# 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/pj/cpjtaps4dusmzrrvkgal3gobvvp6pxqqx5zvwpjyzgdjffmdihfr.py
# Topologically Sorted Source Nodes: [out_masked, out_sum, mask_sum, out_avg_pooling], Original ATen: [aten.mul, aten.sum, aten.div]
# Source node to ATen node mapping:
#   mask_sum => sum_2
#   out_avg_pooling => div
#   out_masked => mul
#   out_sum => sum_1
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%primals_2, %unsqueeze), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1]), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%unsqueeze, [1]), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, %sum_2), kwargs = {})
triton_poi_fused_div_mul_sum_0 = async_compile.triton('triton_poi_fused_div_mul_sum_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: '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_div_mul_sum_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_div_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 64
    x1 = (xindex // 4) % 16
    x2 = (xindex // 64)
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + (x3), xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + x3), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (16 + x1 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (128 + x3), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (32 + x1 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (192 + x3), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (48 + x1 + (64*x2)), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tmp1 + tmp4
    tmp16 = tmp15 + tmp8
    tmp17 = tmp16 + tmp12
    tmp18 = tmp14 / tmp17
    tl.store(out_ptr0 + (x4), tmp18, 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, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 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, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [out_masked, out_sum, mask_sum, out_avg_pooling], Original ATen: [aten.mul, aten.sum, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_div_mul_sum_0.run(primals_2, primals_1, buf0, 256, grid=grid(256), stream=stream0)
        del primals_1
        del primals_2
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        # Topologically Sorted Source Nodes: [projected], Original ATen: [aten.addmm]
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (4, 1), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_3
        del primals_4
    return (reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0), reinterpret_tensor(buf0, (64, 4), (4, 1), 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, 4), (64, 16, 4, 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((1, 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 math
import torch
import torch.nn as nn


class LayerNorm(nn.Module):
    """Construct a layernorm module (See citation for details)."""

    def __init__(self, features, eps=1e-06):
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2


class ScaleNorm(nn.Module):
    """ScaleNorm"""
    """All g’s in SCALE NORM are initialized to sqrt(d)"""

    def __init__(self, scale, eps=1e-05):
        super(ScaleNorm, self).__init__()
        self.scale = nn.Parameter(torch.tensor(math.sqrt(scale)))
        self.eps = eps

    def forward(self, x):
        norm = self.scale / torch.norm(x, dim=-1, keepdim=True).clamp(min=
            self.eps)
        return x * norm


class Generator(nn.Module):
    """Define standard linear + softmax generation step."""

    def __init__(self, d_model, aggregation_type='mean', n_output=1,
        n_layers=1, leaky_relu_slope=0.01, dropout=0.0, scale_norm=False):
        super(Generator, self).__init__()
        if n_layers == 1:
            self.proj = nn.Linear(d_model, n_output)
        else:
            self.proj = []
            for i in range(n_layers - 1):
                self.proj.append(nn.Linear(d_model, d_model))
                self.proj.append(nn.LeakyReLU(leaky_relu_slope))
                self.proj.append(ScaleNorm(d_model) if scale_norm else
                    LayerNorm(d_model))
                self.proj.append(nn.Dropout(dropout))
            self.proj.append(nn.Linear(d_model, n_output))
            self.proj = torch.nn.Sequential(*self.proj)
        self.aggregation_type = aggregation_type

    def forward(self, x, mask):
        mask = mask.unsqueeze(-1).float()
        out_masked = x * mask
        if self.aggregation_type == 'mean':
            out_sum = out_masked.sum(dim=1)
            mask_sum = mask.sum(dim=1)
            out_avg_pooling = out_sum / mask_sum
        elif self.aggregation_type == 'sum':
            out_sum = out_masked.sum(dim=1)
            out_avg_pooling = out_sum
        elif self.aggregation_type == 'dummy_node':
            out_avg_pooling = out_masked[:, 0]
        projected = self.proj(out_avg_pooling)
        return projected


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


def get_init_inputs():
    return [[], {'d_model': 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
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_div_mul_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex % 64
    x1 = xindex // 4 % 16
    x2 = xindex // 64
    x4 = xindex
    tmp0 = tl.load(in_ptr0 + x3, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (64 + x3), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr1 + (16 + x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp7 = tl.load(in_ptr0 + (128 + x3), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr1 + (32 + x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (192 + x3), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr1 + (48 + x1 + 64 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tmp0 * tmp1
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 * tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 * tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = tmp1 + tmp4
    tmp16 = tmp15 + tmp8
    tmp17 = tmp16 + tmp12
    tmp18 = tmp14 / tmp17
    tl.store(out_ptr0 + x4, tmp18, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (1, 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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_mul_sum_0[grid(256)](primals_2, primals_1,
            buf0, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del primals_1
        del primals_2
        buf2 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_4, reinterpret_tensor(buf0, (64, 4), (
            4, 1), 0), reinterpret_tensor(primals_3, (4, 1), (1, 4), 0),
            alpha=1, beta=1, out=buf2)
        del primals_3
        del primals_4
    return reinterpret_tensor(buf2, (4, 4, 4, 1), (16, 4, 1, 1), 0
        ), reinterpret_tensor(buf0, (64, 4), (4, 1), 0)


class LayerNorm(nn.Module):
    """Construct a layernorm module (See citation for details)."""

    def __init__(self, features, eps=1e-06):
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2


class ScaleNorm(nn.Module):
    """ScaleNorm"""
    """All g’s in SCALE NORM are initialized to sqrt(d)"""

    def __init__(self, scale, eps=1e-05):
        super(ScaleNorm, self).__init__()
        self.scale = nn.Parameter(torch.tensor(math.sqrt(scale)))
        self.eps = eps

    def forward(self, x):
        norm = self.scale / torch.norm(x, dim=-1, keepdim=True).clamp(min=
            self.eps)
        return x * norm


class GeneratorNew(nn.Module):
    """Define standard linear + softmax generation step."""

    def __init__(self, d_model, aggregation_type='mean', n_output=1,
        n_layers=1, leaky_relu_slope=0.01, dropout=0.0, scale_norm=False):
        super(GeneratorNew, self).__init__()
        if n_layers == 1:
            self.proj = nn.Linear(d_model, n_output)
        else:
            self.proj = []
            for i in range(n_layers - 1):
                self.proj.append(nn.Linear(d_model, d_model))
                self.proj.append(nn.LeakyReLU(leaky_relu_slope))
                self.proj.append(ScaleNorm(d_model) if scale_norm else
                    LayerNorm(d_model))
                self.proj.append(nn.Dropout(dropout))
            self.proj.append(nn.Linear(d_model, n_output))
            self.proj = torch.nn.Sequential(*self.proj)
        self.aggregation_type = aggregation_type

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