Turn on linting for functorch (#81987)

Test Plan:
- wait for CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81987
Approved by: https://github.com/samdow

.flake8

@@ -22,8 +22,9 @@ exclude =
     ./docs/caffe2,
     ./docs/cpp/src,
     ./docs/src,
-    # See NOTE: [Impending functorch move]
-    ./functorch,
+    ./functorch/docs,
+    ./functorch/examples,
+    ./functorch/notebooks,
     ./scripts,
     ./test/generated_type_hints_smoketest.py,
     ./third_party,

.lintrunner.toml

@@ -9,11 +9,9 @@ exclude_patterns = [
     'docs/caffe2/**',
     'docs/cpp/src/**',
     'docs/src/**',
-    # NOTE: [Impending functorch move]
-    # In preparation for the functorch -> pytorch merge,
-    # we are adding the following excludes so that functorch passes
-    # lint when it gets merged in. Please don't delete.
-    'functorch/**',
+    'functorch/docs/**',
+    'functorch/examples/**',
+    'functorch/notebooks/**',
     'scripts/**',
     'test/generated_type_hints_smoketest.py',
     'third_party/**',
@@ -227,8 +225,6 @@ code = 'TYPEIGNORE'
 include_patterns = ['**/*.py', '**/*.pyi']
 exclude_patterns = [
     'test/test_jit.py',
-    # See NOTE: [Impending functorch move]
-    'functorch/**',
 ]
 command = [
     'python3',
@@ -301,8 +297,6 @@ exclude_patterns=[
     'tools/clang_format_hash/**',
     'test/cpp/jit/upgrader_models/*.ptl',
     'test/cpp/jit/upgrader_models/*.ptl.ff',
-    # See NOTE: [Impending functorch move]
-    'functorch/**',
 ]
 command = [
     'python3',
@@ -322,8 +316,6 @@ exclude_patterns = [
     'aten/src/ATen/native/vulkan/api/vk_mem_alloc.h',
     'test/cpp/jit/upgrader_models/*.ptl',
     'test/cpp/jit/upgrader_models/*.ptl.ff',
-    # See NOTE: [Impending functorch move]
-    'functorch/**',
 ]
 command = [
     'python3',
@@ -353,8 +345,6 @@ exclude_patterns = [
     'test/cpp/jit/upgrader_models/*.ptl',
     'test/cpp/jit/upgrader_models/*.ptl.ff',
     '.lintrunner.toml',
-    # See NOTE: [Impending functorch move]
-    'functorch/**',
 ]
 command = [
     'python3',
@@ -436,8 +426,6 @@ exclude_patterns = [
     '**/git-pre-commit',
     '**/git-clang-format',
     '**/gradlew',
-    # See NOTE: [Impending functorch move]
-    'functorch/**',
 ]
 command = [
     'python3',

functorch/benchmarks/chrome_trace_parser.py

@@ -21,7 +21,7 @@ def get_model_name(filename):
     return modelname
 
 def get_total_length(run_times_df, modelname):
-    return float(run_times_df[run_times_df["name"]==modelname]["runtime"])
+    return float(run_times_df[run_times_df["name"] == modelname]["runtime"])
 
 
 def main():
@@ -51,7 +51,7 @@ def main():
     else:
         print("Please provide a filename or a folder name")
 
-    print(f"modelname, GPU Utilization, MM and Conv time")
+    print("modelname, GPU Utilization, MM and Conv time")
 
     run_times_df = pd.read_csv(args.runtime)
     for filename in filenames:
@@ -60,7 +60,7 @@ def main():
             total_length = get_total_length(run_times_df, modelname) * 1e6
             utilization, mm_conv_utilization = compute_utilization(filenames, total_length)
             print(f"{modelname}, {utilization}, {mm_conv_utilization}")
-        except:
+        except BaseException:
             logging.exception(f"{filename}, ERROR")
             print(f"{filename}, ERROR")
 

functorch/benchmarks/per_sample_grads.py

@@ -2,11 +2,8 @@ import torch
 import torch.nn as nn
 import torchvision.models as models
 from opacus.utils.module_modification import convert_batchnorm_modules
-from torchvision.datasets import CIFAR10
 import time
 
-from functools import partial
-import functorch
 from functorch import vmap, grad
 from functorch import make_functional
 from opacus import PrivacyEngine

functorch/benchmarks/process_scorecard.py

@@ -8,7 +8,7 @@ nops = len(ops)
 pivot_op_shape = df.pivot_table(values="time", index=["operator", "shape"], columns=["fuser"])
 pivot_speedups = (pivot_op_shape.T / pivot_op_shape["eager"]).T
 
-plt.rcParams["figure.figsize"] = (20,100)
+plt.rcParams["figure.figsize"] = (20, 100)
 fig, axs = plt.subplots(nops)
 plt.subplots_adjust(hspace=0.5)
 for idx, op in enumerate(ops):

functorch/benchmarks/transformer_fusion_patterns/benchmark.py

@@ -1,5 +1,4 @@
 import torch
-import time
 from functorch.compile import memory_efficient_fusion, clear_compile_cache
 import benchmark_helper
 

functorch/benchmarks/transformer_fusion_patterns/benchmark_helper.py

@@ -63,7 +63,9 @@ def profile_cuda_kernels(fn, args, string_id="Model time"):
     print("################################################\n\n\n\n")
 
