| name | ttir-model-op-analysis |
| description | Given a `.mlir` file (or a directory of `.mlir` files) with TTIR ops, run the same TTIR normalization passes as `D2MFrontendPipeline` before D2M, then produce per-file outputs: `preprocessed.mlir`, `ttir-op-report.txt` (op counts from normalized IR), and `ops.mlir` (one func per unique op configuration, golden-style). Optional: per-pass IR dumps.
|
TTIR model op analysis (post-frontend-normalization)
Given a .mlir file (or a directory of .mlir files, e.g. a multi-graph model)
containing TTIR ops (from vLLM, torch-mlir, or tt-forge), normalize it the way the
compiler does before D2M, then inventory which TTIR ops (and shapes) appear. Do not
open TTIRToD2M.cpp, grep populateTTIRToD2MPatterns, or produce "D2M coverage"
unless the user explicitly asks for that comparison.
The input can be:
- A single
.mlir file (e.g. model.mlir)
- A directory of
.mlir files (e.g. vllm_opt/ containing graph1.mlir,
graph2.mlir). Each file is processed independently with per-graph reports.
Step 1: Run the frontend TTIR normalization pipeline
These passes match createD2MFrontendPipeline in
lib/Dialect/D2M/Pipelines/D2MPipelines.cpp:
| # | Pass flag | Short name (for filenames) |
|---|
| 1 | --canonicalize | canonicalize |
| 2 | --ttir-predicate-type-alignment | predicate-type-alignment |
| 3 | --ttir-element-type-normalization | element-type-normalization |
| 4 | --ttir-to-ttir-decomposition | decomposition |
| 5 | --ttir-explicate-tms | explicate-tms |
| 6 | --ttir-erase-inverse-ops | erase-inverse-ops |
| 7 | --ttir-move-reshape-to-constant | move-reshape-to-constant |
| 8 | --ttir-fold-constant-reshape-broadcast | fold-constant-reshape-broadcast |
| 9 | --ttir-implicit-broadcast-fold | implicit-broadcast-fold |
Single-file mode
Create <basename>/ next to the input (basename = filename without .mlir):
<basename>/
preprocessed.mlir # IR after all passes above
ttir-op-report.txt # counts + sorted mnemonic list (see below)
ops.mlir # one func per unique TTIR op configuration
source env/activate
INPUT="<input.mlir>"
BASENAME="$(basename "$INPUT" .mlir)"
mkdir -p "$BASENAME"
ttmlir-opt \
--canonicalize \
--ttir-predicate-type-alignment \
--ttir-element-type-normalization \
--ttir-to-ttir-decomposition \
--ttir-explicate-tms \
--ttir-erase-inverse-ops \
--ttir-move-reshape-to-constant \
--ttir-fold-constant-reshape-broadcast \
--ttir-implicit-broadcast-fold \
-o "$BASENAME/preprocessed.mlir" \
"$INPUT"
Directory mode (multi-graph models)
When the input is a directory like vllm_opt/ containing graph1.mlir,
graph2.mlir, etc., loop over each file and create a subdirectory per graph.
Each subdirectory uses clean names -- the folder provides the graph context:
vllm_opt/
graph1.mlir # original input
graph2.mlir
ttir-op-report.txt # combined report across all graphs
graph1/ # per-graph output folder
preprocessed.mlir
ttir-op-report.txt
ops.mlir
graph2/
preprocessed.mlir
ttir-op-report.txt
ops.mlir
source env/activate
INPUT_DIR="<dir>"
for f in "$INPUT_DIR"/*.mlir; do
STEM="$(basename "$f" .mlir)"
mkdir -p "$INPUT_DIR/$STEM"
ttmlir-opt \
--canonicalize \
--ttir-predicate-type-alignment \
--ttir-element-type-normalization \
--ttir-to-ttir-decomposition \
--ttir-explicate-tms \
--ttir-erase-inverse-ops \
--ttir-move-reshape-to-constant \
--ttir-fold-constant-reshape-broadcast \
--ttir-implicit-broadcast-fold \
-o "$INPUT_DIR/$STEM/preprocessed.mlir" \
"$f" || { echo "FAILED on $f"; continue; }
done
ttir-op-report.txt and ops.mlir
Do not hand-roll parsing. From the repo root (any Python 3.12+; no venv import deps).
