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@ -112,7 +112,7 @@ class TestMKLDNNReluPrimitivesAlreadyExist(unittest.TestCase):
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out_grad = np.random.random_sample(x.shape).astype(np.float32)
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out_grad = np.random.random_sample(x.shape).astype(np.float32)
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x_grad = out_grad * np.sign(x) # Abs grad calculation
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x_grad = out_grad * np.sign(x) # Abs grad calculation
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var_dict = {'x':x, 'out':out, 'out@GRAD':out_grad, 'x@GRAD':x_grad}
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var_dict = {'x': x, 'out': out, 'out@GRAD': out_grad, 'x@GRAD': x_grad}
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var_names = list(var_dict.keys())
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var_names = list(var_dict.keys())
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ground_truth = {name: var_dict[name] for name in var_names}
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ground_truth = {name: var_dict[name] for name in var_names}
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@ -121,14 +121,12 @@ class TestMKLDNNReluPrimitivesAlreadyExist(unittest.TestCase):
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block = program.global_block()
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block = program.global_block()
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for name in ground_truth:
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for name in ground_truth:
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block.create_var(
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block.create_var(
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name=name,
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name=name, dtype='float32', shape=ground_truth[name].shape)
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dtype='float32',
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shape=ground_truth[name].shape)
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relu_op = block.append_op(
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relu_op = block.append_op(
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type="abs",
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type="abs",
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inputs={"X": block.var('x'),},
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inputs={"X": block.var('x'), },
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outputs={"Out": block.var('out') },
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outputs={"Out": block.var('out')},
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attrs={"use_mkldnn": True})
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attrs={"use_mkldnn": True})
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# Generate backward op_desc
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# Generate backward op_desc
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@ -149,8 +147,10 @@ class TestMKLDNNReluPrimitivesAlreadyExist(unittest.TestCase):
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# Do at least 2 iterations
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# Do at least 2 iterations
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for i in range(2):
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for i in range(2):
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out = exe.run(program,
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out = exe.run(
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feed={name: var_dict[name] for name in ['x', 'out@GRAD']},
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program,
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feed={name: var_dict[name]
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for name in ['x', 'out@GRAD']},
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fetch_list=['x@GRAD'])
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fetch_list=['x@GRAD'])
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self.__assert_close(x_grad, out[0], "x@GRAD")
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self.__assert_close(x_grad, out[0], "x@GRAD")
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