TensorRT-LLMs/tensorrt_llm/models/phi3/phi3small/model.py

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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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import json
import os
import traceback
from concurrent.futures import ThreadPoolExecutor, as_completed

import numpy as np
import safetensors
from transformers import AutoModelForCausalLM

from ...._utils import pad_vocab_size
from ....functional import PositionEmbeddingType, Tensor
from ....layers import (MLP, Attention, AttentionMaskType,
                        BlockSparseAttnParams, Embedding, LayerNorm,
                        ParallelLMHead)
from ....module import Module
from ...modeling_utils import (DecoderLayerList, DecoderModelForCausalLM,
                               PretrainedConfig)
from .convert import convert_hf_config, convert_hf_weights


class Phi3SmallDecoderLayer(Module):

    def __init__(self, config: PretrainedConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        tp_group = config.mapping.tp_group
        tp_size = config.mapping.tp_size
        self.gegelu_limit = config.gegelu_limit

        self.input_layernorm = LayerNorm(normalized_shape=config.hidden_size,
                                         dtype=config.dtype)

        # MuP uses norm_factor=attention_head_size (rather than sqrt(attention_head_size))
        # We achieve this using q_scaling = sqrt(attention_head_size)
        hidden_size = config.hidden_size
        num_attention_heads = config.num_attention_heads
        attention_head_size = hidden_size / num_attention_heads
        q_scaling = attention_head_size**.5

        block_sparse = (
            (layer_idx + 1) % config.dense_attention_every_n_layers) != 0
        attention_mask_type = AttentionMaskType.blocksparse if block_sparse else AttentionMaskType.causal

        block_sparse_attn_params = BlockSparseAttnParams(
            config.blocksparse_block_size, config.blocksparse_homo_head_pattern,
            config.blocksparse_num_local_blocks,
            config.blocksparse_vertical_stride)

        layers_range = config.mapping.pp_layers(config.num_hidden_layers)
        local_layer_idx = layer_idx - layers_range[0]

        position_embedding_type = PositionEmbeddingType.rope_gpt_neox
        original_max_position_embeddings = config.max_position_embeddings

        rope_scaling_short_factors, rope_scaling_long_factors = 1.0, 1.0
        rope_scaling_short_mscale, rope_scaling_long_mscale = 1.0, 1.0

        if hasattr(config, "longrope_scaling_short_factors"):
            rope_scaling_short_factors = np.asarray(
                config.longrope_scaling_short_factors).astype(np.float32)
            rope_scaling_long_factors = np.asarray(
                config.longrope_scaling_long_factors).astype(np.float32)
            rope_scaling_short_mscale = config.longrope_short_mscale
            rope_scaling_long_mscale = config.longrope_long_mscale

            position_embedding_type = PositionEmbeddingType.long_rope
            original_max_position_embeddings = config.original_max_position_embeddings

        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_kv_heads,
            position_embedding_type=position_embedding_type,
            rotary_embedding_base=config.rotary_embedding_base,
            max_position_embeddings=config.max_position_embeddings,
            original_max_position_embeddings=original_max_position_embeddings,
            dtype=config.dtype,
            attention_mask_type=attention_mask_type,
            bias=True,
            q_scaling=q_scaling,
            tp_group=tp_group,
            tp_size=tp_size,
            quant_mode=config.quant_mode,
            rope_scaling_short_factors=rope_scaling_short_factors,
            rope_scaling_long_factors=rope_scaling_long_factors,
            rope_scaling_short_mscale=rope_scaling_short_mscale,
            rope_scaling_long_mscale=rope_scaling_long_mscale,
            block_sparse_params=block_sparse_attn_params)

        self.post_layernorm = LayerNorm(normalized_shape=config.hidden_size,
                                        dtype=config.dtype)

        self.mlp = MLP(hidden_size=config.hidden_size,
                       ffn_hidden_size=config.intermediate_size,
                       hidden_act=config.hidden_act,
                       dtype=config.dtype,
                       tp_group=tp_group,
                       tp_size=tp_size,
                       quant_mode=config.quant_mode)

    def forward(
        self,
        hidden_states: Tensor,
        attention_mask=None,
        use_cache=False,
        kv_cache_params=None,
        attention_params=None,
    ):
        residual = hidden_states
        input_layernorm_output = self.input_layernorm(hidden_states)

