Note
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Using External Libraries in Relay¶
Author: Masahiro Masuda, Truman Tian
This is a short tutorial on how to use external libraries such as cuDNN, or cuBLAS with Relay.
Relay uses TVM internally to generate target specific code. For example, with cuda backend TVM generates cuda kernels for all layers in the user provided network. But sometimes it is also helpful to incorporate external libraries developed by various vendors into Relay. Luckily, TVM has a mechanism to transparently call into these libraries. For Relay users, all we need to do is just to set a target string appropriately.
Before we can use external libraries from Relay, your TVM needs to be built with libraries you want to use. For example, to use cuDNN, USE_CUDNN option in cmake/config.cmake needs to be enabled, and cuDNN include and library directories need to be specified if necessary.
To begin with, we import Relay and TVM.
import tvm
from tvm import te
import numpy as np
from tvm.contrib import graph_executor as runtime
from tvm import relay
from tvm.relay import testing
import tvm.testing
Create a simple network¶
Let’s create a very simple network for demonstration. It consists of convolution, batch normalization, and ReLU activation.
out_channels = 16
batch_size = 1
data = relay.var("data", relay.TensorType((batch_size, 3, 224, 224), "float32"))
weight = relay.var("weight")
bn_gamma = relay.var("bn_gamma")
bn_beta = relay.var("bn_beta")
bn_mmean = relay.var("bn_mean")
bn_mvar = relay.var("bn_var")
simple_net = relay.nn.conv2d(
data=data, weight=weight, kernel_size=(3, 3), channels=out_channels, padding=(1, 1)
)
simple_net = relay.nn.batch_norm(simple_net, bn_gamma, bn_beta, bn_mmean, bn_mvar)[0]
simple_net = relay.nn.relu(simple_net)
simple_net = relay.Function(relay.analysis.free_vars(simple_net), simple_net)
data_shape = (batch_size, 3, 224, 224)
net, params = testing.create_workload(simple_net)
Build and run with cuda backend¶
We build and run this network with cuda backend, as usual. By setting the logging level to DEBUG, the result of Relay graph compilation will be dumped as pseudo code.
import logging
logging.basicConfig(level=logging.DEBUG) # to dump TVM IR after fusion
target = "cuda"
lib = relay.build_module.build(net, target, params=params)
dev = tvm.device(target, 0)
data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
module = runtime.GraphModule(lib["default"](dev))
module.set_input("data", data)
module.run()
out_shape = (batch_size, out_channels, 224, 224)
out = module.get_output(0, tvm.nd.empty(out_shape))
out_cuda = out.numpy()
The generated pseudo code should look something like below. Note how bias add, batch normalization, and ReLU activation are fused into the convolution kernel. TVM generates a single, fused kernel from this representation.
produce tensor {
// attr [iter_var(blockIdx.z, , blockIdx.z)] thread_extent = 1
// attr [compute] storage_scope = "local"
allocate compute[float32 * 32]
// attr [pad_temp.shared] storage_scope = "shared"
allocate pad_temp.shared[float32 * 180]
// attr [placeholder.shared] storage_scope = "shared"
allocate placeholder.shared[float32 * 144]
// attr [iter_var(blockIdx.y, , blockIdx.y)] thread_extent = 28
// attr [iter_var(blockIdx.x, , blockIdx.x)] thread_extent = 14
// attr [iter_var(threadIdx.z, , threadIdx.z)] thread_extent = 4
// attr [iter_var(threadIdx.y, , threadIdx.y)] thread_extent = 1
// attr [iter_var(threadIdx.x, , threadIdx.x)] thread_extent = 16
produce compute {
compute[0] = 0.000000f
compute[1] = 0.000000f
compute[2] = 0.000000f
compute[3] = 0.000000f
compute[4] = 0.000000f
compute[5] = 0.000000f
compute[6] = 0.000000f
compute[7] = 0.000000f
compute[8] = 0.000000f
compute[9] = 0.000000f
compute[10] = 0.000000f
compute[11] = 0.000000f
compute[12] = 0.000000f
compute[13] = 0.000000f
compute[14] = 0.000000f
compute[15] = 0.000000f
compute[16] = 0.000000f
compute[17] = 0.000000f
compute[18] = 0.000000f
compute[19] = 0.000000f
compute[20] = 0.000000f
compute[21] = 0.000000f
compute[22] = 0.000000f
compute[23] = 0.000000f
compute[24] = 0.000000f
compute[25] = 0.000000f
compute[26] = 0.000000f
compute[27] = 0.000000f
compute[28] = 0.000000f
compute[29] = 0.000000f
compute[30] = 0.000000f
compute[31] = 0.000000f
for (rc.outer, 0, 3) {
produce pad_temp.shared {
// attr [iter_var(threadIdx.z, , threadIdx.z)] thread_extent = 4
// attr [iter_var(threadIdx.y, , threadIdx.y)] thread_extent = 1
// attr [iter_var(threadIdx.x, , threadIdx.x)] thread_extent = 16
if (likely(((threadIdx.z*15) < (60 - threadIdx.x)))) {
if (likely((threadIdx.x < 15))) {
pad_temp.shared[(((((threadIdx.z*15) + threadIdx.x)/60)*180) + ((((((threadIdx.z*15) + threadIdx.x)/6) % 10)*18) + ((((threadIdx.z*3) + threadIdx.x)*3) % 18)))] = tvm_if_then_else((((((1 - ((((threadIdx.z*15) + threadIdx.x)/6) % 10)) <= (blockIdx.y*8)) && ((blockIdx.y*8) < (225 - ((((threadIdx.z*15) + threadIdx.x)/6) % 10)))) && ((1 - ((((threadIdx.z*3) + threadIdx.x)*3) % 18)) <= (blockIdx.x*16))) && ((blockIdx.x*16) < (225 - ((((threadIdx.z*3) + threadIdx.x)*3) % 18)))), placeholder[((((((((blockIdx.y*112) + blockIdx.x) + (rc.outer*3136)) + ((((threadIdx.z*15) + threadIdx.x)/60)*9408))*16) + ((((threadIdx.z*3) + threadIdx.x)*3) % 18)) + (((((threadIdx.z*15) + threadIdx.x)/6) % 10)*224)) + -225)], 0.000000f)
