# 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/v2/cv26xmd6uhnpasjiayrxny6gmnwndswt7vj57asgoudfza6xzwle.py
# Topologically Sorted Source Nodes: [contiguous_1, matmul], Original ATen: [aten.clone, aten.view]
# Source node to ATen node mapping:
#   contiguous_1 => clone_2
#   matmul => view_1
# Graph fragment:
#   %clone_2 : [num_users=1] = call_function[target=torch.ops.aten.clone.default](args = (%expand,), kwargs = {memory_format: torch.contiguous_format})
#   %view_1 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%clone_2, [64, 4]), kwargs = {})
triton_poi_fused_clone_view_0 = async_compile.triton('triton_poi_fused_clone_view_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_view_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_view_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = (xindex // 4)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (4*(x1 // 4))), xmask)
    tl.store(out_ptr0 + (x2), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/7q/c7qgwbp7h2p2gm2mjyswszqjvgaiq3hw6ul3ljm3kx5kacqyu56x.py
# Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
# Source node to ATen node mapping:
#   contiguous => 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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_clone_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, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x2 = (xindex // 64)
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (16*x2)), xmask, eviction_policy='evict_last')
    tl.store(out_ptr0 + (x3), tmp0, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_6/inductor_cache/nt/cntmnffcozkeu7dmnap4ub3giwedzhutvy2zd57fhzbmevu6xzld.py
# Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
# Source node to ATen node mapping:
#   add => add
# Graph fragment:
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%bmm, %primals_4), kwargs = {})
triton_poi_fused_add_2 = async_compile.triton('triton_poi_fused_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: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_2', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr0 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + (x0), tmp3, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [proj], Original ATen: [aten.mm]
        extern_kernels.mm(primals_2, primals_3, out=buf0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous_1, matmul], Original ATen: [aten.clone, aten.view]
        stream0 = get_raw_stream(0)
        triton_poi_fused_clone_view_0.run(primals_1, buf1, 256, grid=grid(256), stream=stream0)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(buf1, buf0, out=buf2)
        del buf0
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [contiguous], Original ATen: [aten.clone]
        triton_poi_fused_clone_1.run(primals_1, buf3, 256, grid=grid(256), stream=stream0)
        del primals_1
        buf4 = empty_strided_cuda((64, 1, 1), (1, 1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bmm], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 1, 4), (4, 4, 1), 0), reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 0), 0), out=buf4)
        del buf2
        buf5 = buf4; del buf4  # reuse
        # Topologically Sorted Source Nodes: [add], Original ATen: [aten.add]
        triton_poi_fused_add_2.run(buf5, primals_4, 64, grid=grid(64), stream=stream0)
        del primals_4
    return (reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf3, (64, 1, 4), (4, 1, 1), 0), reinterpret_tensor(buf1, (4, 64), (1, 4), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), reinterpret_tensor(primals_3, (4, 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((4, 4), (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((1, ), (1, ), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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.utils.data.dataloader


class TwoWordBilinearLabelProbe(nn.Module):
    """ Computes a bilinear function of pairs of vectors.
    For a batch of sentences, computes all n^2 pairs of scores
    for each sentence in the batch.
    """

    def __init__(self, model_dim, rank, prob, device):
        super(TwoWordBilinearLabelProbe, self).__init__()
        self.maximum_rank = rank
        self.model_dim = model_dim
        self.proj_L = nn.Parameter(data=torch.zeros(self.model_dim, self.
            maximum_rank))
        self.proj_R = nn.Parameter(data=torch.zeros(self.maximum_rank, self
            .model_dim))
        self.bias = nn.Parameter(data=torch.zeros(1))
        nn.init.uniform_(self.proj_L, -0.05, 0.05)
        nn.init.uniform_(self.proj_R, -0.05, 0.05)
        nn.init.uniform_(self.bias, -0.05, 0.05)
        self
        self.dropout = nn.Dropout(p=prob)

    def forward(self, batch):
        """ Computes all n^2 pairs of attachment scores
        for each sentence in a batch.
        Computes h_i^TAh_j for all i,j
        where A = LR, L in R^{model_dim x maximum_rank}; R in R^{maximum_rank x model_rank}
        hence A is rank-constrained to maximum_rank.
        Args:
          batch: a batch of word representations of the shape
            (batch_size, max_seq_len, representation_dim)
        Returns:
          A tensor of scores of shape (batch_size, max_seq_len, max_seq_len)
        """
        batchlen, seqlen, rank = batch.size()
        batch = self.dropout(batch)
        proj = torch.mm(self.proj_L, self.proj_R)
        batch_square = batch.unsqueeze(2).expand(batchlen, seqlen, seqlen, rank
            )
        batch_transposed = batch.unsqueeze(1).expand(batchlen, seqlen,
            seqlen, rank).contiguous().view(batchlen * seqlen * seqlen, rank, 1
            )
        psd_transformed = torch.matmul(batch_square.contiguous(), proj).view(
            batchlen * seqlen * seqlen, 1, rank)
        logits = (torch.bmm(psd_transformed, batch_transposed) + self.bias
            ).view(batchlen, seqlen, seqlen)
        return logits


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


def get_init_inputs():
    return [[], {'model_dim': 4, 'rank': 4, 'prob': 0.5, 'device': 0}]

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
import torch.nn as nn
import torch.utils.data.dataloader
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_view_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * (x1 // 4)), xmask)
    tl.store(out_ptr0 + x2, tmp0, xmask)


@triton.jit
def triton_poi_fused_clone_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
    x0 = xindex % 16
    x2 = xindex // 64
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(primals_2, primals_3, out=buf0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_view_0[grid(256)](primals_1, buf1, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf1, buf0, out=buf2)
        del buf0
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_clone_1[grid(256)](primals_1, buf3, 256, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_1
        buf4 = empty_strided_cuda((64, 1, 1), (1, 1, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (64, 1, 4), (4, 4, 1), 
            0), reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 0), 0), out=buf4)
        del buf2
        buf5 = buf4
        del buf4
        triton_poi_fused_add_2[grid(64)](buf5, primals_4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_4
    return reinterpret_tensor(buf5, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(buf3, (64, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf1, (4, 64), (1, 4), 0), reinterpret_tensor(
        primals_2, (4, 4), (1, 4), 0), reinterpret_tensor(primals_3, (4, 4),
        (1, 4), 0)


class TwoWordBilinearLabelProbeNew(nn.Module):
    """ Computes a bilinear function of pairs of vectors.
    For a batch of sentences, computes all n^2 pairs of scores
    for each sentence in the batch.
    """

    def __init__(self, model_dim, rank, prob, device):
        super(TwoWordBilinearLabelProbeNew, self).__init__()
        self.maximum_rank = rank
        self.model_dim = model_dim
        self.proj_L = nn.Parameter(data=torch.zeros(self.model_dim, self.
            maximum_rank))
        self.proj_R = nn.Parameter(data=torch.zeros(self.maximum_rank, self
            .model_dim))
        self.bias = nn.Parameter(data=torch.zeros(1))
        nn.init.uniform_(self.proj_L, -0.05, 0.05)
        nn.init.uniform_(self.proj_R, -0.05, 0.05)
        nn.init.uniform_(self.bias, -0.05, 0.05)
        self
        self.dropout = nn.Dropout(p=prob)

    def forward(self, input_0):
        primals_2 = self.proj_L
        primals_3 = self.proj_R
        primals_4 = self.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
