I have just been migrating some code to PyTorch 0.4.0.
For some background - My model outputs scores that I put into a list as part of the forward pass. I then need to torch.cat the list so I can later call .sum() and then .backward()
Since updating to 0.4.0 I am finding that the torch.cat causes:
“RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation”
I tested by taking a single score out of the list and doing .backward(), and this does work, so I am fairly certain that it is the torch.cat() operation that is triggering this inplace problem.
def forward(self, clause_dsets={}, ont_clause_dsets={}, aggregator='keep_all',
                inference=False, verbose=False):
        outputs = []
        for clause_name in clause_dsets.keys():
            clause = self.clauses[clause_name]
            if verbose:
                log.debug('===Training Clause: {}==='.format(clause.label))
            # 1) Pass clause's input dictionary as input, which contains the data
            #    per clause literal, with the literal label as key.
            # 2) Compute clause grounding by triggering underlying machinery.
            # 3) Output is a batch column vector, via unsqueeze(1). Append to list
            #    of outputs
            outputs.append(clause.compute(
                inference, clause_dsets[clause_name]).unsqueeze(1))
        # Ontology clauses
        for clause_name in ont_clause_dsets.keys():
            clause = self.clauses[clause_name]
            if verbose:
                log.debug('===Training Ontology Clause: {}==='.format(clause.label))
            outputs.append(clause.compute(
                inference, ont_clause_dsets[clause.label]).unsqueeze(1))

        if verbose:
            log.debug('===Aggregating Clauses with {}==='.format(aggregator))
        # Concatenate clauses into big column vector. All clauses have same
        # output expectations (high score), so no need to know which clause is
        # which (unless we want to have that insight in investigation phase)
        clauses_value_tensor = torch.cat(outputs, dim=0)
        ...

I can call backward on everything before the last line. The error only triggers after the cat operation.
I may have messed something up elsewhere during the migration, but given that the error only occurs after the cat op and beforehand I can do .backward(), meaning that there are no inplace operations prior, is something up?