From 3ad5451f3b75809e3033e4e577b9f60bcaf6676a Mon Sep 17 00:00:00 2001 From: uvos Date: Wed, 27 Nov 2024 17:10:08 +0100 Subject: [PATCH 01/29] Add some minimal optimizations for CDNA (#10498) * Add some minimal optimizations for CDNA * ggml_cuda: set launch bounds also for GCN as it helps there too --- ggml/src/ggml-cuda/common.cuh | 17 ++++++++++++++--- ggml/src/ggml-cuda/ggml-cuda.cu | 11 ++++++++++- ggml/src/ggml-cuda/mmq.cu | 2 +- ggml/src/ggml-cuda/mmq.cuh | 4 ++-- ggml/src/ggml-cuda/mmvq.cu | 2 +- ggml/src/ggml-cuda/vendors/hip.h | 8 ++++++++ 6 files changed, 36 insertions(+), 8 deletions(-) diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index b0dd16066..535118d87 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -47,9 +47,20 @@ #define CC_TURING 750 #define CC_AMPERE 800 #define CC_OFFSET_AMD 1000000 -#define CC_RDNA1 (CC_OFFSET_AMD + 1010) -#define CC_RDNA2 (CC_OFFSET_AMD + 1030) -#define CC_RDNA3 (CC_OFFSET_AMD + 1100) + +// GCN/CNDA, wave size is 64 +#define CC_GCN4 (CC_OFFSET_AMD + 803) // Tonga, Fiji, Polaris, minimum for fast fp16 +#define CC_VEGA (CC_OFFSET_AMD + 900) // Vega56/64, minimum for fp16 dual issue +#define CC_VEGA20 (CC_OFFSET_AMD + 906) // MI50/Radeon VII, minimum for dp4a +#define CC_CDNA (CC_OFFSET_AMD + 908) // MI100, minimum for MFMA, acc registers +#define CC_CDNA2 (CC_OFFSET_AMD + 910) // MI210, minimum acc register renameing +#define CC_CDNA3 (CC_OFFSET_AMD + 942) // MI300 + +// RNDA removes MFMA, dp4a, xnack, acc registers, wave size is 32 +#define CC_RDNA1 (CC_OFFSET_AMD + 1010) // RX 5000 +#define CC_RDNA2 (CC_OFFSET_AMD + 1030) // RX 6000, minimum for dp4a +#define CC_RDNA3 (CC_OFFSET_AMD + 1100) // RX 7000, minimum for WMMA + #define CC_QY1 210 #define CC_QY2 220 diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 2a78a4393..d6e4bfdd0 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -1107,6 +1107,11 @@ static void ggml_cuda_op_mul_mat_cublas( const half alpha_f16 = 1.0f; const half beta_f16 = 0.0f; + cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F; + if (ggml_cuda_info().devices[ctx.device].cc == CC_CDNA) { + cu_compute_type = CUBLAS_COMPUTE_32F; + } + CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream)); CUBLAS_CHECK( cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N, @@ -1114,7 +1119,7 @@ static void ggml_cuda_op_mul_mat_cublas( &alpha_f16, src0_ptr, CUDA_R_16F, ne00, src1_ptr, CUDA_R_16F, ne10, &beta_f16, dst_f16.get(), CUDA_R_16F, ldc, - CUBLAS_COMPUTE_16F, + cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)); const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); @@ -1607,6 +1612,10 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F; cudaDataType_t cu_data_type = CUDA_R_16F; + if (ggml_cuda_info().devices[ctx.device].cc == CC_CDNA) { + cu_compute_type = CUBLAS_COMPUTE_32F; + } + // dst strides size_t nbd2 = dst->nb[2]; size_t nbd3 = dst->nb[3]; diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index ae5c68ab3..7f7c8c90b 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -148,5 +148,5 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) { return cc < CC_VOLTA || ne11 < MMQ_DP4A_MAX_BATCH_SIZE; } - return cc < CC_RDNA3 || ne11 < MMQ_DP4A_MAX_BATCH_SIZE; + return (cc < CC_RDNA3 && cc != CC_CDNA && cc != CC_VEGA20) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE; } diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index 425acb20d..8d8867121 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -2570,9 +2570,9 @@ static __device__ void mul_mat_q_process_tile( template #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) +#if defined(RDNA3) || defined(RDNA2) || defined(CDNA) || defined(GCN) __launch_bounds__(WARP_SIZE*nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) +#endif // defined(RDNA3) || defined(RDNA2) || defined(CDNA) || defined(GCN) #else #if __CUDA_ARCH__ >= CC_VOLTA __launch_bounds__(WARP_SIZE*nwarps, 1) diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu index 735975c16..02d150983 100644 --- a/ggml/src/ggml-cuda/mmvq.cu +++ b/ggml/src/ggml-cuda/mmvq.cu @@ -142,7 +142,7 @@ static void mul_mat_vec_q_cuda( int64_t nwarps = 1; int64_t rows_per_cuda_block = 1; - if (ggml_cuda_info().devices[id].cc < CC_RDNA2) { // NVIDIA and AMD older than RDNA2 + if (ggml_cuda_info().devices[id].cc < CC_CDNA || ggml_cuda_info().devices[id].cc == CC_RDNA1) { // NVIDIA and AMD older than RDNA2 but not CDNA switch(ncols_y) { case 1: nwarps = 4; diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h index 1f3c70c2e..3205534d6 100644 --- a/ggml/src/ggml-cuda/vendors/hip.h +++ b/ggml/src/ggml-cuda/vendors/hip.h @@ -95,6 +95,14 @@ #define __CUDA_ARCH__ 1300 +#if defined(__gfx803__) || defined(__gfx900__) || defined(__gfx906__) +#define GCN +#endif + +#if defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx942__) +#define CDNA +#endif + #if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \ defined(__gfx1150__) || defined(__gfx1151__) #define RDNA3 From 9f912511bc9414fa7a3c521378b6388cd932b58d Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Wed, 27 Nov 2024 22:30:52 +0100 Subject: [PATCH 02/29] common : fix duplicated file name with hf_repo and hf_file (#10550) --- common/arg.cpp | 6 +++- common/common.cpp | 54 +++++++++++++++++----------------- common/common.h | 13 ++++++-- examples/server/tests/utils.py | 1 - 4 files changed, 43 insertions(+), 31 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 272492e50..a6b7a1394 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -128,7 +128,11 @@ static void common_params_handle_model_default(common_params & params) { } params.hf_file = params.model; } else if (params.model.empty()) { - params.model = fs_get_cache_file(string_split(params.hf_file, '/').back()); + // this is to avoid different repo having same file name, or same file name in different subdirs + std::string filename = params.hf_repo + "_" + params.hf_file; + // to make sure we don't have any slashes in the filename + string_replace_all(filename, "/", "_"); + params.model = fs_get_cache_file(filename); } } else if (!params.model_url.empty()) { if (params.model.empty()) { diff --git a/common/common.cpp b/common/common.cpp index 09ec9f238..2b2f00098 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -829,9 +829,9 @@ struct common_init_result common_init_from_params(common_params & params) { llama_model * model = nullptr; if (!params.hf_repo.empty() && !params.hf_file.empty()) { - model = common_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams); + model = common_load_model_from_hf(params.hf_repo, params.hf_file, params.model, params.hf_token, mparams); } else if (!params.model_url.empty()) { - model = common_load_model_from_url(params.model_url.c_str(), params.model.c_str(), params.hf_token.c_str(), mparams); + model = common_load_model_from_url(params.model_url, params.model, params.hf_token, mparams); } else { model = llama_load_model_from_file(params.model.c_str(), mparams); } @@ -1342,17 +1342,17 @@ static bool common_download_file(const std::string & url, const std::string & pa } struct llama_model * common_load_model_from_url( - const char * model_url, - const char * path_model, - const char * hf_token, + const std::string & model_url, + const std::string & local_path, + const std::string & hf_token, const struct llama_model_params & params) { // Basic validation of the model_url - if (!model_url || strlen(model_url) == 0) { + if (model_url.empty()) { LOG_ERR("%s: invalid model_url\n", __func__); return NULL; } - if (!common_download_file(model_url, path_model, hf_token)) { + if (!common_download_file(model_url, local_path, hf_token)) { return NULL; } @@ -1363,9 +1363,9 @@ struct llama_model * common_load_model_from_url( /*.no_alloc = */ true, /*.ctx = */ NULL, }; - auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params); + auto * ctx_gguf = gguf_init_from_file(local_path.c_str(), gguf_params); if (!ctx_gguf) { - LOG_ERR("\n%s: failed to load input GGUF from %s\n", __func__, path_model); + LOG_ERR("\n%s: failed to load input GGUF from %s\n", __func__, local_path.c_str()); return NULL; } @@ -1384,13 +1384,13 @@ struct llama_model * common_load_model_from_url( // Verify the first split file format // and extract split URL and PATH prefixes { - if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) { - LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, path_model, n_split); + if (!llama_split_prefix(split_prefix, sizeof(split_prefix), local_path.c_str(), 0, n_split)) { + LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, local_path.c_str(), n_split); return NULL; } - if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) { - LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model_url, n_split); + if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url.c_str(), 0, n_split)) { + LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model_url.c_str(), n_split); return NULL; } } @@ -1417,14 +1417,14 @@ struct llama_model * common_load_model_from_url( } } - return llama_load_model_from_file(path_model, params); + return llama_load_model_from_file(local_path.c_str(), params); } struct llama_model * common_load_model_from_hf( - const char * repo, - const char * model, - const char * path_model, - const char * hf_token, + const std::string & repo, + const std::string & remote_path, + const std::string & local_path, + const std::string & hf_token, const struct llama_model_params & params) { // construct hugging face model url: // @@ -1438,27 +1438,27 @@ struct llama_model * common_load_model_from_hf( std::string model_url = "https://huggingface.co/"; model_url += repo; model_url += "/resolve/main/"; - model_url += model; + model_url += remote_path; - return common_load_model_from_url(model_url.c_str(), path_model, hf_token, params); + return common_load_model_from_url(model_url, local_path, hf_token, params); } #else struct llama_model * common_load_model_from_url( - const char * /*model_url*/, - const char * /*path_model*/, - const char * /*hf_token*/, + const std::string & /*model_url*/, + const std::string & /*local_path*/, + const std::string & /*hf_token*/, const struct llama_model_params & /*params*/) { LOG_WRN("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__); return nullptr; } struct llama_model * common_load_model_from_hf( - const char * /*repo*/, - const char * /*model*/, - const char * /*path_model*/, - const char * /*hf_token*/, + const std::string & /*repo*/, + const std::string & /*remote_path*/, + const std::string & /*local_path*/, + const std::string & /*hf_token*/, const struct llama_model_params & /*params*/) { LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__); return nullptr; diff --git a/common/common.h b/common/common.h index 286642db2..9b1508a15 100644 --- a/common/common.h +++ b/common/common.h @@ -470,8 +470,17 @@ struct llama_model_params common_model_params_to_llama ( common_params struct llama_context_params common_context_params_to_llama(const common_params & params); struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params); -struct llama_model * common_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params); -struct llama_model * common_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params); +struct llama_model * common_load_model_from_url( + const std::string & model_url, + const std::string & local_path, + const std::string & hf_token, + const struct llama_model_params & params); +struct llama_model * common_load_model_from_hf( + const std::string & repo, + const std::string & remote_path, + const std::string & local_path, + const std::string & hf_token, + const struct llama_model_params & params); // clear LoRA adapters from context, then apply new list of adapters void common_lora_adapters_apply(struct llama_context * ctx, std::vector & lora_adapters); diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py index bc590bcb3..e31743c50 100644 --- a/examples/server/tests/utils.py +++ b/examples/server/tests/utils.py @@ -319,7 +319,6 @@ class ServerPreset: server.model_hf_repo = "ggml-org/models" server.model_hf_file = "jina-reranker-v1-tiny-en/ggml-model-f16.gguf" server.model_alias = "jina-reranker" - server.model_file = "./tmp/jina-reranker-v1-tiny-en.gguf" server.n_ctx = 512 server.n_batch = 512 server.n_slots = 1 From b7420131bf8ab3e067bc660439ab1ab18be7edbd Mon Sep 17 00:00:00 2001 From: Chenguang Li <87689256+noemotiovon@users.noreply.github.com> Date: Thu, 28 Nov 2024 14:24:46 +0800 Subject: [PATCH 03/29] CANN: ROPE operator optimization (#10540) * [cann] ROPE operator optimization Co-authored-by: noemotiovon --- ggml/src/ggml-cann/aclnn_ops.cpp | 308 +++++++++++++++++++++---------- ggml/src/ggml-cann/ggml-cann.cpp | 9 - 2 files changed, 211 insertions(+), 106 deletions(-) diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index d7472ee3a..d707efc5d 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -21,22 +21,23 @@ */ #include "aclnn_ops.h" -#include "ggml-impl.h" +#include #include +#include #include #include #include #include +#include #include #include #include #include #include -#include -#include #include #include +#include #include #include #include @@ -56,6 +57,7 @@ #include #include +#include "ggml-impl.h" #include "kernels/ascendc_kernels.h" #define GGML_COMMON_DECL_C @@ -1103,9 +1105,9 @@ static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer, } /** - * @brief Creates an ACL tensor initialized with ones using a provided buffer. + * @brief Creates an ACL tensor initialized with value using a provided buffer. * - * This function initializes a tensor with ones using the specified buffer and + * This function initializes a tensor with value using the specified buffer and * tensor parameters. * * @param ctx The context for the CANN backend operations. @@ -1118,12 +1120,12 @@ static aclTensor* aclnn_zero(ggml_backend_cann_context& ctx, void* buffer, * @param type_size The size of each element in the tensor data type. * @param value The value to be used for initializing the tensor (default * is 1.0). - * @return An ACL tensor initialized with ones. + * @return An ACL tensor initialized with value. */ -static aclTensor* aclnn_ones(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) { +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; @@ -1165,7 +1167,7 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) { size_t one_tensor_n_bytes = src->ne[0] * ggml_element_size(src); ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes); - aclTensor* acl_gamma = aclnn_ones( + aclTensor* acl_gamma = aclnn_values( ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, 1, ggml_cann_type_mapping(src->type), ggml_element_size(src)); @@ -1209,9 +1211,9 @@ void ggml_cann_diag_mask(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_cann_pool_alloc one_tensor_allocator(ctx.pool(), one_tensor_n_bytes); aclTensor* mask_tensor = - aclnn_ones(ctx, one_tensor_allocator.get(), one_tensor_n_bytes, src->ne, - GGML_MAX_DIMS, ggml_cann_type_mapping(src->type), - ggml_element_size(src), value); + aclnn_values(ctx, one_tensor_allocator.get(), one_tensor_n_bytes, + src->ne, GGML_MAX_DIMS, ggml_cann_type_mapping(src->type), + ggml_element_size(src), value); uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -1768,6 +1770,92 @@ static void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src, ACL_CHECK(aclnnSin(workspaceAddr, workspaceSize, executor, ctx.stream())); } +/** + * @brief Performs element-wise division of tensor1 by tensor2 , multiplies the + result by the scalar value and adds it to self . + * + * Performs element-wise division of tensor1 by tensor2, + * multiplies the result by the scalar value and adds it to self . + * The operation is defined as: + * \f[ + * \text{out}_i = \text{selft}_i + \text{value} \times + \frac{\text{tensor1}_i}{\text{tensor2}_i} + * \f] + + * @param ctx The context for the CANN backend operations. + * @param acl_self The source tensor on which the addcdiv function will be + applied. + * @param tensor1 Numerator tensor. + * @param tensor2 Denominator tensor. + * @param value The value to be used for coefficient. + */ +static void aclnn_inplace_addcdiv(ggml_backend_cann_context& ctx, + aclTensor* acl_self, aclTensor* tensor1, + aclTensor* tensor2, float value) { + uint64_t workspaceSize = 0; + aclOpExecutor* executor; + void* workspaceAddr = nullptr; + aclScalar* acl_value = aclCreateScalar(&value, aclDataType::ACL_FLOAT); + + ACL_CHECK(aclnnInplaceAddcdivGetWorkspaceSize( + acl_self, tensor1, tensor2, acl_value, &workspaceSize, &executor)); + if (workspaceSize > 0) { + ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); + workspaceAddr = workspace_allocator.get(); + } + + ACL_CHECK(aclnnInplaceAddcdiv(workspaceAddr, workspaceSize, executor, + ctx.stream())); +} + +/** + * @brief Matrix division, optionally in-place. + * + * This function division each element of the source tensor `acl_src` by the + * tensor `acl_other` and stores the result in the destination tensor `acl_dst`. + * If `inplace` is true, `acl_dst` will not be used and the operation is + * performed in-place on `acl_src`. The operation is defined as: \f[ + * \text{dst}_i = \frac{\text{acl_src}_i}{\text{acl_other}_i} + * \f] + * + * @param ctx The context for the CANN backend operations. + * @param acl_src Numerator tensor.. + * @param acl_other Denominator tensor. + * @param acl_dst The destination tensor where the result will be stored if + * `inplace` is false. + * @param inplace Flag indicating whether to perform the operation in-place on + * `acl_src`. + */ +static void aclnn_div_tensor(ggml_backend_cann_context& ctx, aclTensor* acl_src, + aclTensor* acl_other, aclTensor* acl_dst, + bool inplace) { + uint64_t workspaceSize = 0; + aclOpExecutor* executor; + void* workspaceAddr = nullptr; + + if (inplace) { + ACL_CHECK(aclnnInplaceDivGetWorkspaceSize(acl_src, acl_other, + &workspaceSize, &executor)); + if (workspaceSize > 0) { + ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); + workspaceAddr = workspace_allocator.get(); + } + + ACL_CHECK(aclnnInplaceDiv(workspaceAddr, workspaceSize, executor, + ctx.stream())); + } else { + ACL_CHECK(aclnnDivGetWorkspaceSize(acl_src, acl_other, acl_dst, + &workspaceSize, &executor)); + if (workspaceSize > 0) { + ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); + workspaceAddr = workspace_allocator.get(); + } + + ACL_CHECK( + aclnnDiv(workspaceAddr, workspaceSize, executor, ctx.stream())); + } +} + void ggml_cann_timestep_embedding(ggml_backend_cann_context& ctx, ggml_tensor* dst) { const ggml_tensor* src = dst->src[0]; @@ -2311,12 +2399,13 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ctx.stream())); switch (src0->type) { - case GGML_TYPE_F32: - { + case GGML_TYPE_F32: { #ifdef ASCEND_310P - // Special operation for get_row_f32 kernel of 310P: clear the content of dest data buffer when row is not aligned to 32 bytes + // Special operation for get_row_f32 kernel of 310P: clear the + // content of dest data buffer when row is not aligned to 32 bytes if ((src0->ne[0] % 8) != 0) { - size_t dst_len = src1->ne[0] * src1->ne[1] * src1->ne[2] * src0->ne[0] * ggml_type_size(GGML_TYPE_F32); + size_t dst_len = src1->ne[0] * src1->ne[1] * src1->ne[2] * + src0->ne[0] * ggml_type_size(GGML_TYPE_F32); ACL_CHECK(aclrtMemset((char*)dst->data, dst_len, 0, dst_len)); } #endif @@ -2329,12 +2418,15 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ((ggml_tensor*)dst->extra)->nb); break; } - case GGML_TYPE_F16: - { + case GGML_TYPE_F16: { #ifdef ASCEND_310P - // Special operation for get_row_f16 kernel of 310P: clear the content of dest data buffer when row is not aligned to 32 bytes + // Special operation for get_row_f16 kernel of 310P: clear the + // content of dest data buffer when row is not aligned to 32 bytes if ((src0->ne[0] % 16) != 0) { - size_t dst_len = src1->ne[0] * src1->ne[1] * src1->ne[2] * src0->ne[0] * ggml_type_size(GGML_TYPE_F32); // out is also f32, even input is f16 + size_t dst_len = + src1->ne[0] * src1->ne[1] * src1->ne[2] * src0->ne[0] * + ggml_type_size( + GGML_TYPE_F32); // out is also f32, even input is f16 ACL_CHECK(aclrtMemset((char*)dst->data, dst_len, 0, dst_len)); } #endif @@ -2459,8 +2551,9 @@ static void aclnn_mat_mul(ggml_backend_cann_context& ctx, aclTensor* acl_input, * @param acl_dst The destination tensor where the result of the matrix * multiplication will be stored. */ -static void aclnn_mat_mul_2d(ggml_backend_cann_context& ctx, aclTensor* acl_input, - aclTensor* acl_weight, aclTensor* acl_dst) { +static void aclnn_mat_mul_2d(ggml_backend_cann_context& ctx, + aclTensor* acl_input, aclTensor* acl_weight, + aclTensor* acl_dst) { int8_t cube_math_type = 2; uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -2475,8 +2568,7 @@ static void aclnn_mat_mul_2d(ggml_backend_cann_context& ctx, aclTensor* acl_inpu workspaceAddr = workspace_allocator.get(); } - ACL_CHECK( - aclnnMm(workspaceAddr, workspaceSize, executor, ctx.stream())); + ACL_CHECK(aclnnMm(workspaceAddr, workspaceSize, executor, ctx.stream())); } /** @@ -2496,8 +2588,9 @@ static void aclnn_mat_mul_2d(ggml_backend_cann_context& ctx, aclTensor* acl_inpu * @param acl_dst The destination tensor where the result of the matrix * multiplication will be stored. */ -static void aclnn_mat_mul_3d(ggml_backend_cann_context& ctx, aclTensor* acl_input, - aclTensor* acl_weight, aclTensor* acl_dst) { +static void aclnn_mat_mul_3d(ggml_backend_cann_context& ctx, + aclTensor* acl_input, aclTensor* acl_weight, + aclTensor* acl_dst) { int8_t cube_math_type = 2; uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -2548,31 +2641,27 @@ 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] - }; + 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 = ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims); aclTensor* acl_dst = ggml_cann_create_tensor(dst, bcast_dst_ne, bcast_dst_nb, n_dims); switch (n_dims) { - case 2: - aclnn_mat_mul_2d(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); - break; - case 3: - aclnn_mat_mul_3d(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); - break; - default: - aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); - break; + case 2: + aclnn_mat_mul_2d(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); + break; + case 3: + aclnn_mat_mul_3d(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); + break; + default: + aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst); + break; } ACL_CHECK(aclDestroyTensor(acl_weight_tensor)); @@ -2594,8 +2683,8 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx, * 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* dst, + const enum ggml_type type) { ggml_tensor* src0 = dst->src[0]; // weight ggml_tensor* src1 = dst->src[1]; // input @@ -2617,14 +2706,15 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, // 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_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_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; @@ -2632,7 +2722,8 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, // 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); + 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]; @@ -2642,9 +2733,8 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, } aclTensor* acl_input_tensor = ggml_cann_create_tensor( - input_buffer, - ACL_FLOAT16, - input_elem_size, input_cast_ne, input_cast_nb, GGML_MAX_DIMS); + 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); ACL_CHECK(aclDestroyTensor(acl_input_tensor)); @@ -2655,7 +2745,8 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, 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); + 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 @@ -2679,7 +2770,9 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, // 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 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; @@ -2687,24 +2780,21 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, 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); + 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); + 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); + (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16, + output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, + output_ne_offset); ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize( - acl_input_tensor, acl_weight_tensor, acl_scale_tensor, - nullptr, nullptr, nullptr, nullptr, QK8_0, - acl_output_tensor, &workspaceSize, &executor)); + acl_input_tensor, acl_weight_tensor, acl_scale_tensor, nullptr, + nullptr, nullptr, nullptr, QK8_0, acl_output_tensor, + &workspaceSize, &executor)); if (workspaceAddr == nullptr) { workspaceAddr = workspace_allocator.alloc(workspaceSize); } @@ -2717,28 +2807,29 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, // 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); + 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); + 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); + (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16, + output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND, + output_ne_offset); ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize( acl_input_tensor, acl_weight_tensor, acl_scale_tensor, @@ -2766,11 +2857,11 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx, } aclTensor* acl_output_tensor = ggml_cann_create_tensor( - output_buffer, - ACL_FLOAT16, - output_elem_size, output_cast_ne, output_cast_nb, GGML_MAX_DIMS); + 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)); + aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, + ggml_cann_type_mapping(dst->type)); ACL_CHECK(aclDestroyTensor(acl_output_tensor)); ACL_CHECK(aclDestroyTensor(acl_dst_tensor)); @@ -2873,12 +2964,14 @@ static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx, static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, aclTensor* acl_cos_repeat_tensor, aclTensor* acl_sin_repeat_tensor, - float theta_scale, bool is_neox) { + float theta_scale, float freq_scale, + bool is_neox) { // int sin/cos cache, cache has different repeat method depond on // @param.is_neox ggml_tensor* src0 = dst->src[0]; // input ggml_tensor* src1 = dst->src[1]; // position + ggml_tensor* src2 = dst->src[2]; // freq_factors // arange, [0,1,...,ne0/2] int64_t arange_length = src0->ne[0] / 2; @@ -2907,11 +3000,25 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_cann_pool_alloc theta_scale_allocator(ctx.pool(), arange_length * sizeof(float_t)); void* theta_scale_buffer = theta_scale_allocator.get(); - aclTensor* acl_theta_scale_tensor = aclnn_ones( + aclTensor* acl_theta_scale_tensor = aclnn_values( ctx, theta_scale_buffer, arange_length * sizeof(float_t), arange_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), theta_scale); aclnn_pow_tensor_tensor(ctx, acl_theta_scale_tensor, acl_arange_tensor); + // freq_scale + if (freq_scale != 1) { + aclnn_muls(ctx, acl_theta_scale_tensor, freq_scale, nullptr, true); + } + + // freq_factors + if (src2) { + aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor( + src2->data, ggml_cann_type_mapping(src2->type), + ggml_type_size(src2->type), arange_ne, arange_nb, GGML_MAX_DIMS); + aclnn_div_tensor(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor, + nullptr, true); + } + // position GGML_ASSERT(src1->type == GGML_TYPE_I32); int64_t position_length = src1->ne[0]; @@ -2940,6 +3047,16 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor, acl_theta_tensor); + // // power[] * position[] * freq_scale / freq_factors[] + // ggml_cann_pool_alloc theta_final_allocator(ctx.pool(), + // theta_length * + // sizeof(float_t)); + // aclTensor* acl_theat_final_tensor = aclnn_zero( + // ctx, theta_final_allocator.get(), sizeof(float_t) * theta_length, + // theta_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t)); + // aclnn_inplace_addcdiv(ctx, acl_theat_final_tensor, acl_theta_tensor, + // acl_freq_factors_tensor, freq_scale); + // permute: [0,1,2,3]->[0,2,1,3] int64_t permute_ne[] = {arange_length, 1, position_length, 1}; size_t permute_nb[GGML_MAX_DIMS]; @@ -3038,8 +3155,6 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { memcpy(&beta_fast, (int32_t*)dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t*)dst->op_params + 10, sizeof(float)); - // TODO: with freq_factors - GGML_ASSERT(src2 == NULL); // TODO: attn_factor != 1 GGML_ASSERT(attn_factor == 1); // TODO: n_dims <= ne0 @@ -3047,8 +3162,6 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { GGML_ASSERT(n_dims % 2 == 0); // TODO: ext_factor != 0 GGML_ASSERT(ext_factor == 0); - // TODO: freq_scale != 1 - GGML_ASSERT(freq_scale == 1); // TODO: type == GGML_TYPE_F16 GGML_ASSERT(src0->type == GGML_TYPE_F32); @@ -3081,7 +3194,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t), sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); aclnn_cache_init(ctx, dst, acl_cos_reshape_tensor, acl_sin_reshape_tensor, - theta_scale, is_neox); + theta_scale, freq_scale, is_neox); uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -3096,7 +3209,8 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { aclTensor* acl_x = ggml_cann_create_tensor(src0); aclTensor* acl_dst = ggml_cann_create_tensor(dst); ACL_CHECK(aclnnRotaryPositionEmbeddingGetWorkspaceSize( - acl_x, acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, acl_dst, &workspaceSize, &executor)); + acl_x, acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, + acl_dst, &workspaceSize, &executor)); if (workspaceSize > 0) { ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); workspaceAddr = workspace_allocator.get(); diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp index c7a3419c7..bcb54e444 100644 --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -1738,13 +1738,8 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, } case GGML_OP_ROPE: { // TODO: with ops-test v == 1 - float * freq_scale = (float*)((int32_t*)op->op_params + 6); float * ext_factor = (float*)((int32_t*)op->op_params + 7); float * attn_factor = (float*)((int32_t*)op->op_params + 8); - // TODO: with freq_factors - if (op->src[2] != NULL) { - return false; - } // TODO: n_dims <= ne0 if (op->src[0]->ne[0] != op->op_params[1]) { return false; @@ -1753,10 +1748,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, if (*ext_factor != 0) { return false; } - // TODO: freq_scale != 1 - if (*freq_scale != 1) { - return false; - } // TODO: attn_factor != 1 if (*attn_factor != 1) { return false; From 605fa66c509f9f117bd654cf0b9b3ea08bb86e80 Mon Sep 17 00:00:00 2001 From: leo-pony Date: Thu, 28 Nov 2024 15:25:24 +0800 Subject: [PATCH 04/29] CANN: Fix SOC_TYPE compile bug (#10519) * CANN: Fix the bug build fail on Ascend310P under two cases: 1) Manual specify SOC_TYPE 2) Under some unusual compile environment * Update the cann backend News content: Support F16 and F32 data type model for Ascend 310P NPU. * fix CANN compile fail bug: the assert in ascend kernel function doesn't supportted on some CANN version --- docs/backend/CANN.md | 3 +++ ggml/src/ggml-cann/CMakeLists.txt | 7 ++++--- ggml/src/ggml-cann/kernels/CMakeLists.txt | 2 +- ggml/src/ggml-cann/kernels/dup.cpp | 1 - ggml/src/ggml-cann/kernels/get_row_q4_0.cpp | 16 ++++++++++++---- .../src/ggml-cann/kernels/quantize_f16_q8_0.cpp | 10 ++++++++++ .../src/ggml-cann/kernels/quantize_f32_q8_0.cpp | 10 ++++++++++ .../kernels/quantize_float_to_q4_0.cpp | 17 +++++++++++++++++ 8 files changed, 57 insertions(+), 9 deletions(-) diff --git a/docs/backend/CANN.md b/docs/backend/CANN.md index 6bdd9d2da..496e05807 100644 --- a/docs/backend/CANN.md +++ b/docs/backend/CANN.md @@ -23,6 +23,8 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi ## News +- 2024.11 + - Support F16 and F32 data type model for Ascend 310P NPU. - 2024.8 - Support `Q4_0` and `Q8_0` data type for Ascend NPU. - 2024.7 @@ -43,6 +45,7 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi | Ascend NPU | Status | |:-----------------------------:|:-------:| | Atlas 300T A2 | Support | +| Atlas 300I Duo | Support | *Notes:* diff --git a/ggml/src/ggml-cann/CMakeLists.txt b/ggml/src/ggml-cann/CMakeLists.txt index 901327185..05cf06bfa 100644 --- a/ggml/src/ggml-cann/CMakeLists.txt +++ b/ggml/src/ggml-cann/CMakeLists.txt @@ -22,13 +22,14 @@ if(NOT SOC_TYPE) detect_ascend_soc_type(SOC_VERSION) set(SOC_TYPE "${SOC_VERSION}") message(STATUS "CANN: SOC_VERSION auto-detected is:${SOC_VERSION}") -else() - string(TOLOWER ${SOC_TYPE} SOC_VERSION) endif() -# Construct Soc specify compile option: ASCEND_#Soc_Major_SN. Such as ASCEND_910B, ASCEND310P. +string(TOLOWER ${SOC_TYPE} SOC_VERSION) # SOC_VERSION need lower + +# Construct Soc specify compile option: ASCEND_#Soc_Major_SN. Such as ASCEND_910B, ASCEND_310P. string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}") set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}") +string(TOUPPER ${SOC_TYPE_COMPILE_OPTION} SOC_TYPE_COMPILE_OPTION) if (CANN_INSTALL_DIR) # Only Support Linux. diff --git a/ggml/src/ggml-cann/kernels/CMakeLists.txt b/ggml/src/ggml-cann/kernels/CMakeLists.txt index 6a4e17cce..d687220c3 100644 --- a/ggml/src/ggml-cann/kernels/CMakeLists.txt +++ b/ggml/src/ggml-cann/kernels/CMakeLists.txt @@ -25,6 +25,6 @@ ascendc_library(ascendc_kernels STATIC ${SRC_FILES} ) -message(STATUS "CANN: compile ascend kernels witch SOC_VERSION:${SOC_VERSION}.") +message(STATUS "CANN: compile ascend kernels witch SOC_TYPE:${SOC_TYPE}, SOC_VERSION:${SOC_VERSION}, compile macro:-D${SOC_TYPE_COMPILE_OPTION}.") ascendc_compile_definitions(ascendc_kernels PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}") # ascendc_compile_definitions(ascendc_kernels PRIVATE -DASCENDC_DUMP) diff --git a/ggml/src/ggml-cann/kernels/dup.cpp b/ggml/src/ggml-cann/kernels/dup.cpp index 99f03e058..c7ba38d10 100644 --- a/ggml/src/ggml-cann/kernels/dup.cpp +++ b/ggml/src/ggml-cann/kernels/dup.cpp @@ -20,7 +20,6 @@ class DupByRows { // Input has four dims. int64_t op_block_num = GetBlockNum(); int64_t op_block_idx = GetBlockIdx(); - assert(op_block_idx < SUPPORTED_MAX_DIM && op_block_idx >= 0, "Invalid block index:%d, max is:%d\n", op_block_idx, SUPPORTED_MAX_DIM); // param num_rows = input_ne_ub[1] * input_ne_ub[2] * input_ne_ub[3]; diff --git a/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp b/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp index 377211096..4fbe72208 100644 --- a/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp +++ b/ggml/src/ggml-cann/kernels/get_row_q4_0.cpp @@ -2,6 +2,15 @@ // optimize me. Use template to avoid copy code. using namespace AscendC; +#ifdef ASCEND_310P // 310P not support 4bit get row + extern "C" __global__ __aicore__ void ascendc_get_row_q4_0( + GM_ADDR input_gm, GM_ADDR indices_gm, GM_ADDR output_gm, + GM_ADDR input_ne_gm, GM_ADDR indices_ne_gm, GM_ADDR indices_nb_gm, + GM_ADDR output_ne_gm, GM_ADDR output_nb_gm) { + // let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed. + printf("Ascend310P not support 4bit get row.