The script accepts multiple paths, so pass all preprocessed files in one command:
python tools/scripts/model_breakdown/ttir_model_op_inventory.py "$INPUT_DIR"/*/preprocessed.mlir
This writes ttir-op-report.txt and ops.mlir next to each preprocessed.mlir,
plus a combined ttir-op-report.txt at the common parent directory with per-file
summary and merged mnemonic counts.
Optional flags: --report PATH, --ops PATH (single-file only), -v.
The script reports: total "ttir.*" instances, per-mnemonic counts (with optional percentages),
distinct mnemonic count, and distinct op configurations (SSA-normalized op lines). ops.mlir
follows the same shape as test/python/golden/mlir_snippets/models/qwen3_4b/ops.mlir: one
func.func per unique configuration (<mnemonic>_<index>), args %arg0, ..., single op + return.
For multi-result ops such as ttir.sort and ttir.topk, the generated op must use MLIR
multi-result binding syntax (%0:2 = ...) and return projected results (return %0#0, %0#1 : ...). If you see operation defines 2 results but was provided 1 to bind, regenerate with
tools/scripts/model_breakdown/ttir_model_op_inventory.py instead of hand-editing every snippet.
ttir.full and ttir.constant are excluded from ops.mlir unless their result is directly
returned by the module -- they almost always just produce values consumed by other ops, and are
already inlined as const producers inside those ops' test functions. The report still counts them.
It assumes one generic TTIR op per line (normal ttmlir-opt output). If needed, sanity-check
parse with ttmlir-opt on ops.mlir.
Optional: dump IR after each pass
Only if the user asks. Same pass loop as above; write
<basename>/<basename>.<short-name>.mlir for each stage. Final stage is the source for
ttir-op-report.txt and ops.mlir (same as *.implicit-broadcast-fold.mlir when you ran
all nine).
source env/activate
INPUT="<input.mlir>"
BASENAME="$(basename "$INPUT" .mlir)"
mkdir -p "$BASENAME"
PREV="$INPUT"
declare -a PASSES=(
"--canonicalize|canonicalize"
"--ttir-predicate-type-alignment|predicate-type-alignment"
"--ttir-element-type-normalization|element-type-normalization"
"--ttir-to-ttir-decomposition|decomposition"
"--ttir-explicate-tms|explicate-tms"
"--ttir-erase-inverse-ops|erase-inverse-ops"
"--ttir-move-reshape-to-constant|move-reshape-to-constant"
"--ttir-fold-constant-reshape-broadcast|fold-constant-reshape-broadcast"
"--ttir-implicit-broadcast-fold|implicit-broadcast-fold"
)
for entry in "${PASSES[@]}"; do
IFS='|' read -r FLAG SHORTNAME <<< "$entry"
OUT="$BASENAME/$BASENAME.$SHORTNAME.mlir"
ttmlir-opt $FLAG -o "$OUT" "$PREV" || { echo "FAILED at: $FLAG"; break; }
PREV="$OUT"
done
NOTE: If a pass fails, check include/ttmlir/Dialect/TTIR/Transforms/Passes.td for the
registered flag. Some setups need
--ttcore-register-device="system-desc-path=..." before other passes.
Step 2: Fill the report and ops.mlir
Run tools/scripts/model_breakdown/ttir_model_op_inventory.py on each preprocessed.mlir file
(single-file mode). See above.
Next: compile/run the snippets
To compile or execute the generated ops.mlir on device, see the
run-ops-mlir-snippets skill.
