# 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/7r/c7rxg3w7rlhss757iglx5dqb5t2p34mqwk3l3hkxvoensbd2ljoi.py
# Topologically Sorted Source Nodes: [add, div, log_pos, sum_1], Original ATen: [aten.add, aten.div, aten.log, aten.sum]
# Source node to ATen node mapping:
#   add => add
#   div => div
#   log_pos => log
#   sum_1 => sum_1
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%select, 0.7500001), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%select, %add), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%log,), kwargs = {})
triton_per_fused_add_div_log_sum_0 = async_compile.triton('triton_per_fused_add_div_log_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: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_log_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_div_log_sum_0(in_ptr0, out_ptr0, 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 % 16
    r1 = (rindex // 16)
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None)
    tmp1 = 0.7500001
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 / tmp2
    tmp4 = tl_math.log(tmp3)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.sum(tmp5, 1)[:, None]
    tl.store(out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp7, None)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3d/c3dzijfii4noxr5gi7lywikx64ycs3btsqsriojg542uck7hxs4y.py
# Topologically Sorted Source Nodes: [fill_, add_1, div_1, log_neg, sum_2, add_2, neg, loss], Original ATen: [aten.fill, aten.add, aten.div, aten.log, aten.sum, aten.neg]
# Source node to ATen node mapping:
#   add_1 => add_1
#   add_2 => add_2
#   div_1 => div_1
#   fill_ => full_default
#   log_neg => log_1
#   loss => div_2
#   neg => neg
#   sum_2 => sum_2
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([4, 3, 4, 4], 0.75), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%slice_1, 0.7500001), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%full_default, %add_1), kwargs = {})
#   %log_1 : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%div_1,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.default](args = (%log_1,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %sum_2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%add_2,), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, 4), kwargs = {})
triton_per_fused_add_div_fill_log_neg_sum_1 = async_compile.triton('triton_per_fused_add_div_fill_log_neg_sum_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_fill_log_neg_sum_1', '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_add_div_fill_log_neg_sum_1(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    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 = rindex < rnumel
    r0 = rindex % 48
    r1 = (rindex // 48)
    tmp0 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), rmask, other=0.0)
    tmp10 = tl.load(in_out_ptr0 + (0))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, 1])
    tmp1 = 0.7500001
    tmp2 = tmp0 + tmp1
    tmp3 = 0.75
    tmp4 = tmp3 / tmp2
    tmp5 = tl_math.log(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp12 = tmp11 + tmp9
    tmp13 = -tmp12
    tmp14 = 0.25
    tmp15 = tmp13 * tmp14
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp15, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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: [add, div, log_pos, sum_1], Original ATen: [aten.add, aten.div, aten.log, aten.sum]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_log_sum_0.run(arg0_1, buf0, 1, 64, grid=grid(1), stream=stream0)
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [fill_, add_1, div_1, log_neg, sum_2, add_2, neg, loss], Original ATen: [aten.fill, aten.add, aten.div, aten.log, aten.sum, aten.neg]
        triton_per_fused_add_div_fill_log_neg_sum_1.run(buf2, arg0_1, 1, 192, grid=grid(1), stream=stream0)
        del arg0_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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_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
from itertools import product as product
import torch.utils.data.distributed


class ContrastLoss(nn.Module):
    """
	contrastive loss, corresponding to Eq.(18)
	"""

    def __init__(self, n_data, eps=1e-07):
        super(ContrastLoss, self).__init__()
        self.n_data = n_data
        self.eps = eps

    def forward(self, x):
        bs = x.size(0)
        N = x.size(1) - 1
        M = float(self.n_data)
        pos_pair = x.select(1, 0)
        log_pos = torch.div(pos_pair, pos_pair.add(N / M + self.eps)).log_()
        neg_pair = x.narrow(1, 1, N)
        log_neg = torch.div(neg_pair.clone().fill_(N / M), neg_pair.add(N /
            M + self.eps)).log_()
        loss = -(log_pos.sum() + log_neg.sum()) / bs
        return loss


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


def get_init_inputs():
    return [[], {'n_data': 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.triton_helpers import math as tl_math
import torch.nn as nn
import torch._utils
from itertools import product as product
import torch.utils.data.distributed
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_div_log_sum_0(in_ptr0, out_ptr0, 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 % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp1 = 0.7500001
    tmp2 = tmp0 + tmp1
    tmp3 = tmp0 / tmp2
    tmp4 = tl_math.log(tmp3)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.sum(tmp5, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp7, None)


@triton.jit
def triton_per_fused_add_div_fill_log_neg_sum_1(in_out_ptr0, in_ptr0,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    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, :]
    rmask = rindex < rnumel
    r0 = rindex % 48
    r1 = rindex // 48
    tmp0 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), rmask, other=0.0)
    tmp10 = tl.load(in_out_ptr0 + 0)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, 1])
    tmp1 = 0.7500001
    tmp2 = tmp0 + tmp1
    tmp3 = 0.75
    tmp4 = tmp3 / tmp2
    tmp5 = tl_math.log(tmp4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, RBLOCK])
    tmp8 = tl.where(rmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp12 = tmp11 + tmp9
    tmp13 = -tmp12
    tmp14 = 0.25
    tmp15 = tmp13 * tmp14
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp15, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_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_div_log_sum_0[grid(1)](arg0_1, buf0, 1, 64,
            XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = buf0
        del buf0
        triton_per_fused_add_div_fill_log_neg_sum_1[grid(1)](buf2, arg0_1, 
            1, 192, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class ContrastLossNew(nn.Module):
    """
	contrastive loss, corresponding to Eq.(18)
	"""

    def __init__(self, n_data, eps=1e-07):
        super(ContrastLossNew, self).__init__()
        self.n_data = n_data
        self.eps = eps

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