\n"); + } +#else #define BUFFER_NUM 2 @@ -110,12 +119,9 @@ class GET_ROW_Q4_0 { LocalTensor output_local = output_queue.AllocTensor(); // TODO: cast more data to speed up. -#ifdef ASCEND_310P - // TODO: 310P support quantification -#else Cast(cast_local, input_local, RoundMode::CAST_NONE, QK4_0); Cast(output_local, cast_local, RoundMode::CAST_NONE, QK4_0); -#endif + // Only mul need compile by group. half scale = scale_gm.GetValue(scale_offset); @@ -194,3 +200,5 @@ extern "C" __global__ __aicore__ void ascendc_get_row_q4_0( indices_nb_ub, output_ne_ub, output_nb_ub); op.calculate(); } + +#endif // #ifdef ASCEND_310P diff --git a/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp b/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp index 8423b3f02..504b43afa 100644 --- a/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp +++ b/ggml/src/ggml-cann/kernels/quantize_f16_q8_0.cpp @@ -1,6 +1,14 @@ #include "kernel_operator.h" using namespace AscendC; +#ifdef ASCEND_310P + extern "C" __global__ __aicore__ void ascendc_quantize_f16_q8_0( + GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm, + GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) { + // let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed. + printf("Ascend310P not support f16->8bit quantization.\n"); + } +#else #define BUFFER_NUM 2 #define QK8_0 32 @@ -206,3 +214,5 @@ extern "C" __global__ __aicore__ void ascendc_quantize_f16_q8_0( op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub); op.calculate(); } + +#endif // #ifdef ASCEND_310P diff --git a/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp b/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp index b7c575093..05b0bc1df 100644 --- a/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp +++ b/ggml/src/ggml-cann/kernels/quantize_f32_q8_0.cpp @@ -1,6 +1,14 @@ #include "kernel_operator.h" using namespace AscendC; +#ifdef ASCEND_310P // 310P not support f32->8bit quantization + extern "C" __global__ __aicore__ void ascendc_quantize_f32_q8_0( + GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm, + GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) { + // let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed. + printf("Ascend310P not support f32->8bit quantization.\n"); + } +#else #define BUFFER_NUM 2 #define QK8_0 32 @@ -204,3 +212,5 @@ extern "C" __global__ __aicore__ void ascendc_quantize_f32_q8_0( op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub); op.calculate(); } + +#endif // #ifdef ASCEND_310P diff --git a/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp b/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp index 9c8c86b66..1188937b7 100644 --- a/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp +++ b/ggml/src/ggml-cann/kernels/quantize_float_to_q4_0.cpp @@ -1,6 +1,21 @@ #include "kernel_operator.h" using namespace AscendC; +#ifdef ASCEND_310P // 310P not support float->4bit quantization + extern "C" __global__ __aicore__ void ascendc_quantize_f32_to_q4_0( + GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm, + GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) { + // let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed. + printf("Ascend310P not support f32->4bit quantization.\n"); + } + + extern "C" __global__ __aicore__ void ascendc_quantize_f16_to_q4_0( + GM_ADDR input_gm, GM_ADDR output_gm, GM_ADDR input_ne_gm, + GM_ADDR input_nb_gm, GM_ADDR output_ne_gm) { + // let following test cases can continue run, here just print error information. Of Cource the test case that call this operator is failed. + printf("Ascend310P not support f16->4bit quantization.\n"); + } +#else #define BUFFER_NUM 2 #define Group_Size 32 @@ -276,3 +291,5 @@ extern "C" __global__ __aicore__ void ascendc_quantize_f32_to_q4_0( op.init(input_gm, output_gm, input_ne_ub, input_nb_ub, output_ne_ub); op.calculate(); } + +#endif // #ifdef ASCEND_310P From c6bc73951ed52466392b1abda98c28ecbe522c7f Mon Sep 17 00:00:00 2001 From: Ruixin Huang <18860020911@163.com> Date: Thu, 28 Nov 2024 15:27:11 +0800 Subject: [PATCH 05/29] CANN: Update cann.md to display correctly in CLion (#10538) --- docs/backend/CANN.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/backend/CANN.md b/docs/backend/CANN.md index 496e05807..23f10175a 100644 --- a/docs/backend/CANN.md +++ b/docs/backend/CANN.md @@ -42,6 +42,7 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi ### Ascend NPU **Verified devices** + | Ascend NPU | Status | |:-----------------------------:|:-------:| | Atlas 300T A2 | Support | From 2025fa67e94358deda4740a74fe9803916cb2f60 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sergio=20L=C3=B3pez?= Date: Thu, 28 Nov 2024 12:51:38 +0100 Subject: [PATCH 06/29] kompute : improve backend to pass test_backend_ops (#10542) * kompute: op_unary: reject unsupported parameters Signed-off-by: Sergio Lopez * kompute: softmax: implement ALiBi support Signed-off-by: Sergio Lopez * kompute: rope: implement neox and phi3 support Signed-off-by: Sergio Lopez * kompute: op_mul_mat_q4_k permutted support Signed-off-by: Sergio Lopez * kompute: op_mul_mat_[q4_0|q4_1|q8_0] permutted support Signed-off-by: Sergio Lopez * kompute: op_mul_mat_f16 permutted support Signed-off-by: Sergio Lopez * kompute: op_mul_mat_q6_k permutted support Signed-off-by: Sergio Lopez --------- Signed-off-by: Sergio Lopez --- ggml/src/ggml-kompute/CMakeLists.txt | 12 +- ggml/src/ggml-kompute/ggml-kompute.cpp | 176 ++++++++++++------ .../ggml-kompute/kompute-shaders/common.comp | 1 + .../kompute-shaders/op_mul_mat_f16.comp | 6 +- .../kompute-shaders/op_mul_mat_q4_k.comp | 19 +- .../kompute-shaders/op_mul_mat_q6_k.comp | 24 ++- .../kompute-shaders/op_mul_mv_q_n.comp | 14 +- .../kompute-shaders/op_mul_mv_q_n_pre.comp | 8 +- .../kompute-shaders/op_rope_f16.comp | 73 -------- .../kompute-shaders/op_rope_f32.comp | 73 -------- .../kompute-shaders/op_rope_neox_f16.comp | 52 ++++++ .../kompute-shaders/op_rope_neox_f32.comp | 52 ++++++ .../kompute-shaders/op_rope_norm_f16.comp | 52 ++++++ .../kompute-shaders/op_rope_norm_f32.comp | 52 ++++++ .../kompute-shaders/op_softmax.comp | 20 +- .../kompute-shaders/rope_common.comp | 2 + 16 files changed, 403 insertions(+), 233 deletions(-) delete mode 100644 ggml/src/ggml-kompute/kompute-shaders/op_rope_f16.comp delete mode 100644 ggml/src/ggml-kompute/kompute-shaders/op_rope_f32.comp create mode 100644 ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f16.comp create mode 100644 ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f32.comp create mode 100644 ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f16.comp create mode 100644 ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f32.comp diff --git a/ggml/src/ggml-kompute/CMakeLists.txt b/ggml/src/ggml-kompute/CMakeLists.txt index dc623926c..c9109d5e8 100644 --- a/ggml/src/ggml-kompute/CMakeLists.txt +++ b/ggml/src/ggml-kompute/CMakeLists.txt @@ -105,8 +105,10 @@ if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt") kompute-shaders/op_getrows_q4_0.comp kompute-shaders/op_getrows_q4_1.comp kompute-shaders/op_getrows_q6_k.comp - kompute-shaders/op_rope_f16.comp - kompute-shaders/op_rope_f32.comp + kompute-shaders/op_rope_norm_f16.comp + kompute-shaders/op_rope_norm_f32.comp + kompute-shaders/op_rope_neox_f16.comp + kompute-shaders/op_rope_neox_f32.comp kompute-shaders/op_cpy_f16_f16.comp kompute-shaders/op_cpy_f16_f32.comp kompute-shaders/op_cpy_f32_f16.comp @@ -139,8 +141,10 @@ if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt") shaderop_getrows_q4_0.h shaderop_getrows_q4_1.h shaderop_getrows_q6_k.h - shaderop_rope_f16.h - shaderop_rope_f32.h + shaderop_rope_norm_f16.h + shaderop_rope_norm_f32.h + shaderop_rope_neox_f16.h + shaderop_rope_neox_f32.h shaderop_cpy_f16_f16.h shaderop_cpy_f16_f32.h shaderop_cpy_f32_f16.h diff --git a/ggml/src/ggml-kompute/ggml-kompute.cpp b/ggml/src/ggml-kompute/ggml-kompute.cpp index 24566404d..28ceecfc4 100644 --- a/ggml/src/ggml-kompute/ggml-kompute.cpp +++ b/ggml/src/ggml-kompute/ggml-kompute.cpp @@ -28,8 +28,10 @@ #include "shaderop_getrows_q4_0.h" #include "shaderop_getrows_q4_1.h" #include "shaderop_getrows_q6_k.h" -#include "shaderop_rope_f16.h" -#include "shaderop_rope_f32.h" +#include "shaderop_rope_norm_f16.h" +#include "shaderop_rope_norm_f32.h" +#include "shaderop_rope_neox_f16.h" +#include "shaderop_rope_neox_f32.h" #include "shaderop_cpy_f16_f16.h" #include "shaderop_cpy_f16_f32.h" #include "shaderop_cpy_f32_f16.h" @@ -345,7 +347,7 @@ void ggml_vk_allocate_descriptor_pool(struct ggml_kompute_context * ctx, size_t std::vector descriptorPoolSizes = { vk::DescriptorPoolSize( vk::DescriptorType::eStorageBuffer, - 3 * size // Descriptor count is number of possible tensors to pass into an algorithm + 4 * size // Descriptor count is number of possible tensors to pass into an algorithm ) }; @@ -788,7 +790,8 @@ static void ggml_vk_soft_max( const std::shared_ptr& out, uint32_t inAOff, uint32_t inBOff, uint32_t outOff, int32_t ne00, int32_t ne01, int32_t ne02, uint32_t ne03, - float scale + float scale, float max_bias, float m0, float m1, + uint32_t n_head_log2 ) { const static auto spirv = getSpirvShader(kp::shader_data::op_softmax_comp_spv, kp::shader_data::op_softmax_comp_spv_len); @@ -796,12 +799,14 @@ static void ggml_vk_soft_max( struct PushConstants { uint32_t inAOff, inBOff, outOff; int32_t ne00, ne01, ne02; - float scale; + float scale, max_bias, m0, m1; + uint32_t n_head_log2; int32_t mask; } pushConsts { safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), ne00, ne01, ne02, - scale, + scale, max_bias, m0, m1, + n_head_log2, bool(inB) }; @@ -911,9 +916,9 @@ static void ggml_vk_mul_mat_f16( const std::shared_ptr& out, uint32_t inAOff, uint32_t inBOff, uint32_t outOff, int32_t ne00, int32_t ne01, int32_t ne02, - uint32_t nb00, uint32_t nb01, uint32_t nb02, + uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03, int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, - uint32_t nb10, uint32_t nb11, uint32_t nb12, + uint32_t nb10, uint32_t nb11, uint32_t nb12, uint32_t nb13, int32_t ne0, int32_t ne1, uint32_t r2, uint32_t r3 ) { @@ -923,17 +928,17 @@ static void ggml_vk_mul_mat_f16( struct PushConstants { uint32_t inAOff, inBOff, outOff; int32_t ne00, ne01, ne02; - uint32_t nb00, nb01, nb02; + uint32_t nb00, nb01, nb02, nb03; int32_t ne10, ne11, ne12; - uint32_t nb10, nb11, nb12; + uint32_t nb10, nb11, nb12, nb13; int32_t ne0, ne1; uint32_t r2, r3; } pushConsts { safe_divide(inAOff, 2), safe_divide(inBOff, 4), safe_divide(outOff, 4), ne00, ne01, ne02, - nb00, nb01, nb02, + nb00, nb01, nb02, nb03, ne10, ne11, ne12, - nb10, nb11, nb12, + nb10, nb11, nb12, nb13, ne0, ne1, r2, r3 }; @@ -1013,6 +1018,8 @@ static void ggml_vk_mul_mat_impl( int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, int32_t ne0, int32_t ne1, + uint32_t nb01, uint32_t nb02, uint32_t nb03, + uint32_t nb11, uint32_t nb12, uint32_t nb13, uint32_t r2, uint32_t r3 ) { struct PushConstants { @@ -1020,19 +1027,23 @@ static void ggml_vk_mul_mat_impl( int32_t ne00, ne01, ne02; int32_t ne10, ne12; int32_t ne0, ne1; + uint32_t nb01, nb02, nb03; + uint32_t nb11, nb12, nb13; uint32_t r2, r3; } pushConsts { safe_divide(inAOff, block_size), safe_divide(inBOff, 4), safe_divide(outOff, 4), ne00, ne01, ne02, ne10, ne12, ne0, ne1, + nb01, nb02, nb03, + nb11, nb12, nb13, r2, r3 }; auto name = std::string(__func__) + "_" + suffix; std::shared_ptr s_algo = nullptr; if (!komputeManager()->hasAlgorithm(name)) { - const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2; + const uint32_t local_x = (ggml_vk_current_device().subgroupSize * 2) / 8; s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 7)/8), unsigned(ne11), unsigned(ne12*ne13)}, {local_x}, {pushConsts}); } else { s_algo = komputeManager()->getAlgorithm(name); @@ -1074,19 +1085,26 @@ static void ggml_vk_mul_mat_q4_k( const std::shared_ptr& inB, const std::shared_ptr& out, uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne10, - int32_t ne11, int32_t ne12, int32_t ne13, int32_t ne0, - int32_t ne1, int32_t r2, int32_t r3 + int32_t ne00, int32_t ne01, int32_t ne02, + int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, + int32_t ne0, int32_t ne1, + uint32_t nb01, uint32_t nb02, uint32_t nb03, + uint32_t nb11, uint32_t nb12, uint32_t nb13, + uint32_t r2, uint32_t r3 ) { const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_k_comp_spv, kp::shader_data::op_mul_mat_q4_k_comp_spv_len); struct PushConstants { uint32_t inAOff, inBOff, outOff; - int32_t ne00, ne10, ne0, ne1, ne01, ne02, ne12, r2, r3; + int32_t ne00, ne10, ne0, ne1, ne01, ne02, ne12; + uint32_t nb01, nb02, nb03, nb11, nb12, nb13; + uint32_t r2, r3; } pushConsts { - 0, 0, 0, - ne00, ne10, ne0, ne1, ne01, ne02, ne12, r2, r3 + inAOff, safe_divide(inBOff, 4), safe_divide(outOff, 4), + ne00, ne10, ne0, ne1, ne01, ne02, ne12, + nb01, nb02, nb03, nb11, nb12, nb13, + r2, r3 }; std::shared_ptr s_algo = nullptr; @@ -1108,28 +1126,37 @@ static void ggml_vk_mul_mat_q6_k( const std::shared_ptr& inB, const std::shared_ptr& out, uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - int32_t ne00, int32_t ne10, int32_t ne0, int32_t ne1, - int32_t ne01, int32_t ne11, int32_t ne12, int32_t ne02 + int32_t ne00, int32_t ne01, int32_t ne02, + int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, + int32_t ne0, int32_t ne1, + uint32_t nb01, uint32_t nb02, uint32_t nb03, + uint32_t nb11, uint32_t nb12, uint32_t nb13, + uint32_t r2, uint32_t r3 ) { const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q6_k_comp_spv, kp::shader_data::op_mul_mat_q6_k_comp_spv_len); struct PushConstants { uint32_t inAOff, inBOff, outOff; - int32_t ne00, ne10, ne0, ne1, ne01, gqa; + int32_t ne00, ne10, ne0, ne1, ne01, ne02, ne12; + uint32_t nb01, nb02, nb03, nb11, nb12, nb13; + uint32_t r2, r3; } pushConsts { inAOff, safe_divide(inBOff, 4), safe_divide(outOff, 4), - ne00, ne10, ne0, ne1, ne01, ne12/ne02 + ne00, ne10, ne0, ne1, ne01, ne02, ne12, + nb01, nb02, nb03, nb11, nb12, nb13, + r2, r3 }; std::shared_ptr s_algo = nullptr; if (!komputeManager()->hasAlgorithm(__func__)) { - const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2; - s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)}, {local_x}, {pushConsts}); + const uint32_t local_x = 2; + const uint32_t local_y = ggml_vk_current_device().subgroupSize; + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)*unsigned(ne13)}, {local_x, local_y}, {pushConsts}); } else { s_algo = komputeManager()->getAlgorithm(__func__); s_algo->setTensors({inA, inB, out}); - s_algo->setWorkgroup({unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)}); + s_algo->setWorkgroup({unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)*unsigned(ne13)}); s_algo->setPushConstants({pushConsts}); s_algo->updateDescriptors(s_kompute_context->pool.get()); } @@ -1217,10 +1244,11 @@ static void ggml_vk_rope( kp::Sequence& seq, const std::shared_ptr& inA, const std::shared_ptr& inB, + const std::shared_ptr& inC, const std::shared_ptr& out, - uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + uint32_t inAOff, uint32_t inBOff, uint32_t inCOff, uint32_t outOff, ggml_type src0t, int32_t n_dims, int32_t mode, int32_t n_ctx_orig, - float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow, + float freq_base, float freq_scale, bool has_freq_factors, float ext_factor, float attn_factor, float beta_fast, float beta_slow, int32_t ne01, int32_t ne02, int32_t ne03, uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03, int32_t ne0, @@ -1228,11 +1256,17 @@ static void ggml_vk_rope( ) { GGML_ASSERT(src0t == GGML_TYPE_F16 || src0t == GGML_TYPE_F32); - static const auto spirv_f16 = getSpirvShader( - kp::shader_data::op_rope_f16_comp_spv, kp::shader_data::op_rope_f16_comp_spv_len + static const auto spirv_norm_f16 = getSpirvShader( + kp::shader_data::op_rope_norm_f16_comp_spv, kp::shader_data::op_rope_norm_f16_comp_spv_len ); - static const auto spirv_f32 = getSpirvShader( - kp::shader_data::op_rope_f32_comp_spv, kp::shader_data::op_rope_f32_comp_spv_len + static const auto spirv_norm_f32 = getSpirvShader( + kp::shader_data::op_rope_norm_f32_comp_spv, kp::shader_data::op_rope_norm_f32_comp_spv_len + ); + static const auto spirv_neox_f16 = getSpirvShader( + kp::shader_data::op_rope_neox_f16_comp_spv, kp::shader_data::op_rope_neox_f16_comp_spv_len + ); + static const auto spirv_neox_f32 = getSpirvShader( + kp::shader_data::op_rope_neox_f32_comp_spv, kp::shader_data::op_rope_neox_f32_comp_spv_len ); int type_size = src0t == GGML_TYPE_F16 ? 2 : 4; @@ -1247,32 +1281,40 @@ static void ggml_vk_rope( GGML_ASSERT(nb0 % type_size == 0); struct PushConstants { - uint32_t inAOff, inBOff, outOff; + uint32_t inAOff, inBOff, inCOff, outOff; int32_t n_dims, mode, n_ctx_orig; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + float freq_base, freq_scale; + bool has_freq_factors; + float ext_factor, attn_factor, beta_fast, beta_slow; uint32_t nb00, nb01, nb02, nb03; int32_t ne0; uint32_t nb0, nb1, nb2, nb3; } pushConsts { - safe_divide(inAOff, type_size), safe_divide(inBOff, 4), safe_divide(outOff, type_size), + safe_divide(inAOff, type_size), safe_divide(inBOff, 4), safe_divide(inCOff, type_size), safe_divide(outOff, type_size), n_dims, mode, n_ctx_orig, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, + freq_base, freq_scale, + has_freq_factors, + ext_factor, attn_factor, beta_fast, beta_slow, nb00, nb01, nb02, nb03, ne0, nb0, nb1, nb2, nb3 }; - auto name = std::string(__func__) + (src0t == GGML_TYPE_F16 ? "_f16" : "_f32"); + auto & inC_ = inC ? inC : inA; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + const bool is_f16 = src0t == GGML_TYPE_F16; + + auto name = std::string(__func__) + (is_neox ? "_neox" : "_norm") + (src0t == GGML_TYPE_F16 ? "_f16" : "_f32"); std::shared_ptr s_algo = nullptr; if (!komputeManager()->hasAlgorithm(name)) { + auto & spirv = is_neox ? is_f16 ? spirv_neox_f16 : spirv_neox_f32 : is_f16 ? spirv_norm_f16 : spirv_norm_f32; s_algo = komputeManager()->algorithm( - name, s_kompute_context->pool.get(), {inA, inB, out}, - src0t == GGML_TYPE_F16 ? spirv_f16 : spirv_f32, + name, s_kompute_context->pool.get(), {inA, inB, inC_, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts} ); } else { s_algo = komputeManager()->getAlgorithm(name); - s_algo->setTensors({inA, inB, out}); + s_algo->setTensors({inA, inB, inC_, out}); s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); s_algo->setPushConstants({pushConsts}); s_algo->updateDescriptors(s_kompute_context->pool.get()); @@ -1351,11 +1393,15 @@ static void ggml_vk_cpy_f16_f32(Args&&... args) { } static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { + int64_t n = ggml_nelements(op); switch (op->op) { case GGML_OP_UNARY: + if (n % 4 != 0) return false; switch (ggml_get_unary_op(op)) { - case GGML_UNARY_OP_RELU: case GGML_UNARY_OP_GELU: + if (n % 8 != 0) return false; + // fall through + case GGML_UNARY_OP_RELU: case GGML_UNARY_OP_SILU: return ggml_is_contiguous(op->src[0]); default: @@ -1413,8 +1459,8 @@ static bool ggml_backend_kompute_device_supports_op(ggml_backend_dev_t dev, cons switch (op->src[0]->type) { case GGML_TYPE_F32: - case GGML_TYPE_Q6_K: return op->ne[3] == 1; + case GGML_TYPE_Q6_K: case GGML_TYPE_F16: case GGML_TYPE_Q8_0: case GGML_TYPE_Q4_0: @@ -1515,9 +1561,11 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml const static std::shared_ptr nullTensor = nullptr; uint32_t off_src0 = 0; uint32_t off_src1 = 0; + uint32_t off_src2 = 0; uint32_t off_dst = 0; const std::shared_ptr& id_src0 = src0 ? ggml_vk_get_tensor(src0, &off_src0) : nullTensor; const std::shared_ptr& id_src1 = src1 ? ggml_vk_get_tensor(src1, &off_src1) : nullTensor; + const std::shared_ptr& id_src2 = src2 ? ggml_vk_get_tensor(src2, &off_src2) : nullTensor; const std::shared_ptr& id_dst = dst ? ggml_vk_get_tensor(dst, &off_dst) : nullTensor; switch (dst->op) { @@ -1593,11 +1641,16 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32); -#pragma message("TODO: add ALiBi support") -#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/7192") - GGML_ASSERT(max_bias == 0.0f); + const int64_t nrows_x = ggml_nrows(src0); + const int64_t nrows_y = src0->ne[1]; - ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale); + const uint32_t n_head = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale, max_bias, m0, m1, n_head_log2); } break; case GGML_OP_DIAG_MASK_INF: { @@ -1649,38 +1702,44 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml case GGML_TYPE_F16: ggml_vk_mul_mat_f16( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, ne13, nb10, nb11, nb12, + ne00, ne01, ne02, nb00, nb01, nb02, nb03, + ne10, ne11, ne12, ne13, nb10, nb11, nb12, nb13, ne0, ne1, r2, r3 ); break; case GGML_TYPE_Q8_0: ggml_vk_mul_mat_q8_0( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, + nb01, nb02, nb03, nb11, nb12, nb13, r2, r3 ); break; case GGML_TYPE_Q4_0: ggml_vk_mul_mat_q4_0( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, + nb01, nb02, nb03, nb11, nb12, nb13, r2, r3 ); break; case GGML_TYPE_Q4_1: ggml_vk_mul_mat_q4_1( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, + nb01, nb02, nb03, nb11, nb12, nb13, r2, r3 ); break; case GGML_TYPE_Q4_K: ggml_vk_mul_mat_q4_k( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, ne12/ne02, ne13/ne03 + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, + nb01, nb02, nb03, nb11, nb12, nb13, r2, r3 ); break; case GGML_TYPE_Q6_K: ggml_vk_mul_mat_q6_k( seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, - ne00, ne10, ne0, ne1, ne01, ne11, ne12, ne02 + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, + nb01, nb02, nb03, nb11, nb12, nb13, r2, r3 ); break; default: { @@ -1709,13 +1768,6 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml } break; case GGML_OP_ROPE: { -#pragma message("TODO: implement phi3 frequency factors support") -#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") - GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); - -#pragma message("TODO: update rope NORM mode to match NEOX mode") -#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7634") - GGML_ASSERT(ne10 == ne02); GGML_ASSERT(src0t == dstt); // const int n_past = ((int32_t *) dst->op_params)[0]; @@ -1724,6 +1776,8 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml // skip 3, n_ctx used in GLM RoPE, unimplemented in Vulkan const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; + const bool has_freq_factors = dst->src[2] != nullptr; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); @@ -1732,8 +1786,8 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); ggml_vk_rope( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, src0t, n_dims, mode, n_ctx_orig, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, + seq, id_src0, id_src1, id_src2, id_dst, off_src0, off_src1, off_src2, off_dst, src0t, n_dims, mode, n_ctx_orig, + freq_base, freq_scale, has_freq_factors, ext_factor, attn_factor, beta_fast, beta_slow, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, nb0, nb1, nb2, nb3 ); } break; diff --git a/ggml/src/ggml-kompute/kompute-shaders/common.comp b/ggml/src/ggml-kompute/kompute-shaders/common.comp index 2aaddf704..dbe4cf804 100644 --- a/ggml/src/ggml-kompute/kompute-shaders/common.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/common.comp @@ -3,6 +3,7 @@ #extension GL_EXT_shader_explicit_arithmetic_types_float16: require #extension GL_EXT_shader_explicit_arithmetic_types_int8: require #extension GL_EXT_shader_explicit_arithmetic_types_int16: require +#extension GL_EXT_shader_explicit_arithmetic_types_int64: require #extension GL_EXT_control_flow_attributes: enable #extension GL_KHR_shader_subgroup_arithmetic : require #extension GL_EXT_debug_printf : enable diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_f16.comp index 8f0a9031f..0ab1b2fc2 100644 --- a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_f16.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_f16.comp @@ -20,12 +20,14 @@ layout (push_constant) uniform parameter { uint nb00; uint nb01; uint nb02; + uint nb03; int ne10; int ne11; int ne12; uint nb10; uint nb11; uint nb12; + uint nb13; int ne0; int ne1; uint r2; @@ -42,7 +44,7 @@ void main() { const uint i12 = im%pcs.ne12; const uint i13 = im/pcs.ne12; - const uint offset0 = r0*pcs.nb01 + (i12/pcs.r2)*pcs.nb02 + (i13/pcs.r3)*pcs.nb02*pcs.ne02; + const uint offset0 = r0*pcs.nb01 + (i12/pcs.r2)*pcs.nb02 + (i13/pcs.r3)*pcs.nb03; const uint x = offset0 / 2 + pcs.inAOff; // Based from inA @@ -52,7 +54,7 @@ void main() { break; } - const uint y = (r1*pcs.nb11 + im*pcs.nb12) / 4 + pcs.inBOff; // Based from inB + const uint y = (r1*pcs.nb11 + i12*pcs.nb12 + i13*pcs.nb13) / 4 + pcs.inBOff; float sumf = 0; for (uint i = gl_SubgroupInvocationID.x; i < pcs.ne00; i += gl_SubgroupSize) { diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_k.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_k.comp index fc8e45aa9..a5752a3a0 100644 --- a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_k.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q4_k.comp @@ -24,8 +24,14 @@ layout (push_constant) uniform parameter { int ne01; int ne02; int ne12; - int r2; - int r3; + uint nb01; + uint nb02; + uint nb03; + uint nb11; + uint nb12; + uint nb13; + uint r2; + uint r3; } pcs; void main() { @@ -50,10 +56,11 @@ void main() { const uint i12 = im%pcs.ne12; const uint i13 = im/pcs.ne12; - const uint offset0 = (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02); + const uint offset0 = first_row*(pcs.nb01/SIZE_OF_BLOCK) + (i12/pcs.r2)*(pcs.nb02/SIZE_OF_BLOCK) + (i13/pcs.r3)*(pcs.nb03/SIZE_OF_BLOCK); + const uint offset1 = r1*pcs.nb11 + (i12 )*pcs.nb12 + (i13 )*pcs.nb13; - const uint xblk = ib_row + offset0 + pcs.inAOff; - const uint y = r1*pcs.ne10 + im*pcs.ne00*pcs.ne1 + pcs.inBOff; + const uint xblk = offset0 + pcs.inAOff; + const uint y = (offset1 / 4) + pcs.inBOff; float yl[16]; float yh[16]; @@ -74,7 +81,7 @@ void main() { } for (int row = 0; row < N_DST; row++) { - uint row_idx = row * nb; + uint row_idx = row * (pcs.nb01 / SIZE_OF_BLOCK); uint16_t sc_0 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 0); uint16_t sc_1 = u8BufToU16(inA[blk_idx + row_idx].scales, iq * 2 + 2); diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q6_k.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q6_k.comp index c9baebdf4..d331d1a70 100644 --- a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q6_k.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mat_q6_k.comp @@ -21,7 +21,16 @@ layout (push_constant) uniform parameter { int ne0; int ne1; int ne01; - int gqa; + int ne02; + int ne12; + uint nb01; + uint nb02; + uint nb03; + uint nb11; + uint nb12; + uint nb13; + uint r2; + uint r3; } pcs; void main() { @@ -34,12 +43,15 @@ void main() { const uint r0 = gl_WorkGroupID.x; const uint r1 = gl_WorkGroupID.y; - const uint r2 = gl_WorkGroupID.z; + const uint im = gl_WorkGroupID.z; const uint row = (r0 * gl_NumSubgroups + gl_SubgroupID); - const uint offset0 = r2/pcs.gqa*(nb*pcs.ne0); - const uint x = row * nb + offset0; // Based from inA without base offset - const uint yy = r1*pcs.ne10 + r2*pcs.ne00*pcs.ne1+pcs.inBOff; // Based from inB + + const uint i12 = im%pcs.ne12; + const uint i13 = im/pcs.ne12; + + const uint x = row*(pcs.nb01/SIZE_OF_BLOCK) + (i12/pcs.r2)*(pcs.nb02/SIZE_OF_BLOCK) + (i13/pcs.r3)*(pcs.nb03/SIZE_OF_BLOCK); + const uint yy = (r1*pcs.nb11 + i12*pcs.nb12 + i13*pcs.nb13) / 4 + pcs.inBOff; float sumf = 0; @@ -89,6 +101,6 @@ void main() { const float tot = subgroupAdd(sumf); if (subgroupElect()) { - out_[r1*pcs.ne0 + r2*pcs.ne0*pcs.ne1 + row + pcs.outOff] = tot; + out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + row + pcs.outOff] = tot; } } diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n.comp index 440b5ab2c..a6517cc1f 100644 --- a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n.comp @@ -14,10 +14,15 @@ void main() { const uint i12 = im%pcs.ne12; const uint i13 = im/pcs.ne12; - const uint offset0 = first_row * nb + (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02); + // pointers to src0 rows + uint ax[N_ROWS]; + for (int row = 0; row < N_ROWS; ++row) { + const uint offset0 = (first_row + row)*(pcs.nb01/SIZE_OF_BLOCK) + (i12/pcs.r2)*(pcs.nb02/SIZE_OF_BLOCK) + (i13/pcs.r3)*(pcs.nb03/SIZE_OF_BLOCK); - const uint x = offset0; // Based from inA without base offset - const uint y = r1*uint(pcs.ne10)+im*pcs.ne00*pcs.ne1+pcs.inBOff; // Based from inB + ax[row] = offset0 + pcs.inAOff; + } + + const uint y = (r1*pcs.nb11 + i12*pcs.nb12 + i13*pcs.nb13) / 4 + pcs.inBOff; float sumf[N_ROWS] = {0.0f, 0.0f, 0.0f, 0.0f}; @@ -32,8 +37,7 @@ void main() { for (uint ib = ix; ib < nb; ib += 16) { for (int row = 0; row < N_ROWS; row++) { - const uint block_index = x + ib + row * nb; - sumf[row] += block_q_n_dot_y(block_index, yb, il); + sumf[row] += block_q_n_dot_y(ax[row] + ib, yb, il); } yb += BLOCKS_IN_QUANT * 16; diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n_pre.comp b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n_pre.comp index 7912b09ac..a9a2f2218 100644 --- a/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n_pre.