97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
import torch
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import torch.distributed
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from opentelemetry import trace
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from transformers import AutoConfig, AutoTokenizer, GenerationConfig
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from transformers.models.llama import LlamaTokenizer
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from typing import Optional
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_llama_modeling import (
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FlashLlamaForCausalLM,
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)
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from text_generation_server.utils import (
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initialize_torch_distributed,
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weight_files,
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Weights,
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)
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tracer = trace.get_tracer(__name__)
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from text_generation_server.utils.import_utils import SYSTEM
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class FlashLlama(FlashCausalLM):
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def __init__(
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self,
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model_id: str,
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revision: Optional[str] = None,
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quantize: Optional[str] = None,
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use_medusa: Optional[str] = None,
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dtype: Optional[torch.dtype] = None,
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trust_remote_code: bool = False,
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):
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self.process_group, rank, world_size = initialize_torch_distributed()
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if torch.cuda.is_available():
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device = torch.device(f"cuda:{rank}")
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dtype = torch.float16 if dtype is None else dtype
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elif SYSTEM == "xpu":
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device = torch.device(f"xpu:{rank}")
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dtype = torch.float16 if dtype is None else dtype
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else:
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raise NotImplementedError("FlashLlama is only available on GPU")
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try:
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tokenizer = LlamaTokenizer.from_pretrained(
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model_id,
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revision=revision,
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padding_side="left",
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truncation_side="left",
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trust_remote_code=trust_remote_code,
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)
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except Exception:
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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revision=revision,
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padding_side="left",
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truncation_side="left",
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trust_remote_code=trust_remote_code,
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)
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try:
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generation_config = GenerationConfig.from_pretrained(
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model_id, revision=revision, trust_remote_code=trust_remote_code
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)
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if isinstance(generation_config.eos_token_id, (list, set)):
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# TODO Huge hack
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tokenizer._eos_token_ids = set(generation_config.eos_token_id)
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except Exception:
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pass
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config = AutoConfig.from_pretrained(
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model_id, revision=revision, trust_remote_code=trust_remote_code
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)
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config.quantize = quantize
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config.use_medusa = use_medusa
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torch.distributed.barrier(group=self.process_group)
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filenames = weight_files(model_id, revision=revision, extension=".safetensors")
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weights = Weights(filenames, device, dtype, process_group=self.process_group)
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if config.quantize in ["gptq", "awq"]:
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weights._set_gptq_params(model_id, revision)
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prefix = ""
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model = FlashLlamaForCausalLM(prefix, config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(FlashLlama, self).__init__(
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model=model,
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tokenizer=tokenizer,
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num_layers=len(model.model.layers),
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num_kv_heads=model.model.num_key_value_heads,
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head_size=model.model.head_size,
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dtype=dtype,
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device=device,
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rank=rank,
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world_size=world_size,
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)
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