mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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f7eca56161
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com> Co-authored-by: zhang-ge-hao <842720660@qq.com>
397 lines
16 KiB
Python
397 lines
16 KiB
Python
import copy
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import json
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import os
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import safetensors
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from .._common import default_net
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from .._utils import str_dtype_to_trt
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from ..functional import PositionEmbeddingType, Tensor, gather_last_token_logits
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from ..layers import AttentionParams, KeyValueCacheParams
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from ..mapping import Mapping
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from ..module import Module, ModuleList
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from ..quantization import QuantMode
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from ..quantization.quantize import quantize
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from .generation_mixin import GenerationMixin
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class PretrainedConfig:
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def __init__(self, architecture: str, dtype: str, logits_dtype: str,
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vocab_size: int, max_position_embeddings: int,
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hidden_size: int, num_hidden_layers: int,
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num_attention_heads: int, num_key_value_heads: int,
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hidden_act: str, intermediate_size: int, norm_epsilon: float,
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position_embedding_type: str, world_size: int, tp_size: int,
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pp_size: int, quant_mode: QuantMode, quant_kwargs: dict,
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use_prompt_tuning: bool, **kwargs):
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self.architecture = architecture
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self.dtype = dtype
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self.logits_dtype = logits_dtype
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.norm_epsilon = norm_epsilon
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self.position_embedding_type = PositionEmbeddingType.from_string(
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position_embedding_type)
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self.use_prompt_tuning = use_prompt_tuning
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self.mapping = Mapping(world_size=world_size,
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tp_size=tp_size,
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pp_size=pp_size)
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self.quant_mode = quant_mode
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self.quant_kwargs = quant_kwargs
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self.kv_dtype = self.dtype
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if self.quant_mode.has_int8_kv_cache():
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self.kv_dtype = 'int8'
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elif self.quant_mode.has_fp8_kv_cache():
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self.kv_dtype = 'fp8'
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for key, value in kwargs.items():
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try:
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setattr(self, key, value)
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except AttributeError as err:
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raise err
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@classmethod
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def from_dict(cls, config):
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architecture = config.pop('architecture')
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dtype = config.pop('dtype')
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vocab_size = config.pop('vocab_size')
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hidden_size = config.pop('hidden_size')
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num_hidden_layers = config.pop('num_hidden_layers')
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num_attention_heads = config.pop('num_attention_heads')
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hidden_act = config.pop('hidden_act')
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norm_epsilon = config.pop('norm_epsilon', 1e-5)
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position_embedding_type = config.pop('position_embedding_type',
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'learned_absolute')
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logits_dtype = config.pop('logits_dtype', 'float32')
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num_key_value_heads = config.pop('num_key_value_heads',
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num_attention_heads)
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intermediate_size = config.pop('intermediate_size', None)
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max_position_embeddings = config.pop('max_position_embeddings', None)
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use_prompt_tuning = config.pop('use_prompt_tuning', False)
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mapping = config.pop('mapping', {
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'world_size': 1,
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'tp_size': 1,
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'pp_size': 0
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})
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world_size = mapping.get('world_size', 1)
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tp_size = mapping.get('tp_size', 1)
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pp_size = mapping.get('pp_size', 1)
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quantization = config.pop(
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'quantization', {
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'use_smooth_quant': False,
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'per_channel': False,
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'per_token': False,
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'per_group': False,
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'group_size': 128,
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'int8_kv_cache': False,
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'enable_fp8': False,
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'fp8_kv_cache': False,
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'use_weight_only': False,
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'weight_only_precision': 'int8'
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})
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use_smooth_quant = quantization.get('use_smooth_quant', False)
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per_channel = quantization.get('per_channel', False)
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per_token = quantization.get('per_token', False)
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per_group = quantization.get('per_group', False)
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group_size = quantization.get('group_size', 128)
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int8_kv_cache = quantization.get('int8_kv_cache', False)
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enable_fp8 = quantization.get('enable_fp8', False)
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fp8_kv_cache = quantization.get('fp8_kv_cache', False)
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use_weight_only = quantization.get('use_weight_only', False)
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weight_only_precision = quantization.get('weight_only_precision',
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'int8')
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quantize_weights, quantize_activations = False, False
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if use_smooth_quant:
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quantize_weights = True
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quantize_activations = True
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elif use_weight_only:
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quantize_weights = True
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per_token = False
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per_channel = False
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quant_mode = QuantMode.from_description(
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quantize_weights=quantize_weights,
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quantize_activations=quantize_activations,
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per_token=per_token,
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per_channel=per_channel,
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per_group=per_group,
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use_int4_weights=(weight_only_precision == 'int4'),
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use_int8_kv_cache=int8_kv_cache,
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use_fp8_kv_cache=fp8_kv_cache,
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use_fp8_qdq=enable_fp8,
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)
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quant_kwargs = {'group_size': group_size}
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return cls(architecture, dtype, logits_dtype, vocab_size,
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max_position_embeddings, hidden_size, num_hidden_layers,
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num_attention_heads, num_key_value_heads, hidden_act,
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intermediate_size, norm_epsilon, position_embedding_type,
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world_size, tp_size, pp_size, quant_mode, quant_kwargs,
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use_prompt_tuning, **config)
