Exllama integration to run GPTQ models

[AmineDjeghri]

Feature request

An integration of exllama in Langchain to be able to use 4-bit GPTQ weights, designed to be fast and memory-efficient on modern GPUs.

Motivation

The benchmarks on the official repo speak for themselves: https://github.com/turboderp/exllama#results-so-far

Your contribution

There is a fork that uses exllama with langchain here : https://github.com/CoffeeVampir3/exllama-langchain-example/tree/master

[dosubot[bot]]

Hi, @AmineDjeghri! I'm Dosu, and I'm here to help the LangChain team manage their backlog. I wanted to let you know that we are marking this issue as stale.

From what I understand, you opened this issue requesting an integration of exllama in Langchain to enable the use of 4-bit GPTQ weights. You provided a link to a fork that already demonstrates the integration. However, there hasn't been any activity on this issue yet.

Could you please let us know if this issue is still relevant to the latest version of the LangChain repository? If it is, please comment on the issue to let the LangChain team know. Otherwise, feel free to close the issue yourself, or the issue will be automatically closed in 7 days.

Thank you for your understanding and contribution to the LangChain project. If you have any further questions or need assistance, please don't hesitate to reach out.

[AmineDjeghri]

Hello ! Yes it's still relevant. Specially for ExllamaV2. Here are some benchmarks provided by Oobabooga

https://oobabooga.github.io/blog/posts/gptq-awq-exl2-llamacpp/

https://www.reddit.com/r/LocalLLaMA/s/3en2xHV5bd

[dosubot[bot]]

@baskaryan Could you please help @AmineDjeghri with this issue? They have indicated that it is still relevant, especially for ExllamaV2, and have provided some benchmarks for reference. Thank you!

[AmineDjeghri]

still no update about the integration of exllama/ exllamaV2 in langchain ?

[pabl-o-ce]

https://pypi.org/project/exllamav2/ Is exclusive model that use https://huggingface.co/LoneStriker exl2 format

[AmineDjeghri]

@pabl-o-ce exllama2 supports both GPTQ & EXL2

[jnash10]

Looking forward to official integration.

this guy does have a version that works with older version of langchain incase anyone want to take up itnegrating it: https://pastebin.com/p9KwXSSD

[dosubot[bot]]

🤖

Thank you for your interest in integrating Exllama into LangChain. I understand the potential benefits of using Exllama for handling 4-bit GPTQ weights, especially in terms of speed and memory efficiency.

However, as a maintainer of the LangChain repository, I must clarify that the integration of Exllama into LangChain would require significant changes to the current architecture of the framework. LangChain uses a modular approach to handle language models, with a separate class for each language model. This is different from the Exllama method, which typically uses a single class or a few classes to handle all language models.

To integrate Exllama into LangChain, we would need to create a new class for Exllama that inherits from the BaseLLM class, similar to how other language models are handled. This class would need to provide access to the APIs and services of Exllama, and it would need to be able to handle 4-bit GPTQ weights.

However, without more specific information about how Exllama handles 4-bit GPTQ weights, it's difficult to provide a detailed plan for this integration. If you could provide more details about the Exllama method, it would be very helpful.

Regarding the fork that uses Exllama with LangChain, it's important to note that forks are separate projects that are not officially supported by the LangChain team. While they can provide valuable insights and potential solutions, they are not guaranteed to be compatible with the latest version of LangChain or to meet the same standards of quality and reliability.

Finally, I would like to clarify that I am not able to create pull requests or issues on the LangChain repository. However, I encourage you to share your ideas and suggestions with the LangChain community. Your contributions are greatly appreciated and can help to improve the framework for everyone.

For more information about the current architecture of LangChain and how it handles language models, you can refer to the following sources:

• libs/langchain/langchain/agents/load_tools.py
• libs/langchain/langchain/llms/init.py

I hope this information is helpful. If you have any further questions or need more clarification, please don't hesitate to ask.

Sources

• libs/langchain/langchain/agents/load_tools.py
• libs/langchain/langchain/llms/init.py

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[AmineDjeghri]

I will start working on adding Exllamav2 to langchain Meanwhile, here is a complete script that will let you use the ExllamaV1 with langchain written by @EkChinHui & adapted by me almost all GPTQ models work with that, except some latest models that have a wider vocab like 'CapybaraHermes-2.5-Mistral-7B-GPTQ' which raises an 'IndexError: Out of range: piece id is out of range'. I will handle that in the v2 script.

import glob
import os
from typing import Any, Callable, Dict, Iterator, List, Optional

from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.chains import LLMChain
from langchain.llms.base import LLM
from langchain.schema.output import GenerationChunk
from langchain_core.callbacks import StreamingStdOutCallbackHandler
from langchain_core.prompts import PromptTemplate
from pydantic.v1 import Field, root_validator

class Exllama(LLM):
    """Exllama API.

