# AOT ID: ['0_inference']
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/xq/cxqveorha4d4vzcsm62i7ynweqdfb2tqd6zqlpmh6ptxiazzumzp.py
# Topologically Sorted Source Nodes: [masked_fill_, alpha_flat], Original ATen: [aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
#   alpha_flat => amax, exp, sub, sum_1
#   masked_fill_ => full_default, where
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -inf), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%expand, %full_default, %bmm), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %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 = {})
triton_poi_fused__softmax_masked_fill_0 = async_compile.triton('triton_poi_fused__softmax_masked_fill_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], 
    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_masked_fill_0', '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_masked_fill_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4*x2), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr1 + (2 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp18 = tl.load(in_ptr1 + (3 + (4*x2)), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = float("-inf")
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tmp6 == tmp1
    tmp9 = tl.where(tmp7, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp12 = tmp11 == tmp1
    tmp14 = tl.where(tmp12, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp17 = tmp16 == tmp1
    tmp19 = tl.where(tmp17, tmp4, tmp18)
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp5 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp9 - tmp20
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp14 - tmp20
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp19 - tmp20
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tl.store(out_ptr0 + (x2), tmp20, xmask)
    tl.store(out_ptr1 + (x2), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_8/inductor_cache/3x/c3xrc6c4gif26vtq7rbugtcdffh27r6ke6eg4xz5o2mzyta5lktk.py
# Topologically Sorted Source Nodes: [masked_fill_, alpha_flat], Original ATen: [aten.masked_fill, aten._softmax]
# Source node to ATen node mapping:
#   alpha_flat => amax, div, exp, sub
#   masked_fill_ => full_default, where
# Graph fragment:
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], -inf), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %where : [num_users=2] = call_function[target=torch.ops.aten.where.self](args = (%expand, %full_default, %bmm), kwargs = {})
#   %amax : [num_users=1] = call_function[target=torch.ops.aten.amax.default](args = (%where, [-1], True), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%where, %amax), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub,), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_poi_fused__softmax_masked_fill_1 = async_compile.triton('triton_poi_fused__softmax_masked_fill_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=[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_masked_fill_1', '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_masked_fill_1(in_out_ptr0, in_ptr0, in_ptr1, in_ptr2, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 16
    x4 = xindex
    x5 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + (x4), xmask)
    tmp6 = tl.load(in_ptr1 + (x5), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + (x5), xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = float("-inf")
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + (x4), tmp10, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [scores], Original ATen: [aten.bmm]
        extern_kernels.bmm(arg0_1, reinterpret_tensor(arg1_1, (4, 4, 4), (16, 1, 4), 0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        # Topologically Sorted Source Nodes: [masked_fill_, alpha_flat], Original ATen: [aten.masked_fill, aten._softmax]
        stream0 = get_raw_stream(0)
        triton_poi_fused__softmax_masked_fill_0.run(arg2_1, buf0, buf1, buf2, 16, grid=grid(16), stream=stream0)
        buf3 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [masked_fill_, alpha_flat], Original ATen: [aten.masked_fill, aten._softmax]
        triton_poi_fused__softmax_masked_fill_1.run(buf3, arg2_1, buf1, buf2, 64, grid=grid(64), stream=stream0)
        del arg2_1
        del buf1
        del buf2
    return (buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import torch.nn as nn
import torch.nn.functional as F


class AttentionScore(nn.Module):
    """
    correlation_func = 1, sij = x1^Tx2
    correlation_func = 2, sij = (Wx1)D(Wx2)
    correlation_func = 3, sij = Relu(Wx1)DRelu(Wx2)
    correlation_func = 4, sij = x1^TWx2
    correlation_func = 5, sij = Relu(Wx1)DRelu(Wx2)
    """

    def __init__(self, input_size, hidden_size, correlation_func=1,
        do_similarity=False):
        super(AttentionScore, self).__init__()
        self.correlation_func = correlation_func
        self.hidden_size = hidden_size
        if correlation_func == 2 or correlation_func == 3:
            self.linear = nn.Linear(input_size, hidden_size, bias=False)
            if do_similarity:
                self.diagonal = Parameter(torch.ones(1, 1, 1) / hidden_size **
                    0.5, requires_grad=False)
            else:
                self.diagonal = Parameter(torch.ones(1, 1, hidden_size),
                    requires_grad=True)
        if correlation_func == 4:
            self.linear = nn.Linear(input_size, input_size, bias=False)
        if correlation_func == 5:
            self.linear = nn.Linear(input_size, hidden_size, bias=False)

