ipex-llm/python/llm/src/ipex_llm/vllm/config.py

#
# Copyright 2016 The BigDL Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
# Some parts of this file is adapted from
# https://github.com/vllm-project/vllm/blob/v0.2.1.post1/vllm/config.py
# which is licensed under Apache License 2.0
#
# Copyright 2023 The vLLM team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Optional
import torch
from transformers import AutoConfig, PretrainedConfig
from ipex_llm.vllm.logger import init_logger
from ipex_llm.utils.common import invalidInputError

logger = init_logger(__name__)


class ModelConfig:
    """Configuration for the model.

    Args:
        model: Name or path of the huggingface model to use.
        tokenizer: Name or path of the huggingface tokenizer to use.
        tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
            available, and "slow" will always use the slow tokenizer.
        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
            downloading the model and tokenizer.
        download_dir: Directory to download and load the weights, default to the
            default cache directory of huggingface.
        load_format: The format of the model weights to load:
            "auto" will try to load the weights in the safetensors format and
                fall back to the pytorch bin format if safetensors format is
                not available.
            "pt" will load the weights in the pytorch bin format.
            "safetensors" will load the weights in the safetensors format.
            "npcache" will load the weights in pytorch format and store
                a numpy cache to speed up the loading.
            "dummy" will initialize the weights with random values, which is
                mainly for profiling.
        dtype: Data type for model weights and activations. The "auto" option
            will use FP16 precision for FP32 and FP16 models, and BF16 precision
            for BF16 models.
        seed: Random seed for reproducibility.
        revision: The specific model version to use. It can be a branch name,
            a tag name, or a commit id. If unspecified, will use the default
            version.
        tokenizer_revision: The specific tokenizer version to use. It can be a
            branch name, a tag name, or a commit id. If unspecified, will use
            the default version.
        max_model_len: Maximum length of a sequence (including prompt and
            output). If None, will be derived from the model.
        quantization: Quantization method that was used to quantize the model
            weights. If None, we assume the model weights are not quantized.
        device: The device to be used for the model. If None, we will default
            to use CPU as the device.
        load_in_low_bit: The low-bit quantization for model to be loaded. Default int4.
    """

    def __init__(
        self,
        model: str,
        tokenizer: str,
        tokenizer_mode: str,
        trust_remote_code: bool,
        download_dir: Optional[str],
        load_format: str,
        dtype: str,
        seed: int,
        revision: Optional[str] = None,
        tokenizer_revision: Optional[str] = None,
        max_model_len: Optional[int] = None,
        quantization: Optional[str] = None,
        device: Optional[str] = 'cpu',
        load_in_low_bit: str = 'sym_int4',
    ) -> None:
        self.model = model
        self.tokenizer = tokenizer
        self.tokenizer_mode = tokenizer_mode
        self.trust_remote_code = trust_remote_code
        self.download_dir = download_dir
        self.load_format = load_format
        self.seed = seed
        self.revision = revision
        self.tokenizer_revision = tokenizer_revision
        self.quantization = quantization
        self.device = device
        self.load_in_low_bit = load_in_low_bit

        self.hf_config = get_config(model, trust_remote_code, revision)
        self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
        self.max_model_len = _get_and_verify_max_len(self.hf_config,
                                                     max_model_len)
        self._verify_load_format()
        self._verify_tokenizer_mode()
        self._verify_quantization()

    def _verify_load_format(self) -> None:
        load_format = self.load_format.lower()
        if load_format not in [
                "auto", "pt", "safetensors", "npcache", "dummy"
        ]:
            invalidInputError(
                False,
                f"Unknown load format: {self.load_format}. Must be one of "
                "'auto', 'pt', 'safetensors', 'npcache', or 'dummy'.")
        self.load_format = load_format

    def _verify_tokenizer_mode(self) -> None:
        tokenizer_mode = self.tokenizer_mode.lower()
        if tokenizer_mode not in ["auto", "slow"]:
            invalidInputError(
                False,
                f"Unknown tokenizer mode: {self.tokenizer_mode}. Must be "
                "either 'auto' or 'slow'.")
        self.tokenizer_mode = tokenizer_mode

    def _verify_quantization(self) -> None:
        supported_quantization = ["awq"]
        if self.quantization is None:
            return
        quantization = self.quantization.lower()
        if quantization not in supported_quantization:
            invalidInputError(
                False,
                f"Unknown quantization: {self.quantization}. Must be one of "
                f"{supported_quantization}.")
        self.quantization = quantization

    def verify_with_parallel_config(
        self,
        parallel_config: "ParallelConfig",
    ) -> None:
        total_num_attention_heads = self.hf_config.num_attention_heads
        tensor_parallel_size = parallel_config.tensor_parallel_size
        if total_num_attention_heads % tensor_parallel_size != 0:
            invalidInputError(
                False,
                f"Total number of attention heads ({total_num_attention_heads})"
                " must be divisible by tensor parallel size "
                f"({tensor_parallel_size}).")

