mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 20:04:35 +00:00
9986 lines
364 KiB
Plaintext
9986 lines
364 KiB
Plaintext
#include <algorithm>
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#include <assert.h>
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#include <atomic>
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#include <cinttypes>
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#include <cstddef>
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#include <cstdint>
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#include <float.h>
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#include <limits>
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#include <stdint.h>
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#include <stdio.h>
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#include <vector>
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#if defined(GGML_USE_HIPBLAS)
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#include <hip/hip_runtime.h>
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#include <hipblas/hipblas.h>
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#include <hip/hip_fp16.h>
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#ifdef __HIP_PLATFORM_AMD__
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// for rocblas_initialize()
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#include "rocblas/rocblas.h"
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#endif // __HIP_PLATFORM_AMD__
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#define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
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#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
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#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
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#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
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#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
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#define CUBLAS_OP_N HIPBLAS_OP_N
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#define CUBLAS_OP_T HIPBLAS_OP_T
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#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
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#define CUBLAS_TF32_TENSOR_OP_MATH 0
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#define CUDA_R_16F HIPBLAS_R_16F
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#define CUDA_R_32F HIPBLAS_R_32F
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#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
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#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
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#define cublasCreate hipblasCreate
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#define cublasGemmEx hipblasGemmEx
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#define cublasGemmBatchedEx hipblasGemmBatchedEx
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#define cublasGemmStridedBatchedEx hipblasGemmStridedBatchedEx
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#define cublasHandle_t hipblasHandle_t
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#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
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#define cublasSetStream hipblasSetStream
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#define cublasSgemm hipblasSgemm
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#define cublasStatus_t hipblasStatus_t
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#define cudaDataType_t hipblasDatatype_t //deprecated, new hipblasDatatype not in 5.6
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#define cudaDeviceCanAccessPeer hipDeviceCanAccessPeer
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#define cudaDeviceDisablePeerAccess hipDeviceDisablePeerAccess
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#define cudaDeviceEnablePeerAccess hipDeviceEnablePeerAccess
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#define cudaDeviceProp hipDeviceProp_t
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#define cudaDeviceSynchronize hipDeviceSynchronize
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#define cudaError_t hipError_t
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#define cudaEventCreateWithFlags hipEventCreateWithFlags
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#define cudaEventDisableTiming hipEventDisableTiming
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#define cudaEventRecord hipEventRecord
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#define cudaEvent_t hipEvent_t
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#define cudaEventDestroy hipEventDestroy
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#define cudaFree hipFree
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#define cudaFreeHost hipHostFree
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#define cudaGetDevice hipGetDevice
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#define cudaGetDeviceCount hipGetDeviceCount
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#define cudaGetDeviceProperties hipGetDeviceProperties
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#define cudaGetErrorString hipGetErrorString
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#define cudaGetLastError hipGetLastError
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#define cudaMalloc hipMalloc
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#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
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#define cudaMemcpy hipMemcpy
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#define cudaMemcpy2DAsync hipMemcpy2DAsync
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#define cudaMemcpyAsync hipMemcpyAsync
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#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
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#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
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#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
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#define cudaMemcpyKind hipMemcpyKind
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#define cudaMemset hipMemset
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#define cudaMemsetAsync hipMemsetAsync
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#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
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#define cudaSetDevice hipSetDevice
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#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
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#define cudaStreamFireAndForget hipStreamFireAndForget
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#define cudaStreamNonBlocking hipStreamNonBlocking
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#define cudaStreamSynchronize hipStreamSynchronize
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#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
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#define cudaStream_t hipStream_t
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#define cudaSuccess hipSuccess
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#else
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#include <cuda_runtime.h>
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#include <cublas_v2.h>
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#include <cuda_fp16.h>
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#endif // defined(GGML_USE_HIPBLAS)
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#include "ggml-cuda.h"
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#include "ggml.h"
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#include "ggml-backend-impl.h"
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#define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
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#define CC_VOLTA 700
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#define CC_OFFSET_AMD 1000000
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#define CC_RDNA2 (CC_OFFSET_AMD + 1030)
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#define GGML_CUDA_MAX_NODES 8192
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// define this if you want to always fallback to MMQ kernels and not use cuBLAS for matrix multiplication
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// on modern hardware, using cuBLAS is recommended as it utilizes F16 tensor cores which are very performant
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// for large computational tasks. the drawback is that this requires some extra amount of VRAM:
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// - 7B quantum model: +100-200 MB
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// - 13B quantum model: +200-400 MB
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//
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//#define GGML_CUDA_FORCE_MMQ
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// TODO: improve this to be correct for more hardware
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// for example, currently fails for GeForce GTX 1660 which is TURING arch (> VOLTA) but does not have tensor cores
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// probably other such cases, and not sure what happens on AMD hardware
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#if !defined(GGML_CUDA_FORCE_MMQ)
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#define CUDA_USE_TENSOR_CORES
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#endif
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// max batch size to use MMQ kernels when tensor cores are available
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#define MMQ_MAX_BATCH_SIZE 32
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#if defined(GGML_USE_HIPBLAS)
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#define __CUDA_ARCH__ 1300
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#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
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defined(__gfx1150__) || defined(__gfx1151__)
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#define RDNA3
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#endif
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#if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \
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defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__)
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#define RDNA2
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#endif
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#ifndef __has_builtin
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#define __has_builtin(x) 0
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#endif
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typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
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static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
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const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
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const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
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#if __has_builtin(__builtin_elementwise_sub_sat)
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const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
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return reinterpret_cast<const int&>(c);
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#else
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int8x4_t c;
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int16_t tmp;
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#pragma unroll
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for (int i = 0; i < 4; i++) {
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tmp = va[i] - vb[i];
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if(tmp > std::numeric_limits<int8_t>::max()) tmp = std::numeric_limits<int8_t>::max();
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if(tmp < std::numeric_limits<int8_t>::min()) tmp = std::numeric_limits<int8_t>::min();
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c[i] = tmp;
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}
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return reinterpret_cast<int&>(c);
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#endif // __has_builtin(__builtin_elementwise_sub_sat)
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}
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static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
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#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
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c = __builtin_amdgcn_sdot4(a, b, c, false);
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#elif defined(__gfx1100__)
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c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
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#elif defined(__gfx1010__) || defined(__gfx900__)
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int tmp1;
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int tmp2;
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asm("\n \
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v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
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v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
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v_add3_u32 %0, %1, %2, %0 \n \
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v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
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v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
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v_add3_u32 %0, %1, %2, %0 \n \
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"
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: "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
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: "v"(a), "v"(b)
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);
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#else
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const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
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const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
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c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
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#endif
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return c;
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}
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#endif // defined(GGML_USE_HIPBLAS)
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size");
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#define CUDA_CHECK(err) \
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do { \
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cudaError_t err_ = (err); \
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if (err_ != cudaSuccess) { \
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int id; \
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cudaGetDevice(&id); \
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fprintf(stderr, "\nCUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
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cudaGetErrorString(err_)); \
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fprintf(stderr, "current device: %d\n", id); \
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GGML_ASSERT(!"CUDA error"); \
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} \
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} while (0)
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#if CUDART_VERSION >= 12000
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#define CUBLAS_CHECK(err) \
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do { \
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cublasStatus_t err_ = (err); \
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if (err_ != CUBLAS_STATUS_SUCCESS) { \
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int id; \
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cudaGetDevice(&id); \
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fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n", \
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err_, __FILE__, __LINE__, cublasGetStatusString(err_)); \
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fprintf(stderr, "current device: %d\n", id); \
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GGML_ASSERT(!"cuBLAS error"); \
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} \
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} while (0)
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#else
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#define CUBLAS_CHECK(err) \
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do { \
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cublasStatus_t err_ = (err); \
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if (err_ != CUBLAS_STATUS_SUCCESS) { \
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int id; \
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cudaGetDevice(&id); \
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fprintf(stderr, "\ncuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
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fprintf(stderr, "current device: %d\n", id); \
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GGML_ASSERT(!"cuBLAS error"); \
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} \
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} while (0)
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#endif // CUDART_VERSION >= 11
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#if CUDART_VERSION >= 11100
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#define GGML_CUDA_ASSUME(x) __builtin_assume(x)
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#else
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#define GGML_CUDA_ASSUME(x)
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#endif // CUDART_VERSION >= 11100
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#ifdef GGML_CUDA_F16
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typedef half dfloat; // dequantize float
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typedef half2 dfloat2;
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#else
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typedef float dfloat; // dequantize float
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typedef float2 dfloat2;
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#endif //GGML_CUDA_F16
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static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
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const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
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int x32 = 0;
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x32 |= x16[0] << 0;
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x32 |= x16[1] << 16;
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return x32;
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}
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static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) {
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const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
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int x32 = 0;
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x32 |= x16[0] << 0;
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x32 |= x16[1] << 16;
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return x32;
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}
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static __device__ __forceinline__ int get_int_from_int8_aligned(const int8_t * x8, const int & i32) {
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return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
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}
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static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t * x8, const int & i32) {
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return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
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}
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template<typename T>
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using to_t_cuda_t = void (*)(const void * __restrict__ x, T * __restrict__ y, int k, cudaStream_t stream);
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typedef to_t_cuda_t<float> to_fp32_cuda_t;
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typedef to_t_cuda_t<half> to_fp16_cuda_t;
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typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
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typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v);
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typedef void (*cpy_kernel_t)(const char * cx, char * cdst);
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typedef void (*ggml_cuda_func_t)(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst);
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typedef void (*ggml_cuda_op_mul_mat_t)(
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const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
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const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
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const int64_t src1_padded_row_size, const cudaStream_t & stream);
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typedef void (*ggml_cuda_op_flatten_t)(
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const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
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const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream);
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// QK = number of values after dequantization
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// QR = QK / number of values before dequantization
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// QI = number of 32 bit integers before dequantization
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#define QK4_0 32
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#define QR4_0 2
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#define QI4_0 (QK4_0 / (4 * QR4_0))
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typedef struct {
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half d; // delta
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uint8_t qs[QK4_0 / 2]; // nibbles / quants
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} block_q4_0;
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static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
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#define QK4_1 32
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#define QR4_1 2
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#define QI4_1 (QK4_1 / (4 * QR4_1))
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typedef struct {
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half2 dm; // dm.x = delta, dm.y = min
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uint8_t qs[QK4_1 / 2]; // nibbles / quants
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} block_q4_1;
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static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
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#define QK5_0 32
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#define QR5_0 2
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#define QI5_0 (QK5_0 / (4 * QR5_0))
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typedef struct {
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half d; // delta
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uint8_t qh[4]; // 5-th bit of quants
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uint8_t qs[QK5_0 / 2]; // nibbles / quants
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} block_q5_0;
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static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
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#define QK5_1 32
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#define QR5_1 2
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#define QI5_1 (QK5_1 / (4 * QR5_1))
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typedef struct {
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half2 dm; // dm.x = delta, dm.y = min
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uint8_t qh[4]; // 5-th bit of quants
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uint8_t qs[QK5_1 / 2]; // nibbles / quants
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} block_q5_1;
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static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
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#define QK8_0 32
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#define QR8_0 1
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#define QI8_0 (QK8_0 / (4 * QR8_0))
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typedef struct {
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half d; // delta
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int8_t qs[QK8_0]; // quants
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} block_q8_0;
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static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
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#define QK8_1 32
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#define QR8_1 1
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#define QI8_1 (QK8_1 / (4 * QR8_1))
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typedef struct {
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half2 ds; // ds.x = delta, ds.y = sum
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int8_t qs[QK8_0]; // quants
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} block_q8_1;
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static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding");
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typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
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typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
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typedef void (*load_tiles_cuda_t)(
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const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
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int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row);
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typedef float (*vec_dot_q_mul_mat_cuda_t)(
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const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
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const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k);
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//================================= k-quants
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#ifdef GGML_QKK_64
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#define QK_K 64
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#define K_SCALE_SIZE 4
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#else
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#define QK_K 256
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#define K_SCALE_SIZE 12
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#endif
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#define QR2_K 4
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#define QI2_K (QK_K / (4*QR2_K))
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typedef struct {
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uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
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uint8_t qs[QK_K/4]; // quants
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half2 dm; // super-block scale for quantized scales/mins
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} block_q2_K;
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static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
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#define QR3_K 4
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#define QI3_K (QK_K / (4*QR3_K))
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typedef struct {
|
|
uint8_t hmask[QK_K/8]; // quants - high bit
|
|
uint8_t qs[QK_K/4]; // quants - low 2 bits
|
|
#ifdef GGML_QKK_64
|
|
uint8_t scales[2]; // scales, quantized with 8 bits
|
|
#else
|
|
uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
|
|
#endif
|
|
half d; // super-block scale
|
|
} block_q3_K;
|
|
//static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + K_SCALE_SIZE, "wrong q3_K block size/padding");
|
|
|
|
#define QR4_K 2
|
|
#define QI4_K (QK_K / (4*QR4_K))
|
|
#ifdef GGML_QKK_64
|
|
typedef struct {
|
|
half dm[2]; // super-block scales/mins
|
|
uint8_t scales[2]; // 4-bit block scales/mins
|
|
uint8_t qs[QK_K/2]; // 4--bit quants
|
|
} block_q4_K;
|
|
static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding");
|
|
#else
|
|
typedef struct {
|
|
half2 dm; // super-block scale for quantized scales/mins
|
|
uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
|
|
uint8_t qs[QK_K/2]; // 4--bit quants
|
|
} block_q4_K;
|
|
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding");
|
|
#endif
|
|
|
|
#define QR5_K 2
|
|
#define QI5_K (QK_K / (4*QR5_K))
|
|
#ifdef GGML_QKK_64
|
|
typedef struct {
|
|
half d; // super-block scale
|
|
int8_t scales[QK_K/16]; // block scales
|
|
uint8_t qh[QK_K/8]; // quants, high bit
|
|
uint8_t qs[QK_K/2]; // quants, low 4 bits
|
|
} block_q5_K;
|
|
static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
|
|
#else
|
|
typedef struct {
|
|
half2 dm; // super-block scale for quantized scales/mins
|
|
uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
|
|
uint8_t qh[QK_K/8]; // quants, high bit
|
|
uint8_t qs[QK_K/2]; // quants, low 4 bits
|
|
} block_q5_K;
|
|
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
|
|
#endif
|
|
|
|
#define QR6_K 2
|
|
#define QI6_K (QK_K / (4*QR6_K))
|
|
typedef struct {
|
|
uint8_t ql[QK_K/2]; // quants, lower 4 bits
|
|
uint8_t qh[QK_K/4]; // quants, upper 2 bits
|
|
int8_t scales[QK_K/16]; // scales
|
|
half d; // delta
|
|
} block_q6_K;
|
|
static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding");
|
|
|
|
#define WARP_SIZE 32
|
|
#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
|
|
|
|
#define CUDA_GELU_BLOCK_SIZE 256
|
|
#define CUDA_SILU_BLOCK_SIZE 256
|
|
#define CUDA_TANH_BLOCK_SIZE 256
|
|
#define CUDA_RELU_BLOCK_SIZE 256
|
|
#define CUDA_SQR_BLOCK_SIZE 256
|
|
#define CUDA_CPY_BLOCK_SIZE 32
|
|
#define CUDA_SCALE_BLOCK_SIZE 256
|
|
#define CUDA_CLAMP_BLOCK_SIZE 256
|
|
#define CUDA_ROPE_BLOCK_SIZE 256
|
|
#define CUDA_SOFT_MAX_BLOCK_SIZE 1024
|
|
#define CUDA_ALIBI_BLOCK_SIZE 32
|
|
#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
|
|
#define CUDA_QUANTIZE_BLOCK_SIZE 256
|
|
#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
|
|
#define CUDA_GET_ROWS_BLOCK_SIZE 256
|
|
#define CUDA_UPSCALE_BLOCK_SIZE 256
|
|
#define CUDA_CONCAT_BLOCK_SIZE 256
|
|
#define CUDA_PAD_BLOCK_SIZE 256
|
|
#define CUDA_ACC_BLOCK_SIZE 256
|
|
#define CUDA_IM2COL_BLOCK_SIZE 256
|
|
|
|
// dmmv = dequantize_mul_mat_vec
|
|
#ifndef GGML_CUDA_DMMV_X
|
|
#define GGML_CUDA_DMMV_X 32
|
|
#endif
|
|
#ifndef GGML_CUDA_MMV_Y
|
|
#define GGML_CUDA_MMV_Y 1
|
|
#endif
|
|
|
|
#ifndef K_QUANTS_PER_ITERATION
|
|
#define K_QUANTS_PER_ITERATION 2
|
|
#else
|
|
static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
|
|
#endif
|
|
|
|
#ifndef GGML_CUDA_PEER_MAX_BATCH_SIZE
|
|
#define GGML_CUDA_PEER_MAX_BATCH_SIZE 128
|
|
#endif // GGML_CUDA_PEER_MAX_BATCH_SIZE
|
|
|
|
#define MUL_MAT_SRC1_COL_STRIDE 128
|
|
|
|
#define MAX_STREAMS 8
|
|
static cudaStream_t g_cudaStreams[GGML_CUDA_MAX_DEVICES][MAX_STREAMS] = { { nullptr } };
|
|
|
|
struct ggml_tensor_extra_gpu {
|
|
void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
|
|
cudaEvent_t events[GGML_CUDA_MAX_DEVICES][MAX_STREAMS]; // events for synchronizing multiple GPUs
|
|
};
|
|
|
|
// this is faster on Windows
|
|
// probably because the Windows CUDA libraries forget to make this check before invoking the drivers
|
|
inline cudaError_t ggml_cuda_set_device(const int device) {
|
|
int current_device;
|
|
CUDA_CHECK(cudaGetDevice(¤t_device));
|
|
|
|
if (device == current_device) {
|
|
return cudaSuccess;
|
|
}
|
|
|
|
return cudaSetDevice(device);
|
|
}
|
|
|
|
static int g_device_count = -1;
|
|
static int g_main_device = 0;
|
|
static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
|
|
static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
|
|
|
|
static void * g_scratch_buffer = nullptr;
|
|
static size_t g_scratch_size = 0; // disabled by default
|
|
static size_t g_scratch_offset = 0;
|
|
|
|
static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
|
|
|
|
[[noreturn]]
|
|
static __device__ void bad_arch() {
|
|
printf("ERROR: ggml-cuda was compiled without support for the current GPU architecture.\n");
|
|
__trap();
|
|
|
|
(void) bad_arch; // suppress unused function warning
|
|
}
|
|
|
|
static __device__ __forceinline__ float warp_reduce_sum(float x) {
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
|
|
}
|
|
return x;
|
|
}
|
|
|
|
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
|
|
a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
|
|
}
|
|
return a;
|
|
}
|
|
|
|
static __device__ __forceinline__ float warp_reduce_max(float x) {
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
|
|
}
|
|
return x;
|
|
}
|
|
|
|
static __device__ __forceinline__ float op_repeat(const float a, const float b) {
|
|
return b;
|
|
}
|
|
|
|
static __device__ __forceinline__ float op_add(const float a, const float b) {
|
|
return a + b;
|
|
}
|
|
|
|
static __device__ __forceinline__ float op_mul(const float a, const float b) {
|
|
return a * b;
|
|
}
|
|
|
|
static __device__ __forceinline__ float op_div(const float a, const float b) {
|
|
return a / b;
|
|
}
|
|
|
|
template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
|
|
static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
|
|
int ne0, int ne1, int ne2, int ne3,
|
|
int ne10, int ne11, int ne12, int ne13,
|
|
/*int s0, */ int s1, int s2, int s3,
|
|
/*int s10,*/ int s11, int s12, int s13) {
|
|
const int i0s = blockDim.x*blockIdx.x + threadIdx.x;
|
|
const int i1 = (blockDim.y*blockIdx.y + threadIdx.y);
|
|
const int i2 = (blockDim.z*blockIdx.z + threadIdx.z) / ne3;
|
|
const int i3 = (blockDim.z*blockIdx.z + threadIdx.z) % ne3;
|
|
|
|
if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
|
|
return;
|
|
}
|
|
|
|
const int i11 = i1 % ne11;
|
|
const int i12 = i2 % ne12;
|
|
const int i13 = i3 % ne13;
|
|
|
|
const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
|
|
const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
|
|
const size_t i_dst = i_src0;
|
|
|
|
const src0_t * src0_row = src0 + i_src0;
|
|
const src1_t * src1_row = src1 + i_src1;
|
|
dst_t * dst_row = dst + i_dst;
|
|
|
|
for (int i0 = i0s; i0 < ne0; i0 += blockDim.x*gridDim.x) {
|
|
const int i10 = i0 % ne10;
|
|
dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
|
|
}
|
|
}
|
|
|
|
template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
|
|
static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst,
|
|
int ne0, int ne1, int ne2, int ne3,
|
|
int ne10, int ne11, int ne12, int ne13,
|
|
/*int s0, */ int s1, int s2, int s3,
|
|
/*int s10,*/ int s11, int s12, int s13) {
|
|
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
const int i3 = i/(ne2*ne1*ne0);
|
|
const int i2 = (i/(ne1*ne0)) % ne2;
|
|
const int i1 = (i/ne0) % ne1;
|
|
const int i0 = i % ne0;
|
|
|
|
if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
|
|
return;
|
|
}
|
|
|
|
const int i11 = i1 % ne11;
|
|
const int i12 = i2 % ne12;
|
|
const int i13 = i3 % ne13;
|
|
|
|
const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
|
|
const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
|
|
const size_t i_dst = i_src0;
|
|
|
|
const src0_t * src0_row = src0 + i_src0;
|
|
const src1_t * src1_row = src1 + i_src1;
|
|
dst_t * dst_row = dst + i_dst;
|
|
|
|
const int i10 = i0 % ne10;
|
|
dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
|
|
}
|
|
|
|
static __global__ void acc_f32(const float * x, const float * y, float * dst, const int ne,
|
|
const int ne10, const int ne11, const int ne12,
|
|
const int nb1, const int nb2, int offset) {
|
|
const int i = blockDim.x * blockIdx.x + threadIdx.x;
|
|
if (i >= ne) {
|
|
return;
|
|
}
|
|
int src1_idx = i - offset;
|
|
int oz = src1_idx / nb2;
|
|
int oy = (src1_idx - (oz * nb2)) / nb1;
|
|
int ox = src1_idx % nb1;
|
|
if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) {
|
|
dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11];
|
|
} else {
|
|
dst[i] = x[i];
|
|
}
|
|
}
|
|
|
|
static __global__ void gelu_f32(const float * x, float * dst, const int k) {
|
|
const float GELU_COEF_A = 0.044715f;
|
|
const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (i >= k) {
|
|
return;
|
|
}
|
|
|
|
float xi = x[i];
|
|
dst[i] = 0.5f*xi*(1.0f + tanhf(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)));
|
|
}
|
|
|
|
static __global__ void silu_f32(const float * x, float * dst, const int k) {
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (i >= k) {
|
|
return;
|
|
}
|
|
dst[i] = x[i] / (1.0f + expf(-x[i]));
|
|
}
|
|
|
|
static __global__ void gelu_quick_f32(const float *x, float *dst, int k) {
|
|
const float GELU_QUICK_COEF = -1.702f;
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
if (i >= k) {
|
|
return;
|
|
}
|
|
dst[i] = x[i] * (1.0f / (1.0f + expf(GELU_QUICK_COEF * x[i])));
|
|
}
|
|
|
|
static __global__ void tanh_f32(const float *x, float *dst, int k) {
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
if (i >= k) {
|
|
return;
|
|
}
|
|
dst[i] = tanhf(x[i]);
|
|
}
|
|
|
|
static __global__ void relu_f32(const float * x, float * dst, const int k) {
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (i >= k) {
|
|
return;
|
|
}
|
|
dst[i] = fmaxf(x[i], 0);
|
|
}
|
|
|
|
static __global__ void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope) {
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
if (i >= k) {
|
|
return;
|
|
}
|
|
dst[i] = fmaxf(x[i], 0) + fminf(x[i], 0.0f) * negative_slope;
|
|
}
|
|
|
|
static __global__ void sqr_f32(const float * x, float * dst, const int k) {
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (i >= k) {
|
|
return;
|
|
}
|
|
dst[i] = x[i] * x[i];
|
|
}
|
|
|
|
template <int block_size>
|
|
static __global__ void norm_f32(const float * x, float * dst, const int ncols, const float eps) {
|
|
