TensorRT-LLMs/tensorrt_llm/models/llama/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, recv, send
from ...layers import (Attention, AttentionMaskType, AttentionParams,
                       ColumnLinear, Embedding, FusedGatedMLP, GatedMLP,
                       KeyValueCacheParams, PositionEmbeddingType,
                       PromptTuningEmbedding, RmsNorm)
from ...mapping import Mapping
from ...module import Module, ModuleList
from ...quantization import QuantMode
from ..generation_mixin import GenerationMixin


class LLaMADecoderLayer(Module):

    def __init__(self,
                 layer_id,
                 hidden_size,
                 num_attention_heads,
                 num_kv_heads=None,
                 max_position_embeddings=2048,
                 dtype=None,
                 attention_mask_type=AttentionMaskType.causal,
                 hidden_act='silu',
                 position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                 rotary_base=10000.0,
                 rotary_scaling=None,
                 mlp_hidden_size=None,
                 tp_group=None,
                 tp_size=1,
                 quant_mode=QuantMode(0),
                 rms_norm_eps=1e-06,
                 attn_bias=False,
                 mlp_bias=False,
                 use_fused_mlp=False):
        super().__init__()
        self._layer_id = layer_id  # useful for debugging
        # used for quantizing model
        self.hidden_size = hidden_size
        self.num_attention_heads = num_attention_heads
        self.num_kv_heads = num_kv_heads
        self.max_position_embeddings = max_position_embeddings
        self.dtype = dtype
        self.hidden_act = hidden_act
        self.tp_group = tp_group
        self.tp_size = tp_size
        self.mlp_hidden_size = mlp_hidden_size
        self.attention_mask_type = attention_mask_type
        self.position_embedding_type = position_embedding_type
        self.input_layernorm = RmsNorm(normalized_shape=hidden_size,
                                       eps=rms_norm_eps,
                                       dtype=dtype)

        self.attention = Attention(
            hidden_size,
            num_attention_heads,
            num_kv_heads,
            max_position_embeddings,
            dtype=dtype,
            attention_mask_type=AttentionMaskType.causal,
            bias=attn_bias,
            position_embedding_type=position_embedding_type,
            rotary_embedding_base=rotary_base,
            rotary_embedding_scaling=rotary_scaling,
            tp_group=tp_group,
            tp_size=tp_size,
            use_int8_kv_cache=quant_mode.has_int8_kv_cache(),
            quant_mode=quant_mode,
            instance_id=2 * layer_id,
        )
        if not mlp_hidden_size:
            self.mlp_hidden_size = hidden_size * 4
        ClsMLP = FusedGatedMLP if use_fused_mlp is True else GatedMLP
        self.mlp = ClsMLP(hidden_size=hidden_size,
                          ffn_hidden_size=self.mlp_hidden_size,
                          hidden_act=hidden_act,
                          dtype=dtype,
                          bias=mlp_bias,
                          tp_group=tp_group,
                          tp_size=tp_size,
                          quant_mode=quant_mode,
                          instance_id=2 * layer_id + 1)
        self.post_layernorm = RmsNorm(normalized_shape=hidden_size,
                                      eps=rms_norm_eps,
                                      dtype=dtype)

    def forward(self,
                hidden_states,
                attention_mask=None,
                use_cache=False,
                kv_cache_params=None,
                attention_params=None,
                all_reduce_workspace=None):
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        if self._layer_id == 0:
            self.register_network_output(f"norm0", 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=all_reduce_workspace)

        if use_cache:
            attention_output, presents = attention_output
        if self._layer_id == 0:
            self.register_network_output(f"attn", attention_output)

        hidden_states = residual + attention_output

        residual = hidden_states
        hidden_states = self.post_layernorm(hidden_states)
        if self._layer_id == 0:
            self.register_network_output(f"norm1", hidden_states)

        hidden_states = self.mlp(hidden_states, all_reduce_workspace)
        if self._layer_id == 0:
            self.register_network_output(f"mlp", hidden_states)

