# 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/px/cpxgh7rygczo7s5i6nrfdx75l2hi6cjdfexsy2ctyfflclxdb7ye.py
# Topologically Sorted Source Nodes: [r, pow_1, add, neg, pow_2, sum_1, mul, w], Original ATen: [aten.sub, aten.pow, aten.add, aten.neg, aten.sum, aten.mul, aten._softmax]
# Source node to ATen node mapping:
#   add => add
#   mul => mul
#   neg => neg
#   pow_1 => pow_1
#   pow_2 => pow_2
#   r => sub
#   sum_1 => sum_1
#   w => amax, exp, sub_1, sum_2
# Graph fragment:
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %primals_2), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%primals_3, 2), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_1, 1e-10), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%add,), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_2, [2], True), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %sum_1), kwargs = {})
#   %amax : [num_users=2] = call_function[target=torch.ops.aten.amax.default](args = (%mul, [3], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_2 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [3], True), kwargs = {})
triton_per_fused__softmax_add_mul_neg_pow_sub_sum_0 = async_compile.triton('triton_per_fused__softmax_add_mul_neg_pow_sub_sum_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.persistent_reduction(
    size_hints=[16, 64],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__softmax_add_mul_neg_pow_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_add_mul_neg_pow_sub_sum_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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)
    r2 = rindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (r2), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr2 + (r2), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (4 + x0 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (64 + r2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (8 + x0 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr2 + (128 + r2), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (12 + x0 + (16*x1)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr2 + (192 + r2), None, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = 1e-10
    tmp3 = tmp1 + tmp2
    tmp4 = -tmp3
    tmp7 = tmp5 - tmp6
    tmp8 = tmp7 * tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp21 = tmp19 - tmp20
    tmp22 = tmp21 * tmp21
    tmp23 = tmp18 + tmp22
    tmp24 = tmp4 * tmp23
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.where(xmask, tmp25, float("-inf"))
    tmp28 = triton_helpers.max2(tmp27, 1)[:, None]
    tmp29 = tmp24 - tmp28
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
    tmp33 = tl.where(xmask, tmp31, 0)
    tmp34 = tl.sum(tmp33, 1)[:, None]
    tl.store(out_ptr0 + (r2 + (64*x3)), tmp24, xmask)
    tl.store(out_ptr1 + (x3), tmp28, xmask)
    tl.store(out_ptr2 + (x3), tmp34, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/5c/c5cyg6tmgybx2znb56tli3mxu474a5mhwo2sfz4cyzeil7dyv77v.py
# Topologically Sorted Source Nodes: [r, w, mul_1, e], Original ATen: [aten.sub, aten._softmax, aten.mul, aten.mean]
# Source node to ATen node mapping:
#   e => mean
#   mul_1 => mul_1
#   r => sub
#   w => div, exp, sub_1
# Graph fragment:
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %primals_2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div, %sub), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%mul_1, [1]), kwargs = {})
triton_poi_fused__softmax_mean_mul_sub_1 = async_compile.triton('triton_poi_fused__softmax_mean_mul_sub_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=[1024], 
    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__softmax_mean_mul_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 17, '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_mean_mul_sub_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x2 = (xindex // 256)
    x4 = (xindex // 64)
    x3 = xindex % 256
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (256*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*x2), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (4*x2), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (4*x4), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x3), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (64 + x0 + (256*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr2 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr3 + (1 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (128 + x0 + (256*x2)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr1 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr2 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr3 + (2 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (192 + x0 + (256*x2)), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr1 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp34 = tl.load(in_ptr2 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp36 = tl.load(in_ptr3 + (3 + (4*x4)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 / tmp4
    tmp8 = tmp6 - tmp7
    tmp9 = tmp5 * tmp8
    tmp12 = tmp10 - tmp11
    tmp13 = tl_math.exp(tmp12)
    tmp15 = tmp13 / tmp14
    tmp17 = tmp16 - tmp7
    tmp18 = tmp15 * tmp17
    tmp19 = tmp9 + tmp18
    tmp22 = tmp20 - tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp25 = tmp23 / tmp24
    tmp27 = tmp26 - tmp7
    tmp28 = tmp25 * tmp27
    tmp29 = tmp19 + tmp28
    tmp32 = tmp30 - tmp31
    tmp33 = tl_math.exp(tmp32)
    tmp35 = tmp33 / tmp34
    tmp37 = tmp36 - tmp7
    tmp38 = tmp35 * tmp37
    tmp39 = tmp29 + tmp38
    tmp40 = 4.0
    tmp41 = tmp39 / tmp40
    tl.store(out_ptr0 + (x5), tmp41, xmask)
''', 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), (16, 4, 1))
    assert_size_stride(primals_2, (4, 64), (64, 1))
    assert_size_stride(primals_3, (64, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 64), (256, 64, 1024, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [r, pow_1, add, neg, pow_2, sum_1, mul, w], Original ATen: [aten.sub, aten.pow, aten.add, aten.neg, aten.sum, aten.mul, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_per_fused__softmax_add_mul_neg_pow_sub_sum_0.run(primals_3, primals_1, primals_2, buf0, buf1, buf2, 16, 64, grid=grid(16), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.float32)
        # Topologically Sorted Source Nodes: [r, w, mul_1, e], Original ATen: [aten.sub, aten._softmax, aten.mul, aten.mean]
        triton_poi_fused__softmax_mean_mul_sub_1.run(buf0, buf1, buf2, primals_1, primals_2, buf3, 1024, grid=grid(1024), stream=stream0)
        del buf0
    return (reinterpret_tensor(buf3, (4, 256, 1), (256, 1, 1), 0), primals_1, primals_2, primals_3, buf1, buf2, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, ), (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 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
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_per_fused__softmax_add_mul_neg_pow_sub_sum_0(in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 16
    RBLOCK: tl.constexpr = 64
    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)
    r2 = rindex
    x0 = xindex % 4
    x1 = xindex // 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + r2, None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x0 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr2 + r2, None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (4 + x0 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr2 + (64 + r2), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr1 + (8 + x0 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr2 + (128 + r2), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (12 + x0 + 16 * x1), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr2 + (192 + r2), None, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = 1e-10
    tmp3 = tmp1 + tmp2
    tmp4 = -tmp3
    tmp7 = tmp5 - tmp6
    tmp8 = tmp7 * tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp21 = tmp19 - tmp20
    tmp22 = tmp21 * tmp21
    tmp23 = tmp18 + tmp22
    tmp24 = tmp4 * tmp23
    tmp25 = tl.broadcast_to(tmp24, [XBLOCK, RBLOCK])
    tmp27 = tl.where(xmask, tmp25, float('-inf'))
    tmp28 = triton_helpers.max2(tmp27, 1)[:, None]
    tmp29 = tmp24 - tmp28
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
    tmp33 = tl.where(xmask, tmp31, 0)
    tmp34 = tl.sum(tmp33, 1)[:, None]
    tl.store(out_ptr0 + (r2 + 64 * x3), tmp24, xmask)
    tl.store(out_ptr1 + x3, tmp28, xmask)
    tl.store(out_ptr2 + x3, tmp34, xmask)