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
|
const int tid = threadIdx.x;
|
|
|
|
float2 mean_var = make_float2(0.f, 0.f);
|
|
|
|
for (int col = tid; col < ncols; col += block_size) {
|
|
const float xi = x[row*ncols + col];
|
|
mean_var.x += xi;
|
|
mean_var.y += xi * xi;
|
|
}
|
|
|
|
// sum up partial sums
|
|
mean_var = warp_reduce_sum(mean_var);
|
|
if (block_size > WARP_SIZE) {
|
|
__shared__ float2 s_sum[32];
|
|
int warp_id = threadIdx.x / WARP_SIZE;
|
|
int lane_id = threadIdx.x % WARP_SIZE;
|
|
if (lane_id == 0) {
|
|
s_sum[warp_id] = mean_var;
|
|
}
|
|
__syncthreads();
|
|
mean_var = s_sum[lane_id];
|
|
mean_var = warp_reduce_sum(mean_var);
|
|
}
|
|
|
|
const float mean = mean_var.x / ncols;
|
|
const float var = mean_var.y / ncols - mean * mean;
|
|
const float inv_std = rsqrtf(var + eps);
|
|
|
|
for (int col = tid; col < ncols; col += block_size) {
|
|
dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
|
|
}
|
|
}
|
|
|
|
static __global__ void concat_f32(const float *x,const float *y, float *dst, const int ne0, const int ne02) {
|
|
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
|
|
if (nidx >= ne0) {
|
|
return;
|
|
}
|
|
// operation
|
|
int offset_dst =
|
|
nidx +
|
|
blockIdx.y * ne0 +
|
|
blockIdx.z * ne0 * gridDim.y;
|
|
if (blockIdx.z < ne02) { // src0
|
|
int offset_src =
|
|
nidx +
|
|
blockIdx.y * ne0 +
|
|
blockIdx.z * ne0 * gridDim.y;
|
|
dst[offset_dst] = x[offset_src];
|
|
} else {
|
|
int offset_src =
|
|
nidx +
|
|
blockIdx.y * ne0 +
|
|
(blockIdx.z - ne02) * ne0 * gridDim.y;
|
|
dst[offset_dst] = y[offset_src];
|
|
}
|
|
}
|
|
|
|
static __global__ void upscale_f32(const float *x, float *dst, const int ne00, const int nb02, const int scale_factor) {
|
|
int ne0 = ne00 * scale_factor;
|
|
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
|
|
if (nidx >= ne0) {
|
|
return;
|
|
}
|
|
// operation
|
|
int i00 = nidx / scale_factor;
|
|
int i01 = blockIdx.y / scale_factor;
|
|
int offset_src =
|
|
i00 +
|
|
i01 * ne00 +
|
|
blockIdx.z * nb02;
|
|
int offset_dst =
|
|
nidx +
|
|
blockIdx.y * ne0 +
|
|
blockIdx.z * ne0 * gridDim.y;
|
|
dst[offset_dst] = x[offset_src];
|
|
}
|
|
|
|
static __global__ void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02) {
|
|
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
|
|
if (nidx >= ne0) {
|
|
return;
|
|
}
|
|
|
|
// operation
|
|
int offset_dst =
|
|
nidx +
|
|
blockIdx.y * ne0 +
|
|
blockIdx.z * ne0 * gridDim.y;
|
|
if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02) {
|
|
int offset_src =
|
|
nidx +
|
|
blockIdx.y * ne00 +
|
|
blockIdx.z * ne00 * ne01;
|
|
dst[offset_dst] = x[offset_src];
|
|
} else {
|
|
dst[offset_dst] = 0.0f;
|
|
}
|
|
}
|
|
|
|
template <int block_size>
|
|
static __global__ void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps) {
|
|
int start = blockIdx.x * group_size;
|
|
int end = start + group_size;
|
|
|
|
start += threadIdx.x;
|
|
|
|
if (end >= ne_elements) {
|
|
end = ne_elements;
|
|
}
|
|
|
|
float tmp = 0.0f; // partial sum for thread in warp
|
|
|
|
for (int j = start; j < end; j += block_size) {
|
|
tmp += x[j];
|
|
}
|
|
|
|
tmp = warp_reduce_sum(tmp);
|
|
if (block_size > WARP_SIZE) {
|
|
__shared__ float s_sum[32];
|
|
int warp_id = threadIdx.x / WARP_SIZE;
|
|
int lane_id = threadIdx.x % WARP_SIZE;
|
|
if (lane_id == 0) {
|
|
s_sum[warp_id] = tmp;
|
|
}
|
|
__syncthreads();
|
|
tmp = s_sum[lane_id];
|
|
tmp = warp_reduce_sum(tmp);
|
|
}
|
|
|
|
float mean = tmp / group_size;
|
|
tmp = 0.0f;
|
|
|
|
for (int j = start; j < end; j += block_size) {
|
|
float xi = x[j] - mean;
|
|
dst[j] = xi;
|
|
tmp += xi * xi;
|
|
}
|
|
|
|
tmp = warp_reduce_sum(tmp);
|
|
if (block_size > WARP_SIZE) {
|
|
__shared__ float s_sum[32];
|
|
int warp_id = threadIdx.x / WARP_SIZE;
|
|
int lane_id = threadIdx.x % WARP_SIZE;
|
|
if (lane_id == 0) {
|
|
s_sum[warp_id] = tmp;
|
|
}
|
|
__syncthreads();
|
|
tmp = s_sum[lane_id];
|
|
tmp = warp_reduce_sum(tmp);
|
|
}
|
|
|
|
float variance = tmp / group_size;
|
|
float scale = rsqrtf(variance + eps);
|
|
for (int j = start; j < end; j += block_size) {
|
|
dst[j] *= scale;
|
|
}
|
|
}
|
|
|
|
template <int block_size>
|
|
static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
|
|
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
|
const int tid = threadIdx.x;
|
|
|
|
float tmp = 0.0f; // partial sum for thread in warp
|
|
|
|
for (int col = tid; col < ncols; col += block_size) {
|
|
const float xi = x[row*ncols + col];
|
|
tmp += xi * xi;
|
|
}
|
|
|
|
// sum up partial sums
|
|
tmp = warp_reduce_sum(tmp);
|
|
if (block_size > WARP_SIZE) {
|
|
__shared__ float s_sum[32];
|
|
int warp_id = threadIdx.x / WARP_SIZE;
|
|
int lane_id = threadIdx.x % WARP_SIZE;
|
|
if (lane_id == 0) {
|
|
s_sum[warp_id] = tmp;
|
|
}
|
|
__syncthreads();
|
|
tmp = s_sum[lane_id];
|
|
tmp = warp_reduce_sum(tmp);
|
|
}
|
|
|
|
const float mean = tmp / ncols;
|
|
const float scale = rsqrtf(mean + eps);
|
|
|
|
for (int col = tid; col < ncols; col += block_size) {
|
|
dst[row*ncols + col] = scale * x[row*ncols + col];
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
|
|
const block_q4_0 * x = (const block_q4_0 *) vx;
|
|
|
|
const dfloat d = x[ib].d;
|
|
|
|
const int vui = x[ib].qs[iqs];
|
|
|
|
v.x = vui & 0xF;
|
|
v.y = vui >> 4;
|
|
|
|
#ifdef GGML_CUDA_F16
|
|
v = __hsub2(v, {8.0f, 8.0f});
|
|
v = __hmul2(v, {d, d});
|
|
#else
|
|
v.x = (v.x - 8.0f) * d;
|
|
v.y = (v.y - 8.0f) * d;
|
|
#endif // GGML_CUDA_F16
|
|
}
|
|
|
|
static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
|
|
const block_q4_1 * x = (const block_q4_1 *) vx;
|
|
|
|
const dfloat d = __low2half(x[ib].dm);
|
|
const dfloat m = __high2half(x[ib].dm);
|
|
|
|
const int vui = x[ib].qs[iqs];
|
|
|
|
v.x = vui & 0xF;
|
|
v.y = vui >> 4;
|
|
|
|
#ifdef GGML_CUDA_F16
|
|
v = __hmul2(v, {d, d});
|
|
v = __hadd2(v, {m, m});
|
|
#else
|
|
v.x = (v.x * d) + m;
|
|
v.y = (v.y * d) + m;
|
|
#endif // GGML_CUDA_F16
|
|
}
|
|
|
|
static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
|
|
const block_q5_0 * x = (const block_q5_0 *) vx;
|
|
|
|
const dfloat d = x[ib].d;
|
|
|
|
uint32_t qh;
|
|
memcpy(&qh, x[ib].qh, sizeof(qh));
|
|
|
|
const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10;
|
|
const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10;
|
|
|
|
v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
|
|
v.y = ((x[ib].qs[iqs] >> 4) | xh_1);
|
|
|
|
#ifdef GGML_CUDA_F16
|
|
v = __hsub2(v, {16.0f, 16.0f});
|
|
v = __hmul2(v, {d, d});
|
|
#else
|
|
v.x = (v.x - 16.0f) * d;
|
|
v.y = (v.y - 16.0f) * d;
|
|
#endif // GGML_CUDA_F16
|
|
}
|
|
|
|
static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
|
|
const block_q5_1 * x = (const block_q5_1 *) vx;
|
|
|
|
const dfloat d = __low2half(x[ib].dm);
|
|
const dfloat m = __high2half(x[ib].dm);
|
|
|
|
uint32_t qh;
|
|
memcpy(&qh, x[ib].qh, sizeof(qh));
|
|
|
|
const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10;
|
|
const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10;
|
|
|
|
v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
|
|
v.y = ((x[ib].qs[iqs] >> 4) | xh_1);
|
|
|
|
#ifdef GGML_CUDA_F16
|
|
v = __hmul2(v, {d, d});
|
|
v = __hadd2(v, {m, m});
|
|
#else
|
|
v.x = (v.x * d) + m;
|
|
v.y = (v.y * d) + m;
|
|
#endif // GGML_CUDA_F16
|
|
}
|
|
|
|
static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
|
|
const block_q8_0 * x = (const block_q8_0 *) vx;
|
|
|
|
const dfloat d = x[ib].d;
|
|
|
|
v.x = x[ib].qs[iqs + 0];
|
|
v.y = x[ib].qs[iqs + 1];
|
|
|
|
#ifdef GGML_CUDA_F16
|
|
v = __hmul2(v, {d, d});
|
|
#else
|
|
v.x *= d;
|
|
v.y *= d;
|
|
#endif // GGML_CUDA_F16
|
|
}
|
|
|
|
//================================== k-quants
|
|
|
|
template<typename dst_t>
|
|
static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
|
|
|
|
const int i = blockIdx.x;
|
|
const block_q2_K * x = (const block_q2_K *) vx;
|
|
|
|
const int tid = threadIdx.x;
|
|
#if QK_K == 256
|
|
const int n = tid/32;
|
|
const int l = tid - 32*n;
|
|
const int is = 8*n + l/16;
|
|
|
|
const uint8_t q = x[i].qs[32*n + l];
|
|
dst_t * y = yy + i*QK_K + 128*n;
|
|
|
|
float dall = __low2half(x[i].dm);
|
|
float dmin = __high2half(x[i].dm);
|
|
y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
|
|
y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
|
|
y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
|
|
y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
|
|
#else
|
|
const int is = tid/16; // 0 or 1
|
|
const int il = tid%16; // 0...15
|
|
const uint8_t q = x[i].qs[il] >> (2*is);
|
|
dst_t * y = yy + i*QK_K + 16*is + il;
|
|
float dall = __low2half(x[i].dm);
|
|
float dmin = __high2half(x[i].dm);
|
|
y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
|
|
y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
|
|
#endif
|
|
|
|
}
|
|
|
|
template<typename dst_t>
|
|
static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
|
|
|
|
const int i = blockIdx.x;
|
|
const block_q3_K * x = (const block_q3_K *) vx;
|
|
|
|
#if QK_K == 256
|
|
const int r = threadIdx.x/4;
|
|
const int tid = r/2;
|
|
const int is0 = r%2;
|
|
const int l0 = 16*is0 + 4*(threadIdx.x%4);
|
|
const int n = tid / 4;
|
|
const int j = tid - 4*n;
|
|
|
|
uint8_t m = 1 << (4*n + j);
|
|
int is = 8*n + 2*j + is0;
|
|
int shift = 2*j;
|
|
|
|
int8_t us = is < 4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
|
|
is < 8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) :
|
|
is < 12 ? (x[i].scales[is-8] >> 4) | (((x[i].scales[is+0] >> 4) & 3) << 4) :
|
|
(x[i].scales[is-8] >> 4) | (((x[i].scales[is-4] >> 6) & 3) << 4);
|
|
float d_all = x[i].d;
|
|
float dl = d_all * (us - 32);
|
|
|
|
dst_t * y = yy + i*QK_K + 128*n + 32*j;
|
|
const uint8_t * q = x[i].qs + 32*n;
|
|
const uint8_t * hm = x[i].hmask;
|
|
|
|
for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
|
|
#else
|
|
const int tid = threadIdx.x;
|
|
const int is = tid/16; // 0 or 1
|
|
const int il = tid%16; // 0...15
|
|
const int im = il/8; // 0...1
|
|
const int in = il%8; // 0...7
|
|
|
|
dst_t * y = yy + i*QK_K + 16*is + il;
|
|
|
|
const uint8_t q = x[i].qs[il] >> (2*is);
|
|
const uint8_t h = x[i].hmask[in] >> (2*is + im);
|
|
const float d = (float)x[i].d;
|
|
|
|
if (is == 0) {
|
|
y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
|
|
y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
|
|
} else {
|
|
y[ 0] = d * ((x[i].scales[0] >> 4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4));
|
|
y[32] = d * ((x[i].scales[1] >> 4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4));
|
|
}
|
|
#endif
|
|
|
|
}
|
|
|
|
#if QK_K == 256
|
|
static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
|
|
if (j < 4) {
|
|
d = q[j] & 63; m = q[j + 4] & 63;
|
|
} else {
|
|
d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
|
|
m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4);
|
|
}
|
|
}
|
|
#endif
|
|
|
|
template<typename dst_t>
|
|
static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
|
|
const block_q4_K * x = (const block_q4_K *) vx;
|
|
|
|
const int i = blockIdx.x;
|
|
|
|
#if QK_K == 256
|
|
// assume 32 threads
|
|
const int tid = threadIdx.x;
|
|
const int il = tid/8;
|
|
const int ir = tid%8;
|
|
const int is = 2*il;
|
|
const int n = 4;
|
|
|
|
dst_t * y = yy + i*QK_K + 64*il + n*ir;
|
|
|
|
const float dall = __low2half(x[i].dm);
|
|
const float dmin = __high2half(x[i].dm);
|
|
|
|
const uint8_t * q = x[i].qs + 32*il + n*ir;
|
|
|
|
uint8_t sc, m;
|
|
get_scale_min_k4(is + 0, x[i].scales, sc, m);
|
|
const float d1 = dall * sc; const float m1 = dmin * m;
|
|
get_scale_min_k4(is + 1, x[i].scales, sc, m);
|
|
const float d2 = dall * sc; const float m2 = dmin * m;
|
|
for (int l = 0; l < n; ++l) {
|
|
y[l + 0] = d1 * (q[l] & 0xF) - m1;
|
|
y[l +32] = d2 * (q[l] >> 4) - m2;
|
|
}
|
|
#else
|
|
const int tid = threadIdx.x;
|
|
const uint8_t * q = x[i].qs;
|
|
dst_t * y = yy + i*QK_K;
|
|
const float d = (float)x[i].dm[0];
|
|
const float m = (float)x[i].dm[1];
|
|
y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
|
|
y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4);
|
|
#endif
|
|
}
|
|
|
|
template<typename dst_t>
|
|
static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
|
|
const block_q5_K * x = (const block_q5_K *) vx;
|
|
|
|
const int i = blockIdx.x;
|
|
|
|
#if QK_K == 256
|
|
// assume 64 threads - this is very slightly better than the one below
|
|
const int tid = threadIdx.x;
|
|
const int il = tid/16; // il is in 0...3
|
|
const int ir = tid%16; // ir is in 0...15
|
|
const int is = 2*il; // is is in 0...6
|
|
|
|
dst_t * y = yy + i*QK_K + 64*il + 2*ir;
|
|
|
|
const float dall = __low2half(x[i].dm);
|
|
const float dmin = __high2half(x[i].dm);
|
|
|
|
const uint8_t * ql = x[i].qs + 32*il + 2*ir;
|
|
const uint8_t * qh = x[i].qh + 2*ir;
|
|
|
|
uint8_t sc, m;
|
|
get_scale_min_k4(is + 0, x[i].scales, sc, m);
|
|
const float d1 = dall * sc; const float m1 = dmin * m;
|
|
get_scale_min_k4(is + 1, x[i].scales, sc, m);
|
|
const float d2 = dall * sc; const float m2 = dmin * m;
|
|
|
|
uint8_t hm = 1 << (2*il);
|
|
y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1;
|
|
y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1;
|
|
hm <<= 1;
|
|
y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2;
|
|
y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2;
|
|
#else
|
|
const int tid = threadIdx.x;
|
|
const uint8_t q = x[i].qs[tid];
|
|
const int im = tid/8; // 0...3
|
|
const int in = tid%8; // 0...7
|
|
const int is = tid/16; // 0 or 1
|
|
const uint8_t h = x[i].qh[in] >> im;
|
|
const float d = x[i].d;
|
|
dst_t * y = yy + i*QK_K + tid;
|
|
y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16));
|
|
y[32] = d * x[i].scales[is+2] * ((q >> 4) - ((h >> 4) & 1 ? 0 : 16));
|
|
#endif
|
|
}
|
|
|
|
template<typename dst_t>
|
|
static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
|
|
const block_q6_K * x = (const block_q6_K *) vx;
|
|
|
|
const int i = blockIdx.x;
|
|
#if QK_K == 256
|
|
|
|
// assume 64 threads - this is very slightly better than the one below
|
|
const int tid = threadIdx.x;
|
|
const int ip = tid/32; // ip is 0 or 1
|
|
const int il = tid - 32*ip; // 0...32
|
|
const int is = 8*ip + il/16;
|
|
|
|
dst_t * y = yy + i*QK_K + 128*ip + il;
|
|
|
|
const float d = x[i].d;
|
|
|
|
const uint8_t * ql = x[i].ql + 64*ip + il;
|
|
const uint8_t qh = x[i].qh[32*ip + il];
|
|
const int8_t * sc = x[i].scales + is;
|
|
|
|
y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
|
|
y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
|
|
y[64] = d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32);
|
|
y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
|
|
#else
|
|
|
|
// assume 32 threads
|
|
const int tid = threadIdx.x;
|
|
const int ip = tid/16; // 0 or 1
|
|
const int il = tid - 16*ip; // 0...15
|
|
|
|
dst_t * y = yy + i*QK_K + 16*ip + il;
|
|
|
|
const float d = x[i].d;
|
|
|
|
const uint8_t ql = x[i].ql[16*ip + il];
|
|
const uint8_t qh = x[i].qh[il] >> (2*ip);
|
|
const int8_t * sc = x[i].scales;
|
|
|
|
y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
|
|
y[32] = d * sc[ip+2] * ((int8_t)((ql >> 4) | (((qh >> 4) & 3) << 4)) - 32);
|
|
#endif
|
|
}
|
|
|
|
static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
|
|
|
|
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
|
|
|
|
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
|
if (row > nrows) return;
|
|
|
|
const int num_blocks_per_row = ncols / QK_K;
|
|
const int ib0 = row*num_blocks_per_row;
|
|
|
|
const block_q2_K * x = (const block_q2_K *)vx + ib0;
|
|
|
|
float tmp = 0; // partial sum for thread in warp
|
|
|
|
#if QK_K == 256
|
|
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15
|
|
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1
|
|
|
|
const int step = 16/K_QUANTS_PER_ITERATION;
|
|
|
|
const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
|
|
const int in = tid - step*im; // 0...15 or 0...7
|
|
|
|
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2
|
|
const int q_offset = 32*im + l0;
|
|
const int s_offset = 8*im;
|
|
const int y_offset = 128*im + l0;
|
|
|
|
uint32_t aux[4];
|
|
const uint8_t * d = (const uint8_t *)aux;
|
|
const uint8_t * m = (const uint8_t *)(aux + 2);
|
|
|
|
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
|
|
|
|
const float * y = yy + i * QK_K + y_offset;
|
|
const uint8_t * q = x[i].qs + q_offset;
|
|
|
|
const float dall = __low2half(x[i].dm);
|
|
const float dmin = __high2half(x[i].dm);
|
|
|
|
const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
|
|
aux[0] = a[0] & 0x0f0f0f0f;
|
|
aux[1] = a[1] & 0x0f0f0f0f;
|
|
aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
|
|
aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
|
|
|
|
float sum1 = 0, sum2 = 0;
|
|
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
|
|
sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
|
|
+ y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
|
|
+ y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
|
|
+ y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
|
|
+ y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
|
|
+ y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
|
|
+ y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
|
|
+y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
|
|
sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
|
|
+ y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
|
|
|
|
}
|
|
tmp += dall * sum1 - dmin * sum2;
|
|
|
|
}
|
|
#else
|
|
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7
|
|
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3
|
|
const int offset = tid * K_QUANTS_PER_ITERATION;
|
|
|
|
uint32_t uaux[2];
|
|
const uint8_t * d = (const uint8_t *)uaux;
|
|
|
|
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
|
|
|
|
const float * y = yy + i * QK_K + offset;
|
|
const uint8_t * q = x[i].qs + offset;
|
|
const uint32_t * s = (const uint32_t *)x[i].scales;
|
|
|
|
uaux[0] = s[0] & 0x0f0f0f0f;
|
|
uaux[1] = (s[0] >> 4) & 0x0f0f0f0f;
|
|
|
|
const float2 dall = __half22float2(x[i].dm);
|
|
|
|
float sum1 = 0, sum2 = 0;
|
|
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
|
|
const uint8_t ql = q[l];
|
|
sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3)
|
|
+ y[l+16] * d[1] * ((ql >> 2) & 3)
|
|
+ y[l+32] * d[2] * ((ql >> 4) & 3)
|
|
+ y[l+48] * d[3] * ((ql >> 6) & 3);
|
|
sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7];
|
|
}
|
|
tmp += dall.x * sum1 - dall.y * sum2;
|
|
}
|
|
#endif
|
|
|
|
// sum up partial sums and write back result
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
|
}
|
|
|
|
if (threadIdx.x == 0) {
|
|
dst[row] = tmp;
|
|
}
|
|
}
|
|
|
|
static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
|
|
|
|
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
|
if (row > nrows) return;
|
|
|
|
const int num_blocks_per_row = ncols / QK_K;
|
|
const int ib0 = row*num_blocks_per_row;
|
|
|
|
const block_q3_K * x = (const block_q3_K *)vx + ib0;
|
|
|
|
float tmp = 0; // partial sum for thread in warp
|
|
|
|
#if QK_K == 256
|
|
|
|
const uint16_t kmask1 = 0x0303;
|
|
const uint16_t kmask2 = 0x0f0f;
|
|
|
|
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
|
|
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1
|
|
|
|
const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop
|
|
const int step = 16/K_QUANTS_PER_ITERATION;
|
|
const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
|
|
const int in = tid - step*im; // 0....15 or 0...7
|
|
|
|
const uint8_t m = 1 << (4*im);
|
|
|
|
const int l0 = n*in; // 0...15 or 0...14 in steps of 2
|
|
const int q_offset = 32*im + l0;
|
|
const int y_offset = 128*im + l0;
|
|
|
|
uint16_t utmp[4];
|
|
const int8_t * s = (const int8_t *)utmp;
|
|
|
|
const uint16_t s_shift = 4*im;
|
|
|
|
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
|
|
|
|
const float * y = yy + i * QK_K + y_offset;
|
|
const uint8_t * q = x[i].qs + q_offset;
|
|
const uint8_t * h = x[i].hmask + l0;
|
|
|
|
const uint16_t * a = (const uint16_t *)x[i].scales;
|
|
utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
|
|
utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
|
|
utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
|
|
utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
|
|
|
|
const float d = x[i].d;
|
|
|
|
float sum = 0;
|
|
for (int l = 0; l < n; ++l) {
|
|
sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
|
|
+ y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
|
|
+ y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
|
|
+ y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
|
|
sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
|
|
+ y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
|
|
+ y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
|
|
+ y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
|
|
}
|
|
tmp += d * sum;
|
|
|
|
}
|
|
#else
|
|
|
|
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7
|
|
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3
|
|
const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14
|
|
const int in = offset/8; // 0 or 1
|
|
const int im = offset%8; // 0...7
|
|
|
|
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
|
|
|
|
const float * y = yy + i * QK_K + offset;
|
|
const uint8_t * q = x[i].qs + offset;
|
|
const uint8_t * s = x[i].scales;
|
|
|
|
const float dall = (float)x[i].d;
|
|
|
|
float sum = 0;
|
|
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
|
|
const uint8_t hl = x[i].hmask[im+l] >> in;
|
|
const uint8_t ql = q[l];
|
|
sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4))
|
|
+ y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4))
|
|
+ y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4))
|
|
+ y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4));
|
|
}
|
|
tmp += sum;
|
|
}
|
|
#endif
|
|
|
|
// sum up partial sums and write back result
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
|
}
|
|
|
|
if (threadIdx.x == 0) {
|
|
dst[row] = tmp;
|
|
}
|
|
}
|
|
|
|
static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
|
|
|
|
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
|
if (row > nrows) return;
|
|
const int num_blocks_per_row = ncols / QK_K;
|
|
const int ib0 = row*num_blocks_per_row;
|
|
|
|
const block_q4_K * x = (const block_q4_K *)vx + ib0;
|
|
|
|
#if QK_K == 256
|
|
const uint16_t kmask1 = 0x3f3f;
|
|
const uint16_t kmask2 = 0x0f0f;
|
|
const uint16_t kmask3 = 0xc0c0;
|
|
|
|
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
|
|
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1
|
|
|
|
const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4
|
|
|
|
const int il = tid/step; // 0...3
|
|
const int ir = tid - step*il; // 0...7 or 0...3
|
|
const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4
|
|
|
|
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
|
|
const int in = il%2;
|
|
|
|
const int l0 = n*(2*ir + in);
|
|
const int q_offset = 32*im + l0;
|
|
const int y_offset = 64*im + l0;
|
|
|
|
uint16_t aux[4];
|
|
const uint8_t * sc = (const uint8_t *)aux;
|
|
|
|
#if K_QUANTS_PER_ITERATION == 2
|
|
uint32_t q32[4];
|
|
const uint8_t * q4 = (const uint8_t *)q32;
|
|
#else
|
|
uint16_t q16[4];
|
|
const uint8_t * q4 = (const uint8_t *)q16;
|
|
#endif
|
|
|
|
float tmp = 0; // partial sum for thread in warp
|
|
|
|
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
|
|
|
|
const float * y1 = yy + i*QK_K + y_offset;
|
|
const float * y2 = y1 + 128;
|
|
|
|
const float dall = __low2half(x[i].dm);
|
|
const float dmin = __high2half(x[i].dm);
|
|
|
|
const uint16_t * a = (const uint16_t *)x[i].scales;
|
|
aux[0] = a[im+0] & kmask1;
|
|
aux[1] = a[im+2] & kmask1;
|
|
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
|
|
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
|
|
|
|
#if K_QUANTS_PER_ITERATION == 2
|
|
const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset);
|
|
const uint32_t * q2 = q1 + 16;
|
|
|
|
q32[0] = q1[0] & 0x0f0f0f0f;
|
|
q32[1] = q1[0] & 0xf0f0f0f0;
|
|
q32[2] = q2[0] & 0x0f0f0f0f;
|
|
q32[3] = q2[0] & 0xf0f0f0f0;
|
|
|
|
float4 s = {0.f, 0.f, 0.f, 0.f};
|
|
float smin = 0;
|
|
for (int l = 0; l < 4; ++l) {
|
|
s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+ 4];
|
|
s.z += y2[l] * q4[l+8]; s.w += y2[l+32] * q4[l+12];
|
|
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
|
|
}
|
|
tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
|
|
#else
|
|
const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset);
|
|
const uint16_t * q2 = q1 + 32;
|
|
|
|
q16[0] = q1[0] & 0x0f0f;
|
|
q16[1] = q1[0] & 0xf0f0;
|
|
q16[2] = q2[0] & 0x0f0f;
|
|
q16[3] = q2[0] & 0xf0f0;
|
|
|
|
float4 s = {0.f, 0.f, 0.f, 0.f};
|
|
float smin = 0;
|
|
for (int l = 0; l < 2; ++l) {
|
|
s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
|
|
s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
|
|
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
|
|
}
|
|
tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
|
|
#endif
|
|
|
|
}
|
|
#else
|
|
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15
|
|
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
|
|
|
|
const int step = tid * K_QUANTS_PER_ITERATION;
|
|
|
|
uint16_t aux16[2];
|
|
const uint8_t * s = (const uint8_t *)aux16;
|
|
|
|
float tmp = 0;
|
|
|
|
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
|
|
const uint8_t * q = x[i].qs + step;
|
|
const float * y = yy + i*QK_K + step;
|
|
const uint16_t * a = (const uint16_t *)x[i].scales;
|
|
aux16[0] = a[0] & 0x0f0f;
|
|
aux16[1] = (a[0] >> 4) & 0x0f0f;
|
|
const float d = (float)x[i].dm[0];
|
|
const float m = (float)x[i].dm[1];
|
|
float sum = 0.f;
|
|
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
|
|
sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
|
|
+ y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
|
|
+ y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3])
|
|
+ y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]);
|
|
}
|
|
tmp += sum;
|
|
}
|
|
|
|
#endif
|
|
|
|
// sum up partial sums and write back result
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
|
}
|
|
|
|
if (tid == 0) {
|
|
dst[row] = tmp;
|
|
}
|
|
}
|
|
|
|
static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols) {
|
|
|
|
const int row = blockIdx.x;
|
|
const int num_blocks_per_row = ncols / QK_K;
|
|
const int ib0 = row*num_blocks_per_row;
|
|
|
|
const block_q5_K * x = (const block_q5_K *)vx + ib0;
|
|
|
|
float tmp = 0; // partial sum for thread in warp
|
|
|
|
#if QK_K == 256
|
|
const uint16_t kmask1 = 0x3f3f;
|
|
const uint16_t kmask2 = 0x0f0f;
|
|
const uint16_t kmask3 = 0xc0c0;
|
|
|
|
const int tid = threadIdx.x/2; // 0...15
|
|
const int ix = threadIdx.x%2;
|
|
|
|
const int il = tid/4; // 0...3
|
|
const int ir = tid - 4*il;// 0...3
|
|
const int n = 2;
|
|
|
|
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
|
|
const int in = il%2;
|
|
|
|
const int l0 = n*(2*ir + in);
|
|
const int q_offset = 32*im + l0;
|
|
const int y_offset = 64*im + l0;
|
|
|
|
const uint8_t hm1 = 1 << (2*im);
|
|
const uint8_t hm2 = hm1 << 4;
|
|
|
|
uint16_t aux[4];
|
|
const uint8_t * sc = (const uint8_t *)aux;
|
|
|
|
uint16_t q16[8];
|
|
const uint8_t * q4 = (const uint8_t *)q16;
|
|
|
|
for (int i = ix; i < num_blocks_per_row; i += 2) {
|
|
|
|
const uint8_t * ql1 = x[i].qs + q_offset;
|
|
const uint8_t * qh = x[i].qh + l0;
|
|
const float * y1 = yy + i*QK_K + y_offset;
|
|
const float * y2 = y1 + 128;
|
|
|
|
const float dall = __low2half(x[i].dm);
|
|
const float dmin = __high2half(x[i].dm);
|
|
|
|
const uint16_t * a = (const uint16_t *)x[i].scales;
|
|
aux[0] = a[im+0] & kmask1;
|
|
aux[1] = a[im+2] & kmask1;
|
|
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
|
|
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
|
|
|
|
float4 sum = {0.f, 0.f, 0.f, 0.f};
|
|
float smin = 0;
|
|
const uint16_t * q1 = (const uint16_t *)ql1;
|
|
const uint16_t * q2 = q1 + 32;
|
|
q16[0] = q1[0] & 0x0f0f;
|
|
q16[1] = q1[8] & 0x0f0f;
|
|
q16[2] = (q1[0] >> 4) & 0x0f0f;
|
|
q16[3] = (q1[8] >> 4) & 0x0f0f;
|
|
q16[4] = q2[0] & 0x0f0f;
|
|
q16[5] = q2[8] & 0x0f0f;
|
|
q16[6] = (q2[0] >> 4) & 0x0f0f;
|
|
q16[7] = (q2[8] >> 4) & 0x0f0f;
|
|
for (int l = 0; l < n; ++l) {
|
|
sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
|
|
+ y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0));
|
|
sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
|
|
+ y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0));
|
|
sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
|
|
+ y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0));
|
|
sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
|
|
+ y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0));
|
|
smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
|
|
+ (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
|
|
}
|
|
tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
|
|
}
|
|
|
|
#else
|
|
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15
|
|
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION);
|
|
const int step = tid * K_QUANTS_PER_ITERATION;
|
|
const int im = step/8;
|
|
const int in = step%8;
|
|
|
|
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
|
|
const uint8_t * q = x[i].qs + step;
|
|
const int8_t * s = x[i].scales;
|
|
const float * y = yy + i*QK_K + step;
|
|
const float d = x[i].d;
|
|
float sum = 0.f;
|
|
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
|
|
const uint8_t h = x[i].qh[in+j] >> im;
|
|
sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16))
|
|
+ y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16))
|
|
+ y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16))
|
|
+ y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16));
|
|
}
|
|
tmp += sum;
|
|
}
|
|
#endif
|
|
|
|
// sum up partial sums and write back result
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
|
}
|
|
|
|
if (threadIdx.x == 0) {
|
|
dst[row] = tmp;
|
|
}
|
|
}
|
|
|
|
static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) {
|
|
|
|
static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
|
|
|
|
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
|
if (row > nrows) return;
|
|
|
|
const int num_blocks_per_row = ncols / QK_K;
|
|
const int ib0 = row*num_blocks_per_row;
|
|
|
|
const block_q6_K * x = (const block_q6_K *)vx + ib0;
|
|
|
|
#if QK_K == 256
|
|
|
|
const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
|
|
const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1
|
|
|
|
const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8
|
|
|
|
const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
|
|
const int in = tid - step*im; // 0...15 or 0...7
|
|
|
|
#if K_QUANTS_PER_ITERATION == 1
|
|
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15
|
|
const int is = 0;
|
|
#else
|
|
const int l0 = 4 * in; // 0, 4, 8, ..., 28
|
|
const int is = in / 4;
|
|
#endif
|
|
const int ql_offset = 64*im + l0;
|
|
const int qh_offset = 32*im + l0;
|
|
const int s_offset = 8*im + is;
|
|
const int y_offset = 128*im + l0;
|
|
|
|
float tmp = 0; // partial sum for thread in warp
|
|
|
|
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
|
|
|
|
const float * y = yy + i * QK_K + y_offset;
|
|
const uint8_t * ql = x[i].ql + ql_offset;
|
|
const uint8_t * qh = x[i].qh + qh_offset;
|
|
const int8_t * s = x[i].scales + s_offset;
|
|
|
|
const float d = x[i].d;
|
|
|
|
#if K_QUANTS_PER_ITERATION == 1
|
|
float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
|
|
+ y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
|
|
+ y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
|
|
+ y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
|
|
+ y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
|
|
+ y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
|
|
+ y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
|
|
+y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
|
|
tmp += sum;
|
|
#else
|
|
float sum = 0;
|
|
for (int l = 0; l < 4; ++l) {
|
|
sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
|
|
+ y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
|
|
+ y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
|
|
+ y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
|
|
}
|
|
tmp += sum;
|
|
#endif
|
|
|
|
}
|
|
|
|
#else
|
|
|
|
const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7
|
|
const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3
|
|
|
|
const int step = tid * K_QUANTS_PER_ITERATION;
|
|
|
|
float tmp = 0; // partial sum for thread in warp
|
|
|
|
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
|
|
|
|
const float * y = yy + i * QK_K + step;
|
|
const uint8_t * ql = x[i].ql + step;
|
|
const uint8_t * qh = x[i].qh + step;
|
|
const int8_t * s = x[i].scales;
|
|
|
|
const float d = x[i+0].d;
|
|
|
|
float sum = 0;
|
|
for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
|
|
sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
|
|
+ y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
|
|
+ y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32)
|
|
+ y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32);
|
|
}
|
|
tmp += sum;
|
|
|
|
}
|
|
|
|
#endif
|
|
|
|
// sum up partial sums and write back result
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
|
}
|
|
|
|
if (tid == 0) {
|
|
dst[row] = tmp;
|
|
}
|
|
}
|
|
|
|
static __device__ void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){
|
|
const half * x = (const half *) vx;
|
|
|
|
// automatic half -> float type cast if dfloat == float
|
|
v.x = x[ib + iqs + 0];
|
|
v.y = x[ib + iqs + 1];
|
|
}
|
|
|
|
static __device__ void convert_f32(const void * vx, const int ib, const int iqs, dfloat2 & v){
|
|
const float * x = (const float *) vx;
|
|
|
|
// automatic half -> float type cast if dfloat == float
|
|
v.x = x[ib + iqs + 0];
|
|
v.y = x[ib + iqs + 1];
|
|
}
|
|
|
|
static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded) {
|
|
const int ix = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (ix >= kx_padded) {
|
|
return;
|
|
}
|
|
|
|
const int iy = blockDim.y*blockIdx.y + threadIdx.y;
|
|
|
|
const int i_padded = iy*kx_padded + ix;
|
|
|
|
block_q8_1 * y = (block_q8_1 *) vy;
|
|
|
|
const int ib = i_padded / QK8_1; // block index
|
|
const int iqs = i_padded % QK8_1; // quant index
|
|
|
|
const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
|
|
float amax = fabsf(xi);
|
|
float sum = xi;
|
|
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32));
|
|
sum += __shfl_xor_sync(0xffffffff, sum, mask, 32);
|
|
}
|
|
|
|
const float d = amax / 127;
|
|
const int8_t q = amax == 0.0f ? 0 : roundf(xi / d);
|
|
|
|
y[ib].qs[iqs] = q;
|
|
|
|
if (iqs > 0) {
|
|
return;
|
|
}
|
|
|
|
reinterpret_cast<half&>(y[ib].ds.x) = d;
|
|
reinterpret_cast<half&>(y[ib].ds.y) = sum;
|
|
}
|
|
|
|
template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
|
|
static __global__ void k_get_rows(
|
|
const void * src0, const int32_t * src1, dst_t * dst,
|
|
int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
|
|
/*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
|
|
/*size_t s0,*/ size_t s1, size_t s2, size_t s3,
|
|
/*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
|
|
size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
|
|
|
|
const int i00 = (blockIdx.x*blockDim.x + threadIdx.x)*2;
|
|
const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
|
|
const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
|
|
const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
|
|
|
|
if (i00 >= ne00) {
|
|
return;
|
|
}
|
|
|
|
const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
|
|
|
|
dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
|
|
const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03;
|
|
|
|
const int ib = i00/qk; // block index
|
|
const int iqs = (i00%qk)/qr; // quant index
|
|
const int iybs = i00 - i00%qk; // dst block start index
|
|
const int y_offset = qr == 1 ? 1 : qk/2;
|
|
|
|
// dequantize
|
|
dfloat2 v;
|
|
dequantize_kernel(src0_row, ib, iqs, v);
|
|
|
|
dst_row[iybs + iqs + 0] = v.x;
|
|
dst_row[iybs + iqs + y_offset] = v.y;
|
|
}
|
|
|
|
template<typename src0_t, typename dst_t>
|
|
static __global__ void k_get_rows_float(
|
|
const src0_t * src0, const int32_t * src1, dst_t * dst,
|
|
int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/
|
|
/*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/
|
|
/*size_t s0,*/ size_t s1, size_t s2, size_t s3,
|
|
/*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03,
|
|
size_t s10, size_t s11, size_t s12/*, size_t s13*/) {
|
|
|
|
const int i00 = blockIdx.x*blockDim.x + threadIdx.x;
|
|
const int i10 = blockDim.y*blockIdx.y + threadIdx.y;
|
|
const int i11 = (blockIdx.z*blockDim.z + threadIdx.z)/ne12;
|
|
const int i12 = (blockIdx.z*blockDim.z + threadIdx.z)%ne12;
|
|
|
|
if (i00 >= ne00) {
|
|
return;
|
|
}
|
|
|
|
const int i01 = src1[i10*s10 + i11*s11 + i12*s12];
|
|
|
|
dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
|
|
const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03);
|
|
|
|
dst_row[i00] = src0_row[i00];
|
|
}
|
|
|
|
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
|
|
static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k) {
|
|
const int i = blockDim.x*blockIdx.x + 2*threadIdx.x;
|
|
|
|
if (i >= k) {
|
|
return;
|
|
}
|
|
|
|
const int ib = i/qk; // block index
|
|
const int iqs = (i%qk)/qr; // quant index
|
|
const int iybs = i - i%qk; // y block start index
|
|
const int y_offset = qr == 1 ? 1 : qk/2;
|
|
|
|
// dequantize
|
|
dfloat2 v;
|
|
dequantize_kernel(vx, ib, iqs, v);
|
|
|
|
y[iybs + iqs + 0] = v.x;
|
|
y[iybs + iqs + y_offset] = v.y;
|
|
}
|
|
|
|
// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
|
|
// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
|
|
|
|
#define VDR_Q4_0_Q8_1_MMVQ 2
|
|
#define VDR_Q4_0_Q8_1_MMQ 4
|
|
|
|
template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
|
|
const int * v, const int * u, const float & d4, const half2 & ds8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
int sumi = 0;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < vdr; ++i) {
|
|
const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
|
|
const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
|
|
|
|
// SIMD dot product of quantized values
|
|
sumi = __dp4a(vi0, u[2*i+0], sumi);
|
|
sumi = __dp4a(vi1, u[2*i+1], sumi);
|
|
}
|
|
|
|
const float2 ds8f = __half22float2(ds8);
|
|
|
|
// second part effectively subtracts 8 from each quant value
|
|
return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
#define VDR_Q4_1_Q8_1_MMVQ 2
|
|
#define VDR_Q4_1_Q8_1_MMQ 4
|
|
|
|
template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
|
|
const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
int sumi = 0;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < vdr; ++i) {
|
|
const int vi0 = (v[i] >> 0) & 0x0F0F0F0F;
|
|
const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
|
|
|
|
// SIMD dot product of quantized values
|
|
sumi = __dp4a(vi0, u[2*i+0], sumi);
|
|
sumi = __dp4a(vi1, u[2*i+1], sumi);
|
|
}
|
|
|
|
#ifdef GGML_CUDA_F16
|
|
const float2 tmp = __half22float2(__hmul2(dm4, ds8));
|
|
const float d4d8 = tmp.x;
|
|
const float m4s8 = tmp.y;
|
|
#else
|
|
const float2 dm4f = __half22float2(dm4);
|
|
const float2 ds8f = __half22float2(ds8);
|
|
const float d4d8 = dm4f.x * ds8f.x;
|
|
const float m4s8 = dm4f.y * ds8f.y;
|
|
#endif // GGML_CUDA_F16
|
|
|
|
// scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
|
|
return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
#define VDR_Q5_0_Q8_1_MMVQ 2
|
|
#define VDR_Q5_0_Q8_1_MMQ 4
|
|
|
|
template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
|
|
const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
int sumi = 0;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < vdr; ++i) {
|
|
int vi0 = (vl[i] >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
|
|
vi0 |= (vh[i] << 4) & 0x00000010; // 0 -> 4
|
|
vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12
|
|
vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20
|
|
vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28
|
|
sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
|
|
|
|
int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
|
|
vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4
|
|
vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12
|
|
vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20
|
|
vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28
|
|
sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
|
|
}
|
|
|
|
const float2 ds8f = __half22float2(ds8);
|
|
|
|
// second part effectively subtracts 16 from each quant value
|
|
return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
#define VDR_Q5_1_Q8_1_MMVQ 2
|
|
#define VDR_Q5_1_Q8_1_MMQ 4
|
|
|
|
template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
|
|
const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
int sumi = 0;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < vdr; ++i) {
|
|
int vi0 = (vl[i] >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits
|
|
vi0 |= (vh[i] << 4) & 0x00000010; // 0 -> 4
|
|
vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12
|
|
vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20
|
|
vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28
|
|
sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
|
|
|
|
int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
|
|
vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4
|
|
vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12
|
|
vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20
|
|
vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28
|
|
sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
|
|
}
|
|
|
|
#ifdef GGML_CUDA_F16
|
|
const float2 tmp = __half22float2(__hmul2(dm5, ds8));
|
|
const float d5d8 = tmp.x;
|
|
const float m5s8 = tmp.y;
|
|
#else
|
|
const float2 dm5f = __half22float2(dm5);
|
|
const float2 ds8f = __half22float2(ds8);
|
|
const float d5d8 = dm5f.x * ds8f.x;
|
|
const float m5s8 = dm5f.y * ds8f.y;
|
|
#endif // GGML_CUDA_F16
|
|
|
|
// scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
|
|
return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
|
|
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
#define VDR_Q8_0_Q8_1_MMVQ 2
|
|
#define VDR_Q8_0_Q8_1_MMQ 8
|
|
|
|
template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl(
|
|
const int * v, const int * u, const float & d8_0, const float & d8_1) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
int sumi = 0;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < vdr; ++i) {
|
|
// SIMD dot product of quantized values
|
|
sumi = __dp4a(v[i], u[i], sumi);
|
|
}
|
|
|
|
return d8_0*d8_1 * sumi;
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
|
|
const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
int sumi = 0;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < vdr; ++i) {
|
|
// SIMD dot product of quantized values
|
|
sumi = __dp4a(v[i], u[i], sumi);
|
|
}
|
|
|
|
#ifdef GGML_CUDA_F16
|
|
const float2 tmp = __half22float2(__hmul2(dm8, ds8));
|
|
const float d8d8 = tmp.x;
|
|
const float m8s8 = tmp.y;
|
|
#else
|
|
const float2 dm8f = __half22float2(dm8);
|
|
const float2 ds8f = __half22float2(ds8);
|
|
const float d8d8 = dm8f.x * ds8f.x;
|
|
const float m8s8 = dm8f.y * ds8f.y;
|
|
#endif // GGML_CUDA_F16
|
|
|
|
// scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
|
|
return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
#define VDR_Q2_K_Q8_1_MMVQ 1
|
|
#define VDR_Q2_K_Q8_1_MMQ 2
|
|
|
|
// contiguous v/x values
|
|
static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
|
|
const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
|
|
const half2 & dm2, const float * __restrict__ d8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
float sumf_d = 0.0f;
|
|
float sumf_m = 0.0f;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR2_K; ++i) {
|
|
const int sc = scales[2*i];
|
|
|
|
const int vi = (v >> (2*i)) & 0x03030303;
|
|
|
|
sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
|
|
|
|
// fill int with 4x m
|
|
int m = sc >> 4;
|
|
m |= m << 8;
|
|
m |= m << 16;
|
|
sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
|
|
}
|
|
|
|
const float2 dm2f = __half22float2(dm2);
|
|
|
|
return dm2f.x*sumf_d - dm2f.y*sumf_m;
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
// contiguous u/y values
|
|
static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
|
|
const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
|
|
const half2 & dm2, const float & d8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
int sumi_d = 0;
|
|
int sumi_m = 0;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) {
|
|
int sumi_d_sc = 0;
|
|
|
|
const int sc = scales[i0 / (QI8_1/2)];
|
|
|
|
// fill int with 4x m
|
|
int m = sc >> 4;
|
|
m |= m << 8;
|
|
m |= m << 16;
|
|
|
|
#pragma unroll
|
|
for (int i = i0; i < i0 + QI8_1/2; ++i) {
|
|
sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product
|
|
sumi_m = __dp4a(m, u[i], sumi_m); // multiply sum of q8_1 values with m
|
|
}
|
|
|
|
sumi_d += sumi_d_sc * (sc & 0xF);
|
|
}
|
|
|
|
const float2 dm2f = __half22float2(dm2);
|
|
|
|
return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m);
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
#define VDR_Q3_K_Q8_1_MMVQ 1
|
|
#define VDR_Q3_K_Q8_1_MMQ 2
|
|
|
|
// contiguous v/x values
|
|
static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
|
|
const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
|
|
const int & scale_offset, const float & d3, const float * __restrict__ d8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
float sumf = 0.0f;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR3_K; ++i) {
|
|
const int isc = scale_offset + 2*i;
|
|
|
|
const int isc_low = isc % (QK_K/32);
|
|
const int sc_shift_low = 4 * (isc / (QK_K/32));
|
|
const int sc_low = (scales[isc_low] >> sc_shift_low) & 0xF;
|
|
|
|
const int isc_high = isc % (QK_K/64);
|
|
const int sc_shift_high = 2 * (isc / (QK_K/64));
|
|
const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
|
|
|
|
const int sc = (sc_low | sc_high) - 32;
|
|
|
|
const int vil = (vl >> (2*i)) & 0x03030303;
|
|
|
|
const int vih = ((vh >> i) << 2) & 0x04040404;
|
|
|
|
const int vi = __vsubss4(vil, vih);
|
|
|
|
sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
|
|
}
|
|
|
|
return d3 * sumf;
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
// contiguous u/y values
|
|
static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
|
|
const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
|
|
const float & d3, const float & d8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
int sumi = 0;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) {
|
|
int sumi_sc = 0;
|
|
|
|
for (int i = i0; i < i0 + QI8_1/2; ++i) {
|
|
sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product
|
|
}
|
|
|
|
sumi += sumi_sc * scales[i0 / (QI8_1/2)];
|
|
}
|
|
|
|
return d3*d8 * sumi;
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
#define VDR_Q4_K_Q8_1_MMVQ 2
|
|
#define VDR_Q4_K_Q8_1_MMQ 8
|
|
|
|
// contiguous v/x values
|
|
static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
|
|
const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
|
|
const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
float sumf_d = 0.0f;
|
|
float sumf_m = 0.0f;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR4_K; ++i) {
|
|
const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
|
|
const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
|
|
|
|
const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
|
|
const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u
|
|
|
|
sumf_d += d8[i] * (dot1 * sc[i]);
|
|
sumf_m += d8[i] * (dot2 * m[i]); // multiply constant part of q4_K with sum of q8_1 values
|
|
}
|
|
|
|
const float2 dm4f = __half22float2(dm4);
|
|
|
|
return dm4f.x*sumf_d - dm4f.y*sumf_m;
|
|
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
// contiguous u/y values
|
|
static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
|
|
const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
|
|
const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
float sumf_d = 0.0f;
|
|
float sumf_m = 0.0f;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) {
|
|
int sumi_d = 0;
|
|
|
|
#pragma unroll
|
|
for (int j = 0; j < QI8_1; ++j) {
|
|
sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
|
|
}
|
|
|
|
const float2 ds8f = __half22float2(ds8[i]);
|
|
|
|
sumf_d += ds8f.x * (sc[i] * sumi_d);
|
|
sumf_m += ds8f.y * m[i]; // sum of q8_1 block * q4_K min val
|
|
}
|
|
|
|
const float2 dm4f = __half22float2(dm4);
|
|
|
|
return dm4f.x*sumf_d - dm4f.y*sumf_m;
|
|
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
#define VDR_Q5_K_Q8_1_MMVQ 2
|
|
#define VDR_Q5_K_Q8_1_MMQ 8
|
|
|
|
// contiguous v/x values
|
|
static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
|
|
const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
|
|
const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
float sumf_d = 0.0f;
|
|
float sumf_m = 0.0f;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR5_K; ++i) {
|
|
const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F;
|
|
const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F;
|
|
|
|
const int vh0i = ((vh[0] >> i) << 4) & 0x10101010;
|
|
const int vh1i = ((vh[1] >> i) << 4) & 0x10101010;
|
|
|
|
const int v0i = vl0i | vh0i;
|
|
const int v1i = vl1i | vh1i;
|
|
|
|
const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
|
|
const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u
|
|
|
|
sumf_d += d8[i] * (dot1 * sc[i]);
|
|
sumf_m += d8[i] * (dot2 * m[i]);
|
|
|
|
}
|
|
|
|
const float2 dm5f = __half22float2(dm5);
|
|
|
|
return dm5f.x*sumf_d - dm5f.y*sumf_m;
|
|
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
// contiguous u/y values
|
|
static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
|
|
const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
|
|
const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
float sumf_d = 0.0f;
|
|
float sumf_m = 0.0f;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) {
|
|
int sumi_d = 0;
|
|
|
|
#pragma unroll
|
|
for (int j = 0; j < QI8_1; ++j) {
|
|
sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
|
|
}
|
|
|
|
const float2 ds8f = __half22float2(ds8[i]);
|
|
|
|
sumf_d += ds8f.x * (sc[i] * sumi_d);
|
|
sumf_m += ds8f.y * m[i]; // sum of q8_1 block * q4_K min val
|
|
}
|
|
|
|
const float2 dm4f = __half22float2(dm4);
|
|
|
|
return dm4f.x*sumf_d - dm4f.y*sumf_m;
|
|
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
#define VDR_Q6_K_Q8_1_MMVQ 1
|
|
#define VDR_Q6_K_Q8_1_MMQ 8
|
|
|
|
// contiguous v/x values
|
|
static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
|
|
const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
|
|
const float & d, const float * __restrict__ d8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
float sumf = 0.0f;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR6_K; ++i) {
|
|
const int sc = scales[4*i];
|
|
|
|
const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
|
|
|
|
const int vih = ((vh >> (4*i)) << 4) & 0x30303030;
|
|
|
|
const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
|
|
|
|
sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
|
|
}
|
|
|
|
return d*sumf;
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
// contiguous u/y values
|
|
static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
|
|
const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
|
|
const float & d6, const float * __restrict__ d8) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
float sumf_d = 0.0f;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) {
|
|
int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale
|
|
|
|
#pragma unroll
|
|
for (int i = i0; i < i0 + 2; ++i) {
|
|
sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
|
|
sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
|
|
|
|
sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
|
|
sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
|
|
}
|
|
|
|
sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
|
|
}
|
|
|
|
return d6 * sumf_d;
|
|
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
|
|
|
|
const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
|
|
|
|
int v[VDR_Q4_0_Q8_1_MMVQ];
|
|
int u[2*VDR_Q4_0_Q8_1_MMVQ];
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
|
|
v[i] = get_int_from_uint8(bq4_0->qs, iqs + i);
|
|
u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
|
|
u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
|
|
}
|
|
|
|
return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
__shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + mmq_y];
|
|
__shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0];
|
|
|
|
*x_ql = tile_x_qs;
|
|
*x_dm = (half2 *) tile_x_d;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
(void)x_qh; (void)x_sc;
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI4_0;
|
|
const int kqsx = k % QI4_0;
|
|
|
|
const block_q4_0 * bx0 = (const block_q4_0 *) vx;
|
|
|
|
float * x_dmf = (float *) x_dm;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
|
|
// x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) {
|
|
int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
|
|
const float * x_dmf = (const float *) x_dm;
|
|
|
|
int u[2*VDR_Q4_0_Q8_1_MMQ];
|
|
|
|
#pragma unroll
|
|
for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) {
|
|
u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
|
|
u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE];
|
|
}
|
|
|
|
return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMQ>
|
|
(&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0],
|
|
y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
|
|
|
|
const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
|
|
|
|
int v[VDR_Q4_1_Q8_1_MMVQ];
|
|
int u[2*VDR_Q4_1_Q8_1_MMVQ];
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
|
|
v[i] = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
|
|
u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
|
|
u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
|
|
}
|
|
|
|
return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
__shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1];
|
|
|
|
*x_ql = tile_x_qs;
|
|
*x_dm = tile_x_dm;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI4_1;
|
|
const int kqsx = k % QI4_1;
|
|
|
|
const block_q4_1 * bx0 = (const block_q4_1 *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) {
|
|
int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
|
|
|
|
int u[2*VDR_Q4_1_Q8_1_MMQ];
|
|
|
|
#pragma unroll
|
|
for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) {
|
|
u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
|
|
u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE];
|
|
}
|
|
|
|
return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMQ>
|
|
(&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1],
|
|
y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
|
|
|
|
const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
|
|
|
|
int vl[VDR_Q5_0_Q8_1_MMVQ];
|
|
int vh[VDR_Q5_0_Q8_1_MMVQ];
|
|
int u[2*VDR_Q5_0_Q8_1_MMVQ];
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
|
|
vl[i] = get_int_from_uint8(bq5_0->qs, iqs + i);
|
|
vh[i] = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
|
|
u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
|
|
u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
|
|
}
|
|
|
|
return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y];
|
|
__shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = (half2 *) tile_x_d;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI5_0;
|
|
const int kqsx = k % QI5_0;
|
|
|
|
const block_q5_0 * bx0 = (const block_q5_0 *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
const int ql = get_int_from_uint8(bxi->qs, kqsx);
|
|
const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0));
|
|
|
|
int qs0 = (ql >> 0) & 0x0F0F0F0F;
|
|
qs0 |= (qh << 4) & 0x00000010; // 0 -> 4
|
|
qs0 |= (qh << 11) & 0x00001000; // 1 -> 12
|
|
qs0 |= (qh << 18) & 0x00100000; // 2 -> 20
|
|
qs0 |= (qh << 25) & 0x10000000; // 3 -> 28
|
|
qs0 = __vsubss4(qs0, 0x10101010); // subtract 16
|
|
|
|
x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
|
|
|
|
int qs1 = (ql >> 4) & 0x0F0F0F0F;
|
|
qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4
|
|
qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12
|
|
qs1 |= (qh << 2) & 0x00100000; // 18 -> 20
|
|
qs1 |= (qh << 9) & 0x10000000; // 19 -> 28
|
|
qs1 = __vsubss4(qs1, 0x10101010); // subtract 16
|
|
|
|
x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
float * x_dmf = (float *) x_dm;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) {
|
|
int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
|
|
const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0;
|
|
const float * x_dmf = (const float *) x_dm;
|
|
const float * y_df = (const float *) y_ds;
|
|
|
|
int u[2*VDR_Q5_0_Q8_1_MMQ];
|
|
|
|
#pragma unroll
|
|
for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) {
|
|
u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
|
|
u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE];
|
|
}
|
|
|
|
return vec_dot_q8_0_q8_1_impl<QR5_0*VDR_Q5_0_Q8_1_MMQ>
|
|
(&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
|
|
|
|
const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
|
|
|
|
int vl[VDR_Q5_1_Q8_1_MMVQ];
|
|
int vh[VDR_Q5_1_Q8_1_MMVQ];
|
|
int u[2*VDR_Q5_1_Q8_1_MMVQ];
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
|
|
vl[i] = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
|
|
vh[i] = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
|
|
u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
|
|
u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
|
|
}
|
|
|
|
return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI5_1;
|
|
const int kqsx = k % QI5_1;
|
|
|
|
const block_q5_1 * bx0 = (const block_q5_1 *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
|
|
const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
|
|
|
|
int qs0 = (ql >> 0) & 0x0F0F0F0F;
|
|
qs0 |= (qh << 4) & 0x00000010; // 0 -> 4
|
|
qs0 |= (qh << 11) & 0x00001000; // 1 -> 12
|
|
qs0 |= (qh << 18) & 0x00100000; // 2 -> 20
|
|
qs0 |= (qh << 25) & 0x10000000; // 3 -> 28
|
|
|
|
x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0;
|
|
|
|
int qs1 = (ql >> 4) & 0x0F0F0F0F;
|
|
qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4
|
|
qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12
|
|
qs1 |= (qh << 2) & 0x00100000; // 18 -> 20
|
|
qs1 |= (qh << 9) & 0x10000000; // 19 -> 28
|
|
|
|
x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1;
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) {
|
|
int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2));
|
|
const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1;
|
|
|
|
int u[2*VDR_Q5_1_Q8_1_MMQ];
|
|
|
|
#pragma unroll
|
|
for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) {
|
|
u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE];
|
|
u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE];
|
|
}
|
|
|
|
return vec_dot_q8_1_q8_1_impl<QR5_1*VDR_Q5_1_Q8_1_MMQ>
|
|
(&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]);
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
|
|
|
|
const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
|
|
|
|
int v[VDR_Q8_0_Q8_1_MMVQ];
|
|
int u[VDR_Q8_0_Q8_1_MMVQ];
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
|
|
v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
|
|
u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
|
|
}
|
|
|
|
return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
__shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + mmq_y];
|
|
__shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0];
|
|
|
|
*x_ql = tile_x_qs;
|
|
*x_dm = (half2 *) tile_x_d;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI8_0;
|
|
const int kqsx = k % QI8_0;
|
|
float * x_dmf = (float *) x_dm;
|
|
|
|
const block_q8_0 * bx0 = (const block_q8_0 *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI8_0;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) {
|
|
int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
(void)x_qh; (void)x_sc;
|
|
|
|
const float * x_dmf = (const float *) x_dm;
|
|
const float * y_df = (const float *) y_ds;
|
|
|
|
return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMQ>
|
|
(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0],
|
|
y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]);
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
|
|
|
|
const block_q2_K * bq2_K = (const block_q2_K *) vbq;
|
|
|
|
const int bq8_offset = QR2_K * (iqs / QI8_1);
|
|
const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
|
|
|
|
const uint8_t * scales = bq2_K->scales + scale_offset;
|
|
|
|
const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
|
|
int u[QR2_K];
|
|
float d8[QR2_K];
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR2_K; ++ i) {
|
|
u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
|
|
d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
|
|
}
|
|
|
|
return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
(void)x_qh;
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K];
|
|
__shared__ int tile_x_sc[mmq_y * (WARP_SIZE/4) + mmq_y/4];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
*x_sc = tile_x_sc;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
(void)x_qh;
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI2_K;
|
|
const int kqsx = k % QI2_K;
|
|
|
|
const block_q2_K * bx0 = (const block_q2_K *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI2_K;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) {
|
|
int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
|
|
int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4);
|
|
|
|
x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4));
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
(void)x_qh;
|
|
|
|
const int kbx = k / QI2_K;
|
|
const int ky = (k % QI2_K) * QR2_K;
|
|
const float * y_df = (const float *) y_ds;
|
|
|
|
int v[QR2_K*VDR_Q2_K_Q8_1_MMQ];
|
|
|
|
const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2);
|
|
const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2));
|
|
|
|
#pragma unroll
|
|
for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) {
|
|
v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303;
|
|
}
|
|
|
|
const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4;
|
|
|
|
const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE;
|
|
return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]);
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
|
|
|
|
const block_q3_K * bq3_K = (const block_q3_K *) vbq;
|
|
|
|
const int bq8_offset = QR3_K * (iqs / (QI3_K/2));
|
|
const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2);
|
|
|
|
const float d = bq3_K->d;
|
|
|
|
const int vl = get_int_from_uint8(bq3_K->qs, iqs);
|
|
|
|
// invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
|
|
const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
|
|
|
|
int u[QR3_K];
|
|
float d8[QR3_K];
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR3_K; ++i) {
|
|
u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
|
|
d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
|
|
}
|
|
|
|
return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K];
|
|
__shared__ int tile_x_qh[mmq_y * (WARP_SIZE/2) + mmq_y/2];
|
|
__shared__ int tile_x_sc[mmq_y * (WARP_SIZE/4) + mmq_y/4];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
*x_qh = tile_x_qh;
|
|
*x_sc = tile_x_sc;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI3_K;
|
|
const int kqsx = k % QI3_K;
|
|
|
|
const block_q3_K * bx0 = (const block_q3_K *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI3_K;
|
|
const int kbxd = k % blocks_per_tile_x_row;
|
|
float * x_dmf = (float *) x_dm;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) {
|
|
int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) {
|
|
int i = i0 + i_offset * 2 + k / (WARP_SIZE/2);
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2);
|
|
|
|
// invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
|
|
x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2));
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) {
|
|
int i = i0 + i_offset * 4 + k / (WARP_SIZE/4);
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4);
|
|
|
|
const int ksc = k % (QI3_K/4);
|
|
|
|
const int ksc_low = ksc % (QI3_K/8);
|
|
const int shift_low = 4 * (ksc / (QI3_K/8));
|
|
const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F;
|
|
|
|
const int ksc_high = QI3_K/8;
|
|
const int shift_high = 2 * ksc;
|
|
const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030;
|
|
|
|
const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
|
|
|
|
x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
|
|
const int kbx = k / QI3_K;
|
|
const int ky = (k % QI3_K) * QR3_K;
|
|
const float * x_dmf = (const float *) x_dm;
|
|
const float * y_df = (const float *) y_ds;
|
|
|
|
const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4;
|
|
|
|
int v[QR3_K*VDR_Q3_K_Q8_1_MMQ];
|
|
|
|
#pragma unroll
|
|
for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) {
|
|
const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2);
|
|
const int shift = 2 * ((ky % 32) / 8);
|
|
const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303;
|
|
|
|
const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8);
|
|
const int vlh = (vh << 2) & 0x04040404;
|
|
|
|
v[l] = __vsubss4(vll, vlh);
|
|
}
|
|
|
|
const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE;
|
|
return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]);
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
|
|
|
|
#ifndef GGML_QKK_64
|
|
const block_q4_K * bq4_K = (const block_q4_K *) vbq;
|
|
|
|
int v[2];
|
|
int u[2*QR4_K];
|
|
float d8[QR4_K];
|
|
|
|
// iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6
|
|
const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2));
|
|
|
|
// iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
|
|
// iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
|
|
// iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
|
|
// iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
|
|
|
|
const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
|
|
v[0] = q4[0];
|
|
v[1] = q4[4];
|
|
|
|
const uint16_t * scales = (const uint16_t *)bq4_K->scales;
|
|
uint16_t aux[2];
|
|
const int j = bq8_offset/2;
|
|
if (j < 2) {
|
|
aux[0] = scales[j+0] & 0x3f3f;
|
|
aux[1] = scales[j+2] & 0x3f3f;
|
|
} else {
|
|
aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
|
|
aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
|
|
}
|
|
const uint8_t * sc = (const uint8_t *)aux;
|
|
const uint8_t * m = sc + 2;
|
|
|
|
for (int i = 0; i < QR4_K; ++i) {
|
|
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
|
|
d8[i] = __low2half(bq8i->ds);
|
|
|
|
const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
|
|
u[2*i+0] = q8[0];
|
|
u[2*i+1] = q8[4];
|
|
}
|
|
|
|
return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8);
|
|
|
|
#else
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
const block_q4_K * bq4_K = (const block_q4_K *) vbq;
|
|
|
|
float sumf_d = 0.0f;
|
|
float sumf_m = 0.0f;
|
|
|
|
uint16_t aux16[2];
|
|
const uint8_t * s = (const uint8_t *)aux16;
|
|
|
|
const uint16_t * a = (const uint16_t *)bq4_K->scales;
|
|
aux16[0] = a[0] & 0x0f0f;
|
|
aux16[1] = (a[0] >> 4) & 0x0f0f;
|
|
|
|
const float dall = bq4_K->dm[0];
|
|
const float dmin = bq4_K->dm[1];
|
|
|
|
const float d8_1 = __low2float(bq8_1[0].ds);
|
|
const float d8_2 = __low2float(bq8_1[1].ds);
|
|
|
|
const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
|
|
const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
|
|
const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
|
|
const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
|
|
|
|
const int * q4 = (const int *)bq4_K->qs + (iqs/2);
|
|
const int v1 = q4[0];
|
|
const int v2 = q4[4];
|
|
|
|
const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0));
|
|
const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0));
|
|
const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
|
|
const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0));
|
|
|
|
sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]);
|
|
sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]);
|
|
|
|
return dall * sumf_d - dmin * sumf_m;
|
|
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
|
|
#endif
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
(void)x_qh;
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K];
|
|
__shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
*x_sc = tile_x_sc;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
(void)x_qh;
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI4_K; // == 0 if QK_K == 256
|
|
const int kqsx = k % QI4_K; // == k if QK_K == 256
|
|
|
|
const block_q4_K * bx0 = (const block_q4_K *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx;
|
|
|
|
x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256
|
|
const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) {
|
|
int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
#if QK_K == 256
|
|
x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
|
|
#else
|
|
x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
|
|
#endif
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
|
|
int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8);
|
|
|
|
const int * scales = (const int *) bxi->scales;
|
|
|
|
const int ksc = k % (WARP_SIZE/8);
|
|
|
|
// scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
|
|
int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
|
|
scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits
|
|
|
|
x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
(void)x_qh;
|
|
|
|
const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8);
|
|
|
|
const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE;
|
|
return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8,
|
|
x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]);
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
|
|
|
|
#ifndef GGML_QKK_64
|
|
const block_q5_K * bq5_K = (const block_q5_K *) vbq;
|
|
|
|
int vl[2];
|
|
int vh[2];
|
|
int u[2*QR5_K];
|
|
float d8[QR5_K];
|
|
|
|
const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2));
|
|
const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4));
|
|
const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4));
|
|
|
|
vl[0] = ql[0];
|
|
vl[1] = ql[4];
|
|
|
|
vh[0] = qh[0] >> bq8_offset;
|
|
vh[1] = qh[4] >> bq8_offset;
|
|
|
|
const uint16_t * scales = (const uint16_t *)bq5_K->scales;
|
|
uint16_t aux[2];
|
|
const int j = bq8_offset/2;
|
|
if (j < 2) {
|
|
aux[0] = scales[j+0] & 0x3f3f;
|
|
aux[1] = scales[j+2] & 0x3f3f;
|
|
} else {
|
|
aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
|
|
aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
|
|
}
|
|
const uint8_t * sc = (const uint8_t *)aux;
|
|
const uint8_t * m = sc + 2;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR5_K; ++i) {
|
|
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
|
|
d8[i] = __low2float(bq8i->ds);
|
|
|
|
const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
|
|
u[2*i+0] = q8[0];
|
|
u[2*i+1] = q8[4];
|
|
}
|
|
|
|
return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8);
|
|
|
|
#else
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
const block_q5_K * bq5_K = (const block_q5_K *) vbq;
|
|
|
|
const int8_t * s = bq5_K->scales;
|
|
|
|
const float d = bq5_K->d;
|
|
|
|
const float d8_1 = __low2half(bq8_1[0].ds);
|
|
const float d8_2 = __low2half(bq8_1[1].ds);
|
|
|
|
const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
|
|
const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
|
|
const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2));
|
|
const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4);
|
|
|
|
const int * ql = (const int *)bq5_K->qs + (iqs/2);
|
|
const int vl1 = ql[0];
|
|
const int vl2 = ql[4];
|
|
|
|
const int step = 4 * (iqs/2); // 0, 4, 8, 12
|
|
const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6
|
|
const int in = step%8; // 0, 4, 0, 4
|
|
const int vh = (*((const int *)(bq5_K->qh + in))) >> im;
|
|
|
|
const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f);
|
|
const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f);
|
|
const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f);
|
|
const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f);
|
|
|
|
const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1])
|
|
+ d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]);
|
|
|
|
return d * sumf_d;
|
|
|
|
#else
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
|
|
#endif
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
(void)x_qh;
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K];
|
|
__shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
*x_sc = tile_x_sc;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
(void)x_qh;
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI5_K; // == 0 if QK_K == 256
|
|
const int kqsx = k % QI5_K; // == k if QK_K == 256
|
|
|
|
const block_q5_K * bx0 = (const block_q5_K *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx;
|
|
const int ky = QR5_K*kqsx;
|
|
|
|
const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
|
|
const int ql0 = (ql >> 0) & 0x0F0F0F0F;
|
|
const int ql1 = (ql >> 4) & 0x0F0F0F0F;
|
|
|
|
const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4));
|
|
const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010;
|
|
const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010;
|
|
|
|
const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0;
|
|
const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4);
|
|
|
|
x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
|
|
x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256
|
|
const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) {
|
|
int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
#if QK_K == 256
|
|
x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
|
|
#endif
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
|
|
int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8);
|
|
|
|
const int * scales = (const int *) bxi->scales;
|
|
|
|
const int ksc = k % (WARP_SIZE/8);
|
|
|
|
// scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8
|
|
int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits
|
|
scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits
|
|
|
|
x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8;
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
(void)x_qh;
|
|
|
|
const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8);
|
|
|
|
const int index_x = i * (QR5_K*WARP_SIZE + 1) + QR5_K*k;
|
|
const int index_y = j * WARP_SIZE + (QR5_K*k) % WARP_SIZE;
|
|
return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8,
|
|
x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]);
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) {
|
|
|
|
const block_q6_K * bq6_K = (const block_q6_K *) vbq;
|
|
|
|
const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4);
|
|
const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
|
|
const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
|
|
|
|
const int vl = get_int_from_uint8(bq6_K->ql, iqs);
|
|
const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
|
|
|
|
const int8_t * scales = bq6_K->scales + scale_offset;
|
|
|
|
int u[QR6_K];
|
|
float d8[QR6_K];
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < QR6_K; ++i) {
|
|
u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
|
|
d8[i] = __low2half(bq8_1[bq8_offset + 2*i].ds);
|
|
}
|
|
|
|
return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
|
|
}
|
|
|
|
template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
|
|
(void)x_qh;
|
|
|
|
__shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y];
|
|
__shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K];
|
|
__shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8];
|
|
|
|
*x_ql = tile_x_ql;
|
|
*x_dm = tile_x_dm;
|
|
*x_sc = tile_x_sc;
|
|
}
|
|
|
|
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
|
|
const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh,
|
|
int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) {
|
|
(void)x_qh;
|
|
|
|
GGML_CUDA_ASSUME(i_offset >= 0);
|
|
GGML_CUDA_ASSUME(i_offset < nwarps);
|
|
GGML_CUDA_ASSUME(k >= 0);
|
|
GGML_CUDA_ASSUME(k < WARP_SIZE);
|
|
|
|
const int kbx = k / QI6_K; // == 0 if QK_K == 256
|
|
const int kqsx = k % QI6_K; // == k if QK_K == 256
|
|
|
|
const block_q6_K * bx0 = (const block_q6_K *) vx;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
|
|
int i = i0 + i_offset;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx;
|
|
const int ky = QR6_K*kqsx;
|
|
|
|
const int ql = get_int_from_uint8(bxi->ql, kqsx);
|
|
const int ql0 = (ql >> 0) & 0x0F0F0F0F;
|
|
const int ql1 = (ql >> 4) & 0x0F0F0F0F;
|
|
|
|
const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4));
|
|
const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030;
|
|
const int qh1 = (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) & 0x30303030;
|
|
|
|
const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0;
|
|
const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2);
|
|
|
|
x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
|
|
x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
|
|
}
|
|
|
|
const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
|
|
const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256
|
|
float * x_dmf = (float *) x_dm;
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) {
|
|
int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd;
|
|
|
|
x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) {
|
|
int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y;
|
|
|
|
if (need_check) {
|
|
i = min(i, i_max);
|
|
}
|
|
|
|
const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4;
|
|
|
|
x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8));
|
|
}
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat(
|
|
const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc,
|
|
const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) {
|
|
(void)x_qh;
|
|
|
|
const float * x_dmf = (const float *) x_dm;
|
|
const float * y_df = (const float *) y_ds;
|
|
|
|
const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]);
|
|
|
|
const int index_x = i * (QR6_K*WARP_SIZE + 1) + QR6_K*k;
|
|
const int index_y = j * WARP_SIZE + (QR6_K*k) % WARP_SIZE;
|
|
return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]);
|
|
}
|
|
|
|
template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
|
|
allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
|
|
static __device__ __forceinline__ void mul_mat_q(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
const block_q_t * x = (const block_q_t *) vx;
|
|
const block_q8_1 * y = (const block_q8_1 *) vy;
|
|
|
|
const int blocks_per_row_x = ncols_x / qk;
|
|
const int blocks_per_col_y = nrows_y / QK8_1;
|
|
const int blocks_per_warp = WARP_SIZE / qi;
|
|
|
|
const int & ncols_dst = ncols_y;
|
|
|
|
const int row_dst_0 = blockIdx.x*mmq_y;
|
|
const int & row_x_0 = row_dst_0;
|
|
|
|
const int col_dst_0 = blockIdx.y*mmq_x;
|
|
const int & col_y_0 = col_dst_0;
|
|
|
|
int * tile_x_ql = nullptr;
|
|
half2 * tile_x_dm = nullptr;
|
|
int * tile_x_qh = nullptr;
|
|
int * tile_x_sc = nullptr;
|
|
|
|
allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);
|
|
|
|
__shared__ int tile_y_qs[mmq_x * WARP_SIZE];
|
|
__shared__ half2 tile_y_ds[mmq_x * WARP_SIZE/QI8_1];
|
|
|
|
float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}};
|
|
|
|
for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
|
|
|
|
load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc,
