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add reduce_all and reduce_any #49765

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add reduce_all and reduce_any #49765

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955b0ab
add reduce_all and reduce_any
sanbuphy Jan 12, 2023
409f3c8
add reduce_all and reduce_any
sanbuphy Jan 12, 2023
0a4650c
add reduce_all and reduce_any
sanbuphy Jan 12, 2023
f79cc0d
add reduce_all and reduce_any
sanbuphy Jan 12, 2023
4d5e89e
add reduce_all and reduce_any
sanbuphy Jan 12, 2023
e7ef35b
add reduce_all and reduce_any
sanbuphy Jan 12, 2023
5257b7b
fix ops_config by outputs_dtype
sanbuphy Jan 12, 2023
5475262
fix ops_config by outputs_dtype
sanbuphy Jan 12, 2023
8ee53a9
fix bool_
sanbuphy Jan 12, 2023
8662195
fix op_teller
sanbuphy Jan 14, 2023
47e3822
fix op_teller
sanbuphy Jan 14, 2023
8094c09
fix op_teller dtype
sanbuphy Jan 14, 2023
3e4745d
fix op_teller dtype
sanbuphy Jan 14, 2023
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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 @@ -2356,6 +2356,8 @@ USE_TRT_CONVERTER(reduce_max);
USE_TRT_CONVERTER(reduce_min);
USE_TRT_CONVERTER(reduce_sum);
USE_TRT_CONVERTER(reduce_prod);
USE_TRT_CONVERTER(reduce_any);
USE_TRT_CONVERTER(reduce_all);
USE_TRT_CONVERTER(tile);
USE_TRT_CONVERTER(conv3d);
USE_TRT_CONVERTER(conv3d_transpose);
Expand Down
78 changes: 78 additions & 0 deletions paddle/fluid/inference/tensorrt/convert/reduce_op.cc
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Expand Up @@ -105,6 +105,8 @@ const std::unordered_map<std::string, std::vector<nvinfer1::ReduceOperation>>
{"reduce_max", {nvinfer1::ReduceOperation::kMAX}},
{"reduce_min", {nvinfer1::ReduceOperation::kMIN}},
{"reduce_prod", {nvinfer1::ReduceOperation::kPROD}},
{"reduce_any", {nvinfer1::ReduceOperation::kMAX}},
{"reduce_all", {nvinfer1::ReduceOperation::kMIN}},
};

class ReduceSumOpConverter : public ReduceOpConverter {
Expand Down Expand Up @@ -132,6 +134,80 @@ class ReduceProdOpConverter : public ReduceOpConverter {
ReduceProdOpConverter() { op_type = "reduce_prod"; }
};

class ReduceAnyOpConverter : public ReduceOpConverter {
public:
ReduceAnyOpConverter() { op_type = "reduce_any"; }
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope,
bool test_mode) override {
VLOG(4) << "convert a paddle " << op_type << " op to tensorrt reduce layer";
framework::OpDesc op_desc(op, nullptr);
auto reduce_type = ops_.find(op_type);
auto* x = engine_->GetITensor(op_desc.Input("X").front());
// Cast the DataType to float
nvinfer1::IReduceLayer* reduce_layer = nullptr;
auto* cast_layer = TRT_ENGINE_ADD_LAYER(engine_, Identity, *x);
cast_layer->setOutputType(0, nvinfer1::DataType::kINT32);
cast_layer->getOutput(0)->setType(nvinfer1::DataType::kINT32);

nvinfer1::Dims input_shape = x->getDimensions();
int input_dims = input_shape.nbDims;
// Discriminate DataType between int and bool.
bool keep_dim = PADDLE_GET_CONST(bool, op_desc.GetAttr("keep_dim"));
std::vector<int32_t> dim =
PADDLE_GET_CONST(std::vector<int32_t>, op_desc.GetAttr("dim"));
bool reduce_all = PADDLE_GET_CONST(bool, op_desc.GetAttr("reduce_all"));

