fixing pylint version skew. rewrote some dicts as literals before dis…

…abling that check.

.github/workflows/linting.yml

@@ -14,7 +14,7 @@ jobs:
     - name: Install pylint
       run: |
         python -m pip install --upgrade pip
-        pip install pylint
+        pip install pylint==2.16.1
     - name: Run pylint
       run: |
         pylint algorithmic_efficiency

.pre-commit-config.yaml

@@ -9,6 +9,6 @@ repos:
     hooks:
       - id: isort
   - repo: https://github.com/pycqa/pylint
-    rev: v2.14.5
+    rev: v2.16.1
     hooks:
       - id: pylint

algorithmic_efficiency/checkpoint_utils.py

@@ -68,14 +68,15 @@ def maybe_restore_checkpoint(framework: str,
 
   uninitialized_global_step = -1
   uninitialized_preemption_count = -1
-  checkpoint_state = dict(
-      model_params=model_params,
-      optimizer_state=opt_state,
-      model_state=model_state,
-      train_state=train_state,
-      eval_results=None,
-      global_step=uninitialized_global_step,
-      preemption_count=uninitialized_preemption_count)
+  checkpoint_state = {
+      'model_params': model_params,
+      'optimizer_state': opt_state,
+      'model_state': model_state,
+      'train_state': train_state,
+      'eval_results': None,
+      'global_step': uninitialized_global_step,
+      'preemption_count': uninitialized_preemption_count,
+  }
 
   if framework == 'jax':
     latest_ckpt = flax_checkpoints.restore_checkpoint(
@@ -90,7 +91,7 @@ def maybe_restore_checkpoint(framework: str,
 
   # Load_latest_checkpoint() will return checkpoint_state if
   # checkpoint_dir does not exist or if it exists and contains no checkpoints.
-  found_checkpoint = (latest_ckpt['global_step'] != uninitialized_global_step)
+  found_checkpoint = latest_ckpt['global_step'] != uninitialized_global_step
 
   if not found_checkpoint:
     return (optimizer_state,
@@ -209,14 +210,15 @@ def save_checkpoint(framework: str,
             'method.')
     opt_state = optimizer_state_dict
 
-  checkpoint_state = dict(
-      model_params=model_params,
-      optimizer_state=opt_state,
-      model_state=model_state,
-      train_state=train_state,
-      eval_results=tuple(eval_results),
-      global_step=global_step,
-      preemption_count=preemption_count)
+  checkpoint_state = {
+      'model_params': model_params,
+      'optimizer_state': opt_state,
+      'model_state': model_state,
+      'train_state': train_state,
+      'eval_results': tuple(eval_results),
+      'global_step': global_step,
+      'preemption_count': preemption_count,
+  }
 
   save_path = os.path.join(checkpoint_dir, f'checkpoint_{global_step}')
   if framework == 'jax':

algorithmic_efficiency/workloads/imagenet_vit/imagenet_jax/models.py

@@ -39,10 +39,10 @@ class MlpBlock(nn.Module):
   @nn.compact
   def __call__(self, x: spec.Tensor, train: bool = True) -> spec.Tensor:
     """Applies Transformer MlpBlock module."""
-    inits = dict(
-        kernel_init=nn.initializers.xavier_uniform(),
-        bias_init=nn.initializers.normal(stddev=1e-6),
-    )
+    inits = {
+        'kernel_init': nn.initializers.xavier_uniform(),
+        'bias_init': nn.initializers.normal(stddev=1e-6),
+    }
 
     d = x.shape[2]
     x = nn.Dense(self.mlp_dim or 4 * d, **inits)(x)

algorithmic_efficiency/workloads/librispeech_conformer/librispeech_pytorch/workload.py

@@ -239,7 +239,7 @@ def greedy_decode(
         fin_result.shape[1], device=result.device).view(
             1, -1) < result.count_nonzero(dim=1).view(-1, 1)
     fin_result.view(-1)[idxs[mask != 0]] = result[result != blank_id]
-    padding = (fin_result == 0)
+    padding = fin_result == 0
     return fin_result, padding
 
   def sync_sd(self, params: spec.ParameterContainer) -> None:

algorithmic_efficiency/workloads/ogbg/metrics.py

@@ -22,7 +22,7 @@ def predictions_match_labels(*,
                              **kwargs) -> jnp.ndarray:
   """Returns a binary array indicating where predictions match the labels."""
   del kwargs  # Unused.
-  preds = (logits > 0)
+  preds = logits > 0
   return (preds == labels).astype(jnp.float32)
 
