# 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/3u/c3ui6pflkqqmwiicu3k3k6nxfn3zxrzgar4nyb7sxfkreg6ab7we.py
# Topologically Sorted Source Nodes: [attn_weights], Original ATen: [aten._softmax]
# Source node to ATen node mapping:
#   attn_weights => div, exp, sum_1
# Graph fragment:
#   %mul_tensor : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%bmm, 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=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_tensor, 0.5), kwargs = {})
#   %exp : [num_users=2] = 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 = {})
#   %div : [num_users=3] = call_function[target=torch.ops.aten.div.Tensor](args = (%exp, %sum_1), kwargs = {})
triton_per_fused__softmax_0 = async_compile.triton('triton_per_fused__softmax_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.persistent_reduction(
    size_hints=[64, 16],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: 'i32', 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_per_fused__softmax_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 1, 'num_reduction': 2, '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}
)
@triton.jit
def triton_per_fused__softmax_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    rnumel = 16
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    roffset = 0
    rmask = tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + (16*x0)), xmask, other=0.0)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, float("-inf"))
    tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
    tmp7 = tmp2 - tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tmp10 / tmp14
    tl.store(out_ptr2 + (r1 + (16*x0)), tmp15, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_1], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 16), out=buf1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_2], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 32), out=buf2)
        del primals_2
        buf3 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [bmm], Original ATen: [aten.bmm]
        extern_kernels.bmm(reinterpret_tensor(buf2, (4, 16, 4), (64, 4, 1), 0), reinterpret_tensor(buf1, (4, 4, 16), (64, 1, 4), 0), out=buf3)
        buf6 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attn_weights], Original ATen: [aten._softmax]
        stream0 = get_raw_stream(0)
        triton_per_fused__softmax_0.run(buf3, buf6, 64, 16, grid=grid(64), stream=stream0)
        del buf3
        buf7 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [attended], Original ATen: [aten.bmm]
        extern_kernels.bmm(buf6, reinterpret_tensor(buf0, (4, 16, 4), (64, 4, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [linear_3], Original ATen: [aten.mm]
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0), reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf8)
    return (reinterpret_tensor(buf8, (4, 16, 4), (64, 4, 1), 0), reinterpret_tensor(buf6, (1, 4, 16, 16), (256, 256, 16, 1), 0), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0), buf6, reinterpret_tensor(buf7, (64, 4), (4, 1), 0), primals_5, reinterpret_tensor(buf0, (4, 4, 16), (64, 1, 4), 0), reinterpret_tensor(buf2, (4, 4, 16), (64, 1, 4), 0), reinterpret_tensor(buf1, (4, 16, 4), (64, 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((12, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((4, 4, 4, 4), (64, 16, 4, 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((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4, primals_5])
    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
from torch import nn
from torch.nn import functional as F


def same_tensor(tensor, *args):
    """ Do the input tensors all point to the same underlying data """
    for other in args:
        if not torch.is_tensor(other):
            return False
        if tensor.device != other.device:
            return False
        if tensor.dtype != other.dtype:
            return False
        if tensor.data_ptr() != other.data_ptr():
            return False
    return True


class MultiHeadedAttention(nn.Module):
    """ Implement a multi-headed attention module """

    def __init__(self, embed_dim, num_heads=1):
        """ Initialize the attention module """
        super(MultiHeadedAttention, self).__init__()
        assert embed_dim % num_heads == 0, f'num_heads={num_heads} should evenly divide embed_dim={embed_dim}'
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.projection_dim = embed_dim // num_heads
        self.scale = self.projection_dim ** -0.5
        self.input_weights = nn.Parameter(torch.Tensor(3 * embed_dim,
            embed_dim))
        self.output_projection = nn.Linear(embed_dim, embed_dim, bias=False)
        self.reset_parameters()

