# 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/yd/cydbtjoq352gcolmflbvu2nqkda7xg7q5hnvltb47jsg5dbmubym.py
# Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   matmul => 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 + (4*x2) + (16*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_4/inductor_cache/s2/cs2rk3o3kmhydx4oijp6rsdb5atcrq5axy4adadrpl7gkt7scies.py
# Topologically Sorted Source Nodes: [p_attn], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   p_attn => exp
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_11, 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 = {})
#   %div_tensor : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub_tensor, 1.0), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%div_tensor,), kwargs = {})
triton_poi_fused__softmax_1 = async_compile.triton('triton_poi_fused__softmax_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=[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__softmax_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__softmax_1(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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp1
    tmp16 = tl_math.exp(tmp15)
    tl.store(out_ptr0 + (x2), tmp16, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/3f/c3fx6bzkalkw7u7askqdnz4rzlcoyqiec4r434sjc5x3axxgkrmr.py
# Topologically Sorted Source Nodes: [p_attn], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   p_attn => div_1, sum_1
# Graph fragment:
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %div_1 : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_2 = async_compile.triton('triton_poi_fused__softmax_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__softmax_2', '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__softmax_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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 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, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_0.run(buf0, buf3, 16, 4, grid=grid(16, 4), stream=stream0)
        buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(buf1, buf4, 16, 4, grid=grid(16, 4), stream=stream0)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [p_attn], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_1.run(buf5, buf6, 256, grid=grid(256), stream=stream0)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0); del buf5  # reuse
        # Topologically Sorted Source Nodes: [p_attn], Original ATen: [aten._softmax]
        triton_poi_fused__softmax_2.run(buf6, buf7, 256, grid=grid(256), stream=stream0)
        del buf6
        buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0); del buf1  # reuse
        # Topologically Sorted Source Nodes: [x], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.run(buf2, buf8, 16, 4, grid=grid(16, 4), stream=stream0)
        buf9 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0); del buf2  # reuse
        # Topologically Sorted Source Nodes: [x], 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, 1), (16, 4, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
        triton_poi_fused_clone_0.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: [linear_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf10, (16, 4), (4, 1), 0), reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf11)
    return (reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0), buf7, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0), reinterpret_tensor(primals_4, (16, 4), (4, 1), 0), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), buf7, reinterpret_tensor(buf10, (16, 4), (4, 1), 0), primals_7, reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf4, (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, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4), (4, 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, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4), (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])
    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 math
import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.onnx.operators


def combine_heads(X):
    """
    Combine heads (the inverse of split heads):
    1) Transpose X from (batch size, nheads, sequence length, d_head) to
        (batch size, sequence length, nheads, d_head)
    2) Combine (reshape) last 2 dimensions (nheads, d_head) into 1 (d_model)

    Inputs:
      X : [batch size * nheads, sequence length, d_head]
      nheads : integer
      d_head : integer

    Outputs:
      [batch_size, seq_len, d_model]

    """
    X = X.transpose(1, 2)
    nheads, d_head = X.shape[-2:]
    return X.contiguous().view(list(X.shape[:-2]) + [nheads * d_head])


def create_src_lengths_mask(batch_size, src_lengths):
    max_srclen = src_lengths.max()
    src_indices = torch.arange(0, max_srclen).unsqueeze(0).type_as(src_lengths)
    src_indices = src_indices.expand(batch_size, max_srclen)
    src_lengths = src_lengths.unsqueeze(dim=1).expand(batch_size, max_srclen)
    return (src_indices < src_lengths).int().detach()


def apply_masks(scores, batch_size, unseen_mask, src_lengths):
    seq_len = scores.shape[-1]
    sequence_mask = torch.ones(seq_len, seq_len).unsqueeze(0).int()
    if unseen_mask:
        sequence_mask = torch.tril(torch.ones(seq_len, seq_len), diagonal=0
            ).unsqueeze(0).int()
    if src_lengths is not None:
        src_lengths_mask = create_src_lengths_mask(batch_size=batch_size,
            src_lengths=src_lengths).unsqueeze(-2)
        sequence_mask = sequence_mask & src_lengths_mask
    sequence_mask = sequence_mask.unsqueeze(1)
    scores = scores.masked_fill(sequence_mask == 0, -np.inf)
    return scores


def scaled_dot_prod_attn(query, key, value, unseen_mask=False, src_lengths=None
    ):
    """
    Scaled Dot Product Attention

    Implements equation:
    Attention(Q, K, V) = softmax(QK^T/\\sqrt{d_k})V

    Inputs:
      query : [batch size, nheads, sequence length, d_k]
      key : [batch size, nheads, sequence length, d_k]
      value : [batch size, nheads, sequence length, d_v]
      unseen_mask: if True, only attend to previous sequence positions
      src_lengths_mask: if True, mask padding based on src_lengths

