Wjy dev

.github/workflows/ci.yml

@@ -2,7 +2,7 @@ name: CI
 
 on:
   push:
-    branches: [main]
+    branches: [main, wjy_dev, jzj_dev]
   pull_request:
     branches: [main]
 
@@ -17,79 +17,70 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-
       - name: Install
         run: |
-          python -m pip install --upgrade pip
-          pip install -e ".[all]"
+          python${{ matrix.python-version }} -m pip install --upgrade pip
+          python${{ matrix.python-version }} -m pip install -e ".[all]"
 
       - name: Run tests
-        run: python -m pytest tests/ -v --tb=short
+        run: python${{ matrix.python-version }} -m pytest tests/ -v --tb=short
 
   lint:
     runs-on: self-hosted
     steps:
       - uses: actions/checkout@v4
 
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
       - name: Install
         run: |
-          python -m pip install --upgrade pip
-          pip install -e ".[all]"
-          pip install flake8 mypy
+          python3.12 -m pip install --upgrade pip
+          python3.12 -m pip install -e ".[all]"
+          python3.12 -m pip install flake8 mypy
 
       - name: flake8
-        run: python -m flake8 scratchv/ scratchv_dag/ tests/
+        run: python3.12 -m flake8 scratchv/ scratchv_dag/ tests/
 
       - name: mypy
-        run: python -m mypy scratchv/ scratchv_dag/ --ignore-missing-imports
+        run: python3.12 -m mypy scratchv/ scratchv_dag/ --ignore-missing-imports
 
   coverage:
     runs-on: self-hosted
     steps:
       - uses: actions/checkout@v4
 
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
       - name: Install
         run: |
-          python -m pip install --upgrade pip
-          pip install -e ".[all]"
-          pip install pytest-cov
+          python3.12 -m pip install --upgrade pip
+          python3.12 -m pip install -e ".[all]"
+          python3.12 -m pip install pytest-cov
 
       - name: Run tests with coverage
-        run: python -m pytest tests/ --cov=scratchv --cov=scratchv_dag --cov-report=term --cov-report=xml
-
-      - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v5
-        with:
-          files: ./coverage.xml
-          fail_ci_if_error: false
+        run: python3.12 -m pytest tests/ --cov=scratchv --cov=scratchv_dag --cov-report=term --cov-report=xml
 
   smoke:
     runs-on: self-hosted
     steps:
       - uses: actions/checkout@v4
 
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
       - name: Install
         run: |
-          python -m pip install --upgrade pip
-          pip install -e ".[all]"
+          python3.12 -m pip install --upgrade pip
+          python3.12 -m pip install -e ".[all]"
 
       - name: Smoke test - DSL compilation
         run: |
-          python -m scratchv examples/simple_add.dsl -o /tmp/simple_add.s --dump-ir
-          python -m scratchv examples/relu_test.dsl -o /tmp/relu.s --optimize all
-          python -m scratchv examples/matmul_test.dsl -o /tmp/matmul.s --optimize all
+          python3.12 -m scratchv examples/simple_add.dsl -o /tmp/simple_add.s --dump-ir
+          python3.12 -m scratchv examples/relu_test.dsl -o /tmp/relu.s --optimize all
+          python3.12 -m scratchv examples/matmul_test.dsl -o /tmp/matmul.s --optimize all
+
+  benchmark:
+    runs-on: self-hosted
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install
+        run: |
+          python3.12 -m pip install --upgrade pip
+          python3.12 -m pip install -e ".[all]"
+
+      - name: Run benchmark tests
+        run: python3.12 -m pytest benchmarks/test_benchmark.py -v --tb=short

