model.py

# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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

import tensorrt as trt

from ..._common import default_net
from ..._utils import pad_vocab_size, str_dtype_to_trt
from ...functional import (Tensor, gather_last_token_logits,
                           is_gated_activation, non_gated_version)
from ...layers import (MLP, Attention, AttentionMaskType, AttentionParams,
                       ColumnLinear, Embedding, GatedMLP, KeyValueCacheParams,
                       LayerNorm, LoraParams, PositionEmbeddingType,
                       PromptTuningEmbedding)
from ...mapping import Mapping
from ...module import Module, ModuleList
from ...quantization import QuantMode
from ..generation_mixin import GenerationMixin


def MLPFactory(hidden_size,
               ffn_hidden_size,
               hidden_act,
               bias=True,
               dtype=None,
               tp_group=None,
               tp_size=1,
               quant_mode=QuantMode(0),
               instance_id: int = 0):
    MLPClass = GatedMLP if is_gated_activation(hidden_act) else MLP
    hidden_act = non_gated_version(hidden_act)
    return MLPClass(hidden_size, ffn_hidden_size, hidden_act, bias, dtype,
                    tp_group, tp_size, quant_mode, instance_id)


class GPTEmbedding(Module):

    def __init__(self,
                 vocab_size,
                 hidden_size,
                 max_position_embeddings,
                 position_embedding_type=PositionEmbeddingType.learned_absolute,
                 dtype=None,
                 use_prompt_tuning=False,
                 tensor_parallel=1,
                 tensor_parallel_group=None,
                 sharding_dim=0,
                 tp_rank=None,
                 instance_id: int = 0):
        super().__init__()
        self.max_position_embeddings = max_position_embeddings
        self.position_embedding_type = position_embedding_type
        self.use_prompt_tuning = use_prompt_tuning

        EmbeddingCls = PromptTuningEmbedding if use_prompt_tuning else Embedding
        self.vocab_embedding = EmbeddingCls(vocab_size,
                                            hidden_size,
                                            dtype=dtype,
                                            tp_size=tensor_parallel,
                                            tp_group=tensor_parallel_group,
                                            sharding_dim=sharding_dim,
                                            tp_rank=tp_rank,
                                            instance_id=instance_id)

        if self.position_embedding_type == PositionEmbeddingType.learned_absolute:
            self.position_embedding = Embedding(max_position_embeddings,
                                                hidden_size,
                                                dtype=dtype)

    def forward(self,
                input_ids,
                position_ids,
                prompt_embedding_table=None,
                prompt_tasks=None,
                prompt_vocab_size=None,
                workspace: Optional[Tensor] = None):
        args = []
        if self.use_prompt_tuning:
            args = [prompt_embedding_table, prompt_tasks, prompt_vocab_size]
        x = self.vocab_embedding(input_ids, *args, workspace=workspace)
        if self.position_embedding_type == PositionEmbeddingType.learned_absolute:
            x = x + self.position_embedding(position_ids)

        return x


class GPTDecoderLayer(Module):

    def __init__(self,
                 hidden_size,
                 num_attention_heads,
                 max_position_embeddings,
                 num_layers,
                 dtype=None,
                 apply_query_key_layer_scaling=False,
                 attention_mask_type=AttentionMaskType.causal,
                 hidden_act='relu',
                 position_embedding_type=PositionEmbeddingType.learned_absolute,
                 quant_mode=QuantMode(0),
                 rotary_embedding_percentage=1.0,
                 rotary_base=10000.0,
                 rotary_scaling=None,
                 inter_size=None,
                 bias=True,
                 num_kv_heads=None,
                 tp_group=None,
                 tp_size=1,
                 tp_rank=0,
                 instance_id: int = 0):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        self.max_position_embeddings = max_position_embeddings
        self.num_layers = num_layers
        self.dtype = dtype
        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
        self.attention_mask_type = attention_mask_type
        self.hidden_act = hidden_act
        self.position_embedding_type = position_embedding_type
        self.tp_group = tp_group
        self.tp_size = tp_size
        self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
                                         dtype=dtype)

        self.attention = Attention(
            hidden_size,
            num_attention_heads,
            num_kv_heads,
            max_position_embeddings,
            num_layers,
            apply_query_key_layer_scaling,
            dtype=dtype,
            attention_mask_type=attention_mask_type,
            position_embedding_type=position_embedding_type,
            rotary_embedding_percentage=rotary_embedding_percentage,
            rotary_embedding_base=rotary_base,
            rotary_embedding_scaling=rotary_scaling,
            bias=bias,
            tp_group=tp_group,
            tp_size=tp_size,
            tp_rank=tp_rank,
            use_int8_kv_cache=quant_mode.has_int8_kv_cache(),
            quant_mode=quant_mode,
            instance_id=2 * instance_id)

