[Refactor] Rename TVMContext to Device (#7721)

apps/android_camera/app/src/main/java/org/apache/tvm/android/androidcamerademo/Camera2BasicFragment.java

@@ -57,7 +57,7 @@
 import org.apache.tvm.Function;
 import org.apache.tvm.Module;
 import org.apache.tvm.NDArray;
-import org.apache.tvm.TVMContext;
+import org.apache.tvm.Device;
 import org.apache.tvm.TVMType;
 import org.apache.tvm.TVMValue;
 import org.json.JSONException;
@@ -571,9 +571,9 @@ protected Integer doInBackground(Void... args) {
                 return -1;//failure
             }
 
-            Log.i(TAG, "creating java tvm context...");
-            // create java tvm context
-            TVMContext tvmCtx = EXE_GPU ? TVMContext.opencl() : TVMContext.cpu();
+            Log.i(TAG, "creating java tvm device...");
+            // create java tvm device
+            Device tvmDev = EXE_GPU ? Device.opencl() : Device.cpu();
 
             Log.i(TAG, "loading compiled functions...");
             Log.i(TAG, libCacheFilePath);
@@ -587,13 +587,13 @@ protected Integer doInBackground(Void... args) {
             Function runtimeCreFun = Function.getFunction("tvm.graph_runtime.create");
             Log.i(TAG, "creating graph runtime...");
 
-            Log.i(TAG, "ctx type: " + tvmCtx.deviceType);
-            Log.i(TAG, "ctx id: " + tvmCtx.deviceId);
+            Log.i(TAG, "device type: " + tvmDev.deviceType);
+            Log.i(TAG, "device id: " + tvmDev.deviceId);
 
             TVMValue runtimeCreFunRes = runtimeCreFun.pushArg(modelGraph)
                     .pushArg(modelLib)
-                    .pushArg(tvmCtx.deviceType)
-                    .pushArg(tvmCtx.deviceId)
+                    .pushArg(tvmDev.deviceType)
+                    .pushArg(tvmDev.deviceId)
                     .invoke();
 
             Log.i(TAG, "as module...");

apps/android_deploy/app/src/main/java/org/apache/tvm/android/demo/MainActivity.java

@@ -56,7 +56,7 @@
 import org.apache.tvm.Function;
 import org.apache.tvm.Module;
 import org.apache.tvm.NDArray;
-import org.apache.tvm.TVMContext;
+import org.apache.tvm.Device;
 import org.apache.tvm.TVMValue;
 import org.apache.tvm.TVMType;
 
@@ -177,8 +177,8 @@ protected Integer doInBackground(Void... args) {
                 return -1;//failure
             }
 
-            // create java tvm context
-            TVMContext tvmCtx = EXE_GPU ? TVMContext.opencl() : TVMContext.cpu();
+            // create java tvm device
+            Device tvmDev = EXE_GPU ? Device.opencl() : Device.cpu();
 
             // tvm module for compiled functions
             Module modelLib = Module.load(libCacheFilePath);
@@ -187,8 +187,8 @@ protected Integer doInBackground(Void... args) {
             Function runtimeCreFun = Function.getFunction("tvm.graph_runtime.create");
             TVMValue runtimeCreFunRes = runtimeCreFun.pushArg(modelGraph)
                     .pushArg(modelLib)
-                    .pushArg(tvmCtx.deviceType)
-                    .pushArg(tvmCtx.deviceId)
+                    .pushArg(tvmDev.deviceType)
+                    .pushArg(tvmDev.deviceId)
                     .invoke();
             graphRuntimeModule = runtimeCreFunRes.asModule();
 

apps/android_rpc/tests/android_rpc_test.py

@@ -68,12 +68,12 @@ def test_rpc_module():
 
