# AOT ID: ['0_forward']
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/le/clemd3fjnkby5w36zezyjaq4ygcdl6zeuha7yam2oijv4yjilswb.py
# Topologically Sorted Source Nodes: [mean, var, add, std, sub, x, x_1, x_2], Original ATen: [aten.mean, aten.var, aten.add, aten.sqrt, aten.sub, aten.mul, aten.div]
# Source node to ATen node mapping:
#   add => add
#   mean => mean
#   std => sqrt
#   sub => sub
#   var => var
#   x => mul
#   x_1 => div
#   x_2 => add_1
# Graph fragment:
#   %mean : [num_users=2] = call_function[target=torch.ops.aten.mean.dim](args = (%view_4, [-1], True), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%view_4, [-1]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%var, 1e-05), kwargs = {})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%view_4, %mean), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %sub), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %sqrt), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %primals_4), kwargs = {})
triton_per_fused_add_div_mean_mul_sqrt_sub_var_0 = async_compile.triton('triton_per_fused_add_div_mean_mul_sqrt_sub_var_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, 16],
    reduction_hint=ReductionHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_div_mean_mul_sqrt_sub_var_0', 'mutated_arg_names': ['in_out_ptr0', 'in_out_ptr1'], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 4, '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_mean_mul_sqrt_sub_var_0(in_out_ptr0, in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    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)
    r2 = rindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + ((4*x0) + (16*(r2 // 4)) + (64*x1) + (r2 % 4)), xmask, other=0.0)
    tmp25 = tl.load(in_ptr1 + (r2), None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr2 + (r2), None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp4 / tmp19
    tmp21 = tmp18 / tmp19
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp26 = tmp0 - tmp20
    tmp27 = tmp25 * tmp26
    tmp28 = tmp27 / tmp24
    tmp30 = tmp28 + tmp29
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x3), tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + (x3), tmp24, xmask)
    tl.store(out_ptr0 + (r2 + (16*x3)), tmp30, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (16, ), (1, ))
    assert_size_stride(primals_4, (16, ), (1, ))
    assert_size_stride(primals_5, (4, 16), (16, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pair], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 16, 4), (64, 1, 16), 0), reinterpret_tensor(primals_2, (4, 4, 16), (64, 16, 1), 0), out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf1  # reuse
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf4  # reuse
        buf7 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mean, var, add, std, sub, x, x_1, x_2], Original ATen: [aten.mean, aten.var, aten.add, aten.sqrt, aten.sub, aten.mul, aten.div]
        stream0 = get_raw_stream(0)
        triton_per_fused_add_div_mean_mul_sqrt_sub_var_0.run(buf2, buf6, buf0, primals_3, primals_4, buf7, 64, 16, grid=grid(64), stream=stream0)
        del primals_3
        del primals_4
        buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pair_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf7, (64, 16), (16, 1), 0), reinterpret_tensor(primals_5, (16, 4), (1, 16), 0), alpha=1, beta=1, out=buf8)
        del primals_6
    return (reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf0, buf2, buf6, reinterpret_tensor(buf7, (64, 16), (16, 1), 0), primals_5, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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


class LayerNorm(nn.Module):

    def __init__(self, d_model, eps=1e-05):
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(d_model))
        self.b_2 = nn.Parameter(torch.zeros(d_model))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = torch.sqrt(x.var(dim=-1, keepdim=True, unbiased=False) + self.eps
            )
        x = self.a_2 * (x - mean)
        x /= std
        x += self.b_2
        return x


class CoevolExtractor(nn.Module):

    def __init__(self, n_feat_proj, n_feat_out, p_drop=0.1):
        super(CoevolExtractor, self).__init__()
        self.norm_2d = LayerNorm(n_feat_proj * n_feat_proj)
        self.proj_2 = nn.Linear(n_feat_proj ** 2, n_feat_out)

    def forward(self, x_down, x_down_w):
        B, _N, L = x_down.shape[:3]
        pair = torch.einsum('abij,ablm->ailjm', x_down, x_down_w)
        pair = pair.reshape(B, L, L, -1)
        pair = self.norm_2d(pair)
        pair = self.proj_2(pair)
        return pair


