1071 lines
34 KiB
Python
1071 lines
34 KiB
Python
#!/usr/bin/env python3#
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# The Initial DNC Copyright 2017 Robert Csordas. All Rights Reserved.
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# The modification of the initial DNC implementation by Ari Azarafrooz.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# ==============================================================================
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import functools
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import os
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import torch.utils.data
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import Utils.Debug as debug
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from Dataset.Bitmap.AssociativeRecall import AssociativeRecall
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from Dataset.Bitmap.BitmapTaskRepeater import BitmapTaskRepeater
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from Dataset.Bitmap.KeyValue import KeyValue
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from Dataset.Bitmap.CopyTask import CopyData
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from Dataset.Bitmap.KeyValue2Way import KeyValue2Way
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from Dataset.NLP.bAbi import bAbiDataset
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from Models.DNCA import DNC, LSTMController, FeedforwardController
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from Models.Information_Agents import RolloutStorage, Demon, FNet, ZNet
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from Utils import Visdom
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from Utils.ArgumentParser import ArgumentParser
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from Utils.Index import index_by_dim
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from Utils.Saver import Saver, GlobalVarSaver, StateSaver
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from Utils.Collate import MetaCollate
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from Utils import gpu_allocator
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from Dataset.NLP.NLPTask import NLPTask
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from tqdm import tqdm
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from Visualize.preview import preview
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from Utils.timer import OnceEvery
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from Utils import Seed
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import time
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import sys
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import signal
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import math
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from Utils import Profile
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import shutil
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import math
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#from torch.utils.tensorboard import SummaryWriter
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import numpy as np
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model_dir = ""
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Profile.ENABLED = False
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random_seed = 1
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if random_seed:
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print("Random Seed: {}".format(random_seed))
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torch.manual_seed(random_seed)
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np.random.seed(random_seed)
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if os.path.exists("tmp_train_dir"):
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shutil.rmtree("tmp_train_dir")
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action_std = 0.1 # constant std for action distribution (Multivariate Normal)
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K_epochs = 1 # update policy for K epochs
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eps_clip = 0.2 # clip parameter for PPO
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gamma = 1 # discount factor
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lr = 0.001 # parameters for Adam optimizer #0.01
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betas = (0.9, 0.999)
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def main():
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global i
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global loss_sum
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global running
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parser = ArgumentParser()
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parser.add_argument(
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"-bit_w", type=int, default=8, help="Bit vector length for copy task"
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)
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parser.add_argument(
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"-block_w", type=int, default=3, help="Block width to associative recall task"
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)
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parser.add_argument(
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"-len",
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type=str,
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default="4",
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help="Sequence length for copy task",
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parser=lambda x: [int(a) for a in x.split("-")],
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)
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parser.add_argument(
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"-repeat",
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type=str,
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default="1",
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help="Sequence length for copy task",
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parser=lambda x: [int(a) for a in x.split("-")],
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)
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parser.add_argument(
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"-batch_size", type=int, default=16, help="Sequence length for copy task"
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)
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parser.add_argument(
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"-n_subbatch",
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type=str,
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default="auto",
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help="Average this much forward passes to a backward pass",
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)
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parser.add_argument(
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"-max_input_count_per_batch",
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type=int,
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default=6000,
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help="Max batch_size*len that can fit into memory",
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)
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parser.add_argument("-lr", type=float, default=0.0001, help="Learning rate")
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parser.add_argument("-wd", type=float, default=1e-5, help="Weight decay")
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parser.add_argument(
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"-optimizer", type=str, default="rmsprop", help="Optimizer algorithm"
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)
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parser.add_argument("-name", type=str, help="Save training to this directory")
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parser.add_argument(
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"-preview_interval",
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type=int,
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default=10,
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help="Show preview every nth iteration",
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)
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parser.add_argument(
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"-info_interval", type=int, default=10, help="Show info every nth iteration"
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)
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parser.add_argument(
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"-save_interval", type=int, default=500, help="Save network every nth iteration"
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)
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parser.add_argument(
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"-masked_lookup", type=bool, default=1, help="Enable masking in content lookups"
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)
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parser.add_argument(
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"-visport",
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type=int,
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default=-1,
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help="Port to run Visdom server on. -1 to disable",
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)
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parser.add_argument("-gpu", default="auto", type=str, help="Run on this GPU.")
