[LLM] support Qwen2

llm/data.py

@@ -44,11 +44,12 @@ def get_convert_example(model):
 
     if base_model_prefix == "chatglm":
         return convert_example_chatglm
-    elif base_model_prefix in ["chatglm_v2", "llama", "bloom", "opt", "qwen", "mixtral", "gemma"]:
+    elif base_model_prefix in ["chatglm_v2", "llama", "bloom", "opt", "qwen", "mixtral", "gemma", "qwen2moe"]:
         return convert_example_common
     else:
         raise ValueError(
-            f"Unknown base_model_prefix: {model.base_model_prefix}. Supported base_model_prefix list: chatglm, bloom, llama, qwen, mixtral, gemma"
+            f"Unknown base_model_prefix: {model.base_model_prefix}. Supported base_model_prefix list: chatglm, bloom, llama, qwen, mixtral, gemma",
+            "qwen2moe",
         )
 
 

llm/qwen2moe/lora_argument.json

@@ -0,0 +1,32 @@
+{
+    "model_name_or_path": "qwen/Qwen1.5-MoE-A2.7B",
+    "dataset_name_or_path": "./data",
+    "output_dir": "./checkpoints/qwen2moe_lora_ckpts",
+    "per_device_train_batch_size": 4,
+    "gradient_accumulation_steps": 4,
+    "per_device_eval_batch_size": 8,
+    "eval_accumulation_steps":16,
+    "num_train_epochs": 3,
+    "learning_rate": 3e-04,
+    "warmup_steps": 30,
+    "logging_steps": 1,
+    "evaluation_strategy": "epoch",
+    "save_strategy": "epoch",
+    "src_length": 1024,
+    "max_length": 32768,
+    "bf16": true,
+    "fp16_opt_level": "O2",
+    "do_train": true,
+    "do_eval": true,
+    "disable_tqdm": true,
+    "load_best_model_at_end": true,
+    "eval_with_do_generation": false,
+    "metric_for_best_model": "accuracy",
+    "recompute": true,
+    "save_total_limit": 1,
+    "tensor_parallel_degree": 8,
+    "pipeline_parallel_degree": 1,
+    "lora": true,
+    "zero_padding": false,
+    "use_flash_attention": false
+  }

llm/qwen2moe/sft_argument.json

@@ -0,0 +1,30 @@
+{
+    "model_name_or_path": "qwen/Qwen1.5-MoE-A2.7B",
+    "dataset_name_or_path": "./data",
+    "output_dir": "./checkpoints/qwen2moe_sft_ckpts",
+    "per_device_train_batch_size": 4,
+    "gradient_accumulation_steps": 4,
+    "per_device_eval_batch_size": 8,
+    "eval_accumulation_steps":16,
+    "num_train_epochs": 3,
+    "learning_rate": 3e-05,
+    "warmup_steps": 30,
+    "logging_steps": 1,
+    "evaluation_strategy": "epoch",
+    "save_strategy": "epoch",
+    "src_length": 1024,
+    "max_length": 32768,
+    "bf16": true,
+    "fp16_opt_level": "O2",
+    "do_train": true,
+    "do_eval": true,
+    "disable_tqdm": true,
+    "load_best_model_at_end": true,
+    "eval_with_do_generation": false,
+    "metric_for_best_model": "accuracy",
+    "recompute": true,
+    "save_total_limit": 1,
+    "tensor_parallel_degree": 8,
+    "sharding": "stage2",
+    "pipeline_parallel_degree": 1
+}

llm/utils.py

@@ -41,7 +41,6 @@
 
 
 def compute_metrics(eval_preds):
-
     flattened_preds = np.array(eval_preds.predictions).flatten()
     flattened_labels = np.array(eval_preds.label_ids).flatten()
     filtered_preds = flattened_preds[flattened_labels != -100]
@@ -155,10 +154,22 @@ def get_lora_target_modules(model):
             ".*k_proj.*",
             ".*v_proj.*",
             ".*o_proj.*",
+            # ".*gate.*", # TODO(DrownFish19): Does the gate weight require training?
             ".*w1.*",
             ".*w2.*",
             ".*w3.*",
         ]
+    elif model.base_model_prefix == "qwen2moe":
+        target_modules = [
+            ".*q_proj.*",
+            ".*k_proj.*",
+            ".*v_proj.*",
+            ".*o_proj.*",
+            # ".*gate.*", # TODO(DrownFish19): Does the gate weight require training?
+            ".*gate_proj.*",
+            ".*up_proj.*",
+            ".*down_proj.*",
+        ]
     else:
         raise ValueError(f"Unknown base_model_prefix: {model.base_model_prefix}.")
     return target_modules

paddlenlp/transformers/__init__.py

@@ -294,6 +294,9 @@
 from .deberta_v2.modeling import *
 from .deberta_v2.tokenizer import *
 from .deberta_v2.configuration import *
+from .qwen2moe.modeling import *
+from .qwen2moe.configuration import *
+from .qwen2moe.tokenizer import *
 
