support quantization of conv2d_transpose (#34547)

python/paddle/fluid/contrib/slim/quantization/imperative/qat.py

@@ -42,17 +42,18 @@ class ImperativeQuantAware(object):
     Applying quantization aware training (QAT) to the dgraph model.
     """
 
-    def __init__(self,
-                 quantizable_layer_type=['Conv2D', 'Linear'],
-                 weight_quantize_type='abs_max',
-                 activation_quantize_type='moving_average_abs_max',
-                 weight_bits=8,
-                 activation_bits=8,
-                 moving_rate=0.9,
-                 weight_preprocess_layer=None,
-                 act_preprocess_layer=None,
-                 weight_quantize_layer=None,
-                 act_quantize_layer=None):
+    def __init__(
+            self,
+            quantizable_layer_type=['Conv2D', 'Linear', 'Conv2DTranspose'],
+            weight_quantize_type='abs_max',
+            activation_quantize_type='moving_average_abs_max',
+            weight_bits=8,
+            activation_bits=8,
+            moving_rate=0.9,
+            weight_preprocess_layer=None,
+            act_preprocess_layer=None,
+            weight_quantize_layer=None,
+            act_quantize_layer=None):
         """
         The constructor for ImperativeQuantAware.
 
@@ -212,9 +213,44 @@ def quantize(self, model):
         the out_scale value of outputs would be calculated.
 
         Args:
-            model(fluid.dygraph.Layer): the model to be quantized.
+            model(paddle.nn.Layer): the model to be quantized.
         Returns:
             None
+
+        Examples:
+        .. code-block:: python
+
+            import paddle
+            from paddle.fluid.contrib.slim.quantization \
+                import ImperativeQuantAware
+
+            class ImperativeModel(paddle.nn.Layer):
+                def __init__(self):
+                    super(ImperativeModel, self).__init__()
+                    # self.linear_0 would skip the quantization.
+                    self.linear_0 = paddle.nn.Linear(784, 400)
+                    self.linear_0.skip_quant = True
+
+                    # self.linear_1 would not skip the quantization.
+                    self.linear_1 = paddle.nn.Linear(400, 10)
+                    self.linear_1.skip_quant = False
+
+                def forward(self, inputs):
+                    x = self.linear_0(inputs)
+                    x = self.linear_1(inputs)
+                    return x
+
+            model = ImperativeModel()
+            imperative_qat = ImperativeQuantAware(
+                weight_quantize_type='abs_max',
+                activation_quantize_type='moving_average_abs_max')
+
+            # Add the fake quant logical.
+            # The original model will be rewrite.
+            #
+            # There is only one Layer(self.linear1) would be added the
+            # fake quant logical.
+            imperative_qat.quantize(model)
         """
         assert isinstance(model, dygraph.Layer), \
             "The model must be the instance of dygraph.Layer."
@@ -232,17 +268,18 @@ class ImperativeQuantizeInputs(object):
     logic both for activation inputs and weight inputs.
     """
 
-    def __init__(self,
-                 quantizable_layer_type=['Conv2D', 'Linear'],
-                 weight_quantize_type='abs_max',
-                 activation_quantize_type='moving_average_abs_max',
-                 weight_bits=8,
-                 activation_bits=8,
-                 moving_rate=0.9,
-                 weight_preprocess_layer=None,
-                 act_preprocess_layer=None,
-                 weight_quantize_layer=None,
-                 act_quantize_layer=None):
+    def __init__(
+            self,
+            quantizable_layer_type=['Conv2D', 'Linear', 'Conv2DTranspose'],
+            weight_quantize_type='abs_max',
+            activation_quantize_type='moving_average_abs_max',
+            weight_bits=8,
+            activation_bits=8,
+            moving_rate=0.9,
+            weight_preprocess_layer=None,
+            act_preprocess_layer=None,
+            weight_quantize_layer=None,
+            act_quantize_layer=None):
         """
         The constructor for ImperativeQuantizeInputs. 
 
@@ -303,6 +340,18 @@ def __init__(self,
         }
 
     def apply(self, model):
+        """
+        Quantize the weights and activations to calculate for specific 
+        layers.
+
+        Args:
+            model(paddle.nn.Layer): The target model which would
+                calculate the input quantization scale.
+
+        Returns:
+            None
+        """
+
         assert isinstance(model, dygraph.Layer), \
             "The model must be the instance of dygraph.Layer."
 
@@ -354,7 +403,7 @@ def apply(self, model):
         output scales for specific layers in the dygraph model.
 
         Args:
-            model(fluid.dygraph.Layer): The target model which would be
+            model(paddle.nn.Layer): The target model which would be
                 calculate the output quantization scale.
 