    Outputs:
      attn: [batch size, sequence length, d_v]

    Note that in this implementation d_q = d_k = d_v = dim
    """
    d_k = query.shape[-1]
    scores = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(d_k)
    if unseen_mask or src_lengths is not None:
        scores = apply_masks(scores=scores, batch_size=query.shape[0],
            unseen_mask=unseen_mask, src_lengths=src_lengths)
    p_attn = F.softmax(scores, dim=-1)
    return torch.matmul(p_attn, value), p_attn


def split_heads(X, nheads):
    """
    Split heads:
    1) Split (reshape) last dimension (size d_model) into nheads, d_head
    2) Transpose X from (batch size, sequence length, nheads, d_head) to
        (batch size, nheads, sequence length, d_head)

    Inputs:
      X : [batch size, sequence length, nheads * d_head]
      nheads : integer
    Outputs:
      [batch size,  nheads, sequence length, d_head]

    """
    last_dim = X.shape[-1]
    assert last_dim % nheads == 0
    X_last_dim_split = X.view(list(X.shape[:-1]) + [nheads, last_dim // nheads]
        )
    return X_last_dim_split.transpose(1, 2)


class MultiheadAttention(nn.Module):
    """
    Multiheaded Scaled Dot Product Attention

    Implements equation:
    MultiHead(Q, K, V) = Concat(head_1,...,head_h)W^O
        where head_i = Attention(QW_i^Q, KW_i^K, VW_i^V)

    Similarly to the above, d_k = d_v = d_model / h

    Inputs
      init:
        nheads : integer # of attention heads
        d_model : model dimensionality
        d_head : dimensionality of a single head

      forward:
        query : [batch size, sequence length, d_model]
        key: [batch size, sequence length, d_model]
        value: [batch size, sequence length, d_model]
      unseen_mask: if True, only attend to previous sequence positions
      src_lengths_mask: if True, mask padding based on src_lengths

    Output
      result : [batch_size, sequence length, d_model]
    """

    def __init__(self, nheads, d_model):
        """Take in model size and number of heads."""
        super(MultiheadAttention, self).__init__()
        assert d_model % nheads == 0
        self.d_head = d_model // nheads
        self.nheads = nheads
        self.Q_fc = nn.Linear(d_model, d_model, bias=False)
        self.K_fc = nn.Linear(d_model, d_model, bias=False)
        self.V_fc = nn.Linear(d_model, d_model, bias=False)
        self.output_fc = nn.Linear(d_model, d_model, bias=False)
        self.attn = None

    def forward(self, query, key, value, unseen_mask=False, src_lengths=None):
        query = split_heads(self.Q_fc(query), self.nheads)
        key = split_heads(self.K_fc(key), self.nheads)
        value = split_heads(self.V_fc(value), self.nheads)
        x, self.attn = scaled_dot_prod_attn(query=query, key=key, value=
            value, unseen_mask=unseen_mask, src_lengths=src_lengths)
        x = combine_heads(x)
        return self.output_fc(x)


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


def get_init_inputs():
    return [[], {'nheads': 4, 'd_model': 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 math
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch.jit
import torch.onnx.operators
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 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused__softmax_1(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp1
    tmp16 = tl_math.exp(tmp15)
    tl.store(out_ptr0 + x2, tmp16, xmask)


@triton.jit
def triton_poi_fused__softmax_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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 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, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = 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, buf3, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf0
        triton_poi_fused_clone_0[grid(16, 4)](buf1, buf4, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused__softmax_1[grid(256)](buf5, buf6, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf7 = reinterpret_tensor(buf5, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf5
        triton_poi_fused__softmax_2[grid(256)](buf6, buf7, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf6
        buf8 = reinterpret_tensor(buf1, (4, 4, 4, 1), (16, 4, 1, 1), 0)
        del buf1
        triton_poi_fused_clone_0[grid(16, 4)](buf2, buf8, 16, 4, XBLOCK=4,
            YBLOCK=16, num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
        del buf2
        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, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_clone_0[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.mm(reinterpret_tensor(buf10, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf11)
    return reinterpret_tensor(buf11, (4, 4, 4), (16, 4, 1), 0
        ), buf7, reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_4, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_6, (16, 4), (4, 1), 0
        ), buf7, reinterpret_tensor(buf10, (16, 4), (4, 1), 0
        ), primals_7, reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)


def combine_heads(X):
    """
    Combine heads (the inverse of split heads):
    1) Transpose X from (batch size, nheads, sequence length, d_head) to
        (batch size, sequence length, nheads, d_head)
    2) Combine (reshape) last 2 dimensions (nheads, d_head) into 1 (d_model)

    Inputs:
      X : [batch size * nheads, sequence length, d_head]
      nheads : integer
      d_head : integer

