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dnc/main.py
Robert Csordas 2a2b6bfd78 Initial commit
2018-11-15 20:31:23 +01:00

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#!/usr/bin/env python3
#
# Copyright 2017 Robert Csordas. All Rights Reserved.
#
# 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.
#
# ==============================================================================
import functools
import os
import torch.utils.data
import Utils.Debug as debug
from Dataset.Bitmap.AssociativeRecall import AssociativeRecall
from Dataset.Bitmap.BitmapTaskRepeater import BitmapTaskRepeater
from Dataset.Bitmap.KeyValue import KeyValue
from Dataset.Bitmap.CopyTask import CopyData
from Dataset.Bitmap.KeyValue2Way import KeyValue2Way
from Dataset.NLP.bAbi import bAbiDataset
from Models.DNC import DNC, LSTMController, FeedforwardController
from Utils import Visdom
from Utils.ArgumentParser import ArgumentParser
from Utils.Index import index_by_dim
from Utils.Saver import Saver, GlobalVarSaver, StateSaver
from Utils.Collate import MetaCollate
from Utils import gpu_allocator
from Dataset.NLP.NLPTask import NLPTask
from tqdm import tqdm
from Visualize.preview import preview
from Utils.timer import OnceEvery
from Utils import Seed
import time
import sys
import signal
import math
from Utils import Profile
Profile.ENABLED=False
def main():
global i
global loss_sum
global running
parser = ArgumentParser()
parser.add_argument("-bit_w", type=int, default=8, help="Bit vector length for copy task")
parser.add_argument("-block_w", type=int, default=3, help="Block width to associative recall task")
parser.add_argument("-len", type=str, default="4", help="Sequence length for copy task", parser=lambda x: [int(a) for a in x.split("-")])
parser.add_argument("-repeat", type=str, default="1", help="Sequence length for copy task", parser=lambda x: [int(a) for a in x.split("-")])
parser.add_argument("-batch_size", type=int, default=16, help="Sequence length for copy task")
parser.add_argument("-n_subbatch", type=str, default="auto", help="Average this much forward passes to a backward pass")
parser.add_argument("-max_input_count_per_batch", type=int, default=6000, help="Max batch_size*len that can fit into memory")
parser.add_argument("-lr", type=float, default=0.0001, help="Learning rate")
parser.add_argument("-wd", type=float, default=1e-5, help="Weight decay")
parser.add_argument("-optimizer", type=str, default="rmsprop", help="Optimizer algorithm")
parser.add_argument("-name", type=str, help="Save training to this directory")
parser.add_argument("-preview_interval", type=int, default=10, help="Show preview every nth iteration")
parser.add_argument("-info_interval", type=int, default=10, help="Show info every nth iteration")
parser.add_argument("-save_interval", type=int, default=500, help="Save network every nth iteration")
parser.add_argument("-masked_lookup", type=bool, default=1, help="Enable masking in content lookups")
parser.add_argument("-visport", type=int, default=-1, help="Port to run Visdom server on. -1 to disable")
parser.add_argument("-gpu", default="auto", type=str, help="Run on this GPU.")
