After noticing that my custom implementation of first order MAML might be wrong I decided to google how the official way to do first order MAML is. I found a useful gitissue that suggests to stop tracking the higher order gradients. Which makes complete sense to me. No more derivatives over the derivatives. But when I tried setting it to false (so that no higher derivatives are tracked) I got that there was no more training of my models and the .grad fiedl was None. Which is obviously wrong.
Is this a bug in higher or what is going on?
To reproduce run the official MAML example higher has but slightly modified here. The main code is this though:
#!/usr/bin/env python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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.
"""
This example shows how to use higher to do Model Agnostic Meta Learning (MAML)
for few-shot Omniglot classification.
For more details see the original MAML paper:
https://arxiv.org/abs/1703.03400
This code has been modified from Jackie Loong's PyTorch MAML implementation:
https://github.com/dragen1860/MAML-Pytorch/blob/master/omniglot_train.py
Our MAML++ fork and experiments are available at:
https://github.com/bamos/HowToTrainYourMAMLPytorch
"""
import argparse
import time
import typing
import pandas as pd
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('bmh')
import torch
from torch import nn
import torch.nn.functional as F
import torch.optim as optim
import higher
from support.omniglot_loaders import OmniglotNShot
def main():
argparser = argparse.ArgumentParser()
argparser.add_argument('--n_way', type=int, help='n way', default=5)
argparser.add_argument(
'--k_spt', type=int, help='k shot for support set', default=5)
argparser.add_argument(
'--k_qry', type=int, help='k shot for query set', default=15)
argparser.add_argument(
'--task_num',
type=int,
help='meta batch size, namely task num',
default=32)
argparser.add_argument('--seed', type=int, help='random seed', default=1)
args = argparser.parse_args()
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
# Set up the Omniglot loader.
# device = torch.device('cuda')
# from uutils.torch_uu import get_device
# device = get_device()
device = torch.device(f"cuda:{gpu_idx}" if torch.cuda.is_available() else "cpu")
db = OmniglotNShot(
'/tmp/omniglot-data',
batchsz=args.task_num,
n_way=args.n_way,
k_shot=args.k_spt,
k_query=args.k_qry,
imgsz=28,
device=device,
)
# Create a vanilla PyTorch neural network that will be
# automatically monkey-patched by higher later.
# Before higher, models could *not* be created like this
# and the parameters needed to be manually updated and copied
# for the updates.
net = nn.Sequential(
nn.Conv2d(1, 64, 3),
nn.BatchNorm2d(64, momentum=1, affine=True),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2),
nn.Conv2d(64, 64, 3),
nn.BatchNorm2d(64, momentum=1, affine=True),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2),
nn.Conv2d(64, 64, 3),
nn.BatchNorm2d(64, momentum=1, affine=True),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2),
Flatten(),
nn.Linear(64, args.n_way)).to(device)
# We will use Adam to (meta-)optimize the initial parameters
# to be adapted.
meta_opt = optim.Adam(net.parameters(), lr=1e-3)
log = []
for epoch in range(100):
train(db, net, device, meta_opt, epoch, log)
test(db, net, device, epoch, log)
# plot(log)
def train(db, net, device, meta_opt, epoch, log):
net.train()
n_train_iter = db.x_train.shape[0] // db.batchsz
for batch_idx in range(n_train_iter):
start_time = time.time()
# Sample a batch of support and query images and labels.
x_spt, y_spt, x_qry, y_qry = db.next()
task_num, setsz, c_, h, w = x_spt.size()
querysz = x_qry.size(1)
# TODO: Maybe pull this out into a separate module so it
# doesn't have to be duplicated between `train` and `test`?
# Initialize the inner optimizer to adapt the parameters to
# the support set.
n_inner_iter = 5
inner_opt = torch.optim.SGD(net.parameters(), lr=1e-1)
qry_losses = []
qry_accs = []
meta_opt.zero_grad()
for i in range(task_num):
with higher.innerloop_ctx(
net, inner_opt, copy_initial_weights=False,
# track_higher_grads=True,
track_higher_grads=False,
) as (fnet, diffopt):
# Optimize the likelihood of the support set by taking
# gradient steps w.r.t. the model's parameters.
# This adapts the model's meta-parameters to the task.
# higher is able to automatically keep copies of
# your network's parameters as they are being updated.
for _ in range(n_inner_iter):
spt_logits = fnet(x_spt[i])
spt_loss = F.cross_entropy(spt_logits, y_spt[i])
diffopt.step(spt_loss)
# The final set of adapted parameters will induce some
# final loss and accuracy on the query dataset.
# These will be used to update the model's meta-parameters.
qry_logits = fnet(x_qry[i])
qry_loss = F.cross_entropy(qry_logits, y_qry[i])
qry_losses.append(qry_loss.detach())
qry_acc = (qry_logits.argmax(
dim=1) == y_qry[i]).sum().item() / querysz
qry_accs.append(qry_acc)
