Shameless copy

paddle/fluid/imperative/layer.cc

@@ -188,11 +188,13 @@ std::vector<Variable*> OpBase::ApplyGrad(framework::Scope* scope) {
   std::vector<Variable*> ret;
   for (size_t i = 0; i < input_vars_->size(); ++i) {
     bool found = false;
+    VarBase* origin_var = (*input_vars_)[i];
     for (const std::string& outvar : grad_op_desc_->OutputArgumentNames()) {
       Variable* var = scope->FindVar(outvar);
-      VarBase* origin_var = (*input_vars_)[i];
       std::string orig_var = grad_to_var_->at(outvar);
-      PADDLE_ENFORCE(origin_var->var_desc_->Name() == orig_var);
+      if (origin_var->var_desc_->Name() != orig_var) {
+        continue;
+      }
       VLOG(3) << "apply grad " << outvar << " with origin " << orig_var;
       origin_var->ApplyGrad(scope, var);
       found = true;

paddle/fluid/imperative/tracer.h

@@ -43,9 +43,12 @@ void CreateGradOp(const framework::OpDesc& op_desc,
 
 class Tracer {
  public:
-  explicit Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {
+  explicit Tracer(framework::BlockDesc* root_block,
+                  framework::BlockDesc* startup_block)
+      : root_block_(root_block), startup_block_(startup_block) {
     root_scope_ = new framework::Scope();
     scopes_[root_block_] = root_scope_;
+    scopes_[startup_block_] = root_scope_;
   }
 
   virtual ~Tracer() { delete root_scope_; }
@@ -80,6 +83,8 @@ class Tracer {
       } else {
         op->pre_ops_->push_back(nullptr);
       }
+      VLOG(3) << "input vname " << vname << " "
+              << var->Get<framework::LoDTensor>().dims().size();
     }
 
     *op->output_vars_ = outputs;
@@ -98,12 +103,19 @@ class Tracer {
       outputs[i]->pre_op_ = op;
       outputs[i]->pre_op_out_idx_ = i;
     }
+
+    VLOG(3) << "tracer running " << op_desc->Type();
     op_base->Run(*scope, platform::CPUPlace());
-    framework::OpDesc* grad_op_desc;
-    auto grad_to_var = new std::unordered_map<std::string, std::string>();
-    CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var);
-    op->grad_op_desc_ = grad_op_desc;
-    op->grad_to_var_ = grad_to_var;
+    if (block == startup_block_) {
+      op->grad_op_desc_ = nullptr;
+      op->grad_to_var_ = nullptr;
+    } else {
+      framework::OpDesc* grad_op_desc;
+      auto grad_to_var = new std::unordered_map<std::string, std::string>();
+      CreateGradOp(*op_desc, {}, {block}, &grad_op_desc, grad_to_var);
+      op->grad_op_desc_ = grad_op_desc;
+      op->grad_to_var_ = grad_to_var;
+    }
     op->block_ = block;
   }
 
@@ -121,6 +133,7 @@ class Tracer {
  private:
   std::map<framework::BlockDesc*, framework::Scope*> scopes_;
   framework::BlockDesc* root_block_;
+  framework::BlockDesc* startup_block_;
   framework::Scope* root_scope_;
 };
 

paddle/fluid/operators/mul_op.cc

@@ -49,7 +49,8 @@ class MulOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_GT(
         y_dims.size(), y_num_col_dims,
         "The input tensor Y's rank of MulOp should be larger than "
-        "y_num_col_dims.");
+        "y_num_col_dims: %ld vs %ld",
+        y_dims.size(), y_num_col_dims);
 
     auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
     auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);

paddle/fluid/pybind/imperative.cc

@@ -24,8 +24,9 @@ namespace pybind {
 void BindTracer(pybind11::module *m) {
   pybind11::class_<imperative::Tracer>(*m, "Tracer", "")
       .def("__init__",
-           [](imperative::Tracer &self, framework::BlockDesc *root_block) {
-             new (&self) imperative::Tracer(root_block);
+           [](imperative::Tracer &self, framework::BlockDesc *root_block,
+              framework::BlockDesc *startup_block) {
+             new (&self) imperative::Tracer(root_block, startup_block);
            })
       .def("trace", &imperative::Tracer::Trace)
       .def("get_scope", &imperative::Tracer::GetScope,

