def compile(self, inputs):
xmodel = ch2o.compile_model(self.mc, inputs)
f = tempfile.NamedTemporaryFile(delete=False)
f.write(xmodel.SerializeToString())
f.close()
del xmodel
graph = chainer_compiler_core.load(f.name)
os.unlink(f.name)
self.orig_output_names = graph.output_names()
fwd_graph, bwd_graph = graph.backward_to(graph.input_names())
if self.dump_onnx:
sys.stderr.write('=== vvv forward vvv ===\n' + fwd_graph.dump() +
'\n=== ^^^ forward ^^^ ===\n')
sys.stderr.write('=== vvv backward vvv ===\n' + bwd_graph.dump() +
'\n=== ^^^ backward ^^^ ===\n')
assert graph.input_names() == fwd_graph.input_names()
self.fwd_input_names = fwd_graph.input_names()
self.fwd_output_names = fwd_graph.output_names()
self.bwd_input_names = bwd_graph.input_names()
self.bwd_output_names = bwd_graph.output_names()
# TODO(hamaji): Revive shape inference.
self.fwd = fwd_graph.compile(skip_inference=True)
self.bwd = bwd_graph.compile(skip_inference=True)
self.param_names = self.fwd_input_names[len(inputs):]
self.compiled = True
def test_backprop():
graph = chainer_compiler_core.load(
'out/ch2o_node_Linear_backprop/model.onnx')
params = graph.params()
input_names = graph.input_names()
output_names = graph.output_names()
assert len(input_names) == 1
assert len(output_names) == 1
fwd_graph, bwd_graph = graph.backward()
assert len(fwd_graph.input_names()) == 1
assert len(fwd_graph.output_names()) == 3
assert len(bwd_graph.input_names()) == 3
assert len(bwd_graph.output_names()) == 2
fwd = fwd_graph.compile()
bwd = bwd_graph.compile()
fwd_inputs = dict(params)
t1 = aranges(5, 7)
fwd_inputs[input_names[0]] = chainer_compiler_core.value(t1)
loss = (chainerx.dot(t1, params['/l1/W'].array().T) +
params['/l1/b'].array())
fwd_outputs = fwd.run(fwd_inputs)
assert len(fwd_outputs) == 3
chainerx.testing.assert_allclose(loss,
fwd_outputs[output_names[0]].array())
grad_loss = aranges(*loss.shape) + 4.2
bwd_inputs = {}
for name in fwd_graph.output_names():
iname = name
value = fwd_outputs[name]
if name in output_names:
iname = 'grad_in@' + name
value = chainer_compiler_core.value(grad_loss)
bwd_inputs[iname] = value
bwd_outputs = bwd.run(bwd_inputs)
grad_w = chainerx.dot(grad_loss.T, t1)
chainerx.testing.assert_allclose(grad_w,
bwd_outputs['grad_out@/l1/W'].array())
grad_b = chainerx.sum(grad_loss, axis=0)
chainerx.testing.assert_allclose(grad_b,
bwd_outputs['grad_out@/l1/b'].array())
def test_dropout_inference():
graph = chainer_compiler_core.load(
os.path.join(ONNX_TEST_DATA, 'node/test_dropout_random/model.onnx'))
input_names = graph.input_names()
output_names = graph.output_names()
assert len(input_names) == 1
assert len(output_names) == 1
xcvm = graph.compile()
input = chainerx.array(np.random.normal(size=(3, 4, 5)))
inputs = {input_names[0]: chainer_compiler_core.value(input)}
outputs = xcvm.run(inputs)
output = outputs[output_names[0]].array()
assert bool(chainerx.sum(input != output) == 0)
def compile(self, inputs):
if self.translator == 'ch2o':
xmodel = ch2o.compile_model(self.mc, inputs)
f = tempfile.NamedTemporaryFile(delete=False)
f.write(xmodel.SerializeToString())
f.close()
del xmodel
elif self.translator == 'onnx_chainer':
import onnx_chainer
f = tempfile.NamedTemporaryFile(delete=False)
onnx_chainer.export(self.mc, inputs, filename=f)
f.close()
else:
raise NotImplementedError('Unsupported translator:',
self.translator)
graph = chainer_compiler_core.load(f.name)
os.unlink(f.name)
self.orig_output_names = graph.output_names()
if self.computation_order is None:
fwd_graph, bwd_graph = graph.backward_to(graph.input_names() +
graph.param_names())
else:
fwd_graph, bwd_graph = graph.backward_to_with_order(
self.computation_order)
if self.dump_onnx:
sys.stderr.write('=== vvv forward vvv ===\n' + fwd_graph.dump() +
'\n=== ^^^ forward ^^^ ===\n')
sys.stderr.write('=== vvv backward vvv ===\n' + bwd_graph.dump() +
'\n=== ^^^ backward ^^^ ===\n')
assert graph.input_names() == fwd_graph.input_names()
self.fwd_input_names = fwd_graph.input_names()
self.fwd_output_names = fwd_graph.output_names()
self.bwd_input_names = bwd_graph.input_names()
self.bwd_output_names = bwd_graph.output_names()
