# 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_7/inductor_cache/sm/csmkspcdnf5w43fjp343mzxpz7juyyqsdczqdavgbz7pebek43dy.py
# Topologically Sorted Source Nodes: [add, add_1], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view, %unsqueeze_5), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view, %unsqueeze_7), kwargs = {})
triton_poi_fused_add_0 = async_compile.triton('triton_poi_fused_add_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_0', 'mutated_arg_names': [], '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_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp2 + tmp5
    tl.store(out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr1 + (x2), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2m/c2mt7vvxypcyg4roj4r3bns7fqblbouce2ybektelf7rsc62boym.py
# Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv1d_1 => convolution_1
# Graph fragment:
#   %convolution_1 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%unsqueeze_1, %primals_5, %primals_6, [1], [0], [1], False, [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=[16], 
    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_convolution_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/cw/ccwzvcpu3vqd5oszyqyyx4r44brxhmaycrhz6pu6ysnxdyfjifln.py
# Topologically Sorted Source Nodes: [add_2, attn_map], Original ATen: [aten.add, aten._softmax]
# Source node to ATen node mapping:
#   add_2 => add_2
#   attn_map => div_1, exp, sum_1
# Graph fragment:
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_6, %slice_7), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 2.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_add_2 = async_compile.triton('triton_poi_fused__softmax_add_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: '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_add_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_add_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    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.full([1], 1, tl.int64)
    tmp2 = tl.full([1], 0, tl.int64)
    tmp3 = tmp1 >= tmp2
    tmp4 = tmp1 < tmp1
    tmp5 = 0.0
    tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
    tmp7 = tl.where(tmp4, tmp5, tmp6)
    tmp8 = tmp1 >= tmp1
    tmp9 = tl.full([1], 2, tl.int64)
    tmp10 = tmp1 < tmp9
    tmp11 = tl.load(in_ptr0 + (x0), tmp8 & xmask, other=0.0)
    tmp12 = tl.where(tmp4, tmp7, tmp11)
    tmp13 = tmp0 + tmp12
    tmp14 = 1.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp15 - tmp15
    tmp17 = 0.5
    tmp18 = tmp16 * tmp17
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp19 / tmp19
    tl.store(in_out_ptr0 + (x0), tmp20, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5v/c5vp5kwiougsjrt24sbuebc3tdgtaqyy6acw5g4woberjney646p.py
# Topologically Sorted Source Nodes: [conv1d_4], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   conv1d_4 => convolution_4
# Graph fragment:
#   %convolution_4 : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%view_15, %primals_14, %primals_15, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_3 = async_compile.triton('triton_poi_fused_convolution_3', '''
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: '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_convolution_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), tmp2, 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, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_12, (4, 1), (1, 1))
    assert_size_stride(primals_13, (4, 1), (1, 1))
    assert_size_stride(primals_14, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_15, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Topologically Sorted Source Nodes: [conv1d], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_4, (1, 4, 4), (16, 4, 1), 0), primals_2, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (1, 4, 4), (16, 4, 1))
