# 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_4/inductor_cache/rm/crmjcbrhesyjltwjwo2gy5ktnw7trv24ctlurkfme6ykhtfquq32.py
# Topologically Sorted Source Nodes: [attn_weights], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   attn_weights => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_0 = async_compile.triton('triton_poi_fused_clone_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, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_0', 'mutated_arg_names': [], '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_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/rb/crbncgepp7pchewiviz2ecap4hkql77bxprjbw2ciuujmpu57s6c.py
# Topologically Sorted Source Nodes: [attn_weights], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   attn_weights => clone_4
# Graph fragment:
#   %clone_4 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_1,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_1 = async_compile.triton('triton_poi_fused_clone_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=[16, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_1', 'mutated_arg_names': [], '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_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 + y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/o3/co3izn5ncj4srf2yyvlna6wjchri34pntj2gc5wjgn7jlnnz6con.py
# Topologically Sorted Source Nodes: [attn_weights_1, mul_1, sub, mul_2, attn_weights_2, attn_weights_3], Original ATen: [aten.mul, aten.sub, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   attn_weights_1 => mul
#   attn_weights_2 => add
#   attn_weights_3 => amax, exp, sub_1, sum_1
#   mul_1 => mul_1
#   mul_2 => mul_2
#   sub => sub
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, 1.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %unsqueeze), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, 1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 10000.0), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%add, [-1], True), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %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_sub_2 = async_compile.triton('triton_poi_fused__softmax_add_mul_sub_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=[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_sub_2', '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_sub_2(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
    x3 = xindex
    x0 = xindex % 4
    x2 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (4*x3), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + ((4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (1 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp19 = tl.load(in_ptr1 + (2 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (3 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (3 + (4*x0) + (16*x2)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tmp3 - tmp1
    tmp6 = 10000.0
    tmp7 = tmp5 * tmp6
    tmp8 = tmp4 + tmp7
    tmp10 = tmp9 * tmp1
    tmp12 = tmp10 * tmp11
    tmp13 = tmp11 - tmp1
    tmp14 = tmp13 * tmp6
    tmp15 = tmp12 + tmp14
    tmp16 = triton_helpers.maximum(tmp8, tmp15)
    tmp18 = tmp17 * tmp1
    tmp20 = tmp18 * tmp19
    tmp21 = tmp19 - tmp1
    tmp22 = tmp21 * tmp6
    tmp23 = tmp20 + tmp22
    tmp24 = triton_helpers.maximum(tmp16, tmp23)
    tmp26 = tmp25 * tmp1
    tmp28 = tmp26 * tmp27
    tmp29 = tmp27 - tmp1
    tmp30 = tmp29 * tmp6
    tmp31 = tmp28 + tmp30
    tmp32 = triton_helpers.maximum(tmp24, tmp31)
    tmp33 = tmp8 - tmp32
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp15 - tmp32
    tmp36 = tl_math.exp(tmp35)
    tmp37 = tmp34 + tmp36
    tmp38 = tmp23 - tmp32
    tmp39 = tl_math.exp(tmp38)
    tmp40 = tmp37 + tmp39
    tmp41 = tmp31 - tmp32
    tmp42 = tl_math.exp(tmp41)
    tmp43 = tmp40 + tmp42
    tl.store(out_ptr0 + (x3), tmp32, xmask)
    tl.store(out_ptr1 + (x3), tmp43, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/v4/cv4vg32uvk6n3kmqvtqdk2um3dvs5hp236tvdfmeead6n5d2kw5g.py
# Topologically Sorted Source Nodes: [attn_weights_1, mul_1, sub, mul_2, attn_weights_2, attn_weights_3], Original ATen: [aten.mul, aten.sub, aten.add, aten._softmax]
# Source node to ATen node mapping:
#   attn_weights_1 => mul
#   attn_weights_2 => add
#   attn_weights_3 => div, exp, sub_1
#   mul_1 => mul_1
#   mul_2 => mul_2
#   sub => sub
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_7, 1.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %unsqueeze), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, 1), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub, 10000.0), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_1, %mul_2), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add, %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_sub_3 = async_compile.triton('triton_poi_fused__softmax_add_mul_sub_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=[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_sub_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__softmax_add_mul_sub_3(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
    x4 = xindex
    x3 = (xindex // 64)
    x5 = xindex % 16
    x6 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x4), xmask)
    tmp3 = tl.load(in_ptr0 + (x5 + (16*x3)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr1 + (x6), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (x6), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tmp3 - tmp1
    tmp6 = 10000.0
    tmp7 = tmp5 * tmp6
    tmp8 = tmp4 + tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tl_math.exp(tmp10)
    tmp13 = tmp11 / tmp12
    tl.store(in_out_ptr0 + (x4), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/bb/cbby6op7dmkjsypxm4o3urasth73g6q5oi4ddo6uk6dsuv6off2v.py
# Topologically Sorted Source Nodes: [attn_output], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   attn_output => clone_6
# Graph fragment:
#   %clone_6 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_3,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_4 = async_compile.triton('triton_poi_fused_clone_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=[16, 4], tile_hint=TileHint.DEFAULT,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_4', 'mutated_arg_names': [], '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_clone_4(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (8 + y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (8 + y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp2, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/we/cwe54p4p4jvwbdktkpj3wy2coheu6f3r3dgvi7ozm7xjfk4mgbwx.py
# Topologically Sorted Source Nodes: [contiguous_3], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   contiguous_3 => clone_7
# Graph fragment:
#   %clone_7 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%permute_4,), kwargs = {memory_format: torch.contiguous_format})
triton_poi_fused_clone_5 = async_compile.triton('triton_poi_fused_clone_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=[16, 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_5', '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_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (4*x2) + (16*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (12, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 12), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_weights], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf0, primals_3, buf1, 16, 4, grid=grid(16, 4), stream=stream0)
        buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_weights], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf0, primals_3, buf2, 16, 4, grid=grid(16, 4), stream=stream0)
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_weights], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf2, (16, 1, 4), (4, 0, 1), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [attn_weights_1, mul_1, sub, mul_2, attn_weights_2, attn_weights_3], Original ATen: [aten.mul, aten.sub, aten.add, aten._softmax]
        triton_poi_fused__softmax_add_mul_sub_2.run(buf3, primals_1, buf4, buf5, 64, grid=grid(64), stream=stream0)
        buf6 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [attn_weights_1, mul_1, sub, mul_2, attn_weights_2, attn_weights_3], Original ATen: [aten.mul, aten.sub, aten.add, aten._softmax]
        triton_poi_fused__softmax_add_mul_sub_3.run(buf6, primals_1, buf4, buf5, 256, grid=grid(256), stream=stream0)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [attn_output], Original ATen: [aten.clone]
        triton_poi_fused_clone_4.run(buf0, primals_3, buf7, 16, 4, grid=grid(16, 4), stream=stream0)
        del buf0
        del primals_3
        buf8 = reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 1), 0); del buf4  # reuse
        # Topologically Sorted Source Nodes: [attn_output], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf7, (16, 4, 1), (4, 1, 0), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous_3], Original ATen: [aten.clone]
        triton_poi_fused_clone_5.run(buf8, buf9, 16, 4, grid=grid(16, 4), stream=stream0)
        buf10 = reinterpret_tensor(buf8, (16, 4), (4, 1), 0); del buf8  # reuse
        # Topologically Sorted Source Nodes: [attn_output_2], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf9, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf10)
        del primals_6
    return (reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0), primals_1, reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), buf6, reinterpret_tensor(buf9, (16, 4), (4, 1), 0), primals_5, reinterpret_tensor(buf7, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf1, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 4), 0), )


