# 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/nk/cnkn4azjs7p23wvexd663q3licov6bhbhf2h5uynp4fgwugfkg6g.py
# Topologically Sorted Source Nodes: [cat, x, roll, k, sum_1, add, truediv], Original ATen: [aten.cat, aten.relu, aten.roll, aten.mul, aten.sum, aten.add, aten.div]
# Source node to ATen node mapping:
#   add => add_1
#   cat => cat
#   k => mul
#   roll => index
#   sum_1 => sum_1
#   truediv => div
#   x => relu
# Graph fragment:
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%arg0_1, %neg], -1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%cat,), kwargs = {})
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%relu, [None, None, None, %fmod]), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%relu, %index), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1], True), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, 1e-06), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %add_1), kwargs = {})
triton_per_fused_add_cat_div_mul_relu_roll_sum_0 = async_compile.triton('triton_per_fused_add_cat_div_mul_relu_roll_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=[64, 8],
    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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_cat_div_mul_relu_roll_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_cat_div_mul_relu_roll_sum_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 8
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = r1
    tmp1 = tl.full([1, 1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1, 1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + ((4*x0) + r1), tmp4 & xmask, eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1, 1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tl.load(in_ptr0 + ((4*x0) + ((-4) + r1)), tmp6 & xmask, eviction_policy='evict_last', other=0.0)
    tmp10 = -tmp9
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp6, tmp10, tmp11)
    tmp13 = tl.where(tmp4, tmp5, tmp12)
    tmp14 = tl.full([1, 1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = (7 + r1) % 8
    tmp17 = tmp16 >= tmp1
    tmp18 = tmp16 < tmp3
    tmp19 = tl.load(in_ptr0 + ((4*x0) + ((7 + r1) % 8)), tmp18 & xmask, eviction_policy='evict_last', other=0.0)
    tmp20 = tmp16 >= tmp3
    tmp21 = tmp16 < tmp7
    tmp22 = tl.load(in_ptr0 + ((4*x0) + ((-4) + ((7 + r1) % 8))), tmp20 & xmask, eviction_policy='evict_last', other=0.0)
    tmp23 = -tmp22
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp20, tmp23, tmp24)
    tmp26 = tl.where(tmp18, tmp19, tmp25)
    tmp27 = triton_helpers.maximum(tmp14, tmp26)
    tmp28 = tmp15 * tmp27
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.where(xmask, tmp29, 0)
    tmp32 = tl.sum(tmp31, 1)[:, None]
    tmp33 = 1e-06
    tmp34 = tmp32 + tmp33
    tmp35 = tmp28 / tmp34
    tl.store(out_ptr2 + (r1 + (8*x0)), tmp35, xmask)
''', 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)
        buf2 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [cat, x, roll, k, sum_1, add, truediv], Original ATen: [aten.cat, aten.relu, aten.roll, aten.mul, aten.sum, aten.add, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_cat_div_mul_relu_roll_sum_0.run(arg0_1, buf2, 64, 8, grid=grid(64), 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)

from torch.nn import Module
import torch
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd


class DPFP(Module):
    """
    ## Deterministic Parameter Free Project (DPFP)

    This is the new projection function $	extcolor{lightgreen}{\\phi}$ introduced in the paper.
    DPFP projects $k$ of dimensionality $d_{key}$ to dimensionality $d_{dot} = 2 d_{key} 
u$,
    where $
u \\in \\{1, 2, ..., 2 d_{key} - 1 \\}$ is a hyper-parameter.

    $$	extcolor{lightgreen}{\\phi_{2 d_{key} (i - 1)  + j}(k)}
     = 	ext{ReLU}\\Big(ig[k, -kig]\\Big)_{j}
                        	ext{ReLU}\\Big(ig[k, -kig]\\Big)_{i + j}$$

    where $ig[k, -kig]$ is the concatenation of $k$ and $-k$ to give a vector of
    size $2 d_{key}$, $i \\in \\{1, 2, ..., 
u \\}$, and $j \\in \\{1, 2, ..., 2 d_{key}\\}$.
    $x_i$ is the $i$-th element of vector $x$ and is rolled around if
    $i$ is larger than the number of elements in $x$.

    Basically, it creates a new vector by multiplying elements of $[k, -k]$ shifted by $i$.

    This produces projections that are sparse (only a few elements of $phi$ are non-zero) and
    orthogonal ($	extcolor{lightgreen}{\\phi(k^{(i)})} \\cdot 	extcolor{lightgreen}{\\phi(k^{(j)})}
     pprox 0$ for most $i, j$
    unless $k^{(i)}$ and $k^{(j)}$ are very similar.

    ### Normalization

    Paper introduces a simple normalization for $	extcolor{lightgreen}{\\phi}$,

    $$	extcolor{lightgreen}{\\phi '(k)} =
     rac{	extcolor{lightgreen}{\\phi(k)}}{\\sum^{d_{dot}}_{j=1} 	extcolor{lightgreen}{\\phi(k)_j}}$$

