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# pylint: disable=invalid-name, import-outside-toplevel
""" Functions to convert quantized torch models to QNN """
import logging

import numpy as np

import tvm
from tvm import relay
from tvm.relay import expr as _expr
from tvm.relay import op as _op
from tvm.relay.frontend.common import infer_shape


class QNNParam:
    """ A placeholder for weight quantization parameters """

    def __init__(self, weight, bias, scale, zero_point, param_key):
        param_prefix = param_key[:-len("._packed_params")]
        self.weight_var = _expr.var(param_prefix + "_weight",
                                    shape=weight.shape)
        self.weight = weight

        if bias is not None:
            self.bias_var = _expr.var(param_prefix + "_bias",
                                      shape=bias.shape)
            self.bias = bias.detach().numpy()
        else:
            self.bias_var = None
            self.bias = None

        self.scale = _expr.const(scale)
        self.zero_point = _expr.const(zero_point, dtype="int32")


def _unpack_quant_params(param_name, packed_params, unpack_func):
    # Torch stores quantized params in a custom packed format,
    # need to unpack and retrieve them as numpy arrays
    qweight, bias = unpack_func(packed_params)
    weight_np = qweight.dequantize().numpy()

    import torch
    if qweight.qscheme() == torch.per_tensor_affine:
        param = QNNParam(weight_np, bias, qweight.q_scale(),
                         int(qweight.q_zero_point()), param_name)
    else:
        scales = qweight.q_per_channel_scales().numpy()
        zero_points = qweight.q_per_channel_zero_points().numpy()
        # This is an assumption posed by QNN
        msg = "The values of zero points should be all zero for per channel"
        assert np.all(zero_points == 0), msg
        param = QNNParam(weight_np, bias, scales, 0, param_name)

    return param


def get_weight_quant_params(script_module):
    """ Retrive and unpack weight parameters from quantized modules """
    conv_packed_params = []
    linear_packed_params = []

    import torch
    # conv and linear requires different unpacking function
    # extract all conv and linear parameters separately to distinguish them
    for name, m in script_module.named_modules():
        if isinstance(m, torch.jit.RecursiveScriptModule):
            if "Conv" in m.original_name:
                conv_packed_params.append((name, m.state_dict()))
            elif m.original_name == "LinearPackedParams":
                linear_packed_params.append((name, m.state_dict()))

    pairs = [(torch.ops.quantized.conv2d_unpack, conv_packed_params),
             (torch.ops.quantized.linear_unpack, linear_packed_params)]

    quant_params = {}
    param_name = "_packed_params"
    for unpack_func, params in pairs:
        for name, state_dict in params:
            assert len(state_dict) == 1
            assert param_name in state_dict
            key = name + "." + param_name
            packed_param = state_dict[param_name]
            quant_params[key] = _unpack_quant_params(key, packed_param,
                                                     unpack_func)

    return quant_params


def add_quant_params_to_outputs(outputs, packed_param_map,
                                quant_params):
    """
    Add quant params to outputs so that they can be referenced by other
    ops later. Weights are quantized here.
    """
    for node_name, packed_param_name in packed_param_map.items():
        qparam = quant_params[packed_param_name]
        qweight = relay.qnn.op.quantize(qparam.weight_var, qparam.scale,
                                        qparam.zero_point, out_dtype="int8",
                                        axis=0)
        param_tup = (qweight, qparam.scale, qparam.zero_point, qparam.bias_var)
        outputs[node_name] = param_tup


def _get_quant_param_for_input(input_value):
    """
    We want to know the input scale and zp of this input_value, since
    input quant params are not explicitly passed around in torch (they
    are embeded in a QTensor data structure, not visible statically).
    We know that it is quantized using output scale and zp
    of some previous quantized op. The purpose of this function
    is to find that pair of parameters.
    """
    # Indices for output scale and zp
    # For example, in quantized::conv2d(%input, %1, %2, %3, %4, %5, %6, %7),
    # 6th and 7th arg are output scale and zp respectively.
    output_quant_param_indices = {
        "aten::quantize_per_tensor": (1, 2),
        "quantized::conv2d": (6, 7),
        "quantized::conv2d_relu": (6, 7),
        "quantized::linear": (2, 3),
        "quantized::linear_relu": (2, 3),
        "quantized::add_relu": (2, 3),
        "quantized::add": (2, 3),
        "quantized::mul_relu": (2, 3),
        "quantized::mul": (2, 3),
        "quantized::cat": (2, 3),
        "quantized::mul_scalar": (2, 3),
        "quantized::add_scalar": (2, 3)
    }

    def dfs(current_node):
        # trace back to find the producer of this input value
        current_op = current_node.kind()
        if current_op in output_quant_param_indices:
            indices = output_quant_param_indices[current_op]
            scale = current_node.inputsAt(indices[0])
            zp = current_node.inputsAt(indices[1])
            return scale, zp

