# 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 Bamba model.""" import inspect import tempfile import unittest import pytest from pytest import mark from transformers import ( AutoTokenizer, BambaConfig, DataCollatorWithFlattening, is_torch_available, ) from transformers.testing_utils import ( DeviceProperties, Expectations, get_device_properties, require_deterministic_for_xpu, require_flash_attn, 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 ( BambaForCausalLM, BambaModel, ) from transformers.models.bamba.modeling_bamba import HybridMambaAttentionDynamicCache class BambaModelTester: config_class = BambaConfig if is_torch_available(): model_class = BambaModel for_causal_lm_class = BambaForCausalLM def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, num_key_value_heads=2, intermediate_size=64, hidden_act="silu", attention_dropout=0.0, attn_layer_indices=None, attn_rotary_emb=8, max_position_embeddings=512, type_vocab_size=16, initializer_range=0.02, num_labels=3, pad_token_id=0, mamba_n_groups=1, mamba_n_heads=16, mamba_d_state=16, mamba_d_conv=4, mamba_expand=2, mamba_chunk_size=16, 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_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.num_key_value_heads = num_key_value_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.attention_dropout = attention_dropout self.attn_layer_indices = attn_layer_indices self.attn_rotary_emb = attn_rotary_emb self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.num_labels = num_labels self.pad_token_id = pad_token_id self.scope = scope self.mamba_n_groups = mamba_n_groups self.mamba_n_heads = mamba_n_heads self.mamba_d_state = mamba_d_state self.mamba_d_conv = mamba_d_conv self.mamba_expand = mamba_expand self.mamba_chunk_size = mamba_chunk_size 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_labels = None if self.use_labels: token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) self._update_layer_configs() config = self.get_config() return config, input_ids, input_mask, token_labels def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, input_mask, token_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict def _update_layer_configs(self): """Configures hidden layers and attn layer indices if they are not set.""" # Fix for SDPA tests, force at least 4 layers if self.num_hidden_layers < 4: self.num_hidden_layers = 4 if self.attn_layer_indices is None: d = [x for x in range(2, self.num_hidden_layers) if self.num_hidden_layers % x == 0] if len(d) == 0: raise ValueError("num_hidden_layers is prime, cannot automatically set attn_layer_indices.") d = d[-1] # get the largest divisor self.attn_layer_indices = [x + 1 for x in range(0, self.num_hidden_layers, d)] def get_config(self, **kwargs): return self.config_class( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, attention_dropout=self.attention_dropout, attn_layer_indices=self.attn_layer_indices, attn_rotary_emb=self.attn_rotary_emb, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, mamba_n_groups=self.mamba_n_groups, mamba_n_heads=self.mamba_n_heads, mamba_d_state=self.mamba_d_state, mamba_d_conv=self.mamba_d_conv, mamba_expand=self.mamba_expand, mamba_chunk_size=self.mamba_chunk_size, **kwargs, ) def create_and_check_model( self, config, input_ids, input_mask, token_labels, ): model = self.model_class(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, token_labels, ): model = self.for_causal_lm_class(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, token_labels, ): # config.is_decoder = True # config.add_cross_attention = True model = self.for_causal_lm_class(config=config) model.to(torch_device) model.eval() # first forward pass # Attention: Jamba needs the cache to be initialized to return a cache! past_key_values = HybridMambaAttentionDynamicCache( 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, 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, 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[:, -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)) @require_torch class BambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): model_tester_class = BambaModelTester all_model_classes = (BambaModel, BambaForCausalLM) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": BambaModel, "text-generation": BambaForCausalLM, } 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 _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config): self.assertIsInstance(past_key_values, HybridMambaAttentionDynamicCache) # (batch, kv heads, seq_length, head_dim) num_heads = getattr(config, "num_key_value_heads", config.num_attention_heads) head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) attention_shape = (batch_size, num_heads, seq_length, head_dim) conv_shape = ( batch_size, config.mamba_expand * config.hidden_size + 2 * config.mamba_n_groups * config.mamba_d_state, config.mamba_d_conv, ) ssm_shape = (batch_size, config.mamba_n_heads, config.mamba_d_head, config.mamba_d_state) self.assertTrue(config.num_hidden_layers, len(past_key_values)) for idx in range(len(past_key_values)): if