"""Computation of reductions on vectors."""



__copyright__ = "Copyright (C) 2009 Andreas Kloeckner"

__license__ = """
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Based on code/ideas by Mark Harris <mharris@nvidia.com>.

Original License:

Copyright 1993-2007 NVIDIA Corporation.  All rights reserved.

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"""




from pycuda.tools import context_dependent_memoize
from pycuda.tools import dtype_to_ctype
import numpy as np




def get_reduction_module(out_type, block_size,
        neutral, reduce_expr, map_expr, arguments,
        name="reduce_kernel", keep=False, options=None, preamble=""):

    from pycuda.compiler import SourceModule
    src = """
        #include <pycuda-complex.hpp>

        #define BLOCK_SIZE %(block_size)d
        #define READ_AND_MAP(i) (%(map_expr)s)
        #define REDUCE(a, b) (%(reduce_expr)s)

        %(preamble)s

        typedef %(out_type)s out_type;

        extern "C"
        __global__
        void %(name)s(out_type *out, %(arguments)s,
          unsigned int seq_count, unsigned int n)
        {
          // Needs to be variable-size to prevent the braindead CUDA compiler from
          // running constructors on this array. Grrrr.
          extern __shared__ out_type sdata[];

          unsigned int tid = threadIdx.x;

          unsigned int i = blockIdx.x*BLOCK_SIZE*seq_count + tid;

          out_type acc = %(neutral)s;
          for (unsigned s = 0; s < seq_count; ++s)
          {
            if (i >= n)
              break;
            acc = REDUCE(acc, READ_AND_MAP(i));

            i += BLOCK_SIZE;
          }

          sdata[tid] = acc;

          __syncthreads();

          #if (BLOCK_SIZE >= 512)
            if (tid < 256) { sdata[tid] = REDUCE(sdata[tid], sdata[tid + 256]); }
            __syncthreads();
          #endif

          #if (BLOCK_SIZE >= 256)
            if (tid < 128) { sdata[tid] = REDUCE(sdata[tid], sdata[tid + 128]); }
            __syncthreads();
          #endif

          #if (BLOCK_SIZE >= 128)
            if (tid < 64) { sdata[tid] = REDUCE(sdata[tid], sdata[tid + 64]); }
            __syncthreads();
          #endif

          if (tid < 32)
          {
            // 'volatile' required according to Fermi compatibility guide 1.2.2
            volatile out_type *smem = sdata;
            if (BLOCK_SIZE >= 64) smem[tid] = REDUCE(smem[tid], smem[tid + 32]);
            if (BLOCK_SIZE >= 32) smem[tid] = REDUCE(smem[tid], smem[tid + 16]);
            if (BLOCK_SIZE >= 16) smem[tid] = REDUCE(smem[tid], smem[tid + 8]);
            if (BLOCK_SIZE >= 8)  smem[tid] = REDUCE(smem[tid], smem[tid + 4]);
            if (BLOCK_SIZE >= 4)  smem[tid] = REDUCE(smem[tid], smem[tid + 2]);
            if (BLOCK_SIZE >= 2)  smem[tid] = REDUCE(smem[tid], smem[tid + 1]);
          }

          if (tid == 0) out[blockIdx.x] = sdata[0];
        }
        """ % {
            "out_type": out_type,
            "arguments": arguments,
            "block_size": block_size,
            "neutral": neutral,
            "reduce_expr": reduce_expr,
            "map_expr": map_expr,
            "name": name,
            "preamble": preamble
            }
    return SourceModule(src, options=options, keep=keep, no_extern_c=True)




def get_reduction_kernel_and_types(stage, out_type, block_size,
        neutral, reduce_expr, map_expr=None, arguments=None,
        name="reduce_kernel", keep=False, options=None, preamble=""):

    if stage == 1:
        if map_expr is None:
            map_expr = "in[i]"

    elif stage == 2:
        if map_expr is None:
            map_expr = "pycuda_reduction_inp[i]"

        in_arg = "const %s *pycuda_reduction_inp" % out_type
        if arguments:
            arguments = in_arg + ", " + arguments
        else:
            arguments = in_arg

