# 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/mo/cmoh7o4cxc7ciy47lvubs3qyhyjzcdxvcowoeso42n7ttsk4rmwm.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
# Source node to ATen node mapping:
#   x => convolution
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [1], [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=[256, 64], 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 = 240
    xnumel = 60
    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 % 60
    y1 = (yindex // 60)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (60*x2) + (3600*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (60*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/ts/cts3ekm3ffln5jz7hqfjg5zj7bgpiimefqincreweblqfkvel2nd.py
# Topologically Sorted Source Nodes: [x, x_1, x_2], Original ATen: [aten.convolution, aten.relu, aten.unsqueeze, aten.threshold_backward]
# Source node to ATen node mapping:
#   x => convolution
#   x_1 => relu
#   x_2 => unsqueeze
# Graph fragment:
#   %convolution : [num_users=1] = call_function[target=torch.ops.aten.convolution.default](args = (%permute, %primals_2, %primals_3, [1], [1], [1], False, [0], 1), kwargs = {})
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%convolution,), kwargs = {})
#   %unsqueeze : [num_users=2] = call_function[target=torch.ops.aten.unsqueeze.default](args = (%relu, -2), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_1 = async_compile.triton('triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_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=[1024, 64], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*i1', 4: 'i32', 5: '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_convolution_relu_threshold_backward_unsqueeze_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_relu_threshold_backward_unsqueeze_1(in_ptr0, in_ptr1, out_ptr1, out_ptr2, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 920
    xnumel = 60
    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
    y3 = yindex
    y0 = yindex % 230
    y1 = (yindex // 230)
    tmp0 = tl.load(in_ptr0 + (x2 + (60*y3)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr1 + (y0 + (230*x2) + (13800*y1)), tmp4, xmask & ymask)
    tl.store(out_ptr2 + (x2 + (60*y0) + (13824*y1)), tmp6, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/6r/c6rqnmhhfmorjkuzqsc3hkikjpes5fclzv2vyhca4bnkm2ppcsua.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
# Source node to ATen node mapping:
#   x_2 => _low_memory_max_pool2d_with_offsets, getitem_1
# Graph fragment:
#   %_low_memory_max_pool2d_with_offsets : [num_users=2] = call_function[target=torch.ops.prims._low_memory_max_pool2d_with_offsets.default](args = (%unsqueeze, [1, 4], [1, 4], [0, 0], [1, 1], False), kwargs = {})
#   %getitem_1 : [num_users=1] = call_function[target=operator.getitem](args = (%_low_memory_max_pool2d_with_offsets, 1), kwargs = {})
triton_poi_fused_max_pool2d_with_indices_2 = async_compile.triton('triton_poi_fused_max_pool2d_with_indices_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=[64, 256], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*i8', 2: '*fp32', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_max_pool2d_with_indices_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_max_pool2d_with_indices_2(in_ptr0, out_ptr0, out_ptr1, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 60
    xnumel = 230
    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
    x1 = xindex
    y0 = yindex
    y2 = yindex % 15
    y3 = (yindex // 15)
    tmp0 = tl.load(in_ptr0 + (x1 + (920*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (230 + x1 + (920*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (460 + x1 + (920*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (690 + x1 + (920*y0)), xmask & ymask, eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1, 1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1, 1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x1 + (230*y0)), tmp15, xmask & ymask)
    tl.store(out_ptr1 + (y2 + (15*x1) + (3450*y3)), tmp16, xmask & ymask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 60, 60), (3600, 60, 1))
    assert_size_stride(primals_2, (230, 60, 3), (180, 3, 1))
    assert_size_stride(primals_3, (230, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 60, 60), (3600, 60, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        stream0 = get_raw_stream(0)
        triton_poi_fused_convolution_0.run(primals_1, buf0, 240, 60, grid=grid(240, 60), stream=stream0)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.convolution]
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 230, 60), (13800, 60, 1))
        del buf0
        buf3 = empty_strided_cuda((4, 230, 1, 60), (13800, 1, 13800, 230), torch.float32)
        buf6 = empty_strided_cuda((4, 230, 60), (13824, 60, 1), torch.bool)
        # Topologically Sorted Source Nodes: [x, x_1, x_2], Original ATen: [aten.convolution, aten.relu, aten.unsqueeze, aten.threshold_backward]
        triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_1.run(buf1, primals_3, buf3, buf6, 920, 60, grid=grid(920, 60), stream=stream0)
        del buf1
        del primals_3
        buf4 = empty_strided_cuda((4, 230, 1, 15), (3450, 1, 3450, 230), torch.int8)
        buf5 = empty_strided_cuda((4, 230, 1, 15), (3450, 15, 15, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.max_pool2d_with_indices]
        triton_poi_fused_max_pool2d_with_indices_2.run(buf3, buf4, buf5, 60, 230, grid=grid(60, 230), stream=stream0)
    return (reinterpret_tensor(buf5, (4, 230, 15), (3450, 15, 1), 0), primals_2, reinterpret_tensor(primals_1, (4, 60, 60), (3600, 1, 60), 0), buf3, buf4, buf6, )


