# 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/cn/ccnexfmna4ur4wyqs74gsfxj7bs426zzjfy6me36neoffbevgmat.py
# Topologically Sorted Source Nodes: [eq, target_1, sum_1], Original ATen: [aten.eq, aten._to_copy, aten.sum]
# Source node to ATen node mapping:
#   eq => eq
#   sum_1 => sum_1
#   target_1 => convert_element_type
# Graph fragment:
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%arg1_1, %permute), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%convert_element_type, [1], True), kwargs = {})
triton_poi_fused__to_copy_eq_sum_0 = async_compile.triton('triton_poi_fused__to_copy_eq_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_eq_sum_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__to_copy_eq_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp5 = tl.load(in_ptr0 + (1))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp10 = tl.load(in_ptr0 + (2))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp15 = tl.load(in_ptr0 + (3))
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp3 = tmp0 == tmp2
    tmp4 = tmp3.to(tl.float32)
    tmp7 = tmp0 == tmp6
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp4 + tmp8
    tmp12 = tmp0 == tmp11
    tmp13 = tmp12.to(tl.float32)
    tmp14 = tmp9 + tmp13
    tmp17 = tmp0 == tmp16
    tmp18 = tmp17.to(tl.float32)
    tmp19 = tmp14 + tmp18
    tl.store(out_ptr0 + (x0), tmp19, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/j4/cj43tvt2ej5tb4b45yykcp3vlqrhx3dhcrcrr3xxldx5ndw4whmn.py
# Topologically Sorted Source Nodes: [logit], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   logit => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16) % 4
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2) + (64*x1)), xmask)
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/2k/c2kmhltecesti2f5yrqwoec6qb3ppxjhmqwsomzpamlxgm7ierrr.py
# Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => amax, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_3, [-1], True), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_3, %amax), kwargs = {})
triton_poi_fused__log_softmax_2 = async_compile.triton('triton_poi_fused__log_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_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__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), 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
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/gz/cgzemqldpqnlddiws6bvttmwh47dbbdhjzyo2db56tysdxvmv2tv.py
# Topologically Sorted Source Nodes: [eq, target_1, sum_1, target_2, neg, log_softmax, mul, sum_2, loss_ce], Original ATen: [aten.eq, aten._to_copy, aten.sum, aten.div, aten.neg, aten._log_softmax, aten.mul, aten.mean]
# Source node to ATen node mapping:
#   eq => eq
#   log_softmax => exp, log, sub_1, sum_2
#   loss_ce => mean
#   mul => mul
#   neg => neg
#   sum_1 => sum_1
#   sum_2 => sum_3
#   target_1 => convert_element_type
#   target_2 => div
# Graph fragment:
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%arg1_1, %permute), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%eq, torch.float32), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%convert_element_type, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%convert_element_type, %sum_1), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%div,), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_2,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %log), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %sub_1), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_3,), kwargs = {})
triton_per_fused__log_softmax__to_copy_div_eq_mean_mul_neg_sum_3 = async_compile.triton('triton_per_fused__log_softmax__to_copy_div_eq_mean_mul_neg_sum_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.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__log_softmax__to_copy_div_eq_mean_mul_neg_sum_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, '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__to_copy_div_eq_mean_mul_neg_sum_3(in_ptr0, in_ptr1, in_ptr2, out_ptr1, 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 % 4
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.load(in_ptr1 + (r0), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (4*r2), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (1 + (4*r2)), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr2 + (2 + (4*r2)), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + (3 + (4*r2)), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (1))
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK, RBLOCK])
    tmp31 = tl.load(in_ptr0 + (2))
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp40 = tl.load(in_ptr0 + (3))
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp3 = tmp0 == tmp2
    tmp4 = tmp3.to(tl.float32)
    tmp6 = tmp4 / tmp5
    tmp7 = -tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp9 + tmp11
