + ggml_cann_create_tensor(tmp_mul_buffer, ggml_cann_type_mapping(src->type), ggml_type_size(src->type), + tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_mul(ctx, tmp_permute_tensor, tmp_arange_tensor, tmp_mul_tensor); // cos - ggml_cann_pool_alloc cos_allocator( - ctx.pool(), mul_nelements * ggml_type_size(src->type)); - void* tmp_cos_buffer = cos_allocator.get(); - aclTensor* tmp_cos_tensor = ggml_cann_create_tensor( - tmp_cos_buffer, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, - ACL_FORMAT_ND); + ggml_cann_pool_alloc cos_allocator(ctx.pool(), mul_nelements * ggml_type_size(src->type)); + void * tmp_cos_buffer = cos_allocator.get(); + aclTensor * tmp_cos_tensor = + ggml_cann_create_tensor(tmp_cos_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_cos(ctx, tmp_mul_tensor, tmp_cos_tensor); // sin - ggml_cann_pool_alloc sin_allocator( - ctx.pool(), mul_nelements * ggml_type_size(src->type)); - void* tmp_sin_buffer = sin_allocator.get(); - aclTensor* tmp_sin_tensor = ggml_cann_create_tensor( - tmp_sin_buffer, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, - ACL_FORMAT_ND); + ggml_cann_pool_alloc sin_allocator(ctx.pool(), mul_nelements * ggml_type_size(src->type)); + void * tmp_sin_buffer = sin_allocator.get(); + aclTensor * tmp_sin_tensor = + ggml_cann_create_tensor(tmp_sin_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + tmp_mul_ne, tmp_mul_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_sin(ctx, tmp_mul_tensor, tmp_sin_tensor); // concat - int64_t concat_dim = 3; - aclTensor* acl_dst = ggml_cann_create_tensor(dst); - aclTensor* tensors[] = {tmp_cos_tensor, tmp_sin_tensor}; - aclTensorList* tensor_list = aclCreateTensorList(tensors, 2); + int64_t concat_dim = 3; + aclTensor * acl_dst = ggml_cann_create_tensor(dst); + aclTensor * tensors[] = { tmp_cos_tensor, tmp_sin_tensor }; + aclTensorList * tensor_list = aclCreateTensorList(tensors, 2); aclnn_concat(ctx, tensor_list, acl_dst, concat_dim); // release // segmentation fault when delete both tensorList and his elements. - ggml_cann_release_resources(ctx, tensor_list, acl_src, tmp_arange_tensor, - tmp_permute_tensor, tmp_mul_tensor, acl_dst); + ggml_cann_release_resources(ctx, tensor_list, acl_src, tmp_arange_tensor, tmp_permute_tensor, tmp_mul_tensor, + acl_dst); } /** @@ -1410,8 +1363,7 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, * @param acl_exp The exponent tensor, each element of which is used to raise * the corresponding element in the destination tensor. */ -static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx, - aclTensor* acl_dst, aclTensor* acl_exp) { +static void aclnn_pow_tensor_tensor(ggml_backend_cann_context & ctx, aclTensor * acl_dst, aclTensor * acl_exp) { GGML_CANN_CALL_ACLNN_OP(ctx, InplacePowTensorTensor, acl_dst, acl_exp); } @@ -1436,25 +1388,29 @@ static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx, * @param step Step size for the exponent increment. * @param dtype Data type for slope tensor. */ -static void aclnn_get_slope_inner(ggml_backend_cann_context& ctx, void* slope_buffer, - float m, int64_t size, float start, float stop, float step, ggml_type dtype){ - aclDataType acl_type = ggml_cann_type_mapping(dtype); - size_t type_size = ggml_type_size(dtype); - - int64_t ne[] = {size}; - size_t nb[] = {type_size}; +static void aclnn_get_slope_inner(ggml_backend_cann_context & ctx, + void * slope_buffer, + float m, + int64_t size, + float start, + float stop, + float step, + ggml_type dtype) { + aclDataType acl_type = ggml_cann_type_mapping(dtype); + size_t type_size = ggml_type_size(dtype); + + int64_t ne[] = { size }; + size_t nb[] = { type_size }; ggml_cann_pool_alloc arange_allocator(ctx.pool(), size * type_size); - void* arange_buffer = arange_allocator.get(); + void * arange_buffer = arange_allocator.get(); - aclTensor* arange_tensor = ggml_cann_create_tensor( - arange_buffer, acl_type, type_size, ne, nb, 1); + aclTensor * arange_tensor = ggml_cann_create_tensor(arange_buffer, acl_type, type_size, ne, nb, 1); aclnn_arange(ctx, arange_tensor, start, stop, step, size); - aclTensor* slope_tensor = ggml_cann_create_tensor( - slope_buffer, acl_type, type_size, ne, nb, 1); + aclTensor * slope_tensor = ggml_cann_create_tensor(slope_buffer, acl_type, type_size, ne, nb, 1); - aclScalar* sc = aclCreateScalar(&m, aclDataType::ACL_FLOAT); + aclScalar * sc = aclCreateScalar(&m, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, sc, arange_tensor, slope_tensor); ggml_cann_release_resources(ctx, sc, arange_tensor, slope_tensor); @@ -1486,8 +1442,11 @@ static void aclnn_get_slope_inner(ggml_backend_cann_context& ctx, void* slope_bu * @param dtype Data type for slope tensor. * */ -static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head, - void* slope_buffer, float max_bias, ggml_type dtype) { +static void aclnn_get_slope(ggml_backend_cann_context & ctx, + int64_t n_head, + void * slope_buffer, + float max_bias, + ggml_type dtype) { const int n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); float m0 = powf(2.0f, -(max_bias) / n_head_log2); @@ -1511,9 +1470,8 @@ static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head, end = 2 * ((n_head - 1) - n_head_log2) + 1; step = 2; count = n_head - n_head_log2; - aclnn_get_slope_inner( - ctx, (char *) slope_buffer + n_head_log2 * sizeof(float), - m1, count, start, end + 1, step, dtype); + aclnn_get_slope_inner(ctx, (char *) slope_buffer + n_head_log2 * sizeof(float), m1, count, start, end + 1, step, + dtype); } } @@ -1538,17 +1496,19 @@ static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head, * - Write data into dst_ptr using only the shape information of the dst tensor. * - `GGML_MAX_DIMS + 2` is used to extend tensor dimensions for broadcasting. */ -static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask, - ggml_tensor* dst, void* dst_ptr, float max_bias) { - void* slope_buffer = nullptr; - void* bias_buffer = nullptr; +static void aclnn_add_alibi(ggml_backend_cann_context & ctx, + ggml_tensor * mask, + ggml_tensor * dst, + void * dst_ptr, + float max_bias) { + void * slope_buffer = nullptr; + void * bias_buffer = nullptr; if (max_bias > 0.0f) { - int64_t n_heads = dst->ne[2]; + int64_t n_heads = dst->ne[2]; ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(float)); slope_buffer = slope_allocator.get(); - ggml_cann_pool_alloc bias_allocator( - ctx.pool(), ggml_nelements(dst) * ggml_element_size(dst)); + ggml_cann_pool_alloc bias_allocator(ctx.pool(), ggml_nelements(dst) * ggml_element_size(dst)); bias_buffer = bias_allocator.get(); aclnn_get_slope(ctx, n_heads, slope_buffer, max_bias, GGML_TYPE_F32); } @@ -1559,16 +1519,12 @@ static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask, // broadcast the mask across rows int64_t mask_ne[] = { mask->ne[0], dst->ne[1], mask->ne[2], 1, mask->ne[3], 1 }; - size_t mask_nb[] = { - mask_nb[0] = mask->nb[0], mask_nb[1] = mask->nb[1], mask_nb[2] = mask->nb[2], - mask_nb[3] = mask->nb[2], mask_nb[4] = mask->nb[3], mask_nb[5] = mask->nb[3] - }; + size_t mask_nb[] = { mask_nb[0] = mask->nb[0], mask_nb[1] = mask->nb[1], mask_nb[2] = mask->nb[2], + mask_nb[3] = mask->nb[2], mask_nb[4] = mask->nb[3], mask_nb[5] = mask->nb[3] }; int64_t dst_ne[] = { dst->ne[0], dst->ne[1], mask->ne[2], nr2, mask->ne[3], nr3 }; - size_t dst_nb[] = { - dst_nb[0] = dst->nb[0], dst_nb[1] = dst->nb[1], dst_nb[2] = dst->nb[2], - dst_nb[3] = dst->nb[2], dst_nb[4] = dst->nb[3], dst_nb[5] = dst->nb[3] - }; + size_t dst_nb[] = { dst_nb[0] = dst->nb[0], dst_nb[1] = dst->nb[1], dst_nb[2] = dst->nb[2], + dst_nb[3] = dst->nb[2], dst_nb[4] = dst->nb[3], dst_nb[5] = dst->nb[3] }; // slope is a 1 dim tensor, slope.ne2 == dst.ne2 int64_t slope_ne[] = { 1, 1, mask->ne[2], nr2, 1, 1 }; @@ -1578,17 +1534,13 @@ static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask, slope_nb[i] = slope_nb[i - 1] * slope_ne[i - 1]; } - aclTensor* acl_slope = ggml_cann_create_tensor( - slope_buffer, ACL_FLOAT, sizeof(float), - slope_ne, slope_nb, GGML_MAX_DIMS + 2); - aclTensor* acl_mask = ggml_cann_create_tensor( - mask, mask_ne, mask_nb, GGML_MAX_DIMS + 2); + aclTensor * acl_slope = + ggml_cann_create_tensor(slope_buffer, ACL_FLOAT, sizeof(float), slope_ne, slope_nb, GGML_MAX_DIMS + 2); + aclTensor * acl_mask = ggml_cann_create_tensor(mask, mask_ne, mask_nb, GGML_MAX_DIMS + 2); // write data into dst_ptr using only the shape information of the dst tensor. - aclTensor* acl_dst = ggml_cann_create_tensor( - dst_ptr, ggml_cann_type_mapping(dst->type), - ggml_type_size(dst->type), dst_ne, dst_nb, - GGML_MAX_DIMS + 2); + aclTensor * acl_dst = ggml_cann_create_tensor(dst_ptr, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), + dst_ne, dst_nb, GGML_MAX_DIMS + 2); if (max_bias > 0.0f) { int64_t bias_ne[] = { mask->ne[0], dst->ne[1], mask->ne[2], nr2, mask->ne[3], 1 }; @@ -1597,9 +1549,8 @@ static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask, for (int i = 1; i < GGML_MAX_DIMS + 2; i++) { bias_nb[i] = bias_nb[i - 1] * bias_ne[i - 1]; } - aclTensor* bias_tensor = ggml_cann_create_tensor( - bias_buffer, ACL_FLOAT, sizeof(float), - bias_ne, bias_nb, GGML_MAX_DIMS + 2); + aclTensor * bias_tensor = + ggml_cann_create_tensor(bias_buffer, ACL_FLOAT, sizeof(float), bias_ne, bias_nb, GGML_MAX_DIMS + 2); aclnn_mul(ctx, acl_slope, acl_mask, bias_tensor); aclnn_add(ctx, acl_dst, bias_tensor); @@ -1628,17 +1579,16 @@ void ggml_cann_cpy(ggml_backend_cann_context & ctx, ggml_tensor * dst) { * @param acl_dst The destination tensor where the softmax results will be * stored. */ -static void aclnn_softmax(ggml_backend_cann_context & ctx, - aclTensor* acl_src, int64_t dim, aclTensor * acl_dst) { +static void aclnn_softmax(ggml_backend_cann_context & ctx, aclTensor * acl_src, int64_t dim, aclTensor * acl_dst) { GGML_CANN_CALL_ACLNN_OP(ctx, Softmax, acl_src, dim, acl_dst); } void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; // mask + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; // mask - aclTensor* acl_src0 = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src0 = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); float scale = 1.0f; float max_bias = 0.0f; @@ -1647,12 +1597,11 @@ void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) { memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); // input mul scale - aclScalar* acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT); + aclScalar * acl_scale = aclCreateScalar(&scale, aclDataType::ACL_FLOAT); ggml_cann_pool_alloc src_tensor_allocator(ctx.pool(), ggml_nbytes(src0)); - void* src_tensor_buffer = src_tensor_allocator.get(); - aclTensor* softmax_tensor = ggml_cann_create_tensor( - src_tensor_buffer, ggml_cann_type_mapping(src0->type), - ggml_element_size(src0), src0->ne, src0->nb,GGML_MAX_DIMS); + void * src_tensor_buffer = src_tensor_allocator.get(); + aclTensor * softmax_tensor = ggml_cann_create_tensor(src_tensor_buffer, ggml_cann_type_mapping(src0->type), + ggml_element_size(src0), src0->ne, src0->nb, GGML_MAX_DIMS); aclnn_muls(ctx, acl_src0, scale, softmax_tensor, false); @@ -1684,29 +1633,31 @@ void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) { * @param index The index tensor specifying the indices to select from the source tensor. * @param type The data type of the source and destination tensors. */ -static void aclnn_index_select_4d(ggml_backend_cann_context& ctx, - void* src_buffer,int64_t* src_ne, size_t* src_nb, - void* dst_buffer, int64_t* dst_ne, size_t* dst_nb, - ggml_tensor* index, ggml_type type) { +static void aclnn_index_select_4d(ggml_backend_cann_context & ctx, + void * src_buffer, + int64_t * src_ne, + size_t * src_nb, + void * dst_buffer, + int64_t * dst_ne, + size_t * dst_nb, + ggml_tensor * index, + ggml_type type) { for (int64_t i = 0; i < src_ne[3]; i++) { for (int64_t j = 0; j < src_ne[2]; j++) { // src - aclTensor* acl_src_tensor = ggml_cann_create_tensor( - (char*)src_buffer + i * src_nb[3] + j * src_nb[2], - ggml_cann_type_mapping(type), ggml_type_size(type), - src_ne, src_nb, 2); + aclTensor * acl_src_tensor = + ggml_cann_create_tensor((char *) src_buffer + i * src_nb[3] + j * src_nb[2], + ggml_cann_type_mapping(type), ggml_type_size(type), src_ne, src_nb, 2); // index - aclTensor* acl_index = ggml_cann_create_tensor( - (char*)index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1], - ggml_cann_type_mapping(index->type), ggml_element_size(index), - index->ne, index->nb, 1); + aclTensor * acl_index = ggml_cann_create_tensor( + (char *) index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1], + ggml_cann_type_mapping(index->type), ggml_element_size(index), index->ne, index->nb, 1); // out - aclTensor* acl_out = ggml_cann_create_tensor( - (char*)dst_buffer + i * dst_nb[3] + j * dst_nb[2], - ggml_cann_type_mapping(type), ggml_type_size(type), - dst_ne, dst_nb, 2); + aclTensor * acl_out = + ggml_cann_create_tensor((char *) dst_buffer + i * dst_nb[3] + j * dst_nb[2], + ggml_cann_type_mapping(type), ggml_type_size(type), dst_ne, dst_nb, 2); GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, acl_src_tensor, 0, acl_index, acl_out); ggml_cann_release_resources(ctx, acl_src_tensor, acl_index, acl_out); } @@ -1733,162 +1684,154 @@ static void aclnn_index_select_4d(ggml_backend_cann_context& ctx, * @param index The index tensor specifying target positions in the destination tensor. * @param type The data type of the source and destination tensors. */ -static void aclnn_index_copy_4d(ggml_backend_cann_context& ctx, - void* src_buffer,int64_t* src_ne, size_t* src_nb, - void* dst_buffer, int64_t* dst_ne, size_t* dst_nb, - ggml_tensor* index, ggml_type type) { +static void aclnn_index_copy_4d(ggml_backend_cann_context & ctx, + void * src_buffer, + int64_t * src_ne, + size_t * src_nb, + void * dst_buffer, + int64_t * dst_ne, + size_t * dst_nb, + ggml_tensor * index, + ggml_type type) { for (int64_t i = 0; i < src_ne[3]; i++) { for (int64_t j = 0; j < src_ne[2]; j++) { // src - aclTensor* acl_src_tensor = ggml_cann_create_tensor( - (char*)src_buffer + i * src_nb[3] + j * src_nb[2], - ggml_cann_type_mapping(type), ggml_type_size(type), - src_ne, src_nb, 2); + aclTensor * acl_src_tensor = + ggml_cann_create_tensor((char *) src_buffer + i * src_nb[3] + j * src_nb[2], + ggml_cann_type_mapping(type), ggml_type_size(type), src_ne, src_nb, 2); // index - aclTensor* acl_index = ggml_cann_create_tensor( - (char*)index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1], - ggml_cann_type_mapping(index->type), ggml_element_size(index), - index->ne, index->nb, 1); + aclTensor * acl_index = ggml_cann_create_tensor( + (char *) index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1], + ggml_cann_type_mapping(index->type), ggml_element_size(index), index->ne, index->nb, 1); // out - aclTensor* acl_out = ggml_cann_create_tensor( - (char*)dst_buffer + i * dst_nb[3] + j * dst_nb[2], - ggml_cann_type_mapping(type), ggml_type_size(type), - dst_ne, dst_nb, 2); + aclTensor * acl_out = + ggml_cann_create_tensor((char *) dst_buffer + i * dst_nb[3] + j * dst_nb[2], + ggml_cann_type_mapping(type), ggml_type_size(type), dst_ne, dst_nb, 2); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexCopy, acl_out, 0, acl_index, acl_src_tensor); ggml_cann_release_resources(ctx, acl_src_tensor, acl_index, acl_out); } } } -void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; // src - ggml_tensor* src1 = dst->src[1]; // index +void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; // src + ggml_tensor * src1 = dst->src[1]; // index GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); switch (src0->type) { case GGML_TYPE_F16: case GGML_TYPE_F32: - if(src0->type == dst->type) { - aclnn_index_select_4d(ctx, src0->data, src0->ne, src0->nb, - dst->data, dst->ne, dst->nb, - src1, dst->type); + if (src0->type == dst->type) { + aclnn_index_select_4d(ctx, src0->data, src0->ne, src0->nb, dst->data, dst->ne, dst->nb, src1, + dst->type); } else { - aclTensor* acl_src0 = ggml_cann_create_tensor(src0); - ggml_cann_pool_alloc src_buffer_allocator( - ctx.pool(), ggml_nelements(src0) * ggml_element_size(dst)); - void* src_trans_buffer = src_buffer_allocator.get(); - size_t src_trans_nb[GGML_MAX_DIMS]; + aclTensor * acl_src0 = ggml_cann_create_tensor(src0); + ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * ggml_element_size(dst)); + void * src_trans_buffer = src_buffer_allocator.get(); + size_t src_trans_nb[GGML_MAX_DIMS]; src_trans_nb[0] = dst->nb[0]; for (int i = 1; i < GGML_MAX_DIMS; i++) { src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; } - aclTensor* src_trans_tensor = ggml_cann_create_tensor( - src_trans_buffer, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), - src0->ne, src_trans_nb, GGML_MAX_DIMS); + aclTensor * src_trans_tensor = + ggml_cann_create_tensor(src_trans_buffer, ggml_cann_type_mapping(dst->type), + ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS); aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type)); - aclnn_index_select_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, - dst->data, dst->ne, dst->nb, - src1, dst->type); + aclnn_index_select_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1, + dst->type); ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor); } break; - case GGML_TYPE_Q8_0: { - // add 1 dim for bcast mul. - size_t weight_nb[GGML_MAX_DIMS + 1], scale_nb[GGML_MAX_DIMS + 1], - dequant_nb[GGML_MAX_DIMS + 1]; - int64_t weight_ne[GGML_MAX_DIMS + 1], scale_ne[GGML_MAX_DIMS + 1], - *dequant_ne; - int64_t scale_offset = 0; - // [3,4,5,64] -> [3,4,5,2,32] - weight_ne[0] = QK8_0; - weight_ne[1] = src0->ne[0] / QK8_0; - weight_nb[0] = sizeof(int8_t); - weight_nb[1] = weight_nb[0] * weight_ne[0]; - for (int i = 2; i < GGML_MAX_DIMS + 1; i++) { - weight_ne[i] = src0->ne[i - 1]; - weight_nb[i] = weight_nb[i - 1] * weight_ne[i - 1]; - } - // [3,4,5,64] -> [3,4,5,2,1] - scale_ne[0] = 1; - scale_ne[1] = src0->ne[0] / QK8_0; - scale_nb[0] = sizeof(uint16_t); - scale_nb[1] = scale_nb[0] * scale_ne[0]; - for (int i = 2; i < GGML_MAX_DIMS + 1; i++) { - scale_ne[i] = src0->ne[i - 1]; - scale_nb[i] = scale_nb[i - 1] * scale_ne[i - 1]; - } - // [3,4,5,64] -> [3,4,5,2,32] - dequant_ne = weight_ne; - dequant_nb[0] = ggml_type_size(dst->type); - for (int i = 1; i < GGML_MAX_DIMS + 1; i++) { - dequant_nb[i] = dequant_nb[i - 1] * dequant_ne[i - 1]; - } - scale_offset = ggml_nelements(src0) * sizeof(int8_t); - ggml_cann_pool_alloc dequant_buffer_allocator( - ctx.pool(), ggml_nelements(src0) * ggml_type_size(dst->type)); - aclTensor* acl_weight_tensor = ggml_cann_create_tensor( - src0->data, ACL_INT8, sizeof(int8_t), weight_ne, weight_nb, - GGML_MAX_DIMS + 1); - aclTensor* acl_scale_tensor = ggml_cann_create_tensor( - src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb, - GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset); - aclTensor* dequant_tensor = ggml_cann_create_tensor( - dequant_buffer_allocator.get(), ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), - dequant_ne, dequant_nb, GGML_MAX_DIMS + 1); - aclnn_mul(ctx, acl_weight_tensor, acl_scale_tensor, dequant_tensor); - dequant_nb[0] = ggml_type_size(dst->type); - dequant_ne = src0->ne; - for (int i = 1; i < GGML_MAX_DIMS; i++) { - dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1]; - } - aclnn_index_select_4d(ctx, dequant_buffer_allocator.get(), - dequant_ne, dequant_nb, - dst->data, dst->ne, dst->nb, - src1, dst->type); + case GGML_TYPE_Q8_0: + { + // add 1 dim for bcast mul. + size_t weight_nb[GGML_MAX_DIMS + 1], scale_nb[GGML_MAX_DIMS + 1], dequant_nb[GGML_MAX_DIMS + 1]; + int64_t weight_ne[GGML_MAX_DIMS + 1], scale_ne[GGML_MAX_DIMS + 1], *dequant_ne; + int64_t scale_offset = 0; + // [3,4,5,64] -> [3,4,5,2,32] + weight_ne[0] = QK8_0; + weight_ne[1] = src0->ne[0] / QK8_0; + weight_nb[0] = sizeof(int8_t); + weight_nb[1] = weight_nb[0] * weight_ne[0]; + for (int i = 2; i < GGML_MAX_DIMS + 1; i++) { + weight_ne[i] = src0->ne[i - 1]; + weight_nb[i] = weight_nb[i - 1] * weight_ne[i - 1]; + } + // [3,4,5,64] -> [3,4,5,2,1] + scale_ne[0] = 1; + scale_ne[1] = src0->ne[0] / QK8_0; + scale_nb[0] = sizeof(uint16_t); + scale_nb[1] = scale_nb[0] * scale_ne[0]; + for (int i = 2; i < GGML_MAX_DIMS + 1; i++) { + scale_ne[i] = src0->ne[i - 1]; + scale_nb[i] = scale_nb[i - 1] * scale_ne[i - 1]; + } + // [3,4,5,64] -> [3,4,5,2,32] + dequant_ne = weight_ne; + dequant_nb[0] = ggml_type_size(dst->type); + for (int i = 1; i < GGML_MAX_DIMS + 1; i++) { + dequant_nb[i] = dequant_nb[i - 1] * dequant_ne[i - 1]; + } + scale_offset = ggml_nelements(src0) * sizeof(int8_t); + ggml_cann_pool_alloc dequant_buffer_allocator(ctx.pool(), + ggml_nelements(src0) * ggml_type_size(dst->type)); + aclTensor * acl_weight_tensor = ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t), weight_ne, + weight_nb, GGML_MAX_DIMS + 1); + aclTensor * acl_scale_tensor = + ggml_cann_create_tensor(src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb, + GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset); + aclTensor * dequant_tensor = + ggml_cann_create_tensor(dequant_buffer_allocator.get(), ggml_cann_type_mapping(dst->type), + ggml_type_size(dst->type), dequant_ne, dequant_nb, GGML_MAX_DIMS + 1); + aclnn_mul(ctx, acl_weight_tensor, acl_scale_tensor, dequant_tensor); + dequant_nb[0] = ggml_type_size(dst->type); + dequant_ne = src0->ne; + for (int i = 1; i < GGML_MAX_DIMS; i++) { + dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1]; + } + aclnn_index_select_4d(ctx, dequant_buffer_allocator.get(), dequant_ne, dequant_nb, dst->data, dst->ne, + dst->nb, src1, dst->type); - ggml_cann_release_resources(ctx, acl_weight_tensor, acl_scale_tensor, dequant_tensor); - break; - } + ggml_cann_release_resources(ctx, acl_weight_tensor, acl_scale_tensor, dequant_tensor); + break; + } default: GGML_ABORT("Unsupported tensor type for GGML_OP_GET_ROWS"); break; } } -void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; // src - ggml_tensor* src1 = dst->src[1]; // index +void ggml_cann_set_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; // src + ggml_tensor * src1 = dst->src[1]; // index switch (dst->type) { - case GGML_TYPE_F32: { - aclnn_index_copy_4d(ctx, src0->data, src0->ne, src0->nb, - dst->data, dst->ne, dst->nb, - src1, dst->type); - break; - } - case GGML_TYPE_F16: { - aclTensor* acl_src0 = ggml_cann_create_tensor(src0); - ggml_cann_pool_alloc src_buffer_allocator( - ctx.pool(), ggml_nelements(src0) * sizeof(uint16_t)); - void* src_trans_buffer = src_buffer_allocator.get(); - size_t src_trans_nb[GGML_MAX_DIMS]; - src_trans_nb[0] = sizeof(uint16_t); - for (int i = 1; i < GGML_MAX_DIMS; i++) { - src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; + case GGML_TYPE_F32: + { + aclnn_index_copy_4d(ctx, src0->data, src0->ne, src0->nb, dst->data, dst->ne, dst->nb, src1, dst->type); + break; + } + case GGML_TYPE_F16: + { + aclTensor * acl_src0 = ggml_cann_create_tensor(src0); + ggml_cann_pool_alloc src_buffer_allocator(ctx.pool(), ggml_nelements(src0) * sizeof(uint16_t)); + void * src_trans_buffer = src_buffer_allocator.get(); + size_t src_trans_nb[GGML_MAX_DIMS]; + src_trans_nb[0] = sizeof(uint16_t); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; + } + aclTensor * src_trans_tensor = ggml_cann_create_tensor( + src_trans_buffer, ACL_FLOAT16, ggml_type_size(dst->type), src0->ne, src_trans_nb, GGML_MAX_DIMS); + aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type)); + aclnn_index_copy_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, dst->data, dst->ne, dst->nb, src1, + dst->type); + ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor); + break; } - aclTensor* src_trans_tensor = ggml_cann_create_tensor( - src_trans_buffer, ACL_FLOAT16, ggml_type_size(dst->type), - src0->ne, src_trans_nb, GGML_MAX_DIMS); - aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type)); - aclnn_index_copy_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb, - dst->data, dst->ne, dst->nb, - src1, dst->type); - ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor); - break; - } default: GGML_ABORT("Unsupported tensor type for GGML_OP_SET_ROWS"); break; @@ -1910,12 +1853,13 @@ void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param repeats The number of times each element will be repeated. * @param output_size The size of the output tensor. */ -static void aclnn_repeat_interleave(ggml_backend_cann_context& ctx, - aclTensor* acl_src, aclTensor* acl_dst, - int64_t dim, int64_t repeats, - int64_t output_size) { - GGML_CANN_CALL_ACLNN_OP(ctx, RepeatInterleaveIntWithDim, acl_src, repeats, dim, - output_size, acl_dst); +static void aclnn_repeat_interleave(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + int64_t dim, + int64_t repeats, + int64_t output_size) { + GGML_CANN_CALL_ACLNN_OP(ctx, RepeatInterleaveIntWithDim, acl_src, repeats, dim, output_size, acl_dst); } /** @@ -1930,10 +1874,9 @@ static void aclnn_repeat_interleave(ggml_backend_cann_context& ctx, * @param dst The destination tensor where the result of the matrix * multiplication will be stored. */ -static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, - ggml_tensor* dst) { - ggml_tensor* weight = dst->src[0]; // weight - ggml_tensor* input = dst->src[1]; // input +static void ggml_cann_mat_mul_fp(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * weight = dst->src[0]; // weight + ggml_tensor * input = dst->src[1]; // input // when weight ne2 or ne3 is 1, aclnnMatmulGetWorkspaceSize will auto // broadcast, when weight ne2 or ne3 is not 1, weight need repeat. @@ -1948,27 +1891,21 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, } } - aclTensor* acl_input_tensor = - ggml_cann_create_tensor(input, bcast_input_ne, bcast_input_nb, n_dims); - int64_t transpose_ne[] = {bcast_weight_ne[1], bcast_weight_ne[0], - bcast_weight_ne[2], bcast_weight_ne[3], - bcast_weight_ne[4], bcast_weight_ne[5]}; - size_t transpose_nb[] = {bcast_weight_nb[1], bcast_weight_nb[0], - bcast_weight_nb[2], bcast_weight_nb[3], - bcast_weight_nb[4], bcast_weight_nb[5]}; - aclTensor* acl_weight_tensor; + aclTensor * acl_input_tensor = ggml_cann_create_tensor(input, bcast_input_ne, bcast_input_nb, n_dims); + int64_t transpose_ne[] = { bcast_weight_ne[1], bcast_weight_ne[0], bcast_weight_ne[2], + bcast_weight_ne[3], bcast_weight_ne[4], bcast_weight_ne[5] }; + size_t transpose_nb[] = { bcast_weight_nb[1], bcast_weight_nb[0], bcast_weight_nb[2], + bcast_weight_nb[3], bcast_weight_nb[4], bcast_weight_nb[5] }; + aclTensor * acl_weight_tensor; // Only check env once. static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on")); if (weight_to_nz && is_matmul_weight(weight)) { - acl_weight_tensor = - ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_FRACTAL_NZ); + acl_weight_tensor = ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_FRACTAL_NZ); } else { - acl_weight_tensor = - ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_ND); + acl_weight_tensor = ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims, ACL_FORMAT_ND); } - aclTensor* acl_dst = - ggml_cann_create_tensor(dst, bcast_dst_ne, bcast_dst_nb, n_dims); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, bcast_dst_ne, bcast_dst_nb, n_dims); switch (n_dims) { case 2: @@ -2000,11 +1937,9 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, * @param dst The destination tensor where the result of the matrix * multiplication will be stored. */ -static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, - ggml_tensor* dst, - const enum ggml_type type) { - ggml_tensor* src0 = dst->src[0]; // weight - ggml_tensor* src1 = dst->src[1]; // input +static void ggml_cann_mul_mat_quant(ggml_backend_cann_context & ctx, ggml_tensor * dst, const enum ggml_type type) { + ggml_tensor * src0 = dst->src[0]; // weight + ggml_tensor * src1 = dst->src[1]; // input // The shape of the weight is NCHW. // Matrix multiplication uses HW dims. @@ -2018,56 +1953,52 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, } else { GGML_ABORT("Only support Q4_0 and Q8_0 MUL_MAT"); } - float weight_nb[] = {src0->ne[0] * weight_elem_size, weight_elem_size}; + float weight_nb[] = { src0->ne[0] * weight_elem_size, weight_elem_size }; size_t weight_stride = src0->ne[1] * src0->ne[0] * weight_elem_size; - size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3]; + size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3]; // scale stored at the end of weight. Also need transpose. size_t scale_elem_size = sizeof(uint16_t); - size_t scale_nb[] = {src0->ne[0] / QK8_0 * scale_elem_size, - scale_elem_size}; - size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size; - char* scale_offset = (char*)src0->data + weight_size; + size_t scale_nb[] = { src0->ne[0] / QK8_0 * scale_elem_size, scale_elem_size }; + size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size; + char * scale_offset = (char *) src0->data + weight_size; // input - size_t input_elem_size = sizeof(uint16_t); - int64_t input_ne[] = {src1->ne[0], src1->ne[1]}; - size_t input_nb[] = {input_elem_size, input_ne[0] * input_elem_size}; - size_t input_stride = input_ne[0] * input_ne[1] * input_elem_size; + size_t input_elem_size = sizeof(uint16_t); + int64_t input_ne[] = { src1->ne[0], src1->ne[1] }; + size_t input_nb[] = { input_elem_size, input_ne[0] * input_elem_size }; + size_t input_stride = input_ne[0] * input_ne[1] * input_elem_size; ggml_cann_pool_alloc input_alloctor(ctx.pool()); - void* input_buffer = src1->data; + void * input_buffer = src1->data; // case in if (src1->type != GGML_TYPE_F16) { - aclTensor* acl_src1_tensor = ggml_cann_create_tensor(src1); - input_buffer = - input_alloctor.alloc(ggml_nelements(src1) * input_elem_size); + aclTensor * acl_src1_tensor = ggml_cann_create_tensor(src1); + input_buffer = input_alloctor.alloc(ggml_nelements(src1) * input_elem_size); - int64_t* input_cast_ne = src1->ne; - size_t input_cast_nb[GGML_MAX_DIMS]; + int64_t * input_cast_ne = src1->ne; + size_t input_cast_nb[GGML_MAX_DIMS]; input_cast_nb[0] = sizeof(uint16_t); for (int i = 1; i < GGML_MAX_DIMS; i++) { input_cast_nb[i] = input_cast_nb[i - 1] * input_cast_ne[i - 1]; } - aclTensor* acl_input_tensor = ggml_cann_create_tensor( - input_buffer, ACL_FLOAT16, input_elem_size, input_cast_ne, - input_cast_nb, GGML_MAX_DIMS); + aclTensor * acl_input_tensor = ggml_cann_create_tensor(input_buffer, ACL_FLOAT16, input_elem_size, + input_cast_ne, input_cast_nb, GGML_MAX_DIMS); aclnn_cast(ctx, acl_src1_tensor, acl_input_tensor, ACL_FLOAT16); ggml_cann_release_resources(ctx, acl_input_tensor, acl_src1_tensor); } // output - size_t output_elem_size = sizeof(uint16_t); - size_t output_nb[] = {output_elem_size, dst->ne[0] * output_elem_size}; + size_t output_elem_size = sizeof(uint16_t); + size_t output_nb[] = { output_elem_size, dst->ne[0] * output_elem_size }; ggml_cann_pool_alloc output_allocator(ctx.pool()); - void* output_buffer = - output_allocator.alloc(ggml_nelements(dst) * output_elem_size); - size_t output_stride = dst->ne[0] * dst->ne[1] * output_elem_size; + void * output_buffer = output_allocator.alloc(ggml_nelements(dst) * output_elem_size); + size_t output_stride = dst->ne[0] * dst->ne[1] * output_elem_size; // aclnn - int64_t max_elem_size = 65535; - int64_t split_size = (src0->ne[1] / max_elem_size) + 1; + int64_t max_elem_size = 65535; + int64_t split_size = (src0->ne[1] / max_elem_size) + 1; ggml_cann_pool_alloc workspace_allocator(ctx.pool()); for (int64_t n1 = 0; n1 < src1->ne[3]; n1++) { for (int64_t c1 = 0; c1 < src1->ne[2]; c1++) { @@ -2077,71 +2008,57 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, int64_t batch1 = (n1 * src1->ne[2]) + c1; int64_t batch0 = (n0 * src0->ne[2]) + c0; - aclTensor* acl_input_tensor = ggml_cann_create_tensor( - (char*)input_buffer + batch1 * input_stride, ACL_FLOAT16, - input_elem_size, input_ne, input_nb, 2); + aclTensor * acl_input_tensor = ggml_cann_create_tensor((char *) input_buffer + batch1 * input_stride, + ACL_FLOAT16, input_elem_size, input_ne, input_nb, 2); // first split int64_t weight_ne_offset = 0; - int64_t weight_ne[2] = { - max_elem_size > src0->ne[1] ? src0->ne[1] : max_elem_size, - src0->ne[0]}; - int64_t scale_ne_offset = 0; - int64_t scale_ne[2] = {weight_ne[0], weight_ne[1] / QK8_0}; + int64_t weight_ne[2] = { max_elem_size > src0->ne[1] ? src0->ne[1] : max_elem_size, src0->ne[0] }; + int64_t scale_ne_offset = 0; + int64_t scale_ne[2] = { weight_ne[0], weight_ne[1] / QK8_0 }; int64_t output_ne_offset = 0; - int64_t output_ne[2] = {weight_ne[0], dst->ne[1]}; - - aclTensor* acl_weight_tensor = ggml_cann_create_tensor( - (char*)src0->data + batch0 * weight_stride, - ggml_cann_type_mapping(type), weight_elem_size, weight_ne, - weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset); - aclTensor* acl_scale_tensor = ggml_cann_create_tensor( - scale_offset + batch0 * scale_stride, ACL_FLOAT16, - scale_elem_size, scale_ne, scale_nb, 2, ACL_FORMAT_ND, - scale_ne_offset); - aclTensor* acl_output_tensor = ggml_cann_create_tensor( - (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16, - output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, - output_ne_offset); + int64_t output_ne[2] = { weight_ne[0], dst->ne[1] }; + + aclTensor * acl_weight_tensor = + ggml_cann_create_tensor((char *) src0->data + batch0 * weight_stride, ggml_cann_type_mapping(type), + weight_elem_size, weight_ne, weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset); + aclTensor * acl_scale_tensor = + ggml_cann_create_tensor(scale_offset + batch0 * scale_stride, ACL_FLOAT16, scale_elem_size, scale_ne, + scale_nb, 2, ACL_FORMAT_ND, scale_ne_offset); + aclTensor * acl_output_tensor = + ggml_cann_create_tensor((char *) output_buffer + batch1 * output_stride, ACL_FLOAT16, output_elem_size, + output_ne, output_nb, 2, ACL_FORMAT_ND, output_ne_offset); int64_t antiquantGroupSize = 0; if (src0->ne[0] > QK8_0) { antiquantGroupSize = QK8_0; } - GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, acl_input_tensor, - acl_weight_tensor, acl_scale_tensor, nullptr, - nullptr, nullptr, nullptr, antiquantGroupSize, - acl_output_tensor); + GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, acl_input_tensor, acl_weight_tensor, + acl_scale_tensor, nullptr, nullptr, nullptr, nullptr, antiquantGroupSize, + acl_output_tensor); ggml_cann_release_resources(ctx, acl_weight_tensor, acl_scale_tensor, acl_output_tensor); // other splits for (int64_t split = 1; split < split_size; split++) { - weight_ne_offset += - weight_elem_size * weight_ne[0] * weight_ne[1]; - weight_ne[0] = max_elem_size * (split + 1) > src0->ne[1] - ? src0->ne[1] - (max_elem_size * split) - : max_elem_size; + weight_ne_offset += weight_elem_size * weight_ne[0] * weight_ne[1]; + weight_ne[0] = + max_elem_size * (split + 1) > src0->ne[1] ? src0->ne[1] - (max_elem_size * split) : max_elem_size; scale_ne_offset += scale_elem_size * scale_ne[0] * scale_ne[1]; scale_ne[0] = weight_ne[0]; - output_ne_offset += - output_elem_size * output_ne[0] * output_ne[1]; + output_ne_offset += output_elem_size * output_ne[0] * output_ne[1]; output_ne[0] = weight_ne[0]; - acl_weight_tensor = ggml_cann_create_tensor( - (char*)src0->data + batch0 * weight_stride, - ggml_cann_type_mapping(type), weight_elem_size, weight_ne, - weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset); - acl_scale_tensor = ggml_cann_create_tensor( - scale_offset + batch0 * scale_stride, ACL_FLOAT16, - scale_elem_size, scale_ne, scale_nb, 2, ACL_FORMAT_ND, - scale_ne_offset); - acl_output_tensor = ggml_cann_create_tensor( - (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16, - output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, - output_ne_offset); - GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, acl_input_tensor, - acl_weight_tensor, acl_scale_tensor, nullptr, - nullptr, nullptr, nullptr, antiquantGroupSize, - acl_output_tensor); + acl_weight_tensor = + ggml_cann_create_tensor((char *) src0->data + batch0 * weight_stride, ggml_cann_type_mapping(type), + weight_elem_size, weight_ne, weight_nb, 2, ACL_FORMAT_ND, weight_ne_offset); + acl_scale_tensor = + ggml_cann_create_tensor(scale_offset + batch0 * scale_stride, ACL_FLOAT16, scale_elem_size, + scale_ne, scale_nb, 2, ACL_FORMAT_ND, scale_ne_offset); + acl_output_tensor = + ggml_cann_create_tensor((char *) output_buffer + batch1 * output_stride, ACL_FLOAT16, + output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, output_ne_offset); + GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, acl_input_tensor, acl_weight_tensor, + acl_scale_tensor, nullptr, nullptr, nullptr, nullptr, antiquantGroupSize, + acl_output_tensor); ggml_cann_release_resources(ctx, acl_weight_tensor, acl_scale_tensor, acl_output_tensor); } @@ -2151,24 +2068,23 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, // cast out if (dst->type != GGML_TYPE_F16) { - int64_t* output_cast_ne = dst->ne; - size_t output_cast_nb[GGML_MAX_DIMS]; + int64_t * output_cast_ne = dst->ne; + size_t output_cast_nb[GGML_MAX_DIMS]; output_cast_nb[0] = sizeof(uint16_t); for (int i = 1; i < GGML_MAX_DIMS; i++) { output_cast_nb[i] = output_cast_nb[i - 1] * output_cast_ne[i - 1]; } - aclTensor* acl_output_tensor = ggml_cann_create_tensor( - output_buffer, ACL_FLOAT16, output_elem_size, output_cast_ne, - output_cast_nb, GGML_MAX_DIMS); - aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst); + aclTensor * acl_output_tensor = ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, output_elem_size, + output_cast_ne, output_cast_nb, GGML_MAX_DIMS); + aclTensor * acl_dst_tensor = ggml_cann_create_tensor(dst); aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type)); ggml_cann_release_resources(ctx, acl_output_tensor, acl_dst_tensor); } } -void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +void ggml_cann_mul_mat(ggml_backend_cann_context & ctx, ggml_tensor * dst) { const enum ggml_type type = dst->src[0]->type; switch (type) { case GGML_TYPE_F32: @@ -2201,10 +2117,13 @@ void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param dims An array specifying the dimensions along which elements are * shifted. */ -static void aclnn_roll(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst, int64_t* shifts, int64_t* dims) { - aclIntArray* acl_shifts = aclCreateIntArray(shifts, 1); - aclIntArray* acl_dims = aclCreateIntArray(dims, 1); +static void aclnn_roll(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_dst, + int64_t * shifts, + int64_t * dims) { + aclIntArray * acl_shifts = aclCreateIntArray(shifts, 1); + aclIntArray * acl_dims = aclCreateIntArray(dims, 1); GGML_CANN_CALL_ACLNN_OP(ctx, Roll, acl_src, acl_shifts, acl_dims, acl_dst); ggml_cann_release_resources(ctx, acl_shifts, acl_dims); } @@ -2222,12 +2141,14 @@ static void aclnn_roll(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param index_num The number of positions specified in the index array. * @param value The scalar value used to fill the specified positions. */ -static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx, - aclTensor* acl_src, int64_t dim, - int64_t* index, int64_t index_num, - float value) { - aclIntArray* acl_index = aclCreateIntArray(index, index_num); - aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); +static void aclnn_index_fill_tensor(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + int64_t dim, + int64_t * index, + int64_t index_num, + float value) { + aclIntArray * acl_index = aclCreateIntArray(index, index_num); + aclScalar * acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexFillTensor, acl_src, dim, acl_index, acl_value); ggml_cann_release_resources(ctx, acl_index, acl_value); } @@ -2262,85 +2183,82 @@ static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx, * @param is_neox Whether to use Neox-style repeat strategy * (dim expansion vs repeat_interleave). */ -static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, - float* corr_dims, float ext_factor, - float theta_scale, float freq_scale, - float attn_factor, bool is_neox) { - ggml_tensor* src0 = dst->src[0]; // input - ggml_tensor* src1 = dst->src[1]; // position - ggml_tensor* src2 = dst->src[2]; // freq_factors - - if(src2 == nullptr && ctx.rope_cache.cached - && ctx.rope_cache.ext_factor == ext_factor - && ctx.rope_cache.theta_scale == theta_scale - && ctx.rope_cache.freq_scale == freq_scale - && ctx.rope_cache.attn_factor == attn_factor - && ctx.rope_cache.is_neox == is_neox) { +static void aclnn_cache_init(ggml_backend_cann_context & ctx, + ggml_tensor * dst, + float * corr_dims, + float ext_factor, + float theta_scale, + float freq_scale, + float attn_factor, + bool is_neox) { + ggml_tensor * src0 = dst->src[0]; // input + ggml_tensor * src1 = dst->src[1]; // position + ggml_tensor * src2 = dst->src[2]; // freq_factors + + if (src2 == nullptr && ctx.rope_cache.cached && ctx.rope_cache.ext_factor == ext_factor && + ctx.rope_cache.theta_scale == theta_scale && ctx.rope_cache.freq_scale == freq_scale && + ctx.rope_cache.attn_factor == attn_factor && ctx.rope_cache.is_neox == is_neox) { // use cache. return; } int64_t theta_scale_length = src0->ne[0] / 2; - int64_t theta_scale_ne[] = {theta_scale_length, 1, 1, 1}; - size_t theta_scale_nb[] = {sizeof(float), sizeof(float), sizeof(float), - theta_scale_length * sizeof(float)}; + int64_t theta_scale_ne[] = { theta_scale_length, 1, 1, 1 }; + size_t theta_scale_nb[] = { sizeof(float), sizeof(float), sizeof(float), theta_scale_length * sizeof(float) }; GGML_ASSERT(src1->type == GGML_TYPE_I32); int64_t position_length = src1->ne[0]; - int64_t position_ne[] = {1, 1, position_length, 1}; - size_t position_nb[] = {sizeof(int32_t), sizeof(int32_t), sizeof(int32_t), - sizeof(int32_t) * position_length}; + int64_t position_ne[] = { 1, 1, position_length, 1 }; + size_t position_nb[] = { sizeof(int32_t), sizeof(int32_t), sizeof(int32_t), sizeof(int32_t) * position_length }; - int64_t theta_ne[] = {theta_scale_length, 1, position_length, 1}; - size_t theta_nb[GGML_MAX_DIMS]; + int64_t theta_ne[] = { theta_scale_length, 1, position_length, 1 }; + size_t theta_nb[GGML_MAX_DIMS]; theta_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1]; } // theta_scale arange, [0,1,...,ne00/2 - 1] - aclTensor* acl_theta_scale_tensor = nullptr; + aclTensor * acl_theta_scale_tensor = nullptr; // cache theta scale if (ctx.rope_cache.theta_scale_length != theta_scale_length || // theta_scale and freq_scale should not change during the current token inference process, // so we can directly use == here instead of comparing the absolute difference. - ctx.rope_cache.theta_scale != theta_scale || - ctx.rope_cache.freq_scale != freq_scale) { - + ctx.rope_cache.theta_scale != theta_scale || ctx.rope_cache.freq_scale != freq_scale) { ctx.rope_cache.theta_scale_length = theta_scale_length; if (ctx.rope_cache.theta_scale_cache != nullptr) { ACL_CHECK(aclrtFree(ctx.rope_cache.theta_scale_cache)); } - ACL_CHECK(aclrtMalloc(&ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); + ACL_CHECK(aclrtMalloc(&ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float), + ACL_MEM_MALLOC_HUGE_FIRST)); - acl_theta_scale_tensor = - ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float), - theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); + acl_theta_scale_tensor = ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float), + theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); - float start = 0; - float step = 1; - float stop = theta_scale_length; + float start = 0; + float step = 1; + float stop = theta_scale_length; float n_elements = theta_scale_length; aclnn_arange(ctx, acl_theta_scale_tensor, start, stop, step, n_elements); ggml_cann_pool_alloc yarn_ramp_allocator(ctx.pool()); - aclTensor* acl_yarn_ramp_tensor = nullptr; + aclTensor * acl_yarn_ramp_tensor = nullptr; if (ext_factor != 0) { // -rope_yarn_ramp // const float y = (i0 / 2 - low) / MAX(0.001f, high - low); // return MIN(1, MAX(0, y)) - 1; yarn_ramp_allocator.alloc(theta_scale_length * sizeof(float)); - void* yarn_ramp_buffer = yarn_ramp_allocator.get(); - acl_yarn_ramp_tensor = ggml_cann_create_tensor(yarn_ramp_buffer, ACL_FLOAT, sizeof(float), - theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); - float zero_value = 0, one_value = 1; - float denom_safe_value = MAX(0.001f, corr_dims[1] - corr_dims[0]); - aclScalar* low = aclCreateScalar(&corr_dims[0], aclDataType::ACL_FLOAT); - aclScalar* zero = aclCreateScalar(&zero_value, aclDataType::ACL_FLOAT); - aclScalar* one = aclCreateScalar(&one_value, aclDataType::ACL_FLOAT); - aclScalar* denom_safe = aclCreateScalar(&denom_safe_value, aclDataType::ACL_FLOAT); - aclScalar* ext_factor_sc = aclCreateScalar(&ext_factor, aclDataType::ACL_FLOAT); + void * yarn_ramp_buffer = yarn_ramp_allocator.get(); + acl_yarn_ramp_tensor = ggml_cann_create_tensor(yarn_ramp_buffer, ACL_FLOAT, sizeof(float), theta_scale_ne, + theta_scale_nb, GGML_MAX_DIMS); + float zero_value = 0, one_value = 1; + float denom_safe_value = MAX(0.001f, corr_dims[1] - corr_dims[0]); + aclScalar * low = aclCreateScalar(&corr_dims[0], aclDataType::ACL_FLOAT); + aclScalar * zero = aclCreateScalar(&zero_value, aclDataType::ACL_FLOAT); + aclScalar * one = aclCreateScalar(&one_value, aclDataType::ACL_FLOAT); + aclScalar * denom_safe = aclCreateScalar(&denom_safe_value, aclDataType::ACL_FLOAT); + aclScalar * ext_factor_sc = aclCreateScalar(&ext_factor, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, Subs, acl_theta_scale_tensor, low, one, acl_yarn_ramp_tensor); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceDivs, acl_yarn_ramp_tensor, denom_safe); @@ -2357,9 +2275,9 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, // // we cache (freq_scale - freq_scale * ramp_mix + ramp_mix), Considering that the rope_yarn_ramp here is the inverse // cache freq_scale + (freq_scale - 1) * ramp_mix - float freq_scale_1 = freq_scale - 1; - aclScalar* freq_scale_sc = aclCreateScalar(&freq_scale, aclDataType::ACL_FLOAT); - aclScalar* freq_scale_1_sc = aclCreateScalar(&freq_scale_1, aclDataType::ACL_FLOAT); + float freq_scale_1 = freq_scale - 1; + aclScalar * freq_scale_sc = aclCreateScalar(&freq_scale, aclDataType::ACL_FLOAT); + aclScalar * freq_scale_1_sc = aclCreateScalar(&freq_scale_1, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_yarn_ramp_tensor, freq_scale_1_sc); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdds, acl_yarn_ramp_tensor, freq_scale_sc, one); @@ -2367,9 +2285,8 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, } // power - aclScalar* acl_theta_scale = aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT); - GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, acl_theta_scale, acl_theta_scale_tensor, - acl_theta_scale_tensor); + aclScalar * acl_theta_scale = aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT); + GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, acl_theta_scale, acl_theta_scale_tensor, acl_theta_scale_tensor); if (ext_factor != 0) { aclnn_mul(ctx, acl_theta_scale_tensor, acl_yarn_ramp_tensor); @@ -2380,22 +2297,20 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_cann_release_resources(ctx, acl_yarn_ramp_tensor, acl_theta_scale); } else { // use cache - acl_theta_scale_tensor = - ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float), - theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); + acl_theta_scale_tensor = ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float), + theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); } ggml_cann_pool_alloc freq_fac_res_allocator(ctx.pool()); // freq_factors if (src2) { freq_fac_res_allocator.alloc(theta_scale_length * sizeof(float)); - void* freq_fac_res_ptr = freq_fac_res_allocator.get(); - aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor( - src2->data, ggml_cann_type_mapping(src2->type), - ggml_type_size(src2->type), theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); - aclTensor* acl_freq_fac_res_tensor = ggml_cann_create_tensor( - freq_fac_res_ptr, ACL_FLOAT, sizeof(float), - theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); + void * freq_fac_res_ptr = freq_fac_res_allocator.get(); + aclTensor * acl_freq_factors_tensor = + ggml_cann_create_tensor(src2->data, ggml_cann_type_mapping(src2->type), ggml_type_size(src2->type), + theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); + aclTensor * acl_freq_fac_res_tensor = ggml_cann_create_tensor(freq_fac_res_ptr, ACL_FLOAT, sizeof(float), + theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS); aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor, acl_freq_fac_res_tensor); std::swap(acl_theta_scale_tensor, acl_freq_fac_res_tensor); ggml_cann_release_resources(ctx, acl_freq_factors_tensor, acl_freq_fac_res_tensor); @@ -2411,42 +2326,37 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ACL_CHECK(aclrtFree(ctx.rope_cache.cos_cache)); } int64_t repeat_theta_length = theta_scale_length * position_length * 2; - ACL_CHECK(aclrtMalloc(&ctx.rope_cache.sin_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); - ACL_CHECK(aclrtMalloc(&ctx.rope_cache.cos_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); + ACL_CHECK( + aclrtMalloc(&ctx.rope_cache.sin_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); + ACL_CHECK( + aclrtMalloc(&ctx.rope_cache.cos_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); } // position - aclTensor* acl_position_tensor = ggml_cann_create_tensor( - src1->data, ggml_cann_type_mapping(src1->type), - ggml_type_size(src1->type), position_ne, position_nb, GGML_MAX_DIMS); + aclTensor * acl_position_tensor = + ggml_cann_create_tensor(src1->data, ggml_cann_type_mapping(src1->type), ggml_type_size(src1->type), position_ne, + position_nb, GGML_MAX_DIMS); // power * position - int64_t theta_length = theta_scale_length * position_length; - ggml_cann_pool_alloc theta_allocator(ctx.pool(), - theta_length * sizeof(float)); - void* theta_buffer = theta_allocator.get(); + int64_t theta_length = theta_scale_length * position_length; + ggml_cann_pool_alloc theta_allocator(ctx.pool(), theta_length * sizeof(float)); + void * theta_buffer = theta_allocator.get(); - aclTensor* acl_theta_tensor = - ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float), - theta_ne, theta_nb, GGML_MAX_DIMS); - aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor, - acl_theta_tensor); + aclTensor * acl_theta_tensor = + ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb, GGML_MAX_DIMS); + aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor, acl_theta_tensor); // sin/cos - ggml_cann_pool_alloc sin_allocator(ctx.pool(), - theta_length * sizeof(float)); - void* sin_buffer = sin_allocator.get(); - aclTensor* acl_sin_tensor = ggml_cann_create_tensor( - sin_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb, - GGML_MAX_DIMS, ACL_FORMAT_ND); + ggml_cann_pool_alloc sin_allocator(ctx.pool(), theta_length * sizeof(float)); + void * sin_buffer = sin_allocator.get(); + aclTensor * acl_sin_tensor = + ggml_cann_create_tensor(sin_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor); - ggml_cann_pool_alloc cos_allocator(ctx.pool(), - theta_length * sizeof(float)); - void* cos_buffer = cos_allocator.get(); - aclTensor* acl_cos_tensor = ggml_cann_create_tensor( - cos_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb, - GGML_MAX_DIMS, ACL_FORMAT_ND); + ggml_cann_pool_alloc cos_allocator(ctx.pool(), theta_length * sizeof(float)); + void * cos_buffer = cos_allocator.get(); + aclTensor * acl_cos_tensor = + ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor); if (ext_factor != 0) { @@ -2459,81 +2369,79 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, aclnn_muls(ctx, acl_cos_tensor, attn_factor, nullptr, true); } - int64_t sin_reshape_ne[4] = {src0->ne[0], 1, src0->ne[2], 1}; - size_t sin_reshape_nb[GGML_MAX_DIMS]; + int64_t sin_reshape_ne[4] = { src0->ne[0], 1, src0->ne[2], 1 }; + size_t sin_reshape_nb[GGML_MAX_DIMS]; sin_reshape_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1]; } - aclTensor* acl_sin_repeat_tensor = - ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float), - sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); - aclTensor* acl_cos_repeat_tensor = - ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float), - sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); + aclTensor * acl_sin_repeat_tensor = ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float), + sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); + aclTensor * acl_cos_repeat_tensor = ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float), + sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); // repeat if (is_neox) { - int64_t repeatsArray[] = {1, 1, 1, 2}; + int64_t repeatsArray[] = { 1, 1, 1, 2 }; aclnn_repeat(ctx, acl_sin_tensor, acl_sin_repeat_tensor, repeatsArray); aclnn_repeat(ctx, acl_cos_tensor, acl_cos_repeat_tensor, repeatsArray); } else { int64_t num_repeats = 2; - int64_t dim = 3; + int64_t dim = 3; int64_t output_size = theta_scale_length * num_repeats; - aclnn_repeat_interleave(ctx, acl_sin_tensor, acl_sin_repeat_tensor, dim, - num_repeats, output_size); - aclnn_repeat_interleave(ctx, acl_cos_tensor, acl_cos_repeat_tensor, dim, - num_repeats, output_size); + aclnn_repeat_interleave(ctx, acl_sin_tensor, acl_sin_repeat_tensor, dim, num_repeats, output_size); + aclnn_repeat_interleave(ctx, acl_cos_tensor, acl_cos_repeat_tensor, dim, num_repeats, output_size); } // Other layers use cache except first layer. - ctx.rope_cache.cached = true; - ctx.rope_cache.ext_factor = ext_factor; + ctx.rope_cache.cached = true; + ctx.rope_cache.ext_factor = ext_factor; ctx.rope_cache.theta_scale = theta_scale; - ctx.rope_cache.freq_scale = freq_scale; + ctx.rope_cache.freq_scale = freq_scale; ctx.rope_cache.attn_factor = attn_factor; - ctx.rope_cache.is_neox = is_neox; + ctx.rope_cache.is_neox = is_neox; - ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor, - acl_theta_tensor, acl_sin_tensor, acl_sin_repeat_tensor, acl_cos_tensor, - acl_cos_repeat_tensor); + ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor, acl_theta_tensor, acl_sin_tensor, + acl_sin_repeat_tensor, acl_cos_tensor, acl_cos_repeat_tensor); } #ifdef __cplusplus extern "C" { #endif -aclnnStatus aclnnRotaryPositionEmbeddingGetWorkspaceSize( - const aclTensor* x, const aclTensor* cos, const aclTensor* sin, - int64_t mode, const aclTensor* yOut, uint64_t* workspaceSize, - aclOpExecutor** executor); -aclnnStatus aclnnRotaryPositionEmbedding(void* workspace, - uint64_t workspaceSize, - aclOpExecutor* executor, - aclrtStream stream); +aclnnStatus aclnnRotaryPositionEmbeddingGetWorkspaceSize(const aclTensor * x, + const aclTensor * cos, + const aclTensor * sin, + int64_t mode, + const aclTensor * yOut, + uint64_t * workspaceSize, + aclOpExecutor ** executor); +aclnnStatus aclnnRotaryPositionEmbedding(void * workspace, + uint64_t workspaceSize, + aclOpExecutor * executor, + aclrtStream stream); #ifdef __cplusplus } #endif -void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; // input +void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; // input // param - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; // const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t*)dst->op_params)[1]; - const int mode = ((int32_t*)dst->op_params)[2]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; // const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_ctx_orig = ((int32_t*)dst->op_params)[4]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; GGML_TENSOR_UNARY_OP_LOCALS - memcpy(&freq_base, (int32_t*)dst->op_params + 5, sizeof(float)); - memcpy(&freq_scale, (int32_t*)dst->op_params + 6, sizeof(float)); - memcpy(&ext_factor, (int32_t*)dst->op_params + 7, sizeof(float)); - memcpy(&attn_factor, (int32_t*)dst->op_params + 8, sizeof(float)); - memcpy(&beta_fast, (int32_t*)dst->op_params + 9, sizeof(float)); - memcpy(&beta_slow, (int32_t*)dst->op_params + 10, sizeof(float)); + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); // TODO: n_dims <= ne0 GGML_ASSERT(n_dims == ne0); @@ -2542,123 +2450,111 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { const float theta_scale = powf(freq_base, -2.0f / n_dims); float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, - beta_slow, corr_dims); + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; // init ctx.rope_cos/rope_sin cache - aclnn_cache_init(ctx, dst, corr_dims, ext_factor, - theta_scale, freq_scale, attn_factor, is_neox); + aclnn_cache_init(ctx, dst, corr_dims, ext_factor, theta_scale, freq_scale, attn_factor, is_neox); - int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1}; - size_t sin_reshape_nb[GGML_MAX_DIMS]; + int64_t sin_reshape_ne[4] = { ne00, 1, ne02, 1 }; + size_t sin_reshape_nb[GGML_MAX_DIMS]; sin_reshape_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1]; } - aclTensor* acl_sin_reshape_tensor = - ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float), - sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); - aclTensor* acl_cos_reshape_tensor = - ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float), - sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); + aclTensor * acl_sin_reshape_tensor = ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float), + sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); + aclTensor * acl_cos_reshape_tensor = ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float), + sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); #ifdef ASCEND_310P // Special ROPE operation for 310P // roll input - void* input_roll_buffer; - aclTensor* acl_minus_one_tensor; - void* minus_one_scale_buffer = nullptr; + void * input_roll_buffer; + aclTensor * acl_minus_one_tensor; + void * minus_one_scale_buffer = nullptr; ggml_cann_pool_alloc roll_allocator(ctx.pool(), ggml_nbytes(src0)); - ggml_cann_pool_alloc minus_one_scale_allocator( - ctx.pool(), sizeof(float) * src0->ne[0]); + ggml_cann_pool_alloc minus_one_scale_allocator(ctx.pool(), sizeof(float) * src0->ne[0]); if (!is_neox) { // roll input: [q0,q1,q2,q3,...] -> [q1,q0,q3,q2,...] - input_roll_buffer = roll_allocator.get(); - int64_t input_roll_ne[4] = {2, src0->ne[1] * (src0->ne[0] / 2), - src0->ne[2], src0->ne[3]}; - size_t input_roll_nb[GGML_MAX_DIMS]; + input_roll_buffer = roll_allocator.get(); + int64_t input_roll_ne[4] = { 2, src0->ne[1] * (src0->ne[0] / 2), src0->ne[2], src0->ne[3] }; + size_t input_roll_nb[GGML_MAX_DIMS]; input_roll_nb[0] = ggml_type_size(src0->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { input_roll_nb[i] = input_roll_nb[i - 1] * input_roll_ne[i - 1]; } - aclTensor* acl_input_roll_tensor = ggml_cann_create_tensor( - input_roll_buffer, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), input_roll_ne, input_roll_nb, - GGML_MAX_DIMS); - aclTensor* acl_input_tensor = ggml_cann_create_tensor( - src0->data, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), input_roll_ne, input_roll_nb, - GGML_MAX_DIMS); - - int64_t shifts[] = {1}; - int64_t dims[] = {3}; + aclTensor * acl_input_roll_tensor = + ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), + input_roll_ne, input_roll_nb, GGML_MAX_DIMS); + aclTensor * acl_input_tensor = + ggml_cann_create_tensor(src0->data, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), + input_roll_ne, input_roll_nb, GGML_MAX_DIMS); + + int64_t shifts[] = { 1 }; + int64_t dims[] = { 3 }; aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims); ggml_cann_release_resources(ctx, acl_input_roll_tensor, acl_input_tensor); // init [-1, 1, -1, 1, ...] minus_one_scale_buffer = minus_one_scale_allocator.get(); - int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1}; - size_t minus_one_nb[GGML_MAX_DIMS]; + int64_t minus_one_ne[4] = { src0->ne[0], 1, 1, 1 }; + size_t minus_one_nb[GGML_MAX_DIMS]; minus_one_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1]; } - acl_minus_one_tensor = aclnn_values( - ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0], - minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1); - int64_t dim = 3; - int64_t* index = new int64_t[src0->ne[0]]; + acl_minus_one_tensor = aclnn_values(ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0], minus_one_ne, + GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1); + int64_t dim = 3; + int64_t * index = new int64_t[src0->ne[0]]; for (int i = 0; i < src0->ne[0]; i++) { index[i] = i / 2 * 2; } int64_t index_num = src0->ne[0]; - float value = -1; - aclnn_index_fill_tensor(ctx, acl_minus_one_tensor, dim, index, - index_num, value); + float value = -1; + aclnn_index_fill_tensor(ctx, acl_minus_one_tensor, dim, index, index_num, value); } else { // roll input: [q0,q1,q2,...] -> // [q_half,q_half+1,...,q_end,q0,q1,...q_half-1] input_roll_buffer = roll_allocator.get(); - aclTensor* acl_input_roll_tensor = ggml_cann_create_tensor( - input_roll_buffer, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), src0->ne, src0->nb, GGML_MAX_DIMS); - aclTensor* acl_input_tensor = ggml_cann_create_tensor(src0); + aclTensor * acl_input_roll_tensor = + ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), + src0->ne, src0->nb, GGML_MAX_DIMS); + aclTensor * acl_input_tensor = ggml_cann_create_tensor(src0); - int64_t shifts[] = {src0->ne[0] / 2}; - int64_t dims[] = {3}; + int64_t shifts[] = { src0->ne[0] / 2 }; + int64_t dims[] = { 3 }; aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims); ggml_cann_release_resources(ctx, acl_input_roll_tensor, acl_input_tensor); // init [-1, -1, -1, 1, 1，1，...] - minus_one_scale_buffer = minus_one_scale_allocator.get(); - int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1}; - size_t minus_one_nb[GGML_MAX_DIMS]; + minus_one_scale_buffer = minus_one_scale_allocator.get(); + int64_t minus_one_ne[4] = { src0->ne[0], 1, 1, 1 }; + size_t minus_one_nb[GGML_MAX_DIMS]; minus_one_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1]; } - acl_minus_one_tensor = aclnn_values( - ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0], - minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1); + acl_minus_one_tensor = aclnn_values(ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0], minus_one_ne, + GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1); // -1 * first half - int64_t first_half_ne[4] = {src0->ne[0] / 2, 1, 1, 1}; - size_t first_half_nb[GGML_MAX_DIMS]; + int64_t first_half_ne[4] = { src0->ne[0] / 2, 1, 1, 1 }; + size_t first_half_nb[GGML_MAX_DIMS]; first_half_nb[0] = sizeof(float); for (int i = 1; i < GGML_MAX_DIMS; i++) { first_half_nb[i] = first_half_nb[i - 1] * first_half_ne[i - 1]; } - aclTensor* acl_first_half_tensor = ggml_cann_create_tensor( - minus_one_scale_buffer, ACL_FLOAT, sizeof(float), first_half_ne, - first_half_nb, GGML_MAX_DIMS); - bool inplace = true; - float scale = -1; + aclTensor * acl_first_half_tensor = ggml_cann_create_tensor(minus_one_scale_buffer, ACL_FLOAT, sizeof(float), + first_half_ne, first_half_nb, GGML_MAX_DIMS); + bool inplace = true; + float scale = -1; aclnn_muls(ctx, acl_first_half_tensor, scale, nullptr, inplace); ggml_cann_release_resources(ctx, acl_first_half_tensor); } @@ -2667,30 +2563,27 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { GGML_ASSERT(n_dims == src0->ne[0]); // input * scale - ggml_cann_pool_alloc roll_mul_scale_allocator(ctx.pool(), - ggml_nbytes(src0)); - void* input_roll_mul_scale_buffer = roll_mul_scale_allocator.get(); - size_t input_nb[GGML_MAX_DIMS]; + ggml_cann_pool_alloc roll_mul_scale_allocator(ctx.pool(), ggml_nbytes(src0)); + void * input_roll_mul_scale_buffer = roll_mul_scale_allocator.get(); + size_t input_nb[GGML_MAX_DIMS]; input_nb[0] = ggml_type_size(src0->type); for (int i = 1; i < GGML_MAX_DIMS; i++) { input_nb[i] = input_nb[i - 1] * src0->ne[i - 1]; } - aclTensor* acl_input_roll_mul_scale_tensor = ggml_cann_create_tensor( - input_roll_mul_scale_buffer, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS); - aclTensor* acl_input_roll_reshape_tensor = ggml_cann_create_tensor( - input_roll_buffer, ggml_cann_type_mapping(src0->type), - ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS); + aclTensor * acl_input_roll_mul_scale_tensor = + ggml_cann_create_tensor(input_roll_mul_scale_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS); + aclTensor * acl_input_roll_reshape_tensor = + ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), + src0->ne, input_nb, GGML_MAX_DIMS); - aclnn_mul(ctx, acl_input_roll_reshape_tensor, acl_minus_one_tensor, - acl_input_roll_mul_scale_tensor); + aclnn_mul(ctx, acl_input_roll_reshape_tensor, acl_minus_one_tensor, acl_input_roll_mul_scale_tensor); // output - void* output_fp32_buffer; + void * output_fp32_buffer; if (src0->type == GGML_TYPE_F32) { aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor); - aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, - acl_sin_reshape_tensor); + aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor); aclnn_add(ctx, acl_src, acl_input_roll_mul_scale_tensor, acl_dst); // TODO: ne0 != n_dims in mode2 } else if (src0->type == GGML_TYPE_F16) { @@ -2699,36 +2592,27 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { for (int i = 1; i < GGML_MAX_DIMS; i++) { input_fp32_nb[i] = input_fp32_nb[i - 1] * dst->ne[i - 1]; } - ggml_cann_pool_alloc fp32_allocator1( - ctx.pool(), ggml_nelements(dst) * sizeof(float)); - void* input_fp32_buffer1 = fp32_allocator1.get(); - aclTensor* input_fp32_tensor1 = ggml_cann_create_tensor( - input_fp32_buffer1, ACL_FLOAT, sizeof(float), dst->ne, - input_fp32_nb, GGML_MAX_DIMS); - ggml_cann_pool_alloc fp32_allocator2( - ctx.pool(), ggml_nelements(dst) * sizeof(float)); - void* input_fp32_buffer2 = fp32_allocator2.get(); - aclTensor* input_fp32_tensor2 = ggml_cann_create_tensor( - input_fp32_buffer2, ACL_FLOAT, sizeof(float), dst->ne, - input_fp32_nb, GGML_MAX_DIMS); - - ggml_cann_pool_alloc fp32_allocator( - ctx.pool(), ggml_nelements(dst) * sizeof(float)); - output_fp32_buffer = fp32_allocator.get(); - aclTensor* output_fp32_tensor = ggml_cann_create_tensor( - output_fp32_buffer, ACL_FLOAT, sizeof(float), dst->ne, - input_fp32_nb, GGML_MAX_DIMS); + ggml_cann_pool_alloc fp32_allocator1(ctx.pool(), ggml_nelements(dst) * sizeof(float)); + void * input_fp32_buffer1 = fp32_allocator1.get(); + aclTensor * input_fp32_tensor1 = ggml_cann_create_tensor(input_fp32_buffer1, ACL_FLOAT, sizeof(float), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); + ggml_cann_pool_alloc fp32_allocator2(ctx.pool(), ggml_nelements(dst) * sizeof(float)); + void * input_fp32_buffer2 = fp32_allocator2.get(); + aclTensor * input_fp32_tensor2 = ggml_cann_create_tensor(input_fp32_buffer2, ACL_FLOAT, sizeof(float), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); + + ggml_cann_pool_alloc fp32_allocator(ctx.pool(), ggml_nelements(dst) * sizeof(float)); + output_fp32_buffer = fp32_allocator.get(); + aclTensor * output_fp32_tensor = ggml_cann_create_tensor(output_fp32_buffer, ACL_FLOAT, sizeof(float), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor, input_fp32_tensor1); - aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor, - input_fp32_tensor2); - aclnn_add(ctx, input_fp32_tensor1, input_fp32_tensor2, - output_fp32_tensor); + aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor, input_fp32_tensor2); + aclnn_add(ctx, input_fp32_tensor1, input_fp32_tensor2, output_fp32_tensor); aclnn_cast(ctx, output_fp32_tensor, acl_dst, ACL_FLOAT16); - ggml_cann_release_resources(ctx, input_fp32_tensor1, input_fp32_tensor2, - output_fp32_tensor, acl_sin_reshape_tensor, - acl_minus_one_tensor, acl_input_roll_mul_scale_tensor, - acl_input_roll_reshape_tensor, acl_src); + ggml_cann_release_resources(ctx, input_fp32_tensor1, input_fp32_tensor2, output_fp32_tensor, + acl_sin_reshape_tensor, acl_minus_one_tensor, acl_input_roll_mul_scale_tensor, + acl_input_roll_reshape_tensor, acl_src); } return; #endif @@ -2737,155 +2621,146 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { int64_t acl_mode = mode == 0 ? 1 : mode; switch (src0->type) { - case GGML_TYPE_F32: { - GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src, - acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, acl_dst); - break; - } - case GGML_TYPE_F16: { - ggml_cann_pool_alloc src_trans_allocator( - ctx.pool(), ggml_nelements(src0) * sizeof(float)); - void* src_trans_buffer = src_trans_allocator.get(); - ggml_cann_pool_alloc dst_trans_allocator( - ctx.pool(), ggml_nelements(dst) * sizeof(float)); - void* dst_trans_buffer = dst_trans_allocator.get(); - - size_t src_trans_nb[GGML_MAX_DIMS]; - src_trans_nb[0] = sizeof(float); - for (int i = 1; i < GGML_MAX_DIMS; i++) { - src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; + case GGML_TYPE_F32: + { + GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src, acl_cos_reshape_tensor, + acl_sin_reshape_tensor, acl_mode, acl_dst); + break; } + case GGML_TYPE_F16: + { + ggml_cann_pool_alloc src_trans_allocator(ctx.pool(), ggml_nelements(src0) * sizeof(float)); + void * src_trans_buffer = src_trans_allocator.get(); + ggml_cann_pool_alloc dst_trans_allocator(ctx.pool(), ggml_nelements(dst) * sizeof(float)); + void * dst_trans_buffer = dst_trans_allocator.get(); - aclTensor* acl_src_trans_tensor = ggml_cann_create_tensor( - src_trans_buffer, ACL_FLOAT, sizeof(float), src0->ne, src_trans_nb, - GGML_MAX_DIMS); - aclTensor* acl_dst_trans_tensor = ggml_cann_create_tensor( - dst_trans_buffer, ACL_FLOAT, sizeof(float), dst->ne, src_trans_nb, - GGML_MAX_DIMS); + size_t src_trans_nb[GGML_MAX_DIMS]; + src_trans_nb[0] = sizeof(float); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1]; + } - aclnn_cast(ctx, acl_src, acl_src_trans_tensor, ACL_FLOAT); + aclTensor * acl_src_trans_tensor = ggml_cann_create_tensor(src_trans_buffer, ACL_FLOAT, sizeof(float), + src0->ne, src_trans_nb, GGML_MAX_DIMS); + aclTensor * acl_dst_trans_tensor = ggml_cann_create_tensor(dst_trans_buffer, ACL_FLOAT, sizeof(float), + dst->ne, src_trans_nb, GGML_MAX_DIMS); - GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_trans_tensor, - acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, - acl_dst_trans_tensor); + aclnn_cast(ctx, acl_src, acl_src_trans_tensor, ACL_FLOAT); - aclnn_cast(ctx, acl_dst_trans_tensor, acl_dst, ACL_FLOAT16); + GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_trans_tensor, acl_cos_reshape_tensor, + acl_sin_reshape_tensor, acl_mode, acl_dst_trans_tensor); - ggml_cann_release_resources(ctx, acl_src_trans_tensor, - acl_dst_trans_tensor); - break; - } + aclnn_cast(ctx, acl_dst_trans_tensor, acl_dst, ACL_FLOAT16); + + ggml_cann_release_resources(ctx, acl_src_trans_tensor, acl_dst_trans_tensor); + break; + } default: GGML_ABORT("Unsupported tensor type for GGML_OP_ROPE"); break; } - ggml_cann_release_resources(ctx, acl_cos_reshape_tensor, - acl_sin_reshape_tensor, acl_src, acl_dst); + ggml_cann_release_resources(ctx, acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_src, acl_dst); } - - void ggml_cann_argmax(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_argmax(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3); GGML_CANN_CALL_ACLNN_OP(ctx, ArgMax, acl_src, 3, false, acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst); } -void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_conv_transpose_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; ggml_tensor * src1 = dst->src[1]; // stride - int64_t s0 = ((const int32_t*)(dst->op_params))[0]; + int64_t s0 = ((const int32_t *) (dst->op_params))[0]; - aclTensor* acl_input = ggml_cann_create_tensor(src1, src1->ne, src1->nb, 3, ACL_FORMAT_NCL); - aclTensor* acl_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3, ACL_FORMAT_NCL); - aclTensor* acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3, ACL_FORMAT_NCL); + aclTensor * acl_input = ggml_cann_create_tensor(src1, src1->ne, src1->nb, 3, ACL_FORMAT_NCL); + aclTensor * acl_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3, ACL_FORMAT_NCL); + aclTensor * acl_dst = ggml_cann_create_tensor(dst, dst->ne, dst->nb, 3, ACL_FORMAT_NCL); int64_t strideVal[1]; - strideVal[0] = s0; - aclIntArray *stride = aclCreateIntArray(strideVal, 1); - int64_t paddingVal[] = {0}; - aclIntArray *padding = aclCreateIntArray(paddingVal, 1); - int64_t dilationVal[] = {1}; - aclIntArray *dilation = aclCreateIntArray(dilationVal, 1); - int8_t cubeMathType = 0; + strideVal[0] = s0; + aclIntArray * stride = aclCreateIntArray(strideVal, 1); + int64_t paddingVal[] = { 0 }; + aclIntArray * padding = aclCreateIntArray(paddingVal, 1); + int64_t dilationVal[] = { 1 }; + aclIntArray * dilation = aclCreateIntArray(dilationVal, 1); + int8_t cubeMathType = 0; #ifdef ASCEND_310P cubeMathType = 1; #endif - GGML_CANN_CALL_ACLNN_OP(ctx, Convolution, acl_input, acl_weight, nullptr, stride, - padding, dilation, true, padding, 1, acl_dst, cubeMathType); + GGML_CANN_CALL_ACLNN_OP(ctx, Convolution, acl_input, acl_weight, nullptr, stride, padding, dilation, true, padding, + 1, acl_dst, cubeMathType); ggml_cann_release_resources(ctx, acl_weight, acl_dst, stride, padding, dilation); } -void ggml_cann_elu(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_elu(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; - aclTensor* acl_input = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_input = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - float alphaValue = 1.0f; - aclScalar* alpha = nullptr; - alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + float alphaValue = 1.0f; + aclScalar * alpha = nullptr; + alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); - GGML_CANN_CALL_ACLNN_OP(ctx, Elu, acl_input, alpha, alpha, alpha, - acl_dst); + GGML_CANN_CALL_ACLNN_OP(ctx, Elu, acl_input, alpha, alpha, alpha, acl_dst); ggml_cann_release_resources(ctx, acl_input, acl_dst, alpha); } -void ggml_cann_mean(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_mean(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - int64_t reduceDimValue[] = {3}; - aclIntArray* reduceDim = aclCreateIntArray(reduceDimValue, 1); - bool keepDim = true; + int64_t reduceDimValue[] = { 3 }; + aclIntArray * reduceDim = aclCreateIntArray(reduceDimValue, 1); + bool keepDim = true; GGML_CANN_CALL_ACLNN_OP(ctx, Mean, acl_src, reduceDim, keepDim, ACL_FLOAT, acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst, reduceDim); } -void ggml_cann_pad_reflect_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst){ - ggml_tensor * src0 = dst->src[0]; - int32_t *opts = (int32_t *) dst->op_params; - int64_t paddingsArray[2] = {opts[0], opts[1]}; - aclIntArray* paddings = aclCreateIntArray(paddingsArray, 2); +void ggml_cann_pad_reflect_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; + int32_t * opts = (int32_t *) dst->op_params; + int64_t paddingsArray[2] = { opts[0], opts[1] }; + aclIntArray * paddings = aclCreateIntArray(paddingsArray, 2); for (int64_t i = 0; i < src0->ne[3]; i++) { - aclTensor* acl_src = ggml_cann_create_tensor( - (char*)src0->data + i * src0->ne[3], - ggml_cann_type_mapping(src0->type), ggml_element_size(src0), - src0->ne, src0->nb, 3); + aclTensor * acl_src = + ggml_cann_create_tensor((char *) src0->data + i * src0->ne[3], ggml_cann_type_mapping(src0->type), + ggml_element_size(src0), src0->ne, src0->nb, 3); - aclTensor* acl_dst = ggml_cann_create_tensor( - (char*)dst->data + i * src0->ne[3], - ggml_cann_type_mapping(dst->type), ggml_element_size(dst), - dst->ne, dst->nb, 3); + aclTensor * acl_dst = + ggml_cann_create_tensor((char *) dst->data + i * src0->ne[3], ggml_cann_type_mapping(dst->type), + ggml_element_size(dst), dst->ne, dst->nb, 3); - GGML_CANN_CALL_ACLNN_OP(ctx, ReflectionPad1d, acl_src, paddings, acl_dst); + GGML_CANN_CALL_ACLNN_OP(ctx, ReflectionPad1d, acl_src, paddings, acl_dst); - ggml_cann_release_resources(ctx, acl_src, acl_dst); + ggml_cann_release_resources(ctx, acl_src, acl_dst); } ggml_cann_release_resources(ctx, paddings); } -void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_count_equal(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; ggml_tensor * src1 = dst->src[1]; - aclTensor* acl_self = ggml_cann_create_tensor(src0); - aclTensor* acl_other = ggml_cann_create_tensor(src1); + aclTensor * acl_self = ggml_cann_create_tensor(src0); + aclTensor * acl_other = ggml_cann_create_tensor(src1); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceEqTensor, acl_self, acl_other); @@ -2894,15 +2769,15 @@ void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst){ ggml_cann_release_resources(ctx, acl_self, acl_other); } -void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst){ +void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst) { ggml_tensor * src0 = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src0); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); - float alphaValue = 0.0f; - aclScalar* alpha = nullptr; - alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); + float alphaValue = 0.0f; + aclScalar * alpha = nullptr; + alpha = aclCreateScalar(&alphaValue, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, GtScalar, acl_src, alpha, acl_dst); @@ -2927,7 +2802,7 @@ void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst){ * @note This function assumes floating-point data types and is designed for * MoE architectures, possibly involving sparse expert routing. */ -static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context & ctx, ggml_tensor * dst) { //dst [M, K, N, 1] ggml_tensor * src0 = dst->src[0]; //src0 [D, M, A, 1] -> [D, M, K, 1] ggml_tensor * src1 = dst->src[1]; //src1 [D, B, N, 1], B = K or B = 1 -> [D, 1, K, 1] @@ -2941,36 +2816,42 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor* GGML_ASSERT(batch == ids->ne[1]); ggml_cann_pool_alloc export_allocator(ctx.pool(), src0->ne[0] * src0->ne[1] * ids->ne[0] * ggml_element_size(src0)); - void* export_ptr = export_allocator.get(); + void * export_ptr = export_allocator.get(); for (int64_t i = 0; i < batch; i++) { - aclTensor *select_index = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, i * ids->nb[1]); - aclTensor *export_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3); + aclTensor * select_index = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, i * ids->nb[1]); + aclTensor * export_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3); - int64_t select_export_ne[] = {src0->ne[0], src0->ne[1], ids->ne[0]}; - size_t select_export_nb[3]; + int64_t select_export_ne[] = { src0->ne[0], src0->ne[1], ids->ne[0] }; + size_t select_export_nb[3]; select_export_nb[0] = src0->nb[0]; - for (int k = 1;k < 3; k++) { - select_export_nb[k] = select_export_nb[k-1] * select_export_ne[k-1]; + for (int k = 1; k < 3; k++) { + select_export_nb[k] = select_export_nb[k - 1] * select_export_ne[k - 1]; } - aclTensor *select_export = ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), select_export_ne, select_export_nb, 3); + aclTensor * select_export = + ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), + select_export_ne, select_export_nb, 3); GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, export_weight, 0, select_index, select_export); - int64_t select_transpose_ne[] = {select_export_ne[1], select_export_ne[0], select_export_ne[2]}; - size_t select_transpose_nb[] = {select_export_nb[1], select_export_nb[0], select_export_nb[2]}; - aclTensor *select_export_transpose = ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), select_transpose_ne, select_transpose_nb, 3); + int64_t select_transpose_ne[] = { select_export_ne[1], select_export_ne[0], select_export_ne[2] }; + size_t select_transpose_nb[] = { select_export_nb[1], select_export_nb[0], select_export_nb[2] }; + aclTensor * select_export_transpose = + ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), + select_transpose_ne, select_transpose_nb, 3); - int64_t active_tensor_ne[] = {src1->ne[0], 1, src1->ne[1]}; - size_t active_tensor_nb[] = {src1->nb[0], src1->nb[1], src1->nb[1]}; - aclTensor *active_tensor = ggml_cann_create_tensor(src1, active_tensor_ne, active_tensor_nb, 3, ACL_FORMAT_ND, i * src1->nb[2]); + int64_t active_tensor_ne[] = { src1->ne[0], 1, src1->ne[1] }; + size_t active_tensor_nb[] = { src1->nb[0], src1->nb[1], src1->nb[1] }; + aclTensor * active_tensor = + ggml_cann_create_tensor(src1, active_tensor_ne, active_tensor_nb, 3, ACL_FORMAT_ND, i * src1->nb[2]); - int64_t dst_ne[] = {dst->ne[0], 1, dst->ne[1]}; - size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[1]}; - aclTensor *acl_dst = ggml_cann_create_tensor(dst, dst_ne,dst_nb, 3, ACL_FORMAT_ND, i * dst->nb[2]); + int64_t dst_ne[] = { dst->ne[0], 1, dst->ne[1] }; + size_t dst_nb[] = { dst->nb[0], dst->nb[1], dst->nb[1] }; + aclTensor * acl_dst = ggml_cann_create_tensor(dst, dst_ne, dst_nb, 3, ACL_FORMAT_ND, i * dst->nb[2]); GGML_CANN_CALL_ACLNN_OP(ctx, BatchMatMul, active_tensor, select_export_transpose, acl_dst, 2); - ggml_cann_release_resources(ctx, select_index, export_weight, select_export, active_tensor, acl_dst, select_export_transpose); + ggml_cann_release_resources(ctx, select_index, export_weight, select_export, active_tensor, acl_dst, + select_export_transpose); } } @@ -2997,7 +2878,7 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor* * @note This function assumes quantized data types and is designed for * MoE architectures with potential sparse expert routing. */ -static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context & ctx, ggml_tensor * dst) { // TODO: Use aclnnGroupedMatMul //dst [M, K, N, 1] ggml_tensor * src0 = dst->src[0]; //src0 [D, M, A, 1] @@ -3007,24 +2888,23 @@ static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tens GGML_TENSOR_BINARY_OP_LOCALS // copy index from npu to cpu - int64_t n_as = ne02; // A - int64_t n_ids = ids->ne[0]; // K + int64_t n_as = ne02; // A + int64_t n_ids = ids->ne[0]; // K std::vector ids_host(ggml_nbytes(ids)); - ggml_cann_async_memcpy(ctx, ids_host.data(), ids->data, ggml_nbytes(ids), - ACL_MEMCPY_DEVICE_TO_HOST); + ggml_cann_async_memcpy(ctx, ids_host.data(), ids->data, ggml_nbytes(ids), ACL_MEMCPY_DEVICE_TO_HOST); ACL_CHECK(aclrtSynchronizeStream(ctx.stream())); char * src0_original = (char *) src0->data; char * src1_original = (char *) src1->data; - char * dst_original = (char *) dst->data; + char * dst_original = (char *) dst->data; ggml_tensor src0_row = *src0; ggml_tensor src1_row = *src1; - ggml_tensor dst_row = *dst; + ggml_tensor dst_row = *dst; const enum ggml_type type = dst->src[0]->type; - float weight_elem_size; + float weight_elem_size; if (type == GGML_TYPE_Q4_0) { weight_elem_size = float(sizeof(uint8_t)) / 2; } else if (type == GGML_TYPE_Q8_0) { @@ -3034,18 +2914,18 @@ static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tens } // src0_row [D, M, 1, 1] weight without permute - src0_row.ne[2] = 1; - src0_row.ne[3] = 1; - src0_row.nb[0] = weight_elem_size; - src0_row.nb[1] = weight_elem_size * ne00; - src0_row.nb[2] = weight_elem_size * ne00; - src0_row.nb[3] = weight_elem_size * ne00; + src0_row.ne[2] = 1; + src0_row.ne[3] = 1; + src0_row.nb[0] = weight_elem_size; + src0_row.nb[1] = weight_elem_size * ne00; + src0_row.nb[2] = weight_elem_size * ne00; + src0_row.nb[3] = weight_elem_size * ne00; size_t weight_stride = ne00 * ne01 * weight_elem_size; - size_t weight_size = weight_stride * ne02 * ne03; + size_t weight_size = weight_stride * ne02 * ne03; // scale [D, M, 1, 1] -> scale && permute size_t scale_elem_size = sizeof(uint16_t); - size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size; + size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size; // src1_row [D, 1, 1, 1] -> input src1_row.ne[1] = 1; @@ -3063,11 +2943,11 @@ static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tens //create weight for one row ggml_cann_pool_alloc weight_allocator(ctx.pool()); - void* weight_buffer = weight_allocator.alloc(nb02); + void * weight_buffer = weight_allocator.alloc(nb02); for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { for (int64_t id = 0; id < n_ids; id++) { // expert index - int32_t i02 = *(int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); + int32_t i02 = *(int32_t *) (ids_host.data() + iid1 * ids->nb[1] + id * ids->nb[0]); GGML_ASSERT(i02 >= 0 && i02 < n_as); // If B = 1 (broadcast), always use 0; otherwise, use id. @@ -3077,21 +2957,19 @@ static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tens int64_t i1 = id; int64_t i2 = i12; - void* src0_tmp_ptr = src0_original + i02*weight_stride; - void* scale_tmp_ptr = src0_original + weight_size + i02*scale_stride; - void* src1_tmp_ptr = src1_original + i11*nb11 + i12*nb12; - void* dst_tmp_ptr = dst_original + i1*nb1 + i2*nb2; + void * src0_tmp_ptr = src0_original + i02 * weight_stride; + void * scale_tmp_ptr = src0_original + weight_size + i02 * scale_stride; + void * src1_tmp_ptr = src1_original + i11 * nb11 + i12 * nb12; + void * dst_tmp_ptr = dst_original + i1 * nb1 + i2 * nb2; // mem cpy - ggml_cann_async_memcpy(ctx, weight_buffer, src0_tmp_ptr, weight_stride, - ACL_MEMCPY_DEVICE_TO_DEVICE); - void* scale_buffer = (char*)weight_buffer + weight_stride; - ggml_cann_async_memcpy(ctx, scale_buffer, scale_tmp_ptr, scale_stride, - ACL_MEMCPY_DEVICE_TO_DEVICE); - - src0_row.data = weight_buffer; - src1_row.data = src1_tmp_ptr; - dst_row.data = dst_tmp_ptr; + ggml_cann_async_memcpy(ctx, weight_buffer, src0_tmp_ptr, weight_stride, ACL_MEMCPY_DEVICE_TO_DEVICE); + void * scale_buffer = (char *) weight_buffer + weight_stride; + ggml_cann_async_memcpy(ctx, scale_buffer, scale_tmp_ptr, scale_stride, ACL_MEMCPY_DEVICE_TO_DEVICE); + + src0_row.data = weight_buffer; + src1_row.data = src1_tmp_ptr; + dst_row.data = dst_tmp_ptr; dst_row.src[0] = &src0_row; dst_row.src[1] = &src1_row; @@ -3101,7 +2979,7 @@ static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context& ctx, ggml_tens return; } -void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst) { +void ggml_cann_mul_mat_id(ggml_backend_cann_context & ctx, ggml_tensor * dst) { const enum ggml_type type = dst->src[0]->type; switch (type) { case GGML_TYPE_F32: @@ -3118,12 +2996,11 @@ void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst) { } } -void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){ - - ggml_tensor* src0 = dst->src[0]; // q, fp32 | B, N, S, D (uncont) -> B, S, N, D (cont) - ggml_tensor* src1 = dst->src[1]; // k, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont) - ggml_tensor* src2 = dst->src[2]; // v, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont) - ggml_tensor* src3 = dst->src[3]; // mask, fp16 +void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; // q, fp32 | B, N, S, D (uncont) -> B, S, N, D (cont) + ggml_tensor * src1 = dst->src[1]; // k, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont) + ggml_tensor * src2 = dst->src[2]; // v, fp16 | B, N, S, D (uncont) -> B, S, N, D (cont) + ggml_tensor * src3 = dst->src[3]; // mask, fp16 // B, N, S, D (uncont) -> B, S, N, D (cont) int64_t src0_bsnd_ne[GGML_MAX_DIMS]; @@ -3139,107 +3016,96 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){ size_t src2_bsnd_nb[GGML_MAX_DIMS]; memcpy(src2_bsnd_nb, src2->nb, GGML_MAX_DIMS * sizeof(size_t)); - auto transpose12 = [](int64_t* ne, size_t* nb) { + auto transpose12 = [](int64_t * ne, size_t * nb) { int64_t ne_tmp = ne[1]; size_t nb_tmp = nb[1]; - ne[1] = ne[2]; - nb[1] = nb[2]; - ne[2] = ne_tmp; - nb[2] = nb_tmp; + ne[1] = ne[2]; + nb[1] = nb[2]; + ne[2] = ne_tmp; + nb[2] = nb_tmp; }; transpose12(src0_bsnd_ne, src0_bsnd_nb); transpose12(src1_bsnd_ne, src1_bsnd_nb); transpose12(src2_bsnd_ne, src2_bsnd_nb); - float maxBias = 0.0f; - float scaleValue = 1.0f; + float maxBias = 0.0f; + float scaleValue = 1.0f; float logitSoftcap = 0.0f; - memcpy(&scaleValue, (float*)dst->op_params + 0, sizeof(float)); - memcpy(&maxBias, (float*)dst->op_params + 1, sizeof(float)); - memcpy(&logitSoftcap, (float*)dst->op_params + 2, sizeof(float)); + memcpy(&scaleValue, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&maxBias, (float *) dst->op_params + 1, sizeof(float)); + memcpy(&logitSoftcap, (float *) dst->op_params + 2, sizeof(float)); - if(logitSoftcap == 0.0f){ + if (logitSoftcap == 0.0f) { size_t faElemSize = sizeof(uint16_t); - auto faDataType = ACL_FLOAT16; //ACL_BF16; + auto faDataType = ACL_FLOAT16; //ACL_BF16; - aclTensor* acl_src0_f16_tensor = nullptr; - aclTensor* acl_src1_f16_tensor = nullptr; - aclTensor* acl_src2_f16_tensor = nullptr; + aclTensor * acl_src0_f16_tensor = nullptr; + aclTensor * acl_src1_f16_tensor = nullptr; + aclTensor * acl_src2_f16_tensor = nullptr; // Step 1: cast the src0 (Query) to fp16 if needed ggml_cann_pool_alloc src0_f16_allocator(ctx.pool()); - void* src0_f16_buffer = nullptr; + void * src0_f16_buffer = nullptr; - if(ggml_cann_type_mapping(src0->type) != faDataType){ - aclTensor* acl_src0_f32_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne, - src0_bsnd_nb, GGML_MAX_DIMS); - src0_f16_buffer = src0_f16_allocator.alloc( - ggml_nelements(src0) * faElemSize); + if (ggml_cann_type_mapping(src0->type) != faDataType) { + aclTensor * acl_src0_f32_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne, src0_bsnd_nb, GGML_MAX_DIMS); + src0_f16_buffer = src0_f16_allocator.alloc(ggml_nelements(src0) * faElemSize); - int64_t* src0_f16_ne = src0_bsnd_ne; - size_t src0_f16_nb[GGML_MAX_DIMS]; + int64_t * src0_f16_ne = src0_bsnd_ne; + size_t src0_f16_nb[GGML_MAX_DIMS]; src0_f16_nb[0] = sizeof(uint16_t); - for(int i = 1; i < GGML_MAX_DIMS; ++i){ + for (int i = 1; i < GGML_MAX_DIMS; ++i) { src0_f16_nb[i] = src0_f16_nb[i - 1] * src0_f16_ne[i - 1]; } - acl_src0_f16_tensor = ggml_cann_create_tensor( - src0_f16_buffer, faDataType, faElemSize, - src0_f16_ne, src0_f16_nb, GGML_MAX_DIMS - ); + acl_src0_f16_tensor = ggml_cann_create_tensor(src0_f16_buffer, faDataType, faElemSize, src0_f16_ne, + src0_f16_nb, GGML_MAX_DIMS); aclnn_cast(ctx, acl_src0_f32_tensor, acl_src0_f16_tensor, faDataType); ggml_cann_release_resources(ctx, acl_src0_f32_tensor); - }else{ - acl_src0_f16_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne, - src0_bsnd_nb, GGML_MAX_DIMS); + } else { + acl_src0_f16_tensor = ggml_cann_create_tensor(src0, src0_bsnd_ne, src0_bsnd_nb, GGML_MAX_DIMS); } // Step 2: create the acl tensors for src1 (Key), src2 (Value), // and the direct output from FusedInferAttention - acl_src1_f16_tensor = ggml_cann_create_tensor(src1, src1_bsnd_ne, - src1_bsnd_nb, GGML_MAX_DIMS); - acl_src2_f16_tensor = ggml_cann_create_tensor(src2, src2_bsnd_ne, - src2_bsnd_nb, GGML_MAX_DIMS); + acl_src1_f16_tensor = ggml_cann_create_tensor(src1, src1_bsnd_ne, src1_bsnd_nb, GGML_MAX_DIMS); + acl_src2_f16_tensor = ggml_cann_create_tensor(src2, src2_bsnd_ne, src2_bsnd_nb, GGML_MAX_DIMS); // Step 3: create the PSEShift tensor if needed // this tensor is considered as mask (f16) in the llama.cpp - aclTensor* bcast_pse_tensor = nullptr; + aclTensor * bcast_pse_tensor = nullptr; ggml_cann_pool_alloc bcast_pse_allocator(ctx.pool()); - if(src3 != nullptr){ + if (src3 != nullptr) { // Construct the truncated pse tensor (common for prefill/decode) int64_t trunc_pse_ne[GGML_MAX_DIMS] = { - src3->ne[0], // D - src0->ne[1], // S (number of Q tokens) - src3->ne[2], // mask N - src3->ne[3] // B + src3->ne[0], // D + src0->ne[1], // S (number of Q tokens) + src3->ne[2], // mask N + src3->ne[3] // B }; - size_t* trunc_pse_nb = src3->nb; + size_t * trunc_pse_nb = src3->nb; - aclTensor* acl_mask_f16_trunc_tensor = ggml_cann_create_tensor( - src3->data, ACL_FLOAT16, sizeof(uint16_t), - trunc_pse_ne, trunc_pse_nb, GGML_MAX_DIMS - ); + aclTensor * acl_mask_f16_trunc_tensor = ggml_cann_create_tensor(src3->data, ACL_FLOAT16, sizeof(uint16_t), + trunc_pse_ne, trunc_pse_nb, GGML_MAX_DIMS); int64_t bcast_pse_ne[GGML_MAX_DIMS]; - size_t bcast_pse_nb[GGML_MAX_DIMS]; - bcast_pse_ne[0] = src3->ne[0]; // D - bcast_pse_ne[1] = src0->ne[1]; // S - bcast_pse_ne[2] = src0->ne[2]; // N (num_heads) - bcast_pse_ne[3] = src3->ne[3]; // B + size_t bcast_pse_nb[GGML_MAX_DIMS]; + bcast_pse_ne[0] = src3->ne[0]; // D + bcast_pse_ne[1] = src0->ne[1]; // S + bcast_pse_ne[2] = src0->ne[2]; // N (num_heads) + bcast_pse_ne[3] = src3->ne[3]; // B if (maxBias == 0.0f) { // When maxBias == 0.0f, use nb = 0 reduce once repeat (Qwen2) // Construct the bcast tensor (simulate repeat on the head dimension using stride=0) bcast_pse_nb[0] = sizeof(uint16_t); bcast_pse_nb[1] = bcast_pse_nb[0] * bcast_pse_ne[0]; - bcast_pse_nb[2] = 0; // <---- the head dimension shares the same data + bcast_pse_nb[2] = 0; // <---- the head dimension shares the same data bcast_pse_nb[3] = src3->nb[3]; - bcast_pse_tensor = ggml_cann_create_tensor( - src3->data, ACL_FLOAT16, sizeof(uint16_t), - bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS - ); + bcast_pse_tensor = ggml_cann_create_tensor(src3->data, ACL_FLOAT16, sizeof(uint16_t), bcast_pse_ne, + bcast_pse_nb, GGML_MAX_DIMS); ggml_cann_release_resources(ctx, acl_mask_f16_trunc_tensor); } else { @@ -3248,35 +3114,31 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){ bcast_pse_nb[i] = bcast_pse_nb[i - 1] * bcast_pse_ne[i - 1]; } - void* bcast_pse_buffer = bcast_pse_allocator.alloc( - ggml_nelements(src3) * src0->ne[2] * sizeof(uint16_t) - ); + void * bcast_pse_buffer = + bcast_pse_allocator.alloc(ggml_nelements(src3) * src0->ne[2] * sizeof(uint16_t)); - bcast_pse_tensor = ggml_cann_create_tensor( - bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t), - bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS - ); + bcast_pse_tensor = ggml_cann_create_tensor(bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t), + bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS); - int64_t repeats[] = {1, src0->ne[2], 1, 1}; + int64_t repeats[] = { 1, src0->ne[2], 1, 1 }; aclnn_repeat(ctx, acl_mask_f16_trunc_tensor, bcast_pse_tensor, repeats); // alibi // Compute the slope if needed. Derived from ggml_cann_softmax(). - const int64_t n_heads = src0->ne[2]; + const int64_t n_heads = src0->ne[2]; ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(uint16_t)); - void* slope_buffer = slope_allocator.get(); + void * slope_buffer = slope_allocator.get(); aclnn_get_slope(ctx, n_heads, slope_buffer, maxBias, GGML_TYPE_F16); - int64_t slope_ne[] = {1, 1, n_heads, 1}; - size_t slope_nb[GGML_MAX_DIMS]; + int64_t slope_ne[] = { 1, 1, n_heads, 1 }; + size_t slope_nb[GGML_MAX_DIMS]; slope_nb[0] = sizeof(uint16_t); - for(int i = 1;ine[2]; // N - int64_t numKeyValueHeads = src1->ne[2]; + int kvTensorNum = 1; + aclTensor * acl_q_tensor = acl_src0_f16_tensor; + aclTensor * acl_k_tensors[] = { acl_src1_f16_tensor }; + aclTensor * acl_v_tensors[] = { acl_src2_f16_tensor }; + aclTensorList * acl_k_tensor_list = aclCreateTensorList(acl_k_tensors, kvTensorNum); + aclTensorList * acl_v_tensor_list = aclCreateTensorList(acl_v_tensors, kvTensorNum); + + int64_t numHeads = src0->ne[2]; // N + int64_t numKeyValueHeads = src1->ne[2]; // double scaleValue = 1 / sqrt(src0->ne[0]); // 1/sqrt(d) - int64_t preTokens = 65535; - int64_t nextTokens = 65535; - char layout[5] = {'B', 'S', 'N', 'D', 0}; - int64_t sparseMode = 0; - int64_t innerPrecise = (src0->ne[1] == 1) ? 