pad_temp.shared[(((((((threadIdx.z*15) + threadIdx.x)*3) + 1)/180)*180) + ((((((((threadIdx.z*15) + threadIdx.x)*3) + 1)/18) % 10)*18) + (((((threadIdx.z*3) + threadIdx.x)*3) + 1) % 18)))] = tvm_if_then_else((((((1 - ((((((threadIdx.z*15) + threadIdx.x)*3) + 1)/18) % 10)) <= (blockIdx.y*8)) && ((blockIdx.y*8) < (225 - ((((((threadIdx.z*15) + threadIdx.x)*3) + 1)/18) % 10)))) && ((1 - (((((threadIdx.z*3) + threadIdx.x)*3) + 1) % 18)) <= (blockIdx.x*16))) && ((blockIdx.x*16) < (225 - (((((threadIdx.z*3) + threadIdx.x)*3) + 1) % 18)))), placeholder[((((((((blockIdx.y*112) + blockIdx.x) + (rc.outer*3136)) + ((((((threadIdx.z*15) + threadIdx.x)*3) + 1)/180)*9408))*16) + (((((threadIdx.z*3) + threadIdx.x)*3) + 1) % 18)) + (((((((threadIdx.z*15) + threadIdx.x)*3) + 1)/18) % 10)*224)) + -225)], 0.000000f)
pad_temp.shared[(((((((threadIdx.z*15) + threadIdx.x)*3) + 2)/180)*180) + ((((((((threadIdx.z*15) + threadIdx.x)*3) + 2)/18) % 10)*18) + (((((threadIdx.z*3) + threadIdx.x)*3) + 2) % 18)))] = tvm_if_then_else((((((1 - ((((((threadIdx.z*15) + threadIdx.x)*3) + 2)/18) % 10)) <= (blockIdx.y*8)) && ((blockIdx.y*8) < (225 - ((((((threadIdx.z*15) + threadIdx.x)*3) + 2)/18) % 10)))) && ((1 - (((((threadIdx.z*3) + threadIdx.x)*3) + 2) % 18)) <= (blockIdx.x*16))) && ((blockIdx.x*16) < (225 - (((((threadIdx.z*3) + threadIdx.x)*3) + 2) % 18)))), placeholder[((((((((blockIdx.y*112) + blockIdx.x) + (rc.outer*3136)) + ((((((threadIdx.z*15) + threadIdx.x)*3) + 2)/180)*9408))*16) + (((((threadIdx.z*3) + threadIdx.x)*3) + 2) % 18)) + (((((((threadIdx.z*15) + threadIdx.x)*3) + 2)/18) % 10)*224)) + -225)], 0.000000f)
}
}
}
produce placeholder.shared {
// attr [iter_var(threadIdx.z, , threadIdx.z)] thread_extent = 4
// attr [iter_var(threadIdx.y, , threadIdx.y)] thread_extent = 1
// attr [iter_var(threadIdx.x, , threadIdx.x)] thread_extent = 16
if (likely(((threadIdx.z*4) < (16 - (threadIdx.x/3))))) {
if (likely(((threadIdx.z*12) < (48 - threadIdx.x)))) {
if (likely((threadIdx.x < 12))) {
placeholder.shared[(((((threadIdx.z*4) + (threadIdx.x/3))*3) + (threadIdx.x % 3))*3)] = placeholder[(((((rc.outer + (threadIdx.z*12)) + ((threadIdx.x/3)*3))*3) + (threadIdx.x % 3))*3)]
placeholder.shared[((((((threadIdx.z*4) + (threadIdx.x/3))*3) + (threadIdx.x % 3))*3) + 1)] = placeholder[((((((rc.outer + (threadIdx.z*12)) + ((threadIdx.x/3)*3))*3) + (threadIdx.x % 3))*3) + 1)]
placeholder.shared[((((((threadIdx.z*4) + (threadIdx.x/3))*3) + (threadIdx.x % 3))*3) + 2)] = placeholder[((((((rc.outer + (threadIdx.z*12)) + ((threadIdx.x/3)*3))*3) + (threadIdx.x % 3))*3) + 2)]
}
}
}
}
compute[0] = (compute[0] + (pad_temp.shared[threadIdx.x]*placeholder.shared[(threadIdx.z*36)]))
compute[1] = (compute[1] + (pad_temp.shared[(threadIdx.x + 18)]*placeholder.shared[(threadIdx.z*36)]))
compute[2] = (compute[2] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[(threadIdx.z*36)]))
compute[3] = (compute[3] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[(threadIdx.z*36)]))
compute[4] = (compute[4] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[(threadIdx.z*36)]))
compute[5] = (compute[5] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[(threadIdx.z*36)]))
compute[6] = (compute[6] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[(threadIdx.z*36)]))
compute[7] = (compute[7] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[(threadIdx.z*36)]))
compute[8] = (compute[8] + (pad_temp.shared[threadIdx.x]*placeholder.shared[((threadIdx.z*36) + 9)]))
compute[9] = (compute[9] + (pad_temp.shared[(threadIdx.x + 18)]*placeholder.shared[((threadIdx.z*36) + 9)]))
compute[10] = (compute[10] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 9)]))
compute[11] = (compute[11] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 9)]))
compute[12] = (compute[12] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 9)]))
compute[13] = (compute[13] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 9)]))
compute[14] = (compute[14] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 9)]))
compute[15] = (compute[15] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 9)]))
compute[16] = (compute[16] + (pad_temp.shared[threadIdx.x]*placeholder.shared[((threadIdx.z*36) + 18)]))
compute[17] = (compute[17] + (pad_temp.shared[(threadIdx.x + 18)]*placeholder.shared[((threadIdx.z*36) + 18)]))
compute[18] = (compute[18] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 18)]))
compute[19] = (compute[19] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 18)]))
compute[20] = (compute[20] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 18)]))
compute[21] = (compute[21] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 18)]))
compute[22] = (compute[22] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 18)]))
compute[23] = (compute[23] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 18)]))
compute[24] = (compute[24] + (pad_temp.shared[threadIdx.x]*placeholder.shared[((threadIdx.z*36) + 27)]))
compute[25] = (compute[25] + (pad_temp.shared[(threadIdx.x + 18)]*placeholder.shared[((threadIdx.z*36) + 27)]))
compute[26] = (compute[26] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 27)]))
compute[27] = (compute[27] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 27)]))
compute[28] = (compute[28] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 27)]))
compute[29] = (compute[29] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 27)]))
compute[30] = (compute[30] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 27)]))
compute[31] = (compute[31] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 27)]))
compute[0] = (compute[0] + (pad_temp.shared[(threadIdx.x + 1)]*placeholder.shared[((threadIdx.z*36) + 1)]))
compute[1] = (compute[1] + (pad_temp.shared[(threadIdx.x + 19)]*placeholder.shared[((threadIdx.z*36) + 1)]))
compute[2] = (compute[2] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 1)]))
compute[3] = (compute[3] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 1)]))
compute[4] = (compute[4] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 1)]))
compute[5] = (compute[5] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 1)]))
