# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from typing import Optional
from ..._utils import pad_vocab_size
from ...functional import Tensor, recv, send
from ...layers import (Attention, AttentionMaskType, ColumnLinear, Embedding,
GatedMLP, PositionEmbeddingType, RmsNorm)
from ...mapping import Mapping
from ...module import Module
from ...top_model_mixin import TopModelMixin
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
PretrainedConfig, QuantConfig)
from .weight import load_from_hf_gemma
class GemmaDecoderLayer(Module):
def __init__(self, config: PretrainedConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
self.config = config
self.input_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]
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,
attention_head_size=config.head_size,
max_position_embeddings=config.max_position_embeddings,
dtype=config.dtype,
attention_mask_type=AttentionMaskType.causal,
bias=config.attn_bias,
position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
rotary_embedding_base=config.rotary_base,
rotary_embedding_scaling=config.rotary_scaling,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
quant_mode=config.quant_mode,
)
mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
self.mlp = GatedMLP(hidden_size=config.hidden_size,
ffn_hidden_size=mlp_hidden_size,
hidden_act=config.hidden_act,
dtype=config.dtype,
bias=config.mlp_bias,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
quant_mode=config.quant_mode)
self.post_layernorm = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
def forward(
self,
hidden_states,
attention_mask=None,
medusa_packed_mask=None, # For Medusa support
medusa_position_offsets=None,
use_cache=False,
kv_cache_params=None,
attention_params=None,
lora_layer_params=None):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
attention_output = self.attention(
hidden_states,
attention_mask=attention_mask,
medusa_packed_mask=medusa_packed_mask, # For Medusa support
medusa_position_offsets=medusa_position_offsets,
use_cache=use_cache,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
lora_layer_params=lora_layer_params)
if use_cache:
attention_output, presents = attention_output
hidden_states = residual + attention_output
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states,
lora_layer_params=lora_layer_params)
hidden_states = residual + hidden_states
if use_cache:
return (hidden_states, presents)
return hidden_states
class GemmaModel(Module):
def __init__(self, config: PretrainedConfig) -> None:
super().__init__()
self.mapping = config.mapping
if self.mapping.is_first_pp_rank():
self.vocab_embedding = Embedding(config.vocab_size,
config.hidden_size,
dtype=config.dtype)
self.layers = DecoderLayerList(GemmaDecoderLayer, config)
if self.mapping.is_last_pp_rank():
self.ln_f = RmsNorm(normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
dtype=config.dtype)
def forward(self,
input_ids,
position_ids=None,
use_cache=False,
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):
ptuning_args = [
prompt_embedding_table, prompt_tasks, prompt_vocab_size
] if prompt_embedding_table is not None else []
if self.mapping.is_first_pp_rank():
hidden_states = self.vocab_embedding(input_ids, *ptuning_args)
else:
hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
hidden_states = self.layers.forward(
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
if self.mapping.is_last_pp_rank():
hidden_states = self.ln_f(hidden_states)
else:
hidden_states = send(hidden_states, self.mapping.next_pp_rank())
if use_cache:
return (hidden_states, tuple(presents))
return hidden_states
[docs]
class GemmaForCausalLM(DecoderModelForCausalLM, TopModelMixin):
def __init__(self, config: PretrainedConfig):
self.check_config(config)
transformer = GemmaModel(config)
vocab_size_padded = pad_vocab_size(config.vocab_size,
config.mapping.tp_size)
if config.mapping.is_last_pp_rank():
lm_head = ColumnLinear(config.hidden_size,
vocab_size_padded,
bias=False,
dtype=config.dtype,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
gather_output=True)
else:
lm_head = None
self.quant_mode = config.quant_mode
self.mapping = config.mapping
super().__init__(config, transformer, lm_head)
[docs]
@classmethod
def from_hugging_face(cls,
hf_model_dir,
dtype='float16',
mapping: Optional[Mapping] = None,
**kwargs):
import transformers
from transformers import GemmaConfig
from ...models.modeling_utils import PretrainedConfig
cfg = GemmaConfig.from_pretrained(hf_model_dir)
num_kv_heads = cfg.num_key_value_heads if hasattr(cfg, "num_key_value_heads") \
else cfg.num_attention_heads
quantization = kwargs.get('quantization', QuantConfig())
if mapping is None:
mapping = Mapping()
cfg.mapping = mapping
cfg.dtype = dtype
cfg.norm_epsilon = cfg.rms_norm_eps
config = {
'architecture': cfg.architectures[0],
'dtype': cfg.dtype,
'logits_dtype': 'float32',
'num_hidden_layers': cfg.num_hidden_layers,
'num_attention_heads': cfg.num_attention_heads,
'head_size': cfg.head_dim,
'hidden_size': cfg.hidden_size,
'intermediate_size': cfg.intermediate_size,
'num_key_value_heads': num_kv_heads,
'vocab_size': cfg.vocab_size,
'position_embedding_type': 'rope_gpt_neox',
'max_position_embeddings': cfg.max_position_embeddings,
'hidden_act': cfg.hidden_act,
'rotary_base': getattr(cfg, 'rotary_base', 10000.0),
'rotary_scaling': getattr(cfg, 'rotary_scaling', None),
'norm_epsilon': cfg.rms_norm_eps,
'quantization': quantization.asdict(),
'mapping': {
'world_size': mapping.world_size,
'tp_size': mapping.world_size,
},
'use_parallel_embedding': kwargs.get("use_parallel_embedding",
False),
'embedding_sharding_dim': kwargs.get("embedding_sharding_dim", 0),
'use_fused_mlp': kwargs.get("use_fused_mlp", False),
}
assert not quantization.quant_mode.has_any_quant()
tllm_llama = GemmaForCausalLM(PretrainedConfig.from_dict(config))
hf_model = transformers.GemmaForCausalLM
hf_llama = hf_model.from_pretrained(
hf_model_dir,
device_map={
"model": "cpu",
"lm_head": "cpu",
"embed_tokens": "cpu",
"layers": "cpu",
"norm": "cpu",
}, # Load to CPU memory
torch_dtype='auto',
)
weights = load_from_hf_gemma(
tllm_llama,
hf_llama,
mapping=mapping,
dtype=dtype,
# TODO: these shall be outside from_hugging_face too.
use_gemm_woq_plugin=kwargs.get("use_gemm_woq_plugin", False),
)
del hf_llama
tllm_llama.load(weights)
return tllm_llama
[docs]
def check_config(self, config):
config.set_if_not_exist('use_parallel_embedding', False)
config.set_if_not_exist('embedding_sharding_dim', 0)
config.set_if_not_exist('mlp_bias', False)
config.set_if_not_exist('attn_bias', False)
config.set_if_not_exist('rotary_base', 10000.0)
config.set_if_not_exist('rotary_scaling', None)
config.set_if_not_exist('use_fused_mlp', False)