# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional, Union from ..._common import default_net from ..._utils import pad_vocab_size from ...functional import (AllReduceFusionOp, AllReduceFusionParams, Tensor, non_gated_version, recv, send) from ...layers import (MOE, Attention, AttentionMaskType, ColumnLinear, Embedding, GatedMLP, PositionEmbeddingType, RmsNorm) from ...lora_manager import LoraConfig, use_lora from ...mapping import Mapping from ...module import Module from ...quantization import W8A8_SQ_PLUGIN_LIST, QuantAlgo from ..convert_utils import has_safetensors from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM, QuantConfig, check_share_embedding) from .config import LLaMAConfig from .convert import (load_hf_llama, load_weights_from_hf_by_shard, load_weights_from_hf_model, load_weights_from_hf_safetensors, load_weights_from_meta_ckpt) class LLaMADecoderLayer(Module): def __init__(self, config: LLaMAConfig, layer_idx: int): super().__init__() self.layer_idx = layer_idx self.config = config self.input_layernorm = RmsNorm(normalized_shape=config.hidden_size, eps=config.norm_epsilon, dtype=config.dtype) layers_range = config.mapping.pp_layers(config.num_hidden_layers) self.local_layer_idx = layer_idx - layers_range[0] self.attention = Attention( local_layer_idx=self.local_layer_idx, hidden_size=config.hidden_size, attention_head_size=config.head_size, num_attention_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, max_position_embeddings=config.max_position_embeddings, dtype=config.dtype, attention_mask_type=AttentionMaskType.causal, bias=config.attn_bias, position_embedding_type=PositionEmbeddingType.rope_gpt_neox, rotary_embedding_base=config.rotary_base, rotary_embedding_scaling=config.rotary_scaling, tp_group=config.mapping.tp_group, tp_size=config.mapping.tp_size, tp_rank=config.mapping.tp_rank, quant_mode=config.quant_mode) mlp_hidden_size = config.hidden_size * 4 if config.intermediate_size is None else config.intermediate_size ClsMLP = GatedMLP mlp_kwargs = {} if config.moe.has_moe(): ClsMLP = MOE mlp_kwargs = { "moe_config": config.moe, "mapping": config.mapping, } self.mlp = ClsMLP(hidden_size=config.hidden_size, ffn_hidden_size=mlp_hidden_size, hidden_act=config.hidden_act, dtype=config.dtype, bias=config.mlp_bias, tp_group=config.mapping.tp_group, tp_size=config.mapping.tp_size, quant_mode=config.quant_mode, **mlp_kwargs) self.post_layernorm = RmsNorm(normalized_shape=config.hidden_size, eps=config.norm_epsilon, dtype=config.dtype) # Residual MLP that applies on pre-attention input # TODO: change to self.has_residual_mlp = self.config.residual_mlp after ModelOpt quantize config is updated self.has_residual_mlp = False if hasattr(self.config, "residual_mlp") and self.config.residual_mlp is True: self.has_residual_mlp = True if self.has_residual_mlp: self.residual_layernorm = RmsNorm( normalized_shape=config.hidden_size, eps=config.norm_epsilon, dtype=config.dtype) ClsMLP = GatedMLP # TODO: may use FusedGatedMLP to further speedup self.residual_mlp = ClsMLP( hidden_size=config.hidden_size, ffn_hidden_size=config. hidden_size, # residual mlp uses hidden_size hidden_act=non_gated_version( config.hidden_act), # back to non-gated dtype=config.dtype, bias=config.mlp_bias, tp_group=config.mapping.tp_group, tp_size=config.mapping.tp_size, quant_mode=config.quant_mode) def forward(self, hidden_states, attention_mask=None, use_cache=False, spec_decoding_params=None, kv_cache_params=None, attention_params=None, lora_layer_params=None, next_layer_input_layernorm_args=None): assert not ( default_net().plugin_config.reduce_fusion and self.has_residual_mlp ), "Custom all reduce and residual mlp can't be enabled at the same time." if default_net( ).plugin_config.reduce_fusion and self.local_layer_idx > 0: hidden_states, residual = hidden_states else: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) attention_output = self.attention( hidden_states, attention_mask=attention_mask, use_cache=use_cache, spec_decoding_params=spec_decoding_params, kv_cache_params=kv_cache_params, attention_params=attention_params, lora_layer_params=lora_layer_params, reduce_fusion_params=AllReduceFusionParams( fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM if default_net().plugin_config.reduce_fusion else AllReduceFusionOp.NONE, residual=residual, norm_weight=self.post_layernorm.weight.value, eps=self.post_layernorm.eps)) if use_cache: attention_output, presents = attention_output if self.has_residual_mlp: hidden_states = residual + attention_output residual_attn = hidden_states # arctic layer w/ residual mlp # residual mlp hidden_states = self.residual_layernorm(hidden_states) hidden_states = self.residual_mlp(hidden_states) residual_mlp = residual_attn + hidden_states # parallel moe # parallel moe layers applies on PRE-ATTENTION input residual, therefore achieving pre-fetching and better parallelism hidden_states = self.post_layernorm(residual) hidden_states = self.mlp(hidden_states, lora_layer_params=lora_layer_params) hidden_states = residual_mlp + hidden_states else: if default_net().plugin_config.reduce_fusion: hidden_states, residual = attention_output else: hidden_states = residual + attention_output residual = hidden_states hidden_states = self.post_layernorm(hidden_states) if next_layer_input_layernorm_args is not None: hidden_states = self.mlp( hidden_states, lora_layer_params=lora_layer_params, reduce_fusion_params=AllReduceFusionParams( fusion_op=AllReduceFusionOp.RESIDUAL_RMS_NORM if default_net().plugin_config.reduce_fusion else AllReduceFusionOp.NONE, residual=residual, norm_weight=next_layer_input_layernorm_args[0], eps=next_layer_input_layernorm_args[1])) else: hidden_states = self.mlp(hidden_states, lora_layer_params=lora_layer_params) hidden_states = residual + hidden_states if use_cache: return (hidden_states, presents) return hidden_states class LLaMAModel(Module): def __init__(self, config: LLaMAConfig) -> None: super().__init__() self.mapping = config.mapping if self.mapping.is_first_pp_rank(): self.vocab_embedding = Embedding(config.vocab_size, config.hidden_size, dtype=config.dtype) self.layers = DecoderLayerList(LLaMADecoderLayer, config) if self.mapping.is_last_pp_rank(): self.ln_f = RmsNorm(normalized_shape=config.hidden_size, eps=config.norm_epsilon, dtype=config.dtype) def forward(self, input_ids, position_ids=None, use_cache=False, attention_mask=None, spec_decoding_params=None, kv_cache_params=None, attention_params=None, hidden_states=None, prompt_embedding_table: Optional[Tensor] = None, prompt_tasks: Optional[Tensor] = None, prompt_vocab_size: Optional[Tensor] = None, lora_params=None): ptuning_args = [ prompt_embedding_table, prompt_tasks, prompt_vocab_size ] if prompt_embedding_table is not None else [] if self.mapping.is_first_pp_rank(): hidden_states = self.vocab_embedding(input_ids, *ptuning_args) else: hidden_states = recv(hidden_states, self.mapping.prev_pp_rank()) hidden_states = self.layers.forward( hidden_states, use_cache=use_cache, attention_mask=attention_mask, kv_cache_params=kv_cache_params, attention_params=attention_params, lora_params=lora_params, spec_decoding_params=spec_decoding_params) if use_cache: hidden_states, presents = hidden_states if self.mapping.is_last_pp_rank(): hidden_states = self.ln_f(hidden_states) else: hidden_states = send(hidden_states, self.mapping.next_pp_rank()) if use_cache: return (hidden_states, tuple(presents)) return hidden_states class LLaMAForCausalLM(DecoderModelForCausalLM): config_class = LLaMAConfig def __init__(self, config: LLaMAConfig): transformer = LLaMAModel(config) vocab_size_padded = pad_vocab_size(config.vocab_size, config.mapping.tp_size) if config.mapping.is_last_pp_rank(): lm_head = ColumnLinear(config.hidden_size, vocab_size_padded, bias=False, dtype=config.dtype, tp_group=config.mapping.tp_group, tp_size=config.mapping.tp_size, gather_output=True) else: lm_head = None self.quant_mode = config.quant_mode self.mapping = config.mapping super().