# Update the model's meta-parameters to optimize the query
# losses across all of the tasks sampled in this batch.
# This unrolls through the gradient steps.
qry_loss.backward()
assert meta_opt.param_groups[0]['params'][0].grad is not None
meta_opt.step()
qry_losses = sum(qry_losses) / task_num
qry_accs = 100. * sum(qry_accs) / task_num
i = epoch + float(batch_idx) / n_train_iter
iter_time = time.time() - start_time
if batch_idx % 4 == 0:
print(
f'[Epoch {i:.2f}] Train Loss: {qry_losses:.2f} | Acc: {qry_accs:.2f} | Time: {iter_time:.2f}'
)
log.append({
'epoch': i,
'loss': qry_losses,
'acc': qry_accs,
'mode': 'train',
'time': time.time(),
})
def test(db, net, device, epoch, log):
# Crucially in our testing procedure here, we do *not* fine-tune
# the model during testing for simplicity.
# Most research papers using MAML for this task do an extra
# stage of fine-tuning here that should be added if you are
# adapting this code for research.
net.train()
n_test_iter = db.x_test.shape[0] // db.batchsz
qry_losses = []
qry_accs = []
for batch_idx in range(n_test_iter):
x_spt, y_spt, x_qry, y_qry = db.next('test')
task_num, setsz, c_, h, w = x_spt.size()
querysz = x_qry.size(1)
# doesn't have to be duplicated between `train` and `test`?
n_inner_iter = 5
inner_opt = torch.optim.SGD(net.parameters(), lr=1e-1)
for i in range(task_num):
with higher.innerloop_ctx(net, inner_opt, track_higher_grads=False) as (fnet, diffopt):
# Optimize the likelihood of the support set by taking
# gradient steps w.r.t. the model's parameters.
# This adapts the model's meta-parameters to the task.
for _ in range(n_inner_iter):
spt_logits = fnet(x_spt[i])
spt_loss = F.cross_entropy(spt_logits, y_spt[i])
diffopt.step(spt_loss)
# The query loss and acc induced by these parameters.
qry_logits = fnet(x_qry[i]).detach()
qry_loss = F.cross_entropy(
qry_logits, y_qry[i], reduction='none')
qry_losses.append(qry_loss.detach())
qry_accs.append(
(qry_logits.argmax(dim=1) == y_qry[i]).detach())
qry_losses = torch.cat(qry_losses).mean().item()
qry_accs = 100. * torch.cat(qry_accs).float().mean().item()
print(
f'[Epoch {epoch + 1:.2f}] Test Loss: {qry_losses:.2f} | Acc: {qry_accs:.2f}'
)
log.append({
'epoch': epoch + 1,
'loss': qry_losses,
'acc': qry_accs,
'mode': 'test',
'time': time.time(),
})
def plot(log):
# Generally you should pull your plotting code out of your training
# script but we are doing it here for brevity.
df = pd.DataFrame(log)
fig, ax = plt.subplots(figsize=(6, 4))
train_df = df[df['mode'] == 'train']
test_df = df[df['mode'] == 'test']
ax.plot(train_df['epoch'], train_df['acc'], label='Train')
ax.plot(test_df['epoch'], test_df['acc'], label='Test')
ax.set_xlabel('Epoch')
ax.set_ylabel('Accuracy')
ax.set_ylim(70, 100)
fig.legend(ncol=2, loc='lower right')
fig.tight_layout()
fname = 'maml-accs.png'
print(f'--- Plotting accuracy to {fname}')
fig.savefig(fname)
plt.close(fig)
# Won't need this after this PR is merged in:
# https://github.com/pytorch/pytorch/pull/22245
class Flatten(nn.Module):
def forward(self, input):
return input.view(input.size(0), -1)
if __name__ == '__main__':
main()
Note:
I asked a similar question here Would making the gradient "data" by detaching them implement first order MAML using PyTorch's higher library? but that one is slightly different. It is asking about a custom implementation that detaches the gradients directly to make them "data". This one is asking why the setting track_higher_grads=False screws up the population of gradients -- which as I understand should not.
related:
Explain the reasoning of why the solution here works i.e. why
track_higher_grads = True
...
diffopt.step(inner_loss, grad_callback=lambda grads: [g.detach() for g in grads])
computed FO maml but:
new_params = params[:]
for group, mapping in zip(self.param_groups, self._group_to_param_list):
for p, index in zip(group['params'], mapping):
if self._track_higher_grads:
new_params[index] = p
else:
new_params[index] = p.detach().requires_grad_() # LIKELY THIS LINE!!!
does not allow FO to work properly and sets .grads to None (not populate the grad field). The assignment with p.detach().requires_grad_() honestly looks the same to me. This .requires_grad_() evens seems extra "safe".
The reason why track_higher_grads=False doesn't actually work is that it detaches the gradients of the post-adaptation parameters rather than just the gradients (see here). So you get no gradient at all from your outer loop loss. What you really want is just to detach the gradients on just the inner loop-computed gradients, but leave the (otherwise trivial) computation graph between model initialization and adapted parameters intact.