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[inference zero dim] softmax, stack op trt converter support zero dim #53729

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Merged
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May 12, 2023
Merged

[inference zero dim] softmax, stack op trt converter support zero dim #53729

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a2a7e9c
softmax support
yuanlehome May 11, 2023
97cbce7
support stack
yuanlehome May 11, 2023
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5 changes: 4 additions & 1 deletion paddle/fluid/inference/tensorrt/convert/expand_v2_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -83,7 +83,10 @@ class ExpandOpConverter : public OpConverter {
input_shape_tensor = Shape(input);
}

auto* newInputTensor = Reshape(input, input_shape_tensor);
auto* newInputTensor =
Reshape(input,
input_shape_tensor,
("expand_v2: reshape: (Output(" + output_name + ")").c_str());

std::vector<int32_t> start_vec(shape_rank, 0);
nvinfer1::Dims start;
Expand Down
8 changes: 3 additions & 5 deletions paddle/fluid/inference/tensorrt/convert/op_converter.h
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Expand Up @@ -407,13 +407,11 @@ class OpConverter {
}

nvinfer1::ITensor* Reshape(nvinfer1::ITensor* input,
nvinfer1::ITensor* newShape) {
nvinfer1::ITensor* oldShape = Shape(input);
if (oldShape == newShape) {
return input;
}
nvinfer1::ITensor* newShape,
const std::string& name = "reshape") {
auto* shuffle = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
shuffle->setInput(1, *newShape);
shuffle->setName(name.c_str());
return shuffle->getOutput(0);
}

Expand Down
34 changes: 30 additions & 4 deletions paddle/fluid/inference/tensorrt/convert/softmax_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -38,8 +38,23 @@ class SoftMaxOpConverter : public OpConverter {
? PADDLE_GET_CONST(int, op_desc.GetAttr("axis"))
: -1;

auto* layer = TRT_ENGINE_ADD_LAYER(
engine_, SoftMax, *const_cast<nvinfer1::ITensor*>(input1));
// support 0 or 1 dims input
bool is_0_dims = input_dims == 0;
bool is_1_dims = input_dims == 1;
if (is_0_dims || is_1_dims) {
auto reshaped_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input1);
nvinfer1::Dims reshaped_dims;
reshaped_dims.nbDims = 2;
reshaped_dims.d[0] = 1;
reshaped_dims.d[1] = is_0_dims ? 1 : input_shape.d[0];
reshaped_layer->setReshapeDimensions(reshaped_dims);
input1 = reshaped_layer->getOutput(0);
input_shape = input1->getDimensions();
input_dims = input_shape.nbDims;
axis = -1;
}

auto* layer = TRT_ENGINE_ADD_LAYER(engine_, SoftMax, *input1);
uint32_t axes = std::max(0, input_dims - 3);
// TODO(cryoco): Poor workaround. Fix padded dims problem when TRT layers
// support Nd.
Expand Down Expand Up @@ -68,11 +83,22 @@ class SoftMaxOpConverter : public OpConverter {
}
}
layer->setAxes(1 << axes);
auto output_name = op_desc.Output("Out")[0];
RreplenishLayerAndOutput(layer, "softmax", {output_name}, test_mode);

// The trt will not run int for softmax.
engine_->SetTensorDynamicRange(input1, 1.0);
auto output_name = op_desc.Output("Out")[0];

// support 0 or 1 dims input
if (is_0_dims || is_1_dims) {
auto reshaped_layer =
TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *layer->getOutput(0));
reshaped_layer->setReshapeDimensions(
engine_->GetITensor(op_desc.Input("X")[0])->getDimensions());
RreplenishLayerAndOutput(
reshaped_layer, "reshape_softmax_reshape", {output_name}, test_mode);
} else {
RreplenishLayerAndOutput(layer, "softmax", {output_name}, test_mode);
}
}
};

Expand Down
11 changes: 5 additions & 6 deletions paddle/fluid/inference/tensorrt/convert/stack_op.cc
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Expand Up @@ -65,12 +65,11 @@ class StackOpConverter : public OpConverter {
auto* after_shape_tensor = Concat(shape_tensor_vec);

for (int i = 0; i < input_num; ++i) {
auto* reshape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *inputs[i]);
reshape_layer->setInput(1, *after_shape_tensor);
inputs[i] = reshape_layer->getOutput(0);
reshape_layer->setName(("stack: reshape: (Output( " + std::to_string(i) +
" )" + output_name + ")")
.c_str());
inputs[i] = Reshape(inputs[i],
after_shape_tensor,
("stack: reshape: (Output( " + std::to_string(i) +
" )" + output_name + ")")
.c_str());
}

auto* layer = TRT_ENGINE_ADD_LAYER(
Expand Down
35 changes: 35 additions & 0 deletions paddle/fluid/inference/tensorrt/op_teller.cc
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Expand Up @@ -409,6 +409,7 @@ struct SimpleOpTypeSetTeller : public Teller {
return false;
#endif
}

if (op_type == "softmax") {
auto* block = desc.Block();
if (block == nullptr) {
Expand All @@ -420,7 +421,23 @@ struct SimpleOpTypeSetTeller : public Teller {
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 "
"with static shape.";
return false;
}

