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"""Some of the tests using CUDNN require a special GPU instruction called dp4a.
Ref: http://images.nvidia.com/content/pdf/tesla/184457-Tesla-P4-Datasheet-NV-Final-Letter-Web.pdf
"""
import os
import mxnet as mx
import numpy as np
from mxnet.test_utils import assert_almost_equal, assert_exception, rand_ndarray, rand_shape_nd, same, DummyIter
from common import with_seed
from mxnet.module import Module
from mxnet.io import NDArrayIter
import unittest

def is_test_for_gpu():
    return mx.current_context().device_type == 'gpu'

def is_test_for_mkldnn():
    return (mx.current_context().device_type == 'cpu'
            and os.environ.get('ENABLE_MKLDNN_QUANTIZATION_TEST') == '1')

def is_test_for_native_cpu():
    return (mx.current_context().device_type == 'cpu'
            and os.environ.get('ENABLE_MKLDNN_QUANTIZATION_TEST') == None)

@with_seed()
def test_quantize_float32_to_int8():
    shape = rand_shape_nd(4)
    data = rand_ndarray(shape, 'default', dtype='float32')
    min_range = mx.nd.min(data)
    max_range = mx.nd.max(data)
    qdata, min_val, max_val = mx.nd.contrib.quantize(data, min_range, max_range, out_type='int8')
    data_np = data.asnumpy()
    min_range = min_range.asscalar()
    max_range = max_range.asscalar()
    real_range = np.maximum(np.abs(min_range), np.abs(max_range))
    quantized_range = 127.0
    scale = quantized_range / real_range
    assert qdata.dtype == np.int8
    assert min_val.dtype == np.float32
    assert max_val.dtype == np.float32
    assert same(min_val.asscalar(), -real_range)
    assert same(max_val.asscalar(), real_range)
    qdata_np = (np.sign(data_np) * np.minimum(np.abs(data_np) * scale + 0.5, quantized_range)).astype(np.int8)
    assert same(qdata.asnumpy(), qdata_np)


@with_seed()
def test_dequantize_int8_to_float32():
    shape = rand_shape_nd(4)
    qdata_np = np.random.uniform(low=-127, high=127, size=shape).astype(dtype=np.int8)
    qdata = mx.nd.array(qdata_np, dtype=np.int8)
    real_range = 402.3347
    min_range = mx.nd.array([-real_range], dtype=np.float32)
    max_range = mx.nd.array([real_range], dtype=np.float32)
    data = mx.nd.contrib.dequantize(qdata, min_range, max_range, out_type='float32')
    quantized_range = 127.0
    scale = real_range / quantized_range
    assert data.dtype == np.float32
    data_np = qdata_np * scale
    assert_almost_equal(data.asnumpy(), data_np)


@with_seed()
@unittest.skip('Flaky test, tracked in: https://github.com/apache/incubator-mxnet/issues/11747')
def test_requantize_int32_to_int8():
    def quantized_int32_to_float(qdata, min_range, max_range):
        assert qdata.dtype == 'int32'
        quantized_range = np.iinfo('int32').max
        real_range = np.maximum(np.abs(min_range), np.abs(max_range))
        scale = float(real_range) / float(quantized_range)
        return qdata.astype('float32') * scale

    def float_to_quantized_int8(data, min_range, max_range):
        assert data.dtype == 'float32'
        real_range = np.maximum(np.abs(min_range), np.abs(max_range))
        quantized_range = np.iinfo('int8').max
        scale = float(quantized_range) / float(real_range)
        return (np.sign(data) * np.minimum(np.abs(data) * scale + 0.5, quantized_range)).astype('int8')

    def requantize(qdata, min_data, max_data, real_range):
        data = quantized_int32_to_float(qdata, min_data, max_data)
        output = float_to_quantized_int8(data, -real_range, real_range)
        return output, -real_range, real_range

