add missing files
This commit is contained in:
Felix Marty
parent
770975fa81
commit
3760102077
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@ -0,0 +1,787 @@
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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 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.utils.chunks import concat_text_chunks
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from text_generation_server.utils.tokens import batch_top_tokens
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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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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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top_n_tokens: List[int]
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top_n_tokens_tensor: torch.Tensor
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# Metadata used for padding
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max_input_length: int
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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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top_n_tokens = []
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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(concat_text_chunks(r.input_chunks.chunks))
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next_token_choosers.append(
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NextTokenChooser.from_pb(r.parameters, device, tokenizer)
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)
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stopping_criteria = StoppingCriteria.from_pb(
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r.stopping_parameters, tokenizer
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)
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stopping_criterias.append(stopping_criteria)
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top_n_tokens.append(r.top_n_tokens)
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max_truncation = max(max_truncation, r.truncate)
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max_decode_tokens += stopping_criteria.max_new_tokens
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padding_right_offset = max(
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padding_right_offset, stopping_criteria.max_new_tokens
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)
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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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top_n_tokens_tensor = torch.tensor(
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top_n_tokens, device=device, dtype=torch.int64
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)
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max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
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return cls(
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batch_id=pb.id,
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requests=pb.requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=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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top_n_tokens=top_n_tokens,
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top_n_tokens_tensor=top_n_tokens_tensor,
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max_input_length=max_input_length.item(),
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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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top_n_tokens = []
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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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top_n_tokens.append(self.top_n_tokens[idx])
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remaining_decode_tokens = (
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stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
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)
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total_remaining_decode_tokens += remaining_decode_tokens
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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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top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices]
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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.top_n_tokens = top_n_tokens
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self.top_n_tokens_tensor = top_n_tokens_tensor
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self.max_input_length = max_input_length
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self.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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top_n_tokens = []
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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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top_n_tokens_tensor = None
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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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top_n_tokens.extend(batch.top_n_tokens)
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if i == 0:
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requests_idx_mapping = batch.requests_idx_mapping
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else:
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# We need to offset the mapping for each batch by the cumulative batch size
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for k, v in batch.requests_idx_mapping.items():
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requests_idx_mapping[k] = v + start_index
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# Slicing end index for this batch
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end_index = start_index + len(batch)
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# We only concatenate batches that did at least one step
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if batch.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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if top_n_tokens_tensor is None:
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top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros(
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total_batch_size,
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)
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top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor
