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composable_kernel/dispatcher/tests/test_depthwise_tile_math.py
Ville Pietilä 60b276647b [rocm-libraries] ROCm/rocm-libraries#8157 (commit b0d9d39)
[CK Tile] Rule-based configuration generation in CK
 Dispatcher codegen (#8157)
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## Motivation

The CK Tile Dispatcher code generation for CK Tile Profiler relies on
flat JSON files to list the generated configurations. This approach has
the following problems

- The JSON files are verbose
- The JSON files get easily out of sync with the CK Builder .config
files from which they were generated from.
- The JSON file based configuration make it hard to list explicitly the
rules that govern the instance generation.

## Technical Details

Replaced the JSON files with a rule based configuration. To preserve the
existing functionality, the `profiler` and the `tests` instance sets are
generated directly from the CK Builder config files. The JSON config
files are removed from source control, and the "on-the-fly" generation
guarantees that the Dispatcher codegen uses up to date configurations.

This is PR introduces six different rule sets for the CK Tile Dispatcher
code generation

1. `profiler`: matches with the old JSON set of profiler configurations.
2. `tests`: matches with the old JSON set of tests configurations.
3. `full`: full configuration set created from a rule-based config
selection
4. `full-tests`: a subset of `full` for generating configurations for
convolution integration tests.
5. `tiny`: a subset of `full-tests` to produce the minimal set of
configurations to test the Dispatcher codegen.
6. `default`: the default rules, which corresponds to the existing
heuristic rules for configuration selection. This ensures that ML based
kernel selection doesn't get broken.

The main use of the `full` rule set is to define a reasonable solution
space for the possible implicit GEMM configurations. We start from the
configurations that allowed by the device architecture. The `full` rule
set defines the relevant tile sizes for each convolution direction. From
the tile size we have a curated mapping to the number of waves over the
different GEMM axes, i.e., we describe how many waves each GEMM
dimensions corresponds to. The GEMM-K wave tile dimension can be
computed from the other parameters and does not need to be listed
explicitly.

An orthogonal axis to the tiling strategy is the vectorization strategy.
This mainly defined by the data type and hardware as in general, we want
to use the maximum possible load widths. The maximum sizes for each
convolution direction variant are defined by the implicit GEMM matrix
dimensions. For cases where have a low number of channels per
convolution group, we need smaller vector load sizes. These are captured
by the `VecStrategy` enumeration in the codegen rules.

The problem with the rule based configuration selection is that we "over
generate" configurations. The old JSON configurations compose
approximately 25% of all configuration that the `full` rule set creates.
The additional configurations are valid, but they many not provide any
performance benefits. Hence, we keep the `profiler` and `tests` rule set
for now to avoid building an excessive amount configurations by default.
The `full` rule set can be taken into use by specifying CMake
configuration flag `-D DISPATCHER_RULE_SET=full`. By default, the
`tests` rule set is used, i.e., we don't change the existing bahaviour.

## Test Plan

Added a new stage in the CI/CD pipeline that ensures the Dispatcher
codegen rules are up to date. Otherwise the functionality is covered by
the existing CI/CD tests. There are no functional changes to the
convolution kernels. Only how the different instances are generated.

## Test Result

If the CK Tile conv instances build without errors, the Dispatcher
codegen is generating valid code. If all tests in CI/CD pipeline are
passing, the Dispatcher codegen generates valid instances.

## Submission Checklist

- [x] Look over the contributing guidelines at
https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
2026-06-18 01:22:50 +00:00

