933 lines
34 KiB
Python
933 lines
34 KiB
Python
import torch
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import torch.distributed
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import time
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from dataclasses import dataclass
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from opentelemetry import trace
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from transformers import (
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AutoTokenizer,
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AutoModelForSeq2SeqLM,
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PreTrainedTokenizerBase,
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AutoConfig,
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)
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from typing import Optional, Tuple, List, Type, Dict
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from text_generation_server.utils.import_utils import SYSTEM
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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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from text_generation_server.utils.chunks import concat_text_chunks
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from text_generation_server.utils.quantization import get_loader
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from text_generation_server.utils.tokens import batch_top_tokens
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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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Tokens,
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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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prefix_offsets: List[int]
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read_offsets: List[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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top_n_tokens: List[int]
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top_n_tokens_tensor: torch.Tensor
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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.CachedBatch:
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"""Convert a Seq2SeqLMBatch to a text_generation_server.v1.CachedBatch protobuf"""
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return generate_pb2.CachedBatch(
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id=self.batch_id,
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request_ids=[r.id for r in 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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dtype: torch.dtype,
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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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top_n_tokens = []
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decoder_input_lengths = []
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prefix_offsets = []
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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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inputs.append(concat_text_chunks(r.input_chunks.chunks))
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requests_idx_mapping[r.id] = i
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decoder_input_lengths.append(1)
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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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# 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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for _ in pb.requests:
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prefix_offsets.append(0)
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read_offsets.append(1)
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all_decoder_input_ids = decoder_input_ids.view(-1).split(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=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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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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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(self, request_ids: List[int]) -> Optional["Seq2SeqLMBatch"]:
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if len(request_ids) == 0:
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raise ValueError("Batch must have at least one request")
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if len(request_ids) == 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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requests = []
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input_lengths = []
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decoder_input_lengths = []
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prefix_offsets = []
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read_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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top_n_tokens = []
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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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total_remaining_decode_tokens = 0
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for i, request_id in enumerate(request_ids):
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idx = self.requests_idx_mapping[request_id]
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requests_idx_mapping[request_id] = i
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keep_indices.append(idx)
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requests.append(self.requests[idx])
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prefix_offsets.append(self.prefix_offsets[idx])
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read_offsets.append(self.read_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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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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top_n_tokens.append(self.top_n_tokens[idx])
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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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total_remaining_decode_tokens += remaining_decode_tokens
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padding_right_offset = max(padding_right_offset, remaining_decode_tokens)
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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]) is 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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top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices]
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max_tokens = (
