[dy2static]implement tensor.cuda() in static graph

paddle/fluid/framework/new_executor/interpreter/interpreter_util.cc

@@ -476,6 +476,56 @@ void ApplyDeviceGuard(const OperatorBase* op_base,
   }
 }
 
+platform::DeviceContext* ConstructDeviceContext(const OperatorBase* op,
+                                                const platform::Place& place) {
+  auto& pool = platform::DeviceContextPool::Instance();
+  auto* default_dev_ctx = pool.Get(place);
+
+  // Replace the default_dev_ctx according to dst_place_type for memcpy op if
+  // needed.
+
+  // NOTE(liudongxue01):
+  //  Please apply the following logic in other Executor/Interpreter modules
+  //  likewise.
+  //
+
+  // NOTE(liudongxue01):
+  // The following code aims to fixup the memcpy kernel which does not handle
+  // some rare case. The case is:
+  //  1. The default place in the current execution context is not CUDAPlace,
+  //  such as CPUPlace,
+  //  2. The dst_place_type is 1 which means CUDAPlace,
+  //  3. The expected result place is CUDAPlace but the actual result is
+  //  CPUPlace.
+  // When the default place is CPUPlace, we call the tensor.cuda() would
+  // simply hit such case.
+  //
+  // Q: Why we do not add such logic in the memcpy kernel?
+  // A: (1) To fixup the memcpy kernel, we need to construct a CUDAPlace() and
+  //    corresponding DeviceContext instance which used by the phi::Copy(...)
+  //    api to perform the real memcpy action. (2) We should not access the
+  //    singleton of the DeviceContextPool object in the PHI framework which
+  //    is designed as a standalone module and all context data should passed
+  //    into the kernel API through arguments. (3) So we have no way to
+  //    construct a CUDAPlace() in the memcpy kernel and then pass it
+  //     to the phi::Copy(...) api.
+  if (!platform::is_gpu_place(place)) {
+    const auto& op_type = op->Type();
+    if (op_type == "memcpy") {
+      int dst_place_type = op->Attr<int>("dst_place_type");
+      if (dst_place_type == 1) {  // 1 : CUDAPlace
+        auto dev_ctx = pool.Get(paddle::DefaultGPUPlace());
+        VLOG(4) << "Change the device context for memcpy OP: ("
+                << default_dev_ctx->type_info().name() << ") -> ("
+                << dev_ctx->type_info().name() << ")";
+        return dev_ctx;
+      }
+    }
+  }
+
+  return default_dev_ctx;
+}
+
 void HandleOperatorBase(const platform::Place& place,
                         std::shared_ptr<OperatorBase> op,
                         OpFuncNode* op_func_node,
@@ -537,6 +587,8 @@ void BuildOpFuncList(const platform::Place& place,
   }
   auto unused_var_map = GetUnusedVars(block, ops);
 
+  platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
+
   bool flag_log_is_printed = false;
   for (size_t i = 0; i < ops.size(); ++i) {
     auto op = ops[i].get();
@@ -655,8 +707,9 @@ void BuildOpFuncList(const platform::Place& place,
           runtime_scope = local_scope;
         }
 
-        auto& pool = platform::DeviceContextPool::Instance();
-        auto* dev_ctx = pool.Get(place);
+        // construct the device context
+        auto* dev_ctx = ConstructDeviceContext(op, place);
+
         SetDeviceCommContext(op, dev_ctx);
         auto exec_ctx = ExecutionContext(
             *op_with_kernel, *runtime_scope, *dev_ctx, runtime_context);

python/paddle/fluid/layers/math_op_patch.py

@@ -146,6 +146,7 @@ def cpu(self):
             In Static Graph Mode:
 
             .. code-block:: python
+
                 import paddle
                 paddle.enable_static()
 
@@ -162,7 +163,7 @@ def cpu(self):
             persistable=False,
             stop_gradient=True,
         )
-        # 0 means cpu place, see paddle/fluid/operators/memcpy_op.h
+        # 0 means cpu place, see paddle/phi/kernels/memcpy_kernel.cc
         attrs = {'dst_place_type': 0}
         block.append_op(
             type='memcpy',
@@ -173,13 +174,57 @@ def cpu(self):
         return output
 
