# AOT ID: ['0_inference']
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/sz/cszdqebg5pns6veqfzmjstoqvgro5kzpfkqix2nafzrmf7gzh3he.py
# Topologically Sorted Source Nodes: [abs_1, amax, used, length, length_1, sequence_length, setitem], Original ATen: [aten.abs, aten.amax, aten.sign, aten.sum, aten._to_copy, aten.sub, aten.lift_fresh, aten.index_put]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   amax => amax
#   length => sum_1
#   length_1 => convert_element_type
#   sequence_length => sub
#   setitem => full_default, index_put
#   used => sign
# Graph fragment:
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%arg0_1,), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%abs_1, [2]), kwargs = {})
#   %sign : [num_users=1] = call_function[target=torch.ops.aten.sign.default](args = (%amax,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%sign, [1]), kwargs = {})
#   %convert_element_type : [num_users=1] = call_function[target=torch.ops.prims.convert_element_type.default](args = (%sum_1, torch.int32), kwargs = {})
#   %sub : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%convert_element_type, 1), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0), kwargs = {dtype: torch.int32, layout: torch.strided, device: cpu, pin_memory: False})
#   %index_put : [num_users=1] = call_function[target=torch.ops.aten.index_put_.default](args = (%sub, [%lt], %full_default), kwargs = {})
triton_poi_fused__to_copy_abs_amax_index_put_lift_fresh_sign_sub_sum_0 = async_compile.triton('triton_poi_fused__to_copy_abs_amax_index_put_lift_fresh_sign_sub_sum_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: '*i32', 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__to_copy_abs_amax_index_put_lift_fresh_sign_sub_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 16, '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__to_copy_abs_amax_index_put_lift_fresh_sign_sub_sum_0(in_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp2 = tl.load(in_ptr0 + (4 + x0 + (64*x1)), xmask)
    tmp5 = tl.load(in_ptr0 + (8 + x0 + (64*x1)), xmask)
    tmp8 = tl.load(in_ptr0 + (12 + x0 + (64*x1)), xmask)
    tmp18 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp20 = tl.load(in_ptr0 + (20 + x0 + (64*x1)), xmask)
    tmp23 = tl.load(in_ptr0 + (24 + x0 + (64*x1)), xmask)
    tmp26 = tl.load(in_ptr0 + (28 + x0 + (64*x1)), xmask)
    tmp36 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp38 = tl.load(in_ptr0 + (36 + x0 + (64*x1)), xmask)
    tmp41 = tl.load(in_ptr0 + (40 + x0 + (64*x1)), xmask)
    tmp44 = tl.load(in_ptr0 + (44 + x0 + (64*x1)), xmask)
    tmp54 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp56 = tl.load(in_ptr0 + (52 + x0 + (64*x1)), xmask)
    tmp59 = tl.load(in_ptr0 + (56 + x0 + (64*x1)), xmask)
    tmp62 = tl.load(in_ptr0 + (60 + x0 + (64*x1)), xmask)
    tmp1 = tl_math.abs(tmp0)
    tmp3 = tl_math.abs(tmp2)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp6 = tl_math.abs(tmp5)
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tl_math.abs(tmp8)
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = tmp11 < tmp10
    tmp13 = tmp12.to(tl.int8)
    tmp14 = tmp10 < tmp11
    tmp15 = tmp14.to(tl.int8)
    tmp16 = tmp13 - tmp15
    tmp17 = tmp16.to(tmp10.dtype)
    tmp19 = tl_math.abs(tmp18)
    tmp21 = tl_math.abs(tmp20)
    tmp22 = triton_helpers.maximum(tmp19, tmp21)