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_mul_mv_q_n_pre.comp @@ -1,5 +1,5 @@ layout(local_size_x_id = 0) in; -layout(local_size_y = 1) in; +layout(local_size_y = 8) in; layout(local_size_z = 1) in; layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; @@ -17,6 +17,12 @@ layout (push_constant) uniform parameter { int ne12; int ne0; int ne1; + uint nb01; + uint nb02; + uint nb03; + uint nb11; + uint nb12; + uint nb13; uint r2; uint r3; } pcs; diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_rope_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_f16.comp deleted file mode 100644 index 0ecfb2eab..000000000 --- a/ggml/src/ggml-kompute/kompute-shaders/op_rope_f16.comp +++ /dev/null @@ -1,73 +0,0 @@ -#version 450 - -#include "rope_common.comp" - -layout(binding = 0) buffer restrict readonly tensorInA { float16_t inA[]; }; -layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; -layout(binding = 2) buffer restrict writeonly tensorOut { float16_t out_[]; }; - -void main() { - const uint i3 = gl_WorkGroupID.z; - const uint i2 = gl_WorkGroupID.y; - const uint i1 = gl_WorkGroupID.x; - - const bool is_neox = (pcs.mode & GGML_ROPE_TYPE_NEOX) != 0; - - float corr_dims[2]; - rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); - - const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); - - const int p = inB[pcs.inBOff + i2]; - - float theta = float(p); - - if (!is_neox) { - for (uint i0 = 0; i0 < pcs.ne0; i0 += 2) { - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ - - const float x0 = float(inA[src]); - const float x1 = float(inA[src+1]); - - out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); - out_[dst_data+1] = float16_t(x0*sin_theta + x1*cos_theta); - } - } else { - const float inv_ndims = -1.f/pcs.n_dims; - for (uint ic = 0; ic < pcs.n_dims; ic += 2) { - const uint cur_rot = ic; - - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, cur_rot, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint i0 = ic/2; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ - - const float x0 = float(inA[src]); - const float x1 = float(inA[src+pcs.n_dims/2]); - - out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); - out_[dst_data+pcs.n_dims/2] = float16_t(x0*sin_theta + x1*cos_theta); - } - - for (uint ic = pcs.n_dims; ic < pcs.ne0; ic += 2) { - const uint i0 = ic; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ - - out_[dst_data + 0] = inA[src + 0]; - out_[dst_data + 1] = inA[src + 1]; - } - } -} diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_rope_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_f32.comp deleted file mode 100644 index cec0fd9a5..000000000 --- a/ggml/src/ggml-kompute/kompute-shaders/op_rope_f32.comp +++ /dev/null @@ -1,73 +0,0 @@ -#version 450 - -#include "rope_common.comp" - -layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; -layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; -layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; - -void main() { - const uint i3 = gl_WorkGroupID.z; - const uint i2 = gl_WorkGroupID.y; - const uint i1 = gl_WorkGroupID.x; - - const bool is_neox = (pcs.mode & GGML_ROPE_TYPE_NEOX) != 0; - - float corr_dims[2]; - rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); - - const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); - - const int p = inB[pcs.inBOff + i2]; - - float theta = float(p); - - if (!is_neox) { - for (uint i0 = 0; i0 < pcs.ne0; i0 += 2) { - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ - - const float x0 = inA[src]; - const float x1 = inA[src+1]; - - out_[dst_data] = x0*cos_theta - x1*sin_theta; - out_[dst_data+1] = x0*sin_theta + x1*cos_theta; - } - } else { - const float inv_ndims = -1.f/pcs.n_dims; - for (uint ic = 0; ic < pcs.n_dims; ic += 2) { - const uint cur_rot = ic; - - float cos_theta, sin_theta; - rope_yarn(theta, pcs.freq_scale, corr_dims, cur_rot, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); - - theta *= theta_scale; - - const uint i0 = ic/2; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ - - const float x0 = inA[src]; - const float x1 = inA[src+pcs.n_dims/2]; - - out_[dst_data] = x0*cos_theta - x1*sin_theta; - out_[dst_data+pcs.n_dims/2] = x0*sin_theta + x1*cos_theta; - } - - for (uint ic = pcs.n_dims; ic < pcs.ne0; ic += 2) { - const uint i0 = ic; - - const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in - const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ - - out_[dst_data + 0] = inA[src + 0]; - out_[dst_data + 1] = inA[src + 1]; - } - } -} diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f16.comp new file mode 100644 index 000000000..63659cbfe --- /dev/null +++ b/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f16.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "rope_common.comp" + +layout(binding = 0) buffer restrict readonly tensorInA { float16_t inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; +layout(binding = 2) buffer restrict readonly tensorInC { float inC[]; }; +layout(binding = 3) buffer restrict writeonly tensorOut { float16_t out_[]; }; + +void main() { + const uint i3 = gl_WorkGroupID.z; + const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_WorkGroupID.x; + + float corr_dims[2]; + rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + + const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); + + float theta_base = float(inB[pcs.inBOff + i2]); + float inv_ndims = -1.f/pcs.n_dims; + + float cos_theta; + float sin_theta; + + for (uint i0 = 2*gl_LocalInvocationIndex; i0 < pcs.ne0; i0 += 2*gl_WorkGroupSize.x) { + if (i0 < pcs.n_dims) { + uint ic = i0/2; + + float theta = theta_base * pow(pcs.freq_base, inv_ndims*i0); + + const float freq_factor = pcs.has_freq_factors ? inC[pcs.inCOff + ic] : 1.0f; + + rope_yarn(theta/freq_factor, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + ic*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + ic*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + const float x0 = float(inA[src]); + const float x1 = float(inA[src+pcs.n_dims/2]); + + out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); + out_[dst_data+pcs.n_dims/2] = float16_t(x0*sin_theta + x1*cos_theta); + } else { + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + out_[dst_data] = inA[src]; + out_[dst_data+1] = inA[src+1]; + } + } +} diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f32.comp new file mode 100644 index 000000000..4df56204d --- /dev/null +++ b/ggml/src/ggml-kompute/kompute-shaders/op_rope_neox_f32.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "rope_common.comp" + +layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; +layout(binding = 2) buffer restrict readonly tensorInC { float inC[]; }; +layout(binding = 3) buffer restrict writeonly tensorOut { float out_[]; }; + +void main() { + const uint i3 = gl_WorkGroupID.z; + const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_WorkGroupID.x; + + float corr_dims[2]; + rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + + const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); + + float theta_base = float(inB[pcs.inBOff + i2]); + float inv_ndims = -1.f/pcs.n_dims; + + float cos_theta; + float sin_theta; + + for (uint i0 = 2*gl_LocalInvocationIndex; i0 < pcs.ne0; i0 += 2*gl_WorkGroupSize.x) { + if (i0 < pcs.n_dims) { + uint ic = i0/2; + + float theta = theta_base * pow(pcs.freq_base, inv_ndims*i0); + + const float freq_factor = pcs.has_freq_factors ? inC[pcs.inCOff + ic] : 1.0f; + + rope_yarn(theta/freq_factor, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + ic*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + ic*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + const float x0 = inA[src]; + const float x1 = inA[src+pcs.n_dims/2]; + + out_[dst_data] = x0*cos_theta - x1*sin_theta; + out_[dst_data+pcs.n_dims/2] = x0*sin_theta + x1*cos_theta; + } else { + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + out_[dst_data] = inA[src]; + out_[dst_data+1] = inA[src+1]; + } + } +} diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f16.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f16.comp new file mode 100644 index 000000000..a3c0eda8b --- /dev/null +++ b/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f16.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "rope_common.comp" + +layout(binding = 0) buffer restrict readonly tensorInA { float16_t inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; +layout(binding = 2) buffer restrict readonly tensorInC { float inC[]; }; +layout(binding = 3) buffer restrict writeonly tensorOut { float16_t out_[]; }; + +void main() { + const uint i3 = gl_WorkGroupID.z; + const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_WorkGroupID.x; + + float corr_dims[2]; + rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + + const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); + + float theta_base = float(inB[pcs.inBOff + i2]); + float inv_ndims = -1.f/pcs.n_dims; + + float cos_theta; + float sin_theta; + + for (uint i0 = 2*gl_LocalInvocationIndex; i0 < pcs.ne0; i0 += 2*gl_WorkGroupSize.x) { + if (i0 < pcs.n_dims) { + uint ic = i0/2; + + float theta = theta_base * pow(pcs.freq_base, inv_ndims*i0); + + const float freq_factor = pcs.has_freq_factors ? inC[pcs.inCOff + ic] : 1.0f; + + rope_yarn(theta/freq_factor, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + const float x0 = float(inA[src]); + const float x1 = float(inA[src+1]); + + out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); + out_[dst_data+1] = float16_t(x0*sin_theta + x1*cos_theta); + } else { + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + out_[dst_data] = inA[src]; + out_[dst_data+1] = inA[src+1]; + } + } +} diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f32.comp b/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f32.comp new file mode 100644 index 000000000..b7963ae72 --- /dev/null +++ b/ggml/src/ggml-kompute/kompute-shaders/op_rope_norm_f32.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "rope_common.comp" + +layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; +layout(binding = 2) buffer restrict readonly tensorInC { float inC[]; }; +layout(binding = 3) buffer restrict writeonly tensorOut { float out_[]; }; + +void main() { + const uint i3 = gl_WorkGroupID.z; + const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_WorkGroupID.x; + + float corr_dims[2]; + rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + + const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); + + float theta_base = float(inB[pcs.inBOff + i2]); + float inv_ndims = -1.f/pcs.n_dims; + + float cos_theta; + float sin_theta; + + for (uint i0 = 2*gl_LocalInvocationIndex; i0 < pcs.ne0; i0 += 2*gl_WorkGroupSize.x) { + if (i0 < pcs.n_dims) { + uint ic = i0/2; + + float theta = theta_base * pow(pcs.freq_base, inv_ndims*i0); + + const float freq_factor = pcs.has_freq_factors ? inC[pcs.inCOff + ic] : 1.0f; + + rope_yarn(theta/freq_factor, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + const float x0 = inA[src]; + const float x1 = inA[src+1]; + + out_[dst_data] = x0*cos_theta - x1*sin_theta; + out_[dst_data+1] = x0*sin_theta + x1*cos_theta; + } else { + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + out_[dst_data] = inA[src]; + out_[dst_data+1] = inA[src+1]; + } + } +} diff --git a/ggml/src/ggml-kompute/kompute-shaders/op_softmax.comp b/ggml/src/ggml-kompute/kompute-shaders/op_softmax.comp index 7bc9176ca..4165295bf 100644 --- a/ggml/src/ggml-kompute/kompute-shaders/op_softmax.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/op_softmax.comp @@ -18,6 +18,10 @@ layout(push_constant) uniform PushConstants { int ne01; int ne02; float scale; + float max_bias; + float m0; + float m1; + uint n_head_log2; int mask; } pcs; @@ -34,17 +38,29 @@ void main() { const uint pmask = i01*pcs.ne00 + pcs.inBOff; // Based from inB const uint pdst = extra_off + pcs.outOff; // Based from out_ + float slope = 1.0f; + + // ALiBi + if (pcs.max_bias > 0.0f) { + int64_t h = i02; + + float base = h < pcs.n_head_log2 ? pcs.m0 : pcs.m1; + int64_t exp = h < pcs.n_head_log2 ? h + 1 : 2*(h - pcs.n_head_log2) + 1; + + slope = pow(base, float(exp)); + } + // parallel max float localMax = uintBitsToFloat(0xFF800000); for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) { - localMax = max(localMax, inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? inB[pmask + i00] : 0.0f)); + localMax = max(localMax, inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? slope*inB[pmask + i00] : 0.0f)); } float max_ = subgroupMax(localMax); // parallel sum float localSum = 0.0f; for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) { - const float exp_psrc0 = exp(inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? inB[pmask + i00] : 0.0f) - max_); + const float exp_psrc0 = exp(inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? slope*inB[pmask + i00] : 0.0f) - max_); localSum += exp_psrc0; out_[pdst + i00] = exp_psrc0; } diff --git a/ggml/src/ggml-kompute/kompute-shaders/rope_common.comp b/ggml/src/ggml-kompute/kompute-shaders/rope_common.comp index df4702896..0fca640dc 100644 --- a/ggml/src/ggml-kompute/kompute-shaders/rope_common.comp +++ b/ggml/src/ggml-kompute/kompute-shaders/rope_common.comp @@ -8,12 +8,14 @@ layout(local_size_x = 1) in; layout (push_constant) uniform parameter { uint inAOff; uint inBOff; + uint inCOff; uint outOff; int n_dims; int mode; int n_ctx_orig; float freq_base; float freq_scale; + bool has_freq_factors; float ext_factor; float attn_factor; float beta_fast; From c202cef1686182a78f8f4e253ab8d0c0ffe2fcc8 Mon Sep 17 00:00:00 2001 From: Shupei Fan Date: Thu, 28 Nov 2024 20:52:03 +0800 Subject: [PATCH 07/29] ggml-cpu: support IQ4_NL_4_4 by runtime repack (#10541) * ggml-cpu: support IQ4_NL_4_4 by runtime repack * ggml-cpu: add __ARM_FEATURE_DOTPROD guard --- ggml/include/ggml-cpu.h | 1 + ggml/include/ggml.h | 3 + ggml/src/ggml-common.h | 6 + ggml/src/ggml-cpu/ggml-cpu-aarch64.c | 321 +++++++++++++++++++++++++-- ggml/src/ggml-cpu/ggml-cpu-aarch64.h | 2 + ggml/src/ggml-cpu/ggml-cpu.c | 27 ++- ggml/src/ggml-cpu/ggml-cpu.cpp | 2 +- ggml/src/ggml.c | 9 + 8 files changed, 352 insertions(+), 19 deletions(-) diff --git a/ggml/include/ggml-cpu.h b/ggml/include/ggml-cpu.h index a5358d047..e14ea9ea5 100644 --- a/ggml/include/ggml-cpu.h +++ b/ggml/include/ggml-cpu.h @@ -91,6 +91,7 @@ extern "C" { GGML_BACKEND_API int ggml_cpu_has_neon (void); GGML_BACKEND_API int ggml_cpu_has_arm_fma (void); GGML_BACKEND_API int ggml_cpu_has_fp16_va (void); + GGML_BACKEND_API int ggml_cpu_has_dotprod (void); GGML_BACKEND_API int ggml_cpu_has_matmul_int8(void); GGML_BACKEND_API int ggml_cpu_has_sve (void); GGML_BACKEND_API int ggml_cpu_get_sve_cnt (void); // sve vector length in bytes diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 9843b09fb..65cb92c44 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -389,6 +389,9 @@ extern "C" { GGML_TYPE_Q4_0_8_8 = 33, GGML_TYPE_TQ1_0 = 34, GGML_TYPE_TQ2_0 = 35, + GGML_TYPE_IQ4_NL_4_4 = 36, + // GGML_TYPE_IQ4_NL_4_8 = 37, + // GGML_TYPE_IQ4_NL_8_8 = 38, GGML_TYPE_COUNT, }; diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h index 050161393..27253a6c2 100644 --- a/ggml/src/ggml-common.h +++ b/ggml/src/ggml-common.h @@ -418,6 +418,12 @@ typedef struct { } block_iq4_xs; static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); +typedef struct { + ggml_half d[4]; // deltas for 4 iq4_nl blocks + uint8_t qs[QK4_NL * 2];// nibbles / quants for 4 iq4_nl blocks +} block_iq4_nlx4; +static_assert(sizeof(block_iq4_nlx4) == 4 * sizeof(ggml_half) + QK4_NL * 2, "wrong iq4_nlx4 block size/padding"); + #endif // GGML_COMMON_DECL #endif // GGML_COMMON_DECL diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.c b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c index 96a16dfba..ced378879 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.c +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c @@ -187,6 +187,8 @@ static inline __m256i mul_sum_i8_pairs_int32x8(const __m256i x, const __m256i y) } #endif +static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; + static void quantize_q8_0_4x4(const float * restrict x, void * restrict vy, int64_t k) { assert(QK8_0 == 32); assert(k % QK8_0 == 0); @@ -528,7 +530,7 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * UNUSED(blocklen); #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - if (ggml_cpu_has_neon()) { + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { const void * b_ptr = vx; const void * a_ptr = vy; float * res_ptr = s; @@ -996,6 +998,102 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * } } +void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert (n % qk == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + const int8x16_t kvalues = vld1q_s8(kvalues_iq4nl); + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + float * res_ptr = s; + + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + + float32x4_t sumf = vdupq_n_f32(0); + for (int l = 0; l < nb; l++) { + uint8x16_t b_0 = vld1q_u8(b_ptr[l].qs + 0); + uint8x16_t b_1 = vld1q_u8(b_ptr[l].qs + 16); + uint8x16_t b_2 = vld1q_u8(b_ptr[l].qs + 32); + uint8x16_t b_3 = vld1q_u8(b_ptr[l].qs + 48); + + int8x16_t b_0_hi = vqtbl1q_s8(kvalues, b_0 >> 4); + int8x16_t b_0_lo = vqtbl1q_s8(kvalues, b_0 & 0x0F); + int8x16_t b_1_hi = vqtbl1q_s8(kvalues, b_1 >> 4); + int8x16_t b_1_lo = vqtbl1q_s8(kvalues, b_1 & 0x0F); + int8x16_t b_2_hi = vqtbl1q_s8(kvalues, b_2 >> 4); + int8x16_t b_2_lo = vqtbl1q_s8(kvalues, b_2 & 0x0F); + int8x16_t b_3_hi = vqtbl1q_s8(kvalues, b_3 >> 4); + int8x16_t b_3_lo = vqtbl1q_s8(kvalues, b_3 & 0x0F); + + int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 0); + int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16); + + int32x4_t sumi = vdupq_n_s32(0); + sumi = vdotq_laneq_s32(sumi, b_0_lo, a_0, 0); + sumi = vdotq_laneq_s32(sumi, b_0_hi, a_1, 0); + sumi = vdotq_laneq_s32(sumi, b_1_lo, a_0, 1); + sumi = vdotq_laneq_s32(sumi, b_1_hi, a_1, 1); + sumi = vdotq_laneq_s32(sumi, b_2_lo, a_0, 2); + sumi = vdotq_laneq_s32(sumi, b_2_hi, a_1, 2); + sumi = vdotq_laneq_s32(sumi, b_3_lo, a_0, 3); + sumi = vdotq_laneq_s32(sumi, b_3_hi, a_1, 3); + + float32x4_t a_d = vcvt_f32_f16(vld1_dup_f16((const float16_t *)&a_ptr[l].d)); + float32x4_t b_d = vcvt_f32_f16(vld1_f16((const float16_t *)b_ptr[l].d)); + float32x4_t d = a_d * b_d; + + sumf = vmlaq_f32(sumf, d, vcvtq_f32_s32(sumi)); + } + + vst1q_f32(res_ptr + x * 4, sumf); + } + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + { + float sumf[4]; + int sumi; + + const block_q8_0 * a_ptr = (const block_q8_0 *) vy; + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); + + for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0; + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F]; + const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4]; + sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])); + } + sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d); + } + } + } + for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; + } + } +} + void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { const int qk = QK8_0; const int nb = n / qk; @@ -1017,7 +1115,7 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * restrict s, size_t bs, const void * UNUSED(blocklen); #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) - if (ggml_cpu_has_neon()) { + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { const void * b_ptr = vx; const void * a_ptr = vy; float * res_ptr = s; @@ -3386,6 +3484,117 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * restrict s, size_t bs, const void * } } +void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, const void * restrict vy, int nr, int nc) { + const int qk = QK8_0; + const int nb = n / qk; + const int ncols_interleaved = 4; + const int blocklen = 4; + + assert (n % qk == 0); + assert (nr % 4 == 0); + assert (nc % ncols_interleaved == 0); + + UNUSED(s); + UNUSED(bs); + UNUSED(vx); + UNUSED(vy); + UNUSED(nr); + UNUSED(nc); + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD) + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + const int8x16_t kvalues = vld1q_s8(kvalues_iq4nl); + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + + float32x4_t sumf[4]; + for (int m = 0; m < 4; m++) { + sumf[m] = vdupq_n_f32(0); + } + + for (int l = 0; l < nb; l++) { + float32x4_t a_d = vcvt_f32_f16(vld1_f16((const float16_t *)a_ptr[l].d)); + float32x4_t b_d = vcvt_f32_f16(vld1_f16((const float16_t *)b_ptr[l].d)); + + int32x4_t sumi_0 = vdupq_n_s32(0); + int32x4_t sumi_1 = vdupq_n_s32(0); + int32x4_t sumi_2 = vdupq_n_s32(0); + int32x4_t sumi_3 = vdupq_n_s32(0); + + for (int k = 0; k < 4; k++) { + int8x16_t a_0 = vld1q_s8(a_ptr[l].qs + 16 * k + 0); + int8x16_t a_1 = vld1q_s8(a_ptr[l].qs + 16 * k + 64); + + uint8x16_t b = vld1q_u8(b_ptr[l].qs + 16 * k); + int8x16_t b_hi = vqtbl1q_s8(kvalues, b >> 4); + int8x16_t b_lo = vqtbl1q_s8(kvalues, b & 0xF); + + sumi_0 = vdotq_laneq_s32(sumi_0, b_lo, a_0, 0); + sumi_1 = vdotq_laneq_s32(sumi_1, b_lo, a_0, 1); + sumi_2 = vdotq_laneq_s32(sumi_2, b_lo, a_0, 2); + sumi_3 = vdotq_laneq_s32(sumi_3, b_lo, a_0, 3); + sumi_0 = vdotq_laneq_s32(sumi_0, b_hi, a_1, 0); + sumi_1 = vdotq_laneq_s32(sumi_1, b_hi, a_1, 1); + sumi_2 = vdotq_laneq_s32(sumi_2, b_hi, a_1, 2); + sumi_3 = vdotq_laneq_s32(sumi_3, b_hi, a_1, 3); + } + + sumf[0] = vmlaq_f32(sumf[0], vmulq_laneq_f32(b_d, a_d, 0), vcvtq_f32_s32(sumi_0)); + sumf[1] = vmlaq_f32(sumf[1], vmulq_laneq_f32(b_d, a_d, 1), vcvtq_f32_s32(sumi_1)); + sumf[2] = vmlaq_f32(sumf[2], vmulq_laneq_f32(b_d, a_d, 2), vcvtq_f32_s32(sumi_2)); + sumf[3] = vmlaq_f32(sumf[3], vmulq_laneq_f32(b_d, a_d, 3), vcvtq_f32_s32(sumi_3)); + } + + for (int m = 0; m < 4; m++) { + vst1q_f32(s + (y * 4 + m) * bs + x * 4, sumf[m]); + } + } + } + return; + } +#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) + { + float sumf[4][4]; + int sumi; + + for (int y = 0; y < nr / 4; y++) { + const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0; + } + for (int l = 0; l < nb; l++) { + for (int k = 0; k < (qk / (2 * blocklen)); k++) { + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) { + sumi = 0; + for (int i = 0; i < blocklen; ++i) { + const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F]; + const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4]; + sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) + + (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])); + } + sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_FP16_TO_FP32(a_ptr[l].d[m]); + } + } + } + } + for (int m = 0; m < 4; m++) { + for (int j = 0; j < ncols_interleaved; j++) + s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j]; + } + } + } + } +} + // FIXME: this code is duplicated from ggml-aarch64.c static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) { block_q4_0x4 out; @@ -3518,6 +3727,70 @@ static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor *t, int interleave_block, GGML_UNUSED(data_size); } +static block_iq4_nlx4 make_block_iq4_nlx4(block_iq4_nl * in, unsigned int blck_size_interleave) { + block_iq4_nlx4 out; + + for (int i = 0; i < 4; i++) { + out.d[i] = in[i].d; + } + + const int end = QK4_NL * 2 / blck_size_interleave; + + if (blck_size_interleave == 8) { + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + // Using memcpy to avoid unaligned memory accesses + memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint64_t)); + } + } else if (blck_size_interleave == 4) { + for (int i = 0; i < end; ++i) { + int src_id = i % 4; + int src_offset = (i / 4) * blck_size_interleave; + int dst_offset = i * blck_size_interleave; + + memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], sizeof(uint32_t)); + } + } else { + GGML_ASSERT(false); + } + + return out; +} + +static int repack_iq4_nl_to_iq4_nl_4_bl(struct ggml_tensor * t, int interleave_block, const void * restrict data, size_t data_size) { + GGML_ASSERT(t->type == GGML_TYPE_IQ4_NL); + GGML_ASSERT(interleave_block == 4 || interleave_block == 8); + + block_iq4_nlx4 * dst = (block_iq4_nlx4 *)t->data; + const block_iq4_nl * src = (const block_iq4_nl *)data; + block_iq4_nl dst_tmp[4]; + int nrow = t->ne[1]; // Number of rows + int nrows_interleaved = 4; + int nblocks = t->ne[0] / QK4_0; + + GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_iq4_nl)); + + if (nrow % nrows_interleaved != 0 || t->ne[0] % 8 != 0) { + return -1; + } + + for (int b = 0; b < nrow; b += nrows_interleaved) { + for (int64_t x = 0; x < nblocks; x++) { + for (int i = 0; i < nrows_interleaved; i++) { + dst_tmp[i] = src[x + i * nblocks]; + } + *dst++ = make_block_iq4_nlx4(dst_tmp, interleave_block); + } + src += nrows_interleaved * nblocks; + } + return 0; + + GGML_UNUSED(data_size); +} + // Prepare for optimized kernels if applicable void ggml_aarch64_repack_tensor(struct ggml_tensor * cur, enum ggml_type repack_type, const void * restrict data, size_t data_size) { if (cur->type == repack_type) { @@ -3525,20 +3798,30 @@ void ggml_aarch64_repack_tensor(struct ggml_tensor * cur, enum ggml_type repack_ return; } - GGML_ASSERT(cur->type == GGML_TYPE_Q4_0); - - switch (repack_type) { - case GGML_TYPE_Q4_0_8_8: - repack_q4_0_to_q4_0_8_bl(cur, 8, data, data_size); - break; - case GGML_TYPE_Q4_0_4_8: - repack_q4_0_to_q4_0_4_bl(cur, 8, data, data_size); - break; - case GGML_TYPE_Q4_0_4_4: - repack_q4_0_to_q4_0_4_bl(cur, 4, data, data_size); - break; - default: - GGML_ABORT("Unsupported type"); + if (cur->type == GGML_TYPE_Q4_0) { + switch (repack_type) { + case GGML_TYPE_Q4_0_8_8: + repack_q4_0_to_q4_0_8_bl(cur, 8, data, data_size); + break; + case GGML_TYPE_Q4_0_4_8: + repack_q4_0_to_q4_0_4_bl(cur, 8, data, data_size); + break; + case GGML_TYPE_Q4_0_4_4: + repack_q4_0_to_q4_0_4_bl(cur, 4, data, data_size); + break; + default: + GGML_ABORT("Unsupported type"); + } + } else if (cur->type == GGML_TYPE_IQ4_NL) { + switch (repack_type) { + case GGML_TYPE_IQ4_NL_4_4: + repack_iq4_nl_to_iq4_nl_4_bl(cur, 4, data, data_size); + break; + default: + GGML_ABORT("Unsupported type"); + } + } else { + GGML_ABORT("Unsupported type"); } } @@ -3551,9 +3834,13 @@ enum ggml_type ggml_aarch64_get_optimal_repack_type(const struct ggml_tensor * c if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { return GGML_TYPE_Q4_0_4_8; } - if (ggml_cpu_has_neon()) { + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { return GGML_TYPE_Q4_0_4_4; } + } else if (cur->type == GGML_TYPE_IQ4_NL) { + if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) { + return GGML_TYPE_IQ4_NL_4_4; + } } return cur->type; diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.h b/ggml/src/ggml-cpu/ggml-cpu-aarch64.h index 53b30c1dd..3d9db6a19 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.h +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.h @@ -15,11 +15,13 @@ void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); // GEMM void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); +void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_aarch64_repack_tensor(struct ggml_tensor * cur, enum ggml_type repack_type, const void * data, size_t data_size); enum ggml_type ggml_aarch64_get_optimal_repack_type(const struct ggml_tensor * cur); diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index c6ede19d9..fea867440 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -109,10 +109,11 @@ static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; #if defined(__ARM_ARCH) struct ggml_arm_arch_features_type { int has_neon; + int has_dotprod; int has_i8mm; int has_sve; int sve_cnt; -} ggml_arm_arch_features = {-1, -1, -1, 0}; +} ggml_arm_arch_features = {-1, -1, -1, -1, 0}; #endif @@ -446,6 +447,15 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = { .vec_dot_type = GGML_TYPE_Q8_K, .nrows = 1, }, + [GGML_TYPE_IQ4_NL_4_4] = { + .from_float = NULL, + .vec_dot = NULL, + .vec_dot_type = GGML_TYPE_Q8_0, + .nrows = 1, + .ncols = 4, + .gemv = ggml_gemv_iq4_nl_4x4_q8_0, + .gemm = ggml_gemm_iq4_nl_4x4_q8_0, + }, }; const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) { @@ -2439,6 +2449,7 @@ static void ggml_init_arm_arch_features(void) { uint32_t hwcap2 = getauxval(AT_HWCAP2); ggml_arm_arch_features.has_neon = !!(hwcap & HWCAP_ASIMD); + ggml_arm_arch_features.has_dotprod = !!(hwcap && HWCAP_ASIMDDP); ggml_arm_arch_features.has_i8mm = !!(hwcap2 & HWCAP2_I8MM); ggml_arm_arch_features.has_sve = !!(hwcap & HWCAP_SVE); @@ -2453,6 +2464,11 @@ static void ggml_init_arm_arch_features(void) { } ggml_arm_arch_features.has_neon = oldp; + if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, NULL, 0) != 0) { + oldp = 0; + } + ggml_arm_arch_features.has_dotprod = oldp; + if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) != 0) { oldp = 0; } @@ -9133,6 +9149,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_Q4_0_4_4: case GGML_TYPE_Q4_0_4_8: case GGML_TYPE_Q4_0_8_8: + case GGML_TYPE_IQ4_NL_4_4: case GGML_TYPE_I8: case GGML_TYPE_I16: case GGML_TYPE_I32: @@ -13880,6 +13897,14 @@ int ggml_cpu_has_neon(void) { #endif } +int ggml_cpu_has_dotprod(void) { +#if defined(__ARM_ARCH) && defined(__ARM_FEATURE_DOTPROD) + return ggml_arm_arch_features.has_dotprod; +#else + return 0; +#endif +} + int ggml_cpu_has_sve(void) { #if defined(__ARM_ARCH) && defined(__ARM_FEATURE_SVE) return ggml_arm_arch_features.has_sve; diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index febed433a..44d99089a 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -457,7 +457,7 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st const struct ggml_tensor * src1 = op->src[1]; if (src0 && src0->buffer && ggml_backend_cpu_buft_is_aarch64(src0->buffer->buft)) { - if (op->op != GGML_OP_MUL_MAT || src0->type != GGML_TYPE_Q4_0 || ggml_aarch64_get_optimal_repack_type(src0) == GGML_TYPE_Q4_0) { + if (op->op != GGML_OP_MUL_MAT || src0->type == ggml_aarch64_get_optimal_repack_type(src0)) { return false; } } diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 1a2318cb1..1a9a7efaf 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -831,6 +831,15 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { .to_float = (ggml_to_float_t) dequantize_row_tq2_0, .from_float_ref = (ggml_from_float_t) quantize_row_tq2_0_ref, }, + [GGML_TYPE_IQ4_NL_4_4] = { + .type_name = "iq4_nl_4x4", + .blck_size = QK4_NL, + .blck_size_interleave = 4, + .type_size = sizeof(block_iq4_nl), + .is_quantized = true, + .to_float = NULL, + .from_float_ref = NULL, + }, }; const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type) { From eea986f215e1dc490654d012ccf2ab62fe8f606d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 28 Nov 2024 14:56:23 +0200 Subject: [PATCH 08/29] cmake : fix ARM feature detection (#10543) ggml-ci --- ggml/src/ggml-cpu/CMakeLists.txt | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index ddc05ecef..4dbc1f75b 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -82,17 +82,23 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) if (GGML_COMPILER_SUPPORT_DOTPROD) add_compile_definitions(__ARM_FEATURE_DOTPROD) + + message(STATUS "ARM feature DOTPROD enabled") endif () - check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) if (GGML_COMPILER_SUPPORT_MATMUL_INT8) add_compile_definitions(__ARM_FEATURE_MATMUL_INT8) + + message(STATUS "ARM feature MATMUL_INT8 enabled") endif () check_cxx_source_compiles("#include \nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + + message(STATUS "ARM feature FP16_VECTOR_ARITHMETIC enabled") endif () set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_PREV}) @@ -113,17 +119,23 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR if (GGML_COMPILER_SUPPORT_DOTPROD) set(MARCH_FLAGS "${MARCH_FLAGS}+dotprod") add_compile_definitions(__ARM_FEATURE_DOTPROD) + + message(STATUS "ARM feature DOTPROD enabled") endif () set(TEST_I8MM_FLAGS "-march=armv8.2a+i8mm") set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS}) - set(CMAKE_REQUIRED_FLAGS "${CMAKE_REQUIRED_FLAGS} ${TEST_I8MM_FLAGS}") + set(CMAKE_REQUIRED_FLAGS "${CMAKE_REQUIRED_FLAGS} ${TEST_I8MM_FLAGS}") + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) if (GGML_COMPILER_SUPPORT_MATMUL_INT8) set(MARCH_FLAGS "${MARCH_FLAGS}+i8mm") add_compile_definitions(__ARM_FEATURE_MATMUL_INT8) + + message(STATUS "ARM feature MATMUL_INT8 enabled") endif () + set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE}) list(APPEND ARCH_FLAGS "${MARCH_FLAGS}") From 76b27d29c22af03172cf211a8a31025c7c828a57 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 28 Nov 2024 14:56:37 +0200 Subject: [PATCH 09/29] ggml : fix row condition for i8mm kernels (#10561) ggml-ci --- ggml/src/ggml-cpu/ggml-cpu-quants.c | 6 ++++-- ggml/src/ggml-cpu/ggml-cpu.c | 17 +++++++++-------- 2 files changed, 13 insertions(+), 10 deletions(-) diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.c b/ggml/src/ggml-cpu/ggml-cpu-quants.c index f0e276b69..11e8df253 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-quants.c +++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c @@ -1813,11 +1813,13 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), l1, r1)), l2, r2)), l3, r3))), scale); } - float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); + + float32x4_t sumv1 = vextq_f32 (sumv0, sumv0, 2); float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - vst1_f32(s, vget_low_f32(sumv2)); + vst1_f32(s, vget_low_f32 (sumv2)); vst1_f32(s + bs, vget_high_f32(sumv2)); + return; } #endif diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index fea867440..1c88e5d81 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -7576,14 +7576,6 @@ UseGgmlGemm2:; // This is the size of the rest of the dimensions of the result const int64_t nr1 = ne1 * ne2 * ne3; - // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols - int64_t num_rows_per_vec_dot = vec_dot_num_rows; - // TODO: currently the mmla kernels support only even numbered rows/cols. - // this check can be removed once they are extended to support odd numbered rows/cols too - if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) { - num_rows_per_vec_dot = 1; - } - // Now select a reasonable chunk size. int chunk_size = 16; @@ -7646,6 +7638,15 @@ UseGgmlGemm2:; const int64_t ir1_start = dr1 * ith1; const int64_t ir1_end = MIN(ir1_start + dr1, nr1); + // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols + int64_t num_rows_per_vec_dot = vec_dot_num_rows; + + // TODO: currently the mmla kernels support only even numbered rows/cols. + // this check can be removed once they are extended to support odd numbered rows/cols too + if ((nr0 % 2 != 0) || (ne11 % 2 != 0) || ((ir0_end - ir0_start) % 2 != 0) || ((ir1_end - ir1_start) % 2 != 0)) { + num_rows_per_vec_dot = 1; + } + ggml_compute_forward_mul_mat_one_chunk(params, dst, type, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end); if (nth >= nchunk0 * nchunk1) { From e90688edd004fdb7063f463bd18408ba9ae008dd Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Thu, 28 Nov 2024 15:58:54 +0100 Subject: [PATCH 10/29] ci : fix tag name in cuda and hip releases (#10566) --- .github/workflows/build.