# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Convert Mega pretrained checkpoint. Built to convert the Masked LM checkpoint located at https://huggingface.co/mnaylor/mega-wikitext-103 Requirements: - clone the Mega repo and install fairseq from there 1. git clone https://github.com/facebookresearch/mega.git 2. cd mega && pip install -e - clone the pretrained weights for the original implementation from the hugging face repo * use this location as the path for pretrained weights """ import argparse # utilities to import the model weights and config file import os import pickle as pkl # PyTorch + new model classes import torch from torch import nn from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM # import the EncoderLayer class used to pretrain # !! NOTE !! this requires the version of fairseq that is built when you install the Mega source try: from fairseq.modules.mega_layer import MegaEncoderLayer except ImportError: raise ImportError("You need to install the version of fairseq from the Mega repo!") # define the wrapper classes used to train the MLM (see colab notebook below) # https://colab.research.google.com/drive/1qfUO6o5HRdxBblWlw058HVyvaEPhPpH8?usp=sharing # MegaLM outputs hidden states class MegaLM(nn.Module): "The base class for our Mega encoder - given input IDs, embed text and return encoder output" def __init__(self, mega_args, depth, vocab_size): super().__init__() self.mega_args = mega_args self.embedding_layer = nn.Embedding(vocab_size, self.mega_args.encoder_embed_dim) self.encoders = nn.ModuleList([MegaEncoderLayer(self.mega_args) for _ in range(depth)]) self.depth = depth def forward(self, input_ids, attention_mask, batch_first=True, ignore_mask_value=0): """ Code for a forward pass - expects input_ids and attention_mask to come from a Hugging Face tokenizer as PyTorch tensors, and returns a tensor of size (batch, n_classes) containing classification logits Other options: - batch_first: boolean indicating whether the batch dimension is first in input_ids (default: True, which aligns with the HF tokenizer behavior) - ignore_mask_value: the value in attention_mask that identifies tokens that should be ignored (default: 0, which aligns with HF tokenizer) """ # Mega expects embeddings to be (time, batch, embedding size), but # Hugging Face returns tokens as (batch, time) if batch_first: input_ids = input_ids.T # to make things more confusing, Mega expects the attention mask to # be (batch, time), but with values of 0 (normal token) and 1 (ignore token) # which is the opposite of what HF returns if ignore_mask_value == 0: attention_mask = 1 - attention_mask # get token embeddings from IDs embeds = self.embedding_layer(input_ids) # pass through the Mega layers # input is (time, batch, encoder dim) and output is the same for encoder in self.encoders: embeds = encoder(embeds, attention_mask) # return according to the shape specified if batch_first: # (T, B, H) --> (B, T, H) return torch.transpose(embeds, 0, 1) else: return embeds # renamed from MegaForMaskedLM to avoid confusion with new module class OriginalMegaForMaskedLM(nn.Module): "A wrapper class for doing masked language modeling with Mega" def __init__(self, mega_args, depth, vocab_size): super().__init__() self.mega = MegaLM(mega_args, depth, vocab_size) self.mlm_head = nn.Linear(mega_args.encoder_embed_dim, vocab_size) self.dropout = nn.Dropout(p=0.1) def forward(self, input_ids, attention_mask, batch_first=True, ignore_mask_value=0): """ Perform a forward pass through the Mega encoder and the masked LM head. Returns logits for each vocabulary entry. If `batch_first` (default to align with Hugging Face tokenizer behavior), output will have the shape (Batch size, Sequence length, Vocab size); otherwise (S, B, V) """ encoder_output = self.mega(input_ids, attention_mask, batch_first, ignore_mask_value) return self.mlm_head(self.dropout(encoder_output)) # code to convert the checkpoint located in the user-specified location def convert_checkpoint_to_huggingface(pretrained_checkpoint_path, output_path, includes_tokenizer): with open(os.path.join(pretrained_checkpoint_path, "model_args.pkl"), "rb") as f: mega_original_args = pkl.load(f) # load the original encoder original_mlm = OriginalMegaForMaskedLM(**mega_original_args).eval() # load its weights print( "Original Mega encoder:", original_mlm.mega.load_state_dict( torch.load(os.path.join(pretrained_checkpoint_path, "encoder_weights.pt"), map_location="cpu") ), ) print( "Original Mega MLM layer:", original_mlm.mlm_head.load_state_dict( torch.load(os.path.join(pretrained_checkpoint_path, "mlm_head_weights.pt"), map_location="cpu") ), ) # create a new config from the old one hf_config = MegaConfig( num_hidden_layers=mega_original_args["depth"], vocab_size=mega_original_args["vocab_size"], hidden_size=mega_original_args["mega_args"].encoder_embed_dim, shared_representation_size=mega_original_args["mega_args"].encoder_z_dim, intermediate_size=mega_original_args["mega_args"].encoder_hidden_dim, ema_projection_size=mega_original_args["mega_args"].encoder_n_dim, dropout_prob=mega_original_args["mega_args"].dropout, attention_probs_dropout_prob=mega_original_args["mega_args"].attention_dropout, hidden_dropout_prob=mega_original_args["mega_args"].hidden_dropout, activation=mega_original_args["mega_args"].activation_fn, attention_activation=mega_original_args["mega_args"].attention_activation_fn, bidirectional=mega_original_args["mega_args"].bidirectional, use_chunking=mega_original_args["mega_args"].encoder_chunk_size > 0, chunk_size=mega_original_args["mega_args"].encoder_chunk_size, truncation=mega_original_args["mega_args"].truncation_length, normalization_type=mega_original_args["mega_args"].normalization_type, normalize_before_mega=True, norm_affine=True, use_feature_dropout=mega_original_args["mega_args"].feature_dropout, relative_positional_bias=mega_original_args["mega_args"].rel_pos_bias, max_positions=mega_original_args["mega_args"].max_source_positions, nffn_hidden_size=mega_original_args["mega_args"].encoder_ffn_embed_dim, normalize_before_ffn=mega_original_args["mega_args"].normalize_before, # new arguments added for HF implementation nffn_activation_dropout_prob=0.0, add_token_type_embeddings=False, add_lm_hidden_dense_layer=False, ) hf_mlm = MegaForMaskedLM(hf_config).eval() # the originl checkpoint just uses nn.Embedding for the word embeddings # we use a wrapper module for embeddings to add support for positional embeddings hf_mlm.mega.embedding_layer.word_embeddings.weight = original_mlm.mega.embedding_layer.weight # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained, # also renaming previously confusing parameter names original_state_dict = original_mlm.mega.encoders.state_dict() updated_keys = {} for module_name in original_state_dict.keys(): new_module_name = None # have to handle gamma, beta, and alpha differently due to their use # in multiple modules within the original repository; # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here if "beta" in module_name: # EMA sub-layers were always called "move" in the original repo if "move.beta" in module_name: new_module_name = module_name.replace("move.beta", "ema_gate.ema_expansion_matrix") elif "mega_layer.beta" in module_name: new_module_name = module_name.replace("beta", "qk_bias") else: new_module_name = module_name.replace("beta", "b_param") # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights elif "gamma" in module_name: if "move.gamma" in module_name: new_module_name = module_name.replace("move.gamma", "ema_gate.kernel_projection_matrix") elif "mega_layer.gamma" in module_name: new_module_name = module_name.replace("gamma", "qk_weight") else: new_module_name = module_name.replace("gamma", "g_param") # alpha is used in EMA and positional bias; renaming to improve readability elif "move.alpha" in module_name: new_module_name = module_name.replace("move.alpha", "ema_gate.decay_factor") # delta is only used in EMA; renaming to improve readability elif "move.delta" in module_name: new_module_name = module_name.replace("move.delta", "ema_gate.damping_factor") # omega is only used in EMA; renaming to improve readability elif "omega" in module_name: new_module_name = module_name.replace("move.omega", "ema_gate.residual_weight") if new_module_name: updated_keys[module_name] = new_module_name if len(updated_keys) != 0: print(f"Renaming these keys: {updated_keys.keys()}") else: print("No need to rename state dict entries") for old, new in updated_keys.items(): original_state_dict[new] = original_state_dict.pop(old) # now attempt to load the state dictionary with updated names # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style print("HF Mega encoder:", hf_mlm.mega.layers.load_state_dict(original_state_dict)) # load the MLM head weights directly print( "HF Mega MLM layer:", hf_mlm.mlm_head.load_state_dict( torch.load(os.path.join(pretrained_checkpoint_path, "mlm_head_weights.pt"), map_location="cpu") ), ) # test on a randomly generated input sequence input_ids = torch.randint(0, hf_config.vocab_size, size=(4, 256)) input_mask = torch.ones_like(input_ids) # mask a few tokens to make sure masking is applied appropriately :) input_mask[:, -10:] = 0 # run forward passes original_output = original_mlm(input_ids, input_mask, batch_first=True, ignore_mask_value=0) hf_output = hf_mlm(input_ids, input_mask)[0] # print shapes and diff print(f"original output {original_output.shape}") print(f"hf output {hf_output.shape}") print(f"max diff: {(original_output - hf_output).max()}") # 0.0 success = torch.allclose(original_output, hf_output, atol=1e-3) if success: print("Yay!") hf_mlm.save_pretrained(output_path) else: raise RuntimeError(f"Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}") if includes_tokenizer: print("Transferring tokenizer") tokenizer = AutoTokenizer.from_pretrained(pretrained_checkpoint_path) tokenizer.save_pretrained(output_path) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--pretrained_checkpoint_path", default=None, type=str, required=True, help="Point to the directory containing your model weights using the official Mega repo", ) parser.add_argument( "--output_path", default=None, type=str, required=True, help="Location to save the Hugging Face version" ) parser.add_argument( "--includes_tokenizer", action="store_true", help="Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo", ) args = parser.parse_args() convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)