# coding=utf-8 # Copyright 2023 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 Falcon model. """ import unittest from parameterized import parameterized from transformers import ( AutoModelForCausalLM, AutoTokenizer, FalconConfig, is_torch_available, set_seed, ) from transformers.testing_utils import require_bitsandbytes, require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( FalconForCausalLM, FalconForQuestionAnswering, FalconForSequenceClassification, FalconForTokenClassification, FalconModel, ) class FalconModelTester: def __init__( self, parent, batch_size=3, 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, 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.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 = random_attention_mask([self.batch_size, self.seq_length]) token_type_ids = None 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 FalconConfig( 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=1, new_decoder_architecture=True, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = FalconModel(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 = FalconModel(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 = FalconForCausalLM(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 = FalconForCausalLM(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 FalconModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( FalconModel, FalconForCausalLM, FalconForSequenceClassification, FalconForTokenClassification, FalconForQuestionAnswering, ) if is_torch_available() else () ) all_generative_model_classes = (FalconForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": FalconModel, "question-answering": FalconForQuestionAnswering, "text-classification": FalconForSequenceClassification, "text-generation": FalconForCausalLM, "token-classification": FalconForTokenClassification, "zero-shot": FalconForSequenceClassification, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False def setUp(self): self.model_tester = FalconModelTester(self) self.config_tester = ConfigTester(self, config_class=FalconConfig, 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) def test_position_embedding_types(self): config, *inputs = self.model_tester.prepare_config_and_inputs() for alibi in [True, False]: config.alibi = alibi self.model_tester.create_and_check_model(config, *inputs) def test_falcon_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 = FalconForSequenceClassification(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)) def test_falcon_sequence_classification_model_for_single_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "single_label_classification" 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 = FalconForSequenceClassification(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)) def test_cache_conversions(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() input_ids = input_dict["input_ids"] model = FalconForCausalLM(config) model.to(torch_device) model.eval() result = model(input_ids, use_cache=True) batch_size = input_ids.shape[0] rw_cache = model._convert_to_rw_cache(result.past_key_values) standard_cache = model._convert_cache_to_standard_format(rw_cache, batch_size) for layer in range(len(rw_cache)): for tensor_idx in range(2): self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3) self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4) self.assertTrue( torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]) ) def test_falcon_sequence_classification_model_for_multi_label(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.problem_type = "multi_label_classification" input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor( [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size ).to(torch.float) model = FalconForSequenceClassification(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)) def test_past_key_values_format(self): # Falcon can have different numbers of KV-heads than the number of query heads, so we need # to override this test to use the right head counts. for model_class in self.all_generative_model_classes: config, inputs = self.model_tester.prepare_config_and_inputs_for_common() # If it doesn't support cache, pass the test if not hasattr(config, "use_cache"): return model = model_class(config).to(torch_device) if "use_cache" not in inputs: inputs["use_cache"] = True outputs = model(**inputs) # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format) if "past_key_values" not in outputs: return num_hidden_layers = ( getattr(config, "decoder_layers", None) or getattr(config, "num_decoder_layers", None) or config.num_hidden_layers ) num_attention_heads = getattr(config, "num_kv_heads", config.num_attention_heads) embed_dim = getattr(config, "d_model", config.hidden_size) per_head_embed_dim = embed_dim // num_attention_heads past_kv = outputs["past_key_values"] self.assertEqual(len(past_kv), num_hidden_layers) batch_size, seq_length = inputs["input_ids"].shape for i in range(num_hidden_layers): if config.new_decoder_architecture: num_attention_heads = config.num_attention_heads elif config.multi_query: num_attention_heads = 1 self.assertEqual(len(past_kv[0]), 2) # K V for the decoder = 2 self.assertEqual( past_kv[i][0].