Add DeviceGuard

fast_rnnt/csrc/CMakeLists.txt

@@ -11,6 +11,7 @@ if(FT_WITH_CUDA)
   set(cuda_srcs mutual_information_cuda.cu)
   add_library(mutual_information_core_cuda ${cuda_srcs})
   target_link_libraries(mutual_information_core_cuda PUBLIC ${TORCH_LIBRARIES})
+  # for <torch/extension.h>
   target_include_directories(mutual_information_core_cuda PUBLIC ${PYTHON_INCLUDE_DIRS})
   target_link_libraries(mutual_information_core PUBLIC mutual_information_core_cuda)
 endif()

fast_rnnt/csrc/device_guard.h

+/**
+ * Copyright      2022  Xiaomi Corporation (authors: Wei Kang)
+ *
+ * See LICENSE for clarification regarding multiple authors
+ *
+ * Licensed 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.
+ */
+
+#ifndef FAST_RNNT_CSRC_DEVICE_GUARD_H_
+#define FAST_RNNT_CSRC_DEVICE_GUARD_H_
+
+#include <torch/script.h>
+
+// This file is modified from
+// https://github.com/k2-fsa/k2/blob/master/k2/csrc/device_guard.h
+namespace fast_rnnt {
+
+// DeviceGuard is an RAII class. Its sole purpose is to restore
+// the previous default cuda device if a CUDA context changes the
+// current default cuda device.
+class DeviceGuard {
+public:
+  explicit DeviceGuard(torch::Device device) {
+    if (device.type() == torch::kCUDA) {
+      old_device_ = GetDevice();
+      new_device_ = device.index();
+      if (old_device_ != new_device_)
+        SetDevice(new_device_);
+    }
+    // else do nothing
+  }
+
+  explicit DeviceGuard(int32_t new_device) : new_device_(new_device) {
+    if (new_device != -1) {
+      old_device_ = GetDevice();
+      if (old_device_ != new_device)
+        SetDevice(new_device);
+    }
+  }
+
+  ~DeviceGuard() {
+    if (old_device_ != -1 && old_device_ != new_device_) {
+      // restore the previous device
+      SetDevice(old_device_);
+    }
+    // else it was either a CPU context or the device IDs
+    // were the same
+  }
+
+  DeviceGuard(const DeviceGuard &) = delete;
+  DeviceGuard &operator=(const DeviceGuard &) = delete;
+
+  DeviceGuard(DeviceGuard &&) = delete;
+  DeviceGuard &operator=(DeviceGuard &&) = delete;
+
+private:
+  static int32_t GetDevice() {
+    int32_t device;
+    auto s = cudaGetDevice(&device);
+    TORCH_CHECK(s == cudaSuccess, cudaGetErrorString(s));
+    return device;
+  }
+
+  static void SetDevice(int32_t device) {
+    auto s = cudaSetDevice(device);
+    TORCH_CHECK(s == cudaSuccess, cudaGetErrorString(s));
+  }
+
+private:
+  int32_t old_device_ = -1;
+  int32_t new_device_ = -1;
+};
+
+} // namespace fast_rnnt
+
+#endif // FAST_RNNT_CSRC_DEVICE_GUARD_H_

fast_rnnt/csrc/mutual_information_cpu.cc

@@ -18,6 +18,7 @@
  * limitations under the License.
  */
 
+#include <iostream>
 #include "fast_rnnt/csrc/mutual_information.h"
 
 namespace fast_rnnt {
@@ -241,11 +242,11 @@ MutualInformationBackwardCpu(torch::Tensor px, torch::Tensor py,
           if (ans_grad_a[b] != 0.0) {
             float grad_ratio = p_grad_a[b][s_begin][t_begin] / ans_grad_a[b];
             if (fabs(grad_ratio - 1.0) > 0.01) {
-              // K2_LOG(WARNING)
-              //<< "Warning: mutual_information backprop: expected these "
-              //<< "numbers to be the same:"
-              //<< static_cast<float>(p_grad_a[b][s_begin][t_begin]) << " vs "
-              //<< static_cast<float>(ans_grad_a[b]);
+              std::cout
+                  << "Warning: mutual_information backprop: expected these "
+                  << "numbers to be the same:"
+                  << static_cast<float>(p_grad_a[b][s_begin][t_begin]) << " vs "
+                  << static_cast<float>(ans_grad_a[b]);
             }
           }
         }

fast_rnnt/python/csrc/mutual_information.cu

@@ -18,6 +18,7 @@
  * limitations under the License.
  */
 
+#include "fast_rnnt/csrc/device_guard.h"
 #include "fast_rnnt/csrc/mutual_information.h"
 #include "fast_rnnt/python/csrc/mutual_information.h"
 
@@ -29,14 +30,15 @@ PYBIND11_MODULE(_fast_rnnt, m) {
       [](torch::Tensor px, torch::Tensor py,
          torch::optional<torch::Tensor> boundary,
          torch::Tensor p) -> torch::Tensor {
+        fast_rnnt::DeviceGuard guard(px.device());
         if (px.device().is_cpu()) {
           return fast_rnnt::MutualInformationCpu(px, py, boundary, p);
         } else {
 #ifdef FT_WITH_CUDA
           return fast_rnnt::MutualInformationCuda(px, py, boundary, p);
 #else
-          //K2_LOG(FATAL) << "Failed to find native CUDA module, make sure "
-                        //<< "that you compiled the code with K2_WITH_CUDA.";
+          TORCH_CHECK(false, "Failed to find native CUDA module, make sure "
+                             "that you compiled the code with K2_WITH_CUDA.");
           return torch::Tensor();
 #endif
         }
@@ -48,6 +50,7 @@ PYBIND11_MODULE(_fast_rnnt, m) {
       [](torch::Tensor px, torch::Tensor py,
          torch::optional<torch::Tensor> boundary, torch::Tensor p,
          torch::Tensor ans_grad) -> std::vector<torch::Tensor> {
+        fast_rnnt::DeviceGuard guard(px.device());
         if (px.device().is_cpu()) {
           return fast_rnnt::MutualInformationBackwardCpu(px, py, boundary, p,
                                                          ans_grad);
@@ -56,8 +59,8 @@ PYBIND11_MODULE(_fast_rnnt, m) {
           return fast_rnnt::MutualInformationBackwardCuda(px, py, boundary, p,
                                                           ans_grad, true);
 #else
-          //K2_LOG(FATAL) << "Failed to find native CUDA module, make sure "
-                        //<< "that you compiled the code with K2_WITH_CUDA.";
+          TORCH_CHECK(false, "Failed to find native CUDA module, make sure "
+                             "that you compiled the code with K2_WITH_CUDA.");
           return std::vector<torch::Tensor>();
 #endif
         }