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 libdevice, math as tl_math
import numpy as np
import torch.nn as nn
import torch.distributions
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, in_ptr1, out_ptr0, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp2, xmask & ymask)


@triton.jit
def triton_per_fused_div_max_1(in_ptr0, out_ptr0, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 1024
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = tl.broadcast_to(tmp2, [RBLOCK])
    tmp5 = triton_helpers.promote_to_tensor(triton_helpers.max2(tmp3, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp5, None)


@triton.jit
def triton_poi_fused__softmax_div_sub_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, 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 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp6 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp14 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp5 = tmp2 - tmp4
    tmp7 = tmp6 * tmp1
    tmp8 = tmp7 - tmp4
    tmp9 = triton_helpers.maximum(tmp5, tmp8)
    tmp11 = tmp10 * tmp1
    tmp12 = tmp11 - tmp4
    tmp13 = triton_helpers.maximum(tmp9, tmp12)
    tmp15 = tmp14 * tmp1
    tmp16 = tmp15 - tmp4
    tmp17 = triton_helpers.maximum(tmp13, tmp16)
    tmp18 = tmp5 - tmp17
    tmp19 = tl_math.exp(tmp18)
    tmp20 = tmp8 - tmp17
    tmp21 = tl_math.exp(tmp20)
    tmp22 = tmp19 + tmp21
    tmp23 = tmp12 - tmp17
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp22 + tmp24
    tmp26 = tmp16 - tmp17
    tmp27 = tl_math.exp(tmp26)
    tmp28 = tmp25 + tmp27
    tl.store(out_ptr0 + x0, tmp17, xmask)
    tl.store(out_ptr1 + x0, tmp28, xmask)


@triton.jit
def triton_poi_fused__softmax_div_isnan_masked_fill_sub_3(in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 1024
    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)
    tmp3 = tl.load(in_ptr1 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp6 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr3 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp5 = tmp2 - tmp4
    tmp7 = tmp5 - tmp6
    tmp8 = tl_math.exp(tmp7)
    tmp10 = tmp8 / tmp9
    tmp11 = libdevice.isnan(tmp10).to(tl.int1)
    tmp12 = 0.0
    tmp13 = tl.where(tmp11, tmp12, tmp10)
    tl.store(out_ptr0 + x2, tmp13, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_4(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, 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 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + 4 * x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp4 = tl.load(in_ptr1 + (1 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp8 = tl.load(in_ptr1 + (2 + 4 * x2), xmask, eviction_policy='evict_last')
    tmp11 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    tmp12 = tl.load(in_ptr1 + (3 + 4 * x2), xmask, eviction_policy='evict_last'
        )
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp2 + tmp5
    tmp9 = tmp7 + tmp8
    tmp10 = tmp6 + tmp9
    tmp13 = tmp11 + tmp12
    tmp14 = tmp10 + tmp13
    tmp15 = 4.0
    tmp16 = tmp14 / tmp15
    tmp17 = tmp2 - tmp16
    tmp18 = tmp17 * tmp17
    tmp19 = tmp5 - tmp16
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = tmp9 - tmp16
    tmp23 = tmp22 * tmp22
    tmp24 = tmp21 + tmp23
    tmp25 = tmp13 - tmp16
    tmp26 = tmp25 * tmp25
    tmp27 = tmp24 + tmp26
    tmp28 = tmp27 / tmp15
    tl.store(out_ptr0 + x2, tmp16, xmask)
    tl.store(out_ptr1 + x2, tmp28, xmask)


@triton.jit
def triton_poi_fused_add_native_layer_norm_5(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, in_ptr4, in_ptr5, out_ptr0, ynumel, xnumel, YBLOCK: tl.
    constexpr, XBLOCK: tl.constexpr):
    ynumel = 64
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y0 = yindex % 4
    y1 = yindex // 4
    y3 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 4 * x2 + 16 * y1), xmask & ymask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + y3, ymask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + y3, ymask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr4 + x2, xmask, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr5 + x2, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 - tmp3
    tmp6 = 1e-05
    tmp7 = tmp5 + tmp6
    tmp8 = libdevice.rsqrt(tmp7)
    tmp9 = tmp4 * tmp8
    tmp11 = tmp9 * tmp10
    tmp13 = tmp11 + tmp12
    tl.store(out_ptr0 + (x2 + 4 * y3), tmp13, xmask & ymask)


