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 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_native_layer_norm_0(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_1(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)


@triton.jit
def triton_poi_fused_stack_2(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 // 16
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x3 = xindex
    tmp0 = x2
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 + x0 + 12 * x1 + 48 * x2), tmp4 & xmask,
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 8, tl.int64)
    tmp9 = tl.load(in_ptr0 + (8 + x0 + 12 * x1 + 48 * (-4 + x2)), tmp6 &
        xmask, other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_tril_5(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 % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0 + -1 * x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 <= tmp1
    tl.store(out_ptr0 + x2, tmp2, xmask)


@triton.jit
def triton_poi_fused__softmax_div_mul_rsub_sub_6(in_ptr0, 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 % 4
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp23 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp33 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = -1 * x0
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 <= tmp4
    tmp6 = 0.0
    tmp7 = tl.where(tmp5, tmp1, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tmp1 - tmp7
    tmp10 = 10000.0
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp13 * tmp1
    tmp15 = 1 + -1 * x0
    tmp16 = tmp15 <= tmp4
    tmp17 = tl.where(tmp16, tmp1, tmp6)
    tmp18 = tmp14 * tmp17
    tmp19 = tmp1 - tmp17
    tmp20 = tmp19 * tmp10
    tmp21 = tmp18 - tmp20
    tmp22 = triton_helpers.maximum(tmp12, tmp21)
    tmp24 = tmp23 * tmp1
    tmp25 = 2 + -1 * x0
    tmp26 = tmp25 <= tmp4
    tmp27 = tl.where(tmp26, tmp1, tmp6)
    tmp28 = tmp24 * tmp27
    tmp29 = tmp1 - tmp27
    tmp30 = tmp29 * tmp10
    tmp31 = tmp28 - tmp30
    tmp32 = triton_helpers.maximum(tmp22, tmp31)
    tmp34 = tmp33 * tmp1
    tmp35 = 3 + -1 * x0
    tmp36 = tmp35 <= tmp4
    tmp37 = tl.where(tmp36, tmp1, tmp6)
    tmp38 = tmp34 * tmp37
    tmp39 = tmp1 - tmp37
    tmp40 = tmp39 * tmp10
    tmp41 = tmp38 - tmp40
    tmp42 = triton_helpers.maximum(tmp32, tmp41)
    tmp43 = tmp12 - tmp42
    tmp44 = tl_math.exp(tmp43)
    tmp45 = tmp21 - tmp42
    tmp46 = tl_math.exp(tmp45)
    tmp47 = tmp44 + tmp46
    tmp48 = tmp31 - tmp42
    tmp49 = tl_math.exp(tmp48)
    tmp50 = tmp47 + tmp49
    tmp51 = tmp41 - tmp42
    tmp52 = tl_math.exp(tmp51)
    tmp53 = tmp50 + tmp52
    tl.store(out_ptr0 + x2, tmp42, xmask)
    tl.store(out_ptr1 + x2, tmp53, xmask)


@triton.jit
def triton_poi_fused__softmax_div_mul_rsub_sub_7(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x4 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp13 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = x0 + -1 * x1
    tmp4 = tl.full([1], 0, tl.int64)
    tmp5 = tmp3 <= tmp4
    tmp6 = 0.0
    tmp7 = tl.where(tmp5, tmp1, tmp6)
    tmp8 = tmp2 * tmp7
    tmp9 = tmp1 - tmp7
    tmp10 = 10000.0
    tmp11 = tmp9 * tmp10
    tmp12 = tmp8 - tmp11
    tmp14 = tmp12 - tmp13
    tmp15 = tl_math.exp(tmp14)
    tmp17 = tmp15 / tmp16
    tl.store(in_out_ptr0 + x3, tmp17, xmask)


