871 lines
32 KiB
Python
871 lines
32 KiB
Python
import torch
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import torch
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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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AutoProcessor,
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AutoTokenizer,
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PreTrainedTokenizerBase,
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ProcessorMixin,
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)
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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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Batch,
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Tokens,
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Generation,
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GeneratedText,
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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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from text_generation_server.models.vlm_causal_lm import split
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import re
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IMAGES = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)")
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tracer = trace.get_tracer(__name__)
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@dataclass
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class IdeficsCausalLMBatch(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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# Decoder values
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input_ids: torch.Tensor
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attention_mask: torch.Tensor
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position_ids: torch.Tensor
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pixel_values: Optional[torch.Tensor]
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image_hidden_states: Optional[torch.Tensor]
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image_attention_mask: Optional[torch.Tensor]
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past_key_values: Optional[List[Tuple]]
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# All tokens
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all_input_ids: List[torch.Tensor]
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# Lengths of all generations present in the batch
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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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# Metadata used for padding
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max_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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# Past metadata
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keys_head_dim_last: bool = True
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def to_pb(self) -> generate_pb2.CachedBatch:
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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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) -> "IdeficsCausalLMBatch":
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raise NotImplementedError
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@classmethod
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def from_pb_processor(
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cls,
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pb: generate_pb2.Batch,
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tokenizer: PreTrainedTokenizerBase,
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processor: ProcessorMixin, # Hack
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config,
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dtype: torch.dtype,
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device: torch.device,
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) -> "IdeficsCausalLMBatch":
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inputs = []
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next_token_choosers = []
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stopping_criterias = []
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prefix_offsets = []
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read_offsets = []
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requests_idx_mapping = {}
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# Parse batch
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max_truncation = 0
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padding_right_offset = 0
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max_decode_tokens = 0
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for i, r in enumerate(pb.requests):
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requests_idx_mapping[r.id] = i
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inputs.append(r.inputs)
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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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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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# TODO Check impact on idefics
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prompts = []
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for inp in inputs:
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# Each input is encoded into a list, where each element of this input list is either a string or a URL
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prompt = []
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for chunk in split(inp):
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prompt.append(chunk["content"])
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prompts.append(prompt)
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# The processor replaces the call to tokenizer, and
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# a/ takes care of fetching images from the URL
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# b/ generate the correct input_ids, attention_mask, pixel_values, image_attention_mask to feed to the model
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tokenized_inputs = processor(
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prompts,
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=max_truncation,
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# TODO Check impact on idefics
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# add_end_of_utterance_token=False, # Already taken care of inside the prompts, so bypassing the processor's handling of this token
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).to(device)
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for _ in pb.requests:
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input_len = tokenized_inputs["input_ids"].shape[1]
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prefix_offsets.append(
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input_len - 5
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) # To decode without potential fallbacks errors
