# 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_4/inductor_cache/hd/chdl6pmcku25ogjqf2jico75q3zbf4ildxlg5z7qzg22kzut6hqq.py
# Topologically Sorted Source Nodes: [v_proj, add, h], Original ATen: [aten.convolution, aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add => add
#   h => tanh
#   v_proj => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%primals_1, %primals_2, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%convolution, %expand), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
triton_poi_fused_add_convolution_tanh_0 = async_compile.triton('triton_poi_fused_add_convolution_tanh_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_add_convolution_tanh_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, '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_add_convolution_tanh_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = (xindex // 16) % 4
    x4 = (xindex // 16)
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + (x3), tmp7, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/k6/ck6noxh32dcboenlnpypgwejuatvdhjguazlw4zfuz5pjvuoo55s.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, div, exp, sub, sum_1
# 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 = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_1 = async_compile.triton('triton_per_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.persistent_reduction(
    size_hints=[4, 16],
    reduction_hint=ReductionHint.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, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__softmax_1(in_ptr0, in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    rnumel = 16
    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, :]
    roffset = 0
    rmask = 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.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, float("-inf"))
    tmp7 = triton_helpers.max2(tmp6, 1)[:, None]
    tmp8 = tmp3 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tmp14 = tmp9 / tmp13
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp14, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/vf/cvfkeernfkoxw64mey4uhun4hqqdtsmlllbjohoyzdjuv3fghuwt.py
# Topologically Sorted Source Nodes: [mul, sum_1, sum_2, next_u], Original ATen: [aten.mul, aten.sum, aten.add]
# Source node to ATen node mapping:
#   mul => mul
#   next_u => add_1
#   sum_1 => sum_2
#   sum_2 => sum_3
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_1, %primals_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [2]), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sum_2, [2]), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_6, %sum_3), kwargs = {})
triton_poi_fused_add_mul_sum_2 = async_compile.triton('triton_poi_fused_add_mul_sum_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_add_mul_sum_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 33, '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_add_mul_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (16*x1), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (16*x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (4 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (8 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (8 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (12 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (12 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (1 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (1 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (5 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (5 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (9 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + (9 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr0 + (13 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (13 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr0 + (2 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr1 + (2 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr0 + (6 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp35 = tl.load(in_ptr1 + (6 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp38 = tl.load(in_ptr0 + (10 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp39 = tl.load(in_ptr1 + (10 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp42 = tl.load(in_ptr0 + (14 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp43 = tl.load(in_ptr1 + (14 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp47 = tl.load(in_ptr0 + (3 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp48 = tl.load(in_ptr1 + (3 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp50 = tl.load(in_ptr0 + (7 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp51 = tl.load(in_ptr1 + (7 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp54 = tl.load(in_ptr0 + (11 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp55 = tl.load(in_ptr1 + (11 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp58 = tl.load(in_ptr0 + (15 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp59 = tl.load(in_ptr1 + (15 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp63 = tl.load(in_ptr2 + (x2), xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp17 = tmp15 * tmp16
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp28 = tmp26 * tmp27
    tmp29 = tmp25 + tmp28
    tmp30 = tmp14 + tmp29
    tmp33 = tmp31 * tmp32
    tmp36 = tmp34 * tmp35
    tmp37 = tmp33 + tmp36
    tmp40 = tmp38 * tmp39
    tmp41 = tmp37 + tmp40
    tmp44 = tmp42 * tmp43
    tmp45 = tmp41 + tmp44
    tmp46 = tmp30 + tmp45
    tmp49 = tmp47 * tmp48
    tmp52 = tmp50 * tmp51
    tmp53 = tmp49 + tmp52
    tmp56 = tmp54 * tmp55
    tmp57 = tmp53 + tmp56
    tmp60 = tmp58 * tmp59
    tmp61 = tmp57 + tmp60
    tmp62 = tmp46 + tmp61
    tmp64 = tmp63 + tmp62
    tl.store(in_out_ptr0 + (x2), tmp64, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, ), (1, ))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_8, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [v_proj], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(primals_1, primals_2, 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, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(primals_6, reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [v_proj, add, h], Original ATen: [aten.convolution, aten.add, aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_convolution_tanh_0.run(buf2, primals_3, buf1, primals_5, 256, grid=grid(256), stream=stream0)
        del primals_3
        del primals_5
        # Topologically Sorted Source Nodes: [conv2d_1], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf2, primals_7, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf6 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_per_fused__softmax_1.run(buf3, primals_8, buf6, 4, 16, grid=grid(4), stream=stream0)
        del buf3
        del primals_8
        buf7 = buf1; del buf1  # reuse
        buf8 = buf7; del buf7  # reuse
        # Topologically Sorted Source Nodes: [mul, sum_1, sum_2, next_u], Original ATen: [aten.mul, aten.sum, aten.add]
        triton_poi_fused_add_mul_sum_2.run(buf8, buf6, primals_1, primals_6, 16, grid=grid(16), stream=stream0)
    return (buf8, reinterpret_tensor(buf6, (4, 1, 4, 4), (16, 16, 4, 1), 0), primals_1, primals_2, primals_6, primals_7, buf2, 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, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((1, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8])
    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.functional as F
import torch.nn


