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
import math
from torch import nn
import torch.nn.functional as F
import torch.cuda
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.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_add_relu_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, None)
    tmp4 = tl.load(in_ptr2 + x0, None, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x2, None)
    tmp8 = tl.load(in_ptr4 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tl.store(in_out_ptr0 + x2, tmp12, None)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 1024
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13) = args
    args.clear()
    assert_size_stride(primals_1, (4, 3, 1024), (3072, 1024, 1))
    assert_size_stride(primals_2, (1024, 1024), (1024, 1))
    assert_size_stride(primals_3, (1024,), (1,))
    assert_size_stride(primals_4, (1024, 1024), (1024, 1))
    assert_size_stride(primals_5, (1024,), (1,))
    assert_size_stride(primals_6, (1024, 1024), (1024, 1))
    assert_size_stride(primals_7, (1024,), (1,))
    assert_size_stride(primals_8, (1024, 1024), (1024, 1))
    assert_size_stride(primals_9, (1024,), (1,))
    assert_size_stride(primals_10, (1024, 1024), (1024, 1))
    assert_size_stride(primals_11, (1024,), (1,))
    assert_size_stride(primals_12, (10, 1024), (1024, 1))
    assert_size_stride(primals_13, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 1024), (3072, 1
            ), 0), reinterpret_tensor(primals_2, (1024, 1024), (1, 1024), 0
            ), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 1024), (3072, 1
            ), 1024), reinterpret_tensor(primals_4, (1024, 1024), (1, 1024),
            0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 1024), (1024, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (4, 1024), (3072, 1
            ), 2048), reinterpret_tensor(primals_6, (1024, 1024), (1, 1024),
            0), out=buf2)
        del primals_6
        buf3 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_relu_0[grid(4096)](buf3, primals_3, buf1,
            primals_5, buf2, primals_7, 4096, XBLOCK=256, num_warps=4,
            num_stages=1)
        del primals_3
        del primals_5
        del primals_7
        buf4 = buf2
        del buf2
        extern_kernels.mm(buf3, reinterpret_tensor(primals_8, (1024, 1024),
            (1, 1024), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_relu_1[grid(4096)](buf5, primals_9, 4096, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_9
        buf6 = buf1
        del buf1
        extern_kernels.mm(buf5, reinterpret_tensor(primals_10, (1024, 1024),
            (1, 1024), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_relu_1[grid(4096)](buf7, primals_11, 4096, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_11
        buf8 = empty_strided_cuda((4, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_13, buf7, reinterpret_tensor(
            primals_12, (1024, 10), (1, 1024), 0), alpha=1, beta=1, out=buf8)
        del primals_13
    return buf8, reinterpret_tensor(primals_1, (4, 1024), (3072, 1), 0
        ), reinterpret_tensor(primals_1, (4, 1024), (3072, 1), 1024
        ), reinterpret_tensor(primals_1, (4, 1024), (3072, 1), 2048
        ), buf3, buf5, buf7, primals_12, primals_10, primals_8


class Butterfly(nn.Module):
    """Product of log N butterfly factors, each is a block 2x2 of diagonal matrices.
    Compatible with torch.nn.Linear.

    Parameters:
        in_size: size of input
        out_size: size of output
        bias: If set to False, the layer will not learn an additive bias.
                Default: ``True``
        complex: whether complex or real
        increasing_stride: whether the first butterfly block will multiply with increasing stride
            (e.g. 1, 2, ..., n/2) or decreasing stride (e.g., n/2, n/4, ..., 1).
        init: 'randn', 'ortho', or 'identity'. Whether the weight matrix should be initialized to
            from randn twiddle, or to be randomly orthogonal/unitary, or to be the identity matrix.
        nblocks: number of B or B^T blocks. The B and B^T will alternate.
    """

