# 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_7/inductor_cache/6l/c6l77yqizb2ussrwpql2sgy6aibsneaicpoedl5xmi5qhui5vhw4.py
# Topologically Sorted Source Nodes: [abs_1, mean, mean_1, loss1], Original ATen: [aten.abs, aten.mean, aten.stack]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   loss1 => cat
#   mean => mean
#   mean_1 => mean_1
# Graph fragment:
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%select,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_1,), kwargs = {})
#   %mean_1 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%select,), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3],), kwargs = {})
triton_per_fused_abs_mean_stack_0 = async_compile.triton('triton_per_fused_abs_mean_stack_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=[1, 64],
    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_abs_mean_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_abs_mean_stack_0(in_ptr0, in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/c4/cc4tj5vi742je3y7znxvoicmpmdhmlfc25j2auyjf6n6654deott.py
# Topologically Sorted Source Nodes: [abs_3, mean_2, mean_3, loss1], Original ATen: [aten.abs, aten.mean, aten.stack]
# Source node to ATen node mapping:
#   abs_3 => abs_3
#   loss1 => cat
#   mean_2 => mean_2
#   mean_3 => mean_3
# Graph fragment:
#   %abs_3 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%select_1,), kwargs = {})
#   %mean_2 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_3,), kwargs = {})
#   %mean_3 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%select_1,), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3],), kwargs = {})
triton_per_fused_abs_mean_stack_1 = async_compile.triton('triton_per_fused_abs_mean_stack_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.persistent_reduction(
    size_hints=[1, 64],
    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, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_mean_stack_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_abs_mean_stack_1(in_ptr0, in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 + (64 + r0), None)
    tmp1 = tl.load(in_ptr1 + (64 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/4b/c4bckej6djvjnbkodmvc7m5kmqjulnsjemywn7ygch5hhtqptqa7.py
# Topologically Sorted Source Nodes: [abs_5, mean_4, mean_5, loss1], Original ATen: [aten.abs, aten.mean, aten.stack]
# Source node to ATen node mapping:
#   abs_5 => abs_5
#   loss1 => cat
#   mean_4 => mean_4
#   mean_5 => mean_5
# Graph fragment:
#   %abs_5 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%select_2,), kwargs = {})
#   %mean_4 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_5,), kwargs = {})
#   %mean_5 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%select_2,), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3],), kwargs = {})
triton_per_fused_abs_mean_stack_2 = async_compile.triton('triton_per_fused_abs_mean_stack_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.persistent_reduction(
    size_hints=[1, 64],
    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, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_mean_stack_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_abs_mean_stack_2(in_ptr0, in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 + (128 + r0), None)
    tmp1 = tl.load(in_ptr1 + (128 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/mu/cmu3psc3iyhuo2tyzjfqgyu6vwyob6ysqcpnehonoiu6swzme645.py
# Topologically Sorted Source Nodes: [abs_7, mean_6, mean_7, loss1], Original ATen: [aten.abs, aten.mean, aten.stack]
# Source node to ATen node mapping:
#   abs_7 => abs_7
#   loss1 => cat
#   mean_6 => mean_6
#   mean_7 => mean_7
# Graph fragment:
#   %abs_7 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%select_3,), kwargs = {})
#   %mean_6 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_7,), kwargs = {})
#   %mean_7 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%select_3,), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%unsqueeze, %unsqueeze_1, %unsqueeze_2, %unsqueeze_3],), kwargs = {})
triton_per_fused_abs_mean_stack_3 = async_compile.triton('triton_per_fused_abs_mean_stack_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.persistent_reduction(
    size_hints=[1, 64],
    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, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_mean_stack_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_abs_mean_stack_3(in_ptr0, in_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    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 + (192 + r0), None)
