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Merged
Shixiaowei02 merged 1 commit into from
Apr 11, 2019
Merged

update fc convert #16807

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58 changes: 13 additions & 45 deletions paddle/fluid/inference/anakin/convert/fc.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
// limitations under the License.

#include "paddle/fluid/inference/anakin/convert/fc.h"
#include "paddle/fluid/inference/anakin/convert/helper.h"
#include <algorithm>
#include <string>
#include <vector>
Expand Down Expand Up @@ -45,72 +46,39 @@ void FcBaseOpConverter<TargetT>::operator()(
// get weights
auto *y_v = scope.FindVar(op_desc.Input(w_name).front());
PADDLE_ENFORCE_NOT_NULL(y_v);
auto *y_t = y_v->GetMutable<framework::LoDTensor>();
auto weight_tensor = tensor_from_var(*y_v, platform::CPUPlace());
auto weight_shape = framework::vectorize2int(weight_tensor->dims());

int out_dim = weight_shape[1];
const int w_m = weight_shape[0];
const int w_k = weight_shape[1];

auto input_name = op_desc.Input(i_name).front();
auto output_name = op_desc.Output("Out").front();

this->engine_->AddOp(op_name, "Dense", {input_name}, {output_name});
this->engine_->AddOpAttr(op_name, "bias_term", with_bias);
this->engine_->AddOpAttr(op_name, "axis", 1);

auto weight_shape = framework::vectorize2int(y_t->dims());
int out_dim = weight_shape[1];
this->engine_->AddOpAttr(op_name, "out_dim", out_dim);
const int w_m = weight_shape[0];
const int w_k = weight_shape[1];

if (weight_shape.size() < 4UL) {
weight_shape.insert(weight_shape.begin(), 4UL - weight_shape.size(), 1);
}
Shape anakin_shape(weight_shape);
auto *weight_data = weight_tensor->data<float>();
PADDLE_ENFORCE(w_m * w_k == weight_tensor->numel());

framework::LoDTensor weight_tensor;
weight_tensor.Resize(y_t->dims());
TensorCopySync((*y_t), platform::CPUPlace(), &weight_tensor);
auto *weight_data = weight_tensor.data<float>();
PADDLE_ENFORCE(w_m * w_k == weight_tensor.numel());

std::vector<float> trans_weight_data(weight_tensor.numel());
std::vector<float> trans_weight_data(weight_tensor->numel());
for (int i = 0; i < w_m; i++) {
for (int j = 0; j < w_k; j++) {
trans_weight_data[i + j * w_m] = weight_data[i * w_k + j];
}
}
auto *weight1 =
GraphGlobalMem<TargetT>::Global().template new_block<AK_FLOAT>(
anakin_shape);
float *cpu_data = static_cast<float *>(weight1->h_tensor().mutable_data());
std::copy_n(trans_weight_data.data(), weight_tensor.numel(), cpu_data);
weight1->d_tensor().set_shape(anakin_shape);
weight1->d_tensor().copy_from(weight1->h_tensor());

auto *weight1 = pblock_from_vector<TargetT>(trans_weight_data);
this->engine_->AddOpAttr(op_name, "weight_1", *weight1);

// get bias
if (with_bias) {
auto *b_v = scope.FindVar(op_desc.Input("Bias").front());
PADDLE_ENFORCE_NOT_NULL(b_v);
auto *b_t = b_v->GetMutable<framework::LoDTensor>();

auto bias_shape = framework::vectorize2int(b_t->dims());
framework::LoDTensor bias_tensor;
bias_tensor.Resize(b_t->dims());
TensorCopySync((*b_t), platform::CPUPlace(), &bias_tensor);
auto *bias_data = bias_tensor.data<float>();
bias_shape.insert(bias_shape.begin(), 1);
bias_shape.insert(bias_shape.begin(), 1);
bias_shape.insert(bias_shape.begin(), 1);
// bias_shape.push_back(1);
// bias_shape.push_back(1);
Shape anakin_bias_shape(bias_shape);

auto *weight2 =
GraphGlobalMem<TargetT>::Global().template new_block<AK_FLOAT>(
anakin_bias_shape);
float *cpu_data2 = static_cast<float *>(weight2->h_tensor().mutable_data());
std::copy_n(bias_data, bias_tensor.numel(), cpu_data2);
weight2->d_tensor().set_shape(anakin_bias_shape);
weight2->d_tensor().copy_from(weight2->h_tensor());
auto weight2 = pblock_from_var<TargetT>(*b_v);
this->engine_->AddOpAttr(op_name, "weight_2", *weight2);
}
}
Expand Down
7 changes: 6 additions & 1 deletion paddle/fluid/inference/anakin/convert/helper.h
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Original file line number Diff line number Diff line change
Expand Up @@ -55,12 +55,17 @@ PBlock<T>* pblock_from_tensor(const framework::LoDTensor& tensor,

template <typename T>
PBlock<T>* pblock_from_vector(const std::vector<float>& vec,
const std::vector<int>& shape_vec) {
std::vector<int> shape_vec) {
while (shape_vec.size() < 4) {
shape_vec.insert(shape_vec.begin(), 1);
}
Shape shape(shape_vec);
auto *weight =
GraphGlobalMem<T>::Global().template new_block<AK_FLOAT>(shape);
auto *weight_data = static_cast<float *>(weight->h_tensor().mutable_data());
std::copy(std::begin(vec), std::end(vec), weight_data);
weight->d_tensor().set_shape(shape);
weight->d_tensor().copy_from(weight->h_tensor());
return weight;
}

Expand Down