# coding=utf-8 # Copyright 2025 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 PLM model.""" import unittest from packaging import version from parameterized import parameterized from transformers import AutoTokenizer, PLMConfig, is_torch_available from transformers.testing_utils import ( 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( PLMForCausalLM, PLMForSequenceClassification, PLMForTokenClassification, PLMModel, ) class PLMModelTester: 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, intermediate_size=37, num_hidden_layers=5, num_attention_heads=4, num_key_value_heads=4, kv_lora_rank=16, q_lora_rank=32, qk_rope_head_dim=16, v_head_dim=32, qk_nope_head_dim=32, n_group=2, first_k_dense_replace=2, norm_topk_prob=True, hidden_act="relu2", max_position_embeddings=512, initializer_range=0.02, attention_probs_dropout_prob=0.1, type_vocab_size=16, type_sequence_label_size=2, 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.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.kv_lora_rank = kv_lora_rank self.q_lora_rank = q_lora_rank self.qk_rope_head_dim = qk_rope_head_dim self.v_head_dim = v_head_dim self.qk_nope_head_dim = qk_nope_head_dim self.hidden_act = hidden_act self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size 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 PLMConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, intermediate_size=self.intermediate_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_heads, kv_lora_rank=self.kv_lora_rank, q_lora_rank=self.q_lora_rank, qk_rope_head_dim=self.qk_rope_head_dim, v_head_dim=self.v_head_dim, qk_nope_head_dim=self.qk_nope_head_dim, hidden_act=self.hidden_act, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, use_cache=True, pad_token_id=self.pad_token_id, attention_dropout=self.attention_probs_dropout_prob, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = PLMModel(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 create_and_check_model_as_decoder( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.add_cross_attention = True model = PLMModel(config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, ) result = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, ) result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size), ) def create_and_check_for_causal_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): model = PLMForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_decoder_model_past_large_inputs( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ): config.is_decoder = True config.add_cross_attention = True model = PLMForCausalLM(config=config) model.to(torch_device) model.eval() # first forward pass outputs = model( input_ids, attention_mask=input_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, use_cache=True, ) past_key_values = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size) next_mask = ids_tensor((self.batch_size, 3), vocab_size=2) # append to next input_ids and next_input_ids = torch.cat([input_ids, next_tokens], dim=-1) next_attention_mask = torch.cat([input_mask, next_mask], dim=-1) output_from_no_past = model( next_input_ids, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, output_hidden_states=True, )["hidden_states"][0] # select random slice random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item() output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach() output_from_past_slice = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3)) 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 PLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): # breakpoint() all_model_classes = ( ( PLMModel, PLMForCausalLM, PLMForSequenceClassification, PLMForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = (PLMForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": PLMModel, "text-classification": PLMForSequenceClassification, "token-classification": PLMForTokenClassification, "text-generation": PLMForCausalLM, "zero-shot": PLMForSequenceClassification, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False fx_compatible = False # 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] # used in `test_torch_compile_for_training` _torch_compile_train_cls = PLMForCausalLM if is_torch_available() else None def setUp(self): self.model_tester = PLMModelTester(self) self.config_tester = ConfigTester(self, config_class=PLMConfig, hidden_size=37) @unittest.skip("Failing because of unique cache (HybridCache)") def test_model_outputs_equivalence(self, **kwargs): pass @parameterized.expand([("random",), ("same",)]) @unittest.skip("PLM has HybridCache which is not compatible with assisted decoding") def test_assisted_decoding_matches_greedy_search(self, assistant_type): pass @unittest.skip("PLM has HybridCache which is not compatible with assisted decoding") def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type): pass @unittest.skip("PLM has HybridCache which is not compatible with assisted decoding") def test_assisted_decoding_sample(self): pass @unittest.skip("PLM