# 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/6v/c6vzcw3gyn5uqhyxbbwmpum2zzhvhs66tjq2oznzcap5zo7izpvb.py
# Topologically Sorted Source Nodes: [g, x], Original ATen: [aten.sigmoid, aten.mul]
# Source node to ATen node mapping:
#   g => sigmoid
#   x => mul
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %view_3), kwargs = {})
triton_poi_fused_mul_sigmoid_0 = async_compile.triton('triton_poi_fused_mul_sigmoid_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sigmoid_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_mul_sigmoid_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp2 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [g, x], Original ATen: [aten.sigmoid, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_sigmoid_0.run(buf0, buf1, buf2, 256, grid=grid(256), stream=stream0)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
    return (reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0), reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), buf0, buf1, reinterpret_tensor(buf2, (64, 4), (4, 1), 0), primals_4, )


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, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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
from torch import Tensor
from torch import nn as nn
import torch.nn.functional as F


class MonteCarloDropout(nn.Dropout):
    """
    Defines Monte Carlo dropout Module as defined
    in the paper https://arxiv.org/pdf/1506.02142.pdf.
    In summary, This technique uses the regular dropout
    which can be interpreted as a Bayesian approximation of
    a well-known probabilistic model: the Gaussian process.
    We can treat the many different networks
    (with different neurons dropped out) as Monte Carlo samples
    from the space of all available models. This provides mathematical
    grounds to reason about the model’s uncertainty and, as it turns out,
    often improves its performance.
    """
    mc_dropout_enabled: 'bool' = False

    def train(self, mode: 'bool'=True):
        if mode:
            self.mc_dropout_enabled = True

    def forward(self, input: 'Tensor') ->Tensor:
        return F.dropout(input, self.p, self.mc_dropout_enabled, self.inplace)


class FeedForward(nn.Module):
    """
    ## FFN module

    source
    [FeedForward network](https://arxiv.org/abs/2002.05202)
    """

    def __init__(self, d_model: 'int', d_ff: 'int', dropout: 'float'=0.1,
        activation=nn.ReLU(), is_gated: 'bool'=False, bias1: 'bool'=True,
        bias2: 'bool'=True, bias_gate: 'bool'=True):
        """
        * `d_model` is the number of features in a token embedding
        * `d_ff` is the number of features in the hidden layer of the FFN
        * `dropout` is dropout probability for the hidden layer,
           compatible with Monte Carlo dropout at inference time
        * `is_gated` specifies whether the hidden layer is gated
        * `bias1` specified whether the first fully connected layer should have a learnable bias
        * `bias2` specified whether the second fully connected layer should have a learnable bias
        * `bias_gate` specified whether the fully connected layer for the gate should have a learnable bias
        """
        super().__init__()
        self.layer1 = nn.Linear(d_model, d_ff, bias=bias1)
        self.layer2 = nn.Linear(d_ff, d_model, bias=bias2)
        self.dropout = MonteCarloDropout(dropout)
        self.activation = activation
        self.is_gated = is_gated
        if is_gated:
            self.linear_v = nn.Linear(d_model, d_ff, bias=bias_gate)

    def forward(self, x: 'torch.Tensor'):
        g = self.activation(self.layer1(x))
        if self.is_gated:
            x = g * self.linear_v(x)
        else:
            x = g
        x = self.dropout(x)
        return self.layer2(x)


class GLU(nn.Module):

    def __init__(self, d_model: 'int', d_ff: 'int', dropout: 'float'=0.1):
        super().__init__()
        self.ffn = FeedForward(d_model, d_ff, dropout, nn.Sigmoid(), True, 
            False, False, False)

    def forward(self, x: 'torch.Tensor'):
        return self.ffn(x)


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


def get_init_inputs():
    return [[], {'d_model': 4, 'd_ff': 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 import Tensor
from torch import nn as nn
import torch.nn.functional as F
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_mul_sigmoid_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_sigmoid_0[grid(256)](buf0, buf1, buf2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf3)
    return reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), reinterpret_tensor(primals_2, (64, 4), (4, 1), 0
        ), buf0, buf1, reinterpret_tensor(buf2, (64, 4), (4, 1), 0), primals_4


class MonteCarloDropout(nn.Dropout):
    """
    Defines Monte Carlo dropout Module as defined
    in the paper https://arxiv.org/pdf/1506.02142.pdf.
    In summary, This technique uses the regular dropout
    which can be interpreted as a Bayesian approximation of
    a well-known probabilistic model: the Gaussian process.
    We can treat the many different networks
    (with different neurons dropped out) as Monte Carlo samples
    from the space of all available models. This provides mathematical
    grounds to reason about the model’s uncertainty and, as it turns out,
    often improves its performance.
    """
    mc_dropout_enabled: 'bool' = False

    def train(self, mode: 'bool'=True):
        if mode:
            self.mc_dropout_enabled = True

    def forward(self, input: 'Tensor') ->Tensor:
        return F.dropout(input, self.p, self.mc_dropout_enabled, self.inplace)


class FeedForward(nn.Module):
    """
    ## FFN module

    source
    [FeedForward network](https://arxiv.org/abs/2002.05202)
    """

    def __init__(self, d_model: 'int', d_ff: 'int', dropout: 'float'=0.1,
        activation=nn.ReLU(), is_gated: 'bool'=False, bias1: 'bool'=True,
        bias2: 'bool'=True, bias_gate: 'bool'=True):
        """
        * `d_model` is the number of features in a token embedding
        * `d_ff` is the number of features in the hidden layer of the FFN
        * `dropout` is dropout probability for the hidden layer,
           compatible with Monte Carlo dropout at inference time
        * `is_gated` specifies whether the hidden layer is gated
        * `bias1` specified whether the first fully connected layer should have a learnable bias
        * `bias2` specified whether the second fully connected layer should have a learnable bias
        * `bias_gate` specified whether the fully connected layer for the gate should have a learnable bias
        """
        super().__init__()
        self.layer1 = nn.Linear(d_model, d_ff, bias=bias1)
        self.layer2 = nn.Linear(d_ff, d_model, bias=bias2)
        self.dropout = MonteCarloDropout(dropout)
        self.activation = activation
        self.is_gated = is_gated
        if is_gated:
            self.linear_v = nn.Linear(d_model, d_ff, bias=bias_gate)

    def forward(self, x: 'torch.Tensor'):
        g = self.activation(self.layer1(x))
        if self.is_gated:
            x = g * self.linear_v(x)
        else:
            x = g
        x = self.dropout(x)
        return self.layer2(x)


class GLUNew(nn.Module):

    def __init__(self, d_model: 'int', d_ff: 'int', dropout: 'float'=0.1):
        super().__init__()
        self.ffn = FeedForward(d_model, d_ff, dropout, nn.Sigmoid(), True, 
            False, False, False)

    def forward(self, input_0):
        primals_1 = self.ffn.layer1.weight
        primals_3 = self.ffn.layer2.weight
        primals_4 = self.ffn.linear_v.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
