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[Hackathon NO.76] 为 Paddle-TRT 添加elementwise_mod 算子 (#50974)
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@AndSonder
AndSonder authored Mar 6, 2023
1 parent 69ccbec commit cf1a1c4
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Showing 5 changed files with 89 additions and 9 deletions.
2 changes: 2 additions & 0 deletions paddle/fluid/inference/api/analysis_predictor.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -2385,6 +2385,7 @@ USE_TRT_CONVERTER(elementwise_div_weight);
USE_TRT_CONVERTER(elementwise_min_weight);
USE_TRT_CONVERTER(elementwise_max_weight);
USE_TRT_CONVERTER(elementwise_pow_weight);
USE_TRT_CONVERTER(elementwise_mod_weight);
USE_TRT_CONVERTER(elementwise_floordiv_weight);
USE_TRT_CONVERTER(elementwise_add_tensor);
USE_TRT_CONVERTER(elementwise_sub_tensor);
Expand All @@ -2394,6 +2395,7 @@ USE_TRT_CONVERTER(elementwise_max_tensor);
USE_TRT_CONVERTER(elementwise_min_tensor);
USE_TRT_CONVERTER(elementwise_pow_tensor);
USE_TRT_CONVERTER(elementwise_floordiv_tensor);
USE_TRT_CONVERTER(elementwise_mod_tensor);
USE_TRT_CONVERTER(less_than);
USE_TRT_CONVERTER(greater_than);
USE_TRT_CONVERTER(logical_or);
Expand Down
27 changes: 27 additions & 0 deletions paddle/fluid/inference/tensorrt/convert/elementwise_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -163,6 +163,25 @@ class ElementwiseTensorOpConverter : public OpConverter {
nvinfer1::ElementWiseOperation::kOR);

RreplenishLayerAndOutput(layer, "elementwise", {output_name}, test_mode);
} else if (op_type_ == "mod") {
auto* div_layer =
TRT_ENGINE_ADD_LAYER(engine_,
ElementWise,
*X,
*reshape_y_tensor,
nvinfer1::ElementWiseOperation::kFLOOR_DIV);
auto* mul_layer =
TRT_ENGINE_ADD_LAYER(engine_,
ElementWise,
*(div_layer->getOutput(0)),
*reshape_y_tensor,
nvinfer1::ElementWiseOperation::kPROD);
auto* layer = TRT_ENGINE_ADD_LAYER(engine_,
ElementWise,
*X,
*(mul_layer->getOutput(0)),
nvinfer1::ElementWiseOperation::kSUB);
RreplenishLayerAndOutput(layer, "elementwise", {output_name}, test_mode);
} else {
auto op_pair = ops.find(op_type_);
PADDLE_ENFORCE_NE(
Expand Down Expand Up @@ -271,6 +290,10 @@ class ElementwiseTensorLessEqualOpConverter
public:
ElementwiseTensorLessEqualOpConverter() { op_type_ = "less_equal"; }
};
class ElementwiseTensorModOpConverter : public ElementwiseTensorOpConverter {
public:
ElementwiseTensorModOpConverter() { op_type_ = "mod"; }
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
Expand All @@ -291,6 +314,8 @@ REGISTER_TRT_OP_CONVERTER(elementwise_pow_weight,
ElementwiseTensorPowOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_floordiv_weight,
ElementwiseTensorFloorDivOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_mod_weight,
ElementwiseTensorModOpConverter);

REGISTER_TRT_OP_CONVERTER(elementwise_add_tensor,
ElementwiseTensorAddOpConverter);
Expand All @@ -308,6 +333,8 @@ REGISTER_TRT_OP_CONVERTER(elementwise_pow_tensor,
ElementwiseTensorPowOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_floordiv_tensor,
ElementwiseTensorFloorDivOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_mod_tensor,
ElementwiseTensorModOpConverter);
REGISTER_TRT_OP_CONVERTER(less_than, ElementwiseTensorLessThanOpConverter);
REGISTER_TRT_OP_CONVERTER(greater_than,
ElementwiseTensorGreaterThanOpConverter);
Expand Down
4 changes: 2 additions & 2 deletions paddle/fluid/inference/tensorrt/convert/op_converter.h
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Original file line number Diff line number Diff line change
Expand Up @@ -74,9 +74,9 @@ class OpConverter {
}
if (op_desc.Type().find("elementwise") != std::string::npos) {
static std::unordered_set<std::string> add_tensor_op_set{
"add", "mul", "sub", "div", "max", "min", "pow"};
"add", "mul", "sub", "div", "max", "min", "pow", "mod"};
static std::unordered_set<std::string> add_weight_op_set{
"add", "mul", "sub", "div", "max", "min", "pow"};
"add", "mul", "sub", "div", "max", "min", "pow", "mod"};
PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(),
1UL,
platform::errors::InvalidArgument(
Expand Down
15 changes: 10 additions & 5 deletions paddle/fluid/inference/tensorrt/op_teller.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -1418,7 +1418,8 @@ struct SimpleOpTypeSetTeller : public Teller {
if (op_type == "elementwise_add" || op_type == "elementwise_mul" ||
op_type == "elementwise_sub" || op_type == "elementwise_div" ||
op_type == "elementwise_pow" || op_type == "elementwise_min" ||
op_type == "elementwise_max" || op_type == "elementwise_floordiv") {
op_type == "elementwise_max" || op_type == "elementwise_floordiv" ||
op_type == "elementwise_mod") {
if (desc.Input("X").size() != 1) {
VLOG(3) << "The input op's Input(\"X\").size() "
"should equal to 1, but received Input(\"X\").size() = "
Expand Down Expand Up @@ -1453,12 +1454,14 @@ struct SimpleOpTypeSetTeller : public Teller {
if (op_type == "elementwise_add" || op_type == "elementwise_mul" ||
op_type == "elementwise_sub" || op_type == "elementwise_div" ||
op_type == "elementwise_pow" || op_type == "elementwise_min" ||
op_type == "elementwise_max" || op_type == "elementwise_floordiv") {
op_type == "elementwise_max" || op_type == "elementwise_floordiv" ||
op_type == "elementwise_mod") {
if (x_var_desc->GetDataType() ==
paddle::framework::proto::VarType_Type::VarType_Type_BOOL) {
VLOG(3) << "These operations "
"(elementwise_add/mul/sub/div/pow/min/max/floordiv) do "
"not support boolean datatype.";
VLOG(3)
<< "These operations "
"(elementwise_add/mul/sub/div/pow/min/max/floordiv/mod) do "
"not support boolean datatype.";
return false;
}
}
Expand Down Expand Up @@ -2643,6 +2646,7 @@ struct SimpleOpTypeSetTeller : public Teller {
"elementwise_min",
"elementwise_max",
"elementwise_floordiv",
"elementwise_mod",
"equal",
"not_equal",
"less_than",
Expand Down Expand Up @@ -2796,6 +2800,7 @@ struct SimpleOpTypeSetTeller : public Teller {
"elementwise_min",
"elementwise_max",
"elementwise_floordiv",
"elementwise_mod",
"equal",
"not_equal",
"less_than",
Expand Down
50 changes: 48 additions & 2 deletions python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,10 @@ def generate_input(shape, op_type):
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)

