839 lines
29 KiB
Python
839 lines
29 KiB
Python
import torch
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import torch.distributed
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import numpy as np
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from torch.nn import functional as F
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from dataclasses import dataclass
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from opentelemetry import trace
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from transformers import AutoTokenizer, PreTrainedTokenizerBase, PreTrainedModel
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from typing import Optional, Tuple, List, Type, Union, Dict
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from text_generation_server.models import Model
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from text_generation_server.models.types import (
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Batch,
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PrefillTokens,
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Generation,
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GeneratedText,
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)
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from text_generation_server.pb import generate_pb2
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from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
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tracer = trace.get_tracer(__name__)
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@dataclass
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class FlashCausalLMBatch(Batch):
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batch_id: int
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requests: List[generate_pb2.Request]
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# request id -> idx in list mapping
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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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position_ids: torch.Tensor
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# cumulative sequence lengths
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cu_seqlens: torch.Tensor
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# cumulative query sequence lengths, only used in decode
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cu_seqlens_q: Optional[torch.Tensor]
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# past key values, only used in decode
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past_key_values: Optional[torch.Tensor]
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max_seqlen: int
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# Prefill metadata tensors to efficiently compute logprobs
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prefill_head_indices: Optional[torch.Tensor]
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prefill_next_token_indices: Optional[torch.tensor]
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prefill_cu_outlens: Optional[List[int]]
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# All tokens
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all_input_ids: List[List[int]]
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all_input_ids_tensor: 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[Optional[int]]
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read_offsets: List[Optional[int]]
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# Generation helpers
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next_token_chooser: HeterogeneousNextTokenChooser
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stopping_criterias: List[StoppingCriteria]
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# Maximum number of tokens this batch will grow to
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max_tokens: int
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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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) -> "FlashCausalLMBatch":
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position_ids = []
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cu_seqlens = [0]
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max_seqlen = 0
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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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requests_idx_mapping = {}
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all_prefill_logprobs = True
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no_prefill_logprobs = True
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prefill_head_indices = []
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prefill_next_token_indices = []
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prefill_cu_outlens = [0]
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next_token_chooser_parameters = []
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stopping_criterias = []
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# Cumulative length
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cumulative_length = 0
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prefill_out_cumulative_length = 0
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max_tokens = 0
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max_length = 0
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# Parse batch
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for i, r in enumerate(pb.requests):
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# request id -> idx in list mapping
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requests_idx_mapping[r.id] = i
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tokenized_input = tokenizer(
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r.inputs, truncation=True, max_length=r.truncate
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)["input_ids"]
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input_length = len(tokenized_input)
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max_seqlen = max(max_seqlen, input_length)
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input_lengths.append(input_length)
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prefix_offsets.append(input_length - 5)
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read_offsets.append(input_length)
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all_input_ids.append(tokenized_input)
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# Position ids
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request_position_ids = torch.arange(0, input_length, dtype=torch.int32)
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position_ids.append(request_position_ids)
