Refactor workers to use backend interfaces for quantization, compilation, and evaluation; add optional flags for simulation in request schemas and update documentation accordingly.

README.md

@@ -27,6 +27,28 @@ ONNX → BIE → NEF。系統以 Scheduler 為控制面，搭配 Worker Pool 與
 7) NEF Worker 執行完成  
 8) Scheduler 標記 COMPLETED
 
+## Worker API Flags（可選）
+這些旗標用於控制 evaluator / simulator 步驟。皆有預設值，不填不影響既有流程。
+
+- ONNX `/api/onnx/process`
+  - `enable_evaluate` (default: `false`): 是否執行 IP evaluator（原 Web GUI 流程為 OFF）
+  - `enable_sim_fp` (default: `false`): 是否執行浮點 E2E 模擬（尚未接線）
+- BIE `/api/bie/process`
+  - `enable_sim_fixed` (default: `false`): 是否執行定點 E2E 模擬（尚未接線）
+- NEF `/api/nef/process`
+  - `enable_sim_hw` (default: `false`): 是否執行硬體 E2E 模擬（尚未接線）
+
+## 流程預設開關對照（原 Web GUI vs 現在 Workers）
+| 步驟 | 原 Web GUI 預設 | 現在 Workers 預設 | 開關 |
+|---|---|---|---|
+| ONNX 轉換/最佳化 | ON | ON | 無 |
+| IP Evaluator | OFF | OFF | `enable_evaluate` |
+| FP E2E 模擬 | OFF | OFF | `enable_sim_fp` |
+| BIE 量化 | ON | ON | 無 |
+| Fixed-Point E2E 模擬 | OFF | OFF | `enable_sim_fixed` |
+| NEF Compile | ON | ON | 無 |
+| HW E2E 模擬 | OFF | OFF | `enable_sim_hw` |
+
 ## 非目標
 - 不做任務持久化  
 - 不做 crash 後 resume  

docs/Design.md

@@ -165,16 +165,34 @@ error:
 - 輸入：工作目錄下的唯一檔案（不假設檔名 / 副檔名）
 - 輸出：`out.onnx`
 - 輸出位置：同一工作目錄
+- 可選旗標：
+  - `enable_evaluate` (default: `false`)：是否執行 IP evaluator（原 Web GUI 流程為 OFF）
+  - `enable_sim_fp` (default: `false`)：是否執行浮點 E2E 模擬（尚未接線）
 
 ### 4.1.3 BIE Worker
 - 輸入：`out.onnx` + `ref_images/*`
 - 輸出：`out.bie`
 - 輸出位置：同一工作目錄
+- 可選旗標：
+  - `enable_sim_fixed` (default: `false`)：是否執行定點 E2E 模擬（尚未接線）
 
 ### 4.1.4 NEF Worker
 - 輸入：`out.bie`
 - 輸出：`out.nef`
 - 輸出位置：同一工作目錄
+- 可選旗標：
+  - `enable_sim_hw` (default: `false`)：是否執行硬體 E2E 模擬（尚未接線）
+
+### 4.1.6 流程預設開關對照（原 Web GUI vs 現在 Workers）
+| 步驟 | 原 Web GUI 預設 | 現在 Workers 預設 | 開關 |
+|---|---|---|---|
+| ONNX 轉換/最佳化 | ON | ON | 無 |
+| IP Evaluator | OFF | OFF | `enable_evaluate` |
+| FP E2E 模擬 | OFF | OFF | `enable_sim_fp` |
+| BIE 量化 | ON | ON | 無 |
+| Fixed-Point E2E 模擬 | OFF | OFF | `enable_sim_fixed` |
+| NEF Compile | ON | ON | 無 |
+| HW E2E 模擬 | OFF | OFF | `enable_sim_hw` |
 
 ### 4.1.5 Core / Toolchain 路徑一致性
 - Worker 需將工作目錄 path 傳給 core

docs/flow_modularization_notes.md

@@ -245,7 +245,41 @@ If a file currently mixes multiple module responsibilities:
 - Move high-risk dependencies first (prebuilt calls, sys_flow usage).
 - After each phase, re-check boundaries and adjust.
 
