add rotation ut for xpu.

test/test_xpu/transform/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 2026 Intel Corporation
+#
+# Licensed 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.

test/test_xpu/transform/test_hadamard.py

@@ -0,0 +1,92 @@
+# Copyright (c) 2026 Intel Corporation
+#
+# Licensed 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.
+
+"""XPU CI tests for the Hadamard rotation transform.
+
+XPU counterpart of test/test_cuda/transform/test_mxfp4_transform.py. Covers:
+- Direct ``apply_rotation`` (hadamard backend) on an XPU-resident model
+- AutoRound pipeline with ``rotation_config="default"`` / ``"random_hadamard"``
+  on XPU: quantize → save → load → generate
+"""
+
+import shutil
+
+import pytest
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from auto_round import AutoRound
+from auto_round.algorithms.transforms import apply_rotation, normalize_rotation_config
+
+from ...helpers import generate_prompt
+
+DEVICE = "xpu"
+
+pytestmark = pytest.mark.skipif(
+    not (hasattr(torch, "xpu") and torch.xpu.is_available()),
+    reason="XPU is not available on this host",
+)
+
+
+class TestHadamardApplyXPU:
+    """Direct apply_rotation on an XPU-resident model."""
+
+    def test_apply_rotation_forward(self, tiny_opt_model_path):
+        """Hadamard rotation applied on XPU should produce valid logits."""
+        model = AutoModelForCausalLM.from_pretrained(tiny_opt_model_path, dtype=torch.float32).to(DEVICE).eval()
+        cfg = normalize_rotation_config("hadamard")
+        model = apply_rotation(model, cfg)
+
+        tokenizer = AutoTokenizer.from_pretrained(tiny_opt_model_path)
+        inputs = tokenizer("The capital of France is", return_tensors="pt").to(DEVICE)
+        with torch.no_grad():
+            logits = model(**inputs).logits
+
+        assert logits.device.type == DEVICE
+        assert not torch.isnan(logits).any(), "NaN logits after hadamard rotation on XPU"
+        assert not torch.isinf(logits).any(), "Inf logits after hadamard rotation on XPU"
+        assert logits.abs().sum() > 0, "All-zero logits after hadamard rotation on XPU"
+        del model
+        torch.xpu.empty_cache()
+
+
+class TestHadamardPipelineXPU:
+    """AutoRound pipeline with hadamard rotation on XPU (MXFP4 scheme)."""
+
+    @pytest.fixture(autouse=True)
+    def _save_dir(self, tmp_path):
+        self.save_dir = str(tmp_path / "saved")
+        yield
+        shutil.rmtree(self.save_dir, ignore_errors=True)
+
+    @pytest.mark.parametrize("rotation", ["default", "random_hadamard"])
+    def test_transform_mxfp4_quant_infer(self, tiny_opt_model_path, rotation):
+        """MXFP4 + hadamard rotation: quantize → save → load on XPU → generate."""
+        ar = AutoRound(
+            model=tiny_opt_model_path,
+            iters=0,
+            seqlen=8,
+            nsamples=2,
+            scheme="MXFP4",
+            rotation_config=rotation,
+            device_map=DEVICE,
+        )
+        _, quantized_model_path = ar.quantize_and_save(output_dir=self.save_dir, format="auto_round")
+
+        model = AutoModelForCausalLM.from_pretrained(quantized_model_path, dtype="auto", device_map=DEVICE)
+        tokenizer = AutoTokenizer.from_pretrained(quantized_model_path)
+        output = generate_prompt(model, tokenizer, device=DEVICE)
+        assert len(output) > 0, "Quantized model should produce non-empty output"
+        del model
+        torch.xpu.empty_cache()

