[ETHOSN] Fix tests pylint errors (#12649)

This pr fixes pylint errors in tests/python/contrib/test_ethosn as reported in issue #11414.

tests/lint/pylint.sh

@@ -21,6 +21,7 @@ python3 -m pylint python/tvm --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint vta/python/vta --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/unittest/test_tvmscript_type.py --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/contrib/test_cmsisnn --rcfile="$(dirname "$0")"/pylintrc
+python3 -m pylint tests/python/contrib/test_ethosn --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/relay/aot/*.py --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/ci --rcfile="$(dirname "$0")"/pylintrc
 python3 -m pylint tests/python/integration/ --rcfile="$(dirname "$0")"/pylintrc

tests/python/contrib/test_ethosn/infrastructure.py

@@ -18,17 +18,17 @@
 """Arm(R) Ethos(TM)-N test functions"""
 
 from __future__ import absolute_import, print_function
-import tvm
-from tvm import relay
-from tvm.contrib import utils, graph_executor, download
 from hashlib import md5
 from itertools import zip_longest, combinations
+import os
 import numpy as np
 from PIL import Image
-import os
 
-from . import _infrastructure
+import tvm
+from tvm import relay
+from tvm.contrib import utils, graph_executor, download
 from tvm.relay.op.contrib import partition_for_ethosn
+from . import _infrastructure
 
 
 def get_real_image(im_height, im_width):
@@ -82,43 +82,47 @@ def make_module(func, params):
 
 
 def make_ethosn_composite(ethosn_expr, name):
-    vars = relay.analysis.free_vars(ethosn_expr)
-    inner_vars = [relay.Var(v.name_hint, v.type_annotation) for v in vars]
+    variables = relay.analysis.free_vars(ethosn_expr)
+    inner_vars = [relay.Var(v.name_hint, v.type_annotation) for v in variables]
     func = relay.Function(inner_vars, ethosn_expr)
     func = func.with_attr("Composite", name)
-    call = relay.Call(func, vars)
+    call = relay.Call(func, variables)
     return call
 
 
 def make_ethosn_partition(ethosn_expr):
+    """Make an Ethos(TM)-N partition."""
+
     # Create an Ethos-N global function
     mod = tvm.IRModule({})
-    vars = relay.analysis.free_vars(ethosn_expr)
+    variables = relay.analysis.free_vars(ethosn_expr)
     # NB: it is illegal to reuse variables inside and outside a scope in Relay
     # if you want to duplicate types and names you must re-allocate them.
-    fresh_vars = [relay.Var(v.name_hint, v.type_annotation) for v in vars]
+    fresh_vars = [relay.Var(v.name_hint, v.type_annotation) for v in variables]
     binds = {}
-    for var, fresh_var in zip(vars, fresh_vars):
+    for var, fresh_var in zip(variables, fresh_vars):
         binds[var] = fresh_var
     ethosn_expr_fresh = relay.bind(ethosn_expr, binds)
     func = relay.Function(fresh_vars, ethosn_expr_fresh)
     func = func.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
     func = func.with_attr("Inline", tvm.tir.IntImm("int32", 1))
     func = func.with_attr("Compiler", "ethos-n")
     func = func.with_attr("global_symbol", "ethos-n_0")
-    g1 = relay.GlobalVar("ethos-n_0")
-    mod[g1] = func
+    global_var = relay.GlobalVar("ethos-n_0")
+    mod[global_var] = func
     mod = relay.transform.InferType()(mod)
 
     # These are the vars to call the Ethos-N partition with
     more_vars = relay.analysis.free_vars(ethosn_expr)
     # Call the Ethos-N partition in main
-    call_fn1 = g1(*more_vars)
+    call_fn1 = global_var(*more_vars)
     mod["main"] = relay.Function(more_vars, call_fn1)
     return relay.transform.InferType()(mod)
 
 
 def get_host_op_count(mod):
+    """Return the number of host operators."""
+
     class Counter(tvm.relay.ExprVisitor):
         def __init__(self):
             super().__init__()
@@ -219,9 +223,7 @@ def run(lib, inputs, outputs, npu=True):
     return out
 