@triton.jit
def triton_poi_fused_clone_8(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_9(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    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
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_10(in_ptr0, in_ptr1, in_ptr2,
    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_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + 0)
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + 1)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp13 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr2 + 2)
    tmp16 = tl.broadcast_to(tmp15, [XBLOCK])
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp21 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr2 + 3)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK])
    tmp4 = tmp1 + tmp3
    tmp5 = tmp0 + tmp4
    tmp10 = tmp7 + tmp9
    tmp11 = tmp6 + tmp10
    tmp12 = tmp5 + tmp11
    tmp17 = tmp14 + tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tmp12 + tmp18
    tmp24 = tmp21 + tmp23
    tmp25 = tmp20 + tmp24
    tmp26 = tmp19 + tmp25
    tmp27 = 4.0
    tmp28 = tmp26 / tmp27
    tmp29 = tmp5 - tmp28
    tmp30 = tmp29 * tmp29
    tmp31 = tmp11 - tmp28
    tmp32 = tmp31 * tmp31
    tmp33 = tmp30 + tmp32
    tmp34 = tmp18 - tmp28
    tmp35 = tmp34 * tmp34
    tmp36 = tmp33 + tmp35
    tmp37 = tmp25 - tmp28
    tmp38 = tmp37 * tmp37
    tmp39 = tmp36 + tmp38
    tmp40 = tmp39 / tmp27
    tl.store(out_ptr0 + x0, tmp28, xmask)
    tl.store(out_ptr1 + x0, tmp40, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_11(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, in_ptr6, 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
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x1, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x0, xmask, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr6 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp6 = tmp4 - tmp5
    tmp8 = 1e-05
    tmp9 = tmp7 + tmp8
    tmp10 = libdevice.rsqrt(tmp9)
    tmp11 = tmp6 * tmp10
    tmp13 = tmp11 * tmp12
    tmp15 = tmp13 + tmp14
    tl.store(out_ptr0 + x2, tmp15, xmask)


@triton.jit
def triton_poi_fused_gelu_12(in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, xmask)


@triton.jit
def triton_poi_fused_add_13(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    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_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_out_ptr0 + x2, xmask)
    tmp6 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tmp7 = tmp5 + tmp6
    tmp8 = tmp4 + tmp7
    tl.store(in_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) = args
    args.clear()
    assert_size_stride(primals_1, (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, (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, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(16)](primals_3, buf0,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](primals_3, buf0,
            buf1, primals_1, primals_2, buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_4, (4, 12), (1, 4), 0),
            alpha=1, beta=1, out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((8, 4, 4, 1), (16, 1, 4, 16), torch.float32)
        triton_poi_fused_stack_2[grid(128)](buf3, buf4, 128, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_3[grid(16, 4)](buf3, buf5, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf6 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_clone_4[grid(16, 4)](buf3, buf6, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf6, (16, 1, 4), (4, 0, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        triton_poi_fused_tril_5[grid(16)](buf8, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_div_mul_rsub_sub_6[grid(64)](buf7, buf9,
            buf10, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf11 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf7
        triton_poi_fused__softmax_div_mul_rsub_sub_7[grid(256)](buf11, buf9,
            buf10, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf12 = reinterpret_tensor(buf9, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf9
        triton_poi_fused_clone_8[grid(16, 4)](buf3, buf12, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        del buf3
        buf13 = reinterpret_tensor(buf10, (16, 4, 1), (4, 1, 1), 0)
        del buf10
        extern_kernels.bmm(reinterpret_tensor(buf11, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf12, (16, 4, 1), (4, 1, 0), 0), out=buf13)
        buf14 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_9[grid(16, 4)](buf13, buf14, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf15 = reinterpret_tensor(buf13, (16, 4), (4, 1), 0)
        del buf13
        extern_kernels.mm(reinterpret_tensor(buf14, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf15)
        buf16 = buf1
        del buf1
        buf17 = buf0
        del buf0
        triton_poi_fused_add_native_layer_norm_10[grid(16)](primals_3,
            buf15, primals_7, buf16, buf17, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_add_native_layer_norm_11[grid(64)](primals_3,
            buf15, primals_7, buf16, buf17, primals_8, primals_9, buf18, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf16
        del buf17
        del primals_9
        buf19 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf18, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_10, (4, 16), (1, 4), 0),
            alpha=1, beta=1, out=buf19)
        del primals_11
        buf20 = empty_strided_cuda((4, 4, 16), (64, 16, 1), torch.float32)
        triton_poi_fused_gelu_12[grid(256)](buf19, buf20, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf21 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf20, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_12, (16, 4), (1, 16), 0), out=buf21)
        buf22 = reinterpret_tensor(buf21, (4, 4, 4), (16, 4, 1), 0)
        del buf21
        triton_poi_fused_add_13[grid(64)](buf22, primals_3, buf15,
            primals_7, primals_13, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_13
    return buf22, reinterpret_tensor(buf4, (2, 4, 4, 4, 1), (64, 16, 1, 4, 
        4), 0), primals_3, primals_7, primals_8, reinterpret_tensor(buf2, (
        16, 4), (4, 1), 0), buf8, buf11, reinterpret_tensor(buf14, (16, 4),
        (4, 1), 0), buf15, reinterpret_tensor(buf18, (16, 4), (4, 1), 0
        ), buf19, reinterpret_tensor(buf20, (16, 16), (16, 1), 0
        ), primals_12, primals_10, primals_6, reinterpret_tensor(buf12, (16,
        1, 4), (4, 1, 1), 0), reinterpret_tensor(buf5, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 4), 0), primals_4