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parser.add_argument("-debug", type=bool, default=0, help="Enable debugging")
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parser.add_argument("-task", type=str, default="copy", help="Task to learn")
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parser.add_argument(
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"-mem_count", type=int, default=16, help="Number of memory cells"
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)
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parser.add_argument(
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"-data_word_size", type=int, default=128, help="Memory word size"
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)
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parser.add_argument(
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"-n_read_heads", type=int, default=1, help="Number of read heads"
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)
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parser.add_argument(
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"-layer_sizes",
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type=str,
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default="256",
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help="Controller layer sizes. Separate with ,. For example 512,256,256",
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parser=lambda x: [int(y) for y in x.split(",") if y],
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)
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parser.add_argument("-debug_log", type=bool, default=0, help="Enable debug log")
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parser.add_argument(
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"-controller_type",
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type=str,
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default="lstm",
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help="Controller type: lstm or linear",
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)
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parser.add_argument(
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"-lstm_use_all_outputs",
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type=bool,
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default=1,
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help="Use all LSTM outputs as controller output vs use only the last layer",
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)
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parser.add_argument(
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"-momentum", type=float, default=0.9, help="Momentum for optimizer"
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)
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parser.add_argument(
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"-embedding_size",
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type=int,
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default=256,
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help="Size of word embedding for NLP tasks",
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)
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parser.add_argument(
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"-test_interval", type=int, default=10, help="Run test in this interval"
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)
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parser.add_argument(
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"-dealloc_content",
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type=bool,
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default=1,
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help="Deallocate memory content, unlike DNC, which leaves it unchanged, just decreases the usage counter, causing problems with lookup",
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)
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parser.add_argument(
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"-sharpness_control",
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type=bool,
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default=1,
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help="Distribution sharpness control for forward and backward links",
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)
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parser.add_argument(
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"-think_steps",
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type=int,
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default=0,
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help="Iddle steps before requiring the answer (for bAbi)",
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)
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parser.add_argument("-dump_profile", type=str, save=False)
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parser.add_argument("-test_on_start", default="0", save=False)
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parser.add_argument("-dump_heatmaps", default=False, save=False)
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parser.add_argument("-test_batch_size", default=16)
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parser.add_argument("-mask_min", default=0.0)
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parser.add_argument("-load", type=str, save=False)
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parser.add_argument(
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"-dataset_path",
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type=str,
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default="none",
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parser=ArgumentParser.str_or_none(),
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help="Specify babi path manually",
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)
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parser.add_argument(
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"-babi_train_tasks",
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type=str,
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default="none",
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parser=ArgumentParser.list_or_none(type=str),
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help="babi task list to use for training",
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)
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parser.add_argument(
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"-babi_test_tasks",
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type=str,
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default="none",
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parser=ArgumentParser.list_or_none(type=str),
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help="babi task list to use for testing",
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)