    def requantize_baseline(qdata, min_data, max_data, min_calib_range=None, max_calib_range=None):
        if min_calib_range is not None and max_calib_range is not None:
            real_range = np.maximum(np.abs(min_calib_range), np.abs(max_calib_range))
            return requantize(qdata, min_data, max_data, real_range)
        else:
            min_range = quantized_int32_to_float(np.min(qdata), min_data, max_data)
            max_range = quantized_int32_to_float(np.max(qdata), min_data, max_data)
            return requantize(qdata, min_data, max_data, np.maximum(np.abs(min_range), np.abs(max_range)))

    def check_requantize(shape, min_calib_range=None, max_calib_range=None):
        qdata = mx.nd.random.uniform(low=-1000.0, high=1000.0, shape=shape).astype('int32')
        min_range = mx.nd.array([-1010.0])
        max_range = mx.nd.array([1020.0])
        if min_calib_range is None or max_calib_range is None:
            qdata_int8, min_output, max_output = mx.nd.contrib.requantize(qdata, min_range, max_range)
        else:
            qdata_int8, min_output, max_output = mx.nd.contrib.requantize(qdata, min_range, max_range,
                                                                          min_calib_range, max_calib_range)

        qdata_int8_np, min_output_np, max_output_np = requantize_baseline(qdata.asnumpy(), min_range.asscalar(),
                                                                          max_range.asscalar(),
                                                                          min_calib_range=min_calib_range,
                                                                          max_calib_range=max_calib_range)
        assert_almost_equal(qdata_int8.asnumpy(), qdata_int8_np)
        assert_almost_equal(min_output.asnumpy(), np.array([min_output_np]))
        assert_almost_equal(max_output.asnumpy(), np.array([max_output_np]))

    check_requantize((3, 4, 10, 10))
    check_requantize((32, 3, 23, 23))
    check_requantize((3, 4, 10, 10), min_calib_range=-1050.0, max_calib_range=1040.0)
    check_requantize((32, 3, 23, 23), min_calib_range=-134.349, max_calib_range=523.43)


@with_seed()
def test_quantized_conv():
    def check_quantized_conv(data_shape, kernel, num_filter, pad, stride, no_bias, qdtype):
        if is_test_for_native_cpu():
            print('skipped testing quantized_conv for native cpu since it is not supported yet')
            return
        elif qdtype == 'int8' and is_test_for_mkldnn():
            print('skipped testing quantized_conv for mkldnn cpu int8 since it is not supported yet')
            return
        elif qdtype == 'uint8' and is_test_for_gpu():
            print('skipped testing quantized_conv for gpu uint8 since it is not supported yet')
            return

        # run fp32 conv
        data = mx.sym.Variable(name='data', shape=data_shape, dtype='float32')
        conv2d = mx.sym.Convolution(data=data, kernel=kernel, num_filter=num_filter, pad=pad, stride=stride,
                                    no_bias=no_bias, cudnn_off=False, name='conv2d')
        arg_shapes, _, _ = conv2d.infer_shape(data=data_shape)
        arg_names = conv2d.list_arguments()
        conv_exe_fp32 = conv2d.simple_bind(ctx=mx.current_context(), grad_req='null')
        if qdtype == 'uint8':
            data_low = 0.0
            data_high = 127.0
        else:
            data_low = -127.0
            data_high = 127.0
        conv_exe_fp32.arg_dict[arg_names[0]][:] = mx.nd.random.uniform(low=data_low, high=data_high,
                                                                        shape=data_shape).astype('int32')
        conv_exe_fp32.arg_dict[arg_names[1]][:] = mx.nd.random.uniform(low=-127.0, high=127.0,
                                                                        shape=arg_shapes[1]).astype('int32')
        if not no_bias:
            conv_exe_fp32.arg_dict[arg_names[2]][:] = mx.nd.random.uniform(low=-127.0, high=127.0,
                                                                            shape=arg_shapes[2]).astype('int32')
        output = conv_exe_fp32.forward()[0]

