# auto-generated file
import ggml.ffi as ffi
import numpy as np
class lib:
@property
def GGML_BACKEND_CPU(self) -> int: ...
@property
def GGML_BACKEND_GPU(self) -> int: ...
@property
def GGML_BACKEND_GPU_SPLIT(self) -> int: ...
@property
def GGML_FTYPE_ALL_F32(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_F16(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q2_K(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q3_K(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q4_0(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q4_1(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q4_1_SOME_F16(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q4_K(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q5_0(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q5_1(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q5_K(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q6_K(self) -> int: ...
@property
def GGML_FTYPE_MOSTLY_Q8_0(self) -> int: ...
@property
def GGML_FTYPE_UNKNOWN(self) -> int: ...
@property
def GGML_LINESEARCH_BACKTRACKING_ARMIJO(self) -> int: ...
@property
def GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE(self) -> int: ...
@property
def GGML_LINESEARCH_BACKTRACKING_WOLFE(self) -> int: ...
@property
def GGML_LINESEARCH_DEFAULT(self) -> int: ...
@property
def GGML_LINESEARCH_FAIL(self) -> int: ...
@property
def GGML_LINESEARCH_INVALID_PARAMETERS(self) -> int: ...
@property
def GGML_LINESEARCH_MAXIMUM_ITERATIONS(self) -> int: ...
@property
def GGML_LINESEARCH_MAXIMUM_STEP(self) -> int: ...
@property
def GGML_LINESEARCH_MINIMUM_STEP(self) -> int: ...
@property
def GGML_OBJECT_GRAPH(self) -> int: ...
@property
def GGML_OBJECT_TENSOR(self) -> int: ...
@property
def GGML_OBJECT_WORK_BUFFER(self) -> int: ...
@property
def GGML_OPT_ADAM(self) -> int: ...
@property
def GGML_OPT_DID_NOT_CONVERGE(self) -> int: ...
@property
def GGML_OPT_FAIL(self) -> int: ...
@property
def GGML_OPT_INVALID_WOLFE(self) -> int: ...
@property
def GGML_OPT_LBFGS(self) -> int: ...
@property
def GGML_OPT_NO_CONTEXT(self) -> int: ...
@property
def GGML_OPT_OK(self) -> int: ...
@property
def GGML_OP_ACC(self) -> int: ...
@property
def GGML_OP_ADD(self) -> int: ...
@property
def GGML_OP_ADD1(self) -> int: ...
@property
def GGML_OP_ALIBI(self) -> int: ...
@property
def GGML_OP_ARGMAX(self) -> int: ...
@property
def GGML_OP_CLAMP(self) -> int: ...
@property
def GGML_OP_CONT(self) -> int: ...
@property
def GGML_OP_CONV_1D(self) -> int: ...
@property
def GGML_OP_CONV_2D(self) -> int: ...
@property
def GGML_OP_COUNT(self) -> int: ...
@property
def GGML_OP_CPY(self) -> int: ...
@property
def GGML_OP_CROSS_ENTROPY_LOSS(self) -> int: ...
@property
def GGML_OP_CROSS_ENTROPY_LOSS_BACK(self) -> int: ...
@property
def GGML_OP_DIAG(self) -> int: ...
@property
def GGML_OP_DIAG_MASK_INF(self) -> int: ...
@property
def GGML_OP_DIAG_MASK_ZERO(self) -> int: ...
@property
def GGML_OP_DIV(self) -> int: ...
@property
def GGML_OP_DUP(self) -> int: ...
@property
def GGML_OP_FLASH_ATTN(self) -> int: ...
@property
def GGML_OP_FLASH_ATTN_BACK(self) -> int: ...
@property
def GGML_OP_FLASH_FF(self) -> int: ...
@property
def GGML_OP_GET_ROWS(self) -> int: ...
@property
def GGML_OP_GET_ROWS_BACK(self) -> int: ...
@property
def GGML_OP_LOG(self) -> int: ...
@property
def GGML_OP_MAP_BINARY(self) -> int: ...
@property
def GGML_OP_MAP_CUSTOM1(self) -> int: ...
@property
def GGML_OP_MAP_CUSTOM1_F32(self) -> int: ...
@property
def GGML_OP_MAP_CUSTOM2(self) -> int: ...
@property
def GGML_OP_MAP_CUSTOM2_F32(self) -> int: ...
@property
def GGML_OP_MAP_CUSTOM3(self) -> int: ...
@property
def GGML_OP_MAP_CUSTOM3_F32(self) -> int: ...
@property
def GGML_OP_MAP_UNARY(self) -> int: ...
@property
def GGML_OP_MEAN(self) -> int: ...
@property
def GGML_OP_MUL(self) -> int: ...
@property
def GGML_OP_MUL_MAT(self) -> int: ...
@property
def GGML_OP_NONE(self) -> int: ...
@property
def GGML_OP_NORM(self) -> int: ...
@property
def GGML_OP_OUT_PROD(self) -> int: ...
@property
def GGML_OP_PERMUTE(self) -> int: ...
@property
def GGML_OP_POOL_1D(self) -> int: ...
@property
def GGML_OP_POOL_2D(self) -> int: ...
@property
def GGML_OP_POOL_AVG(self) -> int: ...
@property
def GGML_OP_POOL_COUNT(self) -> int: ...
@property
def GGML_OP_POOL_MAX(self) -> int: ...
@property
def GGML_OP_REPEAT(self) -> int: ...
@property
def GGML_OP_REPEAT_BACK(self) -> int: ...
@property
def GGML_OP_RESHAPE(self) -> int: ...
@property
def GGML_OP_RMS_NORM(self) -> int: ...
@property
def GGML_OP_RMS_NORM_BACK(self) -> int: ...
@property
def GGML_OP_ROPE(self) -> int: ...
@property
def GGML_OP_ROPE_BACK(self) -> int: ...
@property
def GGML_OP_SCALE(self) -> int: ...
@property
def GGML_OP_SET(self) -> int: ...
@property
def GGML_OP_SILU_BACK(self) -> int: ...
@property
def GGML_OP_SOFT_MAX(self) -> int: ...
@property
def GGML_OP_SOFT_MAX_BACK(self) -> int: ...
@property
def GGML_OP_SQR(self) -> int: ...
@property
def GGML_OP_SQRT(self) -> int: ...
@property
def GGML_OP_SUB(self) -> int: ...
@property
def GGML_OP_SUM(self) -> int: ...
@property
def GGML_OP_SUM_ROWS(self) -> int: ...
@property
def GGML_OP_TRANSPOSE(self) -> int: ...
@property
def GGML_OP_UNARY(self) -> int: ...
@property
def GGML_OP_VIEW(self) -> int: ...
@property
def GGML_OP_WIN_PART(self) -> int: ...
@property
def GGML_OP_WIN_UNPART(self) -> int: ...
@property
def GGML_TASK_COMPUTE(self) -> int: ...
@property
def GGML_TASK_FINALIZE(self) -> int: ...
@property
def GGML_TASK_INIT(self) -> int: ...
@property
def GGML_TYPE_COUNT(self) -> int: ...
@property
def GGML_TYPE_F16(self) -> int: ...
@property
def GGML_TYPE_F32(self) -> int: ...
@property
def GGML_TYPE_I16(self) -> int: ...
@property
def GGML_TYPE_I32(self) -> int: ...
@property
def GGML_TYPE_I8(self) -> int: ...
@property
def GGML_TYPE_Q2_K(self) -> int: ...
@property
def GGML_TYPE_Q3_K(self) -> int: ...
@property
def GGML_TYPE_Q4_0(self) -> int: ...
@property
def GGML_TYPE_Q4_1(self) -> int: ...
@property
def GGML_TYPE_Q4_K(self) -> int: ...
