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Merged
fc500110 merged 4 commits into from
Mar 7, 2019
Merged

batch norm #16110

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2cc0df3
use anakin batch norm and scale implement fluid batch norm
fc500110 Mar 7, 2019
bf8a99e
update
fc500110 Mar 7, 2019
6d984f8
update
fc500110 Mar 7, 2019
a822a18
update
fc500110 Mar 7, 2019
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97 changes: 50 additions & 47 deletions paddle/fluid/inference/anakin/convert/batch_norm.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -41,16 +41,15 @@ void BatchNormOpConverter::operator()(const framework::proto::OpDesc &op,

auto output = op_desc.Output("Y").front();
auto op_name = op_desc.Type() + ":" + op_desc.Output("Y").front();
engine_->AddOp(op_name, "Scale", {inputs["X"]}, {output});
engine_->AddOpAttr(op_name, "bias_term", true);
engine_->AddOpAttr(op_name, "axis", 1);
engine_->AddOpAttr(op_name, "num_axes", 1);

bool is_test = boost::get<bool>(op_desc.GetAttr("is_test"));
PADDLE_ENFORCE(is_test);
float epsilon = boost::get<float>(op_desc.GetAttr("epsilon"));
engine_->AddOpAttr(op_name, "epsilon", epsilon);
auto epsilon = boost::get<float>(op_desc.GetAttr("epsilon"));

auto bn_op_name = op_name + ":bn";
auto bn_output = bn_op_name + "_output";
engine_->AddOp(bn_op_name, "BatchNorm", {inputs["X"]}, {bn_output});
engine_->AddOpAttr(bn_op_name, "epsilon", epsilon);

auto scale_op_name = op_name + ":scale";
auto get_lod_tensor = [this, &scope, &op_name](const std::string &var_name,
framework::LoDTensor *tensor) {
auto *v = scope.FindVar(var_name);
Expand All @@ -69,50 +68,54 @@ void BatchNormOpConverter::operator()(const framework::proto::OpDesc &op,
get_lod_tensor(inputs["Scale"], &scale_t);
get_lod_tensor(inputs["Variance"], &variance_t);

auto *bias = bias_t.mutable_data<float>(platform::CPUPlace());
auto *mean = mean_t.mutable_data<float>(platform::CPUPlace());
auto *scale = scale_t.mutable_data<float>(platform::CPUPlace());
auto *variance = variance_t.mutable_data<float>(platform::CPUPlace());

framework::LoDTensor combile_scale_t;
framework::LoDTensor combile_bias_t;
combile_scale_t.Resize(scale_t.dims());
combile_bias_t.Resize(bias_t.dims());

auto *combile_scale =
combile_scale_t.mutable_data<float>(platform::CPUPlace());
auto *combile_bias = combile_bias_t.mutable_data<float>(platform::CPUPlace());

size_t elem_num = combile_scale_t.memory_size() / sizeof(float);
for (size_t i = 0; i < elem_num; i++) {
combile_scale[i] = scale[i] / sqrtf(variance[i] + epsilon);
combile_bias[i] = bias[i] - mean[i] * combile_scale[i];
}

auto fill_shape = [](size_t n, std::vector<int> *shape) {
shape->insert(shape->begin(), 1);
if (shape->size() < n) {
shape->insert(shape->end(), n - shape->size(), 1);
auto fill_shape = [](size_t n, std::vector<int> shape) {
shape.insert(shape.begin(), 1);
if (shape.size() < n) {
shape.insert(shape.end(), n - shape.size(), 1);
}
return shape;
};
auto scale_shape = framework::vectorize2int(combile_scale_t.dims());
auto bias_shape = framework::vectorize2int(combile_bias_t.dims());
fill_shape(4, &scale_shape);
fill_shape(4, &bias_shape);
Shape weight1_shape(scale_shape);
Shape weight2_shape(bias_shape);
Shape shape1(fill_shape(4, framework::vectorize2int(mean_t.dims())));
Shape shape2(fill_shape(4, framework::vectorize2int(variance_t.dims())));
auto *weight1 =
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(weight1_shape);
auto *scale_data = static_cast<float *>(weight1->h_tensor().mutable_data());
std::copy_n(combile_scale_t.data<float>(), combile_scale_t.numel(),
scale_data);
engine_->AddOpAttr(op_name, "weight_1", *weight1);
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(shape1);
auto *mean_data = static_cast<float *>(weight1->h_tensor().mutable_data());
std::copy_n(mean_t.data<float>(), mean_t.numel(), mean_data);
engine_->AddOpAttr(bn_op_name, "weight_1", *weight1);

auto *weight2 =
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(weight2_shape);
auto *bias_data = static_cast<float *>(weight2->h_tensor().mutable_data());
std::copy_n(combile_bias_t.data<float>(), combile_bias_t.numel(), bias_data);
engine_->AddOpAttr(op_name, "weight_2", *weight2);
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(shape2);
auto *variance_data =
static_cast<float *>(weight2->h_tensor().mutable_data());
std::copy_n(variance_t.data<float>(), variance_t.numel(), variance_data);
engine_->AddOpAttr(bn_op_name, "weight_2", *weight2);

Shape shape3(std::vector<int>({1, 1, 1, 1}));
auto *weight3 =
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(shape3);
auto *alpha_data = static_cast<float *>(weight3->h_tensor().mutable_data());
float weight3_data[] = {1};
std::copy(std::begin(weight3_data), std::end(weight3_data), alpha_data);
engine_->AddOpAttr(bn_op_name, "weight_3", *weight3);

Shape scale_shape(fill_shape(4, framework::vectorize2int(scale_t.dims())));
auto *scale =
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(scale_shape);
auto *scale_data = static_cast<float *>(scale->h_tensor().mutable_data());
std::copy_n(scale_t.data<float>(), scale_t.numel(), scale_data);

Shape bias_shape(fill_shape(4, framework::vectorize2int(bias_t.dims())));
auto *bias =
GraphGlobalMem<NV>::Global().template new_block<AK_FLOAT>(bias_shape);
auto *bias_data = static_cast<float *>(bias->h_tensor().mutable_data());
std::copy_n(bias_t.data<float>(), bias_t.numel(), bias_data);

engine_->AddOp(scale_op_name, "Scale", {bn_output}, {output});
engine_->AddOpAttr(scale_op_name, "axis", 1);
engine_->AddOpAttr(scale_op_name, "num_axes", 1);
engine_->AddOpAttr(scale_op_name, "bias_term", true);
engine_->AddOpAttr(scale_op_name, "weight_1", *scale);
engine_->AddOpAttr(scale_op_name, "weight_2", *bias);
}

} // namespace anakin
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1 change: 0 additions & 1 deletion paddle/fluid/inference/anakin/convert/test_batch_norm_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,6 @@ TEST(batch_norm_op, test) {
float eps = 1e-5f;
desc.SetAttr("epsilon", eps);
desc.SetAttr("is_test", true);
// desc.SetAttr("momentum", 0.8f);

validator.SetOp(*desc.Proto());

Expand Down