# 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_6/inductor_cache/t4/ct4t2fmlrmfxptdiid3i6pgnapm2liftmal2stemtgxuxoou5pcw.py
# Topologically Sorted Source Nodes: [out], Original ATen: [aten.mean, aten.std, aten.mul, aten.add, aten.sqrt, aten.sub, aten.div]
# Source node to ATen node mapping:
#   out => add, add_1, div, mean, mul, mul_1, sqrt, sqrt_1, sub, var
# Graph fragment:
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%primals_1, [0]), kwargs = {})
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%primals_1, [0]), kwargs = {correction: 0.0})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sqrt, %sqrt), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 1e-05), kwargs = {})
#   %sqrt_1 : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %mean), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %sqrt_1), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %div), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_3), kwargs = {})
triton_poi_fused_add_div_mean_mul_sqrt_std_sub_0 = async_compile.triton('triton_poi_fused_add_div_mean_mul_sqrt_std_sub_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_add_div_mean_mul_sqrt_std_sub_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 7, '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_div_mean_mul_sqrt_std_sub_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2), xmask)
    tmp2 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 4.0
    tmp10 = tmp8 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp2 - tmp10
    tmp13 = tmp12 * tmp12
    tmp14 = tmp3 - tmp10
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp10
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp7 - tmp10
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp23 = tmp22 / tmp9
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tmp24 * tmp24
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = tmp11 / tmp28
    tmp30 = tmp0 * tmp29
    tmp32 = tmp30 + tmp31
    tl.store(in_out_ptr0 + (x2), tmp32, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.mean, aten.std, aten.mul, aten.add, aten.sqrt, aten.sub, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_mean_mul_sqrt_std_sub_0.run(buf1, primals_2, primals_1, primals_3, 16, grid=grid(16), stream=stream0)
        del primals_2
        del primals_3
    return (buf1, primals_1, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 CustomBatchNormManualFunction(torch.autograd.Function):
    """
    This torch.autograd.Function implements a functional custom version of the batch norm operation for MLPs.
    Using torch.autograd.Function allows you to write a custom backward function.
    The function will be called from the nn.Module CustomBatchNormManualModule
    Inside forward the tensors are (automatically) not recorded for automatic differentiation since the backward
    pass is done via the backward method.
    The forward pass is not called directly but via the apply() method. This makes sure that the context objects
    are dealt with correctly. Example:
      my_bn_fct = CustomBatchNormManualFunction()
      normalized = fct.apply(input, gamma, beta, eps)
    """

    @staticmethod
    def forward(ctx, input, gamma, beta, eps=1e-05):
        """
        Compute the batch normalization
        
        Args:
          ctx: context object handling storing and retrival of tensors and constants and specifying
               whether tensors need gradients in backward pass
          input: input tensor of shape (n_batch, n_neurons)
          gamma: variance scaling tensor, applied per neuron, shpae (n_neurons)
          beta: mean bias tensor, applied per neuron, shpae (n_neurons)
          eps: small float added to the variance for stability
        Returns:
          out: batch-normalized tensor
    
        TODO:
          Implement the forward pass of batch normalization
          Store constant non-tensor objects via ctx.constant=myconstant
          Store tensors which you need in the backward pass via ctx.save_for_backward(tensor1, tensor2, ...)
          Intermediate results can be decided to be either recomputed in the backward pass or to be stored
          for the backward pass. Do not store tensors which are unnecessary for the backward pass to save memory!
          For the case that you make use of torch.var be aware that the flag unbiased=False should be set.
        """
        x = input
        mu = x.mean(dim=0)
        std = x.std(dim=0, unbiased=False)
        var = torch.sqrt(std * std + eps)
        x_centered = x - mu
        x_hat = x_centered / var
        out = gamma * x_hat + beta
        ctx.save_for_backward(x, mu, std, var, gamma)
        return out

    @staticmethod
    def backward(ctx, grad_output):
        """
        Compute backward pass of the batch normalization.
        
