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Use dereference operator in IR Values. #222

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Merged
merged 1 commit into from
Feb 11, 2019
Merged

Use dereference operator in IR Values. #222

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4 changes: 2 additions & 2 deletions torch_xla/csrc/ir.cpp
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Expand Up @@ -47,8 +47,8 @@ Node::Node(OpKind op, OpList operands, xla::Shape shape, size_t num_outputs,
hash_(xla::util::HashCombine(op_.hash(), hash_seed)) {
for (auto& operand : operands) {
AddOperand(operand.node, operand.index);
graph_size_ += operand.node->graph_size();
hash_ = xla::util::HashCombine(hash_, operand.node->hash());
graph_size_ += operand->graph_size();
hash_ = xla::util::HashCombine(hash_, operand->hash());
}
}

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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/avg_pool2d.cpp
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Expand Up @@ -26,7 +26,7 @@ xla::Shape NodeOutputShape(
return BuildAvgPool2d(operands[0], kernel_size, stride, padding,
count_include_pad);
};
return InferOutputShape({input.node->shape()}, lower_for_shape_fn);
return InferOutputShape({input->shape()}, lower_for_shape_fn);
}

} // namespace
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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/avg_pool2d_backward.cpp
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Expand Up @@ -26,7 +26,7 @@ xla::Shape NodeOutputShape(
/*input=*/operands[1], kernel_size, stride,
padding, count_include_pad);
};
return InferOutputShape({grad_output.node->shape(), input.node->shape()},
return InferOutputShape({grad_output->shape(), input->shape()},
lower_for_shape_fn);
}

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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/conv2d.cpp
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Expand Up @@ -26,7 +26,7 @@ xla::Shape NodeOutputShape(
return BuildConvolution(operands[0], operands[1], stride, padding,
xla::PrecisionConfig::DEFAULT);
};
return InferOutputShape({input.node->shape(), weight.node->shape()},
return InferOutputShape({input->shape(), weight->shape()},
lower_for_shape_fn);
}

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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/conv2d_backward.cpp
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Expand Up @@ -29,7 +29,7 @@ xla::Shape NodeOutputShape(
{grads.grad_input, grads.grad_weight, grads.grad_bias});
};
return InferOutputShape(
{grad_output.node->shape(), input.node->shape(), weight.node->shape()},
{grad_output->shape(), input->shape(), weight->shape()},
lower_for_shape_fn);
}

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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/cross_replica_sum.cpp
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Expand Up @@ -10,7 +10,7 @@ namespace ops {

CrossReplicaSum::CrossReplicaSum(const Value& operand,
std::vector<std::vector<xla::int64>> groups)
: Node(xla_cross_replica_sum, {operand}, operand.node->shape(),
: Node(xla_cross_replica_sum, {operand}, operand->shape(),
/*num_outputs=*/1, xla::util::MHash(groups)),
groups_(std::move(groups)) {}

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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/max_pool2d.cpp
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Expand Up @@ -24,7 +24,7 @@ xla::Shape NodeOutputShape(
<< "Unexpected number of operands: " << operands.size();
return BuildMaxPool2d(operands[0], kernel_size, stride, padding);
};
return InferOutputShape({input.node->shape()}, lower_for_shape_fn);
return InferOutputShape({input->shape()}, lower_for_shape_fn);
}

} // namespace
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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/max_pool2d_backward.cpp
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Expand Up @@ -24,7 +24,7 @@ xla::Shape NodeOutputShape(
/*input=*/operands[1], kernel_size, stride,
padding);
};
return InferOutputShape({grad_output.node->shape(), input.node->shape()},
return InferOutputShape({grad_output->shape(), input->shape()},
lower_for_shape_fn);
}

