# 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/ny/cny2lybv3eo5gjecnhptlj5fm6l572ym36s5acsilpa4ilaczkmy.py
# Topologically Sorted Source Nodes: [pred_sigmoid, sub, pow_1, ne, mask, mul_1, sub_1, pos_part, mul_4, pow_2, neg, max_val, neg_1, exp, neg_2, sub_2, exp_1, add, log, add_1, neg_part, mul_5, add_2, sum_1, neg_3, loss, loss_1], Original ATen: [aten.sigmoid, aten.rsub, aten.pow, aten.ne, aten._to_copy, aten.mul, aten.sub, aten.neg, aten.clamp, aten.exp, aten.add, aten.log, aten.sum, aten.mean]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   exp => exp
#   exp_1 => exp_1
#   log => log
#   loss => mul_6
#   loss_1 => mean
#   mask => convert_element_type
#   max_val => clamp_min
#   mul_1 => mul_1
#   mul_4 => mul_4
#   mul_5 => mul_5
#   ne => ne
#   neg => neg
#   neg_1 => neg_1
#   neg_2 => neg_2
#   neg_3 => neg_3
#   neg_part => mul_3
#   pos_part => mul_2
#   pow_1 => pow_1
#   pow_2 => pow_2
#   pred_sigmoid => sigmoid
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
#   sum_1 => sum_1
# Graph fragment:
#   %sigmoid : [num_users=6] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view,), 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, 2.0), kwargs = {})
#   %ne : [num_users=1] = call_function[target=torch.ops.aten.ne.Scalar](args = (%view_1, 4), kwargs = {})
#   %convert_element_type : [num_users=2] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%ne, torch.float32), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %view_2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sigmoid, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_1, %sub_1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, 0.25), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sigmoid, 2.0), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sigmoid,), kwargs = {})
#   %clamp_min : [num_users=3] = call_function[target=torch.ops.aten.clamp_min.default](args = (%neg, 0), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%clamp_min,), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%neg_1,), kwargs = {})
#   %neg_2 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sigmoid,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%neg_2, %clamp_min), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%exp, %exp_1), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%add,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%clamp_min, %log), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%pow_2, %add_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, 0.75), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %mul_5), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%add_2, [-1]), kwargs = {})
#   %neg_3 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_1,), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg_3, %convert_element_type), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%mul_6,), kwargs = {})
triton_per_fused__to_copy_add_clamp_exp_log_mean_mul_ne_neg_pow_rsub_sigmoid_sub_sum_0 = async_compile.triton('triton_per_fused__to_copy_add_clamp_exp_log_mean_mul_ne_neg_pow_rsub_sigmoid_sub_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, 64],
    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__to_copy_add_clamp_exp_log_mean_mul_ne_neg_pow_rsub_sigmoid_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, '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__to_copy_add_clamp_exp_log_mean_mul_ne_neg_pow_rsub_sigmoid_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp5 = tl.load(in_ptr1 + (r0), None)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = tmp3 * tmp3
    tmp6 = 4.0
    tmp7 = tmp5 != tmp6
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp8 * tmp5
    tmp10 = tmp1 * tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp4 * tmp11
    tmp13 = 0.25
    tmp14 = tmp12 * tmp13
    tmp15 = tmp1 * tmp1
    tmp16 = -tmp1
    tmp17 = 0.0
    tmp18 = triton_helpers.maximum(tmp16, tmp17)
    tmp19 = -tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp16 - tmp18
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tl_math.log(tmp23)
    tmp25 = tmp18 + tmp24
    tmp26 = tmp15 * tmp25
    tmp27 = 0.75
    tmp28 = tmp26 * tmp27
    tmp29 = tmp14 + tmp28
    tmp30 = -tmp29
    tmp31 = tmp30 * tmp8
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp34 = tl.sum(tmp32, 1)[:, None]
    tmp35 = 64.0
    tmp36 = tmp34 / tmp35
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp36, 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), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [pred_sigmoid, sub, pow_1, ne, mask, mul_1, sub_1, pos_part, mul_4, pow_2, neg, max_val, neg_1, exp, neg_2, sub_2, exp_1, add, log, add_1, neg_part, mul_5, add_2, sum_1, neg_3, loss, loss_1], Original ATen: [aten.sigmoid, aten.rsub, aten.pow, aten.ne, aten._to_copy, aten.mul, aten.sub, aten.neg, aten.clamp, aten.exp, aten.add, aten.log, aten.sum, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused__to_copy_add_clamp_exp_log_mean_mul_ne_neg_pow_rsub_sigmoid_sub_sum_0.run(buf2, arg1_1, arg0_1, 1, 64, grid=grid(1), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf2, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 16), (16, 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
import torch.utils