@triton.jit
def triton_poi_fused__softmax_mean_mul_sub_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x2 = xindex // 256
    x4 = xindex // 64
    x3 = xindex % 256
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 256 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + 4 * x4, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x3, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (64 + x0 + 256 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr2 + (1 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr3 + (1 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (128 + x0 + 256 * x2), xmask, eviction_policy
        ='evict_last')
    tmp21 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr2 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp26 = tl.load(in_ptr3 + (2 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp30 = tl.load(in_ptr0 + (192 + x0 + 256 * x2), xmask, eviction_policy
        ='evict_last')
    tmp31 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp34 = tl.load(in_ptr2 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp36 = tl.load(in_ptr3 + (3 + 4 * x4), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp5 = tmp3 / tmp4
    tmp8 = tmp6 - tmp7
    tmp9 = tmp5 * tmp8
    tmp12 = tmp10 - tmp11
    tmp13 = tl_math.exp(tmp12)
    tmp15 = tmp13 / tmp14
    tmp17 = tmp16 - tmp7
    tmp18 = tmp15 * tmp17
    tmp19 = tmp9 + tmp18
    tmp22 = tmp20 - tmp21
    tmp23 = tl_math.exp(tmp22)
    tmp25 = tmp23 / tmp24
    tmp27 = tmp26 - tmp7
    tmp28 = tmp25 * tmp27
    tmp29 = tmp19 + tmp28
    tmp32 = tmp30 - tmp31
    tmp33 = tl_math.exp(tmp32)
    tmp35 = tmp33 / tmp34
    tmp37 = tmp36 - tmp7
    tmp38 = tmp35 * tmp37
    tmp39 = tmp29 + tmp38
    tmp40 = 4.0
    tmp41 = tmp39 / tmp40
    tl.store(out_ptr0 + x5, tmp41, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 64), (64, 1))
    assert_size_stride(primals_3, (64,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 64), (256, 64, 1024, 1), torch.
            float32)
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__softmax_add_mul_neg_pow_sub_sum_0[grid(16)](primals_3
            , primals_1, primals_2, buf0, buf1, buf2, 16, 64, XBLOCK=8,
            num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.float32)
        triton_poi_fused__softmax_mean_mul_sub_1[grid(1024)](buf0, buf1,
            buf2, primals_1, primals_2, buf3, 1024, XBLOCK=128, num_warps=4,
            num_stages=1)
        del buf0
    return reinterpret_tensor(buf3, (4, 256, 1), (256, 1, 1), 0
        ), primals_1, primals_2, primals_3, buf1, buf2


class LDEPoolingNew(torch.nn.Module):
    """A novel learnable dictionary encoding layer.
    Reference: Weicheng Cai, etc., "A NOVEL LEARNABLE DICTIONARY ENCODING LAYER FOR END-TO-END 
               LANGUAGE IDENTIFICATION", icassp, 2018
    """

    def __init__(self, input_dim, c_num=64, eps=1e-10):
        super(LDEPoolingNew, self).__init__()
        self.input_dim = input_dim
        self.output_dim = input_dim * c_num
        self.eps = eps
        self.mu = torch.nn.Parameter(torch.randn(input_dim, c_num))
        self.s = torch.nn.Parameter(torch.ones(c_num))
        self.softmax_for_w = torch.nn.Softmax(dim=3)

    def get_output_dim(self):
        return self.output_dim

    def forward(self, input_0):
        primals_2 = self.mu
        primals_3 = self.s
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