@property
def GGML_TYPE_Q5_0(self) -> int: ...
@property
def GGML_TYPE_Q5_1(self) -> int: ...
@property
def GGML_TYPE_Q5_K(self) -> int: ...
@property
def GGML_TYPE_Q6_K(self) -> int: ...
@property
def GGML_TYPE_Q8_0(self) -> int: ...
@property
def GGML_TYPE_Q8_1(self) -> int: ...
@property
def GGML_TYPE_Q8_K(self) -> int: ...
@property
def GGML_UNARY_OP_ABS(self) -> int: ...
@property
def GGML_UNARY_OP_ELU(self) -> int: ...
@property
def GGML_UNARY_OP_GELU(self) -> int: ...
@property
def GGML_UNARY_OP_GELU_QUICK(self) -> int: ...
@property
def GGML_UNARY_OP_NEG(self) -> int: ...
@property
def GGML_UNARY_OP_RELU(self) -> int: ...
@property
def GGML_UNARY_OP_SGN(self) -> int: ...
@property
def GGML_UNARY_OP_SILU(self) -> int: ...
@property
def GGML_UNARY_OP_STEP(self) -> int: ...
@property
def GGML_UNARY_OP_TANH(self) -> int: ...
@property
def GGUF_TYPE_ARRAY(self) -> int: ...
@property
def GGUF_TYPE_BOOL(self) -> int: ...
@property
def GGUF_TYPE_COUNT(self) -> int: ...
@property
def GGUF_TYPE_FLOAT32(self) -> int: ...
@property
def GGUF_TYPE_INT16(self) -> int: ...
@property
def GGUF_TYPE_INT32(self) -> int: ...
@property
def GGUF_TYPE_INT8(self) -> int: ...
@property
def GGUF_TYPE_STRING(self) -> int: ...
@property
def GGUF_TYPE_UINT16(self) -> int: ...
@property
def GGUF_TYPE_UINT32(self) -> int: ...
@property
def GGUF_TYPE_UINT8(self) -> int: ...
def abort_callback(data: ffi.CData) -> bool:
"""
abort ggml_graph_compute when true
bool (*abort_callback)(void * data);
"""
...
def dequantize_row_q2_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""
Dequantization
void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
"""
...
def dequantize_row_q3_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);"""
...
def dequantize_row_q4_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);"""
...
def dequantize_row_q5_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k);"""
...
def dequantize_row_q6_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k);"""
...
def dequantize_row_q8_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);"""
...
def ggml_abs(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_abs(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_abs_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_abs_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_acc(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, nb1: int, nb2: int, nb3: int, offset: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_acc(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
size_t nb1,
size_t nb2,
size_t nb3,
size_t offset);
"""
...
def ggml_acc_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, nb1: int, nb2: int, nb3: int, offset: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_acc_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
size_t nb1,
size_t nb2,
size_t nb3,
size_t offset);
"""
...
def ggml_add(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_add(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_add1(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_add1(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_add1_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_add1_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_add_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_add_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_alibi(ctx: ffi.CData, a: ffi.CData, n_past: int, n_head: int, bias_max: float) -> ffi.CData:
"""
alibi position embedding
in-place, returns view(a)
struct ggml_tensor * ggml_alibi(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_head,
float bias_max);
"""
...
def ggml_allocr_alloc(alloc: ffi.CData, tensor: ffi.CData) -> None:
"""GGML_API void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);"""
...
def ggml_allocr_alloc_graph(alloc: ffi.CData, graph: ffi.CData) -> int:
"""GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);"""
...
def ggml_allocr_free(alloc: ffi.CData) -> None:
"""GGML_API void ggml_allocr_free(struct ggml_allocr * alloc);"""
...
def ggml_allocr_is_measure(alloc: ffi.CData) -> bool:
"""GGML_API bool ggml_allocr_is_measure(struct ggml_allocr * alloc);"""
...
def ggml_allocr_new(data: ffi.CData, size: int, alignment: int) -> ffi.CData:
"""GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);"""
...
def ggml_allocr_new_measure(alignment: int) -> ffi.CData:
"""GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);"""
...
def ggml_allocr_reset(alloc: ffi.CData) -> None:
"""GGML_API void ggml_allocr_reset(struct ggml_allocr * alloc);"""
...
def ggml_allocr_set_parse_seq(alloc: ffi.CData, list: ffi.CData, n: int) -> None:
"""
tell the allocator to parse nodes following the order described in the list
you should call this if your graph are optimized to execute out-of-order
GGML_API void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, int * list, int n);
"""
...
def ggml_are_same_shape(t0: ffi.CData, t1: ffi.CData) -> bool:
""" GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);"""
...
def ggml_argmax(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
argmax along rows
GGML_API struct ggml_tensor * ggml_argmax(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_blck_size(type: int) -> int:
""" GGML_API int ggml_blck_size (enum ggml_type type);"""
...
def ggml_build_backward(ctx: ffi.CData, gf: ffi.CData, keep: bool) -> ffi.CData:
""" GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);"""
...
def ggml_build_forward(tensor: ffi.CData) -> ffi.CData:
""" GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);"""
...
def ggml_build_forward_ctx(ctx: ffi.CData, tensor: ffi.CData) -> ffi.CData:
""" GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor);"""
...
def ggml_build_forward_expand(cgraph: ffi.CData, tensor: ffi.CData) -> None:
""" GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);"""
...
def ggml_cl_can_mul_mat(src0: ffi.CData, src1: ffi.CData, dst: ffi.CData) -> bool:
"""bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);"""
...
def ggml_cl_free_data(tensor: ffi.CData) -> None:
"""void ggml_cl_free_data(const struct ggml_tensor* tensor);"""
...
def ggml_cl_host_free(ptr: ffi.CData) -> None:
"""void ggml_cl_host_free(void * ptr);"""
...
def ggml_cl_host_malloc(size: int) -> ffi.CData:
"""void * ggml_cl_host_malloc(size_t size);"""
...
def ggml_cl_init() -> None:
"""void ggml_cl_init(void);"""
...
def ggml_cl_mul(src0: ffi.CData, src1: ffi.CData, dst: ffi.CData) -> None:
"""void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);"""
...
def ggml_cl_mul_mat(src0: ffi.CData, src1: ffi.CData, dst: ffi.CData, wdata: ffi.CData, wsize: int) -> None:
"""void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);"""
...
def ggml_cl_mul_mat_get_wsize(src0: ffi.CData, src1: ffi.CData, dst: ffi.CData) -> int:
"""size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);"""
...
def ggml_cl_transform_tensor(data: ffi.CData, tensor: ffi.CData) -> None:
"""void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);"""
...
def ggml_clamp(ctx: ffi.CData, a: ffi.CData, min: float, max: float) -> ffi.CData:
"""
clamp
in-place, returns view(a)
struct ggml_tensor * ggml_clamp(
struct ggml_context * ctx,
struct ggml_tensor * a,
float min,
float max);
"""
...
def ggml_cont(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
make contiguous
GGML_API struct ggml_tensor * ggml_cont(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_conv_1d(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, s0: int, p0: int, d0: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_conv_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0, // stride
int p0, // padding
int d0); // dilation
"""
...
def ggml_conv_1d_ph(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, s: int, d: int) -> ffi.CData:
"""
conv_1d with padding = half
alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
GGML_API struct ggml_tensor * ggml_conv_1d_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s,
int d);
"""
...
def ggml_conv_2d(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, s0: int, s1: int, p0: int, p1: int, d0: int, d1: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_conv_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1);
"""
...
def ggml_cpu_has_arm_fma() -> int:
""" GGML_API int ggml_cpu_has_arm_fma (void);"""
...
def ggml_cpu_has_avx() -> int:
""" GGML_API int ggml_cpu_has_avx (void);"""
...
