# 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/yz/cyzvc2lavs7gif3drpgmv6u3vaeybasxoxhjuhsybhfhgrpitob7.py
# Topologically Sorted Source Nodes: [res], Original ATen: [aten.view]
# Source node to ATen node mapping:
#   res => view_16
# Graph fragment:
#   %view_16 : [num_users=2] = call_function[target=torch.ops.aten.reshape.default](args = (%view_15, [16, 16]), kwargs = {})
triton_poi_fused_view_0 = async_compile.triton('triton_poi_fused_view_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: '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_view_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, '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_view_0(in_ptr0, 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 + (x0), xmask)
    tl.store(out_ptr0 + (x0), tmp0, 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, primals_7 = args
    args.clear()
    assert_size_stride(primals_1, (4, 16), (16, 1))
    assert_size_stride(primals_2, (4, 16), (16, 1))
    assert_size_stride(primals_3, (4, 16), (16, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [Q], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_5, (16, 4), (4, 1), 0), primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [K], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0), primals_2, out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [V], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_7, (16, 4), (4, 1), 0), primals_3, out=buf2)
        del primals_3
        # Topologically Sorted Source Nodes: [], Original ATen: []
        buf3 = torch.ops.aten._scaled_dot_product_efficient_attention.default(reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 4, 16, 1), 0), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 4, 16, 1), 0), reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 4, 16, 1), 0), None, True, scale=0.5)
        buf4 = buf3[0]
        buf5 = buf3[1]
        buf6 = buf3[2]
        buf7 = buf3[3]
        del buf3
        buf8 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [res], Original ATen: [aten.view]
        stream0 = get_raw_stream(0)
        triton_poi_fused_view_0.run(buf4, buf8, 256, grid=grid(256), stream=stream0)
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [res], Original ATen: [aten.mm]
        extern_kernels.mm(buf8, primals_4, out=buf9)
    return (reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0), reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 4, 16, 1), 0), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 4, 16, 1), 0), reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 4, 16, 1), 0), buf4, buf5, buf6, buf7, reinterpret_tensor(buf8, (16, 16), (1, 16), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0), reinterpret_tensor(primals_7, (4, 16), (1, 4), 0), reinterpret_tensor(primals_6, (4, 16), (1, 4), 0), reinterpret_tensor(primals_5, (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, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 16), (16, 1), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((16, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_5 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_6 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_7 = rand_strided((4, 4, 4), (16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5, primals_6, primals_7])
    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
from torch import nn


class ScaledDotProductAttention(nn.Module):
    """
    Scaled Dot-Product Attention Layer

    Attributes
    ----------
    softmax : nn.Functional
        softmax function applied at the last dimension
    """

    def __init__(self, dropout=0.1):
        super(ScaledDotProductAttention, self).__init__()
        self.softmax = nn.Softmax(dim=-1)
        self.dropout = nn.Dropout(dropout)

    def forward(self, Q, K, V, mask=None):
        """
        Parameters
        ----------
        Q    : 4d tensor (batch_size, h, seq_len, d_K)
        K    : 4d tensor (batch_size, h, seq_len, d_K)
        V    : 4d tensor (batch_size, h, seq_len, d_V)
        mask : 2d tensor (seq_len, seq_len)
            2d binary tensor, where 1 means connection should be blocked and 
            0 means values can be fed forward to softmax
        
        Returns
        -------
        4d tensor (batch_size, h, seq_len, d_V)
        """
        scaled = torch.matmul(Q, K.transpose_(2, 3))
        scaled = scaled / math.sqrt(K.shape[3])
        if mask is not None:
            scaled.masked_fill_(mask, float('-inf'))
        scaled = self.softmax(scaled)
        scaled = self.dropout(scaled)
        attn = torch.matmul(scaled, V)
        return attn


class MultiHeadAttention(nn.Module):
    """
    Multi-Head Attention Layer

    Attributes
    ----------
    h       : int
        number of parallel heads
    d_K     : int
        dimension of features in both query and key
    d_V     : int
        dimension of features in value
    d_model : int
        dimension of token embedding
    spd_attn: ScaledDotProductattention layer
        sub module to apply scaled dot product
    W_Q     : 2d tensor (d_model, d_K * h)
        learned parameters used to linearly project query to Q
    W_K     : 2d tensor (d_model, d_K * h)
        learned parameters used to linearly project key to K
    W_V     : 2d tensor (d_model, d_V * h)
        learned parameters used to linearly project val to V
    W_O     : 2d tensor (d_V * h, d_model)
        learned parameters used to linearly project scaled attention
        to the output tensor with the same dimension as input
    """

