109 lines
3.1 KiB
Python
109 lines
3.1 KiB
Python
import torch
|
|
import torch.distributed
|
|
|
|
from typing import Optional, Type
|
|
|
|
from transformers import (
|
|
AutoTokenizer,
|
|
AutoConfig,
|
|
PreTrainedTokenizerBase,
|
|
)
|
|
|
|
from text_generation_server.models.custom_modeling.bloom_modeling import (
|
|
BloomForCausalLM,
|
|
)
|
|
from text_generation_server.models import CausalLM
|
|
from text_generation_server.models.causal_lm import CausalLMBatch
|
|
from text_generation_server.pb import generate_pb2
|
|
from text_generation_server.utils import (
|
|
initialize_torch_distributed,
|
|
weight_files,
|
|
Weights,
|
|
)
|
|
|
|
|
|
class BloomCausalLMBatch(CausalLMBatch):
|
|
@classmethod
|
|
def from_pb(
|
|
cls,
|
|
pb: generate_pb2.Batch,
|
|
tokenizer: PreTrainedTokenizerBase,
|
|
dtype: torch.dtype,
|
|
device: torch.device,
|
|
) -> "CausalLMBatch":
|
|
batch = super().from_pb(pb=pb, tokenizer=tokenizer, dtype=dtype, device=device)
|
|
batch.keys_head_dim_last = False
|
|
return batch
|
|
|
|
|
|
class BLOOMSharded(CausalLM):
|
|
def __init__(
|
|
self,
|
|
model_id: str,
|
|
revision: Optional[str] = None,
|
|
quantize: Optional[str] = None,
|
|
trust_remote_code: bool = False,
|
|
):
|
|
self.process_group, rank, world_size = initialize_torch_distributed()
|
|
if torch.cuda.is_available():
|
|
device = torch.device(f"cuda:{rank}")
|
|
dtype = torch.float16
|
|
else:
|
|
device = torch.device("cpu")
|
|
dtype = torch.float32
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
padding_side="left",
|
|
truncation_side="left",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
|
|
config = AutoConfig.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
slow_but_exact=False,
|
|
tp_parallel=True,
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
config.pad_token_id = 3
|
|
config.quantize = quantize
|
|
|
|
torch.distributed.barrier(group=self.process_group)
|
|
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
|
|
weights = Weights(
|
|
filenames, device=device, dtype=dtype, process_group=self.process_group
|
|
)
|
|
|
|
model = BloomForCausalLM(config, weights)
|
|
|
|
torch.distributed.barrier(group=self.process_group)
|
|
super(CausalLM, self).__init__(
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
requires_padding=True,
|
|
dtype=dtype,
|
|
device=device,
|
|
rank=rank,
|
|
world_size=world_size,
|
|
)
|
|
|
|
@property
|
|
def batch_type(self) -> Type[CausalLMBatch]:
|
|
return BloomCausalLMBatch
|
|
|
|
def forward(
|
|
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
|
|
):
|
|
outputs = self.model.forward(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
use_cache=True,
|
|
)
|
|
|
|
logits = outputs.logits
|
|
return logits, outputs.past_key_values
|