 
-def build_and_run(
-    mod, inputs, outputs, params, device=tvm.cpu(), npu=True, expected_host_ops=0, npu_partitions=1
-):
+def build_and_run(mod, inputs, outputs, params, npu=True, expected_host_ops=0, npu_partitions=1):
     lib = build(mod, params, npu, expected_host_ops, npu_partitions)
     return run(lib, inputs, outputs, npu)
 
@@ -254,6 +256,8 @@ def inference_result(outputs):
 
 
 def test_error(mod, params, err_msg):
+    """Test an operator error message."""
+
     caught = None
     with tvm.transform.PassContext(
         opt_level=3, config={"relay.ext.ethos-n.options": {"variant": get_ethosn_variant()}}
@@ -262,8 +266,8 @@ def test_error(mod, params, err_msg):
             try:
                 mod = relay.transform.InferType()(mod)
                 relay.build(mod, params=params)
-            except tvm.error.TVMError as e:
-                caught = e.args[0]
+            except tvm.error.TVMError as error:
+                caught = error.args[0]
             finally:
                 relay.backend.te_compiler.get().clear()
 
@@ -275,8 +279,8 @@ def get_conv2d(var, shape, dtype):
     """Standard convolution to test activation functions"""
 
     weight_shape = (1, 1, shape[3], 1)
-    w = tvm.nd.array(np.ones(weight_shape, dtype))
-    weights = relay.const(w, dtype)
+    weights_array = tvm.nd.array(np.ones(weight_shape, dtype))
+    weights = relay.const(weights_array, dtype)
     conv = relay.qnn.op.conv2d(
         var,
         weights,
@@ -300,13 +304,15 @@ def get_conv2d(var, shape, dtype):
         relay.const(0, "int32"),  # output zero point
         out_dtype=dtype,
     )
-    params = {"w": w, "b": b}
+    params = {"w": weights_array, "b": b}
     return req, params
 
 
 def get_conv2d_qnn_params(
     dtype, input_zp, input_sc, kernel_zp, kernel_sc, kernel_h, kernel_w, channels
 ):
+    """Return Conv2D QNN params."""
+
     kernel_sc = (
         kernel_sc.numpy() if isinstance(kernel_sc, tvm.runtime.ndarray.NDArray) else [kernel_sc]
     )

tests/python/contrib/test_ethosn/test_concatenate.py

@@ -57,6 +57,8 @@ def _get_model(shapes, dtype, axis):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_concatenate(dtype):
+    """Compare Concatenate output with TVM."""
+
     trials = [
         ([(1, 4), (1, 6)], 1),
         ([(1, 16, 4), (1, 16, 4)], 1),
@@ -78,19 +80,23 @@ def test_concatenate(dtype):
 
 @requires_ethosn
 def test_concatenate_failure():
+    """Check Concatenate error messages."""
+
     trials = [
         ([(1, 4, 4, 4, 4), (1, 4, 4, 4, 4)], "uint8", 1, "dimensions=5, dimensions must be <= 4;"),
         (
             [(1, 4, 4, 4), (1, 4, 4, 4)],
             "uint8",
             3,
-            "Concatenation along the channels dimension (axis 3) requires input tensors with a multiple of 16 channels;",
+            "Concatenation along the channels dimension (axis 3) "
+            "requires input tensors with a multiple of 16 channels;",
         ),
         (
             [(1, 4, 4, 4), (1, 4, 4, 4)],
             "int16",
             2,
-            "dtype='int16', dtype must be either uint8, int8 or int32; dtype='int16', dtype must be either uint8, int8 or int32;",
+            "dtype='int16', dtype must be either uint8, int8 or int32; dtype='int16', "
+            "dtype must be either uint8, int8 or int32;",
         ),
         (
             [(2, 4, 4, 4), (2, 4, 4, 4)],

tests/python/contrib/test_ethosn/test_constant_duplication.py

@@ -36,8 +36,10 @@ def _get_model():
     add_const = relay.const(add_const_value, "uint8")
     a = relay.add(a, add_const)
     weight_shape = (kernel_h, kernel_w, shape[3], out_channels)
-    w = tvm.nd.array(np.random.randint(low=0, high=255, size=weight_shape, dtype="uint8"))
-    weights = relay.const(w, "uint8")
+    weights_array = tvm.nd.array(
+        np.random.randint(low=0, high=255, size=weight_shape, dtype="uint8")
+    )
+    weights = relay.const(weights_array, "uint8")
     conv = relay.qnn.op.conv2d(
         a,
         weights,
@@ -66,12 +68,14 @@ def _get_model():
         relay.const(0, "int32"),  # output zero point
         out_dtype="uint8",
     )
-    params = {"w": w, "b": b}
+    params = {"w": weights_array, "b": b}
     return req, params
 
