mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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655524dd82
* Update TensorRT-LLM --------- Co-authored-by: Bhuvanesh Sridharan <bhuvan.sridharan@gmail.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
474 lines
20 KiB
Python
474 lines
20 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 tempfile
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from pathlib import Path
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from typing import Optional
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from transformers import AutoConfig, AutoModelForCausalLM
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from tensorrt_llm.models.llama.weight import (load_from_awq_llama,
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load_from_fp8_llama)
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from ... import profiler
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from ..._utils import pad_vocab_size
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from ...functional import RotaryScalingType, Tensor, recv, send
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from ...layers import (MOE, Attention, AttentionMaskType, ColumnLinear,
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Embedding, GatedMLP, MoeConfig, PositionEmbeddingType,
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PromptTuningEmbedding, RmsNorm)
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from ...mapping import Mapping
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from ...module import Module
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from ...plugin import init_all_reduce_helper
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from ...quantization import QuantMode
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from ...runtime.lora_manager import LoraConfig
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from ...top_model_mixin import TopModelMixin
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from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
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PretrainedConfig)
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from .weight import load_from_hf_llama
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class LLaMADecoderLayer(Module):
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def __init__(self, config: PretrainedConfig, 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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self.input_layernorm = 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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self.attention = Attention(
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layer_idx=self.layer_idx,
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hidden_size=config.hidden_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=config.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=PositionEmbeddingType.rope_gpt_neox,
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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=config.mapping.tp_group,
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tp_size=config.mapping.tp_size,
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tp_rank=config.mapping.tp_rank,
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quant_mode=config.quant_mode,
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enable_pos_shift=config.enable_pos_shift,
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dense_context_fmha=config.dense_context_fmha,
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max_lora_rank=config.max_lora_rank)
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mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size
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ClsMLP = GatedMLP
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mlp_kwargs = {}
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if config.moe_num_experts > 1:
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ClsMLP = MOE
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mlp_kwargs = {
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"moe_config":
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MoeConfig(
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config.moe_num_experts,
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config.moe_top_k,
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config.moe_tp_mode,
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config.moe_normalization_mode,
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),
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"tp_rank":
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config.mapping.tp_rank,
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}
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self.mlp = ClsMLP(hidden_size=config.hidden_size,
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ffn_hidden_size=mlp_hidden_size,
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hidden_act=config.hidden_act,
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dtype=config.dtype,
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bias=config.mlp_bias,
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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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max_lora_rank=config.max_lora_rank,
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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=config.dtype)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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medusa_packed_mask=None, # For Medusa support
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medusa_position_offsets=None,
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use_cache=False,
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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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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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medusa_packed_mask=medusa_packed_mask, # For Medusa support
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medusa_position_offsets=medusa_position_offsets,
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use_cache=use_cache,
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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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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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hidden_states = self.mlp(hidden_states,
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lora_layer_params=lora_layer_params)
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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 LLaMAModel(Module):
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def __init__(self, config: PretrainedConfig) -> None:
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super().__init__()
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init_all_reduce_helper()
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self.mapping = config.mapping
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self.use_prompt_tuning = config.use_prompt_tuning
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EmbeddingCls = PromptTuningEmbedding if config.use_prompt_tuning else Embedding
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if self.mapping.is_first_pp_rank():
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self.vocab_embedding = EmbeddingCls(
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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=self.mapping.tp_size
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if config.use_parallel_embedding else 1,
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tp_group=self.mapping.tp_group
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if config.use_parallel_embedding else None,
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sharding_dim=config.embedding_sharding_dim,
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tp_rank=self.mapping.tp_rank,
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)
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self.layers = DecoderLayerList(LLaMADecoderLayer, 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(
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self,
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input_ids,
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position_ids=None,
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use_cache=False,
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attention_mask=None,
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medusa_position_offsets=None, # For Medusa support
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medusa_packed_mask=None, # For Medusa support
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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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kv_cache_params.fill_none_tensor_list(len(self.layers))
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if use_cache:
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presents = []
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ptuning_args = [
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prompt_embedding_table, prompt_tasks, prompt_vocab_size
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] if self.use_prompt_tuning 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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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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medusa_position_offsets=medusa_position_offsets,
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medusa_packed_mask=medusa_packed_mask)
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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 LLaMAForCausalLM(DecoderModelForCausalLM, TopModelMixin):
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def __init__(self, config: PretrainedConfig):
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self.check_config(config)
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transformer = LLaMAModel(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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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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super().__init__(config, transformer, lm_head)
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def check_config(self, config):
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config.set_if_not_exist('mlp_bias', False)
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config.set_if_not_exist('attn_bias', False)
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config.set_if_not_exist('rotary_base', 10000.0)
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config.set_if_not_exist('rotary_scaling', None)
