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 libdevice, math as tl_math
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__softmax_0(in_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_out_ptr0 + x0, xmask)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tmp2 - tmp2
    tmp4 = tmp3 * tmp1
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp5 / tmp5
    tl.store(in_out_ptr0 + x0, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_1(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
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + x2, tmp16, xmask)
    tl.store(out_ptr1 + x2, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_2(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, 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 % 16
    x4 = xindex // 4
    x5 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr1 + x3, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x4, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x4, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + x5, tmp13, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_3(in_out_ptr0, in_ptr0,
    out_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
    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)


@triton.jit
def triton_poi_fused_add_4(in_out_ptr0, in_ptr0, in_ptr1, 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')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_5(in_ptr0, 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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_6(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (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,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4), (4, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4, 4), (4, 1))
    assert_size_stride(primals_15, (4,), (1,))
    assert_size_stride(primals_16, (4,), (1,))
    assert_size_stride(primals_17, (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_1, 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, 1), torch.float32)
        extern_kernels.addmm(primals_5, primals_1, reinterpret_tensor(
            primals_4, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf1)
        del primals_4
        del primals_5
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_7, primals_1, reinterpret_tensor(
            primals_6, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_6
        del primals_7
        buf3 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (16, 1, 1), (1, 1, 1), 
            0), reinterpret_tensor(buf1, (16, 1, 1), (1, 1, 1), 0), out=buf3)
        buf4 = buf3
        del buf3
        get_raw_stream(0)
        triton_poi_fused__softmax_0[grid(16)](buf4, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((16, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(buf4, reinterpret_tensor(buf2, (16, 1, 1), (1, 1,
            1), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_8, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf6)
        del primals_9
        buf7 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf8 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_add_native_layer_norm_1[grid(16)](buf6, primals_1,
            buf7, buf8, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_2[grid(64)](buf6, primals_1,
            buf7, buf8, primals_10, primals_11, buf9, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_11
        buf10 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf9, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_12, (4, 4), (1, 4), 0), out=buf10)
        buf11 = reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0)
        del buf10
        buf17 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_3[grid(64)](buf11,
            primals_13, buf17, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_13
        buf12 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf11, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_14, (4, 4), (1, 4), 0), out=buf12)
        buf13 = reinterpret_tensor(buf12, (4, 4, 4), (16, 4, 1), 0)
        del buf12
        triton_poi_fused_add_4[grid(64)](buf13, primals_15, buf9, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_15
        buf14 = buf8
        del buf8
        buf15 = buf7
        del buf7
        triton_poi_fused_native_layer_norm_5[grid(16)](buf13, buf14, buf15,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_6[grid(64)](buf13, buf14, buf15,
            primals_16, primals_17, buf16, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf14
        del buf15
        del primals_17
    return buf16, primals_1, primals_10, primals_16, buf4, reinterpret_tensor(
        buf5, (4, 4), (4, 1), 0), buf6, reinterpret_tensor(buf9, (16, 4), (
        4, 1), 0), reinterpret_tensor(buf11, (16, 4), (4, 1), 0
        ), buf13, primals_14, buf17, primals_12, primals_8, reinterpret_tensor(
        buf2, (16, 1, 1), (1, 1, 1), 0), reinterpret_tensor(buf0, (16, 1, 1
        ), (1, 1, 1), 0), reinterpret_tensor(buf1, (16, 1, 1), (1, 1, 1), 0)


class Scaled_Dot_Product_Attention(nn.Module):
    """Scaled Dot-Product Attention """

    def __init__(self):
        super(Scaled_Dot_Product_Attention, self).__init__()

    def forward(self, Q, K, V, scale=None):
        """
        Args:
            Q: [batch_size, len_Q, dim_Q]
            K: [batch_size, len_K, dim_K]
            V: [batch_size, len_V, dim_V]
            scale: 缩放因子 论文为根号dim_K
        Return:
            self-attention后的张量，以及attention张量
        """
        attention = torch.matmul(Q, K.permute(0, 2, 1))
        if scale:
            attention = attention * scale
        attention = F.softmax(attention, dim=-1)
        context = torch.matmul(attention, V)
        return context


class Multi_Head_Attention(nn.Module):

    def __init__(self, dim_model, num_head, dropout=0.0):
        super(Multi_Head_Attention, self).__init__()
        self.num_head = num_head
        assert dim_model % num_head == 0
        self.dim_head = dim_model // self.num_head
        self.fc_Q = nn.Linear(dim_model, num_head * self.dim_head)
        self.fc_K = nn.Linear(dim_model, num_head * self.dim_head)
        self.fc_V = nn.Linear(dim_model, num_head * self.dim_head)
        self.attention = Scaled_Dot_Product_Attention()
        self.fc = nn.Linear(num_head * self.dim_head, dim_model)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(dim_model)

    def forward(self, x):
        batch_size = x.size(0)
        Q = self.fc_Q(x)
        K = self.fc_K(x)
        V = self.fc_V(x)
        Q = Q.view(batch_size * self.num_head, -1, self.dim_head)
        K = K.view(batch_size * self.num_head, -1, self.dim_head)
        V = V.view(batch_size * self.num_head, -1, self.dim_head)
        scale = K.size(-1) ** -0.5
        context = self.attention(Q, K, V, scale)
        context = context.view(batch_size, -1, self.dim_head * self.num_head)
        out = self.fc(context)
        out = self.dropout(out)
        out = out + x
        out = self.layer_norm(out)
        return out


class Position_wise_Feed_Forward(nn.Module):

    def __init__(self, dim_model, hidden, dropout=0.0):
        super(Position_wise_Feed_Forward, self).__init__()
        self.fc1 = nn.Linear(dim_model, hidden)
        self.fc2 = nn.Linear(hidden, dim_model)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(dim_model)

    def forward(self, x):
        out = self.fc1(x)
        out = F.relu(out)
        out = self.fc2(out)
        out = self.dropout(out)
        out = out + x
        out = self.layer_norm(out)
        return out


class EncoderNew(nn.Module):

    def __init__(self, dim_model, num_head, hidden, dropout):
        super(EncoderNew, self).__init__()
        self.attention = Multi_Head_Attention(dim_model, num_head, dropout)
        self.feed_forward = Position_wise_Feed_Forward(dim_model, hidden,
            dropout)

    def forward(self, input_0):
        primals_1 = self.attention.fc_Q.weight
        primals_3 = self.attention.fc_Q.bias
        primals_2 = self.attention.fc_K.weight
        primals_5 = self.attention.fc_K.bias
        primals_4 = self.attention.fc_V.weight
        primals_7 = self.attention.fc_V.bias
        primals_6 = self.attention.fc.weight
        primals_9 = self.attention.fc.bias
        primals_10 = self.attention.layer_norm.weight
        primals_11 = self.attention.layer_norm.bias
        primals_8 = self.feed_forward.fc1.weight
        primals_13 = self.feed_forward.fc1.bias
        primals_12 = self.feed_forward.fc2.weight
        primals_15 = self.feed_forward.fc2.bias
        primals_16 = self.feed_forward.layer_norm.weight
        primals_17 = self.feed_forward.layer_norm.bias
        primals_14 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17])
        return output[0]


Measured latency_ms: 0.096236