 
 @requires_ethosn
 def test_constant_duplication():
+    """Test that constants are not duplicated."""
+
     np.random.seed(0)
     model, params = _get_model()
     mod = tei.make_module(model, params)

tests/python/contrib/test_ethosn/test_conv2d.py

@@ -17,9 +17,9 @@
 
 """Arm(R) Ethos(TM)-N integration conv2d tests"""
 
+import math
 import numpy as np
 import pytest
-import math
 import tvm
 from tvm import relay
 from tvm.testing import requires_ethosn
@@ -61,7 +61,7 @@ def _get_model(
 ):
     """Return a model and any parameters it may have"""
     a = relay.var("a", shape=shape, dtype=dtype)
-    if pad == "op" or pad == "both":
+    if pad in ("op", "both"):
         p = _get_same_padding((shape[1], shape[2]), (kernel_h, kernel_w), dilation, strides)
         a = relay.nn.pad(
             a,
@@ -76,12 +76,12 @@ def _get_model(
         weight_shape = (kernel_h, kernel_w, shape[3] // groups, out_channels)
     else:
         weight_shape = (kernel_h, kernel_w, out_channels, 1)
-    w = tvm.nd.array(
+    weights_array = tvm.nd.array(
         np.random.randint(
             np.iinfo(dtype).min, high=np.iinfo(dtype).max + 1, size=weight_shape, dtype=dtype
         )
     )
-    weights = relay.const(w, dtype)
+    weights = relay.const(weights_array, dtype)
     conv = relay.qnn.op.conv2d(
         a,
         weights,
@@ -96,7 +96,7 @@ def _get_model(
         strides=strides,
         groups=groups,
         channels=out_channels,
-        padding=p if pad == "attr" or pad == "both" else (0, 0, 0, 0),
+        padding=p if pad in ("attr", "both") else (0, 0, 0, 0),
         out_dtype="int32",
     )
     b = tvm.nd.array(
@@ -118,14 +118,16 @@ def _get_model(
         relay.const(output_zp, "int32"),  # output zero point
         out_dtype=dtype,
     )
-    params = {"w": w, "b": b}
+    params = {"w": weights_array, "b": b}
     return req, params
 
 
 @requires_ethosn
 @pytest.mark.parametrize("depthwise", [False, True])
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_conv2d(dtype, depthwise):
+    """Compare Conv2D output with TVM."""
+
     trials = [
         [(1, 17, 20, 26), 4, 3, 1, "attr", (2, 2), (1, 1), False],
         [(1, 30, 27, 30), 5, 5, 3, "none", (1, 1), (1, 1), False],
@@ -208,6 +210,8 @@ def test_conv2d(dtype, depthwise):
 
 @requires_ethosn
 def test_conv2d_failure():
+    """Check Conv2D error messages."""
+
     trials = [
         (
             (1, 4, 4, 4),
@@ -326,7 +330,7 @@ def test_conv2d_failure():
         weight_format,
         err_msg,
     ) in trials:
-        model, params = _get_model(
+        model, _ = _get_model(
             shape,
             kernel_h,
             kernel_w,

tests/python/contrib/test_ethosn/test_depth_to_space.py

@@ -34,6 +34,8 @@ def _get_model(shape, block, dtype, layout):
 @requires_ethosn
 @pytest.mark.parametrize("dtype", ["uint8", "int8"])
 def test_depth_to_space(dtype):
+    """Compare Depth To Space output with TVM."""
+
     trials = [
         (1, 16, 16, 16),
         (1, 64, 32, 16),
@@ -59,6 +61,8 @@ def test_depth_to_space(dtype):
 
