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]


Measured latency_ms: 0.026223