        # run quantized conv
        qdata = mx.sym.Variable(name='qdata', shape=data_shape, dtype=qdtype)
        qweight = mx.sym.Variable(name='qweight', dtype='int8')
        min_data = mx.sym.Variable(name='min_data')
        max_data = mx.sym.Variable(name='max_data')
        min_weight = mx.sym.Variable(name='min_weight')
        max_weight = mx.sym.Variable(name='max_weight')
        quantized_conv2d = mx.sym.contrib.quantized_conv(data=qdata, weight=qweight, min_data=min_data,
                                                            max_data=max_data, min_weight=min_weight,
                                                            max_weight=max_weight, kernel=kernel,
                                                            num_filter=num_filter, pad=pad, stride=stride,
                                                            no_bias=no_bias)
        qarg_names = quantized_conv2d.list_arguments()
        type_dict = None
        if not no_bias:
            type_dict = {qarg_names[2]: 'int8'}
        conv_exe_int8 = quantized_conv2d.simple_bind(ctx=mx.current_context(), type_dict=type_dict, grad_req='null')
        conv_exe_int8.arg_dict[qarg_names[0]][:] = conv_exe_fp32.arg_dict[arg_names[0]].astype(qdtype)
        conv_exe_int8.arg_dict[qarg_names[1]][:] = conv_exe_fp32.arg_dict[arg_names[1]].astype('int8')
        quantized_range = 127.0
        if no_bias:
            conv_exe_int8.arg_dict[qarg_names[2]][:] = -quantized_range
            conv_exe_int8.arg_dict[qarg_names[3]][:] = quantized_range
            conv_exe_int8.arg_dict[qarg_names[4]][:] = -quantized_range
            conv_exe_int8.arg_dict[qarg_names[5]][:] = quantized_range
        else:
            conv_exe_int8.arg_dict[qarg_names[2]][:] = conv_exe_fp32.arg_dict[arg_names[2]].astype('int8')
            conv_exe_int8.arg_dict[qarg_names[3]][:] = -quantized_range
            conv_exe_int8.arg_dict[qarg_names[4]][:] = quantized_range
            conv_exe_int8.arg_dict[qarg_names[5]][:] = -quantized_range
            conv_exe_int8.arg_dict[qarg_names[6]][:] = quantized_range
            conv_exe_int8.arg_dict[qarg_names[7]][:] = -quantized_range
            conv_exe_int8.arg_dict[qarg_names[8]][:] = quantized_range
        qoutput, min_range, max_range = conv_exe_int8.forward()

        if no_bias:
            assert_almost_equal(output.asnumpy(), qoutput.asnumpy())
        else:
            # with adding bias, accuracy loss should not be greater than one
            diff = mx.nd.abs(output - qoutput.astype(output.dtype))
            cond = mx.nd.lesser(2, diff).sum().asscalar()
            assert cond == 0

    for qdtype in ['int8', 'uint8']:
        check_quantized_conv((3, 4, 28, 28), (3, 3), 128, (1, 1), (1, 1), True, qdtype)
        check_quantized_conv((3, 4, 28, 28), (3, 3), 128, (1, 1), (1, 1), False, qdtype)

@with_seed()
def test_quantized_pooling():
    def check_quantized_pooling(data_shape, kernel, pool_type, pad, stride, global_pool, qdtype):
        if is_test_for_native_cpu():
            print('skipped testing quantized_pooling for native cpu since it is not supported yet')
            return
        elif qdtype == 'uint8' and is_test_for_gpu():
            print('skipped testing quantized_pooling for gpu uint8 since it is not supported yet')
            return

        data = mx.sym.Variable(name='data', shape=data_shape, dtype='float32')
        pooling_fp32 = mx.sym.Pooling(data=data, kernel=kernel, pad=pad, stride=stride,
                                        pool_type=pool_type, global_pool=global_pool, cudnn_off=False)
        arg_shapes, _, _ = pooling_fp32.infer_shape(data=data_shape)
        arg_names = pooling_fp32.list_arguments()
        pooling_fp32_exe = pooling_fp32.simple_bind(ctx=mx.current_context(), grad_req='null')
        if qdtype == 'uint8':
            data_low = 0.0
            data_high = 127.0
        else:
            data_low = -127.0
            data_high = 127.0
        pooling_fp32_exe.arg_dict[arg_names[0]][:] = mx.nd.random.uniform(low=data_low, high=data_high,
                                                                            shape=data_shape).astype('int32')
        output = pooling_fp32_exe.forward()[0]

