hf_text-generation-inference/server/text_generation_server/models/flash_mistral.py

import torch
import torch.distributed

from opentelemetry import trace
from transformers import AutoTokenizer, AutoConfig
from typing import Optional, Tuple, Dict, List

from text_generation_server.models import FlashCausalLM
from text_generation_server.models.flash_causal_lm import set_sliding_window
from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
    FlashMistralForCausalLM,
    MistralConfig,
)
from text_generation_server.utils import (
    initialize_torch_distributed,
    weight_files,
    Weights,
)
from text_generation_server.utils.import_utils import SYSTEM

tracer = trace.get_tracer(__name__)


ADAPTER_LAYERS = [
    "q_proj",
    "k_proj",
    "v_proj",
    "o_proj",
    "gate_proj",
    "up_proj",
    "down_proj",
]
ROW_PARALLEL = {"o_proj", "down_proj", "lm_head"}


class BaseFlashMistral(FlashCausalLM):
    def __init__(
        self,
        model_cls,
        model_id: str,
        config_cls=AutoConfig,
        revision: Optional[str] = None,
        quantize: Optional[str] = None,
        speculator: Optional[str] = None,
        dtype: Optional[torch.dtype] = None,
        trust_remote_code: bool = False,
        tokenizer_class=AutoTokenizer,
    ):
        self.process_group, rank, world_size = initialize_torch_distributed()
        if torch.cuda.is_available():
            device = torch.device(f"cuda:{rank}")
            dtype = torch.float16 if dtype is None else dtype
        elif SYSTEM == "ipex":
            if hasattr(torch, "xpu") and torch.xpu.is_available():
                device = torch.device(f"xpu:{rank}")
                dtype = torch.float16 if dtype is None else dtype
            else:
                device = torch.device("cpu")
                dtype = torch.bfloat16 if dtype is None else dtype
        else:
            raise NotImplementedError("FlashMistral is only available on GPU")

        tokenizer = tokenizer_class.from_pretrained(
            model_id,
            revision=revision,
            padding_side="left",
            truncation_side="left",
            trust_remote_code=trust_remote_code,
        )

        config = config_cls.from_pretrained(
            model_id, revision=revision, trust_remote_code=trust_remote_code
        )
        config.quantize = quantize
        config.speculator = speculator

        # Set context windows
        if getattr(config, "sliding_window", None) is not None:
            set_sliding_window(config.sliding_window)
        else:
            config.sliding_window = None

        torch.distributed.barrier(group=self.process_group)

        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
        weights = Weights(filenames, device, dtype, process_group=self.process_group)
        if config.quantize in ["gptq", "awq", "marlin"]:
            weights._set_gptq_params(model_id, revision)

        prefix = ""
        model = model_cls(prefix, config, weights)

        self.cuda_graphs = {}

        torch.distributed.barrier(group=self.process_group)
        num_layers, num_kv_heads, head_size = self.get_layer_config(model)
        super().__init__(
            model_id=model_id,
            model=model,
            tokenizer=tokenizer,
            num_layers=num_layers,
            num_kv_heads=num_kv_heads,
            head_size=head_size,
            dtype=dtype,
            device=device,
            rank=rank,
            world_size=world_size,
            sliding_window=config.sliding_window,
        )

    def get_layer_config(self, model) -> Tuple[int, int, int]:
        return (
            len(model.model.layers),
            model.model.num_key_value_heads,
            model.model.head_size,
        )

    @property
    def supports_adapter_loading(self) -> bool:
        return True

    def adapter_target_to_layer(self) -> Dict[str, Tuple[str, torch.Tensor]]:
        layer_weights = {}

        prefix = "model.layers"

        # This accounts for VLMs (e.g. LlavaNext, Idefics2)
        # that have a language_model inside of the larger model.
        if hasattr(self.model, "language_model"):
            _model = self.model.language_model
        elif hasattr(self.model, "text_model"):
            _model = self.model.text_model
        else:
            _model = self.model

        for i, layer in enumerate(_model.model.layers):
            layer_weights[(i, "q_proj")] = (
                f"{prefix}.{i}.self_attn.q_proj",
                layer.self_attn.query_key_value,
            )
            layer_weights[(i, "k_proj")] = (
                f"{prefix}.{i}.self_attn.k_proj",
                layer.self_attn.query_key_value,
            )
            layer_weights[(i, "v_proj")] = (
                f"{prefix}.{i}.self_attn.v_proj",
                layer.self_attn.query_key_value,
            )
            layer_weights[(i, "o_proj")] = (
                f"{prefix}.{i}.self_attn.o_proj",
                layer.self_attn.o_proj,
            )

            # TODO: this is a hack to avoid the gate_proj for
            # FlashStarcoder2 that doesnt have these layers
            if hasattr(layer, "mlp") and hasattr(layer.mlp, "gate_up_proj"):
                layer_weights[(i, "gate_proj")] = (
                    f"{prefix}.{i}.mlp.gate_proj",
                    layer.mlp.gate_up_proj,
                )
                layer_weights[(i, "up_proj")] = (
                    f"{prefix}.{i}.mlp.up_proj",
                    layer.mlp.gate_up_proj,
                )
                layer_weights[(i, "down_proj")] = (
                    f"{prefix}.{i}.mlp.down_proj",
                    layer.mlp.down_proj,
                )

        layer_weights[(0, "lm_head")] = ("lm_head", _model.lm_head)
        return layer_weights

    @property
    def adapter_layers(self) -> List[str]:
        return ADAPTER_LAYERS

    @property
    def default_traced_adapter_layers(self) -> List[str]:
        return ["q_proj", "v_proj"]

    def get_num_layers_for_type(self, layer_type: str) -> int:
        return 1 if layer_type == "lm_head" else len(self.model.model.layers)

    def is_row_parallel(self, layer_type: str) -> bool:
        return layer_type in ROW_PARALLEL


class FlashMistral(BaseFlashMistral):
    def __init__(
        self,
        model_id: str,
        revision: Optional[str] = None,
        quantize: Optional[str] = None,
        speculator: Optional[str] = None,
        dtype: Optional[torch.dtype] = None,
        trust_remote_code: bool = False,
    ):
        super(FlashMistral, self).__init__(
            config_cls=MistralConfig,
            model_cls=FlashMistralForCausalLM,
            model_id=model_id,
            revision=revision,
            quantize=quantize,
            speculator=speculator,
            dtype=dtype,
            trust_remote_code=trust_remote_code,
        )