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@classmethod
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def from_json_file(cls, config_file: str):
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with open(config_file) as f:
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config = json.load(f)
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return PretrainedConfig.from_dict(config)
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def to_dict(self):
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output = copy.deepcopy(self.__dict__)
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output['position_embedding_type'] = str(self.position_embedding_type)
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output['mapping'] = {
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'world_size': self.mapping.world_size,
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'tp_size': self.mapping.tp_size,
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'pp_size': self.mapping.pp_size,
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}
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output.pop('quant_mode')
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output.pop('quant_kwargs')
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output['quantization'] = {
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'use_smooth_quant':
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self.quant_mode.has_act_and_weight_quant(),
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'per_channel':
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self.quant_mode.has_per_channel_scaling(),
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'per_token':
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self.quant_mode.has_per_token_dynamic_scaling(),
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'per_group':
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self.quant_mode.has_per_group_scaling(),
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'group_size':
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self.quant_kwargs.get('group_size', 128),
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'int8_kv_cache':
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self.quant_mode.has_int8_kv_cache(),
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'enable_fp8':
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self.quant_mode.has_fp8_qdq(),
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'fp8_kv_cache':
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self.quant_mode.has_fp8_kv_cache(),
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'use_weight_only':
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self.quant_mode.is_weight_only(),
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'weight_only_precision':
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'int8' if self.quant_mode.is_int8_weight_only() else 'int4',
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}
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return output
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def set_rank(self, rank):
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self.mapping = Mapping(self.mapping.world_size,
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rank=rank,
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tp_size=self.mapping.tp_size,
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pp_size=self.mapping.pp_size)
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class DecoderLayerList(ModuleList):
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def __init__(self, cls, config):
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self.layer_list = config.mapping.pp_layers(config.num_hidden_layers)
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super().__init__([cls(config, idx) for idx in self.layer_list])
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def forward(self,
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hidden_states,
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use_cache=False,
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attention_mask=None,
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kv_cache_params=None,
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attention_params=None):
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if kv_cache_params.past_key_value is None:
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kv_cache_params.past_key_value = tuple([None] *
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len(self.layer_list))
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if kv_cache_params.host_max_attention_window_sizes is None:
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kv_cache_params.host_max_attention_window_sizes = tuple(
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[None] * len(self.layer_list))
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if use_cache:
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presents = []
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for layer, past, pointer, host_pointer, max_attention_window_size in zip(
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self, kv_cache_params.past_key_value,
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kv_cache_params.kv_cache_block_pointers,
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kv_cache_params.host_kv_cache_block_pointers,
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kv_cache_params.host_max_attention_window_sizes):
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hidden_states = layer(
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hidden_states,
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use_cache=use_cache,
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attention_mask=attention_mask,
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kv_cache_params=KeyValueCacheParams(
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past_key_value=[past],
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host_past_key_value_lengths=kv_cache_params.
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host_past_key_value_lengths,
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host_max_attention_window_sizes=max_attention_window_size,
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kv_cache_block_pointers=[pointer],
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host_kv_cache_block_pointers=[host_pointer],
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cache_indirection=kv_cache_params.cache_indirection),
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attention_params=attention_params)
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if use_cache:
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presents.append(hidden_states[1])
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hidden_states = hidden_states[0]
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if use_cache:
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return hidden_states, presents
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return hidden_states
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class PostInitCaller(type):
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def __call__(cls, *args, **kwargs):
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obj = type.__call__(cls, *args, **kwargs)
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obj.__post_init__()
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return obj
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class PretrainedModel(Module, GenerationMixin, metaclass=PostInitCaller):
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def __init__(self, config):
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super().__init__()
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self.config = config
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def __post_init__(self):
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quantize(self, self.config.quant_mode)
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@classmethod
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def from_config(cls, config: PretrainedConfig):
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return cls(config)
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@classmethod
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def from_checkpoint(cls, ckpt_dir: str, rank: int = 0):
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config = PretrainedConfig.from_json_file(
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os.path.join(ckpt_dir, 'config.json'))
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config.set_rank(rank)
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model = cls.from_config(config)
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weights = {}
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with safetensors.safe_open(os.path.join(ckpt_dir,
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f'rank{rank}.safetensors'),
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framework='pt',
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device='cpu') as f:
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for key in f.keys():
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weights[key] = f.get_tensor(key)
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model.load(weights)
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return model
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def load(self, weights):
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for name, param in self.named_parameters():
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if name not in weights:
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continue
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param.value = weights[name]
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def prepare_inputs(self,
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max_batch_size,
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max_input_len,
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max_new_tokens,
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use_cache,
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max_beam_width: int = 1,
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max_num_tokens: int = None,
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prompt_embedding_table_size: int = 0):
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'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
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ranges of the dimensions of when using TRT dynamic shapes.
