# 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/vw/cvwqcezjgw4hcr2zvuks3igdsd4iniq3ke5z3ruhzsvxj35eqh2j.py
# Topologically Sorted Source Nodes: [attn_1, x, x_1, max_1, sub, e_x, e_x_1, sum_1], Original ATen: [aten.div, aten.clamp, aten.mul, aten.max, aten.sub, aten.exp, aten.sum]
# Source node to ATen node mapping:
#   attn_1 => div
#   e_x => exp
#   e_x_1 => mul_1
#   max_1 => max_1
#   sub => sub
#   sum_1 => sum_1
#   x => clamp_max, clamp_min
#   x_1 => mul
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%bmm, 4), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%div, -15.0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 15.0), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max, %arg2_1), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul, 2, True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %getitem), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %arg2_1), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [2], True), kwargs = {})
triton_poi_fused_clamp_div_exp_max_mul_sub_sum_0 = async_compile.triton('triton_poi_fused_clamp_div_exp_max_mul_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clamp_div_exp_max_mul_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_clamp_div_exp_max_mul_sub_sum_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = -15.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 15.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp8 = tmp6 * tmp7
    tmp10 = tmp9 * tmp1
    tmp11 = triton_helpers.maximum(tmp10, tmp3)
    tmp12 = triton_helpers.minimum(tmp11, tmp5)
    tmp14 = tmp12 * tmp13
    tmp15 = triton_helpers.maximum(tmp8, tmp14)
    tmp17 = tmp16 * tmp1
    tmp18 = triton_helpers.maximum(tmp17, tmp3)
    tmp19 = triton_helpers.minimum(tmp18, tmp5)
    tmp21 = tmp19 * tmp20
    tmp22 = triton_helpers.maximum(tmp15, tmp21)
    tmp24 = tmp23 * tmp1
    tmp25 = triton_helpers.maximum(tmp24, tmp3)
    tmp26 = triton_helpers.minimum(tmp25, tmp5)
    tmp28 = tmp26 * tmp27
    tmp29 = triton_helpers.maximum(tmp22, tmp28)
    tmp30 = tmp8 - tmp29
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp31 * tmp7
    tmp33 = tmp14 - tmp29
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp34 * tmp13
    tmp36 = tmp32 + tmp35
    tmp37 = tmp21 - tmp29
    tmp38 = tl_math.exp(tmp37)
    tmp39 = tmp38 * tmp20
    tmp40 = tmp36 + tmp39
    tmp41 = tmp28 - tmp29
    tmp42 = tl_math.exp(tmp41)
    tmp43 = tmp42 * tmp27
    tmp44 = tmp40 + tmp43
    tl.store(out_ptr0 + (x0), tmp29, xmask)
    tl.store(out_ptr1 + (x0), tmp44, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/73/c73ulpsraxahj2gseyz7pjma7y7eg6gvtqpca55bh6jaui66cdc5.py
# Topologically Sorted Source Nodes: [attn_1, x, x_1, max_1, sub, e_x, e_x_1, add, softmax], Original ATen: [aten.div, aten.clamp, aten.mul, aten.max, aten.sub, aten.exp, aten.add]
# Source node to ATen node mapping:
#   add => add
#   attn_1 => div
#   e_x => exp
#   e_x_1 => mul_1
#   max_1 => max_1
#   softmax => div_1
#   sub => sub
#   x => clamp_max, clamp_min
#   x_1 => mul
# Graph fragment:
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%bmm, 4), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%div, -15.0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 15.0), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max, %arg2_1), kwargs = {})
#   %max_1 : [num_users=1] = call_function[target=torch.ops.aten.max.dim](args = (%mul, 2, True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul, %getitem), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%exp, %arg2_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-06), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %add), kwargs = {})
triton_poi_fused_add_clamp_div_exp_max_mul_sub_1 = async_compile.triton('triton_poi_fused_add_clamp_div_exp_max_mul_sub_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_div_exp_max_mul_sub_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, '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},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_clamp_div_exp_max_mul_sub_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp7 = tl.load(in_ptr0 + (x2), xmask)
    tmp9 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = -15.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 15.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp11 * tmp7
    tmp14 = 1e-06
    tmp15 = tmp13 + tmp14
    tmp16 = tmp12 / tmp15
    tl.store(in_out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn], Original ATen: [aten.bmm]
        extern_kernels.bmm(arg1_1, reinterpret_tensor(arg0_1, (4, 4, 4), (16, 1, 4), 0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [attn_1, x, x_1, max_1, sub, e_x, e_x_1, sum_1], Original ATen: [aten.div, aten.clamp, aten.mul, aten.max, aten.sub, aten.exp, aten.sum]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_div_exp_max_mul_sub_sum_0.run(buf0, arg2_1, buf1, buf2, 16, grid=grid(16), stream=stream0)
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [attn_1, x, x_1, max_1, sub, e_x, e_x_1, add, softmax], Original ATen: [aten.div, aten.clamp, aten.mul, aten.max, aten.sub, aten.exp, aten.add]
        triton_poi_fused_add_clamp_div_exp_max_mul_sub_1.run(buf3, arg2_1, buf1, buf2, 64, grid=grid(64), stream=stream0)
        del arg2_1
        del buf1
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf3, arg3_1, out=buf4)
        del arg3_1
    return (buf4, buf3, )


