Python test.is_gpu_available函数代码示例

  def testTraining(self):
    x_shape = [1, 1, 6, 1]
    for dtype in [np.float16, np.float32]:
      if test.is_gpu_available(cuda_only=True):
        self._test_training(
            x_shape, dtype, [1], np.float32, use_gpu=True, data_format='NHWC')
        self._test_training(
            x_shape, dtype, [1], np.float32, use_gpu=True, data_format='NCHW')
      self._test_training(
          x_shape, dtype, [1], np.float32, use_gpu=False, data_format='NHWC')

    x_shape = [1, 1, 6, 2]
    for dtype in [np.float16, np.float32]:
      if test.is_gpu_available(cuda_only=True):
        self._test_training(
            x_shape, dtype, [2], np.float32, use_gpu=True, data_format='NHWC')
      self._test_training(
          x_shape, dtype, [2], np.float32, use_gpu=False, data_format='NHWC')

    x_shape = [1, 2, 1, 6]
    if test.is_gpu_available(cuda_only=True):
      for dtype in [np.float16, np.float32]:
        self._test_training(
            x_shape, dtype, [2], np.float32, use_gpu=True, data_format='NCHW')

    x_shape = [27, 131, 127, 6]
    for dtype in [np.float16, np.float32]:
      if test.is_gpu_available(cuda_only=True):
        self._test_training(
            x_shape, dtype, [131], np.float32, use_gpu=True, data_format='NCHW')
        self._test_training(
            x_shape, dtype, [6], np.float32, use_gpu=True, data_format='NHWC')
      self._test_training(
          x_shape, dtype, [6], np.float32, use_gpu=False, data_format='NHWC')

  def testSelectEverthingDetail(self):
    ops.reset_default_graph()
    dev = '/gpu:0' if test.is_gpu_available() else '/cpu:0'
    outfile = os.path.join(test.get_temp_dir(), 'dump')
    opts = (builder(builder.trainable_variables_parameter())
            .with_file_output(outfile)
            .with_accounted_types(['.*'])
            .select(['micros', 'bytes', 'params', 'float_ops', 'occurrence',
                     'device', 'op_types', 'input_shapes']).build())

    config = config_pb2.ConfigProto()
    with session.Session(config=config) as sess, ops.device(dev):
      x = lib.BuildSmallModel()

      sess.run(variables.global_variables_initializer())
      run_meta = config_pb2.RunMetadata()
      _ = sess.run(x,
                   options=config_pb2.RunOptions(
                       trace_level=config_pb2.RunOptions.FULL_TRACE),
                   run_metadata=run_meta)

      model_analyzer.profile(
          sess.graph, run_meta, options=opts)

      with gfile.Open(outfile, 'r') as f:
        # pylint: disable=line-too-long
        outputs = f.read().split('\n')

        self.assertEqual(outputs[0],
                         'node name | # parameters | # float_ops | requested bytes | total execution time | accelerator execution time | cpu execution time | assigned devices | op types | op count (run|defined) | input shapes')
        for o in outputs[1:]:
          if o.find('Conv2D ') > 0:
            metrics = o[o.find('(') +1: o.find(')')].split(',')
            # Make sure time is profiled.
            gap = 1 if test.is_gpu_available() else 2
            for i in range(3, 6, gap):
              mat = re.search('(.*)[um]s/(.*)[um]s', metrics[i])
              self.assertGreater(float(mat.group(1)), 0.0)
              self.assertGreater(float(mat.group(2)), 0.0)
            # Make sure device is profiled.
            if test.is_gpu_available():
              self.assertTrue(metrics[6].find('gpu') > 0)
              self.assertFalse(metrics[6].find('cpu') > 0)
            else:
              self.assertFalse(metrics[6].find('gpu') > 0)
              self.assertTrue(metrics[6].find('cpu') > 0)
            # Make sure float_ops is profiled.
            mat = re.search('(.*)k/(.*)k flops', metrics[1].strip())
            self.assertGreater(float(mat.group(1)), 0.0)
            self.assertGreater(float(mat.group(2)), 0.0)
            # Make sure op_count is profiled.
            self.assertEqual(metrics[8].strip(), '1/1|1/1')
            # Make sure input_shapes is profiled.
            self.assertEqual(metrics[9].strip(), '0:2x6x6x3|1:3x3x3x6')

