# 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_6/inductor_cache/h5/ch5lfvnllcvazjuezszebfgon7p2ejca3vxfydnmq7vrxlqz74lc.py
# Topologically Sorted Source Nodes: [mul, output, truediv, sign, abs_1, add, floor, output_1, output_2], Original ATen: [aten.mul, aten.clamp, aten.div, aten.sign, aten.abs, aten.add, aten.floor]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   add => add
#   floor => floor
#   mul => mul
#   output => clamp_max, clamp_min
#   output_1 => mul_1
#   output_2 => mul_2
#   sign => sign
#   truediv => div
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_1, 0.1), kwargs = {})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%mul, 0), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 1), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%clamp_max, 0.00392156862745098), kwargs = {})
#   %sign : [num_users=1] = call_function[target=torch.ops.aten.sign.default](args = (%div,), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%div,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%abs_1, 0.5), kwargs = {})
#   %floor : [num_users=1] = call_function[target=torch.ops.aten.floor.default](args = (%add,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sign, %floor), kwargs = {})
#   %mul_2 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_1, 0.00392156862745098), kwargs = {})
triton_poi_fused_abs_add_clamp_div_floor_mul_sign_0 = async_compile.triton('triton_poi_fused_abs_add_clamp_div_floor_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.pointwise(
    size_hints=[256], 
    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_abs_add_clamp_div_floor_mul_sign_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_abs_add_clamp_div_floor_mul_sign_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    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 = 0.1
    tmp2 = tmp0 * tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 1.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = 255.0
    tmp8 = tmp6 * tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = tmp9 < tmp8
    tmp11 = tmp10.to(tl.int8)
    tmp12 = tmp8 < tmp9
    tmp13 = tmp12.to(tl.int8)
    tmp14 = tmp11 - tmp13
    tmp15 = tmp14.to(tmp8.dtype)
    tmp16 = tl_math.abs(tmp8)
    tmp17 = 0.5
    tmp18 = tmp16 + tmp17
    tmp19 = libdevice.floor(tmp18)
    tmp20 = tmp15 * tmp19
    tmp21 = 0.00392156862745098
    tmp22 = tmp20 * tmp21
    tl.store(out_ptr0 + (x0), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/cu/ccu2oa5e6uaucwepitulp5ppunndrp4etqw7x3565dbmjgvli4jk.py
# Topologically Sorted Source Nodes: [output_3, truediv_1, abs_2, max_1, truediv_2, output_4, truediv_3, output_5, output_6, mul_4, output_7], Original ATen: [aten.tanh, aten.div, aten.abs, aten.max, aten.add, aten.sign, aten.floor, aten.mul, aten.sub]
# Source node to ATen node mapping:
#   abs_2 => abs_2
#   max_1 => max_1
#   mul_4 => mul_5
#   output_3 => tanh
#   output_4 => add_1
#   output_5 => abs_3, add_2, floor_1, mul_3, sign_1
#   output_6 => mul_4
#   output_7 => sub
#   truediv_1 => div_1
#   truediv_2 => div_2
#   truediv_3 => div_3
# Graph fragment:
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%primals_2,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%tanh, 2), kwargs = {})
#   %abs_2 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%tanh,), kwargs = {})
#   %max_1 : [num_users=2] = call_function[target=torch.ops.aten.max.default](args = (%abs_2,), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%div_1, %max_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div_2, 0.5), kwargs = {})
#   %div_3 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%add_1, 0.00392156862745098), kwargs = {})
#   %sign_1 : [num_users=1] = call_function[target=torch.ops.aten.sign.default](args = (%div_3,), kwargs = {})
#   %abs_3 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%div_3,), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%abs_3, 0.5), kwargs = {})
#   %floor_1 : [num_users=1] = call_function[target=torch.ops.aten.floor.default](args = (%add_2,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sign_1, %floor_1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_3, 0.00392156862745098), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, 2), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_5, 1), kwargs = {})
triton_per_fused_abs_add_div_floor_max_mul_sign_sub_tanh_1 = async_compile.triton('triton_per_fused_abs_add_div_floor_max_mul_sign_sub_tanh_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_abs_add_div_floor_max_mul_sign_sub_tanh_1', '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_add_div_floor_max_mul_sign_sub_tanh_1(in_ptr0, out_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 = libdevice.tanh(tmp0)
    tmp2 = tl_math.abs(tmp1)
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tmp6 = 0.5
    tmp7 = tmp1 * tmp6
    tmp8 = tmp7 / tmp5
    tmp9 = tmp8 + tmp6
    tmp10 = 255.0
    tmp11 = tmp9 * tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = tmp12 < tmp11
    tmp14 = tmp13.to(tl.int8)
    tmp15 = tmp11 < tmp12
    tmp16 = tmp15.to(tl.int8)
    tmp17 = tmp14 - tmp16
    tmp18 = tmp17.to(tmp11.dtype)
    tmp19 = tl_math.abs(tmp11)
    tmp20 = tmp19 + tmp6
    tmp21 = libdevice.floor(tmp20)
    tmp22 = tmp18 * tmp21
    tmp23 = 0.00392156862745098
    tmp24 = tmp22 * tmp23
    tmp25 = 2.0
    tmp26 = tmp24 * tmp25
    tmp27 = 1.0
    tmp28 = tmp26 - tmp27
    tl.store(out_ptr1 + (tl.broadcast_to(r0, [RBLOCK])), tmp28, None)
    tl.store(out_ptr0 + (tl.full([1], 0, tl.int32)), tmp5, None)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/k2/ck2mamkqpmuzem4n3p4ij6fmfpy2bcbblg6sx6wwslgqwuqq5ifh.py
# Topologically Sorted Source Nodes: [output_8], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   output_8 => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%mul_2, %sub, %primals_3, [1, 1], [0, 0], [1, 1], False, [0, 0], 1), kwargs = {})
triton_poi_fused_convolution_2 = async_compile.triton('triton_poi_fused_convolution_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: '*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_convolution_2', 'mutated_arg_names': ['in_out_ptr0'], '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_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x2), 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, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, output, truediv, sign, abs_1, add, floor, output_1, output_2], Original ATen: [aten.mul, aten.clamp, aten.div, aten.sign, aten.abs, aten.add, aten.floor]
        stream0 = get_raw_stream(0)
        triton_poi_fused_abs_add_clamp_div_floor_mul_sign_0.run(primals_1, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [output_3, truediv_1, abs_2, max_1, truediv_2, output_4, truediv_3, output_5, output_6, mul_4, output_7], Original ATen: [aten.tanh, aten.div, aten.abs, aten.max, aten.add, aten.sign, aten.floor, aten.mul, aten.sub]
        triton_per_fused_abs_add_div_floor_max_mul_sign_sub_tanh_1.run(primals_2, buf1, buf2, 1, 256, grid=grid(1), stream=stream0)
        # Topologically Sorted Source Nodes: [output_8], Original ATen: [aten.convolution]
        buf3 = extern_kernels.convolution(buf0, buf2, stride=(1, 1), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 1, 1), (4, 1, 1, 1))
        buf4 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [output_8], Original ATen: [aten.convolution]
        triton_poi_fused_convolution_2.run(buf4, primals_3, 16, grid=grid(16), stream=stream0)
        del primals_3
    return (buf4, primals_2, buf0, buf1, 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, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, ), (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)

