Upgrade black to version ~=22.0 (#15565)

* Upgrade black to version ~=22.0

* Check copies

* Fix code

examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py

@@ -350,7 +350,7 @@ def get_grad_norm(params, scale=1):
         if p.grad is not None:
             param_norm = (p.grad.detach().data / scale).norm(2)
             total_norm += param_norm.item() ** 2
-    total_norm = total_norm ** 0.5
+    total_norm = total_norm**0.5
     return total_norm
 
 
@@ -619,7 +619,7 @@ def main():
 
                 # update gumbel temperature
                 gumbel_temperature = max(
-                    args.max_gumbel_temperature * args.gumbel_temperature_decay ** completed_steps,
+                    args.max_gumbel_temperature * args.gumbel_temperature_decay**completed_steps,
                     args.min_gumbel_temperature,
                 )
                 if hasattr(model, "module"):

examples/research_projects/distillation/run_squad_w_distillation.py

@@ -229,20 +229,14 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
                 assert end_logits_tea.size() == end_logits_stu.size()
 
                 loss_fct = nn.KLDivLoss(reduction="batchmean")
-                loss_start = (
-                    loss_fct(
-                        nn.functional.log_softmax(start_logits_stu / args.temperature, dim=-1),
-                        nn.functional.softmax(start_logits_tea / args.temperature, dim=-1),
-                    )
-                    * (args.temperature ** 2)
-                )
-                loss_end = (
-                    loss_fct(
-                        nn.functional.log_softmax(end_logits_stu / args.temperature, dim=-1),
-                        nn.functional.softmax(end_logits_tea / args.temperature, dim=-1),
-                    )
-                    * (args.temperature ** 2)
-                )
+                loss_start = loss_fct(
+                    nn.functional.log_softmax(start_logits_stu / args.temperature, dim=-1),
+                    nn.functional.softmax(start_logits_tea / args.temperature, dim=-1),
+                ) * (args.temperature**2)
+                loss_end = loss_fct(
+                    nn.functional.log_softmax(end_logits_stu / args.temperature, dim=-1),
+                    nn.functional.softmax(end_logits_tea / args.temperature, dim=-1),
+                ) * (args.temperature**2)
                 loss_ce = (loss_start + loss_end) / 2.0
 
                 loss = args.alpha_ce * loss_ce + args.alpha_squad * loss

examples/research_projects/jax-projects/wav2vec2/run_wav2vec2_pretrain_flax.py

@@ -450,7 +450,7 @@ def main():
             negative_indices = batch.pop("sampled_negative_indices")
 
             gumbel_temperature = jnp.clip(
-                model_args.max_gumbel_temperature * model_args.gumbel_temperature_decay ** state.step,
+                model_args.max_gumbel_temperature * model_args.gumbel_temperature_decay**state.step,
                 a_min=model_args.min_gumbel_temperature,
             )
 

examples/research_projects/lxmert/modeling_frcnn.py

@@ -1264,7 +1264,7 @@ class Res5ROIHeads(nn.Module):
         self.feature_strides = {k: v.stride for k, v in input_shape.items()}
         self.feature_channels = {k: v.channels for k, v in input_shape.items()}
         self.cls_agnostic_bbox_reg = cfg.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG
-        self.stage_channel_factor = 2 ** 3  # res5 is 8x res2
+        self.stage_channel_factor = 2**3  # res5 is 8x res2
         self.out_channels = cfg.RESNETS.RES2_OUT_CHANNELS * self.stage_channel_factor
 
         # self.proposal_matcher = Matcher(
@@ -1419,7 +1419,7 @@ class AnchorGenerator(nn.Module):
 
         anchors = []
         for size in sizes:
-            area = size ** 2.0
+            area = size**2.0
             for aspect_ratio in aspect_ratios:
                 w = math.sqrt(area / aspect_ratio)
                 h = aspect_ratio * w

examples/research_projects/movement-pruning/masked_run_glue.py

@@ -84,7 +84,7 @@ def schedule_threshold(
         spars_warmup_steps = initial_warmup * warmup_steps
         spars_schedu_steps = (final_warmup + initial_warmup) * warmup_steps
         mul_coeff = 1 - (step - spars_warmup_steps) / (total_step - spars_schedu_steps)
-        threshold = final_threshold + (initial_threshold - final_threshold) * (mul_coeff ** 3)
+        threshold = final_threshold + (initial_threshold - final_threshold) * (mul_coeff**3)
     regu_lambda = final_lambda * threshold / final_threshold
     return threshold, regu_lambda
 
@@ -285,14 +285,11 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
                         attention_mask=inputs["attention_mask"],
                     )
 