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parser.add_argument(
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"-babi_train_sets",
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type=str,
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default="train",
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parser=ArgumentParser.list_or_none(type=str),
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help="babi train sets to use",
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)
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parser.add_argument(
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"-babi_test_sets",
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type=str,
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default="test",
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parser=ArgumentParser.list_or_none(type=str),
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help="babi test sets to use",
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)
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parser.add_argument(
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"-noargsave",
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type=bool,
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default=False,
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help="Do not save modified arguments",
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save=False,
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)
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parser.add_argument(
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"-demo",
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type=bool,
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default=False,
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help="Do a single step with fixed seed",
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save=False,
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)
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parser.add_argument(
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"-exit_after",
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type=int,
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help="Exit after this amount of steps. Useful for debugging.",
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save=False,
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)
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parser.add_argument(
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"-grad_clip", type=float, default=10.0, help="Max gradient norm"
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)
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parser.add_argument(
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"-clip_controller", type=float, default=20.0, help="Max gradient norm"
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)
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parser.add_argument("-print_test", default=False, save=False)
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parser.add_profile(
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[
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ArgumentParser.Profile(
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"babi",
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{
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"preview_interval": 10,
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"save_interval": 500,
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"task": "babi",
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"mem_count": 64,
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"data_word_size": 64,
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"n_read_heads": 4,
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"layer_sizes": "128",
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"controller_type": "lstm",
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"lstm_use_all_outputs": True,
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"momentum": 0.9,
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"embedding_size": 128,
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"test_interval": 10000,
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"think_steps": 3,
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"batch_size": 4,
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},
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include=["dnc-msd"],
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),
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ArgumentParser.Profile(
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"repeat_copy",
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{
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"bit_w": 8,
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"repeat": "1-8",
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"len": "2-14",
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"task": "copy",
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"think_steps": 1,
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"preview_interval": 10,
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"info_interval": 10,
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"save_interval": 100,
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"data_word_size": 16,
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"layer_sizes": "32",
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"n_subbatch": 1,
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"controller_type": "lstm",
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},
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),
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ArgumentParser.Profile(
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"repeat_copy_simple",
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{
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"repeat": "1-3",
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},
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include="repeat_copy",
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),
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ArgumentParser.Profile(
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"dnc",
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{
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"masked_lookup": False,
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"sharpness_control": False,
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"dealloc_content": False,
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},
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),
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ArgumentParser.Profile(
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"dnc-m",
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{
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"masked_lookup": True,
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"sharpness_control": False,
