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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/06/06 13:27:40 UTC
[GitHub] [incubator-tvm] maheshambule commented on a change in pull request #5052: [TARGET] ONNX codegen
maheshambule commented on a change in pull request #5052:
URL: https://github.com/apache/incubator-tvm/pull/5052#discussion_r436269084
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File path: tests/python/contrib/test_onnx.py
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@@ -0,0 +1,393 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+
+"""Relay to ONNX serialization test cases"""
+import pytest
+pytest.importorskip('onnx')
+pytest.importorskip('onnxruntime')
+
+import numpy as np
+import tvm
+from tvm import relay
+from tvm.contrib.target.onnx import to_onnx
+import onnxruntime as rt
+
+
+def func_to_onnx(func, name):
+ mod = tvm.IRModule()
+ mod['main'] = func
+ onnx_model = to_onnx(mod, {}, name, path=None)
+ return onnx_model.SerializeToString()
+
+
+def run_onnx(onnx_model, input_data):
+ sess = rt.InferenceSession(onnx_model)
+ input_names = {}
+ for input, data in zip(sess.get_inputs(), input_data):
+ input_names[input.name] = data
+ output_name = sess.get_outputs()[0].name
+ res = sess.run([output_name], input_names)
+ return res[0]
+
+
+def run_relay(func, data_tuple):
+ target = 'llvm'
+ ctx = tvm.context('llvm', 0)
+ intrp = relay.create_executor("graph", ctx=ctx, target=target)
+ relay_res = intrp.evaluate(func)(*data_tuple)
+ return relay_res.asnumpy()
+
+
+def verify_results(relay_func, indata, test_name, rtol=1e-7, atol=0):
+ relay_res = run_relay(relay_func, indata)
+ onnx_res = run_onnx(func_to_onnx(relay_func, test_name), indata)
+ np.testing.assert_allclose(relay_res, onnx_res, rtol=rtol, atol=atol)
+
+
+def test_add():
+ dtype = 'float32'
+ t1 = relay.TensorType((5, 10, 5))
+ t2 = relay.TensorType((5, 10, 5))
+ x = relay.var("x", t1, dtype=dtype)
+ y = relay.var("y", t2, dtype=dtype)
+ z = relay.add(x, y)
+ func = relay.Function([x, y], z)
+
+ x_data = np.random.rand(5, 10, 5).astype(dtype)
+ y_data = np.random.rand(5, 10, 5).astype(dtype)
+
+ verify_results(func, [x_data, y_data], 'test_add')
+
+
+def test_bias_add():
+ for dtype in ['float16', 'float32']:
+ xshape = (10, 2, 3, 4)
+ bshape = (2,)
+ rtol = 1e-2 if dtype == 'float16' else 1e-5
+ x = relay.var("x", shape=xshape, dtype=dtype)
+ bias = relay.var("bias", dtype=dtype)
+ z = relay.nn.bias_add(x, bias)
+ func = relay.Function([x, bias], z)
+
+ x_data = np.random.uniform(size=xshape).astype(dtype)
+ y_data = np.random.uniform(size=bshape).astype(dtype)
+
+ verify_results(func, [x_data, y_data], 'test_bias_add', rtol=rtol)
+
+
+def test_conv2d():
+ def verify_conv2d(dtype, scale, dshape, kshape,
+ padding=(1, 1),
+ groups=1,
+ dilation=(1, 1),
+ **attrs):
+ x = relay.var("x", shape=dshape, dtype=dtype)
+ w = relay.var("w", shape=kshape, dtype=dtype)
+ y = relay.nn.conv2d(x, w,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ **attrs)
+ func = relay.Function([x, w], y)
+ data = np.random.uniform(-scale, scale, size=dshape).astype(dtype)
+ kernel = np.random.uniform(-scale, scale, size=kshape).astype(dtype)
+ verify_results(func, [data, kernel], 'test_conv2d', rtol=1e-5, atol=1e-5)
+
+ dshape = (1, 32, 18, 18)
+ kshape = (32, 1, 3, 3)
+ verify_conv2d("float32", 1, dshape, kshape,
+ padding=(1, 1), channels=32, groups=32, kernel_size=(3, 3))
+
+ dshape = (1, 32, 18, 18)
+ kshape = (32, 4, 3, 3)
+ verify_conv2d("float32", 1, dshape, kshape,
+ padding=(1, 1), channels=32, groups=8, kernel_size=(3, 3))
+
+ # also group conv2d
+ dshape = (1, 32, 18, 18)
+ kshape = (64, 1, 3, 3)
+ verify_conv2d("float32", 1, dshape, kshape,
+ padding=(1, 1), channels=64, groups=32, kernel_size=(3, 3))
+
+ # normal conv2d
+ dshape = (1, 3, 224, 224)