        qdata = mx.sym.Variable(name='qdata', shape=data_shape, dtype=qdtype)
        min_data = mx.sym.Variable(name='min_data')
        max_data = mx.sym.Variable(name='max_data')
        quantized_pooling = mx.sym.contrib.quantized_pooling(data=qdata, min_data=min_data,
                                                                max_data=max_data, kernel=kernel,
                                                                pad=pad, stride=stride, pool_type=pool_type,
                                                                global_pool=global_pool)
        pooling_int8_exe = quantized_pooling.simple_bind(ctx=mx.current_context(), grad_req='null')
        qarg_names = quantized_pooling.list_arguments()
        pooling_int8_exe.arg_dict[qarg_names[0]][:] = pooling_fp32_exe.arg_dict[arg_names[0]].astype(qdtype)
        quantized_range = 127.0
        pooling_int8_exe.arg_dict[qarg_names[1]][:] = -quantized_range
        pooling_int8_exe.arg_dict[qarg_names[2]][:] = quantized_range
        qoutput, min_range, max_range = pooling_int8_exe.forward()

        if pool_type == 'max':
            assert_almost_equal(output.asnumpy(), qoutput.asnumpy())
        elif pool_type == 'avg':  # for avg pooling, fp32 and int8 may be different due to rounding errors
            diff = mx.nd.abs(output - qoutput.astype(output.dtype))
            cond = mx.nd.lesser(2, diff).sum().asscalar()
            assert cond == 0

    for qdtype in ['int8', 'uint8']:
        check_quantized_pooling((3, 4, 56, 56), (3, 3), 'max', (0, 0), (2, 2), False, qdtype)
        check_quantized_pooling((3, 4, 56, 56), (3, 3), 'max', (0, 0), (2, 2), True, qdtype)
        check_quantized_pooling((3, 512, 7, 7), (7, 7), 'avg', (0, 0), (1, 1), False, qdtype)
        check_quantized_pooling((3, 512, 7, 7), (7, 7), 'avg', (0, 0), (1, 1), True, qdtype)

@with_seed()
def test_quantized_fc():
    def check_quantized_fc(data_shape, num_hidden, no_bias, qdtype, flatten=True):
        if mx.current_context().device_type != 'gpu':
            print('skipped testing quantized_fc on cpu since it is not supported yet')
            return
        elif qdtype == 'uint8' and is_test_for_gpu():
            print('skipped testing quantized_fc for gpu uint8 since it is not supported yet')
            return

        data = mx.sym.Variable(name='data', shape=data_shape, dtype='float32')
        fc_fp32 = mx.sym.FullyConnected(data=data, num_hidden=num_hidden, no_bias=no_bias, flatten=flatten)
        arg_shapes, _, _ = fc_fp32.infer_shape(data=data_shape)
        arg_names = fc_fp32.list_arguments()
        fc_fp32_exe = fc_fp32.simple_bind(ctx=mx.current_context(), grad_req='null')
        if qdtype == 'uint8':
            data_low = 0.0
            data_high = 127.0
        else:
            data_low = -127.0
            data_high = 127.0
        fc_fp32_exe.arg_dict[arg_names[0]][:] = mx.nd.random.uniform(low=data_low, high=data_high,
                                                                     shape=data_shape).astype('int32')
        fc_fp32_exe.arg_dict[arg_names[1]][:] = mx.nd.random.uniform(low=-127.0, high=127.0,
                                                                     shape=arg_shapes[1]).astype('int32')
        if not no_bias:
            fc_fp32_exe.arg_dict[arg_names[2]][:] = mx.nd.random.uniform(low=-127.0, high=127.0,
                                                                         shape=arg_shapes[2]).astype('int32')
        output = fc_fp32_exe.forward()[0]