         Returns:
@@ -544,7 +593,9 @@ def _is_skip_quant_op(self, block, in_op):
         1. the type of input op should be conv2d, depthwise_conv2d or matmul
         2. the previous ops of the input op are not fake_quantize_dequantize ops
         """
-        target_op_types = ["conv2d", "depthwise_conv2d", "matmul"]
+        target_op_types = [
+            "conv2d", "depthwise_conv2d", "matmul", "conv2d_transpose"
+        ]
         if in_op.type not in target_op_types:
             return False
 

python/paddle/fluid/contrib/slim/quantization/imperative/utils.py

@@ -24,6 +24,7 @@
 from ..quantization_pass import _get_input_name_index
 
 layer_name_map = {
+    'Conv2DTranspose': paddle.nn.Conv2DTranspose,
     'Conv2D': paddle.nn.Conv2D,
     'Linear': paddle.nn.Linear,
     'AdaptiveAvgPool2D': paddle.nn.AdaptiveAvgPool2D,
@@ -46,8 +47,9 @@
 }
 
 # Apply fake quant for the inputs of these layers
-# TODO (jc): support paddle.nn.Conv2DTranspose
-fake_quant_input_layers = [paddle.nn.Conv2D, paddle.nn.Linear]
+fake_quant_input_layers = [
+    paddle.nn.Conv2D, paddle.nn.Linear, paddle.nn.Conv2DTranspose
+]
 
 # Apply fake quant for the output of these layers
 # TODO(jc): fix the problem of adding duplicate fake_quant ops
@@ -65,7 +67,8 @@
 ]
 
 fake_quant_wrap_layers = [
-    quant_layers.QuantizedConv2D, quant_layers.QuantizedLinear
+    quant_layers.QuantizedConv2D, quant_layers.QuantizedLinear,
+    quant_layers.QuantizedConv2DTranspose
 ]
 
 # The weight format of these layers is Cin * Cout * H * W 
@@ -84,9 +87,9 @@
 
 
 def load_variable_data(scope, var_name):
-    '''
+    """
     Load variable value from scope
-    '''
+    """
     var_node = scope.find_var(var_name)
     assert var_node is not None, \
         "Can not find " + var_name + " in the scope."
@@ -120,6 +123,12 @@ def find_parent_layer_and_sub_name(model, name):
     the sub_name of the layer.
     For example, if name is 'block_1/convbn_1/conv_1', the parent layer is
     'block_1/convbn_1' and the sub_name is `conv_1`.
+    Args:
+        model(paddle.nn.Layer): the model to be quantized.
+        name(string): the name of a layer
+
+    Returns:
+        parent_layer, subname
     """
     assert isinstance(model, paddle.nn.Layer), \
             "The model must be the instance of paddle.nn.Layer."

python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py

@@ -28,10 +28,10 @@
 from paddle.fluid.optimizer import AdamOptimizer
 from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
 from paddle.fluid.dygraph.container import Sequential
-from paddle.nn import Linear, Conv2D, Softmax
+from paddle.nn import Linear, Conv2D, Softmax, Conv2DTranspose
 from paddle.fluid.log_helper import get_logger
 from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
-from paddle.nn.quant.quant_layers import QuantizedConv2D
+from paddle.nn.quant.quant_layers import QuantizedConv2D, QuantizedConv2DTranspose
 
 from imperative_test_utils import fix_model_dict, ImperativeLenet
 
@@ -75,6 +75,12 @@ def test_qat(self):
             data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
             quant_conv1(fluid.dygraph.to_variable(data))
 
+            conv_transpose = Conv2DTranspose(4, 6, (3, 3))
+            quant_conv_transpose = QuantizedConv2DTranspose(conv_transpose)
+            x_var = paddle.uniform(
+                (2, 4, 8, 8), dtype='float32', min=-1.0, max=1.0)
+            quant_conv_transpose(x_var)
+
             seed = 1
             np.random.seed(seed)
             fluid.default_main_program().random_seed = seed

python/paddle/fluid/contrib/slim/tests/test_imperative_qat_user_defined.py

@@ -28,6 +28,7 @@
 from paddle.fluid.dygraph import Conv2D
 from paddle.fluid.dygraph import Pool2D
 from paddle.fluid.dygraph import Linear
+from paddle.nn.quant.quant_layers import QuantizedConv2DTranspose
 from paddle.fluid.log_helper import get_logger
 
 os.environ["CPU_NUM"] = "1"
@@ -100,6 +101,19 @@ def dequantize(x, lower_bound, delta, interval):
         return x
 
 
+class ModelForConv2dT(nn.Layer):
+    def __init__(self, num_classes=10):
+        super(ModelForConv2dT, self).__init__()
+        self.features = nn.Conv2DTranspose(4, 6, (3, 3))
+        self.fc = Linear(input_dim=600, output_dim=num_classes)
+
+    def forward(self, inputs):
+        x = self.features(inputs)
+        x = paddle.flatten(x, 1)
+        x = self.fc(x)
+        return x
+
+
 class ImperativeLenet(paddle.nn.Layer):
     def __init__(self, num_classes=10, classifier_activation='softmax'):
         super(ImperativeLenet, self).__init__()
@@ -168,6 +182,11 @@ def test_quant_aware_training(self):
         imperative_qat.quantize(lenet)
         adam = Adam(learning_rate=0.001, parameters=lenet.parameters())
         dynamic_loss_rec = []
+        #for CI coverage
+        conv_transpose = ModelForConv2dT()
+        imperative_qat.quantize(conv_transpose)
+        x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.)
+        conv_transpose(x_var)
 