class SigmoidFocalLoss(nn.Module):

    def __init__(self, ignore_label, gamma=2.0, alpha=0.25, reduction='mean'):
        super(SigmoidFocalLoss, self).__init__()
        self.ignore_label = ignore_label
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = reduction

    def forward(self, pred, target):
        b, _h, _w = target.size()
        pred = pred.view(b, -1, 1)
        pred_sigmoid = pred.sigmoid()
        target = target.view(b, -1).float()
        mask = target.ne(self.ignore_label).float()
        target = mask * target
        onehot = target.view(b, -1, 1)
        max_val = (-pred_sigmoid).clamp(min=0)
        pos_part = (1 - pred_sigmoid) ** self.gamma * (pred_sigmoid - 
            pred_sigmoid * onehot)
        neg_part = pred_sigmoid ** self.gamma * (max_val + ((-max_val).exp(
            ) + (-pred_sigmoid - max_val).exp()).log())
        loss = -(self.alpha * pos_part + (1 - self.alpha) * neg_part).sum(dim
            =-1) * mask
        if self.reduction == 'mean':
            loss = loss.mean()
        return loss


def get_inputs():
    return [torch.rand([4, 16]), torch.rand([4, 4, 4])]


def get_init_inputs():
    return [[], {'ignore_label': 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 math as tl_math
import torch.nn as nn
import torch.utils
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__to_copy_add_clamp_exp_log_mean_mul_ne_neg_pow_rsub_sigmoid_sub_sum_0(
    in_out_ptr0, in_ptr0, in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp5 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.sigmoid(tmp0)
    tmp2 = 1.0
    tmp3 = tmp2 - tmp1
    tmp4 = tmp3 * tmp3
    tmp6 = 4.0
    tmp7 = tmp5 != tmp6
    tmp8 = tmp7.to(tl.float32)
    tmp9 = tmp8 * tmp5
    tmp10 = tmp1 * tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp4 * tmp11
    tmp13 = 0.25
    tmp14 = tmp12 * tmp13
    tmp15 = tmp1 * tmp1
    tmp16 = -tmp1
    tmp17 = 0.0
    tmp18 = triton_helpers.maximum(tmp16, tmp17)
    tmp19 = -tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp16 - tmp18
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tl_math.log(tmp23)
    tmp25 = tmp18 + tmp24
    tmp26 = tmp15 * tmp25
    tmp27 = 0.75
    tmp28 = tmp26 * tmp27
    tmp29 = tmp14 + tmp28
    tmp30 = -tmp29
    tmp31 = tmp30 * tmp8
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK, RBLOCK])
    tmp34 = tl.sum(tmp32, 1)[:, None]
    tmp35 = 64.0
    tmp36 = tmp34 / tmp35
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp36, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 16), (16, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused__to_copy_add_clamp_exp_log_mean_mul_ne_neg_pow_rsub_sigmoid_sub_sum_0[
            grid(1)](buf2, arg1_1, arg0_1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class SigmoidFocalLossNew(nn.Module):

    def __init__(self, ignore_label, gamma=2.0, alpha=0.25, reduction='mean'):
        super(SigmoidFocalLossNew, self).__init__()
        self.ignore_label = ignore_label
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = reduction

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