          if o.find('DW (3x3x3x6') > 0:
            metrics = o[o.find('(') +1: o.find(')')].split(',')
            mat = re.search('(.*)/(.*) params', metrics[1].strip())
            self.assertGreater(float(mat.group(1)), 0.0)
            self.assertGreater(float(mat.group(2)), 0.0)

  def testBatchNormGrad(self):
    for is_training in [True, False]:
      x_shape = [1, 1, 6, 1]
      if test.is_gpu_available(cuda_only=True):
        self._test_gradient(
            x_shape, [1],
            use_gpu=True,
            data_format='NHWC',
            is_training=is_training)
        self._test_gradient(
            x_shape, [1],
            use_gpu=True,
            data_format='NCHW',
            is_training=is_training)
      self._test_gradient(
          x_shape, [1],
          use_gpu=False,
          data_format='NHWC',
          is_training=is_training)

      x_shape = [1, 1, 6, 2]
      if test.is_gpu_available(cuda_only=True):
        self._test_gradient(
            x_shape, [2],
            use_gpu=True,
            data_format='NHWC',
            is_training=is_training)
      self._test_gradient(
          x_shape, [2],
          use_gpu=False,
          data_format='NHWC',
          is_training=is_training)

      x_shape = [1, 2, 1, 6]
      if test.is_gpu_available(cuda_only=True):
        self._test_gradient(
            x_shape, [2],
            use_gpu=True,
            data_format='NCHW',
            is_training=is_training)

      x_shape = [7, 9, 13, 6]
      if test.is_gpu_available(cuda_only=True):
        self._test_gradient(
            x_shape, [9],
            use_gpu=True,
            data_format='NCHW',
            is_training=is_training)
        self._test_gradient(
            x_shape, [6],
            use_gpu=True,
            data_format='NHWC',
            is_training=is_training)
      self._test_gradient(
          x_shape, [6],
          use_gpu=False,
          data_format='NHWC',
          is_training=is_training)

  def test_convolution_2d(self):
    num_samples = 2
    filters = 2
    stack_size = 3
    kernel_size = (3, 2)
    num_row = 7
    num_col = 6

    for padding in ['valid', 'same']:
      for strides in [(1, 1), (2, 2)]:
        if padding == 'same' and strides != (1, 1):
          continue

        with self.test_session(use_gpu=True):
          # Only runs on GPU with CUDA, channels_first is not supported on CPU.
          # TODO(b/62340061): Support channels_first on CPU.
          if test.is_gpu_available(cuda_only=True):
            testing_utils.layer_test(
                keras.layers.Conv2D,
                kwargs={
                    'filters': filters,
                    'kernel_size': kernel_size,
                    'padding': padding,
                    'strides': strides,
                    'data_format': 'channels_first'
                },
                input_shape=(num_samples, stack_size, num_row, num_col))

  def testReverseWithConstDims(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      dims = constant_op.constant([3, 1], name='DimsConst')
      reverse = array_ops.reverse(conv, dims)
      output = array_ops.identity(reverse)

      with session.Session() as sess:
        output_val_ref = sess.run(output)

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(output, run_metadata=metadata)

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if _is_transpose(node.name):
          num_transposes += 1
        nodes.append(node.name)

