[vLLM IR] 1/N Implement IR skeleton and rms_norm op (#33825)

Signed-off-by: Luka Govedič <lgovedic@redhat.com>
Signed-off-by: Xinyu Chen <xinyu1.chen@intel.com>
Signed-off-by: chzhang <chaojun.zhang@intel.com>
Signed-off-by: Luka Govedic <luka.govedic@gmail.com>
Co-authored-by: Xinyu Chen <xinyu1.chen@intel.com>
Co-authored-by: Chaojun Zhang <chaojun.zhang@intel.com>
Co-authored-by: Luka Govedič <ProExpertProg@h100-01.nemg-001.lab.rdu2.dc.redhat.com>

tests/compile/passes/ir/test_lowering.py

@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+import torch
+from torch import nn
+
+import vllm.kernels  # noqa: F401 to register kernels
+from vllm import ir
+from vllm.compilation.passes.ir.lowering_pass import (
+    VllmIRLoweringPass,
+)
+from vllm.config import get_current_vllm_config
+from vllm.ir import ops
+from vllm.platforms import current_platform
+
+from ...backend import TestBackend
+
+
+class Model(nn.Module):
+    def __init__(self, hidden_size=16, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.hidden_size = hidden_size
+        self.weight = torch.ones(hidden_size, dtype=torch.bfloat16)
+
+    def forward(self, x):
+        x1 = x + 4.0
+        x2 = ops.rms_norm(x1, self.weight, 1e-5)
+        x3 = x2 * 5.0
+        # no weight
+        x4 = ops.rms_norm(x3, None, 1e-5)
+        x5 = x4 / 2.0
+        # dispatch to native due to variance_size parameter
+        x6 = ops.rms_norm(x5, self.weight, 1e-5, self.hidden_size // 2)
+        return x6 + 3.0
+
+
+@pytest.mark.parametrize("rms_provider", ops.rms_norm.supported_providers())
+def test_lowering_rms_norm(rms_provider, default_vllm_config):
+    torch.set_default_device(current_platform.device_type)
+
+    lowering_pass = VllmIRLoweringPass(get_current_vllm_config())
+    backend = TestBackend(lowering_pass)
+    backend_unlowered = TestBackend()
+
+    model = Model()
+    x = torch.randn(8, 16, dtype=torch.bfloat16)
+    with (
+        ops.rms_norm.set_priority([rms_provider, "native"]),
+        ir.enable_torch_wrap(True),
+    ):
+        compiled_model = torch.compile(model, backend=backend, fullgraph=True)
+        compiled_unlowered_model = torch.compile(
+            model, backend=backend_unlowered, fullgraph=True
+        )
+        output = compiled_model(x)
+        output_unlowered = compiled_unlowered_model(x)
+
+    selected = lowering_pass.selected_impls["rms_norm"]
+    assert len(selected) == 3
+    assert selected["rms_norm"] == rms_provider
+    assert selected["rms_norm_1"] == rms_provider
+    assert selected["rms_norm_2"] == "native"
+
+    # Compiled function guards on global value, avoid recompilation
+    with ir.enable_torch_wrap(True):
+        output2 = compiled_model(x)
+
+    torch.testing.assert_close(output_unlowered, output)
+    torch.testing.assert_close(output_unlowered, output2)