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


def get_init_inputs():
    return [[], {'n_feat_proj': 4, 'n_feat_out': 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.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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_add_div_mean_mul_sqrt_sub_var_0(in_out_ptr0,
    in_out_ptr1, in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 16
    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)
    r2 = rindex
    x0 = xindex % 4
    x1 = xindex // 4
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (4 * x0 + 16 * (r2 // 4) + 64 * x1 + r2 % 4),
        xmask, other=0.0)
    tmp25 = tl.load(in_ptr1 + r2, None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr2 + r2, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp6 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp8 = tl.where(xmask, tmp6, 0)
    tmp9 = tl.sum(tmp8, 1)[:, None]
    tmp10 = tl.full([XBLOCK, 1], 16, tl.int32)
    tmp11 = tmp10.to(tl.float32)
    tmp12 = tmp9 / tmp11
    tmp13 = tmp1 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.where(xmask, tmp15, 0)
    tmp18 = tl.sum(tmp17, 1)[:, None]
    tmp19 = 16.0
    tmp20 = tmp4 / tmp19
    tmp21 = tmp18 / tmp19
    tmp22 = 1e-05
    tmp23 = tmp21 + tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp26 = tmp0 - tmp20
    tmp27 = tmp25 * tmp26
    tmp28 = tmp27 / tmp24
    tmp30 = tmp28 + tmp29
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x3, tmp20, xmask)
    tl.debug_barrier()
    tl.store(in_out_ptr1 + x3, tmp24, xmask)
    tl.store(out_ptr0 + (r2 + 16 * x3), tmp30, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (16,), (1,))
    assert_size_stride(primals_4, (16,), (1,))
    assert_size_stride(primals_5, (4, 16), (16, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 16, 4), (64, 1,
            16), 0), reinterpret_tensor(primals_2, (4, 4, 16), (64, 16, 1),
            0), out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf2 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf1
        buf6 = reinterpret_tensor(buf4, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf4
        buf7 = empty_strided_cuda((4, 4, 4, 16), (256, 64, 16, 1), torch.
            float32)
        get_raw_stream(0)
        triton_per_fused_add_div_mean_mul_sqrt_sub_var_0[grid(64)](buf2,
            buf6, buf0, primals_3, primals_4, buf7, 64, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del primals_3
        del primals_4
        buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf7, (64, 16),
            (16, 1), 0), reinterpret_tensor(primals_5, (16, 4), (1, 16), 0),
            alpha=1, beta=1, out=buf8)
        del primals_6
    return reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf0, buf2, buf6, reinterpret_tensor(buf7, (64, 16), (16, 1), 0
        ), primals_5


class LayerNorm(nn.Module):

    def __init__(self, d_model, eps=1e-05):
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(d_model))
        self.b_2 = nn.Parameter(torch.zeros(d_model))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = torch.sqrt(x.var(dim=-1, keepdim=True, unbiased=False) + self.eps
            )
        x = self.a_2 * (x - mean)
        x /= std
        x += self.b_2
        return x


class CoevolExtractorNew(nn.Module):

    def __init__(self, n_feat_proj, n_feat_out, p_drop=0.1):
        super(CoevolExtractorNew, self).__init__()
        self.norm_2d = LayerNorm(n_feat_proj * n_feat_proj)
        self.proj_2 = nn.Linear(n_feat_proj ** 2, n_feat_out)

    def forward(self, input_0, input_1):
        primals_3 = self.norm_2d.a_2
        primals_4 = self.norm_2d.b_2
        primals_5 = self.proj_2.weight
        primals_6 = self.proj_2.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