parser.add_argument("-debug", type=bool, default=1, help="Enable debugging")
parser.add_argument("-task", type=str, default="copy", help="Task to learn")
parser.add_argument("-mem_count", type=int, default=16, help="Number of memory cells")
parser.add_argument("-data_word_size", type=int, default=128, help="Memory word size")
parser.add_argument("-n_read_heads", type=int, default=1, help="Number of read heads")
parser.add_argument("-layer_sizes", type=str, default="256", help="Controller layer sizes. Separate with ,. For example 512,256,256", parser=lambda x: [int(y) for y in x.split(",") if y])
parser.add_argument("-debug_log", type=bool, default=0, help="Enable debug log")
parser.add_argument("-controller_type", type=str, default="lstm", help="Controller type: lstm or linear")
parser.add_argument("-lstm_use_all_outputs", type=bool, default=1, help="Use all LSTM outputs as controller output vs use only the last layer")
parser.add_argument("-momentum", type=float, default=0.9, help="Momentum for optimizer")
parser.add_argument("-embedding_size", type=int, default=256, help="Size of word embedding for NLP tasks")
parser.add_argument("-test_interval", type=int, default=10000, help="Run test in this interval")
parser.add_argument("-dealloc_content", type=bool, default=1, help="Deallocate memory content, unlike DNC, which leaves it unchanged, just decreases the usage counter, causing problems with lookup")
parser.add_argument("-sharpness_control", type=bool, default=1, help="Distribution sharpness control for forward and backward links")
parser.add_argument("-think_steps", type=int, default=0, help="Iddle steps before requiring the answer (for bAbi)")
parser.add_argument("-dump_profile", type=str, save=False)
parser.add_argument("-test_on_start", default="0", save=False)
parser.add_argument("-dump_heatmaps", default=False, save=False)
parser.add_argument("-test_batch_size", default=16)
parser.add_argument("-mask_min", default=0.0)
parser.add_argument("-load", type=str, save=False)
parser.add_argument("-dataset_path", type=str, default="none", parser=ArgumentParser.str_or_none(), help="Specify babi path manually")
parser.add_argument("-babi_train_tasks", type=str, default="none", parser=ArgumentParser.list_or_none(type=str), help="babi task list to use for training")
parser.add_argument("-babi_test_tasks", type=str, default="none", parser=ArgumentParser.list_or_none(type=str), help="babi task list to use for testing")
parser.add_argument("-babi_train_sets", type=str, default="train", parser=ArgumentParser.list_or_none(type=str), help="babi train sets to use")
parser.add_argument("-babi_test_sets", type=str, default="test", parser=ArgumentParser.list_or_none(type=str), help="babi test sets to use")
parser.add_argument("-noargsave", type=bool, default=False, help="Do not save modified arguments", save=False)
parser.add_argument("-demo", type=bool, default=False, help="Do a single step with fixed seed", save=False)
parser.add_argument("-exit_after", type=int, help="Exit after this amount of steps. Useful for debugging.", save=False)
parser.add_argument("-grad_clip", type=float, default=10.0, help="Max gradient norm")
parser.add_argument("-clip_controller", type=float, default=20.0, help="Max gradient norm")
parser.add_argument("-print_test", default=False, save=False)
parser.add_profile([
ArgumentParser.Profile("babi", {
"preview_interval": 10,
"save_interval": 500,
"task": "babi",
"mem_count": 256,
"data_word_size": 64,
"n_read_heads": 4,
"layer_sizes": "256",
"controller_type": "lstm",
"lstm_use_all_outputs": True,
"momentum": 0.9,
"embedding_size": 128,
"test_interval": 5000,
"think_steps": 3,
"batch_size": 2
}, include=["dnc-msd"]),
ArgumentParser.Profile("repeat_copy", {
"bit_w": 8,
"repeat": "1-8",
"len": "2-14",
"task": "copy",
"think_steps": 1,
"preview_interval": 10,
"info_interval": 10,
"save_interval": 100,
"data_word_size": 16,
"layer_sizes": "32",
"n_subbatch": 1,
"controller_type": "lstm",
}),
ArgumentParser.Profile("repeat_copy_simple", {
"repeat": "1-3",
}, include="repeat_copy"),
ArgumentParser.Profile("dnc", {
"masked_lookup": False,
"sharpness_control": False,
"dealloc_content": False
}),
ArgumentParser.Profile("dnc-m", {
"masked_lookup": True,
"sharpness_control": False,
"dealloc_content": False
}),
ArgumentParser.Profile("dnc-s", {
"masked_lookup": False,
"sharpness_control": True,
"dealloc_content": False
}),
ArgumentParser.Profile("dnc-d", {
"masked_lookup": False,
"sharpness_control": False,
"dealloc_content": True
}),
ArgumentParser.Profile("dnc-md", {
"masked_lookup": True,
"sharpness_control": False,
"dealloc_content": True
}),
ArgumentParser.Profile("dnc-ms", {
"masked_lookup": True,
"sharpness_control": True,
"dealloc_content": False
}),
ArgumentParser.Profile("dnc-sd", {
"masked_lookup": False,
"sharpness_control": True,
