Compare the gradient consistency between GPU and CPU calculations.

paddle/operators/sigmoid_op.cc

@@ -44,7 +44,8 @@ class SigmoidOpGrad : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
-    ctx.Output<Tensor>(0)->Resize(ctx.Input<Tensor>(0)->dims());
+    ctx.Output<Tensor>(framework::GradVarName("X"))
+        ->Resize(ctx.Input<Tensor>("Y")->dims());
   }
 };
 

python/paddle/v2/framework/tests/CMakeLists.txt

@@ -25,3 +25,4 @@ py_test(test_operator SRCS test_operator.py)
 # py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py)
 py_test(test_uniform_random_op SRCS test_uniform_random_op.py)
 py_test(test_recurrent_op SRCS test_recurrent_op.py)
+py_test(test_gradient_checker SRCS test_gradient_checker.py)

python/paddle/v2/framework/tests/gradient_checker.py

 import unittest
 
 import numpy
+import itertools
 import paddle.v2.framework.core as core
 from paddle.v2.framework.op import Operator
 
@@ -8,6 +9,7 @@ __all__ = ['get_numeric_gradient']
 
 
 def create_op(op_type):
+    # TODO need to set attrs
     kwargs = dict()
     for in_name in Operator.get_op_input_names(op_type):
         kwargs[in_name] = in_name
@@ -66,7 +68,6 @@ def get_numeric_gradient(op,
             local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace(
             ))
 
-    # TODO(yuyang18): Only CPU is support now.
     cpu_ctx = core.DeviceContext.create(core.CPUPlace())
 
     def get_output():
@@ -109,12 +110,71 @@ def get_numeric_gradient(op,
 
 
 class GradientChecker(unittest.TestCase):
-    def assert_is_close(self, numeric_grads, scope, max_relative_error,
-                        msg_prefix):
-        for name in numeric_grads:
-            b = numpy.array(scope.find_var(grad_var_name(name)).get_tensor())
-            a = numeric_grads[name]
+    def get_grad(self, forward_op, backward_op, input_vars, grad_names, place):
+        scope = core.Scope()
+        ctx = core.DeviceContext.create(place)
 
+        inputs = forward_op.inputs()
+        in_names = [item for k in inputs for item in inputs[k]]
+        outputs = forward_op.outputs()
+        out_names = [item for k in outputs for item in outputs[k]]
+
+        # create input var and set value
+        for name, value in input_vars.iteritems():
+            if name not in in_names:
+                raise ValueError(name + "does not exist in Op's inputs.")
+            var = scope.new_var(name).get_tensor()
+            var.set_dims(value.shape)
+            var.set(value, place)
+
+        # run forward op
+        for out_name in out_names:
+            scope.new_var(out_name)
+        forward_op.infer_shape(scope)
+        forward_op.run(scope, ctx)
+
+        # set output var's shape
+        # set output grad to ones
+        for name in out_names:
+            out_tensor = scope.find_var(name).get_tensor()
+            grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
+            grad_tensor.set_dims(out_tensor.shape())
+            data = numpy.ones(out_tensor.shape(), dtype=numpy.float32)
+            grad_tensor.set(data, place)
+
+        # run backward op
+        for name in backward_op.outputs():
+            scope.new_var(name)
+        backward_op.infer_shape(scope)
+        backward_op.run(scope, ctx)
+
+        outs = [
+            numpy.array(scope.find_var(name).get_tensor())
+            for name in grad_names
+        ]
+        return outs
+
+    def compare_grad(self, forward_op, inputs):
+        backward_op = core.Operator.backward(forward_op, set())
+        if not (core.is_compile_gpu() and backward_op.support_gpu()):
+            return
+
+        outputs = backward_op.outputs()
+        out_names = [item for k in outputs for item in outputs[k]]
+        cpu_grads = self.get_grad(forward_op, backward_op, inputs, out_names,
+                                  core.CPUPlace())
+        gpu_grads = self.get_grad(forward_op, backward_op, inputs, out_names,
+                                  core.GPUPlace(0))
+
+        for c_grad, g_grad, name in itertools.izip(cpu_grads, gpu_grads,
+                                                   out_names):
+            self.assertTrue(
+                numpy.allclose(c_grad, g_grad),
+                "output name: " + name + " has diff")
+
+    def assert_is_close(self, numeric_grads, analytic_grads, names,
+                        max_relative_error, msg_prefix):
+        for a, b, name in itertools.izip(numeric_grads, analytic_grads, names):
             abs_a = numpy.abs(a)
             # if abs_a is nearly zero, then use abs error for a, not relative
             # error.
@@ -159,106 +219,27 @@ class GradientChecker(unittest.TestCase):
 
         inputs = forward_op.inputs()
         in_names = [item for k in inputs for item in inputs[k]]
-        outputs = forward_op.outputs()
-        out_names = [item for k in outputs for item in outputs[k]]
-
         for no_grad in no_grad_set:
             if no_grad not in in_names:
                 raise ValueError("no_grad should be in in_names")
 
         backward_op = core.Operator.backward(forward_op, no_grad_set)
 
