| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| """ |
| Using External Libraries in Relay |
| ================================= |
| **Author**: `Masahiro Masuda <https://github.com/masahi>`_, `Truman Tian <https://github.com/SiNZeRo>`_ |
| |
| 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_runtime as runtime |
| from tvm import relay |
| from tvm.relay import 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) |
| |
| ctx = tvm.context(target, 0) |
| data = np.random.uniform(-1, 1, size=data_shape).astype("float32") |
| module = runtime.GraphModule(lib["default"](ctx)) |
| 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.asnumpy() |
| ###################################################################### |
| # 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. |
| # |
| # .. code-block:: text |
| # |
| # 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) |
| |
| ctx = tvm.context(target, 0) |
| data = np.random.uniform(-1, 1, size=data_shape).astype("float32") |
| module = runtime.GraphModule(lib["default"](ctx)) |
| 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.asnumpy() |
| |
| ###################################################################### |
| # 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. |
| # |
| # .. code-block:: text |
| # |
| # // 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. |
