#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
from .... import core
from ....framework import IrGraph

__all__ = ['Qat2Int8MkldnnPass']

OpRole = core.op_proto_and_checker_maker.OpRole


class Qat2Int8MkldnnPass(object):
    """
    Transform a QAT model IrGraph into MKL-DNN supported INT8 IrGraph.
    The pass consists of the following transformations:
        1. gather scale values from fake quantize/dequantize operators,
        2. extract FP32 inference model graph from the QAT graph, i.e.
            a.  remove fake quantize/dequantize operators,
            b.  dequantize conv2d and mul's weights,
        3. optimize the FP32 graph using standard FP32 optimization fuses
            (e.g. `conv2d`+`bn` -> `conv2d`),
        4. quantize the optimized FP32 graph using standard INT8v2 quantization
            passes (`cpu_quantize_pass`, `cpu_quantize_squash_pass`).
    """

    def __init__(self,
                 _quantized_ops,
                 _scope=None,
                 _place=None,
                 _core=None,
                 _debug=False):
        self._scope = _scope
        self._place = _place
        self._core = _core
        self._debug = _debug
        self._fake_quantize_types = [
            'fake_quantize_moving_average_abs_max',
            'fake_quantize_range_abs_max',
            'fake_quantize_dequantize_moving_average_abs_max'
        ]
        self._fake_dequantize_types = [
            'fake_dequantize_max_abs', 'fake_channel_wise_dequantize_max_abs'
        ]
        self._quantized_ops = _quantized_ops
        self._scale_immutable_ops = [
            'transpose2', 'reshape2', 'pool2d', 'scale'
        ]
        self._conv_ops = ['conv2d', 'depthwise_conv2d']
        self._pool_ops = ['pool2d']
        self._mul_ops = ['mul']
        self._fc_ops = ['fc']
        self._matmul_ops = ['matmul']
        self._weight_scales = {}
        # Collect the Input and Output sclaes from Fake QAT models
        self._var_quant_scales = {}
        self._max_range = {}
        self._s8_max = 127

    def apply(self, graph):
        assert isinstance(graph,
                          IrGraph), 'graph must be the instance of IrGraph.'

        graph = self._gather_weight_scales_from_fake(graph)
        graph = self._gather_output_scales_from_attr(graph)
        graph = self._gather_input_scales_from_fake(graph)
        graph = self._remove_fake_ops(graph)
        graph = self._dequantize_weights(graph)
        graph = self._optimize_fp32_graph(graph)
        graph = self._compute_weight_scales(graph)
        graph = self._update_relu_output_scales(graph)
        graph = self._propagate_scales(graph)
        graph = self._set_dummy_out_scales(graph)
        graph = self._quantize_fp32_graph(graph)
        graph = self._optimize_int8_graph(graph)
        graph = self._cleanup(graph)
        return graph

    def apply_fp32(self, graph):
        assert isinstance(graph,
                          IrGraph), 'graph must be the instance of IrGraph.'

        graph = self._optimize_fp32_graph(graph)
        graph = self._cleanup(graph)
        return graph

    def _convert_scale2tensor(self, scale):
        tensor = core.LoDTensor()
        tensor.set(scale, core.CPUPlace())
        return tensor

    def _is_conv_quantized(self):
        return any(op_type in self._quantized_ops for op_type in self._conv_ops)

    def _is_fc_quantized(self):
        return 'fc' in self._quantized_ops

    def _gather_input_scales_from_fake(self, graph):
        def _add_scale_for_vars(var_names, use_unsigned_int, lod_tensor):
            scales = self._var_quant_scales
            for var_name in var_names:
                scales[var_name] = (use_unsigned_int, lod_tensor)

        for op in graph.all_op_nodes():
            if op.name() in self._fake_quantize_types:
                bit_length = op.op().attr("bit_length")
                assert bit_length == 8, 'Unsupported number quantization bits ({}). Only 8 is supported now.'.format(
                    bit_length)

