[STYLE] check python with flake8 (#424)

I've been using this locally to find errors without running tests, and now that we're using autopep8, it passes with minimal suppressions. This is also what turned up the issues with the tutorials, which were fixed in #422.

.github/workflows/integration-tests.yml

@@ -36,6 +36,9 @@ jobs:
       - name: Check style
         run: "autopep8 -a -r -d --exit-code ./python || ( echo '::error title=Style issues::Please run \"autopep8 -a -r -i ./python\"' ; exit 1 )"
 
+      - name: Flake8
+        run: "flake8 --config ./python/setup.cfg ./python || ( echo '::error::Flake8 failed; see logs for errors.' ; exit 1 )"
+
       - name: Unit tests
         run: |
           cd python/test/unit

python/bench/bench_matmul.py

@@ -50,7 +50,6 @@ def bench_op(M, N, K, AT, BT, dtype, provider, warmup=25, rep=75):
         a = a.t()
     if BT:
         b = b.t()
-    num_flops = 2 * M * N * K
     tflops = lambda ms: 2. * M * N * K / ms * 1e-9
     if provider == "cublas":
         ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b), warmup=warmup, rep=rep)

python/setup.cfg

@@ -3,3 +3,6 @@ description_file = README.md
 
 [pycodestyle]
 ignore = E501,E701,E731
+
+[flake8]
+ignore = E501,E701,E731

python/setup.py

@@ -149,6 +149,7 @@ setup(
     extras_require={
         "tests": [
             "autopep8",
+            "flake8",
             "isort",
             "numpy",
             "pytest",

python/test/regression/test_performance.py

@@ -3,7 +3,6 @@ import sys
 
 import pytest
 import torch
-from numpy import record
 
 import triton
 import triton.language as tl

python/test/unit/language/test_core.py

@@ -1,3 +1,4 @@
+# flake8: noqa: F821,F841
 import copy
 import itertools
 import re

python/test/unit/language/test_random.py

@@ -2,7 +2,6 @@ import numpy as np
 import pytest
 import scipy.stats
 import torch
-from numpy.random import Philox
 
 import triton
 import triton.language as tl

python/triton/__init__.py

@@ -1,4 +1,5 @@
 """isort:skip_file"""
+# flake8: noqa: F401
 __version__ = '2.0.0'
 
 # TODO: torch needs to be imported first

python/triton/code_gen.py

@@ -1,19 +1,17 @@
 import ast
 import builtins
-import dbm
 import functools
 import hashlib
 import inspect
 import os
 import pickle
-import struct
 import subprocess
 import sys
 import tempfile
 import textwrap
 import time
 import warnings
-from typing import Dict, Optional
+from typing import Dict
 
 import torch
 from filelock import FileLock
@@ -406,7 +404,7 @@ class CodeGenerator(ast.NodeVisitor):
                                                    self.visit(pos_cond_node),
                                                    self.visit(neg_cond_node),
                                                    _builder=self.builder)
-        #cond_node = neg_cond_node
+        # cond_node = neg_cond_node
         step_node = ast.AugAssign(target=st_target, op=ast.Add(), value=arg_2)
         # code generation
         current_bb = self.builder.get_insert_block()

python/triton/language/__init__.py

@@ -1,3 +1,4 @@
+# flake8: noqa: F401
 from . import core, random
 from .core import *
 from .random import *

python/triton/language/core.py

@@ -802,14 +802,6 @@ def max_contiguous(input, value, _builder=None):
     return frontend.max_contiguous(input, value, _builder)
 
 
-@builtin
-def max_contiguous(input, value, _builder=None):
-    """
-    Let the compiler knows that the `value` first values in :code:`input` are contiguous.
-    """
-    return frontend.max_contiguous(input, value, _builder)
-
-
 # -----------------------
 # Standard library
 # -----------------------

python/triton/ops/__init__.py

@@ -1,3 +1,4 @@
+# flake8: noqa: F401
 #from .conv import _conv, conv
 from . import blocksparse
 from .cross_entropy import _cross_entropy, cross_entropy

python/triton/ops/blocksparse/__init__.py

@@ -1,2 +1,3 @@
+# flake8: noqa: F401
 from .matmul import matmul
 from .softmax import softmax

python/triton/ops/cross_entropy.py

@@ -1,5 +1,3 @@
-import os
-
 import torch
 
 import triton
@@ -96,11 +94,9 @@ class _cross_entropy(torch.autograd.Function):
         """
         # load saved tensors
         neg_logprobs, indices = ctx.saved_tensors
-        # make kernel
-        device, dtype = neg_logprobs.device, neg_logprobs.dtype
-        n_cols = neg_logprobs.shape[-1]
         # run the kernel
         # neg_logprobs will be modified in place to become our gradient:
+        n_cols = neg_logprobs.shape[-1]
         grid = lambda opt: (neg_logprobs.numel() // n_cols, )
         _backward[grid](neg_logprobs, indices, dneg_logprobs, n_cols)
         return neg_logprobs, None

python/triton/ops/matmul.py

@@ -2,7 +2,7 @@ import torch
 
 import triton
 import triton.language as tl
-from .matmul_perf_model import *
+from .matmul_perf_model import estimate_matmul_time, prune_num_stages
 
 
 def init_to_zero(name):

python/triton/tools/disasm.py

@@ -20,7 +20,6 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-import argparse
 import re
 import subprocess
 
@@ -75,7 +74,7 @@ def extract(file_path, fun):
         # .headerflags: ...
         # /*0000*/ asmstr /*0x...*/
         #                 /*0x...*/
-        fname_match = FNAME_RE.match(line)
+
         # Looking for new function header (function: <name>)
         while FNAME_RE.match(line) is None:
             line_idx += 1

python/tutorials/01-vector-add.py

@@ -65,7 +65,7 @@ def add(x: torch.Tensor, y: torch.Tensor):
     #  - each torch.tensor object is implicitly converted into a pointer to its first element.
     #  - `triton.jit`'ed functions can be index with a launch grid to obtain a callable GPU kernel
     #  - don't forget to pass meta-parameters as keywords arguments
-    pgm = add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
+    add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
     # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
     # running asynchronously at this point.
     return output