Layer norm fp16 (#29169)

* add fp16 for layer_norm op

* revert layernorm api

* fix forward

* fix forward

* fix backward for layernorm with fp16

* fix unit test for layernorm with fp16

* fix with_mkldnn compile error for layernorm with fp16

* 1. revert to PADDLE_ENFORCE_NOT_NULL, 2. change static_cast<float> to static_cast<U>

* fix with_mkldnn compile error for layernorm with fp16

* fix with_mkldnn compile error for layernorm with fp16
Co-authored-by: Nzhiqiu <chenqiuliang@baidu.com>

paddle/fluid/operators/layer_norm_op.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include "paddle/fluid/operators/layer_norm_op.h"
 #include <memory>
+#include <string>
 
 #ifdef PADDLE_WITH_MKLDNN
 #include "paddle/fluid/platform/mkldnn_helper.h"
@@ -98,7 +99,26 @@ class LayerNormOp : public framework::OperatorWithKernel {
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext &ctx) const {
+      const framework::ExecutionContext &ctx) const override {
+    auto input_data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
+    // By default, the type of the scale, bias, mean,
+    // and var tensors should both be float. (For float or float16 input tensor)
+    // or double (For double input tensor).
+    auto ln_param_type = framework::proto::VarType::FP32;
+    if (input_data_type == framework::proto::VarType::FP64) {
+      ln_param_type = framework::proto::VarType::FP64;
+    }
+    if (ctx.HasInput("Scale")) {
+      PADDLE_ENFORCE_EQ(ln_param_type, ctx.Input<Tensor>("Scale")->type(),
+                        platform::errors::InvalidArgument(
+                            "Scale input should be of float type"));
+    }
+    if (ctx.HasInput("Bias")) {
+      PADDLE_ENFORCE_EQ(ln_param_type, ctx.Input<Tensor>("Bias")->type(),
+                        platform::errors::InvalidArgument(
+                            "Bias input should be of float type"));
+    }
+
     framework::LibraryType library = framework::LibraryType::kPlain;
     framework::DataLayout layout = framework::DataLayout::kAnyLayout;
 
@@ -110,9 +130,8 @@ class LayerNormOp : public framework::OperatorWithKernel {
     }
 #endif
 
-    return framework::OpKernelType(
-        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace(),
-        layout, library);
+    return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
+                                   library);
   }
 };
 
@@ -224,7 +243,13 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
     }
     PADDLE_ENFORCE_NOT_NULL(
         t, platform::errors::NotFound("Y@GRAD of LayerNorm Op is not found."));
-    return framework::OpKernelType(t->type(), ctx.GetPlace());
+
+    framework::LibraryType library = framework::LibraryType::kPlain;
+    framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace(),
+        layout, library);
   }
 };
 

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

@@ -109,9 +109,11 @@ gray_list = {
     'elementwise_mod',
     'elementwise_floordiv',
     'batch_norm',
+    'layer_norm',
     'tanh',
     'sigmoid',
     'lookup_table',
+    'lookup_table_v2',
     'top_k',
     'pool2d',
     'pool3d',
@@ -123,6 +125,7 @@ gray_list = {
     'flatten2',
     'stack',
     'unstack',
+    'uniform_random',
     'uniform_random_batch_size_like',
     'gaussian_random',
     'gaussian_random_batch_size_like',
@@ -192,7 +195,6 @@ unsupported_fp16_list = {
     'sequence_concat',
     'sequence_slice',
     'data_norm',
-    'layer_norm',
     'group_norm',
     'spectral_norm',
     'depthwise_conv2d_transpose',

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

@@ -70,7 +70,7 @@ def _insert_cast_op(block, op, idx, src_dtype, dest_dtype):
 
     for in_name in op.input_names:
         if src_dtype == core.VarDesc.VarType.FP32 and op.type in [
-                'batch_norm', 'fused_bn_add_activation'
+                'batch_norm', 'fused_bn_add_activation', 'layer_norm'
         ]:
             if in_name not in {'X', 'Z'}:
                 continue
@@ -104,7 +104,8 @@ def _insert_cast_op(block, op, idx, src_dtype, dest_dtype):
                     op._set_attr('in_dtype', dest_dtype)
     if src_dtype == core.VarDesc.VarType.FP32 and dest_dtype == core.VarDesc.VarType.FP16:
         for out_name in op.output_names:
-            if op.type in ['batch_norm', 'fused_bn_add_activation'
+            if op.type in [
+                    'batch_norm', 'fused_bn_add_activation', 'layer_norm'
             ] and out_name != 'Y':
                 continue
             for out_var_name in op.output(out_name):

python/paddle/fluid/tests/unittests/test_layer_norm_op.py

@@ -15,6 +15,7 @@
 from __future__ import print_function
 import unittest
 import numpy as np
+import paddle
 
 from operator import mul
 import paddle.fluid.core as core
@@ -210,7 +211,7 @@ class TestLayerNormOp(unittest.TestCase):
                                   for name in ['x', 'scale', 'bias', 'y@GRAD']
                               },
                               fetch_list=fetch_list)
-                self.__assert_close(y, out[0], "y")
+                self.__assert_close(y, out[0], "y", 1e-3)
                 self.__assert_close(mean, out[1], "mean")
                 self.__assert_close(variance, out[2], "variance", 1e-3)
                 self.__assert_close(x_grad, out[3], "x_grad")
@@ -310,6 +311,8 @@ class TestLayerNormAPI(unittest.TestCase):
 class TestDygraphLayerNormAPIError(unittest.TestCase):
     def test_errors(self):
         with program_guard(Program(), Program()):
+            paddle.enable_static()
+
             layer_norm = fluid.LayerNorm([32, 32])
             # the input of LayerNorm must be Variable.
             x1 = np.random.random((3, 32, 32)).astype('float32')

python/paddle/nn/functional/norm.py

@@ -293,7 +293,8 @@ def layer_norm(x,
                                             'begin_norm_axis', begin_norm_axis)
         return dygraph_utils._append_activation_in_dygraph(pre_act, act=None)
 
-    check_variable_and_dtype(x, 'input', ['float32', 'float64'], 'LayerNorm')
+    check_variable_and_dtype(x, 'input', ['float16', 'float32', 'float64'],
+                             'LayerNorm')
 
     inputs = dict()
     inputs['X'] = [x]
@@ -305,11 +306,13 @@ def layer_norm(x,
 
     # create output
     helper = LayerHelper('layer_norm', **locals())
+
+    dtype = x.dtype
     mean_out = helper.create_variable_for_type_inference(
-        dtype=x.dtype, stop_gradient=True)
+        dtype=dtype, stop_gradient=True)
     variance_out = helper.create_variable_for_type_inference(
-        dtype=x.dtype, stop_gradient=True)
-    layer_norm_out = helper.create_variable_for_type_inference(x.dtype)
+        dtype=dtype, stop_gradient=True)
+    layer_norm_out = helper.create_variable_for_type_inference(dtype)
 
     helper.append_op(
         type="layer_norm",