|
|
threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x);
|
|
|
|
#pragma unroll
|
|
for (int ir = 0; ir < qr; ++ir) {
|
|
const int kqs = ir*WARP_SIZE + threadIdx.x;
|
|
const int kbxd = kqs / QI8_1;
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < mmq_x; i += nwarps) {
|
|
const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses
|
|
|
|
const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd];
|
|
|
|
const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE;
|
|
tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) {
|
|
const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x;
|
|
const int kby = threadIdx.x % (WARP_SIZE/QI8_1);
|
|
const int col_y_eff = min(col_y_0 + ids, ncols_y-1);
|
|
|
|
// if the sum is not needed it's faster to transform the scale to f32 ahead of time
|
|
const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds;
|
|
half2 * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby];
|
|
if (need_sum) {
|
|
*dsi_dst = *dsi_src;
|
|
} else {
|
|
float * dfi_dst = (float *) dsi_dst;
|
|
*dfi_dst = __low2half(*dsi_src);
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
// #pragma unroll // unrolling this loop causes too much register pressure
|
|
for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) {
|
|
#pragma unroll
|
|
for (int j = 0; j < mmq_x; j += nwarps) {
|
|
#pragma unroll
|
|
for (int i = 0; i < mmq_y; i += WARP_SIZE) {
|
|
sum[i/WARP_SIZE][j/nwarps] += vec_dot(
|
|
tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds,
|
|
threadIdx.x + i, threadIdx.y + j, k);
|
|
}
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
}
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int j = 0; j < mmq_x; j += nwarps) {
|
|
const int col_dst = col_dst_0 + j + threadIdx.y;
|
|
|
|
if (col_dst >= ncols_dst) {
|
|
return;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i = 0; i < mmq_y; i += WARP_SIZE) {
|
|
const int row_dst = row_dst_0 + threadIdx.x + i;
|
|
|
|
if (row_dst >= nrows_dst) {
|
|
continue;
|
|
}
|
|
|
|
dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps];
|
|
}
|
|
}
|
|
}
|
|
|
|
#define MMQ_X_Q4_0_RDNA2 64
|
|
#define MMQ_Y_Q4_0_RDNA2 128
|
|
#define NWARPS_Q4_0_RDNA2 8
|
|
#define MMQ_X_Q4_0_RDNA1 64
|
|
#define MMQ_Y_Q4_0_RDNA1 64
|
|
#define NWARPS_Q4_0_RDNA1 8
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
#define MMQ_X_Q4_0_AMPERE 4
|
|
#define MMQ_Y_Q4_0_AMPERE 32
|
|
#define NWARPS_Q4_0_AMPERE 4
|
|
#else
|
|
#define MMQ_X_Q4_0_AMPERE 64
|
|
#define MMQ_Y_Q4_0_AMPERE 128
|
|
#define NWARPS_Q4_0_AMPERE 4
|
|
#endif
|
|
#define MMQ_X_Q4_0_PASCAL 64
|
|
#define MMQ_Y_Q4_0_PASCAL 64
|
|
#define NWARPS_Q4_0_PASCAL 8
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q4_0(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
const int mmq_x = MMQ_X_Q4_0_RDNA2;
|
|
const int mmq_y = MMQ_Y_Q4_0_RDNA2;
|
|
const int nwarps = NWARPS_Q4_0_RDNA2;
|
|
#else
|
|
const int mmq_x = MMQ_X_Q4_0_RDNA1;
|
|
const int mmq_y = MMQ_Y_Q4_0_RDNA1;
|
|
const int nwarps = NWARPS_Q4_0_RDNA1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
|
|
load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= CC_VOLTA
|
|
const int mmq_x = MMQ_X_Q4_0_AMPERE;
|
|
const int mmq_y = MMQ_Y_Q4_0_AMPERE;
|
|
const int nwarps = NWARPS_Q4_0_AMPERE;
|
|
|
|
mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
|
|
load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
const int mmq_x = MMQ_X_Q4_0_PASCAL;
|
|
const int mmq_y = MMQ_Y_Q4_0_PASCAL;
|
|
const int nwarps = NWARPS_Q4_0_PASCAL;
|
|
|
|
mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
|
|
load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
(void) vec_dot_q4_0_q8_1_mul_mat;
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
}
|
|
|
|
#define MMQ_X_Q4_1_RDNA2 64
|
|
#define MMQ_Y_Q4_1_RDNA2 128
|
|
#define NWARPS_Q4_1_RDNA2 8
|
|
#define MMQ_X_Q4_1_RDNA1 64
|
|
#define MMQ_Y_Q4_1_RDNA1 64
|
|
#define NWARPS_Q4_1_RDNA1 8
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
#define MMQ_X_Q4_1_AMPERE 4
|
|
#define MMQ_Y_Q4_1_AMPERE 32
|
|
#define NWARPS_Q4_1_AMPERE 4
|
|
#else
|
|
#define MMQ_X_Q4_1_AMPERE 64
|
|
#define MMQ_Y_Q4_1_AMPERE 128
|
|
#define NWARPS_Q4_1_AMPERE 4
|
|
#endif
|
|
#define MMQ_X_Q4_1_PASCAL 64
|
|
#define MMQ_Y_Q4_1_PASCAL 64
|
|
#define NWARPS_Q4_1_PASCAL 8
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#elif __CUDA_ARCH__ < CC_VOLTA
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2)
|
|
#endif // __CUDA_ARCH__ < CC_VOLTA
|
|
mul_mat_q4_1(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
const int mmq_x = MMQ_X_Q4_1_RDNA2;
|
|
const int mmq_y = MMQ_Y_Q4_1_RDNA2;
|
|
const int nwarps = NWARPS_Q4_1_RDNA2;
|
|
#else
|
|
const int mmq_x = MMQ_X_Q4_1_RDNA1;
|
|
const int mmq_y = MMQ_Y_Q4_1_RDNA1;
|
|
const int nwarps = NWARPS_Q4_1_RDNA1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
|
|
load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= CC_VOLTA
|
|
const int mmq_x = MMQ_X_Q4_1_AMPERE;
|
|
const int mmq_y = MMQ_Y_Q4_1_AMPERE;
|
|
const int nwarps = NWARPS_Q4_1_AMPERE;
|
|
|
|
mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
|
|
load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
const int mmq_x = MMQ_X_Q4_1_PASCAL;
|
|
const int mmq_y = MMQ_Y_Q4_1_PASCAL;
|
|
const int nwarps = NWARPS_Q4_1_PASCAL;
|
|
|
|
mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
|
|
load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
(void) vec_dot_q4_1_q8_1_mul_mat;
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
}
|
|
|
|
#define MMQ_X_Q5_0_RDNA2 64
|
|
#define MMQ_Y_Q5_0_RDNA2 128
|
|
#define NWARPS_Q5_0_RDNA2 8
|
|
#define MMQ_X_Q5_0_RDNA1 64
|
|
#define MMQ_Y_Q5_0_RDNA1 64
|
|
#define NWARPS_Q5_0_RDNA1 8
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
#define MMQ_X_Q5_0_AMPERE 4
|
|
#define MMQ_Y_Q5_0_AMPERE 32
|
|
#define NWARPS_Q5_0_AMPERE 4
|
|
#else
|
|
#define MMQ_X_Q5_0_AMPERE 128
|
|
#define MMQ_Y_Q5_0_AMPERE 64
|
|
#define NWARPS_Q5_0_AMPERE 4
|
|
#endif
|
|
#define MMQ_X_Q5_0_PASCAL 64
|
|
#define MMQ_Y_Q5_0_PASCAL 64
|
|
#define NWARPS_Q5_0_PASCAL 8
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q5_0(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
const int mmq_x = MMQ_X_Q5_0_RDNA2;
|
|
const int mmq_y = MMQ_Y_Q5_0_RDNA2;
|
|
const int nwarps = NWARPS_Q5_0_RDNA2;
|
|
#else
|
|
const int mmq_x = MMQ_X_Q5_0_RDNA1;
|
|
const int mmq_y = MMQ_Y_Q5_0_RDNA1;
|
|
const int nwarps = NWARPS_Q5_0_RDNA1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
|
|
load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= CC_VOLTA
|
|
const int mmq_x = MMQ_X_Q5_0_AMPERE;
|
|
const int mmq_y = MMQ_Y_Q5_0_AMPERE;
|
|
const int nwarps = NWARPS_Q5_0_AMPERE;
|
|
|
|
mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
|
|
load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
const int mmq_x = MMQ_X_Q5_0_PASCAL;
|
|
const int mmq_y = MMQ_Y_Q5_0_PASCAL;
|
|
const int nwarps = NWARPS_Q5_0_PASCAL;
|
|
|
|
mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
|
|
load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
(void) vec_dot_q5_0_q8_1_mul_mat;
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
}
|
|
|
|
#define MMQ_X_Q5_1_RDNA2 64
|
|
#define MMQ_Y_Q5_1_RDNA2 128
|
|
#define NWARPS_Q5_1_RDNA2 8
|
|
#define MMQ_X_Q5_1_RDNA1 64
|
|
#define MMQ_Y_Q5_1_RDNA1 64
|
|
#define NWARPS_Q5_1_RDNA1 8
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
#define MMQ_X_Q5_1_AMPERE 4
|
|
#define MMQ_Y_Q5_1_AMPERE 32
|
|
#define NWARPS_Q5_1_AMPERE 4
|
|
#else
|
|
#define MMQ_X_Q5_1_AMPERE 128
|
|
#define MMQ_Y_Q5_1_AMPERE 64
|
|
#define NWARPS_Q5_1_AMPERE 4
|
|
#endif
|
|
#define MMQ_X_Q5_1_PASCAL 64
|
|
#define MMQ_Y_Q5_1_PASCAL 64
|
|
#define NWARPS_Q5_1_PASCAL 8
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q5_1(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
const int mmq_x = MMQ_X_Q5_1_RDNA2;
|
|
const int mmq_y = MMQ_Y_Q5_1_RDNA2;
|
|
const int nwarps = NWARPS_Q5_1_RDNA2;
|
|
#else
|
|
const int mmq_x = MMQ_X_Q5_1_RDNA1;
|
|
const int mmq_y = MMQ_Y_Q5_1_RDNA1;
|
|
const int nwarps = NWARPS_Q5_1_RDNA1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
|
|
load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= CC_VOLTA
|
|
const int mmq_x = MMQ_X_Q5_1_AMPERE;
|
|
const int mmq_y = MMQ_Y_Q5_1_AMPERE;
|
|
const int nwarps = NWARPS_Q5_1_AMPERE;
|
|
|
|
mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
|
|
load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
const int mmq_x = MMQ_X_Q5_1_PASCAL;
|
|
const int mmq_y = MMQ_Y_Q5_1_PASCAL;
|
|
const int nwarps = NWARPS_Q5_1_PASCAL;
|
|
|
|
mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
|
|
load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
(void) vec_dot_q5_1_q8_1_mul_mat;
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
}
|
|
|
|
#define MMQ_X_Q8_0_RDNA2 64
|
|
#define MMQ_Y_Q8_0_RDNA2 128
|
|
#define NWARPS_Q8_0_RDNA2 8
|
|
#define MMQ_X_Q8_0_RDNA1 64
|
|
#define MMQ_Y_Q8_0_RDNA1 64
|
|
#define NWARPS_Q8_0_RDNA1 8
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
#define MMQ_X_Q8_0_AMPERE 4
|
|
#define MMQ_Y_Q8_0_AMPERE 32
|
|
#define NWARPS_Q8_0_AMPERE 4
|
|
#else
|
|
#define MMQ_X_Q8_0_AMPERE 128
|
|
#define MMQ_Y_Q8_0_AMPERE 64
|
|
#define NWARPS_Q8_0_AMPERE 4
|
|
#endif
|
|
#define MMQ_X_Q8_0_PASCAL 64
|
|
#define MMQ_Y_Q8_0_PASCAL 64
|
|
#define NWARPS_Q8_0_PASCAL 8
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q8_0(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
const int mmq_x = MMQ_X_Q8_0_RDNA2;
|
|
const int mmq_y = MMQ_Y_Q8_0_RDNA2;
|
|
const int nwarps = NWARPS_Q8_0_RDNA2;
|
|
#else
|
|
const int mmq_x = MMQ_X_Q8_0_RDNA1;
|
|
const int mmq_y = MMQ_Y_Q8_0_RDNA1;
|
|
const int nwarps = NWARPS_Q8_0_RDNA1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
|
|
load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= CC_VOLTA
|
|
const int mmq_x = MMQ_X_Q8_0_AMPERE;
|
|
const int mmq_y = MMQ_Y_Q8_0_AMPERE;
|
|
const int nwarps = NWARPS_Q8_0_AMPERE;
|
|
|
|
mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
|
|
load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
const int mmq_x = MMQ_X_Q8_0_PASCAL;
|
|
const int mmq_y = MMQ_Y_Q8_0_PASCAL;
|
|
const int nwarps = NWARPS_Q8_0_PASCAL;
|
|
|
|
mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
|
|
load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
(void) vec_dot_q8_0_q8_1_mul_mat;
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
}
|
|
|
|
#define MMQ_X_Q2_K_RDNA2 64
|
|
#define MMQ_Y_Q2_K_RDNA2 128
|
|
#define NWARPS_Q2_K_RDNA2 8
|
|
#define MMQ_X_Q2_K_RDNA1 128
|
|
#define MMQ_Y_Q2_K_RDNA1 32
|
|
#define NWARPS_Q2_K_RDNA1 8
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
#define MMQ_X_Q2_K_AMPERE 4
|
|
#define MMQ_Y_Q2_K_AMPERE 32
|
|
#define NWARPS_Q2_K_AMPERE 4
|
|
#else
|
|
#define MMQ_X_Q2_K_AMPERE 64
|
|
#define MMQ_Y_Q2_K_AMPERE 128
|
|
#define NWARPS_Q2_K_AMPERE 4
|
|
#endif
|
|
#define MMQ_X_Q2_K_PASCAL 64
|
|
#define MMQ_Y_Q2_K_PASCAL 64
|
|
#define NWARPS_Q2_K_PASCAL 8
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q2_K(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
const int mmq_x = MMQ_X_Q2_K_RDNA2;
|
|
const int mmq_y = MMQ_Y_Q2_K_RDNA2;
|
|
const int nwarps = NWARPS_Q2_K_RDNA2;
|
|
#else
|
|
const int mmq_x = MMQ_X_Q2_K_RDNA1;
|
|
const int mmq_y = MMQ_Y_Q2_K_RDNA1;
|
|
const int nwarps = NWARPS_Q2_K_RDNA1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
|
|
load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= CC_VOLTA
|
|
const int mmq_x = MMQ_X_Q2_K_AMPERE;
|
|
const int mmq_y = MMQ_Y_Q2_K_AMPERE;
|
|
const int nwarps = NWARPS_Q2_K_AMPERE;
|
|
|
|
mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
|
|
load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
const int mmq_x = MMQ_X_Q2_K_PASCAL;
|
|
const int mmq_y = MMQ_Y_Q2_K_PASCAL;
|
|
const int nwarps = NWARPS_Q2_K_PASCAL;
|
|
|
|
mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
|
|
load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
(void) vec_dot_q2_K_q8_1_mul_mat;
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
}
|
|
|
|
#define MMQ_X_Q3_K_RDNA2 128
|
|
#define MMQ_Y_Q3_K_RDNA2 64
|
|
#define NWARPS_Q3_K_RDNA2 8
|
|
#define MMQ_X_Q3_K_RDNA1 32
|
|
#define MMQ_Y_Q3_K_RDNA1 128
|
|
#define NWARPS_Q3_K_RDNA1 8
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
#define MMQ_X_Q3_K_AMPERE 4
|
|
#define MMQ_Y_Q3_K_AMPERE 32
|
|
#define NWARPS_Q3_K_AMPERE 4
|
|
#else
|
|
#define MMQ_X_Q3_K_AMPERE 128
|
|
#define MMQ_Y_Q3_K_AMPERE 128
|
|
#define NWARPS_Q3_K_AMPERE 4
|
|
#endif
|
|
#define MMQ_X_Q3_K_PASCAL 64
|
|
#define MMQ_Y_Q3_K_PASCAL 64
|
|
#define NWARPS_Q3_K_PASCAL 8
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#elif __CUDA_ARCH__ < CC_VOLTA
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2)
|
|
#endif // __CUDA_ARCH__ < CC_VOLTA
|
|
mul_mat_q3_K(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
const int mmq_x = MMQ_X_Q3_K_RDNA2;
|
|
const int mmq_y = MMQ_Y_Q3_K_RDNA2;
|
|
const int nwarps = NWARPS_Q3_K_RDNA2;
|
|
#else
|
|
const int mmq_x = MMQ_X_Q3_K_RDNA1;
|
|
const int mmq_y = MMQ_Y_Q3_K_RDNA1;
|
|
const int nwarps = NWARPS_Q3_K_RDNA1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
|
|
load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= CC_VOLTA
|
|
const int mmq_x = MMQ_X_Q3_K_AMPERE;
|
|
const int mmq_y = MMQ_Y_Q3_K_AMPERE;
|
|
const int nwarps = NWARPS_Q3_K_AMPERE;
|
|
|
|
mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
|
|
load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
const int mmq_x = MMQ_X_Q3_K_PASCAL;
|
|
const int mmq_y = MMQ_Y_Q3_K_PASCAL;
|
|
const int nwarps = NWARPS_Q3_K_PASCAL;
|
|
|
|
mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
|
|
load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
(void) vec_dot_q3_K_q8_1_mul_mat;
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
}
|
|
|
|
#define MMQ_X_Q4_K_RDNA2 64
|
|
#define MMQ_Y_Q4_K_RDNA2 128
|
|
#define NWARPS_Q4_K_RDNA2 8
|
|
#define MMQ_X_Q4_K_RDNA1 32
|
|
#define MMQ_Y_Q4_K_RDNA1 64
|
|
#define NWARPS_Q4_K_RDNA1 8
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
#define MMQ_X_Q4_K_AMPERE 4
|
|
#define MMQ_Y_Q4_K_AMPERE 32
|
|
#define NWARPS_Q4_K_AMPERE 4
|
|
#else
|
|
#define MMQ_X_Q4_K_AMPERE 64
|
|
#define MMQ_Y_Q4_K_AMPERE 128
|
|
#define NWARPS_Q4_K_AMPERE 4
|
|
#endif
|
|
#define MMQ_X_Q4_K_PASCAL 64
|
|
#define MMQ_Y_Q4_K_PASCAL 64
|
|
#define NWARPS_Q4_K_PASCAL 8
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#elif __CUDA_ARCH__ < CC_VOLTA
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2)
|
|
#endif // __CUDA_ARCH__ < CC_VOLTA
|
|
mul_mat_q4_K(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
const int mmq_x = MMQ_X_Q4_K_RDNA2;
|
|
const int mmq_y = MMQ_Y_Q4_K_RDNA2;
|
|
const int nwarps = NWARPS_Q4_K_RDNA2;
|
|
#else
|
|
const int mmq_x = MMQ_X_Q4_K_RDNA1;
|
|
const int mmq_y = MMQ_Y_Q4_K_RDNA1;
|
|
const int nwarps = NWARPS_Q4_K_RDNA1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
|
|
load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= CC_VOLTA
|
|
const int mmq_x = MMQ_X_Q4_K_AMPERE;
|
|
const int mmq_y = MMQ_Y_Q4_K_AMPERE;
|
|
const int nwarps = NWARPS_Q4_K_AMPERE;
|
|
|
|
mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
|
|
load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
const int mmq_x = MMQ_X_Q4_K_PASCAL;
|
|
const int mmq_y = MMQ_Y_Q4_K_PASCAL;
|
|
const int nwarps = NWARPS_Q4_K_PASCAL;
|
|
|
|
mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
|
|
load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
(void) vec_dot_q4_K_q8_1_mul_mat;
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
}
|
|
|
|
#define MMQ_X_Q5_K_RDNA2 64
|
|
#define MMQ_Y_Q5_K_RDNA2 128
|
|
#define NWARPS_Q5_K_RDNA2 8
|
|
#define MMQ_X_Q5_K_RDNA1 32
|
|
#define MMQ_Y_Q5_K_RDNA1 64
|
|
#define NWARPS_Q5_K_RDNA1 8
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
#define MMQ_X_Q5_K_AMPERE 4
|
|
#define MMQ_Y_Q5_K_AMPERE 32
|
|
#define NWARPS_Q5_K_AMPERE 4
|
|
#else
|
|
#define MMQ_X_Q5_K_AMPERE 64
|
|
#define MMQ_Y_Q5_K_AMPERE 128
|
|
#define NWARPS_Q5_K_AMPERE 4
|
|
#endif
|
|
#define MMQ_X_Q5_K_PASCAL 64
|
|
#define MMQ_Y_Q5_K_PASCAL 64
|
|
#define NWARPS_Q5_K_PASCAL 8
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
mul_mat_q5_K(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
const int mmq_x = MMQ_X_Q5_K_RDNA2;
|
|
const int mmq_y = MMQ_Y_Q5_K_RDNA2;
|
|
const int nwarps = NWARPS_Q5_K_RDNA2;
|
|
#else
|
|
const int mmq_x = MMQ_X_Q5_K_RDNA1;
|
|
const int mmq_y = MMQ_Y_Q5_K_RDNA1;
|
|
const int nwarps = NWARPS_Q5_K_RDNA1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
|
|
load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= CC_VOLTA
|
|
const int mmq_x = MMQ_X_Q5_K_AMPERE;
|
|
const int mmq_y = MMQ_Y_Q5_K_AMPERE;
|
|
const int nwarps = NWARPS_Q5_K_AMPERE;
|
|
|
|
mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
|
|
load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
const int mmq_x = MMQ_X_Q5_K_PASCAL;
|
|
const int mmq_y = MMQ_Y_Q5_K_PASCAL;
|
|
const int nwarps = NWARPS_Q5_K_PASCAL;
|
|
|
|
mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
|
|
load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
(void) vec_dot_q5_K_q8_1_mul_mat;
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
}
|
|
|
|
#define MMQ_X_Q6_K_RDNA2 64
|
|
#define MMQ_Y_Q6_K_RDNA2 128
|
|
#define NWARPS_Q6_K_RDNA2 8
|
|
#define MMQ_X_Q6_K_RDNA1 32
|
|
#define MMQ_Y_Q6_K_RDNA1 64
|
|
#define NWARPS_Q6_K_RDNA1 8
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
#define MMQ_X_Q6_K_AMPERE 4
|
|
#define MMQ_Y_Q6_K_AMPERE 32
|
|
#define NWARPS_Q6_K_AMPERE 4
|
|
#else
|
|
#define MMQ_X_Q6_K_AMPERE 64
|
|
#define MMQ_Y_Q6_K_AMPERE 64
|
|
#define NWARPS_Q6_K_AMPERE 4
|
|
#endif
|
|
#define MMQ_X_Q6_K_PASCAL 64
|
|
#define MMQ_Y_Q6_K_PASCAL 64
|
|
#define NWARPS_Q6_K_PASCAL 8
|
|
|
|
template <bool need_check> static __global__ void
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2)
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
#elif __CUDA_ARCH__ < CC_VOLTA
|
|
__launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2)
|
|
#endif // __CUDA_ARCH__ < CC_VOLTA
|
|
mul_mat_q6_K(
|
|
const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
#if defined(RDNA3) || defined(RDNA2)
|
|
const int mmq_x = MMQ_X_Q6_K_RDNA2;
|
|
const int mmq_y = MMQ_Y_Q6_K_RDNA2;
|
|
const int nwarps = NWARPS_Q6_K_RDNA2;
|
|
#else
|
|
const int mmq_x = MMQ_X_Q6_K_RDNA1;
|
|
const int mmq_y = MMQ_Y_Q6_K_RDNA1;
|
|
const int nwarps = NWARPS_Q6_K_RDNA1;
|
|
#endif // defined(RDNA3) || defined(RDNA2)
|
|
|
|
mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
|
|
load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= CC_VOLTA
|
|
const int mmq_x = MMQ_X_Q6_K_AMPERE;
|
|
const int mmq_y = MMQ_Y_Q6_K_AMPERE;
|
|
const int nwarps = NWARPS_Q6_K_AMPERE;
|
|
|
|
mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
|
|
load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
|
|
#elif __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
const int mmq_x = MMQ_X_Q6_K_PASCAL;
|
|
const int mmq_y = MMQ_Y_Q6_K_PASCAL;
|
|
const int nwarps = NWARPS_Q6_K_PASCAL;
|
|
|
|
mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
|
|
load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
#else
|
|
(void) vec_dot_q6_K_q8_1_mul_mat;
|
|
bad_arch();
|
|
#endif // __CUDA_ARCH__ >= CC_VOLTA
|
|
}
|
|
|
|
template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
|
|
static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) {
|
|
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
|
|
|
if (row >= nrows) {
|
|
return;
|
|
}
|
|
|
|
const int blocks_per_row = ncols / qk;
|
|
const int blocks_per_warp = vdr * WARP_SIZE / qi;
|
|
|
|
// partial sum for each thread
|
|
float tmp = 0.0f;
|
|
|
|
const block_q_t * x = (const block_q_t *) vx;
|
|
const block_q8_1 * y = (const block_q8_1 *) vy;
|
|
|
|
for (int i = 0; i < blocks_per_row; i += blocks_per_warp) {
|
|
const int ibx = row*blocks_per_row + i + threadIdx.x / (qi/vdr); // x block index
|
|
|
|
const int iby = (i + threadIdx.x / (qi/vdr)) * (qk/QK8_1); // y block index that aligns with ibx
|
|
|
|
const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int
|
|
|
|
tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs);
|
|
}
|
|
|
|
// sum up partial sums and write back result
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
|
}
|
|
|
|
if (threadIdx.x == 0) {
|
|
dst[row] = tmp;
|
|
}
|
|
}
|
|
|
|
template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
|
|
static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) {
|
|
// qk = quantized weights per x block
|
|
// qr = number of quantized weights per data value in x block
|
|
const int row = blockIdx.x*blockDim.y + threadIdx.y;
|
|
|
|
if (row >= nrows) {
|
|
return;
|
|
}
|
|
|
|
const int tid = threadIdx.x;
|
|
|
|
const int iter_stride = 2*GGML_CUDA_DMMV_X;
|
|
const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter
|
|
const int y_offset = qr == 1 ? 1 : qk/2;
|
|
|
|
// partial sum for each thread
|
|
#ifdef GGML_CUDA_F16
|
|
half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics
|
|
#else
|
|
float tmp = 0.0f;
|
|
#endif // GGML_CUDA_F16
|
|
|
|
for (int i = 0; i < ncols; i += iter_stride) {
|
|
const int col = i + vals_per_iter*tid;
|
|
const int ib = (row*ncols + col)/qk; // x block index
|
|
const int iqs = (col%qk)/qr; // x quant index
|
|
const int iybs = col - col%qk; // y block start index
|
|
|
|
// processing >2 values per i iter is faster for fast GPUs
|
|
#pragma unroll
|
|
for (int j = 0; j < vals_per_iter; j += 2) {
|
|
// process 2 vals per j iter
|
|
|
|
// dequantize
|
|
// for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val
|
|
dfloat2 v;
|
|
dequantize_kernel(vx, ib, iqs + j/qr, v);
|
|
|
|
// matrix multiplication
|
|
// for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2
|
|
#ifdef GGML_CUDA_F16
|
|
tmp += __hmul2(v, {
|
|
y[iybs + iqs + j/qr + 0],
|
|
y[iybs + iqs + j/qr + y_offset]
|
|
});
|
|
#else
|
|
tmp += v.x * y[iybs + iqs + j/qr + 0];
|
|
tmp += v.y * y[iybs + iqs + j/qr + y_offset];
|
|
#endif // GGML_CUDA_F16
|
|
}
|
|
}
|
|
|
|
// sum up partial sums and write back result
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
|
}
|
|
|
|
if (tid == 0) {
|
|
#ifdef GGML_CUDA_F16
|
|
dst[row] = tmp.x + tmp.y;
|
|
#else
|
|
dst[row] = tmp;
|
|
#endif // GGML_CUDA_F16
|
|
}
|
|
}
|
|
|
|
static __global__ void mul_mat_p021_f16_f32(
|
|
const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst,
|
|
const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y) {
|
|
|
|
const half * x = (const half *) vx;
|
|
|
|
const int row_x = blockDim.y*blockIdx.y + threadIdx.y;
|
|
const int channel = blockDim.z*blockIdx.z + threadIdx.z;
|
|
const int channel_x = channel / (nchannels_y / nchannels_x);
|
|
|
|
const int nrows_y = ncols_x;
|
|
const int nrows_dst = nrows_x;
|
|
const int row_dst = row_x;
|
|
|
|
float tmp = 0.0f;
|
|
|
|
for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) {
|
|
const int col_x = col_x0 + threadIdx.x;
|
|
|
|
if (col_x >= ncols_x) {
|
|
break;
|
|
}
|
|
|
|
// x is transposed and permuted
|
|
const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x;
|
|
const float xi = __half2float(x[ix]);
|
|
|
|
const int row_y = col_x;
|
|
|
|
|
|
// y is not transposed but permuted
|
|
const int iy = channel*nrows_y + row_y;
|
|
|
|
tmp += xi * y[iy];
|
|
}
|
|
|
|
// dst is not transposed and not permuted
|
|
const int idst = channel*nrows_dst + row_dst;
|
|
|
|
// sum up partial sums and write back result
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
|
}
|
|
|
|
if (threadIdx.x == 0) {
|
|
dst[idst] = tmp;
|
|
}
|
|
}
|
|
|
|
static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
|
|
const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x,
|
|
const int row_stride_x, const int channel_stride_x, const int channel_x_divisor) {
|
|
|
|
const half * x = (const half *) vx;
|
|
|
|
const int row_x = blockDim.y*blockIdx.y + threadIdx.y;
|
|
const int channel = blockDim.z*blockIdx.z + threadIdx.z;
|
|
const int channel_x = channel / channel_x_divisor;
|
|
|
|
const int nrows_y = ncols_x;
|
|
const int nrows_dst = nrows_x;
|
|
const int row_dst = row_x;
|
|
|
|
const int idst = channel*nrows_dst + row_dst;
|
|
|
|
float tmp = 0.0f;
|
|
|
|
for (int col_x0 = 0; col_x0 < ncols_x; col_x0 += blockDim.x) {
|
|
const int col_x = col_x0 + threadIdx.x;
|
|
|
|
if (col_x >= ncols_x) {
|
|
break;
|
|
}
|
|
|
|
const int row_y = col_x;
|
|
|
|
const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x;
|
|
const int iy = channel*nrows_y + row_y;
|
|
|
|
const float xi = __half2float(x[ix]);
|
|
|
|
tmp += xi * y[iy];
|
|
}
|
|
|
|
// sum up partial sums and write back result
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
|
}
|
|
|
|
if (threadIdx.x == 0) {
|
|
dst[idst] = tmp;
|
|
}
|
|
}
|
|
|
|
static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) {
|
|
const float * xi = (const float *) cxi;
|
|
float * dsti = (float *) cdsti;
|
|
|
|
*dsti = *xi;
|
|
}
|
|
|
|
static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
|
|
const float * xi = (const float *) cxi;
|
|
half * dsti = (half *) cdsti;
|
|
|
|
*dsti = __float2half(*xi);
|
|
}
|
|
|
|
static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) {
|
|
const half * xi = (const half *) cxi;
|
|
half * dsti = (half *) cdsti;
|
|
|
|
*dsti = *xi;
|
|
}
|
|
|
|
template <cpy_kernel_t cpy_1>
|
|
static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
|
|
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12) {
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (i >= ne) {
|
|
return;
|
|
}
|
|
|
|
// determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor
|
|
// then combine those indices with the corresponding byte offsets to get the total offsets
|
|
const int i02 = i / (ne00*ne01);
|
|
const int i01 = (i - i02*ne01*ne00) / ne00;
|
|
const int i00 = i - i02*ne01*ne00 - i01*ne00;
|
|
const int x_offset = i00*nb00 + i01*nb01 + i02*nb02;
|
|
|
|
const int i12 = i / (ne10*ne11);
|
|
const int i11 = (i - i12*ne10*ne11) / ne10;
|
|
const int i10 = i - i12*ne10*ne11 - i11*ne10;
|
|
const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12;
|
|
|
|
cpy_1(cx + x_offset, cdst + dst_offset);
|
|
}
|
|
|
|
static __device__ void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) {
|
|
const float * xi = (const float *) cxi;
|
|
block_q8_0 * dsti = (block_q8_0 *) cdsti;
|
|
|
|
float amax = 0.0f; // absolute max
|
|
|
|
for (int j = 0; j < QK8_0; j++) {
|
|
const float v = xi[j];
|
|
amax = fmaxf(amax, fabsf(v));
|
|
}
|
|
|
|
const float d = amax / ((1 << 7) - 1);
|
|
const float id = d ? 1.0f/d : 0.0f;
|
|
|
|
dsti->d = d;
|
|
|
|
for (int j = 0; j < QK8_0; ++j) {
|
|
const float x0 = xi[j]*id;
|
|
|
|
dsti->qs[j] = roundf(x0);
|
|
}
|
|
}
|
|
|
|
static __device__ void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) {
|
|
const float * xi = (const float *) cxi;
|
|
block_q4_0 * dsti = (block_q4_0 *) cdsti;
|
|
|
|
float amax = 0.0f;
|
|
float vmax = 0.0f;
|
|
|
|
for (int j = 0; j < QK4_0; ++j) {
|
|
const float v = xi[j];
|
|
if (amax < fabsf(v)) {
|
|
amax = fabsf(v);
|
|
vmax = v;
|
|
}
|
|
}
|
|
|
|
const float d = vmax / -8;
|
|
const float id = d ? 1.0f/d : 0.0f;
|
|
|
|
dsti->d = d;
|
|
|
|
for (int j = 0; j < QK4_0/2; ++j) {
|
|
const float x0 = xi[0 + j]*id;
|
|
const float x1 = xi[QK4_0/2 + j]*id;
|
|
|
|
const uint8_t xi0 = min(15, (int8_t)(x0 + 8.5f));
|
|
const uint8_t xi1 = min(15, (int8_t)(x1 + 8.5f));
|
|
|
|
dsti->qs[j] = xi0;
|
|
dsti->qs[j] |= xi1 << 4;
|
|
}
|
|
}
|
|
|
|
static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) {
|
|
const float * xi = (const float *) cxi;
|
|
block_q4_1 * dsti = (block_q4_1 *) cdsti;
|
|
|
|
float vmin = FLT_MAX;
|
|
float vmax = -FLT_MAX;
|
|
|
|
for (int j = 0; j < QK4_1; ++j) {
|
|
const float v = xi[j];
|
|
|
|
if (v < vmin) vmin = v;
|
|
if (v > vmax) vmax = v;
|
|
}
|
|
|
|
const float d = (vmax - vmin) / ((1 << 4) - 1);
|
|
const float id = d ? 1.0f/d : 0.0f;
|
|
|
|
dsti->dm.x = d;
|
|
dsti->dm.y = vmin;
|
|
|
|
for (int j = 0; j < QK4_1/2; ++j) {
|
|
const float x0 = (xi[0 + j] - vmin)*id;
|
|
const float x1 = (xi[QK4_1/2 + j] - vmin)*id;
|
|
|
|
const uint8_t xi0 = min(15, (int8_t)(x0 + 0.5f));
|
|
const uint8_t xi1 = min(15, (int8_t)(x1 + 0.5f));
|
|
|
|
dsti->qs[j] = xi0;
|
|
dsti->qs[j] |= xi1 << 4;
|
|
}
|
|
}
|
|
|
|
template <cpy_kernel_t cpy_blck, int qk>
|
|
static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
|
|
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12) {
|
|
const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
|
|
|
|
if (i >= ne) {
|
|
return;
|
|
}
|
|
|
|
const int i02 = i / (ne00*ne01);
|
|
const int i01 = (i - i02*ne01*ne00) / ne00;
|
|
const int i00 = (i - i02*ne01*ne00 - i01*ne00);
|
|
const int x_offset = i00*nb00 + i01*nb01 + i02*nb02;
|
|
|
|
const int i12 = i / (ne10*ne11);
|
|
const int i11 = (i - i12*ne10*ne11) / ne10;
|
|
const int i10 = (i - i12*ne10*ne11 - i11*ne10)/qk;
|
|
const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12;
|
|
|
|
cpy_blck(cx + x_offset, cdst + dst_offset);
|
|
}
|
|
|
|
static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) {
|
|
const float y = (i0 / 2 - low) / max(0.001f, high - low);
|
|
return 1.0f - min(1.0f, max(0.0f, y));
|
|
}
|
|
|
|
struct rope_corr_dims {
|
|
float v[4];
|
|
};
|
|
|
|
// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
|
|
// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
|
|
static __device__ void rope_yarn(
|
|
float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
|
|
float * cos_theta, float * sin_theta
|
|
) {
|
|
// Get n-d rotational scaling corrected for extrapolation
|
|
float theta_interp = freq_scale * theta_extrap;
|
|
float theta = theta_interp;
|
|
if (ext_factor != 0.0f) {
|
|
float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
|
|
theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
|
|
|
|
// Get n-d magnitude scaling corrected for interpolation
|
|
mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
|
|
}
|
|
*cos_theta = cosf(theta) * mscale;
|
|
*sin_theta = sinf(theta) * mscale;
|
|
}
|
|
|
|
// rope == RoPE == rotary positional embedding
|
|
template<typename T, bool has_pos>
|
|
static __global__ void rope(
|
|
const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
|
|
float ext_factor, float attn_factor, rope_corr_dims corr_dims
|
|
) {
|
|
const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
|
|
|
|
if (col >= ncols) {
|
|
return;
|
|
}
|
|
|
|
const int row = blockDim.x*blockIdx.x + threadIdx.x;
|
|
const int i = row*ncols + col;
|
|
const int i2 = row/p_delta_rows;
|
|
|
|
const int p = has_pos ? pos[i2] : 0;
|
|
const float theta_base = p*powf(freq_base, -float(col)/ncols);
|
|
|
|
float cos_theta, sin_theta;
|
|
rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
|
|
|
|
const float x0 = x[i + 0];
|
|
const float x1 = x[i + 1];
|
|
|
|
dst[i + 0] = x0*cos_theta - x1*sin_theta;
|
|
dst[i + 1] = x0*sin_theta + x1*cos_theta;
|
|
}
|
|
|
|
template<typename T, bool has_pos>
|
|
static __global__ void rope_neox(
|
|
const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
|
|
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims
|
|
) {
|
|
const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
|
|
|
|
if (col >= ncols) {
|
|
return;
|
|
}
|
|
|
|
const int row = blockDim.x*blockIdx.x + threadIdx.x;
|
|
const int ib = col / n_dims;
|
|
const int ic = col % n_dims;
|
|
|
|
if (ib > 0) {
|
|
const int i = row*ncols + ib*n_dims + ic;
|
|
|
|
dst[i + 0] = x[i + 0];
|
|
dst[i + 1] = x[i + 1];
|
|
|
|
return;
|
|
}
|
|
|
|
const int i = row*ncols + ib*n_dims + ic/2;
|
|
const int i2 = row/p_delta_rows;
|
|
|
|
float cur_rot = inv_ndims * ic - ib;
|
|
|
|
const int p = has_pos ? pos[i2] : 0;
|
|
const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f);
|
|
|
|
float cos_theta, sin_theta;
|
|
rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
|
|
|
|
const float x0 = x[i + 0];
|
|
const float x1 = x[i + n_dims/2];
|
|
|
|
dst[i + 0] = x0*cos_theta - x1*sin_theta;
|
|
dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
|
|
}
|
|
|
|
static __global__ void rope_glm_f32(
|
|
const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
|
|
int n_ctx
|
|
) {
|
|
const int col = blockDim.x*blockIdx.x + threadIdx.x;
|
|
const int half_n_dims = ncols/4;
|
|
|
|
if (col >= half_n_dims) {
|
|
return;
|
|
}
|
|
|
|
const int row = blockDim.y*blockIdx.y + threadIdx.y;
|
|
const int i = row*ncols + col;
|
|
const int i2 = row/p_delta_rows;
|
|
|
|
const float col_theta_scale = powf(freq_base, -2.0f*col/ncols);
|
|
// FIXME: this is likely wrong
|
|
const int p = pos != nullptr ? pos[i2] : 0;
|
|
|
|
const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale;
|
|
const float sin_theta = sinf(theta);
|
|
const float cos_theta = cosf(theta);
|
|
|
|
const float x0 = x[i + 0];
|
|
const float x1 = x[i + half_n_dims];
|
|
|
|
dst[i + 0] = x0*cos_theta - x1*sin_theta;
|
|
dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta;
|
|
|
|
const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale;
|
|
const float sin_block_theta = sinf(block_theta);
|
|
const float cos_block_theta = cosf(block_theta);
|
|
|
|
const float x2 = x[i + half_n_dims * 2];
|
|
const float x3 = x[i + half_n_dims * 3];
|
|
|
|
dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta;
|
|
dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
|
|
}
|
|
|
|
static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows,
|
|
const int n_heads_log2_floor, const float m0, const float m1) {
|
|
const int col = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (col >= ncols) {
|
|
return;
|
|
}
|
|
|
|
const int row = blockDim.y*blockIdx.y + threadIdx.y;
|
|
const int i = row*ncols + col;
|
|
|
|
const int k = row/k_rows;
|
|
|
|
float m_k;
|
|
if (k < n_heads_log2_floor) {
|
|
m_k = powf(m0, k + 1);
|
|
} else {
|
|
m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
|
|
}
|
|
|
|
dst[i] = col * m_k + x[i];
|
|
}
|
|
|
|
static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) {
|
|
const int row = blockIdx.y;
|
|
const int col = threadIdx.x;
|
|
|
|
float sum = 0.0f;
|
|
for (int i = col; i < ncols; i += blockDim.x) {
|
|
sum += x[row * ncols + i];
|
|
}
|
|
|
|
sum = warp_reduce_sum(sum);
|
|
|
|
if (col == 0) {
|
|
dst[row] = sum;
|
|
}
|
|
}
|
|
|
|
template<typename T>
|
|
static inline __device__ void swap(T & a, T & b) {
|
|
T tmp = a;
|
|
a = b;
|
|
b = tmp;
|
|
}
|
|
|
|
template<ggml_sort_order order>
|
|
static __global__ void k_argsort_f32_i32(const float * x, int * dst, const int ncols) {
|
|
// bitonic sort
|
|
int col = threadIdx.x;
|
|
int row = blockIdx.y;
|
|
|
|
if (col >= ncols) return;
|
|
|
|
const float * x_row = x + row * ncols;
|
|
int * dst_row = dst + row * ncols;
|
|
|
|
// initialize indices
|
|
if (col < ncols) {
|
|
dst_row[col] = col;
|
|
}
|
|
__syncthreads();
|
|
|
|
for (int k = 2; k <= ncols; k *= 2) {
|
|
for (int j = k / 2; j > 0; j /= 2) {
|
|
int ixj = col ^ j;
|
|
if (ixj > col) {
|
|
if ((col & k) == 0) {
|
|
if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) {
|
|
swap(dst_row[col], dst_row[ixj]);
|
|
}
|
|
} else {
|
|
if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) {
|
|
swap(dst_row[col], dst_row[ixj]);
|
|
}
|
|
}
|
|
}
|
|
__syncthreads();
|
|
}
|
|
}
|
|
}
|
|
|
|
static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) {
|
|
const int col = blockDim.y*blockIdx.y + threadIdx.y;
|
|
const int row = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (col >= ncols) {
|
|
return;
|
|
}
|
|
|
|
const int i = row*ncols + col;
|
|
//dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i];
|
|
//dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU
|
|
dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX;
|
|
}
|
|
|
|
static __global__ void soft_max_f32(const float * x, const float * y, float * dst, const int ncols, const int nrows_y, const float scale) {
|
|
const int tid = threadIdx.x;
|
|
const int rowx = blockIdx.x;
|
|
const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension
|
|
|
|
const int block_size = blockDim.x;
|
|
|
|
const int warp_id = threadIdx.x / WARP_SIZE;
|
|
const int lane_id = threadIdx.x % WARP_SIZE;
|
|
|
|
__shared__ float buf[CUDA_SOFT_MAX_BLOCK_SIZE/WARP_SIZE];
|
|
|
|
float max_val = -INFINITY;
|
|
|
|
for (int col = tid; col < ncols; col += block_size) {
|
|
const int ix = rowx*ncols + col;
|
|
const int iy = rowy*ncols + col;
|
|
max_val = max(max_val, x[ix]*scale + (y ? y[iy] : 0.0f));
|
|
}
|
|
|
|
// find the max value in the block
|
|
max_val = warp_reduce_max(max_val);
|
|
if (block_size > WARP_SIZE) {
|
|
if (warp_id == 0) {
|
|
buf[lane_id] = -INFINITY;
|
|
}
|
|
__syncthreads();
|
|
|
|
if (lane_id == 0) {
|
|
buf[warp_id] = max_val;
|
|
}
|
|
__syncthreads();
|
|
|
|
max_val = buf[lane_id];
|
|
max_val = warp_reduce_max(max_val);
|
|
}
|
|
|
|
float tmp = 0.f;
|
|
|
|
for (int col = tid; col < ncols; col += block_size) {
|
|
const int ix = rowx*ncols + col;
|
|
const int iy = rowy*ncols + col;
|
|
const float val = expf((x[ix]*scale + (y ? y[iy] : 0.0f)) - max_val);
|
|
tmp += val;
|
|
dst[ix] = val;
|
|
}
|
|
|
|
// find the sum of exps in the block
|
|
tmp = warp_reduce_sum(tmp);
|
|
if (block_size > WARP_SIZE) {
|
|
if (warp_id == 0) {
|
|
buf[lane_id] = 0.f;
|
|
}
|
|
__syncthreads();
|
|
|
|
if (lane_id == 0) {
|
|
buf[warp_id] = tmp;
|
|
}
|
|
__syncthreads();
|
|
|
|
tmp = buf[lane_id];
|
|
tmp = warp_reduce_sum(tmp);
|
|
}
|
|
|
|
const float inv_tmp = 1.f / tmp;
|
|
|
|
for (int col = tid; col < ncols; col += block_size) {
|
|
const int i = rowx*ncols + col;
|
|
dst[i] *= inv_tmp;
|
|
}
|
|
}
|
|
|
|
static __global__ void scale_f32(const float * x, float * dst, const float scale, const int k) {
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (i >= k) {
|
|
return;
|
|
}
|
|
|
|
dst[i] = scale * x[i];
|
|
}
|
|
|
|
static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) {
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (i >= k) {
|
|
return;
|
|
}
|
|
|
|
dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
|
|
}
|
|
|
|
static __global__ void im2col_f32_f16(
|
|
const float * x, half * dst,
|
|
int offset_delta, int IW, int IH, int OW, int KW, int KH, int pelements, int CHW,
|
|
int s0, int s1, int p0, int p1, int d0, int d1) {
|
|
const int i = threadIdx.x + blockIdx.x * blockDim.x;
|
|
if (i >= pelements) {
|
|
return;
|
|
}
|
|
|
|
const int ksize = OW * (KH > 1 ? KW : 1);
|
|
const int kx = i / ksize;
|
|
const int kd = kx * ksize;
|
|
const int ky = (i - kd) / OW;
|
|
const int ix = i % OW;
|
|
|
|
const int iiw = ix * s0 + kx * d0 - p0;
|
|
const int iih = blockIdx.y * s1 + ky * d1 - p1;
|
|
|
|
const int offset_dst =
|
|
(blockIdx.y * OW + ix) * CHW +
|
|
(blockIdx.z * (KW * KH) + ky * KW + kx);
|
|
|
|
if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
|
|
dst[offset_dst] = __float2half(0.0f);
|
|
} else {
|
|
const int offset_src = blockIdx.z * offset_delta;
|
|
dst[offset_dst] = __float2half(x[offset_src + iih * IW + iiw]);
|
|
}
|
|
}
|
|
|
|
template<int qk, int qr, dequantize_kernel_t dq>
|
|
static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
|
|
|
|
GGML_TENSOR_BINARY_OP_LOCALS
|
|
|
|
const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
|
|
const int block_num_x = (ne00 + 2*CUDA_GET_ROWS_BLOCK_SIZE - 1) / (2*CUDA_GET_ROWS_BLOCK_SIZE);
|
|
const dim3 block_nums(block_num_x, ne10, ne11*ne12);
|
|
|
|
// strides in elements
|
|
//const size_t s0 = nb0 / ggml_element_size(dst);
|
|
const size_t s1 = nb1 / ggml_element_size(dst);
|
|
const size_t s2 = nb2 / ggml_element_size(dst);
|
|
const size_t s3 = nb3 / ggml_element_size(dst);
|
|
|
|
const size_t s10 = nb10 / ggml_element_size(src1);
|
|
const size_t s11 = nb11 / ggml_element_size(src1);
|
|
const size_t s12 = nb12 / ggml_element_size(src1);
|
|
//const size_t s13 = nb13 / ggml_element_size(src1);
|
|
|
|
GGML_ASSERT(ne00 % 2 == 0);
|
|
|
|
k_get_rows<qk, qr, dq><<<block_nums, block_dims, 0, stream>>>(
|
|
src0_dd, src1_dd, dst_dd,
|
|
