# AOT ID: ['0_forward']
from ctypes import c_void_p, c_long, c_int
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.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
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
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_7/inductor_cache/zw/czwemfbc42kjdc6k3rt2lguzq7gywvrwr3b4jc5tlwvxk2bj7thg.py
# Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   layer_norm => add, add_1, mul, mul_1, rsqrt, sub, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [3]), kwargs = {correction: 0, keepdim: True})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem, 1e-05), kwargs = {})
#   %rsqrt : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_3, %getitem_1), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, %rsqrt), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_1), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %primals_2), kwargs = {})
triton_per_fused_native_layer_norm_0 = async_compile.triton('triton_per_fused_native_layer_norm_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[8192, 512],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: 'i32', 7: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_native_layer_norm_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 3, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_native_layer_norm_0(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 8192
    XBLOCK: tl.constexpr = 1
    rnumel = 512
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (512*x0)), None)
    tmp21 = tl.load(in_ptr1 + (r1), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr2 + (r1), None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 512, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 512.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tmp19 = tmp0 - tmp8
    tmp20 = tmp19 * tmp18
    tmp22 = tmp20 * tmp21
    tmp24 = tmp22 + tmp23
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp18, None)
    tl.store(out_ptr1 + (r1 + (512*x0)), tmp24, None)
    tl.store(out_ptr0 + (x0), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/65/c65pi2qtb6eyfexgoigw5cn2nkouol3ju46n5j4xo2rc4qkpo3ge.py
# Topologically Sorted Source Nodes: [x_1, layer_norm_1], Original ATen: [aten.add, aten.native_layer_norm]
# Source node to ATen node mapping:
#   layer_norm_1 => add_3, add_4, mul_2, mul_3, rsqrt_1, sub_1, var_mean_1
#   x_1 => add_2
# Graph fragment:
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %view_1), kwargs = {})
#   %var_mean_1 : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%add_2, [3]), kwargs = {correction: 0, keepdim: True})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_2, 1e-05), kwargs = {})
#   %rsqrt_1 : [num_users=2] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add_3,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %getitem_3), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %rsqrt_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_2, %primals_6), kwargs = {})
#   %add_4 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, %primals_7), kwargs = {})
triton_per_fused_add_native_layer_norm_1 = async_compile.triton('triton_per_fused_add_native_layer_norm_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.persistent_reduction(
    size_hints=[8192, 512],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '*fp32', 7: 'i32', 8: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4, 5, 6, 7, 8), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_add_native_layer_norm_1', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 4, 'num_reduction': 4, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False}
)
@triton.jit
def triton_per_fused_add_native_layer_norm_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 8192
    XBLOCK: tl.constexpr = 1
    rnumel = 512
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (512*x0)), None)
    tmp1 = tl.load(in_ptr1 + (r1 + (512*x0)), None)
    tmp23 = tl.load(in_ptr2 + (r1), None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr3 + (r1), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 512, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 512.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tmp21 = tmp2 - tmp10
    tmp22 = tmp21 * tmp20
    tmp24 = tmp22 * tmp23
    tmp26 = tmp24 + tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (x0), tmp20, None)
    tl.store(out_ptr1 + (r1 + (512*x0)), tmp26, None)
    tl.store(out_ptr0 + (x0), tmp10, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/jg/cjghtxku4uxojc6qot4mjj5pxz2z5t6h4tdjsogj7sz5tuogsufs.py
# Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.gelu]
# Source node to ATen node mapping:
#   x_3 => add_5, erf, mul_4, mul_5, mul_6
# Graph fragment:
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 0.5), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_3, 0.7071067811865476), kwargs = {})
#   %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%mul_5,), kwargs = {})
#   %add_5 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_4, %add_5), kwargs = {})
triton_poi_fused_gelu_2 = async_compile.triton('triton_poi_fused_gelu_2', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[16777216], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_gelu_2', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_gelu_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16777216
