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https://github.com/huggingface/text-generation-inference.git
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abd58ff82c
# What does this PR do? Reworked the loading logic. Idea is to use cleaner loading code: - Remove need for `no_init_weights` - Remove all weird `bnb_linear` and `load_weights` and `post_load_weights`. New code layout: - New class `Weights` in charge of handling loading the weights from multiple files into appropiate tensors (potentially sharded) - TP layers now are "shells", they contain the code to know what kind of sharding we need + eventual `all_reduce`. They do not inherit from linear, but they contain some kind of Linear instead - the contained linear can be either FastLinear, BnbLinear or GPTq Linear next. - All modeling code is explictly made for sharding, process group is just no-ops for non sharded code (removes a lot of test cases)  --------- Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.taildb5d.ts.net> Co-authored-by: Ubuntu <ubuntu@ip-172-31-41-161.ec2.internal> Co-authored-by: OlivierDehaene <olivier@huggingface.co> Co-authored-by: OlivierDehaene <23298448+OlivierDehaene@users.noreply.github.com>
102 lines
2.9 KiB
Python
102 lines
2.9 KiB
Python
import torch
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import torch.distributed
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from typing import List, Optional, Tuple
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from transformers import (
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AutoTokenizer,
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AutoConfig,
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)
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from text_generation_server.models import Seq2SeqLM
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from text_generation_server.models.custom_modeling.t5_modeling import (
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T5ForConditionalGeneration,
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)
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from text_generation_server.utils import (
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initialize_torch_distributed,
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weight_files,
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Weights,
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)
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class T5Sharded(Seq2SeqLM):
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def __init__(
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self,
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model_id: str,
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revision: Optional[str] = None,
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quantize: Optional[str] = None,
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trust_remote_code: bool = False,
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):
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self.process_group, rank, world_size = initialize_torch_distributed()
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if torch.cuda.is_available():
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device = torch.device(f"cuda:{rank}")
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dtype = torch.float16
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else:
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device = torch.device("cpu")
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dtype = torch.float32
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config = AutoConfig.from_pretrained(
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model_id,
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revision=revision,
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trust_remote_code=trust_remote_code,
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)
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config.quantize = quantize
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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revision=revision,
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padding_side="left",
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truncation_side="left",
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trust_remote_code=trust_remote_code,
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)
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tokenizer.bos_token_id = config.decoder_start_token_id
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torch.distributed.barrier(group=self.process_group)
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filenames = weight_files(model_id, revision=revision, extension=".safetensors")
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weights = Weights(
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filenames, device=device, dtype=dtype, process_group=self.process_group
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)
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model = T5ForConditionalGeneration(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(Seq2SeqLM, self).__init__(
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model=model,
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tokenizer=tokenizer,
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requires_padding=True,
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dtype=dtype,
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device=device,
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rank=rank,
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world_size=world_size,
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)
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def forward(
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self,
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input_ids,
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attention_mask,
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decoder_input_ids,
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decoder_attention_mask: Optional,
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encoder_last_hidden_state: Optional,
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past_key_values: Optional = None,
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) -> Tuple[
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torch.Tensor,
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torch.Tensor,
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List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]],
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]:
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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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decoder_input_ids=decoder_input_ids,
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decoder_attention_mask=decoder_attention_mask,
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encoder_outputs=encoder_last_hidden_state,
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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 (
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outputs.logits,
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outputs.encoder_last_hidden_state,
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outputs.past_key_values,
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)
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