# 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 DeepseekV3 model.""" import unittest import pytest from packaging import version from parameterized import parameterized from transformers import AutoTokenizer, DeepseekV3Config, is_torch_available from transformers.testing_utils import ( cleanup, require_read_token, require_torch, require_torch_accelerator, require_torch_large_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 ( Cache, DeepseekV3ForCausalLM, DeepseekV3ForSequenceClassification, DeepseekV3ForTokenClassification, DeepseekV3Model, ) class DeepseekV3ModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, use_labels=True, vocab_size=99, hidden_size=32, intermediate_size=37, moe_intermediate_size=12, num_hidden_layers=2, num_attention_heads=4, num_key_value_heads=4, n_shared_experts=1, n_routed_experts=8, routed_scaling_factor=2.5, kv_lora_rank=16, q_lora_rank=32, qk_rope_head_dim=16, v_head_dim=32, qk_nope_head_dim=32, n_group=2, topk_group=1, num_experts_per_tok=8, first_k_dense_replace=2, norm_topk_prob=True, aux_loss_alpha=0.001, hidden_act="silu", max_position_embeddings=512, initializer_range=0.02, attention_probs_dropout_prob=0.1, type_vocab_size=16, type_sequence_label_size=2, num_labels=3, num_choices=4, pad_token_id=0, 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_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.moe_intermediate_size = moe_intermediate_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.n_shared_experts = n_shared_experts self.n_routed_experts = n_routed_experts self.routed_scaling_factor = routed_scaling_factor self.kv_lora_rank = kv_lora_rank self.q_lora_rank = q_lora_rank self.qk_rope_head_dim = qk_rope_head_dim self.v_head_dim = v_head_dim self.qk_nope_head_dim = qk_nope_head_dim self.n_group = n_group self.topk_group = topk_group self.num_experts_per_tok = num_experts_per_tok self.first_k_dense_replace = first_k_dense_replace self.norm_topk_prob = norm_topk_prob self.aux_loss_alpha = aux_loss_alpha self.hidden_act = hidden_act self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.num_labels = num_labels self.num_choices = num_choices self.pad_token_id = pad_token_id 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 = torch.tril(torch.ones_like(input_ids).to(torch_device)) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) 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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return DeepseekV3Config( vocab_size=self.vocab_size, hidden_size=self.hidden_size, intermediate_size=self.intermediate_size, moe_intermediate_size=self.moe_intermediate_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, num_key_value_heads=self.num_key_value_heads, n_shared_experts=self.n_shared_experts, n_routed_experts=self.n_routed_experts, routed_scaling_factor=self.routed_scaling_factor, kv_lora_rank=self.kv_lora_rank, q_lora_rank=self.q_lora_rank, qk_rope_head_dim=self.qk_rope_head_dim, v_head_dim=self.v_head_dim, qk_nope_head_dim=self.qk_nope_head_dim, n_group=self.n_group, topk_group=self.topk_group, num_experts_per_tok=self.num_experts_per_tok, first_k_dense_replace=self.first_k_dense_replace, norm_topk_prob=self.norm_topk_prob, aux_loss_alpha=self.aux_loss_alpha, hidden_act=self.hidden_act, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, use_cache=True, pad_token_id=self.pad_token_id, attention_dropout=self.attention_probs_dropout_prob, ) def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DeepseekV3Model(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 prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_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 DeepseekV3ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( DeepseekV3Model, DeepseekV3ForCausalLM, DeepseekV3ForSequenceClassification, DeepseekV3ForTokenClassification, ) if is_torch_available() else () ) all_generative_model_classes = (DeepseekV3ForCausalLM,) if is_torch_available() else () pipeline_model_mapping = ( { "feature-extraction": DeepseekV3Model, "text-classification": DeepseekV3ForSequenceClassification, "token-classification": DeepseekV3ForTokenClassification, "text-generation": DeepseekV3ForCausalLM, "zero-shot": DeepseekV3ForSequenceClassification, } 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] # used in `test_torch_compile_for_training` _torch_compile_train_cls = DeepseekV3ForCausalLM if is_torch_available() else None def setUp(self): self.model_tester = DeepseekV3ModelTester(self) self.config_tester = ConfigTester(self, config_class=DeepseekV3Config, hidden_size=37) def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config): """Needs to be overridden as deepseek has special MLA cache format (though we don't really use the MLA)""" self.assertIsInstance(past_key_values, Cache) # (batch, head, seq_length, head_features) expected_common_shape = ( batch_size, getattr(config, "num_key_value_heads", config.num_attention_heads), seq_length, ) expected_key_shape = expected_common_shape + (config.qk_nope_head_dim + config.qk_rope_head_dim,) expected_value_shape = expected_common_shape + (config.v_head_dim,) for layer in past_key_values.layers: self.assertEqual(layer.keys.shape, expected_key_shape) self.assertEqual(layer.values.shape, expected_value_shape) @parameterized.expand([("random",), ("same",)]) @unittest.skip("DeepseekV3 is not compatible with assisted decoding") def test_assisted_decoding_matches_greedy_search(self, assistant_type): pass @unittest.skip("DeepseekV3 is not compatible with assisted decoding") def test_prompt_lookup_decoding_matches_greedy_search(self, assistant_type): pass @unittest.skip("DeepseekV3 is not compatible with assisted decoding") def test_assisted_decoding_sample(self): pass @unittest.skip("Deepseek-V3 uses MLA so it is not compatible with the standard cache format") def test_beam_search_generate_dict_outputs_use_cache(self): pass @unittest.skip("Deepseek-V3 uses MLA so it is not compatible