# 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 Jamba model.""" import math import tempfile import unittest import pytest from parameterized import parameterized from transformers import AutoTokenizer, JambaConfig, is_torch_available from transformers.testing_utils import ( 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 ( JambaForCausalLM, JambaForSequenceClassification, JambaModel, ) from transformers.models.jamba.modeling_jamba import ( HybridMambaAttentionDynamicCache, ) 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=5, attn_layer_offset=1, attn_layer_period=8, num_attention_heads=4, 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 () ) all_generative_model_classes = (JambaForCausalLM,) 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 {} ) test_headmasking = False test_pruning = False def setUp(self): self.model_tester = JambaModelTester(self) self.config_tester = ConfigTester(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_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_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_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 = 16 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)) 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) pad_length = 1000 # 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 self.assertNotAlmostEqual(include_padding_result.aux_loss.item(), result.aux_loss.item()) 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.mamba_d_state + 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)) 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 Mamba block are initialized differently and we tested that in test_initialization """ self.skipTest(reason="Cumbersome and redundant for Jamba") 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(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_left_padding_compatibility(self): r""" Overriding the test_left_padding_compatibility test as the mamba layers accentuate the numerical differences effect of the left padding discussed in the issue in the note. Using a more permissive tolerance value. """ import inspect # 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 - generative and decoder-only. # Jamba is a decoder-only architecture decoder_only_classes = self.all_generative_model_classes # 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, input_ids, attention_mask = self._get_input_ids_and_config() model = model_class(config).to(torch_device).eval() signature = inspect.signature(model.forward).parameters.keys() # 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) pad_size = (input_ids.shape[0], 32) padding = torch.ones(pad_size, dtype=input_ids.dtype, device=torch_device) * config.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 self.assertTrue(torch.allclose(next_logits_wo_padding, next_logits_with_padding, atol=3e-3)) @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 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, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, 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) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_padding_right(self): r""" Overriding the test_flash_attn_2_generate_padding_right test as the Jamba model, like Mixtral, doesn't support right padding + use cache with FA2 """ import torch for model_class in self.all_generative_model_classes: config, _ = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, low_cpu_mem_usage=True).to( torch_device ) dummy_input = torch.LongTensor([[0, 2, 3, 4], [0, 2, 3, 4]]).to(torch_device) dummy_attention_mask = torch.LongTensor([[1, 1, 1, 1], [1, 1, 1, 0]]).to(torch_device) model.generate(dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False) model = model_class.from_pretrained( tmpdirname, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) with self.assertRaises(ValueError): _ = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=1, do_sample=False ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_generate_use_cache(self): r""" Overriding the test_flash_attn_2_generate_use_cache test as the Jamba model, like Mixtral, doesn't support right padding + use cache with FA2 """ import torch max_new_tokens = 30 for model_class in self.all_generative_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() 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) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) 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, torch_dtype=torch.float16, attn_implementation="flash_attention_2", low_cpu_mem_usage=True, ).to(torch_device) # Just test that a large cache works as expected _ = model.generate( dummy_input, attention_mask=dummy_attention_mask, max_new_tokens=max_new_tokens, do_sample=False, use_cache=True, ) @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence_right_padding(self): r""" Overriding the test_flash_attn_2_inference_padding_right test as the Jamba model, like Mixtral, doesn't support right padding + use cache with FA2 """ self.skipTest(reason="Jamba flash attention does not support right padding") @unittest.skip(reason="Jamba 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 class JambaModelIntegrationTest(unittest.TestCase): model = None tokenizer = None @classmethod def setUpClass(cls): model_id = "ai21labs/Jamba-tiny-random" cls.model = JambaForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True) cls.tokenizer = AutoTokenizer.from_pretrained(model_id) @slow def test_simple_generate(self): 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, "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats", ) with torch.no_grad(): logits = self.model(input_ids=input_ids).logits EXPECTED_LOGITS_NO_GRAD = torch.tensor( [ 0.0140, -0.2246, 0.0408, -0.1016, 0.0471, 0.2715, -0.1465, 0.1631, -0.2949, -0.0297, 0.0250, -0.5586, -0.2139, -0.1426, -0.1602, 0.1309, 0.0703, 0.2236, 0.1729, -0.2285, -0.1152, -0.1177, -0.1367, 0.0289, 0.1245, 0.2363, 0.0442, 0.1094, -0.1348, -0.2295, 0.1494, -0.3945, 0.1777, -0.4570, -0.0408, 0.2412, 0.1562, -0.1943, 0.2373, -0.0593 ] , dtype=torch.float32) # fmt: skip torch.testing.assert_close(logits[0, -1, :40].cpu(), EXPECTED_LOGITS_NO_GRAD, rtol=1e-3, atol=1e-3) @slow def test_simple_batched_generate_with_padding(self): 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], "<|startoftext|>Hey how are you doing on this lovely evening? Canyon rins hugaughter glamour Rutgers Singh Hebrew cases Cats", ) self.assertEqual( output_sentences[1], "<|pad|><|pad|><|pad|><|pad|><|pad|><|pad|><|startoftext|>Tell me a storyptus Nets Madison El chamadamodern updximVaparsed", ) with torch.no_grad(): logits = self.model(input_ids=inputs["input_ids"]).logits EXPECTED_LOGITS_NO_GRAD_0 = torch.tensor( [ 0.0140, -0.2246, 0.0408, -0.1016, 0.0471, 0.2715, -0.1465, 0.1631, -0.2949, -0.0297, 0.0250, -0.5586, -0.2139, -0.1426, -0.1602, 0.1309, 0.0703, 0.2236, 0.1729, -0.2285, -0.1152, -0.1177, -0.1367, 0.0289, 0.1245, 0.2363, 0.0442, 0.1094, -0.1348, -0.2295, 0.1494, -0.3945, 0.1777, -0.4570, -0.0408, 0.2412, 0.1562, -0.1943, 0.2373, -0.0593 ] , dtype=torch.float32) # fmt: skip EXPECTED_LOGITS_NO_GRAD_1 = torch.tensor( [ -0.1289, 0.2363, -0.4180, -0.0302, -0.0476, 0.0327, 0.2578, 0.0874, 0.1484, 0.2305, -0.1152, -0.1396, -0.1494, -0.1113, -0.0021, -0.2832, 0.2002, -0.2676, 0.0598, -0.1982, -0.2539, -0.1133, -0.1973, 0.2148, 0.0559, 0.1670, 0.1846, 0.1270, 0.1680, -0.1250, -0.2656, -0.2871, 0.2344, 0.2637, 0.0510, -0.1855, 0.2158, -0.1289, 0.1758, 0.0074 ] , dtype=torch.float32) # fmt: skip 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=1e-3)