+ kshape = (10, 3, 3, 3)
+ verify_conv2d("float32", 1, dshape, kshape,
+ padding=(1, 1), channels=10, kernel_size=(3, 3))
+
+ dshape = (1, 3, 224, 224)
+ kshape = (10, 3, 3, 3)
+ verify_conv2d("float32", 1, dshape, kshape,
+ padding=(2, 2), channels=10, kernel_size=(3, 3))
+
+ dshape = (1, 3, 18, 18)
+ kshape = (10, 3, 3, 3)
+ verify_conv2d("float32", 1, dshape, kshape,
+ padding=(1, 1), channels=10, kernel_size=(3, 3), dilation=(3, 3))
+
+ dshape = (1, 3, 18, 18)
+ kshape = (10, 3, 2, 2)
+ verify_conv2d("float32", 1, dshape, kshape,
+ padding=(2, 2), channels=10, kernel_size=(2, 2), dilation=(1, 1))
+
+ dshape = (1, 3, 18, 18)
+ kshape = (10, 3, 4, 4)
+ verify_conv2d("float32", 1, dshape, kshape,
+ padding=(1, 1), channels=10, kernel_size=(4, 4))
+
+ dshape = (1, 3, 18, 18)
+ kshape = (10, 3, 4, 4)
+ verify_conv2d("float32", 1, dshape, kshape,
+ padding=(1, 1), channels=10, kernel_size=(4, 4))
+
+
+def test_reshape():
+ def verify_reshape(shape, newshape):
+ x = relay.var("x", relay.TensorType(shape, "float32"))
+ z = relay.reshape(x, newshape=newshape)
+
+ func = relay.Function([x], z)
+ x_data = np.random.uniform(low=-1, high=1, size=shape).astype("float32")
+ verify_results(func, [x_data], 'test_reshape', rtol=1e-5, atol=1e-5)
+
+ verify_reshape((2, 3, 4), tuple(np.array([4, 2, 3], dtype=np.int64)))
+ verify_reshape((2, 3, 4), tuple(np.array([2, 0, 0], dtype=np.int64)))
+ verify_reshape((2, 3, 4), tuple(np.array([0, -1], dtype=np.int64)))
+ verify_reshape((2, 3, 4), tuple(np.array([-1, 0], dtype=np.int64)))
+
+
+def test_transpose():
+ def verify_reshape(shape, newshape):
+ x = relay.var("x", relay.TensorType(shape, "float32"))
+ z = relay.transpose(x, newshape)
+ func = relay.Function([x], z)
+ x_data = np.random.uniform(low=-1, high=1, size=shape).astype("float32")
+ verify_results(func, [x_data], 'test_transpose', rtol=1e-5, atol=1e-5)
+
+ verify_reshape((1, 2, 3, 4), (0, 2, 3, 1))
+ verify_reshape((1, 2, 3, 4), (0, 3, 2, 1))
+
+
+def test_dense():
+ def verify_dense(d_shape, w_shape):
+ data = relay.var("data", relay.TensorType(d_shape, "float32"))
+ weight = relay.var("weight", relay.TensorType(w_shape, "float32"))
+ func = relay.Function([data, weight], relay.nn.dense(data, weight))
+ x_data = np.random.uniform(size=d_shape).astype("float32")
+ w_data = np.random.uniform(size=w_shape).astype("float32")
+ verify_results(func, [x_data, w_data], 'test_dense', rtol=1e-5, atol=1e-5)
+
+ verify_dense((1, 8), (16, 8))
+ verify_dense((1, 4), (3, 4))
+
+
+def test_max_pool():
+ def verify_max_pool(x_shape, pool_size, strides, padding, ceil_mode):
+ x = relay.var("x", relay.TensorType(x_shape, "float32"))
+ y = tvm.relay.nn.max_pool2d(x, pool_size=pool_size, strides=strides, padding=padding,
+ ceil_mode=ceil_mode)
+ func = relay.Function([x], y)
+ x_data = np.random.uniform(size=x_shape).astype("float32")
+ verify_results(func, [x_data], 'test_max_pool', rtol=1e-5, atol=1e-5)
+
+ verify_max_pool((1, 4, 16, 16), pool_size=(2, 2), strides=(2, 2), padding=(0, 0), ceil_mode=False)
+
+
+def test_batch_flatten():
+ def verify_test_batch_flatten(d_shape):
+ data = relay.var("data", relay.TensorType(d_shape, "float32"))
+ func = relay.Function([data], relay.nn.batch_flatten(data))
+ x_data = np.random.uniform(size=d_shape).astype("float32")
+ verify_results(func, [x_data], 'test_batch_flatten', rtol=1e-5, atol=1e-5)
+
+ verify_test_batch_flatten((1, 2, 3, 4))
+ verify_test_batch_flatten((1, 8))
+
+
+def test_batch_norm():
+ def verify_batch_norm(axis=1):
+ for dtype in ['float16', 'float32']:
+ data = relay.var("data", relay.TensorType((2, 4, 4, 1), dtype))
+ gamma_shape = (data.type_annotation.shape[axis].value,)
+ beta = relay.var("beta", relay.TensorType(gamma_shape, dtype))
+ gamma = relay.var("gamma", relay.TensorType(gamma_shape, dtype))