    To use, you should have the exllama library installed, and provide the
    path to the Llama model as a named parameter to the constructor.
    Check out: https://github.com/jllllll/exllama

    Example:
        .. code-block:: python

        from langchain.llms import Exllama
        llm = Exllama(model_path="/path/to/llama/model")
    """

    client: Any  #: :meta private:
    model_path: str
    """The path to the GPTQ model folder."""
    exllama_cache: Any = None  #: :meta private:
    config: Any = None  #: :meta private:
    generator: Any = None  #: :meta private:
    tokenizer: Any = None  #: :meta private:

    # Langchain parameters
    logfunc = print
    stop_sequences: List[str] = Field("")
    """Sequences that immediately will stop the generator."""

    streaming: Optional[bool] = Field(True)
    """Whether to stream the results, token by token."""

    # Generator parameters
    disallowed_tokens: Optional[List[int]] = Field(None)
    """List of tokens to disallow during generation."""

    temperature: Optional[float] = Field(None)
    """Temperature for sampling diversity."""

    top_k: Optional[int] = Field(None)
    """Consider the most probable top_k samples, 0 to disable top_k sampling."""

    top_p: Optional[float] = Field(None)
    """Consider tokens up to a cumulative probabiltiy of top_p,
    0.0 to disable top_p sampling."""

    min_p: Optional[float] = Field(None)
    """Do not consider tokens with probability less than this."""

    typical: Optional[float] = Field(None)
    """Locally typical sampling threshold, 0.0 to disable typical sampling."""

    token_repetition_penalty_max: Optional[float] = Field(None)
    """Repetition penalty for most recent tokens."""

    token_repetition_penalty_sustain: Optional[int] = Field(None)
    """No. most recent tokens to repeat penalty for, -1 to apply to whole context."""

    token_repetition_penalty_decay: Optional[int] = Field(None)
    """Gradually decrease penalty over this many tokens."""

    beams: Optional[int] = Field(None)
    """Number of beams for beam search."""

    beam_length: Optional[int] = Field(None)
    """Length of beams for beam search."""

    # Config overrides
    max_seq_len: int = Field(2048)
    """Reduce to save memory. Can also be increased,
    ideally while also using compress_pos_emn and a compatible model/LoRA"""

    compress_pos_emb: Optional[float] = Field(1.0)
    """Amount of compression to apply to the positional embedding."""

    set_auto_map: Optional[str] = Field(None)
    """Comma-separated list of VRAM (in GB) to use per GPU device for model layers,
    e.g. 20,7,7"""

    gpu_peer_fix: Optional[bool] = Field(None)
    """Prevent direct copies of data between GPUs"""

    alpha_value: Optional[float] = Field(1.0)
    """Rope context extension alpha"""

    # Tuning
    matmul_recons_thd: Optional[int] = Field(None)
    fused_mlp_thd: Optional[int] = Field(None)
    sdp_thd: Optional[int] = Field(None)
    fused_attn: Optional[bool] = Field(None)
    matmul_fused_remap: Optional[bool] = Field(None)
    rmsnorm_no_half2: Optional[bool] = Field(None)
    rope_no_half2: Optional[bool] = Field(None)
    matmul_no_half2: Optional[bool] = Field(None)
    silu_no_half2: Optional[bool] = Field(None)
    concurrent_streams: Optional[bool] = Field(None)

    # Lora Parameters
    lora_path: Optional[str] = Field(None, description="Path to your lora.")

    @staticmethod
    def get_model_path_at(path: str) -> Optional[str]:
        patterns = ["*.safetensors", "*.bin", "*.pt"]
        model_paths = []
        for pattern in patterns:
            full_pattern = os.path.join(path, pattern)
            model_paths = glob.glob(full_pattern)
            if model_paths:  # If there are any files matching the current pattern
                break  # Exit the loop as soon as we find a matching file
        if model_paths:  # If there are any files matching any of the patterns
            return model_paths[0]
        else:
            return None  # Return None if no matching files were found

