tensorflow - Nvidia TX1上的TensorFlow

Question

有人在Nvidia Tegra X1上使用了tensorflow吗？

我发现一些资料表明TK1上可能存在这种情况，或者TX1上存在严重的黑客入侵/错误，但尚无确定的配方。

http://cudamusing.blogspot.de/2015/11/building-tensorflow-for-jetson-tk1.html

https://github.com/tensorflow/tensorflow/issues/851

我正在使用Jetson 2.3安装程序，但尚未使它工作-最感谢任何提示。

Answer 1

TensorFlow R0.9在带有Bazel 0.2.1，CUDA 8.0，CUDNN5.1，L4T24.2和全新JetPack 2.3的TX1上运行。我已经使用BN，Sigmoid，ReLU等使用基本的MLP，Conv和LSTM网络对其进行了测试，并且没有错误。我删除了sparse_matmul_op，但否则认为编译应该可以完全正常运行。其中许多步骤直接来自MaxCuda's excellent blog，非常感谢他们的提供。

计划继续对R0.10/R0.11进行锤击(gRPC二进制文件现在阻止了Bazel 0.3.0)，但直到那时我才确定要发布R0.9公式。如下:

先得到java

sudo add-apt-repository ppa:webupd8team/java
sudo apt-get update
sudo apt-get install oracle-java8-installer

安装其他一些部门

sudo apt-get install git zip unzip autoconf automake libtool curl zlib1g-dev maven swig

需要自己构建protobuf 3.0.0-beta-2 jar

git clone https://github.com/google/protobuf.git
cd protobuf
# autogen.sh downloads broken gmock.zip in d5fb408d
git checkout master
./autogen.sh
git checkout d5fb408d
./configure --prefix=/usr
make -j 4
sudo make install
cd java
mvn package

换上榛子。我们需要版本0.2.1，与0.3.0不同，它不需要gRPC二进制文件，而我还无法构建(可能很快!)

git clone https://github.com/bazelbuild/bazel.git
cd bazel
git checkout 0.2.1
cp /usr/bin/protoc third_party/protobuf/protoc-linux-arm32.exe
cp ../protobuf/java/target/protobuf-java-3.0.0-beta-2.jar third_party/protobuf/protobuf-java-3.0.0-beta-1.jar

需要编辑bazel文件以将aarch64识别为ARM

--- a/src/main/java/com/google/devtools/build/lib/util/CPU.java
+++ b/src/main/java/com/google/devtools/build/lib/util/CPU.java
@@ -25,7 +25,7 @@ import java.util.Set;
 public enum CPU {
   X86_32("x86_32", ImmutableSet.of("i386", "i486", "i586", "i686", "i786", "x86")),
   X86_64("x86_64", ImmutableSet.of("amd64", "x86_64", "x64")),
-  ARM("arm", ImmutableSet.of("arm", "armv7l")),
+  ARM("arm", ImmutableSet.of("arm", "armv7l", "aarch64")),
   UNKNOWN("unknown", ImmutableSet.<String>of());

现在编译

./compile.sh

并安装

sudo cp output/bazel /usr/local/bin

获取tensorflow R0.9。高于R0.9需要Bazel 0.3.0，由于gRPC问题，我还没有弄清楚该如何构建。

git clone -b r0.9 https://github.com/tensorflow/tensorflow.git

建立一次。它将失败，但是现在您有了bazel .cache目录，您可以在其中放置更新的config.guess和config.sub文件，这些文件将确定您正在运行的体系结构

./configure
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package

cd ~
wget -O config.guess 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.guess;hb=HEAD'
wget -O config.sub 'http://git.savannah.gnu.org/gitweb/?p=config.git;a=blob_plain;f=config.sub;hb=HEAD'

# below are commands I ran, yours will vary depending on .cache details. `find` is your friend
cp config.guess ./.cache/bazel/_bazel_socialh/742c01ff0765b098544431b60b1eed9f/external/farmhash_archive/farmhash-34c13ddfab0e35422f4c3979f360635a8c050260/config.guess
cp config.sub ./.cache/bazel/_bazel_socialh/742c01ff0765b098544431b60b1eed9f/external/farmhash_archive/farmhash-34c13ddfab0e35422f4c3979f360635a8c050260/config.sub

sparse_matmul_op有几个错误，我走了怯弱的路线，从构建中删除了

--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -985,7 +985,7 @@ tf_kernel_libraries(
         "reduction_ops",
         "segment_reduction_ops",
         "sequence_ops",
-        "sparse_matmul_op",
+        #DC "sparse_matmul_op",
     ],
     deps = [
         ":bounds_check",

--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -1110,7 +1110,7 @@ medium_kernel_test_list = glob([
     "kernel_tests/seq2seq_test.py",
     "kernel_tests/slice_op_test.py",
     "kernel_tests/sparse_ops_test.py",
-    "kernel_tests/sparse_matmul_op_test.py",
+    #DC "kernel_tests/sparse_matmul_op_test.py",
     "kernel_tests/sparse_tensor_dense_matmul_op_test.py",
 ])

TX1无法在cwise_op_gpu_select.cu.cc中执行漂亮的构造函数

--- a/tensorflow/core/kernels/cwise_op_gpu_select.cu.cc
+++ b/tensorflow/core/kernels/cwise_op_gpu_select.cu.cc
@@ -43,8 +43,14 @@ struct BatchSelectFunctor<GPUDevice, T> {
     const int all_but_batch = then_flat_outer_dims.dimension(1);