Expand All @@ -44,6 +48,10 @@ def generate_weight(op_type):
return np.random.randint(
low=1, high=10000, size=[1, 32, 1, 1], dtype=np.int32
)
elif op_type == "elementwise_mod":
return np.random.uniform(
low=0.1, high=1.0, size=[1, 32, 1, 1]
).astype(np.float32)
else:
return np.random.randn(1, 32, 1, 1).astype(np.float32)

Expand All @@ -58,6 +66,7 @@ def generate_weight(op_type):
"elementwise_min",
"elementwise_max",
"elementwise_floordiv",
"elementwise_mod",
]:
for axis in [-1]:
self.dims = len(shape)
Expand Down Expand Up @@ -169,6 +178,10 @@ def generate_input(shape, op_type):
return np.random.randint(
low=1, high=10000, size=shape, dtype=np.int32
)
if 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)

Expand All @@ -178,6 +191,10 @@ def generate_weight(op_type):
return np.random.randint(
low=1, high=10000, size=[1], dtype=np.int32
)
elif op_type == "elementwise_mod":
return np.random.uniform(low=0.1, high=1.0, size=[1]).astype(
np.float32
)
else:
return np.random.randn(1).astype(np.float32)

Expand All @@ -191,6 +208,7 @@ def generate_weight(op_type):
"elementwise_min",
"elementwise_max",
"elementwise_floordiv",
"elementwise_mod",
]:
for axis in [-1]:
self.dims = len(shape)
Expand Down Expand Up @@ -290,6 +308,10 @@ def generate_input(shape, op_type):
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)

Expand All @@ -299,6 +321,10 @@ def generate_weight(op_type):
return np.random.randint(
low=1, high=10000, size=[32], dtype=np.int32
)
elif op_type == "elementwise_mod":
return np.random.uniform(low=0.1, high=1.0, size=[32]).astype(
np.float32
)
else:
return np.random.randn(32).astype(np.float32)

Expand All @@ -318,6 +344,7 @@ def generate_weight(op_type):
"elementwise_min",
"elementwise_max",
"elementwise_floordiv",
"elementwise_mod",
]:
for axis in [-1 if len(shape) == 1 else 1]:
self.dims = len(shape)
Expand Down Expand Up @@ -442,6 +469,10 @@ def generate_input(shape, op_type):
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)

Expand All @@ -455,6 +486,7 @@ def generate_input(shape, op_type):
"elementwise_min",
"elementwise_max",
"elementwise_floordiv",
"elementwise_mod",
]:
for axis in [0, -1]:
self.dims = len(shape)
Expand Down Expand Up @@ -605,6 +637,10 @@ def generate_input(shape, op_type):
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)

Expand Down Expand Up @@ -651,6 +687,7 @@ def generate_input(shape, op_type):
"elementwise_min",
"elementwise_max",
"elementwise_floordiv",
"elementwise_mod",
]:
for axis in axis_list[j][i]:
self.shape1 = input1_shape
Expand Down Expand Up @@ -763,6 +800,10 @@ def generate_input(shape, op_type):
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)

Expand All @@ -772,6 +813,10 @@ def generate_weight(op_type):
return np.random.randint(
low=1, high=10000, size=[32], dtype=np.int32
)
elif op_type == "elementwise_mod":
return np.random.uniform(low=0.1, high=1.0, size=[32]).astype(
np.float32
)
else:
return np.random.rand(32).astype(np.float32)

Expand All @@ -791,6 +836,7 @@ def generate_weight(op_type):
"elementwise_min",
"elementwise_max",
"elementwise_floordiv",
"elementwise_mod",
]:
self.op_type = op_type
for axis in [-1 if len(shape) == 1 else 1]:
Expand Down Expand Up @@ -840,8 +886,8 @@ 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]}
self.dynamic_shape.max_input_shape = {"input_data": [64]}
self.dynamic_shape.opt_input_shape = {"input_data": [32]}
elif self.dims == 2:
self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [4, 32]}
Expand Down

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