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  • TVM优化Deep Learning GPU算子

    TVM优化Deep Learning GPU算子

    高效的深度学习算子是深度学习系统的核心。通常,这些算子很难优化,需要HPC专家付出巨大的努力。 端到端张量IR / DSL堆栈TVM使这一过程变得更加容易。

    如何在TVM的帮助下编写高性能GPU运算符内核。本文以深度卷积(即topi.nn.depthwise_conv2d_nchw)为例,并演示如何在tensorflow中改进已经手工优化的CUDA内核。在不同的工作负载下,最终版本比tf-1.2中优化的内核快2到4倍,在启用了算子融合的情况下,最终版本快3到7倍。以下是在GTX1080上测试的结果,filter size = [1, 256, 3, 3], stride = [1, 1], padding = ‘SAME’:

     

     深度卷积简介

    深度卷积是现代体系结构的重要组成部分,例如Xception和MobileNet。这是降低深度神经网络计算复杂度的有效方法。

     

     在TVM中,深度卷积可以声明为:

    # padding stage

    PaddedInput = tvm.compute(

        (batch, in_channel, height_after_pad, width_after_pad),

        lambda b, c, i, j: tvm.select(

            tvm.all(i >= pad_top, i - pad_top < in_height, j >= pad_left, j - pad_left < in_width),

            Input[b, c, i - pad_top, j - pad_left], tvm.const(0.0)),

        name="PaddedInput")

    # depthconv stage

    di = tvm.reduce_axis((0, filter_height), name='di')

    dj = tvm.reduce_axis((0, filter_width), name='dj')

    Output = tvm.compute(

        (batch, out_channel, out_height, out_width),

        lambda b, c, i, j: tvm.sum(

            PaddedInput[b, c/channel_multiplier, i*stride_h + di, j*stride_w + dj] * Filter[c/channel_multiplier, c%channel_multiplier, di, dj],

            axis=[di, dj]),

        name='DepthwiseConv2d')

    通用GPU优化准则

    本部分简要讨论了优化CUDA代码时应了解的三个概念:数据重用,共享内存和存储冲突。

    数据重用

    在现代计算体系结构中,从内存中加载数据的成本要比进行单个浮点计算高得多。因此,始终希望在将输入数据加载到寄存器或共享内存(高速缓存)后重用。

    深度卷积有两种形式的数据重用:filter过滤器重用和输入重用。当filter过滤器在输入通道上滑动并多次计算时,会发生filter过滤器重用。输入重用是通过平铺实现的,以3x3深度转换为例:

    General GPU Optimization Guidelines

     

     在不进行平铺的情况下,每个线程计算1个输出元素并加载3x3输入数据。16个线程加在一起有9x16的负载。

     

     通过平铺,每个线程计算2x2输出元素并加载4x4输入数据。4个线程加在一起有16x4的负载。

    共享内存和bank冲突

    共享内存可以看作是GPU中的缓存。这是片上的,并且比全局存储快得多。

     

     共享内存按block块分配。通常的做法是将数据从全局内存加载到共享内存中, block块中的所有线程都从共享内存中读取数据。

    共享内存的大小是有限的(通常为48K),必须谨慎对待共享内存的溢出。此外,分配给一个block块的共享内存过多,限制了每个多处理器的活动块数。

    共享内存的另一个性能问题是存储区冲突。共享内存分为大小相等的内存模块(可同时访问),但是,如果多个线程访问同一内存库(导致内存库冲突),则访问将被序列化,从而降低了有效带宽。

    共享存储体的组织方式是将连续的地址分配给连续的存储体。为避免存储区冲突,最好连续的线程,访问连续的内存地址,如下图所示(每种颜色代表一个共享存储区):

     

     开始优化TVM中的深度卷积。

    调度优化

    计算PaddedInput内联以节省内存分配

    从第1部分中可以看到,padding填充被显式声明为一个单独的阶段。内联计算以避免冗余的内存分配:

    s = tvm.create_schedule(Output.op)
    s[PaddedInput].compute_inline()

    将一个大通道划分为较小的块

    深度卷积的一个简单明了的调度表是,一个cuda块负责一个输入通道和相应的filter过滤器,加载到共享内存中,然后进行计算:

    IS = s.cache_read(PaddedInput, "shared", [DepthwiseConv2d])

    FS = s.cache_read(Filter, "shared", [DepthwiseConv2d])

    block_y = tvm.thread_axis("blockIdx.y")

    block_x = tvm.thread_axis("blockIdx.x")

    # bind the dimension of batch (N in NCHW) with block_y

    s[Output].bind(Output.op.axis[0], block_y)

    # bind the dimension of channel (C in NCHW) with block_x

    s[Output].bind(Output.op.axis[1], block_x)

    Here is the result: 测试了在GTX 1080上运行1000次的平均时间成本,并与tensorflow中的depthwise_conv2d进行了比较。结果如下:

    Input

    Filter

    stride

    tf-1.2 SAME pad (us)

    TVM SAME pad (us)

    [1, 256, 21, 21]

    [256, 1, 3, 3]

    [1, 1]

    16.1

    9.1

    [1, 256, 32, 32]

    [256, 1, 3, 3]

    [1, 1]

    34.8

    14.5

    [1, 256, 64, 64]

    [256, 1, 3, 3]

    [1, 1]

    130.9

    98.9

    [1, 256, 96, 96]

    [256, 1, 3, 3]

    [1, 1]

    251.6

    387.4

     

     

     

     

     

     

     

     

     

    As we can see, this schedule performs well with small channel size like 21 x 21 or 32 x 32, however, its performance drops seriously as the channel size increases to larger than 64 x 64. One main reason is that too much shared memory allocated to one block limits the number of active blocks per multiprocessor.

    此调度在较小的通道大小(例如21 x 21或32 x 32)下表现良好,但是,当通道大小增加到大于64 x 64时,其性能会严重下降。一个主要原因是分配的共享内存过多分配到一块,限制每个多处理器的活动块数。

    修改了调度表,将一个大频道划分为多个较小的块。例如,一个通道(64 x 64或96 x 96)被分成32 x 32的块,而一个cuda块负责一个32 x 32的块:

     

    blocking_h = 32

    blocking_w = 32

    # split the dimension of height (H in NCHW)

    bx1, _ = s[Output].split(Output.op.axis[2], factor=blocking_h)

    # split the dimension of width (W in NCHW)

    bx2, _ = s[Output].split(Output.op.axis[3], factor=blocking_w)

    # assign one 32 x 32 block to one cuda block

    by = s[Output].fuse(Output.op.axis[0], Output.op.axis[1])

    s[Output].bind(by, block_y)

    bx = s[Output].fuse(bx1, bx2)

    s[Output].bind(bx, block_x)

    结果如下:

    Input

    [blocking_h, blocking_w]

    tf-1.2 SAME pad (us)

    TVM SAME pad (us)

    [1, 256, 64, 64]

    [32, 32]

    130.9

    63.4

    [1, 256, 96, 96]

    [32, 32]

    251.6

    132.5

     

     

     

     

     

     

    封锁策略有效!对于64 x 64通道大小,带来1.6倍加速(98.9us-> 63.4us);对于96 x 96通道大小,带来2.9倍加速(387.4us-> 132.5us)。

    线程的调整参数

    如何在一个cuda块的线程之间调度工作负载(例如32x32)?直观地,应该是这样的:

    num_thread_y = 8

    num_thread_x = 8

    thread_y = tvm.thread_axis((0, num_thread_y), "threadIdx.y")

    thread_x = tvm.thread_axis((0, num_thread_x), "threadIdx.x")

    ty, yi = s[Output].split(h_dim, nparts=num_thread_y)

    tx, xi = s[Output].split(w_dim, nparts=num_thread_x)

    s[Output].reorder(ty, tx, yi, xi)

    s[Output].bind(ty, thread_y)

    s[Output].bind(tx, thread_x)

    调度表中有两个参数:num_thread_ynum_thread_x。如何确定最佳组合?先做一些实验。以下是Filter = [256,1,3,3]和stride = [1,1]的结果:

    Case

    Input

    num_thread_y

    num_thread_x

    TVM SAME pad (us)

