[ Layer ] Move the Weight Read Function to Layer object #2856
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It is more flexible to moe Weight Read Fuction to Layer Object itself, rather than having layer_node. Sometimes, it is necessary to change the read weight depending on Layer Type, such as Batch normalization layer. This PR move general read implementation to Layer_devel and layer is able to override it as it needs. **Self evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: jijoong.moon <[email protected]>
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djeong20
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Jan 15, 2025
| } else { | ||
| for (unsigned int i = 0; i < run_context.getNumWeights(); ++i) { | ||
| /// @note shared weights are only be read at the first acecss | ||
| // if (run_context->isGradientLastAccess(i)) { |
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Suggested change
| // if (run_context->isGradientLastAccess(i)) { |
let's remove this line
Comment on lines
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to
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| TensorDim dim = run_context.getWeight(i).getDim(); | ||
| dim.setDataType(definedWeightDataType); | ||
| Tensor T_read(dim, true); |
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Suggested change
| TensorDim dim = run_context.getWeight(i).getDim(); | |
| dim.setDataType(definedWeightDataType); | |
| Tensor T_read(dim, true); | |
| Tensor T_read = run_context.getWeight(i).clone(definedWeightDataType); |
we can utilize Tensor::clone here :)
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It is more flexible to moe Weight Read Fuction to Layer Object itself,
rather than having layer_node. Sometimes, it is necessary to change
the read weight depending on Layer Type, such as Batch normalization
layer.
This PR move general read implementation to Layer_devel and layer is
able to override it as it needs.
Self evaluation:
Signed-off-by: jijoong.moon [email protected]