# 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/gy/cgyeivaj5xohig6bokzkqeo2uatkrsikluo6qxgc3gxiv2okwbcm.py
# Topologically Sorted Source Nodes: [W], Original ATen: [aten.sigmoid]
# Source node to ATen node mapping:
#   W => sigmoid
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%primals_1,), kwargs = {})
triton_poi_fused_sigmoid_0 = async_compile.triton('triton_poi_fused_sigmoid_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 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_sigmoid_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_sigmoid_0(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 = tl.sigmoid(tmp0)
    tl.store(out_ptr0 + (x0), tmp1, 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, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [W], Original ATen: [aten.sigmoid]
        stream0 = get_raw_stream(0)
        triton_poi_fused_sigmoid_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0)
        buf1 = 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(buf0, (4, 4), (1, 4), 0), out=buf1)
        del buf0
    return (reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_1, reinterpret_tensor(primals_2, (64, 4), (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((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)
    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
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
import collections
import torch.utils.data
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_sigmoid_0(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 = tl.sigmoid(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


def call(args):
    primals_1, primals_2 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_sigmoid_0[grid(16)](primals_1, buf0, 16, XBLOCK=16,
            num_warps=1, num_stages=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(buf0, (4, 4), (1, 4), 0), out=buf1)
        del buf0
    return reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_1, reinterpret_tensor(primals_2, (64, 4), (4, 1), 0)


def sparsity_error(W):
    W_error = torch.min(torch.abs(W), torch.abs(1 - torch.abs(W)))
    return torch.max(W_error)


class SummaryWriterNamespaceNoLoggingScope:

    def __init__(self, writer):
        self._writer = writer

    def __enter__(self):
        self._writer._logging_enabled = False

    def __exit__(self, type, value, traceback):
        self._writer._logging_enabled = True
        return False


class DummySummaryWriter:

    def __init__(self, **kwargs):
        self._logging_enabled = False
        pass

    def add_scalar(self, name, value, verbose_only=True):
        pass

    def add_summary(self, name, tensor, verbose_only=True):
        pass

    def add_histogram(self, name, tensor, verbose_only=True):
        pass

    def add_tensor(self, name, tensor, verbose_only=True):
        pass

    def print(self, name, tensor, verbose_only=True):
        pass

    def namespace(self, name):
        return self

    def every(self, epoch_interval):
        return self

    def verbose(self, verbose):
        return self

    def no_logging(self):
        return SummaryWriterNamespaceNoLoggingScope(self)


class NoRandomScope:

    def __init__(self, module):
        self._module = module

    def __enter__(self):
        self._module._disable_random()

    def __exit__(self, type, value, traceback):
        self._module._enable_random()
        return False


class ExtendedTorchModule(torch.nn.Module):

    def __init__(self, default_name, *args, writer=None, name=None, **kwargs):
        super().__init__()
        if writer is None:
            writer = DummySummaryWriter()
        self.writer = writer.namespace(default_name if name is None else name)
        self.allow_random = True

    def set_parameter(self, name, value):
        parameter = getattr(self, name, None)
        if isinstance(parameter, torch.nn.Parameter):
            parameter.fill_(value)
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module.set_parameter(name, value)

    def regualizer(self, merge_in=None):
        regualizers = collections.defaultdict(int)
        if merge_in is not None:
            for key, value in merge_in.items():
                self.writer.add_scalar(f'regualizer/{key}', value)
                regualizers[key] += value
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                for key, value in module.regualizer().items():
                    regualizers[key] += value
        return regualizers

    def optimize(self, loss):
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module.optimize(loss)

    def log_gradients(self):
        for name, parameter in self.named_parameters(recurse=False):
            if parameter.requires_grad:
                gradient, *_ = parameter.grad.data
                self.writer.add_summary(f'{name}/grad', gradient)
                self.writer.add_histogram(f'{name}/grad', gradient)
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module.log_gradients()

    def no_internal_logging(self):
        return self.writer.no_logging()

    def _disable_random(self):
        self.allow_random = False
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module._disable_random()

    def _enable_random(self):
        self.allow_random = True
        for module in self.children():
            if isinstance(module, ExtendedTorchModule):
                module._enable_random()

    def no_random(self):
        return NoRandomScope(self)


class PosNACLayerNew(ExtendedTorchModule):
    """Implements the NAC (Neural Accumulator)

    Arguments:
        in_features: number of ingoing features
        out_features: number of outgoing features
    """

    def __init__(self, in_features, out_features, **kwargs):
        super().__init__('nac', **kwargs)
        self.in_features = in_features
        self.out_features = out_features
        self.W_hat = torch.nn.Parameter(torch.Tensor(out_features, in_features)
            )
        self.register_parameter('bias', None)

    def reset_parameters(self):
        torch.nn.init.xavier_normal_(self.W_hat)

    def extra_repr(self):
        return 'in_features={}, out_features={}'.format(self.in_features,
            self.out_features)

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