mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-31 00:01:22 +08:00
528 lines
21 KiB
Python
528 lines
21 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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import copy
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import os
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from typing import Optional, Union
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import torch
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from tqdm import tqdm
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from ..._utils import pad_vocab_size
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from ...functional import Tensor, allreduce, recv, send, sigmoid
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from ...layers import (MLP, MOE, Attention, AttentionMaskType, ColumnLinear,
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Embedding, GatedMLP, RmsNorm, RowLinear)
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from ...layers.moe import MOEWeightWrapper
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from ...logger import logger
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from ...lora_manager import (LoraConfig,
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get_default_trtllm_modules_to_hf_modules, use_lora)
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from ...mapping import Mapping
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from ...module import Module
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from ..model_weights_loader import ModelWeightsLoader
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from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
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QuantConfig, check_share_embedding)
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from .config import QWenConfig
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from .convert import (load_hf_qwen, load_weights_from_hf_gptq_model,
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load_weights_from_hf_model)
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class QWenDecoderLayer(Module):
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def __init__(self, config: QWenConfig, layer_idx: int):
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super().__init__()
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self.layer_idx = layer_idx
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self.config = config
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dtype = config.dtype
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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.input_layernorm = RmsNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=dtype)
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layers_range = config.mapping.pp_layers(config.num_hidden_layers)
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local_layer_idx = layer_idx - layers_range[0]
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self.attention = Attention(
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local_layer_idx=local_layer_idx,
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hidden_size=config.hidden_size,
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attention_head_size=config.head_size,
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num_attention_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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max_position_embeddings=config.max_position_embeddings,
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dtype=dtype,
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attention_mask_type=AttentionMaskType.causal,
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bias=config.attn_bias,
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position_embedding_type=config.position_embedding_type,
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rotary_embedding_base=config.rotary_base,
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rotary_embedding_scaling=config.rotary_scaling,
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tp_group=self.tp_group,
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tp_size=self.tp_size,
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quant_mode=config.quant_mode,
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dense_bias=False)
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ClsMLP = GatedMLP
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mlp_kwargs = {}
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if config.moe.has_moe():
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ClsMLP = MOE
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mlp_kwargs = {
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"moe_config": config.moe,
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"mapping": config.mapping,
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}
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if config.qwen_type == 'qwen2_moe':
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self.shared_expert = MLP(
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hidden_size=config.hidden_size,
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ffn_hidden_size=config.moe_shared_expert_intermediate_size,
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hidden_act=config.hidden_act,
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dtype=dtype,
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bias=False,
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tp_group=self.tp_group,
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tp_size=self.tp_size,
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quant_mode=config.quant_mode,
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is_expert=True)
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self.shared_expert_gate = RowLinear(config.hidden_size,
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1,
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bias=False,
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dtype=dtype,
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tp_group=None,
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tp_size=1)
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mlp_kwargs['use_all_reduce'] = False
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# Qwen's real inter_size depends on qwen_type
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if self.config.qwen_type == 'qwen':
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intermediate_size = config.intermediate_size // 2
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elif self.config.qwen_type == 'qwen2_moe':
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intermediate_size = config.moe_intermediate_size
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else:
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intermediate_size = config.intermediate_size
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self.mlp = ClsMLP(hidden_size=config.hidden_size,
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ffn_hidden_size=intermediate_size,
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hidden_act=config.hidden_act,
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dtype=dtype,
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bias=config.mlp_bias,
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tp_group=self.tp_group,
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tp_size=self.tp_size,
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quant_mode=config.quant_mode,
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**mlp_kwargs)
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self.post_layernorm = RmsNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=dtype)
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def forward(
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self,
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hidden_states: Tensor,
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attention_mask=None,
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use_cache=False,
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spec_decoding_params=None,
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kv_cache_params=None,
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attention_params=None,
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lora_layer_params=None,
