def test_merge_graph_defs_mismatch_version(self):
graph_def_a = GraphDef()
text_format.Parse(
"""
node {
name: "A"
op: "Input"
}
versions {
producer: 21
}
""",
graph_def_a,
)
graph_def_b = GraphDef()
text_format.Parse(
"""
node {
name: "A"
op: "Input"
}
versions {
producer: 100
}
""",
graph_def_b,
)
with self.assertRaisesRegex(
ValueError, "Cannot combine GraphDefs of different versions"
):
graph_util.merge_graph_defs([graph_def_a, graph_def_b])
def test_merge_graph_defs_gradient(self):
expected_proto = """
library {
gradient {
function_name: "graph_1_foo"
gradient_func: "graph_1_foo_grad"
}
gradient {
function_name: "graph_2_foo"
gradient_func: "graph_2_foo_grad"
}
gradient {
function_name: "graph_2_bar"
gradient_func: "graph_2_bar_grad"
}
}
"""
graph_def_a = GraphDef()
text_format.Parse(
"""
library {
gradient {
function_name: "foo"
gradient_func: "foo_grad"
}
}
""",
graph_def_a,
)
graph_def_b = GraphDef()
text_format.Parse(
"""
library {
gradient {
function_name: "foo"
gradient_func: "foo_grad"
}
gradient {
function_name: "bar"
gradient_func: "bar_grad"
}
}
""",
graph_def_b,
)
self.assertProtoEquals(
expected_proto,
graph_util.merge_graph_defs([graph_def_a, graph_def_b]),
)
def graph_impl(
self,
ctx,
run,
tag,
is_conceptual,
experiment=None,
limit_attr_size=None,
large_attrs_key=None,
):
"""Result of the form `(body, mime_type)`; may raise `NotFound`."""
if is_conceptual:
keras_model_config = json.loads(
self._read_blob(
ctx,
experiment,
[metadata.PLUGIN_NAME_KERAS_MODEL],
run,
tag,
))
graph = keras_util.keras_model_to_graph_def(keras_model_config)
elif tag is None:
graph_raw = self._read_blob(
ctx,
experiment,
[metadata.PLUGIN_NAME],
run,
metadata.RUN_GRAPH_NAME,
)
graph = graph_pb2.GraphDef.FromString(graph_raw)
else:
# Op graph: could be either of two plugins. (Cf. `info_impl`.)
plugins = [
metadata.PLUGIN_NAME_RUN_METADATA,
metadata.PLUGIN_NAME_RUN_METADATA_WITH_GRAPH,
]
raw_run_metadata = self._read_blob(ctx, experiment, plugins, run,
tag)
run_metadata = config_pb2.RunMetadata.FromString(raw_run_metadata)
graph = graph_util.merge_graph_defs([
func_graph.pre_optimization_graph
for func_graph in run_metadata.function_graphs
])
# This next line might raise a ValueError if the limit parameters
# are invalid (size is negative, size present but key absent, etc.).
process_graph.prepare_graph_for_ui(graph, limit_attr_size,
large_attrs_key)
return (str(graph), "text/x-protobuf") # pbtxt
def test_merge_graph_defs_single_graph_def_no_prefix(self):
graph_def_a = GraphDef()
text_format.Parse(
"""
node {
name: "A"
op: "Input"
}
versions {
producer: 21
}
""",
graph_def_a,
)
self.assertProtoEquals(
graph_def_a,
graph_util.merge_graph_defs([graph_def_a]),
)
def graph_impl(
self,
ctx,
run,
tag,
is_conceptual,
experiment=None,
limit_attr_size=None,
large_attrs_key=None,
):
"""Result of the form `(body, mime_type)`, or `None` if no graph
exists."""
