From 23bc779a6e58762ea892eca1801b2ea1b9050c00 Mon Sep 17 00:00:00 2001 From: ubergarm Date: Fri, 21 Nov 2025 08:51:38 -0500 Subject: [PATCH 01/37] model : detect GigaChat3-10-A1.8B as deepseek lite (#17420) * Detect GigaChat3-10-A1.8B as deepseek lite Hardcodes checking number of layers to detect if lite version of deepseek. * Add commnent identifying deepseek lite variants deepseek lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B --- src/llama-model.cpp | 6 ++++-- src/models/deepseek2.cpp | 3 ++- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index e703181a198..175549a9e30 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -1593,7 +1593,8 @@ void llama_model::load_hparams(llama_model_loader & ml) { } break; case LLM_ARCH_DEEPSEEK2: { - bool is_lite = (hparams.n_layer == 27); + // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B + bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); if (!is_lite) { @@ -4581,7 +4582,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_DEEPSEEK2: { - const bool is_lite = (hparams.n_layer == 27); + // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B + const bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26); const bool is_mla = (hparams.n_embd_head_k_mla != 0 && hparams.n_embd_head_v_mla != 0); diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp index 68f72f72bb6..0b41f7ba8eb 100644 --- a/src/models/deepseek2.cpp +++ b/src/models/deepseek2.cpp @@ -4,7 +4,8 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { - bool is_lite = (hparams.n_layer == 27); + // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B + bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26); const bool is_mla = (hparams.n_embd_head_k_mla != 0 && hparams.n_embd_head_v_mla != 0); From 8e9ddba610e8c86361e068117a1935cde94b8903 Mon Sep 17 00:00:00 2001 From: lhez Date: Fri, 21 Nov 2025 14:34:48 -0800 Subject: [PATCH 02/37] opencl: refine condition for kqv mm (#17392) --- ggml/src/ggml-opencl/ggml-opencl.cpp | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 4cb6afe9271..2319f7a9e25 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -6895,9 +6895,23 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co cl_context context = backend_ctx->context; if(src0t == GGML_TYPE_F16 && src1t == GGML_TYPE_F32){ - if (ne01 >= 64 && ne1 >= 32 && ne00 >= 16 && (ne12 % ne02) == 0){ - ggml_cl_mul_mat_kq_kqv_adreno(backend, src0, src1, dst); - return; + if (ne01 >= 64 && ne1 >= 32 && ne00 >= 16 && (ne12 % ne02) == 0) { + // For KQ + if (ggml_is_permuted(src0) && ggml_is_permuted(src1) && + nb00 <= nb02 && + nb02 <= nb01 && + nb01 <= nb03 && + nb10 <= nb12 && + nb12 <= nb11 && + nb11 <= nb13) { + ggml_cl_mul_mat_kq_kqv_adreno(backend, src0, src1, dst); + return; + } + // For KQV + if (!ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { + ggml_cl_mul_mat_kq_kqv_adreno(backend, src0, src1, dst); + return; + } } } From 028f93ef9819d1a039f97d74d72380c986cd69aa Mon Sep 17 00:00:00 2001 From: yulo <77381088+zhang-hui-yulo@users.noreply.github.com> Date: Sat, 22 Nov 2025 07:03:24 +0800 Subject: [PATCH 03/37] HIP: RDNA4 tensor core support for MMF (#17077) * mmf for rdna4 * align the padding for rdna4 * forbit mul_mat_f for rdna4 * fix as comment * remove device kernels * add constexpr for early return * update based on review comment * change based on the review comment * pass compile error * keep code consistency --------- Co-authored-by: zhang hui --- ggml/src/ggml-cuda/common.cuh | 8 +++ ggml/src/ggml-cuda/convert.cuh | 9 +++ ggml/src/ggml-cuda/mma.cuh | 107 +++++++++++++++++++++++++++++++++ ggml/src/ggml-cuda/mmf.cu | 6 +- ggml/src/ggml-cuda/mmf.cuh | 73 ++++++++++++++++------ 5 files changed, 180 insertions(+), 23 deletions(-) diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index 25e9308d756..99ec96869a7 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -224,6 +224,10 @@ static const char * cu_get_error_str(CUresult err) { #define AMD_MFMA_AVAILABLE #endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA) +#if defined(GGML_USE_HIP) && defined(RDNA4) +#define AMD_WMMA_AVAILABLE +#endif // defined(GGML_USE_HIP) && defined(RDNA4) + // The Volta instructions are in principle available on Turing or newer but they are effectively unusable: #if !defined(GGML_USE_HIP) && __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA #define VOLTA_MMA_AVAILABLE @@ -283,6 +287,10 @@ static bool amd_mfma_available(const int cc) { #endif //!defined(GGML_HIP_NO_MMQ_MFMA) } +static bool amd_wmma_available(const int cc) { + return GGML_CUDA_CC_IS_RDNA4(cc); +} + static bool volta_mma_available(const int cc) { return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_VOLTA; } diff --git a/ggml/src/ggml-cuda/convert.cuh b/ggml/src/ggml-cuda/convert.cuh index 8a5e08ef667..09f9a33f909 100644 --- a/ggml/src/ggml-cuda/convert.cuh +++ b/ggml/src/ggml-cuda/convert.cuh @@ -39,6 +39,15 @@ template return __float2bfloat16(float(x)); } else if constexpr(std::is_same_v) { return __bfloat162float(x); + } else if constexpr(std::is_same_v && std::is_same_v) { + return __float22half2_rn(x); + } else if constexpr(std::is_same_v && std::is_same_v) { + // bypass compile error on cuda 12.0.1 +#ifdef GGML_USE_HIP + return __float22bfloat162_rn(x); +#else + return {x.x, x.y}; +#endif // GGML_USE_HIP } else if constexpr(std::is_same_v) { return int32_t(x); } else { diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh index a7a28fd1ae6..c3c4b779965 100644 --- a/ggml/src/ggml-cuda/mma.cuh +++ b/ggml/src/ggml-cuda/mma.cuh @@ -74,6 +74,33 @@ namespace ggml_cuda_mma { static constexpr int J = J_; #if defined(GGML_USE_HIP) +#if defined(RDNA4) + static constexpr int ne = I * J / 32; + T x[ne] = {0}; + + static constexpr __device__ bool supported() { + if (I == 16 && J == 16) return true; + return false; + } + + static __device__ __forceinline__ int get_i(const int l) { + if constexpr (I == 16 && J == 16) { + return 8 * (threadIdx.x / 16) + l; + } else { + NO_DEVICE_CODE; + return -1; + } + } + + static __device__ __forceinline__ int get_j(const int l) { + if constexpr (I == 16 && J == 16) { + return threadIdx.x % 16; + } else { + NO_DEVICE_CODE; + return -1; + } + } +#else static constexpr int ne = I * J / 64; T x[ne] = {0}; @@ -119,6 +146,7 @@ namespace ggml_cuda_mma { return -1; } } +#endif // defined(RDNA4) #elif __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA static constexpr int ne = I * J / 32; T x[ne] = {0}; @@ -236,6 +264,32 @@ namespace ggml_cuda_mma { return -1; } } +#elif defined(AMD_WMMA_AVAILABLE) + static constexpr int ne = I * J / 32; + half2 x[ne] = {{0.0f, 0.0f}}; + + static constexpr __device__ bool supported() { + if (I == 16 && J == 8) return true; + return false; + } + + static __device__ __forceinline__ int get_i(const int l) { + if constexpr (I == 16 && J == 8) { + return threadIdx.x % 16; + } else { + NO_DEVICE_CODE; + return -1; + } + } + + static __device__ __forceinline__ int get_j(const int l) { + if constexpr (I == 16 && J == 8) { + return 4 * (threadIdx.x / 16) + l; + } else { + NO_DEVICE_CODE; + return -1; + } + } #else static constexpr int ne = I * J / WARP_SIZE; half2 x[ne] = {{0.0f, 0.0f}}; @@ -285,6 +339,34 @@ namespace ggml_cuda_mma { struct tile { static constexpr int I = I_; static constexpr int J = J_; + +#if defined(AMD_WMMA_AVAILABLE) + static constexpr int ne = I * J / 32; + nv_bfloat162 x[ne] = {{0.0f, 0.0f}}; + + static constexpr __device__ bool supported() { + if (I == 16 && J == 8) return true; + return false; + } + + static __device__ __forceinline__ int get_i(const int l) { + if constexpr (I == 16 && J == 8) { + return threadIdx.x % 16; + } else { + NO_DEVICE_CODE; + return -1; + } + } + + static __device__ __forceinline__ int get_j(const int l) { + if constexpr (I == 16 && J == 8) { + return 4 * (threadIdx.x / 16) + l; + } else { + NO_DEVICE_CODE; + return -1; + } + } +#else static constexpr int ne = I * J / WARP_SIZE; nv_bfloat162 x[ne] = {{0.0f, 0.0f}}; @@ -320,6 +402,7 @@ namespace ggml_cuda_mma { return -1; } } +#endif // defined(AMD_WMMA_AVAILABLE) }; template @@ -353,6 +436,8 @@ namespace ggml_cuda_mma { const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 2 * (threadIdx.x / t.I)); xi[0] = xs[0]; } +#elif defined(AMD_WMMA_AVAILABLE) + ggml_cuda_memcpy_1(t.x, xs0 + t.get_i(0) * stride + t.get_j(0)); #else #pragma unroll for (int l = 0; l < t.ne; ++l) { @@ -639,12 +724,34 @@ namespace ggml_cuda_mma { : "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7]) : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[3])); #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE +#elif defined(AMD_WMMA_AVAILABLE) + using halfx8_t = __attribute__((ext_vector_type(8))) _Float16; + using floatx8_t = __attribute__((ext_vector_type(8))) float; + floatx8_t& acc_frag = reinterpret_cast(D.x[0]); + const halfx8_t& a_frag = reinterpret_cast(A.x[0]); + const halfx8_t& b_frag = reinterpret_cast(B.x[0]); + acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(a_frag, b_frag, acc_frag); #else GGML_UNUSED_VARS(D, A, B); NO_DEVICE_CODE; #endif // TURING_MMA_AVAILABLE } + static __device__ __forceinline__ void mma( + tile<16, 16, float> & D, const tile<16, 8, nv_bfloat162> & A, const tile<16, 8, nv_bfloat162> & B) { +#if defined(AMD_WMMA_AVAILABLE) + using bf16x8_t = __attribute__((ext_vector_type(8))) __bf16; + using floatx8_t = __attribute__((ext_vector_type(8))) float; + floatx8_t& acc_frag = reinterpret_cast(D.x[0]); + const bf16x8_t& a_frag = reinterpret_cast(A.x[0]); + const bf16x8_t& b_frag = reinterpret_cast(B.x[0]); + acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_bf16_w32_gfx12(a_frag, b_frag, acc_frag); +#else + GGML_UNUSED_VARS(D, A, B); + NO_DEVICE_CODE; +#endif // AMPERE_MMA_AVAILABLE + } + static __device__ __forceinline__ void mma( tile<16, 16, int> & D, const tile<16, 8, int> & A, const tile<16, 8, int> & B) { #if defined(AMD_MFMA_AVAILABLE) diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu index 153dd5a97d5..5c51a22256a 100644 --- a/ggml/src/ggml-cuda/mmf.cu +++ b/ggml/src/ggml-cuda/mmf.cu @@ -151,7 +151,7 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const return false; } } else { - if (src1_ncols > 16) { + if (src1_ncols > 16 || GGML_CUDA_CC_IS_RDNA4(cc)) { return false; } } @@ -160,9 +160,9 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const case GGML_TYPE_F32: return ampere_mma_available(cc); case GGML_TYPE_F16: - return volta_mma_available(cc) || turing_mma_available(cc); + return volta_mma_available(cc) || turing_mma_available(cc) || amd_wmma_available(cc); case GGML_TYPE_BF16: - return ampere_mma_available(cc); + return ampere_mma_available(cc) || amd_wmma_available(cc); default: return false; } diff --git a/ggml/src/ggml-cuda/mmf.cuh b/ggml/src/ggml-cuda/mmf.cuh index 45724e0911e..c2a0a2e42fe 100644 --- a/ggml/src/ggml-cuda/mmf.cuh +++ b/ggml/src/ggml-cuda/mmf.cuh @@ -2,6 +2,7 @@ #include "mma.cuh" #include "common.cuh" +#include "convert.cuh" using namespace ggml_cuda_mma; @@ -27,20 +28,35 @@ static __global__ void mul_mat_f( const int stride_col_id, const int stride_row_id, const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst, const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) { -#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) +// TODO: handle this in a consistent and simpler way after AMD MFMA support has been added +#if (!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)) || defined(AMD_WMMA_AVAILABLE) +#if defined(AMD_WMMA_AVAILABLE) + // Special case for tf32, just dummy mma layout as wmma doesn't support it. + constexpr int tile_B_I = std::is_same_v ? 8 : 16; + constexpr int tile_C_J = std::is_same_v ? 8 : 16; + typedef tile<16, 8, T> tile_A; + typedef tile tile_B; + typedef tile<16, tile_C_J, float> tile_C; + + constexpr bool a_supported = tile_A::supported(); + constexpr bool b_supported = tile_B::supported(); + constexpr bool c_supported = tile_C::supported(); + constexpr bool supported = a_supported && b_supported && c_supported; +#else constexpr bool I_16_supported = tile<16, 8, T>::supported() && tile<16, 8, float>::supported(); constexpr bool I_32_supported = tile<32, 8, T>::supported() && tile<32, 8, float>::supported(); - - if (!I_16_supported && !I_32_supported) { - NO_DEVICE_CODE; - return; - } + constexpr bool supported = I_16_supported || I_32_supported; constexpr int I_preferred = I_16_supported ? 16 : 32; // For Turing MMA both work but 16 is ~1% faster. typedef tile tile_A; typedef tile<8, 8, T> tile_B; typedef tile tile_C; +#endif // defined(AMD_WMMA_AVAILABLE) + if constexpr (!supported) { + NO_DEVICE_CODE; + return; + } constexpr int warp_size = ggml_cuda_get_physical_warp_size(); constexpr int tile_k_padded = warp_size + 4; @@ -161,11 +177,11 @@ static __global__ void mul_mat_f( if constexpr (!has_ids) { const float2 tmp = j < cols_per_block ? y2[j*stride_col_y + col] : make_float2(0.0f, 0.0f); - tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y}; + tile_xy[j0*tile_k_padded + threadIdx.x] = ggml_cuda_cast(tmp); } else { const bool valid = j < cols_per_block && (col_base + j) < ncols_dst_total && slot_map[j] >= 0; float2 tmp = valid ? *(const float2*) &y[slot_map[j]*stride_channel_y + 2*(j*stride_col_y + col)] : make_float2(0.0f, 0.0f); - tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y}; + tile_xy[j0*tile_k_padded + threadIdx.x] = ggml_cuda_cast(tmp); } } } else { @@ -239,7 +255,7 @@ static __global__ void mul_mat_f( channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst); NO_DEVICE_CODE; -#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) +#endif // (!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)) || defined(AMD_WMMA_AVAILABLE) } //This kernel is for larger batch sizes of mul_mat_id @@ -253,20 +269,35 @@ static __global__ void mul_mat_f_ids( const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst, const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst, const uint3 sis1_fd, const uint3 nch_fd) { -#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) +// TODO: handle this in a consistent and simpler way after AMD MFMA support has been added +#if (!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)) || defined(AMD_WMMA_AVAILABLE) +#if defined(AMD_WMMA_AVAILABLE) + // Special case for tf32, just dummy mma layout as wmma doesn't support it. + constexpr int tile_B_I = std::is_same_v ? 8 : 16; + constexpr int tile_C_J = std::is_same_v ? 8 : 16; + typedef tile<16, 8, T> tile_A; + typedef tile tile_B; + typedef tile<16, tile_C_J, float> tile_C; + + constexpr bool a_supported = tile_A::supported(); + constexpr bool b_supported = tile_B::supported(); + constexpr bool c_supported = tile_C::supported(); + constexpr bool supported = a_supported && b_supported && c_supported; +#else constexpr bool I_16_supported = tile<16, 8, T>::supported() && tile<16, 8, float>::supported(); constexpr bool I_32_supported = tile<32, 8, T>::supported() && tile<32, 8, float>::supported(); + constexpr bool supported = I_16_supported || I_32_supported; - if (!I_16_supported && !I_32_supported) { - NO_DEVICE_CODE; - return; - } - - constexpr int I_preferred = I_16_supported ? 16 : 32; // For Turing MMA both work butr 16 is ~1% faster. + constexpr int I_preferred = I_16_supported ? 16 : 32; // For Turing MMA both work but 16 is ~1% faster. typedef tile tile_A; typedef tile<8, 8, T> tile_B; typedef tile tile_C; +#endif // defined(AMD_WMMA_AVAILABLE) + if constexpr (!supported) { + NO_DEVICE_CODE; + return; + } constexpr int warp_size = ggml_cuda_get_physical_warp_size(); constexpr int tile_k_padded = warp_size + 4; @@ -408,7 +439,7 @@ static __global__ void mul_mat_f_ids( #pragma unroll for (int j0 = 0; j0 < tile_B::I; ++j0) { const float2 tmp = vals_buf[curr_buf][j0]; - tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y}; + tile_xy[j0*tile_k_padded + threadIdx.x] = ggml_cuda_cast(tmp); } if (itB + 1 < ntB) { @@ -492,7 +523,7 @@ static __global__ void mul_mat_f_ids( channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst, sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst, sis1_fd, nch_fd); NO_DEVICE_CODE; -#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) +#endif // (!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)) || defined(AMD_WMMA_AVAILABLE) } template @@ -554,7 +585,8 @@ void mul_mat_f_cuda( cudaStream_t stream, const mmf_ids_data * ids_data) { typedef tile<16, 8, T> tile_A_16; typedef tile<32, 8, T> tile_A_32; - typedef tile< 8, 8, T> tile_B; + typedef tile<16, 8, T> tile_B_16; + typedef tile< 8, 8, T> tile_B_8; GGML_ASSERT(ncols_x % 2 == 0); GGML_ASSERT(stride_row % 2 == 0); @@ -581,7 +613,8 @@ void mul_mat_f_cuda( constexpr int rows_per_block = MMF_ROWS_PER_BLOCK; const int nbytes_shared_iter = nwarps_best * (volta_mma_available(cc) ? tile_A_32::I : tile_A_16::I) * (warp_size + 4) * 4; - const int nbytes_shared_combine = GGML_PAD(cols_per_block, tile_B::I) * (nwarps_best*rows_per_block + 4) * 4; + const int nbytes_cols_per_block_pad = amd_wmma_available(cc) ? tile_B_16::I : tile_B_8::I; + const int nbytes_shared_combine = GGML_PAD(cols_per_block, nbytes_cols_per_block_pad) * (nwarps_best*rows_per_block + 4) * 4; const int nbytes_shared = std::max(nbytes_shared_iter, nbytes_shared_combine); const int nbytes_slotmap = ids ? GGML_PAD(cols_per_block, 16) * sizeof(int) : 0; const int nbytes_shared_total = nbytes_shared + nbytes_slotmap; From 3f3a4fb9c3b907c68598363b204e6f58f4757c8c Mon Sep 17 00:00:00 2001 From: Masato Nakasaka Date: Sat, 22 Nov 2025 18:55:43 +0900 Subject: [PATCH 04/37] Revive MUL_MAT_ID to perf testing (#17397) --- tests/test-backend-ops.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 2bb4b122247..90bdb80e5ec 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -7819,6 +7819,7 @@ static std::vector> make_test_cases_perf() { for (int bs : {1, 4, 8, 32, 64, 128, 256, 512}) { for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q4_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XS}) { for (ggml_type type_b : {GGML_TYPE_F32}) { + test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 128, 8, false, 768, bs, 2048)); test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 128, 8, false, 768, bs, 2048, 1)); } } @@ -7827,6 +7828,7 @@ static std::vector> make_test_cases_perf() { for (int bs : {1, 4, 8, 32, 64, 128, 256, 512}) { for (ggml_type type_a : {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0, GGML_TYPE_Q4_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XS}) { for (ggml_type type_b : {GGML_TYPE_F32}) { + test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 32, 4, false, 1792, bs, 2048)); test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 32, 4, false, 1792, bs, 2048, 1)); } } @@ -7837,6 +7839,7 @@ static std::vector> make_test_cases_perf() { for (int bs : {1, 4, 8, 512}) { for (ggml_type type_a : {GGML_TYPE_MXFP4}) { for (ggml_type type_b : {GGML_TYPE_F32}) { + test_cases.emplace_back(new test_mul_mat_id(type_a, type_b, 32, 4, false, 2880, bs, 2880)); test_cases.emplace_back(new test_mul_mat_id_fusion(type_a, type_b, 32, 4, false, 2880, bs, 2880, 1)); } } From 4949ac0f18dd6f4a1fb1757f2152bd682fefae54 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= Date: Sat, 22 Nov 2025 21:38:19 +0100 Subject: [PATCH 05/37] ci : switch to BoringSSL on Server workflow (#17441) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Adrien Gallouët --- .github/workflows/server.yml | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index be4e23bc813..a57d0e8b1cb 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -351,16 +351,10 @@ jobs: fetch-depth: 0 ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }} - - name: libCURL - id: get_libcurl - uses: ./.github/actions/windows-setup-curl - - name: Build id: cmake_build - env: - CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} run: | - cmake -B build -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" + cmake -B build -DLLAMA_CURL=OFF -DLLAMA_BUILD_BORINGSSL=ON cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server - name: Python setup @@ -374,13 +368,6 @@ jobs: run: | pip install -r tools/server/tests/requirements.txt - - name: Copy Libcurl - id: prepare_libcurl - env: - CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }} - run: | - cp $env:CURL_PATH/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll - - name: Tests id: server_integration_tests if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }} From 54d83bbe85c1f1ec7f4f61e719231a95101c1182 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sat, 22 Nov 2025 23:29:40 -0600 Subject: [PATCH 06/37] vulkan: remove a couple unnecessary switches (#17419) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 280 ++------------------------- 1 file changed, 17 insertions(+), 263 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index d4f27af8fc6..bc8d3cdcb59 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -11381,13 +11381,13 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_contex } } -static bool ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_cgraph * cgraph, ggml_tensor* tensor, int tensor_idx, bool almost_ready); +static void ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_cgraph * cgraph, ggml_tensor* tensor, int tensor_idx, bool almost_ready); // Returns true if node has enqueued work into the queue, false otherwise // If submit is true the current all operations queued so far are being submitted to Vulkan to overlap cmdlist creation and GPU execution. static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int node_idx, ggml_tensor *node_begin, int node_idx_begin, bool last_node, bool almost_ready, bool submit){ ggml_tensor * node = cgraph->nodes[node_idx]; - if (ggml_is_empty(node) || !node->buffer) { + if (ggml_is_empty(node) || ggml_op_is_empty(node->op) || !node->buffer) { return false; } @@ -11399,132 +11399,19 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr ggml_tensor * src2 = node->src[2]; ggml_tensor * src3 = node->src[3]; - switch (node->op) { - // Return on empty ops to avoid generating a compute_ctx and setting exit_tensor - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_NONE: - return false; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(node)) { - case GGML_UNARY_OP_EXP: - case GGML_UNARY_OP_SILU: - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_ERF: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_NEG: - case GGML_UNARY_OP_TANH: - case GGML_UNARY_OP_SIGMOID: - case GGML_UNARY_OP_HARDSIGMOID: - case GGML_UNARY_OP_HARDSWISH: - case GGML_UNARY_OP_ABS: - case GGML_UNARY_OP_SOFTPLUS: - case GGML_UNARY_OP_STEP: - case GGML_UNARY_OP_ROUND: - case GGML_UNARY_OP_CEIL: - case GGML_UNARY_OP_FLOOR: - case GGML_UNARY_OP_TRUNC: - break; - default: - return false; - } - break; - case GGML_OP_GLU: - switch (ggml_get_glu_op(node)) { - case GGML_GLU_OP_GEGLU: - case GGML_GLU_OP_REGLU: - case GGML_GLU_OP_SWIGLU: - case GGML_GLU_OP_SWIGLU_OAI: - case GGML_GLU_OP_GEGLU_ERF: - case GGML_GLU_OP_GEGLU_QUICK: - break; - default: - return false; - } - break; - case GGML_OP_ADD: - { - int next_node_idx = node_idx + 1 + ctx->num_additional_fused_ops; - if (next_node_idx < cgraph->n_nodes && - cgraph->nodes[next_node_idx]->op == GGML_OP_RMS_NORM && - cgraph->nodes[next_node_idx]->src[0] == cgraph->nodes[next_node_idx - 1] && - ggml_nrows(cgraph->nodes[next_node_idx]) == 1 && - ctx->device->add_rms_fusion) { - uint32_t size = ggml_vk_rms_partials_size(ctx, cgraph->nodes[node_idx]); - ctx->do_add_rms_partials_offset_calculation = true; - if (ctx->prealloc_size_add_rms_partials_offset + size <= ctx->prealloc_size_add_rms_partials) { - ctx->do_add_rms_partials = true; - } + if (node->op == GGML_OP_ADD) { + int next_node_idx = node_idx + 1 + ctx->num_additional_fused_ops; + if (next_node_idx < cgraph->n_nodes && + cgraph->nodes[next_node_idx]->op == GGML_OP_RMS_NORM && + cgraph->nodes[next_node_idx]->src[0] == cgraph->nodes[next_node_idx - 1] && + ggml_nrows(cgraph->nodes[next_node_idx]) == 1 && + ctx->device->add_rms_fusion) { + uint32_t size = ggml_vk_rms_partials_size(ctx, cgraph->nodes[node_idx]); + ctx->do_add_rms_partials_offset_calculation = true; + if (ctx->prealloc_size_add_rms_partials_offset + size <= ctx->prealloc_size_add_rms_partials) { + ctx->do_add_rms_partials = true; } - } break; - case GGML_OP_REPEAT: - case GGML_OP_REPEAT_BACK: - case GGML_OP_GET_ROWS: - case GGML_OP_ADD_ID: - case GGML_OP_ACC: - case GGML_OP_SUB: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_ADD1: - case GGML_OP_ARANGE: - case GGML_OP_FILL: - case GGML_OP_CONCAT: - case GGML_OP_UPSCALE: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_SQRT: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_LOG: - case GGML_OP_CLAMP: - case GGML_OP_PAD: - case GGML_OP_ROLL: - case GGML_OP_CPY: - case GGML_OP_SET_ROWS: - case GGML_OP_CONT: - case GGML_OP_DUP: - case GGML_OP_SILU_BACK: - case GGML_OP_NORM: - case GGML_OP_GROUP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_RMS_NORM_BACK: - case GGML_OP_L2_NORM: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - case GGML_OP_SOFT_MAX_BACK: - case GGML_OP_ROPE: - case GGML_OP_ROPE_BACK: - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - case GGML_OP_ARGSORT: - case GGML_OP_SUM: - case GGML_OP_SUM_ROWS: - case GGML_OP_MEAN: - case GGML_OP_ARGMAX: - case GGML_OP_COUNT_EQUAL: - case GGML_OP_IM2COL: - case GGML_OP_IM2COL_3D: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_CONV_TRANSPOSE_1D: - case GGML_OP_POOL_2D: - case GGML_OP_CONV_2D: - case GGML_OP_CONV_TRANSPOSE_2D: - case GGML_OP_CONV_2D_DW: - case GGML_OP_RWKV_WKV6: - case GGML_OP_RWKV_WKV7: - case GGML_OP_SSM_SCAN: - case GGML_OP_SSM_CONV: - case GGML_OP_LEAKY_RELU: - case GGML_OP_FLASH_ATTN_EXT: - case GGML_OP_OPT_STEP_ADAMW: - case GGML_OP_OPT_STEP_SGD: - break; - default: - std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl; - GGML_ABORT("fatal error"); + } } vk_context compute_ctx; @@ -11961,145 +11848,14 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr ctx->compute_ctx.reset(); - bool ok = ggml_vk_compute_forward(ctx, cgraph, node_begin, node_idx_begin, almost_ready); - if (!ok) { - if (node->op == GGML_OP_UNARY) { - std::cerr << __func__ << ": error: op not supported UNARY " << node->name << " (" << ggml_unary_op_name(static_cast(node->op_params[0])) << ")" << std::endl; - } else if (node->op == GGML_OP_GLU) { - std::cerr << __func__ << ": error: op not supported GLU " << node->name << " (" << ggml_glu_op_name(static_cast(node->op_params[0])) << ")" << std::endl; - } else { - std::cerr << __func__ << ": error: op not supported " << node->name << " (" << ggml_op_name(node->op) << ")" << std::endl; - } - } - + ggml_vk_compute_forward(ctx, cgraph, node_begin, node_idx_begin, almost_ready); } return true; } -static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, ggml_tensor * tensor, int tensor_idx, bool almost_ready = false) { +static void ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, ggml_tensor * tensor, int tensor_idx, bool almost_ready = false) { GGML_UNUSED(cgraph); - ggml_backend_buffer * buf = nullptr; - - switch (tensor->op) { - case GGML_OP_ADD: - case GGML_OP_ACC: - case GGML_OP_GET_ROWS: - case GGML_OP_SUB: - case GGML_OP_MUL: - case GGML_OP_DIV: - case GGML_OP_ADD1: - case GGML_OP_ARANGE: - case GGML_OP_FILL: - case GGML_OP_ADD_ID: - case GGML_OP_CONCAT: - case GGML_OP_UPSCALE: - case GGML_OP_SCALE: - case GGML_OP_SQR: - case GGML_OP_SQRT: - case GGML_OP_SIN: - case GGML_OP_COS: - case GGML_OP_LOG: - case GGML_OP_CLAMP: - case GGML_OP_PAD: - case GGML_OP_ROLL: - case GGML_OP_CPY: - case GGML_OP_SET_ROWS: - case GGML_OP_CONT: - case GGML_OP_DUP: - case GGML_OP_SILU_BACK: - case GGML_OP_NORM: - case GGML_OP_GROUP_NORM: - case GGML_OP_RMS_NORM: - case GGML_OP_RMS_NORM_BACK: - case GGML_OP_L2_NORM: - case GGML_OP_DIAG_MASK_INF: - case GGML_OP_SOFT_MAX: - case GGML_OP_SOFT_MAX_BACK: - case GGML_OP_ROPE: - case GGML_OP_ROPE_BACK: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_NONE: - case GGML_OP_ARGSORT: - case GGML_OP_SUM: - case GGML_OP_SUM_ROWS: - case GGML_OP_MEAN: - case GGML_OP_ARGMAX: - case GGML_OP_COUNT_EQUAL: - case GGML_OP_IM2COL: - case GGML_OP_IM2COL_3D: - case GGML_OP_TIMESTEP_EMBEDDING: - case GGML_OP_CONV_TRANSPOSE_1D: - case GGML_OP_POOL_2D: - case GGML_OP_CONV_2D: - case GGML_OP_CONV_TRANSPOSE_2D: - case GGML_OP_CONV_2D_DW: - case GGML_OP_RWKV_WKV6: - case GGML_OP_RWKV_WKV7: - case GGML_OP_SSM_SCAN: - case GGML_OP_SSM_CONV: - case GGML_OP_LEAKY_RELU: - case GGML_OP_REPEAT: - case GGML_OP_REPEAT_BACK: - case GGML_OP_OPT_STEP_ADAMW: - case GGML_OP_OPT_STEP_SGD: - buf = tensor->buffer; - break; - case GGML_OP_UNARY: - switch (ggml_get_unary_op(tensor)) { - case GGML_UNARY_OP_EXP: - case GGML_UNARY_OP_SILU: - case GGML_UNARY_OP_GELU: - case GGML_UNARY_OP_GELU_ERF: - case GGML_UNARY_OP_GELU_QUICK: - case GGML_UNARY_OP_RELU: - case GGML_UNARY_OP_NEG: - case GGML_UNARY_OP_TANH: - case GGML_UNARY_OP_SIGMOID: - case GGML_UNARY_OP_HARDSIGMOID: - case GGML_UNARY_OP_HARDSWISH: - case GGML_UNARY_OP_ABS: - case GGML_UNARY_OP_SOFTPLUS: - case GGML_UNARY_OP_STEP: - case GGML_UNARY_OP_ROUND: - case GGML_UNARY_OP_CEIL: - case GGML_UNARY_OP_FLOOR: - case GGML_UNARY_OP_TRUNC: - buf = tensor->buffer; - break; - default: - return false; - } - break; - case GGML_OP_GLU: - switch (ggml_get_glu_op(tensor)) { - case GGML_GLU_OP_GEGLU: - case GGML_GLU_OP_REGLU: - case GGML_GLU_OP_SWIGLU: - case GGML_GLU_OP_SWIGLU_OAI: - case GGML_GLU_OP_GEGLU_ERF: - case GGML_GLU_OP_GEGLU_QUICK: - buf = tensor->buffer; - break; - default: - return false; - } - break; - case GGML_OP_MUL_MAT: - case GGML_OP_MUL_MAT_ID: - case GGML_OP_FLASH_ATTN_EXT: - buf = tensor->buffer; - - break; - default: - return false; - } - - if (buf == nullptr) { - return false; - } + GGML_UNUSED(tensor); VK_LOG_DEBUG("ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")"); @@ -12143,8 +11899,6 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph * subctx->out_memcpys.clear(); subctx->memsets.clear(); } - - return true; } // Clean up after graph processing is done From bc809e9c53ee34fe059d7b820072e2a22c51b9e2 Mon Sep 17 00:00:00 2001 From: Eric Curtin Date: Sun, 23 Nov 2025 09:29:36 +0000 Subject: [PATCH 07/37] vulkan: Update docker image to Ubuntu 26.04 to enable glslc features (#17439) 26.04 provides these Signed-off-by: Eric Curtin --- .devops/vulkan.Dockerfile | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile index b6b802a7c6e..ebf23ba5cff 100644 --- a/.devops/vulkan.Dockerfile +++ b/.devops/vulkan.Dockerfile @@ -1,9 +1,7 @@ -ARG UBUNTU_VERSION=25.10 +ARG UBUNTU_VERSION=26.04 FROM ubuntu:$UBUNTU_VERSION AS build -# Ref: https://vulkan.lunarg.com/doc/sdk/latest/linux/getting_started.html - # Install build tools RUN apt update && apt install -y git build-essential cmake wget xz-utils From 96ac5a2329029dfc35c9cbbb24c09fd91ae9416b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sun, 23 Nov 2025 11:13:34 +0100 Subject: [PATCH 08/37] cuda : support non-contiguous i32 to i32 copy (#17326) * support non-contiguous i32 to i32 copy * add tests * rename cpy_flt to cpy_scalar and reindent params --- ggml/src/ggml-cuda/cpy-utils.cuh | 2 +- ggml/src/ggml-cuda/cpy.cu | 132 ++++++++++++++++++++----------- ggml/src/ggml-cuda/ggml-cuda.cu | 3 + tests/test-backend-ops.cpp | 4 +- 4 files changed, 92 insertions(+), 49 deletions(-) diff --git a/ggml/src/ggml-cuda/cpy-utils.cuh b/ggml/src/ggml-cuda/cpy-utils.cuh index e621cb9811a..7697c292dd6 100644 --- a/ggml/src/ggml-cuda/cpy-utils.cuh +++ b/ggml/src/ggml-cuda/cpy-utils.cuh @@ -212,6 +212,6 @@ static __device__ void cpy_blck_f32_iq4_nl(const char * cxi, char * cdsti) { } template -static __device__ void cpy_1_flt(const char * cxi, char * cdsti) { +static __device__ void cpy_1_scalar(const char * cxi, char * cdsti) { *(dst_t *) cdsti = ggml_cuda_cast(*(const src_t *) cxi); } diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu index c1afde9627f..82367ad3fb0 100644 --- a/ggml/src/ggml-cuda/cpy.cu +++ b/ggml/src/ggml-cuda/cpy.cu @@ -12,10 +12,10 @@ const int CUDA_CPY_BLOCK_NM = 8; // block size of 3rd dimension if available const int CUDA_CPY_BLOCK_ROWS = 8; // block dimension for marching through rows template -static __global__ void cpy_flt(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, - const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, - const int nb12, const int nb13) { +static __global__ void cpy_scalar(const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13) { const int64_t i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= ne) { @@ -40,7 +40,7 @@ static __global__ void cpy_flt(const char * cx, char * cdst, const int ne, } template -static __global__ void cpy_flt_transpose(const char * cx, char * cdst, const int ne, +static __global__ void cpy_scalar_transpose(const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13) { @@ -166,7 +166,7 @@ static __global__ void cpy_q_f32(const char * cx, char * cdst, const int ne, } template -static __global__ void cpy_flt_contiguous(const char * cx, char * cdst, const int64_t ne) { +static __global__ void cpy_scalar_contiguous(const char * cx, char * cdst, const int64_t ne) { const int64_t i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= ne) { @@ -180,17 +180,17 @@ static __global__ void cpy_flt_contiguous(const char * cx, char * cdst, const in } template -static void ggml_cpy_flt_contiguous_cuda( +static void ggml_cpy_scalar_contiguous_cuda( const char * cx, char * cdst, const int64_t ne, cudaStream_t stream) { const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; - cpy_flt_contiguous<<>> + cpy_scalar_contiguous<<>> (cx, cdst, ne); } template -static void ggml_cpy_flt_cuda( +static void ggml_cpy_scalar_cuda( const char * cx, char * cdst, const int ne, const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { @@ -212,11 +212,11 @@ static void ggml_cpy_flt_cuda( (ne00n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D, (ne/(ne01n*ne00n) + CUDA_CPY_BLOCK_NM - 1) / CUDA_CPY_BLOCK_NM); dim3 dimBlock(CUDA_CPY_TILE_DIM_2D, CUDA_CPY_BLOCK_ROWS, 1); - cpy_flt_transpose<<>> + cpy_scalar_transpose<<>> (cx, cdst, ne, ne00n, ne01n, ne02n, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } else { const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; - cpy_flt><<>> + cpy_scalar><<>> (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } } @@ -399,94 +399,132 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg } } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { if (can_be_transposed) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_BF16) { if (contiguous_srcs) { - ggml_cpy_flt_contiguous_cuda (src0_ddc, src1_ddc, ne, main_stream); + ggml_cpy_scalar_contiguous_cuda + (src0_ddc, src1_ddc, ne, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { if (contiguous_srcs) { - ggml_cpy_flt_contiguous_cuda (src0_ddc, src1_ddc, ne, main_stream); + ggml_cpy_scalar_contiguous_cuda + (src0_ddc, src1_ddc, ne, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { - ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_f32_q8_0_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) { - ggml_cpy_q8_0_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_q8_0_f32_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { - ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_f32_q4_0_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q4_0 && src1->type == GGML_TYPE_F32) { - ggml_cpy_q4_0_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, - nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_q4_0_f32_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { - ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_f32_q4_1_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q4_1 && src1->type == GGML_TYPE_F32) { - ggml_cpy_q4_1_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, - nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_q4_1_f32_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) { - ggml_cpy_f32_q5_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_f32_q5_0_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q5_0 && src1->type == GGML_TYPE_F32) { - ggml_cpy_q5_0_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, - nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_q5_0_f32_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) { - ggml_cpy_f32_iq4_nl_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_f32_iq4_nl_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) { - ggml_cpy_f32_q5_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_f32_q5_1_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_Q5_1 && src1->type == GGML_TYPE_F32) { - ggml_cpy_q5_1_f32_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_q5_1_f32_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { if (can_be_transposed) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_BF16) { if (contiguous_srcs) { - ggml_cpy_flt_contiguous_cuda (src0_ddc, src1_ddc, ne, main_stream); + ggml_cpy_scalar_contiguous_cuda + (src0_ddc, src1_ddc, ne, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { if (contiguous_srcs) { - ggml_cpy_flt_contiguous_cuda (src0_ddc, src1_ddc, ne, main_stream); + ggml_cpy_scalar_contiguous_cuda + (src0_ddc, src1_ddc, ne, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_BF16) { if (can_be_transposed) { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F16) { if (contiguous_srcs) { - ggml_cpy_flt_contiguous_cuda (src0_ddc, src1_ddc, ne, main_stream); + ggml_cpy_scalar_contiguous_cuda + (src0_ddc, src1_ddc, ne, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32) { if (contiguous_srcs) { - ggml_cpy_flt_contiguous_cuda (src0_ddc, src1_ddc, ne, main_stream); + ggml_cpy_scalar_contiguous_cuda + (src0_ddc, src1_ddc, ne, main_stream); + } else { + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } + } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) { + if (can_be_transposed) { + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_I32) { if (contiguous_srcs) { - ggml_cpy_flt_contiguous_cuda (src0_ddc, src1_ddc, ne, main_stream); + ggml_cpy_scalar_contiguous_cuda + (src0_ddc, src1_ddc, ne, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_F32) { if (contiguous_srcs) { - ggml_cpy_flt_contiguous_cuda (src0_ddc, src1_ddc, ne, main_stream); + ggml_cpy_scalar_contiguous_cuda + (src0_ddc, src1_ddc, ne, main_stream); } else { - ggml_cpy_flt_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + ggml_cpy_scalar_cuda + (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } } else { GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__, diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index 8fe0899bb5a..0b29074f33d 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -4115,6 +4115,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g if (src0_type == GGML_TYPE_I32 && src1_type == GGML_TYPE_F32) { return true; } + if (src0_type == GGML_TYPE_I32 && src1_type == GGML_TYPE_I32) { + return true; + } if (src0_type == src1_type && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1])) { return true; } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 90bdb80e5ec..ce8c068d7aa 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -6953,9 +6953,11 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_cpy(GGML_TYPE_F16, GGML_TYPE_F16, {256, 4, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true)); test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 4, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true)); test_cases.emplace_back(new test_cpy(GGML_TYPE_BF16, GGML_TYPE_BF16, {256, 4, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true)); + test_cases.emplace_back(new test_cpy(GGML_TYPE_I32, GGML_TYPE_I32, {256, 4, 1, 1}, {0, 0, 0, 0}, {0, 0, 0, 0}, true)); + test_cases.emplace_back(new test_cpy(GGML_TYPE_I32, GGML_TYPE_I32, {256, 1, 4, 1}, {1, 2, 0, 3}, {0, 0, 0, 0})); test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, GGML_TYPE_F32, {256, 1, 4, 1}, {1, 2, 0, 3}, {0, 0, 0, 0})); - for (ggml_type type_dst : { GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16 }) { + for (ggml_type type_dst : { GGML_TYPE_F32, GGML_TYPE_I32, GGML_TYPE_F16, GGML_TYPE_BF16 }) { for (bool use_view_slice : { true, false }) { for (std::array ne : std::initializer_list>{ {2, 1, 1, 1}, {2, 1, 3, 5}, {2, 3, 5, 7}, {1, 4, 4, 1}, {1, 8, 17, 1}, {10, 10, 10, 1} }) { From 0c7220db56525d40177fcce3baa0d083448ec813 Mon Sep 17 00:00:00 2001 From: Pascal Date: Sun, 23 Nov 2025 18:42:00 +0100 Subject: [PATCH 09/37] webui: minor settings reorganization and add disable autoscroll option (#17452) * webui: added a dedicated 'Display' settings section that groups visualization options * webui: added a Display setting to toggle automatic chat scrolling * chore: update webui build output --- tools/server/public/index.html.gz | Bin 1122346 -> 1122464 bytes .../app/chat/ChatScreen/ChatScreen.svelte | 37 +++++++++--- 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z`Mttc$mG_J-r#PJbYV+;7X2Ws3gZd&BJjr{mv-RMr1qdVq3SrDLsch^AV`ksCM5gb zvAe@n+yxW;f5mmGW6o7wsKv(OyI>%W;#Y_`e-ciy+H(ZK#*)DyiM`D33(I2i&$&+H z?q$u1a=8P)H1wtA=NG33)01HKc+o%VniaGI?e_v<@5yK%N!F6xZ~n83zl2Ne z9(~m{Nl?8ou>j)p1~GQ6w$T#eT45^u5pL_QX-vKiJ zIt*q+3BG|d7sVVDXW1qH``|NNk;iA}5Aox{XT$M3{PEzkgX!?%;U^D1ql?eOPyP=c K*Qm%ZnlS)Br_`SS diff --git a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte b/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte index e891f7efdc5..c736178fe22 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatScreen/ChatScreen.svelte @@ -29,6 +29,7 @@ sendMessage, stopGeneration } from '$lib/stores/chat.svelte'; + import { config } from '$lib/stores/settings.svelte'; import { supportsVision, supportsAudio, @@ -47,6 +48,7 @@ let { showCenteredEmpty = false } = $props(); + let disableAutoScroll = $derived(Boolean(config().disableAutoScroll)); let autoScrollEnabled = $state(true); let chatScrollContainer: HTMLDivElement | undefined = $state(); let dragCounter = $state(0); @@ -149,7 +151,7 @@ } function handleScroll() { - if (!chatScrollContainer) return; + if (disableAutoScroll || !chatScrollContainer) return; const { scrollTop, scrollHeight, clientHeight } = chatScrollContainer; const distanceFromBottom = scrollHeight - scrollTop - clientHeight; @@ -194,8 +196,10 @@ const extras = result?.extras; // Enable autoscroll for user-initiated message sending - userScrolledUp = false; - autoScrollEnabled = true; + if (!disableAutoScroll) { + userScrolledUp = false; + autoScrollEnabled = true; + } await sendMessage(message, extras); scrollChatToBottom(); @@ -241,6 +245,8 @@ } function scrollChatToBottom(behavior: ScrollBehavior = 'smooth') { + if (disableAutoScroll) return; + chatScrollContainer?.scrollTo({ top: chatScrollContainer?.scrollHeight, behavior @@ -248,14 +254,27 @@ } afterNavigate(() => { - setTimeout(() => scrollChatToBottom('instant'), INITIAL_SCROLL_DELAY); + if (!disableAutoScroll) { + setTimeout(() => scrollChatToBottom('instant'), INITIAL_SCROLL_DELAY); + } }); onMount(() => { - setTimeout(() => scrollChatToBottom('instant'), INITIAL_SCROLL_DELAY); + if (!disableAutoScroll) { + setTimeout(() => scrollChatToBottom('instant'), INITIAL_SCROLL_DELAY); + } }); $effect(() => { + if (disableAutoScroll) { + autoScrollEnabled = false; + if (scrollInterval) { + clearInterval(scrollInterval); + scrollInterval = undefined; + } + return; + } + if (isCurrentConversationLoading && autoScrollEnabled) { scrollInterval = setInterval(scrollChatToBottom, AUTO_SCROLL_INTERVAL); } else if (scrollInterval) { @@ -289,9 +308,11 @@ class="mb-16 md:mb-24" messages={activeMessages()} onUserAction={() => { - userScrolledUp = false; - autoScrollEnabled = true; - scrollChatToBottom(); + if (!disableAutoScroll) { + userScrolledUp = false; + autoScrollEnabled = true; + scrollChatToBottom(); + } }} /> diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte index d00ae128538..204f0d7551e 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte @@ -3,7 +3,6 @@ Settings, Funnel, AlertTriangle, - Brain, Code, Monitor, Sun, @@ -58,6 +57,33 @@ label: 'Paste long text to file length', type: 'input' }, + { + key: 'enableContinueGeneration', + label: 'Enable "Continue" button', + type: 'checkbox', + isExperimental: true + }, + { + key: 'pdfAsImage', + label: 'Parse PDF as image', + type: 'checkbox' + }, + { + key: 'askForTitleConfirmation', + label: 'Ask for confirmation before changing conversation title', + type: 'checkbox' + } + ] + }, + { + title: 'Display', + icon: Monitor, + fields: [ + { + key: 'showThoughtInProgress', + label: 'Show thought in progress', + type: 'checkbox' + }, { key: 'showMessageStats', label: 'Show message generation statistics', @@ -79,25 +105,14 @@ type: 'checkbox' }, { - key: 'enableContinueGeneration', - label: 'Enable "Continue" button', - type: 'checkbox', - isExperimental: true - }, - { - key: 'pdfAsImage', - label: 'Parse PDF as image', + key: 'disableAutoScroll', + label: 'Disable automatic scroll', type: 'checkbox' }, { key: 'renderUserContentAsMarkdown', label: 'Render user content as Markdown', type: 'checkbox' - }, - { - key: 'askForTitleConfirmation', - label: 'Ask for confirmation before changing conversation title', - type: 'checkbox' } ] }, @@ -208,17 +223,6 @@ } ] }, - { - title: 'Reasoning', - icon: Brain, - fields: [ - { - key: 'showThoughtInProgress', - label: 'Show thought in progress', - type: 'checkbox' - } - ] - }, { title: 'Import/Export', icon: Database, diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts index c25ea23f37b..6783757e6b4 100644 --- a/tools/server/webui/src/lib/constants/settings-config.ts +++ b/tools/server/webui/src/lib/constants/settings-config.ts @@ -14,6 +14,7 @@ export const SETTING_CONFIG_DEFAULT: Record = pasteLongTextToFileLen: 2500, pdfAsImage: false, showModelInfo: false, + disableAutoScroll: false, renderUserContentAsMarkdown: false, modelSelectorEnabled: false, // make sure these default values are in sync with `common.h` @@ -93,6 +94,8 @@ export const SETTING_CONFIG_INFO: Record = { 'Ask for confirmation before automatically changing conversation title when editing the first message.', pdfAsImage: 'Parse PDF as image instead of text (requires vision-capable model).', showModelInfo: 'Display the model name used to generate each message below the message content.', + disableAutoScroll: + 'Disable automatic scrolling while messages stream so you can control the viewport position manually.', renderUserContentAsMarkdown: 'Render user messages using markdown formatting in the chat.', modelSelectorEnabled: 'Enable the model selector in the chat input to choose the inference model. Sends the associated model field in API requests.', From d5bc1ad11062563faa3e57320399c0938c4803fa Mon Sep 17 00:00:00 2001 From: nullname Date: Mon, 24 Nov 2025 06:26:36 +0800 Subject: [PATCH 10/37] ggml-hexagon: add `hex_supported_buffer` for better buffer supported check (#17212) * hexagon: add buffer support checks for hexagon sessions * refactor: simplify buffer support checks in hexagon operations * hexagon: update buffer support checks to use tensor structure * refactor: streamline buffer initialization for DSP queue in hexagon operations * refactor: simplify buffer initialization in DSP queue for hexagon operations * refactor: optimize hex_supported_buffer function by fold expression * wip * refactor: simplify dspqueue_buffers_init function and its usage in hexagon operations * fix: improve nan handling at hvx_vec_fast_sigmoid_fp32_guard * refactor: optimize hvx_vec_inverse_fp32_guard for better nan handling * refactor: update hvx_vec_fast_sigmoid_fp32_guard to use adjusted exponent limits * refactor: modify hvx_vec_fast_sigmoid_fp32_guard to accept parameters for improved flexibility * refactor: update hvx_vec_exp_fp32_guard to accept max_exp and inf parameters to save some instructions * refactor: move hvx_vec_inverse_fp32_guard implementation to hvx-inverse.c for better perf --- ggml/src/ggml-hexagon/ggml-hexagon.cpp | 460 +++++------------------- ggml/src/ggml-hexagon/htp/hvx-exp.c | 27 +- ggml/src/ggml-hexagon/htp/hvx-inverse.c | 18 +- ggml/src/ggml-hexagon/htp/hvx-utils.h | 41 +-- 4 files changed, 142 insertions(+), 404 deletions(-) diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index cabd301ad35..0b4e2c3d4df 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -240,6 +240,23 @@ struct ggml_hexagon_session { uint32_t prof_pkts; }; +static inline void hex_print_op_info(const ggml_tensor * op, ggml_hexagon_session * sess, const uint32_t req_flags) { + char dims[64 * GGML_MAX_SRC]; + char strides[64 * GGML_MAX_SRC]; + char types[16 * GGML_MAX_SRC]; + char buffs[64 * GGML_MAX_SRC]; + char names[64 * GGML_MAX_SRC]; + + hex_format_op_dims(dims, op); + hex_format_op_strides(strides, op); + hex_format_op_types(types, op); + hex_format_op_buffs(buffs, op); + hex_format_op_names(names, op); + + HEX_VERBOSE("ggml-hex: %s %s: %s : %s : %s : %s : %s: flags 0x%x\n", sess->name.c_str(), ggml_op_name(op->op), + names, dims, types, strides, buffs, req_flags); +} + void ggml_hexagon_session::enqueue(struct htp_general_req &req, struct dspqueue_buffer *bufs, uint32_t n_bufs, bool sync) { // Bump pending flag (cleared in the session::flush