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[Paddle-TRT]fix trt-converter-fc_op #32671

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merged 10 commits into from
May 8, 2021
Merged

[Paddle-TRT]fix trt-converter-fc_op #32671

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e0d016b
[Paddle-TRT]fix fc_op
Wangzheee Apr 29, 2021
fee0846
[Paddle-TRT]fix fc_op
Wangzheee Apr 29, 2021
e87259d
[Paddle-TRT]fix fc_op
Wangzheee Apr 29, 2021
10b5df4
test_trt_subgraph_pass.py
Wangzheee Apr 30, 2021
fb11f85
fix elementwise_op
Wangzheee May 6, 2021
986cd0e
fix elementwise_op
Wangzheee May 6, 2021
42d5450
fix elementwise_op
Wangzheee May 6, 2021
a8c5eea
fix elementwise_op.cc
Wangzheee May 7, 2021
cb6819e
op_teller.cc
Wangzheee May 7, 2021
c4207e5
Merge branch 'fc_op' of https://github.com/Wangzheee/Paddle into fc_op
Wangzheee May 7, 2021
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31 changes: 30 additions & 1 deletion paddle/fluid/inference/tensorrt/convert/elementwise_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,25 @@ class ElementwiseWeightOpConverter : public OpConverter {
0};
TensorRTEngine::Weight power_weights{nvinfer1::DataType::kFLOAT, nullptr,
0};

nvinfer1::IShuffleLayer* expand_layer = nullptr;
nvinfer1::IShuffleLayer* squeeze_layer = nullptr;
int dynamic_shape_offset = engine_->with_dynamic_shape() ? 1 : 0;
auto input_dim = X->getDimensions();
if (input_dim.nbDims < 3 + dynamic_shape_offset) {
nvinfer1::Dims expand_shape;
expand_shape.nbDims = 3 + dynamic_shape_offset;
for (int i = 0; i < expand_shape.nbDims; i++) {
if (i < input_dim.nbDims) {
expand_shape.d[i] = input_dim.d[i] < 0 ? 0 : input_dim.d[i];
} else {
expand_shape.d[i] = 1;
}
}
expand_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
expand_layer->setReshapeDimensions(expand_shape);
X = expand_layer->getOutput(0);
}
if (op_type_ == "add") {
nvinfer1::IScaleLayer* scale_layer = TRT_ENGINE_ADD_LAYER(
engine_, Scale, *X, scale_mode, shift_weights.get(),
Expand All @@ -77,7 +96,17 @@ class ElementwiseWeightOpConverter : public OpConverter {
shift_weights.get(), power_weights.get());
layer = scale_layer;
}

if (input_dim.nbDims < 3 + dynamic_shape_offset) {
nvinfer1::Dims squeeze_shape;
squeeze_shape.nbDims = input_dim.nbDims;
for (int i = 0; i < squeeze_shape.nbDims; i++) {
squeeze_shape.d[i] = input_dim.d[i] < 0 ? 0 : input_dim.d[i];
}
squeeze_layer =
TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *(layer->getOutput(0)));
squeeze_layer->setReshapeDimensions(squeeze_shape);
layer = static_cast<nvinfer1::ILayer*>(squeeze_layer);
}
auto output_name = op_desc.Output("Out")[0];
RreplenishLayerAndOutput(layer, "elementwise_" + op_type_, {output_name},
test_mode);
Expand Down
236 changes: 75 additions & 161 deletions paddle/fluid/inference/tensorrt/convert/fc_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ class FcOpConverter : public OpConverter {
const framework::Scope& scope, bool test_mode) override {
VLOG(3) << "convert a fluid fc op to tensorrt fc layer without bias";
framework::OpDesc op_desc(op, nullptr);

