mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-29 08:41:22 +00:00
129d844c87
* Fix Q4_K and Q5_K for QK_K = 64 * Very slightly better Q5_K bit fiddling --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
4136 lines
154 KiB
Plaintext
4136 lines
154 KiB
Plaintext
#include <cstddef>
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#include <cstdint>
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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 <atomic>
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#include <assert.h>
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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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#include "ggml-cuda.h"
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#include "ggml.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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#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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fprintf(stderr, "CUDA error %d at %s:%d: %s\n", err_, __FILE__, __LINE__, \
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cudaGetErrorString(err_)); \
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exit(1); \
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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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fprintf(stderr, "\ncuBLAS error %d at %s:%d: %s\n", \
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err_, __FILE__, __LINE__, cublasGetStatusString(err_)); \
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exit(1); \
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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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fprintf(stderr, "\ncuBLAS error %d at %s:%d\n", err_, __FILE__, __LINE__); \
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exit(1); \
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} \
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} while (0)
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#endif // CUDART_VERSION >= 11
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#ifdef GGML_CUDA_DMMV_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_DMMV_F16
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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 (*to_fp32_cuda_t)(const void * __restrict__ x, float * __restrict__ y, int k, cudaStream_t stream);
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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_t)(
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const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i, float * src0_ddf_i,
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float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
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cudaStream_t & cudaStream_main);
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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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half d; // delta
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half m; // 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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half d; // delta
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half m; // 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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half d; // delta
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half s; // unquantized 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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//================================= 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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half d; // super-block scale for quantized scales
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half dmin; // super-block scale for quantized 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 {
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uint8_t hmask[QK_K/8]; // quants - high bit
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uint8_t qs[QK_K/4]; // quants - low 2 bits
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#ifdef GGML_QKK_64
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uint8_t scales[2]; // scales, quantized with 8 bits
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#else
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uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits
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#endif
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half d; // super-block scale
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} block_q3_K;
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//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");
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#define QR4_K 2
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#define QI4_K (QK_K / (4*QR4_K))
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#ifdef GGML_QKK_64
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typedef struct {
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half d[2]; // super-block scales/mins
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uint8_t scales[2]; // 4-bit block scales/mins
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uint8_t qs[QK_K/2]; // 4--bit quants
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} block_q4_K;
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static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
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#else
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typedef struct {
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half d; // super-block scale for quantized scales
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half dmin; // super-block scale for quantized mins
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uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
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uint8_t qs[QK_K/2]; // 4--bit quants
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} block_q4_K;
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static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding");
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#endif
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#define QR5_K 2
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#define QI5_K (QK_K / (4*QR5_K))
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#ifdef GGML_QKK_64
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typedef struct {
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half d; // super-block scale
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int8_t scales[QK_K/16]; // block scales
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uint8_t qh[QK_K/8]; // quants, high bit
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uint8_t qs[QK_K/2]; // quants, low 4 bits
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} block_q5_K;
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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");
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#else
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typedef struct {
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half d; // super-block scale for quantized scales
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half dmin; // super-block scale for quantized mins
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uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
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uint8_t qh[QK_K/8]; // quants, high bit
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uint8_t qs[QK_K/2]; // quants, low 4 bits
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} block_q5_K;
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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");
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#endif
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#define QR6_K 2
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#define QI6_K (QK_K / (4*QR6_K))
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typedef struct {
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uint8_t ql[QK_K/2]; // quants, lower 4 bits
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uint8_t qh[QK_K/4]; // quants, upper 2 bits
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int8_t scales[QK_K/16]; // scales
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half d; // delta
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} block_q6_K;
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static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding");
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#define WARP_SIZE 32
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#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
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#define CUDA_ADD_BLOCK_SIZE 256
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#define CUDA_MUL_BLOCK_SIZE 256
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#define CUDA_GELU_BLOCK_SIZE 256
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#define CUDA_SILU_BLOCK_SIZE 256
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#define CUDA_CPY_BLOCK_SIZE 32
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#define CUDA_SCALE_BLOCK_SIZE 256
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#define CUDA_ROPE_BLOCK_SIZE 256
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#define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
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#define CUDA_QUANTIZE_BLOCK_SIZE 256
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#define CUDA_DEQUANTIZE_BLOCK_SIZE 256
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// dmmv = dequantize_mul_mat_vec
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#ifndef GGML_CUDA_DMMV_X
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#define GGML_CUDA_DMMV_X 32
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#endif
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#ifndef GGML_CUDA_MMV_Y
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#define GGML_CUDA_MMV_Y 1
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#endif
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#ifndef K_QUANTS_PER_ITERATION
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#define K_QUANTS_PER_ITERATION 2
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#else
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static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
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#endif
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struct ggml_tensor_extra_gpu {
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void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
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cudaEvent_t events[GGML_CUDA_MAX_DEVICES]; // events for synchronizing multiple GPUs
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};
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static __global__ void add_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
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const int i = blockDim.x*blockIdx.x + threadIdx.x;
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if (i >= kx) {
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return;
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}
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dst[i] = x[i] + y[i%ky];
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}
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static __global__ void add_f16_f32_f16(const half * x, const float * y, half * dst, const int k) {
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const int i = blockDim.x*blockIdx.x + threadIdx.x;
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if (i >= k) {
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return;
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}
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dst[i] = __hadd(x[i], __float2half(y[i]));
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}
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static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
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const int i = blockDim.x*blockIdx.x + threadIdx.x;
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if (i >= kx) {
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return;
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}
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dst[i] = x[i] * y[i%ky];
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}
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static __global__ void gelu_f32(const float * x, float * dst, const int k) {
