# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/qm/cqmm47zum25ouiix4f7wvfuakk6y4jnirb6ca36sewd4mw62ehm7.py
# Topologically Sorted Source Nodes: [P, sub_1, pow_2, mul_5, sub_2, log_1, mul_6, sub_3, mul_7, alpha_mask, sub_4, loss_neg, sub, pow_1, mul_1, log, mul_2, mul_3, loss_pos, batch_loss, loss], Original ATen: [aten.sigmoid, aten.rsub, aten.pow, aten.mul, aten.log, aten.add, aten.sum]
# Source node to ATen node mapping:
#   P => sigmoid
#   alpha_mask => mul
#   batch_loss => add
#   log => log
#   log_1 => log_1
#   loss => sum_1
#   loss_neg => mul_8
#   loss_pos => mul_4
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_5 => mul_5
#   mul_6 => mul_6
#   mul_7 => mul_7
#   pow_1 => pow_1
#   pow_2 => pow_2
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   sub_4 => sub_4
# Graph fragment:
#   %sigmoid : [num_users=4] = call_function[target=torch.ops.aten.sigmoid.default](args = (%arg0_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_1, 2), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, -1.0), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sub_2,), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_5, %log_1), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %arg1_1), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, %sub_3), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 0.25), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %mul), kwargs = {})
#   %mul_8 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_7, %sub_4), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %sigmoid), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, -1.0), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sigmoid,), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, %log), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %arg1_1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, %mul), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_8, %mul_4), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%add,), kwargs = {})
triton_per_fused_add_log_mul_pow_rsub_sigmoid_sum_0 = async_compile.triton('triton_per_fused_add_log_mul_pow_rsub_sigmoid_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.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_log_mul_pow_rsub_sigmoid_sum_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 1, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_log_mul_pow_rsub_sigmoid_sum_0(in_ptr0, in_ptr1, out_ptr0, 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)
    tmp9 = tl.load(in_ptr1 + (r0), None)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = -1.0
    tmp6 = tmp4 * tmp5
    tmp7 = tl_math.log(tmp3)
    tmp8 = tmp6 * tmp7
    tmp10 = tmp2 - tmp9
    tmp11 = tmp8 * tmp10
    tmp12 = 0.25
    tmp13 = tmp9 * tmp12
    tmp14 = tmp2 - tmp13
    tmp15 = tmp11 * tmp14
    tmp16 = tl_math.log(tmp1)
    tmp17 = tmp6 * tmp16
    tmp18 = tmp17 * tmp9
    tmp19 = tmp18 * tmp13
    tmp20 = tmp15 + tmp19
    tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp21, 0))
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp23, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        # Topologically Sorted Source Nodes: [P, sub_1, pow_2, mul_5, sub_2, log_1, mul_6, sub_3, mul_7, alpha_mask, sub_4, loss_neg, sub, pow_1, mul_1, log, mul_2, mul_3, loss_pos, batch_loss, loss], Original ATen: [aten.sigmoid, aten.rsub, aten.pow, aten.mul, aten.log, aten.add, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_log_mul_pow_rsub_sigmoid_sum_0.run(arg0_1, arg1_1, buf0, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product


class FocalLossSigmoid(nn.Module):
    """
    sigmoid version focal loss
    """

    def __init__(self, alpha=0.25, gamma=2, size_average=False):
        super(FocalLossSigmoid, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.size_average = size_average

    def forward(self, inputs, targets):
        inputs.size(0)
        inputs.size(1)
        P = torch.sigmoid(inputs)
        alpha_mask = self.alpha * targets
        loss_pos = -1.0 * torch.pow(1 - P, self.gamma) * torch.log(P
            ) * targets * alpha_mask
        loss_neg = -1.0 * torch.pow(1 - P, self.gamma) * torch.log(1 - P) * (
            1 - targets) * (1 - alpha_mask)
        batch_loss = loss_neg + loss_pos
        if self.size_average:
            loss = batch_loss.mean()
        else:
            loss = batch_loss.sum()
        return loss


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


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

import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
from math import sqrt as sqrt
from itertools import product as product
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_add_log_mul_pow_rsub_sigmoid_sum_0(in_ptr0, in_ptr1,
    out_ptr0, 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)
    tmp9 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = -1.0
    tmp6 = tmp4 * tmp5
    tmp7 = tl_math.log(tmp3)
    tmp8 = tmp6 * tmp7
    tmp10 = tmp2 - tmp9
    tmp11 = tmp8 * tmp10
    tmp12 = 0.25
    tmp13 = tmp9 * tmp12
    tmp14 = tmp2 - tmp13
    tmp15 = tmp11 * tmp14
    tmp16 = tl_math.log(tmp1)
    tmp17 = tmp6 * tmp16
    tmp18 = tmp17 * tmp9
    tmp19 = tmp18 * tmp13
    tmp20 = tmp15 + tmp19
    tmp21 = tl.broadcast_to(tmp20, [RBLOCK])
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp21, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp23, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_log_mul_pow_rsub_sigmoid_sum_0[grid(1)](arg0_1,
            arg1_1, buf0, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class FocalLossSigmoidNew(nn.Module):
    """
    sigmoid version focal loss
    """

    def __init__(self, alpha=0.25, gamma=2, size_average=False):
        super(FocalLossSigmoidNew, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.size_average = size_average

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