with the standard cache format") def test_greedy_generate_dict_outputs_use_cache(self): pass @unittest.skip(reason="SDPA can't dispatch on flash due to unsupported head dims") def test_sdpa_can_dispatch_on_flash(self): pass 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) @require_torch_large_accelerator @slow def test_eager_matches_sdpa_generate(self): """ Overwriting the common test as the test is flaky on tiny models """ max_new_tokens = 30 tokenizer = AutoTokenizer.from_pretrained("bzantium/tiny-deepseek-v3") model_sdpa = DeepseekV3ForCausalLM.from_pretrained( "bzantium/tiny-deepseek-v3", dtype=torch.float16, ).to(torch_device) self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") model_eager = DeepseekV3ForCausalLM.from_pretrained( "bzantium/tiny-deepseek-v3", dtype=torch.float16, attn_implementation="eager", ).to(torch_device) self.assertTrue(model_eager.config._attn_implementation == "eager") texts = [ "hi here's a longer context, getting longer and", "Hello this is a very long sentence my friend, very long for real", "Today I am in Paris and", ] for padding_side in ["left", "right"]: tokenizer.padding_side = padding_side tokenizer.pad_token = tokenizer.eos_token inputs = tokenizer(texts, return_tensors="pt", padding=True).to(torch_device) res_eager = model_eager.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False) res_sdpa = model_sdpa.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False) with self.subTest(f"{padding_side}"): torch.testing.assert_close( res_eager, res_sdpa, msg=f"\n{tokenizer.batch_decode(res_eager)} \nvs\n{tokenizer.batch_decode(res_sdpa)}", ) @require_torch_accelerator def test_flex_attention_with_grads(self): """ Overwriting as the namings/functionality on the attention part are different; for now it's more of a unique model. Original issue is also due to dimensionalities, here specifically due to dims not being a multiple of 2. """ for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config._attn_implementation = "flex_attention" # Disable dropout config.attention_dropout = 0.0 # Deepseek 3 specific - manipulate nope and adjust calculated total head dim config.qk_nope_head_dim = 16 config.qk_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim model = model_class(config).to(device=torch_device) self.assertTrue(model.config._attn_implementation == "flex_attention") # Elaborate workaround for encoder-decoder models as some do not specify their main input dummy_inputs = {model.main_input_name: inputs_dict[model.main_input_name].to(torch_device)} if config.is_encoder_decoder: dummy_inputs["decoder_input_ids"] = inputs_dict["decoder_input_ids"].to(torch_device) dummy_inputs["decoder_attention_mask"] = inputs_dict["decoder_attention_mask"].to(torch_device) # If this does not raise an error, the test passes (see https://github.com/huggingface/transformers/pull/35605) _ = model(**dummy_inputs) def test_deepseek_v3_sequence_classification_model(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_labels = 3 input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(1).to(torch_device) sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.num_labels) model = DeepseekV3ForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels) self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels)) @require_torch_accelerator class DeepseekV3IntegrationTest(unittest.TestCase): def tearDown(self): # See LlamaIntegrationTest.tearDown(). Can be removed once LlamaIntegrationTest.tearDown() is removed. cleanup(torch_device, gc_collect=False) @slow @require_torch_accelerator @pytest.mark.torch_compile_test @require_read_token def test_compile_static_cache(self): # `torch==2.2` will throw an error on this test (as in other compilation tests), but torch==2.1.2 and torch>2.2 # work as intended. See https://github.com/pytorch/pytorch/issues/121943 if version.parse(torch.__version__) < version.parse("2.3.0"): self.skipTest(reason="This test requires torch >= 2.3 to run.") NUM_TOKENS_TO_GENERATE = 40 # https://github.com/huggingface/transformers/pull/38562#issuecomment-2939209171 # The reason why the output is gibberish is because the testing model bzantium/tiny-deepseek-v3 is not trained # one. Since original DeepSeek-V3 model is too big to debug and test, there was no testing with the original one. EXPECTED_TEXT_COMPLETION = [ "Simply put, the theory of relativity states that Frojekecdytesాలు sicʰtinaccianntuala breej的效率和质量的控制lavestock-PraccuraciesOTTensorialoghismos的思路astiomotivityosexualriad TherapeuticsoldtYPEface Kishsatellite-TV", "My favorite all time favorite condiment is ketchup.ieden沟渠係室温 Fryrok般地Segmentation Cycle/physicalwarenkrautempsాలు蹈梗 Mesomac一等asan lethality suspended Causewaydreamswith Fossilsdorfాలు蹈 ChristiansenHOMEbrew", ] prompts = [ "Simply put, the theory of relativity states that ", "My favorite all time favorite condiment is ketchup.", ] tokenizer = AutoTokenizer.from_pretrained("bzantium/tiny-deepseek-v3", pad_token="", padding_side="right") model = DeepseekV3ForCausalLM.from_pretrained( "bzantium/tiny-deepseek-v3", device_map=torch_device, dtype=torch.float16 ) inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device) # Dynamic Cache generated_ids = model.generate(**inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False) dynamic_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, dynamic_text) # Static Cache generated_ids = model.generate( **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" ) static_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_text) # Static Cache + compile model._cache = None # clear cache object, initialized when we pass `cache_implementation="static"` model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True) generated_ids = model.generate( **inputs, max_new_tokens=NUM_TOKENS_TO_GENERATE, do_sample=False, cache_implementation="static" ) static_compiled_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, static_compiled_text)