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index e02b5c620..48953dafa 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -904,6 +904,8 @@ jobs: - name: Clone id: checkout uses: actions/checkout@v4 + with: + fetch-depth: 0 - name: Install Cuda Toolkit 11.7 if: ${{ matrix.cuda == '11.7' }} @@ -1139,6 +1141,8 @@ jobs: - name: Clone id: checkout uses: actions/checkout@v4 + with: + fetch-depth: 0 - name: Install id: depends From 7281cf13addfae9b64bb2be87e3b5b1914505d63 Mon Sep 17 00:00:00 2001 From: Random Fly Date: Thu, 28 Nov 2024 23:03:11 +0800 Subject: [PATCH 11/29] docs: fix outdated usage of llama-simple (#10565) --- docs/android.md | 4 ++-- examples/simple/README.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/android.md b/docs/android.md index 320b62240..47530c6c1 100644 --- a/docs/android.md +++ b/docs/android.md @@ -23,10 +23,10 @@ $ curl -L {model-url} -o ~/{model}.gguf Then, if you are not already in the repo directory, `cd` into `llama.cpp` and: ``` -$ ./build/bin/llama-simple -m ~/{model}.gguf -c {context-size} -p "{your-prompt}" +$ ./build/bin/llama-cli -m ~/{model}.gguf -c {context-size} -p "{your-prompt}" ``` -Here, we show `llama-simple`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal. +Here, we show `llama-cli`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal. To see what it might look like visually, here's an old demo of an interactive session running on a Pixel 5 phone: diff --git a/examples/simple/README.md b/examples/simple/README.md index 0ff342535..937008b24 100644 --- a/examples/simple/README.md +++ b/examples/simple/README.md @@ -3,7 +3,7 @@ The purpose of this example is to demonstrate a minimal usage of llama.cpp for generating text with a given prompt. ```bash -./llama-simple -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" +./llama-simple -m ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" ... From 890719311b6535e572f15965c6d7ec4ac2537f60 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 28 Nov 2024 18:15:25 +0100 Subject: [PATCH 12/29] common: fix warning message when no GPU found (#10564) --- common/arg.cpp | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index a6b7a1394..32d9a964c 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1370,8 +1370,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, int value) { params.n_gpu_layers = value; if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + fprintf(stderr, "warning: no usable GPU found, --gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n"); + fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); } } ).set_env("LLAMA_ARG_N_GPU_LAYERS")); @@ -2104,8 +2105,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, int value) { params.speculative.n_gpu_layers = value; if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + fprintf(stderr, "warning: no usable GPU found, --gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: one possible reason is that llama.cpp was compiled without GPU support\n"); + fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n"); } } ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER})); From 6c595676899013102fdb0aa4b06a49954300c94a Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Thu, 28 Nov 2024 19:17:49 +0100 Subject: [PATCH 13/29] server : (tests) don't use thread for capturing stdout/stderr, bump openai client library (#10568) * server : (tests) don't use thread for capturing stdout/stderr * test: bump openai to 1.55.2 * bump openai to 1.55.3 --- examples/server/tests/requirements.txt | 2 +- examples/server/tests/utils.py | 19 ++----------------- 2 files changed, 3 insertions(+), 18 deletions(-) diff --git a/examples/server/tests/requirements.txt b/examples/server/tests/requirements.txt index 935a79114..074b9d47b 100644 --- a/examples/server/tests/requirements.txt +++ b/examples/server/tests/requirements.txt @@ -2,6 +2,6 @@ aiohttp~=3.9.3 pytest~=8.3.3 huggingface_hub~=0.23.2 numpy~=1.26.4 -openai~=1.30.3 +openai~=1.55.3 prometheus-client~=0.20.0 requests~=2.32.3 diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py index e31743c50..a831f113f 100644 --- a/examples/server/tests/utils.py +++ b/examples/server/tests/utils.py @@ -8,7 +8,6 @@ import os import re import json import sys -import threading import requests import time from concurrent.futures import ThreadPoolExecutor, as_completed @@ -161,26 +160,12 @@ class ServerProcess: self.process = subprocess.Popen( [str(arg) for arg in [server_path, *server_args]], creationflags=flags, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, + stdout=sys.stdout, + stderr=sys.stdout, env={**os.environ, "LLAMA_CACHE": "tmp"}, ) server_instances.add(self) - def server_log(in_stream, out_stream): - for line in iter(in_stream.readline, b""): - print(line.decode("utf-8"), end="", file=out_stream) - - thread_stdout = threading.Thread( - target=server_log, args=(self.process.stdout, sys.stdout), daemon=True - ) - thread_stdout.start() - - thread_stderr = threading.Thread( - target=server_log, args=(self.process.stderr, sys.stderr), daemon=True - ) - thread_stderr.start() - print(f"server pid={self.process.pid}, pytest pid={os.getpid()}") # wait for server to start From 4c0a95b1074907ce7efe6f5bb6ae3351c01429ab Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 28 Nov 2024 20:45:07 +0200 Subject: [PATCH 14/29] llama : add missing model types --- src/llama.cpp | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/llama.cpp b/src/llama.cpp index af5e686e0..22b951ba2 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -2341,6 +2341,7 @@ enum e_model { MODEL_16B, MODEL_20B, MODEL_30B, + MODEL_32B, MODEL_34B, MODEL_35B, MODEL_40B, @@ -5330,6 +5331,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_16B: return "16B"; case MODEL_20B: return "20B"; case MODEL_30B: return "30B"; + case MODEL_32B: return "32B"; case MODEL_34B: return "34B"; case MODEL_35B: return "35B"; case MODEL_40B: return "40B"; @@ -5690,7 +5692,10 @@ static void llm_load_hparams( case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break; case 28: model.type = hparams.n_embd == 1536 ? e_model::MODEL_1_5B : e_model::MODEL_7B; break; case 32: model.type = e_model::MODEL_7B; break; + case 36: model.type = e_model::MODEL_3B; break; case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break; + case 48: model.type = e_model::MODEL_14B; break; + case 64: model.type = e_model::MODEL_32B; break; case 80: model.type = e_model::MODEL_70B; break; default: model.type = e_model::MODEL_UNKNOWN; } From dc22344088a7ee81a1e4f096459b03a72f24ccdc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 28 Nov 2024 20:46:40 +0200 Subject: [PATCH 15/29] ggml : remove redundant copyright notice + update authors --- AUTHORS | 186 ++++++++++++++++++++++++++- ggml/src/ggml-cpu/ggml-cpu-aarch64.c | 4 - 2 files changed, 185 insertions(+), 5 deletions(-) diff --git a/AUTHORS b/AUTHORS index 1bd36158a..2eb60806a 100644 --- a/AUTHORS +++ b/AUTHORS @@ -1,4 +1,4 @@ -# date: Wed Jun 26 19:36:34 EEST 2024 +# date: Thu Nov 28 20:46:15 EET 2024 # this file is auto-generated by scripts/gen-authors.sh 0cc4m @@ -7,6 +7,7 @@ 2f38b454 3ooabkhxtn <31479382+3ooabkhxtn@users.noreply.github.com> 44670 <44670@users.noreply.github.com> +65a <10104049+65a@users.noreply.github.com> AN Long AT Aarni Koskela @@ -19,20 +20,28 @@ Adithya Balaji AdithyanI Adrian Adrian Hesketh +Ahmad Tameem <113388789+Tameem-10xE@users.noreply.github.com> Ahmet Zeer AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> +AidanBeltonS Aisuko +Akarshan Biswas Akarshan Biswas +Al Mochkin <14274697+amochkin@users.noreply.github.com> Albert Jin Alberto <57916483+albbus-stack@users.noreply.github.com> +Alberto Cabrera Pérez +Alberto Cabrera Pérez Alex Alex Azarov Alex Azarov Alex Klinkhamer Alex Klinkhamer Alex Nguyen +Alex O'Connell <35843486+acon96@users.noreply.github.com> Alex Petenchea Alex Renda +Alex Tuddenham <61622354+AlexsCode@users.noreply.github.com> Alex von Gluck IV Alexey Parfenov Ali Chraghi <63465728+alichraghi@users.noreply.github.com> @@ -45,18 +54,25 @@ AmirAli Mirian <37371367+amiralimi@users.noreply.github.com> Ananta Bastola Anas Ahouzi <112881240+aahouzi@users.noreply.github.com> András Salamon +Andreas (Andi) Kunar Andrei Andrew Canis Andrew Downing Andrew Duffy Andrew Godfrey +Andrew Minh Nguyen <40281306+amqdn@users.noreply.github.com> +Andy Salerno Andy Tai +Anthony Van de Gejuchte +Antonis Makropoulos Arik Poznanski +Armen Kaleshian Artem Artem Zinnatullin Artyom Lebedev Asbjørn Olling Ásgeir Bjarni Ingvarsson +Asghar Ghorbani Ashish <1856117+ashishdatta@users.noreply.github.com> Ashok Gelal <401055+ashokgelal@users.noreply.github.com> Ashraful Islam @@ -76,12 +92,16 @@ Ben Williams Benjamin Findley <39356821+Kartoffelsaft@users.noreply.github.com> Benjamin Lecaillon <84293038+blecaillon@users.noreply.github.com> Bernat Vadell +Bert Wagner Bingan <70050083+binganao@users.noreply.github.com> +Bjarke Viksøe <164612031+bviksoe@users.noreply.github.com> Bodo Graumann Bono Lv Borislav Stanimirov Branden Butler +Brandon Squizzato <35474886+bsquizz@users.noreply.github.com> Brian +Brian Cunnie Bruce MacDonald Bryan Honof CJ Pais @@ -90,32 +110,47 @@ Calvin Laurenson Cameron Cameron Kaiser Carolinabanana <140120812+Carolinabanana@users.noreply.github.com> +CarryFun <76023481+CarryFun@users.noreply.github.com> +Carsten Kragelund Jørgensen +CarterLi999 <664681047@qq.com> Casey Primozic Casey Primozic CausalLM <148736309+CausalLM@users.noreply.github.com> Cebtenzzre Chad Brewbaker +Changyeon Kim Chao Jiang +Charles Xu <63788048+chaxu01@users.noreply.github.com> +Charles Xu +Chen Xi +Chen Xi Cheng Shao +Chenguang Li <87689256+noemotiovon@users.noreply.github.com> Chris Elrod Chris Kuehl Christian Demsar Christian Demsar Christian Falch <875252+chrfalch@users.noreply.github.com> Christian Kögler +Christian Köhnenkamp Christian Zhou-Zheng <59622928+christianazinn@users.noreply.github.com> Clark Saben <76020733+csaben@users.noreply.github.com> Clint Herron +Conrad Kramer CrispStrobe <154636388+CrispStrobe@users.noreply.github.com> +Csaba Kecskemeti Cuong Trinh Manh DAN™ Damian Stewart +Dan Johansson <164997844+eddnjjn@users.noreply.github.com> +Dan Johansson Dane Madsen DaniAndTheWeb <57776841+DaniAndTheWeb@users.noreply.github.com> Daniel Bevenius Daniel Drake Daniel Hiltgen Daniel Illescas Romero +Daniel Kleine <53251018+d-kleine@users.noreply.github.com> Daniele <57776841+daniandtheweb@users.noreply.github.com> DannyDaemonic Dat Quoc Nguyen <2412555+datquocnguyen@users.noreply.github.com> @@ -129,19 +164,28 @@ David Pflug David Renshaw David Sommers <12738+databyte@users.noreply.github.com> David Yang +DavidKorczynski Dawid Potocki Dawid Wysocki <62249621+TortillaZHawaii@users.noreply.github.com> Dean Deins +Denis Spasyuk <34203011+dspasyuk@users.noreply.github.com> +Derrick T. Woolworth Deven Mistry <31466137+deven367@users.noreply.github.com> +Dibakar Gope Didzis Gosko +Diego Devesa +Diogo Teles Sant'Anna Djip007 Don Mahurin DooWoong Lee (David) Doomsdayrs <38189170+Doomsdayrs@users.noreply.github.com> +Dou Xinpeng <15529241576@163.com> +Dou Xinpeng <81913537+Dou-Git@users.noreply.github.com> Douglas Hanley Dr. Tom Murphy VII Ph.D <499244+tom7@users.noreply.github.com> Ebey Abraham +Echo Nolan Ed Lee Ed Lepedus Eddie-Wang @@ -151,10 +195,13 @@ Elbios <141279586+Elbios@users.noreply.github.com> Elton Kola Engininja2 <139037756+Engininja2@users.noreply.github.com> Equim +Eric Curtin +Eric Curtin Eric Sommerlade Eric Zhang <34133756+EZForever@users.noreply.github.com> Erik Garrison Erik Scholz +Esko Toivonen Ettore Di Giacinto Evan Jones Evan Miller @@ -166,19 +213,26 @@ FK Fabian Fabio R. Sluzala Faez Shakil +Faisal Zaghloul +Faisal Zaghloul +Fan Shupei FantasyGmm <16450052+FantasyGmm@users.noreply.github.com> +Farbod Bijary <110523279+farbodbj@users.noreply.github.com> Fattire <528174+fat-tire@users.noreply.github.com> Felix Finn Voorhees Firat +FirstTimeEZ <179362031+FirstTimeEZ@users.noreply.github.com> Folko-Ven <71110216+Folko-Ven@users.noreply.github.com> Foul-Tarnished <107711110+Foul-Tarnished@users.noreply.github.com> Francisco Melo <43780565+francis2tm@users.noreply.github.com> Frank Mai FrankHB +Frankie Robertson Fred Douglas <43351173+fredlas@users.noreply.github.com> Frederik Vogel Gabe Goodhart +Gabe Goodhart GainLee Galunid Gary Linscott @@ -187,11 +241,13 @@ Gavin Zhao Genkagaku.GPT Georgi Gerganov Gilad S +Gilad S. <7817232+giladgd@users.noreply.github.com> Giuseppe Scrivano GiviMAD Govlzkoy Guillaume "Vermeille" Sanchez Guillaume Wenzek +Guoliang Hua <32868157+nbcsm@users.noreply.github.com> Guoteng <32697156+SolenoidWGT@users.noreply.github.com> Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com> Haggai Nuchi @@ -213,11 +269,14 @@ Hong Bo PENG Hongyu Ouyang <96765450+casavaca@users.noreply.github.com> Howard Su Hua Jiang +Huang Qi Huawei Lin Hugo Roussel +Huifeng Ou <79071290+ho2103@users.noreply.github.com> Ian Bull Ian Bull Ian Scrivener +Icecream95 Ido S IgnacioFDM Igor Okulist @@ -226,11 +285,15 @@ Ilya Kurdyukov <59548320+ilyakurdyukov@users.noreply.github.com> Ionoclast Laboratories Isaac McFadyen IsaacDynamo <61521674+IsaacDynamo@users.noreply.github.com> +Ivan +Ivan Filipov <159561759+vanaka11@users.noreply.github.com> Ivan Komarov Ivan Stepanov JH23X <165871467+JH23X@users.noreply.github.com> +Jack Mousseau Jack Mousseau JackJollimore <130917767+JackJollimore@users.noreply.github.com> +Jaeden Amero Jaemin Son Jag Chadha Jakub N @@ -243,10 +306,14 @@ Jannis Schönleber Jared Van Bortel Jared Van Bortel Jason McCartney +Jason Stillerman Jean-Christophe Hoelt Jean-Michaël Celerier Jed Fox +Jeff Bolz +Jeffrey Morgan Jeffrey Quesnelle +Jeroen Mostert Jesse Jojo Johnson Jeximo Jhen-Jie Hong @@ -258,6 +325,9 @@ Jiří Podivín <66251151+jpodivin@users.noreply.github.com> Jiří Sejkora Joan Fontanals Joan Fontanals +João Dinis Ferreira +Joe Eli McIlvain +Joe Todd Johan Johannes Gäßler Johannes Rudolph @@ -274,7 +344,9 @@ Joyce Juan Calderon-Perez <835733+gaby@users.noreply.github.com> Judd Julius Arkenberg +Jun Hee Yoo Jun Jie <71215065+junnjiee16@users.noreply.github.com> +Junil Kim Junyang Lin Juraj Bednar Justin Parker @@ -292,12 +364,14 @@ Karthik Sethuraman Kasumi <90275229+kasumi-1@users.noreply.github.com> Kawrakow <48489457+ikawrakow@users.noreply.github.com> Keiichi Tabata +Keke Han Kenvix ⭐ Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Kevin Gibbons Kevin Ji <1146876+kevinji@users.noreply.github.com> Kevin Kwok Kevin Lo +Kevin Wang Kolen Cheung Konstantin Herud Konstantin Zhuravlyov @@ -315,22 +389,29 @@ LeonEricsson <70749762+LeonEricsson@users.noreply.github.com> Leonardo Neumann Li Tan Linwei Wang +Liu Jia <109258120+Septa2112@users.noreply.github.com> +Liu Jia LoganDark +Loïc Carrère LostRuins <39025047+LostRuins@users.noreply.github.com> Luciano Luo Tian Lyle Dean +M-A M. Yusuf Sarıgöz +Ma Mingfei Maarten ter Huurne Mack Straight Maël Kerbiriou MaggotHATE +Mahesh Madhav <67384846+heshpdx@users.noreply.github.com> Manuel <44313466+makuche@users.noreply.github.com> Marc Köhlbrugge Marco Matthies <71844+marcom@users.noreply.github.com> Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com> Marian Cepok Mark Fairbairn +Mark Zhuang Marko Tasic Markus Tavenrath Martin Delille @@ -342,11 +423,15 @@ MasterYi1024 <39848311+MasterYi1024@users.noreply.github.com> Mateusz Charytoniuk Matheus C. França Matheus Gabriel Alves Silva +Mathieu Geli Mathieu Nayrolles +Mathijs Henquet Mathijs de Bruin Matt Clayton <156335168+mattjcly@users.noreply.github.com> Matt Pulver +Matt Stephenson Matteo Boschini <12133566+mbosc@users.noreply.github.com> +Matteo Mortari Mattheus Chediak Matthew Tejo Matvey Soloviev @@ -356,8 +441,10 @@ Maxime <672982+maximegmd@users.noreply.github.com> Maximilian Winter Meng Zhang Meng, Hengyu +Mengqing Cao Merrick Christensen Michael Coppola +Michael Francis Michael Hueschen Michael Kesper Michael Klimenko @@ -365,41 +452,57 @@ Michael Podvitskiy Michael Potter Michael de Gans Michaël de Vries +Michał Tuszyński Mihai Mike Mikko Juola Minsoo Cheong <54794500+mscheong01@users.noreply.github.com> +Minsoo Cheong Mirko185 Mirror Azure <54669636+MirrorAzure@users.noreply.github.com> +MistApproach <98988043+MistApproach@users.noreply.github.com> Miwa / Ensan <63481257+ensan-hcl@users.noreply.github.com> Mohammadreza Hendiani Mohammadreza Hendiani +Molly Sophia +MorganRO8 <47795945+MorganRO8@users.noreply.github.com> Murilo Santana Musab Gultekin Nam D. Tran <42194884+namtranase@users.noreply.github.com> Nathan Epstein +Natsu NawafAlansari <72708095+NawafAlansari@users.noreply.github.com> Nebula Neo Zhang <14088817+arthw@users.noreply.github.com> Neo Zhang Neo Zhang Jianyu Neuman Vong +Nexes the Old <124105151+Nexesenex@users.noreply.github.com> Nexesenex <124105151+Nexesenex@users.noreply.github.com> Niall Coates <1349685+Niall-@users.noreply.github.com> +Nicholai Tukanov +Nico Bosshard Nicolai Weitkemper Nicolás Pérez Nigel Bosch Niklas Korz +NikolaiLyssogor <59844691+NikolaiLyssogor@users.noreply.github.com> Nikolas <127742645+nneubacher@users.noreply.github.com> Nindaleth +OSecret <135510162+OLSecret@users.noreply.github.com> Oleksandr Nikitin Oleksii Maryshchenko Olivier Chafik Ondřej Čertík Ouadie EL FAROUKI +PAB +Pablo Duboue +Pascal Patry Patrice Ferlet Paul Tsochantaris +Pavel Zloi Pavol Rusnak +Paweł Wodnicki <151604+32bitmicro@users.noreply.github.com> Pedro Cuenca Peter Sugihara Phil H <5756783+phiharri@users.noreply.github.com> @@ -407,10 +510,15 @@ Philip Taron Phillip Kravtsov Pierre Alexandre SCHEMBRI Pierrick Hymbert +Pieter Ouwerkerk +Plamen Minev +Prashant Vithule <119530321+Vithulep@users.noreply.github.com> Przemysław Pawełczyk Qin Yue Chen <71813199+chenqiny@users.noreply.github.com> Qingyou Meng Qu Zongfu <43257352+yancaoweidaode@users.noreply.github.com> +R0CKSTAR +R0CKSTAR RJ Adriaansen Radoslav Gerganov Radosław Gryta @@ -419,11 +527,13 @@ Raj Hammeer Singh Hada Ralph Soika Rand Xie Randall Fitzgerald +Random Fly Reinforce-II Ren Xuancheng Rene Leonhardt <65483435+reneleonhardt@users.noreply.github.com> RhinoDevel Riceball LEE +Rich Dougherty Richard Kiss Richard Roberson Rick G <26732651+TheFlipbook@users.noreply.github.com> @@ -439,21 +549,30 @@ Robey Holderith Robyn Roger Meier Roland <14355895+rbur0425@users.noreply.github.com> +Romain Biessy Romain D <90720+Artefact2@users.noreply.github.com> Romain Neutron Roman Parykin Ron Evans Ron Jailall +Roni Ronny Brendel Ronsor Rowan Hart +Ruchira Hasaranga +Ruixin Huang <18860020911@163.com> Rune <43761327+Rune-AI@users.noreply.github.com> +RunningLeon +RunningLeon Ryan Landay Ryder Wishart Ryuei Rőczey Barnabás <31726601+An0nie@users.noreply.github.com> +SRHMorris <69468379+SRHMorris@users.noreply.github.com> +SXX SakuraUmi Salvador E. Tropea +Salvatore Mesoraca Sam Spilsbury Sami Farin <3876865+Safari77@users.noreply.github.com> Samuel Maynard @@ -463,23 +582,29 @@ Sebastián A SebastianApel <13675545+SebastianApel@users.noreply.github.com> Senemu <10880819+Senemu@users.noreply.github.com> Sergey Alirzaev +Sergio López Sergio López Sertaç Özercan <852750+sozercan@users.noreply.github.com> SeungWon Jeong <65549245+redlion0929@users.noreply.github.com> ShadovvBeast Shakhar Dasgupta +Shane A Shangning Xu <32517059+xushangning@users.noreply.github.com> +Shankar +Shanshan Shen <467638484@qq.com> Shijie <821898965@qq.com> Shintarou Okada Shouzheng Liu <61452103+lshzh-ww@users.noreply.github.com> Shouzheng Liu Shuichi Tsutsumi +Shupei Fan Sigbjørn Skjæret Simon Willison Siwen Yu Sky Yan Slaren <2141330+slaren@users.noreply.github.com> Slava Primenko +Small Grass Forest SoftwareRenderer <138734813+SoftwareRenderer@users.noreply.github.com> Someone Someone Serge @@ -491,12 +616,15 @@ Stefan Sydow Steffen Röcker Stephan Walter Stephen Nichols +Steve Bonds Steve Grubb Steven Prichard Steven Roussey Steward Garcia <57494570+FSSRepo@users.noreply.github.com> +StrangeBytesDev <141275258+StrangeBytesDev@users.noreply.github.com> Suaj Carrot <72162667+SuajCarrot@users.noreply.github.com> SuperUserNameMan +Sutou Kouhei Tai Duc Nguyen Taikono-Himazin Tameem <113388789+AhmadTameem@users.noreply.github.com> @@ -507,7 +635,9 @@ Theia Vogel Thérence <13496987+Royalphax@users.noreply.github.com> Thibault Terrasson Thomas Klausner +Thorsten Sommer Tim Miller +Tim Wang Timmy Knight Timothy Cronin <40186632+4imothy@users.noreply.github.com> Ting Lou @@ -517,24 +647,31 @@ Tom C Tom Jobbins <784313+TheBloke@users.noreply.github.com> Tomas Tomáš Pazdiora +Tony Wasserka <4840017+neobrain@users.noreply.github.com> Tristan Druyen Tristan Ross +Trivikram Kamat <16024985+trivikr@users.noreply.github.com> Tungsten842 <886724vf@anonaddy.me> Tungsten842 Tushar UEXTM.com <84163508+uextm@users.noreply.github.com> +Ujjawal Panchal <31011628+Ujjawal-K-Panchal@users.noreply.github.com> Ulrich Drepper Uzo Nweke Vaibhav Srivastav Val Kharitonov Valentin Konovalov Valentyn Bezshapkin <61702053+valentynbez@users.noreply.github.com> +Vali Malinoiu <0x4139@gmail.com> Victor Nogueira Victor Z. Peng +Viet-Anh NGUYEN (Andrew) +Vinesh Janarthanan <36610342+VJHack@users.noreply.github.com> Vlad Vladimir Vladimir Malyutin Vladimir Zorin +VoidIsVoid <343750470@qq.com> Volodymyr Vitvitskyi <72226+signalpillar@users.noreply.github.com> WangHaoranRobin <56047610+WangHaoranRobin@users.noreply.github.com> Weird Constructor @@ -551,15 +688,22 @@ Xiang (Kevin) Li Xiao-Yong Jin XiaotaoChen Xiaoyi Chen +Xie Yanbo Xingchen Song(宋星辰) +Xinpeng Dou <81913537+Dou-Git@users.noreply.github.com> Xuan Son Nguyen +Yaiko Yann Follet <131855179+YannFollet@users.noreply.github.com> Yaroslav Yazan Agha-Schrader Yiming Cui Yishuo Wang +Yoshi Suhara +Yoshi Suhara +Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Yueh-Po Peng <94939112+y10ab1@users.noreply.github.com> Yui +Yuri Khrustalev Yusuf Kağan Hanoğlu Yuval Peled <31162840+Yuval-Peled@users.noreply.github.com> ZHAOKAI WANG @@ -568,6 +712,8 @@ Zay <95888118+isaiahbjork@users.noreply.github.com> Zenix Zhang Peiyuan Zheng.Deng <32841220+dengzheng-cloud@users.noreply.github.com> +Zhenwei Jin <109658203+kylo5aby@users.noreply.github.com> +Zhiyuan Li ZhouYuChen Ziad Ben Hadj-Alouane Ziang Wu <97337387+ZiangWu-77@users.noreply.github.com> @@ -581,6 +727,7 @@ alexpinel <93524949+alexpinel@users.noreply.github.com> alonfaraj alwqx amd-lalithnc +amritahs-ibm andrijdavid anon998 <131767832+anon998@users.noreply.github.com> anzz1 @@ -588,14 +735,18 @@ apaz apcameron <37645737+apcameron@users.noreply.github.com> arch-btw <57669023+arch-btw@users.noreply.github.com> arcrank +ardfork <134447697+ardfork@users.noreply.github.com> arlo-phoenix <140345165+arlo-phoenix@users.noreply.github.com> at8u <129688334+at8u@users.noreply.github.com> automaticcat +awatuna <23447591+awatuna@users.noreply.github.com> +b4b4o bandoti <141645996+bandoti@users.noreply.github.com> beiller bhubbb <79117352+bhubbb@users.noreply.github.com> bmwl bobqianic <129547291+bobqianic@users.noreply.github.com> +brucepro bryanSwk <93190252+bryanSwk@users.noreply.github.com> bsilvereagle bssrdf @@ -614,10 +765,14 @@ cpumaxx <163466046+cpumaxx@users.noreply.github.com> crasm crasm daboe01 +daghanerdonmez <44506702+daghanerdonmez@users.noreply.github.com> +daminho <37615795+daminho@users.noreply.github.com> david raistrick ddh0 ddpasa <112642920+ddpasa@users.noreply.github.com> deepdiffuser <112834445+deepdiffuser@users.noreply.github.com> +devojony <61173062+devojony@users.noreply.github.com> +ditsuke divinity76 dm4 dotpy314 <33351922+dotpy314@users.noreply.github.com> @@ -629,14 +784,18 @@ ebraminio eiery <19350831+eiery@users.noreply.github.com> eric8607242 fairydreaming <166155368+fairydreaming@users.noreply.github.com> +fengerhu1 <2748250768@qq.com> fraxy-v <65565042+fraxy-v@users.noreply.github.com> github-actions[bot] gliptic goerch grahameth <96447521+grahameth@users.noreply.github.com> +gtygo gwjr <502526+gwjr@users.noreply.github.com> h-h-h-h <13482553+h-h-h-h@users.noreply.github.com> hankcs +haopeng <657407891@qq.com> +hipudding hoangmit hongbo.mo <352280764@qq.com> hopkins385 <98618192+hopkins385@users.noreply.github.com> @@ -649,12 +808,14 @@ hxer7963 hydai iSma iacore <74560659+iacore@users.noreply.github.com> +icppWorld <124377669+icppWorld@users.noreply.github.com> igarnier intelmatt <61025942+intelmatt@users.noreply.github.com> iohub jacobi petrucciani <8117202+jpetrucciani@users.noreply.github.com> jaime-m-p <167997752+jaime-m-p@users.noreply.github.com> jameswu2014 <545426914@qq.com> +jdomke <28772296+jdomke@users.noreply.github.com> jiez <373447296@qq.com> jneem joecryptotoo <80373433+joecryptotoo@users.noreply.github.com> @@ -677,28 +838,35 @@ klosax <131523366+klosax@users.noreply.github.com> kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> kunnis kuronekosaiko +kustaaya <58045274+kustaaya@users.noreply.github.com> kuvaus <22169537+kuvaus@users.noreply.github.com> kwin1412 <42286931+kwin1412@users.noreply.github.com> l3utterfly +laik ldwang le.chang leejet +leo-pony limitedAtonement liuwei-git <14815172+liuwei-git@users.noreply.github.com> lon <114724657+longregen@users.noreply.github.com> loonerin <132926317+loonerin@users.noreply.github.com> +ltoniazzi <61414566+ltoniazzi@users.noreply.github.com> luoyu-intel m3ndax maddes8cht <55592906+maddes8cht@users.noreply.github.com> makomk manikbhandari maor-ps <154728172+maor-ps@users.noreply.github.com> +matiaslin <45382001+matiaslin@users.noreply.github.com> +matteo mdrokz mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com> minarchist mj-shifu <77107165+mj-shifu@users.noreply.github.com> mmyjona momonga <115213907+mmnga@users.noreply.github.com> +momonga <146910567+mmngays@users.noreply.github.com> moritzbrantner <31051084+moritzbrantner@users.noreply.github.com> mzcu nanahi <130121847+na-na-hi@users.noreply.github.com> @@ -716,8 +884,10 @@ omahs <73983677+omahs@users.noreply.github.com> oobabooga <112222186+oobabooga@users.noreply.github.com> opparco ostix360 <55257054+ostix360@users.noreply.github.com> +pculliton pengxin99 perserk +piDack <104877312+piDack@users.noreply.github.com> pmysl postmasters pudepiedj @@ -733,6 +903,7 @@ runfuture sandyiscool sasha0552 semidark +serhii-nakon <57632032+serhii-nakon@users.noreply.github.com> sharpHL <132747147+sharpHL@users.noreply.github.com> shibe2 singularity <12184989+singularity-s0@users.noreply.github.com> @@ -741,42 +912,55 @@ sjxx <63994076+ylsdamxssjxxdd@users.noreply.github.com> slaren <2141330+slaren@users.noreply.github.com> slaren snadampal <87143774+snadampal@users.noreply.github.com> +standby24x7 staviq stduhpf strawberrymelonpanda <152940198+strawberrymelonpanda@users.noreply.github.com> swittk takov751 <40316768+takov751@users.noreply.github.com> tarcey +tc-mb <157115220+tc-mb@users.noreply.github.com> texmex76 <40733439+texmex76@users.noreply.github.com> thement <40525767+thement@users.noreply.github.com> +thewh1teagle <61390950+thewh1teagle@users.noreply.github.com> tjohnman +toyer <2042519524@qq.com> tslmy ubik2 uint256_t uint256_t unbounded +uvos valiray <133289098+valiray@users.noreply.github.com> +vb vik viric vodkaslime <646329483@qq.com> vvhg1 <94630311+vvhg1@users.noreply.github.com> vxiiduu <73044267+vxiiduu@users.noreply.github.com> +wangshuai09 <391746016@qq.com> wbpxre150 <100937007+wbpxre150@users.noreply.github.com> whoreson <139810751+whoreson@users.noreply.github.com> woachk <24752637+woachk@users.noreply.github.com> wonjun Jang woodx <124784234+woodx9@users.noreply.github.com> +wwoodsTM <104587230+wwoodsTM@users.noreply.github.com> wzy <32936898+Freed-Wu@users.noreply.github.com> xaedes xaedes +xctan xloem <0xloem@gmail.com> yangli2 yuiseki +yuri@FreeBSD zakkor zhangkaihuo +zhentaoyu zhouwg <6889919+zhouwg@users.noreply.github.com> zhouwg zrm Ștefan-Gabriel Muscalu +杨朱 · Kiki 源文雨 <41315874+fumiama@users.noreply.github.com> +蕭澧邦 <45505768+shou692199@users.noreply.github.com> Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com> diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.c b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c index ced378879..69d3d327d 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.c +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c @@ -1,7 +1,3 @@ -// SPDX-FileCopyrightText: Copyright 2024 Arm Limited and/or its affiliates -// SPDX-License-Identifier: MIT -// - #define GGML_COMMON_IMPL_C #include "ggml-common.h" From 678d7994f4da0af3d29046be99950ac999ee9762 Mon Sep 17 00:00:00 2001 From: Ting Lou Date: Fri, 29 Nov 2024 08:09:46 +0800 Subject: [PATCH 16/29] llava: return false instead of exit (#10546) --- examples/llava/clip.cpp | 15 +++++++++++---- examples/llava/llava.cpp | 28 +++++++++++++++++++--------- 2 files changed, 30 insertions(+), 13 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index aae49c965..7ba4cea58 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -40,10 +40,17 @@ #include #include -#define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) -#define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_DBG(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +#if defined(LLAVA_LOG_OFF) +# define LOG_INF(...) +# define LOG_WRN(...) +# define LOG_ERR(...) +# define LOG_DBG(...) +#else // defined(LLAVA_LOG_OFF) +# define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +# define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +#endif // defined(LLAVA_LOG_OFF) //#define CLIP_DEBUG_FUNCTIONS diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index be6988540..4ca53a0b8 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -11,13 +11,17 @@ #include #include -#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0) -#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) - -#define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) -#define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) -#define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +#if defined(LLAVA_LOG_OFF) +# define LOG_INF(...) +# define LOG_WRN(...) +# define LOG_ERR(...) +# define LOG_DBG(...) +#else // defined(LLAVA_LOG_OFF) +# define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +# define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0) +# define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0) +#endif // defined(LLAVA_LOG_OFF) // RGB uint8 image struct clip_image_u8 { @@ -498,10 +502,16 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long