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"dealloc_content": False,
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},
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),
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ArgumentParser.Profile(
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"dnc-s",
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{
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"masked_lookup": False,
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"sharpness_control": True,
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"dealloc_content": False,
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},
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),
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ArgumentParser.Profile(
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"dnc-d",
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{
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"masked_lookup": False,
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"sharpness_control": False,
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"dealloc_content": True,
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},
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),
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ArgumentParser.Profile(
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"dnc-md",
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{
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"masked_lookup": True,
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"sharpness_control": False,
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"dealloc_content": True,
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},
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),
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ArgumentParser.Profile(
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"dnc-ms",
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{
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"masked_lookup": True,
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"sharpness_control": True,
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"dealloc_content": False,
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},
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),
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ArgumentParser.Profile(
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"dnc-sd",
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{
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"masked_lookup": False,
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"sharpness_control": True,
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"dealloc_content": True,
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},
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),
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ArgumentParser.Profile(
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"dnc-msd",
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{
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"masked_lookup": True,
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"sharpness_control": True,
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"dealloc_content": True,
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},
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),
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ArgumentParser.Profile(
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"keyvalue",
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{
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"repeat": "1",
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"len": "2-16",
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"mem_count": 16,
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"task": "keyvalue",
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"think_steps": 1,
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"preview_interval": 10,
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"info_interval": 10,
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"data_word_size": 32,
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"bit_w": 12,
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"save_interval": 1000,
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"layer_sizes": "32",
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},
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),
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ArgumentParser.Profile(
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"keyvalue2way",
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{
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"task": "keyvalue2way",
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},
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include="keyvalue",
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),
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ArgumentParser.Profile(
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"associative_recall",
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{
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"task": "recall",
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"bit_w": 8,
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"len": "2-16",
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"mem_count": 64,
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"data_word_size": 32,
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"n_read_heads": 1,
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"layer_sizes": "128",
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"controller_type": "lstm",
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"lstm_use_all_outputs": 1,
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"think_steps": 1,
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"mask_min": 0.1,
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"info_interval": 10,
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"save_interval": 1000,
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"preview_interval": 10,
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"n_subbatch": 1,
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},
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),
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]
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)
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opt = parser.parse()
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assert opt.name is not None, "Training dir (-name parameter) not given"
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opt = parser.sync(os.path.join(opt.name, "args.json"), save=not opt.noargsave)
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if opt.demo:
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Seed.fix()
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os.makedirs(os.path.join(opt.name, "save"), exist_ok=True)
