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https://github.com/NVIDIA/TensorRT-LLM.git
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c89653021e
* Update TensorRT-LLM --------- Co-authored-by: Eddie-Wang1120 <81598289+Eddie-Wang1120@users.noreply.github.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
692 lines
25 KiB
Python
692 lines
25 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from copy import deepcopy
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from types import SimpleNamespace
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import tensorrt as trt
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from tensorrt_llm.mapping import Mapping
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from tensorrt_llm.quantization import QuantMode
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from ..._common import default_net
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from ..._utils import pad_vocab_size, str_dtype_to_trt
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from ...functional import (PositionEmbeddingType, Tensor, concat,
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gather_last_token_logits, shape)
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from ...layers import (MLP, Attention, AttentionMaskType, AttentionParams,
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ColumnLinear, Embedding, KeyValueCacheParams, LayerNorm,
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RmsNorm)
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from ...module import Module, ModuleList
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from ..generation_mixin import GenerationMixin
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class ChatGLMParams:
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apply_query_key_layer_scaling: bool = None
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apply_residual_connection_post_layernorm: bool = None
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dtype: str = None
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enable_debug_output: bool = None
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ffn_hidden_size: int = None
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hidden_act: str = None
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hidden_size: int = None
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linear_bias: bool = None
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logits_dtype: str = None
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mapping: Mapping = None
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max_batch_size: int = None
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max_beam_width: int = None
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max_input_len: int = None
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max_num_tokens: int = None
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max_output_len: int = None
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max_seq_length: int = None
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model_name: str = None
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norm_epsilon: float = None
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num_heads: int = None
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num_kv_heads: int = None
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num_layers: int = None
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qkv_bias: bool = None
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quant_mode: QuantMode = None
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rmsnorm: bool = None
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rotary_embedding_scaling: float = None
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tokens_per_block: int = None
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use_cache: bool = None
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vocab_size: int = None
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# default values
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default_config = {}
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default_config["chatglm_6b"] = SimpleNamespace(
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apply_query_key_layer_scaling=False,
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apply_residual_connection_post_layernorm=False,
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dtype="float16",
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ffn_hidden_size=16384,
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hidden_act='gelu',
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hidden_size=4096,
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linear_bias=True,
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logits_dtype="float16",
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mapping=Mapping(),
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max_batch_size=256,
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max_beam_width=1,
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max_input_len=512,
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max_num_tokens=256 * 512,
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max_output_len=512,
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max_seq_length=2048,
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norm_epsilon=1.0e-5,
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num_heads=32,
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num_kv_heads=32,
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num_layers=28,
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qkv_bias=True,
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quant_mode=QuantMode(0),
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rmsnorm=False,
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rotary_embedding_scaling=1.0,
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use_cache=True,
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vocab_size=130528,
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)
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default_config["chatglm2_6b"] = SimpleNamespace(
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apply_query_key_layer_scaling=False,
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apply_residual_connection_post_layernorm=False,
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dtype="float16",
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ffn_hidden_size=13696,
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hidden_act='swiglu',
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hidden_size=4096,