    def __init__(self, in_size, out_size, bias=True, complex=False,
        increasing_stride=True, init='randn', nblocks=1):
        super().__init__()
        self.in_size = in_size
        log_n = int(math.ceil(math.log2(in_size)))
        self.log_n = log_n
        size = self.in_size_extended = 1 << log_n
        self.out_size = out_size
        self.nstacks = int(math.ceil(out_size / self.in_size_extended))
        self.complex = complex
        self.increasing_stride = increasing_stride
        assert nblocks >= 1
        self.nblocks = nblocks
        dtype = torch.get_default_dtype(
            ) if not self.complex else real_dtype_to_complex[torch.
            get_default_dtype()]
        twiddle_shape = self.nstacks, nblocks, log_n, size // 2, 2, 2
        assert init in ['randn', 'ortho', 'identity']
        self.init = init
        self.twiddle = nn.Parameter(torch.empty(twiddle_shape, dtype=dtype))
        if bias:
            self.bias = nn.Parameter(torch.empty(out_size, dtype=dtype))
        else:
            self.register_parameter('bias', None)
        self.twiddle._is_structured = True
        if self.complex:
            self.twiddle = nn.Parameter(view_as_real(self.twiddle))
            if self.bias is not None:
                self.bias = nn.Parameter(view_as_real(self.bias))
        self.reset_parameters()

    def reset_parameters(self):
        """Initialize bias the same way as torch.nn.Linear."""
        twiddle = self.twiddle if not self.complex else view_as_complex(self
            .twiddle)
        if self.init == 'randn':
            scaling = 1.0 / math.sqrt(2)
            with torch.no_grad():
                twiddle.copy_(torch.randn(twiddle.shape, dtype=twiddle.
                    dtype) * scaling)
        elif self.init == 'ortho':
            twiddle_core_shape = twiddle.shape[:-2]
            if not self.complex:
                theta = torch.rand(twiddle_core_shape) * math.pi * 2
                c, s = torch.cos(theta), torch.sin(theta)
                det = torch.randint(0, 2, twiddle_core_shape, dtype=c.dtype
                    ) * 2 - 1
                with torch.no_grad():
                    twiddle.copy_(torch.stack((torch.stack((det * c, -det *
                        s), dim=-1), torch.stack((s, c), dim=-1)), dim=-2))
            else:
                phi = torch.asin(torch.sqrt(torch.rand(twiddle_core_shape)))
                c, s = torch.cos(phi), torch.sin(phi)
                alpha, psi, chi = torch.rand((3,) + twiddle_core_shape
                    ) * math.pi * 2
                A = torch.exp(1.0j * (alpha + psi)) * c
                B = torch.exp(1.0j * (alpha + chi)) * s
                C = -torch.exp(1.0j * (alpha - chi)) * s
                D = torch.exp(1.0j * (alpha - psi)) * c
                with torch.no_grad():
                    twiddle.copy_(torch.stack((torch.stack((A, B), dim=-1),
                        torch.stack((C, D), dim=-1)), dim=-2))
        elif self.init == 'identity':
            twiddle_new = torch.eye(2, dtype=twiddle.dtype).reshape(1, 1, 1,
                1, 2, 2)
            twiddle_new = twiddle_new.expand(*twiddle.shape).contiguous()
            with torch.no_grad():
                twiddle.copy_(twiddle_new)
        if self.bias is not None:
            bound = 1 / math.sqrt(self.in_size)
            nn.init.uniform_(self.bias, -bound, bound)