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os.makedirs(os.path.join(opt.name, "preview"), exist_ok=True)
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gpu_allocator.use_gpu(opt.gpu)
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debug.enableDebug = opt.debug_log
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if opt.visport > 0:
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Visdom.start(opt.visport)
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Visdom.Text("Name").set(opt.name)
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class LengthHackSampler:
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def __init__(self, batch_size, length):
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self.length = length
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self.batch_size = batch_size
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def __iter__(self):
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while True:
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len = self.length() if callable(self.length) else self.length
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yield [len] * self.batch_size
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def __len__(self):
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return 0x7FFFFFFF
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embedding = None
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test_set = None
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curriculum = None
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loader_reset = False
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if opt.task == "copy":
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dataset = CopyData(bit_w=opt.bit_w)
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in_size = opt.bit_w + 1
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out_size = in_size
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elif opt.task == "recall":
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dataset = AssociativeRecall(bit_w=opt.bit_w, block_w=opt.block_w)
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in_size = opt.bit_w + 2
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out_size = in_size
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elif opt.task == "keyvalue":
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assert opt.bit_w % 2 == 0, "Key-value datasets works only with even bit_w"
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dataset = KeyValue(bit_w=opt.bit_w)
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in_size = opt.bit_w + 1
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out_size = opt.bit_w // 2
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elif opt.task == "keyvalue2way":
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assert opt.bit_w % 2 == 0, "Key-value datasets works only with even bit_w"
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dataset = KeyValue2Way(bit_w=opt.bit_w)
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in_size = opt.bit_w + 2
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out_size = opt.bit_w // 2
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elif opt.task == "babi":
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dataset = bAbiDataset(think_steps=opt.think_steps, dir_name=opt.dataset_path)
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test_set = bAbiDataset(
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think_steps=opt.think_steps, dir_name=opt.dataset_path, name="test"
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)
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dataset.use(opt.babi_train_tasks, opt.babi_train_sets)
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in_size = opt.embedding_size
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print("bAbi: loaded total of %d sequences." % len(dataset))
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test_set.use(opt.babi_test_tasks, opt.babi_test_sets)
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out_size = len(dataset.vocabulary)
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print(
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"bAbi: using %d sequences for training, %d for testing"
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% (len(dataset), len(test_set))
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)
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else:
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assert False, "Invalid task: %s" % opt.task
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if opt.task in ["babi"]:
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data_loader = torch.utils.data.DataLoader(
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dataset,
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batch_size=opt.batch_size,
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num_workers=4,
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pin_memory=True,
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shuffle=True,
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collate_fn=MetaCollate(),
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)
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test_loader = (
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torch.utils.data.DataLoader(
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test_set,
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batch_size=opt.test_batch_size,
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num_workers=opt.test_batch_size,
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pin_memory=True,
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shuffle=False,
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collate_fn=MetaCollate(),
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)
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if test_set is not None
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else None
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)
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else:
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dataset = BitmapTaskRepeater(dataset)
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data_loader = torch.utils.data.DataLoader(
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dataset,
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batch_sampler=LengthHackSampler(
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opt.batch_size, BitmapTaskRepeater.key_sampler(opt.len, opt.repeat)
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),
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num_workers=1,
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pin_memory=True,
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)
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|
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if opt.controller_type == "lstm":
|
|
controller_constructor = functools.partial(
|
|
LSTMController, out_from_all_layers=opt.lstm_use_all_outputs
|
|
)
|
|