    tmp1 = tl.load(in_ptr1 + (192 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp16, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ry/cryijeflk5r7nerkz63c6t3pdp4ki6lwy6rx7y3nfbwihcgqyjev.py
# Topologically Sorted Source Nodes: [loss1_1], Original ATen: [aten.mean]
# Source node to ATen node mapping:
#   loss1_1 => mean_9
# Graph fragment:
#   %mean_9 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%cat,), kwargs = {})
triton_per_fused_mean_4 = async_compile.triton('triton_per_fused_mean_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, 4],
    reduction_hint=ReductionHint.INNER,
    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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_mean_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_mean_4(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 4
    RBLOCK: tl.constexpr = 4
    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 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp3, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yd/cyd45563hxh7zu4baq6rnch5pz6nf4lfcve3aitxbieufmqj3fff.py
# Topologically Sorted Source Nodes: [loss1_1, loss2, add], Original ATen: [aten.mean, aten.mse_loss, aten.add]
# Source node to ATen node mapping:
#   add => add
#   loss1_1 => mean_9
#   loss2 => mean_8, pow_1, sub_5
# Graph fragment:
#   %mean_9 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%cat,), kwargs = {})
#   %sub_5 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, %arg1_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_5, 2), kwargs = {})
#   %mean_8 : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%pow_1,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean_9, %mean_8), kwargs = {})
triton_per_fused_add_mean_mse_loss_5 = async_compile.triton('triton_per_fused_add_mean_mse_loss_5', '''
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, 256],
    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_add_mean_mse_loss_5', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, '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_add_mean_mse_loss_5(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl.load(in_ptr1 + (r0), None)
    tmp7 = tl.load(in_out_ptr0 + (0))
    tmp8 = tl.broadcast_to(tmp7, [1])
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp9 = 4.0
    tmp10 = tmp8 / tmp9
    tmp11 = 256.0
    tmp12 = tmp6 / tmp11
    tmp13 = tmp10 + tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp13, 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)
        buf12 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf8 = reinterpret_tensor(buf12, (1, ), (1, ), 0)  # alias
        # Topologically Sorted Source Nodes: [abs_1, mean, mean_1, loss1], Original ATen: [aten.abs, aten.mean, aten.stack]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_mean_stack_0.run(arg0_1, arg1_1, buf8, 1, 64, grid=grid(1), stream=stream0)
        buf9 = reinterpret_tensor(buf12, (1, ), (1, ), 1)  # alias
        # Topologically Sorted Source Nodes: [abs_3, mean_2, mean_3, loss1], Original ATen: [aten.abs, aten.mean, aten.stack]
        triton_per_fused_abs_mean_stack_1.run(arg0_1, arg1_1, buf9, 1, 64, grid=grid(1), stream=stream0)
        buf10 = reinterpret_tensor(buf12, (1, ), (1, ), 2)  # alias
        # Topologically Sorted Source Nodes: [abs_5, mean_4, mean_5, loss1], Original ATen: [aten.abs, aten.mean, aten.stack]
        triton_per_fused_abs_mean_stack_2.run(arg0_1, arg1_1, buf10, 1, 64, grid=grid(1), stream=stream0)
        buf11 = reinterpret_tensor(buf12, (1, ), (1, ), 3)  # alias
        # Topologically Sorted Source Nodes: [abs_7, mean_6, mean_7, loss1], Original ATen: [aten.abs, aten.mean, aten.stack]
        triton_per_fused_abs_mean_stack_3.run(arg0_1, arg1_1, buf11, 1, 64, grid=grid(1), stream=stream0)
        buf13 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [loss1_1], Original ATen: [aten.mean]
        triton_per_fused_mean_4.run(buf12, buf13, 1, 4, grid=grid(1), stream=stream0)
        del buf10
        del buf11
        del buf12
        del buf8
        del buf9
        buf15 = buf13; del buf13  # reuse
        # Topologically Sorted Source Nodes: [loss1_1, loss2, add], Original ATen: [aten.mean, aten.mse_loss, aten.add]
        triton_per_fused_add_mean_mse_loss_5.run(buf15, arg0_1, arg1_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf15, )


