Hello,
I am training model 1 (using train1) with a specific loss function that involves tensor A. I am accumulating the loss and then want to perform an update. Next I am training a second model 2 (train2) in which I want to calculate the gradients wrt A using the loss calculated in train2. Thus I am adding loss 1 to loss2.
#reproduce error
from transformers import BertModel, BertForMaskedLM, BertConfig, EncoderDecoderModel
import torch
import torch.nn.functional as F
model1 = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert from pre-trained checkpoints
model2 = EncoderDecoderModel.from_encoder_decoder_pretrained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert from pre-trained checkpoints
optimizer1 = torch.optim.Adam(model1.parameters(), lr=0.001)
A=torch.rand(1, requires_grad=True)
optimizer3 = torch.optim.SGD([A], lr=0.1)
en_input=torch.tensor([[1,2], [3,4]])
en_masks=torch.tensor([[0,0], [0,0]])
de_output=torch.tensor([[3,1], [4,2]])
de_masks=torch.tensor([[0,0], [0,0]])
lm_labels=torch.tensor([[5,7], [6,8]])
torch.autograd.set_detect_anomaly(True)
def train1():
acc=torch.zeros(1)
for i in range(2):
optimizer1.zero_grad()
out = model1(input_ids=en_input, attention_mask=en_masks, decoder_input_ids=de_output,
decoder_attention_mask=de_masks, labels=lm_labels.clone())
prediction_scores = out[1]
predictions = F.log_softmax(prediction_scores, dim=2)
p=((predictions.sum() - de_output.sum())*A).sum()
p=torch.unsqueeze(p, dim=0)
acc = torch.cat((p,acc)) # accumulating the loss
loss=acc.sum()
A.retain_grad()
loss.backward(retain_graph=True)
optimizer1.step()
return loss
def train2(loss1):
for i in range (2):
optimizer3.zero_grad()
output = model2(input_ids=en_input, attention_mask=en_masks,
decoder_input_ids=de_output,
decoder_attention_mask=de_masks, labels=lm_labels.clone())
prediction_scores_ = output[1]
predictions_= F.log_softmax(prediction_scores_, dim=2)
loss2=((predictions_.sum() - de_output.sum())).sum()+loss1 # want to calculate gradients
wrt A
A.retain_grad()
loss2.backward(inputs=[A], retain_graph=True)
optimizer3.step() #update A based on calculated gradients
loss1=train1()
train2(loss1)
If this is the right method, I am not understanding whats wrong in my code? If its not right, I would appreciate if someone pointed me in the right direction.
error trace
/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py:147: UserWarning: Error detected in MulBackward0. Traceback of forward call that caused the error:
File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py", line 845, in launch_instance
app.start()
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py", line 499, in start
self.io_loop.start()
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 132, in start
self.asyncio_loop.run_forever()
File "/usr/lib/python3.7/asyncio/base_events.py", line 541, in run_forever
self._run_once()
File "/usr/lib/python3.7/asyncio/base_events.py", line 1786, in _run_once
handle._run()
File "/usr/lib/python3.7/asyncio/events.py", line 88, in _run
self._context.run(self._callback, *self._args)
File "/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py", line 122, in _handle_events
handler_func(fileobj, events)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 451, in _handle_events
self._handle_recv()
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 480, in _handle_recv
self._run_callback(callback, msg)
File "/usr/local/lib/python3.7/dist-packages/zmq/eventloop/zmqstream.py", line 434, in _run_callback
callback(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py", line 300, in null_wrapper
return fn(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2718, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2822, in run_ast_nodes
if self.run_code(code, result):
File "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py", line 2882, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-66-c603f915c713>", line 78, in <module>
loss1=train1()
File "<ipython-input-66-c603f915c713>", line 25, in train1
p=((predictions.sum() - de_output.sum())*A).sum()
(Triggered internally at /pytorch/torch/csrc/autograd/python_anomaly_mode.cpp:104.)
allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-66-c603f915c713> in <module>()
77 for i in range(2):
78 loss1=train1()
---> 79 train2(loss1)
2 frames
<ipython-input-66-c603f915c713> in train2(loss1)
69 print(A.grad)
70 #loss2.grad(inputs=A,outputs=A, only_inputs=True)
---> 71 loss2.backward(inputs=[A],retain_graph=True) #calculates gradients # retain_graph=True #list(dec.parameters())
72 print(A.grad)
73 # torch.nn.utils.clip_grad_norm_(model1.parameters(), 1.0)
/usr/local/lib/python3.7/dist-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph, inputs)
243 create_graph=create_graph,
244 inputs=inputs)
--> 245 torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
246
247 def register_hook(self, hook):
/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)
145 Variable._execution_engine.run_backward(
146 tensors, grad_tensors_, retain_graph, create_graph, inputs,
--> 147 allow_unreachable=True, accumulate_grad=True) # allow_unreachable flag
148
149
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [1]] is at version 1; expected version 0 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!
. I understood my mistake. I had another ques, Is it possible for me to calculate the gradients wrt A, if I don’t add loss1 to loss2 and simply do a loss2.backward(inputs=[A]) ? Thanks.