elif opt.controller_type == "linear":
|
|
controller_constructor = FeedforwardController
|
|
else:
|
|
assert False, "Invalid controller: %s" % opt.controller_type
|
|
|
|
parity_size = 0
|
|
|
|
model = DNC(
|
|
in_size + parity_size,
|
|
out_size,
|
|
opt.data_word_size,
|
|
opt.mem_count,
|
|
opt.n_read_heads,
|
|
controller_constructor(opt.layer_sizes),
|
|
batch_first=True,
|
|
mask=opt.masked_lookup,
|
|
dealloc_content=opt.dealloc_content,
|
|
link_sharpness_control=opt.sharpness_control,
|
|
mask_min=opt.mask_min,
|
|
clip_controller=opt.clip_controller,
|
|
)
|
|
|
|
# model.load_state_dict(torch.load(model_dir, map_location="cpu")["model"])
|
|
|
|
print("data_word_size: {}".format(opt.data_word_size))
|
|
rollout_storage = RolloutStorage()
|
|
demon_state_dim = (
|
|
in_size + opt.mem_count * opt.data_word_size
|
|
) #:TODO opt.mem_count * opt.data_word_size
|
|
demon_action_dim = in_size
|
|
|
|
demon = Demon(
|
|
demon_state_dim,
|
|
demon_action_dim,
|
|
action_std,
|
|
lr,
|
|
betas,
|
|
gamma,
|
|
K_epochs,
|
|
eps_clip,
|
|
)
|
|
|
|
fnet = FNet()
|
|
fnet.init(2 * opt.mem_count * opt.data_word_size)
|
|
#fnet.load_state_dict(torch.load(model_dir, map_location="cpu")["FNet"])
|
|
|
|
znet = ZNet()
|
|
znet.init(opt.mem_count * opt.data_word_size)
|
|
#znet.load_state_dict(torch.load(model_dir, map_location="cpu")["ZNet"])
|
|
|
|
params = [
|
|
{"params": [p for n, p in model.named_parameters() if not n.endswith(".bias")]},
|
|
{
|
|
"params": [p for n, p in model.named_parameters() if n.endswith(".bias")],
|
|
"weight_decay": 0,
|
|
},
|
|
]
|
|
|
|
device = (
|
|
torch.device("cuda")
|
|
if opt.gpu != "none" and torch.cuda.is_available()
|
|
else torch.device("cpu")
|
|
)
|
|
print("DEVICE: ", device)
|
|
|
|
if isinstance(dataset, NLPTask):
|
|
embedding = torch.nn.Embedding(len(dataset.vocabulary), opt.embedding_size).to(
|
|
device
|
|
)
|
|
params.append({"params": embedding.parameters(), "weight_decay": 0})
|
|
# embedding.load_state_dict(
|
|
# torch.load(model_dir, map_location="cpu")["word_embeddings"]
|
|
# )
|
|
|
|
if opt.optimizer == "sgd":
|
|
optimizer = torch.optim.SGD(
|
|
params, lr=opt.lr, weight_decay=opt.wd, momentum=opt.momentum
|
|
)
|
|
elif opt.optimizer == "adam":
|
|
optimizer = torch.optim.Adam(params, lr=opt.lr, weight_decay=opt.wd)
|
|
elif opt.optimizer == "rmsprop":
|
|
optimizer = torch.optim.RMSprop(
|
|
params, lr=opt.lr, weight_decay=opt.wd, momentum=opt.momentum, eps=1e-10
|
|
)
|
|
else:
|
|
assert "Invalid optimizer: %s" % opt.optimizer
|
|
|
|
n_params = sum([sum([t.numel() for t in d["params"]]) for d in params])
|
|
print("Number of parameters: %d" % n_params)
|
|
|
|
model = model.to(device)
|
|
fnet = fnet.to(device)
|
|
znet = znet.to(device)
|
|
znet_optim = torch.optim.Adam(znet.parameters(), lr=0.001)
|
|
fnet_optim = torch.optim.Adam(fnet.parameters(), lr=0.001)
|
|
|
|
if embedding is not None and hasattr(embedding, "to"):
|
|
embedding = embedding.to(device)
|
|
|
|
i = 0
|
|
loss_sum = 0
|
|
|
|
loss_plot = Visdom.Plot2D(
|
|
"loss", store_interval=opt.info_interval, xlabel="iterations", ylabel="loss"
|
|
)
|
|
|
|
if curriculum is not None:
|
|
curriculum_plot = Visdom.Plot2D(
|
|
"curriculum lesson"
|
|
+ (
|
|
" (last %d)" % (curriculum.n_lessons - 1)
|
|
if curriculum.n_lessons is not None
|
|
else ""
|
|
),
|
|
xlabel="iterations",
|
|
ylabel="lesson",
|
|
)
|
|
curriculum_accuracy = Visdom.Plot2D(
|
|
"curriculum accuracy", xlabel="iterations", ylabel="accuracy"
|
|
)
|
|
|
|
saver = Saver(os.path.join(opt.name, "save"), short_interval=opt.save_interval)
|
|
saver.register("model", StateSaver(model))
|
|
saver.register("optimizer", StateSaver(optimizer))
|
|
saver.register("i", GlobalVarSaver("i"))
|
|
saver.register("loss_sum", GlobalVarSaver("loss_sum"))
|
|
saver.register("loss_plot", StateSaver(loss_plot))
|
|
saver.register("dataset", StateSaver(dataset))
|
|
if test_set:
|
|
saver.register("test_set", StateSaver(test_set))
|
|
|
|
if curriculum is not None:
|
|
saver.register("curriculum", StateSaver(curriculum))
|
|
saver.register("curriculum_plot", StateSaver(curriculum_plot))
|
|
saver.register("curriculum_accuracy", StateSaver(curriculum_accuracy))
|
|
|
|
if isinstance(dataset, NLPTask):
|
|
saver.register("word_embeddings", StateSaver(embedding))
|
|
elif embedding is not None:
|
|
saver.register("embeddings", StateSaver(embedding))
|
|
|
|
visualizers = {}
|
|
|
|
debug_schemas = {
|
|
"read_head": {"list_dim": 2},
|
|
"temporal_links/forward_dists": {"list_dim": 2},
|
|
"temporal_links/backward_dists": {"list_dim": 2},
|
|
}
|
|
|
|
def plot_debug(debug, prefix="", schema={}):
|
|
if debug is None:
|
|
return
|
|
|
|
for k, v in debug.items():
|
|
curr_name = prefix + k
|
|
if curr_name in debug_schemas:
|
|
curr_schema = schema.copy()
|
|
curr_schema.update(debug_schemas[curr_name])
|
|
else:
|
|
curr_schema = schema
|
|
|
|
if isinstance(v, dict):
|
|
plot_debug(v, curr_name + "/", curr_schema)
|
|
continue
|
|
|
|
data = v[0]
|
|
|
|
if curr_schema.get("list_dim", -1) > 0:
|
|
if data.ndim != 3:
|
|
print(
|
|
"WARNING: unknown data shape for array display: %s, tensor %s"
|
|
% (data.shape, curr_name)
|
|
)
|
|
continue
|
|
|
|
n_steps = data.shape[curr_schema["list_dim"] - 1]
|
|
if curr_name not in visualizers:
|
|
visualizers[curr_name] = [
|
|
Visdom.Heatmap(
|
|
curr_name + "_%d" % i,
|
|
dumpdir=os.path.join(opt.name, "preview")
|
|
if opt.dump_heatmaps
|
|
else None,
|
|
)
|
|
for i in range(n_steps)
|
|
]
|
|
|
|
for i in range(n_steps):
|
|
visualizers[curr_name][i].draw(
|
|
index_by_dim(data, curr_schema["list_dim"] - 1, i)
|
|
)
|
|
else:
|
|
if data.ndim != 2:
|
|
print(
|
|
"WARNING: unknown data shape for simple display: %s, tensor %s"
|
|
% (data.shape, curr_name)
|
|
)
|
|
continue
|
|
|
|
if curr_name not in visualizers:
|
|
visualizers[curr_name] = Visdom.Heatmap(
|
|
curr_name,
|
|
dumpdir=os.path.join(opt.name, "preview")
|
|
if opt.dump_heatmaps
|
|
else None,
|
|
)
|
|
|
|
visualizers[curr_name].draw(data)
|
|
|
|
def run_model(input, debug=None, demon=None, rollout_storage=None):
|
|
if isinstance(dataset, NLPTask):
|
|
input = embedding(input["input"])
|
|
else:
|
|
input = input["input"] * 2.0 - 1.0
|
|
|
|
return model(input, debug=debug, demon=demon, rollout_storage=rollout_storage)
|
|
|
|
def run_znet(mem_state):
|
|
mem_state = mem_state[:, 1:, :]
|
|
return znet(mem_state)
|
|
|
|
def run_fnet(mem_state, marginal=False):
|
|
shuffled_mem_state = None
|
|
if not marginal:
|
|
input = torch.cat((mem_state[:, :-1, :], mem_state[:, 1:, :]), dim=2)
|
|
else:
|
|
shuffled_indx = torch.randperm(mem_state.size(0)).to(
|
|
device
|
|
) # random index for shuffling the elements of batch
|
|
shuffled_mem_state = mem_state.index_select(0, shuffled_indx)
|
|
input = torch.cat(
|
|
(mem_state[:, :-1, :], shuffled_mem_state[:, 1:, :]), dim=2
|
|
)
|
|
|
|
return fnet(input), shuffled_mem_state
|
|
|
|
def multiply_grads(params, mul):
|
|
if mul == 1:
|
|
return
|
|
|
|
for pa in params:
|
|
for p in pa["params"]:
|
|
p.grad.data *= mul
|
|
|
|
def test():
|
|
if test_set is None:
|
|
return
|
|
|
|
print("TESTING...")