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len(request_ids) * (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.prefix_offsets = prefix_offsets
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self.read_offsets = read_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.top_n_tokens = top_n_tokens
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self.top_n_tokens_tensor = top_n_tokens_tensor
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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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prefix_offsets = []
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read_offsets = []
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next_token_choosers = []
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stopping_criterias = []
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top_n_tokens = []
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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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top_n_tokens_tensor = 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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prefix_offsets.extend(batch.prefix_offsets)
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read_offsets.extend(batch.read_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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top_n_tokens.extend(batch.top_n_tokens)
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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[start_index:end_index, -batch.max_input_length :] = (
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batch.attention_mask[:, -batch.max_input_length :]
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)
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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
|
|
- 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
|
|
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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|
|
if top_n_tokens_tensor is None:
|
|
top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros(
|
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total_batch_size,
|
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)
|
|
top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor
|
|
|
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# Copy to correct indices
|
|
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 :, :]
|
|
batch.encoder_last_hidden_state = None
|
|
|
|
# Ensure that we can update tensors in-place
|
|
if isinstance(batch.past_key_values[0], tuple):
|
|
batch.past_key_values = [
|
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[t for t in layer] for layer in batch.past_key_values
|
|
]
|
|
|
|
# Add eventual padding tokens that were added while concatenating
|
|
max_tokens += batch.max_tokens + (
|
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max_input_length
|
|
- batch.max_input_length
|
|
+ max_decoder_input_length
|
|
- batch.max_decoder_input_length
|
|
) * len(batch)
|
|
|
|
start_index = end_index
|
|
|
|
# Determine shapes for new past kv tensors
|
|
first_past_kvs = batches[0].past_key_values
|
|
_, num_heads, _, head_dim = first_past_kvs[0][0].shape
|
|
|
|
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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|
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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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)
|
|
|
|
# Iterate over attention layers
|
|
for j in range(len(first_past_kvs)):
|
|
past_key_values.append([])
|
|
|
|
# Decoder past
|
|
for k in range(0, 2):
|
|
# Initialize tensors
|
|
padded_past_values = first_past_kvs[j][k].new_zeros(padded_dec_t_shape)
|
|
past_key_values[j].append(padded_past_values)
|
|
|
|
start_index = 0
|
|
for batch in batches:
|
|
t = batch.past_key_values[j][k]
|
|
# Clear reference to the original tensor
|
|
batch.past_key_values[j][k] = None
|
|
# Slicing end index for this batch
|
|
end_index = start_index + len(batch)
|
|
# We slice the past keys and values to remove the padding from previous batches
|
|
past_seq_len = batch.max_decoder_input_length - 1
|
|
padded_past_values[start_index:end_index, :, -past_seq_len:, :] = t[
|
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:, :, -past_seq_len:, :
|
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]
|
|
del t
|
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|
|
start_index = end_index
|
|
|
|
# Encoder past
|
|
for k in range(2, 4):
|
|
# Initialize tensors
|
|
padded_past_values = first_past_kvs[j][k].new_zeros(padded_enc_t_shape)
|
|
past_key_values[j].append(padded_past_values)
|
|
|
|
start_index = 0
|
|
for batch in batches:
|
|
t = batch.past_key_values[j][k]
|
|
# Clear reference to the original tensor
|
|
batch.past_key_values[j][k] = None
|
|
# Slicing end index for this batch
|
|
end_index = start_index + len(batch)
|
|
# We slice the past keys and values to remove the padding from previous batches
|
|
padded_past_values[
|
|
start_index:end_index, :, -batch.max_input_length :, :
|
|
] = t[:, :, -batch.max_input_length :, :]
|
|
del t
|
|
|
|
start_index = end_index
|
|
|
|
return cls(
|
|
batch_id=batches[0].batch_id,
|
|
requests=requests,
|
|
requests_idx_mapping=requests_idx_mapping,
|
|
input_ids=None,
|
|
attention_mask=attention_mask,
|
|
decoder_input_ids=decoder_input_ids,
|
|
all_decoder_input_ids=all_decoder_input_ids,
|
|
decoder_attention_mask=decoder_attention_mask,
|
|
encoder_last_hidden_state=encoder_last_hidden_state,
|
|
past_key_values=past_key_values,
|
|
input_lengths=input_lengths,
|
|
decoder_input_lengths=decoder_input_lengths,
|
|
prefix_offsets=prefix_offsets,
|
|
read_offsets=read_offsets,
|
|
next_token_choosers=next_token_choosers,
|
|
stopping_criterias=stopping_criterias,
|
|
top_n_tokens=top_n_tokens,
|
|
top_n_tokens_tensor=top_n_tokens_tensor,
|
|
max_input_length=max_input_length,
|
|
max_decoder_input_length=max_decoder_input_length,
|
|
padding_right_offset=padding_right_offset,
|
|
max_tokens=max_tokens,
|
|
)
|
|
|
|
def __len__(self):
|
|
return len(self.requests)
|
|
|
|
|
|
class Seq2SeqLM(Model):
|
|
def __init__(
|
|
self,
|
|
model_id: str,
|
|
model_class,
|
|
revision: Optional[str] = None,
|
|
quantize: Optional[str] = None,
|
|
speculator: Optional[str] = None,
|
|
dtype: Optional[torch.dtype] = None,
|
|
default_dtype=torch.float16,
|
|
trust_remote_code: bool = False,
|
|
config_class=AutoConfig,
|
|
tokenizer_class=AutoTokenizer,
|
|
aliases=None,
|
|
):
|
|
self.quantize = quantize
|
|
self.process_group, rank, world_size = initialize_torch_distributed()
|
|
if torch.cuda.is_available():
|
|
device = torch.device(f"cuda:{rank}")
|
|
dtype = default_dtype if dtype is None else dtype
|
|
elif SYSTEM == "ipex":
|
|
if hasattr(torch, "xpu") and torch.xpu.is_available():
|
|
device = torch.device(f"xpu:{rank}")
|
|
dtype = default_dtype if dtype is None else dtype
|
|
else:
|
|
device = torch.device("cpu")