once we get the responce) this->op_pending++; // atomic inc @@ -1912,6 +1929,15 @@ static bool hex_supported_dims(const struct ggml_tensor * x, const struct ggml_t return true; } +template +static inline bool hex_supported_buffer(const struct ggml_hexagon_session * sess, _TTensor... tensors) { + return ([&]() -> bool { + return !tensors || !tensors->buffer || + (ggml_backend_buffer_is_hexagon(tensors->buffer) && + ggml_backend_hexagon_buffer_get_sess(tensors->buffer) == sess); + }() && ...); +} + static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * sess, const struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; @@ -1959,16 +1985,7 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s } // src0 & src1 & dst must be mapped to the same session - if (src0->buffer && - (!ggml_backend_buffer_is_hexagon(src0->buffer) || ggml_backend_hexagon_buffer_get_sess(src0->buffer) != sess)) { - return false; - } - if (src1->buffer && - (!ggml_backend_buffer_is_hexagon(src1->buffer) || ggml_backend_hexagon_buffer_get_sess(src1->buffer) != sess)) { - return false; - } - if (dst->buffer && - (!ggml_backend_buffer_is_hexagon(dst->buffer) || ggml_backend_hexagon_buffer_get_sess(dst->buffer) != sess)) { + if (!hex_supported_buffer(sess, src0, src1, dst)) { return false; } @@ -2016,20 +2033,7 @@ static bool ggml_hexagon_supported_mul_mat_id(const struct ggml_hexagon_session // src0 (weights) must be repacked and mapped to the same session // src1 & sr2 & dst must be mapped to the same session - if (src0->buffer && - (!ggml_backend_buffer_is_hexagon(src0->buffer) || ggml_backend_hexagon_buffer_get_sess(src0->buffer) != sess)) { - return false; - } - if (src1->buffer && - (!ggml_backend_buffer_is_hexagon(src1->buffer) || ggml_backend_hexagon_buffer_get_sess(src1->buffer) != sess)) { - return false; - } - if (src2->buffer && - (!ggml_backend_buffer_is_hexagon(src2->buffer) || ggml_backend_hexagon_buffer_get_sess(src2->buffer) != sess)) { - return false; - } - if (dst->buffer && - (!ggml_backend_buffer_is_hexagon(dst->buffer) || ggml_backend_hexagon_buffer_get_sess(dst->buffer) != sess)) { + if (!hex_supported_buffer(sess, src0, src1, src2, dst)) { return false; } @@ -2063,16 +2067,7 @@ static bool ggml_hexagon_supported_binary(const struct ggml_hexagon_session * se } // src0, src1 & dst must be mapped to the same session - if (src0->buffer && - (!ggml_backend_buffer_is_hexagon(src0->buffer) || ggml_backend_hexagon_buffer_get_sess(src0->buffer) != sess)) { - return false; - } - if (src1->buffer && - (!ggml_backend_buffer_is_hexagon(src1->buffer) || ggml_backend_hexagon_buffer_get_sess(src1->buffer) != sess)) { - return false; - } - if (dst->buffer && - (!ggml_backend_buffer_is_hexagon(dst->buffer) || ggml_backend_hexagon_buffer_get_sess(dst->buffer) != sess)) { + if (!hex_supported_buffer(sess, src0, src1, dst)) { return false; } @@ -2104,20 +2099,7 @@ static bool ggml_hexagon_supported_add_id(const struct ggml_hexagon_session * se } // src0, src1 & dst must be mapped to the same session - if (src0->buffer && - (!ggml_backend_buffer_is_hexagon(src0->buffer) || ggml_backend_hexagon_buffer_get_sess(src0->buffer) != sess)) { - return false; - } - if (src1->buffer && - (!ggml_backend_buffer_is_hexagon(src1->buffer) || ggml_backend_hexagon_buffer_get_sess(src1->buffer) != sess)) { - return false; - } - if (src2->buffer && - (!ggml_backend_buffer_is_hexagon(src2->buffer) || ggml_backend_hexagon_buffer_get_sess(src2->buffer) != sess)) { - return false; - } - if (dst->buffer && - (!ggml_backend_buffer_is_hexagon(dst->buffer) || ggml_backend_hexagon_buffer_get_sess(dst->buffer) != sess)) { + if (!hex_supported_buffer(sess, src0, src1, src2, dst)) { return false; } @@ -2144,12 +2126,7 @@ static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * ses } // src0 & dst must be mapped to the same session - if (src0->buffer && - (!ggml_backend_buffer_is_hexagon(src0->buffer) || ggml_backend_hexagon_buffer_get_sess(src0->buffer) != sess)) { - return false; - } - if (dst->buffer && - (!ggml_backend_buffer_is_hexagon(dst->buffer) || ggml_backend_hexagon_buffer_get_sess(dst->buffer) != sess)) { + if (!hex_supported_buffer(sess, src0, dst)) { return false; } @@ -2186,16 +2163,7 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session } // src0, src1 & dst must be mapped to the same session - if (src0->buffer && - (!ggml_backend_buffer_is_hexagon(src0->buffer) || ggml_backend_hexagon_buffer_get_sess(src0->buffer) != sess)) { - return false; - } - if (src1 && src1->buffer && - (!ggml_backend_buffer_is_hexagon(src1->buffer) || ggml_backend_hexagon_buffer_get_sess(src1->buffer) != sess)) { - return false; - } - if (dst->buffer && - (!ggml_backend_buffer_is_hexagon(dst->buffer) || ggml_backend_hexagon_buffer_get_sess(dst->buffer) != sess)) { + if (!hex_supported_buffer(sess, src0, src1, dst)) { return false; } @@ -2248,16 +2216,7 @@ static bool ggml_hexagon_supported_softmax(const struct ggml_hexagon_session * s } // src0, src1 & dst must be mapped to the same session - if (src0->buffer && - (!ggml_backend_buffer_is_hexagon(src0->buffer) || ggml_backend_hexagon_buffer_get_sess(src0->buffer) != sess)) { - return false; - } - if (src1 && src1->buffer && - (!ggml_backend_buffer_is_hexagon(src1->buffer) || ggml_backend_hexagon_buffer_get_sess(src1->buffer) != sess)) { - return false; - } - if (dst->buffer && - (!ggml_backend_buffer_is_hexagon(dst->buffer) || ggml_backend_hexagon_buffer_get_sess(dst->buffer) != sess)) { + if (!hex_supported_buffer(sess, src0, src1, dst)) { return false; } @@ -2312,20 +2271,7 @@ static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess } // src0, src1, src2 & dst must be mapped to the same session - if (src0->buffer && - (!ggml_backend_buffer_is_hexagon(src0->buffer) || ggml_backend_hexagon_buffer_get_sess(src0->buffer) != sess)) { - return false; - } - if (src1->buffer && - (!ggml_backend_buffer_is_hexagon(src1->buffer) || ggml_backend_hexagon_buffer_get_sess(src1->buffer) != sess)) { - return false; - } - if (src2 && src2->buffer && - (!ggml_backend_buffer_is_hexagon(src2->buffer) || ggml_backend_hexagon_buffer_get_sess(src2->buffer) != sess)) { - return false; - } - if (dst->buffer && - (!ggml_backend_buffer_is_hexagon(dst->buffer) || ggml_backend_hexagon_buffer_get_sess(dst->buffer) != sess)) { + if (!hex_supported_buffer(sess, src0, src1, src2, dst)) { return false; } @@ -2346,6 +2292,26 @@ static void init_htp_tensor(htp_tensor * h, const ggml_tensor * t) { h->nb[3] = t->nb[3]; } +static size_t dspqueue_buffers_init(dspqueue_buffer * buf, const ggml_tensor * t, bool flush_host, bool flush_htp) { + if (!t) { + return 0; + } + + memset(buf, 0, sizeof(*buf)); + auto tensor_buf = static_cast(t->buffer->context); + buf->fd = tensor_buf->fd; + buf->ptr = t->data; + buf->offset = (uint8_t *) t->data - tensor_buf->base; + buf->size = ggml_nbytes(t); + buf->flags = (flush_host ? DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER : 0); // Flush CPU + buf->flags |= (flush_htp ? DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT : 0); // Invalidate DSP + return 1; +} + +static ggml_hexagon_session * get_session_from_tensor(const ggml_tensor * t) { + return static_cast(t->buffer->context)->sess; +} + static void hex_dump_dspbuf(const struct ggml_tensor * t, const dspqueue_buffer * d) { auto buf = static_cast(t->buffer->context); auto sess = buf->sess; @@ -2360,10 +2326,6 @@ static void ggml_hexagon_mul_mat(const struct ggml_tensor * op, uint32_t flags) const struct ggml_tensor * src1 = op->src[1]; const struct ggml_tensor * dst = op; - auto src0_buf = static_cast(src0->buffer->context); - auto src1_buf = static_cast(src1->buffer->context); - auto dst_buf = static_cast(dst->buffer->context); - uint64_t t1, t2; t1 = ggml_time_us(); @@ -2385,55 +2347,27 @@ static void ggml_hexagon_mul_mat(const struct ggml_tensor * op, uint32_t flags) } dspqueue_buffer bufs[3]; - memset(bufs, 0, sizeof(bufs)); // First buffer Weights. // The content is static, there is no need to do any cache management - bufs[0].fd = src0_buf->fd; - bufs[0].ptr = src0->data; - bufs[0].offset = (uint8_t *) src0->data - src0_buf->base; - bufs[0].size = ggml_nbytes(src0); - bufs[0].flags = 0; + dspqueue_buffers_init(bufs, src0, false, false); // Second buffer Input Activations. This is a buffer that the CPU // writes and the DSP reads, so we'll need to flush CPU caches and // invalidate DSP ones. On platforms with I/O coherency support the // framework will automatically skip cache operations where possible. - bufs[1].fd = src1_buf->fd; - bufs[1].ptr = src1->data; - bufs[1].offset = (uint8_t *) src1->data - src1_buf->base; - bufs[1].size = ggml_nbytes(src1); - bufs[1].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP + dspqueue_buffers_init(&bufs[1], src1, true, true); // Third buffer Output Activations. We'll handle DSP // cache maintenance in the response message but need to flush // CPU caches to ensure any previously written dirty lines are // written out before writes from the DSP start. - bufs[2].fd = dst_buf->fd; - bufs[2].ptr = dst->data; - bufs[2].offset = (uint8_t *) dst->data - dst_buf->base; - bufs[2].size = ggml_nbytes(dst); - bufs[2].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER); + dspqueue_buffers_init(&bufs[2], dst, true, false); - // Primary DSP session from the src0 (normally weight) tensor - auto sess = src0_buf->sess; + auto * sess = get_session_from_tensor(src0); if (opt_verbose) { - char dims[64 * GGML_MAX_SRC]; - char strides[64 * GGML_MAX_SRC]; - char types[16 * GGML_MAX_SRC]; - char buffs[64 * GGML_MAX_SRC]; - char names[64 * GGML_MAX_SRC]; - - hex_format_op_dims(dims, op); - hex_format_op_strides(strides, op); - hex_format_op_types(types, op); - hex_format_op_buffs(buffs, op); - hex_format_op_names(names, op); - - HEX_VERBOSE("ggml-hex: %s %s: %s : %s : %s : %s : %s: flags 0x%x\n", sess->name.c_str(), ggml_op_name(op->op), - names, dims, types, strides, buffs, req.flags); + hex_print_op_info(op, sess, req.flags); if (opt_verbose > 1) { hex_dump_dspbuf(src0, &bufs[0]); hex_dump_dspbuf(src1, &bufs[1]); @@ -2463,11 +2397,6 @@ static void ggml_hexagon_mul_mat_id(const struct ggml_tensor * op, uint32_t flag const struct ggml_tensor * src2 = op->src[2]; const struct ggml_tensor * dst = op; - auto src0_buf = static_cast(src0->buffer->context); - auto src1_buf = static_cast(src1->buffer->context); - auto src2_buf = static_cast(src2->buffer->context); - auto dst_buf = static_cast(dst->buffer->context); - uint64_t t1, t2; t1 = ggml_time_us(); @@ -2490,66 +2419,32 @@ static void ggml_hexagon_mul_mat_id(const struct ggml_tensor * op, uint32_t flag } dspqueue_buffer bufs[4]; - memset(bufs, 0, sizeof(bufs)); - // First buffer Weights. // The content is static, there is no need to do any cache management - bufs[0].fd = src0_buf->fd; - bufs[0].ptr = src0->data; - bufs[0].offset = (uint8_t *) src0->data - src0_buf->base; - bufs[0].size = ggml_nbytes(src0); - bufs[0].flags = 0; + dspqueue_buffers_init(bufs, src0, false, false); // Second buffer Input Activations. This is a buffer that the CPU // writes and the DSP reads, so we'll need to flush CPU caches and // invalidate DSP ones. On platforms with I/O coherency support the // framework will automatically skip cache operations where possible. - bufs[1].fd = src1_buf->fd; - bufs[1].ptr = src1->data; - bufs[1].offset = (uint8_t *) src1->data - src1_buf->base; - bufs[1].size = ggml_nbytes(src1); - bufs[1].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP + dspqueue_buffers_init(&bufs[1], src1, true, true); // Third buffer expert IDs. This is a buffer that the CPU // writes and the DSP reads, so we'll need to flush CPU caches and // invalidate DSP ones. On platforms with I/O coherency support the // framework will automatically skip cache operations where possible. - bufs[2].fd = src2_buf->fd; - bufs[2].ptr = src2->data; - bufs[2].offset = (uint8_t *) src2->data - src2_buf->base; - bufs[2].size = ggml_nbytes(src2); - bufs[2].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP + dspqueue_buffers_init(&bufs[2], src2, true, true); // Forth buffer Output Activations. We'll handle DSP // cache maintenance in the response message but need to flush // CPU caches to ensure any previously written dirty lines are // written out before writes from the DSP start. - bufs[3].fd = dst_buf->fd; - bufs[3].ptr = dst->data; - bufs[3].offset = (uint8_t *) dst->data - dst_buf->base; - bufs[3].size = ggml_nbytes(dst); - bufs[3].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER); + dspqueue_buffers_init(&bufs[3], dst, true, false); - // Primary DSP session from the src0 (normally weight) tensor - auto sess = src0_buf->sess; + auto * sess = get_session_from_tensor(src0); if (opt_verbose) { - char dims[64 * GGML_MAX_SRC]; - char strides[64 * GGML_MAX_SRC]; - char types[16 * GGML_MAX_SRC]; - char buffs[64 * GGML_MAX_SRC]; - char names[64 * GGML_MAX_SRC]; - - hex_format_op_dims(dims, op); - hex_format_op_types(types, op); - hex_format_op_buffs(buffs, op); - hex_format_op_names(names, op); - - HEX_VERBOSE("ggml-hex: %s %s: %s : %s : %s : %s : %s: flags 0x%x\n", sess->name.c_str(), ggml_op_name(op->op), - names, dims, types, strides, buffs, req.flags); - + hex_print_op_info(op, sess, req.flags); if (opt_verbose > 1) { hex_dump_dspbuf(src0, &bufs[0]); hex_dump_dspbuf(src1, &bufs[1]); @@ -2581,10 +2476,6 @@ static void ggml_hexagon_binary(const struct ggml_tensor * op, uint32_t flags) { const struct ggml_tensor * src1 = node->src[1]; const struct ggml_tensor * dst = node; - auto src0_buf = static_cast(src0->buffer->context); - auto src1_buf = static_cast(src1->buffer->context); - auto dst_buf = static_cast(dst->buffer->context); - uint64_t t1 = 0; uint64_t t2 = 0; @@ -2621,60 +2512,30 @@ static void ggml_hexagon_binary(const struct ggml_tensor * op, uint32_t flags) { init_htp_tensor(&req.dst, dst); dspqueue_buffer bufs[3]; - memset(bufs, 0, sizeof(bufs)); - // First buffer = First Operand of Binary op // This is a buffer that the CPU writes and the DSP reads, so we'll // need to flush CPU caches and invalidate DSP ones. On platforms // with I/O coherency support the framework will automatically skip // cache operations where possible. - bufs[0].fd = src0_buf->fd; - bufs[0].ptr = src0->data; - bufs[0].offset = (uint8_t *) src0->data - src0_buf->base; - bufs[0].size = ggml_nbytes(src0); - bufs[0].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP; + dspqueue_buffers_init(bufs, src0, true, true); // Second buffer = Second Operand of Binary op // This is a buffer that the CPU writes and the DSP reads, so we'll // need to flush CPU caches and invalidate DSP ones. On platforms // with I/O coherency support the framework will automatically skip // cache operations where possible. - bufs[1].fd = src1_buf->fd; - bufs[1].ptr = src1->data; - bufs[1].offset = (uint8_t *) src1->data - src1_buf->base; - bufs[1].size = ggml_nbytes(src1); - bufs[1].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP + dspqueue_buffers_init(&bufs[1], src1, true, true); // Third buffer = Output Activations. We'll handle DSP // cache maintenance in the response message but need to flush // CPU caches to ensure any previously written dirty lines are // written out before writes from the DSP start. - bufs[2].fd = dst_buf->fd; - bufs[2].ptr = dst->data; - bufs[2].offset = (uint8_t *) dst->data - dst_buf->base; - bufs[2].size = ggml_nbytes(dst); - bufs[2].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER); + dspqueue_buffers_init(&bufs[2], dst, true, false); - // Primary DSP session from the src0 tensor - ggml_hexagon_session * sess = src0_buf->sess; + auto * sess = get_session_from_tensor(src0); if (opt_verbose) { - char dims[64 * GGML_MAX_SRC]; - char strides[16 * GGML_MAX_SRC]; - char types[16 * GGML_MAX_SRC]; - char buffs[64 * GGML_MAX_SRC]; - char names[64 * GGML_MAX_SRC]; - - hex_format_op_dims(dims, op); - hex_format_op_strides(strides, op); - hex_format_op_types(types, op); - hex_format_op_buffs(buffs, op); - hex_format_op_names(names, op); - - HEX_VERBOSE("ggml-hex: %s %s : %s : %s : %s : %s : %s : flags 0x%x\n", sess->name.c_str(), - ggml_op_name(node->op), names, dims, types, strides, buffs, req.flags); + hex_print_op_info(op, sess, req.flags); if (opt_verbose > 1) { hex_dump_dspbuf(src0, &bufs[0]); hex_dump_dspbuf(src1, &bufs[1]); @@ -2705,11 +2566,6 @@ static void ggml_hexagon_add_id(const struct ggml_tensor * op, uint32_t flags) { const struct ggml_tensor * src2 = node->src[2]; const struct ggml_tensor * dst = node; - auto src0_buf = static_cast(src0->buffer->context); - auto src1_buf = static_cast(src1->buffer->context); - auto src2_buf = static_cast(src2->buffer->context); - auto dst_buf = static_cast(dst->buffer->context); - uint64_t t1 = 0; uint64_t t2 = 0; @@ -2741,58 +2597,19 @@ static void ggml_hexagon_add_id(const struct ggml_tensor * op, uint32_t flags) { init_htp_tensor(&req.dst, dst); dspqueue_buffer bufs[4]; - memset(bufs, 0, sizeof(bufs)); - // First buffer = input activations - bufs[0].fd = src0_buf->fd; - bufs[0].ptr = src0->data; - bufs[0].offset = (uint8_t *) src0->data - src0_buf->base; - bufs[0].size = ggml_nbytes(src0); - bufs[0].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP; - + dspqueue_buffers_init(bufs, src0, true, true); // Second buffer = experts bias - bufs[1].fd = src1_buf->fd; - bufs[1].ptr = src1->data; - bufs[1].offset = (uint8_t *) src1->data - src1_buf->base; - bufs[1].size = ggml_nbytes(src1); - bufs[1].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP - + dspqueue_buffers_init(&bufs[1], src1, true, true); // Third buffer = activated experts - bufs[2].fd = src2_buf->fd; - bufs[2].ptr = src2->data; - bufs[2].offset = (uint8_t *) src2->data - src2_buf->base; - bufs[2].size = ggml_nbytes(src2); - bufs[2].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP - + dspqueue_buffers_init(&bufs[2], src2, true, true); // Forth buffer = output activations - bufs[3].fd = dst_buf->fd; - bufs[3].ptr = dst->data; - bufs[3].offset = (uint8_t *) dst->data - dst_buf->base; - bufs[3].size = ggml_nbytes(dst); - bufs[3].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER); + dspqueue_buffers_init(&bufs[3], dst, true, true); - // Primary DSP session from the src0 tensor - ggml_hexagon_session * sess = src0_buf->sess; + auto * sess = get_session_from_tensor(src0); if (opt_verbose) { - char dims[64 * GGML_MAX_SRC]; - char strides[16 * GGML_MAX_SRC]; - char types[16 * GGML_MAX_SRC]; - char buffs[64 * GGML_MAX_SRC]; - char names[64 * GGML_MAX_SRC]; - - hex_format_op_dims(dims, op); - hex_format_op_strides(strides, op); - hex_format_op_types(types, op); - hex_format_op_buffs(buffs, op); - hex_format_op_names(names, op); - - HEX_VERBOSE("ggml-hex: %s %s : %s : %s : %s : %s : %s : flags 0x%x\n", sess->name.c_str(), - ggml_op_name(node->op), names, dims, types, strides, buffs, req.flags); - + hex_print_op_info(op, sess, req.flags); if (opt_verbose > 1) { hex_dump_dspbuf(src0, &bufs[0]); hex_dump_dspbuf(src1, &bufs[1]); @@ -2886,71 +2703,33 @@ static void ggml_hexagon_unary(const struct ggml_tensor * op, uint32_t flags) { } dspqueue_buffer bufs[3]; - int n_bufs = 0; - - memset(bufs, 0, sizeof(bufs)); // First buffer = Only Operand of Unary op // This is a buffer that the CPU writes and the DSP reads, so we'll // need to flush CPU caches and invalidate DSP ones. On platforms // with I/O coherency support the framework will automatically skip // cache operations where possible. - auto src0_buf = static_cast(src0->buffer->context); - bufs[n_bufs].fd = src0_buf->fd; - bufs[n_bufs].ptr = src0->data; - bufs[n_bufs].offset = (uint8_t *) src0->data - src0_buf->base; - bufs[n_bufs].size = ggml_nbytes(src0); - bufs[n_bufs].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP; - ++n_bufs; + size_t n_bufs = dspqueue_buffers_init(bufs, src0, true, true); - if (src1) { - // Second buffer = Second Operand of Binary op - // This is a buffer that the CPU writes and the DSP reads, so we'll - // need to flush CPU caches and invalidate DSP ones. On platforms - // with I/O coherency support the framework will automatically skip - // cache operations where possible. - auto src1_buf = static_cast(src1->buffer->context); - bufs[n_bufs].fd = src1_buf->fd; - bufs[n_bufs].ptr = src1->data; - bufs[n_bufs].offset = (uint8_t *) src1->data - src1_buf->base; - bufs[n_bufs].size = ggml_nbytes(src1); - bufs[n_bufs].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP - ++n_bufs; - } + // Second buffer(nullable) = Second Operand of Binary op + // This is a buffer that the CPU writes and the DSP reads, so we'll + // need to flush CPU caches and invalidate DSP ones. On platforms + // with I/O coherency support the framework will automatically skip + // cache operations where possible. + n_bufs += dspqueue_buffers_init(&bufs[n_bufs], src1, true, true); // Second or third buffer = Output Activations. We'll handle DSP // Second buffer = Output Activations. We'll handle DSP // cache maintenance in the response message but need to flush // CPU caches to ensure any previously written dirty lines are // written out before writes from the DSP start. - auto dst_buf = static_cast(dst->buffer->context); - bufs[n_bufs].fd = dst_buf->fd; - bufs[n_bufs].ptr = dst->data; - bufs[n_bufs].offset = (uint8_t *) dst->data - dst_buf->base; - bufs[n_bufs].size = ggml_nbytes(dst); - bufs[n_bufs].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER); - ++n_bufs; + n_bufs += dspqueue_buffers_init(&bufs[n_bufs], dst, true, false); // Primary DSP session from the src0 tensor - ggml_hexagon_session * sess = src0_buf->sess; + auto * sess = get_session_from_tensor(src0); if (opt_verbose) { - char dims[64 * GGML_MAX_SRC]; - char strides[64 * GGML_MAX_SRC]; - char types[16 * GGML_MAX_SRC]; - char buffs[64 * GGML_MAX_SRC]; - char names[64 * GGML_MAX_SRC]; - - hex_format_op_dims(dims, op); - hex_format_op_strides(strides, op); - hex_format_op_types(types, op); - hex_format_op_buffs(buffs, op); - hex_format_op_names(names, op); - - HEX_VERBOSE("ggml-hex: %s %s : %s : %s : %s : %s : %s : flags 0x%x\n", sess->name.c_str(), ggml_op_name(op->op), - names, dims, types, strides, buffs, req.flags); + hex_print_op_info(op, sess, req.flags); if (opt_verbose > 1) { hex_dump_dspbuf(src0, &bufs[0]); if (src1) { @@ -3023,85 +2802,40 @@ static void ggml_hexagon_rope(const struct ggml_tensor * op, uint32_t flags) { } dspqueue_buffer bufs[4]; - int n_bufs = 0; - - memset(bufs, 0, sizeof(bufs)); // First buffer // This is a buffer that the CPU writes and the DSP reads, so we'll // need to flush CPU caches and invalidate DSP ones. On platforms // with I/O coherency support the framework will automatically skip // cache operations where possible. - auto src0_buf = static_cast(src0->buffer->context); - bufs[n_bufs].fd = src0_buf->fd; - bufs[n_bufs].ptr = src0->data; - bufs[n_bufs].offset = (uint8_t *) src0->data - src0_buf->base; - bufs[n_bufs].size = ggml_nbytes(src0); - bufs[n_bufs].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP; - ++n_bufs; + size_t n_bufs = dspqueue_buffers_init(bufs, src0, true, true); // Second buffer // This is a buffer that the CPU writes and the DSP reads, so we'll // need to flush CPU caches and invalidate DSP ones. On platforms // with I/O coherency support the framework will automatically skip // cache operations where possible. - auto src1_buf = static_cast(src1->buffer->context); - bufs[n_bufs].fd = src1_buf->fd; - bufs[n_bufs].ptr = src1->data; - bufs[n_bufs].offset = (uint8_t *) src1->data - src1_buf->base; - bufs[n_bufs].size = ggml_nbytes(src1); - bufs[n_bufs].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP - ++n_bufs; + n_bufs += dspqueue_buffers_init(&bufs[n_bufs], src1, true, true); - if (src2) { - // Third buffer - // This is a buffer that the CPU writes and the DSP reads, so we'll - // need to flush CPU caches and invalidate DSP ones. On platforms - // with I/O coherency support the framework will automatically skip - // cache operations where possible. - auto src2_buf = static_cast(src2->buffer->context); - bufs[n_bufs].fd = src2_buf->fd; - bufs[n_bufs].ptr = src2->data; - bufs[n_bufs].offset = (uint8_t *) src2->data - src2_buf->base; - bufs[n_bufs].size = ggml_nbytes(src2); - bufs[n_bufs].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | // Flush CPU - DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT); // Invalidate DSP - ++n_bufs; - } + // Third buffer(nullable) + // This is a buffer that the CPU writes and the DSP reads, so we'll + // need to flush CPU caches and invalidate DSP ones. On platforms + // with I/O coherency support the framework will automatically skip + // cache operations where possible. + n_bufs += dspqueue_buffers_init(&bufs[n_bufs], src2, true, true); // Final buffer = Output Activations. We'll handle DSP // Second buffer = Output Activations. We'll handle DSP // cache maintenance in the response message but need to flush // CPU caches to ensure any previously written dirty lines are // written out before writes from the DSP start. - auto dst_buf = static_cast(dst->buffer->context); - bufs[n_bufs].fd = dst_buf->fd; - bufs[n_bufs].ptr = dst->data; - bufs[n_bufs].offset = (uint8_t *) dst->data - dst_buf->base; - bufs[n_bufs].size = ggml_nbytes(dst); - bufs[n_bufs].flags = (DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER); - ++n_bufs; + n_bufs += dspqueue_buffers_init(&bufs[n_bufs], dst, true, false); // Primary DSP session from the src0 tensor - ggml_hexagon_session * sess = src0_buf->sess; + auto * sess = get_session_from_tensor(src0); if (opt_verbose) { - char dims[64 * GGML_MAX_SRC]; - char strides[64 * GGML_MAX_SRC]; - char types[16 * GGML_MAX_SRC]; - char buffs[64 * GGML_MAX_SRC]; - char names[64 * GGML_MAX_SRC]; - - hex_format_op_dims(dims, op); - hex_format_op_strides(strides, op); - hex_format_op_types(types, op); - hex_format_op_buffs(buffs, op); - hex_format_op_names(names, op); - - HEX_VERBOSE("ggml-hex: %s %s : %s : %s : %s : %s : %s : flags 0x%x\n", sess->name.c_str(), ggml_op_name(op->op), - names, dims, types, strides, buffs, req.flags); + hex_print_op_info(op, sess, req.flags); if (opt_verbose > 1) { hex_dump_dspbuf(src0, &bufs[0]); if (src1) { diff --git a/ggml/src/ggml-hexagon/htp/hvx-exp.c b/ggml/src/ggml-hexagon/htp/hvx-exp.c index d0735e9325e..21bf46a542f 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-exp.c +++ b/ggml/src/ggml-hexagon/htp/hvx-exp.c @@ -16,13 +16,8 @@ #include "hvx-utils.h" #include "ops-utils.h" -static inline HVX_Vector hvx_vec_exp_fp32_guard(HVX_Vector in_vec) { - static const float kInf = INFINITY; - static const float kMaxExp = 88.02f; // log(INF) - - const HVX_Vector max_exp = hvx_vec_splat_fp32(kMaxExp); - const HVX_Vector inf = hvx_vec_splat_fp32(kInf); - const HVX_VectorPred pred0 = Q6_Q_vcmp_gt_VsfVsf(in_vec, max_exp); +static inline HVX_Vector hvx_vec_exp_fp32_guard(HVX_Vector in_vec, HVX_Vector max_exp, HVX_Vector inf) { + const HVX_VectorPred pred0 = Q6_Q_vcmp_gt_VsfVsf(in_vec, max_exp); HVX_Vector out = hvx_vec_exp_fp32(in_vec); @@ -47,6 +42,12 @@ void hvx_exp_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int HVX_Vector vec_out = Q6_V_vzero(); + static const float kInf = INFINITY; + static const float kMaxExp = 88.02f; // log(INF) + + const HVX_Vector max_exp = hvx_vec_splat_fp32(kMaxExp); + const HVX_Vector inf = hvx_vec_splat_fp32(kInf); + if (0 == unaligned_loop) { HVX_Vector * p_vec_in1 = (HVX_Vector *) src; HVX_Vector * p_vec_out = (HVX_Vector *) dst; @@ -55,9 +56,9 @@ void hvx_exp_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int for (int i = 0; i < num_elems_whole; i += VLEN_FP32) { if (true == negate) { HVX_Vector neg_vec_in = hvx_vec_neg_fp32(*p_vec_in1++); - *p_vec_out++ = hvx_vec_exp_fp32_guard(neg_vec_in); + *p_vec_out++ = hvx_vec_exp_fp32_guard(neg_vec_in, max_exp, inf); } else { - *p_vec_out++ = hvx_vec_exp_fp32_guard(*p_vec_in1++); + *p_vec_out++ = hvx_vec_exp_fp32_guard(*p_vec_in1++, max_exp, inf); } } } else { @@ -67,9 +68,9 @@ void hvx_exp_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int if (true == negate) { HVX_Vector neg_vec_in = hvx_vec_neg_fp32(in); - *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_exp_fp32_guard(neg_vec_in); + *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_exp_fp32_guard(neg_vec_in, max_exp, inf); } else { - *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_exp_fp32_guard(in); + *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_exp_fp32_guard(in, max_exp, inf); } } } @@ -83,9 +84,9 @@ void hvx_exp_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int if (true == negate) { HVX_Vector neg_vec_in = hvx_vec_neg_fp32(in); - vec_out = hvx_vec_exp_fp32_guard(neg_vec_in); + vec_out = hvx_vec_exp_fp32_guard(neg_vec_in, max_exp, inf); } else { - vec_out = hvx_vec_exp_fp32_guard(in); + vec_out = hvx_vec_exp_fp32_guard(in, max_exp, inf); } hvx_vec_store_u((void *) dstf, left_over * SIZEOF_FP32, vec_out); diff --git a/ggml/src/ggml-hexagon/htp/hvx-inverse.c b/ggml/src/ggml-hexagon/htp/hvx-inverse.c index 953d3e6c167..4d70634fcd4 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-inverse.c +++ b/ggml/src/ggml-hexagon/htp/hvx-inverse.c @@ -16,6 +16,15 @@ #include "hvx-utils.h" #include "ops-utils.h" +static inline HVX_Vector hvx_vec_inverse_fp32_guard(HVX_Vector v_sf, HVX_Vector nan_inf_mask) { + HVX_Vector out = hvx_vec_inverse_fp32(v_sf); + + HVX_Vector masked_out = Q6_V_vand_VV(out, nan_inf_mask); + const HVX_VectorPred pred = Q6_Q_vcmp_eq_VwVw(nan_inf_mask, masked_out); + + return Q6_V_vmux_QVV(pred, Q6_V_vzero(), out); +} + void hvx_inverse_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int num_elems) { int left_over = num_elems & (VLEN_FP32 - 1); int num_elems_whole = num_elems - left_over; @@ -32,19 +41,22 @@ void hvx_inverse_f32(const uint8_t * restrict src, uint8_t * restrict dst, const FARF(HIGH, "hvx_inverse_f32: unaligned loop in hvx op, possibly slower execution\n"); } + static const uint32_t kNanInfMask = 0x7f800000; + const HVX_Vector nan_inf_mask = Q6_V_vsplat_R(kNanInfMask); + if (0 == unaligned_loop) { HVX_Vector * p_vec_in = (HVX_Vector *) src; HVX_Vector * p_vec_out = (HVX_Vector *) dst; #pragma unroll(4) for (int i = 0; i < num_elems_whole; i += VLEN_FP32) { - *p_vec_out++ = hvx_vec_inverse_fp32_guard(*p_vec_in++); + *p_vec_out++ = hvx_vec_inverse_fp32_guard(*p_vec_in++, nan_inf_mask); } } else { #pragma unroll(4) for (int i = 0; i < num_elems_whole; i += VLEN_FP32) { HVX_Vector in = *(HVX_UVector *) (src + i * SIZEOF_FP32); - *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_inverse_fp32_guard(in); + *(HVX_UVector *) (dst + i * SIZEOF_FP32) = hvx_vec_inverse_fp32_guard(in, nan_inf_mask); } } @@ -53,7 +65,7 @@ void hvx_inverse_f32(const uint8_t * restrict src, uint8_t * restrict dst, const float * dstf = (float *) dst + num_elems_whole; HVX_Vector in = *(HVX_UVector *) srcf; - HVX_Vector out = hvx_vec_inverse_fp32_guard(in); + HVX_Vector out = hvx_vec_inverse_fp32_guard(in, nan_inf_mask); hvx_vec_store_u((void *) dstf, left_over * SIZEOF_FP32, out); } diff --git a/ggml/src/ggml-hexagon/htp/hvx-utils.h b/ggml/src/ggml-hexagon/htp/hvx-utils.h index 5f94645cde3..28b0014fb5a 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-utils.h +++ b/ggml/src/ggml-hexagon/htp/hvx-utils.h @@ -726,24 +726,6 @@ static inline HVX_Vector hvx_vec_inverse_fp32(HVX_Vector v_sf) { return Q6_Vsf_equals_Vqf32(r_qf); } -static inline HVX_Vector hvx_vec_inverse_fp32_guard(HVX_Vector v_sf) { - static const float kInf = INFINITY; - static const uint32_t kNanMask = 0x7fffffff; - static const uint32_t kNanMin = 0x7f800000; - - const HVX_Vector inf = hvx_vec_splat_fp32(kInf); - const HVX_VectorPred pred_inf = Q6_Q_vcmp_gt_VsfVsf(inf, v_sf); - - HVX_Vector out = hvx_vec_inverse_fp32(v_sf); - - const HVX_Vector nan_mask = Q6_V_vsplat_R(kNanMask); - const HVX_Vector nan_min = Q6_V_vsplat_R(kNanMin); - HVX_Vector masked_out = Q6_V_vand_VV(out, nan_mask); - const HVX_VectorPred pred = Q6_Q_vcmp_gtand_QVuwVuw(pred_inf, nan_min, masked_out); - - return Q6_V_vmux_QVV(pred, out, Q6_V_vzero()); -} - #define FAST_SIGMOID_LOG2F (0x3fb8aa3b) // 1.442695022 #define FAST_SIGMOID_C1 (0x3d009076) // 0.03138777 #define FAST_SIGMOID_C2 (0x3e8d74bd) // 0.276281267 @@ -958,14 +940,16 @@ static inline HVX_Vector hvx_vec_rsqrt_fp32(HVX_Vector in_vec) { return Q6_Vsf_equals_Vqf32(temp); } -static inline HVX_Vector hvx_vec_fast_sigmoid_fp32_guard(HVX_Vector v) { - static const float kMaxExp = -88.02f; // log(INF) - - const HVX_Vector max_exp = Q6_V_vsplat_R(*((uint32_t *) &kMaxExp)); - const HVX_VectorPred pred_inf = Q6_Q_vcmp_gt_VsfVsf(v, max_exp); +static inline HVX_Vector hvx_vec_fast_sigmoid_fp32_guard(HVX_Vector v, + HVX_Vector one, + HVX_Vector max_exp, + HVX_Vector min_exp) { + const HVX_VectorPred pred_max = Q6_Q_vcmp_gt_VsfVsf(max_exp, v); + const HVX_VectorPred pred_min = Q6_Q_vcmp_gt_VsfVsf(v, min_exp); HVX_Vector out = hvx_vec_fast_sigmoid_fp32(v); - return Q6_V_vmux_QVV(pred_inf, out, Q6_V_vzero()); + out = Q6_V_vmux_QVV(pred_max, out, one); + return Q6_V_vmux_QVV(pred_min, out, Q6_V_vzero()); } static inline void hvx_fast_sigmoid_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int num_elems) { @@ -977,9 +961,16 @@ static inline void hvx_fast_sigmoid_f32(const uint8_t * restrict src, uint8_t * const HVX_Vector * restrict v_src = (HVX_Vector *) src; HVX_Vector * restrict v_dst = (HVX_Vector *) dst; + static const float kMinExp = -87.f; // 0 + static const float kMaxExp = 87.f; // 1 + + const HVX_Vector one = hvx_vec_splat_fp32(1.f); + const HVX_Vector max_exp = hvx_vec_splat_fp32(kMaxExp); + const HVX_Vector min_exp = hvx_vec_splat_fp32(kMinExp); + #pragma unroll(4) for (int i = 0; i < step_of_1; i++) { - v_dst[i] = hvx_vec_fast_sigmoid_fp32_guard(v_src[i]); + v_dst[i] = hvx_vec_fast_sigmoid_fp32_guard(v_src[i], one, max_exp, min_exp); } } From fcb013847c2c983967e9d8c9a13b16829fb799e6 Mon Sep 17 00:00:00 2001 From: "M. Mediouni" Date: Mon, 24 Nov 2025 01:54:49 +0100 Subject: [PATCH 11/37] ggml-hexagon: Initial Hexagon v68/v69 support (#17394) * ggml-hexagon: fix build error with GCC Add stdexcept include to fix GCC build errors Signed-off-by: Mohamed Mediouni * ggml-hexagon: check VTCM acquire failures Signed-off-by: Mohamed Mediouni * ggml-hexagon: disable destination bypass on older than v73 v68 errors out if having bypass enabled when the VTCM is the destination. At least on v68 this made things actually work... not a proper fix though, so to look at later... Signed-off-by: Mohamed Mediouni * ggml-hexagon: add initial v68/v69 support v68 is the Hexagon revision notably used on the Snapdragon 8cx Gen 3 and the QCM6490. Also add support for v69. 8MB isn't a supported page size, so relax asked for page size constraint for HAP_compute_res_attr_set_vtcm_param_v2 to optimal. Signed-off-by: Mohamed Mediouni --------- Signed-off-by: Mohamed Mediouni --- ggml/src/ggml-hexagon/CMakeLists.txt | 10 ++++++++++ ggml/src/ggml-hexagon/ggml-hexagon.cpp | 1 + ggml/src/ggml-hexagon/htp-utils.c | 6 ++++++ ggml/src/ggml-hexagon/htp/htp-dma.h | 7 +++++++ ggml/src/ggml-hexagon/htp/hvx-utils.h | 20 ++++++++++++++++++++ ggml/src/ggml-hexagon/htp/main.c | 15 ++++++++++++--- 6 files changed, 56 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-hexagon/CMakeLists.txt b/ggml/src/ggml-hexagon/CMakeLists.txt index 166825c2c5f..ac422027b91 100644 --- a/ggml/src/ggml-hexagon/CMakeLists.txt +++ b/ggml/src/ggml-hexagon/CMakeLists.txt @@ -43,6 +43,14 @@ set(HTP_CMAKE_ARGS -DHEXAGON_TOOLS_ROOT=$ENV{HEXAGON_TOOLS_ROOT} -DHEXAGON_HTP_DEBUG=${GGML_HEXAGON_HTP_DEBUG}) +ExternalProject_Add(htp-v68 + SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON + CMAKE_ARGS ${HTP_CMAKE_ARGS} -DDSP_VERSION=v68 -DPREBUILT_LIB_DIR="toolv19_v68") + +ExternalProject_Add(htp-v69 + SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON + CMAKE_ARGS ${HTP_CMAKE_ARGS} -DDSP_VERSION=v69 -DPREBUILT_LIB_DIR="toolv19_v69") + ExternalProject_Add(htp-v73 SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON CMAKE_ARGS ${HTP_CMAKE_ARGS} -DDSP_VERSION=v73 -DPREBUILT_LIB_DIR="toolv19_v73") @@ -61,6 +69,8 @@ ExternalProject_Add(htp-v81 # Install Hexagon skels required at runtime install(FILES + ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v68.so + ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v69.so ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v73.so ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v75.so ${CMAKE_CURRENT_BINARY_DIR}/libggml-htp-v79.so diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index 0b4e2c3d4df..881ed39ae3e 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -9,6 +9,7 @@ #include #include #include +#include #ifdef _WIN32 # include diff --git a/ggml/src/ggml-hexagon/htp-utils.c b/ggml/src/ggml-hexagon/htp-utils.c index e8a035af8c6..3f335bf71c0 100644 --- a/ggml/src/ggml-hexagon/htp-utils.c +++ b/ggml/src/ggml-hexagon/htp-utils.c @@ -390,6 +390,12 @@ int get_hex_arch_ver(int domain, int * arch) { } switch (arch_ver.capability & 0xff) { + case 0x68: + *arch = 68; + return 0; + case 0x69: + *arch = 69; + return 0; case 0x73: *arch = 73; return 0; diff --git a/ggml/src/ggml-hexagon/htp/htp-dma.h b/ggml/src/ggml-hexagon/htp/htp-dma.h index 4d0d54ce859..7d3fc4078cc 100644 --- a/ggml/src/ggml-hexagon/htp/htp-dma.h +++ b/ggml/src/ggml-hexagon/htp/htp-dma.h @@ -66,6 +66,13 @@ static inline bool dma_queue_push(dma_queue * q, desc->desctype = HEXAGON_UDMA_DESC_DESCTYPE_TYPE1; desc->dstbypass = 1; desc->srcbypass = 1; +#if __HVX_ARCH__ >= 73 + desc->dstbypass = 1; + desc->srcbypass = 1; +#else + desc->dstbypass = 0; + desc->srcbypass = 1; +#endif desc->order = 0; desc->dstate = HEXAGON_UDMA_DESC_DSTATE_INCOMPLETE; desc->src = (void *) src; diff --git a/ggml/src/ggml-hexagon/htp/hvx-utils.h b/ggml/src/ggml-hexagon/htp/hvx-utils.h index 28b0014fb5a..80658105c55 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-utils.h +++ b/ggml/src/ggml-hexagon/htp/hvx-utils.h @@ -21,6 +21,26 @@ typedef union { float fp32[VLEN_FP32]; } __attribute__((aligned(VLEN), packed)) HVX_VectorAlias; +/* Q6_Vsf_equals_Vw is only available on v73+.*/ +#if __HVX_ARCH__ < 73 +static inline HVX_Vector int32_to_qfloat(HVX_Vector const in) +{ + HVX_Vector const vzero = Q6_V_vzero(); + HVX_VectorPred is_zero = Q6_Q_vcmp_eq_VwVw(in, vzero); + HVX_Vector lshift = Q6_Vw_vnormamt_Vw(in); + HVX_Vector normalized = Q6_Vw_vasl_VwVw(in, lshift); + HVX_Vector vexp = Q6_Vw_vsub_VwVw(Q6_V_vsplat_R(0x7f + 30), lshift); + HVX_Vector mant = Q6_V_vand_VV(Q6_V_vsplat_R(0xFFFFFF00), normalized); + HVX_Vector ret = Q6_V_vmux_QVV(is_zero, vzero, Q6_Vw_vadd_VwVw(mant, vexp)); + return ret; +} + +static inline HVX_Vector Q6_Vsf_equals_Vw(HVX_Vector const in) +{ + return Q6_Vsf_equals_Vqf32(int32_to_qfloat(in)); +} +#endif + static inline HVX_Vector hvx_vec_splat_fp32(float i) { union { float f; diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c index 10e27333243..b60b352a7b4 100644 --- a/ggml/src/ggml-hexagon/htp/main.c +++ b/ggml/src/ggml-hexagon/htp/main.c @@ -143,16 +143,25 @@ AEEResult htp_iface_disable_etm(remote_handle64 handle) { } static int vtcm_acquire(struct htp_context * ctx) { + int err; if (!ctx->vtcm_valid) { // Temporarily bump thread priority to make sure it's higher than other sessions. // This way the resource manager will notify the other thread to release VTCM. // Note that we need to reaquire VTCM at normal priority for this to work next time. qurt_thread_set_priority(qurt_thread_get_id(), ctx->thread_prio - 10); - HAP_compute_res_acquire_cached(ctx->vtcm_rctx, 1000000); + err = HAP_compute_res_acquire_cached(ctx->vtcm_rctx, 1000000); + if (err != 0) { + FARF(ERROR, "Failed to acquire VTCM: 0x%08x", (unsigned)err); + abort(); + } HAP_compute_res_release_cached(ctx->vtcm_rctx); qurt_thread_set_priority(qurt_thread_get_id(), ctx->thread_prio); - HAP_compute_res_acquire_cached(ctx->vtcm_rctx, 1000000); + err = HAP_compute_res_acquire_cached(ctx->vtcm_rctx, 1000000); + if (err != 0) { + FARF(ERROR, "Failed to acquire VTCM: 0x%08x", (unsigned)err); + abort(); + } ctx->vtcm_valid = true; } @@ -201,7 +210,7 @@ static int vtcm_alloc(struct htp_context * ctx) { HAP_compute_res_attr_init(&attr); HAP_compute_res_attr_set_serialize(&attr, 0); HAP_compute_res_attr_set_cache_mode(&attr, 1); - HAP_compute_res_attr_set_vtcm_param_v2(&attr, vtcm_size, vtcm_size, vtcm_size); + HAP_compute_res_attr_set_vtcm_param_v2(&attr, vtcm_size, 0, vtcm_size); HAP_compute_res_attr_set_release_callback(&attr, vtcm_release_callback, (void *) ctx); HAP_compute_res_attr_set_hmx_param(&attr, 1); From 01ad35e6d65916c74fb3edfbab474c569d818294 Mon Sep 17 00:00:00 2001 From: Raul Torres <138264735+rauletorresc@users.noreply.github.com> Date: Mon, 24 Nov 2025 02:02:52 +0000 Subject: [PATCH 12/37] CANN: Define `cann_graph_update_required` before macro (#17434) **Description of the problem** `cann_graph_update_required` is redundantly defined and initialized as `false` inside two mutually exclusive macro branches. **Proposed solution** Define it right before the macro so that it could serve both branches. --- ggml/src/ggml-cann/ggml-cann.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp index 5cbf5683e1d..3c67c48ffa1 100644 --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -2303,9 +2303,9 @@ static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend, // calculate rope cache for fist layer in current device. cann_ctx->rope_cache.cached = false; + bool cann_graph_update_required = false; #ifdef USE_ACL_GRAPH bool use_cann_graph = true; - bool cann_graph_update_required = false; static bool prefill_use_graph = parse_bool(get_env("GGML_CANN_PREFILL_USE_GRAPH").value_or("")); if (!prefill_use_graph) { @@ -2336,7 +2336,6 @@ static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend, } #else bool use_cann_graph = false; - bool cann_graph_update_required = false; #endif // USE_ACL_GRAPH evaluate_and_capture_cann_graph(cann_ctx, cgraph, use_cann_graph, cann_graph_update_required); From 923ae3c61983e60a2324ae56b412be5b8b511a53 Mon Sep 17 00:00:00 2001 From: Max Krasnyansky Date: Sun, 23 Nov 2025 18:55:56 -0800 Subject: [PATCH 13/37] hexagon: add support for ROPE_NEOX (#17458) --- ggml/src/ggml-hexagon/ggml-hexagon.cpp | 2 +- ggml/src/ggml-hexagon/htp/rope-ops.c | 87 +++++++++++++++++++++++--- 2 files changed, 81 insertions(+), 8 deletions(-) diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index 881ed39ae3e..72a82a89116 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -2229,7 +2229,7 @@ static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess int mode = op_params[2]; - if ((mode & GGML_ROPE_TYPE_NEOX) || (mode & GGML_ROPE_TYPE_MROPE) || (mode & GGML_ROPE_TYPE_VISION)) { + if ((mode & GGML_ROPE_TYPE_MROPE) || (mode & GGML_ROPE_TYPE_VISION)) { return false; } if (mode & 1) { diff --git a/ggml/src/ggml-hexagon/htp/rope-ops.c b/ggml/src/ggml-hexagon/htp/rope-ops.c index 16afa50f5b0..00419bcba6b 100644 --- a/ggml/src/ggml-hexagon/htp/rope-ops.c +++ b/ggml/src/ggml-hexagon/htp/rope-ops.c @@ -24,6 +24,10 @@ #include "hvx-utils.h" #include "ops-utils.h" +// Redefined the types GGML_ROPE_TYPE_NORMAL & GGML_ROPE_TYPE_NEOX as we cant include ggml.h +#define HTP_ROPE_TYPE_NORMAL 0 +#define HTP_ROPE_TYPE_NEOX 2 + #define htp_rope_preamble \ const uint32_t ne00 = src0->ne[0]; \ const uint32_t ne01 = src0->ne[1]; \ @@ -146,6 +150,57 @@ static void init_rope_ctx(struct rope_th_ctx * rope_ctx, struct htp_ops_context rope_ctx->ext_factor, rope_ctx->theta_scale, rope_ctx->attn_factor); } +static void hvx_calc_rope_neox_f32(const float * restrict src0, + float * restrict dst, + const int num_elems, + const float * restrict theta_cache) { + // for (int i = 0; i < num_elems; i += 2) { + //const float cos_theta = theta_cache[i + 0]; + //const float sin_theta = theta_cache[i + 1]; + + //const float x0 = src[0]; + //const float x1 = src[num_elems/2]; + + //dst[0] = x0*cos_theta - x1*sin_theta; + //dst[num_elems/2] = x0*sin_theta + x1*cos_theta; + + //src += 1; + //dst += 1; + // } + + const uint8_t * restrict src0_curr = (const uint8_t *) src0; + const uint8_t * restrict theta_curr = (const uint8_t *) theta_cache; + uint8_t * restrict dst_curr = (uint8_t *) dst; + + int step_of_1 = num_elems >> 6; // 6 because we process two vectors at once + int half_size = (sizeof(float) * (num_elems / 2)); + + for (int i = 0; i < step_of_1; i++) { + HVX_Vector v0 = *(HVX_Vector *) src0_curr; + HVX_Vector v1 = *(HVX_Vector *) (src0_curr + half_size); + + HVX_Vector v2 = *(HVX_Vector *) theta_curr; + HVX_Vector v3 = *(HVX_Vector *) (theta_curr + VLEN); + + HVX_VectorPair vcos_sin = Q6_W_vdeal_VVR(v3, v2, -4); // vcos_sin[0] = cos_theta, vcos_sin[1] = sin_theta + + HVX_Vector vx0_c = Q6_Vqf32_vmpy_VsfVsf(v0, Q6_V_lo_W(vcos_sin)); + HVX_Vector vx0_s = Q6_Vqf32_vmpy_VsfVsf(v0, Q6_V_hi_W(vcos_sin)); + HVX_Vector vx1_c = Q6_Vqf32_vmpy_VsfVsf(v1, Q6_V_lo_W(vcos_sin)); + HVX_Vector vx1_s = Q6_Vqf32_vmpy_VsfVsf(v1, Q6_V_hi_W(vcos_sin)); + + HVX_Vector v4 = Q6_Vqf32_vsub_Vqf32Vqf32(vx0_c, vx1_s); + HVX_Vector v5 = Q6_Vqf32_vadd_Vqf32Vqf32(vx0_s, vx1_c); + + *(HVX_Vector *) dst_curr = Q6_Vsf_equals_Vqf32(v4); + *(HVX_Vector *) (dst_curr + half_size) = Q6_Vsf_equals_Vqf32(v5); + + src0_curr += VLEN; + theta_curr += 2 * VLEN; + dst_curr += VLEN; + } +} + static void hvx_calc_rope_f32(const float * restrict src0, float * restrict dst, const int num_elems, @@ -212,6 +267,9 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx, const struct htp_tensor * src2 = &octx->src2; struct htp_tensor * dst = &octx->dst; + const int32_t mode = rope_ctx->mode; + const bool is_neox = mode & HTP_ROPE_TYPE_NEOX; + htp_rope_preamble; const int32_t * pos = (const int32_t *) src1->data; @@ -247,20 +305,35 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx, float * dst_data_loc = dst_data; if (1 == opt_path) { - hvx_calc_rope_f32(src_loc, dst_data_loc, rope_ctx->n_dims, wp0); + if (is_neox) { + hvx_calc_rope_neox_f32(src_loc, dst_data_loc, rope_ctx->n_dims, wp0); + } else { + hvx_calc_rope_f32(src_loc, dst_data_loc, rope_ctx->n_dims, wp0); + } } else { for (uint32_t i0 = 0; i0 < rope_ctx->n_dims; i0 += 2) { const float cos_theta = wp0[i0 + 0]; const float sin_theta = wp0[i0 + 1]; - const float x0 = src_loc[0]; - const float x1 = src_loc[1]; + if (is_neox) { + const float x0 = src_loc[0]; + const float x1 = src_loc[rope_ctx->n_dims/2]; + + dst_data_loc[0] = x0 * cos_theta - x1 * sin_theta; + dst_data_loc[rope_ctx->n_dims/2] = x0 * sin_theta + x1 * cos_theta; + + src_loc += 1; + dst_data_loc += 1; + } else { + const float x0 = src_loc[0]; + const float x1 = src_loc[1]; - dst_data_loc[0] = x0 * cos_theta - x1 * sin_theta; - dst_data_loc[1] = x0 * sin_theta + x1 * cos_theta; + dst_data_loc[0] = x0 * cos_theta - x1 * sin_theta; + dst_data_loc[1] = x0 * sin_theta + x1 * cos_theta; - src_loc += 2; - dst_data_loc += 2; + src_loc += 2; + dst_data_loc += 2; + } } } From 4902eebe33ed2341ef9bd7c80195bbd9d24f4d5f Mon Sep 17 00:00:00 2001 From: william pan <61359596+wp4032@users.noreply.github.com> Date: Sun, 23 Nov 2025 22:16:56 -0800 Subject: [PATCH 14/37] models : Added support for RND1 Diffusion Language Model (#17433) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Converted RND1 model to GGUF weights * RND1 llama.cpp support v1 * RND1 llama.cpp support v2 non causal bug * RND1 llama.cpp support v3 doccumentation * RND1 llama.cpp support v4 clean code * linting issues * RND1 pr fixes v1 * RND1 pr fixes v2 Co-authored-by: Sigbjørn Skjæret * Diffusion documentation edits --------- Co-authored-by: Sigbjørn Skjæret --- convert_hf_to_gguf.py | 15 +++++ examples/diffusion/README.md | 50 +++++++++++++- gguf-py/gguf/constants.py | 19 ++++++ src/CMakeLists.txt | 1 + src/llama-arch.cpp | 22 ++++++ src/llama-arch.h | 1 + src/llama-model.cpp | 22 +++++- src/models/models.h | 4 ++ src/models/rnd1.cpp | 126 +++++++++++++++++++++++++++++++++++ 9 files changed, 257 insertions(+), 3 deletions(-) create mode 100644 src/models/rnd1.cpp diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 8743202ad6c..6cbaee03dfd 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -4183,6 +4183,21 @@ def set_vocab(self): super().set_vocab() +@ModelBase.register("RND1") +class RND1Model(Qwen2MoeModel): + model_arch = gguf.MODEL_ARCH.RND1 + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + # RND1 specific parameters + # RND1 uses bidirectional attention + self.gguf_writer.add_causal_attention(False) + + if (mask_token_id := self.hparams.get("mask_token_id")) is not None: + self.gguf_writer.add_mask_token_id(mask_token_id) + + @ModelBase.register("Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration") class Qwen3VLVisionModel(MmprojModel): def __init__(self, *args, **kwargs): diff --git a/examples/diffusion/README.md b/examples/diffusion/README.md index 26de5668aa8..f71d2413193 100644 --- a/examples/diffusion/README.md +++ b/examples/diffusion/README.md @@ -6,8 +6,54 @@ More Info: - https://github.com/ggml-org/llama.cpp/pull/14644 - https://github.com/ggml-org/llama.cpp/pull/14771 +## Parameters +The diffusion CLI supports various parameters to control the generation process: -Example of using Dream architechture: `llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual` +### Core Diffusion Parameters +- `--diffusion-steps`: Number of diffusion steps (default: 256) +- `--diffusion-algorithm`: Algorithm for token selection + - `0`: ORIGIN - Token will be generated in a purely random order from https://arxiv.org/abs/2107.03006. + - `1`: ENTROPY_BASED - Entropy-based selection + - `2`: MARGIN_BASED - Margin-based selection + - `3`: RANDOM - Random selection + - `4`: CONFIDENCE_BASED - Confidence-based selection (default) + - More documentation here https://github.com/DreamLM/Dream +- `--diffusion-visual`: Enable live visualization during generation -Example of using LLaDA architechture: `llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual` +### Scheduling Parameters +Choose one of the following scheduling methods: +**Timestep-based scheduling:** +- `--diffusion-eps`: Epsilon value for timestep scheduling (e.g., 0.001) + +**Block-based scheduling:** +- `--diffusion-block-length`: Block size for block-based scheduling (e.g., 32) + +### Sampling Parameters +- `--temp`: Temperature for sampling (0.0 = greedy/deterministic, higher = more random) +- `--top-k`: Top-k filtering for sampling +- `--top-p`: Top-p (nucleus) filtering for sampling +- `--seed`: Random seed for reproducibility + +### Model Parameters +- `-m`: Path to the GGUF model file +- `-p`: Input prompt text +- `-ub`: Maximum sequence length (ubatch size) +- `-c`: Context size +- `-b`: Batch size + +### Examples +#### Dream architechture: +``` +llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual +``` + +#### LLaDA architechture: +``` +llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual +``` + +#### RND1 architecture: +``` +llama-diffusion-cli -m RND1-Base-0910.