compute[6] = (compute[6] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 1)]))
compute[7] = (compute[7] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 1)]))
compute[8] = (compute[8] + (pad_temp.shared[(threadIdx.x + 1)]*placeholder.shared[((threadIdx.z*36) + 10)]))
compute[9] = (compute[9] + (pad_temp.shared[(threadIdx.x + 19)]*placeholder.shared[((threadIdx.z*36) + 10)]))
compute[10] = (compute[10] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 10)]))
compute[11] = (compute[11] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 10)]))
compute[12] = (compute[12] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 10)]))
compute[13] = (compute[13] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 10)]))
compute[14] = (compute[14] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 10)]))
compute[15] = (compute[15] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 10)]))
compute[16] = (compute[16] + (pad_temp.shared[(threadIdx.x + 1)]*placeholder.shared[((threadIdx.z*36) + 19)]))
compute[17] = (compute[17] + (pad_temp.shared[(threadIdx.x + 19)]*placeholder.shared[((threadIdx.z*36) + 19)]))
compute[18] = (compute[18] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 19)]))
compute[19] = (compute[19] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 19)]))
compute[20] = (compute[20] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 19)]))
compute[21] = (compute[21] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 19)]))
compute[22] = (compute[22] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 19)]))
compute[23] = (compute[23] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 19)]))
compute[24] = (compute[24] + (pad_temp.shared[(threadIdx.x + 1)]*placeholder.shared[((threadIdx.z*36) + 28)]))
compute[25] = (compute[25] + (pad_temp.shared[(threadIdx.x + 19)]*placeholder.shared[((threadIdx.z*36) + 28)]))
compute[26] = (compute[26] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 28)]))
compute[27] = (compute[27] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 28)]))
compute[28] = (compute[28] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 28)]))
compute[29] = (compute[29] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 28)]))
compute[30] = (compute[30] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 28)]))
compute[31] = (compute[31] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 28)]))
compute[0] = (compute[0] + (pad_temp.shared[(threadIdx.x + 2)]*placeholder.shared[((threadIdx.z*36) + 2)]))
compute[1] = (compute[1] + (pad_temp.shared[(threadIdx.x + 20)]*placeholder.shared[((threadIdx.z*36) + 2)]))
compute[2] = (compute[2] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 2)]))
compute[3] = (compute[3] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 2)]))
compute[4] = (compute[4] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 2)]))
compute[5] = (compute[5] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 2)]))
compute[6] = (compute[6] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 2)]))
compute[7] = (compute[7] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 2)]))
compute[8] = (compute[8] + (pad_temp.shared[(threadIdx.x + 2)]*placeholder.shared[((threadIdx.z*36) + 11)]))
compute[9] = (compute[9] + (pad_temp.shared[(threadIdx.x + 20)]*placeholder.shared[((threadIdx.z*36) + 11)]))
compute[10] = (compute[10] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 11)]))
compute[11] = (compute[11] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 11)]))
compute[12] = (compute[12] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 11)]))
compute[13] = (compute[13] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 11)]))
compute[14] = (compute[14] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 11)]))
compute[15] = (compute[15] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 11)]))
compute[16] = (compute[16] + (pad_temp.shared[(threadIdx.x + 2)]*placeholder.shared[((threadIdx.z*36) + 20)]))
compute[17] = (compute[17] + (pad_temp.shared[(threadIdx.x + 20)]*placeholder.shared[((threadIdx.z*36) + 20)]))
compute[18] = (compute[18] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 20)]))
compute[19] = (compute[19] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 20)]))
compute[20] = (compute[20] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 20)]))
compute[21] = (compute[21] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 20)]))
compute[22] = (compute[22] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 20)]))
compute[23] = (compute[23] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 20)]))
compute[24] = (compute[24] + (pad_temp.shared[(threadIdx.x + 2)]*placeholder.shared[((threadIdx.z*36) + 29)]))
compute[25] = (compute[25] + (pad_temp.shared[(threadIdx.x + 20)]*placeholder.shared[((threadIdx.z*36) + 29)]))
compute[26] = (compute[26] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 29)]))
compute[27] = (compute[27] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 29)]))
compute[28] = (compute[28] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 29)]))
compute[29] = (compute[29] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 29)]))
compute[30] = (compute[30] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 29)]))
compute[31] = (compute[31] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 29)]))
compute[0] = (compute[0] + (pad_temp.shared[(threadIdx.x + 18)]*placeholder.shared[((threadIdx.z*36) + 3)]))
compute[1] = (compute[1] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 3)]))
compute[2] = (compute[2] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 3)]))
compute[3] = (compute[3] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 3)]))
