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
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__softmax_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 64
    x2 = xindex // 256
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 256 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (64 + x0 + 256 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (128 + x0 + 256 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (192 + x0 + 256 * x2), 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 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_sum_1(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 64
    x1 = xindex // 64
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 256 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (64 + x0 + 256 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0 + 256 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x0 + 256 * x1), xmask)
    tmp8 = tl.load(in_ptr1 + (x0 + 256 * x1), xmask)
    tmp11 = tl.load(in_ptr1 + (64 + x0 + 256 * x1), xmask)
    tmp15 = tl.load(in_ptr1 + (128 + x0 + 256 * x1), xmask)
    tmp19 = tl.load(in_ptr1 + (192 + x0 + 256 * x1), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = tmp0 / tmp6
    tmp9 = tmp7 * tmp8
    tmp10 = tmp1 / tmp6
    tmp12 = tmp10 * tmp11
    tmp13 = tmp9 + tmp12
    tmp14 = tmp3 / tmp6
    tmp16 = tmp14 * tmp15
    tmp17 = tmp13 + tmp16
    tmp18 = tmp5 / tmp6
    tmp20 = tmp18 * tmp19
    tmp21 = tmp17 + tmp20
    tl.store(out_ptr0 + x2, tmp21, xmask)


@triton.jit
def triton_poi_fused__softmax_mul_sigmoid_sum_2(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 64
    x2 = xindex // 256
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp2 = tl.load(in_ptr1 + (x0 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tl.store(out_ptr0 + x3, tmp3, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = 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, 64), (64, 1))
    assert_size_stride(primals_9, (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((4, 4, 64), (256, 64, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(1024)](buf1, buf3, 1024, XBLOCK=
            128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((4, 1, 64), (64, 256, 1), torch.float32)
        triton_poi_fused__softmax_mul_sum_1[grid(256)](buf3, buf2, buf4, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        buf5 = buf3
        del buf3
        triton_poi_fused__softmax_mul_sigmoid_sum_2[grid(1024)](buf0, buf4,
            buf5, 1024, XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        buf6 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (16, 64),
            (64, 1), 0), reinterpret_tensor(primals_8, (64, 4), (1, 64), 0),
            alpha=1, beta=1, out=buf6)
        del primals_9
    return reinterpret_tensor(buf6, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (16, 4), (4, 1), 0
        ), buf0, buf1, buf2, reinterpret_tensor(buf5, (16, 64), (64, 1), 0
        ), primals_8


class AFTSimpleNew(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)

    def forward(self, input_0):
        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_8 = self.project.weight
        primals_9 = 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])
        return output[0]


Measured latency_ms: 0.074728