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#!/usr/bin/env python3
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
# SPDX-License-Identifier: MIT
"""
Unit tests for depthwise convolution rules in codegen/tile_math.py.
Ground truth: all 20 profiler depthwise configs extracted from
configs/grouped_conv/forward/profiler/ngchw_fp16.json (identical
across fp16/bf16/fp32).
Run:
cd projects/composablekernel/dispatcher
python3 -m unittest tests/test_depthwise_tile_math.py -v
"""
import sys
import unittest
from pathlib import Path
SCRIPT_DIR = Path(__file__).parent.resolve()
DISPATCHER_DIR = SCRIPT_DIR.parent
sys.path.insert(0, str(DISPATCHER_DIR / "codegen"))
from grouped_conv.tile_math import ( # noqa: E402
DepthwiseConfig,
is_valid_depthwise_config,
get_valid_depthwise_configs,
)
# =============================================================================
# Reference configs from JSON ground truth
# =============================================================================
# Tuple order: (tile_h, tile_w, filt, str_h, str_w, pad_h, pad_w,
# nbatch, sub_h, sub_w, in_vec, out_vec)
DEPTHWISE_PROFILER_CONFIGS = [
(8, 8, 3, 1, 1, 1, 1, 8, 2, 2, 2, 2),
(16, 16, 3, 1, 1, 1, 1, 8, 1, 4, 8, 8),
(16, 16, 3, 1, 1, 1, 1, 1, 2, 2, 2, 2),
(28, 28, 3, 1, 1, 1, 1, 1, 4, 4, 8, 8),
(32, 32, 3, 1, 1, 1, 1, 1, 4, 4, 8, 8),
(16, 16, 3, 2, 2, 1, 1, 2, 1, 4, 8, 8),
(16, 16, 3, 2, 2, 1, 1, 1, 1, 4, 8, 8),
(16, 16, 3, 2, 2, 1, 1, 1, 2, 2, 8, 8),
(16, 16, 3, 2, 2, 1, 1, 1, 2, 2, 2, 2),
(14, 28, 3, 2, 2, 1, 1, 1, 2, 4, 8, 8),
(32, 32, 3, 2, 2, 1, 1, 2, 4, 4, 8, 8),
(32, 32, 3, 2, 2, 1, 1, 1, 4, 4, 4, 4),
(32, 32, 3, 2, 2, 1, 1, 1, 4, 4, 8, 8),
(32, 32, 3, 2, 2, 1, 1, 1, 2, 8, 8, 8),
(8, 8, 5, 1, 1, 2, 2, 1, 1, 1, 1, 1),
(8, 8, 5, 1, 1, 2, 2, 8, 2, 2, 2, 2),
(16, 16, 5, 1, 1, 2, 2, 1, 1, 4, 8, 8),
(16, 16, 5, 1, 1, 2, 2, 8, 1, 4, 8, 8),
(28, 28, 5, 1, 1, 2, 2, 8, 4, 4, 8, 8),
(32, 32, 5, 1, 1, 2, 2, 4, 4, 4, 8, 8),
]
DEPTHWISE_TEST_CONFIGS = [
(8, 8, 3, 1, 1, 1, 1, 8, 2, 2, 2, 2),
(32, 32, 3, 1, 1, 1, 1, 1, 4, 4, 8, 8),
(16, 16, 3, 2, 2, 1, 1, 2, 1, 4, 8, 8),
(32, 32, 3, 2, 2, 1, 1, 1, 2, 8, 8, 8),
(8, 8, 5, 1, 1, 2, 2, 1, 1, 1, 1, 1),
(32, 32, 5, 1, 1, 2, 2, 4, 4, 4, 8, 8),
]
# Tile/filter/stride space matching grouped_config_rules_default.py
TILE_SIZES = [(8, 8), (14, 28), (16, 16), (28, 28), (32, 32)]
FILTER_SIZES = [3, 5]
STRIDES = [(1, 1), (2, 2)]
def _tuple_to_cfg(t):
"""Convert a 12-tuple to a DepthwiseConfig."""
return DepthwiseConfig(*t)
def _cfg_to_tuple(c):
"""Convert a DepthwiseConfig to a 12-tuple."""
return (c.tile_h, c.tile_w, c.filt, c.str_h, c.str_w,
c.pad_h, c.pad_w, c.nbatch, c.sub_h, c.sub_w,
c.in_vec, c.out_vec)
# =============================================================================
# Tests
# =============================================================================
class TestIsValidDepthwiseConfig(unittest.TestCase):
"""Tests for is_valid_depthwise_config()."""
def test_all_reference_configs_valid(self):
"""Every JSON reference config must pass validation."""
for t in DEPTHWISE_PROFILER_CONFIGS:
cfg = _tuple_to_cfg(t)
self.assertTrue(
is_valid_depthwise_config(cfg),
f"Reference config should be valid: {t}",
)
def test_all_reference_configs_valid_fp32(self):
"""Reference configs must also be valid with fp32 dtype_size=4."""
for t in DEPTHWISE_PROFILER_CONFIGS:
cfg = _tuple_to_cfg(t)
self.assertTrue(is_valid_depthwise_config(cfg, dtype_size=4))
def test_odd_filter_required(self):
"""Even filter size must be rejected."""
cfg = DepthwiseConfig(8, 8, 4, 1, 1, 1, 1, 8, 2, 2, 2, 2)
self.assertFalse(is_valid_depthwise_config(cfg))
def test_pad_w_must_be_positive(self):
"""PadW=0 must be rejected."""
cfg = DepthwiseConfig(8, 8, 3, 1, 1, 1, 0, 8, 2, 2, 2, 2)