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# Add cumulative lengths of all previous inputs
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cu_seqlens.append(cumulative_length + input_length)
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next_token_chooser_parameters.append(r.parameters)
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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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max_new_tokens = stopping_criteria.max_new_tokens
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stopping_criterias.append(stopping_criteria)
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all_prefill_logprobs = all_prefill_logprobs and r.prefill_logprobs
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no_prefill_logprobs = no_prefill_logprobs and not r.prefill_logprobs
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if r.prefill_logprobs:
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prefill_head_indices.append(request_position_ids + cumulative_length)
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prefill_next_token_indices.append(
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prefill_out_cumulative_length + input_length - 1
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)
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prefill_cu_outlens.append(prefill_out_cumulative_length + input_length)
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prefill_out_cumulative_length += input_length
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else:
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prefill_head_indices.append(
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torch.tensor(
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[cumulative_length + input_length - 1], dtype=torch.int32
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)
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)
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prefill_next_token_indices.append(prefill_out_cumulative_length)
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prefill_cu_outlens.append(prefill_out_cumulative_length + 1)
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prefill_out_cumulative_length += 1
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# Update
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cumulative_length += input_length
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max_tokens += input_length + max_new_tokens
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max_length = max(max_length, input_length + max_new_tokens)
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next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
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next_token_chooser_parameters, dtype, device
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)
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# Padded all_input_ids_tensor
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all_input_ids_tensor = np.zeros(
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(len(all_input_ids), max_length), dtype=np.int64
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)
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for i, input_ids in enumerate(all_input_ids):
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all_input_ids_tensor[i, : len(input_ids)] = input_ids
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if len(pb.requests) > 1:
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input_ids = np.concatenate(all_input_ids, dtype=np.int64)
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position_ids = torch.cat(position_ids)
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else:
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input_ids = all_input_ids[0]
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position_ids = position_ids[0]
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# Create tensors on device
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input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
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all_input_ids_tensor = torch.tensor(
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all_input_ids_tensor, dtype=torch.int64, device=device
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)
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position_ids = torch.tensor(position_ids, dtype=torch.int32, device=device)
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cu_seqlens = torch.tensor(cu_seqlens, device=device, dtype=torch.int32)
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if all_prefill_logprobs:
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prefill_head_indices = None
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prefill_next_token_indices = cu_seqlens[1:] - 1
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elif no_prefill_logprobs:
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prefill_head_indices = cu_seqlens[1:] - 1
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prefill_next_token_indices = None
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else:
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prefill_head_indices = torch.tensor(
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torch.cat(prefill_head_indices), dtype=torch.int64, device=device
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)
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prefill_next_token_indices = torch.tensor(
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prefill_next_token_indices, dtype=torch.int64, device=device
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)
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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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position_ids=position_ids,
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cu_seqlens=cu_seqlens,
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cu_seqlens_q=None,
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max_seqlen=max_seqlen,
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prefill_head_indices=prefill_head_indices,
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prefill_next_token_indices=prefill_next_token_indices,
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prefill_cu_outlens=prefill_cu_outlens,
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past_key_values=None,
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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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all_input_ids=all_input_ids,
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all_input_ids_tensor=all_input_ids_tensor,
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next_token_chooser=next_token_chooser,