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linear_bias=False,
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logits_dtype="float16",
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mapping=Mapping(),
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max_batch_size=256,
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max_beam_width=1,
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max_input_len=512,
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max_num_tokens=256 * 512,
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max_output_len=512,
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max_seq_length=32768,
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norm_epsilon=1.0e-5,
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num_heads=32,
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num_kv_heads=2,
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num_layers=28,
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qkv_bias=True,
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quant_mode=QuantMode(0),
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rmsnorm=True,
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rotary_embedding_scaling=1.0,
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use_cache=True,
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vocab_size=65024,
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)
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default_config["chatglm3_6b"] = default_config["chatglm2_6b"]
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default_config["chatglm3_6b_base"] = default_config["chatglm2_6b"]
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default_config["chatglm2_6b_32k"] = deepcopy(default_config["chatglm2_6b"])
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default_config["chatglm2_6b_32k"].rotary_embedding_scaling = 50.0
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default_config["chatglm3_6b_32k"] = default_config["chatglm2_6b_32k"]
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default_config["glm_10b"] = SimpleNamespace(
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apply_query_key_layer_scaling=False,
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apply_residual_connection_post_layernorm=False,
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dtype="float16",
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ffn_hidden_size=16384,
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hidden_act='gelu',
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hidden_size=4096,
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linear_bias=True,
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logits_dtype="float16",
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mapping=Mapping(),
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max_batch_size=256,
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max_beam_width=1,
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max_input_len=1024,
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max_num_tokens=256 * 1024,
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max_output_len=1024,
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max_seq_length=2048,
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norm_epsilon=1.0e-5,
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num_heads=32,
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num_kv_heads=32,
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num_layers=48,
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qkv_bias=True,
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quant_mode=QuantMode(0),
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rmsnorm=False,
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rotary_embedding_scaling=1.0,
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use_cache=True,
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vocab_size=50304,
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)
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default_config["glm_2b"] = deepcopy(default_config["glm_10b"])
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default_config["glm_2b"].num_layers = 36
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default_config["glm_2b"].num_heads = 32
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default_config["glm_2b"].hidden_size = 2048
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default_config["glm_2b"].ffn_hidden_size = 8192
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default_config["glm_10b_chinese"] = deepcopy(default_config["glm_10b"])
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default_config["glm_10b_chinese"].vocab_size = 50048
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def __init__(self, **args):
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for key, value in args.items():
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assert key in dir(
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self), f"{key} is not in configuration of ChatGLMHeadModel"
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if value is not None:
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self.__setattr__(key, value)
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assert self.model_name is not None, "model_name must be set for ChatGLMHeadModel"
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# fill other parameters as default values
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for key, value in self.default_config[self.model_name].__dict__.items():
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if self.__getattribute__(key) is None:
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self.__setattr__(key, value)
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def report(self):
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for key, value in self.__dict__.items():
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print(f"{key} = {value}")
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class ChatGLMDecoderLayer(Module):
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def __init__(self, layer_id, config):
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super().__init__()
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rotary_embedding_scaling = None
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self.model_name = config.model_name
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self.rotary_embedding_base = 10000.0
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self.use_cache = config.use_cache
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# Save for Smooth Quantization