ne00, /*ne01, ne02, ne03,*/
|
|
/*ne10, ne11,*/ ne12, /*ne13,*/
|
|
/* s0,*/ s1, s2, s3,
|
|
/* nb00,*/ nb01, nb02, nb03,
|
|
s10, s11, s12/*, s13*/);
|
|
|
|
(void) dst;
|
|
}
|
|
|
|
template<typename src0_t>
|
|
static void get_rows_cuda_float(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const src0_t * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) {
|
|
|
|
GGML_TENSOR_BINARY_OP_LOCALS
|
|
|
|
const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
|
|
const int block_num_x = (ne00 + CUDA_GET_ROWS_BLOCK_SIZE - 1) / CUDA_GET_ROWS_BLOCK_SIZE;
|
|
const dim3 block_nums(block_num_x, ne10, ne11*ne12);
|
|
|
|
// strides in elements
|
|
//const size_t s0 = nb0 / ggml_element_size(dst);
|
|
const size_t s1 = nb1 / ggml_element_size(dst);
|
|
const size_t s2 = nb2 / ggml_element_size(dst);
|
|
const size_t s3 = nb3 / ggml_element_size(dst);
|
|
|
|
const size_t s10 = nb10 / ggml_element_size(src1);
|
|
const size_t s11 = nb11 / ggml_element_size(src1);
|
|
const size_t s12 = nb12 / ggml_element_size(src1);
|
|
//const size_t s13 = nb13 / ggml_element_size(src1);
|
|
|
|
k_get_rows_float<<<block_nums, block_dims, 0, stream>>>(
|
|
src0_dd, src1_dd, dst_dd,
|
|
ne00, /*ne01, ne02, ne03,*/
|
|
/*ne10, ne11,*/ ne12, /*ne13,*/
|
|
/* s0,*/ s1, s2, s3,
|
|
/* nb00,*/ nb01, nb02, nb03,
|
|
s10, s11, s12/*, s13*/);
|
|
|
|
(void) dst;
|
|
}
|
|
|
|
template<float (*bin_op)(const float, const float)>
|
|
struct bin_bcast_cuda {
|
|
template<typename src0_t, typename src1_t, typename dst_t>
|
|
void operator()(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst,
|
|
const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd,
|
|
cudaStream_t stream) {
|
|
|
|
GGML_TENSOR_BINARY_OP_LOCALS
|
|
|
|
int nr0 = ne10/ne0;
|
|
int nr1 = ne11/ne1;
|
|
int nr2 = ne12/ne2;
|
|
int nr3 = ne13/ne3;
|
|
|
|
int nr[4] = { nr0, nr1, nr2, nr3 };
|
|
|
|
// collapse dimensions until first broadcast dimension
|
|
int64_t cne0[] = {ne0, ne1, ne2, ne3};
|
|
int64_t cne1[] = {ne10, ne11, ne12, ne13};
|
|
size_t cnb0[] = {nb0, nb1, nb2, nb3};
|
|
size_t cnb1[] = {nb10, nb11, nb12, nb13};
|
|
auto collapse = [](int64_t cne[]) {
|
|
cne[0] *= cne[1];
|
|
cne[1] = cne[2];
|
|
cne[2] = cne[3];
|
|
cne[3] = 1;
|
|
};
|
|
|
|
auto collapse_nb = [](size_t cnb[], int64_t cne[]) {
|
|
cnb[1] *= cne[1];
|
|
cnb[2] *= cne[2];
|
|
cnb[3] *= cne[3];
|
|
};
|
|
|
|
for (int i = 0; i < 4; i++) {
|
|
if (nr[i] != 1) {
|
|
break;
|
|
}
|
|
if (i > 0) {
|
|
collapse_nb(cnb0, cne0);
|
|
collapse_nb(cnb1, cne1);
|
|
collapse(cne0);
|
|
collapse(cne1);
|
|
}
|
|
}
|
|
{
|
|
int64_t ne0 = cne0[0];
|
|
int64_t ne1 = cne0[1];
|
|
int64_t ne2 = cne0[2];
|
|
int64_t ne3 = cne0[3];
|
|
|
|
int64_t ne10 = cne1[0];
|
|
int64_t ne11 = cne1[1];
|
|
int64_t ne12 = cne1[2];
|
|
int64_t ne13 = cne1[3];
|
|
|
|
size_t nb0 = cnb0[0];
|
|
size_t nb1 = cnb0[1];
|
|
size_t nb2 = cnb0[2];
|
|
size_t nb3 = cnb0[3];
|
|
|
|
size_t nb10 = cnb1[0];
|
|
size_t nb11 = cnb1[1];
|
|
size_t nb12 = cnb1[2];
|
|
size_t nb13 = cnb1[3];
|
|
|
|
size_t s0 = nb0 / sizeof(dst_t);
|
|
size_t s1 = nb1 / sizeof(dst_t);
|
|
size_t s2 = nb2 / sizeof(dst_t);
|
|
size_t s3 = nb3 / sizeof(dst_t);
|
|
|
|
size_t s10 = nb10 / sizeof(src1_t);
|
|
size_t s11 = nb11 / sizeof(src1_t);
|
|
size_t s12 = nb12 / sizeof(src1_t);
|
|
size_t s13 = nb13 / sizeof(src1_t);
|
|
|
|
GGML_ASSERT(s0 == 1);
|
|
GGML_ASSERT(s10 == 1);
|
|
|
|
const int block_size = 128;
|
|
|
|
int64_t hne0 = std::max(ne0/2LL, 1LL);
|
|
|
|
dim3 block_dims;
|
|
block_dims.x = std::min<unsigned int>(hne0, block_size);
|
|
block_dims.y = std::min<unsigned int>(ne1, block_size / block_dims.x);
|
|
block_dims.z = std::min(std::min<unsigned int>(ne2*ne3, block_size / block_dims.x / block_dims.y), 64U);
|
|
|
|
dim3 block_nums(
|
|
(hne0 + block_dims.x - 1) / block_dims.x,
|
|
(ne1 + block_dims.y - 1) / block_dims.y,
|
|
(ne2*ne3 + block_dims.z - 1) / block_dims.z
|
|
);
|
|
|
|
if (block_nums.z > 65535) {
|
|
// this is the maximum number of blocks in z direction, fallback to 1D grid kernel
|
|
int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
|
|
k_bin_bcast_unravel<bin_op><<<block_num, block_size, 0, stream>>>(
|
|
src0_dd, src1_dd, dst_dd,
|
|
ne0, ne1, ne2, ne3,
|
|
ne10, ne11, ne12, ne13,
|
|
/* s0, */ s1, s2, s3,
|
|
/* s10, */ s11, s12, s13);
|
|
} else {
|
|
k_bin_bcast<bin_op><<<block_nums, block_dims, 0, stream>>>(
|
|
src0_dd, src1_dd, dst_dd,
|
|
ne0, ne1, ne2, ne3,
|
|
ne10, ne11, ne12, ne13,
|
|
/* s0, */ s1, s2, s3,
|
|
/* s10, */ s11, s12, s13);
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
static void acc_f32_cuda(const float * x, const float * y, float * dst, const int n_elements,
|
|
const int ne10, const int ne11, const int ne12,
|
|
const int nb1, const int nb2, const int offset, cudaStream_t stream) {
|
|
int num_blocks = (n_elements + CUDA_ACC_BLOCK_SIZE - 1) / CUDA_ACC_BLOCK_SIZE;
|
|
acc_f32<<<num_blocks, CUDA_ACC_BLOCK_SIZE, 0, stream>>>(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset);
|
|
}
|
|
|
|
static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
|
|
gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
|
}
|
|
|
|
static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_SILU_BLOCK_SIZE - 1) / CUDA_SILU_BLOCK_SIZE;
|
|
silu_f32<<<num_blocks, CUDA_SILU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
|
}
|
|
|
|
static void gelu_quick_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
|
|
gelu_quick_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
|
}
|
|
|
|
static void tanh_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_TANH_BLOCK_SIZE - 1) / CUDA_TANH_BLOCK_SIZE;
|
|
tanh_f32<<<num_blocks, CUDA_TANH_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
|
}
|
|
|
|
static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
|
|
relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
|
}
|
|
|
|
static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
|
|
leaky_relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k, negative_slope);
|
|
}
|
|
|
|
static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE;
|
|
sqr_f32<<<num_blocks, CUDA_SQR_BLOCK_SIZE, 0, stream>>>(x, dst, k);
|
|
}
|
|
|
|
static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % WARP_SIZE == 0);
|
|
if (ncols < 1024) {
|
|
const dim3 block_dims(WARP_SIZE, 1, 1);
|
|
norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
|
|
} else {
|
|
const dim3 block_dims(1024, 1, 1);
|
|
norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
|
|
}
|
|
}
|
|
|
|
static void group_norm_f32_cuda(const float * x, float * dst, const int num_groups, const int group_size, const int ne_elements, cudaStream_t stream) {
|
|
static const float eps = 1e-6f;
|
|
if (group_size < 1024) {
|
|
const dim3 block_dims(WARP_SIZE, 1, 1);
|
|
group_norm_f32<WARP_SIZE><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
|
|
} else {
|
|
const dim3 block_dims(1024, 1, 1);
|
|
group_norm_f32<1024><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
|
|
}
|
|
}
|
|
|
|
static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) {
|
|
int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
|
|
dim3 gridDim(num_blocks, ne1, ne2);
|
|
concat_f32<<<gridDim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne0, ne02);
|
|
}
|
|
|
|
static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int scale_factor, cudaStream_t stream) {
|
|
int ne0 = (ne00 * scale_factor);
|
|
int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
|
|
dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02);
|
|
upscale_f32<<<gridDim, CUDA_UPSCALE_BLOCK_SIZE, 0, stream>>>(x, dst, ne00, ne00 * ne01, scale_factor);
|
|
}
|
|
|
|
static void pad_f32_cuda(const float * x, float * dst,
|
|
const int ne00, const int ne01, const int ne02,
|
|
const int ne0, const int ne1, const int ne2, cudaStream_t stream) {
|
|
int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
|
|
dim3 gridDim(num_blocks, ne1, ne2);
|
|
pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(x, dst, ne0, ne00, ne01, ne02);
|
|
}
|
|
|
|
static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % WARP_SIZE == 0);
|
|
if (ncols < 1024) {
|
|
const dim3 block_dims(WARP_SIZE, 1, 1);
|
|
rms_norm_f32<WARP_SIZE><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
|
|
} else {
|
|
const dim3 block_dims(1024, 1, 1);
|
|
rms_norm_f32<1024><<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
|
|
}
|
|
}
|
|
|
|
static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) {
|
|
const int block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
|
|
const dim3 num_blocks(block_num_x, ky, 1);
|
|
const dim3 block_size(CUDA_DEQUANTIZE_BLOCK_SIZE, 1, 1);
|
|
quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
|
|
}
|
|
|
|
template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
|
|
static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
|
|
dequantize_block<qk, qr, dequantize_kernel><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
|
|
}
|
|
|
|
template<typename dst_t>
|
|
static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
|
|
const int nb = k / QK_K;
|
|
#if QK_K == 256
|
|
dequantize_block_q2_K<<<nb, 64, 0, stream>>>(vx, y);
|
|
#else
|
|
dequantize_block_q2_K<<<nb, 32, 0, stream>>>(vx, y);
|
|
#endif
|
|
}
|
|
|
|
template<typename dst_t>
|
|
static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
|
|
const int nb = k / QK_K;
|
|
#if QK_K == 256
|
|
dequantize_block_q3_K<<<nb, 64, 0, stream>>>(vx, y);
|
|
#else
|
|
dequantize_block_q3_K<<<nb, 32, 0, stream>>>(vx, y);
|
|
#endif
|
|
}
|
|
|
|
template<typename dst_t>
|
|
static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
|
|
const int nb = k / QK_K;
|
|
dequantize_block_q4_K<<<nb, 32, 0, stream>>>(vx, y);
|
|
}
|
|
|
|
template<typename dst_t>
|
|
static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
|
|
const int nb = k / QK_K;
|
|
#if QK_K == 256
|
|
dequantize_block_q5_K<<<nb, 64, 0, stream>>>(vx, y);
|
|
#else
|
|
dequantize_block_q5_K<<<nb, 32, 0, stream>>>(vx, y);
|
|
#endif
|
|
}
|
|
|
|
template<typename dst_t>
|
|
static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) {
|
|
const int nb = k / QK_K;
|
|
#if QK_K == 256
|
|
dequantize_block_q6_K<<<nb, 64, 0, stream>>>(vx, y);
|
|
#else
|
|
dequantize_block_q6_K<<<nb, 32, 0, stream>>>(vx, y);
|
|
#endif
|
|
}
|
|
|
|
static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
|
|
switch (type) {
|
|
case GGML_TYPE_Q4_0:
|
|
return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
|
|
case GGML_TYPE_Q4_1:
|
|
return dequantize_block_cuda<QK4_1, QR4_1, dequantize_q4_1>;
|
|
case GGML_TYPE_Q5_0:
|
|
return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
|
|
case GGML_TYPE_Q5_1:
|
|
return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
|
|
case GGML_TYPE_Q8_0:
|
|
return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
|
|
case GGML_TYPE_Q2_K:
|
|
return dequantize_row_q2_K_cuda;
|
|
case GGML_TYPE_Q3_K:
|
|
return dequantize_row_q3_K_cuda;
|
|
case GGML_TYPE_Q4_K:
|
|
return dequantize_row_q4_K_cuda;
|
|
case GGML_TYPE_Q5_K:
|
|
return dequantize_row_q5_K_cuda;
|
|
case GGML_TYPE_Q6_K:
|
|
return dequantize_row_q6_K_cuda;
|
|
case GGML_TYPE_F32:
|
|
return dequantize_block_cuda<1, 1, convert_f32>;
|
|
default:
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
|
|
switch (type) {
|
|
case GGML_TYPE_Q4_0:
|
|
return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
|
|
case GGML_TYPE_Q4_1:
|
|
return dequantize_block_cuda<QK4_1, QR4_1, dequantize_q4_1>;
|
|
case GGML_TYPE_Q5_0:
|
|
return dequantize_block_cuda<QK5_0, QR5_0, dequantize_q5_0>;
|
|
case GGML_TYPE_Q5_1:
|
|
return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>;
|
|
case GGML_TYPE_Q8_0:
|
|
return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>;
|
|
case GGML_TYPE_Q2_K:
|
|
return dequantize_row_q2_K_cuda;
|
|
case GGML_TYPE_Q3_K:
|
|
return dequantize_row_q3_K_cuda;
|
|
case GGML_TYPE_Q4_K:
|
|
return dequantize_row_q4_K_cuda;
|
|
case GGML_TYPE_Q5_K:
|
|
return dequantize_row_q5_K_cuda;
|
|
case GGML_TYPE_Q6_K:
|
|
return dequantize_row_q6_K_cuda;
|
|
case GGML_TYPE_F16:
|
|
return dequantize_block_cuda<1, 1, convert_f16>;
|
|
default:
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
// the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
|
}
|
|
|
|
static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
|
}
|
|
|
|
static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
|
}
|
|
|
|
static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
|
}
|
|
|
|
static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
|
}
|
|
|
|
static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK_K == 0);
|
|
const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
|
|
const int block_num_y = (nrows + ny - 1) / ny;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(32, ny, 1);
|
|
dequantize_mul_mat_vec_q2_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
|
}
|
|
|
|
static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK_K == 0);
|
|
const int ny = 2 / K_QUANTS_PER_ITERATION;
|
|
const int block_num_y = (nrows + ny - 1) / ny;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(32, ny, 1);
|
|
dequantize_mul_mat_vec_q3_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
|
}
|
|
|
|
static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK_K == 0);
|
|
const int ny = 2 / K_QUANTS_PER_ITERATION;
|
|
const int block_num_y = (nrows + ny - 1) / ny;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(32, ny, 1);
|
|
dequantize_mul_mat_vec_q4_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
|
}
|
|
|
|
static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK_K == 0);
|
|
const dim3 block_dims(32, 1, 1);
|
|
dequantize_mul_mat_vec_q5_k<<<nrows, block_dims, 0, stream>>>(vx, y, dst, ncols);
|
|
}
|
|
|
|
static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK_K == 0);
|
|
const int ny = 2 / K_QUANTS_PER_ITERATION;
|
|
const int block_num_y = (nrows + ny - 1) / ny;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(32, ny, 1);
|
|
dequantize_mul_mat_vec_q6_k<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
|
}
|
|
|
|
static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
dequantize_mul_mat_vec<1, 1, convert_f16>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols, nrows);
|
|
}
|
|
|
|
static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK4_0 == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK4_1 == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK5_0 == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK5_1 == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK8_0 == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK_K == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK_K == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK_K == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK_K == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % QK_K == 0);
|
|
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
|
|
const dim3 block_nums(block_num_y, 1, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void ggml_mul_mat_q4_0_q8_1_cuda(
|
|
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
|
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
int mmq_x, mmq_y, nwarps;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
mmq_x = MMQ_X_Q4_0_RDNA2;
|
|
mmq_y = MMQ_Y_Q4_0_RDNA2;
|
|
nwarps = NWARPS_Q4_0_RDNA2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
mmq_x = MMQ_X_Q4_0_RDNA1;
|
|
mmq_y = MMQ_Y_Q4_0_RDNA1;
|
|
nwarps = NWARPS_Q4_0_RDNA1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
mmq_x = MMQ_X_Q4_0_AMPERE;
|
|
mmq_y = MMQ_Y_Q4_0_AMPERE;
|
|
nwarps = NWARPS_Q4_0_AMPERE;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
mmq_x = MMQ_X_Q4_0_PASCAL;
|
|
mmq_y = MMQ_Y_Q4_0_PASCAL;
|
|
nwarps = NWARPS_Q4_0_PASCAL;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
|
|
const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, nwarps, 1);
|
|
|
|
if (nrows_x % mmq_y == 0) {
|
|
const bool need_check = false;
|
|
mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
} else {
|
|
const bool need_check = true;
|
|
mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
}
|
|
}
|
|
|
|
static void ggml_mul_mat_q4_1_q8_1_cuda(
|
|
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
|
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
int mmq_x, mmq_y, nwarps;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
mmq_x = MMQ_X_Q4_1_RDNA2;
|
|
mmq_y = MMQ_Y_Q4_1_RDNA2;
|
|
nwarps = NWARPS_Q4_1_RDNA2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
mmq_x = MMQ_X_Q4_1_RDNA1;
|
|
mmq_y = MMQ_Y_Q4_1_RDNA1;
|
|
nwarps = NWARPS_Q4_1_RDNA1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
mmq_x = MMQ_X_Q4_1_AMPERE;
|
|
mmq_y = MMQ_Y_Q4_1_AMPERE;
|
|
nwarps = NWARPS_Q4_1_AMPERE;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
mmq_x = MMQ_X_Q4_1_PASCAL;
|
|
mmq_y = MMQ_Y_Q4_1_PASCAL;
|
|
nwarps = NWARPS_Q4_1_PASCAL;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
|
|
const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, nwarps, 1);
|
|
|
|
if (nrows_x % mmq_y == 0) {
|
|
const bool need_check = false;
|
|
mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
} else {
|
|
const bool need_check = true;
|
|
mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
}
|
|
}
|
|
|
|
static void ggml_mul_mat_q5_0_q8_1_cuda(
|
|
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
|
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
int mmq_x, mmq_y, nwarps;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
mmq_x = MMQ_X_Q5_0_RDNA2;
|
|
mmq_y = MMQ_Y_Q5_0_RDNA2;
|
|
nwarps = NWARPS_Q5_0_RDNA2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
mmq_x = MMQ_X_Q5_0_RDNA1;
|
|
mmq_y = MMQ_Y_Q5_0_RDNA1;
|
|
nwarps = NWARPS_Q5_0_RDNA1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
mmq_x = MMQ_X_Q5_0_AMPERE;
|
|
mmq_y = MMQ_Y_Q5_0_AMPERE;
|
|
nwarps = NWARPS_Q5_0_AMPERE;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
mmq_x = MMQ_X_Q5_0_PASCAL;
|
|
mmq_y = MMQ_Y_Q5_0_PASCAL;
|
|
nwarps = NWARPS_Q5_0_PASCAL;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
|
|
const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, nwarps, 1);
|
|
|
|
if (nrows_x % mmq_y == 0) {
|
|
const bool need_check = false;
|
|
mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
} else {
|
|
const bool need_check = true;
|
|
mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
}
|
|
}
|
|
|
|
static void ggml_mul_mat_q5_1_q8_1_cuda(
|
|
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
|
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
int mmq_x, mmq_y, nwarps;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
mmq_x = MMQ_X_Q5_1_RDNA2;
|
|
mmq_y = MMQ_Y_Q5_1_RDNA2;
|
|
nwarps = NWARPS_Q5_1_RDNA2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
mmq_x = MMQ_X_Q5_1_RDNA1;
|
|
mmq_y = MMQ_Y_Q5_1_RDNA1;
|
|
nwarps = NWARPS_Q5_1_RDNA1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
mmq_x = MMQ_X_Q5_1_AMPERE;
|
|
mmq_y = MMQ_Y_Q5_1_AMPERE;
|
|
nwarps = NWARPS_Q5_1_AMPERE;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
mmq_x = MMQ_X_Q5_1_PASCAL;
|
|
mmq_y = MMQ_Y_Q5_1_PASCAL;
|
|
nwarps = NWARPS_Q5_1_PASCAL;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
|
|
const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, nwarps, 1);
|
|
|
|
if (nrows_x % mmq_y == 0) {
|
|
const bool need_check = false;
|
|
mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
} else {
|
|
const bool need_check = true;
|
|
mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
}
|
|
}
|
|
|
|
static void ggml_mul_mat_q8_0_q8_1_cuda(
|
|
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
|
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
int mmq_x, mmq_y, nwarps;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
mmq_x = MMQ_X_Q8_0_RDNA2;
|
|
mmq_y = MMQ_Y_Q8_0_RDNA2;
|
|
nwarps = NWARPS_Q8_0_RDNA2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
mmq_x = MMQ_X_Q8_0_RDNA1;
|
|
mmq_y = MMQ_Y_Q8_0_RDNA1;
|
|
nwarps = NWARPS_Q8_0_RDNA1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
mmq_x = MMQ_X_Q8_0_AMPERE;
|
|
mmq_y = MMQ_Y_Q8_0_AMPERE;
|
|
nwarps = NWARPS_Q8_0_AMPERE;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
mmq_x = MMQ_X_Q8_0_PASCAL;
|
|
mmq_y = MMQ_Y_Q8_0_PASCAL;
|
|
nwarps = NWARPS_Q8_0_PASCAL;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
|
|
const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, nwarps, 1);
|
|
|
|
if (nrows_x % mmq_y == 0) {
|
|
const bool need_check = false;
|
|
mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
} else {
|
|
const bool need_check = true;
|
|
mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
}
|
|
}
|
|
|
|
static void ggml_mul_mat_q2_K_q8_1_cuda(
|
|
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
|
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
int mmq_x, mmq_y, nwarps;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
mmq_x = MMQ_X_Q2_K_RDNA2;
|
|
mmq_y = MMQ_Y_Q2_K_RDNA2;
|
|
nwarps = NWARPS_Q2_K_RDNA2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
mmq_x = MMQ_X_Q2_K_RDNA1;
|
|
mmq_y = MMQ_Y_Q2_K_RDNA1;
|
|
nwarps = NWARPS_Q2_K_RDNA1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
mmq_x = MMQ_X_Q2_K_AMPERE;
|
|
mmq_y = MMQ_Y_Q2_K_AMPERE;
|
|
nwarps = NWARPS_Q2_K_AMPERE;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
mmq_x = MMQ_X_Q2_K_PASCAL;
|
|
mmq_y = MMQ_Y_Q2_K_PASCAL;
|
|
nwarps = NWARPS_Q2_K_PASCAL;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
|
|
const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, nwarps, 1);
|
|
|
|
if (nrows_x % mmq_y == 0) {
|
|
const bool need_check = false;
|
|
mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
} else {
|
|
const bool need_check = true;
|
|
mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
}
|
|
}
|
|
|
|
static void ggml_mul_mat_q3_K_q8_1_cuda(
|
|
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
|
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
|
|
|
#if QK_K == 256
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
int mmq_x, mmq_y, nwarps;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
mmq_x = MMQ_X_Q3_K_RDNA2;
|
|
mmq_y = MMQ_Y_Q3_K_RDNA2;
|
|
nwarps = NWARPS_Q3_K_RDNA2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
mmq_x = MMQ_X_Q3_K_RDNA1;
|
|
mmq_y = MMQ_Y_Q3_K_RDNA1;
|
|
nwarps = NWARPS_Q3_K_RDNA1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
mmq_x = MMQ_X_Q3_K_AMPERE;
|
|
mmq_y = MMQ_Y_Q3_K_AMPERE;
|
|
nwarps = NWARPS_Q3_K_AMPERE;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
mmq_x = MMQ_X_Q3_K_PASCAL;
|
|
mmq_y = MMQ_Y_Q3_K_PASCAL;
|
|
nwarps = NWARPS_Q3_K_PASCAL;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
|
|
const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, nwarps, 1);
|
|
|
|
if (nrows_x % mmq_y == 0) {
|
|
const bool need_check = false;
|
|
mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
} else {
|
|
const bool need_check = true;
|
|
mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
}
|
|
#endif
|
|
}
|
|
|
|
static void ggml_mul_mat_q4_K_q8_1_cuda(
|
|
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
|
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
int mmq_x, mmq_y, nwarps;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
mmq_x = MMQ_X_Q4_K_RDNA2;
|
|
mmq_y = MMQ_Y_Q4_K_RDNA2;
|
|
nwarps = NWARPS_Q4_K_RDNA2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
mmq_x = MMQ_X_Q4_K_RDNA1;
|
|
mmq_y = MMQ_Y_Q4_K_RDNA1;
|
|
nwarps = NWARPS_Q4_K_RDNA1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
mmq_x = MMQ_X_Q4_K_AMPERE;
|
|
mmq_y = MMQ_Y_Q4_K_AMPERE;
|
|
nwarps = NWARPS_Q4_K_AMPERE;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
mmq_x = MMQ_X_Q4_K_PASCAL;
|
|
mmq_y = MMQ_Y_Q4_K_PASCAL;
|
|
nwarps = NWARPS_Q4_K_PASCAL;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
|
|
const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, nwarps, 1);
|
|
|
|
if (nrows_x % mmq_y == 0) {
|
|
const bool need_check = false;
|
|
mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
} else {
|
|
const bool need_check = true;
|
|
mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
}
|
|
}
|
|
|
|
static void ggml_mul_mat_q5_K_q8_1_cuda(
|
|
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
|
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
int mmq_x, mmq_y, nwarps;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
mmq_x = MMQ_X_Q5_K_RDNA2;
|
|
mmq_y = MMQ_Y_Q5_K_RDNA2;
|
|
nwarps = NWARPS_Q5_K_RDNA2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
mmq_x = MMQ_X_Q5_K_RDNA1;
|
|
mmq_y = MMQ_Y_Q5_K_RDNA1;
|
|
nwarps = NWARPS_Q5_K_RDNA1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
mmq_x = MMQ_X_Q5_K_AMPERE;
|
|
mmq_y = MMQ_Y_Q5_K_AMPERE;
|
|
nwarps = NWARPS_Q5_K_AMPERE;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
mmq_x = MMQ_X_Q5_K_PASCAL;
|
|
mmq_y = MMQ_Y_Q5_K_PASCAL;
|
|
nwarps = NWARPS_Q5_K_PASCAL;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
|
|
const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, nwarps, 1);
|
|
|
|
if (nrows_x % mmq_y == 0) {
|
|
const bool need_check = false;
|
|
mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
} else {
|
|
const bool need_check = true;
|
|
mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
}
|
|
}
|
|
|
|
static void ggml_mul_mat_q6_K_q8_1_cuda(
|
|
const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
|
|
const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
int mmq_x, mmq_y, nwarps;
|
|
if (compute_capability >= CC_RDNA2) {
|
|
mmq_x = MMQ_X_Q6_K_RDNA2;
|
|
mmq_y = MMQ_Y_Q6_K_RDNA2;
|
|
nwarps = NWARPS_Q6_K_RDNA2;
|
|
} else if (compute_capability >= CC_OFFSET_AMD) {
|
|
mmq_x = MMQ_X_Q6_K_RDNA1;
|
|
mmq_y = MMQ_Y_Q6_K_RDNA1;
|
|
nwarps = NWARPS_Q6_K_RDNA1;
|
|
} else if (compute_capability >= CC_VOLTA) {
|
|
mmq_x = MMQ_X_Q6_K_AMPERE;
|
|
mmq_y = MMQ_Y_Q6_K_AMPERE;
|
|
nwarps = NWARPS_Q6_K_AMPERE;
|
|
} else if (compute_capability >= MIN_CC_DP4A) {
|
|
mmq_x = MMQ_X_Q6_K_PASCAL;
|
|
mmq_y = MMQ_Y_Q6_K_PASCAL;
|
|
nwarps = NWARPS_Q6_K_PASCAL;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
|
|
const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x;
|
|
const dim3 block_nums(block_num_x, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, nwarps, 1);
|
|
|
|
if (nrows_x % mmq_y == 0) {
|
|
const bool need_check = false;
|
|
mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
} else {
|
|
const bool need_check = true;
|
|
mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
|
|
(vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
|
|
}
|
|
}
|
|
|
|
static void ggml_mul_mat_p021_f16_f32_cuda(
|
|
const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x,
|
|
const int nchannels_x, const int nchannels_y, cudaStream_t stream) {
|
|
|
|
const dim3 block_nums(1, nrows_x, nchannels_y);
|
|
const dim3 block_dims(WARP_SIZE, 1, 1);
|
|
mul_mat_p021_f16_f32<<<block_nums, block_dims, 0, stream>>>(vx, y, dst, ncols_x, nrows_x, nchannels_x, nchannels_y);
|
|
}
|
|
|
|
static void ggml_mul_mat_vec_nc_f16_f32_cuda(
|
|
const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int row_stride_x,
|
|
const int nchannels_x, const int nchannels_y, const int channel_stride_x, cudaStream_t stream) {
|
|
|
|
const dim3 block_nums(1, nrows_x, nchannels_y);
|
|
const dim3 block_dims(WARP_SIZE, 1, 1);
|
|
mul_mat_vec_nc_f16_f32<<<block_nums, block_dims, 0, stream>>>
|
|
(vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y/nchannels_x);
|
|
}
|
|
|
|
static void ggml_cpy_f32_f32_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
|
|
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
|
|
|
|
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
|
cpy_f32_f16<cpy_1_f32_f32><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
|
|
}
|
|
|
|
static void ggml_cpy_f32_f16_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
|
|
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
|
|
|
|
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
|
cpy_f32_f16<cpy_1_f32_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
|
|
}
|
|
|
|
static void ggml_cpy_f32_q8_0_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
|
|
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK8_0 == 0);
|
|
const int num_blocks = ne / QK8_0;
|
|
cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
|
|
}
|
|
|
|
static void ggml_cpy_f32_q4_0_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
|
|
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK4_0 == 0);
|
|
const int num_blocks = ne / QK4_0;
|
|
cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
|
|
}
|
|
|
|
static void ggml_cpy_f32_q4_1_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
|
|
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
|
|
|
|
GGML_ASSERT(ne % QK4_1 == 0);
|
|
const int num_blocks = ne / QK4_1;
|
|
cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
|
|
}
|
|
|
|
static void ggml_cpy_f16_f16_cuda(
|
|
const char * cx, char * cdst, const int ne,
|
|
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
|
|
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
|
|
|
|
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
|
cpy_f32_f16<cpy_1_f16_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
|
|
(cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
|
|
}
|
|
|
|
static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
|
|
scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
|
|
}
|
|
|
|
static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE;
|
|
clamp_f32<<<num_blocks, CUDA_CLAMP_BLOCK_SIZE, 0, stream>>>(x, dst, min, max, k);
|
|
}
|
|
|
|
template<typename T>
|
|
static void rope_cuda(
|
|
const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
|
|
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
|
|
) {
|
|
GGML_ASSERT(ncols % 2 == 0);
|
|
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
|
|
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
|
|
const dim3 block_nums(nrows, num_blocks_x, 1);
|
|
if (pos == nullptr) {
|
|
rope<T, false><<<block_nums, block_dims, 0, stream>>>(
|
|
x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
|
|
);
|
|
} else {
|
|
rope<T, true><<<block_nums, block_dims, 0, stream>>>(
|
|
x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims
|
|
);
|
|
}
|
|
}
|
|
|
|
template<typename T>
|
|
static void rope_neox_cuda(
|
|
const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
|
|
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream
|
|
) {
|
|
GGML_ASSERT(ncols % 2 == 0);
|
|
const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
|
|
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
|
|
const dim3 block_nums(nrows, num_blocks_x, 1);
|
|
|
|
const float theta_scale = powf(freq_base, -2.0f/n_dims);
|
|
const float inv_ndims = -1.0f / n_dims;
|
|
|
|
if (pos == nullptr) {
|
|
rope_neox<T, false><<<block_nums, block_dims, 0, stream>>>(
|
|
x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
|
|
theta_scale, inv_ndims
|
|
);
|
|
} else {
|
|
rope_neox<T, true><<<block_nums, block_dims, 0, stream>>>(
|
|
x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
|
|
theta_scale, inv_ndims
|
|
);
|
|
}
|
|
}
|
|
|
|
static void rope_glm_f32_cuda(
|
|
const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows,
|
|
float freq_base, int n_ctx, cudaStream_t stream
|
|
) {
|
|
GGML_ASSERT(ncols % 4 == 0);
|
|
const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1);
|
|
const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE;
|
|
const dim3 block_nums(num_blocks_x, nrows, 1);
|
|
rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx);
|
|
}
|
|
|
|
static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
|
|
const int k_rows, const int n_heads_log2_floor, const float m0,
|
|
const float m1, cudaStream_t stream) {
|
|
const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1);
|
|
const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE);
|
|
const dim3 block_nums(num_blocks_x, nrows, 1);
|
|
alibi_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1);
|
|
}
|
|
|
|
static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
const dim3 block_dims(WARP_SIZE, 1, 1);
|
|
const dim3 block_nums(1, nrows, 1);
|
|
k_sum_rows_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
|
|
}
|
|
|
|
static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, const int nrows, ggml_sort_order order, cudaStream_t stream) {
|
|
// bitonic sort requires ncols to be power of 2
|
|
GGML_ASSERT((ncols & (ncols - 1)) == 0);
|
|
|
|
const dim3 block_dims(ncols, 1, 1);
|
|
const dim3 block_nums(1, nrows, 1);
|
|
if (order == GGML_SORT_ASC) {
|
|
k_argsort_f32_i32<GGML_SORT_ASC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
|
|
} else if (order == GGML_SORT_DESC) {
|
|
k_argsort_f32_i32<GGML_SORT_DESC><<<block_nums, block_dims, 0, stream>>>(x, dst, ncols);
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) {
|
|
const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1);
|
|
const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE;
|
|
const dim3 block_nums(nrows_x, block_num_x, 1);
|
|
diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past);
|
|
}
|
|
|
|
static void soft_max_f32_cuda(const float * x, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, cudaStream_t stream) {
|
|
int nth = WARP_SIZE;
|
|
while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
|
|
const dim3 block_dims(nth, 1, 1);
|
|
const dim3 block_nums(nrows_x, 1, 1);
|
|
soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, y, dst, ncols_x, nrows_y, scale);
|
|
}
|
|
|
|
static void im2col_f32_f16_cuda(const float* x, half* dst,
|
|
int IW, int IH, int OW, int OH, int KW, int KH, int IC,
|
|
int offset_delta,
|
|
int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
|
|
const int parallel_elements = OW * KW * KH;
|
|
const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
|
|
dim3 block_nums(num_blocks, OH, IC);
|
|
im2col_f32_f16<<<block_nums, CUDA_IM2COL_BLOCK_SIZE, 0, stream>>>(x, dst, offset_delta, IW, IH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1);
|
|
}
|
|
|
|
// buffer pool for cuda
|
|
#define MAX_CUDA_BUFFERS 256
|
|
|
|
struct scoped_spin_lock {
|
|
std::atomic_flag& lock;
|
|
scoped_spin_lock(std::atomic_flag& lock) : lock(lock) {
|
|
while (lock.test_and_set(std::memory_order_acquire)) {
|
|
; // spin
|
|
}
|
|
}
|
|
~scoped_spin_lock() {
|
|
lock.clear(std::memory_order_release);
|
|
}
|
|
scoped_spin_lock(const scoped_spin_lock&) = delete;
|
|
scoped_spin_lock& operator=(const scoped_spin_lock&) = delete;
|
|
};
|
|
|
|
struct cuda_buffer {
|
|
void * ptr = nullptr;
|
|
size_t size = 0;
|
|
};
|
|
|
|
static cuda_buffer g_cuda_buffer_pool[GGML_CUDA_MAX_DEVICES][MAX_CUDA_BUFFERS];
|
|
static std::atomic_flag g_cuda_pool_lock = ATOMIC_FLAG_INIT;
|
|
|
|
static void * ggml_cuda_pool_malloc(size_t size, size_t * actual_size) {
|
|
scoped_spin_lock lock(g_cuda_pool_lock);
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
#ifdef DEBUG_CUDA_MALLOC
|
|
int nnz = 0;
|
|
size_t max_size = 0, tot_size = 0;
|
|
#endif
|
|
size_t best_diff = 1ull << 36;
|
|
int ibest = -1;
|
|
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
|
|
cuda_buffer& b = g_cuda_buffer_pool[id][i];
|
|
if (b.ptr != nullptr) {
|
|
#ifdef DEBUG_CUDA_MALLOC
|
|
++nnz;
|
|
tot_size += b.size;
|
|
if (b.size > max_size) max_size = b.size;
|
|
#endif
|
|
if (b.size >= size) {
|
|
size_t diff = b.size - size;
|
|
if (diff < best_diff) {
|
|
best_diff = diff;
|
|
ibest = i;
|
|
if (!best_diff) {
|
|
void * ptr = b.ptr;
|
|
*actual_size = b.size;
|
|
b.ptr = nullptr;
|
|
b.size = 0;
|
|
return ptr;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
if (ibest >= 0) {
|
|
cuda_buffer& b = g_cuda_buffer_pool[id][ibest];
|
|
void * ptr = b.ptr;
|
|
*actual_size = b.size;
|
|
b.ptr = nullptr;
|
|
b.size = 0;
|
|
return ptr;
|
|
}
|
|
#ifdef DEBUG_CUDA_MALLOC
|
|
fprintf(stderr, "%s: %d buffers, max_size = %u MB, tot_size = %u MB, requested %u MB\n", __func__, nnz,
|
|
(uint32_t)(max_size/1024/1024), (uint32_t)(tot_size/1024/1024), (uint32_t)(size/1024/1024));
|
|
#endif
|
|
void * ptr;
|
|
size_t look_ahead_size = (size_t) (1.05 * size);
|
|
look_ahead_size = 256 * ((look_ahead_size + 255)/256);
|
|
CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size));
|
|
*actual_size = look_ahead_size;
|
|
return ptr;
|
|
}
|
|
|
|
static void ggml_cuda_pool_free(void * ptr, size_t size) {
|
|
scoped_spin_lock lock(g_cuda_pool_lock);
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
|
|
for (int i = 0; i < MAX_CUDA_BUFFERS; ++i) {
|
|
cuda_buffer& b = g_cuda_buffer_pool[id][i];
|
|
if (b.ptr == nullptr) {
|
|
b.ptr = ptr;
|
|
b.size = size;
|
|
return;
|
|
}
|
|
}
|
|
fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n");
|
|
CUDA_CHECK(cudaFree(ptr));
|
|
}
|
|
|
|
static bool g_cublas_loaded = false;
|
|
|
|
bool ggml_cublas_loaded(void) {
|
|
return g_cublas_loaded;
|
|
}
|
|
|
|
void ggml_init_cublas() {
|
|
static bool initialized = false;
|
|
|
|
if (!initialized) {
|
|
|
|
#ifdef __HIP_PLATFORM_AMD__
|
|
// Workaround for a rocBLAS bug when using multiple graphics cards:
|
|
// https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
|
|
rocblas_initialize();
|
|
CUDA_CHECK(cudaDeviceSynchronize());
|
|
#endif
|
|
|
|
if (cudaGetDeviceCount(&g_device_count) != cudaSuccess) {
|
|
initialized = true;
|
|
g_cublas_loaded = false;
|
|
return;
|
|
}
|
|
|
|
GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
|
|
int64_t total_vram = 0;
|
|
#if defined(GGML_CUDA_FORCE_MMQ)
|
|
fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__);
|
|
#else
|
|
fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: no\n", __func__);
|
|
#endif
|
|
#if defined(CUDA_USE_TENSOR_CORES)
|
|
fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__);
|
|
#else
|
|
fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__);
|
|
#endif
|
|
fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count);
|
|
for (int id = 0; id < g_device_count; ++id) {
|
|
cudaDeviceProp prop;
|
|
CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
|
|
fprintf(stderr, " Device %d: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor);
|
|
|
|
g_tensor_split[id] = total_vram;
|
|
total_vram += prop.totalGlobalMem;
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
g_compute_capabilities[id] = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD;
|
|
#else
|
|
g_compute_capabilities[id] = 100*prop.major + 10*prop.minor;
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
}
|
|
for (int id = 0; id < g_device_count; ++id) {
|
|
g_tensor_split[id] /= total_vram;
|
|
}
|
|
|
|
for (int id = 0; id < g_device_count; ++id) {
|
|
CUDA_CHECK(ggml_cuda_set_device(id));
|
|
|
|
// create cuda streams
|
|
for (int is = 0; is < MAX_STREAMS; ++is) {
|
|
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams[id][is], cudaStreamNonBlocking));
|
|
}
|
|
|
|
// create cublas handle
|
|
CUBLAS_CHECK(cublasCreate(&g_cublas_handles[id]));
|
|
CUBLAS_CHECK(cublasSetMathMode(g_cublas_handles[id], CUBLAS_TF32_TENSOR_OP_MATH));
|
|
}
|
|
|
|
// configure logging to stdout
|
|
// CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
|
|
|
|
initialized = true;
|
|
g_cublas_loaded = true;
|
|
}
|
|
}
|
|
|
|
void ggml_cuda_set_tensor_split(const float * tensor_split) {
|
|
if (tensor_split == nullptr) {
|
|
return;
|
|
}
|
|
bool all_zero = true;
|
|
for (int i = 0; i < g_device_count; ++i) {
|
|
if (tensor_split[i] != 0.0f) {
|
|
all_zero = false;
|
|
break;
|
|
}
|
|
}
|
|
if (all_zero) {
|
|
return;
|
|
}
|
|
float split_sum = 0.0f;
|
|
for (int i = 0; i < g_device_count; ++i) {
|
|
g_tensor_split[i] = split_sum;
|
|
split_sum += tensor_split[i];
|
|
}
|
|
for (int i = 0; i < g_device_count; ++i) {
|
|
g_tensor_split[i] /= split_sum;
|
|
}
|
|
}
|
|
|
|
void * ggml_cuda_host_malloc(size_t size) {
|
|
if (getenv("GGML_CUDA_NO_PINNED") != nullptr) {
|
|
return nullptr;
|
|
}
|
|
|
|
void * ptr = nullptr;
|
|
cudaError_t err = cudaMallocHost((void **) &ptr, size);
|
|
if (err != cudaSuccess) {
|
|
// The allocation error can be bypassed. A null ptr will assigned out of this function.
