456 lines
17 KiB
Python
456 lines
17 KiB
Python
import torch
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import torch.distributed
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from dataclasses import dataclass
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from pathlib import Path
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from typing import List, Tuple, Optional, Dict
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
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from transformers.modeling_utils import no_init_weights
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from bloom_inference.pb import generate_pb2
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from bloom_inference.prepare_weights import prepare_weights, match_suffix
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from bloom_inference.utils import (
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StoppingCriteria,
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NextTokenChooser,
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initialize_torch_distributed,
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set_default_dtype,
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)
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torch.manual_seed(0)
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@dataclass
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class Batch:
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batch_id: int
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requests: List[generate_pb2.Request]
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input_ids: Dict[str, torch.Tensor]
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all_input_ids: List[torch.Tensor]
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next_token_choosers: List[NextTokenChooser]
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stopping_criterias: List[StoppingCriteria]
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size: int
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max_sequence_length: int
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def to_pb(self):
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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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max_sequence_length=self.max_sequence_length,
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)
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@classmethod
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def from_pb(
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cls, pb: generate_pb2.Batch, tokenizer: AutoTokenizer, device: torch.device
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) -> "Batch":
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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(
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NextTokenChooser(
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temperature=r.parameters.temperature,
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top_k=r.parameters.top_k,
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top_p=r.parameters.top_p,
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do_sample=r.parameters.do_sample,
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)
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)
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stopping_criterias.append(StoppingCriteria(max_new_tokens=r.max_new_tokens))
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input_ids = tokenizer(inputs, return_tensors="pt", padding=True).to(device)
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# Remove padding from all_input_ids
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all_input_ids = [
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input_ids.squeeze(0)[-length:].unsqueeze(-1)
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for length, input_ids in zip(
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input_lengths, input_ids["input_ids"].split(1, dim=0)
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)
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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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input_ids=input_ids,
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all_input_ids=all_input_ids,
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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=pb.max_sequence_length,
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)
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@classmethod
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def concatenate(cls, batches: List["Batch"]) -> "Batch":
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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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input_ids = {"input_ids": None, "attention_mask": None, "past_key_values": []}
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requests = []
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all_input_ids = []
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next_token_choosers = []
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stopping_criterias = []
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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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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.input_ids["input_ids"].shape[1] > 1:
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raise ValueError("Batch input_ids should be of shape (batch_size, 1)")
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# Initialize tensors
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if i == 0:
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input_ids["input_ids"] = torch.empty(
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(total_batch_size, 1),
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dtype=batch.input_ids["input_ids"].dtype,
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device=batch.input_ids["input_ids"].device,
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)
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input_ids["attention_mask"] = torch.zeros(
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(total_batch_size, max_sequence_length),
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dtype=batch.input_ids["attention_mask"].dtype,
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device=batch.input_ids["attention_mask"].device,
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)
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# input_ids["input_ids"] is always of shape [batch_size, 1]
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# We do not need to pad it
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input_ids["input_ids"][start_index:end_index] = batch.input_ids["input_ids"]
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# We need to slice the attention mask to remove padding from previous steps
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input_ids["attention_mask"][
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start_index:end_index, -batch.max_sequence_length :
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] = batch.input_ids["attention_mask"][:, -batch.max_sequence_length :]
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for j, past in enumerate(batch.input_ids["past_key_values"]):
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past_keys = past[0]
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past_values = past[1]
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_, head_dim, padded_sequence_length = past_keys.shape
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# Reshape the tensors to make slicing easier
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past_keys = past_keys.view(
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batch.size, -1, head_dim, padded_sequence_length
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)
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past_values = past_values.view(
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batch.size, -1, padded_sequence_length, head_dim
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)
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num_heads = past_keys.shape[1]