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const float GELU_COEF_A = 0.044715f;
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const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
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const int i = blockDim.x*blockIdx.x + threadIdx.x;
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if (i >= k) {
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return;
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}
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float xi = x[i];
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dst[i] = 0.5f*xi*(1.0f + tanhf(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)));
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}
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static __global__ void silu_f32(const float * x, float * dst, const int k) {
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const int i = blockDim.x*blockIdx.x + threadIdx.x;
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if (i >= k) {
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return;
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}
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dst[i] = x[i] / (1.0f + expf(-x[i]));
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}
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static __global__ void norm_f32(const float * x, float * dst, const int ncols) {
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const int row = blockIdx.x*blockDim.y + threadIdx.y;
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const int tid = threadIdx.x;
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const float eps = 1e-5f;
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float mean = 0.0f;
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float var = 0.0f;
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for (int col = tid; col < ncols; col += WARP_SIZE) {
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const float xi = x[row*ncols + col];
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mean += xi;
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var += xi * xi;
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}
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// sum up partial sums
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#pragma unroll
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for (int mask = 16; mask > 0; mask >>= 1) {
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mean += __shfl_xor_sync(0xffffffff, mean, mask, 32);
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var += __shfl_xor_sync(0xffffffff, var, mask, 32);
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}
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mean /= ncols;
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var = var / ncols - mean * mean;
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const float inv_var = rsqrtf(var + eps);
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for (int col = tid; col < ncols; col += WARP_SIZE) {
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dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_var;
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}
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}
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static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) {
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const int row = blockIdx.x*blockDim.y + threadIdx.y;
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const int tid = threadIdx.x;
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float tmp = 0.0f; // partial sum for thread in warp
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for (int col = tid; col < ncols; col += WARP_SIZE) {
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const float xi = x[row*ncols + col];
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tmp += xi * xi;
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}
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// sum up partial sums
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#pragma unroll
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for (int mask = 16; mask > 0; mask >>= 1) {
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tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
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}
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const float mean = tmp / ncols;
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const float scale = rsqrtf(mean + eps);
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for (int col = tid; col < ncols; col += WARP_SIZE) {
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dst[row*ncols + col] = scale * x[row*ncols + col];
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}
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}
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static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
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const block_q4_0 * x = (const block_q4_0 *) vx;
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const dfloat d = x[ib].d;
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const int vui = x[ib].qs[iqs];
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v.x = vui & 0xF;
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v.y = vui >> 4;
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#ifdef GGML_CUDA_DMMV_F16
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v = __hsub2(v, {8.0f, 8.0f});
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v = __hmul2(v, {d, d});
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#else
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v.x = (v.x - 8.0f) * d;
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v.y = (v.y - 8.0f) * d;
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#endif // GGML_CUDA_DMMV_F16
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}
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static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
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const block_q4_1 * x = (const block_q4_1 *) vx;
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const dfloat d = x[ib].d;
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const dfloat m = x[ib].m;
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const int vui = x[ib].qs[iqs];
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v.x = vui & 0xF;
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v.y = vui >> 4;
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#ifdef GGML_CUDA_DMMV_F16
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v = __hmul2(v, {d, d});
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v = __hadd2(v, {m, m});
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#else
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v.x = (v.x * d) + m;
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v.y = (v.y * d) + m;
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#endif // GGML_CUDA_DMMV_F16
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}
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static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
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const block_q5_0 * x = (const block_q5_0 *) vx;
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const dfloat d = x[ib].d;
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uint32_t qh;
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memcpy(&qh, x[ib].qh, sizeof(qh));
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const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10;
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const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10;
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v.x = ((x[ib].qs[iqs] & 0xf) | xh_0);
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v.y = ((x[ib].qs[iqs] >> 4) | xh_1);
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#ifdef GGML_CUDA_DMMV_F16
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v = __hsub2(v, {16.0f, 16.0f});
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v = __hmul2(v, {d, d});
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#else
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|
v.x = (v.x - 16.0f) * d;
|
|
v.y = (v.y - 16.0f) * d;
|
|
#endif // GGML_CUDA_DMMV_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 = x[ib].d;
|
|
const dfloat m = x[ib].m;
|
|
|
|
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_DMMV_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_DMMV_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_DMMV_F16
|
|
v = __hmul2(v, {d, d});
|
|
#else
|
|
v.x *= d;
|
|
v.y *= d;
|
|
#endif // GGML_CUDA_DMMV_F16
|
|
}
|
|
|
|
//================================== k-quants
|
|
|
|
static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, float * __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];
|
|
float * y = yy + i*QK_K + 128*n;
|
|
|
|
float dall = x[i].d;
|
|
float dmin = x[i].dmin;
|
|
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);
|
|
float * y = yy + i*QK_K + 16*is + il;
|
|
float dall = x[i].d;
|
|
float dmin = x[i].dmin;
|
|
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
|
|
|
|
}
|
|
|
|
static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, float * __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);
|
|
|
|
float * 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
|
|
|
|
float * 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
|
|
|
|
static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, float * __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;
|
|
|
|
float * y = yy + i*QK_K + 64*il + n*ir;
|
|
|
|
const float dall = x[i].d;
|
|
const float dmin = x[i].dmin;
|
|
|
|
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;
|
|
float * y = yy + i*QK_K;
|
|
const float d = (float)x[i].d[0];
|
|
const float m = (float)x[i].d[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
|
|
}
|
|
|
|
static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, float * __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
|
|
|
|
float * y = yy + i*QK_K + 64*il + 2*ir;
|
|
|
|
const float dall = x[i].d;
|
|
const float dmin = x[i].dmin;
|
|
|
|
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;
|
|
float * 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
|
|
}
|
|
|
|
static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, float * __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;
|
|
|
|
float * 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
|
|
|
|
float * 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.y*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 = x[i].d;
|
|
const float dmin = x[i].dmin;
|
|
|
|
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 half2 * dh = (const half2 *)&x[i].d;
|
|
|
|
const float2 dall = __half22float2(dh[0]);
|
|
|
|
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.y*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.y*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 = x[i].d;
|
|
const float dmin = x[i].dmin;
|
|
|
|
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].d[0];
|
|
const float m = (float)x[i].d[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 = x[i].d;
|
|
const float dmin = x[i].dmin;
|
|
|
|
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) {
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static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
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const int row = blockIdx.y*blockDim.y + threadIdx.y;
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if (row > nrows) return;
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const int num_blocks_per_row = ncols / QK_K;
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const int ib0 = row*num_blocks_per_row;
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const block_q6_K * x = (const block_q6_K *)vx + ib0;
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#if QK_K == 256
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const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
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const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1
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const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8
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const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
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const int in = tid - step*im; // 0...15 or 0...7
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#if K_QUANTS_PER_ITERATION == 1
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const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15
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const int is = 0;
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#else