def ggml_cpu_has_avx2() -> int:
""" GGML_API int ggml_cpu_has_avx2 (void);"""
...
def ggml_cpu_has_avx512() -> int:
""" GGML_API int ggml_cpu_has_avx512 (void);"""
...
def ggml_cpu_has_avx512_vbmi() -> int:
""" GGML_API int ggml_cpu_has_avx512_vbmi(void);"""
...
def ggml_cpu_has_avx512_vnni() -> int:
""" GGML_API int ggml_cpu_has_avx512_vnni(void);"""
...
def ggml_cpu_has_blas() -> int:
""" GGML_API int ggml_cpu_has_blas (void);"""
...
def ggml_cpu_has_clblast() -> int:
""" GGML_API int ggml_cpu_has_clblast (void);"""
...
def ggml_cpu_has_cublas() -> int:
""" GGML_API int ggml_cpu_has_cublas (void);"""
...
def ggml_cpu_has_f16c() -> int:
""" GGML_API int ggml_cpu_has_f16c (void);"""
...
def ggml_cpu_has_fma() -> int:
""" GGML_API int ggml_cpu_has_fma (void);"""
...
def ggml_cpu_has_fp16_va() -> int:
""" GGML_API int ggml_cpu_has_fp16_va (void);"""
...
def ggml_cpu_has_gpublas() -> int:
""" GGML_API int ggml_cpu_has_gpublas (void);"""
...
def ggml_cpu_has_neon() -> int:
""" GGML_API int ggml_cpu_has_neon (void);"""
...
def ggml_cpu_has_sse3() -> int:
""" GGML_API int ggml_cpu_has_sse3 (void);"""
...
def ggml_cpu_has_vsx() -> int:
""" GGML_API int ggml_cpu_has_vsx (void);"""
...
def ggml_cpu_has_wasm_simd() -> int:
""" GGML_API int ggml_cpu_has_wasm_simd (void);"""
...
def ggml_cpy(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
a -> b, return view(b)
GGML_API struct ggml_tensor * ggml_cpy(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_cross_entropy_loss(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_cross_entropy_loss_back(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, c: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c);
"""
...
def ggml_cuda_assign_buffers(tensor: ffi.CData) -> None:
"""GGML_API void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);"""
...
def ggml_cuda_assign_buffers_force_inplace(tensor: ffi.CData) -> None:
"""GGML_API void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);"""
...
def ggml_cuda_assign_buffers_no_scratch(tensor: ffi.CData) -> None:
"""GGML_API void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);"""
...
def ggml_cuda_can_mul_mat(src0: ffi.CData, src1: ffi.CData, dst: ffi.CData) -> bool:
"""GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);"""
...
def ggml_cuda_compute_forward(params: ffi.CData, tensor: ffi.CData) -> bool:
"""GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);"""
...
def ggml_cuda_free_data(tensor: ffi.CData) -> None:
"""GGML_API void ggml_cuda_free_data(struct ggml_tensor * tensor);"""
...
def ggml_cuda_free_scratch() -> None:
"""GGML_API void ggml_cuda_free_scratch(void);"""
...
def ggml_cuda_get_device_count() -> int:
"""GGML_API int ggml_cuda_get_device_count(void);"""
...
def ggml_cuda_get_device_description(device: int, description: ffi.CData, description_size: int) -> None:
"""GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size);"""
...
def ggml_cuda_host_free(ptr: ffi.CData) -> None:
"""GGML_API void ggml_cuda_host_free(void * ptr);"""
...
def ggml_cuda_host_malloc(size: int) -> ffi.CData:
"""GGML_API void * ggml_cuda_host_malloc(size_t size);"""
...
def ggml_cuda_set_main_device(main_device: int) -> None:
"""GGML_API void ggml_cuda_set_main_device(int main_device);"""
...
def ggml_cuda_set_mul_mat_q(mul_mat_q: bool) -> None:
"""GGML_API void ggml_cuda_set_mul_mat_q(bool mul_mat_q);"""
...
def ggml_cuda_set_scratch_size(scratch_size: int) -> None:
"""GGML_API void ggml_cuda_set_scratch_size(size_t scratch_size);"""
...
def ggml_cuda_set_tensor_split(tensor_split: ffi.CData) -> None:
"""GGML_API void ggml_cuda_set_tensor_split(const float * tensor_split);"""
...
def ggml_cuda_transform_tensor(data: ffi.CData, tensor: ffi.CData) -> None:
"""GGML_API void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);"""
...
def ggml_cycles() -> int:
""" GGML_API int64_t ggml_cycles(void);"""
...
def ggml_cycles_per_ms() -> int:
""" GGML_API int64_t ggml_cycles_per_ms(void);"""
...
def ggml_diag(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_diag(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_diag_mask_inf(ctx: ffi.CData, a: ffi.CData, n_past: int) -> ffi.CData:
"""
set elements above the diagonal to -INF
GGML_API struct ggml_tensor * ggml_diag_mask_inf(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past);
"""
...
def ggml_diag_mask_inf_inplace(ctx: ffi.CData, a: ffi.CData, n_past: int) -> ffi.CData:
"""
in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_diag_mask_inf_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past);
"""
...
def ggml_diag_mask_zero(ctx: ffi.CData, a: ffi.CData, n_past: int) -> ffi.CData:
"""
set elements above the diagonal to 0
GGML_API struct ggml_tensor * ggml_diag_mask_zero(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past);
"""
...
def ggml_diag_mask_zero_inplace(ctx: ffi.CData, a: ffi.CData, n_past: int) -> ffi.CData:
"""
in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_diag_mask_zero_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past);
"""
...
def ggml_div(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_div(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_div_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_div_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_dup(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_dup(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_dup_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_dup_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_dup_tensor(ctx: ffi.CData, src: ffi.CData) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);"""
...
def ggml_element_size(tensor: ffi.CData) -> int:
""" GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);"""
...
def ggml_elu(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_elu(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_elu_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_elu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_flash_attn(ctx: ffi.CData, q: ffi.CData, k: ffi.CData, v: ffi.CData, masked: bool) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_flash_attn(
struct ggml_context * ctx,
struct ggml_tensor * q,
struct ggml_tensor * k,
struct ggml_tensor * v,
bool masked);
"""
...
def ggml_flash_attn_back(ctx: ffi.CData, q: ffi.CData, k: ffi.CData, v: ffi.CData, d: ffi.CData, masked: bool) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_flash_attn_back(
struct ggml_context * ctx,
struct ggml_tensor * q,
struct ggml_tensor * k,
struct ggml_tensor * v,
struct ggml_tensor * d,
bool masked);
"""
...
def ggml_flash_ff(ctx: ffi.CData, a: ffi.CData, b0: ffi.CData, b1: ffi.CData, c0: ffi.CData, c1: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_flash_ff(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b0,
struct ggml_tensor * b1,
struct ggml_tensor * c0,
struct ggml_tensor * c1);
"""
...
def ggml_format_name(tensor: ffi.CData, fmt: ffi.CData, *args2) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_format_name( struct ggml_tensor * tensor, const char * fmt, ...);"""
...
def ggml_fp16_to_fp32(x: np.float16) -> float:
"""
convert FP16 <-> FP32
GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x);
"""
...
def ggml_fp16_to_fp32_row(x: ffi.CData, y: ffi.CData, n: int) -> None:
""" GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n);"""
...
def ggml_fp32_to_fp16(x: float) -> np.float16:
""" GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);"""
...
def ggml_fp32_to_fp16_row(x: ffi.CData, y: ffi.CData, n: int) -> None:
""" GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n);"""
...
def ggml_free(ctx: ffi.CData) -> None:
""" GGML_API void ggml_free(struct ggml_context * ctx);"""
...
