# 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/ed/cedsxs5xqaahup2w2reh6kpyrindonknn2rajkznat2ii7e4fwqe.py
# Topologically Sorted Source Nodes: [loss], Original ATen: [aten.eq, aten.fill, aten.mul, aten.sum, aten.add, aten.sqrt, aten.div, aten.sub, aten.zeros_like, aten.where, aten.clamp_min, aten.mean]
# Source node to ATen node mapping:
#   loss => add, add_1, add_2, clamp_min, div, full_default, full_default_1, full_default_2, full_default_3, mean, mul, mul_1, mul_2, mul_3, sqrt, sub, sub_1, sum_1, sum_2, sum_3, where, where_1
# Graph fragment:
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([64], True), kwargs = {dtype: torch.bool, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([64], 1), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view, %view), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Scalar](args = (%sum_2, 9.999999960041972e-13), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_1, %view_1), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [1]), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Scalar](args = (%sum_3, 9.999999960041972e-13), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, %add_1), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%mul_3,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %sqrt), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%full_default_1, %div), kwargs = {})
#   %full_default : [num_users=2] = call_function[target=torch.ops.aten.full.default](args = ([64], 0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%full_default_2, %sub, %full_default), kwargs = {})
#   %full_default_3 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([64], False), kwargs = {dtype: torch.bool, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Scalar](args = (%div, 0.0), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%sub_1, 0), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%full_default_3, %clamp_min, %full_default), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where, %where_1), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%add_2,), kwargs = {})
triton_per_fused_add_clamp_min_div_eq_fill_mean_mul_sqrt_sub_sum_where_zeros_like_0 = async_compile.triton('triton_per_fused_add_clamp_min_div_eq_fill_mean_mul_sqrt_sub_sum_where_zeros_like_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.DEFAULT,
    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_clamp_min_div_eq_fill_mean_mul_sqrt_sub_sum_where_zeros_like_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 8, '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_clamp_min_div_eq_fill_mean_mul_sqrt_sub_sum_where_zeros_like_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 + (4*r0), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4*r0), None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + (4*r0)), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + (4*r0)), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + (4*r0)), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tmp0 * tmp0
    tmp16 = tmp3 * tmp3
    tmp17 = tmp15 + tmp16
    tmp18 = tmp7 * tmp7
    tmp19 = tmp17 + tmp18
    tmp20 = tmp11 * tmp11
    tmp21 = tmp19 + tmp20
    tmp22 = 9.999999960041972e-13
    tmp23 = tmp21 + tmp22
    tmp24 = tmp1 * tmp1
    tmp25 = tmp4 * tmp4
    tmp26 = tmp24 + tmp25
    tmp27 = tmp8 * tmp8
    tmp28 = tmp26 + tmp27
    tmp29 = tmp12 * tmp12
    tmp30 = tmp28 + tmp29
    tmp31 = tmp30 + tmp22
    tmp32 = tmp23 * tmp31
    tmp33 = libdevice.sqrt(tmp32)
    tmp34 = tmp14 / tmp33
    tmp35 = 1.0
    tmp36 = tmp35 - tmp34
    tmp37 = tl.full([1, 1], True, tl.int1)
    tmp38 = 0.0
    tmp39 = tl.where(tmp37, tmp36, tmp38)
    tmp40 = tmp34 - tmp38
    tmp41 = triton_helpers.maximum(tmp40, tmp38)
    tmp42 = tl.full([1, 1], False, tl.int1)
    tmp43 = tl.where(tmp42, tmp41, tmp38)
    tmp44 = tmp39 + tmp43
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp47 = tl.sum(tmp45, 1)[:, None]
    tmp48 = 64.0
    tmp49 = tmp47 / tmp48
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([XBLOCK, 1], 0, tl.int32)), tmp49, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [loss], Original ATen: [aten.eq, aten.fill, aten.mul, aten.sum, aten.add, aten.sqrt, aten.div, aten.sub, aten.zeros_like, aten.where, aten.clamp_min, aten.mean]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_clamp_min_div_eq_fill_mean_mul_sqrt_sub_sum_where_zeros_like_0.run(buf2, arg0_1, arg1_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, 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
import torch.nn.functional as F


class CosLoss(nn.Module):

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

    def forward(self, state_S, state_T, mask=None):
        """
        This is the loss used in DistilBERT

        :param state_S: Tensor of shape  (batch_size, length, hidden_size)
        :param state_T: Tensor of shape  (batch_size, length, hidden_size)
        :param mask:    Tensor of shape  (batch_size, length)
        """
        if mask is None:
            state_S = state_S.view(-1, state_S.size(-1))
            state_T = state_T.view(-1, state_T.size(-1))
        else:
            mask = mask.to(state_S).unsqueeze(-1).expand_as(state_S)
            state_S = torch.masked_select(state_S, mask).view(-1, mask.size(-1)
                )
            state_T = torch.masked_select(state_T, mask).view(-1, mask.size(-1)
                )
        target = state_S.new(state_S.size(0)).fill_(1)
        loss = F.cosine_embedding_loss(state_S, state_T, target, reduction=
            'mean')
        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 libdevice
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_per_fused_add_clamp_min_div_eq_fill_mean_mul_sqrt_sub_sum_where_zeros_like_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 + 4 * r0, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * r0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * r0), None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * r0), None, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (3 + 4 * r0), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tmp0 * tmp0
    tmp16 = tmp3 * tmp3
    tmp17 = tmp15 + tmp16
    tmp18 = tmp7 * tmp7
    tmp19 = tmp17 + tmp18
    tmp20 = tmp11 * tmp11
    tmp21 = tmp19 + tmp20
    tmp22 = 9.999999960041972e-13
    tmp23 = tmp21 + tmp22
    tmp24 = tmp1 * tmp1
    tmp25 = tmp4 * tmp4
    tmp26 = tmp24 + tmp25
    tmp27 = tmp8 * tmp8
    tmp28 = tmp26 + tmp27
    tmp29 = tmp12 * tmp12
    tmp30 = tmp28 + tmp29
    tmp31 = tmp30 + tmp22
    tmp32 = tmp23 * tmp31
    tmp33 = libdevice.sqrt(tmp32)
    tmp34 = tmp14 / tmp33
    tmp35 = 1.0
    tmp36 = tmp35 - tmp34
    tmp37 = tl.full([1, 1], True, tl.int1)
    tmp38 = 0.0
    tmp39 = tl.where(tmp37, tmp36, tmp38)
    tmp40 = tmp34 - tmp38
    tmp41 = triton_helpers.maximum(tmp40, tmp38)
    tmp42 = tl.full([1, 1], False, tl.int1)
    tmp43 = tl.where(tmp42, tmp41, tmp38)
    tmp44 = tmp39 + tmp43
    tmp45 = tl.broadcast_to(tmp44, [XBLOCK, RBLOCK])
    tmp47 = tl.sum(tmp45, 1)[:, None]
    tmp48 = 64.0
    tmp49 = tmp47 / tmp48
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp49, 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)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_add_clamp_min_div_eq_fill_mean_mul_sqrt_sub_sum_where_zeros_like_0[
            grid(1)](buf2, arg0_1, arg1_1, 1, 64, XBLOCK=1, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class CosLossNew(nn.Module):

    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]
