Support float64 for TruncatedNormal and Assign

paddle/fluid/operators/assign_value_op.h

@@ -110,6 +110,9 @@ class AssignValueKernel : public framework::OpKernel<T> {
       case framework::proto::VarType::FP32:
         value_name = "fp32_values";
         break;
+      case framework::proto::VarType::FP64:
+        value_name = "fp64_values";
+        break;
       case framework::proto::VarType::INT64:
         value_name = "int64_values";
       case framework::proto::VarType::INT8:
@@ -118,7 +121,7 @@ class AssignValueKernel : public framework::OpKernel<T> {
       default:
         PADDLE_THROW(platform::errors::Unimplemented(
             "Unsupported data type(code %d) for AssignValue operator, only "
-            "supports bool, int32, float32, int8 and int64.",
+            "supports bool, int32, float32, float64, int8 and int64.",
             dtype));
         break;
     }

paddle/phi/api/yaml/static_ops.yaml

@@ -90,7 +90,7 @@
   backward : assign_grad
 
 - op : assign_value
-  args : (int[] shape, DataType dtype, int[] bool_values = {}, float[] fp32_values = {}, int[] int32_values = {}, int64_t[] int64_values = {})
+  args : (int[] shape, DataType dtype, int[] bool_values = {}, float[] fp32_values = {}, double[] fp64_values = {}, int[] int32_values = {}, int64_t[] int64_values = {})
   output : Tensor(out)
   infer_meta :
     func : AssignValueInferMeta

paddle/phi/kernels/assign_kernel.cc

@@ -132,6 +132,7 @@ PD_REGISTER_KERNEL(assign_value,
                    bool,
                    int,
                    float,
+                   double,
                    int8_t,
                    int64_t) {}
 
@@ -159,6 +160,7 @@ PD_REGISTER_KERNEL(assign_value,
                    bool,
                    int,
                    float,
+                   double,
                    int8_t,
                    int64_t) {}
 #endif

paddle/phi/kernels/cpu/truncated_gaussian_random_kernel.cc

@@ -59,4 +59,5 @@ PD_REGISTER_KERNEL(truncated_gaussian_random,
                    CPU,
                    ALL_LAYOUT,
                    phi::TruncatedGaussianRandomKernel,
-                   float) {}
+                   float,
+                   double) {}

paddle/phi/kernels/gpu/truncated_gaussian_random_kernel.cu

@@ -124,4 +124,5 @@ PD_REGISTER_KERNEL(truncated_gaussian_random,
                    GPU,
                    ALL_LAYOUT,
                    phi::TruncatedGaussianRandomKernel,
-                   float) {}
+                   float,
+                   double) {}

paddle/phi/ops/compat/assign_value_sig.cc

@@ -33,6 +33,9 @@ KernelSignature AssignValueOpArgumentMapping(
   } else if (dtype == /*FP32*/ 5) {
     return KernelSignature(
         "assign_value", {}, {"shape", "dtype", "fp32_values"}, {"Out"});
+  } else if (dtype == /*FP64*/ 6) {
+    return KernelSignature(
+        "assign_value", {}, {"shape", "dtype", "fp64_values"}, {"Out"});
   } else if (dtype == /*INT64*/ 3) {
     return KernelSignature(
         "assign_value", {}, {"shape", "dtype", "int64_values"}, {"Out"});

python/paddle/nn/initializer/assign.py

@@ -78,6 +78,9 @@ def forward(self, var, block=None):
         if out_dtype == core.VarDesc.VarType.FP32:
             value_name = "fp32_values"
             values = [float(v) for v in np_value.flat]
+        elif out_dtype == core.VarDesc.VarType.FP64:
+            value_name = "fp64_values"
+            values = [float(v) for v in np_value.flat]
         elif out_dtype == core.VarDesc.VarType.INT32:
             value_name = "int32_values"
             values = [int(v) for v in np_value.flat]

test/legacy_test/test_initializer.py

@@ -16,6 +16,8 @@
 import unittest
 
 import numpy as np
+from scipy import special
+from utils import dygraph_guard, static_guard
 
 import paddle
 from paddle import base
@@ -796,7 +798,7 @@ def test_order(self):
         paddle.set_device('cpu')
         SEED = 123
         weight_attr = paddle.framework.ParamAttr(
-            name="linear_weight",
+            name="linear_weight2",
             learning_rate=1.0,
             trainable=False,
             regularizer=None,
@@ -805,7 +807,7 @@ def test_order(self):
             ),
         )
         bias_attr = paddle.framework.ParamAttr(
-            name="linear_bias",
+            name="linear_bias2",
             learning_rate=1.0,
             trainable=False,
             regularizer=None,
@@ -815,33 +817,126 @@ def test_order(self):
         )
 
