Merge branch 'develop' into 'master'

update for paddle 1.8.0

See merge request !51

README.md

@@ -40,7 +40,7 @@ sudo apt-get install libsndfile1
 
 ### Install PaddlePaddle
 
-See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires PaddlePaddle **1.7.1** or above.
+See [install](https://www.paddlepaddle.org.cn/install/quick) for more details. This repo requires PaddlePaddle **1.8.0** or above.
 
 ### Install Parakeet
 

examples/clarinet/train.py

@@ -163,11 +163,11 @@ if __name__ == "__main__":
         anneal_interval = train_config["anneal_interval"]
         lr_scheduler = dg.ExponentialDecay(
             learning_rate, anneal_interval, anneal_rate, staircase=True)
-        optim = fluid.optimizer.Adam(
-            lr_scheduler, parameter_list=model.parameters())
         gradiant_max_norm = train_config["gradient_max_norm"]
-        clipper = fluid.dygraph_grad_clip.GradClipByGlobalNorm(
-            gradiant_max_norm)
+        optim = fluid.optimizer.Adam(
+            lr_scheduler,
+            parameter_list=model.parameters(),
+            grad_clip=fluid.clip.ClipByGlobalNorm(gradiant_max_norm))
 
         # train
         max_iterations = train_config["max_iterations"]
@@ -229,7 +229,7 @@ if __name__ == "__main__":
                                                                   step_loss))
 
             l.backward()
-            optim.minimize(l, grad_clip=clipper)
+            optim.minimize(l)
             optim.clear_gradients()
 
             if global_step % eval_interval == 0:

examples/deepvoice3/train.py

@@ -196,8 +196,8 @@ if __name__ == "__main__":
             beta1,
             beta2,
             epsilon=epsilon,
-            parameter_list=dv3.parameters())
-        gradient_clipper = fluid.dygraph_grad_clip.GradClipByGlobalNorm(0.1)
+            parameter_list=dv3.parameters(),
+            grad_clip=fluid.clip.GradientClipByGlobalNorm(0.1))
 
         # generation
         synthesis_config = config["synthesis"]
@@ -258,15 +258,19 @@ if __name__ == "__main__":
                                text_lengths, frames)
             l = losses["loss"]
             l.backward()
+
             # record learning rate before updating
             writer.add_scalar("learning_rate",
                               optim._learning_rate.step().numpy(), global_step)
-            optim.minimize(l, grad_clip=gradient_clipper)
+            optim.minimize(l)
             optim.clear_gradients()
 
             # ==================all kinds of tedious things=================
             # record step loss into tensorboard
-            step_loss = {k: v.numpy()[0] for k, v in losses.items()}
+            step_loss = {
+                k: v.numpy()[0]
+                for k, v in losses.items() if v is not None
+            }
             tqdm.tqdm.write("global_step: {}\tloss: {}".format(
                 global_step, step_loss["loss"]))
             for k, v in step_loss.items():

examples/wavenet/train.py

@@ -126,12 +126,11 @@ if __name__ == "__main__":
         anneal_interval = train_config["anneal_interval"]
         lr_scheduler = dg.ExponentialDecay(
             learning_rate, anneal_interval, anneal_rate, staircase=True)
-        optim = fluid.optimizer.Adam(
-            lr_scheduler, parameter_list=model.parameters())
-
         gradiant_max_norm = train_config["gradient_max_norm"]
-        clipper = fluid.dygraph_grad_clip.GradClipByGlobalNorm(
-            gradiant_max_norm)
+        optim = fluid.optimizer.Adam(
+            lr_scheduler,
+            parameter_list=model.parameters(),
+            grad_clip=fluid.clip.ClipByGlobalNorm(gradiant_max_norm))
 
         train_loader = fluid.io.DataLoader.from_generator(
             capacity=10, return_list=True)
@@ -149,7 +148,7 @@ if __name__ == "__main__":
         log_dir = os.path.join(args.output, "log")
         writer = SummaryWriter(log_dir)
 
-        # load parameters and optimizer, and opdate iterations done sofar
+        # load parameters and optimizer, and update iterations done so far
         if args.checkpoint is not None:
             iteration = io.load_parameters(
                 model, optim, checkpoint_path=args.checkpoint)
@@ -181,7 +180,7 @@ if __name__ == "__main__":
             writer.add_scalar("learning_rate",
                               optim._learning_rate.step().numpy()[0],
                               global_step)
-            optim.minimize(loss_var, grad_clip=clipper)
+            optim.minimize(loss_var)
             optim.clear_gradients()
             print("global_step: {}\tloss: {:<8.6f}".format(global_step,
                                                            loss_np[0]))

parakeet/models/clarinet/utils.py

@@ -29,22 +29,10 @@ def conv2d(input,
            data_format="NCHW"):
     padding = tuple(pad for pad_dim in padding for pad in pad_dim)
 
