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Summary: Special executor for RNNs which can exploit parallelism over timesteps. For CPU we use multi-threading, achiving 3x or so improved on 4-layers LSTMs. With CUDA, perf improvements are more modest, but the structure allows for optimizing it further. For CUDA, we use multiple streams and events if there is parallellism over timesteps. In my experiments, it was not good to use more than 2 streams, though. Flag --caffe2_rnn_executor can be used to switch the executor off. Reviewed By: salexspb Differential Revision: D5749304 fbshipit-source-id: d6f76b3e16598be5b4e8188aff031671ebafaa4c
331 lines
13 KiB
Python
331 lines
13 KiB
Python
## @package recurrent
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# Module caffe2.python.recurrent
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core, workspace
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from caffe2.python.scope import CurrentNameScope
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from future.utils import viewitems, viewkeys
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def recurrent_net(
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net, cell_net, inputs, initial_cell_inputs,
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links, timestep=None, scope=None, outputs_with_grads=(0,),
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recompute_blobs_on_backward=None, forward_only=False,
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):
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'''
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net: the main net operator should be added to
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cell_net: cell_net which is executed in a recurrent fasion
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inputs: sequences to be fed into the recurrent net. Currently only one input
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is supported. It has to be in a format T x N x (D1...Dk) where T is lengths
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of the sequence. N is a batch size and (D1...Dk) are the rest of dimentions
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initial_cell_inputs: inputs of the cell_net for the 0 timestamp.
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Format for each input is:
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(cell_net_input_name, external_blob_with_data)
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links: a dictionary from cell_net input names in moment t+1 and
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output names of moment t. Currently we assume that each output becomes
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an input for the next timestep.
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timestep: name of the timestep blob to be used. If not provided "timestep"
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is used.
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scope: Internal blobs are going to be scoped in a format
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<scope_name>/<blob_name>
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If not provided we generate a scope name automatically
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outputs_with_grads : position indices of output blobs which will receive
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error gradient (from outside recurrent network) during backpropagation
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recompute_blobs_on_backward: specify a list of blobs that will be
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recomputed for backward pass, and thus need not to be
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stored for each forward timestep.
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forward_only: if True, only forward steps are executed
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'''
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assert len(inputs) == 1, "Only one input blob is supported so far"
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# Validate scoping
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for einp in cell_net.Proto().external_input:
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assert einp.startswith(CurrentNameScope()), \
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'''
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Cell net external inputs are not properly scoped, use
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AddScopedExternalInputs() when creating them
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'''
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input_blobs = [str(i[0]) for i in inputs]
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initial_input_blobs = [str(x[1]) for x in initial_cell_inputs]
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op_name = net.NextName('recurrent')
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def s(name):
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# We have to manually scope due to our internal/external blob
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# relationships.
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scope_name = op_name if scope is None else scope
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return "{}/{}".format(str(scope_name), str(name))
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# determine inputs that are considered to be references
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# it is those that are not referred to in inputs or initial_cell_inputs
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known_inputs = [str(b) for b in input_blobs + initial_input_blobs]
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known_inputs += [str(x[0]) for x in initial_cell_inputs]
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if timestep is not None:
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known_inputs.append(str(timestep))
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references = [
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core.BlobReference(b) for b in cell_net.Proto().external_input
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if b not in known_inputs]
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inner_outputs = list(cell_net.Proto().external_output)
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# These gradients are expected to be available during the backward pass
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inner_outputs_map = {o: o + '_grad' for o in inner_outputs}
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# compute the backward pass of the cell net
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if not forward_only:
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backward_ops, backward_mapping = core.GradientRegistry.GetBackwardPass(
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cell_net.Proto().op, inner_outputs_map)
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backward_mapping = {str(k): v for k, v in viewitems(backward_mapping)}
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backward_cell_net = core.Net("RecurrentBackwardStep")
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del backward_cell_net.Proto().op[:]
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if recompute_blobs_on_backward is not None:
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# Insert operators to re-compute the specified blobs.
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# They are added in the same order as for the forward pass, thus
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# the order is correct.
