722 lines
26 KiB
Python
722 lines
26 KiB
Python
import torch
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from dataclasses import dataclass
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from opentelemetry import trace
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, PreTrainedTokenizerBase
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from typing import Optional, Tuple, List, Type, Dict
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from text_generation_server.models import Model
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from text_generation_server.models.types import (
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GeneratedText,
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Batch,
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Generation,
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PrefillTokens,
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)
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from text_generation_server.pb import generate_pb2
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from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
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tracer = trace.get_tracer(__name__)
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@dataclass
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class Seq2SeqLMBatch(Batch):
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batch_id: int
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requests: List[generate_pb2.Request]
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requests_idx_mapping: Dict[int, int]
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# Encoder values
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input_ids: Optional[torch.Tensor]
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attention_mask: torch.Tensor
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# Decoder values
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decoder_input_ids: torch.Tensor
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decoder_attention_mask: Optional[torch.Tensor]
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encoder_last_hidden_state: Optional[torch.Tensor]
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# All tokens
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all_decoder_input_ids: List[torch.Tensor]
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# Seq2SeqLM keeps track of both encoder and decoder attention keys and values
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past_key_values: Optional[List[Tuple]]
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# Lengths of all generations present in the batch
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input_lengths: List[int]
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decoder_input_lengths: List[int]
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offsets: List[Optional[int]]
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token_offsets: List[Optional[int]]
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# Generation helpers
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next_token_choosers: List[NextTokenChooser]
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stopping_criterias: List[StoppingCriteria]
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# Metadata used for padding
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max_input_length: int
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max_decoder_input_length: int
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padding_right_offset: int
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# Maximum number of tokens this batch will grow to
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max_tokens: int
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def to_pb(self) -> generate_pb2.Batch:
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"""Convert a Seq2SeqLMBatch to a text_generation_server.v1.Batch protobuf"""
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return generate_pb2.Batch(
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id=self.batch_id,
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requests=self.requests,
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size=len(self),
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max_tokens=self.max_tokens,
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)
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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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device: torch.device,
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) -> "Seq2SeqLMBatch":
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"""Convert a text_generation_server.v1.Batch protobuf to a Seq2SeqLMBatch"""
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inputs = []
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next_token_choosers = []
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stopping_criterias = []
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decoder_input_lengths = []
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offsets = []
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token_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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inputs.append(r.inputs)
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requests_idx_mapping[r.id] = i
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decoder_input_lengths.append(1)
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offsets.append(None)
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token_offsets.append(None)
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next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
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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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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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# Tokenize batch
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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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input_lengths = tokenized_inputs["attention_mask"].sum(1)
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max_input_length = input_lengths.max()
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# Decoder sequence only contains the bos_token
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decoder_input_ids = (
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torch.tensor(tokenizer.bos_token_id, device=device)
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.repeat(len(pb.requests))
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.view(-1, 1)
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)
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all_decoder_input_ids = decoder_input_ids.view(-1).split(1)
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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=tokenized_inputs["input_ids"],
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attention_mask=tokenized_inputs["attention_mask"],
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decoder_input_ids=decoder_input_ids,
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all_decoder_input_ids=list(all_decoder_input_ids),
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decoder_attention_mask=None,
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encoder_last_hidden_state=None,
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past_key_values=None,
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input_lengths=input_lengths.tolist(),
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decoder_input_lengths=decoder_input_lengths,
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offsets=offsets,
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token_offsets=token_offsets,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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max_input_length=max_input_length.item(),
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max_decoder_input_length=1,
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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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@tracer.start_as_current_span("filter")
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def filter(
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self, requests: List[generate_pb2.Request]
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) -> Optional["Seq2SeqLMBatch"]:
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if len(requests) == 0:
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raise ValueError("Batch must have at least one request")
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if len(requests) == len(self):
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return self
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keep_indices = []
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# New values after filtering
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requests_idx_mapping = {}
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input_lengths = []
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decoder_input_lengths = []
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offsets = []
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token_offsets = []
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all_decoder_input_ids = []
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next_token_choosers = []
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stopping_criterias = []
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max_input_length = 0
