# 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/c5/cc5hiyrqfmfxmipscxrurz47qz3x3e4v7b3qrmfw4clinzd5btca.py
# Topologically Sorted Source Nodes: [exp], Original ATen: [aten.exp]
# Source node to ATen node mapping:
#   exp => exp
# Graph fragment:
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%unsqueeze,), kwargs = {})
triton_poi_fused_exp_0 = async_compile.triton('triton_poi_fused_exp_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_exp_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_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)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/ct/cctsgn4jbttnem4cpsk4ma7nkks3cjt6ed3vf6o7kmyudmhhtegs.py
# Topologically Sorted Source Nodes: [temp], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   temp => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=[16, 64], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_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)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/a4/ca4q7myqhmy7hwzc4tjozpqaktxuuca5pemaz7fnjpsfntt7qv5w.py
# Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   matmul_1 => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_6,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_2', '''
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=[256, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_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)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/v6/cv63i6zsdop24hcb7etspotmlequk5sybcjcn2344r7uw2gwuxkk.py
# Topologically Sorted Source Nodes: [Q_sig, temp, matmul_1, weighted, Yt], Original ATen: [aten.sigmoid, aten.clone, aten.div, aten.mul]
# Source node to ATen node mapping:
#   Q_sig => sigmoid
#   Yt => mul_1
#   matmul_1 => clone_3
#   temp => clone_1
#   weighted => div
# Graph fragment:
#   %sigmoid : [num_users=1] = call_function[target=torch.ops.aten.sigmoid.default](args = (%view_1,), kwargs = {})
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_5,), kwargs = {memory_format: torch.contiguous_format})
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_8,), kwargs = {memory_format: torch.contiguous_format})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%clone_1, %clone_3), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sigmoid, %div), kwargs = {})
triton_poi_fused_clone_div_mul_sigmoid_3 = async_compile.triton('triton_poi_fused_clone_div_mul_sigmoid_3', '''
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=[16, 64], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32', 5: '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': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_div_mul_sigmoid_3', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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_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)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

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)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        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)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.addmm]
        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)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.addmm]
        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)
        # Topologically Sorted Source Nodes: [exp], Original ATen: [aten.exp]
        stream0 = get_raw_stream(0)
        triton_poi_fused_exp_0.run(primals_8, buf3, 16, grid=grid(16), stream=stream0)
        del primals_8
        buf4 = empty_strided_cuda((4, 64, 4), (256, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [temp], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf1, buf2, buf4, 16, 64, grid=grid(16, 64), stream=stream0)
        buf5 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [temp], Original ATen: [aten.mm]
        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)
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf1, buf6, 256, 4, grid=grid(256, 4), stream=stream0)
        buf7 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.mm]
        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)
        # Topologically Sorted Source Nodes: [Q_sig, temp, matmul_1, weighted, Yt], Original ATen: [aten.sigmoid, aten.clone, aten.div, aten.mul]
        triton_poi_fused_clone_div_mul_sigmoid_3.run(buf0, buf5, buf7, buf8, 16, 64, grid=grid(16, 64), stream=stream0)
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [Yt_2], Original ATen: [aten.addmm]
        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, )


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), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((64, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((64, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, 64), (64, 1), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10])
    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 torch
from torch import nn


class AFTFull(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, x):
        B, T, _ = x.shape
        Q = self.to_q(x).view(B, T, self.hidden_dim)
        K = self.to_k(x).view(B, T, self.hidden_dim)
        V = self.to_v(x).view(B, T, self.hidden_dim)
        temp_wbias = self.wbias[:T, :T].unsqueeze(0)
        """
        From the paper
        """
        Q_sig = torch.sigmoid(Q)
        temp = torch.exp(temp_wbias) @ torch.mul(torch.exp(K), V)
        weighted = temp / (torch.exp(temp_wbias) @ torch.exp(K))
        Yt = torch.mul(Q_sig, weighted)
        Yt = Yt.view(B, T, self.hidden_dim)
        Yt = self.project(Yt)
        return Yt


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


def get_init_inputs():
    return [[], {'max_seqlen': 4, 'dim': 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
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]
