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# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import logging
from typing import Any

import numpy as np
import onnx
import onnx.numpy_helper

try:
    from onnx.reference.op_run import to_array_extended
except ImportError:
    # old version of onnx.
    to_array_extended = None

from .calibrate import TensorData
from .onnx_model import ONNXModel
from .quant_utils import (
    DEQUANT_OP_NAME,
    ONNX_TYPE_TO_NP_TYPE,
    QUANT_OP_NAME,
    TENSOR_NAME_QUANT_SUFFIX,
    find_by_name,
    get_opset_version,
    model_has_infer_metadata,
    normalize_axis,
    pack_bytes_to_4bit,
    quantize_data,
    quantize_nparray,
    save_and_reload_model_with_shape_infer,
    tensor_proto_to_array,
)
from .tensor_quant_overrides import TensorQuantOverridesHelper


class QuantizationParams:
    def __init__(self, **data: dict[str, Any]):
        self.data = {}
        for k, v in data.items():
            if not isinstance(k, str):
                raise TypeError(f"Keys must be strings not {type(k)} for k={k!r}.")
            if k != "axis" and not isinstance(v, (int, str, np.ndarray, float)):
                raise TypeError(f"Values must be numpy arrays, int, float, str not {type(v)} for k={k!r}.")
            if k == "axis" and not isinstance(v, int) and v is not None:
                raise TypeError(f"Axis value must be an int or None, not {type(v)}.")
            if k == "scale" and v.dtype not in (np.float32, np.float16):
                raise ValueError(f"scale must a float32 or float16 numpy element but is {v.dtype} for k={k!r}")
            self.data[k] = v

    def get(self, key, default_value=None):
        return self.data.get(key, default_value)

    def __iter__(self):
        yield from self.data

    def __getitem__(self, key):
        return self.data[key]

    def __setitem__(self, key, value):
        self.data[key] = value

    def __len__(self):
        return len(self.data)


class BaseQuantizer:
    def __init__(
        self,
        model,
        per_channel,
        reduce_range,
        weight_qType,
        activation_qType,
        tensors_range,
        nodes_to_quantize,
        nodes_to_exclude,
        op_types_to_quantize,
        extra_options=None,
    ):
        if not model_has_infer_metadata(model):
            model = save_and_reload_model_with_shape_infer(model)
        self.value_infos = {vi.name: vi for vi in model.graph.value_info}
        self.value_infos.update({ot.name: ot for ot in model.graph.output})
        self.value_infos.update({it.name: it for it in model.graph.input})

        self.model = ONNXModel(model)
        self.opset_version = get_opset_version(model)
        self.per_channel = per_channel  # weight-pack per channel
        self.reduce_range = reduce_range

        self.extra_options = extra_options if extra_options else {}
        self.enable_subgraph_quantization = (
            "EnableSubgraph" in self.extra_options and self.extra_options["EnableSubgraph"]
        )
        self.parent = None
        self.force_quantize_no_input_check = (
            "ForceQuantizeNoInputCheck" in self.extra_options and self.extra_options["ForceQuantizeNoInputCheck"]
        )

        # If user does not explicitly set "WeightSymmetric", then the weight's quantization type determines
        # the symmetry (i.e., signed integer types will use symmetric quantization). See `def is_weight_symmetric()`
        self._is_weight_symmetric: bool | None = self.extra_options.get("WeightSymmetric", None)
        self.is_activation_symmetric = self.extra_options.get("ActivationSymmetric", False)
        self.is_activation_restricted_asymmetric = self.extra_options.get("ActivationRestrictedAsymmetric", False)
        self.min_real_range = self.extra_options.get("MinimumRealRange")

        self.activation_qType = getattr(activation_qType, "tensor_type", activation_qType)
        self.weight_qType = getattr(weight_qType, "tensor_type", weight_qType)

