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https://github.com/NVIDIA/TensorRT-LLM.git
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deaae40bd7
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
599 lines
24 KiB
Python
599 lines
24 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List, Optional
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import tensorrt as trt
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from ..._common import default_net
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from ..._utils import pad_vocab_size, str_dtype_to_trt
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from ...functional import (Tensor, gather_last_token_logits,
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is_gated_activation, non_gated_version)
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from ...layers import (MLP, MOE, Attention, AttentionMaskType, AttentionParams,
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ColumnLinear, Embedding, GatedMLP, KeyValueCacheParams,
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LayerNorm, LoraParams, MoeConfig, PositionEmbeddingType,
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PromptTuningEmbedding)
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from ...mapping import Mapping
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from ...module import Module, ModuleList
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from ...quantization import QuantMode
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from ..generation_mixin import GenerationMixin
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def MLPFactory(hidden_size,
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ffn_hidden_size,
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hidden_act,
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bias=True,
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dtype=None,
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moe_config: MoeConfig = MoeConfig(),
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tp_group=None,
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tp_size=1,
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tp_rank=0,
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quant_mode=QuantMode(0),
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instance_id: int = 0):
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if moe_config.has_moe():
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return MOE(moe_config,
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hidden_size,
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ffn_hidden_size,
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hidden_act,
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bias,
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dtype,
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tp_group,
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tp_size,
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tp_rank,
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quant_mode=quant_mode)
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MLPClass = GatedMLP if is_gated_activation(hidden_act) else MLP
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hidden_act = non_gated_version(hidden_act)
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return MLPClass(hidden_size, ffn_hidden_size, hidden_act, bias, dtype,
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tp_group, tp_size, quant_mode, instance_id)
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class GPTEmbedding(Module):
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def __init__(self,
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vocab_size,
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hidden_size,
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max_position_embeddings,
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position_embedding_type=PositionEmbeddingType.learned_absolute,
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dtype=None,
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use_prompt_tuning=False,
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tensor_parallel=1,
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tensor_parallel_group=None,
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sharding_dim=0,
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tp_rank=None,
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instance_id: int = 0):
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super().__init__()
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self.max_position_embeddings = max_position_embeddings
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self.position_embedding_type = position_embedding_type
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self.use_prompt_tuning = use_prompt_tuning
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EmbeddingCls = PromptTuningEmbedding if use_prompt_tuning else Embedding
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self.vocab_embedding = EmbeddingCls(vocab_size,
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hidden_size,
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dtype=dtype,
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tp_size=tensor_parallel,
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tp_group=tensor_parallel_group,
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sharding_dim=sharding_dim,
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tp_rank=tp_rank,
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instance_id=instance_id)
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if self.position_embedding_type == PositionEmbeddingType.learned_absolute:
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self.position_embedding = Embedding(max_position_embeddings,
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hidden_size,
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dtype=dtype)
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def forward(self,
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input_ids,
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position_ids,
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prompt_embedding_table=None,
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prompt_tasks=None,
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prompt_vocab_size=None,
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workspace: Optional[Tensor] = None):
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args = []
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if self.use_prompt_tuning:
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args = [prompt_embedding_table, prompt_tasks, prompt_vocab_size]
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x = self.vocab_embedding(input_ids, *args, workspace=workspace)
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if self.position_embedding_type == PositionEmbeddingType.learned_absolute:
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x = x + self.position_embedding(position_ids)
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return x
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class GPTDecoderLayer(Module):
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def __init__(self,
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hidden_size,
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num_attention_heads,
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max_position_embeddings,
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num_layers,
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dtype=None,
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apply_query_key_layer_scaling=False,
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attention_mask_type=AttentionMaskType.causal,
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hidden_act='relu',
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position_embedding_type=PositionEmbeddingType.learned_absolute,
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quant_mode=QuantMode(0),
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rotary_embedding_percentage=1.0,
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rotary_base=10000.0,
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rotary_scaling=None,
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inter_size=None,
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bias=True,
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num_kv_heads=None,
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moe_config: MoeConfig = MoeConfig(),
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use_auto_parallel=False,
