554 lines
21 KiB
Python
554 lines
21 KiB
Python
import torch
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from dataclasses import dataclass
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, PreTrainedTokenizerBase
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from typing import Optional, Tuple, List, Type
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from text_generation.models import Model
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from text_generation.models.types import GeneratedText, Batch
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from text_generation.pb import generate_pb2
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from text_generation.utils import NextTokenChooser, StoppingCriteria
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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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# Encoder values
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input_ids: 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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# 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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decoder_logprobs: List[Optional[torch.Tensor]]
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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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size: int
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max_input_length: int
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max_decoder_input_length: int
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def to_pb(self) -> generate_pb2.Batch:
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"""Convert a Seq2SeqLMBatch to a text_generation.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=self.size,
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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.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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input_lengths = []
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decoder_input_ids = []
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decoder_input_lengths = []
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decoder_logprobs = []
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# Parse batch
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for r in pb.requests:
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inputs.append(r.inputs)
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input_lengths.append(r.input_length)
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# Decoder sequence only contains the bos_token
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decoder_input_ids.append(tokenizer.bos_token_id)
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decoder_input_lengths.append(1)
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next_token_choosers.append(NextTokenChooser.from_pb(r.parameters))
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stopping_criterias.append(
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StoppingCriteria.from_pb(r.stopping_parameters, tokenizer)
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)
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decoder_logprobs.append(None)
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# Tokenize batch
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pad_to_multiple_of = 8 if device.type == "cuda" else None
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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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pad_to_multiple_of=pad_to_multiple_of,
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return_token_type_ids=False,
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).to(device)
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# Convert decoder_input_ids to torch tensor of size [batch_size, 1]
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decoder_input_ids = torch.tensor(decoder_input_ids, device=device).unsqueeze(-1)
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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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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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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,
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decoder_input_lengths=decoder_input_lengths,
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decoder_logprobs=decoder_logprobs,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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size=len(pb.requests),
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max_input_length=max(input_lengths),
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max_decoder_input_length=1,
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)
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@classmethod
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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 = sum(batch.size for batch in batches)
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max_input_length = max(batch.max_input_length for batch in batches)
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max_decoder_input_length = max(
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batch.max_decoder_input_length for batch in batches
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)
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# Batch attributes
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requests = []
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input_lengths = []
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decoder_input_lengths = []
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decoder_logprobs = []
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next_token_choosers = []
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stopping_criterias = []
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# Batch tensors
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input_ids = None
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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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input_lengths.extend(batch.input_lengths)
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decoder_input_lengths.extend(batch.decoder_input_lengths)
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decoder_logprobs.extend(batch.decoder_logprobs)
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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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# Slicing end index for this batch
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end_index = start_index + batch.size
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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 input_ids is None:
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input_ids = batch.input_ids.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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input_ids[
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start_index:end_index, -batch.max_input_length :
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] = batch.input_ids[:, -batch.max_input_length :]
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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, max_decoder_input_length),
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)
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# Copy to correct indices
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decoder_input_ids[
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start_index:end_index, -batch.max_decoder_input_length :
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] = batch.decoder_input_ids[:, -batch.max_decoder_input_length :]
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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),
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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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if batch.decoder_attention_mask is None:
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decoder_attention_mask[
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start_index:end_index, -batch.max_decoder_input_length :
