# 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/wd/cwdz7kqs3uwyg53zsyekt77eye7yjl6v7vulow2q6ni534mkf6zw.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   x => add, rsqrt, var_mean
# Graph fragment:
#   %var_mean : [num_users=2] = call_function[target=torch.ops.aten.var_mean.correction](args = (%primals_3, [2]), 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=1] = call_function[target=torch.ops.aten.rsqrt.default](args = (%add,), kwargs = {})
triton_poi_fused_native_layer_norm_0 = async_compile.triton('triton_poi_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.pointwise(
    size_hints=[16], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_0', 'mutated_arg_names': [], '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_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + (x0), tmp8, xmask)
    tl.store(out_ptr1 + (x0), tmp23, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/vs/cvsfvbs4wlaqvwxm3svg65dnhcq336ptudvn6xetnbnrtzj7xssn.py
# Topologically Sorted Source Nodes: [x], Original ATen: [aten.native_layer_norm]
# Source node to ATen node mapping:
#   x => 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, [2]), 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=1] = 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_poi_fused_native_layer_norm_1 = async_compile.triton('triton_poi_fused_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.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '*fp32', 6: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_native_layer_norm_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 5, '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_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 4)
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr1 + (x1), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x0), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/n3/cn3h43fi4m7oq2vwlktxfhxi3dzck4gnc765fyme47rufsuxazkg.py
# Topologically Sorted Source Nodes: [product], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   product => clone
# Graph fragment:
#   %clone : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_2 = async_compile.triton('triton_poi_fused_clone_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=[256], 
    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_clone_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_clone_2(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*x2) + (16*x1) + (64*x3)), xmask)
    tl.store(out_ptr0 + (x4), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/2s/c2s3zo6qtbodb6bdwv46ozxj4nxxymp76igm7emvdafvrj3673sn.py
# Topologically Sorted Source Nodes: [product], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   product => clone_1
# Graph fragment:
#   %clone_1 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_3 = async_compile.triton('triton_poi_fused_clone_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=[64, 4], tile_hint=TileHint.SQUARE,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 3: '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_clone_3', '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_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = (yindex // 16)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (16*x2) + (64*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/yw/cyw3ff4nmszw3dpfuipofodyezjcpjoru35h7fhkaosfnlrctm2g.py
# Topologically Sorted Source Nodes: [product_1, product_2, weights], Original ATen: [aten.mul, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   product_1 => mul_2
#   product_2 => add_2
#   weights => amax, exp, sub_1, sum_1
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_11, 0.5), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %primals_7), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add_2, [-1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
triton_poi_fused__softmax_add_mul_4 = async_compile.triton('triton_poi_fused__softmax_add_mul_4', '''
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=[64], 
    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__softmax_add_mul_4', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, '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__softmax_add_mul_4(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + (4*x2), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp5 * tmp1
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp11 = tmp10 * tmp1
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 * tmp1
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp4 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp8 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + (x2), tmp19, xmask)
    tl.store(out_ptr1 + (x2), tmp30, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/4j/c4js4wnrmajokobx5l4yjjeu36aktrpkep2mzo6qtwttwlqodwbm.py
