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jerryzh

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Mon Apr 29, 2024 12:35pm

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from ctypes import c_void_p, c_long

import torch

import math

import random

import os

import tempfile

from math import inf, nan

from torch._inductor.hooks import run_intermediate_hooks

from torch._inductor.utils import maybe_profile

from torch._inductor.codegen.memory_planning import _align as align

from torch import device, empty_strided

from torch._inductor.codecache import AsyncCompile

from torch._inductor.select_algorithm import extern_kernels

from torch._inductor.codegen.multi_kernel import MultiKernelCall

aten = torch.ops.aten

inductor_ops = torch.ops.inductor

assert_size_stride = torch._C._dynamo.guards.assert_size_stride

empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu

empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda

alloc_from_pool = torch.ops.inductor._alloc_from_pool

reinterpret_tensor = torch.ops.inductor._reinterpret_tensor

async_compile = AsyncCompile()

# kernel path: /tmp/torchinductor_jerryzh/3m/c3mdxfx77gic7e55bygnquq4oboho4wkm2knnxiy3fbmx6ka5zxd.py

# Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]

# cat_1 => cat

# input_1 => add

# x_5 => convert_element_type, var_mean

triton_red_fused_add_cat_native_layer_norm_0 = async_compile.triton('triton_', '''

import triton

import triton.language as tl

from triton.compiler.compiler import AttrsDescriptor

from torch._inductor import triton_helpers, triton_heuristics

from torch._inductor.ir import ReductionHint, TileHint

from torch._inductor.triton_helpers import libdevice, math as tl_math

from torch._inductor.triton_heuristics import AutotuneHint

from torch._inductor.utils import instance_descriptor

@triton_heuristics.reduction(

size_hints=[2048, 128],

reduction_hint=ReductionHint.OUTER,

filename=__file__,

triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 8), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(8,))]},

inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_add_cat_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}

)

@triton.jit

def triton_(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):

xnumel = 1182

rnumel = 128

xoffset = tl.program_id(0) * XBLOCK

xindex = xoffset + tl.arange(0, XBLOCK)[:, None]

xmask = xindex < xnumel

rbase = tl.arange(0, RBLOCK)[None, :]

x1 = (xindex // 6)

x0 = xindex % 6

x3 = xindex

tmp21_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)

tmp21_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)

tmp21_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)

for roffset in range(0, rnumel, RBLOCK):

rindex = roffset + rbase

rmask = rindex < rnumel

r2 = rindex

tmp17 = tl.load(in_ptr3 + (r2 + (128*x3)), rmask & xmask, eviction_policy='evict_first', other=0.0).to(tl.float32)

tmp0 = x1

tmp1 = tl.full([1, 1], 0, tl.int64)

tmp2 = tmp0 >= tmp1

tmp3 = tl.full([1, 1], 1, tl.int64)

tmp4 = tmp0 < tmp3

tmp5 = tl.load(in_ptr0 + (r2 + (128*x0)), rmask & tmp4 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)

tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)

tmp7 = tl.where(tmp4, tmp5, tmp6)

tmp8 = tmp0 >= tmp3

tmp9 = tl.full([1, 1], 197, tl.int64)

tmp10 = tmp0 < tmp9

tmp11 = tl.load(in_ptr1 + ((196*r2) + (25088*x0) + (((-1) + x1) % 196)), rmask & tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)

tmp12 = tl.load(in_ptr2 + (r2 + (128*x0)), rmask & tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)

tmp13 = tmp11 + tmp12

tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)

tmp15 = tl.where(tmp8, tmp13, tmp14)

tmp16 = tl.where(tmp4, tmp7, tmp15)

tmp18 = tmp16 + tmp17

tmp19 = tmp18.to(tl.float32)

tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])

tmp21_mean_next, tmp21_m2_next, tmp21_weight_next = triton_helpers.welford_reduce(

tmp20, tmp21_mean, tmp21_m2, tmp21_weight, roffset == 0

)

tmp21_mean = tl.where(rmask & xmask, tmp21_mean_next, tmp21_mean)

tmp21_m2 = tl.where(rmask & xmask, tmp21_m2_next, tmp21_m2)

tmp21_weight = tl.where(rmask & xmask, tmp21_weight_next, tmp21_weight)

tmp21_tmp, tmp22_tmp, tmp23_tmp = triton_helpers.welford(

tmp21_mean, tmp21_m2, tmp21_weight, 1

)

tmp21 = tmp21_tmp[:, None]

tmp22 = tmp22_tmp[:, None]

tmp23 = tmp23_tmp[:, None]

tl.store(out_ptr0 + (x3), tmp21, xmask)

tl.store(out_ptr1 + (x3), tmp22, xmask)

tl.store(out_ptr2 + (x3), tmp23, xmask)

''', device_str='cuda')

import triton

import triton.language as tl

from torch._inductor.triton_heuristics import grid, split_scan_grid, start_graph, end_graph

from torch._C import _cuda_getCurrentRawStream as get_raw_stream

# kernel path: /tmp/torchinductor_jerryzh/rp/crprwyxhxbgfxjioi6d64h5rouzl2mdpzshcznykn37q2luuupzp.py

# Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]

# cat_1 => cat

# input_1 => add

# x_5 => convert_element_type, var_mean

triton_per_fused_add_cat_native_layer_norm_1 = async_compile.triton('triton_', '''

import triton

import triton.language as tl

from triton.compiler.compiler import AttrsDescriptor

from torch._inductor import triton_helpers, triton_heuristics

from torch._inductor.ir import ReductionHint, TileHint

from torch._inductor.triton_helpers import libdevice, math as tl_math

from torch._inductor.triton_heuristics import AutotuneHint

from torch._inductor.utils import instance_descriptor

@triton_heuristics.persistent_reduction(

size_hints=[256, 8],

reduction_hint=ReductionHint.INNER,

filename=__file__,

triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},

inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_cat_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}

)

@triton.jit

def triton_(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):

xnumel = 197

rnumel = 6

RBLOCK: tl.constexpr = 8

xoffset = tl.program_id(0) * XBLOCK

xindex = xoffset + tl.arange(0, XBLOCK)[:, None]

xmask = xindex < xnumel

rindex = tl.arange(0, RBLOCK)[None, :]

roffset = 0

rmask = rindex < rnumel

r1 = rindex

x0 = xindex

tmp0 = tl.load(in_ptr0 + (r1 + (6*x0)), rmask & xmask, other=0.0)

tmp1 = tl.load(in_ptr1 + (r1 + (6*x0)), rmask & xmask, other=0.0)

tmp2 = tl.load(in_ptr2 + (r1 + (6*x0)), rmask & xmask, other=0.0)

tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])

tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])

tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])

tmp7 = tl.where(rmask & xmask, tmp3, 0)

tmp8 = tl.where(rmask & xmask, tmp4, 0)

tmp9 = tl.where(rmask & xmask, tmp5, 0)

tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)

tmp13 = tmp10[:, None]

tmp14 = tmp11[:, None]

tmp15 = tmp12[:, None]

tl.store(out_ptr0 + (x0), tmp13, xmask)

tl.store(out_ptr1 + (x0), tmp14, xmask)

''', device_str='cuda')

# kernel path: /tmp/torchinductor_jerryzh/4g/c4gelhmxwybhe35chfwqwajj7xo2bonxrt4coqtti75qtsxpc3dd.py

# Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]

# cat_1 => cat

# input_1 => add

# x_5 => add_1, add_2, convert_element_type, convert_element_type_1, mul, mul_1, rsqrt, sub, var_mean

triton_poi_fused_add_cat_native_layer_norm_2 = async_compile.triton('triton_', '''

import triton

import triton.language as tl

from triton.compiler.compiler import AttrsDescriptor

from torch._inductor import triton_helpers, triton_heuristics

from torch._inductor.ir import ReductionHint, TileHint

from torch._inductor.triton_helpers import libdevice, math as tl_math

from torch._inductor.triton_heuristics import AutotuneHint

from torch._inductor.utils import instance_descriptor

@triton_heuristics.pointwise(

size_hints=[262144],

filename=__file__,

triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*fp32', 5: '*fp32', 6: '*bf16', 7: '*bf16', 8: '*bf16', 9: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(9,))]},

inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_cat_native_layer_norm_2', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},

min_elem_per_thread=0

)

@triton.jit

def triton_(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr1, xnumel, XBLOCK : tl.constexpr):

xnumel = 151296

xoffset = tl.program_id(0) * XBLOCK

xindex = xoffset + tl.arange(0, XBLOCK)[:]

xmask = xindex < xnumel

x1 = (xindex // 768)

x0 = xindex % 768

x2 = xindex

tmp17 = tl.load(in_ptr3 + (x2), xmask).to(tl.float32)

tmp20 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')

tmp22 = tl.load(in_ptr5 + (x1), xmask, eviction_policy='evict_last')

tmp29 = tl.load(in_ptr6 + (x0), xmask, eviction_policy='evict_last').to(tl.float32)

tmp32 = tl.load(in_ptr7 + (x0), xmask, eviction_policy='evict_last').to(tl.float32)

tmp0 = x1

tmp1 = tl.full([1], 0, tl.int64)

tmp2 = tmp0 >= tmp1

tmp3 = tl.full([1], 1, tl.int64)

tmp4 = tmp0 < tmp3

tmp5 = tl.load(in_ptr0 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)

tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)

tmp7 = tl.where(tmp4, tmp5, tmp6)

tmp8 = tmp0 >= tmp3

tmp9 = tl.full([1], 197, tl.int64)

tmp10 = tmp0 < tmp9

tmp11 = tl.load(in_ptr1 + ((196*x0) + (((-1) + x1) % 196)), tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)

tmp12 = tl.load(in_ptr2 + (x0), tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)

tmp13 = tmp11 + tmp12

tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)

tmp15 = tl.where(tmp8, tmp13, tmp14)

tmp16 = tl.where(tmp4, tmp7, tmp15)

tmp18 = tmp16 + tmp17

tmp19 = tmp18.to(tl.float32)

tmp21 = tmp19 - tmp20

tmp23 = 768.0

tmp24 = tmp22 / tmp23

tmp25 = 1e-06

tmp26 = tmp24 + tmp25

tmp27 = libdevice.rsqrt(tmp26)

tmp28 = tmp21 * tmp27

tmp30 = tmp29.to(tl.float32)

tmp31 = tmp28 * tmp30

tmp33 = tmp32.to(tl.float32)

tmp34 = tmp31 + tmp33

tmp35 = tmp34.to(tl.float32)

tl.store(out_ptr1 + (x2), tmp35, xmask)

''', device_str='cuda')

# kernel path: /tmp/torchinductor_jerryzh/f6/cf6bv6b4vfuvbectvwbrf5n7kguat2tmat24wzdu32injuk3czgi.py

# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]

# l__self___encoder_layers_encoder_layer_0_self_attention => _scaled_dot_product_flash_attention

triton_poi_fused__scaled_dot_product_flash_attention_3 = async_compile.triton('triton_', '''

import triton

import triton.language as tl

from triton.compiler.compiler import AttrsDescriptor

from torch._inductor import triton_helpers, triton_heuristics

from torch._inductor.ir import ReductionHint, TileHint

from torch._inductor.triton_helpers import libdevice, math as tl_math

from torch._inductor.triton_heuristics import AutotuneHint

from torch._inductor.utils import instance_descriptor

@triton_heuristics.pointwise(

size_hints=[262144],

filename=__file__,

triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(3,))]},

inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__scaled_dot_product_flash_attention_3', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},

min_elem_per_thread=0

)

@triton.jit

def triton_(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):

xnumel = 151296

xoffset = tl.program_id(0) * XBLOCK

xindex = xoffset + tl.arange(0, XBLOCK)[:]

xmask = xindex < xnumel

x0 = xindex % 768

x1 = (xindex // 768)

x2 = xindex

tmp0 = tl.load(in_ptr0 + (x0 + (2304*x1)), xmask).to(tl.float32)

tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last').to(tl.float32)

tmp2 = tmp0 + tmp1

tl.store(out_ptr0 + (x2), tmp2, xmask)

''', device_str='cuda')

# kernel path: /tmp/torchinductor_jerryzh/ni/cnibta3iuvpy5fbssjg635n7b6h3fa5rwyhvzhm3ouugyisfup6w.py

# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]

# l__self___encoder_layers_encoder_layer_0_self_attention => _scaled_dot_product_flash_attention

triton_poi_fused__scaled_dot_product_flash_attention_4 = async_compile.triton('triton_', '''

import triton

import triton.language as tl

from triton.compiler.compiler import AttrsDescriptor

from torch._inductor import triton_helpers, triton_heuristics

from torch._inductor.ir import ReductionHint, TileHint

from torch._inductor.triton_helpers import libdevice, math as tl_math

from torch._inductor.triton_heuristics import AutotuneHint

from torch._inductor.utils import instance_descriptor

@triton_heuristics.pointwise(

size_hints=[262144],

filename=__file__,

inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__scaled_dot_product_flash_attention_4', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},

min_elem_per_thread=0

)

@triton.jit

def triton_(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):

xnumel = 151296

xoffset = tl.program_id(0) * XBLOCK

xindex = xoffset + tl.arange(0, XBLOCK)[:]

xmask = xindex < xnumel

x0 = xindex % 768

x1 = (xindex // 768)

x2 = xindex

tmp0 = tl.load(in_ptr0 + (768 + x0 + (2304*x1)), xmask).to(tl.float32)

tmp1 = tl.load(in_ptr1 + (768 + x0), xmask, eviction_policy='evict_last').to(tl.float32)

tmp2 = tmp0 + tmp1

tl.store(out_ptr0 + (x2), tmp2, xmask)

''', device_str='cuda')

# kernel path: /tmp/torchinductor_jerryzh/vm/cvm3ucqwmyyakgkf2cfvaeylq3ktp3i7s5emlef7zyz44bzlk4jt.py

# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]

# l__self___encoder_layers_encoder_layer_0_self_attention => _scaled_dot_product_flash_attention

triton_poi_fused__scaled_dot_product_flash_attention_5 = async_compile.triton('triton_', '''

import triton

import triton.language as tl

from triton.compiler.compiler import AttrsDescriptor

from torch._inductor import triton_helpers, triton_heuristics

from torch._inductor.ir import ReductionHint, TileHint

from torch._inductor.triton_helpers import libdevice, math as tl_math

from torch._inductor.triton_heuristics import AutotuneHint

from torch._inductor.utils import instance_descriptor

@triton_heuristics.pointwise(

size_hints=[262144],

filename=__file__,

inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__scaled_dot_product_flash_attention_5', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},

min_elem_per_thread=0

)

@triton.jit

def triton_(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):

xnumel = 151296

xoffset = tl.program_id(0) * XBLOCK

xindex = xoffset + tl.arange(0, XBLOCK)[:]

xmask = xindex < xnumel

x0 = xindex % 768

x1 = (xindex // 768)

x2 = xindex

tmp0 = tl.load(in_ptr0 + (1536 + x0 + (2304*x1)), xmask).to(tl.float32)

tmp1 = tl.load(in_ptr1 + (1536 + x0), xmask, eviction_policy='evict_last').to(tl.float32)

tmp2 = tmp0 + tmp1

tl.store(out_ptr0 + (x2), tmp2, xmask)

''', device_str='cuda')

# kernel path: /tmp/torchinductor_jerryzh/fv/cfv2vtxyxm4ieahpqcdzbdf2o2u6h4ju6comfyd5t3tuvdlqmnv5.py

# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]

# l__self___encoder_layers_encoder_layer_0_self_attention => abs_1, amax, clamp_max, clamp_min, clamp_min_1, convert_element_type_5, convert_element_type_6, convert_element_type_7, convert_element_type_8, convert_element_type_9, div, div_1, round_1

triton_per_fused__to_copy_abs_amax_clamp_div_round_6 = async_compile.triton('triton_', '''

import triton

import triton.language as tl

from triton.compiler.compiler import AttrsDescriptor

from torch._inductor import triton_helpers, triton_heuristics

from torch._inductor.ir import ReductionHint, TileHint

from torch._inductor.triton_helpers import libdevice, math as tl_math

from torch._inductor.triton_heuristics import AutotuneHint

from torch._inductor.utils import instance_descriptor

@triton_heuristics.persistent_reduction(

size_hints=[256, 1024],

reduction_hint=ReductionHint.INNER,

filename=__file__,

triton_meta={'signature': {0: '*bf16', 1: '*i8', 2: '*bf16', 3: 'i32', 4: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(4,))]},

inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_abs_amax_clamp_div_round_6', 'mutated_arg_names': [], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}

)

@triton.jit

def triton_(in_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):

xnumel = 197

XBLOCK: tl.constexpr = 1

rnumel = 768

RBLOCK: tl.constexpr = 1024

xoffset = tl.program_id(0) * XBLOCK

xindex = tl.full([1], xoffset, tl.int32)

xmask = xindex < xnumel

rindex = tl.arange(0, RBLOCK)[:]

roffset = 0

rmask = rindex < rnumel

r1 = rindex

x0 = xindex

tmp0 = tl.load(in_ptr0 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)

tmp1 = tl_math.abs(tmp0)

tmp2 = tl.broadcast_to(tmp1, [RBLOCK])

tmp4 = tl.where(rmask & xmask, tmp2, float("-inf"))

tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp4, 0))

tmp6 = tmp5.to(tl.float32)

tmp7 = 1e-05

tmp8 = triton_helpers.maximum(tmp6, tmp7)

tmp9 = tmp8.to(tl.float32)

tmp10 = 127.0

tmp11 = tmp9 / tmp10

tmp12 = tmp0 / tmp11

tmp13 = libdevice.nearbyint(tmp12)

tmp14 = tmp13.to(tl.float32)

tmp15 = -127.0

tmp16 = triton_helpers.maximum(tmp14, tmp15)

tmp17 = triton_helpers.minimum(tmp16, tmp10)

tmp18 = tmp17.to(tl.float32)

tmp19 = tmp18.to(tl.int8)

tl.store(out_ptr1 + (r1 + (768*x0)), tmp19, rmask & xmask)

tl.store(out_ptr2 + (x0), tmp11, xmask)

''', device_str='cuda')

# kernel path: /tmp/torchinductor_jerryzh/tb/ctbetqksq5wdquspt6aqeaeczbvkly47okhymmcfwjlfwji4avos.py

# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]

# l__self___encoder_layers_encoder_layer_0_self_attention => clamp_max, clamp_min, clamp_min_1, convert_element_type_5, convert_element_type_6, convert_element_type_7, convert_element_type_8, convert_element_type_9, div, div_1, fused_int_mm_mul_36, round_1, view_11

triton_tem_fused__to_copy_clamp_div_mul_round_view_7 = async_compile.triton('triton_', '''

import triton

import triton.language as tl

from triton.compiler.compiler import AttrsDescriptor

from torch._inductor import triton_helpers, triton_heuristics

from torch._inductor.ir import ReductionHint, TileHint

from torch._inductor.triton_helpers import libdevice, math as tl_math

from torch._inductor.triton_heuristics import AutotuneHint

from torch._inductor.utils import instance_descriptor

@triton_heuristics.template(

num_stages=5,

num_warps=8,

triton_meta={'signature': {0: '*i8', 1: '*i8', 2: '*bf16', 3: '*bf16'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},

inductor_meta={'kernel_name': 'triton_tem_fused__to_copy_clamp_div_mul_round_view_7', 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},

)

@triton.jit

def triton_(arg_A, arg_B, in_ptr2, out_ptr0):

GROUP_M : tl.constexpr = 8

EVEN_K : tl.constexpr = True

ALLOW_TF32 : tl.constexpr = False

ACC_TYPE : tl.constexpr = tl.int32

B_PROLOGUE_CAST_TYPE : tl.constexpr = None

BLOCK_M : tl.constexpr = 64

BLOCK_N : tl.constexpr = 32

BLOCK_K : tl.constexpr = 32

A = arg_A

B = arg_B

M = 197

N = 768

K = 768

if M * N == 0:

# early exit due to zero-size input(s)

return

stride_am = 768

stride_ak = 1

stride_bk = 1

stride_bn = 768

# based on triton.ops.matmul

pid = tl.program_id(0)

grid_m = (M + BLOCK_M - 1) // BLOCK_M

grid_n = (N + BLOCK_N - 1) // BLOCK_N

# re-order program ID for better L2 performance

width = GROUP_M * grid_n

group_id = pid // width

group_size = min(grid_m - group_id * GROUP_M, GROUP_M)

pid_m = group_id * GROUP_M + (pid % group_size)

pid_n = (pid % width) // (group_size)

rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)

rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)

rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)

rk = tl.arange(0, BLOCK_K)

A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)

B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)

acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)

for k in range(K, 0, -BLOCK_K):

if EVEN_K:

a = tl.load(A)

b = tl.load(B)

else:

a = tl.load(A, mask=rk[None, :] < k, other=0.)

b = tl.load(B, mask=rk[:, None] < k, other=0.)

if B_PROLOGUE_CAST_TYPE is not None:

b = b.to(B_PROLOGUE_CAST_TYPE)

acc += tl.dot(a, b, allow_tf32=ALLOW_TF32)

A += BLOCK_K * stride_ak

B += BLOCK_K * stride_bk

# rematerialize rm and rn to save registers

rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)

rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)

idx_m = rm[:, None]

idx_n = rn[None, :]

mask = (idx_m < M) & (idx_n < N)

# inductor generates a suffix

xindex = idx_n + (768*idx_m)

tmp0 = tl.load(in_ptr2 + (tl.broadcast_to(idx_m, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)

tl.store(out_ptr0 + (tl.broadcast_to(xindex, mask.shape)), acc * tmp0, mask)

''', device_str='cuda')

import torch._inductor.kernel.mm_common

meta0 = {'GROUP_M': 8, 'EVEN_K': True, 'ALLOW_TF32': False, 'ACC_TYPE': 'tl.int32', 'B_PROLOGUE_CAST_TYPE': None, 'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32}

# kernel path: /tmp/torchinductor_jerryzh/cr/ccr74bfho3ok5o2tyb5aq3refnm7mumphtnf7anixsxqq2ylylnx.py

# Source Nodes: [cat_1, input_1, l__self___encoder_layers_encoder_layer_0_mlp_0, x_7, x_8, y], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.cat, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]

# cat_1 => cat

# input_1 => add

# l__self___encoder_layers_encoder_layer_0_mlp_0 => abs_2, amax_1, clamp_max_1, clamp_min_2, clamp_min_3, convert_element_type_12, convert_element_type_13, convert_element_type_14, convert_element_type_15, convert_element_type_16, div_2, div_3, round_2

