# 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 Jamba model.""" import math import tempfile import unittest import pytest from transformers import AutoTokenizer, BitsAndBytesConfig, JambaConfig, is_torch_available from transformers.testing_utils import ( DeviceProperties, Expectations, get_device_properties, is_flaky, require_bitsandbytes, 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, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( JambaForCausalLM, JambaForSequenceClassification, JambaModel, ) from transformers.models.jamba.modeling_jamba import ( HybridMambaAttentionDynamicCache, ) class JambaConfigTester(ConfigTester): def _create_attn_config(self, attn_layer_offset: int, attn_layer_period: int): _input_dict = self.inputs_dict.copy() _input_dict["attn_layer_offset"] = attn_layer_offset _input_dict["attn_layer_period"] = attn_layer_period return self.config_class(**_input_dict) def _create_expert_config(self, expert_layer_offset: int, expert_layer_period: int): _input_dict = self.inputs_dict.copy() _input_dict["expert_layer_offset"] = expert_layer_offset _input_dict["expert_layer_period"] = expert_layer_period return self.config_class(**_input_dict) def test_attn_offsets(self): self._create_attn_config(attn_layer_offset=0, attn_layer_period=4) self._create_attn_config(attn_layer_offset=1, attn_layer_period=4) self._create_attn_config(attn_layer_offset=2, attn_layer_period=4) self._create_attn_config(attn_layer_offset=3, attn_layer_period=4) with self.parent.assertRaises(ValueError): self._create_attn_config(attn_layer_offset=4, attn_layer_period=4) with self.parent.assertRaises(ValueError): self._create_attn_config(attn_layer_offset=5, attn_layer_period=4) def test_expert_offsets(self): self._create_expert_config(expert_layer_offset=0, expert_layer_period=4) self._create_expert_config(expert_layer_offset=1, expert_layer_period=4) self._create_expert_config(expert_layer_offset=2, expert_layer_period=4) self._create_expert_config(expert_layer_offset=3, expert_layer_period=4) with self.parent.assertRaises(ValueError): self._create_expert_config(expert_layer_offset=4, expert_layer_period=4) with self.parent.assertRaises(ValueError): self._create_expert_config(expert_layer_offset=5, expert_layer_period=4) def test_jamba_offset_properties(self): self.test_attn_offsets() self.test_expert_offsets() def run_common_tests(self): self.test_jamba_offset_properties() return super().run_common_tests() class JambaModelTester: 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, attn_layer_offset=1, attn_layer_period=8, num_attention_heads=2, num_key_value_heads=2, 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_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.attn_layer_offset = attn_layer_offset self.attn_layer_period = attn_layer_period 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.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]) 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 JambaConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, attn_layer_offset=self.attn_layer_offset, attn_layer_period=self.attn_layer_period, 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, 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, num_experts=2, ) 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 = JambaModel(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 = JambaForCausalLM(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 = True model = JambaForCausalLM(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)) 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 = JambaForSequenceClassification(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 JambaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( JambaModel, JambaForCausalLM, JambaForSequenceClassification, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": JambaModel, "text-classification": JambaForSequenceClassification, "text-generation": JambaForCausalLM, "zero-shot": JambaForSequenceClassification, } if is_torch_available() else {} ) 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, config.mamba_d_conv) ssm_shape = (batch_size, config.mamba_expand * config.hidden_size, 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 = JambaModelTester(self) self.config_tester = JambaConfigTester(self, config_class=JambaConfig, 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_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) # After #40617, we still have 0.01 % of failure rate here. @is_flaky(max_attempts=2) def test_load_balancing_loss(self): r""" Let's make sure we can actually compute the loss and do a backward on it. """ config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 config.num_experts = 3 config.output_router_logits = True