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rwkv.cpp(ggml) support

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josc146
2023-12-12 20:29:55 +08:00
parent 6e29f97881
commit b14fbc29b7
26 changed files with 1234 additions and 102 deletions
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from typing import Any, List
from . import rwkv_cpp_model
from . import rwkv_cpp_shared_library
class RWKV:
def __init__(self, model_path: str, strategy=None):
self.library = rwkv_cpp_shared_library.load_rwkv_shared_library()
self.model = rwkv_cpp_model.RWKVModel(self.library, model_path)
self.w = {} # fake weight
self.w["emb.weight"] = [0] * self.model.n_vocab
def forward(self, tokens: List[int], state: Any | None):
return self.model.eval_sequence_in_chunks(tokens, state, use_numpy=True)

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import os
import multiprocessing
# Pre-import PyTorch, if available.
# This fixes "OSError: [WinError 127] The specified procedure could not be found".
try:
import torch
except ModuleNotFoundError:
pass
# I'm sure this is not strictly correct, but let's keep this crutch for now.
try:
import rwkv_cpp_shared_library
except ModuleNotFoundError:
from . import rwkv_cpp_shared_library
from typing import TypeVar, Optional, Tuple, List
# A value of this type is either a numpy's ndarray or a PyTorch's Tensor.
NumpyArrayOrPyTorchTensor: TypeVar = TypeVar('NumpyArrayOrPyTorchTensor')
class RWKVModel:
"""
An RWKV model managed by rwkv.cpp library.
"""
def __init__(
self,
shared_library: rwkv_cpp_shared_library.RWKVSharedLibrary,
model_path: str,
thread_count: int = max(1, multiprocessing.cpu_count() // 2),
gpu_layer_count: int = 0,
**kwargs
) -> None:
"""
Loads the model and prepares it for inference.
In case of any error, this method will throw an exception.
Parameters
----------
shared_library : RWKVSharedLibrary
rwkv.cpp shared library.
model_path : str
Path to RWKV model file in ggml format.
thread_count : int
Thread count to use. If not set, defaults to CPU count / 2.
gpu_layer_count : int
Count of layers to offload onto the GPU, must be >= 0.
See documentation of `gpu_offload_layers` for details about layer offloading.
"""
if 'gpu_layers_count' in kwargs:
gpu_layer_count = kwargs['gpu_layers_count']
assert os.path.isfile(model_path), f'{model_path} is not a file'
assert thread_count > 0, 'Thread count must be > 0'
assert gpu_layer_count >= 0, 'GPU layer count must be >= 0'
self._library: rwkv_cpp_shared_library.RWKVSharedLibrary = shared_library
self._ctx: rwkv_cpp_shared_library.RWKVContext = self._library.rwkv_init_from_file(model_path, thread_count)
if gpu_layer_count > 0:
self.gpu_offload_layers(gpu_layer_count)
self._state_buffer_element_count: int = self._library.rwkv_get_state_buffer_element_count(self._ctx)
self._logits_buffer_element_count: int = self._library.rwkv_get_logits_buffer_element_count(self._ctx)
self._valid: bool = True
def gpu_offload_layers(self, layer_count: int) -> bool:
"""
Offloads specified count of model layers onto the GPU. Offloaded layers are evaluated using cuBLAS or CLBlast.
For the purposes of this function, model head (unembedding matrix) is treated as an additional layer:
- pass `model.n_layer` to offload all layers except model head
- pass `model.n_layer + 1` to offload all layers, including model head
Returns true if at least one layer was offloaded.
If rwkv.cpp was compiled without cuBLAS and CLBlast support, this function is a no-op and always returns false.
Parameters
----------
layer_count : int
Count of layers to offload onto the GPU, must be >= 0.