# x_7 => clon

# x_7 => clone

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from ctypes import c_void_p, c_long
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.codecache import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
alloc_from_pool = torch.ops.inductor._alloc_from_pool
reinterpret_tensor = torch.ops.inductor._reinterpret_tensor
async_compile = AsyncCompile()
# kernel path: /tmp/torchinductor_jerryzh/3m/c3mdxfx77gic7e55bygnquq4oboho4wkm2knnxiy3fbmx6ka5zxd.py
# Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
# cat_1 => cat
# input_1 => add
# x_5 => convert_element_type, var_mean
triton_red_fused_add_cat_native_layer_norm_0 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.reduction(
    size_hints=[2048, 128],
    reduction_hint=ReductionHint.OUTER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 8), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(8,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_add_cat_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1182
    rnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x1 = (xindex // 6)
    x0 = xindex % 6
    x3 = xindex
    tmp21_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp21_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp21_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp17 = tl.load(in_ptr3 + (r2 + (128*x3)), rmask & xmask, eviction_policy='evict_first', other=0.0).to(tl.float32)
        tmp0 = x1
        tmp1 = tl.full([1, 1], 0, tl.int64)
        tmp2 = tmp0 >= tmp1
        tmp3 = tl.full([1, 1], 1, tl.int64)
        tmp4 = tmp0 < tmp3
        tmp5 = tl.load(in_ptr0 + (r2 + (128*x0)), rmask & tmp4 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
        tmp7 = tl.where(tmp4, tmp5, tmp6)
        tmp8 = tmp0 >= tmp3
        tmp9 = tl.full([1, 1], 197, tl.int64)
        tmp10 = tmp0 < tmp9
        tmp11 = tl.load(in_ptr1 + ((196*r2) + (25088*x0) + (((-1) + x1) % 196)), rmask & tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp12 = tl.load(in_ptr2 + (r2 + (128*x0)), rmask & tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp13 = tmp11 + tmp12
        tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
        tmp15 = tl.where(tmp8, tmp13, tmp14)
        tmp16 = tl.where(tmp4, tmp7, tmp15)
        tmp18 = tmp16 + tmp17
        tmp19 = tmp18.to(tl.float32)
        tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
        tmp21_mean_next, tmp21_m2_next, tmp21_weight_next = triton_helpers.welford_reduce(
            tmp20, tmp21_mean, tmp21_m2, tmp21_weight, roffset == 0
        )
        tmp21_mean = tl.where(rmask & xmask, tmp21_mean_next, tmp21_mean)
        tmp21_m2 = tl.where(rmask & xmask, tmp21_m2_next, tmp21_m2)
        tmp21_weight = tl.where(rmask & xmask, tmp21_weight_next, tmp21_weight)
    tmp21_tmp, tmp22_tmp, tmp23_tmp = triton_helpers.welford(
        tmp21_mean, tmp21_m2, tmp21_weight, 1
    )
    tmp21 = tmp21_tmp[:, None]
    tmp22 = tmp22_tmp[:, None]
    tmp23 = tmp23_tmp[:, None]
    tl.store(out_ptr0 + (x3), tmp21, xmask)
    tl.store(out_ptr1 + (x3), tmp22, xmask)
    tl.store(out_ptr2 + (x3), tmp23, xmask)
''', device_str='cuda')
import triton
import triton.language as tl
from torch._inductor.triton_heuristics import grid, split_scan_grid, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
# kernel path: /tmp/torchinductor_jerryzh/rp/crprwyxhxbgfxjioi6d64h5rouzl2mdpzshcznykn37q2luuupzp.py
# Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
# cat_1 => cat
# input_1 => add
# x_5 => convert_element_type, var_mean
triton_per_fused_add_cat_native_layer_norm_1 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[256, 8],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_cat_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 197
    rnumel = 6
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (6*x0)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + (6*x0)), rmask & xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (r1 + (6*x0)), rmask & xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(rmask & xmask, tmp3, 0)
    tmp8 = tl.where(rmask & xmask, tmp4, 0)
    tmp9 = tl.where(rmask & xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp15 = tmp12[:, None]
    tl.store(out_ptr0 + (x0), tmp13, xmask)
    tl.store(out_ptr1 + (x0), tmp14, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/4g/c4gelhmxwybhe35chfwqwajj7xo2bonxrt4coqtti75qtsxpc3dd.py
# Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
# cat_1 => cat
# input_1 => add
# x_5 => add_1, add_2, convert_element_type, convert_element_type_1, mul, mul_1, rsqrt, sub, var_mean
triton_poi_fused_add_cat_native_layer_norm_2 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*fp32', 5: '*fp32', 6: '*bf16', 7: '*bf16', 8: '*bf16', 9: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(9,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_cat_native_layer_norm_2', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
    min_elem_per_thread=0
)
@triton.jit
def triton_(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 151296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 768)
    x0 = xindex % 768
    x2 = xindex
    tmp17 = tl.load(in_ptr3 + (x2), xmask).to(tl.float32)
    tmp20 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + (x1), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr6 + (x0), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp32 = tl.load(in_ptr7 + (x0), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
    tmp7 = tl.where(tmp4, tmp5, tmp6)
    tmp8 = tmp0 >= tmp3
    tmp9 = tl.full([1], 197, tl.int64)
    tmp10 = tmp0 < tmp9
    tmp11 = tl.load(in_ptr1 + ((196*x0) + (((-1) + x1) % 196)), tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp12 = tl.load(in_ptr2 + (x0), tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp8, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp7, tmp15)
    tmp18 = tmp16 + tmp17
    tmp19 = tmp18.to(tl.float32)
    tmp21 = tmp19 - tmp20
    tmp23 = 768.0
    tmp24 = tmp22 / tmp23
    tmp25 = 1e-06
    tmp26 = tmp24 + tmp25
    tmp27 = libdevice.rsqrt(tmp26)
    tmp28 = tmp21 * tmp27
    tmp30 = tmp29.to(tl.float32)
    tmp31 = tmp28 * tmp30
    tmp33 = tmp32.to(tl.float32)
    tmp34 = tmp31 + tmp33
    tmp35 = tmp34.to(tl.float32)
    tl.store(out_ptr1 + (x2), tmp35, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/f6/cf6bv6b4vfuvbectvwbrf5n7kguat2tmat24wzdu32injuk3czgi.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
# l__self___encoder_layers_encoder_layer_0_self_attention => _scaled_dot_product_flash_attention
triton_poi_fused__scaled_dot_product_flash_attention_3 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__scaled_dot_product_flash_attention_3', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
    min_elem_per_thread=0
)
@triton.jit
def triton_(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 151296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 768
    x1 = (xindex // 768)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (2304*x1)), xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/ni/cnibta3iuvpy5fbssjg635n7b6h3fa5rwyhvzhm3ouugyisfup6w.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
# l__self___encoder_layers_encoder_layer_0_self_attention => _scaled_dot_product_flash_attention
triton_poi_fused__scaled_dot_product_flash_attention_4 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__scaled_dot_product_flash_attention_4', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
    min_elem_per_thread=0
)
@triton.jit
def triton_(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 151296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 768
    x1 = (xindex // 768)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (768 + x0 + (2304*x1)), xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (768 + x0), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/vm/cvm3ucqwmyyakgkf2cfvaeylq3ktp3i7s5emlef7zyz44bzlk4jt.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
# l__self___encoder_layers_encoder_layer_0_self_attention => _scaled_dot_product_flash_attention
triton_poi_fused__scaled_dot_product_flash_attention_5 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__scaled_dot_product_flash_attention_5', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
    min_elem_per_thread=0
)
@triton.jit
def triton_(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 151296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 768
    x1 = (xindex // 768)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (1536 + x0 + (2304*x1)), xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (1536 + x0), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/fv/cfv2vtxyxm4ieahpqcdzbdf2o2u6h4ju6comfyd5t3tuvdlqmnv5.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
# l__self___encoder_layers_encoder_layer_0_self_attention => abs_1, amax, clamp_max, clamp_min, clamp_min_1, convert_element_type_5, convert_element_type_6, convert_element_type_7, convert_element_type_8, convert_element_type_9, div, div_1, round_1
triton_per_fused__to_copy_abs_amax_clamp_div_round_6 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*i8', 2: '*bf16', 3: 'i32', 4: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_abs_amax_clamp_div_round_6', 'mutated_arg_names': [], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    xnumel = 197
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = tl.where(rmask & xmask, tmp2, float("-inf"))
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp4, 0))
    tmp6 = tmp5.to(tl.float32)
    tmp7 = 1e-05
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = 127.0
    tmp11 = tmp9 / tmp10
    tmp12 = tmp0 / tmp11
    tmp13 = libdevice.nearbyint(tmp12)
    tmp14 = tmp13.to(tl.float32)
    tmp15 = -127.0
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = triton_helpers.minimum(tmp16, tmp10)
    tmp18 = tmp17.to(tl.float32)
    tmp19 = tmp18.to(tl.int8)
    tl.store(out_ptr1 + (r1 + (768*x0)), tmp19, rmask & xmask)
    tl.store(out_ptr2 + (x0), tmp11, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/tb/ctbetqksq5wdquspt6aqeaeczbvkly47okhymmcfwjlfwji4avos.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
# l__self___encoder_layers_encoder_layer_0_self_attention => clamp_max, clamp_min, clamp_min_1, convert_element_type_5, convert_element_type_6, convert_element_type_7, convert_element_type_8, convert_element_type_9, div, div_1, fused_int_mm_mul_36, round_1, view_11
triton_tem_fused__to_copy_clamp_div_mul_round_view_7 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.template(
    num_stages=5,
    num_warps=8,
    triton_meta={'signature': {0: '*i8', 1: '*i8', 2: '*bf16', 3: '*bf16'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'kernel_name': 'triton_tem_fused__to_copy_clamp_div_mul_round_view_7', 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
)
@triton.jit
def triton_(arg_A, arg_B, in_ptr2, out_ptr0):
    GROUP_M : tl.constexpr = 8
    EVEN_K : tl.constexpr = True
    ALLOW_TF32 : tl.constexpr = False
    ACC_TYPE : tl.constexpr = tl.int32
    B_PROLOGUE_CAST_TYPE : tl.constexpr = None
    BLOCK_M : tl.constexpr = 64
    BLOCK_N : tl.constexpr = 32
    BLOCK_K : tl.constexpr = 32
    A = arg_A
    B = arg_B
    M = 197
    N = 768
    K = 768
    if M * N == 0:
        # early exit due to zero-size input(s)
        return
    stride_am = 768
    stride_ak = 1
    stride_bk = 1
    stride_bn = 768
    # based on triton.ops.matmul
    pid = tl.program_id(0)
    grid_m = (M + BLOCK_M - 1) // BLOCK_M
    grid_n = (N + BLOCK_N - 1) // BLOCK_N
    # re-order program ID for better L2 performance
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // (group_size)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
    for k in range(K, 0, -BLOCK_K):
        if EVEN_K:
            a = tl.load(A)
            b = tl.load(B)
        else:
            a = tl.load(A, mask=rk[None, :] < k, other=0.)
            b = tl.load(B, mask=rk[:, None] < k, other=0.)
        if B_PROLOGUE_CAST_TYPE is not None:
            b = b.to(B_PROLOGUE_CAST_TYPE)
        acc += tl.dot(a, b, allow_tf32=ALLOW_TF32)
        A += BLOCK_K * stride_ak
        B += BLOCK_K * stride_bk
    # rematerialize rm and rn to save registers
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    idx_m = rm[:, None]
    idx_n = rn[None, :]
    mask = (idx_m < M) & (idx_n < N)
    # inductor generates a suffix
    xindex = idx_n + (768*idx_m)
    tmp0 = tl.load(in_ptr2 + (tl.broadcast_to(idx_m, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tl.store(out_ptr0 + (tl.broadcast_to(xindex, mask.shape)), acc * tmp0, mask)
''', device_str='cuda')
import torch._inductor.kernel.mm_common
meta0 = {'GROUP_M': 8, 'EVEN_K': True, 'ALLOW_TF32': False, 'ACC_TYPE': 'tl.int32', 'B_PROLOGUE_CAST_TYPE': None, 'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32}
# kernel path: /tmp/torchinductor_jerryzh/cr/ccr74bfho3ok5o2tyb5aq3refnm7mumphtnf7anixsxqq2ylylnx.py
# Source Nodes: [cat_1, input_1, l__self___encoder_layers_encoder_layer_0_mlp_0, x_7, x_8, y], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.cat, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
# cat_1 => cat
# input_1 => add
# l__self___encoder_layers_encoder_layer_0_mlp_0 => abs_2, amax_1, clamp_max_1, clamp_min_2, clamp_min_3, convert_element_type_12, convert_element_type_13, convert_element_type_14, convert_element_type_15, convert_element_type_16, div_2, div_3, round_2
# x_7 => clone_2
# x_8 => add_4
# y => add_5, add_6, convert_element_type_10, convert_element_type_11, mul_4, mul_5, rsqrt_1, sub_1, var_mean_1
triton_per_fused__to_copy_abs_add_amax_cat_clamp_clone_div_native_layer_norm_round_8 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*bf16', 5: '*bf16', 6: '*bf16', 7: '*bf16', 8: '*bf16', 9: '*i8', 10: '*bf16', 11: 'i32', 12: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(12,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_abs_add_amax_cat_clamp_clone_div_native_layer_norm_round_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr4, out_ptr5, xnumel, rnumel):
    xnumel = 197
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp1 = tl.load(in_ptr0 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp3 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp22 = tl.load(in_ptr5 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp49 = tl.load(in_ptr6 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp52 = tl.load(in_ptr7 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = x0
    tmp6 = tl.full([1], 0, tl.int64)
    tmp7 = tmp5 >= tmp6
    tmp8 = tl.full([1], 1, tl.int64)
    tmp9 = tmp5 < tmp8
    tmp10 = tl.load(in_ptr2 + (tl.broadcast_to(r1, [RBLOCK])), rmask & tmp9 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp9, tmp10, tmp11)
    tmp13 = tmp5 >= tmp8
    tmp14 = tl.full([1], 197, tl.int64)
    tmp15 = tmp5 < tmp14
    tmp16 = tl.load(in_ptr3 + ((196*r1) + (((-1) + x0) % 196)), rmask & tmp13 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp17 = tl.load(in_ptr4 + (tl.broadcast_to(r1, [RBLOCK])), rmask & tmp13 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp18 = tmp16 + tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp13, tmp18, tmp19)
    tmp21 = tl.where(tmp9, tmp12, tmp20)
    tmp23 = tmp21 + tmp22
    tmp24 = tmp4 + tmp23
    tmp25 = tmp24.to(tl.float32)
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = tl.where(rmask & xmask, tmp26, 0)
    tmp29 = tl.broadcast_to(tmp26, [RBLOCK])
    tmp31 = tl.where(rmask & xmask, tmp29, 0)
    tmp32 = triton_helpers.promote_to_tensor(tl.sum(tmp31, 0))
    tmp33 = tl.full([1], 768, tl.int32)
    tmp34 = tmp33.to(tl.float32)
    tmp35 = tmp32 / tmp34
    tmp36 = tmp26 - tmp35
    tmp37 = tmp36 * tmp36
    tmp38 = tl.broadcast_to(tmp37, [RBLOCK])
    tmp40 = tl.where(rmask & xmask, tmp38, 0)
    tmp41 = triton_helpers.promote_to_tensor(tl.sum(tmp40, 0))
    tmp42 = tmp25 - tmp35
    tmp43 = 768.0
    tmp44 = tmp41 / tmp43
    tmp45 = 1e-06
    tmp46 = tmp44 + tmp45
    tmp47 = libdevice.rsqrt(tmp46)
    tmp48 = tmp42 * tmp47
    tmp50 = tmp49.to(tl.float32)
    tmp51 = tmp48 * tmp50
    tmp53 = tmp52.to(tl.float32)
    tmp54 = tmp51 + tmp53
    tmp55 = tmp54.to(tl.float32)
    tmp56 = tl_math.abs(tmp55)
    tmp57 = tl.broadcast_to(tmp56, [RBLOCK])
    tmp59 = tl.where(rmask & xmask, tmp57, float("-inf"))
    tmp60 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp59, 0))
    tmp61 = tmp60.to(tl.float32)
    tmp62 = 1e-05
    tmp63 = triton_helpers.maximum(tmp61, tmp62)
    tmp64 = tmp63.to(tl.float32)
    tmp65 = 127.0
    tmp66 = tmp64 / tmp65
    tmp67 = tmp55 / tmp66
    tmp68 = libdevice.nearbyint(tmp67)
    tmp69 = tmp68.to(tl.float32)
    tmp70 = -127.0
    tmp71 = triton_helpers.maximum(tmp69, tmp70)
    tmp72 = triton_helpers.minimum(tmp71, tmp65)
    tmp73 = tmp72.to(tl.float32)
    tmp74 = tmp73.to(tl.int8)
    tl.store(in_out_ptr0 + (r1 + (768*x0)), tmp24, rmask & xmask)
    tl.store(out_ptr4 + (r1 + (768*x0)), tmp74, rmask & xmask)
    tl.store(out_ptr5 + (x0), tmp66, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/jx/cjxx4domyf5fqnwxmul6z4wzyoglsc24vcwrgybgrtp2t54xbq4v.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_0], Original ATen: [aten.mul]
# l__self___encoder_layers_encoder_layer_0_mlp_0 => fused_int_mm_mul_35
triton_tem_fused_mul_9 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.template(
    num_stages=3,
    num_warps=8,
    triton_meta={'signature': {0: '*i8', 1: '*i8', 2: '*bf16', 3: '*bf16'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'kernel_name': 'triton_tem_fused_mul_9', 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
)
@triton.jit
def triton_(arg_A, arg_B, in_ptr2, out_ptr0):
    GROUP_M : tl.constexpr = 8
    EVEN_K : tl.constexpr = True
    ALLOW_TF32 : tl.constexpr = False
    ACC_TYPE : tl.constexpr = tl.int32
    B_PROLOGUE_CAST_TYPE : tl.constexpr = None
    BLOCK_M : tl.constexpr = 64
    BLOCK_N : tl.constexpr = 64
    BLOCK_K : tl.constexpr = 64
    A = arg_A
    B = arg_B
    M = 197
    N = 3072
    K = 768
    if M * N == 0:
        # early exit due to zero-size input(s)
        return
    stride_am = 768
    stride_ak = 1
    stride_bk = 1
    stride_bn = 768
    # based on triton.ops.matmul
    pid = tl.program_id(0)
    grid_m = (M + BLOCK_M - 1) // BLOCK_M
    grid_n = (N + BLOCK_N - 1) // BLOCK_N
    # re-order program ID for better L2 performance
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // (group_size)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
    for k in range(K, 0, -BLOCK_K):
        if EVEN_K:
            a = tl.load(A)
            b = tl.load(B)
        else:
            a = tl.load(A, mask=rk[None, :] < k, other=0.)
            b = tl.load(B, mask=rk[:, None] < k, other=0.)
        if B_PROLOGUE_CAST_TYPE is not None:
            b = b.to(B_PROLOGUE_CAST_TYPE)
        acc += tl.dot(a, b, allow_tf32=ALLOW_TF32)
        A += BLOCK_K * stride_ak
        B += BLOCK_K * stride_bk
    # rematerialize rm and rn to save registers
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    idx_m = rm[:, None]
    idx_n = rn[None, :]
    mask = (idx_m < M) & (idx_n < N)
    # inductor generates a suffix
    xindex = idx_n + (3072*idx_m)
    tmp0 = tl.load(in_ptr2 + (tl.broadcast_to(idx_m, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tl.store(out_ptr0 + (tl.broadcast_to(xindex, mask.shape)), acc * tmp0, mask)
''', device_str='cuda')
meta1 = {'GROUP_M': 8, 'EVEN_K': True, 'ALLOW_TF32': False, 'ACC_TYPE': 'tl.int32', 'B_PROLOGUE_CAST_TYPE': None, 'BLOCK_M': 64, 'BLOCK_N': 64, 'BLOCK_K': 64}
# kernel path: /tmp/torchinductor_jerryzh/iy/ciyvyjnui7iwwlmxkprc7lnz7wv3wqmha4nv43gesczchuffgkse.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_1, l__self___encoder_layers_encoder_layer_0_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
# l__self___encoder_layers_encoder_layer_0_mlp_1 => add_8, convert_element_type_17, convert_element_type_18, erf, mul_10, mul_8, mul_9
# l__self___encoder_layers_encoder_layer_0_mlp_3 => abs_3, amax_2, clamp_max_2, clamp_min_4, clamp_min_5, convert_element_type_19, convert_element_type_20, convert_element_type_21, convert_element_type_22, convert_element_type_23, div_4, div_5, round_3
triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.reduction(
    size_hints=[256, 4096],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*i8', 4: '*bf16', 5: 'i32', 6: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(6,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, in_ptr1, in_ptr2, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 197
    rnumel = 3072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp17 = tl.full([XBLOCK, RBLOCK], float("-inf"), tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + (3072*x0)), rmask & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp1 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp3 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp2 = tmp0 * tmp1
        tmp4 = tmp2 + tmp3
        tmp5 = tmp4.to(tl.float32)
        tmp6 = 0.5
        tmp7 = tmp5 * tmp6
        tmp8 = 0.7071067811865476
        tmp9 = tmp5 * tmp8
        tmp10 = libdevice.erf(tmp9)
        tmp11 = 1.0
        tmp12 = tmp10 + tmp11
        tmp13 = tmp7 * tmp12
        tmp14 = tmp13.to(tl.float32)
        tmp15 = tl_math.abs(tmp14)
        tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
        tmp18 = triton_helpers.maximum(_tmp17, tmp16)
        _tmp17 = tl.where(rmask & xmask, tmp18, _tmp17)
    tmp17 = triton_helpers.max2(_tmp17, 1)[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp19 = tl.load(in_ptr0 + (r1 + (3072*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0).to(tl.float32)
        tmp20 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp22 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp21 = tmp19 * tmp20
        tmp23 = tmp21 + tmp22
        tmp24 = tmp23.to(tl.float32)
        tmp25 = 0.5
        tmp26 = tmp24 * tmp25
        tmp27 = 0.7071067811865476
        tmp28 = tmp24 * tmp27
        tmp29 = libdevice.erf(tmp28)
        tmp30 = 1.0
        tmp31 = tmp29 + tmp30
        tmp32 = tmp26 * tmp31
        tmp33 = tmp32.to(tl.float32)
        tmp34 = tmp17.to(tl.float32)
        tmp35 = 1e-05
        tmp36 = triton_helpers.maximum(tmp34, tmp35)
        tmp37 = tmp36.to(tl.float32)
        tmp38 = 127.0
        tmp39 = tmp37 / tmp38
        tmp40 = tmp33 / tmp39
        tmp41 = libdevice.nearbyint(tmp40)
        tmp42 = tmp41.to(tl.float32)
        tmp43 = -127.0
        tmp44 = triton_helpers.maximum(tmp42, tmp43)
        tmp45 = triton_helpers.minimum(tmp44, tmp38)
        tmp46 = tmp45.to(tl.float32)
        tmp47 = tmp46.to(tl.int8)
        tl.store(out_ptr1 + (r1 + (3072*x0)), tmp47, rmask & xmask)
    tmp48 = tmp17.to(tl.float32)
    tmp49 = 1e-05
    tmp50 = triton_helpers.maximum(tmp48, tmp49)
    tmp51 = tmp50.to(tl.float32)
    tmp52 = 127.0
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr2 + (x0), tmp53, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/ps/cpsyn7vzywekfevmwvtf55dbk53i5wmhkwkayz7ootpwltznssnw.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_3], Original ATen: [aten.mul]
# l__self___encoder_layers_encoder_layer_0_mlp_3 => fused_int_mm_mul_34
triton_tem_fused_mul_11 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.template(
    num_stages=3,
    num_warps=8,
    triton_meta={'signature': {0: '*i8', 1: '*i8', 2: '*bf16', 3: '*bf16'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'kernel_name': 'triton_tem_fused_mul_11', 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
)
@triton.jit
def triton_(arg_A, arg_B, in_ptr2, out_ptr0):
    GROUP_M : tl.constexpr = 8
    EVEN_K : tl.constexpr = True
    ALLOW_TF32 : tl.constexpr = False
    ACC_TYPE : tl.constexpr = tl.int32
    B_PROLOGUE_CAST_TYPE : tl.constexpr = None
    BLOCK_M : tl.constexpr = 64
    BLOCK_N : tl.constexpr = 64
    BLOCK_K : tl.constexpr = 64
    A = arg_A
    B = arg_B
    M = 197
    N = 768
    K = 3072
    if M * N == 0:
        # early exit due to zero-size input(s)
        return
    stride_am = 3072
    stride_ak = 1
    stride_bk = 1
    stride_bn = 3072
    # based on triton.ops.matmul
    pid = tl.program_id(0)
    grid_m = (M + BLOCK_M - 1) // BLOCK_M
    grid_n = (N + BLOCK_N - 1) // BLOCK_N
    # re-order program ID for better L2 performance
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // (group_size)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
    for k in range(K, 0, -BLOCK_K):
        if EVEN_K:
            a = tl.load(A)
            b = tl.load(B)
        else:
            a = tl.load(A, mask=rk[None, :] < k, other=0.)
            b = tl.load(B, mask=rk[:, None] < k, other=0.)
        if B_PROLOGUE_CAST_TYPE is not None:
            b = b.to(B_PROLOGUE_CAST_TYPE)
        acc += tl.dot(a, b, allow_tf32=ALLOW_TF32)
        A += BLOCK_K * stride_ak
        B += BLOCK_K * stride_bk
    # rematerialize rm and rn to save registers
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    idx_m = rm[:, None]
    idx_n = rn[None, :]
    mask = (idx_m < M) & (idx_n < N)
    # inductor generates a suffix
    xindex = idx_n + (768*idx_m)
    tmp0 = tl.load(in_ptr2 + (tl.broadcast_to(idx_m, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tl.store(out_ptr0 + (tl.broadcast_to(xindex, mask.shape)), acc * tmp0, mask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/vy/cvycfkdip26kupsh6qqdyw6u6occez4idkkoffi37ti5bizxwchp.py
# Source Nodes: [add_2, x_9], Original ATen: [aten.add, aten.native_layer_norm]
# add_2 => add_10
# x_9 => add_11, add_12, convert_element_type_24, convert_element_type_25, mul_13, mul_14, rsqrt_2, sub_2, var_mean_2
triton_per_fused_add_native_layer_norm_12 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*bf16', 5: '*bf16', 6: '*bf16', 7: 'i32', 8: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 8), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(8,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_native_layer_norm_12', 'mutated_arg_names': [], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr2, xnumel, rnumel):
    xnumel = 197
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp2 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp4 = tl.load(in_ptr3 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp31 = tl.load(in_ptr4 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp34 = tl.load(in_ptr5 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 + tmp4
    tmp6 = tmp0 + tmp5
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = tl.where(rmask & xmask, tmp8, 0)
    tmp11 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp13 = tl.where(rmask & xmask, tmp11, 0)
    tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp15 = tl.full([1], 768, tl.int32)
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp14 / tmp16
    tmp18 = tmp8 - tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
    tmp22 = tl.where(rmask & xmask, tmp20, 0)
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp24 = tmp7 - tmp17
    tmp25 = 768.0
    tmp26 = tmp23 / tmp25
    tmp27 = 1e-06
    tmp28 = tmp26 + tmp27
    tmp29 = libdevice.rsqrt(tmp28)
    tmp30 = tmp24 * tmp29
    tmp32 = tmp31.to(tl.float32)
    tmp33 = tmp30 * tmp32
    tmp35 = tmp34.to(tl.float32)
    tmp36 = tmp33 + tmp35
    tmp37 = tmp36.to(tl.float32)
    tl.store(out_ptr2 + (r1 + (768*x0)), tmp37, rmask & xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/qq/cqq6ykhkbe3vxe2rgr4vqhn7noiowpjojt2ccs4uylq23avmipb3.py
# Source Nodes: [add_2, l__self___encoder_layers_encoder_layer_1_mlp_0, x_11, x_12, y_2], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
# add_2 => add_10
# l__self___encoder_layers_encoder_layer_1_mlp_0 => abs_5, amax_4, clamp_max_4, clamp_min_8, clamp_min_9, convert_element_type_36, convert_element_type_37, convert_element_type_38, convert_element_type_39, convert_element_type_40, div_8, div_9, round_5
# x_11 => clone_6
# x_12 => add_14
# y_2 => add_15, add_16, convert_element_type_34, convert_element_type_35, mul_17, mul_18, rsqrt_3, sub_3, var_mean_3
triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*bf16', 5: '*bf16', 6: '*bf16', 7: '*bf16', 8: '*bf16', 9: '*i8', 10: '*bf16', 11: 'i32', 12: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(12,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr4, out_ptr5, xnumel, rnumel):
    xnumel = 197
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp1 = tl.load(in_ptr0 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp3 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp5 = tl.load(in_ptr2 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp6 = tl.load(in_ptr3 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp7 = tl.load(in_ptr4 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp9 = tl.load(in_ptr5 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp37 = tl.load(in_ptr6 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp40 = tl.load(in_ptr7 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = tmp5 + tmp10
    tmp12 = tmp4 + tmp11
    tmp13 = tmp12.to(tl.float32)
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = tl.where(rmask & xmask, tmp14, 0)
    tmp17 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp19 = tl.where(rmask & xmask, tmp17, 0)
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp21 = tl.full([1], 768, tl.int32)
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp20 / tmp22
    tmp24 = tmp14 - tmp23
    tmp25 = tmp24 * tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = tl.where(rmask & xmask, tmp26, 0)
    tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp28, 0))
    tmp30 = tmp13 - tmp23
    tmp31 = 768.0
    tmp32 = tmp29 / tmp31
    tmp33 = 1e-06
    tmp34 = tmp32 + tmp33
    tmp35 = libdevice.rsqrt(tmp34)
    tmp36 = tmp30 * tmp35
    tmp38 = tmp37.to(tl.float32)
    tmp39 = tmp36 * tmp38
    tmp41 = tmp40.to(tl.float32)
    tmp42 = tmp39 + tmp41
    tmp43 = tmp42.to(tl.float32)
    tmp44 = tl_math.abs(tmp43)
    tmp45 = tl.broadcast_to(tmp44, [RBLOCK])
    tmp47 = tl.where(rmask & xmask, tmp45, float("-inf"))
    tmp48 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp47, 0))
    tmp49 = tmp48.to(tl.float32)
    tmp50 = 1e-05
    tmp51 = triton_helpers.maximum(tmp49, tmp50)
    tmp52 = tmp51.to(tl.float32)
    tmp53 = 127.0
    tmp54 = tmp52 / tmp53
    tmp55 = tmp43 / tmp54
    tmp56 = libdevice.nearbyint(tmp55)
    tmp57 = tmp56.to(tl.float32)
    tmp58 = -127.0
    tmp59 = triton_helpers.maximum(tmp57, tmp58)
    tmp60 = triton_helpers.minimum(tmp59, tmp53)
    tmp61 = tmp60.to(tl.float32)
    tmp62 = tmp61.to(tl.int8)
    tl.store(in_out_ptr0 + (r1 + (768*x0)), tmp12, rmask & xmask)
    tl.store(out_ptr4 + (r1 + (768*x0)), tmp62, rmask & xmask)
    tl.store(out_ptr5 + (x0), tmp54, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/wd/cwdqc7dmebh3v2xd6cfey6yui43rvmyhummf4vqofw63udidqwyg.py
# Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
# x_55 => abs_37, amax_36, clamp_max_36, clamp_min_72, clamp_min_73, convert_element_type_290, convert_element_type_291, convert_element_type_292, convert_element_type_293, convert_element_type_294, div_72, div_73, round_37
triton_per_fused__to_copy_abs_amax_clamp_div_round_14 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[1, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*i8', 2: '*bf16', 3: 'i32', 4: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,), ids_of_folded_args=(3,), divisible_by_8=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_abs_amax_clamp_div_round_14', 'mutated_arg_names': [], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), rmask, other=0.0).to(tl.float32)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = tl.where(rmask, tmp2, float("-inf"))
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp4, 0))
    tmp6 = tmp5.to(tl.float32)
    tmp7 = 1e-05
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = 127.0
    tmp11 = tmp9 / tmp10
    tmp12 = tmp0 / tmp11
    tmp13 = libdevice.nearbyint(tmp12)
    tmp14 = tmp13.to(tl.float32)
    tmp15 = -127.0
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = triton_helpers.minimum(tmp16, tmp10)
    tmp18 = tmp17.to(tl.float32)
    tmp19 = tmp18.to(tl.int8)
    tl.store(out_ptr1 + (tl.broadcast_to(r0, [RBLOCK])), tmp19, rmask)
    tl.store(out_ptr2 + (tl.full([1], 0, tl.int32)), tmp11, None)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/q7/cq7l22t3jg2gl4suedtqectjqqnzo2au36jgqky34jaylvqzbpe6.py
# Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
# x_55 => clamp_max_36, clamp_min_72, clamp_min_73, convert_element_type_290, convert_element_type_291, convert_element_type_292, convert_element_type_293, convert_element_type_294, div_72, div_73, fused_int_mm_mul, round_37, view_337
triton_poi_fused__to_copy_clamp_div_mul_round_view_15 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_clamp_div_mul_round_view_15', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
    min_elem_per_thread=0
)
@triton.jit
def triton_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (0)).to(tl.float32)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/ok/cokrq32j6selob5j2cg3mdihs6r7dcbuoikxiheczlgornscdupy.py
# Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.add, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
# x_55 => add_123, clamp_max_36, clamp_min_72, clamp_min_73, convert_element_type_290, convert_element_type_291, convert_element_type_292, convert_element_type_293, convert_element_type_294, div_72, div_73, fused_int_mm_mul, mul_159, round_37, view_337
triton_tem_fused__to_copy_add_clamp_div_mul_round_view_16 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.template(
    num_stages=3,
    num_warps=8,
    triton_meta={'signature': {0: '*i8', 1: '*i8', 2: '*bf16', 3: '*bf16', 4: '*bf16', 5: '*bf16'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'kernel_name': 'triton_tem_fused__to_copy_add_clamp_div_mul_round_view_16', 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
)