    1

    [1, 256, 32, 32]

    8

    32

    9.7

    2

    [1, 256, 32, 32]

    4

    32

    8.8

    3

    [1, 256, 32, 32]

    1

    32

    17.7

    4

    [1, 256, 32, 32]

    32

    1

    32.5

     

     

     

     

     

     

     

     

     

    从以上结果中可以得到:

    • 情况2比情况1快。在情况2中,每个线程在输出中计算一个8x1的图块,对应于输入中的10x3的图块。比情况1的4x1 tile具有更好的数据重用性。
    • 情况3比情况2慢。这是因为在情况3中,每个线程的工作量太大,导致读取本地内存的成本较高。
    • 情况4比情况3慢。这是因为num_thread_x = 32确保没有bank冲突,而num_thread_y = 32没有。

    总结一下:

    • 大图块有利于数据重用,但不利于本地内存读取。
    • num_thread_y和num_thread_x对bank冲突的影响是不对称的。
    • 为了找到num_thread_y和num_thread_x的最佳组合,实现高效共享存储器访问(避免组冲突),数据复用,本地存储器read的平衡。

    如何才能找到最佳组合呢?答案是蛮力搜索。可以将num_thread_y和num_thread_x作为参数传递给schedule函数,并尝试所有可能的组合以找到最佳组合。这可以在TVM中轻松完成:

    def schedule_depthwise_conv2d(..., num_thread_y=8, num_thread_x=8):

        num_thread_y = num_thread_y

        num_thread_x = num_thread_x

        do_schedule_as_usual

        return schedule

     

    min_time_cost = inf

    for num_thread_y, num_thread_x in all_possible_combinations:

        schedule = schedule_depthwise_conv2d(..., num_thread_y=num_thread_y, num_thread_x=num_thread_x)

        time_cost = test_depthwise_conv2d(..., schedule)

        if time_cost < min_time_cost:

            min_time_cost = time_cost

            optimal_combination = [num_thread_y, num_thread_x]

    实际上,可以看作是一个简单的自动调度程序。

    Vthread和交叉模式

    引入TVM中的Vthread(虚拟线程),支持跨步模式。可以这样使用:

    num_vthread_y = 2

    num_vthread_x = 2

    num_thread_y = 8

    num_thread_x = 8

    thread_vy = tvm.thread_axis((0, num_vthread_y), "vthread", name="vy")

    thread_vx = tvm.thread_axis((0, num_vthread_x), "vthread", name="vx")

    thread_y = tvm.thread_axis((0, num_thread_y), "threadIdx.y")

    thread_x = tvm.thread_axis((0, num_thread_x), "threadIdx.x")

    # split the dimension of height (H in NCHW) twice

    tvy, vyi = s[Output].split(h_dim, nparts=num_vthread_y)

    ty, yi = s[Output].split(vyi, nparts=num_thread_y)

    # split the dimension of width (W in NCHW) twice

    tvx, vxi = s[Output].split(w_dim, nparts=num_vthread_x)

    tx, xi = s[Output].split(vxi, nparts=num_thread_x)

    # bind thread and vthread respectively

    s[Output].bind(tvy, thread_vy)

    s[Output].bind(tvx, thread_vx)

    s[Output].bind(ty, thread_y)

    s[Output].bind(tx, thread_x)

    s[Output].reorder(tvy, tvx, ty, tx, yi, xi)

    Let’s print the IR to see what vthread does:

    /* Input = [1, 1, 32, 32], Filter = [1, 1, 3, 3], stride = [1, 1], padding = 'SAME' */

    produce DepthwiseConv2d {

      // attr [iter_var(blockIdx.y, , blockIdx.y)] thread_extent = 1

      // attr [iter_var(blockIdx.x, , blockIdx.x)] thread_extent = 1

      // attr [iter_var(threadIdx.y, Range(min=0, extent=8), threadIdx.y)] thread_extent = 8

      // attr [iter_var(threadIdx.x, Range(min=0, extent=8), threadIdx.x)] thread_extent = 8

      for (i.inner.inner.inner, 0, 2) {

        for (j.inner.inner.inner, 0, 2) {

          DepthwiseConv2d[((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner)] = 0.000000f

