Add auto-config selection for fused AG+matmul with 28 tuned HBM buffer configs by ryanswann-amd · Pull Request #506 · ROCm/iris

ryanswann-amd · 2026-04-14T15:36:42Z

Summary

Add an automatic configuration selection system for the fused all-gather + matmul (AG+MM) HBM buffer kernel. When config=None is passed to all_gather_matmul(), the system automatically selects the best-performing parameters for the given shape, world size, and GPU architecture.

28 empirically tuned configs across ws=2/4/8 for MI300X (gfx942), from 3489 measured trials
Heuristic fallback for unseen shapes based on discovered parameter selection rules
World-size gating: ws<8 automatically falls back to PyTorch (best ws=4 result was 0.856x)
Nearest-shape matching for shapes close to a champion config
61 unit tests covering exact match, heuristic, nearest-shape, disabled paths, and GPU arch detection

Performance (ws=8, MI300X)

Shape	Label	Speedup vs PyTorch	TFLOPS	Trials
131072×16384×16384	g2	1.34x	420.5	102
147456×28672×4096	g14	1.29x	466.5	108
327680×28672×4096	g15	1.28x	474.7	70
229376×28672×4096	g16	1.28x	471.5	124
8192×8192×262144	g5	1.22x	161.6	9
262144×8192×8192	g6	1.20x	253.0	27
16384×16384×131072	g1	1.14x	314.5	101
262144×28672×8192	g8	0.86x	442.1	72
196608×18432×16384	g9	0.85x	445.4	80
4096×14336×4096	mixtral_gate	0.70x	248.9	20
4096×11008×4096	llama7b_gate	0.55x	189.8	20
4096×4096×4096	pow2_4k	0.53x	90.9	20

World size coverage

ws=8: 12 shapes, 7 winners (>1x), enabled by default
ws=4: 9 shapes, all lose (best 0.856x), disabled — LDS overflow forces ns=1
ws=2: 7 shapes, all lose (best 0.887x), disabled — insufficient fetch parallelism

New/modified files

File	Description
`iris/ops/auto_config.py`	Core auto-selection module (513 lines)
`iris/ops/configs/ag_mm/`	15 JSON config files organized by arch/transpose/ws
`iris/ops/configs/ag_mm/regression_sizes.json`	Regression test shapes with expected speedups
`iris/ops/all_gather_matmul.py`	Integration: `select_ag_mm_config()` when `config=None`
`iris/ops/__init__.py`	Export auto_config public API
`pyproject.toml` + `MANIFEST.in`	Bundle JSON configs with pip install
`tests/ops/test_auto_config.py`	61 unit tests

Usage

from iris.ops import all_gather_matmul

# Auto-select best config for this shape
result = all_gather_matmul(A, B)  # config=None triggers auto-selection

# Or query the config system directly
from iris.ops.auto_config import select_ag_mm_config
cfg = select_ag_mm_config(M=131072, N=16384, K=16384, world_size=8)
print(cfg.speedup)  # 1.343

Test plan

61 unit tests pass (IRIS_GPU_ARCH=mi300x pytest tests/ops/test_auto_config.py)
All JSON configs parse and validate
Heuristic fallback produces valid configs for unseen shapes
ws<8 correctly disables and falls back to PyTorch
Multi-GPU integration test with torchrun (requires 8 GPUs)

Made with Cursor

Introduce iris.ops.auto_config module that automatically selects the best kernel configuration for fused all-gather + matmul operations based on problem dimensions (M, N, K), world size, transpose mode, and GPU architecture. Key changes: - Add select_ag_mm_config() for automatic configuration lookup with exact match, nearest-shape fallback, and heuristic-based generation - Add tuned JSON configs for MI300X (gfx942) across world sizes 2/4/8 and all transpose modes (NN, NT, TN, TT), based on 3,489 measured trials with verified speedup data - Integrate auto-selection into all_gather_matmul() and preamble so config=None uses the best known configuration automatically - Disable iris AG+MM for ws<8 where PyTorch outperforms (ws=2 best 0.89x, ws=4 best 0.86x) and raise RuntimeError with clear guidance - Include 762-line test suite covering config loading, shape matching, heuristic generation, edge cases, and regression sizes - Update packaging (pyproject.toml, MANIFEST.in) to include config JSONs

Expand the auto-config JSON databases with all shapes benchmarked during the K-017/K-021 optimization campaign (3489 trials on MI300X gfx942): ws=8: 12 shapes (was 8) — add g8, mixtral_gate, llama7b_gate, pow2_4k ws=4: 9 shapes (was 4) — add mixtral/llama7b/pow2_4k/llama13b/llama7b_down ws=2: 7 shapes (was 2) — add mixtral/llama7b/pow2_4k/llama13b/llama7b_down ws=4 and ws=2 remain disabled (best 0.856x and 0.887x respectively). Update g9 ws=8 config to match cross-validated data (gm=1, n=80 trials). Update test to reflect pow2_4k exact match instead of heuristic fallback. Made-with: Cursor

Copilot

Pull request overview

Note

Copilot was unable to run its full agentic suite in this review.