test/test_xpu/transform/test_spinquant.py

@@ -0,0 +1,222 @@
+# Copyright (c) 2026 Intel Corporation
+#
+# Licensed 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.
+
+"""XPU CI tests for the SpinQuant/QuaRot rotation transform.
+
+XPU counterpart of test/test_cuda/transform/test_spinquant.py. Covers:
+- Rotation correctness: R1, R1+R2, R1+R2+R3+R4 produce valid logits on XPU
+- Rotation equivalence: rotation preserves model output (cosine similarity)
+- Hook lifecycle: SpinQuant-tagged hooks are registered and selectively removed
+- Pipeline integration via AutoRound(rotation_config="quarot") on XPU
+"""
+
+import shutil
+
+import pytest
+import torch
+import torch.nn.functional as F
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from auto_round import AutoRound
+from auto_round.algorithms.transforms import apply_rotation
+from auto_round.algorithms.transforms.spinquant import SpinQuantConfig
+from auto_round.algorithms.transforms.spinquant.preprocessor import remove_spinquant_hooks_from_model
+
+from ...helpers import generate_prompt
+
+DEVICE = "xpu"
+
+pytestmark = pytest.mark.skipif(
+    not (hasattr(torch, "xpu") and torch.xpu.is_available()),
+    reason="XPU is not available on this host",
+)
+
+PROMPT = "The capital of France is"
+
+
+def _load_model(model_path, dtype=torch.float32):
+    return AutoModelForCausalLM.from_pretrained(model_path, dtype=dtype).to(DEVICE).eval()
+
+
+def _get_logits(model, tokenizer, text=PROMPT):
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        return model(**inputs).logits.cpu()
+
+
+class TestRotationCorrectnessXPU:
+    """Rotation configurations should produce valid (non-NaN, non-Inf) logits on XPU."""
+
+    @pytest.mark.parametrize(
+        "r1,r2,r3,r4,label",
+        [
+            (True, False, False, False, "R1"),
+            (True, True, False, False, "R1+R2"),
+            (True, True, True, True, "R1+R2+R3+R4"),
+        ],
+    )
+    def test_rotation_produces_valid_logits(self, tiny_qwen_model_path, r1, r2, r3, r4, label):
+        model = _load_model(tiny_qwen_model_path)
+        cfg = SpinQuantConfig(
+            r1=r1,
+            r2=r2,
+            r3=r3,
+            r4=r4,
+            online_r1_rotation=True,
+            trainable_rotation=False,
+            trainable_smooth=False,
+        )
+        model = apply_rotation(model, cfg)
+        tokenizer = AutoTokenizer.from_pretrained(tiny_qwen_model_path)
+        logits = _get_logits(model, tokenizer)
+
+        assert not torch.isnan(logits).any(), f"{label} rotation produced NaN logits"
+        assert not torch.isinf(logits).any(), f"{label} rotation produced Inf logits"
+        assert logits.abs().sum() > 0, f"{label} rotation produced all-zero logits"
+        del model
+        torch.xpu.empty_cache()
+
+
+class TestRotationEquivalenceXPU:
+    """Rotation should preserve model output (functional equivalence) on XPU.
+
+    R1 (online): activation hook + weight compensation → x·R·(R^T·W)^T = x·W^T
+    R2 (offline): head rotation fused into o_proj/next-layer weights
+    R3 (online): same rotation on Q and K after RoPE → (Q@R)(K@R)^T = Q@K^T
+    R4 (online + offline fuse): activation rotation + down_proj compensation
+    """
+
+    @pytest.mark.parametrize(
+        "r1,r2,r3,r4,label",
+        [
+            (True, False, False, False, "R1"),
+            (False, True, False, False, "R2"),
+            (False, False, True, False, "R3"),
+            (False, False, False, True, "R4"),
+            (True, True, True, True, "R1+R2+R3+R4"),
+        ],
+    )
+    def test_rotation_equivalence(self, tiny_qwen_model_path, r1, r2, r3, r4, label):
+        tokenizer = AutoTokenizer.from_pretrained(tiny_qwen_model_path)
+
+        baseline_model = _load_model(tiny_qwen_model_path)
+        baseline_logits = _get_logits(baseline_model, tokenizer)