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, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_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


def masked_softmax(x, m=None, dim=-1):
    """
    Softmax with mask (optional)
    """
    x = torch.clamp(x, min=-15.0, max=15.0)
    if m is not None:
        m = m.float()
        x = x * m
    e_x = torch.exp(x - torch.max(x, dim=dim, keepdim=True)[0])
    if m is not None:
        e_x = e_x * m
    softmax = e_x / (torch.sum(e_x, dim=dim, keepdim=True) + 1e-06)
    return softmax


class ScaledDotProductAttention(torch.nn.Module):
    """ Scaled Dot-Product Attention """

    def __init__(self, temperature, dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = torch.nn.Dropout(dropout)

    def forward(self, q, k, v, mask):
        attn = torch.bmm(q, k.transpose(1, 2))
        attn = attn / self.temperature
        attn = masked_softmax(attn, mask, 2)
        attn = self.dropout(attn)
        output = torch.bmm(attn, v)
        return output, attn


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


def get_init_inputs():
    return [[], {'temperature': 4}]

import torch
from torch._inductor.select_algorithm import extern_kernels
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
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_poi_fused_clamp_div_exp_max_mul_sub_sum_0(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp27 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = -15.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 15.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp8 = tmp6 * tmp7
    tmp10 = tmp9 * tmp1
    tmp11 = triton_helpers.maximum(tmp10, tmp3)
    tmp12 = triton_helpers.minimum(tmp11, tmp5)
    tmp14 = tmp12 * tmp13
    tmp15 = triton_helpers.maximum(tmp8, tmp14)
    tmp17 = tmp16 * tmp1
    tmp18 = triton_helpers.maximum(tmp17, tmp3)
    tmp19 = triton_helpers.minimum(tmp18, tmp5)
    tmp21 = tmp19 * tmp20
    tmp22 = triton_helpers.maximum(tmp15, tmp21)
    tmp24 = tmp23 * tmp1
    tmp25 = triton_helpers.maximum(tmp24, tmp3)
    tmp26 = triton_helpers.minimum(tmp25, tmp5)
    tmp28 = tmp26 * tmp27
    tmp29 = triton_helpers.maximum(tmp22, tmp28)
    tmp30 = tmp8 - tmp29
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp31 * tmp7
    tmp33 = tmp14 - tmp29
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp34 * tmp13
    tmp36 = tmp32 + tmp35
    tmp37 = tmp21 - tmp29
    tmp38 = tl_math.exp(tmp37)
    tmp39 = tmp38 * tmp20
    tmp40 = tmp36 + tmp39
    tmp41 = tmp28 - tmp29
    tmp42 = tl_math.exp(tmp41)
    tmp43 = tmp42 * tmp27
    tmp44 = tmp40 + tmp43
    tl.store(out_ptr0 + x0, tmp29, xmask)
    tl.store(out_ptr1 + x0, tmp44, xmask)


@triton.jit
def triton_poi_fused_add_clamp_div_exp_max_mul_sub_1(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp7 = tl.load(in_ptr0 + x2, xmask)
    tmp9 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = -15.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 15.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tl_math.exp(tmp10)
    tmp12 = tmp11 * tmp7
    tmp14 = 1e-06
    tmp15 = tmp13 + tmp14
    tmp16 = tmp12 / tmp15
    tl.store(in_out_ptr0 + x2, tmp16, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg2_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(arg1_1, reinterpret_tensor(arg0_1, (4, 4, 4), (
            16, 1, 4), 0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_div_exp_max_mul_sub_sum_0[grid(16)](buf0,
            arg2_1, buf1, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = buf0
        del buf0
        triton_poi_fused_add_clamp_div_exp_max_mul_sub_1[grid(64)](buf3,
            arg2_1, buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg2_1
        del buf1
        del buf2
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf3, arg3_1, out=buf4)
        del arg3_1
    return buf4, buf3


def masked_softmax(x, m=None, dim=-1):
    """
    Softmax with mask (optional)
    """
    x = torch.clamp(x, min=-15.0, max=15.0)
    if m is not None:
        m = m.float()
        x = x * m
    e_x = torch.exp(x - torch.max(x, dim=dim, keepdim=True)[0])
    if m is not None:
        e_x = e_x * m
    softmax = e_x / (torch.sum(e_x, dim=dim, keepdim=True) + 1e-06)
    return softmax


class ScaledDotProductAttentionNew(torch.nn.Module):
    """ Scaled Dot-Product Attention """

    def __init__(self, temperature, dropout=0.1):
        super().__init__()
        self.temperature = temperature
        self.dropout = torch.nn.Dropout(dropout)

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0], output[1]