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tp_group=None,
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tp_size=1,
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tp_rank=0,
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instance_id: int = 0):
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super().__init__()
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.num_layers = num_layers
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self.dtype = dtype
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self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
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self.attention_mask_type = attention_mask_type
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self.hidden_act = hidden_act
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self.position_embedding_type = position_embedding_type
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self.tp_group = tp_group
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self.tp_size = tp_size
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self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
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dtype=dtype)
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self.attention = Attention(
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hidden_size,
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num_attention_heads,
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num_kv_heads,
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max_position_embeddings,
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num_layers,
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apply_query_key_layer_scaling,
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dtype=dtype,
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attention_mask_type=attention_mask_type,
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position_embedding_type=position_embedding_type,
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rotary_embedding_percentage=rotary_embedding_percentage,
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rotary_embedding_base=rotary_base,
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rotary_embedding_scaling=rotary_scaling,
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bias=bias,
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tp_group=tp_group,
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tp_size=tp_size,
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use_auto_parallel=use_auto_parallel,
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tp_rank=tp_rank,
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quant_mode=quant_mode,
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instance_id=2 * instance_id)
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if inter_size is None:
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inter_size = hidden_size * 4
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self.mlp = MLPFactory(hidden_size=hidden_size,
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ffn_hidden_size=inter_size,
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hidden_act=hidden_act,
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dtype=dtype,
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bias=bias,
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moe_config=moe_config,
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tp_group=tp_group,
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tp_size=tp_size,
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tp_rank=tp_rank,
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quant_mode=quant_mode,
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instance_id=2 * instance_id + 1)
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self.post_layernorm = LayerNorm(normalized_shape=hidden_size,
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dtype=dtype)
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def forward(self,
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hidden_states: Tensor,
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attention_mask=None,
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use_cache=False,
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kv_cache_params=None,
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attention_params=None,
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workspace=None,
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lora_layer_params=None):
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assert isinstance(hidden_states, Tensor)
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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attention_output = self.attention(hidden_states,
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attention_mask=attention_mask,
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use_cache=use_cache,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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workspace=workspace,
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lora_layer_params=lora_layer_params)
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if use_cache:
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attention_output, presents = attention_output
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hidden_states = residual + attention_output
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residual = hidden_states
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hidden_states = self.post_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states, workspace=workspace)
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hidden_states = residual + hidden_states
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if use_cache:
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return (hidden_states, presents)
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return hidden_states
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class GPTModel(Module):
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def __init__(self,
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num_layers,
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num_heads,
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hidden_size,
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vocab_size,
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hidden_act,
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max_position_embeddings,
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dtype=None,
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mapping=Mapping(),
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use_auto_parallel=False,
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apply_query_key_layer_scaling=False,
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position_embedding_type=PositionEmbeddingType.learned_absolute,
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rotary_embedding_percentage=1.0,
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rotary_base=10000.0,
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rotary_scaling=None,
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inter_size=None,
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bias=True,
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quant_mode=QuantMode(0),
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num_kv_heads=None,
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use_prompt_tuning=False,
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use_parallel_embedding=False,
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embedding_sharding_dim=0,
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moe_config=MoeConfig()):
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super().__init__()
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self.mapping = mapping
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self.embedding = GPTEmbedding(
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vocab_size,
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hidden_size,
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max_position_embeddings,
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position_embedding_type,
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dtype=dtype,
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use_prompt_tuning=use_prompt_tuning,
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tensor_parallel=mapping.tp_size if use_parallel_embedding else 1,
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tensor_parallel_group=mapping.tp_group
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if use_parallel_embedding else None,
