Hello PyTorch users,
I have been trying to solve Exercise 3, Chapter 8.5 from Dive into Deep Learning book. I got stuck on that exercise and I was hoping you can help me. I will explain the exercise below.
Exercise goes as follows:
Modify the prediction function such as to use sampling rather than picking the most likely next character.
What happens?
I tried to do that. Below is the original code from the book, which works as expected (the training goes well). I separated every code cell in its code block.
%matplotlib inline
import math
import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
def get_params(vocab_size, num_hiddens, device):
num_inputs = num_outputs = vocab_size
def normal(shape):
return torch.randn(size=shape, device=device) * 0.01
# Hidden layer parameters
W_xh = normal((num_inputs, num_hiddens))
W_hh = normal((num_hiddens, num_hiddens))
b_h = torch.zeros(num_hiddens, device=device)
# Output layer parameters
W_hq = normal((num_hiddens, num_outputs))
b_q = torch.zeros(num_outputs, device=device)
# Attach gradients
params = [W_xh, W_hh, b_h, W_hq, b_q]
for param in params:
param.requires_grad_(True)
return params
def init_rnn_state(batch_size, num_hiddens, device):
return (torch.zeros((batch_size, num_hiddens), device=device),)
def rnn(inputs, state, params):
# Here `inputs` shape: (`num_steps`, `batch_size`, `vocab_size`)
W_xh, W_hh, b_h, W_hq, b_q = params
H, = state
outputs = []
# Shape of `X`: (`batch_size`, `vocab_size`)
for X in inputs:
H = torch.tanh(torch.mm(X, W_xh) + torch.mm(H, W_hh) + b_h)
Y = torch.mm(H, W_hq) + b_q
outputs.append(Y)
return torch.cat(outputs, dim=0), (H,)
class RNNModelScratch: #@save
"""A RNN Model implemented from scratch."""
def __init__(self, vocab_size, num_hiddens, device, get_params,
init_state, forward_fn):
self.vocab_size, self.num_hiddens = vocab_size, num_hiddens
self.params = get_params(vocab_size, num_hiddens, device)
self.init_state, self.forward_fn = init_state, forward_fn
def __call__(self, X, state):
X = F.one_hot(X.T, self.vocab_size).type(torch.float32)
return self.forward_fn(X, state, self.params)
def begin_state(self, batch_size, device):
return self.init_state(batch_size, self.num_hiddens, device)
def predict_ch8(prefix, num_preds, net, vocab, device): #@save
"""Generate new characters following the `prefix`."""
state = net.begin_state(batch_size=1, device=device)
outputs = [vocab[prefix[0]]]
get_input = lambda: torch.tensor([outputs[-1]], device=device).reshape(
(1, 1))
for y in prefix[1:]: # Warm-up period
_, state = net(get_input(), state)
outputs.append(vocab[y])
for _ in range(num_preds): # Predict `num_preds` steps
y, state = net(get_input(), state)
print("int(y.argmax(dim=1).reshape(1)):")
print(int(y.argmax(dim=1).reshape(1)))
outputs.append(int(y.argmax(dim=1).reshape(1)))
return ''.join([vocab.idx_to_token[i] for i in outputs])
Now take a look at the next code cell and its output right after it (again, this is the code from the book):
predict_ch8('time traveller ', 10, net, vocab, d2l.try_gpu())
int(y.argmax(dim=1).reshape(1)):
16
int(y.argmax(dim=1).reshape(1)):
9
int(y.argmax(dim=1).reshape(1)):
27
int(y.argmax(dim=1).reshape(1)):
7
int(y.argmax(dim=1).reshape(1)):
18
int(y.argmax(dim=1).reshape(1)):
20
int(y.argmax(dim=1).reshape(1)):
12
int(y.argmax(dim=1).reshape(1)):
1
int(y.argmax(dim=1).reshape(1)):
16
int(y.argmax(dim=1).reshape(1)):
9
'time traveller autovkurho'
The non-sensical output is expected, but note that there are different letters present in the output. Now follows the rest of the code from the book:
def grad_clipping(net, theta): #@save
"""Clip the gradient."""
if isinstance(net, nn.Module):
params = [p for p in net.parameters() if p.requires_grad]
else:
params = net.params
norm = torch.sqrt(sum(torch.sum((p.grad**2)) for p in params))
if norm > theta:
for param in params:
param.grad[:] *= theta / norm
#@save
def train_epoch_ch8(net, train_iter, loss, updater, device, use_random_iter):
"""Train a net within one epoch (defined in Chapter 8)."""
