# 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/p7/cp76hkbjjz2jdkas7tdu32sl6rpjyrps4pgdlpuwisqcemaemj6f.py
# Topologically Sorted Source Nodes: [cosine_similarity], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
# Source node to ATen node mapping:
#   cosine_similarity => clamp_min, clamp_min_1, div, div_1, mul, pow_1, pow_2, pow_3, pow_4, sum_1, sum_2
# Graph fragment:
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%expand_1, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_1, 0.5), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_2, 1e-08), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%expand_1, %clamp_min), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%expand, 2), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_3, [-1], True), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sum_2, 0.5), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%pow_4, 1e-08), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%expand, %clamp_min_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%div_1, %div), kwargs = {})
triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0 = async_compile.triton('triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*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_clamp_min_div_linalg_vector_norm_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 10, '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_clamp_min_div_linalg_vector_norm_mul_0(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
    x0 = xindex % 4
    x2 = (xindex // 16)
    x3 = xindex % 16
    x1 = (xindex // 4) % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + (x3), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (4*x1), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr1 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr1 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-08
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = triton_helpers.maximum(tmp28, tmp13)
    tmp30 = tmp16 / tmp29
    tmp31 = tmp15 * tmp30
    tl.store(out_ptr0 + (x4), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/hx/chx3gwrmc25lbwfd76ito5jmotizhy7ktyu3jjyw7sktlc7pg4m4.py
# Topologically Sorted Source Nodes: [cosine_similarity], Original ATen: [aten.sum]
# Source node to ATen node mapping:
#   cosine_similarity => sum_3
# Graph fragment:
#   %sum_3 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_3, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [1], True), kwargs = {})
triton_poi_fused_sum_1 = async_compile.triton('triton_poi_fused_sum_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=[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_sum_1', '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_sum_1(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 + (16*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (4 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (5 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (6 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (7 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (8 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (9 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (10 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (11 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (12 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (13 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (14 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr0 + (15 + (16*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 1.0
    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 + (x0), tmp35, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/62/c62jy6v3opderdbb4odtpcjc73sqzaolwdfikhsiz4elxxul4ck3.py
# Topologically Sorted Source Nodes: [cross_entropy_1], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   cross_entropy_1 => amax_1, clone_2
# Graph fragment:
#   %clone_2 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%clone_2, [1], True), 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=[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__log_softmax_2', '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__log_softmax_2(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 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (16 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (17 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (18 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (19 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr0 + (32 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr0 + (33 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp21 = tl.load(in_ptr0 + (34 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (35 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr0 + (48 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp28 = tl.load(in_ptr0 + (49 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp30 = tl.load(in_ptr0 + (50 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp32 = tl.load(in_ptr0 + (51 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 0.25
    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 + (x0), tmp35, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/dv/cdvb362ojkbtkishyu4cqizfwftfzazuakrlf6a2y3lhv5p5vjw4.py
# Topologically Sorted Source Nodes: [cosine_similarity, cross_entropy_1], Original ATen: [aten.sum, aten._log_softmax]
# Source node to ATen node mapping:
#   cosine_similarity => sum_3
#   cross_entropy_1 => clone_2, sub_2
# Graph fragment:
#   %sum_3 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sum_3, 1), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %div_tensor : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 4), kwargs = {})
#   %clone_2 : [num_users=2] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
#   %sub_2 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clone_2, %amax_1), kwargs = {})
triton_poi_fused__log_softmax_sum_3 = async_compile.triton('triton_poi_fused__log_softmax_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax_sum_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 6, '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_sum_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 1.0