          DepthwiseConv2d[(((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + 512)] = 0.000000f

          DepthwiseConv2d[(((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + 16)] = 0.000000f

          DepthwiseConv2d[(((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + 528)] = 0.000000f

          for (di, 0, 3) {

            for (dj, 0, 3) {

              DepthwiseConv2d[((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner)] = (DepthwiseConv2d[((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner)] + (tvm_if_then_else(((((((1 - di) - i.inner.inner.inner) <= (((blockIdx.x*16) + threadIdx.y)*2)) && ((((blockIdx.x*16) + threadIdx.y)*2) < ((33 - di) - i.inner.inner.inner))) && (((1 - dj) - j.inner.inner.inner) <= (threadIdx.x*2))) && ((threadIdx.x*2) < ((33 - dj) - j.inner.inner.inner))), Input[(((((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + (di*32)) + dj) + -33)], 0.000000f)*Filter[((di*3) + dj)]))

              DepthwiseConv2d[(((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + 512)] = (DepthwiseConv2d[(((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + 512)] + (tvm_if_then_else(((((((-15 - di) - i.inner.inner.inner) <= (((blockIdx.x*16) + threadIdx.y)*2)) && ((((blockIdx.x*16) + threadIdx.y)*2) < ((17 - di) - i.inner.inner.inner))) && (((1 - dj) - j.inner.inner.inner) <= (threadIdx.x*2))) && ((threadIdx.x*2) < ((33 - dj) - j.inner.inner.inner))), Input[(((((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + (di*32)) + dj) + 479)], 0.000000f)*Filter[((di*3) + dj)]))

              DepthwiseConv2d[(((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + 16)] = (DepthwiseConv2d[(((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + 16)] + (tvm_if_then_else(((((((1 - di) - i.inner.inner.inner) <= (((blockIdx.x*16) + threadIdx.y)*2)) && ((((blockIdx.x*16) + threadIdx.y)*2) < ((33 - di) - i.inner.inner.inner))) && (((-15 - dj) - j.inner.inner.inner) <= (threadIdx.x*2))) && ((threadIdx.x*2) < ((17 - dj) - j.inner.inner.inner))), Input[(((((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + (di*32)) + dj) + -17)], 0.000000f)*Filter[((di*3) + dj)]))

              DepthwiseConv2d[(((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + 528)] = (DepthwiseConv2d[(((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + 528)] + (tvm_if_then_else(((((((-15 - di) - i.inner.inner.inner) <= (((blockIdx.x*16) + threadIdx.y)*2)) && ((((blockIdx.x*16) + threadIdx.y)*2) < ((17 - di) - i.inner.inner.inner))) && (((-15 - dj) - j.inner.inner.inner) <= (threadIdx.x*2))) && ((threadIdx.x*2) < ((17 - dj) - j.inner.inner.inner))), Input[(((((((((((blockIdx.y + blockIdx.x)*16) + threadIdx.y)*32) + threadIdx.x)*2) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + (di*32)) + dj) + 495)], 0.000000f)*Filter[((di*3) + dj)]))

            }

          }

        }

      }

    }

    Without vthread (just set to 1), the IR is:

    /* Input = [1, 1, 32, 32], Filter = [1, 1, 3, 3], stride = [1, 1], padding = 'SAME' */

    produce DepthwiseConv2d {

      // attr [iter_var(blockIdx.y, , blockIdx.y)] thread_extent = 1

      // attr [iter_var(blockIdx.x, , blockIdx.x)] thread_extent = 1

      // attr [iter_var(threadIdx.y, Range(min=0, extent=8), threadIdx.y)] thread_extent = 8

