# AOT ID: ['0_forward']
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/fh/cfhnguw4v6uy4ysjg54ojclakwi3bj2lte6oqizl4rpf4lcxpiyp.py
# Topologically Sorted Source Nodes: [X], Original ATen: [aten.div]
# Source node to ATen node mapping:
#   X => div
# Graph fragment:
#   %div : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_1, %expand), kwargs = {})
triton_poi_fused_div_0 = async_compile.triton('triton_poi_fused_div_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=[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_div_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_div_0(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
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), 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-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + (x3), tmp15, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/xk/cxkugsynlmnyrjhah42fewrhwovuvurnuv2qimo2qhxq27wjmq7q.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, exp, sub
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%view_1, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 16
    x2 = (xindex // 64)
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/tw/ctwkw35qgrxnplwjus5xpzkjda4uxgj3ectshin3uc6hmrbxdxt6.py
# Topologically Sorted Source Nodes: [softmax, max_1], Original ATen: [aten._softmax, aten.max]
# Source node to ATen node mapping:
#   max_1 => max_1
#   softmax => div_1, sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_2), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%div_1, 1), kwargs = {})
triton_poi_fused__softmax_max_2 = async_compile.triton('triton_poi_fused__softmax_max_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i64', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_max_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused__softmax_max_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp8 = tmp1 / tmp6
    tmp9 = tmp7 > tmp8
    tmp10 = tmp7 == tmp8
    tmp11 = tmp7 != tmp7
    tmp12 = tmp8 != tmp8
    tmp13 = tmp11 > tmp12
    tmp14 = tmp9 | tmp13
    tmp15 = tmp11 & tmp12
    tmp16 = tmp10 | tmp15
    tmp17 = tl.full([1], 0, tl.int64)
    tmp18 = tl.full([1], 1, tl.int64)
    tmp19 = tmp17 < tmp18
    tmp20 = tmp16 & tmp19
    tmp21 = tmp14 | tmp20
    tmp22 = tl.where(tmp21, tmp7, tmp8)
    tmp23 = tl.where(tmp21, tmp17, tmp18)
    tmp24 = tmp3 / tmp6
    tmp25 = tmp22 > tmp24
    tmp26 = tmp22 == tmp24
    tmp27 = tmp22 != tmp22
    tmp28 = tmp24 != tmp24
    tmp29 = tmp27 > tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tmp27 & tmp28
    tmp32 = tmp26 | tmp31
    tmp33 = tl.full([1], 2, tl.int64)
    tmp34 = tmp23 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tmp30 | tmp35
    tmp37 = tl.where(tmp36, tmp22, tmp24)
    tmp38 = tl.where(tmp36, tmp23, tmp33)
    tmp39 = tmp5 / tmp6
    tmp40 = tmp37 > tmp39
    tmp41 = tmp37 == tmp39
    tmp42 = tmp37 != tmp37
    tmp43 = tmp39 != tmp39
    tmp44 = tmp42 > tmp43
    tmp45 = tmp40 | tmp44
    tmp46 = tmp42 & tmp43
    tmp47 = tmp41 | tmp46
    tmp48 = tl.full([1], 3, tl.int64)
    tmp49 = tmp38 < tmp48
    tmp50 = tmp47 & tmp49
    tmp51 = tmp45 | tmp50
    tmp52 = tl.where(tmp51, tmp37, tmp39)
    tmp53 = tl.where(tmp51, tmp38, tmp48)
    tl.store(out_ptr0 + (x2), tmp53, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ld/cldoapnebb2yhaanl4ibvt7hg5txag3xsrmgntown2nx2tizfox4.py
# Topologically Sorted Source Nodes: [neighbor_bool, mul, inner, clamp, acos, geo_desic, square, sub, relu, square_1, where, geo_losses, mean], Original ATen: [aten.eq, aten.mul, aten.sum, aten.clamp, aten.acos, aten.pow, aten.rsub, aten.relu, aten.where, aten.mean]
# Source node to ATen node mapping:
#   acos => acos
#   clamp => clamp_max, clamp_min_1
#   geo_desic => mul_1
#   geo_losses => clamp_min_2
#   inner => sum_3
#   mean => mean
#   mul => mul
#   neighbor_bool => eq
#   relu => relu
#   square => pow_3
#   square_1 => pow_4
#   sub => sub_1
#   where => where
# Graph fragment:
#   %eq : [num_users=1] = call_function[target=torch.ops.aten.eq.Tensor](args = (%slice_1, %slice_2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%slice_3, %slice_4), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sum_3, -1e-07), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 1e-07), kwargs = {})
#   %acos : [num_users=1] = call_function[target=torch.ops.aten.acos.default](args = (%clamp_max,), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%acos, 1.0), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mul_1, 2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (0.5, %mul_1), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%sub_1,), kwargs = {})
#   %pow_4 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%relu, 2), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%eq, %pow_3, %pow_4), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%where, 1e-12), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%clamp_min_2,), kwargs = {})
triton_per_fused_acos_clamp_eq_mean_mul_pow_relu_rsub_sum_where_3 = async_compile.triton('triton_per_fused_acos_clamp_eq_mean_mul_pow_relu_rsub_sum_where_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, 32],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i64', 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_acos_clamp_eq_mean_mul_pow_relu_rsub_sum_where_3', 'mutated_arg_names': ['in_out_ptr0'], '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_acos_clamp_eq_mean_mul_pow_relu_rsub_sum_where_3(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 32
    RBLOCK: tl.constexpr = 32
    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 % 16
    r1 = (rindex // 16)
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp1 = tl.load(in_ptr0 + (128 + r0 + (64*r1)), None)
    tmp3 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None)
    tmp4 = tl.load(in_ptr0 + (144 + r0 + (64*r1)), None)
    tmp7 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None)
    tmp8 = tl.load(in_ptr0 + (160 + r0 + (64*r1)), None)
    tmp11 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None)
    tmp12 = tl.load(in_ptr0 + (176 + r0 + (64*r1)), None)
    tmp22 = tl.load(in_ptr1 + (r2), None)
    tmp23 = tl.load(in_ptr1 + (32 + r2), None)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = -1e-07
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = 1e-07
    tmp18 = triton_helpers.minimum(tmp16, tmp17)
    tmp19 = libdevice.acos(tmp18)
    tmp20 = 1.0
    tmp21 = tmp19 * tmp20
    tmp24 = tmp22 == tmp23
    tmp25 = tmp21 * tmp21
    tmp26 = 0.5
    tmp27 = tmp26 - tmp21
    tmp28 = tl.full([1, 1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp30 = tmp29 * tmp29
    tmp31 = tl.where(tmp24, tmp25, tmp30)
    tmp32 = 1e-12
    tmp33 = triton_helpers.maximum(tmp31, tmp32)
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK, RBLOCK])
    tmp36 = tl.sum(tmp34, 1)[:, None]
    tmp37 = 32.0
    tmp38 = tmp36 / tmp37
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp38, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [X], Original ATen: [aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [wf], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf1, buf2, 256, grid=grid(256), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        # Topologically Sorted Source Nodes: [softmax, max_1], Original ATen: [aten._softmax, aten.max]
        triton_poi_fused__softmax_max_2.run(buf2, buf3, 64, grid=grid(64), stream=stream0)
        del buf2
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5; del buf5  # reuse
        # Topologically Sorted Source Nodes: [neighbor_bool, mul, inner, clamp, acos, geo_desic, square, sub, relu, square_1, where, geo_losses, mean], Original ATen: [aten.eq, aten.mul, aten.sum, aten.clamp, aten.acos, aten.pow, aten.rsub, aten.relu, aten.where, aten.mean]
        triton_per_fused_acos_clamp_eq_mean_mul_pow_relu_rsub_sum_where_3.run(buf6, buf0, buf3, 1, 32, grid=grid(1), stream=stream0)
        del buf3
    return (buf6, reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(buf0, (64, 4), (4, 1), 0), )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 AMCLoss(nn.Module):