     @static_only
-    def cuda(self):
+    def cuda(self, device_id=None, blocking=True):
         """
-        Variable should not have cpu() and cuda() interface.
-        But this interface can greatly facilitate dy2static.
-        We do nothing here.
+        In dy2static, Variable also needs cpu() and cuda() interface.
+        But, the underneath operator has only forward op but not backward one.
+
+        Args:
+            self(Variable): The variable itself.
+            device_id(int, optional): The destination GPU device id. Default: None, means current device.
+                We add this argument for dy2static translation, please do not use it.
+            blocking(bool, optional): Whether blocking or not, Default: True.
+                We add this argument for dy2static translation, please do not use it.
+
+        Returns:
+            The tensor which has copied to cuda place.
+
+        Examples:
+            In Static Graph Mode:
+
+            .. code-block:: python
+
+                import paddle
+                paddle.enable_static()
+
+                x = paddle.static.data(name="x", shape=[2,2], dtype='float32')
+                y = x.cpu()
+                z = y.cuda()
         """
-        return self
+        if device_id is not None:
+            warnings.warn("device_id is not supported, and it will be ignored.")
+        if blocking is not True:
+            warnings.warn("blocking is not supported, and it will be ignored.")
+
+        block = current_block(self)
+        tmp_name = unique_tmp_name()
+        output = block.create_var(
+            name=tmp_name,
+            dtype=self.dtype,
+            shape=self.shape,
+            type=self.type,
+            persistable=False,
+            stop_gradient=True,
+        )
+        # 1 means cuda place, see paddle/phi/kernels/memcpy_kernel.cc
+        attrs = {'dst_place_type': 1}
+        block.append_op(
+            type='memcpy',
+            inputs={'X': [self]},
+            outputs={'Out': [output]},
+            attrs=attrs,
+        )
+        return output
 
     @static_only
     def place(self):

test/dygraph_to_static/test_tensor_memcpy_on_cpu.py

@@ -26,6 +26,20 @@ def tensor_copy_to_cpu(x):
     return y
 
 
+@paddle.jit.to_static
+def tensor_copy_to_cuda(x):
+    x = paddle.to_tensor(x)
+    y = x.cuda()
+    return y
+
+
+@paddle.jit.to_static
+def tensor_copy_to_cuda_with_warning(x, device_id=None, blocking=True):
+    x = paddle.to_tensor(x)
+    y = x.cuda(device_id, blocking)
+    return y
+
+
 class TestTensorCopyToCpuOnDefaultCPU(unittest.TestCase):
     def _run(self, to_static):
         paddle.jit.enable_to_static(to_static)
@@ -46,5 +60,67 @@ def test_tensor_cpu_on_default_cpu(self):
         self.assertTrue(static_place.is_cpu_place())
 
 
+class TestTensorCopyToCUDAOnDefaultCPU(unittest.TestCase):
+    def _run(self, to_static):
+        paddle.jit.enable_to_static(to_static)
+        x1 = paddle.ones([1, 2, 3])
+        x2 = tensor_copy_to_cuda(x1)
+        return x1.place, x2.place, x2.numpy()
+
+    def test_tensor_cuda_on_default_cpu(self):
+        if not paddle.fluid.is_compiled_with_cuda():
+            return
+
+        """
+        Note(liudongxue01): If the following asserts fail to run,
+        please check the workaround logic for memcpy OP
+        whether is still taking effect or not.
+        See ConstructDeviceContext() in interpreter_util.cc.
+        """
+        paddle.fluid.framework._set_expected_place(paddle.CPUPlace())
+        dygraph_x1_place, dygraph_place, dygraph_res = self._run(
+            to_static=False
+        )
+        static_x1_place, static_place, static_res = self._run(to_static=True)
+        np.testing.assert_allclose(dygraph_res, static_res, rtol=1e-05)
+        self.assertTrue(dygraph_x1_place.is_cpu_place())
+        self.assertTrue(static_x1_place.is_cpu_place())
+        self.assertTrue(dygraph_place.is_gpu_place())
+        self.assertTrue(static_place.is_gpu_place())
+
+
+class TestTensorCopyToCUDAWithWarningOnCPU(unittest.TestCase):
+    def _run(self, to_static):
+        paddle.jit.enable_to_static(to_static)
+        x1 = paddle.ones([1, 2, 3])
+        x2 = tensor_copy_to_cuda_with_warning(x1, device_id=1, blocking=False)
+        return x1.place, x2.place, x2.numpy()
+
+    def test_with_warning_on_cpu(self):
+        if not paddle.fluid.is_compiled_with_cuda():
+            return
+
+        paddle.fluid.framework._set_expected_place(paddle.CPUPlace())
+
+        x1 = paddle.ones([1, 2, 3])
+        with self.assertWarns(UserWarning, msg="ignored") as cm:
+            x2 = tensor_copy_to_cuda_with_warning(
+                x1, device_id=1, blocking=True
+            )
+        self.assertIn('math_op_patch.py', cm.filename)
+
+        with self.assertWarns(UserWarning, msg="ignored") as cm:
+            x2 = tensor_copy_to_cuda_with_warning(
+                x1, device_id=None, blocking=False
+            )
+        self.assertIn('math_op_patch.py', cm.filename)
+
+        with self.assertWarns(UserWarning, msg="ignored") as cm:
+            x2 = tensor_copy_to_cuda_with_warning(
+                x1, device_id=2, blocking=False
+            )
+        self.assertIn('math_op_patch.py', cm.filename)
+
+
 if __name__ == '__main__':
     unittest.main()