        qdata = mx.sym.Variable(name='qdata', shape=data_shape, dtype='int8')
        fc_int8 = mx.sym.contrib.quantized_fully_connected(data=qdata, num_hidden=num_hidden,
                                                           no_bias=no_bias, flatten=flatten)
        qarg_names = fc_int8.list_arguments()
        type_dict = {qarg_names[1]: 'int8'}
        if not no_bias:
            type_dict.update({qarg_names[2]: 'int8'})
        fc_int8_exe = fc_int8.simple_bind(ctx=mx.current_context(), type_dict=type_dict, grad_req='null')
        fc_int8_exe.arg_dict[qarg_names[0]][:] = fc_fp32_exe.arg_dict[arg_names[0]].astype(qdtype)
        fc_int8_exe.arg_dict[qarg_names[1]][:] = fc_fp32_exe.arg_dict[arg_names[1]].astype('int8')
        quantized_range = 127.0
        if no_bias:
            fc_int8_exe.arg_dict[qarg_names[2]][:] = -quantized_range
            fc_int8_exe.arg_dict[qarg_names[3]][:] = quantized_range
            fc_int8_exe.arg_dict[qarg_names[4]][:] = -quantized_range
            fc_int8_exe.arg_dict[qarg_names[5]][:] = quantized_range
        else:
            fc_int8_exe.arg_dict[qarg_names[2]][:] = fc_fp32_exe.arg_dict[arg_names[2]].astype('int8')
            fc_int8_exe.arg_dict[qarg_names[3]][:] = -quantized_range
            fc_int8_exe.arg_dict[qarg_names[4]][:] = quantized_range
            fc_int8_exe.arg_dict[qarg_names[5]][:] = -quantized_range
            fc_int8_exe.arg_dict[qarg_names[6]][:] = quantized_range
            fc_int8_exe.arg_dict[qarg_names[7]][:] = -quantized_range
            fc_int8_exe.arg_dict[qarg_names[8]][:] = quantized_range
        qoutput, min_range, max_range = fc_int8_exe.forward()

        if no_bias:
            assert_almost_equal(output.asnumpy(), qoutput.asnumpy())
        else:
            # with adding bias, accuracy loss should not be greater than one
            diff = mx.nd.abs(output - qoutput.astype(output.dtype))
            cond = mx.nd.lesser(2, diff).sum().asscalar()
            assert cond == 0

    for qdtype in ['int8', 'uint8']:
        check_quantized_fc((32, 512, 2, 2), 100, True, qdtype)
        check_quantized_fc((32, 111, 2, 2), 100, True, qdtype)
        check_quantized_fc((32, 512, 2, 2), 100, False, qdtype)
        check_quantized_fc((32, 111, 2, 2), 100, False, qdtype)

@with_seed()
def test_quantized_flatten():
    def check_quantized_flatten(shape, qdtype):
        if qdtype == 'uint8':
            data_low = 0.0
            data_high = 127.0
        else:
            data_low = -127.0
            data_high = 127.0
        qdata = mx.nd.random.uniform(low=data_low, high=data_high, shape=shape).astype(qdtype)
        min_data = mx.nd.array([-1023.343], dtype='float32')
        max_data = mx.nd.array([2343.324275], dtype='float32')
        qoutput, min_output, max_output = mx.nd.contrib.quantized_flatten(qdata, min_data, max_data)
        assert qoutput.ndim == 2
        assert qoutput.shape[0] == qdata.shape[0]
        assert qoutput.shape[1] == np.prod(qdata.shape[1:])
        assert same(qdata.asnumpy().flatten(), qoutput.asnumpy().flatten())
        assert same(min_data.asnumpy(), min_output.asnumpy())
        assert same(max_data.asnumpy(), max_output.asnumpy())

    for qdtype in ['int8', 'uint8']:
        check_quantized_flatten((10,), qdtype)
        check_quantized_flatten((10, 15), qdtype)
        check_quantized_flatten((10, 15, 18), qdtype)
        check_quantized_flatten((3, 4, 23, 23), qdtype)


