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"""
Copyright 2020 The OneFlow Authors. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import os
import unittest
import numpy as np
import oneflow
import oneflow as flow
import oneflow.framework.graph_build_util as graph_build_util
import oneflow.unittest
class SubModule(flow.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = flow.nn.Conv2d(1, 1, 5)
self.relu = flow.nn.ReLU()
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
return x
class CustomModule(flow.nn.Module):
def __init__(self):
super().__init__()
self.layer = SubModule()
self.fc1 = flow.nn.Linear(36, 4)
self.register_buffer("dummy_buff", flow.Tensor(1, 4))
def forward(self, x):
x = self.layer(x)
x = oneflow.F.flatten(x, 1)
x = self.fc1(x) + self.dummy_buff
return x
@unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases")
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@flow.unittest.skip_unless_1n1d()
class TestGraph(flow.unittest.TestCase):
def test_add_nested_module(test_case):
x = flow.Tensor(1, 1, 10, 10)
flow.nn.init.uniform_(x, a=-1.0, b=1.0)
m = CustomModule()
y = m(x)
class CustomGraphNestedModule(flow.nn.Graph):
def __init__(self):
super().__init__()
self.m = m
def build(self, x):
return self.m(x)
g = CustomGraphNestedModule()
test_case.assertEqual(g.name, g._c_nn_graph.name)
test_case.assertTrue(isinstance(g.m, flow.nn.graph.Block))
test_case.assertEqual(g.m.type, "MODULE")
test_case.assertEqual(g.m.name, "m")
test_case.assertTrue(isinstance(g.m.dummy_buff, flow.nn.graph.Block))
test_case.assertEqual(g.m.dummy_buff.type, "BUFFER")
test_case.assertTrue(isinstance(g.m.layer.conv1, flow.nn.graph.Block))
test_case.assertEqual(g.m.layer.conv1.name, "conv1")
test_case.assertEqual(g.m.layer.conv1.name_prefix, "m.layer.")
test_case.assertTrue(isinstance(g.m.layer.conv1.weight, flow.nn.graph.Block))
test_case.assertEqual(g.m.layer.conv1.weight.type, "PARAMETER")
g.m.layer.conv1._is_executing_forward = True
test_case.assertTrue(isinstance(g.m.layer.conv1.weight, flow.Tensor))
g.m.layer.conv1._is_executing_forward = False
test_case.assertEqual(g.m.layer.conv1.kernel_size, (5, 5))
z = g.build(x)
test_case.assertTrue(np.array_equal(y.numpy(), z.numpy()))
def test_graph_config(test_case):
print("cclog: CustomGraphConfig begin")
class CustomGraphConfig(flow.nn.Graph):
def __init__(self):
super().__init__()
self.m = CustomModule()
self.config.enable_auto_mixed_precision(True)
def build(self, x):
x = self.m(x)
return x
g = CustomGraphConfig()
test_case.assertEqual(g.config.training, False)
g.config.enable_fuse_add_to_output(True)
g.config.enable_fuse_add_to_output(False)
for s in g._state():
print("g state: ", repr(s))
print(repr(g))
print("cclog: CustomGraphConfig done")
def test_graph_name(test_case):
print("cclog: GraphName begin")
class ACustomGraph(flow.nn.Graph):
def __init__(self):
super().__init__()
def build(self, x):
return x
class BCustomGraph(flow.nn.Graph):
def __init__(self):
super().__init__()
def build(self, x):
return x
class CBCustomGraph(BCustomGraph):
def __init__(self):
super().__init__()
def create_graph(cnt):
a = ACustomGraph()
test_case.assertEqual(a.name, "ACustomGraph_" + str(cnt))
b = BCustomGraph()
test_case.assertEqual(b.name, "BCustomGraph_" + str(cnt))
cb = CBCustomGraph()
test_case.assertEqual(cb.name, "CBCustomGraph_" + str(cnt))
flow.nn.Graph._child_init_cnt.clear()
for i in range(0, 3):
create_graph(i)
flow.nn.Graph._child_init_cnt.clear()
for i in range(0, 3):
create_graph(i)
print("cclog: GraphName done")
def test_graph_build_ctx(test_case):
test_case.assertEqual(graph_build_util.lazy_mode.is_enabled(), False)
with graph_build_util.lazy_mode.gard(True):
test_case.assertEqual(graph_build_util.lazy_mode.is_enabled(), True)
