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Results 1 - 2 of 2 for affine_user (0.22 sec)

  1. tensorflow/compiler/mlir/lite/transforms/prepare_quantize_dynamic_range.cc

        int quantize_operand_num = quant_op.second;
    
        auto affine_user = dyn_cast<AffineQuantizedOpInterface>(quantize_op);
    
        bool op_with_per_axis_support = false;
    
        if (!llvm::dyn_cast_or_null<CustomOp>(quantize_op)) {
          bool op_with_narrow_range =
              affine_user &&
              affine_user.GetAffineOperandIndex() == quantize_operand_num &&
              affine_user.RequiredNarrowRangeAffineOperand();
    
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Thu Apr 25 16:01:03 UTC 2024
    - 20.8K bytes
    - Viewed (0)
  2. tensorflow/compiler/mlir/lite/flatbuffer_import.cc

          Operation* user = use.getOwner();
          if (user->hasTrait<mlir::OpTrait::IsTerminator>()) continue;
    
          auto affine_user = llvm::dyn_cast<mlir::AffineQuantizedOpInterface>(user);
          if (affine_user &&
              affine_user.GetAffineOperandIndex() == use.getOperandNumber() &&
              affine_user.RequiredNarrowRangeAffineOperand())
            continue;
          // Create a fully range quantized constant.
    Registered: Sun Jun 16 05:45:23 UTC 2024
    - Last Modified: Tue May 21 18:21:50 UTC 2024
    - 66.8K bytes
    - Viewed (0)
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