[Cherry-pick]The unsupported_fp16_list using in AMP will be created...

[Cherry-pick]The unsupported_fp16_list using in AMP will be created automatically during the runtime. (#32102) (#32217)

* The unsupported_fp16_list using in AMP will be created automatically during the runtime. (#32102)

* Use the runtime to create the unsupported_fp16_list using in AMP.

* Add more infos about supported ops.

* Add some comments for the function of OpSupportedInfos.

* Fix the unit test of test_multi_precision_fp16_train.

* Remove unnecessary codes.

paddle/fluid/pybind/pybind.cc

@@ -14,11 +14,15 @@ limitations under the License. */
 #include <Python.h>
 
 #include <algorithm>
+#include <cctype>
 #include <cstdlib>
+#include <iterator>
 #include <map>
 #include <memory>
 #include <mutex>  // NOLINT // for call_once
 #include <string>
+#include <tuple>
+#include <type_traits>
 #include <unordered_map>
 #include <unordered_set>
 #include <utility>
@@ -162,6 +166,64 @@ bool SupportsBfloat16() {
 #endif
 }
 
+// According to the input `place` and `dtype`, this function returns a tuple
+// consists of three sets:
+// 1) All operators registered in the Paddle framework.
+// 2) All operators supported for `place` and `dtype`.
+// 3) All operators unsupported for `place` and `dtype`.
+// The input `place` is a type of string, which can only be `GPU` or `CPU`.
+// The input `dtype` is a type of paddle::framework::proto::VarType::Type,
+// which can be paddle::framework::proto::VarType::FP16,
+// paddle::framework::proto::VarType::FP32 and so on.
+std::tuple<std::unordered_set<std::string>, std::unordered_set<std::string>,
+           std::unordered_set<std::string>>
+OpSupportedInfos(const std::string &place,
+                 framework::proto::VarType::Type dtype) {
+  std::string query_place;
+  std::transform(place.begin(), place.end(), std::back_inserter(query_place),
+                 [](unsigned char c) { return std::toupper(c); });
+  using fn_type = std::add_pointer<bool(const platform::Place &)>::type;
+  std::unordered_map<std::string, fn_type> is_target_place{
+      {"GPU", &platform::is_gpu_place}, {"CPU", &platform::is_cpu_place},
+  };
+  PADDLE_ENFORCE_NE(
+      is_target_place.count(query_place), 0,
+      platform::errors::InvalidArgument(
+          "The argument `place` should be 'GPU' or 'CPU', but get '%s'.",
+          place));
+
+  std::unordered_set<std::string> all_ops;
+  const auto &op_info = framework::OpInfoMap::Instance().map();
+  for (auto it = op_info.begin(); it != op_info.end(); it++) {
+    all_ops.emplace(it->first);
+  }
+
+  std::unordered_set<std::string> supported_ops;
+  auto &all_kernels = framework::OperatorWithKernel::AllOpKernels();
+  for (auto it = all_kernels.begin(); it != all_kernels.end(); it++) {
+    for (auto &kernel_type : it->second) {
+      if (is_target_place[query_place](kernel_type.first.place_) &&
+          kernel_type.first.data_type_ == dtype) {
+        supported_ops.emplace(it->first);
+      }
+    }
+  }
+
+  std::unordered_set<std::string> unsupported_ops;
+  for (auto &op : all_ops) {
+    if (!supported_ops.count(op)) {
+      unsupported_ops.emplace(op);
+    }
+  }
+
+  VLOG(4) << "-- The size of all_ops: " << all_ops.size() << " --";
+  VLOG(4) << "-- The size of supported_ops: " << supported_ops.size() << " --";
+  VLOG(4) << "-- The size of unsupported_ops: " << unsupported_ops.size()
+          << " --";
+  return std::make_tuple(std::move(all_ops), std::move(supported_ops),
+                         std::move(unsupported_ops));
+}
+
 bool IsCompiledWithBrpc() {
 #ifndef PADDLE_WITH_DISTRIBUTE
   return false;
@@ -1741,6 +1803,7 @@ All parameter, weight, gradient are variables in Paddle.
   m.def("is_compiled_with_xpu", IsCompiledWithXPU);
   m.def("is_compiled_with_mkldnn", IsCompiledWithMKLDNN);
   m.def("supports_bfloat16", SupportsBfloat16);
+  m.def("op_supported_infos", OpSupportedInfos);
   m.def("is_compiled_with_brpc", IsCompiledWithBrpc);
   m.def("is_compiled_with_dist", IsCompiledWithDIST);
   m.def("_cuda_synchronize", [](const platform::CUDAPlace &place) {

python/paddle/fluid/contrib/mixed_precision/fp16_lists.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import copy
+from ... import core
 