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recompute_blobs_on_backward = {str(b) for b in
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recompute_blobs_on_backward}
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for op in cell_net.Proto().op:
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if not recompute_blobs_on_backward.isdisjoint(set(op.output)):
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backward_cell_net.Proto().op.extend([op])
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# This fires if other outputs than the declared
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# are computed by the ops that are recomputed
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assert set(op.output).issubset(recompute_blobs_on_backward)
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backward_cell_net.Proto().op.extend(backward_ops)
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# compute blobs used but not defined in the backward pass
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backward_ssa, backward_blob_versions = core.get_ssa(
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backward_cell_net.Proto())
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undefined = core.get_undefined_blobs(backward_ssa)
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# also add to the output list the intermediate outputs of fwd_step that
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# are used by backward.
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ssa, blob_versions = core.get_ssa(cell_net.Proto())
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scratches = [
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blob
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for blob, ver in viewitems(blob_versions)
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if (ver > 0 and
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blob in undefined and
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blob not in cell_net.Proto().external_output)
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]
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backward_cell_net.Proto().external_input.extend(scratches)
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backward_cell_net.Proto().type = 'simple'
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else:
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backward_cell_net = None
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all_inputs = [i[1] for i in inputs] + [
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x[1] for x in initial_cell_inputs] + references
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all_outputs = []
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cell_net.Proto().type = 'simple'
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# Internal arguments used by RecurrentNetwork operator
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# Links are in the format blob_name, recurrent_states, offset.
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# In the moment t we know that corresponding data block is at
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# t + offset position in the recurrent_states tensor
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forward_links = []
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backward_links = []
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# Aliases are used to expose outputs to external world
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# Format (internal_blob, external_blob, offset)
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# Negative offset stands for going from the end,
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# positive - from the beginning
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aliases = []
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# States held inputs to the cell net
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recurrent_states = []
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backward_recurrent_mapping = {}
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for cell_input, _ in initial_cell_inputs:
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cell_input = str(cell_input)
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# Recurrent_states is going to be (T + 1) x ...
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# It stores all inputs and outputs of the cell net over time.
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# Or their gradients in the case of the backward pass.
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state = s(cell_input + "_states")
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states_grad = state + "_grad"
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cell_output = links[str(cell_input)]
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forward_links.append((cell_input, state, 0))
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forward_links.append((cell_output, state, 1))
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aliases.append((state, cell_output + "_all", 1))
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aliases.append((state, cell_output + "_last", -1))
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all_outputs.extend([cell_output + "_all", cell_output + "_last"])
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recurrent_states.append(state)
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if backward_cell_net is not None:
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backward_links.append((cell_output + "_grad", states_grad, 1))
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backward_cell_net.Proto().external_input.append(
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str(cell_output) + "_grad")
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recurrent_input_grad = cell_input + "_grad"
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if not backward_blob_versions.get(recurrent_input_grad, 0):
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# If nobody writes to this recurrent input gradient, we need
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# to make sure it gets to the states grad blob after all.