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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[start_index:end_index, :, -past_seq_len:, :] = (
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past_keys[:, :, -past_seq_len:, :]
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)
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else:
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# BLOOM case
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padded_past_keys[start_index:end_index, :, :, -past_seq_len:] = (
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past_keys[:, :, :, -past_seq_len:]
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)
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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[start_index:end_index, :, -past_seq_len:, :] = (
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past_values[:, :, -past_seq_len:, :]
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)
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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])
|
||||
|
||||
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,
|
||||
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,
|
||||
top_n_tokens=top_n_tokens,
|
||||
top_n_tokens_tensor=top_n_tokens_tensor,
|
||||
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 TransformersCausalLM(Model):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
speculator: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
if speculator:
|
||||
raise RuntimeError("Speculator decoding is not enabled for AutoModel")
|
||||
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
else:
|
||||
if quantize:
|
||||
raise ValueError("quantization is not available on CPU")
|
||||
|
||||
device = torch.device("cpu")
|
||||
dtype = torch.float32 if dtype is None else dtype
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
model = AutoModelForCausalLM.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
|
||||
and quantize != "bitsandbytes"
|
||||
):
|
||||
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": "[PAD]"})
|
||||
|
||||
super(CausalLM, self).__init__(
|
||||
model_id=model_id,
|
||||
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, Optional[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)
|
||||
if isinstance(outputs, tuple):
|
||||
outputs, speculative_logits = outputs
|
||||
else:
|
||||
speculative_logits = None
|
||||
return outputs.logits, speculative_logits, outputs.past_key_values
|
||||
|
||||
@tracer.start_as_current_span("generate_token")
|
||||
def generate_token(
|
||||
self, batch: CausalLMBatch
|
||||
) -> Tuple[List[Generation], Optional[CausalLMBatch], Tuple[int, int]]:
|
||||
start = time.time_ns()
|
||||
# slice the attention mask to the correct shape
|
||||
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
|
||||
|
||||
logits, speculative_logits, past = self.forward(
|
||||
batch.input_ids,
|
||||
attention_mask,
|
||||
batch.position_ids,
|
||||
batch.past_key_values,
|
||||
)
|
||||
|
||||
# Results
|
||||
generations: List[Generation] = []
|
||||
stopped = True
|
||||
|
||||
# Speculation is not active for causal
|
||||
accepted_ids = torch.ones_like(batch.input_ids)[:, 0]
|
||||
batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
|
||||
batch.top_n_tokens,
|
||||
batch.top_n_tokens_tensor,
|
||||
torch.log_softmax(logits[:, -1], -1),
|
||||
accepted_ids,
|
||||
)
|
||||
|
||||
start_decode = time.time_ns()
|
||||
|
||||
# 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,
|
||||
batch.top_n_tokens,
|
||||
batch_top_token_ids,
|
||||
batch_top_token_logprobs,
|
||||
)
|
||||
|
||||
# For each member of the batch
|
||||
for i, (
|
||||
request,
|
||||
input_length,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
logits,
|
||||
next_token_chooser,
|
||||
stopping_criteria,
|
||||
all_input_ids,
|
||||
top_n_tokens,
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
) in enumerate(iterator):
|
||||
# Select next token
|
||||
next_token_id, logprobs = next_token_chooser(
|
||||
all_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
|
||||
|
||||
if top_n_tokens > 0:
|
||||
all_top_tokens = []
|
||||
for top_token_ids, top_token_logprobs in zip(
|
||||
top_token_ids, top_token_logprobs
|
||||
):
|
||||
toptoken_texts = self.tokenizer.batch_decode(
|
||||
top_token_ids,
|
||||
clean_up_tokenization_spaces=False,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
special_toptokens = [
|
||||
token_id in self.all_special_ids
|
||||
for token_id in top_token_ids
|
||||
]
|
||||
top_tokens = Tokens(
|
||||
top_token_ids,
|
||||
top_token_logprobs,
|
||||
toptoken_texts,
|
||||
special_toptokens,
|
||||
)
|
||||
all_top_tokens.append(top_tokens)
|
||||
top_tokens = all_top_tokens
|
||||
else:
|
||||
top_tokens = None
|
||||
|
||||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
Tokens(
|
||||
[next_token_id_squeezed],
|
||||
[next_token_logprob],
|
||||
[next_token_text],
|
||||
[next_token_id_squeezed.item() in self.all_special_ids],
|
||||
),
|
||||
generated_text,
|
||||
top_tokens,
|
||||
)
|
||||
|
||||
generations.append(generation)
|
||||
|
||||
# Update values
|
||||
batch.next_token_choosers[i] = batch.next_token_choosers[i].advance_grammar(
|
||||
next_token_id_squeezed.item()
|
||||
)
|
||||
batch.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
|
||||
# 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
|
||||
|
||||
forward_ns = start_decode - start
|
||||
decode_ns = time.time_ns() - start_decode
|
||||
return generations, batch, (forward_ns, decode_ns)
|
||||
|
|
@ -0,0 +1,359 @@
|
|||
import torch
|
||||
import time
|
||||
import sys
|
||||
from dataclasses import dataclass
|
||||
from opentelemetry import trace
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
|
||||
from typing import Optional, Tuple, List, Type, Dict, Any
|
||||
from text_generation_server.utils.import_utils import SYSTEM
|
||||
from text_generation_server.models import Model
|
||||
from text_generation_server.utils.chunks import concat_text_chunks
|
||||
from text_generation_server.utils.tokens import batch_top_tokens
|
||||
from text_generation_server.models.types import (
|
||||
Batch,
|
||||
Tokens,
|
||||
Generation,
|
||||
GeneratedText,
|
||||
)
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
|
||||
from text_generation_server.models.flash_causal_lm import (
|
||||
FlashCausalLMBatch,
|
||||
FlashCausalLM,
|
||||
)
|
||||
|
||||
from text_generation_server.utils.import_utils import (
|
||||
empty_cache,
|
||||
synchronize,
|
||||
get_free_memory,
|
||||
)
|
||||
from text_generation_server.utils.speculate import get_speculate
|
||||
from text_generation_server.utils.dist import MEMORY_FRACTION
|
||||
|
||||
tracer = trace.get_tracer(__name__)
