Enable eager mode on xpu (#46227)

* enable eager mode on xpu, test=kunlun

* add numpy support to xpu

* fix tensor using error

* fix  error, test=kunlun

* fix failed tests, test=kunlun

paddle/fluid/pybind/eager_method.cc

@@ -215,6 +215,33 @@ static PyObject* tensor_method_numpy(TensorObject* self,
           kind);
     }
 #endif
+#if defined(PADDLE_WITH_XPU)
+  } else if (self->tensor.is_xpu()) {
+    platform::CPUPlace place;
+    if (self->tensor.is_selected_rows()) {
+      VLOG(6) << "Getting SelectedRows's numpy value";
+      auto* selected_rows =
+          static_cast<phi::SelectedRows*>(self->tensor.impl().get());
+      auto* dense_tensor = static_cast<paddle::framework::LoDTensor*>(
+          selected_rows->mutable_value());
+      paddle::memory::Copy(
+          place,
+          reinterpret_cast<void*>(pybind11::detail::array_proxy(array)->data),
+          dense_tensor->place(),
+          dense_tensor->data(),
+          sizeof_dtype * numel);
+    } else {
+      VLOG(6) << "Getting DenseTensor's numpy value";
+      auto dense_tensor =
+          std::dynamic_pointer_cast<phi::DenseTensor>(self->tensor.impl());
+      paddle::memory::Copy(
+          place,
+          reinterpret_cast<void*>(pybind11::detail::array_proxy(array)->data),
+          dense_tensor->place(),
+          dense_tensor->data(),
+          sizeof_dtype * numel);
+    }
+#endif
 #ifdef PADDLE_WITH_CUSTOM_DEVICE
   } else if (self->tensor.is_custom_device()) {
     if (self->tensor.is_selected_rows()) {

paddle/phi/api/include/tensor.h

@@ -286,6 +286,14 @@ class PADDLE_API Tensor final {
    */
   bool is_gpu_pinned() const;
 
+  /**
+   * @brief Determine whether the tensor device is XPU
+   *
+   * @return true
+   * @return false
+   */
+  bool is_xpu() const;
+
   /**
    * @brief Determine whether the tensor device is CustomDevice
    *

paddle/phi/api/lib/tensor.cc

@@ -157,6 +157,8 @@ bool Tensor::is_gpu_pinned() const {
   return paddle::platform::is_cuda_pinned_place(place());
 }
 
+bool Tensor::is_xpu() const { return paddle::platform::is_xpu_place(place()); }
+
 bool Tensor::is_custom_device() const {
   return paddle::platform::is_custom_place(place());
 }

python/paddle/fluid/framework.py

@@ -181,9 +181,9 @@ def _fallback_legacy_dygraph():
     global _in_eager_mode_
     global _is_first_import_
     need_fallback = False
-    # Only enable eager on CPU/GPU
-    is_not_support = core.is_compiled_with_xpu() or core.is_compiled_with_npu(
-    ) or core.is_compiled_with_ipu() or core.is_compiled_with_mlu()
+    # Only enable eager on CPU/GPU/XPU
+    is_not_support = core.is_compiled_with_npu() or core.is_compiled_with_ipu(
+    ) or core.is_compiled_with_mlu()
 
     if _in_eager_mode_ and is_not_support:
         # switch into legacy dygraph mode
@@ -245,8 +245,8 @@ def _non_static_mode():
 
 @signature_safe_contextmanager
 def _test_eager_guard(place=None):
-    # FIXME(dev): We haven't fully verified eager mode on XPU/NPU et.al but
-    # only GPU/CPU. Remove this after we improve this feature.
+    # FIXME(dev): We haven't fully verified eager mode on NPU et.al but
+    # only GPU/CPU/XPU. Remove this after we improve this feature.
     already_fallback = _fallback_legacy_dygraph()
     if not already_fallback:
         _disable_legacy_dygraph()

python/paddle/fluid/tests/unittests/xpu/test_dropout_op_xpu.py

@@ -19,6 +19,7 @@ sys.path.append("..")
 import unittest
 import numpy as np
 import paddle.fluid.core as core
+from paddle import _legacy_C_ops
 from op_test import OpTest, skip_check_grad_ci
 import paddle
 import paddle.fluid as fluid
@@ -185,7 +186,8 @@ class XPUTestDropoutOp(XPUOpTestWrapper):
 