0 : 2; - int64_t blockSize = 0; - int64_t antiquantMode = 0; - bool softmaxLseFlag = false; - int64_t keyAntiquantMode = 0; + int64_t preTokens = 65535; + int64_t nextTokens = 65535; + char layout[5] = { 'B', 'S', 'N', 'D', 0 }; + int64_t sparseMode = 0; + int64_t innerPrecise = (src0->ne[1] == 1) ? 0 : 2; + int64_t blockSize = 0; + int64_t antiquantMode = 0; + bool softmaxLseFlag = false; + int64_t keyAntiquantMode = 0; int64_t valueAntiquantMode = 0; GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); - aclTensor * fa_dst_tensor = nullptr; - aclTensor * acl_dst_tensor = nullptr; + aclTensor * fa_dst_tensor = nullptr; + aclTensor * acl_dst_tensor = nullptr; ggml_cann_pool_alloc out_f16_allocator(ctx.pool()); if (dst->type == GGML_TYPE_F32) { - void* out_f16_buffer = out_f16_allocator.alloc( - ggml_nelements(dst) * faElemSize); + void * out_f16_buffer = out_f16_allocator.alloc(ggml_nelements(dst) * faElemSize); - int64_t* out_f16_ne = src0_bsnd_ne; - size_t out_f16_nb[GGML_MAX_DIMS]; + int64_t * out_f16_ne = src0_bsnd_ne; + size_t out_f16_nb[GGML_MAX_DIMS]; out_f16_nb[0] = faElemSize; - for(int i = 1; i < GGML_MAX_DIMS; ++i){ + for (int i = 1; i < GGML_MAX_DIMS; ++i) { out_f16_nb[i] = out_f16_nb[i - 1] * out_f16_ne[i - 1]; } - fa_dst_tensor = ggml_cann_create_tensor( - out_f16_buffer, faDataType, faElemSize, - out_f16_ne, out_f16_nb, GGML_MAX_DIMS - ); - } - else { + fa_dst_tensor = + ggml_cann_create_tensor(out_f16_buffer, faDataType, faElemSize, out_f16_ne, out_f16_nb, GGML_MAX_DIMS); + } else { fa_dst_tensor = ggml_cann_create_tensor(dst); } - GGML_CANN_CALL_ACLNN_OP(ctx, FusedInferAttentionScoreV2, - acl_q_tensor, acl_k_tensor_list, acl_v_tensor_list, // q, k, v - bcast_pse_tensor, nullptr, // pse, mask - nullptr, nullptr, // actSeqLen, actSeqLenkv - nullptr, nullptr, // deqScale1, quantScale1 - nullptr, nullptr, nullptr, // deqScale2, quantScale2, quantOffset2 - nullptr, nullptr, // antiquantScale, antiquantOffset - nullptr, // blockTable - nullptr, nullptr, // qPadSize, kvPadSize - nullptr, nullptr, // kAntiquantScale, kAntiQuantOffset - nullptr, nullptr, // vAntiquantScale, vAntiQuantOffset - nullptr, nullptr, nullptr, // kSharedPrefix, vSharedPrefix, actSharedLen - numHeads, scaleValue, // heads, scaleValue - preTokens, nextTokens, // preTokens, nextTokens - layout, // inputLayout - numKeyValueHeads, // numKVHeads - sparseMode, innerPrecise, // sparseMode, innerPrecise - blockSize, antiquantMode, // blockSize, antiquantMode - softmaxLseFlag, // softmaxLseFlag - keyAntiquantMode, valueAntiquantMode, // keyAntiqMode, valueAntiqMode - fa_dst_tensor, // attentionOut - nullptr // softmaxLse + GGML_CANN_CALL_ACLNN_OP(ctx, FusedInferAttentionScoreV2, acl_q_tensor, acl_k_tensor_list, + acl_v_tensor_list, // q, k, v + bcast_pse_tensor, nullptr, // pse, mask + nullptr, nullptr, // actSeqLen, actSeqLenkv + nullptr, nullptr, // deqScale1, quantScale1 + nullptr, nullptr, nullptr, // deqScale2, quantScale2, quantOffset2 + nullptr, nullptr, // antiquantScale, antiquantOffset + nullptr, // blockTable + nullptr, nullptr, // qPadSize, kvPadSize + nullptr, nullptr, // kAntiquantScale, kAntiQuantOffset + nullptr, nullptr, // vAntiquantScale, vAntiQuantOffset + nullptr, nullptr, nullptr, // kSharedPrefix, vSharedPrefix, actSharedLen + numHeads, scaleValue, // heads, scaleValue + preTokens, nextTokens, // preTokens, nextTokens + layout, // inputLayout + numKeyValueHeads, // numKVHeads + sparseMode, innerPrecise, // sparseMode, innerPrecise + blockSize, antiquantMode, // blockSize, antiquantMode + softmaxLseFlag, // softmaxLseFlag + keyAntiquantMode, valueAntiquantMode, // keyAntiqMode, valueAntiqMode + fa_dst_tensor, // attentionOut + nullptr // softmaxLse ); if (dst->type == GGML_TYPE_F32) { // Step 6: post-processing, permute and cast to f32 - aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst); + aclTensor * acl_dst_tensor = ggml_cann_create_tensor(dst); aclnn_cast(ctx, fa_dst_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type)); } - ggml_cann_release_resources(ctx, acl_src0_f16_tensor, - acl_k_tensor_list, - acl_v_tensor_list, - fa_dst_tensor, - acl_dst_tensor, - bcast_pse_tensor); + ggml_cann_release_resources(ctx, acl_src0_f16_tensor, acl_k_tensor_list, acl_v_tensor_list, fa_dst_tensor, + acl_dst_tensor, bcast_pse_tensor); } else { GGML_ABORT("Function is not implemented."); diff --git a/ggml/src/ggml-cann/aclnn_ops.h b/ggml/src/ggml-cann/aclnn_ops.h old mode 100755 new mode 100644 index 5c510cc9932e8..ec7455af88cd5 --- a/ggml/src/ggml-cann/aclnn_ops.h +++ b/ggml/src/ggml-cann/aclnn_ops.h @@ -62,7 +62,7 @@ * @param dst The ggml tensor representing the destination, which op is * GGML_OP_REPEAT and specifies the desired dimensions. */ -void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_repeat(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies the Leaky ReLU activation function to a tensor using the CANN @@ -82,7 +82,7 @@ void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result of the Leaky ReLU * activation is stored, which op is `GGML_OP_LEAKY_RELU` */ -void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_leaky_relu(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Concatenates multiple tensors along a specified dimension using the @@ -97,7 +97,7 @@ void ggml_cann_leaky_relu(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @attention tensorList length should be 2 and the dimension using for concat * default to 1. */ -void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_concat(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Generates a sequence of evenly spaced values within a specified @@ -113,7 +113,7 @@ void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst); * `start`, 'stop' and 'step' are in dst->op_params and dst->op is * `GGML_OP_ARANGE`. */ -void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_arange(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies a clamp operation to the elements of a ggml tensor using the @@ -131,7 +131,7 @@ void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the clamped values will be stored. * dst->op is `GGML_OP_CLAMP`, `min` and `max` value is in dst->params. */ -void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_clamp(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Scales the elements of a ggml tensor by a constant factor using the @@ -148,7 +148,7 @@ void ggml_cann_clamp(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the scaled values will be stored. * dst->op is `GGML_OP_SCALE` and `scale` value is in dst->params. */ -void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_scale(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Sorts the elements of a ggml tensor and returns the indices that @@ -163,7 +163,7 @@ void ggml_cann_scale(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the sorted indices will be stored. * dst->op is `GGML_OP_ARGSORT`. */ -void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_argsort(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the Layer Normalization for a ggml tensor using the CANN @@ -185,7 +185,7 @@ void ggml_cann_argsort(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the normalized values will be stored. * @attention `Var` defaults to dst->ne[0]. */ -void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the Group Normalization for a ggml tensor using the CANN @@ -209,7 +209,7 @@ void ggml_cann_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); * * @attention eps defaults to 1e-6f. */ -void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_group_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the accumulation of tensors using the CANN backend. @@ -228,7 +228,7 @@ void ggml_cann_group_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the accumulated values will be stored. * `inplace` is in dst->params, and dst->op is `GGML_OP_ACC`. */ -void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_acc(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the sum of elements along the last dimension of a ggml tensor @@ -244,7 +244,7 @@ void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst); * * @attention `reduce_dims` defaults to 3, which means the last dimension. */ -void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_sum_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the sum of elements in a ggml tensor. @@ -258,7 +258,7 @@ void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); * */ -void ggml_cann_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Upsamples a ggml tensor using nearest neighbor interpolation using @@ -274,8 +274,7 @@ void ggml_cann_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the upsampled values will be stored. * dst->op is `GGML_OP_UPSCALE`. */ -void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, - ggml_tensor* dst); +void ggml_cann_upsample_nearest2d(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Pads a ggml tensor to match the dimensions of the destination tensor @@ -290,7 +289,7 @@ void ggml_cann_upsample_nearest2d(ggml_backend_cann_context& ctx, * @param dst The destination tensor, which specifies the target dimensions for * padding. dst->op is `GGML_OP_PAD`. */ -void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_pad(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Executes a 2D pooling operation on a ggml tensor using the CANN @@ -307,7 +306,7 @@ void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor on which the pooling operation is to be * performed. dst->op is `GGML_OP_POOL_2D`. */ -void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_pool2d(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Duplicates a ggml tensor using the CANN backend. @@ -326,7 +325,7 @@ void ggml_cann_pool2d(ggml_backend_cann_context& ctx, ggml_tensor* dst); * different shape and dst is no-contiguous. * @note: This func need to simplify. */ -void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_dup(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the Root Mean Square (RMS) normalization of a ggml tensor @@ -348,7 +347,7 @@ void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the normalized values will be stored. * dst->op is `GGML_OP_RMS_NORM`. */ -void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_rms_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies a diagonal mask to the tensor with a specified value. @@ -363,7 +362,7 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst); * `GGML_OP_DIAG_MASK` * @param value The value to use for masking. */ -void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, float value); +void ggml_cann_diag_mask(ggml_backend_cann_context & ctx, ggml_tensor * dst, float value); /** * @brief Performs an image-to-column transformation on the input tensor. @@ -378,7 +377,7 @@ void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, float * @param dst The destination tensor that stores the result of the operation. * dst->op is `GGML_OP_IM2COL`. */ -void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_im2col(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes time step embeddings using sine and cosine functions. @@ -392,10 +391,10 @@ void ggml_cann_im2col(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result of the embedding operation * will be stored. dst->op is `GGML_OP_TIMESTEP_EMBEDDING`. */ -void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_timestep_embedding(ggml_backend_cann_context & ctx, ggml_tensor * dst); // @see ggml_cann_dup. -void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_cpy(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the softmax activation with optional masking. @@ -417,7 +416,7 @@ void ggml_cann_cpy(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result will be stored. dst->op is * `GGML_OP_SOFTMAX`. */ -void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_softmax(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Extracts specific rows from a tensor based on indices. @@ -429,7 +428,7 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param ctx The backend CANN context for executing operations. * @param dst The destination tensor where the extracted rows will be stored. */ -void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_get_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Writes specific rows into a tensor at positions specified by indices. @@ -441,7 +440,7 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param ctx The backend CANN context for executing operations. * @param dst The destination tensor where the specified rows will be updated. */ -void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_set_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Executes matrix multiplication for the given tensor. @@ -454,7 +453,7 @@ void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor for storing the result of the matrix * multiplication. dst->op is `GGML_OP_MUL_MAT`. */ -void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_mul_mat(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies Rotary Positional Embedding (RoPE) to the input tensor. @@ -477,7 +476,7 @@ void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @note The function currently does not support cases where the freq_scale is * not equal 1. */ -void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the index of the maximum value along the specified dimension @@ -492,7 +491,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the indices of the maximum values will * be stored. dst->op is `GGML_OP_ARGMAX`. */ -void ggml_cann_argmax(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_argmax(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Adds two tensors element-wise and stores the result in a destination @@ -509,8 +508,10 @@ void ggml_cann_argmax(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param acl_src1 The second source tensor. * @param acl_dst The destination tensor where the result will be stored. */ -void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0, - aclTensor* acl_src1, aclTensor* acl_dst = nullptr); +void aclnn_add(ggml_backend_cann_context & ctx, + aclTensor * acl_src0, + aclTensor * acl_src1, + aclTensor * acl_dst = nullptr); /** * @brief Sub two tensors element-wise and stores the result in a destination @@ -527,8 +528,10 @@ void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0, * @param acl_src1 The second source tensor. * @param acl_dst The destination tensor where the result will be stored. */ -void aclnn_sub(ggml_backend_cann_context& ctx, aclTensor* acl_src0, - aclTensor* acl_src1, aclTensor* acl_dst = nullptr); +void aclnn_sub(ggml_backend_cann_context & ctx, + aclTensor * acl_src0, + aclTensor * acl_src1, + aclTensor * acl_dst = nullptr); /** * @brief Performs element-wise multiplication of two tensors and stores the @@ -546,8 +549,10 @@ void aclnn_sub(ggml_backend_cann_context& ctx, aclTensor* acl_src0, * @param acl_other The second tensor for element-wise multiplication. * @param acl_dst The destination tensor where the result will be stored. */ -void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_other, aclTensor* acl_dst = nullptr); +void aclnn_mul(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_other, + aclTensor * acl_dst = nullptr); /** * @brief Matrix division, optionally in-place. @@ -567,8 +572,10 @@ void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param inplace Flag indicating whether to perform the operation in-place on * `acl_src`. */ -void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_other, aclTensor* acl_dst = nullptr); +void aclnn_div(ggml_backend_cann_context & ctx, + aclTensor * acl_src, + aclTensor * acl_other, + aclTensor * acl_dst = nullptr); /** * @brief Applies element-wise cosine function to the elements of a tensor. @@ -584,8 +591,7 @@ void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param acl_dst The destination tensor where the cosine results will be * stored. */ -void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst); +void aclnn_cos(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst); /** * @brief Applies element-wise sine function to the elements of a tensor. @@ -602,8 +608,7 @@ void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param acl_src The source tensor on which the sine function will be applied. * @param acl_dst The destination tensor where the sine results will be stored. */ -void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, - aclTensor* acl_dst); +void aclnn_sin(ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst); /** * @brief Prepares broadcast-compatible ACL tensors for two input tensors and one @@ -621,8 +626,12 @@ void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, * @param acl_src1 Output pointer to the created ACL tensor corresponding to src1. * @param acl_dst Output pointer to the created ACL tensor corresponding to dst. */ -void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, - aclTensor ** acl_src0, aclTensor ** acl_src1, aclTensor ** acl_dst); +void bcast_shape(ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst, + aclTensor ** acl_src0, + aclTensor ** acl_src1, + aclTensor ** acl_dst); /** * @brief Computes the 1D transposed convolution (deconvolution) of a ggml @@ -637,7 +646,7 @@ void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, * @param dst The destination tensor where the transposed convolution result * will be stored. dst->op is `GGML_OP_CONV_TRANSPOSE_1D`. */ -void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_conv_transpose_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies the ELU (Exponential Linear Unit) activation to a ggml tensor @@ -662,7 +671,7 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* ds * @param dst The destination tensor where the ELU-activated result will be stored. * dst->op is expected to be `GGML_OP_ELU`. */ -void ggml_cann_elu(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_elu(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Computes the mean of a ggml tensor element-wise using the CANN backend. @@ -677,7 +686,7 @@ void ggml_cann_elu(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the mean result will be stored. * dst->op is expected to be `GGML_OP_MEAN`. */ -void ggml_cann_mean(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_mean(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies 1D reflect padding to a ggml tensor using the CANN backend. @@ -692,7 +701,7 @@ void ggml_cann_mean(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the padded result will be stored. * dst->op is expected to be `GGML_OP_PAD_REFLECT_1D`. */ -void ggml_cann_pad_reflect_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_pad_reflect_1d(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Counts the number of equal elements in two ggml tensors using the CANN backend. @@ -708,7 +717,7 @@ void ggml_cann_pad_reflect_1d(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result will be stored. * dst->op is expected to be `GGML_OP_COUNT_EQUAL`. */ -void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_count_equal(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Applies the Step activation function to a ggml tensor using the CANN backend. @@ -723,7 +732,7 @@ void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result will be stored. * dst->op is expected to be `GGML_OP_STEP`. */ -void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_step(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Performs the Flash Attention extended operator using the CANN backend. @@ -738,59 +747,46 @@ void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst); * @param dst The destination tensor where the result will be stored. * dst->op is expected to be `GGML_OP_FLASH_ATTN_EXT`. */ -void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst); /* * @brief A generic wrapper for ACL resources with custom deleter support. */ -using any_acl_resource = std::unique_ptr>; +using any_acl_resource = std::unique_ptr>; /** * @brief Trait structure used to define how to destroy a given ACL resource type. * * @tparam T ACL resource type. */ -template -struct acl_resource_traits; +template struct acl_resource_traits; /** * @brief Specialization for aclTensor, defines how to destroy an aclTensor resource. */ -template<> -struct acl_resource_traits { - static void destroy(void* p) { - ACL_CHECK(aclDestroyTensor(static_cast(p))); - } +template <> struct acl_resource_traits { + static void destroy(void * p) { ACL_CHECK(aclDestroyTensor(static_cast(p))); } }; /** * @brief Specialization for aclIntArray, defines how to destroy an aclIntArray resource. */ -template<> -struct acl_resource_traits { - static void destroy(void* p) { - ACL_CHECK(aclDestroyIntArray(static_cast(p))); - } +template <> struct acl_resource_traits { + static void destroy(void * p) { ACL_CHECK(aclDestroyIntArray(static_cast(p))); } }; /** * @brief Specialization for aclScalar, defines how to destroy an aclScalar resource. */ -template<> -struct acl_resource_traits { - static void destroy(void* p) { - ACL_CHECK(aclDestroyScalar(static_cast(p))); - } +template <> struct acl_resource_traits { + static void destroy(void * p) { ACL_CHECK(aclDestroyScalar(static_cast(p))); } }; /** * @brief Specialization for aclTensorList, defines how to destroy an aclTensorList resource. */ -template<> -struct acl_resource_traits { - static void destroy(void* p) { - ACL_CHECK(aclDestroyTensorList(static_cast(p))); - } +template <> struct acl_resource_traits { + static void destroy(void * p) { ACL_CHECK(aclDestroyTensorList(static_cast(p))); } }; /** @@ -800,14 +796,8 @@ struct acl_resource_traits { * @param ptr Raw pointer to ACL resource. * @return any_acl_resource Smart pointer that handles destruction. */ -template -any_acl_resource make_acl_resource(T* ptr) { - return any_acl_resource( - static_cast(ptr), - [](void* p) { - acl_resource_traits::destroy(p); - } - ); +template any_acl_resource make_acl_resource(T * ptr) { + return any_acl_resource(static_cast(ptr), [](void * p) { acl_resource_traits::destroy(p); }); } /** @@ -817,8 +807,7 @@ any_acl_resource make_acl_resource(T* ptr) { * @param vec Target vector to hold ACL resources. * @param args Raw pointers to ACL resources. */ -template -void register_acl_resources(std::vector& vec, Args*... args) { +template void register_acl_resources(std::vector & vec, Args *... args) { (vec.emplace_back(make_acl_resource(args)), ...); } @@ -826,39 +815,36 @@ void register_acl_resources(std::vector& vec, Args*... args) { * @brief Task class that wraps the execution of an aclnn function call. */ class aclnn_task : public cann_task { - public: - aclnn_task(aclnn_func_t aclnn_func, void * workspace_addr, - uint64_t workspace_size, aclOpExecutor * executor, - aclrtStream stream) : - aclnn_func_(aclnn_func), - workspace_addr_(workspace_addr), - workspace_size_(workspace_size), - executor_(executor), - stream_(stream) {} - virtual void run_task() override { - ACL_CHECK(aclnn_func_(workspace_addr_, workspace_size_, executor_, stream_)); - } - private: - aclnn_func_t aclnn_func_; - void * workspace_addr_; - uint64_t workspace_size_; - aclOpExecutor * executor_; - aclrtStream stream_; + public: + aclnn_task(aclnn_func_t aclnn_func, + void * workspace_addr, + uint64_t workspace_size, + aclOpExecutor * executor, + aclrtStream stream) : + aclnn_func_(aclnn_func), + workspace_addr_(workspace_addr), + workspace_size_(workspace_size), + executor_(executor), + stream_(stream) {} + + virtual void run_task() override { ACL_CHECK(aclnn_func_(workspace_addr_, workspace_size_, executor_, stream_)); } + private: + aclnn_func_t aclnn_func_; + void * workspace_addr_; + uint64_t workspace_size_; + aclOpExecutor * executor_; + aclrtStream stream_; }; /** * @brief Task class that releases ACL resources after usage. */ class release_resource_task : public cann_task { -public: - release_resource_task(std::vector&& resources){ - resource_ = std::move(resources); - } + public: + release_resource_task(std::vector && resources) { resource_ = std::move(resources); } - virtual void run_task() override { - resource_.clear(); - } -private: + virtual void run_task() override { resource_.clear(); } + private: std::vector resource_; }; @@ -866,38 +852,40 @@ class release_resource_task : public cann_task { * @brief Task class for performing asynchronous memory copy operations. */ class async_memcpy_task : public cann_task { -public: - async_memcpy_task(void* dst, const void* src, size_t size, - aclrtMemcpyKind kind, aclrtStream stream) - : dst_(dst), src_(src), size_(size), kind_(kind), stream_(stream) {} - - virtual void run_task() override { - ACL_CHECK(aclrtMemcpyAsync(dst_, size_, src_, size_, kind_, stream_)); - } -private: - void* dst_; - const void* src_; - size_t size_; + public: + async_memcpy_task(void * dst, const void * src, size_t size, aclrtMemcpyKind kind, aclrtStream stream) : + dst_(dst), + src_(src), + size_(size), + kind_(kind), + stream_(stream) {} + + virtual void run_task() override { ACL_CHECK(aclrtMemcpyAsync(dst_, size_, src_, size_, kind_, stream_)); } + private: + void * dst_; + const void * src_; + size_t size_; aclrtMemcpyKind kind_; - aclrtStream stream_; + aclrtStream stream_; }; /** * @brief Task class for performing asynchronous memory set operations. */ class async_memset_task : public cann_task { - public: - async_memset_task(void* buffer, size_t size, int32_t value, aclrtStream stream) - : buffer_(buffer), size_(size), value_(value), stream_(stream) {} - - virtual void run_task() override { - ACL_CHECK(aclrtMemsetAsync(buffer_, size_, value_, size_, stream_)); - } - private: - void* buffer_; - size_t size_; - int32_t value_; - aclrtStream stream_; + public: + async_memset_task(void * buffer, size_t size, int32_t value, aclrtStream stream) : + buffer_(buffer), + size_(size), + value_(value), + stream_(stream) {} + + virtual void run_task() override { ACL_CHECK(aclrtMemsetAsync(buffer_, size_, value_, size_, stream_)); } + private: + void * buffer_; + size_t size_; + int32_t value_; + aclrtStream stream_; }; /** @@ -918,25 +906,24 @@ class async_memset_task : public cann_task { * same stream are executed in queue order. */ -#define GGML_CANN_CALL_ACLNN_OP(CTX, OP_NAME, ...) \ - do { \ - uint64_t workspaceSize = 0; \ - aclOpExecutor * executor; \ - void * workspaceAddr = nullptr; \ - ACL_CHECK(aclnn##OP_NAME##GetWorkspaceSize(__VA_ARGS__, &workspaceSize, &executor));\ - /* workspace should alloced in main thread to keep malloc order when using vmm. */ \ - if (workspaceSize > 0) { \ - ggml_cann_pool_alloc workspace_allocator(CTX.pool(), workspaceSize); \ - workspaceAddr = workspace_allocator.get(); \ - } \ - if (CTX.async_mode) { \ - auto task = \ - std::make_unique(aclnn##OP_NAME, workspaceAddr, workspaceSize, \ - executor, CTX.stream()); \ - CTX.task_queue.submit_task(std::move(task)); \ - } else { \ - ACL_CHECK(aclnn##OP_NAME(workspaceAddr, workspaceSize, executor, CTX.stream()));\ - } \ +#define GGML_CANN_CALL_ACLNN_OP(CTX, OP_NAME, ...) \ + do { \ + uint64_t workspaceSize = 0; \ + aclOpExecutor * executor; \ + void * workspaceAddr = nullptr; \ + ACL_CHECK(aclnn##OP_NAME##GetWorkspaceSize(__VA_ARGS__, &workspaceSize, &executor)); \ + /* workspace should alloced in main thread to keep malloc order when using vmm. */ \ + if (workspaceSize > 0) { \ + ggml_cann_pool_alloc workspace_allocator(CTX.pool(), workspaceSize); \ + workspaceAddr = workspace_allocator.get(); \ + } \ + if (CTX.async_mode) { \ + auto task = \ + std::make_unique(aclnn##OP_NAME, workspaceAddr, workspaceSize, executor, CTX.stream()); \ + CTX.task_queue.submit_task(std::move(task)); \ + } else { \ + ACL_CHECK(aclnn##OP_NAME(workspaceAddr, workspaceSize, executor, CTX.stream())); \ + } \ } while (0) /** @@ -947,11 +934,10 @@ class async_memset_task : public cann_task { * @param ctx Backend context which manages task submission and async mode. * @param args Pointers to ACL resources to be released. */ -template -void ggml_cann_release_resources(ggml_backend_cann_context & ctx, Args &&... args) { +template void ggml_cann_release_resources(ggml_backend_cann_context & ctx, Args &&... args) { std::vector resources; register_acl_resources(resources, std::forward(args)...); - if(ctx.async_mode) { + if (ctx.async_mode) { auto task = std::make_unique(std::move(resources)); ctx.task_queue.submit_task(std::move(task)); } @@ -966,8 +952,11 @@ void ggml_cann_release_resources(ggml_backend_cann_context & ctx, Args &&... arg * @param len Size of memory to copy (in bytes). * @param kind Type of memory copy (host-to-device, device-to-host, etc). */ -inline void ggml_cann_async_memcpy(ggml_backend_cann_context & ctx, void * dst, - const void * src, size_t len, aclrtMemcpyKind kind) { +inline void ggml_cann_async_memcpy(ggml_backend_cann_context & ctx, + void * dst, + const void * src, + size_t len, + aclrtMemcpyKind kind) { if (ctx.async_mode) { auto task = std::make_unique(dst, const_cast(src), len, kind, ctx.stream()); ctx.task_queue.submit_task(std::move(task)); @@ -976,8 +965,11 @@ inline void ggml_cann_async_memcpy(ggml_backend_cann_context & ctx, void * dst, } } -inline void ggml_cann_async_memcpy(ggml_backend_cann_context * ctx, void * dst, - const void * src, size_t len, aclrtMemcpyKind kind) { +inline void ggml_cann_async_memcpy(ggml_backend_cann_context * ctx, + void * dst, + const void * src, + size_t len, + aclrtMemcpyKind kind) { if (ctx->async_mode) { auto task = std::make_unique(dst, const_cast(src), len, kind, ctx->stream()); ctx->task_queue.submit_task(std::move(task)); @@ -994,8 +986,7 @@ inline void ggml_cann_async_memcpy(ggml_backend_cann_context * ctx, void * dst, * @param size Size of the memory buffer (in bytes). * @param value Value to set in the buffer. */ -inline void ggml_cann_async_memset(ggml_backend_cann_context & ctx, void * buffer, - size_t size, int value) { +inline void ggml_cann_async_memset(ggml_backend_cann_context & ctx, void * buffer, size_t size, int value) { if (ctx.async_mode) { auto task = std::make_unique(buffer, size, value, ctx.stream()); ctx.task_queue.submit_task(std::move(task)); @@ -1029,7 +1020,7 @@ inline void ggml_cann_async_memset(ggml_backend_cann_context & ctx, void * buffe * @param dst The destination tensor where the expert-weighted token outputs are stored. * Expected to be of shape [M, K, N, 1]. */ -void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_mul_mat_id(ggml_backend_cann_context & ctx, ggml_tensor * dst); /** * @brief Check whether a tensor is a weight tensor for matrix multiplication. @@ -1041,20 +1032,14 @@ void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst); * * @param tensor Pointer to the target ggml_tensor object (const-qualified). */ -static bool is_matmul_weight(const ggml_tensor* tensor) { - std::string name = ggml_get_name(tensor); - static const std::unordered_set weight_suffixes{ - "output.weight", - "attn_q.weight", - "attn_k.weight", - "attn_v.weight", - "attn_output.weight", - "ffn_gate.weight", - "ffn_up.weight", - "ffn_down.weight" - }; - - for (const auto& suffix : weight_suffixes) { +static bool is_matmul_weight(const ggml_tensor * tensor) { + std::string name = ggml_get_name(tensor); + static const std::unordered_set weight_suffixes{ "output.weight", "attn_q.weight", + "attn_k.weight", "attn_v.weight", + "attn_output.weight", "ffn_gate.weight", + "ffn_up.weight", "ffn_down.weight" }; + + for (const auto & suffix : weight_suffixes) { if (name.find(suffix) != std::string::npos) { return true; } @@ -1078,14 +1063,13 @@ static bool is_matmul_weight(const ggml_tensor* tensor) { * @param ctx The CANN backend context used to manage execution and resources. * @param dst The destination tensor. */ -template -void ggml_cann_binary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src0 = dst->src[0]; - ggml_tensor* src1 = dst->src[1]; +template void ggml_cann_binary_op(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; - aclTensor* acl_src0; - aclTensor* acl_src1; - aclTensor* acl_dst; + aclTensor * acl_src0; + aclTensor * acl_src1; + aclTensor * acl_dst; // Need bcast bcast_shape(src0, src1, dst, &acl_src0, &acl_src1, &acl_dst); @@ -1094,7 +1078,6 @@ void ggml_cann_binary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_release_resources(ctx, acl_src0, acl_src1, acl_dst); } - /** * @brief Applies a unary operation to an input tensor using the CANN backend. * @@ -1107,12 +1090,12 @@ void ggml_cann_binary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) { * @param ctx The CANN backend context for managing resources and execution. * @param dst The destination tensor. Its src[0] is treated as the input tensor. */ -template - void ggml_cann_op_unary(ggml_backend_cann_context& ctx, ggml_tensor* dst) { - ggml_tensor* src = dst->src[0]; +template +void ggml_cann_op_unary(ggml_backend_cann_context & ctx, ggml_tensor * dst) { + ggml_tensor * src = dst->src[0]; - aclTensor* acl_src = ggml_cann_create_tensor(src); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); + aclTensor * acl_src = ggml_cann_create_tensor(src); + aclTensor * acl_dst = ggml_cann_create_tensor(dst); unary_op(ctx, acl_src, acl_dst); ggml_cann_release_resources(ctx, acl_src, acl_dst); @@ -1138,9 +1121,9 @@ template * * @see GGML_CANN_CALL_OP_UNARY */ -void ggml_cann_op_unary( - std::function unary_op, - ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_op_unary(std::function unary_op, + ggml_backend_cann_context & ctx, + ggml_tensor * dst); /** * @brief Applies a gated (GLU-style) unary operation using the CANN backend. @@ -1172,9 +1155,9 @@ void ggml_cann_op_unary( * * @see GGML_CANN_CALL_OP_UNARY_GATED */ -void ggml_cann_op_unary_gated( - std::function unary_op, - ggml_backend_cann_context& ctx, ggml_tensor* dst); +void ggml_cann_op_unary_gated(std::function unary_op, + ggml_backend_cann_context & ctx, + ggml_tensor * dst); /** * @brief Helper macro to call a unary ACL operator via ggml_cann_op_unary. @@ -1197,16 +1180,13 @@ void ggml_cann_op_unary_gated( * @see ggml_cann_op_unary * @see GGML_CANN_CALL_ACLNN_OP */ -#define GGML_CANN_CALL_OP_UNARY(OP_NAME) \ - do { \ - auto lambda = [](ggml_backend_cann_context& ctx, \ - aclTensor* acl_src, \ - aclTensor* acl_dst) { \ - GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst); \ - }; \ - ggml_cann_op_unary(lambda, ctx, dst); \ - } \ - while (0) +#define GGML_CANN_CALL_OP_UNARY(OP_NAME) \ + do { \ + auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { \ + GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst); \ + }; \ + ggml_cann_op_unary(lambda, ctx, dst); \ + } while (0) /** * @brief Helper macro to call a gated unary ACL operator via ggml_cann_op_unary_gated. @@ -1229,15 +1209,12 @@ void ggml_cann_op_unary_gated( * @see ggml_cann_op_unary_gated * @see GGML_CANN_CALL_ACLNN_OP */ -#define GGML_CANN_CALL_OP_UNARY_GATED(OP_NAME) \ - do { \ - auto lambda = [](ggml_backend_cann_context& ctx, \ - aclTensor* acl_src, \ - aclTensor* acl_dst) { \ - GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst); \ - }; \ - ggml_cann_op_unary_gated(lambda, ctx, dst); \ - } \ - while (0) +#define GGML_CANN_CALL_OP_UNARY_GATED(OP_NAME) \ + do { \ + auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { \ + GGML_CANN_CALL_ACLNN_OP(ctx, OP_NAME, acl_src, acl_dst); \ + }; \ + ggml_cann_op_unary_gated(lambda, ctx, dst); \ + } while (0) #endif // CANN_ACLNN_OPS diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h old mode 100755 new mode 100644 index debbcadc1e4c5..e87dbcf329ff2 --- a/ggml/src/ggml-cann/common.h +++ b/ggml/src/ggml-cann/common.h @@ -44,7 +44,7 @@ #include "../include/ggml.h" #include "../ggml-impl.h" -#define MATRIX_ROW_PADDING 512 +#define MATRIX_ROW_PADDING 512 #define GGML_CANN_MAX_STREAMS 8 /** @@ -56,8 +56,7 @@ * @param line The line number at which the error occurred. * @param msg The error message. */ -[[noreturn]] void ggml_cann_error(const char* stmt, const char* func, - const char* file, int line, const char* msg); +[[noreturn]] void ggml_cann_error(const char * stmt, const char * func, const char * file, int line, const char * msg); /** * @brief Checks the result of a CANN function call and invokes the error @@ -89,25 +88,24 @@ struct ggml_cann_device_info { * @brief Information about a single CANN device. */ struct cann_device_info { - int cc; /**< Compute capability. */ + int cc; /**< Compute capability. */ size_t smpb; /**< Maximum shared memory per block. */ - bool vmm; /**< Virtual memory support. */ + bool vmm; /**< Virtual memory support. */ size_t vmm_granularity; /**< Granularity of virtual memory. */ size_t total_vram; /**< Total video RAM available on the device. */ }; - cann_device_info devices[GGML_CANN_MAX_DEVICES] = - {}; /**< Array of CANN device information. */ + cann_device_info devices[GGML_CANN_MAX_DEVICES] = {}; /**< Array of CANN device information. */ }; -const ggml_cann_device_info& ggml_cann_info(); +const ggml_cann_device_info & ggml_cann_info(); -void ggml_cann_set_device(int32_t device); +void ggml_cann_set_device(int32_t device); int32_t ggml_cann_get_device(); -std::optional get_env(const std::string& name); -bool parse_bool(const std::string& value); -int parse_integer(const std::string& value); +std::optional get_env(const std::string & name); +bool parse_bool(const std::string & value); +int parse_integer(const std::string & value); /** * @brief Abstract base class for memory pools used by CANN. @@ -126,7 +124,7 @@ struct ggml_cann_pool { * will be stored. * @return Pointer to the allocated memory block. */ - virtual void* alloc(size_t size, size_t* actual_size) = 0; + virtual void * alloc(size_t size, size_t * actual_size) = 0; /** * @brief Frees a previously allocated memory block. @@ -136,16 +134,16 @@ struct ggml_cann_pool { * @note Note that all CANN opertors are running async. Make sure memory is * still avaiable before this operator finished. */ - virtual void free(void* ptr, size_t size) = 0; + virtual void free(void * ptr, size_t size) = 0; }; /** * @brief RAII wrapper for managing memory allocations from a CANN memory pool. */ struct ggml_cann_pool_alloc { - ggml_cann_pool* pool = nullptr; /**< Pointer to the memory pool. */ - void* ptr = nullptr; /**< Pointer to the allocated memory block. */ - size_t actual_size = 0; /**< Actual size of the allocated memory block. */ + ggml_cann_pool * pool = nullptr; /**< Pointer to the memory pool. */ + void * ptr = nullptr; /**< Pointer to the allocated memory block. */ + size_t actual_size = 0; /**< Actual size of the allocated memory block. */ /** * @brief Default constructor. @@ -156,16 +154,14 @@ struct ggml_cann_pool_alloc { * @brief Constructor that initializes the memory pool. * @param pool Reference to the memory pool. */ - explicit ggml_cann_pool_alloc(ggml_cann_pool& pool) : pool(&pool) {} + explicit ggml_cann_pool_alloc(ggml_cann_pool & pool) : pool(&pool) {} /** * @brief Constructor that initializes the memory pool and allocates memory. * @param pool Reference to the memory pool. * @param size Size of the memory block to allocate. */ - ggml_cann_pool_alloc(ggml_cann_pool& pool, size_t size) : pool(&pool) { - alloc(size); - } + ggml_cann_pool_alloc(ggml_cann_pool & pool, size_t size) : pool(&pool) { alloc(size); } /** * @brief Destructor that frees the allocated memory block. @@ -181,7 +177,7 @@ struct ggml_cann_pool_alloc { * @param size Size of the memory block to allocate. * @return Pointer to the allocated memory block. */ - void* alloc(size_t size) { + void * alloc(size_t size) { GGML_ASSERT(pool != nullptr); GGML_ASSERT(ptr == nullptr); ptr = pool->alloc(size, &this->actual_size); @@ -194,7 +190,7 @@ struct ggml_cann_pool_alloc { * @param size Size of the memory block to allocate. * @return Pointer to the allocated memory block. */ - void* alloc(ggml_cann_pool& pool, size_t size) { + void * alloc(ggml_cann_pool & pool, size_t size) { this->pool = &pool; return alloc(size); } @@ -203,25 +199,25 @@ struct ggml_cann_pool_alloc { * @brief Gets the pointer to the allocated memory block. * @return Pointer to the allocated memory block. */ - void* get() { return ptr; } + void * get() { return ptr; } // Deleted copy constructor - ggml_cann_pool_alloc(const ggml_cann_pool_alloc&) = delete; + ggml_cann_pool_alloc(const ggml_cann_pool_alloc &) = delete; // Deleted move constructor - ggml_cann_pool_alloc(ggml_cann_pool_alloc&&) = delete; + ggml_cann_pool_alloc(ggml_cann_pool_alloc &&) = delete; // Deleted copy assignment operator - ggml_cann_pool_alloc& operator=(const ggml_cann_pool_alloc&) = delete; + ggml_cann_pool_alloc & operator=(const ggml_cann_pool_alloc &) = delete; // Deleted move assignment operator - ggml_cann_pool_alloc& operator=(ggml_cann_pool_alloc&&) = delete; + ggml_cann_pool_alloc & operator=(ggml_cann_pool_alloc &&) = delete; }; /** * @brief Function pointer type for ACLNN operator calls. */ -using aclnn_func_t = aclnnStatus (*)(void*, uint64_t, aclOpExecutor*, aclrtStream); +using aclnn_func_t = aclnnStatus (*)(void *, uint64_t, aclOpExecutor *, aclrtStream); /** * @brief Base class for all CANN tasks to be submitted to the task queue. @@ -229,7 +225,7 @@ using aclnn_func_t = aclnnStatus (*)(void*, uint64_t, aclOpExecutor*, aclrtStrea * Users should override the run_task() method with actual task logic. */ class cann_task { -public: + public: virtual void run_task() {} }; @@ -237,16 +233,20 @@ class cann_task { * @brief A lock-free ring-buffer based task queue for asynchronously executing cann_task instances. */ class cann_task_queue { -public: + public: /** * @brief Constructs a task queue with a fixed power-of-two capacity for a specific device. * * @param capacity Queue capacity. Must be a power of 2. * @param device Target device ID (used for context setting). */ - explicit cann_task_queue(size_t capacity, int32_t device) - : buffer_(capacity), capacity_(capacity), head_(0), tail_(0), - running_(false), device_(device) { + explicit cann_task_queue(size_t capacity, int32_t device) : + buffer_(capacity), + capacity_(capacity), + head_(0), + tail_(0), + running_(false), + device_(device) { GGML_ASSERT((capacity & (capacity - 1)) == 0 && "capacity must be power of 2"); mask_ = capacity_ - 1; } @@ -257,7 +257,7 @@ class cann_task_queue { * @param item Unique pointer to the task. * @return true if the task was successfully enqueued, false if the queue was full. */ - bool enqueue(std::unique_ptr&& item) { + bool enqueue(std::unique_ptr && item) { size_t next_tail = (tail_ + 1) & mask_; if (next_tail == head_) { @@ -276,17 +276,16 @@ class cann_task_queue { * * @param task Task to be submitted. */ - void submit_task(std::unique_ptr&& task) { - while(!enqueue(std::move(task))) { + void submit_task(std::unique_ptr && task) { + while (!enqueue(std::move(task))) { std::this_thread::yield(); continue; } if (!running_) { running_ = true; - thread_ = std::thread(&cann_task_queue::execute, this); + thread_ = std::thread(&cann_task_queue::execute, this); } - } /** @@ -309,7 +308,7 @@ class cann_task_queue { } } -private: + private: /** * @brief Worker thread function that continuously dequeues and executes tasks. */ @@ -317,7 +316,7 @@ class cann_task_queue { ggml_cann_set_device(device_); while (running_) { - if(head_ == tail_) { + if (head_ == tail_) { std::this_thread::yield(); continue; } @@ -330,24 +329,24 @@ class cann_task_queue { } std::vector> buffer_; - const size_t capacity_; - size_t mask_; - size_t head_; - size_t tail_; - bool running_; - std::thread thread_; - int32_t device_; + const size_t capacity_; + size_t mask_; + size_t head_; + size_t tail_; + bool running_; + std::thread thread_; + int32_t device_; }; #ifdef USE_ACL_GRAPH struct ggml_graph_node_properties { // dst tensor - void * node_address; + void * node_address; int64_t ne[GGML_MAX_DIMS]; - size_t nb[GGML_MAX_DIMS]; + size_t nb[GGML_MAX_DIMS]; // src tensor - void * src_address[GGML_MAX_SRC]; + void * src_address[GGML_MAX_SRC]; int64_t src_ne[GGML_MAX_SRC][GGML_MAX_DIMS]; size_t src_nb[GGML_MAX_SRC][GGML_MAX_DIMS]; @@ -376,13 +375,11 @@ struct ggml_cann_graph { * move existing graphs to the front (most recently used), and clear the cache. */ struct ggml_cann_graph_lru_cache { - size_t capacity; /**< Maximum number of graphs in the cache. */ + size_t capacity; /**< Maximum number of graphs in the cache. */ - std::list cache_list; /**< List storing cached graphs as raw pointers. */ + std::list cache_list; /**< List storing cached graphs as raw pointers. */ - ggml_cann_graph_lru_cache() { - capacity = parse_integer(get_env("GGML_CANN_GRAPH_CACHE_CAPACITY").value_or("12")); - } + ggml_cann_graph_lru_cache() { capacity = parse_integer(get_env("GGML_CANN_GRAPH_CACHE_CAPACITY").value_or("12")); } /** * @brief Push a new graph to the front of the cache. @@ -390,11 +387,11 @@ struct ggml_cann_graph_lru_cache { * @param new_node Pointer to the new ggml_cann_graph to cache. * Ownership is transferred to the cache (cache will delete it). */ - void push(ggml_cann_graph* new_node) { + void push(ggml_cann_graph * new_node) { if (cache_list.size() >= capacity) { - ggml_cann_graph* old = cache_list.back(); + ggml_cann_graph * old = cache_list.back(); cache_list.pop_back(); - delete old; // free the old graph + delete old; // free the old graph } cache_list.push_front(new_node); } @@ -403,7 +400,7 @@ struct ggml_cann_graph_lru_cache { * @brief Move an existing graph to the front of the cache. * @param node Pointer to the ggml_cann_graph to move. */ - void move_to_front(ggml_cann_graph* node) { + void move_to_front(ggml_cann_graph * node) { cache_list.remove(node); cache_list.push_front(node); } @@ -421,92 +418,89 @@ struct ggml_cann_graph_lru_cache { /** * @brief Destructor that clears