# 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/qk/cqktl6a5kztlpt4hsz4gfob2rxmtfurvnwvwoend2jzbg534exfw.py
# Topologically Sorted Source Nodes: [g, x], Original ATen: [aten.relu, aten.mul]
# Source node to ATen node mapping:
#   g => relu
#   x => mul
# Graph fragment:
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%view_1,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%relu, %view_3), kwargs = {})
triton_poi_fused_mul_relu_0 = async_compile.triton('triton_poi_fused_mul_relu_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_relu_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_relu_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)
    tmp3 = tl.load(in_ptr1 + (x0), xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + (x0), tmp4, 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.relu, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_relu_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
import torch.nn as nn


class PositionWiseFeedForward(nn.Module):
    """
    title: Position-wise Feed-Forward Network (FFN)
    summary: Documented reusable implementation of the position wise feedforward network.

    # Position-wise Feed-Forward Network (FFN)
    This is a [PyTorch](https://pytorch.org)  implementation
    of position-wise feedforward network used in transformer.
    FFN consists of two fully connected layers.
    Number of dimensions in the hidden layer $d_{ff}$, is generally set to around
    four times that of the token embedding $d_{model}$.
    So it is sometime also called the expand-and-contract network.
    There is an activation at the hidden layer, which is
    usually set to ReLU (Rectified Linear Unit) activation, $$\\max(0, x)$$
    That is, the FFN function is,
    $$FFN(x, W_1, W_2, b_1, b_2) = \\max(0, x W_1 + b_1) W_2 + b_2$$
    where $W_1$, $W_2$, $b_1$ and $b_2$ are learnable parameters.
    Sometimes the
    GELU (Gaussian Error Linear Unit) activation is also used instead of ReLU.
    $$x \\Phi(x)$$ where $\\Phi(x) = P(X \\le x), X \\sim \\mathcal{N}(0,1)$
    ### Gated Linear Units
    This is a generic implementation that supports different variants including
    [Gated Linear Units](https://arxiv.org/abs/2002.05202) (GLU).
    We have also implemented experiments on these:
    * [experiment that uses `labml.configs`](glu_variants/experiment.html)
    * [simpler version from scratch](glu_variants/simple.html)
    """

    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
        * `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 = nn.Dropout(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 ReGLU(nn.Module):

    def __init__(self, d_model: 'int', d_ff: 'int', dropout: 'float'=0.1):
        super().__init__()
        self.ffn = PositionWiseFeedForward(d_model, d_ff, dropout, nn.ReLU(
            ), 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._inductor.runtime import triton_helpers
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_poi_fused_mul_relu_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)
    tmp3 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = tmp2 * tmp3
    tl.store(out_ptr0 + x0, tmp4, 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_relu_0[grid(256)](buf0, buf1, buf2, 256,
            XBLOCK=128, 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 PositionWiseFeedForward(nn.Module):
    """
    title: Position-wise Feed-Forward Network (FFN)
    summary: Documented reusable implementation of the position wise feedforward network.

    # Position-wise Feed-Forward Network (FFN)
    This is a [PyTorch](https://pytorch.org)  implementation
    of position-wise feedforward network used in transformer.
    FFN consists of two fully connected layers.
    Number of dimensions in the hidden layer $d_{ff}$, is generally set to around
    four times that of the token embedding $d_{model}$.
    So it is sometime also called the expand-and-contract network.
    There is an activation at the hidden layer, which is
    usually set to ReLU (Rectified Linear Unit) activation, $$\\max(0, x)$$
    That is, the FFN function is,
    $$FFN(x, W_1, W_2, b_1, b_2) = \\max(0, x W_1 + b_1) W_2 + b_2$$
    where $W_1$, $W_2$, $b_1$ and $b_2$ are learnable parameters.
    Sometimes the
    GELU (Gaussian Error Linear Unit) activation is also used instead of ReLU.
    $$x \\Phi(x)$$ where $\\Phi(x) = P(X \\le x), X \\sim \\mathcal{N}(0,1)$
    ### Gated Linear Units
    This is a generic implementation that supports different variants including
    [Gated Linear Units](https://arxiv.org/abs/2002.05202) (GLU).
    We have also implemented experiments on these:
    * [experiment that uses `labml.configs`](glu_variants/experiment.html)
    * [simpler version from scratch](glu_variants/simple.html)
    """

    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
        * `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 = nn.Dropout(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 ReGLUNew(nn.Module):

    def __init__(self, d_model: 'int', d_ff: 'int', dropout: 'float'=0.1):
        super().__init__()
        self.ffn = PositionWiseFeedForward(d_model, d_ff, dropout, nn.ReLU(
            ), 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]