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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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decoder_attention_mask[
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start_index:end_index, -batch.max_decoder_input_length :
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] = batch.decoder_attention_mask[:, -batch.max_decoder_input_length :]
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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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# Iterate over attention layers
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for j, past in enumerate(batch.past_key_values):
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_, num_heads, _, head_dim = past[0].shape
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# This will run only once per layer
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if j == len(past_key_values):
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past_key_values.append([])
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# Decoder past
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for k, t in enumerate(past[:2]):
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padded_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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# Initialize tensors
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# This will run only once per layer and per past tensor
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if k == len(past_key_values[j]):
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past_key_values[j].append(t.new_zeros(padded_t_shape))
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# We slice the past keys and values to remove the padding from previous batches
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past_key_values[j][k][
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start_index:end_index,
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:,
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-(batch.max_decoder_input_length - 1) :,
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:,
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] = t[:, :, -(batch.max_decoder_input_length - 1) :, :]
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# encoder past
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for k, t in enumerate(past[2:]):
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padded_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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idx = k + 2
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# Initialize tensors
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# This will run only once per layer and per past tensor
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if idx == len(past_key_values[j]):
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past_key_values[j].append(t.new_zeros(padded_t_shape))
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past_key_values[j][idx][
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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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start_index += batch.size
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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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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_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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decoder_logprobs=decoder_logprobs,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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size=total_batch_size,
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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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)
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class Seq2SeqLM(Model):
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def __init__(self, model_name: str, quantize=False):
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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_name,
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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(model_name, padding_side="left")
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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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device=device,
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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(decoder_ids, skip_special_tokens=True)
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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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if past_key_values is not None:
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decoder_input_ids = decoder_input_ids[:, -1].unsqueeze(-1)
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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 encoder_last_hidden_state is not None:
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encoder_last_hidden_state = [encoder_last_hidden_state]
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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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def generate_token(
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self, batch: Seq2SeqLMBatch
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) -> Tuple[List[GeneratedText], Optional[Seq2SeqLMBatch]]:
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# For some reason, inference_mode does not work well with GLOO which we use on CPU
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context_manager = (
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torch.no_grad if self.device.type == "cpu" else torch.inference_mode
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)
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with context_manager():
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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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batch.decoder_attention_mask,
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batch.encoder_last_hidden_state,
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batch.past_key_values,
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)
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# List of indices to cache
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next_batch_keep_indices = []
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# New values for next forward
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next_batch_input_lengths = []
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next_batch_decoder_input_ids = []
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next_batch_decoder_input_lengths = []
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next_batch_decoder_logprobs = []
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# Metadata
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next_batch_size = 0
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next_batch_max_input_length = 0
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next_batch_max_decoder_input_length = 0
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# Finished requests
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generated_texts: List[GeneratedText] = []
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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.decoder_input_lengths,
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batch.decoder_logprobs,
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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.input_ids,
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batch.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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decoder_input_length,
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decoder_logprobs,
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logits,
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next_token_chooser,
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stopping_criteria,
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input_tokens,