class StackedAttention(nn.Module):

    def __init__(self, input_dim, hidden_dim):
        super(StackedAttention, self).__init__()
        self.Wv = nn.Conv2d(input_dim, hidden_dim, kernel_size=(1, 1),
            padding=(0, 0))
        self.Wu = nn.Linear(input_dim, hidden_dim)
        self.Wp = nn.Conv2d(hidden_dim, 1, kernel_size=(1, 1), padding=(0, 0))
        self.hidden_dim = hidden_dim
        self.attention_maps = None

    def forward(self, v, u):
        """
        Input:
        - v: N x D x H x W
        - u: N x D

        Returns:
        - next_u: N x D
        """
        N, K = v.size(0), self.hidden_dim
        D, H, W = v.size(1), v.size(2), v.size(3)
        v_proj = self.Wv(v)
        u_proj = self.Wu(u)
        u_proj_expand = u_proj.view(N, K, 1, 1).expand(N, K, H, W)
        h = torch.tanh(v_proj + u_proj_expand)
        p = F.softmax(self.Wp(h).view(N, H * W), dim=1).view(N, 1, H, W)
        self.attention_maps = p.data.clone()
        v_tilde = (p.expand_as(v) * v).sum(2).sum(2).view(N, D)
        next_u = u + v_tilde
        return next_u


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


def get_init_inputs():
    return [[], {'input_dim': 4, 'hidden_dim': 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 libdevice, math as tl_math
import torch.nn as nn
import torch.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_add_convolution_tanh_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 16 % 4
    x4 = xindex // 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = libdevice.tanh(tmp6)
    tl.store(in_out_ptr0 + x3, tmp7, xmask)


@triton.jit
def triton_per_fused__softmax_1(in_ptr0, in_ptr1, 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.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.where(xmask, tmp4, float('-inf'))
    tmp7 = triton_helpers.max2(tmp6, 1)[:, None]
    tmp8 = tmp3 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK, RBLOCK])
    tmp12 = tl.where(xmask, tmp10, 0)
    tmp13 = tl.sum(tmp12, 1)[:, None]
    tmp14 = tmp9 / tmp13
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp14, xmask)


