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未验证 提交 245005d4 编写于 作者: Z zhoutianzi666 提交者： GitHub
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[Paddle-TRT][cherry pick] Slice to 2.3 (#44757) 

* slice_to_2.3
上级 7cdce09b
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Showing with 494 addition and 252 deletion
paddle/fluid/inference/tensorrt/convert/fc_op.cc
浏览文件 @ 245005d4
......@@ -34,12 +34,17 @@ namespace tensorrt {
class FcOpConverter : public OpConverter {
public:
nvinfer1::ILayer* reshape_before_fc(nvinfer1::ITensor* before_fc,
nvinfer1::Dims x_dim, int x_num_col_dims,
nvinfer1::Dims x_dim,
int x_num_col_dims,
std::string output_name) {
// 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"
nvinfer1::ITensor* filal_reshape_before_fc_shape_tensor = nullptr;
if (!engine_->with_dynamic_shape()) {
for (int i = 0; i < reshape_before_fc_dim.nbDims; i++) {
reshape_before_fc_dim.d[i] = 1;
}
......@@ -47,16 +52,41 @@ class FcOpConverter : public OpConverter {
if (i < x_num_col_dims) {
reshape_before_fc_dim.d[i] = 0;
} else {
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];
}
}
} else {
std::vector<nvinfer1::ITensor*> reshape_before_fc_shape_tensor;
nvinfer1::ITensor* input_shape_tensor = Shape(before_fc);
for (int i = 0; i < reshape_before_fc_dim.nbDims; i++) {
reshape_before_fc_shape_tensor.push_back(Add1DConstantLayer(1));
}
for (int i = 0; i < x_dim.nbDims; i++) {
if (i < x_num_col_dims) {
reshape_before_fc_shape_tensor[i] =
GetEleTensorOfShape(input_shape_tensor, i);
} else {
reshape_before_fc_shape_tensor[x_num_col_dims] =
Prod(GetEleTensorOfShape(input_shape_tensor, i),
reshape_before_fc_shape_tensor[x_num_col_dims]);
// If not set, test_trt_matmul_quant_dequant in trt 6015 will fail
reshape_before_fc_shape_tensor[x_num_col_dims]->setType(
nvinfer1::DataType::kINT32);
}
}
filal_reshape_before_fc_shape_tensor =
Concat(reshape_before_fc_shape_tensor);
}
auto* reshape_before_fc_layer =
TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *before_fc);
if (!engine_->with_dynamic_shape()) {
reshape_before_fc_layer->setReshapeDimensions(reshape_before_fc_dim);
} else {
reshape_before_fc_layer->setInput(1,
*filal_reshape_before_fc_shape_tensor);
}
reshape_before_fc_layer->setName(
("fc_op_reshape_before_fc: Shuffle (Output: " + output_name + ")")
.c_str());
......@@ -64,21 +94,37 @@ class FcOpConverter : public OpConverter {
}
nvinfer1::ILayer* reshape_after_fc(nvinfer1::ITensor* after_fc,
nvinfer1::Dims x_dim, int x_num_col_dims) {
nvinfer1::Dims x_dim,
int x_num_col_dims) {
// add shuffle after fc
nvinfer1::Dims reshape_after_fc_dim;
reshape_after_fc_dim.nbDims = x_num_col_dims + 1;
nvinfer1::ITensor* filal_reshape_after_fc_shape_tensor = nullptr;
if (!engine_->with_dynamic_shape()) {
for (int i = 0; i < reshape_after_fc_dim.nbDims; i++) {
reshape_after_fc_dim.d[i] = 0;
}
} else {
std::vector<int> gather_indices(x_num_col_dims + 1);
std::iota(gather_indices.begin(), gather_indices.end(), 0);
filal_reshape_after_fc_shape_tensor =
Gather(Shape(after_fc), gather_indices);
}
auto* reshape_after_fc_layer =
TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *after_fc);
if (!engine_->with_dynamic_shape()) {
reshape_after_fc_layer->setReshapeDimensions(reshape_after_fc_dim);
} else {
reshape_after_fc_layer->setInput(1, *filal_reshape_after_fc_shape_tensor);
}
return reshape_after_fc_layer;
}
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
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();
......@@ -96,7 +142,8 @@ class FcOpConverter : public OpConverter {
// Declare weights
auto* Y_v = scope.FindVar(op_desc.Input(w_name).front());
PADDLE_ENFORCE_NOT_NULL(
Y_v, platform::errors::NotFound(
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>();
int x_num_col_dims =
......@@ -128,7 +175,8 @@ class FcOpConverter : public OpConverter {
}
weight_data = engine_->GetWeightCPUData(op_desc.Input(w_name).front(), Y_t);
PADDLE_ENFORCE_EQ(Y_t->dims().size(), 2UL,
PADDLE_ENFORCE_EQ(Y_t->dims().size(),
2UL,
platform::errors::InvalidArgument(
"The fc's weight should be a matrix with 2 dims, but "
"it's %d-dimensional.",
......@@ -143,7 +191,8 @@ class FcOpConverter : public OpConverter {
}
};
auto regist_fc = [&](nvinfer1::ITensor* inputs, int n_output,
auto regist_fc = [&](nvinfer1::ITensor* inputs,
int n_output,
TensorRTEngine::Weight& weight,
TensorRTEngine::Weight& bias) {
if (enable_int8 || support_int8) {
......@@ -151,7 +200,8 @@ class FcOpConverter : public OpConverter {
float out_scale = 0;
if (enable_int8) {
PADDLE_ENFORCE_EQ(
op_desc.HasAttr("out_threshold"), true,
op_desc.HasAttr("out_threshold"),
true,
platform::errors::InvalidArgument(
"must have out threshold in fc layers in int8 mode"));
out_scale = BOOST_GET_CONST(float, op_desc.GetAttr("out_threshold"));
......@@ -159,9 +209,13 @@ class FcOpConverter : public OpConverter {