@triton.jit
def triton_poi_fused_relu_threshold_backward_6(in_out_ptr0, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_7(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_8(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp23, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_9(in_ptr0, in_ptr1, in_ptr2, in_ptr3,
    in_ptr4, 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
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x0, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4,), (1,))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4, 4), (4, 1))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4, 4), (4, 1))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4,), (1,))
    assert_size_stride(primals_15, (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)
        del primals_2
        buf1 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_4, (4, 4), (1, 4), 0), out=buf1)
        del primals_4
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_6, (4, 4), (1, 4), 0), out=buf2)
        del primals_6
        buf3 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 1), torch
            .float32)
        get_raw_stream(0)
        triton_poi_fused_clone_0[grid(64, 4)](buf0, primals_3, buf3, 64, 4,
            XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_3
        buf4 = reinterpret_tensor(buf0, (4, 4, 4, 1, 4), (64, 16, 4, 4, 1), 0)
        del buf0
        triton_poi_fused_clone_0[grid(64, 4)](buf1, primals_5, buf4, 64, 4,
            XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_5
        buf5 = empty_strided_cuda((64, 4, 4), (16, 4, 1), torch.float32)
        extern_kernels.bmm(reinterpret_tensor(buf3, (64, 4, 1), (4, 1, 0), 
            0), reinterpret_tensor(buf4, (64, 1, 4), (4, 0, 1), 0), out=buf5)
        buf6 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_div_max_1[grid(1)](buf5, buf6, 1, 1024, num_warps=
            8, num_stages=1)
        buf7 = reinterpret_tensor(buf1, (4, 4, 4, 4, 1), (64, 16, 4, 1, 256), 0
            )
        del buf1
        buf8 = empty_strided_cuda((4, 4, 4, 4, 1), (64, 16, 4, 1, 256),
            torch.float32)
        triton_poi_fused__softmax_div_sub_2[grid(256)](buf5, buf6, buf7,
            buf8, 256, XBLOCK=128, num_warps=4, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4, 4, 4), (256, 64, 16, 4, 1),
            torch.float32)
        triton_poi_fused__softmax_div_isnan_masked_fill_sub_3[grid(1024)](buf5,
            buf6, buf7, buf8, buf9, 1024, XBLOCK=128, num_warps=4, num_stages=1
            )
        buf10 = reinterpret_tensor(buf8, (4, 4, 4, 4, 1), (64, 16, 4, 1, 1), 0)
        del buf8
        triton_poi_fused_clone_0[grid(64, 4)](buf2, primals_7, buf10, 64, 4,
            XBLOCK=4, YBLOCK=64, num_warps=4, num_stages=1)
        del primals_7
        buf11 = reinterpret_tensor(buf2, (64, 4, 1), (4, 1, 1), 0)
        del buf2
        extern_kernels.bmm(reinterpret_tensor(buf9, (64, 4, 4), (16, 4, 1),
            0), reinterpret_tensor(buf10, (64, 4, 1), (4, 1, 0), 0), out=buf11)
        buf12 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        buf13 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        triton_poi_fused_add_native_layer_norm_4[grid(64)](buf11, primals_1,
            buf12, buf13, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf14 = reinterpret_tensor(buf7, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf7
        triton_poi_fused_add_native_layer_norm_5[grid(64, 4)](buf11,
            primals_1, buf12, buf13, primals_8, primals_9, buf14, 64, 4,
            XBLOCK=4, YBLOCK=32, num_warps=4, num_stages=1)
        del primals_9
        buf15 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf14, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_10, (4, 4), (1, 4), 0), out=buf15)
        buf16 = reinterpret_tensor(buf15, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf15
        buf22 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        triton_poi_fused_relu_threshold_backward_6[grid(256)](buf16,
            primals_11, buf22, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_11
        buf17 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf16, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_12, (4, 4), (1, 4), 0), out=buf17)
        buf18 = reinterpret_tensor(buf17, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf17
        triton_poi_fused_add_7[grid(256)](buf18, primals_13, buf14, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_13
        buf19 = buf13
        del buf13
        buf20 = buf12
        del buf12
        triton_poi_fused_native_layer_norm_8[grid(64)](buf18, buf19, buf20,
            64, XBLOCK=64, num_warps=1, num_stages=1)
        buf21 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_native_layer_norm_9[grid(256)](buf18, buf19, buf20,
            primals_14, primals_15, buf21, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
        del buf19
        del buf20
        del primals_15
    return (buf21, primals_1, primals_8, primals_14, buf5, buf6, buf11,
        reinterpret_tensor(buf14, (64, 4), (4, 1), 0), reinterpret_tensor(
        buf16, (64, 4), (4, 1), 0), buf18, primals_12, buf22, primals_10,
        reinterpret_tensor(buf9, (64, 4, 4), (16, 1, 4), 0),
        reinterpret_tensor(buf10, (64, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf3, (64, 1, 4), (4, 1, 1), 0),
        reinterpret_tensor(buf4, (64, 4, 1), (4, 1, 4), 0))