|
|
start_time = time.time()
|
|
t = test_set.start_test()
|
|
with torch.no_grad():
|
|
for data in tqdm(test_loader):
|
|
data = {
|
|
k: v.to(device) if torch.is_tensor(v) else v
|
|
for k, v in data.items()
|
|
}
|
|
if hasattr(dataset, "prepare"):
|
|
data = dataset.prepare(data)
|
|
|
|
net_out = run_model(data, demon=demon)
|
|
test_set.veify_result(t, data, net_out)
|
|
|
|
test_set.show_test_results(i, t)
|
|
print("Test done in %gs" % (time.time() - start_time))
|
|
|
|
if opt.test_on_start.lower() in ["on", "1", "true", "quit"]:
|
|
test()
|
|
if opt.test_on_start.lower() == "quit":
|
|
saver.write(i)
|
|
sys.exit(-1)
|
|
|
|
if opt.print_test:
|
|
model.eval()
|
|
total = 0
|
|
correct = 0
|
|
with torch.no_grad():
|
|
for data in tqdm(test_loader):
|
|
if not running:
|
|
return
|
|
|
|
data = {
|
|
k: v.to(device) if torch.is_tensor(v) else v
|
|
for k, v in data.items()
|
|
}
|
|
if hasattr(test_set, "prepare"):
|
|
data = test_set.prepare(data)
|
|
|
|
net_out = run_model(data, demon)
|
|
|
|
c, t = test_set.curriculum_measure(net_out, data["output"])
|
|
total += t
|
|
correct += c
|
|
|
|
print(
|
|
"Test result: %2.f%% (%d out of %d correct)"
|
|
% (100.0 * correct / total, correct, total)
|
|
)
|
|
model.train()
|
|
return
|
|
|
|
iter_start_time = time.time() if i % opt.info_interval == 0 else None
|
|
data_load_total_time = 0
|
|
|
|
start_i = i
|
|
|
|
if opt.dump_profile:
|
|
profiler = torch.autograd.profiler.profile(use_cuda=True)
|
|
|
|
if opt.dump_heatmaps:
|
|
dataset.set_dump_dir(os.path.join(opt.name, "preview"))
|
|
|
|
@preview()
|
|
def do_visualize(raw_data, output, pos_map, debug):
|
|
if pos_map is not None:
|
|
output = embedding.backmap_output(
|
|
output, pos_map, raw_data["output"].shape[1]
|
|
)
|
|
dataset.visualize_preview(raw_data, output)
|
|
|
|
if debug is not None:
|
|
plot_debug(debug)
|
|
|
|
preview_timer = OnceEvery(opt.preview_interval)
|
|
|
|
pos_map = None
|
|
start_iter = i
|
|
|
|
if curriculum is not None:
|
|
curriculum.init()
|
|
|
|
ma_et = 1.0
|
|
while running:
|
|
data_load_timer = time.time()
|
|
for data in data_loader:
|
|
if not running:
|
|
break
|
|
|
|
if loader_reset:
|
|
print("Loader reset requested. Resetting...")
|
|
loader_reset = False
|
|
if curriculum is not None:
|
|
curriculum.lesson_started()
|
|
break
|
|
|
|
if opt.dump_profile:
|
|
if i == start_i + 1:
|
|
print("Starting profiler")
|
|
profiler.__enter__()
|
|
elif i == start_i + 5 + 1:
|
|
print("Stopping profiler")
|
|
profiler.__exit__(None, None, None)
|
|
print("Average stats")
|
|
print(profiler.key_averages().table("cpu_time_total"))
|
|
print("Writing trace to file")
|
|
profiler.export_chrome_trace(opt.dump_profile)
|
|
print("Done.")
|
|
sys.exit(0)
|
|
else:
|
|
print("Step %d out of 5" % (i - start_i))
|
|
|
|
debug.dbg_print("-------------------------------------")
|
|
raw_data = data
|
|
|
|
data = {
|
|
k: v.to(device) if torch.is_tensor(v) else v for k, v in data.items()
|
|
}
|
|
if hasattr(dataset, "prepare"):
|
|
data = dataset.prepare(data)
|
|
|
|
data_load_total_time += time.time() - data_load_timer
|
|
|
|
need_preview = preview_timer()
|
|
debug_data = {} if opt.debug and need_preview else None
|
|
|
|
optimizer.zero_grad()
|
|
# demon_optim.zero_grad()
|
|
# znet_optim.zero_grad()
|
|
# fnet_optim.zero_grad()
|
|
|
|
if opt.n_subbatch == "auto":
|
|
n_subbatch = math.ceil(
|
|
data["input"].numel() / opt.max_input_count_per_batch
|
|
)
|
|
else:
|
|
n_subbatch = int(opt.n_subbatch)
|
|
|
|
real_batch = max(math.floor(opt.batch_size / n_subbatch), 1)
|
|
n_subbatch = math.ceil(opt.batch_size / real_batch)
|
|
remaning_batch = opt.batch_size % real_batch
|
|
|
|
for subbatch in range(n_subbatch):
|
|
if not running:
|
|
break
|
|
input = data["input"]
|
|
target = data["output"]
|
|
|
|
if n_subbatch != 1:
|
|
input = input[subbatch * real_batch : (subbatch + 1) * real_batch]
|
|
target = target[subbatch * real_batch : (subbatch + 1) * real_batch]
