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
from torch._inductor.runtime.triton_helpers import math as tl_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_clone_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused__to_copy_1(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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tmp0 != 0
    tl.store(out_ptr0 + x0, tmp1, xmask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last').to(tl
        .int1)
    tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp5 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp9 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp13 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = -1.0000000116860974e-07
    tmp3 = tl.where(tmp0, tmp2, tmp1)
    tmp6 = tl.where(tmp4, tmp2, tmp5)
    tmp7 = triton_helpers.maximum(tmp3, tmp6)
    tmp10 = tl.where(tmp8, tmp2, tmp9)
    tmp11 = triton_helpers.maximum(tmp7, tmp10)
    tmp14 = tl.where(tmp12, tmp2, tmp13)
    tmp15 = triton_helpers.maximum(tmp11, tmp14)
    tmp16 = tmp3 - tmp15
    tmp17 = tl_math.exp(tmp16)
    tmp18 = tmp6 - tmp15
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp17 + tmp19
    tmp21 = tmp10 - tmp15
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp20 + tmp22
    tmp24 = tmp14 - tmp15
    tmp25 = tl_math.exp(tmp24)
    tmp26 = tmp23 + tmp25
    tl.store(out_ptr0 + x2, tmp15, xmask)
    tl.store(out_ptr1 + x2, tmp26, xmask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_3(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    x4 = xindex // 4
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        ).to(tl.int1)
    tmp1 = tl.load(in_out_ptr0 + x3, xmask)
    tmp4 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp2 = -1.0000000116860974e-07
    tmp3 = tl.where(tmp0, tmp2, tmp1)
    tmp5 = tmp3 - tmp4
    tmp6 = tl_math.exp(tmp5)
    tmp8 = tmp6 / tmp7
    tl.store(in_out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_4(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x1 + x0), tmp4 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr1 + (4 * x1 + (-4 + x0)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tl.load(in_ptr1 + (4 * x1 + (-8 + x0)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp17 = tmp15 - tmp16
    tmp18 = tl.full(tmp17.shape, 0.0, tmp17.dtype)
    tmp19 = tl.where(tmp14, tmp17, tmp18)
    tmp20 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp23 = tl.load(in_ptr0 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp24 = tl.load(in_ptr1 + (4 * x1 + (-12 + x0)), tmp20 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp25 = tmp23 * tmp24
    tmp26 = tl.full(tmp25.shape, 0.0, tmp25.dtype)
    tmp27 = tl.where(tmp20, tmp25, tmp26)
    tmp28 = tl.where(tmp14, tmp19, tmp27)
    tmp29 = tl.where(tmp9, tmp10, tmp28)
    tmp30 = tl.where(tmp4, tmp5, tmp29)
    tl.store(out_ptr0 + x2, tmp30, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_5(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 8
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, 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, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (8, 16), (16, 1))
    assert_size_stride(primals_7, (8,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](buf1, primals_2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(primals_4, buf1, out=buf2)
        buf3 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.bool)
        triton_poi_fused__to_copy_1[grid(16)](primals_5, buf3, 16, XBLOCK=
            16, num_warps=1, num_stages=1)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused__softmax_masked_fill_2[grid(16)](buf3, buf2, buf4,
            buf5, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf6 = buf2
        del buf2
        triton_poi_fused__softmax_masked_fill_3[grid(64)](buf6, buf3, buf4,
            buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf4
        del buf5
        buf7 = reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0)
        del buf1
        extern_kernels.bmm(buf6, primals_3, out=buf7)
        buf8 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_cat_4[grid(256)](primals_4, buf7, buf8, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del buf7
        buf9 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf8, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_6, (16, 8), (1, 16), 0), out=buf9)
        buf10 = reinterpret_tensor(buf9, (4, 4, 8), (32, 8, 1), 0)
        del buf9
        buf11 = empty_strided_cuda((4, 4, 8), (32, 8, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_5[grid(128)](buf10,
            primals_7, buf11, 128, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_7
    return buf10, primals_3, primals_4, buf3, buf6, reinterpret_tensor(buf8,
        (16, 16), (16, 1), 0), buf11, primals_6


class MatchModuleNew(nn.Module):
    """
    Computing the match representation for Match LSTM.

    :param hidden_size: Size of hidden vectors.
    :param dropout_rate: Dropout rate of the projection layer. Defaults to 0.

    Examples:
        >>> import torch
        >>> attention = MatchModule(hidden_size=10)
        >>> v1 = torch.randn(4, 5, 10)
        >>> v1.shape
        torch.Size([4, 5, 10])
        >>> v2 = torch.randn(4, 5, 10)
        >>> v2_mask = torch.ones(4, 5).to(dtype=torch.uint8)
        >>> attention(v1, v2, v2_mask).shape
        torch.Size([4, 5, 20])


    """

    def __init__(self, hidden_size, dropout_rate=0):
        """Init."""
        super().__init__()
        self.v2_proj = nn.Linear(hidden_size, hidden_size)
        self.proj = nn.Linear(hidden_size * 4, hidden_size * 2)
        self.dropout = nn.Dropout(p=dropout_rate)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.v2_proj.weight
        primals_2 = self.v2_proj.bias
        primals_6 = self.proj.weight
        primals_7 = self.proj.bias
        primals_3 = input_0
        primals_4 = input_1
        primals_5 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]


Measured latency_ms: 0.109513