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 math as tl_math
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__softmax_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_ptr0, ynumel, xnumel, YBLOCK: tl
    .constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_out_ptr0 + (x1 + 4 * y0), xmask & ymask)
    tmp1 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x1 + 4 * y0), tmp2, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = 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,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 1, 4))
    assert_size_stride(primals_5, (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.addmm(primals_3, primals_2, primals_1, alpha=1, beta
            =1, out=buf0)
        del primals_1
        del primals_3
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16)](buf0, buf1, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf2 = buf0
        del buf0
        triton_poi_fused__softmax_1[grid(16)](buf1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(16, 4)](primals_4, buf3, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        del primals_4
        buf4 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (16, 4), (4, 1), 0),
            reinterpret_tensor(buf2, (4, 4), (1, 4), 0), out=buf4)
        buf5 = buf1
        del buf1
        extern_kernels.mm(primals_5, reinterpret_tensor(buf2, (4, 4), (1, 4
            ), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf4, (4, 4, 4), (1, 16, 4), 
            0), reinterpret_tensor(primals_2, (4, 4, 1), (4, 1, 1), 0), out
            =buf6)
        del buf4
        buf7 = reinterpret_tensor(buf6, (4, 4), (4, 1), 0)
        del buf6
        triton_poi_fused_add_3[grid(4, 4)](buf7, buf5, 4, 4, XBLOCK=4,
            YBLOCK=4, num_warps=1, num_stages=1)
        del buf5
    return buf7, primals_2, primals_5, buf2, reinterpret_tensor(buf3, (16, 
        4), (4, 1), 0)


class MENew(torch.nn.Module):

    def __init__(self, in_features, out_features, n_leaf, projection=
        'linear', dropout=0.0):
        super(MENew, self).__init__()
        self.proj = projection
        self.n_leaf = n_leaf
        self.in_features = in_features
        self.out_features = out_features
        self.gw = torch.nn.Parameter(torch.nn.init.xavier_normal_(torch.
            empty(in_features, n_leaf)))
        self.gb = torch.nn.Parameter(torch.zeros(n_leaf))
        if self.proj == 'linear':
            self.pw = torch.nn.init.kaiming_normal_(torch.empty(
                out_features * n_leaf, in_features), nonlinearity='linear')
            self.pw = torch.nn.Parameter(self.pw.reshape(out_features,
                n_leaf, in_features).permute(0, 2, 1))
            self.pb = torch.nn.Parameter(torch.zeros(out_features, n_leaf))
        elif self.proj == 'constant':
            self.z = torch.nn.Parameter(torch.randn(out_features, n_leaf))

    def extra_repr(self):
        return 'in_features=%d, out_features=%d, n_leaf=%d, projection=%s' % (
            self.in_features, self.out_features, self.n_leaf, self.proj)

    def forward(self, input_0):
        primals_1 = self.gw
        primals_3 = self.gb
        primals_4 = self.pw
        primals_2 = self.pb
        primals_5 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.086546