# Topologically Sorted Source Nodes: [product_1, product_2, weights], Original ATen: [aten.mul, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   product_1 => mul_2
#   product_2 => add_2
#   weights => amax, div, exp, sub_1
# Graph fragment:
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_11, 0.5), kwargs = {})
#   %add_2 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %primals_7), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add_2, [-1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_2, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_add_mul_5 = async_compile.triton('triton_poi_fused__softmax_add_mul_5', '''
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=[256], 
    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__softmax_add_mul_5', '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__softmax_add_mul_5(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex % 64
    x5 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x3), xmask)
    tmp3 = tl.load(in_ptr0 + (x4), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + (x5), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + (x5), xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(in_out_ptr0 + (x3), tmp9, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/zi/czic6s5idip57syewxigjtom43flziklldd4ea2qpsxjorxgbunq.py
# Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   x_2 => add_3
# Graph fragment:
#   %add_3 : [num_users=4] = call_function[target=torch.ops.aten.add.Tensor](args = (%getitem_4, %primals_3), kwargs = {})
triton_poi_fused_add_6 = async_compile.triton('triton_poi_fused_add_6', '''
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=[64], 
    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_add_6', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + (x0), tmp2, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_7/inductor_cache/u4/cu4mvhweewrefdurxuza5qfbqlwomkc67kmxkkaurh6luaf2e2fz.py
# Topologically Sorted Source Nodes: [hidden_1], Original ATen: [aten.relu, aten.threshold_backward]
# Source node to ATen node mapping:
#   hidden_1 => relu
# Graph fragment:
#   %relu : [num_users=2] = call_function[target=torch.ops.aten.relu.default](args = (%view_19,), kwargs = {})
#   %le : [num_users=1] = call_function[target=torch.ops.aten.le.Scalar](args = (%relu, 0), kwargs = {})
triton_poi_fused_relu_threshold_backward_7 = async_compile.triton('triton_poi_fused_relu_threshold_backward_7', '''
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=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*i1', 3: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_relu_threshold_backward_7', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, '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_relu_threshold_backward_7(in_out_ptr0, in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + (x2), tmp4, xmask)
    tl.store(out_ptr0 + (x2), tmp6, xmask)
''', 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, primals_12, primals_13, primals_14 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, ), (1, ))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16, 4), (4, 1))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_8, (4, 16), (16, 1))
    assert_size_stride(primals_9, (4, ), (1, ))
    assert_size_stride(primals_10, (4, ), (1, ))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4, ), (1, ))
    assert_size_stride(primals_13, (4, 4), (4, 1))
    assert_size_stride(primals_14, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.native_layer_norm]
        stream0 = get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0.run(primals_3, buf0, buf1, 16, grid=grid(16), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_1.run(primals_3, buf0, buf1, primals_1, primals_2, buf2, 64, grid=grid(64), stream=stream0)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [q], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [k], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 16), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [v], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [product], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf3, buf6, 256, grid=grid(256), stream=stream0)
        buf7 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [product], Original ATen: [aten.clone]
        triton_poi_fused_clone_3.run(buf4, buf7, 64, 4, grid=grid(64, 4), stream=stream0)
        buf8 = reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [product], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [product_1, product_2, weights], Original ATen: [aten.mul, aten.add, aten._softmax]
        triton_poi_fused__softmax_add_mul_4.run(buf8, primals_7, buf9, buf10, 64, grid=grid(64), stream=stream0)
        buf11 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [product_1, product_2, weights], Original ATen: [aten.mul, aten.add, aten._softmax]
        triton_poi_fused__softmax_add_mul_5.run(buf11, primals_7, buf9, buf10, 256, grid=grid(256), stream=stream0)
        del primals_7
        # Topologically Sorted Source Nodes: [weights_1], Original ATen: [aten.native_dropout]
