diff --git a/paddle/fluid/inference/tensorrt/op_teller.cc b/paddle/fluid/inference/tensorrt/op_teller.cc index 13c16ab6897e378eca113e3e408c4a4455f049e5..cfdccecb5c8f768146d5e5bb5adcea5eb2254c68 100644 --- a/paddle/fluid/inference/tensorrt/op_teller.cc +++ b/paddle/fluid/inference/tensorrt/op_teller.cc @@ -1011,6 +1011,21 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8, VLOG(3) << "Now trt may not support two 1d tensor elementwise op."; return false; } + if (op_type == "elementwise_add" || op_type == "elementwise_mul") { + if (x_var_desc->Persistable()) { + VLOG(3) << "Input X is a parameter which is not supported for " + "elementwise_add/elementwise_mul in tensorrt, swap x and " + "y will work"; + return false; + } + } + if (op_type == "elementwise_sub" || op_type == "elementwise_div") { + if (x_var_desc->Persistable() || y_var_desc->Persistable()) { + VLOG(3) << "Input X or Input Y is a parameter which is not supported " + "for elementwise_sub/elementwise_div in tensorrt"; + return false; + } + } } if (op_type == "stack") { diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py index 047a6094ec1e189fc739211b410cb935c4e98b0b..e849496621a10e580f312892a3ca636b0ad76910 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py @@ -397,5 +397,139 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest): self.run_test() +class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest): + def is_program_valid(self, program_config: ProgramConfig) -> bool: + return True + + def sample_program_configs(self): + def generate_input(shape): + return np.random.random(shape).astype(np.float32) + + def generate_weight(): + return np.random.randn(32).astype(np.float32) + + for batch in [1, 2, 4]: + for shape in [[32], [batch, 32], [batch, 32, 32], + [batch, 32, 16, 32]]: + for op_type in [ + "elementwise_add", "elementwise_mul", "elementwise_sub", + "elementwise_div" + ]: + for axis in [-1 if len(shape) == 1 else 1]: + self.dims = len(shape) + dics = [{"axis": axis}] + ops_config = [{ + "op_type": op_type, + "op_inputs": { + "X": ["weight"], + "Y": ["input_data"] + }, + "op_outputs": { + "Out": ["output_data"] + }, + "op_attrs": dics[0] + }] + ops = self.generate_op_config(ops_config) + + program_config = ProgramConfig( + ops=ops, + weights={ + "weight": + TensorConfig(data_gen=partial(generate_weight)) + }, + inputs={ + "input_data": 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 == 1: + self.dynamic_shape.min_input_shape = {"input_data": [4]} + self.dynamic_shape.max_input_shape = {"input_data": [256]} + self.dynamic_shape.opt_input_shape = {"input_data": [16]} + elif self.dims == 2: + self.dynamic_shape.min_input_shape = {"input_data": [1, 32]} + self.dynamic_shape.max_input_shape = {"input_data": [4, 32]} + self.dynamic_shape.opt_input_shape = {"input_data": [2, 32]} + elif self.dims == 3: + self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 4]} + self.dynamic_shape.max_input_shape = { + "input_data": [4, 32, 256] + } + self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 16]} + elif self.dims == 4: + self.dynamic_shape.min_input_shape = { + "input_data": [1, 32, 4, 4] + } + self.dynamic_shape.max_input_shape = { + "input_data": [4, 32, 128, 256] + } + self.dynamic_shape.opt_input_shape = { + "input_data": [2, 32, 32, 16] + } + + 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 self.dims == 1: + 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 + self.trt_param.precision = paddle_infer.PrecisionType.Half + yield self.create_inference_config(), generate_trt_nodes_num( + attrs, False), 1e-5 + + # 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 + self.trt_param.precision = paddle_infer.PrecisionType.Half + yield self.create_inference_config(), generate_trt_nodes_num(attrs, + True), 1e-5 + + def add_skip_trt_case(self): + def teller1(program_config, predictor_config): + input_x_names = program_config.ops[0].inputs["X"] + for weight_name in program_config.weights: + if weight_name in input_x_names: + return True + op_type = program_config.ops[0].type + if op_type in ["elementwise_sub", "elementwise_div"]: + input_y_names = program_config.ops[0].inputs["Y"] + for weight_name in program_config.weights: + if weight_name in input_y_names: + return True + return False + + self.add_skip_case( + teller1, SkipReasons.TRT_NOT_SUPPORT, + "Input X should not be parameters in elementwise op and Input Y should not be parameters in elementwise_sub or elementwise_div op" + ) + + def test(self): + self.add_skip_trt_case() + self.run_test() + + if __name__ == "__main__": unittest.main()