@triton.jit
def triton_(arg_A, arg_B, in_ptr2, in_ptr3, in_ptr4, out_ptr1):
    GROUP_M : tl.constexpr = 8
    EVEN_K : tl.constexpr = True
    ALLOW_TF32 : tl.constexpr = False
    ACC_TYPE : tl.constexpr = tl.int32
    B_PROLOGUE_CAST_TYPE : tl.constexpr = None
    BLOCK_M : tl.constexpr = 16
    BLOCK_N : tl.constexpr = 128
    BLOCK_K : tl.constexpr = 128
    A = arg_A
    B = arg_B
    M = 1
    N = 1000
    K = 768
    if M * N == 0:
        # early exit due to zero-size input(s)
        return
    stride_am = 768
    stride_ak = 1
    stride_bk = 1
    stride_bn = 768
    # based on triton.ops.matmul
    pid = tl.program_id(0)
    grid_m = (M + BLOCK_M - 1) // BLOCK_M
    grid_n = (N + BLOCK_N - 1) // BLOCK_N
    # re-order program ID for better L2 performance
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // (group_size)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
    for k in range(K, 0, -BLOCK_K):
        if EVEN_K:
            a = tl.load(A)
            b = tl.load(B)
        else:
            a = tl.load(A, mask=rk[None, :] < k, other=0.)
            b = tl.load(B, mask=rk[:, None] < k, other=0.)
        if B_PROLOGUE_CAST_TYPE is not None:
            b = b.to(B_PROLOGUE_CAST_TYPE)
        acc += tl.dot(a, b, allow_tf32=ALLOW_TF32)
        A += BLOCK_K * stride_ak
        B += BLOCK_K * stride_bk
    # rematerialize rm and rn to save registers
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    idx_m = rm[:, None]
    idx_n = rn[None, :]
    mask = (idx_m < M) & (idx_n < N)
    # inductor generates a suffix
    xindex = idx_n + (1000*idx_m)
    tmp0 = tl.load(in_ptr2 + (tl.broadcast_to(idx_n, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tmp1 = tl.load(in_ptr3 + (tl.broadcast_to(xindex, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tmp3 = tl.load(in_ptr4 + (tl.broadcast_to(xindex, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tmp2 = acc * tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr1 + (tl.broadcast_to(xindex, mask.shape)), tmp4, mask)
''', device_str='cuda')
meta2 = {'GROUP_M': 8, 'EVEN_K': True, 'ALLOW_TF32': False, 'ACC_TYPE': 'tl.int32', 'B_PROLOGUE_CAST_TYPE': None, 'BLOCK_M': 16, 'BLOCK_N': 128, 'BLOCK_K': 128}
async_compile.wait(globals())
del async_compile
def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1, arg15_1, arg16_1, arg17_1, arg18_1, arg19_1, arg20_1, arg21_1, arg22_1, arg23_1, arg24_1, arg25_1, arg26_1, arg27_1, arg28_1, arg29_1, arg30_1, arg31_1, arg32_1, arg33_1, arg34_1, arg35_1, arg36_1, arg37_1, arg38_1, arg39_1, arg40_1, arg41_1, arg42_1, arg43_1, arg44_1, arg45_1, arg46_1, arg47_1, arg48_1, arg49_1, arg50_1, arg51_1, arg52_1, arg53_1, arg54_1, arg55_1, arg56_1, arg57_1, arg58_1, arg59_1, arg60_1, arg61_1, arg62_1, arg63_1, arg64_1, arg65_1, arg66_1, arg67_1, arg68_1, arg69_1, arg70_1, arg71_1, arg72_1, arg73_1, arg74_1, arg75_1, arg76_1, arg77_1, arg78_1, arg79_1, arg80_1, arg81_1, arg82_1, arg83_1, arg84_1, arg85_1, arg86_1, arg87_1, arg88_1, arg89_1, arg90_1, arg91_1, arg92_1, arg93_1, arg94_1, arg95_1, arg96_1, arg97_1, arg98_1, arg99_1, arg100_1, arg101_1, arg102_1, arg103_1, arg104_1, arg105_1, arg106_1, arg107_1, arg108_1, arg109_1, arg110_1, arg111_1, arg112_1, arg113_1, arg114_1, arg115_1, arg116_1, arg117_1, arg118_1, arg119_1, arg120_1, arg121_1, arg122_1, arg123_1, arg124_1, arg125_1, arg126_1, arg127_1, arg128_1, arg129_1, arg130_1, arg131_1, arg132_1, arg133_1, arg134_1, arg135_1, arg136_1, arg137_1, arg138_1, arg139_1, arg140_1, arg141_1, arg142_1, arg143_1, arg144_1, arg145_1, arg146_1, arg147_1, arg148_1, arg149_1, arg150_1, arg151_1, arg152_1, arg153_1, arg154_1, arg155_1, arg156_1, arg157_1, arg158_1, arg159_1, arg160_1, arg161_1, arg162_1, arg163_1, arg164_1, arg165_1, arg166_1, arg167_1, arg168_1, arg169_1, arg170_1, arg171_1, arg172_1, arg173_1, arg174_1, arg175_1, arg176_1, arg177_1, arg178_1, arg179_1, arg180_1, arg181_1, arg182_1, arg183_1, arg184_1, arg185_1, arg186_1, arg187_1, arg188_1, arg189_1 = args
    args.clear()
    assert_size_stride(arg0_1, (1, 1, 768), (768, 768, 1))
    assert_size_stride(arg1_1, (1, 197, 768), (151296, 768, 1))
    assert_size_stride(arg2_1, (768, 3, 16, 16), (768, 256, 16, 1))
    assert_size_stride(arg3_1, (768, ), (1, ))
    assert_size_stride(arg4_1, (768, ), (1, ))
    assert_size_stride(arg5_1, (768, ), (1, ))
    assert_size_stride(arg6_1, (2304, 768), (768, 1))
    assert_size_stride(arg7_1, (2304, ), (1, ))
    assert_size_stride(arg8_1, (768, 768), (1, 768))
    assert_size_stride(arg9_1, (768, ), (1, ))
    assert_size_stride(arg10_1, (768, ), (1, ))
    assert_size_stride(arg11_1, (768, ), (1, ))
    assert_size_stride(arg12_1, (768, ), (1, ))
    assert_size_stride(arg13_1, (768, 3072), (1, 768))
    assert_size_stride(arg14_1, (3072, ), (1, ))
    assert_size_stride(arg15_1, (3072, ), (1, ))
    assert_size_stride(arg16_1, (3072, 768), (1, 3072))
    assert_size_stride(arg17_1, (768, ), (1, ))
    assert_size_stride(arg18_1, (768, ), (1, ))
    assert_size_stride(arg19_1, (768, ), (1, ))
    assert_size_stride(arg20_1, (768, ), (1, ))
    assert_size_stride(arg21_1, (2304, 768), (768, 1))
    assert_size_stride(arg22_1, (2304, ), (1, ))
    assert_size_stride(arg23_1, (768, 768), (1, 768))
    assert_size_stride(arg24_1, (768, ), (1, ))
    assert_size_stride(arg25_1, (768, ), (1, ))
    assert_size_stride(arg26_1, (768, ), (1, ))
    assert_size_stride(arg27_1, (768, ), (1, ))
    assert_size_stride(arg28_1, (768, 3072), (1, 768))
    assert_size_stride(arg29_1, (3072, ), (1, ))
    assert_size_stride(arg30_1, (3072, ), (1, ))
    assert_size_stride(arg31_1, (3072, 768), (1, 3072))
    assert_size_stride(arg32_1, (768, ), (1, ))
    assert_size_stride(arg33_1, (768, ), (1, ))
    assert_size_stride(arg34_1, (768, ), (1, ))
    assert_size_stride(arg35_1, (768, ), (1, ))
    assert_size_stride(arg36_1, (2304, 768), (768, 1))
    assert_size_stride(arg37_1, (2304, ), (1, ))
    assert_size_stride(arg38_1, (768, 768), (1, 768))
    assert_size_stride(arg39_1, (768, ), (1, ))
    assert_size_stride(arg40_1, (768, ), (1, ))
    assert_size_stride(arg41_1, (768, ), (1, ))
    assert_size_stride(arg42_1, (768, ), (1, ))
    assert_size_stride(arg43_1, (768, 3072), (1, 768))
    assert_size_stride(arg44_1, (3072, ), (1, ))
    assert_size_stride(arg45_1, (3072, ), (1, ))
    assert_size_stride(arg46_1, (3072, 768), (1, 3072))
    assert_size_stride(arg47_1, (768, ), (1, ))
    assert_size_stride(arg48_1, (768, ), (1, ))
    assert_size_stride(arg49_1, (768, ), (1, ))
    assert_size_stride(arg50_1, (768, ), (1, ))
    assert_size_stride(arg51_1, (2304, 768), (768, 1))
    assert_size_stride(arg52_1, (2304, ), (1, ))
    assert_size_stride(arg53_1, (768, 768), (1, 768))
    assert_size_stride(arg54_1, (768, ), (1, ))
    assert_size_stride(arg55_1, (768, ), (1, ))
    assert_size_stride(arg56_1, (768, ), (1, ))
    assert_size_stride(arg57_1, (768, ), (1, ))
    assert_size_stride(arg58_1, (768, 3072), (1, 768))
    assert_size_stride(arg59_1, (3072, ), (1, ))
    assert_size_stride(arg60_1, (3072, ), (1, ))
    assert_size_stride(arg61_1, (3072, 768), (1, 3072))
    assert_size_stride(arg62_1, (768, ), (1, ))
    assert_size_stride(arg63_1, (768, ), (1, ))
    assert_size_stride(arg64_1, (768, ), (1, ))
    assert_size_stride(arg65_1, (768, ), (1, ))
    assert_size_stride(arg66_1, (2304, 768), (768, 1))
    assert_size_stride(arg67_1, (2304, ), (1, ))
    assert_size_stride(arg68_1, (768, 768), (1, 768))
    assert_size_stride(arg69_1, (768, ), (1, ))
    assert_size_stride(arg70_1, (768, ), (1, ))
    assert_size_stride(arg71_1, (768, ), (1, ))
    assert_size_stride(arg72_1, (768, ), (1, ))
    assert_size_stride(arg73_1, (768, 3072), (1, 768))
    assert_size_stride(arg74_1, (3072, ), (1, ))
    assert_size_stride(arg75_1, (3072, ), (1, ))
    assert_size_stride(arg76_1, (3072, 768), (1, 3072))
    assert_size_stride(arg77_1, (768, ), (1, ))
    assert_size_stride(arg78_1, (768, ), (1, ))
    assert_size_stride(arg79_1, (768, ), (1, ))
    assert_size_stride(arg80_1, (768, ), (1, ))
    assert_size_stride(arg81_1, (2304, 768), (768, 1))
    assert_size_stride(arg82_1, (2304, ), (1, ))
    assert_size_stride(arg83_1, (768, 768), (1, 768))
    assert_size_stride(arg84_1, (768, ), (1, ))
    assert_size_stride(arg85_1, (768, ), (1, ))
    assert_size_stride(arg86_1, (768, ), (1, ))
    assert_size_stride(arg87_1, (768, ), (1, ))
    assert_size_stride(arg88_1, (768, 3072), (1, 768))
    assert_size_stride(arg89_1, (3072, ), (1, ))
    assert_size_stride(arg90_1, (3072, ), (1, ))
    assert_size_stride(arg91_1, (3072, 768), (1, 3072))
    assert_size_stride(arg92_1, (768, ), (1, ))
    assert_size_stride(arg93_1, (768, ), (1, ))
    assert_size_stride(arg94_1, (768, ), (1, ))
    assert_size_stride(arg95_1, (768, ), (1, ))
    assert_size_stride(arg96_1, (2304, 768), (768, 1))
    assert_size_stride(arg97_1, (2304, ), (1, ))
    assert_size_stride(arg98_1, (768, 768), (1, 768))
    assert_size_stride(arg99_1, (768, ), (1, ))
    assert_size_stride(arg100_1, (768, ), (1, ))
    assert_size_stride(arg101_1, (768, ), (1, ))
    assert_size_stride(arg102_1, (768, ), (1, ))
    assert_size_stride(arg103_1, (768, 3072), (1, 768))
    assert_size_stride(arg104_1, (3072, ), (1, ))
    assert_size_stride(arg105_1, (3072, ), (1, ))
    assert_size_stride(arg106_1, (3072, 768), (1, 3072))
    assert_size_stride(arg107_1, (768, ), (1, ))
    assert_size_stride(arg108_1, (768, ), (1, ))
    assert_size_stride(arg109_1, (768, ), (1, ))
    assert_size_stride(arg110_1, (768, ), (1, ))
    assert_size_stride(arg111_1, (2304, 768), (768, 1))
    assert_size_stride(arg112_1, (2304, ), (1, ))
    assert_size_stride(arg113_1, (768, 768), (1, 768))
    assert_size_stride(arg114_1, (768, ), (1, ))
    assert_size_stride(arg115_1, (768, ), (1, ))
    assert_size_stride(arg116_1, (768, ), (1, ))
    assert_size_stride(arg117_1, (768, ), (1, ))
    assert_size_stride(arg118_1, (768, 3072), (1, 768))
    assert_size_stride(arg119_1, (3072, ), (1, ))
    assert_size_stride(arg120_1, (3072, ), (1, ))
    assert_size_stride(arg121_1, (3072, 768), (1, 3072))
    assert_size_stride(arg122_1, (768, ), (1, ))
    assert_size_stride(arg123_1, (768, ), (1, ))
    assert_size_stride(arg124_1, (768, ), (1, ))
    assert_size_stride(arg125_1, (768, ), (1, ))
    assert_size_stride(arg126_1, (2304, 768), (768, 1))
    assert_size_stride(arg127_1, (2304, ), (1, ))
    assert_size_stride(arg128_1, (768, 768), (1, 768))
    assert_size_stride(arg129_1, (768, ), (1, ))
    assert_size_stride(arg130_1, (768, ), (1, ))
    assert_size_stride(arg131_1, (768, ), (1, ))
    assert_size_stride(arg132_1, (768, ), (1, ))
    assert_size_stride(arg133_1, (768, 3072), (1, 768))
    assert_size_stride(arg134_1, (3072, ), (1, ))
    assert_size_stride(arg135_1, (3072, ), (1, ))
    assert_size_stride(arg136_1, (3072, 768), (1, 3072))
    assert_size_stride(arg137_1, (768, ), (1, ))
    assert_size_stride(arg138_1, (768, ), (1, ))
    assert_size_stride(arg139_1, (768, ), (1, ))
    assert_size_stride(arg140_1, (768, ), (1, ))
    assert_size_stride(arg141_1, (2304, 768), (768, 1))
    assert_size_stride(arg142_1, (2304, ), (1, ))
    assert_size_stride(arg143_1, (768, 768), (1, 768))
    assert_size_stride(arg144_1, (768, ), (1, ))
    assert_size_stride(arg145_1, (768, ), (1, ))
    assert_size_stride(arg146_1, (768, ), (1, ))
    assert_size_stride(arg147_1, (768, ), (1, ))
    assert_size_stride(arg148_1, (768, 3072), (1, 768))
    assert_size_stride(arg149_1, (3072, ), (1, ))
    assert_size_stride(arg150_1, (3072, ), (1, ))
    assert_size_stride(arg151_1, (3072, 768), (1, 3072))
    assert_size_stride(arg152_1, (768, ), (1, ))
    assert_size_stride(arg153_1, (768, ), (1, ))
    assert_size_stride(arg154_1, (768, ), (1, ))
    assert_size_stride(arg155_1, (768, ), (1, ))
    assert_size_stride(arg156_1, (2304, 768), (768, 1))
    assert_size_stride(arg157_1, (2304, ), (1, ))
    assert_size_stride(arg158_1, (768, 768), (1, 768))
    assert_size_stride(arg159_1, (768, ), (1, ))
    assert_size_stride(arg160_1, (768, ), (1, ))
    assert_size_stride(arg161_1, (768, ), (1, ))
    assert_size_stride(arg162_1, (768, ), (1, ))
    assert_size_stride(arg163_1, (768, 3072), (1, 768))
    assert_size_stride(arg164_1, (3072, ), (1, ))
    assert_size_stride(arg165_1, (3072, ), (1, ))
    assert_size_stride(arg166_1, (3072, 768), (1, 3072))
    assert_size_stride(arg167_1, (768, ), (1, ))
    assert_size_stride(arg168_1, (768, ), (1, ))
    assert_size_stride(arg169_1, (768, ), (1, ))
    assert_size_stride(arg170_1, (768, ), (1, ))
    assert_size_stride(arg171_1, (2304, 768), (768, 1))
    assert_size_stride(arg172_1, (2304, ), (1, ))
    assert_size_stride(arg173_1, (768, 768), (1, 768))
    assert_size_stride(arg174_1, (768, ), (1, ))
    assert_size_stride(arg175_1, (768, ), (1, ))
    assert_size_stride(arg176_1, (768, ), (1, ))
    assert_size_stride(arg177_1, (768, ), (1, ))
    assert_size_stride(arg178_1, (768, 3072), (1, 768))
    assert_size_stride(arg179_1, (3072, ), (1, ))
    assert_size_stride(arg180_1, (3072, ), (1, ))
    assert_size_stride(arg181_1, (3072, 768), (1, 3072))
    assert_size_stride(arg182_1, (768, ), (1, ))
    assert_size_stride(arg183_1, (768, ), (1, ))
    assert_size_stride(arg184_1, (768, ), (1, ))
    assert_size_stride(arg185_1, (768, ), (1, ))
    assert_size_stride(arg186_1, (768, 1000), (1, 768))
    assert_size_stride(arg187_1, (1000, ), (1, ))
    assert_size_stride(arg188_1, (1000, ), (1, ))
    assert_size_stride(arg189_1, (1, 3, 224, 224), (150528, 50176, 224, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(arg189_1, arg2_1, stride=(16, 16), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 768, 14, 14), (150528, 196, 14, 1))
        del arg189_1
        del arg2_1
        buf1 = empty_strided_cuda((1, 197, 1, 6), (1182, 6, 1182, 1), torch.float32)
        buf2 = empty_strided_cuda((1, 197, 1, 6), (1182, 6, 1182, 1), torch.float32)
        buf3 = empty_strided_cuda((1, 197, 1, 6), (1182, 6, 1182, 1), torch.float32)
        # Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_red_fused_add_cat_native_layer_norm_0.run(arg0_1, buf0, arg3_1, arg1_1, buf1, buf2, buf3, 1182, 128, grid=grid(1182), stream=stream0)
        buf4 = empty_strided_cuda((1, 197, 1), (197, 1, 197), torch.float32)
        buf5 = empty_strided_cuda((1, 197, 1), (197, 1, 197), torch.float32)
        # Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
        triton_per_fused_add_cat_native_layer_norm_1.run(buf1, buf2, buf3, buf4, buf5, 197, 6, grid=grid(197), stream=stream0)
        del buf1
        del buf2
        del buf3
        buf8 = empty_strided_cuda((1, 197, 768), (151296, 768, 1), torch.bfloat16)
        # Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
        triton_poi_fused_add_cat_native_layer_norm_2.run(arg0_1, buf0, arg3_1, arg1_1, buf4, buf5, arg4_1, arg5_1, buf8, 151296, grid=grid(151296), stream=stream0)
        del arg4_1
        del arg5_1
        del buf4
        del buf5
        buf9 = empty_strided_cuda((197, 2304), (2304, 1), torch.bfloat16)
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf8, (197, 768), (768, 1), 0), reinterpret_tensor(arg6_1, (768, 2304), (1, 768), 0), out=buf9)
        del arg6_1
        buf10 = reinterpret_tensor(buf8, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf8  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf9, arg7_1, buf10, 151296, grid=grid(151296), stream=stream0)
        buf11 = empty_strided_cuda((1, 12, 197, 64), (151296, 64, 768, 1), torch.bfloat16)
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf9, arg7_1, buf11, 151296, grid=grid(151296), stream=stream0)
        buf12 = empty_strided_cuda((1, 12, 197, 64), (151296, 64, 768, 1), torch.bfloat16)
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf9, arg7_1, buf12, 151296, grid=grid(151296), stream=stream0)
        del arg7_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf13 = aten._scaled_dot_product_flash_attention.default(buf10, buf11, buf12, scale=0.125)
        buf14 = buf13[0]
        del buf13
        buf20 = empty_strided_cuda((197, 768), (768, 1), torch.int8)
        buf21 = empty_strided_cuda((197, 1), (1, 1), torch.bfloat16)
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf14, buf20, buf21, 197, 768, grid=grid(197), stream=stream0)
        buf22 = reinterpret_tensor(buf14, (197, 768), (768, 1), 0); del buf14  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        stream0 = get_raw_stream(0)
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf20, arg8_1, buf21, buf22, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg8_1
        buf23 = reinterpret_tensor(buf22, (1, 197, 768), (151296, 768, 1), 0); del buf22  # reuse
        buf29 = reinterpret_tensor(buf20, (1, 197, 768), (151296, 768, 1), 0); del buf20  # reuse
        buf30 = reinterpret_tensor(buf21, (1, 197, 1), (197, 1, 1), 0); del buf21  # reuse
        # Source Nodes: [cat_1, input_1, l__self___encoder_layers_encoder_layer_0_mlp_0, x_7, x_8, y], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.cat, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_cat_clamp_clone_div_native_layer_norm_round_8.run(buf23, arg9_1, arg10_1, arg0_1, buf0, arg3_1, arg1_1, arg11_1, arg12_1, buf29, buf30, 197, 768, grid=grid(197), stream=stream0)
        del arg0_1
        del arg10_1
        del arg11_1
        del arg12_1
        del arg1_1
        del arg3_1
        del arg9_1
        del buf0
        buf31 = empty_strided_cuda((197, 3072), (3072, 1), torch.bfloat16)
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf29, arg13_1, buf30, buf31, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg13_1
        buf33 = empty_strided_cuda((1, 197, 3072), (605184, 3072, 1), torch.int8)
        buf34 = buf30; del buf30  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_1, l__self___encoder_layers_encoder_layer_0_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf31, arg14_1, arg15_1, buf33, buf34, 197, 3072, grid=grid(197), stream=stream0)
        del arg14_1
        del arg15_1
        buf35 = reinterpret_tensor(buf12, (197, 768), (768, 1), 0); del buf12  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf33, arg16_1, buf34, buf35, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg16_1
        buf39 = reinterpret_tensor(buf11, (1, 197, 768), (151296, 768, 1), 0); del buf11  # reuse
        # Source Nodes: [add_2, x_9], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf23, buf35, arg17_1, arg18_1, arg19_1, arg20_1, buf39, 197, 768, grid=grid(197), stream=stream0)
        del arg19_1
        del arg20_1
        buf40 = buf9; del buf9  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf39, (197, 768), (768, 1), 0), reinterpret_tensor(arg21_1, (768, 2304), (1, 768), 0), out=buf40)
        del arg21_1
        buf41 = reinterpret_tensor(buf39, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf39  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf40, arg22_1, buf41, 151296, grid=grid(151296), stream=stream0)
        buf42 = buf10; del buf10  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf40, arg22_1, buf42, 151296, grid=grid(151296), stream=stream0)
        buf43 = empty_strided_cuda((1, 12, 197, 64), (151296, 64, 768, 1), torch.bfloat16)
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf40, arg22_1, buf43, 151296, grid=grid(151296), stream=stream0)
        del arg22_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf44 = aten._scaled_dot_product_flash_attention.default(buf41, buf42, buf43, scale=0.125)
        del buf41
        buf45 = buf44[0]
        del buf44
        buf51 = reinterpret_tensor(buf29, (197, 768), (768, 1), 0); del buf29  # reuse
        buf52 = reinterpret_tensor(buf34, (197, 1), (1, 1), 0); del buf34  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf45, buf51, buf52, 197, 768, grid=grid(197), stream=stream0)
        buf53 = reinterpret_tensor(buf45, (197, 768), (768, 1), 0); del buf45  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf51, arg23_1, buf52, buf53, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg23_1
        buf54 = reinterpret_tensor(buf53, (1, 197, 768), (151296, 768, 1), 0); del buf53  # reuse
        buf60 = reinterpret_tensor(buf51, (1, 197, 768), (151296, 768, 1), 0); del buf51  # reuse
        buf61 = reinterpret_tensor(buf52, (1, 197, 1), (197, 1, 1), 0); del buf52  # reuse
        # Source Nodes: [add_2, l__self___encoder_layers_encoder_layer_1_mlp_0, x_11, x_12, y_2], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf54, arg24_1, arg25_1, buf23, buf35, arg17_1, arg18_1, arg26_1, arg27_1, buf60, buf61, 197, 768, grid=grid(197), stream=stream0)
        del arg17_1
        del arg18_1
        del arg24_1
        del arg25_1
        del arg26_1
        del arg27_1
        buf62 = buf31; del buf31  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf60, arg28_1, buf61, buf62, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg28_1
        buf64 = buf33; del buf33  # reuse
        buf65 = buf61; del buf61  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_mlp_1, l__self___encoder_layers_encoder_layer_1_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf62, arg29_1, arg30_1, buf64, buf65, 197, 3072, grid=grid(197), stream=stream0)
        del arg29_1
        del arg30_1
        buf66 = buf35; del buf35  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf64, arg31_1, buf65, buf66, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg31_1
        buf70 = buf23; del buf23  # reuse
        # Source Nodes: [add_4, x_13], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf54, buf66, arg32_1, arg33_1, arg34_1, arg35_1, buf70, 197, 768, grid=grid(197), stream=stream0)
        del arg34_1
        del arg35_1
        buf71 = buf40; del buf40  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf70, (197, 768), (768, 1), 0), reinterpret_tensor(arg36_1, (768, 2304), (1, 768), 0), out=buf71)
        del arg36_1
        buf72 = reinterpret_tensor(buf70, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf70  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf71, arg37_1, buf72, 151296, grid=grid(151296), stream=stream0)
        buf73 = buf43; del buf43  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf71, arg37_1, buf73, 151296, grid=grid(151296), stream=stream0)
        buf74 = buf42; del buf42  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf71, arg37_1, buf74, 151296, grid=grid(151296), stream=stream0)
        del arg37_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf75 = aten._scaled_dot_product_flash_attention.default(buf72, buf73, buf74, scale=0.125)
        del buf72
        buf76 = buf75[0]
        del buf75
        buf82 = reinterpret_tensor(buf60, (197, 768), (768, 1), 0); del buf60  # reuse
        buf83 = reinterpret_tensor(buf65, (197, 1), (1, 1), 0); del buf65  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf76, buf82, buf83, 197, 768, grid=grid(197), stream=stream0)
        buf84 = reinterpret_tensor(buf76, (197, 768), (768, 1), 0); del buf76  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf82, arg38_1, buf83, buf84, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg38_1
        buf85 = reinterpret_tensor(buf84, (1, 197, 768), (151296, 768, 1), 0); del buf84  # reuse
        buf91 = reinterpret_tensor(buf82, (1, 197, 768), (151296, 768, 1), 0); del buf82  # reuse
        buf92 = reinterpret_tensor(buf83, (1, 197, 1), (197, 1, 1), 0); del buf83  # reuse
        # Source Nodes: [add_4, l__self___encoder_layers_encoder_layer_2_mlp_0, x_15, x_16, y_4], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf85, arg39_1, arg40_1, buf54, buf66, arg32_1, arg33_1, arg41_1, arg42_1, buf91, buf92, 197, 768, grid=grid(197), stream=stream0)
        del arg32_1
        del arg33_1
        del arg39_1
        del arg40_1
        del arg41_1
        del arg42_1
        buf93 = buf62; del buf62  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf91, arg43_1, buf92, buf93, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg43_1
        buf95 = buf64; del buf64  # reuse
        buf96 = buf92; del buf92  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_mlp_1, l__self___encoder_layers_encoder_layer_2_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf93, arg44_1, arg45_1, buf95, buf96, 197, 3072, grid=grid(197), stream=stream0)
        del arg44_1
        del arg45_1
        buf97 = buf66; del buf66  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf95, arg46_1, buf96, buf97, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg46_1
        buf101 = buf54; del buf54  # reuse
        # Source Nodes: [add_6, x_17], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf85, buf97, arg47_1, arg48_1, arg49_1, arg50_1, buf101, 197, 768, grid=grid(197), stream=stream0)
        del arg49_1
        del arg50_1
        buf102 = buf71; del buf71  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf101, (197, 768), (768, 1), 0), reinterpret_tensor(arg51_1, (768, 2304), (1, 768), 0), out=buf102)
        del arg51_1
        buf103 = reinterpret_tensor(buf101, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf101  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf102, arg52_1, buf103, 151296, grid=grid(151296), stream=stream0)
        buf104 = buf74; del buf74  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf102, arg52_1, buf104, 151296, grid=grid(151296), stream=stream0)
        buf105 = buf73; del buf73  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf102, arg52_1, buf105, 151296, grid=grid(151296), stream=stream0)
        del arg52_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf106 = aten._scaled_dot_product_flash_attention.default(buf103, buf104, buf105, scale=0.125)
        del buf103
        buf107 = buf106[0]
        del buf106
        buf113 = reinterpret_tensor(buf91, (197, 768), (768, 1), 0); del buf91  # reuse
        buf114 = reinterpret_tensor(buf96, (197, 1), (1, 1), 0); del buf96  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf107, buf113, buf114, 197, 768, grid=grid(197), stream=stream0)
        buf115 = reinterpret_tensor(buf107, (197, 768), (768, 1), 0); del buf107  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf113, arg53_1, buf114, buf115, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg53_1
        buf116 = reinterpret_tensor(buf115, (1, 197, 768), (151296, 768, 1), 0); del buf115  # reuse
        buf122 = reinterpret_tensor(buf113, (1, 197, 768), (151296, 768, 1), 0); del buf113  # reuse
        buf123 = reinterpret_tensor(buf114, (1, 197, 1), (197, 1, 1), 0); del buf114  # reuse
        # Source Nodes: [add_6, l__self___encoder_layers_encoder_layer_3_mlp_0, x_19, x_20, y_6], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf116, arg54_1, arg55_1, buf85, buf97, arg47_1, arg48_1, arg56_1, arg57_1, buf122, buf123, 197, 768, grid=grid(197), stream=stream0)
        del arg47_1
        del arg48_1
        del arg54_1
        del arg55_1
        del arg56_1
        del arg57_1
        buf124 = buf93; del buf93  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf122, arg58_1, buf123, buf124, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg58_1
        buf126 = buf95; del buf95  # reuse
        buf127 = buf123; del buf123  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_mlp_1, l__self___encoder_layers_encoder_layer_3_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf124, arg59_1, arg60_1, buf126, buf127, 197, 3072, grid=grid(197), stream=stream0)
        del arg59_1
        del arg60_1
        buf128 = buf97; del buf97  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf126, arg61_1, buf127, buf128, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg61_1
        buf132 = buf85; del buf85  # reuse
        # Source Nodes: [add_8, x_21], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf116, buf128, arg62_1, arg63_1, arg64_1, arg65_1, buf132, 197, 768, grid=grid(197), stream=stream0)
        del arg64_1
        del arg65_1
        buf133 = buf102; del buf102  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf132, (197, 768), (768, 1), 0), reinterpret_tensor(arg66_1, (768, 2304), (1, 768), 0), out=buf133)
        del arg66_1
        buf134 = reinterpret_tensor(buf132, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf132  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf133, arg67_1, buf134, 151296, grid=grid(151296), stream=stream0)
        buf135 = buf105; del buf105  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf133, arg67_1, buf135, 151296, grid=grid(151296), stream=stream0)
        buf136 = buf104; del buf104  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf133, arg67_1, buf136, 151296, grid=grid(151296), stream=stream0)
        del arg67_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf137 = aten._scaled_dot_product_flash_attention.default(buf134, buf135, buf136, scale=0.125)
        del buf134
        buf138 = buf137[0]
        del buf137
        buf144 = reinterpret_tensor(buf122, (197, 768), (768, 1), 0); del buf122  # reuse
        buf145 = reinterpret_tensor(buf127, (197, 1), (1, 1), 0); del buf127  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf138, buf144, buf145, 197, 768, grid=grid(197), stream=stream0)
        buf146 = reinterpret_tensor(buf138, (197, 768), (768, 1), 0); del buf138  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf144, arg68_1, buf145, buf146, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg68_1
        buf147 = reinterpret_tensor(buf146, (1, 197, 768), (151296, 768, 1), 0); del buf146  # reuse
        buf153 = reinterpret_tensor(buf144, (1, 197, 768), (151296, 768, 1), 0); del buf144  # reuse
        buf154 = reinterpret_tensor(buf145, (1, 197, 1), (197, 1, 1), 0); del buf145  # reuse
        # Source Nodes: [add_8, l__self___encoder_layers_encoder_layer_4_mlp_0, x_23, x_24, y_8], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf147, arg69_1, arg70_1, buf116, buf128, arg62_1, arg63_1, arg71_1, arg72_1, buf153, buf154, 197, 768, grid=grid(197), stream=stream0)
        del arg62_1
        del arg63_1
        del arg69_1
        del arg70_1
        del arg71_1
        del arg72_1
        buf155 = buf124; del buf124  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf153, arg73_1, buf154, buf155, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg73_1
        buf157 = buf126; del buf126  # reuse
        buf158 = buf154; del buf154  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_mlp_1, l__self___encoder_layers_encoder_layer_4_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf155, arg74_1, arg75_1, buf157, buf158, 197, 3072, grid=grid(197), stream=stream0)
        del arg74_1
        del arg75_1
        buf159 = buf128; del buf128  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf157, arg76_1, buf158, buf159, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg76_1
        buf163 = buf116; del buf116  # reuse
        # Source Nodes: [add_10, x_25], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf147, buf159, arg77_1, arg78_1, arg79_1, arg80_1, buf163, 197, 768, grid=grid(197), stream=stream0)
        del arg79_1
        del arg80_1
        buf164 = buf133; del buf133  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf163, (197, 768), (768, 1), 0), reinterpret_tensor(arg81_1, (768, 2304), (1, 768), 0), out=buf164)
        del arg81_1
        buf165 = reinterpret_tensor(buf163, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf163  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf164, arg82_1, buf165, 151296, grid=grid(151296), stream=stream0)
        buf166 = buf136; del buf136  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf164, arg82_1, buf166, 151296, grid=grid(151296), stream=stream0)
        buf167 = buf135; del buf135  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf164, arg82_1, buf167, 151296, grid=grid(151296), stream=stream0)
        del arg82_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf168 = aten._scaled_dot_product_flash_attention.default(buf165, buf166, buf167, scale=0.125)
        del buf165
        buf169 = buf168[0]
        del buf168
        buf175 = reinterpret_tensor(buf153, (197, 768), (768, 1), 0); del buf153  # reuse
        buf176 = reinterpret_tensor(buf158, (197, 1), (1, 1), 0); del buf158  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf169, buf175, buf176, 197, 768, grid=grid(197), stream=stream0)
        buf177 = reinterpret_tensor(buf169, (197, 768), (768, 1), 0); del buf169  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf175, arg83_1, buf176, buf177, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg83_1
        buf178 = reinterpret_tensor(buf177, (1, 197, 768), (151296, 768, 1), 0); del buf177  # reuse
        buf184 = reinterpret_tensor(buf175, (1, 197, 768), (151296, 768, 1), 0); del buf175  # reuse
        buf185 = reinterpret_tensor(buf176, (1, 197, 1), (197, 1, 1), 0); del buf176  # reuse
        # Source Nodes: [add_10, l__self___encoder_layers_encoder_layer_5_mlp_0, x_27, x_28, y_10], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf178, arg84_1, arg85_1, buf147, buf159, arg77_1, arg78_1, arg86_1, arg87_1, buf184, buf185, 197, 768, grid=grid(197), stream=stream0)
        del arg77_1
        del arg78_1
        del arg84_1
        del arg85_1
        del arg86_1
        del arg87_1
        buf186 = buf155; del buf155  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf184, arg88_1, buf185, buf186, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg88_1
        buf188 = buf157; del buf157  # reuse
        buf189 = buf185; del buf185  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_mlp_1, l__self___encoder_layers_encoder_layer_5_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf186, arg89_1, arg90_1, buf188, buf189, 197, 3072, grid=grid(197), stream=stream0)
        del arg89_1