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):
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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attention_output = self.attention(
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hidden_states,
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attention_mask=attention_mask,
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use_cache=use_cache,
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spec_decoding_params=spec_decoding_params,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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lora_layer_params=lora_layer_params,
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)
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if use_cache:
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attention_output, presents = attention_output
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hidden_states = residual + attention_output
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residual = hidden_states
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hidden_states = self.post_layernorm(hidden_states)
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shared_output = None
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if self.config.qwen_type == 'qwen2_moe':
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shared_output = self.shared_expert(
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hidden_states, lora_layer_params=lora_layer_params)
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if self.shared_expert_gate is not None:
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gate_lora_params = None
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if lora_layer_params is not None:
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gate_lora_params = lora_layer_params.get_runtime_params(
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0, "mlp_router")
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shared_output = sigmoid(
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self.shared_expert_gate(hidden_states,
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gate_lora_params)) * shared_output
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hidden_states = self.mlp(hidden_states,
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lora_layer_params=lora_layer_params)
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if shared_output is not None:
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hidden_states = hidden_states + shared_output
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if self.tp_size > 1 and self.tp_group is not None:
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hidden_states = allreduce(hidden_states, self.tp_group)
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hidden_states = residual + hidden_states
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if use_cache:
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return (hidden_states, presents)
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return hidden_states
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class QWenModel(Module):
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def __init__(self, config: QWenConfig) -> None:
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super().__init__()
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self.mapping = config.mapping
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if self.mapping.is_first_pp_rank():
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self.vocab_embedding = Embedding(config.vocab_size,
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config.hidden_size,
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dtype=config.dtype)
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self.layers = DecoderLayerList(QWenDecoderLayer, config)
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if self.mapping.is_last_pp_rank():
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self.ln_f = RmsNorm(normalized_shape=config.hidden_size,
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eps=config.norm_epsilon,
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dtype=config.dtype)
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def forward(self,
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input_ids: Tensor,
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position_ids=None,
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use_cache=False,
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spec_decoding_params=None,
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attention_mask=None,
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kv_cache_params=None,
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attention_params=None,
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hidden_states=None,
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prompt_embedding_table: Optional[Tensor] = None,
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prompt_tasks: Optional[Tensor] = None,
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prompt_vocab_size: Optional[Tensor] = None,
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lora_params=None):
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ptuning_args = [
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prompt_embedding_table, prompt_tasks, prompt_vocab_size
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] if prompt_embedding_table is not None else []
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if self.mapping.is_first_pp_rank():
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hidden_states = self.vocab_embedding(input_ids, *ptuning_args)
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else:
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hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
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hidden_states = self.layers.forward(
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hidden_states,
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use_cache=use_cache,
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spec_decoding_params=spec_decoding_params,
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attention_mask=attention_mask,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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lora_params=lora_params)
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if use_cache:
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hidden_states, presents = hidden_states
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if self.mapping.is_last_pp_rank():
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hidden_states = self.ln_f(hidden_states)
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else:
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hidden_states = send(hidden_states, self.mapping.next_pp_rank())
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if use_cache:
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return (hidden_states, tuple(presents))
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return hidden_states
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class QWenForCausalLM(DecoderModelForCausalLM):
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config_class = QWenConfig
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def __init__(self, config: QWenConfig):
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transformer = QWenModel(config)
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vocab_size_padded = pad_vocab_size(config.vocab_size,
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config.mapping.tp_size)
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if config.mapping.is_last_pp_rank():
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if config.architecture == 'Qwen2ForSequenceClassification':
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lm_head = ColumnLinear(config.hidden_size,
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config.num_labels,
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bias=False,
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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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gather_output=True)
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else:
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lm_head = ColumnLinear(config.hidden_size,
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vocab_size_padded,