out_scale = BOOST_GET_CONST(float, op_desc.GetAttr("Out"));
}
nvinfer1::DimsHW nv_ksize(1, 1);
auto* fc_layer_int8 =
TRT_ENGINE_ADD_LAYER(engine_, Convolution, *inputs, n_output,
nv_ksize, weight.get(), bias.get());
auto* fc_layer_int8 = TRT_ENGINE_ADD_LAYER(engine_,
Convolution,
*inputs,
n_output,
nv_ksize,
weight.get(),
bias.get());
fc_layer_int8->setName(
("fc_op_int8_conv1x1: Convolution (Output: " + output_name + ")")
.c_str());
......@@ -174,21 +228,29 @@ class FcOpConverter : public OpConverter {
.c_str());
engine_->SetTensorDynamicRange(fc_after_reshape_int8->getOutput(0),
out_scale);
nvinfer1::IActivationLayer* relu_layer_int8 = TRT_ENGINE_ADD_LAYER(
engine_, Activation, *(fc_after_reshape_int8->getOutput(0)),
nvinfer1::IActivationLayer* relu_layer_int8 =
TRT_ENGINE_ADD_LAYER(engine_,
Activation,
*(fc_after_reshape_int8->getOutput(0)),
nvinfer1::ActivationType::kRELU);
RreplenishLayerAndOutput(relu_layer_int8, "relu_after_fc_shuffle",
{output_name}, test_mode);
RreplenishLayerAndOutput(relu_layer_int8,
"relu_after_fc_shuffle",
{output_name},
test_mode);
} else {
RreplenishLayerAndOutput(fc_after_reshape_int8,
"fc_op_int8_reshape_after_fc: Shuffle",
{output_name}, test_mode);
{output_name},
test_mode);
}
} else {
// add fc layer
auto* fc_layer_float =
TRT_ENGINE_ADD_LAYER(engine_, FullyConnected, *inputs, n_output,
weight.get(), bias.get());
auto* fc_layer_float = TRT_ENGINE_ADD_LAYER(engine_,
FullyConnected,
*inputs,
n_output,
weight.get(),
bias.get());
fc_layer_float->setName(
("fc_op_float: FullyConnected (Output: " + output_name + ")")
.c_str());
......@@ -198,14 +260,20 @@ class FcOpConverter : public OpConverter {
fc_after_reshape_float->setName(
("float_reshape_after_fc: Shuffle (Output: " + output_name + ")")
.c_str());
nvinfer1::IActivationLayer* relu_layer_float = TRT_ENGINE_ADD_LAYER(
engine_, Activation, *(fc_after_reshape_float->getOutput(0)),
nvinfer1::IActivationLayer* relu_layer_float =
TRT_ENGINE_ADD_LAYER(engine_,
Activation,
*(fc_after_reshape_float->getOutput(0)),
nvinfer1::ActivationType::kRELU);
RreplenishLayerAndOutput(relu_layer_float, "relu_after_fc_shuffle",
{output_name}, test_mode);
RreplenishLayerAndOutput(relu_layer_float,
"relu_after_fc_shuffle",
{output_name},
test_mode);
} else {
RreplenishLayerAndOutput(fc_after_reshape_float, "shuffle_after_fc",
{output_name}, test_mode);
RreplenishLayerAndOutput(fc_after_reshape_float,
"shuffle_after_fc",
{output_name},
test_mode);
}
}
};
......@@ -255,15 +323,20 @@ class FcOpConverter : public OpConverter {
if (enable_int8 || support_int8) {
// add conv1x1 layer
nvinfer1::DimsHW nv_ksize(1, 1);
auto* fc_layer_int8 =
TRT_ENGINE_ADD_LAYER(engine_, Convolution, *X, n_output, nv_ksize,
weight.get(), bias.get());
auto* fc_layer_int8 = TRT_ENGINE_ADD_LAYER(engine_,
Convolution,
*X,
n_output,
nv_ksize,
weight.get(),
bias.get());
if (activation_type == "relu") {
fc_layer_int8->setName(
("ernie_fc_op_int8: Convolution (Output: " + output_name + ")")
.c_str());
PADDLE_ENFORCE_EQ(
op_desc.HasAttr("out_threshold"), true,
op_desc.HasAttr("out_threshold"),
true,
platform::errors::InvalidArgument(
"must have out threshold in fc layers in int8 mode"));
float out_scale = 0;
......@@ -275,15 +348,20 @@ class FcOpConverter : public OpConverter {
}
engine_->SetTensorDynamicRange(fc_layer_int8->getOutput(0),
out_scale);
nvinfer1::IActivationLayer* relu_layer_int8 = TRT_ENGINE_ADD_LAYER(
engine_, Activation, *(fc_layer_int8->getOutput(0)),
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_ernie_fc_int8",
{output_name}, test_mode);
RreplenishLayerAndOutput(relu_layer_int8,
"relu_after_ernie_fc_int8",
{output_name},
test_mode);
} else {
RreplenishLayerAndOutput(fc_layer_int8,
"ernie_fc_op_int8: Convolution",
{output_name}, test_mode);
{output_name},
test_mode);
}
} else {
// add fc layer
......@@ -292,25 +370,30 @@ class FcOpConverter : public OpConverter {
if (activation_type == "relu") {
fc_layer_float->setName(
("ernie_fc_op_float: (Output: " + output_name + ")").c_str());
nvinfer1::IActivationLayer* relu_layer_float = TRT_ENGINE_ADD_LAYER(
engine_, Activation, *(fc_layer_float->getOutput(0)),
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_ernie_fc_float", {output_name},
"relu_after_ernie_fc_float",
{output_name},
test_mode);
} else {
RreplenishLayerAndOutput(fc_layer_float, "ernie_fc_op_float",
{output_name}, test_mode);
RreplenishLayerAndOutput(
fc_layer_float, "ernie_fc_op_float", {output_name}, test_mode);
}
}
} else { // need reshape input before and after fc
PADDLE_ENFORCE_GT(
x_dim.nbDims, x_num_col_dims,
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));
x_dim.nbDims,
x_num_col_dims));
auto* reshape_before_fc_layer =
reshape_before_fc(X, x_dim, x_num_col_dims, output_name);
auto* reshape_itensor = reshape_before_fc_layer->getOutput(0);
......