      # Four transposes were initially added in the Expand phase of
      # LayoutOptimizer; two of them are cancelled out in the Collapse phase.
      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
      self._assert_trans_nchw_to_nhwc('ReverseV2-0-0', nodes)
      self.assertIn('ReverseV2-1-LayoutOptimizer', nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

 def test_cudnn_rnn_basics(self):
   if test.is_gpu_available(cuda_only=True):
     with self.test_session(use_gpu=True):
       input_size = 10
       timesteps = 6
       units = 2
       num_samples = 32
       for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
         for return_sequences in [True, False]:
           with keras.utils.CustomObjectScope(
               {'keras.layers.CuDNNGRU': keras.layers.CuDNNGRU,
                'keras.layers.CuDNNLSTM': keras.layers.CuDNNLSTM}):
             testing_utils.layer_test(
                 layer_class,
                 kwargs={'units': units,
                         'return_sequences': return_sequences},
                 input_shape=(num_samples, timesteps, input_size))
         for go_backwards in [True, False]:
           with keras.utils.CustomObjectScope(
               {'keras.layers.CuDNNGRU': keras.layers.CuDNNGRU,
                'keras.layers.CuDNNLSTM': keras.layers.CuDNNLSTM}):
             testing_utils.layer_test(
                 layer_class,
                 kwargs={'units': units,
                         'go_backwards': go_backwards},
                 input_shape=(num_samples, timesteps, input_size))

  def testTwoConvLayers(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      output = two_layer_model(x)

      with session.Session() as sess:
        output_val_ref = sess.run(output)

      with session.Session(config=get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(output, run_metadata=metadata)

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if node.name.startswith('LayoutOptimizerTranspose'):
          num_transposes += 1
        nodes.append(node.name)

      # Four transposes were initially added in the Expand phase of
      # LayoutOptimizer; two of them are cancelled out in the Collapse phase.
      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self.assertIn('LayoutOptimizerTransposeNHWCToNCHW-Conv2D-Reshape-0',
                    nodes)
      self.assertIn('LayoutOptimizerTransposeNCHWToNHWC-Relu_1-MaxPool_1',
                    nodes)

      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

  def testAllocationHistory(self):
    if not test.is_gpu_available(cuda_only=True):
      return

    gpu_dev = test.gpu_device_name()
    ops.reset_default_graph()
    with ops.device(gpu_dev):
      _, run_meta = _run_model()

    mm = _extract_node(run_meta, 'MatMul')['gpu:0'][0]
    mm_allocs = mm.memory[0].allocation_records
    # has allocation and deallocation.
    self.assertEqual(len(mm_allocs), 2)
    # first allocated.
    self.assertGreater(mm_allocs[1].alloc_micros, mm_allocs[0].alloc_micros)
    self.assertGreater(mm_allocs[0].alloc_bytes, 0)
    # Then deallocated.
    self.assertLess(mm_allocs[1].alloc_bytes, 0)
    # All memory deallocated.
    self.assertEqual(mm_allocs[0].alloc_bytes + mm_allocs[1].alloc_bytes, 0)

    rand = _extract_node(
        run_meta, 'random_normal/RandomStandardNormal')['gpu:0'][0]
    random_allocs = rand.memory[0].allocation_records
    # random normal must allocated first since matmul depends on it.
    self.assertLess(random_allocs[0].alloc_micros, mm.all_start_micros)
    # deallocates the memory after matmul started.
    self.assertGreater(random_allocs[1].alloc_micros, mm.all_start_micros)

  def benchmarkMatrixExponentialOp(self):
    for shape in self.shapes:
      with ops.Graph().as_default(), \
          session.Session() as sess, \
          ops.device("/cpu:0"):
        matrix = self._GenerateMatrix(shape)
        expm = linalg_impl.matrix_exponential(matrix)
        variables.global_variables_initializer().run()
        self.run_op_benchmark(
            sess,
            control_flow_ops.group(expm),
            min_iters=25,
            name="matrix_exponential_cpu_{shape}".format(
                shape=shape))

