mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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501 lines
21 KiB
Python
501 lines
21 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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, recv, send
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from ...layers import (Attention, AttentionMaskType, AttentionParams,
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ColumnLinear, Embedding, FusedGatedMLP, GatedMLP,
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KeyValueCacheParams, LoraParams, PositionEmbeddingType,
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PromptTuningEmbedding, RmsNorm)
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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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class LLaMADecoderLayer(Module):
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def __init__(self,
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layer_id,
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hidden_size,
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num_attention_heads,
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num_kv_heads=None,
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max_position_embeddings=2048,
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dtype=None,
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attention_mask_type=AttentionMaskType.causal,
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hidden_act='silu',
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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rotary_base=10000.0,
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rotary_scaling=None,
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mlp_hidden_size=None,
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tp_group=None,
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tp_size=1,
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quant_mode=QuantMode(0),
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rms_norm_eps=1e-06,
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attn_bias=False,
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mlp_bias=False,
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use_fused_mlp=False):
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super().__init__()
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self._layer_id = layer_id # useful for debugging
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# used for quantizing model
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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.num_kv_heads = num_kv_heads
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self.max_position_embeddings = max_position_embeddings
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self.dtype = dtype
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self.hidden_act = hidden_act
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self.tp_group = tp_group
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self.tp_size = tp_size
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self.mlp_hidden_size = mlp_hidden_size
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self.attention_mask_type = attention_mask_type
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self.position_embedding_type = position_embedding_type
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self.input_layernorm = RmsNorm(normalized_shape=hidden_size,
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eps=rms_norm_eps,
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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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dtype=dtype,
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attention_mask_type=AttentionMaskType.causal,
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bias=attn_bias,
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position_embedding_type=position_embedding_type,
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rotary_embedding_base=rotary_base,
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rotary_embedding_scaling=rotary_scaling,
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tp_group=tp_group,
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tp_size=tp_size,
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use_int8_kv_cache=quant_mode.has_int8_kv_cache(),
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quant_mode=quant_mode,
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instance_id=2 * layer_id,
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)
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if not mlp_hidden_size:
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self.mlp_hidden_size = hidden_size * 4
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ClsMLP = FusedGatedMLP if use_fused_mlp is True else GatedMLP
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self.mlp = ClsMLP(hidden_size=hidden_size,
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ffn_hidden_size=self.mlp_hidden_size,
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hidden_act=hidden_act,
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dtype=dtype,
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bias=mlp_bias,
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tp_group=tp_group,
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tp_size=tp_size,
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quant_mode=quant_mode,
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instance_id=2 * layer_id + 1)
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self.post_layernorm = RmsNorm(normalized_shape=hidden_size,
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eps=rms_norm_eps,
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dtype=dtype)
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def forward(self,
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hidden_states,
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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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all_reduce_workspace=None,
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lora_param=None):
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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if self._layer_id == 0:
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self.register_network_output(f"norm0", 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=all_reduce_workspace,
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lora_param=lora_param)
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if use_cache:
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attention_output, presents = attention_output
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if self._layer_id == 0:
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self.register_network_output(f"attn", 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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if self._layer_id == 0:
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self.register_network_output(f"norm1", hidden_states)
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hidden_states = self.mlp(hidden_states,
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all_reduce_workspace,
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lora_param=lora_param)
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if self._layer_id == 0:
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self.register_network_output(f"mlp", hidden_states)
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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 LLaMAModel(Module):
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def __init__(self,
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num_layers,
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num_heads,
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num_kv_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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mlp_hidden_size=None,
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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rotary_base=10000.0,
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rotary_scaling=None,
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mapping=Mapping(),
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quant_mode=QuantMode(0),
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use_parallel_embedding=False,
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embedding_sharding_dim=0,
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rms_norm_eps=1e-06,
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use_fused_mlp=False,
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attn_bias=False,
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mlp_bias=False,
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use_prompt_tuning: bool = False):
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super().__init__()
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self.mapping = mapping
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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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if self.mapping.is_first_pp_rank():
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self.vocab_embedding = EmbeddingCls(
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num_embeddings=vocab_size,
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embedding_dim=hidden_size,
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dtype=dtype,
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tp_size=mapping.tp_size if use_parallel_embedding else 1,
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tp_group=mapping.tp_group 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 *
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num_layers, # ids in [0, 2 * (num_layers - 1) + 1] already used
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)
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self.layers = ModuleList([
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LLaMADecoderLayer(layer_id=i,
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hidden_size=hidden_size,
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num_attention_heads=num_heads,
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num_kv_heads=num_kv_heads,
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max_position_embeddings=max_position_embeddings,