state, timer = None, d2l.Timer()
metric = d2l.Accumulator(2) # Sum of training loss, no. of tokens
for X, Y in train_iter:
if state is None or use_random_iter:
# Initialize `state` when either it is the first iteration or
# using random sampling
state = net.begin_state(batch_size=X.shape[0], device=device)
else:
if isinstance(net, nn.Module) and not isinstance(state, tuple):
# `state` is a tensor for `nn.GRU`
state.detach_()
else:
# `state` is a tuple of tensors for `nn.LSTM` and
# for our custom scratch implementation
for s in state:
s.detach_()
y = Y.T.reshape(-1)
X, y = X.to(device), y.to(device)
y_hat, state = net(X, state)
l = loss(y_hat, y.long()).mean()
if isinstance(updater, torch.optim.Optimizer):
updater.zero_grad()
l.backward()
grad_clipping(net, 1)
updater.step()
else:
l.backward()
grad_clipping(net, 1)
# Since the `mean` function has been invoked
updater(batch_size=1)
metric.add(l * y.numel(), y.numel())
return math.exp(metric[0] / metric[1]), metric[1] / timer.stop()
#@save
def train_ch8(net, train_iter, vocab, lr, num_epochs, device,
use_random_iter=False):
"""Train a model (defined in Chapter 8)."""
loss = nn.CrossEntropyLoss()
animator = d2l.Animator(xlabel='epoch', ylabel='perplexity',
legend=['train'], xlim=[10, num_epochs])
# Initialize
if isinstance(net, nn.Module):
updater = torch.optim.SGD(net.parameters(), lr)
else:
updater = lambda batch_size: d2l.sgd(net.params, lr, batch_size)
predict = lambda prefix: predict_ch8(prefix, 50, net, vocab, device)
# Train and predict
for epoch in range(num_epochs):
ppl, speed = train_epoch_ch8(net, train_iter, loss, updater, device,
use_random_iter)
if (epoch + 1) % 10 == 0:
print(predict('time traveller'))
animator.add(epoch + 1, [ppl])
print(f'perplexity {ppl:.1f}, {speed:.1f} tokens/sec on {str(device)}')
print(predict('time traveller'))
print(predict('traveller'))
What follows is the code I modified:
import numpy as np
def predict_ch8(prefix, num_preds, net, vocab, device): #@save
"""Generate new characters following the `prefix`."""
state = net.begin_state(batch_size=1, device=device)
outputs = [vocab[prefix[0]]]
get_input = lambda: torch.tensor([outputs[-1]], device=device).reshape(
(1, 1))
for y in prefix[1:]: # Warm-up period
_, state = net(get_input(), state)
outputs.append(vocab[y])
for _ in range(num_preds): # Predict `num_preds` steps
y, state = net(get_input(), state)
print("int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):")
print(int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)))
outputs.append(int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1))) # here is the modification
return ''.join([vocab.idx_to_token[i] for i in outputs])
Now look at the following code cells and their outputs:
predict_ch8('time traveller ', 10, net, vocab, d2l.try_gpu())
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
'time traveller <unk><unk><unk><unk><unk><unk><unk><unk><unk><unk>'
Note how my outputs are all 0. I don’t know why is that.
When I try to train the model, I get the following:
num_epochs, lr = 500, 1
train_ch8(net, train_iter, vocab, lr, num_epochs, d2l.try_gpu())
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
0
int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):
/pytorch/aten/src/ATen/native/cuda/ScatterGatherKernel.cu:312: operator(): block: [0,0,0], thread: [0,0,0] Assertion `idx_dim >= 0 && idx_dim < index_size && "index out of bounds"` failed.
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
/tmp/ipykernel_28343/3385443296.py in <module>
1 num_epochs, lr = 500, 1
----> 2 train_ch8(net, train_iter, vocab, lr, num_epochs, d2l.try_gpu())
/tmp/ipykernel_28343/636485725.py in train_ch8(net, train_iter, vocab, lr, num_epochs, device, use_random_iter)
17 use_random_iter)
18 if (epoch + 1) % 10 == 0:
---> 19 print(predict('time traveller'))
20 animator.add(epoch + 1, [ppl])
21 print(f'perplexity {ppl:.1f}, {speed:.1f} tokens/sec on {str(device)}')
/tmp/ipykernel_28343/636485725.py in <lambda>(prefix)
11 else:
12 updater = lambda batch_size: d2l.sgd(net.params, lr, batch_size)
---> 13 predict = lambda prefix: predict_ch8(prefix, 50, net, vocab, device)
14 # Train and predict
15 for epoch in range(num_epochs):
/tmp/ipykernel_28343/2030242475.py in predict_ch8(prefix, num_preds, net, vocab, device)
13 y, state = net(get_input(), state)
14 print("int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)):")
---> 15 print(int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1)))
16 outputs.append(int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1))) # here is the modification
17 return ''.join([vocab.idx_to_token[i] for i in outputs])
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
I tried re-defning the train_epoch_ch8 and train_ch8 functions after the redefining the predict_ch8 function. The only difference is that besides the 0’s I sometimes get -2’s as outputs.
Can someone please tell me what is going on here? All I changed was the line in predict_ch8 that was:
outputs.append(int(y.argmax(dim=1).reshape(1)))
to:
outputs.append(int(np.random.choice(y.cpu().detach().numpy().ravel(), size=1).reshape(1))) # here is the modification
I don’t know what went wrong. The entire .cpu().detach().numpy().ravel() chain is due to getting errors that I have to move the tensor to the CPU.
Can someone tell me what is going on here?
Thank you in advance!