# 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/b3/cb37iguppx2eoxitvn46oo7e7oz42hkvq7ryhcjen2434tmqgxth.py
# Topologically Sorted Source Nodes: [mul, mul_1, truediv, x], Original ATen: [aten.mul, aten.div, aten.sub]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   truediv => div
#   x => sub
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select, 2), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %view_5), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_1, %select_2), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, %div), kwargs = {})
triton_poi_fused_div_mul_sub_0 = async_compile.triton('triton_poi_fused_div_mul_sub_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_mul_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_div_mul_sub_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (0))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 - tmp8
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/n7/cn7wdcrkq3ubtqqcfgqw4rzkrl5pxw5sddwxxwrcpimstw2fw7va.py
# Topologically Sorted Source Nodes: [mul_2, mul_3, truediv_1, x_1], Original ATen: [aten.mul, aten.div, aten.sub]
# Source node to ATen node mapping:
#   mul_2 => mul_2
#   mul_3 => mul_3
#   truediv_1 => div_1
#   x_1 => sub_1
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_3, 2), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %view_7), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_3, %select_5), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub, %div_1), kwargs = {})
triton_poi_fused_div_mul_sub_1 = async_compile.triton('triton_poi_fused_div_mul_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_mul_sub_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_div_mul_sub_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (1))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 - tmp8
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/el/cel44ebm3ngf7gqpcctuo5zc2zmjocpghv5wfjcfq3ii3etw5u4o.py
# Topologically Sorted Source Nodes: [mul_4, mul_5, truediv_2, x_2], Original ATen: [aten.mul, aten.div, aten.sub]
# Source node to ATen node mapping:
#   mul_4 => mul_4
#   mul_5 => mul_5
#   truediv_2 => div_2
#   x_2 => sub_2
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_6, 2), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %view_9), kwargs = {})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_5, %select_8), kwargs = {})
#   %sub_2 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, %div_2), kwargs = {})
triton_poi_fused_div_mul_sub_2 = async_compile.triton('triton_poi_fused_div_mul_sub_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_div_mul_sub_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_div_mul_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (2))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 - tmp8
    tl.store(out_ptr0 + (x2), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/tq/ctqh5dmn6qjfr6e3lj6dns4j66q63yifqui56nix6le3qsmsvzn6.py
# Topologically Sorted Source Nodes: [mul_6, mul_7, truediv_3, x_3, add], Original ATen: [aten.mul, aten.div, aten.sub, aten.add]
# Source node to ATen node mapping:
#   add => add
#   mul_6 => mul_6
#   mul_7 => mul_7
#   truediv_3 => div_3
#   x_3 => sub_3
# Graph fragment:
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_9, 2), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_6, %view_11), kwargs = {})
#   %div_3 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul_7, %select_11), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_2, %div_3), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sub_3, %primals_3), kwargs = {})
triton_poi_fused_add_div_mul_sub_3 = async_compile.triton('triton_poi_fused_add_div_mul_sub_3', '''
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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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_mul_sub_3', '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_mul_sub_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + (3))
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp10 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 - tmp8
    tmp11 = tmp9 + tmp10
    tl.store(out_ptr0 + (x2), tmp11, 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, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [norms_sq], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 1, 4), (4, 4, 1), 0), reinterpret_tensor(primals_1, (4, 4, 1), (4, 1, 1), 0), out=buf0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 1), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, truediv, x], Original ATen: [aten.mul, aten.div, aten.sub]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_mul_sub_0.run(primals_2, primals_1, buf1, buf0, buf2, 256, grid=grid(256), stream=stream0)
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 1), 4), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_2, mul_3, truediv_1, x_1], Original ATen: [aten.mul, aten.div, aten.sub]
        triton_poi_fused_div_mul_sub_1.run(buf2, primals_1, buf3, buf0, buf4, 256, grid=grid(256), stream=stream0)
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 1), 8), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_4, mul_5, truediv_2, x_2], Original ATen: [aten.mul, aten.div, aten.sub]
        triton_poi_fused_div_mul_sub_2.run(buf4, primals_1, buf5, buf0, buf6, 256, grid=grid(256), stream=stream0)
        buf7 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf6, (64, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 1), 12), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_6, mul_7, truediv_3, x_3, add], Original ATen: [aten.mul, aten.div, aten.sub, aten.add]
        triton_poi_fused_add_div_mul_sub_3.run(buf6, primals_1, buf7, buf0, primals_3, buf8, 256, grid=grid(256), stream=stream0)
        del primals_3
    return (buf8, primals_1, buf1, reinterpret_tensor(buf0, (), (), 0), reinterpret_tensor(primals_1, (4, ), (1, ), 4), buf3, reinterpret_tensor(buf0, (), (), 1), reinterpret_tensor(primals_1, (4, ), (1, ), 8), buf5, reinterpret_tensor(buf0, (), (), 2), reinterpret_tensor(primals_1, (4, ), (1, ), 12), buf7, reinterpret_tensor(buf0, (), (), 3), reinterpret_tensor(buf6, (4, 64), (1, 4), 0), reinterpret_tensor(buf4, (4, 64), (1, 4), 0), reinterpret_tensor(buf2, (4, 64), (1, 4), 0), reinterpret_tensor(primals_2, (4, 64), (1, 4), 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 torch.nn as nn