has HybridCache which is not compatible with dola decoding") def test_dola_decoding_sample(self): pass @unittest.skip("PLM has HybridCache and doesn't support continue from past kv") def test_generate_continue_from_past_key_values(self): pass @unittest.skip("PLM has HybridCache and doesn't support low_memory generation") def test_beam_search_low_memory(self): pass @unittest.skip("PLM has HybridCache and doesn't support contrastive generation") def test_contrastive_generate(self): pass @unittest.skip("PLM has HybridCache and doesn't support contrastive generation") def test_contrastive_generate_dict_outputs_use_cache(self): pass @unittest.skip("PLM has HybridCache and doesn't support contrastive generation") def test_contrastive_generate_low_memory(self): pass @unittest.skip("PLM has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.") def test_generate_with_static_cache(self): pass @unittest.skip("PLM has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.") def test_generate_from_inputs_embeds_with_static_cache(self): pass @unittest.skip("PLM has HybridCache and doesn't support StaticCache. Though it could, it shouldn't support.") def test_generate_continue_from_inputs_embeds(self): pass @unittest.skip("PLM's eager attn/sdpa attn outputs are expected to be different") def test_sdpa_equivalence(self): pass @unittest.skip("PLM uses MLA so it is not compatible with the standard cache format") def test_beam_search_generate_dict_outputs_use_cache(self): pass @unittest.skip("PLM uses MLA so it is not compatible with the standard cache format") def test_generate_compilation_all_outputs(self): pass @unittest.skip("PLM uses MLA so it is not compatible with the standard cache format") def test_generate_compile_model_forward(self): pass @unittest.skip("PLM uses MLA so it is not compatible with the standard cache format") def test_greedy_generate_dict_outputs_use_cache(self): pass def test_config(self): self.config_tester.run_common_tests() def test_PLM_token_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) token_labels = ids_tensor([self.model_tester.batch_size, self.model_tester.seq_length], config.num_labels) model = PLMForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=token_labels) self.assertEqual( result.logits.shape, (self.model_tester.batch_size, self.model_tester.seq_length, self.model_tester.num_labels), ) def test_PLM_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size) model = PLMForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @require_torch_accelerator class PLMIntegrationTest(unittest.TestCase): # This variable is used to determine which CUDA device are we using for our runners (A10 or T4) # Depending on the hardware we get different logits / generations cuda_compute_capability_major_version = None @classmethod def setUpClass(cls): if is_torch_available() and torch.cuda.is_available(): cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] @slow @require_torch_accelerator @require_read_token def test_compile_static_cache(self): # `torch==2.2` will throw an error on this test (as in other compilation tests), but torch==2.1.2 and torch>2.2 # work as intended. See https://github.com/pytorch/pytorch/issues/121943 if version.parse(torch.__version__) < version.parse("2.3.0"): self.skipTest(reason="This test requires torch >= 2.3 to run.") NUM_TOKENS_TO_GENERATE = 40 # Note on `EXPECTED_TEXT_COMPLETION`'s diff: the current value matches the original test if the original test # was changed to have a cache of 53 tokens (as opposed to 4096), on Ampere GPUs. EXPECTED_TEXT_COMPLETION = [ "Simply put, the theory of relativity states that 1) the speed of light is constant in all inertial " "reference frames, and 2) the laws of physics are the same for all inertial reference frames.\nThe " "theory of relativ", "My favorite all time favorite condiment is ketchup. I love it on everything. I love it on my eggs, " "my fries, my chicken, my burgers, my hot dogs, my sandwiches, my salads, my p", ] prompts = [ "Simply put, the theory of relativity states that ", "My favorite all time favorite condiment is ketchup.", ] tokenizer = AutoTokenizer.from_pretrained("PLM-Team/PLM-1.8B-Base", use_fast=False) model = PLMForCausalLM.from_pretrained( "PLM-Team/PLM-1.8B-Base", device_map=torch_device, torch_dtype=torch.float16 ) inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device) # Dynamic Cache generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False) dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, dynamic_text) # Static Cache generated_ids = model.generate( **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" ) static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text) # Static Cache + compile model._cache = None # clear cache object, initialized when we pass `cache_implementation="static"` model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) generated_ids = model.generate( **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" ) static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text)