# 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_6/inductor_cache/36/c367gi4ltlx2fcm53k7ugnhtylq6wxwfjfmkd5fa434ar2vnqu2t.py
# Topologically Sorted Source Nodes: [matmul, matmul_2, matmul_4, matmul_6], Original ATen: [aten.mv]
# Source node to ATen node mapping:
#   matmul => mul, sum_1
#   matmul_2 => mul_1, sum_2
#   matmul_4 => mul_2, sum_3
#   matmul_6 => mul_3, sum_4
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %select), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %select_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1]), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %select_2), kwargs = {})
#   %sum_3 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_2, [1]), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %select_3), kwargs = {})
#   %sum_4 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_3, [1]), kwargs = {})
triton_poi_fused_mv_0 = async_compile.triton('triton_poi_fused_mv_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=[4], 
    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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mv_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 20, '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_mv_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2, out_ptr3, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (4))
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (8))
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr1 + (12))
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp19 = tl.load(in_ptr1 + (1))
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp22 = tl.load(in_ptr1 + (5))
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp26 = tl.load(in_ptr1 + (9))
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK])
    tmp30 = tl.load(in_ptr1 + (13))
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK])
    tmp34 = tl.load(in_ptr1 + (2))
    tmp35 = tl.broadcast_to(tmp34, [XBLOCK])
    tmp37 = tl.load(in_ptr1 + (6))
    tmp38 = tl.broadcast_to(tmp37, [XBLOCK])
    tmp41 = tl.load(in_ptr1 + (10))
    tmp42 = tl.broadcast_to(tmp41, [XBLOCK])
    tmp45 = tl.load(in_ptr1 + (14))
    tmp46 = tl.broadcast_to(tmp45, [XBLOCK])
    tmp49 = tl.load(in_ptr1 + (3))
    tmp50 = tl.broadcast_to(tmp49, [XBLOCK])
    tmp52 = tl.load(in_ptr1 + (7))
    tmp53 = tl.broadcast_to(tmp52, [XBLOCK])
    tmp56 = tl.load(in_ptr1 + (11))
    tmp57 = tl.broadcast_to(tmp56, [XBLOCK])
    tmp60 = tl.load(in_ptr1 + (15))
    tmp61 = tl.broadcast_to(tmp60, [XBLOCK])
    tmp3 = tmp0 * tmp2
    tmp7 = tmp4 * tmp6
    tmp8 = tmp3 + tmp7
    tmp12 = tmp9 * tmp11
    tmp13 = tmp8 + tmp12
    tmp17 = tmp14 * tmp16
    tmp18 = tmp13 + tmp17
    tmp21 = tmp0 * tmp20
    tmp24 = tmp4 * tmp23
    tmp25 = tmp21 + tmp24
    tmp28 = tmp9 * tmp27
    tmp29 = tmp25 + tmp28
    tmp32 = tmp14 * tmp31
    tmp33 = tmp29 + tmp32
    tmp36 = tmp0 * tmp35
    tmp39 = tmp4 * tmp38
    tmp40 = tmp36 + tmp39
    tmp43 = tmp9 * tmp42
    tmp44 = tmp40 + tmp43
    tmp47 = tmp14 * tmp46
    tmp48 = tmp44 + tmp47
    tmp51 = tmp0 * tmp50
    tmp54 = tmp4 * tmp53
    tmp55 = tmp51 + tmp54
    tmp58 = tmp9 * tmp57
    tmp59 = tmp55 + tmp58
    tmp62 = tmp14 * tmp61
    tmp63 = tmp59 + tmp62
    tl.store(out_ptr0 + (x0), tmp18, xmask)
    tl.store(out_ptr1 + (x0), tmp33, xmask)
    tl.store(out_ptr2 + (x0), tmp48, xmask)
    tl.store(out_ptr3 + (x0), tmp63, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/i6/ci6dtushg5vdgefkf6eab4wefulbaymttbdtkgg4gnnr5oxehggo.py
# Topologically Sorted Source Nodes: [matmul, add, add_1, h_t], Original ATen: [aten.mv, aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add => add
#   add_1 => add_1
#   h_t => tanh
#   matmul => mul, sum_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %select), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_1, %mm), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add, %primals_5), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_1,), kwargs = {})
triton_poi_fused_add_mv_tanh_1 = async_compile.triton('triton_poi_fused_add_mv_tanh_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], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 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_add_mv_tanh_1', '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_add_mv_tanh_1(in_ptr0, in_ptr1, in_ptr2, 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 % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(out_ptr0 + (x2), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/r2/cr2ck23oter5t6vtfz3l4nrevhadroflg3xzdssmzkxjjd7qmxqa.py
