// custom op is quantized only for storage and dequantized at runtime.
// 'no_side_effect' is used to determine whether the op can be pruned if
// considered as trivially dead.
struct CustomOpInfo {
  std::vector<std::int32_t> quantizable_input_indices;
  bool is_weight_only = false;
  bool no_side_effect = true;
};

using BuiltinOperatorSet = absl::flat_hash_set<tflite::BuiltinOperator>;

        entry.second.quantizable_input_indices;
    quant_specs.custom_map[entry.first].is_weight_only =
        entry.second.is_weight_only;
    quant_specs.custom_map[entry.first].no_side_effect =
        entry.second.no_side_effect;
  }

  if (quant_specs.inference_type == tensorflow::DT_INT8)
    quant_specs.inference_type = tensorflow::DT_QINT8;

  if (!(quant_specs.inference_type == tensorflow::DT_HALF ||

        custom_op_map[node_name].is_weight_only =
            GetBooleanSpecs(node_specification);
        break;
      case CustomOpUpdateOptions::kNoSideEffect:
        custom_op_map[node_name].no_side_effect =
            GetBooleanSpecs(node_specification);
        break;
    }
  }
}

bool ParseInputNodeQuantSpecs(const absl::string_view node_names,

namespace quant {

// Stores information about how to quantize a user-specified custom operation.
struct CustomOpInfo {
  std::vector<std::int32_t> quantizable_input_indices;
  bool is_weight_only = false;
  bool no_side_effect = true;
};

using CustomOpMap = std::unordered_map<std::string, CustomOpInfo>;
enum CustomOpUpdateOptions { kInputIndices, kWeightOnly, kNoSideEffect };
enum class QDQConversionMode { kQDQNone, kQDQStatic, kQDQDynamic };

    }
    // Remove if the custom op is in the provided map and is NoSideEffect.
    auto custom_op = llvm::isa<CustomOp>(op);
    if (custom_op) {
      auto q = llvm::cast<CustomOp>(op);
      std::string op_name = q.getCustomCode().str();
      if ((custom_op_map.find(op_name) == custom_op_map.end()) ||
          !custom_op_map.find(op_name)->second.no_side_effect)
        return failure();
    }
    rewriter.eraseOp(op);

Tres Popp <******@****.***> 1648100645 -0700

			Name:         "webhook.k8s.io",
			ClientConfig: validClientConfig,
			SideEffects:  &noSideEffect,
		}, {
			Name:         "k8s.io",
			ClientConfig: validClientConfig,
			SideEffects:  &noSideEffect,
		}, {
			Name:         "",
			ClientConfig: validClientConfig,
			SideEffects:  &noSideEffect,
		},
		}, true),

// NoSideEffect: %[[q_w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
// NoSideEffect: %[[custom:.*]] = "tfl.custom"(%arg0, %[[q_w:.*]]) <{custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">}
// NoSideEffect: return %[[custom:.*]]

             "Enable canonicalization during optimization pass.">,
      Option<"enable_custom_op_no_side_effect_", "enable-no-side-effect",
            "std::string", "", "Specifies which custom ops are NoSideEffect.">,
  ];
}

def PostQuantizeRemoveQDQPass : Pass<"tfl-post-quantize-remove-qdq", "mlir::func::FuncOp"> {
  let summary = "Remove qdq from input and output nodes after quantization.";