        buf12 = torch.ops.aten.native_dropout.default(buf11, 0.5, True)
        buf13 = buf12[0]
        buf14 = buf12[1]
        del buf12
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf5, buf15, 256, grid=grid(256), stream=stream0)
        buf16 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [out], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf13, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf15, (16, 4, 4), (16, 4, 1), 0), out=buf16)
        buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_2], Original ATen: [aten.clone]
        triton_poi_fused_clone_2.run(buf16, buf17, 256, grid=grid(256), stream=stream0)
        del buf16
        buf18 = reinterpret_tensor(buf9, (16, 4), (4, 1), 0); del buf9  # reuse
        # Topologically Sorted Source Nodes: [out_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf17, (16, 16), (16, 1), 0), reinterpret_tensor(primals_8, (16, 4), (1, 16), 0), out=buf18)
        # Topologically Sorted Source Nodes: [x_1], Original ATen: [aten.native_dropout]
        buf19 = torch.ops.aten.native_dropout.default(reinterpret_tensor(buf18, (4, 4, 4), (16, 4, 1), 0), 0.5, True)
        buf20 = buf19[0]
        buf21 = buf19[1]
        del buf19
        buf22 = buf20; del buf20  # reuse
        # Topologically Sorted Source Nodes: [x_2], Original ATen: [aten.add]
        triton_poi_fused_add_6.run(buf22, primals_3, 64, grid=grid(64), stream=stream0)
        buf23 = buf1; del buf1  # reuse
        buf24 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_0.run(buf22, buf23, buf24, 16, grid=grid(16), stream=stream0)
        buf25 = reinterpret_tensor(buf18, (4, 4, 4), (16, 4, 1), 0); del buf18  # reuse
        # Topologically Sorted Source Nodes: [x_3], Original ATen: [aten.native_layer_norm]
        triton_poi_fused_native_layer_norm_1.run(buf22, buf23, buf24, primals_9, primals_10, buf25, 64, grid=grid(64), stream=stream0)
        del buf23
        del buf24
        del primals_10
        buf26 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0); del buf10  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(buf25, (16, 4), (4, 1), 0), reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), out=buf26)
        buf27 = reinterpret_tensor(buf26, (4, 4, 4), (16, 4, 1), 0); del buf26  # reuse
        buf36 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        # Topologically Sorted Source Nodes: [hidden_1], Original ATen: [aten.relu, aten.threshold_backward]
        triton_poi_fused_relu_threshold_backward_7.run(buf27, primals_12, buf36, 64, grid=grid(64), stream=stream0)
        del primals_12
        # Topologically Sorted Source Nodes: [hidden_1, hidden_2], Original ATen: [aten.relu, aten.native_dropout]
        buf28 = torch.ops.aten.native_dropout.default(buf27, 0.5, True)
        buf29 = buf28[0]
        buf30 = buf28[1]
        del buf28
        buf31 = reinterpret_tensor(buf27, (16, 4), (4, 1), 0); del buf27  # reuse
        # Topologically Sorted Source Nodes: [out_4], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_14, reinterpret_tensor(buf29, (16, 4), (4, 1), 0), reinterpret_tensor(primals_13, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf31)
        del primals_14
        # Topologically Sorted Source Nodes: [x_4], Original ATen: [aten.native_dropout]
        buf32 = torch.ops.aten.native_dropout.default(reinterpret_tensor(buf31, (4, 4, 4), (16, 4, 1), 0), 0.5, True)
        del buf31
        buf33 = buf32[0]
        buf34 = buf32[1]
        del buf32
        buf35 = buf33; del buf33  # reuse
        # Topologically Sorted Source Nodes: [x_5], Original ATen: [aten.add]
        triton_poi_fused_add_6.run(buf35, buf22, 64, grid=grid(64), stream=stream0)
    return (buf35, primals_3, primals_9, reinterpret_tensor(buf2, (16, 4), (4, 1), 0), buf11, buf14, reinterpret_tensor(buf17, (16, 16), (16, 1), 0), buf21, buf22, reinterpret_tensor(buf25, (16, 4), (4, 1), 0), buf30, reinterpret_tensor(buf29, (16, 4), (4, 1), 0), buf34, primals_13, buf36, primals_11, primals_8, reinterpret_tensor(buf13, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf15, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf6, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0), primals_6, primals_5, 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((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_8 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_9 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_10 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_11 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_12 = rand_strided((4, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_13 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_14 = rand_strided((4, ), (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, primals_12, primals_13, primals_14])
    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.nn as nn
import torch.nn.functional as F