def ggml_ftype_to_ggml_type(ftype: int) -> int:
"""
TODO: temporary until model loading of ggml examples is refactored
GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
"""
...
def ggml_gelu(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
TODO: double-check this computation is correct
GGML_API struct ggml_tensor * ggml_gelu(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_gelu_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_gelu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_gelu_quick(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_gelu_quick(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_gelu_quick_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_gelu_quick_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_get_data(tensor: ffi.CData) -> ffi.CData:
""" GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);"""
...
def ggml_get_data_f32(tensor: ffi.CData) -> ffi.CData:
""" GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);"""
...
def ggml_get_f32_1d(tensor: ffi.CData, i: int) -> float:
""" GGML_API float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);"""
...
def ggml_get_i32_1d(tensor: ffi.CData, i: int) -> int:
""" GGML_API int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);"""
...
def ggml_get_max_tensor_size(ctx: ffi.CData) -> int:
""" GGML_API size_t ggml_get_max_tensor_size(const struct ggml_context * ctx);"""
...
def ggml_get_mem_buffer(ctx: ffi.CData) -> ffi.CData:
""" GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx);"""
...
def ggml_get_mem_size(ctx: ffi.CData) -> int:
""" GGML_API size_t ggml_get_mem_size (const struct ggml_context * ctx);"""
...
def ggml_get_name(tensor: ffi.CData) -> ffi.CData:
""" GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor);"""
...
def ggml_get_no_alloc(ctx: ffi.CData) -> bool:
""" GGML_API bool ggml_get_no_alloc(struct ggml_context * ctx);"""
...
def ggml_get_rows(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_get_rows(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_get_rows_back(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, c: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_get_rows_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c);
"""
...
def ggml_get_tensor(ctx: ffi.CData, name: ffi.CData) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);"""
...
def ggml_get_unary_op(tensor: ffi.CData) -> int:
""" GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);"""
...
def ggml_graph_compute(cgraph: ffi.CData, cplan: ffi.CData) -> int:
""" GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);"""
...
def ggml_graph_compute_with_ctx(ctx: ffi.CData, cgraph: ffi.CData, n_threads: int) -> None:
"""
same as ggml_graph_compute() but the work data is allocated as a part of the context
note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
"""
...
def ggml_graph_dump_dot(gb: ffi.CData, gf: ffi.CData, filename: ffi.CData) -> None:
"""
dump the graph into a file using the dot format
GGML_API void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename);
"""
...
def ggml_graph_export(cgraph: ffi.CData, fname: ffi.CData) -> None:
""" GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname);"""
...
def ggml_graph_get_tensor(cgraph: ffi.CData, name: ffi.CData) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);"""
...
def ggml_graph_import(fname: ffi.CData, ctx_data: ffi.CData, ctx_eval: ffi.CData) -> ffi.CData:
""" GGML_API struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval);"""
...
def ggml_graph_overhead() -> int:
""" GGML_API size_t ggml_graph_overhead(void);"""
...
def ggml_graph_plan(cgraph: ffi.CData, n_threads: int) -> ffi.CData:
"""
ggml_graph_plan() has to be called before ggml_graph_compute()
when plan.work_size > 0, caller must allocate memory for plan.work_data
GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
"""
...
def ggml_graph_print(cgraph: ffi.CData) -> None:
"""
print info and performance information for the graph
GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);
"""
...
def ggml_graph_reset(cgraph: ffi.CData) -> None:
""" GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph);"""
...
def ggml_init(params: ffi.CData) -> ffi.CData:
""" GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);"""
...
def ggml_init_cublas() -> None:
"""GGML_API void ggml_init_cublas(void);"""
...
def ggml_internal_get_type_traits(type: int) -> ffi.CData:
""" ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);"""
...
def ggml_is_contiguous(tensor: ffi.CData) -> bool:
""" GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);"""
...
def ggml_is_numa() -> bool:
""" GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node"""
...
def ggml_is_permuted(tensor: ffi.CData) -> bool:
""" GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);"""
...
def ggml_is_quantized(type: int) -> bool:
""" GGML_API bool ggml_is_quantized(enum ggml_type type);"""
...
def ggml_is_transposed(tensor: ffi.CData) -> bool:
""" GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);"""
...
def ggml_log(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_log(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_log_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_log_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_map_binary_f32(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, fun: ffi.CData) -> ffi.CData:
"""
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun),
"use ggml_map_custom2 instead");
"""
...
def ggml_map_binary_inplace_f32(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, fun: ffi.CData) -> ffi.CData:
"""
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun),
"use ggml_map_custom2_inplace instead");
"""
...
def ggml_map_custom1(ctx: ffi.CData, a: ffi.CData, fun: ffi.CData, n_tasks: int, userdata: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_map_custom1(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_t fun,
int n_tasks,
void * userdata);
"""
...
def ggml_map_custom1_f32(ctx: ffi.CData, a: ffi.CData, fun: ffi.CData) -> ffi.CData:
"""
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_f32_t fun),
"use ggml_map_custom1 instead");
"""
...
def ggml_map_custom1_inplace(ctx: ffi.CData, a: ffi.CData, fun: ffi.CData, n_tasks: int, userdata: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_map_custom1_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_t fun,
int n_tasks,
void * userdata);
"""
...
def ggml_map_custom1_inplace_f32(ctx: ffi.CData, a: ffi.CData, fun: ffi.CData) -> ffi.CData:
"""
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_f32_t fun),
"use ggml_map_custom1_inplace instead");
"""
...
def ggml_map_custom2(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, fun: ffi.CData, n_tasks: int, userdata: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_map_custom2(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_t fun,
int n_tasks,
void * userdata);
"""
...
def ggml_map_custom2_f32(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, fun: ffi.CData) -> ffi.CData:
"""
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_f32_t fun),
"use ggml_map_custom2 instead");
"""
...
def ggml_map_custom2_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, fun: ffi.CData, n_tasks: int, userdata: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_map_custom2_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_t fun,
int n_tasks,
void * userdata);
"""
...
def ggml_map_custom2_inplace_f32(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, fun: ffi.CData) -> ffi.CData:
"""
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_f32_t fun),
"use ggml_map_custom2_inplace instead");
"""
...
def ggml_map_custom3(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, c: ffi.CData, fun: ffi.CData, n_tasks: int, userdata: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_map_custom3(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_t fun,
int n_tasks,
void * userdata);
"""
...
def ggml_map_custom3_f32(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, c: ffi.CData, fun: ffi.CData) -> ffi.CData:
"""
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_f32_t fun),
"use ggml_map_custom3 instead");
"""
...
def ggml_map_custom3_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, c: ffi.CData, fun: ffi.CData, n_tasks: int, userdata: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_map_custom3_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_t fun,
int n_tasks,
void * userdata);
"""
...
def ggml_map_custom3_inplace_f32(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, c: ffi.CData, fun: ffi.CData) -> ffi.CData:
"""
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_f32_t fun),
"use ggml_map_custom3_inplace instead");
"""
...
def ggml_map_unary_f32(ctx: ffi.CData, a: ffi.CData, fun: ffi.CData) -> ffi.CData:
"""
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun),
"use ggml_map_custom1 instead");
"""
...
def ggml_map_unary_inplace_f32(ctx: ffi.CData, a: ffi.CData, fun: ffi.CData) -> ffi.CData:
"""
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun),
"use ggml_map_custom1_inplace instead");
"""
...
def ggml_mean(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
mean along rows
GGML_API struct ggml_tensor * ggml_mean(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_metal_add_buffer(ctx: ffi.CData, name: ffi.CData, data: ffi.CData, size: int, max_size: int) -> bool:
"""
creates a mapping between a host memory buffer and a device memory buffer
- make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
- the mapping is used during computation to determine the arguments of the compute kernels
- you don't need to keep the host memory buffer allocated as it is never accessed by Metal
- max_size specifies the maximum size of a tensor and is used to create shared views such
that it is guaranteed that the tensor will fit in at least one of the views
bool ggml_metal_add_buffer(
struct ggml_metal_context * ctx,
const char * name,
void * data,
size_t size,
size_t max_size);
"""
...