    def __init__(self, h, d_model, d_K, d_V, dropout=0.1):
        super(MultiHeadAttention, self).__init__()
        self.h = h
        self.d_K = d_K
        self.d_V = d_V
        self.d_model = d_model
        self.sdp_attn = ScaledDotProductAttention(dropout=dropout)
        self.W_Q = nn.Parameter(torch.Tensor(d_model, d_K * h))
        self.W_K = nn.Parameter(torch.Tensor(d_model, d_K * h))
        self.W_V = nn.Parameter(torch.Tensor(d_model, d_V * h))
        self.W_O = nn.Parameter(torch.Tensor(d_V * h, d_model))
        self.dropout = nn.Dropout(dropout)
        self.reset_parameters()

    def reset_parameters(self):
        slope = math.sqrt(5)
        nn.init.kaiming_uniform_(self.W_Q, a=slope)
        nn.init.kaiming_uniform_(self.W_K, a=slope)
        nn.init.kaiming_uniform_(self.W_V, a=slope)
        nn.init.kaiming_uniform_(self.W_O, a=slope)

    def forward(self, query, key, val, mask=None):
        """
        Parameters
        ----------
        query : 3d tensor (batch_size, seq_len, d_model)
            embedded query sequence
        key   : 3d tensor (batch_size, seq_len, d_model)
            embedded key sequence
        val   : 3d tensor (batch_size, seq_len, d_model)
            embedded value sequence
        mask  : 2d tensor (seq_len, seq_len)
            2d binary tensor, where 1 means pass, 0 means block

        Returns
        -------
        3d tensor (batch_size, seq_len, d_model)
        """
        h, d_K, d_V, _d_model = self.h, self.d_K, self.d_V, self.d_model
        W_Q, W_K, W_V, W_O = self.W_Q, self.W_K, self.W_V, self.W_O
        bs_q, l_q = query.shape[0], query.shape[1]
        bs_k, l_k = key.shape[0], key.shape[1]
        bs_v, l_v = val.shape[0], val.shape[1]
        Q = torch.matmul(query, W_Q)
        K = torch.matmul(key, W_K)
        V = torch.matmul(val, W_V)
        Q = Q.view(bs_q, l_q, h, d_K)
        K = K.view(bs_k, l_k, h, d_K)
        V = V.view(bs_v, l_v, h, d_V)
        Q.transpose_(1, 2)
        K.transpose_(1, 2)
        V.transpose_(1, 2)
        head = self.sdp_attn(Q, K, V, mask)
        head.transpose_(1, 2)
        head = head.reshape(bs_q, l_q, h * d_V)
        res = torch.matmul(head, W_O)
        return self.dropout(res)


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


def get_init_inputs():
    return [[], {'h': 4, 'd_model': 4, 'd_K': 4, 'd_V': 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
from torch import 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_view_0(in_ptr0, 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 + x0, xmask)
    tl.store(out_ptr0 + x0, tmp0, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7) = args
    args.clear()
    assert_size_stride(primals_1, (4, 16), (16, 1))
    assert_size_stride(primals_2, (4, 16), (16, 1))
    assert_size_stride(primals_3, (4, 16), (16, 1))
    assert_size_stride(primals_4, (16, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_6, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_7, (4, 4, 4), (16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_5, (16, 4), (4, 1), 0),
            primals_1, out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_6, (16, 4), (4, 1), 0),
            primals_2, out=buf1)
        del primals_2
        buf2 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_7, (16, 4), (4, 1), 0),
            primals_3, out=buf2)
        del primals_3
        buf3 = torch.ops.aten._scaled_dot_product_efficient_attention.default(
            reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 4, 16, 1), 0),
            reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 4, 16, 1), 0),
            reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 4, 16, 1), 0), None,
            True, scale=0.5)
        buf4 = buf3[0]
        buf5 = buf3[1]
        buf6 = buf3[2]
        buf7 = buf3[3]
        del buf3
        buf8 = empty_strided_cuda((16, 16), (16, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_view_0[grid(256)](buf4, buf8, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf8, primals_4, out=buf9)
    return reinterpret_tensor(buf9, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(buf0, (4, 4, 4, 4), (64, 4, 16, 1), 0
        ), reinterpret_tensor(buf1, (4, 4, 4, 4), (64, 4, 16, 1), 0
        ), reinterpret_tensor(buf2, (4, 4, 4, 4), (64, 4, 16, 1), 0
        ), buf4, buf5, buf6, buf7, reinterpret_tensor(buf8, (16, 16), (1, 
        16), 0), reinterpret_tensor(primals_4, (4, 16), (1, 4), 0
        ), reinterpret_tensor(primals_7, (4, 16), (1, 4), 0
        ), reinterpret_tensor(primals_6, (4, 16), (1, 4), 0
        ), reinterpret_tensor(primals_5, (4, 16), (1, 4), 0)


