102 lines
3.4 KiB
Python
102 lines
3.4 KiB
Python
import torch
|
|
import torch.distributed
|
|
|
|
from opentelemetry import trace
|
|
from transformers import AutoConfig, AutoTokenizer
|
|
from transformers.models.llama import LlamaTokenizer
|
|
from typing import Optional
|
|
|
|
from text_generation_server.models import FlashCausalLM
|
|
from text_generation_server.models.custom_modeling.flash_llama_modeling import (
|
|
FlashLlamaForCausalLM,
|
|
LlamaConfig,
|
|
)
|
|
from text_generation_server.utils import (
|
|
initialize_torch_distributed,
|
|
weight_files,
|
|
Weights,
|
|
)
|
|
|
|
tracer = trace.get_tracer(__name__)
|
|
|
|
|
|
class FlashLlama(FlashCausalLM):
|
|
def __init__(
|
|
self,
|
|
model_id: str,
|
|
revision: Optional[str] = None,
|
|
quantize: Optional[str] = None,
|
|
dtype: Optional[torch.dtype] = None,
|
|
trust_remote_code: bool = False,
|
|
use_medusa: Optional[str] = None,
|
|
):
|
|
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
|
|
else:
|
|
raise NotImplementedError("FlashLlama is only available on GPU")
|
|
|
|
try:
|
|
tokenizer = LlamaTokenizer.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
padding_side="left",
|
|
truncation_side="left",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
except Exception:
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
padding_side="left",
|
|
truncation_side="left",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
|
|
config = LlamaConfig.from_pretrained(
|
|
model_id, revision=revision, trust_remote_code=trust_remote_code
|
|
)
|
|
config.quantize = quantize
|
|
|
|
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"]:
|
|
weights._set_gptq_params(model_id)
|
|
|
|
model = FlashLlamaForCausalLM(config, weights)
|
|
if use_medusa:
|
|
from text_generation_server.utils.medusa import MedusaModel
|
|
from huggingface_hub import hf_hub_download
|
|
import json
|
|
|
|
medusa_config = hf_hub_download(
|
|
use_medusa, revision=revision, filename="config.json"
|
|
)
|
|
with open(medusa_config, "r") as f:
|
|
config = json.load(f)
|
|
medusa_head = hf_hub_download(
|
|
use_medusa, revision=revision, filename="medusa_lm_head.pt"
|
|
)
|
|
medusa_sf = medusa_head[: -len(".pt")] + ".safetensors"
|
|
weights = Weights(
|
|
[medusa_sf], device, dtype, process_group=self.process_group
|
|
)
|
|
lm_head = model.lm_head
|
|
model.lm_head = MedusaModel(config, weights, lm_head)
|
|
|
|
torch.distributed.barrier(group=self.process_group)
|
|
super(FlashLlama, self).__init__(
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
num_layers=len(model.model.layers),
|
|
num_kv_heads=model.model.num_key_value_heads,
|
|
head_size=model.model.head_size,
|
|
dtype=dtype,
|
|
device=device,
|
|
rank=rank,
|
|
world_size=world_size,
|
|
)
|