# 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/yd/cydbtjoq352gcolmflbvu2nqkda7xg7q5hnvltb47jsg5dbmubym.py
# Topologically Sorted Source Nodes: [linear], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   linear => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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=[16, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', '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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/qu/cqukmrxa6etdpgvb2u23hdcpcjzslz6rrkiupkx3762mhtweeoww.py
# Topologically Sorted Source Nodes: [alpha, delta, rho, add, m, add_1, pow_1, truediv, add_2, pow_2, pow_3, pcen_out], Original ATen: [aten.exp, aten.add, aten.relu, aten.pow, aten.div, aten.sub]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   add_2 => add_2
#   alpha => exp
#   delta => exp_1
#   m => relu
#   pcen_out => sub
#   pow_1 => pow_1
#   pow_2 => pow_2
#   pow_3 => pow_3
#   rho => exp_2
#   truediv => div
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%expand,), kwargs = {})
#   %exp_1 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%expand_1,), kwargs = {})
#   %exp_2 : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%expand_2,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%permute_3, %primals_2), kwargs = {})
#   %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%add,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%relu, 0.1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Tensor](args = (%add_1, %exp), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%primals_2, %pow_1), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, %exp_1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Tensor](args = (%add_2, %exp_2), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Tensor](args = (%exp_1, %exp_2), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%pow_2, %pow_3), kwargs = {})
triton_poi_fused_add_div_exp_pow_relu_sub_1 = async_compile.triton('triton_poi_fused_add_div_exp_pow_relu_sub_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, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: '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, 5, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_exp_pow_relu_sub_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_div_exp_pow_relu_sub_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2 + (4*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + (x2), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr3 + (x2), xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + (x2), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.1
    tmp6 = tmp4 + tmp5
    tmp8 = tl_math.exp(tmp7)
    tmp9 = libdevice.pow(tmp6, tmp8)
    tmp10 = tmp0 / tmp9
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp15 = tl_math.exp(tmp14)
    tmp16 = libdevice.pow(tmp13, tmp15)
    tmp17 = libdevice.pow(tmp12, tmp15)
    tmp18 = tmp16 - tmp17
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp18, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, ), (1, ))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    assert_size_stride(primals_6, (4, 1), (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: [linear], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(primals_2, buf0, 16, 4, grid=grid(16, 4), stream=stream0)
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(buf1, reinterpret_tensor(primals_6, (1, 4), (1, 1), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [alpha, delta, rho, add, m, add_1, pow_1, truediv, add_2, pow_2, pow_3, pcen_out], Original ATen: [aten.exp, aten.add, aten.relu, aten.pow, aten.div, aten.sub]
        triton_poi_fused_add_div_exp_pow_relu_sub_1.run(primals_2, buf2, primals_1, primals_3, primals_4, buf3, 16, 4, grid=grid(16, 4), stream=stream0)
    return (buf3, primals_1, primals_2, primals_3, primals_4, reinterpret_tensor(buf0, (16, 4), (4, 1), 0), buf1, buf2, primals_6, )


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, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 1), (1, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6])
    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 PCENlr(nn.Module):
    """
        A Low-rank version for per-channel energy normalization.
    """

    def __init__(self, N, T):
        super(PCENlr, self).__init__()
        self.N = N
        self.T = T
        self.lr_enc = nn.Linear(self.T, 1, bias=False)
        self.lr_dec = nn.Linear(1, self.T, bias=False)
        self.log_alpha = nn.Parameter((torch.randn(self.N) * 0.1 + 1.0).log_())
        self.log_delta = nn.Parameter((torch.randn(self.N) * 0.1 + 2.0).log_())
        self.log_rho = nn.Parameter((torch.randn(self.N) * 0.1 + 0.6).log_())
        self.relu = nn.ReLU()
        self.eps = 0.1
        self.initialize_parameters()

    def initialize_parameters(self):
        torch.nn.init.xavier_normal(self.lr_enc.weight)

    def forward(self, x):
        alpha = self.log_alpha.expand_as(x).exp()
        delta = self.log_delta.expand_as(x).exp()
        rho = self.log_rho.expand_as(x).exp()
        m = self.relu(self.lr_dec(self.lr_enc(x.permute(0, 2, 1))).permute(
            0, 2, 1) + x)
        pcen_out = (x / (m + self.eps) ** alpha + delta) ** rho - delta ** rho
        return pcen_out


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


def get_init_inputs():
    return [[], {'N': 4, 'T': 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
from torch._inductor.runtime.triton_helpers import libdevice, 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_clone_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_div_exp_pow_relu_sub_1(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr,
    XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + x2, xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr3 + x2, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.1
    tmp6 = tmp4 + tmp5
    tmp8 = tl_math.exp(tmp7)
    tmp9 = libdevice.pow(tmp6, tmp8)
    tmp10 = tmp0 / tmp9
    tmp12 = tl_math.exp(tmp11)
    tmp13 = tmp10 + tmp12
    tmp15 = tl_math.exp(tmp14)
    tmp16 = libdevice.pow(tmp13, tmp15)
    tmp17 = libdevice.pow(tmp12, tmp15)
    tmp18 = tmp16 - tmp17
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp18, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4,), (1,))
    assert_size_stride(primals_5, (1, 4), (4, 1))
    assert_size_stride(primals_6, (4, 1), (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_clone_0[grid(16, 4)](primals_2, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((16, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 1), (1, 4), 0), out=buf1)
        del primals_5
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf1, reinterpret_tensor(primals_6, (1, 4), (1, 1
            ), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_div_exp_pow_relu_sub_1[grid(16, 4)](primals_2,
            buf2, primals_1, primals_3, primals_4, buf3, 16, 4, XBLOCK=4,
            YBLOCK=8, num_warps=1, num_stages=1)
    return (buf3, primals_1, primals_2, primals_3, primals_4,
        reinterpret_tensor(buf0, (16, 4), (4, 1), 0), buf1, buf2, primals_6)


class PCENlrNew(nn.Module):
    """
        A Low-rank version for per-channel energy normalization.
    """

    def __init__(self, N, T):
        super(PCENlrNew, self).__init__()
        self.N = N
        self.T = T
        self.lr_enc = nn.Linear(self.T, 1, bias=False)
        self.lr_dec = nn.Linear(1, self.T, bias=False)
        self.log_alpha = nn.Parameter((torch.randn(self.N) * 0.1 + 1.0).log_())
        self.log_delta = nn.Parameter((torch.randn(self.N) * 0.1 + 2.0).log_())
        self.log_rho = nn.Parameter((torch.randn(self.N) * 0.1 + 0.6).log_())
        self.relu = nn.ReLU()
        self.eps = 0.1
        self.initialize_parameters()

    def initialize_parameters(self):
        torch.nn.init.xavier_normal(self.lr_enc.weight)

    def forward(self, input_0):
        primals_1 = self.log_alpha
        primals_3 = self.log_delta
        primals_4 = self.log_rho
        primals_5 = self.lr_enc.weight
        primals_6 = self.lr_dec.weight
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
