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 torch.utils.data
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_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
    x3 = xindex // 4
    x0 = xindex % 4
    x1 = xindex // 4 % 4
    x2 = xindex // 16
    x4 = xindex
    tmp0 = x3
    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 + 4 * x2)), 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 + 4 * x2)), 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 + 4 * x2)), 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 + 4 * x2)), 
        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)
    tmp23 = 0.7071067811865476
    tmp24 = tmp22 * tmp23
    tl.store(out_ptr0 + x4, tmp24, xmask)


@triton.jit
def triton_poi_fused_1(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp3 = triton_helpers.maximum(tmp1, tmp2)
    tmp5 = triton_helpers.maximum(tmp3, tmp4)
    tmp7 = triton_helpers.maximum(tmp5, tmp6)
    tmp8 = tmp0 - tmp7
    tmp9 = tl_math.exp(tmp8)
    tl.store(out_ptr0 + x2, tmp9, xmask)


@triton.jit
def triton_poi_fused_2(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
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp18 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp25 = tl.load(in_ptr1 + x2, xmask)
    tmp26 = tl.load(in_ptr1 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp27 = tl.load(in_ptr1 + (1 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp29 = tl.load(in_ptr1 + (2 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp31 = tl.load(in_ptr1 + (3 + 4 * x1), xmask, eviction_policy='evict_last'
        )
    tmp1 = float('-inf')
    tmp2 = tmp0 == tmp1
    tmp3 = tmp2 == 0
    tmp4 = tmp3.to(tl.int64)
    tmp5 = tmp4 != 0
    tmp7 = tmp6 == tmp1
    tmp8 = tmp7 == 0
    tmp9 = tmp8.to(tl.int64)
    tmp10 = tmp9 != 0
    tmp11 = tmp5 | tmp10
    tmp13 = tmp12 == tmp1
    tmp14 = tmp13 == 0
    tmp15 = tmp14.to(tl.int64)
    tmp16 = tmp15 != 0
    tmp17 = tmp11 | tmp16
    tmp19 = tmp18 == tmp1
    tmp20 = tmp19 == 0
    tmp21 = tmp20.to(tl.int64)
    tmp22 = tmp21 != 0
    tmp23 = tmp17 | tmp22
    tmp24 = tmp23 == 0
    tmp28 = tmp26 + tmp27
    tmp30 = tmp28 + tmp29
    tmp32 = tmp30 + tmp31
    tmp33 = tmp25 / tmp32
    tmp34 = 0.0
    tmp35 = tl.where(tmp24, tmp34, tmp33)
    tl.store(out_ptr0 + x2, tmp35, xmask)


@triton.jit
def triton_poi_fused_stack_3(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_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 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4, 4), (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((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 4), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_3, (4, 4), (1, 4), 0), out=buf1)
        del primals_3
        buf2 = empty_strided_cuda((16, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_4, (16, 4), (4, 1), 0),
            reinterpret_tensor(primals_5, (4, 4), (1, 4), 0), out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_0[grid(64)](buf0, buf3, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        buf4 = reinterpret_tensor(buf0, (4, 4, 1, 4), (16, 4, 4, 1), 0)
        del buf0
        triton_poi_fused_0[grid(64)](buf1, buf4, 64, XBLOCK=64, num_warps=1,
            num_stages=1)
        buf5 = empty_strided_cuda((16, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (16, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf4, (16, 1, 4), (4, 0, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_1[grid(256)](buf5, buf6, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf7 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_2[grid(256)](buf5, buf6, buf7, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del buf5
        del buf6
        buf8 = reinterpret_tensor(buf1, (16, 4, 1), (4, 1, 1), 0)
        del buf1
        triton_poi_fused_stack_3[grid(64)](buf2, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf9 = reinterpret_tensor(buf2, (16, 4, 1), (4, 1, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf7, (16, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf8, (16, 4, 1), (4, 1, 0), 0), out=buf9)
        buf10 = empty_strided_cuda((1, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_cat_4[grid(64)](buf9, buf10, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del buf9
    return reinterpret_tensor(buf10, (4, 4, 4), (16, 4, 1), 0
        ), reinterpret_tensor(primals_2, (16, 4), (4, 1), 0
        ), reinterpret_tensor(primals_4, (16, 4), (4, 1), 0
        ), buf7, reinterpret_tensor(buf8, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf3, (16, 1, 4), (4, 1, 1), 0
        ), reinterpret_tensor(buf4, (16, 4, 1), (4, 1, 4), 0)


class MultiHeadAttentionNew(nn.Module):
    """
    input:
        query --- [N, T_q, query_dim]
        key --- [N, T_k, key_dim]
    output:
        out --- [N, T_q, num_units]
    """

    def __init__(self, query_dim, key_dim, num_units, num_heads):
        super().__init__()
        self.num_units = num_units
        self.num_heads = num_heads
        self.key_dim = key_dim
        self.W_query = nn.Linear(in_features=query_dim, out_features=
            num_units, bias=False)
        self.W_key = nn.Linear(in_features=key_dim, out_features=num_units,
            bias=False)
        self.W_value = nn.Linear(in_features=key_dim, out_features=
            num_units, bias=False)

    def forward(self, input_0, input_1):
        primals_1 = self.W_query.weight
        primals_3 = self.W_key.weight
        primals_5 = self.W_value.weight
        primals_2 = input_0
        primals_4 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.014142