479 lines
18 KiB
Python
479 lines
18 KiB
Python
import torch
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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
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from text_generation.models import Model
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from text_generation.models.types import Batch, PrefillTokens, Generation, GeneratedText
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from text_generation.pb import generate_pb2
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from text_generation.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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# 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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# Generation helpers
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next_token_choosers: List[NextTokenChooser]
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stopping_criterias: List[StoppingCriteria]
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# Metadata used for padding
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size: int
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max_sequence_length: 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.Batch:
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return generate_pb2.Batch(
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id=self.batch_id,
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requests=self.requests,
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size=self.size,
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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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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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input_lengths = []
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# Parse batch
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for r in pb.requests:
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inputs.append(r.inputs)
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input_lengths.append(r.input_length)
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next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
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stopping_criterias.append(
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StoppingCriteria.from_pb(r.stopping_parameters, tokenizer)
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)
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pad_to_multiple_of = 8 if device.type == "cuda" else None
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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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pad_to_multiple_of=pad_to_multiple_of,
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return_token_type_ids=False,
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).to(device)
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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"].unsqueeze(-1)
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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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input_ids=tokenized_inputs["input_ids"],
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attention_mask=tokenized_inputs["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=all_input_ids,
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input_lengths=input_lengths,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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size=pb.size,
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max_sequence_length=max(input_lengths),
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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["CausalLMBatch"]) -> "CausalLMBatch":
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# Used for padding
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total_batch_size = sum(batch.size for batch in batches)
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max_sequence_length = max(batch.max_sequence_length for batch in batches)
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# Batch attributes
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requests = []
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input_lengths = []
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all_input_ids = []
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next_token_choosers = []
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stopping_criterias = []
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# Batch tensors
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input_ids = None
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attention_mask = None
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position_ids = None
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past_key_values = []
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# Used for slicing correctly inside the tensors
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# Equivalent to a cumsum on batch sizes
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start_index = 0
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for i, batch in enumerate(batches):
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requests.extend(batch.requests)
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input_lengths.extend(batch.input_lengths)
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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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# Slicing end index for this batch
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end_index = start_index + batch.size
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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_sequence_length),
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)
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# We need to slice the attention mask to remove padding from previous steps
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attention_mask[
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start_index:end_index, -batch.max_sequence_length :
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] = batch.attention_mask[:, -batch.max_sequence_length :]
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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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for j, past in enumerate(batch.past_key_values):
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past_keys, past_values = past
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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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past_keys = past_keys.view(batch.size, -1, *past_keys.shape[-2:])
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past_values = past_values.view(batch.size, -1, *past_values.shape[-2:])
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_, num_heads, padded_sequence_length, head_dim = past_values.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_sequence_length - 1,
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head_dim,
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)
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if batch.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_sequence_length - 1,
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)
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# This will run only once per layer
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if j == len(past_key_values):
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padded_past_keys = past_keys.new_zeros(padded_past_keys_shape)
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padded_past_values = past_values.new_zeros(padded_past_values_shape)
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past_key_values.append((padded_past_keys, padded_past_values))
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# We slice the past keys and values to remove the padding from previous batches
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if batch.keys_head_dim_last:
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past_key_values[j][0][
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start_index:end_index,
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:,
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-(batch.max_sequence_length - 1) :,
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:,
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] = past_keys[:, :, -(batch.max_sequence_length - 1) :, :]
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else:
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past_key_values[j][0][
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start_index:end_index,
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:,
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:,
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-(batch.max_sequence_length - 1) :,
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] = past_keys[:, :, :, -(batch.max_sequence_length - 1) :]
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past_key_values[j][1][
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start_index:end_index, :, -(batch.max_sequence_length - 1) :, :
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] = past_values[:, :, -(batch.max_sequence_length - 1) :, :]
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start_index += batch.size
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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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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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all_input_ids=all_input_ids,
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input_lengths=input_lengths,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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size=total_batch_size,
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max_sequence_length=max_sequence_length,
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keys_head_dim_last=batches[0].keys_head_dim_last,
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)
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def __len__(self):
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return len(self.requests)
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class CausalLM(Model):
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def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
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if torch.cuda.is_available():
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device = torch.device("cuda")
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dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
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else:
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if quantize:
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raise ValueError("quantization is not available on CPU")
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device = torch.device("cpu")
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dtype = torch.float32
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tokenizer = AutoTokenizer.from_pretrained(
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model_id, revision=revision, padding_side="left"
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)
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self.model = AutoModelForCausalLM.from_pretrained(
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model_id,
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revision=revision,
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torch_dtype=dtype,
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device_map="auto" if torch.cuda.is_available() else None,
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load_in_8bit=quantize,
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).eval()
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tokenizer.pad_token_id = (
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self.model.config.pad_token_id
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if self.model.config.pad_token_id is not None
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else self.model.config.eos_token_id
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)
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super(CausalLM, self).__init__(
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tokenizer=tokenizer,
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device=device,
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)
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@property
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def batch_type(self) -> Type[CausalLMBatch]:
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return CausalLMBatch
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def decode(self, generated_ids: List[int]) -> str:
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return self.tokenizer.decode(
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generated_ids, skip_special_tokens=True, cleanup_tokenization_spaces=False
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)
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def forward(
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self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
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) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
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# Model Forward
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outputs = self.model.forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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use_cache=True,