compute[4] = (compute[4] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 3)]))
compute[5] = (compute[5] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 3)]))
compute[6] = (compute[6] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 3)]))
compute[7] = (compute[7] + (pad_temp.shared[(threadIdx.x + 144)]*placeholder.shared[((threadIdx.z*36) + 3)]))
compute[8] = (compute[8] + (pad_temp.shared[(threadIdx.x + 18)]*placeholder.shared[((threadIdx.z*36) + 12)]))
compute[9] = (compute[9] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 12)]))
compute[10] = (compute[10] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 12)]))
compute[11] = (compute[11] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 12)]))
compute[12] = (compute[12] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 12)]))
compute[13] = (compute[13] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 12)]))
compute[14] = (compute[14] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 12)]))
compute[15] = (compute[15] + (pad_temp.shared[(threadIdx.x + 144)]*placeholder.shared[((threadIdx.z*36) + 12)]))
compute[16] = (compute[16] + (pad_temp.shared[(threadIdx.x + 18)]*placeholder.shared[((threadIdx.z*36) + 21)]))
compute[17] = (compute[17] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 21)]))
compute[18] = (compute[18] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 21)]))
compute[19] = (compute[19] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 21)]))
compute[20] = (compute[20] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 21)]))
compute[21] = (compute[21] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 21)]))
compute[22] = (compute[22] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 21)]))
compute[23] = (compute[23] + (pad_temp.shared[(threadIdx.x + 144)]*placeholder.shared[((threadIdx.z*36) + 21)]))
compute[24] = (compute[24] + (pad_temp.shared[(threadIdx.x + 18)]*placeholder.shared[((threadIdx.z*36) + 30)]))
compute[25] = (compute[25] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 30)]))
compute[26] = (compute[26] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 30)]))
compute[27] = (compute[27] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 30)]))
compute[28] = (compute[28] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 30)]))
compute[29] = (compute[29] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 30)]))
compute[30] = (compute[30] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 30)]))
compute[31] = (compute[31] + (pad_temp.shared[(threadIdx.x + 144)]*placeholder.shared[((threadIdx.z*36) + 30)]))
compute[0] = (compute[0] + (pad_temp.shared[(threadIdx.x + 19)]*placeholder.shared[((threadIdx.z*36) + 4)]))
compute[1] = (compute[1] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 4)]))
compute[2] = (compute[2] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 4)]))
compute[3] = (compute[3] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 4)]))
compute[4] = (compute[4] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 4)]))
compute[5] = (compute[5] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 4)]))
compute[6] = (compute[6] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 4)]))
compute[7] = (compute[7] + (pad_temp.shared[(threadIdx.x + 145)]*placeholder.shared[((threadIdx.z*36) + 4)]))
compute[8] = (compute[8] + (pad_temp.shared[(threadIdx.x + 19)]*placeholder.shared[((threadIdx.z*36) + 13)]))
compute[9] = (compute[9] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 13)]))
compute[10] = (compute[10] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 13)]))
compute[11] = (compute[11] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 13)]))
compute[12] = (compute[12] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 13)]))
compute[13] = (compute[13] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 13)]))
compute[14] = (compute[14] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 13)]))
compute[15] = (compute[15] + (pad_temp.shared[(threadIdx.x + 145)]*placeholder.shared[((threadIdx.z*36) + 13)]))
compute[16] = (compute[16] + (pad_temp.shared[(threadIdx.x + 19)]*placeholder.shared[((threadIdx.z*36) + 22)]))
compute[17] = (compute[17] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 22)]))
compute[18] = (compute[18] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 22)]))
compute[19] = (compute[19] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 22)]))
compute[20] = (compute[20] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 22)]))
compute[21] = (compute[21] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 22)]))
compute[22] = (compute[22] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 22)]))
compute[23] = (compute[23] + (pad_temp.shared[(threadIdx.x + 145)]*placeholder.shared[((threadIdx.z*36) + 22)]))
compute[24] = (compute[24] + (pad_temp.shared[(threadIdx.x + 19)]*placeholder.shared[((threadIdx.z*36) + 31)]))
compute[25] = (compute[25] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 31)]))
compute[26] = (compute[26] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 31)]))
compute[27] = (compute[27] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 31)]))
compute[28] = (compute[28] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 31)]))
compute[29] = (compute[29] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 31)]))
compute[30] = (compute[30] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 31)]))
compute[31] = (compute[31] + (pad_temp.shared[(threadIdx.x + 145)]*placeholder.shared[((threadIdx.z*36) + 31)]))
compute[0] = (compute[0] + (pad_temp.shared[(threadIdx.x + 20)]*placeholder.shared[((threadIdx.z*36) + 5)]))
compute[1] = (compute[1] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 5)]))