    def forward(self, input, transpose=False, conjugate=False, subtwiddle=False
        ):
        """
        Parameters:
            input: (batch, *, in_size)
            transpose: whether the butterfly matrix should be transposed.
            conjugate: whether the butterfly matrix should be conjugated.
            subtwiddle: allow using only part of the parameters for smaller input.
                Could be useful for weight sharing.
                out_size is set to self.nstacks * self.in_size_extended in this case
        Return:
            output: (batch, *, out_size)
        """
        twiddle = self.twiddle if not self.complex else view_as_complex(self
            .twiddle)
        if not subtwiddle:
            output = self.pre_process(input)
        else:
            log_n = int(math.ceil(math.log2(input.size(-1))))
            n = 1 << log_n
            output = self.pre_process(input, padded_size=n)
            twiddle = twiddle[:, :, :log_n, :n // 2
                ] if self.increasing_stride else twiddle[:, :, -log_n:, :n // 2
                ]
        if conjugate and self.complex:
            twiddle = twiddle.conj()
        if not transpose:
            output = butterfly_multiply(twiddle, output, self.increasing_stride
                )
        else:
            twiddle = twiddle.transpose(-1, -2).flip([1, 2])
            last_increasing_stride = self.increasing_stride != ((self.
                nblocks - 1) % 2 == 1)
            output = butterfly_multiply(twiddle, output, not
                last_increasing_stride)
        if not subtwiddle:
            return self.post_process(input, output)
        else:
            return self.post_process(input, output, out_size=output.size(-1))

    def pre_process(self, input, padded_size=None):
        if padded_size is None:
            padded_size = self.in_size_extended
        input_size = input.size(-1)
        output = input.reshape(-1, input_size)
        batch = output.shape[0]
        if input_size != padded_size:
            output = F.pad(output, (0, padded_size - input_size))
        output = output.unsqueeze(1).expand(batch, self.nstacks, padded_size)
        return output

    def post_process(self, input, output, out_size=None):
        if out_size is None:
            out_size = self.out_size
        batch = output.shape[0]
        output = output.view(batch, self.nstacks * output.size(-1))
        out_size_extended = 1 << int(math.ceil(math.log2(output.size(-1))))
        if out_size != out_size_extended:
            output = output[:, :out_size]
        if self.bias is not None:
            bias = self.bias if not self.complex else view_as_complex(self.bias
                )
            output = output + bias[:out_size]
        return output.view(*input.size()[:-1], out_size)

    def extra_repr(self):
        s = (
            'in_size={}, out_size={}, bias={}, complex={}, increasing_stride={}, init={}, nblocks={}'
            .format(self.in_size, self.out_size, self.bias is not None,
            self.complex, self.increasing_stride, self.init, self.nblocks))
        return s


class MLPNew(nn.Module):

    def __init__(self, method='linear', **kwargs):
        super().__init__()
        if method == 'linear':

            def make_layer(name):
                return self.add_module(name, nn.Linear(1024, 1024, bias=True))
        elif method == 'butterfly':

            def make_layer(name):
                return self.add_module(name, Butterfly(1024, 1024, bias=
                    True, **kwargs))
        elif method == 'low-rank':

            def make_layer(name):
                return self.add_module(name, nn.Sequential(nn.Linear(1024,
                    kwargs['rank'], bias=False), nn.Linear(kwargs['rank'], 
                    1024, bias=True)))
        elif method == 'toeplitz':

            def make_layer(name):
                return self.add_module(name, sl.ToeplitzLikeC(layer_size=
                    1024, bias=True, **kwargs))
        else:
            assert False, f'method {method} not supported'
        make_layer('fc10')
        make_layer('fc11')
        make_layer('fc12')
        make_layer('fc2')
        make_layer('fc3')
        self.logits = nn.Linear(1024, 10)

    def forward(self, input_0):
        primals_2 = self.fc10.weight
        primals_3 = self.fc10.bias
        primals_4 = self.fc11.weight
        primals_5 = self.fc11.bias
        primals_6 = self.fc12.weight
        primals_7 = self.fc12.bias
        primals_8 = self.fc2.weight
        primals_9 = self.fc2.bias
        primals_10 = self.fc3.weight
        primals_11 = self.fc3.bias
        primals_12 = self.logits.weight
        primals_13 = self.logits.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13])
        return output[0]


Measured latency_ms: 0.092196