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decoder_input_ids,
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) in enumerate(iterator):
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# Select next token
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next_token, logprobs = next_token_chooser(decoder_input_ids, logits)
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# Append next token to decoder tokens
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decoder_input_ids = torch.cat([decoder_input_ids, next_token])
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new_decoder_input_length = decoder_input_length + 1
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next_token_logprob = logprobs[-1, next_token]
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if decoder_logprobs is None:
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decoder_logprobs = next_token_logprob
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else:
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decoder_logprobs = torch.cat([decoder_logprobs, next_token_logprob])
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# Evaluate stopping criteria
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stop, reason = stopping_criteria(
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next_token.squeeze(),
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self.tokenizer.decode(
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next_token.squeeze(), clean_up_tokenization_spaces=False
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),
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)
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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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token_ids = decoder_input_ids[-new_decoder_input_length:]
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output_text = self.decode(token_ids)
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tokens = self.tokenizer.batch_decode(token_ids)
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# Add NaN for the bos token
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logprobs = [float("nan")] + decoder_logprobs[
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-decoder_input_length:
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].tolist()
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# Add to the list of finished generations with the original request
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generated_texts.append(
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GeneratedText(
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request=request,
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output_text=output_text,
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generated_tokens=stopping_criteria.current_tokens,
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tokens=tokens,
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token_ids=token_ids.tolist(),
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logprobs=logprobs,
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reason=reason,
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)
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)
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# add to the next batch
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else:
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next_batch_keep_indices.append(i)
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next_batch_decoder_input_ids.append(decoder_input_ids.unsqueeze(0))
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next_batch_size += 1
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next_batch_input_lengths.append(input_length)
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next_batch_decoder_input_lengths.append(new_decoder_input_length)
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next_batch_decoder_logprobs.append(decoder_logprobs)
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next_batch_max_input_length = max(
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next_batch_max_input_length, input_length
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)
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next_batch_max_decoder_input_length = max(
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next_batch_max_decoder_input_length, new_decoder_input_length
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)
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# We finished all generations in the batch; there is no next batch
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if not next_batch_keep_indices:
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return generated_texts, None
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next_batch_decoder_input_ids = torch.cat(next_batch_decoder_input_ids)
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# If we finished at least one generation, we need to evict the indices of the generations that finished
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# from the values of the next batch
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if generated_texts:
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# Apply indices to attention mask, past key values and other items that need to be cached
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next_batch_input_ids = batch.input_ids[next_batch_keep_indices]
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next_batch_attention_mask = batch.attention_mask[next_batch_keep_indices]
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if batch.decoder_attention_mask is not None:
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next_batch_decoder_attention_mask = batch.decoder_attention_mask[
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next_batch_keep_indices
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]
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else:
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next_batch_decoder_attention_mask = None
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next_batch_encoder_last_hidden_state = encoder_last_hidden_state[
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next_batch_keep_indices
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]
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next_batch_past_key_values = [
|
|
[t[next_batch_keep_indices] for t in layer] for layer in past
|
|
]
|
|
next_batch_requests = [batch.requests[i] for i in next_batch_keep_indices]
|
|
next_batch_next_token_choosers = [
|
|
batch.next_token_choosers[i] for i in next_batch_keep_indices
|
|
]
|
|
next_batch_stopping_criterias = [
|
|
batch.stopping_criterias[i] for i in next_batch_keep_indices
|
|
]
|
|
else:
|
|
next_batch_input_ids = batch.input_ids
|
|
next_batch_attention_mask = batch.attention_mask
|
|
next_batch_decoder_attention_mask = batch.decoder_attention_mask
|
|
next_batch_encoder_last_hidden_state = encoder_last_hidden_state
|
|
next_batch_past_key_values = past
|
|
|
|
next_batch_requests = batch.requests
|
|
next_batch_next_token_choosers = batch.next_token_choosers
|
|
next_batch_stopping_criterias = batch.stopping_criterias
|
|
|
|
# Update decoder_attention_mask with padding as we added a new token to input_ids
|
|
if next_batch_decoder_attention_mask is not None:
|
|
next_batch_decoder_attention_mask = torch.cat(
|
|
[
|
|
next_batch_decoder_attention_mask,
|
|
next_batch_decoder_attention_mask.new_ones(next_batch_size, 1),
|
|
],
|
|
dim=1,
|
|
)
|
|
|
|
next_batch = Seq2SeqLMBatch(
|
|
batch_id=batch.batch_id,
|
|
requests=next_batch_requests,
|
|
input_ids=next_batch_input_ids,
|
|
attention_mask=next_batch_attention_mask,
|
|
decoder_input_ids=next_batch_decoder_input_ids,
|
|
decoder_attention_mask=next_batch_decoder_attention_mask,
|
|
encoder_last_hidden_state=next_batch_encoder_last_hidden_state,
|
|
past_key_values=next_batch_past_key_values,
|
|
input_lengths=next_batch_input_lengths,
|
|
decoder_input_lengths=next_batch_decoder_input_lengths,
|
|
decoder_logprobs=next_batch_decoder_logprobs,
|
|
next_token_choosers=next_batch_next_token_choosers,
|
|
stopping_criterias=next_batch_stopping_criterias,
|
|
size=next_batch_size,
|
|
max_input_length=next_batch_max_input_length,
|
|
max_decoder_input_length=next_batch_max_decoder_input_length,
|
|
)
|
|
return generated_texts, next_batch
|