paddle/fluid/inference/tensorrt/convert/multihead_matmul_op.cc
浏览文件 @ 245005d4
......@@ -22,7 +22,8 @@ namespace tensorrt {
class MultiheadMatMulOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
const framework::Scope& scope,
bool test_mode) override {
VLOG(3) << "convert a fluid multihead_mamul op to a corresponding tensorrt "
"network structure";
framework::OpDesc op_desc(op, nullptr);
......@@ -52,8 +53,8 @@ class MultiheadMatMulOpConverter : public OpConverter {
float* bias_data = engine_->GetWeightCPUData(bias_name, bias_t);
std::vector<float> weight_data_tmp;
weight_data_tmp.reserve(weight_t->numel());
memcpy(weight_data_tmp.data(), weight_data,
weight_t->numel() * sizeof(float));
memcpy(
weight_data_tmp.data(), weight_data, weight_t->numel() * sizeof(float));
// (hidden_in, 3, hidden_out)
auto weight_dims = weight_t->dims();
......@@ -98,14 +99,15 @@ class MultiheadMatMulOpConverter : public OpConverter {
nvinfer1::ILayer* fc_layer = nullptr;
float dp_probs = 1.0 / 127.0;
nvinfer1::DimsHW nv_ksize(1, 1);
fc_layer = TRT_ENGINE_ADD_LAYER(engine_, Convolution, *input, n,
nv_ksize, weight, bias);
fc_layer = TRT_ENGINE_ADD_LAYER(
engine_, Convolution, *input, n, nv_ksize, weight, bias);
fc_layer->setName(
("Multihead: Convolution/FullyConnected: (Output: " +
output_name + ")")
.c_str());
PADDLE_ENFORCE_EQ(
op_desc.HasAttr("fc_out_threshold"), true,
op_desc.HasAttr("fc_out_threshold"),
true,
platform::errors::InvalidArgument(
"must have out_threshold in multihead layers in int8 mode"));
float out_scale =
......@@ -119,13 +121,19 @@ class MultiheadMatMulOpConverter : public OpConverter {
"CustomQKVToContextPluginDynamic", "3");
assert(creator != nullptr);
std::vector<nvinfer1::PluginField> fields{
{"hidden_size", &hidden_out, nvinfer1::PluginFieldType::kINT32,
{"hidden_size",
&hidden_out,
nvinfer1::PluginFieldType::kINT32,
1},
{"num_heads", &head_number, nvinfer1::PluginFieldType::kINT32,
{"num_heads",
&head_number,
nvinfer1::PluginFieldType::kINT32,
1}};
if (qkv2context_plugin_int8) {
fields.push_back({"dq_probs", &dp_probs,
nvinfer1::PluginFieldType::kFLOAT32, 1});
fields.push_back({"dq_probs",
&dp_probs,
nvinfer1::PluginFieldType::kFLOAT32,
1});
}
nvinfer1::PluginFieldCollection* plugin_collection =
static_cast<nvinfer1::PluginFieldCollection*>(malloc(
......@@ -154,7 +162,8 @@ class MultiheadMatMulOpConverter : public OpConverter {
engine_->GetITensor(engine_->network()->getInput(3)->getName());
engine_->SetTensorDynamicRange(max_seqlen_tensor, 1.0f);
auto* shuffle_layer = TRT_ENGINE_ADD_LAYER(
engine_, Shuffle,
engine_,
Shuffle,
*const_cast<nvinfer1::ITensor*>(max_seqlen_tensor));
nvinfer1::Dims shape_dim;
shape_dim.nbDims = 1;
......@@ -173,8 +182,11 @@ class MultiheadMatMulOpConverter : public OpConverter {
// [3, head_number, head_size, hidden_in] -> [head_number, 3,
// head_size,
// hidden_in]
auto transpose_weight_v2 = [](const float* src, float* dst, int three,
int head_number, int head_size,
auto transpose_weight_v2 = [](const float* src,
float* dst,
int three,
int head_number,
int head_size,
int hidden_in) {
const int HH = head_size * hidden_in;
for (int i = 0; i < three; ++i) {
......@@ -187,8 +199,8 @@ class MultiheadMatMulOpConverter : public OpConverter {
}
};
// [3, head_number, head_size] -> [head_number, 3, head_size]
auto transpose_bias_v2 = [](const float* src, float* dst, int N,
int H) {
auto transpose_bias_v2 =
[](const float* src, float* dst, int N, int H) {
for (int i = 0; i < 3; ++i) {
for (int n = 0; n < N; ++n) {
for (int h = 0; h < H; ++h) {
......@@ -197,31 +209,37 @@ class MultiheadMatMulOpConverter : public OpConverter {
}
}
};
memcpy(weight_data_tmp.data(), weight_data,
memcpy(weight_data_tmp.data(),
weight_data,
weight_t->numel() * sizeof(float));
transpose_weight_v2(weight_data_tmp.data(), weight_data, three,
head_number, head_size, hidden_in);
transpose_weight_v2(weight_data_tmp.data(),
weight_data,
three,
head_number,
head_size,
hidden_in);
std::vector<float> bias_data_tmp;
bias_data_tmp.reserve(bias_t->numel());
memcpy(bias_data_tmp.data(), bias_data,
bias_t->numel() * sizeof(float));
transpose_bias_v2(bias_data_tmp.data(), bias_data, head_number,
head_size);
memcpy(
bias_data_tmp.data(), bias_data, bias_t->numel() * sizeof(float));
transpose_bias_v2(
bias_data_tmp.data(), bias_data, head_number, head_size);
nvinfer1::ILayer* fc_layer = nullptr;
float dp_probs = 1.0 / 127.0;
if (op_desc.HasAttr("Input_scale")) {
nvinfer1::DimsHW nv_ksize(1, 1);
fc_layer = TRT_ENGINE_ADD_LAYER(engine_, Convolution, *input, n,
nv_ksize, weight, bias);
fc_layer = TRT_ENGINE_ADD_LAYER(
engine_, Convolution, *input, n, nv_ksize, weight, bias);
} else {
fc_layer = TRT_ENGINE_ADD_LAYER(engine_, FullyConnected, *input, n,
weight, bias);
fc_layer = TRT_ENGINE_ADD_LAYER(
engine_, FullyConnected, *input, n, weight, bias);
}
if (op_desc.HasAttr("fc_out_threshold")) {
PADDLE_ENFORCE_EQ(op_desc.HasAttr("fc_out_threshold"), true,
PADDLE_ENFORCE_EQ(op_desc.HasAttr("fc_out_threshold"),
true,
platform::errors::InvalidArgument(
"must have out threshold in multihead layers "
"in int8 mode"));
