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#include "elementwise_linear_op.h"
namespace caffe2 {
template<>
bool ElementwiseLinearOp<float, CPUContext>::RunOnDevice(){
const auto& X = Input(0);
const auto& a = Input(1);
const auto& b = Input(2);
const auto canonical_axis = X.canonical_axis_index(axis_);
const int N = X.size_to_dim(canonical_axis);
const int D = X.size_from_dim(canonical_axis);
CAFFE_ENFORCE_EQ(a.dim(), 1, a.dim());
CAFFE_ENFORCE_EQ(a.size(0), D, a.dim());
CAFFE_ENFORCE_EQ(b.dim(), 1, b.dim());
CAFFE_ENFORCE_EQ(b.size(0), D, b.dim());
auto* Y = Output(0, X.sizes(), at::dtype<float>());
const float* X_data = X.data<float>();
const float* a_data = a.data<float>();
const float* b_data = b.data<float>();
float* Y_data = Y->template mutable_data<float>();
int p = 0;
for (int n = 0; n < N; ++n) {
for (int d = 0; d < D; ++d) {
Y_data[p] = X_data[p] * a_data[d] + b_data[d];
p++;
}
}
return true;
}
template<>
bool ElementwiseLinearGradientOp<float, CPUContext>::RunOnDevice(){
const auto& g_o = Input(0);
const auto& X = Input(1);
const auto& a = Input(2);
const auto canonical_axis = X.canonical_axis_index(axis_);
const int N = X.size_to_dim(canonical_axis);
const int D = X.size_from_dim(canonical_axis);
CAFFE_ENFORCE_EQ(a.dim(), 1, a.dim());
CAFFE_ENFORCE_EQ(a.size(0), D, a.dim());
auto* g_X = Output(0, X.sizes(), at::dtype<float>());
auto* g_a = Output(1, a.sizes(), at::dtype<float>());
auto* g_b = Output(2, a.sizes(), at::dtype<float>());
const float* g_o_data = g_o.data<float>();
const float* X_data = X.data<float>();
const float* a_data = a.data<float>();
float* g_X_data = g_X->template mutable_data<float>();
float* g_a_data = g_a->template mutable_data<float>();
float* g_b_data = g_b->template mutable_data<float>();
math::Set<float, CPUContext>(g_a->numel(), 0.f, g_a_data, &context_);
math::Set<float, CPUContext>(g_b->numel(), 0.f, g_b_data, &context_);
int p = 0;
for (int n = 0; n < N; ++n) {
for (int d = 0; d < D; ++d) {
g_X_data[p] = g_o_data[p] * a_data[d];
g_a_data[d] += g_o_data[p] * X_data[p];
g_b_data[d] += g_o_data[p];
p++;
}
}
return true;
}
REGISTER_CPU_OPERATOR(
ElementwiseLinear,
ElementwiseLinearOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(
ElementwiseLinearGradient,
ElementwiseLinearGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(ElementwiseLinear)
.NumInputs(3)
.NumOutputs(1)
.SetDoc(R"DOC(
This op computes the elementwise linear combination of a batch of input vectors with a weight vector and bias vector. As input, the op takes an input tensor $X$ of shape $NxD$, a weight vector $w$ of length $D$, and a bias vector $b$ of length $D$. Here, $N$ represents the batch size and $D$ represents the length of the feature vectors. The output, $Y$, is a tensor of shape $NxD$ and is calculated as
$$Y_{ij} = X_{ij}w_j + b_j \ for \ i\in{N}, j\in{D}$$
Github Links:
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/elementwise_linear_op.h
- https://github.com/pytorch/pytorch/blob/master/caffe2/operators/elementwise_linear_op.cc
<details>
<summary> <b>Example</b> </summary>
**Code**
```
workspace.ResetWorkspace()
op = core.CreateOperator(
"ElementwiseLinear",
["X", "w", "b"],
["Y"]
)
// Create X
X = np.array([[1,2,3,4,5],[6,8,9,16,10]])
print("X:\n",X)
// Create w
w = np.array([1,1/2.,1/3.,1/4.,1/5.])
print("w:\n",w)
// Create b
b = np.array([1.,1.,1.,1.,1.])
print("b:\n",b)
// Feed X & w & b into workspace
workspace.FeedBlob("X", X.astype(np.float32))
workspace.FeedBlob("w", w.astype(np.float32))
workspace.FeedBlob("b", b.astype(np.float32))
// Run op
workspace.RunOperatorOnce(op)
// Collect Output
print("Y:\n", workspace.FetchBlob("Y"))
```
**Result**
```
X:
[[ 1 2 3 4 5]
[ 6 8 9 16 10]]
w:
[1. 0.5 0.33333333 0.25 0.2]
b:
[1. 1. 1. 1. 1.]
Y:
[[2. 2. 2. 2. 2.]
[7. 5. 4. 5. 3.]]
```
</details>
)DOC")
.Input(
0,
"X",
"2D input tensor of size $NxD$. This input represents the input data to be operated on.")
.Input(
1,
"w",
"1D scaling factors, or weights, of size $D$. This input contains the weights that will be multiplied by the data.")
.Input(
2,
"b",
"1D biases of size $D$. This input contains the biases that will be added to the products of the weights and data.")
.Output(
0,
"Y",
"2D output tensor of size $NxD$. Calculated as described above.")
.Arg(
"axis",
"*(type: int; default: 1)* Describes the axis of the inputs; defaults to one because the 0th axis most likely describes the batch size.")
.InheritOnnxSchema();
OPERATOR_SCHEMA(ElementwiseLinearGradient)
.NumInputs(3)
.NumOutputs(3);
struct GetElementwiseLinearGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"ElementwiseLinearGradient",
"",
vector<string>{GO(0), I(0), I(1)},
vector<string>{GI(0), GI(1), GI(2)});
}
};
REGISTER_GRADIENT(
ElementwiseLinear,
GetElementwiseLinearGradient
);
} // namespace caffe2
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