class Lambda(nn.Module):
    """An easy way to create a pytorch layer for a simple `func`."""

    def __init__(self, func):
        """create a layer that simply calls `func` with `x`"""
        super().__init__()
        self.func = func

    def forward(self, x):
        return self.func(x)


class FFN(nn.Module):
    """
    Feed-Forward Network
    """

    def __init__(self, d_inner_hid, d_model, dropout_rate):
        super(FFN, self).__init__()
        self.dropout_rate = dropout_rate
        self.fc1 = torch.nn.Linear(in_features=d_model, out_features=
            d_inner_hid)
        self.fc2 = torch.nn.Linear(in_features=d_inner_hid, out_features=
            d_model)

    def forward(self, x):
        hidden = self.fc1(x)
        hidden = F.relu(hidden)
        if self.dropout_rate:
            hidden = F.dropout(hidden, p=self.dropout_rate)
        out = self.fc2(hidden)
        return out


class MultiHeadAttention(nn.Module):
    """
    Multi-Head Attention
    """

    def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.0):
        super(MultiHeadAttention, self).__init__()
        self.n_head = n_head
        self.d_key = d_key
        self.d_value = d_value
        self.d_model = d_model
        self.dropout_rate = dropout_rate
        self.q_fc = torch.nn.Linear(in_features=d_model, out_features=d_key *
            n_head, bias=False)
        self.k_fc = torch.nn.Linear(in_features=d_model, out_features=d_key *
            n_head, bias=False)
        self.v_fc = torch.nn.Linear(in_features=d_model, out_features=
            d_value * n_head, bias=False)
        self.proj_fc = torch.nn.Linear(in_features=d_value * n_head,
            out_features=d_model, bias=False)

    def _prepare_qkv(self, queries, keys, values, cache=None):
        if keys is None:
            keys, values = queries, queries
            static_kv = False
        else:
            static_kv = True
        q = self.q_fc(queries)
        q = torch.reshape(q, shape=[q.size(0), q.size(1), self.n_head, self
            .d_key])
        q = q.permute(0, 2, 1, 3)
        if cache is not None and static_kv and 'static_k' in cache:
            k = cache['static_k']
            v = cache['static_v']
        else:
            k = self.k_fc(keys)
            v = self.v_fc(values)
            k = torch.reshape(k, shape=[k.size(0), k.size(1), self.n_head,
                self.d_key])
            k = k.permute(0, 2, 1, 3)
            v = torch.reshape(v, shape=[v.size(0), v.size(1), self.n_head,
                self.d_value])
            v = v.permute(0, 2, 1, 3)
        if cache is not None:
            if static_kv and 'static_k' not in cache:
                cache['static_k'], cache['static_v'] = k, v
            elif not static_kv:
                cache_k, cache_v = cache['k'], cache['v']
                k = torch.cat([cache_k, k], dim=2)
                v = torch.cat([cache_v, v], dim=2)
                cache['k'], cache['v'] = k, v
        return q, k, v

    def forward(self, queries, keys, values, attn_bias, cache=None):
        keys = queries if keys is None else keys
        values = keys if values is None else values
        q, k, v = self._prepare_qkv(queries, keys, values, cache)
        product = torch.matmul(q, k.transpose(2, 3))
        product = product * self.d_model ** -0.5
        if attn_bias is not None:
            product += attn_bias
        weights = F.softmax(product, dim=-1)
        if self.dropout_rate:
            weights = F.dropout(weights, p=self.dropout_rate)
        out = torch.matmul(weights, v)
        out = out.permute(0, 2, 1, 3)
        out = torch.reshape(out, shape=[out.size(0), out.size(1), out.shape
            [2] * out.shape[3]])
        out = self.proj_fc(out)
        return out


class LambdaXY(nn.Module):
    """An easy way to create a pytorch layer for a simple `func`."""

    def __init__(self, func):
        """create a layer that simply calls `func` with `x`"""
        super().__init__()
        self.func = func

    def forward(self, x, y):
        return self.func(x, y)


class PrePostProcessLayer(nn.Module):
    """
    PrePostProcessLayer
    """

