# 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_8/inductor_cache/is/cisc2qptf5u5c6jlvkji4njhxsbq22eukpz7kuldfdoxadbbikoy.py
# Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
# Source node to ATen node mapping:
#   mul => mul
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
triton_poi_fused_mul_0 = async_compile.triton('triton_poi_fused_mul_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=[32768], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mul_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 24576
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = tl.full([XBLOCK], True, tl.int1)
    x3 = (xindex // 8)
    x0 = xindex % 8
    x2 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + (8*x2)), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + (x4), tmp2, None)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/xi/cxi3ma2zsffgaa6murtux7xtgsgq2wj7vteix33b4iitg327qtvp.py
# Topologically Sorted Source Nodes: [softmax_logits, log_softmax], Original ATen: [aten._softmax, aten._log_softmax]
# Source node to ATen node mapping:
#   log_softmax => amax_1, exp_1, log, sub_1, sub_2, sum_2
#   softmax_logits => amax, div, exp, sub, sum_1
# Graph fragment:
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%addmm, [1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
#   %amax_1 : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%addmm, [-1], True), kwargs = {})
#   %sub_1 : [num_users=2] = call_function[target=torch.ops.aten.sub.Tensor](args = (%addmm, %amax_1), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%sub_1,), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp_1, [-1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_2,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%sub_1, %log), kwargs = {})
triton_poi_fused__log_softmax__softmax_1 = async_compile.triton('triton_poi_fused__log_softmax__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=[8], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused__log_softmax__softmax_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, '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__log_softmax__softmax_1(in_ptr0, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = (xindex // 2)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp1 = tl.load(in_ptr0 + (2*x1), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + (2*x1)), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp1 - tmp3
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp2 - tmp3
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tmp5 / tmp10
    tmp12 = tl_math.log(tmp10)
    tmp13 = tmp4 - tmp12
    tl.store(out_ptr0 + (x2), tmp11, xmask)
    tl.store(out_ptr1 + (x2), tmp13, 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, 96, 8), (768, 8, 1))
    assert_size_stride(primals_2, (4, 96, 8), (768, 8, 1))
    assert_size_stride(primals_3, (2, 6144), (6144, 1))
    assert_size_stride(primals_4, (2, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 96, 8, 8), (6144, 64, 8, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul], Original ATen: [aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_0.run(primals_1, primals_2, buf0, 24576, grid=grid(24576), stream=stream0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [logits], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (4, 6144), (6144, 1), 0), reinterpret_tensor(primals_3, (6144, 2), (1, 6144), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [softmax_logits, log_softmax], Original ATen: [aten._softmax, aten._log_softmax]
        triton_poi_fused__log_softmax__softmax_1.run(buf1, buf2, buf3, 8, grid=grid(8), stream=stream0)
        del buf1
    return (buf2, buf3, reinterpret_tensor(buf0, (4, 6144), (6144, 1), 0), buf2, buf3, )


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, 96, 8), (768, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 96, 8), (768, 8, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((2, 6144), (6144, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((2, ), (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
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F


class BilinearRanking(nn.Module):

    def __init__(self, n_classes: 'int'=2, emb_size: 'int'=768, block_size:
        'int'=8):
        super().__init__()
        self.n_classes = n_classes
        self.emb_size = emb_size
        self.block_size = block_size
        self.bilinear = nn.Linear(self.emb_size * self.block_size, self.
            n_classes)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, text1: 'Tensor', text2: 'Tensor'):
        b1 = text1.view(-1, self.emb_size // self.block_size, self.block_size)
        b2 = text2.view(-1, self.emb_size // self.block_size, self.block_size)
        bl = (b1.unsqueeze(3) * b2.unsqueeze(2)).view(-1, self.emb_size *
            self.block_size)
        logits = self.bilinear(bl)
        softmax_logits = self.softmax(logits)
        log_softmax = F.log_softmax(logits, dim=-1)
        return softmax_logits, log_softmax


def get_inputs():
    return [torch.rand([4, 96, 8]), torch.rand([4, 96, 8])]


def get_init_inputs():
    return [[], {}]

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_mul_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x3 = xindex // 8
    x0 = xindex % 8
    x2 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x0 + 8 * x2), None, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x4, tmp2, None)


@triton.jit
def triton_poi_fused__log_softmax__softmax_1(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 8
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 2
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 2 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 2 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp4 = tmp0 - tmp3
    tmp5 = tl_math.exp(tmp4)
    tmp6 = tmp1 - tmp3
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp2 - tmp3
    tmp9 = tl_math.exp(tmp8)
    tmp10 = tmp7 + tmp9
    tmp11 = tmp5 / tmp10
    tmp12 = tl_math.log(tmp10)
    tmp13 = tmp4 - tmp12
    tl.store(out_ptr0 + x2, tmp11, xmask)
    tl.store(out_ptr1 + x2, tmp13, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 96, 8), (768, 8, 1))
    assert_size_stride(primals_2, (4, 96, 8), (768, 8, 1))
    assert_size_stride(primals_3, (2, 6144), (6144, 1))
    assert_size_stride(primals_4, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 96, 8, 8), (6144, 64, 8, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_mul_0[grid(24576)](primals_1, primals_2, buf0, 
            24576, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (4, 6144),
            (6144, 1), 0), reinterpret_tensor(primals_3, (6144, 2), (1, 
            6144), 0), alpha=1, beta=1, out=buf1)
        del primals_3
        del primals_4
        buf2 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        buf3 = empty_strided_cuda((4, 2), (2, 1), torch.float32)
        triton_poi_fused__log_softmax__softmax_1[grid(8)](buf1, buf2, buf3,
            8, XBLOCK=8, num_warps=1, num_stages=1)
        del buf1
    return buf2, buf3, reinterpret_tensor(buf0, (4, 6144), (6144, 1), 0
        ), buf2, buf3


class BilinearRankingNew(nn.Module):

    def __init__(self, n_classes: 'int'=2, emb_size: 'int'=768, block_size:
        'int'=8):
        super().__init__()
        self.n_classes = n_classes
        self.emb_size = emb_size
        self.block_size = block_size
        self.bilinear = nn.Linear(self.emb_size * self.block_size, self.
            n_classes)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, input_0, input_1):
        primals_3 = self.bilinear.weight
        primals_4 = self.bilinear.bias
        primals_1 = input_0
        primals_2 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0], output[1]
