# 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_8/inductor_cache/2s/c2srlvu3ig3l4rt4br537nskxrm55ugw7myg2outhdk64rd63rae.py
# Topologically Sorted Source Nodes: [sub_1, mul_2, h_3, stack], Original ATen: [aten.rsub, aten.mul, aten.add, aten.stack]
# Source node to ATen node mapping:
#   h_3 => add_1
#   mul_2 => mul_2
#   stack => cat
#   sub_1 => sub_1
# Graph fragment:
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (1, %getitem_2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %view_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_6, %mul_2), kwargs = {})
#   %cat : [num_users=1] = call_function[target=torch.ops.aten.cat.default](args = ([%view, %view_1, %view_2, %view_3],), kwargs = {})
triton_poi_fused_add_mul_rsub_stack_0 = async_compile.triton('triton_poi_fused_add_mul_rsub_stack_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_rsub_stack_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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_add_mul_rsub_stack_0(in_ptr0, in_ptr1, out_ptr1, out_ptr2, out_ptr3, out_ptr4, 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 + (128 + x0), xmask)
    tmp1 = tl.load(in_ptr1 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp6 = tl.load(in_ptr1 + (64 + x0), xmask)
    tmp9 = tl.load(in_ptr0 + (x0), xmask)
    tmp10 = tl.load(in_ptr1 + (x0), xmask)
    tmp16 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp17 = tl.load(in_ptr1 + (192 + x0), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 - tmp5
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 * tmp11
    tmp13 = tmp7 + tmp12
    tmp14 = tmp4 * tmp13
    tmp15 = tmp2 + tmp14
    tmp18 = tmp16 * tmp17
    tmp19 = tmp3 - tmp16
    tmp20 = tmp19 * tmp15
    tmp21 = tmp18 + tmp20
    tl.store(out_ptr1 + (x0), tmp13, xmask)
    tl.store(out_ptr2 + (x0), tmp11, xmask)
    tl.store(out_ptr3 + (x0), tmp15, xmask)
    tl.store(out_ptr4 + (x0), tmp21, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 64)  # alias
        buf1 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0)  # alias
        buf3 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 128)  # alias
        buf4 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 192)  # alias
        # Topologically Sorted Source Nodes: [sub_1, mul_2, h_3, stack], Original ATen: [aten.rsub, aten.mul, aten.add, aten.stack]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_rsub_stack_0.run(arg0_1, arg1_1, buf2, buf1, buf3, buf4, 64, grid=grid(64), stream=stream0)
        del arg0_1
        del arg1_1
    return (reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0), )


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)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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.optim import *


class ForgetMult(torch.nn.Module):
    """ForgetMult computes a simple recurrent equation:
    h_t = f_t * x_t + (1 - f_t) * h_{t-1}

    This equation is equivalent to dynamic weighted averaging.

    Inputs: X, hidden
        - X (seq_len, batch, input_size): tensor containing the features of the input sequence.
        - F (seq_len, batch, input_size): tensor containing the forget gate values, assumed in range [0, 1].
        - hidden_init (batch, input_size): tensor containing the initial hidden state for the recurrence (h_{t-1}).
    """

    def __init__(self):
        super(ForgetMult, self).__init__()

    def forward(self, f, x, hidden_init=None):
        result = []
        forgets = f.split(1, dim=0)
        prev_h = hidden_init
        for i, h in enumerate((f * x).split(1, dim=0)):
            if prev_h is not None:
                h = h + (1 - forgets[i]) * prev_h
            h = h.view(h.size()[1:])
            result.append(h)
            prev_h = h
        return torch.stack(result)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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.optim import *
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_add_mul_rsub_stack_0(in_ptr0, in_ptr1, out_ptr1,
    out_ptr2, out_ptr3, out_ptr4, 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 + (128 + x0), xmask)
    tmp1 = tl.load(in_ptr1 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (64 + x0), xmask)
    tmp6 = tl.load(in_ptr1 + (64 + x0), xmask)
    tmp9 = tl.load(in_ptr0 + x0, xmask)
    tmp10 = tl.load(in_ptr1 + x0, xmask)
    tmp16 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp17 = tl.load(in_ptr1 + (192 + x0), xmask)
    tmp2 = tmp0 * tmp1
    tmp3 = 1.0
    tmp4 = tmp3 - tmp0
    tmp7 = tmp5 * tmp6
    tmp8 = tmp3 - tmp5
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 * tmp11
    tmp13 = tmp7 + tmp12
    tmp14 = tmp4 * tmp13
    tmp15 = tmp2 + tmp14
    tmp18 = tmp16 * tmp17
    tmp19 = tmp3 - tmp16
    tmp20 = tmp19 * tmp15
    tmp21 = tmp18 + tmp20
    tl.store(out_ptr1 + x0, tmp13, xmask)
    tl.store(out_ptr2 + x0, tmp11, xmask)
    tl.store(out_ptr3 + x0, tmp15, xmask)
    tl.store(out_ptr4 + x0, tmp21, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        buf2 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 64)
        buf1 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0)
        buf3 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 128)
        buf4 = reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 192)
        get_raw_stream(0)
        triton_poi_fused_add_mul_rsub_stack_0[grid(64)](arg0_1, arg1_1,
            buf2, buf1, buf3, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg0_1
        del arg1_1
    return reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0),


class ForgetMultNew(torch.nn.Module):
    """ForgetMult computes a simple recurrent equation:
    h_t = f_t * x_t + (1 - f_t) * h_{t-1}

    This equation is equivalent to dynamic weighted averaging.

    Inputs: X, hidden
        - X (seq_len, batch, input_size): tensor containing the features of the input sequence.
        - F (seq_len, batch, input_size): tensor containing the forget gate values, assumed in range [0, 1].
        - hidden_init (batch, input_size): tensor containing the initial hidden state for the recurrence (h_{t-1}).
    """

    def __init__(self):
        super(ForgetMultNew, self).__init__()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