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self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
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self.dtype = config.dtype
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self.hidden_size = config.hidden_size
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self.ffn_hidden_size = config.ffn_hidden_size
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self.max_seq_length = config.max_seq_length
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self.num_heads = config.num_heads
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self.num_kv_heads = config.num_kv_heads
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self.num_layers = config.num_layers
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self.tp_group = config.mapping.tp_group
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self.tp_size = config.mapping.tp_size
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self.hidden_act = config.hidden_act
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self.bias = config.qkv_bias
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self.dense_bias = config.linear_bias
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if self.model_name in ["chatglm_6b"]:
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self.alpha = (2 * config.num_layers)**0.5
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self.norm = LayerNorm
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self.attention_mask_type = AttentionMaskType.bidirectional
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self.position_embedding_type = PositionEmbeddingType.chatglm
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elif config.model_name in [
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"chatglm2_6b", "chatglm2_6b_32k", "chatglm3_6b",
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"chatglm3_6b_base", "chatglm3_6b_32k"
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]:
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self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
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self.norm = RmsNorm if config.rmsnorm else LayerNorm
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self.attention_mask_type = AttentionMaskType.causal
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self.position_embedding_type = PositionEmbeddingType.rope_gptj
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if config.model_name in ["chatglm2_6b_32k", "chatglm3_6b_32k"]:
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self.rotary_embedding_base *= config.rotary_embedding_scaling
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elif config.model_name in ["glm_2b", "glm_10b", "glm_10b_chinese"]:
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self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
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self.norm = LayerNorm
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self.attention_mask_type = AttentionMaskType.bidirectionalglm
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self.position_embedding_type = PositionEmbeddingType.learned_absolute
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self.pre_norm = self.norm(
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normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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elementwise_affine=True,
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dtype=config.dtype,
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)
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self.attention = Attention(
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hidden_size=config.hidden_size,
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num_attention_heads=config.num_heads,
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num_kv_heads=config.num_kv_heads,
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max_position_embeddings=config.max_seq_length,
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num_layers=config.num_layers,
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apply_query_key_layer_scaling=config.apply_query_key_layer_scaling,
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attention_mask_type=self.attention_mask_type,
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bias=config.qkv_bias,
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dtype=config.dtype,
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position_embedding_type=self.position_embedding_type,
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rotary_embedding_base=self.rotary_embedding_base,
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rotary_embedding_scaling=rotary_embedding_scaling,
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rotary_embedding_percentage=0.5,
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tp_group=config.mapping.tp_group,
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tp_size=config.mapping.tp_size,
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tp_rank=config.mapping.rank,
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quant_mode=config.quant_mode,
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q_scaling=1.0,
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cross_attention=False,
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relative_attention=False,
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max_distance=0,
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num_buckets=0,
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instance_id=layer_id * 2,
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dense_bias=config.linear_bias,
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)
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self.mlp = MLP(
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hidden_size=config.hidden_size,
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ffn_hidden_size=config.ffn_hidden_size,
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hidden_act=config.hidden_act,
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bias=config.linear_bias,
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dtype=config.dtype,
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tp_group=config.mapping.tp_group,
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tp_size=config.mapping.tp_size,
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quant_mode=config.quant_mode,
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instance_id=layer_id * 2 + 1,
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)
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self.post_norm = self.norm(
