pymllm.ffi

Submodules

• pymllm.ffi.base

Attributes

MLLM_FIND_TORCH_AVAILABLE
MLLM_FIND_NUMPY_AVAILABLE
float32
float16
bfloat16
cpu
cuda
qnn

Classes

Device
DType
Tensor
Session

Functions

echo(rec)
initialize_context()
shutdown_context()
float32_()
float16_()
bfloat16_()
cpu_()
cuda_()
qnn_()
device(device_type)
empty(shape[, dtype, device_type])
zeros(shape[, dtype, device_type])
ones(shape[, dtype, device_type])
arange(start, end[, step, dtype, device_type])
random(shape[, start, end, dtype, device_type])
is_torch_available()
is_numpy_available()
from_torch(torch_tensor)
from_numpy(numpy_tensor)

Package Contents

pymllm.ffi.MLLM_FIND_TORCH_AVAILABLE

pymllm.ffi.MLLM_FIND_NUMPY_AVAILABLE

pymllm.ffi.echo(rec)

Parameters:

rec (str)

Return type:

None

pymllm.ffi.initialize_context()

Return type:

None

pymllm.ffi.shutdown_context()

Return type:

None

class pymllm.ffi.Device

Bases: tvm_ffi.Object

to_pod()

Return type:

int

class pymllm.ffi.DType

Bases: tvm_ffi.Object

to_pod()

Return type:

int

pymllm.ffi.float32_()

Return type:

DType

pymllm.ffi.float16_()

Return type:

DType

pymllm.ffi.bfloat16_()

Return type:

DType

pymllm.ffi.cpu_()

Return type:

Device

pymllm.ffi.cuda_()

Return type:

Device

pymllm.ffi.qnn_()

Return type:

Device

class pymllm.ffi.Tensor

Bases: tvm_ffi.Object

__str__()

Return type:

str

property shape: tvm_ffi.Shape

Return type:

tvm_ffi.Shape

property dtype: DType

Return type:

DType

property device: Device

Return type:

Device

tobytes()

Return type:

tvm_ffi.Array

__add__(other)

Return type:

Tensor

__sub__(other)

__mul__(other)

Return type:

Tensor

__div__(other)

Return type:

Tensor

__neg__(other)

Return type:

Tensor

abs()

Return type:

Tensor

clip(min_val, max_val)

Parameters:

• min_val (float)

• max_val (float)

Return type:

Tensor

min(dim=-1, keep_dim=False)

Parameters:

• dim (int)

• keep_dim (bool)

Return type:

Tensor

max(dim=-1, keep_dim=False)

Parameters:

• dim (int)

• keep_dim (bool)

Return type:

Tensor

sum(dim=-1, keep_dim=False)

Parameters:

• dim (int)

• keep_dim (bool)

Return type:

Tensor

mean(dim=-1, keep_dim=False)

Parameters:

• dim (int)

• keep_dim (bool)

Return type:

Tensor

transpose(dim0, dim1)

Parameters:

• dim0 (int)

• dim1 (int)

Return type:

Tensor

property T: Tensor

Return type:

Tensor

view(shape)

Return type:

Tensor

unsqueeze(dim)

Parameters:

dim (int)

Return type:

Tensor

squeeze(dim)

Parameters:

dim (int)

Return type:

Tensor

permute(dims)

Return type:

Tensor

contiguous()

Return type:

Tensor

clone()

Return type:

Tensor

repeat(multiplier, dim)

Return type:

Tensor

to(dd)

Parameters:

dd (Union[Device, DType])

Return type:

Tensor

cpu()

Return type:

Tensor

cuda()

Return type:

Tensor

property name

set_name(name)

numel()

property rank

is_contiguous()

pymllm.ffi.float32: DType

pymllm.ffi.float16: DType

pymllm.ffi.bfloat16: DType

pymllm.ffi.cpu: Device

pymllm.ffi.cuda: Device

pymllm.ffi.qnn: Device

pymllm.ffi.device(device_type)

Parameters:

device_type (str)

Return type:

Device

pymllm.ffi.empty(shape, dtype=float32, device_type=cpu)

Parameters:

• shape (tvm_ffi.Shape)

• dtype (DType)

• device_type (Union[Device, str])

Return type:

Tensor

pymllm.ffi.zeros(shape, dtype=float32, device_type=cpu)

Parameters:

• shape (tvm_ffi.Shape)

• dtype (DType)

• device_type (Union[Device, str])

Return type:

Tensor

pymllm.ffi.ones(shape, dtype=float32, device_type=cpu)

Parameters:

• shape (tvm_ffi.Shape)

• dtype (DType)

• device_type (Union[Device, str])

Return type:

Tensor

pymllm.ffi.arange(start, end, step=1, dtype=float32, device_type=cpu)

Parameters:

• start (float)

• end (float)

• step (float)

• dtype (DType)

• device_type (Union[Device, str])

Return type:

Tensor

pymllm.ffi.random(shape, start=-1.0, end=1.0, dtype=float32, device_type=cpu)

Parameters:

• shape (tvm_ffi.Shape)

• start (float)

• end (float)

• dtype (DType)

• device_type (Union[Device, str])

Return type:

Tensor

pymllm.ffi.is_torch_available()

Return type:

bool

pymllm.ffi.is_numpy_available()

Return type:

bool

pymllm.ffi.from_torch(torch_tensor)

pymllm.ffi.from_numpy(numpy_tensor)

class pymllm.ffi.Session

Bases: tvm_ffi.Object