# 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. 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 from ...layers import (Attention, AttentionMaskType, AttentionParams, ColumnLinear, Embedding, GatedMLP, KeyValueCacheParams, RmsNorm) from ...mapping import Mapping from ...module import Module, ModuleList from ..generation_mixin import GenerationMixin class BaichuanDecoderLayer(Module): def __init__(self, hidden_size, num_attention_heads, max_position_embeddings, position_embedding_type, dtype=None, hidden_act='silu', mlp_hidden_size=None, tp_group=None, tp_size=1, tp_rank=0): super().__init__() self.input_layernorm = RmsNorm(normalized_shape=hidden_size, dtype=dtype) assert position_embedding_type is not None self.attention = Attention( hidden_size, num_attention_heads, max_position_embeddings=max_position_embeddings, dtype=dtype, attention_mask_type=AttentionMaskType.causal, bias=False, position_embedding_type=position_embedding_type, tp_group=tp_group, tp_size=tp_size, tp_rank=tp_rank) if not mlp_hidden_size: mlp_hidden_size = hidden_size * 4 self.mlp = GatedMLP(hidden_size=hidden_size, ffn_hidden_size=mlp_hidden_size, hidden_act=hidden_act, dtype=dtype, bias=False, tp_group=tp_group, tp_size=tp_size) self.post_layernorm = RmsNorm(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): 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) 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) hidden_states = residual + hidden_states if use_cache: return (hidden_states, presents) return hidden_states class BaichuanModel(Module): def __init__(self, num_layers, num_heads, hidden_size, vocab_size, hidden_act, max_position_embeddings, position_embedding_type, dtype, mlp_hidden_size=None, mapping=Mapping()): super().__init__() self.num_layers = num_layers self.vocab_embedding = Embedding(vocab_size, hidden_size, dtype=dtype) self.layers = ModuleList([ BaichuanDecoderLayer( hidden_size=hidden_size, num_attention_heads=num_heads, max_position_embeddings=max_position_embeddings, position_embedding_type=position_embedding_type, dtype=dtype, hidden_act=hidden_act, mlp_hidden_size=mlp_hidden_size, tp_group=mapping.tp_group, tp_size=mapping.tp_size, tp_rank=mapping.tp_rank) for _ in range(num_layers) ]) self.ln_f = RmsNorm(normalized_shape=hidden_size, dtype=dtype) def forward(self, input_ids: Tensor, position_ids=None, use_cache=False, attention_mask=None, kv_cache_params=None, attention_params=None): hidden_states = self.vocab_embedding(input_ids) if kv_cache_params.past_key_value is None: kv_cache_params.past_key_value = tuple([None] * len(self.layers)) if use_cache: presents = [] 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) 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 BaichuanForCausalLM(BaichuanModel, GenerationMixin): def __init__(self, num_layers, num_heads, hidden_size, vocab_size, hidden_act, max_position_embeddings, position_embedding_type, dtype, mlp_hidden_size=None, mapping=Mapping()): if isinstance(dtype, str): self._kv_dtype = str_dtype_to_trt(dtype) else: assert isinstance(dtype, trt.DataType) self._kv_dtype = dtype self._num_layers = num_layers self.num_heads = num_heads self.num_kv_heads = num_heads self.hidden_size = hidden_size self.vocab_size = vocab_size self.tp_size = mapping.tp_size super().__init__(num_layers, num_heads, hidden_size, vocab_size, hidden_act, max_position_embeddings, position_embedding_type, dtype, mlp_hidden_size, mapping) vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size) 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: Tensor, position_ids=None, use_cache=False, last_token_ids=None, attention_mask=None, kv_cache_params=None, attention_params=None): hidden_states = super().forward(input_ids, position_ids, use_cache, attention_mask, kv_cache_params, attention_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._kv_dtype) if use_cache and 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, max_num_tokens: int = None): '''@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 + self.tp_size - 1) // self.tp_size 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 model_inputs = self.prepare_basic_inputs( max_batch_size, max_beam_width, max_input_len, max_new_tokens, num_heads_kv, 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, paged_kv_cache=paged_kv_cache, tokens_per_block=tokens_per_block, max_num_tokens=max_num_tokens) 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']))