-                loss_logits = (
-                    nn.functional.kl_div(
-                        input=nn.functional.log_softmax(logits_stu / args.temperature, dim=-1),
-                        target=nn.functional.softmax(logits_tea / args.temperature, dim=-1),
-                        reduction="batchmean",
-                    )
-                    * (args.temperature ** 2)
-                )
+                loss_logits = nn.functional.kl_div(
+                    input=nn.functional.log_softmax(logits_stu / args.temperature, dim=-1),
+                    target=nn.functional.softmax(logits_tea / args.temperature, dim=-1),
+                    reduction="batchmean",
+                ) * (args.temperature**2)
 
                 loss = args.alpha_distil * loss_logits + args.alpha_ce * loss
 

examples/research_projects/movement-pruning/masked_run_squad.py

@@ -88,7 +88,7 @@ def schedule_threshold(
         spars_warmup_steps = initial_warmup * warmup_steps
         spars_schedu_steps = (final_warmup + initial_warmup) * warmup_steps
         mul_coeff = 1 - (step - spars_warmup_steps) / (total_step - spars_schedu_steps)
-        threshold = final_threshold + (initial_threshold - final_threshold) * (mul_coeff ** 3)
+        threshold = final_threshold + (initial_threshold - final_threshold) * (mul_coeff**3)
     regu_lambda = final_lambda * threshold / final_threshold
     return threshold, regu_lambda
 
@@ -306,22 +306,16 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
                         attention_mask=inputs["attention_mask"],
                     )
 
-                loss_start = (
-                    nn.functional.kl_div(
-                        input=nn.functional.log_softmax(start_logits_stu / args.temperature, dim=-1),
-                        target=nn.functional.softmax(start_logits_tea / args.temperature, dim=-1),
-                        reduction="batchmean",
-                    )
-                    * (args.temperature ** 2)
-                )
-                loss_end = (
-                    nn.functional.kl_div(
-                        input=nn.functional.log_softmax(end_logits_stu / args.temperature, dim=-1),
-                        target=nn.functional.softmax(end_logits_tea / args.temperature, dim=-1),
-                        reduction="batchmean",
-                    )
-                    * (args.temperature ** 2)
-                )
+                loss_start = nn.functional.kl_div(
+                    input=nn.functional.log_softmax(start_logits_stu / args.temperature, dim=-1),
+                    target=nn.functional.softmax(start_logits_tea / args.temperature, dim=-1),
+                    reduction="batchmean",
+                ) * (args.temperature**2)
+                loss_end = nn.functional.kl_div(
+                    input=nn.functional.log_softmax(end_logits_stu / args.temperature, dim=-1),
+                    target=nn.functional.softmax(end_logits_tea / args.temperature, dim=-1),
+                    reduction="batchmean",
+                ) * (args.temperature**2)
                 loss_logits = (loss_start + loss_end) / 2.0
 
                 loss = args.alpha_distil * loss_logits + args.alpha_ce * loss

examples/research_projects/onnx/summarization/bart_onnx/generation_onnx.py

@@ -442,7 +442,7 @@ class BeamSearchScorerTS(torch.nn.Module):
         elif self.do_early_stopping:
             return True
         else:
-            cur_score = best_sum_logprobs / cur_len ** self.length_penalty
+            cur_score = best_sum_logprobs / cur_len**self.length_penalty
             ret = self._beam_hyps_worst_scores[hypo_idx].item() >= cur_score
             return ret
 

examples/research_projects/pplm/run_pplm.py

@@ -550,7 +550,7 @@ def generate_text_pplm(
 
             unpert_probs = nn.functional.softmax(unpert_logits[:, -1, :], dim=-1)
 
-            pert_probs = (pert_probs ** gm_scale) * (unpert_probs ** (1 - gm_scale))  # + SMALL_CONST
+            pert_probs = (pert_probs**gm_scale) * (unpert_probs ** (1 - gm_scale))  # + SMALL_CONST
             pert_probs = top_k_filter(pert_probs, k=top_k, probs=True)  # + SMALL_CONST
 
             # rescale

examples/research_projects/visual_bert/modeling_frcnn.py

@@ -1264,7 +1264,7 @@ class Res5ROIHeads(nn.Module):
         self.feature_strides = {k: v.stride for k, v in input_shape.items()}
         self.feature_channels = {k: v.channels for k, v in input_shape.items()}
         self.cls_agnostic_bbox_reg = cfg.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG
-        self.stage_channel_factor = 2 ** 3  # res5 is 8x res2
+        self.stage_channel_factor = 2**3  # res5 is 8x res2
         self.out_channels = cfg.RESNETS.RES2_OUT_CHANNELS * self.stage_channel_factor
 
         # self.proposal_matcher = Matcher(
@@ -1419,7 +1419,7 @@ class AnchorGenerator(nn.Module):
 
         anchors = []
         for size in sizes:
-            area = size ** 2.0
+            area = size**2.0
             for aspect_ratio in aspect_ratios:
                 w = math.sqrt(area / aspect_ratio)
                 h = aspect_ratio * w

examples/research_projects/wav2vec2/run_pretrain.py

@@ -273,11 +273,11 @@ class Wav2Vec2PreTrainer(Trainer):
         # make sure gumbel softmax temperature is decayed
         if self.args.n_gpu > 1 or self.deepspeed:
             model.module.set_gumbel_temperature(
-                max(self.max_gumbel_temp * self.gumbel_temp_decay ** self.num_update_step, self.min_gumbel_temp)
+                max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step, self.min_gumbel_temp)
             )
         else:
             model.set_gumbel_temperature(
-                max(self.max_gumbel_temp * self.gumbel_temp_decay ** self.num_update_step, self.min_gumbel_temp)
+                max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step, self.min_gumbel_temp)
             )
 
         return loss.detach()