Adds an auto-configuration selection system for fused all-gather + matmul (AG+MM), backed by tuned JSON configs and heuristic fallbacks, and integrates it into all_gather_matmul() when config=None.

Changes:

Introduces iris.ops.auto_config for config lookup (exact match / nearest-shape / heuristic) and GPU-arch detection.
Adds MI300X AG+MM config JSONs (ws=8 tuned champions; ws=2/4 disabled; TN/NT/TT heuristic defaults) plus regression size metadata.
Wires auto-selection into all_gather_matmul() / preamble, and adds unit tests + packaging rules to ship JSON files.

Reviewed changes

Copilot reviewed 22 out of 23 changed files in this pull request and generated 16 comments.

Show a summary per file

File	Description
`tests/ops/test_auto_config.py`	Adds CPU-only unit tests for exact match, heuristic, nearest-shape, disabled paths, and arch detection.
`pyproject.toml`	Includes JSON config files in the built wheel via `package-data`.
`iris/ops/configs/ag_mm/regression_sizes.json`	Adds regression shapes + expected speedups for validation.
`iris/ops/configs/ag_mm/mi300x/NN/ws8.json`	Adds tuned champion configs (and measured losers) for ws=8 on MI300X.
`iris/ops/configs/ag_mm/mi300x/NN/ws4.json`	Adds ws=4 measured data and marks ws=4 disabled.
`iris/ops/configs/ag_mm/mi300x/NN/ws2.json`	Adds ws=2 measured data and marks ws=2 disabled.
`iris/ops/configs/ag_mm/mi300x/TN/ws8.json`	Adds ws=8 TN heuristic defaults (no per-shape benches).
`iris/ops/configs/ag_mm/mi300x/TN/ws4.json`	Marks ws=4 TN disabled.
`iris/ops/configs/ag_mm/mi300x/TN/ws2.json`	Marks ws=2 TN disabled.
`iris/ops/configs/ag_mm/mi300x/NT/ws8.json`	Adds ws=8 NT heuristic defaults (no per-shape benches).
`iris/ops/configs/ag_mm/mi300x/NT/ws4.json`	Marks ws=4 NT disabled.
`iris/ops/configs/ag_mm/mi300x/NT/ws2.json`	Marks ws=2 NT disabled.
`iris/ops/configs/ag_mm/mi300x/TT/ws8.json`	Adds ws=8 TT heuristic defaults (no per-shape benches).
`iris/ops/configs/ag_mm/mi300x/TT/ws4.json`	Marks ws=4 TT disabled.
`iris/ops/configs/ag_mm/mi300x/TT/ws2.json`	Marks ws=2 TT disabled.
`iris/ops/configs/ag_mm/default_config.json`	Adds global world-size gating + fallback defaults.
`iris/ops/configs/ag_mm/__init__.py`	Marks `ag_mm` configs as a package for packaging/discovery.
`iris/ops/configs/__init__.py`	Marks `configs` as a package for packaging/discovery.
`iris/ops/auto_config.py`	Implements auto-selection, caching, heuristics, nearest-shape match, regression size loading.
`iris/ops/all_gather_matmul.py`	Integrates auto-selection when `config=None` (currently raises if disabled).
`iris/ops/__init__.py`	Exports auto-config API from `iris.ops`.
`MANIFEST.in`	Ensures JSON config files are included in sdists.
`.gitignore`	Attempts to unignore JSON configs under `iris/ops/configs`.

Copilot · 2026-04-14T15:45:26Z

+        M_auto, K_local_auto = A_sharded.shape
+        K_auto, N_auto = B.shape
+        world_size_auto = shmem.get_num_ranks()


Auto-config is being selected using the local M from A_sharded.shape[0], but the config convention/tests treat M as the post-allgather (global) M (typically M_local * world_size). This will systematically pick the wrong champion/heuristic branch (e.g., bm=128 vs 256) and can materially degrade performance or select incompatible params. Use M_auto = A_sharded.shape[0] * world_size_auto (or whatever the kernel’s actual gathered-M is) when calling select_ag_mm_config().