-## 6) Minimum Viable API Proposal
+## 7) Current Structure and Replacement Strategy (As-Is)
+Based on the refactor just completed, the effective call chain is:
+
+```
+workers (ONNX/BIE/NEF)
+  -> backends (interfaces + Kneron implementations)
+    -> ktc (toolchain python API)
+      -> vendor sys_flow / libs / libs_V2 / prebuilt binaries
+```
+
+### 7.1 What this means today
+- Workers only depend on **backend interfaces**. They no longer call `ktc.ModelConfig` directly.
+- Kneron specifics are concentrated in backend implementations.
+- `ktc` still wraps the Kneron toolchain and binaries; that dependency remains, but it is **now isolated**.
+
+### 7.2 How to replace later
+1) **Replace backend implementations** (lowest-risk)
+   - Keep backend interfaces stable.
+   - Swap `Kneron*Backend` for `Your*Backend` without touching workers.
+
+2) **Keep backend layer, but replace `ktc` calls**
+   - Modify `Kneron*Backend` to call your own library instead of `ktc`.
+   - Workers stay unchanged; only backend code moves.
+
+3) **Introduce multiple backends**
+   - Add `get_*_backend(name=...)` selection based on config/env.
+   - Allows mixed runs: Kneron for NEF, OSS for ONNX, etc.
+
+### 7.3 Where to implement replacements
+- `services/backends/quantization.py`
+- `services/backends/compiler.py`
+- `services/backends/evaluator.py`
+- `services/backends/simulator.py`
+
+## 8) Minimum Viable API Proposal
 Keep it minimal to avoid churn:
 
 ```python
@@ -260,6 +294,24 @@ class CompilerBackend:
 
 Then the pipeline is just a pure composition of these two + ONNX ops.
 
+## 9) What This Enables
+- Replace ONNX converters / optimizers without touching quantization.
+- Run ONNX flow in pure OSS environments (CI, dev) without Kneron binaries.
+- Swap in future Kneron versions only inside backend adapters.
+- Experiment with alternative quantization or compiler backends.
+
+---
+
+## 10) Next Steps (if you want)
+I can draft the following next:
+1) A small refactor plan with concrete file edits and minimal API changes.
+2) A diagram (Mermaid) of the new modular flow.
+3) A compatibility matrix (current vs target dependencies per module).
+
+Tell me which one you want, and I’ll prepare it.
+
+Then the pipeline is just a pure composition of these two + ONNX ops.
+
 ## 7) What This Enables
 - Replace ONNX converters / optimizers without touching quantization.
 - Run ONNX flow in pure OSS environments (CI, dev) without Kneron binaries.

docs/refactor_progress.md

@@ -0,0 +1,45 @@
+# Refactor Progress Log
+
+## 2026-02-05
+- Started modularization refactor per `docs/flow_modularization_notes.md`.
+- Goal: introduce backend interfaces, decouple ONNX evaluation, keep behavior stable.
+
+### Planned Steps
+1) Create backend interfaces (quantization/compiler, optional evaluator/simulator).
+2) Update ONNX/BIE/NEF workers to use backends and make eval optional.
+3) Review boundaries and document issues.
+
+### Issues / Risks
+- None yet.
+
+## 2026-02-05 Update
+- Added backend interfaces under `services/backends`.
+- ONNX worker now makes IP evaluation optional via `parameters.enable_evaluate`.
+- BIE/NEF workers now call backend interfaces instead of direct `ModelConfig` usage.
+
+### Issues / Risks
+- `services/workers/onnx/core.py` now sets `eval_report` to empty string when disabled; check callers if they rely on non-empty.
+- Quantization backend supports optional `onnx_model` to avoid duplicate optimization.
+
+## 2026-02-05 Update 2
+- Added explicit request flags for evaluator/simulator toggles in worker schemas:
+  - ONNX: `enable_evaluate`, `enable_sim_fp`
+  - BIE: `enable_sim_fixed`
+  - NEF: `enable_sim_hw`
+
+### Issues / Risks
+- Simulator flags are defined but not yet wired to execution paths.
+
+## 2026-02-05 Update 3
+- Documented worker API flags in `README.md` and `docs/Design.md`.
+
+## 2026-02-05 Update 4
+- Set `enable_evaluate` default to `false` to match original Web GUI flow.
+- Documented original Web GUI ON/OFF expectations in `README.md` and `docs/Design.md`.
+
+## 2026-02-05 Update 5
+- Added ON/OFF comparison table for original Web GUI vs current workers in `README.md` and `docs/Design.md`.
+
+## 2026-02-05 Update 6
+- Default `enable_evaluate` in `process_onnx_core` set to `False` to match Web GUI defaults.
+- Full worker test set passed (onnx/bie/nef/e2e/e2e-tflite).

services/backends/__init__.py

@@ -0,0 +1 @@
+"""Backend interfaces and implementations."""

services/backends/compiler.py

@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+from typing import Protocol
+
+
+class CompilerBackend(Protocol):
+    def compile(self, bie_path: str, output_dir: str, **kwargs) -> str:
+        """Compile BIE into NEF and return the generated NEF path."""
+
+
+class KneronCompilerBackend:
+    def compile(self, bie_path: str, output_dir: str, **kwargs) -> str:
+        import ktc
+
+        km = ktc.ModelConfig(
+            kwargs["model_id"],
+            kwargs["version"],
+            kwargs["platform"],
+            bie_path=bie_path,
+        )
+        return ktc.compile([km], output_dir=output_dir or None)
+
+
+def get_compiler_backend(name: str | None = None) -> CompilerBackend:
+    _ = name
+    return KneronCompilerBackend()

services/backends/evaluator.py

@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+from typing import Protocol
+
+
+class EvaluatorBackend(Protocol):
+    def evaluate(self, onnx_path: str, **kwargs) -> str:
+        """Run IP evaluation and return a report string."""
+
+
+class KneronEvaluatorBackend:
+    def evaluate(self, onnx_path: str, **kwargs) -> str:
+        import ktc
+
+        km = ktc.ModelConfig(
+            kwargs["model_id"],
+            kwargs["version"],
+            kwargs["platform"],
+            onnx_path=onnx_path,
+        )
+        return km.evaluate()
+
+
+def get_evaluator_backend(name: str | None = None) -> EvaluatorBackend:
+    _ = name
+    return KneronEvaluatorBackend()

services/backends/quantization.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+from typing import Dict, Protocol
+
+
+class QuantizationBackend(Protocol):
+    def analyze(
+        self,
+        onnx_path: str,
+        input_mapping: Dict,
+        output_dir: str,
+        **kwargs,
+    ) -> str:
+        """Run quantization and return the generated BIE path."""
+
+
+class KneronQuantizationBackend:
+    def analyze(
+        self,
+        onnx_path: str,
+        input_mapping: Dict,
+        output_dir: str,
+        **kwargs,
+    ) -> str:
+        import ktc
+
+        model = kwargs.get("onnx_model")
+        if model is None:
+            import onnx
+
+            model = onnx.load(onnx_path)
+            model = ktc.onnx_optimizer.onnx2onnx_flow(model, eliminate_tail=True, opt_matmul=True)
+
+        km = ktc.ModelConfig(
+            kwargs["model_id"],
+            kwargs["version"],
+            kwargs["platform"],
+            onnx_model=model,
+        )
+        return km.analysis(input_mapping, output_dir=output_dir)
+
+
+def get_quantization_backend(name: str | None = None) -> QuantizationBackend:
+    # Placeholder for future backend selection logic.
+    _ = name
+    return KneronQuantizationBackend()

services/backends/simulator.py

@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from typing import Protocol, Sequence
+
+
+class SimulatorBackend(Protocol):
+    def simulate(self, input_data: Sequence, **kwargs):
+        """Run E2E simulation and return results."""
+
+
+class KneronSimulatorBackend:
+    def simulate(self, input_data: Sequence, **kwargs):
+        import ktc
+
+        return ktc.kneron_inference(
+            input_data,
+            onnx_file=kwargs.get("onnx_file"),
+            bie_file=kwargs.get("bie_file"),
+            nef_file=kwargs.get("nef_file"),
+            input_names=kwargs.get("input_names"),
+            platform=kwargs.get("platform"),
+            model_id=kwargs.get("model_id"),
+        )
+
+
+def get_simulator_backend(name: str | None = None) -> SimulatorBackend:
+    _ = name
+    return KneronSimulatorBackend()

services/workers/bie/core.py

@@ -46,13 +46,6 @@ def process_bie_core(
     input_node_height = input_node.type.tensor_type.shape.dim[2].dim_value
     input_node_width = input_node.type.tensor_type.shape.dim[3].dim_value
 
-    km = ktc.ModelConfig(
-        parameters["model_id"],
-        parameters["version"],
-        parameters["platform"],
-        onnx_model=model,
-    )
-
     img_list = []
     for dir_path, _, file_names in os.walk(data_dir):
         for file_name in file_names:
@@ -66,7 +59,18 @@ def process_bie_core(
             )
             img_list.append(img_data)
 
-    bie_model_path = km.analysis({input_node_name: img_list}, output_dir=output_dir or ".")
+    from services.backends.quantization import get_quantization_backend
+
+    backend = get_quantization_backend()
+    bie_model_path = backend.analyze(
+        onnx_file_path,
+        {input_node_name: img_list},
+        output_dir or ".",
+        onnx_model=model,
+        model_id=parameters["model_id"],
+        version=parameters["version"],
+        platform=parameters["platform"],
+    )
 
     if os.path.abspath(bie_model_path) != os.path.abspath(output_path):
         # Move to avoid keeping duplicate large binaries on disk.