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, 60, 60), (3600, 60, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((230, 60, 3), (180, 3, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((230, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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
import torch.nn.functional as F


class Encoder(nn.Module):
    """利用卷积 + 最大池化得到句子嵌入"""

    def __init__(self, max_length, word_embedding_dim=50, pos_embedding_dim
        =5, hidden_size=230):
        nn.Module.__init__(self)
        self.max_length = max_length
        self.hidden_size = hidden_size
        self.embedding_dim = word_embedding_dim + pos_embedding_dim * 2
        self.conv = nn.Conv1d(self.embedding_dim, self.hidden_size, 3,
            padding=1)
        self.pool = nn.MaxPool1d(max_length)

    def forward(self, inputs):
        return self.cnn(inputs)

    def cnn(self, inputs):
        x = self.conv(inputs.transpose(1, 2))
        x = F.relu(x)
        x = self.pool(x)
        return x.squeeze(2)


def get_inputs():
    return [torch.rand([4, 60, 60])]


def get_init_inputs():
    return [[], {'max_length': 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 import triton_helpers
import torch.nn as nn
import torch.nn.functional as F
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 = 240
    xnumel = 60
    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 % 60
    y1 = yindex // 60
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 60 * x2 + 3600 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 60 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_1(in_ptr0,
    in_ptr1, out_ptr1, out_ptr2, ynumel, xnumel, YBLOCK: tl.constexpr,
    XBLOCK: tl.constexpr):
    ynumel = 920
    xnumel = 60
    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
    y3 = yindex
    y0 = yindex % 230
    y1 = yindex // 230
    tmp0 = tl.load(in_ptr0 + (x2 + 60 * y3), xmask & ymask, eviction_policy
        ='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1, 1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(out_ptr1 + (y0 + 230 * x2 + 13800 * y1), tmp4, xmask & ymask)
    tl.store(out_ptr2 + (x2 + 60 * y0 + 13824 * y1), tmp6, xmask & ymask)


@triton.jit
def triton_poi_fused_max_pool2d_with_indices_2(in_ptr0, out_ptr0, out_ptr1,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 60
    xnumel = 230
    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
    x1 = xindex
    y0 = yindex
    y2 = yindex % 15
    y3 = yindex // 15
    tmp0 = tl.load(in_ptr0 + (x1 + 920 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (230 + x1 + 920 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (460 + x1 + 920 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (690 + x1 + 920 * y0), xmask & ymask,
        eviction_policy='evict_last')
    tmp2 = tmp1 > tmp0
    tmp3 = tl.full([1, 1], 1, tl.int8)
    tmp4 = tl.full([1, 1], 0, tl.int8)
    tmp5 = tl.where(tmp2, tmp3, tmp4)
    tmp6 = triton_helpers.maximum(tmp1, tmp0)
    tmp8 = tmp7 > tmp6
    tmp9 = tl.full([1, 1], 2, tl.int8)
    tmp10 = tl.where(tmp8, tmp9, tmp5)
    tmp11 = triton_helpers.maximum(tmp7, tmp6)
    tmp13 = tmp12 > tmp11
    tmp14 = tl.full([1, 1], 3, tl.int8)
    tmp15 = tl.where(tmp13, tmp14, tmp10)
    tmp16 = triton_helpers.maximum(tmp12, tmp11)
    tl.store(out_ptr0 + (x1 + 230 * y0), tmp15, xmask & ymask)
    tl.store(out_ptr1 + (y2 + 15 * x1 + 3450 * y3), tmp16, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 60, 60), (3600, 60, 1))
    assert_size_stride(primals_2, (230, 60, 3), (180, 3, 1))
    assert_size_stride(primals_3, (230,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 60, 60), (3600, 60, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(240, 60)](primals_1, buf0, 240,
            60, XBLOCK=32, YBLOCK=32, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 230, 60), (13800, 60, 1))
        del buf0
        buf3 = empty_strided_cuda((4, 230, 1, 60), (13800, 1, 13800, 230),
            torch.float32)
        buf6 = empty_strided_cuda((4, 230, 60), (13824, 60, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_unsqueeze_1[grid
            (920, 60)](buf1, primals_3, buf3, buf6, 920, 60, XBLOCK=32,
            YBLOCK=32, num_warps=4, num_stages=1)
        del buf1
        del primals_3
        buf4 = empty_strided_cuda((4, 230, 1, 15), (3450, 1, 3450, 230),
            torch.int8)
        buf5 = empty_strided_cuda((4, 230, 1, 15), (3450, 15, 15, 1), torch
            .float32)
        triton_poi_fused_max_pool2d_with_indices_2[grid(60, 230)](buf3,
            buf4, buf5, 60, 230, XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1
            )
    return reinterpret_tensor(buf5, (4, 230, 15), (3450, 15, 1), 0
        ), primals_2, reinterpret_tensor(primals_1, (4, 60, 60), (3600, 1, 
        60), 0), buf3, buf4, buf6


class EncoderNew(nn.Module):
    """利用卷积 + 最大池化得到句子嵌入"""

    def __init__(self, max_length, word_embedding_dim=50, pos_embedding_dim
        =5, hidden_size=230):
        nn.Module.__init__(self)
        self.max_length = max_length
        self.hidden_size = hidden_size
        self.embedding_dim = word_embedding_dim + pos_embedding_dim * 2
        self.conv = nn.Conv1d(self.embedding_dim, self.hidden_size, 3,
            padding=1)
        self.pool = nn.MaxPool1d(max_length)

    def cnn(self, inputs):
        x = self.conv(inputs.transpose(1, 2))
        x = F.relu(x)
        x = self.pool(x)
        return x.squeeze(2)

    def forward(self, input_0):
        primals_2 = self.conv.weight
        primals_3 = self.conv.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
