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fix ut
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@yuanlehome
yuanlehome committed Jul 11, 2023
1 parent 265f3c9 commit 0ae9257
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Showing 2 changed files with 128 additions and 26 deletions.
39 changes: 20 additions & 19 deletions paddle/fluid/framework/ir/identity_op_clean_pass.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,19 @@ FindUselessOpPattern::FindUselessOpPattern(PDPattern* pattern,
: PatternBase(pattern, name_scope, name_scope) {
auto* useless_op_in = pattern->NewNode(useless_op_in_repr())
->assert_is_var()
->assert_var_not_persistable();
->assert_var_not_persistable()
->assert_has_n_outputs(1)
->assert_more([](Node* x) {
for (auto* op : x->inputs) {
CHECK_EQ(op->IsOp(), true);
const auto& op_type = op->Op()->Type();
if (op_type == "conditional_block" ||
op_type == "while" || op_type == "feed") {
return false;
}
}
return true;
});

// This useless_op must have only one input and one output!
auto* useless_op =
Expand Down Expand Up @@ -72,18 +84,7 @@ FindUselessOpPattern::FindUselessOpPattern(PDPattern* pattern,
});

auto* useless_op_out =
pattern->NewNode(useless_op_out_repr())
->assert_is_var()
->assert_more([](Node* x) {
for (auto* op : x->outputs) {
CHECK_EQ(op->IsOp(), true);
const auto& op_type = op->Op()->Type();
if (op_type == "conditional_block" || op_type == "while") {
return false;
}
}
return true;
});
pattern->NewNode(useless_op_out_repr())->assert_is_var();

useless_op->LinksFrom({useless_op_in}).LinksTo({useless_op_out});
}
Expand All @@ -106,14 +107,14 @@ void IdentityOpCleanPass::ApplyImpl(ir::Graph* graph) const {
CHECK_EQ(useless_op_out->IsVar(), true);
CHECK_EQ(useless_op->IsOp(), true);

for (auto* next_op : useless_op_out->outputs) {
CHECK_EQ(next_op->IsOp(), true);
next_op->Op()->RenameInput(useless_op_out->Var()->Name(),
useless_op_in->Var()->Name());
IR_NODE_LINK_TO(useless_op_in, next_op);
for (auto* prev_op : useless_op_in->inputs) {
CHECK_EQ(prev_op->IsOp(), true);
prev_op->Op()->RenameOutput(useless_op_in->Var()->Name(),
useless_op_out->Var()->Name());
IR_NODE_LINK_TO(prev_op, useless_op_out);
}

GraphSafeRemoveNodes(graph, {useless_op, useless_op_out});
GraphSafeRemoveNodes(graph, {useless_op_in, useless_op});
found_count++;
};

Expand Down
115 changes: 108 additions & 7 deletions test/ir/inference/test_identity_clean_pass.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,54 @@


class TestIdentityScaleCleanPass(PassAutoScanTest):
def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_gpu=True)
yield config, ['relu', 'relu', 'scale'], (1e-5, 1e-5)

def sample_program_config(self, draw):
bias_after_scale = draw(st.booleans())
n = draw(st.integers(min_value=1, max_value=4))
c = draw(st.integers(min_value=1, max_value=20))
h = draw(st.integers(min_value=1, max_value=20))
w = draw(st.integers(min_value=1, max_value=20))

relu_op1 = OpConfig(
"relu", inputs={"X": ["relu_x"]}, outputs={"Out": ["relu_op1_out"]}
)
scale_op1 = OpConfig(
"scale",
inputs={"X": ["relu_op1_out"]},
outputs={"Out": ["scale_op1_out"]},
bias=0.0,
scale=1.0,
bias_after_scale=True,
)
scale_op2 = OpConfig(
"scale",
inputs={"X": ["scale_op1_out"]},
outputs={"Out": ["scale_op2_out"]},
bias=0.0,
scale=1.0,
bias_after_scale=True,
)
relu_op2 = OpConfig(
"relu",
inputs={"X": ["relu_op1_out"]},
outputs={"Out": ["relu_op2_out"]},
)
program_config = ProgramConfig(
ops=[relu_op1, relu_op2, scale_op1, scale_op2],
weights={},
inputs={"relu_x": TensorConfig(shape=[n, c, h, w])},
outputs=["scale_op2_out", "relu_op2_out"],
)
return program_config

def test(self):
self.run_and_statis(max_examples=25, passes=["identity_op_clean_pass"])


class TestIdentityScaleCleanPass_V1(PassAutoScanTest):
def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_gpu=True)
yield config, ['relu'], (1e-5, 1e-5)
Expand All @@ -31,22 +79,75 @@ def sample_program_config(self, draw):
h = draw(st.integers(min_value=1, max_value=20))
w = draw(st.integers(min_value=1, max_value=20))

relu_op = OpConfig(
"relu", inputs={"X": ["relu_x"]}, outputs={"Out": ["relu_out"]}
relu_op1 = OpConfig(
"relu", inputs={"X": ["relu_x"]}, outputs={"Out": ["relu_op1_out"]}
)
scale_op = OpConfig(
scale_op1 = OpConfig(
"scale",
inputs={"X": ["relu_out"]},
outputs={"Out": ["scale_out"]},
inputs={"X": ["relu_op1_out"]},
outputs={"Out": ["scale_op1_out"]},
bias=0.0,
scale=1.0,
bias_after_scale=True,
)
scale_op2 = OpConfig(
"scale",
inputs={"X": ["scale_op1_out"]},
outputs={"Out": ["scale_op2_out"]},
bias=0.0,
scale=1.0,
bias_after_scale=True,
)
program_config = ProgramConfig(
ops=[relu_op, scale_op],
ops=[relu_op1, scale_op1, scale_op2],
weights={},
inputs={"relu_x": TensorConfig(shape=[n, c, h, w])},
outputs=["scale_out"],
outputs=["scale_op2_out"],
)
return program_config

def test(self):
self.run_and_statis(max_examples=25, passes=["identity_op_clean_pass"])


class TestIdentityScaleCleanPass_V2(PassAutoScanTest):
def sample_predictor_configs(self, program_config):
config = self.create_inference_config(use_gpu=True)
yield config, ['scale', 'relu'], (1e-5, 1e-5)

def sample_program_config(self, draw):
bias_after_scale = draw(st.booleans())
n = draw(st.integers(min_value=1, max_value=4))
c = draw(st.integers(min_value=1, max_value=20))
h = draw(st.integers(min_value=1, max_value=20))
w = draw(st.integers(min_value=1, max_value=20))

scale_op1 = OpConfig(
"scale",
inputs={"X": ["scale_op1_in"]},
outputs={"Out": ["scale_op1_out"]},
bias=0.0,
scale=1.0,
bias_after_scale=True,
)
scale_op2 = OpConfig(
"scale",
inputs={"X": ["scale_op1_out"]},
outputs={"Out": ["scale_op2_out"]},
bias=0.0,
scale=1.0,
bias_after_scale=True,
)
relu_op1 = OpConfig(
"relu",
inputs={"X": ["scale_op2_out"]},
outputs={"Out": ["relu_op1_out"]},
)
program_config = ProgramConfig(
ops=[scale_op1, scale_op2, relu_op1],
weights={},
inputs={"scale_op1_in": TensorConfig(shape=[n, c, h, w])},
outputs=["relu_op1_out"],
)
return program_config

Expand Down

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