diff --git a/paddle/fluid/framework/ir/fusion_group/code_generator.cc b/paddle/fluid/framework/ir/fusion_group/code_generator.cc
index 2d21d15f45..af75f93392 100644
--- a/paddle/fluid/framework/ir/fusion_group/code_generator.cc
+++ b/paddle/fluid/framework/ir/fusion_group/code_generator.cc
@@ -24,7 +24,7 @@ namespace framework {
namespace ir {
namespace fusion_group {
-std::string ExtractDataType(const std::vector<Node*> nodes) {
+std::string ExtractDataType(const std::vector<Node*>& nodes) {
std::string dtype_str = "float";
auto data_type = nodes.back()->Var()->GetDataType();
@@ -98,6 +98,7 @@ std::vector<OperationExpression> CodeGenerator::ConvertToExpressions(
std::vector<int> output_ids;
std::vector<std::string> output_names =
OperationMap::Instance().Get(op->Type()).output_names;
+
for (auto& name : output_names) {
PADDLE_ENFORCE_EQ(
op->Output(name).size(), 1U,
@@ -125,9 +126,10 @@ std::string CodeGenerator::Generate(
std::string func_name,
const std::vector<OperationExpression>& expressions) {
// TODO(liuyiqun): Check whether all expressions are elementwise operations.
- std::set<int> input_ids = DistilInputIds(expressions);
- std::set<int> output_ids = DistilOutputIds(expressions);
- std::unordered_map<int, std::string> dtypes = DistilDtypes(expressions);
+ std::set<int> input_ids = std::move(DistilInputIds(expressions));
+ std::set<int> output_ids = std::move(DistilOutputIds(expressions));
+ std::unordered_map<int, std::string> dtypes =
+ std::move(DistilDtypes(expressions));
TemplateVariable template_var;
template_var.Add("func_name", func_name);
template_var.Add("parameters", EmitParameters(input_ids, output_ids, dtypes));
@@ -211,7 +213,7 @@ std::unordered_map<int, std::string> CodeGenerator::DistilDtypes(
// we get the parameter list code for the expression information
std::string CodeGenerator::EmitParameters(
const std::set<int>& input_ids, const std::set<int>& output_ids,
- std::unordered_map<int, std::string> dtypes) {
+ const std::unordered_map<int, std::string>& dtypes) const {
std::stringstream ret;
ret << "int N, ";
@@ -219,13 +221,13 @@ std::string CodeGenerator::EmitParameters(
// from the input list.
for (auto id : input_ids) {
if (output_ids.find(id) == output_ids.end()) {
- ret << dtypes[id] << "* " << ArgName(id) << ", ";
+ ret << dtypes.at(id) << "* " << ArgName(id) << ", ";
}
}
size_t index = 0;
for (auto id : output_ids) {
- ret << dtypes[id] << "* " << ArgName(id);
+ ret << dtypes.at(id) << "* " << ArgName(id);
if (index != output_ids.size() - 1) {
ret << ", ";
}
@@ -238,7 +240,7 @@ std::string CodeGenerator::EmitParameters(
std::string CodeGenerator::EmitComputeBody(
const std::vector<OperationExpression>& expressions,
const std::set<int>& input_ids, const std::set<int>& output_ids,
- std::unordered_map<int, std::string> dtypes) {
+ const std::unordered_map<int, std::string>& dtypes) const {
std::ostringstream compute;
std::unordered_set<int> used;
for (size_t i = 0; i < expressions.size(); i++) {
@@ -251,7 +253,8 @@ std::string CodeGenerator::EmitComputeBody(
for (auto id : input_ids) {
if (output_ids.find(id) == output_ids.end() &&
used.find(id) != used.end()) {
- load << dtypes[id] << " " << TmpName(id) << " = " << VarName(id) << ";";
+ load << dtypes.at(id) << " " << TmpName(id) << " = " << VarName(id)
+ << ";";
}
}
// Store temporal variables to memory.
