import copy
import json
import os
from typing import List, Optional
import numpy as np
import safetensors
import torch
from .._common import default_net
from .._utils import (numpy_to_torch, str_dtype_to_torch, str_dtype_to_trt,
trt_dtype_to_torch)
from ..functional import PositionEmbeddingType, Tensor, gather_last_token_logits
from ..layers import (AttentionParams, FusedGatedMLP, GatedMLP,
KeyValueCacheParams, LoraParams)
from ..mapping import Mapping
from ..module import Module, ModuleList
from ..quantization import QuantMode
from ..quantization.quantize import quantize
from .generation_mixin import GenerationMixin
[docs]
class PretrainedConfig:
def __init__(self,
architecture: str,
dtype: str,
logits_dtype: str,
vocab_size: int,
max_position_embeddings: int,
hidden_size: int,
num_hidden_layers: int,
num_attention_heads: int,
num_key_value_heads: int,
hidden_act: str,
intermediate_size: int,
norm_epsilon: float,
position_embedding_type: str,
world_size: int,
tp_size: int,
pp_size: int,
quant_mode: QuantMode,
quant_kwargs: dict,
use_prompt_tuning: bool = False,
use_parallel_embedding: bool = False,
embedding_sharding_dim: int = 0,
share_embedding_table: bool = False,
max_lora_rank: int = 64,
head_size: int = None,
**kwargs):
self.architecture = architecture
self.dtype = dtype
self.logits_dtype = logits_dtype
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_size = hidden_size // num_attention_heads if head_size is None else head_size
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.norm_epsilon = norm_epsilon
self.position_embedding_type = PositionEmbeddingType.from_string(
position_embedding_type)
self.use_prompt_tuning = use_prompt_tuning
self.use_parallel_embedding = use_parallel_embedding
self.embedding_sharding_dim = embedding_sharding_dim
self.share_embedding_table = share_embedding_table
self.mapping = Mapping(world_size=world_size,
tp_size=tp_size,
pp_size=pp_size)
self.quant_mode = quant_mode
self.quant_kwargs = quant_kwargs
self.kv_dtype = self.dtype
self.max_lora_rank = max_lora_rank
if self.quant_mode.has_int8_kv_cache():
self.kv_dtype = 'int8'
elif self.quant_mode.has_fp8_kv_cache():
self.kv_dtype = 'fp8'
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
raise err
[docs]
def set_if_not_exist(self, key, value):
if not hasattr(self, key):
setattr(self, key, value)
[docs]
@classmethod
def from_dict(cls, config):
architecture = config.pop('architecture')
dtype = config.pop('dtype')
vocab_size = config.pop('vocab_size')
hidden_size = config.pop('hidden_size')
num_hidden_layers = config.pop('num_hidden_layers')
num_attention_heads = config.pop('num_attention_heads')
hidden_act = config.pop('hidden_act')
norm_epsilon = config.pop('norm_epsilon', 1e-5)
position_embedding_type = config.pop('position_embedding_type',
'learned_absolute')
logits_dtype = config.pop('logits_dtype', 'float32')
num_key_value_heads = config.pop('num_key_value_heads',
num_attention_heads)
intermediate_size = config.pop('intermediate_size', None)
max_position_embeddings = config.pop('max_position_embeddings', None)
use_prompt_tuning = config.pop('use_prompt_tuning', False)
use_parallel_embedding = config.pop('use_parallel_embedding', False)
embedding_sharding_dim = config.pop('embedding_sharding_dim', 0)
share_embedding_table = config.pop('share_embedding_table', False)
mapping = config.pop('mapping', {
'world_size': 1,
'tp_size': 1,
'pp_size': 1
})
world_size = mapping.get('world_size', 1)
tp_size = mapping.get('tp_size', 1)
pp_size = mapping.get('pp_size', 1)
if share_embedding_table and mapping.tp_size > 1:
if (not use_parallel_embedding) or (use_parallel_embedding and
embedding_sharding_dim == 1):
raise NotImplementedError(
"For multiple-processes cases, sharing the embedding table must set" \
"use_parallel_embedding=True and embedding_sharding_dim=0"
)
quantization = config.pop(
