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
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):
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y3 = yindex
    y0 = yindex % 1024
    y1 = yindex // 1024
    tmp0 = tl.load(in_ptr0 + (x2 + 4096 * y3), None)
    tl.store(out_ptr0 + (y0 + 1024 * x2 + 4194304 * y1), tmp0, None)


@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    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]
    tl.full([XBLOCK, YBLOCK], True, tl.int1)
    x2 = xindex
    y0 = yindex % 64
    y1 = yindex // 64
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 64 * x2 + 262144 * y1), ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4096 * y3), tmp0, ymask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (64, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_2, (4, 1024, 64, 64), (4194304, 4096, 64, 1))
    assert_size_stride(primals_3, (64, 1024, 1, 1), (1024, 1, 1, 1))
    assert_size_stride(primals_4, (4, 1024, 64, 64), (4194304, 4096, 64, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1024, 64, 64), (4194304, 1, 65536, 
            1024), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(4096, 4096)](primals_2, buf0, 4096, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_2
        buf1 = empty_strided_cuda((4, 1024, 64, 64), (4194304, 1, 65536, 
            1024), torch.float32)
        triton_poi_fused_0[grid(4096, 4096)](primals_4, buf1, 4096, 4096,
            XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_4
        buf2 = extern_kernels.convolution(buf0, primals_1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf3 = empty_strided_cuda((4, 64, 64, 64), (262144, 4096, 64, 1),
            torch.float32)
        triton_poi_fused_convolution_1[grid(256, 4096)](buf2, buf3, 256, 
            4096, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        buf4 = extern_kernels.convolution(buf1, primals_3, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (4, 64, 64, 64), (262144, 1, 4096, 64))
        buf5 = reinterpret_tensor(buf2, (4, 64, 64, 64), (262144, 4096, 64,
            1), 0)
        del buf2
        triton_poi_fused_convolution_1[grid(256, 4096)](buf4, buf5, 256, 
            4096, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        del buf4
    return buf3, buf5, primals_1, buf0, primals_3, buf1


class PredictorCNNNew(nn.Module):

    def __init__(self, latent_dim=1024, reduced_dim=64):
        super(PredictorCNNNew, self).__init__()
        self.latent_dim = latent_dim
        self.reduced_dim = reduced_dim
        self.conv1 = nn.Conv2d(self.latent_dim, self.reduced_dim, 1, bias=False
            )
        self.conv2 = nn.Conv2d(self.latent_dim, self.reduced_dim, 1, bias=False
            )

    def train_order_block_ids(self):
        return [[0, 1]]

    def forward(self, input_0, input_1):
        primals_1 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]


Measured latency_ms: 0.605128