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read_offsets.append(
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input_len
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) # To decode without potential fallbacks errors
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input_lengths = tokenized_inputs["attention_mask"].sum(1)
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max_input_length = input_lengths.max()
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input_ids = tokenized_inputs["input_ids"]
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pixel_values = tokenized_inputs.get("pixel_values", None)
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image_hidden_states = None
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# Allocate maximum attention_mask
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attention_mask = input_ids.new_zeros(
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(pb.size, max_input_length + padding_right_offset)
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)
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# Copy tokenizer attention_mask into fully allocated attention_mask
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attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
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# Do the same for image_attention_mask
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if pixel_values is None:
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image_attention_mask = None
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else:
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image_attention_mask = input_ids.new_zeros(
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(
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pb.size,
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max_input_length + padding_right_offset,
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pixel_values.size(1),
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)
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)
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image_attention_mask[:, :max_input_length, :] = tokenized_inputs[
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"image_attention_mask"
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]
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position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
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position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
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all_input_ids = tokenized_inputs["input_ids"].T.split(
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1, dim=1
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) # It's input_ids but splitted into a tuple of tensors where each tensor is (seq_len, 1) size. It is then transformed into a list
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max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
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return cls(
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batch_id=pb.id,
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requests=pb.requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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pixel_values=pixel_values,
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image_hidden_states=image_hidden_states,
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image_attention_mask=image_attention_mask,
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past_key_values=None,
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all_input_ids=list(all_input_ids),
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input_lengths=input_lengths.tolist(),
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prefix_offsets=prefix_offsets,
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read_offsets=read_offsets,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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max_input_length=max_input_length.item(),
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padding_right_offset=padding_right_offset,
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max_tokens=max_tokens,
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)
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@tracer.start_as_current_span("filter")
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def filter(self, request_ids: List[int]) -> Optional["IdeficsCausalLMBatch"]:
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# It deletes requests from the batch. For instance when client lost connection
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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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prefix_offsets = []
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read_offsets = []
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all_input_ids = []
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max_input_length = 0
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next_token_choosers = []
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stopping_criterias = []
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total_remaining_decode_tokens = 0
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new_padding_right_offset = 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_input_ids.append(self.all_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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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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total_remaining_decode_tokens += remaining_decode_tokens
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new_padding_right_offset = max(
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new_padding_right_offset, remaining_decode_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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input_ids = self.input_ids[keep_indices]
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position_ids = self.position_ids[keep_indices]
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self.attention_mask = self.attention_mask[
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keep_indices,
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-(self.padding_right_offset + max_input_length) : (
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self.attention_mask.shape[1] - self.padding_right_offset
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)
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+ new_padding_right_offset,
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]
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# Do the same for pixel_values and image_attention_mask
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pixel_values = self.pixel_values[keep_indices]
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self.image_attention_mask = self.image_attention_mask[
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keep_indices,
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-(self.padding_right_offset + max_input_length) : (
