# 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
import numpy as np
from ..._common import default_net
from ..._utils import pad_vocab_size
from ...functional import (Tensor, concat, constant, expand, op_and, recv, send,
shape, slice, unsqueeze, where)
from ...layers import (AttentionMaskType, CogVLMAttention, ColumnLinear,
Embedding, GatedMLP, PromptTuningEmbedding, RmsNorm)
from ...mapping import Mapping
from ...module import Module
# this is to use to module global algo string with a quant_algo prefix
from ...quantization import QuantMode
from ...top_model_mixin import TopModelMixin
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
QuantConfig)
from .config import CogVLMConfig
class CogvlmDecoderLayer(Module):
def __init__(self, config: CogVLMConfig, 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 = CogVLMAttention(
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,
max_position_embeddings=config.max_position_embeddings,
dtype=config.dtype,
attention_mask_type=AttentionMaskType.causal,
bias=config.attn_bias,
position_embedding_type=config.position_embedding_type,
rotary_embedding_base=config.rotary_base,
rotary_embedding_scaling=config.rotary_scaling,
tp_group=config.mapping.tp_group,
tp_size=config.mapping.tp_size,
tp_rank=config.mapping.tp_rank,
quant_mode=config.quant_mode)
mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
self.hidden_size = config.hidden_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.vis_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,
use_cache=False,
kv_cache_params=None,
attention_params=None,
lora_layer_params=None,
vision_token_mask=None,
position_ids=None,
):
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
attention_output = self.attention(hidden_states,
use_cache=use_cache,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
vision_token_mask=vision_token_mask,
position_embedding=position_ids)
if use_cache:
attention_output, presents = attention_output
hidden_states = residual + attention_output
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
vision_mlp_out = self.vis_mlp(hidden_states)
language_mlp_out = self.mlp(hidden_states)
hidden_states = where(vision_token_mask, vision_mlp_out,
language_mlp_out)
# 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 CogvlmModel(Module):
def __init__(self, config: CogVLMConfig) -> None:
super().__init__()
self.mapping = config.mapping
self.use_prompt_tuning = config.use_prompt_tuning
self.vocab_size = config.vocab_size
EmbeddingCls = PromptTuningEmbedding if config.use_prompt_tuning else Embedding
if self.mapping.is_first_pp_rank():
self.vocab_embedding = EmbeddingCls(
num_embeddings=config.vocab_size,
embedding_dim=config.hidden_size,
dtype=config.dtype,
tp_size=self.mapping.tp_size
if config.use_parallel_embedding else 1,
tp_group=self.mapping.tp_group
if config.use_parallel_embedding else None,
sharding_dim=config.embedding_sharding_dim,
tp_rank=self.mapping.tp_rank,
)
self.layers = DecoderLayerList(CogvlmDecoderLayer, 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):
kv_cache_params.fill_none_tensor_list(len(self.layers))
if use_cache:
presents = []
ptuning_args = [
prompt_embedding_table, prompt_tasks, prompt_vocab_size
] if self.use_prompt_tuning 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())
vision_mask = input_ids > (self.vocab_size - 1)
if default_net().plugin_config.remove_input_padding:
seq_length = shape(vision_mask, 0) # lvvvvvvllvvvvlll
zero = constant(np.ascontiguousarray(np.zeros([1], dtype=bool)))
one = constant(np.ascontiguousarray(np.ones([1], dtype=bool)))
t1 = slice(vision_mask, [0], seq_length - 1)
t2 = slice(vision_mask, [1], seq_length - 1)
vision_token_mask = concat([op_and(t1 == one, t2 == one),
zero]) # 0111110001110000
vision_token_mask = unsqueeze(vision_token_mask,
-1) # [num_tokens, 1]
else:
seq_length = shape(vision_mask,
1) # lvvvvvvllvvvvlll, lvvvvvvllvvvvlll
batch_size = shape(vision_mask, 0)
t1 = slice(vision_mask, [0, 0], concat([batch_size,
seq_length - 1]))
t2 = slice(vision_mask, [0, 1], concat([batch_size,
seq_length - 1]))
zero = expand(
constant(np.ascontiguousarray(np.zeros([1, 1], dtype=bool))),
concat([batch_size, 1]))
one = constant(np.ascontiguousarray(np.ones([1, 1], dtype=bool)))
vision_token_mask = concat([op_and(t1 == one, t2 == one), zero],
dim=1) # 0111110001110000 [bs, seqlen]
vision_token_mask = unsqueeze(vision_token_mask,
-1) # [bs, seqlen, 1]
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,
vision_token_mask=vision_token_mask,
position_ids=position_ids)
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 CogVLMForCausalLM(DecoderModelForCausalLM, TopModelMixin):
config_class = CogVLMConfig
def __init__(self, config: CogVLMConfig):
transformer = CogvlmModel(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,
quant_mode: Optional[QuantMode] = None,
**kwargs):
pass
[docs]
def default_plugin_config(self, **kwargs):
plugin_config = super().default_plugin_config(**kwargs)
if self.quant_mode.is_int4_weight_only_per_group():
plugin_config.weight_only_groupwise_quant_matmul_plugin = 'auto'
return plugin_config
[docs]
@classmethod
def quantize(
cls,
hf_model_dir,
output_dir,
quant_config: QuantConfig,
*,
dtype='float16',
mapping: Optional[Mapping] = None,
calib_batches=512,
calib_batch_size=1,
random_seed=1234,
tokenizer_max_seq_length=2048,
**kwargs,
):
pass