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[inference Zero-Dim]add equal, elementwise_op trt 0d #53704

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May 11, 2023
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[inference Zero-Dim]add equal, elementwise_op trt 0d #53704

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886a933
add square, elementwise_op trt 0d
zhangjun May 10, 2023
01fba03
update teller.cc
zhangjun May 10, 2023
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50 changes: 23 additions & 27 deletions paddle/fluid/inference/tensorrt/op_teller.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -114,6 +114,25 @@ struct SimpleOpTypeSetTeller : public Teller {
"sign", "silu", "logical_not", "reciprocal", "tanh_shrink",
"logsigmoid", "erf", "bitwise_not", "equal", "not_equal",
"rsqrt"};

// Static shape does not support 0 or 1 dim's input.
if (!with_dynamic_shape) {
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这一块是抽出来统一判断吗

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是的 每个OP判断有点冗余

auto inputs = desc.Inputs();
for (auto iter : inputs) {
for (auto var_name : iter.second) {
auto* block = desc.Block();
if (block) {
auto* var_desc = block->FindVar(var_name);
// Can't get feed op's TensorDesc
if (op_type != "feed" && var_desc && !var_desc->Persistable()) {
const auto shape = var_desc->GetShape();
if (shape.size() == 1 || shape.size() == 0) return false;
}
}
}
}
}

if (act_op_list.find(op_type) != act_op_list.end()) {
auto* block = desc.Block();
if (block == nullptr) {
Expand All @@ -122,47 +141,23 @@ struct SimpleOpTypeSetTeller : public Teller {
"the pass.";
return false;
}
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVar(x_var_name);
const auto x_shape = x_var_desc->GetShape();
if (!with_dynamic_shape && (x_shape.size() == 1 || x_shape.size() == 0)) {
VLOG(3) << op_type
<< " op does not support input's dim is 1 or 0 in tensorrt "
"static shape mode.";
return false;
}
#if !IS_TRT_VERSION_GE(7000)
if (op_type == "erf") {
VLOG(3) << op_type << " op does not support tensorrt.";
return false;
}
#endif
#if !IS_TRT_VERSION_GE(8600)
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVar(x_var_name);
const auto x_shape = x_var_desc->GetShape();
if (x_shape.size() == 0 && unary_list.find(op_type) != unary_list.end()) {
VLOG(3) << op_type
<< " op does not support 0 dim input when TensorRT < 8.6.";
return false;
}
#endif
}
// In static shape in Paddle-TRT, we can't allow that one op has a
// 1D intermediate tensor as input.
if (!with_dynamic_shape) {
auto inputs = desc.Inputs();
for (auto iter : inputs) {
for (auto var_name : iter.second) {
auto* block = desc.Block();
if (block) {
auto* var_desc = block->FindVar(var_name);
// Can't get feed op's TensorDesc
if (op_type != "feed" && var_desc && !var_desc->Persistable()) {
const auto shape = var_desc->GetShape();
if (shape.size() == 1) return false;
}
}
}
}
}

if (op_type == "dropout") {
/*
Expand Down Expand Up @@ -1505,6 +1500,7 @@ struct SimpleOpTypeSetTeller : public Teller {
"elementwise op.";
return false;
}

if (x_var_desc->Persistable() && !with_dynamic_shape) {
VLOG(3)
<< "Input X is a parameter which is not supported for "
Expand Down
156 changes: 156 additions & 0 deletions test/ir/inference/test_trt_convert_elementwise.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1214,5 +1214,161 @@ def test(self):
self.run_test()


class TrtConvertElementwise0D(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
return True

def sample_program_configs(self):
def generate_input(dims, op_type):
shape = []
if dims == 0:
shape = []
elif dims == 1:
shape = [8]
elif dims == 2:
shape = [1, 8]
elif dims == 3:
shape = [1, 8, 8]
else:
shape = [1, 8, 8, 8]

# elementwise_floordiv is integer only
if op_type == "elementwise_floordiv":
return np.random.randint(
low=1, high=10000, size=shape, dtype=np.int32
)
elif op_type == "elementwise_mod":
return np.random.uniform(low=0.1, high=1.0, size=shape).astype(
np.float32
)
else:
return np.random.random(shape).astype(np.float32)

for dims in [[0, 0], [0, 1], [0, 2], [1, 0], [2, 0]]:
for op_type in [
"elementwise_add",
"elementwise_mul",
"elementwise_sub",
"elementwise_div",
"elementwise_pow",
"elementwise_min",
"elementwise_max",
"elementwise_floordiv",
"elementwise_mod",
]:
for axis in [-1 if dims[0] == 1 or dims[0] == 0 else 1]:
self.dims = dims[0]
dics = [{"axis": axis}]
ops_config = [
{
"op_type": op_type,
"op_inputs": {
"X": ["input_data"],
"Y": ["weight"],
},
"op_outputs": {"Out": ["output_data"]},
"op_attrs": dics[0],
"outputs_dtype": {
"output_data": np.float32
if op_type != "elementwise_floordiv"
else np.int32
},
}
]
ops = self.generate_op_config(ops_config)