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp12 + tmp14
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp15 + tmp17
    tmp19 = tl_math.log(tmp18)
    tmp20 = tmp8 - tmp19
    tmp21 = tmp7 * tmp20
    tmp24 = tmp0 == tmp23
    tmp25 = tmp24.to(tl.float32)
    tmp26 = tmp25 / tmp5
    tmp27 = -tmp26
    tmp28 = tmp10 - tmp19
    tmp29 = tmp27 * tmp28
    tmp30 = tmp21 + tmp29
    tmp33 = tmp0 == tmp32
    tmp34 = tmp33.to(tl.float32)
    tmp35 = tmp34 / tmp5
    tmp36 = -tmp35
    tmp37 = tmp13 - tmp19
    tmp38 = tmp36 * tmp37
    tmp39 = tmp30 + tmp38
    tmp42 = tmp0 == tmp41
    tmp43 = tmp42.to(tl.float32)
    tmp44 = tmp43 / tmp5
    tmp45 = -tmp44
    tmp46 = tmp16 - tmp19
    tmp47 = tmp45 * tmp46
    tmp48 = tmp39 + tmp47
    tmp49 = tl.broadcast_to(tmp48, [XBLOCK, RBLOCK])
    tmp51 = tl.sum(tmp49, 1)[:, None]
    tl.store(out_ptr1 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp51, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/re/cre57ul3zil5quz6dxfdl5fnoktlmyazrkihdhgrknp5rsrobsbm.py
# Topologically Sorted Source Nodes: [loss_ce, pow_1, sum_3, truediv_1, pow_2, sum_4, truediv_2, l2_loss, mul_1, mul_2, loss], Original ATen: [aten.mean, aten.pow, aten.sum, aten.div, aten.add, aten.mul]
# Source node to ATen node mapping:
#   l2_loss => add
#   loss => add_1
#   loss_ce => mean
#   mul_1 => mul_1
#   mul_2 => mul_2
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sum_3 => sum_4
#   sum_4 => sum_5
#   truediv_1 => div_1
#   truediv_2 => div_2
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_3,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg0_1, 2), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_1,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_4, 4), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg2_1, 2), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%pow_2,), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_5, 4), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_1, %div_2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.02), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, 0.25), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mean, %mul_2), kwargs = {})
triton_per_fused_add_div_mean_mul_pow_sum_4 = async_compile.triton('triton_per_fused_add_div_mean_mul_pow_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, 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_div_mean_mul_pow_sum_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, '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_add_div_mean_mul_pow_sum_4(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)
    tmp5 = tl.load(in_ptr1 + (r0), None)
    tmp10 = tl.load(in_out_ptr0 + (0))
    tmp11 = tl.broadcast_to(tmp10, [1])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = triton_helpers.promote_to_tensor(tl.sum(tmp2, 0))
    tmp6 = tmp5 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp12 = 64.0
    tmp13 = tmp11 / tmp12
    tmp14 = 0.25
    tmp15 = tmp4 * tmp14
    tmp16 = tmp9 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = 0.02
    tmp19 = tmp17 * tmp18
    tmp20 = tmp19 * tmp14
    tmp21 = tmp13 + tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp21, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 1), (1, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [eq, target_1, sum_1], Original ATen: [aten.eq, aten._to_copy, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_eq_sum_0.run(arg1_1, buf0, 4, grid=grid(4), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logit], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(arg2_1, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logit], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(arg0_1, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0), out=buf2)
        buf3 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [log_softmax], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_2.run(buf2, buf3, 256, grid=grid(256), stream=stream0)
        del buf2
        buf5 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [eq, target_1, sum_1, target_2, neg, log_softmax, mul, sum_2, loss_ce], Original ATen: [aten.eq, aten._to_copy, aten.sum, aten.div, aten.neg, aten._log_softmax, aten.mul, aten.mean]
        triton_per_fused__log_softmax__to_copy_div_eq_mean_mul_neg_sum_3.run(arg1_1, buf0, buf3, buf5, 1, 64, grid=grid(1), stream=stream0)
        del arg1_1
        del buf0
        del buf3
        buf8 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [loss_ce, pow_1, sum_3, truediv_1, pow_2, sum_4, truediv_2, l2_loss, mul_1, mul_2, loss], Original ATen: [aten.mean, aten.pow, aten.sum, aten.div, aten.add, aten.mul]
        triton_per_fused_add_div_mean_mul_pow_sum_4.run(buf8, arg0_1, arg2_1, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg2_1
    return (buf8, )