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = 0.25
    tmp12 = tmp10 * tmp11
    tmp13 = tmp6 * tmp11
    tmp15 = tmp13 - tmp14
    tl.store(out_ptr0 + (x2), tmp12, xmask)
    tl.store(out_ptr1 + (x2), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ig/cigrvadnvweekwtgmcoa5tujfd5vshnbqlcakxjd5a3u5phi5udn.py
# Topologically Sorted Source Nodes: [label, cross_entropy, cross_entropy_1, loss, mul], Original ATen: [aten.arange, aten.nll_loss_forward, aten.add, aten.mul]
# Source node to ATen node mapping:
#   cross_entropy => convert_element_type, div_3, full_default_1, ne_1, ne_2, neg, sum_5, sum_6, where_1
#   cross_entropy_1 => convert_element_type_1, div_4, full_default_3, ne_4, ne_5, neg_1, sum_8, sum_9, where_3
#   label => iota
#   loss => add
#   mul => mul_1
# Graph fragment:
#   %iota : [num_users=8] = call_function[target=torch.ops.prims.iota.default](args = (4,), kwargs = {start: 0, step: 1, dtype: torch.int64, device: cuda:0, requires_grad: False})
#   %ne_1 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%iota, -100), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%squeeze,), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%ne_1, %neg, %full_default_1), kwargs = {})
#   %sum_6 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%where_1,), kwargs = {})
#   %ne_2 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%iota, -100), kwargs = {})
#   %sum_5 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%ne_2,), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%sum_5, torch.float32), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_6, %convert_element_type), kwargs = {})
#   %ne_4 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%iota, -100), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%squeeze_1,), kwargs = {})
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where_3 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%ne_4, %neg_1, %full_default_3), kwargs = {})
#   %sum_9 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%where_3,), kwargs = {})
#   %ne_5 : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%iota, -100), kwargs = {})
#   %sum_8 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%ne_5,), kwargs = {})
#   %convert_element_type_1 : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%sum_8, torch.float32), kwargs = {})
#   %div_4 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_9, %convert_element_type_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_3, %div_4), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.5), kwargs = {})
triton_per_fused_add_arange_mul_nll_loss_forward_4 = async_compile.triton('triton_per_fused_add_arange_mul_nll_loss_forward_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: '*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), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_arange_mul_nll_loss_forward_4', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 4, '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_arange_mul_nll_loss_forward_4(in_out_ptr0, in_ptr0, in_ptr1, 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
    tmp6 = tl.load(in_ptr0 + (4*r0), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr1 + (r0), None)
    tmp28 = tl.load(in_ptr1 + (4 + r0), None)
    tmp31 = tl.load(in_ptr1 + (8 + r0), None)
    tmp34 = tl.load(in_ptr1 + (12 + r0), None)
    tmp0 = r0
    tmp1 = tl.full([1, 1], -100, tl.int64)
    tmp2 = tmp0 != tmp1
    tmp3 = tl.full([1, 1], 0, tl.int64)
    tmp4 = tl.where(tmp2, tmp0, tmp3)
    tmp5 = tl.load(in_ptr0 + (tmp4 + (4*r0)), None, eviction_policy='evict_last')
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tl_math.log(tmp16)
    tmp18 = tmp5 - tmp17
    tmp19 = -tmp18
    tmp20 = 0.0
    tmp21 = tl.where(tmp2, tmp19, tmp20)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tmp24 = tl.sum(tmp22, 1)[:, None]
    tmp25 = tl.load(in_ptr1 + (r0 + (4*tmp4)), None)
    tmp27 = tl_math.exp(tmp26)
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp30 + tmp32
    tmp35 = tl_math.exp(tmp34)
    tmp36 = tmp33 + tmp35
    tmp37 = tl_math.log(tmp36)
    tmp38 = tmp25 - tmp37
    tmp39 = -tmp38
    tmp40 = tl.where(tmp2, tmp39, tmp20)
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43 = tl.sum(tmp41, 1)[:, None]
    tmp44 = tmp2.to(tl.int64)
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp47 = tl.sum(tmp45, 1)[:, None]
    tmp48 = tmp47.to(tl.float32)
    tmp49 = tmp24 / tmp48
    tmp50 = tmp43 / tmp48
    tmp51 = tmp49 + tmp50
    tmp52 = 0.5
    tmp53 = tmp51 * tmp52
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp53, 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, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cosine_similarity], Original ATen: [aten.linalg_vector_norm, aten.clamp_min, aten.div, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0.run(arg0_1, arg1_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [cosine_similarity], Original ATen: [aten.sum]
        triton_poi_fused_sum_1.run(buf0, buf1, 4, grid=grid(4), stream=stream0)
        buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [cross_entropy_1], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_2.run(buf0, buf5, 4, grid=grid(4), stream=stream0)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (1, 4), torch.float32)
        # Topologically Sorted Source Nodes: [cosine_similarity, cross_entropy_1], Original ATen: [aten.sum, aten._log_softmax]
        triton_poi_fused__log_softmax_sum_3.run(buf0, buf1, buf5, buf2, buf6, 16, grid=grid(16), stream=stream0)
        del buf0
        del buf1
        del buf5
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf9 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [label, cross_entropy, cross_entropy_1, loss, mul], Original ATen: [aten.arange, aten.nll_loss_forward, aten.add, aten.mul]
        triton_per_fused_add_arange_mul_nll_loss_forward_4.run(buf9, buf2, buf6, 1, 4, grid=grid(1), stream=stream0)
        del buf2
        del buf6
    return (buf9, )


