🚨 🚨 Inherited CausalLM Tests (#37590)
* stash commit * Experiment 1: Try just Gemma * Experiment 1: Just try Gemma * make fixup * Trigger tests * stash commit * Try adding Gemma3 as well * make fixup * Correct attrib names * Correct pipeline model mapping * Add in all_model_classes for Gemma1 again * Move the pipeline model mapping around again * make fixup * Revert Gemma3 changes since it's a VLM * Let's try Falcon * Correct attributes * Correct attributes * Let's try just overriding get_config() for now * Do Nemotron too * And Llama! * Do llama/persimmon * Correctly skip tests * Fix Persimmon * Include Phimoe * Fix Gemma2 * Set model_tester_class correctly * Add GLM * More models! * models models models * make fixup * Add Qwen3 + Qwen3MoE * Correct import * make fixup * Add the QuestionAnswering classes * Add the QuestionAnswering classes * Move pipeline mapping to the right place * Jetmoe too * Stop RoPE testing models with no RoPE * Fix up JetMOE a bit * Fix up JetMOE a bit * Can we just force pad_token_id all the time? * make fixup * fix starcoder2 * Move pipeline mapping * Fix RoPE skipping * Fix RecurrentGemma tests * Fix Falcon tests * Add MoE attributes * Fix values for RoPE testing * Make sure we set bos_token_id and eos_token_id in an appropriate range * make fixup * Fix GLM4 * Add mamba attributes * Revert bits of JetMOE * Re-add the JetMOE skips * Update tests/causal_lm_tester.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Add licence --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
This commit is contained in:
Matt
@@ -16,6 +16,7 @@
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import unittest
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import pytest
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from parameterized import parameterized
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from transformers import AutoModelForCausalLM, AutoTokenizer, RecurrentGemmaConfig, is_torch_available, set_seed
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from transformers.testing_utils import (
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@@ -27,151 +28,26 @@ from transformers.testing_utils import (
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torch_device,
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)
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import RecurrentGemmaForCausalLM, RecurrentGemmaModel
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from transformers import RecurrentGemmaConfig, RecurrentGemmaForCausalLM, RecurrentGemmaModel
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class RecurrentGemmaModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=12,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=False,
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use_labels=True,
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num_hidden_layers=3,
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vocab_size=99,
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hidden_size=32,
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intermediate_size=3 * 32,
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num_attention_heads=2,
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lru_width=2 * 32,
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embeddings_scale_by_sqrt_dim=True,
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attention_window_size=16,
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conv1d_width=4,
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logits_soft_cap=30.0,
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rms_norm_eps=1e-6,
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use_cache=True,
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rope_theta=10000.0,
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type_vocab_size=16,
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type_sequence_label_size=2,
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num_labels=3,
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num_choices=4,
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pad_token_id=0,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
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self.num_hidden_layers = num_hidden_layers
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_attention_heads = num_attention_heads
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self.lru_width = lru_width if lru_width is not None else hidden_size
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self.embeddings_scale_by_sqrt_dim = embeddings_scale_by_sqrt_dim
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self.attention_window_size = attention_window_size
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self.conv1d_width = conv1d_width
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self.logits_soft_cap = logits_soft_cap
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.pad_token_id = pad_token_id
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self.scope = scope
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# Copied from tests.models.mistral.test_modeling_mistral.MistralModelTester.prepare_config_and_inputs
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = torch.tril(torch.ones_like(input_ids).to(torch_device))
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def get_config(self):
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return RecurrentGemmaConfig(
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num_hidden_layers=self.num_hidden_layers,
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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intermediate_size=self.intermediate_size,
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num_attention_heads=self.num_attention_heads,
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lru_width=self.lru_width,
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embeddings_scale_by_sqrt_dim=self.embeddings_scale_by_sqrt_dim,
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attention_window_size=self.attention_window_size,
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conv1d_width=self.conv1d_width,
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logits_soft_cap=self.logits_soft_cap,
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rms_norm_eps=self.rms_norm_eps,
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use_cache=self.use_cache,
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rope_theta=self.rope_theta,
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pad_token_id=self.pad_token_id,
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output_attentions=False,
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)
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.create_and_check_model with Llama->RecurrentGemma
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def create_and_check_model(
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self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = RecurrentGemmaModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester.prepare_config_and_inputs_for_common with Llama->RecurrentGemma
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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class RecurrentGemmaModelTester(CausalLMModelTester):
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config_class = RecurrentGemmaConfig
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if is_torch_available():
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base_model_class = RecurrentGemmaModel
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causal_lm_class = RecurrentGemmaForCausalLM
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@require_torch
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class RecurrentGemmaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (RecurrentGemmaForCausalLM,) if is_torch_available() else ()
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# Doesn't run generation tests. TODO @gante not fully supported
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all_generative_model_classes = ()
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class RecurrentGemmaModelTest(CausalLMModelTest, unittest.TestCase):
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all_model_classes = (RecurrentGemmaModel, RecurrentGemmaForCausalLM) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": RecurrentGemmaModel,
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@@ -180,48 +56,10 @@ class RecurrentGemmaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Te
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if is_torch_available()
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else {}
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)
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fx_compatible = False # FIXME let's try to support this @ArthurZucker
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test_torchscript = False # FIXME let's try to support this @ArthurZucker
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test_missing_keys = False
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test_model_parallel = False
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test_headmasking = False
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test_pruning = False
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test_head_masking = False # RecurrentGemma does not have attention heads
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# Need to remove 0.9 in `test_cpu_offload`
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# This is because we are hitting edge cases with the causal_mask buffer
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model_split_percents = [0.5, 0.6]
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# TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
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def is_pipeline_test_to_skip(
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self,
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pipeline_test_case_name,
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config_class,
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model_architecture,
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tokenizer_name,
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image_processor_name,
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feature_extractor_name,
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processor_name,
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):
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return True
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def setUp(self):
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# We don't output attentions
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self.has_attentions = False
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self.model_tester = RecurrentGemmaModelTester(self)
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self.config_tester = ConfigTester(self, config_class=RecurrentGemmaConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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self.model_tester.create_and_check_model(*config_and_inputs)
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has_attentions = False
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model_tester_class = RecurrentGemmaModelTester
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@unittest.skip(reason="RecurrentGemma only supports sdpa")
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def test_eager_matches_sdpa_generate(self):
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@@ -255,6 +93,7 @@ class RecurrentGemmaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Te
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def test_model_parallel_beam_search(self):
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pass
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@parameterized.expand([("random",), ("same",)])
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@pytest.mark.generate
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@unittest.skip(reason="Rely on `past_key_values` to crop the assistant pkv. Not supported")
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def test_assisted_decoding_matches_greedy_search(self):
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@@ -273,6 +112,65 @@ class RecurrentGemmaModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Te
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def test_initialization(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_beam_sample_generate_dict_output(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_beam_search_generate_dict_output(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_beam_search_generate_dict_outputs_use_cache(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_constrained_beam_search_generate_dict_output(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_dola_decoding_sample(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_generate_without_input_ids(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_group_beam_search_generate(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_group_beam_search_generate_dict_output(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_constrained_beam_search_generate(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_greedy_generate_dict_outputs(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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@pytest.mark.generate
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def test_greedy_generate_dict_outputs_use_cache(self):
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pass
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@unittest.skip(reason="RecurrentGemma is unusual and fails a lot of generation tests")
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def test_model_outputs_equivalence(self):
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pass
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@require_torch_accelerator
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@slow
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