+        del baseline_model
+        torch.xpu.empty_cache()
+
+        model = _load_model(tiny_qwen_model_path)
+        cfg = SpinQuantConfig(
+            r1=r1,
+            r2=r2,
+            r3=r3,
+            r4=r4,
+            online_r1_rotation=True,
+            trainable_rotation=False,
+            trainable_smooth=False,
+        )
+        model = apply_rotation(model, cfg)
+        logits = _get_logits(model, tokenizer)
+        del model
+        torch.xpu.empty_cache()
+
+        cos_sim = F.cosine_similarity(
+            baseline_logits.flatten().unsqueeze(0).float(),
+            logits.flatten().unsqueeze(0).float(),
+        ).item()
+        max_diff = (baseline_logits - logits).abs().max().item()
+        assert (
+            cos_sim > 0.9999
+        ), f"{label} rotation broke model equivalence: cos_sim = {cos_sim:.6f}, max_diff = {max_diff:.4f}"
+
+
+class TestHookLifecycleXPU:
+    """SpinQuant hooks are properly tagged and selectively removed on XPU."""
+
+    def test_remove_only_spinquant_hooks(self, tiny_qwen_model_path):
+        model = _load_model(tiny_qwen_model_path, dtype=torch.float16)
+
+        # Register a foreign hook
+        def foreign_hook(module, input):
+            return input
+
+        first_linear = None
+        for m in model.modules():
+            if isinstance(m, torch.nn.Linear):
+                first_linear = m
+                break
+        handle = first_linear.register_forward_pre_hook(foreign_hook)
+
+        cfg = SpinQuantConfig(
+            r1=True,
+            r2=False,
+            r3=False,
+            r4=False,
+            online_r1_rotation=True,
+            trainable_rotation=False,
+            trainable_smooth=False,
+        )
+        model = apply_rotation(model, cfg)
+
+        # SpinQuant-tagged hooks should exist
+        tagged_hooks = 0
+        for module in model.modules():
+            for hook in module._forward_pre_hooks.values():
+                if getattr(hook, "_spinquant_hook", False):
+                    tagged_hooks += 1
+        assert tagged_hooks > 0, "No SpinQuant-tagged hooks found"
+
+        # Remove only spinquant hooks
+        remove_spinquant_hooks_from_model(model)
+
+        # Foreign hook should still exist
+        assert handle.id in first_linear._forward_pre_hooks, "Foreign hook was incorrectly removed"
+
+        # SpinQuant hooks should be gone
+        for module in model.modules():
+            for hook in module._forward_pre_hooks.values():
+                assert not getattr(hook, "_spinquant_hook", False), "SpinQuant hook was not removed"
+
+        handle.remove()
+        del model
+        torch.xpu.empty_cache()
+
+
+class TestPipelineIntegrationXPU:
+    """AutoRound(rotation_config='quarot') end-to-end on XPU."""
+
+    @pytest.fixture(autouse=True)
+    def _save_dir(self, tmp_path):
+        self.save_dir = str(tmp_path / "saved")
+        yield
+        shutil.rmtree(self.save_dir, ignore_errors=True)
+
+    def test_pipeline_quarot_string(self, tiny_qwen_model_path):
+        """AutoRound(rotation_config='quarot') should work end-to-end on XPU."""
+        ar = AutoRound(
+            model=tiny_qwen_model_path,
+            iters=0,
+            seqlen=8,
+            nsamples=2,
+            scheme="W4A16",
+            rotation_config="quarot",
+            device_map=DEVICE,
+        )
+        _, quantized_model_path = ar.quantize_and_save(output_dir=self.save_dir, format="auto_round")
+
+        model = AutoModelForCausalLM.from_pretrained(quantized_model_path, dtype="auto", device_map=DEVICE)
+        tokenizer = AutoTokenizer.from_pretrained(quantized_model_path)
+        output = generate_prompt(model, tokenizer, device=DEVICE)
+        assert len(output) > 0, "Quantized model should produce non-empty output"
+        del model
+        torch.xpu.empty_cache()