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_7, (1, 4, 4), (16, 4, 1), 0), primals_5, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 4), (16, 4, 1))
        # Topologically Sorted Source Nodes: [conv1d_2], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1, 4, 4), (16, 4, 1), 0), primals_8, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf2, (1, 4, 4), (16, 4, 1))
        # Topologically Sorted Source Nodes: [conv1d_3], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_10, (1, 4, 4), (16, 4, 1), 0), primals_11, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf3, (1, 4, 4), (16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        # Topologically Sorted Source Nodes: [add, add_1], Original ATen: [aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_0.run(buf0, primals_3, primals_12, primals_13, buf4, buf7, 16, grid=grid(16), stream=stream0)
        del primals_12
        del primals_13
        del primals_3
        buf5 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [conv1d_1], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf5, primals_6, 16, grid=grid(16), stream=stream0)
        del primals_6
        buf6 = reinterpret_tensor(buf0, (16, 1, 1), (1, 1, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [content_score], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 1, 1), (1, 0, 0), 0), reinterpret_tensor(buf5, (16, 1, 1), (1, 0, 0), 0), out=buf6)
        buf8 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pos_score], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 1, 1), (1, 0, 0), 0), reinterpret_tensor(buf3, (16, 1, 1), (1, 1, 1), 0), out=buf8)
        buf9 = reinterpret_tensor(buf6, (4, 4, 1, 1), (4, 1, 1, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [add_2, attn_map], Original ATen: [aten.add, aten._softmax]
        triton_poi_fused__softmax_add_2.run(buf9, buf8, 16, grid=grid(16), stream=stream0)
        buf10 = buf2; del buf2  # reuse
        # Topologically Sorted Source Nodes: [conv1d_2], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_1.run(buf10, primals_9, 16, grid=grid(16), stream=stream0)
        del primals_9
        buf11 = buf8; del buf8  # reuse
        # Topologically Sorted Source Nodes: [context], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 1, 1), (1, 0, 0), 0), reinterpret_tensor(buf9, (16, 1, 1), (1, 1, 1), 0), out=buf11)
        # Topologically Sorted Source Nodes: [conv1d_4], Original ATen: [aten.convolution]
        buf12 = extern_kernels.convolution(reinterpret_tensor(buf11, (4, 4, 1), (4, 1, 1), 0), primals_14, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 1), (4, 1, 1))
        buf13 = buf12; del buf12  # reuse
        # Topologically Sorted Source Nodes: [conv1d_4], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_3.run(buf13, primals_15, 16, grid=grid(16), stream=stream0)
        del primals_15
    return (buf13, primals_2, primals_5, primals_8, primals_11, primals_14, reinterpret_tensor(primals_4, (1, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_7, (1, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_1, (1, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_10, (1, 4, 4), (16, 4, 1), 0), buf9, reinterpret_tensor(buf11, (4, 4, 1), (4, 1, 1), 0), reinterpret_tensor(buf10, (16, 1, 1), (1, 1, 1), 0), reinterpret_tensor(buf7, (16, 1, 1), (1, 1, 1), 0), reinterpret_tensor(buf3, (16, 1, 1), (1, 1, 1), 0), reinterpret_tensor(buf4, (16, 1, 1), (1, 1, 1), 0), reinterpret_tensor(buf5, (16, 1, 1), (1, 1, 1), 0), )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 1), (4, 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, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_15 = rand_strided((4, ), (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, primals_9, primals_10, primals_11, primals_12, primals_13, primals_14, primals_15])
    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 math
import torch
import torch.nn.functional as F
import torch.nn as nn


