# 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_4/inductor_cache/5g/c5gkk32spz5figisqcc7f5n3dtjhtus3ow7bq2ks33nukfh7wdw4.py
# Topologically Sorted Source Nodes: [pow_1], Original ATen: [aten.pow]
# Source node to ATen node mapping:
#   pow_1 => pow_1
# Graph fragment:
#   %pow_1 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Tensor](args = (%unsqueeze, %primals_2), kwargs = {})
triton_poi_fused_pow_0 = async_compile.triton('triton_poi_fused_pow_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=[1024], 
    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_pow_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_pow_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x4 = (xindex // 64)
    x5 = (xindex // 16) % 16
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x4)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x5), xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.1
    tmp4 = tmp2 + tmp3
    tmp6 = libdevice.pow(tmp4, tmp5)
    tl.store(out_ptr0 + (x6), tmp6, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/ps/cpsrsgxeivql4cnkvpnwvwm7hpo2xhy5n7cebpa5de6vg5vjrd5a.py
# Topologically Sorted Source Nodes: [prod, add_1], Original ATen: [aten.prod, aten.add]
# Source node to ATen node mapping:
#   add_1 => add_1
#   prod => prod
# Graph fragment:
#   %prod : [num_users=1] = call_function[target=torch.ops.aten.prod.dim_int](args = (%pow_1, 1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%prod, %primals_3), kwargs = {})
triton_poi_fused_add_prod_1 = async_compile.triton('triton_poi_fused_add_prod_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=[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_add_prod_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_prod_1(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
    x2 = (xindex // 64)
    x3 = xindex % 64
    x1 = (xindex // 16) % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + (256*x2)), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x3 + (256*x2)), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x3 + (256*x2)), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x3 + (256*x2)), xmask)
    tmp7 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x4), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/5x/c5x6bvrnx7ypssnzbt5e5f2hyblgzqdfrxfbbeg2bi3d6xrd4a2w.py
# Topologically Sorted Source Nodes: [], Original ATen: [aten.ge]
# Source node to ATen node mapping:
# Graph fragment:
#   %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Scalar](args = (%primals_2, 0), kwargs = {})
triton_poi_fused_ge_2 = async_compile.triton('triton_poi_fused_ge_2', '''
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: '*i1', 2: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_ge_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_ge_2(in_ptr0, 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 = 0.0
    tmp2 = tmp0 >= tmp1
    tl.store(out_ptr0 + (x0), tmp2, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4, 1, 1), (1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_1], Original ATen: [aten.pow]
        stream0 = get_raw_stream(0)
        triton_poi_fused_pow_0.run(primals_1, primals_2, buf0, 1024, grid=grid(1024), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [prod, add_1], Original ATen: [aten.prod, aten.add]
        triton_poi_fused_add_prod_1.run(buf0, primals_3, buf1, 256, grid=grid(256), stream=stream0)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
        # Topologically Sorted Source Nodes: [], Original ATen: [aten.ge]
        triton_poi_fused_ge_2.run(primals_2, buf2, 16, grid=grid(16), stream=stream0)
        del primals_2
    return (buf1, primals_1, buf0, buf2, )


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, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 1, 1), (4, 1, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 1, 1), (1, 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.utils.data
import torch
from torch import nn


class Mult(nn.Module):

    def __init__(self, nc):
        super(Mult, self).__init__()
        self.register_parameter(name='exp', param=torch.nn.Parameter(torch.
            diag(torch.ones(nc)).unsqueeze(-1).unsqueeze(-1)))
        """self.register_parameter(name='weight',
                                param=torch.nn.Parameter(torch.ones(nc).unsqueeze(-1).unsqueeze(-1)))
                                """
        self.register_parameter(name='bias', param=torch.nn.Parameter(torch
            .zeros(nc).unsqueeze(-1).unsqueeze(-1)))
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.relu(x) + 0.1
        return x.unsqueeze(-3).pow(self.exp).prod(1) + self.bias


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


def get_init_inputs():
    return [[], {'nc': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.data
import torch
from torch import 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_pow_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x4 = xindex // 64
    x5 = xindex // 16 % 16
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x4), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = 0.1
    tmp4 = tmp2 + tmp3
    tmp6 = libdevice.pow(tmp4, tmp5)
    tl.store(out_ptr0 + x6, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_prod_1(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
    x2 = xindex // 64
    x3 = xindex % 64
    x1 = xindex // 16 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3 + 256 * x2), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x3 + 256 * x2), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x3 + 256 * x2), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x3 + 256 * x2), xmask)
    tmp7 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x4, tmp8, xmask)


@triton.jit
def triton_poi_fused_ge_2(in_ptr0, 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 = 0.0
    tmp2 = tmp0 >= tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 1, 1), (4, 1, 1, 1))
    assert_size_stride(primals_3, (4, 1, 1), (1, 1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pow_0[grid(1024)](primals_1, primals_2, buf0, 1024,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_prod_1[grid(256)](buf0, primals_3, buf1, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.bool)
        triton_poi_fused_ge_2[grid(16)](primals_2, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_2
    return buf1, primals_1, buf0, buf2


class MultNew(nn.Module):

    def __init__(self, nc):
        super(MultNew, self).__init__()
        self.register_parameter(name='exp', param=torch.nn.Parameter(torch.
            diag(torch.ones(nc)).unsqueeze(-1).unsqueeze(-1)))
        """self.register_parameter(name='weight',
                                param=torch.nn.Parameter(torch.ones(nc).unsqueeze(-1).unsqueeze(-1)))
                                """
        self.register_parameter(name='bias', param=torch.nn.Parameter(torch
            .zeros(nc).unsqueeze(-1).unsqueeze(-1)))
        self.relu = nn.ReLU()

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