if self._data_provider:
if tag is None:
tag = metadata.RUN_GRAPH_NAME
graph_blob_sequences = self._data_provider.read_blob_sequences(
ctx,
experiment_id=experiment,
plugin_name=metadata.PLUGIN_NAME,
run_tag_filter=provider.RunTagFilter(runs=[run], tags=[tag]),
downsample=1,
)
blob_datum_list = graph_blob_sequences.get(run, {}).get(tag, ())
try:
blob_ref = blob_datum_list[0].values[0]
except IndexError:
return None
# Always use the blob_key approach for now, even if there is a direct url.
graph_raw = self._data_provider.read_blob(
ctx, blob_key=blob_ref.blob_key
)
# This method ultimately returns pbtxt, but we have to deserialize and
# later reserialize this anyway, because a) this way we accept binary
# protobufs too, and b) below we run `prepare_graph_for_ui` on the graph.
graph = graph_pb2.GraphDef.FromString(graph_raw)
elif is_conceptual:
tensor_events = self._multiplexer.Tensors(run, tag)
# Take the first event if there are multiple events written from different
# steps.
keras_model_config = json.loads(
tensor_events[0].tensor_proto.string_val[0]
)
graph = keras_util.keras_model_to_graph_def(keras_model_config)
elif tag:
tensor_events = self._multiplexer.Tensors(run, tag)
# Take the first event if there are multiple events written from different
# steps.
run_metadata = config_pb2.RunMetadata.FromString(
tensor_events[0].tensor_proto.string_val[0]
)
graph = graph_util.merge_graph_defs(
[
func_graph.pre_optimization_graph
for func_graph in run_metadata.function_graphs
]
)
else:
graph = self._multiplexer.Graph(run)
# This next line might raise a ValueError if the limit parameters
# are invalid (size is negative, size present but key absent, etc.).
process_graph.prepare_graph_for_ui(
graph, limit_attr_size, large_attrs_key
)
return (str(graph), "text/x-protobuf") # pbtxt
def test_merge_graph_defs_partitioned_call_remap(self):
expected_proto = GraphDef()
text_format.Parse(
"""
node {
name: "graph_1/X"
op: "PartitionedCall"
attr {
key: "f"
value {
func {
name: "graph_1_foo"
}
}
}
}
library {
function {
signature {
name: "graph_1_foo"
input_arg {
name: "x"
type: DT_HALF
}
output_arg {
name: "identity"
type: DT_HALF
}
}
}
}
""",
expected_proto,
)
graph_def_a = GraphDef()
text_format.Parse(
"""
node {
name: "X"
op: "PartitionedCall"
attr {
key: "f"
value {
func {
name: "foo"
}
}
}
}
library {
function {
signature {
name: "foo"
input_arg {
name: "x"
type: DT_HALF
}
output_arg {
name: "identity"
type: DT_HALF
}
}
}
}
""",
graph_def_a,
)
graph_def_b = GraphDef()
self.assertProtoEquals(
expected_proto,
graph_util.merge_graph_defs([graph_def_a, graph_def_b]),
)
def test_merge_graph_defs_function(self):
expected_proto = """
library {
function {
signature {
name: "graph_1_foo"
input_arg {
name: "x"
type: DT_HALF
}
output_arg {
name: "identity"
type: DT_HALF
}
}
node_def {
name: "add"
op: "Add"
input: "x"
input: "y"
}
}
function {
signature {
name: "graph_2_foo"
input_arg {
name: "x"
type: DT_INT32
}
output_arg {
name: "identity"
type: DT_INT32
}
}
node_def {
name: "add"
op: "Add"
input: "x"
input: "y"
}
}
function {
signature {