                input_name = op.input("X")[0]
                scale_name = op.input("InScale")[0]
                output_name = op.output("Out")[0]
                # Gather new weights scale after folding batchnorm in convolution
                scale = np.array(1.0 / self._load_param(
                    self._scope, scale_name)[0]).astype(np.float64)
                lod_tensor = self._convert_scale2tensor(scale)
                use_unsigned_int = False
                _add_scale_for_vars([input_name, output_name], use_unsigned_int,
                                    lod_tensor)

        return graph

    def _gather_weight_scales_from_fake(self, graph):
        for op in graph.all_op_nodes():
            if op.name() in self._fake_dequantize_types:
                input_name = op.input("X")[0]
                if op.op().has_attr("max_range"):
                    _max_range = np.array(op.op().attr("max_range")).astype(
                        np.float64)
                    self._weight_scales[input_name] = _max_range
                else:
                    scale_name = op.input("Scales")[0]
                    scale = np.array(
                        self._s8_max * self._s8_max / self._load_param(
                            self._scope, scale_name)).astype(np.float64)
                    self._weight_scales[input_name] = scale

        return graph

    def _gather_output_scales_from_attr(self, graph):
        for op in graph.all_op_nodes():
            if op.op().has_attr("out_threshold"):
                attr_scale = op.op().attr("out_threshold")
                if attr_scale == 0.0: continue
                scale = np.array(1.0 / attr_scale).astype(np.float64)
                scale_lod_tensor = self._convert_scale2tensor(scale)
                use_unsigned_int = False
                for output_name in op.op().outputs():
                    for out_var_name in op.op().output(output_name):
                        self._var_quant_scales[out_var_name] = (
                            use_unsigned_int, scale_lod_tensor)

        return graph

    def _propagate_scales(self, graph):
        def _update_scale_op_in_scale(op, input, output):
            unsigned, tensor = self._var_quant_scales[output]
            scale = np.array(tensor) * op.op().attr("scale")
            new_tensor = self._convert_scale2tensor(scale.astype(np.float64))
            self._var_quant_scales[input] = (unsigned, new_tensor)

        def _update_scales(graph):
            waiting_for_scale = set()
            for op in graph.all_op_nodes():
                if op.name() in self._scale_immutable_ops:
                    input_name = op.input("X")[0]
                    output_name = op.output("Out")[0]
                    tensor_names = [input_name, output_name]

                    # Scale is not quantized, so if it doesn't have any scales
                    # to propagate, its tensors won't be added to the waiting list.
                    if all(name not in self._var_quant_scales for name in tensor_names) \
                            and op.name() != 'scale':
                        waiting_for_scale.update(tensor_names)
                        continue

                    if input_name in self._var_quant_scales:
                        self._var_quant_scales[
                            output_name] = self._var_quant_scales[input_name]
                    elif output_name in self._var_quant_scales:
                        if op.name() == 'scale':
                            _update_scale_op_in_scale(op, input_name,
                                                      output_name)
                        else:
                            self._var_quant_scales[
                                input_name] = self._var_quant_scales[
                                    output_name]
            return waiting_for_scale

        waiting_for_scale = _update_scales(graph)
        waiting_for_scale_prev = set()

        while len(waiting_for_scale
                  ) != 0 and waiting_for_scale != waiting_for_scale_prev:
            waiting_for_scale_prev = waiting_for_scale
            waiting_for_scale = _update_scales(graph)

        return graph

    def _set_dummy_out_scales(self, graph):
        '''
        For the output tensors of fc, conv2d and matmul ops that do not have an assigned scale,
        assign a dummy scale (same scale as input), so that the quantize pass
        won't fail. In the end these scales aren't used, since the ops that
        have an unassigend output scale will have a force_fp32_output attr
        set to True.
        '''

        def _set_scale(op, op_types, input_names, output_name):
            scales = self._var_quant_scales
            should_set = op.name() in op_types \
                and op.output(output_name)[0] not in scales \
                and all(op.input(input_name)[0] in scales for input_name in input_names)
            if should_set:
                output_var_name = op.output(output_name)[0]
                input_var_name = op.input(input_names[0])[0]
                scales[output_var_name] = scales[input_var_name]