'quantization', {
'quant_algo': None,
'kv_cache_quant_algo': None,
'group_size': 128,
'has_zero_point': False,
'pre_quant_scale': False,
'exclude_modules': None,
'sq_use_plugin': False,
})
quant_algo = quantization.get('quant_algo', None)
kv_cache_quant_algo = quantization.get('kv_cache_quant_algo', None)
group_size = quantization.get('group_size', 128)
has_zero_point = quantization.get('has_zero_point', False)
pre_quant_scale = quantization.get('pre_quant_scale', False)
exclude_modules = quantization.get('exclude_modules', None)
sq_use_plugin = quantization.get('sq_use_plugin', False)
quant_mode = QuantMode.from_quant_algo(quant_algo, kv_cache_quant_algo)
quant_kwargs = {
'quant_algo': quant_algo,
'kv_cache_quant_algo': kv_cache_quant_algo,
'group_size': group_size,
'zero': has_zero_point,
'pre_quant_scale': pre_quant_scale,
'exclude_modules': exclude_modules,
'sq_use_plugin': sq_use_plugin,
}
max_lora_rank = config.pop('max_lora_rank', 64)
return cls(architecture, dtype, logits_dtype, vocab_size,
max_position_embeddings, hidden_size, num_hidden_layers,
num_attention_heads, num_key_value_heads, hidden_act,
intermediate_size, norm_epsilon, position_embedding_type,
world_size, tp_size, pp_size, quant_mode, quant_kwargs,
use_prompt_tuning, use_parallel_embedding,
embedding_sharding_dim, share_embedding_table, max_lora_rank,
**config)
[docs]
@classmethod
def from_json_file(cls, config_file: str):
with open(config_file) as f:
config = json.load(f)
return PretrainedConfig.from_dict(config)
[docs]
def to_dict(self):
output = copy.deepcopy(self.__dict__)
output['position_embedding_type'] = str(self.position_embedding_type)
output['mapping'] = {
'world_size': self.mapping.world_size,
'tp_size': self.mapping.tp_size,
'pp_size': self.mapping.pp_size,
}
output.pop('quant_mode')
output.pop('quant_kwargs')
output['quantization'] = {
'quant_algo':
self.quant_kwargs.get('quant_algo', None),
'kv_cache_quant_algo':
self.quant_kwargs.get('kv_cache_quant_algo', None),
'group_size':
self.quant_kwargs.get('group_size', 128),
'has_zero_point':
self.quant_kwargs.get('zero', False),
'pre_quant_scale':
self.quant_kwargs.get('pre_quant_scale', False),
'exclude_modules':
self.quant_kwargs.get('exclude_modules', None),
'sq_use_plugin':
self.quant_kwargs.get('sq_use_plugin', False),
}
return output
[docs]
def set_rank(self, rank):
self.mapping = Mapping(self.mapping.world_size,
rank=rank,
tp_size=self.mapping.tp_size,
pp_size=self.mapping.pp_size)
class DecoderLayerList(ModuleList):
def __init__(self, cls, config):
self.layer_list = config.mapping.pp_layers(config.num_hidden_layers)
super().__init__([cls(config, idx) for idx in self.layer_list])
def forward(self,
hidden_states,
use_cache=False,
attention_mask=None,
kv_cache_params=None,
attention_params=None,
lora_params=None,
medusa_position_offsets=None,
medusa_packed_mask=None):
kv_cache_params.fill_none_tensor_list(len(self.layer_list))
if use_cache:
presents = []
for layer_idx, (
layer, past, pointer, host_pointer,
max_attention_window_size) in enumerate(
zip(self, kv_cache_params.past_key_value,
kv_cache_params.kv_cache_block_pointers,
kv_cache_params.host_kv_cache_block_pointers,
kv_cache_params.host_max_attention_window_sizes)):
lora_layer_params = None
if lora_params is not None and lora_params.lora_ranks is not None:
lora_layer_params = lora_params.get_layer_params(layer_idx)
kwargs = {}
if lora_layer_params is not None:
kwargs['lora_layer_params'] = lora_layer_params
if medusa_position_offsets is not None:
kwargs['medusa_position_offsets'] = medusa_position_offsets
if medusa_packed_mask is not None:
kwargs['medusa_packed_mask'] = medusa_packed_mask
hidden_states = layer(
hidden_states,
use_cache=use_cache,
attention_mask=attention_mask,
kv_cache_params=KeyValueCacheParams(
past_key_value=[past],
host_past_key_value_lengths=kv_cache_params.
