678 lines
25 KiB
Python
678 lines
25 KiB
Python
import torch
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import inspect
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from dataclasses import dataclass
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from opentelemetry import trace
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from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
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from typing import Optional, Tuple, List, Type, Dict
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from text_generation_server.models import Model
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from text_generation_server.models.types import (
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Batch,
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PrefillTokens,
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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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tracer = trace.get_tracer(__name__)
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@dataclass
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class CausalLMBatch(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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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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) -> "CausalLMBatch":
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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(NextTokenChooser.from_pb(r.parameters, device))
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stopping_criteria = StoppingCriteria.from_pb(
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r.stopping_parameters, tokenizer
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)
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stopping_criterias.append(stopping_criteria)
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max_truncation = max(max_truncation, r.truncate)
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max_decode_tokens += stopping_criteria.max_new_tokens
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padding_right_offset = max(
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padding_right_offset, stopping_criteria.max_new_tokens
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)
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tokenized_inputs = tokenizer(
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inputs,
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return_tensors="pt",
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padding=True,
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return_token_type_ids=False,
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truncation=True,
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max_length=max_truncation,
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).to(device)
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for _ in pb.requests:
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input_len = tokenized_inputs["input_ids"].shape[1]
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prefix_offsets.append(input_len - 5)
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read_offsets.append(input_len)
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input_lengths = tokenized_inputs["attention_mask"].sum(1)
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max_input_length = input_lengths.max()
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input_ids = tokenized_inputs["input_ids"]
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# Allocate maximum attention_mask
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attention_mask = input_ids.new_zeros(
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(pb.size, max_input_length + padding_right_offset)
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)
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# Copy tokenizer attention_mask into fully allocated attention_mask
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attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
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position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
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position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
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all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
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max_tokens = len(inputs) * max_input_length + max_decode_tokens
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return cls(
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batch_id=pb.id,
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requests=pb.requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=None,
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all_input_ids=list(all_input_ids),
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input_lengths=input_lengths.tolist(),
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prefix_offsets=prefix_offsets,
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read_offsets=read_offsets,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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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["CausalLMBatch"]:
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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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# 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.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(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
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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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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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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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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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# 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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# 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
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# Concatenate past key values layer by layer to allow incremental garbage collection
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for j in range(len(first_past_kvs)):
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padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
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start_index = 0
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for batch in batches:
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past_keys = batch.past_key_values[j][0]
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# Clear reference to the original tensor
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batch.past_key_values[j][0] = None
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# Slicing end index for this batch
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end_index = start_index + len(batch)
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# We slice the keys to remove the padding from previous batches
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past_seq_len = batch.max_input_length - 1
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if batch.keys_head_dim_last:
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padded_past_keys[
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start_index:end_index, :, -past_seq_len:, :
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] = past_keys[:, :, -past_seq_len:, :]
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else:
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# BLOOM case
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padded_past_keys[
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start_index:end_index, :, :, -past_seq_len:
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] = past_keys[:, :, :, -past_seq_len:]
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del past_keys
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start_index = end_index
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padded_past_values = first_past_kvs[j][1].new_zeros(
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padded_past_values_shape
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)
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start_index = 0
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for batch in batches:
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past_values = batch.past_key_values[j][1]
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# Clear reference to the original tensor
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batch.past_key_values[j][1] = None
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# Slicing end index for this batch
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end_index = start_index + len(batch)
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# We slice the past values to remove the padding from previous batches
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past_seq_len = batch.max_input_length - 1
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padded_past_values[
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start_index:end_index, :, -past_seq_len:, :
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] = past_values[:, :, -past_seq_len:, :]
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del past_values
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# Update values
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start_index = end_index
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past_key_values.append([padded_past_keys, padded_past_values])
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return cls(
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batch_id=batches[0].batch_id,
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requests=requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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all_input_ids=all_input_ids,
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input_lengths=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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max_input_length=max_input_length,
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padding_right_offset=padding_right_offset,
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keys_head_dim_last=batches[0].keys_head_dim_last,
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max_tokens=max_tokens,
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)
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def __len__(self):
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return len(self.requests)
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class CausalLM(Model):
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def __init__(
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self,
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model_id: str,
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revision: Optional[str] = None,
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quantize: Optional[str] = None,
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trust_remote_code: bool = False,
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):
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if torch.cuda.is_available():
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device = torch.device("cuda")
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dtype = torch.float16
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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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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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revision=revision,
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padding_side="left",
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truncation_side="left",
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trust_remote_code=trust_remote_code,
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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revision=revision,
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torch_dtype=dtype,
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device_map="auto"
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if torch.cuda.is_available() and torch.cuda.device_count() > 1
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else None,
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load_in_8bit=quantize == "bitsandbytes",
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trust_remote_code=trust_remote_code,
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)
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if torch.cuda.is_available() and torch.cuda.device_count() == 1:
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model = model.cuda()
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if tokenizer.pad_token_id is None:
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if model.config.pad_token_id is not None:
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tokenizer.pad_token_id = model.config.pad_token_id
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|
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": "[PAD]"})
|
|
|
|
super(CausalLM, self).__init__(
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
requires_padding=True,
|
|
dtype=dtype,
|
|
device=device,
|
|
)
|
|
|
|
@property
|
|
def batch_type(self) -> Type[CausalLMBatch]:
|
|
return CausalLMBatch
|
|
|
|
def decode(self, generated_ids: List[int]) -> str:
|
|
return self.tokenizer.decode(
|
|
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
|
|
)
|
|
|
|
def forward(
|
|
self, input_ids, attention_mask, position_ids, 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,
|
|
"past_key_values": past_key_values,
|
|
"use_cache": True,
|
|
"return_dict": True,
|
|
}
|
|
if self.has_position_ids:
|
|
kwargs["position_ids"] = position_ids
|
|
|
|
outputs = self.model.forward(**kwargs)
|
|
return outputs.logits, outputs.past_key_values
|
|
|
|
@tracer.start_as_current_span("generate_token")
|
|
def generate_token(
|
|
self, batch: CausalLMBatch
|
|
) -> Tuple[List[Generation], Optional[CausalLMBatch]]:
|
|
# slice the attention mask to the correct shape
|
|
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
|
|
|
|
logits, past = self.forward(
|
|
batch.input_ids,
|
|
attention_mask,
|
|
batch.position_ids,
|
|
batch.past_key_values,
|
|
)
|
|
|
|
# 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(
|
|
all_input_ids[-stopping_criteria.current_tokens :, 0]
|
|
)
|
|
# 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 = PrefillTokens(
|
|
prefill_token_ids, prefill_logprobs, prefill_texts
|
|
)
|
|
else:
|
|
prefill_tokens = None
|
|
|
|
generation = Generation(
|
|
request.id,
|
|
prefill_tokens,
|
|
next_token_id_squeezed,
|
|
next_token_logprob,
|
|
next_token_text,
|
|
next_token_id_squeezed.item() in self.all_special_ids,
|
|
generated_text,
|
|
)
|
|
|
|
generations.append(generation)
|
|
|
|
# Update values
|
|
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:
|
|
return generations, None
|
|
|
|
# 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
|
|
# 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
|
|
|
|
return generations, batch
|