# 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_4/inductor_cache/em/cemtbxj46jzc7s3a3pexakdfg5dv3w7chdxynyt6hemfgtua425c.py
# Topologically Sorted Source Nodes: [attention_T, probs_T], Original ATen: [aten.mean, aten._softmax]
# Source node to ATen node mapping:
#   attention_T => mean_1
#   probs_T => amax
# Graph fragment:
#   %mean_1 : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%arg1_1, [1]), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mean_1, [-1], True), kwargs = {})
triton_poi_fused__softmax_mean_0 = async_compile.triton('triton_poi_fused__softmax_mean_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: '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_mean_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (16 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (48 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (17 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (33 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (49 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (2 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (18 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (34 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (50 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (3 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (19 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (35 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr0 + (51 + (4*x0) + (64*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = triton_helpers.maximum(tmp8, tmp16)
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = triton_helpers.maximum(tmp17, tmp25)
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = triton_helpers.maximum(tmp26, tmp34)
    tl.store(out_ptr0 + (x2), tmp35, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/z7/cz76prezjig7qgxgvguiadouzcb27eld52u7pnldapfz7dkkbbyq.py
# Topologically Sorted Source Nodes: [attention_T, probs_T], Original ATen: [aten.mean, aten._softmax]
# Source node to ATen node mapping:
#   attention_T => mean_1
#   probs_T => exp, sub
# Graph fragment:
#   %mean_1 : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%arg1_1, [1]), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_mean_1 = async_compile.triton('triton_poi_fused__softmax_mean_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: '*fp32', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_mean_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_mean_1(in_ptr0, in_ptr1, 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 // 16)
    x3 = xindex % 16
    x4 = (xindex // 4)
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (64*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x3 + (64*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x3 + (64*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x3 + (64*x2)), xmask)
    tmp9 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tl_math.exp(tmp10)
    tl.store(out_ptr0 + (x5), tmp11, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/yq/cyqmbvhphd3h2m25wdbpkegnclceflojku56mtlgw532pxonenqo.py
# Topologically Sorted Source Nodes: [attention_T, le, zeros_like, probs_T, probs_T_select], Original ATen: [aten.mean, aten.le, aten.zeros_like, aten._softmax, aten.where]
# Source node to ATen node mapping:
#   attention_T => mean_1
#   le => le
#   probs_T => div, sum_1
#   probs_T_select => where
#   zeros_like => full_default
# Graph fragment:
#   %mean_1 : [num_users=3] = call_function[target=torch.ops.aten.mean.dim](args = (%arg1_1, [1]), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%mean_1, -0.001), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 4, 4], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%le, %full_default, %div), kwargs = {})
triton_poi_fused__softmax_le_mean_where_zeros_like_2 = async_compile.triton('triton_poi_fused__softmax_le_mean_where_zeros_like_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: '*fp32', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_le_mean_where_zeros_like_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 9, '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_le_mean_where_zeros_like_2(in_ptr0, in_ptr1, 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 // 16)
    x3 = xindex % 16
    x4 = xindex
    x5 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x3 + (64*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x3 + (64*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x3 + (64*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x3 + (64*x2)), xmask)
    tmp11 = tl.load(in_ptr1 + (x4), xmask)
    tmp12 = tl.load(in_ptr1 + (4*x5), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + (4*x5)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (2 + (4*x5)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (3 + (4*x5)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = -0.001