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# Initialize tensors
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# This will run only once per layer
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if j == len(input_ids["past_key_values"]):
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padded_past_keys = torch.zeros(
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(
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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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dtype=past_keys.dtype,
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device=past_keys.device,
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)
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padded_past_values = torch.zeros(
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(
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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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dtype=past_values.dtype,
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device=past_values.device,
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)
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input_ids["past_key_values"].append(
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[padded_past_keys, padded_past_values]
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)
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# We slice the past keys and values to remove the padding from previous batches
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input_ids["past_key_values"][j][0][
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start_index:end_index, :, :, -(batch.max_sequence_length - 1) :
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] = past_keys[:, :, :, -(batch.max_sequence_length - 1) :]
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input_ids["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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# If we are on the last batch, we need to reshape the tensors
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if (i + 1) == len(batches):
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input_ids["past_key_values"][j][0] = input_ids["past_key_values"][
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j
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][0].view(total_batch_size * num_heads, head_dim, -1)
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input_ids["past_key_values"][j][1] = input_ids["past_key_values"][
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j
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][1].view(total_batch_size * num_heads, -1, head_dim)
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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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all_input_ids=all_input_ids,
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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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)
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@dataclass
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class GeneratedText:
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request: generate_pb2.Request
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output: str
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def to_pb(self) -> generate_pb2.GeneratedText:
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return generate_pb2.GeneratedText(request=self.request, output=self.output)
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class BLOOM:
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def __init__(self, model_name: str):
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if torch.cuda.is_available():
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self.device = torch.device("cuda")
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dtype = torch.bfloat16
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else:
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self.device = torch.device("cpu")
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dtype = torch.float32
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self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
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self.model = (
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AutoModelForCausalLM.from_pretrained(model_name).eval().to(self.device).to(dtype)
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)
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self.num_heads = self.model.base_model.num_heads
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def forward(self, input_ids, attention_mask, past_key_values: Optional = None):
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# Model Forward
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return self.model.forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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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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def generate_token(
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self, batch: Batch
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) -> Tuple[List[GeneratedText], Optional[Batch]]:
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with torch.no_grad():
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outputs = self.forward(**batch.input_ids)
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# List of indices to cache
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next_batch_keep_indices = []
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next_batch_past_keep_indices = []
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# New input_ids for next forward
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next_batch_input_ids = []
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next_batch_all_input_ids = []
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next_batch_size = 0
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next_batch_max_sequence_length = 0
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# Finished requests
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generated_texts: List[GeneratedText] = []
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# Zipped iterator
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iterator = zip(
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batch.requests,
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outputs.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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logits,
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next_token_chooser,
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stopping_criteria,
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all_tokens,
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) in enumerate(iterator):
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# Select next token
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next_token = next_token_chooser(all_tokens, logits.unsqueeze(0)[:, -1])
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# Append next token to all tokens
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all_tokens = torch.cat([all_tokens, next_token])
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# Evaluate stopping criteria
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if stopping_criteria(all_tokens):
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# Decode all tokens
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output = self.tokenizer.decode(
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all_tokens.squeeze(-1), skip_special_tokens=True
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)
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# Add to the list of finished generations with the original request
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generated_texts.append(GeneratedText(request, output))
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# add to the next batch
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else:
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next_batch_keep_indices.append(i)
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# past_key_values is of shape [batch_size * num_heads, ...]