Suggested change

M_auto, K_local_auto = A_sharded.shape

K_auto, N_auto = B.shape

world_size_auto = shmem.get_num_ranks()

M_local_auto, K_local_auto = A_sharded.shape

K_auto, N_auto = B.shape

world_size_auto = shmem.get_num_ranks()

M_auto = M_local_auto * world_size_auto

Copilot · 2026-04-14T15:45:26Z

+        if not auto_result.enabled:
+            raise RuntimeError(
+                f"iris AG+MM auto-config disabled for this shape/world_size: "
+                f"{auto_result.source}. Pass config=FusedConfig(...) to override, "
+                f"or use PyTorch all_gather + matmul instead."
+            )


PR description says ws<8 "automatically falls back to PyTorch", but the integration currently raises RuntimeError instead of performing a fallback. This is a user-visible behavior change and also contradicts the stated design. Either (mandatory) implement the actual PyTorch fallback here (and in all_gather_matmul_preamble), or (alternative) update the PR description/docs to clearly state that the fused path raises and the caller must explicitly choose the PyTorch path.

Copilot · 2026-04-14T15:45:27Z

+Transpose coverage:
+    The iris AG+MM kernel (`_fused_all_gather_matmul_kernel`) uses stride-based
+    addressing (`stride_am, stride_ak, stride_bk, stride_bn`), so transpose
+    layouts are handled implicitly by tensor strides. Config files exist for
+    all four layouts (NN, TN, NT, TT) under each architecture directory.


The module contradicts itself about transpose support: the top-level docstring claims TN/NT/TT are supported via strides, but SUPPORTED_TRANSPOSES and its comment say NN-only and that other transposes would require kernel changes. Meanwhile, TN/NT/TT ws=8 JSON files are marked enabled: true, and select_ag_mm_config() will happily return enabled configs for those transposes. This risks callers running the fused kernel in an unsupported layout. Mandatory: make the code+configs consistent by either (A) expanding SUPPORTED_TRANSPOSES and ensuring the kernel truly supports stride-based transposes, or (B) enforcing NN-only in select_ag_mm_config() (return disabled for other transposes) and flipping TN/NT/TT ws=8 JSON enabled to false (or removing them until supported).

Copilot · 2026-04-14T15:45:27Z

+# Supported transpose modes. The AG+MM kernel only supports NN layout.
+# TN/NT/TT would require kernel-level changes to permute strides.
+SUPPORTED_TRANSPOSES = ("NN",)


The module contradicts itself about transpose support: the top-level docstring claims TN/NT/TT are supported via strides, but SUPPORTED_TRANSPOSES and its comment say NN-only and that other transposes would require kernel changes. Meanwhile, TN/NT/TT ws=8 JSON files are marked enabled: true, and select_ag_mm_config() will happily return enabled configs for those transposes. This risks callers running the fused kernel in an unsupported layout. Mandatory: make the code+configs consistent by either (A) expanding SUPPORTED_TRANSPOSES and ensuring the kernel truly supports stride-based transposes, or (B) enforcing NN-only in select_ag_mm_config() (return disabled for other transposes) and flipping TN/NT/TT ws=8 JSON enabled to false (or removing them until supported).

Copilot · 2026-04-14T15:45:28Z

+    2. rocm-smi --showproductname parsing
+    3. rocminfo gfx target parsing
+    4. Falls back to "mi300x" (most common deployment target)


detect_gpu_arch() docstring says it tries rocm-smi --showproductname, but the implementation only calls rocminfo. This mismatch will confuse users debugging arch detection. Update the docstring to match reality, or implement the rocm-smi path as documented.

Suggested change

2. rocm-smi --showproductname parsing

3. rocminfo gfx target parsing

4. Falls back to "mi300x" (most common deployment target)

2. rocminfo gfx target parsing

3. Falls back to "mi300x" (most common deployment target)

Copilot · 2026-04-14T15:45:31Z

+    def test_heuristic_medium_m_large_k(self):
+        """M=16384, K=131072: bm=128, gm=16, kpf=16."""


Several test docstrings/comments state incorrect expected values (e.g., kpf=1/16/4) that don’t match the assertions (kpf==8/64/8), and the g9 speedup comment says 0.950 but the config JSON has 0.854. Update these docstrings/comments so they reflect the actual behavior being tested—this will reduce confusion when future changes break the heuristic.