the cache and frees all cached graphs. */ - ~ggml_cann_graph_lru_cache() { - clear(); - } + ~ggml_cann_graph_lru_cache() { clear(); } }; #endif // USE_ACL_GRAPH struct ggml_cann_rope_cache { ~ggml_cann_rope_cache() { - if(theta_scale_cache != nullptr) { + if (theta_scale_cache != nullptr) { ACL_CHECK(aclrtFree(theta_scale_cache)); } - if(sin_cache != nullptr) { + if (sin_cache != nullptr) { ACL_CHECK(aclrtFree(sin_cache)); } - if(cos_cache != nullptr) { + if (cos_cache != nullptr) { ACL_CHECK(aclrtFree(cos_cache)); } } - void* theta_scale_cache = nullptr; + void * theta_scale_cache = nullptr; int64_t theta_scale_length = 0; // sin/cos cache, used only to accelerate first layer on each device - void* sin_cache = nullptr; - void* cos_cache = nullptr; - int64_t position_length = 0; + void * sin_cache = nullptr; + void * cos_cache = nullptr; + int64_t position_length = 0; // Properties to check before reusing the sincos cache - bool cached = false; - float ext_factor = 0.0f; - float theta_scale = 0.0f; - float freq_scale = 0.0f; - float attn_factor = 0.0f; - bool is_neox = false; + bool cached = false; + float ext_factor = 0.0f; + float theta_scale = 0.0f; + float freq_scale = 0.0f; + float attn_factor = 0.0f; + bool is_neox = false; }; struct ggml_cann_tensor_cache { ~ggml_cann_tensor_cache() { - if(cache != nullptr) { + if (cache != nullptr) { ACL_CHECK(aclrtFree(cache)); } } - void* cache = nullptr; - int64_t size = 0; + void * cache = nullptr; + int64_t size = 0; }; /** * @brief Context for managing CANN backend operations. */ struct ggml_backend_cann_context { - int32_t device; /**< Device ID. */ - std::string name; /**< Name of the device. */ - std::string description; /**< Description of the device. */ - aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */ + int32_t device; /**< Device ID. */ + std::string name; /**< Name of the device. */ + std::string description; /**< Description of the device. */ + aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */ #ifdef USE_ACL_GRAPH /// Cached CANN ACL graph used for executing the current ggml computation graph. ggml_cann_graph_lru_cache graph_lru_cache; - bool acl_graph_mode = true; + bool acl_graph_mode = true; #endif - cann_task_queue task_queue; - bool async_mode; + cann_task_queue task_queue; + bool async_mode; // Rope Cache - ggml_cann_rope_cache rope_cache; + ggml_cann_rope_cache rope_cache; // Constant Pool ggml_cann_tensor_cache rms_norm_one_tensor_cache; ggml_cann_tensor_cache rms_norm_zero_tensor_cache; - aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */ + aclrtStream streams[GGML_CANN_MAX_STREAMS] = { nullptr }; /**< Array of streams for the device. */ /** * @brief Constructor for initializing the context with a given device. * @param device Device ID. */ - explicit ggml_backend_cann_context(int device) - : device(device), name("CANN" + std::to_string(device)), task_queue(1024, device) { + explicit ggml_backend_cann_context(int device) : + device(device), + name("CANN" + std::to_string(device)), + task_queue(1024, device) { ggml_cann_set_device(device); description = aclrtGetSocName(); async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or("")); - GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__, - device, async_mode ? "ON" : "OFF"); + GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__, device, async_mode ? "ON" : "OFF"); #ifdef USE_ACL_GRAPH acl_graph_mode = parse_bool(get_env("GGML_CANN_ACL_GRAPH").value_or("on")); - GGML_LOG_INFO("%s: device %d execution mode is %s (%s)\n", - __func__, device, - acl_graph_mode ? "GRAPH" : "EAGER", - acl_graph_mode ? "acl graph enabled" : "acl graph disabled"); + GGML_LOG_INFO("%s: device %d execution mode is %s (%s)\n", __func__, device, acl_graph_mode ? "GRAPH" : "EAGER", + acl_graph_mode ? "acl graph enabled" : "acl graph disabled"); #endif } @@ -549,8 +543,7 @@ struct ggml_backend_cann_context { aclrtStream stream() { return stream(0); } // TODO: each stream should have a memory pool. - std::unique_ptr - mem_pool; /**< Memory pool for the device. */ + std::unique_ptr mem_pool; /**< Memory pool for the device. */ /** * @brief Create a new memory pool for a given device. @@ -563,7 +556,7 @@ struct ggml_backend_cann_context { * @brief Get or create the memory pool for the context. * @return Reference to the memory pool. */ - ggml_cann_pool& pool() { + ggml_cann_pool & pool() { if (mem_pool == nullptr) { mem_pool = new_pool_for_device(device); } diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp old mode 100755 new mode 100644 index ad1adba6b3a8a..8bd5449f1f75f --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -56,14 +56,12 @@ * @param line The line number where the error occurred. * @param msg The error message. */ -[[noreturn]] void ggml_cann_error(const char* stmt, const char* func, - const char* file, int line, const char* msg) { +[[noreturn]] void ggml_cann_error(const char * stmt, const char * func, const char * file, int line, const char * msg) { int32_t id = -1; aclrtGetDevice(&id); GGML_LOG_ERROR("CANN error: %s\n", msg); - GGML_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, - file, line); + GGML_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, file, line); GGML_LOG_ERROR(" %s\n", stmt); // abort with GGML_ASSERT to get a stack trace GGML_ABORT("CANN error"); @@ -79,7 +77,7 @@ void ggml_cann_set_device(const int32_t device) { aclrtGetDevice(¤t_device); if (device == current_device) { - return; + return; } ACL_CHECK(aclrtSetDevice(device)); } @@ -99,9 +97,11 @@ int32_t ggml_cann_get_device() { * @brief Get the value of the specified environment variable (name). * if not empty, return a std::string object */ -std::optional get_env(const std::string& name) { - const char* val = std::getenv(name.c_str()); - if (!val) return std::nullopt; +std::optional get_env(const std::string & name) { + const char * val = std::getenv(name.c_str()); + if (!val) { + return std::nullopt; + } std::string res = std::string(val); std::transform(res.begin(), res.end(), res.begin(), ::tolower); return res; @@ -110,8 +110,8 @@ std::optional get_env(const std::string& name) { /** * @brief Verify whether the environment variable is a valid value. */ -bool parse_bool(const std::string& value) { - std::unordered_set valid_values = {"on", "1", "yes", "y", "enable", "true"}; +bool parse_bool(const std::string & value) { + std::unordered_set valid_values = { "on", "1", "yes", "y", "enable", "true" }; return valid_values.find(value) != valid_values.end(); } @@ -125,7 +125,7 @@ bool parse_bool(const std::string& value) { * @param value The string to parse. * @return The parsed integer, or 0 if conversion fails. */ -int parse_integer(const std::string& value) { +int parse_integer(const std::string & value) { try { return std::stoi(value); } catch (...) { @@ -144,11 +144,10 @@ int parse_integer(const std::string& value) { static ggml_cann_device_info ggml_cann_init() { ggml_cann_device_info info = {}; - aclError err = aclrtGetDeviceCount((uint32_t*)&info.device_count); + aclError err = aclrtGetDeviceCount((uint32_t *) &info.device_count); if (err != ACL_SUCCESS) { - GGML_LOG_ERROR("%s: failed to initialize CANN: %s\n", - __func__, aclGetRecentErrMsg()); + GGML_LOG_ERROR("%s: failed to initialize CANN: %s\n", __func__, aclGetRecentErrMsg()); return info; } @@ -156,16 +155,15 @@ static ggml_cann_device_info ggml_cann_init() { for (int id = 0; id < info.device_count; ++id) { aclrtPhysicalMemProp prop = {}; - prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; - prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; - prop.memAttr = ACL_HBM_MEM_HUGE; - prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = id; - prop.reserve = 0; - err = aclrtMemGetAllocationGranularity( - &prop, ACL_RT_MEM_ALLOC_GRANULARITY_RECOMMENDED, - &info.devices[id].vmm_granularity); - info.devices[id].vmm = err == ACL_SUCCESS; + prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; + prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; + prop.memAttr = ACL_HBM_MEM_HUGE; + prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; + prop.location.id = id; + prop.reserve = 0; + err = aclrtMemGetAllocationGranularity(&prop, ACL_RT_MEM_ALLOC_GRANULARITY_RECOMMENDED, + &info.devices[id].vmm_granularity); + info.devices[id].vmm = err == ACL_SUCCESS; size_t free, total; ggml_backend_cann_get_device_memory(id, &free, &total); @@ -185,7 +183,7 @@ static ggml_cann_device_info ggml_cann_init() { * * @return A reference to the structure containing the device information. */ -const ggml_cann_device_info& ggml_cann_info() { +const ggml_cann_device_info & ggml_cann_info() { static ggml_cann_device_info info = ggml_cann_init(); return info; } @@ -205,7 +203,7 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { /** * @brief The minimum free margin for a buffer. */ - static const size_t min_free_margin = 1ull << 20; // 1MB + static const size_t min_free_margin = 1ull << 20; // 1MB /** * @brief The alignment for buffer allocation. @@ -226,22 +224,18 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { * @brief Structure representing a CANN buffer. */ struct ggml_cann_buffer { - void* ptr = nullptr; ///< Pointer to the buffer. - size_t size = 0; ///< Size of the buffer. - std::chrono::steady_clock::time_point last_used; ///< Last used time. + void * ptr = nullptr; ///< Pointer to the buffer. + size_t size = 0; ///< Size of the buffer. + std::chrono::steady_clock::time_point last_used; ///< Last used time. - bool operator>(const ggml_cann_buffer& other) const { - return size > other.size; - } + bool operator>(const ggml_cann_buffer & other) const { return size > other.size; } }; /** * @brief Array of CANN buffers in the pool. */ - std::unordered_map buffer_pool; - std::priority_queue, - std::greater<>> free_buffers ; + std::unordered_map buffer_pool; + std::priority_queue, std::greater<>> free_buffers; /** * @brief Total size of all buffers in the pool. @@ -262,7 +256,7 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { */ ~ggml_cann_pool_buf_prio() { ggml_cann_set_device(device); - for (auto& [b_ptr, b_size] : buffer_pool) { + for (auto & [b_ptr, b_size] : buffer_pool) { aclrtFree(b_ptr); pool_size -= b_size; } @@ -278,14 +272,14 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { * the allocated buffer. * @return A pointer to the allocated buffer. */ - void* alloc(size_t size, size_t* actual_size) override { + void * alloc(size_t size, size_t * actual_size) override { size = GGML_PAD(size, alignment); if (size == 0) { size = alignment; } - void* ptr = nullptr; - auto now = std::chrono::steady_clock::now(); + void * ptr = nullptr; + auto now = std::chrono::steady_clock::now(); std::vector free_buffers_rest; free_buffers_rest.reserve(free_buffers.size()); @@ -298,24 +292,22 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { const size_t margin = b.size - size; if (margin <= max_reuse_margin) { *actual_size = b.size; - ptr = b.ptr; + ptr = b.ptr; #ifdef DEBUG_CANN_MALLOC GGML_LOG_INFO( "cann pool[%d]: reused %p, " "pool_size = %5u MB, " "size = %5u MB, " "margin = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(margin, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(margin, 1048576) / 1048576)); #endif break; } } - bool should_clean = !disable_clean && - b.size > min_free_margin && + bool should_clean = !disable_clean && b.size > min_free_margin && std::chrono::duration_cast(now - b.last_used).count() > 100; if (should_clean) { // free the buffer if the size is needed to be freed @@ -327,20 +319,20 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { "cann pool[%d]: clean %p, " "pool_size = %5u MB, " "size = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(b.size, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(b.size, 1048576) / 1048576)); #endif continue; } free_buffers_rest.push_back(b); } - for (ggml_cann_buffer &b : free_buffers_rest) { + for (ggml_cann_buffer & b : free_buffers_rest) { free_buffers.push(std::move(b)); } #ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO("cann pool[%d] free pool_size = %5u MB\n\n", device, (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576)); + GGML_LOG_INFO("cann pool[%d] free pool_size = %5u MB\n\n", device, + (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576)); #endif if (ptr != nullptr) { return ptr; @@ -356,8 +348,8 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { "cann pool[%d]: allocate %p, " "pool_size = %5u MB, " "size = %5u MB\n", - device, ptr, (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(size, 1048576) / 1048576)); + device, ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(size, 1048576) / 1048576)); #endif buffer_pool.emplace(ptr, size); return ptr; @@ -369,7 +361,7 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { * @param ptr Pointer to the buffer to free. * @param size Size of the buffer to free. */ - void free(void* ptr, size_t size) override { + void free(void * ptr, size_t size) override { GGML_UNUSED(size); auto it = buffer_pool.find(ptr); if (it == buffer_pool.end()) { @@ -377,13 +369,12 @@ struct ggml_cann_pool_buf_prio : public ggml_cann_pool { } auto now = std::chrono::steady_clock::now(); - free_buffers.emplace(ggml_cann_buffer{ptr, it->second, now}); + free_buffers.emplace(ggml_cann_buffer{ ptr, it->second, now }); #ifdef DEBUG_CANN_MALLOC GGML_LOG_INFO( "cann pool[%d]: return %p, " "pool_size = %5u MB\n", - device, ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576)); + device, ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576)); #endif } }; @@ -402,7 +393,7 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { /** * @brief The minimum free margin for a buffer. */ - static const size_t min_free_margin = 1ull << 20; // 1MB + static const size_t min_free_margin = 1ull << 20; // 1MB /** * @brief The alignment for buffer allocation. @@ -428,10 +419,10 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { * @brief Structure representing a CANN buffer. */ struct ggml_cann_buffer { - void* ptr = nullptr; ///< Pointer to the buffer memory. - size_t size = 0; ///< Size of the buffer. - bool used = false; ///< Whether the buffer is currently in use. - std::chrono::steady_clock::time_point last_used; ///< Last used time. + void * ptr = nullptr; ///< Pointer to the buffer memory. + size_t size = 0; ///< Size of the buffer. + bool used = false; ///< Whether the buffer is currently in use. + std::chrono::steady_clock::time_point last_used; ///< Last used time. }; /** @@ -459,7 +450,7 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { ~ggml_cann_pool_buf() { ggml_cann_set_device(device); for (int i = 0; i < MAX_BUFFERS; ++i) { - ggml_cann_buffer& b = buffer_pool[i]; + ggml_cann_buffer & b = buffer_pool[i]; if (b.ptr != nullptr) { aclrtFree(b.ptr); pool_size -= b.size; @@ -476,18 +467,18 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { * the allocated buffer. * @return A pointer to the allocated buffer. */ - void* alloc(size_t size, size_t* actual_size) override { + void * alloc(size_t size, size_t * actual_size) override { size = GGML_PAD(size, alignment); if (size == 0) { size = alignment; } - void* ptr = nullptr; - auto now = std::chrono::steady_clock::now(); + void * ptr = nullptr; + auto now = std::chrono::steady_clock::now(); int i = 0; for (; i < MAX_BUFFERS; ++i) { - ggml_cann_buffer& b = buffer_pool[i]; + ggml_cann_buffer & b = buffer_pool[i]; if (b.ptr == nullptr) { break; } @@ -499,25 +490,23 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { const size_t margin = b.size - size; if (margin <= max_reuse_margin) { *actual_size = b.size; - b.used = true; - ptr = b.ptr; + b.used = true; + ptr = b.ptr; #ifdef DEBUG_CANN_MALLOC GGML_LOG_INFO( "cann pool[%d]: reused %p, " "pool_size = %5u MB, " "size = %5u MB, " "margin = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(margin, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(margin, 1048576) / 1048576)); #endif break; } } - bool should_clean = !disable_clean && - b.size > min_free_margin && + bool should_clean = !disable_clean && b.size > min_free_margin && std::chrono::duration_cast(now - b.last_used).count() > 100; if (should_clean) { // free the buffer if the size is needed to be freed @@ -528,9 +517,8 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { "cann pool[%d]: clean %p, " "pool_size = %5u MB, " "size = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(b.size, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(b.size, 1048576) / 1048576)); #endif b.ptr = nullptr; } @@ -541,13 +529,13 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { if (i < MAX_BUFFERS) { // allocate a new buffer if no buffer can be reused - ggml_cann_buffer& b = buffer_pool[i]; + ggml_cann_buffer & b = buffer_pool[i]; ggml_cann_set_device(device); ACL_CHECK(aclrtMalloc(&b.ptr, size, ACL_MEM_MALLOC_HUGE_FIRST)); pool_size += size; *actual_size = size; - b.size = size; - b.used = true; + b.size = size; + b.used = true; if (i >= MAX_BUFFERS - 8) { GGML_LOG_WARN("cann pool[%d]: slots almost full\n", device); } @@ -556,9 +544,8 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { "cann pool[%d]: allocate %p, " "pool_size = %5u MB, " "size = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576), - (uint32_t)(GGML_PAD(b.size, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576), + (uint32_t) (GGML_PAD(b.size, 1048576) / 1048576)); #endif return b.ptr; } @@ -572,21 +559,20 @@ struct ggml_cann_pool_buf : public ggml_cann_pool { * @param ptr Pointer to the buffer to free. * @param size Size of the buffer to free. */ - void free(void* ptr, size_t size) override { + void free(void * ptr, size_t size) override { GGML_UNUSED(size); for (int i = 0; i < MAX_BUFFERS; ++i) { - ggml_cann_buffer& b = buffer_pool[i]; + ggml_cann_buffer & b = buffer_pool[i]; if (b.ptr != ptr) { continue; } - b.used = false; + b.used = false; b.last_used = std::chrono::steady_clock::now(); #ifdef DEBUG_CANN_MALLOC GGML_LOG_INFO( "cann pool[%d]: return %p, " "pool_size = %5u MB\n", - device, b.ptr, - (uint32_t)(GGML_PAD(pool_size, 1048576) / 1048576)); + device, b.ptr, (uint32_t) (GGML_PAD(pool_size, 1048576) / 1048576)); #endif return; } @@ -614,7 +600,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { /** * @brief Pointer to the start of the virtual memory pool. */ - void* pool_addr = 0; + void * pool_addr = 0; /** * @brief Amount of virtual memory used in the pool. @@ -639,7 +625,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { /** * @brief Offsets for the mapped memory regions. */ - std::vector map_offsets; + std::vector map_offsets; /** * @brief Constructor to initialize the buffer pool with virtual memory for @@ -647,11 +633,10 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { * * @param device The device ID to associate with this buffer pool. */ - explicit ggml_cann_pool_vmm(int device) - : device(device) { - auto dev = ggml_cann_info().devices[device]; + explicit ggml_cann_pool_vmm(int device) : device(device) { + auto dev = ggml_cann_info().devices[device]; granularity = dev.vmm_granularity; - max_size = dev.total_vram; + max_size = dev.total_vram; } /** @@ -659,10 +644,10 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { */ ~ggml_cann_pool_vmm() { if (pool_addr != 0) { - for (auto& offset : map_offsets) { + for (auto & offset : map_offsets) { ACL_CHECK(aclrtUnmapMem(offset)); } - for (auto& handle : handles) { + for (auto & handle : handles) { ACL_CHECK(aclrtFreePhysical(handle)); } ACL_CHECK(aclrtReleaseMemAddress(pool_addr)); @@ -677,11 +662,11 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { * the allocated buffer. * @return A pointer to the allocated buffer. */ - void* alloc(size_t size, size_t* actual_size) override { + void * alloc(size_t size, size_t * actual_size) override { // round up the allocation size to the alignment to ensure that all // allocations are aligned for all data types const size_t alignment = 128; - size = GGML_PAD(size, alignment); + size = GGML_PAD(size, alignment); if (size == 0) { size = alignment; } @@ -691,53 +676,51 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { if (size > avail) { // round up to the next multiple of the granularity size_t reserve_size = size - avail; - reserve_size = GGML_PAD(reserve_size, granularity); + reserve_size = GGML_PAD(reserve_size, granularity); GGML_ASSERT(pool_size + reserve_size <= max_size); // allocate more physical memory aclrtPhysicalMemProp prop = {}; - prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; - prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; - prop.memAttr = ACL_HBM_MEM_HUGE; - prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; - prop.location.id = device; - prop.reserve = 0; + prop.handleType = ACL_MEM_HANDLE_TYPE_NONE; + prop.allocationType = ACL_MEM_ALLOCATION_TYPE_PINNED; + prop.memAttr = ACL_HBM_MEM_HUGE; + prop.location.type = ACL_MEM_LOCATION_TYPE_DEVICE; + prop.location.id = device; + prop.reserve = 0; aclrtDrvMemHandle handle; ACL_CHECK(aclrtMallocPhysical(&handle, reserve_size, &prop, 0)); // reserve virtual address space (if not already reserved) if (pool_addr == 0) { - ACL_CHECK(aclrtReserveMemAddress( - &pool_addr, max_size, 0, NULL, 1)); + ACL_CHECK(aclrtReserveMemAddress(&pool_addr, max_size, 0, NULL, 1)); } // map at the end of the pool - ACL_CHECK(aclrtMapMem((char*)pool_addr + pool_size, reserve_size, 0, - handle, 0)); + ACL_CHECK(aclrtMapMem((char *) pool_addr + pool_size, reserve_size, 0, handle, 0)); handles.push_back(handle); - map_offsets.push_back((char*)pool_addr + pool_size); + map_offsets.push_back((char *) pool_addr + pool_size); // add to the pool pool_size += reserve_size; #ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO("cann pool[%d]: size increased to %llu MB (reserved %llu MB)\n", - device, (unsigned long long) (pool_size/1024/1024), - (unsigned long long) (reserve_size/1024/1024)); + GGML_LOG_INFO("cann pool[%d]: size increased to %llu MB (reserved %llu MB)\n", device, + (unsigned long long) (pool_size / 1024 / 1024), + (unsigned long long) (reserve_size / 1024 / 1024)); #endif } GGML_ASSERT(pool_addr != 0); - void* ptr = (void*)((char*)pool_addr + pool_used); + void * ptr = (void *) ((char *) pool_addr + pool_used); *actual_size = size; pool_used += size; #ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO("cann pool[%d]: allocated %llu bytes at %llx\n", device, - (unsigned long long)size, (unsigned long long)ptr); + GGML_LOG_INFO("cann pool[%d]: allocated %llu bytes at %llx\n", device, (unsigned long long) size, + (unsigned long long) ptr); #endif return ptr; } @@ -748,16 +731,16 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { * @param ptr Pointer to the buffer to free. * @param size Size of the buffer to free. */ - void free(void* ptr, size_t size) override { + void free(void * ptr, size_t size) override { #ifdef DEBUG_CANN_MALLOC - GGML_LOG_INFO("cann pool[%d]: freed %llu bytes at %llx\n", device, - (unsigned long long)size, (unsigned long long)ptr); + GGML_LOG_INFO("cann pool[%d]: freed %llu bytes at %llx\n", device, (unsigned long long) size, + (unsigned long long) ptr); #endif pool_used -= size; // all deallocations must be in reverse order of the allocations - GGML_ASSERT(ptr == (void*)((char*)pool_addr + pool_used)); + GGML_ASSERT(ptr == (void *) ((char *) pool_addr + pool_used)); } }; @@ -769,8 +752,7 @@ struct ggml_cann_pool_vmm : public ggml_cann_pool { * @param device The device ID for which to create the pool. * @return A unique pointer to the created CANN pool. */ -std::unique_ptr ggml_backend_cann_context::new_pool_for_device( - int device) { +std::unique_ptr ggml_backend_cann_context::new_pool_for_device(int device) { std::string mem_pool_type = get_env("GGML_CANN_MEM_POOL").value_or(""); if (mem_pool_type == "prio") { @@ -795,9 +777,8 @@ std::unique_ptr ggml_backend_cann_context::new_pool_for_device( * ID, device pointer, and a name derived from GGML_CANN_NAME and the device ID. */ struct ggml_backend_cann_buffer_context { - int32_t device; ///< The device ID associated with this buffer context. - void* dev_ptr = - nullptr; ///< Pointer to the device memory allocated for the buffer. + int32_t device; ///< The device ID associated with this buffer context. + void * dev_ptr = nullptr; ///< Pointer to the device memory allocated for the buffer. /** * @brief Constructor to initialize the CANN buffer context. @@ -805,9 +786,7 @@ struct ggml_backend_cann_buffer_context { * @param device The device ID associated with this buffer context. * @param dev_ptr Pointer to the device memory allocated for the buffer. */ - ggml_backend_cann_buffer_context(int32_t device, void* dev_ptr) - : device(device), - dev_ptr(dev_ptr) {} + ggml_backend_cann_buffer_context(int32_t device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {} /** * @brief Destructor to free the device memory allocated for the buffer. @@ -825,8 +804,8 @@ struct ggml_backend_cann_buffer_context { * @return true if the buffer is a CANN buffer, false otherwise. */ static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft); -static bool ggml_backend_buffer_is_cann( - ggml_backend_buffer_t buffer) { + +static bool ggml_backend_buffer_is_cann(ggml_backend_buffer_t buffer) { return ggml_backend_buft_is_cann(buffer->buft); } @@ -838,10 +817,8 @@ static bool ggml_backend_buffer_is_cann( * * @param buffer The CANN buffer to free. */ -static void ggml_backend_cann_buffer_free_buffer( - ggml_backend_buffer_t buffer) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; +static void ggml_backend_cann_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context; delete ctx; } @@ -854,10 +831,8 @@ static void ggml_backend_cann_buffer_free_buffer( * @param buffer The CANN buffer whose base pointer is to be retrieved. * @return A pointer to the base of the device memory allocated for the buffer. */ -static void* ggml_backend_cann_buffer_get_base( - ggml_backend_buffer_t buffer) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; +static void * ggml_backend_cann_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context; return ctx->dev_ptr; } @@ -874,21 +849,17 @@ static void* ggml_backend_cann_buffer_get_base( * @param dst Pointer to the destination buffer where transformed data will be * stored. */ -static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor, - const void* src, - void* dst) { - - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK4_0; - size_t quant_bytes = n_elems * sizeof(uint8_t) / 2; +static void ggml_backend_cann_transform_q4_0(ggml_tensor * tensor, const void * src, void * dst) { + int64_t n_elems = ggml_nelements(tensor); + int64_t groups = n_elems / QK4_0; + size_t quant_bytes = n_elems * sizeof(uint8_t) / 2; - uint8_t* quant_offset = (uint8_t*)dst; - uint16_t* scale_offset = (uint16_t*)((char*)dst + quant_bytes); + uint8_t * quant_offset = (uint8_t *) dst; + uint16_t * scale_offset = (uint16_t *) ((char *) dst + quant_bytes); for (int i = 0; i < groups; i++) { - const block_q4_0* group = - (const block_q4_0*)((const char*)src + i * sizeof(block_q4_0)); - *scale_offset = group->d; + const block_q4_0 * group = (const block_q4_0 *) ((const char *) src + i * sizeof(block_q4_0)); + *scale_offset = group->d; scale_offset++; // 0-15 @@ -907,8 +878,7 @@ static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor, } // put (uint4b_t -8) into int4b_t - for (quant_offset = (uint8_t*)dst; - quant_offset < (uint8_t*)dst + quant_bytes; quant_offset++) { + for (quant_offset = (uint8_t *) dst; quant_offset < (uint8_t *) dst + quant_bytes; quant_offset++) { (*quant_offset) ^= 0x88; } } @@ -926,29 +896,27 @@ static void ggml_backend_cann_transform_q4_0(ggml_tensor* tensor, * @param dst Pointer to the destination buffer where the Q4.0 formatted data * will be stored. */ -static void ggml_backend_cann_transform_back_q4_0( - const ggml_tensor* tensor, void* src, void* dst) { +static void ggml_backend_cann_transform_back_q4_0(const ggml_tensor * tensor, void * src, void * dst) { + int64_t n_elems = ggml_nelements(tensor); + int64_t groups = n_elems / QK4_0; + size_t quant_bytes = n_elems * sizeof(uint8_t) / 2; - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK4_0; - size_t quant_bytes = n_elems * sizeof(uint8_t) / 2; + uint8_t * quant_offset = (uint8_t *) src; + uint16_t * scale_offset = (uint16_t *) ((char *) src + quant_bytes); - uint8_t* quant_offset = (uint8_t*)src; - uint16_t* scale_offset = (uint16_t*)((char*)src + quant_bytes); - - for (; quant_offset < (uint8_t*)src + quant_bytes; quant_offset++) { + for (; quant_offset < (uint8_t *) src + quant_bytes; quant_offset++) { (*quant_offset) ^= 0x88; } - quant_offset = (uint8_t*)src; + quant_offset = (uint8_t *) src; for (int i = 0; i < groups; i++) { - block_q4_0* group = (block_q4_0*)((char*)dst + i * sizeof(block_q4_0)); - group->d = *scale_offset; + block_q4_0 * group = (block_q4_0 *) ((char *) dst + i * sizeof(block_q4_0)); + group->d = *scale_offset; scale_offset++; // 0-15 for (int j = 0; j < QK4_0 / 2; j += 2) { - group->qs[j] = ((*quant_offset) & 0x0F); + group->qs[j] = ((*quant_offset) & 0x0F); group->qs[j + 1] = ((*quant_offset) >> 4); quant_offset++; } @@ -975,20 +943,17 @@ static void ggml_backend_cann_transform_back_q4_0( * @param dst Pointer to the destination buffer where transformed data will be * stored. */ -static void ggml_backend_cann_transform_q8_0(ggml_tensor* tensor, - const void* src, - void* dst) { - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK8_0; - size_t quant_bytes = n_elems * sizeof(uint8_t); +static void ggml_backend_cann_transform_q8_0(ggml_tensor * tensor, const void * src, void * dst) { + int64_t n_elems = ggml_nelements(tensor); + int64_t groups = n_elems / QK8_0; + size_t quant_bytes = n_elems * sizeof(uint8_t); - uint8_t* quant_offset = (uint8_t*)dst; - uint16_t* scale_offset = (uint16_t*)((char*)dst + quant_bytes); + uint8_t * quant_offset = (uint8_t *) dst; + uint16_t * scale_offset = (uint16_t *) ((char *) dst + quant_bytes); for (int i = 0; i < groups; i++) { - const block_q8_0* group = - (const block_q8_0*)((const char*)src + i * sizeof(block_q8_0)); - *scale_offset = group->d; + const block_q8_0 * group = (const block_q8_0 *) ((const char *) src + i * sizeof(block_q8_0)); + *scale_offset = group->d; scale_offset++; size_t group_quant_size = QK8_0 * sizeof(uint8_t); memcpy(quant_offset, group->qs, group_quant_size); @@ -1009,19 +974,17 @@ static void ggml_backend_cann_transform_q8_0(ggml_tensor* tensor, * @param dst Pointer to the destination buffer where the Q8.0 formatted data * will be stored. */ -static void ggml_backend_cann_transform_back_q8_0( - const ggml_tensor* tensor, const void* src, void* dst) { - int64_t n_elems = ggml_nelements(tensor); - int64_t groups = n_elems / QK8_0; - size_t quant_bytes = n_elems * sizeof(uint8_t); +static void ggml_backend_cann_transform_back_q8_0(const ggml_tensor * tensor, const void * src, void * dst) { + int64_t n_elems = ggml_nelements(tensor); + int64_t groups = n_elems / QK8_0; + size_t quant_bytes = n_elems * sizeof(uint8_t); - const uint8_t* quant_offset = (const uint8_t*)src; - const uint16_t* scale_offset = - (const uint16_t*)((const char*)src + quant_bytes); + const uint8_t * quant_offset = (const uint8_t *) src; + const uint16_t * scale_offset = (const uint16_t *) ((const char *) src + quant_bytes); for (int i = 0; i < groups; i++) { - block_q8_0* group = (block_q8_0*)((char*)dst + i * sizeof(block_q8_0)); - group->d = *scale_offset; + block_q8_0 * group = (block_q8_0 *) ((char *) dst + i * sizeof(block_q8_0)); + group->d = *scale_offset; scale_offset++; size_t group_quant_size = QK8_0 * sizeof(uint8_t); memcpy(group->qs, quant_offset, group_quant_size); @@ -1041,8 +1004,7 @@ static void ggml_backend_cann_transform_back_q8_0( * @param dst Pointer to the destination buffer where transformed data will be * stored. */ -static void ggml_backend_cann_transform(ggml_tensor* tensor, - const void* src, void* dst) { +static void ggml_backend_cann_transform(ggml_tensor * tensor, const void * src, void * dst) { switch (tensor->type) { case GGML_TYPE_Q4_0: ggml_backend_cann_transform_q4_0(tensor, src, dst); @@ -1067,8 +1029,7 @@ static void ggml_backend_cann_transform(ggml_tensor* tensor, * @param dst Pointer to the destination buffer where transformed tensor data * will be stored. */ -static void ggml_backend_cann_transform_back( - const ggml_tensor* tensor, void* src, void* dst) { +static void ggml_backend_cann_transform_back(const ggml_tensor * tensor, void * src, void * dst) { switch (tensor->type) { case GGML_TYPE_Q4_0: ggml_backend_cann_transform_back_q4_0(tensor, src, dst); @@ -1109,8 +1070,7 @@ static bool need_transform(ggml_type type) { * @param buffer The CANN buffer from which to initialize the tensor. * @param tensor Pointer to the tensor to be initialized. */ -static enum ggml_status ggml_backend_cann_buffer_init_tensor( - ggml_backend_buffer_t buffer, ggml_tensor* tensor) { +static enum ggml_status ggml_backend_cann_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { if (tensor->view_src != NULL && tensor->view_offs == 0) { GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft); return GGML_STATUS_SUCCESS; @@ -1121,13 +1081,11 @@ static enum ggml_status ggml_backend_cann_buffer_init_tensor( if (ggml_is_quantized(tensor->type)) { // Initialize padding to 0 to avoid possible NaN values size_t original_size = ggml_nbytes(tensor); - size_t padded_size = - ggml_backend_buft_get_alloc_size(buffer->buft, tensor); + size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); if (padded_size > original_size && tensor->view_src == nullptr) { size_t memset_size = padded_size - original_size; - ACL_CHECK(aclrtMemset((char*)tensor->data + original_size, - memset_size, 0, memset_size)); + ACL_CHECK(aclrtMemset((char *) tensor->data + original_size, memset_size, 0, memset_size)); } } return GGML_STATUS_SUCCESS; @@ -1141,8 +1099,8 @@ static enum ggml_status ggml_backend_cann_buffer_init_tensor( * designed to be used with a global array, one per device. */ struct ggml_cann_nz_workspace { - void* ptr; // Pointer to allocated device buffer - size_t allocated; // Size of currently allocated buffer in bytes + void * ptr; // Pointer to allocated device buffer + size_t allocated; // Size of currently allocated buffer in bytes /** * @brief Constructor. Initializes the workspace with no allocated memory. @@ -1158,7 +1116,7 @@ struct ggml_cann_nz_workspace { void clear() { if (ptr) { ACL_CHECK(aclrtFree(ptr)); - ptr = nullptr; + ptr = nullptr; allocated = 0; } } @@ -1185,7 +1143,7 @@ struct ggml_cann_nz_workspace { * * @return Pointer to the allocated buffer, or nullptr if not allocated. */ - void* get() const { return ptr; } + void * get() const { return ptr; } }; /** @@ -1207,19 +1165,17 @@ static ggml_cann_nz_workspace g_nz_workspaces[GGML_CANN_MAX_DEVICES]; * @note The workspace buffer used in this function is managed globally and reused * across calls. This reduces overhead from repeated memory allocation and deallocation. */ -static void weight_format_to_nz(ggml_tensor *tensor, size_t offset, int device) { - aclTensor* weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne, - tensor->nb, 2, ACL_FORMAT_ND, offset); - uint64_t workspaceSize = 0; - aclOpExecutor *executor; +static void weight_format_to_nz(ggml_tensor * tensor, size_t offset, int device) { + aclTensor * weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne, tensor->nb, 2, ACL_FORMAT_ND, offset); + uint64_t workspaceSize = 0; + aclOpExecutor * executor; // TransMatmulWeight - ACL_CHECK(aclnnTransMatmulWeightGetWorkspaceSize(weightTransposed, - &workspaceSize, &executor)); + ACL_CHECK(aclnnTransMatmulWeightGetWorkspaceSize(weightTransposed, &workspaceSize, &executor)); // Avoid frequent malloc/free of the workspace. g_nz_workspaces[device].realloc(workspaceSize); - void* g_nz_workspace = g_nz_workspaces[device].get(); + void * g_nz_workspace = g_nz_workspaces[device].get(); ACL_CHECK(aclnnTransMatmulWeight(g_nz_workspace, workspaceSize, executor, nullptr)); ACL_CHECK(aclDestroyTensor(weightTransposed)); @@ -1238,11 +1194,12 @@ static void weight_format_to_nz(ggml_tensor *tensor, size_t offset, int device) * @param offset Offset in the source data from where to start copying. * @param size Size of the data to be copied, in bytes. */ -static void ggml_backend_cann_buffer_set_tensor( - ggml_backend_buffer_t buffer, ggml_tensor *tensor, const void *data, - size_t offset, size_t size) { - ggml_backend_cann_buffer_context *ctx = - (ggml_backend_cann_buffer_context *)buffer->context; +static void ggml_backend_cann_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context; ggml_cann_set_device(ctx->device); // TODO: refer to cann(#6017), it use thread's default stream. @@ -1252,20 +1209,17 @@ static void ggml_backend_cann_buffer_set_tensor( // Only check env once. static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on")); if (!need_transform(tensor->type)) { - ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, data, size, - ACL_MEMCPY_HOST_TO_DEVICE)); - if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) { + ACL_CHECK(aclrtMemcpy((char *) tensor->data + offset, size, data, size, ACL_MEMCPY_HOST_TO_DEVICE)); + if (weight_to_nz && is_matmul_weight((const ggml_tensor *) tensor)) { GGML_ASSERT(tensor->ne[2] == 1); GGML_ASSERT(tensor->ne[3] == 1); weight_format_to_nz(tensor, offset, ctx->device); } } else { - void *transform_buffer = malloc(size); + void * transform_buffer = malloc(size); ggml_backend_cann_transform(tensor, data, transform_buffer); - ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, - transform_buffer, size, - ACL_MEMCPY_HOST_TO_DEVICE)); + ACL_CHECK(aclrtMemcpy((char *) tensor->data + offset, size, transform_buffer, size, ACL_MEMCPY_HOST_TO_DEVICE)); free(transform_buffer); } } @@ -1283,22 +1237,20 @@ static void ggml_backend_cann_buffer_set_tensor( * @param offset Offset in the destination buffer where to start copying. * @param size Size of the data to be copied, in bytes. */ -static void ggml_backend_cann_buffer_get_tensor( - ggml_backend_buffer_t buffer, const ggml_tensor* tensor, void* data, - size_t offset, size_t size) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; +static void ggml_backend_cann_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size) { + ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context; ggml_cann_set_device(ctx->device); if (!need_transform(tensor->type)) { - ACL_CHECK(aclrtMemcpy(data, size, (char*)tensor->data + offset, size, - ACL_MEMCPY_DEVICE_TO_HOST)); + ACL_CHECK(aclrtMemcpy(data, size, (char *) tensor->data + offset, size, ACL_MEMCPY_DEVICE_TO_HOST)); } else { - void* transform_buffer = malloc(size); - ACL_CHECK(aclrtMemcpy(transform_buffer, size, - (char*)tensor->data + offset, size, - ACL_MEMCPY_DEVICE_TO_HOST)); + void * transform_buffer = malloc(size); + ACL_CHECK(aclrtMemcpy(transform_buffer, size, (char *) tensor->data + offset, size, ACL_MEMCPY_DEVICE_TO_HOST)); ggml_backend_cann_transform_back(tensor, transform_buffer, data); free(transform_buffer); } @@ -1317,19 +1269,17 @@ static void ggml_backend_cann_buffer_get_tensor( * @param dst Pointer to the destination tensor where the data will be copied. * @return true if the copy operation succeeded, false otherwise. */ -static bool ggml_backend_cann_buffer_cpy_tensor( - ggml_backend_buffer_t buffer, const ggml_tensor* src, ggml_tensor* dst) { +static bool ggml_backend_cann_buffer_cpy_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor * src, + ggml_tensor * dst) { if (ggml_backend_buffer_is_cann(src->buffer)) { - ggml_backend_cann_buffer_context* src_ctx = - (ggml_backend_cann_buffer_context*)src->buffer->context; - ggml_backend_cann_buffer_context* dst_ctx = - (ggml_backend_cann_buffer_context*)buffer->context; + ggml_backend_cann_buffer_context * src_ctx = (ggml_backend_cann_buffer_context *) src->buffer->context; + ggml_backend_cann_buffer_context * dst_ctx = (ggml_backend_cann_buffer_context *) buffer->context; size_t memcpy_size = ggml_nbytes(src); // Same device. if (src_ctx->device == dst_ctx->device) { - ACL_CHECK(aclrtMemcpy((char*)dst->data, memcpy_size, - (const char*)src->data, memcpy_size, + ACL_CHECK(aclrtMemcpy((char *) dst->data, memcpy_size, (const char *) src->data, memcpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE)); return true; } else { @@ -1339,13 +1289,11 @@ static bool ggml_backend_cann_buffer_cpy_tensor( #endif // Different device but can access by peer. int32_t canAccessPeer = 0; - ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, src_ctx->device, - dst_ctx->device)); + ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, src_ctx->device, dst_ctx->device)); if (canAccessPeer) { ggml_cann_set_device(src_ctx->device); ACL_CHECK(aclrtDeviceEnablePeerAccess(dst_ctx->device, 0)); - ACL_CHECK(aclrtMemcpy((char*)dst->data, memcpy_size, - (const char*)src->data, memcpy_size, + ACL_CHECK(aclrtMemcpy((char *) dst->data, memcpy_size, (const char *) src->data, memcpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE)); return true; } @@ -1363,10 +1311,8 @@ static bool ggml_backend_cann_buffer_cpy_tensor( * @param buffer The CANN buffer to be cleared. * @param value The value to which each byte in the buffer will be set. */ -static void ggml_backend_cann_buffer_clear( - ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_cann_buffer_context* ctx = - (ggml_backend_cann_buffer_context*)buffer->context; +static void ggml_backend_cann_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context; ggml_cann_set_device(ctx->device); ACL_CHECK(aclrtMemset(ctx->dev_ptr, buffer->size, value, buffer->size)); @@ -1396,9 +1342,8 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = { * buffer type. */ struct ggml_backend_cann_buffer_type_context { - int32_t - device; /**< Device identifier associated with the buffer context. */ - std::string name; /**< Name associated with the buffer context. */ + int32_t device; /**< Device identifier associated with the buffer context. */ + std::string name; /**< Name associated with the buffer context. */ }; /** @@ -1410,10 +1355,8 @@ struct ggml_backend_cann_buffer_type_context { * @param buft Pointer to the buffer type context. * @return Const pointer to the C-style string containing the name. */ -static const char* ggml_backend_cann_buffer_type_name( - ggml_backend_buffer_type_t buft) { - ggml_backend_cann_buffer_type_context* buft_ctx = - (ggml_backend_cann_buffer_type_context*)buft->context; +static const char * ggml_backend_cann_buffer_type_name(ggml_backend_buffer_type_t buft) { + ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context; return buft_ctx->name.c_str(); } @@ -1428,34 +1371,27 @@ static const char* ggml_backend_cann_buffer_type_name( * @param size Size in bytes of the buffer to allocate. * @return Pointer to the allocated buffer, or nullptr if allocation fails. */ -static ggml_backend_buffer_t -ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, - size_t size) { - ggml_backend_cann_buffer_type_context* buft_ctx = - (ggml_backend_cann_buffer_type_context*)buft->context; +static ggml_backend_buffer_t ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context; ggml_cann_set_device(buft_ctx->device); const size_t alignment = 128; - size = GGML_PAD(size, alignment); + size = GGML_PAD(size, alignment); if (size == 0) { size = alignment; } - void* dev_ptr; + void * dev_ptr; aclError err = aclrtMalloc(&dev_ptr, size, ACL_MEM_MALLOC_HUGE_FIRST); if (err != ACL_SUCCESS) { - GGML_LOG_ERROR( - "%s: allocating %.2f MiB on device %d: aclrtMalloc failed: %s\n", - __func__, size / 1024.0 / 1024.0, buft_ctx->device, - aclGetRecentErrMsg()); + GGML_LOG_ERROR("%s: allocating %.2f MiB on device %d: aclrtMalloc failed: %s\n", __func__, + size / 1024.0 / 1024.0, buft_ctx->device, aclGetRecentErrMsg()); return nullptr; } - ggml_backend_cann_buffer_context* ctx = - new ggml_backend_cann_buffer_context(buft_ctx->device, dev_ptr); + ggml_backend_cann_buffer_context * ctx = new ggml_backend_cann_buffer_context(buft_ctx->device, dev_ptr); - return ggml_backend_buffer_init(buft, ggml_backend_cann_buffer_interface, - ctx, size); + return ggml_backend_buffer_init(buft, ggml_backend_cann_buffer_interface, ctx, size); } /** @@ -1470,8 +1406,7 @@ ggml_backend_cann_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, * @return The alignment requirement in bytes (fixed at 128 bytes for CANN * buffers). */ -static size_t ggml_backend_cann_buffer_type_get_alignment( - ggml_backend_buffer_type_t buft) { +static size_t ggml_backend_cann_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { return 128; GGML_UNUSED(buft); @@ -1491,10 +1426,10 @@ static size_t ggml_backend_cann_buffer_type_get_alignment( * @return The total allocation size in bytes required for the tensor in the * CANN buffer. */ -static size_t ggml_backend_cann_buffer_type_get_alloc_size( - ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) { - size_t size = ggml_nbytes(tensor); - int64_t ne0 = tensor->ne[0]; +static size_t ggml_backend_cann_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, + const ggml_tensor * tensor) { + size_t size = ggml_nbytes(tensor); + int64_t ne0 = tensor->ne[0]; // Only check env once. static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on")); @@ -1507,19 +1442,17 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size( // size += (line_size_align_32 - line_size); if (ggml_is_quantized(tensor->type)) { if (ne0 % MATRIX_ROW_PADDING != 0) { - size += ggml_row_size( - tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); } - } else if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) { + } else if (weight_to_nz && is_matmul_weight((const ggml_tensor *) tensor)) { // NZ format weight are not support quantized yet. // If ND tensor transform to NZ, size may changed. - int64_t shape[] = {tensor->ne[1], tensor->ne[0]}; + int64_t shape[] = { tensor->ne[1], tensor->ne[0] }; GGML_ASSERT(tensor->ne[2] == 1); GGML_ASSERT(tensor->ne[3] == 1); - const aclIntArray *acl_shape = aclCreateIntArray(shape, 2); - size_t new_size; - ACL_CHECK(aclnnCalculateMatmulWeightSizeV2(acl_shape, - ggml_cann_type_mapping(tensor->type), &new_size)); + const aclIntArray * acl_shape = aclCreateIntArray(shape, 2); + size_t new_size; + ACL_CHECK(aclnnCalculateMatmulWeightSizeV2(acl_shape, ggml_cann_type_mapping(tensor->type), &new_size)); ACL_CHECK(aclDestroyIntArray(acl_shape)); size = std::max(size, new_size); } @@ -1560,17 +1493,15 @@ static const ggml_backend_buffer_type_i ggml_backend_cann_buffer_type_interface * @return A pointer to the buffer type interface for the specified device, or * nullptr if the device index is out of range. */ -ggml_backend_buffer_type_t -ggml_backend_cann_buffer_type(int32_t device) { - static std::mutex mutex; +ggml_backend_buffer_type_t ggml_backend_cann_buffer_type(int32_t device) { + static