test(traced_module): add some testcases for traced module

GitOrigin-RevId: 0d6bb20b2b

imperative/python/megengine/experimental/traced_module/expr.py

@@ -201,7 +201,8 @@ class Apply(Expr):
                 NodeMixin.wrap_safe(i, Constant.make(i))
         apply_node = cls.make(opdef)
         for i in inputs:
-            apply_node.add_input(NodeMixin.get(i))
+            assert isinstance(i, RawTensor)
+            apply_node.inputs.append(NodeMixin.get(i))
 
         unset_module_tracing()
         outputs = apply(opdef, *inputs)

imperative/python/megengine/experimental/traced_module/pytree.py

@@ -1,3 +1,13 @@
+# -*- coding: utf-8 -*-
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+
+import collections
 from typing import Callable, NamedTuple
 
 SUPPORTED_TYPE = {}
@@ -9,11 +19,22 @@ def register_supported_type(type, flatten, unflatten):
     SUPPORTED_TYPE[type] = NodeType(flatten, unflatten)
 
 
+def _dict_flatten(inp):
+    aux_data = []
+    results = []
+    for key, value in sorted(inp.items()):
+        results.append(value)
+        aux_data.append(key)
+    return results, aux_data
+
+
+def _dict_unflatten(inps, aux_data):
+    return dict(zip(aux_data, inps))
+
+
 register_supported_type(list, lambda x: (x, None), lambda x, aux_data: list(x))
 register_supported_type(tuple, lambda x: (x, None), lambda x, aux_data: list(x))
-register_supported_type(
-    dict, lambda x: (list(x.values()), list(x.keys())), lambda x, y: dict(zip(y, x))
-)
+register_supported_type(dict, _dict_flatten, _dict_unflatten)
 register_supported_type(
     slice,
     lambda x: ([x.start, x.stop, x.step], None),
@@ -68,6 +89,8 @@ class TreeDef:
 
 class LeafDef(TreeDef):
     def __init__(self, type):
+        if not isinstance(type, collections.abc.Sequence):
+            type = (type,)
         super().__init__(type, None, [])
         self.num_leaves = 1
 
@@ -77,4 +100,4 @@ class LeafDef(TreeDef):
         return leaves[0]
 
     def __repr__(self):
-        return "Leaf({})".format(self.type.__name__)
+        return "Leaf({})".format(", ".join(t.__name__ for t in self.type))

imperative/python/test/integration/test_converge.py

@@ -14,6 +14,7 @@ import megengine as mge
 import megengine.autodiff as ad
 import megengine.functional as F
 from megengine import Tensor
+from megengine.experimental.traced_module import trace_module
 from megengine.module import Linear, Module
 from megengine.optimizer import SGD
 
@@ -71,8 +72,13 @@ class XORNet(Module):
         return x
 
 
-def test_training_converge():
+@pytest.mark.parametrize("test_traced_module", [True, False])
+def test_training_converge(test_traced_module):
     net = XORNet()
+    if test_training_converge:
+        inp = Tensor(np.random.random((14, 2)))
+        net = trace_module(net, inp)
+
     opt = SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
     gm = ad.GradManager().attach(net.parameters())
 
@@ -105,9 +111,8 @@ def test_training_converge():
     xx = xx.reshape((ngrid * ngrid, 1))
     yy = yy.reshape((ngrid * ngrid, 1))
     data = mge.tensor(np.concatenate((xx, yy), axis=1).astype(np.float32))
-
-    pred = infer(data).numpy()
-    precision = calculate_precision(data.numpy(), pred)
+    pred = infer(data)
+    precision = calculate_precision(data.numpy(), pred.numpy())
     assert precision == 1.0, "Test precision must be high enough, get {}".format(
         precision
     )

imperative/python/test/integration/test_converge_with_gradient_clip.py

@@ -15,6 +15,7 @@ import megengine.autodiff as ad
 import megengine.functional as F
 import megengine.optimizer as optim
 from megengine import Tensor
+from megengine.experimental.traced_module import trace_module
 from megengine.jit import trace
 from megengine.module import Linear, Module
 from megengine.optimizer import SGD
@@ -73,8 +74,12 @@ class XORNet(Module):
         return x
 