input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(config.pad_token_id).to(torch_device) model = JambaForCausalLM(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask) bs, seqlen = input_ids.shape self.assertEqual(result.router_logits[0].shape, (bs * seqlen, config.num_experts)) # After #40617, we still have 0.01 % of failure rate here. torch.testing.assert_close(result.aux_loss.cpu(), torch.tensor(2, dtype=torch.float32), rtol=1e-2, atol=1e-2) # First, we make sure that adding padding tokens doesn't change the loss # loss(input_ids, attention_mask=None) == loss(input_ids + padding, attention_mask=attention_mask_with_padding) # (This length is selected from experiments) pad_length = input_ids.shape[1] * 4 # Add padding tokens to input_ids padding_block = config.pad_token_id * torch.ones(input_ids.shape[0], pad_length, dtype=torch.int32).to( torch_device ) padded_input_ids = torch.cat((padding_block, input_ids), dim=1) # this is to simulate padding to the left padded_attention_mask = padded_input_ids.ne(config.pad_token_id).to(torch_device) padded_result = model(padded_input_ids, attention_mask=padded_attention_mask) torch.testing.assert_close(result.aux_loss.cpu(), padded_result.aux_loss.cpu(), rtol=1e-4, atol=1e-4) # We make sure that the loss of including padding tokens != the loss without padding tokens # if attention_mask=None --> we don't exclude padding tokens include_padding_result = model(padded_input_ids, attention_mask=None) # This is to mimic torch.testing.assert_not_close # After #40617, we still have 0.003 % of failure rate here. self.assertNotAlmostEqual(include_padding_result.aux_loss.item(), result.aux_loss.item()) def test_attention_outputs(self): r""" Overriding the test_attention_outputs test as the Jamba 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 = math.ceil( (self.model_tester.num_hidden_layers - self.model_tester.attn_layer_offset) / self.model_tester.attn_layer_period ) 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], ) @require_flash_attn @require_torch_accelerator @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 Jamba 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: Jamba 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", quantization_config=BitsAndBytesConfig(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("Jamba has a non standard cache which is not compatible with dp/ddp") def test_multi_gpu_data_parallel_forward(self): pass @require_torch @slow class JambaModelIntegrationTest(unittest.TestCase): model = None tokenizer = None # This variable is used to determine which acclerator are we using for our runners (e.g. A10 or T4) # Depending on the hardware we get different logits / generations device_properties: DeviceProperties = (None, None, None) @classmethod def setUpClass(cls): model_id = "ai21labs/Jamba-tiny-dev" cls.model = JambaForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, use_mamba_kernels=False, ) cls.tokenizer = AutoTokenizer.from_pretrained(model_id) cls.device_properties = get_device_properties() def test_simple_generate(self): # ("cuda", 8) for A100/A10, and ("cuda", 7) for T4. # # considering differences in hardware processing and potential deviations in generated text. # fmt: off EXPECTED_TEXTS = Expectations( { ("cuda", 7): "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh<|reserved_797|>cw algunas", ("cuda", 8): "<|startoftext|>Hey how are you doing on this lovely evening? I'm so glad you're here.", ("rocm", 9): "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew llam bb", } ) # fmt: on expected_sentence = EXPECTED_TEXTS.get_expectation() 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_sentence) def test_simple_batched_generate_with_padding(self): # ("cuda", 8) for A100/A10, and ("cuda", 7) for T4. # # considering differences in hardware processing and potential deviations in generated text. # fmt: off EXPECTED_TEXTS = Expectations( { ("cuda", 7): ["<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats", "<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",], ("cuda", 8): ["<|startoftext|>Hey how are you doing on this lovely evening? I'm so glad you're here.", "<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a story about a woman who was born in the United States",], ("rocm", 9): ["<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh<|reserved_797|>cw algunas", "<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed",], } ) # fmt: on expected_sentences = EXPECTED_TEXTS.get_expectation() self.model.to(torch_device) inputs = self.tokenizer( ["Hey how are you doing on this lovely evening?", "Tell me a story"], 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_sentences[0]) self.assertEqual(output_sentences[1], expected_sentences[1])