    def __init__(self, in_features, out_features, s=None, m=None, device='cuda'
        ):
        """
        Angular Margin Contrastive Loss

        https://arxiv.org/pdf/2004.09805.pdf
        
        Code converted over from Tensorflow to Pytorch

        """
        super(AMCLoss, self).__init__()
        self.m = 0.5 if not m else m
        self.s = 1.0 if not s else s
        self.in_features = in_features
        self.out_features = out_features
        self.fc = nn.Linear(in_features, out_features, bias=False)
        self.device = device

    def forward(self, X, labels=None):
        """
        input shape (N, in_features)
        """
        X = F.normalize(X, p=2, dim=1)
        batch_size = X.shape[0]
        wf = self.fc(X)
        half = int(batch_size / 2)
        _, target_hard = torch.max(F.softmax(wf, dim=1), 1)
        try:
            neighbor_bool = torch.eq(target_hard[:half], target_hard[half:])
            inner = torch.sum(X[:half] * X[half:], axis=1)
        except:
            neighbor_bool = torch.eq(target_hard[:half + 1], target_hard[half:]
                )
            inner = torch.sum(X[:half + 1] * X[half:], axis=1)
        geo_desic = torch.acos(torch.clamp(inner, -1e-07, 1e-07)) * self.s
        geo_losses = torch.where(neighbor_bool, torch.square(geo_desic),
            torch.square(F.relu(self.m - geo_desic))).clamp(min=1e-12)
        return torch.mean(geo_losses), wf


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


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

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 libdevice, math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_div_0(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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), 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-12
    tmp14 = triton_helpers.maximum(tmp12, tmp13)
    tmp15 = tmp0 / tmp14
    tl.store(out_ptr0 + x3, tmp15, xmask)