     # Execute the portable graph on cpu target
     print("Run CPU test ...")
-    ctx = remote.cpu(0)
+    dev = remote.cpu(0)
     remote.upload(path_dso_cpu)
     f2 = remote.load_module("cpu_lib.so")
-    a = tvm.nd.array(a_np, ctx)
-    b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
-    time_f = f2.time_evaluator(f2.entry_name, ctx, number=10)
+    a = tvm.nd.array(a_np, dev)
+    b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), dev)
+    time_f = f2.time_evaluator(f2.entry_name, dev, number=10)
     cost = time_f(a, b).mean
     print("%g secs/op\n" % cost)
     np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
@@ -91,12 +91,12 @@ def test_rpc_module():
         f.export_library(path_dso_cl, ndk.create_shared)
 
         print("Run GPU(OpenCL Flavor) test ...")
-        ctx = remote.cl(0)
+        dev = remote.cl(0)
         remote.upload(path_dso_cl)
         f1 = remote.load_module("dev_lib_cl.so")
-        a = tvm.nd.array(a_np, ctx)
-        b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
-        time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
+        a = tvm.nd.array(a_np, dev)
+        b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), dev)
+        time_f = f1.time_evaluator(f1.entry_name, dev, number=10)
         cost = time_f(a, b).mean
         print("%g secs/op\n" % cost)
         np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
@@ -114,12 +114,12 @@ def test_rpc_module():
         f.export_library(path_dso_vulkan, ndk.create_shared)
 
         print("Run GPU(Vulkan Flavor) test ...")
-        ctx = remote.vulkan(0)
+        dev = remote.vulkan(0)
         remote.upload(path_dso_vulkan)
         f1 = remote.load_module("dev_lib_vulkan.so")
-        a = tvm.nd.array(a_np, ctx)
-        b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
-        time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
+        a = tvm.nd.array(a_np, dev)
+        b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), dev)
+        time_f = f1.time_evaluator(f1.entry_name, dev, number=10)
         cost = time_f(a, b).mean
         print("%g secs/op\n" % cost)
         np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)

apps/benchmark/arm_cpu_imagenet_bench.py

@@ -54,17 +54,17 @@ def evaluate_network(network, target, target_host, repeat):
 
     # upload library and params
     print_progress("%-20s uploading..." % network)
-    ctx = remote.context(str(target), 0)
+    dev = remote.device(str(target), 0)
     remote.upload(tmp.relpath(filename))
 
     rlib = remote.load_module(filename)
-    module = runtime.GraphModule(rlib["default"](ctx))
+    module = runtime.GraphModule(rlib["default"](dev))
     data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
     module.set_input("data", data_tvm)
 
     # evaluate
     print_progress("%-20s evaluating..." % network)
-    ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=repeat)
+    ftimer = module.module.time_evaluator("run", dev, number=1, repeat=repeat)
     prof_res = np.array(ftimer().results) * 1000  # multiply 1000 for converting to millisecond
     print(
         "%-20s %-19s (%s)" % (network, "%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res))

apps/benchmark/gpu_imagenet_bench.py

@@ -37,13 +37,13 @@ def benchmark(network, target):
         lib = relay.build(net, target=target, params=params)
 
     # create runtime
-    ctx = tvm.context(str(target), 0)
-    module = runtime.GraphModule(lib["default"](ctx))
+    dev = tvm.device(str(target), 0)
+    module = runtime.GraphModule(lib["default"](dev))
     data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
     module.set_input("data", data_tvm)
 
     # evaluate
-    ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=args.repeat)
+    ftimer = module.module.time_evaluator("run", dev, number=1, repeat=args.repeat)
     prof_res = np.array(ftimer().results) * 1000  # multiply 1000 for converting to millisecond
     print(
         "%-20s %-19s (%s)" % (network, "%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res))

apps/benchmark/mobile_gpu_imagenet_bench.py

@@ -54,17 +54,17 @@ def evaluate_network(network, target, target_host, dtype, repeat):
 
     # upload library and params
     print_progress("%-20s uploading..." % network)
-    ctx = remote.context(str(target), 0)
+    dev = remote.device(str(target), 0)
     remote.upload(tmp.relpath(filename))
 
     rlib = remote.load_module(filename)
-    module = runtime.GraphModule(rlib["default"](ctx))
+    module = runtime.GraphModule(rlib["default"](dev))
     data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
     module.set_input("data", data_tvm)
 