"""
assert layer_count >= 0, 'Layer count must be >= 0'
return self._library.rwkv_gpu_offload_layers(self._ctx, layer_count)
@property
def n_vocab(self) -> int:
return self._library.rwkv_get_n_vocab(self._ctx)
@property
def n_embed(self) -> int:
return self._library.rwkv_get_n_embed(self._ctx)
@property
def n_layer(self) -> int:
return self._library.rwkv_get_n_layer(self._ctx)
def eval(
self,
token: int,
state_in: Optional[NumpyArrayOrPyTorchTensor],
state_out: Optional[NumpyArrayOrPyTorchTensor] = None,
logits_out: Optional[NumpyArrayOrPyTorchTensor] = None,
use_numpy: bool = False
) -> Tuple[NumpyArrayOrPyTorchTensor, NumpyArrayOrPyTorchTensor]:
"""
Evaluates the model for a single token.
In case of any error, this method will throw an exception.
Parameters
----------
token : int
Index of next token to be seen by the model. Must be in range 0 <= token < n_vocab.
state_in : Optional[NumpyArrayOrTorchTensor]
State from previous call of this method. If this is a first pass, set it to None.
state_out : Optional[NumpyArrayOrTorchTensor]
Optional output tensor for state. If provided, must be of type float32, contiguous and of shape (state_buffer_element_count).
logits_out : Optional[NumpyArrayOrTorchTensor]
Optional output tensor for logits. If provided, must be of type float32, contiguous and of shape (logits_buffer_element_count).
use_numpy : bool
If set to True, numpy's ndarrays will be created instead of PyTorch's Tensors.
This parameter is ignored if any tensor parameter is not None; in such case,
type of returned tensors will match the type of received tensors.
Returns
-------
logits, state
Logits vector of shape (n_vocab); state for the next step.
"""
assert self._valid, 'Model was freed'
use_numpy = self._detect_numpy_usage([state_in, state_out, logits_out], use_numpy)
if state_in is not None:
self._validate_tensor(state_in, 'state_in', self._state_buffer_element_count)
state_in_ptr = self._get_data_ptr(state_in)
else:
state_in_ptr = 0
if state_out is not None:
self._validate_tensor(state_out, 'state_out', self._state_buffer_element_count)
else:
state_out = self._zeros_float32(self._state_buffer_element_count, use_numpy)
if logits_out is not None:
self._validate_tensor(logits_out, 'logits_out', self._logits_buffer_element_count)
else:
logits_out = self._zeros_float32(self._logits_buffer_element_count, use_numpy)
self._library.rwkv_eval(
self._ctx,
token,
state_in_ptr,
self._get_data_ptr(state_out),
self._get_data_ptr(logits_out)
)
return logits_out, state_out
def eval_sequence(
self,
tokens: List[int],
state_in: Optional[NumpyArrayOrPyTorchTensor],
state_out: Optional[NumpyArrayOrPyTorchTensor] = None,
logits_out: Optional[NumpyArrayOrPyTorchTensor] = None,
use_numpy: bool = False
) -> Tuple[NumpyArrayOrPyTorchTensor, NumpyArrayOrPyTorchTensor]:
"""
Evaluates the model for a sequence of tokens.
NOTE ON GGML NODE LIMIT
ggml has a hard-coded limit on max amount of nodes in a computation graph. The sequence graph is built in a way that quickly exceedes
this limit when using large models and/or large sequence lengths.
Fortunately, rwkv.cpp's fork of ggml has increased limit which was tested to work for sequence lengths up to 64 for 14B models.
If you get `GGML_ASSERT: ...\\ggml.c:16941: cgraph->n_nodes < GGML_MAX_NODES`, this means you've exceeded the limit.
To get rid of the assertion failure, reduce the model size and/or sequence length.
In case of any error, this method will throw an exception.
