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tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py

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+# Copyright 2024 The HuggingFace Inc. 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 GraniteMoeHybrid model."""
+
+import inspect
+import tempfile
+import unittest
+
+import pytest
+from huggingface_hub.errors import StrictDataclassClassValidationError
+from parameterized import parameterized
+from pytest import mark
+
+from transformers import (
+    AutoTokenizer,
+    DataCollatorWithFlattening,
+    GraniteMoeHybridConfig,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    require_flash_attn,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...models.bamba.test_modeling_bamba import BambaModelTester
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import GraniteMoeHybridForCausalLM, GraniteMoeHybridModel
+
+
+class GraniteMoeHybridModelTester(BambaModelTester):
+    config_class = GraniteMoeHybridConfig
+    if is_torch_available():
+        model_class = GraniteMoeHybridModel
+        for_causal_lm_class = GraniteMoeHybridForCausalLM
+
+    def __init__(
+        self,
+        parent,
+        use_cache=False,
+        shared_intermediate_size=174,
+        layer_types=None,
+    ):
+        super().__init__(parent)
+        self.shared_intermediate_size = shared_intermediate_size
+        self.layer_types = layer_types
+        self.use_cache = use_cache
+
+    def _update_layer_configs(self):
+        super()._update_layer_configs()
+        # GraniteMoeHybrid uses layer_types instead of attn_layer_indices
+        self.layer_types = ["mamba"] * self.num_hidden_layers
+        for idx in self.attn_layer_indices:
+            self.layer_types[idx] = "attention"
+
+    def get_config(self):
+        return super().get_config(
+            shared_intermediate_size=self.shared_intermediate_size,
+            layer_types=self.layer_types,
+        )
+
+
+@require_torch
+class GraniteMoeHybridModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    model_tester_class = GraniteMoeHybridModelTester
+    all_model_classes = (
+        (
+            GraniteMoeHybridModel,
+            GraniteMoeHybridForCausalLM,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": GraniteMoeHybridModel,
+            "text-generation": GraniteMoeHybridForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    # Need to use `0.8` instead of `0.9` for `test_cpu_offload`
+    # This is because we are hitting edge cases with the causal_mask buffer
+    model_split_percents = [0.5, 0.7, 0.8]
+
+    def setUp(self):
+        self.model_tester = self.model_tester_class(self)
+        self.config_tester = ConfigTester(self, config_class=self.model_tester.config_class, hidden_size=64)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_causal_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
+
+    def test_decoder_model_past_with_large_inputs(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
+
+    def test_attention_outputs(self):
+        r"""
+        Overriding the test_attention_outputs test as the Bamba model outputs attention only for its attention layers
+        """
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+
+        seq_len = getattr(self.model_tester, "seq_length", None)
+        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
+        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
+
+        expected_num_attentions = self.model_tester.num_hidden_layers - len(self.model_tester.attn_layer_indices)
+
+        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), expected_num_attentions)
+
+            # 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), expected_num_attentions)
+
+            self.assertListEqual(
+                list(attentions[0].shape[-3:]),
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
+            )
+            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))
+
+            added_hidden_states = 1
+            self.assertEqual(out_len + added_hidden_states, len(outputs))
+
+            self_attentions = outputs.attentions
+
+            self.assertEqual(len(self_attentions), expected_num_attentions)
+            self.assertListEqual(
+                list(self_attentions[0].shape[-3:]),
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
+            )
+
+    def test_batching_equivalence(self):
+        # need to disable the tril input mask
+        orig = self.model_tester.use_input_mask
+        self.model_tester.use_input_mask = False
+        super().test_batching_equivalence()
+        self.model_tester.use_input_mask = orig
+
+    @pytest.mark.generate
+    def test_left_padding_compatibility(self):
+        # TODO: document why a random attention mask causes this test to fail, but a full mask doesn't
+        unpadded_custom_inputs = {"attention_mask": None}
+        super().test_left_padding_compatibility(unpadded_custom_inputs=unpadded_custom_inputs)
+
+    @unittest.skip(
+        "Bamba requires additionally specifying position_ids, seq_idx, and FlashAttentionKwargs for padding-free training."
