quant export fix up for atc tools

mindspore/common/parameter.py

@@ -270,6 +270,7 @@ class Parameter(MetaTensor):
         "Update the parameter by a Tensor."
         if isinstance(self, Tensor):
             # for Tensor same shape:
+            self.init_flag = False
             return self.assign_value(data)
         # create a new tensor
         return Parameter(data, self.name, self.requires_grad)

mindspore/train/quant/quant.py

@@ -29,6 +29,7 @@ from ...common import dtype as mstype
 from ...common.api import _executor
 from ...nn.layer import quant
 from ...ops import functional as F
+from ...ops import operations as P
 from ...ops.operations import _inner_ops as inner
 from ...train import serialization
 from . import quant_utils
@@ -366,8 +367,6 @@ class ExportToQuantInferNetwork:
             sqrt_mode = True
         dequant_op = inner.Dequant(sqrt_mode)
 
-        # get op
-        op_core = cell_core.matmul if isinstance(cell_core, quant.DenseQuant) else cell_core.conv
         if isinstance(activation, _AddFakeQuantAfterSubCell):
             activation = activation.subcell
         elif hasattr(activation, "get_origin"):
@@ -383,10 +382,17 @@ class ExportToQuantInferNetwork:
             weight, bias = quant_utils.fold_batchnorm(weight, cell_core)
 
         # apply the quant
-        weight = Tensor(quant_utils.weight2int(weight, scale_w, zp_w), self.data_type)
+        weight = quant_utils.weight2int(weight, scale_w, zp_w)
         if bias is not None:
             bias = Tensor(scale_a_in * scale_w * bias, mstype.int32)
         scale_deq = Tensor(scale_deq, mstype.float16)
+        # get op
+        if isinstance(cell_core, quant.DenseQuant):
+            op_core = P.MatMul()
+            weight = np.transpose(weight)
+        else:
+            op_core = cell_core.conv
+        weight = Tensor(weight, self.data_type)
         block = quant.QuantBlock(op_core, weight, quant_op, dequant_op, scale_deq, bias, activation)
         return block
 

model_zoo/official/nlp/bert/run_classifier.py

@@ -50,7 +50,7 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
                                        power=optimizer_cfg.AdamWeightDecay.power)
         params = net_with_loss.trainable_params()
         decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-        other_params = list(filter(lambda x: x not in decay_params, params))
+        other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
         group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
                         {'params': other_params, 'weight_decay': 0.0}]
 

model_zoo/official/nlp/bert/run_ner.py

@@ -52,7 +52,7 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
                                        power=optimizer_cfg.AdamWeightDecay.power)
         params = network.trainable_params()
         decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-        other_params = list(filter(lambda x: x not in decay_params, params))
+        other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
         group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
                         {'params': other_params, 'weight_decay': 0.0}]
         optimizer = AdamWeightDecay(group_params, lr_schedule, eps=optimizer_cfg.AdamWeightDecay.eps)

model_zoo/official/nlp/bert/run_pretrain.py

@@ -116,7 +116,7 @@ def run_pretrain():
                                        power=cfg.Lamb.power)
         params = net_with_loss.trainable_params()
         decay_params = list(filter(cfg.Lamb.decay_filter, params))
-        other_params = list(filter(lambda x: x not in decay_params, params))
+        other_params = list(filter(lambda x: not cfg.Lamb.decay_filter(x), params))
         group_params = [{'params': decay_params, 'weight_decay': cfg.Lamb.weight_decay},
                         {'params': other_params},
                         {'order_params': params}]
@@ -132,7 +132,7 @@ def run_pretrain():
                                        power=cfg.AdamWeightDecay.power)
         params = net_with_loss.trainable_params()
         decay_params = list(filter(cfg.AdamWeightDecay.decay_filter, params))
-        other_params = list(filter(lambda x: x not in decay_params, params))
+        other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
         group_params = [{'params': decay_params, 'weight_decay': cfg.AdamWeightDecay.weight_decay},
                         {'params': other_params, 'weight_decay': 0.0},
                         {'order_params': params}]

model_zoo/official/nlp/bert/run_squad.py

@@ -52,7 +52,7 @@ def do_train(dataset=None, network=None, load_checkpoint_path="", save_checkpoin
                                        power=optimizer_cfg.AdamWeightDecay.power)
         params = network.trainable_params()
         decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-        other_params = list(filter(lambda x: x not in decay_params, params))
+        other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
         group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
                         {'params': other_params, 'weight_decay': 0.0}]
 

model_zoo/official/nlp/bert_thor/run_pretrain.py

@@ -137,7 +137,7 @@ def run_pretrain():
                                        power=cfg.Lamb.power)
         params = net_with_loss.trainable_params()
         decay_params = list(filter(cfg.Lamb.decay_filter, params))
-        other_params = list(filter(lambda x: x not in decay_params, params))
+        other_params = list(filter(lambda x: not cfg.Lamb.decay_filter(x), params))
         group_params = [{'params': decay_params, 'weight_decay': cfg.Lamb.weight_decay},
                         {'params': other_params},
                         {'order_params': params}]
@@ -153,7 +153,7 @@ def run_pretrain():
                                        power=cfg.AdamWeightDecay.power)
         params = net_with_loss.trainable_params()
         decay_params = list(filter(cfg.AdamWeightDecay.decay_filter, params))
-        other_params = list(filter(lambda x: x not in decay_params, params))
+        other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
         group_params = [{'params': decay_params, 'weight_decay': cfg.AdamWeightDecay.weight_decay},
                         {'params': other_params, 'weight_decay': 0.0},
                         {'order_params': params}]

model_zoo/official/nlp/tinybert/run_general_distill.py

@@ -99,7 +99,7 @@ def run_general_distill():
                                    power=common_cfg.AdamWeightDecay.power)
     params = netwithloss.trainable_params()
     decay_params = list(filter(common_cfg.AdamWeightDecay.decay_filter, params))
-    other_params = list(filter(lambda x: x not in decay_params, params))
+    other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
     group_params = [{'params': decay_params, 'weight_decay': common_cfg.AdamWeightDecay.weight_decay},
                     {'params': other_params, 'weight_decay': 0.0},
                     {'order_params': params}]

model_zoo/official/nlp/tinybert/run_task_distill.py

@@ -107,7 +107,7 @@ def run_predistill():
                                    power=optimizer_cfg.AdamWeightDecay.power)
     params = netwithloss.trainable_params()
     decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-    other_params = list(filter(lambda x: x not in decay_params, params))
+    other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
     group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
                     {'params': other_params, 'weight_decay': 0.0},
                     {'order_params': params}]
@@ -165,7 +165,7 @@ def run_task_distill(ckpt_file):
                                    power=optimizer_cfg.AdamWeightDecay.power)
     params = netwithloss.trainable_params()
     decay_params = list(filter(optimizer_cfg.AdamWeightDecay.decay_filter, params))
-    other_params = list(filter(lambda x: x not in decay_params, params))
+    other_params = list(filter(lambda x: not cfg.AdamWeightDecay.decay_filter(x), params))
     group_params = [{'params': decay_params, 'weight_decay': optimizer_cfg.AdamWeightDecay.weight_decay},
                     {'params': other_params, 'weight_decay': 0.0},
                     {'order_params': params}]