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, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((12, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((12, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = 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])
    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 MultiHeadSelfAttention(nn.Module):

    def __init__(self, d_ipt: 'int', n_head: 'int', dropout_p: 'float'=0.1):
        super(MultiHeadSelfAttention, self).__init__()
        self.qkv_linear = nn.Linear(d_ipt, d_ipt * 3, True)
        self.n_head = n_head
        self.output_linear = nn.Linear(d_ipt, d_ipt, True)
        self.dropout = nn.Dropout(dropout_p)

    def forward(self, src: 'torch.FloatTensor', attn_mask: 'torch.FloatTensor'
        ) ->torch.FloatTensor:
        if attn_mask.dim() == 2:
            attn_mask = attn_mask.unsqueeze(0)
        if attn_mask.dim() == 3:
            attn_mask = attn_mask.unsqueeze(1)
        q, k, v = self.qkv_linear(src).chunk(3, dim=-1)
        q = q.contiguous().view(src.shape[0], src.shape[1], self.n_head, 
            src.shape[2] // self.n_head).permute(0, 2, 1, 3)
        k = k.contiguous().view(src.shape[0], src.shape[1], self.n_head, 
            src.shape[2] // self.n_head).permute(0, 2, 3, 1)
        v = v.contiguous().view(src.shape[0], src.shape[1], self.n_head, 
            src.shape[2] // self.n_head).permute(0, 2, 1, 3)
        attn_weights = torch.matmul(q, k)
        attn_weights = attn_weights * float(src.shape[2] // self.n_head
            ) ** -0.5
        attn_weights = attn_weights * attn_mask + (attn_mask - 1) * 10000.0
        attn_weights = F.softmax(attn_weights, dim=-1)
        attn_weights = self.dropout(attn_weights)
        attn_output = torch.matmul(attn_weights, v)
        attn_output = attn_output.permute(0, 2, 1, 3).contiguous().view(src
            .shape)
        attn_output = self.output_linear(attn_output)
        return attn_output


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


def get_init_inputs():
    return [[], {'d_ipt': 4, 'n_head': 4}]