|
|
// This can fixed the OOM error in WSL.
|
|
cudaGetLastError();
|
|
fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
|
|
size/1024.0/1024.0, cudaGetErrorString(err));
|
|
return nullptr;
|
|
}
|
|
|
|
return ptr;
|
|
}
|
|
|
|
void ggml_cuda_host_free(void * ptr) {
|
|
CUDA_CHECK(cudaFreeHost(ptr));
|
|
}
|
|
|
|
static cudaError_t ggml_cuda_cpy_tensor_2d(
|
|
void * dst, const struct ggml_tensor * src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, cudaStream_t stream) {
|
|
|
|
cudaMemcpyKind kind;
|
|
char * src_ptr;
|
|
if (src->backend == GGML_BACKEND_CPU) {
|
|
kind = cudaMemcpyHostToDevice;
|
|
src_ptr = (char *) src->data;
|
|
} else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) {
|
|
GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1]));
|
|
kind = cudaMemcpyDeviceToDevice;
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra;
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
src_ptr = (char *) extra->data_device[id];
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
char * dst_ptr = (char *) dst;
|
|
|
|
const int64_t ne0 = src->ne[0];
|
|
const int64_t nb0 = src->nb[0];
|
|
const int64_t nb1 = src->nb[1];
|
|
const int64_t nb2 = src->nb[2];
|
|
const int64_t nb3 = src->nb[3];
|
|
const enum ggml_type type = src->type;
|
|
const int64_t ts = ggml_type_size(type);
|
|
const int64_t bs = ggml_blck_size(type);
|
|
int64_t i1_diff = i1_high - i1_low;
|
|
|
|
const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3;
|
|
if (nb0 == ts && nb1 == ts*ne0/bs) {
|
|
return cudaMemcpyAsync(dst_ptr, x, i1_diff*nb1, kind, stream);
|
|
} else if (nb0 == ts) {
|
|
return cudaMemcpy2DAsync(dst_ptr, ts*ne0/bs, x, nb1, ts*ne0/bs, i1_diff, kind, stream);
|
|
} else {
|
|
for (int64_t i1 = 0; i1 < i1_diff; i1++) {
|
|
const void * rx = (const void *) ((const char *) x + i1*nb1);
|
|
void * rd = (void *) (dst_ptr + i1*ts*ne0/bs);
|
|
// pretend the row is a matrix with cols=1
|
|
cudaError_t r = cudaMemcpy2DAsync(rd, ts/bs, rx, nb0, ts/bs, ne0, kind, stream);
|
|
if (r != cudaSuccess) return r;
|
|
}
|
|
return cudaSuccess;
|
|
}
|
|
}
|
|
|
|
static void ggml_cuda_op_get_rows(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_d, const float * src1_d, float * dst_d, const cudaStream_t & stream) {
|
|
|
|
GGML_ASSERT(src1->type == GGML_TYPE_I32);
|
|
GGML_ASSERT(dst->type == GGML_TYPE_F32);
|
|
|
|
GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
|
|
GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
|
|
GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type));
|
|
|
|
const int32_t * src1_i32 = (const int32_t *) src1_d;
|
|
|
|
switch (src0->type) {
|
|
case GGML_TYPE_F16:
|
|
get_rows_cuda_float(src0, src1, dst, (const half *)src0_d, src1_i32, dst_d, stream);
|
|
break;
|
|
case GGML_TYPE_F32:
|
|
get_rows_cuda_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
|
|
break;
|
|
case GGML_TYPE_Q4_0:
|
|
get_rows_cuda<QK4_0, QR4_0, dequantize_q4_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
|
|
break;
|
|
case GGML_TYPE_Q4_1:
|
|
get_rows_cuda<QK4_1, QR4_1, dequantize_q4_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_0:
|
|
get_rows_cuda<QK5_0, QR5_0, dequantize_q5_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_1:
|
|
get_rows_cuda<QK5_1, QR5_1, dequantize_q5_1>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
|
|
break;
|
|
case GGML_TYPE_Q8_0:
|
|
get_rows_cuda<QK8_0, QR8_0, dequantize_q8_0>(src0, src1, dst, src0_d, src1_i32, dst_d, stream);
|
|
break;
|
|
default:
|
|
// TODO: k-quants
|
|
fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type));
|
|
GGML_ASSERT(false);
|
|
break;
|
|
}
|
|
}
|
|
|
|
template<class op>
|
|
inline void ggml_cuda_op_bin_bcast(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
op()(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
|
|
op()(src0, src1, dst, (const half *) src0_dd, src1_dd, (half *) dst_dd, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
|
|
op()(src0, src1, dst, (const half *) src0_dd, src1_dd, dst_dd, main_stream);
|
|
} else {
|
|
fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
|
|
ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
static void ggml_cuda_op_repeat(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_d, const float * src1_d, float * dst_d, const cudaStream_t & main_stream) {
|
|
|
|
ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_repeat>>(dst, src0, dst, nullptr, src0_d, dst_d, main_stream);
|
|
|
|
(void) src1;
|
|
(void) src1_d;
|
|
}
|
|
|
|
inline void ggml_cuda_op_add(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_add>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
|
|
}
|
|
|
|
inline void ggml_cuda_op_acc(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported
|
|
|
|
int nb1 = dst->op_params[0] / 4; // 4 bytes of float32
|
|
int nb2 = dst->op_params[1] / 4; // 4 bytes of float32
|
|
// int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused
|
|
int offset = dst->op_params[3] / 4; // offset in bytes
|
|
|
|
acc_f32_cuda(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream);
|
|
|
|
(void) dst;
|
|
}
|
|
|
|
inline void ggml_cuda_op_mul(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_mul>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
|
|
}
|
|
|
|
inline void ggml_cuda_op_div(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_div>>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
|
|
}
|
|
|
|
inline void ggml_cuda_op_gelu(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
gelu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_silu(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
silu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_gelu_quick(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
gelu_quick_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_tanh(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
tanh_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_relu(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_leaky_relu(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
float negative_slope;
|
|
memcpy(&negative_slope, dst->op_params, sizeof(float));
|
|
|
|
leaky_relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_sqr(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
sqr_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_norm(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t nrows = ggml_nrows(src0);
|
|
|
|
float eps;
|
|
memcpy(&eps, dst->op_params, sizeof(float));
|
|
|
|
norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
|
|
inline void ggml_cuda_op_group_norm(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
int num_groups = dst->op_params[0];
|
|
int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
|
|
group_norm_f32_cuda(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_concat(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
GGML_ASSERT(dst->type == GGML_TYPE_F32);
|
|
|
|
for (int i3 = 0; i3 < dst->ne[3]; i3++) {
|
|
concat_f32_cuda(src0_dd + i3 * (src0->nb[3] / 4), src1_dd + i3 * (src1->nb[3] / 4), dst_dd + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], main_stream);
|
|
}
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
}
|
|
|
|
inline void ggml_cuda_op_upscale(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT(dst->type == GGML_TYPE_F32);
|
|
GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
|
|
|
|
const int scale_factor = dst->op_params[0];
|
|
|
|
upscale_f32_cuda(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_pad(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT(dst->type == GGML_TYPE_F32);
|
|
GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
|
|
|
|
pad_f32_cuda(src0_dd, dst_dd,
|
|
src0->ne[0], src0->ne[1], src0->ne[2],
|
|
dst->ne[0], dst->ne[1], dst->ne[2], main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_rms_norm(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t nrows = ggml_nrows(src0);
|
|
|
|
float eps;
|
|
memcpy(&eps, dst->op_params, sizeof(float));
|
|
|
|
rms_norm_f32_cuda(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_mul_mat_q(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
|
|
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
|
|
const int64_t src1_padded_row_size, const cudaStream_t & stream) {
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
GGML_ASSERT(ne10 % QK8_1 == 0);
|
|
|
|
const int64_t ne0 = dst->ne[0];
|
|
|
|
const int64_t row_diff = row_high - row_low;
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
|
|
// the main device has a larger memory buffer to hold the results from all GPUs
|
|
// nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into
|
|
const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
|
|
|
|
switch (src0->type) {
|
|
case GGML_TYPE_Q4_0:
|
|
ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
|
|
break;
|
|
case GGML_TYPE_Q4_1:
|
|
ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_0:
|
|
ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_1:
|
|
ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
|
|
break;
|
|
case GGML_TYPE_Q8_0:
|
|
ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
|
|
break;
|
|
case GGML_TYPE_Q2_K:
|
|
ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
|
|
break;
|
|
case GGML_TYPE_Q3_K:
|
|
ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
|
|
break;
|
|
case GGML_TYPE_Q4_K:
|
|
ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_K:
|
|
ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
|
|
break;
|
|
case GGML_TYPE_Q6_K:
|
|
ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream);
|
|
break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
break;
|
|
}
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_ddf_i;
|
|
}
|
|
|
|
static int64_t get_row_rounding(ggml_type type) {
|
|
int64_t min_compute_capability = INT_MAX;
|
|
int64_t max_compute_capability = INT_MIN;
|
|
for (int64_t id = 0; id < g_device_count; ++id) {
|
|
if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
|
|
if (min_compute_capability > g_compute_capabilities[id]) {
|
|
min_compute_capability = g_compute_capabilities[id];
|
|
}
|
|
if (max_compute_capability < g_compute_capabilities[id]) {
|
|
max_compute_capability = g_compute_capabilities[id];
|
|
}
|
|
}
|
|
}
|
|
|
|
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
switch(type) {
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_Q5_0:
|
|
case GGML_TYPE_Q5_1:
|
|
case GGML_TYPE_Q8_0:
|
|
return max_compute_capability >= CC_RDNA2 ? 128 : 64;
|
|
case GGML_TYPE_F16:
|
|
case GGML_TYPE_F32:
|
|
return 1;
|
|
case GGML_TYPE_Q2_K:
|
|
return max_compute_capability >= CC_RDNA2 ? 128 : 32;
|
|
case GGML_TYPE_Q3_K:
|
|
return min_compute_capability < CC_RDNA2 ? 128 : 64;
|
|
case GGML_TYPE_Q4_K:
|
|
case GGML_TYPE_Q5_K:
|
|
case GGML_TYPE_Q6_K:
|
|
return max_compute_capability >= CC_RDNA2 ? 128 : 64;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
#else
|
|
switch(type) {
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
return max_compute_capability >= CC_VOLTA ? 128 : 64;
|
|
case GGML_TYPE_Q5_0:
|
|
case GGML_TYPE_Q5_1:
|
|
case GGML_TYPE_Q8_0:
|
|
return 64;
|
|
case GGML_TYPE_F16:
|
|
case GGML_TYPE_F32:
|
|
return 1;
|
|
case GGML_TYPE_Q2_K:
|
|
case GGML_TYPE_Q3_K:
|
|
case GGML_TYPE_Q4_K:
|
|
case GGML_TYPE_Q5_K:
|
|
return max_compute_capability >= CC_VOLTA ? 128 : 64;
|
|
case GGML_TYPE_Q6_K:
|
|
return 64;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
|
}
|
|
|
|
inline void ggml_cuda_op_mul_mat_vec_q(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
|
|
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
|
|
const int64_t src1_padded_row_size, const cudaStream_t & stream) {
|
|
|
|
GGML_ASSERT(ggml_nrows(src1) == 1);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t row_diff = row_high - row_low;
|
|
|
|
switch (src0->type) {
|
|
case GGML_TYPE_Q4_0:
|
|
mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q4_1:
|
|
mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_0:
|
|
mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_1:
|
|
mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q8_0:
|
|
mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q2_K:
|
|
mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q3_K:
|
|
mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q4_K:
|
|
mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_K:
|
|
mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q6_K:
|
|
mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
break;
|
|
}
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_ddf_i;
|
|
(void) src1_ncols;
|
|
(void) src1_padded_row_size;
|
|
}
|
|
|
|
inline void ggml_cuda_op_dequantize_mul_mat_vec(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
|
|
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
|
|
const int64_t src1_padded_row_size, const cudaStream_t & stream) {
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t row_diff = row_high - row_low;
|
|
|
|
// on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
|
|
#ifdef GGML_CUDA_F16
|
|
size_t ash;
|
|
dfloat * src1_dfloat = nullptr; // dfloat == half
|
|
|
|
bool src1_convert_f16 =
|
|
src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 ||
|
|
src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 ||
|
|
src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;
|
|
|
|
if (src1_convert_f16) {
|
|
src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash);
|
|
ggml_cpy_f32_f16_cuda((const char *) src1_ddf_i, (char *) src1_dfloat, ne00,
|
|
ne00, 1, sizeof(float), 0, 0,
|
|
ne00, 1, sizeof(half), 0, 0, stream);
|
|
}
|
|
#else
|
|
const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion
|
|
#endif // GGML_CUDA_F16
|
|
|
|
switch (src0->type) {
|
|
case GGML_TYPE_Q4_0:
|
|
dequantize_mul_mat_vec_q4_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q4_1:
|
|
dequantize_mul_mat_vec_q4_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_0:
|
|
dequantize_mul_mat_vec_q5_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_1:
|
|
dequantize_mul_mat_vec_q5_1_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q8_0:
|
|
dequantize_mul_mat_vec_q8_0_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q2_K:
|
|
dequantize_mul_mat_vec_q2_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q3_K:
|
|
dequantize_mul_mat_vec_q3_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q4_K:
|
|
dequantize_mul_mat_vec_q4_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q5_K:
|
|
dequantize_mul_mat_vec_q5_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_Q6_K:
|
|
dequantize_mul_mat_vec_q6_K_cuda(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
case GGML_TYPE_F16:
|
|
convert_mul_mat_vec_f16_cuda(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
|
|
break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
break;
|
|
}
|
|
|
|
#ifdef GGML_CUDA_F16
|
|
if (src1_convert_f16) {
|
|
ggml_cuda_pool_free(src1_dfloat, ash);
|
|
}
|
|
#endif // GGML_CUDA_F16
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_ddq_i;
|
|
(void) src1_ncols;
|
|
(void) src1_padded_row_size;
|
|
}
|
|
|
|
inline void ggml_cuda_op_mul_mat_cublas(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
|
|
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
|
|
const int64_t src1_padded_row_size, const cudaStream_t & stream) {
|
|
|
|
GGML_ASSERT(src0_dd_i != nullptr);
|
|
GGML_ASSERT(src1_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_dd_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t ne10 = src1->ne[0];
|
|
|
|
const int64_t ne0 = dst->ne[0];
|
|
|
|
const int64_t row_diff = row_high - row_low;
|
|
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
|
|
// the main device has a larger memory buffer to hold the results from all GPUs
|
|
// ldc == nrows of the matrix that cuBLAS writes into
|
|
int ldc = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff;
|
|
|
|
const int compute_capability = g_compute_capabilities[id];
|
|
|
|
if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
|
|
// convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
|
|
half * src0_as_f16 = nullptr;
|
|
size_t src0_as = 0;
|
|
if (src0->type != GGML_TYPE_F16) {
|
|
const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type);
|
|
GGML_ASSERT(to_fp16_cuda != nullptr);
|
|
size_t ne = row_diff*ne00;
|
|
src0_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src0_as);
|
|
to_fp16_cuda(src0_dd_i, src0_as_f16, ne, stream);
|
|
}
|
|
const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16;
|
|
|
|
half * src1_as_f16 = nullptr;
|
|
size_t src1_as = 0;
|
|
if (src1->type != GGML_TYPE_F16) {
|
|
const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
|
|
GGML_ASSERT(to_fp16_cuda != nullptr);
|
|
size_t ne = src1_ncols*ne10;
|
|
src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as);
|
|
to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream);
|
|
}
|
|
const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16;
|
|
size_t dst_as = 0;
|
|
half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as);
|
|
|
|
const half alpha_f16 = 1.0f;
|
|
const half beta_f16 = 0.0f;
|
|
|
|
CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
|
|
CUBLAS_CHECK(
|
|
cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
|
|
row_diff, src1_ncols, ne10,
|
|
&alpha_f16, src0_ptr, CUDA_R_16F, ne00,
|
|
src1_ptr, CUDA_R_16F, ne10,
|
|
&beta_f16, dst_f16, CUDA_R_16F, ldc,
|
|
CUBLAS_COMPUTE_16F,
|
|
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
|
|
|
|
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
|
|
to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream);
|
|
|
|
ggml_cuda_pool_free(dst_f16, dst_as);
|
|
|
|
if (src0_as != 0) {
|
|
ggml_cuda_pool_free(src0_as_f16, src0_as);
|
|
}
|
|
|
|
if (src1_as != 0) {
|
|
ggml_cuda_pool_free(src1_as_f16, src1_as);
|
|
}
|
|
}
|
|
else {
|
|
float * src0_ddq_as_f32 = nullptr;
|
|
size_t src0_as = 0;
|
|
|
|
if (src0->type != GGML_TYPE_F32) {
|
|
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type);
|
|
GGML_ASSERT(to_fp32_cuda != nullptr);
|
|
src0_ddq_as_f32 = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_as); // NOLINT
|
|
to_fp32_cuda(src0_dd_i, src0_ddq_as_f32, row_diff*ne00, stream);
|
|
}
|
|
const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32;
|
|
|
|
const float alpha = 1.0f;
|
|
const float beta = 0.0f;
|
|
|
|
CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
|
|
CUBLAS_CHECK(
|
|
cublasSgemm(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
|
|
row_diff, src1_ncols, ne10,
|
|
&alpha, src0_ddf_i, ne00,
|
|
src1_ddf_i, ne10,
|
|
&beta, dst_dd_i, ldc));
|
|
|
|
if (src0_as != 0) {
|
|
ggml_cuda_pool_free(src0_ddq_as_f32, src0_as);
|
|
}
|
|
}
|
|
|
|
(void) dst;
|
|
(void) src1_ddq_i;
|
|
(void) src1_padded_row_size;
|
|
}
|
|
|
|
inline void ggml_cuda_op_rope(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
|
|
GGML_ASSERT(src0->type == dst->type);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t ne01 = src0->ne[1];
|
|
const int64_t ne2 = dst->ne[2];
|
|
const int64_t nrows = ggml_nrows(src0);
|
|
|
|
//const int n_past = ((int32_t *) dst->op_params)[0];
|
|
const int n_dims = ((int32_t *) dst->op_params)[1];
|
|
const int mode = ((int32_t *) dst->op_params)[2];
|
|
const int n_ctx = ((int32_t *) dst->op_params)[3];
|
|
const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
|
|
|
|
// RoPE alteration for extended context
|
|
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
|
|
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
|
|
memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
|
|
memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
|
|
memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
|
|
memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
|
|
memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
|
|
|
|
const int32_t * pos = nullptr;
|
|
if ((mode & 1) == 0) {
|
|
GGML_ASSERT(src1->type == GGML_TYPE_I32);
|
|
GGML_ASSERT(src1->ne[0] == ne2);
|
|
pos = (const int32_t *) src1_dd;
|
|
}
|
|
|
|
const bool is_neox = mode & 2;
|
|
const bool is_glm = mode & 4;
|
|
|
|
rope_corr_dims corr_dims;
|
|
ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);
|
|
|
|
// compute
|
|
if (is_glm) {
|
|
GGML_ASSERT(false);
|
|
rope_glm_f32_cuda(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream);
|
|
} else if (is_neox) {
|
|
if (src0->type == GGML_TYPE_F32) {
|
|
rope_neox_cuda(
|
|
(const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
|
|
attn_factor, corr_dims, main_stream
|
|
);
|
|
} else if (src0->type == GGML_TYPE_F16) {
|
|
rope_neox_cuda(
|
|
(const half *)src0_dd, (half *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
|
|
attn_factor, corr_dims, main_stream
|
|
);
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
} else {
|
|
if (src0->type == GGML_TYPE_F32) {
|
|
rope_cuda(
|
|
(const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
|
|
attn_factor, corr_dims, main_stream
|
|
);
|
|
} else if (src0->type == GGML_TYPE_F16) {
|
|
rope_cuda(
|
|
(const half *)src0_dd, (half *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
|
|
attn_factor, corr_dims, main_stream
|
|
);
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_alibi(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t ne01 = src0->ne[1];
|
|
const int64_t ne02 = src0->ne[2];
|
|
const int64_t nrows = ggml_nrows(src0);
|
|
|
|
//const int n_past = ((int32_t *) dst->op_params)[0];
|
|
const int n_head = ((int32_t *) dst->op_params)[1];
|
|
float max_bias;
|
|
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
|
|
|
|
//GGML_ASSERT(ne01 + n_past == ne00);
|
|
GGML_ASSERT(n_head == ne02);
|
|
|
|
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
|
|
|
|
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
|
|
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
|
|
|
|
alibi_f32_cuda(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream);
|
|
|
|
(void) src1;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_im2col(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F16);
|
|
|
|
const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
|
|
const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
|
|
const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
|
|
const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
|
|
const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
|
|
const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
|
|
|
|
const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
|
|
|
|
const int64_t IC = src1->ne[is_2D ? 2 : 1];
|
|
const int64_t IH = is_2D ? src1->ne[1] : 1;
|
|
const int64_t IW = src1->ne[0];
|
|
|
|
const int64_t KH = is_2D ? src0->ne[1] : 1;
|
|
const int64_t KW = src0->ne[0];
|
|
|
|
const int64_t OH = is_2D ? dst->ne[2] : 1;
|
|
const int64_t OW = dst->ne[1];
|
|
|
|
const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
|
|
|
|
im2col_f32_f16_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream);
|
|
|
|
(void) src0;
|
|
(void) src0_dd;
|
|
}
|
|
|
|
|
|
inline void ggml_cuda_op_sum_rows(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
const int64_t ncols = src0->ne[0];
|
|
const int64_t nrows = ggml_nrows(src0);
|
|
|
|
sum_rows_f32_cuda(src0_dd, dst_dd, ncols, nrows, main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_argsort(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_I32);
|
|
|
|
const int64_t ncols = src0->ne[0];
|
|
const int64_t nrows = ggml_nrows(src0);
|
|
|
|
enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0];
|
|
|
|
argsort_f32_i32_cuda(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_diag_mask_inf(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t ne01 = src0->ne[1];
|
|
const int nrows0 = ggml_nrows(src0);
|
|
|
|
const int n_past = ((int32_t *) dst->op_params)[0];
|
|
|
|
diag_mask_inf_f32_cuda(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_soft_max(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t nrows_x = ggml_nrows(src0);
|
|
const int64_t nrows_y = src1 ? ggml_nrows(src1) : 1;
|
|
|
|
float scale = 1.0f;
|
|
memcpy(&scale, dst->op_params, sizeof(float));
|
|
|
|
soft_max_f32_cuda(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream);
|
|
|
|
(void) dst;
|
|
}
|
|
|
|
inline void ggml_cuda_op_scale(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
const float scale = ((float *) dst->op_params)[0];
|
|
|
|
scale_f32_cuda(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream);
|
|
CUDA_CHECK(cudaGetLastError());
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
inline void ggml_cuda_op_clamp(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
|
|
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
|
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
|
|
|
float min;
|
|
float max;
|
|
memcpy(&min, dst->op_params, sizeof(float));
|
|
memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
|
|
|
|
clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream);
|
|
CUDA_CHECK(cudaGetLastError());
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src1_dd;
|
|
}
|
|
|
|
static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const ggml_cuda_op_flatten_t op) {
|
|
const int64_t nrows0 = ggml_nrows(src0);
|
|
|
|
const bool use_src1 = src1 != nullptr;
|
|
const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
|
|
|
|
GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
|
|
GGML_ASSERT( dst->backend != GGML_BACKEND_GPU_SPLIT);
|
|
|
|
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
|
|
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
|
|
const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
|
|
const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU;
|
|
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU;
|
|
|
|
// dd = data device
|
|
float * src0_ddf = nullptr;
|
|
float * src1_ddf = nullptr;
|
|
float * dst_ddf = nullptr;
|
|
|
|
// as = actual size
|
|
size_t src0_asf = 0;
|
|
size_t src1_asf = 0;
|
|
size_t dst_asf = 0;
|
|
|
|
ggml_cuda_set_device(g_main_device);
|
|
const cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
|
|
|
|
if (src0_on_device) {
|
|
src0_ddf = (float *) src0_extra->data_device[g_main_device];
|
|
} else {
|
|
src0_ddf = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_asf);
|
|
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream));
|
|
}
|
|
|
|
if (use_src1) {
|
|
if (src1_on_device) {
|
|
src1_ddf = (float *) src1_extra->data_device[g_main_device];
|
|
} else {
|
|
src1_ddf = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf);
|
|
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream));
|
|
}
|
|
}
|
|
if (dst_on_device) {
|
|
dst_ddf = (float *) dst_extra->data_device[g_main_device];
|
|
} else {
|
|
dst_ddf = (float *) ggml_cuda_pool_malloc(ggml_nbytes(dst), &dst_asf);
|
|
}
|
|
|
|
// do the computation
|
|
op(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
|
|
CUDA_CHECK(cudaGetLastError());
|
|
|
|
// copy dst to host if necessary
|
|
if (!dst_on_device) {
|
|
CUDA_CHECK(cudaMemcpyAsync(dst->data, dst_ddf, ggml_nbytes(dst), cudaMemcpyDeviceToHost, main_stream));
|
|
}
|
|
|
|
if (src0_asf > 0) {
|
|
ggml_cuda_pool_free(src0_ddf, src0_asf);
|
|
}
|
|
if (src1_asf > 0) {
|
|
ggml_cuda_pool_free(src1_ddf, src1_asf);
|
|
}
|
|
if (dst_asf > 0) {
|
|
ggml_cuda_pool_free(dst_ddf, dst_asf);
|
|
}
|
|
|
|
if (dst->backend == GGML_BACKEND_CPU) {
|
|
CUDA_CHECK(cudaDeviceSynchronize());
|
|
}
|
|
}
|
|
|
|
static void ggml_cuda_set_peer_access(const int n_tokens) {
|
|
static bool peer_access_enabled = false;
|
|
|
|
const bool enable_peer_access = n_tokens <= GGML_CUDA_PEER_MAX_BATCH_SIZE;
|
|
|
|
if (peer_access_enabled == enable_peer_access) {
|
|
return;
|
|
}
|
|
|
|
#ifdef NDEBUG
|
|
for (int id = 0; id < g_device_count; ++id) {
|
|
CUDA_CHECK(ggml_cuda_set_device(id));
|
|
CUDA_CHECK(cudaDeviceSynchronize());
|
|
}
|
|
|
|
for (int id = 0; id < g_device_count; ++id) {
|
|
CUDA_CHECK(ggml_cuda_set_device(id));
|
|
|
|
for (int id_other = 0; id_other < g_device_count; ++id_other) {
|
|
if (id == id_other) {
|
|
continue;
|
|
}
|
|
if (id != g_main_device && id_other != g_main_device) {
|
|
continue;
|
|
}
|
|
|
|
int can_access_peer;
|
|
CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other));
|
|
if (can_access_peer) {
|
|
if (enable_peer_access) {
|
|
CUDA_CHECK(cudaDeviceEnablePeerAccess(id_other, 0));
|
|
} else {
|
|
CUDA_CHECK(cudaDeviceDisablePeerAccess(id_other));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#endif // NDEBUG
|
|
|
|
peer_access_enabled = enable_peer_access;
|
|
}
|
|
|
|
static void ggml_cuda_op_mul_mat(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op,
|
|
const bool convert_src1_to_q8_1) {
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t ne01 = src0->ne[1];
|
|
const int64_t ne02 = src0->ne[2];
|
|
const int64_t ne03 = src0->ne[3];
|
|
const int64_t nrows0 = ggml_nrows(src0);
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
const int64_t ne11 = src1->ne[1];
|
|
const int64_t ne12 = src1->ne[2];
|
|
const int64_t ne13 = src1->ne[3];
|
|
const int64_t nrows1 = ggml_nrows(src1);
|
|
|
|
GGML_ASSERT(ne03 == ne13);
|
|
|
|
const int64_t ne0 = dst->ne[0];
|
|
const int64_t ne1 = dst->ne[1];
|
|
|
|
const int nb2 = dst->nb[2];
|
|
const int nb3 = dst->nb[3];
|
|
|
|
GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT);
|
|
GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT);
|
|
|
|
GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0);
|
|
|
|
const int64_t i02_divisor = ne12 / ne02;
|
|
|
|
const size_t src0_ts = ggml_type_size(src0->type);
|
|
const size_t src0_bs = ggml_blck_size(src0->type);
|
|
const size_t q8_1_ts = sizeof(block_q8_1);
|
|
const size_t q8_1_bs = QK8_1;
|
|
|
|
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
|
|
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
|
|
const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT;
|
|
const bool src0_is_contiguous = ggml_is_contiguous(src0);
|
|
const bool src1_is_contiguous = ggml_is_contiguous(src1);
|
|
|
|
const int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING);
|
|
|
|
const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
|
|
GGML_ASSERT(!(split && ne02 > 1));
|
|
GGML_ASSERT(!(split && ne03 > 1));
|
|
GGML_ASSERT(!(split && ne02 < ne12));
|
|
|
|
// dd = data device
|
|
char * src0_dd[GGML_CUDA_MAX_DEVICES] = {nullptr};
|
|
float * src1_ddf[GGML_CUDA_MAX_DEVICES] = {nullptr}; // float
|
|
char * src1_ddq[GGML_CUDA_MAX_DEVICES] = {nullptr}; // q8_1
|
|
float * dst_dd[GGML_CUDA_MAX_DEVICES] = {nullptr};
|
|
|
|
// as = actual size
|
|
size_t src0_as[GGML_CUDA_MAX_DEVICES] = {0};
|
|
size_t src1_asf[GGML_CUDA_MAX_DEVICES] = {0};
|
|
size_t src1_asq[GGML_CUDA_MAX_DEVICES] = {0};
|
|
size_t dst_as[GGML_CUDA_MAX_DEVICES] = {0};
|
|
|
|
int64_t row_low[GGML_CUDA_MAX_DEVICES];
|
|
int64_t row_high[GGML_CUDA_MAX_DEVICES];
|
|
|
|
int used_devices = 0;
|
|
|
|
for (int64_t id = 0; id < g_device_count; ++id) {
|
|
// by default, use all rows
|
|
row_low[id] = 0;
|
|
row_high[id] = ne01;
|
|
|
|
// for multi GPU, get the row boundaries from tensor split
|
|
// and round to mul_mat_q tile sizes
|
|
if (split) {
|
|
const int64_t rounding = get_row_rounding(src0->type);
|
|
|
|
if (id != 0) {
|
|
row_low[id] = ne01*g_tensor_split[id];
|
|
row_low[id] -= row_low[id] % rounding;
|
|
}
|
|
|
|
if (id != g_device_count - 1) {
|
|
row_high[id] = ne01*g_tensor_split[id + 1];
|
|
row_high[id] -= row_high[id] % rounding;
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int64_t id = 0; id < g_device_count; ++id) {
|
|
if ((!split && id != g_main_device) || row_low[id] == row_high[id]) {
|
|
continue;
|
|
}
|
|
|
|
used_devices++;
|
|
|
|
const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
|
|
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device;
|
|
|
|
ggml_cuda_set_device(id);
|
|
const cudaStream_t stream = g_cudaStreams[id][0];
|
|
|
|
if (src0_on_device && src0_is_contiguous) {
|
|
src0_dd[id] = (char *) src0_extra->data_device[id];
|
|
} else {
|
|
// const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0);
|
|
src0_dd[id] = (char *) ggml_cuda_pool_malloc(ggml_nbytes(src0), &src0_as[id]);
|
|
}
|
|
|
|
if (src1_on_device && src1_is_contiguous) {
|
|
src1_ddf[id] = (float *) src1_extra->data_device[id];
|
|
} else {
|
|
src1_ddf[id] = (float *) ggml_cuda_pool_malloc(ggml_nbytes(src1), &src1_asf[id]);
|
|
}
|
|
|
|
if (convert_src1_to_q8_1) {
|
|
src1_ddq[id] = (char *) ggml_cuda_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]);
|
|
|
|
if (src1_on_device && src1_is_contiguous) {
|
|
quantize_row_q8_1_cuda(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream);
|
|
CUDA_CHECK(cudaGetLastError());
|
|
}
|
|
}
|
|
|
|
if (dst_on_device) {
|
|
dst_dd[id] = (float *) dst_extra->data_device[id];
|
|
} else {
|
|
const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst);
|
|
dst_dd[id] = (float *) ggml_cuda_pool_malloc(size_dst_ddf, &dst_as[id]);
|
|
}
|
|
}
|
|
|
|
// if multiple devices are used they need to wait for the main device
|
|
// here an event is recorded that signals that the main device has finished calculating the input data
|
|
if (split && used_devices > 1) {
|
|
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
|
|
CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device][0], g_cudaStreams[g_main_device][0]));
|
|
}
|
|
|
|
const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11;
|
|
for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) {
|
|
const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0;
|
|
const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride;
|
|
|
|
for (int64_t id = 0; id < g_device_count; ++id) {
|
|
if ((!split && id != g_main_device) || row_low[id] == row_high[id]) {
|
|
continue;
|
|
}
|
|
|
|
const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device;
|
|
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device;
|
|
const int64_t row_diff = row_high[id] - row_low[id];
|
|
|
|
ggml_cuda_set_device(id);
|
|
const cudaStream_t stream = g_cudaStreams[id][is];
|
|
|
|
// wait for main GPU data if necessary
|
|
if (split && (id != g_main_device || is != 0)) {
|
|
CUDA_CHECK(cudaStreamWaitEvent(stream, src0_extra->events[g_main_device][0], 0));
|
|
}
|
|
|
|
for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) {
|
|
const int64_t i03 = i0 / ne12;
|
|
const int64_t i02 = i0 % ne12;
|
|
|
|
const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs;
|
|
|
|
// for split tensors the data begins at i0 == i0_offset_low
|
|
char * src0_dd_i = src0_dd[id] + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs;
|
|
float * src1_ddf_i = src1_ddf[id] + (i0*ne11 + src1_col_0) * ne10;
|
|
char * src1_ddq_i = src1_ddq[id] + src1_ddq_i_offset;
|
|
float * dst_dd_i = dst_dd[id] + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff);
|
|
|
|
// the main device memory buffer can be on VRAM scratch, with space for all partial results
|
|
// in that case an offset on dst_ddf_i is needed
|
|
if (dst->backend == GGML_BACKEND_GPU && id == g_main_device) {
|
|
dst_dd_i += row_low[id]; // offset is 0 if no tensor split
|
|
}
|
|
|
|
// copy src0, src1 to device if necessary
|
|
if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
|
|
if (id != g_main_device) {
|
|
if (convert_src1_to_q8_1) {
|
|
char * src1_ddq_i_source = src1_ddq[g_main_device] + src1_ddq_i_offset;
|
|
CUDA_CHECK(cudaMemcpyAsync(src1_ddq_i, src1_ddq_i_source, src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs,
|
|
cudaMemcpyDeviceToDevice, stream));
|
|
} else {
|
|
float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device];
|
|
src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10;
|
|
CUDA_CHECK(cudaMemcpyAsync(src1_ddf_i, src1_ddf_i_source, src1_ncols*ne10*sizeof(float),
|
|
cudaMemcpyDeviceToDevice, stream));
|
|
}
|
|
}
|
|
} else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) {
|
|
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(
|
|
src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream));
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) {
|
|
quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
|
|
CUDA_CHECK(cudaGetLastError());
|
|
}
|
|
|
|
if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) {
|
|
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, row_low[id], row_high[id], stream));
|
|
}
|
|
|
|
// do the computation
|
|
op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i,
|
|
row_low[id], row_high[id], src1_ncols, src1_padded_col_size, stream);
|
|
CUDA_CHECK(cudaGetLastError());
|
|
|
|
// copy dst to host or other device if necessary
|
|
if (!dst_on_device) {
|
|
void * dst_off_device;
|
|
cudaMemcpyKind kind;
|
|
if (dst->backend == GGML_BACKEND_CPU) {
|
|
dst_off_device = dst->data;
|
|
kind = cudaMemcpyDeviceToHost;
|
|
} else if (dst->backend == GGML_BACKEND_GPU) {
|
|
dst_off_device = dst_extra->data_device[g_main_device];
|
|
kind = cudaMemcpyDeviceToDevice;
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
if (split) {
|
|
// src0 = weight matrix is saved as a transposed matrix for better memory layout.
