def rewrite(self, context):
logger.debug("enter rewrite function")
try:
scan_props = context.loop_properties
state_inputs_initial_values = []
for state_input in scan_props.state_inputs_initial_values:
if self.g.opset == 8:
nodes = self._adapt_scan_sequence_input_or_output(
"input", state_input, False)
state_inputs_initial_values.append(nodes[-1].output[0])
else: # since opset 9
state_inputs_initial_values.append(state_input)
scan_inputs_initial_values = []
scan_length = -1
for scan_input in scan_props.scan_inputs_initial_values:
if self.g.opset == 8:
nodes = self._adapt_scan_sequence_input_or_output(
"input", scan_input, False)
scan_inputs_initial_values.append(nodes[-1].output[0])
else: # since opset 9
scan_inputs_initial_values.append(scan_input)
scan_shape = self.g.get_shape(scan_input)
if scan_shape is not None and len(scan_shape) > 0:
scan_length = scan_shape[0]
cell_g_info = context.cell_graph
scan_body_g = LoopRewriterBase.construct_graph_from_nodes(
self.g, cell_g_info.nodes, cell_g_info.outputs)
for input_tensor_info in scan_props.state_inputs:
scan_body_g.add_graph_input(input_tensor_info.id,
input_tensor_info.dtype,
input_tensor_info.shape)
for input_tensor_info in scan_props.scan_inputs:
scan_body_g.add_graph_input(input_tensor_info.id,
input_tensor_info.dtype,
input_tensor_info.shape)
scan_node = self._create_scan_node(
context, scan_props,
state_inputs_initial_values + scan_inputs_initial_values,
scan_body_g, scan_length)
if not scan_node:
logger.error("failed to create scan node during rewrite")
return REWRITER_RESULT.FAIL
self._connect_scan_with_output(context, scan_node)
return REWRITER_RESULT.OK
except Exception as ex:
tb = traceback.format_exc()
logger.error(
"custom rnn rewrite failed, due to exception: %s, details:%s",
ex, tb)
return REWRITER_RESULT.FAIL
def rewrite(self, context):
log.debug("enter rewrite function")
try:
scan_props = context.loop_properties
nodes_to_append = []
state_inputs_initial_values = []
for state_input in scan_props.state_inputs_initial_values:
nodes = self._adapt_scan_sequence_input_or_output(
"input", state_input, False)
state_inputs_initial_values.append(nodes[-1].output[0])
nodes_to_append.extend(nodes)
scan_inputs_initial_values = []
for scan_input in scan_props.scan_inputs_initial_values:
nodes = self._adapt_scan_sequence_input_or_output(
"input", scan_input, False)
scan_inputs_initial_values.append(nodes[-1].output[0])
nodes_to_append.extend(nodes)
cell_g_info = context.cell_graph
scan_body_g = LoopRewriterBase.construct_graph_from_nodes(
self.g, cell_g_info.nodes, cell_g_info.outputs)
for input_tensor_info in scan_props.state_inputs:
scan_body_g.add_graph_input(input_tensor_info.id,
input_tensor_info.dtype,
input_tensor_info.shape)
for input_tensor_info in scan_props.scan_inputs:
scan_body_g.add_graph_input(input_tensor_info.id,
input_tensor_info.dtype,
input_tensor_info.shape)
scan_node = self._create_scan_node(
context, scan_props,
state_inputs_initial_values + scan_inputs_initial_values)
if not scan_node:
log.error("failed to create scan node during rewrite")
return REWRITER_RESULT.FAIL
scan_node.set_body_graph_as_attr("body", scan_body_g)
nodes_to_append.append(scan_node)
to_append = self._connect_scan_with_output(context, scan_node)
nodes_to_append.extend(to_append)
all_nodes = self.g.get_nodes()
all_nodes.extend(nodes_to_append)
self.g.set_nodes(all_nodes)
return REWRITER_RESULT.OK
except Exception as ex:
tb = traceback.format_exc()
log.error(
"custom rnn rewrite failed, due to exception: %s, details:%s",
ex, tb)
return REWRITER_RESULT.FAIL
def _match_cell(self, context, unittype):
"""match unit cell"""
for cell_pattern in get_pattern(unittype):
matcher = GraphMatcher(cell_pattern, allow_reorder=True)
loop_props = context.loop_properties
inputs = loop_props.state_inputs + loop_props.scan_inputs
input_ids = [input_tensor_value_info.id for input_tensor_value_info in inputs]
outputs = loop_props.state_outputs + loop_props.scan_outputs
output_ids = [out_tensor_value_info.id for out_tensor_value_info in outputs]
body_graph_ops, _, _ = LoopRewriterBase.find_subgraph(
set(input_ids),
set(output_ids),
self.g, merge_as_end=True
)
match_results = list(matcher.match_ops(body_graph_ops))
if len(match_results) == 1:
return match_results[0]
return None
def rewrite(self):
log.debug("enter custom rnn late rewriter")
nodes = self.g.get_nodes()
nodes_to_remove = []
for scan_node in nodes:
if scan_node.type != "Scan":
continue
log.debug("late write for scan node %s", scan_node.name)
num_scan_inputs = scan_node.get_attr("num_scan_inputs").i
if not BodyGraphDict.has_body_graph_info(scan_node.name):
continue
body_graph_meta = BodyGraphDict.pop_body_graph_info(scan_node.name)
onnx_nodes, _ = LoopRewriterBase.find_subgraph(
body_graph_meta, self.g)
nodes_to_remove.extend(onnx_nodes)
log.debug("start creating body graph for scan node %s ",
scan_node.name)
body_graph_initializers = {}
const_nodes = [
n for n in onnx_nodes if n.type in ("Const", "ConstV2")
]
for n in const_nodes:
# when set nodes, Const should be removed, they need be replaced as initializers.
body_graph_initializers[n.output[0]] = self.g.initializers[
n.output[0]]
onnx_nodes.remove(n)
onnx_nodes = set(onnx_nodes)
ops = []
for op in onnx_nodes:
onnx_op = op.op
ops.append(onnx_op)
body_g = Graph(ops,
output_shapes=self.g._output_shapes,
dtypes=self.g._dtypes)
body_g._initializers = body_graph_initializers
log.debug("start preparing body graph inputs nodes")
temp_nodes = body_g.get_nodes()
i = 0
input_count = len(body_graph_meta.input_ids)
for input_name, init_input_id in zip(
body_graph_meta.input_ids,
body_graph_meta.initial_input_ids):
shape = body_g.get_shape(input_name)
dtype = body_g.get_dtype(input_name)
if shape is None:
shape = self.g.get_shape(init_input_id)
if i >= input_count - num_scan_inputs:
loop_input_shape = list(shape)[2:] # delete [1, time,]
else:
loop_input_shape = list(shape)
else:
loop_input_shape = list(shape)
onnx_input_shape = utils.make_onnx_shape(loop_input_shape)
val = helper.make_tensor_value_info(input_name, dtype,
onnx_input_shape)
body_g.add_model_input(input_name, val)
i += 1
log.debug("start preparing body graph outputs nodes")
new_output_names = []
for o in body_graph_meta.output_ids:
# insert identity node, since sometimes we need output same output_id as state_output
# and scan_out, but ONNX don't allow the same output_id appeared more than once as
# output node.
identity_name = utils.make_name("Identity")
identity_output = utils.port_name(identity_name)
node = Node(
helper.make_node("Identity", [o], [identity_output],
name=identity_name), body_g)
body_g.set_dtype(identity_output, body_g.get_dtype(o))
body_g.copy_shape(o, identity_output)
new_output_names.append(identity_output)
temp_nodes.append(node)
body_g.set_nodes(temp_nodes)
body_g.topological_sort(body_g.get_nodes())
log.debug("start make graph based on body graph nodes")
body_g.output_names = new_output_names
graph = body_g.make_graph("scan body graph")
scan_node.set_attr("body", graph)
# remove nodes in body graph from g
for n in set(nodes_to_remove):
if n in nodes:
nodes.remove(n)
elif self.g.is_initializer(n.output[0]):
del self.g.initializers[n.output[0]]
else:
raise ValueError("error when removing nodes")
return nodes
def rewrite(self, context):
logger.debug("enter rewrite function")
loop_node = None
try:
loop_props = context.loop_properties
cell_g_info = context.cell_graph
cond_g_info = context.cond_graph
# create a dummy loop to calculate the init condition
init_cond_output = self._create_subgraph_initial_cond(cond_g_info)
## create Loop body graph with existing nodes
body_nodes = set(cell_g_info.nodes + cond_g_info.nodes)
body_outputs = cond_g_info.outputs + cell_g_info.outputs
for out_tensor_value_info in body_outputs:
shape = out_tensor_value_info.shape
utils.make_sure(
shape is not None,
"Conversion of Loop requries output shape [{}] exists".