"dealloc_content": True
}),
ArgumentParser.Profile("dnc-msd", {
"masked_lookup": True,
"sharpness_control": True,
"dealloc_content": True
}),
ArgumentParser.Profile("keyvalue", {
"repeat": "1",
"len": "2-16",
"mem_count": 16,
"task": "keyvalue",
"think_steps": 1,
"preview_interval": 10,
"info_interval": 10,
"data_word_size": 32,
"bit_w": 12,
"save_interval": 1000,
"layer_sizes": "32"
}),
ArgumentParser.Profile("keyvalue2way", {
"task": "keyvalue2way",
}, include="keyvalue"),
ArgumentParser.Profile("associative_recall",{
"task": "recall",
"bit_w": 8,
"len": "2-16",
"mem_count": 64,
"data_word_size": 32,
"n_read_heads": 1,
"layer_sizes": "128",
"controller_type": "lstm",
"lstm_use_all_outputs": 1,
"think_steps": 1,
"mask_min": 0.1,
"info_interval": 10,
"save_interval": 1000,
"preview_interval": 10,
"n_subbatch": 1,
})
])
opt = parser.parse()
assert opt.name is not None, "Training dir (-name parameter) not given"
opt = parser.sync(os.path.join(opt.name, "args.json"), save=not opt.noargsave)
if opt.demo:
Seed.fix()
os.makedirs(os.path.join(opt.name,"save"), exist_ok=True)
os.makedirs(os.path.join(opt.name,"preview"), exist_ok=True)
gpu_allocator.use_gpu(opt.gpu)
debug.enableDebug = opt.debug_log
if opt.visport>0:
Visdom.start(opt.visport)
Visdom.Text("Name").set(opt.name)
class LengthHackSampler:
def __init__(self, batch_size, length):
self.length = length
self.batch_size = batch_size
def __iter__(self):
while True:
len = self.length() if callable(self.length) else self.length
yield [len] * self.batch_size
def __len__(self):
return 0x7FFFFFFF
embedding = None
test_set = None
curriculum = None
loader_reset = False
if opt.task=="copy":
dataset = CopyData(bit_w=opt.bit_w)
in_size = opt.bit_w + 1
out_size = in_size
elif opt.task=="recall":
dataset = AssociativeRecall(bit_w=opt.bit_w, block_w=opt.block_w)
in_size = opt.bit_w + 2
out_size = in_size
elif opt.task=="keyvalue":
assert opt.bit_w % 2==0, "Key-value datasets works only with even bit_w"
dataset = KeyValue(bit_w=opt.bit_w)
in_size = opt.bit_w + 1
out_size = opt.bit_w//2
elif opt.task=="keyvalue2way":
assert opt.bit_w % 2==0, "Key-value datasets works only with even bit_w"
dataset = KeyValue2Way(bit_w=opt.bit_w)
in_size = opt.bit_w + 2
out_size = opt.bit_w//2
elif opt.task=="babi":
dataset = bAbiDataset(think_steps=opt.think_steps, dir_name=opt.dataset_path)
test_set = bAbiDataset(think_steps=opt.think_steps, dir_name=opt.dataset_path, name="test")
dataset.use(opt.babi_train_tasks, opt.babi_train_sets)
in_size = opt.embedding_size
print("bAbi: loaded total of %d sequences." % len(dataset))
test_set.use(opt.babi_test_tasks, opt.babi_test_sets)
out_size = len(dataset.vocabulary)
print("bAbi: using %d sequences for training, %d for testing" % (len(dataset), len(test_set)))
else:
assert False, "Invalid task: %s" % opt.task
if opt.task in ["babi"]:
data_loader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, num_workers=4, pin_memory=True, shuffle=True, collate_fn=MetaCollate())
test_loader = torch.utils.data.DataLoader(test_set, batch_size=opt.test_batch_size, num_workers=opt.test_batch_size, pin_memory=True, shuffle=False, collate_fn=MetaCollate()) if test_set is not None else None
else:
dataset = BitmapTaskRepeater(dataset)
data_loader = torch.utils.data.DataLoader(dataset, batch_sampler=LengthHackSampler(opt.batch_size, BitmapTaskRepeater.key_sampler(opt.len, opt.repeat)), num_workers=1, pin_memory=True)
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
model = DNC(in_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)
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" 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})
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)
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))
if not saver.load(opt.load):
model.reset_parameters()
if embedding is not None:
embedding.reset_parameters()
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):
if isinstance(dataset, NLPTask):
input = embedding(input["input"])
else:
input = input["input"] * 2.0 - 1.0
return model(input, debug=debug)
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)
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)
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()
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()
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
output = run_model(f2, debug=debug_data if subbatch==n_subbatch-1 else None)
l = dataset.loss(output, target)
debug.nan_check(l, force=True)
l.backward()
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)
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__":
global running
running = True
def signal_handler(signal, frame):
global running
print('You pressed Ctrl+C!')
running = False
signal.signal(signal.SIGINT, signal_handler)
main()
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