class ScaledDotProductAttention(nn.Module):
    """
    Scaled Dot-Product Attention Layer

    Attributes
    ----------
    softmax : nn.Functional
        softmax function applied at the last dimension
    """

    def __init__(self, dropout=0.1):
        super(ScaledDotProductAttention, self).__init__()
        self.softmax = nn.Softmax(dim=-1)
        self.dropout = nn.Dropout(dropout)

    def forward(self, Q, K, V, mask=None):
        """
        Parameters
        ----------
        Q    : 4d tensor (batch_size, h, seq_len, d_K)
        K    : 4d tensor (batch_size, h, seq_len, d_K)
        V    : 4d tensor (batch_size, h, seq_len, d_V)
        mask : 2d tensor (seq_len, seq_len)
            2d binary tensor, where 1 means connection should be blocked and 
            0 means values can be fed forward to softmax
        
        Returns
        -------
        4d tensor (batch_size, h, seq_len, d_V)
        """
        scaled = torch.matmul(Q, K.transpose_(2, 3))
        scaled = scaled / math.sqrt(K.shape[3])
        if mask is not None:
            scaled.masked_fill_(mask, float('-inf'))
        scaled = self.softmax(scaled)
        scaled = self.dropout(scaled)
        attn = torch.matmul(scaled, V)
        return attn


class MultiHeadAttentionNew(nn.Module):
    """
    Multi-Head Attention Layer

    Attributes
    ----------
    h       : int
        number of parallel heads
    d_K     : int
        dimension of features in both query and key
    d_V     : int
        dimension of features in value
    d_model : int
        dimension of token embedding
    spd_attn: ScaledDotProductattention layer
        sub module to apply scaled dot product
    W_Q     : 2d tensor (d_model, d_K * h)
        learned parameters used to linearly project query to Q
    W_K     : 2d tensor (d_model, d_K * h)
        learned parameters used to linearly project key to K
    W_V     : 2d tensor (d_model, d_V * h)
        learned parameters used to linearly project val to V
    W_O     : 2d tensor (d_V * h, d_model)
        learned parameters used to linearly project scaled attention
        to the output tensor with the same dimension as input
    """

    def __init__(self, h, d_model, d_K, d_V, dropout=0.1):
        super(MultiHeadAttentionNew, self).__init__()
        self.h = h
        self.d_K = d_K
        self.d_V = d_V
        self.d_model = d_model
        self.sdp_attn = ScaledDotProductAttention(dropout=dropout)
        self.W_Q = nn.Parameter(torch.Tensor(d_model, d_K * h))
        self.W_K = nn.Parameter(torch.Tensor(d_model, d_K * h))
        self.W_V = nn.Parameter(torch.Tensor(d_model, d_V * h))
        self.W_O = nn.Parameter(torch.Tensor(d_V * h, d_model))
        self.dropout = nn.Dropout(dropout)
        self.reset_parameters()

    def reset_parameters(self):
        slope = math.sqrt(5)
        nn.init.kaiming_uniform_(self.W_Q, a=slope)
        nn.init.kaiming_uniform_(self.W_K, a=slope)
        nn.init.kaiming_uniform_(self.W_V, a=slope)
        nn.init.kaiming_uniform_(self.W_O, a=slope)

    def forward(self, input_0, input_1, input_2):
        primals_1 = self.W_Q
        primals_2 = self.W_K
        primals_3 = self.W_V
        primals_4 = self.W_O
        primals_5 = input_0
        primals_6 = input_1
        primals_7 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0]