......@@ -248,15 +266,21 @@ class MultiheadMatMulOpConverter : public OpConverter {
int var_seqlen = 1;
std::vector<nvinfer1::PluginField> fields{
{"type_id", &type, nvinfer1::PluginFieldType::kINT32, 1},
{"hidden_size", &hidden_out, nvinfer1::PluginFieldType::kINT32,
{"hidden_size",
&hidden_out,
nvinfer1::PluginFieldType::kINT32,
1},
{"num_heads", &head_number, nvinfer1::PluginFieldType::kINT32, 1},
{"has_mask", &has_mask, nvinfer1::PluginFieldType::kINT32, 1},
{"var_seqlen", &var_seqlen, nvinfer1::PluginFieldType::kINT32,
{"var_seqlen",
&var_seqlen,
nvinfer1::PluginFieldType::kINT32,
1}};
if (qkv2context_plugin_int8) {
fields.push_back({"dq_probs", &dp_probs,
nvinfer1::PluginFieldType::kFLOAT32, 1});
fields.push_back({"dq_probs",
&dp_probs,
nvinfer1::PluginFieldType::kFLOAT32,
1});
}
nvinfer1::PluginFieldCollection* plugin_collection =
static_cast<nvinfer1::PluginFieldCollection*>(malloc(
......@@ -285,7 +309,8 @@ class MultiheadMatMulOpConverter : public OpConverter {
auto max_seqlen_tensor =
engine_->GetITensor(engine_->network()->getInput(3)->getName());
auto* shuffle_layer = TRT_ENGINE_ADD_LAYER(
engine_, Shuffle,
engine_,
Shuffle,
*const_cast<nvinfer1::ITensor*>(max_seqlen_tensor));
nvinfer1::Dims shape_dim;
shape_dim.nbDims = 1;
......@@ -301,7 +326,8 @@ class MultiheadMatMulOpConverter : public OpConverter {
}
} else {
PADDLE_ENFORCE_EQ(
input->getDimensions().nbDims, 3,
input->getDimensions().nbDims,
3,
platform::errors::InvalidArgument(
"The Input dim of the MultiheadMatMul should be 3, "
"but it's (%d) now.",
......@@ -320,20 +346,25 @@ class MultiheadMatMulOpConverter : public OpConverter {
static_cast<size_t>(bias_t->numel())};
// add shuffle before fc
nvinfer1::Dims reshape_before_fc_dim;
reshape_before_fc_dim.nbDims = 5;
reshape_before_fc_dim.d[0] = 0;
reshape_before_fc_dim.d[1] = 0;
reshape_before_fc_dim.d[2] = 0;
reshape_before_fc_dim.d[3] = 1;
reshape_before_fc_dim.d[4] = 1;
std::vector<nvinfer1::ITensor*> reshape_before_fc_shape_tensor;
nvinfer1::ITensor* input_shape_tensor = Shape(input);
for (int i = 0; i < 5; i++) {
reshape_before_fc_shape_tensor.push_back(Add1DConstantLayer(1));
}
for (int i = 0; i < 3; i++) {
reshape_before_fc_shape_tensor[i] =
GetEleTensorOfShape(input_shape_tensor, i);
}
auto* reshape_before_fc_layer =
TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
if (op_desc.HasAttr("Input_scale")) {
engine_->SetTensorDynamicRange(reshape_before_fc_layer->getOutput(0),
in_scale);
}
reshape_before_fc_layer->setReshapeDimensions(reshape_before_fc_dim);
reshape_before_fc_layer->setInput(
1, *Concat(reshape_before_fc_shape_tensor));
reshape_before_fc_layer->setName(
("shuffle_before_multihead_mamul(Output: " + output_name + ")")
.c_str());
......@@ -342,18 +373,28 @@ class MultiheadMatMulOpConverter : public OpConverter {
nvinfer1::ILayer* fc_layer = nullptr;
if (op_desc.HasAttr("Input_scale")) {
nvinfer1::DimsHW nv_ksize(1, 1);
fc_layer = TRT_ENGINE_ADD_LAYER(
engine_, Convolution, *reshape_before_fc_layer->getOutput(0), n,
nv_ksize, weight.get(), bias.get());
fc_layer =
TRT_ENGINE_ADD_LAYER(engine_,
Convolution,
*reshape_before_fc_layer->getOutput(0),
n,
nv_ksize,
weight.get(),
bias.get());
} else {
fc_layer = TRT_ENGINE_ADD_LAYER(
engine_, FullyConnected, *reshape_before_fc_layer->getOutput(0),
n, weight.get(), bias.get());
fc_layer =
TRT_ENGINE_ADD_LAYER(engine_,
FullyConnected,
*reshape_before_fc_layer->getOutput(0),
n,
weight.get(),
bias.get());
}
if (op_desc.HasAttr("fc_out_threshold")) {
PADDLE_ENFORCE_EQ(
op_desc.HasAttr("fc_out_threshold"), true,
op_desc.HasAttr("fc_out_threshold"),
true,
platform::errors::InvalidArgument(
"must have out threshold in multihead layers in int8 mode"));
float out_scale =
......@@ -380,8 +421,8 @@ class MultiheadMatMulOpConverter : public OpConverter {
with_fp16 = true;
}
plugin::DynamicPluginTensorRT* plugin =
new plugin::QkvToContextPluginDynamic(hidden_in, head_number,
head_size, scale, with_fp16);
new plugin::QkvToContextPluginDynamic(
hidden_in, head_number, head_size, scale, with_fp16);
layer = engine_->AddDynamicPlugin(plugin_inputs.data(), 2, plugin);
}
} else {
......@@ -391,8 +432,8 @@ class MultiheadMatMulOpConverter : public OpConverter {
"You can use the config.SetTRTDynamicShapeInfo(...) interface to set "
"the shape information to run the dynamic shape mode."));
}
RreplenishLayerAndOutput(layer, "multihead_matmul", {output_name},
test_mode);
RreplenishLayerAndOutput(
layer, "multihead_matmul", {output_name}, test_mode);
}
};
......
paddle/fluid/inference/tensorrt/convert/slice_op.cc
浏览文件 @ 245005d4
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
......@@ -14,7 +11,6 @@ limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/plugin/slice_op_plugin.h"
#include "paddle/fluid/inference/tensorrt/plugin/special_slice_plugin.h"
namespace paddle {
namespace inference {
......@@ -23,7 +19,8 @@ namespace tensorrt {
class SliceOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
const framework::Scope& scope,
bool test_mode) override {
// This OP is implemented by trt dynamic shpae plugin.
// Dynamic shape plugin requires TRT version greater than 6.0.