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# We do this by using backward_links which triggers an alias
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# This logic is being used for example in a SumOp case
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backward_links.append(
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(backward_mapping[cell_input], states_grad, 0))
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else:
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backward_links.append((recurrent_input_grad, states_grad, 0))
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backward_recurrent_mapping[cell_output +
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"_grad"] = cell_input + "_grad"
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for input_t, input_blob in inputs:
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forward_links.append((str(input_t), str(input_blob), 0))
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if backward_cell_net is not None:
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for input_t, input_blob in inputs:
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backward_links.append((
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backward_mapping[str(input_t)], str(input_blob) + "_grad", 0
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))
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backward_cell_net.Proto().external_input.extend(
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cell_net.Proto().external_input)
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backward_cell_net.Proto().external_input.extend(
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cell_net.Proto().external_output)
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def unpack_triple(x):
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if x:
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a, b, c = zip(*x)
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return a, b, c
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return [], [], []
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# Splitting to separate lists so we can pass them to c++
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# where we ensemle them back
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link_internal, link_external, link_offset = unpack_triple(forward_links)
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alias_src, alias_dst, alias_offset = unpack_triple(aliases)
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recurrent_inputs = [str(x[1]) for x in initial_cell_inputs]
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# Make sure that recurrent gradients accumulate with internal gradients
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# (if a blob in the backward_cell_net receives gradient from both an
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# external connection as well as from within the backward_cell_net,
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# those gradients need to be added together, rather than one overwriting
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# the other)
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if backward_cell_net is not None:
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proto = backward_cell_net.Proto()
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operators = []
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while len(proto.op) > 0:
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op = proto.op[-1]
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proto.op.remove(op)
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operators.append(op)
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for op in operators[::-1]:
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proto.op.extend([op])
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for j, output_blob in enumerate(op.output):
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if output_blob in proto.external_input:
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# In place operation won't cause issues because it takes
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# existing value of a blob into account
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if output_blob in op.input:
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continue
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output_blob = core.BlobReference(output_blob)
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accum_blob = output_blob + "_accum"
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proto.op[-1].output[j] = str(accum_blob)
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backward_cell_net.Sum(
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[output_blob, accum_blob],
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[output_blob],
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)
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def map_to_dual_list(m):
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return [str(x) for x in list(m.keys())] + \
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[str(x) for x in list(m.values())]
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backward_args = {}
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if backward_cell_net is not None:
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backward_mapping_keys = set(viewkeys(backward_mapping))
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backward_link_internal, backward_link_external, backward_link_offset = \
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unpack_triple(backward_links)
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params = [x for x in references if x in backward_mapping_keys]
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param_grads = [
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str(backward_mapping[x])
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for x in references
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if x in backward_mapping_keys
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]
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if recompute_blobs_on_backward is None:
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recompute_blobs_on_backward = set()
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backward_args = {
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'param': [all_inputs.index(p) for p in params],
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'backward_link_internal': [str(l) for l in backward_link_internal],
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'backward_link_external': [str(l) for l in backward_link_external],
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'backward_link_offset': backward_link_offset,
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'backward_step_net': str(backward_cell_net.Proto()),
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'outputs_with_grads': outputs_with_grads,
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'recompute_blobs_on_backward': [
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str(b) for b in recompute_blobs_on_backward
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],
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'backward_recurrent_mapping':
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map_to_dual_list(backward_recurrent_mapping),
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'param_grads': param_grads,
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}
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results = net.RecurrentNetwork(
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all_inputs,
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all_outputs + [s("step_workspaces")],
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alias_src=alias_src,
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alias_dst=[str(a) for a in alias_dst],
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alias_offset=alias_offset,
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recurrent_states=recurrent_states,
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initial_recurrent_state_ids=[
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all_inputs.index(i) for i in recurrent_inputs
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],
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link_internal=[str(l) for l in link_internal],
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link_external=[str(l) for l in link_external],
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link_offset=link_offset,
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enable_rnn_executor=1,
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recurrent_mapping=map_to_dual_list(links),
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step_net=str(cell_net.Proto()),
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timestep="timestep" if timestep is None else str(timestep),
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**backward_args
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)
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# Restore net type since 'rnn' is not recognized outside RNNs
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cell_net.Proto().type = 'simple'
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# The last output is a list of step workspaces,
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# which is only needed internally for gradient propogation
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return results[:-1]
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def retrieve_step_blobs(net, prefix='rnn'):
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'''
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Retrieves blobs from step workspaces (which contain intermediate recurrent
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network computation for each timestep) and puts them in the global
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workspace. This allows access to the contents of this intermediate
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computation in python. Returns the list of extracted blob names.
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net: the net from which the step workspace blobs should be extracted
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prefix: prefix to append to extracted blob names when placing them in the
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global workspace
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'''
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count = 1
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output_list = []
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for op in net.Proto().op:
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if op.type == "RecurrentNetwork":
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blob_name = prefix + "_" + str(count)
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count = count + 1
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scratch_workspaces_blob_name = op.output[-1]
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workspace.RunOperatorOnce(
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core.CreateOperator(
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"RecurrentNetworkBlobFetcher",
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[scratch_workspaces_blob_name],
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[blob_name],
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prefix=prefix
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)
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)
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output_list += workspace.FetchBlob(blob_name).tolist()
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return output_list
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