    tmp10 = tmp8 <= tmp9
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp11 / tmp18
    tmp20 = 0.0
    tmp21 = tl.where(tmp10, tmp20, tmp19)
    tl.store(out_ptr0 + (x4), tmp21, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ls/clsydhccrbryx7iijzr734m7b6gibnkbl2hyvsz543lktldvszu7.py
# Topologically Sorted Source Nodes: [attention_S, log_softmax], Original ATen: [aten.mean, aten._log_softmax]
# Source node to ATen node mapping:
#   attention_S => mean
#   log_softmax => sub_1
# Graph fragment:
#   %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [1]), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mean, %amax_1), kwargs = {})
triton_poi_fused__log_softmax_mean_3 = async_compile.triton('triton_poi_fused__log_softmax_mean_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: '*fp32', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_mean_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__log_softmax_mean_3(in_ptr0, in_ptr1, 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 // 16)
    x3 = xindex % 16
    x4 = (xindex // 4)
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (64*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x3 + (64*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x3 + (64*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x3 + (64*x2)), xmask)
    tmp9 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp10 = tmp8 - tmp9
    tl.store(out_ptr0 + (x5), tmp10, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/lv/clvmtaauzcncejtlfmicmvxyo42zrk3gwtzdcapoiwcf7iqbykao.py
# Topologically Sorted Source Nodes: [log_softmax, mul, sum_1, mean_2, loss], Original ATen: [aten._log_softmax, aten.mul, aten.sum, aten.mean, aten.neg]
# Source node to ATen node mapping:
#   log_softmax => exp_1, log, sub_2, sum_2
#   loss => neg
#   mean_2 => mean_2
#   mul => mul
#   sum_1 => sum_3
# Graph fragment:
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [-1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_2,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, %log), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%where, %sub_2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_3,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%mean_2,), kwargs = {})
triton_per_fused__log_softmax_mean_mul_neg_sum_4 = async_compile.triton('triton_per_fused__log_softmax_mean_mul_neg_sum_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.persistent_reduction(
    size_hints=[1, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__log_softmax_mean_mul_neg_sum_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 1, '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__log_softmax_mean_mul_neg_sum_4(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = tmp0 * tmp13
    tmp16 = tmp3 - tmp12
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp20 = tmp6 - tmp12
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp24 = tmp9 - tmp12
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp30 = 16.0
    tmp31 = tmp29 / tmp30
    tmp32 = -tmp31
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp32, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_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, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [attention_T, probs_T], Original ATen: [aten.mean, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_mean_0.run(arg1_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_T, probs_T], Original ATen: [aten.mean, aten._softmax]
        triton_poi_fused__softmax_mean_1.run(arg1_1, buf0, buf1, 64, grid=grid(64), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention_T, le, zeros_like, probs_T, probs_T_select], Original ATen: [aten.mean, aten.le, aten.zeros_like, aten._softmax, aten.where]
        triton_poi_fused__softmax_le_mean_where_zeros_like_2.run(arg1_1, buf1, buf2, 64, grid=grid(64), stream=stream0)
        del arg1_1
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [attention_S, log_softmax], Original ATen: [aten.mean, aten._log_softmax]
        triton_poi_fused__softmax_mean_0.run(arg0_1, buf3, 16, grid=grid(16), stream=stream0)
        buf4 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [attention_S, log_softmax], Original ATen: [aten.mean, aten._log_softmax]
        triton_poi_fused__log_softmax_mean_3.run(arg0_1, buf3, buf4, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del buf3
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [log_softmax, mul, sum_1, mean_2, loss], Original ATen: [aten._log_softmax, aten.mul, aten.sum, aten.mean, aten.neg]
        triton_per_fused__log_softmax_mean_mul_neg_sum_4.run(buf6, buf2, buf4, 1, 16, grid=grid(1), stream=stream0)
        del buf2
        del buf4
    return (buf6, )