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max_decoder_input_length = 0
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padding_right_offset = 0
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remaining_decode_tokens = 0
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for i, r in enumerate(requests):
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idx = self.requests_idx_mapping[r.id]
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requests_idx_mapping[r.id] = i
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keep_indices.append(idx)
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offsets.append(self.offsets[idx])
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token_offsets.append(self.token_offsets[idx])
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all_decoder_input_ids.append(self.all_decoder_input_ids[idx])
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request_input_length = self.input_lengths[idx]
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input_lengths.append(request_input_length)
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max_input_length = max(max_input_length, request_input_length)
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request_decoder_input_length = self.decoder_input_lengths[idx]
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decoder_input_lengths.append(request_decoder_input_length)
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max_decoder_input_length = max(
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max_decoder_input_length, request_decoder_input_length
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)
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padding_right_offset = max(
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padding_right_offset,
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self.stopping_criterias[idx].max_new_tokens
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- self.stopping_criterias[idx].current_tokens,
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)
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next_token_choosers.append(self.next_token_choosers[idx])
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stopping_criteria = self.stopping_criterias[idx]
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stopping_criterias.append(stopping_criteria)
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remaining_decode_tokens += (
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stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
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)
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# Apply indices to input_ids, attention mask, past key values and other items that need to be cached
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self.decoder_input_ids = self.decoder_input_ids[keep_indices]
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self.attention_mask = self.attention_mask[keep_indices, -max_input_length:]
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if self.decoder_attention_mask is not None:
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self.decoder_attention_mask = self.decoder_attention_mask[
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keep_indices,
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-(self.padding_right_offset + max_decoder_input_length) : (
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self.decoder_attention_mask.shape[1] - self.padding_right_offset
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)
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+ padding_right_offset,
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]
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self.encoder_last_hidden_state = self.encoder_last_hidden_state[
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keep_indices, -max_input_length:
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]
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# Ensure that past_key_values tensors can be updated in-place
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if type(self.past_key_values[0]) == tuple:
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self.past_key_values = [
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[t for t in layer] for layer in self.past_key_values
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]
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decoder_past_seq_len = max_decoder_input_length - 1
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for layer in self.past_key_values:
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layer[0] = layer[0][keep_indices, :, -decoder_past_seq_len:]
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layer[1] = layer[1][keep_indices, :, -decoder_past_seq_len:]
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layer[2] = layer[2][keep_indices, :, -max_input_length:]
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layer[3] = layer[3][keep_indices, :, -max_input_length:]
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max_tokens = (
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len(requests) * (max_input_length + max_decoder_input_length)
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+ remaining_decode_tokens
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)
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self.requests = requests
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self.requests_idx_mapping = requests_idx_mapping
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self.input_ids = None
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self.all_decoder_input_ids = all_decoder_input_ids
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self.input_lengths = input_lengths
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self.decoder_input_lengths = decoder_input_lengths
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self.offsets = offsets
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self.token_offsets = token_offsets
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self.next_token_choosers = next_token_choosers
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self.stopping_criterias = stopping_criterias
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self.max_input_length = max_input_length
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self.max_decoder_input_length = max_decoder_input_length
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self.padding_right_offset = padding_right_offset
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self.max_tokens = max_tokens
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return self
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@classmethod
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@tracer.start_as_current_span("concatenate")
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def concatenate(cls, batches: List["Seq2SeqLMBatch"]) -> "Seq2SeqLMBatch":
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"""Concatenate multiple batches together by padding internal torch tensors"""
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# Used for padding
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total_batch_size = 0
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max_input_length = 0
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max_decoder_input_length = 0
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padding_right_offset = 0
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for batch in batches:
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total_batch_size += len(batch)
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max_input_length = max(max_input_length, batch.max_input_length)
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max_decoder_input_length = max(
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max_decoder_input_length, batch.max_decoder_input_length
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)
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padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
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# Batch attributes
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requests = []
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requests_idx_mapping = {}
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all_decoder_input_ids = []
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input_lengths = []
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decoder_input_lengths = []
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offsets = []
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token_offsets = []
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next_token_choosers = []
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stopping_criterias = []
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max_tokens = 0
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# Batch tensors
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attention_mask = None
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decoder_input_ids = None
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decoder_attention_mask = None
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encoder_last_hidden_state = None
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past_key_values = []
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# Used for slicing correctly inside the tensors
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# Equivalent to a cumsum on batch sizes
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start_index = 0
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for i, batch in enumerate(batches):
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# Extend all list attributes