Parameters
----------
tokens : List[int]
Indices of the next tokens to be seen by the model. Must be in range 0 <= token < n_vocab.
state_in : Optional[NumpyArrayOrTorchTensor]
State from previous call of this method. If this is a first pass, set it to None.
state_out : Optional[NumpyArrayOrTorchTensor]
Optional output tensor for state. If provided, must be of type float32, contiguous and of shape (state_buffer_element_count).
logits_out : Optional[NumpyArrayOrTorchTensor]
Optional output tensor for logits. If provided, must be of type float32, contiguous and of shape (logits_buffer_element_count).
use_numpy : bool
If set to True, numpy's ndarrays will be created instead of PyTorch's Tensors.
This parameter is ignored if any tensor parameter is not None; in such case,
type of returned tensors will match the type of received tensors.
Returns
-------
logits, state
Logits vector of shape (n_vocab); state for the next step.
"""
assert self._valid, 'Model was freed'
use_numpy = self._detect_numpy_usage([state_in, state_out, logits_out], use_numpy)
if state_in is not None:
self._validate_tensor(state_in, 'state_in', self._state_buffer_element_count)
state_in_ptr = self._get_data_ptr(state_in)
else:
state_in_ptr = 0
if state_out is not None:
self._validate_tensor(state_out, 'state_out', self._state_buffer_element_count)
else:
state_out = self._zeros_float32(self._state_buffer_element_count, use_numpy)
if logits_out is not None:
self._validate_tensor(logits_out, 'logits_out', self._logits_buffer_element_count)
else:
logits_out = self._zeros_float32(self._logits_buffer_element_count, use_numpy)
self._library.rwkv_eval_sequence(
self._ctx,
tokens,
state_in_ptr,
self._get_data_ptr(state_out),
self._get_data_ptr(logits_out)
)
return logits_out, state_out
def eval_sequence_in_chunks(
self,
tokens: List[int],
state_in: Optional[NumpyArrayOrPyTorchTensor],
state_out: Optional[NumpyArrayOrPyTorchTensor] = None,
logits_out: Optional[NumpyArrayOrPyTorchTensor] = None,
chunk_size: int = 16,
use_numpy: bool = False
) -> Tuple[NumpyArrayOrPyTorchTensor, NumpyArrayOrPyTorchTensor]:
"""
Evaluates the model for a sequence of tokens using `eval_sequence`, splitting a potentially long sequence into fixed-length chunks.
This function is useful for processing complete prompts and user input in chat & role-playing use-cases.
It is recommended to use this function instead of `eval_sequence` to avoid mistakes and get maximum performance.
Chunking allows processing sequences of thousands of tokens, while not reaching the ggml's node limit and not consuming too much memory.
A reasonable and recommended value of chunk size is 16. If you want maximum performance, try different chunk sizes in range [2..64]
and choose one that works the best in your use case.
In case of any error, this method will throw an exception.
Parameters
----------
tokens : List[int]
Indices of the next tokens to be seen by the model. Must be in range 0 <= token < n_vocab.
chunk_size : int
Size of each chunk in tokens, must be positive.
state_in : Optional[NumpyArrayOrTorchTensor]
State from previous call of this method. If this is a first pass, set it to None.
state_out : Optional[NumpyArrayOrTorchTensor]
Optional output tensor for state. If provided, must be of type float32, contiguous and of shape (state_buffer_element_count).
logits_out : Optional[NumpyArrayOrTorchTensor]
Optional output tensor for logits. If provided, must be of type float32, contiguous and of shape (logits_buffer_element_count).
use_numpy : bool
If set to True, numpy's ndarrays will be created instead of PyTorch's Tensors.
This parameter is ignored if any tensor parameter is not None; in such case,
type of returned tensors will match the type of received tensors.
Returns
-------
logits, state
Logits vector of shape (n_vocab); state for the next step.
