Update BF16 amp list

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

@@ -83,15 +83,18 @@ def _update_list(self):
 bf16_initializer_list = {'fill_constant', 'uniform_random'}
 
 # always bf16
-bf16_list = {'elementwise_add', 'mul'}
+bf16_list = {
+    'conv2d',
+    'matmul',
+    'matmul_v2',
+    'mul',
+}
 
 # depends on the prev_op type
 gray_list = {
-    'cast',
-    'fill_constant',
-    'reduce_mean',
-    'reshape2',
-    'scale',
+    'elementwise_add', 'elementwise_sub', 'elementwise_mul', 'elementwise_div',
+    'relu', 'layer_norm', 'slice', 'concat', 'uniform_random', 'reshape2',
+    'transpose2', 'pool2d', 'sigmoid', 'cast', 'scale', 'fill_constant', 'split'
 }
 
 _, _, _sys_unsupported_bf16_list = core.op_supported_infos(

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

@@ -57,20 +57,20 @@ def test_amp_lists_3(self):
         self.amp_lists_ = amp.bf16.AutoMixedPrecisionListsBF16({'lstm'})
 
     def test_amp_lists_4(self):
-        # 4. w=None, b={'elementwise_add'}
-        self.bf16_list.remove('elementwise_add')
-        self.fp32_list.add('elementwise_add')
+        # 4. w=None, b={'matmul_v2'}
+        self.bf16_list.remove('matmul_v2')
+        self.fp32_list.add('matmul_v2')
 
         self.amp_lists_ = amp.bf16.AutoMixedPrecisionListsBF16(
-            custom_fp32_list={'elementwise_add'})
+            custom_fp32_list={'matmul_v2'})
 
     def test_amp_lists_5(self):
-        # 5. w=None, b={'elementwise_add'}
-        self.fp32_list.add('elementwise_add')
-        self.bf16_list.remove('elementwise_add')
+        # 5. w=None, b={'matmul_v2'}
+        self.fp32_list.add('matmul_v2')
+        self.bf16_list.remove('matmul_v2')
 
         self.amp_lists_ = amp.bf16.AutoMixedPrecisionListsBF16(
-            custom_fp32_list={'elementwise_add'})
+            custom_fp32_list={'matmul_v2'})
 
     def test_amp_lists_6(self):
         # 6. w=None, b={'lstm'}

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

@@ -19,13 +19,28 @@
 import contextlib
 import unittest
 import numpy as np
+import struct
 import paddle.fluid.layers as layers
 import paddle.static.amp as amp
 from paddle.fluid import core
 
 paddle.enable_static()
 
 
+def convert_uint16_to_float(in_list):
+    if in_list.dtype == np.uint16:
+        in_list = np.asarray(in_list)
+        out = np.vectorize(
+            lambda x: struct.unpack('<f', struct.pack('<I', x << 16))[0],
+            otypes=[np.float32])(in_list.flat)
+        return np.reshape(out, in_list.shape)
+    else:
+        return in_list
+
+
+cutf = convert_uint16_to_float
+
+
 @unittest.skipIf(not core.supports_bfloat16(),
                  "place does not support BF16 evaluation")
 class TestModelCastBF16(unittest.TestCase):
@@ -111,10 +126,13 @@ def _graph_common(self, _amp_fun, startup_prog=None):
                     'tt_bf16': nn_bf16,
                 },
                 fetch_list=[ret_bf16, ret, ret_fp32bf16],
-                amp_fun=lambda prog: amp.bf16.rewrite_program_bf16(prog))
+                amp_fun=_amp_fun,
+                startup_prog=startup_prog)
 
-        self.assertTrue(np.allclose(static_ret_bf16, static_ret, 1e-2))
-        self.assertTrue(np.allclose(static_ret_bf16, ret_fp32bf16, 1e-2))
+        self.assertTrue(
+            np.allclose(cutf(static_ret_bf16), cutf(static_ret), 1e-2))
+        self.assertTrue(
+            np.allclose(cutf(static_ret_bf16), cutf(ret_fp32bf16), 1e-2))
 
         with self.static_graph():
             t = layers.data(name='t', shape=[size, size], dtype='float32')
@@ -141,6 +159,7 @@ def test_graph_rewrite(self):
         self._graph_common(lambda prog: amp.bf16.rewrite_program_bf16(
             prog,
             amp.bf16.AutoMixedPrecisionListsBF16(
+                custom_bf16_list={'elementwise_add'},
                 custom_fp32_varnames={'elementwise_add_0.tmp_0'})
         ))
 
@@ -149,6 +168,7 @@ def test_graph_cast(self):
             prog,
             startup_prog,
             amp.bf16.AutoMixedPrecisionListsBF16(
+                custom_bf16_list={'elementwise_add'},
                 custom_fp32_list={'elementwise_mul'}),
             use_bf16_guard=True
         ), startup_prog=fluid.default_startup_program())