      if test.is_gpu_available(True):
        with ops.Graph().as_default(), \
            session.Session() as sess, \
            ops.device("/gpu:0"):
          matrix = self._GenerateMatrix(shape)
          expm = linalg_impl.matrix_exponential(matrix)
          variables.global_variables_initializer().run()
          self.run_op_benchmark(
              sess,
              control_flow_ops.group(expm),
              min_iters=25,
              name="matrix_exponential_gpu_{shape}".format(
                  shape=shape))

  def testConcatLargeNumberOfTensors(self):
    with self.session(use_gpu=True):
      for concat_dim in range(2):
        params = {}
        p = []
        shape = np.array([7, 13])
        if test.is_gpu_available():
          num_tensors = 5000
        else:
          num_tensors = 500
        for i in np.arange(num_tensors):
          input_shape = shape
          placeholder = array_ops.placeholder(dtypes.float32, shape=input_shape)
          p.append(placeholder)

          params[placeholder] = np.random.rand(*input_shape).astype(np.float32)

        concat_inputs = p
        c = array_ops.concat(concat_inputs, concat_dim)
        result = c.eval(feed_dict=params)

        self.assertEqual(result.shape, c.get_shape())
        cur_offset = 0

        for i in np.arange(num_tensors):
          # The index into the result is the ':' along all dimensions
          # except the concat_dim. slice(0, size) is used for ':', and
          # a list of slices is used to index into result.
          index = [slice(0, params[p[i]].shape[j]) for j in np.arange(2)]
          index[concat_dim] = slice(cur_offset,
                                    cur_offset + params[p[i]].shape[concat_dim])
          cur_offset += params[p[i]].shape[concat_dim]
          self.assertAllEqual(result[index], params[p[i]])

  def testLoopGPU(self):
    if not test.is_gpu_available():
      return

    ops.reset_default_graph()
    with ops.device('/gpu:0'):
      tfprof_node, run_meta = _run_loop_model()
      # The while-loop caused a node to appear 4 times in scheduling.
      ret = _extract_node(run_meta,
                          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
      self.assertEqual(len(ret['/job:localhost/replica:0/task:0/gpu:0']), 4)

      total_cpu_execs = 0
      for node in ret['/job:localhost/replica:0/task:0/gpu:0']:
        total_cpu_execs += node.op_end_rel_micros

      ret = _extract_node(
          run_meta,
          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul:MatMul')
      self.assertGreaterEqual(len(ret['/gpu:0/stream:all']), 4)

      total_accelerator_execs = 0
      for node in ret['/gpu:0/stream:all']:
        total_accelerator_execs += node.op_end_rel_micros

      mm_node = lib.SearchTFProfNode(
          tfprof_node,
          'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')

      self.assertEqual(mm_node.run_count, 4)
      self.assertEqual(mm_node.accelerator_exec_micros, total_accelerator_execs)
      self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs)
      self.assertEqual(mm_node.exec_micros,
                       total_cpu_execs + total_accelerator_execs)

  def benchmarkMatrixBandPartOp(self):
    for shape_ in self.shapes:
      for limits in (-1, -1), (-1, 0), (0, -1), (2, 2):
        with ops.Graph().as_default(), \
            session.Session() as sess, \
            ops.device("/cpu:0"):
          matrix = variables.Variable(array_ops.ones(shape_))
          band = array_ops.matrix_band_part(matrix, limits[0], limits[1])
          variables.global_variables_initializer().run()
          self.run_op_benchmark(
              sess,
              control_flow_ops.group(band),
              min_iters=10,
              name="matrix_band_part_cpu_{shape}_{limits}".format(
                  shape=shape_, limits=limits))

        if test_lib.is_gpu_available(True):
          with ops.Graph().as_default(), \
              session.Session() as sess, \
              ops.device("/gpu:0"):
            matrix = variables.Variable(array_ops.ones(shape_))
            band = array_ops.matrix_band_part(matrix, limits[0], limits[1])
            variables.global_variables_initializer().run()
            self.run_op_benchmark(
                sess,
                control_flow_ops.group(band),
                min_iters=10,
                name="matrix_band_part_gpu_{shape}_{limits}".format(
                    shape=shape_, limits=limits))