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const int l0 = 4 * in; // 0, 4, 8, ..., 28
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const int is = in / 4;
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#endif
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const int ql_offset = 64*im + l0;
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const int qh_offset = 32*im + l0;
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const int s_offset = 8*im + is;
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const int y_offset = 128*im + l0;
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float tmp = 0; // partial sum for thread in warp
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for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
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const float * y = yy + i * QK_K + y_offset;
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const uint8_t * ql = x[i].ql + ql_offset;
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const uint8_t * qh = x[i].qh + qh_offset;
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const int8_t * s = x[i].scales + s_offset;
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const float d = x[i].d;
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#if K_QUANTS_PER_ITERATION == 1
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float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
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+ y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
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+ y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
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+ y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
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+ y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
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+ y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
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+ y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
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+y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
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tmp += sum;
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#else
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float sum = 0;
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for (int l = 0; l < 4; ++l) {
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sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
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+ y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
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+ y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
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+ y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
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}
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tmp += sum;
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#endif
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|
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}
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|
#else
|
|
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const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7
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const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3
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|
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const int step = tid * K_QUANTS_PER_ITERATION;
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|
|
float tmp = 0; // partial sum for thread in warp
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|
|
|
for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
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|
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const float * y = yy + i * QK_K + step;
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const uint8_t * ql = x[i].ql + step;
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const uint8_t * qh = x[i].qh + step;
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const int8_t * s = x[i].scales;
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|
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const float d = x[i+0].d;
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|
|
|
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)
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|
+ 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;
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|
|
// 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 ndata, const int k) {
|
|
const int i = blockDim.x*blockIdx.x + threadIdx.x;
|
|
|
|
if (i >= k) {
|
|
return;
|
|
}
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|
|
|
block_q8_1 * y = (block_q8_1 *) vy;
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|
|
|
const int ib = i / QK8_1; // block index
|
|
const int iqs = i % QK8_1; // quant index
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|
|
|
const float xi = i < ndata ? x[i] : 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;
|
|
}
|
|
|
|
y[ib].d = d;
|
|
y[ib].s = sum;
|
|
}
|
|
|
|
template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
|
|
static __global__ void dequantize_block(const void * __restrict__ vx, float * __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);
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|
|
|
y[iybs + iqs + 0] = v.x;
|
|
y[iybs + iqs + y_offset] = v.y;
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq;
|
|
|
|
int vi;
|
|
memcpy(&vi, &bq4_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
|
|
const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
|
|
const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_0)]);
|
|
|
|
const float d = __half2float(bq4_0->d) * __half2float(bq8_1->d);
|
|
|
|
// subtract 8 from each quantized value
|
|
const int vi0 = __vsub4((vi >> 0) & 0x0F0F0F0F, 0x08080808);
|
|
const int vi1 = __vsub4((vi >> 4) & 0x0F0F0F0F, 0x08080808);
|
|
|
|
// SIMD dot product of quantized values
|
|
int sumi = __dp4a(vi0, ui0, 0);
|
|
sumi = __dp4a(vi1, ui1, sumi);
|
|
|
|
return sumi*d;
|
|
#else
|
|
return 0.0f; // only to satisfy the compiler
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq;
|
|
|
|
const int vi = *((int *) &bq4_1->qs[sizeof(int) * (iqs + 0)]);
|
|
const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
|
|
const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_1)]);
|
|
|
|
const float d = __half2float(bq4_1->d) * __half2float(bq8_1->d);
|
|
const float m = bq4_1->m;
|
|
const float s = bq8_1->s;
|
|
|
|
const int vi0 = (vi >> 0) & 0x0F0F0F0F;
|
|
const int vi1 = (vi >> 4) & 0x0F0F0F0F;
|
|
|
|
// SIMD dot product of quantized values
|
|
int sumi = __dp4a(vi0, ui0, 0);
|
|
sumi = __dp4a(vi1, ui1, sumi);
|
|
|
|
return sumi*d + m*s / QI4_1; // scale sum by QI4_1 because there are QI4_1 threads working on this block
|
|
#else
|
|
return 0.0f; // only to satisfy the compiler
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq;
|
|
|
|
int qs;
|
|
memcpy(&qs, &bq5_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
|
|
const int qh0 = bq5_0->qh[iqs/2 + 0] >> 4*(iqs%2);
|
|
const int qh1 = bq5_0->qh[iqs/2 + 2] >> 4*(iqs%2);
|
|
const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
|
|
const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_0)]);
|
|
|
|
const float d = __half2float(bq5_0->d) * __half2float(bq8_1->d);
|
|
|
|
int vi0 = (qs >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
|
|
vi0 |= (qh0 << 4) & 0x00000010; // 1 -> 5
|
|
vi0 |= (qh0 << 11) & 0x00001000; // 2 -> 13
|
|
vi0 |= (qh0 << 18) & 0x00100000; // 3 -> 21
|
|
vi0 |= (qh0 << 25) & 0x10000000; // 4 -> 29
|
|
vi0 = __vsub4(vi0, 0x10101010); // subtract 16 from quantized values
|
|
int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
|
|
|
|
int vi1 = (qs >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
|
|
vi1 |= (qh1 << 4) & 0x00000010; // 1 -> 5
|
|
vi1 |= (qh1 << 11) & 0x00001000; // 2 -> 13
|
|
vi1 |= (qh1 << 18) & 0x00100000; // 3 -> 21
|
|
vi1 |= (qh1 << 25) & 0x10000000; // 4 -> 29
|
|
vi1 = __vsub4(vi1, 0x10101010); // subtract 16 from quantized values
|
|
sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
|
|
|
|
return sumi*d;
|
|
#else
|
|
return 0.0f; // only to satisfy the compiler
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq;
|
|
|
|
const int qs = *((int *) &bq5_1->qs[sizeof(int) * (iqs + 0)]);
|
|
const int qh0 = bq5_1->qh[iqs/2 + 0] >> 4*(iqs%2);
|
|
const int qh1 = bq5_1->qh[iqs/2 + 2] >> 4*(iqs%2);
|
|
const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
|
|
const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_1)]);
|
|
|
|
const float d = __half2float(bq5_1->d) * __half2float(bq8_1->d);
|
|
const float m = bq5_1->m;
|
|
const float s = bq8_1->s;
|
|
|
|
int vi0 = (qs >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits
|
|
vi0 |= (qh0 << 4) & 0x00000010; // 1 -> 5
|
|
vi0 |= (qh0 << 11) & 0x00001000; // 2 -> 13
|
|
vi0 |= (qh0 << 18) & 0x00100000; // 3 -> 21
|
|
vi0 |= (qh0 << 25) & 0x10000000; // 4 -> 29
|
|
int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values
|
|
|
|
int vi1 = (qs >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits
|
|
vi1 |= (qh1 << 4) & 0x00000010; // 1 -> 5
|
|
vi1 |= (qh1 << 11) & 0x00001000; // 2 -> 13
|
|
vi1 |= (qh1 << 18) & 0x00100000; // 3 -> 21
|
|
vi1 |= (qh1 << 25) & 0x10000000; // 4 -> 29
|
|
sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values
|
|
|
|
return sumi*d + m*s / QI5_1; // scale sum by QI5_1 because there are QI5_1 threads working on this block
|
|
#else
|
|
return 0.0f; // only to satisfy the compiler
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq;
|
|
|
|
int vi;
|
|
memcpy(&vi, &bq8_0->qs[sizeof(int) * (iqs + 0)], sizeof(int));
|
|
const int ui = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]);
|
|
|
|
const float d = __half2float(bq8_0->d) * __half2float(bq8_1->d);
|
|
|
|
// SIMD dot product of quantized values
|
|
int sumi = __dp4a(vi, ui, 0);
|
|
|
|
return sumi*d;
|
|
#else
|
|
return 0.0f; // only to satisfy the compiler
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
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);
|
|
|
|
float sumf_d = 0.0f;
|
|
float sumf_m = 0.0f;
|
|
|
|
const float d = bq2_K->d;
|
|
const float dmin = bq2_K->dmin;
|
|
|
|
const int v = *((int *) &bq2_K->qs[sizeof(int) * iqs]);
|
|
|
|
for (int i = 0; i < QR2_K; ++i) {
|
|
const int sc = bq2_K->scales[scale_offset + 2*i];
|
|
|
|
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
|
|
const float d8i = bq8i->d;
|
|
|
|
const int vi = (v >> (2*i)) & 0x03030303;
|
|
const int ui = *((int*) &bq8i->qs[sizeof(int) * (iqs % QI8_1)]);
|
|
|
|
sumf_d += d8i * (__dp4a(vi, ui, 0) * (sc & 0xF)); // SIMD dot product
|
|
sumf_m += d8i * (__dp4a(0x01010101, ui, 0) * (sc >> 4)); // multiply constant q2_K part with sum of q8_1 values
|
|
}
|
|
|
|
return d*sumf_d - dmin*sumf_m;
|
|
#else
|
|
return 0.0f; // only to satisfy the compiler
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
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);
|
|
|
|
float sumf = 0.0f;
|
|
|
|
const float d = bq3_K->d;
|
|
|
|
int vl;
|
|
memcpy(&vl, &bq3_K->qs[sizeof(int) * iqs], sizeof(int));
|
|
|
|
int vh;
|
|
memcpy(&vh, &bq3_K->hmask[sizeof(int) * (iqs % (QI3_K/2))], sizeof(int));
|
|
vh = ~vh; // invert the mask so that a 0/1 results in 4/0 being subtracted
|
|
vh >>= bq8_offset;
|
|
|
|
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 = (bq3_K->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 = ((bq3_K->scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4;
|
|
|
|
const int sc = (sc_low | sc_high) - 32;
|
|
|
|
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
|
|
const int ui = *((int*) &bq8i->qs[sizeof(int) * (iqs % QI8_1)]);
|
|
const float d8i = bq8i->d;
|
|
|
|
const int vil = (vl >> (2*i)) & 0x03030303;
|
|
|
|
const int vih = ((vh >> i) << 2) & 0x04040404;
|
|
|
|
const int vi = __vsubss4(vil, vih);
|
|
|
|
sumf += d8i * (__dp4a(vi, ui, 0) * sc); // SIMD dot product
|
|
}
|
|
|
|
return d*sumf;
|
|
#else
|
|
return 0.0f; // only to satisfy the compiler
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
|
|
|
|
#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;
|
|
|
|
#ifndef GGML_QKK_64
|
|
|
|
// iqs is in 0...15. bq8_offset = 2 * (iqs/4) -> bq8_offset = 0, 2, 4, 6
|
|
const int bq8_offset = QR4_K * (iqs / (QI8_1/2));
|
|
|
|
const float d = bq4_K->d;
|
|
const float dmin = bq4_K->dmin;
|
|
|
|
// 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%4));
|
|
const int v1 = q4[0];
|
|
const int v2 = 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;
|
|
const float d8i = bq8i->d;
|
|
const int * q8 = (const int *)bq8i->qs + (iqs%4);
|
|
const int ui1 = q8[0];
|
|
const int ui2 = q8[4];
|
|
|
|
const int vi1 = (v1 >> (4*i)) & 0x0F0F0F0F;
|
|
const int vi2 = (v2 >> (4*i)) & 0x0F0F0F0F;
