# 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/gd/cgdyncc5fbhtbwk4m3qh4p3orh6ir2n4sbfvfheve5ebdbvm6btr.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   matmul => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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=[64, 4], 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    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
    x3 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4) % 4
    y2 = (yindex // 16)
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x3) + (16*y2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3 + (4*y4)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/2a/c2aleeuqhoz4te7w3qwhl66rqffdzonerxbyu7iyqwrmvsg67b3e.py
# Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   x_1 => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_4,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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_clone_1', '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_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/2x/c2xtedo5flggmbs3xi3ahonfvuhv7jopaiz4kzrgrsin73g5evcv.py
# Topologically Sorted Source Nodes: [x_1, attn], Original ATen: [aten.clone, aten._softmax]
# Source node to ATen node mapping:
#   attn => amax, exp, sub
#   x_1 => clone_3
# Graph fragment:
#   %clone_3 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute_6,), kwargs = {memory_format: torch.contiguous_format})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone_3, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_3, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_clone_2 = async_compile.triton('triton_poi_fused__softmax_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=[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_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__softmax_clone_2(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
    x3 = xindex
    x0 = xindex % 4
    x2 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + (16*x2)), 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 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/6j/c6jxcwk2zob7wa3y7plr7b6myq6bexpfj24ifkfsbtise4t4sq6g.py
# Topologically Sorted Source Nodes: [attn], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn => div, sum_1
# Graph fragment:
#   %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_poi_fused__softmax_3 = async_compile.triton('triton_poi_fused__softmax_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=[64, 4], 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_3', '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_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    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) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (y0 + (16*y1)), ymask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + y0 + (16*y1)), ymask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + y0 + (16*y1)), ymask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + y0 + (16*y1)), ymask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2 + (4*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, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_2, buf0, 64, 4, grid=grid(64, 4), stream=stream0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf1, buf2, 256, grid=grid(256), stream=stream0)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [x_1, attn], Original ATen: [aten.clone, aten._softmax]
        triton_poi_fused__softmax_clone_2.run(buf3, buf4, 256, grid=grid(256), stream=stream0)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [attn], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_3.run(buf4, buf5, 64, 4, grid=grid(64, 4), stream=stream0)
        buf6 = reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_2, (16, 4, 4), (16, 4, 1), 0), out=buf6)
    return (reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf5, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), buf1, reinterpret_tensor(buf2, (64, 4), (4, 1), 0), buf5, reinterpret_tensor(primals_2, (16, 4, 4), (16, 1, 4), 0), primals_3, )


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, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 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
from torch import nn as nn
import torch.utils.data.distributed


class SelfAttention(nn.Module):
    """
       Self SelfAttention Layer
       Given $X\\in \\mathbb{R}^{n 	imes in_feature}$, the attention is calculated by: $a=Softmax(W_2tanh(W_1X))$, where
       $W_1 \\in \\mathbb{R}^{hidden 	imes in_feature}$, $W_2 \\in \\mathbb{R}^{out_feature 	imes hidden}$.
       The final output is: $out=aX$, which is unrelated with input $n$.
    """

    def __init__(self, *, hidden, in_feature, out_feature):
        """
        The init function.
        :param hidden: the hidden dimension, can be viewed as the number of experts.
        :param in_feature: the input feature dimension.
        :param out_feature: the output feature dimension.
        """
        super(SelfAttention, self).__init__()
        self.w1 = torch.nn.Parameter(torch.FloatTensor(hidden, in_feature))
        self.w2 = torch.nn.Parameter(torch.FloatTensor(out_feature, hidden))
        self.reset_parameters()

    def reset_parameters(self):
        """
        Use xavier_normal method to initialize parameters.
        """
        nn.init.xavier_normal_(self.w1)
        nn.init.xavier_normal_(self.w2)

    def forward(self, X):
        """
        The forward function.
        :param X: The input feature map. $X \\in \\mathbb{R}^{n 	imes in_feature}$.
        :return: The final embeddings and attention matrix.
        """
        x = torch.tanh(torch.matmul(self.w1, X.transpose(1, 0)))
        x = torch.matmul(self.w2, x)
        attn = torch.nn.functional.softmax(x, dim=-1)
        x = torch.matmul(attn, X)
        return x, attn


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


def get_init_inputs():
    return [[], {'hidden': 4, 'in_feature': 4, 'out_feature': 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
from torch import nn as nn
import torch.utils.data.distributed
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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    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
    x3 = xindex
    y0 = yindex % 4
    y1 = yindex // 4 % 4
    y2 = yindex // 16
    y4 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x3 + 16 * y2 + 64 * y1), xmask &
        ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3 + 4 * y4), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused__softmax_clone_2(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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), 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 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK:
    tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    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 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + y0 + 16 * y1), ymask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp8, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](primals_2, buf0, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(256)](buf1, buf2, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 1, 4), torch.float32)
        triton_poi_fused__softmax_clone_2[grid(256)](buf3, buf4, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused__softmax_3[grid(64, 4)](buf4, buf5, 64, 4, XBLOCK=
            4, YBLOCK=32, num_warps=4, num_stages=1)
        buf6 = reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0)
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(primals_2, (16, 4, 4), (16, 4, 1), 0),
            out=buf6)
    return reinterpret_tensor(buf6, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf5, reinterpret_tensor(buf0, (64, 4), (4, 1), 0
        ), buf1, reinterpret_tensor(buf2, (64, 4), (4, 1), 0
        ), buf5, reinterpret_tensor(primals_2, (16, 4, 4), (16, 1, 4), 0
        ), primals_3


class SelfAttentionNew(nn.Module):
    """
       Self SelfAttention Layer
       Given $X\\in \\mathbb{R}^{n 	imes in_feature}$, the attention is calculated by: $a=Softmax(W_2tanh(W_1X))$, where
       $W_1 \\in \\mathbb{R}^{hidden 	imes in_feature}$, $W_2 \\in \\mathbb{R}^{out_feature 	imes hidden}$.
       The final output is: $out=aX$, which is unrelated with input $n$.
    """

    def __init__(self, *, hidden, in_feature, out_feature):
        """
        The init function.
        :param hidden: the hidden dimension, can be viewed as the number of experts.
        :param in_feature: the input feature dimension.
        :param out_feature: the output feature dimension.
        """
        super(SelfAttentionNew, self).__init__()
        self.w1 = torch.nn.Parameter(torch.FloatTensor(hidden, in_feature))
        self.w2 = torch.nn.Parameter(torch.FloatTensor(out_feature, hidden))
        self.reset_parameters()

    def reset_parameters(self):
        """
        Use xavier_normal method to initialize parameters.
        """
        nn.init.xavier_normal_(self.w1)
        nn.init.xavier_normal_(self.w2)

    def forward(self, input_0):
        primals_1 = self.w1
        primals_3 = self.w2
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0], output[1]