 

algorithmic_efficiency/workloads/ogbg/ogbg_jax/workload.py

@@ -88,7 +88,7 @@ def _binary_cross_entropy_with_mask(
 
     # Numerically stable implementation of BCE loss.
     # This mimics TensorFlow's tf.nn.sigmoid_cross_entropy_with_logits().
-    positive_logits = (logits >= 0)
+    positive_logits = logits >= 0
     relu_logits = jnp.where(positive_logits, logits, 0)
     abs_logits = jnp.where(positive_logits, logits, -logits)
     losses = relu_logits - (logits * smoothed_labels) + (

algorithmic_efficiency/workloads/ogbg/ogbg_pytorch/workload.py

@@ -192,7 +192,7 @@ def _binary_cross_entropy_with_mask(
 
     # Numerically stable implementation of BCE loss.
     # This mimics TensorFlow's tf.nn.sigmoid_cross_entropy_with_logits().
-    positive_logits = (logits >= 0)
+    positive_logits = logits >= 0
     relu_logits = torch.where(positive_logits, logits, 0)
     abs_logits = torch.where(positive_logits, logits, -logits)
     losses = relu_logits - (logits * smoothed_labels) + (

algorithmic_efficiency/workloads/wmt/wmt_jax/decode.py

@@ -193,7 +193,7 @@ def beam_search(inputs,
   def beam_search_loop_cond_fn(state):
     """Beam search loop termination condition."""
     # Have we reached max decoding length?
-    not_at_end = (state.cur_index < max_decode_len - 1)
+    not_at_end = state.cur_index < max_decode_len - 1
 
     # Is no further progress in the beam search possible?
     # Get the best possible scores from alive sequences.

algorithmic_efficiency/workloads/wmt/wmt_pytorch/decode.py

@@ -219,7 +219,7 @@ def beam_search(
   def beam_search_loop_cond_fn(state: BeamState) -> bool:
     """Beam search loop termination condition."""
     # Have we reached max decoding length?
-    not_at_end = (state.cur_index < max_decode_len - 1)
+    not_at_end = state.cur_index < max_decode_len - 1
 
     # Is no further progress in the beam search possible?
     # Get the best possible scores from alive sequences.

baselines/nadamw/pytorch/submission.py

@@ -52,7 +52,9 @@ def __init__(self,
       raise ValueError(f'Invalid beta parameter at index 1: {betas[1]}')
     if not 0.0 <= weight_decay:
       raise ValueError(f'Invalid weight_decay value: {weight_decay}')
-    defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+    defaults = {
+        'lr': lr, 'betas': betas, 'eps': eps, 'weight_decay': weight_decay
+    }
     super().__init__(params, defaults)
 
   def __setstate__(self, state):

datasets/dataset_setup.py

@@ -181,10 +181,9 @@ def _maybe_prompt_for_deletion(paths, interactive_deletion):
 
 
 def _download_url(url, data_dir):
-  url = url
   data_dir = os.path.expanduser(data_dir)
   file_path = os.path.join(data_dir, url.split('/')[-1])
-  response = requests.get(url, stream=True)
+  response = requests.get(url, stream=True, timeout=600)
   total_size_in_bytes = int(response.headers.get('Content-length', 0))
   total_size_in_mib = total_size_in_bytes / (2**20)
   progress_bar = tqdm.tqdm(total=total_size_in_mib, unit='MiB', unit_scale=True)
@@ -209,7 +208,7 @@ def _download_url(url, data_dir):
       f.write(chunk)
   progress_bar.close()
   if (progress_bar.total != 0 and progress_bar.n != progress_bar.total):
-    raise Exception(
+    raise RuntimeError(
         ('Download corrupted, size {n} MiB from {url} does not match '
          'expected size {size} MiB').format(
              url=url, n=progress_bar.n, size=progress_bar.total))
@@ -402,7 +401,6 @@ def setup_imagenet_pytorch(data_dir):
       os.path.join(imagenet_pytorch_data_dir, IMAGENET_VAL_TAR_FILENAME),
       os.path.join(imagenet_pytorch_data_dir, 'val'))
 