|
|
|
|
const int dot1 = __dp4a(vi2, ui2, __dp4a(vi1, ui1, 0)); // SIMD dot product
|
|
const int dot2 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
|
|
|
|
sumf_d += d8i * (dot1 * sc[i]);
|
|
sumf_m += d8i * (dot2 * m[i]); // multiply constant part of q4_K with sum of q8_1 values
|
|
}
|
|
|
|
return d*sumf_d - dmin*sumf_m;
|
|
|
|
#else
|
|
|
|
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->d[0];
|
|
const float dmin = bq4_K->d[1];
|
|
|
|
const float d8_1 = bq8_1[0].d;
|
|
const float d8_2 = bq8_1[1].d;
|
|
|
|
const int ui1 = *((const int *)bq8_1[0].qs + iqs);
|
|
const int ui2 = *((const int *)bq8_1[0].qs + iqs + 4);
|
|
const int ui3 = *((const int *)bq8_1[1].qs + iqs);
|
|
const int ui4 = *((const int *)bq8_1[1].qs + iqs + 4);
|
|
|
|
const int * q4 = (const int *)bq4_K->qs + iqs;
|
|
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;
|
|
|
|
#endif
|
|
|
|
#else
|
|
return 0.0f; // only to satisfy the compiler
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
const block_q5_K * bq5_K = (const block_q5_K *) vbq;
|
|
|
|
#ifndef GGML_QKK_64
|
|
|
|
const int bq8_offset = QR5_K * (iqs / (QI8_1/2));
|
|
const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * (iqs%4));
|
|
const int * qh = (const int *)(bq5_K->qh + 4 * (iqs%4));
|
|
|
|
float sumf_d = 0.0f;
|
|
float sumf_m = 0.0f;
|
|
|
|
const float d = bq5_K->d;
|
|
const float dmin = bq5_K->dmin;
|
|
|
|
const int vl1 = ql[0];
|
|
const int vl2 = ql[4];
|
|
|
|
const int vh1 = qh[0] >> bq8_offset;
|
|
const int vh2 = 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;
|
|
|
|
for (int i = 0; i < QR5_K; ++i) {
|
|
|
|
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
|
|
const float d8i = bq8i->d;
|
|
const int * q8 = (const int *)bq8i->qs + (iqs%4);
|
|
const int ui1 = q8[0];
|
|
const int ui2 = q8[4];
|
|
|
|
const int vil1 = (vl1 >> (4*i)) & 0x0F0F0F0F;
|
|
const int vil2 = (vl2 >> (4*i)) & 0x0F0F0F0F;
|
|
|
|
const int vih1 = ((vh1 >> i) << 4) & 0x10101010;
|
|
const int vih2 = ((vh2 >> i) << 4) & 0x10101010;
|
|
|
|
const int vi1 = vil1 | vih1;
|
|
const int vi2 = vil2 | vih2;
|
|
|
|
const int dot1 = __dp4a(vi2, ui2, __dp4a(vi1, ui1, 0)); // SIMD dot product
|
|
const int dot2 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
|
|
|
|
sumf_d += d8i * (dot1 * sc[i]);
|
|
sumf_m += d8i * (dot2 * m[i]);
|
|
|
|
}
|
|
|
|
return d*sumf_d - dmin*sumf_m;
|
|
|
|
#else
|
|
|
|
const int8_t * s = bq5_K->scales;
|
|
|
|
const float d = bq5_K->d;
|
|
|
|
const float d8_1 = bq8_1[0].d;
|
|
const float d8_2 = bq8_1[1].d;
|
|
|
|
const int ui1 = *((const int *)bq8_1[0].qs + iqs);
|
|
const int ui2 = *((const int *)bq8_1[0].qs + iqs + 4);
|
|
const int ui3 = *((const int *)bq8_1[1].qs + iqs);
|
|
const int ui4 = *((const int *)bq8_1[1].qs + iqs + 4);
|
|
|
|
const int * ql = (const int *)bq5_K->qs + iqs;
|
|
const int vl1 = ql[0];
|
|
const int vl2 = ql[4];
|
|
|
|
const int step = 4 * iqs; // 0, 4, 8, 12
|
|
const int im = step/8; // = 0 for iqs = 0, 1, = 1 for iqs = 2, 3
|
|
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;
|
|
|
|
#endif
|
|
|
|
#else
|
|
return 0.0f; // only to satisfy the compiler
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
|
|
const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int iqs) {
|
|
|
|
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
|
|
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));
|
|
|
|
float sumf = 0.0f;
|
|
|
|
const float d = bq6_K->d;
|
|
|
|
int vl;
|
|
memcpy(&vl, &bq6_K->ql[sizeof(int) * iqs], sizeof(int));
|
|
|
|
int vh;
|
|
memcpy(&vh, &bq6_K->qh[sizeof(int) * ((QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4))], sizeof(int));
|
|
|
|
for (int i = 0; i < QR6_K; ++i) {
|
|
const int sc = bq6_K->scales[scale_offset + 4*i];
|
|
|
|
const block_q8_1 * bq8i = bq8_1 + bq8_offset + 2*i;
|
|
const int ui = *((int*) &bq8i->qs[sizeof(int) * (iqs % (QI8_1))]);
|
|
const float d8i = bq8i->d;
|
|
|
|
const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
|
|
|
|
const int vih = ((vh >> (vh_shift + 4*i)) << 4) & 0x30303030;
|
|
|
|
const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
|
|
|
|
sumf += d8i * (__dp4a(vi, ui, 0) * sc); // SIMD dot product
|
|
}
|
|
|
|
return d*sumf;
|
|
#else
|
|
return 0.0f; // only to satisfy the compiler
|
|
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
|
|
}
|
|
|
|
template <int qk, int qi, typename block_q_t, 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.y*blockDim.y + threadIdx.y;
|
|
|
|
if (row >= nrows) {
|
|
return;
|
|
}
|
|
|
|
const int blocks_per_row = ncols / qk;
|
|
const int blocks_per_warp = 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; // x block index
|
|
|
|
const int iby = (i + threadIdx.x / qi) * qk/QK8_1; // y block index that aligns with ibx
|
|
|
|
const int iqs = threadIdx.x % qi; // 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.y*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_DMMV_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_DMMV_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_DMMV_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_DMMV_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_DMMV_F16
|
|
dst[row] = tmp.x + tmp.y;
|
|
#else
|
|
dst[row] = tmp;
|
|
#endif // GGML_CUDA_DMMV_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 ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x;
|
|
const float xi = __half2float(x[ix]);
|
|
|
|
const int row_y = col_x;
|
|
|
|
const int iy = channel*nrows_y + row_y;
|
|
|
|
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);
|
|
}
|
|
|
|
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);
|
|
}
|
|
|
|
// rope == RoPE == rotary positional embedding
|
|
static __global__ void rope_f32(const float * x, float * dst, const int ncols, const float p, const float theta_scale) {
|
|
const int col = 2*(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 float theta = p*powf(theta_scale, col/2);
|
|
const float sin_theta = sinf(theta);
|
|
const float cos_theta = cosf(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;
|
|
}
|
|
|
|
static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const float p, const float block_p, const float theta_scale) {
|
|
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 float col_theta_scale = powf(theta_scale, col);
|
|
|
|
const float theta = p*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 = block_p*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 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.x*blockIdx.x + threadIdx.x;
|
|
const int row = blockDim.y*blockIdx.y + threadIdx.y;
|
|
|
|
if (col >= ncols) {
|
|
return;
|
|
}
|
|
|
|
const int i = row*ncols + col;
|
|
// dst[i] = col > n_past + row ? -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
|
|
}
|
|
|
|
// the CUDA soft max implementation differs from the CPU implementation
|
|
// instead of doubles floats are used
|
|
// values are also not normalized to the maximum value by subtracting it in the exponential function
|
|
// theoretically these changes could cause problems with rounding error and arithmetic overflow but for LLaMa it seems to be fine
|
|
static __global__ void soft_max_f32(const float * x, float * dst, const int ncols) {
|
|
const int row = blockDim.y*blockIdx.y + threadIdx.y;
|
|
const int block_size = blockDim.x;
|
|
const int tid = threadIdx.x;
|
|
|
|
float tmp = 0.0;
|
|
|
|
for (int block_start = 0; block_start < ncols; block_start += block_size) {
|
|
const int col = block_start + tid;
|
|
|
|
if (col >= ncols) {
|
|
break;
|
|
}
|
|
|
|
const int i = row*ncols + col;
|
|
const float val = expf(x[i]);
|
|
tmp += val;
|
|
dst[i] = val;
|
|
}
|
|
|
|
// sum up partial sums
|
|
#pragma unroll
|
|
for (int mask = 16; mask > 0; mask >>= 1) {
|
|
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
|
|
}
|
|
|
|
for (int block_start = 0; block_start < ncols; block_start += block_size) {
|
|
const int col = block_start + tid;
|
|
|
|
if (col >= ncols) {
|
|
break;
|
|
}
|
|
|
|
const int i = row*ncols + col;
|
|
dst[i] /= 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 void add_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
|
|
const int num_blocks = (kx + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
|
|
add_f32<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
|
|
}
|
|
|
|
static void add_f16_f32_f16_cuda(const half * x, const float * y, half * dst, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE;
|
|
add_f16_f32_f16<<<num_blocks, CUDA_ADD_BLOCK_SIZE, 0, stream>>>(x, y, dst, k);
|
|
}
|
|
|
|
static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
|
|
const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE;
|
|
mul_f32<<<num_blocks, CUDA_MUL_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
|
|
}
|
|
|
|
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 norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
|
|
GGML_ASSERT(ncols % WARP_SIZE == 0);
|
|
const dim3 block_dims(WARP_SIZE, 1, 1);
|
|
norm_f32<<<nrows, block_dims, 0, stream>>>(x, dst, ncols);
|
|
}
|
|
|
|
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);
|
|
const dim3 block_dims(WARP_SIZE, 1, 1);
|
|
rms_norm_f32<<<nrows, block_dims, 0, stream>>>(x, dst, ncols, eps);
|
|
}
|
|
|
|
static void quantize_row_q8_1_cuda(const float * x, void * vy, const int ndata, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
|
|
quantize_q8_1<<<num_blocks, CUDA_QUANTIZE_BLOCK_SIZE, 0, stream>>>(x, vy, ndata, k);
|
|
}
|
|
|
|
static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
|
|
dequantize_block<QK4_0, QR4_0, dequantize_q4_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
|
|
}
|
|
|
|
static void dequantize_row_q4_1_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
|
|
dequantize_block<QK4_1, QR4_1, dequantize_q4_1><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
|
|
}
|
|
|
|
static void dequantize_row_q5_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
|
|
dequantize_block<QK5_0, QR5_0, dequantize_q5_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
|
|
}
|
|
|
|
static void dequantize_row_q5_1_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
|
|
dequantize_block<QK5_1, QR5_1, dequantize_q5_1><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
|
|
}
|
|
|
|
static void dequantize_row_q8_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
|
|
dequantize_block<QK8_0, QR8_0, dequantize_q8_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
|
|
}
|
|
|
|
static void dequantize_row_q2_K_cuda(const void * vx, float * 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
|
|
}
|
|
|
|
static void dequantize_row_q3_K_cuda(const void * vx, float * 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
|
|
}
|
|
|
|
static void dequantize_row_q4_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
|
|
const int nb = k / QK_K;
|
|
dequantize_block_q4_K<<<nb, 32, 0, stream>>>(vx, y);
|
|
}
|
|
|
|
static void dequantize_row_q5_K_cuda(const void * vx, float * 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
|
|
}
|
|
|
|
static void dequantize_row_q6_K_cuda(const void * vx, float * 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 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;
|
|
const dim3 block_nums(1, block_num_y, 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(1, block_num_y, 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(1, block_num_y, 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(1, block_num_y, 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(1, block_num_y, 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(1, block_num_y, 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(1, block_num_y, 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(1, block_num_y, 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(1, block_num_y, 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 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(1, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, 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(1, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, 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(1, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, 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(1, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, 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(1, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, 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(1, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK_K, QI2_K, block_q2_K, 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(1, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK_K, QI3_K, block_q3_K, 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(1, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
// Note: we use QI4_K/2 instead of QI4_K to make the dot product template require 4 groups of quants to be processed per
|
|
// kernel call instead of 2. This results in a better perfmance because the cost of computing the k-quant scales
|
|
// is better amortized.
