import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 8000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2000
    x1 = xindex // 2000
    tmp0 = tl.load(in_out_ptr0 + (x0 + 2016 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + (x0 + 2016 * x1), tmp4, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 4000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 1000
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_per_fused__log_softmax_exp_relu_2(in_out_ptr0, in_ptr0, out_ptr0,
    out_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    rnumel = 225
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 225 * x0), rmask & xmask, other=0.0)
    tmp1 = tl.full([1, 1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(rmask & xmask, tmp3, float('-inf'))
    tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
    tmp7 = tmp2 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK, RBLOCK])
    tmp11 = tl.where(rmask & xmask, tmp9, 0)
    tmp12 = tl.sum(tmp11, 1)[:, None]
    tmp13 = tl_math.log(tmp12)
    tmp14 = tmp7 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp13, xmask)
    tl.store(out_ptr1 + (r1 + 225 * x0), tmp15, rmask & xmask)
    tl.store(out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_tanh_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = libdevice.tanh(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 225), (225, 1))
    assert_size_stride(primals_2, (2000, 225), (225, 1))
    assert_size_stride(primals_3, (2000,), (1,))
    assert_size_stride(primals_4, (1000, 2000), (2000, 1))
    assert_size_stride(primals_5, (1000,), (1,))
    assert_size_stride(primals_6, (225, 1000), (1000, 1))
    assert_size_stride(primals_7, (225,), (1,))
    assert_size_stride(primals_8, (1, 1000), (1000, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 2000), (2016, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (225, 
            2000), (1, 225), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(8000)](buf1, primals_3, 8000, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 1000), (1000, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_4, (2000, 1000),
            (1, 2000), 0), out=buf2)
        buf3 = buf2
        del buf2
        triton_poi_fused_relu_1[grid(4000)](buf3, primals_5, 4000, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 225), (225, 1), torch.float32)
        extern_kernels.addmm(primals_7, buf3, reinterpret_tensor(primals_6,
            (1000, 225), (1, 1000), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf7 = reinterpret_tensor(buf6, (4, 1), (1, 1), 0)
        del buf6
        buf8 = empty_strided_cuda((4, 225), (225, 1), torch.float32)
        triton_per_fused__log_softmax_exp_relu_2[grid(4)](buf7, buf4, buf5,
            buf8, 4, 225, XBLOCK=1, num_warps=2, num_stages=1)
        buf9 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_8, (1000, 1), (1,
            1000), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_tanh_3[grid(4)](buf10, primals_9, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_9
    return (buf8, buf10, primals_1, buf1, buf3, buf4, buf5, buf7, buf8,
        buf10, primals_8, primals_6, primals_4)


def create_all_possible_moves(m, n):
    """Create all moves on a (m,n) board."""
    moves = []
    for i in range(m):
        for j in range(n):
            moves.append((i, j))
    return list(set(moves))


class MLPNew(nn.Module):
    """3-layer MLP for AlphaZero"""

    def __init__(self, board_size=15, num_hidden1=2000, num_hidden2=1000):
        super(MLPNew, self).__init__()
        self.board_size = board_size
        self.all_moves = create_all_possible_moves(board_size, board_size)
        self.num_hidden1 = num_hidden1
        self.num_hidden2 = num_hidden2
        if len(self.all_moves) != self.board_size ** 2:
            raise ValueError("moves and board don't match")
        self.fc1 = nn.Linear(self.board_size ** 2, self.num_hidden1)
        self.fc2 = nn.Linear(self.num_hidden1, self.num_hidden2)
        self.logsoftmax = nn.LogSoftmax(dim=1)
        self.fc3 = nn.Linear(self.num_hidden2, self.board_size ** 2)
        self.fc4 = nn.Linear(self.num_hidden2, 1)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_8 = self.fc4.weight
        primals_9 = self.fc4.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0], output[1]


Measured latency_ms: 0.086702