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, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_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


def cross_entropy(logits, target, size_average=True):
    if size_average:
        return torch.mean(torch.sum(-target * F.log_softmax(logits, -1), -1))
    else:
        return torch.sum(torch.sum(-target * F.log_softmax(logits, -1), -1))


class NpairLoss(nn.Module):
    """the multi-class n-pair loss"""

    def __init__(self, l2_reg=0.02):
        super(NpairLoss, self).__init__()
        self.l2_reg = l2_reg

    def forward(self, anchor, positive, target):
        batch_size = anchor.size(0)
        target = target.view(target.size(0), 1)
        target = (target == torch.transpose(target, 0, 1)).float()
        target = target / torch.sum(target, dim=1, keepdim=True).float()
        logit = torch.matmul(anchor, torch.transpose(positive, 0, 1))
        loss_ce = cross_entropy(logit, target)
        l2_loss = torch.sum(anchor ** 2) / batch_size + torch.sum(positive ** 2
            ) / batch_size
        loss = loss_ce + self.l2_reg * l2_loss * 0.25
        return loss


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4]), torch.rand(
        [4, 1])]


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

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
import torch.nn.functional as F
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__to_copy_eq_sum_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp5 = tl.load(in_ptr0 + 1)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp10 = tl.load(in_ptr0 + 2)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp15 = tl.load(in_ptr0 + 3)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp3 = tmp0 == tmp2
    tmp4 = tmp3.to(tl.float32)
    tmp7 = tmp0 == tmp6
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp4 + tmp8
    tmp12 = tmp0 == tmp11
    tmp13 = tmp12.to(tl.float32)
    tmp14 = tmp9 + tmp13
    tmp17 = tmp0 == tmp16
    tmp18 = tmp17.to(tl.float32)
    tmp19 = tmp14 + tmp18
    tl.store(out_ptr0 + x0, tmp19, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16 % 4
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2 + 64 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), 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
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_per_fused__log_softmax__to_copy_div_eq_mean_mul_neg_sum_3(in_ptr0,
    in_ptr1, in_ptr2, out_ptr1, 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 % 4
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.load(in_ptr1 + r0, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + 4 * r2, None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr2 + (1 + 4 * r2), None, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr2 + (2 + 4 * r2), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + (3 + 4 * r2), None, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + 1)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK, RBLOCK])
    tmp31 = tl.load(in_ptr0 + 2)
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp40 = tl.load(in_ptr0 + 3)
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp3 = tmp0 == tmp2
    tmp4 = tmp3.to(tl.float32)
    tmp6 = tmp4 / tmp5
    tmp7 = -tmp6
    tmp9 = tl_math.exp(tmp8)
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp9 + tmp11
    tmp14 = tl_math.exp(tmp13)
    tmp15 = tmp12 + tmp14
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp15 + tmp17
    tmp19 = tl_math.log(tmp18)
    tmp20 = tmp8 - tmp19
    tmp21 = tmp7 * tmp20
    tmp24 = tmp0 == tmp23
    tmp25 = tmp24.to(tl.float32)
    tmp26 = tmp25 / tmp5
    tmp27 = -tmp26
    tmp28 = tmp10 - tmp19
    tmp29 = tmp27 * tmp28
    tmp30 = tmp21 + tmp29
    tmp33 = tmp0 == tmp32
    tmp34 = tmp33.to(tl.float32)
    tmp35 = tmp34 / tmp5
    tmp36 = -tmp35
    tmp37 = tmp13 - tmp19
    tmp38 = tmp36 * tmp37
    tmp39 = tmp30 + tmp38
    tmp42 = tmp0 == tmp41
    tmp43 = tmp42.to(tl.float32)
    tmp44 = tmp43 / tmp5
    tmp45 = -tmp44
    tmp46 = tmp16 - tmp19
    tmp47 = tmp45 * tmp46
    tmp48 = tmp39 + tmp47
    tmp49 = tl.broadcast_to(tmp48, [XBLOCK, RBLOCK])
    tmp51 = tl.sum(tmp49, 1)[:, None]
    tl.store(out_ptr1 + tl.full([XBLOCK, 1], 0, tl.int32), tmp51, None)


@triton.jit
def triton_per_fused_add_div_mean_mul_pow_sum_4(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)
    tmp5 = tl.load(in_ptr1 + r0, None)
    tmp10 = tl.load(in_out_ptr0 + 0)
    tmp11 = tl.broadcast_to(tmp10, [1])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = triton_helpers.promote_to_tensor(tl.sum(tmp2, 0))
    tmp6 = tmp5 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [RBLOCK])
    tmp9 = triton_helpers.promote_to_tensor(tl.sum(tmp7, 0))
    tmp12 = 64.0
    tmp13 = tmp11 / tmp12
    tmp14 = 0.25
    tmp15 = tmp4 * tmp14
    tmp16 = tmp9 * tmp14
    tmp17 = tmp15 + tmp16
    tmp18 = 0.02
    tmp19 = tmp17 * tmp18
    tmp20 = tmp19 * tmp14
    tmp21 = tmp13 + tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp21, None)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 1), (1, 1))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__to_copy_eq_sum_0[grid(4)](arg1_1, buf0, 4, XBLOCK
            =4, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(256)](arg2_1, buf1, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(arg0_1, (16, 4, 4), (16, 4, 1
            ), 0), reinterpret_tensor(buf1, (16, 4, 4), (16, 4, 1), 0), out
            =buf2)
        buf3 = buf1
        del buf1
        triton_poi_fused__log_softmax_2[grid(256)](buf2, buf3, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf2
        buf5 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused__log_softmax__to_copy_div_eq_mean_mul_neg_sum_3[grid
            (1)](arg1_1, buf0, buf3, buf5, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg1_1
        del buf0
        del buf3
        buf8 = buf5
        del buf5
        triton_per_fused_add_div_mean_mul_pow_sum_4[grid(1)](buf8, arg0_1,
            arg2_1, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg2_1
    return buf8,


def cross_entropy(logits, target, size_average=True):
    if size_average:
        return torch.mean(torch.sum(-target * F.log_softmax(logits, -1), -1))
    else:
        return torch.sum(torch.sum(-target * F.log_softmax(logits, -1), -1))


class NpairLossNew(nn.Module):
    """the multi-class n-pair loss"""

    def __init__(self, l2_reg=0.02):
        super(NpairLossNew, self).__init__()
        self.l2_reg = l2_reg

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg2_1 = input_1
        arg1_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
