refine dtype use

slm/model_zoo/bert/static/run_glue.py

@@ -159,7 +159,7 @@ def reset_program_state_dict(args, model, state_dict, pretrained_state_dict):
             reset_parameter_names.append(n)
         else:
             dtype_str = "float32"
-            if str(p.dtype) == "VarType.FP64":
+            if p.dtype == paddle.float64:
                 dtype_str = "float64"
             reset_state_dict[p.name] = np.random.normal(loc=0.0, scale=scale, size=p.shape).astype(dtype_str)
     logger.info("the following parameter had reset, please check. {}".format(reset_parameter_names))

slm/model_zoo/bert/static/run_glue_with_sparaity.py

@@ -159,7 +159,7 @@ def reset_program_state_dict(args, model, state_dict, pretrained_state_dict):
             reset_parameter_names.append(n)
         else:
             dtype_str = "float32"
-            if str(p.dtype) == "VarType.FP64":
+            if p.dtype == paddle.float64:
                 dtype_str = "float64"
             reset_state_dict[p.name] = np.random.normal(loc=0.0, scale=scale, size=p.shape).astype(dtype_str)
     logger.info("the following parameter had reset, please check. {}".format(reset_parameter_names))

slm/model_zoo/bert/static/run_pretrain.py

@@ -151,7 +151,7 @@ def reset_program_state_dict(model, state_dict):
     for n, p in state_dict.items():
         if "layer_norm" not in p.name:
             dtype_str = "float32"
-            if str(p.dtype) == "VarType.FP64":
+            if p.dtype == paddle.float64:
                 dtype_str = "float64"
             new_state_dict[p.name] = np.random.normal(loc=0.0, scale=scale, size=p.shape).astype(dtype_str)
     return new_state_dict

slm/model_zoo/gpt-3/ppfleetx/optims/optimizer.py

@@ -98,7 +98,7 @@ def __init__(self, learning_rate, parameters, grad_clip, **config):
     def _add_moments_pows(self, p):
         acc_dtype = p.dtype
         if self._is_dtype_fp16_or_bf16(acc_dtype):
-            acc_dtype = core.VarDesc.VarType.FP32
+            acc_dtype = paddle.float32
         self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype, device="cpu")
         self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype, device="cpu")
         self._add_accumulator(

slm/model_zoo/moe/dygraph/framework/group_sharded.py

@@ -37,7 +37,6 @@
 from paddle.distributed.fleet.meta_parallel.sharding.group_sharded_stage2 import (
     GroupShardedStage2,
 )
-from paddle.framework import core
 from paddle.incubate.distributed.models.moe.grad_clip import ClipGradForMOEByGlobalNorm
 from paddle.optimizer import Optimizer
 
@@ -99,7 +98,7 @@ def _dygraph_clip(self, params_grads):
                 params_and_grads.append((p, g))
                 continue
             # TODO(wangxi): use inplace elementwise_mul
-            clip_input = clip_var.astype("float16") if g.dtype == core.VarDesc.VarType.FP16 else clip_var
+            clip_input = clip_var.astype("float16") if g.dtype == paddle.float16 else clip_var
             new_grad = paddle.multiply(x=g, y=clip_input)
             params_and_grads.append((p, new_grad))
         return params_and_grads

slm/model_zoo/moe/dygraph/run_moe_pretrain.py

@@ -37,7 +37,6 @@
 from paddle.distributed.fleet.meta_parallel.sharding.group_sharded_utils import (
     GroupShardedScaler,
 )
-from paddle.framework import core
 from paddle.incubate.distributed.models import moe
 from utils import get_timers, set_timers
 from visualdl import LogWriter
@@ -158,12 +157,8 @@ def initialize_mp_dp_parameters(model, hcg):
 def unscale_method(self, optimizer):
     if not self._enable:
         return
-    if paddle.framework.use_pir_api():
-        type_float16 = core.DataType.FLOAT16
-        type_float32 = core.DataType.FLOAT32
-    else:
-        type_float16 = core.VarDesc.VarType.FP16
-        type_float32 = core.VarDesc.VarType.FP32
+    type_float16 = paddle.float16
+    type_float32 = paddle.float32
 
     if getattr(optimizer, "_param_groups", None) and isinstance(optimizer._param_groups[0], dict):
         param_grads_fp16 = []