    def __init__(self, process_cmd, d_model, dropout_rate):
        super(PrePostProcessLayer, self).__init__()
        self.process_cmd = process_cmd
        self.functors = nn.ModuleList()
        cur_a_len = 0
        cur_n_len = 0
        cur_d_len = 0
        for cmd in self.process_cmd:
            if cmd == 'a':
                self.functors.add_module('add_res_connect_{}'.format(
                    cur_a_len), LambdaXY(lambda x, y: x + y if y is not
                    None else x))
                cur_a_len += 1
            elif cmd == 'n':
                layerNorm = torch.nn.LayerNorm(normalized_shape=d_model,
                    elementwise_affine=True, eps=1e-05)
                self.functors.add_module('layer_norm_%d' % cur_n_len, layerNorm
                    )
                cur_n_len += 1
            elif cmd == 'd':
                self.functors.add_module('add_drop_{}'.format(cur_d_len),
                    Lambda(lambda x: F.dropout(x, p=dropout_rate) if
                    dropout_rate else x))
                cur_d_len += 1

    def forward(self, x, residual=None):
        for i, (cmd, functor) in enumerate(zip(self.process_cmd, self.functors)
            ):
            if cmd == 'a':
                x = functor(x, residual)
            else:
                x = functor(x)
        return x


class EncoderLayer(nn.Module):
    """
    EncoderLayer
    """

    def __init__(self, n_head, d_key, d_value, d_model, d_inner_hid,
        prepostprocess_dropout, attention_dropout, relu_dropout,
        preprocess_cmd='n', postprocess_cmd='da'):
        super(EncoderLayer, self).__init__()
        self.preprocesser1 = PrePostProcessLayer(preprocess_cmd, d_model,
            prepostprocess_dropout)
        self.self_attn = MultiHeadAttention(d_key, d_value, d_model, n_head,
            attention_dropout)
        self.postprocesser1 = PrePostProcessLayer(postprocess_cmd, d_model,
            prepostprocess_dropout)
        self.preprocesser2 = PrePostProcessLayer(preprocess_cmd, d_model,
            prepostprocess_dropout)
        self.ffn = FFN(d_inner_hid, d_model, relu_dropout)
        self.postprocesser2 = PrePostProcessLayer(postprocess_cmd, d_model,
            prepostprocess_dropout)

    def forward(self, enc_input, attn_bias):
        attn_output = self.self_attn(self.preprocesser1(enc_input), None,
            None, attn_bias)
        attn_output = self.postprocesser1(attn_output, enc_input)
        ffn_output = self.ffn(self.preprocesser2(attn_output))
        ffn_output = self.postprocesser2(ffn_output, attn_output)
        return ffn_output


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


def get_init_inputs():
    return [[], {'n_head': 4, 'd_key': 4, 'd_value': 4, 'd_model': 4,
        'd_inner_hid': 4, 'prepostprocess_dropout': 0.5,
        'attention_dropout': 0.5, 'relu_dropout': 0.5}]

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


@triton.jit
def triton_poi_fused_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_1(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


@triton.jit
def triton_poi_fused_clone_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1 + 64 * x3), xmask)
    tl.store(out_ptr0 + x4, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_3(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 16
    y1 = yindex // 16
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x2 + 64 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_add_mul_4(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 16
    tmp0 = tl.load(in_ptr0 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp12 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp5 * tmp1
    tmp8 = tmp6 + tmp7
    tmp9 = triton_helpers.maximum(tmp4, tmp8)
    tmp11 = tmp10 * tmp1
    tmp13 = tmp11 + tmp12
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 * tmp1
    tmp18 = tmp16 + tmp17
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp20 = tmp4 - tmp19
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp8 - tmp19
    tmp23 = tl_math.exp(tmp22)
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp19
    tmp26 = tl_math.exp(tmp25)
    tmp27 = tmp24 + tmp26
    tmp28 = tmp18 - tmp19
    tmp29 = tl_math.exp(tmp28)
    tmp30 = tmp27 + tmp29
    tl.store(out_ptr0 + x2, tmp19, xmask)
    tl.store(out_ptr1 + x2, tmp30, xmask)


@triton.jit
def triton_poi_fused__softmax_add_mul_5(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x4 = xindex % 64
    x5 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp3 = tl.load(in_ptr0 + x4, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 - tmp5
    tmp7 = tl_math.exp(tmp6)
    tmp9 = tmp7 / tmp8
    tl.store(in_out_ptr0 + x3, tmp9, xmask)