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stopping_criterias=stopping_criterias,
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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]) -> "FlashCausalLMBatch":
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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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# We assume that if len(requests) == len(self) then the requests are the same
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if len(request_ids) == len(self):
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return self
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single_request = len(request_ids) == 1
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# Cumulative length
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cumulative_length = 0
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# New values after filtering
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requests_idx_mapping = {}
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# Used to index into tensors
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indices = []
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# Create on CPU to only move to GPU once instead of at every copy
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cu_seqlens = torch.zeros(len(request_ids) + 1, dtype=torch.int32)
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cu_seqlens_q = self.cu_seqlens_q[: len(request_ids) + 1]
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max_seqlen = 0
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past_key_values = []
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requests = []
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all_input_ids = []
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input_lengths = []
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prefix_offsets = []
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read_offsets = []
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stopping_criterias = []
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max_tokens = 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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indices.append(idx)
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requests_idx_mapping[request_id] = i
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requests.append(self.requests[idx])
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# Get length
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request_input_length = self.input_lengths[idx]
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# Copy to tensor (CPU)
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cu_seqlens[i + 1] = cumulative_length + request_input_length
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max_seqlen = max(max_seqlen, request_input_length)
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# Slice from past
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past_key_values.append(
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self.past_key_values[:, self.cu_seqlens[idx] : self.cu_seqlens[idx + 1]]
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)
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all_input_ids.append(self.all_input_ids[idx])
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input_lengths.append(request_input_length)
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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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stopping_criteria = self.stopping_criterias[idx]
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stopping_criterias.append(stopping_criteria)
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cumulative_length += request_input_length
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max_tokens += request_input_length + (
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stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
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)
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if single_request:
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# Preallocate tensor for bs = 1 case
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past_key_values = F.pad(
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past_key_values[0],
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(
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0,
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0,
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0,
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0,
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0,
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0,
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0,
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stopping_criterias[0].max_new_tokens
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- stopping_criterias[0].current_tokens,
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),
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)
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else:
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# Cat all past
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past_key_values = torch.cat(past_key_values, dim=1)
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# Index into tensors
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input_ids = self.input_ids[indices]
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position_ids = self.position_ids[indices]
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all_input_ids_tensor = self.all_input_ids_tensor[indices]
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next_token_chooser = self.next_token_chooser.filter(indices)
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# Move to GPU now that we have the whole tensor
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cu_seqlens = cu_seqlens.to(self.cu_seqlens.device)
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return FlashCausalLMBatch(
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batch_id=self.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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position_ids=position_ids,
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cu_seqlens=cu_seqlens,
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cu_seqlens_q=cu_seqlens_q,
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max_seqlen=max_seqlen,
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prefill_head_indices=None,
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prefill_next_token_indices=None,
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prefill_cu_outlens=None,