         def run_dynamic_graph():
-            paddle.disable_static()
             paddle.seed(SEED)
             linear = paddle.nn.Linear(
-                1, 1, weight_attr=weight_attr, bias_attr=bias_attr
+                1,
+                1,
+                weight_attr=paddle.framework.ParamAttr(
+                    name="linear_weight1",
+                    learning_rate=1.0,
+                    trainable=False,
+                    regularizer=None,
+                    initializer=paddle.nn.initializer.TruncatedNormal(
+                        mean=0.0, std=2.0
+                    ),
+                ),
+                bias_attr=paddle.framework.ParamAttr(
+                    name="linear_bias1",
+                    learning_rate=1.0,
+                    trainable=False,
+                    regularizer=None,
+                    initializer=paddle.nn.initializer.TruncatedNormal(
+                        mean=0.0, std=2.0
+                    ),
+                ),
             )
             return linear.weight.numpy(), linear.bias.numpy()
-            paddle.enable_static()
 
         def run_static_graph():
-            paddle.enable_static()
             exe = paddle.static.Executor(paddle.CPUPlace())
             paddle.seed(SEED)
             linear = paddle.nn.Linear(
                 1, 1, weight_attr=weight_attr, bias_attr=bias_attr
             )
             res = exe.run(
                 paddle.static.default_startup_program(),
-                fetch_list=['linear_weight', 'linear_bias'],
+                fetch_list=['linear_weight2', 'linear_bias2'],
             )
             return res[0], res[1]
 
-        dynamic_res = run_dynamic_graph()
-        static_res = run_static_graph()
+        with dygraph_guard():
+            dynamic_res = run_dynamic_graph()
+        with static_guard():
+            static_res = run_static_graph()
 
         np.testing.assert_array_equal(dynamic_res[0], static_res[0])
         np.testing.assert_array_equal(dynamic_res[1], static_res[1])
 
+    def test_assign_static_fp32(self):
+        random_value = np.random.randn(128, 128).astype("float32")
+
+        def run_dynamic_graph(dtype):
+            with dygraph_guard():
+                w = paddle.create_parameter(
+                    random_value.shape,
+                    dtype,
+                    default_initializer=paddle.nn.initializer.Assign(
+                        random_value
+                    ),
+                )
+            return w
+
+        def run_static_graph(dtype):
+            with static_guard():
+                exe = paddle.static.Executor(paddle.CPUPlace())
+                w = paddle.create_parameter(
+                    random_value.shape,
+                    dtype,
+                    "w",
+                    default_initializer=paddle.nn.initializer.Assign(
+                        random_value
+                    ),
+                )
+                res = exe.run(
+                    paddle.static.default_startup_program(),
+                    fetch_list=['w'],
+                )
+            return res[0]
+
+        dynamic_res = run_dynamic_graph("float32")
+        static_res = run_static_graph("float32")
+
+        np.testing.assert_array_equal(dynamic_res.numpy(), static_res)
+        np.testing.assert_array_equal(dynamic_res.numpy(), static_res)
+
+    def test_assign_static_fp64(self):
+        random_value = np.random.randn(128, 128).astype("float64")
+
+        def run_dynamic_graph(dtype):
+            with dygraph_guard():
+                w = paddle.create_parameter(
+                    random_value.shape,
+                    dtype,
+                    "www",
+                    default_initializer=paddle.nn.initializer.Assign(
+                        random_value
+                    ),
+                )
+            return w
+
+        def run_static_graph(dtype):
+            with static_guard():
+                exe = paddle.static.Executor(paddle.CPUPlace())
+                w = paddle.create_parameter(
+                    random_value.shape,
+                    dtype,
+                    "ww",
+                    default_initializer=paddle.nn.initializer.Assign(
+                        random_value
+                    ),
+                )
+                res = exe.run(
+                    paddle.static.default_startup_program(),
+                    fetch_list=['ww'],
+                )
+            return res[0]
+
+        dynamic_res = run_dynamic_graph("float64")
+        static_res = run_static_graph("float64")
+
+        np.testing.assert_array_equal(dynamic_res.numpy(), static_res)
+        np.testing.assert_array_equal(dynamic_res.numpy(), static_res)
+
 
 # 2-D Parameter with shape: [10, 15]
 class TestOrthogonalInitializer1(unittest.TestCase):
@@ -1197,6 +1292,133 @@ def test_type_error(self):
         )
 