 
-def test_training_converge():
+@pytest.mark.parametrize("test_traced_module", [True, False])
+def test_training_converge(test_traced_module):
     net = XORNet()
+    if test_traced_module:
+        inp = Tensor(np.random.random((14, 2)))
+        net = trace_module(net, inp)
     opt = SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
     gm = ad.GradManager().attach(net.parameters())
 
@@ -110,9 +115,8 @@ def test_training_converge():
     xx = xx.reshape((ngrid * ngrid, 1))
     yy = yy.reshape((ngrid * ngrid, 1))
     data = mge.tensor(np.concatenate((xx, yy), axis=1).astype(np.float32))
-
-    pred = infer(data).numpy()
-    precision = calculate_precision(data.numpy(), pred)
+    pred = infer(data)
+    precision = calculate_precision(data.numpy(), pred.numpy())
     print("precision=", precision)
     assert precision == 1.0, "Test precision must be high enough, get {}".format(
         precision

imperative/python/test/integration/test_trace_dump.py

@@ -19,6 +19,7 @@ import megengine.module as M
 import megengine.optimizer as optim
 from megengine import tensor
 from megengine.autodiff import GradManager
+from megengine.experimental.traced_module import trace_module
 from megengine.jit import trace
 
 

imperative/python/test/unit/module/test_module.py

@@ -15,6 +15,7 @@ import pytest
 import megengine as mge
 import megengine.functional as F
 from megengine import Parameter, Tensor, tensor
+from megengine.experimental.traced_module import TracedModule, trace_module
 from megengine.module import (
     BatchNorm1d,
     BatchNorm2d,
@@ -67,8 +68,18 @@ class MyModule(Module):
         return x
 
 
-def test_module_api():
+@pytest.mark.parametrize("test_traced_module", [True, False])
+def test_module_api(test_traced_module):
     m = MyModule()
+    if test_traced_module:
+        buff = m.buff
+        param = m.param
+        m = trace_module(m, Tensor(np.random.random((1, 4, 16, 16))))
+        assert "buff" not in m.__dict__
+        assert "param" not in m.__dict__
+        m.buff = buff
+        m.param = param
+
     assert list(m.children()) == [m.bn, m.i]
     assert list(m.named_children()) == [("bn", m.bn), ("i", m.i)]
     assert list(m.modules()) == [m, m.bn, m.i, m.i.bn]
@@ -141,8 +152,11 @@ def test_module_api():
     assert m.bn.training == False and m.i.bn.training == False
 
 
-def test_module_api_reuse_submodule():
+@pytest.mark.parametrize("test_traced_module", [True, False])
+def test_module_api_reuse_submodule(test_traced_module):
     m = MyModule()
+    if test_traced_module:
+        m = trace_module(m, Tensor(np.random.random((1, 4, 16, 16))))
     m.h = m.i  # pylint: disable=attribute-defined-outside-init
     assert list(m.modules()) == [m, m.bn, m.i, m.i.bn]
     assert list(m.named_modules()) == [
@@ -153,15 +167,21 @@ def test_module_api_reuse_submodule():
     ]
 
 
-def test_module_api_iterable_stability():
+@pytest.mark.parametrize("test_traced_module", [True, False])
+def test_module_api_iterable_stability(test_traced_module):
     m = MyModule()
+    if test_traced_module:
+        m = trace_module(m, Tensor(np.random.random((1, 4, 16, 16))))
     l = list(m.modules())
     for _ in range(100):
         assert list(m.modules()) == l
 
