[cherry-pick] Mechanism that converts startup_program initializers to BF16 (#32720) (#32764)

* Add casting initializers for bf16 training

* Changes after review

* Correct test and add comment
Co-authored-by: Njoanna.wozna.intel <joanna.wozna@intel.com>

python/paddle/fluid/contrib/mixed_precision/bf16/amp_lists.py

@@ -49,6 +49,7 @@ class AutoMixedPrecisionListsBF16(object):
         self.bf16_list = copy.copy(bf16_list)
         self.fp32_list = copy.copy(fp32_list)
         self.gray_list = copy.copy(gray_list)
+        self.bf16_initializer_list = copy.copy(bf16_initializer_list)
         self.unsupported_list = copy.copy(unsupported_list)
         self.fp32_varnames = copy.copy(custom_fp32_varnames)
         self._update_list()
@@ -79,6 +80,8 @@ class AutoMixedPrecisionListsBF16(object):
                 self.unsupported_list.add(op_name)
 
 
+bf16_initializer_list = {'fill_constant', 'uniform_random'}
+
 # always bf16
 bf16_list = {'elementwise_add', }
 

python/paddle/fluid/contrib/mixed_precision/bf16/amp_utils.py

@@ -232,7 +232,52 @@ def bf16_guard():
         yield
 
 
-def cast_model_to_bf16(program, amp_lists=None, use_bf16_guard=True):
+def are_post_ops_bf16(post_ops, keep_fp32_ops):
+    for post_op in post_ops:
+        for op in post_op:
+            if op.type in keep_fp32_ops:
+                return False
+    return True
+
+
+def cast_initializers_to_bf16(startup_prog,
+                              amp_lists,
+                              block,
+                              all_ops,
+                              keep_fp32_ops,
+                              to_bf16_var_names=None):
+    prepend_ops = startup_prog.global_block().ops
+    for op in prepend_ops:
+        if str(op.type) in amp_lists.bf16_initializer_list:
+            change_op = True
+            op_post_ops = []
+            op_out_vars = []
+            for out_name in op.output_names:
+                for out_var_name in op.output(out_name):
+                    out_var = block.var(out_var_name)
+                    post_op = find_true_post_op(all_ops, op, out_var_name, True)
+
+                    if out_var is None or out_var.type not in _valid_types:
+                        change_op = False
+                        break
+                    op_post_ops.append(post_op)
+                    op_out_vars.append(out_var)
+
+            if change_op and are_post_ops_bf16(op_post_ops, keep_fp32_ops):
+                for out_var in op_out_vars:
+                    if out_var.dtype == core.VarDesc.VarType.FP32:
+                        out_var.desc.set_dtype(core.VarDesc.VarType.BF16)
+                    if to_bf16_var_names is not None and out_var.name in to_bf16_var_names:
+                        to_bf16_var_names.remove(out_var.name)
+                if op.has_attr('dtype') and op.attr(
+                        'dtype') == core.VarDesc.VarType.FP32:
+                    op._set_attr('dtype', core.VarDesc.VarType.BF16)
+
+
+def cast_model_to_bf16(program,
+                       startup_prog=None,
+                       amp_lists=None,
+                       use_bf16_guard=True):
     """
     Traverse all ops in the whole model and set their inputs and outputs
     to the bf16 data type. This function will do some special processing for
@@ -329,6 +374,10 @@ def cast_model_to_bf16(program, amp_lists=None, use_bf16_guard=True):
             if op.has_attr('mkldnn_data_type'):
                 op._set_attr('mkldnn_data_type', 'bfloat16')
 
+        if startup_prog is not None:
+            cast_initializers_to_bf16(startup_prog, amp_lists, global_block,
+                                      ops, keep_fp32_ops, to_bf16_var_names)
+
     # process ops in keep_fp32_ops
     op_var_rename_map = [
         collections.OrderedDict() for _ in range(len(program.blocks))

python/paddle/fluid/contrib/mixed_precision/bf16/decorator.py

@@ -94,7 +94,8 @@ class OptimizerWithMixedPrecision(object):
 
             if self._use_pure_bf16:
                 self._to_bf16_var_names = cast_model_to_bf16(
-                    self._train_program, self._amp_lists, self._use_bf16_guard)
+                    self._train_program, startup_program, self._amp_lists,
+                    self._use_bf16_guard)
             else:
                 rewrite_program_bf16(self._train_program, self._amp_lists)
 