services/workers/bie/main.py

@@ -64,6 +64,10 @@ class BIEProcessRequest(BaseModel):
     version: str = Field(..., regex=r'^[0-9a-fA-F]{4}$')
     platform: str = Field(..., regex=r'^(520|720|530|630|730)$')
     data_dir: str = Field(..., min_length=1)
+    enable_sim_fixed: bool = Field(
+        False,
+        description="Run fixed-point E2E simulation after quantization (not yet wired).",
+    )
 
 class TaskStatusResponse(BaseModel):
     task_id: str

services/workers/nef/core.py

@@ -23,16 +23,16 @@ def process_nef_core(
         os.environ.setdefault("KTC_WORKDIR", work_dir)
         os.environ.setdefault("KTC_SCRIPT_RES", res_dir)
 
-    import ktc
+    from services.backends.compiler import get_compiler_backend
 
-    km = ktc.ModelConfig(
-        parameters["model_id"],
-        parameters["version"],
-        parameters["platform"],
-        bie_path=bie_file_path,
+    backend = get_compiler_backend()
+    nef_model_path = backend.compile(
+        bie_file_path,
+        output_dir or None,
+        model_id=parameters["model_id"],
+        version=parameters["version"],
+        platform=parameters["platform"],
     )
-
-    nef_model_path = ktc.compile([km], output_dir=output_dir or None)
     if os.path.abspath(nef_model_path) != os.path.abspath(output_path):
         # Move to avoid keeping duplicate large binaries on disk.
         shutil.move(str(nef_model_path), output_path)

services/workers/nef/main.py

@@ -63,6 +63,10 @@ class NEFProcessRequest(BaseModel):
     model_id: int = Field(..., ge=1, le=65535)
     version: str = Field(..., regex=r'^[0-9a-fA-F]{4}$')
     platform: str = Field(..., regex=r'^(520|720|530|630|730)$')
+    enable_sim_hw: bool = Field(
+        False,
+        description="Run hardware E2E simulation after compilation (not yet wired).",
+    )
 
 class TaskStatusResponse(BaseModel):
     task_id: str

services/workers/onnx/core.py

@@ -36,14 +36,18 @@ def process_onnx_core(
     model = ktc.onnx_optimizer.onnx2onnx_flow(model, eliminate_tail=True, opt_matmul=True)
     onnx.save(model, output_path)
 
-    km = ktc.ModelConfig(
-        int(parameters["model_id"]),
-        str(parameters["version"]),
-        str(parameters["platform"]),
-        onnx_model=model,
-    )
-    evaluate_result = km.evaluate()
-    eval_result = evaluate_result.split(",")[0]
+    eval_result = ""
+    if parameters.get("enable_evaluate", False):
+        from services.backends.evaluator import get_evaluator_backend
+
+        evaluator = get_evaluator_backend()
+        evaluate_result = evaluator.evaluate(
+            output_path,
+            model_id=int(parameters["model_id"]),
+            version=str(parameters["version"]),
+            platform=str(parameters["platform"]),
+        )
+        eval_result = evaluate_result.split(",")[0]
 
     return {
         "file_path": output_path,

services/workers/onnx/main.py

@@ -72,6 +72,14 @@ class ONNXProcessRequest(BaseModel):
     model_id: int = Field(..., ge=1, le=65535)
     version: str = Field(..., regex=r'^[0-9a-fA-F]{4}$')
     platform: str = Field(..., regex=r'^(520|720|530|630|730)$')
+    enable_evaluate: bool = Field(
+        False,
+        description="Run IP evaluator (toolchain) after ONNX optimization.",
+    )
+    enable_sim_fp: bool = Field(
+        False,
+        description="Run floating-point E2E simulation (not yet wired).",
+    )
 
 class TaskStatusResponse(BaseModel):
     task_id: str

tests/workers/test_flow_e2e.py

@@ -43,7 +43,13 @@ def test_worker_flow_e2e_uses_single_workdir():
     work_input_file = work_inputs[0]
 
     onnx_output = work_dir / "out.onnx"
-    onnx_params = {"model_id": 10, "version": "e2e", "platform": "520", "work_dir": str(work_dir)}
+    onnx_params = {
+        "model_id": 10,
+        "version": "e2e",
+        "platform": "520",
+        "work_dir": str(work_dir),
+        "enable_evaluate": False,
+    }
     onnx_result = process_onnx_core(
         {"file_path": str(work_input_file)},
         str(onnx_output),

tests/workers/test_flow_e2e_tflite.py

@@ -43,7 +43,13 @@ def test_worker_flow_e2e_tflite_uses_single_workdir():
     work_input_file = work_inputs[0]
 
     onnx_output = work_dir / "out.onnx"
-    onnx_params = {"model_id": 20, "version": "e2e-tflite", "platform": "520", "work_dir": str(work_dir)}
+    onnx_params = {
+        "model_id": 20,
+        "version": "e2e-tflite",
+        "platform": "520",
+        "work_dir": str(work_dir),
+        "enable_evaluate": False,
+    }
     onnx_result = process_onnx_core(
         {"file_path": str(work_input_file)},
         str(onnx_output),