Merge pull request #15343 from panyx0718/imperative3

add a GAN model in imperative mode

paddle/fluid/imperative/layer.cc

@@ -57,15 +57,15 @@ class Autograd {
   Autograd() {}
 
   void RunBackward(VarBase* var) {
-    if (var->stop_gradient_) {
+    if (var->IsStopGradient()) {
       return;
     }
     VLOG(3) << "start autograd";
 
     std::deque<OpBase*> ready;
-    ready.push_back(var->pre_op_);
+    ready.push_back(var->PreOp());
 
-    std::map<OpBase*, int> dep_counts = ComputeDepCounts(var->pre_op_);
+    std::map<OpBase*, int> dep_counts = ComputeDepCounts(var->PreOp());
 
     while (!ready.empty()) {
       OpBase* ready_op = ready.front();
@@ -77,7 +77,7 @@ class Autograd {
         const std::vector<VarBase*>& ingrads = it.second;
         for (size_t i = 0; i < ingrads.size(); ++i) {
           if (!ingrads[i]) continue;
-          if (ready_op->input_vars_[it.first][i]->stop_gradient_) {
+          if (ready_op->input_vars_[it.first][i]->IsStopGradient()) {
             continue;
           }
           OpBase* pre_op = ready_op->pre_ops_[it.first][i];

paddle/fluid/imperative/layer.h

@@ -100,22 +100,20 @@ class VarBase {
 
   // Owns `var` and `grad`
   VarBase(framework::Variable* var, VarBase* grad)
-      : pre_op_(nullptr),
-        pre_op_out_name_(),
-        pre_op_out_idx_(-1),
-        var_desc_(nullptr),
+      : var_desc_(nullptr),
         var_(var),
         grads_(grad),
-        stop_gradient_(false) {}
+        stop_gradient_(false),
+        pre_op_(nullptr),
+        pre_op_out_idx_(-1) {}
 
   explicit VarBase(bool stop_gradient)
-      : pre_op_(nullptr),
-        pre_op_out_name_(),
-        pre_op_out_idx_(-1),
-        var_desc_(nullptr),
+      : var_desc_(nullptr),
         var_(new framework::Variable()),
         grads_(stop_gradient ? nullptr : new VarBase(true)),
-        stop_gradient_(stop_gradient) {}
+        stop_gradient_(stop_gradient),
+        pre_op_(nullptr),
+        pre_op_out_idx_(-1) {}
 
   virtual ~VarBase() {
     if (var_) {
@@ -127,8 +125,27 @@ class VarBase {
     }
   }
 
+  OpBase* PreOp() const { return pre_op_; }
+  int PreOpOutIdx() const { return pre_op_out_idx_; }
+
+  void SetStopGradient(bool stop_gradient) { stop_gradient_ = stop_gradient; }
+  bool IsStopGradient() const { return stop_gradient_; }
+
   void RunBackward();
 
+  void TrackPreOp(OpBase* pre_op, const std::string& pre_op_out_name,
+                  int pre_op_out_idx, bool stop_gradient) {
+    pre_op_ = pre_op;
+    pre_op_out_name_ = pre_op_out_name;
+    pre_op_out_idx_ = pre_op_out_idx;
+    stop_gradient_ = stop_gradient;
+  }
+
+  void ClearGradient() {
+    delete grads_;
+    grads_ = new VarBase(true);
+  }
+
   framework::LoDTensor& GradValue();
 
   inline std::string GradName() const {
@@ -138,16 +155,16 @@ class VarBase {
     return string::Sprintf("%s@IGrad", var_desc_->Name());
   }
 
-  OpBase* pre_op_;
-  std::string pre_op_out_name_;
-  int pre_op_out_idx_;
-
   framework::VarDesc* var_desc_;
 
   framework::Variable* var_;
   VarBase* grads_;
 
+ private:
   bool stop_gradient_;
+  OpBase* pre_op_;
+  std::string pre_op_out_name_;
+  int pre_op_out_idx_;
 };
 
