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diff --git a/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py b/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py
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+# Copyright 2018 The TensorFlow 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.
+# ==============================================================================
+"""Model script to test TF-TensorRT integration."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gen_array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn
+from tensorflow.python.platform import test
+
+
+class BiasaddMatMulTest(trt_test.TfTrtIntegrationTestBase):
+
+ def GetParams(self):
+ """Testing conversion of BiasAdd MatMul in TF-TRT conversion."""
+ dtype = dtypes.float32
+ input_name = "input"
+ input_dims = [48, 12]
+ g = ops.Graph()
+ with g.as_default():
+ x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name)
+
+ b = constant_op.constant(np.random.randn(12, 4), dtype=dtype)
+ x1 = math_ops.matmul(x, b)
+ b = constant_op.constant(np.random.randn(1, 4), dtype=dtype)
+ x1 = x1 + b
+
+ b = constant_op.constant(np.random.randn(48, 4), dtype=dtype)
+ x2 = math_ops.matmul(x, b, transpose_a=True)
+ x2 = gen_array_ops.reshape(x2, [48, 1])
+
+ b = constant_op.constant(np.random.randn(4, 12), dtype=dtype)
+ x3 = math_ops.matmul(x, b, transpose_b=True)
+
+ b = constant_op.constant(np.random.randn(16, 48), dtype=dtype)
+ x4 = math_ops.matmul(x, b, transpose_b=True, transpose_a=True)
+ x4 = gen_array_ops.reshape(x4, [48, 4])
+
+ x5 = gen_array_ops.reshape(x, [4, 144])
+ b = constant_op.constant(np.random.randn(144, 48), dtype=dtype)
+ x5 = math_ops.matmul(x5, b)
+ b = constant_op.constant(np.random.randn(48), dtype=dtype)
+ x5 = nn.bias_add(x5, b)
+ x5 = gen_array_ops.reshape(x5, [48, 4])
+
+ x6 = gen_array_ops.reshape(x, [4, 12, 12])
+ b = constant_op.constant(np.random.randn(12), dtype=dtype)
+ x6 = nn.bias_add(x6, b, data_format="NHWC")
+ x6 = gen_array_ops.reshape(x6, [48, -1])
+
+ x7 = gen_array_ops.reshape(x, [4, 12, 3, 4])
+ b = constant_op.constant(np.random.randn(4), dtype=dtype)
+ x7 = nn.bias_add(x7, b, data_format="NHWC")
+ x7 = gen_array_ops.reshape(x7, [48, -1])
+
+ x8 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2])
+ b = constant_op.constant(np.random.randn(2), dtype=dtype)
+ x8 = nn.bias_add(x8, b, data_format="NHWC")
+ x8 = gen_array_ops.reshape(x8, [48, -1])
+
+ x9 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2])
+ b = constant_op.constant(np.random.randn(3), dtype=dtype)
+ x9 = nn.bias_add(x9, b, data_format="NCHW")
+ x9 = gen_array_ops.reshape(x9, [48, -1])
+
+ x10 = gen_array_ops.reshape(x, [4, 12, 3, 4])
+ b = constant_op.constant(np.random.randn(12), dtype=dtype)
+ x10 = nn.bias_add(x10, b, data_format="NCHW")
+ x10 = gen_array_ops.reshape(x10, [48, -1])
+
+ x11 = gen_array_ops.reshape(x, [4, 12, 12])
+ b = constant_op.constant(np.random.randn(4), dtype=dtype)
+ x11 = nn.bias_add(x11, b, data_format="NCHW")
+ x11 = gen_array_ops.reshape(x11, [48, -1])
+
+ out = array_ops.concat(
+ [x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11], axis=-1)
+ out = array_ops.squeeze(out, name=self.output_name)
+ return trt_test.TfTrtIntegrationTestParams(
+ gdef=g.as_graph_def(),
+ input_names=[input_name],
+ input_dims=[input_dims],
+ num_expected_engines=7,
+ expected_output_dims=(48, 89),
+ allclose_atol=1.e-03,
+ allclose_rtol=1.e-03)
+
+
+if __name__ == "__main__":
+ test.main()