# AOT ID: ['0_inference']
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/rg/crg522m3y4v7k4jllgwpydciu6bjqsfnsxrer5whyf4hotsoe5rw.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%select, [0], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_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], 
    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_0', '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_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/qa/cqafyqzuhzckoqdtli33dfagjrh5mrprozxqnetiembzeg4w7oxs.py
# Topologically Sorted Source Nodes: [softmax_1], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax_1 => amax_1, exp_1, sub_1
# Graph fragment:
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%select_1, [0], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_1, %amax_1), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (80 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (96 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (112 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/bu/cbuxug7lya4eih33uq4mfbh5da6sagxquwrfjrgtafyvfsyilfs6.py
# Topologically Sorted Source Nodes: [softmax_2], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax_2 => amax_2, exp_2, sub_2
# Graph fragment:
#   %amax_2 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%select_2, [0], True), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_2, %amax_2), kwargs = {})
#   %exp_2 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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=[64], 
    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_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_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (128 + x2), xmask)
    tmp1 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (144 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (160 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (176 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/u7/cu74v7lonz2mq7mnyfx2dm5mtzonjucbuxvnfjvhv7cucufpoird.py
# Topologically Sorted Source Nodes: [softmax_3], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax_3 => amax_3, exp_3, sub_3
# Graph fragment:
#   %amax_3 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%select_3, [0], True), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%select_3, %amax_3), kwargs = {})
#   %exp_3 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_3,), 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], 
    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_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, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (192 + x2), xmask)
    tmp1 = tl.load(in_ptr0 + (192 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (208 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (224 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (240 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/2r/c2r7byeley523l73hpccj2xhbirofuf24dg4r7qgj7gvydbk7ytg.py
# Topologically Sorted Source Nodes: [output_], Original ATen: [aten.stack]
# Source node to ATen node mapping:
#   output_ => cat
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%div, %div_1, %div_2, %div_3],), kwargs = {})
triton_poi_fused_stack_4 = async_compile.triton('triton_poi_fused_stack_4', '''
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: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_stack_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 20, '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_stack_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 16)
    x0 = xindex % 16
    x2 = xindex
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + (16*x1)), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp7 = tl.load(in_ptr0 + (16 + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.load(in_ptr0 + (32 + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.load(in_ptr0 + (48 + x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tmp5 / tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp4, tmp13, tmp14)
    tmp16 = tmp0 >= tmp3
    tmp17 = tl.full([1], 8, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr1 + (x0 + (16*((-4) + x1))), tmp19 & xmask, other=0.0)
    tmp21 = tl.load(in_ptr1 + (x0), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp22 = tl.load(in_ptr1 + (16 + x0), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.load(in_ptr1 + (32 + x0), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tl.load(in_ptr1 + (48 + x0), tmp19 & xmask, eviction_policy='evict_last', other=0.0)
    tmp27 = tmp25 + tmp26
    tmp28 = tmp20 / tmp27
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp19, tmp28, tmp29)
    tmp31 = tmp0 >= tmp17
    tmp32 = tl.full([1], 12, tl.int64)
    tmp33 = tmp0 < tmp32
    tmp34 = tmp31 & tmp33
    tmp35 = tl.load(in_ptr2 + (x0 + (16*((-8) + x1))), tmp34 & xmask, other=0.0)
    tmp36 = tl.load(in_ptr2 + (x0), tmp34 & xmask, eviction_policy='evict_last', other=0.0)
    tmp37 = tl.load(in_ptr2 + (16 + x0), tmp34 & xmask, eviction_policy='evict_last', other=0.0)
    tmp38 = tmp36 + tmp37
    tmp39 = tl.load(in_ptr2 + (32 + x0), tmp34 & xmask, eviction_policy='evict_last', other=0.0)
    tmp40 = tmp38 + tmp39
    tmp41 = tl.load(in_ptr2 + (48 + x0), tmp34 & xmask, eviction_policy='evict_last', other=0.0)
    tmp42 = tmp40 + tmp41
    tmp43 = tmp35 / tmp42
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp34, tmp43, tmp44)
    tmp46 = tmp0 >= tmp32
    tmp47 = tl.full([1], 16, tl.int64)
    tmp48 = tmp0 < tmp47
    tmp49 = tl.load(in_ptr3 + (x0 + (16*((-12) + x1))), tmp46 & xmask, other=0.0)
    tmp50 = tl.load(in_ptr3 + (x0), tmp46 & xmask, eviction_policy='evict_last', other=0.0)
    tmp51 = tl.load(in_ptr3 + (16 + x0), tmp46 & xmask, eviction_policy='evict_last', other=0.0)
    tmp52 = tmp50 + tmp51
    tmp53 = tl.load(in_ptr3 + (32 + x0), tmp46 & xmask, eviction_policy='evict_last', other=0.0)
    tmp54 = tmp52 + tmp53
    tmp55 = tl.load(in_ptr3 + (48 + x0), tmp46 & xmask, eviction_policy='evict_last', other=0.0)
    tmp56 = tmp54 + tmp55
    tmp57 = tmp49 / tmp56
    tmp58 = tl.full(tmp57.shape, 0.0, tmp57.dtype)
    tmp59 = tl.where(tmp46, tmp57, tmp58)
    tmp60 = tl.where(tmp34, tmp45, tmp59)
    tmp61 = tl.where(tmp19, tmp30, tmp60)
    tmp62 = tl.where(tmp4, tmp15, tmp61)
    tl.store(out_ptr0 + (x2), tmp62, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(arg0_1, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax_1], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(arg0_1, buf1, 64, grid=grid(64), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax_2], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(arg0_1, buf2, 64, grid=grid(64), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax_3], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_3.run(arg0_1, buf3, 64, grid=grid(64), stream=stream0)
        del arg0_1
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_], Original ATen: [aten.stack]
        triton_poi_fused_stack_4.run(buf0, buf1, buf2, buf3, buf4, 256, grid=grid(256), stream=stream0)
        del buf0
        del buf1
        del buf2
        del buf3
    return (reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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