|
|
|
|
f2 = data.copy()
|
|
f2["input"] = input
|
|
|
|
# Demon modifies the memory before DNC model
|
|
output = run_model(
|
|
f2,
|
|
debug=debug_data if subbatch == n_subbatch - 1 else None,
|
|
demon=demon,
|
|
rollout_storage=rollout_storage,
|
|
)
|
|
l = dataset.loss(output, target)
|
|
l.backward()
|
|
|
|
mem_state = torch.stack(model.mem_state, dim=1)
|
|
t, _ = run_fnet(mem_state.detach())
|
|
z = run_znet(mem_state.detach())
|
|
|
|
et, shuffled_mem = run_fnet(mem_state.detach(), marginal=True)
|
|
et = torch.exp(et)
|
|
mi_lb = t - (torch.mean(et) / z + torch.log(z) - 1)
|
|
|
|
demon_rewards = mi_lb
|
|
info_loss = -mi_lb.mean()
|
|
|
|
info_loss.backward()
|
|
|
|
fnet_optim.step()
|
|
fnet_optim.zero_grad()
|
|
|
|
znet_optim.step()
|
|
znet_optim.zero_grad()
|
|
|
|
del model.mem_state[:] # reset the mem state
|
|
|
|
for j in range(0, demon_rewards.size(1)):
|
|
rollout_storage.rewards.append(demon_rewards[:, j].detach())
|
|
|
|
# update if its time
|
|
if i % 1 == 0: # TODO demon_update_timestep = C
|
|
demon_loss = demon.update(rollout_storage)
|
|
rollout_storage.clear_storage()
|
|
|
|
debug.nan_check(l, force=True)
|
|
|
|
if curriculum is not None:
|
|
curriculum.update(*dataset.curriculum_measure(output, target))
|
|
|
|
if remaning_batch != 0 and subbatch == n_subbatch - 2:
|
|
multiply_grads(params, real_batch / remaning_batch)
|
|
|
|
if n_subbatch != 1:
|
|
if remaning_batch == 0:
|
|
multiply_grads(params, 1 / n_subbatch)
|
|
else:
|
|
multiply_grads(params, remaning_batch / opt.batch_size)
|
|
|
|
for p in params:
|
|
torch.nn.utils.clip_grad_norm_(p["params"], opt.grad_clip)
|
|
|
|
optimizer.step()
|
|
|
|
i += 1
|
|
|
|
curr_loss = l.data.item()
|
|
loss_plot.add_point(i, curr_loss)
|
|
|
|
# writer.add_scalar("associative-recall-loss/dnc-md", curr_loss, i)
|
|
# writer.add_scalar("associative-recall-mutual_info/dnc-md", info_loss, i)
|
|
# writer.add_scalar(
|
|
# "associative-recall-demon_loss/dnc-md", demon_loss.mean(), i
|
|
# )
|
|
|
|
loss_sum += curr_loss
|
|
|
|
if i % opt.info_interval == 0:
|
|
tim = time.time()
|
|
loss_avg = loss_sum / opt.info_interval
|
|
|
|
if curriculum is not None:
|
|
curriculum_accuracy.add_point(i, curriculum.get_accuracy())
|
|
curriculum_plot.add_point(i, curriculum.step)
|
|
|
|
message = "Iteration %d, loss: %.4f" % (i, loss_avg)
|
|
if iter_start_time is not None:
|
|
message += (
|
|
" (%.2f ms/iter, load time %.2g ms/iter, visport: %s)"
|
|
% (
|
|
(tim - iter_start_time) / opt.info_interval * 1000.0,
|
|
data_load_total_time / opt.info_interval * 1000.0,
|
|
Visdom.port,
|
|
)
|
|
)
|
|
print(message)
|
|
iter_start_time = tim
|
|
loss_sum = 0
|
|
data_load_total_time = 0
|
|
|
|
debug.dbg_print("Iteration %d, loss %g" % (i, curr_loss))
|
|
|
|
if need_preview:
|
|
do_visualize(raw_data, output, pos_map, debug_data)
|
|
|
|
if i % opt.test_interval == 0:
|
|
test()
|
|
|
|
saver.tick(i)
|
|
|
|
if opt.demo and opt.exit_after is None:
|
|
running = False
|
|
input("Press enter to quit.")
|
|
|
|
if opt.exit_after is not None and (i - start_iter) >= opt.exit_after:
|
|
running = False
|
|
|
|
data_load_timer = time.time()
|
|
|
|
|
|
if __name__ == "__main__":
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#writer = SummaryWriter()
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global running
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running = True
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def signal_handler(signal, frame):
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global running
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print("You pressed Ctrl+C!")
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running = False
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signal.signal(signal.SIGINT, signal_handler)
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main()
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