@triton.jit
def triton_poi_fused_add_6(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_7(in_out_ptr0, in_ptr0,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14) = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (16, 4), (4, 1))
    assert_size_stride(primals_6, (16, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_8, (4, 16), (16, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4, 4), (4, 1))
    assert_size_stride(primals_12, (4,), (1,))
    assert_size_stride(primals_13, (4, 4), (4, 1))
    assert_size_stride(primals_14, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(16)](primals_3, buf0,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_1[grid(64)](primals_3, buf0,
            buf1, primals_1, primals_2, buf2, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del primals_1
        del primals_2
        buf3 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), out=buf3)
        buf4 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 16), (1, 4), 0), out=buf4)
        buf5 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(256)](buf3, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused_clone_3[grid(64, 4)](buf4, buf7, 64, 4, XBLOCK=4,
            YBLOCK=32, num_warps=4, num_stages=1)
        buf8 = reinterpret_tensor(buf4, (16, 4, 4), (16, 4, 1), 0)
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_add_mul_4[grid(64)](buf8, primals_7, buf9,
            buf10, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf11 = reinterpret_tensor(buf8, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf8
        triton_poi_fused__softmax_add_mul_5[grid(256)](buf11, primals_7,
            buf9, buf10, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_7
        buf12 = torch.ops.aten.native_dropout.default(buf11, 0.5, True)
        buf13 = buf12[0]
        buf14 = buf12[1]
        del buf12
        buf15 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(256)](buf5, buf15, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf16 = reinterpret_tensor(buf5, (16, 4, 4), (16, 4, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf13, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf15, (16, 4, 4), (16, 4, 1), 0), out=buf16
            )
        buf17 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_2[grid(256)](buf16, buf17, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf16
        buf18 = reinterpret_tensor(buf9, (16, 4), (4, 1), 0)
        del buf9
        extern_kernels.mm(reinterpret_tensor(buf17, (16, 16), (16, 1), 0),
            reinterpret_tensor(primals_8, (16, 4), (1, 16), 0), out=buf18)
        buf19 = torch.ops.aten.native_dropout.default(reinterpret_tensor(
            buf18, (4, 4, 4), (16, 4, 1), 0), 0.5, True)
        buf20 = buf19[0]
        buf21 = buf19[1]
        del buf19
        buf22 = buf20
        del buf20
        triton_poi_fused_add_6[grid(64)](buf22, primals_3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf23 = buf1
        del buf1
        buf24 = buf0
        del buf0
        triton_poi_fused_native_layer_norm_0[grid(16)](buf22, buf23, buf24,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf25 = reinterpret_tensor(buf18, (4, 4, 4), (16, 4, 1), 0)
        del buf18
        triton_poi_fused_native_layer_norm_1[grid(64)](buf22, buf23, buf24,
            primals_9, primals_10, buf25, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        del buf23
        del buf24
        del primals_10
        buf26 = reinterpret_tensor(buf10, (16, 4), (4, 1), 0)
        del buf10
        extern_kernels.mm(reinterpret_tensor(buf25, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_11, (4, 4), (1, 4), 0), out=buf26)
        buf27 = reinterpret_tensor(buf26, (4, 4, 4), (16, 4, 1), 0)
        del buf26
        buf36 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_7[grid(64)](buf27,
            primals_12, buf36, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del primals_12
        buf28 = torch.ops.aten.native_dropout.default(buf27, 0.5, True)
        buf29 = buf28[0]
        buf30 = buf28[1]
        del buf28
        buf31 = reinterpret_tensor(buf27, (16, 4), (4, 1), 0)
        del buf27
        extern_kernels.addmm(primals_14, reinterpret_tensor(buf29, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_13, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf31)
        del primals_14
        buf32 = torch.ops.aten.native_dropout.default(reinterpret_tensor(
            buf31, (4, 4, 4), (16, 4, 1), 0), 0.5, True)
        del buf31
        buf33 = buf32[0]
        buf34 = buf32[1]
        del buf32
        buf35 = buf33
        del buf33
        triton_poi_fused_add_6[grid(64)](buf35, buf22, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
    return buf35, primals_3, primals_9, reinterpret_tensor(buf2, (16, 4), (
        4, 1), 0), buf11, buf14, reinterpret_tensor(buf17, (16, 16), (16, 1), 0
        ), buf21, buf22, reinterpret_tensor(buf25, (16, 4), (4, 1), 0
        ), buf30, reinterpret_tensor(buf29, (16, 4), (4, 1), 0
        ), buf34, primals_13, buf36, primals_11, primals_8, reinterpret_tensor(
        buf13, (16, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf15, (16, 4,
        4), (16, 1, 4), 0), reinterpret_tensor(buf6, (16, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf7, (16, 4, 4), (16, 1, 4), 0
        ), primals_6, primals_5, primals_4