@with_seed()
def test_quantize_params():
    data = mx.sym.Variable('data')
    conv = mx.sym.Convolution(data, kernel=(1, 1), num_filter=2048, name='conv')
    sym = mx.sym.BatchNorm(data=conv, eps=2e-05, fix_gamma=False, momentum=0.9, use_global_stats=False, name='bn')
    offline_params = [name for name in sym.list_arguments()
                      if not name.startswith('data') and not name.endswith('label')]
    params = {}
    for name in offline_params:
        params[name] = mx.nd.uniform(shape=(2, 2))
    qsym = mx.contrib.quant._quantize_symbol(sym, offline_params=offline_params)
    qparams = mx.contrib.quant._quantize_params(qsym, params)
    param_names = params.keys()
    qparam_names = qparams.keys()
    for name in qparam_names:
        if name.startswith('bn'):
            assert name in param_names
        elif name.startswith('conv'):
            assert name not in param_names
            assert name.find('quantize') != -1


def get_fp32_sym():
    data = mx.sym.Variable('data')
    conv = mx.sym.Convolution(data, kernel=(1, 1), num_filter=16, name='conv')
    bn = mx.sym.BatchNorm(data=conv, eps=2e-05, fix_gamma=False, momentum=0.9, use_global_stats=False, name='bn')
    act = mx.sym.Activation(data=bn, act_type='relu', name='relu')
    pool = mx.sym.Pooling(act, kernel=(4, 4), pool_type='avg', name='pool')
    fc = mx.sym.FullyConnected(pool, num_hidden=10, flatten=True, name='fc')
    sym = mx.sym.SoftmaxOutput(fc, grad_scale=1, ignore_label=-1, multi_output=False,
                               out_grad=False, preserve_shape=False, use_ignore=False, name='softmax')
    return sym


@with_seed()
def test_quantize_model():
    def check_quantize_model(qdtype):
        def check_params(params, qparams, qsym=None):
            if qsym is None:
                assert len(params) == len(qparams)
                for k, v in params.items():
                    assert k in qparams
                    assert same(v.asnumpy(), qparams[k].asnumpy())
            else:
                qparams_ground_truth = mx.contrib.quant._quantize_params(qsym, params)
                assert len(qparams) == len(qparams_ground_truth)
                for k, v in qparams_ground_truth.items():
                    assert k in qparams
                    assert same(v.asnumpy(), qparams[k].asnumpy())

        def check_qsym_calibrated(qsym):
            attrs = qsym.attr_dict()
            for k, v in attrs.items():
                if k.find('requantize_') != -1:
                    assert 'min_calib_range' in v
                    assert 'max_calib_range' in v

        def check_qsym_qdtype(qsym, qdtype):
            attrs = qsym.attr_dict()
            for k, v in attrs.items():
                if k.find('_quantize') != -1:
                    assert 'out_type' in v
                    assert v['out_type'] == qdtype

        sym = get_fp32_sym()
        mod = Module(symbol=sym)
        batch_size = 4
        data_shape = (batch_size, 4, 10, 10)
        label_shape = (batch_size, 10)
        mod.bind(data_shapes=[('data', data_shape)], label_shapes=[('softmax_label', label_shape)])
        mod.init_params()
        arg_params, aux_params = mod.get_params()
        qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=sym,
                                                                         arg_params=arg_params,
                                                                         aux_params=aux_params,
                                                                         ctx=mx.current_context(),
                                                                         quantized_dtype=qdtype,
                                                                         calib_mode='none')
        check_params(arg_params, qarg_params, qsym)
        check_params(aux_params, qaux_params)

        calib_data = mx.nd.random.uniform(shape=data_shape)
        calib_data = NDArrayIter(data=calib_data)
        calib_data = DummyIter(calib_data)
        qsym, qarg_params, qaux_params = mx.contrib.quant.quantize_model(sym=sym,
                                                                         arg_params=arg_params,
                                                                         aux_params=aux_params,
                                                                         ctx=mx.current_context(),
                                                                         quantized_dtype=qdtype,
                                                                         calib_mode='naive',
                                                                         calib_data=calib_data,
                                                                         num_calib_examples=20)
        check_params(arg_params, qarg_params, qsym)
        check_params(aux_params, qaux_params)
        check_qsym_calibrated(qsym)
        check_qsym_qdtype(qsym, qdtype)

    for qdtype in ['int8', 'uint8']:
        check_quantize_model(qdtype)