class MultiHeadAttention(nn.Module):

    def __init__(self, d_model, n_heads, kq_same=False, bias=True):
        super().__init__()
        """
        It has projection layer for getting keys, queries and values. Followed by attention.
        """
        self.d_model = d_model
        self.h = n_heads
        self.d_k = self.d_model // self.h
        self.kq_same = kq_same
        if not kq_same:
            self.q_linear = nn.Linear(d_model, d_model, bias=bias)
        self.k_linear = nn.Linear(d_model, d_model, bias=bias)
        self.v_linear = nn.Linear(d_model, d_model, bias=bias)

    def head_split(self, x):
        new_x_shape = x.size()[:-1] + (self.h, self.d_k)
        return x.view(*new_x_shape).transpose(-2, -3)

    def forward(self, q, k, v, mask=None):
        origin_shape = q.size()
        if not self.kq_same:
            q = self.head_split(self.q_linear(q))
        else:
            q = self.head_split(self.k_linear(q))
        k = self.head_split(self.k_linear(k))
        v = self.head_split(self.v_linear(v))
        output = self.scaled_dot_product_attention(q, k, v, self.d_k, mask)
        output = output.transpose(-2, -3).reshape(origin_shape)
        return output

    @staticmethod
    def scaled_dot_product_attention(q, k, v, d_k, mask=None):
        """
        This is called by Multi-head attention object to find the values.
        """
        scores = torch.matmul(q, k.transpose(-2, -1)) / d_k ** 0.5
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -np.inf)
        scores = (scores - scores.max()).softmax(dim=-1)
        scores = scores.masked_fill(torch.isnan(scores), 0)
        output = torch.matmul(scores, v)
        return output


class TransformerLayerNew(nn.Module):

    def __init__(self, d_model, d_ff, n_heads, dropout=0, kq_same=False):
        super().__init__()
        """
        This is a Basic Block of Transformer. It contains one Multi-head attention object. 
        Followed by layer norm and position wise feedforward net and dropout layer.
        """
        self.masked_attn_head = MultiHeadAttention(d_model, n_heads,
            kq_same=kq_same)
        self.layer_norm1 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.linear1 = nn.Linear(d_model, d_ff)
        self.linear2 = nn.Linear(d_ff, d_model)
        self.layer_norm2 = nn.LayerNorm(d_model)
        self.dropout2 = nn.Dropout(dropout)

    def forward(self, input_0):
        primals_2 = self.masked_attn_head.q_linear.weight
        primals_3 = self.masked_attn_head.q_linear.bias
        primals_4 = self.masked_attn_head.k_linear.weight
        primals_5 = self.masked_attn_head.k_linear.bias
        primals_6 = self.masked_attn_head.v_linear.weight
        primals_7 = self.masked_attn_head.v_linear.bias
        primals_8 = self.layer_norm1.weight
        primals_9 = self.layer_norm1.bias
        primals_10 = self.linear1.weight
        primals_11 = self.linear1.bias
        primals_12 = self.linear2.weight
        primals_13 = self.linear2.bias
        primals_14 = self.layer_norm2.weight
        primals_15 = self.layer_norm2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15])
        return output[0]


Measured latency_ms: 0.261766