"""
assert self._valid, 'Model was freed'
use_numpy = self._detect_numpy_usage([state_in, state_out, logits_out], use_numpy)
if state_in is not None:
self._validate_tensor(state_in, 'state_in', self._state_buffer_element_count)
state_in_ptr = self._get_data_ptr(state_in)
else:
state_in_ptr = 0
if state_out is not None:
self._validate_tensor(state_out, 'state_out', self._state_buffer_element_count)
else:
state_out = self._zeros_float32(self._state_buffer_element_count, use_numpy)
if logits_out is not None:
self._validate_tensor(logits_out, 'logits_out', self._logits_buffer_element_count)
else:
logits_out = self._zeros_float32(self._logits_buffer_element_count, use_numpy)
self._library.rwkv_eval_sequence_in_chunks(
self._ctx,
tokens,
chunk_size,
state_in_ptr,
self._get_data_ptr(state_out),
self._get_data_ptr(logits_out)
)
return logits_out, state_out
def free(self) -> None:
"""
Frees all allocated resources.
In case of any error, this method will throw an exception.
The object must not be used anymore after calling this method.
"""
assert self._valid, 'Already freed'
self._valid = False
self._library.rwkv_free(self._ctx)
def __del__(self) -> None:
# Free the context on GC in case user forgot to call free() explicitly.
if hasattr(self, '_valid') and self._valid:
self.free()
def _is_pytorch_tensor(self, tensor: NumpyArrayOrPyTorchTensor) -> bool:
return hasattr(tensor, '__module__') and tensor.__module__ == 'torch'
def _detect_numpy_usage(self, tensors: List[Optional[NumpyArrayOrPyTorchTensor]], use_numpy_by_default: bool) -> bool:
for tensor in tensors:
if tensor is not None:
return False if self._is_pytorch_tensor(tensor) else True
return use_numpy_by_default
def _validate_tensor(self, tensor: NumpyArrayOrPyTorchTensor, name: str, size: int) -> None:
if self._is_pytorch_tensor(tensor):
tensor: torch.Tensor = tensor
assert tensor.device == torch.device('cpu'), f'{name} is not on CPU'
assert tensor.dtype == torch.float32, f'{name} is not of type float32'
assert tensor.shape == (size,), f'{name} has invalid shape {tensor.shape}, expected ({size})'
assert tensor.is_contiguous(), f'{name} is not contiguous'
else:
import numpy as np
tensor: np.ndarray = tensor
assert tensor.dtype == np.float32, f'{name} is not of type float32'
assert tensor.shape == (size,), f'{name} has invalid shape {tensor.shape}, expected ({size})'
assert tensor.data.contiguous, f'{name} is not contiguous'
def _get_data_ptr(self, tensor: NumpyArrayOrPyTorchTensor):
if self._is_pytorch_tensor(tensor):
return tensor.data_ptr()
else:
return tensor.ctypes.data
def _zeros_float32(self, element_count: int, use_numpy: bool) -> NumpyArrayOrPyTorchTensor:
if use_numpy:
import numpy as np
return np.zeros(element_count, dtype=np.float32)
else:
return torch.zeros(element_count, dtype=torch.float32, device='cpu')

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import os
import sys
import ctypes
import pathlib
import platform
from typing import Optional, List, Tuple, Callable
QUANTIZED_FORMAT_NAMES: Tuple[str, str, str, str, str] = (
'Q4_0',
'Q4_1',
'Q5_0',
'Q5_1',
'Q8_0'
)
P_FLOAT = ctypes.POINTER(ctypes.c_float)
P_INT = ctypes.POINTER(ctypes.c_int32)
class RWKVContext:
def __init__(self, ptr: ctypes.pointer) -> None:
self.ptr: ctypes.pointer = ptr
class RWKVSharedLibrary:
"""
Python wrapper around rwkv.cpp shared library.
"""
def __init__(self, shared_library_path: str) -> None:
"""
Loads the shared library from specified file.
In case of any error, this method will throw an exception.
Parameters
----------
shared_library_path : str
Path to rwkv.cpp shared library. On Windows, it would look like 'rwkv.dll'. On UNIX, 'rwkv.so'.