errno = 0; size_t ret = fread(buffer, 1, fileSize, file); // Read the file into the buffer if (ferror(file)) { - die_fmt("read error: %s", strerror(errno)); + LOG_ERR("read error: %s", strerror(errno)); + free(buffer); + fclose(file); + return false; } if (ret != (size_t) fileSize) { - die("unexpectedly reached end of file"); + LOG_ERR("unexpectedly reached end of file"); + free(buffer); + fclose(file); + return false; } fclose(file); // Close the file From f095a649ec390e04dfab1b04e646ae8549dafaef Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Fri, 29 Nov 2024 00:18:02 -0600 Subject: [PATCH 17/29] vulkan: get the first command buffer submitted sooner (#10499) This is an incremental improvement over #9118 to get work to the GPU a bit sooner. The first part is to start with a smaller number of nodes before the first submit, and ramp it up to the current 100 nodes/submit. The second part is to reduce the dryrun overhead for all the nodes that just need to request descriptor space. With these changes I get around 1-2% speedup on RTX 4070 combined with my old Haswell-era CPU. --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 60 ++++++++++++++++++++++++++-- 1 file changed, 56 insertions(+), 4 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index a833007fb..849c11923 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -5672,6 +5672,48 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod } else { compute_ctx = ctx->compute_ctx.lock(); } + } else { + switch (node->op) { + case GGML_OP_REPEAT: + case GGML_OP_ACC: + case GGML_OP_GET_ROWS: + case GGML_OP_ADD: + case GGML_OP_MUL: + case GGML_OP_DIV: + case GGML_OP_CONCAT: + case GGML_OP_UPSCALE: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_SIN: + case GGML_OP_COS: + case GGML_OP_CLAMP: + case GGML_OP_PAD: + case GGML_OP_CPY: + case GGML_OP_CONT: + case GGML_OP_DUP: + case GGML_OP_NORM: + case GGML_OP_GROUP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_UNARY: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: + case GGML_OP_ARGSORT: + case GGML_OP_SUM_ROWS: + case GGML_OP_IM2COL: + case GGML_OP_TIMESTEP_EMBEDDING: + case GGML_OP_POOL_2D: + case GGML_OP_LEAKY_RELU: + { + // These operations all go through ggml_vk_op_f32, so short-circuit and + // do the only thing needed for the dryrun. + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, node, node->op); + ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1); + return false; + } + default: + break; + } } switch (node->op) { @@ -6401,16 +6443,17 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg bool first_node_in_batch = true; // true if next node will be first node in a batch int submit_node_idx = 0; // index to first node in a batch - // submit work every submit_count node to overlap CPU cmdbuffer generation with GPU execution - constexpr int submit_count = 100; + // Submit work every nodes_per_submit nodes to overlap CPU cmdbuffer generation with GPU execution. + // Start with a smaller count to get work submitted right away, and increase it after each submit. + int nodes_per_submit = 20; int submitted_nodes = 0; + int submit_count = 0; for (int i = 0; i < cgraph->n_nodes; i++) { if (first_node_in_batch) { submit_node_idx = i; } - bool submit = (submitted_nodes >= submit_count) || (i == last_node); - + bool submit = (submitted_nodes >= nodes_per_submit) || (i == last_node); bool enqueued = ggml_vk_build_graph(ctx, cgraph->nodes[i], i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i == last_node, submit); @@ -6427,6 +6470,15 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg if (submit) { first_node_in_batch = true; submitted_nodes = 0; + switch (submit_count) { + case 0: + nodes_per_submit = 50; + break; + default: + nodes_per_submit = 100; + break; + } + submit_count++; } } From 938f6087421889a3af7d0786c64406ced2be81b8 Mon Sep 17 00:00:00 2001 From: Chenguang Li <87689256+noemotiovon@users.noreply.github.com> Date: Fri, 29 Nov 2024 14:46:55 +0800 Subject: [PATCH 18/29] CANN: RoPE operator optimization (#10563) * [cann] RoPE operator optimization * [CANN]Code Formatting --------- Co-authored-by: noemotiovon --- ggml/src/ggml-cann/aclnn_ops.cpp | 241 ++++++++++++++++++++++++++++--- ggml/src/ggml-cann/ggml-cann.cpp | 13 +- 2 files changed, 222 insertions(+), 32 deletions(-) diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index d707efc5d..b2d857e1e 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -2965,7 +2965,7 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, aclTensor* acl_cos_repeat_tensor, aclTensor* acl_sin_repeat_tensor, float theta_scale, float freq_scale, - bool is_neox) { + float attn_factor, bool is_neox) { // int sin/cos cache, cache has different repeat method depond on // @param.is_neox @@ -3017,6 +3017,7 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_type_size(src2->type), arange_ne, arange_nb, GGML_MAX_DIMS); aclnn_div_tensor(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor, nullptr, true); + ACL_CHECK(aclDestroyTensor(acl_freq_factors_tensor)); } // position @@ -3047,16 +3048,6 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor, acl_theta_tensor); - // // power[] * position[] * freq_scale / freq_factors[] - // ggml_cann_pool_alloc theta_final_allocator(ctx.pool(), - // theta_length * - // sizeof(float_t)); - // aclTensor* acl_theat_final_tensor = aclnn_zero( - // ctx, theta_final_allocator.get(), sizeof(float_t) * theta_length, - // theta_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t)); - // aclnn_inplace_addcdiv(ctx, acl_theat_final_tensor, acl_theta_tensor, - // acl_freq_factors_tensor, freq_scale); - // permute: [0,1,2,3]->[0,2,1,3] int64_t permute_ne[] = {arange_length, 1, position_length, 1}; size_t permute_nb[GGML_MAX_DIMS]; @@ -3092,6 +3083,12 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_cos(ctx, acl_permute_tensor, acl_cos_tensor); + // attn_factor + if (attn_factor != 1) { + aclnn_muls(ctx, acl_sin_tensor, attn_factor, nullptr, true); + aclnn_muls(ctx, acl_cos_tensor, attn_factor, nullptr, true); + } + // repeat if (is_neox) { int64_t repeatsArray[] = {1, 1, 1, 2}; @@ -3155,15 +3152,11 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { memcpy(&beta_fast, (int32_t*)dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t*)dst->op_params + 10, sizeof(float)); - // TODO: attn_factor != 1 - GGML_ASSERT(attn_factor == 1); // TODO: n_dims <= ne0 GGML_ASSERT(n_dims == ne0); GGML_ASSERT(n_dims % 2 == 0); // TODO: ext_factor != 0 GGML_ASSERT(ext_factor == 0); - // TODO: type == GGML_TYPE_F16 - GGML_ASSERT(src0->type == GGML_TYPE_F32); const float theta_scale = powf(freq_base, -2.0f / n_dims); @@ -3194,7 +3187,217 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t), sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); aclnn_cache_init(ctx, dst, acl_cos_reshape_tensor, acl_sin_reshape_tensor, - theta_scale, freq_scale, is_neox); + theta_scale, freq_scale, attn_factor, is_neox); + + 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; + ggml_cann_pool_alloc roll_allocator(ctx.pool(), ggml_nbytes(src0)); + ggml_cann_pool_alloc minus_one_scale_allocator( + ctx.pool(), sizeof(float_t) * 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_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}; + aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims); + ACL_CHECK(aclDestroyTensor(acl_input_roll_tensor)); + ACL_CHECK(aclDestroyTensor(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]; + minus_one_nb[0] = sizeof(float_t); + 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_t) * src0->ne[0], + minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 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); + } 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); + + int64_t shifts[] = {src0->ne[0] / 2}; + int64_t dims[] = {3}; + aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims); + + ACL_CHECK(aclDestroyTensor(acl_input_roll_tensor)); + ACL_CHECK(aclDestroyTensor(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_nb[0] = sizeof(float_t); + 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_t) * src0->ne[0], + minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 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]; + first_half_nb[0] = sizeof(float_t); + 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_t), 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); + ACL_CHECK(aclDestroyTensor(acl_first_half_tensor)); + } + + // TODO: n_dims < ne0 + 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]; + 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); + + aclnn_mul(ctx, acl_input_roll_reshape_tensor, acl_minus_one_tensor, + acl_input_roll_mul_scale_tensor); + + // output + void* output_fp32_buffer; + if (src0->type == GGML_TYPE_F32) { + aclnn_inplace_mul(ctx, acl_src, acl_cos_reshape_tensor); + aclnn_inplace_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) { + size_t input_fp32_nb[GGML_MAX_DIMS]; + input_fp32_nb[0] = sizeof(float_t); + 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_t)); + void* input_fp32_buffer1 = fp32_allocator1.get(); + aclTensor* input_fp32_tensor1 = ggml_cann_create_tensor( + input_fp32_buffer1, ACL_FLOAT, sizeof(float_t), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); + ggml_cann_pool_alloc fp32_allocator2( + ctx.pool(), ggml_nelements(dst) * sizeof(float_t)); + void* input_fp32_buffer2 = fp32_allocator2.get(); + aclTensor* input_fp32_tensor2 = ggml_cann_create_tensor( + input_fp32_buffer2, ACL_FLOAT, sizeof(float_t), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); + + ggml_cann_pool_alloc fp32_allocator( + ctx.pool(), ggml_nelements(dst) * sizeof(float_t)); + output_fp32_buffer = fp32_allocator.get(); + aclTensor* output_fp32_tensor = ggml_cann_create_tensor( + output_fp32_buffer, ACL_FLOAT, sizeof(float_t), 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_cast(ctx, output_fp32_tensor, acl_dst, ACL_FLOAT16); + + ACL_CHECK(aclDestroyTensor(input_fp32_tensor1)); + ACL_CHECK(aclDestroyTensor(input_fp32_tensor2)); + ACL_CHECK(aclDestroyTensor(output_fp32_tensor)); + ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); + ACL_CHECK(aclDestroyTensor(acl_minus_one_tensor)); + ACL_CHECK(aclDestroyTensor(acl_input_roll_mul_scale_tensor)); + ACL_CHECK(aclDestroyTensor(acl_input_roll_reshape_tensor)); + ACL_CHECK(aclDestroyTensor(acl_src)); + } + return; +#endif + + // src0 == GGML_TYPE_F16 + // TODO: optimization this `if` code + if (src0->type == GGML_TYPE_F16) { + ggml_cann_pool_alloc sin_final_allocator( + ctx.pool(), src0->ne[0] * src0->ne[2] * ggml_type_size(src0->type)); + ggml_cann_pool_alloc cos_final_allocator( + ctx.pool(), src0->ne[0] * src0->ne[2] * ggml_type_size(src0->type)); + void* sin_final_buffer = sin_final_allocator.get(); + void* cos_final_buffer = cos_final_allocator.get(); + + int64_t sin_final_ne[4] = {src0->ne[0], 1, src0->ne[2], 1}; + size_t sin_final_nb[GGML_MAX_DIMS]; + sin_final_nb[0] = ggml_type_size(src0->type); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + sin_final_nb[i] = sin_final_nb[i - 1] * sin_final_ne[i - 1]; + } + aclTensor* acl_sin_final_tensor = ggml_cann_create_tensor( + sin_final_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), sin_final_ne, sin_final_nb, + GGML_MAX_DIMS); + aclTensor* acl_cos_final_tensor = ggml_cann_create_tensor( + cos_final_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), sin_final_ne, sin_final_nb, + GGML_MAX_DIMS); + + aclnn_cast(ctx, acl_sin_reshape_tensor, acl_sin_final_tensor, + ggml_cann_type_mapping(src0->type)); + aclnn_cast(ctx, acl_cos_reshape_tensor, acl_cos_final_tensor, + ggml_cann_type_mapping(src0->type)); + ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor)); + ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); + acl_sin_reshape_tensor = acl_sin_final_tensor; + acl_cos_reshape_tensor = acl_cos_final_tensor; + } uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -3206,10 +3409,8 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { acl_mode = 1; } - aclTensor* acl_x = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); ACL_CHECK(aclnnRotaryPositionEmbeddingGetWorkspaceSize( - acl_x, acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, + acl_src, acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, acl_dst, &workspaceSize, &executor)); if (workspaceSize > 0) { ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); @@ -3219,7 +3420,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ACL_CHECK(aclnnRotaryPositionEmbedding(workspaceAddr, workspaceSize, executor, ctx.stream())); - ACL_CHECK(aclDestroyTensor(acl_x)); + ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor)); ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); ACL_CHECK(aclDestroyTensor(acl_dst)); diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp index bcb54e444..04e25b8ab 100644 --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -1739,7 +1739,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, case GGML_OP_ROPE: { // TODO: with ops-test v == 1 float * ext_factor = (float*)((int32_t*)op->op_params + 7); - float * attn_factor = (float*)((int32_t*)op->op_params + 8); // TODO: n_dims <= ne0 if (op->src[0]->ne[0] != op->op_params[1]) { return false; @@ -1748,17 +1747,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, if (*ext_factor != 0) { return false; } - // TODO: attn_factor != 1 - if (*attn_factor != 1) { - return false; - } - //TODO: type == GGML_TYPE_F16 - switch (op->src[0]->type) { - case GGML_TYPE_F32: - return true; - default: - return false; - } + return true; } case GGML_OP_UPSCALE: { // aclnnUpsampleNearest2dGetWorkspaceSize not support From 266b8519ee6d21e7ba2bf56f5629e20a181fee8b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alberto=20Cabrera=20P=C3=A9rez?= Date: Fri, 29 Nov 2024 09:49:43 +0000 Subject: [PATCH 19/29] sycl : Reroute permuted mul_mats through oneMKL (#10408) This PR fixes the failing MUL_MAT tests for the sycl backend. --- ggml/src/ggml-sycl/ggml-sycl.cpp | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index b6392ed8d..aabcdc224 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -3447,8 +3447,15 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q; if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - // KQ single-batch - ggml_sycl_mul_mat_vec_p021(ctx, src0, src1, dst); + // TODO: Refactor and cleanup of mul mat dispatching. + if (src0->ne[3] == 1 && src1->ne[3] == 1) { + // KQ single-batch + // mmv p021 was specific for these dimensions + ggml_sycl_mul_mat_vec_p021(ctx, src0, src1, dst); + } else { + // The kernel from the if path is faster for that specific case, but does not support all mul mats. + ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst); + } } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch ggml_sycl_mul_mat_vec_nc(ctx, src0, src1, dst); From 0f77aae5608f16780a49926b67be6d56ec4b09bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alberto=20Cabrera=20P=C3=A9rez?= Date: Fri, 29 Nov 2024 12:38:45 +0000 Subject: [PATCH 20/29] sycl : offload of get_rows set to 0 (#10432) --- ggml/src/ggml-sycl/ggml-sycl.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index aabcdc224..808f74fa0 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -4493,7 +4493,7 @@ static bool ggml_backend_sycl_device_supports_buft(ggml_backend_dev_t dev, ggml_ static int64_t get_op_batch_size(const ggml_tensor * op) { switch (op->op) { case GGML_OP_GET_ROWS: - return op->ne[1]; // this will increse the speed of prefill in test + return 0; case GGML_OP_MUL_MAT: return op->ne[1]; case GGML_OP_MUL_MAT_ID: From 4b3242bbea172ac0980378496fbc676d44c4f459 Mon Sep 17 00:00:00 2001 From: Shupei Fan Date: Fri, 29 Nov 2024 21:49:02 +0800 Subject: [PATCH 21/29] ggml-cpu: fix typo in gemv/gemm iq4_nl_4_4 (#10580) --- ggml/src/ggml-cpu/ggml-cpu-aarch64.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cpu/ggml-cpu-aarch64.c b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c index 69d3d327d..14a1f00eb 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-aarch64.c +++ b/ggml/src/ggml-cpu/ggml-cpu-aarch64.c @@ -1020,7 +1020,7 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * restrict s, size_t bs, const void float * res_ptr = s; for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); float32x4_t sumf = vdupq_n_f32(0); for (int l = 0; l < nb; l++) { @@ -3507,7 +3507,7 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * restrict s, size_t bs, const void for (int y = 0; y < nr / 4; y++) { const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb); for (int x = 0; x < nc / ncols_interleaved; x++) { - const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb); + const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb); float32x4_t sumf[4]; for (int m = 0; m < 4; m++) { From f0678c5ff4cb8873d6ff48801475ff270db656fa Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 29 Nov 2024 16:25:39 +0200 Subject: [PATCH 22/29] ggml : fix I8MM Q4_1 scaling factor conversion (#10562) ggml-ci --- ggml/src/ggml-cpu/ggml-cpu-quants.c | 57 +++++++++++++++++------------ ggml/src/ggml-cpu/ggml-cpu.c | 4 +- tests/test-backend-ops.cpp | 2 + 3 files changed, 38 insertions(+), 25 deletions(-) diff --git a/ggml/src/ggml-cpu/ggml-cpu-quants.c b/ggml/src/ggml-cpu/ggml-cpu-quants.c index 11e8df253..634c5fa11 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-quants.c +++ b/ggml/src/ggml-cpu/ggml-cpu-quants.c @@ -1791,11 +1791,12 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_l = vld1q_s8(b_y1->qs); const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - float32_t _scale[4] = { GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; - + float32_t _scale[4] = { + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d) + }; float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -1811,7 +1812,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), - l1, r1)), l2, r2)), l3, r3))), scale); + l1, r1)), l2, r2)), l3, r3))), scale); } float32x4_t sumv1 = vextq_f32 (sumv0, sumv0, 2); @@ -2347,10 +2348,12 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const block_q8_1 * restrict b_y0 = &vy0[i]; const block_q8_1 * restrict b_y1 = &vy1[i]; - float32_t summs_t[4] = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; + float32_t summs_t[4] = { + GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s) + }; summs0 = vaddq_f32(summs0, vld1q_f32(summs_t)); const uint8x16_t m4b = vdupq_n_u8(0x0F); @@ -2371,10 +2374,12 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); // mmla into int32x4_t - float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; + float32_t _scale[4] = { + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d) + }; float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -2389,15 +2394,17 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r int8x16_t r2 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), - l1, r1)), l2, r2)), l3, r3))), scale); + l1, r1)), l2, r2)), l3, r3))), scale); } - float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); + float32x4_t sumv1 = vextq_f32 (sumv0, sumv0, 2); float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); + sumv2 = vaddq_f32(sumv2, summs0); vst1_f32(s, vget_low_f32 (sumv2)); vst1_f32(s + bs, vget_high_f32(sumv2)); + return; } #endif @@ -3374,10 +3381,12 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_l = vld1q_s8(b_y1->qs); const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32_t _scale[4] = { + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d) + }; float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -3393,13 +3402,15 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r int8x16_t r3 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(y0_h), vreinterpretq_s64_s8(y1_h))); sumv0 = vmlaq_f32(sumv0,(vcvtq_f32_s32(vmmlaq_s32((vmmlaq_s32((vmmlaq_s32((vmmlaq_s32(vdupq_n_s32(0), l0, r0)), - l1, r1)), l2, r2)), l3, r3))), scale); + l1, r1)), l2, r2)), l3, r3))), scale); } - float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); + + float32x4_t sumv1 = vextq_f32 (sumv0, sumv0, 2); float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - vst1_f32(s, vget_low_f32(sumv2)); + vst1_f32(s, vget_low_f32 (sumv2)); vst1_f32(s + bs, vget_high_f32(sumv2)); + return; } #endif diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index 1c88e5d81..e0cefc20b 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -7641,8 +7641,8 @@ UseGgmlGemm2:; // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols int64_t num_rows_per_vec_dot = vec_dot_num_rows; - // TODO: currently the mmla kernels support only even numbered rows/cols. - // this check can be removed once they are extended to support odd numbered rows/cols too + // these checks are needed to avoid crossing dim1 boundaries + // can be optimized, but the logic would become more complicated, so keeping it like this for simplicity if ((nr0 % 2 != 0) || (ne11 % 2 != 0) || ((ir0_end - ir0_start) % 2 != 0) || ((ir1_end - ir1_start) % 2 != 0)) { num_rows_per_vec_dot = 1; } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index da66ed856..4d9df1a64 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -3334,7 +3334,9 @@ static const ggml_type all_types[] = { static const ggml_type base_types[] = { GGML_TYPE_F32, GGML_TYPE_F16, + GGML_TYPE_Q8_0, // for I8MM tests GGML_TYPE_Q4_0, + GGML_TYPE_Q4_1, // for I8MM tests GGML_TYPE_Q4_K, GGML_TYPE_IQ2_XXS }; From a3a3048e7a0f9464d0d625a29257d8bce5da5090 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Fri, 29 Nov 2024 17:45:08 +0100 Subject: [PATCH 23/29] cleanup UI link list (#10577) * cleanup UI link list * sort list alphabetically * add missing licenses --- README.md | 58 ++++++++++++++++++++++++------------------------------- 1 file changed, 25 insertions(+), 33 deletions(-) diff --git a/README.md b/README.md index 414c5b1c0..8aa3d1e59 100644 --- a/README.md +++ b/README.md @@ -140,44 +140,36 @@ Typically finetunes of the base models below are supported as well. **UI:** -Unless otherwise noted these projects are open-source with permissive licensing: - -- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT) -- [iohub/collama](https://github.com/iohub/coLLaMA) +- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) +- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) +- [Dot](https://github.com/alexpinel/Dot) (GPL) +- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) +- [iohub/collama](https://github.com/iohub/coLLaMA) (Apache-2.0) - [janhq/jan](https://github.com/janhq/jan) (AGPL) -- [nat/openplayground](https://github.com/nat/openplayground) -- [Faraday](https://faraday.dev/) (proprietary) +- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0) +- [KodiBot](https://github.com/firatkiral/kodibot) (GPL) +- [LARS](https://github.com/abgulati/LARS) (AGPL) +- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) +- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT) +- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT) - [LMStudio](https://lmstudio.ai/) (proprietary) -- [Layla](https://play.google.com/store/apps/details?id=com.laylalite) (proprietary) -- [ramalama](https://github.com/containers/ramalama) (MIT) - [LocalAI](https://github.com/mudler/LocalAI) (MIT) - [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) -- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) -- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) -- [ollama/ollama](https://github.com/ollama/ollama) -- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) -- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) -- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) -- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) -- [pythops/tenere](https://github.com/pythops/tenere) (AGPL) -- [RAGNA Desktop](https://ragna.app/) (proprietary) -- [RecurseChat](https://recurse.chat/) (proprietary) -- [semperai/amica](https://github.com/semperai/amica) -- [withcatai/catai](https://github.com/withcatai/catai) -- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) -- [Msty](https://msty.app) (proprietary) -- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT) -- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file)(Apachev2.0 or later) -- [Dot](https://github.com/alexpinel/Dot) (GPL) - [MindMac](https://mindmac.app) (proprietary) -- [KodiBot](https://github.com/firatkiral/kodibot) (GPL) -- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) -- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) -- [AIKit](https://github.com/sozercan/aikit) (MIT) -- [LARS - The LLM & Advanced Referencing Solution](https://github.com/abgulati/LARS) (AGPL) -- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT) -- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) -- [PocketPal AI - An iOS and Android App](https://github.com/a-ghorbani/pocketpal-ai) (MIT) +- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT) +- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT) +- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) (Apache-2.0) +- [nat/openplayground](https://github.com/nat/openplayground) (MIT) +- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) (MIT) +- [ollama/ollama](https://github.com/ollama/ollama) (MIT) +- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) +- [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai) (MIT) +- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) (MIT) +- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) (MIT) +- [pythops/tenere](https://github.com/pythops/tenere) (AGPL) +- [ramalama](https://github.com/containers/ramalama) (MIT) +- [semperai/amica](https://github.com/semperai/amica) (MIT) +- [withcatai/catai](https://github.com/withcatai/catai) (MIT) *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* From 3a8e9af402f7893423bdab444aa16c5d9a2d429a Mon Sep 17 00:00:00 2001 From: Robert Collins Date: Fri, 29 Nov 2024 12:21:37 -0500 Subject: [PATCH 24/29] imatrix : support combine-only (#10492) * imatrix-combine-only idea * ensured that behavior consistent with log --- examples/imatrix/imatrix.cpp | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 70ff47768..45206f4a7 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -637,10 +637,19 @@ int main(int argc, char ** argv) { LOG_INF("%s\n", common_params_get_system_info(params).c_str()); } - if (!compute_imatrix(ctx, params)) { - return 1; + if (params.prompt.empty()) { + if (params.in_files.empty()) { + LOG_ERR("Error: No prompt provided and no precomputed matrices (--in-file) to combine.\n"); + return 1; + } + LOG_INF("No prompt provided; combining precomputed matrices only.\n"); + } else { + if (!compute_imatrix(ctx, params)) { + return 1; + } } + g_collector.save_imatrix(); LOG("\n"); From b782e5c7d453b3f1fa8dc6c34cde7e2fa946af93 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Fri, 29 Nov 2024 21:48:56 +0100 Subject: [PATCH 25/29] server : add more test cases (#10569) * server : add split model test * add test speculative * add invalid cases --- examples/server/tests/unit/test_basic.py | 14 +++ .../server/tests/unit/test_chat_completion.py | 19 ++++ examples/server/tests/unit/test_infill.py | 22 ++++ examples/server/tests/unit/test_rerank.py | 17 +++ .../server/tests/unit/test_speculative.py | 103 ++++++++++++++++++ examples/server/tests/utils.py | 12 +- 6 files changed, 186 insertions(+), 1 deletion(-) create mode 100644 examples/server/tests/unit/test_speculative.py diff --git a/examples/server/tests/unit/test_basic.py b/examples/server/tests/unit/test_basic.py index 84db5ca1c..d82d54a5a 100644 --- a/examples/server/tests/unit/test_basic.py +++ b/examples/server/tests/unit/test_basic.py @@ -32,3 +32,17 @@ def test_server_models(): assert res.status_code == 200 assert len(res.body["data"]) == 1 assert res.body["data"][0]["id"] == server.model_alias + +def test_load_split_model(): + global server + server.model_hf_repo = "ggml-org/models" + server.model_hf_file = "tinyllamas/split/stories15M-q8_0-00001-of-00003.gguf" + server.model_alias = "tinyllama-split" + server.start() + res = server.make_request("POST", "/completion", data={ + "n_predict": 16, + "prompt": "Hello", + "temperature": 0.0, + }) + assert res.status_code == 200 + assert match_regex("(little|girl)+", res.body["content"]) diff --git a/examples/server/tests/unit/test_chat_completion.py b/examples/server/tests/unit/test_chat_completion.py index d7aeb288d..1048d6fca 100644 --- a/examples/server/tests/unit/test_chat_completion.py +++ b/examples/server/tests/unit/test_chat_completion.py @@ -127,3 +127,22 @@ def test_completion_with_response_format(response_format: dict, n_predicted: int assert res.status_code != 200 assert "error" in res.body + +@pytest.mark.parametrize("messages", [ + None, + "string", + [123], + [{}], + [{"role": 123}], + [{"role": "system", "content": 123}], + # [{"content": "hello"}], # TODO: should not be a valid case + [{"role": "system", "content": "test"}, {}], +]) +def test_invalid_chat_completion_req(messages): + global server + server.start() + res = server.make_request("POST", "/chat/completions", data={ + "messages": messages, + }) + assert res.status_code == 400 or res.status_code == 500 + assert "error" in res.body diff --git a/examples/server/tests/unit/test_infill.py b/examples/server/tests/unit/test_infill.py index 38ce6c429..6a6d40a1c 100644 --- a/examples/server/tests/unit/test_infill.py +++ b/examples/server/tests/unit/test_infill.py @@ -8,6 +8,7 @@ def create_server(): global server server = ServerPreset.tinyllama_infill() + def test_infill_without_input_extra(): global server server.start() @@ -19,6 +20,7 @@ def test_infill_without_input_extra(): assert res.status_code == 200 assert match_regex("(One|day|she|saw|big|scary|bird)+", res.body["content"]) + def test_infill_with_input_extra(): global server server.start() @@ -33,3 +35,23 @@ def test_infill_with_input_extra(): }) assert res.status_code == 200 assert match_regex("(cuts|Jimmy|mom|came|into|the|room)+", res.body["content"]) + + +@pytest.mark.parametrize("input_extra", [ + {}, + {"filename": "ok"}, + {"filename": 123}, + {"filename": 123, "text": "abc"}, + {"filename": 123, "text": 456}, +]) +def test_invalid_input_extra_req(input_extra): + global server + server.start() + res = server.make_request("POST", "/infill", data={ + "prompt": "Complete this", + "input_extra": [input_extra], + "input_prefix": "#include \n#include \"llama.h\"\n\nint main() {\n int n_threads = llama_", + "input_suffix": "}\n", + }) + assert res.status_code == 400 + assert "error" in res.body diff --git a/examples/server/tests/unit/test_rerank.py b/examples/server/tests/unit/test_rerank.py index 3a49fd3ac..189bc4c96 100644 --- a/examples/server/tests/unit/test_rerank.py +++ b/examples/server/tests/unit/test_rerank.py @@ -36,3 +36,20 @@ def test_rerank(): assert most_relevant["relevance_score"] > least_relevant["relevance_score"] assert most_relevant["index"] == 2 assert least_relevant["index"] == 3 + + +@pytest.mark.parametrize("documents", [ + [], + None, + 123, + [1, 2, 3], +]) +def test_invalid_rerank_req(documents): + global server + server.start() + res = server.make_request("POST", "/rerank", data={ + "query": "Machine learning is", + "documents": documents, + }) + assert res.status_code == 400 + assert "error" in res.body diff --git a/examples/server/tests/unit/test_speculative.py b/examples/server/tests/unit/test_speculative.py new file mode 100644 index 000000000..982d6abb4 --- /dev/null +++ b/examples/server/tests/unit/test_speculative.py @@ -0,0 +1,103 @@ +import pytest +from utils import * + +# We use a F16 MOE gguf as main model, and q4_0 as draft model + +server = ServerPreset.stories15m_moe() + +MODEL_DRAFT_FILE_URL = "https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories15M-q4_0.gguf" + +def create_server(): + global server + server = ServerPreset.stories15m_moe() + # download draft model file if needed + file_name = MODEL_DRAFT_FILE_URL.split('/').pop() + model_draft_file = f'../../../{file_name}' + if not os.path.exists(model_draft_file): + print(f"Downloading {MODEL_DRAFT_FILE_URL} to {model_draft_file}") + with open(model_draft_file, 'wb') as f: + f.write(requests.get(MODEL_DRAFT_FILE_URL).content) + print(f"Done downloading draft model file") + # set default values + server.model_draft = model_draft_file + server.draft_min = 4 + server.draft_max = 8 + + +@pytest.fixture(scope="module", autouse=True) +def fixture_create_server(): + return