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), None)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + (x0), tmp8, None)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/pa/cpavnfhh6w5k3ia3bkbpbo6pvpzyknjt46fji7qua27hjunmkmdc.py
# Topologically Sorted Source Nodes: [x_1, x_7], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x_1 => add_2
#   x_7 => add_6
# Graph fragment:
#   %add_2 : [num_users=3] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_3, %view_1), kwargs = {})
#   %add_6 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%add_2, %view_5), kwargs = {})
triton_poi_fused_add_3 = async_compile.triton('triton_poi_fused_add_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[4194304], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_3', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 4, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 4194304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_ptr0 + (x2), None)
    tmp1 = tl.load(in_ptr1 + (x2), None)
    tmp3 = tl.load(in_out_ptr0 + (x2), None)
    tmp4 = tl.load(in_ptr2 + (x0), None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + (x2), tmp6, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11 = args
    args.clear()
    assert_size_stride(primals_1, (512, ), (1, ))
    assert_size_stride(primals_2, (512, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 512, 512), (1048576, 262144, 512, 1))
    assert_size_stride(primals_4, (512, 512), (512, 1))
    assert_size_stride(primals_5, (512, ), (1, ))
    assert_size_stride(primals_6, (512, ), (1, ))
    assert_size_stride(primals_7, (512, ), (1, ))
    assert_size_stride(primals_8, (2048, 512), (512, 1))
    assert_size_stride(primals_9, (2048, ), (1, ))
    assert_size_stride(primals_10, (512, 2048), (2048, 1))
    assert_size_stride(primals_11, (512, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 8192), torch.float32)
        buf3 = reinterpret_tensor(buf1, (4, 4, 512, 1), (2048, 512, 1, 1), 0); del buf1  # reuse
        buf4 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [layer_norm], Original ATen: [aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_per_fused_native_layer_norm_0.run(buf3, primals_3, primals_1, primals_2, buf0, buf4, 8192, 512, grid=grid(8192), stream=stream0)
        del primals_1
        del primals_2
        buf5 = empty_strided_cuda((8192, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf4, (8192, 512), (512, 1), 0), reinterpret_tensor(primals_4, (512, 512), (1, 512), 0), alpha=1, beta=1, out=buf5)
        del primals_5
        buf6 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 1), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 8192), torch.float32)
        buf9 = reinterpret_tensor(buf7, (4, 4, 512, 1), (2048, 512, 1, 1), 0); del buf7  # reuse
        buf10 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_1, layer_norm_1], Original ATen: [aten.add, aten.native_layer_norm]
        triton_per_fused_add_native_layer_norm_1.run(buf9, primals_3, buf5, primals_6, primals_7, buf6, buf10, 8192, 512, grid=grid(8192), stream=stream0)
        del primals_7
        buf11 = empty_strided_cuda((8192, 2048), (2048, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf10, (8192, 512), (512, 1), 0), reinterpret_tensor(primals_8, (512, 2048), (1, 512), 0), alpha=1, beta=1, out=buf11)
        del primals_9
        buf12 = empty_strided_cuda((4, 4, 512, 2048), (4194304, 1048576, 2048, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.gelu]
        triton_poi_fused_gelu_2.run(buf11, buf12, 16777216, grid=grid(16777216), stream=stream0)
        buf13 = empty_strided_cuda((8192, 512), (512, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf12, (8192, 2048), (2048, 1), 0), reinterpret_tensor(primals_10, (2048, 512), (1, 2048), 0), out=buf13)
        buf14 = reinterpret_tensor(buf13, (4, 4, 512, 512), (1048576, 262144, 512, 1), 0); del buf13  # reuse
        # Topologically Sorted Source Nodes: [x_1, x_7], Original ATen: [aten.add]
        triton_poi_fused_add_3.run(buf14, primals_3, buf5, primals_11, 4194304, grid=grid(4194304), stream=stream0)
        del primals_11
    return (buf14, primals_3, primals_6, buf0, buf3, reinterpret_tensor(buf4, (8192, 512), (512, 1), 0), buf5, buf6, buf9, reinterpret_tensor(buf10, (8192, 512), (512, 1), 0), buf11, reinterpret_tensor(buf12, (8192, 2048), (2048, 1), 0), primals_10, primals_8, primals_4, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 512, 512), (1048576, 262144, 512, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((512, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((2048, 512), (512, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((2048, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((512, 2048), (2048, 1), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((512, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7, primals_8, primals_9, primals_10, primals_11])
    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)

