# 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_6/inductor_cache/vw/cvwd3awfb46bqxkrcjujfj2ynwkj3kcousl7ykwdhec2hpstu7wc.py
# Topologically Sorted Source Nodes: [w], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   w => 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_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.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_0', '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_0(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/vb/cvbyysxwwzx66gcjfuilcggw4qhb62pskdxdgwsepqktje7qiwoj.py
# Topologically Sorted Source Nodes: [w], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   w => 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_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.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_1', '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_1(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (4 + y0 + (12*x2) + (48*y1)), xmask & ymask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + (4*y3)), tmp0, xmask & ymask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/q5/cq5txccz3423cuenidgj7uonwcfq67qhzerrzj3ud7utzljmdygb.py
# Topologically Sorted Source Nodes: [w_1, w_3], Original ATen: [aten.add, aten._softmax]
# Source node to ATen node mapping:
#   w_1 => add_1
#   w_3 => exp, sum_1
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_8, %permute_6), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 1), kwargs = {})
#   %amax_default : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%mul_tensor, [-1], True), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %mul_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_tensor, 1.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%mul_tensor_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_2 = async_compile.triton('triton_poi_fused__softmax_add_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_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_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
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16)
    tmp0 = tl.load(in_ptr0 + (4*x3), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (2 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (3 + (4*x3)), xmask, eviction_policy='evict_last')
    tmp1 = (-1) + ((4 + (4*x0)) % 5)
    tmp2 = tl.full([1], 0, tl.int64)
    tmp3 = tmp1 >= tmp2
    tmp4 = tl.load(in_ptr1 + ((-1) + (4*((4 + (4*x0)) // 5)) + (16*x2) + (16*((1 + x0) // 5)) + (64*x1) + (64*((1 + x0 + (5*x2)) // 20)) + ((4 + (4*x0)) % 5)), tmp3 & xmask, eviction_policy='evict_last', other=0.0)
    tmp5 = tmp0 + tmp4
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp9 = (-1) + ((4*x0) % 5)
    tmp10 = tmp9 >= tmp2
    tmp11 = tl.load(in_ptr1 + (3 + (4*((4*x0) // 5)) + (16*x2) + (16*((5 + (4*x0)) // 20)) + (64*x1) + (64*((5 + (4*x0) + (20*x2)) // 80)) + ((4*x0) % 5)), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp8 + tmp11
    tmp13 = tmp12 * tmp6
    tmp14 = triton_helpers.maximum(tmp7, tmp13)
    tmp16 = (-1) + ((6 + (4*x0)) % 5)
    tmp17 = tmp16 >= tmp2
    tmp18 = tl.load(in_ptr1 + ((-1) + (4*((6 + (4*x0)) // 5)) + (16*x2) + (16*((3 + (2*x0)) // 10)) + (64*x1) + (64*((3 + (2*x0) + (10*x2)) // 40)) + ((6 + (4*x0)) % 5)), tmp17 & xmask, eviction_policy='evict_last', other=0.0)
    tmp19 = tmp15 + tmp18
    tmp20 = tmp19 * tmp6
    tmp21 = triton_helpers.maximum(tmp14, tmp20)
    tmp23 = (-1) + ((7 + (4*x0)) % 5)
    tmp24 = tmp23 >= tmp2
    tmp25 = tl.load(in_ptr1 + ((-1) + (4*((7 + (4*x0)) // 5)) + (16*x2) + (16*((7 + (4*x0)) // 20)) + (64*x1) + (64*((7 + (4*x0) + (20*x2)) // 80)) + ((7 + (4*x0)) % 5)), tmp24 & xmask, eviction_policy='evict_last', other=0.0)
    tmp26 = tmp22 + tmp25
    tmp27 = tmp26 * tmp6
    tmp28 = triton_helpers.maximum(tmp21, tmp27)
    tmp29 = tmp7 - tmp28
    tmp30 = tmp29 * tmp6
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp13 - tmp28
    tmp33 = tmp32 * tmp6
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp31 + tmp34
    tmp36 = tmp20 - tmp28
    tmp37 = tmp36 * tmp6
    tmp38 = tl_math.exp(tmp37)
    tmp39 = tmp35 + tmp38
    tmp40 = tmp27 - tmp28
    tmp41 = tmp40 * tmp6
    tmp42 = tl_math.exp(tmp41)
    tmp43 = tmp39 + tmp42
    tl.store(out_ptr0 + (x3), tmp28, xmask)
    tl.store(out_ptr1 + (x3), tmp43, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/uo/cuoq4opu62wnjbty2km65f26c5ewfnf5virutzfemf56n6p2ewli.py
# Topologically Sorted Source Nodes: [w_1, w_3], Original ATen: [aten.add, aten._softmax]
# Source node to ATen node mapping:
#   w_1 => add_1
#   w_3 => div, exp
# Graph fragment:
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%view_8, %permute_6), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_1, 1), kwargs = {})
#   %sub_tensor : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor, %amax_default), kwargs = {})
#   %mul_tensor_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_tensor, 1.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%mul_tensor_1,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_add_3 = async_compile.triton('triton_poi_fused__softmax_add_3', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