 
-def time_with_torch_timer(fn, args, string_id, kwargs={}):
+def time_with_torch_timer(fn, args, string_id, kwargs=None):
+    if kwargs is None:
+        kwargs = {}
     print("################################################")
     print(f"#### Torch Timer for {string_id} starts #########")
     print("################################################")

functorch/benchmarks/transformer_fusion_patterns/bias_gelu_dropout.py

@@ -1,9 +1,8 @@
 import torch
-import time
 from functorch.compile import memory_efficient_pointwise_fusion, clear_compile_cache
 import benchmark_helper
 
-### ALL comments regarding the patetrns
+# ALL comments regarding the patetrns
 
 
 def bias_gelu_dropout(input, bias):

functorch/examples/maml_omniglot/.gitignore

@@ -1,3 +1,2 @@
 omniglot/
 maml-accs.png
-

functorch/functorch/_src/vmap.py

@@ -121,7 +121,7 @@ def _create_batched_inputs(
         flat_in_dims: List[Any], flat_args: List[Any], vmap_level: int, args_spec) -> Tuple:
     # See NOTE [Ignored _remove_batch_dim, _add_batch_dim]
     batched_inputs = [arg if in_dim is None else
-                      _add_batch_dim(arg, in_dim, vmap_level)  # type: ignore
+                      _add_batch_dim(arg, in_dim, vmap_level)
                       for in_dim, arg in zip(flat_in_dims, flat_args)]
     return tree_unflatten(batched_inputs, args_spec)
 

functorch/functorch/csrc/BatchRulesDecompositions.cpp

@@ -259,4 +259,3 @@ TORCH_LIBRARY_IMPL(aten, FT_BATCHED_KEY, m) {
 }
 
 }}
-

functorch/functorch/csrc/BatchRulesFactory.cpp

@@ -97,4 +97,3 @@ TORCH_LIBRARY_IMPL(aten, FT_BATCHED_KEY, m) {
   // Not sure how to add the ones with irregular args to the mix cleanly (i.e. randint takes an extra int parameter)
 }
 }}
-

functorch/functorch/csrc/BatchRulesHelper.h

@@ -470,4 +470,3 @@ inline VmapDimVector range(int64_t start, int64_t stop) {
 }
 
 }}
-

functorch/functorch/csrc/BatchRulesLinearAlgebra.cpp

@@ -216,4 +216,3 @@ TORCH_LIBRARY_IMPL(aten, FT_BATCHED_KEY, m) {
   VARIADIC_BDIMS_BOXED(_lu_with_info);
 }
 }}
-

functorch/functorch/csrc/PlumbingHelper.h

@@ -37,4 +37,3 @@ inline bool ivalueParticipatesInCurrentLevel(const IValue& ivalue) {
 }
 
 }}
-

functorch/notebooks/_src/plot_ensembling.py

@@ -19,7 +19,6 @@ Let's demonstrate how to do this using an ensemble of simple CNNs.
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from functools import partial
 torch.manual_seed(0)
 
 # Here's a simple CNN

functorch/notebooks/_src/plot_jacobians_and_hessians.py

@@ -9,7 +9,6 @@ efficiently using a standard autodiff system like PyTorch Autograd; functorch
 provides ways of computing various higher-order autodiff quantities efficiently.
 """
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
 from functools import partial
 torch.manual_seed(0)

functorch/notebooks/_src/plot_per_sample_gradients.py

@@ -12,7 +12,6 @@ and optimization research.
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from functools import partial
 torch.manual_seed(0)
 
 # Here's a simple CNN

functorch/writing_batching_rules.md

@@ -96,6 +96,3 @@ There's a couple different resources for finding batching rules to write.
 1. [BatchingRegistrations.cpp](functorch/csrc/BatchingRegistrations.cpp): This is probably the easiest place to start. These were batching rules that were written with an old API, and thus have a lot of cruft in them that are no longer necessary. Porting these batching rules to using one of the above options is an easy way to get started and help us reduce tech debt :) Once you've gotten your footing with writing batching rules, you can start helping with writing new batching rules.
 2. Popular operators. See [1](https://github.com/facebookresearch/functorch/issues/112), [2](https://github.com/facebookresearch/functorch/issues/101), [3](https://github.com/facebookresearch/functorch/issues/102), and [4](https://github.com/facebookresearch/functorch/issues/102). These contain lists of (user-facing) PyTorch operators sorted by usages, along with whether they have a batching rule implemented or not.
 3. [Master List](https://docs.google.com/spreadsheets/d/1Sp4HUjxwMifS5oDQg0yvjqk7hKOpCfKO4jWH4MTGP-k/edit#gid=0). This is the master list of vmap operator support :). It's generated by [this script](op_analysis/gen_data.py). Theoretically, we want to support most of the operators in that list (that aren't composite or out variants).
-
-
-