|
|
// dst is NOT transposed.
|
|
// The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU.
|
|
// Instead they need to be copied to the correct slice in ne0 = dst row index.
|
|
// If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results.
|
|
float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
|
|
GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
|
|
dhf_dst_i += src1_col_0*ne0 + row_low[id];
|
|
CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float), dst_dd_i, row_diff*sizeof(float),
|
|
row_diff*sizeof(float), src1_ncols, kind, stream));
|
|
} else {
|
|
float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
|
|
GGML_ASSERT(dst->nb[1] == ne0*sizeof(float));
|
|
dhf_dst_i += src1_col_0*ne0;
|
|
CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_dd_i, src1_ncols*ne0*sizeof(float), kind, stream));
|
|
}
|
|
}
|
|
|
|
// add event for the main device to wait on until other device is done
|
|
if (split && (id != g_main_device || is != 0)) {
|
|
CUDA_CHECK(cudaEventRecord(src0_extra->events[id][is], stream));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int64_t id = 0; id < g_device_count; ++id) {
|
|
if ((!split && id != g_main_device) || row_low[id] == row_high[id]) {
|
|
continue;
|
|
}
|
|
CUDA_CHECK(ggml_cuda_set_device(id));
|
|
|
|
// free buffers again when done
|
|
if (src0_as[id] > 0) {
|
|
ggml_cuda_pool_free(src0_dd[id], src0_as[id]);
|
|
}
|
|
if (src1_asf[id] > 0) {
|
|
ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]);
|
|
}
|
|
if (src1_asq[id] > 0) {
|
|
ggml_cuda_pool_free(src1_ddq[id], src1_asq[id]);
|
|
}
|
|
if (dst_as[id] > 0) {
|
|
ggml_cuda_pool_free(dst_dd[id], dst_as[id]);
|
|
}
|
|
}
|
|
|
|
// main device waits for all other devices to be finished
|
|
if (split && g_device_count > 1) {
|
|
int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE;
|
|
is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS;
|
|
|
|
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
|
|
for (int64_t id = 0; id < g_device_count; ++id) {
|
|
if (row_low[id] == row_high[id]) {
|
|
continue;
|
|
}
|
|
for (int64_t is = 0; is < is_max; ++is) {
|
|
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams[g_main_device][0], src0_extra->events[id][is], 0));
|
|
}
|
|
}
|
|
}
|
|
|
|
if (dst->backend == GGML_BACKEND_CPU) {
|
|
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
|
|
CUDA_CHECK(cudaDeviceSynchronize());
|
|
}
|
|
}
|
|
|
|
static void ggml_cuda_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_repeat);
|
|
}
|
|
|
|
static void ggml_cuda_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_get_rows);
|
|
}
|
|
|
|
static void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_add);
|
|
}
|
|
|
|
static void ggml_cuda_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_acc);
|
|
}
|
|
|
|
static void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_mul);
|
|
}
|
|
|
|
static void ggml_cuda_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_div);
|
|
}
|
|
|
|
static void ggml_cuda_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_gelu);
|
|
}
|
|
|
|
static void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_silu);
|
|
}
|
|
|
|
static void ggml_cuda_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_gelu_quick);
|
|
}
|
|
|
|
static void ggml_cuda_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_tanh);
|
|
}
|
|
|
|
static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu);
|
|
}
|
|
|
|
static void ggml_cuda_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_leaky_relu);
|
|
}
|
|
|
|
static void ggml_cuda_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sqr);
|
|
}
|
|
|
|
static void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_norm);
|
|
}
|
|
|
|
static void ggml_cuda_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_group_norm);
|
|
}
|
|
|
|
static void ggml_cuda_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_concat);
|
|
}
|
|
|
|
static void ggml_cuda_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_upscale);
|
|
}
|
|
|
|
static void ggml_cuda_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pad);
|
|
}
|
|
|
|
static void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rms_norm);
|
|
}
|
|
|
|
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
|
if (!g_cublas_loaded) return false;
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
|
|
const int64_t ne0 = dst->ne[0];
|
|
const int64_t ne1 = dst->ne[1];
|
|
|
|
// TODO: find the optimal values for these
|
|
return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
|
|
src1->type == GGML_TYPE_F32 &&
|
|
dst->type == GGML_TYPE_F32 &&
|
|
(ne0 >= 32 && ne1 >= 32 && ne10 >= 32);
|
|
}
|
|
|
|
static void ggml_cuda_mul_mat_vec_p021(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
|
|
GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1));
|
|
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
|
|
GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation
|
|
GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t ne01 = src0->ne[1];
|
|
const int64_t ne02 = src0->ne[2];
|
|
|
|
const int64_t ne12 = src1->ne[2];
|
|
|
|
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
|
|
cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
|
|
|
|
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
void * src0_ddq = src0_extra->data_device[g_main_device];
|
|
|
|
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
|
|
float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
|
|
|
|
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
|
|
|
|
ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream);
|
|
}
|
|
|
|
static void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
|
|
GGML_ASSERT(!ggml_is_transposed(src0));
|
|
GGML_ASSERT(!ggml_is_transposed(src1));
|
|
GGML_ASSERT(!ggml_is_permuted(src0));
|
|
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t ne01 = src0->ne[1];
|
|
const int64_t ne02 = src0->ne[2];
|
|
|
|
const int64_t nb01 = src0->nb[1];
|
|
const int64_t nb02 = src0->nb[2];
|
|
|
|
const int64_t ne12 = src1->ne[2];
|
|
|
|
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
|
|
cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
|
|
|
|
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
void * src0_ddq = src0_extra->data_device[g_main_device];
|
|
|
|
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
|
|
float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
|
|
|
|
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
|
|
|
|
const int64_t row_stride_x = nb01 / sizeof(half);
|
|
const int64_t channel_stride_x = nb02 / sizeof(half);
|
|
|
|
ggml_mul_mat_vec_nc_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream);
|
|
}
|
|
|
|
static __global__ void k_compute_batched_ptrs(
|
|
const half * src0_as_f16, const half * src1_as_f16, char * dst,
|
|
const void ** ptrs_src, void ** ptrs_dst,
|
|
int64_t ne12, int64_t ne13,
|
|
int64_t ne23,
|
|
size_t nb02, size_t nb03,
|
|
size_t nb12, size_t nb13,
|
|
size_t nbd2, size_t nbd3,
|
|
int64_t r2, int64_t r3) {
|
|
int64_t i13 = blockIdx.x * blockDim.x + threadIdx.x;
|
|
int64_t i12 = blockIdx.y * blockDim.y + threadIdx.y;
|
|
|
|
if (i13 >= ne13 || i12 >= ne12) {
|
|
return;
|
|
}
|
|
|
|
int64_t i03 = i13 / r3;
|
|
int64_t i02 = i12 / r2;
|
|
|
|
ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03;
|
|
ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2;
|
|
ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3;
|
|
}
|
|
|
|
static void ggml_cuda_mul_mat_mat_batched_cublas(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(!ggml_is_transposed(src0));
|
|
GGML_ASSERT(!ggml_is_transposed(src1));
|
|
|
|
GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT);
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
|
|
const int64_t ne00 = src0->ne[0]; GGML_UNUSED(ne00);
|
|
const int64_t ne01 = src0->ne[1];
|
|
const int64_t ne02 = src0->ne[2];
|
|
const int64_t ne03 = src0->ne[3];
|
|
|
|
const int64_t nb01 = src0->nb[1];
|
|
const int64_t nb02 = src0->nb[2]; GGML_UNUSED(nb02);
|
|
const int64_t nb03 = src0->nb[3]; GGML_UNUSED(nb03);
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
const int64_t ne11 = src1->ne[1];
|
|
const int64_t ne12 = src1->ne[2];
|
|
const int64_t ne13 = src1->ne[3];
|
|
|
|
const int64_t nb11 = src1->nb[1];
|
|
const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12);
|
|
const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13);
|
|
|
|
const int64_t ne1 = ggml_nelements(src1);
|
|
const int64_t ne = ggml_nelements(dst);
|
|
|
|
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
|
|
cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
|
|
|
|
CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream));
|
|
|
|
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
void * src0_ddq = src0_extra->data_device[g_main_device];
|
|
half * src0_as_f16 = (half *) src0_ddq;
|
|
|
|
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
|
|
float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
|
|
|
|
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
|
|
|
|
// convert src1 to fp16
|
|
const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
|
|
GGML_ASSERT(to_fp16_cuda != nullptr);
|
|
|
|
size_t src1_as = 0;
|
|
half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as);
|
|
to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
|
|
|
|
size_t dst_as = 0;
|
|
|
|
half * dst_f16 = nullptr;
|
|
char * dst_t = nullptr;
|
|
|
|
cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F;
|
|
cudaDataType_t cu_data_type = CUDA_R_16F;
|
|
|
|
// dst strides
|
|
size_t nbd2 = dst->nb[2];
|
|
size_t nbd3 = dst->nb[3];
|
|
|
|
const half alpha_f16 = 1.0f;
|
|
const half beta_f16 = 0.0f;
|
|
|
|
const float alpha_f32 = 1.0f;
|
|
const float beta_f32 = 0.0f;
|
|
|
|
const void * alpha = &alpha_f16;
|
|
const void * beta = &beta_f16;
|
|
|
|
if (dst->op_params[0] == GGML_PREC_DEFAULT) {
|
|
dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
|
|
dst_t = (char *) dst_f16;
|
|
|
|
nbd2 /= sizeof(float) / sizeof(half);
|
|
nbd3 /= sizeof(float) / sizeof(half);
|
|
} else {
|
|
dst_t = (char *) dst_ddf;
|
|
|
|
cu_compute_type = CUBLAS_COMPUTE_32F;
|
|
cu_data_type = CUDA_R_32F;
|
|
|
|
alpha = &alpha_f32;
|
|
beta = &beta_f32;
|
|
}
|
|
|
|
GGML_ASSERT(ne12 % ne02 == 0);
|
|
GGML_ASSERT(ne13 % ne03 == 0);
|
|
|
|
// broadcast factors
|
|
const int64_t r2 = ne12/ne02;
|
|
const int64_t r3 = ne13/ne03;
|
|
|
|
#if 0
|
|
// use cublasGemmEx
|
|
{
|
|
for (int i13 = 0; i13 < ne13; ++i13) {
|
|
for (int i12 = 0; i12 < ne12; ++i12) {
|
|
int i03 = i13 / r3;
|
|
int i02 = i12 / r2;
|
|
|
|
CUBLAS_CHECK(
|
|
cublasGemmEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
|
|
ne01, ne11, ne10,
|
|
alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , CUDA_R_16F, nb01/sizeof(half),
|
|
(const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, CUDA_R_16F, nb11/sizeof(float),
|
|
beta, ( char *) dst_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01,
|
|
cu_compute_type,
|
|
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
|
|
}
|
|
}
|
|
}
|
|
#else
|
|
if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) {
|
|
// there is no broadcast and src0, src1 are contiguous across dims 2, 3
|
|
// use cublasGemmStridedBatchedEx
|
|
CUBLAS_CHECK(
|
|
cublasGemmStridedBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
|
|
ne01, ne11, ne10,
|
|
alpha, (const char *) src0_as_f16, CUDA_R_16F, nb01/sizeof(half), src0->nb[2]/sizeof(half), // strideA
|
|
(const char *) src1_as_f16, CUDA_R_16F, nb11/sizeof(float), src1->nb[2]/sizeof(float), // strideB
|
|
beta, ( char *) dst_t, cu_data_type, ne01, dst->nb[2]/sizeof(float), // strideC
|
|
ne12*ne13,
|
|
cu_compute_type,
|
|
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
|
|
} else {
|
|
// use cublasGemmBatchedEx
|
|
const int ne23 = ne12*ne13;
|
|
|
|
const void ** ptrs_src = nullptr;
|
|
void ** ptrs_dst = nullptr;
|
|
|
|
size_t ptrs_src_s = 0;
|
|
size_t ptrs_dst_s = 0;
|
|
|
|
ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s);
|
|
ptrs_dst = ( void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s);
|
|
|
|
dim3 block_dims(ne13, ne12);
|
|
k_compute_batched_ptrs<<<1, block_dims, 0, main_stream>>>(
|
|
src0_as_f16, src1_as_f16, dst_t,
|
|
ptrs_src, ptrs_dst,
|
|
ne12, ne13,
|
|
ne23,
|
|
nb02, nb03,
|
|
nb12, nb13,
|
|
nbd2, nbd3,
|
|
r2, r3);
|
|
CUDA_CHECK(cudaGetLastError());
|
|
|
|
CUBLAS_CHECK(
|
|
cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
|
|
ne01, ne11, ne10,
|
|
alpha, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, nb01/sizeof(half),
|
|
(const void **) (ptrs_src + 1*ne23), CUDA_R_16F, nb11/sizeof(float),
|
|
beta, ( void **) (ptrs_dst + 0*ne23), cu_data_type, ne01,
|
|
ne23,
|
|
cu_compute_type,
|
|
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
|
|
|
|
if (ptrs_src_s != 0) {
|
|
ggml_cuda_pool_free(ptrs_src, ptrs_src_s);
|
|
}
|
|
if (ptrs_dst_s != 0) {
|
|
ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s);
|
|
}
|
|
}
|
|
#endif
|
|
|
|
if (dst->op_params[0] == GGML_PREC_DEFAULT) {
|
|
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
|
|
to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
|
|
|
|
ggml_cuda_pool_free(dst_f16, dst_as);
|
|
}
|
|
|
|
ggml_cuda_pool_free(src1_as_f16, src1_as);
|
|
}
|
|
|
|
static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
const bool all_on_device =
|
|
(src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) &&
|
|
(src1->backend == GGML_BACKEND_GPU) &&
|
|
( dst->backend == GGML_BACKEND_GPU);
|
|
|
|
const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
|
|
|
|
int64_t min_compute_capability = INT_MAX;
|
|
for (int64_t id = 0; id < g_device_count; ++id) {
|
|
if (min_compute_capability > g_compute_capabilities[id] && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) {
|
|
min_compute_capability = g_compute_capabilities[id];
|
|
}
|
|
}
|
|
|
|
#ifdef CUDA_USE_TENSOR_CORES
|
|
const bool use_tensor_cores = true;
|
|
#else
|
|
const bool use_tensor_cores = false;
|
|
#endif
|
|
|
|
// debug helpers
|
|
//printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
|
|
//printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
|
|
//printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
|
|
//printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
|
|
//printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
|
|
//printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
|
|
|
|
if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
|
|
// KQ single-batch
|
|
ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
|
|
} else if (!split && all_on_device && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
|
|
// KQV single-batch
|
|
ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
|
|
} else if (!split && all_on_device && use_tensor_cores && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) {
|
|
// KQ + KQV multi-batch
|
|
ggml_cuda_mul_mat_mat_batched_cublas(src0, src1, dst);
|
|
} else if (src0->type == GGML_TYPE_F32) {
|
|
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
|
|
} else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) {
|
|
if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) {
|
|
#ifdef GGML_CUDA_FORCE_DMMV
|
|
const bool use_mul_mat_vec_q = false;
|
|
#else
|
|
const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1;
|
|
#endif // GGML_CUDA_FORCE_DMMV
|
|
|
|
if (use_mul_mat_vec_q) {
|
|
// NOTE: this kernel does not support ggml_nrows(src1) > 1
|
|
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true);
|
|
} else {
|
|
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false);
|
|
}
|
|
} else {
|
|
bool use_mul_mat_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type);
|
|
|
|
// when tensor cores are available, use them for large batch size
|
|
// ref: https://github.com/ggerganov/llama.cpp/pull/3776
|
|
if (use_tensor_cores && min_compute_capability >= CC_VOLTA && src1->ne[1] > MMQ_MAX_BATCH_SIZE) {
|
|
use_mul_mat_q = false;
|
|
}
|
|
|
|
if (use_mul_mat_q) {
|
|
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true);
|
|
} else {
|
|
ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false);
|
|
}
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
#if 0
|
|
template<typename ... Srcs>
|
|
static __global__ void k_compute_batched_ptrs_id(
|
|
const void ** ptrs_src, void ** ptrs_dst,
|
|
int ne12, int ne13,
|
|
int ne23,
|
|
int nb02, int nb03,
|
|
int nb12, int nb13,
|
|
int nb2, int nb3,
|
|
int r2, int r3,
|
|
ggml_type src0_type, half * src0_as_f16, int64_t src0_ne,
|
|
const half * src1_f16, half * dst_f16,
|
|
const int32_t * ids, const int id,
|
|
Srcs... src0s) {
|
|
|
|
int i = ids[id];
|
|
|
|
half * src0_f16;
|
|
const void * srcs_ar[] = { (const half *) src0s... };
|
|
if (src0_type == GGML_TYPE_F16) {
|
|
src0_f16 = (half *) srcs_ar[i];
|
|
} else {
|
|
src0_f16 = src0_as_f16;
|
|
if (threadIdx.x == 0 && threadIdx.y == 0) {
|
|
const to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(src0_type);
|
|
to_fp16(srcs_ar[i], src0_f16, src0_ne, cudaStreamFireAndForget);
|
|
}
|
|
}
|
|
|
|
int i13 = blockIdx.x * blockDim.x + threadIdx.x;
|
|
int i12 = blockIdx.y * blockDim.y + threadIdx.y;
|
|
|
|
if (i13 >= ne13 || i12 >= ne12) {
|
|
return;
|
|
}
|
|
|
|
int i03 = i13 / r3;
|
|
int i02 = i12 / r2;
|
|
|
|
ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_f16 + i02*nb02 + i03*nb03;
|
|
ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_f16 + i12*nb12/2 + i13*nb13/2;
|
|
ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2;
|
|
}
|
|
|
|
static void ggml_cuda_mul_mat_id_cublas(ggml_tensor * dst) {
|
|
const struct ggml_tensor * ids = dst->src[0];
|
|
const struct ggml_tensor * src1 = dst->src[1];
|
|
const struct ggml_tensor * src00 = dst->src[2];
|
|
|
|
const int id = dst->op_params[0];
|
|
|
|
GGML_ASSERT(!ggml_is_transposed(src00));
|
|
GGML_ASSERT(!ggml_is_transposed(src1));
|
|
|
|
GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT);
|
|
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
|
|
|
const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00);
|
|
const int64_t ne01 = src00->ne[1];
|
|
const int64_t ne02 = src00->ne[2];
|
|
const int64_t ne03 = src00->ne[3];
|
|
|
|
//const int64_t nb01 = src00->nb[1];
|
|
const int64_t nb02 = src00->nb[2]; GGML_UNUSED(nb02);
|
|
const int64_t nb03 = src00->nb[3]; GGML_UNUSED(nb03);
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
const int64_t ne11 = src1->ne[1];
|
|
const int64_t ne12 = src1->ne[2];
|
|
const int64_t ne13 = src1->ne[3];
|
|
|
|
//const int64_t nb11 = src1->nb[1];
|
|
const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12);
|
|
const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13);
|
|
|
|
const int64_t ne1 = ggml_nelements(src1);
|
|
const int64_t ne = ggml_nelements(dst);
|
|
|
|
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
|
|
cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
|
|
|
|
CUBLAS_CHECK(cublasSetStream(g_cublas_handles[g_main_device], main_stream));
|
|
|
|
//ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
//void * src0_ddq = src0_extra->data_device[g_main_device];
|
|
//half * src0_as_f16 = (half *) src0_ddq;
|
|
|
|
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
|
|
float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
|
|
|
|
ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
|
|
|
|
// convert src1 to fp16
|
|
const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
|
|
GGML_ASSERT(to_fp16_cuda != nullptr);
|
|
|
|
size_t src1_as = 0;
|
|
half * src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne1 * sizeof(half), &src1_as);
|
|
to_fp16_cuda(src1_ddf, src1_as_f16, ne1, main_stream);
|
|
|
|
size_t dst_as = 0;
|
|
half * dst_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &dst_as);
|
|
|
|
GGML_ASSERT(ne12 % ne02 == 0);
|
|
GGML_ASSERT(ne13 % ne03 == 0);
|
|
|
|
// broadcast factors
|
|
const int64_t r2 = ne12/ne02;
|
|
const int64_t r3 = ne13/ne03;
|
|
|
|
const half alpha_f16 = 1.0f;
|
|
const half beta_f16 = 0.0f;
|
|
|
|
// use cublasGemmBatchedEx
|
|
const int ne23 = ne12*ne13;
|
|
|
|
const void ** ptrs_src = nullptr;
|
|
void ** ptrs_dst = nullptr;
|
|
|
|
size_t ptrs_src_s = 0;
|
|
size_t ptrs_dst_s = 0;
|
|
|
|
ptrs_src = (const void **) ggml_cuda_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s);
|
|
ptrs_dst = ( void **) ggml_cuda_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s);
|
|
|
|
int64_t src0_ne = ggml_nelements(src00);
|
|
half * src0_as_f16 = nullptr;
|
|
size_t src0_as = 0;
|
|
if (src00->type != GGML_TYPE_F16) {
|
|
src0_as_f16 = (half *) ggml_cuda_pool_malloc(src0_ne * sizeof(half), &src0_as);
|
|
}
|
|
|
|
static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6");
|
|
dim3 block_dims(ne13, ne12);
|
|
k_compute_batched_ptrs_id<<<1, block_dims, 0, main_stream>>>(
|
|
ptrs_src, ptrs_dst,
|
|
ne12, ne13,
|
|
ne23,
|
|
ne00*ne01*sizeof(half), ne00*ne01*ne02*sizeof(half),
|
|
nb12, nb13,
|
|
dst->nb[2], dst->nb[3],
|
|
r2, r3,
|
|
src00->type, src0_as_f16, src0_ne,
|
|
src1_as_f16, dst_f16,
|
|
(const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id,
|
|
dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr,
|
|
dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr,
|
|
dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr,
|
|
dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr
|
|
);
|
|
CUDA_CHECK(cudaGetLastError());
|
|
|
|
CUBLAS_CHECK(
|
|
cublasGemmBatchedEx(g_cublas_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N,
|
|
ne01, ne11, ne10,
|
|
&alpha_f16, (const void **) (ptrs_src + 0*ne23), CUDA_R_16F, ne00,
|
|
(const void **) (ptrs_src + 1*ne23), CUDA_R_16F, ne10,
|
|
&beta_f16, ( void **) (ptrs_dst + 0*ne23), CUDA_R_16F, ne01,
|
|
ne23,
|
|
CUBLAS_COMPUTE_16F,
|
|
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
|
|
|
|
if (src0_as != 0) {
|
|
ggml_cuda_pool_free(src0_as_f16, src0_as);
|
|
}
|
|
if (ptrs_src_s != 0) {
|
|
ggml_cuda_pool_free(ptrs_src, ptrs_src_s);
|
|
}
|
|
if (ptrs_dst_s != 0) {
|
|
ggml_cuda_pool_free(ptrs_dst, ptrs_dst_s);
|
|
}
|
|
|
|
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
|
|
to_fp32_cuda(dst_f16, dst_ddf, ne, main_stream);
|
|
|
|
ggml_cuda_pool_free(src1_as_f16, src1_as);
|
|
ggml_cuda_pool_free(dst_f16, dst_as);
|
|
}
|
|
#endif
|
|
|
|
static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
#if 0
|
|
ggml_cuda_mul_mat_id_cublas(dst);
|
|
// TODO: mmq/mmv support
|
|
#endif
|
|
|
|
const int64_t nb11 = src1->nb[1];
|
|
const int64_t nb1 = dst->nb[1];
|
|
|
|
const struct ggml_tensor * ids = src0;
|
|
const int32_t id = ((int32_t *) dst->op_params)[0];
|
|
const int32_t n_as = ((int32_t *) dst->op_params)[1];
|
|
|
|
std::vector<char> ids_host(ggml_nbytes(ids));
|
|
|
|
const cudaStream_t stream = g_cudaStreams[g_main_device][0];
|
|
|
|
if (ids->backend == GGML_BACKEND_GPU) {
|
|
const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device];
|
|
CUDA_CHECK(cudaMemcpyAsync(ids_host.data(), ids_dev, ggml_nbytes(ids), cudaMemcpyDeviceToHost, stream));
|
|
CUDA_CHECK(cudaStreamSynchronize(stream));
|
|
} else {
|
|
memcpy(ids_host.data(), ids->data, ggml_nbytes(ids));
|
|
}
|
|
|
|
const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra;
|
|
const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra;
|
|
|
|
ggml_tensor_extra_gpu src1_row_extra;
|
|
ggml_tensor_extra_gpu dst_row_extra;
|
|
|
|
ggml_tensor src1_row = *src1;
|
|
ggml_tensor dst_row = *dst;
|
|
|
|
src1_row.backend = GGML_BACKEND_GPU;
|
|
dst_row.backend = GGML_BACKEND_GPU;
|
|
|
|
src1_row.extra = &src1_row_extra;
|
|
dst_row.extra = &dst_row_extra;
|
|
|
|
char * src1_original = src1->backend == GGML_BACKEND_CPU ?
|
|
(char *) src1->data : (char *) src1_extra->data_device[g_main_device];
|
|
char * dst_original = dst->backend == GGML_BACKEND_CPU ?
|
|
(char *) dst->data : (char *) dst_extra->data_device[g_main_device];
|
|
|
|
if (src1->ne[1] == 1) {
|
|
GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
|
|
GGML_ASSERT(dst->backend == GGML_BACKEND_GPU);
|
|
|
|
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
|
|
//int32_t row_id;
|
|
//CUDA_CHECK(cudaMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), cudaMemcpyDeviceToHost, g_cudaStreams[g_main_device][0]));
|
|
//CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[g_main_device][0]));
|
|
|
|
const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
|
|
|
|
GGML_ASSERT(row_id >= 0 && row_id < n_as);
|
|
|
|
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
|
|
|
|
src1_row_extra.data_device[g_main_device] = src1_original + i01*src1->nb[1];
|
|
src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set?
|
|
|
|
dst_row_extra.data_device[g_main_device] = dst_original + i01*dst->nb[1];
|
|
dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set?
|
|
|
|
ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row);
|
|
}
|
|
} else {
|
|
size_t as_src1, as_dst;
|
|
char * src1_contiguous = (char *) ggml_cuda_pool_malloc(sizeof(float)*ggml_nelements(src1), &as_src1);
|
|
char * dst_contiguous = (char *) ggml_cuda_pool_malloc(sizeof(float)*ggml_nelements(dst), &as_dst);
|
|
|
|
src1_row_extra.data_device[g_main_device] = src1_contiguous;
|
|
dst_row_extra.data_device[g_main_device] = dst_contiguous;
|
|
|
|
const cudaMemcpyKind src1_kind = src1->backend == GGML_BACKEND_CPU ?
|
|
cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice;
|
|
const cudaMemcpyKind dst_kind = dst->backend == GGML_BACKEND_CPU ?
|
|
cudaMemcpyHostToDevice : cudaMemcpyDeviceToDevice;
|
|
|
|
for (int32_t row_id = 0; row_id < n_as; ++row_id) {
|
|
const struct ggml_tensor * src0_row = dst->src[row_id + 2];
|
|
|
|
int64_t num_src1_rows = 0;
|
|
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
|
|
const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
|
|
|
|
if (row_id_i != row_id) {
|
|
continue;
|
|
}
|
|
|
|
GGML_ASSERT(row_id >= 0 && row_id < n_as);
|
|
|
|
CUDA_CHECK(cudaMemcpyAsync(src1_contiguous + num_src1_rows*nb11, src1_original + i01*nb11,
|
|
nb11, src1_kind, stream));
|
|
num_src1_rows++;
|
|
}
|
|
|
|
if (num_src1_rows == 0) {
|
|
continue;
|
|
}
|
|
|
|
src1_row.ne[1] = num_src1_rows;
|
|
dst_row.ne[1] = num_src1_rows;
|
|
|
|
src1_row.nb[1] = nb11;
|
|
src1_row.nb[2] = num_src1_rows*nb11;
|
|
src1_row.nb[3] = num_src1_rows*nb11;
|
|
|
|
dst_row.nb[1] = nb1;
|
|
dst_row.nb[2] = num_src1_rows*nb1;
|
|
dst_row.nb[3] = num_src1_rows*nb1;
|
|
|
|
ggml_cuda_mul_mat(src0_row, &src1_row, &dst_row);
|
|
|
|
num_src1_rows = 0;
|
|
for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
|
|
const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
|
|
|
|
if (row_id_i != row_id) {
|
|
continue;
|
|
}
|
|
|
|
GGML_ASSERT(row_id >= 0 && row_id < n_as);
|
|
|
|
CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous + num_src1_rows*nb1,
|
|
nb1, dst_kind, stream));
|
|
num_src1_rows++;
|
|
}
|
|
}
|
|
|
|
ggml_cuda_pool_free(src1_contiguous, as_src1);
|
|
ggml_cuda_pool_free(dst_contiguous, as_dst);
|
|
}
|
|
|
|
if (dst->backend == GGML_BACKEND_CPU) {
|
|
CUDA_CHECK(cudaStreamSynchronize(stream));
|
|
}
|
|
}
|
|
|
|
static void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_scale);
|
|
}
|
|
|
|
static void ggml_cuda_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_clamp);
|
|
}
|
|
|
|
static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
const int64_t ne = ggml_nelements(src0);
|
|
GGML_ASSERT(ne == ggml_nelements(src1));
|
|
|
|
GGML_ASSERT(src0->backend == GGML_BACKEND_GPU);
|
|
GGML_ASSERT(src1->backend == GGML_BACKEND_GPU);
|
|
|
|
GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
|
|
GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t ne01 = src0->ne[1];
|
|
GGML_ASSERT(src0->ne[3] == 1);
|
|
|
|
const int64_t nb00 = src0->nb[0];
|
|
const int64_t nb01 = src0->nb[1];
|
|
const int64_t nb02 = src0->nb[2];
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
const int64_t ne11 = src1->ne[1];
|
|
GGML_ASSERT(src1->ne[3] == 1);
|
|
|
|
const int64_t nb10 = src1->nb[0];
|
|
const int64_t nb11 = src1->nb[1];
|
|
const int64_t nb12 = src1->nb[2];
|
|
|
|
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
|
|
cudaStream_t main_stream = g_cudaStreams[g_main_device][0];
|
|
|
|
const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
|
|
|
|
char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
|
|
char * src1_ddc = (char *) src1_extra->data_device[g_main_device];
|
|
|
|
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
|
|
ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
|
|
ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, 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, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, 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, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, 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, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
|
|
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
|
|
ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream);
|
|
} else {
|
|
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
|
|
ggml_type_name(src0->type), ggml_type_name(src1->type));
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
(void) dst;
|
|
}
|
|
|
|
static void ggml_cuda_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
// TODO: why do we pass dst as src1 here?