    *Check the paper for derivation.*
    """

    def __init__(self, nu: 'int'=1, eps: 'float'=1e-06):
        """
        * `nu` is the hyper-parameter $
u$.
        * `eps` is the small value used to make sure there is no division-by-zero when normalizing.
        """
        super().__init__()
        self.nu = nu
        self.relu = nn.ReLU()
        self.eps = eps

    def forward(self, k: 'torch.Tensor'):
        k = self.dpfp(k)
        return k / (torch.sum(k, dim=-1, keepdim=True) + self.eps)

    def dpfp(self, k: 'torch.Tensor'):
        """
        $$	extcolor{lightgreen}{\\phi(k)}$$
        """
        x = self.relu(torch.cat([k, -k], dim=-1))
        x_rolled = [x.roll(shifts=i, dims=-1) for i in range(1, self.nu + 1)]
        x_rolled = torch.cat(x_rolled, dim=-1)
        x_repeat = torch.cat([x] * self.nu, dim=-1)
        return x_repeat * x_rolled


def get_inputs():
    return [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.nn import Module
from torch import nn
import torch.utils.data
import torch.nn.functional
import torch.autograd
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_cat_div_mul_relu_roll_sum_0(in_ptr0, out_ptr2,
    xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = r1
    tl.full([1, 1], 0, tl.int64)
    tmp3 = tl.full([1, 1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x0 + r1), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1, 1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (4 * x0 + (-4 + r1)), tmp6 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp10 = -tmp9
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp6, tmp10, tmp11)
    tmp13 = tl.where(tmp4, tmp5, tmp12)
    tmp14 = tl.full([1, 1], 0, tl.int32)
    tmp15 = triton_helpers.maximum(tmp14, tmp13)
    tmp16 = (7 + r1) % 8
    tmp18 = tmp16 < tmp3
    tmp19 = tl.load(in_ptr0 + (4 * x0 + (7 + r1) % 8), tmp18 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tmp16 >= tmp3
    tmp22 = tl.load(in_ptr0 + (4 * x0 + (-4 + (7 + r1) % 8)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp23 = -tmp22
    tmp24 = tl.full(tmp23.shape, 0.0, tmp23.dtype)
    tmp25 = tl.where(tmp20, tmp23, tmp24)
    tmp26 = tl.where(tmp18, tmp19, tmp25)
    tmp27 = triton_helpers.maximum(tmp14, tmp26)
    tmp28 = tmp15 * tmp27
    tmp29 = tl.broadcast_to(tmp28, [XBLOCK, RBLOCK])
    tmp31 = tl.where(xmask, tmp29, 0)
    tmp32 = tl.sum(tmp31, 1)[:, None]
    tmp33 = 1e-06
    tmp34 = tmp32 + tmp33
    tmp35 = tmp28 / tmp34
    tl.store(out_ptr2 + (r1 + 8 * x0), tmp35, xmask)


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)
        buf2 = empty_strided_cuda((4, 4, 4, 8), (128, 32, 8, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_add_cat_div_mul_relu_roll_sum_0[grid(64)](arg0_1,
            buf2, 64, 8, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
    return buf2,


class DPFPNew(Module):
    """
    ## Deterministic Parameter Free Project (DPFP)

    This is the new projection function $	extcolor{lightgreen}{\\phi}$ introduced in the paper.
    DPFP projects $k$ of dimensionality $d_{key}$ to dimensionality $d_{dot} = 2 d_{key} 
u$,
    where $
u \\in \\{1, 2, ..., 2 d_{key} - 1 \\}$ is a hyper-parameter.

    $$	extcolor{lightgreen}{\\phi_{2 d_{key} (i - 1)  + j}(k)}
     = 	ext{ReLU}\\Big(ig[k, -kig]\\Big)_{j}
                        	ext{ReLU}\\Big(ig[k, -kig]\\Big)_{i + j}$$

    where $ig[k, -kig]$ is the concatenation of $k$ and $-k$ to give a vector of
    size $2 d_{key}$, $i \\in \\{1, 2, ..., 
u \\}$, and $j \\in \\{1, 2, ..., 2 d_{key}\\}$.
    $x_i$ is the $i$-th element of vector $x$ and is rolled around if
    $i$ is larger than the number of elements in $x$.

    Basically, it creates a new vector by multiplying elements of $[k, -k]$ shifted by $i$.

    This produces projections that are sparse (only a few elements of $phi$ are non-zero) and
    orthogonal ($	extcolor{lightgreen}{\\phi(k^{(i)})} \\cdot 	extcolor{lightgreen}{\\phi(k^{(j)})}
     pprox 0$ for most $i, j$
    unless $k^{(i)}$ and $k^{(j)}$ are very similar.

    ### Normalization

    Paper introduces a simple normalization for $	extcolor{lightgreen}{\\phi}$,

    $$	extcolor{lightgreen}{\\phi '(k)} =
     rac{	extcolor{lightgreen}{\\phi(k)}}{\\sum^{d_{dot}}_{j=1} 	extcolor{lightgreen}{\\phi(k)_j}}$$

    *Check the paper for derivation.*
    """

    def __init__(self, nu: 'int'=1, eps: 'float'=1e-06):
        """
        * `nu` is the hyper-parameter $
u$.
        * `eps` is the small value used to make sure there is no division-by-zero when normalizing.
        """
        super().__init__()
        self.nu = nu
        self.relu = nn.ReLU()
        self.eps = eps

    def dpfp(self, k: 'torch.Tensor'):
        """
        $$	extcolor{lightgreen}{\\phi(k)}$$
        """
        x = self.relu(torch.cat([k, -k], dim=-1))
        x_rolled = [x.roll(shifts=i, dims=-1) for i in range(1, self.nu + 1)]
        x_rolled = torch.cat(x_rolled, dim=-1)
        x_repeat = torch.cat([x] * self.nu, dim=-1)
        return x_repeat * x_rolled

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