        # Trace back eariler nodes, dfs order
        # Assume quantized tensor comes earlier in the args
        for arg in current_node.inputs():
            return dfs(arg.node())

        # shouldn't happen
        assert False, "No producer for %s" % (str(current_node))

    return dfs(input_value.node())


def _get_add_scalar_output_quant_param(input_scale, input_zero_point,
                                       scalar):
    """
    Determine the output scale and zp of quantized::add_scalar op
    This is used for mobilenet v3
    Refer to aten/src/ATen/native/quantized/cpu/qadd.cpp
    The names of variables are the same as torch impl
    """
    q_min = 0
    q_max = 255
    s = input_scale
    z = input_zero_point
    c = scalar
    c_q = round(c / s)

    if q_min > z - c_q:
        s_prime = (float(q_max) - (z - c_q)) / (float(q_max) - q_min) * s
        z_prime = q_min
    elif q_max < z - c_q:
        s_prime = (float(z - c_q) - q_min) / (float(q_max) - q_min) * s
        z_prime = q_max
    else:
        s_prime = s
        z_prime = z - c_q

    return s_prime, z_prime


def _get_mul_scalar_output_quant_param(input_scale, input_zero_point,
                                       scalar):
    """
    Determine the output scale and zp of quantized::mul_scalar op
    This is used for mobilenet v3
    Refer to aten/src/ATen/native/quantized/cpu/qmul.cpp
    The names of variables are the same as torch impl
    """
    q_min = 0
    q_max = 255
    self_scale = input_scale
    self_zero_point = input_zero_point
    other_val = scalar

    if other_val > 0.0:
        s_prime = other_val * self_scale
        z_prime = self_zero_point
    elif other_val == 0.0:
        s_prime = 1.0
        z_prime = 0
    else:
        s_prime = abs(other_val) * self_scale
        z_prime = q_max - (self_zero_point - q_min)

    return s_prime, z_prime


def _add_output_quant_params_to_scalar_op(node, graph,
                                          input_scale, input_zero_point,
                                          scalar):
    """
    The output scale and zp of {add,mul}_scalar op are not explicit in the IR
    They are required for _get_quant_param_for_input above to work correctly
    So calculate these params using the same way torch does, and make new
    constant nodes in the input IR. Also add these params to the inputs of
    scalar op.

    For example,
       %6 : float = prim::Constant[value=3.]()
       %input : QUInt8(1, 3, 224, 224) = quantized::add_scalar(%x.1, %6)
    becomes
       %6 : float = prim::Constant[value=3.]()
       %7 : float = prim::Constant[value=0.015686161816120148]()
       %8 : int = prim::Constant[value=0]()
       %input : UInt8(1, 3, 224, 224) = quantized::add_scalar(%x.1, %6, %7, %8)

    %7 and %8 are newly created output scale and zp constant nodes
    """
    import torch
    operator = node.kind()

    if operator == "quantized::mul_scalar":
        out_scale, out_zero_point = \
          _get_mul_scalar_output_quant_param(input_scale, input_zero_point,
                                             scalar)
    elif operator == "quantized::add_scalar":
        out_scale, out_zero_point = \
          _get_add_scalar_output_quant_param(input_scale, input_zero_point,
                                             scalar)
    else:
        raise NotImplementedError("unsupported scalar op: %s" % operator)

    # create new constant nodes and add them to graph
    out_scale_node = graph.create("prim::Constant")
    out_zero_point_node = graph.create("prim::Constant")
    out_scale_node.insertBefore(node)
    out_zero_point_node.insertBefore(node)
    out_scale_node.f_("value", out_scale)
    out_zero_point_node.i_("value", out_zero_point)
    out_scale_node.output().setType(torch._C.FloatType.get())
    out_zero_point_node.output().setType(torch._C.IntType.get())
    node.addInput(out_scale_node.output())
    node.addInput(out_zero_point_node.output())


def add_input_quant_params_to_op_inputs(graph):
    """
    In Torch, input quant params are not explicitly passed around
    Instead, they are stored in QTensor data structure, and retrieved
    at runtime by each quantized ops.
    However, they need to be known statically for QNN translation.
    To workaround and simplify the translation of inputs, we manually add
    input quant params to inputs of Torch quantized operators listed below.
    See _quantized_conv2d() below for example of why this is helpful.

