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_mul_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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 = 1.0
    tmp4 = tmp2 * tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused__softmax_1(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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 4
    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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x1), xmask & ymask)
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_4(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_5(in_ptr0, in_ptr1, out_ptr0, out_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 % 16
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 1.0
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr1 + x2, tmp7, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (12,), (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, (12, 4), (4, 1))
    assert_size_stride(primals_8, (12,), (1,))
    assert_size_stride(primals_9, (4, 4), (4, 1))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (16, 4), (4, 1))
    assert_size_stride(primals_12, (16,), (1,))
    assert_size_stride(primals_13, (4, 16), (16, 1))
    assert_size_stride(primals_14, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_1, reinterpret_tensor(primals_2, (4, 4),
            (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_3, (4,), (1,), 4),
            primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 16),
            alpha=1, beta=1, out=buf1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_3, (4,), (1,), 8),
            primals_1, reinterpret_tensor(primals_2, (4, 4), (1, 4), 32),
            alpha=1, beta=1, out=buf2)
        del primals_2
        buf3 = reinterpret_tensor(buf0, (4, 4, 1), (1, 4, 16), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(16)](buf3, primals_3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_3
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(buf3, reinterpret_tensor(buf1, (4, 1, 4), (1, 1,
            4), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf6 = buf4
        del buf4
        triton_poi_fused__softmax_2[grid(64)](buf5, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf6, reinterpret_tensor(buf2, (4, 4, 1), (1, 4,
            1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(4, 4)](buf7, buf8, 4, 4, XBLOCK=4,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf7, (4, 4), (4, 1), 0)
        del buf7
        extern_kernels.mm(reinterpret_tensor(buf8, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf9)
        buf10 = buf9
        del buf9
        triton_poi_fused_add_4[grid(16)](buf10, primals_1, primals_5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
        buf11 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf10, reinterpret_tensor(primals_7, (4, 4), (1, 
            4), 0), out=buf11)
        buf12 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_8, (4,), (1,), 4),
            primals_6, reinterpret_tensor(primals_7, (4, 4), (1, 4), 16),
            alpha=1, beta=1, out=buf12)
        buf13 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_8, (4,), (1,), 8),
            primals_6, reinterpret_tensor(primals_7, (4, 4), (1, 4), 32),
            alpha=1, beta=1, out=buf13)
        buf14 = reinterpret_tensor(buf11, (4, 4, 1), (1, 4, 16), 0)
        del buf11
        triton_poi_fused_mul_0[grid(16)](buf14, primals_8, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_8
        buf15 = buf5
        del buf5
        extern_kernels.bmm(buf14, reinterpret_tensor(buf12, (4, 1, 4), (1, 
            1, 4), 0), out=buf15)
        buf16 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(64)](buf15, buf16, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf17 = buf15
        del buf15
        triton_poi_fused__softmax_2[grid(64)](buf16, buf17, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf18 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(buf17, reinterpret_tensor(buf13, (4, 4, 1), (1, 
            4, 1), 0), out=buf18)
        buf19 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(4, 4)](buf18, buf19, 4, 4, XBLOCK=4,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf20 = reinterpret_tensor(buf18, (4, 4), (4, 1), 0)
        del buf18
        extern_kernels.mm(reinterpret_tensor(buf19, (4, 4), (4, 1), 0),
            reinterpret_tensor(primals_9, (4, 4), (1, 4), 0), out=buf20)
        buf21 = buf20
        del buf20
        triton_poi_fused_add_4[grid(16)](buf21, buf10, primals_10, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_10
        buf22 = reinterpret_tensor(buf16, (4, 16), (16, 1), 0)
        del buf16
        extern_kernels.mm(buf21, reinterpret_tensor(primals_11, (4, 16), (1,
            4), 0), out=buf22)
        buf23 = empty_strided_cuda((4, 16), (16, 1), torch.bool)
        buf24 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        triton_poi_fused_leaky_relu_5[grid(64)](buf22, primals_12, buf23,
            buf24, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del buf22
        del primals_12
        buf25 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf24, reinterpret_tensor(primals_13, (16, 4), (1,
            16), 0), out=buf25)
        buf26 = buf25
        del buf25
        triton_poi_fused_add_4[grid(16)](buf26, buf21, primals_14, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_14
    return (buf26, primals_1, buf6, reinterpret_tensor(buf8, (4, 4), (4, 1),
        0), buf10, primals_6, buf17, reinterpret_tensor(buf19, (4, 4), (4, 
        1), 0), buf21, buf23, buf24, primals_13, primals_11, primals_9,
        reinterpret_tensor(buf13, (4, 1, 4), (1, 1, 4), 0),
        reinterpret_tensor(buf14, (4, 1, 4), (1, 1, 4), 0),
        reinterpret_tensor(buf12, (4, 4, 1), (1, 4, 1), 0),
        reinterpret_tensor(primals_7, (4, 4), (4, 1), 0), primals_4,
        reinterpret_tensor(buf2, (4, 1, 4), (1, 1, 4), 0),
        reinterpret_tensor(buf3, (4, 1, 4), (1, 1, 4), 0),
        reinterpret_tensor(buf1, (4, 4, 1), (1, 4, 1), 0))


class TransformerDecoderLayerNew(nn.Module):

    def __init__(self, d_model, nhead, dim_feedforward=16, dropout=0):
        super(TransformerDecoderLayerNew, self).__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout
            =dropout)
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)
        self.activation = nn.LeakyReLU(True)

    def forward(self, input_0, input_1):
        primals_2 = self.self_attn.in_proj_weight
        primals_3 = self.self_attn.in_proj_bias
        primals_1 = self.self_attn.out_proj.weight
        primals_5 = self.self_attn.out_proj.bias
        primals_7 = self.multihead_attn.in_proj_weight
        primals_8 = self.multihead_attn.in_proj_bias
        primals_4 = self.multihead_attn.out_proj.weight
        primals_10 = self.multihead_attn.out_proj.bias
        primals_11 = self.linear1.weight
        primals_12 = self.linear1.bias
        primals_13 = self.linear2.weight
        primals_14 = self.linear2.bias
        primals_6 = input_0
        primals_9 = input_1
        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])
        return output[0]


Measured latency_ms: 0.165055