hazyresearch / based Goto Github PK

In [1]: import torch
   ...: from transformers import AutoTokenizer
   ...: from based.models.gpt import GPTLMHeadModel
   ...: 
   ...: tokenizer = AutoTokenizer.from_pretrained("gpt2")
   ...: model = GPTLMHeadModel.from_pretrained_hf("hazyresearch/based-360m").to("cuda", dtype=torch.float
   ...: 16)
tokenizer_config.json: 100%|███████████████████████████████████████████| 26.0/26.0 [00:00<00:00, 260kB/s]
config.json: 100%|██████████████████████████████████████████████████████| 665/665 [00:00<00:00, 8.64MB/s]
vocab.json: 100%|███████████████████████████████████████████████████| 1.04M/1.04M [00:00<00:00, 12.1MB/s]
merges.txt: 100%|█████████████████████████████████████████████████████| 456k/456k [00:00<00:00, 8.99MB/s]
tokenizer.json: 100%|███████████████████████████████████████████████| 1.36M/1.36M [00:00<00:00, 17.8MB/s]
config.json: 100%|██████████████████████████████████████████████████| 2.86k/2.86k [00:00<00:00, 36.7MB/s]
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[1], line 6
      3 from based.models.gpt import GPTLMHeadModel
      5 tokenizer = AutoTokenizer.from_pretrained("gpt2")
----> 6 model = GPTLMHeadModel.from_pretrained_hf("hazyresearch/based-360m").to("cuda", dtype=torch.float16)

File /based/models/gpt.py:468, in GPTPreTrainedModel.from_pretrained_hf(cls, pretrained_model_name, device, **kwargs)
    466 config_data = load_config_hf(pretrained_model_name)
    467 config = GPT2Config(**config_data)
--> 468 model = cls(config, device=device, **kwargs)
    469 state_dict = load_state_dict_hf(pretrained_model_name, device=device)
    471 # remove the 'model.' prefix from the keys

File /based/models/gpt.py:741, in GPTLMHeadModel.__init__(self, config, process_group, device, dtype)
    739 super().__init__(config)
    740 self.process_group = process_group
--> 741 self.transformer = GPTModel(config, process_group=process_group, **factory_kwargs)
    742 self.tie_word_embeddings = getattr(config, "tie_word_embeddings", True)
    743 lm_head_bias = getattr(config, "lm_head_bias", False)

File /based/models/gpt.py:585, in GPTModel.__init__(self, config, process_group, device, dtype)
    569     self.embeddings = ParallelGPT2Embeddings(
    570         config.hidden_size,
    571         vocab_size,
   (...)
    575         **factory_kwargs,
    576     )
    578 # We change the order of dropout, residual and layer norm:
    579 # Instead of LN -> Attn / MLP -> Dropout -> Add, we do:
    580 # Dropout -> Add -> LN -> Attn / MLP, returning both the residual branch (output of Add) and
    581 # the main branch (output of MLP). The model definition is unchanged, but the mapping of the
    582 # nn.Dropout probabilities are changed.
    583 # This is for performance reason: we can fuse dropout + add + layer_norm.
    584 self.layers = nn.ModuleList(
--> 585     [
    586         create_block(config, layer_idx=i, process_group=process_group, **factory_kwargs)
    587         for i in range(config.num_hidden_layers)
    588     ]
    589 )
    590 self.fused_dropout_add_ln = getattr(config, "fused_dropout_add_ln", False)
    591 if self.fused_dropout_add_ln:

File /based/models/gpt.py:586, in <listcomp>(.0)
    569     self.embeddings = ParallelGPT2Embeddings(
    570         config.hidden_size,
    571         vocab_size,
   (...)
    575         **factory_kwargs,
    576     )
    578 # We change the order of dropout, residual and layer norm:
    579 # Instead of LN -> Attn / MLP -> Dropout -> Add, we do:
    580 # Dropout -> Add -> LN -> Attn / MLP, returning both the residual branch (output of Add) and
    581 # the main branch (output of MLP). The model definition is unchanged, but the mapping of the
    582 # nn.Dropout probabilities are changed.
    583 # This is for performance reason: we can fuse dropout + add + layer_norm.
    584 self.layers = nn.ModuleList(
    585     [
--> 586         create_block(config, layer_idx=i, process_group=process_group, **factory_kwargs)
    587         for i in range(config.num_hidden_layers)
    588     ]
    589 )
    590 self.fused_dropout_add_ln = getattr(config, "fused_dropout_add_ln", False)
    591 if self.fused_dropout_add_ln:

File /based/models/gpt.py:371, in create_block(config, layer_idx, process_group, device, dtype, **kwargs)
    369 mlp_cls = create_mlp_cls(config, layer_idx, process_group=process_group, **factory_kwargs)
    370 use_rms_norm = getattr(config, "rms_norm", False)
--> 371 norm_cls = partial(
    372     nn.LayerNorm if not use_rms_norm else RMSNorm,
    373     eps=config.layer_norm_epsilon,
    374     **factory_kwargs,
    375 )
    376 # TD [2022-07-30]: Force residual in fp32, seems to make fp16 training more stable
    377 residual_in_fp32 = getattr(config, "residual_in_fp32", False)

TypeError: the first argument must be callable

FROM nvidia/cuda:11.8.0-devel-ubuntu22.04

RUN apt-get update && apt-get install -y \
    apt-utils \
    python3.10 \
    python3-pip \
    git \
    && rm -rf /var/lib/apt/lists/*

RUN pip install --upgrade pip
RUN pip install \
    torch==2.1.2 \
    torchvision==0.16.2 \
    torchaudio==2.1.2 \
    --index-url https://download.pytorch.org/whl/cu118 # due to observed causal-conv1d dependency

RUN pip install \
    jupyter==1.0.0 \
    hydra-core==1.3.2

RUN pip install jupyter
COPY . .
RUN pip install .