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]


Measured latency_ms: 0.077552