+    )
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip(
+        "Bamba requires additionally specifying position_ids, seq_idx, and FlashAttentionKwargs for padding-free training."
+    )
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids_and_fa_kwargs(self):
+        pass
+
+    @require_flash_attn
+    @require_torch_accelerator
+    @mark.flash_attn_test
+    @slow
+    @unittest.skip(
+        "NotImplementedError: seq_idx support requires fast path support. Please install mamba_ssm and causal_conv1d"
+    )
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids_seq_idx_and_fa_kwargs(self):
+        if not self.has_attentions:
+            self.skipTest(reason="Model architecture does not support attentions")
+
+        max_new_tokens = 30
+
+        for model_class in self.all_generative_model_classes:
+            if not model_class._supports_flash_attn:
+                self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
+
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            if 0 not in inputs_dict.get("attention_mask", []) or "attention_mask" not in inputs_dict:
+                self.skipTest("Model dummy inputs should contain padding in their attention mask")
+
+            dummy_input = inputs_dict[model_class.main_input_name]
+            if dummy_input.dtype in [torch.float32, torch.bfloat16]:
+                dummy_input = dummy_input.to(torch.float16)
+
+            # make sure that all models have enough positions for generation
+            if hasattr(config, "max_position_embeddings"):
+                config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1
+
+            model = model_class(config)
+            if "position_ids" not in inspect.signature(model.forward).parameters:
+                self.skipTest("Model does not support position_ids")
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+
+                # ensure left padding, to adapt for some models
+                if 0 in inputs_dict["attention_mask"][:, -1]:
+                    inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1)
+                dummy_attention_mask = inputs_dict["attention_mask"]
+                inputs_dict["input_ids"][~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id
+                # Ensure inputs_dict also has labels in it, as their presence/absence can induce
+                # dtype conversions. This also lets us compare losses.
+                labels = inputs_dict["input_ids"].clone()
+                # Mask padding tokens
+                labels[~dummy_attention_mask.bool()] = -100
+                # Also need to mask the first non-trivial token to match the padding-free batch.
+                first_nonneg_idx = (labels >= 0).int().argmax(dim=1)
+                labels[torch.arange(labels.size(0), device=labels.device), first_nonneg_idx] = -100
+                inputs_dict["labels"] = labels
+
+                model = (
+                    model_class.from_pretrained(
+                        tmpdirname,
+                        dtype=torch.float16,
+                        attn_implementation="flash_attention_2",
+                    )
+                    .to(torch_device)
+                    .eval()
+                )
+
+                # flatten
+                features = [
+                    {"input_ids": i[a.bool()].tolist()}
+                    for i, a in zip(inputs_dict["input_ids"], inputs_dict["attention_mask"])
+                ]
+
+                # add position_ids + fa_kwargs + seq_idx
+                data_collator = DataCollatorWithFlattening(
+                    return_tensors="pt", return_seq_idx=True, return_flash_attn_kwargs=True
+                )
+                batch = data_collator(features)
+                batch_accelerator = {k: t.to(torch_device) if torch.is_tensor(t) else t for k, t in batch.items()}
+
+                res_padded = model(**inputs_dict)
+                res_padfree = model(**batch_accelerator)
+
+                logits_padded = res_padded.logits[inputs_dict["attention_mask"].bool()]
+                logits_padfree = res_padfree.logits[0]
+
+                torch.testing.assert_close(logits_padded.argmax(-1), logits_padfree.argmax(-1), rtol=0, atol=0)
+                # acceptable numerical instability
+                tol = torch.finfo(torch.float16).eps
+                torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol)
+
+                loss_padded = res_padded.loss
+                loss_padfree = res_padfree.loss
+                torch.testing.assert_close(loss_padded, loss_padfree)
+
+    def _get_conv_state_shape(self, batch_size: int, config):
+        conv_shape = (
+            batch_size,
+            config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * config.mamba_d_state,
+            config.mamba_d_conv,
+        )
+        return conv_shape
+
+    def _get_recurrent_state_shape(self, batch_size: int, config):
+        return (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state)
+
+    def test_attention_only_forward(self):
+        """Ensure forward pass works when all layers are attention (no mamba layers). Regression test for #45507."""
