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.triton_helpers import math as tl_math
from torch import 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_exp_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 = tl_math.exp(tmp0)
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 64
    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
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 64 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tmp2 = tl.load(in_ptr1 + (x2 + 64 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + (y0 + 4 * x2 + 256 * y1), tmp3, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 256
    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 % 64
    y1 = yindex // 64
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 64 * x2 + 256 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl_math.exp(tmp0)
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp1, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_div_mul_sigmoid_3(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 64
    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
    y3 = yindex
    y0 = yindex % 4
    y1 = yindex // 4
    tmp0 = tl.load(in_ptr0 + (x2 + 64 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tmp2 = tl.load(in_ptr1 + (y0 + 4 * x2 + 256 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (y0 + 4 * x2 + 256 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp4 = tmp2 / tmp3
    tmp5 = tmp1 * tmp4
    tl.store(out_ptr0 + (x2 + 64 * y3), tmp5, xmask & ymask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (64, 4), (4, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (64, 4), (4, 1))
    assert_size_stride(primals_5, (64,), (1,))
    assert_size_stride(primals_6, (64, 4), (4, 1))
    assert_size_stride(primals_7, (64,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4, 64), (64, 1))
    assert_size_stride(primals_10, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 64), (1, 4), 
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 64), (1, 4), 
            0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((16, 64), (64, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 64), (1, 4), 
            0), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = empty_strided_cuda((1, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_exp_0[grid(16)](primals_8, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_8
        buf4 = empty_strided_cuda((4, 64, 4), (256, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 64)](buf1, buf2, buf4, 16, 64,
            XBLOCK=64, YBLOCK=4, num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (256, 4), (4, 1), 0),
            reinterpret_tensor(buf3, (4, 4), (1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 64, 4), (256, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(256, 4)](buf1, buf6, 256, 4, XBLOCK=4,
            YBLOCK=256, num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf6, (256, 4), (4, 1), 0),
            reinterpret_tensor(buf3, (4, 4), (1, 4), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 64), (256, 64, 1), torch.float32)
        triton_poi_fused_clone_div_mul_sigmoid_3[grid(16, 64)](buf0, buf5,
            buf7, buf8, 16, 64, XBLOCK=16, YBLOCK=16, num_warps=4, num_stages=1
            )
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_10, reinterpret_tensor(buf8, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_9, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf9)
        del primals_10
    return reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf0, buf1, buf2, buf3, reinterpret_tensor(buf4, (256, 4), (4, 1), 0
        ), buf5, reinterpret_tensor(buf6, (256, 4), (4, 1), 0
        ), buf7, reinterpret_tensor(buf8, (16, 64), (64, 1), 0), primals_9


class AFTFullNew(nn.Module):

    def __init__(self, max_seqlen, dim, hidden_dim=64):
        super().__init__()
        """
        max_seqlen: the maximum number of timesteps (sequence length) to be fed in
        dim: the embedding dimension of the tokens
        hidden_dim: the hidden dimension used inside AFT Full

        Number of heads is 1 as done in the paper
        """
        self.dim = dim
        self.hidden_dim = hidden_dim
        self.to_q = nn.Linear(dim, hidden_dim)
        self.to_k = nn.Linear(dim, hidden_dim)
        self.to_v = nn.Linear(dim, hidden_dim)
        self.project = nn.Linear(hidden_dim, dim)
        self.wbias = nn.Parameter(torch.Tensor(max_seqlen, max_seqlen))
        nn.init.xavier_uniform_(self.wbias)

    def forward(self, input_0):
        primals_8 = self.wbias
        primals_2 = self.to_q.weight
        primals_3 = self.to_q.bias
        primals_4 = self.to_k.weight
        primals_5 = self.to_k.bias
        primals_6 = self.to_v.weight
        primals_7 = self.to_v.bias
        primals_9 = self.project.weight
        primals_10 = self.project.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])
        return output[0]


Measured latency_ms: 0.053886