@triton.jit
def triton_poi_fused__softmax_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
    x3 = xindex
    x0 = xindex % 16
    x2 = xindex // 64
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (16 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (32 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (48 + x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_max_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp8 = tmp1 / tmp6
    tmp9 = tmp7 > tmp8
    tmp10 = tmp7 == tmp8
    tmp11 = tmp7 != tmp7
    tmp12 = tmp8 != tmp8
    tmp13 = tmp11 > tmp12
    tmp14 = tmp9 | tmp13
    tmp15 = tmp11 & tmp12
    tmp16 = tmp10 | tmp15
    tmp17 = tl.full([1], 0, tl.int64)
    tmp18 = tl.full([1], 1, tl.int64)
    tmp19 = tmp17 < tmp18
    tmp20 = tmp16 & tmp19
    tmp21 = tmp14 | tmp20
    tmp22 = tl.where(tmp21, tmp7, tmp8)
    tmp23 = tl.where(tmp21, tmp17, tmp18)
    tmp24 = tmp3 / tmp6
    tmp25 = tmp22 > tmp24
    tmp26 = tmp22 == tmp24
    tmp27 = tmp22 != tmp22
    tmp28 = tmp24 != tmp24
    tmp29 = tmp27 > tmp28
    tmp30 = tmp25 | tmp29
    tmp31 = tmp27 & tmp28
    tmp32 = tmp26 | tmp31
    tmp33 = tl.full([1], 2, tl.int64)
    tmp34 = tmp23 < tmp33
    tmp35 = tmp32 & tmp34
    tmp36 = tmp30 | tmp35
    tmp37 = tl.where(tmp36, tmp22, tmp24)
    tmp38 = tl.where(tmp36, tmp23, tmp33)
    tmp39 = tmp5 / tmp6
    tmp40 = tmp37 > tmp39
    tmp41 = tmp37 == tmp39
    tmp42 = tmp37 != tmp37
    tmp43 = tmp39 != tmp39
    tmp44 = tmp42 > tmp43
    tmp45 = tmp40 | tmp44
    tmp46 = tmp42 & tmp43
    tmp47 = tmp41 | tmp46
    tmp48 = tl.full([1], 3, tl.int64)
    tmp49 = tmp38 < tmp48
    tmp50 = tmp47 & tmp49
    tmp51 = tmp45 | tmp50
    tl.where(tmp51, tmp37, tmp39)
    tmp53 = tl.where(tmp51, tmp38, tmp48)
    tl.store(out_ptr0 + x2, tmp53, xmask)


@triton.jit
def triton_per_fused_acos_clamp_eq_mean_mul_pow_relu_rsub_sum_where_3(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 32
    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 % 16
    r1 = rindex // 16
    r2 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = tl.load(in_ptr0 + (128 + r0 + 64 * r1), None)
    tmp3 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp4 = tl.load(in_ptr0 + (144 + r0 + 64 * r1), None)
    tmp7 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr0 + (160 + r0 + 64 * r1), None)
    tmp11 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp12 = tl.load(in_ptr0 + (176 + r0 + 64 * r1), None)
    tmp22 = tl.load(in_ptr1 + r2, None)
    tmp23 = tl.load(in_ptr1 + (32 + r2), None)
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = -1e-07
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = 1e-07
    tmp18 = triton_helpers.minimum(tmp16, tmp17)
    tmp19 = libdevice.acos(tmp18)
    tmp20 = 1.0
    tmp21 = tmp19 * tmp20
    tmp24 = tmp22 == tmp23
    tmp25 = tmp21 * tmp21
    tmp26 = 0.5
    tmp27 = tmp26 - tmp21
    tmp28 = tl.full([1, 1], 0, tl.int32)
    tmp29 = triton_helpers.maximum(tmp28, tmp27)
    tmp30 = tmp29 * tmp29
    tmp31 = tl.where(tmp24, tmp25, tmp30)
    tmp32 = 1e-12
    tmp33 = triton_helpers.maximum(tmp31, tmp32)
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK, RBLOCK])
    tmp36 = tl.sum(tmp34, 1)[:, None]
    tmp37 = 32.0
    tmp38 = tmp36 / tmp37
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp38, None)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf1, buf2, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.int64)
        triton_poi_fused__softmax_max_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf2
        buf5 = empty_strided_cuda((), (), torch.float32)
        buf6 = buf5
        del buf5
        triton_per_fused_acos_clamp_eq_mean_mul_pow_relu_rsub_sum_where_3[grid
            (1)](buf6, buf0, buf3, 1, 32, XBLOCK=1, num_warps=2, num_stages=1)
        del buf3
    return buf6, reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(buf0, (64, 4), (4, 1), 0)


class AMCLossNew(nn.Module):

    def __init__(self, in_features, out_features, s=None, m=None, device='cuda'
        ):
        """
        Angular Margin Contrastive Loss

        https://arxiv.org/pdf/2004.09805.pdf
        
        Code converted over from Tensorflow to Pytorch

        """
        super(AMCLossNew, self).__init__()
        self.m = 0.5 if not m else m
        self.s = 1.0 if not s else s
        self.in_features = in_features
        self.out_features = out_features
        self.fc = nn.Linear(in_features, out_features, bias=False)
        self.device = device

    def forward(self, input_0):
        primals_2 = self.fc.weight
        primals_1 = input_0
        output = call([primals_1, primals_2])
        return output[0], output[1]