-        bwd_outputs = backward_op.outputs()
-        bwd_out_names = [item for k in bwd_outputs for item in bwd_outputs[k]]
-
         places = [core.CPUPlace()]
         if not only_cpu and core.is_compile_gpu() and backward_op.support_gpu():
             places.append(core.GPUPlace(0))
 
-        numeric_grad = dict()
-        # get numeric gradient
-        for check_name in inputs_to_check:
-            numeric_grad[check_name] = \
-                get_numeric_gradient(forward_op, input_vars, output_name,
-                                     check_name)
+        # get numerical gradients
+        numeric_grads = [
+            get_numeric_gradient(forward_op, input_vars, output_name, name)
+            for name in inputs_to_check
+        ]
 
-        # get operator gradient according to different device
+        check_names = [grad_var_name(name) for name in inputs_to_check]
         for place in places:
-            scope = core.Scope()
-            ctx = core.DeviceContext.create(place)
-
-            # create input var and set value
-            for name, value in input_vars.iteritems():
-                if name not in in_names:
-                    raise ValueError(name + " not in op.inputs_")
-                var = scope.new_var(name).get_tensor()
-                var.set_dims(value.shape)
-                var.set(value, place)
-
-            # create output var
-            for out_name in out_names:
-                scope.new_var(out_name).get_tensor()
-
-            # infer the shape of output var and compute/set value of output var
-            forward_op.infer_shape(scope)
-            forward_op.run(scope, ctx)
-
-            # create output grad var
-            # set shape as the output var
-            # set value of this grad to ones
-            for name in out_names:
-                out_tensor = scope.find_var(name).get_tensor()
-                grad_tensor = scope.new_var(grad_var_name(name)).get_tensor()
-                grad_tensor.set_dims(out_tensor.shape())
-                data = 1.0 * numpy.ones(out_tensor.shape())
-                grad_tensor.set(data, place)
-
-            # create input grad var
-            for name in bwd_out_names:
-                scope.new_var(name).get_tensor()
-
-            # infer the shape of input gradient var and compute/set it's value
-            # with backward op
-            backward_op.infer_shape(scope)
-            backward_op.run(scope, ctx)
-
-            self.assert_is_close(numeric_grad, scope, max_relative_error,
+            # get analytical gradients according to different device
+            analytic_grads = self.get_grad(forward_op, backward_op, input_vars,
+                                           check_grad_names, place)
+            self.assert_is_close(numeric_grads, analytic_grads, check_names,
+                                 max_relative_error,
                                  "Gradient Check On %s" % str(place))
-
-
-if __name__ == '__main__':
-
-    class GetNumericGradientTest(unittest.TestCase):
-        def test_add_op(self):
-            add_op = Operator('add_two', X="X", Y="Y", Out="Z")
-            x = numpy.random.random((10, 1)).astype("float32")
-            y = numpy.random.random((10, 1)).astype("float32")
-
-            arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X')
-            self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-2)
-
-        def test_softmax_op(self):
-            def stable_softmax(x):
-                """Compute the softmax of vector x in a numerically stable way."""
-                shiftx = x - numpy.max(x)
-                exps = numpy.exp(shiftx)
-                return exps / numpy.sum(exps)
-
-            def label_softmax_grad(Y, dY):
-                dX = Y * 0.0
-                for i in range(Y.shape[0]):
-                    d = numpy.dot(Y[i, :], dY[i, :])
-                    dX[i, :] = Y[i, :] * (dY[i, :] - d)
-                return dX
-
-            softmax_op = Operator("softmax", X="X", Y="Y")
-
-            X = numpy.random.random((2, 2)).astype("float32")
-            Y = numpy.apply_along_axis(stable_softmax, 1, X)
-            dY = numpy.ones(Y.shape)
-            dX = label_softmax_grad(Y, dY)
-
-            arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X')
-            numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2)
-
-    unittest.main()

python/paddle/v2/framework/tests/test_sigmoid_op.py

 import unittest
-from op_test_util import OpTestMeta
 import numpy as np
+from op_test_util import OpTestMeta
+from gradient_checker import GradientChecker, create_op
 
 
 class TestSigmoidOp(unittest.TestCase):
@@ -8,12 +9,25 @@ class TestSigmoidOp(unittest.TestCase):
 
     def setUp(self):
         self.type = "sigmoid"
-        self.inputs = {'X': np.random.random((32, 100)).astype("float32")}
+        self.inputs = {'X': np.random.random((15, 31)).astype("float32")}
         self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))}
 
 
-#class TestSigmoidGradOp(unittest.TestCase):
-#TODO(qingqing) add unit test
+class TestSigmoidGradOp(GradientChecker):
+    def test_compare_grad(self):
+        op = create_op("sigmoid")
+        inputs = {"X": np.random.random((11, 17)).astype("float32")}
+
+        # compare gpu and cpu results for backward op
+        self.compare_grad(op, inputs)
+
+    def test_check_grad(self):
+        op = create_op("sigmoid")
+        inputs = {"X": np.random.random((11, 17)).astype("float32")}
+
+        # check gradients
+        self.check_grad(op, inputs, set("X"), "Y")
+
 
 if __name__ == '__main__':
     unittest.main()