tensorrt_llm.models.phi.model — TensorRT-LLM (original) (raw)
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from typing import Optional, Union
from transformers import AutoModelForCausalLM
from ..._utils import pad_vocab_size from ...functional import Tensor from ...layers import (MLP, Attention, AttentionMaskType, ColumnLinear, Embedding, LayerNorm) from ...lora_manager import LoraConfig, use_lora from ...mapping import Mapping from ...module import Module from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM, PretrainedConfig, QuantConfig) from .config import PhiConfig from .convert import load_weights_from_hf_model
class PhiDecoderLayer(Module):
def __init__(self, config: PretrainedConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
tp_group = config.mapping.tp_group
tp_size = config.mapping.tp_size
self.input_layernorm = LayerNorm(normalized_shape=config.hidden_size,
dtype=config.dtype)
layers_range = config.mapping.pp_layers(config.num_hidden_layers)
local_layer_idx = layer_idx - layers_range[0]
self.attention = Attention(
local_layer_idx=local_layer_idx,
hidden_size=config.hidden_size,
num_attention_heads=config.num_attention_heads,
rotary_embedding_percentage=config.rotary_pct,
position_embedding_type=config.position_embedding_type,
rotary_embedding_base=config.rotary_base,
max_position_embeddings=config.max_position_embeddings,
dtype=config.dtype,
attention_mask_type=AttentionMaskType.causal,
bias=True,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=config.quant_mode)
self.mlp = MLP(hidden_size=config.hidden_size,
ffn_hidden_size=config.intermediate_size,
hidden_act=config.hidden_act,
dtype=config.dtype,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=config.quant_mode)
def forward(
self,
hidden_states: Tensor,
attention_mask=None,
use_cache=False,
kv_cache_params=None,
attention_params=None,
lora_layer_params=None,
):
residual = hidden_states
input_layernorm_output = self.input_layernorm(hidden_states)
attention_output = self.attention(
input_layernorm_output,
attention_mask=attention_mask,
use_cache=use_cache,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
norm_before_bmm1=True,
lora_layer_params=lora_layer_params,
)
if use_cache:
attention_output, presents = attention_output
feed_forward_hidden_states = self.mlp(input_layernorm_output, )
hidden_states = attention_output + feed_forward_hidden_states + residual
if use_cache:
return (hidden_states, presents)
return hidden_states[docs] class PhiModel(Module):
def __init__(self, config: PretrainedConfig):
super().__init__()
self.vocab_embedding = Embedding(num_embeddings=config.vocab_size,
embedding_dim=config.hidden_size,
dtype=config.dtype)
self.layers = DecoderLayerList(PhiDecoderLayer, config)
self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
dtype=config.dtype)[docs] def forward( self, input_ids: Tensor, position_ids=None, use_cache=False, attention_mask=None, kv_cache_params=None, attention_params=None, prompt_embedding_table=None, prompt_tasks=None, prompt_vocab_size=None, lora_params=None, ): args = [prompt_embedding_table, prompt_tasks, prompt_vocab_size ] if prompt_embedding_table is not None else [] hidden_states = self.vocab_embedding(input_ids, *args)
hidden_states = self.layers(hidden_states,
use_cache=use_cache,
attention_mask=attention_mask,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
lora_params=lora_params)
if use_cache:
hidden_states, presents = hidden_states
hidden_states = self.ln_f(hidden_states)
if use_cache:
return (hidden_states, tuple(presents))
return hidden_states[docs] class PhiForCausalLM(DecoderModelForCausalLM): config_class = PhiConfig
config_class = PhiConfig
def __init__(self, config: PretrainedConfig):
self.check_config(config)
transformer = PhiModel(config)
vocab_size_padded = pad_vocab_size(config.vocab_size,
config.mapping.tp_size)
lm_head = ColumnLinear(config.hidden_size,
vocab_size_padded,
bias=True,
dtype=config.dtype,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
gather_output=True)
self.trtllm_modules_to_hf_modules = {
"attn_q": "q_proj",
"attn_k": "k_proj",
"attn_v": "v_proj"
}
super().__init__(config, transformer, lm_head)[docs] def check_config(self, config): config.set_if_not_exist('partial_rotary_factor', 0.4) config.set_if_not_exist('rotary_base', 10000.0)
[docs] @classmethod def from_hugging_face( cls, hf_model_or_dir: Union[str, 'transformers.PreTrainedModel'], dtype: str = 'auto', mapping: Optional[Mapping] = None, quant_config: Optional[QuantConfig] = None, **kwargs): import transformers
assert hf_model_or_dir is not None
use_preloading = isinstance(hf_model_or_dir,
transformers.PreTrainedModel)
if use_preloading:
hf_model = hf_model_or_dir
hf_config_or_dir = hf_model.config
else:
hf_model_dir = hf_model_or_dir
hf_config_or_dir = hf_model_or_dir
config = PhiConfig.from_hugging_face(hf_config_or_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
if not use_preloading:
trust_remote_code = kwargs.pop('trust_remote_code', True)
hf_model = AutoModelForCausalLM.from_pretrained(
hf_model_dir,
torch_dtype="auto",
trust_remote_code=trust_remote_code)
assert isinstance(hf_model, transformers.PreTrainedModel)
weights = load_weights_from_hf_model(hf_model, config)
model = cls(config)
model.load(weights)
return model[docs] def use_lora(self, lora_config: LoraConfig): use_lora(self, lora_config, self.trtllm_modules_to_hf_modules)