# 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_6/inductor_cache/qi/cqinh332474qtv7bgen4bcfz2yfclns66jnudr7z7wmvlrgqoduc.py
# Topologically Sorted Source Nodes: [targetT], Original ATen: [aten.clone, aten.transpose]
# Source node to ATen node mapping:
#   targetT => clone
# Graph fragment:
#   %clone : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
#   %permute_5 : [num_users=1] = call_function[target=torch.ops.aten.permute.default](args = (%clone, [0, 2, 1]), kwargs = {})
triton_poi_fused_clone_transpose_0 = async_compile.triton('triton_poi_fused_clone_transpose_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=[16, 16], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_transpose_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', '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_clone_transpose_0(in_ptr0, 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
    x1 = xindex
    y0 = yindex
    y2 = yindex % 4
    y3 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x1 + (16*y0)), xmask & ymask)
    tl.store(out_ptr0 + (x1 + (16*y0)), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y2 + (4*x1) + (64*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/hz/chz2sqsqk26mwhf2dxhgh44jfpu2er5yqjftwkzfav5ctqtx5e7f.py
# Topologically Sorted Source Nodes: [attn_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_2 => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_1, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', '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__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/pm/cpmy57yidxxfl6wmlh5dsizlsat4uz6k43rz6t4r6h2u4z625i5l.py
# Topologically Sorted Source Nodes: [attn_4], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   attn_4 => clone_1
# Graph fragment:
#   %clone_1 : [num_users=3] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', '''
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=[16, 16], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', '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_clone_2(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
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (64*y1)), xmask & ymask)
    tmp1 = tl.load(in_ptr0 + ((4*x2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2 + (16*y3)), tmp8, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv2d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_2, (4, 4, 4, 1), (16, 4, 1, 1), 0), primals_3, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 1), (16, 4, 1, 1))
        buf1 = empty_strided_cuda((4, 16, 4), (64, 1, 16), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 16), (64, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [targetT], Original ATen: [aten.clone, aten.transpose]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_transpose_0.run(primals_1, buf1, buf6, 16, 16, grid=grid(16, 16), stream=stream0)
        del primals_1
        buf2 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [targetT, attn], Original ATen: [aten.clone, aten.bmm]
        extern_kernels.bmm(buf1, reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), out=buf2)
        buf3 = reinterpret_tensor(buf1, (64, 4), (4, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [attn_2], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf2, buf3, 256, grid=grid(256), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_4], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf3, buf4, 16, 16, grid=grid(16, 16), stream=stream0)
        buf5 = reinterpret_tensor(buf3, (4, 4, 16), (64, 16, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [weightedContext], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), buf4, out=buf5)
    return (reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_3, reinterpret_tensor(primals_2, (4, 4, 4, 1), (16, 4, 1, 1), 0), buf2, reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf4, (4, 16, 4), (64, 1, 16), 0), buf6, )


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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
import torch.nn.parallel
import torch.onnx


def conv1x1(in_planes, out_planes, bias=False):
    """1x1 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,
        padding=0, bias=bias)


class GlobalAttentionGeneral(nn.Module):

    def __init__(self, idf, cdf):
        super(GlobalAttentionGeneral, self).__init__()
        self.conv_context = conv1x1(cdf, idf)
        self.sm = nn.Softmax()
        self.mask = None

    def applyMask(self, mask):
        self.mask = mask

    def forward(self, input, context):
        """
            input: batch x idf x ih x iw (queryL=ihxiw)
            context: batch x cdf x sourceL
        """
        ih, iw = input.size(2), input.size(3)
        queryL = ih * iw
        batch_size, sourceL = context.size(0), context.size(2)
        target = input.view(batch_size, -1, queryL)
        targetT = torch.transpose(target, 1, 2).contiguous()
        sourceT = context.unsqueeze(3)
        sourceT = self.conv_context(sourceT).squeeze(3)
        attn = torch.bmm(targetT, sourceT)
        attn = attn.view(batch_size * queryL, sourceL)
        if self.mask is not None:
            mask = self.mask.repeat(queryL, 1)
            attn.data.masked_fill_(mask.data.bool(), -float('inf'))
        attn = self.sm(attn)
        attn = attn.view(batch_size, queryL, sourceL)
        attn = torch.transpose(attn, 1, 2).contiguous()
        weightedContext = torch.bmm(sourceT, attn)
        weightedContext = weightedContext.view(batch_size, -1, ih, iw)
        attn = attn.view(batch_size, -1, ih, iw)
        return weightedContext, attn


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


def get_init_inputs():
    return [[], {'idf': 4, 'cdf': 4}]

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 math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.onnx
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_clone_transpose_0(in_ptr0, 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
    x1 = xindex
    y0 = yindex
    y2 = yindex % 4
    y3 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x1 + 16 * y0), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 16 * y0), tmp0, xmask & ymask)
    tl.store(out_ptr1 + (y2 + 4 * x1 + 64 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_clone_2(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
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 64 * y1), xmask & ymask)
    tmp1 = tl.load(in_ptr0 + (4 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2 + 16 * y3), tmp8, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 1, 1), (4, 1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_2, (4,
            4, 4, 1), (16, 4, 1, 1), 0), primals_3, stride=(1, 1), padding=
            (0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0
            ), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 4, 1), (16, 4, 1, 1))
        buf1 = empty_strided_cuda((4, 16, 4), (64, 1, 16), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 16), (64, 1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_transpose_0[grid(16, 16)](primals_1, buf1,
            buf6, 16, 16, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(buf1, reinterpret_tensor(buf0, (4, 4, 4), (16, 4,
            1), 0), out=buf2)
        buf3 = reinterpret_tensor(buf1, (64, 4), (4, 1), 0)
        del buf1
        triton_poi_fused__softmax_1[grid(256)](buf2, buf3, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 16)](buf3, buf4, 16, 16, XBLOCK=
            16, YBLOCK=16, num_warps=4, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4, 16), (64, 16, 1), 0)
        del buf3
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 
            0), buf4, out=buf5)
    return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_3, reinterpret_tensor(primals_2, (4, 4, 4, 1), (16, 4, 1,
        1), 0), buf2, reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf4, (4, 16, 4), (64, 1, 16), 0), buf6


def conv1x1(in_planes, out_planes, bias=False):
    """1x1 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,
        padding=0, bias=bias)


class GlobalAttentionGeneralNew(nn.Module):

    def __init__(self, idf, cdf):
        super(GlobalAttentionGeneralNew, self).__init__()
        self.conv_context = conv1x1(cdf, idf)
        self.sm = nn.Softmax()
        self.mask = None

    def applyMask(self, mask):
        self.mask = mask

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