host_past_key_value_lengths,
host_max_attention_window_sizes=max_attention_window_size,
host_sink_token_length=kv_cache_params.
host_sink_token_length,
kv_cache_block_pointers=[pointer],
host_kv_cache_block_pointers=[host_pointer],
cache_indirection=kv_cache_params.cache_indirection),
attention_params=attention_params,
**kwargs)
if use_cache:
presents.append(hidden_states[1])
hidden_states = hidden_states[0]
if use_cache:
return hidden_states, presents
return hidden_states
class PostInitCaller(type):
def __call__(cls, *args, **kwargs):
obj = type.__call__(cls, *args, **kwargs)
obj.__post_init__()
return obj
[docs]
class PretrainedModel(Module, GenerationMixin, metaclass=PostInitCaller):
def __init__(self, config: PretrainedConfig):
super().__init__()
self.config = config
def __post_init__(self):
quantize(self, self.config.quant_mode, **self.config.quant_kwargs)
[docs]
def check_config(self, config):
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."
)
[docs]
@classmethod
def from_config(cls, config: PretrainedConfig):
return cls(config)
[docs]
@classmethod
def from_checkpoint(cls,
ckpt_dir: str,
rank: int = 0,
config: PretrainedConfig = None):
if config is None:
config = PretrainedConfig.from_json_file(
os.path.join(ckpt_dir, 'config.json'))
config.set_rank(rank)
model = cls.from_config(config)
weights = {}
with safetensors.safe_open(os.path.join(ckpt_dir,
f'rank{rank}.safetensors'),
framework='pt',
device='cpu') as f:
for key in f.keys():
weights[key] = f.get_tensor(key)
model.load(weights)
return model
[docs]
def load(self, weights):
expected_names = set([name for name, param in self.named_parameters()])
provided_names = set(weights.keys())
if provided_names != expected_names:
err_msg = "Provided tensor names are different from those expected by the engine."
if expected_names.difference(provided_names):
err_msg += f"\nExpected but not provided tensors: {expected_names.difference(provided_names)}"
if provided_names.difference(expected_names):
err_msg += f"\nProvided but not expected tensors: {provided_names.difference(expected_names)}"
raise RuntimeError(err_msg)
for name, param in self.named_parameters():
param.value = weights[name]
class DecoderModelForCausalLM(PretrainedModel):
def __init__(self, config, transformer, lm_head):
super().__init__(config)
self.transformer = transformer
self.lm_head = lm_head
def forward(self,
input_ids: Tensor,
position_ids=None,
use_cache=False,
last_token_ids=None,
attention_mask=None,
kv_cache_params=None,
attention_params=None,
hidden_states=None,
prompt_embedding_table: Optional[Tensor] = None,
prompt_tasks: Optional[Tensor] = None,
prompt_vocab_size: Optional[Tensor] = None,
lora_params=None,
medusa_position_offsets=None,
medusa_packed_mask=None):
kwargs = {
'input_ids': input_ids,
'position_ids': position_ids,
'use_cache': use_cache,
'attention_mask': attention_mask,
'kv_cache_params': kv_cache_params,
'attention_params': attention_params,
}
if lora_params is not None:
kwargs['lora_params'] = lora_params
if hidden_states is not None:
kwargs['hidden_states'] = hidden_states
if prompt_embedding_table is not None:
kwargs['prompt_embedding_table'] = prompt_embedding_table
if prompt_tasks is not None:
kwargs['prompt_tasks'] = prompt_tasks
if prompt_vocab_size is not None:
kwargs['prompt_vocab_size'] = prompt_vocab_size
if medusa_position_offsets is not None:
kwargs['medusa_position_offsets'] = medusa_position_offsets
if medusa_packed_mask is not None:
kwargs['medusa_packed_mask'] = medusa_packed_mask
hidden_states = self.transformer.forward(**kwargs)
if use_cache:
hidden_states, presents = hidden_states
if self.config.mapping.is_last_pp_rank():
hidden_states = gather_last_token_logits(
hidden_states, last_token_ids,
default_net().plugin_config.remove_input_padding)
# [batch_size, hidden_size] -> [batch_size, vocab_size]
lm_logits = self.lm_head(hidden_states)
lm_logits.mark_output('logits', self.config.logits_dtype)
else:
hidden_states.mark_output('hidden_states_output', self.config.dtype)
if use_cache and not default_net().plugin_config.paged_kv_cache:
for i, present in zip(
self.config.mapping.pp_layers(
self.config.num_hidden_layers), presents):
present.mark_output(f'present_key_value_{i}',
self.config.kv_dtype)
if self.config.mapping.is_last_pp_rank():
return (lm_logits, presents, hidden_states)
return (hidden_states, presents)
else:
if self.config.mapping.is_last_pp_rank():
return lm_logits, hidden_states
return hidden_states
def fuse_gate_mlp(model):
for layer in model.transformer.layers:
if not hasattr(layer, 'mlp'):
continue
quant_algo = model.config.quant_kwargs['quant_algo']
if isinstance(layer.mlp, GatedMLP):
fused_layer = FusedGatedMLP(
hidden_size=layer.mlp.hidden_size,
ffn_hidden_size=layer.mlp.ffn_hidden_size,
hidden_act=layer.mlp.hidden_act,
bias=layer.mlp.bias,
dtype=layer.mlp.dtype,
tp_group=layer.mlp.tp_group,
tp_size=layer.mlp.tp_size,
quant_mode=layer.mlp.quant_mode,
max_lora_rank=layer.mlp.max_lora_rank)
if quant_algo == 'FP8':
if isinstance(layer.mlp.dtype, str):
dtype = str_dtype_to_torch(layer.mlp.dtype)
else:
dtype = trt_dtype_to_torch(layer.mlp.dtype)
# dequantize
gate_weight = numpy_to_torch(
layer.mlp.gate.weight.raw_value).to(dtype) * numpy_to_torch(
layer.mlp.gate.weights_scaling_factor.raw_value)
fc_weight = numpy_to_torch(
layer.mlp.fc.weight.raw_value).to(dtype) * numpy_to_torch(
layer.mlp.fc.weights_scaling_factor.raw_value)
# concat
fused_weight = torch.cat([gate_weight, fc_weight], dim=0)
# quantize
fused_weight_scaling_factor = numpy_to_torch(
max(
layer.mlp.gate.weights_scaling_factor.raw_value,
layer.mlp.fc.weights_scaling_factor.raw_value,
))
fused_weight = (fused_weight / fused_weight_scaling_factor).to(
torch.float8_e4m3fn)
fused_layer.fused_fc.weight.value = fused_weight
fused_layer.fused_fc.weights_scaling_factor.value = fused_weight_scaling_factor
fused_layer.fused_fc.activation_scaling_factor.value = \
max(layer.mlp.gate.activation_scaling_factor.raw_value,
layer.mlp.fc.activation_scaling_factor.raw_value
)
elif quant_algo is None:
fused_layer.fused_fc.weight.value = np.concatenate([
layer.mlp.gate.weight.raw_value,
layer.mlp.fc.weight.raw_value
],
axis=0)
if layer.mlp.bias:
fused_layer.fused_fc.bias.value = np.concatenate([
layer.mlp.gate.bias.raw_value,
layer.mlp.fc.bias.raw_value
],
axis=0)
else:
raise ValueError(f'Unsupported quant algo: {quant_algo}')
fused_layer.proj = layer.mlp.proj
layer.mlp = fused_layer
return model
def optimize_model(model, use_fused_mlp=False):
if use_fused_mlp:
model = fuse_gate_mlp(model)
return model