# 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 GPTSANJapanese checkpoints from the original repository to pytorch model.""" import argparse import json import os from collections import OrderedDict import numpy as np import tensorflow as tf import torch def convert_tf_gptsan_to_pt(args): parameter_file = os.path.join(args.tf_model_dir, "parameters.json") params = json.loads(open(parameter_file).read()) if not params: raise ValueError( f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file." ) if not args.output.endswith(".pt"): args.output = args.output + ".pt" new_state = OrderedDict() with tf.device("/CPU:0"): reader = tf.train.load_checkpoint(args.tf_model_dir) shapes = reader.get_variable_to_shape_map() for key_name in shapes.keys(): vnp = reader.get_tensor(key_name).astype(np.float16) if key_name.endswith("/adam_m") or key_name.endswith("/adam_v"): continue if key_name.startswith("pasts/"): if key_name.startswith("pasts/mlp"): player = int(key_name[9]) elif key_name.startswith("pasts/out"): player = 8 name = "model.sqout.%d.weight" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time state = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix new_state[name] = torch.tensor(state) elif key_name.startswith("model/moe"): player = int(key_name[9:].split("/")[0]) if key_name.endswith("/switch_gating/kernel"): name = "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player state = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix new_state[name] = torch.tensor(state) elif key_name.endswith("/softmlp/kernel"): name = "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player state = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix new_state[name] = torch.tensor(state) elif key_name.endswith("/wo/kernel") or key_name.endswith("/wi/kernel"): nlayer = key_name[-9:-7] for i in range(16): name = "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer) state = ( vnp[i].transpose([1, 0]).copy() ) # In Mesh-Tensorflow, it is one array, so it is divided new_state[name] = torch.tensor(state) elif key_name.startswith("model/mlp"): player = int(key_name[9:].split("/")[0]) if key_name.endswith("/p1/kernel"): name = "model.blocks.%d.feed_forward.mlp.wi.weight" % player state = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix new_state[name] = torch.tensor(state) elif key_name.endswith("/p1/bias"): name = "model.blocks.%d.feed_forward.mlp.wi.bias" % player state = vnp.copy() # same because it is one dimensional new_state[name] = torch.tensor(state) elif key_name.endswith("/p2/kernel"): name = "model.blocks.%d.feed_forward.mlp.wo.weight" % player state = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix new_state[name] = torch.tensor(state) elif key_name.endswith("/p2/bias"): name = "model.blocks.%d.feed_forward.mlp.wo.bias" % player state = vnp.copy() # same because it is one dimensional new_state[name] = torch.tensor(state) elif key_name.startswith("model/ln"): player = int(key_name[8:].split("/")[0]) if key_name.endswith("/b"): name = "model.blocks.%d.feed_forward.norm.bias" % player state = vnp.copy() # same because it is one dimensional new_state[name] = torch.tensor(state) elif key_name.endswith("/g"): name = "model.blocks.%d.feed_forward.norm.weight" % player state = vnp.copy() # same because it is one dimensional new_state[name] = torch.tensor(state) elif key_name.startswith("model/att"): player = int(key_name[9:].split("/")[0]) if key_name.endswith("/qkv/kernel"): state = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum state_q = state[:, 0, :, :] state_k = state[:, 1, :, :] state_v = state[:, 2, :, :] state_q = ( state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]) .transpose([1, 0]) .copy() ) # Mesh-Tensorflow is a diagonal matrix state_k = ( state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]) .transpose([1, 0]) .copy() ) # Mesh-Tensorflow is a diagonal matrix state_v = ( state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]) .transpose([1, 0]) .copy() ) # Mesh-Tensorflow is a diagonal matrix name = "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player new_state[name] = torch.tensor(state_q) name = "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player new_state[name] = torch.tensor(state_k) name = "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player new_state[name] = torch.tensor(state_v) elif key_name.endswith("/o/kernel"): name = "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player state = ( vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]).transpose([1, 0]).copy() ) # Mesh-Tensorflow is a diagonal matrix new_state[name] = torch.tensor(state) elif key_name.startswith("model/an"): player = int(key_name[8:].split("/")[0]) if key_name.endswith("/b"): name = "model.blocks.%d.self_attn.norm.bias" % player state = vnp.copy() # same because it is one dimensional new_state[name] = torch.tensor(state) elif key_name.endswith("/g"): name = "model.blocks.%d.self_attn.norm.weight" % player state = vnp.copy() # same because it is one dimensional new_state[name] = torch.tensor(state) elif ( key_name.startswith("model/wte") or key_name.startswith("model/wpe") or key_name.startswith("model/ete") ): nlayer = {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[ key_name[-3:] ] name = "model.%s.weight" % nlayer state = vnp.copy() # same in embedded new_state[name] = torch.tensor(state) if key_name.startswith("model/wte"): name = "lm_head.weight" state = vnp.copy() # same in embedded new_state[name] = torch.tensor(state) elif key_name.startswith("model/wob"): name = "final_logits_bias" state = vnp.copy() # same in embedded state = state.reshape((1, -1)) new_state[name] = torch.tensor(state) elif key_name == "model/dense/kernel": name = "model.last_project.weight" state = vnp.transpose([1, 0]).copy() # Mesh-Tensorflow is a diagonal matrix new_state[name] = torch.tensor(state) elif key_name == "model/dense_1/bias": name = "model.last_project.bias" state = vnp.copy() # same because it is one dimensional new_state[name] = torch.tensor(state) torch.save(new_state, args.output) if __name__ == "__main__": parser = argparse.ArgumentParser( description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model") parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model") args = parser.parse_args() convert_tf_gptsan_to_pt(args)