-    inputs = {
-        'Input': [input],
-        'Filter': [weight],
-    }
-    attrs = {
-        'strides': stride,
-        'paddings': padding,
-        'dilations': dilation,
-        'groups': groups,
-        'use_cudnn': use_cudnn,
-        'use_mkldnn': False,
-        'fuse_relu_before_depthwise_conv': False,
-        "padding_algorithm": "EXPLICIT",
-        "data_format": data_format,
-    }
+    attrs = ('strides', stride, 'paddings', padding, 'dilations', dilation,
+             'groups', groups, 'use_cudnn', use_cudnn, 'use_mkldnn', False,
+             'fuse_relu_before_depthwise_conv', False, "padding_algorithm",
+             "EXPLICIT", "data_format", data_format)
 
-    outputs = ops.conv2d(inputs, attrs)
-    out = outputs["Output"][0]
-    return out
\ No newline at end of file
+    out = ops.conv2d(input, weight, *attrs)
+    return out

parakeet/models/deepvoice3/loss.py

@@ -262,7 +262,7 @@ class TTSLoss(object):
         if compute_lin_loss:
             lin_hyp = lin_hyp[:, :-self.time_shift, :]
             lin_ref = lin_ref[:, self.time_shift:, :]
-            lin_mask = lin_mask[:, self.time_shift:, :]
+            lin_mask = lin_mask[:, self.time_shift:]
             lin_l1_loss = self.l1_loss(
                 lin_hyp, lin_ref, lin_mask, priority_bin=self.priority_bin)
             lin_bce_loss = self.binary_divergence(lin_hyp, lin_ref, lin_mask)
@@ -273,7 +273,7 @@ class TTSLoss(object):
         if compute_mel_loss:
             mel_hyp = mel_hyp[:, :-self.time_shift, :]
             mel_ref = mel_ref[:, self.time_shift:, :]
-            mel_mask = mel_mask[:, self.time_shift:, :]
+            mel_mask = mel_mask[:, self.time_shift:]
             mel_l1_loss = self.l1_loss(mel_hyp, mel_ref, mel_mask)
             mel_bce_loss = self.binary_divergence(mel_hyp, mel_ref, mel_mask)
             # print("=====>", mel_l1_loss.numpy()[0], mel_bce_loss.numpy()[0])

parakeet/models/deepvoice3/position_embedding.py

@@ -31,8 +31,10 @@ def compute_position_embedding(radians, speaker_position_rate):
     """
     _, embed_dim = radians.shape
     batch_size = speaker_position_rate.shape[0]
-    speaker_position_rate = F.unsqueeze(speaker_position_rate, [1, 2])
-    scaled_radians = speaker_position_rate * radians
+    scaled_radians = F.elementwise_mul(
+        F.expand(F.unsqueeze(radians, [0]), [batch_size, 1, 1]),
+        speaker_position_rate,
+        axis=0)
 
     odd_mask = (np.arange(embed_dim) % 2).astype(np.float32)
     odd_mask = dg.to_variable(odd_mask)

parakeet/models/wavenet/wavenet.py

@@ -111,7 +111,7 @@ class ResidualBlock(dg.Layer):
             h = h[:, :, :time_steps]
 
         # condition
-        if condition:
+        if condition is not None:
             h += self.condition_proj(condition)
 
         # gated tanh
@@ -398,7 +398,8 @@ class WaveNet(dg.Layer):
 
         x_std = inv_std * (t - mu)
         exponent = F.exp(-0.5 * x_std * x_std)
-        pdf_x = 1.0 / np.sqrt(2.0 * np.pi) * inv_std * exponent
+        pdf_x = 1.0 / math.sqrt(2.0 * math.pi) * inv_std * exponent
+
         pdf_x = p_mixture * pdf_x
         # pdf_x: [bs, len]
         pdf_x = F.reduce_sum(pdf_x, dim=-1)

parakeet/modules/weight_norm.py

@@ -84,13 +84,15 @@ class WeightNormWrapper(dg.Layer):
             w_v,
             self.create_parameter(
                 shape=original_weight.shape, dtype=original_weight.dtype))
-        F.assign(original_weight, getattr(self, w_v))
+        with dg.no_grad():
+            F.assign(original_weight, getattr(self, w_v))
         delattr(layer, param_name)
         temp = norm_except(getattr(self, w_v), self.dim, self.power)
         self.add_parameter(
             w_g, self.create_parameter(
                 shape=temp.shape, dtype=temp.dtype))
-        F.assign(temp, getattr(self, w_g))
+        with dg.no_grad():
+            F.assign(temp, getattr(self, w_g))
 
         # also set this when setting up
         setattr(self.layer, self.param_name,