__init__(config, transformer, lm_head) @classmethod def from_hugging_face( cls, hf_model_or_dir: Union[str, 'transformers.PreTrainedModel'], dtype: str = 'auto', mapping: Optional[Mapping] = None, quant_config: Optional[QuantConfig] = None, **kwargs): ''' Create a LLaMAForCausalLM object from give parameters ''' import transformers load_by_shard = kwargs.pop('load_by_shard', False) load_model_on_cpu = kwargs.pop('load_model_on_cpu', False) assert hf_model_or_dir is not None use_preloading = isinstance(hf_model_or_dir, transformers.PreTrainedModel) if use_preloading: hf_model = hf_model_or_dir hf_config_or_dir = hf_model.config else: hf_model_dir = hf_model_or_dir hf_config_or_dir = hf_model_or_dir config = LLaMAConfig.from_hugging_face(hf_config_or_dir, dtype=dtype, mapping=mapping, quant_config=quant_config, **kwargs) if use_preloading: assert not load_by_shard weights = load_weights_from_hf_model(hf_model, config) elif load_by_shard: weights = load_weights_from_hf_by_shard(hf_model_dir, config) elif has_safetensors( hf_model_dir) and not config.quant_mode.has_any_quant(): weights = load_weights_from_hf_safetensors(hf_model_dir, config) else: hf_model = load_hf_llama(hf_model_dir, load_model_on_cpu) weights = load_weights_from_hf_model(hf_model, config) check_share_embedding(weights, config) model = LLaMAForCausalLM(config) model.load(weights) return model def default_plugin_config(self, **kwargs): plugin_config = super().default_plugin_config(**kwargs) if self.quant_mode.is_int4_weight_only_per_group(): plugin_config.weight_only_groupwise_quant_matmul_plugin = 'auto' return plugin_config @classmethod def from_meta_ckpt(cls, meta_ckpt_dir: str, dtype: str = 'auto', mapping: Optional[Mapping] = None, quant_config: Optional[QuantConfig] = None, **kwargs): config = LLaMAConfig.from_meta_ckpt(meta_ckpt_dir, dtype=dtype, mapping=mapping, quant_config=quant_config, **kwargs) weights = load_weights_from_meta_ckpt(meta_ckpt_dir, config) check_share_embedding(weights, config) model = LLaMAForCausalLM(config) model.load(weights) return model @classmethod def quantize( cls, hf_model_dir: str, output_dir: str, dtype: str = 'auto', mapping: Optional[Mapping] = None, quant_config: Optional[QuantConfig] = None, *, device: str = 'cuda', calib_dataset: str = 'cnn_dailymail', calib_batches: int = 512, calib_batch_size: int = 1, calib_max_seq_length: int = 512, random_seed: int = 1234, tokenizer_max_seq_length: int = 2048, **kwargs, ): DEFAULT_MODELOPT_FLOW = [ QuantAlgo.W4A16_AWQ, QuantAlgo.FP8, QuantAlgo.W8A8_SQ_PER_CHANNEL, QuantAlgo.W4A8_AWQ ] config = LLaMAConfig.from_hugging_face(hf_model_dir, dtype=dtype, mapping=mapping, quant_config=quant_config, **kwargs) if quant_config.quant_algo in DEFAULT_MODELOPT_FLOW: super().quantize(hf_model_dir, output_dir, dtype=config.dtype, mapping=config.mapping, quant_config=config.quantization, device=device, 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) else: # non-modelopt, the legacy TRT-LLM native quantization algorithm: # sq, int4/int8 weights only, int8 kv cache NATIVE_QUANT_FLOW = [ QuantAlgo.W4A16, QuantAlgo.W8A16, QuantAlgo.FP8_PER_CHANNEL_PER_TOKEN, None ] + W8A8_SQ_PLUGIN_LIST is_valid_native_quant = (quant_config.quant_algo in NATIVE_QUANT_FLOW) and \ (quant_config.kv_cache_quant_algo in [QuantAlgo.INT8, None]) assert quant_config.quant_algo is not None or quant_config.kv_cache_quant_algo is not None, \ "There is no point to call the quantize function if both quant_algo and kv_cache_quant_algo is None" assert is_valid_native_quant, f"Internal error: shall call Modelopt for this quantization {quant_config}" from . import convert convert.quantize(hf_model_dir, output_dir, config=config, device=device, calib_dataset=calib_dataset) def use_lora(self, lora_config: LoraConfig): use_lora(self, lora_config)