    tmp24 = tl_math.abs(tmp23)
    tmp25 = triton_helpers.maximum(tmp22, tmp24)
    tmp27 = tl_math.abs(tmp26)
    tmp28 = triton_helpers.maximum(tmp25, tmp27)
    tmp29 = tmp11 < tmp28
    tmp30 = tmp29.to(tl.int8)
    tmp31 = tmp28 < tmp11
    tmp32 = tmp31.to(tl.int8)
    tmp33 = tmp30 - tmp32
    tmp34 = tmp33.to(tmp28.dtype)
    tmp35 = tmp17 + tmp34
    tmp37 = tl_math.abs(tmp36)
    tmp39 = tl_math.abs(tmp38)
    tmp40 = triton_helpers.maximum(tmp37, tmp39)
    tmp42 = tl_math.abs(tmp41)
    tmp43 = triton_helpers.maximum(tmp40, tmp42)
    tmp45 = tl_math.abs(tmp44)
    tmp46 = triton_helpers.maximum(tmp43, tmp45)
    tmp47 = tmp11 < tmp46
    tmp48 = tmp47.to(tl.int8)
    tmp49 = tmp46 < tmp11
    tmp50 = tmp49.to(tl.int8)
    tmp51 = tmp48 - tmp50
    tmp52 = tmp51.to(tmp46.dtype)
    tmp53 = tmp35 + tmp52
    tmp55 = tl_math.abs(tmp54)
    tmp57 = tl_math.abs(tmp56)
    tmp58 = triton_helpers.maximum(tmp55, tmp57)
    tmp60 = tl_math.abs(tmp59)
    tmp61 = triton_helpers.maximum(tmp58, tmp60)
    tmp63 = tl_math.abs(tmp62)
    tmp64 = triton_helpers.maximum(tmp61, tmp63)
    tmp65 = tmp11 < tmp64
    tmp66 = tmp65.to(tl.int8)
    tmp67 = tmp64 < tmp11
    tmp68 = tmp67.to(tl.int8)
    tmp69 = tmp66 - tmp68
    tmp70 = tmp69.to(tmp64.dtype)
    tmp71 = tmp53 + tmp70
    tmp72 = tmp71.to(tl.int32)
    tmp73 = tl.full([1], 1, tl.int32)
    tmp74 = tmp72 - tmp73
    tmp75 = tmp74 < tmp11
    tmp76 = tl.where(tmp75, tmp11, tmp74)
    tl.store(out_ptr1 + (x2), tmp76, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/rf/crff2v5g62gmwittje4cxykzyvx5ipq7hs2mh2ykeyfvspvt4gxy.py
# Topologically Sorted Source Nodes: [gathered], Original ATen: [aten.index]
# Source node to ATen node mapping:
#   gathered => index
# Graph fragment:
#   %index : [num_users=1] = call_function[target=torch.ops.aten.index.Tensor](args = (%arg0_1, [%iota_default, %convert_element_type_1]), kwargs = {})
triton_poi_fused_index_1 = async_compile.triton('triton_poi_fused_index_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*i32', 1: '*fp32', 2: '*fp32', 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, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_index_1', '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_index_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
    x3 = (xindex // 16)
    x0 = xindex % 16
    x1 = (xindex // 16) % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert(((0 <= tmp5) & (tmp5 < 4)) | ~(xmask), "index out of bounds: 0 <= tmp5 < 4")
    tmp7 = tl.load(in_ptr1 + (x0 + (16*tmp5) + (64*x1)), xmask)
    tl.store(out_ptr0 + (x4), tmp7, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.int32)
        # Topologically Sorted Source Nodes: [abs_1, amax, used, length, length_1, sequence_length, setitem], Original ATen: [aten.abs, aten.amax, aten.sign, aten.sum, aten._to_copy, aten.sub, aten.lift_fresh, aten.index_put]
        stream0 = get_raw_stream(0)
        triton_poi_fused__to_copy_abs_amax_index_put_lift_fresh_sign_sub_sum_0.run(arg0_1, buf1, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [gathered], Original ATen: [aten.index]
        triton_poi_fused_index_1.run(buf1, arg0_1, buf2, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del buf1
    return (buf2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1])
    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