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requests.extend(batch.requests)
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all_decoder_input_ids.extend(batch.all_decoder_input_ids)
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input_lengths.extend(batch.input_lengths)
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decoder_input_lengths.extend(batch.decoder_input_lengths)
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offsets.extend(batch.offsets)
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token_offsets.extend(batch.token_offsets)
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next_token_choosers.extend(batch.next_token_choosers)
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stopping_criterias.extend(batch.stopping_criterias)
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if i == 0:
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requests_idx_mapping = batch.requests_idx_mapping
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else:
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# We need to offset the mapping for each batch by the cumulative batch size
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for k, v in batch.requests_idx_mapping.items():
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requests_idx_mapping[k] = v + start_index
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# Slicing end index for this batch
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end_index = start_index + len(batch)
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# We only concatenate batches that did at least one step
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if batch.encoder_last_hidden_state is None:
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raise ValueError("Batch encoder_last_hidden_state cannot be None")
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# Create padded tensor
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if attention_mask is None:
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attention_mask = batch.attention_mask.new_zeros(
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(total_batch_size, max_input_length),
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)
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# Copy to correct indices
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attention_mask[
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start_index:end_index, -batch.max_input_length :
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] = batch.attention_mask[:, -batch.max_input_length :]
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# Create padded tensor
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if decoder_input_ids is None:
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decoder_input_ids = batch.decoder_input_ids.new_zeros(
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(total_batch_size, 1),
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)
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# Copy to correct indices
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decoder_input_ids[start_index:end_index] = batch.decoder_input_ids
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# Create padded tensor
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if decoder_attention_mask is None:
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# As decoder_attention_mask might not exist, we use `batch.attention_mask` for device here
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decoder_attention_mask = batch.attention_mask.new_zeros(
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(total_batch_size, max_decoder_input_length + padding_right_offset),
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)
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# If the decoder mask does not exist yet, all generations started at the same time and we never concatenated
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# this batch. All generations are of length `batch.max_decoder_input_length`.
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left_offset = max_decoder_input_length - batch.max_decoder_input_length
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if batch.decoder_attention_mask is None:
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decoder_attention_mask[
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start_index:end_index,
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left_offset:-padding_right_offset,
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] = 1
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# If it exists, we need to index
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else:
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batch_left_offset = (
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batch.decoder_attention_mask.shape[1]
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- batch.max_decoder_input_length
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- batch.padding_right_offset
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)
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decoder_attention_mask[
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start_index:end_index,
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left_offset:-padding_right_offset,
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] = batch.decoder_attention_mask[
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:,
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batch_left_offset : -batch.padding_right_offset,
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]
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# Create padded tensor
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if encoder_last_hidden_state is None:
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encoder_last_hidden_state = batch.encoder_last_hidden_state.new_zeros(
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(
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total_batch_size,
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max_input_length,
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batch.encoder_last_hidden_state.shape[-1],
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),
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)
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# Copy to correct indices
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encoder_last_hidden_state[
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start_index:end_index, -batch.max_input_length :, :
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] = batch.encoder_last_hidden_state[:, -batch.max_input_length :, :]
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batch.encoder_last_hidden_state = None
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# Ensure that we can update tensors in-place
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if type(batch.past_key_values[0]) == tuple:
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batch.past_key_values = [
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[t for t in layer] for layer in batch.past_key_values
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]
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start_index = end_index
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# Add eventual padding tokens that were added while concatenating
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max_tokens += batch.max_tokens + (
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max_input_length
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- batch.max_input_length
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+ max_decoder_input_length
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- batch.max_decoder_input_length
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) * len(batch)
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# Determine shapes for new past kv tensors
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first_past_kvs = batches[0].past_key_values
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_, num_heads, _, head_dim = first_past_kvs[0][0].shape
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padded_dec_t_shape = (
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total_batch_size,
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num_heads,
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(max_decoder_input_length - 1),
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head_dim,
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)
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padded_enc_t_shape = (
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total_batch_size,
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num_heads,
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max_input_length,
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head_dim,
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)
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# Iterate over attention layers
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for j in range(len(first_past_kvs)):
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past_key_values.append([])
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# Decoder past
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for k in range(0, 2):
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# Initialize tensors
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padded_past_values = first_past_kvs[j][k].new_zeros(padded_dec_t_shape)
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past_key_values[j].append(padded_past_values)
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start_index = 0
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for batch in batches:
