814 lines
28 KiB
Python
814 lines
28 KiB
Python
import torch
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import torch.distributed
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from transformers import AutoTokenizer, PreTrainedTokenizerBase
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from typing import Optional
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from text_generation_server.models.custom_modeling.mamba_modeling import (
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MambaConfig,
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)
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from loguru import logger
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from text_generation_server.pb import generate_pb2
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from text_generation_server.utils import (
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initialize_torch_distributed,
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weight_files,
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Weights,
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)
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from text_generation_server.models.globals import CUDA_GRAPHS, MEM_POOL
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import time
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from text_generation_server.models.custom_modeling.mamba_modeling import (
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MambaModel,
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InferenceParams,
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)
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from text_generation_server.models import Model
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from typing import Any, List, Tuple, Type, Dict
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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.utils.chunks import concat_text_chunks
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from text_generation_server.utils.quantization import get_loader
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from text_generation_server.utils.tokens import batch_top_tokens, Sampling
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from dataclasses import dataclass
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from text_generation_server.utils import NextTokenChooser, StoppingCriteria
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def new_inference_params(
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n_blocks: int,
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batch_size: int,
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d_inner: int,
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d_conv: int,
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d_state: int,
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seqlen_offset: int,
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dtype: torch.dtype,
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device: torch.device,
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):
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max_seqlen = 0
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conv_states = torch.zeros(
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(
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n_blocks,
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batch_size,
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d_inner,
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d_conv,
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),
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device=device,
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dtype=dtype,
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)
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ssm_states = torch.zeros(
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(
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n_blocks,
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batch_size,
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d_inner,
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d_state,
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),
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device=device,
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dtype=dtype,
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)
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inference_params = InferenceParams(
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max_seqlen=max_seqlen,
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max_batch_size=batch_size,
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seqlen_offset=seqlen_offset,
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conv_states=conv_states,
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ssm_states=ssm_states,
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)
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return inference_params
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@dataclass
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class MambaBatch(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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# 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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# Inference params
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inference_params: Optional[Dict[str, Any]] = None
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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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) -> "MambaBatch":
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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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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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# past_input_ids=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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def filter(self, request_ids: List[int]) -> Optional["MambaBatch"]:
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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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indices = []
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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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indices.append(idx)
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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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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.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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# TODO
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# Kept it simple by just updating the state, maybe updating the other CPU values is necessary.
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self.inference_params.conv_states = self.inference_params.conv_states[
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:, indices
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]
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self.inference_params.ssm_states = self.inference_params.ssm_states[:, indices]
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return self
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@classmethod
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def concatenate(cls, batches: List["MambaBatch"]) -> "MambaBatch":
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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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seqlen_offset = 0
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(n_blocks, _, d_inner, d_conv) = batches[0].inference_params.conv_states.shape
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(_, _, _, d_state) = batches[0].inference_params.ssm_states.shape
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dtype = batches[0].inference_params.conv_states.dtype
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device = batches[0].inference_params.conv_states.device
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inference_params = new_inference_params(
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n_blocks=n_blocks,
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batch_size=total_batch_size,
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d_state=d_state,
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d_conv=d_conv,
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d_inner=d_inner,
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seqlen_offset=seqlen_offset,
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device=device,
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dtype=dtype,
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)
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# Batch tensors
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input_ids = None
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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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# 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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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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# 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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inference_params.max_seqlen = max(
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inference_params.max_seqlen, batch.inference_params.max_seqlen
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)
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assert batch.inference_params.seqlen_offset != 0, "Invalid seqlen offset"
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inference_params.seqlen_offset = max(
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inference_params.seqlen_offset, batch.inference_params.seqlen_offset
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)
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inference_params.conv_states[:, start_index:end_index] = (
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batch.inference_params.conv_states
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)
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inference_params.ssm_states[:, start_index:end_index] = (
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batch.inference_params.ssm_states
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)
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start_index = end_index
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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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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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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,
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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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inference_params=inference_params,
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)
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def __len__(self):
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return len(self.requests)
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class Mamba(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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speculator: Optional[str] = None,
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dtype: Optional[torch.dtype] = None,
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trust_remote_code: bool = False,
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):
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self.process_group, _rank, world_size = initialize_torch_distributed()
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if world_size > 1:
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raise RuntimeError("Mamba does not support Tensor Parallelism (TP)")
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self.cuda_graphs = {}
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if torch.cuda.is_available():
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device = torch.device("cuda")
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# Bf16 is important. In f16 accumulations in the matmul are causing
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# differences while the server is under load.