|
||||
|
||||
from text_generation_server.adapters import AdapterBatchData
|
||||
from text_generation_server.layers.attention import reshape_and_cache
|
||||
from transformers.cache_utils import Cache
|
||||
from transformers.flash_attention_utils import _flash_supports_window_size
|
||||
from flash_attn import flash_attn_varlen_func
|
||||
from text_generation_server.layers.attention import paged_attention
|
||||
|
||||
from loguru import logger
|
||||
|
||||
# Why define it here?
|
||||
BLOCK_SIZE: int = 16
|
||||
|
||||
|
||||
def patch_everywhere(
|
||||
attribute_name: str, patch: Any, module_name_prefix: Optional[str] = None
|
||||
):
|
||||
"""
|
||||
Finds all occurences of `attribute_name` in the loaded modules and patches them with `patch`.
|
||||
|
||||
Args:
|
||||
attribute_name (`str`):
|
||||
The name of attribute to patch.
|
||||
patch (`Any`):
|
||||
The patch for the attribute.
|
||||
module_name_prefix (`Optional[str]`, defaults to `None`):
|
||||
If set, only module names starting with this prefix will be considered for patching.
|
||||
"""
|
||||
# sys.modules may be updated while being iterated over, hence the list copy.
|
||||
for name in list(sys.modules):
|
||||
module = sys.modules[name]
|
||||
if module_name_prefix is not None and not name.startswith(module_name_prefix):
|
||||
continue
|
||||
if hasattr(module, attribute_name):
|
||||
setattr(module, attribute_name, patch)
|
||||
|
||||
|
||||
def _flash_attention_forward_patched(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attention_mask,
|
||||
query_length,
|
||||
layer_idx: int,
|
||||
dropout=0.0,
|
||||
softmax_scale=None,
|
||||
is_causal=False,
|
||||
_flash_attn_uses_top_left_mask=False,
|
||||
sliding_window=None,
|
||||
cache_position=0,
|
||||
**kwargs, #: Unpack[ExtraKwargs],
|
||||
):
|
||||
_flash_attn_uses_top_left_mask = True # TODO felix: fix rocm
|
||||
|
||||
if not _flash_attn_uses_top_left_mask:
|
||||
causal = is_causal
|
||||
else:
|
||||
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
||||
causal = is_causal and query_length != 1
|
||||
|
||||
print(f"causal: {causal}")
|
||||
|
||||
use_sliding_windows = (
|
||||
_flash_supports_window_size
|
||||
and sliding_window is not None
|
||||
and cache_position > sliding_window
|
||||
)
|
||||
flash_kwargs = (
|
||||
{"window_size": (sliding_window, sliding_window)} if use_sliding_windows else {}
|
||||
)
|
||||
|
||||
print(f"kwargs {kwargs.keys()}")
|
||||
|
||||
cu_seqlen_prefill = kwargs.get("cu_seqlen_prefill")
|
||||
max_seq_lens = kwargs.get("max_seq_lens")
|
||||
|
||||
if cu_seqlen_prefill is not None:
|
||||
attn_output = flash_attn_varlen_func(
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
cu_seqlens_q=cu_seqlen_prefill,
|
||||
cu_seqlens_k=cu_seqlen_prefill,
|
||||
max_seqlen_q=kwargs["max_s"],
|
||||
max_seqlen_k=kwargs["max_s"],
|
||||
dropout_p=dropout,
|
||||
softmax_scale=softmax_scale,
|
||||
causal=causal,
|
||||