    else:
        assert False

    mod = get_reduction_module(out_type, block_size,
            neutral, reduce_expr, map_expr, arguments,
            name, keep, options, preamble)

    from pycuda.tools import get_arg_type
    func = mod.get_function(name)
    arg_types = [get_arg_type(arg) for arg in arguments.split(",")]
    func.prepare("P%sII" % "".join(arg_types))

    return func, arg_types




class ReductionKernel:
    def __init__(self, dtype_out,
            neutral, reduce_expr, map_expr=None, arguments=None,
            name="reduce_kernel", keep=False, options=None, preamble=""):

        self.dtype_out = np.dtype(dtype_out)

        self.block_size = 512

        s1_func, self.stage1_arg_types = get_reduction_kernel_and_types(
                1, dtype_to_ctype(dtype_out), self.block_size,
                neutral, reduce_expr, map_expr,
                arguments, name=name+"_stage1", keep=keep, options=options,
                preamble=preamble)
        self.stage1_func = s1_func.prepared_async_call

        # stage 2 has only one input and no map expression
        s2_func, self.stage2_arg_types = get_reduction_kernel_and_types(
                2, dtype_to_ctype(dtype_out), self.block_size,
                neutral, reduce_expr, arguments=arguments,
                name=name+"_stage2", keep=keep, options=options,
                preamble=preamble)
        self.stage2_func = s2_func.prepared_async_call

        assert [i for i, arg_tp in enumerate(self.stage1_arg_types) if arg_tp == "P"], \
                "ReductionKernel can only be used with functions that have at least one " \
                "vector argument"

    def __call__(self, *args, **kwargs):
        MAX_BLOCK_COUNT = 1024
        SMALL_SEQ_COUNT = 4

        s1_func = self.stage1_func
        s2_func = self.stage2_func

        kernel_wrapper = kwargs.get("kernel_wrapper")
        if kernel_wrapper is not None:
            s1_func = kernel_wrapper(s1_func)
            s2_func = kernel_wrapper(s2_func)

        stream = kwargs.get("stream")

        from .gpuarray import empty

        f = s1_func
        arg_types = self.stage1_arg_types

        stage1_args = args

        while True:
            invocation_args = []
            vectors = []

            for arg, arg_tp in zip(args, arg_types):
                if arg_tp == "P":
                    if not arg.flags.forc:
                        raise RuntimeError("ReductionKernel cannot "
                                "deal with non-contiguous arrays")

                    vectors.append(arg)
                    invocation_args.append(arg.gpudata)
                else:
                    invocation_args.append(arg)

            repr_vec = vectors[0]
            sz = repr_vec.size

            if sz <= self.block_size*SMALL_SEQ_COUNT*MAX_BLOCK_COUNT:
                total_block_size = SMALL_SEQ_COUNT*self.block_size
                block_count = (sz + total_block_size - 1) // total_block_size
                seq_count = SMALL_SEQ_COUNT
            else:
                block_count = MAX_BLOCK_COUNT
                macroblock_size = block_count*self.block_size
                seq_count = (sz + macroblock_size - 1) // macroblock_size

            if block_count == 1:
                result = empty((), self.dtype_out, repr_vec.allocator)
            else:
                result = empty((block_count,), self.dtype_out, repr_vec.allocator)

            kwargs = dict(shared_size=self.block_size*self.dtype_out.itemsize)

            #print block_count, seq_count, self.block_size, sz
            f((block_count, 1), (self.block_size, 1, 1), stream,
                    *([result.gpudata]+invocation_args+[seq_count, sz]),
                    **kwargs)

            if block_count == 1:
                return result
            else:
                f = s2_func
                arg_types = self.stage2_arg_types
                args = (result,) + stage1_args




@context_dependent_memoize
def get_sum_kernel(dtype_out, dtype_in):
    if dtype_out is None:
        dtype_out = dtype_in

    return ReductionKernel(dtype_out, "0", "a+b",
            arguments="const %(tp)s *in" % {"tp": dtype_to_ctype(dtype_in)})