std::mutex mutex; std::lock_guard lock(mutex); if (device >= ggml_backend_cann_get_device_count()) { return nullptr; } - static ggml_backend_buffer_type - ggml_backend_cann_buffer_types[GGML_CANN_MAX_DEVICES]; + static ggml_backend_buffer_type ggml_backend_cann_buffer_types[GGML_CANN_MAX_DEVICES]; static bool ggml_backend_cann_buffer_type_initialized = false; @@ -1580,8 +1511,7 @@ ggml_backend_cann_buffer_type(int32_t device) { /* .iface = */ ggml_backend_cann_buffer_type_interface, /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), i), /* .context = */ - new ggml_backend_cann_buffer_type_context{ - i, "CANN" + std::to_string(i)}, + new ggml_backend_cann_buffer_type_context{ i, "CANN" + std::to_string(i) }, }; } ggml_backend_cann_buffer_type_initialized = true; @@ -1645,16 +1575,16 @@ static void * ggml_cann_host_malloc(size_t size) { } const size_t alignment = 128; - size = GGML_PAD(size, alignment); + size = GGML_PAD(size, alignment); if (size == 0) { size = alignment; } - void * hostPtr = nullptr; - aclError err = aclrtMallocHost((void **) &hostPtr, size); + void * hostPtr = nullptr; + aclError err = aclrtMallocHost((void **) &hostPtr, size); if (err != ACL_SUCCESS) { - GGML_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, - size / 1024.0 / 1024.0, aclGetRecentErrMsg()); + GGML_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, size / 1024.0 / 1024.0, + aclGetRecentErrMsg()); return nullptr; } return hostPtr; @@ -1667,7 +1597,8 @@ static void * ggml_cann_host_malloc(size_t size) { * @param size Size in bytes of the host buffer to allocate. * @return Pointer to the allocated host buffer, or CPU buffer pointer if allocation fails. */ -static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, + size_t size) { void * hostPtr = ggml_cann_host_malloc(size); if (hostPtr == nullptr) { @@ -1676,8 +1607,8 @@ static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggm } ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(hostPtr, size); - buffer->buft = buft; - buffer->iface.free_buffer = ggml_backend_cann_host_buffer_free; + buffer->buft = buft; + buffer->iface.free_buffer = ggml_backend_cann_host_buffer_free; return buffer; } @@ -1691,14 +1622,15 @@ static ggml_backend_buffer_t ggml_backend_cann_host_buffer_type_alloc_buffer(ggm ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type() { static struct ggml_backend_buffer_type ggml_backend_cann_buffer_type_host = { /* .iface = */ { - /* .get_name = */ ggml_backend_cann_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_cann_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_name = */ ggml_backend_cann_host_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_cann_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), 0), + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, + /* .device = */ + ggml_backend_reg_dev_get(ggml_backend_cann_reg(), 0), /* .context = */ nullptr, }; @@ -1718,8 +1650,7 @@ ggml_backend_buffer_type_t ggml_backend_cann_host_buffer_type() { * stored. * @return true if the computation was successful; false otherwise. */ -static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, - struct ggml_tensor* dst) { +static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct ggml_tensor * dst) { switch (dst->op) { case GGML_OP_REPEAT: ggml_cann_repeat(ctx, dst); @@ -1765,14 +1696,14 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, case GGML_UNARY_OP_SILU: GGML_CANN_CALL_OP_UNARY(Silu); break; - case GGML_UNARY_OP_GELU_QUICK: { - auto lambda = [](ggml_backend_cann_context& ctx, - aclTensor* acl_src, - aclTensor* acl_dst) { - GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst); - }; - ggml_cann_op_unary(lambda, ctx, dst); - } break; + case GGML_UNARY_OP_GELU_QUICK: + { + auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { + GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst); + }; + ggml_cann_op_unary(lambda, ctx, dst); + } + break; case GGML_UNARY_OP_TANH: GGML_CANN_CALL_OP_UNARY(Tanh); break; @@ -1817,14 +1748,14 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, case GGML_GLU_OP_SWIGLU: GGML_CANN_CALL_OP_UNARY_GATED(Silu); break; - case GGML_GLU_OP_GEGLU_QUICK: { - auto lambda = [](ggml_backend_cann_context& ctx, - aclTensor* acl_src, - aclTensor* acl_dst) { - GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst); - }; - ggml_cann_op_unary_gated(lambda, ctx, dst); - } break; + case GGML_GLU_OP_GEGLU_QUICK: + { + auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) { + GGML_CANN_CALL_ACLNN_OP(ctx, GeluV2, acl_src, 0, acl_dst); + }; + ggml_cann_op_unary_gated(lambda, ctx, dst); + } + break; default: return false; } @@ -1956,9 +1887,8 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx, * @param backend Pointer to the CANN backend structure. * @return A pointer to a constant string representing the backend name. */ -static const char* ggml_backend_cann_name(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; +static const char * ggml_backend_cann_name(ggml_backend_t backend) { + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; return cann_ctx->name.c_str(); } @@ -1972,8 +1902,7 @@ static const char* ggml_backend_cann_name(ggml_backend_t backend) { * @param backend Pointer to the CANN backend structure to be freed. */ static void ggml_backend_cann_free(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; ACL_CHECK(aclrtSynchronizeDevice()); ACL_CHECK(aclrtResetDevice(cann_ctx->device)); @@ -1981,7 +1910,6 @@ static void ggml_backend_cann_free(ggml_backend_t backend) { delete backend; } - /** * @brief Sets tensor data asynchronously in the CANN backend. * @@ -1994,21 +1922,17 @@ static void ggml_backend_cann_free(ggml_backend_t backend) { * @param size Size of the data to copy in bytes. */ static void ggml_backend_cann_set_tensor_async(ggml_backend_t backend, - ggml_tensor *tensor, - const void *data, - size_t offset, - size_t size) { - ggml_backend_cann_context *cann_ctx = - (ggml_backend_cann_context *)backend->context; - ggml_backend_buffer_t buf = - tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - - GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) && - "unsupported buffer type"); + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + + GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) && "unsupported buffer type"); GGML_ASSERT(!ggml_is_quantized(tensor->type)); - ggml_cann_async_memcpy(cann_ctx, (char *)tensor->data + offset, data, size, - ACL_MEMCPY_HOST_TO_DEVICE); + ggml_cann_async_memcpy(cann_ctx, (char *) tensor->data + offset, data, size, ACL_MEMCPY_HOST_TO_DEVICE); } /** @@ -2022,21 +1946,18 @@ static void ggml_backend_cann_set_tensor_async(ggml_backend_t backend, * @param offset Offset in bytes within the host data. * @param size Size of the data to copy in bytes. */ -static void ggml_backend_cann_get_tensor_async( - ggml_backend_t backend, const ggml_tensor *tensor, void *data, - size_t offset, size_t size) { - ggml_backend_cann_context *cann_ctx = - (ggml_backend_cann_context *)backend->context; - ggml_backend_buffer_t buf = - tensor->view_src ? tensor->view_src->buffer : tensor->buffer; +static void ggml_backend_cann_get_tensor_async(ggml_backend_t backend, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size) { + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; - GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) && - "unsupported buffer type"); + GGML_ASSERT(buf->buft == ggml_backend_cann_buffer_type(cann_ctx->device) && "unsupported buffer type"); GGML_ASSERT(!ggml_is_quantized(tensor->type)); - ggml_cann_async_memcpy(cann_ctx, data, (char *)tensor->data + offset, size, - ACL_MEMCPY_DEVICE_TO_HOST); - + ggml_cann_async_memcpy(cann_ctx, data, (char *) tensor->data + offset, size, ACL_MEMCPY_DEVICE_TO_HOST); } /** @@ -2052,28 +1973,23 @@ static void ggml_backend_cann_get_tensor_async( * @param dst Pointer to the destination tensor to copy data to. * @return true if the copy operation succeeds, false otherwise. */ -static bool ggml_backend_cann_cpy_tensor_async( - ggml_backend_t backend_src, ggml_backend_t backend_dst, - const ggml_tensor* src, ggml_tensor* dst) { - GGML_ASSERT(ggml_backend_is_cann(backend_src) || - ggml_backend_is_cann(backend_dst)); +static bool ggml_backend_cann_cpy_tensor_async(ggml_backend_t backend_src, + ggml_backend_t backend_dst, + const ggml_tensor * src, + ggml_tensor * dst) { + GGML_ASSERT(ggml_backend_is_cann(backend_src) || ggml_backend_is_cann(backend_dst)); - GGML_ASSERT(!is_matmul_weight((const ggml_tensor*)src)); + GGML_ASSERT(!is_matmul_weight((const ggml_tensor *) src)); - if (!ggml_backend_buffer_is_cann(src->buffer) || - !ggml_backend_buffer_is_cann(dst->buffer)) { + if (!ggml_backend_buffer_is_cann(src->buffer) || !ggml_backend_buffer_is_cann(dst->buffer)) { return false; } - ggml_backend_buffer_t buf_src = - src->view_src ? src->view_src->buffer : src->buffer; - ggml_backend_buffer_t buf_dst = - dst->view_src ? dst->view_src->buffer : dst->buffer; + ggml_backend_buffer_t buf_src = src->view_src ? src->view_src->buffer : src->buffer; + ggml_backend_buffer_t buf_dst = dst->view_src ? dst->view_src->buffer : dst->buffer; - ggml_backend_cann_context* cann_ctx_src = - (ggml_backend_cann_context*)backend_src->context; - ggml_backend_cann_context* cann_ctx_dst = - (ggml_backend_cann_context*)backend_dst->context; + ggml_backend_cann_context * cann_ctx_src = (ggml_backend_cann_context *) backend_src->context; + ggml_backend_cann_context * cann_ctx_dst = (ggml_backend_cann_context *) backend_dst->context; size_t copy_size = ggml_nbytes(dst); if (copy_size == 0) { @@ -2084,17 +2000,14 @@ static bool ggml_backend_cann_cpy_tensor_async( // TODO: Support 310p P2P copy return false; #endif - ggml_backend_cann_buffer_context* buf_ctx_src = - (ggml_backend_cann_buffer_context*)buf_src->context; - ggml_backend_cann_buffer_context* buf_ctx_dst = - (ggml_backend_cann_buffer_context*)buf_dst->context; + ggml_backend_cann_buffer_context * buf_ctx_src = (ggml_backend_cann_buffer_context *) buf_src->context; + ggml_backend_cann_buffer_context * buf_ctx_dst = (ggml_backend_cann_buffer_context *) buf_dst->context; GGML_ASSERT(cann_ctx_src->device == buf_ctx_src->device); GGML_ASSERT(cann_ctx_dst->device == buf_ctx_dst->device); int32_t canAccessPeer = 0; - ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, cann_ctx_src->device, - cann_ctx_dst->device)); + ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, cann_ctx_src->device, cann_ctx_dst->device)); if (!canAccessPeer) { return false; } @@ -2106,8 +2019,7 @@ static bool ggml_backend_cann_cpy_tensor_async( // wait for task_queue empty to keep task order. cann_ctx_src->task_queue.wait(); - ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size, - ACL_MEMCPY_DEVICE_TO_DEVICE, + ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size, ACL_MEMCPY_DEVICE_TO_DEVICE, cann_ctx_src->stream())); // record event on src stream after the copy // TODO: this event is not effective with acl graph mode, change to use aclrtSynchronizeStream @@ -2122,8 +2034,7 @@ static bool ggml_backend_cann_cpy_tensor_async( ACL_CHECK(aclrtSynchronizeStream(cann_ctx_src->stream())); } else { // src and dst are on the same backend - ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size, - ACL_MEMCPY_DEVICE_TO_DEVICE, + ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size, ACL_MEMCPY_DEVICE_TO_DEVICE, cann_ctx_dst->stream())); } @@ -2139,8 +2050,7 @@ static bool ggml_backend_cann_cpy_tensor_async( * @param backend Pointer to the CANN backend structure to synchronize. */ static void ggml_backend_cann_synchronize(ggml_backend_t backend) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; cann_ctx->task_queue.wait(); ggml_cann_set_device(cann_ctx->device); ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream())); @@ -2168,16 +2078,14 @@ static void ggml_backend_cann_synchronize(ggml_backend_t backend) { * @param cann_ctx The CANN backend context containing the graph cache. * @param cgraph The current ggml computation graph. */ -static void add_lru_matched_graph_node_properties( - ggml_backend_cann_context * cann_ctx, - ggml_cgraph * cgraph) { +static void add_lru_matched_graph_node_properties(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) { // Create a new ggml_cann_graph object on the heap (its lifetime is managed by the cache). ggml_cann_graph * new_graph = new ggml_cann_graph(); new_graph->ggml_graph_properties.resize(cgraph->n_nodes); for (int node_idx = 0; node_idx < cgraph->n_nodes; ++node_idx) { ggml_tensor * node = cgraph->nodes[node_idx]; - auto & prop = new_graph->ggml_graph_properties[node_idx]; + auto & prop = new_graph->ggml_graph_properties[node_idx]; prop.node_address = node->data; prop.node_op = node->op; @@ -2214,11 +2122,9 @@ static void add_lru_matched_graph_node_properties( * @param graph_node_properties The stored properties of a CANN graph node. * @return true if all fields match (excluding GGML_OP_VIEW); false otherwise. */ -static bool ggml_graph_node_has_matching_properties( - ggml_tensor * node, - ggml_graph_node_properties * graph_node_properties) { - if (node->data != graph_node_properties->node_address && - node->op != GGML_OP_VIEW) { +static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, + ggml_graph_node_properties * graph_node_properties) { + if (node->data != graph_node_properties->node_address && node->op != GGML_OP_VIEW) { return false; } @@ -2237,8 +2143,7 @@ static bool ggml_graph_node_has_matching_properties( for (int i = 0; i < GGML_MAX_SRC; i++) { if (node->src[i]) { - if (node->src[i]->data != graph_node_properties->src_address[i] && - node->op != GGML_OP_VIEW) { + if (node->src[i]->data != graph_node_properties->src_address[i] && node->op != GGML_OP_VIEW) { return false; } @@ -2280,8 +2185,8 @@ static bool ggml_graph_node_has_matching_properties( * @return true if a matching cached graph exists; false otherwise. */ static bool is_matched_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) { - ggml_cann_graph_lru_cache &lru_cache = cann_ctx->graph_lru_cache; - for (auto &graph_ptr : lru_cache.cache_list) { + ggml_cann_graph_lru_cache & lru_cache = cann_ctx->graph_lru_cache; + for (auto & graph_ptr : lru_cache.cache_list) { // Skip graphs with a different number of nodes. if (graph_ptr->ggml_graph_properties.size() != static_cast(cgraph->n_nodes)) { continue; @@ -2320,21 +2225,24 @@ static bool is_matched_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * * @param use_cann_graph Whether to use CANN graph execution. * @param cann_graph_update_required Whether graph capture is needed due to graph changes. */ -static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph, - bool & use_cann_graph, bool & cann_graph_update_required) { +static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx, + ggml_cgraph * cgraph, + bool & use_cann_graph, + bool & cann_graph_update_required) { #ifdef USE_ACL_GRAPH - ggml_cann_graph* matched_graph = cann_ctx->graph_lru_cache.cache_list.front(); + ggml_cann_graph * matched_graph = cann_ctx->graph_lru_cache.cache_list.front(); if (use_cann_graph && cann_graph_update_required) { ACL_CHECK(aclmdlRICaptureBegin(cann_ctx->stream(), ACL_MODEL_RI_CAPTURE_MODE_GLOBAL)); } -#endif // USE_ACL_GRAPH +#endif // USE_ACL_GRAPH // Only perform the graph execution if CANN graphs are not enabled, or we are capturing the graph. // With the use of CANN graphs, the execution will be performed by the graph launch. if (!use_cann_graph || cann_graph_update_required) { for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { + if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || + node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { continue; } @@ -2347,7 +2255,7 @@ static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx } #ifdef USE_ACL_GRAPH - if (use_cann_graph && cann_graph_update_required) { // End CANN graph capture + if (use_cann_graph && cann_graph_update_required) { // End CANN graph capture ACL_CHECK(aclmdlRICaptureEnd(cann_ctx->stream(), &matched_graph->graph)); } @@ -2355,10 +2263,9 @@ static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx // Execute graph ACL_CHECK(aclmdlRIExecuteAsync(matched_graph->graph, cann_ctx->stream())); } -#endif // USE_ACL_GRAPH +#endif // USE_ACL_GRAPH } - /** * @brief Computes a computational graph using a CANN backend. * @@ -2371,10 +2278,8 @@ static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx * @return enum ggml_status Returns GGML_STATUS_SUCCESS if computation * completes successfully, otherwise an appropriate error status. */ -static enum ggml_status ggml_backend_cann_graph_compute( - ggml_backend_t backend, ggml_cgraph* cgraph) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; +static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; ggml_cann_set_device(cann_ctx->device); g_nz_workspaces[cann_ctx->device].clear(); @@ -2382,7 +2287,7 @@ static enum ggml_status ggml_backend_cann_graph_compute( cann_ctx->rope_cache.cached = false; #ifdef USE_ACL_GRAPH - bool use_cann_graph = true; + bool use_cann_graph = true; bool cann_graph_update_required = false; static bool prefill_use_graph = parse_bool(get_env("GGML_CANN_PREFILL_USE_GRAPH").value_or("")); @@ -2413,15 +2318,10 @@ static enum ggml_status ggml_backend_cann_graph_compute( } } #else - bool use_cann_graph = false; + bool use_cann_graph = false; bool cann_graph_update_required = false; #endif // USE_ACL_GRAPH - evaluate_and_capture_cann_graph( - cann_ctx, - cgraph, - use_cann_graph, - cann_graph_update_required - ); + evaluate_and_capture_cann_graph(cann_ctx, cgraph, use_cann_graph, cann_graph_update_required); return GGML_STATUS_SUCCESS; } @@ -2438,8 +2338,7 @@ static enum ggml_status ggml_backend_cann_graph_compute( * @return bool Returns true if the operation is supported by the backend, * otherwise false. */ -static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, - const ggml_tensor* op) { +static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -2474,24 +2373,24 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, return false; } break; - case GGML_OP_MUL_MAT: { - switch (op->src[0]->type) { - case GGML_TYPE_F16: - case GGML_TYPE_F32: - return true; - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q4_0: + case GGML_OP_MUL_MAT: + { + switch (op->src[0]->type) { + case GGML_TYPE_F16: + case GGML_TYPE_F32: + return true; + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q4_0: #ifdef ASCEND_310P - // Q4 && Q8 per group is not support on 310p device - return false; + // Q4 && Q8 per group is not support on 310p device + return false; #endif - // only support contiguous for quantized types. - return ggml_is_contiguous(op->src[0]) && - ggml_is_contiguous(op->src[1]); - default: - return false; + // only support contiguous for quantized types. + return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]); + default: + return false; + } } - } case GGML_OP_MUL_MAT_ID: switch (op->src[0]->type) { case GGML_TYPE_F16: @@ -2504,99 +2403,107 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, return false; #endif // only support contiguous for quantized types. - return ggml_is_contiguous(op->src[0]) && - ggml_is_contiguous(op->src[1]); + return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]); default: return false; } // embedding - case GGML_OP_GET_ROWS: { - switch (op->src[0]->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - case GGML_TYPE_Q8_0: - return true; - default: - return false; - } - } break; - case GGML_OP_SET_ROWS: { - switch (op->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - return true; - default: - return false; + case GGML_OP_GET_ROWS: + { + switch (op->src[0]->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_Q8_0: + return true; + default: + return false; + } } - } break; - case GGML_OP_CPY: { - ggml_tensor *src = op->src[0]; - if ((op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16) || - (src->type != GGML_TYPE_F32 && - src->type != GGML_TYPE_F16)) { - // only support F32 and F16. - return false; + break; + case GGML_OP_SET_ROWS: + { + switch (op->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + return true; + default: + return false; + } } - return true; - } break; - case GGML_OP_CONT: { - // TODO: support GGML_TYPE_BF16 - switch (op->src[0]->type) { - case GGML_TYPE_F32: - case GGML_TYPE_F16: - return true; - default: + break; + case GGML_OP_CPY: + { + ggml_tensor * src = op->src[0]; + if ((op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16) || + (src->type != GGML_TYPE_F32 && src->type != GGML_TYPE_F16)) { + // only support F32 and F16. return false; + } + return true; } - } - case GGML_OP_ROPE: { - // TODO: with ops-test v == 1 - // TODO: n_dims <= ne0 - if (op->src[0]->ne[0] != op->op_params[1]) { - return false; + break; + case GGML_OP_CONT: + { + // TODO: support GGML_TYPE_BF16 + switch (op->src[0]->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + return true; + default: + return false; + } } + case GGML_OP_ROPE: + { + // TODO: with ops-test v == 1 + // TODO: n_dims <= ne0 + if (op->src[0]->ne[0] != op->op_params[1]) { + return false; + } - const int mode = ((const int32_t *) op->op_params)[2]; - if (mode & GGML_ROPE_TYPE_MROPE) { - return false; - } - if (mode & GGML_ROPE_TYPE_VISION) { - return false; - } + const int mode = ((const int32_t *) op->op_params)[2]; + if (mode & GGML_ROPE_TYPE_MROPE) { + return false; + } + if (mode & GGML_ROPE_TYPE_VISION) { + return false; + } #ifdef ASCEND_310P - if(!ggml_is_contiguous(op->src[0])){ - return false; - } + if (!ggml_is_contiguous(op->src[0])) { + return false; + } #endif - return true; - } - case GGML_OP_UPSCALE: { - // aclnnUpsampleNearest2dGetWorkspaceSize not support - // selfDimN[2]/outDimN[2] or selfDimC[3]/outDimC[3] not equal - if (op->src[0]->ne[2] * op->ne[3] != op->src[0]->ne[3] * op->ne[2]) { - return false; + return true; } - if (op->op_params[0] != GGML_SCALE_MODE_NEAREST) { - return false; + case GGML_OP_UPSCALE: + { + // aclnnUpsampleNearest2dGetWorkspaceSize not support + // selfDimN[2]/outDimN[2] or selfDimC[3]/outDimC[3] not equal + if (op->src[0]->ne[2] * op->ne[3] != op->src[0]->ne[3] * op->ne[2]) { + return false; + } + if (op->op_params[0] != GGML_SCALE_MODE_NEAREST) { + return false; + } + return true; } - return true; - } - case GGML_OP_POOL_2D: { - const int32_t * opts = (const int32_t *) op->op_params; + case GGML_OP_POOL_2D: + { + const int32_t * opts = (const int32_t *) op->op_params; #ifdef ASCEND_310P - enum ggml_op_pool opt = static_cast(opts[0]); - if(opt == GGML_OP_POOL_MAX){ - return false; - } + enum ggml_op_pool opt = static_cast(opts[0]); + if (opt == GGML_OP_POOL_MAX) { + return false; + } #endif - const int k0 = opts[1]; - const int k1 = opts[2]; - const int p0 = opts[5]; - const int p1 = opts[6]; - // value of paddingH should be at most half of kernelH - // value of paddingW should be at most half of kernelW - return (p0 <= (k0 / 2)) && (p1 <= (k1 / 2)); - } + const int k0 = opts[1]; + const int k1 = opts[2]; + const int p0 = opts[5]; + const int p1 = opts[6]; + // value of paddingH should be at most half of kernelH + // value of paddingW should be at most half of kernelW + return (p0 <= (k0 / 2)) && (p1 <= (k1 / 2)); + } case GGML_OP_DUP: case GGML_OP_SUM: case GGML_OP_IM2COL: @@ -2639,48 +2546,50 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, return (op->src[0]->ne[0] - 1) <= 255; case GGML_OP_SCALE: float bias; - memcpy(&bias, (const float *)(op->op_params) + 1, sizeof(float)); - return bias == 0.0f; // TODO: support bias != 0.0f + memcpy(&bias, (const float *) (op->op_params) + 1, sizeof(float)); + return bias == 0.0f; // TODO: support bias != 0.0f case GGML_OP_SOFT_MAX: // TODO: support attention sinks [TAG_ATTN_SINKS] if (op->src[2]) { return false; } return true; - case GGML_OP_FLASH_ATTN_EXT:{ + case GGML_OP_FLASH_ATTN_EXT: + { #ifdef ASCEND_310P - // FA not support on 310p device - return false; -#endif - // derived from [ggml-cuda.cu] - if(op->src[1]->type != GGML_TYPE_F16 || op->src[2]->type != GGML_TYPE_F16){ - return false; - } - if(op->src[1]->type != GGML_TYPE_F16 && op->src[1]->type != GGML_TYPE_F32 && op->src[1]->type != GGML_TYPE_BF16){ - return false; - } - if(op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_BF16){ - return false; - } - // TODO: support attention sinks [TAG_ATTN_SINKS] - if (op->src[4]) { - return false; - } - if (op->src[1]->ne[0] != op->src[2]->ne[0]) { - // different head sizes of K and V are not supported yet - return false; - } - if (op->src[0]->ne[0] % 16 != 0) { - // TODO: padding to support - return false; - } - float logitSoftcap = 0.0f; - memcpy(&logitSoftcap, (const float *)(op->op_params) + 2, sizeof(float)); - if(logitSoftcap != 0.0f) { + // FA not support on 310p device return false; +#endif + // derived from [ggml-cuda.cu] + if (op->src[1]->type != GGML_TYPE_F16 || op->src[2]->type != GGML_TYPE_F16) { + return false; + } + if (op->src[1]->type != GGML_TYPE_F16 && op->src[1]->type != GGML_TYPE_F32 && + op->src[1]->type != GGML_TYPE_BF16) { + return false; + } + if (op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_BF16) { + return false; + } + // TODO: support attention sinks [TAG_ATTN_SINKS] + if (op->src[4]) { + return false; + } + if (op->src[1]->ne[0] != op->src[2]->ne[0]) { + // different head sizes of K and V are not supported yet + return false; + } + if (op->src[0]->ne[0] % 16 != 0) { + // TODO: padding to support + return false; + } + float logitSoftcap = 0.0f; + memcpy(&logitSoftcap, (const float *) (op->op_params) + 2, sizeof(float)); + if (logitSoftcap != 0.0f) { + return false; + } + return true; } - return true; - } default: return false; } @@ -2717,8 +2626,7 @@ static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) { * @return bool Returns true if the operation should be offloaded, otherwise * false. */ -static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, - const ggml_tensor* op) { +static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { const int min_batch_size = 32; GGML_UNUSED(dev); @@ -2734,9 +2642,8 @@ static bool ggml_backend_cann_offload_op(ggml_backend_dev_t dev, * @param event Pointer to the event structure to be recorded. */ static void ggml_backend_cann_event_record(ggml_backend_t backend, ggml_backend_event_t event) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; - ACL_CHECK(aclrtRecordEvent((aclrtEvent)event->context, cann_ctx->stream())); + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; + ACL_CHECK(aclrtRecordEvent((aclrtEvent) event->context, cann_ctx->stream())); } /** @@ -2749,13 +2656,10 @@ static void ggml_backend_cann_event_record(ggml_backend_t backend, ggml_backend_ * @param event Pointer to the event structure that the backend needs to wait * for. */ -static void ggml_backend_cann_event_wait(ggml_backend_t backend, - ggml_backend_event_t event) { - ggml_backend_cann_context* cann_ctx = - (ggml_backend_cann_context*)backend->context; +static void ggml_backend_cann_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { + ggml_backend_cann_context * cann_ctx = (ggml_backend_cann_context *) backend->context; if (ggml_backend_is_cann(backend)) { - ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(), - (aclrtEvent)event->context)); + ACL_CHECK(aclrtStreamWaitEvent(cann_ctx->stream(), (aclrtEvent) event->context)); } else { GGML_ABORT("fatal error"); } @@ -2794,30 +2698,30 @@ static const ggml_backend_i ggml_backend_cann_interface = { * @return A pointer to the static GUID. */ static ggml_guid_t ggml_backend_cann_guid() { - static ggml_guid guid = {0xa1, 0x94, 0xaf, 0xac, 0xbd, 0x4f, 0x47, 0x34, - 0xbe, 0x1a, 0x9e, 0x71, 0x1f, 0x9e, 0xed, 0x64}; + static ggml_guid guid = { 0xa1, 0x94, 0xaf, 0xac, 0xbd, 0x4f, 0x47, 0x34, + 0xbe, 0x1a, 0x9e, 0x71, 0x1f, 0x9e, 0xed, 0x64 }; return &guid; } // backend device struct ggml_backend_cann_device_context { - int device; + int device; std::string name; std::string description; }; static const char * ggml_backend_cann_device_get_name(ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *) dev->context; return ctx->name.c_str(); } -static const char* ggml_backend_cann_device_get_description(ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; +static const char * ggml_backend_cann_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *) dev->context; return ctx->description.c_str(); } static void ggml_backend_cann_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *) dev->context; ggml_backend_cann_get_device_memory(ctx->device, free, total); } @@ -2844,7 +2748,7 @@ static void ggml_backend_cann_device_get_props(ggml_backend_dev_t dev, ggml_back static ggml_backend_t ggml_backend_cann_device_init(ggml_backend_dev_t dev, const char * params) { GGML_UNUSED(params); - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *) dev->context; return ggml_backend_cann_init(ctx->device); } @@ -2861,19 +2765,17 @@ static ggml_backend_t ggml_backend_cann_device_init(ggml_backend_dev_t dev, cons * @return bool Returns true if the CANN backend supports the buffer type, * otherwise false. */ -static bool ggml_backend_cann_supports_buft( - ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { +static bool ggml_backend_cann_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { if (ggml_backend_buft_is_cann(buft)) { - ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context; - ggml_backend_cann_buffer_type_context * buft_ctx = - (ggml_backend_cann_buffer_type_context *)buft->context; + ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *) dev->context; + ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context; return buft_ctx->device == dev_ctx->device; } return false; } static ggml_backend_buffer_type_t ggml_backend_cann_device_get_buffer_type(ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *)dev->context; + ggml_backend_cann_device_context * ctx = (ggml_backend_cann_device_context *) dev->context; return ggml_backend_cann_buffer_type(ctx->device); } @@ -2892,9 +2794,8 @@ static ggml_backend_buffer_type_t ggml_backend_cann_device_get_host_buffer_type( * @param backend Pointer to the CANN backend. * @return ggml_backend_event_t Returns a pointer to the new event structure. */ -static ggml_backend_event_t ggml_backend_cann_device_event_new( - ggml_backend_dev_t dev) { - ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *)dev->context; +static ggml_backend_event_t ggml_backend_cann_device_event_new(ggml_backend_dev_t dev) { + ggml_backend_cann_device_context * dev_ctx = (ggml_backend_cann_device_context *) dev->context; ggml_cann_set_device(dev_ctx->device); @@ -2916,7 +2817,7 @@ static ggml_backend_event_t ggml_backend_cann_device_event_new( * @param event Pointer to the event structure to be freed. */ static void ggml_backend_cann_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) { - ACL_CHECK(aclrtDestroyEvent((aclrtEvent)event->context)); + ACL_CHECK(aclrtDestroyEvent((aclrtEvent) event->context)); delete event; GGML_UNUSED(dev); @@ -2930,7 +2831,7 @@ static void ggml_backend_cann_device_event_free(ggml_backend_dev_t dev, ggml_bac * @param event Pointer to the event structure to be synchronized. */ static void ggml_backend_cann_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) { - ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent)event->context)); + ACL_CHECK(aclrtSynchronizeEvent((aclrtEvent) event->context)); GGML_UNUSED(dev); } @@ -2941,10 +2842,10 @@ static const ggml_backend_device_i ggml_backend_cann_device_interface = { /* .get_memory = */ ggml_backend_cann_device_get_memory, /* .get_type = */ ggml_backend_cann_device_get_type, /* .get_props = */ ggml_backend_cann_device_get_props, - /* .init_backend = */ ggml_backend_cann_device_init, // called for every card + /* .init_backend = */ ggml_backend_cann_device_init, // called for every card /* .get_buffer_type = */ ggml_backend_cann_device_get_buffer_type, /* .get_host_buffer_type = */ ggml_backend_cann_device_get_host_buffer_type, - /* .buffer_from_host_ptr = */ NULL, // not supported for CANN + /* .buffer_from_host_ptr = */ NULL, // not supported for CANN /* .supports_op = */ ggml_backend_cann_supports_op, /* .supports_buft = */ ggml_backend_cann_supports_buft, /* .offload_op = */ ggml_backend_cann_offload_op, @@ -2953,7 +2854,6 @@ static const ggml_backend_device_i ggml_backend_cann_device_interface = { /* .event_synchronize = */ ggml_backend_cann_device_event_synchronize, }; - // backend reg struct ggml_backend_cann_reg_context { std::vector devices; @@ -2965,12 +2865,12 @@ static const char * ggml_backend_cann_reg_get_name(ggml_backend_reg_t reg) { } static size_t ggml_backend_cann_reg_get_device_count(ggml_backend_reg_t reg) { - ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context; + ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *) reg->context; return ctx->devices.size(); } static ggml_backend_dev_t ggml_backend_cann_reg_get_device(ggml_backend_reg_t reg, size_t index) { - ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *)reg->context; + ggml_backend_cann_reg_context * ctx = (ggml_backend_cann_reg_context *) reg->context; GGML_ASSERT(index < ctx->devices.size()); return ctx->devices[index]; } @@ -2992,34 +2892,30 @@ static const ggml_backend_reg_i ggml_backend_cann_reg_interface = { // backend registry, called only once for cann backend ggml_backend_reg_t ggml_backend_cann_reg() { static ggml_backend_reg reg; - static bool initialized = false; + static bool initialized = false; { - static std::mutex mutex; + static std::mutex mutex; std::lock_guard lock(mutex); if (!initialized) { aclInit(nullptr); ggml_backend_cann_reg_context * ctx = new ggml_backend_cann_reg_context; for (int i = 0; i < ggml_cann_info().device_count; i++) { - ggml_backend_cann_device_context* dev_ctx = new ggml_backend_cann_device_context(); - dev_ctx->description = aclrtGetSocName(); - dev_ctx->device = i; - dev_ctx->name = GGML_CANN_NAME + std::to_string(i); + ggml_backend_cann_device_context * dev_ctx = new ggml_backend_cann_device_context(); + dev_ctx->description = aclrtGetSocName(); + dev_ctx->device = i; + dev_ctx->name = GGML_CANN_NAME + std::to_string(i); ggml_cann_set_device(i); - ggml_backend_dev_t dev = new ggml_backend_device { - /* .iface = */ ggml_backend_cann_device_interface, - /* .reg = */ ®, - /* .context = */ dev_ctx - }; + ggml_backend_dev_t dev = new ggml_backend_device{ /* .iface = */ ggml_backend_cann_device_interface, + /* .reg = */ ®, + /* .context = */ dev_ctx }; ctx->devices.push_back(dev); } - reg = ggml_backend_reg { - /* .api_version = */ GGML_BACKEND_API_VERSION, - /* .iface = */ ggml_backend_cann_reg_interface, - /* .context = */ ctx - }; + reg = ggml_backend_reg{ /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_cann_reg_interface, + /* .context = */ ctx }; } initialized = true; @@ -3035,39 +2931,36 @@ ggml_backend_t ggml_backend_cann_init(int32_t device) { return nullptr; } - ggml_backend_cann_context* ctx = new ggml_backend_cann_context(device); + ggml_backend_cann_context * ctx = new ggml_backend_cann_context(device); if (ctx == nullptr) { GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__); return nullptr; } ggml_cann_set_device(ctx->device); ggml_backend_t cann_backend = - new ggml_backend{/* .guid = */ ggml_backend_cann_guid(), - /* .interface = */ ggml_backend_cann_interface, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), - /* .context = */ ctx}; + new ggml_backend{ /* .guid = */ ggml_backend_cann_guid(), + /* .interface = */ ggml_backend_cann_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cann_reg(), device), + /* .context = */ ctx }; return cann_backend; } bool ggml_backend_is_cann(ggml_backend_t backend) { - return backend != NULL && - ggml_guid_matches(backend->guid, ggml_backend_cann_guid()); + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cann_guid()); } int32_t ggml_backend_cann_get_device_count() { return ggml_cann_info().device_count; } -void ggml_backend_cann_get_device_description( - int32_t device, char* description, size_t description_size) { +void ggml_backend_cann_get_device_description(int32_t device, char * description, size_t description_size) { ggml_cann_set_device(device); - const char* soc_name = aclrtGetSocName(); + const char * soc_name = aclrtGetSocName(); snprintf(description, description_size, "%s", soc_name); } -void ggml_backend_cann_get_device_memory(int32_t device, size_t* free, - size_t* total) { +void ggml_backend_cann_get_device_memory(int32_t device, size_t * free, size_t * total) { ggml_cann_set_device(device); ACL_CHECK(aclrtGetMemInfo(ACL_HBM_MEM, free, total)); } From b22572e97dc51757d3ebe917a5a283385010ec68 Mon Sep 17 00:00:00 2001 From: GittyBurstein Date: Thu, 16 Oct 2025 16:26:21 +0300 Subject: [PATCH 05/77] sycl : add ARANGE operator (#16362) * SYCL: update element-wise ops and presets * clean arange * Re-trigger CI --------- Co-authored-by: Gitty Burstein --- ggml/src/ggml-sycl/element_wise.cpp | 32 +++++++++++++++++++++++++++++ ggml/src/ggml-sycl/element_wise.hpp | 2 ++ ggml/src/ggml-sycl/ggml-sycl.cpp | 5 +++++ ggml/src/ggml-sycl/presets.hpp | 1 + 4 files changed, 40 insertions(+) diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index aeeb387595017..58f5125c9cf6e 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -397,6 +397,14 @@ static void acc_f32_sycl(const float *x, const float *y, float *dst, }); } +template +static void arange_kernel(T * dst, const int k, T start, T step, + const sycl::nd_item<1> &item_ct1) { + SYCL_GLOBAL_ID_LOOP(k, item_ct1) { + dst[i] = start + static_cast(i) * step; + } +} + template static void upscale_sycl(const T *x, T *dst, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, @@ -565,6 +573,25 @@ static inline void dispatch_ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx } +static inline void ggml_sycl_op_arange(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + GGML_ASSERT(dst->type == GGML_TYPE_F32); + float start, stop, step; + memcpy(&start, dst->op_params, sizeof(float)); + memcpy(&stop, (float *) dst->op_params + 1, sizeof(float)); + memcpy(&step, (float *) dst->op_params + 2, sizeof(float)); + dpct::queue_ptr stream = ctx.stream(); + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + float * dst_ptr = (float *)dst->data; + const int k = (int)ggml_nelements(dst); + const int num_blocks = ceil_div(k, SYCL_ARANGE_BLOCK_SIZE); + stream->parallel_for( + sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_ARANGE_BLOCK_SIZE), + sycl::range<1>(SYCL_ARANGE_BLOCK_SIZE)), + [=](sycl::nd_item<1> item_ct1) { + arange_kernel(dst_ptr, k, start, step, item_ct1); + }); +} + } // namespace ggml_sycl_detail @@ -1090,3 +1117,8 @@ void ggml_sycl_geglu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); ggml_sycl_op_geglu_quick(ctx, dst); } + +void ggml_sycl_arange(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/0); + ggml_sycl_detail::ggml_sycl_op_arange(ctx, dst); +} diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp index 434743172876c..ed96c55f75a7a 100644 --- a/ggml/src/ggml-sycl/element_wise.hpp +++ b/ggml/src/ggml-sycl/element_wise.hpp @@ -81,4 +81,6 @@ void ggml_sycl_swiglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_geglu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst); +void ggml_sycl_arange(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + #endif // GGML_SYCL_ELEMENTWISE_HPP diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index f3407a813d731..9e55797233412 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -3832,6 +3832,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_GATED_LINEAR_ATTN: ggml_sycl_op_gated_linear_attn(ctx, dst); break; + case GGML_OP_ARANGE: + ggml_sycl_arange(ctx, dst); + break; default: return false; } @@ -4478,6 +4481,8 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_RWKV_WKV7: case GGML_OP_GATED_LINEAR_ATTN: return true; + case GGML_OP_ARANGE: + return op->type == GGML_TYPE_F32; default: return false; } diff --git a/ggml/src/ggml-sycl/presets.hpp b/ggml/src/ggml-sycl/presets.hpp index af1890727df8f..0814bd79a6504 100644 --- a/ggml/src/ggml-sycl/presets.hpp +++ b/ggml/src/ggml-sycl/presets.hpp @@ -49,6 +49,7 @@ #define SYCL_ARGMAX_BLOCK_SIZE 256 #define SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE 256 #define SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE 256 +#define SYCL_ARANGE_BLOCK_SIZE 256 // dmmv = dequantize_mul_mat_vec #ifndef GGML_SYCL_DMMV_X From 683fa6ba4ed3a23b939d3e11e6dc860bd47a0ccf Mon Sep 17 00:00:00 2001 From: Pascal Date: Thu, 16 Oct 2025 16:28:41 +0200 Subject: [PATCH 06/77] fix: added a normalization step for MathJax-style \[\] and \(\) delimiters (#16599) * fix: added a normalization step for MathJax-style \[\] and \(\) delimiters So inline and block equations are converted before KaTeX rendering, enabling proper display of model-generated LaTeX in the WebUI * chore: update webui build output --- tools/server/public/index.html.gz | Bin 850886 -> 850999 bytes .../app/misc/MarkdownContent.svelte | 13 ++++++++++++- 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 1c62ebe9686057df8254804a8862f2d1eed8db1c..34818012c8e4609a8879c636008bb8d0670bc81a 100644 GIT binary patch delta 741345 zcmW(+Wl$Ym(@fCd!CeARaCeuWL4&)yyK`A2xJ!b&2Y157J-EAF+}$to@qYX3oZi{$ zo|&GiJyp8{_Z-a+95og4Kt_2!3^L`BV7>kqxNy;ypXCJ`FVBC#Np2r5ud;75Jm%Jg4J>@Hbnam{A{_Sr(Hi2%I-Mco4UeK>n*Rl?P_6W!TjH%} zYybENB)ba?CqiT*!`<%Fi-sg8Zib1oEz2+Lyc|Q!X-AK@6yE0C=iR(;zFUZ-KDD)b z53;vgXzT#If+cd*(2K#ZVi`1YnD;IMUlM8|ZTM+^+`_6Z!qkQ;9*%!Yz9gpnUWvbR z?&udN%Y7K2FchPsVZuMx+^9%S2931Hgsxz|@dy+Q8*=Kw^wgCK)YcJ;)cGBc?hWjI zqZ0u$8h9h1crv3pGqli;G5Y>$J*$C+A)>}zP3r)#$&^Dyul$^HJYtmR6yZLFb!H4h z7JmidtCmU0(weqJTx6D#LlCJk1v6I`b|xFD#F}Xpx*iepPPlNYoI!>ia%j6BsEYVL-9QQ|qKI z_m>lJ5}?T-P*&vugZizFaF68-UfCwl*o?H~AB3}wZ)7_1%Co5M&OLoDTjO>zhSK&` zL{Ac2R&FU(s@f_7~GRqH;;8CV==s?W2NLD-czDP1WJ zZ^7bNcGlijXT^21_;`%qrhT`^gN&HGGZz56JTp>Kr}ogBYB>DM1={)C?Z^j*)Y)ww zpZUqakTaJ-ZiC*h*d!%9zKM^m%UF(@(rR&E47wx)MNE zF7v|F?47~QGNJi|THiJE1o(bHWB>N7oF_%B0n?U|sL7;mWzgPu=<(aY-nr0PW*5e? zRb%e-4$r`Mo_h+m=!gcSDmpJxy~hE+2}EbZgEJ_aoXh4);dLY1-&}L;v(A2-NjF7i z6Mel>o~Jd_x*wQwKl)gc>gMY4Rab!wuIX6nf-1+2^VzzEN&lnPwISTIU?_(cvpBer zt9$%>#UgG6Jgj@m9%As^k*uaISWcA(cTb6)w5L z-QXz&sCh{yvnE~GqcB*iByYe_{c>;lNoLKV; 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z4UDFU;ffMYH7`w5WuLYN7XMf$IHdAg#G9Fa7-aQTnNi!(QxV)-HjQ+Qe^S4Sl8lro zMth@^qaEAJe1%MI?b#bV?2!;QgIV+kVf8X@Rxbj7Jmpdkh$i(1#Q{~|bV^lifFMYY z>0L;!y;~25tH1?~{l5a8YCCXM2WGL6U>6*Tqu>e=N5Uaie~vKNxMXlhVlSh6?y{JF z{65!V>_68)l-Aa&u=d#x4*yr z4;$6y_5tSNH3!96cFn(!UqeM6%@*$<@t@<@gV9%fIDUOO9jxB(9ls{T=l$NF)qW!k Hg-a{|`drH7 diff --git a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte index 24d29c2b3e51e..1f4caa9003bce 100644 --- a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte +++ b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte @@ -154,9 +154,20 @@ return mutated ? tempDiv.innerHTML : html; } + function normalizeMathDelimiters(text: string): string { + return text + .replace(/(^|[^\\])\\\[((?:\\.|[\s\S])*?)\\\]/g, (_, prefix: string, content: string) => { + return `${prefix}$$${content}$$`; + }) + .replace(/(^|[^\\])\\\(((?:\\.|[\s\S])*?)