benchmarks/generate_models.py

@@ -0,0 +1,146 @@
+"""Benchmark model generation — uses ScratchV's currently supported ONNX ops.
+
+Supported ops: Add, Mul, Sub, Div, Relu, MatMul, MaxPool, GeLU, Softmax, Neg, Exp
+"""
+
+import os
+
+import numpy as np
+import onnx
+from onnx import helper, TensorProto, numpy_helper
+
+BENCH_DIR = os.path.dirname(__file__)
+MODEL_DIR = os.path.join(BENCH_DIR, "models")
+os.makedirs(MODEL_DIR, exist_ok=True)
+
+
+def _make_model(nodes, inputs, outputs, initializers=None, value_info=None,
+                graph_name="graph"):
+    graph = helper.make_graph(
+        nodes, graph_name, inputs, outputs,
+        initializer=initializers or [], value_info=value_info or []
+    )
+    model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 11)])
+    onnx.checker.check_model(model)
+    return model
+
+
+def make_add_model(path: str | None = None) -> str:
+    """Element-wise Add: A + B → C, shapes [1, 128, 64]."""
+    if path is None:
+        path = os.path.join(MODEL_DIR, "add.onnx")
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 128, 64])
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 128, 64])
+    C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [1, 128, 64])
+    model = _make_model([helper.make_node("Add", ["A", "B"], ["C"])], [A, B], [C])
+    onnx.save(model, path)
+    return path
+
+
+def make_mixed_model(path: str | None = None) -> str:
+    """Mixed ops: Add → Mul → Relu → Sub → Div.
+
+    Input: [1, 256, 256] × 3,  Output: [1, 256, 256]
+    """
+    if path is None:
+        path = os.path.join(MODEL_DIR, "mixed_ops.onnx")
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 256, 256])
+    Y = helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 256, 256])
+    Z = helper.make_tensor_value_info("Z", TensorProto.FLOAT, [1, 256, 256])
+    O = helper.make_tensor_value_info("O", TensorProto.FLOAT, [1, 256, 256])
+
+    vi = [
+        helper.make_tensor_value_info("add_out", TensorProto.FLOAT, [1, 256, 256]),
+        helper.make_tensor_value_info("mul_out", TensorProto.FLOAT, [1, 256, 256]),
+        helper.make_tensor_value_info("relu_out", TensorProto.FLOAT, [1, 256, 256]),
+        helper.make_tensor_value_info("sub_out", TensorProto.FLOAT, [1, 256, 256]),
+    ]
+    nodes = [
+        helper.make_node("Add", ["X", "Y"], ["add_out"]),
+        helper.make_node("Mul", ["add_out", "Z"], ["mul_out"]),
+        helper.make_node("Relu", ["mul_out"], ["relu_out"]),
+        helper.make_node("Sub", ["relu_out", "X"], ["sub_out"]),
+        helper.make_node("Div", ["sub_out", "Y"], ["O"]),
+    ]
+    model = _make_model(nodes, [X, Y, Z], [O], value_info=vi)
+    onnx.save(model, path)
+    return path
+
+
+def make_deep_relu_chain(path: str | None = None, length: int = 50) -> str:
+    """Long chain: Relu → Relu → ... → Relu (50×), stress-test deep graphs.
+
+    Input: [1, 1024], Output: [1, 1024]
+    """
+    if path is None:
+        path = os.path.join(MODEL_DIR, "deep_relu.onnx")
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1024])
+    O = helper.make_tensor_value_info("O", TensorProto.FLOAT, [1, 1024])
+
+    nodes = []
+    vi = []
+    prev = "X"
+    for i in range(length):
+        out = f"r{i}" if i < length - 1 else "O"
+        nodes.append(helper.make_node("Relu", [prev], [out]))
+        if i < length - 1:
+            vi.append(helper.make_tensor_value_info(out, TensorProto.FLOAT, [1, 1024]))
+        prev = out
+
+    model = _make_model(nodes, [X], [O], value_info=vi, graph_name="deep_relu")
+    onnx.save(model, path)
+    return path
+
+
+def make_matmul_model(path: str | None = None) -> str:
+    """MatMul: A @ B → C, shapes [4, 128] × [128, 64] → [4, 64]."""
+    if path is None:
+        path = os.path.join(MODEL_DIR, "matmul.onnx")
+    A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [4, 128])
+    B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [128, 64])
+    C = helper.make_tensor_value_info("C", TensorProto.FLOAT, [4, 64])
+    model = _make_model([helper.make_node("MatMul", ["A", "B"], ["C"])], [A, B], [C])
+    onnx.save(model, path)
+    return path
+
+
+def make_maxpool_relu_model(path: str | None = None) -> str:
+    """MaxPool → Relu: input [1, 8, 32, 32] → MaxPool(2x2, stride 2) → Relu.
+
+    Output: [1, 8, 16, 16]
+    """
+    if path is None:
+        path = os.path.join(MODEL_DIR, "maxpool_relu.onnx")
+    X = helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 8, 32, 32])
+    P = helper.make_tensor_value_info("P", TensorProto.FLOAT, [1, 8, 16, 16])
+    O = helper.make_tensor_value_info("O", TensorProto.FLOAT, [1, 8, 16, 16])
+    model = _make_model([
+        helper.make_node("MaxPool", ["X"], ["pool_out"],
+                         kernel_shape=[2, 2], strides=[2, 2]),
+        helper.make_node("Relu", ["pool_out"], ["O"]),
+    ], [X], [O], value_info=[P])
+    onnx.save(model, path)
+    return path
+
+
+def ensure_all_models() -> dict[str, str]:
+    """Generate all benchmark ONNX models. Returns {model_name: path}."""
+    models: dict[str, str] = {}
+    gens = [
+        ("add", make_add_model),
+        ("mixed_ops", make_mixed_model),
+        ("deep_relu", make_deep_relu_chain),
+        ("matmul", make_matmul_model),
+        ("maxpool_relu", make_maxpool_relu_model),
+    ]
+    for name, gen_func in gens:
+        path = gen_func()
+        models[name] = path
+    return models
+
+
+if __name__ == "__main__":
+    models = ensure_all_models()
+    for name, path in models.items():
+        size_kb = os.path.getsize(path) / 1024
+        print(f"  {name}: {path} ({size_kb:.1f} KB)")