-  cwd = os.path.join(imagenet_pytorch_data_dir, 'train')
   valprep_command = [
       'wget',
       '-qO-',

reference_algorithms/target_setting_algorithms/pytorch_nadamw.py

@@ -55,7 +55,9 @@ def __init__(self,
       raise ValueError(f'Invalid beta parameter at index 1: {betas[1]}')
     if not 0.0 <= weight_decay:
       raise ValueError(f'Invalid weight_decay value: {weight_decay}')
-    defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
+    defaults = {
+        'lr': lr, 'betas': betas, 'eps': eps, 'weight_decay': weight_decay
+    }
     super().__init__(params, defaults)
 
   def __setstate__(self, state):

setup.cfg

@@ -79,7 +79,7 @@ full_dev =
 # Dependencies for developing the package
 dev =
   isort==5.10.1
-  pylint==2.14.5
+  pylint==2.16.1
   pytest==7.1.2
   yapf==0.32.0
   pre-commit==2.20.0
@@ -252,13 +252,13 @@ defining-attr-methods=__init__,__new__,setUp
 # "class_" is also a valid for the first argument to a class method.
 valid-classmethod-first-arg=cls,class_
 [pylint.EXCEPTIONS]
-overgeneral-exceptions=StandardError,Exception,BaseException
+overgeneral-exceptions=builtins.StandardError,builtins.Exception,builtins.BaseException
 [pylint.IMPORTS]
 # Deprecated modules which should not be used, separated by a comma
 deprecated-modules=regsub,TERMIOS,Bastion,rexec,sets
 [pylint.FORMAT]
 # List of checkers and warnings to disable.
-disable=abstract-method,access-member-before-definition,arguments-differ,assignment-from-no-return,attribute-defined-outside-init,bad-mcs-classmethod-argument,bad-option-value,c-extension-no-member,consider-merging-isinstance,consider-using-dict-comprehension,consider-using-enumerate,consider-using-in,consider-using-set-comprehension,consider-using-ternary,deprecated-method,design,file-ignored,fixme,global-statement,import-error,inconsistent-return-statements,invalid-unary-operand-type,len-as-condition,locally-disabled,locally-enabled,misplaced-comparison-constant,missing-docstring,multiple-imports,no-else-return,no-member,no-name-in-module,no-self-use,no-value-for-parameter,not-an-iterable,not-context-manager,pointless-except,protected-access,redefined-argument-from-local,signature-differs,similarities,simplifiable-if-expression,star-args,super-init-not-called,suppressed-message,too-many-function-args,trailing-comma-tuple,trailing-newlines,ungrouped-imports,unnecessary-pass,unsubscriptable-object,unused-argument,useless-object-inheritance,useless-return,useless-suppression,wrong-import-order,wrong-import-position,unneeded-not,unexpected-keyword-arg,redundant-keyword-arg,unspecified-encoding,logging-fstring-interpolation,consider-using-f-string
+disable=abstract-method,access-member-before-definition,arguments-differ,assignment-from-no-return,attribute-defined-outside-init,bad-mcs-classmethod-argument,bad-option-value,c-extension-no-member,consider-merging-isinstance,consider-using-dict-comprehension,consider-using-enumerate,consider-using-in,consider-using-set-comprehension,consider-using-ternary,deprecated-method,design,file-ignored,fixme,global-statement,import-error,inconsistent-return-statements,invalid-unary-operand-type,len-as-condition,locally-disabled,locally-enabled,misplaced-comparison-constant,missing-docstring,multiple-imports,no-else-return,no-member,no-name-in-module,no-self-use,no-value-for-parameter,not-an-iterable,not-context-manager,pointless-except,protected-access,redefined-argument-from-local,signature-differs,similarities,simplifiable-if-expression,star-args,super-init-not-called,suppressed-message,too-many-function-args,trailing-comma-tuple,trailing-newlines,ungrouped-imports,unnecessary-pass,unsubscriptable-object,unused-argument,useless-object-inheritance,useless-return,useless-suppression,wrong-import-order,wrong-import-position,unneeded-not,unexpected-keyword-arg,redundant-keyword-arg,unspecified-encoding,logging-fstring-interpolation,consider-using-f-string,use-dict-literal
 
 # Maximum number of characters on a single line.
 max-line-length=80

tests/reference_algorithm_tests.py

@@ -266,12 +266,13 @@ def _build_input_queue(self, *args, **kwargs):
 
         def _fake_iter():
           while True:
-            fake_batch = dict(
-                num_nodes=tf.ones((1,), dtype=tf.int64),
-                edge_index=tf.ones((1, 2), dtype=tf.int64),
-                node_feat=tf.random.normal((1, 9)),
-                edge_feat=tf.random.normal((1, 3)),
-                labels=tf.ones((self._num_outputs,)))
+            fake_batch = {
+                'num_nodes': tf.ones((1,), dtype=tf.int64),
+                'edge_index': tf.ones((1, 2), dtype=tf.int64),
+                'node_feat': tf.random.normal((1, 9)),
+                'edge_feat': tf.random.normal((1, 3)),
+                'labels': tf.ones((self._num_outputs,)),
+            }
             yield fake_batch
 
         fake_batch_iter = ogbg_input_pipeline._get_batch_iterator(