"""
# When Python is greater than 3.8, we need to reprocess the custom dll
# according to the documentation to prevent loading failure errors.
# https://docs.python.org/3/whatsnew/3.8.html#ctypes
if platform.system().lower() == 'windows':
self.library = ctypes.CDLL(shared_library_path, winmode=0)
else:
self.library = ctypes.cdll.LoadLibrary(shared_library_path)
self.library.rwkv_init_from_file.argtypes = [ctypes.c_char_p, ctypes.c_uint32]
self.library.rwkv_init_from_file.restype = ctypes.c_void_p
self.library.rwkv_gpu_offload_layers.argtypes = [ctypes.c_void_p, ctypes.c_uint32]
self.library.rwkv_gpu_offload_layers.restype = ctypes.c_bool
self.library.rwkv_eval.argtypes = [
ctypes.c_void_p, # ctx
ctypes.c_int32, # token
P_FLOAT, # state_in
P_FLOAT, # state_out
P_FLOAT # logits_out
]
self.library.rwkv_eval.restype = ctypes.c_bool
self.library.rwkv_eval_sequence.argtypes = [
ctypes.c_void_p, # ctx
P_INT, # tokens
ctypes.c_size_t, # token count
P_FLOAT, # state_in
P_FLOAT, # state_out
P_FLOAT # logits_out
]
self.library.rwkv_eval_sequence.restype = ctypes.c_bool
self.library.rwkv_eval_sequence_in_chunks.argtypes = [
ctypes.c_void_p, # ctx
P_INT, # tokens
ctypes.c_size_t, # token count
ctypes.c_size_t, # chunk size
P_FLOAT, # state_in
P_FLOAT, # state_out
P_FLOAT # logits_out
]
self.library.rwkv_eval_sequence_in_chunks.restype = ctypes.c_bool
self.library.rwkv_get_n_vocab.argtypes = [ctypes.c_void_p]
self.library.rwkv_get_n_vocab.restype = ctypes.c_size_t
self.library.rwkv_get_n_embed.argtypes = [ctypes.c_void_p]
self.library.rwkv_get_n_embed.restype = ctypes.c_size_t
self.library.rwkv_get_n_layer.argtypes = [ctypes.c_void_p]
self.library.rwkv_get_n_layer.restype = ctypes.c_size_t
self.library.rwkv_get_state_buffer_element_count.argtypes = [ctypes.c_void_p]
self.library.rwkv_get_state_buffer_element_count.restype = ctypes.c_uint32
self.library.rwkv_get_logits_buffer_element_count.argtypes = [ctypes.c_void_p]
self.library.rwkv_get_logits_buffer_element_count.restype = ctypes.c_uint32
self.library.rwkv_free.argtypes = [ctypes.c_void_p]
self.library.rwkv_free.restype = None
self.library.rwkv_free.argtypes = [ctypes.c_void_p]
self.library.rwkv_free.restype = None
self.library.rwkv_quantize_model_file.argtypes = [ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p]
self.library.rwkv_quantize_model_file.restype = ctypes.c_bool
self.library.rwkv_get_system_info_string.argtypes = []
self.library.rwkv_get_system_info_string.restype = ctypes.c_char_p
self.nullptr = ctypes.cast(0, ctypes.c_void_p)
def rwkv_init_from_file(self, model_file_path: str, thread_count: int) -> RWKVContext:
"""
Loads the model from a file and prepares it for inference.
Throws an exception in case of any error. Error messages would be printed to stderr.
Parameters
----------
model_file_path : str
Path to model file in ggml format.
thread_count : int
Count of threads to use, must be positive.
"""
ptr = self.library.rwkv_init_from_file(model_file_path.encode('utf-8'), ctypes.c_uint32(thread_count))
assert ptr is not None, 'rwkv_init_from_file failed, check stderr'
return RWKVContext(ptr)
def rwkv_gpu_offload_layers(self, ctx: RWKVContext, layer_count: int) -> bool:
"""
Offloads specified count of model layers onto the GPU. Offloaded layers are evaluated using cuBLAS or CLBlast.
