[GitHub] zheng-da commented on a change in pull request #9977: Cpu lstm inference
zheng-da commented on a change in pull request #9977: Cpu lstm inference
URL: https://github.com/apache/incubator-mxnet/pull/9977#discussion_r173038522
##
File path: src/operator/rnn-inl.h
##
@@ -287,8 +507,9 @@ class RNNProp : public OperatorProperty {
const std::vector &out_grad,
const std::vector &in_data,
const std::vector &out_data) const override {
-std::vector dep = {in_data[rnn_enum::kData],
in_data[rnn_enum::kParams],
-in_data[rnn_enum::kState], out_data[rnn_enum::kOut],
out_grad[rnn_enum::kOut]};
+std::vector dep = {in_data[rnn_enum::kData],
+ in_data[rnn_enum::kParams], in_data[rnn_enum::kState],
+ out_data[rnn_enum::kOut], out_grad[rnn_enum::kOut]};
Review comment:
the coding style in mxnet allows up to 100 char per line.
so the original code is fine.
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[GitHub] zheng-da commented on a change in pull request #9977: Cpu lstm inference
zheng-da commented on a change in pull request #9977: Cpu lstm inference
URL: https://github.com/apache/incubator-mxnet/pull/9977#discussion_r172948107
##
File path: src/operator/rnn-inl.h
##
@@ -144,15 +149,224 @@ class RNNOp : public Operator {
const std::vector &req,
const std::vector &in_grad,
const std::vector &aux_args) {
-using namespace mshadow;
-using namespace mshadow::expr;
// TODO(sbodenstein): add MShadow implementation
}
private:
RNNParam param_;
}; // class RNNOp
+template
+class RNNOp : public Operator {
+ public:
+ explicit RNNOp(RNNParam param) {
+this->param_ = param;
+// RNN Mode
+switch (param_.mode) {
+ case rnn_enum::kLstm:
+break;
+ default:
+LOG(FATAL) << "only LSTM is implmented on CPU";
+}
+if (param_.mode == rnn_enum::kLstm)
+ param_.lstm_q_ = true;
+else
+ param_.lstm_q_ = false;
Review comment:
it seems this check can be merged to the switch case statement above.
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[GitHub] zheng-da commented on a change in pull request #9977: Cpu lstm inference
zheng-da commented on a change in pull request #9977: Cpu lstm inference
URL: https://github.com/apache/incubator-mxnet/pull/9977#discussion_r172710204
##
File path: src/operator/rnn-inl.h
##
@@ -78,10 +82,12 @@ inline int rnn_param_size(int layerNum,
int size = rnn_single_param_size(inputSize, hiddenSize, mode);
// get size of remaining layers
if (bidirectional) {
-size += (layerNum - 1) * rnn_single_param_size(2 * hiddenSize, hiddenSize,
mode);
+size += (layerNum - 1) * rnn_single_param_size(2 * hiddenSize,
+hiddenSize, mode);
size *= 2;
} else {
-size += (layerNum - 1) * rnn_single_param_size(hiddenSize, hiddenSize,
mode);
+size += (layerNum - 1) * rnn_single_param_size(hiddenSize, hiddenSize,
+mode);
Review comment:
you are just reformatting the code here?
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[GitHub] zheng-da commented on a change in pull request #9977: Cpu lstm inference
zheng-da commented on a change in pull request #9977: Cpu lstm inference
URL: https://github.com/apache/incubator-mxnet/pull/9977#discussion_r172944904
##
File path: src/operator/rnn-inl.h
##
@@ -287,8 +507,9 @@ class RNNProp : public OperatorProperty {
const std::vector &out_grad,
const std::vector &in_data,
const std::vector &out_data) const override {
-std::vector dep = {in_data[rnn_enum::kData],
in_data[rnn_enum::kParams],
-in_data[rnn_enum::kState], out_data[rnn_enum::kOut],
out_grad[rnn_enum::kOut]};
+std::vector dep = {in_data[rnn_enum::kData],
+ in_data[rnn_enum::kParams], in_data[rnn_enum::kState],
+ out_data[rnn_enum::kOut], out_grad[rnn_enum::kOut]};
Review comment:
i'm not sure why you want to change the code in this function. it seems you
just reorganize the code a little bit.
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[GitHub] zheng-da commented on a change in pull request #9977: Cpu lstm inference
zheng-da commented on a change in pull request #9977: Cpu lstm inference
URL: https://github.com/apache/incubator-mxnet/pull/9977#discussion_r172712476
##
File path: src/operator/rnn-inl.h
##
@@ -144,15 +149,224 @@ class RNNOp : public Operator {
const std::vector &req,
const std::vector &in_grad,
const std::vector &aux_args) {
-using namespace mshadow;
-using namespace mshadow::expr;
// TODO(sbodenstein): add MShadow implementation
}
private:
RNNParam param_;
}; // class RNNOp
+template
+class RNNOp : public Operator {
+ public:
+ explicit RNNOp(RNNParam param) {
+this->param_ = param;
+// RNN Mode
+switch (param_.mode) {
+ case rnn_enum::kLstm:
+break;
+ default:
+LOG(FATAL) << "only LSTM is implmented on CPU";
+}
+if (param_.mode == rnn_enum::kLstm)
+ param_.lstm_q_ = true;
+else
+ param_.lstm_q_ = false;
+ }
+
+ virtual void Forward(const OpContext &ctx,
+ const std::vector &in_data,
+ const std::vector &req,
+ const std::vector &out_data,
+ const std::vector &aux_args) {
+// Layout TNC
+
+size_t in_expected = param_.lstm_q_ ? 4 : 3;
+size_t out_expected = param_.lstm_q_ ? 3 : 2;
+
+if (!param_.state_outputs)
+ LOG(FATAL) << "no state outputs is currently not supported for cpu.";
+
+CHECK_EQ(req[rnn_enum::kOut], kWriteTo);
+CHECK_EQ(in_data.size(), in_expected);
+CHECK_EQ(out_data.size(), out_expected);
+
+mshadow::Stream *s = ctx.get_stream();
+// get input + output tensors
+// w layout i2h_w, h2h_w, i2h_b, h2h_b
+Tensor x =
+in_data[rnn_enum::kData].get(s); // TNC
+Tensor w = in_data[rnn_enum::kParams].get(s);
+Tensor hx =
+in_data[rnn_enum::kState].get(s); // LNC
+Tensor y =
+out_data[rnn_enum::kOut].get(s); // TNC
+int64_t seq_len = x.shape_[0];
+int64_t num_layers = hx.shape_[0];
+int64_t batch_size = x.shape_[1];
+int64_t h_channel = hx.shape_[2];
+int64_t in_channel = x.shape_[2];
+Tensor x_flatten = in_data[rnn_enum::kData]
+ .get_with_shape(
+ mshadow::Shape2(seq_len * batch_size, in_channel), s); // (T*N)C
+Tensor y_flatten = out_data[rnn_enum::kOut]
+ .get_with_shape(
+ mshadow::Shape2(
+ y.shape_[0] * y.shape_[1], y.shape_[2]), s); // (T*N)C
+
+CHECK_EQ(x.CheckContiguous(), true);
+CHECK_EQ(w.CheckContiguous(), true);
+CHECK_EQ(hx.CheckContiguous(), true);
+CHECK_EQ(y.CheckContiguous(), true);
+
+if (ctx.is_train)
+ LOG(FATAL) << "only inference mode is available for cpu at the moment.";
Review comment:
you can do CHECK(!ctx.is_train) << "..."
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