program_config = ProgramConfig(
ops=ops,
weights={
"weight": TensorConfig(
data_gen=partial(
generate_input, dims[1], op_type
)
)
},
inputs={
"input_data": TensorConfig(
data_gen=partial(
generate_input, dims[0], op_type
)
),
},
outputs=["output_data"],
)

yield program_config

def sample_predictor_configs(
self, program_config
) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
# The input.dims[1] must be equal to the weight's length.
if self.dims == 0:
self.dynamic_shape.min_input_shape = {"input_data": []}
self.dynamic_shape.max_input_shape = {"input_data": []}
self.dynamic_shape.opt_input_shape = {"input_data": []}
if self.dims == 1:
self.dynamic_shape.min_input_shape = {"input_data": [1]}
self.dynamic_shape.max_input_shape = {"input_data": [16]}
self.dynamic_shape.opt_input_shape = {"input_data": [8]}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 8]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 8]}
self.dynamic_shape.opt_input_shape = {"input_data": [2, 8]}
elif self.dims == 3:
self.dynamic_shape.min_input_shape = {"input_data": [1, 1, 4]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 16, 16]}
self.dynamic_shape.opt_input_shape = {"input_data": [2, 8, 8]}
elif self.dims == 4:
self.dynamic_shape.min_input_shape = {
"input_data": [1, 8, 8, 8]
}
self.dynamic_shape.max_input_shape = {
"input_data": [4, 8, 8, 8]
}
self.dynamic_shape.opt_input_shape = {
"input_data": [4, 8, 8, 8]
}

def clear_dynamic_shape():
self.dynamic_shape.max_input_shape = {}
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.opt_input_shape = {}

def generate_trt_nodes_num(attrs, dynamic_shape):
if not dynamic_shape and (self.dims == 1 or self.dims == 0):
return 0, 3
return 1, 2

attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]

# for static_shape
clear_dynamic_shape()
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False
), (1e-5, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, False
), (1e-3, 1e-3)

# # for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), (1e-5, 1e-5)
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True
), (1e-3, 1e-3)

def test(self):
self.run_test()


if __name__ == "__main__":
unittest.main()
92 changes: 51 additions & 41 deletions test/ir/inference/test_trt_convert_equal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,54 +40,64 @@ def generate_input(shape):
return np.random.random(shape).astype(np.float32)

for op_type in ["equal", "not_equal"]:
for batch in [1, 2, 4]:
for shape in [[batch, 1], [batch, 1, 32], [batch, 1, 16, 32]]:
for axis in [-1 if len(shape) == 1 else 1]:
self.dims = len(shape)
dics = [{"axis": axis}, {"in_dtype": 0, "out_dtype": 5}]
ops_config = [
{
"op_type": op_type,
"op_inputs": {
"X": ["input_data1"],
"Y": ["input_data2"],
},
"op_outputs": {"Out": ["compare_output_data"]},
"op_attrs": dics[0],
"outputs_dtype": {
"compare_output_data": np.bool_
},
for shape in [[], [1, 1], [1, 1, 32], [1, 1, 16, 32]]:
for axis in [-1 if len(shape) == 1 or len(shape) == 0 else 1]:
self.dims = len(shape)
dics = [{"axis": axis}, {"in_dtype": 0, "out_dtype": 5}]
ops_config = [
{
"op_type": op_type,
"op_inputs": {
"X": ["input_data1"],
"Y": ["input_data2"],
},
{
"op_type": "cast",
"op_inputs": {"X": ["compare_output_data"]},
"op_outputs": {"Out": ["output_data"]},
"op_attrs": dics[1],
"outputs_dtype": {"output_data": np.float32},
},
]
ops = self.generate_op_config(ops_config)

program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data1": TensorConfig(
data_gen=partial(generate_input, shape)
),
"input_data2": TensorConfig(
data_gen=partial(generate_input, shape)
),
},
outputs=["output_data"],
)
yield program_config
"op_outputs": {"Out": ["compare_output_data"]},
"op_attrs": dics[0],
"outputs_dtype": {"compare_output_data": np.bool_},
},
{
"op_type": "cast",
"op_inputs": {"X": ["compare_output_data"]},
"op_outputs": {"Out": ["output_data"]},
"op_attrs": dics[1],
"outputs_dtype": {"output_data": np.float32},
},
]
ops = self.generate_op_config(ops_config)

program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data1": TensorConfig(
data_gen=partial(generate_input, shape)
),
"input_data2": TensorConfig(
data_gen=partial(generate_input, shape)
),
},
outputs=["output_data"],
)
yield program_config

def sample_predictor_configs(
self, program_config
) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
# The input.dims[1] must be equal to the weight's length.
if self.dims == 0:
self.dynamic_shape.min_input_shape = {
"input_data1": [],
"input_data2": [],
}
self.dynamic_shape.max_input_shape = {
"input_data1": [],
"input_data2": [],
}
self.dynamic_shape.opt_input_shape = {
"input_data1": [],
"input_data2": [],
}
if self.dims == 2:
self.dynamic_shape.min_input_shape = {
"input_data1": [1, 1],
Expand Down