# 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 (MLP, Attention, AttentionMaskType, ColumnLinear, Embedding, LayerNorm, PositionEmbeddingType) from ...mapping import Mapping from ...module import Module, ModuleList from ...quantization import QuantMode from ..generation_mixin import GenerationMixin class BloomDecoderLayer(Module): def __init__(self, hidden_size, num_attention_heads, max_position_embeddings, num_layers, dtype=None, attention_mask_type=AttentionMaskType.causal, hidden_act='gelu', quant_mode=QuantMode(0), mlp_hidden_size=None, bias=True, multi_query_mode=False, tp_group=None, tp_size=1, tp_rank=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.attention_mask_type = attention_mask_type self.hidden_act = hidden_act self.tp_group = tp_group self.tp_size = tp_size self.tp_rank = tp_rank self.input_layernorm = LayerNorm(normalized_shape=hidden_size, dtype=dtype) self.attention = Attention( hidden_size, num_attention_heads, 1 if multi_query_mode else num_attention_heads, max_position_embeddings, num_layers, dtype=dtype, attention_mask_type=AttentionMaskType.causal, position_embedding_type=PositionEmbeddingType.alibi, bias=bias, tp_group=tp_group, tp_size=tp_size, tp_rank=tp_rank, use_int8_kv_cache=quant_mode.has_int8_kv_cache()) if mlp_hidden_size is None: mlp_hidden_size = hidden_size * 4 self.mlp = MLP(hidden_size=hidden_size, ffn_hidden_size=mlp_hidden_size, hidden_act=hidden_act, dtype=dtype, bias=bias, tp_group=tp_group, tp_size=tp_size) self.post_layernorm = LayerNorm(normalized_shape=hidden_size, dtype=dtype) def forward( self, hidden_states: Tensor, attention_mask=None, past_key_value=None, sequence_length=None, host_past_key_value_lengths=None, use_cache=False, cache_indirection=None, context_lengths=None, host_context_lengths=None, host_request_types=None, max_context_length: int = 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, past_key_value=past_key_value, sequence_length=sequence_length, host_past_key_value_lengths=host_past_key_value_lengths, use_cache=use_cache, cache_indirection=cache_indirection, context_lengths=context_lengths, host_context_lengths=host_context_lengths, host_request_types=host_request_types, max_context_length=max_context_length) 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 BloomModel(Module): def __init__(self, num_layers, num_heads, hidden_size, vocab_size, hidden_act, max_position_embeddings, dtype=None, mapping=Mapping(), mlp_hidden_size=None, bias=True, quant_mode=QuantMode(0), multi_query_mode=False, use_parallel_embedding=False, embedding_sharding_dim=0): super().__init__() if use_parallel_embedding: self.embedding = Embedding(vocab_size, hidden_size, dtype=dtype, tp_group=mapping.tp_group, tp_size=mapping.tp_size, sharding_dim=embedding_sharding_dim, tp_rank=mapping.tp_rank) else: self.embedding = Embedding(vocab_size, hidden_size, dtype=dtype) self.ln_embed = LayerNorm(normalized_shape=hidden_size, dtype=dtype) self.layers = ModuleList([ BloomDecoderLayer(hidden_size=hidden_size, num_attention_heads=num_heads, max_position_embeddings=max_position_embeddings, num_layers=num_layers, dtype=dtype, attention_mask_type=AttentionMaskType.causal, hidden_act=hidden_act, multi_query_mode=multi_query_mode, tp_group=mapping.tp_group, tp_size=mapping.tp_size, tp_rank=mapping.tp_rank, mlp_hidden_size=mlp_hidden_size, bias=bias, quant_mode=quant_mode) for _ in range(num_layers) ]) self.ln_f = LayerNorm(normalized_shape=hidden_size, dtype=dtype) def forward( self, input_ids=None, position_ids=None, past_key_value=None, sequence_length=None, host_past_key_value_lengths=None, use_cache=False, attention_mask=None, cache_indirection=None, context_lengths=None, host_context_lengths=None, host_request_types=None, max_context_length: int = None, ): hidden_states = self.embedding(input_ids) hidden_states = self.ln_embed(hidden_states) if past_key_value is None: past_key_value = tuple([None] * len(self.layers)) if use_cache: presents = [] for layer, past in zip(self.layers, past_key_value): hidden_states = layer( hidden_states, past_key_value=past, sequence_length=sequence_length, host_past_key_value_lengths=host_past_key_value_lengths, use_cache=use_cache, attention_mask=attention_mask, cache_indirection=cache_indirection, context_lengths=context_lengths, host_context_lengths=host_context_lengths, host_request_types=host_request_types, max_context_length=max_context_length) 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 BloomForCausalLM(BloomModel, GenerationMixin): def __init__(self, num_layers, num_heads, hidden_size, vocab_size, max_position_embeddings, hidden_act='gelu', dtype=None, mapping=Mapping(), mlp_hidden_size=None, bias=True, quant_mode=QuantMode(0), multi_query_mode=False, use_parallel_embedding=False, embedding_sharding_dim=0): if isinstance(dtype, str): self._kv_dtype = str_dtype_to_trt(dtype) else: assert isinstance(dtype, trt.DataType) self._kv_dtype = dtype self._dtype = self._kv_dtype if quant_mode.has_int8_kv_cache(): self._kv_dtype = str_dtype_to_trt('int8') self.quant_mode = quant_mode 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._multi_query_mode = multi_query_mode super().__init__(num_layers, num_heads, hidden_size, vocab_size, hidden_act, max_position_embeddings, dtype, mapping, mlp_hidden_size, bias, quant_mode, multi_query_mode, use_parallel_embedding, embedding_sharding_dim) 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=None, position_ids=None, past_key_value=None, sequence_length=None, host_past_key_value_lengths=None, use_cache=False, last_token_ids=None, attention_mask=None, cache_indirection=None, context_lengths=None, host_context_lengths=None, host_request_types=None, max_context_length=None): hidden_states = super().forward(input_ids, position_ids, past_key_value, sequence_length, host_past_key_value_lengths, use_cache, attention_mask, cache_indirection, context_lengths, host_context_lengths, host_request_types, max_context_length) 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._dtype) if use_cache: 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): '''@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 = self._num_heads // self._tp_size remove_input_padding = default_net().plugin_config.remove_input_padding use_gpt_attention_plugin = default_net( ).plugin_config.gpt_attention_plugin model_inputs = self.prepare_basic_inputs( max_batch_size, max_beam_width, max_input_len, max_new_tokens, num_heads, head_size, self._num_layers, self._kv_dtype, remove_input_padding, use_gpt_attention_plugin) return (model_inputs['input_ids'], model_inputs['position_ids'], model_inputs['past_key_value'], model_inputs['sequence_length'], model_inputs['host_past_key_value_lengths'], True, model_inputs['last_token_ids'], model_inputs['attention_mask'], model_inputs['cache_indirection'], model_inputs['context_lengths'], model_inputs['host_context_lengths'], model_inputs['host_request_types'], max_input_len)