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Add reduce_min prod trt converter #49615

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merged 10 commits into from
Jan 10, 2023
Merged

Add reduce_min prod trt converter #49615

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a574f01
fix reduce_op
sanbuphy Jan 6, 2023
78ab20e
fix reduce_op
sanbuphy Jan 6, 2023
72961b0
fix reduce_op
sanbuphy Jan 6, 2023
a3c8b55
fix reduce_op
sanbuphy Jan 6, 2023
92dbb19
add reduce_any
sanbuphy Jan 7, 2023
d2ea5aa
fix format
sanbuphy Jan 7, 2023
33e3e63
fix format
sanbuphy Jan 7, 2023
7768fbd
fix format
sanbuphy Jan 7, 2023
a3adb0f
fix format
sanbuphy Jan 7, 2023
2df36a2
add dics
sanbuphy Jan 7, 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 @@ -2349,7 +2349,9 @@ USE_TRT_CONVERTER(reshape2);
USE_TRT_CONVERTER(gather_nd);
USE_TRT_CONVERTER(reduce_mean);
USE_TRT_CONVERTER(reduce_max);
USE_TRT_CONVERTER(reduce_min);
USE_TRT_CONVERTER(reduce_sum);
USE_TRT_CONVERTER(reduce_prod);
USE_TRT_CONVERTER(tile);
USE_TRT_CONVERTER(conv3d);
USE_TRT_CONVERTER(conv3d_transpose);
Expand Down
16 changes: 15 additions & 1 deletion paddle/fluid/inference/tensorrt/convert/reduce_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,6 @@ class ReduceOpConverter : public OpConverter {
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());
nvinfer1::Dims input_shape = x->getDimensions();
int input_dims = input_shape.nbDims;
Expand Down Expand Up @@ -104,6 +103,8 @@ const std::unordered_map<std::string, std::vector<nvinfer1::ReduceOperation>>
{"reduce_mean", {nvinfer1::ReduceOperation::kAVG}},
{"reduce_sum", {nvinfer1::ReduceOperation::kSUM}},
{"reduce_max", {nvinfer1::ReduceOperation::kMAX}},
{"reduce_min", {nvinfer1::ReduceOperation::kMIN}},
{"reduce_prod", {nvinfer1::ReduceOperation::kPROD}},
};

class ReduceSumOpConverter : public ReduceOpConverter {
Expand All @@ -120,10 +121,23 @@ class ReduceMaxOpConverter : public ReduceOpConverter {
public:
ReduceMaxOpConverter() { op_type = "reduce_max"; }
};

class ReduceMinOpConverter : public ReduceOpConverter {
public:
ReduceMinOpConverter() { op_type = "reduce_min"; }
};

class ReduceProdOpConverter : public ReduceOpConverter {
public:
ReduceProdOpConverter() { op_type = "reduce_prod"; }
};

} // namespace tensorrt
} // namespace inference
} // namespace paddle

REGISTER_TRT_OP_CONVERTER(reduce_sum, ReduceSumOpConverter);
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);
3 changes: 2 additions & 1 deletion paddle/fluid/inference/tensorrt/op_teller.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -2098,7 +2098,8 @@ struct SimpleOpTypeSetTeller : public Teller {
}

if (op_type == "reduce_sum" || op_type == "reduce_mean" ||
op_type == "reduce_max") {
op_type == "reduce_max" || op_type == "reduce_min" ||
op_type == "reduce_prod") {
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
61 changes: 35 additions & 26 deletions python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reduce.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,10 +68,12 @@ def generate_input1(dtype, attrs: List[Dict[str, Any]]):
for out_dtype in [-1, 2, 5]:
for op_type in [
"reduce_max",
"reduce_min",
"reduce_mean",
"reduce_sum",
"reduce_prod",
]:
dics = [
dics1 = [
{
"keep_dim": keep_dim,
"dim": dim,
Expand All @@ -81,36 +83,43 @@ def generate_input1(dtype, attrs: List[Dict[str, Any]]):
},
{},
]

ops_config = [
dics2 = [
{
"op_type": op_type,
"op_inputs": {"X": ["input_data"]},
"op_outputs": {
"Out": ["reduce_output_data"]
},
"op_attrs": dics[0],
}
"out_dtype": out_dtype,
"in_dtype": out_dtype,
},
{},
]
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
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
)
)
)
},
outputs=["reduce_output_data"],
)
},
outputs=["reduce_output_data"],
)

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