auto output_name = op_desc.Output("Out").front();
auto input_names = op_desc.InputNames();
bool with_bias = input_names.size() >= 3;
std::string w_name = "Y";
Expand All @@ -54,7 +54,7 @@ class FcOpConverter : public OpConverter {
Y_v, platform::errors::NotFound(
"Can not find %s presistale var of fc in scope.", w_name));
auto* Y_t = Y_v->GetMutable<framework::LoDTensor>();
const int x_num_col_dims =
int x_num_col_dims =
op_desc.HasAttr("x_num_col_dims")
? BOOST_GET_CONST(int, op_desc.GetAttr("x_num_col_dims"))
: (op_desc.HasAttr("in_num_col_dims")
Expand Down Expand Up @@ -106,31 +106,55 @@ class FcOpConverter : public OpConverter {
auto regist_fc = [&](nvinfer1::ITensor* inputs, int n_output,
TensorRTEngine::Weight& weight,
TensorRTEngine::Weight& bias) {
nvinfer1::ILayer* fc_layer = nullptr;
if (enable_int8) {
// add conv layer
PADDLE_ENFORCE_EQ(
op_desc.HasAttr("out_threshold"), true,
platform::errors::InvalidArgument(
"must have out threshold in fc layers in int8 mode"));
float out_scale =
BOOST_GET_CONST(float, op_desc.GetAttr("out_threshold"));
nvinfer1::DimsHW nv_ksize(1, 1);
fc_layer = TRT_ENGINE_ADD_LAYER(engine_, Convolution, *inputs, n_output,
nv_ksize, weight.get(), bias.get());
engine_->SetTensorDynamicRange(fc_layer->getOutput(0), out_scale);
} else {
fc_layer = TRT_ENGINE_ADD_LAYER(engine_, FullyConnected, *inputs,
n_output, weight.get(), bias.get());
}

auto output_name = op_desc.Output("Out").front();
if (activation_type == "relu") {
nvinfer1::IActivationLayer* relu_layer =
TRT_ENGINE_ADD_LAYER(engine_, Activation, *(fc_layer->getOutput(0)),
nvinfer1::ActivationType::kRELU);
RreplenishLayerAndOutput(relu_layer, "fc", {output_name}, test_mode);
auto* fc_layer_int8 =
TRT_ENGINE_ADD_LAYER(engine_, Convolution, *inputs, n_output,
nv_ksize, weight.get(), bias.get());
engine_->SetTensorDynamicRange(fc_layer_int8->getOutput(0), out_scale);
if (activation_type == "relu") {
nvinfer1::IActivationLayer* relu_layer_int8 = TRT_ENGINE_ADD_LAYER(
engine_, Activation, *(fc_layer_int8->getOutput(0)),
nvinfer1::ActivationType::kRELU);
RreplenishLayerAndOutput(relu_layer_int8, "relu_after_fc_shuffle",
{output_name}, test_mode);
} else {
RreplenishLayerAndOutput(fc_layer_int8, "shuffle_after_fc",
{output_name}, test_mode);
}
} else {
RreplenishLayerAndOutput(fc_layer, "fc", {output_name}, test_mode);
// add fc layer
auto* fc_layer_before =
TRT_ENGINE_ADD_LAYER(engine_, FullyConnected, *inputs, n_output,
weight.get(), bias.get());
fc_layer_before->setName(
("fc_layer_before(Output: " + output_name + ")").c_str());
// add shuffle after fc
nvinfer1::Dims reshape_after_fc_dim;
reshape_after_fc_dim.nbDims = x_num_col_dims + 1;
for (int i = 0; i < reshape_after_fc_dim.nbDims; i++) {
reshape_after_fc_dim.d[i] = 0;
}
auto* fc_layer_float = TRT_ENGINE_ADD_LAYER(
engine_, Shuffle, *fc_layer_before->getOutput(0));
fc_layer_float->setReshapeDimensions(reshape_after_fc_dim);
if (activation_type == "relu") {
nvinfer1::IActivationLayer* relu_layer_float = TRT_ENGINE_ADD_LAYER(
engine_, Activation, *(fc_layer_float->getOutput(0)),
nvinfer1::ActivationType::kRELU);
RreplenishLayerAndOutput(relu_layer_float, "relu_after_fc_shuffle",
{output_name}, test_mode);
} else {
RreplenishLayerAndOutput(fc_layer_float, "shuffle_after_fc",
{output_name}, test_mode);
}
}
};

