# 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/iu/ciuxern2omgit5ovksuiwlddxkww6e3pkid4q2h3sauzn5rbd35z.py
# Topologically Sorted Source Nodes: [ref_1], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   ref_1 => convolution
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_5, %primals_6, [1], [0], [1], False, [0], 1), kwargs = {})
triton_poi_fused_convolution_0 = async_compile.triton('triton_poi_fused_convolution_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, 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_convolution_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_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/to/ctohnag3s2my72gfje47knfyigkawenmq4hwyddhmhls6qicb3io.py
# Topologically Sorted Source Nodes: [ref_1, expanded_query, add, tanh], Original ATen: [aten.convolution, aten.repeat, aten.add, aten.tanh]
# Source node to ATen node mapping:
#   add => add
#   expanded_query => repeat
#   ref_1 => convolution
#   tanh => tanh
# Graph fragment:
#   %convolution : [num_users=2] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_5, %primals_6, [1], [0], [1], False, [0], 1), kwargs = {})
#   %repeat : [num_users=1] = call_function[target=torch.ops.aten.repeat.default](args = (%unsqueeze, [1, 1, 4]), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%repeat, %convolution), kwargs = {})
#   %tanh : [num_users=2] = call_function[target=torch.ops.aten.tanh.default](args = (%add,), kwargs = {})
triton_poi_fused_add_convolution_repeat_tanh_1 = async_compile.triton('triton_poi_fused_add_convolution_repeat_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=[64], 
    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_convolution_repeat_tanh_1', '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_convolution_repeat_tanh_1(in_out_ptr0, in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = (xindex // 4) % 4
    x3 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x4), xmask)
    tmp1 = tl.load(in_ptr0 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (x3), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp2
    tmp5 = libdevice.tanh(tmp4)
    tl.store(in_out_ptr0 + (x4), tmp2, xmask)
    tl.store(out_ptr0 + (x4), tmp5, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/i5/ci57psuuueutwfqpm57dmpddhnflxjjxpqzf6cwcsnd2zbemfstl.py
# Topologically Sorted Source Nodes: [V], Original ATen: [aten.repeat]
# Source node to ATen node mapping:
#   V => repeat_1
# Graph fragment:
#   %repeat_1 : [num_users=2] = call_function[target=torch.ops.aten.repeat.default](args = (%unsqueeze_2, [4, 1, 1]), kwargs = {})
triton_poi_fused_repeat_2 = async_compile.triton('triton_poi_fused_repeat_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: '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_repeat_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_repeat_2(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 % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2), tmp0, 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, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, ), (1, ))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    assert_size_stride(primals_7, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_3, primals_4, reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [ref_1], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(primals_1, buf1, 16, 4, grid=grid(16, 4), stream=stream0)
        # Topologically Sorted Source Nodes: [ref_1], Original ATen: [aten.convolution]
        buf2 = extern_kernels.convolution(buf1, primals_5, stride=(1,), padding=(0,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4), (16, 4, 1))
        buf3 = buf2; del buf2  # reuse
        buf4 = buf1; del buf1  # reuse
        # Topologically Sorted Source Nodes: [ref_1, expanded_query, add, tanh], Original ATen: [aten.convolution, aten.repeat, aten.add, aten.tanh]
        triton_poi_fused_add_convolution_repeat_tanh_1.run(buf3, primals_6, buf0, buf4, 64, grid=grid(64), stream=stream0)
        del primals_6
        buf5 = reinterpret_tensor(buf0, (4, 1, 4), (4, 16, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [V], Original ATen: [aten.repeat]
        triton_poi_fused_repeat_2.run(primals_7, buf5, 16, grid=grid(16), stream=stream0)
        del primals_7
        buf6 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [V, bmm], Original ATen: [aten.repeat, aten.bmm]
        extern_kernels.bmm(buf5, buf4, out=buf6)
    return (buf3, reinterpret_tensor(buf6, (4, 4), (4, 1), 0), primals_4, primals_5, reinterpret_tensor(primals_1, (4, 4, 4), (16, 1, 4), 0), buf4, reinterpret_tensor(buf5, (4, 4, 1), (4, 1, 4), 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, 4, 4), (16, 4, 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, ), (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, 4, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = 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])
    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 math
import torch
import torch.nn as nn


class Attention(nn.Module):
    """
        Using two types of attention mechanism: "Dot" and "Bahdanau"
    """