test/dygraph_to_static/test_tensor_memcpy_on_gpu.py

@@ -27,6 +27,20 @@ def tensor_copy_to_cpu(x):
     return y
 
 
+@paddle.jit.to_static
+def tensor_copy_to_cuda(x):
+    x = paddle.to_tensor(x)
+    y = x.cuda()
+    return y
+
+
+@paddle.jit.to_static
+def tensor_copy_to_cuda_with_warning(x, device_id=None, blocking=True):
+    x = paddle.to_tensor(x)
+    y = x.cuda(device_id, blocking)
+    return y
+
+
 class TestTensorCopyToCpuOnDefaultGPU(unittest.TestCase):
     def _run(self, to_static):
         paddle.jit.enable_to_static(to_static)
@@ -53,5 +67,67 @@ def test_tensor_cpu_on_default_gpu(self):
         self.assertTrue(static_place.is_cpu_place())
 
 
+class TestTensorCopyToCUDAOnDefaultGPU(unittest.TestCase):
+    def _run(self, to_static):
+        paddle.jit.enable_to_static(to_static)
+        x1 = paddle.ones([1, 2, 3])
+        x2 = tensor_copy_to_cuda(x1)
+        return x1.place, x2.place, x2.numpy()
+
+    def test_tensor_cuda_on_default_gpu(self):
+        if paddle.fluid.is_compiled_with_cuda():
+            place = paddle.CUDAPlace(
+                int(os.environ.get('FLAGS_selected_gpus', 0))
+            )
+        else:
+            return
+        paddle.fluid.framework._set_expected_place(place)
+        dygraph_x1_place, dygraph_place, dygraph_res = self._run(
+            to_static=False
+        )
+        static_x1_place, static_place, static_res = self._run(to_static=True)
+        np.testing.assert_allclose(dygraph_res, static_res, rtol=1e-05)
+        self.assertTrue(dygraph_x1_place.is_gpu_place())
+        self.assertTrue(static_x1_place.is_gpu_place())
+        self.assertTrue(dygraph_place.is_gpu_place())
+        self.assertTrue(static_place.is_gpu_place())
+
+
+class TestTensorCopyToCUDAWithWarningOnGPU(unittest.TestCase):
+    def _run(self, to_static):
+        paddle.jit.enable_to_static(to_static)
+        x1 = paddle.ones([1, 2, 3])
+        x2 = tensor_copy_to_cuda_with_warning(x1, device_id=1, blocking=False)
+        return x1.place, x2.place, x2.numpy()
+
+    def test_with_warning_on_gpu(self):
+        if paddle.fluid.is_compiled_with_cuda():
+            place = paddle.CUDAPlace(
+                int(os.environ.get('FLAGS_selected_gpus', 0))
+            )
+        else:
+            return
+        paddle.fluid.framework._set_expected_place(place)
+
+        x1 = paddle.ones([1, 2, 3])
+        with self.assertWarns(UserWarning, msg="ignored") as cm:
+            x2 = tensor_copy_to_cuda_with_warning(
+                x1, device_id=1, blocking=True
+            )
+        self.assertIn('math_op_patch.py', cm.filename)
+
+        with self.assertWarns(UserWarning, msg="ignored") as cm:
+            x2 = tensor_copy_to_cuda_with_warning(
+                x1, device_id=None, blocking=False
+            )
+        self.assertIn('math_op_patch.py', cm.filename)
+
+        with self.assertWarns(UserWarning, msg="ignored") as cm:
+            x2 = tensor_copy_to_cuda_with_warning(
+                x1, device_id=2, blocking=False
+            )
+        self.assertIn('math_op_patch.py', cm.filename)
+
+
 if __name__ == '__main__':
     unittest.main()