244 lines
8.3 KiB
Python
244 lines
8.3 KiB
Python
import re
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import torch
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import torch.distributed
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from typing import List, Optional, Type
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from transformers import (
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AutoTokenizer,
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AutoConfig,
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PreTrainedTokenizerBase,
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)
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from text_generation_server.models import CausalLM
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from text_generation_server.models.causal_lm import CausalLMBatch
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from text_generation_server.pb import generate_pb2
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from text_generation_server.models.custom_modeling.opt_modeling import OPTForCausalLM
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from text_generation_server.utils import (
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NextTokenChooser,
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StoppingCriteria,
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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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from text_generation_server.utils.chunks import concat_text_chunks
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# CREDIT: Papers with code => https://github.com/paperswithcode/galai/blob/main/galai/utils.py
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# we split individual characters inside special tokens like [START_DNA]
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CUSTOM_SEQ_RE = re.compile(r"(\[START_(DNA|SMILES|I_SMILES|AMINO)])(.*?)(\[END_\2])")
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# token added to implement a custom sequence tokenization. This token is added at
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# corpus cleaning step and removed in pretokenization. The digits are added to increase the chance
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# that they do not occur in the corpus. The digits are escaped so that the token does not appear
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# literally in the source code in case we ever include it in the training data.
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SPLIT_MARKER = f"SPL{1}T-TH{1}S-Pl3A5E"
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def _insert_split_marker(m: re.Match):
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"""
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Applies split marker based on a regex match of special tokens such as
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[START_DNA].
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Parameters
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----------
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n : str
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Input text to split
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Returns
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----------
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str - the text with the split token added
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"""
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start_token, _, sequence, end_token = m.groups()
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sequence = re.sub(r"(.)", rf"{SPLIT_MARKER}\1", sequence, flags=re.DOTALL)
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return f"{start_token}{sequence}{SPLIT_MARKER}{end_token}"
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def escape_custom_split_sequence(text):
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"""
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Applies custom splitting to the text for GALILEO's tokenization
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Parameters
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----------
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text : str
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Input text to split
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Returns
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----------
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str - the text with the split token added
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"""
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return CUSTOM_SEQ_RE.sub(_insert_split_marker, text)
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# END CREDIT
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class GalacticaCausalLMBatch(CausalLMBatch):
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@classmethod
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def from_pb(
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cls,
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pb: generate_pb2.Batch,
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tokenizer: PreTrainedTokenizerBase,
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dtype: torch.dtype,
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device: torch.device,
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) -> "GalacticaCausalLMBatch":
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inputs = []
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next_token_choosers = []
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stopping_criterias = []
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prefix_offsets = []
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top_n_tokens = []
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read_offsets = []
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requests_idx_mapping = {}
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# Parse batch
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max_truncation = 0
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padding_right_offset = 0
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max_decode_tokens = 0
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for i, r in enumerate(pb.requests):
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requests_idx_mapping[r.id] = i
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# Add escape_custom_split_sequence to the CausalLMBatch logic
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inputs.append(
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escape_custom_split_sequence(concat_text_chunks(r.input_chunks.chunks))
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)
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next_token_choosers.append(
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NextTokenChooser.from_pb(r.parameters, device, tokenizer)
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)
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stopping_criteria = StoppingCriteria.from_pb(
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r.stopping_parameters, tokenizer
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)
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stopping_criterias.append(stopping_criteria)
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top_n_tokens.append(r.top_n_tokens)
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max_truncation = max(max_truncation, r.truncate)
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max_decode_tokens += stopping_criteria.max_new_tokens
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padding_right_offset = max(
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padding_right_offset, stopping_criteria.max_new_tokens
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)
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tokenized_inputs = tokenizer(
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inputs,
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return_tensors="pt",
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padding=True,
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return_token_type_ids=False,
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truncation=True,
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max_length=max_truncation,
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).to(device)
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for _ in pb.requests:
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input_len = tokenized_inputs["input_ids"].shape[1]
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prefix_offsets.append(0)
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read_offsets.append(input_len)
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input_lengths = tokenized_inputs["attention_mask"].sum(1)
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max_input_length = input_lengths.max()
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input_ids = tokenized_inputs["input_ids"]
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# Allocate maximum attention_mask
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attention_mask = input_ids.new_zeros(
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(pb.size, max_input_length + padding_right_offset)
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)
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# Copy tokenizer attention_mask into fully allocated attention_mask
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attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
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position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
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position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
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all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
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top_n_tokens_tensor = torch.tensor(
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top_n_tokens, device=device, dtype=torch.int64
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)
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max_tokens = len(inputs) * max_input_length + max_decode_tokens
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return cls(
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batch_id=pb.id,
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requests=pb.requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=None,
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all_input_ids=list(all_input_ids),
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input_lengths=input_lengths.tolist(),
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prefix_offsets=prefix_offsets,
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read_offsets=read_offsets,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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top_n_tokens=top_n_tokens,
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top_n_tokens_tensor=top_n_tokens_tensor,
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max_input_length=max_input_length.item(),
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padding_right_offset=padding_right_offset,
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max_tokens=max_tokens,
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)
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class GalacticaSharded(CausalLM):
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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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speculator: 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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else:
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device = torch.device("cpu")
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dtype = torch.float32 if dtype is None else dtype
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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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config = AutoConfig.from_pretrained(
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model_id,
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revision=revision,
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tp_parallel=True,
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trust_remote_code=trust_remote_code,
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)
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config.quantize = quantize
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tokenizer.pad_token_id = config.pad_token_id
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config.speculator = speculator
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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(
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filenames, device=device, dtype=dtype, process_group=self.process_group
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)
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if config.quantize in ["gptq", "marlin"]:
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weights._set_gptq_params(model_id, revision)
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model = OPTForCausalLM(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(CausalLM, self).__init__(
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model=model,
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tokenizer=tokenizer,
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requires_padding=True,
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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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@property
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def batch_type(self) -> Type[CausalLMBatch]:
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return GalacticaCausalLMBatch
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def decode(self, generated_ids: List[int]) -> str:
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# Do not skip special tokens as they are used for custom parsing rules of the generated text
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return self.tokenizer.decode(
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generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
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)
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def forward(
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self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
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):
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outputs, speculative_logits = self.model.forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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use_cache=True,
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)
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return outputs.logits, speculative_logits, outputs.past_key_values
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