     # evaluate
     print_progress("%-20s evaluating..." % network)
-    ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=repeat)
+    ftimer = module.module.time_evaluator("run", dev, number=1, repeat=repeat)
     prof_res = np.array(ftimer().results) * 1000  # multiply 1000 for converting to millisecond
     print(
         "%-20s %-19s (%s)" % (network, "%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res))

apps/bundle_deploy/bundle.c

@@ -59,9 +59,9 @@ TVM_DLL void* tvm_runtime_create(const char* json_data, const char* params_data,
   params.data = params_data;
   params.size = params_size;
 
-  TVMContext ctx;
-  ctx.device_type = (DLDeviceType)device_type;
-  ctx.device_id = device_id;
+  DLDevice dev;
+  dev.device_type = (DLDeviceType)device_type;
+  dev.device_id = device_id;
 
   // declare pointers
   TVM_CCALL(MemoryManagerCreate(&g_memory_manager, g_crt_memory, sizeof(g_crt_memory),
@@ -76,7 +76,7 @@ TVM_DLL void* tvm_runtime_create(const char* json_data, const char* params_data,
 
   // run modules
   TVMGraphRuntime* graph_runtime = NULL;
-  TVM_CCALL(TVMGraphRuntime_Create(json_data, mod_syslib, &ctx, &graph_runtime));
+  TVM_CCALL(TVMGraphRuntime_Create(json_data, mod_syslib, &dev, &graph_runtime));
   TVM_CCALL(TVMGraphRuntime_LoadParams(graph_runtime, params.data, params.size));
 
   return graph_runtime;
@@ -116,12 +116,12 @@ void __attribute__((noreturn)) TVMPlatformAbort(tvm_crt_error_t error_code) {
   exit(-1);
 }
 
-tvm_crt_error_t TVMPlatformMemoryAllocate(size_t num_bytes, DLContext ctx, void** out_ptr) {
-  return g_memory_manager->Allocate(g_memory_manager, num_bytes, ctx, out_ptr);
+tvm_crt_error_t TVMPlatformMemoryAllocate(size_t num_bytes, DLDevice dev, void** out_ptr) {
+  return g_memory_manager->Allocate(g_memory_manager, num_bytes, dev, out_ptr);
 }
 
-tvm_crt_error_t TVMPlatformMemoryFree(void* ptr, DLContext ctx) {
-  return g_memory_manager->Free(g_memory_manager, ptr, ctx);
+tvm_crt_error_t TVMPlatformMemoryFree(void* ptr, DLDevice dev) {
+  return g_memory_manager->Free(g_memory_manager, ptr, dev);
 }
 
 tvm_crt_error_t TVMPlatformTimerStart() { return kTvmErrorFunctionCallNotImplemented; }

apps/bundle_deploy/bundle_static.c

@@ -59,9 +59,9 @@ TVM_DLL void* tvm_runtime_create(const char* json_data, const char* params_data,
   params.data = params_data;
   params.size = params_size;
 
-  TVMContext ctx;
-  ctx.device_type = (DLDeviceType)device_type;
-  ctx.device_id = device_id;
+  DLDevice dev;
+  dev.device_type = (DLDeviceType)device_type;
+  dev.device_id = device_id;
 
   // get pointers
   TVM_CCALL(MemoryManagerCreate(&g_memory_manager, g_crt_memory, sizeof(g_crt_memory),
@@ -76,7 +76,7 @@ TVM_DLL void* tvm_runtime_create(const char* json_data, const char* params_data,
 