\\\)/g, (_, prefix: string, content: string) => { + return `${prefix}$${content}$`; + }); + } + async function processMarkdown(text: string): Promise { try { - const result = await processor().process(text); + const normalized = normalizeMathDelimiters(text); + const result = await processor().process(normalized); const html = String(result); const enhancedLinks = enhanceLinks(html); From 1bb4f43380944e94c9a86e305789ba103f5e62bd Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Thu, 16 Oct 2025 19:00:31 +0200 Subject: [PATCH 07/77] mtmd : support home-cooked Mistral Small Omni (#14928) --- tools/mtmd/clip-impl.h | 2 ++ tools/mtmd/clip.cpp | 18 +++++++++++++++--- 2 files changed, 17 insertions(+), 3 deletions(-) diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index 7a7523851c117..1669fad99b36b 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -30,6 +30,7 @@ #define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon" // vision-specific +#define KEY_VISION_PROJ_TYPE "clip.vision.projector_type" // for models with mixed modalities #define KEY_IMAGE_SIZE "clip.vision.image_size" #define KEY_PREPROC_IMAGE_SIZE "clip.vision.preproc_image_size" #define KEY_PATCH_SIZE "clip.vision.patch_size" @@ -48,6 +49,7 @@ #define KEY_MINICPMV_QUERY_NUM "clip.minicpmv_query_num" // audio-specific +#define KEY_AUDIO_PROJ_TYPE "clip.audio.projector_type" // for models with mixed modalities #define KEY_A_NUM_MEL_BINS "clip.audio.num_mel_bins" #define KEY_A_PROJ_STACK_FACTOR "clip.audio.projector.stack_factor" diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 98e68af27a690..f2abf88523843 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -2221,15 +2221,27 @@ struct clip_model_loader { // projector type std::string proj_type; { + // default key get_string(KEY_PROJ_TYPE, proj_type, false); - if (!proj_type.empty()) { - model.proj_type = clip_projector_type_from_string(proj_type); + + // for models with mixed modalities + if (proj_type.empty()) { + if (modality == CLIP_MODALITY_VISION) { + get_string(KEY_VISION_PROJ_TYPE, proj_type, false); + } else if (modality == CLIP_MODALITY_AUDIO) { + get_string(KEY_AUDIO_PROJ_TYPE, proj_type, false); + } else { + GGML_ABORT("unknown modality"); + } } + + model.proj_type = clip_projector_type_from_string(proj_type); + if (model.proj_type == PROJECTOR_TYPE_UNKNOWN) { throw std::runtime_error(string_format("%s: unknown projector type: %s\n", __func__, proj_type.c_str())); } - // correct arch for multimodal models + // correct arch for multimodal models (legacy method) if (model.proj_type == PROJECTOR_TYPE_QWEN25O) { model.proj_type = modality == CLIP_MODALITY_VISION ? PROJECTOR_TYPE_QWEN25VL From ceff6bb253dd306f5404d7ccb3f11fadafe71b52 Mon Sep 17 00:00:00 2001 From: GittyBurstein Date: Fri, 17 Oct 2025 05:36:40 +0300 Subject: [PATCH 08/77] SYCL SET operator optimized for F32 tensors (#16350) * SYCL/SET: implement operator + wire-up; docs/ops updates; element_wise & ggml-sycl changes * sycl(SET): re-apply post-rebase; revert manual docs/ops.md; style cleanups * move SET op to standalone file, GPU-only implementation * Update SYCL SET operator for F32 * ci: fix editorconfig issues (LF endings, trailing spaces, final newline) * fixed ggml-sycl.cpp --------- Co-authored-by: Gitty Burstein --- ggml/src/ggml-sycl/ggml-sycl.cpp | 10 +++++ ggml/src/ggml-sycl/presets.hpp | 1 + ggml/src/ggml-sycl/set.cpp | 73 ++++++++++++++++++++++++++++++++ ggml/src/ggml-sycl/set.hpp | 5 +++ 4 files changed, 89 insertions(+) create mode 100644 ggml/src/ggml-sycl/set.cpp create mode 100644 ggml/src/ggml-sycl/set.hpp diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 9e55797233412..a7e077ec8ebe0 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -42,6 +42,7 @@ #include "ggml-sycl/presets.hpp" #include "ggml-sycl/gemm.hpp" #include "ggml-sycl/set_rows.hpp" +#include "ggml-sycl/set.hpp" #include "ggml-sycl/sycl_hw.hpp" #include "ggml-sycl/getrows.hpp" #include "ggml-sycl/quantize.hpp" @@ -3619,6 +3620,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_GET_ROWS: ggml_sycl_get_rows(ctx, dst); break; + case GGML_OP_SET: + ggml_sycl_op_set(ctx, dst); + break; case GGML_OP_SET_ROWS: ggml_sycl_op_set_rows(ctx, dst); break; @@ -4331,6 +4335,12 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g return false; } } + case GGML_OP_SET: + return (op->type == GGML_TYPE_F32) && + (op->src[0] && op->src[1]) && + (op->src[0]->type == GGML_TYPE_F32) && + (op->src[1]->type == GGML_TYPE_F32); + case GGML_OP_SET_ROWS: { return ((op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_BF16 || diff --git a/ggml/src/ggml-sycl/presets.hpp b/ggml/src/ggml-sycl/presets.hpp index 0814bd79a6504..b6517374230a8 100644 --- a/ggml/src/ggml-sycl/presets.hpp +++ b/ggml/src/ggml-sycl/presets.hpp @@ -31,6 +31,7 @@ #define SYCL_SQRT_BLOCK_SIZE 256 #define SYCL_SIN_BLOCK_SIZE 256 #define SYCL_SQR_BLOCK_SIZE 256 +#define SYCL_SET_BLOCK_SIZE 256 #define SYCL_CPY_BLOCK_SIZE 32 #define SYCL_SCALE_BLOCK_SIZE 256 #define SYCL_CLAMP_BLOCK_SIZE 256 diff --git a/ggml/src/ggml-sycl/set.cpp b/ggml/src/ggml-sycl/set.cpp new file mode 100644 index 0000000000000..381326d230ab0 --- /dev/null +++ b/ggml/src/ggml-sycl/set.cpp @@ -0,0 +1,73 @@ +#include "presets.hpp" +#include "common.hpp" +#include "ggml.h" +#include "set.hpp" +#include +#include +using namespace sycl; + +// Internal function: perform element-wise set operation for each thread +inline void set_f32(const float* src, float* dst, + const int64_t ne0, const int64_t ne1, + const int64_t ne2, const int64_t ne3, + const int64_t nb[3], const int64_t src_nb[3], + const int64_t offset_elem, + const nd_item<1>& item) +{ + const size_t idx = item.get_global_id(0); + const size_t total = ne0 * ne1 * ne2 * ne3; + if (idx >= total) return; + + // Convert linear index to 4D indices + const size_t i3 = idx / (ne2 * ne1 * ne0); + const size_t rem = idx % (ne2 * ne1 * ne0); + const size_t i2 = rem / (ne1 * ne0); + const size_t rem2 = rem % (ne1 * ne0); + const size_t i1 = rem2 / ne0; + const size_t i0 = rem2 % ne0; + + // Compute source and destination indices and copy + dst[i0 + i1*nb[0] + i2*nb[1] + i3*nb[2] + offset_elem] = + src[i0 + i1*src_nb[0] + i2*src_nb[1] + i3*src_nb[2]]; +} + +// Main function: prepare GPU queue and launch parallel_for +void ggml_sycl_op_set(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { + const ggml_tensor* src0 = dst->src[0]; + const ggml_tensor* src1 = dst->src[1]; + + // Ensure shapes and types are compatible + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); + GGML_ASSERT(dst->type == src0->type && src0->type == src1->type && dst->type == GGML_TYPE_F32); + + const int32_t* opts = (const int32_t*) dst->op_params; + const int64_t nb[3] = {opts[0]/sizeof(float), opts[1]/sizeof(float), opts[2]/sizeof(float)}; + const int64_t offset_elem = opts[3] / sizeof(float); + const bool inplace = opts[4]; + + float* dst_ptr = (float*) dst->data; + const float* src0_ptr = (const float*) src0->data; + const float* src1_ptr = (const float*) src1->data; + + queue_ptr stream = ctx.stream(); + + // Copy src0 to dst if not inplace + if (!inplace) + stream->memcpy(dst_ptr, src0_ptr, ggml_nbytes(dst)); + + const int64_t ne[4] = {src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]}; + const int64_t src_nb[3] = {src1->nb[1]/sizeof(float), src1->nb[2]/sizeof(float), src1->nb[3]/sizeof(float)}; + + const size_t total_threads = ne[0]*ne[1]*ne[2]*ne[3]; + const size_t grid_size = ((total_threads + SYCL_SET_BLOCK_SIZE - 1) / SYCL_SET_BLOCK_SIZE) * SYCL_SET_BLOCK_SIZE; + + // Copy src0 to dst if not inplace + stream->parallel_for( + nd_range<1>(range<1>(grid_size), range<1>(SYCL_SET_BLOCK_SIZE)), + [=](nd_item<1> item) { + set_f32(src1_ptr, dst_ptr, + ne[0], ne[1], ne[2], ne[3], + nb, src_nb, offset_elem, item); } + ); +} diff --git a/ggml/src/ggml-sycl/set.hpp b/ggml/src/ggml-sycl/set.hpp new file mode 100644 index 0000000000000..657d7ac9a7b07 --- /dev/null +++ b/ggml/src/ggml-sycl/set.hpp @@ -0,0 +1,5 @@ +#pragma once +#include "backend.hpp" +#include "ggml.h" + +void ggml_sycl_op_set(ggml_backend_sycl_context & ctx, ggml_tensor * dst); From 79967ec596c0dacfd2251b085a57e79df292b1cc Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Fri, 17 Oct 2025 06:59:31 +0100 Subject: [PATCH 09/77] grammar : use int64_t to avoid int overflows in int schema to grammar conversion logic (#16626) --- common/json-schema-to-grammar.cpp | 24 ++++++++++++------------ tests/test-grammar-integration.cpp | 24 ++++++++++++++++++++++++ 2 files changed, 36 insertions(+), 12 deletions(-) diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index db1f0b23dd7c2..dd9b51a9e50fd 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -41,9 +41,9 @@ static std::string build_repetition(const std::string & item_rule, int min_items return result; } -static void _build_min_max_int(int min_value, int max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) { - auto has_min = min_value != std::numeric_limits::min(); - auto has_max = max_value != std::numeric_limits::max(); +static void _build_min_max_int(int64_t min_value, int64_t max_value, std::stringstream & out, int decimals_left = 16, bool top_level = true) { + auto has_min = min_value != std::numeric_limits::min(); + auto has_max = max_value != std::numeric_limits::max(); auto digit_range = [&](char from, char to) { out << "["; @@ -159,7 +159,7 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream & if (has_min) { if (min_value < 0) { out << "\"-\" ("; - _build_min_max_int(std::numeric_limits::min(), -min_value, out, decimals_left, /* top_level= */ false); + _build_min_max_int(std::numeric_limits::min(), -min_value, out, decimals_left, /* top_level= */ false); out << ") | [0] | [1-9] "; more_digits(0, decimals_left - 1); } else if (min_value == 0) { @@ -194,7 +194,7 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream & } digit_range(c, c); out << " ("; - _build_min_max_int(std::stoi(min_s.substr(1)), std::numeric_limits::max(), out, less_decimals, /* top_level= */ false); + _build_min_max_int(std::stoll(min_s.substr(1)), std::numeric_limits::max(), out, less_decimals, /* top_level= */ false); out << ")"; if (c < '9') { out << " | "; @@ -216,7 +216,7 @@ static void _build_min_max_int(int min_value, int max_value, std::stringstream & _build_min_max_int(0, max_value, out, decimals_left, /* top_level= */ true); } else { out << "\"-\" ("; - _build_min_max_int(-max_value, std::numeric_limits::max(), out, decimals_left, /* top_level= */ false); + _build_min_max_int(-max_value, std::numeric_limits::max(), out, decimals_left, /* top_level= */ false); out << ")"; } return; @@ -925,17 +925,17 @@ class SchemaConverter { int max_len = schema.contains("maxLength") ? schema["maxLength"].get() : std::numeric_limits::max(); return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space"); } else if (schema_type == "integer" && (schema.contains("minimum") || schema.contains("exclusiveMinimum") || schema.contains("maximum") || schema.contains("exclusiveMaximum"))) { - int min_value = std::numeric_limits::min(); - int max_value = std::numeric_limits::max(); + int64_t min_value = std::numeric_limits::min(); + int64_t max_value = std::numeric_limits::max(); if (schema.contains("minimum")) { - min_value = schema["minimum"].get(); + min_value = schema["minimum"].get(); } else if (schema.contains("exclusiveMinimum")) { - min_value = schema["exclusiveMinimum"].get() + 1; + min_value = schema["exclusiveMinimum"].get() + 1; } if (schema.contains("maximum")) { - max_value = schema["maximum"].get(); + max_value = schema["maximum"].get(); } else if (schema.contains("exclusiveMaximum")) { - max_value = schema["exclusiveMaximum"].get() - 1; + max_value = schema["exclusiveMaximum"].get() - 1; } std::stringstream out; out << "("; diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp index 6d64f07376fb8..82fae671ed00b 100644 --- a/tests/test-grammar-integration.cpp +++ b/tests/test-grammar-integration.cpp @@ -301,6 +301,30 @@ static void test_simple_grammar() { "0123", } ); + test_schema( + "min 1 max 900719925474091", + // Schema + R"""({ + "type": "integer", + "exclusiveMinimum": 0, + "maximum": 900719925474091 + })""", + // Passing strings + { + "1", + "2", + "10", + "900719925474090", + "900719925474091", + }, + // Failing strings + { + "0", + "01", + "900719925474092", + "9007199254740910", + } + ); test_schema( "min -1 max 1", R"""({ From 9ad4f1931ee0f3b41d9355245ef744786aaae0aa Mon Sep 17 00:00:00 2001 From: Ilia Ilmer Date: Fri, 17 Oct 2025 02:33:58 -0400 Subject: [PATCH 10/77] metal : add `CONV_TRANSPOSE_2D` (#16542) * initial: headers and metal-device.cpp updates * adding conv_transpose_2d * fix type * fix type: int32->int64 * Update ggml/src/ggml-metal/ggml-metal.metal Co-authored-by: Georgi Gerganov * Update ggml/src/ggml-metal/ggml-metal.metal Co-authored-by: Georgi Gerganov * Update ggml/src/ggml-metal/ggml-metal.metal Co-authored-by: Georgi Gerganov * add checks for src[0] and src[1]; add type checks * Update ggml-metal.metal Co-authored-by: Georgi Gerganov * add more tests, add optimization to threading * add dynamic memory allocation in metal --------- Co-authored-by: Georgi Gerganov --- ggml/src/ggml-metal/ggml-metal-device.cpp | 25 +++++++ ggml/src/ggml-metal/ggml-metal-device.h | 1 + ggml/src/ggml-metal/ggml-metal-device.m | 5 ++ ggml/src/ggml-metal/ggml-metal-impl.h | 13 ++++ ggml/src/ggml-metal/ggml-metal-ops.cpp | 60 +++++++++++++++ ggml/src/ggml-metal/ggml-metal-ops.h | 1 + ggml/src/ggml-metal/ggml-metal.metal | 91 +++++++++++++++++++++++ tests/test-backend-ops.cpp | 2 + 8 files changed, 198 insertions(+) diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp index 866cd2da58576..75811634227b3 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.cpp +++ b/ggml/src/ggml-metal/ggml-metal-device.cpp @@ -1406,6 +1406,31 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_transpose_1d(ggml_met return res; } +ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_transpose_2d(ggml_metal_library_t lib, const ggml_tensor * op) { + assert(op->op == GGML_OP_CONV_TRANSPOSE_2D); + + GGML_ASSERT(ggml_is_contiguous(op->src[0])); + GGML_ASSERT(ggml_is_contiguous(op->src[1])); + GGML_ASSERT(op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32); + GGML_ASSERT(op->type == GGML_TYPE_F32); + + char base[256]; + char name[256]; + + snprintf(base, 256, "kernel_conv_transpose_2d_%s_%s", ggml_type_name(op->src[0]->type), ggml_type_name(op->src[1]->type)); + snprintf(name, 256, "%s", base); + + ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name); + if (res) { + return res; + } + + res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr); + + return res; +} + ggml_metal_pipeline_t ggml_metal_library_get_pipeline_upscale(ggml_metal_library_t lib, const ggml_tensor * op) { assert(op->op == GGML_OP_UPSCALE); diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index 28ae2e1765146..4d58297481813 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -130,6 +130,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_norm (ggml_me ggml_metal_pipeline_t ggml_metal_library_get_pipeline_rope (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_im2col (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_transpose_1d (ggml_metal_library_t lib, const struct ggml_tensor * op); +ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_transpose_2d (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_upscale (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pad (ggml_metal_library_t lib, const struct ggml_tensor * op); ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pad_reflect_1d (ggml_metal_library_t lib, const struct ggml_tensor * op); diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index c3c83abe4e63e..360fbe19f0fb6 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -653,6 +653,11 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te case GGML_OP_SCALE: case GGML_OP_CONV_TRANSPOSE_1D: return true; + case GGML_OP_CONV_TRANSPOSE_2D: + return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]) && + (op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32) && + op->src[1]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32; case GGML_OP_CLAMP: return op->src[0]->type == GGML_TYPE_F32; case GGML_OP_SQR: diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h index fa2d82cefb40e..96f43d260a3c3 100644 --- a/ggml/src/ggml-metal/ggml-metal-impl.h +++ b/ggml/src/ggml-metal/ggml-metal-impl.h @@ -514,6 +514,19 @@ typedef struct { uint64_t nb1; } ggml_metal_kargs_conv_transpose_1d; +typedef struct { + int32_t IC; + int32_t IH; + int32_t IW; + int32_t KH; + int32_t KW; + int32_t OC; + int32_t s0; + uint64_t nb0; + uint64_t nb1; + uint64_t nb2; +} ggml_metal_kargs_conv_transpose_2d; + typedef struct { uint64_t ofs0; uint64_t ofs1; diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp index 4f9f6bda00a79..7a85edbdcdb84 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.cpp +++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp @@ -368,6 +368,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) { { n_fuse = ggml_metal_op_conv_transpose_1d(ctx, idx); } break; + case GGML_OP_CONV_TRANSPOSE_2D: + { + n_fuse = ggml_metal_op_conv_transpose_2d(ctx, idx); + } break; case GGML_OP_UPSCALE: { n_fuse = ggml_metal_op_upscale(ctx, idx); @@ -3118,6 +3122,62 @@ int ggml_metal_op_conv_transpose_1d(ggml_metal_op_t ctx, int idx) { return 1; } +int ggml_metal_op_conv_transpose_2d(ggml_metal_op_t ctx, int idx) { + ggml_tensor * op = ctx->node(idx); + + ggml_metal_library_t lib = ctx->lib; + ggml_metal_encoder_t enc = ctx->enc; + + GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne); + GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb); + GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne); + GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb); + GGML_TENSOR_LOCALS( int32_t, ne, op, ne); + GGML_TENSOR_LOCALS(uint32_t, nb, op, nb); + + const int32_t s0 = ((const int32_t *)(op->op_params))[0]; + + const int32_t IC = op->src[1]->ne[2]; + const int32_t IH = op->src[1]->ne[1]; + const int32_t IW = op->src[1]->ne[0]; + + const int32_t KH = op->src[0]->ne[1]; + const int32_t KW = op->src[0]->ne[0]; + + const int32_t OW = op->ne[0]; + const int32_t OH = op->ne[1]; + const int32_t OC = op->ne[2]; + + ggml_metal_kargs_conv_transpose_2d args = { + /*.IC =*/ IC, + /*.IH =*/ IH, + /*.IW =*/ IW, + /*.KH =*/ KH, + /*.KW =*/ KW, + /*.OC =*/ OC, + /*.s0 =*/ s0, + /*.nb0 =*/ nb0, + /*.nb1 =*/ nb1, + /*.nb2 =*/ nb2, + }; + + ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_conv_transpose_2d(lib, op); + + ggml_metal_encoder_set_pipeline(enc, pipeline); + ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), 2); + ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 3); + + // Metal requires buffer size to be multiple of 16 bytes + const size_t smem = GGML_PAD(KW * KH * sizeof(float), 16); + ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0); + + ggml_metal_encoder_dispatch_threadgroups(enc, OW, OH, OC, KW, KH, 1); + + return 1; +} + int ggml_metal_op_upscale(ggml_metal_op_t ctx, int idx) { ggml_tensor * op = ctx->node(idx); diff --git a/ggml/src/ggml-metal/ggml-metal-ops.h b/ggml/src/ggml-metal/ggml-metal-ops.h index f352738698beb..0d9cb8af7c1d0 100644 --- a/ggml/src/ggml-metal/ggml-metal-ops.h +++ b/ggml/src/ggml-metal/ggml-metal-ops.h @@ -71,6 +71,7 @@ int ggml_metal_op_norm (ggml_metal_op_t ctx, int idx); int ggml_metal_op_rope (ggml_metal_op_t ctx, int idx); int ggml_metal_op_im2col (ggml_metal_op_t ctx, int idx); int ggml_metal_op_conv_transpose_1d (ggml_metal_op_t ctx, int idx); +int ggml_metal_op_conv_transpose_2d (ggml_metal_op_t ctx, int idx); int ggml_metal_op_upscale (ggml_metal_op_t ctx, int idx); int ggml_metal_op_pad (ggml_metal_op_t ctx, int idx); int ggml_metal_op_pad_reflect_1d (ggml_metal_op_t ctx, int idx); diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal index 496610b154b6d..2c2f0141514ca 100644 --- a/ggml/src/ggml-metal/ggml-metal.metal +++ b/ggml/src/ggml-metal/ggml-metal.metal @@ -4179,6 +4179,97 @@ kernel void kernel_conv_transpose_1d( uint3 tgpig[[threadgroup_position_in_grid]], uint3 tgpg[[threadgroups_per_grid]]); + +typedef void (conv_transpose_2d_t)( + constant ggml_metal_kargs_conv_transpose_2d & args, + device const float * src0, + device const float * src1, + device char * dst, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]]); + +template +kernel void kernel_conv_transpose_2d( + constant ggml_metal_kargs_conv_transpose_2d & args, + device const T * src0, + device const float * src1, + device char * dst, + threadgroup float * shared_sum [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + + const int64_t out_x = tgpig[0]; + const int64_t out_y = tgpig[1]; + const int64_t out_c = tgpig[2]; + + const int64_t kw = tpitg[0]; + const int64_t kh = tpitg[1]; + + float v = 0.0f; + + for (int64_t in_c = 0; in_c < args.IC; in_c++) { + int64_t in_y = out_y - kh; + + if (in_y < 0 || in_y % args.s0) continue; + + in_y /= args.s0; + + if (in_y >= args.IH) continue; + + int64_t in_x = out_x - kw; + + if (in_x < 0 || in_x % args.s0) continue; + + in_x /= args.s0; + + if (in_x >= args.IW) continue; + + const int64_t input_idx = (args.IW * args.IH) * in_c + (args.IW) * in_y + in_x; + const int64_t kernel_idx = (args.KH * args.KW * args.OC) * in_c + (args.KH * args.KW) * out_c + (args.KW) * kh + kw; + + v += (float)src0[kernel_idx] * src1[input_idx]; + } + + const uint tid = tpitg.y * ntg.x + tpitg.x; + shared_sum[tid] = v; + + threadgroup_barrier(mem_flags::mem_threadgroup); + + if (tid == 0) { + float total = 0.0f; + const uint num_threads = ntg.x * ntg.y; + for (uint i = 0; i < num_threads; i++) { + total += shared_sum[i]; + } + + device float * dst_ptr = (device float *) (dst + out_x*args.nb0 + out_y * args.nb1 + out_c*args.nb2); + dst_ptr[0] = total; + } +} + +template [[host_name("kernel_conv_transpose_2d_f32_f32")]] +kernel void kernel_conv_transpose_2d( + constant ggml_metal_kargs_conv_transpose_2d & args, + device const float * src0, + device const float * src1, + device char * dst, + threadgroup float * shared_sum [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + +template [[host_name("kernel_conv_transpose_2d_f16_f32")]] +kernel void kernel_conv_transpose_2d( + constant ggml_metal_kargs_conv_transpose_2d & args, + device const half * src0, + device const float * src1, + device char * dst, + threadgroup float * shared_sum [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + kernel void kernel_upscale_f32( constant ggml_metal_kargs_upscale & args, device const char * src0, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index d5c5a2a6656ee..82bb55ea0e184 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -6989,6 +6989,8 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_conv_2d_dw({512, 512, 256, 1}, {3, 3, 1, 256}, 1, 1, 1, true)); test_cases.emplace_back(new test_conv_transpose_2d({256, 256, 256, 1}, {3, 3, 16, 256}, 1)); + test_cases.emplace_back(new test_conv_transpose_2d({16, 16, 16, 1}, {3, 3, 8, 16}, 1)); + test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2)); test_cases.emplace_back(new test_mean(GGML_TYPE_F32, {256, 256, 3, 1})); From b19491599d4d42c606601d75e95c1d1de3291f8e Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Fri, 17 Oct 2025 02:31:04 -0500 Subject: [PATCH 11/77] vulkan: fix debug build (add_rms_len/data not found) (#16624) --- ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 184f3f3a7db51..32f272e91d466 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -959,7 +959,7 @@ void write_output_files() { } std::string suffixes[2] = {"_f32", "_f16"}; - for (auto op : {"add", "sub", "mul", "div", "add_rms"}) { + for (std::string op : {"add", "sub", "mul", "div", "add_rms"}) { hdr << "extern const void * " << op << "_data[2][2][2][2];\n"; hdr << "extern const uint64_t " << op << "_len[2][2][2][2];\n"; From ababae7e1ec3e9cfdab0322ee55ea3389e82a4d5 Mon Sep 17 00:00:00 2001 From: Pascal Date: Fri, 17 Oct 2025 10:35:03 +0200 Subject: [PATCH 12/77] webui: reorganize settings layout (#16607) * webui: reorganize settings layout * chore: update webui build output * fix: remove unused variable * chore: update webui build output --- tools/server/public/index.html.gz | Bin 850999 -> 850862 bytes .../ChatSettings/ChatSettingsDialog.svelte | 115 +++++++++--------- 2 files changed, 57 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--git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte index d5d4c7fe3f34b..bf17633095242 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte @@ -4,7 +4,7 @@ Funnel, AlertTriangle, Brain, - Cog, + Code, Monitor, Sun, Moon, @@ -88,9 +88,59 @@ ] }, { - title: 'Samplers', + title: 'Sampling', icon: Funnel, fields: [ + { + key: 'temperature', + label: 'Temperature', + type: 'input' + }, + { + key: 'dynatemp_range', + label: 'Dynamic temperature range', + type: 'input' + }, + { + key: 'dynatemp_exponent', + label: 'Dynamic temperature exponent', + type: 'input' + }, + { + key: 'top_k', + label: 'Top K', + type: 'input' + }, + { + key: 'top_p', + label: 'Top P', + type: 'input' + }, + { + key: 'min_p', + label: 'Min P', + type: 'input' + }, + { + key: 'xtc_probability', + label: 'XTC probability', + type: 'input' + }, + { + key: 'xtc_threshold', + label: 'XTC threshold', + type: 'input' + }, + { + key: 'typ_p', + label: 'Typical P', + type: 'input' + }, + { + key: 'max_tokens', + label: 'Max tokens', + type: 'input' + }, { key: 'samplers', label: 'Samplers', @@ -152,68 +202,17 @@ key: 'showThoughtInProgress', label: 'Show thought in progress', type: 'checkbox' - }, - { - key: 'disableReasoningFormat', - label: - 'Show raw LLM output without backend parsing and frontend Markdown rendering to inspect streaming across different models.', - type: 'checkbox' } ] }, { - title: 'Advanced', - icon: Cog, + title: 'Developer', + icon: Code, fields: [ { - key: 'temperature', - label: 'Temperature', - type: 'input' - }, - { - key: 'dynatemp_range', - label: 'Dynamic temperature range', - type: 'input' - }, - { - key: 'dynatemp_exponent', - label: 'Dynamic temperature exponent', - type: 'input' - }, - { - key: 'top_k', - label: 'Top K', - type: 'input' - }, - { - key: 'top_p', - label: 'Top P', - type: 'input' - }, - { - key: 'min_p', - label: 'Min P', - type: 'input' - }, - { - key: 'xtc_probability', - label: 'XTC probability', - type: 'input' - }, - { - key: 'xtc_threshold', - label: 'XTC threshold', - type: 'input' - }, - { - key: 'typ_p', - label: 'Typical P', - type: 'input' - }, - { - key: 'max_tokens', - label: 'Max tokens', - type: 'input' + key: 'disableReasoningFormat', + label: 'Show raw LLM output', + type: 'checkbox' }, { key: 'custom', From 342c728d031d50673feded797520a44127d73379 Mon Sep 17 00:00:00 2001 From: muggle-stack Date: Fri, 17 Oct 2025 18:01:23 +0800 Subject: [PATCH 13/77] ggml : fix SpaceMit IME array out-of-bounds in task assignment (#16629) Fix incorrect task-to-batch index calculation in the quantization phase. The bug caused out-of-bounds access to qnbitgemm_args array when compute_idx exceeded per_gemm_block_count_m, leading to invalid pointer dereferences and SIGBUS errors. Correctly map tasks to batches by dividing compute_idx by per_gemm_block_count_m instead of block_size_m. Example: batch_feature=1, gemm_m=30, block_size_m=4 per_gemm_block_count_m = 8, task_count = 8 Old: gemm_idx = 4/4 = 1 (out of bounds New: gemm_idx = 4/8 = 0 (correct) Tested on SpaceMit K1 RISC-V64 with qwen2.5:0.5b model. Co-authored-by: muggle --- ggml/src/ggml-cpu/spacemit/ime.cpp | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cpu/spacemit/ime.cpp b/ggml/src/ggml-cpu/spacemit/ime.cpp index 54d3dece0e03a..91fe1925eaa3a 100644 --- a/ggml/src/ggml-cpu/spacemit/ime.cpp +++ b/ggml/src/ggml-cpu/spacemit/ime.cpp @@ -485,8 +485,9 @@ template class tensor_ int32_t start = ith * task_per_thread; int32_t end = std::min((ith + 1) * task_per_thread, task_count); for (int32_t compute_idx = start; compute_idx < end; compute_idx++) { - int32_t gemm_idx = compute_idx / block_size_m; - int32_t m_idx = compute_idx % block_size_m * block_size_m; + int32_t gemm_idx = compute_idx / per_gemm_block_count_m; + int32_t block_idx_in_gemm = compute_idx % per_gemm_block_count_m; + int32_t m_idx = block_idx_in_gemm * block_size_m; const qnbitgemm_spacemit_ime_args & data = qnbitgemm_args[gemm_idx]; int32_t rows_tobe_handled = (gemm_m - m_idx) > block_size_m ? block_size_m : (gemm_m - m_idx); From 3d4e86bbeb15f487d6da6174ba6191b7c212cc25 Mon Sep 17 00:00:00 2001 From: Giuseppe Scrivano Date: Fri, 17 Oct 2025 14:23:47 +0200 Subject: [PATCH 14/77] vulkan: Add State Space Model (SSM) Operations Support (#16463) * vulkan: implement SSM scan operation Add State Space Model scan operation to the Vulkan backend. Signed-off-by: Giuseppe Scrivano * vulkan: implement SSM conv operation Add State Space Model conv operation to the Vulkan backend. Signed-off-by: Giuseppe Scrivano --------- Signed-off-by: Giuseppe Scrivano --- docs/ops.md | 4 +- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 227 +++++++++++++++++- .../ggml-vulkan/vulkan-shaders/ssm_conv.comp | 44 ++++ .../ggml-vulkan/vulkan-shaders/ssm_scan.comp | 125 ++++++++++ .../vulkan-shaders/vulkan-shaders-gen.cpp | 4 + 5 files changed, 396 insertions(+), 8 deletions(-) create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/ssm_scan.comp diff --git a/docs/ops.md b/docs/ops.md index 5df72d25015d2..938efac815fc0 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -100,8 +100,8 @@ Legend: | SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | | SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | | SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ | ❌ | -| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | -| SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | +| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | +| SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | | STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | | SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 1674dc66ab912..bc703611f0a94 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -582,6 +582,9 @@ struct vk_device_struct { vk_pipeline pipeline_pool2d_f32; vk_pipeline pipeline_rwkv_wkv6_f32; vk_pipeline pipeline_rwkv_wkv7_f32; + vk_pipeline pipeline_ssm_scan_f32_d128; + vk_pipeline pipeline_ssm_scan_f32_d256; + vk_pipeline pipeline_ssm_conv_f32; vk_pipeline pipeline_opt_step_adamw_f32; vk_pipeline pipeline_opt_step_sgd_f32; vk_pipeline pipeline_conv2d_f32[CONV_SHAPE_COUNT]; @@ -1087,6 +1090,19 @@ struct vk_op_rwkv_wkv7_push_constants { uint32_t C; uint32_t H; }; +struct vk_op_ssm_scan_push_constants { + uint32_t nb02, nb03, nb12, nb13; + uint32_t nb21, nb22, nb31; + uint32_t nb42, nb43, nb52, nb53; + uint32_t s_off; + uint32_t n_head, d_head, n_group, n_tok; +}; +struct vk_op_ssm_conv_push_constants { + uint32_t nb01, nb02; + uint32_t nb11; + uint32_t dst_nb0, dst_nb1, dst_nb2; + uint32_t nc, ncs, nr, n_t, n_s; +}; struct vk_op_conv2d_push_constants { uint32_t Cout; @@ -3591,6 +3607,11 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv7_f32, "rwkv_wkv7_f32", rwkv_wkv7_f32_len, rwkv_wkv7_f32_data, "main", 8, sizeof(vk_op_rwkv_wkv7_push_constants), {1, 1, 1}, {device->subgroup_size}, 1); + ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d128, "ssm_scan_f32", ssm_scan_f32_len, ssm_scan_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {128, device->subgroup_size, 16}, 1); + ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d256, "ssm_scan_f32", ssm_scan_f32_len, ssm_scan_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {256, device->subgroup_size, 16}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_ssm_conv_f32, "ssm_conv_f32", ssm_conv_f32_len, ssm_conv_f32_data, "main", 3, sizeof(vk_op_ssm_conv_push_constants), {32, 1, 1}, {32}, 1); + ggml_vk_create_pipeline(device, device->pipeline_opt_step_adamw_f32, "opt_step_adamw_f32", opt_step_adamw_f32_len, opt_step_adamw_f32_data, "main", 5, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_opt_step_sgd_f32, "opt_step_sgd_f32", opt_step_sgd_f32_len, opt_step_sgd_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); @@ -8098,6 +8119,21 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_rwkv_wkv7_f32; } return nullptr; + case GGML_OP_SSM_SCAN: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + const uint32_t d_state = src0->ne[0]; + if (d_state == 128) { + return ctx->device->pipeline_ssm_scan_f32_d128; + } else if (d_state == 256) { + return ctx->device->pipeline_ssm_scan_f32_d256; + } + } + return nullptr; + case GGML_OP_SSM_CONV: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_ssm_conv_f32; + } + return nullptr; case GGML_OP_OPT_STEP_ADAMW: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_opt_step_adamw_f32; @@ -8592,6 +8628,14 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co } } break; + case GGML_OP_SSM_CONV: + { + const uint32_t nr = src0->ne[1]; + const uint32_t n_t = dst->ne[1]; + const uint32_t n_s = dst->ne[2]; + elements = { nr, n_t, n_s }; + } + break; default: elements = { (uint32_t)ggml_nelements(src0), 1, 1 }; break; @@ -9038,6 +9082,117 @@ static void ggml_vk_rwkv_wkv7(ggml_backend_vk_context * ctx, vk_context& subctx, ); } +static void ggml_vk_ssm_scan(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, bool dryrun = false) { + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + const ggml_tensor * src2 = dst->src[2]; + const ggml_tensor * src3 = dst->src[3]; + const ggml_tensor * src4 = dst->src[4]; + const ggml_tensor * src5 = dst->src[5]; + + GGML_ASSERT(dst->buffer != nullptr); + + const uint32_t head_dim = src0->ne[1]; + const uint32_t n_head = src1->ne[1]; + const uint32_t n_group = src4->ne[1]; + const uint32_t n_tok = src1->ne[2]; + const uint32_t n_seq = src1->ne[3]; + + bool is_mamba2 = (src3->nb[1] == sizeof(float)); + GGML_ASSERT(is_mamba2); + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, dst->op); + GGML_ASSERT(pipeline != nullptr); + + if (dryrun) { + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + return; + } + + const int64_t s_off = ggml_nelements(src1) * sizeof(float); + + const vk_op_ssm_scan_push_constants pc = { + (uint32_t)src0->nb[2], (uint32_t)src0->nb[3], + (uint32_t)src1->nb[2], (uint32_t)src1->nb[3], + (uint32_t)src2->nb[1], (uint32_t)src2->nb[2], + (uint32_t)src3->nb[1], + (uint32_t)src4->nb[2], (uint32_t)src4->nb[3], + (uint32_t)src5->nb[2], (uint32_t)src5->nb[3], + (uint32_t)s_off, + n_head, head_dim, n_group, n_tok + }; + + ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; + ggml_backend_vk_buffer_context * src_buf_ctxs[GGML_MAX_SRC]; + for (int i = 0; i < GGML_MAX_SRC && dst->src[i] != nullptr; i++) { + src_buf_ctxs[i] = (ggml_backend_vk_buffer_context *)dst->src[i]->buffer->context; + } + + vk_buffer d_D = nullptr, d_srcs[GGML_MAX_SRC] = { nullptr }; + size_t dst_offset = 0, src_offsets[GGML_MAX_SRC] = { 0 }; + bool dst_uma = false, srcs_uma[GGML_MAX_SRC] = { false }; + + if (ctx->device->uma) { + for (int i = 0; i < GGML_MAX_SRC && dst->src[i] != nullptr; i++) { + ggml_vk_host_get(ctx->device, dst->src[i]->data, d_srcs[i], src_offsets[i]); + srcs_uma[i] = d_srcs[i] != nullptr; + } + ggml_vk_host_get(ctx->device, dst->data, d_D, dst_offset); + dst_uma = d_D != nullptr; + } + + if (!dst_uma) { + d_D = dst_buf_ctx->dev_buffer; + dst_offset = vk_tensor_offset(dst) + dst->view_offs; + } + for (int i = 0; i < GGML_MAX_SRC && dst->src[i] != nullptr; i++) { + if (!srcs_uma[i]) { + d_srcs[i] = src_buf_ctxs[i]->dev_buffer; + src_offsets[i] = vk_tensor_offset(dst->src[i]) + dst->src[i]->view_offs; + } + } + + size_t dst_size = ggml_nbytes(dst); + size_t src_sizes[GGML_MAX_SRC]; + for (int i = 0; i < GGML_MAX_SRC && dst->src[i] != nullptr; i++) { + src_sizes[i] = ggml_nbytes(dst->src[i]); + } + + std::array elements; + + const int splitH = 16; + const uint32_t num_workgroups_x = CEIL_DIV(n_head * head_dim, splitH); + const uint32_t num_workgroups_y = n_seq; + elements = { num_workgroups_x, num_workgroups_y, 1 }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { + vk_subbuffer{ d_srcs[0], src_offsets[0], src_sizes[0] }, + vk_subbuffer{ d_srcs[1], src_offsets[1], src_sizes[1] }, + vk_subbuffer{ d_srcs[2], src_offsets[2], src_sizes[2] }, + vk_subbuffer{ d_srcs[3], src_offsets[3], src_sizes[3] }, + vk_subbuffer{ d_srcs[4], src_offsets[4], src_sizes[4] }, + vk_subbuffer{ d_srcs[5], src_offsets[5], src_sizes[5] }, + vk_subbuffer{ d_srcs[6], src_offsets[6], src_sizes[6] }, + vk_subbuffer{ d_D, dst_offset, dst_size } + }, pc, elements); +} + +static void ggml_vk_ssm_conv(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, bool dryrun = false) { + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SSM_CONV, { + (uint32_t)src0->nb[1], (uint32_t)src0->nb[2], + (uint32_t)src1->nb[1], + (uint32_t)dst->nb[0], (uint32_t)dst->nb[1], (uint32_t)dst->nb[2], + (uint32_t)src1->ne[0], + (uint32_t)src0->ne[0], + (uint32_t)src0->ne[1], + (uint32_t)dst->ne[1], + (uint32_t)dst->ne[2], + }, dryrun); +} + static void ggml_vk_op_f32_opt_step_adamw(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_push_constants&& pc, bool dryrun = false) { const ggml_tensor * x = dst->src[0]; const ggml_tensor * g = dst->src[1]; @@ -10870,6 +11025,8 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr case GGML_OP_CONV_2D_DW: case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: + case GGML_OP_SSM_SCAN: + case GGML_OP_SSM_CONV: case GGML_OP_LEAKY_RELU: case GGML_OP_FLASH_ATTN_EXT: case GGML_OP_OPT_STEP_ADAMW: @@ -11287,6 +11444,16 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr break; + case GGML_OP_SSM_SCAN: + ggml_vk_ssm_scan(ctx, compute_ctx, node, dryrun); + + break; + + case GGML_OP_SSM_CONV: + ggml_vk_ssm_conv(ctx, compute_ctx, node, dryrun); + + break; + case GGML_OP_OPT_STEP_ADAMW: ggml_vk_opt_step_adamw(ctx, compute_ctx, node, dryrun); @@ -11398,6 +11565,8 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph * case GGML_OP_CONV_2D_DW: case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: + case GGML_OP_SSM_SCAN: + case GGML_OP_SSM_CONV: case GGML_OP_LEAKY_RELU: case GGML_OP_REPEAT: case GGML_OP_REPEAT_BACK: @@ -12879,6 +13048,47 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_RWKV_WKV6: case GGML_OP_RWKV_WKV7: return true; + case GGML_OP_SSM_SCAN: + { + for (int i = 0; i < 6; i++) { + if (op->src[i] && ggml_is_quantized(op->src[i]->type)) { + return false; + } + } + if (op->src[6] && op->src[6]->type != GGML_TYPE_I32) { + return false; + } + if (op->src[0]->type != GGML_TYPE_F32 || op->type != GGML_TYPE_F32) { + return false; + } + + const uint32_t d_state = op->src[0]->ne[0]; + const uint32_t head_dim = op->src[0]->ne[1]; + + bool is_mamba2 = (op->src[3] && op->src[3]->nb[1] == sizeof(float)); + if (!is_mamba2) { + return false; + } + + if ((d_state != 128 && d_state != 256) || head_dim % 16 != 0) { + return false; + } + + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + const vk_device& device = ggml_vk_get_device(ctx->device); + + const uint32_t SPLIT_H = 16; + + size_t stateC_size = SPLIT_H * d_state * sizeof(float); + + if (stateC_size > device->properties.limits.maxComputeSharedMemorySize) { + return false; + } + + return true; + } + case GGML_OP_SSM_CONV: + return true; case GGML_OP_CONV_TRANSPOSE_1D: return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32; case GGML_OP_CONV_2D: @@ -13223,14 +13433,14 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * struct ggml_context * ggml_ctx = ggml_init(iparams); - std::array src_clone = {nullptr, nullptr, nullptr, nullptr, nullptr, nullptr}; - std::array src_size = {0, 0, 0, 0, 0, 0}; - std::array src_buffer = {nullptr, nullptr, nullptr, nullptr, nullptr, nullptr}; - const char * srci_name[6] = {"src0", "src1", "src2", "src3", "src4", "src5"}; + std::array src_clone = {nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr}; + std::array src_size = {}; + std::array src_buffer = {}; + const char * srci_name[GGML_MAX_SRC] = {"src0", "src1", "src2", "src3", "src4", "src5", "src6", "src7", "src8", "src9"}; struct ggml_tensor * tensor_clone = nullptr; - for (int i = 0; i < 6; i++) { + for (int i = 0; i < GGML_MAX_SRC; i++) { ggml_tensor * srci = tensor->src[i]; if (fused_rms_norm_mul) { rms_norm_idx = tensor->src[0]->op == GGML_OP_RMS_NORM ? 0 : 1; @@ -13537,6 +13747,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * src_clone[2]); } else if (tensor->op == GGML_OP_ADD_ID) { tensor_clone = ggml_add_id(ggml_ctx, src_clone[0], src_clone[1], src_clone[2]); + } else if (tensor->op == GGML_OP_SSM_SCAN) { + tensor_clone = ggml_ssm_scan(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], + src_clone[3], src_clone[4], src_clone[5], src_clone[6]); + } else if (tensor->op == GGML_OP_SSM_CONV) { + tensor_clone = ggml_ssm_conv(ggml_ctx, src_clone[0], src_clone[1]); } else { std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; @@ -13558,7 +13773,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * memcpy(comp_result, tensor_clone->data, comp_size); memcpy(comp_nb, tensor_clone->nb, sizeof(size_t) * GGML_MAX_DIMS); - for (int i = 0; i < 6; i++) { + for (int i = 0; i < GGML_MAX_SRC; i++) { if (src_buffer[i] != nullptr) { free(src_buffer[i]); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp b/ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp new file mode 100644 index 0000000000000..d62696bcfaecf --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp @@ -0,0 +1,44 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require + +#include "types.glsl" + +layout(constant_id = 0) const uint BLOCK_SIZE = 32; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout(binding = 0) readonly buffer Src0 { float src0[]; }; +layout(binding = 1) readonly buffer Src1 { float src1[]; }; +layout(binding = 2) buffer Dst { float dst[]; }; + +layout(push_constant) uniform PushConstants { + uint nb01; uint nb02; + uint nb11; + uint dst_nb0; uint dst_nb1; uint dst_nb2; + uint nc; uint ncs; uint nr; uint n_t; uint n_s; +}; + +void main() { + const uint global_thread_id = gl_GlobalInvocationID.x; + const uint i2 = gl_WorkGroupID.y; + const uint i3 = gl_WorkGroupID.z; + + if (global_thread_id >= nr || i2 >= n_t || i3 >= n_s) { + return; + } + + const uint i1 = global_thread_id; + const uint src0_base = i3 * (nb02 / 4) + i2 + i1 * (nb01 / 4); + const uint src1_base = i1 * (nb11 / 4); + const uint dst_idx = i3 * (dst_nb2 / 4) + i2 * (dst_nb1 / 4) + i1; + + float sum = 0.0; + [[unroll]] for (uint i0 = 0; i0 < nc; i0++) { + const uint src0_idx = src0_base + i0; + const uint src1_idx = src1_base + i0; + sum += src0[src0_idx] * src1[src1_idx]; + } + + dst[dst_idx] = sum; +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/ssm_scan.comp b/ggml/src/ggml-vulkan/vulkan-shaders/ssm_scan.comp new file mode 100644 index 0000000000000..12bd174579052 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/ssm_scan.comp @@ -0,0 +1,125 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require + +#include "types.glsl" + +layout(constant_id = 0) const uint D_STATE = 128; +layout(constant_id = 1) const uint SUBGROUP_SIZE = 32; +layout(constant_id = 2) const uint SPLIT_H = 16; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout(binding = 0) readonly buffer Src0 { float s0[]; }; +layout(binding = 1) readonly buffer Src1 { float x[]; }; +layout(binding = 2) readonly buffer Src2 { float dt[]; }; +layout(binding = 3) readonly buffer Src3 { float A[]; }; +layout(binding = 4) readonly buffer Src4 { float B[]; }; +layout(binding = 5) readonly buffer Src5 { float C[]; }; +layout(binding = 6) readonly buffer Src6 { int ids[]; }; +layout(binding = 7) buffer Dst { float d[]; }; + +layout(push_constant) uniform PushConstants { + uint nb02; uint nb03; uint nb12; uint nb13; + uint nb21; uint nb22; uint nb31; + uint nb42; uint nb43; uint nb52; uint nb53; + uint s_off; + uint n_head; + uint d_head; + uint n_group; + uint n_tok; +}; + +float softplus(float x) { + if (x <= 20.0) { + return log(1.0 + exp(x)); + } else { + return x; + } +} + +shared float stateC[SPLIT_H * D_STATE]; + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint head_idx = (gl_WorkGroupID.x * SPLIT_H) / d_head; + const uint head_off = ((gl_WorkGroupID.x * SPLIT_H) % d_head) * 4; + const uint seq_idx = gl_WorkGroupID.y; + + const uint group_off = (head_idx / (n_head / n_group)) * D_STATE * 4; + const uint s0_base_idx = (uint(ids[seq_idx]) * nb03 + head_idx * nb02 + head_off * D_STATE) / 4; + const uint x_base_idx = (seq_idx * nb13 + gl_WorkGroupID.x * SPLIT_H * 4) / 4; + const uint dt_base_idx = (seq_idx * nb22 + head_idx * 4) / 4; + const uint A_base_idx = (head_idx * nb31) / 4; + const uint B_base_idx = (seq_idx * nb43 + group_off) / 4; + const uint C_base_idx = (seq_idx * nb53 + group_off) / 4; + const uint y_base_idx = seq_idx * n_tok * n_head * d_head + gl_WorkGroupID.x * SPLIT_H; + const uint s_base_idx = (s_off + seq_idx * nb03 + head_idx * nb02 + head_off * D_STATE) / 4; + + const uint stride_x = nb12 / 4; + const uint stride_dt = nb21 / 4; + const uint stride_B = nb42 / 4; + const uint stride_C = nb52 / 4; + const uint stride_y = n_head * d_head; + + float state[SPLIT_H]; + [[unroll]] for (uint j = 0; j < SPLIT_H; j++) { + state[j] = s0[s0_base_idx + j * D_STATE + tid]; + } + + for (uint i = 0; i < n_tok; i++) { + const float dt_soft_plus = softplus(dt[dt_base_idx + i * stride_dt]); + + const float dA = exp(dt_soft_plus * A[A_base_idx]); + + const float B_val = B[B_base_idx + i * stride_B + tid]; + const float C_val = C[C_base_idx + i * stride_C + tid]; + + [[unroll]] for (uint j = 0; j < SPLIT_H; j++) { + const float x_dt = x[x_base_idx + i * stride_x + j] * dt_soft_plus; + + state[j] = (state[j] * dA) + (B_val * x_dt); + + stateC[j * D_STATE + tid] = state[j] * C_val; + } + + barrier(); + for (uint w = D_STATE; w > SUBGROUP_SIZE; w >>= 1) { + [[unroll]] for (uint j = 0; j < ((w >> 1) * SPLIT_H + D_STATE - 1) / D_STATE; j++) { + const uint k = (tid % (w >> 1)) + + (D_STATE * (tid / (w >> 1))) + + j * D_STATE * (D_STATE / (w >> 1)); + if (k < SPLIT_H * D_STATE && (k + (w >> 1)) < SPLIT_H * D_STATE) { + stateC[k] += stateC[k + (w >> 1)]; + } + } + barrier(); + } + + [[unroll]] for (uint j = 0; j <= SPLIT_H / (D_STATE / SUBGROUP_SIZE); j++) { + const uint idx = (tid % SUBGROUP_SIZE) + + D_STATE * (tid / SUBGROUP_SIZE) + + j * D_STATE * (D_STATE / SUBGROUP_SIZE); + + uint lane = tid % SUBGROUP_SIZE; + + [[unroll]] for (uint offset = SUBGROUP_SIZE / 2; offset > 0; offset >>= 1) { + if (idx + offset < SPLIT_H * D_STATE) { + stateC[idx] += stateC[idx + offset]; + } + barrier(); + } + + if (idx < SPLIT_H * D_STATE && tid % SUBGROUP_SIZE == 0) { + const uint k = tid / SUBGROUP_SIZE + j * (D_STATE / SUBGROUP_SIZE); + d[y_base_idx + i * stride_y + k] = stateC[idx]; + } + } + + barrier(); + } + + [[unroll]] for (uint j = 0; j < SPLIT_H; j++) { + d[s_base_idx + j * D_STATE + tid] = state[j]; + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 32f272e91d466..1d04a812a038a 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -916,6 +916,10 @@ void process_shaders() { string_to_spv("multi_add_f32", "multi_add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"RTE16", "1"}, {"ADD_RMS" , "0"}}); string_to_spv("multi_add_rms_f32", "multi_add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"RTE16", "1"}, {"ADD_RMS" , "1"}}); + string_to_spv("ssm_scan_f32", "ssm_scan.comp", {{"A_TYPE", "float"}}); + + string_to_spv("ssm_conv_f32", "ssm_conv.comp", {{"A_TYPE", "float"}}); + for (auto &c : compiles) { c.wait(); } From 41386cf365d894134ee0813d15e2f5d76f6a4d8e Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Fri, 17 Oct 2025 18:02:52 +0300 Subject: [PATCH 15/77] rpc : report actual free memory (#16616) * rpc : report actual free memory Start reporting the free memory on every device instead of using fixed values. Now llama-cli users can get a nice memory breakdown when using RPC devices. * drop --mem in rpc-server --- ggml/include/ggml-rpc.h | 3 +-- ggml/src/ggml-rpc/ggml-rpc.cpp | 39 +++++++++++++++++++++------------- tools/rpc/rpc-server.cpp | 34 +---------------------------- 3 files changed, 26 insertions(+), 50 deletions(-) diff --git a/ggml/include/ggml-rpc.h b/ggml/include/ggml-rpc.h index 72eff0027351a..e6dca3f62b09c 100644 --- a/ggml/include/ggml-rpc.h +++ b/ggml/include/ggml-rpc.h @@ -21,8 +21,7 @@ GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const c GGML_BACKEND_API void ggml_backend_rpc_get_device_memory(const char * endpoint, uint32_t device, size_t * free, size_t * total); GGML_BACKEND_API void ggml_backend_rpc_start_server(const char * endpoint, const char * cache_dir, - size_t n_threads, size_t n_devices, - ggml_backend_dev_t * devices, size_t * free_mem, size_t * total_mem); + size_t n_threads, size_t n_devices, ggml_backend_dev_t * devices); GGML_BACKEND_API ggml_backend_reg_t ggml_backend_rpc_reg(void); GGML_BACKEND_API ggml_backend_reg_t ggml_backend_rpc_add_server(const char * endpoint); diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp index aad48d62a850c..a38df5a97e1f0 100644 --- a/ggml/src/ggml-rpc/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -939,6 +939,7 @@ class rpc_server { bool graph_compute(const std::vector & input, rpc_msg_graph_compute_rsp & response); bool init_tensor(const rpc_msg_init_tensor_req & request); bool get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_msg_get_alloc_size_rsp & response); + bool get_device_memory(const rpc_msg_get_device_memory_req & request, rpc_msg_get_device_memory_rsp & response); private: bool get_cached_file(uint64_t hash, std::vector & data); @@ -1458,6 +1459,20 @@ bool rpc_server::graph_compute(const std::vector & input, rpc_msg_graph return true; } +bool rpc_server::get_device_memory(const rpc_msg_get_device_memory_req & request, rpc_msg_get_device_memory_rsp & response) { + uint32_t dev_id = request.device; + if (dev_id >= backends.size()) { + return false; + } + size_t free, total; + ggml_backend_dev_t dev = ggml_backend_get_device(backends[dev_id]); + ggml_backend_dev_memory(dev, &free, &total); + response.free_mem = free; + response.total_mem = total; + LOG_DBG("[%s] device: %u, free_mem: %" PRIu64 ", total_mem: %" PRIu64 "\n", __func__, dev_id, response.free_mem, response.total_mem); + return true; +} + rpc_server::~rpc_server() { for (auto buffer : buffers) { ggml_backend_buffer_free(buffer); @@ -1465,7 +1480,7 @@ rpc_server::~rpc_server() { } static void rpc_serve_client(const std::vector & backends, const char * cache_dir, - sockfd_t sockfd, const std::vector & free_mem, const std::vector & total_mem) { + sockfd_t sockfd) { rpc_server server(backends, cache_dir); uint8_t cmd; if (!recv_data(sockfd, &cmd, 1)) { @@ -1689,15 +1704,10 @@ static void rpc_serve_client(const std::vector & backends, const if (!recv_msg(sockfd, &request, sizeof(request))) { return; } - auto dev_id = request.device; - if (dev_id >= backends.size()) { + rpc_msg_get_device_memory_rsp response; + if (!server.get_device_memory(request, response)) { return; } - rpc_msg_get_device_memory_rsp response; - response.free_mem = free_mem[dev_id]; - response.total_mem = total_mem[dev_id]; - LOG_DBG("[get_device_mem] device: %u, free_mem: %" PRIu64 ", total_mem: %" PRIu64 "\n", dev_id, - response.free_mem, response.total_mem); if (!send_msg(sockfd, &response, sizeof(response))) { return; } @@ -1712,15 +1722,12 @@ static void rpc_serve_client(const std::vector & backends, const } void ggml_backend_rpc_start_server(const char * endpoint, const char * cache_dir, - size_t n_threads, size_t n_devices, - ggml_backend_dev_t * devices, size_t * free_mem, size_t * total_mem) { - if (n_devices == 0 || devices == nullptr || free_mem == nullptr || total_mem == nullptr) { + size_t n_threads, size_t n_devices, ggml_backend_dev_t * devices) { + if (n_devices == 0 || devices == nullptr) { fprintf(stderr, "Invalid arguments to ggml_backend_rpc_start_server\n"); return; } std::vector backends; - std::vector free_mem_vec(free_mem, free_mem + n_devices); - std::vector total_mem_vec(total_mem, total_mem + n_devices); printf("Starting RPC server v%d.