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim) ) self.assertEqual( past_kv[i][1].shape, (batch_size, num_attention_heads, seq_length, per_head_embed_dim) ) @parameterized.expand([("linear",), ("dynamic",)]) def test_model_rope_scaling(self, scaling_type): config, _ = self.model_tester.prepare_config_and_inputs_for_common() short_input = ids_tensor([1, 10], config.vocab_size) long_input = ids_tensor([1, int(config.max_position_embeddings * 1.5)], config.vocab_size) set_seed(42) # Fixed seed at init time so the two models get the same random weights original_model = FalconModel(config) original_model.to(torch_device) original_model.eval() original_short_output = original_model(short_input).last_hidden_state original_long_output = original_model(long_input).last_hidden_state set_seed(42) # Fixed seed at init time so the two models get the same random weights config.rope_scaling = {"type": scaling_type, "factor": 10.0} scaled_model = FalconModel(config) scaled_model.to(torch_device) scaled_model.eval() scaled_short_output = scaled_model(short_input).last_hidden_state scaled_long_output = scaled_model(long_input).last_hidden_state # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original # maximum sequence length, so the outputs for the short input should match. if scaling_type == "dynamic": self.assertTrue(torch.allclose(original_short_output, scaled_short_output, atol=1e-5)) else: self.assertFalse(torch.allclose(original_short_output, scaled_short_output, atol=1e-5)) # The output should be different for long inputs self.assertFalse(torch.allclose(original_long_output, scaled_long_output, atol=1e-5)) @require_torch class FalconLanguageGenerationTest(unittest.TestCase): @slow def test_lm_generate_falcon(self): tokenizer = AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b") model = FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b") model.eval() model.to(torch_device) inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device) EXPECTED_OUTPUT = ( "My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday." ) output_ids = model.generate(**inputs, do_sample=False, max_new_tokens=19) output_str = tokenizer.batch_decode(output_ids)[0] self.assertEqual(output_str, EXPECTED_OUTPUT) @slow def test_lm_generation_big_models(self): # The big models are way too big for the CI, so we use tiny random models that resemble their # architectures but with much smaller and fewer layers for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]: tokenizer = AutoTokenizer.from_pretrained(repo) model = FalconForCausalLM.from_pretrained(repo) model.eval() model.to(torch_device) inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device) # We just test that these run without errors - the models are randomly initialized # and so the actual text outputs will be garbage model.generate(**inputs, do_sample=False, max_new_tokens=4) model.generate(**inputs, do_sample=True, max_new_tokens=4) model.generate(**inputs, num_beams=2, max_new_tokens=4) @slow def test_lm_generation_use_cache(self): # The big models are way too big for the CI, so we use tiny random models that resemble their # architectures but with much smaller and fewer layers with torch.no_grad(): for repo in [ "Rocketknight1/falcon-rw-1b", "Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b", ]: tokenizer = AutoTokenizer.from_pretrained(repo) model = FalconForCausalLM.from_pretrained(repo) model.eval() model.to(device=torch_device) inputs = tokenizer("My favorite food is", return_tensors="pt").to(torch_device) # Test results are the same with and without cache outputs_no_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=False) outputs_cache = model.generate(**inputs, do_sample=False, max_new_tokens=20, use_cache=True) self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0) @require_bitsandbytes @slow def test_batched_generation(self): tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b", padding_side="left") tokenizer.pad_token = tokenizer.eos_token model = AutoModelForCausalLM.from_pretrained( "tiiuae/falcon-7b", device_map="auto", load_in_4bit=True, ) test_text = "A sequence: 1, 2" # should generate the rest of the sequence unpadded_inputs = tokenizer([test_text], return_tensors="pt").to("cuda:0") unpadded_gen_out = model.generate(**unpadded_inputs, max_new_tokens=20) unpadded_gen_text = tokenizer.batch_decode(unpadded_gen_out, skip_special_tokens=True) dummy_text = "This is a longer text " * 2 # forces left-padding on `test_text` padded_inputs = tokenizer([test_text, dummy_text], return_tensors="pt", padding=True).to("cuda:0") padded_gen_out = model.generate(**padded_inputs, max_new_tokens=20) padded_gen_text = tokenizer.batch_decode(padded_gen_out, skip_special_tokens=True) expected_output = "A sequence: 1, 2, 3, 4, 5, 6, 7, 8, " self.assertLess(unpadded_inputs.input_ids.shape[-1], padded_inputs.input_ids.shape[-1]) # left-padding exists self.assertEqual(unpadded_gen_text[0], expected_output) self.assertEqual(padded_gen_text[0], expected_output)