RWKV-Runner/backend-python/rwkv_pip/cpp/rwkv_cpp_shared_library.py

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_arch_version_major.argtypes = [ctypes.c_void_p]
        self.library.rwkv_get_arch_version_major.restype = ctypes.c_uint32

        self.library.rwkv_get_arch_version_minor.argtypes = [ctypes.c_void_p]
        self.library.rwkv_get_arch_version_minor.restype = ctypes.c_uint32

        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)
        )

        if ptr is None:
            raise ValueError("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.
        """

        if not (layer_count >= 0):
            raise ValueError("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.
        """

        if not 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),
        ):
            raise ValueError("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.
        """

        if not 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),
        ):
            raise ValueError("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.
        """

        if not 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),
        ):
            raise ValueError("rwkv_eval_sequence_in_chunks failed, check stderr")

    def rwkv_get_arch_version_major(self, ctx: RWKVContext) -> int:
        """
        Returns the major version used by the given model.

        Parameters
        ----------
        ctx : RWKVContext
            RWKV context obtained from rwkv_init_from_file.
        """

        return self.library.rwkv_get_arch_version_major(ctx.ptr)

    def rwkv_get_arch_version_minor(self, ctx: RWKVContext) -> int:
        """
        Returns the minor version used by the given model.

        Parameters
        ----------
        ctx : RWKVContext
            RWKV context obtained from rwkv_init_from_file.
        """

        return self.library.rwkv_get_arch_version_minor(ctx.ptr)

    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.
        """

        if format_name not in QUANTIZED_FORMAT_NAMES:
            raise ValueError(
                f"Unknown format name {format_name}, use one of {QUANTIZED_FORMAT_NAMES}"
            )

        if not 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"),
        ):
            raise ValueError("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))

    raise ValueError(
        f"Failed to find {file_name} automatically; "
        f"you need to find the library and create RWKVSharedLibrary specifying the path to it"
    )