# 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/x5/cx54xkftpqklbvm7id7ofhuft243ezj5rkv2utlq6n4lgglrfoc4.py
# Topologically Sorted Source Nodes: [attentions], Original ATen: [aten.mv]
# Source node to ATen node mapping:
#   attentions => mul, sum_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%view_4, %primals_6), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
triton_poi_fused_mv_0 = async_compile.triton('triton_poi_fused_mv_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: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_mv_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_mv_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
    tmp0 = tl.load(in_ptr0 + ((2*(x0 % 4)) + (8*(x0 // 16))), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (2*(x0 // 4)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + (0))
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp7 = tl.load(in_ptr3 + (0))
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp10 = tl.load(in_ptr0 + (1 + (2*(x0 % 4)) + (8*(x0 // 16))), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (1 + (2*(x0 // 4))), xmask, eviction_policy='evict_last')
    tmp13 = tl.load(in_ptr2 + (1))
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
    tmp17 = tl.load(in_ptr3 + (1))
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp5 = tmp2 + tmp4
    tmp6 = libdevice.tanh(tmp5)
    tmp9 = tmp6 * tmp8
    tmp12 = tmp10 + tmp11
    tmp15 = tmp12 + tmp14
    tmp16 = libdevice.tanh(tmp15)
    tmp19 = tmp16 * tmp18
    tmp20 = tmp9 + tmp19
    tl.store(out_ptr0 + (x0), tmp20, 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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2), (2, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 2), (2, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (2, ), (1, ))
    assert_size_stride(primals_6, (2, ), (1, ))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [keys], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0), primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        # Topologically Sorted Source Nodes: [queries], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((256, ), (1, ), torch.float32)
        # Topologically Sorted Source Nodes: [attentions], Original ATen: [aten.mv]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mv_0.run(buf1, buf0, primals_5, primals_6, buf2, 256, grid=grid(256), stream=stream0)
    return (reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0), primals_5, primals_6, buf0, buf1, reinterpret_tensor(primals_4, (4, 64), (1, 4), 0), reinterpret_tensor(primals_2, (4, 64), (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, 2), (2, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 2), (2, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((2, ), (1, ), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((2, ), (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.functional


class DrugDrugAttentionLayer(torch.nn.Module):
    """Co-attention layer for drug pairs."""

    def __init__(self, feature_number: 'int'):
        """Initialize the co-attention layer.

        :param feature_number: Number of input features.
        """
        super().__init__()
        self.weight_query = torch.nn.Parameter(torch.zeros(feature_number, 
            feature_number // 2))
        self.weight_key = torch.nn.Parameter(torch.zeros(feature_number, 
            feature_number // 2))
        self.bias = torch.nn.Parameter(torch.zeros(feature_number // 2))
        self.attention = torch.nn.Parameter(torch.zeros(feature_number // 2))
        self.tanh = torch.nn.Tanh()
        torch.nn.init.xavier_uniform_(self.weight_query)
        torch.nn.init.xavier_uniform_(self.weight_key)
        torch.nn.init.xavier_uniform_(self.bias.view(*self.bias.shape, -1))
        torch.nn.init.xavier_uniform_(self.attention.view(*self.attention.
            shape, -1))

    def forward(self, left_representations: 'torch.Tensor',
        right_representations: 'torch.Tensor'):
        """Make a forward pass with the co-attention calculation.

        :param left_representations: Matrix of left hand side representations.
        :param right_representations: Matrix of right hand side representations.
        :returns: Attention scores.
        """
        keys = left_representations @ self.weight_key
        queries = right_representations @ self.weight_query
        e_activations = queries.unsqueeze(-3) + keys.unsqueeze(-2) + self.bias
        attentions = self.tanh(e_activations) @ self.attention
        return attentions


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


def get_init_inputs():
    return [[], {'feature_number': 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.triton_helpers import libdevice
import torch.nn.functional
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_mv_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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
    tmp0 = tl.load(in_ptr0 + (2 * (x0 % 4) + 8 * (x0 // 16)), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + 2 * (x0 // 4), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr2 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp7 = tl.load(in_ptr3 + 0)
    tmp8 = tl.broadcast_to(tmp7, [XBLOCK])
    tmp10 = tl.load(in_ptr0 + (1 + 2 * (x0 % 4) + 8 * (x0 // 16)), xmask,
        eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr1 + (1 + 2 * (x0 // 4)), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr2 + 1)
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK])
    tmp17 = tl.load(in_ptr3 + 1)
    tmp18 = tl.broadcast_to(tmp17, [XBLOCK])
    tmp2 = tmp0 + tmp1
    tmp5 = tmp2 + tmp4
    tmp6 = libdevice.tanh(tmp5)
    tmp9 = tmp6 * tmp8
    tmp12 = tmp10 + tmp11
    tmp15 = tmp12 + tmp14
    tmp16 = libdevice.tanh(tmp15)
    tmp19 = tmp16 * tmp18
    tmp20 = tmp9 + tmp19
    tl.store(out_ptr0 + x0, tmp20, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 2), (2, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 2), (2, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (2,), (1,))
    assert_size_stride(primals_6, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((256,), (1,), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mv_0[grid(256)](buf1, buf0, primals_5, primals_6,
            buf2, 256, XBLOCK=256, num_warps=4, num_stages=1)
    return reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 16, 4, 1), 0
        ), primals_5, primals_6, buf0, buf1, reinterpret_tensor(primals_4,
        (4, 64), (1, 4), 0), reinterpret_tensor(primals_2, (4, 64), (1, 4), 0)


class DrugDrugAttentionLayerNew(torch.nn.Module):
    """Co-attention layer for drug pairs."""

    def __init__(self, feature_number: 'int'):
        """Initialize the co-attention layer.

        :param feature_number: Number of input features.
        """
        super().__init__()
        self.weight_query = torch.nn.Parameter(torch.zeros(feature_number, 
            feature_number // 2))
        self.weight_key = torch.nn.Parameter(torch.zeros(feature_number, 
            feature_number // 2))
        self.bias = torch.nn.Parameter(torch.zeros(feature_number // 2))
        self.attention = torch.nn.Parameter(torch.zeros(feature_number // 2))
        self.tanh = torch.nn.Tanh()
        torch.nn.init.xavier_uniform_(self.weight_query)
        torch.nn.init.xavier_uniform_(self.weight_key)
        torch.nn.init.xavier_uniform_(self.bias.view(*self.bias.shape, -1))
        torch.nn.init.xavier_uniform_(self.attention.view(*self.attention.
            shape, -1))

    def forward(self, input_0, input_1):
        primals_1 = self.weight_query
        primals_3 = self.weight_key
        primals_5 = self.bias
        primals_6 = self.attention
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]
