add minicpm3 model and dynmaic inference demo (#1870)

llm/inference/minicpm3/simple_inference.py

@@ -0,0 +1,28 @@
+import mindspore
+from mindnlp.transformers import MiniCPM3Tokenizer, MiniCPM3Config, MiniCPM3ForCausalLM
+from mindnlp.core import ops
+
+
+model_id = "OpenBMB/MiniCPM3-4B"
+tokenizer = MiniCPM3Tokenizer.from_pretrained(model_id, mirror="modelscope")
+model = MiniCPM3ForCausalLM.from_pretrained(model_id, ms_dtype=mindspore.float16, mirror="modelscope")
+
+
+messages = [
+    {"role": "user", "content": "推荐5个北京的景点。"},
+]
+model_inputs = tokenizer.apply_chat_template(messages, return_tensors="ms", add_generation_prompt=True)
+
+model_outputs = model.generate(
+    model_inputs,
+    max_new_tokens=1024,
+    top_p=0.7,
+    temperature=0.7
+)
+
+output_token_ids = [
+    model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs))
+]
+
+responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
+print(responses)

mindnlp/transformers/models/__init__.py

@@ -146,6 +146,7 @@
     megatron_bert,
     mgp_str,
     minicpm,
+    minicpm3,
     mistral,
     mixtral,
     mobilebert,
@@ -390,6 +391,7 @@
 from .megatron_bert import *
 from .mgp_str import *
 from .minicpm import *
+from .minicpm3 import *
 from .mistral import *
 from .mixtral import *
 from .mobilebert import *
@@ -634,6 +636,7 @@
 __all__.extend(megatron_bert.__all__)
 __all__.extend(mgp_str.__all__)
 __all__.extend(minicpm.__all__)
+__all__.extend(minicpm3.__all__)
 __all__.extend(mistral.__all__)
 __all__.extend(mixtral.__all__)
 __all__.extend(mllama.__all__)

mindnlp/transformers/models/auto/configuration_auto.py

@@ -144,6 +144,7 @@
         ("mctct", "MCTCTConfig"),
         ("megatron-bert", 'MegatronBertConfig'),
         ("minicpm", "MiniCPMConfig"),
+        ("minicpm3", "MiniCPM3Config"),
         ("mistral", "MistralConfig"),
         ("mixtral", "MixtralConfig"),
         ("mllama", "MllamaConfig"),
@@ -360,6 +361,7 @@
         ("mega", "MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("megatron-bert", "MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("mgp-str", "MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP"),
+        ("minicpm3", "MINICPM3_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("mistral", "MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("mixtral", "MIXTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("mobilenet_v1", "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP"),
@@ -619,6 +621,7 @@
         ("megatron_gpt2", "Megatron-GPT2"),
         ("mgp-str", "MGP-STR"),
         ("minicpm", "MiniCPM"),
+        ("minicpm3", "MiniCPM3"),
         ("mistral", "Mistral"),
         ("mixtral", "Mixtral"),
         ("mllama", "Mllama"),