if (reduce_all) {
uint32_t reduce_dim = 0;
for (int i = 0; i < input_dims; ++i) {
reduce_dim |= 1 << i;
}
reduce_layer = TRT_ENGINE_ADD_LAYER(engine_,
Reduce,
*cast_layer->getOutput(0),
reduce_type->second.front(),
reduce_dim,
keep_dim);
} else {
auto CvtToBitMask = [&](const std::vector<int32_t>& dims) -> uint32_t {
uint32_t res = 0;
for (auto x : dims) {
if (x < 0) {
res |= 1 << (x + input_dims);
} else {
if (!engine_->with_dynamic_shape()) x = x - 1;
res |= 1 << x;
}
}
return res;
};
reduce_layer = TRT_ENGINE_ADD_LAYER(engine_,
Reduce,
*cast_layer->getOutput(0),
reduce_type->second.front(),
CvtToBitMask(dim),
keep_dim);
}

auto output_name = op_desc.Output("Out")[0];

auto* layer =
TRT_ENGINE_ADD_LAYER(engine_, Identity, *reduce_layer->getOutput(0));
layer->setOutputType(0, nvinfer1::DataType::kBOOL);
layer->getOutput(0)->setType(nvinfer1::DataType::kBOOL);
// Ensure that the output type and input type are consistent.
layer->getOutput(0)->setType(cast_layer->getInput(0)->getType());

RreplenishLayerAndOutput(layer, op_type, {output_name}, test_mode);
};
};

class ReduceAllOpConverter : public ReduceAnyOpConverter {
public:
ReduceAllOpConverter() { op_type = "reduce_all"; }
};

} // namespace tensorrt
} // namespace inference
} // namespace paddle
Expand All @@ -141,3 +217,5 @@ REGISTER_TRT_OP_CONVERTER(reduce_mean, ReduceMeanOpConverter);
REGISTER_TRT_OP_CONVERTER(reduce_max, ReduceMaxOpConverter);
REGISTER_TRT_OP_CONVERTER(reduce_min, ReduceMinOpConverter);
REGISTER_TRT_OP_CONVERTER(reduce_prod, ReduceProdOpConverter);
REGISTER_TRT_OP_CONVERTER(reduce_any, ReduceAnyOpConverter);
REGISTER_TRT_OP_CONVERTER(reduce_all, ReduceAllOpConverter);
39 changes: 28 additions & 11 deletions paddle/fluid/inference/tensorrt/op_teller.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -2089,7 +2089,8 @@ struct SimpleOpTypeSetTeller : public Teller {

if (op_type == "reduce_sum" || op_type == "reduce_mean" ||
op_type == "reduce_max" || op_type == "reduce_min" ||
op_type == "reduce_prod") {
op_type == "reduce_prod" || op_type == "reduce_any" ||
op_type == "reduce_all") {
if (!desc.HasAttr("dim", /*with_attr_var=*/false)) {
VLOG(3) << "Skip to convert into TRT while found Attribute('dim') is "
"Variable type in "
Expand Down Expand Up @@ -2131,19 +2132,27 @@ struct SimpleOpTypeSetTeller : public Teller {
}

