[Auto Parallel] Add conv2d and pool flops

python/paddle/fluid/tests/unittests/test_profiler.py

@@ -350,6 +350,32 @@ def test_flops(self):
             )
             == 144
         )
+        self.assertTrue(
+            flops(
+                'pool',
+                {'X': [[12, 12]]},
+                {},
+            )
+            == 12 * 12
+        )
+        self.assertTrue(
+            flops(
+                'conv2d',
+                {
+                    'Bias': [],
+                    'Filter': [[3, 3, 2, 2]],
+                    'Input': [[8, 3, 4, 4]],
+                    'ResidualData': [],
+                },
+                {
+                    'dilations': [1, 1],
+                    'groups': 1,
+                    'paddings': [1, 1],
+                    'strides': [1, 1],
+                },
+            )
+            == 14400
+        )
 
 
 if __name__ == '__main__':

python/paddle/utils/flops.py

@@ -69,6 +69,85 @@ def _c_embedding_flops(input_shapes, attrs):
     return 0
 
 
+@register_flops("conv2d")
+def _conv2d_flops(input_shapes, attrs):
+    """FLOPs computation for conv2d op.
+    For conv2d(input,filter):
+        active_elements = batch_size * numel(output)
+        conv_flops = 2 * macs_per_position_conv * active_elements
+        bias_flops = out_channels * active_elements
+        equation: flops = conv_flops + bias_flops
+    """
+
+    bias = (
+        input_shapes.get('Bias')[0]
+        if len(input_shapes.get('Bias')) > 0
+        else None
+    )
+    input = input_shapes.get('Input')[0]
+    weight = input_shapes.get('Filter')[0]
+
+    padding = attrs.get('paddings')
+    stride = attrs.get('strides')
+    dilation = attrs.get('dilations')
+    groups = attrs.get('groups')
+
+    batch_size = input[0]
+    in_channels = input[1]
+    out_channels = weight[0]
+    kernel_dims = list(weight[2:])
+    input_dims = list(input[2:])
+    length = len(input_dims)
+
+    paddings = (
+        padding
+        if isinstance(padding, list)
+        else [
+            padding,
+        ]
+        * length
+    )
+    strides = (
+        stride
+        if isinstance(stride, list)
+        else [
+            stride,
+        ]
+        * length
+    )
+    dilations = (
+        dilation
+        if isinstance(dilation, list)
+        else [
+            dilation,
+        ]
+        * length
+    )
+
+    output_dims = []
+    for idx, input_dim in enumerate(input_dims):
+        output_dim = (
+            input_dim
+            + 2 * paddings[idx]
+            - (dilations[idx] * (kernel_dims[idx] - 1) + 1)
+        ) // strides[idx] + 1
+        output_dims.append(output_dim)
+    filters_per_channel = out_channels // groups
+    macs_conv_per_position = (
+        prod(kernel_dims) * in_channels * filters_per_channel
+    )
+    active_elements = batch_size * prod(output_dims)
+    overall_conv_macs = macs_conv_per_position * active_elements
+    overall_conv_flops = 2 * overall_conv_macs
+
+    overall_bias_flops = 0
+
+    if bias is not None:
+        overall_bias_flops = out_channels * active_elements
+
+    return overall_conv_flops + overall_bias_flops
+
+
 @register_flops("dropout")
 def _dropout_flops(input_shapes, attrs):
     """FLOPs computation for dropout op.
@@ -195,7 +274,7 @@ def _matmul_v2_flops(input_shapes, attrs):
         shape_of_other = [odim1, odim2 ... odim(n-m) ... odim_m_1, dim_m] length:m
         suppose n > m and dim_n = odim_m_1:
         shape_of_output = [dim1, dim2 ... max(dim(n-m), odim(n-m)), max(dim(n-m+1), odim(n-m+1))...dim_n_1, dim_m]
-        equation: flops = 2 * numel(output) * dim_n
+        equation: flops = 2 * numel(outputs) * dim_n
     """
     x_shape = input_shapes.get('X')[0]
     y_shape = input_shapes.get('Y')[0]
@@ -281,3 +360,13 @@ def _transpose2_flops(input_shapes, attrs):
         equation: flops = 0
     """
     return 0
+
+
+@register_flops("pool")
+def _pool_flops(input_shapes, attrs):
+    """FLOPs computation for pool op.
+    For pool(input):
+        equation: flops = (numel)total number of elements in the input tensor.
+    """
+    input = input_shapes.get('X')[0]
+    return prod(input)