|
|
mul_mat_vec_q<QK_K, QI4_K/2, block_q4_K, 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(1, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
// Note: we use QI5_K/2 instead of QI5_K to make the dot product template require 4 groups of quants to be processed per
|
|
// kernel call instead of 2. This results in a better perfmance because the cost of computing the k-quant scales
|
|
// is better amortized.
|
|
mul_mat_vec_q<QK_K, QI5_K/2, block_q5_K, 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(1, block_num_y, 1);
|
|
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
|
|
mul_mat_vec_q<QK_K, QI6_K, block_q6_K, vec_dot_q6_K_q8_1>
|
|
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
|
|
}
|
|
|
|
static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
|
|
const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
|
|
dequantize_block<1, 1, convert_f16><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
|
|
}
|
|
|
|
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(1, block_num_y, 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 to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
|
|
switch (type) {
|
|
case GGML_TYPE_Q4_0:
|
|
return dequantize_row_q4_0_cuda;
|
|
case GGML_TYPE_Q4_1:
|
|
return dequantize_row_q4_1_cuda;
|
|
case GGML_TYPE_Q5_0:
|
|
return dequantize_row_q5_0_cuda;
|
|
case GGML_TYPE_Q5_1:
|
|
return dequantize_row_q5_1_cuda;
|
|
case GGML_TYPE_Q8_0:
|
|
return dequantize_row_q8_0_cuda;
|
|
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 convert_fp16_to_fp32_cuda;
|
|
default:
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
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 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 rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p, const float theta_scale, cudaStream_t stream) {
|
|
GGML_ASSERT(nrows % 2 == 0);
|
|
const dim3 block_dims(2*CUDA_ROPE_BLOCK_SIZE, 1, 1);
|
|
const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
|
|
const dim3 block_nums(num_blocks_x, nrows, 1);
|
|
rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p, theta_scale);
|
|
}
|
|
|
|
static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p, const float block_p, const float theta_scale, cudaStream_t stream) {
|
|
GGML_ASSERT(nrows % 4 == 0);
|
|
const dim3 block_dims(4*CUDA_ROPE_BLOCK_SIZE, 1, 1);
|
|
const int num_blocks_x = (ncols + 4*CUDA_ROPE_BLOCK_SIZE - 1) / (4*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, p, block_p, theta_scale);
|
|
}
|
|
|
|
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(CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1, 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(block_num_x, nrows_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, float * dst, const int ncols_x, const int nrows_x, cudaStream_t stream) {
|
|
const dim3 block_dims(WARP_SIZE, 1, 1);
|
|
const dim3 block_nums(1, nrows_x, 1);
|
|
soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x);
|
|
}
|
|
|
|
// 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 void * g_scratch_buffer = nullptr;
|
|
static size_t g_scratch_size = 1024*1024*1024; // 1 GB by default
|
|
static size_t g_scratch_offset = 0;
|
|
|
|
static int g_device_count = -1;
|
|
static int g_main_device = 0;
|
|
#ifndef GGML_CUDA_FORCE_DMMV
|
|
static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
|
|
#endif
|
|
static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
|
|
|
|
static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr};
|
|
|
|
static cudaStream_t g_cudaStreams_main[GGML_CUDA_MAX_DEVICES] = { nullptr };
|
|
|
|
void ggml_init_cublas() {
|
|
static bool initialized = false;
|
|
|
|
if (!initialized) {
|
|
CUDA_CHECK(cudaGetDeviceCount(&g_device_count));
|
|
GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
|
|
int64_t total_vram = 0;
|
|
fprintf(stderr, "%s: found %d CUDA 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;
|
|
|
|
#ifndef GGML_CUDA_FORCE_DMMV
|
|
g_compute_capabilities[id] = 100*prop.major + 10*prop.minor;
|
|
#endif
|
|
}
|
|
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(cudaSetDevice(id));
|
|
|
|
// create main stream
|
|
CUDA_CHECK(cudaStreamCreateWithFlags(&g_cudaStreams_main[id], 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;
|
|
}
|
|
}
|
|
|
|
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) {
|
|
kind = cudaMemcpyDeviceToDevice;
|
|
struct 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;
|
|
}
|
|
}
|
|
|
|
inline void ggml_cuda_op_add(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddq_i != nullptr || src0_ddf_i != nullptr);
|
|
GGML_ASSERT(src1_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
const int64_t ne11 = src1->ne[1];
|
|
|
|
// compute
|
|
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
|
add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne00*i01_diff, ne10*ne11, cudaStream_main);
|
|
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
|
|
add_f16_f32_f16_cuda((half *) src0_ddq_i, src1_ddf_i, (half *) dst_ddf_i, ne00*i01_diff, cudaStream_main);
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_mul(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddf_i != nullptr);
|
|
GGML_ASSERT(src1_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
const int64_t ne11 = src1->ne[1];
|
|
|
|
mul_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne00*i01_diff, ne10*ne11, cudaStream_main);
|
|
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_gelu(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
|
|
// compute
|
|
gelu_f32_cuda(src0_ddf_i, dst_ddf_i, ne00*i01_diff, cudaStream_main);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) src1_ddf_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_silu(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
|
|
// compute
|
|
silu_f32_cuda(src0_ddf_i, dst_ddf_i, ne00*i01_diff, cudaStream_main);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) src1_ddf_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_norm(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
|
|
// compute
|
|
norm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) src1_ddf_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_rms_norm(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
|
|
float eps;
|
|
memcpy(&eps, dst->op_params, sizeof(float));
|
|
|
|
// compute
|
|
rms_norm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, eps, cudaStream_main);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) src1_ddf_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_mul_mat_vec(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddq_i != nullptr);
|
|
GGML_ASSERT(src1_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t nrows = i01_high - i01_low;
|
|
|
|
#ifdef GGML_CUDA_FORCE_DMMV
|
|
const bool use_mul_mat_vec_q = false;
|
|
#else
|
|
int id;
|
|
CUDA_CHECK(cudaGetDevice(&id));
|
|
|
|
bool mul_mat_vec_q_implemented =
|
|
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;
|
|
#if QK_K == 256
|
|
mul_mat_vec_q_implemented = mul_mat_vec_q_implemented ||
|
|
src0->type == GGML_TYPE_Q2_K ||
|
|
src0->type == GGML_TYPE_Q3_K ||
|
|
src0->type == GGML_TYPE_Q4_K ||
|
|
src0->type == GGML_TYPE_Q5_K ||
|
|
src0->type == GGML_TYPE_Q6_K;
|
|
#endif // QK_K == 256
|
|
|
|
const bool use_mul_mat_vec_q = g_compute_capabilities[id] >= MIN_CC_DP4A && mul_mat_vec_q_implemented;
|
|
#endif
|
|
|
|
if (use_mul_mat_vec_q) {
|
|
const int64_t padded_row_size = ne00 % MATRIX_ROW_PADDING == 0 ?