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t = batch.past_key_values[j][k]
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# Clear reference to the original tensor
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batch.past_key_values[j][k] = None
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# Slicing end index for this batch
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end_index = start_index + len(batch)
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# We slice the past keys and values to remove the padding from previous batches
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past_seq_len = batch.max_decoder_input_length - 1
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padded_past_values[start_index:end_index, :, -past_seq_len:, :] = t[
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:, :, -past_seq_len:, :
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]
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del t
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start_index = end_index
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# Encoder past
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for k in range(2, 4):
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# Initialize tensors
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padded_past_values = first_past_kvs[j][k].new_zeros(padded_enc_t_shape)
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past_key_values[j].append(padded_past_values)
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start_index = 0
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for batch in batches:
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t = batch.past_key_values[j][k]
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# Clear reference to the original tensor
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batch.past_key_values[j][k] = None
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# Slicing end index for this batch
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end_index = start_index + len(batch)
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# We slice the past keys and values to remove the padding from previous batches
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padded_past_values[
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start_index:end_index, :, -batch.max_input_length :, :
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] = t[:, :, -batch.max_input_length :, :]
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del t
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start_index = end_index
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return cls(
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batch_id=batches[0].batch_id,
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requests=requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=None,
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attention_mask=attention_mask,
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decoder_input_ids=decoder_input_ids,
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all_decoder_input_ids=all_decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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encoder_last_hidden_state=encoder_last_hidden_state,
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past_key_values=past_key_values,
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input_lengths=input_lengths,
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decoder_input_lengths=decoder_input_lengths,
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offsets=offsets,
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token_offsets=token_offsets,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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max_input_length=max_input_length,
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max_decoder_input_length=max_decoder_input_length,
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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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def __len__(self):
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return len(self.requests)
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class Seq2SeqLM(Model):
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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: bool = False,
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decode_buffer: int = 3,
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):
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if torch.cuda.is_available():
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device = torch.device("cuda")
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dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
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else:
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if quantize:
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raise ValueError("quantization is not available on CPU")
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device = torch.device("cpu")
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dtype = torch.float32
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self.model = AutoModelForSeq2SeqLM.from_pretrained(
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model_id,
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revision=revision,
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torch_dtype=dtype,
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device_map="auto" if torch.cuda.is_available() else None,
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load_in_8bit=quantize,
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).eval()
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tokenizer = AutoTokenizer.from_pretrained(
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model_id, revision=revision, padding_side="left", truncation_side="left"
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)
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tokenizer.bos_token_id = self.model.config.decoder_start_token_id
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super(Seq2SeqLM, self).__init__(
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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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decode_buffer=decode_buffer,
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)
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@property
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def batch_type(self) -> Type[Seq2SeqLMBatch]:
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return Seq2SeqLMBatch
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def decode(self, decoder_ids: List[int]) -> str:
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return self.tokenizer.decode(
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decoder_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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def forward(
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self,
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input_ids,
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attention_mask,
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decoder_input_ids,
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decoder_attention_mask: Optional,
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encoder_last_hidden_state: Optional,
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past_key_values: Optional = None,
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) -> Tuple[
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torch.Tensor,
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torch.Tensor,
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List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]],
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]:
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# Model Forward
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outputs = self.model.forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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encoder_outputs=encoder_last_hidden_state,
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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 (
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outputs.logits,
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outputs.encoder_last_hidden_state,
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outputs.past_key_values,
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)
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@tracer.start_as_current_span("generate_token")
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def generate_token(
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self, batch: Seq2SeqLMBatch
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) -> Tuple[List[Generation], Optional[Seq2SeqLMBatch]]:
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if batch.decoder_attention_mask is not None:
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# slice to the correct shape
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decoder_attention_mask = batch.decoder_attention_mask[
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:, : -batch.padding_right_offset
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]
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else:
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decoder_attention_mask = None
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# Wrap `encoder_last_hidden_state` because for some reason, Transformers does a `encoder_last_hidden_state[0]`
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# internally...
