xidulu commented on a change in pull request #18852:
URL: https://github.com/apache/incubator-mxnet/pull/18852#discussion_r468292891
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File path: src/operator/numpy/random/np_gamma_op.h
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@@ -394,6 +401,83 @@ void NumpyGammaForward(const nnvm::NodeAttrs &attrs, const
OpContext &ctx,
}
}
+template<typename xpu, int ndim, typename DType>
+inline void GammaReparamBackwardImpl(const OpContext& ctx,
+ const std::vector<TBlob>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<TBlob>& outputs,
+ const mxnet::TShape& new_ishape,
+ const mxnet::TShape& new_oshape,
+ const float scale) {
+ using namespace mshadow;
+ using namespace mshadow::expr;
+ using namespace broadcast;
+ using namespace mxnet_op;
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+ const TBlob igrad = outputs[0].reshape(new_ishape);
+ // inputs: [grad_from_samples, alpha_tensor, samples]
+ const TBlob ograd = inputs[0].reshape(new_oshape);
+ const TBlob alpha = inputs[1].reshape(new_ishape);
+ TBlob samples = inputs[2].reshape(new_oshape);
+ size_t workspace_size =
+ ReduceWorkspaceSize<ndim, DType>(s, igrad.shape_, req[0], ograd.shape_);
+ // Convert samples to standard gamma
+ // Kernel<StandarizeKernel<DType>, xpu>::Launch(
+ // s, samples.Size(), samples.dptr<DType>(), scale);
+ Kernel<op_with_req<mshadow_op::div, kWriteTo>, xpu>::Launch(
+ s, samples.Size(), samples.dptr<DType>(), samples.dptr<DType>(),
DType(scale));
+ Tensor<xpu, 1, char> workspace =
+ ctx.requested[0].get_space_typed<xpu, 1, char>(Shape1(workspace_size),
s);
+ Reduce<red::sum, ndim, DType, op::mshadow_op::mul,
op::mshadow_op::gamma_implicit_grad>(
Review comment:
This stands for the multiplication between d(Gamma(x;\alpha, \beta)) and
gradient from downstream Ops. Similar to
https://github.com/apache/incubator-mxnet/blob/743bbcbc7c8c85661a146d94ebd3196306650677/src/operator/tensor/elemwise_binary_broadcast_op.h#L745
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