def sequence_length_3D(sequence: 'torch.Tensor') ->torch.Tensor:
    used = torch.sign(torch.amax(torch.abs(sequence), dim=2))
    length = torch.sum(used, 1)
    length = length.int()
    return length


class ReduceLast(torch.nn.Module):

    def forward(self, inputs, mask=None):
        batch_size = inputs.shape[0]
        sequence_length = sequence_length_3D(inputs) - 1
        sequence_length[sequence_length < 0] = 0
        gathered = inputs[torch.arange(batch_size), sequence_length.type(
            torch.int64)]
        return gathered


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


def get_init_inputs():
    return [[], {}]

import torch
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


@triton.jit
def triton_poi_fused__to_copy_abs_amax_index_put_lift_fresh_sign_sub_sum_0(
    in_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp18 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp20 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp23 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp26 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp36 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp38 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp41 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp44 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
    tmp54 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp56 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp59 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp62 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
    tmp1 = tl_math.abs(tmp0)
    tmp3 = tl_math.abs(tmp2)
    tmp4 = triton_helpers.maximum(tmp1, tmp3)
    tmp6 = tl_math.abs(tmp5)
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tl_math.abs(tmp8)
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp11 = tl.full([1], 0, tl.int32)
    tmp12 = tmp11 < tmp10
    tmp13 = tmp12.to(tl.int8)
    tmp14 = tmp10 < tmp11
    tmp15 = tmp14.to(tl.int8)
    tmp16 = tmp13 - tmp15
    tmp17 = tmp16.to(tmp10.dtype)
    tmp19 = tl_math.abs(tmp18)
    tmp21 = tl_math.abs(tmp20)
    tmp22 = triton_helpers.maximum(tmp19, tmp21)
    tmp24 = tl_math.abs(tmp23)
    tmp25 = triton_helpers.maximum(tmp22, tmp24)
    tmp27 = tl_math.abs(tmp26)
    tmp28 = triton_helpers.maximum(tmp25, tmp27)
    tmp29 = tmp11 < tmp28
    tmp30 = tmp29.to(tl.int8)
    tmp31 = tmp28 < tmp11
    tmp32 = tmp31.to(tl.int8)
    tmp33 = tmp30 - tmp32
    tmp34 = tmp33.to(tmp28.dtype)
    tmp35 = tmp17 + tmp34
    tmp37 = tl_math.abs(tmp36)
    tmp39 = tl_math.abs(tmp38)
    tmp40 = triton_helpers.maximum(tmp37, tmp39)
    tmp42 = tl_math.abs(tmp41)
    tmp43 = triton_helpers.maximum(tmp40, tmp42)
    tmp45 = tl_math.abs(tmp44)
    tmp46 = triton_helpers.maximum(tmp43, tmp45)
    tmp47 = tmp11 < tmp46
    tmp48 = tmp47.to(tl.int8)
    tmp49 = tmp46 < tmp11
    tmp50 = tmp49.to(tl.int8)
    tmp51 = tmp48 - tmp50
    tmp52 = tmp51.to(tmp46.dtype)
    tmp53 = tmp35 + tmp52
    tmp55 = tl_math.abs(tmp54)
    tmp57 = tl_math.abs(tmp56)
    tmp58 = triton_helpers.maximum(tmp55, tmp57)
    tmp60 = tl_math.abs(tmp59)
    tmp61 = triton_helpers.maximum(tmp58, tmp60)
    tmp63 = tl_math.abs(tmp62)
    tmp64 = triton_helpers.maximum(tmp61, tmp63)
    tmp65 = tmp11 < tmp64
    tmp66 = tmp65.to(tl.int8)
    tmp67 = tmp64 < tmp11
    tmp68 = tmp67.to(tl.int8)
    tmp69 = tmp66 - tmp68
    tmp70 = tmp69.to(tmp64.dtype)
    tmp71 = tmp53 + tmp70
    tmp72 = tmp71.to(tl.int32)
    tmp73 = tl.full([1], 1, tl.int32)
    tmp74 = tmp72 - tmp73
    tmp75 = tmp74 < tmp11
    tmp76 = tl.where(tmp75, tmp11, tmp74)
    tl.store(out_ptr1 + x2, tmp76, xmask)


@triton.jit
def triton_poi_fused_index_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
    x3 = xindex // 16
    x0 = xindex % 16
    x1 = xindex // 16 % 4
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tmp0.to(tl.int64)
    tmp2 = tl.full([XBLOCK], 4, tl.int32)
    tmp3 = tmp1 + tmp2
    tmp4 = tmp1 < 0
    tmp5 = tl.where(tmp4, tmp3, tmp1)
    tl.device_assert((0 <= tmp5) & (tmp5 < 4) | ~xmask,
        'index out of bounds: 0 <= tmp5 < 4')
    tmp7 = tl.load(in_ptr1 + (x0 + 16 * tmp5 + 64 * x1), xmask)
    tl.store(out_ptr0 + x4, tmp7, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.int32)
        get_raw_stream(0)
        triton_poi_fused__to_copy_abs_amax_index_put_lift_fresh_sign_sub_sum_0[
            grid(16)](arg0_1, buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_index_1[grid(256)](buf1, arg0_1, buf2, 256, XBLOCK
            =256, num_warps=4, num_stages=1)
        del arg0_1
        del buf1
    return buf2,


def sequence_length_3D(sequence: 'torch.Tensor') ->torch.Tensor:
    used = torch.sign(torch.amax(torch.abs(sequence), dim=2))
    length = torch.sum(used, 1)
    length = length.int()
    return length


class ReduceLastNew(torch.nn.Module):

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
