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
from collections import OrderedDict
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_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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)


@triton.jit
def triton_poi_fused_mul_2(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_mul_3(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 + (4 + 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__safe_softmax_4(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__safe_softmax_5(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 64
    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')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr1 + x2, xmask)
    tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = float('-inf')
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2 == 0
    tmp4 = tmp3.to(tl.int64)
    tmp5 = tmp4 != 0
    tmp7 = tmp6 == tmp1
    tmp8 = tmp7 == 0
    tmp9 = tmp8.to(tl.int64)
    tmp10 = tmp9 != 0
    tmp11 = tmp5 | tmp10
    tmp13 = tmp12 == tmp1
    tmp14 = tmp13 == 0
    tmp15 = tmp14.to(tl.int64)
    tmp16 = tmp15 != 0
    tmp17 = tmp11 | tmp16
    tmp19 = tmp18 == tmp1
    tmp20 = tmp19 == 0
    tmp21 = tmp20.to(tl.int64)
    tmp22 = tmp21 != 0
    tmp23 = tmp17 | tmp22
    tmp24 = tmp23 == 0
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp32 = tmp30 + tmp31
    tmp33 = tmp25 / tmp32
    tmp34 = 0.0
    tmp35 = tl.where(tmp24, tmp34, tmp33)
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused_clone_6(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_native_layer_norm_7(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    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_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x0), 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 * x0), 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 * x0), 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 + x0, tmp16, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_8(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    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 + x2, xmask)
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, 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 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_mul_sigmoid_9(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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 1.702
    tmp2 = tmp0 * tmp1
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp0 * tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_10(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, 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_ptr1 + x2, xmask)
    tmp3 = tl.load(in_out_ptr0 + x2, xmask)
    tmp4 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, 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) = 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,), (1,))
    assert_size_stride(primals_4, (12, 4), (4, 1))
    assert_size_stride(primals_5, (12,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (16, 4), (4, 1))
    assert_size_stride(primals_11, (16,), (1,))
    assert_size_stride(primals_12, (4, 16), (16, 1))
    assert_size_stride(primals_13, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        buf1 = empty_strided_cuda((4, 1), (1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(4)](primals_1, buf0, buf1,
            4, XBLOCK=4, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(16)](primals_1, buf0,
            buf1, primals_2, primals_3, buf2, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        del primals_2
        del primals_3
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4
            ), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4
            ), 16), out=buf4)
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(reinterpret_tensor(primals_5, (4,), (1,), 8),
            buf2, reinterpret_tensor(primals_4, (4, 4), (1, 4), 32), alpha=
            1, beta=1, out=buf5)
        buf6 = reinterpret_tensor(buf3, (1, 4, 4, 1), (16, 1, 4, 16), 0)
        del buf3
        triton_poi_fused_mul_2[grid(16)](buf6, primals_5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf4, (1, 4, 1, 4), (16, 1, 16, 4), 0)
        del buf4
        triton_poi_fused_mul_3[grid(16)](buf7, primals_5, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del primals_5
        buf8 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf6, (4, 4, 1), (1, 4, 0), 0
            ), reinterpret_tensor(buf7, (4, 1, 4), (1, 0, 4), 0), out=buf8)
        buf9 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__safe_softmax_4[grid(64)](buf8, buf9, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        buf10 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__safe_softmax_5[grid(64)](buf8, buf9, buf10, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        buf11 = empty_strided_cuda((4, 4, 1), (4, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf5, (4, 4, 1), (1, 4, 0), 0), out=buf11)
        buf12 = empty_strided_cuda((4, 1, 4, 1), (4, 1, 1, 4), torch.float32)
        triton_poi_fused_clone_6[grid(4, 4)](buf11, buf12, 4, 4, XBLOCK=4,
            YBLOCK=4, num_warps=1, num_stages=1)
        buf13 = reinterpret_tensor(buf11, (4, 4), (4, 1), 0)
        del buf11
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf12, (4, 4), (
            4, 1), 0), reinterpret_tensor(primals_6, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf13)
        del primals_7
        buf14 = buf1
        del buf1
        buf15 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_7[grid(4)](primals_1, buf13,
            buf14, buf15, 4, XBLOCK=4, num_warps=1, num_stages=1)
        buf16 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_8[grid(16)](primals_1, buf13,
            buf14, buf15, primals_8, primals_9, buf16, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del buf14
        del buf15
        del primals_9
        buf17 = reinterpret_tensor(buf9, (4, 16), (16, 1), 0)
        del buf9
        extern_kernels.addmm(primals_11, buf16, reinterpret_tensor(
            primals_10, (4, 16), (1, 4), 0), alpha=1, beta=1, out=buf17)
        del primals_11
        buf18 = reinterpret_tensor(buf8, (4, 16), (16, 1), 0)
        del buf8
        triton_poi_fused_mul_sigmoid_9[grid(64)](buf17, buf18, 64, XBLOCK=
            64, num_warps=1, num_stages=1)
        buf19 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf18, reinterpret_tensor(primals_12, (16, 4), (1,
            16), 0), out=buf19)
        buf20 = buf19
        del buf19
        triton_poi_fused_add_10[grid(16)](buf20, primals_1, buf13,
            primals_13, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_13
    return (buf20, primals_1, primals_8, buf2, buf10, reinterpret_tensor(
        buf12, (4, 4), (4, 1), 0), buf13, buf16, buf17, buf18, primals_12,
        primals_10, primals_6, reinterpret_tensor(buf5, (4, 1, 4), (1, 1, 4
        ), 0), reinterpret_tensor(buf6, (4, 1, 4), (1, 4, 4), 0),
        reinterpret_tensor(buf7, (4, 4, 1), (1, 4, 16), 0),
        reinterpret_tensor(primals_4, (4, 4), (4, 1), 32),
        reinterpret_tensor(primals_4, (4, 4), (4, 1), 16),
        reinterpret_tensor(primals_4, (4, 4), (4, 1), 0))


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: 'torch.Tensor'):
        orig_type = x.dtype
        ret = super().forward(x.type(torch.float32))
        return ret.type(orig_type)


class QuickGELU(nn.Module):

    def forward(self, x: 'torch.Tensor'):
        return x * torch.sigmoid(1.702 * x)


class ResidualAttentionBlockNew(nn.Module):

    def __init__(self, d_model: 'int', n_head: 'int', attn_mask:
        'torch.Tensor'=None):
        super().__init__()
        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(OrderedDict([('c_fc', nn.Linear(d_model, 
            d_model * 4)), ('gelu', QuickGELU()), ('c_proj', nn.Linear(
            d_model * 4, d_model))]))
        self.ln_2 = LayerNorm(d_model)
        self.attn_mask = attn_mask

    def attention(self, x: 'torch.Tensor'):
        self.attn_mask = self.attn_mask if self.attn_mask is not None else None
        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask
            )[0]

    def forward(self, input_0):
        primals_4 = self.attn.in_proj_weight
        primals_5 = self.attn.in_proj_bias
        primals_1 = self.attn.out_proj.weight
        primals_2 = self.attn.out_proj.bias
        primals_3 = self.ln_1.weight
        primals_7 = self.ln_1.bias
        primals_10 = self.mlp.c_fc.weight
        primals_11 = self.mlp.c_fc.bias
        primals_12 = self.mlp.c_proj.weight
        primals_8 = self.mlp.c_proj.bias
        primals_9 = self.ln_2.weight
        primals_13 = self.ln_2.bias
        primals_6 = 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])
        return output[0]


Measured latency_ms: 0.072787