python/paddle/fluid/backward.py

@@ -564,8 +564,11 @@ def append_backward(loss, parameter_list=None, no_grad_set=None,
     grad_to_var = dict()
 
     op_desc = _create_op_desc_(
-        "fill_constant", {}, {"Out": [_append_grad_suffix_(loss.name)]}, {
-            "shape": [1],
+        "fill_constant",
+        {},
+        {"Out": [_append_grad_suffix_(loss.name)]},
+        {
+            "shape": [1],  # TODO(panyx0718): This can be loss.shape.
             "value": 1.0,
             "dtype": loss.dtype,
             "force_cpu": False,

python/paddle/fluid/framework.py

@@ -1316,6 +1316,9 @@ class Block(object):
     def _prepend_op(self, *args, **kwargs):
         op_desc = self.desc._prepend_op()
         op = Operator(self, op_desc, *args, **kwargs)
+        if _in_imperative_mode():
+            _imperative_tracer().trace(op.iop, [v._ivar for v in op.inputs],
+                                       [v._ivar for v in op.outputs], self.desc)
         self.ops.insert(0, op)
         return op
 

python/paddle/fluid/imperative/base.py

@@ -28,7 +28,8 @@ def enabled():
 def guard():
     train = framework.Program()
     startup = framework.Program()
-    tracer = core.Tracer(train.current_block().desc)
+    tracer = core.Tracer(train.current_block().desc,
+                         startup.current_block().desc)
     with framework.program_guard(train, startup):
         with framework.unique_name.guard():
             with framework._imperative_guard(tracer):

python/paddle/fluid/imperative/layers.py

@@ -25,11 +25,9 @@ __all__ = ['PyLayer']
 
 class PyLayer(core.Layer):
     def __init__(self):
-        pass
+        self._built = False
 
     def __call__(self, inputs):
-        # TODO(panyx0718): Support declarative mode as well.
-        assert base.enabled()
         if not isinstance(inputs, list) and not isinstance(inputs, tuple):
             inputs = [inputs]
 
@@ -37,8 +35,15 @@ class PyLayer(core.Layer):
         for x in inputs:
             py_var = base.to_variable(x)
             var_inputs.append(py_var)
+        if not self._built:
+            self._build_once(inputs)
+            self._built = True
+
         outputs = self.forward(var_inputs)
         return outputs
 
+    def _build_once(self, inputs):
+        pass
+
     def forward(self, inputs):
         return []

python/paddle/fluid/layers/nn.py

@@ -29,6 +29,7 @@ from . import utils
 from .. import unique_name
 from functools import reduce
 from .. import core
+from ..imperative import layers
 
 __all__ = [
     'fc',
@@ -9426,3 +9427,47 @@ def huber_loss(input, label, delta):
                  'Residual': residual},
         attrs={'delta': delta})
     return out
+
+
+class FC(layers.PyLayer):
+    def __init__(self,
+                 size,
+                 param_attr=None,
+                 num_flatten_dims=1,
+                 dtype=core.VarDesc.VarType.FP32):
+        super(FC, self).__init__()
+        self._size = size
+        self._num_flatten_dims = num_flatten_dims
+        self._dtype = dtype
+        self._helper = LayerHelper('FC', param_attr=param_attr)
+
+    def _build_once(self, inputs):
+        input_shape = inputs[0].shape
+        param_shape = [
+            reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:], 1)
+        ] + [self._size]
+        self._w = self._helper.create_parameter(
+            attr=self._helper.param_attr,
+            shape=param_shape,
+            dtype=self._dtype,
+            is_bias=False)
+
+    def forward(self, inputs):
+        tmp = self._helper.create_variable_for_type_inference(self._dtype)
+        self._helper.append_op(
+            type="mul",
+            inputs={"X": inputs[0],
+                    "Y": self._w},
+            outputs={"Out": tmp},
+            attrs={
+                "x_num_col_dims": self._num_flatten_dims,
+                "y_num_col_dims": 1
+            })
+
+        out = self._helper.create_variable_for_type_inference(self._dtype)
+        self._helper.append_op(
+            type="sum",
+            inputs={"X": [tmp]},
+            outputs={"Out": out},
+            attrs={"use_mkldnn": False})
+        return out