name: "graph_2_foo_1"
input_arg {
name: "x"
type: DT_HALF
}
output_arg {
name: "identity"
type: DT_HALF
}
}
node_def {
name: "add"
op: "Add"
input: "x"
input: "y"
}
}
}
"""
graph_def_a = GraphDef()
text_format.Parse(
"""
library {
function {
signature {
name: "foo"
input_arg {
name: "x"
type: DT_HALF
}
output_arg {
name: "identity"
type: DT_HALF
}
}
node_def {
name: "add"
op: "Add"
input: "x"
input: "y"
}
}
}
""",
graph_def_a,
)
graph_def_b = GraphDef()
text_format.Parse(
"""
library {
function {
signature {
name: "foo"
input_arg {
name: "x"
type: DT_INT32
}
output_arg {
name: "identity"
type: DT_INT32
}
}
node_def {
name: "add"
op: "Add"
input: "x"
input: "y"
}
}
function {
signature {
name: "foo_1"
input_arg {
name: "x"
type: DT_HALF
}
output_arg {
name: "identity"
type: DT_HALF
}
}
node_def {
name: "add"
op: "Add"
input: "x"
input: "y"
}
}
}
""",
graph_def_b,
)
self.assertProtoEquals(
expected_proto,
graph_util.merge_graph_defs([graph_def_a, graph_def_b]),
)
def test_merge_graph_defs(self):
expected_proto = """
node {
name: "graph_1/X"
op: "Input"
}
node {
name: "graph_1/W"
op: "Input"
}
node {
name: "graph_1/Y"
op: "MatMul"
input: "graph_1/X"
input: "graph_1/W"
}
node {
name: "graph_2/A"
op: "Input"
}
node {
name: "graph_2/B"
op: "Input"
}
node {
name: "graph_2/C"
op: "MatMul"
input: "graph_2/A"
input: "graph_2/B"
}
node {
name: "graph_3/A"
op: "Input"
}
node {
name: "graph_3/B"
op: "Input"
}
versions {
producer: 21
}
"""
graph_def_a = GraphDef()
text_format.Parse(
"""
node {
name: "X"
op: "Input"
}
node {
name: "W"
op: "Input"
}
node {
name: "Y"
op: "MatMul"
input: "X"
input: "W"
}
versions {
producer: 21
}
""",
graph_def_a,
)
graph_def_b = GraphDef()
text_format.Parse(
"""
node {
name: "A"
op: "Input"
}
node {
name: "B"
op: "Input"
}
node {
name: "C"
op: "MatMul"
input: "A"
input: "B"
}
versions {
producer: 21
}
""",
graph_def_b,
)
graph_def_c = GraphDef()
text_format.Parse(
"""
node {
name: "A"
op: "Input"
}
node {
name: "B"
op: "Input"
}
versions {
producer: 21
}
""",
graph_def_c,
)
self.assertProtoEquals(
expected_proto,
graph_util.merge_graph_defs(
[graph_def_a, graph_def_b, graph_def_c]
),
)
def test_merge_graph_defs_name_collided_with_same_content(self):
expected_proto = """
node {
name: "graph_1/X"
op: "Input"
}
node {
name: "graph_1/W"
op: "Input"
}
node {
name: "graph_1/Y"
op: "MatMul"
input: "graph_1/X"
input: "graph_1/W"
}
node {
name: "graph_2/X"
op: "Input"
}
node {
name: "graph_2/A"
op: "Input"
}
node {
name: "graph_2/Y"
op: "MatMul"
input: "graph_2/X"
input: "graph_2/A"
}
versions {
producer: 21
}
"""
graph_def_a = GraphDef()
text_format.Parse(
"""
node {
name: "X"
op: "Input"
}
node {
name: "W"
op: "Input"
}
node {
name: "Y"
op: "MatMul"
input: "X"
input: "W"
}
versions {
producer: 21
}
""",
graph_def_a,
)
graph_def_b = GraphDef()
text_format.Parse(
"""
node {
name: "X"
op: "Input"
}
node {
name: "A"
op: "Input"
}
node {
name: "Y"
op: "MatMul"
input: "X"
input: "A"
}
versions {
producer: 21
}
""",
graph_def_b,
)
self.assertProtoEquals(
expected_proto,
graph_util.merge_graph_defs([graph_def_a, graph_def_b]),
)