class OrthogonalHouseholder(nn.Module):

    def __init__(self, sz, bias=True):
        super(OrthogonalHouseholder, self).__init__()
        self.sz = sz
        self.bias = bias
        self.A = nn.Parameter(torch.empty((sz, sz)))
        self.b = nn.Parameter(torch.empty(sz)) if bias else 0.0
        self.reset_parameters()

    def reset_parameters(self):
        with torch.no_grad():
            self.A.normal_(0, math.sqrt(2 / self.sz))
            if self.bias:
                self.b.fill_(0.0)

    def forward(self, x):
        norms_sq = torch.einsum('ij,ij->i', self.A, self.A)
        for i in range(self.sz):
            x = x - 2 * self.A[i] * (x @ self.A[i].unsqueeze(1)) / norms_sq[i]
        return x + self.b


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


def get_init_inputs():
    return [[], {'sz': 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
import 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_div_mul_sub_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 - tmp8
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_div_mul_sub_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + 1)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 - tmp8
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_div_mul_sub_2(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + 2)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 - tmp8
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_sub_3(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr3 + 3)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = 2.0
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 * tmp4
    tmp8 = tmp5 / tmp7
    tmp9 = tmp0 - tmp8
    tmp11 = tmp9 + tmp10
    tl.store(out_ptr0 + x2, tmp11, 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, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(primals_1, (4, 1, 4), (4, 4, 
            1), 0), reinterpret_tensor(primals_1, (4, 4, 1), (4, 1, 1), 0),
            out=buf0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1), (1, 1), 0), out=buf1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_mul_sub_0[grid(256)](primals_2, primals_1,
            buf1, buf0, buf2, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf3 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1), (1, 1), 4), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_div_mul_sub_1[grid(256)](buf2, primals_1, buf3,
            buf0, buf4, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1), (1, 1), 8), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_div_mul_sub_2[grid(256)](buf4, primals_1, buf5,
            buf0, buf6, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf6, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1), (1, 1), 12), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_sub_3[grid(256)](buf6, primals_1, buf7,
            buf0, primals_3, buf8, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_3
    return buf8, primals_1, buf1, reinterpret_tensor(buf0, (), (), 0
        ), reinterpret_tensor(primals_1, (4,), (1,), 4
        ), buf3, reinterpret_tensor(buf0, (), (), 1), reinterpret_tensor(
        primals_1, (4,), (1,), 8), buf5, reinterpret_tensor(buf0, (), (), 2
        ), reinterpret_tensor(primals_1, (4,), (1,), 12
        ), buf7, reinterpret_tensor(buf0, (), (), 3), reinterpret_tensor(buf6,
        (4, 64), (1, 4), 0), reinterpret_tensor(buf4, (4, 64), (1, 4), 0
        ), reinterpret_tensor(buf2, (4, 64), (1, 4), 0), reinterpret_tensor(
        primals_2, (4, 64), (1, 4), 0)


class OrthogonalHouseholderNew(nn.Module):

    def __init__(self, sz, bias=True):
        super(OrthogonalHouseholderNew, self).__init__()
        self.sz = sz
        self.bias = bias
        self.A = nn.Parameter(torch.empty((sz, sz)))
        self.b = nn.Parameter(torch.empty(sz)) if bias else 0.0
        self.reset_parameters()

    def reset_parameters(self):
        with torch.no_grad():
            self.A.normal_(0, math.sqrt(2 / self.sz))
            if self.bias:
                self.b.fill_(0.0)

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