# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_1/inductor_cache/qi/cqi3gcnggm7n2eogjvogbs2d42nztjvpa3tgbc7bn2ipfavwmjz2.py
# Unsorted Source Nodes: [], Original ATen: []
# Source node to ATen node mapping:
triton_poi_fused_0 = async_compile.triton('triton_poi_fused_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[4096, 4096], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 4096
    xnumel = 4096
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = tl.full([XBLOCK, YBLOCK], True, tl.int1)
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = 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)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/5p/c5pcngtks4tpy3tsl6qitoja56awxtdmm7my5kwgvvu43iv54a64.py
# Topologically Sorted Source Nodes: [reduced_latents], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   reduced_latents => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_2, %primals_1, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_1 = async_compile.triton('triton_poi_fused_convolution_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256, 4096], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_convolution_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_convolution_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 256
    xnumel = 4096
    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 = 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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Unsorted Source Nodes: [], Original ATen: []
        stream0 = get_raw_stream(0)
        triton_poi_fused_0.run(primals_2, buf0, 4096, 4096, grid=grid(4096, 4096), stream=stream0)
        del primals_2
        buf1 = empty_strided_cuda((4, 1024, 64, 64), (4194304, 1, 65536, 1024), torch.float32)
        # Unsorted Source Nodes: [], Original ATen: []
        triton_poi_fused_0.run(primals_4, buf1, 4096, 4096, grid=grid(4096, 4096), stream=stream0)
        del primals_4
        # Topologically Sorted Source Nodes: [reduced_latents], Original ATen: [aten.convolution]
        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)
        # Topologically Sorted Source Nodes: [reduced_latents], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf2, buf3, 256, 4096, grid=grid(256, 4096), stream=stream0)
        # Topologically Sorted Source Nodes: [prediction], Original ATen: [aten.convolution]
        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  # reuse
        # Topologically Sorted Source Nodes: [prediction], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf4, buf5, 256, 4096, grid=grid(256, 4096), stream=stream0)
        del buf4
    return (buf3, buf5, primals_1, buf0, primals_3, buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((64, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 1024, 64, 64), (4194304, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, 1024, 1, 1), (1024, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 1024, 64, 64), (4194304, 4096, 64, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn


class PredictorCNN(nn.Module):

    def __init__(self, latent_dim=1024, reduced_dim=64):
        super(PredictorCNN, 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 forward(self, latents, context):
        reduced_latents = self.conv1(latents)
        prediction = self.conv2(context)
        return reduced_latents, prediction

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


def get_inputs():
    return [torch.rand([4, 1024, 64, 64]), torch.rand([4, 1024, 64, 64])]


def get_init_inputs():
    return [[], {}]

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]