        if inter_size is None:
            inter_size = hidden_size * 4

        self.mlp = MLPFactory(hidden_size=hidden_size,
                              ffn_hidden_size=inter_size,
                              hidden_act=hidden_act,
                              dtype=dtype,
                              bias=bias,
                              tp_group=tp_group,
                              tp_size=tp_size,
                              quant_mode=quant_mode,
                              instance_id=2 * instance_id + 1)
        self.post_layernorm = LayerNorm(normalized_shape=hidden_size,
                                        dtype=dtype)

    def forward(self,
                hidden_states: Tensor,
                attention_mask=None,
                use_cache=False,
                kv_cache_params=None,
                attention_params=None,
                workspace=None,
                lora_params=None):

        assert isinstance(hidden_states, Tensor)

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        attention_output = self.attention(hidden_states,
                                          attention_mask=attention_mask,
                                          use_cache=use_cache,
                                          kv_cache_params=kv_cache_params,
                                          attention_params=attention_params,
                                          workspace=workspace,
                                          lora_params=lora_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, workspace=workspace)

        hidden_states = residual + hidden_states

        if use_cache:
            return (hidden_states, presents)
        return hidden_states


class GPTModel(Module):

    def __init__(self,
                 num_layers,
                 num_heads,
                 hidden_size,
                 vocab_size,
                 hidden_act,
                 max_position_embeddings,
                 dtype=None,
                 mapping=Mapping(),
                 apply_query_key_layer_scaling=False,
                 position_embedding_type=PositionEmbeddingType.learned_absolute,
                 rotary_embedding_percentage=1.0,
                 rotary_base=10000.0,
                 rotary_scaling=None,
                 inter_size=None,
                 bias=True,
                 quant_mode=QuantMode(0),
                 num_kv_heads=None,
                 use_prompt_tuning=False,
                 use_parallel_embedding=False,
                 embedding_sharding_dim=0):
        super().__init__()
        self.mapping = mapping

        self.embedding = GPTEmbedding(
            vocab_size,
            hidden_size,
            max_position_embeddings,
            position_embedding_type,
            dtype=dtype,
            use_prompt_tuning=use_prompt_tuning,
            tensor_parallel=mapping.tp_size if use_parallel_embedding else 1,
            tensor_parallel_group=mapping.tp_group
            if use_parallel_embedding else None,
            sharding_dim=embedding_sharding_dim,
            tp_rank=mapping.tp_rank,
            instance_id=2 * num_layers,
        )

        self.layers = ModuleList([
            GPTDecoderLayer(
                hidden_size=hidden_size,
                num_attention_heads=num_heads,
                max_position_embeddings=max_position_embeddings,
                num_layers=num_layers,
                dtype=dtype,
                apply_query_key_layer_scaling=apply_query_key_layer_scaling,
                attention_mask_type=AttentionMaskType.causal,
                hidden_act=hidden_act,
                position_embedding_type=position_embedding_type,
                rotary_embedding_percentage=rotary_embedding_percentage,
                rotary_base=rotary_base,
                rotary_scaling=rotary_scaling,
                num_kv_heads=num_kv_heads,
                tp_group=mapping.tp_group,
                tp_size=mapping.tp_size,
                tp_rank=mapping.tp_rank,
                inter_size=inter_size,
                bias=bias,
                quant_mode=quant_mode,
                instance_id=i,
            ) for i in range(num_layers)
        ])

        self.ln_f = LayerNorm(normalized_shape=hidden_size, dtype=dtype)

    def forward(self,
                input_ids,
                position_ids,
                use_cache=False,
                attention_mask=None,
                kv_cache_params=None,
                attention_params=None,
                prompt_embedding_table=None,
                prompt_tasks=None,
                prompt_vocab_size=None,
                workspace=None,
                lora_params=None):

        hidden_states = self.embedding(input_ids,
                                       position_ids,
                                       prompt_embedding_table,
                                       prompt_tasks,
                                       prompt_vocab_size,
                                       workspace=workspace)

        kv_cache_params.fill_none_tensor_list(len(self.layers))

        if use_cache:
            presents = []

