# 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/tt/cttxigtmqhf267hzmb3wzqrn5exb7qqk5xgwzk6dbp4ctdkept2m.py
# Topologically Sorted Source Nodes: [sum_2, gamma, sub, exp, cumsum, add, log, log_cumsum_h, sub_1, mul, sum_1, loss], Original ATen: [aten.sum, aten.max, aten.sub, aten.exp, aten.cumsum, aten.add, aten.log, aten.mul, aten.neg]
# Source node to ATen node mapping:
#   add => add
#   cumsum => cumsum
#   exp => exp
#   gamma => max_1
#   log => log
#   log_cumsum_h => add_1
#   loss => neg
#   mul => mul
#   sub => sub
#   sub_1 => sub_1
#   sum_1 => sum_1
#   sum_2 => sum_2
# Graph fragment:
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%view,), kwargs = {})
#   %max_1 : [num_users=2] = call_function[target=torch.ops.aten.max.default](args = (%view_1,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %max_1), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %cumsum : [num_users=1] = call_function[target=torch.ops.aten.cumsum.default](args = (%exp, 0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%cumsum, 1e-07), 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 = (%log, %max_1), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_1, %add_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %view), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%mul,), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_1,), kwargs = {})
triton_per_fused_add_cumsum_exp_log_max_mul_neg_sub_sum_0 = async_compile.triton('triton_per_fused_add_cumsum_exp_log_max_mul_neg_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.jit
def _triton_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0

@triton_heuristics.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 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': {4: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 5), equal_to_1=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_cumsum_exp_log_max_mul_neg_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 2, 'num_reduction': 3, '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_cumsum_exp_log_max_mul_neg_sub_sum_0(in_out_ptr0, 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)
    tmp4 = tl.load(in_ptr1 + (r0), None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp5, 0))
    tmp8 = tmp4 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp12, = tl.associative_scan((tmp11,), 0, _triton_helper_fn_add0)
    tmp13 = 1e-07
    tmp14 = tmp12 + tmp13
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp15 + tmp7
    tmp17 = tmp4 - tmp16
    tmp18 = tmp17 * tmp0
    tmp19 = tl.broadcast_to(tmp18, [RBLOCK])
    tmp21 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp22 = -tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp22, None)
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp3, 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)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [sum_2, gamma, sub, exp, cumsum, add, log, log_cumsum_h, sub_1, mul, sum_1, loss], Original ATen: [aten.sum, aten.max, aten.sub, aten.exp, aten.cumsum, aten.add, aten.log, aten.mul, aten.neg]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_cumsum_exp_log_max_mul_neg_sub_sum_0.run(buf4, arg0_1, arg1_1, buf0, 1, 256, grid=grid(1), stream=stream0)
        del arg1_1
    return (buf0, reinterpret_tensor(arg0_1, (256, ), (1, ), 0), buf4, )


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
from torch import Tensor
from torch import nn as nn


def cox_ph_loss_sorted(log_h: 'Tensor', events: 'Tensor', eps: 'float'=1e-07
    ) ->Tensor:
    """Requires the input to be sorted by descending duration time.
    See DatasetDurationSorted.
    We calculate the negative log of $(rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
    where h = exp(log_h) are the hazards and R is the risk set, and d is event.
    We just compute a cumulative sum, and not the true Risk sets. This is a
    limitation, but simple and fast.
    """
    events = events.float()
    events = events.view(-1)
    log_h = log_h.view(-1)
    gamma = log_h.max()
    log_cumsum_h = log_h.sub(gamma).exp().cumsum(0).add(eps).log().add(gamma)
    loss = -log_h.sub(log_cumsum_h).mul(events).sum()
    if events.sum().item() == 0:
        return loss
    return loss.div(events.sum())


class CoxPHLossSorted(torch.nn.Module):
    """Loss for CoxPH.
    Requires the input to be sorted by descending duration time.
    See DatasetDurationSorted.
    We calculate the negative log of $(rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
    where h = exp(log_h) are the hazards and R is the risk set, and d is event.
    We just compute a cumulative sum, and not the true Risk sets. This is a
    limitation, but simple and fast.
    """

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

    def forward(self, log_h: 'Tensor', events: 'Tensor') ->Tensor:
        return cox_ph_loss_sorted(log_h, events)


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
from torch import Tensor
from torch import 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_helper_fn_add0(arg0_0, arg1_0):
    tmp0 = arg0_0 + arg1_0
    return tmp0


@triton.jit
def triton_per_fused_add_cumsum_exp_log_max_mul_neg_sub_sum_0(in_out_ptr0,
    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)
    tmp4 = tl.load(in_ptr1 + r0, None)
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tmp5 = tl.broadcast_to(tmp4, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp5, 0))
    tmp8 = tmp4 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp12, = tl.associative_scan((tmp11,), 0, _triton_helper_fn_add0)
    tmp13 = 1e-07
    tmp14 = tmp12 + tmp13
    tmp15 = tl_math.log(tmp14)
    tmp16 = tmp15 + tmp7
    tmp17 = tmp4 - tmp16
    tmp18 = tmp17 * tmp0
    tmp19 = tl.broadcast_to(tmp18, [RBLOCK])
    tmp21 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp22 = -tmp21
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp22, None)
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp3, 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)
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        get_raw_stream(0)
        triton_per_fused_add_cumsum_exp_log_max_mul_neg_sub_sum_0[grid(1)](buf4
            , arg0_1, arg1_1, buf0, 1, 256, num_warps=2, num_stages=1)
        del arg1_1
    return buf0, reinterpret_tensor(arg0_1, (256,), (1,), 0), buf4


def cox_ph_loss_sorted(log_h: 'Tensor', events: 'Tensor', eps: 'float'=1e-07
    ) ->Tensor:
    """Requires the input to be sorted by descending duration time.
    See DatasetDurationSorted.
    We calculate the negative log of $(rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
    where h = exp(log_h) are the hazards and R is the risk set, and d is event.
    We just compute a cumulative sum, and not the true Risk sets. This is a
    limitation, but simple and fast.
    """
    events = events.float()
    events = events.view(-1)
    log_h = log_h.view(-1)
    gamma = log_h.max()
    log_cumsum_h = log_h.sub(gamma).exp().cumsum(0).add(eps).log().add(gamma)
    loss = -log_h.sub(log_cumsum_h).mul(events).sum()
    if events.sum().item() == 0:
        return loss
    return loss.div(events.sum())


class CoxPHLossSortedNew(torch.nn.Module):
    """Loss for CoxPH.
    Requires the input to be sorted by descending duration time.
    See DatasetDurationSorted.
    We calculate the negative log of $(rac{h_i}{\\sum_{j \\in R_i} h_j})^d$,
    where h = exp(log_h) are the hazards and R is the risk set, and d is event.
    We just compute a cumulative sum, and not the true Risk sets. This is a
    limitation, but simple and fast.
    """

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

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