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

@triton_heuristics.pointwise(
    size_hints=[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_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_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
    x0 = xindex % 4
    x1 = (xindex // 4) % 4
    x2 = (xindex // 16) % 4
    x3 = (xindex // 64)
    x6 = (xindex // 4)
    tmp0 = tl.load(in_out_ptr0 + (x4), xmask)
    tmp8 = tl.load(in_ptr1 + (x6), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + (x6), xmask, eviction_policy='evict_last')
    tmp1 = (-1) + ((4 + x0 + (4*x1)) % 5)
    tmp2 = tl.full([1], 0, tl.int64)
    tmp3 = tmp1 >= tmp2
    tmp4 = tl.load(in_ptr0 + ((-1) + (4*((4 + x0 + (4*x1)) // 5)) + (16*x3) + (16*((4 + x0 + (4*x1)) // 20)) + (64*x2) + (64*((4 + x0 + (4*x1) + (20*x3)) // 80)) + ((4 + x0 + (4*x1)) % 5)), tmp3 & xmask, eviction_policy='evict_last', other=0.0)
    tmp5 = tmp0 + tmp4
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = tmp9 * tmp6
    tmp11 = tl_math.exp(tmp10)
    tmp13 = tmp11 / tmp12
    tl.store(in_out_ptr0 + (x4), tmp13, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/no/cnouta5dd4rade2moxf36iemo7zwrdppxzx4t3lxbdxmsz575wvx.py
# Topologically Sorted Source Nodes: [a], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   a => clone_2
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand_5,), 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.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_4', '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_4(in_ptr0, out_ptr0, ynumel, xnumel, YBLOCK : tl.constexpr, XBLOCK : tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = (yindex // 4)
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (8 + y0 + (12*x2) + (48*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_6/inductor_cache/we/cwe54p4p4jvwbdktkpj3wy2coheu6f3r3dgvi7ozm7xjfk4mgbwx.py
# Topologically Sorted Source Nodes: [reshape_10], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   reshape_10 => clone_3
# Graph fragment:
#   %clone_3 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%view_18,), 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, (1, 4, 12), (48, 12, 1))
    assert_size_stride(primals_3, (12, ), (1, ))
    assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (1, 4, 4), (16, 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.addmm(primals_3, reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 12), (12, 1), 0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        buf1 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [w], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf0, 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: [w], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(buf0, 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: [w], 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, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [rel], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 16, 1), (1, 12, 0), 0), reinterpret_tensor(primals_4, (4, 1, 4), (4, 1, 1), 0), out=buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [w_1, w_3], Original ATen: [aten.add, aten._softmax]
        triton_poi_fused__softmax_add_2.run(buf3, buf4, buf5, buf6, 64, grid=grid(64), stream=stream0)
        buf7 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf3  # reuse
        # Topologically Sorted Source Nodes: [w_1, w_3], Original ATen: [aten.add, aten._softmax]
        triton_poi_fused__softmax_add_3.run(buf7, buf4, buf5, buf6, 256, grid=grid(256), stream=stream0)
        del buf4
        buf8 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf6  # reuse
        # Topologically Sorted Source Nodes: [a], Original ATen: [aten.clone]
        triton_poi_fused_clone_4.run(buf0, buf8, 16, 4, grid=grid(16, 4), stream=stream0)
        buf9 = reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [a], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [reshape_10], Original ATen: [aten.clone]
        triton_poi_fused_clone_5.run(buf9, buf10, 16, 4, grid=grid(16, 4), stream=stream0)
        buf11 = reinterpret_tensor(buf9, (16, 4), (4, 1), 0); del buf9  # reuse
        # Topologically Sorted Source Nodes: [], Original ATen: []
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf10, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (4, 1), 0), alpha=1, beta=1, out=buf11)
        del primals_6
    return (reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0), buf7, reinterpret_tensor(buf10, (4, 16), (1, 4), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf0, (4, 1, 16), (1, 1, 12), 0), primals_4, reinterpret_tensor(buf1, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 4), 0), reinterpret_tensor(primals_1, (4, 16), (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((1, 4, 12), (48, 12, 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, 1), (4, 1, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((1, 4, 4), (16, 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