        hidden_states = residual + hidden_states
        if use_cache:
            return (hidden_states, presents)
        return hidden_states


class LLaMAModel(Module):

    def __init__(self,
                 num_layers,
                 num_heads,
                 num_kv_heads,
                 hidden_size,
                 vocab_size,
                 hidden_act,
                 max_position_embeddings,
                 dtype,
                 mlp_hidden_size=None,
                 position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                 rotary_base=10000.0,
                 rotary_scaling=None,
                 mapping=Mapping(),
                 quant_mode=QuantMode(0),
                 use_parallel_embedding=False,
                 embedding_sharding_dim=0,
                 rms_norm_eps=1e-06,
                 use_fused_mlp=False,
                 attn_bias=False,
                 mlp_bias=False,
                 use_prompt_tuning: bool = False):
        super().__init__()
        self.mapping = mapping
        self.use_prompt_tuning = use_prompt_tuning

        EmbeddingCls = PromptTuningEmbedding if use_prompt_tuning else Embedding
        if self.mapping.is_first_pp_rank():
            self.vocab_embedding = EmbeddingCls(
                num_embeddings=vocab_size,
                embedding_dim=hidden_size,
                dtype=dtype,
                tp_size=mapping.tp_size if use_parallel_embedding else 1,
                tp_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,  # ids in [0, 2 * (num_layers - 1) + 1] already used
            )

        self.layers = ModuleList([
            LLaMADecoderLayer(layer_id=i,
                              hidden_size=hidden_size,
                              num_attention_heads=num_heads,
                              num_kv_heads=num_kv_heads,
                              max_position_embeddings=max_position_embeddings,
                              dtype=dtype,
                              hidden_act=hidden_act,
                              mlp_hidden_size=mlp_hidden_size,
                              position_embedding_type=position_embedding_type,
                              rotary_base=rotary_base,
                              rotary_scaling=rotary_scaling,
                              tp_group=mapping.tp_group,
                              tp_size=mapping.tp_size,
                              quant_mode=quant_mode,
                              rms_norm_eps=rms_norm_eps,
                              attn_bias=attn_bias,
                              mlp_bias=mlp_bias,
                              use_fused_mlp=use_fused_mlp)
            for i in self.get_transformer_layers(self.mapping, num_layers)
        ])

        if self.mapping.is_last_pp_rank():
            self.ln_f = RmsNorm(normalized_shape=hidden_size,
                                eps=rms_norm_eps,
                                dtype=dtype)

    def forward(
        self,
        input_ids,
        position_ids=None,
        use_cache=False,
        attention_mask=None,
        kv_cache_params=None,
        attention_params=None,
        hidden_states=None,
        all_reduce_workspace=None,
        prompt_embedding_table: Optional[Tensor] = None,
        prompt_tasks: Optional[Tensor] = None,
        prompt_vocab_size: Optional[Tensor] = None,
    ):

        kv_cache_params.fill_none_tensor_list(len(self.layers))

        if use_cache:
            presents = []

        ptuning_args = []
        if self.use_prompt_tuning:
            ptuning_args = [
                prompt_embedding_table, prompt_tasks, prompt_vocab_size
            ]
        if self.mapping.is_first_pp_rank():
            hidden_states = self.vocab_embedding(input_ids, *ptuning_args,
                                                 all_reduce_workspace)
        else:
            hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
        self.register_network_output(f"embd", hidden_states)

        for layer, past, pointer, max_kv_cache_length in zip(
                self.layers, kv_cache_params.past_key_value,
                kv_cache_params.kv_cache_block_pointers,
                kv_cache_params.host_max_kv_cache_lengths):
            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,
                all_reduce_workspace=all_reduce_workspace)

