# 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_1/inductor_cache/fe/cfectm5bcy5shyxsysnrgesmv5nz3kcc7dmnkoifg25aiy5lkoeg.py
# Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   softmax => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%primals_1, [0], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_1, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [0], True), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_0 = async_compile.triton('triton_poi_fused__softmax_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=[1], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.load(in_ptr0 + (0))
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tmp1 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp3 / tmp3
    tl.store(out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp4, None)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ti/ctirlc5pkmwgnlnthecbyyuplgtjrpbhlixppogsu3g54jlpd7e3.py
# Topologically Sorted Source Nodes: [mul, out], Original ATen: [aten.mul, aten.sum]
# Source node to ATen node mapping:
#   mul => mul
#   out => sum_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%permute, %div), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [-1]), kwargs = {})
triton_poi_fused_mul_sum_1 = async_compile.triton('triton_poi_fused_mul_sum_1', '''
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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_sum_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 * 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 = args
    args.clear()
    assert_size_stride(primals_1, (1, ), (1, ))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [softmax], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_0.run(primals_1, buf0, 1, grid=grid(1), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, out], Original ATen: [aten.mul, aten.sum]
        triton_poi_fused_mul_sum_1.run(primals_2, buf0, buf1, 16, grid=grid(16), stream=stream0)
    return (buf1, buf0, primals_2, buf0, )


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, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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
import torch.optim
import torch.utils.data


class WeightedView(nn.Module):
    """Calculate weighted view
    
    Args:
        num_groups: int, number of groups (views)
        reduce_dimension: bool, default False. If True, reduce dimension dim
        dim: default -1. Only used when reduce_dimension is True
        
    Shape: 
        - Input: if dim is None, (N, num_features*num_groups)
        - Output: (N, num_features)
        
    Attributes:
        weight: (num_groups)
        
    Examples:
    
        >>> model = WeightedView(3)
        >>> x = Variable(torch.randn(1, 6))
        >>> print(model(x))
        >>> model = WeightedView(3, True, 1)
        >>> model(x.view(1,3,2))
    """

    def __init__(self, num_groups, reduce_dimension=False, dim=-1):
        super(WeightedView, self).__init__()
        self.num_groups = num_groups
        self.reduce_dimension = reduce_dimension
        self.dim = dim
        self.weight = nn.Parameter(torch.Tensor(num_groups))
        self.weight.data.uniform_(-1.0 / num_groups, 1.0 / num_groups)

    def forward(self, x):
        self.normalized_weight = nn.functional.softmax(self.weight, dim=0)
        if self.reduce_dimension:
            assert x.size(self.dim) == self.num_groups
            dim = self.dim if self.dim >= 0 else self.dim + x.dim()
            if dim == x.dim() - 1:
                out = (x * self.weight).sum(-1)
            else:
                out = torch.transpose((x.transpose(dim, -1) * self.
                    normalized_weight).sum(-1), dim, -1)
        else:
            assert x.dim() == 2
            num_features = x.size(-1) // self.num_groups
            out = (x.view(-1, self.num_groups, num_features).transpose(1, -
                1) * self.normalized_weight).sum(-1)
        return out


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


def get_init_inputs():
    return [[], {'num_groups': 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
import torch.optim
import torch.utils.data
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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.load(in_ptr0 + 0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tmp2 = tmp1 - tmp1
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp3 / tmp3
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp4, None)


@triton.jit
def triton_poi_fused_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (1,), (1,))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((1,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(1)](primals_1, buf0, 1, XBLOCK=1,
            num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_mul_sum_1[grid(16)](primals_2, buf0, buf1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
    return buf1, buf0, primals_2, buf0


class WeightedViewNew(nn.Module):
    """Calculate weighted view
    
    Args:
        num_groups: int, number of groups (views)
        reduce_dimension: bool, default False. If True, reduce dimension dim
        dim: default -1. Only used when reduce_dimension is True
        
    Shape: 
        - Input: if dim is None, (N, num_features*num_groups)
        - Output: (N, num_features)
        
    Attributes:
        weight: (num_groups)
        
    Examples:
    
        >>> model = WeightedView(3)
        >>> x = Variable(torch.randn(1, 6))
        >>> print(model(x))
        >>> model = WeightedView(3, True, 1)
        >>> model(x.view(1,3,2))
    """

    def __init__(self, num_groups, reduce_dimension=False, dim=-1):
        super(WeightedViewNew, self).__init__()
        self.num_groups = num_groups
        self.reduce_dimension = reduce_dimension
        self.dim = dim
        self.weight = nn.Parameter(torch.Tensor(num_groups))
        self.weight.data.uniform_(-1.0 / num_groups, 1.0 / num_groups)

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