VLOG(4) << "convert slice op to tensorrt layer";
......@@ -64,63 +61,141 @@ class SliceOpConverter : public OpConverter {
}
ends[i] = std::min(ends[i], input_dims.d[axes[i]]);
PADDLE_ENFORCE_GT(
ends[i], starts[i],
ends[i],
starts[i],
platform::errors::InvalidArgument(
"Attr(ends) should be greater than attr(starts) in "
"slice op. But received ends = %d, starts = %d.",
ends[i], starts[i]));
ends[i],
starts[i]));
}
}
nvinfer1::ILayer* layer = nullptr;
if (engine_->with_dynamic_shape()) {
if (engine_->use_oss() && engine_->with_ernie() &&
input_dims.nbDims == 4) {
std::vector<nvinfer1::ITensor*> plugin_inputs;
if (engine_->with_interleaved()) {
auto* shuffler_slice = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
nvinfer1::Permutation transpose_embed{2, 1, 0, 3};
shuffler_slice->setSecondTranspose(transpose_embed);
engine_->SetTensorDynamicRange(shuffler_slice->getOutput(0),
out_scale);
shuffler_slice->setName(
("SpecialSlice_interleaved: transpose: (Output: " + output_name +
")")
.c_str());
plugin_inputs.emplace_back(shuffler_slice->getOutput(0));
} else {
plugin_inputs.emplace_back(input);
#if IS_TRT_VERSION_GE(6000)
auto nchw_input_dims = input->getDimensions();
nvinfer1::Dims trt_start_dims;
trt_start_dims.nbDims = nchw_input_dims.nbDims;
memset(trt_start_dims.d, 0, sizeof(int32_t) * nchw_input_dims.nbDims);
nvinfer1::Dims trt_size_dims = trt_start_dims;
nvinfer1::Dims trt_end_dims = trt_start_dims;
nvinfer1::Dims trt_step_dims = trt_start_dims;
for (int i = 0; i < trt_step_dims.nbDims; i++) trt_step_dims.d[i] = 1;
// input : [N,C,H,W]
bool has_neg_indices = false;
for (size_t i = 0; i < axes.size(); i++) {
int trt_axis = axes[i];
trt_start_dims.d[trt_axis] = starts[i];
trt_end_dims.d[trt_axis] = ends[i];
if (starts[i] < 0 || ends[i] < 0) has_neg_indices = true;
}
std::string pos_name;
if (engine_->Has("ernie_pos_name")) {
pos_name = engine_->Get<std::string>("ernie_pos_name");
} else {
// hard code for compatibility
pos_name = engine_->network()->getInput(2)->getName();
auto* shape_tensor = Shape(input);
auto* start_tensor = Add1DConstantLayer(trt_start_dims);
if (has_neg_indices) {
start_tensor = FixNegIndices(shape_tensor, start_tensor);
}
plugin_inputs.emplace_back(
engine_->GetITensor(pos_name)); // cu_seqlens, eval_placeholder_2
// bool ban_fp16 = engine_->disable_trt_plugin_fp16();
plugin::SpecialSlicePluginDynamic* plugin =
new plugin::SpecialSlicePluginDynamic();
layer = engine_->AddDynamicPlugin(plugin_inputs.data(),
plugin_inputs.size(), plugin);
std::vector<nvinfer1::ITensor*> end_vec_tensor;
for (int i = 0; i < trt_end_dims.nbDims; i++) {
end_vec_tensor.push_back(GetEleTensorOfShape(shape_tensor, i));
}
for (size_t i = 0; i < axes.size(); i++) {
int trt_axis = axes[i];
if (ends[i] >= 0) {
end_vec_tensor[trt_axis] = Add1DConstantLayer(ends[i]);
} else {
end_vec_tensor[trt_axis] =
Sum(end_vec_tensor[trt_axis], Add1DConstantLayer(ends[i]));
}
}
// CI failed in trt 6015 but success in 7134, may be a trt bug
#if IS_TRT_VERSION_GE(7134)
auto* size_tensor =
Sub(Min(Concat(end_vec_tensor), shape_tensor), start_tensor);
#else
auto* size_tensor = Sub(Concat(end_vec_tensor), start_tensor);
#endif
layer = TRT_ENGINE_ADD_LAYER(
engine_, Slice, *input, trt_start_dims, trt_size_dims, trt_step_dims);
layer->setInput(1, *start_tensor);
layer->setInput(2, *size_tensor);
if (decrease_axises.size() > 0) {
std::vector<int32_t> gather_indices;
for (int i = 0; i < trt_size_dims.nbDims; i++) {
if (decrease_axises.end() !=
std::find(decrease_axises.begin(), decrease_axises.end(), i))
continue;
gather_indices.push_back(i);
}
if (gather_indices.empty())
gather_indices.push_back(decrease_axises[0]);
auto real_size_tensor = Gather(size_tensor, gather_indices);
layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *layer->getOutput(0));
layer->setInput(1, *real_size_tensor);
}
#else
bool with_fp16 =
engine_->WithFp16() && !engine_->disable_trt_plugin_fp16();
int decrease_axis =
decrease_axises.size() == 0 ? -1 : decrease_axises[0];
int decrease_axis = decrease_axises.size() == 0 ? -1 : decrease_axises[0];
plugin::SlicePluginDynamic* plugin = new plugin::SlicePluginDynamic(
starts, ends, axes, decrease_axis, with_fp16);
layer = engine_->AddDynamicPlugin(&input, 1, plugin);
}
#endif
} else {
#if IS_TRT_VERSION_GE(6000)
auto chw_input_dims = input->getDimensions();
nvinfer1::Dims trt_start_dims;
trt_start_dims.nbDims = chw_input_dims.nbDims;
memset(trt_start_dims.d, 0, sizeof(int32_t) * chw_input_dims.nbDims);
nvinfer1::Dims trt_size_dims = chw_input_dims;
nvinfer1::Dims trt_step_dims;
trt_step_dims.nbDims = chw_input_dims.nbDims;
for (int i = 0; i < trt_step_dims.nbDims; i++) trt_step_dims.d[i] = 1;
// input : [C,H,W]
for (size_t i = 0; i < axes.size(); i++) {
int trt_axis = axes[i] - 1;
trt_start_dims.d[trt_axis] = starts[i];
trt_size_dims.d[trt_axis] = ends[i] - starts[i];
}
layer = TRT_ENGINE_ADD_LAYER(
engine_, Slice, *input, trt_start_dims, trt_size_dims, trt_step_dims);
nvinfer1::Dims real_trt_size_dims;
real_trt_size_dims.nbDims = 0;
if (decrease_axises.size() > 0) {
for (size_t i = 0; i < decrease_axises.size(); i++) {
decrease_axises[i]--;
}
for (int i = 0; i < trt_size_dims.nbDims; i++) {
if (decrease_axises.end() !=
std::find(decrease_axises.begin(), decrease_axises.end(), i))
continue;
real_trt_size_dims.d[real_trt_size_dims.nbDims] = trt_size_dims.d[i];
real_trt_size_dims.nbDims++;
}
if (real_trt_size_dims.nbDims == 0) {
real_trt_size_dims.nbDims = 1;
real_trt_size_dims.d[0] = 1;
}
auto reshape_layer =
TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *layer->getOutput(0));
reshape_layer->setReshapeDimensions(real_trt_size_dims);
layer = static_cast<nvinfer1::ILayer*>(reshape_layer);
}
#else
bool with_fp16 =
engine_->WithFp16() && !engine_->disable_trt_plugin_fp16();
plugin::SlicePlugin* plugin =
new plugin::SlicePlugin(starts, ends, axes, with_fp16);
layer = engine_->AddPlugin(&input, 1, plugin);
#endif
}
RreplenishLayerAndOutput(layer, "slice", {output_name}, test_mode);
}
......