        Args:
          ctx: context object handling storing and retrival of tensors and constants and specifying
               whether tensors need gradients in backward pass
        Returns:
          out: tuple containing gradients for all input arguments
        
        TODO:
          Retrieve saved tensors and constants via ctx.saved_tensors and ctx.constant
          Compute gradients for inputs where ctx.needs_input_grad[idx] is True. Set gradients for other
          inputs to None. This should be decided dynamically.
        """
        x, mu, _std, var, gamma = ctx.saved_tensors
        grad_input, grad_gamma, grad_beta = None, None, None
        x_hat = (x - mu) / var
        if ctx.needs_input_grad[2]:
            grad_beta = grad_output.sum(0)
        if ctx.needs_input_grad[1]:
            grad_gamma = (grad_output * x_hat).sum(0)
        if ctx.needs_input_grad[0]:
            c1 = grad_output.mean(dim=0)
            c2 = (grad_output * x_hat).mean(dim=0)
            grad_input = gamma / var * (grad_output - c1 - x_hat * c2)
        return grad_input, grad_gamma, grad_beta, None


class CustomBatchNormManualModule(nn.Module):
    """
    This nn.module implements a custom version of the batch norm operation for MLPs.
    In self.forward the functional version CustomBatchNormManualFunction.forward is called.
    The automatic differentiation of PyTorch calls the backward method of this function in the backward pass.
    """

    def __init__(self, n_neurons, eps=1e-05):
        """
        Initializes CustomBatchNormManualModule object.
        
        Args:
          n_neurons: int specifying the number of neurons
          eps: small float to be added to the variance for stability
        
        TODO:
          Save parameters for the number of neurons and eps.
          Initialize parameters gamma and beta via nn.Parameter
        """
        super(CustomBatchNormManualModule, self).__init__()
        self.n_neurons = n_neurons
        self.eps = eps
        self.gammas = torch.nn.Parameter(torch.ones(n_neurons))
        self.betas = torch.nn.Parameter(torch.zeros(n_neurons))

    def forward(self, input):
        """
        Compute the batch normalization via CustomBatchNormManualFunction
        
        Args:
          input: input tensor of shape (n_batch, n_neurons)
        Returns:
          out: batch-normalized tensor
        
        TODO:
          Check for the correctness of the shape of the input tensor.
          Instantiate a CustomBatchNormManualFunction.
          Call it via its .apply() method.
        """
        shape = input.size()
        if len(shape) == 1:
            input.view(1, -1)
            shape = input.size()
        if len(shape) > 2:
            raise ValueError('Expected 1-D or 2-D tensor (got {})'.format(
                str(shape)))
        elif input.shape[1] != self.n_neurons:
            raise ValueError('Expected _ x {} tensor (got {} x {})'.format(
                str(self.n_neurons), str(shape[0]), str(shape[1])))
        fct = CustomBatchNormManualFunction()
        out = fct.apply(input, self.gammas, self.betas, self.eps)
        return out


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


def get_init_inputs():
    return [[], {'n_neurons': 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 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_poi_fused_add_div_mean_mul_sqrt_std_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp31 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 4.0
    tmp10 = tmp8 / tmp9
    tmp11 = tmp1 - tmp10
    tmp12 = tmp2 - tmp10
    tmp13 = tmp12 * tmp12
    tmp14 = tmp3 - tmp10
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp10
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp7 - tmp10
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp23 = tmp22 / tmp9
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tmp24 * tmp24
    tmp26 = 1e-05
    tmp27 = tmp25 + tmp26
    tmp28 = libdevice.sqrt(tmp27)
    tmp29 = tmp11 / tmp28
    tmp30 = tmp0 * tmp29
    tmp32 = tmp30 + tmp31
    tl.store(in_out_ptr0 + x2, tmp32, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_div_mean_mul_sqrt_std_sub_0[grid(16)](buf1,
            primals_2, primals_1, primals_3, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_2
        del primals_3
    return buf1, primals_1


class CustomBatchNormManualFunction(torch.autograd.Function):
    """
    This torch.autograd.Function implements a functional custom version of the batch norm operation for MLPs.
    Using torch.autograd.Function allows you to write a custom backward function.
    The function will be called from the nn.Module CustomBatchNormManualModule
    Inside forward the tensors are (automatically) not recorded for automatic differentiation since the backward
    pass is done via the backward method.
    The forward pass is not called directly but via the apply() method. This makes sure that the context objects
    are dealt with correctly. Example:
      my_bn_fct = CustomBatchNormManualFunction()
      normalized = fct.apply(input, gamma, beta, eps)
    """