         def train(model):
             adam = Adam(learning_rate=0.001, parameters=model.parameters())

python/paddle/nn/quant/quant_layers.py

@@ -31,6 +31,7 @@
     'FakeQuantMovingAverageAbsMax',
     'FakeQuantChannelWiseAbsMax',
     'QuantizedConv2D',
+    'QuantizedConv2DTranspose',
     'QuantizedLinear',
     'MovingAverageAbsMaxScale',
     'MAOutputScaleLayer',
@@ -481,6 +482,112 @@ def forward(self, input):
             data_format=self._data_format)
 
 
+class QuantizedConv2DTranspose(layers.Layer):
+    """
+    The computational logic of QuantizedConv2DTranspose is the same with Conv2DTranspose.
+    The only difference is that its inputs are all fake quantized.
+    
+    Examples:
+       .. code-block:: python
+          import paddle
+          import paddle.nn as nn
+          from paddle.nn.quant.quant_layers import QuantizedConv2DTranspose
+          x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.)
+          conv = nn.Conv2DTranspose(4, 6, (3, 3))
+          conv_quantized = QuantizedConv2DTranspose(conv)
+          y_quantized = conv_quantized(x_var)
+          y_var = conv(x_var)
+          y_quantized_np = y_quantized.numpy()
+          y_np = y_var.numpy()
+          print(y_np.shape, y_quantized_np.shape)
+          # (2, 6, 10, 10), (2, 6, 10, 10)
+    """
+
+    def __init__(self,
+                 layer,
+                 weight_bits=8,
+                 activation_bits=8,
+                 moving_rate=0.9,
+                 weight_quantize_type='abs_max',
+                 activation_quantize_type='abs_max',
+                 weight_pre_layer=None,
+                 act_pre_layer=None,
+                 weight_quant_layer=None,
+                 act_quant_layer=None):
+        r"""
+        Constructor.
+
+        The arguments are the same as ImperativeQuantAware.
+        """
+        super(QuantizedConv2DTranspose, self).__init__()
+        # For Conv2DTranspose
+        self._groups = getattr(layer, '_groups')
+        self._stride = getattr(layer, '_stride')
+        self._padding = getattr(layer, '_padding')
+        self._output_padding = getattr(layer, 'output_padding')
+        self._dilation = getattr(layer, '_dilation')
+        self._data_format = getattr(layer, '_data_format')
+        self.weight = getattr(layer, 'weight')
+        self.bias = getattr(layer, 'bias')
+        # For FakeQuant
+        self._conv2d_transpose_quant_axis = 1
+        if weight_quant_layer is not None:
+            self._fake_quant_weight = weight_quant_layer()
+        else:
+            self._fake_quant_weight = _get_fake_quant_type(
+                weight_quantize_type,
+                name=self.weight.name,
+                moving_rate=moving_rate,
+                quant_bits=weight_bits,
+                dtype=self._dtype,
+                quant_on_weight=True,
+                channel_num=self.weight.shape[
+                    self._conv2d_transpose_quant_axis],
+                quant_axis=self._conv2d_transpose_quant_axis)
+        if act_quant_layer is not None:
+            self._fake_quant_input = act_quant_layer()
+        else:
+            self._fake_quant_input = _get_fake_quant_type(
+                activation_quantize_type,
+                name=layer.full_name(),
+                moving_rate=moving_rate,
+                quant_bits=activation_bits,
+                dtype=self._dtype,
+                quant_on_weight=False)
+
+        self._act_preprocess = act_pre_layer(
+        ) if act_pre_layer is not None else None
+        self._weight_preprocess = weight_pre_layer(
+        ) if weight_pre_layer is not None else None
+
+    def forward(self, input, output_size=None):
+        if self._act_preprocess is not None:
+            input = self._act_preprocess(input)
+        quant_input = self._fake_quant_input(input)
+
+        weight = self.weight
+        if self._weight_preprocess is not None:
+            weight = self._weight_preprocess(self.weight)
+        quant_weight = self._fake_quant_weight(weight)
+
+        if output_size is None:
+            output_padding = self._output_padding
+        else:
+            output_padding = 0
+
+        return F.conv2d_transpose(
+            quant_input,
+            quant_weight,
+            bias=self.bias,
+            padding=self._padding,
+            output_padding=output_padding,
+            stride=self._stride,
+            dilation=self._dilation,
+            groups=self._groups,
+            output_size=output_size,
+            data_format=self._data_format)
+
+
 class QuantizedLinear(layers.Layer):
     """
     The computational logic of QuantizedLinear is the same with Linear.

tools/sampcd_processor.py

@@ -440,6 +440,7 @@ def get_filenames(full_test=False):
     '''
     global whl_error
     import paddle
+    import paddle.fluid.contrib.slim.quantization
     whl_error = []
     if full_test:
         get_full_api_from_pr_spec()