    For example,
      %input : QUInt8(1, 512, 7, 7) = quantized::add(%x.8, %x.9, %434, %435)
    becomes
      %395 : float = prim::Constant[value=0.036212071776390076]()
      %396 : int = prim::Constant[value=0]()
      %430 : float = prim::Constant[value=0.16080744564533234]()
      %431 : int = prim::Constant[value=42]()
      %input : QUInt8(1, 512, 7, 7) = quantized::add(%x.8, %x.9, %434, %435,
                                                     %430, %431, %395, %396)

    %434, %435 are output scale and zp of quantized::add op
    %430, %431, %395, %396 are two pairs of input (scale, zp) for two tensors
    added by this function
    """
    # How many quantized tensors each op takes as inputs?
    # A pair of (scale, zp) for each input quantized tensor will be added
    # to the input nodes
    num_quantized_inputs = {"quantized::conv2d": 1,
                            "quantized::conv2d_relu": 1,
                            "quantized::linear": 1,
                            "quantized::linear_relu": 1,
                            "quantized::add_relu": 2,
                            "quantized::add": 2,
                            "quantized::mul_relu": 2,
                            "quantized::mul": 2,
                            "aten::dequantize": 1,
                            "aten::mean": 1,
                            "aten::upsample_bilinear2d": 1,
                            "aten::relu_": 1,
                            "aten::relu": 1,
                            "quantized::add_scalar": 1,
                            "quantized::mul_scalar": 1,
                            'quantized::relu6': 1}

    need_input_quant_param = set(num_quantized_inputs.keys())
    need_input_quant_param.add("quantized::cat")

    for node in graph.nodes():
        operator = node.kind()
        if operator not in need_input_quant_param:
            continue

        input_scales = []
        input_zero_points = []

        if operator == "quantized::cat":
            # the number of inputs to concat is not constant
            # so handle it separately
            inputs = node.inputsAt(0).node().inputs()
            for inp in inputs:
                scale, zp = _get_quant_param_for_input(inp)
                input_scales.append(scale)
                input_zero_points.append(zp)
        else:
            for i in range(num_quantized_inputs[operator]):
                scale, zp = _get_quant_param_for_input(node.inputsAt(i))
                input_scales.append(scale)
                input_zero_points.append(zp)

        if operator in ["quantized::add_scalar", "quantized::mul_scalar"]:
            scalar = node.inputsAt(1).node().f("value")
            inp_scale = input_scales[0].node().f("value")
            inp_zero_point = input_zero_points[0].node().i("value")

            # see the comments in this function above
            _add_output_quant_params_to_scalar_op(node, graph,
                                                  inp_scale, inp_zero_point,
                                                  scalar)

        for scale, zp in zip(input_scales, input_zero_points):
            node.addInput(scale)
            node.addInput(zp)


def add_quant_params(params, quant_params):
    """ Add quant parameters to TVM param map """
    for qparam in quant_params.values():
        params[qparam.weight_var.name_hint] = tvm.nd.array(qparam.weight)
        if qparam.bias is not None:
            params[qparam.bias_var.name_hint] = tvm.nd.array(qparam.bias)


def apply_with_upcast(data, func):
    inp = _op.cast(data, dtype="int32")
    out = func(inp)
    return _op.cast(out, "uint8")


def quantized_mean(data, input_scale, input_zero_point, func_fp32):
    # refer to aten/src/ATen/native/quantized/cpu/qreduction.cpp
    dequantized = relay.qnn.op.dequantize(data, input_scale, input_zero_point)
    out = func_fp32(dequantized)
    return relay.qnn.op.quantize(out, input_scale, input_zero_point,
                                 out_dtype="uint8", axis=1)


def quantized_upsample(data, input_scale, input_zero_point, func_fp32):
    # currently piggy backs to fp32, it gets identical output as torch
    data = relay.qnn.op.dequantize(data, input_scale, input_zero_point)
    out = func_fp32(data)
    return relay.qnn.op.quantize(out, input_scale, input_zero_point,
                                 out_dtype="uint8", axis=1)


def quantized_relu(data, input_zero_point):
    # refer to aten/src/ATen/native/quantized/cpu/qrelu.cpp
    zp = _op.cast(input_zero_point, dtype="uint8")
    return _op.tensor.maximum(data, zp)