        for op in graph.all_op_nodes():
            _set_scale(op, self._conv_ops, ["Input"], "Output")
            _set_scale(op, self._fc_ops, ["Input"], "Out")
            _set_scale(op, self._matmul_ops, ["X", "Y"], "Out")

        return graph

    def _load_param(self, scope, param_name):
        return np.array(scope.find_var(param_name).get_tensor())

    def _remove_fake_ops(self, graph):
        '''
        When FC isn't quantized:
        Remove fake (de)quantize ops that do not surround mul.
        When FC is quantized:
        Remove all fake (de)quantize ops.
        '''
        is_fc_quantized = self._is_fc_quantized()
        for op in graph.all_op_nodes():
            if op.name() in self._fake_quantize_types:
                op_out = graph._find_node_by_name(op.outputs,
                                                  op.output("Out")[0])
                next_op = op_out.outputs[0]
                if next_op.name() not in self._mul_ops or is_fc_quantized:
                    self._remove_fake_quantize(graph, op)

        for op in graph.all_op_nodes():
            if op.name() in self._fake_dequantize_types:
                op_in = graph._find_node_by_name(op.inputs, op.input("X")[0])
                prev_op = op_in.inputs[0]
                if prev_op.name() not in self._mul_ops or is_fc_quantized:
                    self._remove_fake_dequantize(graph, op)

        return graph

    def _remove_fake_quantize(self, graph, op):
        fake_quant_in = graph._find_node_by_name(op.inputs, op.input("X")[0])
        fake_quant_in_scale = graph._find_node_by_name(op.inputs,
                                                       op.input("InScale")[0])
        fake_quant_out = graph._find_node_by_name(op.outputs,
                                                  op.output("Out")[0])
        fake_quant_out_scale = graph._find_node_by_name(
            op.outputs, op.output("OutScale")[0])

        next_ops = fake_quant_out.outputs
        for next_op in next_ops:
            self._swap_inputs(next_op, fake_quant_out, fake_quant_in)
            graph.link_to(fake_quant_in, next_op)
        graph.safe_remove_nodes(
            {op, fake_quant_in_scale, fake_quant_out, fake_quant_out_scale})

        return graph

    def _remove_fake_dequantize(self, graph, op):
        fake_dequant_in = graph._find_node_by_name(op.inputs, op.input("X")[0])
        fake_dequant_out = graph._find_node_by_name(op.outputs,
                                                    op.output("Out")[0])

        next_ops = fake_dequant_out.outputs
        for next_op in next_ops:
            self._swap_inputs(next_op, fake_dequant_out, fake_dequant_in)
            graph.link_to(fake_dequant_in, next_op)
        graph.safe_remove_nodes({op, fake_dequant_out})

        return graph

    def _swap_inputs(self, op, old_input, new_input):
        for input_name in op.op().input_names():
            if old_input.name() in op.input(input_name):
                op.op().set_input(input_name, [
                    new_input.name() if x == old_input.name() else x
                    for x in op.input(input_name)
                ])

    def _dequantize_weights(self, graph):
        for op in graph.all_op_nodes():
            if op.name() in self._conv_ops:
                self._dequantize_op_weights(graph, op, "Filter", "Output")
            elif self._is_fc_quantized() and op.name() in self._mul_ops:
                self._dequantize_op_weights(graph, op, "Y", "Out")
        return graph

    def _dequantize_op_weights(self, graph, op_node, weight_name, output_name):
        weight_var_name = op_node.input(weight_name)[0]
        output_var_name = op_node.output(output_name)[0]
        # Convert int8 range weights to fp32 range weights
        scales = self._weight_scales[output_var_name]
        weight = self._load_param(self._scope, weight_var_name)
        assert scales.size == 1 or scales.size == len(
            weight
        ), "The size of weight scales vector ({}) does not match the number of output channels ({}) in the weights tensor {}.".format(
            scales.size, len(weight), weight_var_name)
        w_fp32 = np.divide(np.multiply(weight, self._s8_max).T, scales.T).T
        w_fp32 = w_fp32.reshape(weight.shape).astype(np.float32)
        self._restore_var(weight_var_name, w_fp32)