        del arg90_1
        buf190 = buf159; del buf159  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf188, arg91_1, buf189, buf190, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg91_1
        buf194 = buf147; del buf147  # reuse
        # Source Nodes: [add_12, x_29], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf178, buf190, arg92_1, arg93_1, arg94_1, arg95_1, buf194, 197, 768, grid=grid(197), stream=stream0)
        del arg94_1
        del arg95_1
        buf195 = buf164; del buf164  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf194, (197, 768), (768, 1), 0), reinterpret_tensor(arg96_1, (768, 2304), (1, 768), 0), out=buf195)
        del arg96_1
        buf196 = reinterpret_tensor(buf194, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf194  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf195, arg97_1, buf196, 151296, grid=grid(151296), stream=stream0)
        buf197 = buf167; del buf167  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf195, arg97_1, buf197, 151296, grid=grid(151296), stream=stream0)
        buf198 = buf166; del buf166  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf195, arg97_1, buf198, 151296, grid=grid(151296), stream=stream0)
        del arg97_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf199 = aten._scaled_dot_product_flash_attention.default(buf196, buf197, buf198, scale=0.125)
        del buf196
        buf200 = buf199[0]
        del buf199
        buf206 = reinterpret_tensor(buf184, (197, 768), (768, 1), 0); del buf184  # reuse
        buf207 = reinterpret_tensor(buf189, (197, 1), (1, 1), 0); del buf189  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf200, buf206, buf207, 197, 768, grid=grid(197), stream=stream0)
        buf208 = reinterpret_tensor(buf200, (197, 768), (768, 1), 0); del buf200  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf206, arg98_1, buf207, buf208, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg98_1
        buf209 = reinterpret_tensor(buf208, (1, 197, 768), (151296, 768, 1), 0); del buf208  # reuse
        buf215 = reinterpret_tensor(buf206, (1, 197, 768), (151296, 768, 1), 0); del buf206  # reuse
        buf216 = reinterpret_tensor(buf207, (1, 197, 1), (197, 1, 1), 0); del buf207  # reuse
        # Source Nodes: [add_12, l__self___encoder_layers_encoder_layer_6_mlp_0, x_31, x_32, y_12], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf209, arg99_1, arg100_1, buf178, buf190, arg92_1, arg93_1, arg101_1, arg102_1, buf215, buf216, 197, 768, grid=grid(197), stream=stream0)
        del arg100_1
        del arg101_1
        del arg102_1
        del arg92_1
        del arg93_1
        del arg99_1
        buf217 = buf186; del buf186  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf215, arg103_1, buf216, buf217, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg103_1
        buf219 = buf188; del buf188  # reuse
        buf220 = buf216; del buf216  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_mlp_1, l__self___encoder_layers_encoder_layer_6_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf217, arg104_1, arg105_1, buf219, buf220, 197, 3072, grid=grid(197), stream=stream0)
        del arg104_1
        del arg105_1
        buf221 = buf190; del buf190  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf219, arg106_1, buf220, buf221, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg106_1
        buf225 = buf178; del buf178  # reuse
        # Source Nodes: [add_14, x_33], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf209, buf221, arg107_1, arg108_1, arg109_1, arg110_1, buf225, 197, 768, grid=grid(197), stream=stream0)
        del arg109_1
        del arg110_1
        buf226 = buf195; del buf195  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf225, (197, 768), (768, 1), 0), reinterpret_tensor(arg111_1, (768, 2304), (1, 768), 0), out=buf226)
        del arg111_1
        buf227 = reinterpret_tensor(buf225, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf225  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf226, arg112_1, buf227, 151296, grid=grid(151296), stream=stream0)
        buf228 = buf198; del buf198  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf226, arg112_1, buf228, 151296, grid=grid(151296), stream=stream0)
        buf229 = buf197; del buf197  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf226, arg112_1, buf229, 151296, grid=grid(151296), stream=stream0)
        del arg112_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf230 = aten._scaled_dot_product_flash_attention.default(buf227, buf228, buf229, scale=0.125)
        del buf227
        buf231 = buf230[0]
        del buf230
        buf237 = reinterpret_tensor(buf215, (197, 768), (768, 1), 0); del buf215  # reuse
        buf238 = reinterpret_tensor(buf220, (197, 1), (1, 1), 0); del buf220  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf231, buf237, buf238, 197, 768, grid=grid(197), stream=stream0)
        buf239 = reinterpret_tensor(buf231, (197, 768), (768, 1), 0); del buf231  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf237, arg113_1, buf238, buf239, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg113_1
        buf240 = reinterpret_tensor(buf239, (1, 197, 768), (151296, 768, 1), 0); del buf239  # reuse
        buf246 = reinterpret_tensor(buf237, (1, 197, 768), (151296, 768, 1), 0); del buf237  # reuse
        buf247 = reinterpret_tensor(buf238, (1, 197, 1), (197, 1, 1), 0); del buf238  # reuse
        # Source Nodes: [add_14, l__self___encoder_layers_encoder_layer_7_mlp_0, x_35, x_36, y_14], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf240, arg114_1, arg115_1, buf209, buf221, arg107_1, arg108_1, arg116_1, arg117_1, buf246, buf247, 197, 768, grid=grid(197), stream=stream0)
        del arg107_1
        del arg108_1
        del arg114_1
        del arg115_1
        del arg116_1
        del arg117_1
        buf248 = buf217; del buf217  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf246, arg118_1, buf247, buf248, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg118_1
        buf250 = buf219; del buf219  # reuse
        buf251 = buf247; del buf247  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_mlp_1, l__self___encoder_layers_encoder_layer_7_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf248, arg119_1, arg120_1, buf250, buf251, 197, 3072, grid=grid(197), stream=stream0)
        del arg119_1
        del arg120_1
        buf252 = buf221; del buf221  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf250, arg121_1, buf251, buf252, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg121_1
        buf256 = buf209; del buf209  # reuse
        # Source Nodes: [add_16, x_37], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf240, buf252, arg122_1, arg123_1, arg124_1, arg125_1, buf256, 197, 768, grid=grid(197), stream=stream0)
        del arg124_1
        del arg125_1
        buf257 = buf226; del buf226  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf256, (197, 768), (768, 1), 0), reinterpret_tensor(arg126_1, (768, 2304), (1, 768), 0), out=buf257)
        del arg126_1
        buf258 = reinterpret_tensor(buf256, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf256  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf257, arg127_1, buf258, 151296, grid=grid(151296), stream=stream0)
        buf259 = buf229; del buf229  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf257, arg127_1, buf259, 151296, grid=grid(151296), stream=stream0)
        buf260 = buf228; del buf228  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf257, arg127_1, buf260, 151296, grid=grid(151296), stream=stream0)
        del arg127_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf261 = aten._scaled_dot_product_flash_attention.default(buf258, buf259, buf260, scale=0.125)
        del buf258
        buf262 = buf261[0]
        del buf261
        buf268 = reinterpret_tensor(buf246, (197, 768), (768, 1), 0); del buf246  # reuse
        buf269 = reinterpret_tensor(buf251, (197, 1), (1, 1), 0); del buf251  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf262, buf268, buf269, 197, 768, grid=grid(197), stream=stream0)
        buf270 = reinterpret_tensor(buf262, (197, 768), (768, 1), 0); del buf262  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf268, arg128_1, buf269, buf270, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg128_1
        buf271 = reinterpret_tensor(buf270, (1, 197, 768), (151296, 768, 1), 0); del buf270  # reuse
        buf277 = reinterpret_tensor(buf268, (1, 197, 768), (151296, 768, 1), 0); del buf268  # reuse
        buf278 = reinterpret_tensor(buf269, (1, 197, 1), (197, 1, 1), 0); del buf269  # reuse
        # Source Nodes: [add_16, l__self___encoder_layers_encoder_layer_8_mlp_0, x_39, x_40, y_16], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf271, arg129_1, arg130_1, buf240, buf252, arg122_1, arg123_1, arg131_1, arg132_1, buf277, buf278, 197, 768, grid=grid(197), stream=stream0)
        del arg122_1
        del arg123_1
        del arg129_1
        del arg130_1
        del arg131_1
        del arg132_1
        buf279 = buf248; del buf248  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf277, arg133_1, buf278, buf279, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg133_1
        buf281 = buf250; del buf250  # reuse
        buf282 = buf278; del buf278  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_mlp_1, l__self___encoder_layers_encoder_layer_8_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf279, arg134_1, arg135_1, buf281, buf282, 197, 3072, grid=grid(197), stream=stream0)
        del arg134_1
        del arg135_1
        buf283 = buf252; del buf252  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf281, arg136_1, buf282, buf283, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg136_1
        buf287 = buf240; del buf240  # reuse
        # Source Nodes: [add_18, x_41], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf271, buf283, arg137_1, arg138_1, arg139_1, arg140_1, buf287, 197, 768, grid=grid(197), stream=stream0)
        del arg139_1
        del arg140_1
        buf288 = buf257; del buf257  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf287, (197, 768), (768, 1), 0), reinterpret_tensor(arg141_1, (768, 2304), (1, 768), 0), out=buf288)
        del arg141_1
        buf289 = reinterpret_tensor(buf287, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf287  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf288, arg142_1, buf289, 151296, grid=grid(151296), stream=stream0)
        buf290 = buf260; del buf260  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf288, arg142_1, buf290, 151296, grid=grid(151296), stream=stream0)
        buf291 = buf259; del buf259  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf288, arg142_1, buf291, 151296, grid=grid(151296), stream=stream0)
        del arg142_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf292 = aten._scaled_dot_product_flash_attention.default(buf289, buf290, buf291, scale=0.125)
        del buf289
        buf293 = buf292[0]
        del buf292
        buf299 = reinterpret_tensor(buf277, (197, 768), (768, 1), 0); del buf277  # reuse
        buf300 = reinterpret_tensor(buf282, (197, 1), (1, 1), 0); del buf282  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf293, buf299, buf300, 197, 768, grid=grid(197), stream=stream0)
        buf301 = reinterpret_tensor(buf293, (197, 768), (768, 1), 0); del buf293  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf299, arg143_1, buf300, buf301, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg143_1
        buf302 = reinterpret_tensor(buf301, (1, 197, 768), (151296, 768, 1), 0); del buf301  # reuse
        buf308 = reinterpret_tensor(buf299, (1, 197, 768), (151296, 768, 1), 0); del buf299  # reuse
        buf309 = reinterpret_tensor(buf300, (1, 197, 1), (197, 1, 1), 0); del buf300  # reuse
        # Source Nodes: [add_18, l__self___encoder_layers_encoder_layer_9_mlp_0, x_43, x_44, y_18], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf302, arg144_1, arg145_1, buf271, buf283, arg137_1, arg138_1, arg146_1, arg147_1, buf308, buf309, 197, 768, grid=grid(197), stream=stream0)
        del arg137_1
        del arg138_1
        del arg144_1
        del arg145_1
        del arg146_1
        del arg147_1
        buf310 = buf279; del buf279  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf308, arg148_1, buf309, buf310, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg148_1
        buf312 = buf281; del buf281  # reuse
        buf313 = buf309; del buf309  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_mlp_1, l__self___encoder_layers_encoder_layer_9_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf310, arg149_1, arg150_1, buf312, buf313, 197, 3072, grid=grid(197), stream=stream0)
        del arg149_1
        del arg150_1
        buf314 = buf283; del buf283  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf312, arg151_1, buf313, buf314, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg151_1
        buf318 = buf271; del buf271  # reuse
        # Source Nodes: [add_20, x_45], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf302, buf314, arg152_1, arg153_1, arg154_1, arg155_1, buf318, 197, 768, grid=grid(197), stream=stream0)
        del arg154_1
        del arg155_1
        buf319 = buf288; del buf288  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf318, (197, 768), (768, 1), 0), reinterpret_tensor(arg156_1, (768, 2304), (1, 768), 0), out=buf319)
        del arg156_1
        buf320 = reinterpret_tensor(buf318, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf318  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf319, arg157_1, buf320, 151296, grid=grid(151296), stream=stream0)
        buf321 = buf291; del buf291  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf319, arg157_1, buf321, 151296, grid=grid(151296), stream=stream0)
        buf322 = buf290; del buf290  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf319, arg157_1, buf322, 151296, grid=grid(151296), stream=stream0)
        del arg157_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf323 = aten._scaled_dot_product_flash_attention.default(buf320, buf321, buf322, scale=0.125)
        del buf320
        buf324 = buf323[0]
        del buf323
        buf330 = reinterpret_tensor(buf308, (197, 768), (768, 1), 0); del buf308  # reuse
        buf331 = reinterpret_tensor(buf313, (197, 1), (1, 1), 0); del buf313  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf324, buf330, buf331, 197, 768, grid=grid(197), stream=stream0)
        buf332 = reinterpret_tensor(buf324, (197, 768), (768, 1), 0); del buf324  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf330, arg158_1, buf331, buf332, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg158_1
        buf333 = reinterpret_tensor(buf332, (1, 197, 768), (151296, 768, 1), 0); del buf332  # reuse
        buf339 = reinterpret_tensor(buf330, (1, 197, 768), (151296, 768, 1), 0); del buf330  # reuse
        buf340 = reinterpret_tensor(buf331, (1, 197, 1), (197, 1, 1), 0); del buf331  # reuse
        # Source Nodes: [add_20, l__self___encoder_layers_encoder_layer_10_mlp_0, x_47, x_48, y_20], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf333, arg159_1, arg160_1, buf302, buf314, arg152_1, arg153_1, arg161_1, arg162_1, buf339, buf340, 197, 768, grid=grid(197), stream=stream0)
        del arg152_1
        del arg153_1
        del arg159_1
        del arg160_1
        del arg161_1
        del arg162_1
        buf341 = buf310; del buf310  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf339, arg163_1, buf340, buf341, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg163_1
        buf343 = buf312; del buf312  # reuse
        buf344 = buf340; del buf340  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_mlp_1, l__self___encoder_layers_encoder_layer_10_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf341, arg164_1, arg165_1, buf343, buf344, 197, 3072, grid=grid(197), stream=stream0)
        del arg164_1
        del arg165_1
        buf345 = buf314; del buf314  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf343, arg166_1, buf344, buf345, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg166_1
        buf349 = buf302; del buf302  # reuse
        # Source Nodes: [add_22, x_49], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf333, buf345, arg167_1, arg168_1, arg169_1, arg170_1, buf349, 197, 768, grid=grid(197), stream=stream0)
        del arg169_1
        del arg170_1
        buf350 = buf319; del buf319  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf349, (197, 768), (768, 1), 0), reinterpret_tensor(arg171_1, (768, 2304), (1, 768), 0), out=buf350)
        del arg171_1
        buf351 = reinterpret_tensor(buf349, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf349  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf350, arg172_1, buf351, 151296, grid=grid(151296), stream=stream0)
        buf352 = buf322; del buf322  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf350, arg172_1, buf352, 151296, grid=grid(151296), stream=stream0)
        buf353 = buf321; del buf321  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf350, arg172_1, buf353, 151296, grid=grid(151296), stream=stream0)
        del arg172_1
        del buf350
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf354 = aten._scaled_dot_product_flash_attention.default(buf351, buf352, buf353, scale=0.125)
        del buf351
        del buf352
        del buf353
        buf355 = buf354[0]
        del buf354
        buf361 = reinterpret_tensor(buf339, (197, 768), (768, 1), 0); del buf339  # reuse
        buf362 = reinterpret_tensor(buf344, (197, 1), (1, 1), 0); del buf344  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf355, buf361, buf362, 197, 768, grid=grid(197), stream=stream0)
        buf363 = reinterpret_tensor(buf355, (197, 768), (768, 1), 0); del buf355  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf361, arg173_1, buf362, buf363, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg173_1
        buf364 = reinterpret_tensor(buf363, (1, 197, 768), (151296, 768, 1), 0); del buf363  # reuse
        buf370 = reinterpret_tensor(buf361, (1, 197, 768), (151296, 768, 1), 0); del buf361  # reuse
        buf371 = reinterpret_tensor(buf362, (1, 197, 1), (197, 1, 1), 0); del buf362  # reuse
        # Source Nodes: [add_22, l__self___encoder_layers_encoder_layer_11_mlp_0, x_51, x_52, y_22], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf364, arg174_1, arg175_1, buf333, buf345, arg167_1, arg168_1, arg176_1, arg177_1, buf370, buf371, 197, 768, grid=grid(197), stream=stream0)
        del arg167_1
        del arg168_1
        del arg174_1
        del arg175_1
        del arg176_1
        del arg177_1
        buf372 = buf341; del buf341  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf370, arg178_1, buf371, buf372, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg178_1
        del buf370
        buf374 = buf343; del buf343  # reuse
        buf375 = buf371; del buf371  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_mlp_1, l__self___encoder_layers_encoder_layer_11_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf372, arg179_1, arg180_1, buf374, buf375, 197, 3072, grid=grid(197), stream=stream0)
        del arg179_1
        del arg180_1
        del buf372
        buf376 = buf345; del buf345  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf374, arg181_1, buf375, buf376, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg181_1
        del buf374
        del buf375
        buf380 = buf333; del buf333  # reuse
        # Source Nodes: [add_24, x_53], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf364, buf376, arg182_1, arg183_1, arg184_1, arg185_1, buf380, 197, 768, grid=grid(197), stream=stream0)
        del arg182_1
        del arg183_1
        del arg184_1
        del arg185_1
        del buf364
        del buf376
        buf382 = empty_strided_cuda((1, 768), (768, 1), torch.int8)
        buf383 = empty_strided_cuda((1, 1), (1, 1), torch.bfloat16)
        # Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_14.run(buf380, buf382, buf383, 1, 768, grid=grid(1), stream=stream0)
        del buf380
        buf384 = empty_strided_cuda((1, 1000), (1000, 1), torch.bfloat16)
        # Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_poi_fused__to_copy_clamp_div_mul_round_view_15.run(buf383, buf384, 1000, grid=grid(1000), stream=stream0)
        del buf383
        buf386 = empty_strided_cuda((1, 1000), (1000, 1), torch.bfloat16)
        # Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.add, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_add_clamp_div_mul_round_view_16.run(buf382, arg186_1, buf384, arg187_1, arg188_1, buf386, grid=torch._inductor.kernel.mm_common.mm_grid(1, 1000, meta2), stream=stream0)
        del arg186_1
        del arg187_1
        del arg188_1
        del buf382
        del buf384
    return (buf386, )
def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((1, 1, 768), (768, 768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg1_1 = rand_strided((1, 197, 768), (151296, 768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg2_1 = rand_strided((768, 3, 16, 16), (768, 256, 16, 1), device='cuda:0', dtype=torch.bfloat16)
    arg3_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg4_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg5_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg6_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg7_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg8_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg9_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg10_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg11_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg12_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg13_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg14_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg15_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg16_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg17_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg18_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg19_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg20_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg21_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg22_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg23_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg24_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg25_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg26_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg27_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg28_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg29_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg30_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg31_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg32_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg33_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg34_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg35_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg36_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg37_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg38_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg39_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg40_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg41_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg42_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg43_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg44_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg45_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg46_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg47_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg48_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg49_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg50_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg51_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg52_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg53_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg54_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg55_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg56_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg57_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg58_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg59_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg60_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg61_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg62_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg63_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg64_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg65_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg66_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg67_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg68_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg69_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg70_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg71_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg72_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg73_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg74_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg75_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg76_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg77_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg78_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg79_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg80_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg81_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg82_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg83_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg84_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg85_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg86_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg87_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg88_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg89_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg90_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg91_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg92_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg93_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg94_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg95_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg96_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg97_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg98_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg99_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg100_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg101_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg102_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg103_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg104_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg105_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg106_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg107_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg108_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg109_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg110_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg111_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg112_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg113_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg114_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg115_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg116_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg117_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg118_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg119_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg120_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg121_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg122_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg123_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg124_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg125_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg126_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg127_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg128_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg129_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg130_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg131_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg132_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg133_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg134_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg135_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg136_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg137_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg138_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg139_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg140_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg141_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg142_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg143_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg144_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg145_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg146_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg147_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg148_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg149_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg150_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg151_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg152_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg153_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg154_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg155_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg156_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg157_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg158_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg159_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg160_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg161_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg162_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg163_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg164_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg165_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg166_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg167_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg168_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg169_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg170_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg171_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg172_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg173_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg174_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg175_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg176_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg177_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg178_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg179_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg180_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg181_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg182_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg183_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg184_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg185_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg186_1 = rand_strided((768, 1000), (1, 768), device='cuda:0', dtype=torch.int8)
    arg187_1 = rand_strided((1000, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg188_1 = rand_strided((1000, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg189_1 = rand_strided((1, 3, 224, 224), (150528, 50176, 224, 1), device='cuda:0', dtype=torch.bfloat16)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1, arg15_1, arg16_1, arg17_1, arg18_1, arg19_1, arg20_1, arg21_1, arg22_1, arg23_1, arg24_1, arg25_1, arg26_1, arg27_1, arg28_1, arg29_1, arg30_1, arg31_1, arg32_1, arg33_1, arg34_1, arg35_1, arg36_1, arg37_1, arg38_1, arg39_1, arg40_1, arg41_1, arg42_1, arg43_1, arg44_1, arg45_1, arg46_1, arg47_1, arg48_1, arg49_1, arg50_1, arg51_1, arg52_1, arg53_1, arg54_1, arg55_1, arg56_1, arg57_1, arg58_1, arg59_1, arg60_1, arg61_1, arg62_1, arg63_1, arg64_1, arg65_1, arg66_1, arg67_1, arg68_1, arg69_1, arg70_1, arg71_1, arg72_1, arg73_1, arg74_1, arg75_1, arg76_1, arg77_1, arg78_1, arg79_1, arg80_1, arg81_1, arg82_1, arg83_1, arg84_1, arg85_1, arg86_1, arg87_1, arg88_1, arg89_1, arg90_1, arg91_1, arg92_1, arg93_1, arg94_1, arg95_1, arg96_1, arg97_1, arg98_1, arg99_1, arg100_1, arg101_1, arg102_1, arg103_1, arg104_1, arg105_1, arg106_1, arg107_1, arg108_1, arg109_1, arg110_1, arg111_1, arg112_1, arg113_1, arg114_1, arg115_1, arg116_1, arg117_1, arg118_1, arg119_1, arg120_1, arg121_1, arg122_1, arg123_1, arg124_1, arg125_1, arg126_1, arg127_1, arg128_1, arg129_1, arg130_1, arg131_1, arg132_1, arg133_1, arg134_1, arg135_1, arg136_1, arg137_1, arg138_1, arg139_1, arg140_1, arg141_1, arg142_1, arg143_1, arg144_1, arg145_1, arg146_1, arg147_1, arg148_1, arg149_1, arg150_1, arg151_1, arg152_1, arg153_1, arg154_1, arg155_1, arg156_1, arg157_1, arg158_1, arg159_1, arg160_1, arg161_1, arg162_1, arg163_1, arg164_1, arg165_1, arg166_1, arg167_1, arg168_1, arg169_1, arg170_1, arg171_1, arg172_1, arg173_1, arg174_1, arg175_1, arg176_1, arg177_1, arg178_1, arg179_1, arg180_1, arg181_1, arg182_1, arg183_1, arg184_1, arg185_1, arg186_1, arg187_1, arg188_1, arg189_1])
    return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