create_server() + + +def test_with_and_without_draft(): + global server + server.model_draft = None # disable draft model + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }) + assert res.status_code == 200 + content_no_draft = res.body["content"] + server.stop() + + # create new server with draft model + create_server() + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }) + assert res.status_code == 200 + content_draft = res.body["content"] + + assert content_no_draft == content_draft + + +def test_different_draft_min_draft_max(): + global server + test_values = [ + (1, 2), + (1, 4), + (4, 8), + (4, 12), + (8, 16), + ] + last_content = None + for draft_min, draft_max in test_values: + server.stop() + server.draft_min = draft_min + server.draft_max = draft_max + server.start() + res = server.make_request("POST", "/completion", data={ + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }) + assert res.status_code == 200 + if last_content is not None: + assert last_content == res.body["content"] + last_content = res.body["content"] + + +@pytest.mark.parametrize("n_slots,n_requests", [ + (1, 2), + (2, 2), +]) +def test_multi_requests_parallel(n_slots: int, n_requests: int): + global server + server.n_slots = n_slots + server.start() + tasks = [] + for _ in range(n_requests): + tasks.append((server.make_request, ("POST", "/completion", { + "prompt": "I believe the meaning of life is", + "temperature": 0.0, + "top_k": 1, + }))) + results = parallel_function_calls(tasks) + for res in results: + assert res.status_code == 200 + assert match_regex("(wise|kind|owl|answer)+", res.body["content"]) diff --git a/examples/server/tests/utils.py b/examples/server/tests/utils.py index a831f113f..e17a05ff6 100644 --- a/examples/server/tests/utils.py +++ b/examples/server/tests/utils.py @@ -46,6 +46,7 @@ class ServerProcess: model_alias: str | None = None model_url: str | None = None model_file: str | None = None + model_draft: str | None = None n_threads: int | None = None n_gpu_layer: int | None = None n_batch: int | None = None @@ -68,6 +69,8 @@ class ServerProcess: response_format: str | None = None lora_files: List[str] | None = None disable_ctx_shift: int | None = False + draft_min: int | None = None + draft_max: int | None = None # session variables process: subprocess.Popen | None = None @@ -102,6 +105,8 @@ class ServerProcess: server_args.extend(["--model", self.model_file]) if self.model_url: server_args.extend(["--model-url", self.model_url]) + if self.model_draft: + server_args.extend(["--model-draft", self.model_draft]) if self.model_hf_repo: server_args.extend(["--hf-repo", self.model_hf_repo]) if self.model_hf_file: @@ -147,6 +152,10 @@ class ServerProcess: server_args.extend(["--no-context-shift"]) if self.api_key: server_args.extend(["--api-key", self.api_key]) + if self.draft_max: + server_args.extend(["--draft-max", self.draft_max]) + if self.draft_min: + server_args.extend(["--draft-min", self.draft_min]) args = [str(arg) for arg in [server_path, *server_args]] print(f"bench: starting server with: {' '.join(args)}") @@ -185,7 +194,8 @@ class ServerProcess: raise TimeoutError(f"Server did not start within {timeout_seconds} seconds") def stop(self) -> None: - server_instances.remove(self) + if self in server_instances: + server_instances.remove(self) if self.process: print(f"Stopping server with pid={self.process.pid}") self.process.kill() From 7cc2d2c88908fc92b97b28acafb82f7d6e425b85 Mon Sep 17 00:00:00 2001 From: Diego Devesa Date: Fri, 29 Nov 2024 21:54:58 +0100 Subject: [PATCH 26/29] ggml : move AMX to the CPU backend (#10570) * ggml : move AMX to the CPU backend --------- Co-authored-by: Georgi Gerganov --- .clang-tidy | 2 + .github/workflows/build.yml | 5 + Makefile | 9 +- Package.swift | 4 +- common/CMakeLists.txt | 2 +- common/common.cpp | 10 + examples/batched-bench/CMakeLists.txt | 2 +- examples/batched/CMakeLists.txt | 2 +- .../convert-llama2c-to-ggml/CMakeLists.txt | 2 +- examples/cvector-generator/CMakeLists.txt | 2 +- examples/embedding/CMakeLists.txt | 2 +- examples/eval-callback/CMakeLists.txt | 2 +- examples/export-lora/CMakeLists.txt | 2 +- examples/gbnf-validator/CMakeLists.txt | 2 +- examples/gen-docs/CMakeLists.txt | 2 +- examples/gguf-hash/CMakeLists.txt | 2 +- examples/gguf-split/CMakeLists.txt | 2 +- examples/gguf/CMakeLists.txt | 2 +- examples/gritlm/CMakeLists.txt | 2 +- examples/imatrix/CMakeLists.txt | 2 +- examples/infill/CMakeLists.txt | 2 +- examples/llama-bench/CMakeLists.txt | 2 +- examples/llava/CMakeLists.txt | 6 +- examples/lookahead/CMakeLists.txt | 2 +- examples/lookup/CMakeLists.txt | 8 +- examples/main-cmake-pkg/CMakeLists.txt | 2 +- examples/main/CMakeLists.txt | 2 +- examples/parallel/CMakeLists.txt | 2 +- examples/passkey/CMakeLists.txt | 2 +- examples/perplexity/CMakeLists.txt | 2 +- examples/quantize-stats/CMakeLists.txt | 2 +- examples/quantize/CMakeLists.txt | 2 +- examples/retrieval/CMakeLists.txt | 2 +- examples/run/CMakeLists.txt | 2 +- examples/save-load-state/CMakeLists.txt | 2 +- examples/server/CMakeLists.txt | 2 +- examples/simple-chat/CMakeLists.txt | 2 +- examples/simple/CMakeLists.txt | 2 +- examples/speculative-simple/CMakeLists.txt | 2 +- examples/speculative/CMakeLists.txt | 2 +- examples/tokenize/CMakeLists.txt | 2 +- ggml/CMakeLists.txt | 1 - ggml/include/ggml-amx.h | 25 - ggml/src/CMakeLists.txt | 16 +- ggml/src/ggml-amx/CMakeLists.txt | 105 ---- ggml/src/ggml-amx/ggml-amx.cpp | 449 ------------------ ggml/src/ggml-backend-reg.cpp | 7 - ggml/src/ggml-backend.cpp | 3 +- ggml/src/ggml-cpu/CMakeLists.txt | 92 ++-- ggml/src/ggml-cpu/amx/amx.cpp | 196 ++++++++ ggml/src/ggml-cpu/amx/amx.h | 20 + ggml/src/{ggml-amx => ggml-cpu/amx}/common.h | 23 +- ggml/src/{ggml-amx => ggml-cpu/amx}/mmq.cpp | 96 ++-- ggml/src/{ggml-amx => ggml-cpu/amx}/mmq.h | 3 +- ggml/src/ggml-cpu/ggml-cpu-impl.h | 15 + ggml/src/ggml-cpu/ggml-cpu.c | 84 ++-- ggml/src/ggml-cpu/ggml-cpu.cpp | 34 +- ggml/src/ggml-cpu/llamafile/sgemm.cpp | 3 +- ggml/src/ggml-impl.h | 10 +- .../ggml-vulkan/vulkan-shaders/CMakeLists.txt | 2 +- pocs/vdot/CMakeLists.txt | 4 +- src/CMakeLists.txt | 2 +- src/unicode.cpp | 11 + tests/test-sampling.cpp | 2 +- 64 files changed, 514 insertions(+), 801 deletions(-) delete mode 100644 ggml/include/ggml-amx.h delete mode 100644 ggml/src/ggml-amx/CMakeLists.txt delete mode 100644 ggml/src/ggml-amx/ggml-amx.cpp create mode 100644 ggml/src/ggml-cpu/amx/amx.cpp create mode 100644 ggml/src/ggml-cpu/amx/amx.h rename ggml/src/{ggml-amx => ggml-cpu/amx}/common.h (77%) rename ggml/src/{ggml-amx => ggml-cpu/amx}/mmq.cpp (98%) rename ggml/src/{ggml-amx => ggml-cpu/amx}/mmq.h (72%) diff --git a/.clang-tidy b/.clang-tidy index 952c0cca8..310c3d182 100644 --- a/.clang-tidy +++ b/.clang-tidy @@ -17,8 +17,10 @@ Checks: > -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling, performance-*, portability-*, + -portability-simd-intrinsics, misc-*, -misc-const-correctness, -misc-non-private-member-variables-in-classes, -misc-no-recursion, + -misc-use-anonymous-namespace, FormatStyle: none diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 48953dafa..e2291bd34 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1121,6 +1121,11 @@ jobs: run: | & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version + - name: Install ccache + uses: hendrikmuhs/ccache-action@v1.2 + with: + key: ${{ github.job }} + - name: Build id: cmake_build run: | diff --git a/Makefile b/Makefile index 25214ec05..83adcef28 100644 --- a/Makefile +++ b/Makefile @@ -254,8 +254,8 @@ endif # keep standard at C11 and C++11 MK_CPPFLAGS = -Iggml/include -Iggml/src -Iinclude -Isrc -Icommon -DGGML_USE_CPU MK_CFLAGS = -std=c11 -fPIC -MK_CXXFLAGS = -std=c++11 -fPIC -MK_NVCCFLAGS = -std=c++11 +MK_CXXFLAGS = -std=c++17 -fPIC +MK_NVCCFLAGS = -std=c++17 ifdef LLAMA_NO_CCACHE GGML_NO_CCACHE := 1 @@ -575,9 +575,12 @@ endif ifndef GGML_NO_AMX MK_CPPFLAGS += -DGGML_USE_AMX - OBJ_GGML_EXT += ggml/src/ggml-amx/ggml-amx.o ggml/src/ggml-amx/mmq.o + OBJ_GGML_EXT += ggml/src/ggml-cpu/amx/amx.o ggml/src/ggml-cpu/amx/mmq.o endif +# only necessary for the CPU backend files +MK_CPPFLAGS += -Iggml/src/ggml-cpu + ifdef GGML_RPC MK_CPPFLAGS += -DGGML_USE_RPC OBJ_GGML_EXT += ggml/src/ggml-rpc.o diff --git a/Package.swift b/Package.swift index d9e8a4e2d..1e75aa7e2 100644 --- a/Package.swift +++ b/Package.swift @@ -28,13 +28,16 @@ var cSettings: [CSetting] = [ .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), .unsafeFlags(["-fno-objc-arc"]), .headerSearchPath("ggml/src"), + .headerSearchPath("ggml/src/ggml-cpu"), // NOTE: NEW_LAPACK will required iOS version 16.4+ // We should consider add this in the future when we drop support for iOS 14 // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc) // .define("ACCELERATE_NEW_LAPACK"), // .define("ACCELERATE_LAPACK_ILP64") + .define("GGML_USE_CPU"), ] + #if canImport(Darwin) sources.append("ggml/src/ggml-common.h") sources.append("ggml/src/ggml-metal/ggml-metal.m") @@ -44,7 +47,6 @@ cSettings.append( contentsOf: [ .define("GGML_USE_ACCELERATE"), .define("GGML_USE_METAL"), - .define("GGML_USE_CPU") ] ) #endif diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 223174884..89862fe11 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -88,5 +88,5 @@ if (LLAMA_CURL) endif () target_include_directories(${TARGET} PUBLIC .) -target_compile_features (${TARGET} PUBLIC cxx_std_11) +target_compile_features (${TARGET} PUBLIC cxx_std_17) target_link_libraries (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads) diff --git a/common/common.cpp b/common/common.cpp index 2b2f00098..6143516d2 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -652,7 +652,17 @@ bool fs_validate_filename(const std::string & filename) { std::u32string filename_utf32; try { +#if defined(__clang__) + // disable C++17 deprecation warning for std::codecvt_utf8 +# pragma clang diagnostic push +# pragma clang diagnostic ignored "-Wdeprecated-declarations" +#endif std::wstring_convert, char32_t> converter; + +#if defined(__clang__) +# pragma clang diagnostic pop +#endif + filename_utf32 = converter.from_bytes(filename); // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used, diff --git a/examples/batched-bench/CMakeLists.txt b/examples/batched-bench/CMakeLists.txt index 959acaeee..68ad707f3 100644 --- a/examples/batched-bench/CMakeLists.txt +++ b/examples/batched-bench/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-batched-bench) add_executable(${TARGET} batched-bench.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/batched/CMakeLists.txt b/examples/batched/CMakeLists.txt index 77e33343b..0d439f498 100644 --- a/examples/batched/CMakeLists.txt +++ b/examples/batched/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-batched) add_executable(${TARGET} batched.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/convert-llama2c-to-ggml/CMakeLists.txt b/examples/convert-llama2c-to-ggml/CMakeLists.txt index a6790e617..44e5f722a 100644 --- a/examples/convert-llama2c-to-ggml/CMakeLists.txt +++ b/examples/convert-llama2c-to-ggml/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-convert-llama2c-to-ggml) add_executable(${TARGET} convert-llama2c-to-ggml.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/cvector-generator/CMakeLists.txt b/examples/cvector-generator/CMakeLists.txt index 0a559d60c..49ad9561c 100644 --- a/examples/cvector-generator/CMakeLists.txt +++ b/examples/cvector-generator/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-cvector-generator) add_executable(${TARGET} cvector-generator.cpp pca.hpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/embedding/CMakeLists.txt b/examples/embedding/CMakeLists.txt index 8256e789a..809040307 100644 --- a/examples/embedding/CMakeLists.txt +++ b/examples/embedding/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-embedding) add_executable(${TARGET} embedding.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/eval-callback/CMakeLists.txt b/examples/eval-callback/CMakeLists.txt index 5d1048aad..95915ed91 100644 --- a/examples/eval-callback/CMakeLists.txt +++ b/examples/eval-callback/CMakeLists.txt @@ -2,7 +2,7 @@ set(TARGET llama-eval-callback) add_executable(${TARGET} eval-callback.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TEST_TARGET test-eval-callback) add_test(NAME ${TEST_TARGET} diff --git a/examples/export-lora/CMakeLists.txt b/examples/export-lora/CMakeLists.txt index 1cef6e716..310455787 100644 --- a/examples/export-lora/CMakeLists.txt +++ b/examples/export-lora/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-export-lora) add_executable(${TARGET} export-lora.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gbnf-validator/CMakeLists.txt b/examples/gbnf-validator/CMakeLists.txt index 4edd6ec73..d2cb524c0 100644 --- a/examples/gbnf-validator/CMakeLists.txt +++ b/examples/gbnf-validator/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gbnf-validator) add_executable(${TARGET} gbnf-validator.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gen-docs/CMakeLists.txt b/examples/gen-docs/CMakeLists.txt index c94cda776..25de0af35 100644 --- a/examples/gen-docs/CMakeLists.txt +++ b/examples/gen-docs/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gen-docs) add_executable(${TARGET} gen-docs.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gguf-hash/CMakeLists.txt b/examples/gguf-hash/CMakeLists.txt index 7a494ce32..15c5c68c6 100644 --- a/examples/gguf-hash/CMakeLists.txt +++ b/examples/gguf-hash/CMakeLists.txt @@ -19,4 +19,4 @@ add_library(sha256 OBJECT deps/sha256/sha256.c deps/sha256/sha256.h) target_link_libraries(${TARGET} PRIVATE sha256) target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gguf-split/CMakeLists.txt b/examples/gguf-split/CMakeLists.txt index f63887da7..c407e2f0a 100644 --- a/examples/gguf-split/CMakeLists.txt +++ b/examples/gguf-split/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gguf-split) add_executable(${TARGET} gguf-split.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gguf/CMakeLists.txt b/examples/gguf/CMakeLists.txt index a9569b411..fb04eb83f 100644 --- a/examples/gguf/CMakeLists.txt +++ b/examples/gguf/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gguf) add_executable(${TARGET} gguf.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/gritlm/CMakeLists.txt b/examples/gritlm/CMakeLists.txt index 86dfddca3..fa1b4dc70 100644 --- a/examples/gritlm/CMakeLists.txt +++ b/examples/gritlm/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-gritlm) add_executable(${TARGET} gritlm.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/imatrix/CMakeLists.txt b/examples/imatrix/CMakeLists.txt index d4c8265bd..412696c47 100644 --- a/examples/imatrix/CMakeLists.txt +++ b/examples/imatrix/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-imatrix) add_executable(${TARGET} imatrix.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt index 9b1aa3b63..fb26628d8 100644 --- a/examples/infill/CMakeLists.txt +++ b/examples/infill/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-infill) add_executable(${TARGET} infill.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/llama-bench/CMakeLists.txt b/examples/llama-bench/CMakeLists.txt index 5bdbea4e2..17e3b9b87 100644 --- a/examples/llama-bench/CMakeLists.txt +++ b/examples/llama-bench/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-bench) add_executable(${TARGET} llama-bench.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index bbf5fec58..5d32f377f 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -11,7 +11,7 @@ target_include_directories(llava PUBLIC .) target_include_directories(llava PUBLIC ../..) target_include_directories(llava PUBLIC ../../common) -target_compile_features(llava PRIVATE cxx_std_11) +target_compile_features(llava PRIVATE cxx_std_17) add_library(llava_static STATIC $) if (BUILD_SHARED_LIBS) @@ -35,11 +35,11 @@ add_executable(${TARGET} llava-cli.cpp) set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-cli) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-minicpmv-cli) add_executable(${TARGET} minicpmv-cli.cpp) set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-minicpmv-cli) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/lookahead/CMakeLists.txt b/examples/lookahead/CMakeLists.txt index f0ae5cd89..346861314 100644 --- a/examples/lookahead/CMakeLists.txt +++ b/examples/lookahead/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-lookahead) add_executable(${TARGET} lookahead.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/lookup/CMakeLists.txt b/examples/lookup/CMakeLists.txt index ef19fe25e..fba78ceda 100644 --- a/examples/lookup/CMakeLists.txt +++ b/examples/lookup/CMakeLists.txt @@ -2,22 +2,22 @@ set(TARGET llama-lookup) add_executable(${TARGET} lookup.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-lookup-create) add_executable(${TARGET} lookup-create.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-lookup-merge) add_executable(${TARGET} lookup-merge.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-lookup-stats) add_executable(${TARGET} lookup-stats.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt index 3b38db292..5563f4de0 100644 --- a/examples/main-cmake-pkg/CMakeLists.txt +++ b/examples/main-cmake-pkg/CMakeLists.txt @@ -29,4 +29,4 @@ add_executable(${TARGET} ${CMAKE_CURRENT_LIST_DIR}/../main/main.cpp) target_include_directories(${TARGET} PRIVATE ${_common_path}) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/main/CMakeLists.txt b/examples/main/CMakeLists.txt index 5f6efaa9a..af3d9150f 100644 --- a/examples/main/CMakeLists.txt +++ b/examples/main/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-cli) add_executable(${TARGET} main.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/parallel/CMakeLists.txt b/examples/parallel/CMakeLists.txt index c13557bac..847e916de 100644 --- a/examples/parallel/CMakeLists.txt +++ b/examples/parallel/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-parallel) add_executable(${TARGET} parallel.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/passkey/CMakeLists.txt b/examples/passkey/CMakeLists.txt index dc467a5d3..9bc5110c2 100644 --- a/examples/passkey/CMakeLists.txt +++ b/examples/passkey/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-passkey) add_executable(${TARGET} passkey.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt index be0f2fd02..3e6864093 100644 --- a/examples/perplexity/CMakeLists.txt +++ b/examples/perplexity/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-perplexity) add_executable(${TARGET} perplexity.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/quantize-stats/CMakeLists.txt b/examples/quantize-stats/CMakeLists.txt index bb986a716..9a3a0d3cd 100644 --- a/examples/quantize-stats/CMakeLists.txt +++ b/examples/quantize-stats/CMakeLists.txt @@ -3,4 +3,4 @@ add_executable(${TARGET} quantize-stats.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt index 62680cda4..47e5cbe30 100644 --- a/examples/quantize/CMakeLists.txt +++ b/examples/quantize/CMakeLists.txt @@ -3,4 +3,4 @@ add_executable(${TARGET} quantize.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_include_directories(${TARGET} PRIVATE ../../common) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/retrieval/CMakeLists.txt b/examples/retrieval/CMakeLists.txt index 66610f311..512a602ec 100644 --- a/examples/retrieval/CMakeLists.txt +++ b/examples/retrieval/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-retrieval) add_executable(${TARGET} retrieval.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/run/CMakeLists.txt b/examples/run/CMakeLists.txt index 084f1e92d..52add51ef 100644 --- a/examples/run/CMakeLists.txt +++ b/examples/run/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-run) add_executable(${TARGET} run.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/save-load-state/CMakeLists.txt b/examples/save-load-state/CMakeLists.txt index 0fb5e359b..0f50e50de 100644 --- a/examples/save-load-state/CMakeLists.txt +++ b/examples/save-load-state/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-save-load-state) add_executable(${TARGET} save-load-state.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 93e876f5a..e82f91533 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -50,4 +50,4 @@ if (WIN32) TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32) endif() -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/simple-chat/CMakeLists.txt b/examples/simple-chat/CMakeLists.txt index 87723533b..567f7fbbb 100644 --- a/examples/simple-chat/CMakeLists.txt +++ b/examples/simple-chat/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-simple-chat) add_executable(${TARGET} simple-chat.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/simple/CMakeLists.txt b/examples/simple/CMakeLists.txt index b63afbb8b..104ecabfd 100644 --- a/examples/simple/CMakeLists.txt +++ b/examples/simple/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-simple) add_executable(${TARGET} simple.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/speculative-simple/CMakeLists.txt b/examples/speculative-simple/CMakeLists.txt index 7a3a141c2..aeaea74fc 100644 --- a/examples/speculative-simple/CMakeLists.txt +++ b/examples/speculative-simple/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-speculative-simple) add_executable(${TARGET} speculative-simple.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt index aa208e7aa..c84196bd9 100644 --- a/examples/speculative/CMakeLists.txt +++ b/examples/speculative/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-speculative) add_executable(${TARGET} speculative.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/tokenize/CMakeLists.txt b/examples/tokenize/CMakeLists.txt index b704dcae1..1690b53e5 100644 --- a/examples/tokenize/CMakeLists.txt +++ b/examples/tokenize/CMakeLists.txt @@ -2,4 +2,4 @@ set(TARGET llama-tokenize) add_executable(${TARGET} tokenize.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 70b5cfdf7..789fa3b0c 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -161,7 +161,6 @@ set (GGML_METAL_MACOSX_VERSION_MIN "" CACHE STRING set (GGML_METAL_STD "" CACHE STRING "ggml: metal standard version (-std flag)") option(GGML_OPENMP "ggml: use OpenMP" ON) option(GGML_RPC "ggml: use RPC" OFF) -option(GGML_AMX "ggml: use AMX" OFF) option(GGML_SYCL "ggml: use SYCL" OFF) option(GGML_SYCL_F16 "ggml: use 16 bit floats for sycl calculations" OFF) set (GGML_SYCL_TARGET "INTEL" CACHE STRING diff --git a/ggml/include/ggml-amx.h b/ggml/include/ggml-amx.h deleted file mode 100644 index 042d6d919..000000000 --- a/ggml/include/ggml-amx.h +++ /dev/null @@ -1,25 +0,0 @@ -#pragma once - -#include "ggml.h" -#include "ggml-backend.h" - - -#ifdef __cplusplus -extern "C" { -#endif - -// buffer_type API -GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void); - -GGML_BACKEND_API bool ggml_backend_is_amx(ggml_backend_t backend); - -// backend API -GGML_BACKEND_API ggml_backend_t ggml_backend_amx_init(void); - -GGML_BACKEND_API void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads); - -GGML_BACKEND_API ggml_backend_reg_t ggml_backend_amx_reg(void); - -#ifdef __cplusplus -} -#endif diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 9022aa3ae..19289f32b 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -261,21 +261,15 @@ function(ggml_add_backend backend) if (${backend_id}) string(TOLOWER "ggml-${backend}" backend_target) add_subdirectory(${backend_target}) - # check again in case the backend disabled itself - # note that this should NOT be the normal behavior, in case of errors the backend should fail the build - # however, currently it is necessary for AMX, since it is enabled by default on llama.cpp - if (${backend_id}) - message(STATUS "Including ${backend} backend") - if (NOT GGML_BACKEND_DL) - string(TOUPPER "GGML_USE_${backend}" backend_use) - target_compile_definitions(ggml PUBLIC ${backend_use}) - endif() + message(STATUS "Including ${backend} backend") + if (NOT GGML_BACKEND_DL) + string(TOUPPER "GGML_USE_${backend}" backend_use) + target_compile_definitions(ggml PUBLIC ${backend_use}) endif() endif() endfunction() ggml_add_backend(CPU) -ggml_add_backend(AMX) ggml_add_backend(BLAS) ggml_add_backend(CANN) ggml_add_backend(CUDA) @@ -289,7 +283,7 @@ ggml_add_backend(Vulkan) foreach (target ggml-base ggml) target_include_directories(${target} PUBLIC $ $) - target_compile_features (${target} PRIVATE c_std_11) # don't bump + target_compile_features (${target} PRIVATE c_std_11 cxx_std_17) # don't bump endforeach() target_link_libraries(ggml-base PRIVATE Threads::Threads) diff --git a/ggml/src/ggml-amx/CMakeLists.txt b/ggml/src/ggml-amx/CMakeLists.txt deleted file mode 100644 index cf3ade6f0..000000000 --- a/ggml/src/ggml-amx/CMakeLists.txt +++ /dev/null @@ -1,105 +0,0 @@ -if (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR - (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND - CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$") AND - CMAKE_COMPILER_IS_GNUCC AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 11.0) - message(STATUS "Using AMX") - - file(GLOB GGML_HEADERS_AMX "*.h") - list(APPEND GGML_HEADERS_AMX "../../include/ggml-amx.h") - - file(GLOB GGML_SOURCES_AMX "*.cpp") - - ggml_add_backend_library(ggml-amx - ${GGML_HEADERS_AMX} - ${GGML_SOURCES_AMX} - ) - - # this is duplicated from the CPU backend, since the AMX backend also depends on the architecture flags - # TODO: integrate AMX backend into the CPU backend - if (MSVC) - # instruction set detection for MSVC only - if (GGML_NATIVE) - # TODO: improve, should not reference files from the parent folder - include(../ggml-cpu/cmake/FindSIMD.cmake) - endif () - if (GGML_AVX512) - list(APPEND ARCH_FLAGS /arch:AVX512) - # MSVC has no compile-time flags enabling specific - # AVX512 extensions, neither it defines the - # macros corresponding to the extensions. - # Do it manually. - if (GGML_AVX512_VBMI) - add_compile_definitions($<$:__AVX512VBMI__>) - add_compile_definitions($<$:__AVX512VBMI__>) - endif() - if (GGML_AVX512_VNNI) - add_compile_definitions($<$:__AVX512VNNI__>) - add_compile_definitions($<$:__AVX512VNNI__>) - endif() - if (GGML_AVX512_BF16) - add_compile_definitions($<$:__AVX512BF16__>) - add_compile_definitions($<$:__AVX512BF16__>) - endif() - if (GGML_AMX_TILE) - add_compile_definitions($<$:__AMX_TILE__>) - add_compile_definitions($<$:__AMX_TILE__>) - endif() - if (GGML_AMX_INT8) - add_compile_definitions($<$:__AMX_INT8__>) - add_compile_definitions($<$:__AMX_INT8__>) - endif() - if (GGML_AMX_BF16) - add_compile_definitions($<$:__AMX_BF16__>) - add_compile_definitions($<$:__AMX_BF16__>) - endif() - elseif (GGML_AVX2) - list(APPEND ARCH_FLAGS /arch:AVX2) - elseif (GGML_AVX) - list(APPEND ARCH_FLAGS /arch:AVX) - endif() - else() - if (GGML_NATIVE) - list(APPEND ARCH_FLAGS -march=native) - endif() - if (GGML_F16C) - list(APPEND ARCH_FLAGS -mf16c) - endif() - if (GGML_FMA) - list(APPEND ARCH_FLAGS -mfma) - endif() - if (GGML_AVX) - list(APPEND ARCH_FLAGS -mavx) - endif() - if (GGML_AVX2) - list(APPEND ARCH_FLAGS -mavx2) - endif() - if (GGML_AVX512) - list(APPEND ARCH_FLAGS -mavx512f) - list(APPEND ARCH_FLAGS -mavx512dq) - list(APPEND ARCH_FLAGS -mavx512bw) - endif() - if (GGML_AVX512_VBMI) - list(APPEND ARCH_FLAGS -mavx512vbmi) - endif() - if (GGML_AVX512_VNNI) - list(APPEND ARCH_FLAGS -mavx512vnni) - endif() - if (GGML_AVX512_BF16) - list(APPEND ARCH_FLAGS -mavx512bf16) - endif() - if (GGML_AMX_TILE) - list(APPEND ARCH_FLAGS -mamx-tile) - endif() - if (GGML_AMX_INT8) - list(APPEND ARCH_FLAGS -mamx-int8) - endif() - if (GGML_AMX_BF16) - list(APPEND ARCH_FLAGS -mamx-bf16) - endif() - endif() - - target_compile_options(ggml-amx PRIVATE ${ARCH_FLAGS}) -else() - set(GGML_AMX OFF PARENT_SCOPE) - message(WARNING "AMX requires x86 and gcc version > 11.0. Turning off GGML_AMX.") -endif() diff --git a/ggml/src/ggml-amx/ggml-amx.cpp b/ggml/src/ggml-amx/ggml-amx.cpp deleted file mode 100644 index 6bfb3da27..000000000 --- a/ggml/src/ggml-amx/ggml-amx.cpp +++ /dev/null @@ -1,449 +0,0 @@ -#include "ggml-amx.h" -#include "ggml-amx/common.h" -#include "ggml-amx/mmq.h" -#include "ggml-backend-impl.h" -#include "ggml-impl.h" - -#if defined(__gnu_linux__) -#include -#include -#endif - -#include -#include -#include - -#if defined(__AMX_INT8__) - -// AMX buffer interface -static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) { - free(buffer->context); -} - -static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) { - return (void *)(buffer->context); -} - -static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { - memset((char *)tensor->data + offset, value, size); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - if (qtype_has_amx_kernels(tensor->type)) { - ggml_backend_amx_convert_weight(tensor, data, offset, size); - } else { - memcpy((char *)tensor->data + offset, data, size); - } - - GGML_UNUSED(buffer); -} - -static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - GGML_ASSERT(!qtype_has_amx_kernels(tensor->type)); - memcpy(data, (const char *)tensor->data + offset, size); - - GGML_UNUSED(buffer); -} - -static bool ggml_backend_amx_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { - if (ggml_backend_buffer_is_host(src->buffer)) { - if (qtype_has_amx_kernels(src->type)) { - ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_backend_amx_get_alloc_size(dst)); - } else { - memcpy(dst->data, src->data, ggml_nbytes(src)); - } - return true; - } - return false; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - memset(buffer->context, value, buffer->size); -} - -static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = { - /* .free_buffer = */ ggml_backend_amx_buffer_free_buffer, - /* .get_base = */ ggml_backend_amx_buffer_get_base, - /* .init_tensor = */ NULL, // no initialization required - /* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor, - /* .set_tensor = */ ggml_backend_amx_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_amx_buffer_get_tensor, - /* .cpy_tensor = */ ggml_backend_amx_buffer_cpy_tensor, - /* .clear = */ ggml_backend_amx_buffer_clear, - /* .reset = */ NULL, -}; - -static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) { - return "AMX"; - - GGML_UNUSED(buft); -} - -static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * data = aligned_alloc(TENSOR_ALIGNMENT, size); - if (data == NULL) { - fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size); - return NULL; - } - - return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size); -} - -static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return TENSOR_ALIGNMENT; - - GGML_UNUSED(buft); -} - -static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) { - return ggml_backend_amx_get_alloc_size(tensor); - - GGML_UNUSED(buft); -} - -static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft) { - return false; - - GGML_UNUSED(buft); -} - -ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() { - static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = { - /* .iface = */ { - /* .get_name = */ ggml_backend_amx_buffer_type_get_name, - /* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size, - /* .is_host = */ ggml_backend_amx_buffer_type_is_host, - }, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0), - /* .context = */ NULL, - }; - - return &ggml_backend_buffer_type_amx; -} - -// backend interface - -static const char * ggml_backend_amx_name(ggml_backend_t backend) { - return "AMX"; - - GGML_UNUSED(backend); -} - -static void ggml_backend_amx_free(ggml_backend_t backend) { - ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; - delete ctx; - delete backend; -} - -static enum ggml_status ggml_backend_amx_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend->context; - - for (int i = 0; i < cgraph->n_nodes; i++) { - struct ggml_tensor * node = cgraph->nodes[i]; - - switch (node->op) { - case GGML_OP_MUL_MAT: - ggml_backend_amx_mul_mat(ctx, node); - break; - - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - break; - - default: - fprintf(stderr, "%s: unsupported op %s\n", __func__, ggml_op_desc(node)); - GGML_ASSERT(false); - } - } - - return GGML_STATUS_SUCCESS; - - GGML_UNUSED(backend); -} - -static struct ggml_backend_i ggml_backend_amx_i = { - /* .get_name = */ ggml_backend_amx_name, - /* .free = */ ggml_backend_amx_free, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_update = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_amx_graph_compute, - /* .event_record = */ NULL, - /* .event_wait = */ NULL, -}; - -static ggml_guid_t ggml_backend_amx_guid() { - static ggml_guid guid = { 0x13, 0xb8, 0xa4, 0xc4, 0xba, 0xfe, 0x51, 0x67, 0x87, 0x44, 0x55, 0x15, 0xb2, 0x35, 0x62, 0x3e }; - return &guid; -} - -#define ARCH_GET_XCOMP_PERM 0x1022 -#define ARCH_REQ_XCOMP_PERM 0x1023 -#define XFEATURE_XTILECFG 17 -#define XFEATURE_XTILEDATA 18 - -static bool ggml_amx_init() { -#if defined(__gnu_linux__) - if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) { - fprintf(stderr, "AMX is not ready to be used!