paddle/fluid/inference/tensorrt/engine.cc
浏览文件 @ 245005d4
......@@ -48,7 +48,8 @@ void TensorRTEngine::InitNetwork() {
optim_profiles_[i] = infer_builder_->createOptimizationProfile();
}
void TensorRTEngine::Execute(int batch_size, std::vector<void *> *buffers,
void TensorRTEngine::Execute(int batch_size,
std::vector<void *> *buffers,
cudaStream_t stream) {
freshDeviceId();
auto infer_context = context();
......@@ -126,14 +127,32 @@ void TensorRTEngine::FreezeNetwork() {
}
#if IS_TRT_VERSION_GE(5122)
auto is_layer_int8 = [&](nvinfer1::ILayer *layer) -> bool {
auto layer_int8_fallback = [&](nvinfer1::ILayer *layer) -> bool {
if (layer->getType() == nvinfer1::LayerType::kSHAPE) {
return false;
}
bool all_int = true;
for (int j = 0; j < layer->getNbInputs(); j++) {
auto *temp_in = layer->getInput(j);
if (temp_in->getType() != nvinfer1::DataType::kINT32) {
all_int = false;
}
}
for (int j = 0; j < layer->getNbOutputs(); j++) {
auto *temp_out = layer->getOutput(j);
if (temp_out->getType() != nvinfer1::DataType::kINT32) {
all_int = false;
}
}
if (all_int) return false;
for (int j = 0; j < layer->getNbInputs(); j++) {
auto *temp_in = layer->getInput(j);
if (!temp_in->dynamicRangeIsSet()) {
VLOG(1) << "Layer(Name: " << layer->getName()
<< ") is set to float32 because its input("
<< temp_in->getName() << ") doesn't have dynamic range.";
return false;
return true;
}
}
for (int j = 0; j < layer->getNbOutputs(); j++) {
......@@ -142,10 +161,10 @@ void TensorRTEngine::FreezeNetwork() {
VLOG(1) << "Layer(Name: " << layer->getName()
<< ") is set to float32 because its output("
<< temp_out->getName() << ") doesn't have dynamic range.";
return false;
return true;
}
}
return true;
return false;
};
// If a layer's output is the network's output, or not all of its inputs
// and outputs have scales,
......@@ -154,7 +173,7 @@ void TensorRTEngine::FreezeNetwork() {
int layers_no_int8 = 0;
for (int i = 0; i < network()->getNbLayers(); i++) {
auto layer = network()->getLayer(i);
if (!is_layer_int8(layer)) {
if (layer_int8_fallback(layer)) {
layer->setPrecision(nvinfer1::DataType::kFLOAT);
++layers_no_int8;
}
......@@ -205,7 +224,8 @@ void TensorRTEngine::FreezeNetwork() {
for (auto &input : min_input_shape_) {
#if IS_TRT_VERSION_LT(7000)
// trt6 will check all_of input > 0
if (!(std::all_of(input.second.begin(), input.second.end(),
if (!(std::all_of(input.second.begin(),
input.second.end(),
[](int x) { return x > 0; }) &&
std::all_of(max_input_shape_[input.first].begin(),
max_input_shape_[input.first].end(),
......@@ -222,13 +242,16 @@ void TensorRTEngine::FreezeNetwork() {
<< ", opt: " << Vec2Str(optim_input_shape_[input.first]);
optim_profiles_[i]->setDimensions(
input.first.c_str(), nvinfer1::OptProfileSelector::kMIN,
input.first.c_str(),
nvinfer1::OptProfileSelector::kMIN,
Vec2TRT_Dims(input.second, input.first, true));
optim_profiles_[i]->setDimensions(
input.first.c_str(), nvinfer1::OptProfileSelector::kMAX,
input.first.c_str(),
nvinfer1::OptProfileSelector::kMAX,
Vec2TRT_Dims(max_input_shape_[input.first], input.first, true));
optim_profiles_[i]->setDimensions(
input.first.c_str(), nvinfer1::OptProfileSelector::kOPT,
input.first.c_str(),
nvinfer1::OptProfileSelector::kOPT,
Vec2TRT_Dims(optim_input_shape_[input.first], input.first, true));
}
infer_builder_config_->addOptimizationProfile(optim_profiles_[i]);
......@@ -262,7 +285,8 @@ void TensorRTEngine::FreezeNetwork() {
#endif
PADDLE_ENFORCE_NOT_NULL(
infer_engine_, platform::errors::Fatal(
infer_engine_,
platform::errors::Fatal(
"Build TensorRT cuda engine failed! Please recheck "
"you configurations related to paddle-TensorRT."));
......@@ -279,16 +303,19 @@ void TensorRTEngine::FreezeNetwork() {
nvinfer1::ITensor *TensorRTEngine::DeclareInput(const std::string &name,
nvinfer1::DataType dtype,
const nvinfer1::Dims &dims) {
PADDLE_ENFORCE_EQ(network() != nullptr, true,
PADDLE_ENFORCE_EQ(network() != nullptr,
true,
platform::errors::InvalidArgument(
"The TRT network should be initialized first."));
auto *input = network()->addInput(name.c_str(), dtype, dims);
PADDLE_ENFORCE_NOT_NULL(
input, platform::errors::InvalidArgument("Adding input %s failed in "
input,
platform::errors::InvalidArgument("Adding input %s failed in "
"TensorRT inference network. "
"Please recheck your input.",
name));
PADDLE_ENFORCE_EQ(input->isNetworkInput(), true,
PADDLE_ENFORCE_EQ(input->isNetworkInput(),
true,
platform::errors::InvalidArgument(
"Input %s is not the input of TRT inference network. "
"Please recheck your input.",
......@@ -297,22 +324,26 @@ nvinfer1::ITensor *TensorRTEngine::DeclareInput(const std::string &name,
return input;
}
void TensorRTEngine::DeclareOutput(const nvinfer1::ILayer *layer, int offset,