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past_key_values=past_key_values,
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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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all_input_ids=all_input_ids,
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all_input_ids_tensor=all_input_ids_tensor,
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next_token_chooser=next_token_chooser,
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stopping_criterias=stopping_criterias,
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max_tokens=max_tokens,
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)
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@classmethod
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@tracer.start_as_current_span("concatenate")
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def concatenate(cls, batches: List["FlashCausalLMBatch"]) -> "FlashCausalLMBatch":
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# Batch attributes
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requests = []
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requests_idx_mapping = {}
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total_batch_size = sum([len(b) for b in batches])
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dtype = batches[0].past_key_values.dtype
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device = batches[0].input_ids.device
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input_ids = batches[0].input_ids.new_empty(total_batch_size)
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position_ids = batches[0].position_ids.new_empty(total_batch_size)
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cu_seqlens = [0]
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cu_seqlens_q = torch.arange(
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0, total_batch_size + 1, device=device, dtype=torch.int32
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)
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max_seqlen = 0
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past_key_values = []
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all_input_ids = []
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input_lengths = []
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prefix_offsets = []
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read_offsets = []
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next_token_chooser_parameters = []
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stopping_criterias = []
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# Cumulative length
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cumulative_batch_size = 0
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cumulative_length = 0
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max_tokens = 0
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max_length = 0
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for i, batch in enumerate(batches):
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requests.extend(batch.requests)
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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 + cumulative_batch_size
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start_index = cumulative_batch_size
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end_index = cumulative_batch_size + len(batch)
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# Copy tensors (GPU)
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input_ids[start_index:end_index] = batch.input_ids
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position_ids[start_index:end_index] = batch.position_ids
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# Add cumulative lengths of all previous inputs
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cu_seqlens.extend([l + cumulative_length for l in batch.cu_seqlens[1:]])
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max_seqlen = max(max_seqlen, batch.max_seqlen)
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if len(batch) != 1:
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past_key_values.append(batch.past_key_values)
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else:
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# past was pre-allocated for this batch
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# We need to slice to remove the padding
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past_key_values.append(
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batch.past_key_values[:, : batch.input_lengths[0]]
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)
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all_input_ids.extend(batch.all_input_ids)
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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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next_token_chooser_parameters.extend([r.parameters for r in batch.requests])
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stopping_criterias.extend(batch.stopping_criterias)
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# Update
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cumulative_length += batch.cu_seqlens[-1]
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cumulative_batch_size += len(batch)
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max_tokens += batch.max_tokens
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max_length = max(
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max_length,
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max(
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input_length
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+ stopping_criteria.max_new_tokens
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- stopping_criteria.current_tokens
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for input_length, stopping_criteria in zip(
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batch.input_lengths, batch.stopping_criterias
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)
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),
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)
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all_input_ids_tensor = torch.zeros(
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(total_batch_size, max_length), dtype=torch.int64, device=device
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)
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cumulative_batch_size = 0
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for i, batch in enumerate(batches):
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start_index = cumulative_batch_size
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end_index = cumulative_batch_size + len(batch)
