ywkang
dc3fb02aed
- CLI: --verify-data flag enables Phase 2 data verification (ADR-0020) - Tensor.data: returns actual numpy values (verify-data) or zeros placeholder - Tensor.__repr__: shows value summary or data=N/A (placeholder) - DataExecutor: ThreadPoolExecutor for same-timestamp parallel op execution - BenchResult.engine: exposes op_log/memory_store for Phase 2 access Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
227 lines
6.9 KiB
Python
227 lines
6.9 KiB
Python
"""Tests for DataExecutor Phase 2 execution (ADR-0020 D6)."""
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import numpy as np
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from kernbench.sim_engine.data_executor import DataExecutor
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from kernbench.sim_engine.memory_store import MemoryStore
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from kernbench.sim_engine.op_log import OpRecord
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def test_gemm_execution():
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"""Phase 2 GEMM: out = a @ b with f32 accumulation."""
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store = MemoryStore()
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a = np.ones((4, 8), dtype=np.float16)
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b = np.ones((8, 4), dtype=np.float16) * 2.0
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store.write("tcm", 0x0, a)
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store.write("tcm", 0x100, b)
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op = OpRecord(
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t_start=0.0, t_end=100.0,
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component_id="pe_gemm",
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op_kind="gemm", op_name="gemm_f16",
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params={
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"src_a_addr": 0x0, "src_b_addr": 0x100, "dst_addr": 0x200,
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"shape_a": (4, 8), "shape_b": (8, 4), "shape_out": (4, 4),
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"dtype_in": "f16", "dtype_acc": "f32", "dtype_out": "f16",
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"addr_space": "tcm",
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},
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)
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executor = DataExecutor([op], store)
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executor.run()
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result = store.read("tcm", 0x200)
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expected = (a.astype(np.float32) @ b.astype(np.float32)).astype(np.float16)
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assert np.allclose(result, expected)
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def test_math_exp():
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store = MemoryStore()
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x = np.array([0.0, 1.0, 2.0], dtype=np.float32)
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store.write("tcm", 0x0, x)
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op = OpRecord(
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t_start=0.0, t_end=10.0,
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component_id="pe_math",
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op_kind="math", op_name="exp",
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params={
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"op": "exp",
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"input_addrs": [0x0], "input_shapes": [(3,)],
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"dst_addr": 0x100, "shape_out": (3,),
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"dtype": "f32", "axis": None, "addr_space": "tcm",
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},
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)
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executor = DataExecutor([op], store)
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executor.run()
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result = store.read("tcm", 0x100)
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assert np.allclose(result, np.exp(x))
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def test_math_add():
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store = MemoryStore()
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a = np.array([1.0, 2.0], dtype=np.float32)
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b = np.array([3.0, 4.0], dtype=np.float32)
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store.write("tcm", 0x0, a)
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store.write("tcm", 0x100, b)
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op = OpRecord(
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t_start=0.0, t_end=5.0,
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component_id="pe_math",
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op_kind="math", op_name="add",
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params={
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"op": "add",
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"input_addrs": [0x0, 0x100], "input_shapes": [(2,), (2,)],
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"dst_addr": 0x200, "shape_out": (2,),
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"dtype": "f32", "axis": None, "addr_space": "tcm",
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},
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)
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executor = DataExecutor([op], store)
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executor.run()
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result = store.read("tcm", 0x200)
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assert np.array_equal(result, np.array([4.0, 6.0], dtype=np.float32))
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def test_math_sum_reduction():
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store = MemoryStore()
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x = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
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store.write("tcm", 0x0, x)
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op = OpRecord(
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t_start=0.0, t_end=5.0,
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component_id="pe_math",
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op_kind="math", op_name="sum",
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params={
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"op": "sum",
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"input_addrs": [0x0], "input_shapes": [(2, 2)],
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"dst_addr": 0x100, "shape_out": (1, 2),
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"dtype": "f32", "axis": 0, "addr_space": "tcm",
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},
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)
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executor = DataExecutor([op], store)
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executor.run()
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result = store.read("tcm", 0x100)
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assert np.array_equal(result, np.array([[4.0, 6.0]], dtype=np.float32))
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def test_verify_pass():
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store = MemoryStore()
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store.write("hbm", 0x0, np.array([1.0, 2.0], dtype=np.float32))
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executor = DataExecutor([], store)
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results = executor.verify({
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("hbm", 0x0): np.array([1.0, 2.0], dtype=np.float32),
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})
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assert results["hbm:0x0"] is True
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def test_verify_fail():
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store = MemoryStore()
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store.write("hbm", 0x0, np.array([1.0, 2.0], dtype=np.float32))
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executor = DataExecutor([], store)
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results = executor.verify({
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("hbm", 0x0): np.array([9.0, 9.0], dtype=np.float32),
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})
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assert results["hbm:0x0"] is False
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def test_memory_ops_skipped():
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"""Memory ops in op_log should be skipped (handled in Phase 1)."""
