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tests/models/granite/test_modeling_granite.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 Granite model."""
+
+import tempfile
+import unittest
+
+from transformers import GraniteConfig, is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        GraniteForCausalLM,
+        GraniteModel,
+    )
+
+
+class GraniteModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=False,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        pad_token_id=0,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.pad_token_id = pad_token_id
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = torch.tril(torch.ones_like(input_ids).to(torch_device))
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = self.get_config()
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return GraniteConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+            pad_token_id=self.pad_token_id,
+        )
+
+    def create_and_check_model(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = GraniteModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask)
+        result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class GraniteModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            GraniteModel,
+            GraniteForCausalLM,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": GraniteModel,
+            "text-generation": GraniteForCausalLM,
+        }
+        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 = GraniteModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=GraniteConfig, hidden_size=32)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_config_int_multiplier_roundtrip(self):
+        config = GraniteConfig(embedding_multiplier=12, logits_scaling=8)
+        with tempfile.TemporaryDirectory() as tmpdir:
+            config.save_pretrained(tmpdir)
+            loaded = GraniteConfig.from_pretrained(tmpdir)
+        self.assertEqual(loaded.embedding_multiplier, 12)
+        self.assertEqual(loaded.logits_scaling, 8)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+
+@require_torch_accelerator
+class GraniteIntegrationTest(unittest.TestCase):
+    @slow
+    def test_model_3b_logits_bf16(self):
+        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
+
+        model = GraniteForCausalLM.from_pretrained(
+            "ibm/PowerLM-3b", device_map="auto", dtype=torch.bfloat16, attn_implementation="eager"
+        )
+
+        with torch.no_grad():
+            out = model(torch.tensor([input_ids]).to(torch_device))
+        # Expected mean on dim = -1
+
+        # fmt: off
+        EXPECTED_MEANS = Expectations(
+            {
+                ("xpu", 3): torch.tensor([[-3.1406, -2.5469, -2.6250, -2.1250, -2.6250, -2.6562, -2.6875, -2.9688]]),
+                ("cuda", 7): torch.tensor([[-1.9798, -3.1626, -2.8062, -2.3777, -2.7091, -2.2338, -2.5924, -2.3974]]),
+                ("cuda", 8): torch.tensor([[-3.1406, -2.5469, -2.6250, -2.1250, -2.6250, -2.6562, -2.6875, -2.9688]]),
+            }
+        )
+        EXPECTED_MEAN = EXPECTED_MEANS.get_expectation()
+
+        torch.testing.assert_close(EXPECTED_MEAN.to(torch_device), out.logits.mean(-1).float(), rtol=1e-2, atol=1e-2)
+
+        # slicing logits[0, 0, 0:15]
+        EXPECTED_SLICES = Expectations(
+            {
+                ("xpu", 3): torch.tensor([[2.2031, -5.0625, -5.0625, -5.0625, -5.0625, -0.9180, -5.0625, -5.0625, -5.0625, -5.0625, -5.5312, -2.1719, -1.7891, -0.4922, -2.5469]]),
+                ("cuda", 7): torch.tensor([[4.8750, -2.1875, -2.1875, -2.1875, -2.1875, -2.8438, -2.1875, -2.1875, -2.1875, -2.1875, -2.1875, -2.1875, -2.1875, -2.1875, -2.1875]]),
+                ("cuda", 8): torch.tensor([[2.0938, -5.0312, -5.0312, -5.0312, -5.0312, -1.0469, -5.0312, -5.0312, -5.0312, -5.0312, -5.5625, -2.1875, -1.7891, -0.5820, -2.6250]]),
+            }
+        )
+        EXPECTED_SLICE = EXPECTED_SLICES.get_expectation()
+        # fmt: on
+        self.assertTrue(
+            torch.allclose(
+                EXPECTED_SLICE.to(torch_device),
+                out.logits[0, 0, :15].float(),
+                atol=1e-3,
+                rtol=1e-3,
+            )
+        )
+
+    @slow
+    def test_model_3b_logits(self):
+        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
+
+        model = GraniteForCausalLM.from_pretrained("ibm/PowerLM-3b", device_map="auto", dtype=torch.float16)
+
+        with torch.no_grad():
+            out = model(torch.tensor([input_ids]).to(torch_device))
+
+        # fmt: off
+        # Expected mean on dim = -1
+        EXPECTED_MEANS = Expectations(
+            {
+                ("xpu", 3): torch.tensor([[-3.2693, -2.5957, -2.6234, -2.1675, -2.6386, -2.6850, -2.7039, -2.9656]]),
+                ("cuda", 7): torch.tensor([[-2.0984, -3.1294, -2.8153, -2.3568, -2.7337, -2.2624, -2.6016, -2.4022]]),
+                ("cuda", 8): torch.tensor([[-3.2934, -2.6019, -2.6258, -2.1691, -2.6394, -2.6876, -2.7032, -2.9688]]),
+            }
+        )
+        # fmt: on
+        EXPECTED_MEAN = EXPECTED_MEANS.get_expectation()
+
+        torch.testing.assert_close(EXPECTED_MEAN.to(torch_device), out.logits.float().mean(-1), rtol=1e-2, atol=1e-2)