with graph_build_util.lazy_mode.gard(False):
test_case.assertEqual(graph_build_util.lazy_mode.is_enabled(), False)
test_case.assertEqual(graph_build_util.lazy_mode.is_enabled(), True)
test_case.assertEqual(graph_build_util.lazy_mode.is_enabled(), False)
class CustomGraphGraphBuildCtx(flow.nn.Graph):
def __init__(self):
super().__init__()
self.config.enable_auto_mixed_precision(True)
def build(self, x):
test_case.assertEqual(graph_build_util.lazy_mode.is_enabled(), True)
import oneflow.framework.session_context as session_ctx
from oneflow.framework.multi_client_session import MultiClientSession
session = session_ctx.GetDefaultSession()
test_case.assertEqual(type(session), MultiClientSession)
import oneflow.framework.scope_util as scope_util
scope = oneflow.current_scope()
scope_proto = graph_build_util.scope_to_proto(scope)
test_case.assertEqual(session.id, scope_proto.session_id)
test_case.assertEqual(
oneflow._oneflow_internal.JobBuildAndInferCtx_GetCurrentJobName(),
self.name,
)
return x
test_case.assertTrue(oneflow._oneflow_internal.IsMultiClient())
g = CustomGraphGraphBuildCtx()
test_case.assertEqual(graph_build_util.lazy_mode.is_enabled(), False)
data = np.array([2.0, 1.0, 0.0, -1.0, -2.0])
x = flow.tensor(data, dtype=flow.float32)
g._compile(x)
print("graph proto", g._graph_proto)
test_case.assertEqual(graph_build_util.lazy_mode.is_enabled(), False)
def test_block_scope(test_case):
class SubModule0(flow.nn.Module):
def __init__(self):
super().__init__()
self.conv1 = flow.nn.Conv2d(1, 1, 5)
def forward(self, x):
scope = oneflow.current_scope()
scope_proto = graph_build_util.scope_to_proto(scope)
ck_bool = scope_proto.attr_name2attr_value["checkpointing"].at_bool
test_case.assertEqual(ck_bool, True)
stage_int = scope_proto.attr_name2attr_value[
"pipeline_stage_id_hint"
].at_int64
test_case.assertEqual(stage_int, 0)
test_case.assertTrue(weight.is_lazy)
return out
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class SubModule1(flow.nn.Module):
def __init__(self):
super().__init__()
self.fc1 = flow.nn.Linear(36, 4, False)
self.register_buffer("dummy_buff", flow.Tensor(1, 4))
def forward(self, x):
scope = oneflow.current_scope()
scope_proto = graph_build_util.scope_to_proto(scope)
test_case.assertEqual(
scope_proto.parent_scope_symbol_id, self.prev_scope.symbol_id
)
ck_bool = scope_proto.attr_name2attr_value["checkpointing"]
test_case.assertEqual(ck_bool.WhichOneof("value"), None)
stage_int = scope_proto.attr_name2attr_value[
"pipeline_stage_id_hint"
].at_int64
test_case.assertEqual(stage_int, 1)
name = self.name_prefix + self.name
prefixes = []
for prefix in scope_proto.scope_op_name_prefixes:
prefixes.append(prefix)
name_in_scope = ".".join(prefixes)
test_case.assertEqual(name, name_in_scope)
b = self.dummy_buff
dummy_buff_scope_proto = graph_build_util.scope_to_proto(
self._buffers["dummy_buff"].scope
)
test_case.assertEqual(
dummy_buff_scope_proto.parent_scope_symbol_id, scope.symbol_id
)
x = self.fc1(x)
return x + b
class CustomModule1(flow.nn.Module):
def __init__(self):
super().__init__()
self.layer0 = SubModule0()
self.layer1 = SubModule1()
def forward(self, x, y):
print("x0: ", x.shape)
x = self.layer0(x)
print("x1: ", x.shape)
print("y0: ", y.shape)
y = self.layer1(y)
print("y1: ", y.shape)
return (x, y)
m = CustomModule1()
class CustomGraphBlockScope(flow.nn.Graph):
def __init__(self):
super().__init__()
self.m = m
self.m.layer0.config.stage_id = 0
self.m.layer0.config.activation_checkpointing = True
self.m.layer1.config.stage_id = 1
def build(self, x, y):
return self.m(x, y)
g = CustomGraphBlockScope()
x = np.ones((1, 1, 10, 10))
x = flow.tensor(x, dtype=flow.float32)
y = np.ones((16, 36))
y = flow.tensor(y, dtype=flow.float32)
g._compile(x, y)
if __name__ == "__main__":
unittest.main()