    def reset_parameters(self):
        """ Reset parameters using xavier initialization """
        gain = nn.init.calculate_gain('linear')
        nn.init.xavier_uniform_(self.input_weights, gain)
        nn.init.xavier_uniform_(self.output_projection.weight, gain)

    def project(self, inputs, index=0, chunks=1):
        """ Produce a linear projection using the weights """
        batch_size = inputs.shape[0]
        start = index * self.embed_dim
        end = start + chunks * self.embed_dim
        projections = F.linear(inputs, self.input_weights[start:end]).chunk(
            chunks, dim=-1)
        output_projections = []
        for projection in projections:
            output_projections.append(projection.view(batch_size, -1, self.
                num_heads, self.projection_dim).transpose(2, 1).contiguous(
                ).view(batch_size * self.num_heads, -1, self.projection_dim))
        return output_projections

    def attention(self, values, keys, queries, key_mask=None, mask=None):
        """ Scaled dot product attention with optional masks """
        logits = self.scale * torch.bmm(queries, keys.transpose(2, 1))
        if mask is not None:
            logits += mask
        if key_mask is not None:
            logits_shape = logits.shape
            batch_size = logits_shape[0] // self.num_heads
            logits = logits.view(batch_size, self.num_heads, logits_shape[1
                ], logits_shape[2])
            logits.masked_fill_(key_mask[:, None, None], float('-inf'))
            logits = logits.view(logits_shape)
        attn_weights = F.softmax(logits, dim=-1)
        attended = torch.bmm(attn_weights, values)
        batch_size = queries.shape[0] // self.num_heads
        return attended.view(batch_size, self.num_heads, -1, self.
            projection_dim).transpose(2, 1).contiguous().view(batch_size, -
            1, self.num_heads * self.projection_dim), attn_weights.view(
            batch_size, self.num_heads, attn_weights.size()[1], -1).transpose(
            0, 1).contiguous()

    def forward(self, values, keys, queries, key_mask=None, attention_mask=
        None, num_queries=0):
        """ Forward pass of the attention """
        if same_tensor(values, keys, queries):
            values, keys, queries = self.project(values, chunks=3)
        elif same_tensor(values, keys):
            values, keys = self.project(values, chunks=2)
            queries, = self.project(queries, 2)
        else:
            values, = self.project(values, 0)
            keys, = self.project(keys, 1)
            queries, = self.project(queries, 2)
        if num_queries:
            queries = queries[:, -num_queries:]
        attended, attn_weights = self.attention(values, keys, queries,
            key_mask, attention_mask)
        return self.output_projection(attended), attn_weights


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


def get_init_inputs():
    return [[], {'embed_dim': 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
from torch import nn
from torch.nn import functional as F
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_per_fused__softmax_0(in_ptr0, out_ptr2, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 16 * x0), xmask, other=0.0)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, float('-inf'))
    tmp6 = triton_helpers.max2(tmp5, 1)[:, None]
    tmp7 = tmp2 - tmp6
    tmp8 = 0.5
    tmp9 = tmp7 * tmp8
    tmp10 = tl_math.exp(tmp9)
    tmp11 = tl.broadcast_to(tmp10, [XBLOCK, RBLOCK])
    tmp13 = tl.where(xmask, tmp11, 0)
    tmp14 = tl.sum(tmp13, 1)[:, None]
    tmp15 = tmp10 / tmp14
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp15, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (12, 4), (4, 1))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), out=buf0)
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_3, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 16), out=buf1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_2, (4, 4), (1, 4), 32), out=buf2)
        del primals_2
        buf3 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf2, (4, 16, 4), (64, 4, 1),
            0), reinterpret_tensor(buf1, (4, 4, 16), (64, 1, 4), 0), out=buf3)
        buf6 = empty_strided_cuda((4, 16, 16), (256, 16, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused__softmax_0[grid(64)](buf3, buf6, 64, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del buf3
        buf7 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        extern_kernels.bmm(buf6, reinterpret_tensor(buf0, (4, 16, 4), (64, 
            4, 1), 0), out=buf7)
        buf8 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf8)
    return reinterpret_tensor(buf8, (4, 16, 4), (64, 4, 1), 0
        ), reinterpret_tensor(buf6, (1, 4, 16, 16), (256, 256, 16, 1), 0
        ), reinterpret_tensor(primals_1, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), reinterpret_tensor(primals_4, (64, 4), (4, 1), 0
        ), buf6, reinterpret_tensor(buf7, (64, 4), (4, 1), 0
        ), primals_5, reinterpret_tensor(buf0, (4, 4, 16), (64, 1, 4), 0
        ), reinterpret_tensor(buf2, (4, 4, 16), (64, 1, 4), 0
        ), reinterpret_tensor(buf1, (4, 16, 4), (64, 4, 1), 0)