compute[2] = (compute[2] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 5)]))
compute[3] = (compute[3] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 5)]))
compute[4] = (compute[4] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 5)]))
compute[5] = (compute[5] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 5)]))
compute[6] = (compute[6] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 5)]))
compute[7] = (compute[7] + (pad_temp.shared[(threadIdx.x + 146)]*placeholder.shared[((threadIdx.z*36) + 5)]))
compute[8] = (compute[8] + (pad_temp.shared[(threadIdx.x + 20)]*placeholder.shared[((threadIdx.z*36) + 14)]))
compute[9] = (compute[9] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 14)]))
compute[10] = (compute[10] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 14)]))
compute[11] = (compute[11] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 14)]))
compute[12] = (compute[12] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 14)]))
compute[13] = (compute[13] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 14)]))
compute[14] = (compute[14] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 14)]))
compute[15] = (compute[15] + (pad_temp.shared[(threadIdx.x + 146)]*placeholder.shared[((threadIdx.z*36) + 14)]))
compute[16] = (compute[16] + (pad_temp.shared[(threadIdx.x + 20)]*placeholder.shared[((threadIdx.z*36) + 23)]))
compute[17] = (compute[17] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 23)]))
compute[18] = (compute[18] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 23)]))
compute[19] = (compute[19] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 23)]))
compute[20] = (compute[20] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 23)]))
compute[21] = (compute[21] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 23)]))
compute[22] = (compute[22] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 23)]))
compute[23] = (compute[23] + (pad_temp.shared[(threadIdx.x + 146)]*placeholder.shared[((threadIdx.z*36) + 23)]))
compute[24] = (compute[24] + (pad_temp.shared[(threadIdx.x + 20)]*placeholder.shared[((threadIdx.z*36) + 32)]))
compute[25] = (compute[25] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 32)]))
compute[26] = (compute[26] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 32)]))
compute[27] = (compute[27] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 32)]))
compute[28] = (compute[28] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 32)]))
compute[29] = (compute[29] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 32)]))
compute[30] = (compute[30] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 32)]))
compute[31] = (compute[31] + (pad_temp.shared[(threadIdx.x + 146)]*placeholder.shared[((threadIdx.z*36) + 32)]))
compute[0] = (compute[0] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 6)]))
compute[1] = (compute[1] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 6)]))
compute[2] = (compute[2] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 6)]))
compute[3] = (compute[3] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 6)]))
compute[4] = (compute[4] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 6)]))
compute[5] = (compute[5] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 6)]))
compute[6] = (compute[6] + (pad_temp.shared[(threadIdx.x + 144)]*placeholder.shared[((threadIdx.z*36) + 6)]))
compute[7] = (compute[7] + (pad_temp.shared[(threadIdx.x + 162)]*placeholder.shared[((threadIdx.z*36) + 6)]))
compute[8] = (compute[8] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 15)]))
compute[9] = (compute[9] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 15)]))
compute[10] = (compute[10] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 15)]))
compute[11] = (compute[11] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 15)]))
compute[12] = (compute[12] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 15)]))
compute[13] = (compute[13] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 15)]))
compute[14] = (compute[14] + (pad_temp.shared[(threadIdx.x + 144)]*placeholder.shared[((threadIdx.z*36) + 15)]))
compute[15] = (compute[15] + (pad_temp.shared[(threadIdx.x + 162)]*placeholder.shared[((threadIdx.z*36) + 15)]))
compute[16] = (compute[16] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 24)]))
compute[17] = (compute[17] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 24)]))
compute[18] = (compute[18] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 24)]))
compute[19] = (compute[19] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 24)]))
compute[20] = (compute[20] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 24)]))
compute[21] = (compute[21] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 24)]))
compute[22] = (compute[22] + (pad_temp.shared[(threadIdx.x + 144)]*placeholder.shared[((threadIdx.z*36) + 24)]))
compute[23] = (compute[23] + (pad_temp.shared[(threadIdx.x + 162)]*placeholder.shared[((threadIdx.z*36) + 24)]))
compute[24] = (compute[24] + (pad_temp.shared[(threadIdx.x + 36)]*placeholder.shared[((threadIdx.z*36) + 33)]))
compute[25] = (compute[25] + (pad_temp.shared[(threadIdx.x + 54)]*placeholder.shared[((threadIdx.z*36) + 33)]))
compute[26] = (compute[26] + (pad_temp.shared[(threadIdx.x + 72)]*placeholder.shared[((threadIdx.z*36) + 33)]))
compute[27] = (compute[27] + (pad_temp.shared[(threadIdx.x + 90)]*placeholder.shared[((threadIdx.z*36) + 33)]))
compute[28] = (compute[28] + (pad_temp.shared[(threadIdx.x + 108)]*placeholder.shared[((threadIdx.z*36) + 33)]))
compute[29] = (compute[29] + (pad_temp.shared[(threadIdx.x + 126)]*placeholder.shared[((threadIdx.z*36) + 33)]))
compute[30] = (compute[30] + (pad_temp.shared[(threadIdx.x + 144)]*placeholder.shared[((threadIdx.z*36) + 33)]))
