chore: import upstream snapshot with attribution

tests/python/tirx/test_bench_utils.py

@@ -0,0 +1,245 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Tests for tvm.tirx.bench utilities."""
+
+import pytest
+import torch
+
+import tvm.testing
+
+pytest.importorskip("triton")  # tvm.tirx.bench imports triton.profiler
+
+from tvm.testing import env
+from tvm.tirx.bench import _compute_group_count, _parse_proton_tree, bench, tensor_bytes
+
+# ── _parse_proton_tree ──────────────────────────────────────────────────────
+
+
+SAMPLE_TREE = """\
+├─ 1.500 tir
+│  ├─ 1.500 my_kernel_fn
+│  └─ 0.001 vectorized_elementwise_kernel
+└─ 0.800 cublas
+   └─ 0.800 sm90_xmma_gemm_f16f16
+"""
+
+
+def test_parse_proton_tree_basic():
+    impls, errors = _parse_proton_tree(SAMPLE_TREE)
+    assert impls == {"tir": 1.5, "cublas": 0.8}
+    assert errors == {}
+
+
+def test_parse_proton_tree_filters_elementwise():
+    """vectorized_elementwise_kernel and elementwise_kernel_with_index are skipped."""
+    tree = """\
+├─ 0.500 tir
+│  ├─ 0.500 real_kernel
+│  └─ 0.001 elementwise_kernel_with_index
+"""
+    impls, _ = _parse_proton_tree(tree)
+    assert impls == {"tir": 0.5}
+
+
+def test_parse_proton_tree_slowest_child():
+    """Takes the slowest depth-2 child per impl."""
+    tree = """\
+├─ 2.000 tir
+│  ├─ 0.300 kernel_a
+│  └─ 0.700 kernel_b
+"""
+    impls, _ = _parse_proton_tree(tree)
+    assert impls == {"tir": 0.7}
+
+
+def test_parse_proton_tree_baseline_errors():
+    tree = """\
+BASELINE_ERROR: cublas: CUDA OOM
+├─ 1.000 tir
+│  └─ 1.000 my_kernel
+"""
+    impls, errors = _parse_proton_tree(tree)
+    assert impls == {"tir": 1.0}
+    assert errors == {"cublas": "CUDA OOM"}
+
+
+def test_parse_proton_tree_ansi_stripped():
+    """ANSI color codes are stripped before parsing."""
+    tree = "\x1b[32m├─ 1.000 tir\x1b[0m\n│  └─ 1.000 k\n"
+    impls, _ = _parse_proton_tree(tree)
+    assert impls == {"tir": 1.0}
+
+
+def test_parse_proton_tree_empty():
+    impls, errors = _parse_proton_tree("")
+    assert impls == {}
+    assert errors == {}
+
+
+# ── bench ───────────────────────────────────────────────────────────────────
+
+
+@pytest.mark.gpu
+@pytest.mark.skipif(not env.has_cuda(), reason="need cuda")
+def test_bench_basic():
+    """bench returns positive times for each impl."""
+    M, N = 256, 256
+
+    funcs = {"matmul": lambda case: torch.mm(case[0], case[1])}
+
+    def make_input():
+        A = torch.randn(M, N, device="cuda", dtype=torch.float16)
+        B = torch.randn(M, N, device="cuda", dtype=torch.float16)
+        return (A, B), tensor_bytes(A, B)
+
+    def run_and_check():
+        results = bench(funcs, make_input, warmup=5, repeat=10, cooldown_s=0.0, timer="event")
+        assert "matmul" in results["impls"]
+        assert results["impls"]["matmul"] > 0
+
+    tvm.testing.run_with_gpu_lock(run_and_check)
+
+
+@pytest.mark.gpu
+@pytest.mark.skipif(not env.has_cuda(), reason="need cuda")
+def test_bench_multiple_impls():
+    """Multiple impls each get their own timing."""
+    M, N = 128, 128
+    funcs = {
+        "mm": lambda case: torch.mm(case[0], case[1]),
+        "addmm": lambda case: torch.addmm(
+            torch.zeros(M, N, device="cuda", dtype=torch.float16), case[0], case[1]
+        ),
+    }
+
+    def make_input():
+        A = torch.randn(M, N, device="cuda", dtype=torch.float16)
+        B = torch.randn(M, N, device="cuda", dtype=torch.float16)
+        return (A, B), tensor_bytes(A, B)
+
+    def run_and_check():
+        results = bench(funcs, make_input, warmup=5, repeat=10, cooldown_s=0.0, timer="event")
+        assert set(results["impls"].keys()) == {"mm", "addmm"}
+        assert all(v > 0 for v in results["impls"].values())
+
+    tvm.testing.run_with_gpu_lock(run_and_check)
+
+
+@pytest.mark.gpu
+@pytest.mark.skipif(not env.has_cuda(), reason="need cuda")
+def test_bench_multiple_input_groups():
+    """Multiple input groups cycle correctly (L2 eviction)."""