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config.set_if_not_exist('enable_pos_shift', False)
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config.set_if_not_exist('dense_context_fmha', False)
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config.set_if_not_exist('moe_num_experts', 0)
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config.set_if_not_exist('moe_top_k', 0)
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config.set_if_not_exist('moe_tp_mode',
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MoeConfig.ParallelismMode.TENSOR_PARALLEL)
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config.set_if_not_exist(
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'moe_normalization_mode',
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MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE)
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@classmethod
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def from_hugging_face(cls,
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hf_model_dir,
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dtype='float16',
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mapping: Optional[Mapping] = None,
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quant_mode: Optional[QuantMode] = None,
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**kwargs):
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cfg = AutoConfig.from_pretrained(hf_model_dir)
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num_kv_heads = cfg.num_key_value_heads if hasattr(cfg, "num_key_value_heads") \
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else cfg.num_attention_heads
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if mapping is None:
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mapping = Mapping()
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if quant_mode is None:
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quant_mode = QuantMode(0)
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cfg.mapping = mapping
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cfg.dtype = dtype
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cfg.quant_mode = quant_mode
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cfg.norm_epsilon = cfg.rms_norm_eps
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if cfg.model_type == 'mixtral':
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moe_config = MoeConfig(
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num_experts=cfg.num_local_experts,
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top_k=cfg.num_experts_per_tok,
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tp_mode=kwargs.get("moe_tp_mode",
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MoeConfig.ParallelismMode.TENSOR_PARALLEL),
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normalization_mode=kwargs.get(
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"moe_normalization_mode",
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MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE),
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).validate()
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# HF LLaMA-type models are implicitly using gated activation.
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# With our MoE implementation, we must make it explicit
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cfg.hidden_act = 'swiglu'
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cfg.rotary_base = cfg.rope_theta
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else:
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moe_config = MoeConfig()
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config = {
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'architecture': cfg.architectures[0],
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'dtype': cfg.dtype,
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'logits_dtype': 'float32',
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'num_hidden_layers': cfg.num_hidden_layers,
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'num_attention_heads': cfg.num_attention_heads,
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'hidden_size': cfg.hidden_size,
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'intermediate_size': cfg.intermediate_size,
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'num_key_value_heads': cfg.num_key_value_heads,
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'vocab_size': cfg.vocab_size,
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'position_embedding_type': 'rope_gpt_neox',
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'max_position_embeddings': cfg.max_position_embeddings,
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'hidden_act': cfg.hidden_act,
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'rotary_base': getattr(cfg, 'rotary_base', 10000.0),
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'rotary_scaling': getattr(cfg, 'rotary_scaling', None),
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'norm_epsilon': cfg.rms_norm_eps,
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'quantization': {
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'group_size': 128,
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},
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'mapping': {
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'world_size': mapping.world_size,
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'tp_size': mapping.tp_size,
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'pp_size': mapping.pp_size,
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},
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"moe_config": {
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"num_experts": moe_config.num_experts,
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"top_k": moe_config.top_k,
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"tp_mode": moe_config.tp_mode,
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"normalization_mode": moe_config.normalization_mode,
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},
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'use_parallel_embedding': kwargs.get("use_parallel_embedding",
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False),
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'embedding_sharding_dim': kwargs.get("embedding_sharding_dim", 0),
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'use_prompt_tuning': kwargs.get("use_prompt_tuning", False),
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'moe_num_experts': moe_config.num_experts,
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'moe_top_k': moe_config.top_k,
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'moe_tp_mode': moe_config.tp_mode,
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'moe_normalization_mode': moe_config.normalization_mode,
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'use_fused_mlp': kwargs.get("use_fused_mlp", False),
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'enable_pos_shift': kwargs.get("enable_pos_shift", False),
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'dense_context_fmha': kwargs.get("dense_context_fmha", False),
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}
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if quant_mode.is_int4_weight_only_per_group():
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config['quantization'].update({
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'quant_algo': 'W4A16_AWQ',
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'has_zero_point': False,
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'pre_quant_scale': True,
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'exclude_modules': [],
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})
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elif quant_mode.has_fp8_qdq() and quant_mode.has_fp8_kv_cache():
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config['quantization'].update({
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'quant_algo': 'FP8',
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'kv_cache_quant_algo': 'FP8'
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})
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else:
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if quant_mode != QuantMode(0):
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raise ValueError(f"Unsupported quantization mode: {quant_mode}")
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model_config = PretrainedConfig.from_dict(config)
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model_config.set_rank(mapping.tp_rank)
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tllm_llama = LLaMAForCausalLM(model_config)
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q_weights = {}
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if quant_mode.has_any_quant():
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q_weights = tllm_llama._quantize(hf_model_dir, dtype, cfg, **kwargs)
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# For debug purpose, skip weights loading to be faster
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if kwargs.get("skip_loading_weights", False):
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return tllm_llama
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# weights already loaded in _quantize for int4 weight only
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if not quant_mode.is_int4_weight_only_per_group():
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profiler.start("Loading weights from HF")
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hf_llama = AutoModelForCausalLM.from_pretrained(
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hf_model_dir,
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device_map={
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"model": "cpu",
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"lm_head": "cpu",
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"embed_tokens": "cpu",
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"layers": "cpu",
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"norm": "cpu",
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}, # Load to CPU memory
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torch_dtype='auto',
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)
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weights = load_from_hf_llama(
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tllm_llama,
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hf_llama,
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mapping=mapping,
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dtype=dtype,
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# TODO: these shall be outside from_hugging_face too.