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

        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(LLaMAModel, GenerationMixin):

    def __init__(self,
                 num_layers,
                 num_heads,
                 num_kv_heads,
                 hidden_size,
                 vocab_size,
                 hidden_act,
                 max_position_embeddings,
                 dtype,
                 logits_dtype="float32",
                 mlp_hidden_size=None,
                 position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
                 rotary_base=10000.0,
                 rotary_scaling=None,
                 mapping=Mapping(),
                 quant_mode=QuantMode(0),
                 use_parallel_embedding=False,
                 embedding_sharding_dim=0,
                 rms_norm_eps=1e-06,
                 use_fused_mlp=False,
                 attn_bias=False,
                 mlp_bias=False,
                 use_prompt_tuning: bool = False):

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

        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
        if num_kv_heads is None or num_kv_heads <= 0:
            num_kv_heads = num_heads
        self.num_kv_heads = num_kv_heads
        self.hidden_size = hidden_size
        self.vocab_size = vocab_size
        self.tp_size = mapping.tp_size

        self.kv_dtype = self.dtype
        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')

        self.quant_mode = quant_mode
        self.use_parallel_embedding = use_parallel_embedding
        self.embedding_sharding_dim = embedding_sharding_dim

        super().__init__(num_layers, num_heads, num_kv_heads, hidden_size,
                         vocab_size, hidden_act, max_position_embeddings, dtype,
                         mlp_hidden_size, position_embedding_type, rotary_base,
                         rotary_scaling, mapping, quant_mode,
                         use_parallel_embedding, embedding_sharding_dim,
                         rms_norm_eps, use_fused_mlp, attn_bias, mlp_bias,
                         use_prompt_tuning)

        vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
        if self.mapping.is_last_pp_rank():
            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)

    def forward(
        self,
        input_ids,
        position_ids=None,
        use_cache=False,
        last_token_ids=None,
        attention_mask=None,
        kv_cache_params=None,
        attention_params=None,
        hidden_states=None,
        all_reduce_workspace=None,
        prompt_embedding_table: Optional[Tensor] = None,
        prompt_tasks: Optional[Tensor] = None,
        prompt_vocab_size: Optional[Tensor] = None,
    ):
        hidden_states = super().forward(input_ids, position_ids, use_cache,
                                        attention_mask, kv_cache_params,
                                        attention_params, hidden_states,
                                        all_reduce_workspace,
                                        prompt_embedding_table, prompt_tasks,
                                        prompt_vocab_size)

        if use_cache:
            hidden_states, presents = hidden_states

        if self.mapping.is_last_pp_rank():
            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)
        else:
            hidden_states.mark_output('hidden_states_output', self.dtype)

        if use_cache and default_net().plugin_config.paged_kv_cache == False:
            for i, present in zip(
                    self.get_transformer_layers(self.mapping, self.num_layers),
                    presents):
                present.mark_output(f'present_key_value_{i}', self.kv_dtype)
            if self.mapping.is_last_pp_rank():
                return (lm_logits, presents)
            return (hidden_states, presents)
        else:
            if self.mapping.is_last_pp_rank():
                return lm_logits
            return hidden_states

    def prepare_inputs(self,
                       max_batch_size,
                       max_input_len,
                       max_new_tokens,
                       use_cache,
                       max_beam_width,
                       max_num_tokens: int = None,
                       prompt_embedding_table_size: int = 0,
                       gather_all_token_logits: bool = False):
        '''@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
        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

        model_inputs = self.prepare_basic_inputs(
            max_batch_size,
            max_beam_width,
            max_input_len,
            max_new_tokens,
            self.num_kv_heads,
            head_size,
            self.num_layers,
            self.kv_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,
            dtype=self.dtype,
            num_heads=self.num_heads,
            mapping=self.mapping,
            max_num_tokens=max_num_tokens,
            prompt_embedding_table_size=prompt_embedding_table_size,
            gather_all_token_logits=gather_all_token_logits,
        )

        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['hidden_states_input'],
            model_inputs['all_reduce_workspace'],
            model_inputs['prompt_embedding_table'],
            model_inputs['tasks'],
            model_inputs['prompt_vocab_size'],
        )