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normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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elementwise_affine=True,
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dtype=config.dtype,
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)
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def forward(
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self,
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hidden_states: Tensor,
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position_ids: Tensor = None, # only used in ChatGLM-6B
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kv_cache_params: KeyValueCacheParams = None,
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attention_params: AttentionParams = None,
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):
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norm_output = self.pre_norm(hidden_states)
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attention_output = self.attention(
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hidden_states=norm_output,
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attention_mask=None,
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use_cache=self.use_cache,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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encoder_output=None,
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position_embedding=position_ids,
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)
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if self.use_cache:
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attention_output, presents = attention_output
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if self.model_name in ["chatglm_6b"]:
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residual = norm_output
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norm_input = residual * self.alpha + attention_output
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norm_output = self.post_norm(norm_input)
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mlp_output = self.mlp(norm_output)
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residual = norm_output
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output = residual * self.alpha + mlp_output
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elif self.model_name in [
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"chatglm2_6b",
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"chatglm2_6b_32k",
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"chatglm3_6b",
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"chatglm3_6b_base",
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"chatglm3_6b_32k",
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"glm_2b",
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"glm_10b",
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"glm_10b_chinese",
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]:
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residual = norm_output if self.apply_residual_connection_post_layernorm else hidden_states
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norm_input = residual + attention_output
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norm_output = self.post_norm(norm_input)
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mlp_output = self.mlp(norm_output)
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residual = norm_output if self.apply_residual_connection_post_layernorm else norm_input
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output = residual + mlp_output
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return (output, presents) if self.use_cache else output
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class ChatGLMModel(Module):
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def __init__(self, config):
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super().__init__()
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self.model_name = config.model_name
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self.use_cache = config.use_cache
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if config.model_name in [
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"chatglm_6b",
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"glm_2b",
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"glm_10b",
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"glm_10b_chinese",
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]:
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self.norm = LayerNorm
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elif config.model_name in [
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"chatglm2_6b",
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"chatglm2_6b_32k",
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"chatglm3_6b",
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"chatglm3_6b_base",
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"chatglm3_6b_32k",
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]:
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self.norm = RmsNorm
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self.hidden_size = config.hidden_size
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self.vocab_embedding = Embedding(
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num_embeddings=config.vocab_size,
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embedding_dim=config.hidden_size,
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dtype=config.dtype,
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tp_size=1, #config.mapping.tp_size,
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tp_group=None, #config.mapping.tp_group,
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sharding_dim=0,
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tp_rank=0, #config.mapping.rank,
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instance_id=config.num_layers * 2,
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)
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if config.model_name in ["glm_2b", "glm_10b", "glm_10b_chinese"]:
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self.position_embedding = Embedding(
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config.max_seq_length + 1,
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config.hidden_size,
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dtype=config.dtype,
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tp_size=1, #config.mapping.tp_size,
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tp_group=None, #config.mapping.tp_group,
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sharding_dim=0,