# **kwargs,
|
||||
**flash_kwargs,
|
||||
)
|
||||
else:
|
||||
attn_output = torch.empty_like(query_states)
|
||||
|
||||
paged_attention(
|
||||
attn_output,
|
||||
query_states,
|
||||
kwargs["kv_cache"][layer_idx][0],
|
||||
kwargs["kv_cache"][layer_idx][1],
|
||||
kwargs["kv_head_mapping"],
|
||||
softmax_scale,
|
||||
kwargs["block_tables"],
|
||||
kwargs["input_lengths"],
|
||||
kwargs["max_s"],
|
||||
)
|
||||
|
||||
attn_output = attn_output.view(attn_output.shape[0], -1)
|
||||
|
||||
return attn_output
|
||||
|
||||
|
||||
class PagedCache(Cache):
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def update(
|
||||
self,
|
||||
key_states: torch.Tensor,
|
||||
value_states: torch.Tensor,
|
||||
layer_idx: int,
|
||||
cache_kwargs: Optional[Dict[str, Any]] = None,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
|
||||
kv_cache = cache_kwargs["kv_cache"]
|
||||
reshape_and_cache(
|
||||
key_states,
|
||||
value_states,
|
||||
kv_cache[layer_idx][0],
|
||||
kv_cache[layer_idx][1],
|
||||
cache_kwargs["slots"],
|
||||
)
|
||||
|
||||
if cache_kwargs["cu_seqlen_prefill"] is not None:
|
||||
return key_states, value_states
|
||||
else:
|
||||
return None, None
|
||||
|
||||
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
|
||||
raise ValueError(
|
||||
"PagedCache.get_seq_length should never be called, please open an issue."
|
||||
)
|
||||
|
||||
def get_max_length(self) -> Optional[int]:
|
||||
raise ValueError(
|
||||
"PagedCache.get_max_length should never be called, please open an issue."
|
||||
)
|
||||
|
||||
|
||||
class TransformersFlashCausalLM(FlashCausalLM):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
speculator: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
if speculator:
|
||||
raise RuntimeError("Speculator decoding is not enabled for AutoModel")
|
||||
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda:0") # TODO felix: fix support for accelerate
|
||||
dtype = torch.float16 if dtype is None else dtype
|
||||
else:
|
||||
if quantize:
|
||||
raise ValueError("quantization is not available on CPU")
|
||||
|
||||
device = torch.device("cpu")
|
||||
dtype = torch.float32 if dtype is None else dtype
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
torch_dtype=dtype,
|
||||
device_map=None,
|
||||
load_in_8bit=quantize == "bitsandbytes",
|
||||
trust_remote_code=trust_remote_code,
|
||||
attn_implementation="flash_attention_2",
|
||||
)
|
||||
if (
|
||||
torch.cuda.is_available()
|
||||
and torch.cuda.device_count() == 1
|
||||
and quantize != "bitsandbytes"
|
||||
):
|
||||
model = model.cuda()
|
||||
|
||||
self.kv_cache = []
|
||||
|
||||
# TODO felix: make this more general.
|
||||
self.num_layers = len(model.model.layers)
|
||||
self.num_kv_heads = model.config.num_key_value_heads
|
||||
self.head_size = model.config.hidden_size // model.config.num_attention_heads
|
||||
|
||||
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": "[PAD]"})