    @staticmethod
    def configure_object(
        params: List[str], values: Dict[str, Any], logfunc: Callable[[str], None]
    ) -> Callable[[str], None]:
        obj_params = {k: values.get(k) for k in params}

        def apply_to(obj: str) -> None:
            for key, value in obj_params.items():
                if value:
                    if hasattr(obj, key):
                        setattr(obj, key, value)
                        logfunc(f"{key} {value}")
                    else:
                        raise AttributeError(f"{key} does not exist in {obj}")

        return apply_to

    @root_validator()
    def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        try:
            from exllama.generator import ExLlamaGenerator
            from exllama.lora import ExLlamaLora
            from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
            from exllama.tokenizer import ExLlamaTokenizer
        except ImportError:
            raise ImportError(
                "Could not import exllama library. "
                "Please install the exllama library with (cuda 11.8 is required)"
                "!python -m pip install git+https://github.com/jllllll/exllama"
            )
        model_path = values["model_path"]
        lora_path = values["lora_path"]

        tokenizer_path = os.path.join(model_path, "tokenizer.model")
        model_config_path = os.path.join(model_path, "config.json")
        model_path = Exllama.get_model_path_at(model_path)

        config = ExLlamaConfig(model_config_path)
        tokenizer = ExLlamaTokenizer(tokenizer_path)
        config.model_path = model_path

        # Set logging function if verbose or set to empty lambda
        verbose = values["verbose"]
        if not verbose:
            values["logfunc"] = lambda *args, **kwargs: None
        logfunc = values["logfunc"]

        model_param_names = [
            "temperature",
            "top_k",
            "top_p",
            "min_p",
            "typical",
            "token_repetition_penalty_max",
            "token_repetition_penalty_sustain",
            "token_repetition_penalty_decay",
            "beams",
            "beam_length",
        ]

        config_param_names = [
            "max_seq_len",
            "compress_pos_emb",
            "gpu_peer_fix",
            "alpha_value",
        ]

        tuning_parameters = [
            "matmul_recons_thd",
            "fused_mlp_thd",
            "sdp_thd",
            "matmul_fused_remap",
            "rmsnorm_no_half2",
            "rope_no_half2",
            "matmul_no_half2",
            "silu_no_half2",
            "concurrent_streams",
            "fused_attn",
        ]

        configure_config = Exllama.configure_object(config_param_names, values, logfunc)
        configure_config(config)
        configure_tuning = Exllama.configure_object(tuning_parameters, values, logfunc)
        configure_tuning(config)
        configure_model = Exllama.configure_object(model_param_names, values, logfunc)

        # Special parameter, set auto map, it's a function
        if values["set_auto_map"]:
            config.set_auto_map(values["set_auto_map"])
            logfunc(f"set_auto_map {values['set_auto_map']}")

        model = ExLlama(config)
        exllama_cache = ExLlamaCache(model)
        generator = ExLlamaGenerator(model, tokenizer, exllama_cache)

        # Load and apply lora to generator
        if lora_path is not None:
            lora_config_path = os.path.join(lora_path, "adapter_config.json")
            lora_path = Exllama.get_model_path_at(lora_path)
            lora = ExLlamaLora(model, lora_config_path, lora_path)
            generator.lora = lora
            logfunc(f"Loaded LORA @ {lora_path}")

        # Configure the model and generator
        values["stop_sequences"] = [x.strip().lower() for x in values["stop_sequences"]]

        configure_model(generator.settings)
        setattr(generator.settings, "stop_sequences", values["stop_sequences"])
        logfunc(f"stop_sequences {values['stop_sequences']}")

        disallowed = values.get("disallowed_tokens")
        if disallowed:
            generator.disallow_tokens(disallowed)
            print(f"Disallowed Tokens: {generator.disallowed_tokens}")

        values["client"] = model
        values["generator"] = generator
        values["config"] = config
        values["tokenizer"] = tokenizer
        values["exllama_cache"] = exllama_cache

        return values

    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "Exllama"

    def get_num_tokens(self, text: str) -> int:
        """Get the number of tokens present in the text."""
        return self.generator.tokenizer.num_tokens(text)

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        combined_text_output = ""
        for chunk in self._stream(prompt=prompt, stop=stop, run_manager=run_manager):
            combined_text_output += chunk.text
        return combined_text_output

    from enum import Enum

    class MatchStatus(Enum):
        EXACT_MATCH = 1
        PARTIAL_MATCH = 0
        NO_MATCH = 2

    def match_status(self, sequence: str, banned_sequences: List[str]) -> MatchStatus:
        sequence = sequence.strip().lower()
        for banned_seq in banned_sequences:
            if banned_seq == sequence:
                return self.MatchStatus.EXACT_MATCH
            elif banned_seq.startswith(sequence):
                return self.MatchStatus.PARTIAL_MATCH
        return self.MatchStatus.NO_MATCH

    def _stream(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[GenerationChunk]:
        # config = self.config
        generator = self.generator
        beam_search = (
            self.beams
            and self.beams >= 1
            and self.beam_length
            and self.beam_length >= 1
        )

        ids = generator.tokenizer.encode(prompt)
        generator.gen_begin_reuse(ids)

        if beam_search:
            generator.begin_beam_search()
            token_getter = generator.beam_search
        else:
            generator.end_beam_search()
            token_getter = generator.gen_single_token

        last_newline_pos = 0
        match_buffer = ""

        seq_length = len(generator.tokenizer.decode(generator.sequence_actual[0]))
        response_start = seq_length
        cursor_head = response_start

        while generator.gen_num_tokens() <= (
            self.max_seq_len - 4
        ):  # Slight extra padding space as we seem to occasionally get a
            # few more than 1-2 tokens
            # Fetch a token
            token = token_getter()