        """
            Dictionary specifying the min and max values for tensors. It has following format:
                {
                    "param_name": [min, max]
                }
            example:
                {
                    'Conv_3:0': [np.float32(0), np.float32(0.5)],
                    'Conv_4:0': [np.float32(1), np.float32(3.5)]
                }
        """
        if tensors_range is not None and any(not isinstance(t, TensorData) for t in tensors_range.values()):
            raise TypeError(
                f"tensors_range contains unexpected types { {type(v) for v in tensors_range.values()} }, not TensorData."
            )
        self.tensors_range = tensors_range
        self.nodes_to_quantize = nodes_to_quantize  # specific nodes to quantize
        self.nodes_to_exclude = nodes_to_exclude  # specific nodes to exclude
        self.op_types_to_quantize = op_types_to_quantize

        # Get tensor-level quantization overrides and ensure they are valid.
        self.tensor_quant_overrides = TensorQuantOverridesHelper(self.extra_options.get("TensorQuantOverrides", {}))

        self.initializers = {initzer.name: initzer for initzer in self.model.initializer()}
        overrides_valid, overrides_err = self.tensor_quant_overrides.is_valid(
            self.initializers, self.value_infos.keys(), activation_qType
        )
        if not overrides_valid:
            raise ValueError(overrides_err)

        self.tensor_quant_override_qtypes = self.tensor_quant_overrides.get_quant_types()

    def is_weight_symmetric(self, weight_quant_type: onnx.TensorProto.DataType) -> bool:
        if self._is_weight_symmetric is not None:
            return self._is_weight_symmetric  # Return value explicitly set by user.
        return weight_quant_type in (
            onnx.TensorProto.INT4,
            onnx.TensorProto.INT8,
            onnx.TensorProto.INT16,
            onnx.TensorProto.FLOAT8E4M3FN,
        )

    def quantize_model(self):
        raise NotImplementedError

    def is_input_a_initializer(self, input_name):
        initializer = find_by_name(input_name, self.model.initializer())
        return initializer is not None

    def is_per_channel(self):
        return self.per_channel

    def is_valid_quantize_weight(self, weight_name):
        weight = find_by_name(weight_name, self.model.initializer())
        if weight is not None:
            return weight.data_type in (onnx.TensorProto.FLOAT, onnx.TensorProto.FLOAT16)
        if (not self.enable_subgraph_quantization) or (self.parent is None):
            return False
        return self.parent.is_valid_quantize_weight(weight_name)

    def should_quantize_node(self, node):
        if (
            self.nodes_to_quantize is not None
            and len(self.nodes_to_quantize) != 0
            and node.name not in self.nodes_to_quantize
        ):
            return False

        if node.op_type not in self.op_types_to_quantize:
            return False

        if node.op_type in (DEQUANT_OP_NAME, QUANT_OP_NAME):
            return False

        if self.nodes_to_exclude is not None and node.name in self.nodes_to_exclude:
            return False

        return True

    def quantize_bias_static_impl(self, bias_name, input_scale, weight_scale, beta=1.0):
        """
        Quantized the bias. Zero Point == 0 and Scale == Input_Scale * Weight_Scale
        """

        # get bias
        bias_initializer = find_by_name(bias_name, self.model.initializer())
        bias_data = tensor_proto_to_array(bias_initializer)
        quantized_bias_name = bias_name + TENSOR_NAME_QUANT_SUFFIX

        # quantize bias
        if self.weight_qType == onnx.TensorProto.FLOAT8E4M3FN:
            data = np.asarray(bias_data)
            if data.dtype == np.float16:
                node_qtype = onnx.TensorProto.FLOAT16
            elif data.dtype == np.float32:
                node_qtype = onnx.TensorProto.FLOAT
            else:
                raise TypeError(f"Only float16 or float32 are supported with float 8 but bias dtype is {data.dtype}.")
            quantized_data = data.astype(np.float32)
            bias_scale = np.array([1], dtype=quantized_data.dtype)
            bias_scale_data = bias_scale.reshape(-1)
            packed_bias_initializer = onnx.numpy_helper.from_array(quantized_data, quantized_bias_name)
            self.model.initializer_extend([packed_bias_initializer])
            node_type = "Cast"
        else:
            # calculate scale for bias
            # TODO: This formula should be explained including why the scale is not estimated for the bias as well.
            bias_scale = input_scale * weight_scale * beta

