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
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_clone_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
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 4 * x2 + 16 * x1), xmask)
    tl.store(out_ptr0 + x3, tmp0, xmask)


@triton.jit
def triton_poi_fused_cat_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
    x1 = xindex // 4
    x0 = xindex % 4
    x2 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 4, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (4 * x0 + 16 * x1), tmp4 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 8, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (1 + 4 * x0 + 16 * (-4 + x1)), tmp9 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 12, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x0 + 16 * (-8 + x1)), tmp14 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 16, tl.int64)
    tmp19 = tl.load(in_ptr0 + (3 + 4 * x0 + 16 * (-12 + x1)), tmp16 & xmask,
        eviction_policy='evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x2, tmp22, xmask)


@triton.jit
def triton_poi_fused__softmax_2(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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp3 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp8 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp11 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp6 = tmp5 * tmp1
    tmp7 = triton_helpers.maximum(tmp4, tmp6)
    tmp9 = tmp8 * tmp1
    tmp10 = triton_helpers.maximum(tmp7, tmp9)
    tmp12 = tmp11 * tmp1
    tmp13 = triton_helpers.maximum(tmp10, tmp12)
    tmp14 = tmp2 - tmp13
    tmp15 = tmp14 * tmp1
    tmp16 = tl_math.exp(tmp15)
    tl.store(out_ptr0 + x3, tmp16, xmask)


@triton.jit
def triton_poi_fused__softmax_3(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
    x3 = xindex
    x0 = xindex % 4
    x2 = xindex // 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + (x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0 + 16 * x2), xmask, eviction_policy=
        'evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = tmp0 / tmp7
    tl.store(out_ptr0 + x3, tmp8, xmask)


@triton.jit
def triton_poi_fused_cat_4(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
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x1, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tmp7 = tl.full([1], 2, tl.int64)
    tmp8 = tmp0 < tmp7
    tmp9 = tmp6 & tmp8
    tmp10 = tl.load(in_ptr0 + (16 + x1), tmp9 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp11 = tmp0 >= tmp7
    tmp12 = tl.full([1], 3, tl.int64)
    tmp13 = tmp0 < tmp12
    tmp14 = tmp11 & tmp13
    tmp15 = tl.load(in_ptr0 + (32 + x1), tmp14 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp16 = tmp0 >= tmp12
    tl.full([1], 4, tl.int64)
    tmp19 = tl.load(in_ptr0 + (48 + x1), tmp16 & xmask, eviction_policy=
        'evict_last', other=0.0)
    tmp20 = tl.where(tmp14, tmp15, tmp19)
    tmp21 = tl.where(tmp9, tmp10, tmp20)
    tmp22 = tl.where(tmp4, tmp5, tmp21)
    tl.store(out_ptr0 + x2, tmp22, 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, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4, 4), (4, 1))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64)](primals_3, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_3
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_0[grid(64)](primals_2, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_2
        buf3 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf3)
        del primals_5
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_clone_0[grid(64)](primals_1, buf4, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_1
        buf5 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf5)
        del primals_6
        buf6 = empty_strided_cuda((16, 4, 1), (4, 1, 1), torch.float32)
        triton_poi_fused_cat_1[grid(64)](buf3, buf6, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf7 = reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 1), 0)
        del buf3
        triton_poi_fused_cat_1[grid(64)](buf1, buf7, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf8 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf6, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf7, (16, 1, 4), (4, 0, 1), 0), out=buf8)
        buf9 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused__softmax_2[grid(256)](buf8, buf9, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf10 = buf8
        del buf8
        triton_poi_fused__softmax_3[grid(256)](buf9, buf10, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del buf9
        buf11 = reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0)
        del buf1
        triton_poi_fused_cat_1[grid(64)](buf5, buf11, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf12 = reinterpret_tensor(buf5, (16, 1, 4), (4, 4, 1), 0)
        del buf5
        extern_kernels.bmm(reinterpret_tensor(buf11, (16, 1, 4), (4, 0, 1),
            0), buf10, out=buf12)
        buf13 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_cat_4[grid(64)](buf12, buf13, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf14 = reinterpret_tensor(buf12, (16, 4), (4, 1), 0)
        del buf12
        extern_kernels.mm(reinterpret_tensor(buf13, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_7, (4, 4), (1, 4), 0), out=buf14)
    return reinterpret_tensor(buf14, (4, 4, 4), (4, 16, 1), 0
        ), buf10, reinterpret_tensor(buf0, (16, 4), (4, 1), 0
        ), reinterpret_tensor(buf2, (16, 4), (4, 1), 0), reinterpret_tensor(
        buf4, (16, 4), (4, 1), 0), buf10, reinterpret_tensor(buf13, (16, 4),
        (4, 1), 0), primals_7, buf11, reinterpret_tensor(buf6, (16, 1, 4),
        (4, 1, 1), 0), buf7


def dot_scaled_attention(query: 'torch.Tensor', key: 'torch.Tensor', value:
    'torch.Tensor'):
    """ Dot scaled attention
	Implement dot-product scaled attention which takes query, key, value and gives attention scores.

	Arguments:
	query -- Query tensor
				in shape (sequence_length, batch_size, d_k)
	key -- Key tensor
				in shape (sequence_length, batch_size, d_k)
	value -- Value tensor
				in shape (sequence_length, batch_size, d_k)
	padding_mask -- Padding mask tensor in torch.bool type
				in shape (sequence_length, batch_size)
				True for <PAD>, False for non-<PAD>

	Returns:
	attention -- Attention result tensor
				in shape (sequence_length, batch_size, d_k)
	"""
    assert query.shape == key.shape == value.shape
    query_shape = query.shape
    _seq_len, _, d_k = query_shape
    QK_t_scaled = torch.bmm(key.permute(1, 0, 2), query.permute(1, 2, 0)
        ) / math.sqrt(d_k)
    distribution = nn.functional.softmax(QK_t_scaled, dim=1)
    attention = torch.bmm(value.permute(1, 2, 0), distribution).permute(2, 0, 1
        )
    assert attention.shape == query_shape
    return attention, distribution


class MultiHeadAttentionNew(nn.Module):

    def __init__(self, hidden_dim: 'int', n_head: 'int'=4):
        """ Multi-head attention initializer
		Use below attributes to implement the forward function

		Attributes:
		n_head -- the number of heads
		d_k -- Hidden dimension of the dot scaled attention
		V_linear -- Linear function to project hidden_dim of value to d_k
		K_linear -- Linear function to project hidden_dim of key to d_k
		Q_linear -- Linear function to project hidden_dim of query to d_k
		O_linear -- Linear function to project collections of d_k to hidden_dim
		"""
        super().__init__()
        assert hidden_dim % n_head == 0
        self.n_head = n_head
        self.d_k = hidden_dim // n_head
        self.V_linear = nn.Linear(hidden_dim, self.n_head * self.d_k, bias=
            False)
        self.K_linear = nn.Linear(hidden_dim, self.n_head * self.d_k, bias=
            False)
        self.Q_linear = nn.Linear(hidden_dim, self.n_head * self.d_k, bias=
            False)
        self.O_linear = nn.Linear(self.n_head * self.d_k, hidden_dim, bias=
            False)

    def forward(self, input_0, input_1, input_2):
        primals_4 = self.V_linear.weight
        primals_5 = self.K_linear.weight
        primals_6 = self.Q_linear.weight
        primals_7 = self.O_linear.weight
        primals_1 = input_0
        primals_2 = input_1
        primals_3 = input_2
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7])
        return output[0], output[1]


Measured latency_ms: 0.169759