def ggml_metal_free(ctx: ffi.CData) -> None:
"""void ggml_metal_free(struct ggml_metal_context * ctx);"""
...
def ggml_metal_get_concur_list(ctx: ffi.CData) -> ffi.CData:
"""
output the concur_list for ggml_alloc
int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
"""
...
def ggml_metal_get_tensor(ctx: ffi.CData, t: ffi.CData) -> None:
"""
get data from the device into host memory
void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
"""
...
def ggml_metal_graph_compute(ctx: ffi.CData, gf: ffi.CData) -> None:
"""
same as ggml_graph_compute but uses Metal
creates gf->n_threads command buffers in parallel
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
"""
...
def ggml_metal_graph_find_concurrency(ctx: ffi.CData, gf: ffi.CData, check_mem: bool) -> None:
"""
try to find operations that can be run concurrently in the graph
you should run it again if the topology of your graph changes
void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem);
"""
...
def ggml_metal_host_free(data: ffi.CData) -> None:
"""void ggml_metal_host_free (void * data);"""
...
def ggml_metal_host_malloc(n: int) -> ffi.CData:
"""void * ggml_metal_host_malloc(size_t n);"""
...
def ggml_metal_if_optimized(ctx: ffi.CData) -> int:
"""
if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized
int ggml_metal_if_optimized(struct ggml_metal_context * ctx);
"""
...
def ggml_metal_init(n_cb: int) -> ffi.CData:
"""
number of command buffers to use
struct ggml_metal_context * ggml_metal_init(int n_cb);
"""
...
def ggml_metal_set_n_cb(ctx: ffi.CData, n_cb: int) -> None:
"""
set the number of command buffers to use
void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
"""
...
def ggml_metal_set_tensor(ctx: ffi.CData, t: ffi.CData) -> None:
"""
set data from host memory into the device
void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
"""
...
def ggml_mpi_backend_free() -> None:
"""void ggml_mpi_backend_free(void);"""
...
def ggml_mpi_backend_init() -> None:
"""void ggml_mpi_backend_init(void);"""
...
def ggml_mpi_eval_init(ctx_mpi: ffi.CData, n_tokens: ffi.CData, n_past: ffi.CData, n_threads: ffi.CData) -> None:
"""
void ggml_mpi_eval_init(
struct ggml_mpi_context * ctx_mpi,
int * n_tokens,
int * n_past,
int * n_threads);
"""
...
def ggml_mpi_free(ctx: ffi.CData) -> None:
"""void ggml_mpi_free(struct ggml_mpi_context * ctx);"""
...
def ggml_mpi_graph_compute_post(ctx_mpi: ffi.CData, gf: ffi.CData, n_layers: int) -> None:
"""
void ggml_mpi_graph_compute_post(
struct ggml_mpi_context * ctx_mpi,
struct ggml_cgraph * gf,
int n_layers);
"""
...
def ggml_mpi_graph_compute_pre(ctx_mpi: ffi.CData, gf: ffi.CData, n_layers: int) -> None:
"""
void ggml_mpi_graph_compute_pre(
struct ggml_mpi_context * ctx_mpi,
struct ggml_cgraph * gf,
int n_layers);
"""
...
def ggml_mpi_init() -> ffi.CData:
"""struct ggml_mpi_context * ggml_mpi_init(void);"""
...
def ggml_mpi_rank(ctx: ffi.CData) -> int:
"""int ggml_mpi_rank(struct ggml_mpi_context * ctx);"""
...
def ggml_mul(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_mul(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_mul_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_mul_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_mul_mat(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
A: n columns, m rows
B: n columns, p rows (i.e. we transpose it internally)
result is m columns, p rows
GGML_API struct ggml_tensor * ggml_mul_mat(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_nbytes(tensor: ffi.CData) -> int:
""" GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);"""
...
def ggml_nbytes_pad(tensor: ffi.CData) -> int:
""" GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN"""
...
def ggml_nbytes_split(tensor: ffi.CData, nrows_split: int) -> int:
""" GGML_API size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);"""
...
def ggml_neg(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_neg(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_neg_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_neg_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_nelements(tensor: ffi.CData) -> int:
""" GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor);"""
...
def ggml_new_f32(ctx: ffi.CData, value: float) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);"""
...
def ggml_new_graph(ctx: ffi.CData) -> ffi.CData:
"""
graph allocation in a context
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx);
"""
...
def ggml_new_i32(ctx: ffi.CData, value: int) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);"""
...
def ggml_new_tensor(ctx: ffi.CData, type: int, n_dims: int, ne: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_new_tensor(
struct ggml_context * ctx,
enum ggml_type type,
int n_dims,
const int64_t *ne);
"""
...
def ggml_new_tensor_1d(ctx: ffi.CData, type: int, ne0: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_new_tensor_1d(
struct ggml_context * ctx,
enum ggml_type type,
int64_t ne0);
"""
...
def ggml_new_tensor_2d(ctx: ffi.CData, type: int, ne0: int, ne1: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_new_tensor_2d(
struct ggml_context * ctx,
enum ggml_type type,
int64_t ne0,
int64_t ne1);
"""
...
def ggml_new_tensor_3d(ctx: ffi.CData, type: int, ne0: int, ne1: int, ne2: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_new_tensor_3d(
struct ggml_context * ctx,
enum ggml_type type,
int64_t ne0,
int64_t ne1,
int64_t ne2);
"""
...
def ggml_new_tensor_4d(ctx: ffi.CData, type: int, ne0: int, ne1: int, ne2: int, ne3: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_new_tensor_4d(
struct ggml_context * ctx,
enum ggml_type type,
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3);
"""
...
def ggml_norm(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
normalize along rows
TODO: eps is hardcoded to 1e-5 for now
GGML_API struct ggml_tensor * ggml_norm(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_norm_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_nrows(tensor: ffi.CData) -> int:
""" GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor);"""
...
def ggml_numa_init() -> None:
""" GGML_API void ggml_numa_init(void); // call once for better performance on NUMA systems"""
...
def ggml_op_name(op: int) -> ffi.CData:
""" GGML_API const char * ggml_op_name (enum ggml_op op);"""
...
def ggml_op_symbol(op: int) -> ffi.CData:
""" GGML_API const char * ggml_op_symbol(enum ggml_op op);"""
...
def ggml_opt(ctx: ffi.CData, params: ffi.CData, f: ffi.CData) -> int:
"""
optimize the function defined by the tensor f
GGML_API enum ggml_opt_result ggml_opt(
struct ggml_context * ctx,
struct ggml_opt_params params,
struct ggml_tensor * f);
"""
...
def ggml_opt_default_params(type: int) -> ffi.CData:
""" GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);"""
...
def ggml_opt_init(ctx: ffi.CData, opt: ffi.CData, params: ffi.CData, nx: int) -> None:
"""
initialize optimizer context
GGML_API void ggml_opt_init(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_opt_params params,
int64_t nx);
"""
...
def ggml_opt_resume(ctx: ffi.CData, opt: ffi.CData, f: ffi.CData) -> int:
"""
continue optimizing the function defined by the tensor f
GGML_API enum ggml_opt_result ggml_opt_resume(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_tensor * f);
"""
...
def ggml_opt_resume_g(ctx: ffi.CData, opt: ffi.CData, f: ffi.CData, gf: ffi.CData, gb: ffi.CData) -> int:
"""
continue optimizing the function defined by the tensor f
GGML_API enum ggml_opt_result ggml_opt_resume_g(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_tensor * f,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb);
"""
...