def _quantize_per_tensor():
    def _impl(inputs, _):
        return relay.qnn.op.quantize(inputs[0], _expr.const(inputs[1]),
                                     _expr.const(inputs[2]), out_dtype="uint8",
                                     axis=1)
    return _impl


def _dequantize():
    def _impl(inputs, _):
        assert len(inputs) == 3, "Input quant params not found in op inputs"
        inp_scale = _expr.const(inputs[1])
        inp_zero_point = _expr.const(inputs[2])
        return relay.qnn.op.dequantize(inputs[0], inp_scale, inp_zero_point)
    return _impl


def _get_numpy(relay_const_scalar):
    return relay_const_scalar.data.asnumpy()


def _get_scalar(relay_const_scalar):
    return np.asscalar(_get_numpy(relay_const_scalar))


def _do_bias_and_requantize(output, bias, input_scale, weight_scale,
                            output_scale, output_zero_point,
                            with_relu):
    """ Output processing for conv and linear """
    # this is a vector for per channel case
    requant_input_scale = _expr.const(_get_numpy(input_scale) *
                                      _get_numpy(weight_scale))
    # Torch does bias add and requanize scale in fp32
    # refer to third_party/fbgemm/include/fbgemm/OutputProcessing-inl.h
    # Instead, we do bias add in int32 and use qnn requantize, which needs
    # integer input.
    # We observed no loss in accuracy in doing this way, and it is better
    # for tvm because bias quantization can be done at compile time
    # Instead, the torch way requires rounding of activation at runtime

    if bias is not None:
        qbias = relay.qnn.op.quantize(bias, requant_input_scale,
                                      _expr.const(0, "int32"),
                                      out_dtype="int32", axis=0)
        requantize_input = _op.nn.bias_add(output, qbias)
    else:
        requantize_input = output

    requantized = relay.qnn.op.requantize(requantize_input,
                                          requant_input_scale,
                                          relay.const(0, 'int32'),
                                          output_scale, output_zero_point,
                                          out_dtype="int32", axis=1)
    clip_min = 0
    if with_relu:
        clip_min = _get_scalar(output_zero_point)

    clip = _op.tensor.clip(requantized, clip_min, 255.)
    return _op.cast(clip, dtype="uint8")


def _quantized_conv2d(with_relu=False):
    def _impl(inputs, _):
        # refer to src/ATen/native/quantized/cpu/qconv.cpp
        # inputs[0]: input tensor
        # inputs[1]: (weight, scale, zero_point, bias)
        # inputs[2-5]: stride, padding, dilation, groups
        # inputs[6]: output_scale
        # inputs[7]: output_zero_point
        # inputs[8]: input_scale (added manually by frontend)
        # inputs[9]: input_zero_point (added manually by frontend)
        weight = inputs[1][0]
        weight_scale = inputs[1][1]
        weight_zero_point = inputs[1][2]

        output_scale = _expr.const(inputs[6])
        output_zero_point = _expr.const(inputs[7])

        assert len(inputs) == 10, "Input quant params not found in op inputs"
        # These are manually added by add_input_quant_params_to_op_inputs above
        # In torch, they are retrieved from QTensor data structure at runtime
        input_scale = _expr.const(inputs[8])
        input_zero_point = _expr.const(inputs[9])

        strides, padding, dilation = inputs[2], inputs[3], inputs[4]
        strides = infer_shape(inputs[2])
        padding = infer_shape(inputs[3])
        dilation = infer_shape(inputs[4])
        groups = inputs[5]

        weight_shape = infer_shape(weight)
        kernel_size = (weight_shape[2], weight_shape[3])
        out_channels = weight_shape[0]

        if padding[0] != 0 or padding[1] != 0:
            pad_val = _get_scalar(input_zero_point)
            inp = _op.nn.pad(inputs[0], pad_width=((0, 0),
                                                   (0, 0),
                                                   (padding[0], padding[0]),
                                                   (padding[1], padding[1])),
                             pad_value=float(pad_val))
        else:
            inp = inputs[0]

