# 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/va/cvak4tz7y5xkgruz5imh2laix3m7at5tnwvirq3yy2nur2qdcdia.py
# Topologically Sorted Source Nodes: [U, tanh], Original ATen: [aten.mul, aten.tanh]
# Source node to ATen node mapping:
#   U => mul
#   tanh => tanh
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm, 0.5), kwargs = {})
#   %tanh : [num_users=1] = call_function[target=torch.ops.aten.tanh.default](args = (%mul,), kwargs = {})
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%tanh, 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 = {})
#   %mul_tensor_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_tensor, 4), kwargs = {})
triton_poi_fused_mul_tanh_0 = async_compile.triton('triton_poi_fused_mul_tanh_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=[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_mul_tanh_0', '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_mul_tanh_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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)
    tmp6 = tl.load(in_ptr0 + (4*x1), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp20 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp6 * tmp1
    tmp8 = libdevice.tanh(tmp7)
    tmp9 = tmp8 * tmp4
    tmp11 = tmp10 * tmp1
    tmp12 = libdevice.tanh(tmp11)
    tmp13 = tmp12 * tmp4
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 * tmp1
    tmp17 = libdevice.tanh(tmp16)
    tmp18 = tmp17 * tmp4
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp21 = tmp20 * tmp1
    tmp22 = libdevice.tanh(tmp21)
    tmp23 = tmp22 * tmp4
    tmp24 = triton_helpers.maximum(tmp19, tmp23)
    tmp25 = tmp5 - tmp24
    tmp26 = 4.0
    tmp27 = tmp25 * tmp26
    tl.store(out_ptr0 + (x2), tmp27, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/am/cam4sww3ppo4w3jhpfve3iek2mi4cmz7fh25jvjggq52vtu6lahc.py
# Topologically Sorted Source Nodes: [attention], Original ATen: [aten._log_softmax]
# Source node to ATen node mapping:
#   attention => exp, log, sub_1, sum_1
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_tensor_1,), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%exp, [-1], True), kwargs = {})
#   %log : [num_users=1] = call_function[target=torch.ops.aten.log.default](args = (%sum_1,), kwargs = {})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%mul_tensor_1, %log), kwargs = {})
triton_poi_fused__log_softmax_1 = async_compile.triton('triton_poi_fused__log_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=[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__log_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__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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')
    tmp3 = tl.load(in_ptr0 + (1 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + (4*x1)), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + (x2), tmp13, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = 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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0), primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [matmul_2], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [U, tanh], Original ATen: [aten.mul, aten.tanh]
        stream0 = get_raw_stream(0)
        triton_poi_fused_mul_tanh_0.run(buf2, buf3, 64, grid=grid(64), stream=stream0)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attention], Original ATen: [aten._log_softmax]
        triton_poi_fused__log_softmax_1.run(buf3, buf4, 64, grid=grid(64), stream=stream0)
        del buf3
    return (buf4, buf2, buf4, reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0), reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(primals_1, (4, 16), (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)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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)

from _paritybench_helpers import _mock_config
import math
import torch
import torch.nn as nn


class SingleHeadAttention(nn.Module):

    def __init__(self, cfg):
        super(SingleHeadAttention, self).__init__()
        self.input_dim = cfg.embedding_dim
        self.embedding_dim = cfg.embedding_dim
        self.value_dim = self.embedding_dim
        self.key_dim = self.value_dim
        self.tanh_clipping = cfg.tanh_clipping
        self.norm_factor = 1 / math.sqrt(self.key_dim)
        self.w_query = nn.Parameter(torch.Tensor(self.input_dim, self.key_dim))
        self.w_key = nn.Parameter(torch.Tensor(self.input_dim, self.key_dim))
        self.init_parameters()

    def init_parameters(self):
        for param in self.parameters():
            stdv = 1.0 / math.sqrt(param.size(-1))
            param.data.uniform_(-stdv, stdv)

