# 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_8/inductor_cache/ns/cns2z7xwr4w7jrpieb56pibvm53qqpa4wkcuh23u52r6e6pqffvx.py
# Topologically Sorted Source Nodes: [W, mul, round_1, W_1], Original ATen: [aten.clamp, aten.mul, aten.round, aten.div]
# Source node to ATen node mapping:
#   W => clamp_max, clamp_min
#   W_1 => div
#   mul => mul
#   round_1 => round_1
# Graph fragment:
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%primals_1, -128), kwargs = {})
#   %clamp_max : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 128), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max, 128), kwargs = {})
#   %round_1 : [num_users=1] = call_function[target=torch.ops.aten.round.default](args = (%mul,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%round_1, 128), kwargs = {})
triton_poi_fused_clamp_div_mul_round_0 = async_compile.triton('triton_poi_fused_clamp_div_mul_round_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_clamp_div_mul_round_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_clamp_div_mul_round_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 = -128.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 128.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp4 * tmp3
    tmp6 = libdevice.nearbyint(tmp5)
    tmp7 = 0.0078125
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x0), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/gl/cgl5v6psyjnubzjer6zqobl6rzpwpbmtg5yuuv6mwmip56rhpa4o.py
# Topologically Sorted Source Nodes: [W_2, mul_1, round_2, W_3], Original ATen: [aten.clamp, aten.mul, aten.round, aten.div]
# Source node to ATen node mapping:
#   W_2 => clamp_max_1, clamp_min_1
#   W_3 => div_1
#   mul_1 => mul_1
#   round_2 => round_2
# Graph fragment:
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%primals_2, -128), kwargs = {})
#   %clamp_max_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 128), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max_1, 128), kwargs = {})
#   %round_2 : [num_users=1] = call_function[target=torch.ops.aten.round.default](args = (%mul_1,), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%round_2, 128), kwargs = {})
triton_poi_fused_clamp_div_mul_round_1 = async_compile.triton('triton_poi_fused_clamp_div_mul_round_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: '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_clamp_div_mul_round_1', '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_clamp_div_mul_round_1(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 = -128.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 128.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp4 * tmp3
    tmp6 = libdevice.nearbyint(tmp5)
    tmp7 = 0.0078125
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + (x0), tmp8, 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, 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, 1), torch.float32)
        # Topologically Sorted Source Nodes: [W, mul, round_1, W_1], Original ATen: [aten.clamp, aten.mul, aten.round, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clamp_div_mul_round_0.run(primals_1, buf0, 16, grid=grid(16), stream=stream0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [W_2, mul_1, round_2, W_3], Original ATen: [aten.clamp, aten.mul, aten.round, aten.div]
        triton_poi_fused_clamp_div_mul_round_1.run(primals_2, buf1, 256, grid=grid(256), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [rvalue], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf1, (64, 4), (4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_3
    return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), buf1, buf0, reinterpret_tensor(buf1, (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)
    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)

import math
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.cuda
import torch.distributed


def quantize_weights(W, numbits=8):
    W = W.clamp(-2 ** (numbits - 1), 2 ** (numbits - 1))
    W = W.mul(2 ** (numbits - 1)).round().div(2 ** (numbits - 1))
    return W


class quant_weights(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x):
        return quantize_weights(x)

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


class Linear(nn.modules.Linear):

    def __init__(self, in_features, out_features, bias=True):
        super(Linear, self).__init__(in_features, out_features)
        self.in_features = in_features
        self.out_features = out_features
        self.W_LR_scale = np.float32(1.0 / np.sqrt(1.5 / (in_features +
            out_features)))
        self.weight = Parameter(torch.Tensor(out_features, in_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward_t(self, input):
        return F.linear(input, self.weight, self.bias)

    def extra_repr(self):
        return 'in_features={}, out_features={}, bias={}'.format(self.
            in_features, self.out_features, self.bias is not None)

    def forward(self, input):
        Wr = self.weight.data
        self.Wb = quant_weights.apply(self.weight.data)
        self.input_b = quant_weights.apply(input)
        self.weight.data = self.Wb
        rvalue = self.forward_t(self.input_b)
        self.weight.data = Wr
        return rvalue

    def return_W_scale(self):
        return self.W_LR_scale


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


def get_init_inputs():
    return [[], {'in_features': 4, 'out_features': 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
import math
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn import Parameter
import torch.cuda
import torch.distributed
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_clamp_div_mul_round_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 = -128.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 128.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp4 * tmp3
    tmp6 = libdevice.nearbyint(tmp5)
    tmp7 = 0.0078125
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_clamp_div_mul_round_1(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 = -128.0
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = 128.0
    tmp4 = triton_helpers.minimum(tmp2, tmp3)
    tmp5 = tmp4 * tmp3
    tmp6 = libdevice.nearbyint(tmp5)
    tmp7 = 0.0078125
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = 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))
    assert_size_stride(primals_3, (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_clamp_div_mul_round_0[grid(16)](primals_1, buf0, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clamp_div_mul_round_1[grid(256)](primals_2, buf1, 
            256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf1, (64, 4), (
            4, 1), 0), reinterpret_tensor(buf0, (4, 4), (1, 4), 0), alpha=1,
            beta=1, out=buf2)
        del primals_3
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), buf1, buf0, reinterpret_tensor(buf1, (64, 4), (4, 1), 0)


def quantize_weights(W, numbits=8):
    W = W.clamp(-2 ** (numbits - 1), 2 ** (numbits - 1))
    W = W.mul(2 ** (numbits - 1)).round().div(2 ** (numbits - 1))
    return W


class quant_weights(torch.autograd.Function):

    @staticmethod
    def forward(ctx, x):
        return quantize_weights(x)

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


class LinearNew(nn.modules.Linear):

    def __init__(self, in_features, out_features, bias=True):
        super(LinearNew, self).__init__(in_features, out_features)
        self.in_features = in_features
        self.out_features = out_features
        self.W_LR_scale = np.float32(1.0 / np.sqrt(1.5 / (in_features +
            out_features)))
        self.weight = Parameter(torch.Tensor(out_features, in_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.weight.size(1))
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.uniform_(-stdv, stdv)

    def forward_t(self, input):
        return F.linear(input, self.weight, self.bias)

    def extra_repr(self):
        return 'in_features={}, out_features={}, bias={}'.format(self.
            in_features, self.out_features, self.bias is not None)

    def return_W_scale(self):
        return self.W_LR_scale

    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]