 /* The wrapper for OpDesc which holds a OpDesc and a OpDesc of its

paddle/fluid/imperative/tracer.cc

@@ -63,9 +63,9 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
 
       invars.push_back(inp->var_);
       vars[inp->var_desc_->Name()] = inp;
-      if (inp->pre_op_) {
-        op->pre_ops_[it.first].push_back(inp->pre_op_);
-        op->pre_ops_out_idx_[it.first].push_back(inp->pre_op_out_idx_);
+      if (inp->PreOp()) {
+        op->pre_ops_[it.first].push_back(inp->PreOp());
+        op->pre_ops_out_idx_[it.first].push_back(inp->PreOpOutIdx());
       } else {
         op->pre_ops_[it.first].push_back(nullptr);
       }
@@ -89,10 +89,7 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
       } else {
         LOG(ERROR) << "tracer doesn't support yet";
       }
-      out->stop_gradient_ = stop_gradient;
-      out->pre_op_ = op;
-      out->pre_op_out_name_ = it.first;
-      out->pre_op_out_idx_ = i;
+      out->TrackPreOp(op, it.first, i, stop_gradient);
 
       VLOG(3) << "output vname " << out->var_desc_->Name() << " "
               << out->var_->IsInitialized();
@@ -167,9 +164,9 @@ std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
   op->input_vars_[PyLayer::kFwdInp] = inputs;
   op->output_vars_[PyLayer::kFwdOut] = PyLayer::Apply(op->forward_id_, inputs);
   for (VarBase* inp : inputs) {
-    if (inp->pre_op_) {
-      op->pre_ops_[PyLayer::kFwdInp].push_back(inp->pre_op_);
-      op->pre_ops_out_idx_[PyLayer::kFwdInp].push_back(inp->pre_op_out_idx_);
+    if (inp->PreOp()) {
+      op->pre_ops_[PyLayer::kFwdInp].push_back(inp->PreOp());
+      op->pre_ops_out_idx_[PyLayer::kFwdInp].push_back(inp->PreOpOutIdx());
     } else {
       op->pre_ops_[PyLayer::kFwdInp].push_back(nullptr);
     }
@@ -178,10 +175,7 @@ std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
   auto& outputs = op->output_vars_[PyLayer::kFwdOut];
   for (size_t i = 0; i < outputs.size(); ++i) {
     VarBase* out = outputs[i];
-    out->stop_gradient_ = stop_gradient;
-    out->pre_op_ = op;
-    out->pre_op_out_name_ = PyLayer::kFwdOut;
-    out->pre_op_out_idx_ = i;
+    out->TrackPreOp(op, PyLayer::kFwdOut, i, stop_gradient);
   }
   if (!stop_gradient) {
     auto& grad_input_vars =

paddle/fluid/pybind/pybind.cc

@@ -133,6 +133,7 @@ PYBIND11_MODULE(core, m) {
            [](imperative::VarBase &self) { self.RunBackward(); })
       .def("_grad_name", &imperative::VarBase::GradName)
       .def("_grad_value", &imperative::VarBase::GradValue)
+      .def("_clear_gradient", &imperative::VarBase::ClearGradient)
       .def("_grad_ivar",
            [](const imperative::VarBase &self) { return self.grads_; },
            py::return_value_policy::reference)
@@ -147,9 +148,9 @@ PYBIND11_MODULE(core, m) {
           py::return_value_policy::reference)
       .def_property(
           "stop_gradient",
-          [](const imperative::VarBase &self) { return self.stop_gradient_; },
+          [](const imperative::VarBase &self) { return self.IsStopGradient(); },
           [](imperative::VarBase &self, bool stop_gradient) {
-            self.stop_gradient_ = stop_gradient;
+            self.SetStopGradient(stop_gradient);
           });
 
   py::class_<imperative::OpBase, PyOpBase>(m, "OpBase", R"DOC()DOC")

python/paddle/fluid/framework.py

@@ -389,6 +389,9 @@ class Variable(object):
     def _gradient(self):
         return np.array(self._ivar._grad_value())
 
+    def _clear_gradient(self):
+        self._ivar._clear_gradient()
+
     def __str__(self):
         return self.to_string(True)
 

python/paddle/fluid/imperative/layers.py

@@ -27,18 +27,25 @@ class Layer(core.Layer):
     """Layers composed of operators."""
 
     def __init__(self, dtype=core.VarDesc.VarType.FP32, name=None):
-        self._once_built = False
+        self._built = False
         self._dtype = dtype
 
+    def parameters(self):
+        return []
+
+    def clear_gradients(self):
+        for p in self.parameters():
+            p._clear_gradient()
+
     def _build_once(self, inputs):
         pass
 
     def __call__(self, *inputs):
-        if not self._once_built:
+        if not self._built:
             self._build_once(*inputs)
-            self._once_built = True
 
         outputs = self.forward(*inputs)
+        self._built = True
         return outputs
 
     def forward(self, *inputs):

python/paddle/fluid/imperative/nn.py

@@ -48,6 +48,7 @@ class Conv2D(layers.Layer):
         assert param_attr is not False, "param_attr should not be False here."
         super(Conv2D, self).__init__(name=name, dtype=dtype)
 