# Topologically Sorted Source Nodes: [matmul_2, add_2, add_3, h_t_1], Original ATen: [aten.mv, aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add_2 => add_2
#   add_3 => add_3
#   h_t_1 => tanh_1
#   matmul_2 => mul_1, sum_2
# Graph fragment:
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %select_1), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul_1, [1]), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sum_2, %mm_1), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %primals_5), kwargs = {})
#   %tanh_1 : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add_3,), kwargs = {})
triton_poi_fused_add_mv_tanh_2 = async_compile.triton('triton_poi_fused_add_mv_tanh_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=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 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_add_mv_tanh_2', 'mutated_arg_names': ['in_out_ptr0'], '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_add_mv_tanh_2(in_out_ptr0, in_ptr0, in_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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(in_out_ptr0 + (x2), tmp5, xmask)
''', 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 1), (1, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.mm]
        extern_kernels.mm(primals_4, primals_1, out=buf0)
        buf1 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf4 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf7 = empty_strided_cuda((4, ), (1, ), torch.float32)
        buf10 = empty_strided_cuda((4, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [matmul, matmul_2, matmul_4, matmul_6], Original ATen: [aten.mv]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mv_0.run(primals_2, primals_3, buf1, buf4, buf7, buf10, 4, grid=grid(4), stream=stream0)
        del primals_2
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul, add, add_1, h_t], Original ATen: [aten.mv, aten.add, aten.tanh]
        triton_poi_fused_add_mv_tanh_1.run(buf1, buf0, primals_5, buf2, 16, grid=grid(16), stream=stream0)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_3], Original ATen: [aten.mm]
        extern_kernels.mm(primals_4, buf2, out=buf3)
        buf5 = buf3; del buf3  # reuse
        # Topologically Sorted Source Nodes: [matmul_2, add_2, add_3, h_t_1], Original ATen: [aten.mv, aten.add, aten.tanh]
        triton_poi_fused_add_mv_tanh_2.run(buf5, buf4, primals_5, 16, grid=grid(16), stream=stream0)
        del buf4
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_5], Original ATen: [aten.mm]
        extern_kernels.mm(primals_4, buf5, out=buf6)
        buf8 = buf6; del buf6  # reuse
        # Topologically Sorted Source Nodes: [matmul_4, add_4, add_5, h_t_2], Original ATen: [aten.mv, aten.add, aten.tanh]
        triton_poi_fused_add_mv_tanh_2.run(buf8, buf7, primals_5, 16, grid=grid(16), stream=stream0)
        del buf7
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_7], Original ATen: [aten.mm]
        extern_kernels.mm(primals_4, buf8, out=buf9)
        buf11 = buf9; del buf9  # reuse
        # Topologically Sorted Source Nodes: [matmul_6, add_6, add_7, h_t_3], Original ATen: [aten.mv, aten.add, aten.tanh]
        triton_poi_fused_add_mv_tanh_2.run(buf11, buf10, primals_5, 16, grid=grid(16), stream=stream0)
        del buf10
        del primals_5
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.addmm(primals_7, primals_6, buf11, alpha=1, beta=1, out=buf12)
        del primals_7
    return (reinterpret_tensor(buf12, (4, 4), (1, 4), 0), primals_3, buf2, buf5, buf8, buf11, reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), reinterpret_tensor(primals_1, (1, 4), (1, 1), 0), )


