# 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_8/inductor_cache/2d/c2durcbnmxziv6ccmw4s5yb6zlayyf7itflrryxft2ddkko7o6q4.py
# Topologically Sorted Source Nodes: [mul, weighted_sum, mul_1], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   weighted_sum => add
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select, %select_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, 0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_3, %add), kwargs = {})
triton_poi_fused_add_mul_0 = async_compile.triton('triton_poi_fused_add_mul_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=[64], 
    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_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (0))
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + (0))
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp7 = tl.load(in_ptr2 + (x0), xmask)
    tmp4 = tmp3 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 / tmp5
    tmp8 = tmp6 * tmp7
    tmp9 = 0.0
    tmp10 = tmp8 + tmp9
    tmp11 = tmp1 * tmp10
    tl.store(out_ptr0 + (x0), tmp11, 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, (1, ), (1, ))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, weighted_sum, mul_1], Original ATen: [aten.mul, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_0.run(primals_3, primals_1, primals_2, buf0, 64, grid=grid(64), stream=stream0)
    return (buf0, primals_1, primals_3, reinterpret_tensor(primals_2, (4, 4, 4), (16, 4, 1), 0), )


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((1, ), (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((1, ), (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 ScalarMix(nn.Module):
    """
    Computes a parameterised scalar mixture of :math:`N` tensors, :math:`mixture = \\gamma * \\sum_{k}(s_k * tensor_k)`
    where :math:`s = \\mathrm{softmax}(w)`, with :math:`w` and :math:`\\gamma` scalar parameters.

    Args:
        n_layers (int):
            The number of layers to be mixed, i.e., :math:`N`.
        dropout (float):
            The dropout ratio of the layer weights.
            If dropout > 0, then for each scalar weight, adjust its softmax weight mass to 0
            with the dropout probability (i.e., setting the unnormalized weight to -inf).
            This effectively redistributes the dropped probability mass to all other weights.
            Default: 0.
    """

    def __init__(self, n_layers: 'int', dropout: 'float'=0.0):
        super().__init__()
        self.n_layers = n_layers
        self.weights = nn.Parameter(torch.zeros(n_layers))
        self.gamma = nn.Parameter(torch.tensor([1.0]))
        self.dropout = nn.Dropout(dropout)

    def __repr__(self):
        s = f'n_layers={self.n_layers}'
        if self.dropout.p > 0:
            s += f', dropout={self.dropout.p}'
        return f'{self.__class__.__name__}({s})'

    def forward(self, tensors):
        """
        Args:
            tensors (list[~torch.Tensor]):
                :math:`N` tensors to be mixed.

        Returns:
            The mixture of :math:`N` tensors.
        """
        normed_weights = self.dropout(self.weights.softmax(-1))
        weighted_sum = sum(w * h for w, h in zip(normed_weights, tensors))
        return self.gamma * weighted_sum


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


def get_init_inputs():
    return [[], {'n_layers': 1}]

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 math as tl_math
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_add_mul_0(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tl.load(in_ptr1 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp7 = tl.load(in_ptr2 + x0, xmask)
    tmp4 = tmp3 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 / tmp5
    tmp8 = tmp6 * tmp7
    tmp9 = 0.0
    tmp10 = tmp8 + tmp9
    tmp11 = tmp1 * tmp10
    tl.store(out_ptr0 + x0, tmp11, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (1,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(64)](primals_3, primals_1,
            primals_2, buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf0, primals_1, primals_3, reinterpret_tensor(primals_2, (4, 4,
        4), (16, 4, 1), 0)


class ScalarMixNew(nn.Module):
    """
    Computes a parameterised scalar mixture of :math:`N` tensors, :math:`mixture = \\gamma * \\sum_{k}(s_k * tensor_k)`
    where :math:`s = \\mathrm{softmax}(w)`, with :math:`w` and :math:`\\gamma` scalar parameters.

    Args:
        n_layers (int):
            The number of layers to be mixed, i.e., :math:`N`.
        dropout (float):
            The dropout ratio of the layer weights.
            If dropout > 0, then for each scalar weight, adjust its softmax weight mass to 0
            with the dropout probability (i.e., setting the unnormalized weight to -inf).
            This effectively redistributes the dropped probability mass to all other weights.
            Default: 0.
    """

    def __init__(self, n_layers: 'int', dropout: 'float'=0.0):
        super().__init__()
        self.n_layers = n_layers
        self.weights = nn.Parameter(torch.zeros(n_layers))
        self.gamma = nn.Parameter(torch.tensor([1.0]))
        self.dropout = nn.Dropout(dropout)

    def __repr__(self):
        s = f'n_layers={self.n_layers}'
        if self.dropout.p > 0:
            s += f', dropout={self.dropout.p}'
        return f'{self.__class__.__name__}({s})'

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