def ggml_out_prod(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
A: m columns, n rows,
B: p columns, n rows,
result is m columns, p rows
GGML_API struct ggml_tensor * ggml_out_prod(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_permute(ctx: ffi.CData, a: ffi.CData, axis0: int, axis1: int, axis2: int, axis3: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_permute(
struct ggml_context * ctx,
struct ggml_tensor * a,
int axis0,
int axis1,
int axis2,
int axis3);
"""
...
def ggml_pool_1d(ctx: ffi.CData, a: ffi.CData, op: int, k0: int, s0: int, p0: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_pool_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_op_pool op,
int k0, // kernel size
int s0, // stride
int p0); // padding
"""
...
def ggml_pool_2d(ctx: ffi.CData, a: ffi.CData, op: int, k0: int, k1: int, s0: int, s1: int, p0: int, p1: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_pool_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_op_pool op,
int k0,
int k1,
int s0,
int s1,
int p0,
int p1);
"""
...
def ggml_print_object(obj: ffi.CData) -> None:
""" GGML_API void ggml_print_object (const struct ggml_object * obj);"""
...
def ggml_print_objects(ctx: ffi.CData) -> None:
""" GGML_API void ggml_print_objects(const struct ggml_context * ctx);"""
...
def ggml_quantize_chunk(type: int, src: ffi.CData, dst: ffi.CData, start: int, n: int, hist: ffi.CData) -> int:
""" GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);"""
...
def ggml_quantize_q2_K(src: ffi.CData, dst: ffi.CData, n: int, k: int, hist: ffi.CData) -> int:
"""
Quantization with histogram collection
size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
"""
...
def ggml_quantize_q3_K(src: ffi.CData, dst: ffi.CData, n: int, k: int, hist: ffi.CData) -> int:
"""size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);"""
...
def ggml_quantize_q4_0(src: ffi.CData, dst: ffi.CData, n: int, k: int, hist: ffi.CData) -> int:
""" GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);"""
...
def ggml_quantize_q4_1(src: ffi.CData, dst: ffi.CData, n: int, k: int, hist: ffi.CData) -> int:
""" GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);"""
...
def ggml_quantize_q4_K(src: ffi.CData, dst: ffi.CData, n: int, k: int, hist: ffi.CData) -> int:
"""size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);"""
...
def ggml_quantize_q5_0(src: ffi.CData, dst: ffi.CData, n: int, k: int, hist: ffi.CData) -> int:
""" GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);"""
...
def ggml_quantize_q5_1(src: ffi.CData, dst: ffi.CData, n: int, k: int, hist: ffi.CData) -> int:
""" GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);"""
...
def ggml_quantize_q5_K(src: ffi.CData, dst: ffi.CData, n: int, k: int, hist: ffi.CData) -> int:
"""size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);"""
...
def ggml_quantize_q6_K(src: ffi.CData, dst: ffi.CData, n: int, k: int, hist: ffi.CData) -> int:
"""size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);"""
...
def ggml_quantize_q8_0(src: ffi.CData, dst: ffi.CData, n: int, k: int, hist: ffi.CData) -> int:
""" GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);"""
...
def ggml_relu(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_relu(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_relu_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_relu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_repeat(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
if a is the same shape as b, and a is not parameter, return a
otherwise, return a new tensor: repeat(a) to fit in b
GGML_API struct ggml_tensor * ggml_repeat(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_repeat_back(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_repeat_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_reshape(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
return view(a), b specifies the new shape
TODO: when we start computing gradient, make a copy instead of view
GGML_API struct ggml_tensor * ggml_reshape(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_reshape_1d(ctx: ffi.CData, a: ffi.CData, ne0: int) -> ffi.CData:
"""
return view(a)
TODO: when we start computing gradient, make a copy instead of view
GGML_API struct ggml_tensor * ggml_reshape_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0);
"""
...
def ggml_reshape_2d(ctx: ffi.CData, a: ffi.CData, ne0: int, ne1: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_reshape_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1);
"""
...
def ggml_reshape_3d(ctx: ffi.CData, a: ffi.CData, ne0: int, ne1: int, ne2: int) -> ffi.CData:
"""
return view(a)
TODO: when we start computing gradient, make a copy instead of view
GGML_API struct ggml_tensor * ggml_reshape_3d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
int64_t ne2);
"""
...
def ggml_reshape_4d(ctx: ffi.CData, a: ffi.CData, ne0: int, ne1: int, ne2: int, ne3: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_reshape_4d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3);
"""
...
def ggml_rms_norm(ctx: ffi.CData, a: ffi.CData, eps: float) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_rms_norm(
struct ggml_context * ctx,
struct ggml_tensor * a,
float eps);
"""
...
def ggml_rms_norm_back(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
a - x
b - dy
TODO: update with configurable eps
GGML_API struct ggml_tensor * ggml_rms_norm_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_rms_norm_inplace(ctx: ffi.CData, a: ffi.CData, eps: float) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_rms_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
float eps);
"""
...
def ggml_rope(ctx: ffi.CData, a: ffi.CData, n_past: int, n_dims: int, mode: int, n_ctx: int) -> ffi.CData:
"""
rotary position embedding
if mode & 1 == 1, skip n_past elements
if mode & 2 == 1, GPT-NeoX style
if mode & 4 == 1, ChatGLM style
TODO: avoid creating a new tensor every time
GGML_API struct ggml_tensor * ggml_rope(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode,
int n_ctx);
"""
...
def ggml_rope_back(ctx: ffi.CData, a: ffi.CData, n_past: int, n_dims: int, mode: int, n_ctx: int) -> ffi.CData:
"""
rotary position embedding backward, i.e compute dx from dy
a - dy
GGML_API struct ggml_tensor * ggml_rope_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode,
int n_ctx);
"""
...
def ggml_rope_custom(ctx: ffi.CData, a: ffi.CData, n_past: int, n_dims: int, mode: int, n_ctx: int, freq_base: float, freq_scale: float) -> ffi.CData:
"""
custom RoPE
GGML_API struct ggml_tensor * ggml_rope_custom(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode,
int n_ctx,
float freq_base,
float freq_scale);
"""
...
def ggml_rope_custom_inplace(ctx: ffi.CData, a: ffi.CData, n_past: int, n_dims: int, mode: int, n_ctx: int, freq_base: float, freq_scale: float) -> ffi.CData:
"""
in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode,
int n_ctx,
float freq_base,
float freq_scale);
"""
...
def ggml_rope_inplace(ctx: ffi.CData, a: ffi.CData, n_past: int, n_dims: int, mode: int, n_ctx: int) -> ffi.CData:
"""
in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_rope_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode,
int n_ctx);
"""
...
def ggml_scale(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_scale(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_scale_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_scale_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_set(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, nb1: int, nb2: int, nb3: int, offset: int) -> ffi.CData:
"""
b -> view(a,offset,nb1,nb2,3), return modified a
GGML_API struct ggml_tensor * ggml_set(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
size_t nb1,
size_t nb2,
size_t nb3,
size_t offset);
"""
...
def ggml_set_1d(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, offset: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_set_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
size_t offset);
"""
...
def ggml_set_1d_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, offset: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_set_1d_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
size_t offset);
"""
...
def ggml_set_2d(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, nb1: int, offset: int) -> ffi.CData:
"""
b -> view(a,offset,nb1,nb2,3), return modified a
GGML_API struct ggml_tensor * ggml_set_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
size_t nb1,
size_t offset);
"""
...
def ggml_set_2d_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, nb1: int, offset: int) -> ffi.CData:
"""
b -> view(a,offset,nb1,nb2,3), return view(a)
GGML_API struct ggml_tensor * ggml_set_2d_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
size_t nb1,
size_t offset);
"""
...
