diff --git a/router/src/config.rs b/router/src/config.rs index 5d0be9c8..9b770d06 100644 --- a/router/src/config.rs +++ b/router/src/config.rs @@ -159,6 +159,7 @@ pub enum Config { #[serde(rename = "phi-msft")] PhiMsft, Phi3, + PhiMoe, Llama, Baichuan, Paligemma(Paligemma), diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index ee996cc1..99a6ba76 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -32,6 +32,9 @@ from text_generation_server.models.custom_modeling.phi_modeling import ( PhiConfig, PhiForCausalLM, ) +from text_generation_server.models.custom_modeling.flash_phi_moe_modeling import ( + PhiMoEConfig, +) from text_generation_server.models.custom_modeling.t5_modeling import ( T5ForConditionalGeneration, ) @@ -778,6 +781,7 @@ def get_model( return FlashCausalLM( model_id=model_id, model_class=FlashLlamaForCausalLM, + config_class=PhiMoEConfig, revision=revision, quantize=quantize, speculator=speculator, diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py index 522d9b43..5ffcec83 100644 --- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py @@ -457,7 +457,7 @@ class FlashLlamaLayer(nn.Module): weights=weights, ) - if config._name_or_path == "microsoft/Phi-3.5-MoE-instruct": + if config.model_type == "phimoe": self.dense = BlockSparseMoE(f"{prefix}.block_sparse_moe", config, weights) # with moe the layernorms are are not rmsnorms and they have bias self.input_layernorm = FastLayerNorm.load( diff --git a/server/text_generation_server/models/custom_modeling/flash_phi_moe_modeling.py b/server/text_generation_server/models/custom_modeling/flash_phi_moe_modeling.py new file mode 100644 index 00000000..bb585cc4 --- /dev/null +++ b/server/text_generation_server/models/custom_modeling/flash_phi_moe_modeling.py @@ -0,0 +1,254 @@ +# coding=utf-8 +# Copyright 2024 Microsoft 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. + +"""PyTorch Phi-MoE model.""" + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + + +logger = logging.get_logger(__name__) + + +PHIMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "microsoft/Phi-3.5-MoE-instruct": "https://huggingface.co/microsoft/Phi-3.5-MoE-instruct/resolve/main/config.json", +} + + +class PhiMoEConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`PhiMoEModel`]. It is used to instantiate a Phi-MoE + 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 + [microsoft/Phi-3.5-MoE-instruct](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct). + + 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 32064): + Vocabulary size of the PhiMoE model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`PhiMoEModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 6400): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*, defaults to 8): + 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 `8`. + 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 `4096*32`): + The maximum sequence length that this model might ever be used with. Mixtral's sliding window attention + allows sequence of up to 4096*32 tokens. + 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-05): + 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*): + The id of the padding token. + bos_token_id (`int`, *optional*, defaults to 1): + The id of the "beginning-of-sequence" token. + eos_token_id (`int`, *optional*, defaults to 2): + The id of the "end-of-sequence" token. + 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. + rope_scaling (`dict`, *optional*): + The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must + contain the following keys: `type`, `short_factor`, `long_factor`, `short_mscale`, `long_mscale` and + `original_max_position_embeddings`. The `type` must be `longrope`, the `short_mscale` and `long_scale` must + be numbers, the `short_factor` and `long_factor` must be lists of numbers with the same length as half of + the attention head size and the `original_max_position_embeddings` must be an integer. + sliding_window (`int`, *optional*): + Sliding window attention window size. If not specified, will default to `262144`. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + num_experts_per_tok (`int`, *optional*, defaults to 2): + The number of experts to root per-token, can be also interpreted as the `top-p` routing + parameter + num_local_experts (`int`, *optional*, defaults to 16): + Number of experts per Sparse MLP layer. + 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. See [here]() for more details + router_aux_loss_coef (`float`, *optional*, defaults to 0.0): + The aux loss factor for the total loss. + router_jitter_noise (`float`, *optional*, defaults to 0.01): + Amount of noise to add to the router. + + ```python + >>> from transformers import PhiMoEModel, PhiMoEConfig + + >>> # Initializing a Phi-3 style configuration + >>> configuration = PhiMoEConfig.from_pretrained("microsoft/Phi-3.5-MoE-instruct") + + >>> # Initializing a model from the configuration + >>> model = PhiMoEModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "phimoe" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=32064, + hidden_size=4096, + intermediate_size=6400, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=8, + hidden_act="silu", + max_position_embeddings=4096 * 32, + initializer_range=0.02, + rms_norm_eps=1e-5, + use_cache=True, + pad_token_id=None, + bos_token_id=1, + eos_token_id=2, + tie_word_embeddings=False, + rope_theta=1e6, + rope_scaling=None, + sliding_window=None, + attention_dropout=0.0, + num_experts_per_tok=2, + num_local_experts=16, + output_router_logits=False, + router_aux_loss_coef=0.001, + router_jitter_noise=0.01, + input_jitter_noise=0.0, + attention_bias=False, + lm_head_bias=False, + **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.sliding_window = sliding_window + self.attention_bias = attention_bias + self.lm_head_bias = lm_head_bias + # 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.use_cache = use_cache + self.rope_theta = rope_theta + self.attention_dropout = attention_dropout + + self.num_experts_per_tok = num_experts_per_tok + self.num_local_experts = num_local_experts + self.output_router_logits = output_router_logits + self.router_aux_loss_coef = router_aux_loss_coef + self.router_jitter_noise = router_jitter_noise + self.input_jitter_noise = input_jitter_noise + + self.rope_scaling = rope_scaling + self._rope_scaling_validation() + + 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, + ) + + def _rope_scaling_validation(self): + """ + Validate the `rope_scaling` configuration. + """ + if self.rope_scaling is None: + return + + if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 6: + raise ValueError( + "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor`, `long_factor`, " + f"`short_mscale`, `long_mscale` and `original_max_position_embeddings`, got {self.rope_scaling}" + ) + rope_scaling_type = self.rope_scaling.get("type", None) + rope_scaling_short_factor = self.rope_scaling.get("short_factor", None) + rope_scaling_long_factor = self.rope_scaling.get("long_factor", None) + rope_scaling_short_mscale = self.rope_scaling.get("short_mscale", None) + rope_scaling_long_mscale = self.rope_scaling.get("long_mscale", None) + original_max_position_embeddings = self.rope_scaling.get( + "original_max_position_embeddings", None + ) + if rope_scaling_type is None or rope_scaling_type not in ["longrope"]: + raise ValueError( + f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}" + ) + if not ( + isinstance(rope_scaling_short_factor, list) + and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor) + ): + raise ValueError( + f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}" + ) + if ( + not len(rope_scaling_short_factor) + == self.hidden_size // self.num_attention_heads // 2 + ): + raise ValueError( + f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}" + ) + if not ( + isinstance(rope_scaling_long_factor, list) + and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor) + ): + raise ValueError( + f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}" + ) + if ( + not len(rope_scaling_long_factor) + == self.hidden_size // self.num_attention_heads // 2 + ): + raise ValueError( + f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}" + ) + if not isinstance(rope_scaling_short_mscale, (int, float)): + raise ValueError( + f"`rope_scaling`'s short_mscale field must be a number, got {rope_scaling_short_mscale}" + ) + if not isinstance(rope_scaling_long_mscale, (int, float)): + raise ValueError( + f"`rope_scaling`'s long_mscale field must be a number, got {rope_scaling_long_mscale}" + ) + if not isinstance(original_max_position_embeddings, int): + raise ValueError( + f"`rope_scaling`'s original_max_position_embeddings field must be an integer, got {original_max_position_embeddings}" + )