## @package recurrent # Module caffe2.python.recurrent from caffe2.python import core, workspace from future.utils import viewitems, viewkeys def recurrent_net( net, cell_net, inputs, initial_cell_inputs, links, timestep=None, scope=None, outputs_with_grads=(0,), recompute_blobs_on_backward=None, forward_only=False, ): ''' net: the main net operator should be added to cell_net: cell_net which is executed in a recurrent fasion inputs: sequences to be fed into the recurrent net. Currently only one input is supported. It has to be in a format T x N x (D1...Dk) where T is lengths of the sequence. N is a batch size and (D1...Dk) are the rest of dimentions initial_cell_inputs: inputs of the cell_net for the 0 timestamp. Format for each input is: (cell_net_input_name, external_blob_with_data) links: a dictionary from cell_net input names in moment t+1 and output names of moment t. Currently we assume that each output becomes an input for the next timestep. timestep: name of the timestep blob to be used. If not provided "timestep" is used. scope: Internal blobs are going to be scoped in a format / If not provided we generate a scope name automatically outputs_with_grads : position indices of output blobs which will receive error gradient (from outside recurrent network) during backpropagation recompute_blobs_on_backward: specify a list of blobs that will be recomputed for backward pass, and thus need not to be stored for each forward timestep. forward_only: if True, only forward steps are executed ''' assert len(inputs) == 1, "Only one input blob is supported so far" input_blobs = [str(i[0]) for i in inputs] initial_input_blobs = [str(x[1]) for x in initial_cell_inputs] op_name = net.NextName('recurrent') def s(name): # We have to manually scope due to our internal/external blob # relationships. scope_name = op_name if scope is None else scope return "{}/{}".format(str(scope_name), str(name)) # determine inputs that are considered to be references # it is those that are not referred to in inputs or initial_cell_inputs known_inputs = [str(b) for b in input_blobs + initial_input_blobs] known_inputs += [str(x[0]) for x in initial_cell_inputs] if timestep is not None: known_inputs.append(str(timestep)) references = [ core.BlobReference(b) for b in cell_net.Proto().external_input if b not in known_inputs] inner_outputs = list(cell_net.Proto().external_output) # These gradients are expected to be available during the backward pass inner_outputs_map = {o: o + '_grad' for o in inner_outputs} # compute the backward pass of the cell net if not forward_only: backward_ops, backward_mapping = core.GradientRegistry.GetBackwardPass( cell_net.Proto().op, inner_outputs_map) backward_mapping = {str(k): v for k, v in viewitems(backward_mapping)} backward_cell_net = core.Net("RecurrentBackwardStep") del backward_cell_net.Proto().op[:] if recompute_blobs_on_backward is not None: # Insert operators to re-compute the specified blobs. # They are added in the same order as for the forward pass, thus # the order is correct. recompute_blobs_on_backward = {str(b) for b in recompute_blobs_on_backward} for op in cell_net.Proto().op: if not recompute_blobs_on_backward.isdisjoint(set(op.output)): backward_cell_net.Proto().op.extend([op]) # This fires if other outputs than the declared # are computed by the ops that are recomputed assert set(op.output).issubset(recompute_blobs_on_backward) backward_cell_net.Proto().op.extend(backward_ops) # compute blobs used but not defined in the backward pass backward_ssa, backward_blob_versions = core.get_ssa( backward_cell_net.Proto()) undefined = core.get_undefined_blobs(backward_ssa) # also add to the output list the intermediate outputs of fwd_step that # are used by backward. ssa, blob_versions = core.get_ssa(cell_net.Proto()) scratches = [ blob for blob, ver in viewitems(blob_versions) if (ver > 0 and blob in undefined and blob not in cell_net.Proto().external_output) ] backward_cell_net.Proto().external_input.extend(scratches) backward_cell_net.Proto().type = 'simple' else: backward_cell_net = None all_inputs = [i[1] for i in inputs] + [ x[1] for x in initial_cell_inputs] + references all_outputs = [] cell_net.Proto().type = 'simple' # Internal arguments used by RecurrentNetwork operator # Links are in the format blob_name, recurrent_states, offset. # In the moment t we know that corresponding data block is at # t + offset position in the recurrent_states tensor forward_links = [] backward_links = [] # Aliases are used to expose outputs to external world # Format (internal_blob, external_blob, offset) # Negative offset stands for going from the end, # positive - from the beginning aliases = [] # States held inputs to the cell net recurrent_states = [] for cell_input, _ in initial_cell_inputs: cell_input = str(cell_input) # Recurrent_states is going to be (T + 1) x ... # It stores all inputs and outputs of the cell net over time. # Or their gradients in the case of the backward pass. state = s(cell_input + "_states") states_grad = state + "_grad" cell_output = links[str(cell_input)] forward_links.append((cell_input, state, 0)) forward_links.append((cell_output, state, 1)) aliases.append((state, cell_output + "_all", 1)) aliases.append((state, cell_output + "_last", -1)) all_outputs.extend([cell_output + "_all", cell_output + "_last"]) recurrent_states.append(state) if backward_cell_net is not None: backward_links.append((cell_output + "_grad", states_grad, 1)) backward_cell_net.Proto().external_input.append( str(cell_output) + "_grad") recurrent_input_grad = cell_input + "_grad" if not backward_blob_versions.get(recurrent_input_grad, 0): # If nobody writes to this recurrent input gradient, we need # to make sure it gets to the states grad blob after all. # We do this by using backward_links which triggers an alias # This logic is being used for example in a SumOp case backward_links.append( (backward_mapping[cell_input], states_grad, 0)) else: backward_links.append((recurrent_input_grad, states_grad, 0)) for input_t, input_blob in inputs: forward_links.append((str(input_t), str(input_blob), 0)) if backward_cell_net is not None: for input_t, input_blob in inputs: backward_links.append(( backward_mapping[str(input_t)], str(input_blob) + "_grad", 0 )) backward_cell_net.Proto().external_input.extend( cell_net.Proto().external_input) backward_cell_net.Proto().external_input.extend( cell_net.Proto().external_output) def unpack_triple(x): if x: a, b, c = zip(*x) return a, b, c return [], [], [] # Splitting to separate lists so we can pass them to c++ # where we ensemle them back link_internal, link_external, link_offset = unpack_triple(forward_links) alias_src, alias_dst, alias_offset = unpack_triple(aliases) recurrent_inputs = [str(x[1]) for x in initial_cell_inputs] # Make sure that recurrent gradients accumulate with internal gradients # (if a blob in the backward_cell_net receives gradient from both an # external connection as well as from within the backward_cell_net, # those gradients need to be added together, rather than one overwriting # the other) if backward_cell_net is not None: proto = backward_cell_net.Proto() operators = [] while len(proto.op) > 0: op = proto.op[-1] proto.op.remove(op) operators.append(op) for op in operators[::-1]: proto.op.extend([op]) for j, output_blob in enumerate(op.output): if output_blob in proto.external_input: # In place operation won't cause issues because it takes # existing value of a blob into account if output_blob in op.input: continue output_blob = core.BlobReference(output_blob) accum_blob = output_blob + "_accum" proto.op[-1].output[j] = str(accum_blob) backward_cell_net.Sum( [output_blob, accum_blob], [output_blob], ) def map_to_dual_list(m): return [str(x) for x in list(m.keys())] + \ [str(x) for x in list(m.values())] backward_args = {} if backward_cell_net is not None: backward_mapping_keys = set(viewkeys(backward_mapping)) backward_link_internal, backward_link_external, backward_link_offset = \ unpack_triple(backward_links) params = [x for x in references if x in backward_mapping_keys] param_grads = [ str(backward_mapping[x]) for x in references if x in backward_mapping_keys ] if recompute_blobs_on_backward is None: recompute_blobs_on_backward = set() backward_args = { 'param': [all_inputs.index(p) for p in params], 'backward_link_internal': [str(l) for l in backward_link_internal], 'backward_link_external': [str(l) for l in backward_link_external], 'backward_link_offset': backward_link_offset, 'outputs_with_grads': outputs_with_grads, 'recompute_blobs_on_backward': [ str(b) for b in recompute_blobs_on_backward ], 'param_grads': param_grads, } if len(backward_cell_net.Proto().op) != 0: backward_args['backward_step_net'] = backward_cell_net.Proto() results = net.RecurrentNetwork( all_inputs, all_outputs + [s("step_workspaces")], alias_src=alias_src, alias_dst=[str(a) for a in alias_dst], alias_offset=alias_offset, recurrent_states=recurrent_states, initial_recurrent_state_ids=[ all_inputs.index(i) for i in recurrent_inputs ], link_internal=[str(l) for l in link_internal], link_external=[str(l) for l in link_external], link_offset=link_offset, enable_rnn_executor=1, step_net=cell_net.Proto(), timestep="timestep" if timestep is None else str(timestep), **backward_args ) # Restore net type since 'rnn' is not recognized outside RNNs cell_net.Proto().type = 'simple' # The last output is a list of step workspaces, # which is only needed internally for gradient propogation return results[:-1] def set_rnn_executor_config(rnn_op, num_threads=None, max_cuda_streams=None): from caffe2.proto import caffe2_pb2 assert rnn_op.type in {'RecurrentNetwork', 'RecurrentNetworkGradient'} def add_arg(s, v): a = caffe2_pb2.Argument() a.name = "rnn_executor." + s a.i = v rnn_op.arg.extend([a]) if num_threads is not None: add_arg('num_threads', num_threads) if max_cuda_streams is not None: add_arg('max_cuda_streams', max_cuda_streams) def retrieve_step_blobs(net, prefix='rnn'): ''' Retrieves blobs from step workspaces (which contain intermediate recurrent network computation for each timestep) and puts them in the global workspace. This allows access to the contents of this intermediate computation in python. Returns the list of extracted blob names. net: the net from which the step workspace blobs should be extracted prefix: prefix to append to extracted blob names when placing them in the global workspace ''' count = 1 output_list = [] for op in net.Proto().op: if op.type == "RecurrentNetwork": blob_name = prefix + "_" + str(count) count = count + 1 scratch_workspaces_blob_name = op.output[-1] workspace.RunOperatorOnce( core.CreateOperator( "RecurrentNetworkBlobFetcher", [scratch_workspaces_blob_name], [blob_name], prefix=prefix ) ) output_list += workspace.FetchBlob(blob_name).tolist() return output_list