first commit

tests/models/deepseek_v3/test_modeling_deepseek_v3.py

@@ -0,0 +1,440 @@
+# 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 parameterized import parameterized
+
+from transformers import AutoTokenizer, DeepseekV3Config, is_torch_available
+from transformers.testing_utils import (
+    cleanup,
+    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
+from ...test_tensor_parallel_mixin import TensorParallelTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Cache,
+        DeepseekV3ForCausalLM,
+        DeepseekV3ForSequenceClassification,
+        DeepseekV3ForTokenClassification,
+        DeepseekV3Model,
+    )
+
+
+class DeepseekV3ModelTester:
+    if is_torch_available():
+        causal_lm_class = DeepseekV3ForCausalLM
+
+    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=32,
+        moe_intermediate_size=16,
+        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=1,
+        norm_topk_prob=True,
+        aux_loss_alpha=0.001,
+        hidden_act="silu",
+        max_position_embeddings=512,
+        initializer_range=0.02,
+        # NOTE(3outeille): must be 0.0 for TP backward tests. In train mode, non-zero dropout causes
+        # different RNG states between the non-TP and TP model forward passes (they run sequentially),
+        # leading to different dropout masks and mismatched losses.
+        attention_probs_dropout_prob=0.0,
+        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, TensorParallelTesterMixin
+):
+    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=32)
+
+    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
+    def test_compile_static_cache(self):
+        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="</s>", 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)