void TensorRTEngine::DeclareOutput(const nvinfer1::ILayer *layer,
int offset,
const std::string &name) {
auto *output = layer->getOutput(offset);
SetITensor(name, output);
PADDLE_ENFORCE_NOT_NULL(
output, platform::errors::InvalidArgument(
output,
platform::errors::InvalidArgument(
"The output %s of TRT engine should not be null.", name));
output->setName(name.c_str());
PADDLE_ENFORCE_EQ(output->isNetworkInput(), false,
PADDLE_ENFORCE_EQ(output->isNetworkInput(),
false,
platform::errors::InvalidArgument(
"The output %s of TRT engine should not be the input "
"of the network at the same time.",
name));
network()->markOutput(*output);
PADDLE_ENFORCE_EQ(
output->isNetworkOutput(), true,
output->isNetworkOutput(),
true,
platform::errors::InvalidArgument(
"The output %s of TRT engine should be the output of the network.",
name));
......@@ -321,10 +352,12 @@ void TensorRTEngine::DeclareOutput(const nvinfer1::ILayer *layer, int offset,
void TensorRTEngine::DeclareOutput(const std::string &name) {
auto *output = TensorRTEngine::GetITensor(name);
PADDLE_ENFORCE_NOT_NULL(
output, platform::errors::InvalidArgument(
output,
platform::errors::InvalidArgument(
"The output %s of TRT engine should not be null.", name));
output->setName(name.c_str());
PADDLE_ENFORCE_EQ(output->isNetworkInput(), false,
PADDLE_ENFORCE_EQ(output->isNetworkInput(),
false,
platform::errors::InvalidArgument(
"The output %s of TRT engine should not be the input "
"of the network at the same time.",
......@@ -335,17 +368,20 @@ void TensorRTEngine::DeclareOutput(const std::string &name) {
void TensorRTEngine::SetITensor(const std::string &name,
nvinfer1::ITensor *tensor) {
PADDLE_ENFORCE_NOT_NULL(
tensor, platform::errors::InvalidArgument(
tensor,
platform::errors::InvalidArgument(
"Tensor named %s of TRT engine should not be null.", name));
PADDLE_ENFORCE_EQ(
0, itensor_map_.count(name),
0,
itensor_map_.count(name),
platform::errors::InvalidArgument(
"Tensor named %s of TRT engine should not be duplicated", name));
itensor_map_[name] = tensor;
}
nvinfer1::ITensor *TensorRTEngine::GetITensor(const std::string &name) {
PADDLE_ENFORCE_EQ(itensor_map_.count(name), true,
PADDLE_ENFORCE_EQ(itensor_map_.count(name),
true,
platform::errors::NotFound(
"Tensor named %s is not found in TRT engine", name));
return itensor_map_[name];
......@@ -362,15 +398,16 @@ float *TensorRTEngine::GetWeightCPUData(const std::string &name,
std::string splitter = "__";
std::string name_with_suffix = name + splitter + name_suffix;
platform::CPUPlace cpu_place;
PADDLE_ENFORCE_EQ(weight_map.count(name_with_suffix), 0,
PADDLE_ENFORCE_EQ(weight_map.count(name_with_suffix),
0,
platform::errors::AlreadyExists(
"The weight named %s is set into the weight map "
"twice in TRT OP converter.",
name_with_suffix));
weight_map[name_with_suffix].reset(new framework::Tensor());
weight_map[name_with_suffix]->Resize(weight_tensor->dims());
paddle::framework::TensorCopySync(*weight_tensor, cpu_place,
weight_map[name_with_suffix].get());
paddle::framework::TensorCopySync(
*weight_tensor, cpu_place, weight_map[name_with_suffix].get());
float *weight_data =
weight_map[name_with_suffix]->mutable_data<float>(cpu_place);
name_suffix_counter += 1;
......@@ -380,21 +417,24 @@ float *TensorRTEngine::GetWeightCPUData(const std::string &name,
int TensorRTEngine::GetRuntimeBatch() { return runtime_batch_; }
nvinfer1::IPluginV2Layer *TensorRTEngine::AddPlugin(
nvinfer1::ITensor *const *inputs, int num_inputs,
nvinfer1::ITensor *const *inputs,
int num_inputs,
plugin::PluginTensorRT *plugin) {
owned_plugin_.emplace_back(plugin);
return network()->addPluginV2(inputs, num_inputs, *plugin);
}
nvinfer1::IPluginV2Layer *TensorRTEngine::AddPluginV2Ext(
nvinfer1::ITensor *const *inputs, int num_inputs,
nvinfer1::ITensor *const *inputs,
int num_inputs,
plugin::PluginTensorRTV2Ext *plugin) {
owned_plugin_v2ext_.emplace_back(plugin);
return network()->addPluginV2(inputs, num_inputs, *plugin);
}
nvinfer1::IPluginV2Layer *TensorRTEngine::AddPluginV2IOExt(
nvinfer1::ITensor *const *inputs, int num_inputs,
nvinfer1::ITensor *const *inputs,
int num_inputs,
nvinfer1::IPluginV2IOExt *plugin) {
owned_plugin_v2ioext_.emplace_back(plugin);
return network()->addPluginV2(inputs, num_inputs, *plugin);
......@@ -403,10 +443,12 @@ nvinfer1::IPluginV2Layer *TensorRTEngine::AddPluginV2IOExt(
void TensorRTEngine::freshDeviceId() {
int count;
cudaGetDeviceCount(&count);
PADDLE_ENFORCE_LT(device_id_, count,
PADDLE_ENFORCE_LT(device_id_,
count,
platform::errors::OutOfRange(
"Device id %d exceeds the current device count: %d.",
device_id_, count));
device_id_,
count));
platform::SetDeviceId(device_id_);
}
......