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use_gemm_woq_plugin=kwargs.get("use_gemm_woq_plugin", False),
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lora_config=kwargs.get("lora_config", LoraConfig()),
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)
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profiler.stop("Loading weights from HF")
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del hf_llama
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weights.update(q_weights)
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tllm_llama.load(weights)
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else:
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tllm_llama.load(q_weights)
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return tllm_llama
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def _quantize(self, hf_model_dir, dtype, cfg, **kwargs):
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'''Given the quant_mode set in the Module object, read from given hf model
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call AMMO to generate quantization scales, and set the scales back the module parameters.
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'''
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# use self destructed temporary path if kwargs[quantization_cache_dir] is not specified
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# sometimes the quantization checkpoint path needs to be saved for debug purpose
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quantized_temp_dir = tempfile.TemporaryDirectory("llama-quantized")
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quantized_checkpoint_path = kwargs.get("quantization_cache_dir",
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quantized_temp_dir.name)
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quantize_lm_head = kwargs.get("quantize_lm_head", False)
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quant_mode = cfg.quant_mode
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ammo_qformat = None
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calib_size = None
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if quant_mode.has_fp8_qdq() or quant_mode.has_fp8_kv_cache():
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ammo_qformat = 'fp8'
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calib_size = 512
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# TODO: how to distinguish from quant_mode about int4_awq or int4_gptq?
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elif quant_mode.is_int4_weight_only_per_group():
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ammo_qformat = 'int4_awq'
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calib_size = 32
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assert ammo_qformat is not None
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# local import to avoid pytest issue when importing AMMO and transformers lib
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from .quantize import quantize_llama_and_export
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quantize_llama_and_export(hf_model_dir,
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quantized_checkpoint_path,
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ammo_qformat,
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dtype,
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calib_size=calib_size,
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quantize_lm_head=quantize_lm_head)
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ckpt = Path(quantized_checkpoint_path) / "llama_tp1_rank0.npz"
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assert ckpt.exists(), f"The expecting checkpoint path {ckpt} does not exist" \
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"it's likely quantization failed, pls check error logs"
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hf_config = AutoConfig.from_pretrained(hf_model_dir,
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trust_remote_code=True)
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if ammo_qformat == 'fp8':
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return load_from_fp8_llama(
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str(ckpt),
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hf_config.num_hidden_layers,
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cfg.mapping,
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fp8_kv_cache=quant_mode.has_fp8_kv_cache())
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else:
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return load_from_awq_llama(str(ckpt),
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hf_config.num_hidden_layers,
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hf_config.vocab_size,
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cfg.mapping,
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dtype=dtype)
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# llama specific setters, user shall has the chance to change the module attributes after
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# from_hugging_face factory method created the model when these attributes is not included in the huggingface checkpoint
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def rotary_base(self, val):
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for decoder in self.layers:
|
|
decoder.attention.rotary_embedding_base = val
|
|
return self
|
|
|
|
def rotary_scaling(self, scaling_type, factor):
|
|
# TODO: what if there are some other behaviors triggered by the these changes?
|
|
# should implement these assignment as setters of the Attention Module
|
|
assert scaling_type in ("linear", "dynamic"), f"Got {scaling_type}"
|
|
assert factor > 1.0, f"Got {factor}"
|
|
for decoder in self.layers:
|
|
decoder.attention.rotary_embedding_scale_type = RotaryScalingType.linear if scaling_type == "linear" else RotaryScalingType.dynamic
|
|
decoder.attention.rotary_embedding_scale = factor
|
|
return self
|
|
|
|
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.set_weight_only_groupwise_quant_matmul_plugin()
|
|
return plugin_config
|