model.py

# 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,
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# See the License for the specific language governing permissions and
# limitations under the License.
import json
from pathlib import Path
from typing import Optional

from ..._utils import pad_vocab_size
from ...functional import Tensor, recv, send
from ...layers import (MOE, Attention, AttentionMaskType, ColumnLinear,
                       Embedding, GatedMLP, MoeConfig, PositionEmbeddingType,
                       RmsNorm)
from ...lora_manager import LoraBuildConfig, use_lora
from ...mapping import Mapping
from ...module import Module
from ...plugin import init_all_reduce_helper
from ...quantization import W8A8_SQ_PLUGIN_LIST, QuantAlgo
from ..modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
                              PretrainedConfig, QuantConfig)


class LLaMADecoderLayer(Module):

    def __init__(self, config: PretrainedConfig, 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)
        local_layer_idx = layer_idx - layers_range[0]
        self.attention = Attention(
            local_layer_idx=local_layer_idx,
            hidden_size=config.hidden_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_num_experts > 1:
            ClsMLP = MOE
            mlp_kwargs = {
                "moe_config":
                MoeConfig(
                    config.moe_num_experts,
                    config.moe_top_k,
                    config.moe_tp_mode,
                    config.moe_normalization_mode,
                ),
                "tp_rank":
                config.mapping.tp_rank,
            }

        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)

    def forward(
            self,
            hidden_states,
            attention_mask=None,
            medusa_packed_mask=None,  # For Medusa support
            medusa_position_offsets=None,
            use_cache=False,
            kv_cache_params=None,
            attention_params=None,
            lora_layer_params=None):
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)

        attention_output = self.attention(
            hidden_states,
            attention_mask=attention_mask,
            medusa_packed_mask=medusa_packed_mask,  # For Medusa support
            medusa_position_offsets=medusa_position_offsets,
            use_cache=use_cache,
            kv_cache_params=kv_cache_params,
            attention_params=attention_params,
            lora_layer_params=lora_layer_params)

        if use_cache:
            attention_output, presents = attention_output

        hidden_states = residual + attention_output

        residual = hidden_states
        hidden_states = self.post_layernorm(hidden_states)

        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: PretrainedConfig) -> None:
        super().__init__()
        init_all_reduce_helper()

        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,
            medusa_position_offsets=None,  # For Medusa support
            medusa_packed_mask=None,  # For Medusa support
            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,
            medusa_position_offsets=medusa_position_offsets,
            medusa_packed_mask=medusa_packed_mask)

        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):

    def __init__(self, config: PretrainedConfig):
        self.check_config(config)
        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)

    def check_config(self, config):
        config.set_if_not_exist('mlp_bias', False)
        config.set_if_not_exist('attn_bias', False)
        config.set_if_not_exist('rotary_base', 10000.0)
        config.set_if_not_exist('rotary_scaling', None)
        config.set_if_not_exist('moe_num_experts', 0)
        config.set_if_not_exist('moe_top_k', 0)
        config.set_if_not_exist('moe_tp_mode',
                                MoeConfig.ParallelismMode.TENSOR_PARALLEL)
        config.set_if_not_exist(
            'moe_normalization_mode',
            MoeConfig.ExpertScaleNormalizationMode.RENORMALIZE)

    @classmethod
    def from_hugging_face(cls,
                          hf_model_dir,
                          dtype='float16',
                          mapping: Optional[Mapping] = None,
                          **kwargs):
        from . import convert
        if mapping is None:
            mapping = Mapping()
        llama = convert.from_hugging_face(
            cls,
            hf_model_dir,
            dtype,
            mapping=mapping,
            quantization=kwargs.get('quantization', QuantConfig()),
            load_by_shard=kwargs.get('load_by_shard', False),
            load_model_on_cpu=kwargs.get('load_model_on_cpu', False),
            override_fields=kwargs.get('override_fields', {}),
            skip_loading_weights=kwargs.get('skip_loading_weights', False),
            preloaded_model=kwargs.get('preloaded_model', None))
        return llama