@context_dependent_memoize
def get_subset_sum_kernel(dtype_out, dtype_subset, dtype_in):
    if dtype_out is None:
        dtype_out = dtype_in

    return ReductionKernel(dtype_out, "0", "a+b",
            map_expr="in[lookup_tbl[i]]",
            arguments="const %(tp_lut)s *lookup_tbl, const %(tp)s *in"
            % {
                "tp": dtype_to_ctype(dtype_in),
                "tp_lut": dtype_to_ctype(dtype_subset),
                })




@context_dependent_memoize
def get_dot_kernel(dtype_out, dtype_a, dtype_b):
    return ReductionKernel(dtype_out, neutral="0",
            reduce_expr="a+b", map_expr="a[i]*b[i]",
            arguments="const %(tp_a)s *a, const %(tp_b)s *b" % {
                "tp_a": dtype_to_ctype(dtype_a),
                "tp_b": dtype_to_ctype(dtype_b),
                }, keep=True)




@context_dependent_memoize
def get_subset_dot_kernel(dtype_out, dtype_subset, dtype_a=None, dtype_b=None):
    if dtype_out is None:
        dtype_out = dtype_a

    if dtype_b is None:
        if dtype_a is None:
            dtype_b = dtype_out
        else:
            dtype_b = dtype_a

    if dtype_a is None:
        dtype_a = dtype_out

    # important: lookup_tbl must be first--it controls the length
    return ReductionKernel(dtype_out, neutral="0",
            reduce_expr="a+b", map_expr="a[lookup_tbl[i]]*b[lookup_tbl[i]]",
            arguments="const %(tp_lut)s *lookup_tbl, "
            "const %(tp_a)s *a, const %(tp_b)s *b" % {
            "tp_a": dtype_to_ctype(dtype_a),
            "tp_b": dtype_to_ctype(dtype_b),
            "tp_lut": dtype_to_ctype(dtype_subset),
            })




def get_minmax_neutral(what, dtype):
    dtype = np.dtype(dtype)
    if issubclass(dtype.type, np.inexact):
        if what == "min":
            return "MY_INFINITY"
        elif what == "max":
            return "-MY_INFINITY"
        else:
            raise ValueError("what is not min or max.")
    else:
        if what == "min":
            return str(np.iinfo(dtype).max)
        elif what == "max":
            return str(np.iinfo(dtype).min)
        else:
            raise ValueError("what is not min or max.")




@context_dependent_memoize
def get_minmax_kernel(what, dtype):
    if dtype == np.float64:
        reduce_expr = "f%s(a,b)" % what
    elif dtype == np.float32:
        reduce_expr = "f%sf(a,b)" % what
    elif dtype.kind in "iu":
        reduce_expr = "%s(a,b)" % what
    else:
        raise TypeError("unsupported dtype specified")

    return ReductionKernel(dtype,
            neutral=get_minmax_neutral(what, dtype),
            reduce_expr="%(reduce_expr)s" % {"reduce_expr": reduce_expr},
            arguments="const %(tp)s *in" % {
                "tp": dtype_to_ctype(dtype),
                }, preamble="#define MY_INFINITY (1./0)")




@context_dependent_memoize
def get_subset_minmax_kernel(what, dtype, dtype_subset):
    if dtype == np.float64:
        reduce_expr = "f%s(a,b)" % what
    elif dtype == np.float32:
        reduce_expr = "f%sf(a,b)" % what
    elif dtype.kind in "iu":
        reduce_expr = "%s(a,b)" % what
    else:
        raise TypeError("unsupported dtype specified")

    return ReductionKernel(dtype,
            neutral=get_minmax_neutral(what, dtype),
            reduce_expr="%(reduce_expr)s" % {"reduce_expr": reduce_expr},
            map_expr="in[lookup_tbl[i]]",
            arguments="const %(tp_lut)s *lookup_tbl, "
            "const %(tp)s *in"  % {
            "tp": dtype_to_ctype(dtype),
            "tp_lut": dtype_to_ctype(dtype_subset),
            }, preamble="#define MY_INFINITY (1./0)")