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12 changes: 6 additions & 6 deletions torch_xla/csrc/ops/ops.cpp
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Expand Up @@ -24,7 +24,7 @@ NodePtr ReluOp(const Value& input) {
return BuildRelu(operands[0]);
};
xla::Shape output_shape =
ir::ops::InferOutputShape({input.node->shape()}, lower_for_shape_fn);
ir::ops::InferOutputShape({input->shape()}, lower_for_shape_fn);
return ir::ops::GenericOp(ir::OpKind(at::aten::relu), ir::OpList{input},
output_shape, std::move(lower_fn));
}
Expand All @@ -42,7 +42,7 @@ NodePtr TransposeOp(const Value& input) {
return xla::Transpose(operands[0], {1, 0});
};
xla::Shape output_shape =
ir::ops::InferOutputShape({input.node->shape()}, lower_for_shape_fn);
ir::ops::InferOutputShape({input->shape()}, lower_for_shape_fn);
return ir::ops::GenericOp(ir::OpKind(at::aten::t), ir::OpList{input},
output_shape, std::move(lower_fn));
}
Expand Down Expand Up @@ -78,7 +78,7 @@ NodePtr AddMatMulOp(const Value& input, const Value& weight, const Value& bias,
return xla::Dot(operands[0], operands[1]);
};
xla::Shape output_shape = ir::ops::InferOutputShape(
{input.node->shape(), weight.node->shape()}, lower_for_shape_fn);
{input->shape(), weight->shape()}, lower_for_shape_fn);
return ir::ops::GenericOp(ir::OpKind(at::aten::addmm),
ir::OpList{input, weight, bias}, output_shape,
std::move(lower_fn));
Expand Down Expand Up @@ -106,7 +106,7 @@ NodePtr MatMulOp(const Value& input, const Value& weight,
return xla::Dot(operands[0], operands[1]);
};
xla::Shape output_shape = ir::ops::InferOutputShape(
{input.node->shape(), weight.node->shape()}, lower_for_shape_fn);
{input->shape(), weight->shape()}, lower_for_shape_fn);
return ir::ops::GenericOp(ir::OpKind(at::aten::mm), ir::OpList{input, weight},
output_shape, std::move(lower_fn));
}
Expand All @@ -125,7 +125,7 @@ NodePtr NllLossOp(const Value& logits, const Value& labels) {
return BuildNllLoss(/*logits=*/operands[0], /*labels=*/operands[1]);
};
xla::Shape output_shape = ir::ops::InferOutputShape(
{logits.node->shape(), labels.node->shape()}, lower_for_shape_fn);
{logits->shape(), labels->shape()}, lower_for_shape_fn);
return ir::ops::GenericOp(ir::OpKind(at::aten::nll_loss),
ir::OpList{logits, labels}, output_shape,
std::move(lower_fn));
Expand All @@ -145,7 +145,7 @@ NodePtr NllLossBackwardOp(const Value& logits, const Value& labels) {
return BuildNllLossBackward(/*logits=*/operands[0], /*labels=*/operands[1]);
};
xla::Shape output_shape = ir::ops::InferOutputShape(
{logits.node->shape(), labels.node->shape()}, lower_for_shape_fn);
{logits->shape(), labels->shape()}, lower_for_shape_fn);
return ir::ops::GenericOp(ir::OpKind(at::aten::nll_loss_backward),
ir::OpList{logits, labels}, output_shape,
std::move(lower_fn));
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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/softmax.cpp
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Expand Up @@ -19,7 +19,7 @@ xla::Shape NodeOutputShape(const Value& input, xla::int64 dim) {
<< "Unexpected number of operands: " << operands.size();
return BuildLogSoftmax(operands[0], dim);
};
return InferOutputShape({input.node->shape()}, lower_for_shape_fn);
return InferOutputShape({input->shape()}, lower_for_shape_fn);
}

} // namespace
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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/threshold.cpp
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Expand Up @@ -8,7 +8,7 @@ namespace ir {
namespace ops {

Threshold::Threshold(const Value& input, float threshold, float value)
: Node(ir::OpKind(at::aten::threshold), {input}, input.node->shape(),
: Node(ir::OpKind(at::aten::threshold), {input}, input->shape(),
/*num_outputs=*/1, xla::util::MHash(threshold, value)),
threshold_(threshold),
value_(value) {}
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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/threshold_backward.cpp
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Expand Up @@ -10,7 +10,7 @@ namespace ops {
ThresholdBackward::ThresholdBackward(const Value& grad_output,
const Value& input, float threshold)
: Node(ir::OpKind(at::aten::threshold_backward), {grad_output, input},
input.node->shape(), /*num_outputs=*/1, xla::util::MHash(threshold)),
input->shape(), /*num_outputs=*/1, xla::util::MHash(threshold)),
threshold_(threshold) {}

XlaOpVector ThresholdBackward::Lower(LoweringContext* loctx) const {
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2 changes: 1 addition & 1 deletion torch_xla/csrc/ops/view.cpp
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Expand Up @@ -22,7 +22,7 @@ xla::Shape NodeOutputShape(
<< "Unexpected number of operands: " << operands.size();
return BuildView(operands[0], output_sizes);
};
return InferOutputShape({input.node->shape()}, lower_for_shape_fn);
return InferOutputShape({input->shape()}, lower_for_shape_fn);
}

} // namespace
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