\n"); - return false; - } - return true; -#elif defined(_WIN32) - return true; -#endif -} - -ggml_backend_t ggml_backend_amx_init() { - - // invoke a Linux system call to request access to AMX features - ggml_amx_init(); - - // backend context - ggml_backend_amx_context * ctx = new ggml_backend_amx_context; - - // ggml amx backend - ggml_backend_t backend = new ggml_backend { - /* .guid = */ ggml_backend_amx_guid(), - /* .interface = */ ggml_backend_amx_i, - /* .device = */ ggml_backend_reg_dev_get(ggml_backend_amx_reg(), 0), - /* .context = */ ctx, - }; - - return backend; -} - -bool ggml_backend_is_amx(ggml_backend_t backend) { - return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_amx_guid()); -} - -void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) { - GGML_ASSERT(ggml_backend_is_amx(backend_amx)); - - ggml_backend_amx_context * ctx = (ggml_backend_amx_context *)backend_amx->context; - ctx->n_threads = n_threads; -} - -// device interface - -static const char * ggml_backend_amx_device_get_name(ggml_backend_dev_t dev) { - return "AMX"; - - GGML_UNUSED(dev); -} - -static const char * ggml_backend_amx_device_get_description(ggml_backend_dev_t dev) { - return "Intel Advanced Matrix Extensions"; - - GGML_UNUSED(dev); -} - -static void ggml_backend_amx_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - // TODO - *free = 0; - *total = 0; - - GGML_UNUSED(dev); -} - -static enum ggml_backend_dev_type ggml_backend_amx_device_get_type(ggml_backend_dev_t dev) { - return GGML_BACKEND_DEVICE_TYPE_ACCEL; - - GGML_UNUSED(dev); -} - -static void ggml_backend_amx_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { - props->name = ggml_backend_amx_device_get_name(dev); - props->description = ggml_backend_amx_device_get_description(dev); - props->type = ggml_backend_amx_device_get_type(dev); - ggml_backend_amx_device_get_memory(dev, &props->memory_free, &props->memory_total); - - // `buffer_from_host_ptr` is intended to be used in mmap, when memory layout unchanged - props->caps = { - /* .async = */ false, - /* .host_buffer = */ false, - /* .buffer_from_host_ptr = */ false, - /* .events = */ false, - }; -} - -static ggml_backend_t ggml_backend_amx_device_init(ggml_backend_dev_t dev, const char * params) { - return ggml_backend_amx_init(); - - GGML_UNUSED(dev); - GGML_UNUSED(params); -} - -static ggml_backend_buffer_type_t ggml_backend_amx_device_get_buffer_type(ggml_backend_dev_t dev) { - return ggml_backend_amx_buffer_type(); - - GGML_UNUSED(dev); -} - -static bool ggml_backend_amx_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { - - // handle only 2d gemm for now - auto is_contiguous_2d = [](const struct ggml_tensor * t) { - return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1; - }; - - switch (op->op) { - case GGML_OP_NONE: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - return true; - - case GGML_OP_MUL_MAT: { - const struct ggml_tensor * src0 = op->src[0]; - const struct ggml_tensor * src1 = op->src[1]; - - const enum ggml_type type = src0->type; - const int64_t ne0 = op->ne[0]; - - // amx kernels enables for Q4_0, Q4_1, Q8_0, F16 - // Q4_K, Q5_K, Q6_K, IQ4_XS enabled for QK_K = 256 - bool has_amx_kernels = qtype_has_amx_kernels(type) || (type == GGML_TYPE_F16); - - bool can_use_amx = - is_contiguous_2d(src0) && // src0 must be contiguous - is_contiguous_2d(src1) && // src1 must be contiguous - src1->type == GGML_TYPE_F32 && // src1 must be float32 - has_amx_kernels && // with amx kernel impls - ne0 % (TILE_N * 2) == 0; // out_features is 32x - - return can_use_amx; - } - default: - return false; - } - - GGML_UNUSED(dev); -} - -static bool ggml_backend_amx_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return buft->iface.get_name == ggml_backend_amx_buffer_type_get_name; - - GGML_UNUSED(dev); -} - -static const struct ggml_backend_device_i ggml_backend_amx_device_i = { - /* .get_name = */ ggml_backend_amx_device_get_name, - /* .get_description = */ ggml_backend_amx_device_get_description, - /* .get_memory = */ ggml_backend_amx_device_get_memory, - /* .get_type = */ ggml_backend_amx_device_get_type, - /* .get_props = */ ggml_backend_amx_device_get_props, - /* .init_backend = */ ggml_backend_amx_device_init, - /* .get_buffer_type = */ ggml_backend_amx_device_get_buffer_type, - /* .get_host_buffer_type = */ NULL, - /* .buffer_from_host_ptr = */ NULL, - /* .supports_op = */ ggml_backend_amx_device_supports_op, - /* .supports_buft = */ ggml_backend_amx_device_supports_buft, - /* .offload_op = */ NULL, - /* .event_new = */ NULL, - /* .event_free = */ NULL, - /* .event_synchronize = */ NULL, -}; - -// backend reg interface - -static const char * ggml_backend_amx_reg_get_name(ggml_backend_reg_t reg) { - return "AMX"; - - GGML_UNUSED(reg); -} - -static size_t ggml_backend_amx_reg_get_device_count(ggml_backend_reg_t reg) { - return 1; - - GGML_UNUSED(reg); -} - -static ggml_backend_dev_t ggml_backend_amx_reg_get_device(ggml_backend_reg_t reg, size_t index) { - GGML_ASSERT(index == 0); - - static ggml_backend_device ggml_backend_amx_device = { - /* .iface = */ ggml_backend_amx_device_i, - /* .reg = */ reg, - /* .context = */ nullptr, - }; - - return &ggml_backend_amx_device; - - GGML_UNUSED(reg); - GGML_UNUSED(index); -} - -static void * ggml_backend_amx_get_proc_address(ggml_backend_reg_t reg, const char * name) { - if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) { - return (void *)ggml_backend_amx_set_n_threads; - } - return NULL; - - GGML_UNUSED(reg); - GGML_UNUSED(name); -} - -static const struct ggml_backend_reg_i ggml_backend_amx_reg_i = { - /* .get_name = */ ggml_backend_amx_reg_get_name, - /* .get_device_count = */ ggml_backend_amx_reg_get_device_count, - /* .get_device = */ ggml_backend_amx_reg_get_device, - /* .get_proc_address = */ ggml_backend_amx_get_proc_address, -}; - -ggml_backend_reg_t ggml_backend_amx_reg(void) { - static struct ggml_backend_reg ggml_backend_amx_reg = { - /* .api_version = */ GGML_BACKEND_API_VERSION, - /* .iface = */ ggml_backend_amx_reg_i, - /* .context = */ NULL, - }; - - return &ggml_backend_amx_reg; -} - -#else // if defined(__AMX_INT8__) - -ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void) { - return nullptr; -} - -bool ggml_backend_is_amx(ggml_backend_t backend) { - GGML_UNUSED(backend); - return false; -} - -ggml_backend_t ggml_backend_amx_init(void) { - fprintf(stderr, "GGML is not compiled with AMX support!\n"); - return nullptr; -} - -void ggml_backend_amx_set_n_threads(ggml_backend_t backend_amx, int n_threads) { - fprintf(stderr, "GGML is not compiled with AMX support!\n"); - - GGML_UNUSED(backend_amx); - GGML_UNUSED(n_threads); -} - -ggml_backend_reg_t ggml_backend_amx_reg(void) { - return nullptr; -} - -#endif - -GGML_BACKEND_DL_IMPL(ggml_backend_amx_reg) diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp index a0e0e2c58..3182b84f5 100644 --- a/ggml/src/ggml-backend-reg.cpp +++ b/ggml/src/ggml-backend-reg.cpp @@ -49,10 +49,6 @@ #include "ggml-rpc.h" #endif -#ifdef GGML_USE_AMX -# include "ggml-amx.h" -#endif - #ifdef GGML_USE_CANN #include "ggml-cann.h" #endif @@ -92,9 +88,6 @@ struct ggml_backend_registry { #ifdef GGML_USE_RPC register_backend(ggml_backend_rpc_reg()); #endif -#ifdef GGML_USE_AMX - register_backend(ggml_backend_amx_reg()); -#endif #ifdef GGML_USE_KOMPUTE register_backend(ggml_backend_kompute_reg()); #endif diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 45da0c27d..fdb4b986f 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -742,7 +742,8 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st if (tensor->buffer || (tensor->view_src && tensor->view_src->buffer)) { // since the tensor is pre-allocated, it cannot be moved to another backend - GGML_ABORT("pre-allocated tensor (%s) in a backend that cannot run the operation", tensor->name); + ggml_backend_buffer_t buffer = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + GGML_ABORT("pre-allocated tensor (%s) in a buffer (%s) that cannot run the operation (%s)", tensor->name, ggml_backend_buffer_name(buffer), ggml_op_name(tensor->op)); } // graph input diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index 4dbc1f75b..fe2222084 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -1,12 +1,20 @@ -ggml_add_backend_library(ggml-cpu - ggml-cpu.c - ggml-cpu.cpp - ggml-cpu-aarch64.c - ggml-cpu-aarch64.h - ggml-cpu-quants.c - ggml-cpu-quants.h - ) +ggml_add_backend_library(ggml-cpu) +list (APPEND GGML_CPU_SOURCES + ggml-cpu.c + ggml-cpu.cpp + ggml-cpu-aarch64.c + ggml-cpu-aarch64.h + ggml-cpu-quants.c + ggml-cpu-quants.h + amx/amx.cpp + amx/amx.h + amx/mmq.cpp + amx/mmq.h + ggml-cpu-impl.h + ) + +target_compile_features(ggml-cpu PRIVATE c_std_11 cxx_std_17) target_include_directories(ggml-cpu PRIVATE .) if (APPLE AND GGML_ACCELERATE) @@ -14,9 +22,9 @@ if (APPLE AND GGML_ACCELERATE) if (ACCELERATE_FRAMEWORK) message(STATUS "Accelerate framework found") - add_compile_definitions(GGML_USE_ACCELERATE) - add_compile_definitions(ACCELERATE_NEW_LAPACK) - add_compile_definitions(ACCELERATE_LAPACK_ILP64) + target_compile_definitions(ggml-cpu PRIVATE GGML_USE_ACCELERATE) + target_compile_definitions(ggml-cpu PRIVATE ACCELERATE_NEW_LAPACK) + target_compile_definitions(ggml-cpu PRIVATE ACCELERATE_LAPACK_ILP64) target_link_libraries(ggml-cpu PRIVATE ${ACCELERATE_FRAMEWORK}) else() @@ -29,15 +37,9 @@ if (GGML_OPENMP) if (OpenMP_FOUND) message(STATUS "OpenMP found") - add_compile_definitions(GGML_USE_OPENMP) + target_compile_definitions(ggml-cpu PRIVATE GGML_USE_OPENMP) target_link_libraries(ggml-cpu PRIVATE OpenMP::OpenMP_C OpenMP::OpenMP_CXX) - - # FIXME: should be replaced with a compiler id check - #if (GGML_MUSA) - # list(APPEND GGML_CPU_EXTRA_INCLUDES "/usr/lib/llvm-14/lib/clang/14.0.0/include") - # list(APPEND GGML_CPU_EXTRA_LIBS_PRIVATE "/usr/lib/llvm-14/lib/libomp.so") - #endif() else() message(WARNING "OpenMP not found") endif() @@ -46,11 +48,11 @@ endif() if (GGML_LLAMAFILE) message(STATUS "Using llamafile") - add_compile_definitions(GGML_USE_LLAMAFILE) + target_compile_definitions(ggml-cpu PRIVATE GGML_USE_LLAMAFILE) - target_sources(ggml-cpu PRIVATE - llamafile/sgemm.cpp - llamafile/sgemm.h) + list(APPEND GGML_CPU_SOURCES + llamafile/sgemm.cpp + llamafile/sgemm.h) endif() if (GGML_CPU_HBM) @@ -58,7 +60,7 @@ if (GGML_CPU_HBM) message(STATUS "Using memkind for CPU HBM") - add_compile_definitions(GGML_USE_CPU_HBM) + target_compile_definitions(ggml-cpu PRIVATE GGML_USE_CPU_HBM) target_link_libraries(ggml-cpu PUBLIC memkind) endif() @@ -72,16 +74,16 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR message(STATUS "ARM detected") if (MSVC) - add_compile_definitions(__aarch64__) # MSVC defines _M_ARM64 instead - add_compile_definitions(__ARM_NEON) - add_compile_definitions(__ARM_FEATURE_FMA) + list(APPEND ARCH_DEFINITIONS __aarch64__) # MSVC defines _M_ARM64 instead + list(APPEND ARCH_DEFINITIONS __ARM_NEON) + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FMA) set(CMAKE_REQUIRED_FLAGS_PREV ${CMAKE_REQUIRED_FLAGS}) string(JOIN " " CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS} "/arch:armv8.2") check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) if (GGML_COMPILER_SUPPORT_DOTPROD) - add_compile_definitions(__ARM_FEATURE_DOTPROD) + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD) message(STATUS "ARM feature DOTPROD enabled") endif () @@ -89,14 +91,14 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) if (GGML_COMPILER_SUPPORT_MATMUL_INT8) - add_compile_definitions(__ARM_FEATURE_MATMUL_INT8) + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8) message(STATUS "ARM feature MATMUL_INT8 enabled") endif () check_cxx_source_compiles("#include \nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) - add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_FP16_VECTOR_ARITHMETIC) message(STATUS "ARM feature FP16_VECTOR_ARITHMETIC enabled") endif () @@ -118,7 +120,7 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vdotq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_DOTPROD) if (GGML_COMPILER_SUPPORT_DOTPROD) set(MARCH_FLAGS "${MARCH_FLAGS}+dotprod") - add_compile_definitions(__ARM_FEATURE_DOTPROD) + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_DOTPROD) message(STATUS "ARM feature DOTPROD enabled") endif () @@ -131,7 +133,7 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmmlaq_s32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) if (GGML_COMPILER_SUPPORT_MATMUL_INT8) set(MARCH_FLAGS "${MARCH_FLAGS}+i8mm") - add_compile_definitions(__ARM_FEATURE_MATMUL_INT8) + list(APPEND ARCH_DEFINITIONS __ARM_FEATURE_MATMUL_INT8) message(STATUS "ARM feature MATMUL_INT8 enabled") endif () @@ -175,7 +177,6 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW if (MSVC) # instruction set detection for MSVC only if (GGML_NATIVE) - # TODO: improve, should not reference files from the parent folder include(cmake/FindSIMD.cmake) endif () if (GGML_AVX512) @@ -185,37 +186,31 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW # macros corresponding to the extensions. # Do it manually. if (GGML_AVX512_VBMI) - add_compile_definitions($<$:__AVX512VBMI__>) - add_compile_definitions($<$:__AVX512VBMI__>) + list(APPEND ARCH_DEFINITIONS __AVX512VBMI__) if (CMAKE_C_COMPILER_ID STREQUAL "Clang") list(APPEND ARCH_FLAGS -mavx512vbmi) endif() endif() if (GGML_AVX512_VNNI) - add_compile_definitions($<$:__AVX512VNNI__>) - add_compile_definitions($<$:__AVX512VNNI__>) + list(APPEND ARCH_DEFINITIONS __AVX512VNNI__) if (CMAKE_C_COMPILER_ID STREQUAL "Clang") list(APPEND ARCH_FLAGS -mavx512vnni) endif() endif() if (GGML_AVX512_BF16) - add_compile_definitions($<$:__AVX512BF16__>) - add_compile_definitions($<$:__AVX512BF16__>) + list(APPEND ARCH_DEFINITIONS __AVX512BF16__) if (CMAKE_C_COMPILER_ID STREQUAL "Clang") list(APPEND ARCH_FLAGS -mavx512bf16) endif() endif() if (GGML_AMX_TILE) - add_compile_definitions($<$:__AMX_TILE__>) - add_compile_definitions($<$:__AMX_TILE__>) + list(APPEND ARCH_DEFINITIONS __AMX_TILE__) endif() if (GGML_AMX_INT8) - add_compile_definitions($<$:__AMX_INT8__>) - add_compile_definitions($<$:__AMX_INT8__>) + list(APPEND ARCH_DEFINITIONS __AMX_INT8__) endif() if (GGML_AMX_BF16) - add_compile_definitions($<$:__AMX_BF16__>) - add_compile_definitions($<$:__AMX_BF16__>) + list(APPEND ARCH_DEFINITIONS __AMX_BF16__) endif() elseif (GGML_AVX2) list(APPEND ARCH_FLAGS /arch:AVX2) @@ -276,7 +271,7 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") list(APPEND ARCH_FLAGS -mcpu=powerpc64le) else() list(APPEND ARCH_FLAGS -mcpu=native -mtune=native) - #TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be) + # TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be) endif() elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") message(STATUS "loongarch64 detected") @@ -299,11 +294,12 @@ endif() if (GGML_CPU_AARCH64) message(STATUS "Using runtime weight conversion of Q4_0 to Q4_0_x_x to enable optimized GEMM/GEMV kernels") - add_compile_definitions(GGML_USE_CPU_AARCH64) + target_compile_definitions(ggml-cpu PRIVATE GGML_USE_CPU_AARCH64) endif() -target_compile_options(ggml-cpu PRIVATE "$<$:${ARCH_FLAGS}>") -target_compile_options(ggml-cpu PRIVATE "$<$:${ARCH_FLAGS}>") +target_sources(ggml-cpu PRIVATE ${GGML_CPU_SOURCES}) +set_source_files_properties(${GGML_CPU_SOURCES} PROPERTIES COMPILE_OPTIONS "${ARCH_FLAGS}") +set_source_files_properties(${GGML_CPU_SOURCES} PROPERTIES COMPILE_DEFINITIONS "${ARCH_DEFINITIONS}") if (EMSCRIPTEN) set_target_properties(ggml-cpu PROPERTIES COMPILE_FLAGS "-msimd128") diff --git a/ggml/src/ggml-cpu/amx/amx.cpp b/ggml/src/ggml-cpu/amx/amx.cpp new file mode 100644 index 000000000..09c0df0f5 --- /dev/null +++ b/ggml/src/ggml-cpu/amx/amx.cpp @@ -0,0 +1,196 @@ +#include "amx.h" +#include "common.h" +#include "mmq.h" +#include "ggml-backend-impl.h" +#include "ggml-backend.h" +#include "ggml-impl.h" +#include "ggml-cpu.h" + +#if defined(__gnu_linux__) +#include +#include +#endif + +#include +#include +#include + +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + +// AMX buffer interface +static void ggml_backend_amx_buffer_free_buffer(ggml_backend_buffer_t buffer) { + free(buffer->context); +} + +static void * ggml_backend_amx_buffer_get_base(ggml_backend_buffer_t buffer) { + return (void *)(buffer->context); +} + +static void ggml_backend_amx_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + memset((char *)tensor->data + offset, value, size); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + if (qtype_has_amx_kernels(tensor->type)) { + ggml_backend_amx_convert_weight(tensor, data, offset, size); + } else { + memcpy((char *)tensor->data + offset, data, size); + } + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + GGML_ASSERT(!qtype_has_amx_kernels(tensor->type)); + memcpy(data, (const char *)tensor->data + offset, size); + + GGML_UNUSED(buffer); +} + +static bool ggml_backend_amx_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + if (qtype_has_amx_kernels(src->type)) { + ggml_backend_amx_convert_weight(dst, src->data, 0, ggml_nbytes(dst)); + } else { + memcpy(dst->data, src->data, ggml_nbytes(src)); + } + return true; + } + return false; + + GGML_UNUSED(buffer); +} + +static void ggml_backend_amx_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + memset(buffer->context, value, buffer->size); +} + +static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = { + /* .free_buffer = */ ggml_backend_amx_buffer_free_buffer, + /* .get_base = */ ggml_backend_amx_buffer_get_base, + /* .init_tensor = */ NULL, // no initialization required + /* .memset_tensor = */ ggml_backend_amx_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_amx_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_amx_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_amx_buffer_cpy_tensor, + /* .clear = */ ggml_backend_amx_buffer_clear, + /* .reset = */ NULL, +}; + +static const char * ggml_backend_amx_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "AMX"; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_amx_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * data = aligned_alloc(TENSOR_ALIGNMENT, size); + if (data == NULL) { + fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size); + return NULL; + } + + return ggml_backend_buffer_init(buft, ggml_backend_amx_buffer_interface, data, size); +} + +static size_t ggml_backend_amx_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return TENSOR_ALIGNMENT; + + GGML_UNUSED(buft); +} + +static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor* tensor) { + return ggml_backend_amx_get_alloc_size(tensor); + + GGML_UNUSED(buft); +} + +static bool ggml_backend_amx_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return false; + + GGML_UNUSED(buft); +} + +#define ARCH_GET_XCOMP_PERM 0x1022 +#define ARCH_REQ_XCOMP_PERM 0x1023 +#define XFEATURE_XTILECFG 17 +#define XFEATURE_XTILEDATA 18 + +static bool ggml_amx_init() { +#if defined(__gnu_linux__) + if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) { + fprintf(stderr, "AMX is not ready to be used!\n"); + return false; + } + return true; +#elif defined(_WIN32) + return true; +#endif +} +ggml_backend_buffer_type_t ggml_backend_amx_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_buffer_type_amx = { + /* .iface = */ { + /* .get_name = */ ggml_backend_amx_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_amx_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_amx_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_amx_buffer_type_get_alloc_size, + /* .is_host = */ ggml_backend_amx_buffer_type_is_host, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_cpu_reg(), 0), + /* .context = */ NULL, + }; + + if (!ggml_amx_init()) { + return NULL; + } + + return &ggml_backend_buffer_type_amx; +} + +bool ggml_backend_amx_buft_is_amx(ggml_backend_buffer_type_t buft) { + return buft->iface.get_name == ggml_backend_amx_buffer_type_get_name; +} + +bool ggml_backend_amx_device_supports_op(const struct ggml_tensor * op) { + // handle only 2d gemm for now + auto is_contiguous_2d = [](const struct ggml_tensor * t) { + return ggml_is_contiguous(t) && t->ne[3] == 1 && t->ne[2] == 1; + }; + + switch (op->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + return true; + + case GGML_OP_MUL_MAT: { + const struct ggml_tensor * src0 = op->src[0]; + const struct ggml_tensor * src1 = op->src[1]; + + const enum ggml_type type = src0->type; + const int64_t ne0 = op->ne[0]; + + // amx kernels enables for Q4_0, Q4_1, Q8_0, F16 + // Q4_K, Q5_K, Q6_K, IQ4_XS enabled for QK_K = 256 + bool has_amx_kernels = qtype_has_amx_kernels(type) || (type == GGML_TYPE_F16); + + bool can_use_amx = + is_contiguous_2d(src0) && // src0 must be contiguous + is_contiguous_2d(src1) && // src1 must be contiguous + src1->type == GGML_TYPE_F32 && // src1 must be float32 + has_amx_kernels && // with amx kernel impls + ne0 % (TILE_N * 2) == 0; // out_features is 32x + + return can_use_amx; + } + default: + return false; + } +} + +#endif // defined(__AMX_INT8__) && defined(__AVX512VNNI__) diff --git a/ggml/src/ggml-cpu/amx/amx.h b/ggml/src/ggml-cpu/amx/amx.h new file mode 100644 index 000000000..c43546273 --- /dev/null +++ b/ggml/src/ggml-cpu/amx/amx.h @@ -0,0 +1,20 @@ +#include "ggml-backend.h" +#include "ggml-cpu-impl.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + +ggml_backend_buffer_type_t ggml_backend_amx_buffer_type(void); +bool ggml_backend_amx_buft_is_amx(ggml_backend_buffer_type_t buft); +bool ggml_backend_amx_device_supports_op(const struct ggml_tensor * op); +void ggml_backend_amx_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst); +size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst); + +#endif + +#ifdef __cplusplus +} +#endif diff --git a/ggml/src/ggml-amx/common.h b/ggml/src/ggml-cpu/amx/common.h similarity index 77% rename from ggml/src/ggml-amx/common.h rename to ggml/src/ggml-cpu/amx/common.h index 5db8ce30d..0b0657289 100644 --- a/ggml/src/ggml-amx/common.h +++ b/ggml/src/ggml-cpu/amx/common.h @@ -1,8 +1,7 @@ #pragma once #include "ggml.h" -// hack until AMX is moved into the CPU backend -#include "../ggml-cpu/ggml-cpu-impl.h" // +#include "ggml-cpu-impl.h" #include #include @@ -74,16 +73,24 @@ inline void parallel_for(int nth, int n, const func_t& f) { #endif } +template +inline void parallel_for_ggml(const ggml_compute_params * params, int n, const func_t & f) { + int tbegin, tend; + balance211(n, params->nth, params->ith, tbegin, tend); + f(tbegin, tend); + ggml_barrier(params->threadpool); // TODO: might not always be needed +} + // quantized types that have AMX support inline bool qtype_has_amx_kernels(const enum ggml_type type) { // TODO: fix padding for vnni format return (type == GGML_TYPE_Q4_0) || - (type == GGML_TYPE_Q4_1); - //(type == GGML_TYPE_Q8_0) || - //(type == GGML_TYPE_Q4_K) || - //(type == GGML_TYPE_Q5_K) || - //(type == GGML_TYPE_Q6_K) || - //(type == GGML_TYPE_IQ4_XS); + (type == GGML_TYPE_Q4_1) || + (type == GGML_TYPE_Q8_0) || + (type == GGML_TYPE_Q4_K) || + (type == GGML_TYPE_Q5_K) || + (type == GGML_TYPE_Q6_K) || + (type == GGML_TYPE_IQ4_XS); } // ggml backend context diff --git a/ggml/src/ggml-amx/mmq.cpp b/ggml/src/ggml-cpu/amx/mmq.cpp similarity index 98% rename from ggml/src/ggml-amx/mmq.cpp rename to ggml/src/ggml-cpu/amx/mmq.cpp index 529bee25b..6447e73d0 100644 --- a/ggml/src/ggml-amx/mmq.cpp +++ b/ggml/src/ggml-cpu/amx/mmq.cpp @@ -4,8 +4,11 @@ #pragma GCC diagnostic ignored "-Wunused-local-typedefs" #endif +#include "amx.h" #include "mmq.h" #include "ggml-impl.h" +#include "ggml-cpu-impl.h" +#include "ggml-cpu-quants.h" #include "ggml-quants.h" #include #include @@ -33,7 +36,7 @@ #define ALWAYS_INLINE inline #endif -#if defined(__AMX_INT8__) +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) namespace { @@ -496,13 +499,12 @@ inline void from_float(const float * x, char * vy, int64_t k); template <> inline void from_float(const float * x, char * vy, int64_t k) { - // FIXME: using unoptimized reference impl until moved to CPU backend - quantize_row_q8_0_ref(x, (block_q8_0 *)vy, k); + quantize_row_q8_0(x, (block_q8_0 *)vy, k); } template <> inline void from_float(const float * x, char * vy, int64_t k) { - quantize_row_q8_1_ref(x, (block_q8_1 *)vy, k); + quantize_row_q8_1(x, (block_q8_1 *)vy, k); } template <> @@ -950,7 +952,7 @@ template> void unpack_B(packed_B_t * RESTRICT tile, const void * RESTRICT packed_B) { GGML_UNUSED(tile); GGML_UNUSED(packed_B); -}; +} template <> void unpack_B(int8_t * RESTRICT tile, const void * RESTRICT packed_B) { @@ -2327,9 +2329,7 @@ size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor) { // pack weight to vnni format void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - - size_t alloc_size = ggml_backend_amx_get_alloc_size(tensor); - GGML_ASSERT(alloc_size == size); + GGML_ASSERT(offset == 0 && size == ggml_nbytes(tensor)); // only full tensor conversion is supported for now const enum ggml_type TYPE = tensor->type; @@ -2348,6 +2348,29 @@ void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * d }); } +size_t ggml_backend_amx_desired_wsize(const struct ggml_tensor * dst) { + struct ggml_tensor * src0 = dst->src[0]; + + const enum ggml_type TYPE = src0->type; + + const bool is_floating_type = TYPE == GGML_TYPE_F16; + if (is_floating_type) { + return 0; + } + + const int M = dst->ne[1]; + const int K = src0->ne[0]; + + size_t desired_wsize = 0; + + GGML_DISPATCH_QTYPES(TYPE, [&] { + const size_t row_size_A = K / blck_size * sizeof(vec_dot_type); + desired_wsize = M * row_size_A; + }); + + return desired_wsize; +} + // NB: mixed dtype gemm with Advanced Matrix Extensions (Intel AMX) // // src0: weight in shape of {N, K}, quantized @@ -2356,14 +2379,12 @@ void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * d // // the function performs: dst = src1 @ src0.T // -void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst) { +void ggml_backend_amx_mul_mat(const ggml_compute_params * params, struct ggml_tensor * dst) { struct ggml_tensor * src0 = dst->src[0]; struct ggml_tensor * src1 = dst->src[1]; const enum ggml_type TYPE = src0->type; - const int n_threads = ctx->n_threads; - // f16 only has avx512 kernels for now, // amx kernels will be added once 6th gen xeon is released. const bool is_floating_type = TYPE == GGML_TYPE_F16; @@ -2379,7 +2400,7 @@ void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor const int MB = div_up(M, BLOCK_M); const int NB = div_up(N, BLOCK_N); - parallel_for(n_threads, MB * NB, [&](int begin, int end) { + parallel_for_ggml(params, MB * NB, [&](int begin, int end) { GGML_DISPATCH_FLOATING_TYPES(TYPE, [&] { for (int i = begin; i < end; ++i) { int mb = i / NB; @@ -2412,27 +2433,29 @@ void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor } // pointer to work space, used convert A from float to quantized type - void * wdata = nullptr; + void * wdata = params->wdata; //TODO: performance improvement: merge quant A - GGML_DISPATCH_QTYPES(TYPE, [&] { - const size_t row_size_A = K / blck_size * sizeof(vec_dot_type); - const size_t desired_wsize = M * row_size_A; - if (ctx->work_size < desired_wsize) { - ctx->work_data.reset(new char[desired_wsize]); - ctx->work_size = desired_wsize; - } - wdata = ctx->work_data.get(); + if (params->ith == 0) { + GGML_DISPATCH_QTYPES(TYPE, [&] { + const size_t row_size_A = K / blck_size * sizeof(vec_dot_type); + const size_t desired_wsize = M * row_size_A; + if (params->wsize < desired_wsize) { + GGML_ABORT("insufficient work space size"); + } - // Q4_0, Q4_1, Q8_0 handles 1 TILE_K per blck_size - // Q4_K, Q5_K, Q6_K, IQ4_XS handles 8 TILE_K per blck_size - GGML_ASSERT(TILE_K == blck_size || TILE_K * 8 == blck_size); + // Q4_0, Q4_1, Q8_0 handles 1 TILE_K per blck_size + // Q4_K, Q5_K, Q6_K, IQ4_XS handles 8 TILE_K per blck_size + GGML_ASSERT(TILE_K == blck_size || TILE_K * 8 == blck_size); - const float * A_data = static_cast(src1->data); - for (int m = 0; m < M; ++m) { - from_float(A_data + m * K, (char *)wdata + m * row_size_A, K); - } - }); + const float * A_data = static_cast(src1->data); + for (int m = 0; m < M; ++m) { + from_float(A_data + m * K, (char *)wdata + m * row_size_A, K); + } + }); + } + + ggml_barrier(params->threadpool); if (M == 1) { // MB = 1 and handle 8 tiles in each block @@ -2440,7 +2463,7 @@ void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor constexpr int BLOCK_N = TILE_N * kTilesN; const int NB = div_up(N, BLOCK_N); - parallel_for(n_threads, NB, [&](int begin, int end) { + parallel_for_ggml(params, NB, [&](int begin, int end) { GGML_DISPATCH_QTYPES(TYPE, [&] { const int KB = K / blck_size; const int TILE_SIZE = get_tile_size(); @@ -2470,7 +2493,7 @@ void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor const int MB = div_up(M, BLOCK_M); const int NB = div_up(N, BLOCK_N); - parallel_for(n_threads, MB * NB, [&](int begin, int end) { + parallel_for_ggml(params, MB * NB, [&](int begin, int end) { // init tile config for each thread ggml_tile_config_init(); @@ -2498,13 +2521,4 @@ void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor }); } -#else // if defined(__AMX_INT8__) - -void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst) { - fprintf(stderr, "GGML is not compiled with AMX support!