   // run modules
   TVMGraphRuntime* graph_runtime = NULL;
-  TVM_CCALL(TVMGraphRuntime_Create(json_data, mod_syslib, &ctx, &graph_runtime));
+  TVM_CCALL(TVMGraphRuntime_Create(json_data, mod_syslib, &dev, &graph_runtime));
   TVM_CCALL(TVMGraphRuntime_LoadParams(graph_runtime, params.data, params.size));
 
   return graph_runtime;
@@ -117,12 +117,12 @@ void __attribute__((noreturn)) TVMPlatformAbort(tvm_crt_error_t error_code) {
   exit(-1);
 }
 
-tvm_crt_error_t TVMPlatformMemoryAllocate(size_t num_bytes, DLContext ctx, void** out_ptr) {
-  return g_memory_manager->Allocate(g_memory_manager, num_bytes, ctx, out_ptr);
+tvm_crt_error_t TVMPlatformMemoryAllocate(size_t num_bytes, DLDevice dev, void** out_ptr) {
+  return g_memory_manager->Allocate(g_memory_manager, num_bytes, dev, out_ptr);
 }
 
-tvm_crt_error_t TVMPlatformMemoryFree(void* ptr, DLContext ctx) {
-  return g_memory_manager->Free(g_memory_manager, ptr, ctx);
+tvm_crt_error_t TVMPlatformMemoryFree(void* ptr, DLDevice dev) {
+  return g_memory_manager->Free(g_memory_manager, ptr, dev);
 }
 
 tvm_crt_error_t TVMPlatformTimerStart() { return kTvmErrorFunctionCallNotImplemented; }

apps/bundle_deploy/demo.cc

@@ -119,7 +119,7 @@ int main(int argc, char** argv) {
   std::vector<int64_t> input_shape = {1, 3, 224, 224};
   DLTensor input;
   input.data = input_storage;
-  input.ctx = DLContext{kDLCPU, 0};
+  input.device = DLDevice{kDLCPU, 0};
   input.ndim = 4;
   input.dtype = DLDataType{kDLFloat, 32, 1};
   input.shape = input_shape.data();
@@ -138,7 +138,7 @@ int main(int argc, char** argv) {
   std::vector<int64_t> output_shape = {1, 1000};
   DLTensor output;
   output.data = output_storage;
-  output.ctx = DLContext{kDLCPU, 0};
+  output.device = DLDevice{kDLCPU, 0};
   output.ndim = 2;
   output.dtype = DLDataType{kDLFloat, 32, 1};
   output.shape = output_shape.data();

apps/bundle_deploy/demo_static.c

@@ -54,8 +54,8 @@ int main(int argc, char** argv) {
 
   DLTensor input;
   input.data = input_storage;
-  DLContext ctx = {kDLCPU, 0};
-  input.ctx = ctx;
+  DLDevice dev = {kDLCPU, 0};
+  input.device = dev;
   input.ndim = 4;
   DLDataType dtype = {kDLFloat, 32, 1};
   input.dtype = dtype;
@@ -73,8 +73,8 @@ int main(int argc, char** argv) {
   float output_storage[OUTPUT_LEN];
   DLTensor output;
   output.data = output_storage;
-  DLContext out_ctx = {kDLCPU, 0};
-  output.ctx = out_ctx;
+  DLDevice out_dev = {kDLCPU, 0};
+  output.device = out_dev;
   output.ndim = 2;
   DLDataType out_dtype = {kDLFloat, 32, 1};
   output.dtype = out_dtype;

apps/bundle_deploy/test.cc

@@ -114,7 +114,7 @@ int main(int argc, char** argv) {
   std::vector<int64_t> input_shape = {10, 5};
   DLTensor input;
   input.data = input_storage;
-  input.ctx = DLContext{kDLCPU, 0};
+  input.device = DLDevice{kDLCPU, 0};
   input.ndim = 2;
   input.dtype = DLDataType{kDLFloat, 32, 1};
   input.shape = input_shape.data();
@@ -133,7 +133,7 @@ int main(int argc, char** argv) {
   std::vector<int64_t> output_shape = {10, 5};
   DLTensor output;
   output.data = output_storage;
-  output.ctx = DLContext{kDLCPU, 0};
+  output.device = DLDevice{kDLCPU, 0};
   output.ndim = 2;
   output.dtype = DLDataType{kDLFloat, 32, 1};
   output.shape = output_shape.data();