      // attr [iter_var(threadIdx.x, Range(min=0, extent=8), threadIdx.x)] thread_extent = 8

      for (i.inner.inner.inner, 0, 4) {

        for (j.inner.inner.inner, 0, 4) {

          DepthwiseConv2d[((((((((blockIdx.y + blockIdx.x)*8) + threadIdx.y)*32) + threadIdx.x)*4) + (i.inner.inner.inner*32)) + j.inner.inner.inner)] = 0.000000f

          for (di, 0, 3) {

            for (dj, 0, 3) {

              DepthwiseConv2d[((((((((blockIdx.y + blockIdx.x)*8) + threadIdx.y)*32) + threadIdx.x)*4) + (i.inner.inner.inner*32)) + j.inner.inner.inner)] = (DepthwiseConv2d[((((((((blockIdx.y + blockIdx.x)*8) + threadIdx.y)*32) + threadIdx.x)*4) + (i.inner.inner.inner*32)) + j.inner.inner.inner)] + (tvm_if_then_else(((((((1 - di) - i.inner.inner.inner) <= (((blockIdx.x*8) + threadIdx.y)*4)) && ((((blockIdx.x*8) + threadIdx.y)*4) < ((33 - di) - i.inner.inner.inner))) && (((1 - dj) - j.inner.inner.inner) <= (threadIdx.x*4))) && ((threadIdx.x*4) < ((33 - dj) - j.inner.inner.inner))), Input[(((((((((((blockIdx.y + blockIdx.x)*8) + threadIdx.y)*32) + threadIdx.x)*4) + (i.inner.inner.inner*32)) + j.inner.inner.inner) + (di*32)) + dj) + -33)], 0.000000f)*Filter[((di*3) + dj)]))

            }

          }

        }

      }

    }

    可以看到,当num_vthread_y = 2和时num_vthread_x = 2,将32 x 32通道分为四个16 x 16子通道。每个线程一次计算四个输出元素,一个子通道中一个元素。

    以下是Filter = [256,1,3,3],stride = [1,1],blocking_h = 32,blocking_w = 32的结果:

    Case

    Input

    num_thread_y, num_thread_x

    num_vthread_y, num_vthread_x

    TVM SAME pad (us)

    1

    [1, 256, 96, 96]

    8, 8

    1, 1

    132.5

    2

    [1, 256, 96, 96]

    8, 8

    1, 4

    103.1

    3

    [1, 256, 96, 96]

    4, 32

    1, 1

    95.9

    4

    [1, 256, 96, 96]

    8, 16

    1, 2

    90.9

     

     

     

     

     

     

     

     

     

    Case 2比Case 1快。在Case 2中,num_thread_x=8并且num_vthread_x=4一起确保连续的线程访问连续的内存地址,从而避免了存储区冲突,如下所示(每种颜色代表一个线程的工作量):

     

     从理论上讲,case 3和case 4应该同样很快,每个线程的工作量相同,并且都享有有效的共享内存访问。不管怎样,case 4快了一点。

    还记得tensorflow的速度吗?现在是251.6us,现在TVM快了2.8倍。387.4-> 132.5-> 95.9-> 90.9,封锁最有帮助;调整线程数可节省37us;vthread可以节省额外的5us。

    实际上,TVM可以比具有大内核大小或channel_multiplier的tensorflow快得多(因为更多的filter过滤器重用):

    Input

    Filter

    stride

    tf-1.2 SAME pad (us)

    TVM SAME pad (us)

    How faster is TVM

    [1, 256, 96, 96]

    [256, 1, 3, 3]

    [1, 1]

    251.6

    90.9

    2.8x

    [1, 256, 96, 96]

    [256, 1, 5, 5]

    [1, 1]

    597.6

    128.9

    4.6x

    [1, 256, 96, 96]

    [256, 2, 3, 3]

    [1, 1]

    659.9

    143.7

    4.6x

    [1, 256, 96, 96]

    [256, 2, 5, 5]

    [1, 1]

    1203.9

    170.5

    7.1x

     

     

     

     

     

     

     

     

     

    Consider a common pattern in neural networks: depthwise_conv2d + scale_shift + relu. We can fuse the three operators into one, by slightly modifying the original schedule:

    算子融合

    算子融合是可以在深度学习中进行的一种典型优化,可以在单个内核中一起计算多个算子,无需将中间结果保存回全局内存中。TVM对此提供了开箱即用的支持。

    神经网络中的一个常见模式:depthwise_conv2dscale_shiftrelu。稍微修改原始调度表,可以将三个算子融合为一个:

    DepthwiseConv2d = topi.nn.depthwise_conv2d(Input, Filter, stride, padding)

    ScaleShift = topi.nn.scale_shift(DepthwiseConv2d, Scale, Shift)

    Relu = topi.nn.relu(ScaleShift)

     

    Output = Relu # is no longer DepthwiseConv2d

    s[ScaleShift].compute_inline() # this line fuses ScaleShift, explicitly

    s[DepthwiseConv2d].set_scope("local") # this line fuses DepthwiseConv2d, implicitly

    schedule(Output) # schedule for Output the same way we schedule for DepthwiseConv2d as discussed above

    s[DepthwiseConv2d].compute_at(s[Output], tx) # tx is the inner most axis, bound to threadIdx.x

    生成IR,如下所示:

    /* Input = [1, 1, 32, 32], Filter = [1, 1, 3, 3], stride = [1, 1], padding = 'SAME' */

    produce Relu {

      // attr [iter_var(blockIdx.y, , blockIdx.y)] thread_extent = 1

      // attr [DepthwiseConv2d] storage_scope = "local"

      allocate DepthwiseConv2d[float32 * 1 * 1 * 4 * 4]

      // attr [iter_var(blockIdx.x, , blockIdx.x)] thread_extent = 1

      // attr [iter_var(threadIdx.y, Range(min=0, extent=8), threadIdx.y)] thread_extent = 8

      // attr [iter_var(threadIdx.x, Range(min=0, extent=8), threadIdx.x)] thread_extent = 8

      produce DepthwiseConv2d {

        for (i, 0, 4) {

          for (j, 0, 4) {

            DepthwiseConv2d[((i*4) + j)] = 0.000000f

            for (di, 0, 3) {

              for (dj, 0, 3) {

                DepthwiseConv2d[((i*4) + j)] = (DepthwiseConv2d[((i*4) + j)] + (tvm_if_then_else(((((((1 - di) - i) <= (((blockIdx.x*8) + threadIdx.y)*4)) && ((((blockIdx.x*8) + threadIdx.y)*4) < ((33 - di) - i))) && (((1 - dj) - j) <= (threadIdx.x*4))) && ((threadIdx.x*4) < ((33 - dj) - j))), Input[(((((((((((blockIdx.y + blockIdx.x)*8) + threadIdx.y)*32) + threadIdx.x)*4) + (i*32)) + j) + (di*32)) + dj) + -33)], 0.000000f)*Filter[((di*3) + dj)]))

              }

            }

          }

        }

      }

      for (i2.inner.inner.inner, 0, 4) {

        for (i3.inner.inner.inner, 0, 4) {

          Relu[((((((((blockIdx.y + blockIdx.x)*8) + threadIdx.y)*32) + threadIdx.x)*4) + (i2.inner.inner.inner*32)) + i3.inner.inner.inner)] = max(((DepthwiseConv2d[((i2.inner.inner.inner*4) + i3.inner.inner.inner)]*Scale[0]) + Shift[0]), 0.000000f)

        }

      }

    }

    写入depthwise_conv2d全局内存的结果之前,每个线程计算scale_shiftrelu。融合算子的速度与single depthwise_conv2d一样快。以下是输入= [1、256、96、96],filter过滤器= [256、1、3、3],stride步幅= [1、1],padding填充='SAME'的结果:

    • tf-1.2 depthwise_conv2d: 251.6 us
    • tf-1.2 depthwise_conv2d + scale_shift + relu (separate): 419.9 us
    • TVM depthwise_conv2d: 90.9 us
    • TVM depthwise_conv2d + scale_shift + relu (fused): 91.5 us

    The advantage of operator fusion is obvious.

    This is not the end, TVM can do operator fusion in a smarter way. You may refer to this and read the source code provided below.

    Show me the code算子融合的优势显而易见的。

    这不是终点,TVM可以以更智能的方式进行算子融合。参考链接:

     

    人工智能芯片与自动驾驶
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  • 原文地址:https://www.cnblogs.com/wujianming-110117/p/14743401.html
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