if (with_dynamic_shape && (x_shape.size() == 1 || x_shape.size() == 0)) {
int axis = desc.HasAttr("axis")
? PADDLE_GET_CONST(int, desc.GetAttr("axis"))
: -1;
if (axis > 0) {
return false;
}
}
}

if (op_type == "group_norm") {
if (!desc.HasAttr("epsilon") || !desc.HasAttr("groups") ||
!desc.HasAttr("data_layout"))
Expand Down Expand Up @@ -1547,6 +1564,24 @@ struct SimpleOpTypeSetTeller : public Teller {
"mode.";
return false;
}

auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"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();
int rank = x_shape.size();
int axis = desc.HasAttr("axis")
? PADDLE_GET_CONST(int, desc.GetAttr("axis"))
: -1;
if (axis > rank || axis < -(rank + 1)) {
return false;
}
}

if (op_type == "sum") {
Expand Down
16 changes: 15 additions & 1 deletion test/ir/inference/test_trt_convert_softmax.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,8 +47,12 @@ def generate_input1(attrs: List[Dict[str, Any]], batch):
return np.ones([batch, 3, 24]).astype(np.float32)
elif self.dims == 2:
return np.ones([batch, 32]).astype(np.float32)
elif self.dims == 1:
return np.ones([batch]).astype(np.float32)
elif self.dims == 0:
return np.ones([]).astype(np.float32)

for dims in [2, 3, 4]:
for dims in [0, 1, 2, 3, 4]:
for batch in [1, 2, 4]:
for axis in [-1, 0, 1, 2, 3]:
self.dims = dims
Expand Down Expand Up @@ -103,13 +107,23 @@ def generate_dynamic_shape(attrs):
self.dynamic_shape.min_input_shape = {"softmax_input": [1, 32]}
self.dynamic_shape.max_input_shape = {"softmax_input": [4, 64]}
self.dynamic_shape.opt_input_shape = {"softmax_input": [1, 32]}
elif self.dims == 1:
self.dynamic_shape.min_input_shape = {"softmax_input": [1]}
self.dynamic_shape.max_input_shape = {"softmax_input": [4]}
self.dynamic_shape.opt_input_shape = {"softmax_input": [1]}
elif self.dims == 0:
self.dynamic_shape.min_input_shape = {"softmax_input": []}
self.dynamic_shape.max_input_shape = {"softmax_input": []}
self.dynamic_shape.opt_input_shape = {"softmax_input": []}

def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
self.dynamic_shape.max_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 = [
Expand Down
28 changes: 26 additions & 2 deletions test/ir/inference/test_trt_convert_stack.py
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Expand Up @@ -32,9 +32,11 @@ def is_program_valid(self, program_config: ProgramConfig) -> bool:
attrs = [
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
# The input dimension should be less than the set axis.
# axis must be inside [-(rank+1), rank+1)
if len(inputs['stack_input1'].shape) < attrs[0]['axis']:
return False
if -(len(inputs['stack_input1'].shape) + 1) > attrs[0]['axis']:
return False

return True

Expand All @@ -48,6 +50,8 @@ def generate_input1(attrs: List[Dict[str, Any]], batch):
return np.random.random([batch, 24]).astype(np.float32)
elif self.dims == 1:
return np.random.random([24]).astype(np.float32)
elif self.dims == 0:
return np.random.random([]).astype(np.float32)

def generate_input2(attrs: List[Dict[str, Any]], batch):
if self.dims == 4:
Expand All @@ -58,6 +62,8 @@ def generate_input2(attrs: List[Dict[str, Any]], batch):
return np.random.random([batch, 24]).astype(np.float32)
elif self.dims == 1:
return np.random.random([24]).astype(np.float32)
elif self.dims == 0:
return np.random.random([]).astype(np.float32)

def generate_input3(attrs: List[Dict[str, Any]], batch):
if self.dims == 4:
Expand All @@ -68,8 +74,10 @@ def generate_input3(attrs: List[Dict[str, Any]], batch):
return np.random.random([batch, 24]).astype(np.float32)
elif self.dims == 1:
return np.random.random([24]).astype(np.float32)
elif self.dims == 0:
return np.random.random([]).astype(np.float32)

for dims in [1, 2, 3, 4]:
for dims in [0, 1, 2, 3, 4]:
for batch in [1, 4]:
for axis in [-2, -1, 0, 1, 2, 3]:
self.dims = dims
Expand Down Expand Up @@ -176,6 +184,22 @@ def generate_dynamic_shape(attrs):
"stack_input2": [24],
"stack_input3": [24],
}
elif self.dims == 0:
self.dynamic_shape.min_input_shape = {
"stack_input1": [],
"stack_input2": [],
"stack_input3": [],
}
self.dynamic_shape.max_input_shape = {
"stack_input1": [],
"stack_input2": [],
"stack_input3": [],
}
self.dynamic_shape.opt_input_shape = {
"stack_input1": [],
"stack_input2": [],
"stack_input3": [],
}

def clear_dynamic_shape():
self.dynamic_shape.min_input_shape = {}
Expand Down