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tests/models/olmo_hybrid/test_modeling_olmo_hybrid.py

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+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# 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.
+"""Testing suite for the PyTorch OlmoHybrid model."""
+
+import unittest
+
+from transformers import OlmoHybridConfig, is_torch_available
+from transformers.models.auto.tokenization_auto import AutoTokenizer
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+from ...test_modeling_common import ids_tensor
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Cache,
+        OlmoHybridForCausalLM,
+        OlmoHybridModel,
+    )
+    from transformers.models.olmo_hybrid.modeling_olmo_hybrid import (
+        OlmoHybridDynamicCache,
+        OlmoHybridRotaryEmbedding,
+    )
+
+
+class OlmoHybridModelTester(CausalLMModelTester):
+    if is_torch_available():
+        config_class = OlmoHybridConfig
+        base_model_class = OlmoHybridModel
+        causal_lm_class = OlmoHybridForCausalLM
+
+    def __init__(self, parent):
+        super().__init__(parent=parent)
+        self.layer_types = ["linear_attention", "full_attention"]
+        self.linear_num_key_heads = 4
+        self.linear_num_value_heads = 4
+        self.linear_key_head_dim = 8
+        self.linear_value_head_dim = 8
+        self.linear_conv_kernel_dim = 4
+        self.linear_allow_neg_eigval = False
+
+
+@require_torch
+class OlmoHybridModelTest(CausalLMModelTest, unittest.TestCase):
+    model_tester_class = OlmoHybridModelTester
+    rotary_embedding_layer = OlmoHybridRotaryEmbedding if is_torch_available() else None
+
+    @unittest.skip("Float8 quantization + TP numerical noise exceeds match threshold")
+    def test_tp_generation_quantized(self):
+        pass
+
+    def test_linear_attention_multi_token_cached_forward_matches_single_token(self):
+        """
+        OLMo-Hybrid's GatedDeltaNet layers must produce the same output for a token regardless of
+        whether it's fed as a single-token cached forward or as the first token of a multi-token chunk
+        after the cache has been populated (chunked-prefill continuation / speculative verification).
+        A causal LM's logits at position `i` cannot depend on tokens at positions > `i`, even across
+        separate forward calls with a shared cache.
+        """
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+        config._attn_implementation = "eager"
+        model = OlmoHybridModel._from_config(config)
+        model.to(torch_device)
+        model.eval()
+
+        prefill_len = 8
+        prompt = ids_tensor((1, prefill_len), config.vocab_size).to(torch_device)
+        next_token = ids_tensor((1, 1), config.vocab_size).to(torch_device)
+
+        cache_single = OlmoHybridDynamicCache(config=config)
+        with torch.no_grad():
+            model(input_ids=prompt, past_key_values=cache_single, use_cache=True)
+            single_out = model(input_ids=next_token, past_key_values=cache_single, use_cache=True)
+        ref_first = single_out.last_hidden_state[:, 0, :]
+
+        distractors = ids_tensor((1, 7), config.vocab_size).to(torch_device)
+        multi_input = torch.cat([next_token, distractors], dim=1)
+        cache_multi = OlmoHybridDynamicCache(config=config)
+        with torch.no_grad():
+            model(input_ids=prompt, past_key_values=cache_multi, use_cache=True)
+            multi_out = model(input_ids=multi_input, past_key_values=cache_multi, use_cache=True)
+        under_test_first = multi_out.last_hidden_state[:, 0, :]
+
+        torch.testing.assert_close(under_test_first, ref_first, rtol=1e-4, atol=1e-4)
+
+    # === Cache helper methods (same pattern as Qwen3Next) ===
+    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
+        """OlmoHybrid has a special Cache as it alternates with gated deltanet layers"""
+        self.assertIsInstance(past_key_values, OlmoHybridDynamicCache)
+
+        num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
+        head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        expected_shape = (batch_size, num_heads, seq_length, head_dim)
+
+        attention_layer_indices = past_key_values.transformer_layers
+        self.assertListEqual(
+            [past_key_values.key_cache[idx].shape for idx in attention_layer_indices],
+            [expected_shape] * len(attention_layer_indices),
+        )
+        self.assertListEqual(
+            [past_key_values.value_cache[idx].shape for idx in attention_layer_indices],
+            [expected_shape] * len(attention_layer_indices),
+        )
+
+    def _check_caches_are_equal(self, cache1: Cache, cache2: Cache):
+        """OlmoHybrid has a special Cache as it alternates with gated deltanet layers"""
+        if not len(cache1) == len(cache2):
+            raise ValueError("Both caches do not have the same number of layers.")
+
+        num_layers = len(cache1)
+        for idx in range(num_layers):
+            if cache1.key_cache[idx] is not None:
+                torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx])
+                torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx])
+
+    # === Override test_attention_outputs (same pattern as Qwen3Next) ===
+    def test_attention_outputs(self):
+        """Needs to be overwritten as OlmoHybrid alternates between attention layers and gated deltanet layers."""