 @requires_ethosn
 def test_depth_to_space_failure():
+    """Check Depth To Space error messages."""
+
     trials = [
         ((2, 16, 16, 16), 2, "uint8", "NHWC", "batch size=2, batch size must = 1"),
         (

tests/python/contrib/test_ethosn/test_fullyconnected.py

@@ -30,9 +30,9 @@ def _get_model(
 ):
     """Return a model an any parameters it may have"""
     a = relay.var("a", shape=shape, dtype=dtype)
-    w = tvm.nd.array(np.ones(weight_shape, dtype))
-    weights = relay.const(w, dtype)
-    fc = relay.qnn.op.dense(
+    weights_array = tvm.nd.array(np.ones(weight_shape, dtype))
+    weights = relay.const(weights_array, dtype)
+    dense = relay.qnn.op.dense(
         a,
         weights,
         input_zero_point=relay.const(input_zp, "int32"),
@@ -44,7 +44,7 @@ def _get_model(
     )
     b = tvm.nd.array(np.random.randint(0, high=255, size=(weight_shape[0],), dtype="int32"))
     biasc = relay.const(b, "int32")
-    bias = relay.nn.bias_add(fc, biasc)
+    bias = relay.nn.bias_add(dense, biasc)
     req = relay.qnn.op.requantize(
         bias,
         relay.const(input_sc * kernel_sc, "float32"),  # input zero scale
@@ -53,7 +53,7 @@ def _get_model(
         relay.const(output_zp, "int32"),  # output zero point
         out_dtype=dtype,
     )
-    params = {"w": w, "b": b}
+    params = {"w": weights_array, "b": b}
     return req, params
 
 
@@ -76,9 +76,8 @@ def _get_model(
     ],
 )
 def test_fullyconnected(shape, out_channels, dtype, input_zp, input_sc, kernel_zp, kernel_sc):
-    """
-    Test fully connected offloading.
-    """
+    """Compare Fully Connected output with TVM."""
+
     np.random.seed(0)
     inputs = {
         "a": tvm.nd.array(
@@ -116,6 +115,8 @@ def test_fullyconnected(shape, out_channels, dtype, input_zp, input_sc, kernel_z
 
 @requires_ethosn
 def test_fullyconnected_failure():
+    """Check Fully Connected error messages."""
+
     trials = [
         (
             (1, 64),
@@ -139,7 +140,8 @@ def test_fullyconnected_failure():
             0,
             1,
             "uint8",
-            "Weights tensor must have I dimension equal to the number of channels of the input tensor.;",
+            "Weights tensor must have I dimension equal to the number"
+            " of channels of the input tensor.;",
         ),
         ((1024, 64), (1, 64), 0, 1, 0, 1, 0, 1, "uint8", "batch size=1024, batch size must = 1;"),
     ]
@@ -157,10 +159,7 @@ def test_fullyconnected_failure():
         dtype,
         err_msg,
     ) in trials:
-        inputs = {
-            "a": tvm.nd.array(np.random.randint(0, high=255, size=shape, dtype=dtype)),
-        }
-        model, params = _get_model(
+        model, _ = _get_model(
             shape,
             weight_shape,
             input_zp,

tests/python/contrib/test_ethosn/test_leaky_relu.py

@@ -49,6 +49,7 @@ def _get_model(shape, input_zp, input_sc, output_zp, output_sc, dtype, alpha):
 @pytest.mark.parametrize("alpha", [0.001, 0.5678])
 def test_leaky_relu(dtype, shape, alpha):
     """Compare Leaky ReLU output with TVM."""
+
     np.random.seed(0)
 
     iinfo = np.iinfo(dtype)
@@ -75,6 +76,7 @@ def test_leaky_relu(dtype, shape, alpha):
 @pytest.mark.parametrize("alpha", [-1.34, 2.32, 1, 0])
 def test_leaky_relu_unsupported_alpha(dtype, shape, alpha):
     """Test unsupported values of alpha (<= 0, >= 1) in Leaky ReLU."""
+
     iinfo = np.iinfo(dtype)
     zp_min = iinfo.min