            # Quantize by dividing by bias_scale
            quantized_data = np.asarray(bias_data, dtype=np.float64) / np.asarray(bias_scale, dtype=np.float64)
            quantized_data = quantized_data.round()

            # Clip quantized data to the range of a int32
            int32_min = np.float64(np.iinfo(np.int32).min)
            int32_max = np.float64(np.iinfo(np.int32).max)
            if np.any(quantized_data < int32_min) or np.any(quantized_data > int32_max):
                logging.warning(
                    f"Quantized bias `{bias_name}` exceeds the range of a int32. The bias scale is too small."
                )

            quantized_data = np.clip(quantized_data, int32_min, int32_max).astype(np.int32)

            # update bias initializer
            bias_np_data = np.asarray(quantized_data, dtype=np.int32).reshape(bias_initializer.dims)
            packed_bias_initializer = onnx.numpy_helper.from_array(bias_np_data, quantized_bias_name)
            self.model.initializer_extend([packed_bias_initializer])

            # Bias's scale dtype should match the original bias data's unquantized type (float32 or float16).
            bias_scale_data = np.asarray(bias_scale, dtype=bias_data.dtype).reshape(-1)
            node_type = "DequantizeLinear"
            node_qtype = self.weight_qType

        # update scale initializer
        quantized_bias_scale_name = quantized_bias_name + "_scale"
        packed_bias_scale_initializer = onnx.numpy_helper.from_array(bias_scale_data, quantized_bias_scale_name)
        self.model.initializer_extend([packed_bias_scale_initializer])

        # update zero initializer
        if self.weight_qType == onnx.TensorProto.FLOAT8E4M3FN:
            tensor_type = self.weight_qType
        else:
            tensor_type = onnx.TensorProto.INT32

        quantized_bias_zp_name = quantized_bias_name + "_zero_point"
        if self.weight_qType == onnx.TensorProto.FLOAT8E4M3FN:
            packed_bias_zp_initializer = onnx.helper.make_tensor(quantized_bias_zp_name, self.weight_qType, [1], [0.0])
        elif bias_scale.size > 1:
            bias_zp_data = np.zeros(bias_scale.shape, dtype=np.int32).reshape(-1)
            packed_bias_zp_initializer = onnx.numpy_helper.from_array(bias_zp_data, quantized_bias_zp_name)
        else:
            packed_bias_zp_initializer = onnx.helper.make_tensor(quantized_bias_zp_name, tensor_type, [], [0])
        self.model.initializer_extend([packed_bias_zp_initializer])

        return (
            quantized_bias_name,
            quantized_bias_scale_name,
            quantized_bias_zp_name,
            bias_scale_data,
            node_type,
            node_qtype,
        )

    def quantize_initializer_impl(self, weight, qType, reduce_range=False, keep_float_weight=False):
        """
        :param weight: TensorProto initializer
        :param qType: type to quantize to
        :param keep_float_weight: Whether to quantize the weight. In some cases, we only want to qunatize scale and zero point.
                                  If keep_float_weight is False, quantize the weight, or don't quantize the weight.
        :return: quantized weight name, zero point name, scale name
        """
        # TODO(adrianlizarraga): This function is now only used by onnx_quantizer.py, so move it there.
        q_weight_name = weight.name + TENSOR_NAME_QUANT_SUFFIX
        zp_name = weight.name + "_zero_point"
        scale_name = weight.name + "_scale"

