from typing import Optional, Union
import numpy as np
from transformers import AutoModelForCausalLM
from ..._utils import pad_vocab_size
from ...functional import PositionEmbeddingType, Tensor
from ...layers import (MLP, MOE, Attention, AttentionMaskType,
BlockSparseAttnParams, ColumnLinear, Embedding,
LayerNorm, MoeConfig, RmsNorm)
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 Phi3Config
from .convert import load_weights_from_hf_model
class Phi3DecoderLayer(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
attention_mask_type = AttentionMaskType.causal
block_sparse_attn_params = BlockSparseAttnParams()
q_scaling = 1.0
self.gegelu_limit = None
self.small_variant = config.architecture == "Phi3SmallForCausalLM"
self.moe_variant = config.architecture == "PhiMoEForCausalLM"
if self.small_variant:
self.gegelu_limit = config.gegelu_limit
# MuP uses norm_factor=attention_head_size (rather than sqrt(attention_head_size))
# We achieve this using q_scaling = sqrt(attention_head_size)
hidden_size = config.hidden_size
num_attention_heads = config.num_attention_heads
attention_head_size = hidden_size / num_attention_heads
q_scaling = attention_head_size**.5
block_sparse = (
(layer_idx + 1) % config.dense_attention_every_n_layers) != 0
attention_mask_type = AttentionMaskType.blocksparse if block_sparse else AttentionMaskType.causal
block_sparse_attn_params = BlockSparseAttnParams(
config.blocksparse_block_size,
config.blocksparse_homo_head_pattern,
config.blocksparse_num_local_blocks,
config.blocksparse_vertical_stride)
if self.small_variant or self.moe_variant:
self.input_layernorm = LayerNorm(
normalized_shape=config.hidden_size,
dtype=config.dtype,
eps=config.norm_epsilon)
self.post_layernorm = LayerNorm(normalized_shape=config.hidden_size,
dtype=config.dtype,
eps=config.norm_epsilon)
else:
self.input_layernorm = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
self.post_layernorm = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
layers_range = config.mapping.pp_layers(config.num_hidden_layers)
local_layer_idx = layer_idx - layers_range[0]
position_embedding_type = PositionEmbeddingType.rope_gpt_neox
rope_scaling_short_factors, rope_scaling_long_factors = None, None
rope_scaling_short_mscale, rope_scaling_long_mscale = None, None
original_max_position_embeddings = config.max_position_embeddings
if hasattr(config, "longrope_scaling_short_factors"):
rope_scaling_short_factors = np.asarray(
config.longrope_scaling_short_factors).astype(np.float32)
rope_scaling_long_factors = np.asarray(
config.longrope_scaling_long_factors).astype(np.float32)
original_max_position_embeddings = config.original_max_position_embeddings
position_embedding_type = PositionEmbeddingType.long_rope
if self.small_variant or self.moe_variant:
rope_scaling_short_mscale = config.longrope_short_mscale
rope_scaling_long_mscale = config.longrope_long_mscale
self.attention = Attention(
local_layer_idx=local_layer_idx,
hidden_size=config.hidden_size,
num_attention_heads=config.num_attention_heads,
num_kv_heads=config.num_key_value_heads,
position_embedding_type=position_embedding_type,
rotary_embedding_base=config.rotary_base,
max_position_embeddings=config.max_position_embeddings,
dtype=config.dtype,
attention_mask_type=attention_mask_type,
bias=self.small_variant or self.moe_variant,
q_scaling=q_scaling,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=config.quant_mode,
rope_scaling_short_factors=rope_scaling_short_factors,
rope_scaling_long_factors=rope_scaling_long_factors,
rope_scaling_short_mscale=rope_scaling_short_mscale,
rope_scaling_long_mscale=rope_scaling_long_mscale,
original_max_position_embeddings=original_max_position_embeddings,
block_sparse_params=block_sparse_attn_params)
ClsMLP = MLP
mlp_kwargs = {}
if hasattr(config, "moe"):
ClsMLP = MOE
moe_config = MoeConfig()
for key, value in config.moe.items():
setattr(moe_config, key, value)
mlp_kwargs = {
"moe_config": moe_config,
"mapping": config.mapping,
}
self.mlp = ClsMLP(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,
bias=self.small_variant,
**mlp_kwargs)
def forward(
self,
hidden_states: Tensor,
attention_mask=None,
use_cache=False,
kv_cache_params=None,
attention_params=None,
lora_layer_params=None,
):
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=not self.small_variant,
lora_layer_params=lora_layer_params,
)
if use_cache:
attention_output, presents = attention_output
post_attention_input = hidden_states + attention_output
post_attention_output = self.post_layernorm(post_attention_input)
if self.small_variant:
feed_forward_hidden_states = self.mlp(
post_attention_output,
gegelu_limit=self.gegelu_limit,
lora_layer_params=lora_layer_params)
else:
feed_forward_hidden_states = self.mlp(
post_attention_output, lora_layer_params=lora_layer_params)
hidden_states = post_attention_input + feed_forward_hidden_states
if use_cache:
return (hidden_states, presents)
return hidden_states
[docs]
class Phi3Model(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(Phi3DecoderLayer, config)
self.small_variant = config.architecture == "Phi3SmallForCausalLM"
self.moe_variant = config.architecture == "PhiMoEForCausalLM"
if self.small_variant or self.moe_variant:
self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
if self.small_variant:
self.mup_embedding_multiplier = config.mup_embedding_multiplier
else:
self.ln_f = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
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)
if self.small_variant and self.mup_embedding_multiplier > 0.0:
hidden_states = hidden_states * self.mup_embedding_multiplier
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 Phi3ForCausalLM(DecoderModelForCausalLM):
config_class = Phi3Config
def __init__(self, config: PretrainedConfig):
transformer = Phi3Model(config)
vocab_size_padded = pad_vocab_size(config.vocab_size,
config.mapping.tp_size)
self.moe_variant = config.architecture == "PhiMoEForCausalLM"
lm_head = ColumnLinear(config.hidden_size,
vocab_size_padded,
bias=self.moe_variant,
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_qkv": ["qkv_proj", "query_key_value"],
"attn_dense": ["o_proj", "dense"],
"mlp_h_to_4h": ["gate_up_proj", "up_proj"],
"mlp_4h_to_h": "down_proj",
}
super().__init__(config, transformer, lm_head)
[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 = Phi3Config.from_hugging_face(hf_config_or_dir,
dtype=dtype,
mapping=mapping,
quant_config=quant_config,
**kwargs)
if not use_preloading:
hf_model = AutoModelForCausalLM.from_pretrained(
hf_model_dir, torch_dtype="auto", trust_remote_code=True)
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)