# Copyright 2017 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.
# ==============================================================================
"""Tests for contrib.compiler.jit."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.contrib.compiler import jit
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import function
from tensorflow.python.framework import op_def_registry
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.ops import gradients
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
# pylint: enable=g-import-not-at-top


_REGISTERED_OPS = op_def_registry.get_registered_ops()


def enable_jit_nonstateful(node_def):
  try:
    return not _REGISTERED_OPS[node_def.op].is_stateful
  except KeyError:
    raise ValueError("Unregistered op being created: %s" % node_def)


class JITTest(test.TestCase):

  def compute(self, use_jit, compute_fn):
    random_seed.set_random_seed(1234)
    with self.test_session(graph=ops.Graph()) as sess:
      with jit.experimental_jit_scope(use_jit):
        r = compute_fn()
      sess.run(variables.global_variables_initializer())
      return (r, sess.run(r))

  def testJITCreateOpsLambda(self):
    """Test several ways of customizing the compilation attribute."""
    def create_ops():
      with variable_scope.variable_scope(
          "root",
          initializer=init_ops.random_uniform_initializer(
              -0.1, 0.1, seed=2)):
        inputs = random_ops.random_uniform((1,), seed=1)
        return inputs
    v_false_1_t, v_false_1 = self.compute(False, create_ops)
    _, v_false_2 = self.compute(False, create_ops)
    v_true_1_t, v_true_1 = self.compute(enable_jit_nonstateful, create_ops)
    _, v_true_2 = self.compute(enable_jit_nonstateful, create_ops)
    v_all_true_t, _ = self.compute(True, create_ops)
    self.assertFalse(v_false_1_t.op.get_attr("_XlaCompile"))
    v_true_1_t_sampler_op = v_true_1_t.graph.get_operation_by_name(
        "root/random_uniform/RandomUniform")
    v_all_true_t_sampler_op = v_all_true_t.graph.get_operation_by_name(
        "root/random_uniform/RandomUniform")

    self.assertFalse(v_true_1_t_sampler_op.get_attr("_XlaCompile"))
    self.assertTrue(v_all_true_t_sampler_op.get_attr("_XlaCompile"))

    self.assertTrue(v_true_1_t.op.get_attr("_XlaCompile"))
    self.assertTrue(v_all_true_t.op.get_attr("_XlaCompile"))

    # Additionally ensure that where no JIT compilation happens on the
    # random_uniform op, the output values are identical to the case
    # where no JIT compilation happens anywhere.
    self.assertAllClose(v_false_1, v_false_2)
    self.assertAllClose(v_true_1, v_true_2)
    self.assertAllClose(v_false_1, v_true_1)

  def testJITXlaScope(self):
    with self.test_session(graph=ops.Graph()):
      with jit.experimental_jit_scope(True):
        # XlaScope 0
        a1 = constant_op.constant(1)
      with jit.experimental_jit_scope(True):
        # XlaScope 1
        a2 = constant_op.constant(1)
        with jit.experimental_jit_scope(True):
          # XlaScope still 1, depth 1
          a3 = constant_op.constant(1)
          with jit.experimental_jit_scope(True):
            # XlaScope still 1, depth 2
            a4 = constant_op.constant(1)
          # XlaScope still 1, depth 1
          a5 = constant_op.constant(1)
      with jit.experimental_jit_scope(True):
        # XlaScope now 2, depth 0
        a6 = constant_op.constant(1)

    self.assertEqual(b"jit_scope_0", a1.op.get_attr("_XlaScope"))
    self.assertEqual(b"jit_scope_1", a2.op.get_attr("_XlaScope"))
    self.assertEqual(b"jit_scope_1", a3.op.get_attr("_XlaScope"))
    self.assertEqual(b"jit_scope_1", a4.op.get_attr("_XlaScope"))
    self.assertEqual(b"jit_scope_1", a5.op.get_attr("_XlaScope"))
    self.assertEqual(b"jit_scope_2", a6.op.get_attr("_XlaScope"))

  def testJITVariableSeed(self):
    """Test that the stateful initializer is not marked for compilation.

    XLA does not currently support seeded initialization and XLA initializers
    therefore return different values than non-XLA counterparts.  Here
    we ensure that if we can disable JIT compilation for the initializers and
    get the same variable values as if no JIT compilation happened.
    """
    def create_ops():
      with variable_scope.variable_scope(
          "root",
          initializer=init_ops.random_uniform_initializer(
              -0.1, 0.1, seed=2)):
        inputs = variable_scope.get_variable("var", (1,))
        return inputs
    _, v_false_1 = self.compute(False, create_ops)
    _, v_false_2 = self.compute(False, create_ops)
    _, v_true_1 = self.compute(enable_jit_nonstateful, create_ops)
    _, v_true_2 = self.compute(enable_jit_nonstateful, create_ops)
    self.assertAllClose(v_false_1, v_false_2)
    self.assertAllClose(v_true_1, v_true_2)
    self.assertAllClose(v_false_1, v_true_1)


class CompilationEnabledInGradientTest(test.TestCase):

  def testCompilationInGradient(self):
    with self.test_session():
      x = constant_op.constant(3)
      y_nc = math_ops.add(x, x, name="not_compiled")
      with jit.experimental_jit_scope():
        y_c = math_ops.add(y_nc, y_nc, name="compiled")
      x_grads = gradients.gradients([y_c], [x])[0]
      operations = x_grads.graph.get_operations()
      c_grad_ops = [
          op for op in operations if "gradients/compiled" in op.name]
      nc_grad_ops = [
          op for op in operations if "gradients/not_compiled" in op.name]
      self.assertGreater(len(c_grad_ops), 0)
      self.assertGreater(len(nc_grad_ops), 0)
      for cg in c_grad_ops:
        self.assertTrue(cg.get_attr("_XlaCompile"))
      for ncg in nc_grad_ops:
        with self.assertRaisesRegexp(ValueError, "No attr named"):
          ncg.get_attr("_XlaCompile")