format(out_tensor_value_info.id))
out_tensor_value_info.shape = utils.create_vague_shape_like(
shape)
loop_body_g = LoopRewriterBase.construct_graph_from_nodes(
self.g, body_nodes, body_outputs)
# create loop body graph inputs
loop_body_g.add_graph_input(utils.make_name("i"),
TensorProto.INT64, ())
loop_body_g.add_graph_input(utils.make_name("cond"),
TensorProto.BOOL, ())
for i, tensor_value_info in enumerate(loop_props.state_inputs):
input_name = tensor_value_info.id
if input_name is None:
# if the variable is not used in the body graph, then we created a fake one,
# the same type and shape as its corresponding output.
out_tensor_value_info = loop_props.state_outputs[i]
dtype = out_tensor_value_info.dtype
shape = out_tensor_value_info.shape
input_name = utils.make_name("unused_state_input_")
else:
dtype = tensor_value_info.dtype
shape = tensor_value_info.shape
loop_body_g.add_graph_input(
input_name, dtype, utils.create_vague_shape_like(shape))
for input_ta in loop_props.tensor_array_inputs:
# Loop does not have scan inputs, so we use Gather to get data for each iteration.
index_node = loop_body_g.make_node("Unsqueeze",
[input_ta.index_input_id],
attr={"axes": [0]})
gather_node = loop_body_g.make_node(
"Gather", [input_ta.data_input_id, index_node.output[0]])
data_node = loop_body_g.make_node("Squeeze",
[gather_node.output[0]],
attr={"axes": [0]})
loop_body_g.replace_all_inputs(
input_ta.consumer.id,
data_node.output[0]) # ops=loop_body_g.get_nodes()
## create Loop node
branches = {"body": loop_body_g}
loop_node = self._create_loop_node(context,
loop_props,
init_cond_output,
branches=branches)
if not loop_node:
logger.error("failed to create loop node during rewrite")
return REWRITER_RESULT.FAIL
logger.debug("rewrite successfully")
return REWRITER_RESULT.OK
except Exception as ex:
tb = traceback.format_exc()
logger.error(
"loop rewrite failed, due to exception: %s, details:%s", ex,
tb)
return REWRITER_RESULT.FAIL
def rewrite(self, context):
logger.debug("enter rewrite function")
loop_node = None
try:
loop_props = context.loop_properties
cell_g_info = context.cell_graph
cond_g_info = context.cond_graph
# todo(pengwa): we don't check the case where loop body won't be executed at all.
## create Loop body graph with existing nodes
# replace condition graph's inputs to be cell graph's outputs, because we want condition graph
# to consumer cell graph outputs.
for loop_var in cond_g_info.dependent_vars:
self.g.replace_all_inputs(
cond_g_info.nodes, loop_var.switch_true_identity_output.id,
loop_var.next_iteration_input.id)
body_nodes = set(cell_g_info.nodes + cond_g_info.nodes)
body_outputs = cond_g_info.outputs + cell_g_info.outputs
for out_tensor_value_info in body_outputs:
out_tensor_value_info.shape = utils.create_vague_shape_like(
out_tensor_value_info.shape)
loop_body_g = LoopRewriterBase.construct_graph_from_nodes(
self.g, body_nodes, body_outputs)
# create loop body graph inputs
loop_body_g.add_graph_input(utils.make_name("i"),
TensorProto.INT64, ())
loop_body_g.add_graph_input(utils.make_name("cond"),
TensorProto.BOOL, ())
for i, tensor_value_info in enumerate(loop_props.state_inputs):
input_name = tensor_value_info.id
if input_name is None:
# if the variable is not used in the body graph, then we created a fake one,
# the same type and shape as its corresponding output.
out_tensor_value_info = loop_props.state_outputs[i]
dtype = out_tensor_value_info.dtype
shape = out_tensor_value_info.shape
input_name = utils.make_name("unused_state_input_")
else:
dtype = tensor_value_info.dtype
shape = tensor_value_info.shape
loop_body_g.add_graph_input(
input_name, dtype, utils.create_vague_shape_like(shape))
for input_ta in loop_props.tensor_array_inputs:
# Loop does not have scan inputs, so we use Gather to get data for each iteration.
index_node = loop_body_g.make_node("Unsqueeze",
[input_ta.index_input_id],
attr={"axes": [0]})
gather_node = loop_body_g.make_node(
"Gather", [input_ta.data_input_id, index_node.output[0]])
data_node = loop_body_g.make_node("Squeeze",
[gather_node.output[0]],
attr={"axes": [0]})
loop_body_g.replace_all_inputs(loop_body_g.get_nodes(),
input_ta.consumer.id,
data_node.output[0])
## create Loop node
loop_node = self._create_loop_node(context, loop_props)
if not loop_node:
logger.error("failed to create loop node during rewrite")
return REWRITER_RESULT.FAIL
loop_node.set_body_graph_as_attr("body", loop_body_g)
logger.debug("rewrite successfully")
return REWRITER_RESULT.OK
except Exception as ex:
tb = traceback.format_exc()
logger.error(
"loop rewrite failed, due to exception: %s, details:%s", ex,
tb)
return REWRITER_RESULT.FAIL