            # If it's the ending token replace it and end the generation.
            if token.item() == generator.tokenizer.eos_token_id:
                generator.replace_last_token(generator.tokenizer.newline_token_id)
                if beam_search:
                    generator.end_beam_search()
                return

            # Tokenize the string from the last new line, we can't just decode the
            # last token due to how sentencepiece decodes.
            stuff = generator.tokenizer.decode(
                generator.sequence_actual[0][last_newline_pos:]
            )
            cursor_tail = len(stuff)
            chunk = stuff[cursor_head:cursor_tail]
            cursor_head = cursor_tail

            # Append the generated chunk to our stream buffer
            match_buffer = match_buffer + chunk

            if token.item() == generator.tokenizer.newline_token_id:
                last_newline_pos = len(generator.sequence_actual[0])
                cursor_head = 0
                cursor_tail = 0

            # Check if the stream buffer is one of the stop sequences
            status = self.match_status(match_buffer, self.stop_sequences)

            if status == self.MatchStatus.EXACT_MATCH:
                # Encountered a stop, rewind generator to before we hit the match stop.
                rewind_length = generator.tokenizer.encode(match_buffer).shape[-1]
                generator.gen_rewind(rewind_length)
                if beam_search:
                    generator.end_beam_search()
                return
            elif status == self.MatchStatus.PARTIAL_MATCH:
                # Partially matched a stop, continue buffering but don't yield.
                continue
            elif status == self.MatchStatus.NO_MATCH:
                if run_manager:
                    run_manager.on_llm_new_token(
                        token=match_buffer,
                        verbose=self.verbose,
                    )

                # yield match_buffer  # Not a stop, yield the match buffer.
                # match_buffer = ""
                chunk = GenerationChunk(text=match_buffer)
                yield chunk  # Not a stop, yield the match buffer.

                match_buffer = ""
        return

if __name__ == "__main__":
    # Callbacks support token-wise streaming
    callbacks = [StreamingStdOutCallbackHandler()]

    template = """Question: {question}

    Answer: Let's think step by step."""

    prompt = PromptTemplate(template=template, input_variables=["question"])

    # Verbose is required to pass to the callback manager
    llm = Exllama(
        model_path="models/TheBloke_Mistral-7B-Instruct-v0.2-GPTQ",
        callbacks=callbacks,
        verbose=True,
    )
    llm_chain = LLMChain(prompt=prompt, llm=llm)

    question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"

    llm_chain.invoke({"question": question})

[jnash10]

Thanks a lot, looking forward to it!

On Mon, 19 Feb, 2024, 7:08 am Amine Djeghri, @.***> wrote:

I will start working on adding Exllamav2 to langchain Meanwhile, here is a complete script that will let you use the ExllamaV1 with langchain written by @EkChinHui https://github.com/EkChinHui & adapted by me almost all GPTQ models work with that, except some latest models that have a wider vocab like 'CapybaraHermes-2.5-Mistral-7B-GPTQ' which raises an 'IndexError: Out of range: piece id is out of range'. I will handle that in the v2 script.

import globimport osfrom typing import Any, Callable, Dict, Iterator, List, Optional from langchain.callbacks.manager import CallbackManagerForLLMRunfrom langchain.chains import LLMChainfrom langchain.llms.base import LLMfrom langchain.schema.output import GenerationChunkfrom langchain_core.callbacks import StreamingStdOutCallbackHandlerfrom langchain_core.prompts import PromptTemplatefrom pydantic.v1 import Field, root_validator

class Exllama(LLM): """Exllama API. To use, you should have the exllama library installed, and provide the path to the Llama model as a named parameter to the constructor. Check out: https://github.com/jllllll/exllama Example: .. code-block:: python from langchain.llms import Exllama llm = Exllama(model_path="/path/to/llama/model") """

client: Any  #: :meta private:
model_path: str
"""The path to the GPTQ model folder."""
exllama_cache: Any = None  #: :meta private:
config: Any = None  #: :meta private:
generator: Any = None  #: :meta private:
tokenizer: Any = None  #: :meta private:

# Langchain parameters
logfunc = print
stop_sequences: List[str] = Field("")
"""Sequences that immediately will stop the generator."""

streaming: Optional[bool] = Field(True)
"""Whether to stream the results, token by token."""

