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[PIR] Configure support_trans_dtype for ops.yaml according to phi op/dygraph by chen2016013 · Pull Request #60844 · PaddlePaddle/Paddle · GitHub
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[PIR] Configure complex_promote and support_trans_dtype for ops.yaml according to phi op/dygraph
[PIR] Configure support_trans_dtype for ops.yaml according to phi op/dygraph
Jan 17, 2024
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[PIR] Configure support_trans_dtype for ops.yaml according to phi op/dygraph #60844
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[PIR] Configure support_trans_dtype for ops.yaml according to phi op/dygraph #60844
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你的PR提交成功，感谢你对开源项目的贡献! |
paddle/phi/api/yaml/op_compat.yaml
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| @@ -106,6 +106,9 @@ | |||
| bool use_quantizer = false, float Scale_x = 1.0f, float Scale_y = 1.0f, float Scale_out = 1.0f] | |||
| complex_promote : [X, Y] | |||
|
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| - op : add_grad (elementwise_add_grad), add_double_grad (elementwise_add_grad_grad), add_triple_grad (elementwise_add_triple_grad) | |||
| complex_promote : [x, y, out_grad] | |||
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原始静态图逻辑仅仅根据out_grad选kernel的dtype，这里修改后改变了原来的逻辑
phi::KernelKey GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type = OperatorWithKernel::IndicateVarDataType(
ctx, framework::GradVarName("Out"));
return phi::KernelKey(input_data_type, ctx.GetPlace());
}
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Description
Pcard-67164
在yaml中为op配置complex_promote and support_trans_dtype ，对齐动态图和旧IR逻辑，参考paddle/fluid/pybind/eager_math_op_patch.cc和phi::KernelKey GetKernelTypeForVar。其中以下op实际存在dtype transfer逻辑，相应在ops.yaml中配置