@@ -168,10 +169,12 @@ class OptimizerWithMixedPrecision(object):
                                     self._to_bf16_var_names)
         if test_program is not None:
             if self._use_pure_bf16:
-                cast_model_to_bf16(test_program, self._amp_lists,
-                                   self._use_bf16_guard)
+                cast_model_to_bf16(
+                    test_program,
+                    amp_lists=self._amp_lists,
+                    use_bf16_guard=self._use_bf16_guard)
             elif use_bf16_test:
-                rewrite_program_bf16(test_program, self._amp_lists)
+                rewrite_program_bf16(test_program, amp_lists=self._amp_lists)
 
     def apply_gradients(self, params_grads):
         """

python/paddle/fluid/contrib/mixed_precision/fp16_utils.py

@@ -157,7 +157,8 @@ def _insert_cast_post_op(block, op, idx, src_dtype, dest_dtype, target_name,
         return num_cast_ops
 
     assert target_var.dtype == src_dtype, \
-           "The real dtype({}) is not equal to the src dtype({})".format(_dtype_to_str(target_var.dtype), _dtype_to_str(src_dtype))
+        "The real dtype({}) is not equal to the src dtype({})".format(
+            _dtype_to_str(target_var.dtype), _dtype_to_str(src_dtype))
 
     cast_name = target_var.name + '.cast_' + _dtype_to_str(dest_dtype)
     cast_var = block.vars.get(cast_name)
@@ -209,7 +210,7 @@ def find_true_prev_op(ops, cur_op, var_name):
     return None
 
 
-def find_true_post_op(ops, cur_op, var_name):
+def find_true_post_op(ops, cur_op, var_name, search_all=False):
     """
     if there are post ops, return them, if there is no post op,
     return None instead.
@@ -217,8 +218,19 @@ def find_true_post_op(ops, cur_op, var_name):
         ops (list): A list of ops.
         cur_op (Operator): Current operator which has var_name variable.
         var_name (string): Variable name.
+        search_all (bool): The type of operator search. Use if \"cur_op\" is not in the \"ops\" set. 
     """
     post_op = []
+    if search_all:
+        """
+        \"cur_op\" do not have to be in list of \"ops\". E.g. \"cur_op\" can come 
+        from startup_prog block and \"ops\" list from main_prog block. 
+        By setting idx to -1, we'll start looking for post-ops from the top of the list. 
+        If search_all is False, assume that \"cur_op\" is in \"ops\" list, 
+        so to reduce the time of search we can start iterating from \"cur_op\" idx. 
+        """
+        idx = -1
+    else:
         for idx, op in enumerate(ops):
             if op == cur_op:
                 break
@@ -612,7 +624,7 @@ def update_role_var_grad(main_prog, params_grads):
                 raise ValueError("The cast op {0}'s output should not be"
                                  "used by a non-optimize op, however, it"
                                  "is used by {1}".format(op, post_ops[0]))
-            #add new op in the python and cpp at the same time 
+            # add new op in the python and cpp at the same time
             new_op_desc = block.desc.append_op()
             new_op_desc.copy_from(op.desc)
             new_op = framework.Operator(

python/paddle/fluid/contrib/tests/test_bf16_utils.py

@@ -139,6 +139,29 @@ class AMPTest2(unittest.TestCase):
         res = amp.bf16.amp_utils.find_true_post_op(block.ops, op1, "Y")
         assert (res == [op2])
 
+    def test_find_true_post_op_with_search_all(self):
+        program = fluid.Program()
+        block = program.current_block()
+        startup_block = fluid.default_startup_program().global_block()
+
+        var1 = block.create_var(name="X", shape=[3], dtype='float32')
+        var2 = block.create_var(name="Y", shape=[3], dtype='float32')
+        inititializer_op = startup_block._prepend_op(
+            type="fill_constant",
+            outputs={"Out": var1},
+            attrs={"shape": var1.shape,
+                   "dtype": var1.dtype,
+                   "value": 1.0})
+
+        op1 = block.append_op(
+            type="abs", inputs={"X": [var1]}, outputs={"Out": [var2]})
+        result = amp.bf16.amp_utils.find_true_post_op(
+            block.ops, inititializer_op, "X", search_all=False)
+        assert (len(result) == 0)
+        result = amp.bf16.amp_utils.find_true_post_op(
+            block.ops, inititializer_op, "X", search_all=True)
+        assert (result == [op1])
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/contrib/tests/test_model_cast_to_bf16.py