class RelativeMultiHeadAttention(nn.Module):

    def __init__(self, channels, num_heads, dropout):
        super(RelativeMultiHeadAttention, self).__init__()
        assert channels % num_heads == 0, 'd_model % num_heads should be zero.'
        self.channels = channels
        self.inner_channels = channels // num_heads
        self.num_heads = num_heads
        self.sqrt_dim = math.sqrt(channels)
        self.query_proj = nn.Conv1d(channels, channels, 1)
        self.key_proj = nn.Conv1d(channels, channels, 1)
        self.value_proj = nn.Conv1d(channels, channels, 1)
        self.pos_proj = nn.Conv1d(channels, channels, 1, bias=False)
        self.dropout = nn.Dropout(p=dropout)
        self.u_bias = nn.Parameter(torch.Tensor(self.num_heads, self.
            inner_channels))
        self.v_bias = nn.Parameter(torch.Tensor(self.num_heads, self.
            inner_channels))
        torch.nn.init.xavier_uniform_(self.u_bias)
        torch.nn.init.xavier_uniform_(self.v_bias)
        self.out_proj = nn.Conv1d(channels, channels, 1)

    def forward(self, query, key, value, pos_embedding, mask=None):
        B = value.size(0)
        query = self.query_proj(query).view(B, self.num_heads, self.
            inner_channels, -1)
        key = self.key_proj(key).view(B, self.num_heads, self.
            inner_channels, -1)
        value = self.value_proj(value).view(B, self.num_heads, self.
            inner_channels, -1)
        B_pos = pos_embedding.size(0)
        pos_emb = self.pos_proj(pos_embedding).view(B_pos, self.num_heads,
            self.inner_channels, -1)
        content_score = torch.matmul((query + self.u_bias[None, :, :, None]
            ).transpose(-1, -2), key)
        pos_score = torch.matmul((query + self.v_bias[None, :, :, None]).
            transpose(-1, -2), pos_emb)
        pos_score = self.rel_shift(pos_score)
        score = (content_score + pos_score) / self.sqrt_dim
        if mask is not None:
            score = score.masked_fill(mask == 0, -10000.0)
        attn_map = F.softmax(score, -1)
        attn = self.dropout(attn_map)
        context = torch.matmul(value, attn)
        context = context.contiguous().view(B, self.channels, -1)
        return self.out_proj(context)

    @staticmethod
    def rel_shift(x):
        B, H, T1, T2 = x.size()
        zero_pad = torch.zeros((B, H, T1, 1), device=x.device, dtype=x.dtype)
        x_padded = torch.cat([zero_pad, x], dim=-1)
        x_padded = x_padded.view(B, H, T2 + 1, T1)
        x = x_padded[:, :, 1:].view_as(x)[:, :, :, :T2 // 2 + 1]
        return x


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


def get_init_inputs():
    return [[], {'channels': 4, 'num_heads': 4, 'dropout': 0.5}]

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.triton_helpers import math as tl_math
import 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_add_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp2 + tmp5
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_add_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    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.full([1], 1, tl.int64)
    tl.full([1], 0, tl.int64)
    tmp4 = tmp1 < tmp1
    tmp5 = 0.0
    tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
    tmp7 = tl.where(tmp4, tmp5, tmp6)
    tmp8 = tmp1 >= tmp1
    tl.full([1], 2, tl.int64)
    tmp11 = tl.load(in_ptr0 + x0, tmp8 & xmask, other=0.0)
    tmp12 = tl.where(tmp4, tmp7, tmp11)
    tmp13 = tmp0 + tmp12
    tmp14 = 1.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp15 - tmp15
    tmp17 = 0.5
    tmp18 = tmp16 * tmp17
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp19 / tmp19
    tl.store(in_out_ptr0 + x0, tmp20, xmask)