apps/bundle_deploy/test_static.c

@@ -66,8 +66,8 @@ int main(int argc, char** argv) {
 
   DLTensor input;
   input.data = input_storage;
-  DLContext ctx = {kDLCPU, 0};
-  input.ctx = ctx;
+  DLDevice dev = {kDLCPU, 0};
+  input.device = dev;
   input.ndim = 2;
   DLDataType dtype = {kDLFloat, 32, 1};
   input.dtype = dtype;
@@ -85,8 +85,8 @@ int main(int argc, char** argv) {
   float output_storage[10 * 5];
   DLTensor output;
   output.data = output_storage;
-  DLContext out_ctx = {kDLCPU, 0};
-  output.ctx = out_ctx;
+  DLDevice out_dev = {kDLCPU, 0};
+  output.device = out_dev;
   output.ndim = 2;
   DLDataType out_dtype = {kDLFloat, 32, 1};
   output.dtype = out_dtype;

apps/extension/tests/test_ext.py

@@ -40,10 +40,10 @@ def check_llvm():
         if not tvm.testing.device_enabled("llvm"):
             return
         f = tvm.build(s, [A, B], "ext_dev", "llvm")
-        ctx = tvm.ext_dev(0)
+        dev = tvm.ext_dev(0)
         # launch the kernel.
-        a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
-        b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx)
+        a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), dev)
+        b = tvm.nd.array(np.zeros(n, dtype=B.dtype), dev)
         f(a, b)
         tvm.testing.assert_allclose(b.asnumpy(), a.asnumpy() + 1)
 
@@ -87,10 +87,10 @@ def check_llvm():
         if not tvm.testing.device_enabled("llvm"):
             return
         f = tvm.build(s, [A, B], "llvm")
-        ctx = tvm.cpu(0)
+        dev = tvm.cpu(0)
         # launch the kernel.
-        a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
-        b = tvm.nd.array(np.zeros(n, dtype=B.dtype), ctx)
+        a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), dev)
+        b = tvm.nd.array(np.zeros(n, dtype=B.dtype), dev)
         f(a, b)
         tvm.testing.assert_allclose(b.asnumpy(), a.asnumpy() + 1)
 

apps/howto_deploy/cpp_deploy.cc

@@ -86,17 +86,17 @@ void DeploySingleOp() {
 void DeployGraphRuntime() {
   LOG(INFO) << "Running graph runtime...";
   // load in the library
-  DLContext ctx{kDLCPU, 0};
+  DLDevice dev{kDLCPU, 0};
   tvm::runtime::Module mod_factory = tvm::runtime::Module::LoadFromFile("lib/test_relay_add.so");
   // create the graph runtime module
-  tvm::runtime::Module gmod = mod_factory.GetFunction("default")(ctx);
+  tvm::runtime::Module gmod = mod_factory.GetFunction("default")(dev);
   tvm::runtime::PackedFunc set_input = gmod.GetFunction("set_input");
   tvm::runtime::PackedFunc get_output = gmod.GetFunction("get_output");
   tvm::runtime::PackedFunc run = gmod.GetFunction("run");
 
   // Use the C++ API
-  tvm::runtime::NDArray x = tvm::runtime::NDArray::Empty({2, 2}, DLDataType{kDLFloat, 32, 1}, ctx);
-  tvm::runtime::NDArray y = tvm::runtime::NDArray::Empty({2, 2}, DLDataType{kDLFloat, 32, 1}, ctx);
+  tvm::runtime::NDArray x = tvm::runtime::NDArray::Empty({2, 2}, DLDataType{kDLFloat, 32, 1}, dev);
+  tvm::runtime::NDArray y = tvm::runtime::NDArray::Empty({2, 2}, DLDataType{kDLFloat, 32, 1}, dev);
 
   for (int i = 0; i < 2; ++i) {
     for (int j = 0; j < 2; ++j) {