from torch.autograd import Function
import torch
import torch.nn as nn
import torch.nn.functional as F


class Round(Function):

    @staticmethod
    def forward(self, input):
        sign = torch.sign(input)
        output = sign * torch.floor(torch.abs(input) + 0.5)
        return output

    @staticmethod
    def backward(self, grad_output):
        grad_input = grad_output.clone()
        return grad_input


class ActivationQuantizer(nn.Module):

    def __init__(self, a_bits):
        super(ActivationQuantizer, self).__init__()
        self.a_bits = a_bits

    def round(self, input):
        output = Round.apply(input)
        return output

    def forward(self, input):
        if self.a_bits == 32:
            output = input
        elif self.a_bits == 1:
            None
            assert self.a_bits != 1
        else:
            output = torch.clamp(input * 0.1, 0, 1)
            scale = 1 / float(2 ** self.a_bits - 1)
            output = self.round(output / scale) * scale
        return output


class WeightQuantizer(nn.Module):

    def __init__(self, w_bits):
        super(WeightQuantizer, self).__init__()
        self.w_bits = w_bits

    def round(self, input):
        output = Round.apply(input)
        return output

    def forward(self, input):
        if self.w_bits == 32:
            output = input
        elif self.w_bits == 1:
            None
            assert self.w_bits != 1
        else:
            output = torch.tanh(input)
            output = output / 2 / torch.max(torch.abs(output)) + 0.5
            scale = 1 / float(2 ** self.w_bits - 1)
            output = self.round(output / scale) * scale
            output = 2 * output - 1
        return output


class QuantConv2d(nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros',
        a_bits=8, w_bits=8, quant_inference=False):
        super(QuantConv2d, self).__init__(in_channels, out_channels,
            kernel_size, stride, padding, dilation, groups, bias, padding_mode)
        self.quant_inference = quant_inference
        self.activation_quantizer = ActivationQuantizer(a_bits=a_bits)
        self.weight_quantizer = WeightQuantizer(w_bits=w_bits)