compute[31] = (compute[31] + (pad_temp.shared[(threadIdx.x + 162)]*placeholder.shared[((threadIdx.z*36) + 33)]))
compute[0] = (compute[0] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 7)]))
compute[1] = (compute[1] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 7)]))
compute[2] = (compute[2] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 7)]))
compute[3] = (compute[3] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 7)]))
compute[4] = (compute[4] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 7)]))
compute[5] = (compute[5] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 7)]))
compute[6] = (compute[6] + (pad_temp.shared[(threadIdx.x + 145)]*placeholder.shared[((threadIdx.z*36) + 7)]))
compute[7] = (compute[7] + (pad_temp.shared[(threadIdx.x + 163)]*placeholder.shared[((threadIdx.z*36) + 7)]))
compute[8] = (compute[8] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 16)]))
compute[9] = (compute[9] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 16)]))
compute[10] = (compute[10] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 16)]))
compute[11] = (compute[11] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 16)]))
compute[12] = (compute[12] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 16)]))
compute[13] = (compute[13] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 16)]))
compute[14] = (compute[14] + (pad_temp.shared[(threadIdx.x + 145)]*placeholder.shared[((threadIdx.z*36) + 16)]))
compute[15] = (compute[15] + (pad_temp.shared[(threadIdx.x + 163)]*placeholder.shared[((threadIdx.z*36) + 16)]))
compute[16] = (compute[16] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 25)]))
compute[17] = (compute[17] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 25)]))
compute[18] = (compute[18] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 25)]))
compute[19] = (compute[19] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 25)]))
compute[20] = (compute[20] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 25)]))
compute[21] = (compute[21] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 25)]))
compute[22] = (compute[22] + (pad_temp.shared[(threadIdx.x + 145)]*placeholder.shared[((threadIdx.z*36) + 25)]))
compute[23] = (compute[23] + (pad_temp.shared[(threadIdx.x + 163)]*placeholder.shared[((threadIdx.z*36) + 25)]))
compute[24] = (compute[24] + (pad_temp.shared[(threadIdx.x + 37)]*placeholder.shared[((threadIdx.z*36) + 34)]))
compute[25] = (compute[25] + (pad_temp.shared[(threadIdx.x + 55)]*placeholder.shared[((threadIdx.z*36) + 34)]))
compute[26] = (compute[26] + (pad_temp.shared[(threadIdx.x + 73)]*placeholder.shared[((threadIdx.z*36) + 34)]))
compute[27] = (compute[27] + (pad_temp.shared[(threadIdx.x + 91)]*placeholder.shared[((threadIdx.z*36) + 34)]))
compute[28] = (compute[28] + (pad_temp.shared[(threadIdx.x + 109)]*placeholder.shared[((threadIdx.z*36) + 34)]))
compute[29] = (compute[29] + (pad_temp.shared[(threadIdx.x + 127)]*placeholder.shared[((threadIdx.z*36) + 34)]))
compute[30] = (compute[30] + (pad_temp.shared[(threadIdx.x + 145)]*placeholder.shared[((threadIdx.z*36) + 34)]))
compute[31] = (compute[31] + (pad_temp.shared[(threadIdx.x + 163)]*placeholder.shared[((threadIdx.z*36) + 34)]))
compute[0] = (compute[0] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 8)]))
compute[1] = (compute[1] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 8)]))
compute[2] = (compute[2] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 8)]))
compute[3] = (compute[3] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 8)]))
compute[4] = (compute[4] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 8)]))
compute[5] = (compute[5] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 8)]))
compute[6] = (compute[6] + (pad_temp.shared[(threadIdx.x + 146)]*placeholder.shared[((threadIdx.z*36) + 8)]))
compute[7] = (compute[7] + (pad_temp.shared[(threadIdx.x + 164)]*placeholder.shared[((threadIdx.z*36) + 8)]))
compute[8] = (compute[8] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 17)]))
compute[9] = (compute[9] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 17)]))
compute[10] = (compute[10] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 17)]))
compute[11] = (compute[11] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 17)]))
compute[12] = (compute[12] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 17)]))
compute[13] = (compute[13] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 17)]))
compute[14] = (compute[14] + (pad_temp.shared[(threadIdx.x + 146)]*placeholder.shared[((threadIdx.z*36) + 17)]))
compute[15] = (compute[15] + (pad_temp.shared[(threadIdx.x + 164)]*placeholder.shared[((threadIdx.z*36) + 17)]))
compute[16] = (compute[16] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 26)]))
compute[17] = (compute[17] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 26)]))
compute[18] = (compute[18] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 26)]))
compute[19] = (compute[19] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 26)]))
compute[20] = (compute[20] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 26)]))
compute[21] = (compute[21] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 26)]))
compute[22] = (compute[22] + (pad_temp.shared[(threadIdx.x + 146)]*placeholder.shared[((threadIdx.z*36) + 26)]))
compute[23] = (compute[23] + (pad_temp.shared[(threadIdx.x + 164)]*placeholder.shared[((threadIdx.z*36) + 26)]))
compute[24] = (compute[24] + (pad_temp.shared[(threadIdx.x + 38)]*placeholder.shared[((threadIdx.z*36) + 35)]))
compute[25] = (compute[25] + (pad_temp.shared[(threadIdx.x + 56)]*placeholder.shared[((threadIdx.z*36) + 35)]))