    def _restore_var(self, name, array):
        tensor = self._scope.find_var(name).get_tensor()
        tensor.set(array, self._place)

    def _update_activations(self, graph):
        for op in graph.all_op_nodes():
            if op.name() in self._conv_ops and not op.op().has_attr(
                    "fuse_activation"):
                activation = ""
                if op.op().has_attr("fuse_relu") and op.op().attr("fuse_relu"):
                    activation = "relu"
                elif op.op().has_attr("fuse_brelu") and op.op().attr(
                        "fuse_brelu"):
                    activation = "relu6"
                    alpha = 6.0
                    if op.op().has_attr("fuse_brelu_threshold"):
                        alpha = op.op().attr("fuse_brelu_threshold")
                    op.set_attr("fuse_alpha", alpha)
                op.set_attr("fuse_activation", activation)
        return graph

    def _remove_ctrl_vars(self, graph):
        remove_ctr_vars = set()
        for node in graph.all_var_nodes():
            if node.is_ctrl_var():
                remove_ctr_vars.add(node)
        graph.safe_remove_nodes(remove_ctr_vars)
        return graph

    def _optimize_fp32_graph(self, graph):
        graph = self._update_activations(graph)
        graph = self._remove_ctrl_vars(graph)
        graph = self._apply_pass(graph, 'mkldnn_placement_pass',
                                 ['mkldnn_enabled_op_types'], [set()])
        if self._is_conv_quantized():
            graph = self._apply_pass(graph, 'depthwise_conv_mkldnn_pass')
            graph = self._apply_pass(graph, 'conv_bn_fuse_pass')
            graph = self._apply_pass(graph, 'conv_eltwiseadd_bn_fuse_pass')
            graph = self._apply_pass(graph, 'conv_bias_mkldnn_fuse_pass')
            graph = self._apply_pass(graph,
                                     'conv_elementwise_add_mkldnn_fuse_pass')
            graph = self._apply_pass(graph, 'conv_relu_mkldnn_fuse_pass')
            graph = self._apply_pass(graph, 'conv_relu6_mkldnn_fuse_pass')
        if self._is_fc_quantized():
            graph = self._apply_pass(graph, 'fc_fuse_pass',
                                     ['use_gpu', 'use_fc_padding'],
                                     [False, False])
            graph = self._apply_pass(graph, 'fc_mkldnn_pass')
        graph = self._apply_pass(graph, 'matmul_transpose_reshape_fuse_pass')
        return graph

    def _apply_pass(self, graph, pass_name, attrs=None, attr_values=None):
        ir_pass = core.get_pass(pass_name)
        cpp_graph = graph.graph
        if not cpp_graph.has('__param_scope__'):
            cpp_graph.set_not_owned('__param_scope__', self._scope)
        if attrs:
            assert attr_values and len(attrs) == len(
                attr_values
            ), "Different number of pass attributes and their values."
            for attr, value in zip(attrs, attr_values):
                ir_pass.set(attr, value)
        ir_pass.apply(cpp_graph)
        if self._debug:
            graph.draw('.', 'qat_fp32_{}'.format(pass_name),
                       graph.all_op_nodes())
        self._remove_unused_var_nodes(graph)
        return graph

    def _optimize_int8_graph(self, graph):
        # remove dropout ops
        graph = self._apply_pass(graph, 'simplify_with_basic_ops_pass')
        # make some MKL-DNN ops working inplace
        graph = self._apply_pass(graph, 'mkldnn_inplace_pass')
        return graph

    def _cleanup(self, graph):
        graph = self._remove_unused_var_nodes(graph)
        graph = self._set_op_role_forward(graph)
        return graph