        total_num_hidden_layers = self.hf_config.num_hidden_layers
        pipeline_parallel_size = parallel_config.pipeline_parallel_size
        if total_num_hidden_layers % pipeline_parallel_size != 0:
            invalidInputError(
                False,
                f"Total number of hidden layers ({total_num_hidden_layers}) "
                "must be divisible by pipeline parallel size "
                f"({pipeline_parallel_size}).")

    def get_hidden_size(self) -> int:
        return self.hf_config.hidden_size

    def get_head_size(self) -> int:
        # FIXME(woosuk): This may not be true for all models.
        return self.hf_config.hidden_size // self.hf_config.num_attention_heads

    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
        """Returns the number of KV heads per GPU worker."""
        # For GPTBigCode & Falcon:
        # Note: for falcon, when new_decoder_architecture is True, the
        # multi_query flag is ignored and we use n_head_kv for the number of
        # KV heads.
        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
        new_decoder_arch_falcon = (
            self.hf_config.model_type in falcon_model_types
            and getattr(self.hf_config, "new_decoder_architecture", False))
        if not new_decoder_arch_falcon and getattr(self.hf_config,
                                                   "multi_query", False):
            # Multi-query attention, only one KV head.
            # Currently, tensor parallelism is not supported in this case.
            return 1
        # For Falcon:
        if getattr(self.hf_config, "n_head_kv", None) is not None:
            return (self.hf_config.n_head_kv //
                    parallel_config.tensor_parallel_size)
        if getattr(self.hf_config, "num_kv_heads", None) is not None:
            return (self.hf_config.num_kv_heads //
                    parallel_config.tensor_parallel_size)
        # For LLaMA-2:
        if getattr(self.hf_config, "num_key_value_heads", None) is not None:
            return (self.hf_config.num_key_value_heads //
                    parallel_config.tensor_parallel_size)
        total_num_attention_heads = self.hf_config.num_attention_heads
        return total_num_attention_heads // parallel_config.tensor_parallel_size

    def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
        total_num_hidden_layers = self.hf_config.num_hidden_layers
        return total_num_hidden_layers // parallel_config.pipeline_parallel_size


_STR_DTYPE_TO_TORCH_DTYPE = {
    "half": torch.float16,
    "float16": torch.float16,
    "float": torch.float32,
    "float32": torch.float32,
    "bfloat16": torch.bfloat16,
}


def _get_and_verify_dtype(
    config: PretrainedConfig,
    dtype: str,
) -> torch.dtype:
    # NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
    # because config.torch_dtype can be None.
    config_dtype = getattr(config, "torch_dtype", None)
    if config_dtype is None:
        config_dtype = torch.float32

    dtype = dtype.lower()
    if dtype == "auto":
        if config_dtype == torch.float32:
            # Following the common practice, we use float16 for float32 models.
            torch_dtype = torch.float16
        else:
            torch_dtype = config_dtype
    else:
        if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
            invalidInputError(False, f"Unknown dtype: {dtype}")
        torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]

    # Verify the dtype.
    if torch_dtype != config_dtype:
        if torch_dtype == torch.float32:
            # Upcasting to float32 is allowed.
            pass
        elif config_dtype == torch.float32:
            # Downcasting from float32 to float16 or bfloat16 is allowed.
            pass
        else:
            # Casting between float16 and bfloat16 is allowed with a warning.
            logger.warning(f"Casting {config_dtype} to {torch_dtype}.")

    return torch_dtype


def _get_and_verify_max_len(
    hf_config: PretrainedConfig,
    max_model_len: Optional[int],
) -> int:
    """Get and verify the model's maximum length."""
    derived_max_model_len = float("inf")
    possible_keys = [
        # OPT
        "max_position_embeddings",
        # GPT-2
        "n_positions",
        # MPT
        "max_seq_len",
        # Others
        "max_sequence_length",
        "max_seq_length",
        "seq_len",
    ]
    for key in possible_keys:
        max_len_key = getattr(hf_config, key, None)
        if max_len_key is not None:
            derived_max_model_len = min(derived_max_model_len, max_len_key)
    if derived_max_model_len == float("inf"):
        if max_model_len is not None:
            # If max_model_len is specified, we use it.
            return max_model_len