compute[26] = (compute[26] + (pad_temp.shared[(threadIdx.x + 74)]*placeholder.shared[((threadIdx.z*36) + 35)]))
compute[27] = (compute[27] + (pad_temp.shared[(threadIdx.x + 92)]*placeholder.shared[((threadIdx.z*36) + 35)]))
compute[28] = (compute[28] + (pad_temp.shared[(threadIdx.x + 110)]*placeholder.shared[((threadIdx.z*36) + 35)]))
compute[29] = (compute[29] + (pad_temp.shared[(threadIdx.x + 128)]*placeholder.shared[((threadIdx.z*36) + 35)]))
compute[30] = (compute[30] + (pad_temp.shared[(threadIdx.x + 146)]*placeholder.shared[((threadIdx.z*36) + 35)]))
compute[31] = (compute[31] + (pad_temp.shared[(threadIdx.x + 164)]*placeholder.shared[((threadIdx.z*36) + 35)]))
}
}
tensor[(((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x)] = max(((compute[0]*placeholder[(threadIdx.z*4)]) + placeholder[(threadIdx.z*4)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 224)] = max(((compute[1]*placeholder[(threadIdx.z*4)]) + placeholder[(threadIdx.z*4)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 448)] = max(((compute[2]*placeholder[(threadIdx.z*4)]) + placeholder[(threadIdx.z*4)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 672)] = max(((compute[3]*placeholder[(threadIdx.z*4)]) + placeholder[(threadIdx.z*4)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 896)] = max(((compute[4]*placeholder[(threadIdx.z*4)]) + placeholder[(threadIdx.z*4)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 1120)] = max(((compute[5]*placeholder[(threadIdx.z*4)]) + placeholder[(threadIdx.z*4)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 1344)] = max(((compute[6]*placeholder[(threadIdx.z*4)]) + placeholder[(threadIdx.z*4)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 1568)] = max(((compute[7]*placeholder[(threadIdx.z*4)]) + placeholder[(threadIdx.z*4)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 50176)] = max(((compute[8]*placeholder[((threadIdx.z*4) + 1)]) + placeholder[((threadIdx.z*4) + 1)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 50400)] = max(((compute[9]*placeholder[((threadIdx.z*4) + 1)]) + placeholder[((threadIdx.z*4) + 1)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 50624)] = max(((compute[10]*placeholder[((threadIdx.z*4) + 1)]) + placeholder[((threadIdx.z*4) + 1)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 50848)] = max(((compute[11]*placeholder[((threadIdx.z*4) + 1)]) + placeholder[((threadIdx.z*4) + 1)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 51072)] = max(((compute[12]*placeholder[((threadIdx.z*4) + 1)]) + placeholder[((threadIdx.z*4) + 1)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 51296)] = max(((compute[13]*placeholder[((threadIdx.z*4) + 1)]) + placeholder[((threadIdx.z*4) + 1)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 51520)] = max(((compute[14]*placeholder[((threadIdx.z*4) + 1)]) + placeholder[((threadIdx.z*4) + 1)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 51744)] = max(((compute[15]*placeholder[((threadIdx.z*4) + 1)]) + placeholder[((threadIdx.z*4) + 1)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 100352)] = max(((compute[16]*placeholder[((threadIdx.z*4) + 2)]) + placeholder[((threadIdx.z*4) + 2)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 100576)] = max(((compute[17]*placeholder[((threadIdx.z*4) + 2)]) + placeholder[((threadIdx.z*4) + 2)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 100800)] = max(((compute[18]*placeholder[((threadIdx.z*4) + 2)]) + placeholder[((threadIdx.z*4) + 2)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 101024)] = max(((compute[19]*placeholder[((threadIdx.z*4) + 2)]) + placeholder[((threadIdx.z*4) + 2)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 101248)] = max(((compute[20]*placeholder[((threadIdx.z*4) + 2)]) + placeholder[((threadIdx.z*4) + 2)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 101472)] = max(((compute[21]*placeholder[((threadIdx.z*4) + 2)]) + placeholder[((threadIdx.z*4) + 2)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 101696)] = max(((compute[22]*placeholder[((threadIdx.z*4) + 2)]) + placeholder[((threadIdx.z*4) + 2)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 101920)] = max(((compute[23]*placeholder[((threadIdx.z*4) + 2)]) + placeholder[((threadIdx.z*4) + 2)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 150528)] = max(((compute[24]*placeholder[((threadIdx.z*4) + 3)]) + placeholder[((threadIdx.z*4) + 3)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 150752)] = max(((compute[25]*placeholder[((threadIdx.z*4) + 3)]) + placeholder[((threadIdx.z*4) + 3)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 150976)] = max(((compute[26]*placeholder[((threadIdx.z*4) + 3)]) + placeholder[((threadIdx.z*4) + 3)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 151200)] = max(((compute[27]*placeholder[((threadIdx.z*4) + 3)]) + placeholder[((threadIdx.z*4) + 3)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 151424)] = max(((compute[28]*placeholder[((threadIdx.z*4) + 3)]) + placeholder[((threadIdx.z*4) + 3)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 151648)] = max(((compute[29]*placeholder[((threadIdx.z*4) + 3)]) + placeholder[((threadIdx.z*4) + 3)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 151872)] = max(((compute[30]*placeholder[((threadIdx.z*4) + 3)]) + placeholder[((threadIdx.z*4) + 3)]), 0.000000f)
tensor[((((((blockIdx.y*112) + blockIdx.x) + (threadIdx.z*12544))*16) + threadIdx.x) + 152096)] = max(((compute[31]*placeholder[((threadIdx.z*4) + 3)]) + placeholder[((threadIdx.z*4) + 3)]), 0.000000f)
}
Use cuDNN for a convolutional layer¶
We can use cuDNN to replace convolution kernels with cuDNN ones. To do that, all we need to do is to append the option ” -libs=cudnn” to the target string.