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-algorithm 1 --diffusion-steps 256 --diffusion-visual --temp 0.5 --diffusion-eps 0.001 +``` diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 1cd0efad4a8..8bc558fe4b5 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -427,6 +427,7 @@ class MODEL_ARCH(IntEnum): APERTUS = auto() COGVLM = auto() MINIMAXM2 = auto() + RND1 = auto() PANGU_EMBED = auto() @@ -797,6 +798,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.APERTUS: "apertus", MODEL_ARCH.MINIMAXM2: "minimax-m2", MODEL_ARCH.COGVLM: "cogvlm", + MODEL_ARCH.RND1: "rnd1", MODEL_ARCH.PANGU_EMBED: "pangu-embedded", } @@ -2991,6 +2993,23 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.VISEXP_UP, MODEL_TENSOR.VISEXP_DOWN, ], + MODEL_ARCH.RND1: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], MODEL_ARCH.PANGU_EMBED: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 8ec95ee1762..f7a8c9841ec 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -115,6 +115,7 @@ add_library(llama models/qwen3vl-moe.cpp models/qwen3moe.cpp models/refact.cpp + models/rnd1.cpp models/rwkv6-base.cpp models/rwkv6.cpp models/rwkv6qwen2.cpp diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index b2eb2477f93..fc6cddc92f5 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -108,6 +108,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_APERTUS, "apertus" }, { LLM_ARCH_MINIMAX_M2, "minimax-m2" }, { LLM_ARCH_COGVLM, "cogvlm" }, + { LLM_ARCH_RND1, "rnd1" }, { LLM_ARCH_PANGU_EMBED, "pangu-embedded" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -2446,6 +2447,26 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_VISEXP_FFN_UP, "blk.%d.vis_up" }, }, }, + { + LLM_ARCH_RND1, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -2722,6 +2743,7 @@ bool llm_arch_is_diffusion(const llm_arch & arch) { case LLM_ARCH_DREAM: case LLM_ARCH_LLADA: case LLM_ARCH_LLADA_MOE: + case LLM_ARCH_RND1: return true; default: return false; diff --git a/src/llama-arch.h b/src/llama-arch.h index ae7fa222aca..02a1c2dc258 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -112,6 +112,7 @@ enum llm_arch { LLM_ARCH_APERTUS, LLM_ARCH_MINIMAX_M2, LLM_ARCH_COGVLM, + LLM_ARCH_RND1, LLM_ARCH_PANGU_EMBED, LLM_ARCH_UNKNOWN, }; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 175549a9e30..35179a98e0c 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -1036,6 +1036,18 @@ void llama_model::load_hparams(llama_model_loader & ml) { default: type = LLM_TYPE_UNKNOWN; } } break; + case LLM_ARCH_RND1: + { + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false); + + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { + case 48: type = LLM_TYPE_30B_A3B; break; + default: type = LLM_TYPE_UNKNOWN; + } + // Set non-causal attention for diffusion models + hparams.causal_attn = false; + } break; case LLM_ARCH_QWEN2MOE: { ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false); @@ -3402,6 +3414,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } break; case LLM_ARCH_QWEN3MOE: case LLM_ARCH_QWEN3VLMOE: + case LLM_ARCH_RND1: { tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -6720,7 +6733,7 @@ void llama_model::print_info() const { LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp); } - if (arch == LLM_ARCH_QWEN3MOE || arch == LLM_ARCH_OPENAI_MOE || arch == LLM_ARCH_QWEN3VLMOE) { + if (arch == LLM_ARCH_QWEN3MOE || arch == LLM_ARCH_OPENAI_MOE || arch == LLM_ARCH_QWEN3VLMOE || arch == LLM_ARCH_RND1) { LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp); } @@ -6882,6 +6895,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, case LLM_ARCH_DREAM: case LLM_ARCH_LLADA: case LLM_ARCH_LLADA_MOE: + case LLM_ARCH_RND1: { res = nullptr; } break; @@ -7075,6 +7089,11 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { llm = std::make_unique(*this, params); } break; + case LLM_ARCH_RND1: + { + llm = std::make_unique(*this, params); + } + break; case LLM_ARCH_QWEN2VL: { llm = std::make_unique(*this, params); @@ -7595,6 +7614,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_QWEN3: case LLM_ARCH_QWEN3MOE: case LLM_ARCH_LLADA_MOE: + case LLM_ARCH_RND1: case LLM_ARCH_OLMO2: case LLM_ARCH_OLMOE: case LLM_ARCH_PHI2: diff --git a/src/models/models.h b/src/models/models.h index 4d7aeb4f42c..5f019c59be8 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -431,6 +431,10 @@ struct llm_build_refact : public llm_graph_context { llm_build_refact(const llama_model & model, const llm_graph_params & params); }; +struct llm_build_rnd1 : public llm_graph_context { + llm_build_rnd1(const llama_model & model, const llm_graph_params & params); +}; + struct llm_build_rwkv6 : public llm_build_rwkv6_base { llm_build_rwkv6(const llama_model & model, const llm_graph_params & params); }; diff --git a/src/models/rnd1.cpp b/src/models/rnd1.cpp new file mode 100644 index 00000000000..46b3dc3efca --- /dev/null +++ b/src/models/rnd1.cpp @@ -0,0 +1,126 @@ +#include "models.h" + +// RND1 is a Qwen3Moe AR model converted to diffusion model. +llm_build_rnd1::llm_build_rnd1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v; + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); + + // Non-causal attention for diffusion + auto * inp_attn = build_attn_inp_no_cache(); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // norm + cur = build_norm(inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self_attention + { + // compute Q and K and RoPE them + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il); + cb(Qcur, "Qcur_normed", il); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il); + cb(Kcur, "Kcur_normed", il); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, + model.layers[il].wo, model.layers[il].bo, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + } + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // MoE branch + cur = build_norm(ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + ggml_tensor * moe_out = + build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + nullptr, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + false, 0.0, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, + il); + cb(moe_out, "ffn_moe_out", il); + cur = moe_out; + + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + cur = inpL; + + cur = build_norm(cur, + model.output_norm, NULL, + LLM_NORM_RMS, -1); + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur); + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} From 5f55c385cbff7bcee0901782dec6d01de53f4dfe Mon Sep 17 00:00:00 2001 From: ixgbe <1113177880@qq.com> Date: Mon, 24 Nov 2025 19:07:14 +0800 Subject: [PATCH 15/37] ggml: add RISC-V cpu-feats (#17461) * ggml: add RISC-V cpu-feats Signed-off-by: Wang Yang * fix comment[1] --------- Signed-off-by: Wang Yang --- ggml/src/CMakeLists.txt | 15 ++++++++ ggml/src/ggml-cpu/CMakeLists.txt | 41 ++++++++++++++-------- ggml/src/ggml-cpu/arch/riscv/cpu-feats.cpp | 35 ++++++++++++++++++ 3 files changed, 77 insertions(+), 14 deletions(-) create mode 100644 ggml/src/ggml-cpu/arch/riscv/cpu-feats.cpp diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt index 628db3fd655..a4499509ece 100644 --- a/ggml/src/CMakeLists.txt +++ b/ggml/src/CMakeLists.txt @@ -328,6 +328,14 @@ function(ggml_add_cpu_backend_variant tag_name) set(GGML_INTERNAL_${feat} OFF) endforeach() + foreach (feat ${ARGN}) + set(GGML_INTERNAL_${feat} ON) + endforeach() + elseif (GGML_SYSTEM_ARCH STREQUAL "riscv64") + foreach (feat RVV) + set(GGML_INTERNAL_${feat} OFF) + endforeach() + foreach (feat ${ARGN}) set(GGML_INTERNAL_${feat} ON) endforeach() @@ -402,6 +410,13 @@ if (GGML_CPU_ALL_VARIANTS) else() message(FATAL_ERROR "Unsupported s390x target OS: ${CMAKE_SYSTEM_NAME}") endif() + elseif (GGML_SYSTEM_ARCH STREQUAL "riscv64") + if (CMAKE_SYSTEM_NAME MATCHES "Linux") + ggml_add_cpu_backend_variant(riscv64_0) + ggml_add_cpu_backend_variant(riscv64_v RVV) + else() + message(FATAL_ERROR "Unsupported RISC-V target OS: ${CMAKE_SYSTEM_NAME}") + endif() else() message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS not yet supported with ${GGML_SYSTEM_ARCH} on ${CMAKE_SYSTEM_NAME}") endif() diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt index d0cab0bcb9c..feb56173861 100644 --- a/ggml/src/ggml-cpu/CMakeLists.txt +++ b/ggml/src/ggml-cpu/CMakeLists.txt @@ -452,22 +452,35 @@ function(ggml_add_cpu_backend_variant_impl tag_name) ggml-cpu/spacemit/ime_kernels.h ) endif() - set(MARCH_STR "rv64gc") - if (GGML_RV_ZFH) - string(APPEND MARCH_STR "_zfh") - endif() - if (GGML_XTHEADVECTOR) - string(APPEND MARCH_STR "_xtheadvector") - elseif (GGML_RVV) - string(APPEND MARCH_STR "_v") - if (GGML_RV_ZVFH) - string(APPEND MARCH_STR "_zvfh") + if(NOT GGML_CPU_ALL_VARIANTS) + set(MARCH_STR "rv64gc") + if (GGML_RV_ZFH) + string(APPEND MARCH_STR "_zfh") endif() + if (GGML_XTHEADVECTOR) + string(APPEND MARCH_STR "_xtheadvector") + elseif (GGML_RVV) + string(APPEND MARCH_STR "_v") + if (GGML_RV_ZVFH) + string(APPEND MARCH_STR "_zvfh") + endif() + endif() + if (GGML_RV_ZICBOP) + string(APPEND MARCH_STR "_zicbop") + endif() + list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d) + else() + # Begin with the lowest baseline + set(ARCH_DEFINITIONS "") + + if (GGML_INTERNAL_RVV) + message(STATUS "RVV enabled") + list(APPEND ARCH_DEFINITIONS GGML_USE_RVV) + list(APPEND ARCH_FLAGS -march=rv64gc_v -mabi=lp64d) + endif() + + ggml_add_cpu_backend_features(${GGML_CPU_NAME} riscv ${ARCH_DEFINITIONS}) endif() - if (GGML_RV_ZICBOP) - string(APPEND MARCH_STR "_zicbop") - endif() - list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d) elseif (GGML_SYSTEM_ARCH STREQUAL "s390x") message(STATUS "s390x detected") list(APPEND GGML_CPU_SOURCES diff --git a/ggml/src/ggml-cpu/arch/riscv/cpu-feats.cpp b/ggml/src/ggml-cpu/arch/riscv/cpu-feats.cpp new file mode 100644 index 00000000000..b1818988185 --- /dev/null +++ b/ggml/src/ggml-cpu/arch/riscv/cpu-feats.cpp @@ -0,0 +1,35 @@ +#include "ggml-backend-impl.h" + +#if defined(__riscv) && __riscv_xlen == 64 +#include + +//https://github.com/torvalds/linux/blob/master/arch/riscv/include/uapi/asm/hwcap.h#L24 +#ifndef COMPAT_HWCAP_ISA_V +#define COMPAT_HWCAP_ISA_V (1 << ('V' - 'A')) +#endif + +struct riscv64_features { + bool has_rvv = false; + + riscv64_features() { + uint32_t hwcap = getauxval(AT_HWCAP); + + has_rvv = !!(hwcap & COMPAT_HWCAP_ISA_V); + } +}; + +static int ggml_backend_cpu_riscv64_score() { + int score = 1; + riscv64_features rf; + +#ifdef GGML_USE_RVV + if (!rf.has_rvv) { return 0; } + score += 1 << 1; +#endif + + return score; +} + +GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_riscv64_score) + +#endif // __riscv && __riscv_xlen == 64 From dbb852b549adf29609ec53b518f7922a982f14b9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Alberto=20Cabrera=20P=C3=A9rez?= <1478977+Alcpz@users.noreply.github.com> Date: Mon, 24 Nov 2025 11:08:11 +0000 Subject: [PATCH 16/37] ggml-cpu: arm64: q4_K repack gemm and gemv implementations (i8mm) (#16739) * Enabled q4_K_8x8_q8_K path on ARM * wip: I8mm qs multiplication, pending bias * cpu : arm : REPACK gemm q4_K8x8 implementation Signed-off-by: Alberto Cabrera * Guard gemm with proper features, improved superblock scale and min calc Signed-off-by: Alberto Cabrera * cpu: arm: Implemented REPACK gemv for Q4_K Signed-off-by: Alberto Cabrera * Removed completed TODO * Fixed missing guards when selecting optimal repack type for Q4_K Signed-off-by: Alberto Cabrera * Fixed macro guard for gemv * Fixed wrong comment in GEMV * Fixed warning for unused variable * vdotq_s32 -> ggml_vdotq_s32 Signed-off-by: Alberto Cabrera * Clang-format issues * Apply suggestions from code review Co-authored-by: Diego Devesa * Removed unnecessary GGML_UNUSED * Fixed guards in q4_k gemm and gemv (repack) --------- Signed-off-by: Alberto Cabrera Co-authored-by: Diego Devesa --- ggml/src/ggml-cpu/arch-fallback.h | 2 - ggml/src/ggml-cpu/arch/arm/repack.cpp | 388 ++++++++++++++++++++++++++ ggml/src/ggml-cpu/repack.cpp | 5 + 3 files changed, 393 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-cpu/arch-fallback.h b/ggml/src/ggml-cpu/arch-fallback.h index edfd7913903..d27a9697060 100644 --- a/ggml/src/ggml-cpu/arch-fallback.h +++ b/ggml/src/ggml-cpu/arch-fallback.h @@ -51,10 +51,8 @@ #elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64) // repack.cpp #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 -#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K -#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64) diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp index fdd0a513b83..d2adfbea873 100644 --- a/ggml/src/ggml-cpu/arch/arm/repack.cpp +++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp @@ -24,6 +24,29 @@ #define UNUSED GGML_UNUSED +static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in, + int16x8_t * out_mins, + int8_t * out_scales) { + constexpr uint32_t kmask1 = 0x3f3f3f3f; + constexpr uint32_t kmask2 = 0x0f0f0f0f; + constexpr uint32_t kmask3 = 0x03030303; + constexpr uint8_t scales_size = 12; + + uint32_t sm[3]; + memcpy(sm, scales_in, scales_size); + + const uint32_t mins_0_3 = sm[1] & kmask1; + const uint32_t mins_4_7 = ((sm[2] >> 4) & kmask2) | (((sm[1] >> 6) & kmask3) << 4); + const uint32x2_t mins_u32 = { mins_0_3, mins_4_7 }; + + *out_mins = vreinterpretq_s16_u16(vmovl_u8(vreinterpret_u8_u32(mins_u32))); + + uint32_t scales_u32[2]; + scales_u32[0] = sm[0] & kmask1; + scales_u32[1] = (sm[2] & kmask2) | (((sm[0] >> 6) & kmask3) << 4); + memcpy(out_scales, scales_u32, 8); +} + void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) { assert(QK8_0 == 32); assert(k % QK8_0 == 0); @@ -474,6 +497,162 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const ggml_gemv_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc); } +void ggml_gemv_q4_K_8x8_q8_K(int n, + float * GGML_RESTRICT s, + size_t bs, + const void * GGML_RESTRICT vx, + const void * GGML_RESTRICT vy, + int nr, + int nc) { + constexpr int qk = QK_K; + const int nb = n / qk; + + constexpr int ncols_interleaved = 8; + constexpr int blocklen = 8; + + assert(n % qk == 0); + assert(nr % 4 == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined(__aarch64__) && defined(__ARM_NEON) + constexpr int col_pairs = ncols_interleaved / 2; + const uint8x16_t m4b = vdupq_n_u8(0x0f); + + // 1x8 tile = 2 x 4 + float32x4_t acc_f32[ncols_interleaved / 4]; + + const block_q8_K * GGML_RESTRICT q8_ptr = (const block_q8_K *) vy; + + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_Kx8 * GGML_RESTRICT q4_ptr = (const block_q4_Kx8 *) vx + (x * nb); + + for (int i = 0; i < ncols_interleaved / 4; i++) { + acc_f32[i] = vdupq_n_f32(0); + } + + for (int b = 0; b < nb; b++) { + float32x4_t q4_d_0 = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].d)); // d0 d1 d2 d3 + float32x4_t q4_d_1 = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].d + 4)); // d4 d5 d6 d7 + float32x4_t q8_d = vdupq_n_f32(q8_ptr[b].d); + float32x4_t sb_scale_0 = vmulq_f32(q4_d_0, q8_d); + float32x4_t sb_scale_1 = vmulq_f32(q4_d_1, q8_d); + float32x4_t q4_dmin_0 = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].dmin)); // dmin 0..3 + float32x4_t q4_dmin_1 = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].dmin + 4)); // dmin 4..7 + float32x4_t sb_min_0 = vmulq_f32(q4_dmin_0, q8_d); + float32x4_t sb_min_1 = vmulq_f32(q4_dmin_1, q8_d); + + // interleaved bias_acc: [0]->r0 0123, [1]->r0 4567 + int32x4_t bias_acc[2] = { vdupq_n_s32(0), vdupq_n_s32(0) }; + // 2 sb each iteration + int32x4_t acc_lo[col_pairs]; + int32x4_t acc_hi[col_pairs]; + + // Each bsum is 16 elements, pairwise add leaves us with the 8 bsums of the entire block + const int16x8_t bsums = vpaddq_s16(vld1q_s16(q8_ptr[b].bsums), vld1q_s16(q8_ptr[b].bsums + 8)); + int16_t bsums_arr[8]; + vst1q_s16(bsums_arr, bsums); + for (int sb = 0; sb < QK_K / 64; sb++) { + for (int i = 0; i < col_pairs; i++) { + acc_lo[i] = vdupq_n_s32(0); + acc_hi[i] = vdupq_n_s32(0); + } + // Need scales for the low and high nibbles + // 2 * 12 = 24 bytes per subblock, 4 sbs -> 4 * 24 = 96 bytes total + int16x8_t q4sb_mins[2]; // int16 as its needed for bias_acc later + int16x8_t q4sb_scales[2]; + for (int i = 0; i < 2; i++) { + int8_t aux_q4sb[8]; + const int offset = sb * 24 + i * 12; + decode_q4_Kx8_scales_mins(&q4_ptr[b].scales[offset], &q4sb_mins[i], aux_q4sb); + q4sb_scales[i] = vmovl_s8(vld1_s8(aux_q4sb)); + } + + const uint8_t * q4_base = q4_ptr[b].qs + sb * QK_K; + + // Load the 64 quants from q8K duplicated to use vecdots with the interelaved columns + // but still need the qs to use the low and hi bits from q4 + const int8_t * q8_base = q8_ptr[b].qs + sb * 64; + int8x16_t q8_qs[8]; + for (int i = 0; i < 8; i++) { + q8_qs[i] = (int8x16_t) vld1q_dup_s64((const int64_t *) (q8_base + i * 8)); + } + + // Q4s columns iterated in pairs (01, 23, 45, 67) + for (int cp = 0; cp < col_pairs; cp++) { + uint8x16_t q4_qs_cp_0 = vld1q_u8(q4_base + 16 * cp); + uint8x16_t q4_qs_cp_1 = vld1q_u8(q4_base + 16 * cp + 64); + uint8x16_t q4_qs_cp_2 = vld1q_u8(q4_base + 16 * cp + 128); + uint8x16_t q4_qs_cp_3 = vld1q_u8(q4_base + 16 * cp + 192); + + acc_lo[cp] = + ggml_vdotq_s32(acc_lo[cp], vreinterpretq_s8_u8(vandq_u8(q4_qs_cp_0, m4b)), q8_qs[0]); // 0 .. 7 + acc_lo[cp] = + ggml_vdotq_s32(acc_lo[cp], vreinterpretq_s8_u8(vandq_u8(q4_qs_cp_1, m4b)), q8_qs[1]); // 8 ..15 + acc_lo[cp] = + ggml_vdotq_s32(acc_lo[cp], vreinterpretq_s8_u8(vandq_u8(q4_qs_cp_2, m4b)), q8_qs[2]); // 16..23 + acc_lo[cp] = + ggml_vdotq_s32(acc_lo[cp], vreinterpretq_s8_u8(vandq_u8(q4_qs_cp_3, m4b)), q8_qs[3]); // 24..31 + + acc_hi[cp] = + ggml_vdotq_s32(acc_hi[cp], vreinterpretq_s8_u8(vshrq_n_u8(q4_qs_cp_0, 4)), q8_qs[4]); // 32..39 + acc_hi[cp] = + ggml_vdotq_s32(acc_hi[cp], vreinterpretq_s8_u8(vshrq_n_u8(q4_qs_cp_1, 4)), q8_qs[5]); // 40..47 + acc_hi[cp] = + ggml_vdotq_s32(acc_hi[cp], vreinterpretq_s8_u8(vshrq_n_u8(q4_qs_cp_2, 4)), q8_qs[6]); // 48..55 + acc_hi[cp] = + ggml_vdotq_s32(acc_hi[cp], vreinterpretq_s8_u8(vshrq_n_u8(q4_qs_cp_3, 4)), q8_qs[7]); // 56..63 + } + + // Iterates over a pair of column pairs (4 columns) to use a single 128 register + // p = 0 -> 0123 p2 -> 4567 + for (int i = 0, p = 0; p < col_pairs; i++, p += 2) { + int16x4_t group_scales_lo = p == 0 ? vget_low_s16(q4sb_scales[0]) : vget_high_s16(q4sb_scales[0]); + int16x4_t group_scales_hi = p == 0 ? vget_low_s16(q4sb_scales[1]) : vget_high_s16(q4sb_scales[1]); + float32x4_t sb_scale = p == 0 ? sb_scale_0 : sb_scale_1; + + // 0123 or 4567 + // TODO: Single superblock mul at the end of the superblock + float32x4_t sumf_0 = + vcvtq_f32_s32(vmulq_s32(vmovl_s16(group_scales_lo), vpaddq_s32(acc_lo[p], acc_lo[p + 1]))); + acc_f32[i] = vfmaq_f32(acc_f32[i], sb_scale, sumf_0); + + float32x4_t sumf_1 = + vcvtq_f32_s32(vmulq_s32(vmovl_s16(group_scales_hi), vpaddq_s32(acc_hi[p], acc_hi[p + 1]))); + acc_f32[i] = vfmaq_f32(acc_f32[i], sb_scale, sumf_1); + } + + // Multiply Acc bsum + mins + // Each pair of subblocks share the same bsums + // Load scalar bsum → broadcast to a vector (vdupq_n_s16(s)). + int16x4_t bsums_vec_lo = vdup_n_s16(bsums_arr[2 * sb + 0]); + int16x4_t bsums_vec_hi = vdup_n_s16(bsums_arr[2 * sb + 1]); + + // cols 0-3 bias + bias_acc[0] = vmlal_s16(bias_acc[0], bsums_vec_lo, vget_low_s16(q4sb_mins[0])); + bias_acc[0] = vmlal_s16(bias_acc[0], bsums_vec_hi, vget_low_s16(q4sb_mins[1])); + + // cols 4-7 bias + bias_acc[1] = vmlal_s16(bias_acc[1], bsums_vec_lo, vget_high_s16(q4sb_mins[0])); + bias_acc[1] = vmlal_s16(bias_acc[1], bsums_vec_hi, vget_high_s16(q4sb_mins[1])); + } // for sb + + acc_f32[0] = vmlsq_f32(acc_f32[0], vcvtq_f32_s32(bias_acc[0]), sb_min_0); + acc_f32[1] = vmlsq_f32(acc_f32[1], vcvtq_f32_s32(bias_acc[1]), sb_min_1); + } // for b + + int base = x * ncols_interleaved; + vst1q_f32(s + base, acc_f32[0]); + vst1q_f32(s + base + 4, acc_f32[1]); + } // for x + return; +#endif // defined(__aarch64__) && defined(__ARM_NEON) + ggml_gemv_q4_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc); +} + 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) { const int qk = QK8_0; const int nb = n / qk; @@ -1889,3 +2068,212 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) ggml_gemm_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc); } + +void ggml_gemm_q4_K_8x8_q8_K(int n, + float * GGML_RESTRICT s, + size_t bs, + const void * GGML_RESTRICT vx, + const void * GGML_RESTRICT vy, + int nr, + int nc) { + constexpr int qk = QK_K; + const int nb = n / qk; + + constexpr int ncols_interleaved = 8; + constexpr int blocklen = 8; + + assert(n % qk == 0); + assert(nr % 4 == 0); + assert(nc % ncols_interleaved == 0); + + UNUSED(nb); + UNUSED(ncols_interleaved); + UNUSED(blocklen); + +#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + constexpr int q8_k_blocklen = 4; + const uint8x16_t m4b = vdupq_n_u8(0x0f); + + // 8 accumulators: 2 row pairs × 4 col pairs + float32x4_t acc_f32[blocklen]; + + for (int y = 0; y < nr / q8_k_blocklen; y++) { + const block_q8_Kx4 * GGML_RESTRICT q8_ptr = (const block_q8_Kx4 *) vy + (y * nb); + + for (int x = 0; x < nc / ncols_interleaved; x++) { + const block_q4_Kx8 * GGML_RESTRICT q4_ptr = (const block_q4_Kx8 *) vx + (x * nb); + + for (int i = 0; i < blocklen; i++) { + acc_f32[i] = vdupq_n_f32(0); + } + + for (int b = 0; b < nb; b++) { + // bsums pairs belongs to the same q8_k subblock + const int16x8_t bsums[4]{ + vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 0), vld1q_s16(q8_ptr[b].bsums + 16 * 0 + 8)), + vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 1), vld1q_s16(q8_ptr[b].bsums + 16 * 1 + 8)), + vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 2), vld1q_s16(q8_ptr[b].bsums + 16 * 2 + 8)), + vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 3), vld1q_s16(q8_ptr[b].bsums + 16 * 3 + 8)), + }; + int16_t bsums_arr[4][8]; + for (int q8_row = 0; q8_row < 4; q8_row++) { + vst1q_s16(bsums_arr[q8_row], bsums[q8_row]); + } + + int32x4_t sb_acc[4]; // Aux accumulators to store subblock (partial) results + int32x4_t acc[8]; // rows 01 stored in [0][1][2][3] rows 23 stored in [4][5][6][7] + int32x4_t bias_acc[8]; // interleaved bias_acc: [0]->r0 0123, [1]->r0 4567, [2]->r1 0123 ... + for (int i = 0; i < 8; i++) { + acc[i] = vdupq_n_s32(0); + bias_acc[i] = vdupq_n_s32(0); + } + + for (int sb = 0; sb < QK_K / 64; sb++) { + // Need scales for the low and high nibbles + // 2 * 12 = 24 bytes per subblock, 4 sbs -> 4 * 24 = 96 bytes total + int8_t q4sb_scales[2][8]; + int16x8_t q4sb_mins[2]; // int16 as its needed for bias_acc later + for (int i = 0; i < 2; i++) { + const int offset = sb * 24 + i * 12; + decode_q4_Kx8_scales_mins(&q4_ptr[b].scales[offset], &q4sb_mins[i], q4sb_scales[i]); + } + + // q8_ptr[b].qs has interleaved Q8 rows (01, 23) + const int8_t * q8_base = q8_ptr[b].qs + sb * 256; + + int8x16_t q8_qs_01[8]; + int8x16_t q8_qs_23[8]; + + // Load 32-byte per row pair, 1 subblock each time + for (int i = 0; i < 8; i++) { + const int offset = i * 32; // 16 for row 01, 16 for row 23 + q8_qs_01[i] = vld1q_s8(q8_base + offset); + q8_qs_23[i] = vld1q_s8(q8_base + offset + 16); + } + + const int8x16_t q8s[2][8] = { + { q8_qs_01[0], q8_qs_01[1], q8_qs_01[2], q8_qs_01[3], + q8_qs_01[4], q8_qs_01[5], q8_qs_01[6], q8_qs_01[7] }, + { q8_qs_23[0], q8_qs_23[1], q8_qs_23[2], q8_qs_23[3], + q8_qs_23[4], q8_qs_23[5], q8_qs_23[6], q8_qs_23[7] }, + }; + + // Q4s columns iterated in pairs (01, 23, 45, 67) + for (int cp = 0; cp < ncols_interleaved / 2; cp++) { + for (int i = 0; i < 4; i++) { + sb_acc[i] = vdupq_n_s32(0); + } + + uint8x16_t q4_qs_cp_0 = vld1q_u8(q4_ptr[b].qs + sb * QK_K + 16 * cp + 0); // 0 .. 7 & 32..39 + uint8x16_t q4_qs_cp_1 = vld1q_u8(q4_ptr[b].qs + sb * QK_K + 16 * cp + 64); // 8 ..15 & 40..47 + uint8x16_t q4_qs_cp_2 = vld1q_u8(q4_ptr[b].qs + sb * QK_K + 16 * cp + 128); // 16..23 & 48..55 + uint8x16_t q4_qs_cp_3 = vld1q_u8(q4_ptr[b].qs + sb * QK_K + 16 * cp + 192); // 24..31 & 56..63 + const int8x16_t q4_nibbles[2][4] = { + { + vreinterpretq_s8_u8(vandq_u8(q4_qs_cp_0, m4b)), + vreinterpretq_s8_u8(vandq_u8(q4_qs_cp_1, m4b)), + vreinterpretq_s8_u8(vandq_u8(q4_qs_cp_2, m4b)), + vreinterpretq_s8_u8(vandq_u8(q4_qs_cp_3, m4b)), + }, + { + vreinterpretq_s8_u8(vshrq_n_u8(q4_qs_cp_0, 4)), + vreinterpretq_s8_u8(vshrq_n_u8(q4_qs_cp_1, 4)), + vreinterpretq_s8_u8(vshrq_n_u8(q4_qs_cp_2, 4)), + vreinterpretq_s8_u8(vshrq_n_u8(q4_qs_cp_3, 4)), + } + }; + + // Calculates the Qs muladd of every row pair (rp) rows 01 and 23 of q8 + // for each of the internal 32 qs subblock (blk) + for (int rp = 0; rp < 2; rp++) { + for (int blk = 0; blk < 2; blk++) { + const int8x16_t * q8 = &q8s[rp][4 * blk]; + const int8x16_t * q4 = q4_nibbles[blk]; + int32x4_t acc = sb_acc[2 * rp + blk]; + // mul add for each qs in the same subblock + for (int qs_offset = 0; qs_offset < 4; qs_offset++) { + acc = vmmlaq_s32(acc, q4[qs_offset], q8[qs_offset]); + } + sb_acc[2 * rp + blk] = acc; + } + } + + // Scales[i] corresponds to column i + const int scale_offset = cp * 2; + for (int blk = 0; blk < 2; blk++) { + const int32x4_t block_scale = { + (int32_t) q4sb_scales[blk][scale_offset], + (int32_t) q4sb_scales[blk][scale_offset], + (int32_t) q4sb_scales[blk][scale_offset + 1], + (int32_t) q4sb_scales[blk][scale_offset + 1], + }; + acc[cp] = vmlaq_s32(acc[cp], sb_acc[blk], block_scale); + acc[cp + 4] = vmlaq_s32(acc[cp + 4], sb_acc[blk + 2], block_scale); + } + } + + // Multiply Acc bsum + mins + for (int q8_row = 0; q8_row < 4; q8_row++) { + // Each pair of subblocks share the same bsums + // Load scalar bsum → broadcast to a vector (vdupq_n_s16(s)). + int16x4_t bsums_vec_lo = vdup_n_s16(bsums_arr[sb][q8_row * 2]); + int16x4_t bsums_vec_hi = vdup_n_s16(bsums_arr[sb][q8_row * 2 + 1]); + + bias_acc[2 * q8_row] = + vmlal_s16(bias_acc[2 * q8_row], bsums_vec_lo, vget_low_s16(q4sb_mins[0])); + bias_acc[2 * q8_row] = + vmlal_s16(bias_acc[2 * q8_row], bsums_vec_hi, vget_low_s16(q4sb_mins[1])); + bias_acc[2 * q8_row + 1] = + vmlal_s16(bias_acc[2 * q8_row + 1], bsums_vec_lo, vget_high_s16(q4sb_mins[0])); + bias_acc[2 * q8_row + 1] = + vmlal_s16(bias_acc[2 * q8_row + 1], bsums_vec_hi, vget_high_s16(q4sb_mins[1])); + } + } // for sb + + // Reorder of i8mm output with bias and output layout + for (int i = 0; i < 8; i++) { + int32x2x2_t aux = vzip_s32(vget_low_s32(acc[i]), vget_high_s32(acc[i])); + acc[i] = vcombine_s32(aux.val[0], aux.val[1]); + } + int32x4_t reorder_acc[8] = { + vcombine_s32(vget_low_s32(acc[0]), vget_low_s32(acc[1])), + vcombine_s32(vget_low_s32(acc[2]), vget_low_s32(acc[3])), + vcombine_s32(vget_high_s32(acc[0]), vget_high_s32(acc[1])), + vcombine_s32(vget_high_s32(acc[2]), vget_high_s32(acc[3])), + vcombine_s32(vget_low_s32(acc[4]), vget_low_s32(acc[5])), + vcombine_s32(vget_low_s32(acc[6]), vget_low_s32(acc[7])), + vcombine_s32(vget_high_s32(acc[4]), vget_high_s32(acc[5])), + vcombine_s32(vget_high_s32(acc[6]), vget_high_s32(acc[7])), + }; + + for (int i = 0; i < q8_k_blocklen; i++) { + for (int j = 0; j < 2; j++) { + float32x4_t q8_d = vdupq_n_f32(q8_ptr[b].d[i]); + float32x4_t q4_dmin = vcvt_f32_f16(vld1_f16((const __fp16 *) (q4_ptr[b].dmin + j * 4))); + const float32x4_t dmins = vmulq_f32(q4_dmin, q8_d); + + float32x4_t q4_d = vcvt_f32_f16(vld1_f16((const __fp16 *) (q4_ptr[b].d + j * 4))); + const float32x4_t scale = vmulq_f32(q4_d, q8_d); + + acc_f32[2 * i + j] = vmlsq_f32(acc_f32[2 * i + j], vcvtq_f32_s32(bias_acc[2 * i + j]), dmins); + acc_f32[2 * i + j] = + vmlaq_f32(acc_f32[2 * i + j], vcvtq_f32_s32(reorder_acc[2 * i + j]), scale); + } + } + } // for b + + // With the previous reorder, the tile is already in the correct memory layout. + for (int i = 0; i < q8_k_blocklen; i++) { + int row = y * q8_k_blocklen + i; + for (int j = 0; j < 2; j++) { + int col = x * ncols_interleaved + j * 4; + int offset = row * bs + col; + vst1q_f32(s + offset, acc_f32[2 * i + j]); + } + } + } // for x + } // for y + return; +#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) + ggml_gemm_q4_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc); +} diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp index 3db26cff74b..d1321191358 100644 --- a/ggml/src/ggml-cpu/repack.cpp +++ b/ggml/src/ggml-cpu/repack.cpp @@ -1961,6 +1961,11 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons return &q4_K_8x8_q8_K; } } + if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) { + if (cur->ne[1] % 8 == 0) { + return &q4_K_8x8_q8_K; + } + } } else if (cur->type == GGML_TYPE_Q2_K) { if (ggml_cpu_has_avx512()) { if (cur->ne[1] % 8 == 0) { From 697edfeead9769d68387dec6884e0ecac23d2e4e Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Mon, 24 Nov 2025 12:51:50 +0100 Subject: [PATCH 17/37] ggml : remove dirty flag from version string (ggml/1391) This commit removes the "-dirty" suffix from the GGML version string. The motivation for this change is to ensure that the version string works with different ways of checking out ggml and using it in projects. By removing the dirty flag from the version string, we avoid potential artifacts like shared libraries getting a -dirty suffix in their names. Instead, if the project is built from a dirty git state, the dirty flag will be appended to the commit hash in the GGML_BUILD_COMMIT variable. This will enable users to still identify that the build was made from from a modified/dirty state even though the version might match a "real" version. For example, the commit can be produces as follows: ```c++ printf("commit: %s\n", ggml_commit()); ``` Which would print the following for a dirty build: ```console commit: 781baf2a-dirty ``` Refs: https://github.com/ggml-org/ggml/pull/1363#issuecomment-3569691546 --- ggml/CMakeLists.txt | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index 869796f0e3b..0211255a762 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -25,16 +25,17 @@ if(GIT_EXE) ) endif() -# Build the version string with optional dirty flag set(GGML_VERSION "${GGML_VERSION_BASE}") -if(GGML_GIT_DIRTY AND NOT GGML_GIT_DIRTY EQUAL 0) - set(GGML_VERSION "${GGML_VERSION}-dirty") -endif() if(NOT GGML_BUILD_COMMIT) set(GGML_BUILD_COMMIT "unknown") endif() +# Build the commit string with optional dirty flag +if(DEFINED GGML_GIT_DIRTY AND GGML_GIT_DIRTY EQUAL 1) + set(GGML_BUILD_COMMIT "${GGML_BUILD_COMMIT}-dirty") +endif() + include(CheckIncludeFileCXX) set(CMAKE_EXPORT_COMPILE_COMMANDS ON) From 2d50b9d8cb6b6c0ef935809af61ad4958be47648 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 24 Nov 2025 14:28:37 +0200 Subject: [PATCH 18/37] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index c9056b59c70..a879940eaee 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -781baf2a14d9e0aaee542b2e1bb918bfc4132199 +55bc9320a4aae82af18e23eefd5de319a755d7b9 From 6ab8eacddf50cda653b1e27521bd88945c41df1b Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Mon, 24 Nov 2025 14:38:45 +0100 Subject: [PATCH 19/37] examples : add -kvu to batched usage example [no ci] (#17469) This commit adds the --kv-unified flag to the usage example in the README.md file for the batched example. The motivation for this is that without this flag the example will fail with the following error: ```console Hello my name is split_equal: sequential split is not supported when there are coupled sequences in the input batch (you may need to use the -kvu flag) decode: failed to find a memory slot for batch of size 4 main: llama_decode() failed ``` --- examples/batched/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/batched/README.md b/examples/batched/README.md index 6013aab01fd..8cde35dd644 100644 --- a/examples/batched/README.md +++ b/examples/batched/README.md @@ -3,7 +3,7 @@ The example demonstrates batched generation from a given prompt ```bash -./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4 +./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4 --kv-unified ... From b8372eecd94890fd39a59a3a79ab86da1c0db480 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 24 Nov 2025 14:41:53 +0100 Subject: [PATCH 20/37] server: split server.cpp code into server/common/task/queue (#17362) * add server-task, server-common * add server-queue * rm redundant includes * move enum stop_type to server-task * server : headers cleanup --------- Co-authored-by: Georgi Gerganov --- tools/server/CMakeLists.txt | 7 +- tools/server/{utils.hpp => server-common.cpp} | 1707 +++++++-------- tools/server/server-common.h | 349 +++ tools/server/server-http.cpp | 2 +- tools/server/server-queue.cpp | 268 +++ tools/server/server-queue.h | 110 + tools/server/server-task.cpp | 1192 ++++++++++ tools/server/server-task.h | 453 ++++ tools/server/server.cpp | 1934 +---------------- 9 files changed, 3200 insertions(+), 2822 deletions(-) rename tools/server/{utils.hpp => server-common.cpp} (67%) create mode 100644 tools/server/server-common.h create mode 100644 tools/server/server-queue.cpp create mode 100644 tools/server/server-queue.h create mode 100644 tools/server/server-task.cpp create mode 100644 tools/server/server-task.h diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt index 1fccfdd17f1..7fbca320162 100644 --- a/tools/server/CMakeLists.txt +++ b/tools/server/CMakeLists.txt @@ -13,9 +13,14 @@ endif() set(TARGET_SRCS server.cpp - utils.hpp server-http.cpp server-http.h + server-task.cpp + server-task.h + server-queue.cpp + server-queue.h + server-common.cpp + server-common.h ) set(PUBLIC_ASSETS index.html.gz diff --git a/tools/server/utils.hpp b/tools/server/server-common.cpp similarity index 67% rename from tools/server/utils.hpp rename to tools/server/server-common.cpp index bf21726051e..18328f3afbd 100644 --- a/tools/server/utils.hpp +++ b/tools/server/server-common.cpp @@ -1,502 +1,752 @@ -#pragma once - #include "common.h" #include "log.h" #include "llama.h" -#include "arg.h" // common_remote_get_content -#include "base64.hpp" #include "mtmd.h" #include "mtmd-helper.h" #include "chat.h" +#include "arg.h" // for common_remote_get_content; TODO: use download.h only +#include "base64.hpp" -#define JSON_ASSERT GGML_ASSERT -#include +#include "server-common.h" #include #include -#include -#include -#include -#include - -#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo" - -using json = nlohmann::ordered_json; - -#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) -#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) -#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) -#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) - -#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) - -#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) -#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) - -using raw_buffer = std::vector; - -template -static T json_value(const json & body, const std::string & key, const T & default_value) { - // Fallback null to default value - if (body.contains(key) && !body.at(key).is_null()) { - try { - return body.at(key); - } catch (NLOHMANN_JSON_NAMESPACE::detail::type_error const & err) { - LOG_WRN("Wrong type supplied for parameter '%s'. Expected '%s', using default value: %s\n", key.c_str(), json(default_value).type_name(), err.what()); - return default_value; - } - } else { - return default_value; + +json format_error_response(const std::string & message, const enum error_type type) { + std::string type_str; + int code = 500; + switch (type) { + case ERROR_TYPE_INVALID_REQUEST: + type_str = "invalid_request_error"; + code = 400; + break; + case ERROR_TYPE_AUTHENTICATION: + type_str = "authentication_error"; + code = 401; + break; + case ERROR_TYPE_NOT_FOUND: + type_str = "not_found_error"; + code = 404; + break; + case ERROR_TYPE_SERVER: + type_str = "server_error"; + code = 500; + break; + case ERROR_TYPE_PERMISSION: + type_str = "permission_error"; + code = 403; + break; + case ERROR_TYPE_NOT_SUPPORTED: + type_str = "not_supported_error"; + code = 501; + break; + case ERROR_TYPE_UNAVAILABLE: + type_str = "unavailable_error"; + code = 503; + break; + case ERROR_TYPE_EXCEED_CONTEXT_SIZE: + type_str = "exceed_context_size_error"; + code = 400; + break; } + return json { + {"code", code}, + {"message", message}, + {"type", type_str}, + }; } -const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "-" + LLAMA_COMMIT); +// +// random string / id +// -// thin wrapper around common_grammar_trigger with (de)serialization functions -struct server_grammar_trigger { - common_grammar_trigger value; +std::string random_string() { + static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"); - server_grammar_trigger() = default; - server_grammar_trigger(const common_grammar_trigger & value) : value(value) {} - server_grammar_trigger(const json & in) { - value.type = (common_grammar_trigger_type) in.at("type").get(); - value.value = in.at("value").get(); - if (value.type == COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN) { - value.token = (llama_token) in.at("token").get(); - } - } + std::random_device rd; + std::mt19937 generator(rd()); - json to_json() const { - json out { - {"type", (int) value.type}, - {"value", value.value}, - }; - if (value.type == COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN) { - out["token"] = (int) value.token; - } - return out; + std::string result(32, ' '); + + for (int i = 0; i < 32; ++i) { + result[i] = str[generator() % str.size()]; } -}; + + return result; +} + +std::string gen_chatcmplid() { + return "chatcmpl-" + random_string(); +} + +std::string gen_tool_call_id() { + return random_string(); +} // -// tokenizer and input processing utils +// lora utils // -static bool json_is_array_of_numbers(const json & data) { - if (data.is_array()) { - for (const auto & e : data) { - if (!e.is_number_integer()) { +bool lora_all_alora(const std::vector & loras) { + bool found_alora = false; + for (const auto & lora : loras) { + if (lora.scale != 0) { + if (llama_adapter_get_alora_n_invocation_tokens(lora.ptr) == 0) { return false; } + found_alora = true; } - return true; } - return false; + return found_alora; } -// is array having BOTH numbers & strings? -static bool json_is_array_of_mixed_numbers_strings(const json & data) { - bool seen_string = false; - bool seen_number = false; - if (data.is_array()) { - for (const auto & e : data) { - seen_string |= e.is_string(); - seen_number |= e.is_number_integer(); - if (seen_number && seen_string) { - return true; - } +bool lora_should_clear_cache( + const std::vector & current, + const std::vector & next) { + + // This should always be called after determining that the two sets are + // _not_ equal. This assert is therefore some slightly wasted work and + // should be safe to remove as long as this method is called correctly. + GGML_ASSERT(!are_lora_equal(current, next)); + + return ( + !(lora_get_enabled_ids(current).empty() || lora_all_alora(current)) || + !lora_all_alora(next)); +} + +std::vector parse_lora_request( + const std::vector & lora_base, + const json & data) { + std::vector lora(lora_base); + int max_idx = lora.size(); + + // clear existing value + for (auto & entry : lora) { + entry.scale = 0.0f; + } + + // set value + for (const auto & entry : data) { + int id = json_value(entry, "id", -1); + float scale = json_value(entry, "scale", 0.0f); + if (0 <= id && id < max_idx) { + lora[id].scale = scale; + } else { + throw std::runtime_error("invalid adapter id"); } } - return false; + + return lora; } -// does array have any individual integers/tokens? -static bool json_is_array_and_contains_numbers(const json & data) { - if (data.is_array()) { - for (const auto & e : data) { - if (e.is_number_integer()) { - return true; - } - } +bool are_lora_equal( + const std::vector & l1, + const std::vector & l2) { + if (l1.size() != l2.size()) { return false; } - return false; + for (size_t i = 0; i < l1.size(); ++i) { + // we don't check lora.path to reduce the time complexity + if (l1[i].scale != l2[i].scale || l1[i].ptr != l2[i].ptr) { + return false; + } + } + return true; } -// get value by path(key1 / key2) -static json json_get_nested_values(const std::vector & paths, const json & js) { - json result = json::object(); - - for (const std::string & path : paths) { - json current = js; - const auto keys = string_split(path, /*separator*/ '/'); - bool valid_path = true; - for (const std::string & k : keys) { - if (valid_path && current.is_object() && current.contains(k)) { - current = current[k]; - } else { - valid_path = false; - } - } - if (valid_path) { - result[path] = current; +std::vector lora_get_enabled_ids(const std::vector & loras) { + std::vector enabled_ids; + for (size_t i = 0; i < loras.size(); ++i) { + if (loras[i].scale > 0) { + enabled_ids.push_back(i); } } - return result; + return enabled_ids; } -/** - * this handles 2 cases: - * - only string, example: "string" - * - mixed string and tokens, example: [12, 34, "string", 56, 78] - */ -static llama_tokens tokenize_mixed(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) { - // If `add_bos` is true, we only add BOS, when json_prompt is a string, - // or the first element of the json_prompt array is a string. - llama_tokens prompt_tokens; +// +// base64 utils (TODO: use the base64::decode from base64.hpp) +// - if (json_prompt.is_array()) { - bool first = true; - for (const auto & p : json_prompt) { - if (p.is_string()) { - auto s = p.template get(); +static const std::string base64_chars = + "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + "abcdefghijklmnopqrstuvwxyz" + "0123456789+/"; - llama_tokens p; - if (first) { - p = common_tokenize(vocab, s, add_special, parse_special); - first = false; - } else { - p = common_tokenize(vocab, s, false, parse_special); - } +static inline bool is_base64(uint8_t c) { + return (isalnum(c) || (c == '+') || (c == '/')); +} - prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); - } else { - if (first) { - first = false; - } +static inline raw_buffer base64_decode(const std::string & encoded_string) { + int i = 0; + int j = 0; + int in_ = 0; - prompt_tokens.push_back(p.template get()); + int in_len = encoded_string.size(); + + uint8_t char_array_4[4]; + uint8_t char_array_3[3]; + + raw_buffer ret; + + while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) { + char_array_4[i++] = encoded_string[in_]; in_++; + if (i == 4) { + for (i = 0; i < 4; i++) { + char_array_4[i] = base64_chars.find(char_array_4[i]); + } + + char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (i = 0; (i < 3); i++) { + ret.push_back(char_array_3[i]); } + + i = 0; } - } else { - auto s = json_prompt.template get(); - prompt_tokens = common_tokenize(vocab, s, add_special, parse_special); } - return prompt_tokens; -} + if (i) { + for (j = i; j < 4; j++) { + char_array_4[j] = 0; + } -// return the last index of character that can form a valid string -// if the last character is potentially cut in half, return the index before the cut -// if validate_utf8(text) == text.size(), then the whole text is valid utf8 -static size_t validate_utf8(const std::string& text) { - size_t len = text.size(); - if (len == 0) return 0; + for (j = 0; j < 4; j++) { + char_array_4[j] = base64_chars.find(char_array_4[j]); + } - // Check the last few bytes to see if a multi-byte character is cut off - for (size_t i = 1; i <= 4 && i <= len; ++i) { - unsigned char c = text[len - i]; - // Check for start of a multi-byte sequence from the end - if ((c & 0xE0) == 0xC0) { - // 2-byte character start: 110xxxxx - // Needs at least 2 bytes - if (i < 2) return len - i; - } else if ((c & 0xF0) == 0xE0) { - // 3-byte character start: 1110xxxx - // Needs at least 3 bytes - if (i < 3) return len - i; - } else if ((c & 0xF8) == 0xF0) { - // 4-byte character start: 11110xxx - // Needs at least 4 bytes - if (i < 4) return len - i; + char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (j = 0; j < i - 1; j++) { + ret.push_back(char_array_3[j]); } } - // If no cut-off multi-byte character is found, return full length - return len; + return ret; } // -// template utils +// server_tokens implementation // -// format infill task -static llama_tokens format_infill( - const llama_vocab * vocab, - const json & input_prefix, - const json & input_suffix, - const json & input_extra, - const int n_batch, - const int n_predict, - const int n_ctx, - const bool spm_infill, - const llama_tokens & tokens_prompt - ) { - // TODO: optimize this block by reducing memory allocations and movement +server_tokens::server_tokens(mtmd::input_chunks & mtmd_chunks, bool has_mtmd) : has_mtmd(has_mtmd) { + for (size_t i = 0; i < mtmd_chunks.size(); ++i) { + push_back(mtmd_chunks[i]); + } +} - // use FIM repo-level pattern: - // ref: https://arxiv.org/pdf/2409.12186 - // - // [FIM_REP]myproject - // [FIM_SEP]filename0 - // extra chunk 0 - // [FIM_SEP]filename1 - // extra chunk 1 - // ... - // [FIM_SEP]filename - // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt - // - llama_tokens extra_tokens; - extra_tokens.reserve(n_ctx); +server_tokens::server_tokens(const llama_tokens & tokens, bool has_mtmd) : has_mtmd(has_mtmd), tokens(tokens) { +} - auto tokens_prefix = tokenize_mixed(vocab, input_prefix, false, false); - auto tokens_suffix = tokenize_mixed(vocab, input_suffix, false, false); +llama_pos server_tokens::pos_next() const { + if (!has_mtmd) { + return tokens.size(); + } - if (llama_vocab_fim_rep(vocab) != LLAMA_TOKEN_NULL) { - // TODO: make project name an input - static const auto k_fim_repo = common_tokenize(vocab, "myproject\n", false, false); + llama_pos res = tokens.size(); - extra_tokens.push_back(llama_vocab_fim_rep(vocab)); - extra_tokens.insert(extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end()); + for (auto it = map_idx_to_media.begin(); it != map_idx_to_media.end(); ++it) { + const auto & chunk = it->second; + res += mtmd_input_chunk_get_n_pos(chunk.get()) - mtmd_input_chunk_get_n_tokens(chunk.get()); } - for (const auto & chunk : input_extra) { - // { "text": string, "filename": string } - const std::string text = json_value(chunk, "text", std::string()); - const std::string filename = json_value(chunk, "filename", std::string("tmp")); - if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) { - const auto k_fim_file = common_tokenize(vocab, filename + "\n", false, false); + return res; +} - extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab)); - extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); +std::string server_tokens::str() const { + std::ostringstream oss; + oss << "tokens: "; + for (size_t idx = 0; idx < tokens.size(); ++idx) { + llama_token t = tokens[idx]; + oss << "idx:" << idx << " "; + if (t == LLAMA_TOKEN_NULL) { + oss << " "; } else { - // chunk separator in binary form to avoid confusing the AI - static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00}; - static const auto k_chunk_prefix_tokens = common_tokenize(vocab, k_chunk_prefix_str, false, false); - - extra_tokens.insert(extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end()); + oss << t << " "; } - - const auto chunk_tokens = common_tokenize(vocab, text, false, false); - extra_tokens.insert(extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end()); } - - if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) { - // TODO: current filename - static const auto k_fim_file = common_tokenize(vocab, "filename\n", false, false); - - extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab)); - extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); + oss << "\n"; + oss << "image idx: "; + for (const auto & it : map_idx_to_media) { + oss << it.first << ", "; } + return oss.str(); +} - // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?) - const int n_prefix_take = std::min(tokens_prefix.size(), 3*(n_batch/4)); - const int n_suffix_take = std::min(tokens_suffix.size(), std::max(0, (n_batch/4) - (2 + tokens_prompt.size()))); - - SRV_DBG("n_prefix_take = %d, n_suffix_take = %d, total = %d\n", n_prefix_take, n_suffix_take, (n_prefix_take + n_suffix_take)); - - // fill the rest of the context with extra chunks - const int n_extra_take = std::min(std::max(0, n_ctx - (n_batch) - 2*n_predict), extra_tokens.size()); - - tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take); - tokens_suffix.resize(n_suffix_take); - - tokens_prefix.insert(tokens_prefix.begin(), llama_vocab_fim_pre(vocab)); - tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end()); - tokens_suffix.insert(tokens_suffix.begin(), llama_vocab_fim_suf(vocab)); - - auto embd_inp = spm_infill ? tokens_suffix : tokens_prefix; - auto embd_end = spm_infill ? tokens_prefix : tokens_suffix; - - if (llama_vocab_get_add_bos(vocab)) { - embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab)); +const mtmd::input_chunk_ptr & server_tokens::find_chunk(size_t idx) const { + auto it = map_idx_to_media.find(idx); + if (it != map_idx_to_media.end()) { + return it->second; } - - SRV_DBG("extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", n_ctx, n_extra_take, (int) extra_tokens.size()); - - // put the extra context before the FIM prefix - embd_inp.insert(embd_inp.begin(), extra_tokens.end() - n_extra_take, extra_tokens.end()); - - embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); - embd_inp.push_back(llama_vocab_fim_mid(vocab)); - - return embd_inp; + throw std::runtime_error("Chunk not found"); } -// -// base64 utils (TODO: move to common in the future) -// - -static const std::string base64_chars = - "ABCDEFGHIJKLMNOPQRSTUVWXYZ" - "abcdefghijklmnopqrstuvwxyz" - "0123456789+/"; +void server_tokens::push_back(llama_token tok) { + if (tok == LLAMA_TOKEN_NULL) { + throw std::runtime_error("Invalid token"); + } + tokens.emplace_back(tok); +} -static inline bool is_base64(uint8_t c) { - return (isalnum(c) || (c == '+') || (c == '/')); +void server_tokens::push_back(const mtmd_input_chunk * chunk) { + auto type = mtmd_input_chunk_get_type(chunk); + if (type == MTMD_INPUT_CHUNK_TYPE_IMAGE || type == MTMD_INPUT_CHUNK_TYPE_AUDIO) { + GGML_ASSERT(has_mtmd); + const size_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk); + size_t start_idx = tokens.size(); + for (size_t i = 0; i < n_tokens; ++i) { + tokens.emplace_back(LLAMA_TOKEN_NULL); + } + mtmd::input_chunk_ptr new_chunk(mtmd_input_chunk_copy(chunk)); + map_idx_to_media[start_idx] = std::move(new_chunk); + } else if (type == MTMD_INPUT_CHUNK_TYPE_TEXT) { + size_t n_tokens; + const auto * text_tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens); + for (size_t i = 0; i < n_tokens; ++i) { + push_back(text_tokens[i]); + } + } else { + GGML_ABORT("Invalid chunk type"); + } } -static inline raw_buffer base64_decode(const std::string & encoded_string) { - int i = 0; - int j = 0; - int in_ = 0; +void server_tokens::push_back(server_tokens & tokens) { + size_t start_idx = size(); + for (size_t i = 0; i < tokens.size(); i++) { + push_back(tokens[i]); + } + if (tokens.has_mtmd) { + // Assert if we are copying MTMD chunks to a server_tokens that does not have mtmd. + // We could also just check, but this will prevent silently dropping MTMD data. + GGML_ASSERT(has_mtmd); + for (auto it = tokens.map_idx_to_media.begin(); it != tokens.map_idx_to_media.end(); ) { + auto * chunk = tokens.map_idx_to_media[it->first].get(); + mtmd::input_chunk_ptr new_chunk(mtmd_input_chunk_copy(chunk)); + map_idx_to_media[start_idx + it->first] = std::move(new_chunk); + } + } +} - int in_len = encoded_string.size(); +void server_tokens::insert(const llama_tokens & inp_tokens) { + GGML_ASSERT(!has_mtmd); // only allow this if mtmd is disabled + tokens.insert(tokens.end(), inp_tokens.begin(), inp_tokens.end()); +} - uint8_t char_array_4[4]; - uint8_t char_array_3[3]; +const llama_tokens & server_tokens::get_text_tokens() const { + GGML_ASSERT(!has_mtmd); // only allow this if mtmd is disabled + return tokens; +} - raw_buffer ret; +void server_tokens::set_token(llama_pos pos, llama_token id) { + GGML_ASSERT(!has_mtmd); // only allow this if mtmd is disabled + tokens[pos] = id; +} - while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) { - char_array_4[i++] = encoded_string[in_]; in_++; - if (i == 4) { - for (i = 0; i < 4; i++) { - char_array_4[i] = base64_chars.find(char_array_4[i]); +void server_tokens::keep_first(size_t n) { + GGML_ASSERT(n <= tokens.size()); + if (has_mtmd) { + if (n == tokens.size()) { + return; // nothing to do + } + // we throw an error if we try to remove a token in the middle of an image + // for ex. with input of 5 text tokens and 2 images: + // [0] [1] [2] [3] [4] [img0] [img0] [img0] [img1] [img1] + // n 1 2 3 4 5 6 7 8 9 10 + // allowed to resize ^ ^ + // disallowed to resize ^ ^ ^ + if (n > 0) { + // make sure we never remove tokens in the middle of an image + // note that the case where we keep a full image at the end is allowed: + // tokens[n - 1] == LLAMA_TOKEN_NULL && tokens[n] != LLAMA_TOKEN_NULL + if (tokens[n - 1] == LLAMA_TOKEN_NULL && tokens[n] == LLAMA_TOKEN_NULL) { + find_chunk(n - 1); // will throw an error if the token is not begin-of-chunk } - - char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); - char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); - char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; - - for (i = 0; (i < 3); i++) { - ret.push_back(char_array_3[i]); + } + // remove all image chunks that are not used anymore + for (auto it = map_idx_to_media.begin(); it != map_idx_to_media.end(); ) { + size_t idx = it->first; + if (idx >= n) { + it = map_idx_to_media.erase(it); + } else { + ++it; } - - i = 0; } } + tokens.resize(n); +} - if (i) { - for (j = i; j < 4; j++) { - char_array_4[j] = 0; +std::string server_tokens::detokenize(const llama_context * ctx, bool special) const { + llama_tokens text_tokens; + text_tokens.reserve(tokens.size()); + for (const auto & t : tokens) { + if (t != LLAMA_TOKEN_NULL) { + text_tokens.push_back(t); } + } + return common_detokenize(ctx, text_tokens, special); +} - for (j = 0; j < 4; j++) { - char_array_4[j] = base64_chars.find(char_array_4[j]); - } +size_t server_tokens::get_common_prefix(const server_tokens & b) const { + const size_t max_idx = std::min(tokens.size(), b.tokens.size()); - char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); - char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); - char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + if (!has_mtmd) { + for (size_t i = 0; i < max_idx; ++i) { + if (tokens[i] == b.tokens[i]) { + continue; + } - for (j = 0; j < i - 1; j++) { - ret.push_back(char_array_3[j]); + return i; } + + return max_idx; } - return ret; -} + for (size_t i = 0; i < max_idx; ++i) { + const llama_token ai = tokens[i]; + const llama_token bi = b.tokens[i]; -// -// random string / id -// + if (ai == LLAMA_TOKEN_NULL && bi == LLAMA_TOKEN_NULL) { + const auto & a_chunk = find_chunk(i); + const auto & b_chunk = b.find_chunk(i); -static std::string random_string() { - static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"); + GGML_ASSERT(a_chunk && b_chunk); - std::random_device rd; - std::mt19937 generator(rd()); + const std::string id_ai = mtmd_input_chunk_get_id(a_chunk.get()); + const std::string id_bi = mtmd_input_chunk_get_id(b_chunk.get()); - std::string result(32, ' '); + const size_t n_tok_a = mtmd_input_chunk_get_n_tokens(a_chunk.get()); + const size_t n_tok_b = mtmd_input_chunk_get_n_tokens(b_chunk.get()); - for (int i = 0; i < 32; ++i) { - result[i] = str[generator() % str.size()]; + if (id_ai == id_bi && n_tok_a == n_tok_b) { + GGML_ASSERT(n_tok_a > 0 && "Invalid media chunk"); // should never happen + i += n_tok_a - 1; // will be +1 by the for loop + continue; + } + + return i; + } + + if (ai == bi) { + continue; + } + + return i; } - return result; + return max_idx; // all tokens are equal } -static std::string gen_chatcmplid() { - return "chatcmpl-" + random_string(); +bool server_tokens::validate(const struct llama_context * ctx) const { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + const int32_t n_vocab = llama_vocab_n_tokens(vocab); + + for (size_t i = 0; i < tokens.size(); ++i) { + const auto & t = tokens[i]; + if (t == LLAMA_TOKEN_NULL) { + try { + const auto & chunk = find_chunk(i); + size_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk.get()); + i += n_tokens - 1; // will be +1 by the for loop + } catch (const std::exception & e) { + return false; + } + } else if (t < 0 || t >= n_vocab) { + return false; + } + } + return true; } -static std::string gen_tool_call_id() { - return random_string(); +int32_t server_tokens::process_chunk( + llama_context * ctx, + mtmd_context * mctx, + size_t idx, + llama_pos pos, + int32_t seq_id, + size_t & n_tokens_out) const { + const auto & chunk = find_chunk(idx); + const char * name = mtmd_input_chunk_get_type(chunk.get()) == MTMD_INPUT_CHUNK_TYPE_IMAGE + ? "image" : "audio"; + SRV_INF("processing %s...\n", name); + int32_t n_batch = llama_n_batch(ctx); + int64_t t0 = ggml_time_ms(); + llama_pos new_n_past; // unused for now + int32_t result = mtmd_helper_eval_chunk_single(mctx, ctx, + chunk.get(), + pos, + seq_id, + n_batch, + true, // logits last + &new_n_past); + SRV_INF("%s processed in %" PRId64 " ms\n", name, ggml_time_ms() - t0); + if (result != 0) { + LOG_ERR("mtmd_helper_eval failed with status %d", result); + n_tokens_out = 0; + return result; + } + n_tokens_out = mtmd_input_chunk_get_n_tokens(chunk.get()); + return 0; } // -// other common utils +// tokenizer and input processing utils // -static std::string safe_json_to_str(const json & data) { - return data.dump(-1, ' ', false, json::error_handler_t::replace); -} - -// TODO: reuse llama_detokenize -template -static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { - std::string ret; - for (; begin != end; ++begin) { - ret += common_token_to_piece(ctx, *begin); +bool json_is_array_of_numbers(const json & data) { + if (data.is_array()) { + for (const auto & e : data) { + if (!e.is_number_integer()) { + return false; + } + } + return true; } - - return ret; + return false; } -// format incomplete utf-8 multibyte character for output -static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) { - std::string out = token == LLAMA_TOKEN_NULL ? "" : common_token_to_piece(ctx, token); - - // if the size is 1 and first bit is 1, meaning it's a partial character - // (size > 1 meaning it's already a known token) - if (out.size() == 1 && (out[0] & 0x80) == 0x80) { - std::stringstream ss; - ss << std::hex << (out[0] & 0xff); - std::string res(ss.str()); - out = "byte: \\x" + res; +bool json_is_array_of_mixed_numbers_strings(const json & data) { + bool seen_string = false; + bool seen_number = false; + if (data.is_array()) { + for (const auto & e : data) { + seen_string |= e.is_string(); + seen_number |= e.is_number_integer(); + if (seen_number && seen_string) { + return true; + } + } } - - return out; + return false; } -// format server-sent event (SSE), return the formatted string to send -// note: if data is a json array, it will be sent as multiple events, one per item -static std::string format_sse(const json & data) { - std::ostringstream ss; - auto send_single = [&ss](const json & data) { - ss << "data: " << - safe_json_to_str(data) << - "\n\n"; // required by RFC 8895 - A message is terminated by a blank line (two line terminators in a row). - }; - +bool json_is_array_and_contains_numbers(const json & data) { if (data.is_array()) { - for (const auto & item : data) { - send_single(item); + for (const auto & e : data) { + if (e.is_number_integer()) { + return true; + } } - } else { - send_single(data); + return false; } - - return ss.str(); + return false; } -// -// OAI utils -// - -// used by /completions endpoint -static json oaicompat_completion_params_parse(const json & body) { - json llama_params; +json json_get_nested_values(const std::vector & paths, const json & js) { + json result = json::object(); - if (!body.contains("prompt")) { - throw std::runtime_error("\"prompt\" is required"); + for (const std::string & path : paths) { + json current = js; + const auto keys = string_split(path, /*separator*/ '/'); + bool valid_path = true; + for (const std::string & k : keys) { + if (valid_path && current.is_object() && current.contains(k)) { + current = current[k]; + } else { + valid_path = false; + } + } + if (valid_path) { + result[path] = current; + } } + return result; +} - // Handle "stop" field - if (body.contains("stop") && body.at("stop").is_string()) { - llama_params["stop"] = json::array({body.at("stop").get()}); - } else { - llama_params["stop"] = json_value(body, "stop", json::array()); - } +llama_tokens tokenize_mixed(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special) { + // If `add_bos` is true, we only add BOS, when json_prompt is a string, + // or the first element of the json_prompt array is a string. + llama_tokens prompt_tokens; - // Handle "n" field - int n_choices = json_value(body, "n", 1); + if (json_prompt.is_array()) { + bool first = true; + for (const auto & p : json_prompt) { + if (p.is_string()) { + auto s = p.template get(); + + llama_tokens p; + if (first) { + p = common_tokenize(vocab, s, add_special, parse_special); + first = false; + } else { + p = common_tokenize(vocab, s, false, parse_special); + } + + prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end()); + } else { + if (first) { + first = false; + } + + prompt_tokens.push_back(p.template get()); + } + } + } else { + auto s = json_prompt.template get(); + prompt_tokens = common_tokenize(vocab, s, add_special, parse_special); + } + + return prompt_tokens; +} + +size_t validate_utf8(const std::string& text) { + size_t len = text.size(); + if (len == 0) return 0; + + // Check the last few bytes to see if a multi-byte character is cut off + for (size_t i = 1; i <= 4 && i <= len; ++i) { + unsigned char c = text[len - i]; + // Check for start of a multi-byte sequence from the end + if ((c & 0xE0) == 0xC0) { + // 2-byte character start: 110xxxxx + // Needs at least 2 bytes + if (i < 2) return len - i; + } else if ((c & 0xF0) == 0xE0) { + // 3-byte character start: 1110xxxx + // Needs at least 3 bytes + if (i < 3) return len - i; + } else if ((c & 0xF8) == 0xF0) { + // 4-byte character start: 11110xxx + // Needs at least 4 bytes + if (i < 4) return len - i; + } + } + + // If no cut-off multi-byte character is found, return full length + return len; +} + +// Computes FNV-1a hash of the data +static std::string fnv_hash(const uint8_t * data, size_t len) { + const uint64_t fnv_prime = 0x100000001b3ULL; + uint64_t hash = 0xcbf29ce484222325ULL; + + for (size_t i = 0; i < len; ++i) { + hash ^= data[i]; + hash *= fnv_prime; + } + return std::to_string(hash); +} + +server_tokens process_mtmd_prompt(mtmd_context * mctx, std::string prompt, std::vector files) { + mtmd::bitmaps bitmaps; + for (auto & file : files) { + mtmd::bitmap bmp(mtmd_helper_bitmap_init_from_buf(mctx, file.data(), file.size())); + if (!bmp.ptr) { + throw std::runtime_error("Failed to load image or audio file"); + } + // calculate bitmap hash (for KV caching) + std::string hash = fnv_hash(bmp.data(), bmp.n_bytes()); + bmp.set_id(hash.c_str()); + bitmaps.entries.push_back(std::move(bmp)); + } + // process prompt + std::vector inputs; + // multimodal + mtmd_input_text inp_txt = { + prompt.c_str(), + /* add_special */ true, + /* parse_special */ true, + }; + mtmd::input_chunks chunks(mtmd_input_chunks_init()); + auto bitmaps_c_ptr = bitmaps.c_ptr(); + int32_t tokenized = mtmd_tokenize(mctx, + chunks.ptr.get(), + &inp_txt, + bitmaps_c_ptr.data(), + bitmaps_c_ptr.size()); + if (tokenized != 0) { + throw std::runtime_error("Failed to tokenize prompt"); + } + auto result = server_tokens(chunks, true); + return result; +} + +/** + * break the input "prompt" object into multiple prompt if needed, then tokenize them + * use tokenize_input_prompts() if the input could be an array. + * this supports these cases: + * - "prompt": "string" + * - "prompt": [12, 34, 56] + * - "prompt": [12, 34, "string", 56, 78] + * - "prompt": { "prompt_string": "string", "multimodal_data": [ "base64" ] } + */ +static server_tokens tokenize_input_subprompt(const llama_vocab * vocab, mtmd_context * mctx, const json & json_prompt, bool add_special, bool parse_special) { + constexpr char JSON_STRING_PROMPT_KEY[] = "prompt_string"; + constexpr char JSON_MTMD_DATA_KEY[] = "multimodal_data"; + const bool has_mtmd = mctx != nullptr; + if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) { + // string or mixed + llama_tokens tmp = tokenize_mixed(vocab, json_prompt, add_special, parse_special); + return server_tokens(tmp, false); + } else if (json_is_array_of_numbers(json_prompt)) { + // array of tokens + llama_tokens tmp = json_prompt.get(); + return server_tokens(tmp, false); + } else if (json_prompt.contains(JSON_STRING_PROMPT_KEY)) { + // JSON object with prompt key. + if (json_prompt.contains(JSON_MTMD_DATA_KEY)) { + if (!has_mtmd) + throw std::runtime_error("Multimodal data provided, but model does not support multimodal requests."); + + // JSON object with prompt and multimodal key. + std::vector files; + for (const auto & entry : json_prompt.at(JSON_MTMD_DATA_KEY)) { + files.push_back(base64_decode(entry)); + } + return process_mtmd_prompt(mctx, json_prompt.at(JSON_STRING_PROMPT_KEY), files); + } else { + // Not multimodal, but contains a subobject. + llama_tokens tmp = tokenize_mixed(vocab, json_prompt.at(JSON_STRING_PROMPT_KEY), add_special, parse_special); + return server_tokens(tmp, false); + } + } else { + throw std::runtime_error("\"prompt\" elements must be a string, a list of tokens, a JSON object containing a prompt string, or a list of mixed strings & tokens."); + } +} + +std::vector tokenize_input_prompts(const llama_vocab * vocab, mtmd_context * mctx, const json & json_prompt, bool add_special, bool parse_special) { + std::vector result; + if (json_prompt.is_array() && !json_is_array_and_contains_numbers(json_prompt)) { + result.reserve(json_prompt.size()); + for (const auto & p : json_prompt) { + result.push_back(tokenize_input_subprompt(vocab, mctx, p,add_special, parse_special)); + } + } else { + result.push_back(tokenize_input_subprompt(vocab, mctx, json_prompt, add_special, parse_special)); + } + if (result.empty()) { + throw std::runtime_error("\"prompt\" must not be empty"); + } + return result; +} + + +// +// OAI utils +// + +// used by /completions endpoint +json oaicompat_completion_params_parse(const json & body) { + json llama_params; + + if (!body.contains("prompt")) { + throw std::runtime_error("\"prompt\" is required"); + } + + // Handle "stop" field + if (body.contains("stop") && body.at("stop").is_string()) { + llama_params["stop"] = json::array({body.at("stop").get()}); + } else { + llama_params["stop"] = json_value(body, "stop", json::array()); + } + + // Handle "n" field + int n_choices = json_value(body, "n", 1); if (n_choices != 1) { throw std::runtime_error("Only one completion choice is allowed"); } @@ -525,19 +775,8 @@ static json oaicompat_completion_params_parse(const json & body) { return llama_params; } -struct oaicompat_parser_options { - bool use_jinja; - bool prefill_assistant; - common_reasoning_format reasoning_format; - std::map chat_template_kwargs; - common_chat_templates * tmpls; - bool allow_image; - bool allow_audio; - bool enable_thinking = true; -}; - // used by /chat/completions endpoint -static json oaicompat_chat_params_parse( +json oaicompat_chat_params_parse( json & body, /* openai api json semantics */ const oaicompat_parser_options & opt, std::vector & out_files) @@ -809,7 +1048,7 @@ static json oaicompat_chat_params_parse( return llama_params; } -static json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64 = false) { +json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64) { json data = json::array(); int32_t n_tokens = 0; int i = 0; @@ -851,7 +1090,7 @@ static json format_embeddings_response_oaicompat(const json & request, const jso return res; } -static json format_response_rerank( +json format_response_rerank( const json & request, const json & ranks, bool is_tei_format, @@ -896,63 +1135,12 @@ static json format_response_rerank( return res; } -static bool is_valid_utf8(const std::string & str) { - const unsigned char* bytes = reinterpret_cast(str.data()); - const unsigned char* end = bytes + str.length(); - - while (bytes < end) { - if (*bytes <= 0x7F) { - // 1-byte sequence (0xxxxxxx) - bytes++; - } else if ((*bytes & 0xE0) == 0xC0) { - // 2-byte sequence (110xxxxx 10xxxxxx) - if (end - bytes < 2 || (bytes[1] & 0xC0) != 0x80) - return false; - bytes += 2; - } else if ((*bytes & 0xF0) == 0xE0) { - // 3-byte sequence (1110xxxx 10xxxxxx 10xxxxxx) - if (end - bytes < 3 || (bytes[1] & 0xC0) != 0x80 || (bytes[2] & 0xC0) != 0x80) - return false; - bytes += 3; - } else if ((*bytes & 0xF8) == 0xF0) { - // 4-byte sequence (11110xxx 10xxxxxx 10xxxxxx 10xxxxxx) - if (end - bytes < 4 || (bytes[1] & 0xC0) != 0x80 || - (bytes[2] & 0xC0) != 0x80 || (bytes[3] & 0xC0) != 0x80) - return false; - bytes += 4; - } else { - // Invalid UTF-8 lead byte - return false; - } - } - - return true; -} - -static json format_tokenizer_response(const json & tokens) { - return json { - {"tokens", tokens} - }; -} - -static json format_detokenized_response(const std::string & content) { - return json { - {"content", content} - }; -} -static json format_logit_bias(const std::vector & logit_bias) { - json data = json::array(); - for (const auto & lb : logit_bias) { - data.push_back(json{ - {"bias", lb.bias}, - {"token", lb.token}, - }); - } - return data; -} +// +// other utils +// -static std::vector get_token_probabilities(llama_context * ctx, int idx) { +std::vector get_token_probabilities(llama_context * ctx, int idx) { std::vector cur; const auto * logits = llama_get_logits_ith(ctx, idx); @@ -986,538 +1174,203 @@ static std::vector get_token_probabilities(llama_context * ctx return cur; } -static bool are_lora_equal( - const std::vector & l1, - const std::vector & l2) { - if (l1.size() != l2.size()) { - return false; - } - for (size_t i = 0; i < l1.size(); ++i) { - // we don't check lora.path to reduce the time complexity - if (l1[i].scale != l2[i].scale || l1[i].ptr != l2[i].ptr) { - return false; - } - } - return true; +std::string safe_json_to_str(const json & data) { + return data.dump(-1, ' ', false, json::error_handler_t::replace); } -// get the ids of all enabled loras -static std::vector lora_get_enabled_ids(const std::vector & loras) { - std::vector enabled_ids; - for (size_t i = 0; i < loras.size(); ++i) { - if (loras[i].scale > 0) { - enabled_ids.push_back(i); - } +// TODO: reuse llama_detokenize +template +static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { + std::string ret; + for (; begin != end; ++begin) { + ret += common_token_to_piece(ctx, *begin); } - return enabled_ids; + + return ret; } -// check whether the given lora set has only aloras activated (empty => false) -static bool lora_all_alora(const std::vector & loras) { - bool found_alora = false; - for (const auto & lora : loras) { - if (lora.scale != 0) { - if (llama_adapter_get_alora_n_invocation_tokens(lora.ptr) == 0) { - return false; - } - found_alora = true; - } - } - return found_alora; +std::string tokens_to_str(llama_context * ctx, const llama_tokens & tokens) { + return tokens_to_str(ctx, tokens.begin(), tokens.end()); } -// if the two sets of loras are different, they require a cache clear unless the -// change is only from aloras to aloras. -static bool lora_should_clear_cache( - const std::vector & current, - const std::vector & next) { +// format incomplete utf-8 multibyte character for output +std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) { + std::string out = token == LLAMA_TOKEN_NULL ? "" : common_token_to_piece(ctx, token); - // This should always be called after determining that the two sets are - // _not_ equal. This assert is therefore some slightly wasted work and - // should be safe to remove as long as this method is called correctly. - GGML_ASSERT(!are_lora_equal(current, next)); + // if the size is 1 and first bit is 1, meaning it's a partial character + // (size > 1 meaning it's already a known token) + if (out.size() == 1 && (out[0] & 0x80) == 0x80) { + std::stringstream ss; + ss << std::hex << (out[0] & 0xff); + std::string res(ss.str()); + out = "byte: \\x" + res; + } - return ( - !(lora_get_enabled_ids(current).empty() || lora_all_alora(current)) || - !lora_all_alora(next)); + return out; } -// parse lora config from JSON request, returned a copy of lora_base with updated scale -static std::vector parse_lora_request( - const std::vector & lora_base, - const json & data) { - std::vector lora(lora_base); - int max_idx = lora.size(); - - // clear existing value - for (auto & entry : lora) { - entry.scale = 0.0f; - } +// format server-sent event (SSE), return the formatted string to send +// note: if data is a json array, it will be sent as multiple events, one per item +std::string format_sse(const json & data) { + std::ostringstream ss; + auto send_single = [&ss](const json & data) { + ss << "data: " << + safe_json_to_str(data) << + "\n\n"; // required by RFC 8895 - A message is terminated by a blank line (two line terminators in a row). + }; - // set value - for (const auto & entry : data) { - int id = json_value(entry, "id", -1); - float scale = json_value(entry, "scale", 0.0f); - if (0 <= id && id < max_idx) { - lora[id].scale = scale; - } else { - throw std::runtime_error("invalid adapter id"); + if (data.is_array()) { + for (const auto & item : data) { + send_single(item); } + } else { + send_single(data); } - return lora; + return ss.str(); } -// -// utils for interacting with libmtmd -// (may need to refactor in near future) -// +bool is_valid_utf8(const std::string & str) { + const unsigned char* bytes = reinterpret_cast(str.data()); + const unsigned char* end = bytes + str.length(); -/** - * server_tokens is a helper to manage the input tokens and image for the server. - * it is made this way to simplify the logic of KV cache management. - */ -struct server_tokens { - bool has_mtmd = false; - -private: // disallow accessing these members directly, risking out-of-sync - - // map a **start** index in tokens to the image chunk - // note: the order need to be in-sync with tokens - std::map map_idx_to_media; - - // list of tokens - // if the token is LLAMA_TOKEN_NULL, it indicates that this position is occupied by media chunk - // otherwise, it is a normal text token - // note: a non-text chunk can occupy multiple tokens (aka memory cells) in the token list - // note(2): for M-RoPE, an image can occupy different number of pos; do not assume 1-to-1 mapping tokens <-> pos - llama_tokens tokens; - - // for ex. with input of 5 text tokens and 2 images (each image occupies 3 tokens and 2 pos): - // [0] [1] [2] [3] [4] [img0] [img0] [img0] [img1] [img1] [img1] - // idx 0 1 2 3 4 5 6 7 8 9 10 - // pos 0 1 2 3 4 5 5 5 7 7 7 - // map_idx_to_media will contain: {5, img0}, {8, img1} - -public: - server_tokens() = default; - ~server_tokens() = default; - - // Prevent copying - // TODO: server_tokens should be copyable - remove this: - server_tokens(const server_tokens&) = delete; - server_tokens& operator=(const server_tokens&) = delete; - - // Allow moving (usually implicitly generated if members are movable) - server_tokens(server_tokens&&) = default; - server_tokens& operator=(server_tokens&&) = default; - - // Allow accessing elements using [] operator - llama_token operator[](size_t index) { return tokens[index]; } - const llama_token& operator[](size_t index) const { return tokens[index]; } - - server_tokens(mtmd::input_chunks & mtmd_chunks, bool has_mtmd) : has_mtmd(has_mtmd) { - for (size_t i = 0; i < mtmd_chunks.size(); ++i) { - push_back(mtmd_chunks[i]); + while (bytes < end) { + if (*bytes <= 0x7F) { + // 1-byte sequence (0xxxxxxx) + bytes++; + } else if ((*bytes & 0xE0) == 0xC0) { + // 2-byte sequence (110xxxxx 10xxxxxx) + if (end - bytes < 2 || (bytes[1] & 0xC0) != 0x80) + return false; + bytes += 2; + } else if ((*bytes & 0xF0) == 0xE0) { + // 3-byte sequence (1110xxxx 10xxxxxx 10xxxxxx) + if (end - bytes < 3 || (bytes[1] & 0xC0) != 0x80 || (bytes[2] & 0xC0) != 0x80) + return false; + bytes += 3; + } else if ((*bytes & 0xF8) == 0xF0) { + // 4-byte sequence (11110xxx 10xxxxxx 10xxxxxx 10xxxxxx) + if (end - bytes < 4 || (bytes[1] & 0xC0) != 0x80 || + (bytes[2] & 0xC0) != 0x80 || (bytes[3] & 0xC0) != 0x80) + return false; + bytes += 4; + } else { + // Invalid UTF-8 lead byte + return false; } } - server_tokens(const llama_tokens & tokens, bool has_mtmd) : has_mtmd(has_mtmd), tokens(tokens) { - } + return true; +} - llama_pos pos_next() const { - if (!has_mtmd) { - return tokens.size(); - } +llama_tokens format_prompt_infill( + const llama_vocab * vocab, + const json & input_prefix, + const json & input_suffix, + const json & input_extra, + const int n_batch, + const int n_predict, + const int n_ctx, + const bool spm_infill, + const llama_tokens & tokens_prompt + ) { + // TODO: optimize this block by reducing memory allocations and movement - llama_pos res = tokens.size(); + // use FIM repo-level pattern: + // ref: https://arxiv.org/pdf/2409.12186 + // + // [FIM_REP]myproject + // [FIM_SEP]filename0 + // extra chunk 0 + // [FIM_SEP]filename1 + // extra chunk 1 + // ... + // [FIM_SEP]filename + // [FIM_PRE]prefix[FIM_SUF]suffix[FIM_MID]prompt + // + llama_tokens extra_tokens; + extra_tokens.reserve(n_ctx); - for (auto it = map_idx_to_media.begin(); it != map_idx_to_media.end(); ++it) { - const auto & chunk = it->second; - res += mtmd_input_chunk_get_n_pos(chunk.get()) - mtmd_input_chunk_get_n_tokens(chunk.get()); - } + auto tokens_prefix = tokenize_mixed(vocab, input_prefix, false, false); + auto tokens_suffix = tokenize_mixed(vocab, input_suffix, false, false); - return res; - } + if (llama_vocab_fim_rep(vocab) != LLAMA_TOKEN_NULL) { + // TODO: make project name an input + static const auto k_fim_repo = common_tokenize(vocab, "myproject\n", false, false); - // for debugging - std::string str() const { - std::ostringstream oss; - oss << "tokens: "; - for (size_t idx = 0; idx < tokens.size(); ++idx) { - llama_token t = tokens[idx]; - oss << "idx:" << idx << " "; - if (t == LLAMA_TOKEN_NULL) { - oss << " "; - } else { - oss << t << " "; - } - } - oss << "\n"; - oss << "image idx: "; - for (const auto & it : map_idx_to_media) { - oss << it.first << ", "; - } - return oss.str(); + extra_tokens.push_back(llama_vocab_fim_rep(vocab)); + extra_tokens.insert(extra_tokens.end(), k_fim_repo.begin(), k_fim_repo.end()); } + for (const auto & chunk : input_extra) { + // { "text": string, "filename": string } + const std::string text = json_value(chunk, "text", std::string()); + const std::string filename = json_value(chunk, "filename", std::string("tmp")); - const mtmd::input_chunk_ptr & find_chunk(size_t idx) const { - auto it = map_idx_to_media.find(idx); - if (it != map_idx_to_media.end()) { - return it->second; - } - throw std::runtime_error("Chunk not found"); - } + if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) { + const auto k_fim_file = common_tokenize(vocab, filename + "\n", false, false); - void push_back(llama_token tok) { - if (tok == LLAMA_TOKEN_NULL) { - throw std::runtime_error("Invalid token"); - } - tokens.emplace_back(tok); - } - - // will create a copy of the chunk if it contains non-text data - void push_back(const mtmd_input_chunk * chunk) { - auto type = mtmd_input_chunk_get_type(chunk); - if (type == MTMD_INPUT_CHUNK_TYPE_IMAGE || type == MTMD_INPUT_CHUNK_TYPE_AUDIO) { - GGML_ASSERT(has_mtmd); - const size_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk); - size_t start_idx = tokens.size(); - for (size_t i = 0; i < n_tokens; ++i) { - tokens.emplace_back(LLAMA_TOKEN_NULL); - } - mtmd::input_chunk_ptr new_chunk(mtmd_input_chunk_copy(chunk)); - map_idx_to_media[start_idx] = std::move(new_chunk); - } else if (type == MTMD_INPUT_CHUNK_TYPE_TEXT) { - size_t n_tokens; - const auto * text_tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens); - for (size_t i = 0; i < n_tokens; ++i) { - push_back(text_tokens[i]); - } + extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab)); + extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); } else { - GGML_ABORT("Invalid chunk type"); - } - } + // chunk separator in binary form to avoid confusing the AI + static const char k_chunk_prefix_str[] = {0x0a, 0x0a, 0x2d, 0x2d, 0x2d, 0x20, 0x73, 0x6e, 0x69, 0x70, 0x70, 0x65, 0x74, 0x20, 0x2d, 0x2d, 0x2d, 0x0a, 0x0a, 0x00}; + static const auto k_chunk_prefix_tokens = common_tokenize(vocab, k_chunk_prefix_str, false, false); - // appends server tokens, updates the media map. copies media chunks. - void push_back(server_tokens & tokens) { - size_t start_idx = size(); - for (size_t i = 0; i < tokens.size(); i++) { - push_back(tokens[i]); - } - if (tokens.has_mtmd) { - // Assert if we are copying MTMD chunks to a server_tokens that does not have mtmd. - // We could also just check, but this will prevent silently dropping MTMD data. - GGML_ASSERT(has_mtmd); - for (auto it = tokens.map_idx_to_media.begin(); it != tokens.map_idx_to_media.end(); ) { - auto * chunk = tokens.map_idx_to_media[it->first].get(); - mtmd::input_chunk_ptr new_chunk(mtmd_input_chunk_copy(chunk)); - map_idx_to_media[start_idx + it->first] = std::move(new_chunk); - } + extra_tokens.insert(extra_tokens.end(), k_chunk_prefix_tokens.begin(), k_chunk_prefix_tokens.end()); } - } - // for compatibility with context shift and prompt truncation - void insert(const llama_tokens & inp_tokens) { - GGML_ASSERT(!has_mtmd); // only allow this if mtmd is disabled - tokens.insert(tokens.end(), inp_tokens.begin(), inp_tokens.end()); - } - - // for compatibility with speculative decoding, ctx shift, slot save/load - const llama_tokens & get_text_tokens() const { - GGML_ASSERT(!has_mtmd); // only allow this if mtmd is disabled - return tokens; - } - - // for compatibility with speculative decoding - void set_token(llama_pos pos, llama_token id) { - GGML_ASSERT(!has_mtmd); // only allow this if mtmd is disabled - tokens[pos] = id; - } - - size_t size() const { - return tokens.size(); - } - - bool empty() const { - return tokens.empty(); - } - - void clear() { - map_idx_to_media.clear(); - tokens.clear(); + const auto chunk_tokens = common_tokenize(vocab, text, false, false); + extra_tokens.insert(extra_tokens.end(), chunk_tokens.begin(), chunk_tokens.end()); } - void keep_first(size_t n) { - GGML_ASSERT(n <= tokens.size()); - if (has_mtmd) { - if (n == tokens.size()) { - return; // nothing to do - } - // we throw an error if we try to remove a token in the middle of an image - // for ex. with input of 5 text tokens and 2 images: - // [0] [1] [2] [3] [4] [img0] [img0] [img0] [img1] [img1] - // n 1 2 3 4 5 6 7 8 9 10 - // allowed to resize ^ ^ - // disallowed to resize ^ ^ ^ - if (n > 0) { - // make sure we never remove tokens in the middle of an image - // note that the case where we keep a full image at the end is allowed: - // tokens[n - 1] == LLAMA_TOKEN_NULL && tokens[n] != LLAMA_TOKEN_NULL - if (tokens[n - 1] == LLAMA_TOKEN_NULL && tokens[n] == LLAMA_TOKEN_NULL) { - find_chunk(n - 1); // will throw an error if the token is not begin-of-chunk - } - } - // remove all image chunks that are not used anymore - for (auto it = map_idx_to_media.begin(); it != map_idx_to_media.end(); ) { - size_t idx = it->first; - if (idx >= n) { - it = map_idx_to_media.erase(it); - } else { - ++it; - } - } - } - tokens.resize(n); - } + if (llama_vocab_fim_sep(vocab) != LLAMA_TOKEN_NULL) { + // TODO: current filename + static const auto k_fim_file = common_tokenize(vocab, "filename\n", false, false); - std::string detokenize(const llama_context * ctx, bool special) const { - llama_tokens text_tokens; - text_tokens.reserve(tokens.size()); - for (const auto & t : tokens) { - if (t != LLAMA_TOKEN_NULL) { - text_tokens.push_back(t); - } - } - return common_detokenize(ctx, text_tokens, special); + extra_tokens.insert(extra_tokens.end(), llama_vocab_fim_sep(vocab)); + extra_tokens.insert(extra_tokens.end(), k_fim_file.begin(), k_fim_file.end()); } - size_t get_common_prefix(const server_tokens & b) const { - const size_t max_idx = std::min(tokens.size(), b.tokens.size()); - - if (!has_mtmd) { - for (size_t i = 0; i < max_idx; ++i) { - if (tokens[i] == b.tokens[i]) { - continue; - } - - return i; - } - - return max_idx; - } - - for (size_t i = 0; i < max_idx; ++i) { - const llama_token ai = tokens[i]; - const llama_token bi = b.tokens[i]; - - if (ai == LLAMA_TOKEN_NULL && bi == LLAMA_TOKEN_NULL) { - const auto & a_chunk = find_chunk(i); - const auto & b_chunk = b.find_chunk(i); - - GGML_ASSERT(a_chunk && b_chunk); - - const std::string id_ai = mtmd_input_chunk_get_id(a_chunk.get()); - const std::string id_bi = mtmd_input_chunk_get_id(b_chunk.get()); - - const size_t n_tok_a = mtmd_input_chunk_get_n_tokens(a_chunk.get()); - const size_t n_tok_b = mtmd_input_chunk_get_n_tokens(b_chunk.get()); - - if (id_ai == id_bi && n_tok_a == n_tok_b) { - GGML_ASSERT(n_tok_a > 0 && "Invalid media chunk"); // should never happen - i += n_tok_a - 1; // will be +1 by the for loop - continue; - } - - return i; - } - - if (ai == bi) { - continue; - } - - return i; - } + // for now pick FIM context to fit in a batch (ratio prefix:suffix = 3:1, TODO: configurable?) + const int n_prefix_take = std::min(tokens_prefix.size(), 3*(n_batch/4)); + const int n_suffix_take = std::min(tokens_suffix.size(), std::max(0, (n_batch/4) - (2 + tokens_prompt.size()))); - return max_idx; // all tokens are equal - } + SRV_DBG("n_prefix_take = %d, n_suffix_take = %d, total = %d\n", n_prefix_take, n_suffix_take, (n_prefix_take + n_suffix_take)); - // make sure all text tokens are within the vocab range - bool validate(const struct llama_context * ctx) const { - const llama_model * model = llama_get_model(ctx); - const llama_vocab * vocab = llama_model_get_vocab(model); - const int32_t n_vocab = llama_vocab_n_tokens(vocab); + // fill the rest of the context with extra chunks + const int n_extra_take = std::min(std::max(0, n_ctx - (n_batch) - 2*n_predict), extra_tokens.size()); - for (size_t i = 0; i < tokens.size(); ++i) { - const auto & t = tokens[i]; - if (t == LLAMA_TOKEN_NULL) { - try { - const auto & chunk = find_chunk(i); - size_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk.get()); - i += n_tokens - 1; // will be +1 by the for loop - } catch (const std::exception & e) { - return false; - } - } else if (t < 0 || t >= n_vocab) { - return false; - } - } - return true; - } + tokens_prefix.erase(tokens_prefix.begin(), tokens_prefix.begin() + tokens_prefix.size() - n_prefix_take); + tokens_suffix.resize(n_suffix_take); - // encode and decode the image chunk - int32_t process_chunk( - llama_context * ctx, - mtmd_context * mctx, - size_t idx, - llama_pos pos, - int32_t seq_id, - size_t & n_tokens_out) const { - const auto & chunk = find_chunk(idx); - const char * name = mtmd_input_chunk_get_type(chunk.get()) == MTMD_INPUT_CHUNK_TYPE_IMAGE - ? "image" : "audio"; - SRV_INF("processing %s...\n", name); - int32_t n_batch = llama_n_batch(ctx); - int64_t t0 = ggml_time_ms(); - llama_pos new_n_past; // unused for now - int32_t result = mtmd_helper_eval_chunk_single(mctx, ctx, - chunk.get(), - pos, - seq_id, - n_batch, - true, // logits last - &new_n_past); - SRV_INF("%s processed in %" PRId64 " ms\n", name, ggml_time_ms() - t0); - if (result != 0) { - LOG_ERR("mtmd_helper_eval failed with status %d", result); - n_tokens_out = 0; - return result; - } - n_tokens_out = mtmd_input_chunk_get_n_tokens(chunk.get()); - return 0; - } -}; + tokens_prefix.insert(tokens_prefix.begin(), llama_vocab_fim_pre(vocab)); + tokens_prefix.insert(tokens_prefix.end(), tokens_prompt.begin(), tokens_prompt.end()); + tokens_suffix.insert(tokens_suffix.begin(), llama_vocab_fim_suf(vocab)); -// Computes FNV-1a hash of the data -static std::string fnv_hash(const uint8_t * data, size_t len) { - const uint64_t fnv_prime = 0x100000001b3ULL; - uint64_t hash = 0xcbf29ce484222325ULL; + auto embd_inp = spm_infill ? tokens_suffix : tokens_prefix; + auto embd_end = spm_infill ? tokens_prefix : tokens_suffix; - for (size_t i = 0; i < len; ++i) { - hash ^= data[i]; - hash *= fnv_prime; + if (llama_vocab_get_add_bos(vocab)) { + embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab)); } - return std::to_string(hash); -} -static server_tokens process_mtmd_prompt(mtmd_context * mctx, std::string prompt, std::vector files) { - mtmd::bitmaps bitmaps; - for (auto & file : files) { - mtmd::bitmap bmp(mtmd_helper_bitmap_init_from_buf(mctx, file.data(), file.size())); - if (!bmp.ptr) { - throw std::runtime_error("Failed to load image or audio file"); - } - // calculate bitmap hash (for KV caching) - std::string hash = fnv_hash(bmp.data(), bmp.n_bytes()); - bmp.set_id(hash.c_str()); - bitmaps.entries.push_back(std::move(bmp)); - } - // process prompt - std::vector inputs; - // multimodal - mtmd_input_text inp_txt = { - prompt.c_str(), - /* add_special */ true, - /* parse_special */ true, - }; - mtmd::input_chunks chunks(mtmd_input_chunks_init()); - auto bitmaps_c_ptr = bitmaps.c_ptr(); - int32_t tokenized = mtmd_tokenize(mctx, - chunks.ptr.get(), - &inp_txt, - bitmaps_c_ptr.data(), - bitmaps_c_ptr.size()); - if (tokenized != 0) { - throw std::runtime_error("Failed to tokenize prompt"); - } - auto result = server_tokens(chunks, true); - return result; -} + SRV_DBG("extra: n_ctx = %d, n_extra_take = %d, n_extra = %d\n", n_ctx, n_extra_take, (int) extra_tokens.size()); -/** - * break the input "prompt" object into multiple prompt if needed, then tokenize them - * use tokenize_input_prompts() if the input could be an array. - * this supports these cases: - * - "prompt": "string" - * - "prompt": [12, 34, 56] - * - "prompt": [12, 34, "string", 56, 78] - * - "prompt": { "prompt_string": "string", "multimodal_data": [ "base64" ] } - */ -static server_tokens tokenize_input_subprompt(const llama_vocab * vocab, mtmd_context * mctx, const json & json_prompt, bool add_special, bool parse_special) { - constexpr char JSON_STRING_PROMPT_KEY[] = "prompt_string"; - constexpr char JSON_MTMD_DATA_KEY[] = "multimodal_data"; - const bool has_mtmd = mctx != nullptr; - if (json_prompt.is_string() || json_is_array_of_mixed_numbers_strings(json_prompt)) { - // string or mixed - llama_tokens tmp = tokenize_mixed(vocab, json_prompt, add_special, parse_special); - return server_tokens(tmp, false); - } else if (json_is_array_of_numbers(json_prompt)) { - // array of tokens - llama_tokens tmp = json_prompt.get(); - return server_tokens(tmp, false); - } else if (json_prompt.contains(JSON_STRING_PROMPT_KEY)) { - // JSON object with prompt key. - if (json_prompt.contains(JSON_MTMD_DATA_KEY)) { - if (!has_mtmd) - throw std::runtime_error("Multimodal data provided, but model does not support multimodal requests."); + // put the extra context before the FIM prefix + embd_inp.insert(embd_inp.begin(), extra_tokens.end() - n_extra_take, extra_tokens.end()); - // JSON object with prompt and multimodal key. - std::vector files; - for (const auto & entry : json_prompt.at(JSON_MTMD_DATA_KEY)) { - files.push_back(base64_decode(entry)); - } - return process_mtmd_prompt(mctx, json_prompt.at(JSON_STRING_PROMPT_KEY), files); - } else { - // Not multimodal, but contains a subobject. - llama_tokens tmp = tokenize_mixed(vocab, json_prompt.at(JSON_STRING_PROMPT_KEY), add_special, parse_special); - return server_tokens(tmp, false); - } - } else { - throw std::runtime_error("\"prompt\" elements must be a string, a list of tokens, a JSON object containing a prompt string, or a list of mixed strings & tokens."); - } -} + embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); + embd_inp.push_back(llama_vocab_fim_mid(vocab)); -/** - * break the input "prompt" object into multiple prompt if needed, then tokenize them - * this supports these cases: - * - "prompt": "string" - * - "prompt": [12, 34, 56] - * - "prompt": [12, 34, "string", 56, 78] - * - "prompt": { "prompt_string": "string", "multimodal_data": [ "base64" ] } - * and multiple prompts (multi-tasks): - * - "prompt": ["string1", "string2"] - * - "prompt": ["string1", [12, 34, 56]] - * - "prompt": [[12, 34, 56], [78, 90, 12]] - * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56], { "prompt_string": "string", "multimodal_data": [ "base64" ]}] - */ -static std::vector tokenize_input_prompts(const llama_vocab * vocab, mtmd_context * mctx, const json & json_prompt, bool add_special, bool parse_special) { - std::vector result; - if (json_prompt.is_array() && !json_is_array_and_contains_numbers(json_prompt)) { - result.reserve(json_prompt.size()); - for (const auto & p : json_prompt) { - result.push_back(tokenize_input_subprompt(vocab, mctx, p,add_special, parse_special)); - } - } else { - result.push_back(tokenize_input_subprompt(vocab, mctx, json_prompt, add_special, parse_special)); - } - if (result.empty()) { - throw std::runtime_error("\"prompt\" must not be empty"); - } - return result; + return embd_inp; } -// format rerank task: [BOS]query[EOS][SEP]doc[EOS]. -static server_tokens format_rerank(const struct llama_model * model, const struct llama_vocab * vocab, mtmd_context * mctx, const std::string & query, const std::string & doc) { +server_tokens format_prompt_rerank( + const struct llama_model * model, + const struct llama_vocab * vocab, + mtmd_context * mctx, + const std::string & query, + const std::string & doc) { server_tokens result = {}; const char * rerank_prompt = llama_model_chat_template(model, "rerank"); diff --git a/tools/server/server-common.h b/tools/server/server-common.h new file mode 100644 index 00000000000..868c5061031 --- /dev/null +++ b/tools/server/server-common.h @@ -0,0 +1,349 @@ +#pragma once + +#include "common.h" +#include "log.h" +#include "llama.h" +#include "chat.h" +#include "mtmd.h" + +#define JSON_ASSERT GGML_ASSERT +#include + +#include +#include +#include + +#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo" + +const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "-" + LLAMA_COMMIT); + +using json = nlohmann::ordered_json; + +#define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) +#define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) +#define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) +#define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__) + +#define SRV_INF(fmt, ...) LOG_INF("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_WRN(fmt, ...) LOG_WRN("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_ERR(fmt, ...) LOG_ERR("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define SRV_DBG(fmt, ...) LOG_DBG("srv %12.