+ moving_mean = relay.var("moving_mean", relay.TensorType(gamma_shape, dtype))
+ moving_var = relay.var("moving_var", relay.TensorType(gamma_shape, dtype))
+ y = relay.nn.batch_norm(data, gamma, beta, moving_mean, moving_var, axis=axis)
+ func = relay.Function([data, gamma, beta, moving_mean, moving_var], y[0])
+
+ x_data = np.random.uniform(size=(2, 4, 4, 1)).astype(dtype)
+ beta = np.random.uniform(size=gamma_shape).astype(dtype)
+ gamma = np.random.uniform(size=gamma_shape).astype(dtype)
+ moving_mean = np.random.uniform(size=gamma_shape).astype(dtype)
+ moving_var = np.random.uniform(size=gamma_shape).astype(dtype)
+ verify_results(func, [x_data, gamma, beta, moving_mean, moving_var], 'test_batch_norm', rtol=1e-3,
+ atol=1e-3)
+
+ verify_batch_norm(axis=1)
+ verify_batch_norm(axis=3)
+
+
+def test_pad():
+ def verify_pad():
+ for dtype in ['float16', 'float32']:
+ dshape = (4, 10, 7, 7)
+ x = relay.var("x", shape=dshape, dtype=dtype)
+ y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4)))
+ func = relay.Function([x], y)
+ x_data = np.random.uniform(size=dshape).astype(dtype)
+ verify_results(func, [x_data], 'test_pad', rtol=1e-5, atol=1e-5)
+
+ verify_pad()
+
+
+def test_sofmax():
+ def verify_sofmax():
+ for dtype in ['float32']:
+ shape = (10, 4)
+ x = relay.var("x", shape=shape, dtype=dtype)
+ y = relay.nn.softmax(x, axis=1)
+ func = relay.Function([x], y)
+ x_data = np.random.uniform(size=shape).astype(dtype)
+ verify_results(func, [x_data], 'test_softmax', rtol=1e-5, atol=1e-5)
+
+ verify_sofmax()
+
+
+def test_squeeze():
+ def verify_squeeze(shape, dtype, axis):
+ x = relay.var("x", relay.TensorType(shape, dtype))
+ z = relay.squeeze(x, axis=axis)
+ func = relay.Function([x], z)
+ x_data = np.random.random_sample(shape).astype(dtype)
+ verify_results(func, [x_data], 'test_squeeze', rtol=1e-5, atol=1e-5)
+
+ verify_squeeze((1, 3, 2, 5), "float32", None)
+ verify_squeeze((1, 3, 1), "float32", [2, ])
+ verify_squeeze((1, 2, 1, 2, 1), "float32", [0, 2])
+
+
+def test_mean():
+ def verify_mean(data_shape, axis, exclude, keepdims):
+ dtype = "float32"
+ x = relay.var('x', shape=data_shape, dtype=dtype)
+ y = relay.mean(x, axis, keepdims, exclude)
+ func = relay.Function([x], y)
+ x_data = np.random.uniform(size=data_shape).astype(dtype)
+ verify_results(func, [x_data], 'test_mean', rtol=1e-5, atol=1e-5)
+
+ verify_mean((1, 2), 0, False, False)
+ verify_mean((1, 2), 0, True, False)
+ verify_mean((1, 2), 0, True, True)
+ verify_mean((1, 2), 1, True, True)
+ verify_mean((3, 2, 1), 1, False, True)
+
+
+def test_strided_slice():
+ def verify_strided_slice(dshape, begin, end, strides):
+ x = relay.var("x", relay.TensorType(dshape, "float32"))
+ z = relay.strided_slice(x, begin=begin, end=end, strides=strides)
+ func = relay.Function([x], z)
+ x_data = np.random.uniform(size=dshape).astype("float32")
+ verify_results(func, [x_data], 'test_strided_slice', rtol=1e-5, atol=1e-5)
+
+ verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 4, 3], None)
+ verify_strided_slice((3, 4, 3), [0, 0, 0], [4, -5, 4], [1, -1, 2])
+ verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 4, 3], [2, 1, 1])
+ verify_strided_slice((3, 4, 3), [1, -1, 0], [4, -5, 3], [2, -1, 1])
+ verify_strided_slice((3, 4, 3), [1, 0, 0], [2, 2, 3], [1, 1, 2])
+ verify_strided_slice((3, 4, 3), [1, -1, 0], [2, -3, 3], [1, -1, 1])
+
+ verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 1000, 3], None)
+ verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 4], None)
+ verify_strided_slice((3, 4, 3), [1, 1], [4, 4, 3], None)
+
+ # TODO - test cases below fails for TVM itself error -strided_slice get empty slice at axis 1
+ # verify_strided_slice((3, 4, 3), [1, 1, 0], [4, 4], [1, -1, 1])
Review comment:
it seems the test case was invalid..removed it.
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