|
|
# Float16 doesn't exist on target.
|
|
dtype = torch.bfloat16 if dtype is None else dtype
|
|
else:
|
|
device = torch.device("cpu")
|
|
dtype = torch.float32 if dtype is None else dtype
|
|
|
|
config = config_class.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
config.quantize = quantize
|
|
config.speculator = speculator
|
|
|
|
tokenizer = tokenizer_class.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
padding_side="left",
|
|
truncation_side="left",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
tokenizer.bos_token_id = config.decoder_start_token_id
|
|
|
|
weights_loader = get_loader(
|
|
quantize=quantize, model_id=model_id, revision=revision
|
|
)
|
|
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,
|
|
aliases=aliases,
|
|
weights_loader=weights_loader,
|
|
)
|
|
if config.quantize in ["awq", "exl2", "gptq", "marlin"]:
|
|
weights._set_gptq_params(model_id, revision)
|
|
|
|
model = model_class(config, weights)
|
|
|
|
torch.distributed.barrier(group=self.process_group)
|
|
super().__init__(
|
|
model_id=model_id,
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
requires_padding=True,
|
|
dtype=dtype,
|
|
device=device,
|
|
rank=rank,
|
|
world_size=world_size,
|
|
)
|
|
|
|
@classmethod
|
|
def fallback(
|
|
cls,
|
|
model_id: str,
|
|
revision: Optional[str] = None,
|
|
quantize: Optional[str] = None,
|
|
speculator: Optional[str] = None,
|
|
dtype: Optional[torch.dtype] = None,
|
|
trust_remote_code: bool = False,
|
|
):
|
|
if speculator:
|
|
raise RuntimeError("Speculator decoding is not enabled for AutoModel")
|
|
|
|
if torch.cuda.is_available():
|
|
device = torch.device("cuda")
|
|
dtype = torch.float16 if dtype is None else dtype
|
|
else:
|
|
if quantize:
|
|
raise ValueError("quantization is not available on CPU")
|
|
|
|
device = torch.device("cpu")
|
|
dtype = torch.float32 if dtype is None else dtype
|
|
|
|
model = AutoModelForSeq2SeqLM.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
torch_dtype=dtype,
|
|
device_map=(
|
|
"auto"
|
|
if torch.cuda.is_available() and torch.cuda.device_count() > 1
|
|
else None
|
|
),
|
|
load_in_8bit=quantize == "bitsandbytes",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
if torch.cuda.is_available() and torch.cuda.device_count() == 1:
|
|
model = model.cuda()
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
padding_side="left",
|
|
truncation_side="left",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
tokenizer.bos_token_id = model.config.decoder_start_token_id
|
|
|
|
self = cls.__new__(
|
|
cls,
|
|
)
|
|
super().__init__(
|
|
self,
|
|
model_id=model_id,
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
requires_padding=True,
|
|
dtype=dtype,
|
|
device=device,
|
|
)
|
|
self.quantize = quantize
|
|
return self
|
|
|
|
@property
|
|
def batch_type(self) -> Type[Seq2SeqLMBatch]:
|
|
return Seq2SeqLMBatch
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
attention_mask,
|
|
decoder_input_ids,
|
|
decoder_attention_mask: Optional,
|
|
encoder_last_hidden_state: Optional,
|
|
past_key_values: Optional = None,
|
|
) -> Tuple[