import torch
import torch as th
from torch import nn


def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.

    """
    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + th.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()
    output = x.div(keep_prob) * random_tensor
    return output


class Attention(nn.Module):

    def __init__(self, dim=2, num_heads=8, qkv_bias=False, qk_scale=None,
        attn_drop=0.0, proj_drop=0.0):
        super().__init__()
        self.proj = nn.Linear(dim, dim)

    def forward(self, x):
        x = self.proj(x)
        return x


class DropPath(th.nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class Mlp(th.nn.Module):
    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
    """

    def __init__(self, in_features, hidden_features=None, out_features=None,
        act_layer=th.nn.GELU, drop=0.0):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = th.nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = th.nn.Linear(hidden_features, out_features)
        self.drop = th.nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Block(th.nn.Module):

    def __init__(self, dim=512, num_heads=8, mlp_ratio=4.0, qkv_bias=False,
        qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=th
        .nn.GELU, norm_layer=th.nn.LayerNorm):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias,
            qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path
            ) if drop_path > 0.0 else th.nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
            act_layer=act_layer, drop=drop)

    def forward(self, x):
        x = x + self.drop_path(self.attn(self.norm1(x)))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


def get_inputs():
    return [torch.rand([4, 4, 512, 512])]


def get_init_inputs():
    return [[], {}]

import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch as th
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_native_layer_norm_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, out_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 512 * x0), None)
    tmp21 = tl.load(in_ptr1 + r1, None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp1 = tl.broadcast_to(tmp0, [RBLOCK])
    tmp3 = tl.broadcast_to(tmp1, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(tl.sum(tmp3, 0))
    tmp6 = tl.full([1], 512, tl.int32)
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp5 / tmp7
    tmp9 = tmp1 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp14 = 512.0
    tmp15 = tmp13 / tmp14
    tmp16 = 1e-05
    tmp17 = tmp15 + tmp16
    tmp18 = libdevice.rsqrt(tmp17)
    tmp19 = tmp0 - tmp8
    tmp20 = tmp19 * tmp18
    tmp22 = tmp20 * tmp21
    tmp24 = tmp22 + tmp23
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp18, None)
    tl.store(out_ptr1 + (r1 + 512 * x0), tmp24, None)
    tl.store(out_ptr0 + x0, tmp8, None)


@triton.jit
def triton_per_fused_add_native_layer_norm_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 512
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 512 * x0), None)
    tmp1 = tl.load(in_ptr1 + (r1 + 512 * x0), None)
    tmp23 = tl.load(in_ptr2 + r1, None, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr3 + r1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp7 = triton_helpers.promote_to_tensor(tl.sum(tmp5, 0))
    tmp8 = tl.full([1], 512, tl.int32)
    tmp9 = tmp8.to(tl.float32)
    tmp10 = tmp7 / tmp9
    tmp11 = tmp3 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tl.broadcast_to(tmp12, [RBLOCK])
    tmp15 = triton_helpers.promote_to_tensor(tl.sum(tmp13, 0))
    tmp16 = 512.0
    tmp17 = tmp15 / tmp16
    tmp18 = 1e-05
    tmp19 = tmp17 + tmp18
    tmp20 = libdevice.rsqrt(tmp19)
    tmp21 = tmp2 - tmp10
    tmp22 = tmp21 * tmp20
    tmp24 = tmp22 * tmp23
    tmp26 = tmp24 + tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp20, None)
    tl.store(out_ptr1 + (r1 + 512 * x0), tmp26, None)
    tl.store(out_ptr0 + x0, tmp10, None)


@triton.jit
def triton_poi_fused_gelu_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = 0.7071067811865476
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.erf(tmp4)
    tmp6 = 1.0
    tmp7 = tmp5 + tmp6
    tmp8 = tmp2 * tmp7
    tl.store(out_ptr0 + x0, tmp8, None)


