dequantize_kernel(vx, ib, iqs, v);
const int64_t iy0 = ((i03*ne02 + i02)*ne01 + i01)*ne00 + iybs + iqs;
- y[iy0 + 0] = float(v.x);
- y[iy0 + y_offset] = float(v.y);
+ y[iy0 + 0] = ggml_cuda_cast<dst_t>(v.x);
+ y[iy0 + y_offset] = ggml_cuda_cast<dst_t>(v.y);
}
template <bool need_check>
const int64_t ix = i03*s03 + i02*s02 + i01*s01 + i00;
const int64_t iy = ((i03*ne02 + i02)*ne01 + i01)*ne00 + i00;
- y[iy] = float(x[ix]);
+ y[iy] = ggml_cuda_cast<dst_t>(x[ix]);
}
template <typename src_t, typename dst_t>
to_fp32_nc_cuda_t ggml_get_to_fp32_nc_cuda(ggml_type type);
to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type);
to_bf16_nc_cuda_t ggml_get_to_bf16_nc_cuda(ggml_type type);
+
+template<typename dst_t, typename src_t>
+ __host__ __device__ inline dst_t ggml_cuda_cast(src_t x) {
+ if constexpr (std::is_same_v<dst_t, src_t>) {
+ return x;
+ } else if constexpr(std::is_same_v<dst_t, nv_bfloat16>) {
+ return __float2bfloat16(float(x));
+ } else if constexpr(std::is_same_v<src_t, nv_bfloat16>) {
+ return __bfloat162float(x);
+ } else {
+ return float(x);
+ }
+}
#pragma once
#include "ggml-common.h"
-
-template<typename src_t, typename dst_t>
-static __device__ __forceinline__ void convert_flt(const src_t * src, dst_t * dst) {
- if constexpr (std::is_same_v<src_t, dst_t>) {
- *dst = *src;
- } else {
- *dst = float(*src);
- }
-}
+#include "convert.cuh"
static __device__ __forceinline__ int best_index_int8(int n, const int8_t * val, float x) {
if (x <= val[0]) return 0;
template<typename src_t, typename dst_t>
static __device__ void cpy_1_flt(const char * cxi, char * cdsti) {
- convert_flt((const src_t *)cxi, (dst_t *)cdsti);
+ *(dst_t *) cdsti = ggml_cuda_cast<dst_t>(*(const src_t *) cxi);
}
#include "getrows.cuh"
#include "dequantize.cuh"
+#include "convert.cuh"
template<int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
static __global__ void k_get_rows(
dfloat2 v;
dequantize_kernel(src0_row, ib, iqs, v);
- dst_row[iybs + iqs + 0] = float(v.x);
- dst_row[iybs + iqs + y_offset] = float(v.y);
+ dst_row[iybs + iqs + 0] = ggml_cuda_cast<dst_t>(v.x);
+ dst_row[iybs + iqs + y_offset] = ggml_cuda_cast<dst_t>(v.y);
}
template<typename src0_t, typename dst_t>
dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3;
const src0_t * src0_row = (const src0_t *)((const char *) src0 + i01*nb01 + i11*nb02 + i12*nb03);
- dst_row[i00] = float(src0_row[i00]);
+ dst_row[i00] = ggml_cuda_cast<dst_t>(src0_row[i00]);
}
template<typename grad_t, typename dst_t>
#include "ggml.h"
#include "common.cuh"
+#include "convert.cuh"
#include "mmvf.cuh"
template <typename T, typename type_acc, int ncols_dst, int block_size>
#pragma unroll
for (int j = 0; j < ncols_dst; ++j) {
const float2 tmpy = y2[j*stride_col_y2 + col2];
- sumf[j] += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[0]) * tmpy.x;
- sumf[j] += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[1]) * tmpy.y;
+ sumf[j] += ggml_cuda_cast<float>(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[0]) * tmpy.x;
+ sumf[j] += ggml_cuda_cast<float>(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[1]) * tmpy.y;
}
}
} else {
typedef void (*set_rows_kernel_t)(const char * src, char * dst);
-template<typename src_t, typename dst_t>
-__device__ __forceinline__ void set_rows_1(const src_t * src_f, dst_t * dst_f) {
- convert_flt(src_f, dst_f);
-}
-
// Generic quantized set_rows kernel template
template<typename block_type, int qk, void (*quantize_func)(const float*, block_type*)>
static __global__ void k_set_rows_quant(
const src_t * src0_row = src0 + i01*s01 + i02*s02 + i03*s03;
dst_t * dst_row_ptr = dst + dst_row*s1 + i02*s2 + i03*s3;
- const src_t* src_elem = src0_row + i00;
- dst_t* dst_elem = dst_row_ptr + i00;
- set_rows_1(src_elem, dst_elem);
+ dst_row_ptr[i00] = ggml_cuda_cast<dst_t>(src0_row[i00]);
GGML_UNUSED(ne10);
GGML_UNUSED(ne13);
#include <hip/hip_runtime.h>
#include <hipblas/hipblas.h>
#include <hip/hip_fp16.h>
-#include <hip/hip_bfloat16.h>
+#include <hip/hip_bf16.h>
#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
#define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
#define CUBLAS_STATUS_INTERNAL_ERROR HIPBLAS_STATUS_INTERNAL_ERROR
#define CUBLAS_STATUS_NOT_SUPPORTED HIPBLAS_STATUS_NOT_SUPPORTED
-#if HIP_VERSION >= 70000000
+#if HIP_VERSION >= 60500000
#define CUBLAS_COMPUTE_16F HIPBLAS_COMPUTE_16F
#define CUBLAS_COMPUTE_32F HIPBLAS_COMPUTE_32F
#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_COMPUTE_32F_FAST_16F
#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
#define cublasComputeType_t hipblasDatatype_t
#define cudaDataType_t hipblasDatatype_t
-#endif // HIP_VERSION >= 7000000
+#endif // HIP_VERSION >= 6050000
#if !defined(__HIP_PLATFORM_AMD__)
#error "The HIP backend supports only AMD targets"
#define RDNA4
#endif
-#if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
- defined(__gfx1150__) || defined(__gfx1151__)
+#if defined(__GFX11__)
#define RDNA3
#endif
#define __has_builtin(x) 0
#endif
-typedef hip_bfloat16 nv_bfloat16;
-typedef short2 nv_bfloat162; // FIXME there is no 2x BF16 type being defined in bfloat16.h, ad-hoc compilation fix
+typedef __hip_bfloat16 nv_bfloat16;
+typedef __hip_bfloat162 nv_bfloat162;
typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));