        # Quantize weight data. Use quantization overrides if provided by the user.
        weight_data = tensor_proto_to_array(weight)
        quant_overrides = self.tensor_quant_overrides.get_per_tensor_overrides(weight.name, default_val={})
        if "quant_type" in quant_overrides:
            qType = quant_overrides["quant_type"].tensor_type  # noqa: N806

        if "scale" in quant_overrides and "zero_point" in quant_overrides:
            zero_point = np.array(quant_overrides["zero_point"], dtype=ONNX_TYPE_TO_NP_TYPE[qType])
            scale = np.array(quant_overrides["scale"])
            q_weight_data = quantize_nparray(qType, weight_data.flatten(), scale, zero_point)
            assert isinstance(zero_point, np.ndarray), f"Unexpected type {type(zero_point)}"
            assert zero_point.dtype != np.float32 and zero_point.dtype != np.float16, (
                f"Unexpected dtype {zero_point.dtype}"
            )
            assert isinstance(scale, np.ndarray), f"Unexpected type {type(scale)}"

        else:
            symmetric = self.is_weight_symmetric(qType) if qType == self.weight_qType else self.is_activation_symmetric
            zero_point, scale, q_weight_data = quantize_data(
                weight_data.flatten(),
                qType,
                quant_overrides.get("symmetric", symmetric),
                reduce_range=quant_overrides.get("reduce_range", self.reduce_range and reduce_range),
                min_real_range=self.min_real_range,
                rmin_override=quant_overrides.get("rmin"),
                rmax_override=quant_overrides.get("rmax"),
            )

            assert isinstance(zero_point, np.ndarray), f"Unexpected type {type(zero_point)}"
            assert zero_point.dtype != np.float32 and zero_point.dtype != np.float16, (
                f"Unexpected dtype {zero_point.dtype}"
            )
            assert isinstance(scale, np.ndarray), f"Unexpected type {type(scale)}"

        scale_dtype = weight.data_type
        scale_initializer = onnx.helper.make_tensor(scale_name, scale_dtype, [], scale.reshape((-1,)).tolist())
        zero_initializer = onnx.helper.make_tensor(zp_name, qType, [], zero_point.reshape((-1,)).tolist())
        self.model.initializer_extend([scale_initializer, zero_initializer])

        if not keep_float_weight:
            if self.weight_qType == onnx.TensorProto.FLOAT8E4M3FN:
                q_weight_initializer = onnx.TensorProto()
                q_weight_initializer.data_type = self.weight_qType
                q_weight_initializer.dims.extend(weight.dims)
                q_weight_initializer.name = q_weight_name
                # Do not remove .flatten().copy() numpy is not clear about data persistence.
                q_weight_initializer.raw_data = q_weight_data.flatten().copy().tobytes()
                if to_array_extended is not None:
                    # This test should not be needed but it helped catch some issues
                    # with data persistence and tobytes.
                    check = to_array_extended(q_weight_initializer)
                    if check.shape != weight_data.shape or check.tobytes() != q_weight_data.tobytes():
                        raise RuntimeError(
                            f"The initializer of shape {weight_data.shape} could not be created, expecting "
                            f"{q_weight_data.tobytes()[:10]}, got {check.tobytes()[:10]} and shape={weight.shape}"
                            f"\nraw={str(q_weight_initializer)[:200]}."
                        )
            elif qType in (onnx.TensorProto.INT4, onnx.TensorProto.UINT4):
                if q_weight_data.dtype not in (np.int8, np.uint8):
                    raise RuntimeError(
                        f"Quantized weights for {q_weight_name} must be 8-bit before packing as 4-bit values."
                    )

                # We do not use onnx.helper.pack_float32_to_4bit() due to performance.
                # This can be the difference between a large model taking 30 minutes to quantize vs 5 minutes.
                packed_data = bytes(pack_bytes_to_4bit(q_weight_data.tobytes()))