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from ctypes import c_void_p, c_long
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.codecache import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
aten = torch.ops.aten
inductor_ops = torch.ops.inductor
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
alloc_from_pool = torch.ops.inductor._alloc_from_pool
reinterpret_tensor = torch.ops.inductor._reinterpret_tensor
async_compile = AsyncCompile()
# kernel path: /tmp/torchinductor_jerryzh/3m/c3mdxfx77gic7e55bygnquq4oboho4wkm2knnxiy3fbmx6ka5zxd.py
# Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
# cat_1 => cat
# input_1 => add
# x_5 => convert_element_type, var_mean
triton_red_fused_add_cat_native_layer_norm_0 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.reduction(
    size_hints=[2048, 128],
    reduction_hint=ReductionHint.OUTER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 8), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(8,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused_add_cat_native_layer_norm_0', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 1182
    rnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x1 = (xindex // 6)
    x0 = xindex % 6
    x3 = xindex
    tmp21_mean = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp21_m2 = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    tmp21_weight = tl.zeros([XBLOCK, RBLOCK], tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r2 = rindex
        tmp17 = tl.load(in_ptr3 + (r2 + (128*x3)), rmask & xmask, eviction_policy='evict_first', other=0.0).to(tl.float32)
        tmp0 = x1
        tmp1 = tl.full([1, 1], 0, tl.int64)
        tmp2 = tmp0 >= tmp1
        tmp3 = tl.full([1, 1], 1, tl.int64)
        tmp4 = tmp0 < tmp3
        tmp5 = tl.load(in_ptr0 + (r2 + (128*x0)), rmask & tmp4 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
        tmp7 = tl.where(tmp4, tmp5, tmp6)
        tmp8 = tmp0 >= tmp3
        tmp9 = tl.full([1, 1], 197, tl.int64)
        tmp10 = tmp0 < tmp9
        tmp11 = tl.load(in_ptr1 + ((196*r2) + (25088*x0) + (((-1) + x1) % 196)), rmask & tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp12 = tl.load(in_ptr2 + (r2 + (128*x0)), rmask & tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp13 = tmp11 + tmp12
        tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
        tmp15 = tl.where(tmp8, tmp13, tmp14)
        tmp16 = tl.where(tmp4, tmp7, tmp15)
        tmp18 = tmp16 + tmp17
        tmp19 = tmp18.to(tl.float32)
        tmp20 = tl.broadcast_to(tmp19, [XBLOCK, RBLOCK])
        tmp21_mean_next, tmp21_m2_next, tmp21_weight_next = triton_helpers.welford_reduce(
            tmp20, tmp21_mean, tmp21_m2, tmp21_weight, roffset == 0
        )
        tmp21_mean = tl.where(rmask & xmask, tmp21_mean_next, tmp21_mean)
        tmp21_m2 = tl.where(rmask & xmask, tmp21_m2_next, tmp21_m2)
        tmp21_weight = tl.where(rmask & xmask, tmp21_weight_next, tmp21_weight)
    tmp21_tmp, tmp22_tmp, tmp23_tmp = triton_helpers.welford(
        tmp21_mean, tmp21_m2, tmp21_weight, 1
    )
    tmp21 = tmp21_tmp[:, None]
    tmp22 = tmp22_tmp[:, None]
    tmp23 = tmp23_tmp[:, None]
    tl.store(out_ptr0 + (x3), tmp21, xmask)
    tl.store(out_ptr1 + (x3), tmp22, xmask)
    tl.store(out_ptr2 + (x3), tmp23, xmask)
''', device_str='cuda')
import triton
import triton.language as tl
from torch._inductor.triton_heuristics import grid, split_scan_grid, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
# kernel path: /tmp/torchinductor_jerryzh/rp/crprwyxhxbgfxjioi6d64h5rouzl2mdpzshcznykn37q2luuupzp.py
# Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
# cat_1 => cat
# input_1 => add
# x_5 => convert_element_type, var_mean
triton_per_fused_add_cat_native_layer_norm_1 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[256, 8],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: 'i32', 6: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_cat_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 197
    rnumel = 6
    RBLOCK: tl.constexpr = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (6*x0)), rmask & xmask, other=0.0)
    tmp1 = tl.load(in_ptr1 + (r1 + (6*x0)), rmask & xmask, other=0.0)
    tmp2 = tl.load(in_ptr2 + (r1 + (6*x0)), rmask & xmask, other=0.0)
    tmp3 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp4 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp5 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp7 = tl.where(rmask & xmask, tmp3, 0)
    tmp8 = tl.where(rmask & xmask, tmp4, 0)
    tmp9 = tl.where(rmask & xmask, tmp5, 0)
    tmp10, tmp11, tmp12 = triton_helpers.welford(tmp7, tmp8, tmp9, 1)
    tmp13 = tmp10[:, None]
    tmp14 = tmp11[:, None]
    tmp15 = tmp12[:, None]
    tl.store(out_ptr0 + (x0), tmp13, xmask)
    tl.store(out_ptr1 + (x0), tmp14, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/4g/c4gelhmxwybhe35chfwqwajj7xo2bonxrt4coqtti75qtsxpc3dd.py
# Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
# cat_1 => cat
# input_1 => add
# x_5 => add_1, add_2, convert_element_type, convert_element_type_1, mul, mul_1, rsqrt, sub, var_mean
triton_poi_fused_add_cat_native_layer_norm_2 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*fp32', 5: '*fp32', 6: '*bf16', 7: '*bf16', 8: '*bf16', 9: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(9,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_cat_native_layer_norm_2', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
    min_elem_per_thread=0
)
@triton.jit
def triton_(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 151296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = (xindex // 768)
    x0 = xindex % 768
    x2 = xindex
    tmp17 = tl.load(in_ptr3 + (x2), xmask).to(tl.float32)
    tmp20 = tl.load(in_ptr4 + (x1), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr5 + (x1), xmask, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr6 + (x0), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp32 = tl.load(in_ptr7 + (x0), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp0 = x1
    tmp1 = tl.full([1], 0, tl.int64)
    tmp2 = tmp0 >= tmp1
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0), tmp4 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp6 = tl.full(tmp5.shape, 0.0, tmp5.dtype)
    tmp7 = tl.where(tmp4, tmp5, tmp6)
    tmp8 = tmp0 >= tmp3
    tmp9 = tl.full([1], 197, tl.int64)
    tmp10 = tmp0 < tmp9
    tmp11 = tl.load(in_ptr1 + ((196*x0) + (((-1) + x1) % 196)), tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp12 = tl.load(in_ptr2 + (x0), tmp8 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp13 = tmp11 + tmp12
    tmp14 = tl.full(tmp13.shape, 0.0, tmp13.dtype)
    tmp15 = tl.where(tmp8, tmp13, tmp14)
    tmp16 = tl.where(tmp4, tmp7, tmp15)
    tmp18 = tmp16 + tmp17
    tmp19 = tmp18.to(tl.float32)
    tmp21 = tmp19 - tmp20
    tmp23 = 768.0
    tmp24 = tmp22 / tmp23
    tmp25 = 1e-06
    tmp26 = tmp24 + tmp25
    tmp27 = libdevice.rsqrt(tmp26)
    tmp28 = tmp21 * tmp27
    tmp30 = tmp29.to(tl.float32)
    tmp31 = tmp28 * tmp30
    tmp33 = tmp32.to(tl.float32)
    tmp34 = tmp31 + tmp33
    tmp35 = tmp34.to(tl.float32)
    tl.store(out_ptr1 + (x2), tmp35, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/f6/cf6bv6b4vfuvbectvwbrf5n7kguat2tmat24wzdu32injuk3czgi.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
# l__self___encoder_layers_encoder_layer_0_self_attention => _scaled_dot_product_flash_attention
triton_poi_fused__scaled_dot_product_flash_attention_3 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__scaled_dot_product_flash_attention_3', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
    min_elem_per_thread=0
)
@triton.jit
def triton_(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 151296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 768
    x1 = (xindex // 768)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (2304*x1)), xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/ni/cnibta3iuvpy5fbssjg635n7b6h3fa5rwyhvzhm3ouugyisfup6w.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
# l__self___encoder_layers_encoder_layer_0_self_attention => _scaled_dot_product_flash_attention
triton_poi_fused__scaled_dot_product_flash_attention_4 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__scaled_dot_product_flash_attention_4', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
    min_elem_per_thread=0
)
@triton.jit
def triton_(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 151296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 768
    x1 = (xindex // 768)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (768 + x0 + (2304*x1)), xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (768 + x0), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/vm/cvm3ucqwmyyakgkf2cfvaeylq3ktp3i7s5emlef7zyz44bzlk4jt.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
# l__self___encoder_layers_encoder_layer_0_self_attention => _scaled_dot_product_flash_attention
triton_poi_fused__scaled_dot_product_flash_attention_5 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.pointwise(
    size_hints=[262144], 
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__scaled_dot_product_flash_attention_5', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
    min_elem_per_thread=0
)
@triton.jit
def triton_(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 151296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 768
    x1 = (xindex // 768)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (1536 + x0 + (2304*x1)), xmask).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (1536 + x0), xmask, eviction_policy='evict_last').to(tl.float32)
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2), tmp2, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/fv/cfv2vtxyxm4ieahpqcdzbdf2o2u6h4ju6comfyd5t3tuvdlqmnv5.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
# l__self___encoder_layers_encoder_layer_0_self_attention => abs_1, amax, clamp_max, clamp_min, clamp_min_1, convert_element_type_5, convert_element_type_6, convert_element_type_7, convert_element_type_8, convert_element_type_9, div, div_1, round_1
triton_per_fused__to_copy_abs_amax_clamp_div_round_6 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*i8', 2: '*bf16', 3: 'i32', 4: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_abs_amax_clamp_div_round_6', 'mutated_arg_names': [], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    xnumel = 197
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = tl.where(rmask & xmask, tmp2, float("-inf"))
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp4, 0))
    tmp6 = tmp5.to(tl.float32)
    tmp7 = 1e-05
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = 127.0
    tmp11 = tmp9 / tmp10
    tmp12 = tmp0 / tmp11
    tmp13 = libdevice.nearbyint(tmp12)
    tmp14 = tmp13.to(tl.float32)
    tmp15 = -127.0
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = triton_helpers.minimum(tmp16, tmp10)
    tmp18 = tmp17.to(tl.float32)
    tmp19 = tmp18.to(tl.int8)
    tl.store(out_ptr1 + (r1 + (768*x0)), tmp19, rmask & xmask)
    tl.store(out_ptr2 + (x0), tmp11, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/tb/ctbetqksq5wdquspt6aqeaeczbvkly47okhymmcfwjlfwji4avos.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
# l__self___encoder_layers_encoder_layer_0_self_attention => clamp_max, clamp_min, clamp_min_1, convert_element_type_5, convert_element_type_6, convert_element_type_7, convert_element_type_8, convert_element_type_9, div, div_1, fused_int_mm_mul_36, round_1, view_11
triton_tem_fused__to_copy_clamp_div_mul_round_view_7 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.template(
    num_stages=5,
    num_warps=8,
    triton_meta={'signature': {0: '*i8', 1: '*i8', 2: '*bf16', 3: '*bf16'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'kernel_name': 'triton_tem_fused__to_copy_clamp_div_mul_round_view_7', 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
)
@triton.jit
def triton_(arg_A, arg_B, in_ptr2, out_ptr0):
    GROUP_M : tl.constexpr = 8
    EVEN_K : tl.constexpr = True
    ALLOW_TF32 : tl.constexpr = False
    ACC_TYPE : tl.constexpr = tl.int32
    B_PROLOGUE_CAST_TYPE : tl.constexpr = None
    BLOCK_M : tl.constexpr = 64
    BLOCK_N : tl.constexpr = 32
    BLOCK_K : tl.constexpr = 32
    A = arg_A
    B = arg_B
    M = 197
    N = 768
    K = 768
    if M * N == 0:
        # early exit due to zero-size input(s)
        return
    stride_am = 768
    stride_ak = 1
    stride_bk = 1
    stride_bn = 768
    # based on triton.ops.matmul
    pid = tl.program_id(0)
    grid_m = (M + BLOCK_M - 1) // BLOCK_M
    grid_n = (N + BLOCK_N - 1) // BLOCK_N
    # re-order program ID for better L2 performance
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // (group_size)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
    for k in range(K, 0, -BLOCK_K):
        if EVEN_K:
            a = tl.load(A)
            b = tl.load(B)
        else:
            a = tl.load(A, mask=rk[None, :] < k, other=0.)
            b = tl.load(B, mask=rk[:, None] < k, other=0.)
        if B_PROLOGUE_CAST_TYPE is not None:
            b = b.to(B_PROLOGUE_CAST_TYPE)
        acc += tl.dot(a, b, allow_tf32=ALLOW_TF32)
        A += BLOCK_K * stride_ak
        B += BLOCK_K * stride_bk
    # rematerialize rm and rn to save registers
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    idx_m = rm[:, None]
    idx_n = rn[None, :]
    mask = (idx_m < M) & (idx_n < N)
    # inductor generates a suffix
    xindex = idx_n + (768*idx_m)
    tmp0 = tl.load(in_ptr2 + (tl.broadcast_to(idx_m, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tl.store(out_ptr0 + (tl.broadcast_to(xindex, mask.shape)), acc * tmp0, mask)
''', device_str='cuda')
import torch._inductor.kernel.mm_common
meta0 = {'GROUP_M': 8, 'EVEN_K': True, 'ALLOW_TF32': False, 'ACC_TYPE': 'tl.int32', 'B_PROLOGUE_CAST_TYPE': None, 'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32}
# kernel path: /tmp/torchinductor_jerryzh/cr/ccr74bfho3ok5o2tyb5aq3refnm7mumphtnf7anixsxqq2ylylnx.py
# Source Nodes: [cat_1, input_1, l__self___encoder_layers_encoder_layer_0_mlp_0, x_7, x_8, y], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.cat, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
# cat_1 => cat
# input_1 => add
# l__self___encoder_layers_encoder_layer_0_mlp_0 => abs_2, amax_1, clamp_max_1, clamp_min_2, clamp_min_3, convert_element_type_12, convert_element_type_13, convert_element_type_14, convert_element_type_15, convert_element_type_16, div_2, div_3, round_2
# x_7 => clone_2
# x_8 => add_4
# y => add_5, add_6, convert_element_type_10, convert_element_type_11, mul_4, mul_5, rsqrt_1, sub_1, var_mean_1
triton_per_fused__to_copy_abs_add_amax_cat_clamp_clone_div_native_layer_norm_round_8 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*bf16', 5: '*bf16', 6: '*bf16', 7: '*bf16', 8: '*bf16', 9: '*i8', 10: '*bf16', 11: 'i32', 12: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(12,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_abs_add_amax_cat_clamp_clone_div_native_layer_norm_round_8', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr4, out_ptr5, xnumel, rnumel):
    xnumel = 197
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp1 = tl.load(in_ptr0 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp3 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp22 = tl.load(in_ptr5 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp49 = tl.load(in_ptr6 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp52 = tl.load(in_ptr7 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp5 = x0
    tmp6 = tl.full([1], 0, tl.int64)
    tmp7 = tmp5 >= tmp6
    tmp8 = tl.full([1], 1, tl.int64)
    tmp9 = tmp5 < tmp8
    tmp10 = tl.load(in_ptr2 + (tl.broadcast_to(r1, [RBLOCK])), rmask & tmp9 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp11 = tl.full(tmp10.shape, 0.0, tmp10.dtype)
    tmp12 = tl.where(tmp9, tmp10, tmp11)
    tmp13 = tmp5 >= tmp8
    tmp14 = tl.full([1], 197, tl.int64)
    tmp15 = tmp5 < tmp14
    tmp16 = tl.load(in_ptr3 + ((196*r1) + (((-1) + x0) % 196)), rmask & tmp13 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp17 = tl.load(in_ptr4 + (tl.broadcast_to(r1, [RBLOCK])), rmask & tmp13 & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp18 = tmp16 + tmp17
    tmp19 = tl.full(tmp18.shape, 0.0, tmp18.dtype)
    tmp20 = tl.where(tmp13, tmp18, tmp19)
    tmp21 = tl.where(tmp9, tmp12, tmp20)
    tmp23 = tmp21 + tmp22
    tmp24 = tmp4 + tmp23
    tmp25 = tmp24.to(tl.float32)
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = tl.where(rmask & xmask, tmp26, 0)
    tmp29 = tl.broadcast_to(tmp26, [RBLOCK])
    tmp31 = tl.where(rmask & xmask, tmp29, 0)
    tmp32 = triton_helpers.promote_to_tensor(tl.sum(tmp31, 0))
    tmp33 = tl.full([1], 768, tl.int32)
    tmp34 = tmp33.to(tl.float32)
    tmp35 = tmp32 / tmp34
    tmp36 = tmp26 - tmp35
    tmp37 = tmp36 * tmp36
    tmp38 = tl.broadcast_to(tmp37, [RBLOCK])
    tmp40 = tl.where(rmask & xmask, tmp38, 0)
    tmp41 = triton_helpers.promote_to_tensor(tl.sum(tmp40, 0))
    tmp42 = tmp25 - tmp35
    tmp43 = 768.0
    tmp44 = tmp41 / tmp43
    tmp45 = 1e-06
    tmp46 = tmp44 + tmp45
    tmp47 = libdevice.rsqrt(tmp46)
    tmp48 = tmp42 * tmp47
    tmp50 = tmp49.to(tl.float32)
    tmp51 = tmp48 * tmp50
    tmp53 = tmp52.to(tl.float32)
    tmp54 = tmp51 + tmp53
    tmp55 = tmp54.to(tl.float32)
    tmp56 = tl_math.abs(tmp55)
    tmp57 = tl.broadcast_to(tmp56, [RBLOCK])
    tmp59 = tl.where(rmask & xmask, tmp57, float("-inf"))
    tmp60 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp59, 0))
    tmp61 = tmp60.to(tl.float32)
    tmp62 = 1e-05
    tmp63 = triton_helpers.maximum(tmp61, tmp62)
    tmp64 = tmp63.to(tl.float32)
    tmp65 = 127.0
    tmp66 = tmp64 / tmp65
    tmp67 = tmp55 / tmp66
    tmp68 = libdevice.nearbyint(tmp67)
    tmp69 = tmp68.to(tl.float32)
    tmp70 = -127.0
    tmp71 = triton_helpers.maximum(tmp69, tmp70)
    tmp72 = triton_helpers.minimum(tmp71, tmp65)
    tmp73 = tmp72.to(tl.float32)
    tmp74 = tmp73.to(tl.int8)
    tl.store(in_out_ptr0 + (r1 + (768*x0)), tmp24, rmask & xmask)
    tl.store(out_ptr4 + (r1 + (768*x0)), tmp74, rmask & xmask)
    tl.store(out_ptr5 + (x0), tmp66, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/jx/cjxx4domyf5fqnwxmul6z4wzyoglsc24vcwrgybgrtp2t54xbq4v.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_0], Original ATen: [aten.mul]
# l__self___encoder_layers_encoder_layer_0_mlp_0 => fused_int_mm_mul_35
triton_tem_fused_mul_9 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.template(
    num_stages=3,
    num_warps=8,
    triton_meta={'signature': {0: '*i8', 1: '*i8', 2: '*bf16', 3: '*bf16'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'kernel_name': 'triton_tem_fused_mul_9', 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
)
@triton.jit
def triton_(arg_A, arg_B, in_ptr2, out_ptr0):
    GROUP_M : tl.constexpr = 8
    EVEN_K : tl.constexpr = True
    ALLOW_TF32 : tl.constexpr = False
    ACC_TYPE : tl.constexpr = tl.int32
    B_PROLOGUE_CAST_TYPE : tl.constexpr = None
    BLOCK_M : tl.constexpr = 64
    BLOCK_N : tl.constexpr = 64
    BLOCK_K : tl.constexpr = 64
    A = arg_A
    B = arg_B
    M = 197
    N = 3072
    K = 768
    if M * N == 0:
        # early exit due to zero-size input(s)
        return
    stride_am = 768
    stride_ak = 1
    stride_bk = 1
    stride_bn = 768
    # based on triton.ops.matmul
    pid = tl.program_id(0)
    grid_m = (M + BLOCK_M - 1) // BLOCK_M
    grid_n = (N + BLOCK_N - 1) // BLOCK_N
    # re-order program ID for better L2 performance
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // (group_size)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
    for k in range(K, 0, -BLOCK_K):
        if EVEN_K:
            a = tl.load(A)
            b = tl.load(B)
        else:
            a = tl.load(A, mask=rk[None, :] < k, other=0.)
            b = tl.load(B, mask=rk[:, None] < k, other=0.)
        if B_PROLOGUE_CAST_TYPE is not None:
            b = b.to(B_PROLOGUE_CAST_TYPE)
        acc += tl.dot(a, b, allow_tf32=ALLOW_TF32)
        A += BLOCK_K * stride_ak
        B += BLOCK_K * stride_bk
    # rematerialize rm and rn to save registers
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    idx_m = rm[:, None]
    idx_n = rn[None, :]
    mask = (idx_m < M) & (idx_n < N)
    # inductor generates a suffix
    xindex = idx_n + (3072*idx_m)
    tmp0 = tl.load(in_ptr2 + (tl.broadcast_to(idx_m, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tl.store(out_ptr0 + (tl.broadcast_to(xindex, mask.shape)), acc * tmp0, mask)
''', device_str='cuda')
meta1 = {'GROUP_M': 8, 'EVEN_K': True, 'ALLOW_TF32': False, 'ACC_TYPE': 'tl.int32', 'B_PROLOGUE_CAST_TYPE': None, 'BLOCK_M': 64, 'BLOCK_N': 64, 'BLOCK_K': 64}
# kernel path: /tmp/torchinductor_jerryzh/iy/ciyvyjnui7iwwlmxkprc7lnz7wv3wqmha4nv43gesczchuffgkse.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_1, l__self___encoder_layers_encoder_layer_0_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
# l__self___encoder_layers_encoder_layer_0_mlp_1 => add_8, convert_element_type_17, convert_element_type_18, erf, mul_10, mul_8, mul_9
# l__self___encoder_layers_encoder_layer_0_mlp_3 => abs_3, amax_2, clamp_max_2, clamp_min_4, clamp_min_5, convert_element_type_19, convert_element_type_20, convert_element_type_21, convert_element_type_22, convert_element_type_23, div_4, div_5, round_3
triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.reduction(
    size_hints=[256, 4096],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*i8', 4: '*bf16', 5: 'i32', 6: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 6), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(6,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, in_ptr1, in_ptr2, out_ptr1, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr, RBLOCK : tl.constexpr):
    xnumel = 197
    rnumel = 3072
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rbase = tl.arange(0, RBLOCK)[None, :]
    x0 = xindex
    _tmp17 = tl.full([XBLOCK, RBLOCK], float("-inf"), tl.float32)
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp0 = tl.load(in_ptr0 + (r1 + (3072*x0)), rmask & xmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp1 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp3 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp2 = tmp0 * tmp1
        tmp4 = tmp2 + tmp3
        tmp5 = tmp4.to(tl.float32)
        tmp6 = 0.5
        tmp7 = tmp5 * tmp6
        tmp8 = 0.7071067811865476
        tmp9 = tmp5 * tmp8
        tmp10 = libdevice.erf(tmp9)
        tmp11 = 1.0
        tmp12 = tmp10 + tmp11
        tmp13 = tmp7 * tmp12
        tmp14 = tmp13.to(tl.float32)
        tmp15 = tl_math.abs(tmp14)
        tmp16 = tl.broadcast_to(tmp15, [XBLOCK, RBLOCK])
        tmp18 = triton_helpers.maximum(_tmp17, tmp16)
        _tmp17 = tl.where(rmask & xmask, tmp18, _tmp17)
    tmp17 = triton_helpers.max2(_tmp17, 1)[:, None]
    for roffset in range(0, rnumel, RBLOCK):
        rindex = roffset + rbase
        rmask = rindex < rnumel
        r1 = rindex
        tmp19 = tl.load(in_ptr0 + (r1 + (3072*x0)), rmask & xmask, eviction_policy='evict_first', other=0.0).to(tl.float32)
        tmp20 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp22 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
        tmp21 = tmp19 * tmp20
        tmp23 = tmp21 + tmp22
        tmp24 = tmp23.to(tl.float32)
        tmp25 = 0.5
        tmp26 = tmp24 * tmp25
        tmp27 = 0.7071067811865476
        tmp28 = tmp24 * tmp27
        tmp29 = libdevice.erf(tmp28)
        tmp30 = 1.0
        tmp31 = tmp29 + tmp30
        tmp32 = tmp26 * tmp31
        tmp33 = tmp32.to(tl.float32)
        tmp34 = tmp17.to(tl.float32)
        tmp35 = 1e-05
        tmp36 = triton_helpers.maximum(tmp34, tmp35)
        tmp37 = tmp36.to(tl.float32)
        tmp38 = 127.0
        tmp39 = tmp37 / tmp38
        tmp40 = tmp33 / tmp39
        tmp41 = libdevice.nearbyint(tmp40)
        tmp42 = tmp41.to(tl.float32)
        tmp43 = -127.0
        tmp44 = triton_helpers.maximum(tmp42, tmp43)
        tmp45 = triton_helpers.minimum(tmp44, tmp38)
        tmp46 = tmp45.to(tl.float32)
        tmp47 = tmp46.to(tl.int8)
        tl.store(out_ptr1 + (r1 + (3072*x0)), tmp47, rmask & xmask)
    tmp48 = tmp17.to(tl.float32)
    tmp49 = 1e-05
    tmp50 = triton_helpers.maximum(tmp48, tmp49)
    tmp51 = tmp50.to(tl.float32)
    tmp52 = 127.0
    tmp53 = tmp51 / tmp52
    tl.store(out_ptr2 + (x0), tmp53, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/ps/cpsyn7vzywekfevmwvtf55dbk53i5wmhkwkayz7ootpwltznssnw.py
# Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_3], Original ATen: [aten.mul]
# l__self___encoder_layers_encoder_layer_0_mlp_3 => fused_int_mm_mul_34
triton_tem_fused_mul_11 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.template(
    num_stages=3,
    num_warps=8,
    triton_meta={'signature': {0: '*i8', 1: '*i8', 2: '*bf16', 3: '*bf16'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'kernel_name': 'triton_tem_fused_mul_11', 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
)
@triton.jit
def triton_(arg_A, arg_B, in_ptr2, out_ptr0):
    GROUP_M : tl.constexpr = 8
    EVEN_K : tl.constexpr = True
    ALLOW_TF32 : tl.constexpr = False
    ACC_TYPE : tl.constexpr = tl.int32
    B_PROLOGUE_CAST_TYPE : tl.constexpr = None
    BLOCK_M : tl.constexpr = 64
    BLOCK_N : tl.constexpr = 64
    BLOCK_K : tl.constexpr = 64
    A = arg_A
    B = arg_B
    M = 197
    N = 768
    K = 3072
    if M * N == 0:
        # early exit due to zero-size input(s)
        return
    stride_am = 3072
    stride_ak = 1
    stride_bk = 1
    stride_bn = 3072
    # based on triton.ops.matmul
    pid = tl.program_id(0)
    grid_m = (M + BLOCK_M - 1) // BLOCK_M
    grid_n = (N + BLOCK_N - 1) // BLOCK_N
    # re-order program ID for better L2 performance
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // (group_size)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
    for k in range(K, 0, -BLOCK_K):
        if EVEN_K:
            a = tl.load(A)
            b = tl.load(B)
        else:
            a = tl.load(A, mask=rk[None, :] < k, other=0.)
            b = tl.load(B, mask=rk[:, None] < k, other=0.)
        if B_PROLOGUE_CAST_TYPE is not None:
            b = b.to(B_PROLOGUE_CAST_TYPE)
        acc += tl.dot(a, b, allow_tf32=ALLOW_TF32)
        A += BLOCK_K * stride_ak
        B += BLOCK_K * stride_bk
    # rematerialize rm and rn to save registers
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    idx_m = rm[:, None]
    idx_n = rn[None, :]
    mask = (idx_m < M) & (idx_n < N)
    # inductor generates a suffix
    xindex = idx_n + (768*idx_m)
    tmp0 = tl.load(in_ptr2 + (tl.broadcast_to(idx_m, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tl.store(out_ptr0 + (tl.broadcast_to(xindex, mask.shape)), acc * tmp0, mask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/vy/cvycfkdip26kupsh6qqdyw6u6occez4idkkoffi37ti5bizxwchp.py