 def testGradientDilatedConv(self):
   if test.is_gpu_available(cuda_only=True):
     with self.test_session(use_gpu=True):
       for padding in ["SAME", "VALID"]:
         for stride in [1, 2]:
           np.random.seed(1)
           in_shape = [5, 8, 6, 4]
           in_val = constant_op.constant(
               2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32)
           filter_shape = [3, 3, 4, 6]
           # Make a convolution op with the current settings,
           # just to easily get the shape of the output.
           conv_out = nn_ops.conv2d(
               in_val,
               array_ops.zeros(filter_shape),
               dilations=[1, 2, 2, 1],
               strides=[1, stride, stride, 1],
               padding=padding)
           out_backprop_shape = conv_out.get_shape().as_list()
           out_backprop_val = constant_op.constant(
               2 * np.random.random_sample(out_backprop_shape) - 1,
               dtype=dtypes.float32)
           output = nn_ops.conv2d_backprop_filter(
               in_val,
               filter_shape,
               out_backprop_val,
               dilations=[1, 2, 2, 1],
               strides=[1, stride, stride, 1],
               padding=padding)
           err = gradient_checker.compute_gradient_error(
               [in_val, out_backprop_val], [in_shape, out_backprop_shape],
               output, filter_shape)
           print("conv2d_backprop_filter gradient err = %g " % err)
           err_tolerance = 2e-3
           self.assertLess(err, err_tolerance)

  def testStridedSliceWithMask1011(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      # This will generate a StridedSlice op with begin mask and
      # end mask 11(1011).
      s = conv[:, :, 1:-1, :]
      output = array_ops.identity(s)

      with session.Session() as sess:
        output_val_ref = sess.run(output)

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(output, run_metadata=metadata)

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if _is_transpose(node.name):
          num_transposes += 1
        nodes.append(node.name)

      # Four transposes were initially added in the Expand phase of
      # LayoutOptimizer; two of them are cancelled out in the Collapse phase.
      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
      self._assert_trans_nchw_to_nhwc('strided_slice-0-0', nodes)
      self.assertIn('strided_slice-1-LayoutOptimizer', nodes)
      self.assertIn('strided_slice-2-LayoutOptimizer', nodes)
      self.assertIn('strided_slice-3-LayoutOptimizer', nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)

  def testSliceWithNonConstAxis(self):
    if test.is_gpu_available(cuda_only=True):
      random_seed.set_random_seed(0)
      x = random_ops.truncated_normal([1, 784], seed=0)
      conv = _two_layer_model(x)
      size = array_ops.placeholder(dtype='int32')
      s = array_ops.slice(conv, [0, 0, 0, 0], size)
      output = array_ops.identity(s)

      size_val = [1, 2, 3, 4]
      with session.Session() as sess:
        output_val_ref = sess.run(output, feed_dict={size: size_val})

      with session.Session(config=_get_config()) as sess:
        metadata = config_pb2.RunMetadata()
        output_val = sess.run(
            output, run_metadata=metadata, feed_dict={
                size: size_val
            })

      nodes = []
      num_transposes = 0
      for node in metadata.cost_graph.node:
        if _is_transpose(node.name):
          num_transposes += 1
        nodes.append(node.name)

      # Four transposes were initially added in the Expand phase of
      # LayoutOptimizer; two of them are cancelled out in the Collapse phase.
      expected_num_transposes = 2
      self.assertEqual(expected_num_transposes, num_transposes)
      self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes)
      self._assert_trans_nchw_to_nhwc('Slice-0-0', nodes)
      self._assert_vec_nhwc_to_nchw('Slice-2', nodes)
      self.assertAllClose(output_val_ref, output_val, atol=1e-3)