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self.image_attention_mask.shape[1] - self.padding_right_offset
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)
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+ new_padding_right_offset,
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:,
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]
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if self.image_hidden_states is None:
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image_hidden_states = None
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else:
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image_hidden_states = self.image_hidden_states[keep_indices]
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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 = [list(layer) for layer in self.past_key_values]
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# Update tensors in-place to allow incremental garbage collection
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past_kv_length = max_input_length - 1
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for layer in self.past_key_values:
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past_keys, past_values = layer
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if len(past_keys.shape) == 3:
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# Force past to be of dim [self_size, num_heads, ...] for easy indexing
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past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
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past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
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if self.keys_head_dim_last:
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layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
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else:
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layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
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del past_keys
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layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
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del past_values
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max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens
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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 = input_ids
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self.pixel_values = pixel_values
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self.image_hidden_states = image_hidden_states
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self.position_ids = position_ids
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self.all_input_ids = all_input_ids
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self.input_lengths = 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.max_input_length = max_input_length
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self.padding_right_offset = new_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(
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cls, batches: List["IdeficsCausalLMBatch"]
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) -> "IdeficsCausalLMBatch":
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# It adds new requests to the batch
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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_num_images = 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_num_images = max(max_num_images, batch.pixel_values.size(1))
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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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input_lengths = []
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prefix_offsets = []
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read_offsets = []
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all_input_ids = []
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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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input_ids = None
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attention_mask = None
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position_ids = None
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pixel_values = None
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image_hidden_states = None
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image_attention_mask = 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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requests.extend(batch.requests)
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input_lengths.extend(batch.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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all_input_ids.extend(batch.all_input_ids)
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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.past_key_values is None:
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raise ValueError("only concatenate prefilled batches")
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# Create empty tensor
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# input_ids is always of shape [batch_size, 1]
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# We do not need to pad it
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if input_ids is None:
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input_ids = batch.input_ids.new_empty((total_batch_size, 1))
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# Copy to correct indices
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input_ids[start_index:end_index] = batch.input_ids
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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 + padding_right_offset),
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)
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curr_batch_max_num_images = batch.pixel_values.size(1)
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if pixel_values is None:
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pixel_values = batch.pixel_values.new_zeros(
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(total_batch_size, max_num_images, 3, 224, 224)
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)
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pixel_values[start_index:end_index, :curr_batch_max_num_images] = (
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batch.pixel_values
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)
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if image_attention_mask is None:
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image_attention_mask = batch.image_attention_mask.new_zeros(
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(
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total_batch_size,
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max_input_length + padding_right_offset,
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max_num_images,
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)
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)