# Source Nodes: [add_2, x_9], Original ATen: [aten.add, aten.native_layer_norm]
# add_2 => add_10
# x_9 => add_11, add_12, convert_element_type_24, convert_element_type_25, mul_13, mul_14, rsqrt_2, sub_2, var_mean_2
triton_per_fused_add_native_layer_norm_12 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*bf16', 5: '*bf16', 6: '*bf16', 7: 'i32', 8: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 8), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(8,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_native_layer_norm_12', 'mutated_arg_names': [], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, out_ptr2, xnumel, rnumel):
    xnumel = 197
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp1 = tl.load(in_ptr1 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp2 = tl.load(in_ptr2 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp4 = tl.load(in_ptr3 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp31 = tl.load(in_ptr4 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp34 = tl.load(in_ptr5 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp3 = tmp1 * tmp2
    tmp5 = tmp3 + tmp4
    tmp6 = tmp0 + tmp5
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tl.broadcast_to(tmp7, [RBLOCK])
    tmp10 = tl.where(rmask & xmask, tmp8, 0)
    tmp11 = tl.broadcast_to(tmp8, [RBLOCK])
    tmp13 = tl.where(rmask & xmask, tmp11, 0)
    tmp14 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp15 = tl.full([1], 768, tl.int32)
    tmp16 = tmp15.to(tl.float32)
    tmp17 = tmp14 / tmp16
    tmp18 = tmp8 - tmp17
    tmp19 = tmp18 * tmp18
    tmp20 = tl.broadcast_to(tmp19, [RBLOCK])
    tmp22 = tl.where(rmask & xmask, tmp20, 0)
    tmp23 = triton_helpers.promote_to_tensor(tl.sum(tmp22, 0))
    tmp24 = tmp7 - tmp17
    tmp25 = 768.0
    tmp26 = tmp23 / tmp25
    tmp27 = 1e-06
    tmp28 = tmp26 + tmp27
    tmp29 = libdevice.rsqrt(tmp28)
    tmp30 = tmp24 * tmp29
    tmp32 = tmp31.to(tl.float32)
    tmp33 = tmp30 * tmp32
    tmp35 = tmp34.to(tl.float32)
    tmp36 = tmp33 + tmp35
    tmp37 = tmp36.to(tl.float32)
    tl.store(out_ptr2 + (r1 + (768*x0)), tmp37, rmask & xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/qq/cqq6ykhkbe3vxe2rgr4vqhn7noiowpjojt2ccs4uylq23avmipb3.py
# Source Nodes: [add_2, l__self___encoder_layers_encoder_layer_1_mlp_0, x_11, x_12, y_2], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
# add_2 => add_10
# l__self___encoder_layers_encoder_layer_1_mlp_0 => abs_5, amax_4, clamp_max_4, clamp_min_8, clamp_min_9, convert_element_type_36, convert_element_type_37, convert_element_type_38, convert_element_type_39, convert_element_type_40, div_8, div_9, round_5
# x_11 => clone_6
# x_12 => add_14
# y_2 => add_15, add_16, convert_element_type_34, convert_element_type_35, mul_17, mul_18, rsqrt_3, sub_3, var_mean_3
triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[256, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: '*bf16', 3: '*bf16', 4: '*bf16', 5: '*bf16', 6: '*bf16', 7: '*bf16', 8: '*bf16', 9: '*i8', 10: '*bf16', 11: 'i32', 12: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(12,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, in_ptr5, in_ptr6, in_ptr7, out_ptr4, out_ptr5, xnumel, rnumel):
    xnumel = 197
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp1 = tl.load(in_ptr0 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp3 = tl.load(in_ptr1 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp5 = tl.load(in_ptr2 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp6 = tl.load(in_ptr3 + (r1 + (768*x0)), rmask & xmask, other=0.0).to(tl.float32)
    tmp7 = tl.load(in_ptr4 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp9 = tl.load(in_ptr5 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp37 = tl.load(in_ptr6 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp40 = tl.load(in_ptr7 + (r1), rmask, eviction_policy='evict_last', other=0.0).to(tl.float32)
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp8 = tmp6 * tmp7
    tmp10 = tmp8 + tmp9
    tmp11 = tmp5 + tmp10
    tmp12 = tmp4 + tmp11
    tmp13 = tmp12.to(tl.float32)
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = tl.where(rmask & xmask, tmp14, 0)
    tmp17 = tl.broadcast_to(tmp14, [RBLOCK])
    tmp19 = tl.where(rmask & xmask, tmp17, 0)
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp21 = tl.full([1], 768, tl.int32)
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp20 / tmp22
    tmp24 = tmp14 - tmp23
    tmp25 = tmp24 * tmp24
    tmp26 = tl.broadcast_to(tmp25, [RBLOCK])
    tmp28 = tl.where(rmask & xmask, tmp26, 0)
    tmp29 = triton_helpers.promote_to_tensor(tl.sum(tmp28, 0))
    tmp30 = tmp13 - tmp23
    tmp31 = 768.0
    tmp32 = tmp29 / tmp31
    tmp33 = 1e-06
    tmp34 = tmp32 + tmp33
    tmp35 = libdevice.rsqrt(tmp34)
    tmp36 = tmp30 * tmp35
    tmp38 = tmp37.to(tl.float32)
    tmp39 = tmp36 * tmp38
    tmp41 = tmp40.to(tl.float32)
    tmp42 = tmp39 + tmp41
    tmp43 = tmp42.to(tl.float32)
    tmp44 = tl_math.abs(tmp43)
    tmp45 = tl.broadcast_to(tmp44, [RBLOCK])
    tmp47 = tl.where(rmask & xmask, tmp45, float("-inf"))
    tmp48 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp47, 0))
    tmp49 = tmp48.to(tl.float32)
    tmp50 = 1e-05
    tmp51 = triton_helpers.maximum(tmp49, tmp50)
    tmp52 = tmp51.to(tl.float32)
    tmp53 = 127.0
    tmp54 = tmp52 / tmp53
    tmp55 = tmp43 / tmp54
    tmp56 = libdevice.nearbyint(tmp55)
    tmp57 = tmp56.to(tl.float32)
    tmp58 = -127.0
    tmp59 = triton_helpers.maximum(tmp57, tmp58)
    tmp60 = triton_helpers.minimum(tmp59, tmp53)
    tmp61 = tmp60.to(tl.float32)
    tmp62 = tmp61.to(tl.int8)
    tl.store(in_out_ptr0 + (r1 + (768*x0)), tmp12, rmask & xmask)
    tl.store(out_ptr4 + (r1 + (768*x0)), tmp62, rmask & xmask)
    tl.store(out_ptr5 + (x0), tmp54, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/wd/cwdqc7dmebh3v2xd6cfey6yui43rvmyhummf4vqofw63udidqwyg.py
# Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
# x_55 => abs_37, amax_36, clamp_max_36, clamp_min_72, clamp_min_73, convert_element_type_290, convert_element_type_291, convert_element_type_292, convert_element_type_293, convert_element_type_294, div_72, div_73, round_37
triton_per_fused__to_copy_abs_amax_clamp_div_round_14 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.persistent_reduction(
    size_hints=[1, 1024],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*i8', 2: '*bf16', 3: 'i32', 4: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,), ids_of_folded_args=(3,), divisible_by_8=(4,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused__to_copy_abs_amax_clamp_div_round_14', 'mutated_arg_names': [], 'no_x_dim': True, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'}
)
@triton.jit
def triton_(in_ptr0, out_ptr1, out_ptr2, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 768
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = rindex < rnumel
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + (r0), rmask, other=0.0).to(tl.float32)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp4 = tl.where(rmask, tmp2, float("-inf"))
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp4, 0))
    tmp6 = tmp5.to(tl.float32)
    tmp7 = 1e-05
    tmp8 = triton_helpers.maximum(tmp6, tmp7)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = 127.0
    tmp11 = tmp9 / tmp10
    tmp12 = tmp0 / tmp11
    tmp13 = libdevice.nearbyint(tmp12)
    tmp14 = tmp13.to(tl.float32)
    tmp15 = -127.0
    tmp16 = triton_helpers.maximum(tmp14, tmp15)
    tmp17 = triton_helpers.minimum(tmp16, tmp10)
    tmp18 = tmp17.to(tl.float32)
    tmp19 = tmp18.to(tl.int8)
    tl.store(out_ptr1 + (tl.broadcast_to(r0, [RBLOCK])), tmp19, rmask)
    tl.store(out_ptr2 + (tl.full([1], 0, tl.int32)), tmp11, None)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/q7/cq7l22t3jg2gl4suedtqectjqqnzo2au36jgqky34jaylvqzbpe6.py
# Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
# x_55 => clamp_max_36, clamp_min_72, clamp_min_73, convert_element_type_290, convert_element_type_291, convert_element_type_292, convert_element_type_293, convert_element_type_294, div_72, div_73, fused_int_mm_mul, round_37, view_337
triton_poi_fused__to_copy_clamp_div_mul_round_view_15 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.pointwise(
    size_hints=[1024], 
    filename=__file__,
    triton_meta={'signature': {0: '*bf16', 1: '*bf16', 2: 'i32'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=(2,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__to_copy_clamp_div_mul_round_view_15', 'mutated_arg_names': [], 'no_x_dim': False, 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
    min_elem_per_thread=0
)
@triton.jit
def triton_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 1000
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (0)).to(tl.float32)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK])
    tl.store(out_ptr0 + (x0), tmp1, xmask)
''', device_str='cuda')
# kernel path: /tmp/torchinductor_jerryzh/ok/cokrq32j6selob5j2cg3mdihs6r7dcbuoikxiheczlgornscdupy.py
# Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.add, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
# x_55 => add_123, clamp_max_36, clamp_min_72, clamp_min_73, convert_element_type_290, convert_element_type_291, convert_element_type_292, convert_element_type_293, convert_element_type_294, div_72, div_73, fused_int_mm_mul, mul_159, round_37, view_337
triton_tem_fused__to_copy_add_clamp_div_mul_round_view_16 = async_compile.triton('triton_', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor
from torch._inductor import triton_helpers, triton_heuristics
from torch._inductor.ir import ReductionHint, TileHint
from torch._inductor.triton_helpers import libdevice, math as tl_math
from torch._inductor.triton_heuristics import AutotuneHint
from torch._inductor.utils import instance_descriptor
@triton_heuristics.template(
    num_stages=3,
    num_warps=8,
    triton_meta={'signature': {0: '*i8', 1: '*i8', 2: '*bf16', 3: '*bf16', 4: '*bf16', 5: '*bf16'}, 'device': 0, 'device_type': 'cuda', 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5), equal_to_1=(), ids_of_folded_args=(), divisible_by_8=())]},
    inductor_meta={'kernel_name': 'triton_tem_fused__to_copy_add_clamp_div_mul_round_view_16', 'backend_hash': 'd389db67f581f05462832d65867d4147e9aefba5e2e1d883bbadd9e1860863a6'},
)
@triton.jit
def triton_(arg_A, arg_B, in_ptr2, in_ptr3, in_ptr4, out_ptr1):
    GROUP_M : tl.constexpr = 8
    EVEN_K : tl.constexpr = True
    ALLOW_TF32 : tl.constexpr = False
    ACC_TYPE : tl.constexpr = tl.int32
    B_PROLOGUE_CAST_TYPE : tl.constexpr = None
    BLOCK_M : tl.constexpr = 16
    BLOCK_N : tl.constexpr = 128
    BLOCK_K : tl.constexpr = 128
    A = arg_A
    B = arg_B
    M = 1
    N = 1000
    K = 768
    if M * N == 0:
        # early exit due to zero-size input(s)
        return
    stride_am = 768
    stride_ak = 1
    stride_bk = 1
    stride_bn = 768
    # based on triton.ops.matmul
    pid = tl.program_id(0)
    grid_m = (M + BLOCK_M - 1) // BLOCK_M
    grid_n = (N + BLOCK_N - 1) // BLOCK_N
    # re-order program ID for better L2 performance
    width = GROUP_M * grid_n
    group_id = pid // width
    group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
    pid_m = group_id * GROUP_M + (pid % group_size)
    pid_n = (pid % width) // (group_size)
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
    rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
    rk = tl.arange(0, BLOCK_K)
    A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
    B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
    acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
    for k in range(K, 0, -BLOCK_K):
        if EVEN_K:
            a = tl.load(A)
            b = tl.load(B)
        else:
            a = tl.load(A, mask=rk[None, :] < k, other=0.)
            b = tl.load(B, mask=rk[:, None] < k, other=0.)
        if B_PROLOGUE_CAST_TYPE is not None:
            b = b.to(B_PROLOGUE_CAST_TYPE)
        acc += tl.dot(a, b, allow_tf32=ALLOW_TF32)
        A += BLOCK_K * stride_ak
        B += BLOCK_K * stride_bk
    # rematerialize rm and rn to save registers
    rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    idx_m = rm[:, None]
    idx_n = rn[None, :]
    mask = (idx_m < M) & (idx_n < N)
    # inductor generates a suffix
    xindex = idx_n + (1000*idx_m)
    tmp0 = tl.load(in_ptr2 + (tl.broadcast_to(idx_n, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tmp1 = tl.load(in_ptr3 + (tl.broadcast_to(xindex, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tmp3 = tl.load(in_ptr4 + (tl.broadcast_to(xindex, mask.shape)), mask, eviction_policy='evict_last').to(tl.float32)
    tmp2 = acc * tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr1 + (tl.broadcast_to(xindex, mask.shape)), tmp4, mask)
''', device_str='cuda')
meta2 = {'GROUP_M': 8, 'EVEN_K': True, 'ALLOW_TF32': False, 'ACC_TYPE': 'tl.int32', 'B_PROLOGUE_CAST_TYPE': None, 'BLOCK_M': 16, 'BLOCK_N': 128, 'BLOCK_K': 128}
async_compile.wait(globals())
del async_compile
def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1, arg15_1, arg16_1, arg17_1, arg18_1, arg19_1, arg20_1, arg21_1, arg22_1, arg23_1, arg24_1, arg25_1, arg26_1, arg27_1, arg28_1, arg29_1, arg30_1, arg31_1, arg32_1, arg33_1, arg34_1, arg35_1, arg36_1, arg37_1, arg38_1, arg39_1, arg40_1, arg41_1, arg42_1, arg43_1, arg44_1, arg45_1, arg46_1, arg47_1, arg48_1, arg49_1, arg50_1, arg51_1, arg52_1, arg53_1, arg54_1, arg55_1, arg56_1, arg57_1, arg58_1, arg59_1, arg60_1, arg61_1, arg62_1, arg63_1, arg64_1, arg65_1, arg66_1, arg67_1, arg68_1, arg69_1, arg70_1, arg71_1, arg72_1, arg73_1, arg74_1, arg75_1, arg76_1, arg77_1, arg78_1, arg79_1, arg80_1, arg81_1, arg82_1, arg83_1, arg84_1, arg85_1, arg86_1, arg87_1, arg88_1, arg89_1, arg90_1, arg91_1, arg92_1, arg93_1, arg94_1, arg95_1, arg96_1, arg97_1, arg98_1, arg99_1, arg100_1, arg101_1, arg102_1, arg103_1, arg104_1, arg105_1, arg106_1, arg107_1, arg108_1, arg109_1, arg110_1, arg111_1, arg112_1, arg113_1, arg114_1, arg115_1, arg116_1, arg117_1, arg118_1, arg119_1, arg120_1, arg121_1, arg122_1, arg123_1, arg124_1, arg125_1, arg126_1, arg127_1, arg128_1, arg129_1, arg130_1, arg131_1, arg132_1, arg133_1, arg134_1, arg135_1, arg136_1, arg137_1, arg138_1, arg139_1, arg140_1, arg141_1, arg142_1, arg143_1, arg144_1, arg145_1, arg146_1, arg147_1, arg148_1, arg149_1, arg150_1, arg151_1, arg152_1, arg153_1, arg154_1, arg155_1, arg156_1, arg157_1, arg158_1, arg159_1, arg160_1, arg161_1, arg162_1, arg163_1, arg164_1, arg165_1, arg166_1, arg167_1, arg168_1, arg169_1, arg170_1, arg171_1, arg172_1, arg173_1, arg174_1, arg175_1, arg176_1, arg177_1, arg178_1, arg179_1, arg180_1, arg181_1, arg182_1, arg183_1, arg184_1, arg185_1, arg186_1, arg187_1, arg188_1, arg189_1 = args
    args.clear()
    assert_size_stride(arg0_1, (1, 1, 768), (768, 768, 1))
    assert_size_stride(arg1_1, (1, 197, 768), (151296, 768, 1))
    assert_size_stride(arg2_1, (768, 3, 16, 16), (768, 256, 16, 1))
    assert_size_stride(arg3_1, (768, ), (1, ))
    assert_size_stride(arg4_1, (768, ), (1, ))
    assert_size_stride(arg5_1, (768, ), (1, ))
    assert_size_stride(arg6_1, (2304, 768), (768, 1))
    assert_size_stride(arg7_1, (2304, ), (1, ))
    assert_size_stride(arg8_1, (768, 768), (1, 768))
    assert_size_stride(arg9_1, (768, ), (1, ))
    assert_size_stride(arg10_1, (768, ), (1, ))
    assert_size_stride(arg11_1, (768, ), (1, ))
    assert_size_stride(arg12_1, (768, ), (1, ))
    assert_size_stride(arg13_1, (768, 3072), (1, 768))
    assert_size_stride(arg14_1, (3072, ), (1, ))
    assert_size_stride(arg15_1, (3072, ), (1, ))
    assert_size_stride(arg16_1, (3072, 768), (1, 3072))
    assert_size_stride(arg17_1, (768, ), (1, ))
    assert_size_stride(arg18_1, (768, ), (1, ))
    assert_size_stride(arg19_1, (768, ), (1, ))
    assert_size_stride(arg20_1, (768, ), (1, ))
    assert_size_stride(arg21_1, (2304, 768), (768, 1))
    assert_size_stride(arg22_1, (2304, ), (1, ))
    assert_size_stride(arg23_1, (768, 768), (1, 768))
    assert_size_stride(arg24_1, (768, ), (1, ))
    assert_size_stride(arg25_1, (768, ), (1, ))
    assert_size_stride(arg26_1, (768, ), (1, ))
    assert_size_stride(arg27_1, (768, ), (1, ))
    assert_size_stride(arg28_1, (768, 3072), (1, 768))
    assert_size_stride(arg29_1, (3072, ), (1, ))
    assert_size_stride(arg30_1, (3072, ), (1, ))
    assert_size_stride(arg31_1, (3072, 768), (1, 3072))
    assert_size_stride(arg32_1, (768, ), (1, ))
    assert_size_stride(arg33_1, (768, ), (1, ))
    assert_size_stride(arg34_1, (768, ), (1, ))
    assert_size_stride(arg35_1, (768, ), (1, ))
    assert_size_stride(arg36_1, (2304, 768), (768, 1))
    assert_size_stride(arg37_1, (2304, ), (1, ))
    assert_size_stride(arg38_1, (768, 768), (1, 768))
    assert_size_stride(arg39_1, (768, ), (1, ))
    assert_size_stride(arg40_1, (768, ), (1, ))
    assert_size_stride(arg41_1, (768, ), (1, ))
    assert_size_stride(arg42_1, (768, ), (1, ))
    assert_size_stride(arg43_1, (768, 3072), (1, 768))
    assert_size_stride(arg44_1, (3072, ), (1, ))
    assert_size_stride(arg45_1, (3072, ), (1, ))
    assert_size_stride(arg46_1, (3072, 768), (1, 3072))
    assert_size_stride(arg47_1, (768, ), (1, ))
    assert_size_stride(arg48_1, (768, ), (1, ))
    assert_size_stride(arg49_1, (768, ), (1, ))
    assert_size_stride(arg50_1, (768, ), (1, ))
    assert_size_stride(arg51_1, (2304, 768), (768, 1))
    assert_size_stride(arg52_1, (2304, ), (1, ))
    assert_size_stride(arg53_1, (768, 768), (1, 768))
    assert_size_stride(arg54_1, (768, ), (1, ))
    assert_size_stride(arg55_1, (768, ), (1, ))
    assert_size_stride(arg56_1, (768, ), (1, ))
    assert_size_stride(arg57_1, (768, ), (1, ))
    assert_size_stride(arg58_1, (768, 3072), (1, 768))
    assert_size_stride(arg59_1, (3072, ), (1, ))
    assert_size_stride(arg60_1, (3072, ), (1, ))
    assert_size_stride(arg61_1, (3072, 768), (1, 3072))
    assert_size_stride(arg62_1, (768, ), (1, ))
    assert_size_stride(arg63_1, (768, ), (1, ))
    assert_size_stride(arg64_1, (768, ), (1, ))
    assert_size_stride(arg65_1, (768, ), (1, ))
    assert_size_stride(arg66_1, (2304, 768), (768, 1))
    assert_size_stride(arg67_1, (2304, ), (1, ))
    assert_size_stride(arg68_1, (768, 768), (1, 768))
    assert_size_stride(arg69_1, (768, ), (1, ))
    assert_size_stride(arg70_1, (768, ), (1, ))
    assert_size_stride(arg71_1, (768, ), (1, ))
    assert_size_stride(arg72_1, (768, ), (1, ))
    assert_size_stride(arg73_1, (768, 3072), (1, 768))
    assert_size_stride(arg74_1, (3072, ), (1, ))
    assert_size_stride(arg75_1, (3072, ), (1, ))
    assert_size_stride(arg76_1, (3072, 768), (1, 3072))
    assert_size_stride(arg77_1, (768, ), (1, ))
    assert_size_stride(arg78_1, (768, ), (1, ))
    assert_size_stride(arg79_1, (768, ), (1, ))
    assert_size_stride(arg80_1, (768, ), (1, ))
    assert_size_stride(arg81_1, (2304, 768), (768, 1))
    assert_size_stride(arg82_1, (2304, ), (1, ))
    assert_size_stride(arg83_1, (768, 768), (1, 768))
    assert_size_stride(arg84_1, (768, ), (1, ))
    assert_size_stride(arg85_1, (768, ), (1, ))
    assert_size_stride(arg86_1, (768, ), (1, ))
    assert_size_stride(arg87_1, (768, ), (1, ))
    assert_size_stride(arg88_1, (768, 3072), (1, 768))
    assert_size_stride(arg89_1, (3072, ), (1, ))
    assert_size_stride(arg90_1, (3072, ), (1, ))
    assert_size_stride(arg91_1, (3072, 768), (1, 3072))
    assert_size_stride(arg92_1, (768, ), (1, ))
    assert_size_stride(arg93_1, (768, ), (1, ))
    assert_size_stride(arg94_1, (768, ), (1, ))
    assert_size_stride(arg95_1, (768, ), (1, ))
    assert_size_stride(arg96_1, (2304, 768), (768, 1))
    assert_size_stride(arg97_1, (2304, ), (1, ))
    assert_size_stride(arg98_1, (768, 768), (1, 768))
    assert_size_stride(arg99_1, (768, ), (1, ))
    assert_size_stride(arg100_1, (768, ), (1, ))
    assert_size_stride(arg101_1, (768, ), (1, ))
    assert_size_stride(arg102_1, (768, ), (1, ))
    assert_size_stride(arg103_1, (768, 3072), (1, 768))
    assert_size_stride(arg104_1, (3072, ), (1, ))
    assert_size_stride(arg105_1, (3072, ), (1, ))
    assert_size_stride(arg106_1, (3072, 768), (1, 3072))
    assert_size_stride(arg107_1, (768, ), (1, ))
    assert_size_stride(arg108_1, (768, ), (1, ))
    assert_size_stride(arg109_1, (768, ), (1, ))
    assert_size_stride(arg110_1, (768, ), (1, ))
    assert_size_stride(arg111_1, (2304, 768), (768, 1))
    assert_size_stride(arg112_1, (2304, ), (1, ))
    assert_size_stride(arg113_1, (768, 768), (1, 768))
    assert_size_stride(arg114_1, (768, ), (1, ))
    assert_size_stride(arg115_1, (768, ), (1, ))
    assert_size_stride(arg116_1, (768, ), (1, ))
    assert_size_stride(arg117_1, (768, ), (1, ))
    assert_size_stride(arg118_1, (768, 3072), (1, 768))
    assert_size_stride(arg119_1, (3072, ), (1, ))
    assert_size_stride(arg120_1, (3072, ), (1, ))
    assert_size_stride(arg121_1, (3072, 768), (1, 3072))
    assert_size_stride(arg122_1, (768, ), (1, ))
    assert_size_stride(arg123_1, (768, ), (1, ))
    assert_size_stride(arg124_1, (768, ), (1, ))
    assert_size_stride(arg125_1, (768, ), (1, ))
    assert_size_stride(arg126_1, (2304, 768), (768, 1))
    assert_size_stride(arg127_1, (2304, ), (1, ))
    assert_size_stride(arg128_1, (768, 768), (1, 768))
    assert_size_stride(arg129_1, (768, ), (1, ))
    assert_size_stride(arg130_1, (768, ), (1, ))
    assert_size_stride(arg131_1, (768, ), (1, ))
    assert_size_stride(arg132_1, (768, ), (1, ))
    assert_size_stride(arg133_1, (768, 3072), (1, 768))
    assert_size_stride(arg134_1, (3072, ), (1, ))
    assert_size_stride(arg135_1, (3072, ), (1, ))
    assert_size_stride(arg136_1, (3072, 768), (1, 3072))
    assert_size_stride(arg137_1, (768, ), (1, ))
    assert_size_stride(arg138_1, (768, ), (1, ))
    assert_size_stride(arg139_1, (768, ), (1, ))
    assert_size_stride(arg140_1, (768, ), (1, ))
    assert_size_stride(arg141_1, (2304, 768), (768, 1))
    assert_size_stride(arg142_1, (2304, ), (1, ))
    assert_size_stride(arg143_1, (768, 768), (1, 768))
    assert_size_stride(arg144_1, (768, ), (1, ))
    assert_size_stride(arg145_1, (768, ), (1, ))
    assert_size_stride(arg146_1, (768, ), (1, ))
    assert_size_stride(arg147_1, (768, ), (1, ))
    assert_size_stride(arg148_1, (768, 3072), (1, 768))
    assert_size_stride(arg149_1, (3072, ), (1, ))
    assert_size_stride(arg150_1, (3072, ), (1, ))
    assert_size_stride(arg151_1, (3072, 768), (1, 3072))
    assert_size_stride(arg152_1, (768, ), (1, ))
    assert_size_stride(arg153_1, (768, ), (1, ))
    assert_size_stride(arg154_1, (768, ), (1, ))
    assert_size_stride(arg155_1, (768, ), (1, ))
    assert_size_stride(arg156_1, (2304, 768), (768, 1))
    assert_size_stride(arg157_1, (2304, ), (1, ))
    assert_size_stride(arg158_1, (768, 768), (1, 768))
    assert_size_stride(arg159_1, (768, ), (1, ))
    assert_size_stride(arg160_1, (768, ), (1, ))
    assert_size_stride(arg161_1, (768, ), (1, ))
    assert_size_stride(arg162_1, (768, ), (1, ))
    assert_size_stride(arg163_1, (768, 3072), (1, 768))
    assert_size_stride(arg164_1, (3072, ), (1, ))
    assert_size_stride(arg165_1, (3072, ), (1, ))
    assert_size_stride(arg166_1, (3072, 768), (1, 3072))
    assert_size_stride(arg167_1, (768, ), (1, ))
    assert_size_stride(arg168_1, (768, ), (1, ))
    assert_size_stride(arg169_1, (768, ), (1, ))
    assert_size_stride(arg170_1, (768, ), (1, ))
    assert_size_stride(arg171_1, (2304, 768), (768, 1))
    assert_size_stride(arg172_1, (2304, ), (1, ))
    assert_size_stride(arg173_1, (768, 768), (1, 768))
    assert_size_stride(arg174_1, (768, ), (1, ))
    assert_size_stride(arg175_1, (768, ), (1, ))
    assert_size_stride(arg176_1, (768, ), (1, ))
    assert_size_stride(arg177_1, (768, ), (1, ))
    assert_size_stride(arg178_1, (768, 3072), (1, 768))
    assert_size_stride(arg179_1, (3072, ), (1, ))
    assert_size_stride(arg180_1, (3072, ), (1, ))
    assert_size_stride(arg181_1, (3072, 768), (1, 3072))
    assert_size_stride(arg182_1, (768, ), (1, ))
    assert_size_stride(arg183_1, (768, ), (1, ))
    assert_size_stride(arg184_1, (768, ), (1, ))
    assert_size_stride(arg185_1, (768, ), (1, ))
    assert_size_stride(arg186_1, (768, 1000), (1, 768))
    assert_size_stride(arg187_1, (1000, ), (1, ))
    assert_size_stride(arg188_1, (1000, ), (1, ))
    assert_size_stride(arg189_1, (1, 3, 224, 224), (150528, 50176, 224, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        # Source Nodes: [x], Original ATen: [aten.convolution]
        buf0 = extern_kernels.convolution(arg189_1, arg2_1, stride=(16, 16), padding=(0, 0), dilation=(1, 1), transposed=False, output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (1, 768, 14, 14), (150528, 196, 14, 1))
        del arg189_1
        del arg2_1
        buf1 = empty_strided_cuda((1, 197, 1, 6), (1182, 6, 1182, 1), torch.float32)
        buf2 = empty_strided_cuda((1, 197, 1, 6), (1182, 6, 1182, 1), torch.float32)
        buf3 = empty_strided_cuda((1, 197, 1, 6), (1182, 6, 1182, 1), torch.float32)
        # Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_red_fused_add_cat_native_layer_norm_0.run(arg0_1, buf0, arg3_1, arg1_1, buf1, buf2, buf3, 1182, 128, grid=grid(1182), stream=stream0)
        buf4 = empty_strided_cuda((1, 197, 1), (197, 1, 197), torch.float32)
        buf5 = empty_strided_cuda((1, 197, 1), (197, 1, 197), torch.float32)
        # Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
        triton_per_fused_add_cat_native_layer_norm_1.run(buf1, buf2, buf3, buf4, buf5, 197, 6, grid=grid(197), stream=stream0)
        del buf1
        del buf2
        del buf3
        buf8 = empty_strided_cuda((1, 197, 768), (151296, 768, 1), torch.bfloat16)
        # Source Nodes: [cat_1, input_1, x_5], Original ATen: [aten.add, aten.cat, aten.native_layer_norm]
        triton_poi_fused_add_cat_native_layer_norm_2.run(arg0_1, buf0, arg3_1, arg1_1, buf4, buf5, arg4_1, arg5_1, buf8, 151296, grid=grid(151296), stream=stream0)
        del arg4_1
        del arg5_1
        del buf4
        del buf5
        buf9 = empty_strided_cuda((197, 2304), (2304, 1), torch.bfloat16)
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf8, (197, 768), (768, 1), 0), reinterpret_tensor(arg6_1, (768, 2304), (1, 768), 0), out=buf9)
        del arg6_1
        buf10 = reinterpret_tensor(buf8, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf8  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf9, arg7_1, buf10, 151296, grid=grid(151296), stream=stream0)
        buf11 = empty_strided_cuda((1, 12, 197, 64), (151296, 64, 768, 1), torch.bfloat16)
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf9, arg7_1, buf11, 151296, grid=grid(151296), stream=stream0)
        buf12 = empty_strided_cuda((1, 12, 197, 64), (151296, 64, 768, 1), torch.bfloat16)
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf9, arg7_1, buf12, 151296, grid=grid(151296), stream=stream0)
