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
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 128
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + (y0 + 4 * x2 + 36 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 16 * y3), xmask & ymask)
    tl.store(out_ptr0 + (y0 + 4 * x2 + 64 * y1), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 32
    y1 = yindex // 32
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 32 * x2 + 288 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    xnumel = 9
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 64
    y1 = yindex // 64
    tmp0 = tl.load(in_ptr0 + (x2 + 9 * y3), xmask, eviction_policy='evict_last'
        )
    tl.store(out_ptr0 + (y0 + 64 * x2 + 576 * y1), tmp0, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_4(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 32
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)


@triton.jit
def triton_poi_fused_convolution_relu_5(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)


@triton.jit
def triton_poi_fused_convolution_relu_6(in_out_ptr0, in_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 128
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, 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, None)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_7(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    ynumel = 16
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 4 * x2 + 64 * y1), tmp6, xmask & ymask)


@triton.jit
def triton_per_fused__log_softmax_8(in_ptr0, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, float('-inf'))
    tmp4 = triton_helpers.max2(tmp3, 1)[:, None]
    tmp5 = tmp0 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.where(xmask, tmp7, 0)
    tmp10 = tl.sum(tmp9, 1)[:, None]
    tmp11 = tl_math.log(tmp10)
    tmp12 = tmp5 - tmp11
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp12, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_9(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.
    constexpr):
    ynumel = 8
    xnumel = 16
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 2
    y1 = yindex // 2
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 2 * x2 + 32 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp4, xmask & ymask)
    tl.store(out_ptr1 + (y0 + 2 * x2 + 32 * y1), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_relu_10(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    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_poi_fused_tanh_11(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, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17) = args
    args.clear()
    assert_size_stride(primals_1, (32, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (32,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (64, 32, 3, 3), (288, 9, 3, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (128, 64, 3, 3), (576, 9, 3, 1))
    assert_size_stride(primals_7, (128,), (1,))
    assert_size_stride(primals_8, (4, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (16, 64), (64, 1))
    assert_size_stride(primals_11, (16,), (1,))
    assert_size_stride(primals_12, (2, 128, 1, 1), (128, 1, 1, 1))
    assert_size_stride(primals_13, (2,), (1,))
    assert_size_stride(primals_14, (64, 32), (32, 1))
    assert_size_stride(primals_15, (64,), (1,))
    assert_size_stride(primals_16, (1, 64), (64, 1))
    assert_size_stride(primals_17, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((32, 4, 3, 3), (36, 1, 12, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(128, 9)](primals_1, buf0, 128, 9, XBLOCK=16,
            YBLOCK=64, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.float32)
        triton_poi_fused_1[grid(16, 16)](primals_3, buf1, 16, 16, XBLOCK=16,
            YBLOCK=16, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((64, 32, 3, 3), (288, 1, 96, 32), torch.
            float32)
        triton_poi_fused_2[grid(2048, 9)](primals_4, buf2, 2048, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_4
        buf3 = empty_strided_cuda((128, 64, 3, 3), (576, 1, 192, 64), torch
            .float32)
        triton_poi_fused_3[grid(8192, 9)](primals_6, buf3, 8192, 9, XBLOCK=
            16, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_6
        buf4 = extern_kernels.convolution(buf1, buf0, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 32, 4, 4), (512, 1, 128, 32))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_relu_4[grid(2048)](buf5, primals_2, 
            2048, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_2
        buf6 = extern_kernels.convolution(buf5, buf2, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf6, (4, 64, 4, 4), (1024, 1, 256, 64))
        buf7 = buf6
        del buf6
        triton_poi_fused_convolution_relu_5[grid(4096)](buf7, primals_5, 
            4096, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
        buf8 = extern_kernels.convolution(buf7, buf3, stride=(1, 1),
            padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf8, (4, 128, 4, 4), (2048, 1, 512, 128))
        buf9 = buf8
        del buf8
        triton_poi_fused_convolution_relu_6[grid(8192)](buf9, primals_7, 
            8192, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf10 = extern_kernels.convolution(buf9, primals_8, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf10, (4, 4, 4, 4), (64, 1, 16, 4))
        buf11 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        buf23 = empty_strided_cuda((4, 4, 4, 4), (64, 1, 16, 4), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_7[grid(16, 16)](
            buf10, primals_9, buf11, buf23, 16, 16, XBLOCK=16, YBLOCK=16,
            num_warps=4, num_stages=1)
        del primals_9
        buf12 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf11, (4, 64),
            (64, 1), 0), reinterpret_tensor(primals_10, (64, 16), (1, 64), 
            0), alpha=1, beta=1, out=buf12)
        del primals_11
        buf15 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        triton_per_fused__log_softmax_8[grid(4)](buf12, buf15, 4, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf12
        buf16 = extern_kernels.convolution(buf9, primals_12, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf16, (4, 2, 4, 4), (32, 1, 8, 2))
        buf17 = empty_strided_cuda((4, 2, 4, 4), (32, 16, 4, 1), torch.float32)
        buf22 = empty_strided_cuda((4, 2, 4, 4), (32, 1, 8, 2), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_9[grid(8, 16)](
            buf16, primals_13, buf17, buf22, 8, 16, XBLOCK=16, YBLOCK=2,
            num_warps=1, num_stages=1)
        del buf16
        del primals_13
        buf18 = reinterpret_tensor(buf10, (4, 64), (64, 1), 0)
        del buf10
        extern_kernels.mm(reinterpret_tensor(buf17, (4, 32), (32, 1), 0),
            reinterpret_tensor(primals_14, (32, 64), (1, 32), 0), out=buf18)
        buf19 = buf18
        del buf18
        triton_poi_fused_relu_10[grid(256)](buf19, primals_15, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_15
        buf20 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf19, reinterpret_tensor(primals_16, (64, 1), (1,
            64), 0), out=buf20)
        buf21 = buf20
        del buf20
        triton_poi_fused_tanh_11[grid(4)](buf21, primals_17, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        del primals_17
    return (buf15, buf21, buf0, buf1, buf2, buf3, primals_8, primals_12,
        buf5, buf7, buf9, reinterpret_tensor(buf11, (4, 64), (64, 1), 0),
        buf15, reinterpret_tensor(buf17, (4, 32), (32, 1), 0), buf19, buf21,
        primals_16, primals_14, buf22, primals_10, buf23)


class NetNew(nn.Module):
    """policy-value network module"""

    def __init__(self, board_width, board_height):
        super(NetNew, self).__init__()
        self.board_width = board_width
        self.board_height = board_height
        self.conv1 = nn.Conv2d(4, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.act_conv1 = nn.Conv2d(128, 4, kernel_size=1)
        self.act_fc1 = nn.Linear(4 * board_width * board_height, 
            board_width * board_height)
        self.val_conv1 = nn.Conv2d(128, 2, kernel_size=1)
        self.val_fc1 = nn.Linear(2 * board_width * board_height, 64)
        self.val_fc2 = nn.Linear(64, 1)

    def forward(self, input_0):
        primals_1 = self.conv1.weight
        primals_2 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.act_conv1.weight
        primals_9 = self.act_conv1.bias
        primals_10 = self.act_fc1.weight
        primals_11 = self.act_fc1.bias
        primals_12 = self.val_conv1.weight
        primals_13 = self.val_conv1.bias
        primals_14 = self.val_fc1.weight
        primals_15 = self.val_fc1.bias
        primals_16 = self.val_fc2.weight
        primals_17 = self.val_fc2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17])
        return output[0], output[1]


Measured latency_ms: 0.023656