class MySoftmax(nn.Module):

    def forward(self, input_):
        batch_size = input_.size()[0]
        output_ = torch.stack([F.softmax(input_[i]) for i in range(
            batch_size)], 0)
        return output_


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


def get_init_inputs():
    return [[], {}]

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 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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (64 + x2), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (80 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (96 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (112 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (128 + x2), xmask)
    tmp1 = tl.load(in_ptr0 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (144 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (160 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (176 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (192 + x2), xmask)
    tmp1 = tl.load(in_ptr0 + (192 + x0), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (208 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (224 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (240 + x0), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_stack_4(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 16
    x0 = xindex % 16
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 16 * x1), tmp4 & xmask, other=0.0)
    tmp6 = tl.load(in_ptr0 + x0, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp7 = tl.load(in_ptr0 + (16 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp8 = tmp6 + tmp7
    tmp9 = tl.load(in_ptr0 + (32 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp10 = tmp8 + tmp9
    tmp11 = tl.load(in_ptr0 + (48 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp12 = tmp10 + tmp11
    tmp13 = tmp5 / tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp4, tmp13, tmp14)
    tmp16 = tmp0 >= tmp3
    tmp17 = tl.full([1], 8, tl.int64)
    tmp18 = tmp0 < tmp17
    tmp19 = tmp16 & tmp18
    tmp20 = tl.load(in_ptr1 + (x0 + 16 * (-4 + x1)), tmp19 & xmask, other=0.0)
    tmp21 = tl.load(in_ptr1 + x0, tmp19 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp22 = tl.load(in_ptr1 + (16 + x0), tmp19 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp23 = tmp21 + tmp22
    tmp24 = tl.load(in_ptr1 + (32 + x0), tmp19 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp25 = tmp23 + tmp24
    tmp26 = tl.load(in_ptr1 + (48 + x0), tmp19 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp27 = tmp25 + tmp26
    tmp28 = tmp20 / tmp27
    tmp29 = tl.full(tmp28.shape, 0.0, tmp28.dtype)
    tmp30 = tl.where(tmp19, tmp28, tmp29)
    tmp31 = tmp0 >= tmp17
    tmp32 = tl.full([1], 12, tl.int64)
    tmp33 = tmp0 < tmp32
    tmp34 = tmp31 & tmp33
    tmp35 = tl.load(in_ptr2 + (x0 + 16 * (-8 + x1)), tmp34 & xmask, other=0.0)
    tmp36 = tl.load(in_ptr2 + x0, tmp34 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp37 = tl.load(in_ptr2 + (16 + x0), tmp34 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp38 = tmp36 + tmp37
    tmp39 = tl.load(in_ptr2 + (32 + x0), tmp34 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp40 = tmp38 + tmp39
    tmp41 = tl.load(in_ptr2 + (48 + x0), tmp34 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp42 = tmp40 + tmp41
    tmp43 = tmp35 / tmp42
    tmp44 = tl.full(tmp43.shape, 0.0, tmp43.dtype)
    tmp45 = tl.where(tmp34, tmp43, tmp44)
    tmp46 = tmp0 >= tmp32
    tl.full([1], 16, tl.int64)
    tmp49 = tl.load(in_ptr3 + (x0 + 16 * (-12 + x1)), tmp46 & xmask, other=0.0)
    tmp50 = tl.load(in_ptr3 + x0, tmp46 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp51 = tl.load(in_ptr3 + (16 + x0), tmp46 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp52 = tmp50 + tmp51
    tmp53 = tl.load(in_ptr3 + (32 + x0), tmp46 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp54 = tmp52 + tmp53
    tmp55 = tl.load(in_ptr3 + (48 + x0), tmp46 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp56 = tmp54 + tmp55
    tmp57 = tmp49 / tmp56
    tmp58 = tl.full(tmp57.shape, 0.0, tmp57.dtype)
    tmp59 = tl.where(tmp46, tmp57, tmp58)
    tmp60 = tl.where(tmp34, tmp45, tmp59)
    tmp61 = tl.where(tmp19, tmp30, tmp60)
    tmp62 = tl.where(tmp4, tmp15, tmp61)
    tl.store(out_ptr0 + x2, tmp62, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(64)](arg0_1, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](arg0_1, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(64)](arg0_1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_3[grid(64)](arg0_1, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del arg0_1
        buf4 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_stack_4[grid(256)](buf0, buf1, buf2, buf3, buf4, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del buf1
        del buf2
        del buf3
    return reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0),


class MySoftmaxNew(nn.Module):

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