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# We need to slice the attention mask to remove padding from previous steps
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# and to remove unused allocated space
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left_offset = max_input_length - batch.max_input_length
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batch_left_offset = (
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batch.attention_mask.shape[1]
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- batch.max_input_length
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- batch.padding_right_offset
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)
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attention_mask[
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start_index:end_index,
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left_offset:-padding_right_offset,
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] = batch.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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image_attention_mask[
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start_index:end_index,
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left_offset:-padding_right_offset,
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:curr_batch_max_num_images,
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] = batch.image_attention_mask[
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:, batch_left_offset : -batch.padding_right_offset, :
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]
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# Create empty tensor
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# position_ids is always of shape [batch_size, 1]
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if position_ids is None:
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position_ids = batch.position_ids.new_empty((total_batch_size, 1))
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position_ids[start_index:end_index] = batch.position_ids
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# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
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# BLOOM Keys: [batch_size * num_heads, head_dim, seq_length]
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# BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
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# And 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.view(len(batch), -1, *t.shape[-2:]) for t in layer]
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for layer in batch.past_key_values
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]
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elif len(batch.past_key_values[0][0].shape) == 3:
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for layer in batch.past_key_values:
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for k, t in enumerate(layer):
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layer[k] = t.view(len(batch), -1, *t.shape[-2:])
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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 - batch.max_input_length
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) * len(batch)
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start_index = end_index
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first_past_kvs = batches[0].past_key_values
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_, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
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padded_past_values_shape = (
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total_batch_size,
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num_heads,
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max_input_length - 1,
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head_dim,
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)
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if batches[0].keys_head_dim_last:
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padded_past_keys_shape = padded_past_values_shape
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else:
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# seq_length is last for BLOOM
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padded_past_keys_shape = (
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total_batch_size,
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num_heads,
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head_dim,
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max_input_length - 1,
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)
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# Iterate over attention layers
|
|
# Concatenate past key values layer by layer to allow incremental garbage collection
|
|
for j in range(len(first_past_kvs)):
|
|
padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
|
|
start_index = 0
|
|
for batch in batches:
|
|
past_keys = batch.past_key_values[j][0]
|
|
# Clear reference to the original tensor
|
|
batch.past_key_values[j][0] = None
|
|
|
|
# Slicing end index for this batch
|
|
end_index = start_index + len(batch)
|
|
# We slice the keys to remove the padding from previous batches
|
|
past_seq_len = batch.max_input_length - 1
|
|
if batch.keys_head_dim_last:
|
|
padded_past_keys[start_index:end_index, :, -past_seq_len:, :] = (
|
|
past_keys[:, :, -past_seq_len:, :]
|
|
)
|
|
else:
|
|
# BLOOM case
|
|
padded_past_keys[start_index:end_index, :, :, -past_seq_len:] = (
|
|
past_keys[:, :, :, -past_seq_len:]
|
|
)
|
|
del past_keys
|
|
|
|
start_index = end_index
|
|
|
|
padded_past_values = first_past_kvs[j][1].new_zeros(
|
|
padded_past_values_shape
|
|
)
|
|
start_index = 0
|
|
for batch in batches:
|
|
past_values = batch.past_key_values[j][1]
|
|
# Clear reference to the original tensor
|
|
batch.past_key_values[j][1] = None
|
|
|
|
# Slicing end index for this batch
|
|
end_index = start_index + len(batch)
|
|
# We slice the past values to remove the padding from previous batches
|
|
past_seq_len = batch.max_input_length - 1
|
|
padded_past_values[start_index:end_index, :, -past_seq_len:, :] = (
|
|
past_values[:, :, -past_seq_len:, :]
|
|
)
|
|
del past_values
|
|
|
|
# Update values
|
|
start_index = end_index
|
|
|
|
past_key_values.append([padded_past_keys, padded_past_values])
|
|
|
|
return cls(
|
|
batch_id=batches[0].batch_id,
|
|
requests=requests,
|
|
requests_idx_mapping=requests_idx_mapping,
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
pixel_values=pixel_values,
|
|
image_hidden_states=image_hidden_states,
|
|
image_attention_mask=image_attention_mask,
|
|
past_key_values=past_key_values,
|
|
all_input_ids=all_input_ids,
|
|
input_lengths=input_lengths,
|
|
prefix_offsets=prefix_offsets,
|
|
read_offsets=read_offsets,
|
|
next_token_choosers=next_token_choosers,
|
|
stopping_criterias=stopping_criterias,
|
|
max_input_length=max_input_length,
|
|
padding_right_offset=padding_right_offset,
|
|
keys_head_dim_last=batches[0].keys_head_dim_last,
|
|
max_tokens=max_tokens,
|
|
)
|
|
|
|
def __len__(self):
|
|
return len(self.requests)
|
|
|
|
|
|
class IdeficsCausalLM(Model):