self.assertFalse(is_valid_depthwise_config(cfg))
def test_subtile_exceeds_tile(self):
"""sub_h > tile_h must be rejected."""
cfg = DepthwiseConfig(8, 8, 3, 1, 1, 1, 1, 8, 16, 2, 2, 2)
self.assertFalse(is_valid_depthwise_config(cfg))
def test_total_subtiles_exceeds_64(self):
"""Config with too many subtiles must be rejected."""
# sub_h=1, sub_w=1 on tile 16x16 → 256 subtiles > 64
cfg = DepthwiseConfig(16, 16, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1)
self.assertFalse(is_valid_depthwise_config(cfg))
def test_nbatch_divisibility(self):
"""nbatch must be divisible by tile_per_wave."""
# tile_h=8, tile_w=8, sub_h=2, sub_w=2 → 4×4=16 subtiles
# tile_per_wave = 64//16 = 4, so nbatch=3 is invalid
cfg = DepthwiseConfig(8, 8, 3, 1, 1, 1, 1, 3, 2, 2, 2, 2)
self.assertFalse(is_valid_depthwise_config(cfg))
def test_vec_not_power_of_2(self):
"""Non-power-of-2 vector size must be rejected."""
cfg = DepthwiseConfig(8, 8, 3, 1, 1, 1, 1, 8, 2, 2, 3, 2)
self.assertFalse(is_valid_depthwise_config(cfg))
def test_stride_w_constraint(self):
"""Odd StrideW != 1 must be rejected."""
cfg = DepthwiseConfig(8, 8, 3, 1, 3, 1, 1, 8, 2, 2, 2, 2)
self.assertFalse(is_valid_depthwise_config(cfg))
class TestGetValidDepthwiseConfigs(unittest.TestCase):
"""Tests for get_valid_depthwise_configs()."""
@classmethod
def setUpClass(cls):
"""Generate configs once for all tests."""
cls.generated = get_valid_depthwise_configs(
TILE_SIZES, FILTER_SIZES, STRIDES,
)
cls.generated_set = {_cfg_to_tuple(c) for c in cls.generated}
def test_no_false_negatives(self):
"""Every JSON profiler reference config must be generated by rules."""
missing = []
for ref in DEPTHWISE_PROFILER_CONFIGS:
if ref not in self.generated_set:
missing.append(ref)
if missing:
lines = [f" {m}" for m in missing]
self.fail(
f"{len(missing)}/20 profiler configs missing:\n"
+ "\n".join(lines)
)
def test_test_configs_are_subset(self):
"""Test configs must also be in the generated set."""
for ref in DEPTHWISE_TEST_CONFIGS:
self.assertIn(ref, self.generated_set,
f"Test config missing: {ref}")
def test_all_generated_are_valid(self):
"""Every generated config must pass all constraint checks."""
invalid = []
for cfg in self.generated:
if not is_valid_depthwise_config(cfg):
invalid.append(_cfg_to_tuple(cfg))
if invalid:
self.fail(f"{len(invalid)} generated configs are invalid")
def test_no_duplicates(self):
"""Generated list must not contain duplicates."""
self.assertEqual(
len(self.generated), len(self.generated_set),
"Duplicate configs found in generated list",
)
def test_generates_nonzero_configs(self):
"""Must generate at least the 20 reference configs."""
self.assertGreaterEqual(len(self.generated), 20)
def test_coverage_rate(self):
"""Log coverage statistics (informational)."""
n_ref = len(DEPTHWISE_PROFILER_CONFIGS)
n_covered = sum(1 for r in DEPTHWISE_PROFILER_CONFIGS
if r in self.generated_set)
n_total = len(self.generated)
print(f"\n[depthwise coverage] {n_covered}/{n_ref} reference covered, "
f"{n_total} total generated")
self.assertEqual(n_covered, n_ref,
f"Coverage {n_covered}/{n_ref} < 100%")
def test_empty_input(self):
"""Empty tile_sizes should return no configs."""
self.assertEqual(
get_valid_depthwise_configs([], [3], [(1, 1)]),
[],
)
def test_smem_constraint_rejects_huge_tile_fp32(self):
"""Very large tiles with fp32 should produce fewer configs due to SmemSize."""
# fp32 (dtype_size=4) doubles SmemSize, pruning more configs
fp16 = get_valid_depthwise_configs([(32, 32)], [5], [(1, 1)], dtype_size=2)
fp32 = get_valid_depthwise_configs([(32, 32)], [5], [(1, 1)], dtype_size=4)
self.assertLess(len(fp32), len(fp16),
"fp32 should produce fewer valid configs than fp16")
if __name__ == "__main__":
unittest.main(verbosity=2)