Expand All @@ -157,153 +181,43 @@ class FcOpConverter : public OpConverter {
static_cast<void*>(bias_data),
static_cast<size_t>(bias_num)};

if (engine_->with_dynamic_shape()) {
// not NCHW layout, but NLP layout with added 'x 1 x 1'
auto x_dim = X->getDimensions();
if (engine_->use_oss() && engine_->with_ernie() && x_dim.nbDims == 4 &&
x_dim.d[2] == 1 && x_dim.d[3] == 1 && x_num_col_dims == 2) {
// fc which is just after self attention
regist_fc(X, n_output, weight, bias);
return;
}
PADDLE_ENFORCE_LE(
x_dim.nbDims - x_num_col_dims, 3,
platform::errors::InvalidArgument(
"Params and input dims mismatch. Paddle-TRT FC "
"converter expects x_dim.nbDims - x_num_col_dims <= 3, but "
"x_dim.nbDims = %d, x_num_col_dims = %d.",
x_dim.nbDims, x_num_col_dims));
auto output_name = op_desc.Output("Out").front();
// add shuffle before fc
nvinfer1::Dims reshape_before_fc_dim;
// padding shape "x 1 x 1"
int padding_length = 3 - (x_dim.nbDims - x_num_col_dims);
reshape_before_fc_dim.nbDims = x_dim.nbDims + padding_length;
int cur_dim_index = reshape_before_fc_dim.nbDims - 1;
while (padding_length-- > 0) {
reshape_before_fc_dim.d[cur_dim_index--] = 1;
}
while (cur_dim_index >= 0) {
reshape_before_fc_dim.d[cur_dim_index--] = 0;
}

auto* reshape_before_fc_layer =
TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
reshape_before_fc_layer->setReshapeDimensions(reshape_before_fc_dim);
reshape_before_fc_layer->setName(
("shuffle_before_fc(Output: " + output_name + ")").c_str());

// add fc layer
auto* fc_layer = TRT_ENGINE_ADD_LAYER(
engine_, FullyConnected, *reshape_before_fc_layer->getOutput(0),
n_output, weight.get(), bias.get());
fc_layer->setName(("fc_layer(Output: " + output_name + ")").c_str());

// add shuffle after fc
nvinfer1::Dims reshape_after_fc_dim;
reshape_after_fc_dim.nbDims = x_num_col_dims + 1;
for (int i = 0; i < reshape_after_fc_dim.nbDims; i++) {
reshape_after_fc_dim.d[i] = 0;
}

auto* reshape_after_fc_layer =
TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *fc_layer->getOutput(0));
reshape_after_fc_layer->setReshapeDimensions(reshape_after_fc_dim);

if (activation_type == "relu") {
reshape_after_fc_layer->setName(
("shuffle_after_fc(Output: " + output_name + ")").c_str());
nvinfer1::IActivationLayer* relu_layer = TRT_ENGINE_ADD_LAYER(
engine_, Activation, *(reshape_after_fc_layer->getOutput(0)),
nvinfer1::ActivationType::kRELU);
RreplenishLayerAndOutput(relu_layer, "relu_after_fc_shuffle",
{output_name}, test_mode);
} else {
RreplenishLayerAndOutput(reshape_after_fc_layer, "shuffle_after_fc",
{output_name}, test_mode);
}
return;
auto x_dim = X->getDimensions();
// Running the TRT Static Shape mode: x_num_col_dims-1
if (!engine_->with_dynamic_shape()) {
x_num_col_dims--;
}
// in order to handle situations in NLP models(input dims < 3,
// x_num_col_dims != 1, etc.), reshape input to perform FC correctly.
auto* reshape_itensor = X;
int input_dims = X->getDimensions().nbDims;
auto input_d = X->getDimensions().d;
int reshape_dim3[3] = {0};
int reshape_dim4[4] = {0};
PADDLE_ENFORCE_LE(x_num_col_dims, input_dims,
platform::errors::InvalidArgument(
"Params and input dims mismatch. Paddle-TRT FC "
"converter expects x_num_col_dims <= input dims"));
if (x_num_col_dims == 1) {
if (input_dims == 4) {
PADDLE_ENFORCE_EQ(
input_d[3], 1,
platform::errors::InvalidArgument(
"Invalid dimensions. When x_num_col_dims equals to 1 and input "
"dims equals to 4, the last dim of input must be 1, but got %d",
input_d[3]));
}
if (enable_int8) {
reshape_dim3[0] = 1;
for (int i = 0; i < 3; i++) {
reshape_dim3[0] *= input_d[i];
if (i > 0) {
reshape_dim3[i] = 1;
}
}
} else {
for (int i = 0; i < 3; i++) {
if (i < input_dims) {
reshape_dim3[i] = input_d[i];
} else {
reshape_dim3[i] = 1;
}
}
}