def ggml_set_f32(tensor: ffi.CData, value: float) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);"""
...
def ggml_set_f32_1d(tensor: ffi.CData, i: int, value: float) -> None:
""" GGML_API void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);"""
...
def ggml_set_i32(tensor: ffi.CData, value: int) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);"""
...
def ggml_set_i32_1d(tensor: ffi.CData, i: int, value: int) -> None:
""" GGML_API void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);"""
...
def ggml_set_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData, nb1: int, nb2: int, nb3: int, offset: int) -> ffi.CData:
"""
b -> view(a,offset,nb1,nb2,3), return view(a)
GGML_API struct ggml_tensor * ggml_set_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
size_t nb1,
size_t nb2,
size_t nb3,
size_t offset);
"""
...
def ggml_set_name(tensor: ffi.CData, name: ffi.CData) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name);"""
...
def ggml_set_no_alloc(ctx: ffi.CData, no_alloc: bool) -> None:
""" GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);"""
...
def ggml_set_param(ctx: ffi.CData, tensor: ffi.CData) -> None:
"""
GGML_API void ggml_set_param(
struct ggml_context * ctx,
struct ggml_tensor * tensor);
"""
...
def ggml_set_scratch(ctx: ffi.CData, scratch: ffi.CData) -> int:
""" GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch);"""
...
def ggml_set_zero(tensor: ffi.CData) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);"""
...
def ggml_sgn(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_sgn(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_sgn_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_sgn_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_silu(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_silu(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_silu_back(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
a - x
b - dy
GGML_API struct ggml_tensor * ggml_silu_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_silu_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_silu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_soft_max(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_soft_max(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_soft_max_back(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_soft_max_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_soft_max_back_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_soft_max_back_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_soft_max_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_soft_max_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_sqr(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_sqr(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_sqr_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_sqr_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_sqrt(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_sqrt(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_sqrt_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_sqrt_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_step(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_step(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_step_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_step_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_sub(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_sub(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_sub_inplace(ctx: ffi.CData, a: ffi.CData, b: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_sub_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
"""
...
def ggml_sum(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
return scalar
GGML_API struct ggml_tensor * ggml_sum(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_sum_rows(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
sums along rows, with input shape [a,b,c,d] return shape [1,b,c,d]
GGML_API struct ggml_tensor * ggml_sum_rows(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_tanh(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_tanh(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_tanh_inplace(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_tanh_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_tensor_overhead() -> int:
"""
use this to compute the memory overhead of a tensor
GGML_API size_t ggml_tensor_overhead(void);
"""
...
def ggml_time_init() -> None:
""" GGML_API void ggml_time_init(void); // call this once at the beginning of the program"""
...
def ggml_time_ms() -> int:
""" GGML_API int64_t ggml_time_ms(void);"""
...
def ggml_time_us() -> int:
""" GGML_API int64_t ggml_time_us(void);"""
...
def ggml_transpose(ctx: ffi.CData, a: ffi.CData) -> ffi.CData:
"""
alias for ggml_permute(ctx, a, 1, 0, 2, 3)
GGML_API struct ggml_tensor * ggml_transpose(
struct ggml_context * ctx,
struct ggml_tensor * a);
"""
...
def ggml_type_name(type: int) -> ffi.CData:
""" GGML_API const char * ggml_type_name(enum ggml_type type);"""
...
def ggml_type_size(type: int) -> int:
""" GGML_API size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block"""
...
def ggml_type_sizef(type: int) -> float:
""" GGML_API float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float"""
...
def ggml_unary(ctx: ffi.CData, a: ffi.CData, op: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_unary(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_unary_op op);
"""
...
def ggml_unary_inplace(ctx: ffi.CData, a: ffi.CData, op: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_unary_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_unary_op op);
"""
...
def ggml_used_mem(ctx: ffi.CData) -> int:
""" GGML_API size_t ggml_used_mem(const struct ggml_context * ctx);"""
...
def ggml_vec_dot_q2_K_q8_K(n: int, s: ffi.CData, vx: ffi.CData, vy: ffi.CData) -> None:
"""
Dot product
void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
"""
...
def ggml_vec_dot_q3_K_q8_K(n: int, s: ffi.CData, vx: ffi.CData, vy: ffi.CData) -> None:
"""void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);"""
...
def ggml_vec_dot_q4_K_q8_K(n: int, s: ffi.CData, vx: ffi.CData, vy: ffi.CData) -> None:
"""void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);"""
...
def ggml_vec_dot_q5_K_q8_K(n: int, s: ffi.CData, vx: ffi.CData, vy: ffi.CData) -> None:
"""void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);"""
...
def ggml_vec_dot_q6_K_q8_K(n: int, s: ffi.CData, vx: ffi.CData, vy: ffi.CData) -> None:
"""void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);"""
...
def ggml_view_1d(ctx: ffi.CData, a: ffi.CData, ne0: int, offset: int) -> ffi.CData:
"""
offset in bytes
GGML_API struct ggml_tensor * ggml_view_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
size_t offset);
"""
...
def ggml_view_2d(ctx: ffi.CData, a: ffi.CData, ne0: int, ne1: int, nb1: int, offset: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_view_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
size_t nb1, // row stride in bytes
size_t offset);
"""
...
def ggml_view_3d(ctx: ffi.CData, a: ffi.CData, ne0: int, ne1: int, ne2: int, nb1: int, nb2: int, offset: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_view_3d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
int64_t ne2,
size_t nb1, // row stride in bytes
size_t nb2, // slice stride in bytes
size_t offset);
"""
...
def ggml_view_4d(ctx: ffi.CData, a: ffi.CData, ne0: int, ne1: int, ne2: int, ne3: int, nb1: int, nb2: int, nb3: int, offset: int) -> ffi.CData:
"""
GGML_API struct ggml_tensor * ggml_view_4d(
struct ggml_context * ctx,
struct ggml_tensor * a,
int64_t ne0,
int64_t ne1,
int64_t ne2,
int64_t ne3,
size_t nb1, // row stride in bytes
size_t nb2, // slice stride in bytes
size_t nb3,
size_t offset);
"""
...
def ggml_view_tensor(ctx: ffi.CData, src: ffi.CData) -> ffi.CData:
""" GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src);"""
...
def ggml_win_part(ctx: ffi.CData, a: ffi.CData, w: int) -> ffi.CData:
"""
partition into non-overlapping windows with padding if needed
example:
a: 768 64 64 1
w: 14
res: 768 14 14 25
used in sam
GGML_API struct ggml_tensor * ggml_win_part(
struct ggml_context * ctx,
struct ggml_tensor * a,
int w);
"""
...
def ggml_win_unpart(ctx: ffi.CData, a: ffi.CData, w0: int, h0: int, w: int) -> ffi.CData:
"""
reverse of ggml_win_part
used in sam
GGML_API struct ggml_tensor * ggml_win_unpart(
struct ggml_context * ctx,
struct ggml_tensor * a,
int w0,
int h0,
int w);
"""
...
def gguf_add_tensor(ctx: ffi.CData, tensor: ffi.CData) -> None:
"""
manage tensor info
GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
"""
...
def gguf_find_key(ctx: ffi.CData, key: ffi.CData) -> int:
""" GGML_API int gguf_find_key(struct gguf_context * ctx, const char * key);"""
...
def gguf_find_tensor(ctx: ffi.CData, name: ffi.CData) -> int:
""" GGML_API int gguf_find_tensor (struct gguf_context * ctx, const char * name);"""
...
def gguf_free(ctx: ffi.CData) -> None:
""" GGML_API void gguf_free(struct gguf_context * ctx);"""
...
def gguf_get_alignment(ctx: ffi.CData) -> int:
""" GGML_API size_t gguf_get_alignment (struct gguf_context * ctx);"""
...