# Generator parameters
disallowed_tokens: Optional[List[int]] = Field(None)
"""List of tokens to disallow during generation."""

temperature: Optional[float] = Field(None)
"""Temperature for sampling diversity."""

top_k: Optional[int] = Field(None)
"""Consider the most probable top_k samples, 0 to disable top_k sampling."""

top_p: Optional[float] = Field(None)
"""Consider tokens up to a cumulative probabiltiy of top_p,    0.0 to disable top_p sampling."""

min_p: Optional[float] = Field(None)
"""Do not consider tokens with probability less than this."""

typical: Optional[float] = Field(None)
"""Locally typical sampling threshold, 0.0 to disable typical sampling."""

token_repetition_penalty_max: Optional[float] = Field(None)
"""Repetition penalty for most recent tokens."""

token_repetition_penalty_sustain: Optional[int] = Field(None)
"""No. most recent tokens to repeat penalty for, -1 to apply to whole context."""

token_repetition_penalty_decay: Optional[int] = Field(None)
"""Gradually decrease penalty over this many tokens."""

beams: Optional[int] = Field(None)
"""Number of beams for beam search."""

beam_length: Optional[int] = Field(None)
"""Length of beams for beam search."""

# Config overrides
max_seq_len: int = Field(2048)
"""Reduce to save memory. Can also be increased,    ideally while also using compress_pos_emn and a compatible model/LoRA"""

compress_pos_emb: Optional[float] = Field(1.0)
"""Amount of compression to apply to the positional embedding."""

set_auto_map: Optional[str] = Field(None)
"""Comma-separated list of VRAM (in GB) to use per GPU device for model layers,    e.g. 20,7,7"""

gpu_peer_fix: Optional[bool] = Field(None)
"""Prevent direct copies of data between GPUs"""

alpha_value: Optional[float] = Field(1.0)
"""Rope context extension alpha"""

# Tuning
matmul_recons_thd: Optional[int] = Field(None)
fused_mlp_thd: Optional[int] = Field(None)
sdp_thd: Optional[int] = Field(None)
fused_attn: Optional[bool] = Field(None)
matmul_fused_remap: Optional[bool] = Field(None)
rmsnorm_no_half2: Optional[bool] = Field(None)
rope_no_half2: Optional[bool] = Field(None)
matmul_no_half2: Optional[bool] = Field(None)
silu_no_half2: Optional[bool] = Field(None)
concurrent_streams: Optional[bool] = Field(None)

# Lora Parameters
lora_path: Optional[str] = Field(None, description="Path to your lora.")

@staticmethod
def get_model_path_at(path: str) -> Optional[str]:
    patterns = ["*.safetensors", "*.bin", "*.pt"]
    model_paths = []
    for pattern in patterns:
        full_pattern = os.path.join(path, pattern)
        model_paths = glob.glob(full_pattern)
        if model_paths:  # If there are any files matching the current pattern
            break  # Exit the loop as soon as we find a matching file
    if model_paths:  # If there are any files matching any of the patterns
        return model_paths[0]
    else:
        return None  # Return None if no matching files were found

@staticmethod
def configure_object(
    params: List[str], values: Dict[str, Any], logfunc: Callable[[str], None]
) -> Callable[[str], None]:
    obj_params = {k: values.get(k) for k in params}

    def apply_to(obj: str) -> None:
        for key, value in obj_params.items():
            if value:
                if hasattr(obj, key):
                    setattr(obj, key, value)
                    logfunc(f"{key} {value}")
                else:
                    raise AttributeError(f"{key} does not exist in {obj}")

    return apply_to

@root_validator()
def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]:
    try:
        from exllama.generator import ExLlamaGenerator
        from exllama.lora import ExLlamaLora
        from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
        from exllama.tokenizer import ExLlamaTokenizer
    except ImportError:
        raise ImportError(
            "Could not import exllama library. "
            "Please install the exllama library with (cuda 11.8 is required)"
            "!python -m pip install git+https://github.com/jllllll/exllama"
        )
    model_path = values["model_path"]
    lora_path = values["lora_path"]

    tokenizer_path = os.path.join(model_path, "tokenizer.model")
    model_config_path = os.path.join(model_path, "config.json")
    model_path = Exllama.get_model_path_at(model_path)

    config = ExLlamaConfig(model_config_path)
    tokenizer = ExLlamaTokenizer(tokenizer_path)
    config.model_path = model_path