        # Self attention
        attention_output = self.attention(
            input_layernorm_output,
            attention_mask=attention_mask,
            use_cache=use_cache,
            kv_cache_params=kv_cache_params,
            attention_params=attention_params,
        )

        if use_cache:
            attention_output, presents = attention_output

        hidden_states = residual + attention_output

        # Fully connected
        residual = hidden_states
        hidden_states = self.post_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states, gegelu_limit=self.gegelu_limit)
        hidden_states = residual + hidden_states

        if use_cache:
            return (hidden_states, presents)
        return hidden_states


class Phi3SmallModel(Module):

    def __init__(self, config: PretrainedConfig):
        super().__init__()
        self.vocab_embedding = Embedding(num_embeddings=config.vocab_size,
                                         embedding_dim=config.hidden_size,
                                         dtype=config.dtype)

        self.layers = DecoderLayerList(Phi3SmallDecoderLayer, config)
        self.ln_f = LayerNorm(normalized_shape=config.hidden_size,
                              dtype=config.dtype)
        self.mup_embedding_multiplier = config.mup_embedding_multiplier

    def forward(
        self,
        input_ids: Tensor,
        position_ids=None,
        use_cache=False,
        attention_mask=None,
        kv_cache_params=None,
        attention_params=None,
        prompt_embedding_table=None,
        prompt_tasks=None,
        prompt_vocab_size=None,
    ):
        args = [prompt_embedding_table, prompt_tasks, prompt_vocab_size
                ] if prompt_embedding_table is not None else []
        hidden_states = self.vocab_embedding(input_ids, *args)

        if self.mup_embedding_multiplier is not None and self.mup_embedding_multiplier > 0.0:
            hidden_states = hidden_states * self.mup_embedding_multiplier

        hidden_states = self.layers(
            hidden_states,
            use_cache=use_cache,
            attention_mask=attention_mask,
            kv_cache_params=kv_cache_params,
            attention_params=attention_params,
        )
        if use_cache:
            hidden_states, presents = hidden_states

        hidden_states = self.ln_f(hidden_states)

        if use_cache:
            return (hidden_states, tuple(presents))
        return hidden_states


class Phi3SmallForCausalLM(DecoderModelForCausalLM):

    def __init__(self, config: PretrainedConfig):
        transformer = Phi3SmallModel(config)
        vocab_size_padded = pad_vocab_size(config.vocab_size,
                                           config.mapping.tp_size)

        lm_head = ParallelLMHead(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)

        super().__init__(config, transformer, lm_head)

    @classmethod
    def convert_hf_checkpoint(cls, model_dir, dtype, output_dir, args=None):
        '''
        Convert Huggingface checkpoint to TRT-LLM checkpoint
        '''

        hf_model = AutoModelForCausalLM.from_pretrained(model_dir,
                                                        torch_dtype="auto",
                                                        trust_remote_code=True)

        config = convert_hf_config(hf_model.config, dtype, args)
        with open(os.path.join(output_dir, 'config.json'), 'w') as f:
            json.dump(config, f, indent=4)

        def covert_and_save(rank):
            weights = convert_hf_weights(hf_model, config, args, rank)
            safetensors.torch.save_file(
                weights, os.path.join(output_dir, f'rank{rank}.safetensors'))

        world_size = args.tp_size * args.pp_size
        if args.workers == 1:
            for rank in range(world_size):
                covert_and_save(rank)
        else:
            with ThreadPoolExecutor(max_workers=args.workers) as p:
                futures = [
                    p.submit(covert_and_save, rank)
                    for rank in range(world_size)
                ]
                exceptions = []
                for future in as_completed(futures):
                    try:
                        future.result()
                    except Exception as e:
                        traceback.print_exc()
                        exceptions.append(e)
                assert len(
                    exceptions
                ) == 0, "Checkpoint conversion failed, please check error log."