        del arg7_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf13 = aten._scaled_dot_product_flash_attention.default(buf10, buf11, buf12, scale=0.125)
        buf14 = buf13[0]
        del buf13
        buf20 = empty_strided_cuda((197, 768), (768, 1), torch.int8)
        buf21 = empty_strided_cuda((197, 1), (1, 1), torch.bfloat16)
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf14, buf20, buf21, 197, 768, grid=grid(197), stream=stream0)
        buf22 = reinterpret_tensor(buf14, (197, 768), (768, 1), 0); del buf14  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        stream0 = get_raw_stream(0)
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf20, arg8_1, buf21, buf22, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg8_1
        buf23 = reinterpret_tensor(buf22, (1, 197, 768), (151296, 768, 1), 0); del buf22  # reuse
        buf29 = reinterpret_tensor(buf20, (1, 197, 768), (151296, 768, 1), 0); del buf20  # reuse
        buf30 = reinterpret_tensor(buf21, (1, 197, 1), (197, 1, 1), 0); del buf21  # reuse
        # Source Nodes: [cat_1, input_1, l__self___encoder_layers_encoder_layer_0_mlp_0, x_7, x_8, y], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.cat, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_cat_clamp_clone_div_native_layer_norm_round_8.run(buf23, arg9_1, arg10_1, arg0_1, buf0, arg3_1, arg1_1, arg11_1, arg12_1, buf29, buf30, 197, 768, grid=grid(197), stream=stream0)
        del arg0_1
        del arg10_1
        del arg11_1
        del arg12_1
        del arg1_1
        del arg3_1
        del arg9_1
        del buf0
        buf31 = empty_strided_cuda((197, 3072), (3072, 1), torch.bfloat16)
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf29, arg13_1, buf30, buf31, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg13_1
        buf33 = empty_strided_cuda((1, 197, 3072), (605184, 3072, 1), torch.int8)
        buf34 = buf30; del buf30  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_1, l__self___encoder_layers_encoder_layer_0_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf31, arg14_1, arg15_1, buf33, buf34, 197, 3072, grid=grid(197), stream=stream0)
        del arg14_1
        del arg15_1
        buf35 = reinterpret_tensor(buf12, (197, 768), (768, 1), 0); del buf12  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_0_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf33, arg16_1, buf34, buf35, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg16_1
        buf39 = reinterpret_tensor(buf11, (1, 197, 768), (151296, 768, 1), 0); del buf11  # reuse
        # Source Nodes: [add_2, x_9], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf23, buf35, arg17_1, arg18_1, arg19_1, arg20_1, buf39, 197, 768, grid=grid(197), stream=stream0)
        del arg19_1
        del arg20_1
        buf40 = buf9; del buf9  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf39, (197, 768), (768, 1), 0), reinterpret_tensor(arg21_1, (768, 2304), (1, 768), 0), out=buf40)
        del arg21_1
        buf41 = reinterpret_tensor(buf39, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf39  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf40, arg22_1, buf41, 151296, grid=grid(151296), stream=stream0)
        buf42 = buf10; del buf10  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf40, arg22_1, buf42, 151296, grid=grid(151296), stream=stream0)
        buf43 = empty_strided_cuda((1, 12, 197, 64), (151296, 64, 768, 1), torch.bfloat16)
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf40, arg22_1, buf43, 151296, grid=grid(151296), stream=stream0)
        del arg22_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf44 = aten._scaled_dot_product_flash_attention.default(buf41, buf42, buf43, scale=0.125)
        del buf41
        buf45 = buf44[0]
        del buf44
        buf51 = reinterpret_tensor(buf29, (197, 768), (768, 1), 0); del buf29  # reuse
        buf52 = reinterpret_tensor(buf34, (197, 1), (1, 1), 0); del buf34  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf45, buf51, buf52, 197, 768, grid=grid(197), stream=stream0)
        buf53 = reinterpret_tensor(buf45, (197, 768), (768, 1), 0); del buf45  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf51, arg23_1, buf52, buf53, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg23_1
        buf54 = reinterpret_tensor(buf53, (1, 197, 768), (151296, 768, 1), 0); del buf53  # reuse
        buf60 = reinterpret_tensor(buf51, (1, 197, 768), (151296, 768, 1), 0); del buf51  # reuse
        buf61 = reinterpret_tensor(buf52, (1, 197, 1), (197, 1, 1), 0); del buf52  # reuse
        # Source Nodes: [add_2, l__self___encoder_layers_encoder_layer_1_mlp_0, x_11, x_12, y_2], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf54, arg24_1, arg25_1, buf23, buf35, arg17_1, arg18_1, arg26_1, arg27_1, buf60, buf61, 197, 768, grid=grid(197), stream=stream0)
        del arg17_1
        del arg18_1
        del arg24_1
        del arg25_1
        del arg26_1
        del arg27_1
        buf62 = buf31; del buf31  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf60, arg28_1, buf61, buf62, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg28_1
        buf64 = buf33; del buf33  # reuse
        buf65 = buf61; del buf61  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_mlp_1, l__self___encoder_layers_encoder_layer_1_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf62, arg29_1, arg30_1, buf64, buf65, 197, 3072, grid=grid(197), stream=stream0)
        del arg29_1
        del arg30_1
        buf66 = buf35; del buf35  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_1_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf64, arg31_1, buf65, buf66, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg31_1
        buf70 = buf23; del buf23  # reuse
        # Source Nodes: [add_4, x_13], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf54, buf66, arg32_1, arg33_1, arg34_1, arg35_1, buf70, 197, 768, grid=grid(197), stream=stream0)
        del arg34_1
        del arg35_1
        buf71 = buf40; del buf40  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf70, (197, 768), (768, 1), 0), reinterpret_tensor(arg36_1, (768, 2304), (1, 768), 0), out=buf71)
        del arg36_1
        buf72 = reinterpret_tensor(buf70, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf70  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf71, arg37_1, buf72, 151296, grid=grid(151296), stream=stream0)
        buf73 = buf43; del buf43  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf71, arg37_1, buf73, 151296, grid=grid(151296), stream=stream0)
        buf74 = buf42; del buf42  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf71, arg37_1, buf74, 151296, grid=grid(151296), stream=stream0)
        del arg37_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf75 = aten._scaled_dot_product_flash_attention.default(buf72, buf73, buf74, scale=0.125)
        del buf72
        buf76 = buf75[0]
        del buf75
        buf82 = reinterpret_tensor(buf60, (197, 768), (768, 1), 0); del buf60  # reuse
        buf83 = reinterpret_tensor(buf65, (197, 1), (1, 1), 0); del buf65  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf76, buf82, buf83, 197, 768, grid=grid(197), stream=stream0)
        buf84 = reinterpret_tensor(buf76, (197, 768), (768, 1), 0); del buf76  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf82, arg38_1, buf83, buf84, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg38_1
        buf85 = reinterpret_tensor(buf84, (1, 197, 768), (151296, 768, 1), 0); del buf84  # reuse
        buf91 = reinterpret_tensor(buf82, (1, 197, 768), (151296, 768, 1), 0); del buf82  # reuse
        buf92 = reinterpret_tensor(buf83, (1, 197, 1), (197, 1, 1), 0); del buf83  # reuse
        # Source Nodes: [add_4, l__self___encoder_layers_encoder_layer_2_mlp_0, x_15, x_16, y_4], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf85, arg39_1, arg40_1, buf54, buf66, arg32_1, arg33_1, arg41_1, arg42_1, buf91, buf92, 197, 768, grid=grid(197), stream=stream0)
        del arg32_1
        del arg33_1
        del arg39_1
        del arg40_1
        del arg41_1
        del arg42_1
        buf93 = buf62; del buf62  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf91, arg43_1, buf92, buf93, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg43_1
        buf95 = buf64; del buf64  # reuse
        buf96 = buf92; del buf92  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_mlp_1, l__self___encoder_layers_encoder_layer_2_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf93, arg44_1, arg45_1, buf95, buf96, 197, 3072, grid=grid(197), stream=stream0)
        del arg44_1
        del arg45_1
        buf97 = buf66; del buf66  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_2_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf95, arg46_1, buf96, buf97, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg46_1
        buf101 = buf54; del buf54  # reuse
        # Source Nodes: [add_6, x_17], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf85, buf97, arg47_1, arg48_1, arg49_1, arg50_1, buf101, 197, 768, grid=grid(197), stream=stream0)
        del arg49_1
        del arg50_1
        buf102 = buf71; del buf71  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf101, (197, 768), (768, 1), 0), reinterpret_tensor(arg51_1, (768, 2304), (1, 768), 0), out=buf102)
        del arg51_1
        buf103 = reinterpret_tensor(buf101, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf101  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf102, arg52_1, buf103, 151296, grid=grid(151296), stream=stream0)
        buf104 = buf74; del buf74  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf102, arg52_1, buf104, 151296, grid=grid(151296), stream=stream0)
        buf105 = buf73; del buf73  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf102, arg52_1, buf105, 151296, grid=grid(151296), stream=stream0)
        del arg52_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf106 = aten._scaled_dot_product_flash_attention.default(buf103, buf104, buf105, scale=0.125)
        del buf103
        buf107 = buf106[0]
        del buf106
        buf113 = reinterpret_tensor(buf91, (197, 768), (768, 1), 0); del buf91  # reuse
        buf114 = reinterpret_tensor(buf96, (197, 1), (1, 1), 0); del buf96  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf107, buf113, buf114, 197, 768, grid=grid(197), stream=stream0)
        buf115 = reinterpret_tensor(buf107, (197, 768), (768, 1), 0); del buf107  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf113, arg53_1, buf114, buf115, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg53_1
        buf116 = reinterpret_tensor(buf115, (1, 197, 768), (151296, 768, 1), 0); del buf115  # reuse
        buf122 = reinterpret_tensor(buf113, (1, 197, 768), (151296, 768, 1), 0); del buf113  # reuse
        buf123 = reinterpret_tensor(buf114, (1, 197, 1), (197, 1, 1), 0); del buf114  # reuse
        # Source Nodes: [add_6, l__self___encoder_layers_encoder_layer_3_mlp_0, x_19, x_20, y_6], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf116, arg54_1, arg55_1, buf85, buf97, arg47_1, arg48_1, arg56_1, arg57_1, buf122, buf123, 197, 768, grid=grid(197), stream=stream0)
        del arg47_1
        del arg48_1
        del arg54_1
        del arg55_1
        del arg56_1
        del arg57_1
        buf124 = buf93; del buf93  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf122, arg58_1, buf123, buf124, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg58_1
        buf126 = buf95; del buf95  # reuse
        buf127 = buf123; del buf123  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_mlp_1, l__self___encoder_layers_encoder_layer_3_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf124, arg59_1, arg60_1, buf126, buf127, 197, 3072, grid=grid(197), stream=stream0)
        del arg59_1
        del arg60_1
        buf128 = buf97; del buf97  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_3_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf126, arg61_1, buf127, buf128, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg61_1
        buf132 = buf85; del buf85  # reuse
        # Source Nodes: [add_8, x_21], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf116, buf128, arg62_1, arg63_1, arg64_1, arg65_1, buf132, 197, 768, grid=grid(197), stream=stream0)
        del arg64_1
        del arg65_1
        buf133 = buf102; del buf102  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf132, (197, 768), (768, 1), 0), reinterpret_tensor(arg66_1, (768, 2304), (1, 768), 0), out=buf133)
        del arg66_1
        buf134 = reinterpret_tensor(buf132, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf132  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf133, arg67_1, buf134, 151296, grid=grid(151296), stream=stream0)
        buf135 = buf105; del buf105  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf133, arg67_1, buf135, 151296, grid=grid(151296), stream=stream0)
        buf136 = buf104; del buf104  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf133, arg67_1, buf136, 151296, grid=grid(151296), stream=stream0)
        del arg67_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf137 = aten._scaled_dot_product_flash_attention.default(buf134, buf135, buf136, scale=0.125)
        del buf134
        buf138 = buf137[0]
        del buf137
        buf144 = reinterpret_tensor(buf122, (197, 768), (768, 1), 0); del buf122  # reuse
        buf145 = reinterpret_tensor(buf127, (197, 1), (1, 1), 0); del buf127  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf138, buf144, buf145, 197, 768, grid=grid(197), stream=stream0)
        buf146 = reinterpret_tensor(buf138, (197, 768), (768, 1), 0); del buf138  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf144, arg68_1, buf145, buf146, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg68_1
        buf147 = reinterpret_tensor(buf146, (1, 197, 768), (151296, 768, 1), 0); del buf146  # reuse
        buf153 = reinterpret_tensor(buf144, (1, 197, 768), (151296, 768, 1), 0); del buf144  # reuse
        buf154 = reinterpret_tensor(buf145, (1, 197, 1), (197, 1, 1), 0); del buf145  # reuse
        # Source Nodes: [add_8, l__self___encoder_layers_encoder_layer_4_mlp_0, x_23, x_24, y_8], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf147, arg69_1, arg70_1, buf116, buf128, arg62_1, arg63_1, arg71_1, arg72_1, buf153, buf154, 197, 768, grid=grid(197), stream=stream0)
        del arg62_1
        del arg63_1
        del arg69_1
        del arg70_1
        del arg71_1
        del arg72_1
        buf155 = buf124; del buf124  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf153, arg73_1, buf154, buf155, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg73_1
        buf157 = buf126; del buf126  # reuse
        buf158 = buf154; del buf154  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_mlp_1, l__self___encoder_layers_encoder_layer_4_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf155, arg74_1, arg75_1, buf157, buf158, 197, 3072, grid=grid(197), stream=stream0)
        del arg74_1
        del arg75_1
        buf159 = buf128; del buf128  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_4_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf157, arg76_1, buf158, buf159, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg76_1
        buf163 = buf116; del buf116  # reuse
        # Source Nodes: [add_10, x_25], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf147, buf159, arg77_1, arg78_1, arg79_1, arg80_1, buf163, 197, 768, grid=grid(197), stream=stream0)
        del arg79_1
        del arg80_1
        buf164 = buf133; del buf133  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf163, (197, 768), (768, 1), 0), reinterpret_tensor(arg81_1, (768, 2304), (1, 768), 0), out=buf164)
        del arg81_1
        buf165 = reinterpret_tensor(buf163, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf163  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf164, arg82_1, buf165, 151296, grid=grid(151296), stream=stream0)
        buf166 = buf136; del buf136  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf164, arg82_1, buf166, 151296, grid=grid(151296), stream=stream0)
        buf167 = buf135; del buf135  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf164, arg82_1, buf167, 151296, grid=grid(151296), stream=stream0)
        del arg82_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf168 = aten._scaled_dot_product_flash_attention.default(buf165, buf166, buf167, scale=0.125)
        del buf165
        buf169 = buf168[0]
        del buf168
        buf175 = reinterpret_tensor(buf153, (197, 768), (768, 1), 0); del buf153  # reuse
        buf176 = reinterpret_tensor(buf158, (197, 1), (1, 1), 0); del buf158  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf169, buf175, buf176, 197, 768, grid=grid(197), stream=stream0)
        buf177 = reinterpret_tensor(buf169, (197, 768), (768, 1), 0); del buf169  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf175, arg83_1, buf176, buf177, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg83_1
        buf178 = reinterpret_tensor(buf177, (1, 197, 768), (151296, 768, 1), 0); del buf177  # reuse
        buf184 = reinterpret_tensor(buf175, (1, 197, 768), (151296, 768, 1), 0); del buf175  # reuse
        buf185 = reinterpret_tensor(buf176, (1, 197, 1), (197, 1, 1), 0); del buf176  # reuse
        # Source Nodes: [add_10, l__self___encoder_layers_encoder_layer_5_mlp_0, x_27, x_28, y_10], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf178, arg84_1, arg85_1, buf147, buf159, arg77_1, arg78_1, arg86_1, arg87_1, buf184, buf185, 197, 768, grid=grid(197), stream=stream0)
        del arg77_1
        del arg78_1
        del arg84_1
        del arg85_1
        del arg86_1
        del arg87_1
        buf186 = buf155; del buf155  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf184, arg88_1, buf185, buf186, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg88_1
        buf188 = buf157; del buf157  # reuse
        buf189 = buf185; del buf185  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_mlp_1, l__self___encoder_layers_encoder_layer_5_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf186, arg89_1, arg90_1, buf188, buf189, 197, 3072, grid=grid(197), stream=stream0)
        del arg89_1
        del arg90_1
        buf190 = buf159; del buf159  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_5_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf188, arg91_1, buf189, buf190, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg91_1
        buf194 = buf147; del buf147  # reuse
        # Source Nodes: [add_12, x_29], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf178, buf190, arg92_1, arg93_1, arg94_1, arg95_1, buf194, 197, 768, grid=grid(197), stream=stream0)
        del arg94_1
        del arg95_1
        buf195 = buf164; del buf164  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf194, (197, 768), (768, 1), 0), reinterpret_tensor(arg96_1, (768, 2304), (1, 768), 0), out=buf195)
        del arg96_1
        buf196 = reinterpret_tensor(buf194, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf194  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf195, arg97_1, buf196, 151296, grid=grid(151296), stream=stream0)
        buf197 = buf167; del buf167  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf195, arg97_1, buf197, 151296, grid=grid(151296), stream=stream0)
        buf198 = buf166; del buf166  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf195, arg97_1, buf198, 151296, grid=grid(151296), stream=stream0)
        del arg97_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf199 = aten._scaled_dot_product_flash_attention.default(buf196, buf197, buf198, scale=0.125)
        del buf196
        buf200 = buf199[0]
        del buf199
        buf206 = reinterpret_tensor(buf184, (197, 768), (768, 1), 0); del buf184  # reuse
        buf207 = reinterpret_tensor(buf189, (197, 1), (1, 1), 0); del buf189  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf200, buf206, buf207, 197, 768, grid=grid(197), stream=stream0)
        buf208 = reinterpret_tensor(buf200, (197, 768), (768, 1), 0); del buf200  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf206, arg98_1, buf207, buf208, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg98_1
        buf209 = reinterpret_tensor(buf208, (1, 197, 768), (151296, 768, 1), 0); del buf208  # reuse
        buf215 = reinterpret_tensor(buf206, (1, 197, 768), (151296, 768, 1), 0); del buf206  # reuse
        buf216 = reinterpret_tensor(buf207, (1, 197, 1), (197, 1, 1), 0); del buf207  # reuse
        # Source Nodes: [add_12, l__self___encoder_layers_encoder_layer_6_mlp_0, x_31, x_32, y_12], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf209, arg99_1, arg100_1, buf178, buf190, arg92_1, arg93_1, arg101_1, arg102_1, buf215, buf216, 197, 768, grid=grid(197), stream=stream0)
        del arg100_1
        del arg101_1
        del arg102_1
        del arg92_1
        del arg93_1
        del arg99_1
        buf217 = buf186; del buf186  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf215, arg103_1, buf216, buf217, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg103_1
        buf219 = buf188; del buf188  # reuse
        buf220 = buf216; del buf216  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_mlp_1, l__self___encoder_layers_encoder_layer_6_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf217, arg104_1, arg105_1, buf219, buf220, 197, 3072, grid=grid(197), stream=stream0)
        del arg104_1
        del arg105_1
        buf221 = buf190; del buf190  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_6_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf219, arg106_1, buf220, buf221, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg106_1
        buf225 = buf178; del buf178  # reuse
        # Source Nodes: [add_14, x_33], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf209, buf221, arg107_1, arg108_1, arg109_1, arg110_1, buf225, 197, 768, grid=grid(197), stream=stream0)
        del arg109_1
        del arg110_1
        buf226 = buf195; del buf195  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf225, (197, 768), (768, 1), 0), reinterpret_tensor(arg111_1, (768, 2304), (1, 768), 0), out=buf226)
        del arg111_1
        buf227 = reinterpret_tensor(buf225, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf225  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf226, arg112_1, buf227, 151296, grid=grid(151296), stream=stream0)
        buf228 = buf198; del buf198  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf226, arg112_1, buf228, 151296, grid=grid(151296), stream=stream0)
        buf229 = buf197; del buf197  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf226, arg112_1, buf229, 151296, grid=grid(151296), stream=stream0)
        del arg112_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf230 = aten._scaled_dot_product_flash_attention.default(buf227, buf228, buf229, scale=0.125)
        del buf227
        buf231 = buf230[0]
        del buf230
        buf237 = reinterpret_tensor(buf215, (197, 768), (768, 1), 0); del buf215  # reuse
        buf238 = reinterpret_tensor(buf220, (197, 1), (1, 1), 0); del buf220  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf231, buf237, buf238, 197, 768, grid=grid(197), stream=stream0)
        buf239 = reinterpret_tensor(buf231, (197, 768), (768, 1), 0); del buf231  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf237, arg113_1, buf238, buf239, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg113_1
        buf240 = reinterpret_tensor(buf239, (1, 197, 768), (151296, 768, 1), 0); del buf239  # reuse
        buf246 = reinterpret_tensor(buf237, (1, 197, 768), (151296, 768, 1), 0); del buf237  # reuse
        buf247 = reinterpret_tensor(buf238, (1, 197, 1), (197, 1, 1), 0); del buf238  # reuse
        # Source Nodes: [add_14, l__self___encoder_layers_encoder_layer_7_mlp_0, x_35, x_36, y_14], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf240, arg114_1, arg115_1, buf209, buf221, arg107_1, arg108_1, arg116_1, arg117_1, buf246, buf247, 197, 768, grid=grid(197), stream=stream0)
        del arg107_1
        del arg108_1
        del arg114_1
        del arg115_1
        del arg116_1
        del arg117_1
        buf248 = buf217; del buf217  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf246, arg118_1, buf247, buf248, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg118_1
        buf250 = buf219; del buf219  # reuse
        buf251 = buf247; del buf247  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_mlp_1, l__self___encoder_layers_encoder_layer_7_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf248, arg119_1, arg120_1, buf250, buf251, 197, 3072, grid=grid(197), stream=stream0)
        del arg119_1
        del arg120_1
        buf252 = buf221; del buf221  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_7_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf250, arg121_1, buf251, buf252, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg121_1
        buf256 = buf209; del buf209  # reuse
        # Source Nodes: [add_16, x_37], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf240, buf252, arg122_1, arg123_1, arg124_1, arg125_1, buf256, 197, 768, grid=grid(197), stream=stream0)
        del arg124_1
        del arg125_1
        buf257 = buf226; del buf226  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf256, (197, 768), (768, 1), 0), reinterpret_tensor(arg126_1, (768, 2304), (1, 768), 0), out=buf257)
        del arg126_1
        buf258 = reinterpret_tensor(buf256, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf256  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf257, arg127_1, buf258, 151296, grid=grid(151296), stream=stream0)
        buf259 = buf229; del buf229  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf257, arg127_1, buf259, 151296, grid=grid(151296), stream=stream0)
        buf260 = buf228; del buf228  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf257, arg127_1, buf260, 151296, grid=grid(151296), stream=stream0)
        del arg127_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf261 = aten._scaled_dot_product_flash_attention.default(buf258, buf259, buf260, scale=0.125)
        del buf258
        buf262 = buf261[0]
        del buf261
        buf268 = reinterpret_tensor(buf246, (197, 768), (768, 1), 0); del buf246  # reuse
        buf269 = reinterpret_tensor(buf251, (197, 1), (1, 1), 0); del buf251  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf262, buf268, buf269, 197, 768, grid=grid(197), stream=stream0)
        buf270 = reinterpret_tensor(buf262, (197, 768), (768, 1), 0); del buf262  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf268, arg128_1, buf269, buf270, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg128_1
        buf271 = reinterpret_tensor(buf270, (1, 197, 768), (151296, 768, 1), 0); del buf270  # reuse
        buf277 = reinterpret_tensor(buf268, (1, 197, 768), (151296, 768, 1), 0); del buf268  # reuse