        # padding is (0, 0) because we did explicit pad op with
        # pad value being zero point above
        conv_out = relay.qnn.op.conv2d(inp, weight,
                                       input_zero_point, weight_zero_point,
                                       input_scale, weight_scale,
                                       kernel_size=kernel_size,
                                       dilation=dilation, strides=strides,
                                       padding=(0, 0), groups=groups,
                                       channels=out_channels)
        bias_var = inputs[1][3]

        return _do_bias_and_requantize(conv_out, bias_var, input_scale,
                                       weight_scale, output_scale,
                                       output_zero_point, with_relu)

    return _impl


def _linear(with_relu=False):
    # similar to conv
    def _impl(inputs, _):
        weight = inputs[1][0]
        weight_scale = inputs[1][1]
        weight_zero_point = inputs[1][2]
        output_scale = _expr.const(inputs[2])
        output_zero_point = _expr.const(inputs[3])
        assert len(inputs) == 6, "Input quant params not found in op inputs"
        # Manually added by add_input_quant_params_to_op_inputs above
        input_scale = _expr.const(inputs[4])
        input_zero_point = _expr.const(inputs[5])

        weight_shape = infer_shape(weight)
        dense = relay.qnn.op.dense(inputs[0], weight,
                                   input_zero_point, weight_zero_point,
                                   input_scale, weight_scale,
                                   units=weight_shape[0])
        bias_var = inputs[1][3]

        return _do_bias_and_requantize(dense, bias_var, input_scale,
                                       weight_scale, output_scale,
                                       output_zero_point, with_relu)

    return _impl


def _binop(relay_op, with_relu=False, fp32_piggy_back=False):
    def qnn_impl(lhs, rhs, input_scale_lhs, input_zero_point_lhs,
                 input_scale_rhs, input_zero_point_rhs,
                 output_scale, output_zero_point):
        qnn_out = relay_op(lhs, rhs, input_scale_lhs, input_zero_point_lhs,
                           input_scale_rhs, input_zero_point_rhs,
                           output_scale, output_zero_point)
        if with_relu:
            clip_min = _get_scalar(output_zero_point)
            return _op.tensor.clip(qnn_out, clip_min, 255)
        return qnn_out

    # refer to aten/src/ATen/native/quantized/cpu/{qadd, qmul}.cpp
    # they piggy backs to fp32 math by dequantize -> fp32 math -> quantize
    def torch_impl(lhs, rhs, input_scale_lhs, input_zero_point_lhs,
                   input_scale_rhs, input_zero_point_rhs,
                   output_scale, output_zero_point):
        if isinstance(lhs, _expr.Call) and lhs.op.name == 'qnn.quantize':
            lhs = lhs.args[0]
        else:
            lhs = relay.qnn.op.dequantize(lhs,
                                          input_scale_lhs,
                                          input_zero_point_lhs)

        if isinstance(rhs, _expr.Call) and rhs.op.name == 'qnn.quantize':
            rhs = rhs.args[0]
        else:
            rhs = relay.qnn.op.dequantize(rhs,
                                          input_scale_rhs,
                                          input_zero_point_rhs)
        fp32_out = relay_op(lhs, rhs)

        if with_relu:
            fp32_out = _op.nn.relu(fp32_out)

        return relay.qnn.op.quantize(fp32_out,
                                     output_scale,
                                     output_zero_point,
                                     axis=-1,
                                     out_dtype="uint8")

    def _impl(inputs, _):
        lhs = inputs[0]
        rhs = inputs[1]
        output_scale = _expr.const(inputs[2])
        output_zero_point = _expr.const(inputs[3])
        assert len(inputs) == 8, "Input quant params not found in op inputs"
        # Manually added by add_input_quant_params_to_op_inputs above
        input_scale_lhs = _expr.const(inputs[4])
        input_zero_point_lhs = _expr.const(inputs[5])
        input_scale_rhs = _expr.const(inputs[6])
        input_zero_point_rhs = _expr.const(inputs[7])

        if fp32_piggy_back:
            logging.info("Piggy backing to FP32 op (PyTorch way)")
            return torch_impl(lhs, rhs, input_scale_lhs, input_zero_point_lhs,
                              input_scale_rhs, input_zero_point_rhs,
                              output_scale, output_zero_point)

        return qnn_impl(lhs, rhs, input_scale_lhs, input_zero_point_lhs,
                        input_scale_rhs, input_zero_point_rhs,
                        output_scale, output_zero_point)

    return _impl


def _cat(fp32_piggy_back=False):
    # refer to aten/src/ATen/native/quantized/cpu/qconcat.cpp
    # for concat they also piggy backs to fp32(!)
    # dequantize -> fp32 math -> quantize
    def torch_impl(inputs, input_scales, input_zero_points,
                   output_scale, output_zero_point, axis):
        dequantized = []
        for inp, inp_scale, inp_zp in zip(inputs, input_scales,
                                          input_zero_points):
            dequantized.append(relay.qnn.op.dequantize(inp, inp_scale, inp_zp))