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tp_rank=0, #config.mapping.rank,
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instance_id=config.num_layers * 2,
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)
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self.block_embedding = Embedding(
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config.max_seq_length + 1,
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config.hidden_size,
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dtype=config.dtype,
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tp_size=1, #config.mapping.tp_size,
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tp_group=None, #config.mapping.tp_group,
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sharding_dim=0,
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tp_rank=0, #config.mapping.rank,
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instance_id=config.num_layers * 2,
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)
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self.layers = ModuleList(
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ChatGLMDecoderLayer(i, config) for i in range(config.num_layers))
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self.final_norm = self.norm(
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normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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elementwise_affine=True,
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dtype=config.dtype,
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)
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def forward(
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self,
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input_ids: Tensor = None,
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position_ids: Tensor = None, # only used in ChatGLM-6B
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kv_cache_params: KeyValueCacheParams = None,
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attention_params: AttentionParams = None,
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):
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hidden_states = self.vocab_embedding(input_ids)
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if self.model_name in ["glm_2b", "glm_10b", "glm_10b_chinese"]:
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position_ids_list = position_ids.split(1, dim=1)
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position_embedding = self.position_embedding(position_ids_list[0])
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block_embedding = self.block_embedding(position_ids_list[1])
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position_embedding = position_embedding + block_embedding
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position_embedding = position_embedding.view(
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concat([
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shape(input_ids, 0),
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shape(input_ids, 1),
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self.hidden_size,
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]))
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hidden_states = hidden_states + position_embedding
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kv_cache_params.fill_none_tensor_list(len(self.layers))
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if self.use_cache:
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presents = []
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for layer, past, pointer, host_pointer, max_attention_window_size in zip(
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self.layers, kv_cache_params.past_key_value,
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kv_cache_params.kv_cache_block_pointers,
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kv_cache_params.host_kv_cache_block_pointers,
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kv_cache_params.host_max_attention_window_sizes):
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layer_output = layer(
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hidden_states,
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position_ids,
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kv_cache_params=KeyValueCacheParams(
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past_key_value=[past],
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kv_cache_block_pointers=[pointer],
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host_kv_cache_block_pointers=[host_pointer],
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host_past_key_value_lengths=kv_cache_params.
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host_past_key_value_lengths,
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host_max_attention_window_sizes=max_attention_window_size,
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host_sink_token_length=kv_cache_params.
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host_sink_token_length,
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cache_indirection=kv_cache_params.cache_indirection,
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),
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attention_params=attention_params,
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)
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if self.use_cache:
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hidden_states = layer_output[0]
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presents.append(layer_output[1])
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hidden_states = self.final_norm(hidden_states)
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return (hidden_states,
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tuple(presents)) if self.use_cache else hidden_states
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class ChatGLMHeadModel(ChatGLMModel, GenerationMixin):
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def __init__(
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self,
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apply_query_key_layer_scaling: bool = None,
|
|
apply_residual_connection_post_layernorm: bool = None,
|
|
dtype: str = None,
|
|
enable_debug_output: bool = None,
|
|
ffn_hidden_size: int = None,
|
|
hidden_act: str = None,
|
|
hidden_size: int = None,
|
|
linear_bias: bool = None,
|
|
logits_dtype: str = None,
|
|
mapping: Mapping = None,
|
|
max_batch_size: int = None,
|
|
max_beam_width: int = None,
|
|
max_input_len: int = None,
|
|
max_output_len: int = None,