nvinfer1::Dims3 reshape_dim(reshape_dim3[0], reshape_dim3[1],
reshape_dim3[2]);
auto* reshape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
reshape_layer->setReshapeDimensions(reshape_dim);
reshape_itensor = reshape_layer->getOutput(0);
if (enable_int8) {
engine_->SetTensorDynamicRange(reshape_itensor, in_scale);
}
} else {
PADDLE_ENFORCE_NE(input_dims, 1,
platform::errors::InvalidArgument(
"Invalid dimensions. When x_num_col_dims equals to "
"2, input_dims should not be 1"));

if (enable_int8) {
for (int i = 0; i < 4; i++) {
if (i == 0) {
reshape_dim4[i] = input_d[i];
} else {
reshape_dim4[i] = 1;
if (i < input_dims) {
reshape_dim4[1] *= input_d[i];
}
}
}
PADDLE_ENFORCE_GT(
x_dim.nbDims, x_num_col_dims,
platform::errors::InvalidArgument(
"Params and input dims mismatch. Paddle-TRT FC "
"converter expects x_dim.nbDims > x_num_col_dims, but "
"x_dim.nbDims : %d, x_num_col_dims : %d.",
x_dim.nbDims, x_num_col_dims));
// add shuffle before fc
nvinfer1::Dims reshape_before_fc_dim;
reshape_before_fc_dim.nbDims = x_num_col_dims + 3;
// padding shape "* x q x 1 x 1"
for (int i = 0; i < reshape_before_fc_dim.nbDims; i++) {
reshape_before_fc_dim.d[i] = 1;
}
for (int i = 0; i < x_dim.nbDims; i++) {
if (i < x_num_col_dims) {
reshape_before_fc_dim.d[i] = 0;
} else {
for (int i = 0; i < 4; i++) {
if (i < input_dims) {
reshape_dim4[i] = input_d[i];
} else {
reshape_dim4[i] = 1;
}
if (x_dim.d[i] < 0) {
reshape_before_fc_dim.d[x_num_col_dims] = -1;
break;
}
reshape_before_fc_dim.d[x_num_col_dims] *= x_dim.d[i];
}
nvinfer1::Dims4 reshape_dim(reshape_dim4[0], reshape_dim4[1],
reshape_dim4[2], reshape_dim4[3]);
auto* reshape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
reshape_layer->setReshapeDimensions(reshape_dim);
reshape_itensor = reshape_layer->getOutput(0);
if (enable_int8) {
engine_->SetTensorDynamicRange(reshape_itensor, in_scale);
}
}
auto* reshape_before_fc_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
reshape_before_fc_layer->setReshapeDimensions(reshape_before_fc_dim);
reshape_before_fc_layer->setName(
("shuffle_before_fc(Output: " + output_name + ")").c_str());
auto* reshape_itensor = reshape_before_fc_layer->getOutput(0);
if (enable_int8) {
engine_->SetTensorDynamicRange(reshape_itensor, in_scale);
}
regist_fc(reshape_itensor, n_output, weight, bias);
}
Expand Down
14 changes: 14 additions & 0 deletions paddle/fluid/inference/tensorrt/op_teller.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -633,6 +633,20 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
}
}

if (op_type == "fc") {
int x_num_col_dims =
desc.HasAttr("x_num_col_dims")
? BOOST_GET_CONST(int, desc.GetAttr("x_num_col_dims"))
: (desc.HasAttr("in_num_col_dims")
? BOOST_GET_CONST(int, desc.GetAttr("in_num_col_dims"))
: 1);
if (x_num_col_dims < 1) {
VLOG(3) << "converter expects x_num_col_dims >= 1, "
"but x_num_col_dims = %d.";
return false;
}
}

if ((*teller)(op_type, desc, use_no_calib_int8)) return true;
}
return false;
Expand Down
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