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Support phi4 mini (#3467)
* support partial_rotary * support phi-4 * update doc
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‎README.md

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@@ -141,6 +141,7 @@ LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by
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<li>Phi-3-mini (3.8B)</li>
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<li>Phi-3.5-mini (3.8B)</li>
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<li>Phi-3.5-MoE (16x3.8B)</li>
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<li>Phi-4-mini (3.8B)</li>
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<li>MiniCPM3 (4B)</li>
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</ul>
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</td>

‎README_ja.md

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@@ -139,6 +139,7 @@ LMDeploy TurboMindエンジンは卓越した推論能力を持ち、さまざ
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<li>Phi-3-mini (3.8B)</li>
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<li>Phi-3.5-mini (3.8B)</li>
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<li>Phi-3.5-MoE (16x3.8B)</li>
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<li>Phi-4-mini (3.8B)</li>
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<li>MiniCPM3 (4B)</li>
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</ul>
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</td>

‎README_zh-CN.md

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@@ -143,6 +143,7 @@ LMDeploy TurboMind 引擎拥有卓越的推理能力，在各种规模的模型
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<li>Phi-3-mini (3.8B)</li>
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<li>Phi-3.5-mini (3.8B)</li>
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<li>Phi-3.5-MoE (16x3.8B)</li>
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<li>Phi-4-mini (3.8B)</li>
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<li>MiniCPM3 (4B)</li>
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</ul>
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</td>

‎docs/en/supported_models/supported_models.md

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@@ -88,6 +88,7 @@ The following tables detail the models supported by LMDeploy's TurboMind engine
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| StarCoder2 | 3B-15B | LLM | Yes | Yes | Yes | No | No |
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| Phi-3-mini | 3.8B | LLM | Yes | Yes | Yes | Yes | Yes |
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| Phi-3-vision | 4.2B | MLLM | Yes | Yes | Yes | - | - |
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| Phi-4-mini | 3.8B | LLM | Yes | Yes | Yes | Yes | Yes |
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| CogVLM-Chat | 17B | MLLM | Yes | Yes | Yes | - | - |
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| CogVLM2-Chat | 19B | MLLM | Yes | Yes | Yes | - | - |
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| LLaVA(1.5,1.6)<sup>\[2\]</sup> | 7B-34B | MLLM | No | No | No | No | No |
@@ -104,6 +105,7 @@ The following tables detail the models supported by LMDeploy's TurboMind engine
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| Phi-3.5-mini | 3.8B | LLM | Yes | Yes | No | - | - |
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| Phi-3.5-MoE | 16x3.8B | LLM | Yes | Yes | No | - | - |
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| Phi-3.5-vision | 4.2B | MLLM | Yes | Yes | No | - | - |
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| Phi-4-mini | 3.8B | LLM | Yes | Yes | No | - | - |
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```{note}
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* [1] Currently Mono-InternVL does not support FP16 due to numerical instability. Please use BF16 instead.

‎docs/zh_cn/supported_models/supported_models.md

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@@ -88,6 +88,7 @@
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| StarCoder2 | 3B-15B | LLM | Yes | Yes | Yes | No | No |
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| Phi-3-mini | 3.8B | LLM | Yes | Yes | Yes | Yes | Yes |
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| Phi-3-vision | 4.2B | MLLM | Yes | Yes | Yes | - | - |
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| Phi-4-mini | 3.8B | LLM | Yes | Yes | Yes | Yes | Yes |
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| CogVLM-Chat | 17B | MLLM | Yes | Yes | Yes | - | - |
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| CogVLM2-Chat | 19B | MLLM | Yes | Yes | Yes | - | - |
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| LLaVA(1.5,1.6)<sup>\[2\]</sup> | 7B-34B | MLLM | No | No | No | No | No |
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| Phi-3.5-mini | 3.8B | LLM | Yes | Yes | No | - | - |
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| Phi-3.5-MoE | 16x3.8B | LLM | Yes | Yes | No | - | - |
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| Phi-3.5-vision | 4.2B | MLLM | Yes | Yes | No | - | - |
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| Phi-4-mini | 3.8B | LLM | Yes | Yes | No | - | - |
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```{note}
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* [1] 目前，Mono-InternVL不支持FP16，因为数值不稳定。请改用BF16

‎lmdeploy/model.py

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@@ -1633,6 +1633,7 @@ def match(cls, model_path: str) -> Optional[str]:
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return 'internvl-zh-hermes2'
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@MODELS.register_module(name='phi-4')
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@MODELS.register_module(name='phi-3')
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class Phi3Instruct(BaseChatTemplate):
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"""Chat template of InternLM model."""
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path = model_path.lower()
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if all([c in path for c in ['phi-3', 'instruct']]):
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return 'phi-3'
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if all([c in path for c in ['phi-4', 'instruct']]):
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return 'phi-4'
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@MODELS.register_module(name='internvl2-phi3')

‎lmdeploy/pytorch/kernels/cuda/apply_rotary_pos_emb.py

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@@ -124,7 +124,16 @@ def apply_rotary_pos_emb(q: Tensor,
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seq_len = cos.numel() // cos.size(-1)
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BLOCK = 16
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half_size = q.size(-1) // 2
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if q.size(-1) == cos.size(-1):
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half_size = q.size(-1) // 2
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elif q.size(-1) > cos.size(-1):
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# only do rope with rope_dim size
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half_size = cos.size(-1) // 2
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else:
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raise ValueError('Not support head_dim < rope_dim, '
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f'but given head_dim={q.size(-1)} '
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f'rope_dim={cos.size(-1)}')
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BLOCK_N = triton.next_power_of_2(half_size)
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num_heads_q = q.size(-2)
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num_heads_k = k.size(-2)

‎lmdeploy/pytorch/models/phi3.py

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@@ -232,6 +232,7 @@ def __init__(self, config: PretrainedConfig, dtype: torch.dtype = None, device:
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rope_max_pos_emb = config.max_position_embeddings
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rope_base = config.rope_theta
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rope_scaling = config.rope_scaling
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partial_rotary_factor = getattr(config, 'partial_rotary_factor', None)
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if rope_scaling is not None:
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scaling_type = rope_scaling['type']
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assert scaling_type in ['longrope', 'su']
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rope_base,
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longrope_params=longrope_params,
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emb_type=emb_type,
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partial_rotary_factor=partial_rotary_factor,
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)
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else:
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self.rotary_emb = build_rotary_embedding(
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rope_dim,
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rope_max_pos_emb,
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rope_base,
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emb_type=emb_type,
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partial_rotary_factor=partial_rotary_factor,
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)
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def forward(
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"""compute logits of the model output."""
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return self.lm_head(hidden_states)
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def update_weights(self):
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"""update weights."""
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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def get_input_embeddings(self):
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"""get input embeddings."""
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return self.model.get_input_embeddings()

‎lmdeploy/pytorch/nn/rotary_embedding.py

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@@ -85,11 +85,16 @@ def build_rotary_embedding(dim: int,
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yarn_params: YarnParameters = None,
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longrope_params: LongRoPEScalingParameters = None,
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llama3_params: Llama3Parameters = None,
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emb_type: RopeType = RopeType.Default) -> nn.Module:
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emb_type: RopeType = RopeType.Default,
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partial_rotary_factor: float = None) -> nn.Module:
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"""build rotary embedding op."""
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backend = get_backend()
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builder = backend.get_layer_impl_builder(OpType.RotaryEmbedding)
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# update rope_dim
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if partial_rotary_factor is not None:
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dim = int(dim * partial_rotary_factor)
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return builder.build(dim,
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max_position_embeddings,
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base,

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