    def __init__(self, hidden_size, use_tanh=False, C=10, name='Bahdanau',
        use_cuda=True):
        super(Attention, self).__init__()
        self.use_tanh = use_tanh
        self.C = C
        self.name = name
        if name == 'Bahdanau':
            self.W_query = nn.Linear(hidden_size, hidden_size)
            self.W_ref = nn.Conv1d(hidden_size, hidden_size, 1, 1)
            V = torch.FloatTensor(hidden_size)
            if use_cuda:
                V = V
            self.V = nn.Parameter(V)
            self.V.data.uniform_(-(1.0 / math.sqrt(hidden_size)), 1.0 /
                math.sqrt(hidden_size))

    def forward(self, query, ref):
        """
        Args:
            query: [batch_size x hidden_size]
            ref:   [batch_size x seq_len x hidden_size]
        """
        batch_size = ref.size(0)
        seq_len = ref.size(1)
        if self.name == 'Bahdanau':
            ref = ref.permute(0, 2, 1)
            query = self.W_query(query).unsqueeze(2)
            ref = self.W_ref(ref)
            expanded_query = query.repeat(1, 1, seq_len)
            V = self.V.unsqueeze(0).unsqueeze(0).repeat(batch_size, 1, 1)
            logits = torch.bmm(V, torch.tanh(expanded_query + ref)).squeeze(1)
        elif self.name == 'Dot':
            query = query.unsqueeze(2)
            logits = torch.bmm(ref, query).squeeze(2)
            ref = ref.permute(0, 2, 1)
        else:
            raise NotImplementedError
        if self.use_tanh:
            logits = self.C * torch.tanh(logits)
        else:
            logits = logits
        return ref, logits


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


def get_init_inputs():
    return [[], {'hidden_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 math
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_convolution_0(in_ptr0, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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 % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_convolution_repeat_tanh_1(in_out_ptr0, in_ptr0,
    in_ptr1, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x1 = xindex // 4 % 4
    x3 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp2
    tmp5 = libdevice.tanh(tmp4)
    tl.store(in_out_ptr0 + x4, tmp2, xmask)
    tl.store(out_ptr0 + x4, tmp5, xmask)


@triton.jit
def triton_poi_fused_repeat_2(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 % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + x2, tmp0, 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, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, primals_4, reinterpret_tensor(
            primals_2, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf1, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf2 = extern_kernels.convolution(buf1, primals_5, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 4, 4), (16, 4, 1))
        buf3 = buf2
        del buf2
        buf4 = buf1
        del buf1
        triton_poi_fused_add_convolution_repeat_tanh_1[grid(64)](buf3,
            primals_6, buf0, buf4, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_6
        buf5 = reinterpret_tensor(buf0, (4, 1, 4), (4, 16, 1), 0)
        del buf0
        triton_poi_fused_repeat_2[grid(16)](primals_7, buf5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.float32)
        extern_kernels.bmm(buf5, buf4, out=buf6)
    return buf3, reinterpret_tensor(buf6, (4, 4), (4, 1), 0
        ), primals_4, primals_5, reinterpret_tensor(primals_1, (4, 4, 4), (
        16, 1, 4), 0), buf4, reinterpret_tensor(buf5, (4, 4, 1), (4, 1, 4), 0)


class AttentionNew(nn.Module):
    """
        Using two types of attention mechanism: "Dot" and "Bahdanau"
    """

    def __init__(self, hidden_size, use_tanh=False, C=10, name='Bahdanau',
        use_cuda=True):
        super(AttentionNew, self).__init__()
        self.use_tanh = use_tanh
        self.C = C
        self.name = name
        if name == 'Bahdanau':
            self.W_query = nn.Linear(hidden_size, hidden_size)
            self.W_ref = nn.Conv1d(hidden_size, hidden_size, 1, 1)
            V = torch.FloatTensor(hidden_size)
            if use_cuda:
                V = V
            self.V = nn.Parameter(V)
            self.V.data.uniform_(-(1.0 / math.sqrt(hidden_size)), 1.0 /
                math.sqrt(hidden_size))

    def forward(self, input_0, input_1):
        primals_3 = self.V
        primals_2 = self.W_query.weight
        primals_6 = self.W_query.bias
        primals_5 = self.W_ref.weight
        primals_7 = self.W_ref.bias
        primals_4 = input_0
        primals_1 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]
