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# 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 ..._common import default_net
from ..._utils import pad_vocab_size
from ...functional import Tensor, concat, shape
from ...layers import (MLP, Attention, AttentionMaskType, AttentionParams,
ColumnLinear, Embedding, KeyValueCacheParams, LayerNorm,
RmsNorm)
from ...module import Module
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
PretrainedConfig)
class ChatGLMDecoderLayer(Module):
def __init__(self, config: PretrainedConfig, layer_idx: int):
super().__init__()
self.layer_idx = layer_idx
self.config = config
self.chatglm_version = config.chatglm_version
hidden_size = config.hidden_size
dtype = config.dtype
tp_group = config.mapping.tp_group
tp_size = config.mapping.tp_size
tp_rank = config.mapping.tp_rank
layernorm_epsilon = config.norm_epsilon
rope_base = 10000.0
rotary_embedding_scaling = None
self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
self.alpha = (2 * config.num_hidden_layers)**0.5
norm_cls = RmsNorm if config.rmsnorm else LayerNorm
if config.chatglm_version == 'glm':
attention_mask_type = AttentionMaskType.bidirectionalglm
elif config.chatglm_version == 'chatglm':
attention_mask_type = AttentionMaskType.bidirectional
elif config.chatglm_version == 'chatglm2':
attention_mask_type = AttentionMaskType.causal
if config.rope_ratio > 1:
rotary_embedding_scaling = {
'type': 'linear',
'factor': config.rope_ratio
}
elif config.chatglm_version == 'chatglm3':
attention_mask_type = AttentionMaskType.causal
rope_base *= config.rope_ratio
self.input_layernorm = norm_cls(
normalized_shape=hidden_size,
eps=layernorm_epsilon,
elementwise_affine=True,
dtype=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=hidden_size,
num_attention_heads=config.num_attention_heads,
num_kv_heads=config.num_key_value_heads,
max_position_embeddings=config.max_position_embeddings,
num_layers=config.num_hidden_layers,
apply_query_key_layer_scaling=config.apply_query_key_layer_scaling,
attention_mask_type=attention_mask_type,
bias=config.add_qkv_bias,
dense_bias=config.add_bias_linear,
dtype=config.dtype,
position_embedding_type=config.position_embedding_type,
rotary_embedding_base=rope_base,
rotary_embedding_scaling=rotary_embedding_scaling,
rotary_embedding_percentage=0.5,
tp_group=tp_group,
tp_size=tp_size,
tp_rank=tp_rank,
quant_mode=config.quant_mode,
q_scaling=1.0,
cross_attention=False,
relative_attention=False,
max_distance=0,
num_buckets=0,
)
mlp_hidden_size = hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
self.mlp = MLP(
hidden_size=hidden_size,
ffn_hidden_size=mlp_hidden_size,
hidden_act=config.hidden_act,
bias=config.add_bias_linear,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
quant_mode=config.quant_mode,
)
self.post_layernorm = norm_cls(
normalized_shape=hidden_size,
eps=layernorm_epsilon,
elementwise_affine=True,
dtype=dtype,
)
def forward(
self,
hidden_states: Tensor,
attention_mask: Tensor = None,
position_ids: Tensor = None, # only used in ChatGLM-6B
use_cache: bool = False,
kv_cache_params: KeyValueCacheParams = None,
attention_params: AttentionParams = None,
):
norm_output = self.input_layernorm(hidden_states)
attention_output = self.attention(
hidden_states=norm_output,
attention_mask=attention_mask,
use_cache=use_cache,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
encoder_output=None,
position_embedding=position_ids,
)
if use_cache:
attention_output, presents = attention_output
if self.chatglm_version == 'chatglm':
residual = norm_output
norm_input = residual * self.alpha + attention_output
norm_output = self.post_layernorm(norm_input)
mlp_output = self.mlp(norm_output)
residual = norm_output
output = residual * self.alpha + mlp_output
else:
residual = norm_output if self.apply_residual_connection_post_layernorm else hidden_states
norm_input = residual + attention_output
norm_output = self.post_layernorm(norm_input)
mlp_output = self.mlp(norm_output)
residual = norm_output if self.apply_residual_connection_post_layernorm else norm_input
output = residual + mlp_output
if use_cache:
return (output, presents)
return output
class ChatGLMModel(Module):
def __init__(self, config: PretrainedConfig):
super().__init__()
self.chatglm_version = config.chatglm_version
norm_cls = RmsNorm if config.rmsnorm else LayerNorm
self.vocab_embedding = Embedding(config.vocab_size,
config.hidden_size,
dtype=config.dtype)
if config.chatglm_version == 'glm':
self.position_embedding = Embedding(
config.max_position_embeddings + 1,
config.hidden_size,
dtype=config.dtype,
)
self.block_embedding = Embedding(
config.max_position_embeddings + 1,
config.hidden_size,
dtype=config.dtype,
)
self.layers = DecoderLayerList(ChatGLMDecoderLayer, config)
self.ln_f = norm_cls(
normalized_shape=config.hidden_size,
eps=config.norm_epsilon,
elementwise_affine=True,
dtype=config.dtype,
)
def forward(
self,
input_ids: Tensor = None,
position_ids: Tensor = None, # only used in ChatGLM-6B
use_cache: bool = False,
attention_mask: Tensor = None,
kv_cache_params: KeyValueCacheParams = None,
attention_params: AttentionParams = None,
):
hidden_states = self.vocab_embedding(input_ids)
if self.chatglm_version == 'glm':
if default_net().plugin_config.remove_input_padding:
position_ids_list = position_ids.split(1, dim=0)
else:
position_ids_list = position_ids.split(1, dim=1)
position_embedding = self.position_embedding(position_ids_list[0])
block_embedding = self.block_embedding(position_ids_list[1])
position_embedding = position_embedding + block_embedding
if default_net().plugin_config.remove_input_padding:
position_embedding = position_embedding.view(
concat([
shape(position_embedding, 1),
shape(position_embedding, 2)
]))
else:
position_embedding = position_embedding.view(
concat([
shape(position_embedding, 0),
shape(position_embedding, 2),
shape(position_embedding, 3),
]))
hidden_states = hidden_states + position_embedding
hidden_states = self.layers(hidden_states,
use_cache=use_cache,
attention_mask=attention_mask,
kv_cache_params=kv_cache_params,
attention_params=attention_params,
position_ids=position_ids)
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
class ChatGLMForCausalLM(DecoderModelForCausalLM):
def __init__(self, config: PretrainedConfig):
self.check_config(config)
transformer = ChatGLMModel(config)
vocab_size_padded = pad_vocab_size(config.vocab_size,
config.mapping.tp_size)
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)
super().__init__(config, transformer, lm_head)
def check_config(self, config: PretrainedConfig):
config.set_if_not_exist('chatglm_version', 'chatglm3')
config.set_if_not_exist('add_bias_linear', False)
config.set_if_not_exist('add_qkv_bias', True)
config.set_if_not_exist('apply_query_key_layer_scaling', False)
config.set_if_not_exist('apply_residual_connection_post_layernorm',
False)
config.set_if_not_exist('rmsnorm', True)
config.set_if_not_exist('rope_ratio', 1.0)
def prepare_inputs(self, *args, **kwargs):
"""See `PretrainedModel.prepare_inputs` for the detailed parameter list.
"""
if self.transformer.chatglm_version in ['chatglm', 'glm']:
position_encoding_2d = True
else:
position_encoding_2d = False
return super().prepare_inputs(*args,
**kwargs,
position_encoding_2d=position_encoding_2d)
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