# 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 Zamba model.""" import math import tempfile import unittest import pytest from parameterized import parameterized from transformers import AutoTokenizer, Zamba2Config, is_torch_available from transformers.testing_utils import ( Expectations, require_bitsandbytes, require_flash_attn, require_torch, require_torch_gpu, slow, torch_device, ) from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( Zamba2ForCausalLM, Zamba2ForSequenceClassification, Zamba2Model, ) from transformers.models.zamba2.modeling_zamba2 import ( Zamba2HybridDynamicCache, ) class Zamba2ModelTester: def __init__( self, parent, batch_size=14, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=16, mamba_d_state=2, chunk_size=8, mamba_dt_rank="auto", num_hidden_layers=2, num_attention_heads=2, n_mamba_heads=8, mamba_ngroups=8, intermediate_size=4, hidden_act="gelu", hidden_mamba_act="silu", 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, layers_block_type=["mamba", "hybrid"], num_mem_blocks=1, use_mem_rope=True, ): 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_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.mamba_dt_rank = mamba_dt_rank self.mamba_d_state = mamba_d_state self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.n_mamba_heads = n_mamba_heads self.mamba_ngroups = mamba_ngroups self.chunk_size = chunk_size self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_mamba_act = hidden_mamba_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 self.layers_block_type = layers_block_type self.num_mem_blocks = num_mem_blocks self.use_mem_rope = use_mem_rope 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]) 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, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return Zamba2Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, mamba_dt_rank=self.mamba_dt_rank, mamba_d_state=self.mamba_d_state, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, n_mamba_heads=self.n_mamba_heads, intermediate_size=self.intermediate_size, chunk_size=self.chunk_size, hidden_act=self.hidden_act, mamba_ngroups=self.mamba_ngroups, hidden_mamba_act=self.hidden_mamba_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=True, initializer_range=self.initializer_range, use_mamba_kernels=False, layers_block_type=self.layers_block_type, num_mem_blocks=self.num_mem_blocks, use_mem_rope=self.use_mem_rope, ) def prepare_config_and_inputs_for_decoder(self): ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = self.prepare_config_and_inputs() config.is_decoder = True return ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = Zamba2Model(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_for_causal_lm( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ): model = Zamba2ForCausalLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=token_labels) result = model(input_ids, attention_mask=input_mask) result = model(input_ids, labels=token_labels) result = model(input_ids) 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, input_mask, sequence_labels, token_labels, choice_labels, ): config.is_decoder = True config.add_cross_attention = False model = Zamba2ForCausalLM(config=config) model.to(torch_device) model.eval() # first forward pass # Attention: Zamba2 needs the cache to be initialized to return a cache! past_key_values = Zamba2HybridDynamicCache(config, input_ids.shape[0], model.dtype, device=model.device) outputs = model( input_ids, attention_mask=input_mask, past_key_values=past_key_values, 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, 1), config.vocab_size) next_mask = ids_tensor((self.batch_size, 1), 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, output_hidden_states=True, )["hidden_states"][0] output_from_past = model( next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values, output_hidden_states=True, cache_position=torch.arange( input_ids.shape[1], input_ids.shape[1] + next_tokens.shape[1], device=model.device ), )["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[:, -1:, 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 create_and_check_for_sequence_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = Zamba2ForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_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 Zamba2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): test_torchscript = False all_model_classes = ( ( Zamba2Model, Zamba2ForCausalLM, Zamba2ForSequenceClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": Zamba2Model, "text-classification": Zamba2ForSequenceClassification, "text-generation": Zamba2ForCausalLM, "zero-shot": Zamba2ForSequenceClassification, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False def _check_past_key_values_for_generate(self, batch_size, decoder_past_key_values, cache_length, config): self.assertIsInstance(decoder_past_key_values, Zamba2HybridDynamicCache) # (batch, head, seq_length, head_features) expected_shape = ( batch_size, config.num_key_value_heads if hasattr(config, "num_key_value_heads") else config.num_attention_heads, cache_length, config.hidden_size // config.num_attention_heads, ) self.assertListEqual( [key_tensor.shape for key_tensor in decoder_past_key_values.key_cache], [expected_shape] * len(decoder_past_key_values.key_cache), ) self.assertListEqual( [value_cache.shape for value_cache in decoder_past_key_values.value_cache], [expected_shape] * len(decoder_past_key_values.value_cache), ) def setUp(self): self.model_tester = Zamba2ModelTester(self) self.config_tester = ConfigTester(self, config_class=Zamba2Config, hidden_size=37) @unittest.skip("position_ids cannot be used to pad due to Mamba2 layers") def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self): pass def test_past_key_values_format(self): """ Overwriting to pass the expected cache shapes (Zamba2 has cache shape = [batch_size, 0] for mamba layers) """ config, inputs = self.model_tester.prepare_config_and_inputs_for_common() batch_size, seq_length = inputs["input_ids"].shape per_head_embed_dim = config.attention_head_dim # note: this one is not a common attribute name self_attention_cache_shape = (batch_size, config.num_key_value_heads, seq_length, per_head_embed_dim) # build the full cache shapes, including mamba layers all_cache_shapes = [] for i in range(config.num_hidden_layers): if config.layers_block_type[i] == "mamba": all_cache_shapes.append([torch.Size([batch_size, 0]), torch.Size([batch_size, 0])]) else: all_cache_shapes.append([self_attention_cache_shape, self_attention_cache_shape]) super().test_past_key_values_format(custom_all_cache_shapes=all_cache_shapes) @unittest.skip(reason="Zamba2 has hybrid cache.") def test_generate_continue_from_inputs_embeds(self): pass @unittest.skip(reason="A large mamba2 would be necessary (and costly) for that") def test_multi_gpu_data_parallel_forward(self): pass 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_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_initialization(self): r""" Overriding the test_initialization test as the A_log and D params of the Mamba block are initialized differently """ config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: if "A_log" in name: A = torch.arange(1, config.n_mamba_heads + 1, dtype=torch.float32)[None, :] self.assertTrue(torch.allclose(param.data, torch.log(A), atol=1e-5, rtol=1e-5)) elif "D" in name: # check if it's a ones like self.assertTrue(torch.allclose(param.data, torch.ones_like(param.data), atol=1e-5, rtol=1e-5)) elif "dt_bias" in name: dt = torch.exp( torch.tensor([0, 1]) * (math.log(config.time_step_max) - math.log(config.time_step_min)) + math.log(config.time_step_min) ).clamp(min=config.time_step_floor) inv_dt = dt + torch.log(-torch.expm1(-dt)) if param.requires_grad: self.assertTrue(param.data.max().item() <= inv_dt[1]) self.assertTrue(param.data.min().item() >= inv_dt[0]) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_attention_outputs(self): r""" Overriding the test_attention_outputs test as the Zamba2 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) 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 # 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.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.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) def _get_input_ids_and_config(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() ( config, input_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs return config, input_ids, input_mask @require_flash_attn @require_torch_gpu @require_bitsandbytes @pytest.mark.flash_attn_test @slow def test_flash_attn_2_fp32_ln(self): r""" Overriding the test_flash_attn_2_fp32_ln test as the Zamba2 model, like Mixtral, doesn't support right padding + use cache with FA2 """ for model_class in self.all_generative_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) dummy_input = inputs_dict[model.main_input_name] dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(dummy_input)) # NOTE: Zamba2 does not support right padding + use_cache with FA2. dummy_attention_mask[:, -1] = 1 model = model_class.from_pretrained( tmpdirname, dtype=torch.float16, attn_implementation="flash_attention_2", load_in_4bit=True, ) for _, param in model.named_parameters(): # upcast only layer norms if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16): param.data = param.data.to(torch.float32) _ = model(dummy_input) # with attention mask _ = model(dummy_input, attention_mask=dummy_attention_mask) @unittest.skip(reason="Zamba2 has its own special cache type") @parameterized.expand([(1, False), (1, True), (4, False)]) def test_new_cache_format(self, num_beams, do_sample): pass @require_torch_gpu def test_flex_attention_with_grads(self): """ Overwriting as the base hidden size is big enough for compile. Manipulation of dims causes issues due to other constraints not being satisfied anymore. """ for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config._attn_implementation = "flex_attention" model = model_class(config).to(device=torch_device) self.assertTrue(model.config._attn_implementation == "flex_attention") # Elaborate workaround for encoder-decoder models as some do not specify their main input dummy_inputs = {model.main_input_name: inputs_dict[model.main_input_name].to(torch_device)} if config.is_encoder_decoder: dummy_inputs["decoder_input_ids"] = inputs_dict["decoder_input_ids"].to(torch_device) dummy_inputs["decoder_attention_mask"] = inputs_dict["decoder_attention_mask"].to(torch_device) # If this does not raise an error, the test passes (see https://github.com/huggingface/transformers/pull/35605) _ = model(**dummy_inputs) @require_torch class Zamba2ModelIntegrationTest(unittest.TestCase): model = None tokenizer = None @classmethod @slow def setUpClass(cls): model_id = "Zyphra/Zamba2-1.2B" cls.model = Zamba2ForCausalLM.from_pretrained(model_id, dtype=torch.float32, revision="PR") cls.tokenizer = AutoTokenizer.from_pretrained(model_id, revision="PR") @parameterized.expand([(torch_device,), ("cpu",)]) @slow def test_simple_generate(self, torch_device): self.model.to(torch_device) input_ids = self.tokenizer("Hey how are you doing on this lovely evening?", return_tensors="pt")[ "input_ids" ].to(torch_device) out = self.model.generate(input_ids, do_sample=False, max_new_tokens=10) output_sentence = self.tokenizer.decode(out[0, :]) self.assertEqual( output_sentence, " Hey how are you doing on this lovely evening?\n\nI'm doing well, thanks for", ) with torch.no_grad(): logits = self.model(input_ids=input_ids).logits.to(dtype=torch.float32) EXPECTED_LOGITS_NO_GRAD = torch.tensor( [ -5.9587, 10.5152, 7.0382, -2.8728, -4.8143, -4.8142, -4.8142, -4.8144, -4.8143, -4.8143, -4.8142, -4.8142, 6.0185, 18.0037, -4.8142, -4.8144, -4.8143, -4.8142, -4.8143, -4.8143, -4.8143, -4.8143, -4.8142, -4.8143, -4.8144, -4.8143, -4.8143, -4.8141, -4.8142, -4.8142, -4.8142, -4.8144, -4.8143, -4.8143, -4.8143, -4.8142, -4.8144, -4.8144, -4.8142, -4.8142 ] , dtype=torch.float32) # fmt: skip torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3) @parameterized.expand([(torch_device,), ("cpu",)]) @slow def test_simple_batched_generate_with_padding(self, torch_device): self.model.to(torch_device) inputs = self.tokenizer( ["Hey how are you doing on this lovely evening?", "When did the Roman empire "], padding=True, return_tensors="pt", ).to(torch_device) out = self.model.generate(**inputs, do_sample=False, max_new_tokens=10) output_sentences = self.tokenizer.batch_decode(out) self.assertEqual( output_sentences[0], " Hey how are you doing on this lovely evening?\n\nI'm doing well, thanks for", ) self.assertEqual( output_sentences[1], "[PAD][PAD][PAD][PAD] When did the Roman empire 1st fall?\nThe Roman Empire fell in", ) with torch.no_grad(): logits = self.model(input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"]).logits.to( dtype=torch.float32 ) EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor( [ -5.9611, 10.5208, 7.0411, -2.8743, -4.8167, -4.8167, -4.8167, -4.8168, -4.8167, -4.8167, -4.8167, -4.8166, 6.0218, 18.0062, -4.8167, -4.8168, -4.8167, -4.8167, -4.8167, -4.8168, -4.8168, -4.8168, -4.8167, -4.8167, -4.8168, -4.8167, -4.8167, -4.8165, -4.8167, -4.8167, -4.8167, -4.8169, -4.8168, -4.8168, -4.8168, -4.8166, -4.8169, -4.8168, -4.8167, -4.8167 ] , dtype=torch.float32) # fmt: skip EXPECTED_LOGITS_NO_GRAD_1S = Expectations( { ("xpu", 3): torch.tensor([0.2027, 6.3481, 3.8392, -5.7279, -6.5090, -6.5088, -6.5087, -6.5088, -6.5087, -6.5088, -6.5090, -6.5089, 7.8796, 13.5483, -6.5088, -6.5080, -6.5090, -6.5086, -6.5090, -6.5090, -6.5089, -6.5090, -6.5088, -6.5090, -6.5089, -6.5090, -6.5090, -6.5097, -6.5086, -6.5089, -6.5092, -6.5089, -6.5088, -6.5090, -6.5090, -6.5088, -6.5090, -6.5091, -6.5087, -6.5089], dtype=torch.float32), ("cuda", None): torch.tensor([0.1966, 6.3449, 3.8350, -5.7291, -6.5106, -6.5104, -6.5103, -6.5104, -6.5103, -6.5104, -6.5106, -6.5105, 7.8700, 13.5434, -6.5104, -6.5096, -6.5106, -6.5102, -6.5106, -6.5106, -6.5105, -6.5106, -6.5104, -6.5106, -6.5105, -6.5106, -6.5106, -6.5113, -6.5102, -6.5105, -6.5108, -6.5105, -6.5104, -6.5106, -6.5106, -6.5104, -6.5106, -6.5107, -6.5103, -6.5105], dtype=torch.float32), } ) # fmt: skip EXPECTED_LOGITS_NO_GRAD_1 = EXPECTED_LOGITS_NO_GRAD_1S.get_expectation() torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1e-3) torch.testing.assert_close( logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=6e-3 if torch_device == "cpu" else 1e-3, )