class Lambda(nn.Module):
    """An easy way to create a pytorch layer for a simple `func`."""

    def __init__(self, func):
        """create a layer that simply calls `func` with `x`"""
        super().__init__()
        self.func = func

    def forward(self, x):
        return self.func(x)


class FFN(nn.Module):
    """
    Feed-Forward Network
    """

    def __init__(self, d_inner_hid, d_model, dropout_rate):
        super(FFN, self).__init__()
        self.dropout_rate = dropout_rate
        self.fc1 = torch.nn.Linear(in_features=d_model, out_features=
            d_inner_hid)
        self.fc2 = torch.nn.Linear(in_features=d_inner_hid, out_features=
            d_model)

    def forward(self, x):
        hidden = self.fc1(x)
        hidden = F.relu(hidden)
        if self.dropout_rate:
            hidden = F.dropout(hidden, p=self.dropout_rate)
        out = self.fc2(hidden)
        return out


class MultiHeadAttention(nn.Module):
    """
    Multi-Head Attention
    """

    def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.0):
        super(MultiHeadAttention, self).__init__()
        self.n_head = n_head
        self.d_key = d_key
        self.d_value = d_value
        self.d_model = d_model
        self.dropout_rate = dropout_rate
        self.q_fc = torch.nn.Linear(in_features=d_model, out_features=d_key *
            n_head, bias=False)
        self.k_fc = torch.nn.Linear(in_features=d_model, out_features=d_key *
            n_head, bias=False)
        self.v_fc = torch.nn.Linear(in_features=d_model, out_features=
            d_value * n_head, bias=False)
        self.proj_fc = torch.nn.Linear(in_features=d_value * n_head,
            out_features=d_model, bias=False)

    def _prepare_qkv(self, queries, keys, values, cache=None):
        if keys is None:
            keys, values = queries, queries
            static_kv = False
        else:
            static_kv = True
        q = self.q_fc(queries)
        q = torch.reshape(q, shape=[q.size(0), q.size(1), self.n_head, self
            .d_key])
        q = q.permute(0, 2, 1, 3)
        if cache is not None and static_kv and 'static_k' in cache:
            k = cache['static_k']
            v = cache['static_v']
        else:
            k = self.k_fc(keys)
            v = self.v_fc(values)
            k = torch.reshape(k, shape=[k.size(0), k.size(1), self.n_head,
                self.d_key])
            k = k.permute(0, 2, 1, 3)
            v = torch.reshape(v, shape=[v.size(0), v.size(1), self.n_head,
                self.d_value])
            v = v.permute(0, 2, 1, 3)
        if cache is not None:
            if static_kv and 'static_k' not in cache:
                cache['static_k'], cache['static_v'] = k, v
            elif not static_kv:
                cache_k, cache_v = cache['k'], cache['v']
                k = torch.cat([cache_k, k], dim=2)
                v = torch.cat([cache_v, v], dim=2)
                cache['k'], cache['v'] = k, v
        return q, k, v

    def forward(self, queries, keys, values, attn_bias, cache=None):
        keys = queries if keys is None else keys
        values = keys if values is None else values
        q, k, v = self._prepare_qkv(queries, keys, values, cache)
        product = torch.matmul(q, k.transpose(2, 3))
        product = product * self.d_model ** -0.5
        if attn_bias is not None:
            product += attn_bias
        weights = F.softmax(product, dim=-1)
        if self.dropout_rate:
            weights = F.dropout(weights, p=self.dropout_rate)
        out = torch.matmul(weights, v)
        out = out.permute(0, 2, 1, 3)
        out = torch.reshape(out, shape=[out.size(0), out.size(1), out.shape
            [2] * out.shape[3]])
        out = self.proj_fc(out)
        return out


class LambdaXY(nn.Module):
    """An easy way to create a pytorch layer for a simple `func`."""