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all_input_ids_tensor[
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start_index:end_index, : batch.all_input_ids_tensor.shape[1]
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] = batch.all_input_ids_tensor[:, :max_length]
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cumulative_batch_size += len(batch)
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|
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# Cat past
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past_key_values = torch.cat(past_key_values, dim=1)
|
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# Create final tensor on GPU
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cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32, device=device)
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|
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next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
|
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next_token_chooser_parameters, dtype=dtype, device=device
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)
|
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return FlashCausalLMBatch(
|
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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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position_ids=position_ids,
|
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cu_seqlens=cu_seqlens,
|
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cu_seqlens_q=cu_seqlens_q,
|
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max_seqlen=max_seqlen,
|
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prefill_head_indices=None,
|
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prefill_next_token_indices=None,
|
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prefill_cu_outlens=None,
|
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past_key_values=past_key_values,
|
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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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all_input_ids=all_input_ids,
|
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all_input_ids_tensor=all_input_ids_tensor,
|
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next_token_chooser=next_token_chooser,
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stopping_criterias=stopping_criterias,
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max_tokens=max_tokens,
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)
|
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|
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def __len__(self):
|
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return len(self.requests)
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|
|
|
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class FlashCausalLM(Model):
|
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def __init__(
|
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self,
|
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model_cls: Type[PreTrainedModel],
|
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model_id: str,
|
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revision: Optional[str] = None,
|
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quantize: Optional[str] = None,
|
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trust_remote_code: bool = False,
|
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):
|
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if torch.cuda.is_available():
|
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device = torch.device("cuda")
|
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dtype = torch.float16
|
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else:
|
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raise NotImplementedError("FlashCausalLM is only available on GPU")
|
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|
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tokenizer = AutoTokenizer.from_pretrained(
|
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model_id,
|
|
revision=revision,
|
|
padding_side="left",
|
|
truncation_side="left",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
model = model_cls.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
torch_dtype=dtype,
|
|
load_in_8bit=quantize == "bitsandbytes",
|
|
trust_remote_code=trust_remote_code,
|
|
).to(device)
|
|
|
|
super(FlashCausalLM, self).__init__(
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
requires_padding=False,
|
|
dtype=dtype,
|
|
device=device,
|
|
)
|
|
|
|
@property
|
|
def batch_type(self) -> Type[FlashCausalLMBatch]:
|
|
return FlashCausalLMBatch
|
|
|
|
def decode(self, generated_ids: Union[torch.Tensor, List[int]]) -> str:
|
|
return self.tokenizer.decode(
|
|
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
position_ids: torch.Tensor,
|
|
cu_seqlens: torch.Tensor,
|
|
cu_seqlens_q: Optional[torch.Tensor],
|
|
max_s: int,
|
|
past_key_values: Optional = None,
|
|
pre_allocate_past_size: Optional[int] = None,
|
|
lm_head_indices: Optional[torch.Tensor] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
# Model Forward
|
|
return self.model.forward(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
cu_seqlens=cu_seqlens,
|
|
cu_seqlens_q=cu_seqlens_q,
|
|
max_s=max_s,
|
|
past_key_values=past_key_values,
|
|
pre_allocate_past_size=pre_allocate_past_size,
|
|
lm_head_indices=lm_head_indices,
|
|
)
|
|
|
|
@tracer.start_as_current_span("generate_token")
|
|
def generate_token(
|
|
self, batch: FlashCausalLMBatch
|
|
) -> Tuple[List[Generation], Optional[FlashCausalLMBatch]]:
|
|
prefill = batch.past_key_values is None
|
|
prefill_logprobs = batch.prefill_next_token_indices is not None
|
|
single_request = len(batch) == 1
|
|
|
|
if prefill and single_request:
|
|
# Ask to pre-allocate kv to its max size
|
|
# == number of tokens + max_new_tokens
|
|
pre_allocate_past_size = (
|
|
batch.input_lengths[0] + batch.stopping_criterias[0].max_new_tokens
|
|
)
|
|
else:
|
|
pre_allocate_past_size = None
|
|
|
|
out, present = self.forward(
|
|
batch.input_ids,
|
|
batch.position_ids,
|
|
batch.cu_seqlens,
|
|
batch.cu_seqlens_q,
|
|
batch.max_seqlen,
|
|
batch.past_key_values,
|
|
pre_allocate_past_size,
|
|
batch.prefill_head_indices,
|
|
)
|
|
|
|
if prefill:
|
|
next_token_logits = (
|
|
out[batch.prefill_next_token_indices] if prefill_logprobs else out
|
|
)
|
|
else:
|
|
next_token_logits = out
|
|
|
|
next_input_ids, next_token_logprobs = batch.next_token_chooser(
|
|
batch.all_input_ids_tensor[:, : batch.max_seqlen], next_token_logits
|
|
)
|
|
|
|
if prefill:
|
|
if len(batch) > 1 and prefill_logprobs:
|
|
# We create the prefill_tokens_indices tensor that will be used to gather prefill logprobs
|
