# 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/yh/cyhgnoeeispklcpege34eknxzhelhxbgilmkig4obsayv4gvmkeo.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.sign, aten.abs, aten.mean, aten.mul]
# Source node to ATen node mapping:
#   x => abs_1, mean, mul, sign
# Graph fragment:
#   %sign : [num_users=1] = call_function[target=torch.ops.aten.sign.default](args = (%primals_1,), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%primals_1,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%abs_1,), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sign, %mean), kwargs = {})
triton_per_fused_abs_mean_mul_sign_0 = async_compile.triton('triton_per_fused_abs_mean_mul_sign_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    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': {2: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 3), equal_to_1=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_mean_mul_sign_0', 'mutated_arg_names': [], 'no_x_dim': True, 'num_load': 1, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_abs_mean_mul_sign_0(in_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), None)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = triton_helpers.promote_to_tensor(tl.sum(tmp2, 0))
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = tmp5 < tmp0
    tmp7 = tmp6.to(tl.int8)
    tmp8 = tmp0 < tmp5
    tmp9 = tmp8.to(tl.int8)
    tmp10 = tmp7 - tmp9
    tmp11 = tmp10.to(tmp0.dtype)
    tmp12 = 256.0
    tmp13 = tmp4 / tmp12
    tmp14 = tmp11 * tmp13
    tl.store(out_ptr1 + (tl.broadcast_to(r0, [RBLOCK])), tmp14, None)
''', 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, 4), (64, 16, 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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.sign, aten.abs, aten.mean, aten.mul]
        stream0 = get_raw_stream(0)
        triton_per_fused_abs_mean_mul_sign_0.run(primals_1, buf1, 1, 256, grid=grid(1), stream=stream0)
        del primals_1
        # Topologically Sorted Source Nodes: [output], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(primals_2, buf1, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
    return (buf2, primals_2, buf1, )


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, 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
import torch.nn as nn
import torch.nn.functional as F


def quantize_w(x):
    x = Q_W.apply(x)
    return x


def fw(x, bitW):
    if bitW == 32:
        return x
    x = quantize_w(x)
    return x


class Q_W(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x):
        return x.sign() * x.abs().mean()

    @staticmethod
    def backward(ctx, grad):
        return grad


class self_conv(nn.Conv2d):

    def __init__(self, in_channels, out_channels, bitW, kernel_size, stride
        =1, padding=0, bias=False):
        super(self_conv, self).__init__(in_channels, out_channels,
            kernel_size, stride=stride, padding=padding, bias=bias)
        self.bitW = bitW
        self.padding = padding
        self.stride = stride

    def forward(self, input):
        if self.padding > 0:
            padding_shape = (self.padding, self.padding, self.padding, self
                .padding)
            input = F.pad(input, padding_shape, 'constant', 1)
        output = F.conv2d(input, fw(self.weight, self.bitW), bias=self.bias,
            stride=self.stride, dilation=self.dilation, groups=self.groups)
        return output


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


def get_init_inputs():
    return [[], {'in_channels': 4, 'out_channels': 4, 'bitW': 4,
        'kernel_size': 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 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


@triton.jit
def triton_per_fused_abs_mean_mul_sign_0(in_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = triton_helpers.promote_to_tensor(tl.sum(tmp2, 0))
    tmp5 = tl.full([1], 0, tl.int32)
    tmp6 = tmp5 < tmp0
    tmp7 = tmp6.to(tl.int8)
    tmp8 = tmp0 < tmp5
    tmp9 = tmp8.to(tl.int8)
    tmp10 = tmp7 - tmp9
    tmp11 = tmp10.to(tmp0.dtype)
    tmp12 = 256.0
    tmp13 = tmp4 / tmp12
    tmp14 = tmp11 * tmp13
    tl.store(out_ptr1 + tl.broadcast_to(r0, [RBLOCK]), tmp14, None)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_abs_mean_mul_sign_0[grid(1)](primals_1, buf1, 1, 
            256, num_warps=2, num_stages=1)
        del primals_1
        buf2 = extern_kernels.convolution(primals_2, buf1, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 1, 1), (4, 1, 1, 1))
    return buf2, primals_2, buf1


def quantize_w(x):
    x = Q_W.apply(x)
    return x


def fw(x, bitW):
    if bitW == 32:
        return x
    x = quantize_w(x)
    return x


class Q_W(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x):
        return x.sign() * x.abs().mean()

    @staticmethod
    def backward(ctx, grad):
        return grad


class self_convNew(nn.Conv2d):

    def __init__(self, in_channels, out_channels, bitW, kernel_size, stride
        =1, padding=0, bias=False):
        super(self_convNew, self).__init__(in_channels, out_channels,
            kernel_size, stride=stride, padding=padding, bias=bias)
        self.bitW = bitW
        self.padding = padding
        self.stride = stride

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