auto dtype = x_var_desc->GetDataType();
if (op_type == "reduce_all" || op_type == "reduce_any") {
if (dtype != framework::proto::VarType::BOOL) {
VLOG(3)
<< "reduce_all and reduce_any op input data type must be bool";
return false;
}
} else {
#if IS_TRT_VERSION_GE(7000)
if (dtype != framework::proto::VarType::INT32 &&
dtype != framework::proto::VarType::FP32) {
VLOG(3) << "reduce op input data type must be int32 or float32";
return false;
}
if (dtype != framework::proto::VarType::INT32 &&
dtype != framework::proto::VarType::FP32) {
VLOG(3) << "reduce op input data type must be int32 or float32";
return false;
}
#else
if (dtype != framework::proto::VarType::FP32) {
VLOG(3) << "reduce op input data type must be float32 using TensorRT "
"< 7.0";
return false;
}
if (dtype != framework::proto::VarType::FP32) {
VLOG(3) << "reduce op input data type must be float32 using TensorRT "
"< 7.0";
return false;
}
#endif
}
}
#if IS_TRT_VERSION_GE(7000)
if (op_type == "tile") {
Expand Down Expand Up @@ -2534,8 +2543,12 @@ struct SimpleOpTypeSetTeller : public Teller {
"nearest_interp",
"anchor_generator",
"reduce_max",
"reduce_min",
"reduce_mean",
"reduce_sum",
"reduce_prod",
"reduce_any",
"reduce_all",
"conv3d",
"conv3d_transpose",
"mish",
Expand Down Expand Up @@ -2683,8 +2696,12 @@ struct SimpleOpTypeSetTeller : public Teller {
"nearest_interp",
"anchor_generator",
"reduce_max",
"reduce_min",
"reduce_mean",
"reduce_sum",
"reduce_prod",
"reduce_any",
"reduce_all",
"conv3d",
"conv3d_transpose",
"mish",
Expand Down
93 changes: 49 additions & 44 deletions python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reduce.py
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Expand Up @@ -51,6 +51,8 @@ def generate_input1(dtype, attrs: List[Dict[str, Any]]):
return np.random.random([1, 3, 64, 64]).astype(np.float32)
elif dtype == 2:
return np.random.random([1, 3, 64, 64]).astype(np.int32)
elif dtype == 0:
return np.random.random([1, 3, 64, 64]).astype(np.bool_)

for keep_dim in [True, False]:
for dim in [
Expand All @@ -65,15 +67,24 @@ def generate_input1(dtype, attrs: List[Dict[str, Any]]):
[3, 4, 5],
]:
for reduce_all in [True, False]:
for out_dtype in [-1, 2, 5]:
for op_type in [
"reduce_max",
"reduce_min",
"reduce_mean",
"reduce_sum",
"reduce_prod",
]:
dics1 = [
for out_dtype in [-1, 0, 2, 5]:
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generate_input1 也需要添加bool支持

if out_dtype != 0:
reduce_type_list = [
"reduce_max",
"reduce_min",
"reduce_mean",
"reduce_sum",
"reduce_prod",
]
else:
reduce_type_list = [
"reduce_all",
"reduce_any",
]

for op_type in reduce_type_list:

dics = [
{
"keep_dim": keep_dim,
"dim": dim,
Expand All @@ -83,46 +94,40 @@ def generate_input1(dtype, attrs: List[Dict[str, Any]]):
},
{},
]
dics2 = [

ops_config = [
{
"keep_dim": keep_dim,
"dim": dim,
"reduce_all": reduce_all,
Comment on lines -88 to -90
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这里不能去掉，会报错

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这里不能去掉，会报错

ci里面有一个验证(op_teller)是 getAttr 说要没有这些的时候，我想过这个ci，不知道那个是怎么回事。

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现在的覆盖率也是很差，不懂为什么 reduce_any 整个不会进去

"out_dtype": out_dtype,
"in_dtype": out_dtype,
},
{},
"op_type": op_type,
"op_inputs": {"X": ["input_data"]},
"op_outputs": {
"Out": ["reduce_output_data"]
},
"op_attrs": dics[0],
}
]
for dics in [dics1, dics2]:
ops_config = [
{
"op_type": op_type,
"op_inputs": {"X": ["input_data"]},
"op_outputs": {
"Out": ["reduce_output_data"]
},
"op_attrs": dics[0],
}
]
ops = self.generate_op_config(ops_config)

program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(
data_gen=partial(
generate_input1, out_dtype, dics
)
if op_type in ["reduce_any", "reduce_all"]:
ops_config[0]["outputs_dtype"] = {
"reduce_output_data": np.bool_
}
ops = self.generate_op_config(ops_config)

program_config = ProgramConfig(
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(
data_gen=partial(
generate_input1, out_dtype, dics
)
},
outputs=["reduce_output_data"],
)
)
},
outputs=["reduce_output_data"],
)
Comment on lines +124 to +125
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输出为bool类型时，需添加outputs_dtype设定输出类型。可参考test_trt_convert_compare_and_logical.py


if not self.is_program_valid(program_config):
continue
if not self.is_program_valid(program_config):
continue

yield program_config
yield program_config

def sample_predictor_configs(
self, program_config
Expand Down