 
+class TestTruncatedNormalInitializerDygraph(unittest.TestCase):
+    def _trunc_normal_numpy(self, tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
+        # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+        def norm_cdf(x):
+            # Computes standard normal cumulative distribution function
+            return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        _tensor = np.random.uniform(
+            low=2 * l - 1, high=2 * u - 1, size=tensor.shape
+        ).astype(paddle.get_default_dtype())
+        print(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        _tensor = special.erfinv(_tensor)
+
+        # Transform to proper mean, std
+        _tensor = np.multiply(_tensor, std * math.sqrt(2.0))
+        _tensor = np.add(_tensor, mean)
+
+        # Clamp to ensure it"s in the proper range
+        _tensor = np.clip(_tensor, a_min=a, a_max=b)
+        return _tensor
+
+    def test_truncated_normal_initializer_fp32(self):
+        """
+        In dygraph mode, we can use initializer directly to initialize a tensor.
+        """
+        with dygraph_guard():
+            paddle.seed(42)
+            pre_dtype = paddle.get_default_dtype()
+            paddle.set_default_dtype("float32")
+
+            tensor = paddle.zeros([1024, 1024, 8])
+            tensor.stop_gradient = False
+
+            truncated_normal_ = paddle.nn.initializer.TruncatedNormal()
+            truncated_normal_(tensor)
+
+            array = self._trunc_normal_numpy(tensor)
+            np.testing.assert_allclose(
+                array.mean(), tensor.mean().item(), rtol=0.01, atol=0.01
+            )
+            np.testing.assert_allclose(
+                array.std(), tensor.std().item(), rtol=0.01, atol=0.01
+            )
+            paddle.set_default_dtype(pre_dtype)
+
+    def test_truncated_normal_initializer_fp64(self):
+        """
+        In dygraph mode, we can use initializer directly to initialize a tensor.
+        """
+        with dygraph_guard():
+            paddle.seed(42)
+            pre_dtype = paddle.get_default_dtype()
+            paddle.set_default_dtype("float64")
+
+            tensor = paddle.zeros([1024, 1024, 8])
+            tensor.stop_gradient = False
+
+            truncated_normal_ = paddle.nn.initializer.TruncatedNormal()
+            truncated_normal_(tensor)
+
+            array = self._trunc_normal_numpy(tensor)
+            np.testing.assert_allclose(
+                array.mean(), tensor.mean().item(), rtol=0.01, atol=0.01
+            )
+            np.testing.assert_allclose(
+                array.std(), tensor.std().item(), rtol=0.01, atol=0.01
+            )
+            paddle.set_default_dtype(pre_dtype)
+
+
+class TestAssignInitializerDygraph(unittest.TestCase):
+    def test_assign_initializer_fp32(self):
+        """
+        In dygraph mode, we can use initializer directly to initialize a tensor.
+        """
+        with dygraph_guard():
+            pre_dtype = paddle.get_default_dtype()
+            paddle.set_default_dtype("float32")
+
+            tensor = paddle.zeros(
+                [1024, 1024, 8], dtype=paddle.get_default_dtype()
+            )
+            tensor.stop_gradient = False
+            array = np.random.randn(*tensor.shape).astype(
+                paddle.get_default_dtype()
+            )
+
+            assign_ = paddle.nn.initializer.Assign(array)
+            assign_(tensor)
+
+            np.testing.assert_allclose(array, tensor, rtol=1e-6, atol=1e-6)
+            paddle.set_default_dtype(pre_dtype)
+
+    def test_assign_initializer_fp64(self):
+        """
+        In dygraph mode, we can use initializer directly to initialize a tensor.
+        """
+        with dygraph_guard():
+            pre_dtype = paddle.get_default_dtype()
+            paddle.set_default_dtype("float64")
+
+            tensor = paddle.zeros(
+                [1024, 1024, 8], dtype=paddle.get_default_dtype()
+            )
+            tensor.stop_gradient = False
+            array = np.random.randn(*tensor.shape).astype(
+                paddle.get_default_dtype()
+            )
+
+            assign_ = paddle.nn.initializer.Assign(array)
+            assign_(tensor)
+
+            np.testing.assert_allclose(array, tensor, rtol=1e-6, atol=1e-6)
+            paddle.set_default_dtype(pre_dtype)
+
+
 if __name__ == '__main__':
     paddle.enable_static()
     unittest.main()

test/legacy_test/test_initializer_nn.py

@@ -15,6 +15,7 @@
 import unittest
 
 import numpy as np
+from utils import static_guard
 
 import paddle
 from paddle import base, nn
@@ -490,6 +491,12 @@ def test_truncated_normal_initializer_bf16(self):
         block = self.test_truncated_normal_initializer("uint16")  # bfloat16
         self.assertTrue(check_cast_op(block.ops[1]))
 
+    def test_truncated_normal_initializer_fp64(self):
+        """Test truncated normal initializer with float64"""
+        with static_guard():
+            # Only test whether float64 data can be generated without error
+            _ = self.test_truncated_normal_initializer("float64")  # float64
+
     def test_truncated_normal_initializer_dygraph(self):
         """Test truncated normal initializer in dygraph model."""
         paddle.disable_static()