%d.%d\n", RPC_PROTO_MAJOR_VERSION, RPC_PROTO_MINOR_VERSION, @@ -1730,8 +1737,10 @@ void ggml_backend_rpc_start_server(const char * endpoint, const char * cache_dir printf("Devices:\n"); for (size_t i = 0; i < n_devices; i++) { auto dev = devices[i]; + size_t free, total; + ggml_backend_dev_memory(dev, &free, &total); printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), - total_mem[i] / 1024 / 1024, free_mem[i] / 1024 / 1024); + total / 1024 / 1024, free / 1024 / 1024); auto backend = ggml_backend_dev_init(dev, nullptr); if (!backend) { fprintf(stderr, "Failed to create backend for device %s\n", dev->iface.get_name(dev)); @@ -1775,7 +1784,7 @@ void ggml_backend_rpc_start_server(const char * endpoint, const char * cache_dir } printf("Accepted client connection\n"); fflush(stdout); - rpc_serve_client(backends, cache_dir, client_socket->fd, free_mem_vec, total_mem_vec); + rpc_serve_client(backends, cache_dir, client_socket->fd); printf("Client connection closed\n"); fflush(stdout); } diff --git a/tools/rpc/rpc-server.cpp b/tools/rpc/rpc-server.cpp index 088515612772d..58b93c7468ea3 100644 --- a/tools/rpc/rpc-server.cpp +++ b/tools/rpc/rpc-server.cpp @@ -137,7 +137,6 @@ struct rpc_server_params { bool use_cache = false; int n_threads = std::max(1U, std::thread::hardware_concurrency()/2); std::vector devices; - std::vector dev_mem; }; static void print_usage(int /*argc*/, char ** argv, rpc_server_params params) { @@ -148,7 +147,6 @@ static void print_usage(int /*argc*/, char ** argv, rpc_server_params params) { fprintf(stderr, " -d, --device comma-separated list of devices\n"); fprintf(stderr, " -H, --host HOST host to bind to (default: %s)\n", params.host.c_str()); fprintf(stderr, " -p, --port PORT port to bind to (default: %d)\n", params.port); - fprintf(stderr, " -m, --mem memory size for each device (in MB)\n"); fprintf(stderr, " -c, --cache enable local file cache\n"); fprintf(stderr, "\n"); } @@ -197,23 +195,6 @@ static bool rpc_server_params_parse(int argc, char ** argv, rpc_server_params & } } else if (arg == "-c" || arg == "--cache") { params.use_cache = true; - } else if (arg == "-m" || arg == "--mem") { - if (++i >= argc) { - return false; - } - const std::regex regex{ R"([,/]+)" }; - std::string mem_str = argv[i]; - std::sregex_token_iterator iter(mem_str.begin(), mem_str.end(), regex, -1); - std::sregex_token_iterator end; - for ( ; iter != end; ++iter) { - try { - size_t mem = std::stoul(*iter) * 1024 * 1024; - params.dev_mem.push_back(mem); - } catch (const std::exception & ) { - fprintf(stderr, "error: invalid memory size: %s\n", iter->str().c_str()); - return false; - } - } } else if (arg == "-h" || arg == "--help") { print_usage(argc, argv, params); exit(0); @@ -293,18 +274,6 @@ int main(int argc, char * argv[]) { return 1; } std::string endpoint = params.host + ":" + std::to_string(params.port); - std::vector free_mem, total_mem; - for (size_t i = 0; i < devices.size(); i++) { - if (i < params.dev_mem.size()) { - free_mem.push_back(params.dev_mem[i]); - total_mem.push_back(params.dev_mem[i]); - } else { - size_t free, total; - ggml_backend_dev_memory(devices[i], &free, &total); - free_mem.push_back(free); - total_mem.push_back(total); - } - } const char * cache_dir = nullptr; std::string cache_dir_str; if (params.use_cache) { @@ -328,7 +297,6 @@ int main(int argc, char * argv[]) { return 1; } - start_server_fn(endpoint.c_str(), cache_dir, params.n_threads, devices.size(), - devices.data(), free_mem.data(), total_mem.data()); + start_server_fn(endpoint.c_str(), cache_dir, params.n_threads, devices.size(), devices.data()); return 0; } From 66b0dbcb2d462e7b70ba5a69ee8c3899ac2efb1c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 17 Oct 2025 17:41:09 +0200 Subject: [PATCH 16/77] llama-model: fix insonsistent ctxs <-> bufs order (#16581) --- src/llama-model.cpp | 60 +++++++++++++++++++++------------------------ 1 file changed, 28 insertions(+), 32 deletions(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 5002bd42ff04e..522d1f67da3cb 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -421,11 +421,8 @@ struct llama_model::impl { llama_mlocks mlock_bufs; llama_mlocks mlock_mmaps; - // contexts where the model tensors metadata is stored - std::vector ctxs; - - // the model memory buffers for the tensor data - std::vector bufs; + // contexts where the model tensors metadata is stored as well ass the corresponding buffers: + std::vector> ctxs_bufs; buft_list_t cpu_buft_list; std::map gpu_buft_list; @@ -2182,7 +2179,14 @@ bool llama_model::load_tensors(llama_model_loader & ml) { max_n_tensors += n_layer*2; // duplicated rope freq tensors const size_t ctx_size = ggml_tensor_overhead()*max_n_tensors; - std::map ctx_map; + // define a comparator for the buft -> ctx map to ensure that the order is well-defined: + struct ggml_backend_buft_comparator { + bool operator()(const ggml_backend_buffer_type_t & lhs, const ggml_backend_buffer_type_t & rhs) const { + return ggml_backend_buft_name(lhs) < ggml_backend_buft_name(rhs); + } + }; + std::map ctx_map; + auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * { auto it = ctx_map.find(buft); if (it == ctx_map.end()) { @@ -2197,12 +2201,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) { throw std::runtime_error(format("failed to create ggml context")); } - ctx_map[buft] = ctx; - pimpl->ctxs.emplace_back(ctx); + ctx_map.emplace(buft, ctx); return ctx; } - return it->second; + return it->second.get(); }; const auto TENSOR_DUPLICATED = llama_model_loader::TENSOR_DUPLICATED; @@ -6037,16 +6040,15 @@ bool llama_model::load_tensors(llama_model_loader & ml) { pimpl->mappings.reserve(ml.mappings.size()); // create the backend buffers - std::vector> ctx_bufs; - ctx_bufs.reserve(ctx_map.size()); + std::vector> ctx_buf_maps; + ctx_buf_maps.reserve(ctx_map.size()); // Ensure we have enough capacity for the maximum backend buffer we will potentially create const size_t n_max_backend_buffer = ctx_map.size() * ml.files.size(); - pimpl->bufs.reserve(n_max_backend_buffer); + pimpl->ctxs_bufs.reserve(n_max_backend_buffer); - for (auto & it : ctx_map) { - ggml_backend_buffer_type_t buft = it.first; - ggml_context * ctx = it.second; + for (auto & [buft, ctx_ptr] : ctx_map) { + ggml_context * ctx = ctx_ptr.get(); // skip contexts without tensors if (ggml_get_first_tensor(ctx) == nullptr) { @@ -6070,6 +6072,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr; bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev); + ggml_backend_buffer_t buf = nullptr; if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) { for (uint32_t idx = 0; idx < ml.files.size(); idx++) { // only the mmap region containing the tensors in the model is mapped to the backend buffer @@ -6082,20 +6085,18 @@ bool llama_model::load_tensors(llama_model_loader & ml) { continue; } const size_t max_size = ggml_get_max_tensor_size(ctx); - ggml_backend_buffer_t buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size); + buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size); if (buf == nullptr) { throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft))); } - pimpl->bufs.emplace_back(buf); buf_map.emplace(idx, buf); } } else { - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); if (buf == nullptr) { throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft))); } - pimpl->bufs.emplace_back(buf); if (use_mlock && ggml_backend_buffer_is_host(buf)) { pimpl->mlock_bufs.emplace_back(new llama_mlock); auto & mlock_buf = pimpl->mlock_bufs.back(); @@ -6106,10 +6107,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { buf_map.emplace(idx, buf); } } - - if (pimpl->bufs.empty()) { - throw std::runtime_error("failed to allocate buffer"); - } + pimpl->ctxs_bufs.emplace_back(std::move(ctx_ptr), buf); for (auto & buf : buf_map) { // indicate that this buffer contains weights @@ -6117,7 +6115,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS); } - ctx_bufs.emplace_back(ctx, buf_map); + ctx_buf_maps.emplace_back(ctx, buf_map); } if (llama_supports_gpu_offload()) { @@ -6135,22 +6133,20 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } // print memory requirements per buffer type - for (auto & buf : pimpl->bufs) { + for (auto & [_, buf] : pimpl->ctxs_bufs) { LLAMA_LOG_INFO("%s: %12s model buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get()) / 1024.0 / 1024.0); } // populate tensors_by_name - for (auto & ctx : pimpl->ctxs) { + for (auto & [ctx, _] : pimpl->ctxs_bufs) { for (auto * cur = ggml_get_first_tensor(ctx.get()); cur != NULL; cur = ggml_get_next_tensor(ctx.get(), cur)) { tensors_by_name.emplace_back(ggml_get_name(cur), cur); } } // load tensor data - for (auto & it : ctx_bufs) { - ggml_context * ctx = it.first; - auto & bufs = it.second; - if (!ml.load_all_data(ctx, bufs, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) { + for (auto & [ctx, buf_map] : ctx_buf_maps) { + if (!ml.load_all_data(ctx, buf_map, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) { return false; } } @@ -6190,8 +6186,8 @@ size_t llama_model::n_devices() const { std::map llama_model::memory_breakdown() const { std::map ret; - for (const ggml_backend_buffer_ptr & buf_ptr : pimpl->bufs) { - ret[ggml_backend_buffer_get_type(buf_ptr.get())] += ggml_backend_buffer_get_size(buf_ptr.get()); + for (const auto & [_, buf] : pimpl->ctxs_bufs) { + ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get()); } return ret; } From 81387858f1fbcc1acedbd308486e1016618ca8f8 Mon Sep 17 00:00:00 2001 From: Shawn Gu Date: Fri, 17 Oct 2025 17:55:32 -0700 Subject: [PATCH 17/77] opencl: transposed gemm/gemv moe kernel with mxfp4,f32 (#16602) * opencl: transposed gemm/gemv moe kernel with mxfp4,f32 * add restore kernel for moe transpose * fix trailing whitespaces * resolve compilation warnings --- ggml/src/ggml-opencl/CMakeLists.txt | 2 + ggml/src/ggml-opencl/ggml-opencl.cpp | 213 +++++++++++++++++- ggml/src/ggml-opencl/kernels/cvt.cl | 42 ++++ .../ggml-opencl/kernels/gemm_moe_mxfp4_f32.cl | 162 +++++++++++++ .../ggml-opencl/kernels/gemv_moe_mxfp4_f32.cl | 156 +++++++++++++ 5 files changed, 567 insertions(+), 8 deletions(-) create mode 100644 ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32.cl create mode 100644 ggml/src/ggml-opencl/kernels/gemv_moe_mxfp4_f32.cl diff --git a/ggml/src/ggml-opencl/CMakeLists.txt b/ggml/src/ggml-opencl/CMakeLists.txt index 6f6bba55e2805..d3d97f375e8f3 100644 --- a/ggml/src/ggml-opencl/CMakeLists.txt +++ b/ggml/src/ggml-opencl/CMakeLists.txt @@ -91,6 +91,8 @@ set(GGML_OPENCL_KERNELS mul_mv_id_q8_0_f32_flat mul_mv_id_mxfp4_f32 mul_mv_id_mxfp4_f32_flat + gemm_moe_mxfp4_f32 + gemv_moe_mxfp4_f32 mul_mm_f32_f32_l4_lm mul_mm_f16_f32_l4_lm mul_mm_q8_0_f32_l4_lm diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 2ec896fd0e896..d9876e697aae7 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -402,6 +402,7 @@ struct ggml_backend_opencl_context { cl_program program_conv_2d_f32; cl_program program_conv_2d_f16_f32; cl_program program_tsembd; + cl_program program_gemv_moe_mxfp4_f32, program_gemm_moe_mxfp4_f32; cl_program program_mul_mv_id_q4_0_f32_8x_flat; cl_program program_mul_mv_id_q8_0_f32, program_mul_mv_id_q8_0_f32_flat; cl_program program_mul_mv_id_mxfp4_f32; @@ -452,7 +453,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_mul_mat_f16_f32_tiled; cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v; cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0; - cl_kernel kernel_convert_block_mxfp4, kernel_restore_block_mxfp4; + cl_kernel kernel_convert_block_mxfp4, kernel_convert_block_mxfp4_trans, kernel_restore_block_mxfp4, kernel_restore_block_mxfp4_trans; cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0; cl_kernel kernel_mul_mat_q4_0_f32_8x_flat; cl_kernel kernel_convert_block_q4_0_noshuffle; @@ -475,6 +476,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_conv_2d_f32; cl_kernel kernel_conv_2d_f16_f32; cl_kernel kernel_timestep_embedding; + cl_kernel kernel_gemv_moe_mxfp4_f32, kernel_gemm_moe_mxfp4_f32; cl_kernel kernel_mul_mv_id_q4_0_f32_8x_flat; cl_kernel kernel_mul_mv_id_q8_0_f32, kernel_mul_mv_id_q8_0_f32_flat; cl_kernel kernel_mul_mv_id_mxfp4_f32; @@ -559,14 +561,14 @@ struct ggml_backend_opencl_context { fprintf(ftrace, "[\n"); for (const ProfilingInfo & info : profiling_info) { - fprintf(ftrace, "{\"name\": \"%s\", \"cat\": \"OpenCL\", \"ph\": \"B\", \"ts\": %lu, \"pid\": \"\", \"tid\": \"Host\"},\n", + fprintf(ftrace, "{\"name\": \"%s\", \"cat\": \"OpenCL\", \"ph\": \"B\", \"ts\": %llu, \"pid\": \"\", \"tid\": \"Host\"},\n", info.kernel_name.c_str(), info.cmd_queued/1000); - fprintf(ftrace, "{\"name\": \"%s\", \"cat\": \"OpenCL\", \"ph\": \"E\", \"ts\": %lu, \"pid\": \"\", \"tid\": \"Host\"},\n", + fprintf(ftrace, "{\"name\": \"%s\", \"cat\": \"OpenCL\", \"ph\": \"E\", \"ts\": %llu, \"pid\": \"\", \"tid\": \"Host\"},\n", info.kernel_name.c_str(), info.cmd_submit/1000); - fprintf(ftrace, "{\"name\": \"%s\", \"cat\": \"OpenCL\", \"ph\": \"B\", \"ts\": %lu, \"pid\": \"\", \"tid\": \"Device\"},\n", + fprintf(ftrace, "{\"name\": \"%s\", \"cat\": \"OpenCL\", \"ph\": \"B\", \"ts\": %llu, \"pid\": \"\", \"tid\": \"Device\"},\n", info.kernel_name.c_str(), info.cmd_start/1000); - fprintf(ftrace, "{\"name\": \"%s\", \"cat\": \"OpenCL\", \"ph\": \"E\", \"ts\": %lu, \"pid\": \"\", \"tid\": \"Device\"},\n", + fprintf(ftrace, "{\"name\": \"%s\", \"cat\": \"OpenCL\", \"ph\": \"E\", \"ts\": %llu, \"pid\": \"\", \"tid\": \"Device\"},\n", info.kernel_name.c_str(), info.cmd_end/1000); } fclose(ftrace); @@ -777,6 +779,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve CL_CHECK((backend_ctx->kernel_convert_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0", &err), err)); CL_CHECK((backend_ctx->kernel_convert_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err)); + CL_CHECK((backend_ctx->kernel_convert_block_mxfp4_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4_trans", &err), err)); + CL_CHECK((backend_ctx->kernel_restore_block_mxfp4_trans = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4_trans", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4", &err), err)); CL_CHECK((backend_ctx->kernel_convert_block_q8_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q8_0", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_q8_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0", &err), err)); @@ -1991,6 +1995,42 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve CL_CHECK((backend_ctx->CL_mul_mat_Ab_Bi_8x4 = clCreateKernel(backend_ctx->program_CL_gemm, "kernel_mul_mat_Ab_Bi_8x4", &err), err)); GGML_LOG_CONT("."); } + + std::string CL_moe_compile_opts = std::string("-cl-std=") + opencl_c_std + + " -cl-mad-enable " + " -cl-fast-relaxed-math"; + + // gemv_moe_mxfp4_f32 + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "gemv_moe_mxfp4_f32.cl.h" + }; +#else + const std::string kernel_src = read_file("gemv_moe_mxfp4_f32.cl"); +#endif + backend_ctx->program_gemv_moe_mxfp4_f32 = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + + CL_CHECK((backend_ctx->kernel_gemv_moe_mxfp4_f32 = clCreateKernel(backend_ctx->program_gemv_moe_mxfp4_f32, "kernel_gemv_moe_mxfp4_f32", &err), err)); + GGML_LOG_CONT("."); + } + + // gemm_moe_mxfp4_f32 + { +#ifdef GGML_OPENCL_EMBED_KERNELS + const std::string kernel_src { + #include "gemm_moe_mxfp4_f32.cl.h" + }; +#else + const std::string kernel_src = read_file("gemm_moe_mxfp4_f32.cl"); +#endif + backend_ctx->program_gemm_moe_mxfp4_f32 = + build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), CL_moe_compile_opts); + + CL_CHECK((backend_ctx->kernel_gemm_moe_mxfp4_f32 = clCreateKernel(backend_ctx->program_gemm_moe_mxfp4_f32, "kernel_gemm_moe_mxfp4_f32", &err), err)); + GGML_LOG_CONT("."); + } #endif // GGML_OPENCL_USE_ADRENO_KERNELS GGML_LOG_CONT("\n"); } @@ -3299,6 +3339,12 @@ inline bool use_adreno_kernels(const ggml_backend_opencl_context *backend_ctx, c tensor->ne[2] == 1 && tensor->ne[3] == 1; } +inline bool use_adreno_moe_kernels(const ggml_backend_opencl_context *backend_ctx, const ggml_tensor *tensor) { + GGML_UNUSED(backend_ctx); + int ne01 = tensor->ne[1]; + return ((strstr(tensor->name, "ffn") != NULL) || (strstr(tensor->name, "as") != NULL)) && (ne01 % 64 == 0); +} + static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { ggml_backend_opencl_context *backend_ctx = ggml_cl2_init(buffer->buft->device); @@ -3601,14 +3647,39 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err); CL_CHECK(err); +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + if (use_adreno_moe_kernels(backend_ctx, tensor)) { + cl_kernel kernel = backend_ctx->kernel_convert_block_mxfp4_trans; + + int ne00 = tensor->ne[0]; + int ne01 = tensor->ne[1]; + int ne02 = tensor->ne[2]; + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->e)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int), &ne01)); + + size_t global_work_size[3] = {static_cast(((ne01 + 63) / 64) * 64), static_cast(ne00 / 32), static_cast(ne02)}; + size_t local_work_size[3] = {64, 2, 1}; + + cl_event evt; + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt)); + CL_CHECK(clWaitForEvents(1, &evt)); + CL_CHECK(clReleaseMemObject(data_device)); + tensor->extra = extra; + + return; + } +#endif cl_kernel kernel = backend_ctx->kernel_convert_block_mxfp4; CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device)); CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q)); CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->e)); - size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1}; - size_t local_work_size[] = {64, 1, 1}; + size_t global_work_size[3] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1}; + size_t local_work_size[3] = {64, 1, 1}; cl_event evt; CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt)); @@ -3624,7 +3695,6 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, { extra->q } }; extra->q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_format_q, &img_desc_q, NULL, &err); - tensor->extra = extra; return; @@ -3751,6 +3821,33 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, ggml_nbytes(tensor), NULL, &err); CL_CHECK(err); +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + if (use_adreno_moe_kernels(backend_ctx, tensor)) { + cl_kernel kernel = backend_ctx->kernel_restore_block_mxfp4_trans; + + int ne00 = tensor->ne[0]; + int ne01 = tensor->ne[1]; + int ne02 = tensor->ne[2]; + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->e)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_int), &ne01)); + + size_t global_work_size[3] = {static_cast(((ne01 + 63) / 64) * 64), static_cast(ne00 / 32), static_cast(ne02)}; + size_t local_work_size[3] = {64, 2, 1}; + + cl_event evt; + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, + global_work_size, local_work_size, 0, NULL, &evt)); + CL_CHECK(clWaitForEvents(1, &evt)); + CL_CHECK(clEnqueueReadBuffer( + queue, data_device, CL_TRUE, offset, + size, data, 0, NULL, NULL)); + CL_CHECK(clReleaseMemObject(data_device)); + return; + } +#endif cl_kernel kernel = backend_ctx->kernel_restore_block_mxfp4; CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q)); CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->e)); @@ -7553,6 +7650,7 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, const int ne21 = src2->ne[1]; const cl_ulong nb21 = src2->nb[1]; + const cl_ulong nb20 = src2->nb[0]; const int ne0 = dst->ne[0]; const int ne1 = dst->ne[1]; @@ -7692,6 +7790,105 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0, break; } case GGML_TYPE_MXFP4: { +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + if (use_adreno_moe_kernels(backend_ctx, src0)) { + cl_int status; + + size_t local_size[3] = {64, 2, 1}; + size_t global_size[3] = {64, 2, 1}; + + cl_mem src1_sub_buffer, buf_src1_image, buf_src2; + + int tile_size = 320; + if (ne12 == 1) { // for gemv + kernel = backend_ctx->kernel_gemv_moe_mxfp4_f32; + + // create a sub_buffer for src2 + cl_buffer_region region; + region.origin = offset2; + region.size = ne20 * ne21 * sizeof(int); + buf_src2 = clCreateSubBuffer(extra2->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &status); + CL_CHECK(status); + + // set thread grid + global_size[0] = static_cast(ne01); + global_size[1] = 4; + global_size[2] = static_cast(ne20); + local_size[1] = 4; + } else { // for gemm + kernel = backend_ctx->kernel_gemm_moe_mxfp4_f32; + + // preprocess router table + int num_tiles_per_expert = (ne01 + tile_size - 1) / tile_size; + void * host_src2_reorder = malloc(ne20 * ne21 * 4 * num_tiles_per_expert * sizeof(short)); + void * host_src2 = malloc(ne21 * nb21); + CL_CHECK(clEnqueueReadBuffer(backend_ctx->queue, extra2->data_device, CL_TRUE, offset2, ne21 * nb21, host_src2, 0, NULL, NULL)); + int total_experts = nb21 / nb20; + int out_idx = 0; + for (int i_expert = 0; i_expert < ne02; i_expert++) { + for (int i_tile = 0; i_tile < num_tiles_per_expert; i_tile++) { + for (int j = 0; j < ne21; j++) { + for (int i = 0; i < ne20; i++) { + int expert = ((int *)host_src2)[j * total_experts + i]; + if (i_expert == expert) { + ((short *)host_src2_reorder)[out_idx] = static_cast(expert); + ((short *)host_src2_reorder)[out_idx + 1] = static_cast(j * ne11 + (i % ne11)); + ((short *)host_src2_reorder)[out_idx + 2] = static_cast(j * ne20 + i); + ((short *)host_src2_reorder)[out_idx + 3] = static_cast(i_tile); + out_idx += 4; + } + } + } + } + } + buf_src2 = clCreateBuffer(backend_ctx->context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR, ne20 * ne21 * 4 * num_tiles_per_expert * sizeof(short), host_src2_reorder, &status); + CL_CHECK(status); + + // set thread grid + global_size[0] = static_cast(tile_size); + global_size[2] = static_cast(ne20 * ne21 * num_tiles_per_expert); + } + + // create a sub_buffer for src1 + cl_buffer_region region; + region.origin = offset1; + region.size = ne10 * ne11 * ne12 * sizeof(float); + src1_sub_buffer = clCreateSubBuffer(extra1->data_device, 0, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &status); + CL_CHECK(status); + + // create image for src1 + cl_image_format image_format_buf_src1 = {CL_RGBA, CL_FLOAT}; + cl_image_desc image_desc_buf_src1 = {CL_MEM_OBJECT_IMAGE1D_BUFFER, static_cast(ne10 * ne11 * ne12 / 4), 0,0,0,0,0,0,0, {src1_sub_buffer}}; + buf_src1_image = clCreateImage(backend_ctx->context, CL_MEM_READ_ONLY, &image_format_buf_src1, &image_desc_buf_src1, NULL, &status); + CL_CHECK(status); + + // Set kernel args + int arg_idx = 0; + CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem), &extra0_mxfp4->q)); + CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem), &extra0_mxfp4->e)); + CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem), &buf_src1_image)); + CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem), &buf_src2)); + CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_mem), &extrad->data_device)); + CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(cl_ulong), &offsetd)); + CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int), &ne00)); + CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int), &ne01)); + if (ne12 == 1) { + CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int), &ne11)); + } else { + CL_CHECK(clSetKernelArg(kernel, arg_idx++, sizeof(int), &tile_size)); + } + + // launch kernel + backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_size, local_size, dst); + + // deallocate sub buffers and images + CL_CHECK(clReleaseMemObject(src1_sub_buffer)); + CL_CHECK(clReleaseMemObject(buf_src1_image)); + CL_CHECK(clReleaseMemObject(buf_src2)); + return; + } // else fallback to generic kernel +#endif // GGML_OPENCL_USE_ADRENO_KERNELS + #ifdef GGML_OPENCL_SOA_Q kernel = backend_ctx->kernel_mul_mv_id_mxfp4_f32_flat; diff --git a/ggml/src/ggml-opencl/kernels/cvt.cl b/ggml/src/ggml-opencl/kernels/cvt.cl index 045300eb3a537..b26f9c5fb2a31 100644 --- a/ggml/src/ggml-opencl/kernels/cvt.cl +++ b/ggml/src/ggml-opencl/kernels/cvt.cl @@ -147,6 +147,27 @@ kernel void kernel_convert_block_mxfp4( } } +kernel void kernel_convert_block_mxfp4_trans( + global struct block_mxfp4 * src0, + __global uint4 * dst_q, + __global uchar * dst_e, + uint ne00, + uint ne01 +) { + int i00 = get_global_id(1); + uint i01 = get_global_id(0); + uint i02 = get_global_id(2); + + uint ne00_blk = ne00 / QK_MXFP4; + uint src_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01; + uint dst_blk_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01; + + global struct block_mxfp4 * b = src0 + src_blk_offset; + + dst_q[dst_blk_offset] = ((global uint4 *)(&(b->qs[0])))[0]; + dst_e[dst_blk_offset] = b->e; +} + kernel void kernel_restore_block_mxfp4( global uchar * src_q, global half * src_e, @@ -162,6 +183,27 @@ kernel void kernel_restore_block_mxfp4( } } +kernel void kernel_restore_block_mxfp4_trans( + __global uint4 * src_q, + __global uchar * src_e, + global struct block_mxfp4 * dst, + uint ne00, + uint ne01 +) { + int i00 = get_global_id(1); + uint i01 = get_global_id(0); + uint i02 = get_global_id(2); + + uint ne00_blk = ne00 / QK_MXFP4; + uint src_blk_offset = i01 + i00 * ne01 + i02 * ne00_blk * ne01; + uint dst_blk_offset = i00 + i01 * ne00_blk + i02 * ne00_blk * ne01; + + global struct block_mxfp4 * b = dst + dst_blk_offset; + + ((global uint4 *)(&(b->qs[0])))[0] = src_q[src_blk_offset]; + b->e = src_e[src_blk_offset]; +} + //------------------------------------------------------------------------------ // block_q8_0 //------------------------------------------------------------------------------ diff --git a/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32.cl b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32.cl new file mode 100644 index 0000000000000..3917aa3fd9798 --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/gemm_moe_mxfp4_f32.cl @@ -0,0 +1,162 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable +#pragma OPENCL EXTENSION cl_khr_subgroups : enable +#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable + +#define QK_MXFP4 32 +#define N_SIMDGROUP 2 +#define SIMDGROUP_WIDTH 64 + +static inline half8 mxfp4_to_fp16_packed8(ushort2 fp4x8) { //, ushort 0x0E00, ushort 0x8000) { + ushort2 fp16_packed_a_0, fp16_packed_b_0, bias_a, bias_b, sign_a, sign_b; + fp16_packed_a_0.lo = (fp4x8.s0 << 9) & 0x0E00; + fp16_packed_a_0.hi = (fp4x8.s0 << 5) & 0x0E00; + fp16_packed_b_0.lo = (fp4x8.s0 << 1) & 0x0E00; + fp16_packed_b_0.hi = (fp4x8.s0 >> 3) & 0x0E00; + + bias_a.lo = (fp16_packed_a_0.lo != 0) ? 0x3800 : 0x0; + bias_a.hi = (fp16_packed_a_0.hi != 0) ? 0x3800 : 0x0; + bias_b.lo = (fp16_packed_b_0.lo != 0) ? 0x3800 : 0x0; + bias_b.hi = (fp16_packed_b_0.hi != 0) ? 0x3800 : 0x0; + + fp16_packed_a_0.lo = (fp16_packed_a_0.lo != 0x0200) ? fp16_packed_a_0.lo : 0x0; + fp16_packed_a_0.hi = (fp16_packed_a_0.hi != 0x0200) ? fp16_packed_a_0.hi : 0x0; + fp16_packed_b_0.lo = (fp16_packed_b_0.lo != 0x0200) ? fp16_packed_b_0.lo : 0x0; + fp16_packed_b_0.hi = (fp16_packed_b_0.hi != 0x0200) ? fp16_packed_b_0.hi : 0x0; + + sign_a.lo = (fp4x8.s0 << 12) & 0x8000; + sign_a.hi = (fp4x8.s0 << 8) & 0x8000; + sign_b.lo = (fp4x8.s0 << 4) & 0x8000; + sign_b.hi = fp4x8.s0 & 0x8000; + + fp16_packed_a_0 = sign_a + bias_a + fp16_packed_a_0; + fp16_packed_b_0 = sign_b + bias_b + fp16_packed_b_0; + + ushort2 fp16_packed_a_1, fp16_packed_b_1; + fp16_packed_a_1.lo = (fp4x8.s1 << 9) & 0x0E00; + fp16_packed_a_1.hi = (fp4x8.s1 << 5) & 0x0E00; + fp16_packed_b_1.lo = (fp4x8.s1 << 1) & 0x0E00; + fp16_packed_b_1.hi = (fp4x8.s1 >> 3) & 0x0E00; + + bias_a.lo = (fp16_packed_a_1.lo != 0) ? 0x3800 : 0x0; + bias_a.hi = (fp16_packed_a_1.hi != 0) ? 0x3800 : 0x0; + bias_b.lo = (fp16_packed_b_1.lo != 0) ? 0x3800 : 0x0; + bias_b.hi = (fp16_packed_b_1.hi != 0) ? 0x3800 : 0x0; + + fp16_packed_a_1.lo = (fp16_packed_a_1.lo != 0x0200) ? fp16_packed_a_1.lo : 0x0; + fp16_packed_a_1.hi = (fp16_packed_a_1.hi != 0x0200) ? fp16_packed_a_1.hi : 0x0; + fp16_packed_b_1.lo = (fp16_packed_b_1.lo != 0x0200) ? fp16_packed_b_1.lo : 0x0; + fp16_packed_b_1.hi = (fp16_packed_b_1.hi != 0x0200) ? fp16_packed_b_1.hi : 0x0; + + sign_a.lo = (fp4x8.s1 << 12) & 0x8000; + sign_a.hi = (fp4x8.s1 << 8) & 0x8000; + sign_b.lo = (fp4x8.s1 << 4) & 0x8000; + sign_b.hi = fp4x8.s1 & 0x8000; + + fp16_packed_a_1 = sign_a + bias_a + fp16_packed_a_1; + fp16_packed_b_1 = sign_b + bias_b + fp16_packed_b_1; + + return as_half8((ushort8)(fp16_packed_a_0, fp16_packed_b_0, fp16_packed_a_1, fp16_packed_b_1)); +} + +static inline float e8m0_to_fp32(uchar x) { + int bits; + bits = (x == 0) ? 0x00400000 : ((uint) x << 23); + return as_float(bits); +} + + +__attribute__((qcom_reqd_sub_group_size("half"))) +__kernel void kernel_gemm_moe_mxfp4_f32( + __global uint4 * src0_q, + __global uchar * src0_e, + __read_only image1d_buffer_t src1, + __global ushort4 * src2, + __global float * dst, + ulong offsetd, + int ne00, + int ne01, + int tile_size +) { + uint i01 = get_global_id(0); + uint i20 = get_global_id(2); + uint sgid = get_local_id(1); + uint slid = get_sub_group_local_id(); + + ushort4 router = src2[i20]; + ushort expert_id = router.x; + ushort i11 = router.y; + ushort i1 = router.z; + ushort tile_id = router.w; + + if (tile_id * tile_size + i01 >= ne01) { // handle edge case when ne01 is not multiple of tile_size + return; + } + + uint expert_offset = expert_id * ne00 * ne01 / 32; + uint tile_offset = expert_offset + tile_id * tile_size + i01; + + __private float sum = 0.0f; // each thread calculate partial sum of one output + + // loop along ne00 in block granularity, skip 4 blocks every iter + for (uint ib00 = sgid; ib00 < (ne00 / QK_MXFP4); ib00 += N_SIMDGROUP) { + // load one block of q + uint4 regQ = src0_q[tile_offset + ib00 * ne01]; + // convert 8 fp4 to fp16 + half8 fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s0)); + + uint offset = i11 * ne00 / 4 + ib00 * 8; + float4 shared_y4; + shared_y4 = read_imagef(src1, (offset + 0)); + float4 acc = shared_y4 * (float4)(fp16x8.s0, fp16x8.s2, fp16x8.s4, fp16x8.s6); + + shared_y4 = read_imagef(src1, (offset + 4)); + acc += shared_y4 * (float4)(fp16x8.s1, fp16x8.s3, fp16x8.s5, fp16x8.s7); + + + fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s1)); + + shared_y4 = read_imagef(src1, (offset + 1)); + acc += shared_y4 * (float4)(fp16x8.s0, fp16x8.s2, fp16x8.s4, fp16x8.s6); + + shared_y4 = read_imagef(src1, (offset + 5)); + acc += shared_y4 * (float4)(fp16x8.s1, fp16x8.s3, fp16x8.s5, fp16x8.s7); + + + fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s2)); + + shared_y4 = read_imagef(src1, (offset + 2)); + acc += shared_y4 * (float4)(fp16x8.s0, fp16x8.s2, fp16x8.s4, fp16x8.s6); + + shared_y4 = read_imagef(src1, (offset + 6)); + acc += shared_y4 * (float4)(fp16x8.s1, fp16x8.s3, fp16x8.s5, fp16x8.s7); + + + fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s3)); + + shared_y4 = read_imagef(src1, (offset + 3)); + acc += shared_y4 * (float4)(fp16x8.s0, fp16x8.s2, fp16x8.s4, fp16x8.s6); + + shared_y4 = read_imagef(src1, (offset + 7)); + acc += shared_y4 * (float4)(fp16x8.s1, fp16x8.s3, fp16x8.s5, fp16x8.s7); + + uchar regE = src0_e[tile_offset + ib00 * ne01]; + sum += e8m0_to_fp32(regE) * ((acc.s0 + acc.s1) + (acc.s2 + acc.s3)); + } + + // reduction in local memory, assumes #subgroups=4 + __local float reduceLM[SIMDGROUP_WIDTH * (N_SIMDGROUP - 1)]; + if (sgid == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = sum; + // if (sgid == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = sum; + // if (sgid == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = sum; + barrier(CLK_LOCAL_MEM_FENCE); + if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 0 + slid]; + // if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 1 + slid]; + // if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 2 + slid]; + + // 1 outputs per thread in subgroup 0 + if (sgid == 0) { + dst = dst + (offsetd >> 2); + dst[i01 + tile_id * tile_size + i1 * ne01] = sum; + } + +} diff --git a/ggml/src/ggml-opencl/kernels/gemv_moe_mxfp4_f32.cl b/ggml/src/ggml-opencl/kernels/gemv_moe_mxfp4_f32.cl new file mode 100644 index 0000000000000..b4b1e511f945e --- /dev/null +++ b/ggml/src/ggml-opencl/kernels/gemv_moe_mxfp4_f32.cl @@ -0,0 +1,156 @@ +#pragma OPENCL EXTENSION cl_khr_fp16 : enable +#pragma OPENCL EXTENSION cl_khr_subgroups : enable +#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable + +#define QK_MXFP4 32 +#define N_SIMDGROUP 4 +#define SIMDGROUP_WIDTH 64 + +static inline half8 mxfp4_to_fp16_packed8(ushort2 fp4x8) { //, ushort 0x0E00, ushort 0x8000) { + ushort2 fp16_packed_a_0, fp16_packed_b_0, bias_a, bias_b, sign_a, sign_b; + fp16_packed_a_0.lo = (fp4x8.s0 << 9) & 0x0E00; + fp16_packed_a_0.hi = (fp4x8.s0 << 5) & 0x0E00; + fp16_packed_b_0.lo = (fp4x8.s0 << 1) & 0x0E00; + fp16_packed_b_0.hi = (fp4x8.s0 >> 3) & 0x0E00; + + bias_a.lo = (fp16_packed_a_0.lo != 0) ? 0x3800 : 0x0; + bias_a.hi = (fp16_packed_a_0.hi != 0) ? 0x3800 : 0x0; + bias_b.lo = (fp16_packed_b_0.lo != 0) ? 0x3800 : 0x0; + bias_b.hi = (fp16_packed_b_0.hi != 0) ? 0x3800 : 0x0; + + fp16_packed_a_0.lo = (fp16_packed_a_0.lo != 0x0200) ? fp16_packed_a_0.lo : 0x0; + fp16_packed_a_0.hi = (fp16_packed_a_0.hi != 0x0200) ? fp16_packed_a_0.hi : 0x0; + fp16_packed_b_0.lo = (fp16_packed_b_0.lo != 0x0200) ? fp16_packed_b_0.lo : 0x0; + fp16_packed_b_0.hi = (fp16_packed_b_0.hi != 0x0200) ? fp16_packed_b_0.hi : 0x0; + + sign_a.lo = (fp4x8.s0 << 12) & 0x8000; + sign_a.hi = (fp4x8.s0 << 8) & 0x8000; + sign_b.lo = (fp4x8.s0 << 4) & 0x8000; + sign_b.hi = fp4x8.s0 & 0x8000; + + fp16_packed_a_0 = sign_a + bias_a + fp16_packed_a_0; + fp16_packed_b_0 = sign_b + bias_b + fp16_packed_b_0; + + ushort2 fp16_packed_a_1, fp16_packed_b_1; + fp16_packed_a_1.lo = (fp4x8.s1 << 9) & 0x0E00; + fp16_packed_a_1.hi = (fp4x8.s1 << 5) & 0x0E00; + fp16_packed_b_1.lo = (fp4x8.s1 << 1) & 0x0E00; + fp16_packed_b_1.hi = (fp4x8.s1 >> 3) & 0x0E00; + + bias_a.lo = (fp16_packed_a_1.lo != 0) ? 0x3800 : 0x0; + bias_a.hi = (fp16_packed_a_1.hi != 0) ? 0x3800 : 0x0; + bias_b.lo = (fp16_packed_b_1.lo != 0) ? 0x3800 : 0x0; + bias_b.hi = (fp16_packed_b_1.hi != 0) ? 0x3800 : 0x0; + + fp16_packed_a_1.lo = (fp16_packed_a_1.lo != 0x0200) ? fp16_packed_a_1.lo : 0x0; + fp16_packed_a_1.hi = (fp16_packed_a_1.hi != 0x0200) ? fp16_packed_a_1.hi : 0x0; + fp16_packed_b_1.lo = (fp16_packed_b_1.lo != 0x0200) ? fp16_packed_b_1.lo : 0x0; + fp16_packed_b_1.hi = (fp16_packed_b_1.hi != 0x0200) ? fp16_packed_b_1.hi : 0x0; + + sign_a.lo = (fp4x8.s1 << 12) & 0x8000; + sign_a.hi = (fp4x8.s1 << 8) & 0x8000; + sign_b.lo = (fp4x8.s1 << 4) & 0x8000; + sign_b.hi = fp4x8.s1 & 0x8000; + + fp16_packed_a_1 = sign_a + bias_a + fp16_packed_a_1; + fp16_packed_b_1 = sign_b + bias_b + fp16_packed_b_1; + + return as_half8((ushort8)(fp16_packed_a_0, fp16_packed_b_0, fp16_packed_a_1, fp16_packed_b_1)); +} + +static inline float e8m0_to_fp32(uchar x) { + int bits; + bits = (x == 0) ? 0x00400000 : ((uint) x << 23); + return as_float(bits); +} + + +__attribute__((qcom_reqd_sub_group_size("half"))) +__kernel void kernel_gemv_moe_mxfp4_f32( + __global uint4 * src0_q, + __global uchar * src0_e, + __read_only image1d_buffer_t src1, + __global uint * src2, + __global float * dst, + ulong offsetd, + int ne00, + int ne01, + int ne11 +) { + uint i01 = get_global_id(0); + uint i20 = get_global_id(2); + uint sgid = get_local_id(1); + uint slid = get_sub_group_local_id(); + + uint i11 = i20 % ne11; + + uint expert_id = src2[i20]; + uint expert_offset = expert_id * ne00 * ne01 / 32; + + __private float sum = 0.0f; // each thread calculate partial sum of one output + + // loop along ne00 in block granularity, skip 4 blocks every iter + for (uint ib00 = sgid; ib00 < (ne00 / QK_MXFP4); ib00 += N_SIMDGROUP) { + + // load one block of q + uint4 regQ = src0_q[expert_offset + ib00 * ne01 + i01]; + + uint offset = i11 * ne00 / 4 + ib00 * 8; + + half8 fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s0)); + + float4 shared_y4; + shared_y4 = read_imagef(src1, (offset + 0)); + float4 acc = shared_y4 * (float4)(fp16x8.s0, fp16x8.s2, fp16x8.s4, fp16x8.s6); + + shared_y4 = read_imagef(src1, (offset + 4)); + acc += shared_y4 * (float4)(fp16x8.s1, fp16x8.s3, fp16x8.s5, fp16x8.s7); + + + fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s1)); + + shared_y4 = read_imagef(src1, (offset + 1)); + acc += shared_y4 * (float4)(fp16x8.s0, fp16x8.s2, fp16x8.s4, fp16x8.s6); + + shared_y4 = read_imagef(src1, (offset + 5)); + acc += shared_y4 * (float4)(fp16x8.s1, fp16x8.s3, fp16x8.s5, fp16x8.s7); + + + fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s2)); + + shared_y4 = read_imagef(src1, (offset + 2)); + acc += shared_y4 * (float4)(fp16x8.s0, fp16x8.s2, fp16x8.s4, fp16x8.s6); + + shared_y4 = read_imagef(src1, (offset + 6)); + acc += shared_y4 * (float4)(fp16x8.s1, fp16x8.s3, fp16x8.s5, fp16x8.s7); + + + fp16x8 = mxfp4_to_fp16_packed8(as_ushort2(regQ.s3)); + + shared_y4 = read_imagef(src1, (offset + 3)); + acc += shared_y4 * (float4)(fp16x8.s0, fp16x8.s2, fp16x8.s4, fp16x8.s6); + + shared_y4 = read_imagef(src1, (offset + 7)); + acc += shared_y4 * (float4)(fp16x8.s1, fp16x8.s3, fp16x8.s5, fp16x8.s7); + + uchar regE = src0_e[ib00 * ne01 + i01 + expert_offset]; + sum += e8m0_to_fp32(regE) * ((acc.s0 + acc.s1) + (acc.s2 + acc.s3)); + } + + // reduction in local memory, assumes #subgroups=4 + __local float reduceLM[SIMDGROUP_WIDTH * (N_SIMDGROUP - 1)]; + if (sgid == 1) reduceLM[SIMDGROUP_WIDTH * 0 + slid] = sum; + if (sgid == 2) reduceLM[SIMDGROUP_WIDTH * 1 + slid] = sum; + if (sgid == 3) reduceLM[SIMDGROUP_WIDTH * 2 + slid] = sum; + barrier(CLK_LOCAL_MEM_FENCE); + if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 0 + slid]; + if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 1 + slid]; + if (sgid == 0) sum += reduceLM[SIMDGROUP_WIDTH * 2 + slid]; + + // 1 outputs per thread in subgroup 0 + if (sgid == 0) { + dst = dst + (offsetd >> 2); + dst[i01 + i20 * ne01] = sum; + } + +} From 38355c6c8e43204e11a22daa7483082c0ff01e71 Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Sat, 18 Oct 2025 17:52:53 +0800 Subject: [PATCH 18/77] CUDA: use registers instead of smem in topk-moe (#16647) Uses the technique used in the vulkan PR #16641. Neat trick! --- ggml/src/ggml-cuda/topk-moe.cu | 42 +++++++++++++++++++--------------- 1 file changed, 23 insertions(+), 19 deletions(-) diff --git a/ggml/src/ggml-cuda/topk-moe.cu b/ggml/src/ggml-cuda/topk-moe.cu index afe4aee2403b2..c588da2bb9e93 100644 --- a/ggml/src/ggml-cuda/topk-moe.cu +++ b/ggml/src/ggml-cuda/topk-moe.cu @@ -73,8 +73,7 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * float wt_sum = 0.f; - extern __shared__ float data_topk_shared[]; - float * wt_shared_ptr = data_topk_shared + threadIdx.y * n_expert_used; + float output_weights[experts_per_thread]; for (int k = 0; k < n_expert_used; k++) { float max_val = wt[0]; @@ -99,11 +98,14 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * } } + if ((k & (WARP_SIZE - 1)) == threadIdx.x) { + output_weights[k / WARP_SIZE] = max_val; + } + if ((max_expert & (WARP_SIZE - 1)) == threadIdx.x) { wt[max_expert / WARP_SIZE] = -INFINITY; - wt_shared_ptr[k] = max_val; - ids[k] = max_expert; + ids[k] = max_expert; if constexpr (with_norm) { wt_sum += max_val; } @@ -115,12 +117,16 @@ __launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * const float inv_sum = 1.0f / wt_sum; for (int i = threadIdx.x; i < n_expert_used; i += WARP_SIZE) { - wt_shared_ptr[i] = wt_shared_ptr[i] * inv_sum; + output_weights[i] *= inv_sum; } } - for (int i = threadIdx.x; i < n_expert_used; i += WARP_SIZE) { - weights[i] = wt_shared_ptr[i]; +#pragma unroll + for (int i = 0; i < experts_per_thread; i++) { + const int idx = i * WARP_SIZE + threadIdx.x; + if (idx < n_expert_used) { + weights[idx] = output_weights[i]; + } } } @@ -137,48 +143,46 @@ static void launch_topk_moe_cuda(ggml_backend_cuda_context & ctx, dim3 block_dims(WARP_SIZE, rows_per_block, 1); cudaStream_t stream = ctx.stream(); - const int nbytes_shared = n_expert_used * rows_per_block * sizeof(float); - switch (n_expert) { case 1: topk_moe_cuda<1, with_norm> - <<>>(logits, weights, ids, n_rows, n_expert_used); + <<>>(logits, weights, ids, n_rows, n_expert_used); break; case 2: topk_moe_cuda<2, with_norm> - <<>>(logits, weights, ids, n_rows, n_expert_used); + <<>>(logits, weights, ids, n_rows, n_expert_used); break; case 4: topk_moe_cuda<4, with_norm> - <<>>(logits, weights, ids, n_rows, n_expert_used); + <<>>(logits, weights, ids, n_rows, n_expert_used); break; case 8: topk_moe_cuda<8, with_norm> - <<>>(logits, weights, ids, n_rows, n_expert_used); + <<>>(logits, weights, ids, n_rows, n_expert_used); break; case 16: topk_moe_cuda<16, with_norm> - <<>>(logits, weights, ids, n_rows, n_expert_used); + <<>>(logits, weights, ids, n_rows, n_expert_used); break; case 32: topk_moe_cuda<32, with_norm> - <<>>(logits, weights, ids, n_rows, n_expert_used); + <<>>(logits, weights, ids, n_rows, n_expert_used); break; case 64: topk_moe_cuda<64, with_norm> - <<>>(logits, weights, ids, n_rows, n_expert_used); + <<>>(logits, weights, ids, n_rows, n_expert_used); break; case 128: topk_moe_cuda<128, with_norm> - <<>>(logits, weights, ids, n_rows, n_expert_used); + <<>>(logits, weights, ids, n_rows, n_expert_used); break; case 256: topk_moe_cuda<256, with_norm> - <<>>(logits, weights, ids, n_rows, n_expert_used); + <<>>(logits, weights, ids, n_rows, n_expert_used); break; case 512: topk_moe_cuda<512, with_norm> - <<>>(logits, weights, ids, n_rows, n_expert_used); + <<>>(logits, weights, ids, n_rows, n_expert_used); break; default: GGML_ASSERT(false && "fatal error"); From e56abd2098dd2e2b0804691b93c13b48ae421627 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sat, 18 Oct 2025 05:22:57 -0500 Subject: [PATCH 19/77] vulkan: Implement topk_moe fused shader, ported from CUDA (#16641) This is similar to the CUDA shader from #16130, but doesn't use shared memory and handles different subgroup sizes. --- ggml/src/ggml-impl.h | 13 +- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 266 +++++++++++++++++- .../ggml-vulkan/vulkan-shaders/topk_moe.comp | 139 +++++++++ .../vulkan-shaders/vulkan-shaders-gen.cpp | 2 + 4 files changed, 412 insertions(+), 8 deletions(-) create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h index d0fb3bccad225..18f095b896010 100644 --- a/ggml/src/ggml-impl.h +++ b/ggml/src/ggml-impl.h @@ -565,14 +565,23 @@ static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) { #define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x) #define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x) +static inline int32_t ggml_node_get_use_count(const struct ggml_cgraph * cgraph, int node_idx) { + const struct ggml_tensor * node = cgraph->nodes[node_idx]; + + size_t hash_pos = ggml_hash_find(&cgraph->visited_hash_set, node); + if (!ggml_bitset_get(cgraph->visited_hash_set.used, hash_pos)) { + return 0; + } + return cgraph->use_counts[hash_pos]; +} + // return true if the node's results are only used by N other nodes // and can be fused into their calculations. static inline bool ggml_node_has_n_uses(const struct ggml_cgraph * cgraph, int node_idx, int32_t n_uses) { const struct ggml_tensor * node = cgraph->nodes[node_idx]; // check the use count against how many we're replacing - size_t hash_pos = ggml_hash_find(&cgraph->visited_hash_set, node); - if (!ggml_bitset_get(cgraph->visited_hash_set.used, hash_pos) || cgraph->use_counts[hash_pos] != n_uses) { + if (ggml_node_get_use_count(cgraph, node_idx) != n_uses) { return false; } diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index bc703611f0a94..21bd052255564 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -385,6 +385,14 @@ enum shader_reduction_mode { static constexpr uint32_t num_argsort_pipelines = 11; static constexpr uint32_t max_argsort_cols = 1 << (num_argsort_pipelines-1); +static constexpr uint32_t num_topk_moe_pipelines = 10; + +static constexpr std::array topk_moe_norm{ GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT, + GGML_OP_VIEW, GGML_OP_GET_ROWS, GGML_OP_RESHAPE, + GGML_OP_SUM_ROWS, GGML_OP_DIV, GGML_OP_RESHAPE }; +static constexpr std::array topk_moe { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT, + GGML_OP_VIEW, GGML_OP_GET_ROWS }; + struct vk_device_struct { std::recursive_mutex mutex; @@ -598,6 +606,9 @@ struct vk_device_struct { vk_pipeline pipeline_flash_attn_split_k_reduce; + // [2] is {!norm, norm} + vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][2]; + std::vector all_pipelines; std::vector> pinned_memory; @@ -941,6 +952,11 @@ struct vk_op_multi_add_push_constants { static_assert(MAX_PARAMETER_COUNT == 12); static_assert(sizeof(vk_op_multi_add_push_constants) <= 256); +struct vk_op_topk_moe_push_constants { + uint32_t n_rows; + uint32_t n_expert_used; +}; + struct vk_op_add_id_push_constants { uint32_t ne0; uint32_t ne1; @@ -3722,6 +3738,11 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f16_f32, "conv2d_dw_whcn_f16_f32", conv2d_dw_whcn_f16_f32_len, conv2d_dw_whcn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f16_f32, "conv2d_dw_cwhn_f16_f32", conv2d_dw_cwhn_f16_f32_len, conv2d_dw_cwhn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); + for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) { + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][0], "topk_moe_f32_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<pipeline_topk_moe[i][1], "topk_moe_f32_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32); + if (ctx->num_additional_fused_ops) { + uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0]))); + GGML_ASSERT(idx < num_topk_moe_pipelines); + bool with_norm = ctx->num_additional_fused_ops == topk_moe_norm.size() - 1; + return ctx->device->pipeline_topk_moe[idx][with_norm]; + } + if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_wg512 : ctx->device->pipeline_soft_max_f32; } @@ -9589,6 +9617,87 @@ static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& sub ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SOFT_MAX_BACK, { (uint32_t)src0->ne[0], (uint32_t)ggml_nrows(src0), op_params[0], op_params[1] }, dryrun); } +static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx, bool dryrun = false) { + + bool with_norm = ctx->num_additional_fused_ops == topk_moe_norm.size() - 1; + ggml_tensor * logits = cgraph->nodes[node_idx + 0]->src[0]; + ggml_tensor * weights = with_norm ? cgraph->nodes[node_idx + 8] : cgraph->nodes[node_idx + 4]; + ggml_tensor * ids = cgraph->nodes[node_idx + 3]; + + GGML_ASSERT(logits->type == GGML_TYPE_F32); + GGML_ASSERT(weights->type == GGML_TYPE_F32); + GGML_ASSERT(ids->type == GGML_TYPE_I32); + + const int n_experts = logits->ne[0]; + const int n_rows = logits->ne[1]; + const int n_expert_used = weights->ne[1]; + + GGML_ASSERT(ids->nb[1] / ggml_type_size(ids->type) == (size_t) n_experts); + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, nullptr, nullptr, nullptr, cgraph->nodes[node_idx], GGML_OP_SOFT_MAX); + + if (dryrun) { + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + return; + } + + ggml_backend_vk_buffer_context * logits_buf_ctx = (ggml_backend_vk_buffer_context *)logits->buffer->context; + ggml_backend_vk_buffer_context * weights_buf_ctx = (ggml_backend_vk_buffer_context *)weights->buffer->context; + ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context; + + vk_buffer d_logits = nullptr; + size_t logits_buf_offset = 0; + vk_buffer d_weights = nullptr; + size_t weights_buf_offset = 0; + vk_buffer d_ids = nullptr; + size_t ids_buf_offset = 0; + + bool logits_uma = false; + bool weights_uma = false; + bool ids_uma = false; + + if (ctx->device->uma) { + ggml_vk_host_get(ctx->device, logits->data, d_logits, logits_buf_offset); + ggml_vk_host_get(ctx->device, weights->data, d_weights, weights_buf_offset); + ggml_vk_host_get(ctx->device, ids->data, d_ids, ids_buf_offset); + logits_uma = d_logits != nullptr; + weights_uma = d_weights != nullptr; + ids_uma = d_ids != nullptr; + } + + if (!logits_uma) { + d_logits = logits_buf_ctx->dev_buffer; + logits_buf_offset = vk_tensor_offset(logits) + logits->view_offs; + GGML_ASSERT(d_logits != nullptr); + } + if (!weights_uma) { + d_weights = weights_buf_ctx->dev_buffer; + weights_buf_offset = vk_tensor_offset(weights) + weights->view_offs; + GGML_ASSERT(d_weights != nullptr); + } + if (!ids_uma) { + d_ids = ids_buf_ctx->dev_buffer; + ids_buf_offset = vk_tensor_offset(ids) + ids->view_offs; + GGML_ASSERT(d_ids != nullptr); + } + + vk_op_topk_moe_push_constants pc; + pc.n_rows = n_rows; + pc.n_expert_used = n_expert_used; + + GGML_ASSERT(n_expert_used <= n_experts); + + const uint32_t rows_per_block = 4; + std::array elements = { CEIL_DIV(n_rows, rows_per_block), 1, 1 }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + { + ggml_vk_subbuffer(ctx, d_logits, logits_buf_offset), + ggml_vk_subbuffer(ctx, d_weights, weights_buf_offset), + ggml_vk_subbuffer(ctx, d_ids, ids_buf_offset), + }, pc, elements); +} + static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool backprop, bool dryrun = false) { const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; @@ -11174,11 +11283,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr ctx->unsynced_nodes_read.clear(); ggml_vk_sync_buffers(ctx, compute_ctx); } - // Add the last fused node and all fused source nodes to the unsynchronized list. - const ggml_tensor * last_node = cgraph->nodes[node_idx + ctx->num_additional_fused_ops]; - ctx->unsynced_nodes_written.push_back(last_node); + // Add all fused nodes to the unsynchronized lists. for (int32_t i = 0; i < ctx->num_additional_fused_ops + 1; ++i) { const ggml_tensor *cur_node = cgraph->nodes[node_idx + i]; + // Multiple outputs could be written, e.g. in topk_moe. Add them all to the list. + ctx->unsynced_nodes_written.push_back(cur_node); for (uint32_t j = 0; j < GGML_MAX_SRC; ++j) { if (!cur_node->src[j]) { continue; @@ -11345,7 +11454,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr break; case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(ctx, compute_ctx, src0, src1, src2, node, dryrun); + if (ctx->num_additional_fused_ops) { + ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx, dryrun); + } else { + ggml_vk_soft_max(ctx, compute_ctx, src0, src1, src2, node, dryrun); + } break; case GGML_OP_SOFT_MAX_BACK: @@ -12141,6 +12254,120 @@ static bool ggml_vk_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, st return true; } +static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, + int node_idx, bool with_norm) { + + if (with_norm) { + if (node_idx + (int)topk_moe_norm.size() > cgraph->n_nodes) { + return false; + } + for (size_t i = 0; i < topk_moe_norm.size(); ++i) { + if (cgraph->nodes[node_idx + i]->op != topk_moe_norm[i]) { + return false; + } + } + } else { + if (node_idx + (int)topk_moe.size() > cgraph->n_nodes) { + return false; + } + for (size_t i = 0; i < topk_moe.size(); ++i) { + if (cgraph->nodes[node_idx + i]->op != topk_moe[i]) { + return false; + } + } + } + + const ggml_tensor * softmax = cgraph->nodes[node_idx + 0]; + const ggml_tensor * weights = with_norm ? cgraph->nodes[node_idx + 8] : cgraph->nodes[node_idx + 4]; + + const float * op_params = (const float *)softmax->op_params; + + float scale = op_params[0]; + float max_bias = op_params[1]; + + if (!ggml_is_contiguous(softmax->src[0]) || !ggml_is_contiguous(weights)) { + return false; + } + + if (scale != 1.0f || max_bias != 0.0f) { + return false; + } + + // don't fuse when masks or sinks are present + if (softmax->src[1] || softmax->src[2]) { + return false; + } + + const int n_expert = softmax->ne[0]; + // n_expert must be a power of 2 + if (!is_pow2(n_expert) || n_expert > (1 << (num_topk_moe_pipelines-1))) { + return false; + } + + // Check that the nodes don't have any unexpected uses + const ggml_tensor * reshape1 = cgraph->nodes[node_idx + 1]; + const ggml_tensor * argsort = cgraph->nodes[node_idx + 2]; + const ggml_tensor * view = cgraph->nodes[node_idx + 3]; + const ggml_tensor * get_rows = cgraph->nodes[node_idx + 4]; + const ggml_tensor * reshape5 = with_norm ? cgraph->nodes[node_idx + 5] : nullptr; + const ggml_tensor * sum_rows = with_norm ? cgraph->nodes[node_idx + 6] : nullptr; + const ggml_tensor * div = with_norm ? cgraph->nodes[node_idx + 7] : nullptr; + const ggml_tensor * reshape8 = with_norm ? cgraph->nodes[node_idx + 8] : nullptr; + + // softmax is used by reshape and argsort + if (ggml_node_get_use_count(cgraph, node_idx) != 2 || + reshape1->src[0] != softmax || + argsort->src[0] != softmax) { + return false; + } + // reshape is used by get_rows + if (ggml_node_get_use_count(cgraph, node_idx + 1) != 1 || + get_rows->src[0] != reshape1) { + return false; + } + // argsort is used by view + if (ggml_node_get_use_count(cgraph, node_idx + 2) != 1 || + view->src[0] != argsort) { + return false; + } + // view is written (via argsort), we can skip checking it + + if (with_norm) { + // get_rows is used by reshape + if (ggml_node_get_use_count(cgraph, node_idx + 4) != 1 || + reshape5->src[0] != get_rows) { + return false; + } + + // reshape is used by sum_rows and div + if (ggml_node_get_use_count(cgraph, node_idx + 5) != 2 || + sum_rows->src[0] != reshape5 || + div->src[0] != reshape5) { + return false; + } + + // sum_rows is used by div + if (ggml_node_get_use_count(cgraph, node_idx + 6) != 1 || + div->src[1] != sum_rows) { + return false; + } + + // div/reshape are written + if (reshape8->src[0] != div) { + return false; + } + } + + if (!ctx->device->subgroup_arithmetic || + !ctx->device->subgroup_shuffle || + !ctx->device->subgroup_require_full_support || + ctx->device->disable_fusion) { + return false; + } + + return true; +} + static uint32_t ggml_vk_fuse_multi_add(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx) { const ggml_tensor *first_node = cgraph->nodes[node_idx]; @@ -12216,6 +12443,10 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg ctx->num_additional_fused_ops = num_adds - 1; } else if (ggml_vk_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) { ctx->num_additional_fused_ops = 1; + } else if (ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, true)) { + ctx->num_additional_fused_ops = topk_moe_norm.size() - 1; + } else if (ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, false)) { + ctx->num_additional_fused_ops = topk_moe.size() - 1; } } ggml_vk_build_graph(ctx, cgraph, i, nullptr, 0, true, false, false, false); @@ -12313,6 +12544,10 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg ctx->num_additional_fused_ops = num_adds - 1; } else if (ggml_vk_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) { ctx->num_additional_fused_ops = 1; + } else if (ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, true)) { + ctx->num_additional_fused_ops = topk_moe_norm.size() - 1; + } else if (ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, false)) { + ctx->num_additional_fused_ops = topk_moe.size() - 1; } } @@ -12320,10 +12555,10 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg bool almost_ready = (cgraph->n_nodes - i) < cgraph->n_nodes / 5; bool submit = (submitted_nodes >= nodes_per_submit) || (mul_mat_bytes >= mul_mat_bytes_per_submit) || - (i + ctx->num_additional_fused_ops == last_node) || + (i + ctx->num_additional_fused_ops >= last_node) || (almost_ready && !ctx->almost_ready_fence_pending); - bool enqueued = ggml_vk_build_graph(ctx, cgraph, i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i + ctx->num_additional_fused_ops == last_node, almost_ready, submit); + bool enqueued = ggml_vk_build_graph(ctx, cgraph, i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i + ctx->num_additional_fused_ops >= last_node, almost_ready, submit); if (vk_perf_logger_enabled) { if (ctx->compute_ctx.expired()) { @@ -12444,6 +12679,25 @@ static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph * while (first_unused < graph->n_nodes) { std::vector current_set; + // Avoid reordering topk_moe_norm + if (first_unused + (int)topk_moe_norm.size() <= graph->n_nodes) { + bool is_topk_moe_norm = true; + for (size_t j = 0; j < topk_moe_norm.size(); ++j) { + if (graph->nodes[first_unused + j]->op != topk_moe_norm[j] || used[first_unused + j]) { + is_topk_moe_norm = false; + } + } + if (is_topk_moe_norm) { + for (size_t j = 0; j < topk_moe_norm.size(); ++j) { + new_order.push_back(graph->nodes[first_unused + j]); + used[first_unused + j] = true; + } + while (first_unused < graph->n_nodes && used[first_unused]) { + first_unused++; + } + continue; + } + } // First, grab the next unused node. current_set.push_back(first_unused); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp new file mode 100644 index 0000000000000..9e56d5f8a3cc1 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp @@ -0,0 +1,139 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require +#extension GL_KHR_shader_subgroup_basic : enable +#extension GL_KHR_shader_subgroup_arithmetic : enable +#extension GL_KHR_shader_subgroup_shuffle : enable + +#include "types.glsl" + +layout (push_constant) uniform parameter +{ + uint n_rows; + uint n_expert_used; +}; + +layout(local_size_x_id = 0, local_size_y = 4, local_size_z = 1) in; + +layout(constant_id = 0) const uint WARP_SIZE = 32; +layout(constant_id = 1) const uint n_experts = 512; +layout(constant_id = 2) const bool with_norm = true; + +const uint experts_per_thread = (n_experts > WARP_SIZE) ? n_experts / WARP_SIZE : 1; + +layout (binding = 0, std430) readonly buffer Logits {float logits[];}; +layout (binding = 1, std430) writeonly buffer Weights {float weights[];}; +layout (binding = 2, std430) writeonly buffer Ids {uint ids[];}; + +void main() { + const uint row = gl_WorkGroupID.x * gl_WorkGroupSize.y + gl_LocalInvocationID.y; + if (row >= n_rows) { + return; + } + + const uint logits_offset = n_experts * row; + const uint weights_offset = n_expert_used * row; + const uint ids_offset = n_experts * row; + + float logits_r[experts_per_thread]; + + const float INFINITY = 1.0 / 0.0; + + [[unroll]] + for (uint i = 0; i < n_experts; i += WARP_SIZE) { + const uint expert = i + gl_LocalInvocationID.x; + logits_r[i / WARP_SIZE] = n_experts % WARP_SIZE == 0 || expert < n_experts ? logits[logits_offset + expert] : -INFINITY; + } + + float max_val = logits_r[0]; + + [[unroll]] + for (int i = 1; i < experts_per_thread; i++) { + const float val = logits_r[i]; + max_val = max(val, max_val); + } + + max_val = subgroupMax(max_val); + + float wt[experts_per_thread]; + float tmp = 0.f; + + [[unroll]] + for (int i = 0; i < experts_per_thread; i++) { + const float val = logits_r[i]; + wt[i] = exp(val - max_val); + tmp += wt[i]; + } + + tmp = subgroupAdd(tmp); + + const float inv_sum = 1.0f / tmp; + + [[unroll]] + for (int i = 0; i < experts_per_thread; i++) { + wt[i] = wt[i] * inv_sum; + } + + // at this point, each thread holds a portion of softmax, + // we do the argmax reduce over n_expert_used, each time marking + // the expert weight as -inf to exclude from the next iteration + + float wt_sum = 0.f; + + float output_weights[experts_per_thread]; + + for (int k = 0; k < n_expert_used; k++) { + float max_val = wt[0]; + uint max_expert = gl_LocalInvocationID.x; + + [[unroll]] + for (int i = 1; i < experts_per_thread; i++) { + const uint expert = gl_LocalInvocationID.x + i * WARP_SIZE; + if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && wt[i] > max_val) { + max_val = wt[i]; + max_expert = expert; + } + } + + [[unroll]] + for (uint mask = WARP_SIZE / 2; mask > 0; mask /= 2) { + const float val = subgroupShuffleXor(max_val, mask); + const uint expert = subgroupShuffleXor(max_expert, mask); + if (val > max_val || (val == max_val && expert < max_expert)) { + max_val = val; + max_expert = expert; + } + } + + if ((k & (WARP_SIZE - 1)) == gl_LocalInvocationID.x) { + output_weights[k / WARP_SIZE] = max_val; + } + + if ((max_expert & (WARP_SIZE - 1)) == gl_LocalInvocationID.x) { + wt[max_expert / WARP_SIZE] = -INFINITY; + + ids[ids_offset + k] = max_expert; + if (with_norm) { + wt_sum += max_val; + } + } + } + + if (with_norm) { + wt_sum = subgroupAdd(wt_sum); + const float inv_sum = 1.0f / wt_sum; + + [[unroll]] + for (uint i = 0; i < experts_per_thread; ++i) { + output_weights[i] *= inv_sum; + } + } + + [[unroll]] + for (uint i = 0; i < experts_per_thread; ++i) { + uint idx = i * WARP_SIZE + gl_LocalInvocationID.x; + if (idx < n_expert_used) { + weights[weights_offset + idx] = output_weights[i]; + } + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 1d04a812a038a..49bf6c764f726 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -920,6 +920,8 @@ void process_shaders() { string_to_spv("ssm_conv_f32", "ssm_conv.comp", {{"A_TYPE", "float"}}); + string_to_spv("topk_moe_f32", "topk_moe.comp", {}); + for (auto &c : compiles) { c.wait(); } From ee09828cb057460b369576410601a3a09279e23c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 18 Oct 2025 14:47:32 +0200 Subject: [PATCH 20/77] HIP: fix GPU_TARGETS (#16642) --- ci/run.sh | 2 +- ggml/src/ggml-hip/CMakeLists.txt | 6 ++++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/ci/run.sh b/ci/run.sh index bf0d53f20af56..1a4806976aa9a 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -75,7 +75,7 @@ if [ ! -z ${GG_BUILD_ROCM} ]; then exit 1 fi - CMAKE_EXTRA="${CMAKE_EXTRA} -DAMDGPU_TARGETS=${GG_BUILD_AMDGPU_TARGETS}" + CMAKE_EXTRA="${CMAKE_EXTRA} -DGPU_TARGETS=${GG_BUILD_AMDGPU_TARGETS}" fi if [ ! -z ${GG_BUILD_SYCL} ]; then diff --git a/ggml/src/ggml-hip/CMakeLists.txt b/ggml/src/ggml-hip/CMakeLists.txt index 934aefdcb45fa..6b499320e7b12 100644 --- a/ggml/src/ggml-hip/CMakeLists.txt +++ b/ggml/src/ggml-hip/CMakeLists.txt @@ -28,8 +28,10 @@ if (CXX_IS_HIPCC) " Prefer setting the HIP compiler directly. See README for details.") endif() else() - # Forward AMDGPU_TARGETS to CMAKE_HIP_ARCHITECTURES. - if (AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES) + # Forward (AMD)GPU_TARGETS to CMAKE_HIP_ARCHITECTURES. + if(GPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES) + set(CMAKE_HIP_ARCHITECTURES ${GPU_TARGETS}) + elseif(AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES) set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_TARGETS}) endif() cmake_minimum_required(VERSION 3.21) From 55754bebd5d570960cde9c0ba991a0b2991f6b1e Mon Sep 17 00:00:00 2001 From: Aman Gupta Date: Sun, 19 Oct 2025 15:37:12 +0800 Subject: [PATCH 21/77] CODEOWNERS: update for ggml-cuda/mmf (#16660) --- CODEOWNERS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CODEOWNERS b/CODEOWNERS index 3b696bf94a147..f833fb7cf486d 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -55,7 +55,7 @@ /ggml/src/ggml-cuda/common.cuh @slaren /ggml/src/ggml-cuda/fattn* @JohannesGaessler /ggml/src/ggml-cuda/ggml-cuda.cu @slaren -/ggml/src/ggml-cuda/mmf.* @JohannesGaessler +/ggml/src/ggml-cuda/mmf.* @JohannesGaessler @am17an /ggml/src/ggml-cuda/mmq.* @JohannesGaessler /ggml/src/ggml-cuda/mmvf.* @JohannesGaessler /ggml/src/ggml-cuda/mmvq.* @JohannesGaessler From fcb235b46618921cbd826acd49b553b5302233aa Mon Sep 17 00:00:00 2001 From: Aaron Teo Date: Sun, 19 Oct 2025 18:37:47 +0800 Subject: [PATCH 22/77] ci: include s390x release binaries (#16648) Signed-off-by: Aaron Teo --- .github/workflows/release.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 2ad381159409c..cab3ba9e68ee4 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -134,6 +134,8 @@ jobs: include: - build: 'x64' os: ubuntu-22.04 + - build: 's390x-z15' # z15 because our CI runners are on z15 + os: ubuntu-22.04-s390x # GGML_BACKEND_DL and GGML_CPU_ALL_VARIANTS are not currently supported on arm # - build: 'arm64' # os: ubuntu-22.04-arm From cec5edbcaec69bbf6d5851cabce4ac148be41701 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sun, 19 Oct 2025 14:03:25 +0200 Subject: [PATCH 23/77] ci : avoid manual updates of docs/ops.md (#16663) --- .github/workflows/update-ops-docs.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/update-ops-docs.yml b/.github/workflows/update-ops-docs.yml index c0218fa742173..d5e264b34f496 100644 --- a/.github/workflows/update-ops-docs.yml +++ b/.github/workflows/update-ops-docs.yml @@ -3,10 +3,12 @@ name: Update Operations Documentation on: push: paths: + - 'docs/ops.md' - 'docs/ops/**' - 'scripts/create_ops_docs.py' pull_request: paths: + - 'docs/ops.md' - 'docs/ops/**' - 'scripts/create_ops_docs.py' From 4f73d0a95120687e2c527739f771330a5271259a Mon Sep 17 00:00:00 2001 From: Aaron Teo Date: Mon, 20 Oct 2025 05:06:39 +0800 Subject: [PATCH 24/77] ci : fix binaries release failure for s390x (binaries may not work yet) (#16664) * devops: initial patch Signed-off-by: Aaron Teo * devops: forgot the z15 suffix Signed-off-by: Aaron Teo * devops: attempt at impl GGML_CPU_ALL_VARIANTS for s390x Signed-off-by: Aaron Teo * devops: rm baseline version Signed-off-by: Aaron Teo --------- Signed-off-by: Aaron Teo --- ggml/src/CMakeLists.txt | 12 +++++++ ggml/src/ggml-cpu/CMakeLists.txt | 58 ++++++++++++++++++++------------ 2 files changed, 49 insertions(+), 21 deletions(-) diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 892c23318a18e..3356ef550dec0 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -307,6 +307,10 @@ function(ggml_add_cpu_backend_variant tag_name) foreach (feat ${ARGN}) set(GGML_INTERNAL_${feat} ON) endforeach() + elseif (GGML_SYSTEM_ARCH STREQUAL "s390x") + foreach (feat ${ARGN}) + set(GGML_INTERNAL_${feat} ON) + endforeach() endif() ggml_add_cpu_backend_variant_impl(${tag_name}) @@ -371,6 +375,14 @@ if (GGML_CPU_ALL_VARIANTS) else() message(FATAL_ERROR "Unsupported PowerPC target OS: ${CMAKE_SYSTEM_NAME}") endif() + elseif (GGML_SYSTEM_ARCH STREQUAL "s390x") + if (CMAKE_SYSTEM_NAME MATCHES "Linux") + ggml_add_cpu_backend_variant(s390x_z15 Z15 VXE) + # ggml_add_cpu_backend_variant(s390x_z16 Z16 VXE) + # ggml_add_cpu_backend_variant(s390x_z17 Z17 VXE) + else() + message(FATAL_ERROR "Unsupported s390x target OS: ${CMAKE_SYSTEM_NAME}") + endif() else() message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS not yet supported with ${GGML_SYSTEM_ARCH} on ${CMAKE_SYSTEM_NAME}") endif() diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 42041b717aa22..34323afa0762a 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -466,29 +466,45 @@ function(ggml_add_cpu_backend_variant_impl tag_name) list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d) elseif (GGML_SYSTEM_ARCH STREQUAL "s390x") message(STATUS "s390x detected") - list(APPEND GGML_CPU_SOURCES ggml-cpu/arch/s390/quants.c) - file(READ "/proc/cpuinfo" CPUINFO_CONTENTS) - string(REGEX REPLACE "machine[ \t\r\n]*=[ \t\r\n]*([0-9]+)" "\\1" S390X_M ${CPUINFO_CONTENTS}) - - # TODO: Separation to determine activation of VX/VXE/VXE2 - if (${S390X_M} MATCHES "8561|8562") - message(STATUS "z15 target") - list(APPEND ARCH_FLAGS -march=z15) - elseif (${S390X_M} MATCHES "3931") - message(STATUS "z16 target") - list(APPEND ARCH_FLAGS -march=z16) - elseif (${S390X_M} MATCHES "9175|9176") - # NOTE: Only available from GCC 15.1.0 onwards. Any z17 machine with compile issues must first verify their GCC version. - # binutils must also be updated to the latest for the -march=z17 flag to work. Otherwise, use -march=arch15. - message(STATUS "z17 target") - list(APPEND ARCH_FLAGS -march=arch15) - else() - message(STATUS "Unknown target") - message(WARNING "Unknown target. If you are compiling for z14 and earlier, you might have to add -DGGML_VXE=OFF.") - list(APPEND ARCH_FLAGS -march=native -mtune=native) + list(APPEND GGML_CPU_SOURCES + ggml-cpu/arch/s390/quants.c) + + # for native compilation + if (GGML_NATIVE) + # check machine level to determine target + file(READ "/proc/cpuinfo" CPUINFO_CONTENTS) + string(REGEX REPLACE "machine[ \t\r\n]*=[ \t\r\n]*([0-9]+)" "\\1" S390X_M ${CPUINFO_CONTENTS}) + + # TODO: Separation to determine activation of VX/VXE/VXE2 + if (${S390X_M} MATCHES "8561|8562") + message(STATUS "z15 target") + list(APPEND ARCH_FLAGS -march=z15) + elseif (${S390X_M} MATCHES "3931") + message(STATUS "z16 target") + list(APPEND ARCH_FLAGS -march=z16) + elseif (${S390X_M} MATCHES "9175|9176") + # NOTE: Only available from GCC 15.1.0 onwards. Any z17 machine with compile issues must first verify their GCC version. + # binutils must also be updated to the latest for the -march=z17 flag to work. Otherwise, use -march=arch15. + message(STATUS "z17 target") + list(APPEND ARCH_FLAGS -march=arch15) + else() + message(STATUS "Unknown target") + message(WARNING "Unknown target. If you are compiling for z14 and earlier, you might have to add -DGGML_VXE=OFF.") + list(APPEND ARCH_FLAGS -march=native -mtune=native) + endif() + # for cross-compilation + elseif(GGML_CPU_ALL_VARIANTS) + # range through IBM z15 to z17 + # NOTE: update when a new hardware level is released + foreach (ZHW RANGE 15 17) + if(DEFINED GGML_INTERNAL_Z${ZHW}) + message(STATUS "z${ZHW} cross-compile target") + list(APPEND ARCH_FLAGS -march=z${ZHW}) + endif() + endforeach() endif() - if (GGML_VXE) + if (GGML_VXE OR GGML_INTERNAL_VXE) message(STATUS "VX/VXE/VXE2 enabled") list(APPEND ARCH_FLAGS -mvx -mzvector) list(APPEND ARCH_DEFINITIONS GGML_VXE) From 0398752dd450dfabdd1b9e289f6364c2600f6ab5 Mon Sep 17 00:00:00 2001 From: Giuseppe Scrivano Date: Sun, 19 Oct 2025 23:54:31 +0200 Subject: [PATCH 25/77] model : add Granite Hybrid types (#16635) add Granite 4 models mapping their embedding dimensions to the # of parameters. Information taken from https://huggingface.co/ibm-granite/granite-4.0-h-tiny Signed-off-by: Giuseppe Scrivano --- src/llama-model.cpp | 7 +++++-- src/llama-model.h | 1 + 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 522d1f67da3cb..909b49e8e6450 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -114,6 +114,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_17B_16E: return "17Bx16E (Scout)"; case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)"; case LLM_TYPE_A13B: return "A13B"; + case LLM_TYPE_7B_A1B: return "7B.A1B"; case LLM_TYPE_8B_A1B: return "8B.A1B"; case LLM_TYPE_21B_A3B: return "21B.A3B"; case LLM_TYPE_30B_A3B: return "30B.A3B"; @@ -1843,8 +1844,10 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); - switch (hparams.n_layer) { - // TODO: Add llm type label (not sure this is useful) + switch (hparams.n_embd) { + case 1536: type = LLM_TYPE_7B_A1B; break; + case 2048: case 2560: type = LLM_TYPE_3B; break; + case 4096: type = LLM_TYPE_32B; break; default: type = LLM_TYPE_UNKNOWN; } diff --git a/src/llama-model.h b/src/llama-model.h index 7f48662f2807a..05701e7d70c84 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -107,6 +107,7 @@ enum llm_type { LLM_TYPE_17B_16E, // llama4 Scout LLM_TYPE_17B_128E, // llama4 Maverick LLM_TYPE_A13B, + LLM_TYPE_7B_A1B, LLM_TYPE_8B_A1B, // lfm2moe LLM_TYPE_21B_A3B, // Ernie MoE small LLM_TYPE_30B_A3B, From 7062dd8460685d6700ed7621e50a22c6f3400ca3 Mon Sep 17 00:00:00 2001 From: takuya kodama Date: Mon, 20 Oct 2025 15:44:21 +0800 Subject: [PATCH 26/77] llama-context: only warn on pooling_type when user specified (#16674) The unexpeced pooling_type warning was incorrectly shown when users did not specify the --pooling-type parameter. In this case, the parameter defaults to `LLAMA_POOLING_TYPE_UNSPECIFIED (-1)`, and the code automatically applies the model's default pooling type. Example of spurious warning: ``` $ llama-embedding -hf ggml-org/bge-m3-Q8_0-GGUF -p "hello" ... llama_init_from_model: model default pooling_type is [2], but [-1] was specified ... ``` This fix ensures the warning only appears when users explicitly specify a pooling type that differs from the model's default (e.g., using --pooling-type mean on a model that expects CLS pooling). --- src/llama-context.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index e7526e7d0a557..bd348bcad370a 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -2346,7 +2346,8 @@ llama_context * llama_init_from_model( return nullptr; } - if (params.pooling_type != model->hparams.pooling_type) { + if (params.pooling_type != LLAMA_POOLING_TYPE_UNSPECIFIED && + params.pooling_type != model->hparams.pooling_type) { //user-specified pooling-type is different from the model default LLAMA_LOG_WARN("%s: model default pooling_type is [%d], but [%d] was specified\n", __func__, model->hparams.pooling_type, params.pooling_type); From 2330de7b847ca84eac766df372c604c26db72747 Mon Sep 17 00:00:00 2001 From: safranowith Date: Mon, 20 Oct 2025 11:08:32 +0300 Subject: [PATCH 27/77] SYCL: Add support for FLOOR,CEIL,ROUND and TRUNC unary operators (#16613) * SYCL: Add support for FLOOR,CEIL,ROUND and TRUNC unary operators Clean up unrelated changes from previous commit * Chore: remove empty lines and fix indentation * Clean up: remove leftover blank lines and fix spacing * chore: fix trailing whitespace and ensure final newline * Cleanup: remove redundant declarations already defined in header * Sync docs/ops.md with updated backend operation support * docs: update ops.md after rebase * docs: update ops.md - Vulkan supports SSM_CONV and SSM_SCAN --- docs/ops.md | 8 +- docs/ops/SYCL.csv | 16 ++++ docs/ops/Vulkan.csv | 42 ++++----- ggml/src/ggml-sycl/element_wise.cpp | 120 +++++++++++++++++++++++++ ggml/src/ggml-sycl/element_wise.hpp | 4 + ggml/src/ggml-sycl/ggml-sycl.cpp | 16 ++++ tests/test-backend-ops.cpp | 132 ++++++++++++++++++++++++++++ 7 files changed, 313 insertions(+), 25 deletions(-) diff --git a/docs/ops.md b/docs/ops.md index 938efac815fc0..226cd935d698a 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -22,7 +22,7 @@ Legend: | ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | | ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | | ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| CEIL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CEIL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | | CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ❌ | @@ -42,7 +42,7 @@ Legend: | ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | | FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | -| FLOOR | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| FLOOR | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | | GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | | GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | @@ -84,7 +84,7 @@ Legend: | ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | | ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| ROUND | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| ROUND | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | | RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | | SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | @@ -111,6 +111,6 @@ Legend: | TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ | | TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | | TOPK_MOE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | -| TRUNC | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| TRUNC | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | | XIELU | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index d7efa43cdf3da..bc6319f51fa8c 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -31,6 +31,14 @@ "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","XIELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" "SYCL0","XIELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" +"SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" @@ -95,6 +103,14 @@ "SYCL0","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","XIELU","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" "SYCL0","XIELU","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" +"SYCL0","FLOOR","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","FLOOR","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","CEIL","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","CEIL","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","ABS","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" "SYCL0","ABS","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","SYCL" "SYCL0","SGN","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","SYCL" diff --git a/docs/ops/Vulkan.csv b/docs/ops/Vulkan.csv index ea252577280d5..298c2a6ccd5fc 100644 --- a/docs/ops/Vulkan.csv +++ b/docs/ops/Vulkan.csv @@ -3263,27 +3263,27 @@ "Vulkan0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=0","support","1","yes","Vulkan" "Vulkan0","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=1","support","1","yes","Vulkan" "Vulkan0","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[4,1536,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[4,1536,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[4,1536,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","0","no","Vulkan" -"Vulkan0","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","0","no","Vulkan" -"Vulkan0","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","Vulkan" -"Vulkan0","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[4,1536,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","1","yes","Vulkan" +"Vulkan0","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","1","yes","Vulkan" +"Vulkan0","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","1","yes","Vulkan" +"Vulkan0","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","1","yes","Vulkan" "Vulkan0","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=1,n_seqs=1","support","1","yes","Vulkan" "Vulkan0","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","1","yes","Vulkan" "Vulkan0","RWKV_WKV6","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","1","yes","Vulkan" diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index 58f5125c9cf6e..810995d0cbf74 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -150,6 +150,26 @@ static __dpct_inline__ T op_clamp(T x, float min_val, float max_val) { return x < static_cast(min_val) ? static_cast(min_val) : (x > static_cast(max_val) ? static_cast(max_val) : x); } +template +static __dpct_inline__ T op_floor(T x) { + return sycl::floor(x); +} + +template +static __dpct_inline__ T op_ceil(T x) { + return sycl::ceil(x); +} + +template +static __dpct_inline__ T op_round(T x) { + return sycl::round(x); +} + +template +static __dpct_inline__ T op_trunc(T x) { + return sycl::trunc(x); +} + template static void unary_op_sgn_kernel(const T * x, T * dst, const int k, const sycl::nd_item<1> &item_ct1) { SYCL_GLOBAL_ID_LOOP(k, item_ct1) { @@ -304,6 +324,34 @@ static void unary_op_clamp_kernel(const T * x, T * dst, const int k, const sycl: } } +template +static void unary_op_floor_kernel(const T * x, T * dst, const int k, const sycl::nd_item<1> &item_ct1) { + SYCL_GLOBAL_ID_LOOP(k, item_ct1) { + dst[i] = op_floor(x[i]); + } +} + +template +static void unary_op_ceil_kernel(const T * x, T * dst, const int k, const sycl::nd_item<1> &item_ct1) { + SYCL_GLOBAL_ID_LOOP(k, item_ct1) { + dst[i] = op_ceil(x[i]); + } +} + +template +static void unary_op_round_kernel(const T * x, T * dst, const int k, const sycl::nd_item<1> &item_ct1) { + SYCL_GLOBAL_ID_LOOP(k, item_ct1) { + dst[i] = op_round(x[i]); + } +} + +template +static void unary_op_trunc_kernel(const T * x, T * dst, const int k, const sycl::nd_item<1> &item_ct1) { + SYCL_GLOBAL_ID_LOOP(k, item_ct1) { + dst[i] = op_trunc(x[i]); + } +} + template static void upscale(const T *x, T *dst, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, @@ -897,6 +945,58 @@ static inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, ggml_tens }, min_val, max_val); } +static inline void ggml_sycl_op_floor(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst, + [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) { + const int num_blocks = ceil_div(k_elements, 256); + stream->parallel_for( + sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256), + sycl::range<1>(256)), + [=](sycl::nd_item<1> item_ct1) { + unary_op_floor_kernel(src, dst_ptr, k_elements, item_ct1); + }); + }); +} + +static inline void ggml_sycl_op_ceil(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst, + [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) { + const int num_blocks = ceil_div(k_elements, 256); + stream->parallel_for( + sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256), + sycl::range<1>(256)), + [=](sycl::nd_item<1> item_ct1) { + unary_op_ceil_kernel(src, dst_ptr, k_elements, item_ct1); + }); + }); +} + +static inline void ggml_sycl_op_round(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst, + [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) { + const int num_blocks = ceil_div(k_elements, 256); + stream->parallel_for( + sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256), + sycl::range<1>(256)), + [=](sycl::nd_item<1> item_ct1) { + unary_op_round_kernel(src, dst_ptr, k_elements, item_ct1); + }); + }); +} + +static inline void ggml_sycl_op_trunc(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst, + [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) { + const int num_blocks = ceil_div(k_elements, 256); + stream->parallel_for( + sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256), + sycl::range<1>(256)), + [=](sycl::nd_item<1> item_ct1) { + unary_op_trunc_kernel(src, dst_ptr, k_elements, item_ct1); + }); + }); +} + static inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, ggml_tensor *dst) { GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32); GGML_ASSERT(dst->src[1]->type == GGML_TYPE_F32); @@ -1122,3 +1222,23 @@ void ggml_sycl_arange(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/0); ggml_sycl_detail::ggml_sycl_op_arange(ctx, dst); } + +void ggml_sycl_floor(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + ggml_sycl_op_floor(ctx, dst); +} + +void ggml_sycl_ceil(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + ggml_sycl_op_ceil(ctx, dst); +} + +void ggml_sycl_round(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + ggml_sycl_op_round(ctx, dst); +} + +void ggml_sycl_trunc(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + ggml_sycl_op_trunc(ctx, dst); +} diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp index ed96c55f75a7a..fcf93295cb215 100644 --- a/ggml/src/ggml-sycl/element_wise.hpp +++ b/ggml/src/ggml-sycl/element_wise.hpp @@ -80,6 +80,10 @@ void ggml_sycl_reglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_swiglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_geglu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst); +void ggml_sycl_floor(ggml_backend_sycl_context & ctx, ggml_tensor * dst); +void ggml_sycl_ceil(ggml_backend_sycl_context & ctx, ggml_tensor * dst); +void ggml_sycl_round(ggml_backend_sycl_context & ctx, ggml_tensor * dst); +void ggml_sycl_trunc(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_arange(ggml_backend_sycl_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index a7e077ec8ebe0..1a007ffe2bca6 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -3698,6 +3698,18 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_UNARY_OP_ELU: ggml_sycl_elu(ctx, dst); break; + case GGML_UNARY_OP_FLOOR: + ggml_sycl_floor(ctx, dst); + break; + case GGML_UNARY_OP_CEIL: + ggml_sycl_ceil(ctx, dst); + break; + case GGML_UNARY_OP_ROUND: + ggml_sycl_round(ctx, dst); + break; + case GGML_UNARY_OP_TRUNC: + ggml_sycl_trunc(ctx, dst); + break; default: return false; } @@ -4262,6 +4274,10 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_UNARY_OP_SGN: case GGML_UNARY_OP_ABS: case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_FLOOR: + case GGML_UNARY_OP_CEIL: + case GGML_UNARY_OP_ROUND: + case GGML_UNARY_OP_TRUNC: #if defined (GGML_SYCL_F16) return ggml_is_contiguous(op->src[0]) && (op->type == op->src[0]->type); #else diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 82bb55ea0e184..fa98db2982ce7 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -3759,6 +3759,130 @@ struct test_clamp : public test_case { } }; +// GGML_OP_FLOOR +struct test_floor : public test_case { + const ggml_type type; + const std::array ne; + + std::string vars() override { + return VARS_TO_STR2(type, ne); + } + + test_floor(ggml_type type = GGML_TYPE_F32, + std::array ne = {10, 2, 2, 2}) + : type(type), ne(ne) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_set_param(a); + ggml_set_name(a, "a"); + + ggml_tensor * out = ggml_floor(ctx, a); + ggml_set_name(out, "out"); + + return out; + } + + void initialize_tensors(ggml_context * ctx) override { + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + init_tensor_uniform(t, -10.0f, 10.0f); + } + } +}; + +// GGML_OP_CEIL +struct test_ceil : public test_case { + const ggml_type type; + const std::array ne; + + std::string vars() override { + return VARS_TO_STR2(type, ne); + } + + test_ceil(ggml_type type = GGML_TYPE_F32, + std::array ne = {10, 2, 2, 2}) + : type(type), ne(ne) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_set_param(a); + ggml_set_name(a, "a"); + + ggml_tensor * out = ggml_ceil(ctx, a); + ggml_set_name(out, "out"); + + return out; + } + + void initialize_tensors(ggml_context * ctx) override { + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + init_tensor_uniform(t, -10.0f, 10.0f); + } + } +}; + +// GGML_OP_ROUND +struct test_round : public test_case { + const ggml_type type; + const std::array ne; + + std::string vars() override { + return VARS_TO_STR2(type, ne); + } + + test_round(ggml_type type = GGML_TYPE_F32, + std::array ne = {10, 2, 2, 2}) + : type(type), ne(ne) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_set_param(a); + ggml_set_name(a, "a"); + + ggml_tensor * out = ggml_round(ctx, a); + ggml_set_name(out, "out"); + + return out; + } + + void initialize_tensors(ggml_context * ctx) override { + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + init_tensor_uniform(t, -10.0f, 10.0f); + } + } +}; + +// GGML_OP_TRUNC +struct test_trunc : public test_case { + const ggml_type type; + const std::array ne; + + std::string vars() override { + return VARS_TO_STR2(type, ne); + } + + test_trunc(ggml_type type = GGML_TYPE_F32, + std::array ne = {10, 2, 2, 2}) + : type(type), ne(ne) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_set_param(a); + ggml_set_name(a, "a"); + + ggml_tensor * out = ggml_trunc(ctx, a); + ggml_set_name(out, "out"); + + return out; + } + + void initialize_tensors(ggml_context * ctx) override { + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + init_tensor_uniform(t, -10.0f, 10.0f); + } + } +}; + // GGML_OP_DIAG_MASK_INF struct test_diag_mask_inf : public test_case { const ggml_type type; @@ -6585,6 +6709,10 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_cos (type)); test_cases.emplace_back(new test_clamp (type)); test_cases.emplace_back(new test_leaky_relu(type)); + test_cases.emplace_back(new test_floor (type)); + test_cases.emplace_back(new test_ceil (type)); + test_cases.emplace_back(new test_round (type)); + test_cases.emplace_back(new test_trunc (type)); test_cases.emplace_back(new test_sqr (type, {7, 1, 5, 3})); test_cases.emplace_back(new test_sqrt (type, {7, 1, 5, 3})); test_cases.emplace_back(new test_log (type, {7, 1, 5, 3})); @@ -6592,6 +6720,10 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_cos (type, {7, 1, 5, 3})); test_cases.emplace_back(new test_clamp (type, {7, 1, 5, 3})); test_cases.emplace_back(new test_leaky_relu(type, {7, 1, 5, 3})); + test_cases.emplace_back(new test_floor (type, {7, 1, 5, 3})); + test_cases.emplace_back(new test_ceil (type, {7, 1, 5, 3})); + test_cases.emplace_back(new test_round (type, {7, 1, 5, 3})); + test_cases.emplace_back(new test_trunc (type, {7, 1, 5, 3})); } test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10, 1, 1}, 5)); From 72d53e6c4decee8b339e49aed8cc0e234b9639dc Mon Sep 17 00:00:00 2001 From: Ron Evans Date: Mon, 20 Oct 2025 10:20:04 +0200 Subject: [PATCH 28/77] readme: update bindings (#16651) Signed-off-by: deadprogram --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 1c0742370de39..0a755f4800e47 100644 --- a/README.md +++ b/README.md @@ -187,6 +187,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo - Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift) - Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama) - Delphi [Embarcadero/llama-cpp-delphi](https://github.com/Embarcadero/llama-cpp-delphi) +- Go (no CGo needed): [hybridgroup/yzma](https://github.com/hybridgroup/yzma) From 06332e28672356b964d6dfc2ba4657e20581cd43 Mon Sep 17 00:00:00 2001 From: takuya kodama Date: Mon, 20 Oct 2025 16:27:09 +0800 Subject: [PATCH 29/77] llama-batch: fix build fails with `-Werror=missing-braces` (#16614) ## Why it failed When compiling with strict compiler flags (-Wmissing-braces -Werror=missing-braces), the build fails with the following error: ``` cmake \ -S . \ -B ../llama.cpp.build \ --preset=x64-linux-gcc-debug \ -DCMAKE_INSTALL_PREFIX=/tmp/local \ -DCMAKE_CXX_FLAGS="-Wmissing-braces -Werror=missing-braces" && \ cmake --build ../llama.cpp.build/ ... In file included from /home/otegami/work/cpp/llama.cpp/src/llama-graph.h:4, from /home/otegami/work/cpp/llama.cpp/src/llama-model.h:5, from /home/otegami/work/cpp/llama.cpp/src/llama.cpp:8: /home/otegami/work/cpp/llama.cpp/src/llama-batch.h:126:48: error: missing braces around initializer for 'std::__array_traits::_Type' {aka 'int [1]'} [-Werror=missing-braces] 126 | std::array seq_id_0 = { 0 }; // default sequence id | ^ cc1plus: some warnings being treated as errors ``` The issue is that std::array initialization requires double braces. ## How to fix This PR changes `{ 0 }` to `{{ 0 }}` for std::array initialization. This is part of a series of commits to fix missing braces warnings across the codebase. - src/llama-batch.h <- This PR is here. - src/llama-context.cpp - tests/test-backend-ops.cpp - tests/test-gguf.cpp - tools/mtmd/clip.cpp Benefits: - std::array is a struct containing a C-style array, requiring nested braces - Enables stricter compiler warnings to catch potential issues --- src/llama-batch.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/llama-batch.h b/src/llama-batch.h index d563adc66aaf5..0dc8cebd2a7b3 100644 --- a/src/llama-batch.h +++ b/src/llama-batch.h @@ -123,7 +123,7 @@ class llama_batch_allocr { uint32_t n_seq_max; uint32_t n_outputs; - std::array seq_id_0 = { 0 }; // default sequence id + std::array seq_id_0 = {{ 0 }}; // default sequence id std::vector pos; std::vector n_seq_id; From 13f2cfad4170c096c51a02c24a6a158cb47f1480 Mon Sep 17 00:00:00 2001 From: Aleksander Grygier Date: Mon, 20 Oct 2025 12:41:13 +0200 Subject: [PATCH 30/77] Enable per-conversation loading states to allow having parallel conversations (#16327) * feat: Per-conversation loading states and tracking streaming stats * chore: update webui build output * refactor: Chat state management Consolidates loading state management by using a global `isLoading` store synchronized with individual conversation states. This change ensures proper reactivity and avoids potential race conditions when updating the UI based on the loading status of different conversations. It also improves the accuracy of statistics displayed. Additionally, slots service methods are updated to use conversation IDs for per-conversation state management, avoiding global state pollution. * feat: Adds loading indicator to conversation items * chore: update webui build output * fix: Fix aborting chat streaming Improves the chat stream abortion process by ensuring that partial responses are saved before the abort signal is sent. This avoids a race condition where the onError callback could clear the streaming state before the partial response is saved. Additionally, the stream reading loop and callbacks are now checked for abort signals to prevent further processing after abortion. * refactor: Remove redundant comments * chore: build webui static output * refactor: Cleanup * chore: update webui build output * chore: update webui build output * fix: Conversation loading indicator for regenerating messages * chore: update webui static build * feat: Improve configuration * feat: Install `http-server` as dev dependency to not need to rely on `npx` in CI --- tools/server/public/index.html.gz | Bin 850862 -> 851733 bytes tools/server/webui/package-lock.json | 527 ++++++++++++++++++ tools/server/webui/package.json | 1 + tools/server/webui/playwright.config.ts | 6 +- .../app/chat/ChatProcessingInfo.svelte | 42 +- .../app/chat/ChatScreen/ChatScreen.svelte | 8 +- .../ChatSidebarConversationItem.svelte | 21 +- tools/server/webui/src/lib/services/chat.ts | 108 ++-- tools/server/webui/src/lib/services/slots.ts | 102 +++- 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