From 336160e65118354c134274b09bb996b03ddf8460 Mon Sep 17 00:00:00 2001
From: minqiyang <minqiyang@baidu.com>
Date: Thu, 27 Dec 2018 17:26:29 +0800
Subject: [PATCH] Complete imperative optimizer implementation
test=develop
---
paddle/fluid/imperative/tracer.h | 1 -
paddle/fluid/pybind/pybind.cc | 1 -
python/paddle/fluid/framework.py | 30 ++++++---
python/paddle/fluid/layer_helper.py | 5 +-
python/paddle/fluid/optimizer.py | 4 +-
.../tests/unittests/test_imperative_mnist.py | 63 +++++--------------
6 files changed, 39 insertions(+), 65 deletions(-)
diff --git a/paddle/fluid/imperative/tracer.h b/paddle/fluid/imperative/tracer.h
index de7899055d..f6dac762fd 100644
--- a/paddle/fluid/imperative/tracer.h
+++ b/paddle/fluid/imperative/tracer.h
@@ -84,7 +84,6 @@ class Tracer {
*op->output_vars_ = outputs;
for (size_t i = 0; i < outputs.size(); ++i) {
const std::string vname = outputs[i]->var_desc_->Name();
- LOG(ERROR) << "output name: " << vname;
framework::Variable* var = root_scope_->Var(vname);
if (!var->IsInitialized()) {
framework::VarDesc* var_desc = block->FindVar(vname);
diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc
index 23248a5dee..74fee64671 100644
--- a/paddle/fluid/pybind/pybind.cc
+++ b/paddle/fluid/pybind/pybind.cc
@@ -139,7 +139,6 @@ PYBIND11_MODULE(core, m) {
.def_property("value",
[](const imperative::VarBase &self) { return self.var_; },
[](imperative::VarBase &self, framework::Variable *var) {
- LOG(ERROR) << "set var to pointer: " << var;
self.var_ = var;
},
py::return_value_policy::reference)
diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py
index 6c5dd84460..fc00adfbb6 100644
--- a/python/paddle/fluid/framework.py
+++ b/python/paddle/fluid/framework.py
@@ -1289,13 +1289,22 @@ class Block(object):
Operator: the append Operator.
"""
op_desc = self.desc.append_op()
- op = Operator(block=self, desc=op_desc, *args, **kwargs)
+ op = Operator(
+ block=self,
+ desc=op_desc,
+ type=kwargs.get("type", None),
+ inputs=kwargs.get("inputs", None),
+ outputs=kwargs.get("outputs", None),
+ attrs=kwargs.get("attrs", None))
+ self.ops.append(op)
+ self._trace_op(op, kwargs.get("stop_gradient", False))
+ return op
+
+ def _trace_op(self, op, stop_gradient=False):
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,
- kwargs.get("stop_gradient", False))
- self.ops.append(op)
- return op
+ stop_gradient)
def _insert_op(self, index, *args, **kwargs):
"""
@@ -1342,12 +1351,15 @@ 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,
- kwargs.get("stop_gradient", False))
+ op = Operator(
+ self,
+ op_desc,
+ type=kwargs.get("type", None),
+ inputs=kwargs.get("inputs", None),
+ outputs=kwargs.get("outputs", None),
+ attrs=kwargs.get("attrs", None))
self.ops.insert(0, op)
+ self._trace_op(op, kwargs.get("stop_gradient", False))
return op
def _sync_with_cpp(self):
diff --git a/python/paddle/fluid/layer_helper.py b/python/paddle/fluid/layer_helper.py
index 8a8470db46..5429a73533 100644
--- a/python/paddle/fluid/layer_helper.py
+++ b/python/paddle/fluid/layer_helper.py
@@ -23,7 +23,6 @@ import numpy as np
from .framework import Variable, Parameter, default_main_program, default_startup_program, dtype_is_floating
from . import unique_name
from paddle.fluid.imperative import base as imperative_base
-from paddle.fluid.imperative.base import to_variable
from paddle.fluid.initializer import Constant, Xavier