    # Set logging function if verbose or set to empty lambda
    verbose = values["verbose"]
    if not verbose:
        values["logfunc"] = lambda *args, **kwargs: None
    logfunc = values["logfunc"]

    model_param_names = [
        "temperature",
        "top_k",
        "top_p",
        "min_p",
        "typical",
        "token_repetition_penalty_max",
        "token_repetition_penalty_sustain",
        "token_repetition_penalty_decay",
        "beams",
        "beam_length",
    ]

    config_param_names = [
        "max_seq_len",
        "compress_pos_emb",
        "gpu_peer_fix",
        "alpha_value",
    ]

    tuning_parameters = [
        "matmul_recons_thd",
        "fused_mlp_thd",
        "sdp_thd",
        "matmul_fused_remap",
        "rmsnorm_no_half2",
        "rope_no_half2",
        "matmul_no_half2",
        "silu_no_half2",
        "concurrent_streams",
        "fused_attn",
    ]

    configure_config = Exllama.configure_object(config_param_names, values, logfunc)
    configure_config(config)
    configure_tuning = Exllama.configure_object(tuning_parameters, values, logfunc)
    configure_tuning(config)
    configure_model = Exllama.configure_object(model_param_names, values, logfunc)

    # Special parameter, set auto map, it's a function
    if values["set_auto_map"]:
        config.set_auto_map(values["set_auto_map"])
        logfunc(f"set_auto_map {values['set_auto_map']}")

    model = ExLlama(config)
    exllama_cache = ExLlamaCache(model)
    generator = ExLlamaGenerator(model, tokenizer, exllama_cache)

    # Load and apply lora to generator
    if lora_path is not None:
        lora_config_path = os.path.join(lora_path, "adapter_config.json")
        lora_path = Exllama.get_model_path_at(lora_path)
        lora = ExLlamaLora(model, lora_config_path, lora_path)
        generator.lora = lora
        logfunc(f"Loaded LORA @ {lora_path}")

    # Configure the model and generator
    values["stop_sequences"] = [x.strip().lower() for x in values["stop_sequences"]]

    configure_model(generator.settings)
    setattr(generator.settings, "stop_sequences", values["stop_sequences"])
    logfunc(f"stop_sequences {values['stop_sequences']}")

    disallowed = values.get("disallowed_tokens")
    if disallowed:
        generator.disallow_tokens(disallowed)
        print(f"Disallowed Tokens: {generator.disallowed_tokens}")

    values["client"] = model
    values["generator"] = generator
    values["config"] = config
    values["tokenizer"] = tokenizer
    values["exllama_cache"] = exllama_cache

    return values

@property
def _llm_type(self) -> str:
    """Return type of llm."""
    return "Exllama"

def get_num_tokens(self, text: str) -> int:
    """Get the number of tokens present in the text."""
    return self.generator.tokenizer.num_tokens(text)

def _call(
    self,
    prompt: str,
    stop: Optional[List[str]] = None,
    run_manager: Optional[CallbackManagerForLLMRun] = None,
    **kwargs: Any,
) -> str:
    combined_text_output = ""
    for chunk in self._stream(prompt=prompt, stop=stop, run_manager=run_manager):
        combined_text_output += chunk.text
    return combined_text_output

from enum import Enum

class MatchStatus(Enum):
    EXACT_MATCH = 1
    PARTIAL_MATCH = 0
    NO_MATCH = 2

def match_status(self, sequence: str, banned_sequences: List[str]) -> MatchStatus:
    sequence = sequence.strip().lower()
    for banned_seq in banned_sequences:
        if banned_seq == sequence:
            return self.MatchStatus.EXACT_MATCH
        elif banned_seq.startswith(sequence):
            return self.MatchStatus.PARTIAL_MATCH
    return self.MatchStatus.NO_MATCH

def _stream(
    self,
    prompt: str,
    stop: Optional[List[str]] = None,
    run_manager: Optional[CallbackManagerForLLMRun] = None,
    **kwargs: Any,
) -> Iterator[GenerationChunk]:
    # config = self.config
    generator = self.generator
    beam_search = (
        self.beams
        and self.beams >= 1
        and self.beam_length
        and self.beam_length >= 1
    )

    ids = generator.tokenizer.encode(prompt)
    generator.gen_begin_reuse(ids)

    if beam_search:
        generator.begin_beam_search()
        token_getter = generator.beam_search
    else:
        generator.end_beam_search()
        token_getter = generator.gen_single_token

    last_newline_pos = 0
    match_buffer = ""

    seq_length = len(generator.tokenizer.decode(generator.sequence_actual[0]))
    response_start = seq_length
    cursor_head = response_start

    while generator.gen_num_tokens() <= (
        self.max_seq_len - 4
    ):  # Slight extra padding space as we seem to occasionally get a
        # few more than 1-2 tokens
        # Fetch a token
        token = token_getter()

