100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
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import torch
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import torch.distributed
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from opentelemetry import trace
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from typing import Optional
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from transformers import AutoTokenizer
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from transformers.models.gpt2 import GPT2TokenizerFast
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_dbrx_modeling import (
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FlashDbrxForCausalLM,
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DbrxConfig,
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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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class FlashDbrx(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.bfloat16 if dtype is None else dtype
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else:
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raise NotImplementedError("FlashDBRX is only available on GPU")
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try:
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tokenizer = GPT2TokenizerFast.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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use_fast=True,
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from_slow=False,
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)
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except:
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try:
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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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use_fast=True,
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from_slow=False,
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)
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except:
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# FIXME: change back to model id once the tokenizer.json is merged
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tokenizer = GPT2TokenizerFast.from_pretrained(
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"Xenova/dbrx-instruct-tokenizer",
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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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use_fast=True,
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from_slow=False,
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)
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config = DbrxConfig.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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model = FlashDbrxForCausalLM(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(FlashDbrx, 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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