Copilot · 2026-04-14T15:45:31Z

+    def test_heuristic_large_m_small_k(self):
+        """M=327680, K=4096: bm=256, gm=24, kpf=4."""


Several test docstrings/comments state incorrect expected values (e.g., kpf=1/16/4) that don’t match the assertions (kpf==8/64/8), and the g9 speedup comment says 0.950 but the config JSON has 0.854. Update these docstrings/comments so they reflect the actual behavior being tested—this will reduce confusion when future changes break the heuristic.

Copilot · 2026-04-14T15:45:31Z

+        # g9 (196608x18432x16384) has speedup 0.950 — should NOT be matched
+        result = select_ag_mm_config(M=196608, N=18432, K=16384, world_size=8, transpose="NN", arch="mi300x")
+        # g9 is an exact match, but its speedup is 0.950
+        # The exact match path doesn't filter by speedup, but nearest does


Several test docstrings/comments state incorrect expected values (e.g., kpf=1/16/4) that don’t match the assertions (kpf==8/64/8), and the g9 speedup comment says 0.950 but the config JSON has 0.854. Update these docstrings/comments so they reflect the actual behavior being tested—this will reduce confusion when future changes break the heuristic.

Suggested change

# g9 (196608x18432x16384) has speedup 0.950 — should NOT be matched

result = select_ag_mm_config(M=196608, N=18432, K=16384, world_size=8, transpose="NN", arch="mi300x")

# g9 is an exact match, but its speedup is 0.950

# The exact match path doesn't filter by speedup, but nearest does

# g9 (196608x18432x16384) has speedup 0.854 in the config data.

result = select_ag_mm_config(M=196608, N=18432, K=16384, world_size=8, transpose="NN", arch="mi300x")

# This call is an exact match on g9, so it is still returned even though

# nearest-shape matching would skip shapes with speedup <= 1.0.

Copilot · 2026-04-14T15:45:32Z

+# In-memory cache: (arch, transpose, world_size) -> loaded JSON data
+_config_cache: Dict[Tuple[str, str, int], dict] = {}


_config_cache is annotated as storing dict values but the implementation stores None for a non-existent config file. Adjust the type to Dict[Tuple[str, str, int], Optional[dict]] (or avoid caching missing entries as None) to keep type hints accurate and prevent downstream type-checker issues.

Suggested change

# In-memory cache: (arch, transpose, world_size) -> loaded JSON data

_config_cache: Dict[Tuple[str, str, int], dict] = {}

# In-memory cache: (arch, transpose, world_size) -> loaded JSON data or None

_config_cache: Dict[Tuple[str, str, int], Optional[dict]] = {}

Copilot · 2026-04-14T15:45:32Z

+        _config_cache[cache_key] = None
+        return None


_config_cache is annotated as storing dict values but the implementation stores None for a non-existent config file. Adjust the type to Dict[Tuple[str, str, int], Optional[dict]] (or avoid caching missing entries as None) to keep type hints accurate and prevent downstream type-checker issues.

ryanswann-amd and others added 3 commits April 14, 2026 02:08

Apply Ruff auto-fixes

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ryanswann-amd requested review from mawad-amd and neoblizz as code owners April 14, 2026 15:36

Copilot AI review requested due to automatic review settings April 14, 2026 15:36

ryanswann-amd requested a review from BKP as a code owner April 14, 2026 15:36

ryanswann-amd closed this Apr 14, 2026

Copilot AI reviewed Apr 14, 2026

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Add auto-config selection for fused AG+matmul with 28 tuned HBM buffer configs - #506

Add auto-config selection for fused AG+matmul with 28 tuned HBM buffer configs#506
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ryanswann-amd:feature/find-highest-performing-hbm-buffer-all-gather-matm

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		def test_heuristic_medium_m_large_k(self):
		"""M=16384, K=131072: bm=128, gm=16, kpf=16."""

		def test_heuristic_large_m_small_k(self):
		"""M=327680, K=4096: bm=256, gm=24, kpf=4."""

		# In-memory cache: (arch, transpose, world_size) -> loaded JSON data
		_config_cache: Dict[Tuple[str, str, int], dict] = {}

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ryanswann-amd commented Apr 14, 2026

Summary

Performance (ws=8, MI300X)

World size coverage

New/modified files

Usage

Test plan

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