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store = MemoryStore()
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op = OpRecord(
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t_start=0.0, t_end=5.0,
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component_id="pe_dma",
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op_kind="memory", op_name="dma_read",
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params={"src_addr": 0x0, "nbytes": 64, "handle_id": "t0"},
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)
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# Should not raise
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executor = DataExecutor([op], store)
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executor.run()
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def test_sequential_gemm_then_math():
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"""GEMM output feeds into math op."""
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store = MemoryStore()
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a = np.eye(2, dtype=np.float16)
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b = np.ones((2, 2), dtype=np.float16)
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store.write("tcm", 0x0, a)
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store.write("tcm", 0x100, b)
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ops = [
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OpRecord(
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t_start=0.0, t_end=50.0,
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component_id="pe_gemm",
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op_kind="gemm", op_name="gemm_f16",
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params={
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"src_a_addr": 0x0, "src_b_addr": 0x100, "dst_addr": 0x200,
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"shape_a": (2, 2), "shape_b": (2, 2), "shape_out": (2, 2),
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"dtype_in": "f16", "dtype_acc": "f32", "dtype_out": "f32",
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"addr_space": "tcm",
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},
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),
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OpRecord(
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t_start=50.0, t_end=55.0,
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component_id="pe_math",
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op_kind="math", op_name="exp",
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params={
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"op": "exp",
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"input_addrs": [0x200], "input_shapes": [(2, 2)],
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"dst_addr": 0x300, "shape_out": (2, 2),
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"dtype": "f32", "axis": None, "addr_space": "tcm",
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},
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),
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]
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executor = DataExecutor(ops, store)
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executor.run()
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gemm_result = store.read("tcm", 0x200)
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expected_gemm = (a.astype(np.float32) @ b.astype(np.float32)).astype(np.float32)
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assert np.allclose(gemm_result, expected_gemm)
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exp_result = store.read("tcm", 0x300)
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assert np.allclose(exp_result, np.exp(expected_gemm))
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def test_parallel_same_timestamp_ops():
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"""Multiple independent ops at the same t_start produce correct results
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when executed in parallel (ThreadPoolExecutor)."""
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store = MemoryStore()
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n_ops = 8
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# Each op: independent GEMM writing to a different address
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for i in range(n_ops):
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a = np.full((4, 4), float(i + 1), dtype=np.float16)
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b = np.eye(4, dtype=np.float16)
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store.write("tcm", 0x1000 * i, a)
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store.write("tcm", 0x1000 * i + 0x800, b)
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ops = [
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OpRecord(
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t_start=0.0, t_end=100.0,
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component_id=f"pe{i}.pe_gemm",
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op_kind="gemm", op_name="gemm_f16",
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params={
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"src_a_addr": 0x1000 * i,
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"src_b_addr": 0x1000 * i + 0x800,
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"dst_addr": 0x80000 + 0x1000 * i,
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"shape_a": (4, 4), "shape_b": (4, 4), "shape_out": (4, 4),
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"dtype_in": "f16", "dtype_acc": "f32", "dtype_out": "f16",
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"addr_space": "tcm",
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},
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)
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for i in range(n_ops)
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]
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executor = DataExecutor(ops, store)
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executor.run()
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for i in range(n_ops):
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result = store.read("tcm", 0x80000 + 0x1000 * i)
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expected = np.full((4, 4), float(i + 1), dtype=np.float16)
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assert np.allclose(result, expected), f"op {i}: expected {expected}, got {result}"
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