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, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4), (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 numpy as np
import torch.nn as nn
import torch.utils.model_zoo


class SimCLRLoss(nn.Module):

    def __init__(self, temperature):
        super(SimCLRLoss, self).__init__()
        self.T = temperature
        self.ce = nn.CrossEntropyLoss()
        self.norm = nn.functional.normalize
        self.softmax = nn.functional.softmax
        self.cosine = nn.CosineSimilarity(dim=-1)
        self.kl = nn.KLDivLoss(reduction='batchmean')
        self.mse = nn.MSELoss()

    def _get_correlated_mask(self, batch_size):
        diag = np.eye(2 * batch_size)
        l1 = np.eye(2 * batch_size, 2 * batch_size, k=-batch_size)
        l2 = np.eye(2 * batch_size, 2 * batch_size, k=batch_size)
        mask = diag + l1 + l2
        return mask

    def forward(self, f1, f2):
        batch_size = f1.shape[0]
        sim_matrix = self.cosine(f1.unsqueeze(1), f2.unsqueeze(0)) / self.T
        label = torch.arange(0, batch_size, device=sim_matrix.device)
        loss = self.ce(sim_matrix, label) + self.ce(sim_matrix.t(), label)
        return loss * 0.5


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


def get_init_inputs():
    return [[], {'temperature': 4}]

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 libdevice, math as tl_math
import numpy as np
import torch.nn as nn
import torch.utils.model_zoo
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_clamp_min_div_linalg_vector_norm_mul_0(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
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex % 16
    x1 = xindex // 4 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + x3, xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-08
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tmp18 = tmp17 * tmp17
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = triton_helpers.maximum(tmp28, tmp13)
    tmp30 = tmp16 / tmp29
    tmp31 = tmp15 * tmp30
    tl.store(out_ptr0 + x4, tmp31, xmask)


@triton.jit
def triton_poi_fused_sum_1(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 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 1.0
    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 + x0, tmp35, xmask)


@triton.jit
def triton_poi_fused__log_softmax_2(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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (16 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr0 + (17 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp12 = tl.load(in_ptr0 + (18 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (19 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp18 = tl.load(in_ptr0 + (32 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (33 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (34 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (35 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (48 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp28 = tl.load(in_ptr0 + (49 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr0 + (50 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp32 = tl.load(in_ptr0 + (51 + 4 * x0), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 0.25
    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 + x0, tmp35, xmask)


@triton.jit
def triton_poi_fused__log_softmax_sum_3(in_ptr0, in_ptr1, in_ptr2, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 1.0
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = 0.25
    tmp12 = tmp10 * tmp11
    tmp13 = tmp6 * tmp11
    tmp15 = tmp13 - tmp14
    tl.store(out_ptr0 + x2, tmp12, xmask)
    tl.store(out_ptr1 + x2, tmp15, xmask)