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)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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 AttCeMeanLoss(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, attention_S, attention_T, mask=None):
        """
        Calculate the cross entropy  between attention_S and attention_T, the dim of num_heads is averaged

        :param logits_S: Tensor of shape  (batch_size, num_heads, length, length) or (batch_size, length, length)
        :param logits_T: Tensor of shape  (batch_size, num_heads, length, length) or (batch_size, length, length)
        :param mask:     Tensor of shape  (batch_size, length)
        """
        if len(attention_S.size()) == 4:
            attention_S = attention_S.mean(dim=1)
            attention_T = attention_T.mean(dim=1)
        probs_T = F.softmax(attention_T, dim=-1)
        if mask is None:
            probs_T_select = torch.where(attention_T <= -0.001, torch.
                zeros_like(attention_T), probs_T)
            loss = -(probs_T_select * F.log_softmax(attention_S, dim=-1)).sum(
                dim=-1).mean()
        else:
            mask = mask
            loss = -((probs_T * F.log_softmax(attention_S, dim=-1) * mask.
                unsqueeze(1)).sum(dim=-1) * mask).sum() / mask.sum()
        return loss


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


@triton.jit
def triton_poi_fused__softmax_mean_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x0 + 64 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (16 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (32 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (48 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0 + 64 * x1), xmask, eviction_policy
        ='evict_last')
    tmp10 = tl.load(in_ptr0 + (17 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (33 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (49 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (2 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (18 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (34 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (50 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (3 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (19 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (35 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr0 + (51 + 4 * x0 + 64 * x1), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp11 = tmp9 + tmp10
    tmp13 = tmp11 + tmp12
    tmp15 = tmp13 + tmp14
    tmp16 = tmp15 / tmp7
    tmp17 = triton_helpers.maximum(tmp8, tmp16)
    tmp20 = tmp18 + tmp19
    tmp22 = tmp20 + tmp21
    tmp24 = tmp22 + tmp23
    tmp25 = tmp24 / tmp7
    tmp26 = triton_helpers.maximum(tmp17, tmp25)
    tmp29 = tmp27 + tmp28
    tmp31 = tmp29 + tmp30
    tmp33 = tmp31 + tmp32
    tmp34 = tmp33 / tmp7
    tmp35 = triton_helpers.maximum(tmp26, tmp34)
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused__softmax_mean_1(in_ptr0, in_ptr1, 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 // 16
    x3 = xindex % 16
    x4 = xindex // 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp9 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tl_math.exp(tmp10)
    tl.store(out_ptr0 + x5, tmp11, xmask)


@triton.jit
def triton_poi_fused__softmax_le_mean_where_zeros_like_2(in_ptr0, in_ptr1,
    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 // 16
    x3 = xindex % 16
    x4 = xindex
    x5 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp11 = tl.load(in_ptr1 + x4, xmask)
    tmp12 = tl.load(in_ptr1 + 4 * x5, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x5), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr1 + (2 + 4 * x5), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr1 + (3 + 4 * x5), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = -0.001
    tmp10 = tmp8 <= tmp9
    tmp14 = tmp12 + tmp13
    tmp16 = tmp14 + tmp15
    tmp18 = tmp16 + tmp17
    tmp19 = tmp11 / tmp18
    tmp20 = 0.0
    tmp21 = tl.where(tmp10, tmp20, tmp19)
    tl.store(out_ptr0 + x4, tmp21, xmask)


@triton.jit
def triton_poi_fused__log_softmax_mean_3(in_ptr0, in_ptr1, 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 // 16
    x3 = xindex % 16
    x4 = xindex // 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 64 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x3 + 64 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x3 + 64 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x3 + 64 * x2), xmask)
    tmp9 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp10 = tmp8 - tmp9
    tl.store(out_ptr0 + x5, tmp10, xmask)


@triton.jit
def triton_per_fused__log_softmax_mean_mul_neg_sum_4(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp1 - tmp12
    tmp14 = tmp0 * tmp13
    tmp16 = tmp3 - tmp12
    tmp17 = tmp15 * tmp16
    tmp18 = tmp14 + tmp17
    tmp20 = tmp6 - tmp12
    tmp21 = tmp19 * tmp20
    tmp22 = tmp18 + tmp21
    tmp24 = tmp9 - tmp12
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.sum(tmp27, 1)[:, None]
    tmp30 = 16.0
    tmp31 = tmp29 / tmp30
    tmp32 = -tmp31
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp32, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_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, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_mean_0[grid(16)](arg1_1, buf0, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_mean_1[grid(64)](arg1_1, buf0, 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_le_mean_where_zeros_like_2[grid(64)](arg1_1,
            buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg1_1
        buf3 = buf0
        del buf0
        triton_poi_fused__softmax_mean_0[grid(16)](arg0_1, buf3, 16, XBLOCK
            =16, num_warps=1, num_stages=1)
        buf4 = buf1
        del buf1
        triton_poi_fused__log_softmax_mean_3[grid(64)](arg0_1, buf3, buf4, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del buf3
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5
        del buf5
        triton_per_fused__log_softmax_mean_mul_neg_sum_4[grid(1)](buf6,
            buf2, buf4, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
        del buf4
    return buf6,


class AttCeMeanLossNew(nn.Module):

    def __init__(self):
        super().__init__()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