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bias=False,
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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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gather_output=True)
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else:
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lm_head = None
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self.quant_mode = config.quant_mode
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self.mapping = config.mapping
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if config.qwen_type == 'qwen':
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self.trtllm_modules_to_hf_modules = {
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"attn_qkv": "c_attn",
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"attn_dense": "attn.c_proj",
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"mlp_h_to_4h": "w2",
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"mlp_4h_to_h": "mlp.c_proj",
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"mlp_gate": "w1",
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}
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elif config.qwen_type == 'qwen2_moe':
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self.trtllm_modules_to_hf_modules = copy.copy(
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get_default_trtllm_modules_to_hf_modules())
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self.trtllm_modules_to_hf_modules.update({
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"mlp_h_to_4h":
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"mlp.shared_expert.gate_proj",
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"mlp_4h_to_h":
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"mlp.shared_expert.down_proj",
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"mlp_gate":
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"mlp.shared_expert.up_proj",
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"mlp_router":
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"mlp.shared_expert_gate",
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"moe_h_to_4h":
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"mlp.experts.gate_proj",
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"moe_4h_to_h":
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"mlp.experts.down_proj",
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"moe_gate":
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"mlp.experts.up_proj",
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})
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else:
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self.trtllm_modules_to_hf_modules = None
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super().__init__(config, transformer, lm_head)
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@classmethod
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def from_hugging_face(
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cls,
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hf_model_or_dir: Union[str, 'transformers.PreTrainedModel'],
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dtype: str = 'auto',
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mapping: Optional[Mapping] = None,
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quant_config: Optional[QuantConfig] = None,
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**kwargs):
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''' Create a QWenForCausalLM object from give parameters
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'''
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import transformers
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load_model_on_cpu = kwargs.pop('load_model_on_cpu', False)
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use_hf_gptq_checkpoint = kwargs.pop('use_hf_gptq_checkpoint', False)
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assert hf_model_or_dir is not None
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use_preloading = isinstance(hf_model_or_dir,
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transformers.PreTrainedModel)
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if use_preloading:
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hf_model = hf_model_or_dir
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hf_config_or_dir = hf_model.config
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else:
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hf_model_dir = hf_model_or_dir
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hf_config_or_dir = hf_model_or_dir
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config = QWenConfig.from_hugging_face(hf_config_or_dir,
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dtype=dtype,
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mapping=mapping,
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quant_config=quant_config,
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**kwargs)
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if os.environ.get("TRTLLM_DISABLE_UNIFIED_CONVERTER") is None:
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custom_dict = {}
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if config.qwen_type == "qwen":
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custom_dict = {
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"transformer": "transformer",
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"vocab_embedding": "wte",
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"ln_f": "ln_f",
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"layers": "h",
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"attention": "attn",
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"qkv": "c_attn",
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"dense": "c_proj",
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"gate": "w1",
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"proj": "c_proj",
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"fc": "w2",
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"input_layernorm": "ln_1",
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"post_layernorm": "ln_2",
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}
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elif config.qwen_type == "qwen2_moe":
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custom_dict = {
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"shared_expert": "mlp.shared_expert",
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"shared_expert_gate": "mlp.shared_expert_gate",
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"fc": ["up_proj", "gate_proj"],
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}
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elif config.qwen_type == "qwen2" and config.tie_word_embeddings:
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custom_dict = {"lm_head": "model.embed_tokens"}
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elif config.architecture == "Qwen2ForSequenceClassification":
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custom_dict = {
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"lm_head": "score",
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}
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elif config.qwen_type == "qwen2_llava_onevision":
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custom_dict = {
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"transformer": "language_model.model",
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"lm_head": "language_model.lm_head",
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}
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loader = ModelWeightsLoader(hf_model_dir, custom_dict)
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loader.check_share_embedding(config)
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model = cls(config)
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if config.qwen_type == "qwen" and model.config.mapping.has_tp():
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def reshape_qkv(weights):
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if weights is None:
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return weights
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mapping = model.config.mapping