\n"); - - GGML_UNUSED(ctx); - GGML_UNUSED(dst); -} - -#endif // if defined(__AMX_INT8__) +#endif // if defined(__AMX_INT8__) && defined(__AVX512VNNI__) diff --git a/ggml/src/ggml-amx/mmq.h b/ggml/src/ggml-cpu/amx/mmq.h similarity index 72% rename from ggml/src/ggml-amx/mmq.h rename to ggml/src/ggml-cpu/amx/mmq.h index cf0920620..f37366093 100644 --- a/ggml/src/ggml-amx/mmq.h +++ b/ggml/src/ggml-cpu/amx/mmq.h @@ -1,6 +1,5 @@ #pragma once #include "common.h" -#include #ifdef __cplusplus extern "C" { @@ -10,7 +9,7 @@ size_t ggml_backend_amx_get_alloc_size(const struct ggml_tensor * tensor); void ggml_backend_amx_convert_weight(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); -void ggml_backend_amx_mul_mat(ggml_backend_amx_context * ctx, struct ggml_tensor * dst); +void ggml_backend_amx_mul_mat(const struct ggml_compute_params * params, struct ggml_tensor * dst); #ifdef __cplusplus } diff --git a/ggml/src/ggml-cpu/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h index 27a530b22..d71076ad1 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-impl.h +++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h @@ -15,6 +15,18 @@ extern "C" { #endif +struct ggml_compute_params { + // ith = thread index, nth = number of threads + int ith, nth; + + // work buffer for all threads + size_t wsize; + void * wdata; + + struct ggml_threadpool * threadpool; +}; + + #if defined(_MSC_VER) #define m512bh(p) p @@ -366,6 +378,9 @@ static __m256 __lasx_xvreplfr2vr_s(float val) { } #endif +// TODO: move to ggml-threading +void ggml_barrier(struct ggml_threadpool * tp); + #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index e0cefc20b..23ae2e10c 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -10,6 +10,7 @@ #include "ggml-quants.h" #include "ggml-cpu-quants.h" #include "ggml-threading.h" +#include "amx/amx.h" #include "ggml.h" #if defined(_MSC_VER) || defined(__MINGW32__) @@ -624,7 +625,7 @@ do { \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm512_add_ps(x[i], x[offset+i]); \ } \ - res = _mm512_reduce_add_ps(x[0]); \ + res = (ggml_float) _mm512_reduce_add_ps(x[0]); \ } while (0) // TODO: is this optimal ? @@ -674,7 +675,7 @@ do { \ for (int i = 0; i < offset; ++i) { \ x[i] = _mm512_add_ps(x[i], x[offset+i]); \ } \ - res = _mm512_reduce_add_ps(x[0]); \ + res = (ggml_float) _mm512_reduce_add_ps(x[0]); \ } while (0) #define GGML_F16_VEC GGML_F32Cx16 @@ -685,8 +686,8 @@ do { \ #define GGML_F16_VEC_FMA GGML_F32Cx16_FMA #define GGML_F16_VEC_ADD GGML_F32Cx16_ADD #define GGML_F16_VEC_MUL GGML_F32Cx16_MUL -#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE +#define GGML_F16_VEC_REDUCE GGML_F32Cx16_REDUCE #elif defined(__AVX__) #define GGML_SIMD @@ -1178,28 +1179,28 @@ static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { #define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) #define GGML_F32x4_ADD __lsx_vfadd_s #define GGML_F32x4_MUL __lsx_vfmul_s -#define GGML_F32x4_REDUCE(res, x) \ -{ \ - int offset = GGML_F32_ARR >> 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - offset >>= 1; \ - for (int i = 0; i < offset; ++i) { \ - x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ - } \ - __m128i tmp = __lsx_vsrli_d((__m128i)x[0], 32); \ - tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, x[0]); \ - tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ - const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ - tmp = __lsx_vsrli_d((__m128i)t0, 32); \ - tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, t0); \ - tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ - res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset + i]); \ + } \ + __m128i tmp = __lsx_vsrli_d((__m128i) x[0], 32); \ + tmp = (__m128i) __lsx_vfadd_s((__m128) tmp, x[0]); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ + tmp = __lsx_vsrli_d((__m128i) t0, 32); \ + tmp = (__m128i) __lsx_vfadd_s((__m128) tmp, t0); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ } #define GGML_F32_VEC GGML_F32x4 @@ -1367,31 +1368,15 @@ struct ggml_compute_state { int ith; }; -struct ggml_compute_params { - // ith = thread index, nth = number of threads - int ith, nth; - - // work buffer for all threads - size_t wsize; - void * wdata; - - struct ggml_threadpool * threadpool; -}; - // // fundamental operations // inline static void ggml_vec_set_i8(const int n, int8_t * x, const int8_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - inline static void ggml_vec_set_i16(const int n, int16_t * x, const int16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } - inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } @@ -2286,7 +2271,7 @@ struct ggml_state { static struct ggml_state g_state = {0}; -static void ggml_barrier(struct ggml_threadpool * tp) { +void ggml_barrier(struct ggml_threadpool * tp) { int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed); if (n_threads == 1) { return; @@ -7455,6 +7440,13 @@ static void ggml_compute_forward_mul_mat( type = (enum ggml_type)(intptr_t)src0->extra; } +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + if (src0->buffer && ggml_backend_amx_buft_is_amx(src0->buffer->buft)) { + ggml_backend_amx_mul_mat(params, dst); + return; + } +#endif + enum ggml_type const vec_dot_type = type_traits_cpu[type].vec_dot_type; ggml_from_float_t const from_float = type_traits_cpu[vec_dot_type].from_float; ggml_from_float_to_mat_t const from_float_to_mat = type_traits_cpu[vec_dot_type].from_float_to_mat; @@ -13294,10 +13286,16 @@ struct ggml_cplan ggml_graph_plan( } break; case GGML_OP_MUL_MAT: { +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + if (node->src[0]->buffer && ggml_backend_amx_buft_is_amx(node->src[0]->buffer->buft)) { + cur = ggml_backend_amx_desired_wsize(node); + } +#endif const enum ggml_type vec_dot_type = type_traits_cpu[node->src[0]->type].vec_dot_type; if (node->src[1]->type != vec_dot_type) { - cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1])); + size_t cur2 = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1])); + cur = MAX(cur, cur2); } } break; case GGML_OP_MUL_MAT_ID: diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index 44d99089a..77e5d87a8 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -3,6 +3,7 @@ #include "ggml-cpu.h" #include "ggml-cpu-aarch64.h" #include "ggml-impl.h" +#include "amx/amx.h" #include #include #include @@ -134,12 +135,16 @@ static ggml_backend_buffer_type_t * ggml_backend_cpu_get_extra_bufts(ggml_backen static std::vector bufts = []() { std::vector bufts; -#ifdef GGML_USE_CPU_HBM - bufts.push_back(ggml_backend_cpu_hbm_buffer_type()); +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + if (ggml_backend_amx_buffer_type()) { + bufts.push_back(ggml_backend_amx_buffer_type()); + } #endif #ifdef GGML_USE_CPU_AARCH64 - bufts.push_back(ggml_backend_cpu_aarch64_buffer_type()); + if (ggml_backend_cpu_aarch64_buffer_type()) { + bufts.push_back(ggml_backend_cpu_aarch64_buffer_type()); + } #endif bufts.push_back(NULL); @@ -456,12 +461,27 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st const struct ggml_tensor * src0 = op->src[0]; const struct ggml_tensor * src1 = op->src[1]; + if (op->op == GGML_OP_NONE || op->op == GGML_OP_RESHAPE || op->op == GGML_OP_VIEW || op->op == GGML_OP_PERMUTE || op->op == GGML_OP_TRANSPOSE) { + return true; + } + if (src0 && src0->buffer && ggml_backend_cpu_buft_is_aarch64(src0->buffer->buft)) { if (op->op != GGML_OP_MUL_MAT || src0->type == ggml_aarch64_get_optimal_repack_type(src0)) { return false; } } +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + if (src0 && src0->buffer && ggml_backend_amx_buft_is_amx(src0->buffer->buft)) { + return ggml_backend_amx_device_supports_op(op); + } + for (int i = 1; i < GGML_MAX_SRC; i++) { + if (op->src[i] && op->src[i]->buffer && ggml_backend_amx_buft_is_amx(op->src[i]->buffer->buft)) { + return false; + } + } +#endif + for (int i = 1; i < GGML_MAX_SRC; i++) { if (op->src[i] && op->src[i]->buffer && ggml_backend_cpu_buft_is_aarch64(op->src[i]->buffer->buft)) { return false; @@ -491,7 +511,13 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st } static bool ggml_backend_cpu_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { - return ggml_backend_buft_is_host(buft) || ggml_backend_cpu_buft_is_aarch64(buft); + bool supported = ggml_backend_buft_is_host(buft) || ggml_backend_cpu_buft_is_aarch64(buft); + +#if defined(__AMX_INT8__) && defined(__AVX512VNNI__) + supported = supported || ggml_backend_amx_buft_is_amx(buft); +#endif + + return supported; GGML_UNUSED(dev); } diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index b2ce2e664..da4146ec4 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -50,8 +50,7 @@ #include "sgemm.h" #include "ggml-impl.h" -// hack until moved into the CPU backend -#include "../ggml-cpu-impl.h" +#include "ggml-cpu-impl.h" #include "ggml-quants.h" #ifdef _MSC_VER diff --git a/ggml/src/ggml-impl.h b/ggml/src/ggml-impl.h index f39b7a88c..78e3af8f2 100644 --- a/ggml/src/ggml-impl.h +++ b/ggml/src/ggml-impl.h @@ -30,11 +30,13 @@ extern "C" { #endif -#undef MIN -#undef MAX +#ifndef MIN +# define MIN(a, b) ((a) < (b) ? (a) : (b)) +#endif -#define MIN(a, b) ((a) < (b) ? (a) : (b)) -#define MAX(a, b) ((a) > (b) ? (a) : (b)) +#ifndef MAX +# define MAX(a, b) ((a) > (b) ? (a) : (b)) +#endif // required for mmap as gguf only guarantees 32-byte alignment #define TENSOR_ALIGNMENT 32 diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt index 10075db33..51c78b7d2 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt +++ b/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt @@ -3,5 +3,5 @@ find_package (Threads REQUIRED) set(TARGET vulkan-shaders-gen) add_executable(${TARGET} vulkan-shaders-gen.cpp) install(TARGETS ${TARGET} RUNTIME) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) target_link_libraries(vulkan-shaders-gen PUBLIC Threads::Threads) diff --git a/pocs/vdot/CMakeLists.txt b/pocs/vdot/CMakeLists.txt index d5405ad29..6235aec1f 100644 --- a/pocs/vdot/CMakeLists.txt +++ b/pocs/vdot/CMakeLists.txt @@ -1,9 +1,9 @@ set(TARGET llama-vdot) add_executable(${TARGET} vdot.cpp) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) set(TARGET llama-q8dot) add_executable(${TARGET} q8dot.cpp) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 2f581b921..f3b3908b1 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -25,7 +25,7 @@ add_library(llama ) target_include_directories(llama PUBLIC . ../include) -target_compile_features (llama PUBLIC cxx_std_11) # don't bump +target_compile_features (llama PUBLIC cxx_std_17) # don't bump target_link_libraries(llama PUBLIC ggml) diff --git a/src/unicode.cpp b/src/unicode.cpp index 50b35bbbc..3d4592635 100644 --- a/src/unicode.cpp +++ b/src/unicode.cpp @@ -201,7 +201,18 @@ static std::unordered_map unicode_utf8_to_byte_map() { } static inline std::wstring unicode_wstring_from_utf8(const std::string & s) { +#if defined(__clang__) + // disable C++17 deprecation warning for std::codecvt_utf8 +# pragma clang diagnostic push +# pragma clang diagnostic ignored "-Wdeprecated-declarations" +#endif + std::wstring_convert> conv; + +#if defined(__clang__) +# pragma clang diagnostic pop +#endif + return conv.from_bytes(s); } diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index be370044d..e5c9e75e4 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -284,7 +284,7 @@ static void test_perf() { data.reserve(n_vocab); for (int i = 0; i < n_vocab; i++) { - const float logit = 2.0f*((float)(rand())/RAND_MAX - 0.5f); + const float logit = 2.0f*((double)(rand())/RAND_MAX - 0.5); data.emplace_back(llama_token_data{i, logit, 0.0f}); } From 0533e7fb3842a523f64dc533bd7bd7147ec2c63a Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Sat, 30 Nov 2024 07:00:02 +0000 Subject: [PATCH 27/29] vulkan: Dynamic subgroup size support for Q6_K mat_vec (#10536) * subgroup 64 version with subgroup add. 15% faster scalable version tested for subgroup sizes 16-128 * check for subgroup multiple of 16 and greater than 16 * subgroup sizes are always a power of 2 (https://github.com/KhronosGroup/GLSL/issues/45) * force 16 sequential threads per block * make 16 subgroup size a constant --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 13 ++++---- .../vulkan-shaders/mul_mat_vec_q6_k.comp | 30 +++++++++---------- 2 files changed, 23 insertions(+), 20 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 849c11923..df6a659f4 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -1231,6 +1231,9 @@ static void ggml_vk_load_shaders(vk_device& device) { std::cerr << "ggml_vulkan: Compiling shaders"; + // some shaders require the subgroup size to be 16 or larger + const uint32_t subgroup_size_16 = std::max(device->subgroup_size, 16u); + // mulmat std::vector l_warptile, m_warptile, s_warptile, l_warptile_mmq, m_warptile_mmq, s_warptile_mmq; @@ -1240,11 +1243,11 @@ static void ggml_vk_load_shaders(vk_device& device) { l_warptile = { 128, 128, 128, 16, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; m_warptile = { 128, 64, 64, 16, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; - s_warptile = { std::max(device->subgroup_size, 16u), 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; + s_warptile = { subgroup_size_16, 32, 32, 16, 32, 32, 2, 2, 2, device->subgroup_size }; l_warptile_mmq = { 128, 128, 128, 32, device->subgroup_size * 2, 64, 2, 4, 4, device->subgroup_size }; m_warptile_mmq = { 128, 64, 64, 32, device->subgroup_size, 32, 2, 4, 2, device->subgroup_size }; - s_warptile_mmq = { std::max(device->subgroup_size, 16u), 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; + s_warptile_mmq = { subgroup_size_16, 32, 32, 32, 32, 32, 2, 2, 2, device->subgroup_size }; l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 }; m_mmq_wg_denoms = m_wg_denoms = { 64, 64, 1 }; @@ -1431,7 +1434,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f32_f32", mul_mat_vec_iq4_nl_f32_f32_len, mul_mat_vec_iq4_nl_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); @@ -1445,7 +1448,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_iq4_nl_f16_f32", mul_mat_vec_iq4_nl_f16_f32_len, mul_mat_vec_iq4_nl_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1); @@ -1459,7 +1462,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {device->subgroup_size}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {subgroup_size_16}, 1, true); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", mul_mat_vec_id_iq4_nl_f32_len, mul_mat_vec_id_iq4_nl_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1, true); // dequant shaders diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp index 0b392d68d..760aff854 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp @@ -4,9 +4,11 @@ #include "mul_mat_vec_base.comp" -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -shared FLOAT_TYPE tmp[32]; +layout (constant_id = 0) const uint BLOCK_SIZE = 32; + +shared FLOAT_TYPE tmp[BLOCK_SIZE]; void main() { const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z; @@ -21,21 +23,19 @@ void main() { const uint num_blocks_per_row = p.ncols / QUANT_K; const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + // 16 threads are used to process each block + const uint it_size = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid%16; // 0...16 + const uint ix = tid/16; - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + const uint step = 8; - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 + const uint v_im = itid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = itid - step*v_im; // 0...15 or 0...7 -#if K_QUANTS_PER_ITERATION == 1 - const uint l0 = v_in; // 0...15 - const uint is = 0; -#else const uint l0 = 4 * v_in; // 0, 4, 8, ..., 28 const uint is = v_in / 4; -#endif const uint ql_offset = 64*v_im + l0; const uint qh_offset = 32*v_im + l0; @@ -44,7 +44,7 @@ void main() { FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) { const uint y_idx = i * QUANT_K + y_offset; const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); @@ -95,10 +95,10 @@ void main() { } tmp[gl_LocalInvocationID.x] = temp; - // sum up partial sums and write back result + barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { + [[unroll]] for (uint s = gl_WorkGroupSize.x/2; s > 0; s >>= 1) { if (tid < s) { tmp[tid] += tmp[tid + s]; } From abadba05be52cccf6c0da49534e37f6062ce8ded Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 30 Nov 2024 09:47:07 +0200 Subject: [PATCH 28/29] readme : refresh (#10587) * readme : refresh * readme : move section [no ci] * readme : clarify [no ci] * readme : fixes [no ci] * readme : more fixes [no ci] * readme : simplify [no ci] * readme : clarify GGUF --- README.md | 362 +++++++++++++++++++----------------------------------- 1 file changed, 123 insertions(+), 239 deletions(-) diff --git a/README.md b/README.md index 8aa3d1e59..c5321f26c 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ## Description The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide -variety of hardware - locally and in the cloud. +range of hardware - locally and in the cloud. - Plain C/C++ implementation without any dependencies - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks @@ -36,14 +36,17 @@ variety of hardware - locally and in the cloud. - Vulkan and SYCL backend support - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity -Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has -improved significantly thanks to many contributions. It is the main playground for developing new features for the -[ggml](https://github.com/ggerganov/ggml) library. +The `llama.cpp` project is the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library. -**Supported models:** +
+Models Typically finetunes of the base models below are supported as well. +Instructions for adding support for new models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md) + +**Text-only:** + - [X] LLaMA 🦙 - [x] LLaMA 2 🦙🦙 - [x] LLaMA 3 🦙🦙🦙 @@ -97,9 +100,7 @@ Typically finetunes of the base models below are supported as well. - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a) - [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM) -(instructions for supporting more models: [HOWTO-add-model.md](./docs/development/HOWTO-add-model.md)) - -**Multimodal models:** +**Multimodal:** - [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) - [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava) @@ -111,7 +112,10 @@ Typically finetunes of the base models below are supported as well. - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny) -**Bindings:** +
+ +
+Bindings - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) @@ -138,7 +142,12 @@ Typically finetunes of the base models below are supported as well. - Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift) - Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama) -**UI:** +
+ +
+UIs + +*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* - [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) - [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) @@ -148,6 +157,7 @@ Typically finetunes of the base models below are supported as well. - [janhq/jan](https://github.com/janhq/jan) (AGPL) - [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0) - [KodiBot](https://github.com/firatkiral/kodibot) (GPL) +- [llama.vim](https://github.com/ggml-org/llama.vim) (MIT) - [LARS](https://github.com/abgulati/LARS) (AGPL) - [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL) - [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT) @@ -171,241 +181,35 @@ Typically finetunes of the base models below are supported as well. - [semperai/amica](https://github.com/semperai/amica) (MIT) - [withcatai/catai](https://github.com/withcatai/catai) (MIT) -*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* +
-**Tools:** +
+Tools - [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML - [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp - [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption - [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage -- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with prebuild Mobile and Web platform wrappers and a model example) +- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example) -**Infrastructure:** +
+ +
+Infrastructure - [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp - [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs - [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly -**Games:** +
+ +
+Games + - [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you. -## Demo - -
-Typical run using LLaMA v2 13B on M2 Ultra - -``` -$ make -j && ./llama-cli -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -I llama.cpp build info: -I UNAME_S: Darwin -I UNAME_P: arm -I UNAME_M: arm64 -I CFLAGS: -I. -O3 -std=c11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -pthread -DGGML_USE_K_QUANTS -DGGML_USE_ACCELERATE -I CXXFLAGS: -I. -I./common -O3 -std=c++11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -pthread -DGGML_USE_K_QUANTS -I LDFLAGS: -framework Accelerate -I CC: Apple clang version 14.0.3 (clang-1403.0.22.14.1) -I CXX: Apple clang version 14.0.3 (clang-1403.0.22.14.1) - -make: Nothing to be done for `default'. -main: build = 1041 (cf658ad) -main: seed = 1692823051 -llama_model_loader: loaded meta data with 16 key-value pairs and 363 tensors from models/llama-13b-v2/ggml-model-q4_0.gguf (version GGUF V1 (latest)) -llama_model_loader: - type f32: 81 tensors -llama_model_loader: - type q4_0: 281 tensors -llama_model_loader: - type q6_K: 1 tensors -llm_load_print_meta: format = GGUF V1 (latest) -llm_load_print_meta: arch = llama -llm_load_print_meta: vocab type = SPM -llm_load_print_meta: n_vocab = 32000 -llm_load_print_meta: n_merges = 0 -llm_load_print_meta: n_ctx_train = 4096 -llm_load_print_meta: n_ctx = 512 -llm_load_print_meta: n_embd = 5120 -llm_load_print_meta: n_head = 40 -llm_load_print_meta: n_head_kv = 40 -llm_load_print_meta: n_layer = 40 -llm_load_print_meta: n_rot = 128 -llm_load_print_meta: n_gqa = 1 -llm_load_print_meta: f_norm_eps = 1.0e-05 -llm_load_print_meta: f_norm_rms_eps = 1.0e-05 -llm_load_print_meta: n_ff = 13824 -llm_load_print_meta: freq_base = 10000.0 -llm_load_print_meta: freq_scale = 1 -llm_load_print_meta: model type = 13B -llm_load_print_meta: model ftype = mostly Q4_0 -llm_load_print_meta: model size = 13.02 B -llm_load_print_meta: general.name = LLaMA v2 -llm_load_print_meta: BOS token = 1 '' -llm_load_print_meta: EOS token = 2 '' -llm_load_print_meta: UNK token = 0 '' -llm_load_print_meta: LF token = 13 '<0x0A>' -llm_load_tensors: ggml ctx size = 0.11 MB -llm_load_tensors: mem required = 7024.01 MB (+ 400.00 MB per state) -................................................................................................... -llama_new_context_with_model: kv self size = 400.00 MB -llama_new_context_with_model: compute buffer total size = 75.41 MB - -system_info: n_threads = 16 / 24 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | -sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000 -generate: n_ctx = 512, n_batch = 512, n_predict = 400, n_keep = 0 - - - Building a website can be done in 10 simple steps: -Step 1: Find the right website platform. -Step 2: Choose your domain name and hosting plan. -Step 3: Design your website layout. -Step 4: Write your website content and add images. -Step 5: Install security features to protect your site from hackers or spammers -Step 6: Test your website on multiple browsers, mobile devices, operating systems etc… -Step 7: Test it again with people who are not related to you personally – friends or family members will work just fine! -Step 8: Start marketing and promoting the website via social media channels or paid ads -Step 9: Analyze how many visitors have come to your site so far, what type of people visit more often than others (e.g., men vs women) etc… -Step 10: Continue to improve upon all aspects mentioned above by following trends in web design and staying up-to-date on new technologies that can enhance user experience even further! -How does a Website Work? -A website works by having pages, which are made of HTML code. This code tells your computer how to display the content on each page you visit – whether it’s an image or text file (like PDFs). In order for someone else’s browser not only be able but also want those same results when accessing any given URL; some additional steps need taken by way of programming scripts that will add functionality such as making links clickable! -The most common type is called static HTML pages because they remain unchanged over time unless modified manually (either through editing files directly or using an interface such as WordPress). They are usually served up via HTTP protocols – this means anyone can access them without having any special privileges like being part of a group who is allowed into restricted areas online; however, there may still exist some limitations depending upon where one lives geographically speaking. -How to -llama_print_timings: load time = 576.45 ms -llama_print_timings: sample time = 283.10 ms / 400 runs ( 0.71 ms per token, 1412.91 tokens per second) -llama_print_timings: prompt eval time = 599.83 ms / 19 tokens ( 31.57 ms per token, 31.68 tokens per second) -llama_print_timings: eval time = 24513.59 ms / 399 runs ( 61.44 ms per token, 16.28 tokens per second) -llama_print_timings: total time = 25431.49 ms -``` -
-
-Demo of running both LLaMA-7B and whisper.cpp on a single M1 Pro MacBook - -And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook: - -https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4 - -
- -## Usage - -Here are the end-to-end binary build and model conversion steps for most supported models. - -### Basic usage - -Firstly, you need to get the binary. There are different methods that you can follow: -- Method 1: Clone this repository and build locally, see [how to build](./docs/build.md) -- Method 2: If you are using MacOS or Linux, you can install llama.cpp via [brew, flox or nix](./docs/install.md) -- Method 3: Use a Docker image, see [documentation for Docker](./docs/docker.md) -- Method 4: Download pre-built binary from [releases](https://github.com/ggerganov/llama.cpp/releases) - -You can run a basic completion using this command: - -```bash -llama-cli -m your_model.gguf -p "I believe the meaning of life is" -n 128 - -# Output: -# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey. -``` - -See [this page](./examples/main/README.md) for a full list of parameters. - -### Conversation mode - -If you want a more ChatGPT-like experience, you can run in conversation mode by passing `-cnv` as a parameter: - -```bash -llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv - -# Output: -# > hi, who are you? -# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today? -# -# > what is 1+1? -# Easy peasy! The answer to 1+1 is... 2! -``` - -By default, the chat template will be taken from the input model. If you want to use another chat template, pass `--chat-template NAME` as a parameter. See the list of [supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) - -```bash -./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml -``` - -You can also use your own template via in-prefix, in-suffix and reverse-prompt parameters: - -```bash -./llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:' -``` - -### Web server - -[llama.cpp web server](./examples/server/README.md) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients. - -Example usage: - -```bash -./llama-server -m your_model.gguf --port 8080 - -# Basic web UI can be accessed via browser: http://localhost:8080 -# Chat completion endpoint: http://localhost:8080/v1/chat/completions -``` - -### Interactive mode - -> [!NOTE] -> If you prefer basic usage, please consider using conversation mode instead of interactive mode - -In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`. - -Here is an example of a few-shot interaction, invoked with the command - -```bash -# default arguments using a 7B model -./examples/chat.sh - -# advanced chat with a 13B model -./examples/chat-13B.sh - -# custom arguments using a 13B model -./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt -``` - -Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `llama-cli` example program. - -![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png) - -### Persistent Interaction - -The prompt, user inputs, and model generations can be saved and resumed across calls to `./llama-cli` by leveraging `--prompt-cache` and `--prompt-cache-all`. The `./examples/chat-persistent.sh` script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as `chat-13B.sh`. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (`PROMPT_TEMPLATE`) and the model file. - -```bash -# Start a new chat -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh - -# Resume that chat -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh - -# Start a different chat with the same prompt/model -PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh - -# Different prompt cache for different prompt/model -PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \ - CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh -``` - -### Constrained output with grammars - -`llama.cpp` supports grammars to constrain model output. For example, you can force the model to output JSON only: - -```bash -./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' -``` - -The `grammars/` folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](./grammars/README.md). - -For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one. - -## Build - -Please refer to [Build llama.cpp locally](./docs/build.md) - ## Supported backends | Backend | Target devices | @@ -420,23 +224,104 @@ Please refer to [Build llama.cpp locally](./docs/build.md) | [Vulkan](./docs/build.md#vulkan) | GPU | | [CANN](./docs/build.md#cann) | Ascend NPU | -## Tools +## Building and usage -### Prepare and Quantize +The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h). +The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. Possible methods for obtaining the binaries: -> [!NOTE] -> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours. +- Clone this repository and build locally, see [how to build](./docs/build.md) +- On MacOS or Linux, install `llama.cpp` via [brew, flox or nix](./docs/install.md) +- Use a Docker image, see [documentation for Docker](./docs/docker.md) +- Download pre-built binaries from [releases](https://github.com/ggerganov/llama.cpp/releases) -To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. +### Obtaining and quantizing models -Note: `convert.py` has been moved to `examples/convert_legacy_llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives. -It does not support LLaMA 3, you can use `convert_hf_to_gguf.py` with LLaMA 3 downloaded from Hugging Face. +The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`: -To learn more about quantizing model, [read this documentation](./examples/quantize/README.md) +- [Trending](https://huggingface.co/models?library=gguf&sort=trending) +- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf) + +After downloading a model, use the CLI tools to run it locally - see below. + +`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo. + +The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`: + +- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes +- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggerganov/llama.cpp/discussions/10123) +- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggerganov/llama.cpp/discussions/9268) +- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggerganov/llama.cpp/discussions/9669) + +To learn more about model quantization, [read this documentation](./examples/quantize/README.md) + +### Using the `llama-cli` tool + +Run a basic text completion: + +```bash +llama-cli -m your_model.gguf -p "I believe the meaning of life is" -n 128 + +# Output: +# I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey. +``` + +See [this page](./examples/main/README.md) for a full list of parameters. + +### Conversation mode + +Run `llama-cli` in conversation/chat mode by passing the `-cnv` parameter: + +```bash +llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv + +# Output: +# > hi, who are you? +# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today? +# +# > what is 1+1? +# Easy peasy! The answer to 1+1 is... 2! +``` + +By default, the chat template will be taken from the input model. If you want to use another chat template, pass `--chat-template NAME` as a parameter. See the list of [supported templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) + +```bash +llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml +``` + +You can also use your own template via in-prefix, in-suffix and reverse-prompt parameters: + +```bash +llama-cli -m your_model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:' +``` + +### Constrained output with grammars + +`llama.cpp` can constrain the output of the model via custom grammars. For example, you can force the model to output only JSON: + +```bash +llama-cli -m your_model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' +``` + +The `grammars/` folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](./grammars/README.md). + +For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/ + +### Web server (`llama-server`) + +The [llama-server](./examples/server/README.md) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients. + +Example usage: + +```bash +llama-server -m your_model.gguf --port 8080 + +# Basic web UI can be accessed via browser: http://localhost:8080 +# Chat completion endpoint: http://localhost:8080/v1/chat/completions +``` ### Perplexity (measuring model quality) -You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better). +Use the `llama-perplexity` tool to measure perplexity over a given prompt (lower perplexity is better). For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity). To learn more how to measure perplexity using llama.cpp, [read this documentation](./examples/perplexity/README.md) @@ -456,7 +341,6 @@ To learn more how to measure perplexity using llama.cpp, [read this documentatio - [main (cli)](./examples/main/README.md) - [server](./examples/server/README.md) -- [jeopardy](./examples/jeopardy/README.md) - [GBNF grammars](./grammars/README.md) **Development documentation** From 3e0ba0e604b1ac5b2cbca9a3f38f91f2be4ef1cd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 30 Nov 2024 10:09:21 +0200 Subject: [PATCH 29/29] readme : remove old badge --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index c5321f26c..37c6666aa 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,6 @@ [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml) -[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp) [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)