|
|
ne00 : ne00 - ne00 % MATRIX_ROW_PADDING + MATRIX_ROW_PADDING;
|
|
size_t as;
|
|
void * src1_q8_1 = ggml_cuda_pool_malloc(padded_row_size*sizeof(block_q8_1)/QK8_1, &as);
|
|
quantize_row_q8_1_cuda(src1_ddf_i, src1_q8_1, ne00, padded_row_size, cudaStream_main);
|
|
|
|
switch (src0->type) {
|
|
case GGML_TYPE_Q4_0:
|
|
mul_mat_vec_q4_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q4_1:
|
|
mul_mat_vec_q4_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q5_0:
|
|
mul_mat_vec_q5_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q5_1:
|
|
mul_mat_vec_q5_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q8_0:
|
|
mul_mat_vec_q8_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q2_K:
|
|
mul_mat_vec_q2_K_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q3_K:
|
|
mul_mat_vec_q3_K_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q4_K:
|
|
mul_mat_vec_q4_K_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q5_K:
|
|
mul_mat_vec_q5_K_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q6_K:
|
|
mul_mat_vec_q6_K_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
break;
|
|
}
|
|
|
|
ggml_cuda_pool_free(src1_q8_1, as);
|
|
} else {
|
|
// on some GPUs it is faster to convert src1 to half and to use half precision intrinsics
|
|
#ifdef GGML_CUDA_DMMV_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((char *) src1_ddf_i, (char *) src1_dfloat, ne00,
|
|
ne00, 1, sizeof(float), 0, 0,
|
|
ne00, 1, sizeof(half), 0, 0, cudaStream_main);
|
|
}
|
|
#else
|
|
dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion
|
|
#endif // GGML_CUDA_DMMV_F16
|
|
|
|
switch (src0->type) {
|
|
case GGML_TYPE_Q4_0:
|
|
dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q4_1:
|
|
dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q5_0:
|
|
dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q5_1:
|
|
dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q8_0:
|
|
dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q2_K:
|
|
dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q3_K:
|
|
dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q4_K:
|
|
dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q5_K:
|
|
dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_Q6_K:
|
|
dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
case GGML_TYPE_F16:
|
|
convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main);
|
|
break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
break;
|
|
}
|
|
|
|
#ifdef GGML_CUDA_DMMV_F16
|
|
if (src1_convert_f16) {
|
|
ggml_cuda_pool_free(src1_dfloat, ash);
|
|
}
|
|
#endif // GGML_CUDA_DMMV_F16
|
|
}
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src0_ddf_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_mul_mat_cublas(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddf_i != nullptr);
|
|
GGML_ASSERT(src1_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const float alpha = 1.0f;
|
|
const float beta = 0.0f;
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
|
|
const int64_t ne10 = src1->ne[0];
|
|
const int64_t ne11 = src1->ne[1];
|
|
|
|
const int64_t ne0 = dst->ne[0];
|
|
const int64_t i01_diff = i01_high - i01_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 : i01_diff;
|
|
|
|
CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], cudaStream_main));
|
|
CUBLAS_CHECK(
|
|
cublasSgemm(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
|
|
i01_diff, ne11, ne10,
|
|
&alpha, src0_ddf_i, ne00,
|
|
src1_ddf_i, ne10,
|
|
&beta, dst_ddf_i, ldc));
|
|
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_rope(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
|
|
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];
|
|
// RoPE alteration for extended context
|
|
|
|
float freq_base, freq_scale;
|
|
memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
|
|
memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
|
|
|
|
const float theta_scale = powf(freq_base, -2.0f/n_dims);
|
|
const float p = (((mode & 1) == 0 ? n_past + i02 : i02)) * freq_scale;
|
|
|
|
bool is_glm = mode & 4;
|
|
|
|
// compute
|
|
if (is_glm) {
|
|
const float id_p = min(p, n_ctx - 2.f);
|
|
const float block_p = max(p - (n_ctx - 2.f), 0.f);
|
|
rope_glm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, id_p, block_p, theta_scale, cudaStream_main);
|
|
} else {
|
|
rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p, theta_scale, cudaStream_main);
|
|
}
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) src1_ddf_i;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_diag_mask_inf(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t ne01 = src0->ne[1];
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
|
|
const int n_past = ((int32_t *) dst->op_params)[0];
|
|
|
|
// compute
|
|
diag_mask_inf_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, ne01, n_past, cudaStream_main);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) src1_ddf_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_soft_max(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
|
|
// compute
|
|
soft_max_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main);
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) src1_ddf_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
inline void ggml_cuda_op_scale(
|
|
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
|
|
float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
|
|
cudaStream_t & cudaStream_main){
|
|
|
|
GGML_ASSERT(src0_ddf_i != nullptr);
|
|
GGML_ASSERT(dst_ddf_i != nullptr);
|
|
|
|
const float scale = ((float *) src1->data)[0];
|
|
|
|
const int64_t ne00 = src0->ne[0];
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
|
|
// compute
|
|
scale_f32_cuda(src0_ddf_i, dst_ddf_i, scale, ne00*i01_diff, cudaStream_main);
|
|
CUDA_CHECK(cudaGetLastError());
|
|
|
|
(void) src1;
|
|
(void) dst;
|
|
(void) src0_ddq_i;
|
|
(void) src1_ddf_i;
|
|
(void) i02;
|
|
(void) i1;
|
|
}
|
|
|
|
static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
|
|
ggml_cuda_op_t op, bool src0_needs_f32, bool flatten_rows) {
|
|
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 bool use_src1 = src1 != nullptr;
|
|
const int64_t ne10 = use_src1 ? src1->ne[0] : 1;
|
|
const int64_t ne11 = use_src1 ? src1->ne[1] : 1;
|
|
const int64_t ne12 = use_src1 ? src1->ne[2] : 1;
|
|
const int64_t ne13 = use_src1 ? src1->ne[3] : 1;
|
|
const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
|
|
|
|
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(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT);
|
|
|
|
// strides for iteration over dims 3 and 2
|
|
const int64_t num_iters_0 = ne02 >= ne12 ? ne02*ne03 : ne12*ne13;
|
|
const int64_t num_iters = flatten_rows ? 1 : num_iters_0;
|
|
const int64_t stride_mod = flatten_rows ? num_iters_0 : 1;
|
|
const int64_t src0_stride = ne00 * ne01 * stride_mod;
|
|
const int64_t src1_stride = ne10 * ne11 * stride_mod;
|
|
const int64_t dst_stride = ne0 * ne1 * stride_mod;
|
|
|
|
const int64_t rows_per_iter = flatten_rows ? nrows0 : ne01;
|
|
const int64_t i03_max = flatten_rows ? 1 : ne03;
|
|
const int64_t i02_max = flatten_rows ? 1 : (ne02 >= ne12 ? ne02 : ne12);
|
|
const int64_t i02_divisor = ne02 >= ne12 ? 1 : ne12 / ne02;
|
|
GGML_ASSERT(!(flatten_rows && ne02 < ne12));
|
|
|
|
const size_t src0_ts = ggml_type_size(src0->type);
|
|
const size_t src0_bs = ggml_blck_size(src0->type);
|
|
|
|
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
struct ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
|
|
struct 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 src0_is_f32 = src0->type == GGML_TYPE_F32;
|
|
|
|
const bool src1_is_contiguous = use_src1 && ggml_is_contiguous(src1);
|
|
const bool src1_stays_on_host = use_src1 && (
|
|
dst->op == GGML_OP_SCALE || dst->op == GGML_OP_DIAG_MASK_INF || dst->op == GGML_OP_ROPE);
|
|
|
|
const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT;
|
|
GGML_ASSERT(!(split && ne02 < ne12));
|
|
|
|
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(src0->type);
|
|
|
|
// dd = data device
|
|
char * src0_ddq[GGML_CUDA_MAX_DEVICES] = {nullptr}; // quantized
|
|
float * src0_ddf[GGML_CUDA_MAX_DEVICES] = {nullptr}; // float
|
|
float * src1_ddf[GGML_CUDA_MAX_DEVICES] = {nullptr};
|
|
float * dst_ddf[GGML_CUDA_MAX_DEVICES] = {nullptr};
|
|
|
|
// asq = actual size quantized, asf = actual size float
|
|
size_t src0_asq[GGML_CUDA_MAX_DEVICES] = {0};
|
|
size_t src0_asf[GGML_CUDA_MAX_DEVICES] = {0};
|
|
size_t src1_asf[GGML_CUDA_MAX_DEVICES] = {0};
|
|
size_t dst_asf[GGML_CUDA_MAX_DEVICES] = {0};
|
|
|
|
// if multiple devices are used they need to wait for the main device
|
|
// here an event is recorded that signifies that the main device has finished calculating the input data
|
|
if (split && g_device_count > 1) {
|
|
CUDA_CHECK(cudaSetDevice(g_main_device));
|
|
CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device], g_cudaStreams_main[g_main_device]));
|
|
}
|
|
|
|
for (int id = 0; id < g_device_count; ++id) {
|
|
if (!split && id != g_main_device) {
|
|
continue;
|
|
}
|
|
|
|
const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU && id == g_main_device;
|
|
const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device;
|
|
|
|
int64_t row_low, row_high;
|
|
if (split) {
|
|
row_low = id == 0 ? 0 : nrows0*g_tensor_split[id];
|
|
row_high = id == g_device_count - 1 ? nrows0 : nrows0*g_tensor_split[id + 1];
|
|
} else {
|
|
row_low = 0;
|
|
row_high = nrows0*i02_divisor;
|
|
}
|
|
if (row_low == row_high) {
|
|
continue;
|
|
}
|
|
|
|
int64_t row_diff = row_high - row_low;
|
|
|
|
cudaSetDevice(id);
|
|
cudaStream_t cudaStream_main = g_cudaStreams_main[id];
|
|
|
|
// wait for main GPU data if necessary
|
|
if (split && id != g_main_device) {
|
|
CUDA_CHECK(cudaStreamWaitEvent(cudaStream_main, src0_extra->events[g_main_device]));
|
|
}
|
|
|
|
if (src0_on_device && src0_is_contiguous) {
|
|
if (src0_is_f32) {
|
|
src0_ddf[id] = (float *) src0_extra->data_device[id];
|
|
} else {
|
|
src0_ddq[id] = (char *) src0_extra->data_device[id];
|
|
}
|
|
} else {
|
|
if (src0_is_f32) {
|
|
src0_ddf[id] = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_asf[id]);
|
|
} else {
|
|
src0_ddq[id] = (char *) ggml_cuda_pool_malloc(row_diff*ne00 * src0_ts/src0_bs, &src0_asq[id]);
|
|
}
|
|
}
|
|
|
|
if (src0_needs_f32 && !src0_is_f32) {
|
|
src0_ddf[id] = (float *) ggml_cuda_pool_malloc(row_diff*ne00 * sizeof(float), &src0_asf[id]);
|
|
}
|
|
|
|
if (use_src1 && !src1_stays_on_host) {
|
|
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(num_iters*src1_stride * sizeof(float), &src1_asf[id]);
|
|
}
|
|
}
|
|
if (dst_on_device) {
|
|
dst_ddf[id] = (float *) dst_extra->data_device[id];
|
|
} else {
|
|
size_t size_dst_ddf = split ? row_diff*ne1 * sizeof(float) : num_iters*dst_stride * sizeof(float);
|
|
dst_ddf[id] = (float *) ggml_cuda_pool_malloc(size_dst_ddf, &dst_asf[id]);
|
|
}
|
|
|
|
for (int64_t i03 = 0; i03 < i03_max; i03++) {
|
|
const int64_t i13 = i03 % ne13;
|
|
for (int64_t i02 = 0; i02 < i02_max; i02++) {
|
|
const int64_t i12 = i02 % ne12;
|
|
|
|
const int64_t i0 = i03*i02_max + i02;
|
|
|
|
// i0 values that contain the lower/upper rows for a split tensor when using multiple GPUs
|
|
const int64_t i0_offset_low = row_low/rows_per_iter;
|
|
const int64_t i0_offset_high = row_high/rows_per_iter;
|
|
|
|
int64_t i01_low = 0;
|
|
int64_t i01_high = rows_per_iter;
|
|
if (split) {
|
|
if (i0 < i0_offset_low || i0 > i0_offset_high) {
|
|
continue;
|
|
}
|
|
if (i0 == i0_offset_low) {
|
|
i01_low = row_low % rows_per_iter;
|
|
}
|
|
if (i0 == i0_offset_high) {
|
|
i01_high = row_high % rows_per_iter;
|
|
}
|
|
}
|
|
|
|
// There is possibly a bug in the Windows nvcc compiler regarding instruction reordering or optimizing out local variables.