        buf278 = reinterpret_tensor(buf269, (1, 197, 1), (197, 1, 1), 0); del buf269  # reuse
        # Source Nodes: [add_16, l__self___encoder_layers_encoder_layer_8_mlp_0, x_39, x_40, y_16], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf271, arg129_1, arg130_1, buf240, buf252, arg122_1, arg123_1, arg131_1, arg132_1, buf277, buf278, 197, 768, grid=grid(197), stream=stream0)
        del arg122_1
        del arg123_1
        del arg129_1
        del arg130_1
        del arg131_1
        del arg132_1
        buf279 = buf248; del buf248  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf277, arg133_1, buf278, buf279, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg133_1
        buf281 = buf250; del buf250  # reuse
        buf282 = buf278; del buf278  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_mlp_1, l__self___encoder_layers_encoder_layer_8_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf279, arg134_1, arg135_1, buf281, buf282, 197, 3072, grid=grid(197), stream=stream0)
        del arg134_1
        del arg135_1
        buf283 = buf252; del buf252  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_8_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf281, arg136_1, buf282, buf283, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg136_1
        buf287 = buf240; del buf240  # reuse
        # Source Nodes: [add_18, x_41], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf271, buf283, arg137_1, arg138_1, arg139_1, arg140_1, buf287, 197, 768, grid=grid(197), stream=stream0)
        del arg139_1
        del arg140_1
        buf288 = buf257; del buf257  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf287, (197, 768), (768, 1), 0), reinterpret_tensor(arg141_1, (768, 2304), (1, 768), 0), out=buf288)
        del arg141_1
        buf289 = reinterpret_tensor(buf287, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf287  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf288, arg142_1, buf289, 151296, grid=grid(151296), stream=stream0)
        buf290 = buf260; del buf260  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf288, arg142_1, buf290, 151296, grid=grid(151296), stream=stream0)
        buf291 = buf259; del buf259  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf288, arg142_1, buf291, 151296, grid=grid(151296), stream=stream0)
        del arg142_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf292 = aten._scaled_dot_product_flash_attention.default(buf289, buf290, buf291, scale=0.125)
        del buf289
        buf293 = buf292[0]
        del buf292
        buf299 = reinterpret_tensor(buf277, (197, 768), (768, 1), 0); del buf277  # reuse
        buf300 = reinterpret_tensor(buf282, (197, 1), (1, 1), 0); del buf282  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf293, buf299, buf300, 197, 768, grid=grid(197), stream=stream0)
        buf301 = reinterpret_tensor(buf293, (197, 768), (768, 1), 0); del buf293  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf299, arg143_1, buf300, buf301, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg143_1
        buf302 = reinterpret_tensor(buf301, (1, 197, 768), (151296, 768, 1), 0); del buf301  # reuse
        buf308 = reinterpret_tensor(buf299, (1, 197, 768), (151296, 768, 1), 0); del buf299  # reuse
        buf309 = reinterpret_tensor(buf300, (1, 197, 1), (197, 1, 1), 0); del buf300  # reuse
        # Source Nodes: [add_18, l__self___encoder_layers_encoder_layer_9_mlp_0, x_43, x_44, y_18], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf302, arg144_1, arg145_1, buf271, buf283, arg137_1, arg138_1, arg146_1, arg147_1, buf308, buf309, 197, 768, grid=grid(197), stream=stream0)
        del arg137_1
        del arg138_1
        del arg144_1
        del arg145_1
        del arg146_1
        del arg147_1
        buf310 = buf279; del buf279  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf308, arg148_1, buf309, buf310, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg148_1
        buf312 = buf281; del buf281  # reuse
        buf313 = buf309; del buf309  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_mlp_1, l__self___encoder_layers_encoder_layer_9_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf310, arg149_1, arg150_1, buf312, buf313, 197, 3072, grid=grid(197), stream=stream0)
        del arg149_1
        del arg150_1
        buf314 = buf283; del buf283  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_9_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf312, arg151_1, buf313, buf314, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg151_1
        buf318 = buf271; del buf271  # reuse
        # Source Nodes: [add_20, x_45], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf302, buf314, arg152_1, arg153_1, arg154_1, arg155_1, buf318, 197, 768, grid=grid(197), stream=stream0)
        del arg154_1
        del arg155_1
        buf319 = buf288; del buf288  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf318, (197, 768), (768, 1), 0), reinterpret_tensor(arg156_1, (768, 2304), (1, 768), 0), out=buf319)
        del arg156_1
        buf320 = reinterpret_tensor(buf318, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf318  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf319, arg157_1, buf320, 151296, grid=grid(151296), stream=stream0)
        buf321 = buf291; del buf291  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf319, arg157_1, buf321, 151296, grid=grid(151296), stream=stream0)
        buf322 = buf290; del buf290  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf319, arg157_1, buf322, 151296, grid=grid(151296), stream=stream0)
        del arg157_1
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf323 = aten._scaled_dot_product_flash_attention.default(buf320, buf321, buf322, scale=0.125)
        del buf320
        buf324 = buf323[0]
        del buf323
        buf330 = reinterpret_tensor(buf308, (197, 768), (768, 1), 0); del buf308  # reuse
        buf331 = reinterpret_tensor(buf313, (197, 1), (1, 1), 0); del buf313  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf324, buf330, buf331, 197, 768, grid=grid(197), stream=stream0)
        buf332 = reinterpret_tensor(buf324, (197, 768), (768, 1), 0); del buf324  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf330, arg158_1, buf331, buf332, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg158_1
        buf333 = reinterpret_tensor(buf332, (1, 197, 768), (151296, 768, 1), 0); del buf332  # reuse
        buf339 = reinterpret_tensor(buf330, (1, 197, 768), (151296, 768, 1), 0); del buf330  # reuse
        buf340 = reinterpret_tensor(buf331, (1, 197, 1), (197, 1, 1), 0); del buf331  # reuse
        # Source Nodes: [add_20, l__self___encoder_layers_encoder_layer_10_mlp_0, x_47, x_48, y_20], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf333, arg159_1, arg160_1, buf302, buf314, arg152_1, arg153_1, arg161_1, arg162_1, buf339, buf340, 197, 768, grid=grid(197), stream=stream0)
        del arg152_1
        del arg153_1
        del arg159_1
        del arg160_1
        del arg161_1
        del arg162_1
        buf341 = buf310; del buf310  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf339, arg163_1, buf340, buf341, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg163_1
        buf343 = buf312; del buf312  # reuse
        buf344 = buf340; del buf340  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_mlp_1, l__self___encoder_layers_encoder_layer_10_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf341, arg164_1, arg165_1, buf343, buf344, 197, 3072, grid=grid(197), stream=stream0)
        del arg164_1
        del arg165_1
        buf345 = buf314; del buf314  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_10_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf343, arg166_1, buf344, buf345, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg166_1
        buf349 = buf302; del buf302  # reuse
        # Source Nodes: [add_22, x_49], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf333, buf345, arg167_1, arg168_1, arg169_1, arg170_1, buf349, 197, 768, grid=grid(197), stream=stream0)
        del arg169_1
        del arg170_1
        buf350 = buf319; del buf319  # reuse
        # Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf349, (197, 768), (768, 1), 0), reinterpret_tensor(arg171_1, (768, 2304), (1, 768), 0), out=buf350)
        del arg171_1
        buf351 = reinterpret_tensor(buf349, (1, 12, 197, 64), (151296, 64, 768, 1), 0); del buf349  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_3.run(buf350, arg172_1, buf351, 151296, grid=grid(151296), stream=stream0)
        buf352 = buf322; del buf322  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_4.run(buf350, arg172_1, buf352, 151296, grid=grid(151296), stream=stream0)
        buf353 = buf321; del buf321  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        triton_poi_fused__scaled_dot_product_flash_attention_5.run(buf350, arg172_1, buf353, 151296, grid=grid(151296), stream=stream0)
        del arg172_1
        del buf350
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._scaled_dot_product_flash_attention]
        buf354 = aten._scaled_dot_product_flash_attention.default(buf351, buf352, buf353, scale=0.125)
        del buf351
        del buf352
        del buf353
        buf355 = buf354[0]
        del buf354
        buf361 = reinterpret_tensor(buf339, (197, 768), (768, 1), 0); del buf339  # reuse
        buf362 = reinterpret_tensor(buf344, (197, 1), (1, 1), 0); del buf344  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_6.run(buf355, buf361, buf362, 197, 768, grid=grid(197), stream=stream0)
        buf363 = reinterpret_tensor(buf355, (197, 768), (768, 1), 0); del buf355  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_self_attention], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_clamp_div_mul_round_view_7.run(buf361, arg173_1, buf362, buf363, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta0), stream=stream0)
        del arg173_1
        buf364 = reinterpret_tensor(buf363, (1, 197, 768), (151296, 768, 1), 0); del buf363  # reuse
        buf370 = reinterpret_tensor(buf361, (1, 197, 768), (151296, 768, 1), 0); del buf361  # reuse
        buf371 = reinterpret_tensor(buf362, (1, 197, 1), (197, 1, 1), 0); del buf362  # reuse
        # Source Nodes: [add_22, l__self___encoder_layers_encoder_layer_11_mlp_0, x_51, x_52, y_22], Original ATen: [aten._to_copy, aten.abs, aten.add, aten.amax, aten.clamp, aten.clone, aten.div, aten.native_layer_norm, aten.round]
        triton_per_fused__to_copy_abs_add_amax_clamp_clone_div_native_layer_norm_round_13.run(buf364, arg174_1, arg175_1, buf333, buf345, arg167_1, arg168_1, arg176_1, arg177_1, buf370, buf371, 197, 768, grid=grid(197), stream=stream0)
        del arg167_1
        del arg168_1
        del arg174_1
        del arg175_1
        del arg176_1
        del arg177_1
        buf372 = buf341; del buf341  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_mlp_0], Original ATen: [aten.mul]
        triton_tem_fused_mul_9.run(buf370, arg178_1, buf371, buf372, grid=torch._inductor.kernel.mm_common.mm_grid(197, 3072, meta1), stream=stream0)
        del arg178_1
        del buf370
        buf374 = buf343; del buf343  # reuse
        buf375 = buf371; del buf371  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_mlp_1, l__self___encoder_layers_encoder_layer_11_mlp_3], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.gelu, aten.round]
        triton_red_fused__to_copy_abs_amax_clamp_div_gelu_round_10.run(buf372, arg179_1, arg180_1, buf374, buf375, 197, 3072, grid=grid(197), stream=stream0)
        del arg179_1
        del arg180_1
        del buf372
        buf376 = buf345; del buf345  # reuse
        # Source Nodes: [l__self___encoder_layers_encoder_layer_11_mlp_3], Original ATen: [aten.mul]
        triton_tem_fused_mul_11.run(buf374, arg181_1, buf375, buf376, grid=torch._inductor.kernel.mm_common.mm_grid(197, 768, meta1), stream=stream0)
        del arg181_1
        del buf374
        del buf375
        buf380 = buf333; del buf333  # reuse
        # Source Nodes: [add_24, x_53], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_12.run(buf364, buf376, arg182_1, arg183_1, arg184_1, arg185_1, buf380, 197, 768, grid=grid(197), stream=stream0)
        del arg182_1
        del arg183_1
        del arg184_1
        del arg185_1
        del buf364
        del buf376
        buf382 = empty_strided_cuda((1, 768), (768, 1), torch.int8)
        buf383 = empty_strided_cuda((1, 1), (1, 1), torch.bfloat16)
        # Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.abs, aten.amax, aten.clamp, aten.div, aten.round]
        triton_per_fused__to_copy_abs_amax_clamp_div_round_14.run(buf380, buf382, buf383, 1, 768, grid=grid(1), stream=stream0)
        del buf380
        buf384 = empty_strided_cuda((1, 1000), (1000, 1), torch.bfloat16)
        # Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_poi_fused__to_copy_clamp_div_mul_round_view_15.run(buf383, buf384, 1000, grid=grid(1000), stream=stream0)
        del buf383
        buf386 = empty_strided_cuda((1, 1000), (1000, 1), torch.bfloat16)
        # Source Nodes: [x_55], Original ATen: [aten._to_copy, aten.add, aten.clamp, aten.div, aten.mul, aten.round, aten.view]
        triton_tem_fused__to_copy_add_clamp_div_mul_round_view_16.run(buf382, arg186_1, buf384, arg187_1, arg188_1, buf386, grid=torch._inductor.kernel.mm_common.mm_grid(1, 1000, meta2), stream=stream0)
        del arg186_1
        del arg187_1
        del arg188_1
        del buf382
        del buf384
    return (buf386, )
def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((1, 1, 768), (768, 768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg1_1 = rand_strided((1, 197, 768), (151296, 768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg2_1 = rand_strided((768, 3, 16, 16), (768, 256, 16, 1), device='cuda:0', dtype=torch.bfloat16)
    arg3_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg4_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg5_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg6_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg7_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg8_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg9_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg10_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg11_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg12_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg13_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg14_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg15_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg16_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg17_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg18_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg19_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg20_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg21_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg22_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg23_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg24_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg25_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg26_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg27_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg28_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg29_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg30_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg31_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg32_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg33_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg34_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg35_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg36_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg37_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg38_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg39_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg40_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg41_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg42_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg43_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg44_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg45_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg46_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg47_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg48_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg49_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg50_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg51_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg52_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg53_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg54_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg55_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg56_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg57_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg58_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg59_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg60_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg61_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg62_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg63_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg64_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg65_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg66_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg67_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg68_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg69_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg70_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg71_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg72_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg73_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg74_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg75_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg76_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg77_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg78_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg79_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg80_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg81_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg82_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg83_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg84_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg85_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg86_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg87_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg88_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg89_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg90_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg91_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg92_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg93_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg94_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg95_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg96_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg97_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg98_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg99_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg100_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg101_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg102_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg103_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg104_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg105_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg106_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg107_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg108_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg109_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg110_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg111_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg112_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg113_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg114_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg115_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg116_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg117_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg118_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg119_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg120_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg121_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg122_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg123_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg124_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg125_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg126_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg127_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg128_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg129_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg130_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg131_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg132_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg133_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg134_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg135_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg136_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg137_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg138_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg139_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg140_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg141_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg142_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg143_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg144_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg145_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg146_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg147_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg148_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg149_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg150_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg151_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg152_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg153_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg154_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg155_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg156_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg157_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg158_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg159_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg160_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg161_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg162_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg163_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg164_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg165_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg166_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg167_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg168_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg169_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg170_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg171_1 = rand_strided((2304, 768), (768, 1), device='cuda:0', dtype=torch.bfloat16)
    arg172_1 = rand_strided((2304, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg173_1 = rand_strided((768, 768), (1, 768), device='cuda:0', dtype=torch.int8)
    arg174_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg175_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg176_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg177_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg178_1 = rand_strided((768, 3072), (1, 768), device='cuda:0', dtype=torch.int8)
    arg179_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg180_1 = rand_strided((3072, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg181_1 = rand_strided((3072, 768), (1, 3072), device='cuda:0', dtype=torch.int8)
    arg182_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg183_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg184_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg185_1 = rand_strided((768, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg186_1 = rand_strided((768, 1000), (1, 768), device='cuda:0', dtype=torch.int8)
    arg187_1 = rand_strided((1000, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg188_1 = rand_strided((1000, ), (1, ), device='cuda:0', dtype=torch.bfloat16)
    arg189_1 = rand_strided((1, 3, 224, 224), (150528, 50176, 224, 1), device='cuda:0', dtype=torch.bfloat16)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1, arg6_1, arg7_1, arg8_1, arg9_1, arg10_1, arg11_1, arg12_1, arg13_1, arg14_1, arg15_1, arg16_1, arg17_1, arg18_1, arg19_1, arg20_1, arg21_1, arg22_1, arg23_1, arg24_1, arg25_1, arg26_1, arg27_1, arg28_1, arg29_1, arg30_1, arg31_1, arg32_1, arg33_1, arg34_1, arg35_1, arg36_1, arg37_1, arg38_1, arg39_1, arg40_1, arg41_1, arg42_1, arg43_1, arg44_1, arg45_1, arg46_1, arg47_1, arg48_1, arg49_1, arg50_1, arg51_1, arg52_1, arg53_1, arg54_1, arg55_1, arg56_1, arg57_1, arg58_1, arg59_1, arg60_1, arg61_1, arg62_1, arg63_1, arg64_1, arg65_1, arg66_1, arg67_1, arg68_1, arg69_1, arg70_1, arg71_1, arg72_1, arg73_1, arg74_1, arg75_1, arg76_1, arg77_1, arg78_1, arg79_1, arg80_1, arg81_1, arg82_1, arg83_1, arg84_1, arg85_1, arg86_1, arg87_1, arg88_1, arg89_1, arg90_1, arg91_1, arg92_1, arg93_1, arg94_1, arg95_1, arg96_1, arg97_1, arg98_1, arg99_1, arg100_1, arg101_1, arg102_1, arg103_1, arg104_1, arg105_1, arg106_1, arg107_1, arg108_1, arg109_1, arg110_1, arg111_1, arg112_1, arg113_1, arg114_1, arg115_1, arg116_1, arg117_1, arg118_1, arg119_1, arg120_1, arg121_1, arg122_1, arg123_1, arg124_1, arg125_1, arg126_1, arg127_1, arg128_1, arg129_1, arg130_1, arg131_1, arg132_1, arg133_1, arg134_1, arg135_1, arg136_1, arg137_1, arg138_1, arg139_1, arg140_1, arg141_1, arg142_1, arg143_1, arg144_1, arg145_1, arg146_1, arg147_1, arg148_1, arg149_1, arg150_1, arg151_1, arg152_1, arg153_1, arg154_1, arg155_1, arg156_1, arg157_1, arg158_1, arg159_1, arg160_1, arg161_1, arg162_1, arg163_1, arg164_1, arg165_1, arg166_1, arg167_1, arg168_1, arg169_1, arg170_1, arg171_1, arg172_1, arg173_1, arg174_1, arg175_1, arg176_1, arg177_1, arg178_1, arg179_1, arg180_1, arg181_1, arg182_1, arg183_1, arg184_1, arg185_1, arg186_1, arg187_1, arg188_1, arg189_1])
    return print_performance(fn, times=times, repeat=repeat)
if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

Words removed	3
Total words	2478
Words removed (%)	0.12

Words added	3
Total words	2478
Words added (%)	0.12