    @staticmethod
    def forward(ctx, input, gamma, beta, eps=1e-05):
        """
        Compute the batch normalization
        
        Args:
          ctx: context object handling storing and retrival of tensors and constants and specifying
               whether tensors need gradients in backward pass
          input: input tensor of shape (n_batch, n_neurons)
          gamma: variance scaling tensor, applied per neuron, shpae (n_neurons)
          beta: mean bias tensor, applied per neuron, shpae (n_neurons)
          eps: small float added to the variance for stability
        Returns:
          out: batch-normalized tensor
    
        TODO:
          Implement the forward pass of batch normalization
          Store constant non-tensor objects via ctx.constant=myconstant
          Store tensors which you need in the backward pass via ctx.save_for_backward(tensor1, tensor2, ...)
          Intermediate results can be decided to be either recomputed in the backward pass or to be stored
          for the backward pass. Do not store tensors which are unnecessary for the backward pass to save memory!
          For the case that you make use of torch.var be aware that the flag unbiased=False should be set.
        """
        x = input
        mu = x.mean(dim=0)
        std = x.std(dim=0, unbiased=False)
        var = torch.sqrt(std * std + eps)
        x_centered = x - mu
        x_hat = x_centered / var
        out = gamma * x_hat + beta
        ctx.save_for_backward(x, mu, std, var, gamma)
        return out

    @staticmethod
    def backward(ctx, grad_output):
        """
        Compute backward pass of the batch normalization.
        
        Args:
          ctx: context object handling storing and retrival of tensors and constants and specifying
               whether tensors need gradients in backward pass
        Returns:
          out: tuple containing gradients for all input arguments
        
        TODO:
          Retrieve saved tensors and constants via ctx.saved_tensors and ctx.constant
          Compute gradients for inputs where ctx.needs_input_grad[idx] is True. Set gradients for other
          inputs to None. This should be decided dynamically.
        """
        x, mu, _std, var, gamma = ctx.saved_tensors
        grad_input, grad_gamma, grad_beta = None, None, None
        x_hat = (x - mu) / var
        if ctx.needs_input_grad[2]:
            grad_beta = grad_output.sum(0)
        if ctx.needs_input_grad[1]:
            grad_gamma = (grad_output * x_hat).sum(0)
        if ctx.needs_input_grad[0]:
            c1 = grad_output.mean(dim=0)
            c2 = (grad_output * x_hat).mean(dim=0)
            grad_input = gamma / var * (grad_output - c1 - x_hat * c2)
        return grad_input, grad_gamma, grad_beta, None


class CustomBatchNormManualModuleNew(nn.Module):
    """
    This nn.module implements a custom version of the batch norm operation for MLPs.
    In self.forward the functional version CustomBatchNormManualFunction.forward is called.
    The automatic differentiation of PyTorch calls the backward method of this function in the backward pass.
    """

    def __init__(self, n_neurons, eps=1e-05):
        """
        Initializes CustomBatchNormManualModule object.
        
        Args:
          n_neurons: int specifying the number of neurons
          eps: small float to be added to the variance for stability
        
        TODO:
          Save parameters for the number of neurons and eps.
          Initialize parameters gamma and beta via nn.Parameter
        """
        super(CustomBatchNormManualModuleNew, self).__init__()
        self.n_neurons = n_neurons
        self.eps = eps
        self.gammas = torch.nn.Parameter(torch.ones(n_neurons))
        self.betas = torch.nn.Parameter(torch.zeros(n_neurons))

    def forward(self, input_0):
        primals_2 = self.gammas
        primals_3 = self.betas
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