      # d/dx (4 * x)
      self.assertAllClose(4, x_grads.eval())

  def testCompilationGradientScopeNames(self):
    with self.test_session(graph=ops.Graph()):
      with jit.experimental_jit_scope():
        # XlaScope 0
        a1 = constant_op.constant(1)
        a1t = a1 + a1
      with jit.experimental_jit_scope():
        # XlaScope 1
        a2 = constant_op.constant(1)
        a2t = a2 + a2

      self.assertEqual(b"jit_scope_0", a1.op.get_attr("_XlaScope"))
      self.assertEqual(b"jit_scope_1", a2.op.get_attr("_XlaScope"))
      grad_a1 = gradients.gradients(a1t, a1, name="GA")[0]
      grad_a2 = gradients.gradients(a2t, a2, name="GB")[0]
      grad_a1 = grad_a1.op.inputs[0]
      grad_a2 = grad_a2.op.inputs[0]
      self.assertTrue(grad_a1.op.get_attr("_XlaCompile"))
      self.assertTrue(grad_a2.op.get_attr("_XlaCompile"))
      self.assertEqual(b"jit_scope_0", grad_a1.op.get_attr("_XlaScope"))
      self.assertEqual(b"jit_scope_1", grad_a2.op.get_attr("_XlaScope"))

  def testCompilationSeparateGradientScopeNames(self):
    with self.test_session(graph=ops.Graph()):
      with jit.experimental_jit_scope(True, separate_compiled_gradients=True):
        # XlaScope 0
        a1 = constant_op.constant(1)
        a1t = a1 + a1
      with jit.experimental_jit_scope(True, separate_compiled_gradients=True):
        # XlaScope 1
        a2 = constant_op.constant(1)
        a2t = a2 + a2

      self.assertEqual(b"jit_scope_0", a1.op.get_attr("_XlaScope"))
      self.assertEqual(b"jit_scope_1", a2.op.get_attr("_XlaScope"))
      grad_a1 = gradients.gradients(a1t, a1, name="GA")[0]
      grad_a2 = gradients.gradients(a2t, a2, name="GB")[0]
      grad_a1 = grad_a1.op.inputs[0]
      grad_a2 = grad_a2.op.inputs[0]
      self.assertTrue(grad_a1.op.get_attr("_XlaCompile"))
      self.assertTrue(grad_a2.op.get_attr("_XlaCompile"))
      self.assertEqual(b"jit_scope_0_grad_GA",
                       grad_a1.op.get_attr("_XlaScope"))
      self.assertEqual(b"jit_scope_1_grad_GB",
                       grad_a2.op.get_attr("_XlaScope"))

  def testPlaysNicelyWithDefun(self):
    with self.test_session(graph=ops.Graph()) as sess:
      with jit.experimental_jit_scope(True):  # This should be ignored
        @function.Defun(compiled=True, noinline=True)
        def mulop(x1, x2):
          return x1 * x2
        x = constant_op.constant(1.0)
        r = mulop(x, x)
        g_r = gradients.gradients(r, x, name="GA")[0]

      # Ensure the forward function is compiled.
      graph_def = r.graph.as_graph_def()
      func_attrs = graph_def.library.function[0].attr
      self.assertTrue(func_attrs["_XlaCompile"].b)
      self.assertEqual(b"function_mulop", func_attrs["_XlaScope"].s)

      # Ensure the gradient (SymbolicGradient) is compiled, with the same
      # _XlaScope as the function itself.
      grad_op = g_r.op.inputs[0].op
      self.assertTrue(grad_op.get_attr("_XlaCompile"))
      self.assertEqual(b"function_mulop", grad_op.get_attr("_XlaScope"))

      # Ensure the ops run: grad(x1*x1) = 2*x1
      self.assertAllClose([1.0, 1.0, 2.0], sess.run([x, r, g_r]))

  def testPlaysNicelyWithDefunSeparateGradientScope(self):
    with self.test_session(graph=ops.Graph()) as sess:
      with jit.experimental_jit_scope(True):  # This should be ignored

        @function.Defun(
            compiled=True, noinline=True, separate_compiled_gradients=True)
        def mulop(x1, x2):
          return x1 * x2

        x = constant_op.constant(1.0)
        r = mulop(x, x)
        g_r = gradients.gradients(r, x, name="GA")[0]

      # Ensure the forward function is compiled.
      graph_def = r.graph.as_graph_def()
      func_attrs = graph_def.library.function[0].attr
      self.assertTrue(func_attrs["_XlaCompile"].b)
      self.assertEqual(b"function_mulop", func_attrs["_XlaScope"].s)

      # Ensure the gradient (SymbolicGradient) is compiled, with a different
      # _XlaScope from the function itself.
      grad_op = g_r.op.inputs[0].op
      self.assertTrue(grad_op.get_attr("_XlaCompile"))
      self.assertEqual(b"function_mulop_grad_GA",
                       grad_op.get_attr("_XlaScope"))

      # Ensure the ops run: grad(x1*x1) = 2*x1
      self.assertAllClose([1.0, 1.0, 2.0], sess.run([x, r, g_r]))


if __name__ == "__main__":
  test.main()
