# 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_1/inductor_cache/46/c46xb7vmp6upiptnf2nzsamtfqzcr6ve7jp6xugquejgk4ihbzb5.py
# Topologically Sorted Source Nodes: [mul, mul_1, state_1, mul_2, mul_3, state_2, mul_4, mul_5, state_3, mul_6, mul_7, state_4], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   mul_6 => mul_6
#   mul_7 => mul_7
#   state_1 => add
#   state_2 => add_1
#   state_3 => add_2
#   state_4 => add_3
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_1, -3.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select, 4.0), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_4, -3.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 4.0), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_8, -3.0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 4.0), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %mul_5), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_12, -3.0), kwargs = {})
#   %mul_7 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, 4.0), kwargs = {})
#   %add_3 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_6, %mul_7), kwargs = {})
triton_poi_fused_add_mul_0 = async_compile.triton('triton_poi_fused_add_mul_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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp7 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = -3.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp8 = tmp7 * tmp1
    tmp9 = 4.0
    tmp10 = tmp7 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = tmp11 * tmp9
    tmp13 = tmp6 + tmp12
    tmp14 = tmp13 * tmp9
    tmp15 = tmp4 + tmp14
    tmp16 = tmp15 * tmp9
    tmp17 = tmp2 + tmp16
    tl.store(out_ptr0 + (x0), tmp17, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/bh/cbh25okt56l476nhw6rw2nxfx3u3s2joy6rljl3uwg6d724c3vqp.py
# Topologically Sorted Source Nodes: [mul, mul_1, state_1, mul_2, mul_3, state_2, mul_4, mul_5, state_3], Original ATen: [aten.mul, aten.add]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   state_1 => add
#   state_2 => add_1
#   state_3 => add_2
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_1, -3.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select, 4.0), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %select_scatter_default : [num_users=2] = call_function[target=torch.ops.aten.select_scatter.default](args = (%permute, %add, 0, 0), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_4, -3.0), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 4.0), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %select_scatter_default_1 : [num_users=2] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default, %add_1, 0, 1), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%select_8, -3.0), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 4.0), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %mul_5), kwargs = {})
#   %select_scatter_default_2 : [num_users=2] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_1, %add_2, 0, 2), kwargs = {})
#   %select_scatter_default_3 : [num_users=1] = call_function[target=torch.ops.aten.select_scatter.default](args = (%select_scatter_default_2, %add_3, 0, 3), kwargs = {})
triton_poi_fused_add_mul_1 = async_compile.triton('triton_poi_fused_add_mul_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: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', '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_add_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 64)
    x0 = xindex % 64
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr2 + (x2), xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tl.full([1], 2, tl.int32)
    tmp5 = tmp0 == tmp4
    tmp7 = -3.0
    tmp8 = tmp6 * tmp7
    tmp10 = tmp9 * tmp7
    tmp12 = tmp11 * tmp7
    tmp13 = 4.0
    tmp14 = tmp11 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = tmp15 * tmp13
    tmp17 = tmp10 + tmp16
    tmp18 = tmp17 * tmp13
    tmp19 = tmp8 + tmp18
    tmp20 = tl.full([1], 1, tl.int32)
    tmp21 = tmp0 == tmp20
    tmp22 = tl.full([1], 0, tl.int32)
    tmp23 = tmp0 == tmp22
    tmp25 = tl.where(tmp23, tmp15, tmp24)
    tmp26 = tl.where(tmp21, tmp17, tmp25)
    tmp27 = tl.where(tmp5, tmp19, tmp26)
    tmp28 = tl.where(tmp2, tmp3, tmp27)
    tl.store(out_ptr0 + (x2), tmp28, 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))
    buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, state_1, mul_2, mul_3, state_2, mul_4, mul_5, state_3, mul_6, mul_7, state_4], Original ATen: [aten.mul, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_0.run(arg0_1, buf1, 64, grid=grid(64), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, state_1, mul_2, mul_3, state_2, mul_4, mul_5, state_3], Original ATen: [aten.mul, aten.add]
        triton_poi_fused_add_mul_1.run(buf1, arg0_1, buf0, buf2, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del buf0
    return (buf2, buf1, )


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
from torch import Tensor
from torch import nn
from torch.jit import Final


class ExponentialUpdate(nn.Module):
    alpha: 'Final[int]'

    def __init__(self, alpha: 'float'):
        super().__init__()
        self.alpha = float(alpha)

    def forward(self, x: 'Tensor', state: 'Tensor') ->Tensor:
        return x * (1 - self.alpha) + state * self.alpha


class ExponentialDecay(nn.Module):

    def __init__(self, alpha: 'float'):
        super().__init__()
        self.update_rule = ExponentialUpdate(alpha)

    def forward(self, x: 'Tensor', state: 'Optional[Tensor]'=None):
        out = torch.empty_like(x)
        if state is None:
            state = x[0]
        for t in range(x.shape[0]):
            state = self.update_rule(x[t], state)
            out[t] = state
        return out, state


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


def get_init_inputs():
    return [[], {'alpha': 4}]

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 import Tensor
from torch import nn
from torch.jit import Final
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_add_mul_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp7 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = -3.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp8 = tmp7 * tmp1
    tmp9 = 4.0
    tmp10 = tmp7 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = tmp11 * tmp9
    tmp13 = tmp6 + tmp12
    tmp14 = tmp13 * tmp9
    tmp15 = tmp4 + tmp14
    tmp16 = tmp15 * tmp9
    tmp17 = tmp2 + tmp16
    tl.store(out_ptr0 + x0, tmp17, xmask)


@triton.jit
def triton_poi_fused_add_mul_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 64
    x0 = xindex % 64
    x2 = xindex
    tmp3 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr1 + (128 + x0), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (64 + x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr2 + x2, xmask)
    tmp0 = x1
    tmp1 = tl.full([1], 3, tl.int32)
    tmp2 = tmp0 == tmp1
    tmp4 = tl.full([1], 2, tl.int32)
    tmp5 = tmp0 == tmp4
    tmp7 = -3.0
    tmp8 = tmp6 * tmp7
    tmp10 = tmp9 * tmp7
    tmp12 = tmp11 * tmp7
    tmp13 = 4.0
    tmp14 = tmp11 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = tmp15 * tmp13
    tmp17 = tmp10 + tmp16
    tmp18 = tmp17 * tmp13
    tmp19 = tmp8 + tmp18
    tmp20 = tl.full([1], 1, tl.int32)
    tmp21 = tmp0 == tmp20
    tmp22 = tl.full([1], 0, tl.int32)
    tmp23 = tmp0 == tmp22
    tmp25 = tl.where(tmp23, tmp15, tmp24)
    tmp26 = tl.where(tmp21, tmp17, tmp25)
    tmp27 = tl.where(tmp5, tmp19, tmp26)
    tmp28 = tl.where(tmp2, tmp3, tmp27)
    tl.store(out_ptr0 + x2, tmp28, xmask)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_0[grid(64)](arg0_1, buf1, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_mul_1[grid(256)](buf1, arg0_1, buf0, buf2, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del buf0
    return buf2, buf1


class ExponentialUpdate(nn.Module):
    alpha: 'Final[int]'

    def __init__(self, alpha: 'float'):
        super().__init__()
        self.alpha = float(alpha)

    def forward(self, x: 'Tensor', state: 'Tensor') ->Tensor:
        return x * (1 - self.alpha) + state * self.alpha


class ExponentialDecayNew(nn.Module):

    def __init__(self, alpha: 'float'):
        super().__init__()
        self.update_rule = ExponentialUpdate(alpha)

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0], output[1]