    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

    @classmethod
    def from_meta_ckpt(cls,
                       meta_ckpt_dir,
                       dtype,
                       mapping,
                       use_parallel_embedding: Optional[bool] = False,
                       embedding_sharding_dim: Optional[int] = 0):
        meta_config = None
        with open(Path(meta_ckpt_dir, "params.json")) as fp:
            meta_config: dict = json.load(fp)
        assert meta_config is not None
        config = {}
        n_embd = meta_config["dim"]
        n_head = meta_config["n_heads"]
        n_kv_head = meta_config.get("n_kv_heads", n_head)
        if "hidden_dim" in meta_config:
            inter_size = meta_config["hidden_dim"]
        else:
            multiple_of = meta_config.get("multiple_of", 1)
            n_embd_ = int(4 * n_embd * 2 / 3)
            ffn_dim_multiplier = meta_config.get("ffn_dim_multiplier", 1)
            inter_size = multiple_of * (
                (int(n_embd_ * ffn_dim_multiplier) + multiple_of - 1) //
                multiple_of)
        # meta checkpoint don't have vocab_size|hidden_act|rotary_base specified, use same default value as HF
        config.update({
            'architecture': "LlamaForCausalLM",
            'dtype': dtype,
            'logits_dtype': 'float32',
            'num_hidden_layers': meta_config["n_layers"],
            'num_attention_heads': n_head,
            'hidden_size': n_embd,
            'intermediate_size': inter_size,
            'num_key_value_heads': n_kv_head,
            'vocab_size': 32000,
            'position_embedding_type': 'rope_gpt_neox',
            'max_position_embeddings': 2048,
            'hidden_act': 'silu',
            'rotary_base': 10000.0,
            'norm_epsilon': meta_config["norm_eps"],
            'mapping': {
                'world_size': mapping.tp_size * mapping.pp_size,
                'tp_size': mapping.tp_size,
                'pp_size': mapping.pp_size,
            },
        })
        pretrained_config = PretrainedConfig.from_dict(config)
        pretrained_config.use_parallel_embedding = use_parallel_embedding
        pretrained_config.embedding_sharding_dim = embedding_sharding_dim
        pretrained_config.set_rank(mapping.rank)

        llama = cls(pretrained_config)
        from .weight import load_from_meta_llama
        weights = load_from_meta_llama(meta_ckpt_dir, mapping,
                                       pretrained_config)
        llama.load(weights)
        return llama

    @classmethod
    def quantize(
        cls,
        hf_model_dir,
        output_dir,
        quant_config: QuantConfig,
        *,
        dtype='float16',
        mapping: Optional[Mapping] = None,
        calib_batches=512,
        calib_batch_size=1,
        random_seed=1234,
        tokenizer_max_seq_length=2048,
        **kwargs,
    ):
        DEFAULT_AMMO_FLOW = [
            QuantAlgo.W4A16_AWQ, QuantAlgo.FP8, QuantAlgo.W8A8_SQ_PER_CHANNEL,
            QuantAlgo.W4A8_AWQ
        ]
        use_ammo_quantization = quant_config.quant_algo in DEFAULT_AMMO_FLOW
        if use_ammo_quantization:
            super().quantize(hf_model_dir,
                             output_dir,
                             quant_config,
                             dtype=dtype,
                             mapping=mapping,
                             calib_batches=calib_batches,
                             calib_batch_size=calib_batch_size,
                             random_seed=random_seed,
                             tokenizer_max_seq_length=tokenizer_max_seq_length)
        else:
            # non-ammo, the legacy TRT-LLM native quantization algorithm:
            # sq, int4/int8 weights only, int8 kv cache
            NATIVE_QUANT_FLOW = [QuantAlgo.W4A16, QuantAlgo.W8A16, 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 AMMO for this quantization {quant_config}"

            from . import convert
            convert.quantize(
                dtype,
                hf_model_dir,
                output_dir,
                mapping,
                quant_config,
                override_fields=kwargs.get('override_fields', {}),
                dataset_cache_dir=kwargs.get('dataset_cache_dir', None),
            )

    def use_lora(self, lora_config: LoraBuildConfig):
        use_lora(self, lora_config)