 __all__ = ["CustomOpLists", "AutoMixedPrecisionLists"]
 
@@ -147,147 +148,10 @@ gray_list = {
 }
 
 # The set of ops that don't support fp16 calculation
-unsupported_fp16_list = {
-    # from python/paddle/fluid/layers/io.py
-    'send',
-    'send_barrier',
-    'recv',
-    'fetch_barrier',
-    'create_py_reader',
-    'create_double_buffer_reader',
-    'read',
-    'load',
-
-    # from python/paddle/fluid/control_flow.py
-    'increment',
-    'less_than',
-    'less_equal',
-    'greater_than',
-    'greater_equal',
-    'equal',
-    'not_equal',
-    'read_from_array',
-    'shrink_rnn_memory',
-    'lod_array_length',
-    'logical_and',
-    'logical_or',
-    'logical_xor',
-    'logical_not',
-    'print',
-    'conditional_block',
-    'while',
-    'ifelse',
-    'is_empty',
-    'lstm',
-    'cudnn_lstm',
-    'lstmp',
-    'gru',
-    'gru_unit',
-    'linear_chain_crf',
-    'crf_decoding',
-    'bpr_loss',
-    'chunk_eval',
-    'sequence_conv',
-    'sequence_softmax',
-    # Depthwise conv2d isn't fast and safe currently.
-    # ref: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/grappler/optimizers/auto_mixed_precision_lists.h#L79
-    'depthwise_conv2d',
-    # Tensor Core kernels are not available for 3D convolutions currently.
-    'conv3d',
-    'sequence_pool',
-    'sequence_concat',
-    'sequence_slice',
-    'data_norm',
-    'group_norm',
-    'spectral_norm',
-    'depthwise_conv2d_transpose',
-    'sequence_expand',
-    'conv_transposed2d',
-    'conv_transposed3d',
-    'sequence_expand_as',
-    'sequence_pad',
-    'sequence_unpad',
-    'sequence_erase',
-    'beam_search',
-    'beam_search_decode',
-    'lstm_unit',
-    'reduce_sum',
-    'reduce_mean',
-    'reduce_max',
-    'reduce_min',
-    'reduce_prod',
-    'reduce_all',
-    'reduce_any',
-    'split',
-    'edit_distance',
-    'ctc_align',
-    'warpctc',
-    'sequence_reshape',
-    'nce',
-    'hierarchical_sigmoid',
-    'im2sequence',
-    'row_conv',
-    'multiplex',
-    'sample_logits',
-    'one_hot',
-    'smooth_l1_loss',
-    'squeeze2',
-    'unsqueeze2',
-    'lod_reset',
-    'lrn',
-    'pad',
-    'pad_constant_like',
-    'label_smooth',
-    'scatter',
-    'sequence_scatter',
-    'random_crop',
-    'mean_iou',
-    'selu',
-    'crop',
-    'affine_grid',
-    'rank_loss',
-    'margin_rank_loss',
-    'pad2d',
-    'elu',
-    'pow',
-    'stanh',
-    'hard_sigmoid',
-    'swish',
-    'prelu',
-    'brelu',
-    'sequence_enumerate',
-    'sequence_mask',
-    'expand',
-    'sampling_id',
-    'maxout',
-    'space_to_depth',
-    'sequence_reverse',
-    'similarity_focus',
-    'hash',
-    'grid_sampler',
-    'log_loss',
-    'teacher_student_sigmoid_loss',
-    'add_position_encoding',
-    'bilinear_tensor_product',
-    'shuffle_channel',
-    'temporal_shift',
-    'psroi_pool',
-    'huber_loss',
-    'kldiv_loss',
-    'tree_conv',
-    'pixel_shuffle',
-    'fsp',
-    'cvm',
-    'affine_channel',
-    'roi_pool',
-    'roi_align',
-    'anchor_generator',
-    'generate_proposals',
-    'generate_proposal_labels',
-    'generate_mask_labels',
-    # fp16 is slower than fp32, though fp16 is supported.
-    'lookup_table',
-    'lookup_table_v2',
-}
+# lookup_table fp16 is slower than fp32, though fp16 is supported.
+_, _, _sys_unsupported_fp16_list = core.op_supported_infos(
+    'GPU', core.VarDesc.VarType.FP16)
+unsupported_fp16_list = {'lookup_table',
+                         'lookup_table_v2'} | _sys_unsupported_fp16_list
 
 CustomOpLists = AutoMixedPrecisionLists

python/paddle/fluid/contrib/tests/test_multi_precision_fp16_train.py

@@ -258,7 +258,8 @@ class TestAmpWithNonIterableDataLoader(unittest.TestCase):
         cast_model_to_fp16(main_prog, use_fp16_guard=False)
 
     def test_non_iterable_dataloader(self):
-        self.decorate_with_data_loader()
+        if fluid.core.is_compiled_with_cuda():
+            self.decorate_with_data_loader()
 
 
 if __name__ == '__main__':