        # If it's the ending token replace it and end the generation.
        if token.item() == generator.tokenizer.eos_token_id:
            generator.replace_last_token(generator.tokenizer.newline_token_id)
            if beam_search:
                generator.end_beam_search()
            return

        # Tokenize the string from the last new line, we can't just decode the
        # last token due to how sentencepiece decodes.
        stuff = generator.tokenizer.decode(
            generator.sequence_actual[0][last_newline_pos:]
        )
        cursor_tail = len(stuff)
        chunk = stuff[cursor_head:cursor_tail]
        cursor_head = cursor_tail

        # Append the generated chunk to our stream buffer
        match_buffer = match_buffer + chunk

        if token.item() == generator.tokenizer.newline_token_id:
            last_newline_pos = len(generator.sequence_actual[0])
            cursor_head = 0
            cursor_tail = 0

        # Check if the stream buffer is one of the stop sequences
        status = self.match_status(match_buffer, self.stop_sequences)

        if status == self.MatchStatus.EXACT_MATCH:
            # Encountered a stop, rewind generator to before we hit the match stop.
            rewind_length = generator.tokenizer.encode(match_buffer).shape[-1]
            generator.gen_rewind(rewind_length)
            if beam_search:
                generator.end_beam_search()
            return
        elif status == self.MatchStatus.PARTIAL_MATCH:
            # Partially matched a stop, continue buffering but don't yield.
            continue
        elif status == self.MatchStatus.NO_MATCH:
            if run_manager:
                run_manager.on_llm_new_token(
                    token=match_buffer,
                    verbose=self.verbose,
                )

            # yield match_buffer  # Not a stop, yield the match buffer.
            # match_buffer = ""
            chunk = GenerationChunk(text=match_buffer)
            yield chunk  # Not a stop, yield the match buffer.

            match_buffer = ""
    return

if name == "main":

Callbacks support token-wise streaming

callbacks = [StreamingStdOutCallbackHandler()]

template = """Question: {question}    Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])

# Verbose is required to pass to the callback manager
llm = Exllama(
    model_path="models/TheBloke_Mistral-7B-Instruct-v0.2-GPTQ",
    callbacks=callbacks,
    verbose=True,
)
llm_chain = LLMChain(prompt=prompt, llm=llm)

question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"

llm_chain.invoke({"question": question})

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[AmineDjeghri]

Hello, here is a fast adaptation of ExllamaV2. Tested it with :

• python 3.11
• cuda 12.1
• exllamaV2 : https://github.com/turboderp/exllamav2/releases/download/v0.0.12/exllamav2-0.0.12+cu121-cp311-cp311-linux_x86_64.whl

import time
from typing import Optional, List, Any, Dict, Iterator

import torch
from exllamav2.generator import (
    ExLlamaV2Sampler,
    ExLlamaV2StreamingGenerator,
    ExLlamaV2BaseGenerator,
)
from langchain.chains import LLMChain
from langchain_core.callbacks import (
    CallbackManagerForLLMRun,
    StreamingStdOutCallbackHandler,
)
from langchain_core.language_models import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.prompts import PromptTemplate
from pydantic.v1 import root_validator, Field

class ExLlamaV2(LLM):
    """ExllamaV2 API.

    - working only with GPTQ models for now.
    - Lora models are not supported yet.

    To use, you should have the exllamav2 library installed, and provide the
    path to the Llama model as a named parameter to the constructor.
    Check out:

    Example:
        .. code-block:: python
        from langchain_community.llms import Exllamav2
        llm = Exllamav2(model_path="/path/to/llama/model")
    """

    client: Any
    model_path: str
    exllama_cache: Any = None
    config: Any = None
    generator: Any = None
    tokenizer: Any = None
    settings: Any = None  # If not None, it will be used as the default settings for the model. All other parameters won't be used.

    # Langchain parameters
    logfunc = print

    stop_sequences: List[str] = Field("")
    """Sequences that immediately will stop the generator."""

    max_new_tokens: Optional[int] = Field(150)
    """Maximum number of tokens to generate."""

    streaming: Optional[bool] = Field(True)
    """Whether to stream the results, token by token."""

    verbose: Optional[bool] = Field(True)
    """Whether to print debug information."""

    # Generator parameters
    disallowed_tokens: Optional[List[int]] = Field(None)
    """List of tokens to disallow during generation."""