class Attention(nn.Module):

    def __init__(self, heads, n_state):
        super().__init__()
        assert n_state % heads == 0
        self.heads = heads
        self.n_state = n_state
        self.depth = self.n_state // self.heads

    def split_heads(self, x: 'torch.Tensor', batch: 'int', seq_len: 'int'):
        x = x.reshape((batch, seq_len, self.heads, self.depth))
        return x.permute(0, 2, 1, 3)

    def combine_heads(self, x: 'torch.Tensor', batch: 'int', seq_len: 'int'):
        x = x.permute(0, 2, 1, 3)
        return x.reshape((batch, seq_len, self.n_state))


class Conv1d(nn.Module):

    def __init__(self, nf, nx, stdev=0.02):
        super().__init__()
        self.nf = nf
        self.nx = nx
        self.stdev = stdev
        self.w = nn.Parameter(torch.normal(size=[1, self.nx, self.nf], mean
            =0.0, std=self.stdev))
        self.b = nn.Parameter(torch.zeros([self.nf]))

    def forward(self, x: 'torch.Tensor'):
        shape = x.size()
        start, nx = shape[:-1], shape[-1]
        return torch.reshape(torch.matmul(torch.reshape(x, [-1, nx]), torch
            .reshape(self.w, [-1, self.nf])) + self.b, start + (self.nf,))


class RelativeAttention(Attention):

    def __init__(self, heads, n_state, max_sequence):
        super().__init__(heads, n_state)
        self.max_sequence = max_sequence
        self.c_attn = Conv1d(self.n_state * 3, self.n_state)
        self.c_proj = Conv1d(self.n_state, self.n_state)
        self.E = nn.Parameter(torch.Tensor(self.heads, self.max_sequence, 
            n_state // heads))
        nn.init.xavier_normal_(self.E)

    def relative_attn(self, q: 'torch.Tensor', E: 'torch.Tensor', batch:
        'int', seq_len: 'int'):
        q_ = q.permute(1, 0, 2, 3)
        q_ = q_.reshape(self.heads, batch * seq_len, self.depth)
        E = E[:, self.max_sequence - seq_len:]
        rel = q_ @ E.transpose(-1, -2)
        rel = rel.reshape(self.heads, batch, seq_len, seq_len)
        rel = torch.nn.functional.pad(rel, (1, 0), 'constant', 0)
        rel = rel.reshape(self.heads, batch, seq_len + 1, seq_len)
        rel = rel[:, :, 1:]
        rel = rel.permute(1, 0, 2, 3)
        return rel

    def multihead_attn(self, q: 'torch.Tensor', k: 'torch.Tensor', v:
        'torch.Tensor', batch, seq_len, mask=None):
        w = q @ k.transpose(-1, -2)
        w = w + self.relative_attn(q, self.E, batch, seq_len)
        w = w * (1 / self.depth ** (1 / 2))
        if mask is not None:
            w += mask
        w = w.softmax(-1)
        a = w @ v
        return a

    def forward(self, x: 'torch.Tensor', mask=None):
        batch, seq_len, _ = x.size()
        c = self.c_attn(x)
        q, k, v = torch.split(c, self.n_state, dim=2)
        q = self.split_heads(q, batch, seq_len)
        k = self.split_heads(k, batch, seq_len)
        v = self.split_heads(v, batch, seq_len)
        a = self.multihead_attn(q, k, v, batch, seq_len, mask)
        a = self.combine_heads(a, batch, seq_len)
        a = self.c_proj(a)
        return a


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


def get_init_inputs():
    return [[], {'heads': 4, 'n_state': 4, 'max_sequence': 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, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