@triton.jit
def triton_per_fused_add_arange_mul_nll_loss_forward_4(in_out_ptr0, in_ptr0,
    in_ptr1, 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
    tmp6 = tl.load(in_ptr0 + 4 * r0, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp26 = tl.load(in_ptr1 + r0, None)
    tmp28 = tl.load(in_ptr1 + (4 + r0), None)
    tmp31 = tl.load(in_ptr1 + (8 + r0), None)
    tmp34 = tl.load(in_ptr1 + (12 + r0), None)
    tmp0 = r0
    tmp1 = tl.full([1, 1], -100, tl.int64)
    tmp2 = tmp0 != tmp1
    tmp3 = tl.full([1, 1], 0, tl.int64)
    tmp4 = tl.where(tmp2, tmp0, tmp3)
    tmp5 = tl.load(in_ptr0 + (tmp4 + 4 * r0), None, eviction_policy=
        'evict_last')
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp15 = tl_math.exp(tmp14)
    tmp16 = tmp13 + tmp15
    tmp17 = tl_math.log(tmp16)
    tmp18 = tmp5 - tmp17
    tmp19 = -tmp18
    tmp20 = 0.0
    tmp21 = tl.where(tmp2, tmp19, tmp20)
    tmp22 = tl.broadcast_to(tmp21, [XBLOCK, RBLOCK])
    tmp24 = tl.sum(tmp22, 1)[:, None]
    tmp25 = tl.load(in_ptr1 + (r0 + 4 * tmp4), None)
    tmp27 = tl_math.exp(tmp26)
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tmp32 = tl_math.exp(tmp31)
    tmp33 = tmp30 + tmp32
    tmp35 = tl_math.exp(tmp34)
    tmp36 = tmp33 + tmp35
    tmp37 = tl_math.log(tmp36)
    tmp38 = tmp25 - tmp37
    tmp39 = -tmp38
    tmp40 = tl.where(tmp2, tmp39, tmp20)
    tmp41 = tl.broadcast_to(tmp40, [XBLOCK, RBLOCK])
    tmp43 = tl.sum(tmp41, 1)[:, None]
    tmp44 = tmp2.to(tl.int64)
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp47 = tl.sum(tmp45, 1)[:, None]
    tmp48 = tmp47.to(tl.float32)
    tmp49 = tmp24 / tmp48
    tmp50 = tmp43 / tmp48
    tmp51 = tmp49 + tmp50
    tmp52 = 0.5
    tmp53 = tmp51 * tmp52
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp53, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_min_div_linalg_vector_norm_mul_0[grid(64)](
            arg0_1, arg1_1, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused_sum_1[grid(4)](buf0, buf1, 4, XBLOCK=4, num_warps=
            1, num_stages=1)
        buf5 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        triton_poi_fused__log_softmax_2[grid(4)](buf0, buf5, 4, XBLOCK=4,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf6 = empty_strided_cuda((4, 4), (1, 4), torch.float32)
        triton_poi_fused__log_softmax_sum_3[grid(16)](buf0, buf1, buf5,
            buf2, buf6, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del buf1
        del buf5
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf9 = buf3
        del buf3
        triton_per_fused_add_arange_mul_nll_loss_forward_4[grid(1)](buf9,
            buf2, buf6, 1, 4, XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
        del buf6
    return buf9,


class SimCLRLossNew(nn.Module):

    def __init__(self, temperature):
        super(SimCLRLossNew, self).__init__()
        self.T = temperature
        self.ce = nn.CrossEntropyLoss()
        self.norm = nn.functional.normalize
        self.softmax = nn.functional.softmax
        self.cosine = nn.CosineSimilarity(dim=-1)
        self.kl = nn.KLDivLoss(reduction='batchmean')
        self.mse = nn.MSELoss()

    def _get_correlated_mask(self, batch_size):
        diag = np.eye(2 * batch_size)
        l1 = np.eye(2 * batch_size, 2 * batch_size, k=-batch_size)
        l2 = np.eye(2 * batch_size, 2 * batch_size, k=batch_size)
        mask = diag + l1 + l2
        return mask

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