class MLP(nn.Module):

    def __init__(self, embedding_size):
        super(MLP, self).__init__()
        self.dense_h_to_4h = nn.Linear(embedding_size, embedding_size * 4)
        self.dense_4h_to_h = nn.Linear(embedding_size * 4, embedding_size)
        self.act = nn.functional.gelu

    def forward(self, x):
        h = self.act(self.dense_h_to_4h(x))
        h2 = self.dense_4h_to_h(h)
        return h2


class Attention(nn.Module):

    def __init__(self, embedding_size, num_attention_heads,
        attention_dropout, residual_dropout):
        super(Attention, self).__init__()
        self.num_attention_heads = num_attention_heads
        self.size_per_head = embedding_size // num_attention_heads
        self.embedding_size = embedding_size
        self.query_key_value = nn.Linear(embedding_size, embedding_size * 3)
        self.attn_drop = nn.Dropout(attention_dropout)
        self.resid_drop = nn.Dropout(residual_dropout)
        self.dense = nn.Linear(embedding_size, embedding_size)

    def split_heads(self, x):
        x = x.reshape([-1, self.seq_len, self.num_attention_heads, self.
            size_per_head])
        return x.permute(0, 2, 1, 3)

    def forward(self, x, kv_cache=None):
        self.seq_len = x.shape[1]
        x = self.query_key_value(x)
        q, k, v = torch.split(x, split_size_or_sections=self.embedding_size,
            dim=2)
        q = self.split_heads(q)
        k = self.split_heads(k)
        v = self.split_heads(v)
        if kv_cache is not None:
            pk, pv = kv_cache[0], kv_cache[1]
            k = torch.cat([pk, k], dim=-2)
            v = torch.cat([pv, v], dim=-2)
        cached_kv = torch.stack([k, v])
        attn = torch.matmul(q, k.transpose(-1, -2))
        attn = attn / math.sqrt(self.size_per_head)
        attention_mask = torch.tril(torch.ones(self.seq_len, self.seq_len,
            dtype=torch.float32, device=x.device))
        attention_mask = attention_mask.reshape([1, 1, self.seq_len, self.
            seq_len])
        attn = attn * attention_mask - 10000.0 * (1.0 - attention_mask)
        attn = nn.Softmax(dim=-1)(attn)
        attn = self.attn_drop(attn)
        y = torch.matmul(attn, v)
        y = y.permute(0, 2, 1, 3)
        y = torch.reshape(y, [-1, self.seq_len, self.embedding_size])
        y = self.resid_drop(self.dense(y))
        return y, cached_kv


class BlockNew(nn.Module):

    def __init__(self, embedding_size, num_attention_heads,
        attention_dropout, residual_dropout):
        super(BlockNew, self).__init__()
        self.input_layernorm = nn.LayerNorm(embedding_size, eps=1e-05)
        self.attention = Attention(embedding_size, num_attention_heads,
            attention_dropout, residual_dropout)
        self.post_attention_layernorm = nn.LayerNorm(embedding_size, eps=1e-05)
        self.mlp = MLP(embedding_size)

    def forward(self, input_0):
        primals_1 = self.input_layernorm.weight
        primals_2 = self.input_layernorm.bias
        primals_4 = self.attention.query_key_value.weight
        primals_5 = self.attention.query_key_value.bias
        primals_6 = self.attention.dense.weight
        primals_7 = self.attention.dense.bias
        primals_8 = self.post_attention_layernorm.weight
        primals_9 = self.post_attention_layernorm.bias
        primals_10 = self.mlp.dense_h_to_4h.weight
        primals_11 = self.mlp.dense_h_to_4h.bias
        primals_12 = self.mlp.dense_4h_to_h.weight
        primals_13 = self.mlp.dense_4h_to_h.bias
        primals_3 = 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], output[1]


Measured latency_ms: 0.17823