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


@triton.jit
def triton_poi_fused__softmax_add_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
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + 4 * x3, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * x3), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr0 + (3 + 4 * x3), xmask, eviction_policy='evict_last'
        )
    tmp1 = -1 + (4 + 4 * x0) % 5
    tmp2 = tl.full([1], 0, tl.int64)
    tmp3 = tmp1 >= tmp2
    tmp4 = tl.load(in_ptr1 + (-1 + 4 * ((4 + 4 * x0) // 5) + 16 * x2 + 16 *
        ((1 + x0) // 5) + 64 * x1 + 64 * ((1 + x0 + 5 * x2) // 20) + (4 + 4 *
        x0) % 5), tmp3 & xmask, eviction_policy='evict_last', other=0.0)
    tmp5 = tmp0 + tmp4
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp9 = -1 + 4 * x0 % 5
    tmp10 = tmp9 >= tmp2
    tmp11 = tl.load(in_ptr1 + (3 + 4 * (4 * x0 // 5) + 16 * x2 + 16 * ((5 +
        4 * x0) // 20) + 64 * x1 + 64 * ((5 + 4 * x0 + 20 * x2) // 80) + 4 *
        x0 % 5), tmp10 & xmask, eviction_policy='evict_last', other=0.0)
    tmp12 = tmp8 + tmp11
    tmp13 = tmp12 * tmp6
    tmp14 = triton_helpers.maximum(tmp7, tmp13)
    tmp16 = -1 + (6 + 4 * x0) % 5
    tmp17 = tmp16 >= tmp2
    tmp18 = tl.load(in_ptr1 + (-1 + 4 * ((6 + 4 * x0) // 5) + 16 * x2 + 16 *
        ((3 + 2 * x0) // 10) + 64 * x1 + 64 * ((3 + 2 * x0 + 10 * x2) // 40
        ) + (6 + 4 * x0) % 5), tmp17 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp19 = tmp15 + tmp18
    tmp20 = tmp19 * tmp6
    tmp21 = triton_helpers.maximum(tmp14, tmp20)
    tmp23 = -1 + (7 + 4 * x0) % 5
    tmp24 = tmp23 >= tmp2
    tmp25 = tl.load(in_ptr1 + (-1 + 4 * ((7 + 4 * x0) // 5) + 16 * x2 + 16 *
        ((7 + 4 * x0) // 20) + 64 * x1 + 64 * ((7 + 4 * x0 + 20 * x2) // 80
        ) + (7 + 4 * x0) % 5), tmp24 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp26 = tmp22 + tmp25
    tmp27 = tmp26 * tmp6
    tmp28 = triton_helpers.maximum(tmp21, tmp27)
    tmp29 = tmp7 - tmp28
    tmp30 = tmp29 * tmp6
    tmp31 = tl_math.exp(tmp30)
    tmp32 = tmp13 - tmp28
    tmp33 = tmp32 * tmp6
    tmp34 = tl_math.exp(tmp33)
    tmp35 = tmp31 + tmp34
    tmp36 = tmp20 - tmp28
    tmp37 = tmp36 * tmp6
    tmp38 = tl_math.exp(tmp37)
    tmp39 = tmp35 + tmp38
    tmp40 = tmp27 - tmp28
    tmp41 = tmp40 * tmp6
    tmp42 = tl_math.exp(tmp41)
    tmp43 = tmp39 + tmp42
    tl.store(out_ptr0 + x3, tmp28, xmask)
    tl.store(out_ptr1 + x3, tmp43, xmask)


@triton.jit
def triton_poi_fused__softmax_add_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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16 % 4
    x3 = xindex // 64
    x6 = xindex // 4
    tmp0 = tl.load(in_out_ptr0 + x4, xmask)
    tmp8 = tl.load(in_ptr1 + x6, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr2 + x6, xmask, eviction_policy='evict_last')
    tmp1 = -1 + (4 + x0 + 4 * x1) % 5
    tmp2 = tl.full([1], 0, tl.int64)
    tmp3 = tmp1 >= tmp2
    tmp4 = tl.load(in_ptr0 + (-1 + 4 * ((4 + x0 + 4 * x1) // 5) + 16 * x3 +
        16 * ((4 + x0 + 4 * x1) // 20) + 64 * x2 + 64 * ((4 + x0 + 4 * x1 +
        20 * x3) // 80) + (4 + x0 + 4 * x1) % 5), tmp3 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp5 = tmp0 + tmp4
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp9 = tmp7 - tmp8
    tmp10 = tmp9 * tmp6
    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, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (8 + y0 + 12 * x2 + 48 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_clone_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, (1, 4, 12), (48, 12, 1))
    assert_size_stride(primals_3, (12,), (1,))
    assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (1, 4, 4), (16, 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.addmm(primals_3, reinterpret_tensor(primals_1, (16, 
            4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 12), (12, 1),
            0), alpha=1, beta=1, out=buf0)
        del primals_2
        del primals_3
        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, buf1, 16, 4, XBLOCK=4,
            YBLOCK=16, 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, buf2, 16, 4, XBLOCK=4,
            YBLOCK=16, 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, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 16, 1), (1, 12, 0),
            0), reinterpret_tensor(primals_4, (4, 1, 4), (4, 1, 1), 0), out
            =buf4)
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf6 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused__softmax_add_2[grid(64)](buf3, buf4, buf5, buf6, 
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf3, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf3
        triton_poi_fused__softmax_add_3[grid(256)](buf7, buf4, buf5, buf6, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf4
        buf8 = reinterpret_tensor(buf6, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf6
        triton_poi_fused_clone_4[grid(16, 4)](buf0, buf8, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf5, (16, 4, 1), (4, 1, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_5[grid(16, 4)](buf9, buf10, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf11 = reinterpret_tensor(buf9, (16, 4), (4, 1), 0)
        del buf9
        extern_kernels.addmm(primals_6, reinterpret_tensor(buf10, (16, 4),
            (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (4, 1), 0),
            alpha=1, beta=1, out=buf11)
        del primals_6
    return reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0
        ), buf7, reinterpret_tensor(buf10, (4, 16), (1, 4), 0
        ), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0
        ), reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf0, (4, 1, 16), (1, 1, 12), 0
        ), primals_4, reinterpret_tensor(buf1, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 4), 0
        ), reinterpret_tensor(primals_1, (4, 16), (1, 4), 0)


