[AutoParallel]fp16 pass support assign op

python/paddle/distributed/passes/auto_parallel_fp16.py

@@ -156,6 +156,7 @@ def __init__(
             list
         )  # {forward_op_id: [(output_name, input_name, out_dtype, in_dtype, slot_name), ]}
         self.is_train = False
+        self.out_var_op_deps = {}
 
     def _is_fp16_op(self, op_id):
         return self._op_fp16_dict.get(op_id, None)
@@ -169,6 +170,14 @@ def _build_state(self):
         # assume all backward block are behind forward blocks
         for block in self.program.blocks:
             for op in block.ops:
+                for name in op.output_arg_names:
+                    if name not in self.out_var_op_deps:
+                        self.out_var_op_deps[name] = [op.desc.original_id()]
+                    else:
+                        self.out_var_op_deps[name].extend(
+                            [op.desc.original_id()]
+                        )
+
                 self._mark_op(op)
 
         # set forward tensor dtype
@@ -192,6 +201,18 @@ def _mark_op(self, op):
             if op.type == "assign" and "array_" in op.input_arg_names[0]:
                 self._op_fp16_dict[op.desc.original_id()] = False
                 return
+            # If assign op is inplace-operation, assign op exec mode should be same with the created op of output_var.
+            if op.type == "assign":
+                out_name = op.output_arg_names[0]
+                if len(self.out_var_op_deps[out_name]) > 1:
+                    if not self._op_fp16_dict[
+                        self.out_var_op_deps[out_name][0]
+                    ]:
+                        self._op_fp16_dict[op.desc.original_id()] = False
+                    else:
+                        self._op_fp16_dict[op.desc.original_id()] = True
+                    return
+
             if _need_keep_fp32(
                 op, self.amp_list.unsupported_list, self.use_fp16_guard
             ):

python/paddle/fluid/tests/unittests/auto_parallel/CMakeLists.txt

@@ -115,5 +115,6 @@ if(WITH_DISTRIBUTE AND WITH_GPU)
   py_test_modules(test_conditional_block_reshard MODULES
                   test_conditional_block_reshard)
   py_test_modules(test_engine_api_error MODULES test_engine_api_error)
+  py_test_modules(test_fp16_assign MODULES test_fp16_assign)
 
 endif()

python/paddle/fluid/tests/unittests/auto_parallel/test_fp16_assign.py

@@ -0,0 +1,145 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import unittest
+import copy
+
+import paddle
+from paddle.distributed.fleet import auto
+from paddle.distributed.passes import new_pass
+
+paddle.enable_static()
+
+
+def make_program():
+    main_program = paddle.fluid.Program()
+    start_program = paddle.fluid.Program()
+    with paddle.static.program_guard(main_program, start_program):
+        x = paddle.static.data(name='x', shape=[4, 6, 8], dtype='float32')
+        y = paddle.static.data(name='y', shape=[4, 6, 6], dtype='float32')
+        z = paddle.static.data(name='y', shape=[4, 6, 6], dtype='float32')
+
+        auto.shard_tensor(x, auto.ProcessMesh([0], ['d0']), [None, None, None])
+
+        out0 = paddle.static.nn.fc(
+            x,
+            size=6,
+            num_flatten_dims=2,
+            weight_attr=paddle.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(value=0.5)
+            ),
+            bias_attr=paddle.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(value=1.0)
+            ),
+        )
+        where_0 = paddle.where(y > 1, y, out0)
+
+        out1 = paddle.static.nn.fc(
+            out0,
+            size=6,
+            num_flatten_dims=2,
+            weight_attr=paddle.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(value=0.5)
+            ),
+            bias_attr=paddle.ParamAttr(
+                initializer=paddle.nn.initializer.Constant(value=1.0)
+            ),
+        )
+        where_1 = paddle.where(y > 1, y, out1)
+
+        paddle.fluid.layers.assign(where_1, where_0)
+
+    return main_program, start_program
+
+
+def parallelizer(program_func, rank):
+    from paddle.distributed.auto_parallel.completion import Completer
+    from paddle.distributed.auto_parallel.partitioner import Partitioner
+    from paddle.distributed.auto_parallel.dist_context import DistributedContext
+
+    main_program, start_program = program_func()
+
+    dist_context = DistributedContext()
+    completer = Completer(dist_context)
+    completer.complete_forward_annotation(main_program)
+    dist_context.block_state.parse_forward_blocks(main_program)
+
+    strategy = auto.Strategy()
+    amp = strategy.amp
+    amp.enable = True
+    amp.use_pure_fp16 = True
+    amp.init_loss_scaling = 32768
+    amp.use_fp16_guard = False
+    amp.custom_black_list = ['where']
+
+    config = copy.deepcopy(strategy.amp.to_dict())
+    config["dist_context"] = dist_context
+    config["params_grads"] = []
+    config["loss"] = None
+    config["base_opt"] = None
+    auto_parallel_fp16_pass = new_pass("auto_parallel_fp16", config)
+    auto_parallel_fp16_pass.apply([main_program], [start_program], None)
+
+    partitioner = Partitioner(dist_context, rank)
+    dist_main_prog, _, _ = partitioner.partition(
+        main_program, start_program, []
+    )
+
+    return dist_main_prog, dist_context
+
+
+class TestFp16Assign(unittest.TestCase):
+    def assert_fp32_dtype(self, block, op):
+        for slot in op.input_names:
+            for name in op.input(slot):
+                if block.vars[name].dtype == paddle.bool:
+                    continue
+                assert block.vars[name].dtype == paddle.float32
+        for slot in op.output_names:
+            for name in op.output(slot):
+                if block.vars[name].dtype == paddle.bool:
+                    continue
+                assert block.vars[name].dtype == paddle.float32
+
+    def assert_fp16_dtype(self, block, op):
+        for slot in op.input_names:
+            if slot == "Condition":
+                continue
+            for name in op.input(slot):
+                if block.vars[name].dtype == paddle.bool:
+                    continue
+                assert block.vars[name].dtype == paddle.float16
+        for slot in op.output_names:
+            for name in op.output(slot):
+                if block.vars[name].dtype == paddle.bool:
+                    continue
+                assert block.vars[name].dtype == paddle.float16
+
+    def test_fp16_assign(self):
+
+        dist_main_prog, dist_context = parallelizer(make_program, 0)
+        block = dist_main_prog.global_block()
+        for op in block.ops:
+            if op.type == "cast":
+                continue
+            if op.type == "where":
+                self.assert_fp32_dtype(block, op)
+            elif op.type == "assign":
+                self.assert_fp32_dtype(block, op)
+            else:
+                self.assert_fp16_dtype(block, op)
+
+
+if __name__ == "__main__":
+    unittest.main()