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# This is detectable by the integration load test
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dtype = torch.bfloat16 if dtype is None else dtype
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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 if dtype is None else dtype
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tokenizer = AutoTokenizer.from_pretrained(
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"EleutherAI/gpt-neox-20b",
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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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config = MambaConfig.from_pretrained(
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model_id, revision=revision, trust_remote_code=trust_remote_code
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)
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tokenizer.bos_token_id = config.bos_token_id
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tokenizer.eos_token_id = config.eos_token_id
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tokenizer.pad_token = tokenizer.eos_token
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config.quantize = quantize
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config.speculator = speculator
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torch.distributed.barrier(group=self.process_group)
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weights_loader = get_loader(
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quantize=quantize, model_id=model_id, revision=revision
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)
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filenames = weight_files(model_id, revision=revision, extension=".safetensors")
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weights = Weights(
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filenames,
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device,
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dtype,
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process_group=self.process_group,
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weights_loader=weights_loader,
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)
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model = MambaModel(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(Mamba, self).__init__(
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model_id=model_id,
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model=model,
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tokenizer=tokenizer,
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requires_padding=True,
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dtype=dtype,
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device=device,
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)
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@property
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def batch_type(self) -> Type[MambaBatch]:
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return MambaBatch
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def warmup(self, batch) -> Optional[int]:
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# TODO: implement warmup for Mamba if needed
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if CUDA_GRAPHS:
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if self.speculate is None or self.speculate == 0:
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try:
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logger.info(f"Cuda Graphs are enabled for sizes {CUDA_GRAPHS}")
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# Warmup cuda graphs
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for bs in CUDA_GRAPHS:
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self.cuda_graph_warmup(bs)
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except Exception:
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logger.exception("Decode cuda graph warmup failed")
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else:
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logger.info(f"Cuda Graphs are disabled (CUDA_GRAPHS={CUDA_GRAPHS}).")
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return None
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def cuda_graph_warmup(self, batch_size: int):
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input_ids = torch.zeros((batch_size, 1), dtype=torch.int64, device=self.device)
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n_blocks = len(self.model.blocks)
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d_state = self.model.config.d_state
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d_conv = self.model.config.d_conv