        for layer_idx, (layer, past, pointer, max_kv_cache_length) in enumerate(
                zip(self.layers, kv_cache_params.past_key_value,
                    kv_cache_params.kv_cache_block_pointers,
                    kv_cache_params.host_max_kv_cache_lengths)):
            lora_param = None
            if lora_params.lora_ranks is not None:
                lora_param = LoraParams(
                    lora_ranks=lora_params.lora_ranks,
                    lora_weights_pointers_list=[
                        lora_params.lora_weights_pointers_list[layer_idx]
                    ])

            hidden_states = layer(
                hidden_states,
                use_cache=use_cache,
                attention_mask=attention_mask,
                kv_cache_params=KeyValueCacheParams(
                    past_key_value=[past],
                    host_past_key_value_lengths=kv_cache_params.
                    host_past_key_value_lengths,
                    host_max_kv_cache_lengths=max_kv_cache_length,
                    kv_cache_block_pointers=[pointer],
                    cache_indirection=kv_cache_params.cache_indirection),
                attention_params=attention_params,
                workspace=workspace,
                lora_params=lora_param)

            if use_cache:
                presents.append(hidden_states[1])
                hidden_states = hidden_states[0]

        hidden_states = self.ln_f(hidden_states)

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


class GPTLMHeadModel(GPTModel, GenerationMixin):

    def __init__(self,
                 num_layers,
                 num_heads,
                 hidden_size,
                 vocab_size,
                 hidden_act,
                 max_position_embeddings,
                 dtype,
                 logits_dtype='float32',
                 mapping=Mapping(),
                 apply_query_key_layer_scaling=False,
                 position_embedding_type=PositionEmbeddingType.learned_absolute,
                 rotary_embedding_percentage=1.0,
                 rotary_base=10000.0,
                 rotary_scaling=None,
                 inter_size=None,
                 bias=True,
                 quant_mode=QuantMode(0),
                 num_kv_heads=None,
                 use_prompt_tuning=False,
                 use_parallel_embedding=False,
                 embedding_sharding_dim=0,
                 share_embedding_table=False):

        if isinstance(dtype, str):
            self._kv_dtype = str_dtype_to_trt(dtype)
        else:
            assert isinstance(dtype, trt.DataType)
            self._kv_dtype = dtype

        if share_embedding_table and mapping.tp_size > 1:
            if (not use_parallel_embedding) or (use_parallel_embedding and
                                                embedding_sharding_dim == 1):
                raise NotImplementedError(
                    'For multiple-processes cases, sharing the embedding table must set use_parallel_embedding=True and embedding_sharding_dim = 0'
                )

        self._dtype = self._kv_dtype
        self.quant_mode = quant_mode
        if quant_mode.has_int8_kv_cache():
            self._kv_dtype = str_dtype_to_trt('int8')
        elif quant_mode.has_fp8_kv_cache():
            self._kv_dtype = str_dtype_to_trt('fp8')

        if isinstance(logits_dtype, str):
            self._logits_dtype = str_dtype_to_trt(logits_dtype)
        else:
            assert isinstance(logits_dtype, trt.DataType)
            self._logits_dtype = logits_dtype

        self._num_layers = num_layers
        self._num_heads = num_heads
        self._hidden_size = hidden_size
        self._vocab_size = vocab_size
        self._tp_size = mapping.tp_size
        self._num_kv_heads = num_kv_heads if num_kv_heads else num_heads

        super().__init__(
            num_layers=num_layers,
            num_heads=num_heads,
            hidden_size=hidden_size,
            vocab_size=vocab_size,
            hidden_act=hidden_act,
            max_position_embeddings=max_position_embeddings,
            dtype=dtype,
            mapping=mapping,
            apply_query_key_layer_scaling=apply_query_key_layer_scaling,
            position_embedding_type=position_embedding_type,
            rotary_embedding_percentage=rotary_embedding_percentage,
            rotary_base=rotary_base,
            rotary_scaling=rotary_scaling,
            inter_size=inter_size,
            bias=bias,
            quant_mode=quant_mode,
            num_kv_heads=num_kv_heads,
            use_prompt_tuning=use_prompt_tuning,
            use_parallel_embedding=use_parallel_embedding,
            embedding_sharding_dim=embedding_sharding_dim,
        )
        vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)

        share_weight = None
        if share_embedding_table:
            share_weight = self.embedding.vocab_embedding.weight
        self.lm_head = ColumnLinear(hidden_size,
                                    vocab_size_padded,
                                    bias=False,
                                    dtype=dtype,
                                    tp_group=mapping.tp_group,
                                    tp_size=mapping.tp_size,
                                    gather_output=True,
                                    share_weight=share_weight)

