# 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/gh/cghcscvzgpvygoo2cnjxokv7fl3eijtnklfnaci2c6t4a2rvkxs2.py
# Topologically Sorted Source Nodes: [loss, loss_1], Original ATen: [aten.binary_cross_entropy, aten.mean]
# Source node to ATen node mapping:
#   loss => full_default, full_default_1, log, log1p, maximum, maximum_1, mul, mul_1, neg, sub, sub_1
#   loss_1 => mean
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg0_1, 1), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%arg1_1,), kwargs = {})
#   %log1p : [num_users=1] = call_function[target=torch.ops.aten.log1p.default](args = (%neg,), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -100), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %maximum : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%log1p, %full_default), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %maximum), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%arg1_1,), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -100), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %maximum_1 : [num_users=1] = call_function[target=torch.ops.aten.maximum.default](args = (%log, %full_default_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, %maximum_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%sub_1, [1]), kwargs = {})
triton_poi_fused_binary_cross_entropy_mean_0 = async_compile.triton('triton_poi_fused_binary_cross_entropy_mean_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_binary_cross_entropy_mean_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_binary_cross_entropy_mean_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 % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp3 = tl.load(in_ptr1 + (x0 + (64*x1)), xmask)
    tmp13 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp15 = tl.load(in_ptr1 + (16 + x0 + (64*x1)), xmask)
    tmp25 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp27 = tl.load(in_ptr1 + (32 + x0 + (64*x1)), xmask)
    tmp37 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp39 = tl.load(in_ptr1 + (48 + x0 + (64*x1)), xmask)
    tmp1 = 1.0
    tmp2 = tmp0 - tmp1
    tmp4 = -tmp3
    tmp5 = libdevice.log1p(tmp4)
    tmp6 = -100.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tl_math.log(tmp3)
    tmp10 = triton_helpers.maximum(tmp9, tmp6)
    tmp11 = tmp0 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp13 - tmp1
    tmp16 = -tmp15
    tmp17 = libdevice.log1p(tmp16)
    tmp18 = triton_helpers.maximum(tmp17, tmp6)
    tmp19 = tmp14 * tmp18
    tmp20 = tl_math.log(tmp15)
    tmp21 = triton_helpers.maximum(tmp20, tmp6)
    tmp22 = tmp13 * tmp21
    tmp23 = tmp19 - tmp22
    tmp24 = tmp12 + tmp23
    tmp26 = tmp25 - tmp1
    tmp28 = -tmp27
    tmp29 = libdevice.log1p(tmp28)
    tmp30 = triton_helpers.maximum(tmp29, tmp6)
    tmp31 = tmp26 * tmp30
    tmp32 = tl_math.log(tmp27)
    tmp33 = triton_helpers.maximum(tmp32, tmp6)
    tmp34 = tmp25 * tmp33
    tmp35 = tmp31 - tmp34
    tmp36 = tmp24 + tmp35
    tmp38 = tmp37 - tmp1
    tmp40 = -tmp39
    tmp41 = libdevice.log1p(tmp40)
    tmp42 = triton_helpers.maximum(tmp41, tmp6)
    tmp43 = tmp38 * tmp42
    tmp44 = tl_math.log(tmp39)
    tmp45 = triton_helpers.maximum(tmp44, tmp6)
    tmp46 = tmp37 * tmp45
    tmp47 = tmp43 - tmp46
    tmp48 = tmp36 + tmp47
    tmp49 = 4.0
    tmp50 = tmp48 / tmp49
    tl.store(out_ptr0 + (x2), tmp50, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/l3/cl3brlv6v2qpk3opkohktvujrz6abpkv6mtdghb7jpyvg4eoxp2f.py
# Topologically Sorted Source Nodes: [loss_2], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   loss_2 => mul_2
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, %arg2_1), kwargs = {})
triton_poi_fused_mul_1 = async_compile.triton('triton_poi_fused_mul_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: '*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_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_mul_1(in_ptr0, in_ptr1, 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 % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', 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, 4, 4, 4), (64, 16, 4, 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, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [loss, loss_1], Original ATen: [aten.binary_cross_entropy, aten.mean]
        stream0 = get_raw_stream(0)
        triton_poi_fused_binary_cross_entropy_mean_0.run(arg0_1, arg1_1, buf0, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [loss_2], Original ATen: [aten.mul]
        triton_poi_fused_mul_1.run(buf0, arg2_1, buf1, 256, grid=grid(256), stream=stream0)
        del arg2_1
        del buf0
    return (buf1, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    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.utils.data
import torch.nn as nn
import torch.nn.functional as F