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        config = config_and_inputs[0]
+        config.layers_block_type = ["attention"] * config.num_hidden_layers
+
+        for model_class in self.all_model_classes:
+            model = model_class._from_config(config)
+            model.to(torch_device)
+            model.eval()
+            input_ids = config_and_inputs[1]
+            with torch.no_grad():
+                model(input_ids)
+
+    def test_config_requires_mamba_or_attention_layers(self):
+        """Ensure we can't create a config with disallowed layers."""
+        with pytest.raises(StrictDataclassClassValidationError):
+            GraniteMoeHybridConfig(layer_types=["not allowed!"])
+
+
+@require_torch_accelerator
+class GraniteMoeHybridIntegrationTest(unittest.TestCase):
+    @slow
+    @parameterized.expand([("cpu",)])  # runners crash with `cuda`, prob they have mamba kernels installed
+    def test_model_logits(self, device):
+        input_ids = [31390, 631, 4162, 30, 322, 25342, 432, 1875, 43826, 10066, 688, 225]
+
+        model = GraniteMoeHybridForCausalLM.from_pretrained("ibm-granite/granite-4.0-h-tiny", device_map=device)
+
+        with torch.no_grad():
+            out = model(torch.tensor([input_ids]).to(device))
+
+        # fmt: off
+        # Expected mean on dim = -1
+        EXPECTED_MEAN = torch.tensor([
+            [-0.3543, -1.0066, -0.5338, -0.8816, -0.7438,  0.0500, -1.3644, -0.0742, -1.7746, -1.6326, -1.4802, -0.4961]
+        ], device=device)
+
+        torch.testing.assert_close(EXPECTED_MEAN, out.logits.float().mean(-1), rtol=1e-2, atol=1e-2)
+
+        # slicing logits[0, 0, 0:15]
+        EXPECTED_SLICE = torch.tensor([
+            [6.5938,  7.2500,  1.6484,  5.2188,  3.5781,  2.5469,  6.1250,  5.1875, 9.5000,  4.6875,  4.7188, 10.7500, 10.3125,  7.8438,  5.5312]
+        ], device=device)
+        # fmt: on
+
+        self.assertTrue(
+            torch.allclose(
+                EXPECTED_SLICE,
+                out.logits[0, 0, :15].float(),
+                atol=1e-3,
+                rtol=1e-3,
+            )
+        )
+
+    @slow
+    @parameterized.expand([("cpu",)])
+    def test_model_generation(self, device):
+        EXPECTED_TEXT_COMPLETION = "Simply put, the theory of relativity states that 1) the laws of physics are the same for all observers in uniform motion relative"
+        prompt = "Simply put, the theory of relativity states that "
+        tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-4.0-h-tiny")
+        model = GraniteMoeHybridForCausalLM.from_pretrained("ibm-granite/granite-4.0-h-tiny", device_map=device)
+        model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+        # greedy generation outputs
+        generated_ids = model.generate(**model_inputs, max_new_tokens=16, do_sample=False)
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)
+
+
+class GraniteMoeHybridTokenizerTest(unittest.TestCase):
+    @slow
+    def test_tokenizer_encoding_digit_strings(self):
+        tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-4.0-h-tiny")
+        self.assertEqual(tokenizer.encode("2023", add_special_tokens=False), [2366, 18])
+        self.assertEqual(tokenizer.encode("650841823", add_special_tokens=False), [13655, 25496, 23848])
+        self.assertEqual(tokenizer.encode("60-138-3818", add_special_tokens=False), [1399, 12, 10350, 12, 19162, 23])
+        self.assertEqual(tokenizer.encode("d.o.o", add_special_tokens=False), [67, 14778, 14778])
+        self.assertEqual(tokenizer.encode("FY2023", add_special_tokens=False), [82029, 2366, 18])
+        self.assertEqual(
+            tokenizer.encode("ISO 9001:2015", add_special_tokens=False), [25141, 220, 7467, 16, 25, 679, 20]
+        )