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unsqueeze = False
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if isinstance(weights, torch.Tensor):
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unsqueeze = True
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weights = [weights]
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for idx, w in enumerate(weights):
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if use_hf_gptq_checkpoint:
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w = w.reshape(-1, 3, w.shape[-1] // 3)
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w = w.chunk(mapping.tp_size, 2)[mapping.tp_rank]
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if w.shape[0] == 1:
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weights[idx] = w.reshape(-1)
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else:
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weights[idx] = w.reshape(w.shape[0], -1)
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else:
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w = w.reshape(3, w.shape[0] // 3, -1)
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w = w.chunk(mapping.tp_size, 1)[mapping.tp_rank]
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if w.shape[-1] == 1:
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weights[idx] = w.reshape(-1)
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else:
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weights[idx] = w.reshape(-1, w.shape[-1])
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if unsqueeze:
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return weights[0]
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else:
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return weights
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loader.update_key_mapping(model)
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tllm_weights = {}
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for tllm_key, _ in tqdm(model.named_parameters()):
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if "qkv" in tllm_key:
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tllm_weights.update(
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loader.load(tllm_key, reshape_qkv, skip_tp=True))
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else:
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tllm_weights.update(loader.load(tllm_key))
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loader.fill(tllm_weights)
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elif config.qwen_type == "qwen2_moe":
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for tllm_key, _ in model.named_parameters():
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sub_module = model
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for attr in tllm_key.split(".")[:-1]:
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sub_module = getattr(sub_module, attr)
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if "router" in tllm_key or isinstance(
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sub_module, MOEWeightWrapper):
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sub_module_dic = sub_module.tllm_to_externel_key_dict
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sub_module_dic["mlp"] = "mlp"
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if "fc" in sub_module_dic.keys():
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sub_module_dic["fc"] = [
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hf_keyword.replace("w1", "gate_proj")
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for hf_keyword in sub_module_dic["fc"]
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]
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sub_module_dic["fc"] = [
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hf_keyword.replace("w3", "up_proj")
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for hf_keyword in sub_module_dic["fc"]
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]
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if "proj" in sub_module_dic.keys():
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sub_module_dic["proj"] = [
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hf_keyword.replace("w2", "down_proj")
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for hf_keyword in sub_module_dic["proj"]
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]
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sub_module.tllm_to_externel_key_dict = sub_module_dic
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def concat_gate_up_proj(weights):
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return torch.cat(weights, dim=-2)
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loader.update_key_mapping(model)
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tllm_weights = {}
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for tllm_key, _ in tqdm(model.named_parameters()):
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if tllm_key.endswith("shared_expert.fc.weight"):
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tllm_weights.update(
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loader.load(tllm_key, concat_gate_up_proj))
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else:
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tllm_weights.update(loader.load(tllm_key))
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loader.fill(tllm_weights)
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else:
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# For Qwen1 w/o TP, Qwen1.5 and Qwen2 w/o MoE
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loader.generate_tllm_weights(model)
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else:
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if not use_preloading:
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hf_model = load_hf_qwen(hf_model_dir, load_model_on_cpu)
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logger.debug(f"HuggingFace model: {hf_model}")
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model = QWenForCausalLM(config)
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logger.debug(f"TensorRT-LLM model: {model}")
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if use_hf_gptq_checkpoint:
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weights = load_weights_from_hf_gptq_model(hf_model, config)
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else:
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weights = load_weights_from_hf_model(hf_model, config)
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check_share_embedding(weights, config)
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model.load(weights)
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return model
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def default_plugin_config(self, **kwargs):
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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
|
|
|
|
@classmethod
|
|
def quantize(
|
|
cls,
|
|
hf_model_dir: str,
|
|
output_dir: str,
|
|
dtype: str = 'auto',
|
|
mapping: Optional[Mapping] = None,
|
|
quant_config: Optional[QuantConfig] = None,
|
|
*,
|
|
calib_dataset='cnn_dailymail',
|
|
calib_batches=512,
|
|
calib_batch_size=1,
|
|
calib_max_seq_length=512,
|
|
random_seed=1234,
|
|
tokenizer_max_seq_length=2048,
|
|
**kwargs,
|
|
):
|
|
if quant_config.requires_modelopt_quantization:
|
|
# modelopt quantization flow
|
|
super().quantize(hf_model_dir,
|
|
output_dir,
|
|
dtype=dtype,
|
|
mapping=mapping,
|
|
quant_config=quant_config,
|
|
calib_dataset=calib_dataset,
|
|
calib_batches=calib_batches,
|
|
calib_batch_size=calib_batch_size,
|
|
calib_max_seq_length=calib_max_seq_length,
|
|
random_seed=random_seed,
|
|
tokenizer_max_seq_length=tokenizer_max_seq_length)
|
|
elif quant_config.requires_calibration:
|
|
# non-modelopt quantization flow
|
|
from . import convert
|
|
|
|
config = QWenConfig.from_hugging_face(hf_model_dir,
|
|
dtype=dtype,
|
|
mapping=mapping,
|
|
quant_config=quant_config,
|
|
**kwargs)
|
|
convert.quantize(hf_model_dir,
|
|
output_dir,
|
|
config=config,
|
|
calib_dataset=calib_dataset)
|
|
else:
|
|
raise ValueError(
|
|
f"The quant_config ({quant_config}) does not require calibration, try {cls.__name__}.from_hugging_face instead."
|
|
)
|
|
|
|
def use_lora(self, lora_config: LoraConfig):
|
|
use_lora(self, lora_config, self.trtllm_modules_to_hf_modules)
|