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if batch.encoder_last_hidden_state is not None:
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encoder_last_hidden_state = [batch.encoder_last_hidden_state]
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else:
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encoder_last_hidden_state = None
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logits, encoder_last_hidden_state, past = self.forward(
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batch.input_ids,
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batch.attention_mask,
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batch.decoder_input_ids,
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decoder_attention_mask,
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encoder_last_hidden_state,
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batch.past_key_values,
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)
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# Finished requests
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generations: List[Generation] = []
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stopped = True
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# Zipped iterator
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iterator = zip(
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batch.requests,
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batch.input_lengths,
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batch.offsets,
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batch.token_offsets,
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batch.decoder_input_lengths,
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logits,
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batch.next_token_choosers,
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batch.stopping_criterias,
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batch.all_decoder_input_ids,
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)
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# For each member of the batch
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for i, (
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request,
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input_length,
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offset,
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token_offset,
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decoder_input_length,
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|
logits,
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next_token_chooser,
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stopping_criteria,
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all_decoder_input_ids,
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) in enumerate(iterator):
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# Select next token
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next_token_id, logprobs = next_token_chooser(
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all_decoder_input_ids.view(1, -1), logits
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)
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# Append next token to decoder tokens
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|
all_decoder_input_ids = torch.cat(
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|
[all_decoder_input_ids, next_token_id.squeeze(1)]
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)
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new_decoder_input_length = decoder_input_length + 1
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|
|
# Generated token
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|
next_token_logprob = logprobs[-1, next_token_id]
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next_token_id_squeezed = next_token_id.squeeze()
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|
next_token_text, offset, token_offset = self.decode_token(
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all_decoder_input_ids, offset, token_offset
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)
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|
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# Evaluate stopping criteria
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|
stop, reason = stopping_criteria(next_token_id, next_token_text)
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|
|
|
if stop:
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|
# Slice with decoder_input_length to remove padding
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|
# Decode all tokens
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|
output_text = self.decode(all_decoder_input_ids[-decoder_input_length:])
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|
|
|
# Get seed
|
|
if isinstance(next_token_chooser.choice, Sampling):
|
|
seed = next_token_chooser.choice.seed
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|
else:
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|
seed = None
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|
|
|
generated_text = GeneratedText(
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|
output_text, stopping_criteria.current_tokens, reason, seed
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|
)
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else:
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# Keep request in the batch
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|
generated_text = None
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|
stopped = False
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|
|
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# Prefill
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|
if stopping_criteria.current_tokens == 1:
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|
prefill_tokens = PrefillTokens(
|
|
[self.tokenizer.bos_token_id],
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|
[float("nan")],
|
|
[self.tokenizer.bos_token],
|
|
)
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|
else:
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|
prefill_tokens = None
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|
|
|
generation = Generation(
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|
request.id,
|
|
prefill_tokens,
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|
next_token_id_squeezed,
|
|
next_token_logprob,
|
|
next_token_text,
|
|
next_token_id_squeezed.item() in self.all_special_ids,
|
|
generated_text,
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|
)
|
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|
generations.append(generation)
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|
|
# Update values
|
|
batch.decoder_input_ids[i] = next_token_id
|
|
batch.all_decoder_input_ids[i] = all_decoder_input_ids
|
|
batch.input_lengths[i] = input_length
|
|
batch.decoder_input_lengths[i] = new_decoder_input_length
|
|
batch.offsets[i] = offset
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|
batch.token_offsets[i] = token_offset
|
|
batch.max_input_length = max(batch.max_input_length, input_length)
|
|
batch.max_decoder_input_length = max(
|
|
batch.max_decoder_input_length, new_decoder_input_length
|
|
)
|
|
|
|
# We finished all generations in the batch; there is no next batch
|
|
if stopped:
|
|
return generations, None
|
|
|
|
# We don't need input_ids after the prefill forward
|
|
batch.input_ids = None
|
|
batch.encoder_last_hidden_state = encoder_last_hidden_state
|
|
batch.past_key_values = past
|
|
# Update decoder_attention_mask as we added a new token to input_ids
|
|
if batch.decoder_attention_mask is not None:
|
|
batch.decoder_attention_mask[:, -batch.padding_right_offset] = 1
|
|
batch.padding_right_offset -= 1
|
|
|
|
return generations, batch
|