                     input = paddle.uniform([40, 40], dtype=self.in_type)
                     input.stop_gradient = False
-                    out, mask = core.ops.dropout(input, 'dropout_prob', 0.5)
+                    out, mask = _legacy_C_ops.dropout(input, 'dropout_prob',
+                                                      0.5)
                     out.backward()
 
                     np.testing.assert_allclose(
@@ -199,7 +201,8 @@ class XPUTestDropoutOp(XPUOpTestWrapper):
                     prob = 0.5
                     input = paddle.uniform([40, 40], dtype=self.in_type)
                     input.stop_gradient = False
-                    out, mask = core.ops.dropout(input, 'dropout_prob', prob,
+                    out, mask = _legacy_C_ops.dropout(input, 'dropout_prob',
+                                                      prob,
                                                       "dropout_implementation",
                                                       "upscale_in_train")
                     out.backward()
@@ -215,7 +218,8 @@ class XPUTestDropoutOp(XPUOpTestWrapper):
                     prob = 0.3
                     input = paddle.uniform([40, 40], dtype=self.in_type)
                     input.stop_gradient = False
-                    out, mask = core.ops.dropout(input, 'dropout_prob', prob,
+                    out, mask = _legacy_C_ops.dropout(input, 'dropout_prob',
+                                                      prob,
                                                       "dropout_implementation",
                                                       "upscale_in_train")
                     out.backward()

python/paddle/fluid/tests/unittests/xpu/test_fused_gemm_epilogue_op_xpu.py

@@ -21,6 +21,7 @@ import unittest
 import numpy as np
 import paddle
 import paddle.fluid.core as core
+from paddle import _legacy_C_ops
 from op_test_xpu import XPUOpTest
 from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
 
@@ -251,13 +252,13 @@ class TestEagerFusedGemmEpilogue(unittest.TestCase):
         x.stop_gradient = False
         y.stop_gradient = False
 
-        out1 = core.ops.fused_gemm_epilogue(x, y, bias, 'trans_x', False,
+        out1 = _legacy_C_ops.fused_gemm_epilogue(x, y, bias, 'trans_x', False,
                                                  'trans_y', False, 'activation',
                                                  'none')
-        out2 = core.ops.fused_gemm_epilogue(x, y, bias, 'trans_x', False,
+        out2 = _legacy_C_ops.fused_gemm_epilogue(x, y, bias, 'trans_x', False,
                                                  'trans_y', False, 'activation',
                                                  'relu')
-        out3 = core.ops.fused_gemm_epilogue(x, y, bias, 'trans_x', False,
+        out3 = _legacy_C_ops.fused_gemm_epilogue(x, y, bias, 'trans_x', False,
                                                  'trans_y', False, 'activation',
                                                  'gelu')
 

python/paddle/fluid/tests/unittests/xpu/test_fused_resnet_basic_block_op_xpu.py

@@ -24,6 +24,7 @@ import paddle
 import paddle.fluid as fluid
 import paddle.nn as nn
 from paddle.fluid import core
+from paddle import _legacy_C_ops
 from paddle.incubate.xpu.resnet_block import ResNetBasicBlock
 from paddle.fluid.framework import default_main_program
 from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper

python/paddle/incubate/xpu/resnet_block.py

@@ -71,7 +71,7 @@ def resnet_basic_block(x,
                        trainable_statistics=False,
                        find_conv_max=True):
 
-    if fluid.framework.in_dygraph_mode():
+    if fluid.framework._non_static_mode():
         attrs = ('stride1', stride1, 'stride2', stride2, 'stride3', stride3,
                  'padding1', padding1, 'padding2', padding2, 'padding3',
                  padding3, 'dilation1', dilation1, 'dilation2', dilation2,
@@ -83,7 +83,7 @@ def resnet_basic_block(x,
                  find_conv_max)
 
         out, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _, _ = \
-                getattr(_C_ops, "resnet_basic_block")(x, filter1, scale1, bias1, mean1, var1, filter2, scale2, bias2, mean2, var2, \
+                _legacy_C_ops.resnet_basic_block(x, filter1, scale1, bias1, mean1, var1, filter2, scale2, bias2, mean2, var2, \
                 filter3, scale3, bias3, mean3, var3, mean1, var1, mean2, var2, mean3, var3, *attrs)
         return out
     helper = LayerHelper('resnet_basic_block', **locals())