|
||||
|
||||
# Skip FlashCausalLM init.
|
||||
super(FlashCausalLM, self).__init__(
|
||||
model_id=model_id,
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=False,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
)
|
||||
|
||||
def warmup(self, batch: FlashCausalLMBatch):
|
||||
# The warmup batch is the biggest batch we could ever receive
|
||||
empty_cache()
|
||||
|
||||
patch_everywhere("_flash_attention_forward", _flash_attention_forward_patched)
|
||||
|
||||
try:
|
||||
self.init_kv_cache(
|
||||
batch.num_blocks,
|
||||
self.num_layers,
|
||||
self.num_kv_heads,
|
||||
self.head_size,
|
||||
self.dtype,
|
||||
self.device,
|
||||
)
|
||||
max_bt = batch.max_blocks
|
||||
max_s = max_bt * BLOCK_SIZE
|
||||
|
||||
_, batch, _ = self.generate_token(batch)
|
||||
except torch.cuda.OutOfMemoryError as e:
|
||||
raise RuntimeError(
|
||||
f"Not enough memory to handle {len(batch.input_ids)} prefill tokens. "
|
||||
f"You need to decrease `--max-batch-prefill-tokens`"
|
||||
) from e
|
||||
|
||||
synchronize(self.device)
|
||||
|
||||
# Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm)
|
||||
# Calculate the number of blocks that can be allocated with the free memory
|
||||
dtype_size = torch.tensor([], dtype=self.dtype).element_size()
|
||||
cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size
|
||||
total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size
|
||||
|
||||
free_memory = get_free_memory(self.device, MEMORY_FRACTION)
|
||||
batch_num_blocks = batch.num_blocks if batch is not None else 0
|
||||
|
||||
num_blocks = (
|
||||
# Leave 5% for some wiggle room
|
||||
int((free_memory * 0.95) // total_cache_size)
|
||||
# Add batch.num_blocks as we allocated it above, so it is included in the peak memory.
|
||||
+ batch_num_blocks
|
||||
)
|
||||
|
||||
del batch
|
||||
|
||||
self.init_kv_cache(
|
||||
num_blocks,
|
||||
self.num_layers,
|
||||
self.num_kv_heads,
|
||||
self.head_size,
|
||||
self.dtype,
|
||||
self.device,
|
||||
)
|
||||
|
||||
return int(num_blocks * BLOCK_SIZE)
|
||||
|
||||
def forward(
|
||||
self, batch: FlashCausalLMBatch, adapter_data: AdapterBatchData
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
|
||||
# NOTE: adapter_data: not supported
|
||||
|
||||
input_ids = batch.input_ids
|
||||
position_ids = batch.position_ids
|
||||
cu_seqlen_prefill = batch.cu_seqlen_prefill
|
||||
kv_cache = self.kv_cache
|
||||
block_tables = batch.block_tables_tensor
|
||||
slots = batch.slots[batch.slot_indices]
|
||||
input_lengths = batch.input_lengths_tensor
|
||||
max_s = batch.max_seqlen
|
||||
lm_head_indices = batch.prefill_head_indices
|
||||
|
||||
# TODO felix: support window attention
|
||||
# if cu_seqlen_prefill is None and self.max_past() is not None:
|
||||
# # In decode, not prefill, we're actually overwriting the KV-cache
|
||||
# # in a circular buffer mode.
|
||||
# # This makes sure the max_s for the decode pass is correct.
|
||||
# max_s = min(self.max_past(), max_s)
|
||||
|
||||
bs = input_ids.shape[0]
|
||||
|
||||
logits = self.model.forward(
|
||||
input_ids=input_ids,
|
||||
position_ids=position_ids,
|
||||
past_key_values=PagedCache(),
|
||||
cu_seqlen_prefill=cu_seqlen_prefill,
|
||||
kv_cache=kv_cache,
|
||||
block_tables=block_tables,
|
||||
slots=slots,
|
||||
input_lengths=input_lengths,
|
||||
max_s=max_s,
|
||||
prefill_cache_indices=batch.prefill_cache_indices,
|
||||
lm_head_indices=lm_head_indices,
|
||||
cache_position=False,
|
||||
return_dict=False,
|
||||
)[0]
|
||||
|
||||
if lm_head_indices is not None:
|
||||
logits = logits[lm_head_indices]
|
||||
|
||||
if batch.prefill_cache_indices is not None:
|
||||
batch.prefill_cache_indices = None
|
||||
|
||||
speculative_logits = None
|
||||
|
||||
return logits, speculative_logits
|
||||
Loading…
Reference in New Issue