diff --git a/paddle/fluid/framework/ir/fusion_group/code_generator.h b/paddle/fluid/framework/ir/fusion_group/code_generator.h
index ee908b2f57..670fc47a6a 100644
--- a/paddle/fluid/framework/ir/fusion_group/code_generator.h
+++ b/paddle/fluid/framework/ir/fusion_group/code_generator.h
@@ -17,6 +17,7 @@ limitations under the License. */
#include <set>
#include <string>
#include <unordered_map>
+#include <utility>
#include <vector>
#include "paddle/fluid/framework/ir/fusion_group/code_generator_helper.h"
#include "paddle/fluid/framework/ir/fusion_group/subgraph.h"
@@ -46,14 +47,14 @@ class CodeGenerator {
const std::vector<OperationExpression>& expressions);
// we get the parameter list code for the expression information
- std::string EmitParameters(const std::set<int>& input_ids,
- const std::set<int>& output_ids,
- std::unordered_map<int, std::string> dtypes);
+ std::string EmitParameters(
+ const std::set<int>& input_ids, const std::set<int>& output_ids,
+ const std::unordered_map<int, std::string>& dtypes) const;
std::string EmitComputeBody(
const std::vector<OperationExpression>& expressions,
const std::set<int>& input_ids, const std::set<int>& output_ids,
- std::unordered_map<int, std::string> dtypes);
+ const std::unordered_map<int, std::string>& dtypes) const;
// Encode all var nodes in the subgraph with an unique number.
std::unordered_map<std::string, int> EncodeVarNodes(SubGraph* subgraph);
diff --git a/paddle/fluid/operators/fused/fusion_group_op.h b/paddle/fluid/operators/fused/fusion_group_op.h
index ed879cfae5..8449c6b63b 100644
--- a/paddle/fluid/operators/fused/fusion_group_op.h
+++ b/paddle/fluid/operators/fused/fusion_group_op.h
@@ -25,7 +25,7 @@ namespace operators {
static void MutableMultiTypeData(
std::vector<paddle::framework::LoDTensor*>* var,
const std::vector<std::string>& data_type, const platform::Place& place) {
- for (size_t i = 0; i < (*var).size(); i++) {
+ for (size_t i = 0; i < var->size(); i++) {
if (data_type[i] == "float") {
(*var)[i]->mutable_data<float>(place);
} else if (data_type[i] == "double") {
diff --git a/python/paddle/fluid/tests/unittests/ir/pass_test.py b/python/paddle/fluid/tests/unittests/ir/pass_test.py
index 67c569f817..4ee3f26022 100644
--- a/python/paddle/fluid/tests/unittests/ir/pass_test.py
+++ b/python/paddle/fluid/tests/unittests/ir/pass_test.py
@@ -38,6 +38,7 @@ class PassTest(unittest.TestCase):
self.pass_attrs = {}
self.fused_op_type = None
self.num_fused_ops = -1
+ self.backward = True
np.random.seed(123)
random.seed(124)
@@ -48,6 +49,11 @@ class PassTest(unittest.TestCase):
places.append(fluid.CUDAPlace(0))
return places
+ def append_gradinets(self, outs):
+ with fluid.program_guard(self.main_program, self.startup_program):
+ loss = fluid.layers.mean(outs)
+ fluid.backward.append_backward(loss)
+
def check_output(self, startup_on_cpu=False, atol=1e-5):
'''
Check whether the fetched outputs of the origin program and the
@@ -143,7 +149,7 @@ class PassTest(unittest.TestCase):
np.allclose(
outs_opt[i], outs[i], atol=atol),
"Output < {} > has diff at {}, expected {} but got {}".format(
- self.fetch_list[i].name, str(place), outs_opt[i], outs[i]))
+ self.fetch_list[i], str(place), outs_opt[i], outs[i]))