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@return: a list contains values which can be fed into the self.forward()
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'''
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# Prepare inputs
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head_size = self.config.hidden_size // self.config.num_attention_heads
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remove_input_padding = default_net().plugin_config.remove_input_padding
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use_gpt_attention_plugin = default_net(
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).plugin_config.gpt_attention_plugin
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use_gemm_plugin = default_net().plugin_config.gemm_plugin
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paged_kv_cache = default_net().plugin_config.paged_kv_cache
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tokens_per_block = default_net().plugin_config.tokens_per_block
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model_inputs = self.prepare_basic_inputs(
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max_batch_size=max_batch_size,
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max_beam_width=max_beam_width,
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max_input_len=max_input_len,
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max_new_tokens=max_new_tokens,
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num_kv_heads=self.config.num_key_value_heads,
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head_size=head_size,
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num_layers=self.config.num_hidden_layers,
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kv_dtype=str_dtype_to_trt(self.config.kv_dtype),
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remove_input_padding=remove_input_padding,
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use_gpt_attention_plugin=use_gpt_attention_plugin,
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use_gemm_plugin=use_gemm_plugin,
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paged_kv_cache=paged_kv_cache,
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tokens_per_block=tokens_per_block,
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num_heads=self.config.num_attention_heads,
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max_num_tokens=max_num_tokens,
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dtype=str_dtype_to_trt(self.config.dtype),
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prompt_embedding_table_size=prompt_embedding_table_size,
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mapping=self.config.mapping)
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return (model_inputs['input_ids'], model_inputs['position_ids'], True,
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model_inputs['last_token_ids'], model_inputs['attention_mask'],
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KeyValueCacheParams(
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past_key_value=model_inputs['past_key_value'],
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host_past_key_value_lengths=model_inputs[
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'host_past_key_value_lengths'],
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host_max_attention_window_sizes=model_inputs[
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'host_max_attention_window_sizes'],
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kv_cache_block_pointers=model_inputs[
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'kv_cache_block_pointers_list'],
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host_kv_cache_block_pointers=model_inputs[
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'host_kv_cache_block_pointers_list'],
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cache_indirection=model_inputs['cache_indirection'],
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),
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AttentionParams(
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sequence_length=model_inputs['sequence_length'],
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context_lengths=model_inputs['context_lengths'],
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host_context_lengths=model_inputs['host_context_lengths'],
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max_context_length=max_input_len,
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host_request_types=model_inputs['host_request_types']),
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model_inputs['prompt_embedding_table'], model_inputs['tasks'],
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model_inputs['prompt_vocab_size'])
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class DecoderModelForCausalLM(PretrainedModel):
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def __init__(self, config, transformer, lm_head):
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super().__init__(config)
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self.transformer = transformer
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self.lm_head = lm_head
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def forward(self,
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input_ids: Tensor,
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position_ids=None,
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use_cache=False,
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last_token_ids=None,
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attention_mask=None,
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kv_cache_params=None,
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attention_params=None,
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prompt_embedding_table=None,
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prompt_tasks=None,
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prompt_vocab_size=None):
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hidden_states = self.transformer.forward(
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input_ids, position_ids, use_cache, attention_mask, kv_cache_params,
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attention_params, prompt_embedding_table, prompt_tasks,
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prompt_vocab_size)
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if use_cache:
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hidden_states, presents = hidden_states
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hidden_states = gather_last_token_logits(
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hidden_states, last_token_ids,
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default_net().plugin_config.remove_input_padding)
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# [batch_size, hidden_size] -> [batch_size, vocab_size]
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lm_logits = self.lm_head(hidden_states)
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lm_logits.mark_output('logits', self.config.logits_dtype)
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if use_cache and default_net().plugin_config.paged_kv_cache == False:
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for i, present in enumerate(presents):
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present.mark_output(f'present_key_value_{i}',
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self.config.kv_dtype)
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return (lm_logits, presents)
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return lm_logits
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