+        # TODO(minqiyang): Move this to the top.
         from ..layer_helper import LayerHelper
         self._helper = LayerHelper(
             type(self).__name__,
@@ -209,14 +210,25 @@ class FC(layers.Layer):
     def __init__(self,
                  size,
                  param_attr=None,
+                 bias_attr=None,
                  num_flatten_dims=1,
-                 dtype=core.VarDesc.VarType.FP32):
+                 dtype=core.VarDesc.VarType.FP32,
+                 act=None,
+                 name=None):
         super(FC, self).__init__()
         self._size = size
         self._num_flatten_dims = num_flatten_dims
         self._dtype = dtype
         from ..layer_helper import LayerHelper
-        self._helper = LayerHelper('FC', param_attr=param_attr)
+        self._helper = LayerHelper(
+            'FC',
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            act=act,
+            name=name)
+
+    def parameters(self):
+        return [self._w, self._b]
 
     def _build_once(self, input):
         input_shape = input.shape
@@ -247,4 +259,22 @@ class FC(layers.Layer):
             inputs={"X": [tmp]},
             outputs={"Out": out},
             attrs={"use_mkldnn": False})
-        return out
+
+        bias_attr = self._helper.bias_attr
+        if bias_attr:
+            # add bias
+            size = list(out.shape[1:])
+            if not self._built:
+                self._b = self._helper.create_parameter(
+                    attr=bias_attr, shape=size, dtype=out.dtype, is_bias=True)
+            bias_out = self._helper.create_variable_for_type_inference(
+                dtype=out.dtype)
+            self._helper.append_op(
+                type='elementwise_add',
+                inputs={'X': [out],
+                        'Y': [self._b]},
+                outputs={'Out': [bias_out]},
+                attrs={'axis': 1})
+            out = bias_out
+        # add activation
+        return self._helper.append_activation(out)

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

@@ -21,10 +21,11 @@ from paddle.fluid import core
 
 
 @contextlib.contextmanager
-def new_program_scope():
-    prog = fluid.Program()
-    startup_prog = fluid.Program()
-    scope = fluid.core.Scope()
+def new_program_scope(main=None, startup=None, scope=None):
+    prog = main if main else fluid.Program()
+    startup_prog = startup if startup else fluid.Program()
+    scope = scope if scope else fluid.core.Scope()
     with fluid.scope_guard(scope):
         with fluid.program_guard(prog, startup_prog):
+            with fluid.unique_name.guard():
                 yield