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)
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return outputs.logits, outputs.past_key_values
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@tracer.start_as_current_span("generate_token")
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def generate_token(
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self, batch: CausalLMBatch
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) -> Tuple[List[Generation], Optional[CausalLMBatch]]:
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logits, past = self.forward(
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batch.input_ids,
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batch.attention_mask,
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batch.position_ids,
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batch.past_key_values,
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)
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# List of indices to cache
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next_batch_keep_indices = []
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# New values for next forward
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next_batch_input_lengths = []
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next_batch_input_ids = []
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next_batch_all_input_ids = []
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# Metadata
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next_batch_size = 0
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next_batch_max_sequence_length = 0
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# Results
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generations: List[Generation] = []
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# Zipped iterator
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iterator = zip(
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batch.requests,
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batch.input_lengths,
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logits,
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batch.next_token_choosers,
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batch.stopping_criterias,
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batch.all_input_ids,
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)
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# For each member of the batch
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for i, (
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request,
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input_length,
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logits,
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next_token_chooser,
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stopping_criteria,
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all_input_ids,
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) in enumerate(iterator):
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# Select next token
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next_token_id, logprobs = next_token_chooser(
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all_input_ids.view(1, -1), logits
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)
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# Append next token to all tokens
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all_input_ids = torch.cat([all_input_ids, next_token_id])
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new_input_length = input_length + 1
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# Generated token
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next_token_logprob = logprobs[-1, next_token_id]
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next_token_id_squeezed = next_token_id.squeeze()
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next_token_text = self.tokenizer.decode(
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next_token_id_squeezed,
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clean_up_tokenization_spaces=False,
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skip_special_tokens=False,
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)
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# Evaluate stopping criteria
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stop, reason = stopping_criteria(
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next_token_id_squeezed,
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next_token_text,
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)
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if stop:
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# Decode generated tokens
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output_text = self.decode(
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all_input_ids[-stopping_criteria.current_tokens :, 0]
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)
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# Get seed
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if isinstance(next_token_chooser.choice, Sampling):
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seed = next_token_chooser.choice.seed
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else:
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seed = None
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generated_text = GeneratedText(
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output_text, stopping_criteria.current_tokens, reason, seed
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)
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else:
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# Keep request in the batch
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generated_text = None
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next_batch_keep_indices.append(i)
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next_batch_input_ids.append(next_token_id)
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next_batch_all_input_ids.append(all_input_ids)
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next_batch_size += 1
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next_batch_input_lengths.append(new_input_length)
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next_batch_max_sequence_length = max(
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next_batch_max_sequence_length, new_input_length
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)
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# Prefill
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if stopping_criteria.current_tokens == 1:
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# Remove generated token to only have prefill and add nan for first prompt token
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prefill_logprobs = [float("nan")] + logprobs.gather(
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1, all_input_ids[1:]
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).squeeze(1)[-new_input_length:-1].tolist()
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prefill_token_ids = all_input_ids[-new_input_length:-1]
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prefill_texts = self.tokenizer.batch_decode(
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prefill_token_ids,
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clean_up_tokenization_spaces=False,
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skip_special_tokens=False,
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)
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prefill_tokens = PrefillTokens(
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prefill_token_ids, prefill_logprobs, prefill_texts
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)
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else:
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prefill_tokens = None
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generation = Generation(
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request.id,
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prefill_tokens,
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next_token_id_squeezed,
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next_token_logprob,
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next_token_text,
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generated_text,
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)
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generations.append(generation)
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# We finished all generations in the batch; there is no next batch
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if not next_batch_keep_indices:
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return generations, None
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next_batch_input_ids = torch.cat(next_batch_input_ids, dim=0)
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# If we finished at least one generation, we need to evict the indices of the generations that finished
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# from the values of the next batch
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if len(next_batch_keep_indices) != len(batch):
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# Apply indices to attention mask, past key values and other items that need to be cached
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next_batch_attention_mask = batch.attention_mask[next_batch_keep_indices]
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next_batch_position_ids = batch.position_ids[next_batch_keep_indices]
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# Force past to be of dim [batch_size, num_heads, ...] for easy indexing
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next_batch_past_key_values = [
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[
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t.view(batch.size, -1, *t.shape[-2:])[next_batch_keep_indices]
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for t in layer
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]
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for layer in past
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]
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next_batch_requests = [batch.requests[i] for i in next_batch_keep_indices]
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next_batch_next_token_choosers = [
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batch.next_token_choosers[i] for i in next_batch_keep_indices
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]
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next_batch_stopping_criterias = [
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batch.stopping_criterias[i] for i in next_batch_keep_indices
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]
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else:
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next_batch_attention_mask = batch.attention_mask
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next_batch_position_ids = batch.position_ids
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next_batch_past_key_values = past
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next_batch_requests = batch.requests
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next_batch_next_token_choosers = batch.next_token_choosers
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next_batch_stopping_criterias = batch.stopping_criterias
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# Update attention_mask with padding as we added a new token to input_ids
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next_batch_attention_mask = torch.cat(
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[
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next_batch_attention_mask,
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next_batch_attention_mask.new_ones(next_batch_size, 1),
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],
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dim=1,
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)
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# Update position_ids
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next_batch_position_ids = next_batch_position_ids[:, -1:] + 1
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next_batch = CausalLMBatch(
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batch_id=batch.batch_id,
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requests=next_batch_requests,
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input_ids=next_batch_input_ids,
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attention_mask=next_batch_attention_mask,
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position_ids=next_batch_position_ids,
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past_key_values=next_batch_past_key_values,
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all_input_ids=next_batch_all_input_ids,
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input_lengths=next_batch_input_lengths,
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next_token_choosers=next_batch_next_token_choosers,
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stopping_criterias=next_batch_stopping_criterias,
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size=next_batch_size,
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max_sequence_length=next_batch_max_sequence_length,
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keys_head_dim_last=batch.keys_head_dim_last,
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)
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return generations, next_batch
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