*s: " fmt, 12, __func__, __VA_ARGS__) + +using raw_buffer = std::vector; + +template +static T json_value(const json & body, const std::string & key, const T & default_value) { + // Fallback null to default value + if (body.contains(key) && !body.at(key).is_null()) { + try { + return body.at(key); + } catch (NLOHMANN_JSON_NAMESPACE::detail::type_error const & err) { + LOG_WRN("Wrong type supplied for parameter '%s'. Expected '%s', using default value: %s\n", key.c_str(), json(default_value).type_name(), err.what()); + return default_value; + } + } else { + return default_value; + } +} + +// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11 +enum error_type { + ERROR_TYPE_INVALID_REQUEST, + ERROR_TYPE_AUTHENTICATION, + ERROR_TYPE_SERVER, + ERROR_TYPE_NOT_FOUND, + ERROR_TYPE_PERMISSION, + ERROR_TYPE_UNAVAILABLE, // custom error + ERROR_TYPE_NOT_SUPPORTED, // custom error + ERROR_TYPE_EXCEED_CONTEXT_SIZE, // custom error +}; + +// thin wrapper around common_grammar_trigger with (de)serialization functions +struct server_grammar_trigger { + common_grammar_trigger value; + + server_grammar_trigger() = default; + server_grammar_trigger(const common_grammar_trigger & value) : value(value) {} + server_grammar_trigger(const json & in) { + value.type = (common_grammar_trigger_type) in.at("type").get(); + value.value = in.at("value").get(); + if (value.type == COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN) { + value.token = (llama_token) in.at("token").get(); + } + } + + json to_json() const { + json out { + {"type", (int) value.type}, + {"value", value.value}, + }; + if (value.type == COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN) { + out["token"] = (int) value.token; + } + return out; + } +}; + +json format_error_response(const std::string & message, const enum error_type type); + +// +// random string / id +// + +std::string random_string(); +std::string gen_chatcmplid(); +std::string gen_tool_call_id(); + +// +// lora utils +// + +// check whether the given lora set has only aloras activated (empty => false) +bool lora_all_alora(const std::vector & loras); + +// if the two sets of loras are different, they require a cache clear unless the +// change is only from aloras to aloras. +bool lora_should_clear_cache( + const std::vector & current, + const std::vector & next); + +std::vector parse_lora_request( + const std::vector & lora_base, + const json & data); + +bool are_lora_equal( + const std::vector & l1, + const std::vector & l2); + +// get the ids of all enabled loras +std::vector lora_get_enabled_ids(const std::vector & loras); + +// +// server_tokens +// + +/** + * server_tokens is a helper to manage the input tokens and image for the server. + * it is made this way to simplify the logic of KV cache management. + */ +struct server_tokens { + bool has_mtmd = false; + +private: // disallow accessing these members directly, risking out-of-sync + + // map a **start** index in tokens to the image chunk + // note: the order need to be in-sync with tokens + std::map map_idx_to_media; + + // list of tokens + // if the token is LLAMA_TOKEN_NULL, it indicates that this position is occupied by media chunk + // otherwise, it is a normal text token + // note: a non-text chunk can occupy multiple tokens (aka memory cells) in the token list + // note(2): for M-RoPE, an image can occupy different number of pos; do not assume 1-to-1 mapping tokens <-> pos + llama_tokens tokens; + + // for ex. with input of 5 text tokens and 2 images (each image occupies 3 tokens and 2 pos): + // [0] [1] [2] [3] [4] [img0] [img0] [img0] [img1] [img1] [img1] + // idx 0 1 2 3 4 5 6 7 8 9 10 + // pos 0 1 2 3 4 5 5 5 7 7 7 + // map_idx_to_media will contain: {5, img0}, {8, img1} + +public: + server_tokens() = default; + ~server_tokens() = default; + + // Prevent copying + // TODO: server_tokens should be copyable - remove this: + server_tokens(const server_tokens&) = delete; + server_tokens& operator=(const server_tokens&) = delete; + + // Allow moving (usually implicitly generated if members are movable) + server_tokens(server_tokens&&) = default; + server_tokens& operator=(server_tokens&&) = default; + + // Allow accessing elements using [] operator + llama_token operator[](size_t index) { return tokens[index]; } + const llama_token& operator[](size_t index) const { return tokens[index]; } + + server_tokens(mtmd::input_chunks & mtmd_chunks, bool has_mtmd); + server_tokens(const llama_tokens & tokens, bool has_mtmd); + + // for debugging + std::string str() const; + + llama_pos pos_next() const; + const mtmd::input_chunk_ptr & find_chunk(size_t idx) const; + + void push_back(llama_token tok); + + // will create a copy of the chunk if it contains non-text data + void push_back(const mtmd_input_chunk * chunk); + + // appends server tokens, updates the media map. copies media chunks. + void push_back(server_tokens & tokens); + + // for compatibility with context shift and prompt truncation + void insert(const llama_tokens & inp_tokens); + + // for compatibility with speculative decoding, ctx shift, slot save/load + const llama_tokens & get_text_tokens() const; + + // for compatibility with speculative decoding + void set_token(llama_pos pos, llama_token id); + + size_t size() const { return tokens.size(); } + + bool empty() const { return tokens.empty(); } + + void clear() { + map_idx_to_media.clear(); + tokens.clear(); + } + + void keep_first(size_t n); + + std::string detokenize(const llama_context * ctx, bool special) const; + + size_t get_common_prefix(const server_tokens & b) const; + + // make sure all text tokens are within the vocab range + bool validate(const struct llama_context * ctx) const; + + // encode and decode the image chunk + int32_t process_chunk( + llama_context * ctx, + mtmd_context * mctx, + size_t idx, + llama_pos pos, + int32_t seq_id, + size_t & n_tokens_out) const; +}; + + +// +// tokenizer and input processing utils +// + +bool json_is_array_of_numbers(const json & data); + +// is array having BOTH numbers & strings? +bool json_is_array_of_mixed_numbers_strings(const json & data); + +// does array have any individual integers/tokens? +bool json_is_array_and_contains_numbers(const json & data); + +// get value by path(key1 / key2) +json json_get_nested_values(const std::vector & paths, const json & js); + +/** + * this handles 2 cases: + * - only string, example: "string" + * - mixed string and tokens, example: [12, 34, "string", 56, 78] + */ +llama_tokens tokenize_mixed(const llama_vocab * vocab, const json & json_prompt, bool add_special, bool parse_special); + +// return the last index of character that can form a valid string +// if the last character is potentially cut in half, return the index before the cut +// if validate_utf8(text) == text.size(), then the whole text is valid utf8 +size_t validate_utf8(const std::string& text); + +// process mtmd prompt, return the server_tokens containing both text tokens and media chunks +server_tokens process_mtmd_prompt(mtmd_context * mctx, std::string prompt, std::vector files); + +/** + * break the input "prompt" object into multiple prompt if needed, then tokenize them + * this supports these cases: + * - "prompt": "string" + * - "prompt": [12, 34, 56] + * - "prompt": [12, 34, "string", 56, 78] + * - "prompt": { "prompt_string": "string", "multimodal_data": [ "base64" ] } + * and multiple prompts (multi-tasks): + * - "prompt": ["string1", "string2"] + * - "prompt": ["string1", [12, 34, 56]] + * - "prompt": [[12, 34, 56], [78, 90, 12]] + * - "prompt": [[12, 34, "string", 56, 78], [12, 34, 56], { "prompt_string": "string", "multimodal_data": [ "base64" ]}] + */ +std::vector tokenize_input_prompts( + const llama_vocab * vocab, + mtmd_context * mctx, + const json & json_prompt, + bool add_special, + bool parse_special); + +// +// OAI utils +// + +// used by /completions endpoint +json oaicompat_completion_params_parse(const json & body); + +struct oaicompat_parser_options { + bool use_jinja; + bool prefill_assistant; + common_reasoning_format reasoning_format; + std::map chat_template_kwargs; + common_chat_templates * tmpls; + bool allow_image; + bool allow_audio; + bool enable_thinking = true; +}; + +// used by /chat/completions endpoint +json oaicompat_chat_params_parse( + json & body, /* openai api json semantics */ + const oaicompat_parser_options & opt, + std::vector & out_files); + +// TODO: move it to server-task.cpp +json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64 = false); + +// TODO: move it to server-task.cpp +json format_response_rerank( + const json & request, + const json & ranks, + bool is_tei_format, + std::vector & texts, + int top_n); + +// +// other utils +// + +std::vector get_token_probabilities(llama_context * ctx, int idx); + +std::string safe_json_to_str(const json & data); + +std::string tokens_to_str(llama_context * ctx, const llama_tokens & tokens); + +// format incomplete utf-8 multibyte character for output +std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token); + +// format server-sent event (SSE), return the formatted string to send +// note: if data is a json array, it will be sent as multiple events, one per item +std::string format_sse(const json & data); + +bool is_valid_utf8(const std::string & str); + +// +// formatting output responses +// TODO: move these to server-task.cpp +// + +llama_tokens format_prompt_infill( + const llama_vocab * vocab, + const json & input_prefix, + const json & input_suffix, + const json & input_extra, + const int n_batch, + const int n_predict, + const int n_ctx, + const bool spm_infill, + const llama_tokens & tokens_prompt); + +// format rerank task: [BOS]query[EOS][SEP]doc[EOS]. +server_tokens format_prompt_rerank( + const struct llama_model * model, + const struct llama_vocab * vocab, + mtmd_context * mctx, + const std::string & query, + const std::string & doc); diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp index bebe0b49c4f..a82aa86b19e 100644 --- a/tools/server/server-http.cpp +++ b/tools/server/server-http.cpp @@ -1,6 +1,6 @@ -#include "utils.hpp" #include "common.h" #include "server-http.h" +#include "server-common.h" #include diff --git a/tools/server/server-queue.cpp b/tools/server/server-queue.cpp new file mode 100644 index 00000000000..5a74fd76ac3 --- /dev/null +++ b/tools/server/server-queue.cpp @@ -0,0 +1,268 @@ +#include "server-task.h" +#include "server-queue.h" + +#include "log.h" + +#include + +#define QUE_INF(fmt, ...) LOG_INF("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_WRN(fmt, ...) LOG_WRN("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_ERR(fmt, ...) LOG_ERR("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define QUE_DBG(fmt, ...) LOG_DBG("que %12.*s: " fmt, 12, __func__, __VA_ARGS__) + +#define RES_INF(fmt, ...) LOG_INF("res %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define RES_WRN(fmt, ...) LOG_WRN("res %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define RES_ERR(fmt, ...) LOG_ERR("res %12.*s: " fmt, 12, __func__, __VA_ARGS__) +#define RES_DBG(fmt, ...) LOG_DBG("res %12.*s: " fmt, 12, __func__, __VA_ARGS__) + +// +// server_queue +// + +int server_queue::post(server_task && task, bool front) { + std::unique_lock lock(mutex_tasks); + GGML_ASSERT(task.id != -1); + // if this is cancel task make sure to clean up pending tasks + if (task.type == SERVER_TASK_TYPE_CANCEL) { + cleanup_pending_task(task.id_target); + } + const int task_id = task.id; + QUE_DBG("new task, id = %d, front = %d\n", task_id, front); + if (front) { + queue_tasks.push_front(std::move(task)); + } else { + queue_tasks.push_back(std::move(task)); + } + condition_tasks.notify_one(); + return task_id; +} + +int server_queue::post(std::vector && tasks, bool front) { + std::unique_lock lock(mutex_tasks); + for (auto & task : tasks) { + if (task.id == -1) { + task.id = id++; + } + // if this is cancel task make sure to clean up pending tasks + if (task.type == SERVER_TASK_TYPE_CANCEL) { + cleanup_pending_task(task.id_target); + } + QUE_DBG("new task, id = %d/%d, front = %d\n", task.id, (int) tasks.size(), front); + if (front) { + queue_tasks.push_front(std::move(task)); + } else { + queue_tasks.push_back(std::move(task)); + } + } + condition_tasks.notify_one(); + return 0; +} + +void server_queue::defer(server_task && task) { + std::unique_lock lock(mutex_tasks); + QUE_DBG("defer task, id = %d\n", task.id); + queue_tasks_deferred.push_back(std::move(task)); + condition_tasks.notify_one(); +} + +int server_queue::get_new_id() { + std::unique_lock lock(mutex_tasks); + int new_id = id++; + return new_id; +} + +void server_queue::on_new_task(std::function callback) { + callback_new_task = std::move(callback); +} + +void server_queue::on_update_slots(std::function callback) { + callback_update_slots = std::move(callback); +} + +void server_queue::pop_deferred_task() { + std::unique_lock lock(mutex_tasks); + if (!queue_tasks_deferred.empty()) { + queue_tasks.emplace_front(std::move(queue_tasks_deferred.front())); + queue_tasks_deferred.pop_front(); + } + condition_tasks.notify_one(); +} + +void server_queue::terminate() { + std::unique_lock lock(mutex_tasks); + running = false; + condition_tasks.notify_all(); +} + +void server_queue::start_loop() { + running = true; + + while (true) { + QUE_DBG("%s", "processing new tasks\n"); + + while (true) { + std::unique_lock lock(mutex_tasks); + if (!running) { + QUE_DBG("%s", "terminate\n"); + return; + } + if (queue_tasks.empty()) { + lock.unlock(); + break; + } + server_task task = std::move(queue_tasks.front()); + queue_tasks.pop_front(); + lock.unlock(); + + QUE_DBG("processing task, id = %d\n", task.id); + callback_new_task(std::move(task)); + } + + // all tasks in the current loop is processed, slots data is now ready + QUE_DBG("%s", "update slots\n"); + + callback_update_slots(); + + QUE_DBG("%s", "waiting for new tasks\n"); + { + std::unique_lock lock(mutex_tasks); + if (!running) { + QUE_DBG("%s", "terminate\n"); + return; + } + if (queue_tasks.empty()) { + condition_tasks.wait(lock, [&]{ + return (!queue_tasks.empty() || !running); + }); + } + } + } +} + +void server_queue::cleanup_pending_task(int id_target) { + // no need lock because this is called exclusively by post() + auto rm_func = [id_target](const server_task & task) { + return task.id == id_target; + }; + queue_tasks.erase( + std::remove_if(queue_tasks.begin(), queue_tasks.end(), rm_func), + queue_tasks.end()); + queue_tasks_deferred.erase( + std::remove_if(queue_tasks_deferred.begin(), queue_tasks_deferred.end(), rm_func), + queue_tasks_deferred.end()); +} + +// +// server_response +// + +void server_response::add_waiting_task_id(int id_task) { + RES_DBG("add task %d to waiting list. current waiting = %d (before add)\n", id_task, (int) waiting_task_ids.size()); + + std::unique_lock lock(mutex_results); + waiting_task_ids.insert(id_task); +} + +void server_response::add_waiting_tasks(const std::vector & tasks) { + std::unique_lock lock(mutex_results); + + for (const auto & task : tasks) { + RES_DBG("add task %d to waiting list. current waiting = %d (before add)\n", task.id, (int) waiting_task_ids.size()); + waiting_task_ids.insert(task.id); + } +} + +void server_response::remove_waiting_task_id(int id_task) { + RES_DBG("remove task %d from waiting list. current waiting = %d (before remove)\n", id_task, (int) waiting_task_ids.size()); + + std::unique_lock lock(mutex_results); + waiting_task_ids.erase(id_task); + // make sure to clean up all pending results + queue_results.erase( + std::remove_if(queue_results.begin(), queue_results.end(), [id_task](const server_task_result_ptr & res) { + return res->id == id_task; + }), + queue_results.end()); +} + +void server_response::remove_waiting_task_ids(const std::unordered_set & id_tasks) { + std::unique_lock lock(mutex_results); + + for (const auto & id_task : id_tasks) { + RES_DBG("remove task %d from waiting list. current waiting = %d (before remove)\n", id_task, (int) waiting_task_ids.size()); + waiting_task_ids.erase(id_task); + } +} + +server_task_result_ptr server_response::recv(const std::unordered_set & id_tasks) { + while (true) { + std::unique_lock lock(mutex_results); + condition_results.wait(lock, [&]{ + if (!running) { + RES_DBG("%s : queue result stop\n", __func__); + std::terminate(); // we cannot return here since the caller is HTTP code + } + return !queue_results.empty(); + }); + + for (size_t i = 0; i < queue_results.size(); i++) { + if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) { + server_task_result_ptr res = std::move(queue_results[i]); + queue_results.erase(queue_results.begin() + i); + return res; + } + } + } + + // should never reach here +} + +server_task_result_ptr server_response::recv_with_timeout(const std::unordered_set & id_tasks, int timeout) { + while (true) { + std::unique_lock lock(mutex_results); + + for (int i = 0; i < (int) queue_results.size(); i++) { + if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) { + server_task_result_ptr res = std::move(queue_results[i]); + queue_results.erase(queue_results.begin() + i); + return res; + } + } + + std::cv_status cr_res = condition_results.wait_for(lock, std::chrono::seconds(timeout)); + if (!running) { + RES_DBG("%s : queue result stop\n", __func__); + std::terminate(); // we cannot return here since the caller is HTTP code + } + if (cr_res == std::cv_status::timeout) { + return nullptr; + } + } + + // should never reach here +} + +server_task_result_ptr server_response::recv(int id_task) { + std::unordered_set id_tasks = {id_task}; + return recv(id_tasks); +} + +void server_response::send(server_task_result_ptr && result) { + RES_DBG("sending result for task id = %d\n", result->id); + + std::unique_lock lock(mutex_results); + for (const auto & id_task : waiting_task_ids) { + if (result->id == id_task) { + RES_DBG("task id = %d pushed to result queue\n", result->id); + + queue_results.emplace_back(std::move(result)); + condition_results.notify_all(); + return; + } + } +} + +void server_response::terminate() { + running = false; + condition_results.notify_all(); +} diff --git a/tools/server/server-queue.h b/tools/server/server-queue.h new file mode 100644 index 00000000000..47ef58425ea --- /dev/null +++ b/tools/server/server-queue.h @@ -0,0 +1,110 @@ +#pragma once + +#include "server-task.h" + +#include +#include +#include +#include + +struct server_queue { +private: + int id = 0; + bool running; + + // queues + std::deque queue_tasks; + std::deque queue_tasks_deferred; + + std::mutex mutex_tasks; + std::condition_variable condition_tasks; + + // callback functions + std::function callback_new_task; + std::function callback_update_slots; + +public: + // Add a new task to the end of the queue + int post(server_task && task, bool front = false); + + // multi-task version of post() + int post(std::vector && tasks, bool front = false); + + // Add a new task, but defer until one slot is available + void defer(server_task && task); + + // Get the next id for creating a new task + int get_new_id(); + + // Register function to process a new task + void on_new_task(std::function callback); + + // Register the function to be called when all slots data is ready to be processed + void on_update_slots(std::function callback); + + // Call when the state of one slot is changed, it will move one task from deferred to main queue + void pop_deferred_task(); + + // end the start_loop routine + void terminate(); + + /** + * Main loop consists of these steps: + * - Wait until a new task arrives + * - Process the task (i.e. maybe copy data into slot) + * - Check if multitask is finished + * - Update all slots + */ + void start_loop(); + + // for metrics + size_t queue_tasks_deferred_size() { + std::unique_lock lock(mutex_tasks); + return queue_tasks_deferred.size(); + } + +private: + void cleanup_pending_task(int id_target); +}; + +struct server_response { +private: + bool running = true; + + // for keeping track of all tasks waiting for the result + std::unordered_set waiting_task_ids; + + // the main result queue (using ptr for polymorphism) + std::vector queue_results; + + std::mutex mutex_results; + std::condition_variable condition_results; + +public: + // add the id_task to the list of tasks waiting for response + void add_waiting_task_id(int id_task); + + void add_waiting_tasks(const std::vector & tasks); + + // when the request is finished, we can remove task associated with it + void remove_waiting_task_id(int id_task); + + // remove multiple tasks from waiting list + void remove_waiting_task_ids(const std::unordered_set & id_tasks); + + // This function blocks the thread until there is a response for one of the id_tasks + server_task_result_ptr recv(const std::unordered_set & id_tasks); + + // same as recv(), but have timeout in seconds + // if timeout is reached, nullptr is returned + server_task_result_ptr recv_with_timeout(const std::unordered_set & id_tasks, int timeout); + + // single-task version of recv() + server_task_result_ptr recv(int id_task); + + // Send a new result to a waiting id_task + void send(server_task_result_ptr && result); + + // terminate the waiting loop + void terminate(); +}; diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp new file mode 100644 index 00000000000..bc4436ba65b --- /dev/null +++ b/tools/server/server-task.cpp @@ -0,0 +1,1192 @@ +#include "server-common.h" +#include "server-task.h" + +#include "common.h" +#include "llama.h" +#include "chat.h" +#include "sampling.h" +#include "json-schema-to-grammar.h" + +using json = nlohmann::ordered_json; + +// +// task_params +// + +json task_params::format_logit_bias(const std::vector & logit_bias) const { + json data = json::array(); + for (const auto & lb : logit_bias) { + data.push_back(json{ + {"bias", lb.bias}, + {"token", lb.token}, + }); + } + return data; +} + +json task_params::to_json(bool only_metrics) const { + std::vector samplers; + samplers.reserve(sampling.samplers.size()); + for (const auto & sampler : sampling.samplers) { + samplers.emplace_back(common_sampler_type_to_str(sampler)); + } + + json lora = json::array(); + for (size_t i = 0; i < this->lora.size(); ++i) { + lora.push_back({{"id", i}, {"scale", this->lora[i].scale}}); + } + + if (only_metrics) { + return json { + {"seed", sampling.seed}, + {"temperature", sampling.temp}, + {"dynatemp_range", sampling.dynatemp_range}, + {"dynatemp_exponent", sampling.dynatemp_exponent}, + {"top_k", sampling.top_k}, + {"top_p", sampling.top_p}, + {"min_p", sampling.min_p}, + {"top_n_sigma", sampling.top_n_sigma}, + {"xtc_probability", sampling.xtc_probability}, + {"xtc_threshold", sampling.xtc_threshold}, + {"typical_p", sampling.typ_p}, + {"repeat_last_n", sampling.penalty_last_n}, + {"repeat_penalty", sampling.penalty_repeat}, + {"presence_penalty", sampling.penalty_present}, + {"frequency_penalty", sampling.penalty_freq}, + {"dry_multiplier", sampling.dry_multiplier}, + {"dry_base", sampling.dry_base}, + {"dry_allowed_length", sampling.dry_allowed_length}, + {"dry_penalty_last_n", sampling.dry_penalty_last_n}, + {"mirostat", sampling.mirostat}, + {"mirostat_tau", sampling.mirostat_tau}, + {"mirostat_eta", sampling.mirostat_eta}, + {"max_tokens", n_predict}, + {"n_predict", n_predict}, // TODO: deduplicate? + {"n_keep", n_keep}, + {"n_discard", n_discard}, + {"ignore_eos", sampling.ignore_eos}, + {"stream", stream}, + {"n_probs", sampling.n_probs}, + {"min_keep", sampling.min_keep}, + {"chat_format", common_chat_format_name(oaicompat_chat_syntax.format)}, + {"reasoning_format", common_reasoning_format_name(oaicompat_chat_syntax.reasoning_format)}, + {"reasoning_in_content", oaicompat_chat_syntax.reasoning_in_content}, + {"thinking_forced_open", oaicompat_chat_syntax.thinking_forced_open}, + {"samplers", samplers}, + {"speculative.n_max", speculative.n_max}, + {"speculative.n_min", speculative.n_min}, + {"speculative.p_min", speculative.p_min}, + {"timings_per_token", timings_per_token}, + {"post_sampling_probs", post_sampling_probs}, + {"lora", lora}, + }; + } + + auto grammar_triggers = json::array(); + for (const auto & trigger : sampling.grammar_triggers) { + server_grammar_trigger ct(trigger); + grammar_triggers.push_back(ct.to_json()); + } + + return json { + {"seed", sampling.seed}, + {"temperature", sampling.temp}, + {"dynatemp_range", sampling.dynatemp_range}, + {"dynatemp_exponent", sampling.dynatemp_exponent}, + {"top_k", sampling.top_k}, + {"top_p", sampling.top_p}, + {"min_p", sampling.min_p}, + {"top_n_sigma", sampling.top_n_sigma}, + {"xtc_probability", sampling.xtc_probability}, + {"xtc_threshold", sampling.xtc_threshold}, + {"typical_p", sampling.typ_p}, + {"repeat_last_n", sampling.penalty_last_n}, + {"repeat_penalty", sampling.penalty_repeat}, + {"presence_penalty", sampling.penalty_present}, + {"frequency_penalty", sampling.penalty_freq}, + {"dry_multiplier", sampling.dry_multiplier}, + {"dry_base", sampling.dry_base}, + {"dry_allowed_length", sampling.dry_allowed_length}, + {"dry_penalty_last_n", sampling.dry_penalty_last_n}, + {"dry_sequence_breakers", sampling.dry_sequence_breakers}, + {"mirostat", sampling.mirostat}, + {"mirostat_tau", sampling.mirostat_tau}, + {"mirostat_eta", sampling.mirostat_eta}, + {"stop", antiprompt}, + {"max_tokens", n_predict}, + {"n_predict", n_predict}, // TODO: deduplicate? + {"n_keep", n_keep}, + {"n_discard", n_discard}, + {"ignore_eos", sampling.ignore_eos}, + {"stream", stream}, + {"logit_bias", format_logit_bias(sampling.logit_bias)}, + {"n_probs", sampling.n_probs}, + {"min_keep", sampling.min_keep}, + {"grammar", sampling.grammar}, + {"grammar_lazy", sampling.grammar_lazy}, + {"grammar_triggers", grammar_triggers}, + {"preserved_tokens", sampling.preserved_tokens}, + {"chat_format", common_chat_format_name(oaicompat_chat_syntax.format)}, + {"reasoning_format", common_reasoning_format_name(oaicompat_chat_syntax.reasoning_format)}, + {"reasoning_in_content", oaicompat_chat_syntax.reasoning_in_content}, + {"thinking_forced_open", oaicompat_chat_syntax.thinking_forced_open}, + {"samplers", samplers}, + {"speculative.n_max", speculative.n_max}, + {"speculative.n_min", speculative.n_min}, + {"speculative.p_min", speculative.p_min}, + {"timings_per_token", timings_per_token}, + {"post_sampling_probs", post_sampling_probs}, + {"lora", lora}, + }; +} + +// +// server_task +// + +task_params server_task::params_from_json_cmpl( + const llama_context * ctx, + const common_params & params_base, + const json & data) { + const llama_model * model = llama_get_model(ctx); + const llama_vocab * vocab = llama_model_get_vocab(model); + + task_params params; + + // Sampling parameter defaults are loaded from the global server context (but individual requests can still them) + task_params defaults; + defaults.sampling = params_base.sampling; + defaults.speculative = params_base.speculative; + defaults.n_keep = params_base.n_keep; + defaults.n_predict = params_base.n_predict; + defaults.antiprompt = params_base.antiprompt; + + // enabling this will output extra debug information in the HTTP responses from the server + params.verbose = params_base.verbosity > 9; + params.timings_per_token = json_value(data, "timings_per_token", false); + + params.stream = json_value(data, "stream", false); + auto stream_opt = json_value(data, "stream_options", json::object()); + params.include_usage = json_value(stream_opt, "include_usage", false); + params.cache_prompt = json_value(data, "cache_prompt", true); + params.return_tokens = json_value(data, "return_tokens", false); + params.return_progress = json_value(data, "return_progress", false); + params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", defaults.n_predict)); + params.n_indent = json_value(data, "n_indent", defaults.n_indent); + params.n_keep = json_value(data, "n_keep", defaults.n_keep); + params.n_discard = json_value(data, "n_discard", defaults.n_discard); + //params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", defaults.t_max_prompt_ms); // TODO: implement + params.t_max_predict_ms = json_value(data, "t_max_predict_ms", defaults.t_max_predict_ms); + params.response_fields = json_value(data, "response_fields", std::vector()); + + params.sampling.top_k = json_value(data, "top_k", defaults.sampling.top_k); + params.sampling.top_p = json_value(data, "top_p", defaults.sampling.top_p); + params.sampling.min_p = json_value(data, "min_p", defaults.sampling.min_p); + params.sampling.top_n_sigma = json_value(data, "top_n_sigma", defaults.sampling.top_n_sigma); + params.sampling.xtc_probability = json_value(data, "xtc_probability", defaults.sampling.xtc_probability); + params.sampling.xtc_threshold = json_value(data, "xtc_threshold", defaults.sampling.xtc_threshold); + params.sampling.typ_p = json_value(data, "typical_p", defaults.sampling.typ_p); + params.sampling.temp = json_value(data, "temperature", defaults.sampling.temp); + params.sampling.dynatemp_range = json_value(data, "dynatemp_range", defaults.sampling.dynatemp_range); + params.sampling.dynatemp_exponent = json_value(data, "dynatemp_exponent", defaults.sampling.dynatemp_exponent); + params.sampling.penalty_last_n = json_value(data, "repeat_last_n", defaults.sampling.penalty_last_n); + params.sampling.penalty_repeat = json_value(data, "repeat_penalty", defaults.sampling.penalty_repeat); + params.sampling.penalty_freq = json_value(data, "frequency_penalty", defaults.sampling.penalty_freq); + params.sampling.penalty_present = json_value(data, "presence_penalty", defaults.sampling.penalty_present); + params.sampling.dry_multiplier = json_value(data, "dry_multiplier", defaults.sampling.dry_multiplier); + params.sampling.dry_base = json_value(data, "dry_base", defaults.sampling.dry_base); + params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length", defaults.sampling.dry_allowed_length); + params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", defaults.sampling.dry_penalty_last_n); + params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat); + params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau); + params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta); + params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); + params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); + params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep); + params.post_sampling_probs = json_value(data, "post_sampling_probs", defaults.post_sampling_probs); + + params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min); + params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max); + params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min); + + params.speculative.n_min = std::min(params.speculative.n_max, params.speculative.n_min); + params.speculative.n_min = std::max(params.speculative.n_min, 0); + params.speculative.n_max = std::max(params.speculative.n_max, 0); + + // Use OpenAI API logprobs only if n_probs wasn't provided + if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){ + params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs); + } + + if (data.contains("lora")) { + if (data.at("lora").is_array()) { + params.lora = parse_lora_request(params_base.lora_adapters, data.at("lora")); + } else { + throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields"); + } + } else { + params.lora = params_base.lora_adapters; + } + + // TODO: add more sanity checks for the input parameters + + if (params.sampling.penalty_last_n < -1) { + throw std::runtime_error("Error: repeat_last_n must be >= -1"); + } + + if (params.sampling.dry_penalty_last_n < -1) { + throw std::runtime_error("Error: dry_penalty_last_n must be >= -1"); + } + + if (params.sampling.penalty_last_n == -1) { + // note: should be the slot's context and not the full context, but it's ok + params.sampling.penalty_last_n = llama_n_ctx(ctx); + } + + if (params.sampling.dry_penalty_last_n == -1) { + params.sampling.dry_penalty_last_n = llama_n_ctx(ctx); + } + + if (params.sampling.dry_base < 1.0f) { + params.sampling.dry_base = defaults.sampling.dry_base; + } + + // sequence breakers for DRY + { + // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format + // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39 + + if (data.contains("dry_sequence_breakers")) { + params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector()); + if (params.sampling.dry_sequence_breakers.empty()) { + throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings"); + } + } + } + + // process "json_schema" and "grammar" + if (data.contains("json_schema") && !data.contains("grammar")) { + try { + auto schema = json_value(data, "json_schema", json::object()); + SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str()); + params.sampling.grammar = json_schema_to_grammar(schema); + SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str()); + } catch (const std::exception & e) { + throw std::runtime_error(std::string("\"json_schema\": ") + e.what()); + } + } else { + params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar); + SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str()); + params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy); + SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false"); + } + + { + auto it = data.find("chat_format"); + if (it != data.end()) { + params.oaicompat_chat_syntax.format = static_cast(it->get()); + SRV_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_syntax.format)); + } else { + params.oaicompat_chat_syntax.format = defaults.oaicompat_chat_syntax.format; + } + common_reasoning_format reasoning_format = params_base.reasoning_format; + if (data.contains("reasoning_format")) { + reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get()); + } + params.oaicompat_chat_syntax.reasoning_format = reasoning_format; + params.oaicompat_chat_syntax.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY); + params.oaicompat_chat_syntax.thinking_forced_open = json_value(data, "thinking_forced_open", false); + params.oaicompat_chat_syntax.parse_tool_calls = json_value(data, "parse_tool_calls", false); + } + + { + const auto preserved_tokens = data.find("preserved_tokens"); + if (preserved_tokens != data.end()) { + for (const auto & t : *preserved_tokens) { + auto ids = common_tokenize(vocab, t.get(), /* add_special= */ false, /* parse_special= */ true); + if (ids.size() == 1) { + SRV_DBG("Preserved token: %d\n", ids[0]); + params.sampling.preserved_tokens.insert(ids[0]); + } else { + // This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens. + SRV_DBG("Not preserved because more than 1 token: %s\n", t.get().c_str()); + } + } + } + const auto grammar_triggers = data.find("grammar_triggers"); + if (grammar_triggers != data.end()) { + for (const auto & t : *grammar_triggers) { + server_grammar_trigger ct(t); + if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) { + const auto & word = ct.value.value; + auto ids = common_tokenize(vocab, word, /* add_special= */ false, /* parse_special= */ true); + if (ids.size() == 1) { + auto token = ids[0]; + if (std::find(params.sampling.preserved_tokens.begin(), params.sampling.preserved_tokens.end(), (llama_token) token) == params.sampling.preserved_tokens.end()) { + throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word); + } + SRV_DBG("Grammar trigger token: %d (`%s`)\n", token, word.c_str()); + common_grammar_trigger trigger; + trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN; + trigger.value = word; + trigger.token = token; + params.sampling.grammar_triggers.push_back(std::move(trigger)); + } else { + SRV_DBG("Grammar trigger word: `%s`\n", word.c_str()); + params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word}); + } + } else { + if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN) { + SRV_DBG("Grammar trigger pattern: `%s`\n", ct.value.value.c_str()); + } else if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL) { + SRV_DBG("Grammar trigger pattern full: `%s`\n", ct.value.value.c_str()); + } else { + throw std::runtime_error("Unknown grammar trigger type"); + } + params.sampling.grammar_triggers.emplace_back(std::move(ct.value)); + } + } + } + if (params.sampling.grammar_lazy && params.sampling.grammar_triggers.empty()) { + throw std::runtime_error("Error: no triggers set for lazy grammar!"); + } + } + + { + params.sampling.logit_bias.clear(); + + const auto & logit_bias = data.find("logit_bias"); + if (logit_bias != data.end() && logit_bias->is_array()) { + const int n_vocab = llama_vocab_n_tokens(vocab); + for (const auto & el : *logit_bias) { + // TODO: we may want to throw errors here, in case "el" is incorrect + if (el.is_array() && el.size() == 2) { + float bias; + if (el[1].is_number()) { + bias = el[1].get(); + } else if (el[1].is_boolean() && !el[1].get()) { + bias = -INFINITY; + } else { + continue; + } + + if (el[0].is_number_integer()) { + llama_token tok = el[0].get(); + if (tok >= 0 && tok < n_vocab) { + params.sampling.logit_bias.push_back({tok, bias}); + } + } else if (el[0].is_string()) { + auto toks = common_tokenize(vocab, el[0].get(), false); + for (auto tok : toks) { + params.sampling.logit_bias.push_back({tok, bias}); + } + } + } + } + } else if (logit_bias != data.end() && logit_bias->is_object()) { + const int n_vocab = llama_vocab_n_tokens(vocab); + for (const auto & el : logit_bias->items()) { + float bias; + const auto & key = el.key(); + const auto & value = el.value(); + if (value.is_number()) { + bias = value.get(); + } else if (value.is_boolean() && !value.get()) { + bias = -INFINITY; + } else { + continue; + } + + char *end; + llama_token tok = strtol(key.c_str(), &end, 10); + if (*end == 0) { + if (tok >= 0 && tok < n_vocab) { + params.sampling.logit_bias.push_back({tok, bias}); + } + } else { + auto toks = common_tokenize(vocab, key, false); + for (auto tok : toks) { + params.sampling.logit_bias.push_back({tok, bias}); + } + } + } + } + + params.sampling.ignore_eos = json_value(data, "ignore_eos", params_base.sampling.ignore_eos); + if (params.sampling.ignore_eos) { + params.sampling.logit_bias.insert( + params.sampling.logit_bias.end(), + defaults.sampling.logit_bias_eog.begin(), defaults.sampling.logit_bias_eog.end()); + } + } + + { + params.antiprompt.clear(); + + const auto & stop = data.find("stop"); + if (stop != data.end() && stop->is_array()) { + for (const auto & word : *stop) { + if (!word.empty()) { + params.antiprompt.push_back(word); + } + } + } + // set reverse prompt from cli args if not set in the request + if (params.antiprompt.empty()) { + params.antiprompt = defaults.antiprompt; + } + } + + { + const auto samplers = data.find("samplers"); + if (samplers != data.end()) { + if (samplers->is_array()) { + params.sampling.samplers = common_sampler_types_from_names(*samplers, false); + } else if (samplers->is_string()){ + params.sampling.samplers = common_sampler_types_from_chars(samplers->get()); + } + } else { + params.sampling.samplers = defaults.sampling.samplers; + } + } + + std::string model_name = params_base.model_alias.empty() ? DEFAULT_OAICOMPAT_MODEL : params_base.model_alias; + params.oaicompat_model = json_value(data, "model", model_name); + + return params; +} + +// +// result_timings +// + +json result_timings::to_json() const { + json base = { + {"cache_n", cache_n}, + + {"prompt_n", prompt_n}, + {"prompt_ms", prompt_ms}, + {"prompt_per_token_ms", prompt_per_token_ms}, + {"prompt_per_second", prompt_per_second}, + + {"predicted_n", predicted_n}, + {"predicted_ms", predicted_ms}, + {"predicted_per_token_ms", predicted_per_token_ms}, + {"predicted_per_second", predicted_per_second}, + }; + + if (draft_n > 0) { + base["draft_n"] = draft_n; + base["draft_n_accepted"] = draft_n_accepted; + } + + return base; +} + +// +// result_prompt_progress +// +json result_prompt_progress::to_json() const { + return json { + {"total", total}, + {"cache", cache}, + {"processed", processed}, + {"time_ms", time_ms}, + }; +} + +static inline std::string stop_type_to_str(stop_type type) { + switch (type) { + case STOP_TYPE_EOS: return "eos"; + case STOP_TYPE_WORD: return "word"; + case STOP_TYPE_LIMIT: return "limit"; + default: return "none"; + } +} + +// +// completion_token_output +// + +json completion_token_output::to_json(bool post_sampling_probs) const { + json probs_for_token = json::array(); + for (const auto & p : probs) { + std::string txt(p.txt); + txt.resize(validate_utf8(txt)); + probs_for_token.push_back(json { + {"id", p.tok}, + {"token", txt}, + {"bytes", str_to_bytes(p.txt)}, + { + post_sampling_probs ? "prob" : "logprob", + post_sampling_probs ? p.prob : logarithm(p.prob) + }, + }); + } + return probs_for_token; +} + +json completion_token_output::probs_vector_to_json(const std::vector & probs, bool post_sampling_probs) { + json out = json::array(); + for (const auto & p : probs) { + std::string txt(p.text_to_send); + txt.resize(validate_utf8(txt)); + out.push_back(json { + {"id", p.tok}, + {"token", txt}, + {"bytes", str_to_bytes(p.text_to_send)}, + { + post_sampling_probs ? "prob" : "logprob", + post_sampling_probs ? p.prob : logarithm(p.prob) + }, + { + post_sampling_probs ? "top_probs" : "top_logprobs", + p.to_json(post_sampling_probs) + }, + }); + } + return out; +} + +float completion_token_output::logarithm(float x) { + // nlohmann::json converts -inf to null, so we need to prevent that + return x == 0.0f ? std::numeric_limits::lowest() : std::log(x); +} + +std::vector completion_token_output::str_to_bytes(const std::string & str) { + std::vector bytes; + for (unsigned char c : str) { + bytes.push_back(c); + } + return bytes; +} + +// +// server_task_result_cmpl_final +// +json server_task_result_cmpl_final::to_json() { + switch (oaicompat) { + case OAICOMPAT_TYPE_NONE: + return to_json_non_oaicompat(); + case OAICOMPAT_TYPE_COMPLETION: + return to_json_oaicompat(); + case OAICOMPAT_TYPE_CHAT: + return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat(); + default: + GGML_ASSERT(false && "Invalid oaicompat_type"); + } +} + +json server_task_result_cmpl_final::to_json_non_oaicompat() { + json res = json { + {"index", index}, + {"content", stream ? "" : content}, // in stream mode, content is already in last partial chunk + {"tokens", stream ? llama_tokens {} : tokens}, + {"id_slot", id_slot}, + {"stop", true}, + {"model", oaicompat_model}, + {"tokens_predicted", n_decoded}, + {"tokens_evaluated", n_prompt_tokens}, + {"generation_settings", generation_params.to_json()}, + {"prompt", prompt}, + {"has_new_line", has_new_line}, + {"truncated", truncated}, + {"stop_type", stop_type_to_str(stop)}, + {"stopping_word", stopping_word}, + {"tokens_cached", n_tokens_cached}, + {"timings", timings.to_json()}, + }; + if (!stream && !probs_output.empty()) { + res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs); + } + return response_fields.empty() ? res : json_get_nested_values(response_fields, res); +} + +json server_task_result_cmpl_final::to_json_oaicompat() { + std::time_t t = std::time(0); + json logprobs = json(nullptr); // OAI default to null + if (!stream && probs_output.size() > 0) { + logprobs = json{ + {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, + }; + } + json finish_reason = "length"; + if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { + finish_reason = "stop"; + } + json res = json { + {"choices", json::array({ + json{ + {"text", stream ? "" : content}, // in stream mode, content is already in last partial chunk + {"index", index}, + {"logprobs", logprobs}, + {"finish_reason", finish_reason}, + } + })}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "text_completion"}, + {"usage", json { + {"completion_tokens", n_decoded}, + {"prompt_tokens", n_prompt_tokens}, + {"total_tokens", n_decoded + n_prompt_tokens} + }}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + + return res; +} + +json server_task_result_cmpl_final::to_json_oaicompat_chat() { + std::string finish_reason = "length"; + common_chat_msg msg; + if (!oaicompat_msg.empty()) { + msg = oaicompat_msg; + } else { + msg.role = "assistant"; + msg.content = content; + } + if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { + finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls"; + } + + json choice { + {"finish_reason", finish_reason}, + {"index", 0}, + {"message", msg.to_json_oaicompat()}, + }; + + if (!stream && probs_output.size() > 0) { + choice["logprobs"] = json{ + {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, + }; + } + + std::time_t t = std::time(0); + + json res = json { + {"choices", json::array({choice})}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion"}, + {"usage", json { + {"completion_tokens", n_decoded}, + {"prompt_tokens", n_prompt_tokens}, + {"total_tokens", n_decoded + n_prompt_tokens} + }}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + + return res; +} + +json server_task_result_cmpl_final::to_json_oaicompat_chat_stream() { + std::time_t t = std::time(0); + std::string finish_reason = "length"; + if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { + finish_reason = oaicompat_msg.tool_calls.empty() ? "stop" : "tool_calls"; + } + + json deltas = json::array(); + for (const auto & diff : oaicompat_msg_diffs) { + deltas.push_back({ + {"choices", json::array({ + json { + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", common_chat_msg_diff_to_json_oaicompat(diff)}, + }, + })}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion.chunk"}, + }); + } + + deltas.push_back({ + {"choices", json::array({ + json { + {"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()}, + }, + })}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion.chunk"}, + }); + + if (include_usage) { + // OpenAI API spec for chat.completion.chunks specifies an empty `choices` array for the last chunk when including usage + // https://platform.openai.com/docs/api-reference/chat_streaming/streaming#chat_streaming/streaming-choices + deltas.push_back({ + {"choices", json::array()}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion.chunk"}, + {"usage", json { + {"completion_tokens", n_decoded}, + {"prompt_tokens", n_prompt_tokens}, + {"total_tokens", n_decoded + n_prompt_tokens}, + }}, + }); + } + + if (timings.prompt_n >= 0) { + deltas.back().push_back({"timings", timings.to_json()}); + } + + // extra fields for debugging purposes + if (verbose && !deltas.empty()) { + deltas.front()["__verbose"] = to_json_non_oaicompat(); + } + + return deltas; +} + +// +// server_task_result_cmpl_partial +// +json server_task_result_cmpl_partial::to_json() { + switch (oaicompat) { + case OAICOMPAT_TYPE_NONE: + return to_json_non_oaicompat(); + case OAICOMPAT_TYPE_COMPLETION: + return to_json_oaicompat(); + case OAICOMPAT_TYPE_CHAT: + return to_json_oaicompat_chat(); + default: + GGML_ASSERT(false && "Invalid oaicompat_type"); + } +} + +json server_task_result_cmpl_partial::to_json_non_oaicompat() { + // non-OAI-compat JSON + json res = json { + {"index", index}, + {"content", content}, + {"tokens", tokens}, + {"stop", false}, + {"id_slot", id_slot}, + {"tokens_predicted", n_decoded}, + {"tokens_evaluated", n_prompt_tokens}, + }; + // populate the timings object when needed (usually for the last response or with timings_per_token enabled) + if (timings.prompt_n > 0) { + res.push_back({"timings", timings.to_json()}); + } + if (is_progress) { + res.push_back({"prompt_progress", progress.to_json()}); + } + if (!prob_output.probs.empty()) { + res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs); + } + return res; +} + +json server_task_result_cmpl_partial::to_json_oaicompat() { + std::time_t t = std::time(0); + json logprobs = json(nullptr); // OAI default to null + if (prob_output.probs.size() > 0) { + logprobs = json{ + {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, + }; + } + json res = json { + {"choices", json::array({ + json{ + {"text", content}, + {"index", index}, + {"logprobs", logprobs}, + {"finish_reason", nullptr}, + } + })}, + {"created", t}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "text_completion"}, + {"id", oaicompat_cmpl_id} + }; + + // extra fields for debugging purposes + if (verbose) { + res["__verbose"] = to_json_non_oaicompat(); + } + if (timings.prompt_n >= 0) { + res.push_back({"timings", timings.to_json()}); + } + if (is_progress) { + res.push_back({"prompt_progress", progress.to_json()}); + } + + return res; +} + +json server_task_result_cmpl_partial::to_json_oaicompat_chat() { + bool first = n_decoded == 1; + std::time_t t = std::time(0); + json choices; + + std::vector deltas; + auto add_delta = [&](const json & delta) { + deltas.push_back({ + {"choices", json::array({ + json { + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", delta}, + }, + })}, + {"created", t}, + {"id", oaicompat_cmpl_id}, + {"model", oaicompat_model}, + {"system_fingerprint", build_info}, + {"object", "chat.completion.chunk"}, + }); + }; + // We have to send an initial update to conform to openai behavior + if (first || is_progress) { + add_delta({ + {"role", "assistant"}, + {"content", nullptr}, + }); + } + + for (const auto & diff : oaicompat_msg_diffs) { + add_delta(common_chat_msg_diff_to_json_oaicompat(diff)); + } + + if (!deltas.empty()) { + auto & last_json = deltas[deltas.size() - 1]; + GGML_ASSERT(last_json.at("choices").size() >= 1); + + if (prob_output.probs.size() > 0) { + last_json.at("choices").at(0)["logprobs"] = json { + {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, + }; + } + + if (timings.prompt_n >= 0) { + last_json.push_back({"timings", timings.to_json()}); + } + if (is_progress) { + last_json.push_back({"prompt_progress", progress.to_json()}); + } + } + + return deltas; +} + +// +// server_task_result_embd +// +json server_task_result_embd::to_json() { + return oaicompat == OAICOMPAT_TYPE_EMBEDDING + ? to_json_oaicompat() + : to_json_non_oaicompat(); +} + +json server_task_result_embd::to_json_non_oaicompat() { + return json { + {"index", index}, + {"embedding", embedding}, + }; +} + +json server_task_result_embd::to_json_oaicompat() { + return json { + {"index", index}, + {"embedding", embedding[0]}, + {"tokens_evaluated", n_tokens}, + }; +} + +// +// server_task_result_rerank +// +json server_task_result_rerank::to_json() { + return json { + {"index", index}, + {"score", score}, + {"tokens_evaluated", n_tokens}, + }; +} + +// +// server_task_result_error +// +json server_task_result_error::to_json() { + json res = format_error_response(err_msg, err_type); + if (err_type == ERROR_TYPE_EXCEED_CONTEXT_SIZE) { + res["n_prompt_tokens"] = n_prompt_tokens; + res["n_ctx"] = n_ctx; + } + return res; +} + +// +// server_task_result_metrics +// +json server_task_result_metrics::to_json() { + return json { + { "idle", n_idle_slots }, + { "processing", n_processing_slots }, + { "deferred", n_tasks_deferred }, + { "t_start", t_start }, + + { "n_prompt_tokens_processed_total", n_prompt_tokens_processed_total }, + { "t_tokens_generation_total", t_tokens_generation_total }, + { "n_tokens_predicted_total", n_tokens_predicted_total }, + { "t_prompt_processing_total", t_prompt_processing_total }, + + { "n_tokens_max", n_tokens_max }, + + { "n_prompt_tokens_processed", n_prompt_tokens_processed }, + { "t_prompt_processing", t_prompt_processing }, + { "n_tokens_predicted", n_tokens_predicted }, + { "t_tokens_generation", t_tokens_generation }, + + { "n_decode_total", n_decode_total }, + { "n_busy_slots_total", n_busy_slots_total }, + + { "slots", slots_data }, + }; +} + +// +// server_task_result_slot_save_load +// +json server_task_result_slot_save_load::to_json() { + if (is_save) { + return json { + { "id_slot", id_slot }, + { "filename", filename }, + { "n_saved", n_tokens }, + { "n_written", n_bytes }, + { "timings", { + { "save_ms", t_ms } + }}, + }; + } + + return json { + { "id_slot", id_slot }, + { "filename", filename }, + { "n_restored", n_tokens }, + { "n_read", n_bytes }, + { "timings", { + { "restore_ms", t_ms } + }}, + }; +} + +// +// server_task_result_slot_erase +// +json server_task_result_slot_erase::to_json() { + return json { + { "id_slot", id_slot }, + { "n_erased", n_erased }, + }; +} + +// +// server_task_result_apply_lora +// + +json server_task_result_apply_lora::to_json() { + return json {{ "success", true }}; +} + +// +// server_prompt_cache +// +size_t server_prompt_cache::size() const { + size_t res = 0; + + for (const auto & state : states) { + res += state.size(); + } + + return res; +} + +size_t server_prompt_cache::n_tokens() const { + size_t res = 0; + + for (const auto & state : states) { + res += state.n_tokens(); + } + + return res; +} + +server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t state_size) { + // first check if the current state is contained fully in the cache + for (auto it = states.begin(); it != states.end(); ++it) { + const int cur_lcp_len = it->tokens.get_common_prefix(prompt.tokens); + + if (cur_lcp_len == (int) prompt.tokens.size()) { + SRV_WRN("%s", " - prompt is already in the cache, skipping\n"); + return nullptr; + } + } + + // next, remove any cached prompts that are fully contained in the current prompt + for (auto it = states.begin(); it != states.end();) { + const int len = it->tokens.get_common_prefix(prompt.tokens); + + if (len == (int) it->tokens.size()) { + SRV_WRN(" - removing obsolete cached prompt with length %d\n", len); + + it = states.erase(it); + } else { + ++it; + } + } + + std::vector state_data; + + // check if we can allocate enough memory for the new state + try { + state_data.resize(state_size); + } catch (const std::bad_alloc & e) { + SRV_ERR("failed to allocate memory for prompt cache state: %s\n", e.what()); + + limit_size = std::max(1, 0.4*size()); + + SRV_WRN(" - cache size limit reduced to %.3f MiB\n", limit_size / (1024.0 * 1024.0)); + + update(); + + return nullptr; + } + + // TODO: for some reason we can't copy server_tokens, so we have to do this workaround + auto & cur = states.emplace_back(); + cur = { + /*.tokens =*/ server_tokens(prompt.tokens.get_text_tokens(), false), + /*.data =*/ std::move(state_data), + /*.checkpoints =*/ prompt.checkpoints, + }; + + return &cur; +} + +bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx, int32_t id_slot) { + const int lcp_best = prompt.tokens.get_common_prefix(tokens_new); + + float f_keep_best = float(lcp_best) / prompt.tokens.size(); + float sim_best = float(lcp_best) / tokens_new.size(); + + SRV_WRN(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best); + + auto it_best = states.end(); + + // find the most similar cached prompt, that would also preserve the most context + for (auto it = states.begin(); it != states.end(); ++it) { + const int lcp_cur = it->tokens.get_common_prefix(tokens_new); + + const float f_keep_cur = float(lcp_cur) / it->tokens.size(); + const float sim_cur = float(lcp_cur) / tokens_new.size(); + + // don't trash large prompts + if (f_keep_cur < 0.25f) { + continue; + } + + if (f_keep_best < f_keep_cur && sim_best < sim_cur) { + f_keep_best = f_keep_cur; + sim_best = sim_cur; + + it_best = it; + } + } + + if (it_best != states.end()) { + SRV_WRN(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best); + + const size_t size = it_best->data.size(); + const size_t n = llama_state_seq_set_data_ext(ctx, it_best->data.data(), size, id_slot, 0); + if (n != size) { + SRV_WRN("failed to restore state with size %zu\n", size); + + return false; + } + + it_best->data.clear(); + it_best->data.shrink_to_fit(); + + prompt = std::move(*it_best); + + states.erase(it_best); + } + + return true; +} + +void server_prompt_cache::update() { + if (limit_size > 0) { + // always keep at least one state, regardless of the limits + while (states.size() > 1 && size() > limit_size) { + if (states.empty()) { + break; + } + + SRV_WRN(" - cache size limit reached, removing oldest entry (size = %.3f MiB)\n", states.front().size() / (1024.0 * 1024.0)); + + states.pop_front(); + } + } + + // average size per token + const float size_per_token = std::max(1.0f, float(size()) / (std::max(1, n_tokens()))); + + // dynamically increase the token limit if it can fit in the memory limit + const size_t limit_tokens_cur = limit_size > 0 ? std::max(limit_tokens, limit_size/size_per_token) : limit_tokens; + + if (limit_tokens > 0) { + while (states.size() > 1 && n_tokens() > limit_tokens_cur) { + if (states.empty()) { + break; + } + + SRV_WRN(" - cache token limit (%zu, est: %zu) reached, removing oldest entry (size = %.3f MiB)\n", + limit_tokens, limit_tokens_cur, states.front().size() / (1024.0 * 1024.0)); + + states.pop_front(); + } + } + + SRV_WRN(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n", + states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens, limit_tokens_cur); + + for (const auto & state : states) { + SRV_WRN(" - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n", + (const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0)); + } +} diff --git a/tools/server/server-task.h b/tools/server/server-task.h new file mode 100644 index 00000000000..0271caae116 --- /dev/null +++ b/tools/server/server-task.h @@ -0,0 +1,453 @@ +#pragma once + +#include "common.h" +#include "llama.h" + +#include +#include +#include + +// TODO: prevent including the whole server-common.h as we only use server_tokens +#include "server-common.h" + +using json = nlohmann::ordered_json; + +enum server_task_type { + SERVER_TASK_TYPE_COMPLETION, + SERVER_TASK_TYPE_EMBEDDING, + SERVER_TASK_TYPE_RERANK, + SERVER_TASK_TYPE_INFILL, + SERVER_TASK_TYPE_CANCEL, + SERVER_TASK_TYPE_NEXT_RESPONSE, + SERVER_TASK_TYPE_METRICS, + SERVER_TASK_TYPE_SLOT_SAVE, + SERVER_TASK_TYPE_SLOT_RESTORE, + SERVER_TASK_TYPE_SLOT_ERASE, + SERVER_TASK_TYPE_SET_LORA, +}; + +// TODO: change this to more generic "response_format" to replace the "format_response_*" in server-common +enum oaicompat_type { + OAICOMPAT_TYPE_NONE, + OAICOMPAT_TYPE_CHAT, + OAICOMPAT_TYPE_COMPLETION, + OAICOMPAT_TYPE_EMBEDDING, +}; + +enum stop_type { + STOP_TYPE_NONE, + STOP_TYPE_EOS, + STOP_TYPE_WORD, + STOP_TYPE_LIMIT, +}; + +struct task_params { + bool stream = true; + bool include_usage = false; + bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt + bool return_tokens = false; + bool return_progress = false; + + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half + int32_t n_predict = -1; // new tokens to predict + int32_t n_indent = 0; // minimum line indentation for the generated text in number of whitespace characters + + int64_t t_max_prompt_ms = -1; // TODO: implement + int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit + + std::vector lora; + + std::vector antiprompt; + std::vector response_fields; + bool timings_per_token = false; + bool post_sampling_probs = false; + + struct common_params_sampling sampling; + struct common_params_speculative speculative; + + // OAI-compat fields + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; + common_chat_syntax oaicompat_chat_syntax; + + // Embeddings + int32_t embd_normalize = 2; // (-1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm) + + json format_logit_bias(const std::vector & logit_bias) const; + json to_json(bool only_metrics = false) const; +}; + +struct server_task { + int id = -1; // to be filled by server_queue + int index = -1; // used when there are multiple prompts (batch request) + + // used by SERVER_TASK_TYPE_CANCEL + int id_target = -1; + int id_slot = -1; + + // used by SERVER_TASK_TYPE_INFERENCE + task_params params; + server_tokens tokens; + + server_task_type type; + + // used by SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE + struct slot_action { + int slot_id; + std::string filename; + std::string filepath; + }; + slot_action slot_action; + + // used by SERVER_TASK_TYPE_METRICS + bool metrics_reset_bucket = false; + + // used by SERVER_TASK_TYPE_SET_LORA + std::vector set_lora; + + server_task() = default; + + server_task(server_task_type type) : type(type) {} + + int32_t n_tokens() const { + return tokens.size(); + } + + static task_params params_from_json_cmpl( + const llama_context * ctx, + const common_params & params_base, + const json & data); + + // utility function + static std::unordered_set get_list_id(const std::vector & tasks) { + std::unordered_set ids(tasks.size()); + for (size_t i = 0; i < tasks.size(); i++) { + ids.insert(tasks[i].id); + } + return ids; + } +}; + +struct result_timings { + int32_t cache_n = -1; + + int32_t prompt_n = -1; + double prompt_ms; + double prompt_per_token_ms; + double prompt_per_second; + + int32_t predicted_n = -1; + double predicted_ms; + double predicted_per_token_ms; + double predicted_per_second; + + // Optional speculative metrics - only included when > 0 + int32_t draft_n = 0; + int32_t draft_n_accepted = 0; + + json to_json() const; +}; + +struct result_prompt_progress { + int32_t total = 0; + int32_t cache = 0; + int32_t processed = 0; + int64_t time_ms = 0; + + json to_json() const; +}; + +struct server_task_result { + int id = -1; + int id_slot = -1; + virtual bool is_error() { + // only used by server_task_result_error + return false; + } + virtual bool is_stop() { + // only used by server_task_result_cmpl_* + return true; + } + virtual int get_index() { + return -1; + } + virtual json to_json() = 0; + virtual ~server_task_result() = default; +}; + +// using shared_ptr for polymorphism of server_task_result +using server_task_result_ptr = std::unique_ptr; + +struct completion_token_output { + llama_token tok; + float prob; + std::string text_to_send; + struct prob_info { + llama_token tok; + std::string txt; + float prob; + }; + std::vector probs; + + json to_json(bool post_sampling_probs) const; + + static json probs_vector_to_json(const std::vector & probs, bool post_sampling_probs); + + static float logarithm(float x); + + static std::vector str_to_bytes(const std::string & str); + +}; + +struct server_task_result_cmpl_final : server_task_result { + int index = 0; + + std::string content; + llama_tokens tokens; + + bool stream; + bool include_usage; + result_timings timings; + std::string prompt; + + bool truncated; + int32_t n_decoded; + int32_t n_prompt_tokens; + int32_t n_tokens_cached; + bool has_new_line; + std::string stopping_word; + stop_type stop = STOP_TYPE_NONE; + + bool post_sampling_probs; + std::vector probs_output; + std::vector response_fields; + + task_params generation_params; + + // OAI-compat fields + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; + common_chat_msg oaicompat_msg; + + std::vector oaicompat_msg_diffs; + + virtual int get_index() override { + return index; + } + + virtual bool is_stop() override { + return true; // in stream mode, final responses are considered stop + } + + virtual json to_json() override; + + json to_json_non_oaicompat(); + + json to_json_oaicompat(); + + json to_json_oaicompat_chat(); + + json to_json_oaicompat_chat_stream(); +}; + +struct server_task_result_cmpl_partial : server_task_result { + int index = 0; + + std::string content; + llama_tokens tokens; + + int32_t n_decoded; + int32_t n_prompt_tokens; + + bool post_sampling_probs; + bool is_progress = false; + completion_token_output prob_output; + result_timings timings; + result_prompt_progress progress; + + // OAI-compat fields + bool verbose = false; + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + std::string oaicompat_model; + std::string oaicompat_cmpl_id; + std::vector oaicompat_msg_diffs; + + virtual int get_index() override { + return index; + } + + virtual bool is_stop() override { + return false; // in stream mode, partial responses are not considered stop + } + + virtual json to_json() override; + + json to_json_non_oaicompat(); + + json to_json_oaicompat(); + + json to_json_oaicompat_chat(); +}; + +struct server_task_result_embd : server_task_result { + int index = 0; + std::vector> embedding; + + int32_t n_tokens; + + // OAI-compat fields + oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; + + virtual int get_index() override { + return index; + } + + virtual json to_json() override; + + json to_json_non_oaicompat(); + + json to_json_oaicompat(); +}; + +struct server_task_result_rerank : server_task_result { + int index = 0; + float score = -1e6; + + int32_t n_tokens; + + virtual int get_index() override { + return index; + } + + virtual json to_json() override; +}; + +struct server_task_result_error : server_task_result { + int index = 0; + error_type err_type = ERROR_TYPE_SERVER; + std::string err_msg; + + // for ERROR_TYPE_EXCEED_CONTEXT_SIZE + int32_t n_prompt_tokens = 0; + int32_t n_ctx = 0; + + virtual bool is_error() override { + return true; + } + + virtual json to_json() override; +}; + +struct server_task_result_metrics : server_task_result { + int n_idle_slots; + int n_processing_slots; + int n_tasks_deferred; + int64_t t_start; + + // TODO: somehow reuse server_metrics in the future, instead of duplicating the fields + uint64_t n_prompt_tokens_processed_total = 0; + uint64_t t_prompt_processing_total = 0; + uint64_t n_tokens_predicted_total = 0; + uint64_t t_tokens_generation_total = 0; + + uint64_t n_tokens_max = 0; + + uint64_t n_prompt_tokens_processed = 0; + uint64_t t_prompt_processing = 0; + + uint64_t n_tokens_predicted = 0; + uint64_t t_tokens_generation = 0; + + uint64_t n_decode_total = 0; + uint64_t n_busy_slots_total = 0; + + // while we can also use std::vector this requires copying the slot object which can be quite messy + // therefore, we use json to temporarily store the slot.to_json() result + json slots_data = json::array(); + + virtual json to_json() override; +}; + +struct server_task_result_slot_save_load : server_task_result { + std::string filename; + bool is_save; // true = save, false = load + + size_t n_tokens; + size_t n_bytes; + double t_ms; + + virtual json to_json() override; +}; + +struct server_task_result_slot_erase : server_task_result { + size_t n_erased; + + virtual json to_json() override; +}; + +struct server_task_result_apply_lora : server_task_result { + virtual json to_json() override; +}; + +struct server_prompt_checkpoint { + llama_pos pos_min; + llama_pos pos_max; + + std::vector data; + + size_t size() const { + return data.size(); + } +}; + +struct server_prompt { + server_tokens tokens; + + std::vector data; + + std::list checkpoints; + + size_t size() const { + size_t res = data.size(); + + for (const auto & checkpoint : checkpoints) { + res += checkpoint.size(); + } + + return res; + } + + int n_tokens() const { + return tokens.size(); + } +}; + +struct server_prompt_cache { + server_prompt_cache(int32_t limit_size_mib, size_t limit_tokens) { + this->limit_size = 1024ull*1024ull*(limit_size_mib < 0 ? 0 : limit_size_mib); + this->limit_tokens = limit_tokens; + } + + std::list states; + + // in bytes, 0 = no limit + size_t limit_size = 0; + + // in tokens, 0 = no limit + size_t limit_tokens = 0; + + size_t size() const; + + size_t n_tokens() const; + + server_prompt * alloc(const server_prompt & prompt, size_t state_size); + + bool load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx, int32_t id_slot); + + void update(); +}; diff --git a/tools/server/server.cpp b/tools/server/server.cpp index 3750c8fdb60..0f39def3794 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -1,25 +1,21 @@ -#include "chat.h" -#include "utils.hpp" +#include "server-common.h" #include "server-http.h" +#include "server-task.h" +#include "server-queue.h" #include "arg.h" #include "common.h" -#include "json-schema-to-grammar.h" #include "llama.h" #include "log.h" #include "sampling.h" #include "speculative.h" #include "mtmd.h" +#include "mtmd-helper.h" #include -#include -#include #include #include -#include #include -#include -#include #include #include #include @@ -37,1589 +33,39 @@ using json = nlohmann::ordered_json; constexpr int HTTP_POLLING_SECONDS = 1; -enum stop_type { - STOP_TYPE_NONE, - STOP_TYPE_EOS, - STOP_TYPE_WORD, - STOP_TYPE_LIMIT, -}; - -// state diagram: https://github.com/ggml-org/llama.cpp/pull/9283 -enum slot_state { - SLOT_STATE_IDLE, - SLOT_STATE_STARTED, // TODO: this state is only used for setting up the initial prompt processing; maybe merge it with launch_slot_with_task in the future - SLOT_STATE_PROCESSING_PROMPT, - SLOT_STATE_DONE_PROMPT, - SLOT_STATE_GENERATING, -}; - -enum server_state { - SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet - SERVER_STATE_READY, // Server is ready and model is loaded -}; - -enum server_task_type { - SERVER_TASK_TYPE_COMPLETION, - SERVER_TASK_TYPE_EMBEDDING, - SERVER_TASK_TYPE_RERANK, - SERVER_TASK_TYPE_INFILL, - SERVER_TASK_TYPE_CANCEL, - SERVER_TASK_TYPE_NEXT_RESPONSE, - SERVER_TASK_TYPE_METRICS, - SERVER_TASK_TYPE_SLOT_SAVE, - SERVER_TASK_TYPE_SLOT_RESTORE, - SERVER_TASK_TYPE_SLOT_ERASE, - SERVER_TASK_TYPE_SET_LORA, -}; - -enum oaicompat_type { - OAICOMPAT_TYPE_NONE, - OAICOMPAT_TYPE_CHAT, - OAICOMPAT_TYPE_COMPLETION, - OAICOMPAT_TYPE_EMBEDDING, -}; - -// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11 -enum error_type { - ERROR_TYPE_INVALID_REQUEST, - ERROR_TYPE_AUTHENTICATION, - ERROR_TYPE_SERVER, - ERROR_TYPE_NOT_FOUND, - ERROR_TYPE_PERMISSION, - ERROR_TYPE_UNAVAILABLE, // custom error - ERROR_TYPE_NOT_SUPPORTED, // custom error - ERROR_TYPE_EXCEED_CONTEXT_SIZE, // custom error -}; - -static bool server_task_type_need_embd(server_task_type task_type) { - switch (task_type) { - case SERVER_TASK_TYPE_EMBEDDING: - case SERVER_TASK_TYPE_RERANK: - return true; - default: - return false; - } -} - -static bool server_task_type_need_logits(server_task_type task_type) { - switch (task_type) { - case SERVER_TASK_TYPE_COMPLETION: - case SERVER_TASK_TYPE_INFILL: - return true; - default: - return false; - } -} - -struct slot_params { - bool stream = true; - bool include_usage = false; - bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt - bool return_tokens = false; - bool return_progress = false; - - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half - int32_t n_predict = -1; // new tokens to predict - int32_t n_indent = 0; // minimum line indentation for the generated text in number of whitespace characters - - int64_t t_max_prompt_ms = -1; // TODO: implement - int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit - - std::vector lora; - - std::vector antiprompt; - std::vector response_fields; - bool timings_per_token = false; - bool post_sampling_probs = false; - - struct common_params_sampling sampling; - struct common_params_speculative speculative; - - // OAI-compat fields - bool verbose = false; - oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; - std::string oaicompat_model; - std::string oaicompat_cmpl_id; - common_chat_syntax oaicompat_chat_syntax; - - // Embeddings - int32_t embd_normalize = 2; // (-1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm) - - json to_json(bool only_metrics = false) const { - std::vector samplers; - samplers.reserve(sampling.samplers.size()); - for (const auto & sampler : sampling.samplers) { - samplers.emplace_back(common_sampler_type_to_str(sampler)); - } - - json lora = json::array(); - for (size_t i = 0; i < this->lora.size(); ++i) { - lora.push_back({{"id", i}, {"scale", this->lora[i].scale}}); - } - - if (only_metrics) { - return json { - {"seed", sampling.seed}, - {"temperature", sampling.temp}, - {"dynatemp_range", sampling.dynatemp_range}, - {"dynatemp_exponent", sampling.dynatemp_exponent}, - {"top_k", sampling.top_k}, - {"top_p", sampling.top_p}, - {"min_p", sampling.min_p}, - {"top_n_sigma", sampling.top_n_sigma}, - {"xtc_probability", sampling.xtc_probability}, - {"xtc_threshold", sampling.xtc_threshold}, - {"typical_p", sampling.typ_p}, - {"repeat_last_n", sampling.penalty_last_n}, - {"repeat_penalty", sampling.penalty_repeat}, - {"presence_penalty", sampling.penalty_present}, - {"frequency_penalty", sampling.penalty_freq}, - {"dry_multiplier", sampling.dry_multiplier}, - {"dry_base", sampling.dry_base}, - {"dry_allowed_length", sampling.dry_allowed_length}, - {"dry_penalty_last_n", sampling.dry_penalty_last_n}, - {"mirostat", sampling.mirostat}, - {"mirostat_tau", sampling.mirostat_tau}, - {"mirostat_eta", sampling.mirostat_eta}, - {"max_tokens", n_predict}, - {"n_predict", n_predict}, // TODO: deduplicate? - {"n_keep", n_keep}, - {"n_discard", n_discard}, - {"ignore_eos", sampling.ignore_eos}, - {"stream", stream}, - {"n_probs", sampling.n_probs}, - {"min_keep", sampling.min_keep}, - {"chat_format", common_chat_format_name(oaicompat_chat_syntax.format)}, - {"reasoning_format", common_reasoning_format_name(oaicompat_chat_syntax.reasoning_format)}, - {"reasoning_in_content", oaicompat_chat_syntax.reasoning_in_content}, - {"thinking_forced_open", oaicompat_chat_syntax.thinking_forced_open}, - {"samplers", samplers}, - {"speculative.n_max", speculative.n_max}, - {"speculative.n_min", speculative.n_min}, - {"speculative.p_min", speculative.p_min}, - {"timings_per_token", timings_per_token}, - {"post_sampling_probs", post_sampling_probs}, - {"lora", lora}, - }; - } - - auto grammar_triggers = json::array(); - for (const auto & trigger : sampling.grammar_triggers) { - server_grammar_trigger ct(trigger); - grammar_triggers.push_back(ct.to_json()); - } - - return json { - {"seed", sampling.seed}, - {"temperature", sampling.temp}, - {"dynatemp_range", sampling.dynatemp_range}, - {"dynatemp_exponent", sampling.dynatemp_exponent}, - {"top_k", sampling.top_k}, - {"top_p", sampling.top_p}, - {"min_p", sampling.min_p}, - {"top_n_sigma", sampling.top_n_sigma}, - {"xtc_probability", sampling.xtc_probability}, - {"xtc_threshold", sampling.xtc_threshold}, - {"typical_p", sampling.typ_p}, - {"repeat_last_n", sampling.penalty_last_n}, - {"repeat_penalty", sampling.penalty_repeat}, - {"presence_penalty", sampling.penalty_present}, - {"frequency_penalty", sampling.penalty_freq}, - {"dry_multiplier", sampling.dry_multiplier}, - {"dry_base", sampling.dry_base}, - {"dry_allowed_length", sampling.dry_allowed_length}, - {"dry_penalty_last_n", sampling.dry_penalty_last_n}, - {"dry_sequence_breakers", sampling.dry_sequence_breakers}, - {"mirostat", sampling.mirostat}, - {"mirostat_tau", sampling.mirostat_tau}, - {"mirostat_eta", sampling.mirostat_eta}, - {"stop", antiprompt}, - {"max_tokens", n_predict}, - {"n_predict", n_predict}, // TODO: deduplicate? - {"n_keep", n_keep}, - {"n_discard", n_discard}, - {"ignore_eos", sampling.ignore_eos}, - {"stream", stream}, - {"logit_bias", format_logit_bias(sampling.logit_bias)}, - {"n_probs", sampling.n_probs}, - {"min_keep", sampling.min_keep}, - {"grammar", sampling.grammar}, - {"grammar_lazy", sampling.grammar_lazy}, - {"grammar_triggers", grammar_triggers}, - {"preserved_tokens", sampling.preserved_tokens}, - {"chat_format", common_chat_format_name(oaicompat_chat_syntax.format)}, - {"reasoning_format", common_reasoning_format_name(oaicompat_chat_syntax.reasoning_format)}, - {"reasoning_in_content", oaicompat_chat_syntax.reasoning_in_content}, - {"thinking_forced_open", oaicompat_chat_syntax.thinking_forced_open}, - {"samplers", samplers}, - {"speculative.n_max", speculative.n_max}, - {"speculative.n_min", speculative.n_min}, - {"speculative.p_min", speculative.p_min}, - {"timings_per_token", timings_per_token}, - {"post_sampling_probs", post_sampling_probs}, - {"lora", lora}, - }; - } -}; - -struct server_task { - int id = -1; // to be filled by server_queue - int index = -1; // used when there are multiple prompts (batch request) - - // used by SERVER_TASK_TYPE_CANCEL - int id_target = -1; - int id_slot = -1; - - // used by SERVER_TASK_TYPE_INFERENCE - slot_params params; - server_tokens tokens; - - server_task_type type; - - // used by SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE - struct slot_action { - int slot_id; - std::string filename; - std::string filepath; - }; - slot_action slot_action; - - // used by SERVER_TASK_TYPE_METRICS - bool metrics_reset_bucket = false; - - // used by SERVER_TASK_TYPE_SET_LORA - std::vector set_lora; - - server_task() = default; - - server_task(server_task_type type) : type(type) {} - - int32_t n_tokens() const { - return tokens.size(); - } - - static slot_params params_from_json_cmpl( - const llama_context * ctx, - const common_params & params_base, - const json & data) { - const llama_model * model = llama_get_model(ctx); - const llama_vocab * vocab = llama_model_get_vocab(model); - - slot_params params; - - // Sampling parameter defaults are loaded from the global server context (but individual requests can still override them) - slot_params defaults; - defaults.sampling = params_base.sampling; - defaults.speculative = params_base.speculative; - defaults.n_keep = params_base.n_keep; - defaults.n_predict = params_base.n_predict; - defaults.antiprompt = params_base.antiprompt; - - // enabling this will output extra debug information in the HTTP responses from the server - params.verbose = params_base.verbosity > 9; - params.timings_per_token = json_value(data, "timings_per_token", false); - - params.stream = json_value(data, "stream", false); - auto stream_opt = json_value(data, "stream_options", json::object()); - params.include_usage = json_value(stream_opt, "include_usage", false); - params.cache_prompt = json_value(data, "cache_prompt", true); - params.return_tokens = json_value(data, "return_tokens", false); - params.return_progress = json_value(data, "return_progress", false); - params.n_predict = json_value(data, "n_predict", json_value(data, "max_tokens", defaults.n_predict)); - params.n_indent = json_value(data, "n_indent", defaults.n_indent); - params.n_keep = json_value(data, "n_keep", defaults.n_keep); - params.n_discard = json_value(data, "n_discard", defaults.n_discard); - //params.t_max_prompt_ms = json_value(data, "t_max_prompt_ms", defaults.t_max_prompt_ms); // TODO: implement - params.t_max_predict_ms = json_value(data, "t_max_predict_ms", defaults.t_max_predict_ms); - params.response_fields = json_value(data, "response_fields", std::vector()); - - params.sampling.top_k = json_value(data, "top_k", defaults.sampling.top_k); - params.sampling.top_p = json_value(data, "top_p", defaults.sampling.top_p); - params.sampling.min_p = json_value(data, "min_p", defaults.sampling.min_p); - params.sampling.top_n_sigma = json_value(data, "top_n_sigma", defaults.sampling.top_n_sigma); - params.sampling.xtc_probability = json_value(data, "xtc_probability", defaults.sampling.xtc_probability); - params.sampling.xtc_threshold = json_value(data, "xtc_threshold", defaults.sampling.xtc_threshold); - params.sampling.typ_p = json_value(data, "typical_p", defaults.sampling.typ_p); - params.sampling.temp = json_value(data, "temperature", defaults.sampling.temp); - params.sampling.dynatemp_range = json_value(data, "dynatemp_range", defaults.sampling.dynatemp_range); - params.sampling.dynatemp_exponent = json_value(data, "dynatemp_exponent", defaults.sampling.dynatemp_exponent); - params.sampling.penalty_last_n = json_value(data, "repeat_last_n", defaults.sampling.penalty_last_n); - params.sampling.penalty_repeat = json_value(data, "repeat_penalty", defaults.sampling.penalty_repeat); - params.sampling.penalty_freq = json_value(data, "frequency_penalty", defaults.sampling.penalty_freq); - params.sampling.penalty_present = json_value(data, "presence_penalty", defaults.sampling.penalty_present); - params.sampling.dry_multiplier = json_value(data, "dry_multiplier", defaults.sampling.dry_multiplier); - params.sampling.dry_base = json_value(data, "dry_base", defaults.sampling.dry_base); - params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length", defaults.sampling.dry_allowed_length); - params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n", defaults.sampling.dry_penalty_last_n); - params.sampling.mirostat = json_value(data, "mirostat", defaults.sampling.mirostat); - params.sampling.mirostat_tau = json_value(data, "mirostat_tau", defaults.sampling.mirostat_tau); - params.sampling.mirostat_eta = json_value(data, "mirostat_eta", defaults.sampling.mirostat_eta); - params.sampling.seed = json_value(data, "seed", defaults.sampling.seed); - params.sampling.n_probs = json_value(data, "n_probs", defaults.sampling.n_probs); - params.sampling.min_keep = json_value(data, "min_keep", defaults.sampling.min_keep); - params.post_sampling_probs = json_value(data, "post_sampling_probs", defaults.post_sampling_probs); - - params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min); - params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max); - params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min); - - params.speculative.n_min = std::min(params.speculative.n_max, params.speculative.n_min); - params.speculative.n_min = std::max(params.speculative.n_min, 0); - params.speculative.n_max = std::max(params.speculative.n_max, 0); - - // Use OpenAI API logprobs only if n_probs wasn't provided - if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){ - params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs); - } - - if (data.contains("lora")) { - if (data.at("lora").is_array()) { - params.lora = parse_lora_request(params_base.lora_adapters, data.at("lora")); - } else { - throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields"); - } - } else { - params.lora = params_base.lora_adapters; - } - - // TODO: add more sanity checks for the input parameters - - if (params.sampling.penalty_last_n < -1) { - throw std::runtime_error("Error: repeat_last_n must be >= -1"); - } - - if (params.sampling.dry_penalty_last_n < -1) { - throw std::runtime_error("Error: dry_penalty_last_n must be >= -1"); - } - - if (params.sampling.penalty_last_n == -1) { - // note: should be the slot's context and not the full context, but it's ok - params.sampling.penalty_last_n = llama_n_ctx(ctx); - } - - if (params.sampling.dry_penalty_last_n == -1) { - params.sampling.dry_penalty_last_n = llama_n_ctx(ctx); - } - - if (params.sampling.dry_base < 1.0f) { - params.sampling.dry_base = defaults.sampling.dry_base; - } - - // sequence breakers for DRY - { - // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format - // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39 - - if (data.contains("dry_sequence_breakers")) { - params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector()); - if (params.sampling.dry_sequence_breakers.empty()) { - throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings"); - } - } - } - - // process "json_schema" and "grammar" - if (data.contains("json_schema") && !data.contains("grammar")) { - try { - auto schema = json_value(data, "json_schema", json::object()); - SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str()); - params.sampling.grammar = json_schema_to_grammar(schema); - SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str()); - } catch (const std::exception & e) { - throw std::runtime_error(std::string("\"json_schema\": ") + e.what()); - } - } else { - params.sampling.grammar = json_value(data, "grammar", defaults.sampling.grammar); - SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str()); - params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy); - SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false"); - } - - { - auto it = data.find("chat_format"); - if (it != data.end()) { - params.oaicompat_chat_syntax.format = static_cast(it->get()); - SRV_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_syntax.format)); - } else { - params.oaicompat_chat_syntax.format = defaults.oaicompat_chat_syntax.format; - } - common_reasoning_format reasoning_format = params_base.reasoning_format; - if (data.contains("reasoning_format")) { - reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get()); - } - params.oaicompat_chat_syntax.reasoning_format = reasoning_format; - params.oaicompat_chat_syntax.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY); - params.oaicompat_chat_syntax.thinking_forced_open = json_value(data, "thinking_forced_open", false); - params.oaicompat_chat_syntax.parse_tool_calls = json_value(data, "parse_tool_calls", false); - } - - { - const auto preserved_tokens = data.find("preserved_tokens"); - if (preserved_tokens != data.end()) { - for (const auto & t : *preserved_tokens) { - auto ids = common_tokenize(vocab, t.get(), /* add_special= */ false, /* parse_special= */ true); - if (ids.size() == 1) { - SRV_DBG("Preserved token: %d\n", ids[0]); - params.sampling.preserved_tokens.insert(ids[0]); - } else { - // This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens. - SRV_DBG("Not preserved because more than 1 token: %s\n", t.get().c_str()); - } - } - } - const auto grammar_triggers = data.find("grammar_triggers"); - if (grammar_triggers != data.end()) { - for (const auto & t : *grammar_triggers) { - server_grammar_trigger ct(t); - if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) { - const auto & word = ct.value.value; - auto ids = common_tokenize(vocab, word, /* add_special= */ false, /* parse_special= */ true); - if (ids.size() == 1) { - auto token = ids[0]; - if (std::find(params.sampling.preserved_tokens.begin(), params.sampling.preserved_tokens.end(), (llama_token) token) == params.sampling.preserved_tokens.end()) { - throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word); - } - SRV_DBG("Grammar trigger token: %d (`%s`)\n", token, word.c_str()); - common_grammar_trigger trigger; - trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN; - trigger.value = word; - trigger.token = token; - params.sampling.grammar_triggers.push_back(std::move(trigger)); - } else { - SRV_DBG("Grammar trigger word: `%s`\n", word.c_str()); - params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word}); - } - } else { - if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN) { - SRV_DBG("Grammar trigger pattern: `%s`\n", ct.value.value.c_str()); - } else if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL) { - SRV_DBG("Grammar trigger pattern full: `%s`\n", ct.value.value.c_str()); - } else { - throw std::runtime_error("Unknown grammar trigger type"); - } - params.sampling.grammar_triggers.emplace_back(std::move(ct.value)); - } - } - } - if (params.sampling.grammar_lazy && params.sampling.grammar_triggers.empty()) { - throw std::runtime_error("Error: no triggers set for lazy grammar!"); - } - } - - { - params.sampling.logit_bias.clear(); - - const auto & logit_bias = data.find("logit_bias"); - if (logit_bias != data.end() && logit_bias->is_array()) { - const int n_vocab = llama_vocab_n_tokens(vocab); - for (const auto & el : *logit_bias) { - // TODO: we may want to throw errors here, in case "el" is incorrect - if (el.is_array() && el.size() == 2) { - float bias; - if (el[1].is_number()) { - bias = el[1].get(); - } else if (el[1].is_boolean() && !el[1].get()) { - bias = -INFINITY; - } else { - continue; - } - - if (el[0].is_number_integer()) { - llama_token tok = el[0].get(); - if (tok >= 0 && tok < n_vocab) { - params.sampling.logit_bias.push_back({tok, bias}); - } - } else if (el[0].is_string()) { - auto toks = common_tokenize(vocab, el[0].get(), false); - for (auto tok : toks) { - params.sampling.logit_bias.push_back({tok, bias}); - } - } - } - } - } else if (logit_bias != data.end() && logit_bias->is_object()) { - const int n_vocab = llama_vocab_n_tokens(vocab); - for (const auto & el : logit_bias->items()) { - float bias; - const auto & key = el.key(); - const auto & value = el.value(); - if (value.is_number()) { - bias = value.get(); - } else if (value.is_boolean() && !value.get()) { - bias = -INFINITY; - } else { - continue; - } - - char *end; - llama_token tok = strtol(key.c_str(), &end, 10); - if (*end == 0) { - if (tok >= 0 && tok < n_vocab) { - params.sampling.logit_bias.push_back({tok, bias}); - } - } else { - auto toks = common_tokenize(vocab, key, false); - for (auto tok : toks) { - params.sampling.logit_bias.push_back({tok, bias}); - } - } - } - } - - params.sampling.ignore_eos = json_value(data, "ignore_eos", params_base.sampling.ignore_eos); - if (params.sampling.ignore_eos) { - params.sampling.logit_bias.insert( - params.sampling.logit_bias.end(), - defaults.sampling.logit_bias_eog.begin(), defaults.sampling.logit_bias_eog.end()); - } - } - - { - params.antiprompt.clear(); - - const auto & stop = data.find("stop"); - if (stop != data.end() && stop->is_array()) { - for (const auto & word : *stop) { - if (!word.empty()) { - params.antiprompt.push_back(word); - } - } - } - // set reverse prompt from cli args if not set in the request - if (params.antiprompt.empty()) { - params.antiprompt = defaults.antiprompt; - } - } - - { - const auto samplers = data.find("samplers"); - if (samplers != data.end()) { - if (samplers->is_array()) { - params.sampling.samplers = common_sampler_types_from_names(*samplers, false); - } else if (samplers->is_string()){ - params.sampling.samplers = common_sampler_types_from_chars(samplers->get()); - } - } else { - params.sampling.samplers = defaults.sampling.samplers; - } - } - - std::string model_name = params_base.model_alias.empty() ? DEFAULT_OAICOMPAT_MODEL : params_base.model_alias; - params.oaicompat_model = json_value(data, "model", model_name); - - return params; - } - - // utility function - static std::unordered_set get_list_id(const std::vector & tasks) { - std::unordered_set ids(tasks.size()); - for (size_t i = 0; i < tasks.size(); i++) { - ids.insert(tasks[i].id); - } - return ids; - } -}; - -struct result_timings { - int32_t cache_n = -1; - - int32_t prompt_n = -1; - double prompt_ms; - double prompt_per_token_ms; - double prompt_per_second; - - int32_t predicted_n = -1; - double predicted_ms; - double predicted_per_token_ms; - double predicted_per_second; - - // Optional speculative metrics - only included when > 0 - int32_t draft_n = 0; - int32_t draft_n_accepted = 0; - - json to_json() const { - json base = { - {"cache_n", cache_n}, - - {"prompt_n", prompt_n}, - {"prompt_ms", prompt_ms}, - {"prompt_per_token_ms", prompt_per_token_ms}, - {"prompt_per_second", prompt_per_second}, - - {"predicted_n", predicted_n}, - {"predicted_ms", predicted_ms}, - {"predicted_per_token_ms", predicted_per_token_ms}, - {"predicted_per_second", predicted_per_second}, - }; - - if (draft_n > 0) { - base["draft_n"] = draft_n; - base["draft_n_accepted"] = draft_n_accepted; - } - - return base; - } -}; - -struct result_prompt_progress { - int32_t total = 0; - int32_t cache = 0; - int32_t processed = 0; - int64_t time_ms = 0; - - json to_json() const { - return json { - {"total", total}, - {"cache", cache}, - {"processed", processed}, - {"time_ms", time_ms}, - }; - } -}; - -struct server_task_result { - int id = -1; - int id_slot = -1; - virtual bool is_error() { - // only used by server_task_result_error - return false; - } - virtual bool is_stop() { - // only used by server_task_result_cmpl_* - return true; - } - virtual int get_index() { - return -1; - } - virtual json to_json() = 0; - virtual ~server_task_result() = default; -}; - -// using shared_ptr for polymorphism of server_task_result -using server_task_result_ptr = std::unique_ptr; - -static inline std::string stop_type_to_str(stop_type type) { - switch (type) { - case STOP_TYPE_EOS: return "eos"; - case STOP_TYPE_WORD: return "word"; - case STOP_TYPE_LIMIT: return "limit"; - default: return "none"; - } -} - -struct completion_token_output { - llama_token tok; - float prob; - std::string text_to_send; - struct prob_info { - llama_token tok; - std::string txt; - float prob; - }; - std::vector probs; - - json to_json(bool post_sampling_probs) const { - json probs_for_token = json::array(); - for (const auto & p : probs) { - std::string txt(p.txt); - txt.resize(validate_utf8(txt)); - probs_for_token.push_back(json { - {"id", p.tok}, - {"token", txt}, - {"bytes", str_to_bytes(p.txt)}, - { - post_sampling_probs ? "prob" : "logprob", - post_sampling_probs ? p.prob : logarithm(p.prob) - }, - }); - } - return probs_for_token; - } - - static json probs_vector_to_json(const std::vector & probs, bool post_sampling_probs) { - json out = json::array(); - for (const auto & p : probs) { - std::string txt(p.text_to_send); - txt.resize(validate_utf8(txt)); - out.push_back(json { - {"id", p.tok}, - {"token", txt}, - {"bytes", str_to_bytes(p.text_to_send)}, - { - post_sampling_probs ? "prob" : "logprob", - post_sampling_probs ? p.prob : logarithm(p.prob) - }, - { - post_sampling_probs ? "top_probs" : "top_logprobs", - p.to_json(post_sampling_probs) - }, - }); - } - return out; - } - - static float logarithm(float x) { - // nlohmann::json converts -inf to null, so we need to prevent that - return x == 0.0f ? std::numeric_limits::lowest() : std::log(x); - } - - static std::vector str_to_bytes(const std::string & str) { - std::vector bytes; - for (unsigned char c : str) { - bytes.push_back(c); - } - return bytes; - } -}; - -struct server_task_result_cmpl_final : server_task_result { - int index = 0; - - std::string content; - llama_tokens tokens; - - bool stream; - bool include_usage; - result_timings timings; - std::string prompt; - - bool truncated; - int32_t n_decoded; - int32_t n_prompt_tokens; - int32_t n_tokens_cached; - bool has_new_line; - std::string stopping_word; - stop_type stop = STOP_TYPE_NONE; - - bool post_sampling_probs; - std::vector probs_output; - std::vector response_fields; - - slot_params generation_params; - - // OAI-compat fields - bool verbose = false; - oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; - std::string oaicompat_model; - std::string oaicompat_cmpl_id; - common_chat_msg oaicompat_msg; - - std::vector oaicompat_msg_diffs; - - virtual int get_index() override { - return index; - } - - virtual bool is_stop() override { - return true; // in stream mode, final responses are considered stop - } - - virtual json to_json() override { - switch (oaicompat) { - case OAICOMPAT_TYPE_NONE: - return to_json_non_oaicompat(); - case OAICOMPAT_TYPE_COMPLETION: - return to_json_oaicompat(); - case OAICOMPAT_TYPE_CHAT: - return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat(); - default: - GGML_ASSERT(false && "Invalid oaicompat_type"); - } - } - - json to_json_non_oaicompat() { - json res = json { - {"index", index}, - {"content", stream ? "" : content}, // in stream mode, content is already in last partial chunk - {"tokens", stream ? llama_tokens {} : tokens}, - {"id_slot", id_slot}, - {"stop", true}, - {"model", oaicompat_model}, - {"tokens_predicted", n_decoded}, - {"tokens_evaluated", n_prompt_tokens}, - {"generation_settings", generation_params.to_json()}, - {"prompt", prompt}, - {"has_new_line", has_new_line}, - {"truncated", truncated}, - {"stop_type", stop_type_to_str(stop)}, - {"stopping_word", stopping_word}, - {"tokens_cached", n_tokens_cached}, - {"timings", timings.to_json()}, - }; - if (!stream && !probs_output.empty()) { - res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs); - } - return response_fields.empty() ? res : json_get_nested_values(response_fields, res); - } - - json to_json_oaicompat() { - std::time_t t = std::time(0); - json logprobs = json(nullptr); // OAI default to null - if (!stream && probs_output.size() > 0) { - logprobs = json{ - {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, - }; - } - json finish_reason = "length"; - if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { - finish_reason = "stop"; - } - json res = json { - {"choices", json::array({ - json{ - {"text", stream ? "" : content}, // in stream mode, content is already in last partial chunk - {"index", index}, - {"logprobs", logprobs}, - {"finish_reason", finish_reason}, - } - })}, - {"created", t}, - {"model", oaicompat_model}, - {"system_fingerprint", build_info}, - {"object", "text_completion"}, - {"usage", json { - {"completion_tokens", n_decoded}, - {"prompt_tokens", n_prompt_tokens}, - {"total_tokens", n_decoded + n_prompt_tokens} - }}, - {"id", oaicompat_cmpl_id} - }; - - // extra fields for debugging purposes - if (verbose) { - res["__verbose"] = to_json_non_oaicompat(); - } - if (timings.prompt_n >= 0) { - res.push_back({"timings", timings.to_json()}); - } - - return res; - } - - json to_json_oaicompat_chat() { - std::string finish_reason = "length"; - common_chat_msg msg; - if (!oaicompat_msg.empty()) { - msg = oaicompat_msg; - } else { - msg.role = "assistant"; - msg.content = content; - } - if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { - finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls"; - } - - json choice { - {"finish_reason", finish_reason}, - {"index", 0}, - {"message", msg.to_json_oaicompat()}, - }; - - if (!stream && probs_output.size() > 0) { - choice["logprobs"] = json{ - {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)}, - }; - } - - std::time_t t = std::time(0); - - json res = json { - {"choices", json::array({choice})}, - {"created", t}, - {"model", oaicompat_model}, - {"system_fingerprint", build_info}, - {"object", "chat.completion"}, - {"usage", json { - {"completion_tokens", n_decoded}, - {"prompt_tokens", n_prompt_tokens}, - {"total_tokens", n_decoded + n_prompt_tokens} - }}, - {"id", oaicompat_cmpl_id} - }; - - // extra fields for debugging purposes - if (verbose) { - res["__verbose"] = to_json_non_oaicompat(); - } - if (timings.prompt_n >= 0) { - res.push_back({"timings", timings.to_json()}); - } - - return res; - } - - json to_json_oaicompat_chat_stream() { - std::time_t t = std::time(0); - std::string finish_reason = "length"; - if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) { - finish_reason = oaicompat_msg.tool_calls.empty() ? "stop" : "tool_calls"; - } - - json deltas = json::array(); - for (const auto & diff : oaicompat_msg_diffs) { - deltas.push_back({ - {"choices", json::array({ - json { - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", common_chat_msg_diff_to_json_oaicompat(diff)}, - }, - })}, - {"created", t}, - {"id", oaicompat_cmpl_id}, - {"model", oaicompat_model}, - {"system_fingerprint", build_info}, - {"object", "chat.completion.chunk"}, - }); - } - - deltas.push_back({ - {"choices", json::array({ - json { - {"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}, - }, - })}, - {"created", t}, - {"id", oaicompat_cmpl_id}, - {"model", oaicompat_model}, - {"system_fingerprint", build_info}, - {"object", "chat.completion.chunk"}, - }); - - if (include_usage) { - // OpenAI API spec for chat.completion.chunks specifies an empty `choices` array for the last chunk when including usage - // https://platform.openai.com/docs/api-reference/chat_streaming/streaming#chat_streaming/streaming-choices - deltas.push_back({ - {"choices", json::array()}, - {"created", t}, - {"id", oaicompat_cmpl_id}, - {"model", oaicompat_model}, - {"system_fingerprint", build_info}, - {"object", "chat.completion.chunk"}, - {"usage", json { - {"completion_tokens", n_decoded}, - {"prompt_tokens", n_prompt_tokens}, - {"total_tokens", n_decoded + n_prompt_tokens}, - }}, - }); - } - - if (timings.prompt_n >= 0) { - deltas.back().push_back({"timings", timings.to_json()}); - } - - // extra fields for debugging purposes - if (verbose && !deltas.empty()) { - deltas.front()["__verbose"] = to_json_non_oaicompat(); - } - - return deltas; - } -}; - -struct server_task_result_cmpl_partial : server_task_result { - int index = 0; - - std::string content; - llama_tokens tokens; - - int32_t n_decoded; - int32_t n_prompt_tokens; - - bool post_sampling_probs; - bool is_progress = false; - completion_token_output prob_output; - result_timings timings; - result_prompt_progress progress; - - // OAI-compat fields - bool verbose = false; - oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; - std::string oaicompat_model; - std::string oaicompat_cmpl_id; - std::vector oaicompat_msg_diffs; - - virtual int get_index() override { - return index; - } - - virtual bool is_stop() override { - return false; // in stream mode, partial responses are not considered stop - } - - virtual json to_json() override { - switch (oaicompat) { - case OAICOMPAT_TYPE_NONE: - return to_json_non_oaicompat(); - case OAICOMPAT_TYPE_COMPLETION: - return to_json_oaicompat(); - case OAICOMPAT_TYPE_CHAT: - return to_json_oaicompat_chat(); - default: - GGML_ASSERT(false && "Invalid oaicompat_type"); - } - } - - json to_json_non_oaicompat() { - // non-OAI-compat JSON - json res = json { - {"index", index}, - {"content", content}, - {"tokens", tokens}, - {"stop", false}, - {"id_slot", id_slot}, - {"tokens_predicted", n_decoded}, - {"tokens_evaluated", n_prompt_tokens}, - }; - // populate the timings object when needed (usually for the last response or with timings_per_token enabled) - if (timings.prompt_n > 0) { - res.push_back({"timings", timings.to_json()}); - } - if (is_progress) { - res.push_back({"prompt_progress", progress.to_json()}); - } - if (!prob_output.probs.empty()) { - res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs); - } - return res; - } - - json to_json_oaicompat() { - std::time_t t = std::time(0); - json logprobs = json(nullptr); // OAI default to null - if (prob_output.probs.size() > 0) { - logprobs = json{ - {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, - }; - } - json res = json { - {"choices", json::array({ - json{ - {"text", content}, - {"index", index}, - {"logprobs", logprobs}, - {"finish_reason", nullptr}, - } - })}, - {"created", t}, - {"model", oaicompat_model}, - {"system_fingerprint", build_info}, - {"object", "text_completion"}, - {"id", oaicompat_cmpl_id} - }; - - // extra fields for debugging purposes - if (verbose) { - res["__verbose"] = to_json_non_oaicompat(); - } - if (timings.prompt_n >= 0) { - res.push_back({"timings", timings.to_json()}); - } - if (is_progress) { - res.push_back({"prompt_progress", progress.to_json()}); - } - - return res; - } - - json to_json_oaicompat_chat() { - bool first = n_decoded == 1; - std::time_t t = std::time(0); - json choices; - - std::vector deltas; - auto add_delta = [&](const json & delta) { - deltas.push_back({ - {"choices", json::array({ - json { - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", delta}, - }, - })}, - {"created", t}, - {"id", oaicompat_cmpl_id}, - {"model", oaicompat_model}, - {"system_fingerprint", build_info}, - {"object", "chat.completion.chunk"}, - }); - }; - // We have to send an initial update to conform to openai behavior - if (first || is_progress) { - add_delta({ - {"role", "assistant"}, - {"content", nullptr}, - }); - } - - for (const auto & diff : oaicompat_msg_diffs) { - add_delta(common_chat_msg_diff_to_json_oaicompat(diff)); - } - - if (!deltas.empty()) { - auto & last_json = deltas[deltas.size() - 1]; - GGML_ASSERT(last_json.at("choices").size() >= 1); - - if (prob_output.probs.size() > 0) { - last_json.at("choices").at(0)["logprobs"] = json { - {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)}, - }; - } - - if (timings.prompt_n >= 0) { - last_json.push_back({"timings", timings.to_json()}); - } - if (is_progress) { - last_json.push_back({"prompt_progress", progress.to_json()}); - } - } - - return deltas; - } -}; - -struct server_task_result_embd : server_task_result { - int index = 0; - std::vector> embedding; - - int32_t n_tokens; - - // OAI-compat fields - oaicompat_type oaicompat = OAICOMPAT_TYPE_NONE; - - virtual int get_index() override { - return index; - } - - virtual json to_json() override { - return oaicompat == OAICOMPAT_TYPE_EMBEDDING - ? to_json_oaicompat() - : to_json_non_oaicompat(); - } - - json to_json_non_oaicompat() { - return json { - {"index", index}, - {"embedding", embedding}, - }; - } - - json to_json_oaicompat() { - return json { - {"index", index}, - {"embedding", embedding[0]}, - {"tokens_evaluated", n_tokens}, - }; - } -}; - -struct server_task_result_rerank : server_task_result { - int index = 0; - float score = -1e6; - - int32_t n_tokens; - - virtual int get_index() override { - return index; - } - - virtual json to_json() override { - return json { - {"index", index}, - {"score", score}, - {"tokens_evaluated", n_tokens}, - }; - } -}; - -// this function maybe used outside of server_task_result_error -static json format_error_response(const std::string & message, const enum error_type type) { - std::string type_str; - int code = 500; - switch (type) { - case ERROR_TYPE_INVALID_REQUEST: - type_str = "invalid_request_error"; - code = 400; - break; - case ERROR_TYPE_AUTHENTICATION: - type_str = "authentication_error"; - code = 401; - break; - case ERROR_TYPE_NOT_FOUND: - type_str = "not_found_error"; - code = 404; - break; - case ERROR_TYPE_SERVER: - type_str = "server_error"; - code = 500; - break; - case ERROR_TYPE_PERMISSION: - type_str = "permission_error"; - code = 403; - break; - case ERROR_TYPE_NOT_SUPPORTED: - type_str = "not_supported_error"; - code = 501; - break; - case ERROR_TYPE_UNAVAILABLE: - type_str = "unavailable_error"; - code = 503; - break; - case ERROR_TYPE_EXCEED_CONTEXT_SIZE: - type_str = "exceed_context_size_error"; - code = 400; - break; - } - return json { - {"code", code}, - {"message", message}, - {"type", type_str}, - }; -} - -struct server_task_result_error : server_task_result { - int index = 0; - error_type err_type = ERROR_TYPE_SERVER; - std::string err_msg; - - // for ERROR_TYPE_EXCEED_CONTEXT_SIZE - int32_t n_prompt_tokens = 0; - int32_t n_ctx = 0; - - virtual bool is_error() override { - return true; - } - - virtual json to_json() override { - json res = format_error_response(err_msg, err_type); - if (err_type == ERROR_TYPE_EXCEED_CONTEXT_SIZE) { - res["n_prompt_tokens"] = n_prompt_tokens; - res["n_ctx"] = n_ctx; - } - return res; - } -}; - -struct server_task_result_metrics : server_task_result { - int n_idle_slots; - int n_processing_slots; - int n_tasks_deferred; - int64_t t_start; - - // TODO: somehow reuse server_metrics in the future, instead of duplicating the fields - uint64_t n_prompt_tokens_processed_total = 0; - uint64_t t_prompt_processing_total = 0; - uint64_t n_tokens_predicted_total = 0; - uint64_t t_tokens_generation_total = 0; - - uint64_t n_tokens_max = 0; - - uint64_t n_prompt_tokens_processed = 0; - uint64_t t_prompt_processing = 0; - - uint64_t n_tokens_predicted = 0; - uint64_t t_tokens_generation = 0; - - uint64_t n_decode_total = 0; - uint64_t n_busy_slots_total = 0; - - // while we can also use std::vector this requires copying the slot object which can be quite messy - // therefore, we use json to temporarily store the slot.to_json() result - json slots_data = json::array(); - - virtual json to_json() override { - return json { - { "idle", n_idle_slots }, - { "processing", n_processing_slots }, - { "deferred", n_tasks_deferred }, - { "t_start", t_start }, - - { "n_prompt_tokens_processed_total", n_prompt_tokens_processed_total }, - { "t_tokens_generation_total", t_tokens_generation_total }, - { "n_tokens_predicted_total", n_tokens_predicted_total }, - { "t_prompt_processing_total", t_prompt_processing_total }, - - { "n_tokens_max", n_tokens_max }, - - { "n_prompt_tokens_processed", n_prompt_tokens_processed }, - { "t_prompt_processing", t_prompt_processing }, - { "n_tokens_predicted", n_tokens_predicted }, - { "t_tokens_generation", t_tokens_generation }, - - { "n_decode_total", n_decode_total }, - { "n_busy_slots_total", n_busy_slots_total }, - - { "slots", slots_data }, - }; - } -}; - -struct server_task_result_slot_save_load : server_task_result { - std::string filename; - bool is_save; // true = save, false = load - - size_t n_tokens; - size_t n_bytes; - double t_ms; - - virtual json to_json() override { - if (is_save) { - return json { - { "id_slot", id_slot }, - { "filename", filename }, - { "n_saved", n_tokens }, - { "n_written", n_bytes }, - { "timings", { - { "save_ms", t_ms } - }}, - }; - } - - return json { - { "id_slot", id_slot }, - { "filename", filename }, - { "n_restored", n_tokens }, - { "n_read", n_bytes }, - { "timings", { - { "restore_ms", t_ms } - }}, - }; - } -}; - -struct server_task_result_slot_erase : server_task_result { - size_t n_erased; - - virtual json to_json() override { - return json { - { "id_slot", id_slot }, - { "n_erased", n_erased }, - }; - } -}; - -struct server_task_result_apply_lora : server_task_result { - virtual json to_json() override { - return json {{ "success", true }}; - } -}; - -struct server_prompt_checkpoint { - llama_pos pos_min; - llama_pos pos_max; - - std::vector data; - - size_t size() const { - return data.size(); - } +// state diagram: https://github.com/ggml-org/llama.cpp/pull/9283 +enum slot_state { + SLOT_STATE_IDLE, + SLOT_STATE_STARTED, // TODO: this state is only used for setting up the initial prompt processing; maybe merge it with launch_slot_with_task in the future + SLOT_STATE_PROCESSING_PROMPT, + SLOT_STATE_DONE_PROMPT, + SLOT_STATE_GENERATING, }; -struct server_prompt { - server_tokens tokens; - - std::vector data; - - std::list checkpoints; - - size_t size() const { - size_t res = data.size(); - - for (const auto & checkpoint : checkpoints) { - res += checkpoint.size(); - } - - return res; - } - - int n_tokens() const { - return tokens.size(); - } +enum server_state { + SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet + SERVER_STATE_READY, // Server is ready and model is loaded }; -struct server_prompt_cache { - server_prompt_cache(int32_t limit_size_mib, size_t limit_tokens) { - this->limit_size = 1024ull*1024ull*(limit_size_mib < 0 ? 0 : limit_size_mib); - this->limit_tokens = limit_tokens; - } - - std::list states; - - // in bytes, 0 = no limit - size_t limit_size = 0; - - // in tokens, 0 = no limit - size_t limit_tokens = 0; - - size_t size() const { - size_t res = 0; - - for (const auto & state : states) { - res += state.size(); - } - - return res; - } - - size_t n_tokens() const { - size_t res = 0; - - for (const auto & state : states) { - res += state.n_tokens(); - } - - return res; - } - - server_prompt * alloc(const server_prompt & prompt, size_t state_size) { - // first check if the current state is contained fully in the cache - for (auto it = states.begin(); it != states.end(); ++it) { - const int cur_lcp_len = it->tokens.get_common_prefix(prompt.tokens); - - if (cur_lcp_len == (int) prompt.tokens.size()) { - SRV_WRN("%s", " - prompt is already in the cache, skipping\n"); - return nullptr; - } - } - - // next, remove any cached prompts that are fully contained in the current prompt - for (auto it = states.begin(); it != states.end();) { - const int len = it->tokens.get_common_prefix(prompt.tokens); - - if (len == (int) it->tokens.size()) { - SRV_WRN(" - removing obsolete cached prompt with length %d\n", len); - - it = states.erase(it); - } else { - ++it; - } - } - - std::vector state_data; - - // check if we can allocate enough memory for the new state - try { - state_data.resize(state_size); - } catch (const std::bad_alloc & e) { - SRV_ERR("failed to allocate memory for prompt cache state: %s\n", e.what()); - - limit_size = std::max(1, 0.4*size()); - - SRV_WRN(" - cache size limit reduced to %.3f MiB\n", limit_size / (1024.0 * 1024.0)); - - update(); - - return nullptr; - } - - // TODO: for some reason we can't copy server_tokens, so we have to do this workaround - auto & cur = states.emplace_back(); - cur = { - /*.tokens =*/ server_tokens(prompt.tokens.get_text_tokens(), false), - /*.data =*/ std::move(state_data), - /*.checkpoints =*/ prompt.checkpoints, - }; - - return &cur; - } - - bool load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx, int32_t id_slot) { - const int lcp_best = prompt.tokens.get_common_prefix(tokens_new); - - float f_keep_best = float(lcp_best) / prompt.tokens.size(); - float sim_best = float(lcp_best) / tokens_new.size(); - - SRV_WRN(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best); - - auto it_best = states.end(); - - // find the most similar cached prompt, that would also preserve the most context - for (auto it = states.begin(); it != states.end(); ++it) { - const int lcp_cur = it->tokens.get_common_prefix(tokens_new); - - const float f_keep_cur = float(lcp_cur) / it->tokens.size(); - const float sim_cur = float(lcp_cur) / tokens_new.size(); - - // don't trash large prompts - if (f_keep_cur < 0.25f) { - continue; - } - - if (f_keep_best < f_keep_cur && sim_best < sim_cur) { - f_keep_best = f_keep_cur; - sim_best = sim_cur; - - it_best = it; - } - } - - if (it_best != states.end()) { - SRV_WRN(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best); - - const size_t size = it_best->data.size(); - const size_t n = llama_state_seq_set_data_ext(ctx, it_best->data.data(), size, id_slot, 0); - if (n != size) { - SRV_WRN("failed to restore state with size %zu\n", size); - - return false; - } - - it_best->data.clear(); - it_best->data.shrink_to_fit(); - - prompt = std::move(*it_best); - - states.erase(it_best); - } - - return true; +static bool server_task_type_need_embd(server_task_type task_type) { + switch (task_type) { + case SERVER_TASK_TYPE_EMBEDDING: + case SERVER_TASK_TYPE_RERANK: + return true; + default: + return false; } +} - void update() { - if (limit_size > 0) { - // always keep at least one state, regardless of the limits - while (states.size() > 1 && size() > limit_size) { - if (states.empty()) { - break; - } - - SRV_WRN(" - cache size limit reached, removing oldest entry (size = %.3f MiB)\n", states.front().size() / (1024.0 * 1024.0)); - - states.pop_front(); - } - } - - // average size per token - const float size_per_token = std::max(1.0f, float(size()) / (std::max(1, n_tokens()))); - - // dynamically increase the token limit if it can fit in the memory limit - const size_t limit_tokens_cur = limit_size > 0 ? std::max(limit_tokens, limit_size/size_per_token) : limit_tokens; - - if (limit_tokens > 0) { - while (states.size() > 1 && n_tokens() > limit_tokens_cur) { - if (states.empty()) { - break; - } - - SRV_WRN(" - cache token limit (%zu, est: %zu) reached, removing oldest entry (size = %.3f MiB)\n", - limit_tokens, limit_tokens_cur, states.front().size() / (1024.0 * 1024.0)); - - states.pop_front(); - } - } - - SRV_WRN(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n", - states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens, limit_tokens_cur); - - for (const auto & state : states) { - SRV_WRN(" - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n", - (const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0)); - } +static bool server_task_type_need_logits(server_task_type task_type) { + switch (task_type) { + case SERVER_TASK_TYPE_COMPLETION: + case SERVER_TASK_TYPE_INFILL: + return true; + default: + return false; } -}; +} struct server_slot { int id; @@ -2022,303 +468,6 @@ struct server_metrics { } }; -struct server_queue { - int id = 0; - bool running; - - // queues - std::deque queue_tasks; - std::deque queue_tasks_deferred; - - std::mutex mutex_tasks; - std::condition_variable condition_tasks; - - // callback functions - std::function callback_new_task; - std::function callback_update_slots; - - // Add a new task to the end of the queue - int post(server_task && task, bool front = false) { - std::unique_lock lock(mutex_tasks); - GGML_ASSERT(task.id != -1); - // if this is cancel task make sure to clean up pending tasks - if (task.type == SERVER_TASK_TYPE_CANCEL) { - cleanup_pending_task(task.id_target); - } - const int task_id = task.id; - QUE_DBG("new task, id = %d, front = %d\n", task_id, front); - if (front) { - queue_tasks.push_front(std::move(task)); - } else { - queue_tasks.push_back(std::move(task)); - } - condition_tasks.notify_one(); - return task_id; - } - - // multi-task version of post() - int post(std::vector && tasks, bool front = false) { - std::unique_lock lock(mutex_tasks); - for (auto & task : tasks) { - if (task.id == -1) { - task.id = id++; - } - // if this is cancel task make sure to clean up pending tasks - if (task.type == SERVER_TASK_TYPE_CANCEL) { - cleanup_pending_task(task.id_target); - } - QUE_DBG("new task, id = %d/%d, front = %d\n", task.id, (int) tasks.size(), front); - if (front) { - queue_tasks.push_front(std::move(task)); - } else { - queue_tasks.push_back(std::move(task)); - } - } - condition_tasks.notify_one(); - return 0; - } - - // Add a new task, but defer until one slot is available - void defer(server_task && task) { - std::unique_lock lock(mutex_tasks); - QUE_DBG("defer task, id = %d\n", task.id); - queue_tasks_deferred.push_back(std::move(task)); - condition_tasks.notify_one(); - } - - // Get the next id for creating a new task - int get_new_id() { - std::unique_lock lock(mutex_tasks); - int new_id = id++; - return new_id; - } - - // Register function to process a new task - void on_new_task(std::function callback) { - callback_new_task = std::move(callback); - } - - // Register the function to be called when all slots data is ready to be processed - void on_update_slots(std::function callback) { - callback_update_slots = std::move(callback); - } - - // Call when the state of one slot is changed, it will move one task from deferred to main queue - void pop_deferred_task() { - std::unique_lock lock(mutex_tasks); - if (!queue_tasks_deferred.empty()) { - queue_tasks.emplace_front(std::move(queue_tasks_deferred.front())); - queue_tasks_deferred.pop_front(); - } - condition_tasks.notify_one(); - } - - // end the start_loop routine - void terminate() { - std::unique_lock lock(mutex_tasks); - running = false; - condition_tasks.notify_all(); - } - - /** - * Main loop consists of these steps: - * - Wait until a new task arrives - * - Process the task (i.e. maybe copy data into slot) - * - Check if multitask is finished - * - Update all slots - */ - void start_loop() { - running = true; - - while (true) { - QUE_DBG("%s", "processing new tasks\n"); - - while (true) { - std::unique_lock lock(mutex_tasks); - if (!running) { - QUE_DBG("%s", "terminate\n"); - return; - } - if (queue_tasks.empty()) { - lock.unlock(); - break; - } - server_task task = std::move(queue_tasks.front()); - queue_tasks.pop_front(); - lock.unlock(); - - QUE_DBG("processing task, id = %d\n", task.id); - callback_new_task(std::move(task)); - } - - // all tasks in the current loop is processed, slots data is now ready - QUE_DBG("%s", "update slots\n"); - - callback_update_slots(); - - QUE_DBG("%s", "waiting for new tasks\n"); - { - std::unique_lock lock(mutex_tasks); - if (!running) { - QUE_DBG("%s", "terminate\n"); - return; - } - if (queue_tasks.empty()) { - condition_tasks.wait(lock, [&]{ - return (!queue_tasks.empty() || !running); - }); - } - } - } - } - -private: - void cleanup_pending_task(int id_target) { - // no need lock because this is called exclusively by post() - auto rm_func = [id_target](const server_task & task) { - return task.id == id_target; - }; - queue_tasks.erase( - std::remove_if(queue_tasks.begin(), queue_tasks.end(), rm_func), - queue_tasks.end()); - queue_tasks_deferred.erase( - std::remove_if(queue_tasks_deferred.begin(), queue_tasks_deferred.end(), rm_func), - queue_tasks_deferred.end()); - } -}; - -struct server_response { - bool running = true; - - // for keeping track of all tasks waiting for the result - std::unordered_set waiting_task_ids; - - // the main result queue (using ptr for polymorphism) - std::vector queue_results; - - std::mutex mutex_results; - std::condition_variable condition_results; - - // add the id_task to the list of tasks waiting for response - void add_waiting_task_id(int id_task) { - SRV_DBG("add task %d to waiting list. current waiting = %d (before add)\n", id_task, (int) waiting_task_ids.size()); - - std::unique_lock lock(mutex_results); - waiting_task_ids.insert(id_task); - } - - void add_waiting_tasks(const std::vector & tasks) { - std::unique_lock lock(mutex_results); - - for (const auto & task : tasks) { - SRV_DBG("add task %d to waiting list. current waiting = %d (before add)\n", task.id, (int) waiting_task_ids.size()); - waiting_task_ids.insert(task.id); - } - } - - // when the request is finished, we can remove task associated with it - void remove_waiting_task_id(int id_task) { - SRV_DBG("remove task %d from waiting list. current waiting = %d (before remove)\n", id_task, (int) waiting_task_ids.size()); - - std::unique_lock lock(mutex_results); - waiting_task_ids.erase(id_task); - // make sure to clean up all pending results - queue_results.erase( - std::remove_if(queue_results.begin(), queue_results.end(), [id_task](const server_task_result_ptr & res) { - return res->id == id_task; - }), - queue_results.end()); - } - - void remove_waiting_task_ids(const std::unordered_set & id_tasks) { - std::unique_lock lock(mutex_results); - - for (const auto & id_task : id_tasks) { - SRV_DBG("remove task %d from waiting list. current waiting = %d (before remove)\n", id_task, (int) waiting_task_ids.size()); - waiting_task_ids.erase(id_task); - } - } - - // This function blocks the thread until there is a response for one of the id_tasks - server_task_result_ptr recv(const std::unordered_set & id_tasks) { - while (true) { - std::unique_lock lock(mutex_results); - condition_results.wait(lock, [&]{ - if (!running) { - SRV_DBG("%s : queue result stop\n", __func__); - std::terminate(); // we cannot return here since the caller is HTTP code - } - return !queue_results.empty(); - }); - - for (size_t i = 0; i < queue_results.size(); i++) { - if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) { - server_task_result_ptr res = std::move(queue_results[i]); - queue_results.erase(queue_results.begin() + i); - return res; - } - } - } - - // should never reach here - } - - // same as recv(), but have timeout in seconds - // if timeout is reached, nullptr is returned - server_task_result_ptr recv_with_timeout(const std::unordered_set & id_tasks, int timeout) { - while (true) { - std::unique_lock lock(mutex_results); - - for (int i = 0; i < (int) queue_results.size(); i++) { - if (id_tasks.find(queue_results[i]->id) != id_tasks.end()) { - server_task_result_ptr res = std::move(queue_results[i]); - queue_results.erase(queue_results.begin() + i); - return res; - } - } - - std::cv_status cr_res = condition_results.wait_for(lock, std::chrono::seconds(timeout)); - if (!running) { - SRV_DBG("%s : queue result stop\n", __func__); - std::terminate(); // we cannot return here since the caller is HTTP code - } - if (cr_res == std::cv_status::timeout) { - return nullptr; - } - } - - // should never reach here - } - - // single-task version of recv() - server_task_result_ptr recv(int id_task) { - std::unordered_set id_tasks = {id_task}; - return recv(id_tasks); - } - - // Send a new result to a waiting id_task - void send(server_task_result_ptr && result) { - SRV_DBG("sending result for task id = %d\n", result->id); - - std::unique_lock lock(mutex_results); - for (const auto & id_task : waiting_task_ids) { - if (result->id == id_task) { - SRV_DBG("task id = %d pushed to result queue\n", result->id); - - queue_results.emplace_back(std::move(result)); - condition_results.notify_all(); - return; - } - } - } - - // terminate the waiting loop - void terminate() { - running = false; - condition_results.notify_all(); - } -}; - struct server_context { common_params params_base; @@ -3323,7 +1472,7 @@ struct server_context { res->slots_data = std::move(slots_data); res->n_idle_slots = n_idle_slots; res->n_processing_slots = n_processing_slots; - res->n_tasks_deferred = queue_tasks.queue_tasks_deferred.size(); + res->n_tasks_deferred = queue_tasks.queue_tasks_deferred_size(); res->t_start = metrics.t_start; res->n_prompt_tokens_processed_total = metrics.n_prompt_tokens_processed_total; @@ -4645,7 +2794,7 @@ struct server_routes { json default_generation_settings_for_props; { - slot_params params; + task_params params; params.sampling = ctx_server.params_base.sampling; @@ -4784,7 +2933,7 @@ struct server_routes { std::string prompt = json_value(data, "prompt", std::string()); std::vector tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, false, true); SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size()); - data["prompt"] = format_infill( + data["prompt"] = format_prompt_infill( ctx_server.vocab, data.at("input_prefix"), data.at("input_suffix"), @@ -4939,8 +3088,7 @@ struct server_routes { } } - const json data = format_tokenizer_response(tokens_response); - res->ok(data); + res->ok(json{{"tokens", std::move(tokens_response)}}); return res; }; @@ -4951,11 +3099,10 @@ struct server_routes { std::string content; if (body.count("tokens") != 0) { const llama_tokens tokens = body.at("tokens"); - content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend()); + content = tokens_to_str(ctx_server.ctx, tokens); } - const json data = format_detokenized_response(content); - res->ok(data); + res->ok(json{{"content", std::move(content)}}); return res; }; @@ -5009,7 +3156,7 @@ struct server_routes { std::vector tasks; tasks.reserve(documents.size()); for (size_t i = 0; i < documents.size(); i++) { - auto tmp = format_rerank(ctx_server.model, ctx_server.vocab, ctx_server.mctx, query, documents[i]); + auto tmp = format_prompt_rerank(ctx_server.model, ctx_server.vocab, ctx_server.mctx, query, documents[i]); server_task task = server_task(SERVER_TASK_TYPE_RERANK); task.id = ctx_server.queue_tasks.get_new_id(); task.index = i; @@ -5460,10 +3607,10 @@ struct server_routes { } }; -std::function shutdown_handler; -std::atomic_flag is_terminating = ATOMIC_FLAG_INIT; +static std::function shutdown_handler; +static std::atomic_flag is_terminating = ATOMIC_FLAG_INIT; -inline void signal_handler(int signal) { +static inline void signal_handler(int signal) { if (is_terminating.test_and_set()) { // in case it hangs, we can force terminate the server by hitting Ctrl+C twice // this is for better developer experience, we can remove when the server is stable enough @@ -5632,6 +3779,7 @@ int main(int argc, char ** argv) { ctx_server.queue_tasks.terminate(); }; + // TODO: refactor in common/console #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) struct sigaction sigint_action; sigint_action.sa_handler = signal_handler; From b61de2b2df4ff07e6d6de96320fb311d96908b7a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Mon, 24 Nov 2025 15:50:55 +0100 Subject: [PATCH 21/37] convert : allow quantizing lora again (#17453) --- convert_hf_to_gguf.py | 2 +- convert_lora_to_gguf.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 6cbaee03dfd..d24a4682f3d 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -565,7 +565,7 @@ def prepare_tensors(self): gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF, ) ) - or not new_name.endswith(".weight") + or new_name[-7:] not in (".weight", ".lora_a", ".lora_b") ): data_qtype = gguf.GGMLQuantizationType.F32 diff --git a/convert_lora_to_gguf.py b/convert_lora_to_gguf.py index 57c6cd0df1d..b0adde8a8b4 100755 --- a/convert_lora_to_gguf.py +++ b/convert_lora_to_gguf.py @@ -242,7 +242,7 @@ def parse_args() -> argparse.Namespace: help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", ) parser.add_argument( - "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16", + "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f32", help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type", ) parser.add_argument( From 0543f928a3ae576e6e16d3bbf02c0bf9fddba688 Mon Sep 17 00:00:00 2001 From: "Jiacheng (Jason) Chen" <76919340+jiachengjason@users.noreply.github.com> Date: Mon, 24 Nov 2025 14:00:10 -0500 Subject: [PATCH 22/37] HIP: WMMA-MMQ kernels for RDNA 4 (#17156) * first commit naive test to enable mmq for RDNA4 * adding appropriate WMMA instructions * git rebase on top of master: fixing the correctness of the mat mul operations, updating layout mappings for RDNA4 * clean up merge conflicts * add comments and code clean up * PR clean up, addressed comments * enable MMQ fallback on RDNA4 * addressed comments: add guards in load generic, separate wmma branch for use_mmq function * Revert build-xcframework.sh * Formating: remove trailing whitespace * revert CMake files * clean up after rebase: remove duplicated change, revert cmake files * clean up after rebase: revert changes from build-xcframework.sh * clean up: remove extra space line in mma.cuh * Revert "clean up: remove extra space line in mma.cuh" This reverts commit b39ed57c4529906466bd0bc7c2a86e08fc2f8bee. --- ggml/src/ggml-cuda/mma.cuh | 131 ++++++++--- ggml/src/ggml-cuda/mmq.cu | 8 +- ggml/src/ggml-cuda/mmq.cuh | 437 +++++++++++++++++++++++++------------ 3 files changed, 408 insertions(+), 168 deletions(-) diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh index c3c4b779965..caa08b360b5 100644 --- a/ggml/src/ggml-cuda/mma.cuh +++ b/ggml/src/ggml-cuda/mma.cuh @@ -73,34 +73,7 @@ namespace ggml_cuda_mma { static constexpr int I = I_; static constexpr int J = J_; -#if defined(GGML_USE_HIP) -#if defined(RDNA4) - static constexpr int ne = I * J / 32; - T x[ne] = {0}; - - static constexpr __device__ bool supported() { - if (I == 16 && J == 16) return true; - return false; - } - - static __device__ __forceinline__ int get_i(const int l) { - if constexpr (I == 16 && J == 16) { - return 8 * (threadIdx.x / 16) + l; - } else { - NO_DEVICE_CODE; - return -1; - } - } - - static __device__ __forceinline__ int get_j(const int l) { - if constexpr (I == 16 && J == 16) { - return threadIdx.x % 16; - } else { - NO_DEVICE_CODE; - return -1; - } - } -#else +#if defined(AMD_MFMA_AVAILABLE) static constexpr int ne = I * J / 64; T x[ne] = {0}; @@ -146,7 +119,6 @@ namespace ggml_cuda_mma { return -1; } } -#endif // defined(RDNA4) #elif __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA static constexpr int ne = I * J / 32; T x[ne] = {0}; @@ -177,6 +149,34 @@ namespace ggml_cuda_mma { return -1; } } +#elif defined(AMD_WMMA_AVAILABLE) +#if defined(RDNA4) + static constexpr int ne = I * J / 32; + T x[ne] = {0}; + + static constexpr __device__ bool supported() { + if (I == 16 && J == 16) return true; + return false; + } + + static __device__ __forceinline__ int get_i(const int l) { + if constexpr (I == 16 && J == 16) { + return 8 * (threadIdx.x / 16) + l; + } else { + NO_DEVICE_CODE; + return -1; + } + } + + static __device__ __forceinline__ int get_j(const int l) { + if constexpr (I == 16 && J == 16) { + return threadIdx.x % 16; + } else { + NO_DEVICE_CODE; + return -1; + } + } +#endif #else static constexpr int ne = I * J / 32; T x[ne] = {0}; @@ -437,7 +437,20 @@ namespace ggml_cuda_mma { xi[0] = xs[0]; } #elif defined(AMD_WMMA_AVAILABLE) - ggml_cuda_memcpy_1(t.x, xs0 + t.get_i(0) * stride + t.get_j(0)); + if constexpr (I == 16 && J == 4) { + int64_t * xi = (int64_t *) t.x; + const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 2 * (threadIdx.x / t.I)); + xi[0] = xs[0]; + }else if constexpr (I == 16 && J == 8) { + int64_t * xi = (int64_t *) t.x; + const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 4 * (threadIdx.x / t.I)); + xi[0] = xs[0]; + + const int64_t * xs1 = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 4 * (threadIdx.x / t.I) + 2); + xi[1] = xs1[0]; + }else{ + NO_DEVICE_CODE; + } #else #pragma unroll for (int l = 0; l < t.ne; ++l) { @@ -772,6 +785,36 @@ namespace ggml_cuda_mma { acc[0], 0, 0, 0); #endif // defined(CDNA3) + +#elif defined(AMD_WMMA_AVAILABLE) + using int32x2_t = __attribute__((__vector_size__(2 * sizeof(int)))) int; + int32x2_t * a_vec = (int32x2_t *) A.x; + int32x2_t * b_vec = (int32x2_t *) B.x; + + using int32x8_t = __attribute__((__vector_size__(8 * sizeof(int)))) int; + int32x8_t * acc = (int32x8_t *) D.x; + +#if defined(RDNA4) + + acc[0] = __builtin_amdgcn_wmma_i32_16x16x16_iu8_w32_gfx12( + true, + a_vec[0], + true, + b_vec[0], + acc[0], + true + ); + + acc[0] = __builtin_amdgcn_wmma_i32_16x16x16_iu8_w32_gfx12( + true, + a_vec[1], + true, + b_vec[1], + acc[0], + true + ); +#endif // defined(RDNA4) + #else GGML_UNUSED_VARS(D, A, B); NO_DEVICE_CODE; @@ -798,6 +841,7 @@ namespace ggml_cuda_mma { acc[0], 0, 0, 0); #endif // defined(CDNA3) + #else GGML_UNUSED_VARS(D, A, B); NO_DEVICE_CODE; @@ -842,4 +886,31 @@ namespace ggml_cuda_mma { mma(D16[1], A16[1], B); #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE } + +static __device__ __forceinline__ void mma( + tile<16, 16, int> & D, const tile<16, 4, int> & A, const tile<16, 4, int> & B) { +#if defined(AMD_WMMA_AVAILABLE) + using int32x2_t = __attribute__((__vector_size__(2 * sizeof(int)))) int; + int32x2_t * a_vec = (int32x2_t *) A.x; + int32x2_t * b_vec = (int32x2_t *) B.x; + + using int32x8_t = __attribute__((__vector_size__(8 * sizeof(int)))) int; + int32x8_t * acc = (int32x8_t *) D.x; + + acc[0] = __builtin_amdgcn_wmma_i32_16x16x16_iu8_w32_gfx12( + true, + a_vec[0], + true, + b_vec[0], + acc[0], + false + ); +#else + GGML_UNUSED(D); + GGML_UNUSED(A); + GGML_UNUSED(B); + NO_DEVICE_CODE; +#endif + } } + diff --git a/ggml/src/ggml-cuda/mmq.cu b/ggml/src/ggml-cuda/mmq.cu index a2c8760abea..03ceba874d8 100644 --- a/ggml/src/ggml-cuda/mmq.cu +++ b/ggml/src/ggml-cuda/mmq.cu @@ -306,5 +306,11 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) { return false; } - return (!GGML_CUDA_CC_IS_RDNA4(cc) && !GGML_CUDA_CC_IS_RDNA3(cc) && !GGML_CUDA_CC_IS_CDNA(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE; + if (amd_wmma_available(cc)) { + if (GGML_CUDA_CC_IS_RDNA4(cc)) { + return true; + } + } + + return (!GGML_CUDA_CC_IS_RDNA3(cc) && !GGML_CUDA_CC_IS_CDNA(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE; } diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh index 2e133b6bda8..99760d56c72 100644 --- a/ggml/src/ggml-cuda/mmq.cuh +++ b/ggml/src/ggml-cuda/mmq.cuh @@ -92,7 +92,7 @@ struct tile_x_sizes { }; static int get_mmq_x_max_host(const int cc) { - return (amd_mfma_available(cc) || turing_mma_available(cc)) ? 128 : + return (amd_mfma_available(cc) || turing_mma_available(cc) || amd_wmma_available(cc)) ? 128 : GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA ? #ifdef GGML_CUDA_FORCE_MMQ 128 : 64; @@ -102,7 +102,7 @@ static int get_mmq_x_max_host(const int cc) { } static constexpr __device__ int get_mmq_x_max_device() { -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) return 128; #else // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) @@ -121,7 +121,7 @@ static constexpr __device__ int get_mmq_x_max_device() { #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA #endif // defined(GGML_USE_HIP) -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } static int get_mmq_y_host(const int cc) { @@ -231,7 +231,7 @@ static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) { #define MMQ_TILE_Y_K (MMQ_TILE_NE_K + MMQ_TILE_NE_K/QI8_1) static int mmq_get_granularity_host(const int mmq_x, const int cc) { - if (amd_mfma_available(cc)) { + if (amd_mfma_available(cc) || amd_wmma_available(cc)) { return mmq_x >= 128 ? 32 : 16; } else if (turing_mma_available(cc) && mmq_x >= 48) { return 16; @@ -240,7 +240,7 @@ static int mmq_get_granularity_host(const int mmq_x, const int cc) { } } -#if defined(AMD_MFMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) static constexpr __device__ int mmq_get_granularity_device(const int mmq_x) { return mmq_x >= 128 ? 32 : 16; } @@ -265,7 +265,7 @@ static int mmq_get_nwarps_host(const int /*cc*/, const int warp_size) { #endif // (GGML_USE_HIP) static constexpr __device__ int mmq_get_nwarps_device() { -#if defined(AMD_MFMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) return 8; #else return 256/ggml_cuda_get_physical_warp_size(); @@ -279,14 +279,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + 2*MMQ_TILE_NE_K); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_0, mmq_y); int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR4_0); constexpr int nrows = warp_size / threads_per_row; @@ -305,7 +305,7 @@ template static __device__ __forceinline__ void loa const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbx; const int qs0 = get_int_b2(bxi->qs, kqsx); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + 0] = __vsubss4((qs0 >> 0) & 0x0F0F0F0F, 0x08080808); x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + QI4_0] = __vsubss4((qs0 >> 4) & 0x0F0F0F0F, 0x08080808); #else @@ -327,11 +327,11 @@ template static __device__ __forceinline__ void loa const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbxd; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = bxi->d; #else x_df[i*(MMQ_TILE_NE_K/QI4_0) + i/QI4_0 + kbxd] = bxi->d; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -382,14 +382,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; half2 * x_dm = (half2 *) (x_qs + 2*MMQ_TILE_NE_K); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_1, mmq_y); int * x_qs = (int *) x_tile; half2 * x_dm = (half2 *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR4_1); constexpr int nrows = warp_size / threads_per_row; @@ -408,12 +408,12 @@ template static __device__ __forceinline__ void loa const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbx; const int qs0 = get_int_b4(bxi->qs, kqsx); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + 0] = (qs0 >> 0) & 0x0F0F0F0F; x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + QI4_1] = (qs0 >> 4) & 0x0F0F0F0F; #else x_qs[i*(MMQ_TILE_NE_K + 1) + txi] = qs0; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI4_1; @@ -430,11 +430,11 @@ template static __device__ __forceinline__ void loa const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbxd; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + kbxd] = bxi->dm; #else x_dm[i*(MMQ_TILE_NE_K/QI4_1) + i/QI4_1 + kbxd] = bxi->dm; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -485,14 +485,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_0, mmq_y); int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR5_0); constexpr int nrows = warp_size / threads_per_row; @@ -527,13 +527,13 @@ template static __device__ __forceinline__ void loa qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 qs1 = __vsubss4(qs1, 0x10101010); // subtract 16 -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + 0] = qs0; x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + kbx*(2*QI5_0) + kqsx + 0] = qs0; x_qs[i*(2*MMQ_TILE_NE_K + 1) + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI5_0; @@ -550,11 +550,11 @@ template static __device__ __forceinline__ void loa const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbxd; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = bxi->d; #else x_df[i*(MMQ_TILE_NE_K/QI5_0) + i/QI5_0 + kbxd] = bxi->d; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -563,14 +563,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; half2 * x_dm = (half2 *) (x_qs + 2*MMQ_TILE_NE_K); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_1, mmq_y); int * x_qs = (int *) x_tile; half2 * x_dm = (half2 *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR5_1); constexpr int nrows = warp_size / threads_per_row; @@ -603,13 +603,13 @@ template static __device__ __forceinline__ void loa qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + 0] = qs0; x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + kbx*(2*QI5_1) + kqsx + 0] = qs0; x_qs[i*(2*MMQ_TILE_NE_K + 1) + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI5_1; @@ -626,11 +626,11 @@ template static __device__ __forceinline__ void loa const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbxd; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + kbxd] = bxi->dm; #else x_dm[i*(MMQ_TILE_NE_K/QI5_1) + i/QI5_1 + kbxd] = bxi->dm; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -639,14 +639,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_tile + 2*MMQ_TILE_NE_K); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y); int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) // MMQ_ITER_K / (4 * QR8_0) == 64 required. but NV has only 32 threads per warp constexpr int threads_per_row = 32; @@ -665,13 +665,13 @@ template static __device__ __forceinline__ void loa const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbx; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 0 + txi] = get_int_b2(bxi[0].qs, kqsx); x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + MMQ_TILE_NE_K + txi] = get_int_b2(bxi[MMQ_TILE_NE_K/QI8_0].qs, kqsx); #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + 0 + txi] = get_int_b2(bxi[0].qs, kqsx); x_qs[i*(2*MMQ_TILE_NE_K + 1) + MMQ_TILE_NE_K + txi] = get_int_b2(bxi[MMQ_TILE_NE_K/QI8_0].qs, kqsx); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } constexpr int blocks_per_tile_x_row = 2*MMQ_TILE_NE_K / QI8_0; @@ -688,11 +688,11 @@ template static __device__ __forceinline__ void loa const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbxd; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = bxi->d; #else x_df[i*(2*MMQ_TILE_NE_K/QI8_0) + i/(QI8_0/2) + kbxd] = bxi->d; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -701,14 +701,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_MXFP4, mmq_y); int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR_MXFP4); constexpr int nrows = warp_size / threads_per_row; @@ -730,13 +730,13 @@ template static __device__ __forceinline__ void loa const int2 v = get_int_from_table_16(aux_q4, kvalues_mxfp4); const int k0 = kbx * (2 * QI_MXFP4) + kqsx; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + k0 + 0] = v.x; x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + k0 + QI_MXFP4] = v.y; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + 0] = v.x; x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + QI_MXFP4] = v.y; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI_MXFP4; @@ -753,11 +753,11 @@ template static __device__ __forceinline__ void loa const block_mxfp4 * bxi = (const block_mxfp4 *) x + kbx0 + i*stride + kbxd; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q8_1 + kbxd] = ggml_cuda_e8m0_to_fp32(bxi->e)*0.5f; #else x_df[i*(MMQ_TILE_NE_K/QI_MXFP4) + i/QI_MXFP4 + kbxd] = ggml_cuda_e8m0_to_fp32(bxi->e)*0.5f; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -796,7 +796,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a( template static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma( const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { -#if defined(AMD_MFMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) typedef tile<16, 8, int> tile_A; typedef tile<16, 8, int> tile_B; typedef tile<16, 16, int> tile_C; @@ -927,7 +927,7 @@ static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma( } } } -#endif // defined(AMD_MFMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } template @@ -965,7 +965,7 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_dp4a( template static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma( const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) { -#if defined(AMD_MFMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) typedef tile<16, 8, int> tile_A; typedef tile<16, 8, int> tile_B; typedef tile<16, 16, int> tile_C; @@ -1087,7 +1087,7 @@ static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma( } } } -#endif // defined(AMD_MFMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } // Used for Q3_K, IQ2_S, and IQ2_XS @@ -1170,6 +1170,54 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma( tile_C C; mma(C, A[n], B[0]); +#pragma unroll + for (int l = 0; l < tile_C::ne; ++l) { + const int i = i0 + n*tile_C::I + tile_C::get_i(l); + sum[(j0/tile_C::J + n)*tile_C::ne + l] += C.x[l] * x_df[i*MMQ_MMA_TILE_X_K_Q3_K + k0/4] * dB; + } + } + } + } +#elif defined(AMD_WMMA_AVAILABLE) //wmma instructions can handle 16x4 tiles, does not require loading 64x2 tiles + typedef tile<16, 4, int> tile_A; + typedef tile<16, 4, int> tile_B; + typedef tile<16, 16, int> tile_C; + + constexpr int granularity = mmq_get_granularity_device(mmq_x); + constexpr int rows_per_warp = granularity; + constexpr int ntx = rows_per_warp/tile_C::I; // Number of x minitiles per warp. + + y += (threadIdx.y % ntx) * (tile_C::J*MMQ_TILE_Y_K); + + const int * x_qs = (const int *) x; + const float * x_df = (const float *) x_qs + MMQ_TILE_NE_K*2; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + + const int i0 = (threadIdx.y / ntx) * rows_per_warp; + + for (int k01 = 0; k01 < MMQ_TILE_NE_K; k01 += 4) { + const int k0 = k00 + k01; + + tile_A A[ntx]; +#pragma unroll + for (int n = 0; n < ntx; ++n) { + load_generic(A[n], x_qs + (i0 + n*tile_A::I)*MMQ_MMA_TILE_X_K_Q3_K + k0, MMQ_MMA_TILE_X_K_Q3_K); + } + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += ntx*tile_C::J) { + tile_B B; + load_generic(B, y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K); + + const int j = j0 + tile_C::get_j(0); + const float dB = y_df[j*MMQ_TILE_Y_K + k01/QI8_1]; + +#pragma unroll + for (int n = 0; n < ntx; ++n) { + tile_C C; + mma(C, A[n], B); + #pragma unroll for (int l = 0; l < tile_C::ne; ++l) { const int i = i0 + n*tile_C::I + tile_C::get_i(l); @@ -1257,21 +1305,21 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma( #else GGML_UNUSED_VARS(x, y, sum, k00); NO_DEVICE_CODE; -#endif // AMD_MFMA_AVAILABLE +#endif // AMD_MFMA_AVAILABLE || AMD_WMMA_AVAILABLE } template static __device__ __forceinline__ void load_tiles_q2_K( const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) { constexpr int nwarps = mmq_get_nwarps_device(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; half2 * x_dm = (half2 *) (x_qs + 2*MMQ_TILE_NE_K); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q2_K, mmq_y); int * x_qs = (int *) x_tile; half2 * x_dm = (half2 *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR2_K); constexpr int nrows = ggml_cuda_get_physical_warp_size() / threads_per_row; @@ -1295,11 +1343,11 @@ template static __device__ __forceinline__ void loa const int x_qs_k = (x_ql_0 >> (2*l)) & 0x03030303; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q2_K + k] = x_qs_k; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + k] = x_qs_k; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } const int sc_m = bxi->scales[kqsx]; @@ -1310,11 +1358,11 @@ template static __device__ __forceinline__ void loa const half2 x_dm_ik = make_half2(bxi_dmf.x*(sc_m & 0x0F), bxi_dmf.y*(sc_m >> 4)); #endif // FAST_FP16_AVAILABLE -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + kqsx] = x_dm_ik; #else x_dm[i*(MMQ_TILE_NE_K + 1) + kqsx] = x_dm_ik; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -1438,6 +1486,72 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma( tile_C Cd; mma(Cd, A[n], B[0]); +#pragma unroll + for (int l = 0; l < tile_C::ne; ++l) { + const int i = i0 + n*tile_C::I + tile_C::get_i(l); + const float2 dm = __half22float2(x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + k0/4]); + float tmp = Cd.x[l]*dm.x; + if (k01 >= MMQ_TILE_NE_K * 3/4) { + tmp -= Cm.x[l]*dm.y; + } + sum[(j0/tile_C::J + n)*tile_C::ne + l] += tmp*dB; + sum[(j0/tile_C::J + n)*tile_C::ne + l] -= dm.y*sB; + } + } + } + } +#elif defined(AMD_WMMA_AVAILABLE) //wmma instructions can handle 16x4 tiles, does not require loading 64x2 tiles + + typedef tile<16, 4, int> tile_A; + typedef tile<16, 4, int> tile_B; + typedef tile<16, 16, int> tile_C; + + constexpr int granularity = mmq_get_granularity_device(mmq_x); + constexpr int rows_per_warp = granularity; + constexpr int ntx = rows_per_warp/tile_C::I; // Number of x minitiles per warp. + + y += (threadIdx.y % ntx) * (tile_C::J*MMQ_TILE_Y_K); + + const int * x_qs = (const int *) x; + const half2 * x_dm = (const half2 *) x_qs + MMQ_TILE_NE_K*2; + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + + const int i0 = (threadIdx.y / ntx) * rows_per_warp; + + for (int k01 = 0; k01 < MMQ_TILE_NE_K; k01 += 4) { + const int k0 = k00 + k01; + + tile_A A[ntx]; +#pragma unroll + for (int n = 0; n < ntx; ++n) { + load_generic(A[n], x_qs + (i0 + n*tile_A::I)*MMQ_MMA_TILE_X_K_Q2_K + k0, MMQ_MMA_TILE_X_K_Q2_K); + } + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += ntx*tile_C::J) { + tile_B B; + load_generic(B, y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K); + + const int j = j0 + tile_C::get_j(0); + const float dB = (k01 < MMQ_TILE_NE_K/2) ? __half22float2(y_ds[j*MMQ_TILE_Y_K]).x : __half22float2(y_ds[j*MMQ_TILE_Y_K]).y; + const float sB = (k01 >= MMQ_TILE_NE_K * 3/4) ? 0 + : (((k01/4)%2) ? __half22float2(y_ds[j*MMQ_TILE_Y_K + (1 + k01/QI8_1)]).y + : __half22float2(y_ds[j*MMQ_TILE_Y_K + (1 + k01/QI8_1)]).x); + + tile_C Cm; + if (k01 >= MMQ_TILE_NE_K * 3/4) { + tile_A A1; + A1.x[0] = 0x01010101; + A1.x[1] = 0x01010101; + mma(Cm, A1, B); + } + +#pragma unroll + for (int n = 0; n < ntx; ++n) { + tile_C Cd; + mma(Cd, A[n], B); + #pragma unroll for (int l = 0; l < tile_C::ne; ++l) { const int i = i0 + n*tile_C::I + tile_C::get_i(l); @@ -1574,7 +1688,7 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma( #else GGML_UNUSED_VARS(x, y, sum, k00); NO_DEVICE_CODE; -#endif // AMD_MFMA_AVAILABLE +#endif // AMD_MFMA_AVAILABLE || AMD_WMMA_AVAILABLE } template static __device__ __forceinline__ void load_tiles_q3_K( @@ -1582,7 +1696,7 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); #else @@ -1618,11 +1732,11 @@ template static __device__ __forceinline__ void loa const int x_qs_k = __vsubss4(x_ql_k | x_qh_k, 0x04040404); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + k] = x_qs_k; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + k] = x_qs_k; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -1649,7 +1763,7 @@ template static __device__ __forceinline__ void loa const int sc = __vsubss4(sc_low | sc_high, 0x20202020); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) const int8_t * sc8 = (const int8_t *) ≻ const float d = bxi->d; @@ -1659,10 +1773,10 @@ template static __device__ __forceinline__ void loa } #else x_sc[i*(MMQ_TILE_NE_K/8) + i/8 + ksc] = sc; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } -#if !(defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)) +#if !(defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)) #pragma unroll for (int i0 = 0; i0 < mmq_y; i0 += nwarps*warp_size) { int i = (i0 + threadIdx.y*warp_size + threadIdx.x) % mmq_y; @@ -1675,7 +1789,7 @@ template static __device__ __forceinline__ void loa x_df[i] = bxi->d; } -#endif // !(defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)) +#endif // !(defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE)) || defined(AMD_WMMA_AVAILABLE) } template @@ -1728,7 +1842,7 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; half2 * x_dm = (half2 *) (x_qs + 2*MMQ_TILE_NE_K); #else @@ -1736,7 +1850,7 @@ template static __device__ __forceinline__ void loa int * x_qs = (int *) x_tile; half2 * x_dm = (half2 *) (x_qs + txs.qs); int * x_sc = (int *) (x_dm + txs.dm); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR4_K); constexpr int nrows = warp_size / threads_per_row; @@ -1753,19 +1867,19 @@ template static __device__ __forceinline__ void loa const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride; const int qs0 = get_int_b4(bxi->qs, txi); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(txi/8) + txi % 8 + 0] = (qs0 >> 0) & 0x0F0F0F0F; x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(txi/8) + txi % 8 + 8] = (qs0 >> 4) & 0x0F0F0F0F; #else x_qs[i*(MMQ_TILE_NE_K + 1) + txi] = qs0; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int rows_per_warp = warp_size / 2; #pragma unroll for (int i0 = 0; i0 < mmq_y; i0 += nwarps*rows_per_warp) { -#if defined(AMD_MFMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) // Need if on AMD instead of % because warp_size == 64 // This causes double work and throughput loss (MI300X) // H100 loses about 100 t/s with 'if' condition over '%' @@ -1774,7 +1888,7 @@ template static __device__ __forceinline__ void loa #else int i = (i0 + threadIdx.y*rows_per_warp + threadIdx.x/2) % mmq_y; { -#endif // defined(AMD_MFMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) if (need_check) { i = min(i, i_max); } @@ -1829,7 +1943,7 @@ template static __device__ __forceinline__ void loa x_sc[i*(MMQ_TILE_NE_K/8) + i/8 + ksc] = scales8; } -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } template @@ -1872,7 +1986,7 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; half2 * x_dm = (half2 *) (x_qs + MMQ_TILE_NE_K*2); #else @@ -1908,16 +2022,16 @@ template static __device__ __forceinline__ void loa const int kq0 = ky - ky % (QI5_K/2) + txi % (QI5_K/4) + 0; const int kq1 = ky - ky % (QI5_K/2) + txi % (QI5_K/4) + QI5_K/4; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq0] = ql0 | qh0; x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq1] = ql1 | qh1; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + kq0] = ql0 | qh0; x_qs[i*(2*MMQ_TILE_NE_K + 1) + kq1] = ql1 | qh1; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int rows_per_warp = warp_size / 2; #pragma unroll for (int i0 = 0; i0 < mmq_y; i0 += nwarps*rows_per_warp) { @@ -1930,7 +2044,7 @@ template static __device__ __forceinline__ void loa #else int i = (i0 + threadIdx.y*rows_per_warp + threadIdx.x/2) % mmq_y; { -#endif // defined(AMD_MFMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) if (need_check) { i = min(i, i_max); } @@ -1986,7 +2100,7 @@ template static __device__ __forceinline__ void loa x_sc[i*(MMQ_TILE_NE_K/8) + i/8 + ksc] = scales8; } -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } template @@ -2029,7 +2143,7 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); int * x_sc = (int *) (x_df + MMQ_TILE_NE_K/QI6_K); @@ -2038,7 +2152,7 @@ template static __device__ __forceinline__ void loa int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); int * x_sc = (int *) (x_df + txs.dm); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR6_K); constexpr int nrows = warp_size / threads_per_row; @@ -2065,13 +2179,13 @@ template static __device__ __forceinline__ void loa const int kq0 = 2*txi - txi % (QI6_K/2) + 0; const int kq1 = 2*txi - txi % (QI6_K/2) + QI6_K/2; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq0] = __vsubss4(ql0 | qh0, 0x20202020); x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq1] = __vsubss4(ql1 | qh1, 0x20202020); #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020); x_qs[i*(2*MMQ_TILE_NE_K + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } #pragma unroll @@ -2084,11 +2198,11 @@ template static __device__ __forceinline__ void loa const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q6_K] = bxi->d; #else x_df[i*(MMQ_TILE_NE_K/QI6_K) + i/QI6_K] = bxi->d; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } constexpr int rows_per_warp = warp_size / 4; @@ -2102,11 +2216,11 @@ template static __device__ __forceinline__ void loa const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + (threadIdx.x % (MMQ_TILE_NE_K/8)) / 4; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_sc[i*MMQ_MMA_TILE_X_K_Q6_K + threadIdx.x%4] = get_int_b2(bxi->scales, threadIdx.x % (MMQ_TILE_NE_K/8)); #else x_sc[i*(MMQ_TILE_NE_K/8) + i/8 + threadIdx.x%(MMQ_TILE_NE_K/8)] = get_int_b2(bxi->scales, threadIdx.x%(QI6_K/8)); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -2190,6 +2304,56 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma( tile_C C; mma(C, A[n], B[0]); +#pragma unroll + for (int l = 0; l < tile_C::ne; ++l) { + const int i = i0 + n*tile_C::I + tile_C::get_i(l); + const int8_t * sc = (const int8_t *) (x_sc + i*MMQ_MMA_TILE_X_K_Q6_K + k00/16); + sum[(j0/tile_C::J + n)*tile_C::ne + l] += C.x[l] * sc[k01/4] * x_df[i*MMQ_MMA_TILE_X_K_Q6_K] * dB; + } + } + } + } +#elif defined(AMD_WMMA_AVAILABLE) //wmma instructions can handle 16x4 tiles, does not require loading 64x2 tiles + typedef tile<16, 4, int> tile_A; + typedef tile<16, 4, int> tile_B; + typedef tile<16, 16, int> tile_C; + + constexpr int granularity = mmq_get_granularity_device(mmq_x); + constexpr int rows_per_warp = granularity; + constexpr int ntx = rows_per_warp/tile_C::I; // Number of x minitiles per warp. + + y += (threadIdx.y % ntx) * (tile_C::J*MMQ_TILE_Y_K); + + const int * x_qs = (const int *) x; + const float * x_df = (const float *) x_qs + MMQ_TILE_NE_K*2; + const int * x_sc = (const int *) x_df + MMQ_TILE_NE_K/QI6_K; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + + const int i0 = (threadIdx.y / ntx) * rows_per_warp; + + for (int k01 = 0; k01 < MMQ_TILE_NE_K; k01 += 4) { + const int k0 = k00 + k01; + + tile_A A[ntx]; +#pragma unroll + for (int n = 0; n < ntx; ++n) { + load_generic(A[n], x_qs + (i0 + n*tile_A::I)*MMQ_MMA_TILE_X_K_Q6_K + k0, MMQ_MMA_TILE_X_K_Q6_K); + } + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += ntx*tile_C::J) { + tile_B B; + load_generic(B, y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K); + + const int j = j0 + tile_C::get_j(0); + const float dB = y_df[j*MMQ_TILE_Y_K + k01/QI8_1]; + +#pragma unroll + for (int n = 0; n < ntx; ++n) { + tile_C C; + mma(C, A[n], B); + #pragma unroll for (int l = 0; l < tile_C::ne; ++l) { const int i = i0 + n*tile_C::I + tile_C::get_i(l); @@ -2303,7 +2467,7 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma( #else GGML_UNUSED_VARS(x, y, sum, k00); NO_DEVICE_CODE; -#endif // AMD_MFMA_AVAILABLE +#endif // AMD_MFMA_AVAILABLE || AMD_WMMA_AVAILABLE } template static __device__ __forceinline__ void load_tiles_iq4_nl( @@ -2311,14 +2475,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_NL, mmq_y); int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR4_NL); constexpr int nrows = warp_size / threads_per_row; @@ -2340,13 +2504,13 @@ template static __device__ __forceinline__ void loa const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl); const int k0 = kbx * (2 * QI4_NL) + kqsx; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x; x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + QI4_NL] = v.y; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + 0] = v.x; x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + QI4_NL] = v.y; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI4_NL; @@ -2363,11 +2527,11 @@ template static __device__ __forceinline__ void loa const block_iq4_nl * bxi = (const block_iq4_nl *) x + kbx0 + i*stride + kbxd; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = __half2float(bxi->d); #else x_df[i*(MMQ_TILE_NE_K/QI4_NL) + i/QI4_NL + kbxd] = __half2float(bxi->d); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -2376,14 +2540,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_XXS, mmq_y); int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = (MMQ_ITER_K / (4 * QR2_XXS)) / 2; constexpr int nrows = warp_size / threads_per_row; @@ -2414,22 +2578,22 @@ template static __device__ __forceinline__ void loa const int signs1 = __vcmpne4(((signs_packed & 0x30) << 3) | ((signs_packed & 0xC0) << 17), 0x00000000); const int grid1 = __vsub4(grid_pos[1] ^ signs1, signs1); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid0; x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid1; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 0)] = grid0; x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 1)] = grid1; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } const int ls = aux32 >> 28; const float d = bxi->d; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = (ls*d + d/2)/4; #else x_df[i*(MMQ_TILE_NE_K/4) + i/4 + kqsx] = (ls*d + d/2)/4; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -2438,14 +2602,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); #else constexpr tile_x_sizes txs = MMQ_DP4A_TXS_Q8_0_16; int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = (MMQ_ITER_K / (4 * QR2_XS)) / 2; constexpr int nrows = warp_size / threads_per_row; @@ -2472,24 +2636,24 @@ template static __device__ __forceinline__ void loa const int grid_l = __vsub4(grid_pos[0] ^ signs[0], signs[0]); const int grid_h = __vsub4(grid_pos[1] ^ signs[1], signs[1]); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l; x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 0)] = grid_l; x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 1)] = grid_h; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } const int ls = bxi->scales[kqsx]; const float d = bxi->d; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q3_K + 2*kqsx+0] = ((ls & 0x0F)*d + d/2)/4; x_df[i*MMQ_MMA_TILE_X_K_Q3_K + 2*kqsx+1] = ((ls >> 4)*d + d/2)/4; #else x_df[i*(2*MMQ_TILE_NE_K*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls & 0x0F)*d + d/2)/4; x_df[i*(2*MMQ_TILE_NE_K*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >> 4)*d + d/2)/4; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -2498,15 +2662,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_S, mmq_y); int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) - +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = (MMQ_ITER_K / (4 * QR2_S)) / 2; constexpr int nrows = warp_size / threads_per_row; const int kqsx = threadIdx.x % threads_per_row; @@ -2539,24 +2702,24 @@ template static __device__ __forceinline__ void loa const int grid_l = __vsub4(grid_pos[0] ^ signs0, signs0); const int grid_h = __vsub4(grid_pos[1] ^ signs1, signs1); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l; x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 0)] = grid_l; x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 1)] = grid_h; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } const int ls = bxi->scales[kqsx]; const float d = bxi->d; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q3_K + 2*kqsx+0] = ((ls & 0x0F)*d + d/2)/4; x_df[i*MMQ_MMA_TILE_X_K_Q3_K + 2*kqsx+1] = ((ls >> 4)*d + d/2)/4; #else x_df[i*(2*MMQ_TILE_NE_K*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls & 0x0F)*d + d/2)/4; x_df[i*(2*MMQ_TILE_NE_K*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >> 4)*d + d/2)/4; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -2565,14 +2728,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_XXS, mmq_y); int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = (MMQ_ITER_K / (4 * QR3_XXS)) / 2; constexpr int nrows = warp_size / threads_per_row; @@ -2601,22 +2764,22 @@ template static __device__ __forceinline__ void loa const int grid_l = __vsub4(grid_pos.x ^ signs[0], signs[0]); const int grid_h = __vsub4(grid_pos.y ^ signs[1], signs[1]); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid_l; x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid_h; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 0)] = grid_l; x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l + 1)] = grid_h; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } const int ls = aux32 >> 28; const float d = bxi->d; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = (ls*d + d/2)/2; #else x_df[i*(MMQ_TILE_NE_K/4) + i/4 + kqsx] = (ls*d + d/2)/2; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -2625,14 +2788,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y); int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = (MMQ_ITER_K / (4 * QR3_S)) / 2; constexpr int nrows = warp_size / threads_per_row; @@ -2668,22 +2831,22 @@ template static __device__ __forceinline__ void loa const int grid_l = __vsub4(grid_pos.x ^ signs0, signs0); const int grid_h = __vsub4(grid_pos.y ^ signs1, signs1); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+0)] = grid_l; x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+1)] = grid_h; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l+0)] = grid_l; x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l+1)] = grid_h; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } const int ls = 1 + 2*((bxi->scales[kqsx/2] >> (((2*kqsx) << 1) & 0x04)) & 0x0F); const float d = bxi->d; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = ls*d; #else x_df[i*(MMQ_TILE_NE_K/4) + i/4 + kqsx] = ls*d; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -2692,14 +2855,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; half2 * x_ds = (half2 *) (x_qs + MMQ_TILE_NE_K*2); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y); int * x_qs = (int *) x_tile; half2 * x_ds = (half2 *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR1_S); constexpr int nrows = warp_size / threads_per_row; @@ -2727,23 +2890,23 @@ template static __device__ __forceinline__ void loa const int grid0 = (grid >> 0) & 0x0F0F0F0F; const int grid1 = (grid >> 4) & 0x0F0F0F0F; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+0)] = grid0; x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+1)] = grid1; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l+0)] = grid0; x_qs[i*(2*MMQ_TILE_NE_K + 1) + 8*kqsx + (2*l+1)] = grid1; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } const float d1q = __half2float(bxi->d) * (((qh >> 11) & 0x0E) + 1); const float delta = -1.0f + IQ1S_DELTA - (qh & 0x8000) * (2.0f*IQ1S_DELTA/0x8000); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_ds[i*MMQ_MMA_TILE_X_K_Q8_1 + kqsx] = make_half2(d1q, d1q*delta); #else x_ds[i*(MMQ_TILE_NE_K/4) + i/4 + kqsx] = make_half2(d1q, d1q*delta); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -2752,14 +2915,14 @@ template static __device__ __forceinline__ void loa constexpr int nwarps = mmq_get_nwarps_device(); constexpr int warp_size = ggml_cuda_get_physical_warp_size(); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2); #else constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_XS, mmq_y); int * x_qs = (int *) x_tile; float * x_df = (float *) (x_qs + txs.qs); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int threads_per_row = MMQ_ITER_K / (4 * QR4_XS); constexpr int nrows = warp_size / threads_per_row; @@ -2779,13 +2942,13 @@ template static __device__ __forceinline__ void loa const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl); const int k0 = 8 * (kqsx / 4) + kqsx % 4; -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x; x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 4] = v.y; #else x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + 0] = v.x; x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + 4] = v.y; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } constexpr int rows_per_warp = warp_size / 8; @@ -2804,11 +2967,11 @@ template static __device__ __forceinline__ void loa const int ls = ((bxi->scales_l[(threadIdx.x % 8)/2] >> (4*(threadIdx.x % 2))) & 0x0F) | (((bxi->scales_h >> (2*(threadIdx.x % 8))) & 0x03) << 4); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + threadIdx.x % 8] = d * (ls - 32); #else x_df[i*(MMQ_TILE_NE_K/4) + i/4 + threadIdx.x % 8] = d * (ls - 32); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) } } @@ -2848,7 +3011,7 @@ static __device__ __forceinline__ void mmq_write_back_mma( constexpr int granularity = mmq_get_granularity_device(mmq_x); constexpr int nwarps = mmq_get_nwarps_device(); -#if defined(AMD_MFMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int tileC_IJ = mmq_get_granularity_device(0); typedef tile tile_C; constexpr int rows_per_warp = granularity; @@ -2859,11 +3022,11 @@ static __device__ __forceinline__ void mmq_write_back_mma( constexpr int ntx = rows_per_warp/tile_C::I; // Number of x minitiles per warp. const int i0 = (threadIdx.y / ntx) * (ntx*tile_C::I); -#if defined(TURING_MMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) +#if defined(TURING_MMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) static_assert(nwarps*tile_C::I == mmq_y, "nwarps*tile_C::I != mmq_y"); #else GGML_UNUSED(nwarps); -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) #pragma unroll for (int j0 = 0; j0 < mmq_x; j0 += ntx*tile_C::J) { @@ -3063,13 +3226,13 @@ static __device__ __forceinline__ void mul_mat_q_process_tile( int * tile_y = data_mul_mat_q + mmq_x; int * tile_x = tile_y + GGML_PAD(mmq_x*MMQ_TILE_Y_K, nwarps*warp_size); -#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr vec_dot_mmq_t vec_dot = mmq_type_traits::vec_dot_mma; constexpr mmq_write_back_t write_back = mmq_write_back_mma; #else constexpr vec_dot_mmq_t vec_dot = mmq_type_traits::vec_dot_dp4a; constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; -#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) +#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) constexpr int blocks_per_iter = MMQ_ITER_K / qk; From 134e6940caf5c64071b7f3b7bc6c2f32f1b3a5a4 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Mon, 24 Nov 2025 21:06:17 +0100 Subject: [PATCH 23/37] llama : skip output reordering for single token batches (#17466) This commit adds a check to skip the output reordering logic when n_outputs == 1. With a single output token, the data is trivially sorted and the reordering code is currently doing unnecessary work (resetting and rebuilding output_ids to the same values). The motivation for this change is improved code clarity and avoiding confusion when debugging. While the performance impact is probably negligible, this unnecessary work happens on every decode call in llama-server when processing batches with single-token outputs. --- src/llama-context.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 70a3ec62dfc..2aa6d52a242 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -1248,7 +1248,7 @@ int llama_context::decode(const llama_batch & batch_inp) { // make the outputs have the same order they had in the user-provided batch // note: this is mostly relevant for recurrent models atm - if (!sorted_output) { + if (!sorted_output && n_outputs > 1) { GGML_ASSERT((size_t) n_outputs == out_ids.size()); // TODO: is there something more efficient which also minimizes swaps? From 3d07caa99bff9213411202b4063aa2f44e919654 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Mon, 24 Nov 2025 15:25:24 -0600 Subject: [PATCH 24/37] vulkan: more FA details in vk_perf_logger (#17443) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index bc8d3cdcb59..d78c727e53b 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -1629,6 +1629,22 @@ class vk_perf_logger { timings[name].push_back(time); return; } + if (node->op == GGML_OP_FLASH_ATTN_EXT) { + const ggml_tensor * dst = node; + const ggml_tensor * q = node->src[0]; + const ggml_tensor * k = node->src[1]; + const ggml_tensor * v = node->src[2]; + const ggml_tensor * m = node->src[3]; + std::stringstream name; + name << ggml_op_name(node->op) << + " dst(" << dst->ne[0] << "," << dst->ne[1] << "," << dst->ne[2] << "," << dst->ne[3] << "), " << + " q(" << q->ne[0] << "," << q->ne[1] << "," << q->ne[2] << "," << q->ne[3] << "), " << + " k(" << k->ne[0] << "," << k->ne[1] << "," << k->ne[2] << "," << k->ne[3] << "), " << + " v(" << v->ne[0] << "," << v->ne[1] << "," << v->ne[2] << "," << v->ne[3] << "), " << + " m(" << (m?m->ne[0]:0) << "," << (m?m->ne[1]:0) << "," << (m?m->ne[2]:0) << "," << (m?m->ne[3]:0) << ")"; + timings[name.str()].push_back(time); + return; + } timings[ggml_op_name(node->op)].push_back(time); } private: From 877566d512d02a3a288ea38c1f7b1c25ccd6c082 Mon Sep 17 00:00:00 2001 From: Aaron Teo Date: Tue, 25 Nov 2025 09:56:07 +0800 Subject: [PATCH 25/37] llama: introduce support for model-embedded sampling parameters (#17120) --- common/arg.cpp | 12 ++++++ common/common.cpp | 55 ++++++++++++++++++++++++ common/common.h | 18 ++++++++ gguf-py/gguf/constants.py | 14 ++++++ gguf-py/gguf/gguf_writer.py | 36 ++++++++++++++++ gguf-py/gguf/metadata.py | 85 +++++++++++++++++++++++++++++++++++++ include/llama.h | 18 ++++++++ src/llama-arch.cpp | 38 +++++++++++------ src/llama-arch.h | 12 ++++++ src/llama-model.cpp | 18 ++++++++ 10 files changed, 293 insertions(+), 13 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 430ab45dfe2..dd787290d25 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1232,6 +1232,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { const auto sampler_names = string_split(value, ';'); params.sampling.samplers = common_sampler_types_from_names(sampler_names, true); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS; } ).set_sparam()); add_opt(common_arg( @@ -1261,6 +1262,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex [](common_params & params, const std::string & value) { params.sampling.temp = std::stof(value); params.sampling.temp = std::max(params.sampling.temp, 0.0f); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TEMP; } ).set_sparam()); add_opt(common_arg( @@ -1268,6 +1270,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("top-k sampling (default: %d, 0 = disabled)", params.sampling.top_k), [](common_params & params, int value) { params.sampling.top_k = value; + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K; } ).set_sparam()); add_opt(common_arg( @@ -1275,6 +1278,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p), [](common_params & params, const std::string & value) { params.sampling.top_p = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_P; } ).set_sparam()); add_opt(common_arg( @@ -1282,6 +1286,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("min-p sampling (default: %.1f, 0.0 = disabled)", (double)params.sampling.min_p), [](common_params & params, const std::string & value) { params.sampling.min_p = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIN_P; } ).set_sparam()); add_opt(common_arg( @@ -1296,6 +1301,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("xtc probability (default: %.1f, 0.0 = disabled)", (double)params.sampling.xtc_probability), [](common_params & params, const std::string & value) { params.sampling.xtc_probability = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY; } ).set_sparam()); add_opt(common_arg( @@ -1303,6 +1309,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("xtc threshold (default: %.1f, 1.0 = disabled)", (double)params.sampling.xtc_threshold), [](common_params & params, const std::string & value) { params.sampling.xtc_threshold = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD; } ).set_sparam()); add_opt(common_arg( @@ -1321,6 +1328,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } params.sampling.penalty_last_n = value; params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N; } ).set_sparam()); add_opt(common_arg( @@ -1328,6 +1336,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)params.sampling.penalty_repeat), [](common_params & params, const std::string & value) { params.sampling.penalty_repeat = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT; } ).set_sparam()); add_opt(common_arg( @@ -1425,6 +1434,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", params.sampling.mirostat), [](common_params & params, int value) { params.sampling.mirostat = value; + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT; } ).set_sparam()); add_opt(common_arg( @@ -1432,6 +1442,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("Mirostat learning rate, parameter eta (default: %.1f)", (double)params.sampling.mirostat_eta), [](common_params & params, const std::string & value) { params.sampling.mirostat_eta = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA; } ).set_sparam()); add_opt(common_arg( @@ -1439,6 +1450,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex string_format("Mirostat target entropy, parameter tau (default: %.1f)", (double)params.sampling.mirostat_tau), [](common_params & params, const std::string & value) { params.sampling.mirostat_tau = std::stof(value); + params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU; } ).set_sparam()); add_opt(common_arg( diff --git a/common/common.cpp b/common/common.cpp index f3cc55247e7..0d7fd9a9371 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -8,6 +8,7 @@ #include "common.h" #include "log.h" #include "llama.h" +#include "sampling.h" #include #include @@ -949,6 +950,58 @@ std::vector fs_list_files(const std::string & path) { // Model utils // +static inline void common_init_sampler_from_model( + const llama_model * model, + common_params_sampling & sparams) { + + const uint64_t config = sparams.user_sampling_config; + + auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) { + if (config & user_config) return; + + char buf[64] = {0}; + if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) { + char * end = nullptr; + int32_t v = strtol(buf, &end, 10); + if (end && end != buf) dst = v; + } + }; + + auto get_float = [&](const char * key, float & dst, uint64_t user_config) { + if (config & user_config) return; + + char buf[128] = {0}; + if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) { + char * end = nullptr; + float v = strtof(buf, &end); + if (end && end != buf) dst = v; + } + }; + + // Sampling sequence + if (!(config & common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS)) { + char buf[512] = {0}; + if (llama_model_meta_val_str(model, llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE), buf, sizeof(buf)) > 0) { + const std::vector sampler_names = string_split(std::string(buf), ';'); + if (!sampler_names.empty()) { + sparams.samplers = common_sampler_types_from_names(sampler_names, true); + } + } + } + + get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TOP_K), sparams.top_k, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K); + get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TOP_P), sparams.top_p, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_P); + get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIN_P), sparams.min_p, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIN_P); + get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY), sparams.xtc_probability, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY); + get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD), sparams.xtc_threshold, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD); + get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TEMP), sparams.temp, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TEMP); + get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N), sparams.penalty_last_n, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N); + get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT), sparams.penalty_repeat, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT); + get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT), sparams.mirostat, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT); + get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU), sparams.mirostat_tau, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU); + get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA), sparams.mirostat_eta, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA); +} + struct common_init_result common_init_from_params(common_params & params) { common_init_result iparams; auto mparams = common_model_params_to_llama(params); @@ -960,6 +1013,8 @@ struct common_init_result common_init_from_params(common_params & params) { return iparams; } + common_init_sampler_from_model(model, params.sampling); + const llama_vocab * vocab = llama_model_get_vocab(model); auto cparams = common_context_params_to_llama(params); diff --git a/common/common.h b/common/common.h index de5b404dd88..2f23d0baa83 100644 --- a/common/common.h +++ b/common/common.h @@ -140,6 +140,22 @@ struct common_grammar_trigger { llama_token token = LLAMA_TOKEN_NULL; }; +enum common_params_sampling_config : uint64_t { + COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS = 1 << 0, + COMMON_PARAMS_SAMPLING_CONFIG_TOP_K = 1 << 1, + COMMON_PARAMS_SAMPLING_CONFIG_TOP_P = 1 << 2, + COMMON_PARAMS_SAMPLING_CONFIG_MIN_P = 1 << 3, + COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY = 1 << 4, + COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD = 1 << 5, + COMMON_PARAMS_SAMPLING_CONFIG_TEMP = 1 << 6, + COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N = 1 << 7, + COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT = 1 << 8, + COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT = 1 << 9, + COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU = 1 << 10, + COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA = 1 << 11, +}; + + // sampling parameters struct common_params_sampling { uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler @@ -172,6 +188,8 @@ struct common_params_sampling { bool no_perf = false; // disable performance metrics bool timing_per_token = false; + uint64_t user_sampling_config = 0; // bitfield to track user-specified samplers + std::vector dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 8bc558fe4b5..6f5a742e04a 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -25,6 +25,20 @@ class General: ALIGNMENT = "general.alignment" FILE_TYPE = "general.file_type" + # Recommended Sampler Parameters + SAMPLING_SEQUENCE = "general.sampling.sequence" + SAMPLING_TOP_K = "general.sampling.top_k" + SAMPLING_TOP_P = "general.sampling.top_p" + SAMPLING_MIN_P = "general.sampling.min_p" + SAMPLING_XTC_PROBABILITY = "general.sampling.xtc_probability" + SAMPLING_XTC_THRESHOLD = "general.sampling.xtc_threshold" + SAMPLING_TEMP = "general.sampling.temp" + SAMPLING_PENALTY_LAST_N = "general.sampling.penalty_last_n" + SAMPLING_PENALTY_REPEAT = "general.sampling.penalty_repeat" + SAMPLING_MIROSTAT = "general.sampling.mirostat" + SAMPLING_MIROSTAT_TAU = "general.sampling.mirostat_tau" + SAMPLING_MIROSTAT_ETA = "general.sampling.mirostat_eta" + # Authorship Metadata NAME = "general.name" AUTHOR = "general.author" diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index a051daeeb13..642ae2ae596 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -496,6 +496,42 @@ def add_custom_alignment(self, alignment: int) -> None: def add_file_type(self, ftype: int) -> None: self.add_uint32(Keys.General.FILE_TYPE, ftype) + def add_sampling_sequence(self, sequence: str) -> None: + self.add_string(Keys.General.SAMPLING_SEQUENCE, sequence) + + def add_sampling_top_k(self, top_k: int) -> None: + self.add_int32(Keys.General.SAMPLING_TOP_K, top_k) + + def add_sampling_top_p(self, top_p: float) -> None: + self.add_float32(Keys.General.SAMPLING_TOP_P, top_p) + + def add_sampling_min_p(self, min_p: float) -> None: + self.add_float32(Keys.General.SAMPLING_MIN_P, min_p) + + def add_sampling_xtc_probability(self, xtc_probability: float) -> None: + self.add_float32(Keys.General.SAMPLING_XTC_PROBABILITY, xtc_probability) + + def add_sampling_xtc_threshold(self, xtc_threshold: float) -> None: + self.add_float32(Keys.General.SAMPLING_XTC_THRESHOLD, xtc_threshold) + + def add_sampling_temp(self, temp: float) -> None: + self.add_float32(Keys.General.SAMPLING_TEMP, temp) + + def add_sampling_penalty_last_n(self, penalty_last_n: int) -> None: + self.add_int32(Keys.General.SAMPLING_PENALTY_LAST_N, penalty_last_n) + + def add_sampling_penalty_repeat(self, penalty_repeat: float) -> None: + self.add_float32(Keys.General.SAMPLING_PENALTY_REPEAT, penalty_repeat) + + def add_sampling_mirostat(self, mirostat: int) -> None: + self.add_int32(Keys.General.SAMPLING_MIROSTAT, mirostat) + + def add_sampling_mirostat_tau(self, mirostat_tau: float) -> None: + self.add_float32(Keys.General.SAMPLING_MIROSTAT_TAU, mirostat_tau) + + def add_sampling_mirostat_eta(self, mirostat_eta: float) -> None: + self.add_float32(Keys.General.SAMPLING_MIROSTAT_ETA, mirostat_eta) + def add_name(self, name: str) -> None: self.add_string(Keys.General.NAME, name) diff --git a/gguf-py/gguf/metadata.py b/gguf-py/gguf/metadata.py index 67efedbdbc5..e0d478ce95d 100644 --- a/gguf-py/gguf/metadata.py +++ b/gguf-py/gguf/metadata.py @@ -17,6 +17,20 @@ @dataclass class Metadata: + # Recommended Sampler Parameters to be written to GGUF KV Store + sampling_sequence: Optional[str] = None + sampling_top_k: Optional[int] = None + sampling_top_p: Optional[float] = None + sampling_min_p: Optional[float] = None + sampling_xtc_probability: Optional[float] = None + sampling_xtc_threshold: Optional[float] = None + sampling_temp: Optional[float] = None + sampling_penalty_last_n: Optional[int] = None + sampling_penalty_repeat: Optional[float] = None + sampling_mirostat: Optional[int] = None + sampling_mirostat_tau: Optional[float] = None + sampling_mirostat_eta: Optional[float] = None + # Authorship Metadata to be written to GGUF KV Store name: Optional[str] = None author: Optional[str] = None @@ -54,15 +68,43 @@ def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Pat model_card = Metadata.load_model_card(model_path) hf_params = Metadata.load_hf_parameters(model_path) + gen_config = Metadata.load_generation_config(model_path) # TODO: load adapter_config.json when possible, it usually contains the base model of the LoRA adapter # heuristics metadata = Metadata.apply_metadata_heuristic(metadata, model_card, hf_params, model_path, total_params) + if gen_config: + metadata.sampling_sequence = gen_config.get("sequence", metadata.sampling_sequence) + metadata.sampling_top_k = gen_config.get("top_k", metadata.sampling_top_k) + metadata.sampling_top_p = gen_config.get("top_p", metadata.sampling_top_p) + metadata.sampling_min_p = gen_config.get("min_p", metadata.sampling_min_p) + metadata.sampling_xtc_probability = gen_config.get("xtc_probability", metadata.sampling_xtc_probability) + metadata.sampling_xtc_threshold = gen_config.get("xtc_threshold", metadata.sampling_xtc_threshold) + metadata.sampling_temp = gen_config.get("temperature", metadata.sampling_temp) + metadata.sampling_penalty_last_n = gen_config.get("penalty_last_n", metadata.sampling_penalty_last_n) + metadata.sampling_penalty_repeat = gen_config.get("penalty_repeat", metadata.sampling_penalty_repeat) + metadata.sampling_mirostat = gen_config.get("mirostat", metadata.sampling_mirostat) + metadata.sampling_mirostat_tau = gen_config.get("mirostat_tau", metadata.sampling_mirostat_tau) + metadata.sampling_mirostat_eta = gen_config.get("mirostat_eta", metadata.sampling_mirostat_eta) + # Metadata Override File Provided # This is based on LLM_KV_NAMES mapping in llama.cpp metadata_override = Metadata.load_metadata_override(metadata_override_path) + metadata.sampling_sequence = metadata_override.get(Keys.General.SAMPLING_SEQUENCE, metadata.sampling_sequence) + metadata.sampling_top_k = metadata_override.get(Keys.General.SAMPLING_TOP_K, metadata.sampling_top_k) + metadata.sampling_top_p = metadata_override.get(Keys.General.SAMPLING_TOP_P, metadata.sampling_top_p) + metadata.sampling_min_p = metadata_override.get(Keys.General.SAMPLING_MIN_P, metadata.sampling_min_p) + metadata.sampling_xtc_probability = metadata_override.get(Keys.General.SAMPLING_XTC_PROBABILITY, metadata.sampling_xtc_probability) + metadata.sampling_xtc_threshold = metadata_override.get(Keys.General.SAMPLING_XTC_THRESHOLD, metadata.sampling_xtc_threshold) + metadata.sampling_temp = metadata_override.get(Keys.General.SAMPLING_TEMP, metadata.sampling_temp) + metadata.sampling_penalty_last_n = metadata_override.get(Keys.General.SAMPLING_PENALTY_LAST_N, metadata.sampling_penalty_last_n) + metadata.sampling_penalty_repeat = metadata_override.get(Keys.General.SAMPLING_PENALTY_REPEAT, metadata.sampling_penalty_repeat) + metadata.sampling_mirostat = metadata_override.get(Keys.General.SAMPLING_MIROSTAT, metadata.sampling_mirostat) + metadata.sampling_mirostat_tau = metadata_override.get(Keys.General.SAMPLING_MIROSTAT_TAU, metadata.sampling_mirostat_tau) + metadata.sampling_mirostat_eta = metadata_override.get(Keys.General.SAMPLING_MIROSTAT_ETA, metadata.sampling_mirostat_eta) + metadata.name = metadata_override.get(Keys.General.NAME, metadata.name) metadata.author = metadata_override.get(Keys.General.AUTHOR, metadata.author) metadata.version = metadata_override.get(Keys.General.VERSION, metadata.version) @@ -172,6 +214,23 @@ def load_hf_parameters(model_path: Optional[Path] = None) -> dict[str, Any]: with open(config_path, "r", encoding="utf-8") as f: return json.load(f) + @staticmethod + def load_generation_config(model_path: Optional[Path] = None) -> dict[str, Any]: + if model_path is None or not model_path.is_dir(): + return {} + + generation_config_path = model_path / "generation_config.json" + + if not generation_config_path.is_file(): + return {} + + try: + with open(generation_config_path, "r", encoding="utf-8") as f: + return json.load(f) + except (json.JSONDecodeError, IOError): + # not all models have valid generation_config.json + return {} + @staticmethod def id_to_title(string): # Convert capitalization into title form unless acronym or version number @@ -546,6 +605,32 @@ def use_array_model_card_metadata(metadata_key: str, model_card_key: str): def set_gguf_meta_model(self, gguf_writer: gguf.GGUFWriter): assert self.name is not None + + if self.sampling_sequence is not None: + gguf_writer.add_sampling_sequence(self.sampling_sequence) + if self.sampling_top_k is not None: + gguf_writer.add_sampling_top_k(self.sampling_top_k) + if self.sampling_top_p is not None: + gguf_writer.add_sampling_top_p(self.sampling_top_p) + if self.sampling_min_p is not None: + gguf_writer.add_sampling_min_p(self.sampling_min_p) + if self.sampling_xtc_probability is not None: + gguf_writer.add_sampling_xtc_probability(self.sampling_xtc_probability) + if self.sampling_xtc_threshold is not None: + gguf_writer.add_sampling_xtc_threshold(self.sampling_xtc_threshold) + if self.sampling_temp is not None: + gguf_writer.add_sampling_temp(self.sampling_temp) + if self.sampling_penalty_last_n is not None: + gguf_writer.add_sampling_penalty_last_n(self.sampling_penalty_last_n) + if self.sampling_penalty_repeat is not None: + gguf_writer.add_sampling_penalty_repeat(self.sampling_penalty_repeat) + if self.sampling_mirostat is not None: + gguf_writer.add_sampling_mirostat(self.sampling_mirostat) + if self.sampling_mirostat_tau is not None: + gguf_writer.add_sampling_mirostat_tau(self.sampling_mirostat_tau) + if self.sampling_mirostat_eta is not None: + gguf_writer.add_sampling_mirostat_eta(self.sampling_mirostat_eta) + gguf_writer.add_name(self.name) if self.author is not None: diff --git a/include/llama.h b/include/llama.h index 8547226ff21..b52eaacfa7e 100644 --- a/include/llama.h +++ b/include/llama.h @@ -246,6 +246,21 @@ extern "C" { LLAMA_KV_OVERRIDE_TYPE_STR, }; + enum llama_model_meta_key { + LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE, + LLAMA_MODEL_META_KEY_SAMPLING_TOP_K, + LLAMA_MODEL_META_KEY_SAMPLING_TOP_P, + LLAMA_MODEL_META_KEY_SAMPLING_MIN_P, + LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY, + LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD, + LLAMA_MODEL_META_KEY_SAMPLING_TEMP, + LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N, + LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT, + LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT, + LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU, + LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA, + }; + struct llama_model_kv_override { enum llama_model_kv_override_type tag; @@ -518,6 +533,9 @@ extern "C" { // Get the number of metadata key/value pairs LLAMA_API int32_t llama_model_meta_count(const struct llama_model * model); + // Get sampling metadata key name. Returns nullptr if the key is invalid + LLAMA_API const char * llama_model_meta_key_str(enum llama_model_meta_key key); + // Get metadata key name by index LLAMA_API int32_t llama_model_meta_key_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size); diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index fc6cddc92f5..7ef87acf1b3 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -114,19 +114,31 @@ static const std::map LLM_ARCH_NAMES = { }; static const std::map LLM_KV_NAMES = { - { LLM_KV_GENERAL_TYPE, "general.type" }, - { LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" }, - { LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" }, - { LLM_KV_GENERAL_ALIGNMENT, "general.alignment" }, - { LLM_KV_GENERAL_FILE_TYPE, "general.file_type" }, - { LLM_KV_GENERAL_NAME, "general.name" }, - { LLM_KV_GENERAL_AUTHOR, "general.author" }, - { LLM_KV_GENERAL_VERSION, "general.version" }, - { LLM_KV_GENERAL_URL, "general.url" }, - { LLM_KV_GENERAL_DESCRIPTION, "general.description" }, - { LLM_KV_GENERAL_LICENSE, "general.license" }, - { LLM_KV_GENERAL_SOURCE_URL, "general.source.url" }, - { LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" }, + { LLM_KV_GENERAL_TYPE, "general.type" }, + { LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" }, + { LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" }, + { LLM_KV_GENERAL_ALIGNMENT, "general.alignment" }, + { LLM_KV_GENERAL_FILE_TYPE, "general.file_type" }, + { LLM_KV_GENERAL_SAMPLING_SEQUENCE, "general.sampling.sequence" }, + { LLM_KV_GENERAL_SAMPLING_TOP_K, "general.sampling.top_k" }, + { LLM_KV_GENERAL_SAMPLING_TOP_P, "general.sampling.top_p" }, + { LLM_KV_GENERAL_SAMPLING_MIN_P, "general.sampling.min_p" }, + { LLM_KV_GENERAL_SAMPLING_XTC_PROBABILITY, "general.sampling.xtc_probability" }, + { LLM_KV_GENERAL_SAMPLING_XTC_THRESHOLD, "general.sampling.xtc_threshold" }, + { LLM_KV_GENERAL_SAMPLING_TEMP, "general.sampling.temp" }, + { LLM_KV_GENERAL_SAMPLING_PENALTY_LAST_N, "general.sampling.penalty_last_n" }, + { LLM_KV_GENERAL_SAMPLING_PENALTY_REPEAT, "general.sampling.penalty_repeat" }, + { LLM_KV_GENERAL_SAMPLING_MIROSTAT, "general.sampling.mirostat" }, + { LLM_KV_GENERAL_SAMPLING_MIROSTAT_TAU, "general.sampling.mirostat_tau" }, + { LLM_KV_GENERAL_SAMPLING_MIROSTAT_ETA, "general.sampling.mirostat_eta" }, + { LLM_KV_GENERAL_NAME, "general.name" }, + { LLM_KV_GENERAL_AUTHOR, "general.author" }, + { LLM_KV_GENERAL_VERSION, "general.version" }, + { LLM_KV_GENERAL_URL, "general.url" }, + { LLM_KV_GENERAL_DESCRIPTION, "general.description" }, + { LLM_KV_GENERAL_LICENSE, "general.license" }, + { LLM_KV_GENERAL_SOURCE_URL, "general.source.url" }, + { LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" }, { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index 02a1c2dc258..9ad3157bf67 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -123,6 +123,18 @@ enum llm_kv { LLM_KV_GENERAL_QUANTIZATION_VERSION, LLM_KV_GENERAL_ALIGNMENT, LLM_KV_GENERAL_FILE_TYPE, + LLM_KV_GENERAL_SAMPLING_SEQUENCE, + LLM_KV_GENERAL_SAMPLING_TOP_K, + LLM_KV_GENERAL_SAMPLING_TOP_P, + LLM_KV_GENERAL_SAMPLING_MIN_P, + LLM_KV_GENERAL_SAMPLING_XTC_PROBABILITY, + LLM_KV_GENERAL_SAMPLING_XTC_THRESHOLD, + LLM_KV_GENERAL_SAMPLING_TEMP, + LLM_KV_GENERAL_SAMPLING_PENALTY_LAST_N, + LLM_KV_GENERAL_SAMPLING_PENALTY_REPEAT, + LLM_KV_GENERAL_SAMPLING_MIROSTAT, + LLM_KV_GENERAL_SAMPLING_MIROSTAT_TAU, + LLM_KV_GENERAL_SAMPLING_MIROSTAT_ETA, LLM_KV_GENERAL_NAME, LLM_KV_GENERAL_AUTHOR, LLM_KV_GENERAL_VERSION, diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 35179a98e0c..a042ea9632c 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -7687,6 +7687,24 @@ int32_t llama_model_meta_count(const llama_model * model) { return (int)model->gguf_kv.size(); } +const char * llama_model_meta_key_str(llama_model_meta_key key) { + switch (key) { + case LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE: return "general.sampling.sequence"; + case LLAMA_MODEL_META_KEY_SAMPLING_TOP_K: return "general.sampling.top_k"; + case LLAMA_MODEL_META_KEY_SAMPLING_TOP_P: return "general.sampling.top_p"; + case LLAMA_MODEL_META_KEY_SAMPLING_MIN_P: return "general.sampling.min_p"; + case LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY: return "general.sampling.xtc_probability"; + case LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD: return "general.sampling.xtc_threshold"; + case LLAMA_MODEL_META_KEY_SAMPLING_TEMP: return "general.sampling.temp"; + case LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N: return "general.sampling.penalty_last_n"; + case LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT: return "general.sampling.penalty_repeat"; + case LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT: return "general.sampling.mirostat"; + case LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU: return "general.sampling.mirostat_tau"; + case LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA: return "general.sampling.mirostat_eta"; + default: return nullptr; + } +} + int32_t llama_model_meta_key_by_index(const llama_model * model, int i, char * buf, size_t buf_size) { if (i < 0 || i >= (int)model->gguf_kv.size()) { if (buf_size > 0) { From d414db02d3ab3744402bc57a7b3fce7de66e3d5a Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Tue, 25 Nov 2025 00:11:27 -0600 Subject: [PATCH 26/37] vulkan: Use fewer rows for scalar FA when HS is not a multiple of 16 (#17455) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 12 +++++++----- tests/test-backend-ops.cpp | 3 +++ 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index d78c727e53b..6cf15b43bb3 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -2501,9 +2501,11 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector&& events static constexpr uint32_t flash_attention_num_small_rows = 32; static constexpr uint32_t scalar_flash_attention_num_small_rows = 1; -static uint32_t get_fa_scalar_num_large_rows(uint32_t hsv) { +static uint32_t get_fa_scalar_num_large_rows(uint32_t hsk, uint32_t hsv) { if (hsv >= 192) { return 2; + } else if ((hsv | hsk) & 8) { + return 4; } else { return 8; } @@ -2535,9 +2537,9 @@ static std::array fa_rows_cols(FaCodePath path, uint32_t hsk, uint3 if ((hsv | hsk) & 8) { // HSV/HSK not being a multiple of 16 makes D_split smaller, which makes cols_per_iter // larger, and Bc needs to be >= cols_per_thread. 64 is large enough, 32 is not. - return {get_fa_scalar_num_large_rows(hsv), 64}; + return {get_fa_scalar_num_large_rows(hsk, hsv), 64}; } else { - return {get_fa_scalar_num_large_rows(hsv), 32}; + return {get_fa_scalar_num_large_rows(hsk, hsv), 32}; } } } @@ -7740,7 +7742,7 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con // Needs to be kept up to date on shader changes GGML_UNUSED(hsv); const uint32_t wg_size = scalar_flash_attention_workgroup_size; - const uint32_t Br = get_fa_scalar_num_large_rows(hsv); + const uint32_t Br = get_fa_scalar_num_large_rows(hsk, hsv); const uint32_t Bc = scalar_flash_attention_Bc; const uint32_t tmpsh = wg_size * sizeof(float); @@ -7871,7 +7873,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx case FA_SCALAR: case FA_COOPMAT1: // We may switch from coopmat1 to scalar, so use the scalar limit for both - max_gqa = get_fa_scalar_num_large_rows(HSV); + max_gqa = get_fa_scalar_num_large_rows(HSK, HSV); break; case FA_COOPMAT2: max_gqa = get_fa_num_small_rows(FA_COOPMAT2); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index ce8c068d7aa..fd48d254752 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -7859,6 +7859,9 @@ static std::vector> make_test_cases_perf() { } } + // Qwen3-VL-8B https://github.com/ggml-org/llama.cpp/issues/17012 + test_cases.emplace_back(new test_flash_attn_ext(72, 72, 16, {1, 1}, 5776, 5776, false, false, 0, 0, GGML_PREC_F32, GGML_TYPE_F16)); + for (int kv : { 4096, 8192, 16384, }) { for (int hs : { 64, 128, }) { for (int nr : { 1, 4, }) { From b1846f1c8ecd97ee08593e9498ef3244d43c1ad6 Mon Sep 17 00:00:00 2001 From: Pascal Date: Tue, 25 Nov 2025 08:01:02 +0100 Subject: [PATCH 27/37] webui: add rehype plugin to restore HTML in Markdown table cells (#17477) * webui: add rehype plugin to restore HTML in Markdown table cells The remark/rehype pipeline neutralizes inline HTML as literal text (remarkLiteralHtml) so that XML/HTML snippets in LLM responses display as-is instead of being rendered. This causes
and