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# Inner takes the expand multiplication
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d_inner = self.model.config.d_inner
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|
# Important seqlen_offset to go through the update mecanism with the state
|
|
seqlen_offset = 1
|
|
inference_params = new_inference_params(
|
|
n_blocks=n_blocks,
|
|
batch_size=batch_size,
|
|
d_state=d_state,
|
|
d_conv=d_conv,
|
|
d_inner=d_inner,
|
|
seqlen_offset=seqlen_offset,
|
|
device=self.device,
|
|
dtype=self.dtype,
|
|
)
|
|
|
|
graph = torch.cuda.CUDAGraph()
|
|
|
|
torch.cuda.synchronize()
|
|
# Run once outside to warmup
|
|
self.model.forward(input_ids=input_ids, inference_params=inference_params)
|
|
torch.cuda.synchronize()
|
|
|
|
with torch.cuda.graph(graph, pool=MEM_POOL):
|
|
logits, speculative_logits = self.model.forward(
|
|
input_ids=input_ids, inference_params=inference_params
|
|
)
|
|
torch.cuda.synchronize()
|
|
graph_dict = {
|
|
"input_ids": input_ids,
|
|
"inference_params": inference_params,
|
|
"graph": graph,
|
|
"logits": logits,
|
|
"speculative_logits": speculative_logits,
|
|
}
|
|
self.cuda_graphs[batch_size] = graph_dict
|
|
|
|
def tunableop_warmup(self, batch_size: int, seqlen: int):
|
|
input_ids = torch.zeros((batch_size, 1), dtype=torch.int64, device=self.device)
|
|
n_blocks = len(self.model.blocks)
|
|
|
|
d_state = self.model.config.d_state
|
|
d_conv = self.model.config.d_conv
|
|
# Inner takes the expand multiplication
|
|
d_inner = self.model.config.d_inner
|
|
|
|
# Important seqlen_offset to go through the update mecanism with the state
|
|
seqlen_offset = 1
|
|
inference_params = new_inference_params(
|
|
n_blocks=n_blocks,
|
|
batch_size=seqlen,
|
|
d_state=d_state,
|
|
d_conv=d_conv,
|
|
d_inner=d_inner,
|
|
seqlen_offset=seqlen_offset,
|
|
device=self.device,
|
|
dtype=self.dtype,
|
|
)
|
|
|
|
self.model.forward(input_ids=input_ids, inference_params=inference_params)
|
|
|
|
def forward(
|
|
self, input_ids: torch.Tensor, inference_params: Any
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
bs = input_ids.shape[0]
|
|
padded_bs = bs
|
|
if bs == 3:
|
|
padded_bs = 4
|
|
elif 3 < bs <= 8:
|
|
padded_bs = 8
|
|
elif bs > 8:
|
|
padded_bs = (bs + 7) // 8 * 8
|
|
|
|
# Try to find an associated cuda graph
|
|
cuda_graph = self.cuda_graphs.get(padded_bs, None)
|
|
is_prefill = inference_params is None or inference_params.seqlen_offset == 0
|
|
|
|
if is_prefill or cuda_graph is None:
|
|
return self.model(
|
|
input_ids,
|
|
inference_params=inference_params,
|
|
)
|
|
|
|
# Copy inputs to the static inputs of the cuda graph
|
|
# Static inputs are potentially padded
|
|
cuda_graph["input_ids"][:bs] = input_ids
|
|
cuda_graph["inference_params"].conv_states[
|
|
:, :bs
|
|
] = inference_params.conv_states
|
|
cuda_graph["inference_params"].ssm_states[:, :bs] = inference_params.ssm_states
|
|
|
|
# Replay the graph
|
|
cuda_graph["graph"].replay()
|
|
|
|
inference_params.conv_states.copy_(
|
|
cuda_graph["inference_params"].conv_states[:, :bs]
|
|
)
|
|
inference_params.ssm_states.copy_(
|
|
cuda_graph["inference_params"].ssm_states[:, :bs]
|
|
)
|
|
# Slice output to the correct shape
|
|
speculative_logits = (
|
|
cuda_graph["speculative_logits"][:bs]
|
|
if cuda_graph["speculative_logits"] is not None
|
|
else None
|
|
)
|
|
logits = cuda_graph["logits"][:bs]
|
|
return logits, speculative_logits
|
|
|
|
def generate_token(self, batch) -> Tuple[List[Any], Optional[Any], Tuple[int, int]]:
|
|
start = time.time_ns()
|
|
input_ids = (
|
|
batch.input_ids
|
|
) # batch.past_input_ids if batch.past_input_ids is not None else batch.input_ids
|
|
|
|
batch_size, max_seqlen = input_ids.shape
|
|
# Inference params
|
|
|
|
if batch.inference_params is None:
|
|
# 0 is important here
|
|
seqlen_offset = 0
|
|
n_blocks = len(self.model.blocks)
|
|
d_state = self.model.config.d_state
|
|
d_conv = self.model.config.d_conv
|
|
d_inner = self.model.config.d_inner
|
|
inference_params = new_inference_params(
|
|
n_blocks=n_blocks,
|
|
batch_size=batch_size,
|
|
d_state=d_state,
|
|
d_conv=d_conv,
|
|
d_inner=d_inner,
|
|
seqlen_offset=seqlen_offset,
|
|
device=self.device,
|
|
dtype=self.dtype,
|
|
)
|
|
batch.inference_params = inference_params
|
|
|
|
# Forward pass
|
|
logits, speculative_logits = self.forward(
|
|
input_ids, inference_params=batch.inference_params
|
|
)
|
|
|
|
# batch.inference_params = new_inference_params
|
|
# 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
|
|
|
|
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:
|
|
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,
|
|
)
|
|
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]
|
|
|
|
forward_ns = start_decode - start
|
|
decode_ns = time.time_ns() - start_decode
|
|
return generations, batch, (forward_ns, decode_ns)
|