@@ -53,19 +53,27 @@ class TestModelCastBF16(unittest.TestCase):
             with fluid.program_guard(prog, startup_prog):
                 yield
 
-    def get_static_graph_result(self, feed, fetch_list, amp_fun,
-                                with_lod=False):
+    def get_static_graph_result(self,
+                                feed,
+                                fetch_list,
+                                amp_fun,
+                                with_lod=False,
+                                startup_prog=None):
         exe = fluid.Executor(core.CPUPlace())
-        exe.run(fluid.default_startup_program())
+        exe.run(fluid.default_startup_program()
+                if startup_prog is None else startup_prog)
         prog = fluid.default_main_program()
         if amp_fun is not None:
+            if startup_prog is not None:
+                amp_fun(prog, startup_prog)
+            else:
                 amp_fun(prog)
         return exe.run(prog,
                        feed=feed,
                        fetch_list=fetch_list,
                        return_numpy=(not with_lod))
 
-    def _graph_common(self, _amp_fun):
+    def _graph_common(self, _amp_fun, startup_prog=None):
         size = 3
         n = np.ones([size, size], dtype='float32') * 3.2
         nn = np.ones([size, size], dtype='float32') * -2.7
@@ -122,7 +130,8 @@ class TestModelCastBF16(unittest.TestCase):
                 self.get_static_graph_result(
                     feed={'t': n, 'tt': nn},
                     fetch_list=[ret],
-                    amp_fun=_amp_fun
+                    amp_fun=_amp_fun,
+                    startup_prog=startup_prog
                 )
         self.assertTrue(
             static_ret_bf16, np.ones(
@@ -132,16 +141,17 @@ class TestModelCastBF16(unittest.TestCase):
         self._graph_common(lambda prog: amp.bf16.rewrite_program_bf16(
             prog,
             amp.bf16.AutoMixedPrecisionListsBF16(
-                custom_fp32_varnames={'elementwise_add_0.tmp_0'}),
+                custom_fp32_varnames={'elementwise_add_0.tmp_0'})
         ))
 
     def test_graph_cast(self):
-        self._graph_common(lambda prog: amp.bf16.cast_model_to_bf16(
+        self._graph_common(lambda prog, startup_prog: amp.bf16.cast_model_to_bf16(
             prog,
+            startup_prog,
             amp.bf16.AutoMixedPrecisionListsBF16(
                 custom_fp32_list={'elementwise_mul'}),
             use_bf16_guard=True
-        ))
+        ), startup_prog=fluid.default_startup_program())
 
 
 if __name__ == '__main__':

python/paddle/fluid/layers/tensor.py

@@ -231,13 +231,13 @@ def cast(x, dtype):
         out = core.ops.cast(x, 'in_dtype', x.dtype, 'out_dtype', dtype)
         return out
 
-    check_variable_and_dtype(
-        x, 'x',
-        ['bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'uint8'],
-        'cast')
+    check_variable_and_dtype(x, 'x', [
+        'bool', 'float16', 'float32', 'float64', 'int32', 'int64', 'uint8',
+        'uint16'
+    ], 'cast')
     check_dtype(dtype, 'dtype', [
         'bool', 'float16', 'float32', 'float64', 'int8', 'int32', 'int64',
-        'uint8'
+        'uint8', 'uint16'
     ], 'cast')
 
     helper = LayerHelper('cast', **locals())

python/paddle/fluid/tests/book/test_fit_a_line.py

@@ -56,7 +56,8 @@ def train(use_cuda, save_dirname, is_local, use_bf16, pure_bf16):
             amp_lists=amp.bf16.AutoMixedPrecisionListsBF16(),
             use_bf16_guard=False,
             use_pure_bf16=pure_bf16)
-    sgd_optimizer.minimize(avg_cost)
+    sgd_optimizer.minimize(
+        avg_cost, startup_program=fluid.default_startup_program())
 
     BATCH_SIZE = 20
 

python/paddle/fluid/tests/book/test_word2vec_book.py

@@ -115,7 +115,7 @@ def train(target,
             use_bf16_guard=False,
             use_pure_bf16=pure_bf16)
 
-    sgd_optimizer.minimize(avg_cost)
+    sgd_optimizer.minimize(avg_cost, fluid.default_startup_program())
 
     train_reader = paddle.batch(
         paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)