 #if !defined(EIGEN_HAS_INDEX_LIST)
-    Eigen::array<int, 2> broadcast_dims{{ 1, all_but_batch }};
-    Eigen::Tensor<int, 2>::Dimensions reshape_dims{{ batch, 1 }};
+    //DC Eigen::array<int, 2> broadcast_dims{{ 1, all_but_batch }};
+    Eigen::array<int, 2> broadcast_dims;
+    broadcast_dims[0] = 1;
+    broadcast_dims[1] = all_but_batch;
+    //DC Eigen::Tensor<int, 2>::Dimensions reshape_dims{{ batch, 1 }};
+    Eigen::Tensor<int, 2>::Dimensions reshape_dims;
+    reshape_dims[0] = batch;
+    reshape_dims[1] = 1;
 #else
     Eigen::IndexList<Eigen::type2index<1>, int> broadcast_dims;
     broadcast_dims.set(1, all_but_batch);

sparse_tensor_dense_matmul_op_gpu.cu.cc中的内容相同

--- a/tensorflow/core/kernels/sparse_tensor_dense_matmul_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/sparse_tensor_dense_matmul_op_gpu.cu.cc
@@ -104,9 +104,17 @@ struct SparseTensorDenseMatMulFunctor<GPUDevice, T, ADJ_A, ADJ_B> {
     int n = (ADJ_B) ? b.dimension(0) : b.dimension(1);

 #if !defined(EIGEN_HAS_INDEX_LIST)
-    Eigen::Tensor<int, 2>::Dimensions matrix_1_by_nnz{{ 1, nnz }};
-    Eigen::array<int, 2> n_by_1{{ n, 1 }};
-    Eigen::array<int, 1> reduce_on_rows{{ 0 }};
+    //DC Eigen::Tensor<int, 2>::Dimensions matrix_1_by_nnz{{ 1, nnz }};
+    Eigen::Tensor<int, 2>::Dimensions matrix_1_by_nnz;
+    matrix_1_by_nnz[0] = 1;
+    matrix_1_by_nnz[1] = nnz;
+    //DC Eigen::array<int, 2> n_by_1{{ n, 1 }};
+    Eigen::array<int, 2> n_by_1;
+    n_by_1[0] = n;
+    n_by_1[1] = 1;
+    //DC Eigen::array<int, 1> reduce_on_rows{{ 0 }};
+    Eigen::array<int, 1> reduce_on_rows;
+    reduce_on_rows[0] = 0;
 #else
     Eigen::IndexList<Eigen::type2index<1>, int> matrix_1_by_nnz;
     matrix_1_by_nnz.set(1, nnz);

与CUDA 8.0一起运行需要FP16的新宏。非常感谢Kashif/Mrry指出了解决方法!

--- a/tensorflow/stream_executor/cuda/cuda_blas.cc
+++ b/tensorflow/stream_executor/cuda/cuda_blas.cc
@@ -25,6 +25,12 @@ limitations under the License.
 #define EIGEN_HAS_CUDA_FP16
 #endif

+#if CUDA_VERSION >= 8000
+#define SE_CUDA_DATA_HALF CUDA_R_16F
+#else
+#define SE_CUDA_DATA_HALF CUBLAS_DATA_HALF
+#endif
+
 #include "tensorflow/stream_executor/cuda/cuda_blas.h"

 #include <dlfcn.h>
@@ -1680,10 +1686,10 @@ bool CUDABlas::DoBlasGemm(
   return DoBlasInternal(
       dynload::cublasSgemmEx, stream, true /* = pointer_mode_host */,
       CUDABlasTranspose(transa), CUDABlasTranspose(transb), m, n, k, &alpha,
-      CUDAMemory(a), CUBLAS_DATA_HALF, lda,
-      CUDAMemory(b), CUBLAS_DATA_HALF, ldb,
+      CUDAMemory(a), SE_CUDA_DATA_HALF, lda,
+      CUDAMemory(b), SE_CUDA_DATA_HALF, ldb,
       &beta,
-      CUDAMemoryMutable(c), CUBLAS_DATA_HALF, ldc);
+      CUDAMemoryMutable(c), SE_CUDA_DATA_HALF, ldc);
 #else
   LOG(ERROR) << "fp16 sgemm is not implemented in this cuBLAS version "
              << "(need at least CUDA 7.5)";

最后，ARM没有NUMA节点，因此需要添加它，否则在启动tf.Session()时会立即崩溃。

--- a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc
+++ b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc
@@ -888,6 +888,9 @@ CudaContext* CUDAExecutor::cuda_context() { return context_; }
 // For anything more complicated/prod-focused than this, you'll likely want to
 // turn to gsys' topology modeling.
 static int TryToReadNumaNode(const string &pci_bus_id, int device_ordinal) {
+  // DC - make this clever later. ARM has no NUMA node, just return 0
+  LOG(INFO) << "ARM has no NUMA node, hardcoding to return zero";
+  return 0;
 #if defined(__APPLE__)
   LOG(INFO) << "OS X does not support NUMA - returning NUMA node zero";
   return 0;

完成这些更改后，构建并安装!希望这对某些人有用。