    def _remove_unused_var_nodes(self, graph):
        all_used_vars = set()
        ops = graph.all_op_nodes()
        for op_node in ops:
            for input_node in op_node.inputs:
                all_used_vars.add(input_node)
            for output_node in op_node.outputs:
                all_used_vars.add(output_node)

        all_used_vars = {n.node for n in all_used_vars}
        all_unused_vars = {
            n
            for n in filter(lambda node: node.node not in all_used_vars,
                            graph.all_var_nodes())
        }
        graph.safe_remove_nodes(all_unused_vars)
        return graph

    def _set_op_role_forward(self, graph):
        ops = graph.all_op_nodes()
        for op in ops:
            op.set_attr("op_role", OpRole.Forward)
        return graph

    def _compute_weight_scales(self, graph):
        def _compute_var_scales(ops, w_name, axis):
            for op in graph.all_op_nodes():
                if op.op().type() in ops:
                    weight_var_name = op.input(w_name)[0]
                    weights = np.array(
                        self._load_param(self._scope, weight_var_name))
                    scales = 1.0 / np.amax(
                        np.abs(weights.reshape(weights.shape[0], -1)).astype(
                            np.float64),
                        axis=axis)
                    scales[scales == np.Inf] = 0.0

                    lod_tensor = self._convert_scale2tensor(scales)
                    use_unsigned_int = False
                    self._var_quant_scales[weight_var_name] = (use_unsigned_int,
                                                               lod_tensor)

        _compute_var_scales(self._conv_ops, "Filter", axis=1)
        _compute_var_scales(self._fc_ops, "W", axis=0)
        return graph

    def _find_avg_pooling_ids(self, graph):
        ids = []
        for op in graph.all_op_nodes():
            if op.name() in self._pool_ops:
                if op.op().attr("pooling_type") == "avg":
                    ids.append(op.id())
        return set(ids) if len(ids) else set([-1])

    def _update_relu_output_scales(self, graph):
        def _update_scale(graph, ops, op_out_name, predicate):
            '''
            Sets the type of an output scale of a passed op type(s) to 'unsigned int8' if the
            predicate applied on op passes. Typically, the predicate checks if op's
            activation is set to relu.
            '''
            for op in graph.all_op_nodes():
                if op.name() in ops:
                    out_name = op.output(op_out_name)[0]
                    if out_name in self._var_quant_scales and predicate(op.op(
                    )):
                        _, tensor = self._var_quant_scales[out_name]
                        self._var_quant_scales[out_name] = (True, tensor)
            return graph

        if self._is_conv_quantized():
            conv_predicate = lambda op: op.attr("fuse_activation") == 'relu' and \
                op.attr("fuse_residual_connection") == False
            graph = _update_scale(graph, self._conv_ops, "Output",
                                  conv_predicate)

        if self._is_fc_quantized():
            fc_predicate = lambda op: op.attr("activation_type") == 'relu'
            graph = _update_scale(graph, self._fc_ops, "Out", fc_predicate)

        return graph

    def _get_data_layout(self):
        return 'NHWC' if self._is_conv_quantized() else 'NCHW'

    def _quantize_fp32_graph(self, graph):
        ir_pass = self._core.get_pass('cpu_quantize_placement_pass')
        cpp_graph = graph.graph
        ir_pass.set('quantize_enabled_op_types', self._quantized_ops)
        ir_pass.set('quantize_excluded_op_ids',
                    self._find_avg_pooling_ids(graph))
        ir_pass.apply(cpp_graph)
        if self._debug:
            graph.draw('.', 'qat_int8_{}'.format(ir_pass.type()),
                       graph.all_op_nodes())
        graph = self._apply_pass(graph, 'scale_matmul_fuse_pass')
        graph = self._apply_pass(
            graph, 'cpu_quantize_pass', ['quant_var_scales', 'data_layout'],
            [self._var_quant_scales, self._get_data_layout()])
        graph = self._apply_pass(graph, 'cpu_quantize_squash_pass')
        return graph
