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feat(ck-tile): add grouped GEMM variant to TE to dispatcher bridge (#9000) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit > Re-opened from #8130 with a policy-compliant branch name (`users/muozturk/ck-tile/dispatcher-te-bridge-grouped-gemm`). Supersedes #8130. ## What this PR does Routes the **grouped_gemm** variant through the Tile Engine (TE) → Dispatcher **bridge**: TE only generates configs and benchmarks; the Dispatcher owns codegen, build, and runtime. This is the grouped counterpart of the regular-GEMM bridge (#8123/#8479), the fp8/bf8/int8 bridge (#8887), and the Stream-K bridge (#8136). **This PR now also contains the grouped Dispatcher codegen** that previously lived in #8075 — that PR has been **closed in favor of this one** to keep the grouped codegen in a single place (it was otherwise duplicated across both). ## Why grouped needs special handling Grouped GEMM is **multi-problem**: one launch runs a *list* of `(M, N, K)` sub-problems with arrays of A/B/C device pointers. 1. The single-problem run path (`g_dispatcher->run` / `GemmHostArgs`) cannot express a list of problems. 2. The generated registry wrapper (`generated_tile_backend.hpp::run()`) hard-codes the single-problem launch and won't compile against a grouped `SelectedKernel`. So the grouped path **bypasses the registry**: a dedicated ctypes lib calls the generated `SelectedKernel::launch(descs, stream)` directly and reports the name from the compile-time `KERNEL_NAME` macro. ## Changes **Codegen (absorbed from #8075)** - `codegen/arch_filter.py` — `GEMM_GROUPED` operator tile constraints. - `codegen/unified_gemm_codegen.py` — `GemmVariant.GROUPED`, the grouped launch generator (DeviceMem internal workspace via `MakeKargs`, persistent/non-persistent grid), `grouped` in `--variants`. - `examples/gemm/cpp/02_grouped_gemm_driver.cpp` — standalone, layout/dtype-generic grouped driver with per-group reference verification. - `codegen/README.md` + `examples/gemm/cpp/README.md` — grouped sections. **Bridge** - `bindings/ctypes/grouped_gemm_ctypes_lib.cpp` — multi-problem, registry-bypass C ABI; per-group device alloc/copy; strides derived from the compile-time `ALayout/BLayout/CLayout`; warmup/repeat timing matched to Old-TE (`CK_TILE_BENCH_WARMUP/REPEAT`). - `python/gemm_utils.py` — `GroupedGemmProblem`/`GroupedGemmResult`, `GpuGroupedGemmRunner`, `run_grouped`, fp16/bf16/fp8(E4M3 FNUZ)/bf8(E5M2 FNUZ) codecs, output-dtype-aware C buffer. - `tile_engine/ops/gemm/grouped_gemm_full_benchmark.py` + `run_one_grouped_gemm_kernel.py` — TE driver + worker for the parity sweep. - `bindings/ctypes/GROUPED_GEMM_BRIDGE.md` — design README. ## Coverage (= Old-TE grouped runnable set on develop) | Layout \ Dtype | fp16 | bf16 | fp8 (E4M3) | bf8 (E5M2) | |---|---|---|---|---| | rcr / rrr / ccr / crr | ✓ | ✓ | ✓ | ✓ | C is always row-major. `int8` (rejected by the TE grouped builder) and `fp32`/`fp64` (no MFMA warp tiles) are excluded on both sides. ## Parity vs Old-TE (MI300X / gfx942) Apples-to-apples (same warmup=50/repeat=100 both sides, A/B interleaved, single GPU, both engines rebuilt fresh, stale-`.so` guard, matched compile flags): - **Correctness: 64/64 PASS.** - **Performance: 64/64 within ±15%.** - The 5 small-shape (1024³ fp8/bf8) rows that initially read >15% were proven by `rocprof` to be a **measurement-harness artifact** (Old-TE's JSON `latency(ms)` rounded to 2 decimals → 30–50% TFLOPS swing on ~0.02 ms kernels), **not** a kernel/codegen difference — bridge and Old-TE launch byte-identical kernels (same grid/VGPR/SGPR, duration ≤3.22%); full-precision re-measure collapses all 5 to <3%. ## Notes - Targets `develop`. Depends on #8997 (fp16/bf16 bridge) and #8998 (fp8/bf8/int8 bridge) merging to `develop` first; until then this PR's diff also shows their content, after which it reduces to the grouped-only files. - Supersedes #8075 (closed).
CK Tile Dispatcher Python Utilities
This directory contains Python utilities used by the dispatcher examples.
Contents
Shared Utilities (used by both GEMM and Grouped Conv)
dispatcher_common.py- Shared dispatcher infrastructure- Path helpers (
get_dispatcher_root,get_build_dir, etc.) ValidationResultBase- Structured validation feedbackvalidate_wave_config,validate_warp_tile_config,validate_trait_comboauto_correct_wave,auto_correct_trait- Auto-correction helpersColors- Cross-platform ANSI color supportprint_phase,print_success,print_error,print_info- Phased outputcleanup_generated_kernels- Cleanup helper
- Path helpers (
GEMM Utilities
ctypes_utils.py- Core ctypes utilities for GEMM Python examplesKernelConfig- Kernel configuration dataclasssetup_gemm_dispatcher()- Setup dispatcher with auto-correctioncleanup_gemm()- Cleanup dispatcher resourcesGemmRunner- GPU execution helper- Auto-correction and validation utilities
Grouped Convolution Utilities
grouped_conv_utils.py- Utilities for grouped convolutionGroupedConvValidationResult- Validation result (extendsValidationResultBase)validate_grouped_conv_config- Validate a grouped conv configauto_correct_grouped_conv_config- Auto-correct invalid configsget_grouped_conv_default_config- Get default config for a variantGroupedConvDataType- Data type enum (FP16, BF16, FP32, FP8, BF8, INT8)format_grouped_conv_summary- Human-readable config summary
Usage
GEMM Examples
The GEMM Python examples in dispatcher/examples/gemm/python/ import:
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "python"))
from ctypes_utils import (
KernelConfig,
setup_gemm_dispatcher,
cleanup_gemm,
GemmRunner,
)
Grouped Conv Usage
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "python"))
from grouped_conv_utils import (
validate_grouped_conv_config,
auto_correct_grouped_conv_config,
get_grouped_conv_default_config,
GroupedConvDataType,
)
# Get a default config
config = get_grouped_conv_default_config(variant="forward", arch="gfx942")
# Validate
result = validate_grouped_conv_config(config)
print(f"Valid: {result.is_valid}")
Requirements
- Python 3.8+
- NumPy
- HIP runtime (for GPU execution)