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


class VanillaRNN(nn.Module):

    def __init__(self, seq_length, input_dim, num_hidden, num_classes,
        batch_size, device='cpu'):
        super(VanillaRNN, self).__init__()
        self.seq_length = seq_length
        self.h_init = nn.Parameter(torch.zeros(num_hidden, 1),
            requires_grad=False)
        self.w_hx = nn.Parameter(nn.init.orthogonal_(torch.Tensor(
            num_hidden, input_dim).normal_(mean=0, std=0.0001)))
        self.w_hh = nn.Parameter(nn.init.orthogonal_(torch.Tensor(
            num_hidden, num_hidden).normal_(mean=0, std=0.0001)))
        self.b_h = nn.Parameter(torch.Tensor(num_hidden, 1).zero_())
        self.w_ph = nn.Parameter(torch.Tensor(num_classes, num_hidden).
            normal_(mean=0, std=0.0001))
        self.b_p = nn.Parameter(torch.Tensor(num_classes, 1).zero_())

    def forward(self, x):
        h_t = self.h_init
        tanh = nn.Tanh()
        for step in range(self.seq_length):
            h_t = tanh(self.w_hx @ x[:, step].t() + self.w_hh @ h_t + self.b_h)
        p_t = self.w_ph @ h_t + self.b_p
        return p_t.t()


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


def get_init_inputs():
    return [[], {'seq_length': 4, 'input_dim': 4, 'num_hidden': 4,
        'num_classes': 4, 'batch_size': 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 libdevice
import torch.nn as 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_mv_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, out_ptr2,
    out_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + 4)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK])
    tmp9 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + 8)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK])
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr1 + 12)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp19 = tl.load(in_ptr1 + 1)
    tmp20 = tl.broadcast_to(tmp19, [XBLOCK])
    tmp22 = tl.load(in_ptr1 + 5)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp26 = tl.load(in_ptr1 + 9)
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK])
    tmp30 = tl.load(in_ptr1 + 13)
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK])
    tmp34 = tl.load(in_ptr1 + 2)
    tmp35 = tl.broadcast_to(tmp34, [XBLOCK])
    tmp37 = tl.load(in_ptr1 + 6)
    tmp38 = tl.broadcast_to(tmp37, [XBLOCK])
    tmp41 = tl.load(in_ptr1 + 10)
    tmp42 = tl.broadcast_to(tmp41, [XBLOCK])
    tmp45 = tl.load(in_ptr1 + 14)
    tmp46 = tl.broadcast_to(tmp45, [XBLOCK])
    tmp49 = tl.load(in_ptr1 + 3)
    tmp50 = tl.broadcast_to(tmp49, [XBLOCK])
    tmp52 = tl.load(in_ptr1 + 7)
    tmp53 = tl.broadcast_to(tmp52, [XBLOCK])
    tmp56 = tl.load(in_ptr1 + 11)
    tmp57 = tl.broadcast_to(tmp56, [XBLOCK])
    tmp60 = tl.load(in_ptr1 + 15)
    tmp61 = tl.broadcast_to(tmp60, [XBLOCK])
    tmp3 = tmp0 * tmp2
    tmp7 = tmp4 * tmp6
    tmp8 = tmp3 + tmp7
    tmp12 = tmp9 * tmp11
    tmp13 = tmp8 + tmp12
    tmp17 = tmp14 * tmp16
    tmp18 = tmp13 + tmp17
    tmp21 = tmp0 * tmp20
    tmp24 = tmp4 * tmp23
    tmp25 = tmp21 + tmp24
    tmp28 = tmp9 * tmp27
    tmp29 = tmp25 + tmp28
    tmp32 = tmp14 * tmp31
    tmp33 = tmp29 + tmp32
    tmp36 = tmp0 * tmp35
    tmp39 = tmp4 * tmp38
    tmp40 = tmp36 + tmp39
    tmp43 = tmp9 * tmp42
    tmp44 = tmp40 + tmp43
    tmp47 = tmp14 * tmp46
    tmp48 = tmp44 + tmp47
    tmp51 = tmp0 * tmp50
    tmp54 = tmp4 * tmp53
    tmp55 = tmp51 + tmp54
    tmp58 = tmp9 * tmp57
    tmp59 = tmp55 + tmp58
    tmp62 = tmp14 * tmp61
    tmp63 = tmp59 + tmp62
    tl.store(out_ptr0 + x0, tmp18, xmask)
    tl.store(out_ptr1 + x0, tmp33, xmask)
    tl.store(out_ptr2 + x0, tmp48, xmask)
    tl.store(out_ptr3 + x0, tmp63, xmask)