                # We only use onnx.helper.make_tensor with raw data due to bug: https://github.com/onnx/onnx/pull/6161
                q_weight_initializer = onnx.helper.make_tensor(q_weight_name, qType, weight.dims, packed_data, raw=True)
            else:
                q_weight_data = np.asarray(q_weight_data, dtype=onnx.helper.tensor_dtype_to_np_dtype(qType)).reshape(
                    weight.dims
                )
                q_weight_initializer = onnx.numpy_helper.from_array(q_weight_data, q_weight_name)
            self.model.initializer_extend([q_weight_initializer])

        return q_weight_name, zp_name, scale_name

    def quantize_weight_per_channel_impl(
        self,
        weight_name,
        weight_qType,
        channel_axis,
        reduce_range=True,
        keep_float_weight=False,
    ):
        # TODO(adrianlizarraga): This function is now only used by onnx_quantizer.py, so move it there.
        initializer = find_by_name(weight_name, self.model.initializer())
        if initializer is None:
            raise ValueError("{} is not an initializer", weight_name)

        weights = tensor_proto_to_array(initializer)
        weights_rank = len(weights.shape)
        is_axis_valid, axis_norm = normalize_axis(channel_axis, weights_rank)
        if not is_axis_valid:
            raise ValueError(
                f"Weight {weight_name} has a per-channel axis with value {channel_axis} that is "
                f"out-of-bounds for rank {weights_rank}"
            )

        channel_axis = axis_norm
        channel_count = weights.shape[channel_axis]
        quant_overrides_for_channels = self.tensor_quant_overrides.get_per_channel_overrides(
            weight_name, default_val=[{"axis": channel_axis}]
        )

        num_channel_overrides = len(quant_overrides_for_channels)
        if num_channel_overrides != 1 and num_channel_overrides != channel_count:
            raise ValueError(
                f"Per-channel tensor quantization overrides for {weight_name} must have "
                f"either 1 or {channel_count} elements in the list of dictionaries."
            )

        is_axis_override_valid, axis_override = normalize_axis(quant_overrides_for_channels[0]["axis"], weights_rank)
        if not is_axis_override_valid or axis_override != channel_axis:
            raise ValueError(
                f"Tensor quantization overrides for {weight_name} specify an unexpected axis. "
                f"Expected {channel_axis}, but got {quant_overrides_for_channels[0]['axis']}."
            )

        # If user provides per-channel quantization overrides, all channels must use the same quant_type,
        # axis, symmetric, and reduce_range values. So, just use the first channel's values.
        if "quant_type" in quant_overrides_for_channels[0]:
            weight_qType = quant_overrides_for_channels[0]["quant_type"].tensor_type  # noqa: N806

        symmetric = quant_overrides_for_channels[0].get("symmetric", self.is_weight_symmetric(weight_qType))
        reduce_range = quant_overrides_for_channels[0].get("reduce_range", self.reduce_range and reduce_range)
        zero_point_list = []
        scale_list = []
        quantized_per_channel_data_list = []
        weights_shape = list(weights.shape)
        reshape_dims = list(weights_shape)  # deep copy
        reshape_dims[channel_axis] = 1  # only one per channel for reshape
        for i in range(channel_count):
            per_channel_data = weights.take(i, channel_axis)
            channel_override_index = i if i < num_channel_overrides else 0
            channel_quant_overrides = quant_overrides_for_channels[channel_override_index]

            if "scale" in channel_quant_overrides and "zero_point" in channel_quant_overrides:
                zero_point = np.array(channel_quant_overrides["zero_point"], dtype=ONNX_TYPE_TO_NP_TYPE[weight_qType])
                scale = np.array(channel_quant_overrides["scale"])
                quantized_per_channel_data = quantize_nparray(
                    weight_qType, per_channel_data.flatten(), scale, zero_point
                )
                assert isinstance(zero_point, np.ndarray), f"Unexpected type {type(zero_point)}"
                assert zero_point.dtype != np.float32 and zero_point.dtype != np.float16, (
                    f"Unexpected dtype {zero_point.dtype}"
                )
                assert isinstance(scale, np.ndarray), f"Unexpected type {type(scale)}"
                assert isinstance(quantized_per_channel_data, np.ndarray), (
                    f"Unexpected type {type(quantized_per_channel_data)}"
                )