mindnlp/transformers/models/auto/modeling_auto.py

@@ -162,6 +162,7 @@
         ("megatron-bert", "MegatronBertModel"),
         ("mgp-str", "MgpstrForSceneTextRecognition"),
         ('minicpm', 'MiniCPMModel'),
+        ("minicpm3", "MiniCPM3Model"),
         ("mistral", "MistralModel"),
         ("mixtral", "MixtralModel"),
         ("mobilebert", "MobileBertModel"),
@@ -320,6 +321,7 @@
         ("mega", "MegaForMaskedLM"),
         ("megatron-bert", "MegatronBertForPreTraining"),
         ('minicpm', 'MiniCPMForCausalLM'),
+        ('minicpm3', 'MiniCPM3ForCausalLM'),
         ("mllama", "MllamaForConditionalGeneration"),
         ("mobilebert", "MobileBertForPreTraining"),
         ("mpnet", "MPNetForMaskedLM"),
@@ -407,6 +409,7 @@
         ("mega", "MegaForMaskedLM"),
         ("megatron-bert", "MegatronBertForCausalLM"),
         ('minicpm', 'MiniCPMForCausalLM'),
+        ('minicpm3', 'MiniCPM3ForCausalLM'),
         ("mobilebert", "MobileBertForMaskedLM"),
         ("mpnet", "MPNetForMaskedLM"),
         ("mpt", "MptForCausalLM"),
@@ -493,6 +496,7 @@
         ("mega", "MegaForCausalLM"),
         ("megatron-bert", "MegatronBertForCausalLM"),
         ('minicpm', 'MiniCPMForCausalLM'),
+        ('minicpm3', 'MiniCPM3ForCausalLM'),
         ("mistral", "MistralForCausalLM"),
         ("mixtral", "MixtralForCausalLM"),
         ("mllama", "MllamaForCausalLM"),
@@ -943,6 +947,7 @@
         ("mega", "MegaForSequenceClassification"),
         ("megatron-bert", "MegatronBertForSequenceClassification"),
         ('minicpm', 'MiniCPMForSequenceClassification'),
+        ('minicpm3', 'MiniCPM3ForSequenceClassification'),
         ("mistral", "MistralForSequenceClassification"),
         ("mixtral", "MixtralForSequenceClassification"),
         ("mobilebert", "MobileBertForSequenceClassification"),

mindnlp/transformers/models/auto/tokenization_auto.py

@@ -287,6 +287,7 @@
             ("mega", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)),
             ("megatron-bert", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
             ("mgp-str", ("MgpstrTokenizer", None)),
+            ("minicpm3", ("MiniCPMTokenizer", None)),
             (
                 "mistral",
                 (

mindnlp/transformers/models/minicpm3/__init__.py

@@ -0,0 +1,26 @@
+# Copyright 2024 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""
+MiniCPM3 Model.
+"""
+from . import modeling_minicpm3, configuration_minicpm3, tokenization_minicpm3
+from .modeling_minicpm3 import *
+from .configuration_minicpm3 import *
+from .tokenization_minicpm3 import *
+
+__all__ = []
+__all__.extend(modeling_minicpm3.__all__)
+__all__.extend(configuration_minicpm3.__all__)
+__all__.extend(tokenization_minicpm3.__all__)

mindnlp/transformers/models/minicpm3/configuration_minicpm3.py

@@ -0,0 +1,185 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+""" MiniCPM model configuration"""
+
+from mindnlp.utils import logging
+from ...configuration_utils import PretrainedConfig
+
+
+logger = logging.get_logger(__name__)
+
+MINICPM3_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class MiniCPM3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the MiniCPM-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 32000):
+            Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MiniCPMModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            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
+            `num_attention_heads`.
+        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 2048):
+            The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
+            MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
+        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`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+    ```python
+    >>> from transformers import MiniCPMModel, MiniCPMConfig
+    >>> # Initializing a MiniCPM minicpm-7b style configuration
+    >>> configuration = MiniCPMConfig()
+    >>> # Initializing a model from the minicpm-7b style configuration
+    >>> model = MiniCPMModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "minicpm3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        qk_nope_head_dim=64,
+        qk_rope_head_dim=32,
+        q_lora_rank=768,
+        kv_lora_rank=256,
+        v_head_dim=None,
+        head_dim=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        scale_emb=1,
+        dim_model_base=1,
+        scale_depth=1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        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.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.q_lora_rank = q_lora_rank
+        self.kv_lora_rank = kv_lora_rank
+
+        if v_head_dim is None:
+            v_head_dim = qk_nope_head_dim
+        self.v_head_dim = v_head_dim
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        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.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.scale_emb = scale_emb
+        self.dim_model_base = dim_model_base
+        self.scale_depth = scale_depth
+        self.head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
+
+        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,
+            **kwargs,
+        )
+
+__all__ = ["MiniCPM3Config"]