 
-def test_module_api_hooks():
+@pytest.mark.parametrize("test_traced_module", [True, False])
+def test_module_api_hooks(test_traced_module):
     net = MyModule()
+    if test_traced_module:
+        net = trace_module(net, Tensor(np.zeros((1, 4, 1, 1))))
     pre_hook_num = 0
     post_hook_num = 0
     hooks = []
@@ -383,11 +403,16 @@ class Simple(Module):
         self.conv1.weight = self.conv0.weight
 
     def forward(self, inputs):
-        pass
+        x = self.conv0(inputs)
+        y = self.conv1(inputs)
+        return x + y
 
 
-def test_shared_param():
+@pytest.mark.parametrize("test_traced_module", [True, False])
+def test_shared_param(test_traced_module):
     net = Simple()
+    if test_traced_module:
+        net = trace_module(net, tensor(np.random.random((1, 1, 8, 8))))
     assert net.conv0.weight is net.conv1.weight
     data = tensor(np.random.random((1, 1, 8, 8)).astype(np.float32))
     np.testing.assert_allclose(net.conv0(data).numpy(), net.conv1(data).numpy())
@@ -449,15 +474,21 @@ def test_shared_param_1d():
     np.testing.assert_allclose(conv0(data).numpy(), conv1(data).numpy())
 
 
-def test_pickle_module():
+@pytest.mark.parametrize("test_traced_module", [True, False])
+def test_pickle_module(test_traced_module):
     data_shape = (2, 28)
     data = tensor(np.random.random(data_shape))
     mlp = MLP()
+    pred_gt = mlp(data)
+    if test_traced_module:
+        mlp = trace_module(mlp, data)
     # pickle before forward
     with BytesIO() as fout:
         mge.save(mlp, fout)
         fout.seek(0)
         mlp1 = mge.load(fout)
+        if test_traced_module:
+            assert type(mlp1) == TracedModule
         pred0 = mlp1(data)
 
     pred1 = mlp(data)
@@ -467,8 +498,11 @@ def test_pickle_module():
         mge.save(mlp, fout)
         fout.seek(0)
         mlp1 = mge.load(fout)
+        if test_traced_module:
+            assert type(mlp1) == TracedModule
         pred2 = mlp1(data)
 
+    np.testing.assert_allclose(pred_gt.numpy(), pred1.numpy(), atol=5e-6)
     np.testing.assert_allclose(pred0.numpy(), pred1.numpy(), atol=5e-6)
     np.testing.assert_allclose(pred0.numpy(), pred2.numpy(), atol=5e-6)
 

imperative/python/test/unit/traced_module/test_jit_trace.py

@@ -0,0 +1,59 @@
+# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+#
+# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import io
+
+import numpy as np
+
+import megengine.functional as F
+import megengine.module as M
+import megengine.utils.comp_graph_tools as cgtools
+from megengine.experimental.traced_module import trace_module
+from megengine.jit import trace
+from megengine.module import Module
+
+
+class MyBlock(Module):
+    def __init__(self, in_channels, channels):
+        super(MyBlock, self).__init__()
+        self.conv1 = M.Conv2d(in_channels, channels, 3, 1, padding=1, bias=False)
+        self.bn1 = M.BatchNorm2d(channels)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = F.relu(x) + 1
+        return x
+
+
+class MyModule(Module):
+    def __init__(self):
+        super(MyModule, self).__init__()
+        self.block0 = MyBlock(8, 4)
+        self.block1 = MyBlock(4, 2)
+
+    def forward(self, x):
+        x = self.block0(x)
+        x = self.block1(x)
+        return x
+
+
+def test_jit_trace():
+    module = MyModule()
+    module.eval()
+    x = F.ones((1, 8, 14, 14))
+    expect = module(x)
+    traced_module = trace_module(module, x)
+    func = trace(traced_module, capture_as_const=True)
+    np.testing.assert_array_equal(func(x), expect)
+    model = io.BytesIO()
+    func.dump(model)
+    model.seek(0)
+    infer_cg = cgtools.GraphInference(model)
+    np.testing.assert_allclose(
+        list(infer_cg.run(x.numpy()).values())[0], expect, atol=1e-6
+    )