            else:
                zero_point, scale, quantized_per_channel_data = quantize_data(
                    per_channel_data.flatten(),
                    weight_qType,
                    symmetric,
                    reduce_range=reduce_range,
                    min_real_range=self.min_real_range,
                    rmin_override=channel_quant_overrides.get("rmin"),
                    rmax_override=channel_quant_overrides.get("rmax"),
                )

                assert isinstance(zero_point, np.ndarray), f"Unexpected type {type(zero_point)}"
                assert zero_point.dtype != np.float32 and zero_point.dtype != np.float16, (
                    f"Unexpected dtype {zero_point.dtype}"
                )
                assert isinstance(scale, np.ndarray), f"Unexpected type {type(scale)}"
                assert isinstance(quantized_per_channel_data, np.ndarray), (
                    f"Unexpected type {type(quantized_per_channel_data)}"
                )

            zero_point_list.append(zero_point)
            scale_list.append(scale)
            quantized_per_channel_data_list.append(np.asarray(quantized_per_channel_data).reshape(reshape_dims))

        # combine per_channel_data into one
        quantized_weights = np.concatenate(quantized_per_channel_data_list, channel_axis)
        q_weight_name = weight_name + TENSOR_NAME_QUANT_SUFFIX
        zp_name = weight_name + "_zero_point"
        scale_name = weight_name + "_scale"

        # Update packed weight, zero point, and scale initializers
        zero_scale_shape = [initializer.dims[channel_axis]]
        scale_initializer = onnx.helper.make_tensor(
            scale_name, initializer.data_type, zero_scale_shape, np.hstack(scale_list).tolist()
        )
        zero_initializer = onnx.helper.make_tensor(
            zp_name, weight_qType, zero_scale_shape, np.hstack(zero_point_list).tolist()
        )

        self.model.initializer_extend([scale_initializer, zero_initializer])

        if not keep_float_weight:
            if weight_qType in (onnx.TensorProto.INT4, onnx.TensorProto.UINT4):
                if quantized_weights.dtype not in (np.int8, np.uint8):
                    raise RuntimeError(
                        f"Quantized weights for {q_weight_name} must be 8-bit before packing as 4-bit values."
                    )

                # We do not use onnx.helper.pack_float32_to_4bit() due to performance.
                # This can be the difference between a large model taking 30 minutes to quantize vs 5 minutes.
                packed_data = bytes(pack_bytes_to_4bit(quantized_weights.tobytes()))

                # We only use onnx.helper.make_tensor with raw data due to bug: https://github.com/onnx/onnx/pull/6161
                q_weight_initializer = onnx.helper.make_tensor(
                    q_weight_name, weight_qType, weights_shape, packed_data, raw=True
                )
                self.model.initializer_extend([q_weight_initializer])
            else:
                quantized_weights = np.asarray(
                    quantized_weights,
                    dtype=onnx.helper.tensor_dtype_to_np_dtype(weight_qType),
                ).reshape(initializer.dims)
                q_weight_initializer = onnx.numpy_helper.from_array(quantized_weights, q_weight_name)
                self.model.initializer_extend([q_weight_initializer])

        return q_weight_name, zp_name, scale_name

    def adjust_tensor_ranges(self):
        if self.tensors_range is None:
            return

        for node in self.model.nodes():
            # adjust tensor_ranges for input of Clip and Relu node
            if node.op_type in ["Clip", "Relu"]:
                if not self.should_quantize_node(node):
                    continue
                if len(self.model.input_name_to_nodes()[node.input[0]]) != 1:
                    continue
                if node.input[0] not in self.tensors_range or node.output[0] not in self.tensors_range:
                    continue
                td = self.tensors_range[node.output[0]]
                if not isinstance(td, TensorData):
                    raise TypeError(f"Unexpected type {type(td)} for {node.output[0]!r}.")
                self.tensors_range[node.input[0]] = td
            # Adjust Softmax to range from 0.0 to 1.0
            elif node.op_type == "Softmax":
                if not self.should_quantize_node(node):
                    continue
                self.tensors_range[node.output[0]] = TensorData(lowest=np.float32(0.0), highest=np.float32(1.0))