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 math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_clone_0(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_1(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (4 + y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_add_mul_sub_2(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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4 * x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (1 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr0 + (2 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp19 = tl.load(in_ptr1 + (2 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp25 = tl.load(in_ptr0 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp27 = tl.load(in_ptr1 + (3 + 4 * x0 + 16 * x2), xmask,
        eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tmp3 - tmp1
    tmp6 = 10000.0
    tmp7 = tmp5 * tmp6
    tmp8 = tmp4 + tmp7
    tmp10 = tmp9 * tmp1
    tmp12 = tmp10 * tmp11
    tmp13 = tmp11 - tmp1
    tmp14 = tmp13 * tmp6
    tmp15 = tmp12 + tmp14
    tmp16 = triton_helpers.maximum(tmp8, tmp15)
    tmp18 = tmp17 * tmp1
    tmp20 = tmp18 * tmp19
    tmp21 = tmp19 - tmp1
    tmp22 = tmp21 * tmp6
    tmp23 = tmp20 + tmp22
    tmp24 = triton_helpers.maximum(tmp16, tmp23)
    tmp26 = tmp25 * tmp1
    tmp28 = tmp26 * tmp27
    tmp29 = tmp27 - tmp1
    tmp30 = tmp29 * tmp6
    tmp31 = tmp28 + tmp30
    tmp32 = triton_helpers.maximum(tmp24, tmp31)
    tmp33 = tmp8 - tmp32
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp15 - tmp32
    tmp36 = tl_math.exp(tmp35)
    tmp37 = tmp34 + tmp36
    tmp38 = tmp23 - tmp32
    tmp39 = tl_math.exp(tmp38)
    tmp40 = tmp37 + tmp39
    tmp41 = tmp31 - tmp32
    tmp42 = tl_math.exp(tmp41)
    tmp43 = tmp40 + tmp42
    tl.store(out_ptr0 + x3, tmp32, xmask)
    tl.store(out_ptr1 + x3, tmp43, xmask)


@triton.jit
def triton_poi_fused__softmax_add_mul_sub_3(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
    x4 = xindex
    x3 = xindex // 64
    x5 = xindex % 16
    x6 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp3 = tl.load(in_ptr0 + (x5 + 16 * x3), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr1 + x6, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + x6, xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp2 * tmp3
    tmp5 = tmp3 - tmp1
    tmp6 = 10000.0
    tmp7 = tmp5 * tmp6
    tmp8 = tmp4 + tmp7
    tmp10 = tmp8 - tmp9
    tmp11 = tl_math.exp(tmp10)
    tmp13 = tmp11 / tmp12
    tl.store(in_out_ptr0 + x4, tmp13, xmask)


@triton.jit
def triton_poi_fused_clone_4(in_ptr0, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (8 + y0), ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_5(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (12,), (1,))
    assert_size_stride(primals_4, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 12), (12, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 12), (1, 4), 0), out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(16, 4)](buf0, primals_3, buf1, 16, 4,
            XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4, 1, 4), (16, 4, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(16, 4)](buf0, primals_3, buf2, 16, 4,
            XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf2, (16, 1, 4), (4, 0, 1), 0), out=buf3)
        buf4 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_add_mul_sub_2[grid(64)](buf3, primals_1,
            buf4, buf5, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf6 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused__softmax_add_mul_sub_3[grid(256)](buf6, primals_1,
            buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf5
        triton_poi_fused_clone_4[grid(16, 4)](buf0, primals_3, buf7, 16, 4,
            XBLOCK=2, YBLOCK=16, num_warps=1, num_stages=1)
        del buf0
        del primals_3
        buf8 = reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 1), 0)
        del buf4
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf7, (16, 4, 1), (4, 1, 0), 0), out=buf8)
        buf9 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_5[grid(16, 4)](buf8, buf9, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf8, (16, 4), (4, 1), 0)
        del buf8
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf9, (16, 4), (
            4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0),
            alpha=1, beta=1, out=buf10)
        del primals_6
    return reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0
        ), primals_1, reinterpret_tensor(primals_4, (16, 4), (4, 1), 0
        ), buf6, reinterpret_tensor(buf9, (16, 4), (4, 1), 0
        ), primals_5, reinterpret_tensor(buf7, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf1, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 4), 0)


class MultiHeadSelfAttentionNew(nn.Module):

    def __init__(self, d_ipt: 'int', n_head: 'int', dropout_p: 'float'=0.1):
        super(MultiHeadSelfAttentionNew, self).__init__()
        self.qkv_linear = nn.Linear(d_ipt, d_ipt * 3, True)
        self.n_head = n_head
        self.output_linear = nn.Linear(d_ipt, d_ipt, True)
        self.dropout = nn.Dropout(dropout_p)

    def forward(self, input_0, input_1):
        primals_2 = self.qkv_linear.weight
        primals_3 = self.qkv_linear.bias
        primals_5 = self.output_linear.weight
        primals_6 = self.output_linear.bias
        primals_1 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