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

@@ -12,12 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import contextlib
 import unittest
-import sys
 import numpy as np
 
 import paddle.fluid as fluid
 from paddle.fluid import core
+from paddle.fluid.layers.nn import FC
+
+
+@contextlib.contextmanager
+def new_program_scope():
+    prog = fluid.Program()
+    startup_prog = fluid.Program()
+    scope = fluid.core.Scope()
+    with fluid.scope_guard(scope):
+        with fluid.program_guard(prog, startup_prog):
+            yield
 
 
 class MyLayer(fluid.imperative.PyLayer):
@@ -30,6 +41,23 @@ class MyLayer(fluid.imperative.PyLayer):
         return [fluid.layers.elementwise_mul(x, x)]
 
 
+class MLP(fluid.imperative.PyLayer):
+    def __init__(self):
+        super(MLP, self).__init__()
+        self._fc1 = FC(3,
+                       fluid.ParamAttr(
+                           initializer=fluid.initializer.Constant(value=0.1)))
+        self._fc2 = FC(4,
+                       fluid.ParamAttr(
+                           initializer=fluid.initializer.Constant(value=0.1)))
+
+    def forward(self, inputs):
+        x = self._fc1(inputs[0])
+        x = self._fc2(x)
+        x = fluid.layers.reduce_sum(x)
+        return x
+
+
 class TestImperative(unittest.TestCase):
     def test_layer(self):
         with fluid.imperative.guard():
@@ -39,13 +67,56 @@ class TestImperative(unittest.TestCase):
             l.forward([])
 
     def test_layer_in_out(self):
+        np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
         with fluid.imperative.guard():
             l = MyLayer()
-            x = l(np.array([1.0, 2.0, -1.0], dtype=np.float32))[0]
+            x = l(np_inp)[0]
             self.assertIsNotNone(x)
-            sys.stderr.write("%s output: %s\n" % (x, x._numpy()))
+            dy_out = x._numpy()
             x._backward()
-            sys.stderr.write("grad %s\n" % l._x_for_debug._gradient())
+            dy_grad = l._x_for_debug._gradient()
+
+        with new_program_scope():
+            inp = fluid.layers.data(
+                name="inp", shape=[3], append_batch_size=False)
+            l = MyLayer()
+            x = l(inp)[0]
+            param_grads = fluid.backward.append_backward(
+                x, parameter_list=[l._x_for_debug.name])[0]
+            exe = fluid.Executor(fluid.CPUPlace())
+
+            static_out, static_grad = exe.run(
+                feed={inp.name: np_inp},
+                fetch_list=[x.name, param_grads[1].name])
+
+        self.assertTrue(np.allclose(dy_out, static_out))
+        self.assertTrue(np.allclose(dy_grad, static_grad))
+
+    def test_mlp(self):
+        np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
+        with fluid.imperative.guard():
+            mlp = MLP()
+            out = mlp(np_inp)
+            dy_out = out._numpy()
+            out._backward()
+            dy_grad = mlp._fc1._w._gradient()
+
+        with new_program_scope():
+            inp = fluid.layers.data(
+                name="inp", shape=[2, 2], append_batch_size=False)
+            mlp = MLP()
+            out = mlp(inp)
+            param_grads = fluid.backward.append_backward(
+                out, parameter_list=[mlp._fc1._w.name])[0]
+            exe = fluid.Executor(fluid.CPUPlace())
+            exe.run(fluid.default_startup_program())
+
+            static_out, static_grad = exe.run(
+                feed={inp.name: np_inp},
+                fetch_list=[out.name, param_grads[1].name])
+
+        self.assertTrue(np.allclose(dy_out, static_out))
+        self.assertTrue(np.allclose(dy_grad, static_grad))
 
 
 if __name__ == '__main__':