# 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/j2/cj2nf7m36iodxg6bagn7ostrasaexmiy7b3qvqy2fxyprxsalfwt.py
# Topologically Sorted Source Nodes: [], Original ATen: [aten.full]
# Source node to ATen node mapping:
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float64, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_full_0 = async_compile.triton('triton_poi_fused_full_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=[1], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp64', 1: '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': {1: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=(1,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_full_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_full_0(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.full([1], 0.0, tl.float64)
    tl.store(out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp0, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/el/celea7ezymt2wd7zgg6hdmpdspmn3coo7pthct7mxpnf5xoq5ix4.py
# Topologically Sorted Source Nodes: [], Original ATen: [aten.full]
# Source node to ATen node mapping:
# Graph fragment:
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1e-15), kwargs = {dtype: torch.float64, layout: torch.strided, device: cuda:0, pin_memory: False})
triton_poi_fused_full_1 = async_compile.triton('triton_poi_fused_full_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=[1], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp64', 1: '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': {1: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0,), equal_to_1=(1,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_full_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 0, '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_full_1(out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.full([1], 1e-15, tl.float64)
    tl.store(out_ptr0 + (tl.full([XBLOCK], 0, tl.int32)), tmp0, None)
''', 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, 1))
    assert_size_stride(primals_3, (4, 4), (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: [autograd_function_apply], Original ATen: [aten.mm]
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((), (), torch.float64)
        # Topologically Sorted Source Nodes: [], Original ATen: [aten.full]
        stream0 = get_raw_stream(0)
        triton_poi_fused_full_0.run(buf1, 1, grid=grid(1), stream=stream0)
        buf2 = empty_strided_cuda((), (), torch.float64)
        # Topologically Sorted Source Nodes: [], Original ATen: [aten.full]
        triton_poi_fused_full_1.run(buf2, 1, grid=grid(1), stream=stream0)
        # Topologically Sorted Source Nodes: [], Original ATen: [aten.full, aten.linalg_pinv]
        buf3 = torch.ops.aten.linalg_pinv.atol_rtol_tensor(primals_3, atol=buf1, rtol=buf2)
        del buf1
        del buf2
        del primals_3
        buf4 = buf3
        del buf3
    return (buf0, primals_1, buf0, buf4, )


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, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (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
from torch import nn
from torch import autograd
from torch.nn import init


class ExtremeLinearFunction(autograd.Function):

    @staticmethod
    def forward(ctx, input, forward_weight, feedback_weight):
        ctx.save_for_backward(input, forward_weight, feedback_weight)
        output = input.mm(forward_weight.t())
        ctx.output = output
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input, forward_weight, _feedback_weight = ctx.saved_tensors
        grad_input = grad_weight = None
        if ctx.needs_input_grad[1]:
            inv_x = torch.pinverse(input)
            error_weight = inv_x.mm(ctx.output - grad_output)
            grad_weight = forward_weight - error_weight.t()
        if ctx.needs_input_grad[0]:
            grad_input = grad_output.mm(forward_weight)
        return grad_input, grad_weight, None


class ExtremeLinear(nn.Module):

    def __init__(self, input_features, output_features):
        super(ExtremeLinear, self).__init__()
        self.input_features = input_features
        self.output_features = output_features
        self.forward_weight = nn.Parameter(torch.Tensor(output_features,
            input_features))
        self.feedback_weight = nn.Parameter(torch.Tensor(output_features,
            input_features))
        self.reset_parameters()

    def reset_parameters(self) ->None:
        init.kaiming_uniform_(self.forward_weight, a=math.sqrt(5))
        init.kaiming_uniform_(self.feedback_weight, a=math.sqrt(5))

    def forward(self, input):
        return ExtremeLinearFunction.apply(input, self.forward_weight, self
            .feedback_weight)


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


def get_init_inputs():
    return [[], {'input_features': 4, 'output_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
import math
from torch import nn
from torch import autograd
from torch.nn import init
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_full_0(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.full([1], 0.0, tl.float64)
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp0, None)


@triton.jit
def triton_poi_fused_full_1(out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    tmp0 = tl.full([1], 1e-15, tl.float64)
    tl.store(out_ptr0 + tl.full([XBLOCK], 0, tl.int32), tmp0, None)


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, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_3, reinterpret_tensor(primals_1, (4, 4),
            (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((), (), torch.float64)
        get_raw_stream(0)
        triton_poi_fused_full_0[grid(1)](buf1, 1, XBLOCK=1, num_warps=1,
            num_stages=1)
        buf2 = empty_strided_cuda((), (), torch.float64)
        triton_poi_fused_full_1[grid(1)](buf2, 1, XBLOCK=1, num_warps=1,
            num_stages=1)
        buf3 = torch.ops.aten.linalg_pinv.atol_rtol_tensor(primals_3, atol=
            buf1, rtol=buf2)
        del buf1
        del buf2
        del primals_3
        buf4 = buf3
        del buf3
    return buf0, primals_1, buf0, buf4


class ExtremeLinearFunction(autograd.Function):

    @staticmethod
    def forward(ctx, input, forward_weight, feedback_weight):
        ctx.save_for_backward(input, forward_weight, feedback_weight)
        output = input.mm(forward_weight.t())
        ctx.output = output
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input, forward_weight, _feedback_weight = ctx.saved_tensors
        grad_input = grad_weight = None
        if ctx.needs_input_grad[1]:
            inv_x = torch.pinverse(input)
            error_weight = inv_x.mm(ctx.output - grad_output)
            grad_weight = forward_weight - error_weight.t()
        if ctx.needs_input_grad[0]:
            grad_input = grad_output.mm(forward_weight)
        return grad_input, grad_weight, None


class ExtremeLinearNew(nn.Module):

    def __init__(self, input_features, output_features):
        super(ExtremeLinearNew, self).__init__()
        self.input_features = input_features
        self.output_features = output_features
        self.forward_weight = nn.Parameter(torch.Tensor(output_features,
            input_features))
        self.feedback_weight = nn.Parameter(torch.Tensor(output_features,
            input_features))
        self.reset_parameters()

    def reset_parameters(self) ->None:
        init.kaiming_uniform_(self.forward_weight, a=math.sqrt(5))
        init.kaiming_uniform_(self.feedback_weight, a=math.sqrt(5))

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