    def __init__(self, func):
        """create a layer that simply calls `func` with `x`"""
        super().__init__()
        self.func = func

    def forward(self, x, y):
        return self.func(x, y)


class PrePostProcessLayer(nn.Module):
    """
    PrePostProcessLayer
    """

    def __init__(self, process_cmd, d_model, dropout_rate):
        super(PrePostProcessLayer, self).__init__()
        self.process_cmd = process_cmd
        self.functors = nn.ModuleList()
        cur_a_len = 0
        cur_n_len = 0
        cur_d_len = 0
        for cmd in self.process_cmd:
            if cmd == 'a':
                self.functors.add_module('add_res_connect_{}'.format(
                    cur_a_len), LambdaXY(lambda x, y: x + y if y is not
                    None else x))
                cur_a_len += 1
            elif cmd == 'n':
                layerNorm = torch.nn.LayerNorm(normalized_shape=d_model,
                    elementwise_affine=True, eps=1e-05)
                self.functors.add_module('layer_norm_%d' % cur_n_len, layerNorm
                    )
                cur_n_len += 1
            elif cmd == 'd':
                self.functors.add_module('add_drop_{}'.format(cur_d_len),
                    Lambda(lambda x: F.dropout(x, p=dropout_rate) if
                    dropout_rate else x))
                cur_d_len += 1

    def forward(self, x, residual=None):
        for i, (cmd, functor) in enumerate(zip(self.process_cmd, self.functors)
            ):
            if cmd == 'a':
                x = functor(x, residual)
            else:
                x = functor(x)
        return x


class EncoderLayerNew(nn.Module):
    """
    EncoderLayer
    """

    def __init__(self, n_head, d_key, d_value, d_model, d_inner_hid,
        prepostprocess_dropout, attention_dropout, relu_dropout,
        preprocess_cmd='n', postprocess_cmd='da'):
        super(EncoderLayerNew, self).__init__()
        self.preprocesser1 = PrePostProcessLayer(preprocess_cmd, d_model,
            prepostprocess_dropout)
        self.self_attn = MultiHeadAttention(d_key, d_value, d_model, n_head,
            attention_dropout)
        self.postprocesser1 = PrePostProcessLayer(postprocess_cmd, d_model,
            prepostprocess_dropout)
        self.preprocesser2 = PrePostProcessLayer(preprocess_cmd, d_model,
            prepostprocess_dropout)
        self.ffn = FFN(d_inner_hid, d_model, relu_dropout)
        self.postprocesser2 = PrePostProcessLayer(postprocess_cmd, d_model,
            prepostprocess_dropout)

    def forward(self, input_0, input_1):
        primals_1 = self.preprocesser1.functors.layer_norm_0.weight
        primals_2 = self.preprocesser1.functors.layer_norm_0.bias
        primals_4 = self.self_attn.q_fc.weight
        primals_5 = self.self_attn.k_fc.weight
        primals_6 = self.self_attn.v_fc.weight
        primals_8 = self.self_attn.proj_fc.weight
        primals_9 = self.preprocesser2.functors.layer_norm_0.weight
        primals_10 = self.preprocesser2.functors.layer_norm_0.bias
        primals_11 = self.ffn.fc1.weight
        primals_12 = self.ffn.fc1.bias
        primals_13 = self.ffn.fc2.weight
        primals_14 = self.ffn.fc2.bias
        primals_3 = input_0
        primals_7 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14])
        return output[0]