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# so we need to take into account the `num_heads` stride here
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next_batch_past_keep_indices.extend(
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[j for j in range(i * self.num_heads, (i + 1) * self.num_heads)]
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)
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next_batch_input_ids.append(next_token)
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next_batch_all_input_ids.append(all_tokens)
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next_batch_size += 1
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next_batch_max_sequence_length = max(
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next_batch_max_sequence_length, len(all_tokens)
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)
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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 generated_texts, None
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# If we finished at least one generation
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next_batch_input_ids = {"input_ids": torch.cat(next_batch_input_ids, dim=0)}
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if generated_texts:
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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_input_ids["attention_mask"] = batch.input_ids["attention_mask"][
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next_batch_keep_indices
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]
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next_batch_input_ids["past_key_values"] = [
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(
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keys[next_batch_past_keep_indices],
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values[next_batch_past_keep_indices],
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)
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for keys, values in outputs["past_key_values"]
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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_input_ids["attention_mask"] = batch.input_ids["attention_mask"]
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next_batch_input_ids["past_key_values"] = outputs["past_key_values"]
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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_input_ids["attention_mask"] = torch.cat(
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[
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next_batch_input_ids["attention_mask"],
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torch.ones((next_batch_size, 1)).to(self.device),
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],
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dim=1,
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)
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next_batch = Batch(
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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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all_input_ids=next_batch_all_input_ids,
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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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)
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return generated_texts, next_batch
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class BLOOMSharded(BLOOM):
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def __init__(self, model_name: str, shard_directory: Path):
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super(BLOOM, self).__init__()
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self.process_group, self.rank, self.world_size = initialize_torch_distributed()
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self.master = self.rank == 0
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if torch.cuda.is_available():
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self.device = torch.device(f"cuda:{self.rank}")
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dtype = torch.bfloat16
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else:
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self.device = torch.device("cpu")
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dtype = torch.float32
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self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
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# shard state_dict
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if self.master:
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# TODO @thomasw21 do some caching
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shard_state_dict_paths = prepare_weights(
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model_name, shard_directory / "cache", shard_directory, tp_world_size=self.world_size
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)
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shard_state_dict_paths = [
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str(path.absolute()) for path in shard_state_dict_paths
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]
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else:
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shard_state_dict_paths = [None] * self.world_size
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torch.distributed.broadcast_object_list(
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shard_state_dict_paths, src=0, group=self.process_group
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)
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shard_state_dict_path = shard_state_dict_paths[self.rank]
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config = AutoConfig.from_pretrained(
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model_name, slow_but_exact=False, tp_parallel=True
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)
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config.pad_token_id = 3
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# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
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# in PyTorch 1.12 and later.
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torch.backends.cuda.matmul.allow_tf32 = True
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# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
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torch.backends.cudnn.allow_tf32 = True
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with set_default_dtype(dtype):
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with no_init_weights():
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# we can probably set the device to `meta` here?
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model = AutoModelForCausalLM.from_config(config).to(dtype)
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torch.distributed.barrier(group=self.process_group)
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# print_rank_0(f"Initialized model")
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state_dict = torch.load(shard_state_dict_path)
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# TODO @thomasw21: HACK in order to transpose all weight prior
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for key in state_dict.keys():
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do_transpose = False
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if not match_suffix(key, "weight"):
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continue
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for potential_suffix in [
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"self_attention.query_key_value.weight",
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"self_attention.dense.weight",
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"dense_h_to_4h.weight",
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"dense_4h_to_h.weight",
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]:
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if match_suffix(key, potential_suffix):
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do_transpose = True
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if do_transpose:
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state_dict[key] = state_dict[key].transpose(1, 0).contiguous()
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model.load_state_dict(state_dict, strict=False)
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model.tie_weights()
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self.model = model.to(self.device).eval()
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self.num_heads = config.n_head // self.process_group.size()
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torch.distributed.barrier(group=self.process_group)
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def forward(self, input_ids, attention_mask, past_key_values: Optional = None):
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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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past_key_values=past_key_values,
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use_cache=True,
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)
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logits_shard = outputs.logits[:, -1, :].contiguous()
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batch_size, vocab_shard_size = logits_shard.shape
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vocab_size = self.world_size * vocab_shard_size
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logits = [torch.empty_like(logits_shard) for _ in range(self.world_size)]
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torch.distributed.all_gather(logits, logits_shard, group=self.process_group)
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logits = torch.cat(logits, dim=1).view(batch_size, 1, vocab_size)
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outputs.logits = logits
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return outputs
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