@triton.jit
def triton_poi_fused_add_mv_tanh_1(in_ptr0, in_ptr1, in_ptr2, 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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(out_ptr0 + x2, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_mv_tanh_2(in_out_ptr0, in_ptr0, in_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = libdevice.tanh(tmp4)
    tl.store(in_out_ptr0 + x2, tmp5, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 1), (1, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 1), (1, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 1), (1, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        extern_kernels.mm(primals_4, primals_1, out=buf0)
        buf1 = empty_strided_cuda((4,), (1,), torch.float32)
        buf4 = empty_strided_cuda((4,), (1,), torch.float32)
        buf7 = empty_strided_cuda((4,), (1,), torch.float32)
        buf10 = empty_strided_cuda((4,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mv_0[grid(4)](primals_2, primals_3, buf1, buf4,
            buf7, buf10, 4, XBLOCK=4, num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_mv_tanh_1[grid(16)](buf1, buf0, primals_5,
            buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del buf1
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_4, buf2, out=buf3)
        buf5 = buf3
        del buf3
        triton_poi_fused_add_mv_tanh_2[grid(16)](buf5, buf4, primals_5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf4
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_4, buf5, out=buf6)
        buf8 = buf6
        del buf6
        triton_poi_fused_add_mv_tanh_2[grid(16)](buf8, buf7, primals_5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del buf7
        buf9 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_4, buf8, out=buf9)
        buf11 = buf9
        del buf9
        triton_poi_fused_add_mv_tanh_2[grid(16)](buf11, buf10, primals_5, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del buf10
        del primals_5
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, primals_6, buf11, alpha=1, beta=1,
            out=buf12)
        del primals_7
    return reinterpret_tensor(buf12, (4, 4), (1, 4), 0
        ), primals_3, buf2, buf5, buf8, buf11, reinterpret_tensor(primals_6,
        (4, 4), (1, 4), 0), reinterpret_tensor(primals_4, (4, 4), (1, 4), 0
        ), reinterpret_tensor(primals_1, (1, 4), (1, 1), 0)


class VanillaRNNNew(nn.Module):

    def __init__(self, seq_length, input_dim, num_hidden, num_classes,
        batch_size, device='cpu'):
        super(VanillaRNNNew, self).__init__()
        self.seq_length = seq_length
        self.h_init = nn.Parameter(torch.zeros(num_hidden, 1),
            requires_grad=False)
        self.w_hx = nn.Parameter(nn.init.orthogonal_(torch.Tensor(
            num_hidden, input_dim).normal_(mean=0, std=0.0001)))
        self.w_hh = nn.Parameter(nn.init.orthogonal_(torch.Tensor(
            num_hidden, num_hidden).normal_(mean=0, std=0.0001)))
        self.b_h = nn.Parameter(torch.Tensor(num_hidden, 1).zero_())
        self.w_ph = nn.Parameter(torch.Tensor(num_classes, num_hidden).
            normal_(mean=0, std=0.0001))
        self.b_p = nn.Parameter(torch.Tensor(num_classes, 1).zero_())

    def forward(self, input_0):
        primals_1 = self.h_init
        primals_2 = self.w_hx
        primals_3 = self.w_hh
        primals_5 = self.b_h
        primals_4 = self.w_ph
        primals_7 = self.b_p
        primals_6 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
