# 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 GraniteMoe model.""" import unittest from transformers import AutoTokenizer, GraniteMoeConfig, is_torch_available from transformers.testing_utils import ( Expectations, require_read_token, 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 if is_torch_available(): import torch from transformers import ( GraniteMoeForCausalLM, GraniteMoeModel, ) class GraniteMoeModelTester: 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 GraniteMoeConfig( 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 = GraniteMoeModel(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 GraniteMoeModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = ( ( GraniteMoeModel, GraniteMoeForCausalLM, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": GraniteMoeModel, "text-generation": GraniteMoeForCausalLM, } 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 = GraniteMoeModelTester(self) self.config_tester = ConfigTester(self, config_class=GraniteMoeConfig, hidden_size=37) 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) @require_torch_accelerator class GraniteMoeIntegrationTest(unittest.TestCase): @slow @require_read_token def test_model_3b_logits(self): input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] model = GraniteMoeForCausalLM.from_pretrained("ibm/PowerMoE-3b", device_map="auto") 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([[-4.4005, -3.6689, -3.6187, -2.8308, -3.9871, -3.1001, -2.8738, -2.8063]]), ("cuda", 7): torch.tensor([[-2.2122, -1.6632, -2.9269, -2.3344, -2.0143, -3.0146, -2.6839, -2.5610]]), ("cuda", 8): torch.tensor([[-4.4005, -3.6689, -3.6187, -2.8308, -3.9871, -3.1001, -2.8738, -2.8063]]), } ) 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) # slicing logits[0, 0, 0:15] EXPECTED_SLICES = Expectations( { ("xpu", 3): torch.tensor([[2.5479, -9.2123, -9.2121, -9.2175, -9.2122, -1.5024, -9.2121, -9.2122, -9.2161, -9.2122, -6.3100, -3.6223, -3.6377, -5.2542, -5.2523]]), ("cuda", 7): torch.tensor([[4.8785, -2.2890, -2.2892, -2.2885, -2.2890, -3.5007, -2.2897, -2.2892, -2.2895, -2.2891, -2.2887, -2.2882, -2.2889, -2.2898, -2.2892]]), ("cuda", 8): torch.tensor([[2.5479, -9.2124, -9.2121, -9.2175, -9.2122, -1.5024, -9.2121, -9.2122, -9.2162, -9.2122, -6.3101, -3.6224, -3.6377, -5.2542, -5.2524]]), } ) 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_generation(self): # fmt: off EXPECTED_TEXT_COMPLETIONS = Expectations( { ("xpu", 3): ( "Simply put, the theory of relativity states that 1) the speed of light is constant, and 2) the speed of light is the same for all observers.\n\n" "The first part is easy to understand. The second part is a little more difficult.\n\n" "The second part of the theory of relativity is a little more difficult to understand.\n" ), ("cuda", 7): ( "Simply put, the theory of relativity states that \n$$\n\\frac{d^2x^\\mu}{d\\tau^2} = " "\\frac{1}{c^2}\\frac{d^2x^\\mu}{dt^2}\n$$\nwhere $x^\\mu$ is a four-vector, $\\tau$ is the proper time" ), ("cuda", 8): ( "Simply put, the theory of relativity states that 1) the speed of light is constant, and 2) the speed of light is the same for all observers.\n\n" "The first part is easy to understand. The second part is a little more difficult.\n\n" "The second part of the theory of relativity is a little more difficult to understand.\n" ), } ) # fmt: on EXPECTED_TEXT_COMPLETION = EXPECTED_TEXT_COMPLETIONS.get_expectation() prompt = "Simply put, the theory of relativity states that " tokenizer = AutoTokenizer.from_pretrained("ibm/PowerMoE-3b") model = GraniteMoeForCausalLM.from_pretrained("ibm/PowerMoE-3b", device_map="auto") model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device) # greedy generation outputs generated_ids = model.generate(**model_inputs, 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)