    Outputs:
      [batch_size, seq_len, d_model]

    """
    X = X.transpose(1, 2)
    nheads, d_head = X.shape[-2:]
    return X.contiguous().view(list(X.shape[:-2]) + [nheads * d_head])


def create_src_lengths_mask(batch_size, src_lengths):
    max_srclen = src_lengths.max()
    src_indices = torch.arange(0, max_srclen).unsqueeze(0).type_as(src_lengths)
    src_indices = src_indices.expand(batch_size, max_srclen)
    src_lengths = src_lengths.unsqueeze(dim=1).expand(batch_size, max_srclen)
    return (src_indices < src_lengths).int().detach()


def apply_masks(scores, batch_size, unseen_mask, src_lengths):
    seq_len = scores.shape[-1]
    sequence_mask = torch.ones(seq_len, seq_len).unsqueeze(0).int()
    if unseen_mask:
        sequence_mask = torch.tril(torch.ones(seq_len, seq_len), diagonal=0
            ).unsqueeze(0).int()
    if src_lengths is not None:
        src_lengths_mask = create_src_lengths_mask(batch_size=batch_size,
            src_lengths=src_lengths).unsqueeze(-2)
        sequence_mask = sequence_mask & src_lengths_mask
    sequence_mask = sequence_mask.unsqueeze(1)
    scores = scores.masked_fill(sequence_mask == 0, -np.inf)
    return scores


def scaled_dot_prod_attn(query, key, value, unseen_mask=False, src_lengths=None
    ):
    """
    Scaled Dot Product Attention

    Implements equation:
    Attention(Q, K, V) = softmax(QK^T/\\sqrt{d_k})V

    Inputs:
      query : [batch size, nheads, sequence length, d_k]
      key : [batch size, nheads, sequence length, d_k]
      value : [batch size, nheads, sequence length, d_v]
      unseen_mask: if True, only attend to previous sequence positions
      src_lengths_mask: if True, mask padding based on src_lengths

    Outputs:
      attn: [batch size, sequence length, d_v]

    Note that in this implementation d_q = d_k = d_v = dim
    """
    d_k = query.shape[-1]
    scores = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(d_k)
    if unseen_mask or src_lengths is not None:
        scores = apply_masks(scores=scores, batch_size=query.shape[0],
            unseen_mask=unseen_mask, src_lengths=src_lengths)
    p_attn = F.softmax(scores, dim=-1)
    return torch.matmul(p_attn, value), p_attn


def split_heads(X, nheads):
    """
    Split heads:
    1) Split (reshape) last dimension (size d_model) into nheads, d_head
    2) Transpose X from (batch size, sequence length, nheads, d_head) to
        (batch size, nheads, sequence length, d_head)

    Inputs:
      X : [batch size, sequence length, nheads * d_head]
      nheads : integer
    Outputs:
      [batch size,  nheads, sequence length, d_head]

    """
    last_dim = X.shape[-1]
    assert last_dim % nheads == 0
    X_last_dim_split = X.view(list(X.shape[:-1]) + [nheads, last_dim // nheads]
        )
    return X_last_dim_split.transpose(1, 2)


class MultiheadAttentionNew(nn.Module):
    """
    Multiheaded Scaled Dot Product Attention

    Implements equation:
    MultiHead(Q, K, V) = Concat(head_1,...,head_h)W^O
        where head_i = Attention(QW_i^Q, KW_i^K, VW_i^V)

    Similarly to the above, d_k = d_v = d_model / h

    Inputs
      init:
        nheads : integer # of attention heads
        d_model : model dimensionality
        d_head : dimensionality of a single head

      forward:
        query : [batch size, sequence length, d_model]
        key: [batch size, sequence length, d_model]
        value: [batch size, sequence length, d_model]
      unseen_mask: if True, only attend to previous sequence positions
      src_lengths_mask: if True, mask padding based on src_lengths

    Output
      result : [batch_size, sequence length, d_model]
    """

    def __init__(self, nheads, d_model):
        """Take in model size and number of heads."""
        super(MultiheadAttentionNew, self).__init__()
        assert d_model % nheads == 0
        self.d_head = d_model // nheads
        self.nheads = nheads
        self.Q_fc = nn.Linear(d_model, d_model, bias=False)
        self.K_fc = nn.Linear(d_model, d_model, bias=False)
        self.V_fc = nn.Linear(d_model, d_model, bias=False)
        self.output_fc = nn.Linear(d_model, d_model, bias=False)
        self.attn = None

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.Q_fc.weight
        primals_3 = self.K_fc.weight
        primals_5 = self.V_fc.weight
        primals_7 = self.output_fc.weight
        primals_2 = input_0
        primals_4 = input_1
        primals_6 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