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+        config._attn_implementation = "eager"
+        seq_len = getattr(self.model_tester, "seq_length", None)
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class._from_config(config, attn_implementation="eager")
+            config = model.config
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            self.assertEqual(len(attentions), sum(layer == "full_attention" for layer in config.layer_types))
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            self.assertEqual(len(attentions), sum(layer == "full_attention" for layer in config.layer_types))
+            self.assertListEqual(list(attentions[0].shape[-3:]), [config.num_attention_heads, seq_len, seq_len])
+            out_len = len(outputs)
+
+            # Check attention is always last and order is fine
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+                self_attentions = outputs.attentions
+
+            self.assertEqual(out_len + 1, len(outputs))
+            self.assertEqual(len(self_attentions), sum(layer == "full_attention" for layer in config.layer_types))
+            self.assertListEqual(list(self_attentions[0].shape[-3:]), [config.num_attention_heads, seq_len, seq_len])
+
+    @unittest.skip("The specific cache format cannot be instantiated from dp/ddp data.")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+
+@require_torch
+class OlmoHybridIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_model_logits(self):
+        input_ids = [[1, 306, 4658, 278, 6593, 310, 2834, 338]]
+        model = OlmoHybridForCausalLM.from_pretrained("hf-internal-testing/olmo-hybrid").to(
+            torch_device, dtype=torch.bfloat16
+        )
+        out = model(torch.tensor(input_ids, device=torch_device)).logits.float()
+
+        rtol = 3e-2
+        atol = 5e-2
+
+        expectations = Expectations(
+            {
+                ("cuda", 8): [
+                    [
+                        -3.819033145904541,
+                        -3.795485734939575,
+                        -2.975806951522827,
+                        -2.7940011024475098,
+                        -3.548236131668091,
+                        -4.012556552886963,
+                        -4.722480773925781,
+                        -4.015453338623047,
+                    ]
+                ],
+                ("xpu", 3): [
+                    [
+                        -3.799433145904541,
+                        -3.799685734939575,
+                        -2.977006951522827,
+                        -2.7950011024475098,
+                        -3.529636131668091,
+                        -4.018356552886963,
+                        -4.717680773925781,
+                        -3.985853338623047,
+                    ]
+                ],
+            }
+        )
+        EXPECTED_MEAN = torch.tensor(expectations.get_expectation(), device=torch_device)
+        torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=rtol, atol=atol)
+
+        expectations = Expectations(
+            {
+                ("cuda", 8): [
+                    3.828125,
+                    -0.546875,
+                    -1.7578125,
+                    -2.203125,
+                    -2.25,
+                    -2.890625,
+                    -0.87109375,
+                    -1.21875,
+                    -1.65625,
+                    -2.78125,
+                    -1.2890625,
+                    0.8359375,
+                    -2.578125,
+                    0.8125,
+                    -2.1875,
+                    2.921875,
+                    3.671875,
+                    3.5625,
+                    3.109375,
+                    2.78125,
+                    2.703125,
+                    1.7578125,
+                    1.890625,
+                    2.21875,
+                    1.8984375,
+                    -2.5,
+                    -2.03125,
+                    -4.03125,
+                    1.2421875,
+                    -1.1328125,
+                ],
+                ("xpu", 3): [
+                    3.8125,
+                    -0.5391,
+                    -1.7266,
+                    -2.1875,
+                    -2.2344,
+                    -2.8750,
+                    -0.8477,
+                    -1.2266,
+                    -1.6172,
+                    -2.75,
+                    -1.2656,
+                    0.8516,
+                    -2.5469,
+                    0.8281,
+                    -2.1562,
+                    2.9062,
+                    3.6719,
+                    3.5625,
+                    3.1250,
+                    2.7812,
+                    2.7031,
+                    1.7578,
+                    1.9141,
+                    2.2188,
+                    1.8984,
+                    -2.4844,
+                    -2.0156,
+                    -4.0000,
+                    1.2344,
+                    -1.1250,
+                ],
+            }
+        )
+        EXPECTED_SLICE = torch.tensor(expectations.get_expectation(), device=torch_device)
+        torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=rtol, atol=atol)
+
+    @slow
+    def test_model_greedy_generation(self):
+        expectations = Expectations(
+            {
+                (
+                    "cuda",
+                    8,
+                ): "Simply put, the theory of relativity states that \xa0the laws of physics are the same for all non-accelerating observers. This means that the laws of physics are the same for all observers, regardless of their relative motion or the strength of the gravitational field they are in. This theory was first proposed by Albert Einstein in 1905 and has since been confirmed",
+                (
+                    "xpu",
+                    3,
+                ): "Simply put, the theory of relativity states that \xa0the laws of physics are the same for all non-accelerating observers. This means that the laws of physics are the same for all observers, regardless of their relative motion or the strength of the gravitational field they are in. This theory was first proposed by Albert Einstein in 1905 and has since been confirmed",
+            }
+        )
+        EXPECTED_TEXT_COMPLETION = expectations.get_expectation()
+        prompt = "Simply put, the theory of relativity states that "
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/olmo-hybrid")
+        model = OlmoHybridForCausalLM.from_pretrained(
+            "hf-internal-testing/olmo-hybrid", device_map="auto", torch_dtype=torch.bfloat16
+        )
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
+        generated_ids = model.generate(input_ids, max_new_tokens=64, top_p=None, temperature=1, do_sample=False)
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)