# 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, 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=False, 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=False, 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, 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, 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, ): if kv_cache_params.past_key_value is None: tuple([None] * 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 in zip( self.layers, kv_cache_params.past_key_value, kv_cache_params.kv_cache_block_pointers): 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, 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, 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, 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, ): '''@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, ) 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'], 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'], )