@triton.jit
def triton_poi_fused_add_3(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel,
    XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 512
    tmp0 = tl.load(in_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr1 + x2, None)
    tmp3 = tl.load(in_out_ptr0 + x2, None)
    tmp4 = tl.load(in_ptr2 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tl.store(in_out_ptr0 + x2, tmp6, None)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11) = args
    args.clear()
    assert_size_stride(primals_1, (512,), (1,))
    assert_size_stride(primals_2, (512,), (1,))
    assert_size_stride(primals_3, (4, 4, 512, 512), (1048576, 262144, 512, 1))
    assert_size_stride(primals_4, (512, 512), (512, 1))
    assert_size_stride(primals_5, (512,), (1,))
    assert_size_stride(primals_6, (512,), (1,))
    assert_size_stride(primals_7, (512,), (1,))
    assert_size_stride(primals_8, (2048, 512), (512, 1))
    assert_size_stride(primals_9, (2048,), (1,))
    assert_size_stride(primals_10, (512, 2048), (2048, 1))
    assert_size_stride(primals_11, (512,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 1), torch.
            float32)
        buf1 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 8192),
            torch.float32)
        buf3 = reinterpret_tensor(buf1, (4, 4, 512, 1), (2048, 512, 1, 1), 0)
        del buf1
        buf4 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512, 
            1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_native_layer_norm_0[grid(8192)](buf3, primals_3,
            primals_1, primals_2, buf0, buf4, 8192, 512, num_warps=4,
            num_stages=1)
        del primals_1
        del primals_2
        buf5 = empty_strided_cuda((8192, 512), (512, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf4, (8192, 512
            ), (512, 1), 0), reinterpret_tensor(primals_4, (512, 512), (1, 
            512), 0), alpha=1, beta=1, out=buf5)
        del primals_5
        buf6 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 1), torch.
            float32)
        buf7 = empty_strided_cuda((4, 4, 512, 1), (2048, 512, 1, 8192),
            torch.float32)
        buf9 = reinterpret_tensor(buf7, (4, 4, 512, 1), (2048, 512, 1, 1), 0)
        del buf7
        buf10 = empty_strided_cuda((4, 4, 512, 512), (1048576, 262144, 512,
            1), torch.float32)
        triton_per_fused_add_native_layer_norm_1[grid(8192)](buf9,
            primals_3, buf5, primals_6, primals_7, buf6, buf10, 8192, 512,
            num_warps=4, num_stages=1)
        del primals_7
        buf11 = empty_strided_cuda((8192, 2048), (2048, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf10, (8192, 
            512), (512, 1), 0), reinterpret_tensor(primals_8, (512, 2048),
            (1, 512), 0), alpha=1, beta=1, out=buf11)
        del primals_9
        buf12 = empty_strided_cuda((4, 4, 512, 2048), (4194304, 1048576, 
            2048, 1), torch.float32)
        triton_poi_fused_gelu_2[grid(16777216)](buf11, buf12, 16777216,
            XBLOCK=1024, num_warps=4, num_stages=1)
        buf13 = empty_strided_cuda((8192, 512), (512, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf12, (8192, 2048), (2048, 1),
            0), reinterpret_tensor(primals_10, (2048, 512), (1, 2048), 0),
            out=buf13)
        buf14 = reinterpret_tensor(buf13, (4, 4, 512, 512), (1048576, 
            262144, 512, 1), 0)
        del buf13
        triton_poi_fused_add_3[grid(4194304)](buf14, primals_3, buf5,
            primals_11, 4194304, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_11
    return buf14, primals_3, primals_6, buf0, buf3, reinterpret_tensor(buf4,
        (8192, 512), (512, 1), 0), buf5, buf6, buf9, reinterpret_tensor(buf10,
        (8192, 512), (512, 1), 0), buf11, reinterpret_tensor(buf12, (8192, 
        2048), (2048, 1), 0), primals_10, primals_8, primals_4


def drop_path(x, drop_prob: 'float'=0.0, training: 'bool'=False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.

    """
    if drop_prob == 0.0 or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
    random_tensor = keep_prob + th.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()
    output = x.div(keep_prob) * random_tensor
    return output


class Attention(nn.Module):

    def __init__(self, dim=2, num_heads=8, qkv_bias=False, qk_scale=None,
        attn_drop=0.0, proj_drop=0.0):
        super().__init__()
        self.proj = nn.Linear(dim, dim)

    def forward(self, x):
        x = self.proj(x)
        return x


class DropPath(th.nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path(x, self.drop_prob, self.training)


class Mlp(th.nn.Module):
    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
    """

    def __init__(self, in_features, hidden_features=None, out_features=None,
        act_layer=th.nn.GELU, drop=0.0):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = th.nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = th.nn.Linear(hidden_features, out_features)
        self.drop = th.nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class BlockNew(th.nn.Module):

    def __init__(self, dim=512, num_heads=8, mlp_ratio=4.0, qkv_bias=False,
        qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=th
        .nn.GELU, norm_layer=th.nn.LayerNorm):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias,
            qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path
            ) if drop_path > 0.0 else th.nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,
            act_layer=act_layer, drop=drop)

    def forward(self, input_0):
        primals_1 = self.norm1.weight
        primals_2 = self.norm1.bias
        primals_4 = self.attn.proj.weight
        primals_5 = self.attn.proj.bias
        primals_6 = self.norm2.weight
        primals_7 = self.norm2.bias
        primals_8 = self.mlp.fc1.weight
        primals_9 = self.mlp.fc1.bias
        primals_10 = self.mlp.fc2.weight
        primals_11 = self.mlp.fc2.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11])
        return output[0]