        concat = _op.tensor.concatenate(dequantized, axis=axis)
        return relay.qnn.op.quantize(concat, output_scale, output_zero_point,
                                     axis=axis, out_dtype="uint8")

    def _impl(inputs, _):
        axis = inputs[1]
        output_scale = _expr.const(inputs[2])
        output_zero_point = _expr.const(inputs[3])
        num_inputs = (len(inputs) - 4) // 2

        input_scales = []
        input_zero_points = []

        for i in range(0, num_inputs):
            input_scales.append(_expr.const(inputs[4+i*2]))
            input_zero_points.append(_expr.const(inputs[4+i*2+1]))

        if fp32_piggy_back:
            return torch_impl(inputs[0], input_scales, input_zero_points,
                              output_scale, output_zero_point, axis)

        return relay.qnn.op.concatenate(inputs[0],
                                        input_scales, input_zero_points,
                                        output_scale, output_zero_point,
                                        axis)

    return _impl


def _add_scalar():
    # this is used for mobilenet v3
    def _impl(inputs, _):
        # refer to aten/src/ATen/native/quantized/cpu/qadd.cpp
        assert len(inputs) == 6, "Input quant params not found in op inputs"
        s = inputs[4]
        z = inputs[5]
        c = inputs[1]
        c_q = round(c / s)
        q_min = 0
        q_max = 255

        # math for calculating output scale and zp are already done
        # during _add_output_quant_params_to_scalar_op above
        out_scale = _expr.const(inputs[2])
        out_zp = _expr.const(inputs[3])

        if q_min > z - c_q or q_max < z - c_q:
            dequant = relay.qnn.op.dequantize(inputs[0],
                                              _expr.const(s), _expr.const(z))
            dequantized_add = _op.tensor.add(dequant, _expr.const(c_q * s))
            return relay.qnn.op.quantize(dequantized_add, out_scale, out_zp,
                                         axis=1, out_dtype="uint8")
        # only scale change
        return inputs[0]

    return _impl


def quantize_scalar(data, scale, zero_point):
    # used to quantize 6., in mobilenet v3
    transformed = zero_point + data / scale
    return max(0, min(round(transformed), 255))


def _relu6():
    # refer to src/ATen/native/quantized/cpu/qrelu.cpp
    def _impl(inputs, _):
        assert len(inputs) == 4, "Input quant params not found in op inputs"
        input_scale = inputs[2]
        input_zero_point = inputs[3]
        six = quantize_scalar(6., input_scale, input_zero_point)
        return _op.tensor.clip(inputs[0], input_zero_point, six)
    return _impl


def _mul_scalar():
    # this is used for mobilenet v3
    def _impl(inputs, _):
        # refer to aten/src/ATen/native/quantized/cpu/qmul.cpp
        # math for calculating output scale and zp are already done
        # during _add_output_quant_params_to_scalar_op above
        assert len(inputs) == 6, "Input quant params not found in op inputs"
        other_val = inputs[1]  # scalar

        if other_val > 0.0:
            # only scale change
            return inputs[0]
        if other_val == 0.0:
            shape = infer_shape(inputs[0])
            return _op.full(_expr.const(0), shape, dtype="uint8")

        # negative scale case
        q_min = 0
        q_max = 255
        bias = _expr.const(q_max + q_min, dtype="int8")
        int8 = bias - _op.cast(inputs[0], "int8")
        return _op.cast(int8, "uint8")

    return _impl


convert_map = {
    'aten::quantize_per_tensor': _quantize_per_tensor(),
    'quantized::conv2d_relu': _quantized_conv2d(with_relu=True),
    'aten::dequantize': _dequantize(),
    'quantized::conv2d': _quantized_conv2d(),
    'quantized::add_relu': _binop(relay.qnn.op.add, with_relu=True),
    'quantized::add': _binop(relay.qnn.op.add),
    'quantized::mul_relu': _binop(relay.qnn.op.mul, with_relu=True),
    'quantized::mul': _binop(relay.qnn.op.mul),
    'quantized::linear': _linear(),
    'quantized::linear_relu': _linear(with_relu=True),
    'quantized::cat': _cat(),
    'quantized::add_scalar': _add_scalar(),
    'quantized::mul_scalar': _mul_scalar(),
    'quantized::relu6': _relu6()
}