class Attention(nn.Module):

    def __init__(self, heads, n_state):
        super().__init__()
        assert n_state % heads == 0
        self.heads = heads
        self.n_state = n_state
        self.depth = self.n_state // self.heads

    def split_heads(self, x: 'torch.Tensor', batch: 'int', seq_len: 'int'):
        x = x.reshape((batch, seq_len, self.heads, self.depth))
        return x.permute(0, 2, 1, 3)

    def combine_heads(self, x: 'torch.Tensor', batch: 'int', seq_len: 'int'):
        x = x.permute(0, 2, 1, 3)
        return x.reshape((batch, seq_len, self.n_state))


class Conv1d(nn.Module):

    def __init__(self, nf, nx, stdev=0.02):
        super().__init__()
        self.nf = nf
        self.nx = nx
        self.stdev = stdev
        self.w = nn.Parameter(torch.normal(size=[1, self.nx, self.nf], mean
            =0.0, std=self.stdev))
        self.b = nn.Parameter(torch.zeros([self.nf]))

    def forward(self, x: 'torch.Tensor'):
        shape = x.size()
        start, nx = shape[:-1], shape[-1]
        return torch.reshape(torch.matmul(torch.reshape(x, [-1, nx]), torch
            .reshape(self.w, [-1, self.nf])) + self.b, start + (self.nf,))


class RelativeAttentionNew(Attention):

    def __init__(self, heads, n_state, max_sequence):
        super().__init__(heads, n_state)
        self.max_sequence = max_sequence
        self.c_attn = Conv1d(self.n_state * 3, self.n_state)
        self.c_proj = Conv1d(self.n_state, self.n_state)
        self.E = nn.Parameter(torch.Tensor(self.heads, self.max_sequence, 
            n_state // heads))
        nn.init.xavier_normal_(self.E)

    def relative_attn(self, q: 'torch.Tensor', E: 'torch.Tensor', batch:
        'int', seq_len: 'int'):
        q_ = q.permute(1, 0, 2, 3)
        q_ = q_.reshape(self.heads, batch * seq_len, self.depth)
        E = E[:, self.max_sequence - seq_len:]
        rel = q_ @ E.transpose(-1, -2)
        rel = rel.reshape(self.heads, batch, seq_len, seq_len)
        rel = torch.nn.functional.pad(rel, (1, 0), 'constant', 0)
        rel = rel.reshape(self.heads, batch, seq_len + 1, seq_len)
        rel = rel[:, :, 1:]
        rel = rel.permute(1, 0, 2, 3)
        return rel

    def multihead_attn(self, q: 'torch.Tensor', k: 'torch.Tensor', v:
        'torch.Tensor', batch, seq_len, mask=None):
        w = q @ k.transpose(-1, -2)
        w = w + self.relative_attn(q, self.E, batch, seq_len)
        w = w * (1 / self.depth ** (1 / 2))
        if mask is not None:
            w += mask
        w = w.softmax(-1)
        a = w @ v
        return a

    def forward(self, input_0):
        primals_4 = self.E
        primals_2 = self.c_attn.w
        primals_3 = self.c_attn.b
        primals_5 = self.c_proj.w
        primals_6 = self.c_proj.b
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