    def forward(self,
                input_ids: Tensor,
                position_ids=None,
                use_cache=False,
                last_token_ids=None,
                attention_mask=None,
                kv_cache_params=None,
                attention_params=None,
                prompt_embedding_table=None,
                prompt_tasks=None,
                prompt_vocab_size=None,
                workspace=None,
                lora_params=None):

        hidden_states = super().forward(input_ids, position_ids, use_cache,
                                        attention_mask, kv_cache_params,
                                        attention_params,
                                        prompt_embedding_table, prompt_tasks,
                                        prompt_vocab_size, workspace,
                                        lora_params)

        if use_cache:
            hidden_states, presents = hidden_states

        hidden_states = gather_last_token_logits(
            hidden_states, last_token_ids,
            default_net().plugin_config.remove_input_padding)

        # [batch_size, hidden_size] -> [batch_size, vocab_size]
        lm_logits = self.lm_head(hidden_states)
        lm_logits.mark_output('logits', self._logits_dtype)

        if use_cache:
            if default_net().plugin_config.paged_kv_cache == False:
                for i, present in enumerate(presents):
                    present.mark_output(f'present_key_value_{i}',
                                        self._kv_dtype)
            return (lm_logits, presents)

        return lm_logits

    def prepare_inputs(self,
                       max_batch_size,
                       max_input_len,
                       max_new_tokens,
                       use_cache,
                       max_beam_width: int = 1,
                       max_num_tokens: int = None,
                       prompt_embedding_table_size: int = 0,
                       gather_all_token_logits: bool = False,
                       max_draft_len: int = 0):
        '''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
            ranges of the dimensions of when using TRT dynamic shapes.

            @return: a list contains values which can be fed into the self.forward()
        '''

        # Prepare inputs
        head_size = self._hidden_size // self._num_heads
        num_heads_kv = self._num_kv_heads
        remove_input_padding = default_net().plugin_config.remove_input_padding
        use_gpt_attention_plugin = default_net(
        ).plugin_config.gpt_attention_plugin
        use_gemm_plugin = default_net().plugin_config.gemm_plugin
        paged_kv_cache = default_net().plugin_config.paged_kv_cache
        tokens_per_block = default_net().plugin_config.tokens_per_block
        use_custom_all_reduce = default_net(
        ).plugin_config.use_custom_all_reduce
        use_lora_plugin = default_net().plugin_config.lora_plugin

        model_inputs = self.prepare_basic_inputs(
            max_batch_size=max_batch_size,
            max_beam_width=max_beam_width,
            max_input_len=max_input_len,
            max_new_tokens=max_new_tokens,
            num_kv_heads=num_heads_kv,
            head_size=head_size,
            num_layers=self._num_layers,
            kv_dtype=self._kv_dtype,
            num_heads=self._num_heads,
            dtype=self._dtype,
            remove_input_padding=remove_input_padding,
            use_gpt_attention_plugin=use_gpt_attention_plugin,
            use_gemm_plugin=use_gemm_plugin,
            use_custom_all_reduce=use_custom_all_reduce,
            paged_kv_cache=paged_kv_cache,
            tokens_per_block=tokens_per_block,
            gather_all_token_logits=gather_all_token_logits,
            mapping=self.mapping,
            max_num_tokens=max_num_tokens,
            prompt_embedding_table_size=prompt_embedding_table_size,
            use_lora_plugin=use_lora_plugin,
            max_draft_len=max_draft_len)

        return (model_inputs['input_ids'], model_inputs['position_ids'], True,
                model_inputs['last_token_ids'], model_inputs['attention_mask'],
                KeyValueCacheParams(
                    past_key_value=model_inputs['past_key_value'],
                    host_past_key_value_lengths=model_inputs[
                        'host_past_key_value_lengths'],
                    host_max_kv_cache_lengths=model_inputs[
                        'host_max_kv_cache_lengths'],
                    kv_cache_block_pointers=model_inputs[
                        'kv_cache_block_pointers_list'],
                    cache_indirection=model_inputs['cache_indirection'],
                ),
                AttentionParams(
                    sequence_length=model_inputs['sequence_length'],
                    context_lengths=model_inputs['context_lengths'],
                    host_context_lengths=model_inputs['host_context_lengths'],
                    max_context_length=max_input_len,
                    host_request_types=model_inputs['host_request_types']),
                model_inputs['prompt_embedding_table'], model_inputs['tasks'],
                model_inputs['prompt_vocab_size'],
                model_inputs['all_reduce_workspace'],
                LoraParams(model_inputs['lora_ranks'],
                           model_inputs['lora_weights_pointers_list']))