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sharding_dim=embedding_sharding_dim,
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tp_rank=mapping.tp_rank,
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instance_id=2 * num_layers,
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)
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self.layers = ModuleList([
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GPTDecoderLayer(
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hidden_size=hidden_size,
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num_attention_heads=num_heads,
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max_position_embeddings=max_position_embeddings,
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num_layers=num_layers,
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dtype=dtype,
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apply_query_key_layer_scaling=apply_query_key_layer_scaling,
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attention_mask_type=AttentionMaskType.causal,
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hidden_act=hidden_act,
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position_embedding_type=position_embedding_type,
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rotary_embedding_percentage=rotary_embedding_percentage,
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rotary_base=rotary_base,
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rotary_scaling=rotary_scaling,
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num_kv_heads=num_kv_heads,
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tp_group=mapping.tp_group,
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tp_size=mapping.tp_size,
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tp_rank=mapping.tp_rank,
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use_auto_parallel=use_auto_parallel,
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inter_size=inter_size,
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bias=bias,
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quant_mode=quant_mode,
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instance_id=i,
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moe_config=moe_config,
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) for i in range(num_layers)
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])
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self.ln_f = LayerNorm(normalized_shape=hidden_size, dtype=dtype)
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def forward(self,
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input_ids,
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position_ids,
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use_cache=False,
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attention_mask=None,
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kv_cache_params=None,
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attention_params=None,
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prompt_embedding_table=None,
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prompt_tasks=None,
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prompt_vocab_size=None,
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workspace=None,
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lora_params=None):
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hidden_states = self.embedding(input_ids,
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position_ids,
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prompt_embedding_table,
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prompt_tasks,
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prompt_vocab_size,
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workspace=workspace)
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kv_cache_params.fill_none_tensor_list(len(self.layers))
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if use_cache:
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presents = []
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for layer_idx, (
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layer, past, pointer, host_pointer,
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max_attention_window_size) in enumerate(
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zip(self.layers, kv_cache_params.past_key_value,
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kv_cache_params.kv_cache_block_pointers,
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kv_cache_params.host_kv_cache_block_pointers,
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kv_cache_params.host_max_attention_window_sizes)):
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lora_layer_params = None
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if lora_params.lora_ranks is not None:
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lora_layer_params = lora_params.get_layer_params(layer_idx)
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hidden_states = layer(
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hidden_states,
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use_cache=use_cache,
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attention_mask=attention_mask,
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kv_cache_params=KeyValueCacheParams(
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past_key_value=[past],
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host_past_key_value_lengths=kv_cache_params.
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host_past_key_value_lengths,
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host_max_attention_window_sizes=max_attention_window_size,
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host_sink_token_length=kv_cache_params.
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host_sink_token_length,
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kv_cache_block_pointers=[pointer],
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host_kv_cache_block_pointers=[host_pointer],
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cache_indirection=kv_cache_params.cache_indirection),
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attention_params=attention_params,
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workspace=workspace,
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lora_layer_params=lora_layer_params)
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if use_cache:
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presents.append(hidden_states[1])
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hidden_states = hidden_states[0]
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hidden_states = self.ln_f(hidden_states)
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if use_cache:
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return (hidden_states, tuple(presents))
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return hidden_states
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class GPTLMHeadModel(GPTModel, GenerationMixin):
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def __init__(self,
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num_layers,
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num_heads,
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hidden_size,
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vocab_size,
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hidden_act,
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max_position_embeddings,
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dtype,
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logits_dtype='float32',
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mapping=Mapping(),
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use_auto_parallel=False,
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apply_query_key_layer_scaling=False,
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position_embedding_type=PositionEmbeddingType.learned_absolute,
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rotary_embedding_percentage=1.0,
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rotary_base=10000.0,
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rotary_scaling=None,
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inter_size=None,
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bias=True,
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quant_mode=QuantMode(0),
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num_kv_heads=None,
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use_prompt_tuning=False,