For the purposes of this function, model head (unembedding matrix) is treated as an additional layer:
- pass `rwkv_get_n_layer(ctx)` to offload all layers except model head
- pass `rwkv_get_n_layer(ctx) + 1` to offload all layers, including model head
Returns true if at least one layer was offloaded.
If rwkv.cpp was compiled without cuBLAS and CLBlast support, this function is a no-op and always returns false.
Parameters
----------
ctx : RWKVContext
RWKV context obtained from rwkv_init_from_file.
layer_count : int
Count of layers to offload onto the GPU, must be >= 0.
"""
assert layer_count >= 0, 'Layer count must be >= 0'
return self.library.rwkv_gpu_offload_layers(ctx.ptr, ctypes.c_uint32(layer_count))
def rwkv_eval(
self,
ctx: RWKVContext,
token: int,
state_in_address: Optional[int],
state_out_address: int,
logits_out_address: int
) -> None:
"""
Evaluates the model for a single token.
Throws an exception in case of any error. Error messages would be printed to stderr.
Not thread-safe. For parallel inference, call rwkv_clone_context to create one rwkv_context for each thread.
Parameters
----------
ctx : RWKVContext
RWKV context obtained from rwkv_init_from_file.
token : int
Next token index, in range 0 <= token < n_vocab.
state_in_address : int
Address of the first element of a FP32 buffer of size rwkv_get_state_buffer_element_count; or None, if this is a first pass.
state_out_address : int
Address of the first element of a FP32 buffer of size rwkv_get_state_buffer_element_count. This buffer will be written to.
logits_out_address : int
Address of the first element of a FP32 buffer of size rwkv_get_logits_buffer_element_count. This buffer will be written to.
"""
assert self.library.rwkv_eval(
ctx.ptr,
ctypes.c_int32(token),
ctypes.cast(0 if state_in_address is None else state_in_address, P_FLOAT),
ctypes.cast(state_out_address, P_FLOAT),
ctypes.cast(logits_out_address, P_FLOAT)
), 'rwkv_eval failed, check stderr'
def rwkv_eval_sequence(
self,
ctx: RWKVContext,
tokens: List[int],
state_in_address: Optional[int],
state_out_address: int,
logits_out_address: int
) -> None:
"""
Evaluates the model for a sequence of tokens.
Uses a faster algorithm than `rwkv_eval` if you do not need the state and logits for every token. Best used with sequence lengths of 64 or so.
Has to build a computation graph on the first call for a given sequence, but will use this cached graph for subsequent calls of the same sequence length.
NOTE ON GGML NODE LIMIT
ggml has a hard-coded limit on max amount of nodes in a computation graph. The sequence graph is built in a way that quickly exceedes
this limit when using large models and/or large sequence lengths.
Fortunately, rwkv.cpp's fork of ggml has increased limit which was tested to work for sequence lengths up to 64 for 14B models.
If you get `GGML_ASSERT: ...\\ggml.c:16941: cgraph->n_nodes < GGML_MAX_NODES`, this means you've exceeded the limit.
To get rid of the assertion failure, reduce the model size and/or sequence length.
Not thread-safe. For parallel inference, call `rwkv_clone_context` to create one rwkv_context for each thread.
Throws an exception in case of any error. Error messages would be printed to stderr.
Parameters
----------
ctx : RWKVContext
RWKV context obtained from rwkv_init_from_file.
tokens : List[int]
Next token indices, in range 0 <= token < n_vocab.
state_in_address : int
Address of the first element of a FP32 buffer of size rwkv_get_state_buffer_element_count; or None, if this is a first pass.
state_out_address : int
Address of the first element of a FP32 buffer of size rwkv_get_state_buffer_element_count. This buffer will be written to.
logits_out_address : int
Address of the first element of a FP32 buffer of size rwkv_get_logits_buffer_element_count. This buffer will be written to.