|
|
torch.Tensor,
|
|
Optional[torch.Tensor],
|
|
torch.Tensor,
|
|
List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]],
|
|
]:
|
|
# Model Forward
|
|
outputs = self.model.forward(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
decoder_input_ids=decoder_input_ids,
|
|
decoder_attention_mask=decoder_attention_mask,
|
|
encoder_outputs=encoder_last_hidden_state,
|
|
past_key_values=past_key_values,
|
|
use_cache=True,
|
|
)
|
|
if isinstance(outputs, tuple):
|
|
# Our custom models
|
|
outputs, speculative_logits = outputs
|
|
else:
|
|
# Generic transformers models
|
|
speculative_logits = None
|
|
return (
|
|
outputs.logits,
|
|
speculative_logits,
|
|
outputs.encoder_last_hidden_state,
|
|
outputs.past_key_values,
|
|
)
|
|
|
|
@tracer.start_as_current_span("generate_token")
|
|
def generate_token(
|
|
self, batch: Seq2SeqLMBatch
|
|
) -> Tuple[List[Generation], Optional[Seq2SeqLMBatch], Tuple[int, int]]:
|
|
start = time.time_ns()
|
|
if batch.decoder_attention_mask is not None:
|
|
# slice to the correct shape
|
|
decoder_attention_mask = batch.decoder_attention_mask[
|
|
:, : -batch.padding_right_offset
|
|
]
|
|
else:
|
|
decoder_attention_mask = None
|
|
|
|
# Wrap `encoder_last_hidden_state` because for some reason, Transformers does a `encoder_last_hidden_state[0]`
|
|
# internally...
|
|
if batch.encoder_last_hidden_state is not None:
|
|
encoder_last_hidden_state = [batch.encoder_last_hidden_state]
|
|
else:
|
|
encoder_last_hidden_state = None
|
|
|
|
logits, speculative_logits, encoder_last_hidden_state, past = self.forward(
|
|
batch.input_ids,
|
|
batch.attention_mask,
|
|
batch.decoder_input_ids,
|
|
decoder_attention_mask,
|
|
encoder_last_hidden_state,
|
|
batch.past_key_values,
|
|
)
|
|
|
|
# Speculation is not active for seq2seq
|
|
accepted_ids = torch.ones_like(batch.decoder_input_ids)[:, 0]
|
|
batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
|
|
batch.top_n_tokens,
|
|
batch.top_n_tokens_tensor,
|
|
torch.log_softmax(logits[:, -1], -1),
|
|
accepted_ids,
|
|
)
|
|
|
|
start_decode = time.time_ns()
|
|
|
|
# Finished requests
|
|
generations: List[Generation] = []
|
|
stopped = True
|
|
|
|
# Zipped iterator
|
|
iterator = zip(
|
|
batch.requests,
|
|
batch.input_lengths,
|
|
batch.prefix_offsets,
|
|
batch.read_offsets,
|
|
batch.decoder_input_lengths,
|
|
logits,
|
|
batch.next_token_choosers,
|
|
batch.stopping_criterias,
|
|
batch.all_decoder_input_ids,
|
|
batch.top_n_tokens,
|
|
batch_top_token_ids,
|
|
batch_top_token_logprobs,
|
|
)
|
|
|
|
# For each member of the batch
|
|
for i, (
|
|
request,
|
|
input_length,
|
|
prefix_offset,
|
|
read_offset,
|
|
decoder_input_length,
|
|
logits,
|
|
next_token_chooser,
|
|
stopping_criteria,
|
|
all_decoder_input_ids,
|
|
top_n_tokens,
|
|
top_token_ids,
|
|
top_token_logprobs,
|
|
) in enumerate(iterator):
|
|
# Select next token
|
|
next_token_id, logprobs = next_token_chooser(
|
|
all_decoder_input_ids.view(1, -1), logits[-1:, :]
|
|
)
|
|
|
|
# Append next token to decoder tokens
|
|
all_decoder_input_ids = torch.cat(
|
|
[all_decoder_input_ids, next_token_id.squeeze(1)]