    def forward(self, q, h=None, mask=None):
        """
                :param q: queries (batch_size, n_query, input_dim)
                :param h: data (batch_size, graph_size, input_dim)
                :param mask: mask (batch_size, n_query, graph_size) or viewable as that (i.e. can be 2 dim if n_query == 1)
                Mask should contain 1 if attention is not possible (i.e. mask is negative adjacency)
                :return:
                """
        if h is None:
            h = q
        batch_size, target_size, input_dim = h.size()
        n_query = q.size(1)
        assert q.size(0) == batch_size
        assert q.size(2) == input_dim
        assert input_dim == self.input_dim
        h_flat = h.reshape(-1, input_dim)
        q_flat = q.reshape(-1, input_dim)
        shape_k = batch_size, target_size, -1
        shape_q = batch_size, n_query, -1
        Q = torch.matmul(q_flat, self.w_query).view(shape_q)
        K = torch.matmul(h_flat, self.w_key).view(shape_k)
        U = self.norm_factor * torch.matmul(Q, K.transpose(1, 2))
        U = self.tanh_clipping * torch.tanh(U)
        if mask is not None:
            mask = mask.view(batch_size, 1, target_size).expand_as(U)
            U[mask.bool()] = -100000000.0
        attention = torch.log_softmax(U, dim=-1)
        out = attention
        return out


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


def get_init_inputs():
    return [[], {'cfg': _mock_config(embedding_dim=4, tanh_clipping=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 libdevice, math as tl_math
import 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_tanh_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 64
    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)
    tmp6 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp20 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.5
    tmp2 = tmp0 * tmp1
    tmp3 = libdevice.tanh(tmp2)
    tmp4 = 1.0
    tmp5 = tmp3 * tmp4
    tmp7 = tmp6 * tmp1
    tmp8 = libdevice.tanh(tmp7)
    tmp9 = tmp8 * tmp4
    tmp11 = tmp10 * tmp1
    tmp12 = libdevice.tanh(tmp11)
    tmp13 = tmp12 * tmp4
    tmp14 = triton_helpers.maximum(tmp9, tmp13)
    tmp16 = tmp15 * tmp1
    tmp17 = libdevice.tanh(tmp16)
    tmp18 = tmp17 * tmp4
    tmp19 = triton_helpers.maximum(tmp14, tmp18)
    tmp21 = tmp20 * tmp1
    tmp22 = libdevice.tanh(tmp21)
    tmp23 = tmp22 * tmp4
    tmp24 = triton_helpers.maximum(tmp19, tmp23)
    tmp25 = tmp5 - tmp24
    tmp26 = 4.0
    tmp27 = tmp25 * tmp26
    tl.store(out_ptr0 + x2, tmp27, xmask)


@triton.jit
def triton_poi_fused__log_softmax_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 64
    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')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tl_math.exp(tmp1)
    tmp4 = tl_math.exp(tmp3)
    tmp5 = tmp2 + tmp4
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 + tmp7
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tmp8 + tmp10
    tmp12 = tl_math.log(tmp11)
    tmp13 = tmp0 - tmp12
    tl.store(out_ptr0 + x2, tmp13, xmask)


def call(args):
    primals_1, primals_2, primals_3 = 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))
    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_1, (16, 4), (4, 1), 0),
            primals_2, out=buf0)
        del primals_2
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (16, 4), (4, 1), 0),
            primals_3, out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf0, (4, 4, 4), (16, 4, 1), 
            0), reinterpret_tensor(buf1, (4, 4, 4), (16, 1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_tanh_0[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__log_softmax_1[grid(64)](buf3, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf3
    return buf4, buf2, buf4, reinterpret_tensor(buf0, (4, 4, 4), (16, 1, 4), 0
        ), reinterpret_tensor(buf1, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_1, (4, 16), (1, 4), 0)


class SingleHeadAttentionNew(nn.Module):

    def __init__(self, cfg):
        super(SingleHeadAttentionNew, self).__init__()
        self.input_dim = cfg.embedding_dim
        self.embedding_dim = cfg.embedding_dim
        self.value_dim = self.embedding_dim
        self.key_dim = self.value_dim
        self.tanh_clipping = cfg.tanh_clipping
        self.norm_factor = 1 / math.sqrt(self.key_dim)
        self.w_query = nn.Parameter(torch.Tensor(self.input_dim, self.key_dim))
        self.w_key = nn.Parameter(torch.Tensor(self.input_dim, self.key_dim))
        self.init_parameters()

    def init_parameters(self):
        for param in self.parameters():
            stdv = 1.0 / math.sqrt(param.size(-1))
            param.data.uniform_(-stdv, stdv)

    def forward(self, input_0):
        primals_2 = self.w_query
        primals_3 = self.w_key
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