net, params = testing.create_workload(simple_net)
target = "cuda -libs=cudnn" # use cudnn for convolution
lib = relay.build_module.build(net, target, params=params)
dev = tvm.device(target, 0)
data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
module = runtime.GraphModule(lib["default"](dev))
module.set_input("data", data)
module.run()
out_shape = (batch_size, out_channels, 224, 224)
out = module.get_output(0, tvm.nd.empty(out_shape))
out_cudnn = out.numpy()
Note that if you use cuDNN, Relay cannot fuse convolution with layers following it. This is because layer fusion happens at the level of TVM internal representation(IR). Relay treats external libraries as black box, so there is no way to fuse them with TVM IR.
The pseudo code below shows that cuDNN convolution + bias add + batch norm + ReLU turned into two stages of computation, one for cuDNN call and the other for the rest of operations.
// attr [y] storage_scope = "global"
allocate y[float32 * 802816]
produce y {
// attr [0] extern_scope = 0
tvm_call_packed("tvm.contrib.cudnn.conv2d.forward", 1, 0, 1, 1, 1, 1, 1, 1, 1, tvm_stack_make_array(placeholder, tvm_stack_make_shape(1, 3, 224, 224), 0, 4, 0.000000f, 0), tvm_stack_make_array(placeholder, tvm_stack_make_shape(16, 3, 3, 3), 0, 4, 0.000000f, 0), tvm_stack_make_array(y, tvm_stack_make_shape(1, 16, 224, 224), 0, 4, 0.000000f, 0))
}
produce tensor {
// attr [iter_var(blockIdx.x, , blockIdx.x)] thread_extent = 256
// attr [iter_var(threadIdx.x, , threadIdx.x)] thread_extent = 512
for (ax0.ax1.fused.ax2.fused.ax3.fused.outer, 0, 7) {
if (likely(((blockIdx.x*512) < ((802816 - (ax0.ax1.fused.ax2.fused.ax3.fused.outer*131072)) - threadIdx.x)))) {
tensor[(((((((blockIdx.x*512) + threadIdx.x) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer*131072))/802816)*802816) + (((((((blockIdx.x*512) + threadIdx.x) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer*131072))/224) % 224)*224) + ((((blockIdx.x*64) + threadIdx.x) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer*32)) % 224))) + ((((((blockIdx.x*512) + threadIdx.x) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer*131072))/50176) % 16)*50176))] = max(((y[(((((((blockIdx.x*512) + threadIdx.x) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer*131072))/802816)*802816) + (((((((blockIdx.x*512) + threadIdx.x) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer*131072))/224) % 224)*224) + ((((blockIdx.x*64) + threadIdx.x) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer*32)) % 224))) + ((((((blockIdx.x*512) + threadIdx.x) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer*131072))/50176) % 16)*50176))]*placeholder[(((((blockIdx.x*512) + threadIdx.x) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer*131072))/50176) % 16)]) + placeholder[(((((blockIdx.x*512) + threadIdx.x) + (ax0.ax1.fused.ax2.fused.ax3.fused.outer*131072))/50176) % 16)]), 0.000000f)
}
}
}
Verify the result¶
We can check that the results of two runs match.
tvm.testing.assert_allclose(out_cuda, out_cudnn, rtol=1e-5)
Conclusion¶
This tutorial covered the usage of cuDNN with Relay. We also have support for cuBLAS. If cuBLAS is enabled, it will be used inside a fully connected layer (relay.dense). To use cuBLAS, set a target string as “cuda -libs=cublas”. You can use both cuDNN and cuBLAS with “cuda -libs=cudnn,cublas”.
For ROCm backend, we have support for MIOpen and rocBLAS. They can be enabled with target “rocm -libs=miopen,rocblas”.
Being able to use external libraries is great, but we need to keep in mind some cautions.
First, the use of external libraries may restrict your usage of TVM and Relay. For example, MIOpen only supports NCHW layout and fp32 data type at the moment, so you cannot use other layouts or data type in TVM.
Second, and more importantly, external libraries restrict the possibility of operator fusion during graph compilation, as shown above. TVM and Relay aim to achieve the best performance on a variety of hardwares, with joint operator level and graph level optimization. To achieve this goal, we should continue developing better optimizations for TVM and Relay, while using external libraries as a nice way to fall back to existing implementation when necessary.