[Ernie 4.5] Add ernie text models (#39228)

* init

* copied from remote

* add proper structure and llama like structure

* fixup

* revert to state that works

* get closer to llama

* slow and steady

* some removal

* masks work

* it is indeed the rope implementation, how dafuq does it mesh with the cache now hmm

* nice

* getting closer

* closer to transformers style

* let's simplify this, batching works now

* simplified

* working version with modular

* it is indeed the rotation per weights, make it complete llama style

* cleanup conversion, next to look at -> tokenizer

* remove llama artefacts

* fix modeling tests (common ones)

* style

* integration test + first look into tokenization (will need more work, focussing on modeling other models first)

* style

* working moe version, based on remote

* lets keep it simple and go step by step - transformers annotations for modular and transformers style rope (complex view)

* more cleanup

* refactor namings and remove addition forXXX classes

* our moe won't cut it it seems, correction bias seems to be missing in remote code version

* tokenization change (remote)

* our moe version works when adding normalization :D

* cleanup moe

* nits

* cleanup modeling -> let's get to modular next

* style

* modular v1

* minor things + attempt at conversion (which doesn't work)

* no conversion follow glm, fixup modular and other nits

* modular cleanup

* fixes

* tests, tests, tests + some moe dtype forcing

* simplify modular, fix fatal fa2 bug, remaining tests

* fix import issue?

* some initial docs, fix bnb faulty behavior --> needs to fix some tests because of gate needing to be float

* fix sdpa test, load on init dtype only

* fixup post merge

* style

* fix doc links

* tokenization cleanup beginnings

* simplify tokenizer by a lot as its basically llama

* tokenizer is full llama with different defaults + extra special tokens

* sync og special tokens of ernie

* fix decoding with numbers (also in remote done what a timing), begin of tok tests

* align with remote and preserve special tokens, adjust tests to ernie legacy behavior, warning for questionable behavior (also in llama)

* nits

* docs

* my daily post merge it is

* check

* tokenization update with explanations and conversion script

* review on modular (til), revert some tokenizer things i did prior, remove mtp comment (low prio)

* post merge fixes

* fixup tokenization, llama fast is the way to go

* more fixups

* check

* import fixes

* correction bias following the paddle code

* fix

* fix TP plan, fix correction bias sharding during forward

* style

* whoops

* fix tied weights

* docs and last nit

* license

* flasky tests

* move repo id, update when merged on the hub

tests/causal_lm_tester.py

@@ -104,9 +104,11 @@ class CausalLMModelTester:
         is_decoder=False,
         scope=None,
         expert_interval=1,
+        moe_layer_start_index=0,
         moe_intermediate_size=12,
         shared_expert_intermediate_size=36,
         shared_expert_gate=True,
+        moe_num_shared_experts=2,
         num_experts_per_tok=2,
         num_experts=8,
         mamba_n_groups=1,
@@ -146,9 +148,11 @@ class CausalLMModelTester:
         self.head_dim = self.hidden_size // self.num_attention_heads
         self.is_decoder = is_decoder
         self.expert_interval = expert_interval
+        self.moe_layer_start_index = moe_layer_start_index
         self.moe_intermediate_size = moe_intermediate_size
         self.shared_expert_intermediate_size = shared_expert_intermediate_size
         self.shared_expert_gate = shared_expert_gate
+        self.moe_num_shared_experts = moe_num_shared_experts
         self.num_experts_per_tok = num_experts_per_tok
         self.num_experts = num_experts
         self.mamba_n_groups = mamba_n_groups

tests/models/ernie4_5/test_modeling_ernie4_5.py

@@ -0,0 +1,122 @@
+# Copyright 2025 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 Ernie4.5 model."""
+
+import unittest
+
+from transformers import is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        AutoTokenizer,
+        Ernie4_5Config,
+        Ernie4_5ForCausalLM,
+        Ernie4_5Model,
+    )
+    from transformers.models.ernie4_5.modeling_ernie4_5 import Ernie4_5RotaryEmbedding
+
+
+class Ernie4_5ModelTester(CausalLMModelTester):
+    if is_torch_available():
+        config_class = Ernie4_5Config
+        base_model_class = Ernie4_5Model
+        causal_lm_class = Ernie4_5ForCausalLM
+
+
+@require_torch
+class Ernie4_5ModelTest(CausalLMModelTest, unittest.TestCase):
+    all_model_classes = (
+        (
+            Ernie4_5Model,
+            Ernie4_5ForCausalLM,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Ernie4_5Model,
+            "text-generation": Ernie4_5ForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+    fx_compatible = False  # Broken by attention refactor cc @Cyrilvallez
+    model_tester_class = Ernie4_5ModelTester
+    rotary_embedding_layer = Ernie4_5RotaryEmbedding  # Enables RoPE tests if set
+
+    # 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 = Ernie4_5ForCausalLM if is_torch_available() else None
+
+
+@require_torch_accelerator
+class Ernie4_5IntegrationTest(unittest.TestCase):
+    def setup(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_ernie4_5_0p3B(self):
+        """
+        An integration test for Ernie 4.5 0.3B.
+        """
+        expected_texts = Expectations(
+            {
+                ("cuda", None): "User: Hey, are you conscious? Can you talk to me?\nAssistant: Hey! I'm here to help you with whatever you need. Are you feeling a bit overwhelmed or stressed? I'm here to listen and provide support.",
+            }
+        )  # fmt: skip
+        EXPECTED_TEXT = expected_texts.get_expectation()
+
+        tokenizer = AutoTokenizer.from_pretrained("baidu/ERNIE-4.5-0.3B-PT", revision="refs/pr/3")
+        model = Ernie4_5ForCausalLM.from_pretrained(
+            "baidu/ERNIE-4.5-0.3B-PT",
+            revision="refs/pr/3",
+            device_map="auto",
+            torch_dtype=torch.bfloat16,
+        )
+
+        prompt = "Hey, are you conscious? Can you talk to me?"
+        messages = [{"role": "user", "content": prompt}]
+        text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=128,
+            do_sample=False,
+        )
+
+        generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True).strip("\n")
+        self.assertEqual(generated_text, EXPECTED_TEXT)

