# coding=utf-8 # 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 unittest import pytest from transformers import AutoTokenizer, BambaConfig, is_torch_available from transformers.testing_utils import ( 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 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: 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=4, 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) 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 get_config(self): # 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)] return BambaConfig( 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, ) def create_and_check_model( self, config, input_ids, input_mask, token_labels, ): model = BambaModel(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 = BambaForCausalLM(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 = BambaForCausalLM(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): all_model_classes = (BambaModel, BambaForCausalLM) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": BambaModel, "text-generation": BambaForCausalLM, } 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] def setUp(self): self.model_tester = BambaModelTester(self) self.config_tester = ConfigTester(self, config_class=BambaConfig, 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_casual_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_initialization(self): r""" Overriding the test_initialization test as the A_log and D params of the Bamba mixer 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.mamba_n_heads + 1, dtype=torch.float32) torch.testing.assert_close(param.data, torch.log(A), rtol=1e-5, atol=1e-5) elif "D" in name: D = torch.ones(config.mamba_n_heads, dtype=torch.float32) torch.testing.assert_close(param.data, D, rtol=1e-5, atol=1e-5) 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_mismatched_shapes_have_properly_initialized_weights(self): r""" Overriding the test_mismatched_shapes_have_properly_initialized_weights test because A_log and D params of the Bamba mixer are initialized differently and we tested that in test_initialization """ self.skipTest(reason="Cumbersome and redundant for Bamba") 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(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 # essentially the same test in test_utils, just adjustment for rtol for this model @pytest.mark.generate def test_left_padding_compatibility(self): # NOTE: left-padding results in small numerical differences. This is expected. # See https://github.com/huggingface/transformers/issues/25420#issuecomment-1775317535 # First, filter out models that don't support left padding # - The model must have generative capabilities if len(self.all_generative_model_classes) == 0: self.skipTest(reason="No generative architecture available for this model.") # - The model must support padding if not self.has_attentions: self.skipTest(reason="This model doesn't support padding.") # - The model must be a decoder-only architecture (encoder-based architectures use right-padding) decoder_only_classes = [] for model_class in self.all_generative_model_classes: config, _ = self.prepare_config_and_inputs_for_generate() if config.is_encoder_decoder: continue else: decoder_only_classes.append(model_class) if len(decoder_only_classes) == 0: self.skipTest(reason="No decoder-only architecture available for this model.") # - Decoder-only architectures derived from encoder-decoder models could support it in theory, but we haven't # added support for it yet. We skip these models for now. has_encoder_attributes = any( attr_name for attr_name in config.to_dict().keys() if attr_name.startswith("encoder") and attr_name != "encoder_no_repeat_ngram_size" ) if has_encoder_attributes: self.skipTest( reason="The decoder-only derived from encoder-decoder models are not expected to support left-padding." ) # Then, test left-padding def _prepare_model_kwargs(input_ids, attention_mask, signature): model_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask} if "position_ids" in signature: position_ids = torch.cumsum(attention_mask, dim=-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) model_kwargs["position_ids"] = position_ids if "cache_position" in signature: cache_position = torch.arange(input_ids.shape[-1], device=torch_device) model_kwargs["cache_position"] = cache_position return model_kwargs for model_class in decoder_only_classes: config, inputs_dict = self.prepare_config_and_inputs_for_generate() input_ids = inputs_dict["input_ids"] # - for left padding we absolutely need to use an all ones # attention mask, so we do not use the one in inputs_dict attention_mask = torch.ones_like(input_ids) model = model_class(config).to(torch_device).eval() signature = inspect.signature(model.forward).parameters.keys() # no cache as some models require special cache classes to be init outside forward model.generation_config.use_cache = False # Without padding model_kwargs = _prepare_model_kwargs(input_ids, attention_mask, signature) next_logits_wo_padding = model(**model_kwargs).logits[:, -1, :] # With left-padding (length 32) # can hardcode pad_token to be 0 as we'll do attn masking anyway pad_token_id = ( config.get_text_config().pad_token_id if config.get_text_config().pad_token_id is not None else 0 ) pad_size = (input_ids.shape[0], 32) padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * pad_token_id padded_input_ids = torch.cat((padding, input_ids), dim=1) padded_attention_mask = torch.cat((torch.zeros_like(padding), attention_mask), dim=1) model_kwargs = _prepare_model_kwargs(padded_input_ids, padded_attention_mask, signature) next_logits_with_padding = model(**model_kwargs).logits[:, -1, :] # They should result in very similar logits torch.testing.assert_close(next_logits_wo_padding, next_logits_with_padding, rtol=1e-5, atol=1e-5) @slow @require_torch @require_torch_gpu 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 cuda_compute_capability_major_version = None @classmethod def setUpClass(cls): model_id = "ibm-fms/Bamba-9B" cls.model = BambaForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True) 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" if is_torch_available() and torch.cuda.is_available(): # 8 is for A100 / A10 and 7 for T4 cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0] def test_simple_generate(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. EXPECTED_TEXTS = { # 7: "", 8: "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are all having a good time.", 9: "<|begin_of_text|>Hey how are you doing on this lovely evening? I hope you are doing well. I am here", } 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, EXPECTED_TEXTS[self.cuda_compute_capability_major_version]) # TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist if self.cuda_compute_capability_major_version == 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) 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. EXPECTED_TEXTS = { 7: [], 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", ], 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", ], } 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_TEXTS[self.cuda_compute_capability_major_version][0]) self.assertEqual(output_sentences[1], EXPECTED_TEXTS[self.cuda_compute_capability_major_version][1]) # TODO: there are significant differences in the logits across major cuda versions, which shouldn't exist if self.cuda_compute_capability_major_version == 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)