@with_seed()
def test_quantize_sym_with_calib():
    sym = get_fp32_sym()
    offline_params = [name for name in sym.list_arguments()
                      if not name.startswith('data') and not name.endswith('label')]
    qsym = mx.contrib.quant._quantize_symbol(sym, offline_params=offline_params)
    requantize_op_names = ['requantize_conv', 'requantize_fc']
    th_dict = {'conv_output': (np.random.uniform(low=100.0, high=200.0), np.random.uniform(low=100.0, high=200.0)),
               'fc_output': (np.random.uniform(low=100.0, high=200.0), np.random.uniform(low=100.0, high=200.0))}
    op_name_to_th_name = {'requantize_conv': 'conv_output', 'requantize_fc': 'fc_output'}
    cqsym = mx.contrib.quant._calibrate_quantized_sym(qsym, th_dict)
    attr_dict = cqsym.attr_dict()
    for name in requantize_op_names:
        assert name in attr_dict
        lhs = float(attr_dict[name]['min_calib_range'])
        rhs = th_dict[op_name_to_th_name[name]][0]
        assert_almost_equal(np.array([lhs]), np.array([rhs]))
        lhs = float(attr_dict[name]['max_calib_range'])
        rhs = th_dict[op_name_to_th_name[name]][1]
        assert_almost_equal(np.array([lhs]), np.array([rhs]), rtol=1e-3, atol=1e-4)


@with_seed()
def test_smooth_distribution():
    assert_exception(lambda: mx.contrib.quant._smooth_distribution(np.zeros((2,)), eps=1e-3), ValueError)
    dirac_delta = np.zeros((5,))
    dirac_delta[2] = 1
    smooth_dirac_delta = dirac_delta.copy()
    smooth_dirac_delta += 1e-3
    smooth_dirac_delta[2] -= 5e-3
    assert_almost_equal(mx.contrib.quant._smooth_distribution(dirac_delta, eps=1e-3), smooth_dirac_delta)


@with_seed()
def test_optimal_threshold_adversarial_case():
    # The worst case for the optimal_threshold function is when the values are concentrated
    # at one edge: [0, 0, ..., 1000]. (histogram)
    # We want to make sure that the optimal threshold in this case is the max.
    arr = np.array([2]*1000)
    res = mx.contrib.quant._get_optimal_threshold(arr, num_quantized_bins=5)
    # The threshold should be 2.
    assert res[3] - 2 < 1e-5


@with_seed()
@unittest.skip("Flaky test: https://github.com/apache/incubator-mxnet/issues/11456")
def test_get_optimal_thresholds():
    # Given an ndarray with elements following a uniform distribution, the optimal threshold
    # for quantizing the ndarray should be either abs(min(nd)) or abs(max(nd)).
    def get_threshold(nd):
        min_nd = mx.nd.min(nd)
        max_nd = mx.nd.max(nd)
        return mx.nd.maximum(mx.nd.abs(min_nd), mx.nd.abs(max_nd)).asnumpy()

    nd_dict = {'layer1': mx.nd.uniform(low=-10.532, high=11.3432, shape=(8, 3, 23, 23))}
    expected_threshold = get_threshold(nd_dict['layer1'])
    th_dict = mx.contrib.quant._get_optimal_thresholds(nd_dict)
    assert 'layer1' in th_dict
    assert_almost_equal(np.array([th_dict['layer1'][1]]), expected_threshold, rtol=0.001, atol=0.001)


if __name__ == "__main__":
    import nose
    nose.runmodule()