|
|
ggml_cuda_cpy(src0, dst, nullptr);
|
|
(void) src1;
|
|
}
|
|
|
|
static void ggml_cuda_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_diag_mask_inf);
|
|
}
|
|
|
|
static void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_soft_max);
|
|
}
|
|
|
|
static void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_rope);
|
|
}
|
|
|
|
static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi);
|
|
}
|
|
|
|
static void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col);
|
|
}
|
|
|
|
static void ggml_cuda_sum_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(ggml_is_contiguous(src0));
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sum_rows);
|
|
}
|
|
|
|
static void ggml_cuda_argsort(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(ggml_is_contiguous(src0));
|
|
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_argsort);
|
|
}
|
|
|
|
static void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
(void) src0;
|
|
(void) src1;
|
|
(void) dst;
|
|
}
|
|
|
|
static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) {
|
|
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
|
|
|
|
return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]);
|
|
}
|
|
|
|
void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
|
|
const int64_t nrows = ggml_nrows(tensor);
|
|
|
|
const int64_t ne0 = tensor->ne[0];
|
|
|
|
const size_t nb1 = tensor->nb[1];
|
|
|
|
ggml_backend_type backend = tensor->backend;
|
|
ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu;
|
|
memset(extra, 0, sizeof(*extra));
|
|
|
|
for (int64_t id = 0; id < g_device_count; ++id) {
|
|
if (backend == GGML_BACKEND_GPU && id != g_main_device) {
|
|
continue;
|
|
}
|
|
|
|
ggml_cuda_set_device(id);
|
|
|
|
int64_t row_low, row_high;
|
|
if (backend == GGML_BACKEND_GPU) {
|
|
row_low = 0;
|
|
row_high = nrows;
|
|
} else if (backend == GGML_BACKEND_GPU_SPLIT) {
|
|
const int64_t rounding = get_row_rounding(tensor->type);
|
|
|
|
row_low = id == 0 ? 0 : nrows*g_tensor_split[id];
|
|
row_low -= row_low % rounding;
|
|
|
|
if (id == g_device_count - 1) {
|
|
row_high = nrows;
|
|
} else {
|
|
row_high = nrows*g_tensor_split[id + 1];
|
|
row_high -= row_high % rounding;
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
if (row_low == row_high) {
|
|
continue;
|
|
}
|
|
|
|
int64_t nrows_split = row_high - row_low;
|
|
|
|
const size_t offset_split = row_low*nb1;
|
|
size_t size = ggml_nbytes_split(tensor, nrows_split);
|
|
const size_t original_size = size;
|
|
|
|
// pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
|
|
if (ne0 % MATRIX_ROW_PADDING != 0) {
|
|
size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
|
|
}
|
|
|
|
char * buf;
|
|
CUDA_CHECK(cudaMalloc(&buf, size));
|
|
char * buf_host = (char*)data + offset_split;
|
|
|
|
// set padding to 0 to avoid possible NaN values
|
|
if (size > original_size) {
|
|
CUDA_CHECK(cudaMemset(buf + original_size, 0, size - original_size));
|
|
}
|
|
|
|
CUDA_CHECK(cudaMemcpy(buf, buf_host, original_size, cudaMemcpyHostToDevice));
|
|
|
|
extra->data_device[id] = buf;
|
|
|
|
if (backend == GGML_BACKEND_GPU_SPLIT) {
|
|
for (int64_t is = 0; is < MAX_STREAMS; ++is) {
|
|
CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id][is], cudaEventDisableTiming));
|
|
}
|
|
}
|
|
}
|
|
|
|
tensor->extra = extra;
|
|
}
|
|
|
|
void ggml_cuda_free_data(struct ggml_tensor * tensor) {
|
|
if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) {
|
|
return;
|
|
}
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
|
|
for (int64_t id = 0; id < g_device_count; ++id) {
|
|
if (extra->data_device[id] != nullptr) {
|
|
CUDA_CHECK(ggml_cuda_set_device(id));
|
|
CUDA_CHECK(cudaFree(extra->data_device[id]));
|
|
}
|
|
|
|
for (int64_t is = 0; is < MAX_STREAMS; ++is) {
|
|
if (extra->events[id][is] != nullptr) {
|
|
CUDA_CHECK(ggml_cuda_set_device(id));
|
|
CUDA_CHECK(cudaEventDestroy(extra->events[id][is]));
|
|
}
|
|
}
|
|
}
|
|
|
|
delete extra;
|
|
}
|
|
|
|
static ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr;
|
|
static size_t g_temp_tensor_extra_index = 0;
|
|
|
|
static ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
|
|
if (g_temp_tensor_extras == nullptr) {
|
|
g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES];
|
|
}
|
|
|
|
size_t alloc_index = g_temp_tensor_extra_index;
|
|
g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_CUDA_MAX_NODES;
|
|
ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index];
|
|
memset(extra, 0, sizeof(*extra));
|
|
|
|
return extra;
|
|
}
|
|
|
|
static void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace, bool no_alloc) {
|
|
if (scratch && g_scratch_size == 0) {
|
|
return;
|
|
}
|
|
|
|
tensor->backend = GGML_BACKEND_GPU;
|
|
|
|
// recursively assign CUDA buffers until a compute tensor is found
|
|
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
|
|
const ggml_op src0_op = tensor->src[0]->op;
|
|
if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
|
|
ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc);
|
|
}
|
|
}
|
|
if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) {
|
|
ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
|
|
}
|
|
|
|
if (scratch && no_alloc) {
|
|
return;
|
|
}
|
|
|
|
ggml_tensor_extra_gpu * extra;
|
|
|
|
const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
|
|
tensor->op == GGML_OP_VIEW ||
|
|
force_inplace;
|
|
const size_t size = ggml_nbytes(tensor);
|
|
|
|
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
|
|
if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
|
|
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
|
|
char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
|
|
size_t offset = 0;
|
|
if (tensor->op == GGML_OP_VIEW) {
|
|
memcpy(&offset, tensor->op_params, sizeof(size_t));
|
|
}
|
|
extra = ggml_cuda_alloc_temp_tensor_extra();
|
|
extra->data_device[g_main_device] = src0_ddc + offset;
|
|
} else if (tensor->op == GGML_OP_CPY) {
|
|
ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra;
|
|
void * src1_ddv = src1_extra->data_device[g_main_device];
|
|
extra = ggml_cuda_alloc_temp_tensor_extra();
|
|
extra->data_device[g_main_device] = src1_ddv;
|
|
} else if (scratch) {
|
|
GGML_ASSERT(size <= g_scratch_size);
|
|
if (g_scratch_offset + size > g_scratch_size) {
|
|
g_scratch_offset = 0;
|
|
}
|
|
|
|
char * data = (char *) g_scratch_buffer;
|
|
if (data == nullptr) {
|
|
CUDA_CHECK(cudaMalloc(&data, g_scratch_size));
|
|
g_scratch_buffer = data;
|
|
}
|
|
extra = ggml_cuda_alloc_temp_tensor_extra();
|
|
extra->data_device[g_main_device] = data + g_scratch_offset;
|
|
|
|
g_scratch_offset += size;
|
|
|
|
GGML_ASSERT(g_scratch_offset <= g_scratch_size);
|
|
} else { // allocate new buffers outside of scratch
|
|
void * data;
|
|
CUDA_CHECK(cudaMalloc(&data, size));
|
|
CUDA_CHECK(cudaMemset(data, 0, size));
|
|
extra = new ggml_tensor_extra_gpu;
|
|
memset(extra, 0, sizeof(*extra));
|
|
extra->data_device[g_main_device] = data;
|
|
}
|
|
|
|
tensor->extra = extra;
|
|
}
|
|
|
|
void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset) {
|
|
if (g_scratch_size == 0) {
|
|
return;
|
|
}
|
|
if (g_scratch_buffer == nullptr) {
|
|
ggml_cuda_set_device(g_main_device);
|
|
CUDA_CHECK(cudaMalloc(&g_scratch_buffer, g_scratch_size));
|
|
}
|
|
|
|
ggml_tensor_extra_gpu * extra = ggml_cuda_alloc_temp_tensor_extra();
|
|
|
|
const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
|
|
tensor->op == GGML_OP_VIEW;
|
|
|
|
if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
|
|
ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
|
|
char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
|
|
size_t view_offset = 0;
|
|
if (tensor->op == GGML_OP_VIEW) {
|
|
memcpy(&view_offset, tensor->op_params, sizeof(size_t));
|
|
}
|
|
extra->data_device[g_main_device] = src0_ddc + view_offset;
|
|
} else {
|
|
extra->data_device[g_main_device] = (char *) g_scratch_buffer + offset;
|
|
}
|
|
|
|
tensor->extra = extra;
|
|
}
|
|
|
|
void ggml_cuda_copy_to_device(struct ggml_tensor * tensor) {
|
|
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
|
|
GGML_ASSERT(ggml_is_contiguous(tensor));
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
CUDA_CHECK(ggml_cuda_set_device(g_main_device));
|
|
CUDA_CHECK(cudaMemcpy(extra->data_device[g_main_device], tensor->data, ggml_nbytes(tensor), cudaMemcpyHostToDevice));
|
|
}
|
|
|
|
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) {
|
|
ggml_cuda_assign_buffers_impl(tensor, true, false, false);
|
|
}
|
|
|
|
void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor) {
|
|
ggml_cuda_assign_buffers_impl(tensor, true, false, true);
|
|
}
|
|
|
|
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
|
|
ggml_cuda_assign_buffers_impl(tensor, false, false, false);
|
|
}
|
|
|
|
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
|
|
ggml_cuda_assign_buffers_impl(tensor, false, true, false);
|
|
}
|
|
|
|
void ggml_cuda_set_main_device(const int main_device) {
|
|
if (main_device >= g_device_count) {
|
|
fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n",
|
|
main_device, g_device_count, g_main_device);
|
|
return;
|
|
}
|
|
|
|
if (g_main_device != main_device && g_device_count > 1) {
|
|
g_main_device = main_device;
|
|
cudaDeviceProp prop;
|
|
CUDA_CHECK(cudaGetDeviceProperties(&prop, g_main_device));
|
|
fprintf(stderr, "%s: using device %d (%s) as main device\n", __func__, g_main_device, prop.name);
|
|
}
|
|
}
|
|
|
|
void ggml_cuda_set_scratch_size(const size_t scratch_size) {
|
|
// this is a hack to not completely break llama.cpp when using multiple models or contexts simultaneously
|
|
// it still won't always work as expected, but it's better than nothing
|
|
if (scratch_size > g_scratch_size) {
|
|
ggml_cuda_free_scratch();
|
|
}
|
|
g_scratch_size = std::max(g_scratch_size, scratch_size);
|
|
}
|
|
|
|
void ggml_cuda_free_scratch() {
|
|
if (g_scratch_buffer == nullptr) {
|
|
return;
|
|
}
|
|
|
|
CUDA_CHECK(cudaFree(g_scratch_buffer));
|
|
g_scratch_buffer = nullptr;
|
|
}
|
|
|
|
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
|
|
if (!g_cublas_loaded) return false;
|
|
|
|
ggml_cuda_func_t func;
|
|
const bool any_on_device = tensor->backend == GGML_BACKEND_GPU
|
|
|| (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT))
|
|
|| (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU);
|
|
|
|
if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) {
|
|
return false;
|
|
}
|
|
|
|
if (tensor->op == GGML_OP_MUL_MAT) {
|
|
if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
|
|
#ifndef NDEBUG
|
|
fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = " PRId64 ", src1->ne[3] = " PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]);
|
|
#endif
|
|
return false;
|
|
}
|
|
}
|
|
|
|
switch (tensor->op) {
|
|
case GGML_OP_REPEAT:
|
|
func = ggml_cuda_repeat;
|
|
break;
|
|
case GGML_OP_GET_ROWS:
|
|
func = ggml_cuda_get_rows;
|
|
break;
|
|
case GGML_OP_DUP:
|
|
func = ggml_cuda_dup;
|
|
break;
|
|
case GGML_OP_ADD:
|
|
func = ggml_cuda_add;
|
|
break;
|
|
case GGML_OP_ACC:
|
|
func = ggml_cuda_acc;
|
|
break;
|
|
case GGML_OP_MUL:
|
|
func = ggml_cuda_mul;
|
|
break;
|
|
case GGML_OP_DIV:
|
|
func = ggml_cuda_div;
|
|
break;
|
|
case GGML_OP_UNARY:
|
|
switch (ggml_get_unary_op(tensor)) {
|
|
case GGML_UNARY_OP_GELU:
|
|
func = ggml_cuda_gelu;
|
|
break;
|
|
case GGML_UNARY_OP_SILU:
|
|
func = ggml_cuda_silu;
|
|
break;
|
|
case GGML_UNARY_OP_GELU_QUICK:
|
|
func = ggml_cuda_gelu_quick;
|
|
break;
|
|
case GGML_UNARY_OP_TANH:
|
|
func = ggml_cuda_tanh;
|
|
break;
|
|
case GGML_UNARY_OP_RELU:
|
|
func = ggml_cuda_relu;
|
|
break;
|
|
default:
|
|
return false;
|
|
}
|
|
break;
|
|
case GGML_OP_NORM:
|
|
func = ggml_cuda_norm;
|
|
break;
|
|
case GGML_OP_GROUP_NORM:
|
|
func = ggml_cuda_group_norm;
|
|
break;
|
|
case GGML_OP_CONCAT:
|
|
func = ggml_cuda_concat;
|
|
break;
|
|
case GGML_OP_UPSCALE:
|
|
func = ggml_cuda_upscale;
|
|
break;
|
|
case GGML_OP_PAD:
|
|
func = ggml_cuda_pad;
|
|
break;
|
|
case GGML_OP_LEAKY_RELU:
|
|
func = ggml_cuda_leaky_relu;
|
|
break;
|
|
case GGML_OP_RMS_NORM:
|
|
func = ggml_cuda_rms_norm;
|
|
break;
|
|
case GGML_OP_MUL_MAT:
|
|
if (!any_on_device && !ggml_cuda_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_mul_mat;
|
|
break;
|
|
case GGML_OP_MUL_MAT_ID:
|
|
if (!any_on_device && !ggml_cuda_can_mul_mat(tensor->src[2], tensor->src[1], tensor)) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_mul_mat_id;
|
|
break;
|
|
case GGML_OP_SCALE:
|
|
func = ggml_cuda_scale;
|
|
break;
|
|
case GGML_OP_SQR:
|
|
func = ggml_cuda_sqr;
|
|
break;
|
|
case GGML_OP_CLAMP:
|
|
func = ggml_cuda_clamp;
|
|
break;
|
|
case GGML_OP_CPY:
|
|
func = ggml_cuda_cpy;
|
|
break;
|
|
case GGML_OP_CONT:
|
|
func = ggml_cuda_dup;
|
|
break;
|
|
case GGML_OP_NONE:
|
|
case GGML_OP_RESHAPE:
|
|
case GGML_OP_VIEW:
|
|
case GGML_OP_PERMUTE:
|
|
case GGML_OP_TRANSPOSE:
|
|
func = ggml_cuda_nop;
|
|
break;
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
func = ggml_cuda_diag_mask_inf;
|
|
break;
|
|
case GGML_OP_SOFT_MAX:
|
|
func = ggml_cuda_soft_max;
|
|
break;
|
|
case GGML_OP_ROPE:
|
|
func = ggml_cuda_rope;
|
|
break;
|
|
case GGML_OP_ALIBI:
|
|
func = ggml_cuda_alibi;
|
|
break;
|
|
case GGML_OP_IM2COL:
|
|
func = ggml_cuda_im2col;
|
|
break;
|
|
case GGML_OP_SUM_ROWS:
|
|
func = ggml_cuda_sum_rows;
|
|
break;
|
|
case GGML_OP_ARGSORT:
|
|
func = ggml_cuda_argsort;
|
|
break;
|
|
default:
|
|
return false;
|
|
}
|
|
|
|
if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) {
|
|
ggml_cuda_set_peer_access(tensor->src[1]->ne[1]);
|
|
}
|
|
|
|
if (params->ith != 0) {
|
|
return true;
|
|
}
|
|
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
|
|
return true;
|
|
}
|
|
func(tensor->src[0], tensor->src[1], tensor);
|
|
return true;
|
|
}
|
|
|
|
int ggml_cuda_get_device_count() {
|
|
int device_count;
|
|
if (cudaGetDeviceCount(&device_count) != cudaSuccess) {
|
|
return 0;
|
|
}
|
|
return device_count;
|
|
}
|
|
|
|
void ggml_cuda_get_device_description(int device, char * description, size_t description_size) {
|
|
cudaDeviceProp prop;
|
|
CUDA_CHECK(cudaGetDeviceProperties(&prop, device));
|
|
snprintf(description, description_size, "%s", prop.name);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
// backend interface
|
|
|
|
#define UNUSED GGML_UNUSED
|
|
|
|
// cuda buffer
|
|
|
|
struct ggml_backend_buffer_context_cuda {
|
|
int device;
|
|
void * dev_ptr = nullptr;
|
|
ggml_tensor_extra_gpu * temp_tensor_extras = nullptr;
|
|
size_t temp_tensor_extra_index = 0;
|
|
|
|
ggml_backend_buffer_context_cuda(int device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {}
|
|
|
|
~ggml_backend_buffer_context_cuda() {
|
|
delete[] temp_tensor_extras;
|
|
}
|
|
|
|
ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
|
|
if (temp_tensor_extras == nullptr) {
|
|
temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_CUDA_MAX_NODES];
|
|
}
|
|
|
|
size_t alloc_index = temp_tensor_extra_index;
|
|
temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_CUDA_MAX_NODES;
|
|
ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
|
|
memset(extra, 0, sizeof(*extra));
|
|
|
|
return extra;
|
|
}
|
|
};
|
|
|
|
static void ggml_backend_cuda_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
|
ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
|
|
CUDA_CHECK(cudaFree(ctx->dev_ptr));
|
|
delete ctx;
|
|
}
|
|
|
|
static void * ggml_backend_cuda_buffer_get_base(ggml_backend_buffer_t buffer) {
|
|
ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
|
|
return ctx->dev_ptr;
|
|
}
|
|
|
|
static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
|
ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
|
|
|
|
if (tensor->view_src != NULL && tensor->view_offs == 0) {
|
|
assert(tensor->view_src->buffer->buft == buffer->buft); // TODO
|
|
tensor->backend = tensor->view_src->backend;
|
|
tensor->extra = tensor->view_src->extra;
|
|
return;
|
|
}
|
|
|
|
ggml_tensor_extra_gpu * extra = ctx->ggml_cuda_alloc_temp_tensor_extra();
|
|
|
|
extra->data_device[ctx->device] = tensor->data;
|
|
|
|
tensor->backend = GGML_BACKEND_GPU;
|
|
tensor->extra = extra;
|
|
|
|
if (ggml_is_quantized(tensor->type)) {
|
|
// initialize padding to 0 to avoid possible NaN values
|
|
int64_t row_low = 0;
|
|
int64_t row_high = ggml_nrows(tensor);
|
|
int64_t nrows_split = row_high - row_low;
|
|
|
|
size_t original_size = ggml_nbytes_split(tensor, nrows_split);
|
|
size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor);
|
|
|
|
if (padded_size > original_size && tensor->view_src == nullptr) {
|
|
CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + original_size, 0, padded_size - original_size, g_cudaStreams[ctx->device][0]));
|
|
}
|
|
}
|
|
|
|
UNUSED(buffer);
|
|
}
|
|
|
|
static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
|
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
|
|
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
|
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
|
|
|
|
CUDA_CHECK(cudaMemcpy((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice));
|
|
|
|
UNUSED(buffer);
|
|
}
|
|
|
|
static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
|
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
|
|
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
|
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
|
|
|
|
CUDA_CHECK(cudaMemcpy(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost));
|
|
|
|
UNUSED(buffer);
|
|
}
|
|
|
|
static struct ggml_backend_buffer_i cuda_backend_buffer_interface = {
|
|
/* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer,
|
|
/* .get_base = */ ggml_backend_cuda_buffer_get_base,
|
|
/* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor,
|
|
/* .set_tensor = */ ggml_backend_cuda_buffer_set_tensor,
|
|
/* .get_tensor = */ ggml_backend_cuda_buffer_get_tensor,
|
|
/* .cpy_tensor_from = */ NULL,
|
|
/* .cpy_tensor_to = */ NULL,
|
|
};
|
|
|
|
// cuda buffer type
|
|
|
|
static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
|
int device = (int) (intptr_t) buft->context;
|
|
|
|
ggml_cuda_set_device(device);
|
|
|
|
size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0
|
|
|
|
void * dev_ptr;
|
|
CUDA_CHECK(cudaMalloc(&dev_ptr, size));
|
|
|
|
ggml_backend_buffer_context_cuda * ctx = new ggml_backend_buffer_context_cuda(device, dev_ptr);
|
|
|
|
return ggml_backend_buffer_init(buft, cuda_backend_buffer_interface, ctx, size);
|
|
}
|
|
|
|
static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
|
|
return 128;
|
|
|
|
UNUSED(buft);
|
|
}
|
|
|
|
static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, ggml_tensor * tensor) {
|
|
int64_t row_low = 0;
|
|
int64_t row_high = ggml_nrows(tensor);
|
|
int64_t nrows_split = row_high - row_low;
|
|
|
|
size_t size = ggml_nbytes_split(tensor, nrows_split);
|
|
|
|
int64_t ne0 = tensor->ne[0];
|
|
|
|
if (ggml_is_quantized(tensor->type)) {
|
|
if (ne0 % MATRIX_ROW_PADDING != 0) {
|
|
size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
|
|
}
|
|
}
|
|
|
|
return size;
|
|
|
|
UNUSED(buft);
|
|
}
|
|
|
|
static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) {
|
|
return ggml_backend_is_cuda(backend);
|
|
|
|
UNUSED(buft);
|
|
}
|
|
|
|
static ggml_backend_buffer_type_i cuda_backend_buffer_type_interface = {
|
|
/* .alloc_buffer = */ ggml_backend_cuda_buffer_type_alloc_buffer,
|
|
/* .get_alignment = */ ggml_backend_cuda_buffer_type_get_alignment,
|
|
/* .get_alloc_size = */ ggml_backend_cuda_buffer_type_get_alloc_size,
|
|
/* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend,
|
|
};
|
|
|
|
ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) {
|
|
static struct ggml_backend_buffer_type ggml_backend_buffer_type_cuda[GGML_CUDA_MAX_DEVICES];
|
|
static bool ggml_backend_buffer_type_cuda_initialized = false;
|
|
if (!ggml_backend_buffer_type_cuda_initialized) {
|
|
for (int i = 0; i < GGML_CUDA_MAX_DEVICES; i++) {
|
|
ggml_backend_buffer_type_cuda[i] = {
|
|
/* .iface = */ cuda_backend_buffer_type_interface,
|
|
/* .context = */ (ggml_backend_buffer_type_context_t) (intptr_t) i,
|
|
};
|
|
}
|
|
ggml_backend_buffer_type_cuda_initialized = true;
|
|
}
|
|
|
|
return &ggml_backend_buffer_type_cuda[device];
|
|
}
|
|
|
|
// host buffer type
|
|
|
|
static void ggml_backend_cuda_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
|
|
ggml_backend_buffer_context_cuda * ctx = (ggml_backend_buffer_context_cuda *)buffer->context;
|
|
CUDA_CHECK(cudaFreeHost(ctx->dev_ptr));
|
|
delete ctx;
|
|
}
|
|
|
|
static ggml_backend_buffer_t ggml_backend_cuda_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
|
void * ptr;
|
|
CUDA_CHECK(cudaMallocHost(&ptr, size));
|
|
|
|
// FIXME: this is a hack to avoid having to implement a new buffer type
|
|
ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size);
|
|
buffer->buft = buft;
|
|
buffer->iface.free_buffer = ggml_backend_cuda_host_buffer_free_buffer;
|
|
|
|
return buffer;
|
|
|
|
UNUSED(buft);
|
|
}
|
|
|
|
struct ggml_backend_buffer_type_i cuda_backend_host_buffer_type_interface = {
|
|
/* .alloc_buffer = */ ggml_backend_cuda_host_buffer_type_alloc_buffer,
|
|
/* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment,
|
|
/* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size,
|
|
/* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend,
|
|
};
|
|
|
|
ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() {
|
|
static struct ggml_backend_buffer_type ggml_backend_buffer_type_cuda_host = {
|
|
/* .iface = */ cuda_backend_host_buffer_type_interface,
|
|
/* .context = */ nullptr,
|
|
};
|
|
|
|
return &ggml_backend_buffer_type_cuda_host;
|
|
}
|
|
|
|
// backend
|
|
|
|
struct ggml_backend_context_cuda {
|
|
int device;
|
|
};
|
|
|
|
static const char * ggml_backend_cuda_name(ggml_backend_t backend) {
|
|
return GGML_CUDA_NAME;
|
|
|
|
UNUSED(backend);
|
|
}
|
|
|
|
static void ggml_backend_cuda_free(ggml_backend_t backend) {
|
|
ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
|
|
|
|
delete cuda_ctx;
|
|
delete backend;
|
|
}
|
|
|
|
static ggml_backend_buffer_type_t ggml_backend_cuda_get_default_buffer_type(ggml_backend_t backend) {
|
|
ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
|
|
|
|
return ggml_backend_cuda_buffer_type(cuda_ctx->device);
|
|
}
|
|
|
|
static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
|
ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
|
|
|
|
GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
|
|
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
|
|
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
|
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
|
|
|
|
CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, g_cudaStreams[cuda_ctx->device][0]));
|
|
}
|
|
|
|
static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
|
|
ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
|
|
|
|
GGML_ASSERT(tensor->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
|
|
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
|
|
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
|
|
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
|
|
|
|
CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, g_cudaStreams[cuda_ctx->device][0]));
|
|
}
|
|
|
|
static void ggml_backend_cuda_synchronize(ggml_backend_t backend) {
|
|
ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
|
|
|
|
CUDA_CHECK(cudaStreamSynchronize(g_cudaStreams[cuda_ctx->device][0]));
|
|
|
|
UNUSED(backend);
|
|
}
|
|
|
|
static ggml_backend_graph_plan_t ggml_backend_cuda_graph_plan_create(ggml_backend_t backend, ggml_cgraph * cgraph) {
|
|
GGML_ASSERT(!"not implemented");
|
|
|
|
return nullptr;
|
|
|
|
UNUSED(backend);
|
|
UNUSED(cgraph);
|
|
}
|
|
|
|
static void ggml_backend_cuda_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
|
GGML_ASSERT(!"not implemented");
|
|
|
|
UNUSED(backend);
|
|
UNUSED(plan);
|
|
}
|
|
|
|
static void ggml_backend_cuda_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
|
|
GGML_ASSERT(!"not implemented");
|
|
|
|
UNUSED(backend);
|
|
UNUSED(plan);
|
|
}
|
|
|
|
static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
|
|
ggml_backend_context_cuda * cuda_ctx = (ggml_backend_context_cuda *)backend->context;
|
|
|
|
ggml_cuda_set_main_device(cuda_ctx->device);
|
|
|
|
ggml_compute_params params = {};
|
|
params.type = GGML_TASK_COMPUTE;
|
|
params.ith = 0;
|
|
for (int i = 0; i < cgraph->n_nodes; i++) {
|
|
ggml_tensor * node = cgraph->nodes[i];
|
|
|
|
if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE)
|
|
continue;
|
|
|
|
assert(node->backend == GGML_BACKEND_GPU);
|
|
assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
|
|
assert(node->extra != nullptr);
|
|
|
|
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
|
if (node->src[j] != nullptr) {
|
|
assert(node->src[j]->backend == GGML_BACKEND_GPU);
|
|
assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
|
|
assert(node->src[j]->extra != nullptr);
|
|
}
|
|
}
|
|
|
|
bool ok = ggml_cuda_compute_forward(¶ms, node);
|
|
if (!ok) {
|
|
fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
|
|
}
|
|
GGML_ASSERT(ok);
|
|
|
|
#if 0
|
|
if (node->type == GGML_TYPE_F32) {
|
|
cudaDeviceSynchronize();
|
|
std::vector<float> tmp(ggml_nelements(node), 0.0f);
|
|
cudaMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), cudaMemcpyDeviceToHost);
|
|
printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op),
|
|
ggml_type_name(node->src[0]->type),
|
|
node->src[1] ? ggml_type_name(node->src[1]->type) : "none",
|
|
node->src[0]->name,
|
|
node->src[1] ? node->src[1]->name : "none");
|
|
double sum = 0.0;
|
|
double sq_sum = 0.0;
|
|
for (int i = 0; i < ggml_nelements(node); i++) {
|
|
printf("%f ", tmp[i]);
|
|
sum += tmp[i];
|
|
sq_sum += tmp[i]*tmp[i];
|
|
}
|
|
printf("\n");
|
|
printf("sum: %f, ", sum);
|
|
printf("sq_sum: %f\n", sq_sum);
|
|
}
|
|
#endif
|
|
}
|
|
|
|
UNUSED(backend);
|
|
}
|
|
|
|
static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
|
|
switch (op->op) {
|
|
case GGML_OP_UNARY:
|
|
switch (ggml_get_unary_op(op)) {
|
|
case GGML_UNARY_OP_GELU:
|
|
case GGML_UNARY_OP_SILU:
|
|
case GGML_UNARY_OP_RELU:
|
|
case GGML_UNARY_OP_GELU_QUICK:
|
|
case GGML_UNARY_OP_TANH:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
break;
|
|
case GGML_OP_MUL_MAT:
|
|
case GGML_OP_MUL_MAT_ID:
|
|
{
|
|
struct ggml_tensor * a;
|
|
struct ggml_tensor * b;
|
|
if (op->op == GGML_OP_MUL_MAT) {
|
|
a = op->src[0];
|
|
b = op->src[1];
|
|
} else {
|
|
a = op->src[2];
|
|
b = op->src[1];
|
|
}
|
|
if (a->ne[3] != b->ne[3]) {
|
|
return false;
|
|
}
|
|
return true;
|
|
} break;
|
|
case GGML_OP_GET_ROWS:
|
|
{
|
|
switch (op->src[0]->type) {
|
|
case GGML_TYPE_F16:
|
|
case GGML_TYPE_F32:
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_Q5_0:
|
|
case GGML_TYPE_Q5_1:
|
|
case GGML_TYPE_Q8_0:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
} break;
|
|
case GGML_OP_CPY:
|
|
{
|
|
ggml_type src0_type = op->src[0]->type;
|
|
ggml_type src1_type = op->src[1]->type;
|
|
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) {
|
|
return true;
|
|
}
|
|
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) {
|
|
return true;
|
|
}
|
|
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) {
|
|
return true;
|
|
}
|
|
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) {
|
|
return true;
|
|
}
|
|
if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) {
|
|
return true;
|
|
}
|
|
if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) {
|
|
return true;
|
|
}
|
|
return false;
|
|
} break;
|
|
case GGML_OP_NONE:
|
|
case GGML_OP_RESHAPE:
|
|
case GGML_OP_VIEW:
|
|
case GGML_OP_PERMUTE:
|
|
case GGML_OP_TRANSPOSE:
|
|
case GGML_OP_NORM:
|
|
case GGML_OP_REPEAT:
|
|
case GGML_OP_DUP:
|
|
case GGML_OP_ADD:
|
|
case GGML_OP_MUL:
|
|
case GGML_OP_DIV:
|
|
case GGML_OP_RMS_NORM:
|
|
case GGML_OP_SCALE:
|
|
case GGML_OP_SQR:
|
|
case GGML_OP_CLAMP:
|
|
case GGML_OP_CONT:
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
case GGML_OP_SOFT_MAX:
|
|
case GGML_OP_ROPE:
|
|
case GGML_OP_ALIBI:
|
|
case GGML_OP_IM2COL:
|
|
case GGML_OP_SUM_ROWS:
|
|
case GGML_OP_ARGSORT:
|
|
case GGML_OP_ACC:
|
|
case GGML_OP_CONCAT:
|
|
case GGML_OP_GROUP_NORM:
|
|
case GGML_OP_UPSCALE:
|
|
case GGML_OP_PAD:
|
|
case GGML_OP_LEAKY_RELU:
|
|
return true;
|
|
default:
|
|
return false;
|
|
}
|
|
|
|
UNUSED(backend);
|
|
}
|
|
|
|
static ggml_backend_i cuda_backend_i = {
|
|
/* .get_name = */ ggml_backend_cuda_name,
|
|
/* .free = */ ggml_backend_cuda_free,
|
|
/* .get_default_buffer_type = */ ggml_backend_cuda_get_default_buffer_type,
|
|
/* .set_tensor_async = */ ggml_backend_cuda_set_tensor_async,
|
|
/* .get_tensor_async = */ ggml_backend_cuda_get_tensor_async,
|
|
/* .cpy_tensor_from_async = */ NULL,
|
|
/* .cpy_tensor_to_async = */ NULL,
|
|
/* .synchronize = */ ggml_backend_cuda_synchronize,
|
|
/* .graph_plan_create = */ ggml_backend_cuda_graph_plan_create,
|
|
/* .graph_plan_free = */ ggml_backend_cuda_graph_plan_free,
|
|
/* .graph_plan_compute = */ ggml_backend_cuda_graph_plan_compute,
|
|
/* .graph_compute = */ ggml_backend_cuda_graph_compute,
|
|
/* .supports_op = */ ggml_backend_cuda_supports_op,
|
|
};
|
|
|
|
ggml_backend_t ggml_backend_cuda_init(int device) {
|
|
ggml_init_cublas(); // TODO: remove from ggml.c
|
|
|
|
if (device < 0 || device >= ggml_cuda_get_device_count()) {
|
|
fprintf(stderr, "%s: error: invalid device %d\n", __func__, device);
|
|
return nullptr;
|
|
}
|
|
|
|
// not strictly necessary, but it may reduce the overhead of the first graph_compute
|
|
ggml_cuda_set_main_device(device);
|
|
|
|
ggml_backend_context_cuda * ctx = new ggml_backend_context_cuda {
|
|
/* .device = */ device
|
|
};
|
|
|
|
ggml_backend_t cuda_backend = new ggml_backend {
|
|
/* .interface = */ cuda_backend_i,
|
|
/* .context = */ ctx
|
|
};
|
|
|
|
return cuda_backend;
|
|
}
|
|
|
|
bool ggml_backend_is_cuda(ggml_backend_t backend) {
|
|
return backend->iface.get_name == ggml_backend_cuda_name;
|
|
}
|
|
|
|
static ggml_backend_t ggml_backend_reg_cuda_init(const char * params, void * user_data) {
|
|
ggml_backend_t cuda_backend = ggml_backend_cuda_init((int) (intptr_t) user_data);
|
|
return cuda_backend;
|
|
|
|
UNUSED(params);
|
|
}
|
|
|
|
extern "C" int ggml_backend_cuda_reg_devices();
|
|
|
|
int ggml_backend_cuda_reg_devices() {
|
|
int device_count = ggml_cuda_get_device_count();
|
|
//int device_count = 1; // DEBUG: some tools require delaying CUDA initialization
|
|
for (int i = 0; i < device_count; i++) {
|
|
char name[128];
|
|
snprintf(name, sizeof(name), "%s%d", GGML_CUDA_NAME, i);
|
|
ggml_backend_register(name, ggml_backend_reg_cuda_init, ggml_backend_cuda_buffer_type(i), (void *) (intptr_t) i);
|
|
}
|
|
return device_count;
|
|
}
|