"""
assert self.library.rwkv_eval_sequence(
ctx.ptr,
ctypes.cast((ctypes.c_int32 * len(tokens))(*tokens), P_INT),
ctypes.c_size_t(len(tokens)),
ctypes.cast(0 if state_in_address is None else state_in_address, P_FLOAT),
ctypes.cast(state_out_address, P_FLOAT),
ctypes.cast(logits_out_address, P_FLOAT)
), 'rwkv_eval_sequence failed, check stderr'
def rwkv_eval_sequence_in_chunks(
self,
ctx: RWKVContext,
tokens: List[int],
chunk_size: int,
state_in_address: Optional[int],
state_out_address: int,
logits_out_address: int
) -> None:
"""
Evaluates the model for a sequence of tokens using `rwkv_eval_sequence`, splitting a potentially long sequence into fixed-length chunks.
This function is useful for processing complete prompts and user input in chat & role-playing use-cases.
It is recommended to use this function instead of `rwkv_eval_sequence` to avoid mistakes and get maximum performance.
Chunking allows processing sequences of thousands of tokens, while not reaching the ggml's node limit and not consuming too much memory.
A reasonable and recommended value of chunk size is 16. If you want maximum performance, try different chunk sizes in range [2..64]
and choose one that works the best in your use case.
Not thread-safe. For parallel inference, call `rwkv_clone_context` to create one rwkv_context for each thread.
Throws an exception in case of any error. Error messages would be printed to stderr.
Parameters
----------
ctx : RWKVContext
RWKV context obtained from rwkv_init_from_file.
tokens : List[int]
Next token indices, in range 0 <= token < n_vocab.
chunk_size : int
Size of each chunk in tokens, must be positive.
state_in_address : int
Address of the first element of a FP32 buffer of size rwkv_get_state_buffer_element_count; or None, if this is a first pass.
state_out_address : int
Address of the first element of a FP32 buffer of size rwkv_get_state_buffer_element_count. This buffer will be written to.
logits_out_address : int
Address of the first element of a FP32 buffer of size rwkv_get_logits_buffer_element_count. This buffer will be written to.
"""
assert self.library.rwkv_eval_sequence_in_chunks(
ctx.ptr,
ctypes.cast((ctypes.c_int32 * len(tokens))(*tokens), P_INT),
ctypes.c_size_t(len(tokens)),
ctypes.c_size_t(chunk_size),
ctypes.cast(0 if state_in_address is None else state_in_address, P_FLOAT),
ctypes.cast(state_out_address, P_FLOAT),
ctypes.cast(logits_out_address, P_FLOAT)
), 'rwkv_eval_sequence_in_chunks failed, check stderr'
def rwkv_get_n_vocab(self, ctx: RWKVContext) -> int:
"""
Returns the number of tokens in the given model's vocabulary.
Useful for telling 20B_tokenizer models (n_vocab = 50277) apart from World models (n_vocab = 65536).
Parameters
----------
ctx : RWKVContext
RWKV context obtained from rwkv_init_from_file.
"""
return self.library.rwkv_get_n_vocab(ctx.ptr)
def rwkv_get_n_embed(self, ctx: RWKVContext) -> int:
"""
Returns the number of elements in the given model's embedding.
Useful for reading individual fields of a model's hidden state.
Parameters
----------
ctx : RWKVContext
RWKV context obtained from rwkv_init_from_file.
"""
return self.library.rwkv_get_n_embed(ctx.ptr)
def rwkv_get_n_layer(self, ctx: RWKVContext) -> int:
"""
Returns the number of layers in the given model.
A layer is a pair of RWKV and FFN operations, stacked multiple times throughout the model.
Embedding matrix and model head (unembedding matrix) are NOT counted in `n_layer`.
Useful for always offloading the entire model to GPU.
Parameters
----------
ctx : RWKVContext
RWKV context obtained from rwkv_init_from_file.
"""
return self.library.rwkv_get_n_layer(ctx.ptr)
def rwkv_get_state_buffer_element_count(self, ctx: RWKVContext) -> int:
"""
Returns count of FP32 elements in state buffer.