    def forward(self, input):
        quant_input = self.activation_quantizer(input)
        if not self.quant_inference:
            quant_weight = self.weight_quantizer(self.weight)
        else:
            quant_weight = self.weight
        output = F.conv2d(quant_input, quant_weight, self.bias, self.stride,
            self.padding, self.dilation, 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, '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 libdevice, math as tl_math
from torch.autograd import Function
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_poi_fused_abs_add_clamp_div_floor_mul_sign_0(in_ptr0, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    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 = 0.1
    tmp2 = tmp0 * tmp1
    tmp3 = 0.0
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 1.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = 255.0
    tmp8 = tmp6 * tmp7
    tmp9 = tl.full([1], 0, tl.int32)
    tmp10 = tmp9 < tmp8
    tmp11 = tmp10.to(tl.int8)
    tmp12 = tmp8 < tmp9
    tmp13 = tmp12.to(tl.int8)
    tmp14 = tmp11 - tmp13
    tmp15 = tmp14.to(tmp8.dtype)
    tmp16 = tl_math.abs(tmp8)
    tmp17 = 0.5
    tmp18 = tmp16 + tmp17
    tmp19 = libdevice.floor(tmp18)
    tmp20 = tmp15 * tmp19
    tmp21 = 0.00392156862745098
    tmp22 = tmp20 * tmp21
    tl.store(out_ptr0 + x0, tmp22, xmask)


@triton.jit
def triton_per_fused_abs_add_div_floor_max_mul_sign_sub_tanh_1(in_ptr0,
    out_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 = libdevice.tanh(tmp0)
    tmp2 = tl_math.abs(tmp1)
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tmp6 = 0.5
    tmp7 = tmp1 * tmp6
    tmp8 = tmp7 / tmp5
    tmp9 = tmp8 + tmp6
    tmp10 = 255.0
    tmp11 = tmp9 * tmp10
    tmp12 = tl.full([1], 0, tl.int32)
    tmp13 = tmp12 < tmp11
    tmp14 = tmp13.to(tl.int8)
    tmp15 = tmp11 < tmp12
    tmp16 = tmp15.to(tl.int8)
    tmp17 = tmp14 - tmp16
    tmp18 = tmp17.to(tmp11.dtype)
    tmp19 = tl_math.abs(tmp11)
    tmp20 = tmp19 + tmp6
    tmp21 = libdevice.floor(tmp20)
    tmp22 = tmp18 * tmp21
    tmp23 = 0.00392156862745098
    tmp24 = tmp22 * tmp23
    tmp25 = 2.0
    tmp26 = tmp24 * tmp25
    tmp27 = 1.0
    tmp28 = tmp26 - tmp27
    tl.store(out_ptr1 + tl.broadcast_to(r0, [RBLOCK]), tmp28, None)
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp5, None)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, 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, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_abs_add_clamp_div_floor_mul_sign_0[grid(256)](
            primals_1, buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_per_fused_abs_add_div_floor_max_mul_sign_sub_tanh_1[grid(1)](
            primals_2, buf1, buf2, 1, 256, num_warps=2, num_stages=1)
        buf3 = extern_kernels.convolution(buf0, buf2, stride=(1, 1),
            padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf3, (4, 4, 1, 1), (4, 1, 1, 1))
        buf4 = buf3
        del buf3
        triton_poi_fused_convolution_2[grid(16)](buf4, primals_3, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
    return buf4, primals_2, buf0, buf1, buf2


class Round(Function):

    @staticmethod
    def forward(self, input):
        sign = torch.sign(input)
        output = sign * torch.floor(torch.abs(input) + 0.5)
        return output

    @staticmethod
    def backward(self, grad_output):
        grad_input = grad_output.clone()
        return grad_input


class ActivationQuantizer(nn.Module):

    def __init__(self, a_bits):
        super(ActivationQuantizer, self).__init__()
        self.a_bits = a_bits

    def round(self, input):
        output = Round.apply(input)
        return output

    def forward(self, input):
        if self.a_bits == 32:
            output = input
        elif self.a_bits == 1:
            None
            assert self.a_bits != 1
        else:
            output = torch.clamp(input * 0.1, 0, 1)
            scale = 1 / float(2 ** self.a_bits - 1)
            output = self.round(output / scale) * scale
        return output


class WeightQuantizer(nn.Module):

    def __init__(self, w_bits):
        super(WeightQuantizer, self).__init__()
        self.w_bits = w_bits

    def round(self, input):
        output = Round.apply(input)
        return output

    def forward(self, input):
        if self.w_bits == 32:
            output = input
        elif self.w_bits == 1:
            None
            assert self.w_bits != 1
        else:
            output = torch.tanh(input)
            output = output / 2 / torch.max(torch.abs(output)) + 0.5
            scale = 1 / float(2 ** self.w_bits - 1)
            output = self.round(output / scale) * scale
            output = 2 * output - 1
        return output


class QuantConv2dNew(nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
        padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros',
        a_bits=8, w_bits=8, quant_inference=False):
        super(QuantConv2dNew, self).__init__(in_channels, out_channels,
            kernel_size, stride, padding, dilation, groups, bias, padding_mode)
        self.quant_inference = quant_inference
        self.activation_quantizer = ActivationQuantizer(a_bits=a_bits)
        self.weight_quantizer = WeightQuantizer(w_bits=w_bits)

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