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 DocUnetLoss_DL_batch(nn.Module):
    """
    只使用一个unet的loss 目前使用这个loss训练的比较好
    """

    def __init__(self, r=0.0, reduction='mean'):
        super(DocUnetLoss_DL_batch, self).__init__()
        assert reduction in ['mean', 'sum'
            ], " reduction must in ['mean','sum']"
        self.r = r
        self.reduction = reduction

    def forward(self, y, label):
        _bs, _n, _h, _w = y.size()
        d = y - label
        loss1 = []
        for d_i in d:
            loss1.append(torch.abs(d_i).mean() - self.r * torch.abs(d_i.mean())
                )
        loss1 = torch.stack(loss1)
        loss2 = F.mse_loss(y, label, reduction=self.reduction)
        if self.reduction == 'mean':
            loss1 = loss1.mean()
        elif self.reduction == 'sum':
            loss1 = loss1.sum()
        return loss1 + loss2


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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_abs_mean_stack_0(in_ptr0, in_ptr1, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None)


@triton.jit
def triton_per_fused_abs_mean_stack_1(in_ptr0, in_ptr1, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 + (64 + r0), None)
    tmp1 = tl.load(in_ptr1 + (64 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None)


@triton.jit
def triton_per_fused_abs_mean_stack_2(in_ptr0, in_ptr1, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 + (128 + r0), None)
    tmp1 = tl.load(in_ptr1 + (128 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None)


@triton.jit
def triton_per_fused_abs_mean_stack_3(in_ptr0, in_ptr1, out_ptr2, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    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 + (192 + r0), None)
    tmp1 = tl.load(in_ptr1 + (192 + r0), None)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK, RBLOCK])
    tmp6 = tl.sum(tmp4, 1)[:, None]
    tmp7 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = 64.0
    tmp11 = tmp6 / tmp10
    tmp12 = tmp9 / tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = 0.0
    tmp15 = tmp13 * tmp14
    tmp16 = tmp11 - tmp15
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp16, None)


@triton.jit
def triton_per_fused_mean_4(in_ptr0, out_ptr0, xnumel, rnumel, XBLOCK: tl.
    constexpr):
    RBLOCK: tl.constexpr = 4
    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 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.sum(tmp1, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp3, None)


@triton.jit
def triton_per_fused_add_mean_mse_loss_5(in_out_ptr0, in_ptr0, in_ptr1,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl.load(in_ptr1 + r0, None)
    tmp7 = tl.load(in_out_ptr0 + 0)
    tmp8 = tl.broadcast_to(tmp7, [1])
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp9 = 4.0
    tmp10 = tmp8 / tmp9
    tmp11 = 256.0
    tmp12 = tmp6 / tmp11
    tmp13 = tmp10 + tmp12
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp13, 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)
        buf12 = empty_strided_cuda((4,), (1,), torch.float32)
        buf8 = reinterpret_tensor(buf12, (1,), (1,), 0)
        get_raw_stream(0)
        triton_per_fused_abs_mean_stack_0[grid(1)](arg0_1, arg1_1, buf8, 1,
            64, XBLOCK=1, num_warps=2, num_stages=1)
        buf9 = reinterpret_tensor(buf12, (1,), (1,), 1)
        triton_per_fused_abs_mean_stack_1[grid(1)](arg0_1, arg1_1, buf9, 1,
            64, XBLOCK=1, num_warps=2, num_stages=1)
        buf10 = reinterpret_tensor(buf12, (1,), (1,), 2)
        triton_per_fused_abs_mean_stack_2[grid(1)](arg0_1, arg1_1, buf10, 1,
            64, XBLOCK=1, num_warps=2, num_stages=1)
        buf11 = reinterpret_tensor(buf12, (1,), (1,), 3)
        triton_per_fused_abs_mean_stack_3[grid(1)](arg0_1, arg1_1, buf11, 1,
            64, XBLOCK=1, num_warps=2, num_stages=1)
        buf13 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_mean_4[grid(1)](buf12, buf13, 1, 4, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf10
        del buf11
        del buf12
        del buf8
        del buf9
        buf15 = buf13
        del buf13
        triton_per_fused_add_mean_mse_loss_5[grid(1)](buf15, arg0_1, arg1_1,
            1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf15,


class DocUnetLoss_DL_batchNew(nn.Module):
    """
    只使用一个unet的loss 目前使用这个loss训练的比较好
    """

    def __init__(self, r=0.0, reduction='mean'):
        super(DocUnetLoss_DL_batchNew, self).__init__()
        assert reduction in ['mean', 'sum'
            ], " reduction must in ['mean','sum']"
        self.r = r
        self.reduction = reduction

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