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use_parallel_embedding=False,
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embedding_sharding_dim=0,
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moe_config=MoeConfig(),
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share_embedding_table=False):
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if isinstance(dtype, str):
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self._kv_dtype = str_dtype_to_trt(dtype)
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else:
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assert isinstance(dtype, trt.DataType)
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self._kv_dtype = dtype
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if share_embedding_table and mapping.tp_size > 1:
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if (not use_parallel_embedding) or (use_parallel_embedding and
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embedding_sharding_dim == 1):
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raise NotImplementedError(
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'For multiple-processes cases, sharing the embedding table must set use_parallel_embedding=True and embedding_sharding_dim = 0'
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)
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self._dtype = self._kv_dtype
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self.quant_mode = quant_mode
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if quant_mode.has_int8_kv_cache():
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self._kv_dtype = str_dtype_to_trt('int8')
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elif quant_mode.has_fp8_kv_cache():
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self._kv_dtype = str_dtype_to_trt('fp8')
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if isinstance(logits_dtype, str):
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self._logits_dtype = str_dtype_to_trt(logits_dtype)
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else:
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assert isinstance(logits_dtype, trt.DataType)
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self._logits_dtype = logits_dtype
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self._num_layers = num_layers
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self._num_heads = num_heads
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self._hidden_size = hidden_size
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self._vocab_size = vocab_size
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self._tp_size = mapping.tp_size
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self._num_kv_heads = num_kv_heads if num_kv_heads else num_heads
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super().__init__(
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num_layers=num_layers,
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num_heads=num_heads,
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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hidden_act=hidden_act,
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max_position_embeddings=max_position_embeddings,
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dtype=dtype,
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mapping=mapping,
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use_auto_parallel=use_auto_parallel,
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apply_query_key_layer_scaling=apply_query_key_layer_scaling,
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position_embedding_type=position_embedding_type,
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rotary_embedding_percentage=rotary_embedding_percentage,
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rotary_base=rotary_base,
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rotary_scaling=rotary_scaling,
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inter_size=inter_size,
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bias=bias,
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quant_mode=quant_mode,
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num_kv_heads=num_kv_heads,
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use_prompt_tuning=use_prompt_tuning,
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use_parallel_embedding=use_parallel_embedding,
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embedding_sharding_dim=embedding_sharding_dim,
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moe_config=moe_config,
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)
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vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
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share_weight = None
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if share_embedding_table:
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share_weight = self.embedding.vocab_embedding.weight
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self.lm_head = ColumnLinear(hidden_size,
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vocab_size_padded,
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bias=False,
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dtype=dtype,
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tp_group=mapping.tp_group,
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tp_size=mapping.tp_size,
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gather_output=True,
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share_weight=share_weight)
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def forward(self,
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input_ids: Tensor,
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position_ids=None,
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use_cache=False,
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last_token_ids=None,
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attention_mask=None,
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kv_cache_params=None,
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attention_params=None,
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prompt_embedding_table=None,
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prompt_tasks=None,
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prompt_vocab_size=None,
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workspace=None,
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lora_params=None):
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hidden_states = super().forward(input_ids, position_ids, use_cache,
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attention_mask, kv_cache_params,
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attention_params,
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prompt_embedding_table, prompt_tasks,
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prompt_vocab_size, workspace,
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lora_params)
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if use_cache:
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hidden_states, presents = hidden_states
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hidden_states = gather_last_token_logits(
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hidden_states, last_token_ids,
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default_net().plugin_config.remove_input_padding)
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# [batch_size, hidden_size] -> [batch_size, vocab_size]
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lm_logits = self.lm_head(hidden_states)
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lm_logits.mark_output('logits', self._logits_dtype)
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|
|
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,
|
|
lora_target_modules: List[str] = 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
|
|
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,
|
|
lora_target_modules=lora_target_modules)
|
|
|
|
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_attention_window_sizes=model_inputs[
|
|
'host_max_attention_window_sizes'],
|
|
host_sink_token_length=model_inputs['host_sink_token_length'],
|
|
kv_cache_block_pointers=model_inputs[
|
|
'kv_cache_block_pointers_list'],
|
|
host_kv_cache_block_pointers=model_inputs[
|
|
'host_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'],
|
|
host_context_lengths=model_inputs['host_context_lengths'],
|
|
max_context_length=max_input_len,
|
|
host_request_types=model_inputs['host_request_types']),
|
|
)
|