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import numpy as np
import time
import unittest
import onnx
import onnx.defs
from onnx.backend.base import namedtupledict
from onnx.helper import make_node, make_graph, make_tensor_value_info, make_model
from caffe2.proto import caffe2_pb2
from caffe2.python import core, workspace
from caffe2.python.models.download import ModelDownloader
from caffe2.python.onnx.onnxifi import onnxifi_caffe2_net
from caffe2.python.onnx.tests.test_utils import TestCase
ONNXIFI_DATATYPE_FLOAT32 = 1
def _print_net(net):
for i in net.external_input:
print("Input: {}".format(i))
for i in net.external_output:
print("Output: {}".format(i))
for op in net.op:
print("Op {}".format(op.type))
for x in op.input:
print(" input: {}".format(x))
for y in op.output:
print(" output: {}".format(y))
class OnnxifiTest(TestCase):
@unittest.skip("Need ONNXIFI backend support")
def test_relu_graph(self):
batch_size = 1
X = np.random.randn(batch_size, 1, 3, 2).astype(np.float32)
graph_def = make_graph(
[make_node("Relu", ["X"], ["Y"])],
name="test",
inputs=[make_tensor_value_info("X", onnx.TensorProto.FLOAT,
[batch_size, 1, 3, 2])],
outputs=[make_tensor_value_info("Y", onnx.TensorProto.FLOAT,
[batch_size, 1, 3, 2])])
model_def = make_model(graph_def, producer_name='relu-test')
op = core.CreateOperator(
"Onnxifi",
["X"],
["Y"],
onnx_model=model_def.SerializeToString(),
input_names=["X"],
output_names=["Y"],
output_shape_hint_0=[ONNXIFI_DATATYPE_FLOAT32, batch_size, 1, 3, 2])
workspace.FeedBlob("X", X)
workspace.RunOperatorOnce(op)
Y = workspace.FetchBlob("Y")
np.testing.assert_almost_equal(Y, np.maximum(X, 0))
@unittest.skip("Need ONNXIFI backend support")
def test_conv_graph(self):
X = np.array([[[[0., 1., 2., 3., 4.], # (1, 1, 5, 5) input tensor
[5., 6., 7., 8., 9.],
[10., 11., 12., 13., 14.],
[15., 16., 17., 18., 19.],
[20., 21., 22., 23., 24.]]]]).astype(np.float32)
W = np.array([[[[1., 1., 1.], # (1, 1, 3, 3) tensor for convolution weights
[1., 1., 1.],
[1., 1., 1.]]]]).astype(np.float32)
Y_without_padding = np.array([[[[54., 63., 72.], # (1, 1, 3, 3) output tensor
[99., 108., 117.],
[144., 153., 162.]]]]).astype(np.float32)
graph_def = make_graph(
[make_node(
'Conv',
inputs=['X', 'W'],
outputs=['Y'],
kernel_shape=[3, 3],
# Default values for other attributes: strides=[1, 1], dilations=[1, 1], groups=1
pads=[0, 0, 0, 0],
)],
name="test",
inputs=[make_tensor_value_info("X", onnx.TensorProto.FLOAT, [1, 1, 5, 5]),
make_tensor_value_info("W", onnx.TensorProto.FLOAT, [1, 1, 3, 3]),
],
outputs=[make_tensor_value_info("Y", onnx.TensorProto.FLOAT,
[1, 1, 3, 3])])
model_def = make_model(graph_def, producer_name='conv-test')
# We intentional rewrite the input/output name so test that the
# input/output binding of c2 op is positional
op = core.CreateOperator(
"Onnxifi",
["X0"],
["Y0"],
onnx_model=model_def.SerializeToString(),
initializers=["W", "W0"],
input_names=["X"],
output_names=["Y"],
output_shape_hint_0=[ONNXIFI_DATATYPE_FLOAT32, 1, 1, 3, 3])
workspace.FeedBlob("X0", X)
workspace.FeedBlob("W0", W)
workspace.RunOperatorOnce(op)
Y = workspace.FetchBlob("Y0")
np.testing.assert_almost_equal(Y, Y_without_padding)
class OnnxifiTransformTest(TestCase):
def setUp(self):
self.model_downloader = ModelDownloader()
def _add_head_tail(self, pred_net, new_head, new_tail):
orig_head = pred_net.external_input[0]
orig_tail = pred_net.external_output[0]
# Add head
head = caffe2_pb2.OperatorDef()
head.type = "Copy"
head.input.append(new_head)
head.output.append(orig_head)
dummy = caffe2_pb2.NetDef()
dummy.op.extend(pred_net.op)
del pred_net.op[:]
pred_net.op.extend([head])
pred_net.op.extend(dummy.op)
pred_net.external_input[0] = new_head
# Add tail
tail = caffe2_pb2.OperatorDef()
tail.type = "Copy"
tail.input.append(orig_tail)
tail.output.append(new_tail)
pred_net.op.extend([tail])
pred_net.external_output[0] = new_tail
@unittest.skip("Need ONNXIFI backend support")
def test_resnet50_core(self):
N = 1
repeat = 1
print("Batch size: {}, repeat inference {} times".format(N, repeat))
init_net, pred_net, _ = self.model_downloader.get_c2_model('resnet50')
self._add_head_tail(pred_net, 'real_data', 'real_softmax')
input_blob_dims = (N, 3, 224, 224)
input_name = "real_data"
device_option = core.DeviceOption(caffe2_pb2.CPU, 0)
init_net.device_option.CopyFrom(device_option)
pred_net.device_option.CopyFrom(device_option)
for op in pred_net.op:
op.device_option.CopyFrom(device_option)
net_outputs = pred_net.external_output
Y_c2 = None
data = np.random.randn(*input_blob_dims).astype(np.float32)
c2_time = 1
workspace.SwitchWorkspace("onnxifi_test", True)
with core.DeviceScope(device_option):
workspace.FeedBlob(input_name, data)
workspace.RunNetOnce(init_net)
workspace.CreateNet(pred_net)
start = time.time()
for _ in range(repeat):
workspace.RunNet(pred_net.name)
end = time.time()
c2_time = end - start
output_values = [workspace.FetchBlob(name) for name in net_outputs]
Y_c2 = namedtupledict('Outputs', net_outputs)(*output_values)
workspace.ResetWorkspace()
# Fill the workspace with the weights
with core.DeviceScope(device_option):
workspace.RunNetOnce(init_net)
# Cut the graph
start = time.time()
pred_net_cut = onnxifi_caffe2_net(pred_net,
{input_name: input_blob_dims},
infer_shapes=True)
del init_net, pred_net
#_print_net(pred_net_cut)
Y_trt = None
input_name = pred_net_cut.external_input[0]
print("C2 runtime: {}s".format(c2_time))
with core.DeviceScope(device_option):
workspace.FeedBlob(input_name, data)
workspace.CreateNet(pred_net_cut)
end = time.time()
print("Conversion time: {:.2f}s".format(end - start))
start = time.time()
for _ in range(repeat):
workspace.RunNet(pred_net_cut.name)
end = time.time()
trt_time = end - start
print("Onnxifi runtime: {}s, improvement: {}%".format(trt_time, (c2_time - trt_time) / c2_time * 100))
output_values = [workspace.FetchBlob(name) for name in net_outputs]
Y_trt = namedtupledict('Outputs', net_outputs)(*output_values)
np.testing.assert_allclose(Y_c2, Y_trt, rtol=1e-3)
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