class HandTypeLoss(nn.Module):

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

    def forward(self, hand_type_out, hand_type_gt, hand_type_valid):
        loss = F.binary_cross_entropy(hand_type_out, hand_type_gt,
            reduction='none')
        loss = loss.mean(1)
        loss = loss * hand_type_valid
        return loss


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


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

import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torch.utils.data
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_binary_cross_entropy_mean_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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr1 + (x0 + 64 * x1), xmask)
    tmp13 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr1 + (16 + x0 + 64 * x1), xmask)
    tmp25 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr1 + (32 + x0 + 64 * x1), xmask)
    tmp37 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp39 = tl.load(in_ptr1 + (48 + x0 + 64 * x1), xmask)
    tmp1 = 1.0
    tmp2 = tmp0 - tmp1
    tmp4 = -tmp3
    tmp5 = libdevice.log1p(tmp4)
    tmp6 = -100.0
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tl_math.log(tmp3)
    tmp10 = triton_helpers.maximum(tmp9, tmp6)
    tmp11 = tmp0 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp13 - tmp1
    tmp16 = -tmp15
    tmp17 = libdevice.log1p(tmp16)
    tmp18 = triton_helpers.maximum(tmp17, tmp6)
    tmp19 = tmp14 * tmp18
    tmp20 = tl_math.log(tmp15)
    tmp21 = triton_helpers.maximum(tmp20, tmp6)
    tmp22 = tmp13 * tmp21
    tmp23 = tmp19 - tmp22
    tmp24 = tmp12 + tmp23
    tmp26 = tmp25 - tmp1
    tmp28 = -tmp27
    tmp29 = libdevice.log1p(tmp28)
    tmp30 = triton_helpers.maximum(tmp29, tmp6)
    tmp31 = tmp26 * tmp30
    tmp32 = tl_math.log(tmp27)
    tmp33 = triton_helpers.maximum(tmp32, tmp6)
    tmp34 = tmp25 * tmp33
    tmp35 = tmp31 - tmp34
    tmp36 = tmp24 + tmp35
    tmp38 = tmp37 - tmp1
    tmp40 = -tmp39
    tmp41 = libdevice.log1p(tmp40)
    tmp42 = triton_helpers.maximum(tmp41, tmp6)
    tmp43 = tmp38 * tmp42
    tmp44 = tl_math.log(tmp39)
    tmp45 = triton_helpers.maximum(tmp44, tmp6)
    tmp46 = tmp37 * tmp45
    tmp47 = tmp43 - tmp46
    tmp48 = tmp36 + tmp47
    tmp49 = 4.0
    tmp50 = tmp48 / tmp49
    tl.store(out_ptr0 + x2, tmp50, xmask)


@triton.jit
def triton_poi_fused_mul_1(in_ptr0, in_ptr1, 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 % 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


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, 4, 4, 4), (64, 16, 4, 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, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_binary_cross_entropy_mean_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, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_mul_1[grid(256)](buf0, arg2_1, buf1, 256, XBLOCK=
            256, num_warps=4, num_stages=1)
        del arg2_1
        del buf0
    return buf1,


class HandTypeLossNew(nn.Module):

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

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