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# to you under the Apache License, Version 2.0 (the
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#   http://www.apache.org/licenses/LICENSE-2.0
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# pylint: disable=invalid-name, too-many-locals, too-many-arguments
"""Bitserial Dense operator."""
from __future__ import absolute_import
import tvm
from tvm import te
from topi.util import get_const_tuple
from .bitserial_util import bitpack

def bitserial_dense(data, weight, data_bits, weight_bits, pack_dtype='uint32',
                    out_dtype='int16', unipolar=True):
    """The default implementation of bitserial dense in topi.

    Parameters
    ----------
    data : tvm.te.Tensor
        2-D with shape [batch, in_dim]
    weight : tvm.te.Tensor
        2-D with shape [out_dim, in_dim] or
        3-D with shape [out_dim, weight_bits, in_dim]
    Returns
    -------
    output : tvm.te.Tensor
        2-D with shape [batch, out_dim]
    """
    data_packed = bitpack(data, data_bits, pack_axis=1, bit_axis=1, pack_type=pack_dtype)
    if len(weight.shape) == 2:
        weight_packed = bitpack(weight, weight_bits, pack_axis=1, bit_axis=1, pack_type=pack_dtype)
    else:
        weight_packed = weight
    Y, DB, K = get_const_tuple(data_packed.shape)
    X, WB, _ = get_const_tuple(weight_packed.shape)

    oshape = (Y, X)
    k = te.reduce_axis((0, K), name='k')
    db = te.reduce_axis((0, DB), name='db')
    wb = te.reduce_axis((0, WB), name='wb')

    matmul_unipolar = te.compute(oshape, lambda i, j: te.sum(
        (tvm.tir.popcount(weight_packed[j, wb, k] & data_packed[i, db, k]) -
         tvm.tir.popcount(~weight_packed[j, wb, k] & data_packed[i, db, k])).astype(out_dtype)
        << (db+wb).astype(out_dtype), axis=[wb, db, k]),
                                 tag='bitserial_dense_unipolar')

    matmul = te.compute(oshape, lambda i, j: te.sum(
        tvm.tir.popcount(weight_packed[j, wb, k] & data_packed[i, db, k]).astype(out_dtype)
        << (db+wb).astype(out_dtype), axis=[wb, db, k]), tag='bitserial_dense')


    if unipolar:
        return matmul_unipolar
    return matmul