|
|
max_num_tokens: int = None,
|
|
max_seq_length: int = None,
|
|
model_name: str = None,
|
|
norm_epsilon: float = None,
|
|
num_heads: int = None,
|
|
num_kv_heads: int = None,
|
|
num_layers: int = None,
|
|
qkv_bias: bool = None,
|
|
quant_mode: QuantMode = None,
|
|
rmsnorm: bool = None,
|
|
rotary_embedding_scaling: float = None,
|
|
tokens_per_block: int = None,
|
|
use_cache: bool = None,
|
|
vocab_size: int = None,
|
|
max_position_embeddings: int = None,
|
|
):
|
|
|
|
# for benchmark scripts
|
|
if max_seq_length is None and max_position_embeddings is not None:
|
|
max_seq_length = max_position_embeddings
|
|
|
|
config = ChatGLMParams(
|
|
apply_query_key_layer_scaling=apply_query_key_layer_scaling,
|
|
apply_residual_connection_post_layernorm=
|
|
apply_residual_connection_post_layernorm,
|
|
dtype=dtype,
|
|
enable_debug_output=enable_debug_output,
|
|
ffn_hidden_size=ffn_hidden_size,
|
|
hidden_act=hidden_act,
|
|
hidden_size=hidden_size,
|
|
linear_bias=linear_bias,
|
|
logits_dtype=logits_dtype,
|
|
mapping=mapping,
|
|
max_batch_size=max_batch_size,
|
|
max_beam_width=max_beam_width,
|
|
max_input_len=max_input_len,
|
|
max_output_len=max_output_len,
|
|
max_num_tokens=max_num_tokens,
|
|
max_seq_length=max_seq_length,
|
|
model_name=model_name,
|
|
norm_epsilon=norm_epsilon,
|
|
num_heads=num_heads,
|
|
num_kv_heads=num_kv_heads,
|
|
num_layers=num_layers,
|
|
qkv_bias=qkv_bias,
|
|
quant_mode=quant_mode,
|
|
rmsnorm=rmsnorm,
|
|
rotary_embedding_scaling=rotary_embedding_scaling,
|
|
tokens_per_block=tokens_per_block,
|
|
use_cache=use_cache,
|
|
vocab_size=vocab_size,
|
|
)
|
|
|
|
super().__init__(config)
|
|
|
|
if isinstance(config.dtype, str):
|
|
self.kv_dtype = str_dtype_to_trt(config.dtype)
|
|
else:
|
|
assert isinstance(config.dtype, trt.DataType)
|
|
self.kv_dtype = config.dtype
|
|
self.dtype = self.kv_dtype
|
|
|
|
if isinstance(config.logits_dtype, str):
|
|
self.logits_dtype = str_dtype_to_trt(config.logits_dtype)
|
|
else:
|
|
assert isinstance(config.logits_dtype, trt.DataType)
|
|
self.logits_dtype = config.logits_dtype
|
|
|
|
if config.quant_mode.has_int8_kv_cache():
|
|
self.kv_dtype = str_dtype_to_trt('int8')
|
|
elif config.quant_mode.has_fp8_kv_cache():
|
|
self.kv_dtype = str_dtype_to_trt('fp8')
|
|
|
|
self.hidden_size = config.hidden_size
|
|
self.mapping = config.mapping
|
|
self.max_batch_size = config.max_batch_size
|
|
self.max_beam_width = config.max_beam_width
|
|
self.max_input_len = config.max_input_len
|
|
self.max_num_tokens = config.max_num_tokens
|
|
self.max_output_len = config.max_output_len
|
|
self.max_seq_length = config.max_seq_length
|
|
self.model_name = config.model_name
|
|
self.num_heads = config.num_heads
|
|
self.num_kv_heads = config.num_kv_heads
|
|
self.num_layers = config.num_layers
|
|
self.tokens_per_block = config.tokens_per_block
|
|
self.use_cache = config.use_cache
|
|
|
|
self.lm_head = ColumnLinear(
|
|
in_features=self.hidden_size,
|
|
out_features=pad_vocab_size(config.vocab_size,
|
|
self.mapping.tp_size),
|
|
bias=False,
|
|
dtype=self.dtype,
|
|
tp_group=self.mapping.tp_group,
|
|
tp_size=self.mapping.tp_size,
|
|
gather_output=True,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Tensor = None,
|
|
position_ids: Tensor = None, # used in chatglm_6b / glm_*
|
|
last_token_ids: Tensor = None,
|
|
kv_cache_params: KeyValueCacheParams = None,
|
|
attention_params: AttentionParams = None,
|
|
):
|
|
|
|
hidden_states = super().forward(
|
|
input_ids,
|
|
position_ids,
|
|
kv_cache_params,
|
|
attention_params,
|
|
)
|
|
|
|
if self.use_cache:
|
|
hidden_states, presents = hidden_states
|
|
|
|
hidden_states = gather_last_token_logits(
|
|
hidden_states, last_token_ids,
|
|
default_net().plugin_config.remove_input_padding)
|
|
|
|
lm_logits = self.lm_head(hidden_states)
|
|
lm_logits.mark_output('logits', self.logits_dtype)
|
|
|
|
if self.use_cache and default_net(
|
|
).plugin_config.paged_kv_cache == False:
|
|
for i, present in enumerate(presents):
|
|
present.mark_output(f'present_key_value_{i}', self.kv_dtype)
|
|
return (lm_logits, presents)
|
|
|
|
return lm_logits
|
|
|
|
def prepare_inputs(
|
|
self,
|
|
max_batch_size: int = None,
|
|
max_input_len: int = None,
|
|
max_output_len: int = None,
|
|
use_cache: bool = True,
|
|
max_beam_width: int = 1,
|
|
gather_context_logits: bool = False,
|
|
gather_generation_logits: bool = False,
|
|
use_custom_all_reduce: bool = False,
|
|
):
|
|
'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
|
|
ranges of the dimensions of when using TRT dynamic shapes.
|
|
|
|
@return: a list contains values which can be fed into the self.forward()
|
|
'''
|
|
|
|
position_encoding_2d = (self.model_name in [
|
|
"chatglm_6b", "glm_2b", "glm_10b", "glm_10b_chinese"
|
|
])
|
|
|
|
model_inputs = self.prepare_basic_inputs(
|
|
max_batch_size=max_batch_size,
|
|
max_beam_width=max_beam_width,
|
|
max_input_len=max_input_len,
|
|
max_new_tokens=max_output_len,
|
|
num_kv_heads=self.num_kv_heads,
|
|
head_size=self.hidden_size // self.num_heads,
|
|
num_layers=self.num_layers,
|
|
kv_dtype=self.kv_dtype,
|
|
remove_input_padding=default_net(
|
|
).plugin_config.remove_input_padding,
|
|
use_gpt_attention_plugin=default_net().plugin_config.
|
|
gpt_attention_plugin,
|
|
use_gemm_plugin=default_net().plugin_config.gemm_plugin,
|
|
use_custom_all_reduce=use_custom_all_reduce,
|
|
paged_kv_cache=default_net().plugin_config.paged_kv_cache,
|
|
tokens_per_block=self.tokens_per_block,
|
|
gather_context_logits=gather_context_logits,
|
|
gather_generation_logits=gather_generation_logits,
|
|
dtype=self.kv_dtype,
|
|
num_heads=self.num_heads,
|
|
mapping=self.mapping,
|
|
max_num_tokens=self.max_num_tokens,
|
|
prompt_embedding_table_size=0,
|
|
position_encoding_2d=position_encoding_2d,
|
|
)
|
|
|
|
return (
|
|
model_inputs['input_ids'], model_inputs['position_ids'],
|
|
model_inputs['last_token_ids'],
|
|
KeyValueCacheParams(
|
|
past_key_value=model_inputs['past_key_value'],
|
|
host_past_key_value_lengths=model_inputs[
|
|
'host_past_key_value_lengths'],
|
|
host_max_attention_window_sizes=model_inputs[
|
|
'host_max_attention_window_sizes'],
|
|
host_sink_token_length=model_inputs['host_sink_token_length'],
|
|
kv_cache_block_pointers=model_inputs[
|
|
'kv_cache_block_pointers_list'],
|
|
host_kv_cache_block_pointers=model_inputs[
|
|
'host_kv_cache_block_pointers_list'],
|
|
cache_indirection=model_inputs['cache_indirection'],
|
|
),
|
|
AttentionParams(
|
|
sequence_length=model_inputs['sequence_length'],
|
|
context_lengths=model_inputs['context_lengths'],
|
|
host_context_lengths=model_inputs['host_context_lengths'],
|
|
max_context_length=max_input_len,
|
|
host_request_types=model_inputs['host_request_types'],
|
|
))
|