tests/models/ernie4_5_moe/test_modeling_ernie4_5_moe.py

@@ -0,0 +1,199 @@
+# Copyright 2025 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 Ernie4.5 MoE model."""
+
+import tempfile
+import unittest
+
+import pytest
+
+from transformers import Ernie4_5_MoEConfig, is_torch_available
+from transformers.testing_utils import (
+    cleanup,
+    is_flaky,
+    require_bitsandbytes,
+    require_flash_attn,
+    require_torch,
+    require_torch_gpu,
+    require_torch_large_accelerator,
+    require_torch_multi_accelerator,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        AutoTokenizer,
+        Ernie4_5_MoEForCausalLM,
+        Ernie4_5_MoEModel,
+    )
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+class Ernie4_5_MoEModelTester(CausalLMModelTester):
+    config_class = Ernie4_5_MoEConfig
+    if is_torch_available():
+        base_model_class = Ernie4_5_MoEModel
+        causal_lm_class = Ernie4_5_MoEForCausalLM
+
+
+@require_torch
+class Ernie4_5_MoEModelTest(CausalLMModelTest, unittest.TestCase):
+    all_model_classes = (
+        (
+            Ernie4_5_MoEModel,
+            Ernie4_5_MoEForCausalLM,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Ernie4_5_MoEModel,
+            "text-generation": Ernie4_5_MoEForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_headmasking = False
+    test_pruning = False
+    test_all_params_have_gradient = False
+    model_tester_class = Ernie4_5_MoEModelTester
+
+    @require_flash_attn
+    @require_torch_gpu
+    @pytest.mark.flash_attn_test
+    @is_flaky()
+    @slow
+    def test_flash_attn_2_equivalence(self):
+        for model_class in self.all_model_classes:
+            if not model_class._supports_flash_attn_2:
+                self.skipTest(reason="Model does not support Flash Attention 2")
+
+            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)
+                model_fa = model_class.from_pretrained(
+                    tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2"
+                )
+                model_fa.to(torch_device)
+
+                model = model_class.from_pretrained(
+                    tmpdirname, torch_dtype=torch.bfloat16, attn_implementation="eager"
+                )
+                model.to(torch_device)
+
+                dummy_input = inputs_dict[model_class.main_input_name]
+                dummy_input = dummy_input.to(torch_device)
+                outputs = model(dummy_input, output_hidden_states=True)
+                outputs_fa = model_fa(dummy_input, output_hidden_states=True)
+
+                logits = outputs.hidden_states[-1]
+                logits_fa = outputs_fa.hidden_states[-1]
+
+                # higher tolerance, not sure where it stems from
+                assert torch.allclose(logits_fa, logits, atol=1e-2, rtol=1e-2)
+
+    # Ignore copy
+    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 = 8
+        config.expert_interval = 2
+        config.output_router_logits = True
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        model = Ernie4_5_MoEForCausalLM(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask)
+        self.assertEqual(result.router_logits[0].shape, (91, 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 (assume that pad_token_id=1) to input_ids
+        padding_block = 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(1).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())
+
+
+# Run on runners with larger accelerators (for example A10 instead of T4) with a lot of CPU RAM (e.g. g5-12xlarge)
+@require_torch_multi_accelerator
+@require_torch_large_accelerator
+@require_torch
+class Ernie4_5_MoEIntegrationTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = None
+
+    @classmethod
+    def tearDownClass(cls):
+        del cls.model
+        cleanup(torch_device, gc_collect=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @classmethod
+    def get_model(cls):
+        if cls.model is None:
+            cls.model = Ernie4_5_MoEForCausalLM.from_pretrained(
+                "baidu/ERNIE-4.5-21B-A3B-PT",
+                revision="refs/pr/11",
+                device_map="auto",
+                load_in_4bit=True,
+            )
+
+        return cls.model
+
+    @require_bitsandbytes
+    @slow
+    def test_model_21b_a3b_generation(self):
+        EXPECTED_TEXT_COMPLETION = "User: Hey, are you conscious? Can you talk to me?\nAssistant: Yes, I am conscious and I can communicate with you. How can I assist you with any questions or information you need?"  # fmt: skip
+
+        model = self.get_model()
+        tokenizer = AutoTokenizer.from_pretrained("baidu/ERNIE-4.5-21B-A3B-PT", revision="refs/pr/11")
+        prompt = "Hey, are you conscious? Can you talk to me?"
+        messages = [{"role": "user", "content": prompt}]
+        text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=32,
+            do_sample=False,
+        )
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True).strip("\n")
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)

tests/test_modeling_common.py

@@ -258,10 +258,10 @@ def _test_eager_matches_sdpa_inference(
                 model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="sdpa")
             except ValueError:
                 model_sdpa = model_class.from_pretrained(**model_from_pretrained_kwargs)
-            model_sdpa = model_sdpa.eval().to(torch_device, dtype=torch_dtype)
+            model_sdpa = model_sdpa.eval().to(torch_device)
 
             model_eager = model_class.from_pretrained(**model_from_pretrained_kwargs, attn_implementation="eager")
-            model_eager = model_eager.eval().to(torch_device, dtype=torch_dtype)
+            model_eager = model_eager.eval().to(torch_device)
 
         set_model_for_less_flaky_test(model_eager)
         set_model_for_less_flaky_test(model_sdpa)