def same_tensor(tensor, *args):
    """ Do the input tensors all point to the same underlying data """
    for other in args:
        if not torch.is_tensor(other):
            return False
        if tensor.device != other.device:
            return False
        if tensor.dtype != other.dtype:
            return False
        if tensor.data_ptr() != other.data_ptr():
            return False
    return True


class MultiHeadedAttentionNew(nn.Module):
    """ Implement a multi-headed attention module """

    def __init__(self, embed_dim, num_heads=1):
        """ Initialize the attention module """
        super(MultiHeadedAttentionNew, self).__init__()
        assert embed_dim % num_heads == 0, f'num_heads={num_heads} should evenly divide embed_dim={embed_dim}'
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.projection_dim = embed_dim // num_heads
        self.scale = self.projection_dim ** -0.5
        self.input_weights = nn.Parameter(torch.Tensor(3 * embed_dim,
            embed_dim))
        self.output_projection = nn.Linear(embed_dim, embed_dim, bias=False)
        self.reset_parameters()

    def reset_parameters(self):
        """ Reset parameters using xavier initialization """
        gain = nn.init.calculate_gain('linear')
        nn.init.xavier_uniform_(self.input_weights, gain)
        nn.init.xavier_uniform_(self.output_projection.weight, gain)

    def project(self, inputs, index=0, chunks=1):
        """ Produce a linear projection using the weights """
        batch_size = inputs.shape[0]
        start = index * self.embed_dim
        end = start + chunks * self.embed_dim
        projections = F.linear(inputs, self.input_weights[start:end]).chunk(
            chunks, dim=-1)
        output_projections = []
        for projection in projections:
            output_projections.append(projection.view(batch_size, -1, self.
                num_heads, self.projection_dim).transpose(2, 1).contiguous(
                ).view(batch_size * self.num_heads, -1, self.projection_dim))
        return output_projections

    def attention(self, values, keys, queries, key_mask=None, mask=None):
        """ Scaled dot product attention with optional masks """
        logits = self.scale * torch.bmm(queries, keys.transpose(2, 1))
        if mask is not None:
            logits += mask
        if key_mask is not None:
            logits_shape = logits.shape
            batch_size = logits_shape[0] // self.num_heads
            logits = logits.view(batch_size, self.num_heads, logits_shape[1
                ], logits_shape[2])
            logits.masked_fill_(key_mask[:, None, None], float('-inf'))
            logits = logits.view(logits_shape)
        attn_weights = F.softmax(logits, dim=-1)
        attended = torch.bmm(attn_weights, values)
        batch_size = queries.shape[0] // self.num_heads
        return attended.view(batch_size, self.num_heads, -1, self.
            projection_dim).transpose(2, 1).contiguous().view(batch_size, -
            1, self.num_heads * self.projection_dim), attn_weights.view(
            batch_size, self.num_heads, attn_weights.size()[1], -1).transpose(
            0, 1).contiguous()

    def forward(self, input_0, input_1, input_2):
        primals_2 = self.input_weights
        primals_5 = self.output_projection.weight
        primals_1 = input_0
        primals_3 = input_1
        primals_4 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0], output[1]