|
|
// Removing the first assert or changing the order of the arguments causes the second assert to fail.
|
|
// Removing both asserts results in i01_high becoming 0 which in turn results in garbage output.
|
|
// The root cause seems to be a problem with i0_offset_high becoming 0 when it should always be >0 (for single GPU).
|
|
GGML_ASSERT(i01_low == 0 || g_device_count > 1);
|
|
GGML_ASSERT(i01_high == rows_per_iter || g_device_count > 1);
|
|
|
|
const int64_t i01_diff = i01_high - i01_low;
|
|
if (i01_diff == 0) {
|
|
continue;
|
|
}
|
|
const int64_t i11 = i13*ne12 + i12;
|
|
|
|
// for split tensors the data begins at i0 == i0_offset_low
|
|
char * src0_ddq_i = src0_ddq[id] + (i0/i02_divisor - i0_offset_low)*src0_stride*src0_ts/src0_bs;
|
|
float * src0_ddf_i = src0_ddf[id] + (i0/i02_divisor - i0_offset_low)*src0_stride;
|
|
float * src1_ddf_i = src1_ddf[id] + i11*src1_stride;
|
|
float * dst_ddf_i = dst_ddf[id] + (i0 - i0_offset_low)*dst_stride;
|
|
|
|
// for split tensors the data pointer needs to be rounded down
|
|
// to the bin edge for i03, i02 bins beyond the first
|
|
if (i0 - i0_offset_low > 0) {
|
|
GGML_ASSERT(!flatten_rows);
|
|
src0_ddq_i -= (row_low % ne01)*ne00 * src0_ts/src0_bs;
|
|
src0_ddf_i -= (row_low % ne01)*ne00;
|
|
dst_ddf_i -= (row_low % ne0)*ne1;
|
|
}
|
|
|
|
// 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_ddf_i += i01_low; // offset is 0 if no tensor split
|
|
}
|
|
|
|
// copy src0, src1 to device if necessary
|
|
if (use_src1 && !src1_stays_on_host) {
|
|
if (src1->backend == GGML_BACKEND_CPU) {
|
|
GGML_ASSERT(!flatten_rows || nrows0 == ggml_nrows(src1));
|
|
int64_t nrows1 = flatten_rows ? nrows0 : ne11;
|
|
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src1_ddf_i, src1, i03, i02, 0, nrows1, cudaStream_main));
|
|
} else if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) {
|
|
if (id != g_main_device) {
|
|
GGML_ASSERT(!flatten_rows);
|
|
float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device];
|
|
src1_ddf_i_source += i11*src1_stride;
|
|
CUDA_CHECK(cudaMemcpyAsync(src1_ddf_i, src1_ddf_i_source, src1_stride*sizeof(float),
|
|
cudaMemcpyDeviceToDevice, cudaStream_main));
|
|
}
|
|
} else if (src1_on_device && !src1_is_contiguous) {
|
|
GGML_ASSERT(!split);
|
|
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src1_ddf_i, src1, i03, i02, 0, ne11, cudaStream_main));
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
if ((!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) {
|
|
if (src0_is_f32) {
|
|
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_ddf_i, src0, i03, i02/i02_divisor, i01_low, i01_high, cudaStream_main));
|
|
} else {
|
|
CUDA_CHECK(ggml_cuda_cpy_tensor_2d(src0_ddq_i, src0, i03, i02/i02_divisor, i01_low, i01_high, cudaStream_main));
|
|
}
|
|
}
|
|
|
|
// convert src0 to f32 if it is necessary for the ggml_cuda_op
|
|
if (src0_needs_f32 && !src0_is_f32) {
|
|
to_fp32_cuda(src0_ddq_i, src0_ddf_i, i01_diff*ne00, cudaStream_main);
|
|
CUDA_CHECK(cudaGetLastError());
|
|
}
|
|
|
|
// do the computation
|
|
op(src0, src1, dst, src0_ddq_i, src0_ddf_i, src1_ddf_i, dst_ddf_i, i02, i01_low, i01_high, i11, cudaStream_main);
|
|
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 cuBLAS 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.
|
|
for (int64_t j = 0; j < ne1; ++j) {
|
|
float * dhf_dst_i = (float *) ((char *) dst_off_device + (j*ne0 + i01_low)*sizeof(float) + i02*nb2 + i03*nb3);
|
|
CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_ddf_i + j*i01_diff, i01_diff*sizeof(float), kind, cudaStream_main));
|
|
}
|
|
} else {
|
|
float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3);
|
|
CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_ddf_i, dst_stride*sizeof(float), kind, cudaStream_main));
|
|
}
|
|
}
|
|
|
|
// signify to main device that other device is done
|
|
if (split && g_device_count > 1 && id != g_main_device) {
|
|
CUDA_CHECK(cudaEventRecord(src0_extra->events[id], cudaStream_main));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// wait until each device is finished, then free their buffers
|
|
for (int id = 0; id < g_device_count; ++id) {
|
|
if (src0_asq[id] == 0 && src0_asf[id] == 0 && src1_asf[id] == 0 && dst_asf[id] == 0) {
|
|
continue;
|
|
}
|
|
|
|
CUDA_CHECK(cudaSetDevice(id));
|
|
|
|
if (src0_asq[id] > 0) {
|
|
ggml_cuda_pool_free(src0_ddq[id], src0_asq[id]);
|
|
}
|
|
if (src0_asf[id] > 0) {
|
|
ggml_cuda_pool_free(src0_ddf[id], src0_asf[id]);
|
|
}
|
|
if (src1_asf[id] > 0) {
|
|
ggml_cuda_pool_free(src1_ddf[id], src1_asf[id]);
|
|
}
|
|
if (dst_asf[id] > 0) {
|
|
ggml_cuda_pool_free(dst_ddf[id], dst_asf[id]);
|
|
}
|
|
}
|
|
|
|
// main device waits for all other devices to be finished
|
|
if (split && g_device_count > 1) {
|
|
CUDA_CHECK(cudaSetDevice(g_main_device));
|
|
for (int id = 0; id < g_device_count; ++id) {
|
|
if (id != g_main_device) {
|
|
CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams_main[g_main_device], src0_extra->events[id]));
|
|
}
|
|
}
|
|
}
|
|
|
|
if (dst->backend == GGML_BACKEND_CPU) {
|
|
CUDA_CHECK(cudaSetDevice(g_main_device));
|
|
CUDA_CHECK(cudaDeviceSynchronize());
|
|
}
|
|
}
|
|
|
|
void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
// ggml_cuda_add permits f16 dst even though this could in theory cause problems with the pointer arithmetic in ggml_cuda_op.