|
# When batch == 1, we will just use the batch.input_ids values directly
|
|
prefill_tokens_indices = batch.input_ids.new_zeros(len(out))
|
|
|
|
# Create batch.cu_seqlens_q for decode
|
|
batch.cu_seqlens_q = torch.arange(
|
|
0, len(batch) + 1, device=self.device, dtype=torch.int32
|
|
)
|
|
next_position_ids = batch.position_ids.new_empty(len(batch))
|
|
else:
|
|
prefill_logprobs = None
|
|
next_position_ids = batch.position_ids
|
|
|
|
# Prepare past for next decode
|
|
if len(batch) > 1:
|
|
# Used to slice next batch past
|
|
past_indices = torch.empty(
|
|
present.shape[1], dtype=torch.int64, device=self.device
|
|
)
|
|
batch.past_key_values = present.new_empty(
|
|
(
|
|
present.shape[0],
|
|
present.shape[1] + len(batch.requests),
|
|
*present.shape[2:],
|
|
)
|
|
)
|
|
|
|
# It is actually faster to do a whole other for loop here as the copy from present to past is fairly slow
|
|
# and will run asynchronously while we do the next for loop
|
|
cumulative_length = 0
|
|
for i, input_length in enumerate(batch.input_lengths):
|
|
# Indexing metadata
|
|
start_index = cumulative_length
|
|
end_index = cumulative_length + input_length
|
|
|
|
# Indices to copy present at the correct place in past_key_values
|
|
torch.arange(
|
|
start_index + i,
|
|
end_index + i,
|
|
dtype=torch.int64,
|
|
device=self.device,
|
|
out=past_indices[start_index:end_index],
|
|
)
|
|
cumulative_length += input_length
|
|
|
|
# Copy from present to past_key_values
|
|
batch.past_key_values[:, past_indices] = present
|
|
|
|
# Initialize past_key_values in prefill for len(batch) == 1
|
|
elif prefill:
|
|
# present is already pre-padded
|
|
batch.past_key_values = present
|
|
|
|
# Cumulative length
|
|
cumulative_length = 0
|
|
|
|
# Results
|
|
generations: List[Generation] = []
|
|
stopped = True
|
|
|
|
# Zipped iterator
|
|
iterator = zip(
|
|
batch.input_lengths,
|
|
batch.all_input_ids,
|
|
)
|
|
|
|
# We do two for loops as the first one can run completely asynchronously from the GPU while for the second
|
|
# one, we need to first do a GPU <-> CPU sync
|
|
# It is faster if we delay this sync for the maximum amount of time
|
|
|
|
# For each member of the batch
|
|
for i, (
|
|
input_length,
|
|
all_input_ids,
|
|
) in enumerate(iterator):
|
|
start_index = cumulative_length
|
|
end_index = cumulative_length + input_length
|
|
|
|
if prefill:
|
|
# Indexing metadata
|
|
out_start_index = batch.prefill_cu_outlens[i]
|
|
out_end_index = batch.prefill_cu_outlens[i + 1]
|
|
out_length = out_end_index - out_start_index
|
|
|
|
# Initialize position_ids
|
|
# In decode, we do not need this as we can just increment position ids
|
|
next_position_ids[i] = batch.position_ids[end_index - 1]
|
|
|
|
# Used to gather prefill logprobs
|
|
# Copy batch.input_ids to prefill_token_indices
|
|
if prefill_logprobs:
|
|
if len(batch) > 1:
|
|
prefill_tokens_indices[
|
|
out_start_index : out_end_index - 1
|
|
] = batch.input_ids[start_index + 1 : start_index + out_length]
|
|
else:
|
|
# Set prefill_tokens_indices to the correct slice
|
|
prefill_tokens_indices = batch.input_ids[
|
|
start_index + 1 : start_index + out_length
|
|
]
|
|
|
|
batch.all_input_ids_tensor[i, input_length] = next_input_ids[i]
|
|
|
|
cumulative_length += input_length
|
|
|
|
# Set values in batch
|
|
batch.input_ids = next_input_ids
|
|
batch.position_ids = next_position_ids + 1
|
|
batch.cu_seqlens = batch.cu_seqlens + batch.cu_seqlens_q
|
|
|
|
if prefill and prefill_logprobs:
|
|
# Get prefill logprobs
|
|
prefill_logprobs_tensor = torch.log_softmax(out, -1)
|
|
prefill_logprobs = torch.gather(
|
|
prefill_logprobs_tensor, 1, prefill_tokens_indices.view(-1, 1)
|
|
)
|
|
# GPU <-> CPU sync
|
|
prefill_logprobs = prefill_logprobs.view(-1).tolist()
|
|
|
|
# GPU <-> CPU sync
|
|
next_token_logprobs = next_token_logprobs.tolist()
|
|
next_token_ids = batch.input_ids.tolist()
|
|
|
|
# Zipped iterator
|
|
iterator = zip(
|
|
batch.requests,
|
|
batch.input_lengths,
|
|
batch.prefix_offsets,
|
|
batch.read_offsets,
|
|
batch.stopping_criterias,
|
|
batch.all_input_ids,
|
|
batch.all_input_ids_tensor,
|
|
batch.next_token_chooser.do_sample,
|
|
batch.next_token_chooser.seeds,
|
|
next_token_ids,
|
|
next_token_logprobs,
|
|
)
|
|
|
|
# For each member of the batch
|
|
for i, (
|
|
request,
|
|
input_length,
|
|
prefix_offset,
|
|
read_offset,
|
|
stopping_criteria,
|
|
all_input_ids,
|
|
all_input_ids_tensor,
|
|
do_sample,
|
|
seed,
|
|
next_token_id,
|
|
next_token_logprob,
|
|
) in enumerate(iterator):
|
|
# Append next token to all tokens
|
|
all_input_ids.append(next_token_id)
|
|
|
|
# Generated token
|
|
next_token_text, prefix_offset, read_offset = self.decode_token(
|
|
all_input_ids,
|
|
prefix_offset,
|
|
read_offset,
|
|
)
|
|
|
|
# Evaluate stopping criteria
|
|
stop, reason = stopping_criteria(
|
|
next_token_id,
|
|
next_token_text,
|
|
)
|
|
|
|
if not stop:
|
|
stopped = False
|
|
|
|
# Shard generations
|
|
# All generations will be appended in the rust sharded client
|
|
if i % self.world_size == self.rank:
|
|
if stop:
|
|
# Decode generated tokens
|
|
output_text = self.decode(
|
|
all_input_ids[-stopping_criteria.current_tokens :]
|
|
)
|
|
generated_text = GeneratedText(
|
|
output_text,
|
|
stopping_criteria.current_tokens,
|
|
reason,
|
|
seed if do_sample else None,
|
|
)
|
|
else:
|
|
generated_text = None
|
|
|
|
# Prefill
|
|
if prefill and request.prefill_logprobs:
|
|
out_start_index = batch.prefill_cu_outlens[i]
|
|
out_end_index = batch.prefill_cu_outlens[i + 1]
|
|
|
|
# Remove generated token to only have prefill and add nan for first prompt token
|
|
request_prefill_logprobs = [float("nan")] + prefill_logprobs[
|
|
out_start_index : out_end_index - 1
|
|
]
|
|
prefill_token_ids = all_input_ids[:-1]
|
|
prefill_texts = self.tokenizer.batch_decode(
|
|
prefill_token_ids,
|
|
clean_up_tokenization_spaces=False,
|
|
skip_special_tokens=False,
|
|
)
|
|
prefill_tokens = PrefillTokens(
|
|
prefill_token_ids, request_prefill_logprobs, prefill_texts
|
|
)
|
|
else:
|
|
prefill_tokens = None
|
|
|
|
generation = Generation(
|
|
request.id,
|
|
prefill_tokens,
|
|
next_token_id,
|
|
next_token_logprob,
|
|
next_token_text,
|
|
next_token_id in self.all_special_ids,
|
|
generated_text,
|
|
)
|
|
|
|
generations.append(generation)
|
|
|
|
new_input_length = input_length + 1
|
|
|
|
# Update values
|
|
batch.input_lengths[i] = new_input_length
|
|
batch.prefix_offsets[i] = prefix_offset
|
|
batch.read_offsets[i] = read_offset
|
|
batch.all_input_ids[i] = all_input_ids
|
|
|
|
batch.prefill_cu_outlens = None
|
|
batch.prefill_head_indices = None
|
|
batch.prefill_next_token_indices = None
|
|
batch.max_seqlen = batch.max_seqlen + 1
|
|
|
|
# No need to return a batch if we know that all requests stopped
|
|
return generations, batch if not stopped else None
|