    def forward(self, x1, x2, x2_mask):
        """
        Input:
        x1: batch x word_num1 x dim
        x2: batch x word_num2 x dim
        Output:
        scores: batch x word_num1 x word_num2
        """
        x1_rep = x1
        x2_rep = x2
        batch = x1_rep.size(0)
        word_num1 = x1_rep.size(1)
        word_num2 = x2_rep.size(1)
        dim = x1_rep.size(2)
        if self.correlation_func == 2 or self.correlation_func == 3:
            x1_rep = self.linear(x1_rep.contiguous().view(-1, dim)).view(batch,
                word_num1, self.hidden_size)
            x2_rep = self.linear(x2_rep.contiguous().view(-1, dim)).view(batch,
                word_num2, self.hidden_size)
            if self.correlation_func == 3:
                x1_rep = F.relu(x1_rep)
                x2_rep = F.relu(x2_rep)
            x1_rep = x1_rep * self.diagonal.expand_as(x1_rep)
        if self.correlation_func == 4:
            x2_rep = self.linear(x2_rep.contiguous().view(-1, dim)).view(batch,
                word_num2, dim)
        if self.correlation_func == 5:
            x1_rep = self.linear(x1_rep.contiguous().view(-1, dim)).view(batch,
                word_num1, self.hidden_size)
            x2_rep = self.linear(x2_rep.contiguous().view(-1, dim)).view(batch,
                word_num2, self.hidden_size)
            x1_rep = F.relu(x1_rep)
            x2_rep = F.relu(x2_rep)
        scores = x1_rep.bmm(x2_rep.transpose(1, 2))
        empty_mask = x2_mask.eq(0).expand_as(scores)
        scores.data.masked_fill_(empty_mask.data, -float('inf'))
        alpha_flat = F.softmax(scores, dim=-1)
        return alpha_flat


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


def get_init_inputs():
    return [[], {'input_size': 4, 'hidden_size': 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__softmax_masked_fill_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp13 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp16 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = float('-inf')
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tmp6 == tmp1
    tmp9 = tl.where(tmp7, tmp4, tmp8)
    tmp10 = triton_helpers.maximum(tmp5, tmp9)
    tmp12 = tmp11 == tmp1
    tmp14 = tl.where(tmp12, tmp4, tmp13)
    tmp15 = triton_helpers.maximum(tmp10, tmp14)
    tmp17 = tmp16 == tmp1
    tmp19 = tl.where(tmp17, tmp4, tmp18)
    tmp20 = triton_helpers.maximum(tmp15, tmp19)
    tmp21 = tmp5 - tmp20
    tmp22 = tl_math.exp(tmp21)
    tmp23 = tmp9 - tmp20
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp14 - tmp20
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tmp29 = tmp19 - tmp20
    tmp30 = tl_math.exp(tmp29)
    tmp31 = tmp28 + tmp30
    tl.store(out_ptr0 + x2, tmp20, xmask)
    tl.store(out_ptr1 + x2, tmp31, xmask)


@triton.jit
def triton_poi_fused__softmax_masked_fill_1(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 16
    x4 = xindex
    x5 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr0 + x4, xmask)
    tmp6 = tl.load(in_ptr1 + x5, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr2 + x5, xmask, eviction_policy='evict_last')
    tmp1 = 0.0
    tmp2 = tmp0 == tmp1
    tmp4 = float('-inf')
    tmp5 = tl.where(tmp2, tmp4, tmp3)
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tl.store(in_out_ptr0 + x4, tmp10, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg1_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(arg2_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(arg0_1, reinterpret_tensor(arg1_1, (4, 4, 4), (
            16, 1, 4), 0), out=buf0)
        del arg0_1
        del arg1_1
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused__softmax_masked_fill_0[grid(16)](arg2_1, buf0,
            buf1, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = buf0
        del buf0
        triton_poi_fused__softmax_masked_fill_1[grid(64)](buf3, arg2_1,
            buf1, buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        del arg2_1
        del buf1
        del buf2
    return buf3,


class AttentionScoreNew(nn.Module):
    """
    correlation_func = 1, sij = x1^Tx2
    correlation_func = 2, sij = (Wx1)D(Wx2)
    correlation_func = 3, sij = Relu(Wx1)DRelu(Wx2)
    correlation_func = 4, sij = x1^TWx2
    correlation_func = 5, sij = Relu(Wx1)DRelu(Wx2)
    """

    def __init__(self, input_size, hidden_size, correlation_func=1,
        do_similarity=False):
        super(AttentionScoreNew, self).__init__()
        self.correlation_func = correlation_func
        self.hidden_size = hidden_size
        if correlation_func == 2 or correlation_func == 3:
            self.linear = nn.Linear(input_size, hidden_size, bias=False)
            if do_similarity:
                self.diagonal = Parameter(torch.ones(1, 1, 1) / hidden_size **
                    0.5, requires_grad=False)
            else:
                self.diagonal = Parameter(torch.ones(1, 1, hidden_size),
                    requires_grad=True)
        if correlation_func == 4:
            self.linear = nn.Linear(input_size, input_size, bias=False)
        if correlation_func == 5:
            self.linear = nn.Linear(input_size, hidden_size, bias=False)

    def forward(self, input_0, input_1, input_2):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        output = call([arg0_1, arg1_1, arg2_1])
        return output[0]