def _check_fused_ops(self, program):
'''
diff --git a/python/paddle/fluid/tests/unittests/ir/test_ir_fusion_group_pass.py b/python/paddle/fluid/tests/unittests/ir/test_ir_fusion_group_pass.py
index 0ec4a85298..6724b634b7 100644
--- a/python/paddle/fluid/tests/unittests/ir/test_ir_fusion_group_pass.py
+++ b/python/paddle/fluid/tests/unittests/ir/test_ir_fusion_group_pass.py
@@ -35,10 +35,15 @@ class FusionGroupPassTest(PassTest):
# subgraph with 2 op nodes
tmp_2 = layers.relu(tmp_0 + tmp_1)
- self.fetch_list = [tmp_2]
self.num_fused_ops = 1
+ self.fetch_list = [tmp_2.name, tmp_1.name + "@GRAD"]
+
+ if self.backward:
+ self.append_gradinets(tmp_2)
+ self.num_fused_ops = 2
def setUp(self):
+ self.backward = True
self.build_program("float32")
self.feeds = self._feed_random_data(self.feed_vars)
self.pass_names = "fusion_group_pass"
@@ -86,8 +91,13 @@ class FusionGroupPassTest1(FusionGroupPassTest):
self.feed_vars[2]) * layers.tanh(self.feed_vars[3])
tmp_2 = layers.tanh(tmp_1) + layers.sigmoid(self.feed_vars[4])
- self.fetch_list = [tmp_1, tmp_2]
- self.num_fused_ops = 1
+ if self.backward:
+ self.append_gradinets(tmp_2)
+ self.num_fused_ops = 2
+ else:
+ self.num_fused_ops = 1
+
+ self.fetch_list = [tmp_2.name, tmp_0.name + "@GRAD"]
class FusionGroupPassTest2(FusionGroupPassTest):
@@ -98,15 +108,27 @@ class FusionGroupPassTest2(FusionGroupPassTest):
fluid.data(
name="data3", shape=[128, 32], dtype=dtype))
- # subgraph with 3 op nodes
- tmp_1 = layers.relu(
- (self.feed_vars[0] - self.feed_vars[1]) * self.feed_vars[2])
+ # subgraph with 3 op node
+ tmp_0 = self.feed_vars[0] + self.feed_vars[1]
+ tmp_1 = layers.relu(self.feed_vars[2] * tmp_0)
# subgraph with 2 op nodes
tmp_2 = layers.relu(layers.sigmoid(self.feed_vars[3]))
tmp_3 = layers.mul(tmp_1, tmp_2)
- self.fetch_list = [tmp_1, tmp_2, tmp_3]
self.num_fused_ops = 2
+ self.fetch_list = [tmp_3.name]
+
+ #TODO(wangchaochaohu): we need to deal with the condition of stop gradient
+ if self.backward:
+ self.append_gradinets(tmp_3)
+ self.num_fused_ops = 3
+
+ def setUp(self):
+ self.backward = False
+ self.build_program("float32")
+ self.feeds = self._feed_random_data(self.feed_vars)
+ self.pass_names = "fusion_group_pass"
+ self.fused_op_type = "fusion_group"
class FusionGroupPassTestFP64(FusionGroupPassTest):
@@ -132,8 +154,12 @@ class FusionGroupPassTestFP16(FusionGroupPassTest):
tmp_4 = layers.relu(tmp_2 + tmp_3)
tmp_5 = layers.cast(tmp_4, dtype=dtype)
- self.fetch_list = [tmp_0, tmp_1, tmp_2, tmp_3, tmp_4, tmp_5]
- self.num_fused_ops = 2
+ self.num_fused_ops = 1
+ self.fetch_list = [tmp_5.name]
+
+ if self.backward:
+ self.num_fused_ops = 4
+ self.append_gradinets(tmp_5)
class FusionGroupPassSumTest(FusionGroupPassTest):
@@ -158,9 +184,13 @@ class FusionGroupPassCastTest(FusionGroupPassTest):
tmp_1 = layers.cast(tmp_0, dtype="double")
tmp_2 = layers.cast(tmp_1, dtype="float32")
- self.fetch_list = [tmp_0, tmp_1, tmp_2]
+ self.fetch_list = [tmp_2.name, tmp_1.name + "@GRAD"]
self.num_fused_ops = 1
+ if self.backward:
+ self.num_fused_ops = 2
+ self.append_gradinets(tmp_2)
+
def setUp(self):
self.build_program("float64")
self.feeds = self._feed_random_data(self.feed_vars)