@triton.jit
def triton_poi_fused_add_mul_sum_2(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 16 * x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + 16 * x1), xmask, eviction_policy='evict_last'
        )
    tmp4 = tl.load(in_ptr1 + (4 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp7 = tl.load(in_ptr0 + (8 + 16 * x1), xmask, eviction_policy='evict_last'
        )
    tmp8 = tl.load(in_ptr1 + (8 + 16 * x2), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr0 + (12 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr1 + (12 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (1 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp16 = tl.load(in_ptr1 + (1 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr0 + (5 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr1 + (5 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp22 = tl.load(in_ptr0 + (9 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr1 + (9 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp26 = tl.load(in_ptr0 + (13 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr1 + (13 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp31 = tl.load(in_ptr0 + (2 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr1 + (2 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp34 = tl.load(in_ptr0 + (6 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp35 = tl.load(in_ptr1 + (6 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp38 = tl.load(in_ptr0 + (10 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp39 = tl.load(in_ptr1 + (10 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp42 = tl.load(in_ptr0 + (14 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp43 = tl.load(in_ptr1 + (14 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp47 = tl.load(in_ptr0 + (3 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp48 = tl.load(in_ptr1 + (3 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp50 = tl.load(in_ptr0 + (7 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp51 = tl.load(in_ptr1 + (7 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp54 = tl.load(in_ptr0 + (11 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp55 = tl.load(in_ptr1 + (11 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp58 = tl.load(in_ptr0 + (15 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp59 = tl.load(in_ptr1 + (15 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp63 = tl.load(in_ptr2 + x2, xmask)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp17 = tmp15 * tmp16
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tmp24 = tmp22 * tmp23
    tmp25 = tmp21 + tmp24
    tmp28 = tmp26 * tmp27
    tmp29 = tmp25 + tmp28
    tmp30 = tmp14 + tmp29
    tmp33 = tmp31 * tmp32
    tmp36 = tmp34 * tmp35
    tmp37 = tmp33 + tmp36
    tmp40 = tmp38 * tmp39
    tmp41 = tmp37 + tmp40
    tmp44 = tmp42 * tmp43
    tmp45 = tmp41 + tmp44
    tmp46 = tmp30 + tmp45
    tmp49 = tmp47 * tmp48
    tmp52 = tmp50 * tmp51
    tmp53 = tmp49 + tmp52
    tmp56 = tmp54 * tmp55
    tmp57 = tmp53 + tmp56
    tmp60 = tmp58 * tmp59
    tmp61 = tmp57 + tmp60
    tmp62 = tmp46 + tmp61
    tmp64 = tmp63 + tmp62
    tl.store(in_out_ptr0 + x2, tmp64, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (1, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_8, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, 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, 4), (64, 16, 4, 1))
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_6, reinterpret_tensor(primals_4, (4, 4),
            (1, 4), 0), out=buf1)
        del primals_4
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_convolution_tanh_0[grid(256)](buf2, primals_3,
            buf1, primals_5, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        del primals_5
        buf3 = extern_kernels.convolution(buf2, primals_7, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 1, 4, 4), (16, 16, 4, 1))
        buf6 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        triton_per_fused__softmax_1[grid(4)](buf3, primals_8, buf6, 4, 16,
            XBLOCK=1, num_warps=2, num_stages=1)
        del buf3
        del primals_8
        buf7 = buf1
        del buf1
        buf8 = buf7
        del buf7
        triton_poi_fused_add_mul_sum_2[grid(16)](buf8, buf6, primals_1,
            primals_6, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf8, reinterpret_tensor(buf6, (4, 1, 4, 4), (16, 16, 4, 1), 0
        ), primals_1, primals_2, primals_6, primals_7, buf2, buf6


class StackedAttentionNew(nn.Module):

    def __init__(self, input_dim, hidden_dim):
        super(StackedAttentionNew, self).__init__()
        self.Wv = nn.Conv2d(input_dim, hidden_dim, kernel_size=(1, 1),
            padding=(0, 0))
        self.Wu = nn.Linear(input_dim, hidden_dim)
        self.Wp = nn.Conv2d(hidden_dim, 1, kernel_size=(1, 1), padding=(0, 0))
        self.hidden_dim = hidden_dim
        self.attention_maps = None

    def forward(self, input_0, input_1):
        primals_2 = self.Wv.weight
        primals_3 = self.Wv.bias
        primals_4 = self.Wu.weight
        primals_5 = self.Wu.bias
        primals_7 = self.Wp.weight
        primals_8 = self.Wp.bias
        primals_1 = input_0
        primals_6 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8])
        return output[0]