|
|
)
|
|
new_decoder_input_length = decoder_input_length + 1
|
|
|
|
# Generated token
|
|
next_token_logprob = logprobs[-1, next_token_id]
|
|
next_token_id_squeezed = next_token_id.squeeze()
|
|
next_token_text, prefix_offset, read_offset = self.decode_token(
|
|
all_decoder_input_ids, prefix_offset, read_offset
|
|
)
|
|
|
|
# Evaluate stopping criteria
|
|
stop, reason = stopping_criteria(next_token_id, next_token_text)
|
|
|
|
if not stop:
|
|
stopped = False
|
|
|
|
# Shard generations
|
|
# All generations will be appended in the rust sharded client
|
|
if i % self.world_size == self.rank:
|
|
if stop:
|
|
# Slice with decoder_input_length to remove padding
|
|
# Decode all tokens
|
|
output_text, _, _ = self.decode_token(
|
|
all_decoder_input_ids,
|
|
prefix_offset=len(all_decoder_input_ids)
|
|
- decoder_input_length
|
|
- 1,
|
|
read_offset=len(all_decoder_input_ids) - decoder_input_length,
|
|
skip_special_tokens=True,
|
|
)
|
|
|
|
# Get seed
|
|
if isinstance(next_token_chooser.choice, Sampling):
|
|
seed = next_token_chooser.choice.seed
|
|
else:
|
|
seed = None
|
|
|
|
generated_text = GeneratedText(
|
|
output_text, stopping_criteria.current_tokens, reason, seed
|
|
)
|
|
else:
|
|
generated_text = None
|
|
|
|
# Prefill
|
|
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
|
|
prefill_tokens = Tokens(
|
|
[self.tokenizer.bos_token_id],
|
|
[float("nan")],
|
|
[self.tokenizer.bos_token],
|
|
[False],
|
|
)
|
|
else:
|
|
prefill_tokens = None
|
|
|
|
if top_n_tokens > 0:
|
|
all_top_tokens = []
|
|
for top_token_ids, top_token_logprobs in zip(
|
|
top_token_ids, top_token_logprobs
|
|
):
|
|
toptoken_texts = self.tokenizer.batch_decode(
|
|
top_token_ids,
|
|
clean_up_tokenization_spaces=False,
|
|
skip_special_tokens=False,
|
|
)
|
|
special_toptokens = [
|
|
token_id in self.all_special_ids
|
|
for token_id in top_token_ids
|
|
]
|
|
top_tokens = Tokens(
|
|
top_token_ids,
|
|
top_token_logprobs,
|
|
toptoken_texts,
|
|
special_toptokens,
|
|
)
|
|
all_top_tokens.append(top_tokens)
|
|
top_tokens = all_top_tokens
|
|
else:
|
|
top_tokens = None
|
|
|
|
generation = Generation(
|
|
request.id,
|
|
prefill_tokens,
|
|
Tokens(
|
|
[next_token_id_squeezed],
|
|
[next_token_logprob],
|
|
[next_token_text],
|
|
[next_token_id_squeezed.item() in self.all_special_ids],
|
|
),
|
|
generated_text,
|
|
top_tokens,
|
|
)
|
|
|
|
generations.append(generation)
|
|
|
|
# Update values
|
|
batch.next_token_choosers[i] = batch.next_token_choosers[i].advance_grammar(
|
|
next_token_id_squeezed.item()
|
|
)
|
|
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.prefix_offsets[i] = prefix_offset
|
|
batch.read_offsets[i] = read_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:
|
|
forward_ns = start_decode - start
|
|
decode_ns = time.time_ns() - start_decode
|
|
return generations, None, (forward_ns, decode_ns)
|
|
|
|
# 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
|
|
|
|
forward_ns = start_decode - start
|
|
decode_ns = time.time_ns() - start_decode
|
|
return generations, batch, (forward_ns, decode_ns)
|