|
|
// Due to flatten_rows == true this does in practice not make a difference however.
|
|
// Better solution would be nice but right now that would require disproportionate changes.
|
|
GGML_ASSERT(
|
|
(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) &&
|
|
src1->type == GGML_TYPE_F32 &&
|
|
(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16));
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_add, false, true);
|
|
}
|
|
|
|
void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul, true, false); // TODO ggml_cuda_op needs modification for flatten
|
|
}
|
|
|
|
void ggml_cuda_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_gelu, true, true);
|
|
}
|
|
|
|
void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_silu, true, true);
|
|
}
|
|
|
|
void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_norm, true, true);
|
|
}
|
|
|
|
void ggml_cuda_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rms_norm, true, true);
|
|
}
|
|
|
|
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
|
|
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
|
|
if ((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)) {
|
|
return true;
|
|
}
|
|
|
|
return false;
|
|
}
|
|
|
|
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(cudaSetDevice(g_main_device));
|
|
cudaStream_t cudaStream_main = g_cudaStreams_main[g_main_device];
|
|
|
|
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
void * src0_ddq = src0_extra->data_device[g_main_device];
|
|
|
|
struct ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
|
|
float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
|
|
|
|
struct 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, cudaStream_main);
|
|
}
|
|
|
|
void ggml_cuda_mul_mat_vec_nc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){
|
|
GGML_ASSERT(!ggml_is_contiguous(src0) && ggml_is_contiguous(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 ne12 = src1->ne[2];
|
|
|
|
const int64_t nb01 = src0->nb[1];
|
|
const int64_t nb02 = src0->nb[2];
|
|
|
|
CUDA_CHECK(cudaSetDevice(g_main_device));
|
|
cudaStream_t cudaStream_main = g_cudaStreams_main[g_main_device];
|
|
|
|
struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
void * src0_ddq = src0_extra->data_device[g_main_device];
|
|
|
|
struct ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
|
|
float * src1_ddf = (float *) src1_extra->data_device[g_main_device];
|
|
|
|
struct ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra;
|
|
float * dst_ddf = (float *) dst_extra->data_device[g_main_device];
|
|
|
|
const int row_stride_x = nb01 / sizeof(half);
|
|
const int 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, cudaStream_main);
|
|
}
|
|
|
|
void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
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;
|
|
|
|
if (all_on_device && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) {
|
|
ggml_cuda_mul_mat_vec_p021(src0, src1, dst);
|
|
} else if (all_on_device && !ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && src1->ne[1] == 1) {
|
|
ggml_cuda_mul_mat_vec_nc(src0, src1, dst);
|
|
}else if (src0->type == GGML_TYPE_F32) {
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, 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) {
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_vec, false, false);
|
|
} else {
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false);
|
|
}
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_scale, true, true);
|
|
}
|
|
|
|
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(cudaSetDevice(g_main_device));
|
|
cudaStream_t cudaStream_main = g_cudaStreams_main[g_main_device];
|
|
|
|
const struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
|
|
const struct 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, cudaStream_main);
|
|
} 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, cudaStream_main);
|
|
} else {
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
(void) dst;
|
|
}
|
|
|
|
void ggml_cuda_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
ggml_cuda_cpy(src0, dst, nullptr);
|
|
(void) src1;
|
|
}
|
|
|
|
void ggml_cuda_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_diag_mask_inf, true, true);
|
|
}
|
|
|
|
void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_soft_max, true, true);
|
|
}
|
|
|
|
void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
|
|
ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, false); // FIXME flatten changes results
|
|
}
|
|
|
|
void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
|
(void) src0;
|
|
(void) src1;
|
|
(void) dst;
|
|
}
|
|
|
|
void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
|
|
int nrows = ggml_nrows(tensor);
|
|
|
|
const int64_t ne0 = tensor->ne[0];
|
|
|
|
const size_t nb1 = tensor->nb[1];
|
|
|
|
ggml_backend backend = tensor->backend;
|
|
struct ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu;
|
|
memset(extra, 0, sizeof(*extra));
|
|
|
|
for (int id = 0; id < g_device_count; ++id) {
|
|
if (backend == GGML_BACKEND_GPU && id != g_main_device) {
|
|
continue;
|
|
}
|
|
|
|
cudaSetDevice(id);
|
|
|
|
int row_low, row_high;
|
|
if (backend == GGML_BACKEND_GPU) {
|
|
row_low = 0;
|
|
row_high = nrows;
|
|
} else if (backend == GGML_BACKEND_GPU_SPLIT) {
|
|
row_low = id == 0 ? 0 : nrows*g_tensor_split[id];
|
|
row_high = id == g_device_count - 1 ? nrows : nrows*g_tensor_split[id + 1];
|
|
} 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 += (MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING)
|
|
* ggml_type_size(tensor->type)/ggml_blck_size(tensor->type);
|
|
}
|
|
|
|
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) {
|
|
CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id], cudaEventDisableTiming));
|
|
}
|
|
}
|
|
|
|
tensor->extra = extra;
|
|
}
|
|
|
|
void ggml_cuda_free_data(struct ggml_tensor * tensor) {
|
|
if (!tensor || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) {
|
|
return;
|
|
}
|
|
|
|
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
|
|
|
|
for (int id = 0; id < g_device_count; ++id) {
|
|
if (extra->data_device[id] != nullptr) {
|
|
CUDA_CHECK(cudaSetDevice(id));
|
|
CUDA_CHECK(cudaFree(extra->data_device[id]));
|
|
}
|
|
|
|
if (extra->events[id] != nullptr) {
|
|
CUDA_CHECK(cudaSetDevice(id));
|
|
CUDA_CHECK(cudaEventDestroy(extra->events[id]));
|
|
}
|
|
}
|
|
|
|
delete extra;
|
|
}
|
|
|
|
static struct ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr;
|
|
static size_t g_temp_tensor_extra_index = 0;
|
|
|
|
static struct 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_MAX_NODES];
|
|
}
|
|
|
|
size_t alloc_index = g_temp_tensor_extra_index;
|
|
g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_MAX_NODES;
|
|
struct ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index];
|
|
memset(extra, 0, sizeof(*extra));
|
|
|
|
return extra;
|
|
}
|
|
|
|
void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace) {
|
|
if (scratch && g_scratch_size == 0) {
|
|
return;
|
|
}
|
|
|
|
// 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);
|
|
}
|
|
}
|
|
if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) {
|
|
ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace);
|
|
}
|
|
|
|
tensor->backend = GGML_BACKEND_GPU;
|
|
struct 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(cudaSetDevice(g_main_device));
|
|
if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
|
|
struct 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) {
|
|
struct 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_buffers(struct ggml_tensor * tensor) {
|
|
ggml_cuda_assign_buffers_impl(tensor, true, false);
|
|
}
|
|
|
|
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
|
|
ggml_cuda_assign_buffers_impl(tensor, false, false);
|
|
}
|
|
|
|
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
|
|
ggml_cuda_assign_buffers_impl(tensor, false, true);
|
|
}
|
|
|
|
void ggml_cuda_set_main_device(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;
|
|
}
|
|
g_main_device = main_device;
|
|
if (g_device_count > 1) {
|
|
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(size_t scratch_size) {
|
|
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){
|
|
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);
|
|
|
|
switch (tensor->op) {
|
|
case GGML_OP_DUP:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_dup;
|
|
break;
|
|
case GGML_OP_ADD:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_add;
|
|
break;
|
|
case GGML_OP_MUL:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_mul;
|
|
break;
|
|
case GGML_OP_UNARY:
|
|
switch (ggml_get_unary_op(tensor)) {
|
|
case GGML_UNARY_OP_GELU:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_gelu;
|
|
break;
|
|
case GGML_UNARY_OP_SILU:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_silu;
|
|
break;
|
|
default:
|
|
return false;
|
|
} break;
|
|
case GGML_OP_NORM:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_norm;
|
|
break;
|
|
case GGML_OP_RMS_NORM:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
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_SCALE:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_scale;
|
|
break;
|
|
case GGML_OP_CPY:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_cpy;
|
|
break;
|
|
case GGML_OP_CONT:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_dup;
|
|
break;
|
|
case GGML_OP_RESHAPE:
|
|
case GGML_OP_VIEW:
|
|
case GGML_OP_PERMUTE:
|
|
case GGML_OP_TRANSPOSE:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_nop;
|
|
break;
|
|
case GGML_OP_DIAG_MASK_INF:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_diag_mask_inf;
|
|
break;
|
|
case GGML_OP_SOFT_MAX:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_soft_max;
|
|
break;
|
|
case GGML_OP_ROPE:
|
|
if (!any_on_device) {
|
|
return false;
|
|
}
|
|
func = ggml_cuda_rope;
|
|
break;
|
|
default:
|
|
return false;
|
|
}
|
|
|
|
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;
|
|
}
|