def gguf_get_arr_data(ctx: ffi.CData, i: int) -> ffi.CData:
""" GGML_API const void * gguf_get_arr_data(struct gguf_context * ctx, int i);"""
...
def gguf_get_arr_n(ctx: ffi.CData, i: int) -> int:
""" GGML_API int gguf_get_arr_n (struct gguf_context * ctx, int i);"""
...
def gguf_get_arr_str(ctx: ffi.CData, key_id: int, i: int) -> ffi.CData:
""" GGML_API const char * gguf_get_arr_str (struct gguf_context * ctx, int key_id, int i);"""
...
def gguf_get_arr_type(ctx: ffi.CData, i: int) -> int:
""" GGML_API enum gguf_type gguf_get_arr_type(struct gguf_context * ctx, int i);"""
...
def gguf_get_data(ctx: ffi.CData) -> ffi.CData:
""" GGML_API void * gguf_get_data (struct gguf_context * ctx);"""
...
def gguf_get_data_offset(ctx: ffi.CData) -> int:
""" GGML_API size_t gguf_get_data_offset(struct gguf_context * ctx);"""
...
def gguf_get_key(ctx: ffi.CData, i: int) -> ffi.CData:
""" GGML_API const char * gguf_get_key (struct gguf_context * ctx, int i);"""
...
def gguf_get_kv_type(ctx: ffi.CData, i: int) -> int:
""" GGML_API enum gguf_type gguf_get_kv_type (struct gguf_context * ctx, int i);"""
...
def gguf_get_meta_data(ctx: ffi.CData, data: ffi.CData) -> None:
""" GGML_API void gguf_get_meta_data(struct gguf_context * ctx, void * data);"""
...
def gguf_get_meta_size(ctx: ffi.CData) -> int:
"""
get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
GGML_API size_t gguf_get_meta_size(struct gguf_context * ctx);
"""
...
def gguf_get_n_kv(ctx: ffi.CData) -> int:
""" GGML_API int gguf_get_n_kv(struct gguf_context * ctx);"""
...
def gguf_get_n_tensors(ctx: ffi.CData) -> int:
""" GGML_API int gguf_get_n_tensors (struct gguf_context * ctx);"""
...
def gguf_get_tensor_name(ctx: ffi.CData, i: int) -> ffi.CData:
""" GGML_API char * gguf_get_tensor_name (struct gguf_context * ctx, int i);"""
...
def gguf_get_tensor_offset(ctx: ffi.CData, i: int) -> int:
""" GGML_API size_t gguf_get_tensor_offset(struct gguf_context * ctx, int i);"""
...
def gguf_get_val_bool(ctx: ffi.CData, i: int) -> bool:
""" GGML_API bool gguf_get_val_bool(struct gguf_context * ctx, int i);"""
...
def gguf_get_val_f32(ctx: ffi.CData, i: int) -> float:
""" GGML_API float gguf_get_val_f32 (struct gguf_context * ctx, int i);"""
...
def gguf_get_val_i16(ctx: ffi.CData, i: int) -> int:
""" GGML_API int16_t gguf_get_val_i16 (struct gguf_context * ctx, int i);"""
...
def gguf_get_val_i32(ctx: ffi.CData, i: int) -> int:
""" GGML_API int32_t gguf_get_val_i32 (struct gguf_context * ctx, int i);"""
...
def gguf_get_val_i8(ctx: ffi.CData, i: int) -> int:
""" GGML_API int8_t gguf_get_val_i8 (struct gguf_context * ctx, int i);"""
...
def gguf_get_val_str(ctx: ffi.CData, i: int) -> ffi.CData:
""" GGML_API const char * gguf_get_val_str (struct gguf_context * ctx, int i);"""
...
def gguf_get_val_u16(ctx: ffi.CData, i: int) -> int:
""" GGML_API uint16_t gguf_get_val_u16 (struct gguf_context * ctx, int i);"""
...
def gguf_get_val_u32(ctx: ffi.CData, i: int) -> int:
""" GGML_API uint32_t gguf_get_val_u32 (struct gguf_context * ctx, int i);"""
...
def gguf_get_val_u8(ctx: ffi.CData, i: int) -> int:
"""
results are undefined if the wrong type is used for the key
GGML_API uint8_t gguf_get_val_u8 (struct gguf_context * ctx, int i);
"""
...
def gguf_get_version(ctx: ffi.CData) -> int:
""" GGML_API int gguf_get_version (struct gguf_context * ctx);"""
...
def gguf_init_empty() -> ffi.CData:
""" GGML_API struct gguf_context * gguf_init_empty(void);"""
...
def gguf_init_from_file(fname: ffi.CData, params: ffi.CData) -> ffi.CData:
""" GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);"""
...
def gguf_set_arr_data(ctx: ffi.CData, key: ffi.CData, type: int, data: ffi.CData, n: int) -> None:
""" GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);"""
...
def gguf_set_arr_str(ctx: ffi.CData, key: ffi.CData, data: ffi.CData, n: int) -> None:
""" GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, int n);"""
...
def gguf_set_kv(ctx: ffi.CData, src: ffi.CData) -> None:
"""
set or add KV pairs from another context
GGML_API void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src);
"""
...
def gguf_set_tensor_data(ctx: ffi.CData, name: ffi.CData, data: ffi.CData, size: int) -> None:
""" GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size);"""
...
def gguf_set_tensor_type(ctx: ffi.CData, name: ffi.CData, type: int) -> None:
""" GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);"""
...
def gguf_set_val_bool(ctx: ffi.CData, key: ffi.CData, val: bool) -> None:
""" GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val);"""
...
def gguf_set_val_f32(ctx: ffi.CData, key: ffi.CData, val: float) -> None:
""" GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float val);"""
...
def gguf_set_val_i16(ctx: ffi.CData, key: ffi.CData, val: int) -> None:
""" GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t val);"""
...
def gguf_set_val_i32(ctx: ffi.CData, key: ffi.CData, val: int) -> None:
""" GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t val);"""
...
def gguf_set_val_i8(ctx: ffi.CData, key: ffi.CData, val: int) -> None:
""" GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val);"""
...
def gguf_set_val_str(ctx: ffi.CData, key: ffi.CData, val: ffi.CData) -> None:
""" GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);"""
...
def gguf_set_val_u16(ctx: ffi.CData, key: ffi.CData, val: int) -> None:
""" GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val);"""
...
def gguf_set_val_u32(ctx: ffi.CData, key: ffi.CData, val: int) -> None:
""" GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);"""
...
def gguf_set_val_u8(ctx: ffi.CData, key: ffi.CData, val: int) -> None:
"""
overrides existing values or adds a new one
GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val);
"""
...
def gguf_type_name(type: int) -> ffi.CData:
""" GGML_API const char * gguf_type_name(enum gguf_type type);"""
...
def gguf_write_to_file(ctx: ffi.CData, fname: ffi.CData, only_meta: bool) -> None:
"""
write the entire context to a binary file
GGML_API void gguf_write_to_file(struct gguf_context * ctx, const char * fname, bool only_meta);
"""
...
def quantize_row_q2_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);"""
...
def quantize_row_q2_K_reference(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""
Quantization
void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
"""
...
def quantize_row_q3_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);"""
...
def quantize_row_q3_K_reference(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);"""
...
def quantize_row_q4_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);"""
...
def quantize_row_q4_K_reference(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);"""
...
def quantize_row_q5_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);"""
...
def quantize_row_q5_K_reference(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);"""
...
def quantize_row_q6_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);"""
...
def quantize_row_q6_K_reference(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);"""
...
def quantize_row_q8_K(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);"""
...
def quantize_row_q8_K_reference(x: ffi.CData, y: ffi.CData, k: int) -> None:
"""void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);"""
...