        default_max_len = 2048
        logger.warning(
            "The model's config.json does not contain any of the following "
            "keys to determine the original maximum length of the model: "
            f"{possible_keys}. Assuming the model's maximum length is "
            f"{default_max_len}.")
        derived_max_model_len = default_max_len

    rope_scaling = getattr(hf_config, "rope_scaling", None)
    if rope_scaling is not None:
        invalidInputError("factor" in rope_scaling,
                          "invalid hf_config value for rope_scaling")
        scaling_factor = rope_scaling["factor"]
        derived_max_model_len *= scaling_factor

    if max_model_len is None:
        max_model_len = derived_max_model_len
    elif max_model_len > derived_max_model_len:
        invalidInputError(
            False,
            f"User-specified max_model_len ({max_model_len}) is greater than "
            f"the derived max_model_len ({max_len_key}={derived_max_model_len}"
            " in model's config.json). This may lead to incorrect model "
            "outputs or CUDA errors. Make sure the value is correct and "
            "within the model context size.")
    return int(max_model_len)


def get_config(model: str,
               trust_remote_code: bool,
               revision: Optional[str] = None) -> PretrainedConfig:
    # NOTE: Because the Mistral model in HF hub does not have
    # `configuration_mistral.py`, we cannot use `AutoConfig` to load the
    # config. Instead, we use `MistralConfig` directly.
    # NOTE: This is a hack. This does not work for local models.
    # FIXME: Remove this once the Mistral model is available in the stable
    # version of HF transformers.
    if "mistral" in model.lower():
        return MistralConfig.from_pretrained(model, revision=revision)

    try:
        config = AutoConfig.from_pretrained(
            model, trust_remote_code=trust_remote_code, revision=revision)
    except ValueError as e:
        if (not trust_remote_code and
                "requires you to execute the configuration file" in str(e)):
            err_msg = (
                "Failed to load the model config. If the model is a custom "
                "model not yet available in the HuggingFace transformers "
                "library, consider setting `trust_remote_code=True` in LLM "
                "or using the `--trust-remote-code` flag in the CLI.")
            invalidInputError(err_msg)
        else:
            invalidInputError(e)
    return config


class ParallelConfig:
    """Configuration for the distributed execution.

    Args:
        pipeline_parallel_size: Number of pipeline parallel groups.
        tensor_parallel_size: Number of tensor parallel groups.
        worker_use_ray: Whether to use Ray for model workers. Will be set to
            True if either pipeline_parallel_size or tensor_parallel_size is
            greater than 1.
    """

    def __init__(
        self,
        pipeline_parallel_size: int,
        tensor_parallel_size: int,
        worker_use_ray: bool,
    ) -> None:
        self.pipeline_parallel_size = pipeline_parallel_size
        self.tensor_parallel_size = tensor_parallel_size
        self.worker_use_ray = worker_use_ray

        self.world_size = pipeline_parallel_size * tensor_parallel_size
        if self.world_size > 1:
            self.worker_use_ray = True
        self._verify_args()

    def _verify_args(self) -> None:
        if self.pipeline_parallel_size > 1:
            invalidInputError(
                "Pipeline parallelism is not supported yet.")


class SchedulerConfig:
    """Scheduler configuration.

    Args:
        max_num_batched_tokens: Maximum number of tokens to be processed in
            a single iteration.
        max_num_seqs: Maximum number of sequences to be processed in a single
            iteration.
        max_model_len: Maximum length of a sequence (including prompt
            and generated text).
    """

    def __init__(
        self,
        max_num_batched_tokens: Optional[int],
        max_num_seqs: int,
        max_model_len: int,
    ) -> None:
        if max_num_batched_tokens is not None:
            self.max_num_batched_tokens = max_num_batched_tokens
        else:
            # If max_model_len is too short, use 2048 as the default value for
            # higher throughput.
            self.max_num_batched_tokens = max(max_model_len, 2048)
        self.max_num_seqs = max_num_seqs
        self.max_model_len = max_model_len
        self._verify_args()

    def _verify_args(self) -> None:
        if self.max_num_batched_tokens < self.max_model_len:
            invalidInputError(
                False,
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) is "
                f"smaller than max_model_len ({self.max_model_len}). "
                "This effectively limits the maximum sequence length to "
                "max_num_batched_tokens and makes vLLM reject longer "
                "sequences. Please increase max_num_batched_tokens or "
                "decrease max_model_len.")
        if self.max_num_batched_tokens < self.max_num_seqs:
            invalidInputError(
                False,
                f"max_num_batched_tokens ({self.max_num_batched_tokens}) must "
                "be greater than or equal to max_num_seqs "
                f"({self.max_num_seqs}).")