+    M, N = 128, 128
+    call_count = [0]
+
+    def make_input():
+        call_count[0] += 1
+        A = torch.randn(M, N, device="cuda", dtype=torch.float16)
+        B = torch.randn(M, N, device="cuda", dtype=torch.float16)
+        return (A, B), tensor_bytes(A, B)
+
+    funcs = {"mm": lambda case: torch.mm(case[0], case[1])}
+
+    def run_and_check():
+        results = bench(
+            funcs,
+            make_input,
+            warmup=5,
+            repeat=20,
+            cooldown_s=0.0,
+            timer="event",
+            l2_bytes=64 * 1024,
+        )
+        assert results["impls"]["mm"] > 0
+        assert call_count[0] > 1
+
+    tvm.testing.run_with_gpu_lock(run_and_check)
+
+
+# ── _compute_group_count ───────────────────────────────────────────────────
+
+
+def test_compute_groups_small_tensors():
+    """Small tensors need many groups to fill 3x L2."""
+    # 128x128 fp16 = 32KB.  3*128MB / 32KB = 12288, +1 = 12289
+    input_bytes = tensor_bytes(torch.empty(128, 128, dtype=torch.float16))
+    n = _compute_group_count(input_bytes, l2_bytes=128 * 1024 * 1024)
+    assert n == 12289
+
+
+def test_compute_groups_large_tensors():
+    """Inputs >= 3x L2 need only 1 group."""
+    # 16384x16384 fp32 = 1GB >> 3*128MB = 384MB
+    input_bytes = tensor_bytes(torch.empty(16384, 16384, dtype=torch.float32))
+    n = _compute_group_count(input_bytes, l2_bytes=128 * 1024 * 1024)
+    assert n == 1
+
+
+def test_compute_groups_moderate_tensors():
+    """Moderate tensors: floor(3*L2 / input) + 1."""
+    # 8192x8192 bf16 = 128MB.  floor(384M / 128M) + 1 = 4
+    input_bytes = tensor_bytes(torch.empty(8192, 8192, dtype=torch.bfloat16))
+    n = _compute_group_count(input_bytes, l2_bytes=128 * 1024 * 1024)
+    assert n == 4
+
+
+@pytest.mark.gpu
+@pytest.mark.skipif(not env.has_cuda(), reason="need cuda")
+def test_bench_legacy_callable_api():
+    """bench still accepts the existing single-callable API used by TIRx tests."""
+    M, N = 128, 128
+
+    def run_and_check():
+        A = torch.randn(M, N, device="cuda", dtype=torch.float16)
+        B = torch.randn(M, N, device="cuda", dtype=torch.float16)
+        result = bench(
+            lambda: torch.mm(A, B), warmup=1, repeat=2, proton_name="legacy", flush_l2_size=1
+        )
+        assert result > 0
+
+    tvm.testing.run_with_gpu_lock(run_and_check)
+
+
+@pytest.mark.gpu
+@pytest.mark.skipif(not env.has_cuda(), reason="need cuda")
+def test_bench_callable_inputs():
+    """bench accepts a factory callable and auto-computes groups."""
+    M, N = 256, 256
+
+    call_count = [0]
+
+    def make_input():
+        call_count[0] += 1
+        case = (
+            torch.randn(M, N, device="cuda", dtype=torch.float16),
+            torch.randn(M, N, device="cuda", dtype=torch.float16),
+        )
+        return case, tensor_bytes(*case)
+
+    funcs = {"mm": lambda case: torch.mm(case[0], case[1])}
+
+    def run_and_check():
+        results = bench(funcs, make_input, warmup=5, repeat=10, cooldown_s=0.0, timer="event")
+        assert "mm" in results["impls"]
+        assert results["impls"]["mm"] > 0
+        assert call_count[0] >= 2  # at least 2 groups created
+
+    tvm.testing.run_with_gpu_lock(run_and_check)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])