from .param_attr import ParamAttr, WeightNormParamAttr
from . import core
@@ -51,7 +50,7 @@ class LayerHelper(object):
return default_startup_program()
def to_variable(self, x):
- return base.to_variable(x, self.main_program.current_block())
+ return imperative_base.to_variable(x, self.main_program.current_block())
def append_op(self, *args, **kwargs):
return self.main_program.current_block().append_op(*args, **kwargs)
@@ -371,7 +370,7 @@ class LayerHelper(object):
def set_variable_initializer(self, var, initializer):
assert isinstance(var, Variable)
if imperative_base.enabled():
- initializer(var, self.startup_program.global_block())
+ initializer(var, var.block)
else:
self.startup_program.global_block().create_var(
name=var.name,
diff --git a/python/paddle/fluid/optimizer.py b/python/paddle/fluid/optimizer.py
index 5cdbe7c10d..779cb5f961 100644
--- a/python/paddle/fluid/optimizer.py
+++ b/python/paddle/fluid/optimizer.py
@@ -302,7 +302,7 @@ class Optimizer(object):
This method combines interface `append_backward()` and
`create_optimization_pass()` into one.
"""
- if imperative_base.enabled:
+ if imperative_base.enabled():
if parameter_list is not None:
params_grads = parameter_list
else:
@@ -315,7 +315,7 @@ class Optimizer(object):
block=loss.block,
name=param._ivar._grad_name(),
stop_gradient=True)
- grad_var._value = param._ivar.grad_value()
+ grad_var._value = param._ivar.grad_value
params_grads.append((param, grad_var))
optimize_ops = self._create_optimization_pass(params_grads, loss,
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_mnist.py b/python/paddle/fluid/tests/unittests/test_imperative_mnist.py
index 12d605316c..a2e008615c 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_mnist.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_mnist.py
@@ -43,15 +43,6 @@ class SimpleImgConvPool(fluid.imperative.PyLayer):
bias_attr=None):
super(SimpleImgConvPool, self).__init__()
- # groups = 1
- # dilation = [1, 1]
- # pad = [0, 0]
- # stride = [1, 1]
- # input_size = [2, 3, 5, 5] # NCHW
- # assert np.mod(input_size[1], groups) == 0
- # f_c = input_size[1] // groups
- # filter_size = [6, f_c, 3, 3]
-
self._conv2d = Conv2D(
num_channels=num_channels,
num_filters=num_filters,
@@ -108,47 +99,21 @@ class TestImperativeMnist(unittest.TestCase):
def test_mnist_cpu_float32(self):
with fluid.imperative.guard():
mnist = MNIST()
-
- x_data = np.random.rand(128, 1, 28, 28).astype('float32')
- img = to_variable(x_data)
- y_data = np.random.rand(128, 1).astype('int64')
- label = to_variable(y_data)
- label._stop_gradient = True
-
- predict = mnist(img)
- out = fluid.layers.cross_entropy(predict, label)
- out._backward()
- filter_grad = mnist._simple_img_conv_pool_1._conv2d._filter_param._gradient(
- )
- # print(filter_grad)
-
sgd = SGDOptimizer(learning_rate=1e-3)
- sgd.minimize(out)
-
- # 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))
+
+ for i in range(1):
+ x_data = np.random.rand(128, 1, 28, 28).astype('float32')
+ img = to_variable(x_data)
+ y_data = np.random.rand(128, 1).astype('int64')
+ label = to_variable(y_data)
+ label._stop_gradient = True
+
+ predict = mnist(img)
+ out = fluid.layers.cross_entropy(predict, label)
+ out._backward()
+ filter_grad = mnist._simple_img_conv_pool_1._conv2d._filter_param._gradient(
+ )
+ sgd.minimize(out)
if __name__ == '__main__':