# TODO(hamaji): Revive shape inference.
self.fwd = fwd_graph.compile(skip_inference=True)
self.bwd = bwd_graph.compile(skip_inference=True)
self.param_names = fwd_graph.param_names()
self.compiled = True
def test_custom_op():
gb = onnx_script.GraphBuilder('pytest_custom_op')
a = np.array(13)
b = np.array(4)
c = np.array(10)
a_v = gb.input('a', a)
b_v = gb.input('b', b)
c_v = gb.input('c', c)
def custom_func(a, b, c):
return a - b, a * b - c
y, z = custom_func(a, b, c)
y_v = 'y'
z_v = 'z'
gb.ChainerDoSomething([a_v, b_v, c_v],
outputs=[y_v, z_v],
function_name='CustomFunction')
gb.output(y_v, y)
gb.output(z_v, z)
gb.gen_test()
graph = chainer_compiler_core.load('out/pytest_custom_op/model.onnx')
params = graph.params()
input_names = graph.input_names()
output_names = graph.output_names()
assert len(input_names) == 3
assert len(output_names) == 2
xcvm = graph.compile()
inputs = {}
for n, v in [('a', a), ('b', b), ('c', c)]:
inputs[n] = chainer_compiler_core.value(chainerx.array(v))
outputs = xcvm.run(inputs, custom_funcs={'CustomFunction': custom_func})
assert len(outputs) == 2
chainerx.testing.assert_allclose(9, outputs['y'].array())
chainerx.testing.assert_allclose(42, outputs['z'].array())
def test_dropout_training():
graph = chainer_compiler_core.load(
os.path.join(ONNX_TEST_DATA, 'node/test_dropout_random/model.onnx'))
input_names = graph.input_names()
output_names = graph.output_names()
assert len(input_names) == 1
assert len(output_names) == 1
xcvm = graph.compile()
input = chainerx.array(np.random.normal(size=(3, 4, 5)))
inputs = {input_names[0]: chainer_compiler_core.value(input)}
num_retries = 3
for i in range(num_retries):
outputs = xcvm.run(inputs, training=True)
output = outputs[output_names[0]].array()
ok = bool(chainerx.sum(input != output) > 0)
if ok: break
else:
assert False, 'No dropout was observed in %d attempts' % num_retries
def test_inference():
graph = chainer_compiler_core.load('out/ch2o_node_Linear/model.onnx')
params = graph.params()
input_names = graph.input_names()
output_names = graph.output_names()
assert len(input_names) == 1
assert len(output_names) == 2
xcvm = graph.compile()
inputs = dict(params)
t1 = aranges(5, 7)
inputs[input_names[0]] = chainer_compiler_core.value(t1)
y1 = chainerx.dot(t1, params['/l1/W'].array().T) + params['/l1/b'].array()
y2 = chainerx.dot(t1, params['/l2/W'].array().T)
outputs = xcvm.run(inputs)
assert len(outputs) == 2
chainerx.testing.assert_allclose(y1, outputs[output_names[0]].array())
chainerx.testing.assert_allclose(y2, outputs[output_names[1]].array())
assert 'op_type: "ChainerLinear"' in graph.dump()