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

+# Copyright (c) 2018 PaddlePaddle 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.
+
+import contextlib
+import unittest
+import numpy as np
+import six
+import sys
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.optimizer import SGDOptimizer
+from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
+from test_imperative_base import new_program_scope
+from paddle.fluid.imperative.base import to_variable
+
+
+class Discriminator(fluid.imperative.Layer):
+    def __init__(self):
+        super(Discriminator, self).__init__()
+        self._fc1 = FC(size=32, act='elu', name="d_fc1")
+        self._fc2 = FC(size=1, name="d_fc2")
+
+    def parameters(self):
+        return self._fc1.parameters() + self._fc2.parameters()
+
+    def forward(self, inputs):
+        x = self._fc1(inputs)
+        return self._fc2(x)
+
+
+class Generator(fluid.imperative.Layer):
+    def __init__(self):
+        super(Generator, self).__init__()
+        self._fc1 = FC(size=64, act='elu', name="g_fc1")
+        self._fc2 = FC(size=64, act='elu', name="g_fc2")
+        self._fc3 = FC(size=1, name="g_fc3")
+
+    def parameters(self):
+        return self._fc1.parameters() + self._fc2.parameters(
+        ) + self._fc3.parameters()
+
+    def forward(self, inputs):
+        x = self._fc1(inputs)
+        x = self._fc2(x)
+        return self._fc3(x)
+
+
+class TestImperativeMnist(unittest.TestCase):
+    def test_mnist_cpu_float32(self):
+        seed = 90
+
+        startup = fluid.Program()
+        startup.random_seed = seed
+        discriminate_p = fluid.Program()
+        generate_p = fluid.Program()
+        discriminate_p.random_seed = seed
+        generate_p.random_seed = seed
+
+        scope = fluid.core.Scope()
+        with new_program_scope(
+                main=discriminate_p, startup=startup, scope=scope):
+            discriminator = Discriminator()
+            generator = Generator()
+
+            img = fluid.layers.data(
+                name="img", shape=[2, 1], append_batch_size=False)
+            noise = fluid.layers.data(
+                name="noise", shape=[2, 2], append_batch_size=False)
+
+            d_real = discriminator(img)
+            d_loss_real = fluid.layers.reduce_mean(
+                fluid.layers.sigmoid_cross_entropy_with_logits(
+                    x=d_real,
+                    label=fluid.layers.fill_constant(
+                        shape=[2, 1], dtype='float32', value=1.0)))
+
+            d_fake = discriminator(generator(noise))
+            d_loss_fake = fluid.layers.reduce_mean(
+                fluid.layers.sigmoid_cross_entropy_with_logits(
+                    x=d_fake,
+                    label=fluid.layers.fill_constant(
+                        shape=[2, 1], dtype='float32', value=0.0)))
+
+            d_loss = d_loss_real + d_loss_fake
+
+            sgd = SGDOptimizer(learning_rate=1e-3)
+            sgd.minimize(d_loss)
+
+        with new_program_scope(main=generate_p, startup=startup, scope=scope):
+            discriminator = Discriminator()
+            generator = Generator()
+
+            noise = fluid.layers.data(
+                name="noise", shape=[2, 2], append_batch_size=False)
+
+            d_fake = discriminator(generator(noise))
+            g_loss = fluid.layers.reduce_mean(
+                fluid.layers.sigmoid_cross_entropy_with_logits(
+                    x=d_fake,
+                    label=fluid.layers.fill_constant(
+                        shape=[2, 1], dtype='float32', value=1.0)))
+
+            sgd = SGDOptimizer(learning_rate=1e-3)
+            sgd.minimize(g_loss)
+
+        exe = fluid.Executor(fluid.CPUPlace())
+        static_params = dict()
+        with fluid.scope_guard(scope):
+            img = np.ones([2, 1], np.float32)
+            noise = np.ones([2, 2], np.float32)
+            exe.run(startup)
+            static_d_loss = exe.run(discriminate_p,
+                                    feed={'img': img,
+                                          'noise': noise},
+                                    fetch_list=[d_loss])[0]
+            static_g_loss = exe.run(generate_p,
+                                    feed={'noise': noise},
+                                    fetch_list=[g_loss])[0]
+
+            # generate_p contains all parameters needed.
+            for param in generate_p.global_block().all_parameters():
+                static_params[param.name] = np.array(
+                    scope.find_var(param.name).get_tensor())
+
+        dy_params = dict()
+        with fluid.imperative.guard():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+            discriminator = Discriminator()
+            generator = Generator()
+            sgd = SGDOptimizer(learning_rate=1e-3)
+
+            d_real = discriminator(to_variable(np.ones([2, 1], np.float32)))
+            d_loss_real = fluid.layers.reduce_mean(
+                fluid.layers.sigmoid_cross_entropy_with_logits(
+                    x=d_real, label=to_variable(np.ones([2, 1], np.float32))))
+
+            d_fake = discriminator(
+                generator(to_variable(np.ones([2, 2], np.float32))))
+            d_loss_fake = fluid.layers.reduce_mean(
+                fluid.layers.sigmoid_cross_entropy_with_logits(
+                    x=d_fake, label=to_variable(np.zeros([2, 1], np.float32))))
+
+            d_loss = d_loss_real + d_loss_fake
+            d_loss._backward()
+            sgd.minimize(d_loss)
+            discriminator.clear_gradients()
+            generator.clear_gradients()
+
+            d_fake = discriminator(
+                generator(to_variable(np.ones([2, 2], np.float32))))
+            g_loss = fluid.layers.reduce_mean(
+                fluid.layers.sigmoid_cross_entropy_with_logits(
+                    x=d_fake, label=to_variable(np.ones([2, 1], np.float32))))
+            g_loss._backward()
+            sgd.minimize(g_loss)
+            for p in discriminator.parameters():
+                dy_params[p.name] = p._numpy()
+            for p in generator.parameters():
+                dy_params[p.name] = p._numpy()
+
+            dy_g_loss = g_loss._numpy()
+            dy_d_loss = d_loss._numpy()
+
+        self.assertEqual(dy_g_loss, static_g_loss)
+        self.assertEqual(dy_d_loss, static_d_loss)
+        for k, v in six.iteritems(dy_params):
+            self.assertTrue(np.allclose(v, static_params[k]))
+
+
+if __name__ == '__main__':
+    unittest.main()