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @ 245005d4
......@@ -1063,14 +1063,9 @@ bool OpTeller::Tell(const framework::ir::Node* node,
if (desc.HasAttr("decrease_axis")) {
std::vector<int> decrease_axis =
BOOST_GET_CONST(std::vector<int>, desc.GetAttr("decrease_axis"));
if (with_dynamic_shape) {
if (decrease_axis.size() > 1) {
return false;
}
} else {
if (decrease_axis.size() > 0) {
VLOG(3) << "Invalid slice decrease_axis. decrease_axis.size() > 0"
"is not supported in TensorRT";
if (!with_dynamic_shape) {
if (decrease_axis.end() !=
std::find(decrease_axis.begin(), decrease_axis.end(), 0)) {
return false;
}
}
......@@ -1102,15 +1097,28 @@ bool OpTeller::Tell(const framework::ir::Node* node,
return false;
}
}
} else {
for (size_t i = 0; i < axes.size(); i++) {
if (starts[i] < 0 || ends[i] < 0) {
VLOG(3) << "Invalid slice attribute 'starts' or 'ends'. "
"Negative starts or ends not supported in TensorRT "
"when running in dynamic shape mode.";
}
}
// not support following four inputs for slice in paddle-trt
auto slice_inputs = desc.Inputs(); // its size == 5
if (slice_inputs.find("StartsTensor") != slice_inputs.end()) {
if (desc.Input("StartsTensor").size()) {
return false;
}
}
if (slice_inputs.find("EndsTensor") != slice_inputs.end()) {
if (desc.Input("EndsTensor").size()) {
return false;
}
}
if (slice_inputs.find("StartsTensorList") != slice_inputs.end()) {
if (desc.Input("StartsTensorList").size()) {
return false;
}
}
if (slice_inputs.find("EndsTensorList") != slice_inputs.end()) {
if (desc.Input("EndsTensorList").size()) {
return false;
}
}
}
......
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_slice.py
浏览文件 @ 245005d4
......@@ -22,17 +22,14 @@ import unittest
class TrtConvertSliceTest(TrtLayerAutoScanTest):
def is_program_valid(self, program_config: ProgramConfig) -> bool:
inputs = program_config.inputs
weights = program_config.weights
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
for x in attrs[0]["decrease_axis"]:
if x < 0:
return False
out_shape = list(inputs['input_data'].shape)
for x in range(len(attrs[0]["axes"])):
start = 0
end = 0
......@@ -42,24 +39,30 @@ class TrtConvertSliceTest(TrtLayerAutoScanTest):
else:
start = attrs[0]["starts"][x]
if attrs[0]["ends"][x] < 0:
end = attrs[0]["ends"][x] + inputs['input_data'].shape[attrs[0][
"axes"][x]]
end = attrs[0]["ends"][x] + inputs['input_data'].shape[
attrs[0]["axes"][x]]
else:
end = attrs[0]["ends"][x]
start = max(0, start)
end = max(0, end)
out_shape[attrs[0]["axes"][x]] = end - start
if start >= end:
return False
for x in attrs[0]["decrease_axis"]:
if x < 0:
return False
if (out_shape[x] != 1):
return False
return True
def sample_program_configs(self):
def generate_input1(attrs: List[Dict[str, Any]]):
return np.ones([6, 6, 64, 64]).astype(np.float32)
return np.random.random([6, 6, 64, 64]).astype(np.float32)
for axes in [[0, 1], [1, 3], [2, 3]]:
for starts in [[0, 1]]:
for ends in [[2, 2], [5, 5]]:
for ends in [[2, 2], [5, 5], [1, -1]]:
for decrease_axis in [[], [1], [2], [-1], [-100]]:
for infer_flags in [[-1]]:
dics = [{
......@@ -86,8 +89,9 @@ class TrtConvertSliceTest(TrtLayerAutoScanTest):
ops=ops,
weights={},
inputs={
"input_data": TensorConfig(data_gen=partial(
generate_input1, dics))
"input_data":
TensorConfig(
data_gen=partial(generate_input1, dics))
},
outputs=["slice_output_data"])
......@@ -95,6 +99,7 @@ class TrtConvertSliceTest(TrtLayerAutoScanTest):
def sample_predictor_configs(
self, program_config) -> (paddle_infer.Config, List[int], float):
def generate_dynamic_shape(attrs):
self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]}
self.dynamic_shape.max_input_shape = {"input_data": [8, 8, 64, 64]}
......@@ -106,17 +111,6 @@ class TrtConvertSliceTest(TrtLayerAutoScanTest):
self.dynamic_shape.opt_input_shape = {}
def generate_trt_nodes_num(attrs, dynamic_shape):
inputs = program_config.inputs
if dynamic_shape == True and len(attrs[0]["decrease_axis"]) == 0:
return 1, 2
if dynamic_shape == True and len(attrs[0]["decrease_axis"]) != 1:
return 0, 3
if dynamic_shape == False and len(attrs[0]["decrease_axis"]) != 0:
return 0, 3
if dynamic_shape:
for i in range(len(attrs[0]["starts"])):
if attrs[0]["starts"][i] < 0 or attrs[0]["ends"][i] < 0:
return 0, 3
if not dynamic_shape:
for x in attrs[0]["axes"]:
if x == 0:
......@@ -124,8 +118,7 @@ class TrtConvertSliceTest(TrtLayerAutoScanTest):
return 1, 2
attrs = [
program_config.ops[i].attrs
for i in range(len(program_config.ops))
program_config.ops[i].attrs for i in range(len(program_config.ops))
]
self.trt_param.max_batch_size = 9
# for static_shape
......@@ -140,11 +133,11 @@ class TrtConvertSliceTest(TrtLayerAutoScanTest):
# for dynamic_shape
generate_dynamic_shape(attrs)
self.trt_param.precision = paddle_infer.PrecisionType.Float32
yield self.create_inference_config(), generate_trt_nodes_num(attrs,
True), 1e-5
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True), 1e-5
self.trt_param.precision = paddle_infer.PrecisionType.Half
yield self.create_inference_config(), generate_trt_nodes_num(attrs,
True), 1e-4
yield self.create_inference_config(), generate_trt_nodes_num(
attrs, True), 1e-4
def test(self):
# TODO(inference): fix.
......
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