 # For faster tokenizer
 from ..utils.import_utils import is_fast_tokenizer_available

paddlenlp/transformers/auto/modeling.py

@@ -118,6 +118,7 @@
         ("Bloom", "bloom"),
         ("QWen", "qwen"),
         ("Mixtral", "mixtral"),
+        ("QWen2Moe", "qwen2moe"),
         ("Gemma", "gemma"),
     ]
 )
@@ -215,15 +216,20 @@ def _get_model_class_from_config(cls, pretrained_model_name_or_path, config_file
         else:
             init_class = config.pop("init_class", None)
         init_class = init_class[:-5] if init_class is not None and init_class.endswith("Model") else init_class
+
+        # Sort the MAPPING_NAMES to reorder the model class names with longest-first rule
+        # thus the names with same prefix can be correctly inferred
+        # such as QWen and QWen2MOE, QWen2MOE is the longest prefix of QWen2MOEModel
         model_name = None
+        SORTED_MAPPING_NAMES = dict(sorted(MAPPING_NAMES.items(), key=lambda x: len(x[0]), reverse=True))
         if init_class:
-            for model_flag, name in MAPPING_NAMES.items():
+            for model_flag, name in SORTED_MAPPING_NAMES.items():
                 if model_flag in init_class:
                     model_name = model_flag + "Model"
                     break
         else:
             # From pretrained_model_name_or_path
-            for model_flag, name in MAPPING_NAMES.items():
+            for model_flag, name in SORTED_MAPPING_NAMES.items():
                 if name in pretrained_model_name_or_path.lower():
                     model_name = model_flag + "Model"
                     break

paddlenlp/transformers/auto/tokenizer.py

@@ -97,6 +97,7 @@
         ("BloomTokenizer", "bloom"),
         ("SpeechT5Tokenizer", "speecht5"),
         ("QWenTokenizer", "qwen"),
+        ("QWen2MoeTokenizer", "qwen2moe"),
         ("GemmaTokenizer", "gemma"),
     ]
 )

paddlenlp/transformers/qwen2moe/__init__.py

@@ -0,0 +1,17 @@
+# Copyright (c) 2023 PaddlePaddle Authors. 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.
+
+from .configuration import QWen2MoeConfig
+from .modeling import QWen2MoeForCausalLM
+from .tokenizer import QWen2MoeTokenizer

paddlenlp/transformers/qwen2moe/configuration.py

@@ -0,0 +1,203 @@
+# coding=utf-8
+# Copyright 2024 The Qwen team, Alibaba Group and 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.
+""" Qwen2MoE model configuration"""
+
+from paddlenlp.transformers.configuration_utils import PretrainedConfig
+
+__all__ = [
+    "QWen2MoeConfig",
+]
+
+
+class QWen2MoeConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`QWen2MoeModel`]. It is used to instantiate a
+    Qwen2MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen1.5-MoE-A2.7B" [Qwen/Qwen1.5-MoE-A2.7B"](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B").
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 151936):
+            Vocabulary size of the Qwen2MoE model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`QWen2MoeModel`]
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 5632):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 24):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 16):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+        max_window_layers (`int`, *optional*, defaults to 28):
+            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        decoder_sparse_step (`int`, *optional*, defaults to 1):
+            The frequency of the MoE layer.
+        moe_intermediate_size (`int`, *optional*, defaults to 1408):
+            Intermediate size of the routed expert.
+        shared_expert_intermediate_size (`int`, *optional*, defaults to 5632):
+            Intermediate size of the shared expert.
+        num_experts_per_tok (`int`, *optional*, defaults to 4):
+            Number of selected experts.
+        num_experts (`int`, *optional*, defaults to 60):
+            Number of routed experts.
+        norm_topk_prob (`bool`, *optional*, defaults to `False`):
+            Whether to normalize the topk probabilities.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not the router logits should be returned by the model. Enabeling this will also
+            allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+
+    ```python
+    >>> from paddlenlp.transformers import QWen2MoeModel, QWen2MoeConfig
+
+    >>> # Initializing a Qwen2MoE style configuration
+    >>> configuration = QWen2MoeConfig()
+
+    >>> # Initializing a model from the Qwen1.5-MoE-A2.7B" style configuration
+    >>> model = QWen2MoeModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen2moe"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=151936,
+        hidden_size=2048,
+        intermediate_size=5632,
+        num_hidden_layers=24,
+        num_attention_heads=16,
+        num_key_value_heads=16,
+        hidden_act="silu",
+        max_position_embeddings=8192,
+        seq_length=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        use_recompute=False,
+        recompute_granularity="full",
+        no_recompute_layers=None,
+        use_flash_attention=False,
+        attention_dropout=0.0,
+        use_fused_rope=False,
+        rope_theta=1000000.0,
+        tensor_parallel_output=True,
+        sequence_parallel=False,
+        fuse_sequence_parallel_allreduce=False,
+        pad_token_id=0,
+        bos_token_id=151643,
+        eos_token_id=151643,
+        tie_word_embeddings=False,
+        use_sliding_window=False,
+        sliding_window=32768,
+        max_window_layers=28,
+        decoder_sparse_step=1,
+        moe_intermediate_size=1408,
+        shared_expert_intermediate_size=5632,
+        num_experts_per_tok=4,
+        num_experts=60,
+        norm_topk_prob=False,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.seq_length = seq_length
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window
+        self.max_window_layers = max_window_layers
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+
+        self.use_cache = use_cache
+        self.use_recompute = use_recompute
+        self.recompute_granularity = recompute_granularity
+        self.no_recompute_layers = no_recompute_layers
+        self.use_flash_attention = use_flash_attention
+        self.tensor_parallel_output = tensor_parallel_output
+        self.sequence_parallel = sequence_parallel
+        self.fuse_sequence_parallel_allreduce = fuse_sequence_parallel_allreduce
+
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+        self.use_fused_rope = use_fused_rope
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+
+        # MoE arguments
+        self.decoder_sparse_step = decoder_sparse_step
+        self.moe_intermediate_size = moe_intermediate_size
+        self.shared_expert_intermediate_size = shared_expert_intermediate_size
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_experts = num_experts
+        self.norm_topk_prob = norm_topk_prob
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            tensor_parallel_output=tensor_parallel_output,
+            **kwargs,
+        )