@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    assert_size_stride(primals_8, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_12, (4, 1), (1, 1))
    assert_size_stride(primals_13, (4, 1), (1, 1))
    assert_size_stride(primals_14, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_15, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_4, (1,
            4, 4), (16, 4, 1), 0), primals_2, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf0, (1, 4, 4), (16, 4, 1))
        buf1 = extern_kernels.convolution(reinterpret_tensor(primals_7, (1,
            4, 4), (16, 4, 1), 0), primals_5, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf1, (1, 4, 4), (16, 4, 1))
        buf2 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            4, 4), (16, 4, 1), 0), primals_8, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf2, (1, 4, 4), (16, 4, 1))
        buf3 = extern_kernels.convolution(reinterpret_tensor(primals_10, (1,
            4, 4), (16, 4, 1), 0), primals_11, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf3, (1, 4, 4), (16, 4, 1))
        buf4 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_0[grid(16)](buf0, primals_3, primals_12,
            primals_13, buf4, buf7, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_12
        del primals_13
        del primals_3
        buf5 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(16)](buf5, primals_6, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_6
        buf6 = reinterpret_tensor(buf0, (16, 1, 1), (1, 1, 1), 0)
        del buf0
        extern_kernels.bmm(reinterpret_tensor(buf4, (16, 1, 1), (1, 0, 0), 
            0), reinterpret_tensor(buf5, (16, 1, 1), (1, 0, 0), 0), out=buf6)
        buf8 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 1, 1), (1, 0, 0), 
            0), reinterpret_tensor(buf3, (16, 1, 1), (1, 1, 1), 0), out=buf8)
        buf9 = reinterpret_tensor(buf6, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf6
        triton_poi_fused__softmax_add_2[grid(16)](buf9, buf8, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf10 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(16)](buf10, primals_9, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_9
        buf11 = buf8
        del buf8
        extern_kernels.bmm(reinterpret_tensor(buf10, (16, 1, 1), (1, 0, 0),
            0), reinterpret_tensor(buf9, (16, 1, 1), (1, 1, 1), 0), out=buf11)
        buf12 = extern_kernels.convolution(reinterpret_tensor(buf11, (4, 4,
            1), (4, 1, 1), 0), primals_14, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf12, (4, 4, 1), (4, 1, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_3[grid(16)](buf13, primals_15, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_15
    return (buf13, primals_2, primals_5, primals_8, primals_11, primals_14,
        reinterpret_tensor(primals_4, (1, 4, 4), (16, 4, 1), 0),
        reinterpret_tensor(primals_7, (1, 4, 4), (16, 4, 1), 0),
        reinterpret_tensor(primals_1, (1, 4, 4), (16, 4, 1), 0),
        reinterpret_tensor(primals_10, (1, 4, 4), (16, 4, 1), 0), buf9,
        reinterpret_tensor(buf11, (4, 4, 1), (4, 1, 1), 0),
        reinterpret_tensor(buf10, (16, 1, 1), (1, 1, 1), 0),
        reinterpret_tensor(buf7, (16, 1, 1), (1, 1, 1), 0),
        reinterpret_tensor(buf3, (16, 1, 1), (1, 1, 1), 0),
        reinterpret_tensor(buf4, (16, 1, 1), (1, 1, 1), 0),
        reinterpret_tensor(buf5, (16, 1, 1), (1, 1, 1), 0))


class RelativeMultiHeadAttentionNew(nn.Module):

    def __init__(self, channels, num_heads, dropout):
        super(RelativeMultiHeadAttentionNew, self).__init__()
        assert channels % num_heads == 0, 'd_model % num_heads should be zero.'
        self.channels = channels
        self.inner_channels = channels // num_heads
        self.num_heads = num_heads
        self.sqrt_dim = math.sqrt(channels)
        self.query_proj = nn.Conv1d(channels, channels, 1)
        self.key_proj = nn.Conv1d(channels, channels, 1)
        self.value_proj = nn.Conv1d(channels, channels, 1)
        self.pos_proj = nn.Conv1d(channels, channels, 1, bias=False)
        self.dropout = nn.Dropout(p=dropout)
        self.u_bias = nn.Parameter(torch.Tensor(self.num_heads, self.
            inner_channels))
        self.v_bias = nn.Parameter(torch.Tensor(self.num_heads, self.
            inner_channels))
        torch.nn.init.xavier_uniform_(self.u_bias)
        torch.nn.init.xavier_uniform_(self.v_bias)
        self.out_proj = nn.Conv1d(channels, channels, 1)

    @staticmethod
    def rel_shift(x):
        B, H, T1, T2 = x.size()
        zero_pad = torch.zeros((B, H, T1, 1), device=x.device, dtype=x.dtype)
        x_padded = torch.cat([zero_pad, x], dim=-1)
        x_padded = x_padded.view(B, H, T2 + 1, T1)
        x = x_padded[:, :, 1:].view_as(x)[:, :, :, :T2 // 2 + 1]
        return x

    def forward(self, input_0, input_1, input_2, input_3):
        primals_12 = self.u_bias
        primals_13 = self.v_bias
        primals_2 = self.query_proj.weight
        primals_3 = self.query_proj.bias
        primals_5 = self.key_proj.weight
        primals_6 = self.key_proj.bias
        primals_8 = self.value_proj.weight
        primals_9 = self.value_proj.bias
        primals_11 = self.pos_proj.weight
        primals_14 = self.out_proj.weight
        primals_15 = self.out_proj.bias
        primals_1 = input_0
        primals_4 = input_1
        primals_7 = input_2
        primals_10 = input_3
        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])
        return output[0]