|
|
def __init__(
|
|
self,
|
|
model_id: str,
|
|
revision: Optional[str] = None,
|
|
quantize: Optional[str] = None,
|
|
dtype: Optional[torch.dtype] = None,
|
|
trust_remote_code: bool = False,
|
|
):
|
|
from text_generation_server.models.custom_modeling.idefics_modeling import (
|
|
IdeficsForVisionText2Text,
|
|
)
|
|
|
|
if torch.cuda.is_available():
|
|
device = torch.device("cuda")
|
|
dtype = torch.bfloat16 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
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
padding_side="left",
|
|
truncation_side="left",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
self.processor = AutoProcessor.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
padding_side="left",
|
|
truncation_side="left",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
model = IdeficsForVisionText2Text.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()
|
|
|
|
if tokenizer.pad_token_id is None:
|
|
if model.config.pad_token_id is not None:
|
|
tokenizer.pad_token_id = model.config.pad_token_id
|
|
elif model.config.eos_token_id is not None:
|
|
tokenizer.pad_token_id = model.config.eos_token_id
|
|
elif tokenizer.eos_token_id is not None:
|
|
tokenizer.pad_token_id = tokenizer.eos_token_id
|
|
else:
|
|
tokenizer.add_special_tokens({"pad_token": "<unk>"})
|
|
|
|
super(IdeficsCausalLM, self).__init__(
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
requires_padding=True,
|
|
dtype=dtype,
|
|
device=device,
|
|
)
|
|
|
|
@property
|
|
def batch_type(self) -> Type[IdeficsCausalLMBatch]:
|
|
return IdeficsCausalLMBatch
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
attention_mask,
|
|
position_ids,
|
|
pixel_values,
|
|
image_hidden_states,
|
|
image_attention_mask,
|
|
past_key_values: Optional = None,
|
|
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
|
|
# Model Forward
|
|
kwargs = {
|
|
"input_ids": input_ids,
|
|
"attention_mask": attention_mask,
|
|
"pixel_values": pixel_values,
|
|
"image_hidden_states": image_hidden_states,
|
|
"image_attention_mask": image_attention_mask,
|
|
"past_key_values": past_key_values,
|
|
"use_cache": True,
|
|
"return_dict": True,
|
|
}
|
|
if self.has_position_ids:
|
|
kwargs["position_ids"] = position_ids
|
|
|
|
outputs, speculative_logits = self.model.forward(**kwargs)
|
|
return (
|
|
outputs.logits,
|
|
speculative_logits,
|
|
outputs.past_key_values,
|
|
outputs.image_hidden_states,
|
|
)
|
|
|
|
@tracer.start_as_current_span("generate_token")
|
|
def generate_token(
|
|
self, batch: IdeficsCausalLMBatch
|
|
) -> Tuple[List[Generation], Optional[IdeficsCausalLMBatch], Tuple[int, int]]:
|
|
start = time.time_ns()
|
|
# slice the attention mask to the correct shape
|
|
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
|
|
if batch.image_attention_mask is None:
|
|
image_attention_mask = None
|
|
else:
|
|
if batch.input_ids.size(1) == 1:
|
|
# THIS is a hack: when calling idefics.generate, the first time, we need the whole image_attention_mask (size bs x max_seq_len x max_num_images),
|
|
# but the subsequent times, we only need the last attention mask along the `max_seq_len` dimension
|
|
# this is due to the nature IDEFICS: it's an encoder decoder, and so when decoding, only the currently generated
|
|
# token need to attend to the encoder hidden states (i.e. the vision encoder)
|
|
# Also see seq2seq_lm.Seq2SeqLM.generate_token which has roughly the same logic
|
|
image_attention_mask = batch.image_attention_mask[
|
|
:, -(batch.padding_right_offset + 1)
|
|
].unsqueeze(1)
|
|
else:
|
|
image_attention_mask = batch.image_attention_mask[
|
|
:, : -batch.padding_right_offset
|
|
]
|
|
|
|
logits, speculative_logits, past, image_hidden_states = self.forward(
|
|
input_ids=batch.input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=batch.position_ids,
|
|
pixel_values=batch.pixel_values,
|
|
image_hidden_states=batch.image_hidden_states,
|
|
image_attention_mask=image_attention_mask,
|
|
past_key_values=batch.past_key_values,
|
|
)
|
|
# Hardcoded remove image tokens
|
|
logits[:, 32000:32001] = torch.finfo(logits.dtype).min
|
|
|
|
start_decode = time.time_ns()
|
|
|
|
# Results
|
|
generations: List[Generation] = []
|
|
stopped = True
|
|
|
|
# Zipped iterator
|
|
iterator = zip(
|
|
batch.requests,
|
|
batch.input_lengths,
|
|
batch.prefix_offsets,
|
|
batch.read_offsets,
|
|
logits,
|
|
batch.next_token_choosers,
|
|
batch.stopping_criterias,
|
|
batch.all_input_ids,
|
|
)
|
|
|
|
# For each member of the batch
|
|
for i, (
|
|
request,
|
|
input_length,
|
|
prefix_offset,
|
|
read_offset,
|
|
logits,
|
|
next_token_chooser,
|
|
stopping_criteria,
|
|
all_input_ids,
|
|
) in enumerate(iterator):
|
|
# Select next token
|
|
next_token_id, logprobs = next_token_chooser(
|
|
all_input_ids.view(1, -1), logits[-1:, :]
|
|
)
|
|
|
|
# Append next token to all tokens
|
|
all_input_ids = torch.cat([all_input_ids, next_token_id])
|
|
new_input_length = 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_input_ids[:, 0], prefix_offset, read_offset
|
|
)
|
|
|
|
# Evaluate stopping criteria
|
|
stop, reason = stopping_criteria(
|
|
next_token_id_squeezed,
|
|
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:
|
|
# Decode generated tokens
|
|
output_text, _, _ = self.decode_token(
|
|
all_input_ids[:, 0],
|
|
prefix_offset=len(all_input_ids)
|
|
- stopping_criteria.current_tokens
|
|
- 1,
|
|
read_offset=len(all_input_ids)
|
|
- stopping_criteria.current_tokens,
|
|
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:
|
|
# Remove generated token to only have prefill and add nan for first prompt token
|
|
prefill_logprobs = [float("nan")] + torch.log_softmax(
|
|
logits, -1
|
|
).gather(1, all_input_ids[1:]).squeeze(1)[
|
|
-new_input_length:-1
|
|
].tolist()
|
|
prefill_token_ids = all_input_ids[-new_input_length:-1]
|
|
prefill_texts = self.tokenizer.batch_decode(
|
|
prefill_token_ids,
|
|
clean_up_tokenization_spaces=False,
|
|
skip_special_tokens=False,
|
|
)
|
|
prefill_tokens = Tokens(
|
|
prefill_token_ids,
|
|
prefill_logprobs,
|
|
prefill_texts,
|
|
is_special=[],
|
|
)
|
|
else:
|
|
prefill_tokens = None
|
|
|
|
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.input_ids[i, 0] = next_token_id
|
|
batch.all_input_ids[i] = all_input_ids
|
|
batch.input_lengths[i] = new_input_length
|
|
batch.prefix_offsets[i] = prefix_offset
|
|
batch.read_offsets[i] = read_offset
|
|
batch.max_input_length = max(batch.max_input_length, new_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)
|
|
|
|
# Slice unused values from prefill
|
|
batch.input_ids = batch.input_ids[:, :1]
|
|
|
|
# Update attention_mask as we added a new token to input_ids
|
|
batch.attention_mask[:, -batch.padding_right_offset] = 1
|
|
batch.image_attention_mask[:, -batch.padding_right_offset, :] = (
|
|
batch.image_attention_mask[:, -(batch.padding_right_offset + 1), :]
|
|
)
|
|
# Decrease right offset
|
|
batch.padding_right_offset -= 1
|
|
|
|
# Update position_ids
|
|
batch.position_ids = batch.position_ids[:, -1:] + 1
|
|
|
|
# Update past key values
|
|
batch.past_key_values = past
|
|
batch.image_hidden_states = image_hidden_states
|
|
|
|
forward_ns = start_decode - start
|
|
decode_ns = time.time_ns() - start_decode
|
|
return generations, batch, (forward_ns, decode_ns)
|