config.layers_block_type[idx] == "mamba": self.assertEqual(past_key_values.conv_states[idx].shape, conv_shape) self.assertEqual(past_key_values.ssm_states[idx].shape, ssm_shape) else: self.assertEqual(past_key_values.key_cache[idx].shape, attention_shape) self.assertEqual(past_key_values.value_cache[idx].shape, attention_shape) def _check_caches_are_equal( self, cache1: HybridMambaAttentionDynamicCache, cache2: HybridMambaAttentionDynamicCache ): if not isinstance(cache1, HybridMambaAttentionDynamicCache) or not isinstance( cache2, HybridMambaAttentionDynamicCache ): raise ValueError("The wrong cache is being used!") if not len(cache1) == len(cache2): raise ValueError("Both caches do not have the same number of layers.") num_layers = len(cache1) for idx in range(num_layers): torch.testing.assert_close(cache1.key_cache[idx], cache2.key_cache[idx]) torch.testing.assert_close(cache1.value_cache[idx], cache2.value_cache[idx]) torch.testing.assert_close(cache1.conv_states[idx], cache2.conv_states[idx]) torch.testing.assert_close(cache1.ssm_states[idx], cache2.ssm_states[idx]) def setUp(self): self.model_tester = self.model_tester_class(self) self.config_tester = ConfigTester(self, config_class=self.model_tester.config_class, hidden_size=64) 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_decoder_model_past_with_large_inputs(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs) def test_attention_outputs(self): r""" Overriding the test_attention_outputs test as the Bamba 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) expected_num_attentions = self.model_tester.num_hidden_layers - len(self.model_tester.attn_layer_indices) 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 self.assertEqual(len(attentions), expected_num_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.assertEqual(len(attentions), expected_num_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.assertEqual(len(self_attentions), expected_num_attentions) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) def test_batching_equivalence(self): # need to disable the tril input mask orig = self.model_tester.use_input_mask self.model_tester.use_input_mask = False super().test_batching_equivalence() self.model_tester.use_input_mask = orig @pytest.mark.generate def test_left_padding_compatibility(self): # TODO: document why a random attention mask causes this test to fail, but a full mask doesn't unpadded_custom_inputs = {"attention_mask": None} super().test_left_padding_compatibility(unpadded_custom_inputs=unpadded_custom_inputs) @unittest.skip( "Bamba requires additionally specifying position_ids, seq_idx, and FlashAttentionKwargs for padding-free training." ) def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self): pass @unittest.skip( "Bamba requires additionally specifying position_ids, seq_idx, and FlashAttentionKwargs for padding-free training." ) def test_flash_attention_2_padding_matches_padding_free_with_position_ids_and_fa_kwargs(self): pass @require_flash_attn @require_torch_accelerator @mark.flash_attn_test @slow @unittest.skip( "NotImplementedError: seq_idx support requires fast path support. Please install mamba_ssm and causal_conv1d" ) def test_flash_attention_2_padding_matches_padding_free_with_position_ids_seq_idx_and_fa_kwargs(self): if not self.has_attentions: self.skipTest(reason="Model architecture does not support attentions") max_new_tokens = 30 for model_class in self.all_generative_model_classes: if not model_class._supports_flash_attn: self.skipTest(f"{model_class.__name__} does not support Flash Attention 2") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if 0 not in inputs_dict.get("attention_mask", []) or "attention_mask" not in inputs_dict: self.skipTest("Model dummy inputs should contain padding in their attention mask") dummy_input = inputs_dict[model_class.main_input_name] if dummy_input.dtype in [torch.float32, torch.bfloat16]: dummy_input = dummy_input.to(torch.float16) # make sure that all models have enough positions for generation if hasattr(config, "max_position_embeddings"): config.max_position_embeddings = max_new_tokens + dummy_input.shape[1] + 1 model = model_class(config) if "position_ids" not in inspect.signature(model.forward).parameters: self.skipTest("Model does not support position_ids") with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) # ensure left padding, to adapt for some models if 0 in inputs_dict["attention_mask"][:, -1]: inputs_dict["attention_mask"] = inputs_dict["attention_mask"].flip(1) dummy_attention_mask = inputs_dict["attention_mask"] inputs_dict["input_ids"][~dummy_attention_mask.bool()] = config.get_text_config().pad_token_id # Ensure inputs_dict also has labels in it, as their presence/absence can induce # dtype conversions. This also lets us compare losses. labels = inputs_dict["input_ids"].clone() # Mask padding tokens labels[~dummy_attention_mask.bool()] = -100 # Also need to mask the first non-trivial token to match the padding-free batch. first_nonneg_idx = (labels >= 0).int().argmax(dim=1) labels[torch.arange(labels.size(0), device=labels.device), first_nonneg_idx] = -100 inputs_dict["labels"] = labels model = ( model_class.from_pretrained( tmpdirname, dtype=torch.float16, attn_implementation="flash_attention_2", ) .to(torch_device) .eval() ) # flatten features = [ {"input_ids": i[a.bool()].tolist()} for i, a in zip(inputs_dict["input_ids"], inputs_dict["attention_mask"]) ] # add position_ids + fa_kwargs + seq_idx data_collator = DataCollatorWithFlattening( return_tensors="pt", return_seq_idx=True, return_flash_attn_kwargs=True ) batch = data_collator(features) batch_accelerator = {k: t.to(torch_device) if torch.is_tensor(t) else t for k, t in batch.items()} res_padded = model(**inputs_dict) res_padfree = model(**batch_accelerator) logits_padded = res_padded.logits[inputs_dict["attention_mask"].bool()] logits_padfree = res_padfree.logits[0] torch.testing.assert_close(logits_padded.argmax(-1), logits_padfree.argmax(-1), rtol=0, atol=0) # acceptable numerical instability tol = torch.finfo(torch.float16).eps torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol) loss_padded = res_padded.loss loss_padfree = res_padfree.loss torch.testing.assert_close(loss_padded, loss_padfree) @slow @require_torch @require_torch_accelerator class BambaModelIntegrationTest(unittest.TestCase): model = None tokenizer = None # 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 device_properties: DeviceProperties = (None, None, None) @classmethod def setUpClass(cls): model_id = "ibm-fms/Bamba-9B" cls.model = BambaForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16) cls.tokenizer = AutoTokenizer.from_pretrained(model_id) # feels a bit forced to have to do this for the generation test cls.tokenizer.pad_token_id = cls.model.config.pad_token_id cls.tokenizer.padding_side = "left" cls.device_properties = get_device_properties() def test_simple_generate(self): # fmt: off expectations = Expectations( { ("cuda", 8): "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all having a good time.", ("rocm", 9): "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are doing well. I am here", ("xpu", 3): "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all doing well. I am", } ) # fmt: on 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, :]) expected = expectations.get_expectation() self.assertEqual(output_sentence, expected) # TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist if self.device_properties[0] == "cuda" and self.device_properties[1] == 8: with torch.no_grad(): logits = self.model(input_ids=input_ids, logits_to_keep=40).logits EXPECTED_LOGITS_NO_GRAD = torch.tensor( [ 149., 142., 146., 142., 143., 144., 142., 145., 142., 146., 144., 146., 147., 147., 148., 145., 147., 145., 145., 145., 145., 144., 144., 144., 144., 145., 147., 146., 144., 144., 148., 147., 148., 147., 147., 147., 146., 146., 148., 148. ], dtype=torch.bfloat16) # fmt: skip torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1) @require_deterministic_for_xpu def test_simple_batched_generate_with_padding(self): # Key 9 for MI300, Key 8 for A100/A10, and Key 7 for T4. # # Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s, # considering differences in hardware processing and potential deviations in generated text. # fmt: off EXPECTED_TEXTS = Expectations( { ("cuda", 7): [], ("cuda", 8): [ "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are doing well. I am here", "!!!<|begin_of_text|>I am late! I need to get to work! I have to get to the", ], ("rocm", 9): [ "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are doing well. I am here", "!!!<|begin_of_text|>I am late! I need to be at the airport in 20 minutes! I", ], ("xpu", 3): [ "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all doing well. I am", "!!!<|begin_of_text|>I am late! I need to get to work! I have to get to the", ], } ) # fmt: on EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation() self.model.to(torch_device) inputs = self.tokenizer( ["Hey how are you doing on this lovely evening?", "I am late! I need to"], 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], EXPECTED_TEXT[0]) self.assertEqual(output_sentences[1], EXPECTED_TEXT[1]) # TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist if self.device_properties[0] == "cuda" and self.device_properties[1] == 8: with torch.no_grad(): logits = self.model(input_ids=inputs["input_ids"]).logits EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor( [ 149., 142., 146., 142., 143., 144., 142., 145., 142., 146., 144., 146., 147., 147., 148., 145., 147., 145., 145., 145., 145., 144., 144., 144., 144., 145., 147., 146., 144., 144., 148., 147., 148., 147., 147., 147., 146., 146., 148., 148. ], dtype=torch.bfloat16) # fmt: skip EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor( [ 182., 178., 177., 174., 176., 176., 178., 178., 177., 179., 176., 183., 180., 182., 179., 174., 178., 176., 176., 175., 175., 175., 174., 173., 174., 182., 180., 176., 177., 177., 180., 176., 178., 177., 177., 175., 176., 177., 175., 177. ], dtype=torch.bfloat16) # fmt: skip torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_0, rtol=1e-3, atol=1) torch.testing.assert_close(logits[1, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD_1, rtol=1e-3, atol=1)