    @root_validator()
    def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        # check if cuda is available
        if not torch.cuda.is_available():
            raise EnvironmentError("CUDA is not available. ExllamaV2 requires CUDA.")
        try:
            from exllamav2 import (
                ExLlamaV2,
                ExLlamaV2Config,
                ExLlamaV2Cache,
                ExLlamaV2Tokenizer,
            )
        except ImportError:
            raise ImportError(
                "Could not import exllamav2 library. "
                "Please install the exllamav2 library with (cuda 12.1 is required)"
                "example : "
                "!python -m pip install https://github.com/turboderp/exllamav2/releases/download/v0.0.12/exllamav2-0.0.12+cu121-cp311-cp311-linux_x86_64.whl"
            )

        # Set logging function if verbose or set to empty lambda
        verbose = values["verbose"]
        if not verbose:
            values["logfunc"] = lambda *args, **kwargs: None
        logfunc = values["logfunc"]

        if values["settings"]:
            settings = values["settings"]
            logfunc(settings.__dict__)
        else:
            raise NotImplementedError(
                "settings is required. Custom settings are not supported yet."
            )

        config = ExLlamaV2Config()
        config.model_dir = values["model_path"]
        config.prepare()

        model = ExLlamaV2(config)
        print("Loading model: " + values["model_path"])

        exllama_cache = ExLlamaV2Cache(model, lazy=True)
        model.load_autosplit(exllama_cache)

        tokenizer = ExLlamaV2Tokenizer(config)
        if values["streaming"]:
            generator = ExLlamaV2StreamingGenerator(model, exllama_cache, tokenizer)
        else:
            generator = ExLlamaV2BaseGenerator(model, exllama_cache, tokenizer)

        # Configure the model and generator
        values["stop_sequences"] = [x.strip().lower() for x in values["stop_sequences"]]
        setattr(settings, "stop_sequences", values["stop_sequences"])
        logfunc(f"stop_sequences {values['stop_sequences']}")

        disallowed = values.get("disallowed_tokens")
        if disallowed:
            settings.disallow_tokens(tokenizer, disallowed)
            print(f"Disallowed Tokens: {settings.disallowed_tokens}")

        values["client"] = model
        values["generator"] = generator
        values["config"] = config
        values["tokenizer"] = tokenizer
        values["exllama_cache"] = exllama_cache

        return values

    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "ExLlamaV2"

    def get_num_tokens(self, text: str) -> int:
        """Get the number of tokens present in the text."""
        return self.generator.tokenizer.num_tokens(text)

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        generator = self.generator

        if self.streaming:
            combined_text_output = ""
            for chunk in self._stream(
                prompt=prompt, stop=stop, run_manager=run_manager, kwargs=kwargs
            ):
                combined_text_output += chunk
            return combined_text_output
        else:
            output = generator.generate_simple(
                prompt=prompt,
                gen_settings=self.settings,
                num_tokens=self.max_new_tokens,
            )
            return output

    def _stream(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[GenerationChunk]:
        input_ids = self.tokenizer.encode(prompt)
        prompt_tokens = input_ids.shape[-1]
        self.generator.warmup()

        time_begin_prompt = time.time()

        self.generator.set_stop_conditions([])
        self.generator.begin_stream(input_ids, settings)

        time_begin_stream = time.time()
        generated_tokens = 0

        while True:
            chunk, eos, _ = self.generator.stream()
            generated_tokens += 1

            if run_manager:
                run_manager.on_llm_new_token(
                    token=chunk,
                    verbose=self.verbose,
                )
            yield chunk
            if eos or generated_tokens == self.max_new_tokens:
                break

        time_end = time.time()
        time_prompt = time_begin_stream - time_begin_prompt
        time_tokens = time_end - time_begin_stream
        print(
            f"\n\nPrompt processed in {time_prompt:.2f} seconds, {prompt_tokens} tokens, {prompt_tokens / time_prompt:.2f} tokens/second"
            f"\nResponse generated in {time_tokens:.2f} seconds, {generated_tokens} tokens, {generated_tokens / time_tokens:.2f} tokens/second"
        )
        return

if __name__ == "__main__":
    # Callbacks support token-wise streaming
    callbacks = [StreamingStdOutCallbackHandler()]

    template = """Question: {question}

    Answer: Let's think step by step."""

    prompt = PromptTemplate(template=template, input_variables=["question"])

    settings = ExLlamaV2Sampler.Settings()
    settings.temperature = 0.85
    settings.top_k = 50
    settings.top_p = 0.8
    settings.token_repetition_penalty = 1.05

    # Verbose is required to pass to the callback manager
    llm = ExLlamaV2(
        model_path="models/TheBloke_Mistral-7B-Instruct-v0.2-GPTQ",
        callbacks=callbacks,
        verbose=True,
        settings=settings,
        streaming=True,
        max_new_tokens=150,
    )
    llm_chain = LLMChain(prompt=prompt, llm=llm)

    question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"

    out = llm_chain.invoke({"question": question})
    print(out)

[AmineDjeghri]

PR #17817 submitted