Parameters
----------
ctx : RWKVContext
RWKV context obtained from rwkv_init_from_file.
"""
return self.library.rwkv_get_state_buffer_element_count(ctx.ptr)
def rwkv_get_logits_buffer_element_count(self, ctx: RWKVContext) -> int:
"""
Returns count of FP32 elements in logits buffer.
Parameters
----------
ctx : RWKVContext
RWKV context obtained from rwkv_init_from_file.
"""
return self.library.rwkv_get_logits_buffer_element_count(ctx.ptr)
def rwkv_free(self, ctx: RWKVContext) -> None:
"""
Frees all allocated memory and the context.
Parameters
----------
ctx : RWKVContext
RWKV context obtained from rwkv_init_from_file.
"""
self.library.rwkv_free(ctx.ptr)
ctx.ptr = self.nullptr
def rwkv_quantize_model_file(self, model_file_path_in: str, model_file_path_out: str, format_name: str) -> None:
"""
Quantizes FP32 or FP16 model to one of INT4 formats.
Throws an exception in case of any error. Error messages would be printed to stderr.
Parameters
----------
model_file_path_in : str
Path to model file in ggml format, must be either FP32 or FP16.
model_file_path_out : str
Quantized model will be written here.
format_name : str
One of QUANTIZED_FORMAT_NAMES.
"""
assert format_name in QUANTIZED_FORMAT_NAMES, f'Unknown format name {format_name}, use one of {QUANTIZED_FORMAT_NAMES}'
assert self.library.rwkv_quantize_model_file(
model_file_path_in.encode('utf-8'),
model_file_path_out.encode('utf-8'),
format_name.encode('utf-8')
), 'rwkv_quantize_model_file failed, check stderr'
def rwkv_get_system_info_string(self) -> str:
"""
Returns system information string.
"""
return self.library.rwkv_get_system_info_string().decode('utf-8')
def load_rwkv_shared_library() -> RWKVSharedLibrary:
"""
Attempts to find rwkv.cpp shared library and load it.
To specify exact path to the library, create an instance of RWKVSharedLibrary explicitly.
"""
file_name: str
if 'win32' in sys.platform or 'cygwin' in sys.platform:
file_name = 'rwkv.dll'
elif 'darwin' in sys.platform:
file_name = 'librwkv.dylib'
else:
file_name = 'librwkv.so'
# Possible sub-paths to the library relative to the repo dir.
child_paths: List[Callable[[pathlib.Path], pathlib.Path]] = [
# No lookup for Debug config here.
# I assume that if a user wants to debug the library,
# they will be able to find the library and set the exact path explicitly.
lambda p: p / 'backend-python' / 'rwkv_pip' / 'cpp' / file_name,
lambda p: p / 'bin' / 'Release' / file_name,
lambda p: p / 'bin' / file_name,
# Some people prefer to build in the "build" subdirectory.
lambda p: p / 'build' / 'bin' / 'Release' / file_name,
lambda p: p / 'build' / 'bin' / file_name,
lambda p: p / 'build' / file_name,
# Fallback.
lambda p: p / file_name
]
working_dir: pathlib.Path = pathlib.Path(os.path.abspath(os.getcwd()))
parent_paths: List[pathlib.Path] = [
# Possible repo dirs relative to the working dir.
# ./python/rwkv_cpp
working_dir.parent.parent,
# ./python
working_dir.parent,
# .
working_dir,
# Repo dir relative to this Python file.
pathlib.Path(os.path.abspath(__file__)).parent.parent.parent
]
for parent_path in parent_paths:
for child_path in child_paths:
full_path: pathlib.Path = child_path(parent_path)
if os.path.isfile(full_path):
return RWKVSharedLibrary(str(full_path))
assert False, (f'Failed to find {file_name} automatically; '
f'you need to find the library and create RWKVSharedLibrary specifying the path to it')