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dtype=dtype,
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hidden_act=hidden_act,
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mlp_hidden_size=mlp_hidden_size,
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position_embedding_type=position_embedding_type,
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rotary_base=rotary_base,
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rotary_scaling=rotary_scaling,
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tp_group=mapping.tp_group,
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tp_size=mapping.tp_size,
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quant_mode=quant_mode,
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rms_norm_eps=rms_norm_eps,
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attn_bias=attn_bias,
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mlp_bias=mlp_bias,
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use_fused_mlp=use_fused_mlp)
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for i in self.get_transformer_layers(self.mapping, num_layers)
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])
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if self.mapping.is_last_pp_rank():
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self.ln_f = RmsNorm(normalized_shape=hidden_size,
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eps=rms_norm_eps,
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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=None,
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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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hidden_states=None,
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all_reduce_workspace=None,
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prompt_embedding_table: Optional[Tensor] = None,
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prompt_tasks: Optional[Tensor] = None,
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prompt_vocab_size: Optional[Tensor] = None,
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lora_params=None):
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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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ptuning_args = []
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if self.use_prompt_tuning:
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ptuning_args = [
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prompt_embedding_table, prompt_tasks, prompt_vocab_size
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]
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if self.mapping.is_first_pp_rank():
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hidden_states = self.vocab_embedding(input_ids, *ptuning_args,
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all_reduce_workspace)
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else:
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hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
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self.register_network_output(f"embd", hidden_states)
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for layer_idx, (layer, past, pointer, host_pointer,
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max_kv_cache_length) 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_kv_cache_lengths)):
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lora_param = None
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if lora_params.lora_ranks is not None:
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lora_param = 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_kv_cache_lengths=max_kv_cache_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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all_reduce_workspace=all_reduce_workspace,
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lora_param=lora_param)
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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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if self.mapping.is_last_pp_rank():
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hidden_states = self.ln_f(hidden_states)
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else:
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hidden_states = send(hidden_states, self.mapping.next_pp_rank())
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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 LLaMAForCausalLM(LLaMAModel, GenerationMixin):
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def __init__(self,
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num_layers,
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num_heads,
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num_kv_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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mlp_hidden_size=None,
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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rotary_base=10000.0,
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rotary_scaling=None,
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mapping=Mapping(),
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quant_mode=QuantMode(0),
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use_parallel_embedding=False,
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embedding_sharding_dim=0,
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rms_norm_eps=1e-06,
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use_fused_mlp=False,
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attn_bias=False,
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mlp_bias=False,
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use_prompt_tuning: bool = False):
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if isinstance(dtype, str):
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self.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.dtype = dtype
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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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if num_kv_heads is None or num_kv_heads <= 0:
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num_kv_heads = num_heads
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self.num_kv_heads = num_kv_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.kv_dtype = self.dtype
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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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self.quant_mode = quant_mode
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self.use_parallel_embedding = use_parallel_embedding
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self.embedding_sharding_dim = embedding_sharding_dim
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super().__init__(num_layers, num_heads, num_kv_heads, hidden_size,
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vocab_size, hidden_act, max_position_embeddings, dtype,
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mlp_hidden_size, position_embedding_type, rotary_base,
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rotary_scaling, mapping, quant_mode,
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use_parallel_embedding, embedding_sharding_dim,
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rms_norm_eps, use_fused_mlp, attn_bias, mlp_bias,
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use_prompt_tuning)
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vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
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if self.mapping.is_last_pp_rank():
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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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def forward(self,
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input_ids,
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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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hidden_states=None,
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all_reduce_workspace=None,
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prompt_embedding_table: Optional[Tensor] = None,
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prompt_tasks: Optional[Tensor] = None,
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prompt_vocab_size: Optional[Tensor] = 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, hidden_states,
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all_reduce_workspace,
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prompt_embedding_table, prompt_tasks,
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prompt_vocab_size, lora_params)
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if use_cache:
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hidden_states, presents = hidden_states
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if self.mapping.is_last_pp_rank():
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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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else:
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hidden_states.mark_output('hidden_states_output', self.dtype)
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if use_cache and default_net().plugin_config.paged_kv_cache == False:
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for i, present in zip(
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self.get_transformer_layers(self.mapping, self.num_layers),
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presents):
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present.mark_output(f'present_key_value_{i}', self.kv_dtype)
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if self.mapping.is_last_pp_rank():
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return (lm_logits, presents)
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return (hidden_states, presents)
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else:
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if self.mapping.is_last_pp_rank():
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return lm_logits
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return hidden_states
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def prepare_inputs(self,
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max_batch_size,
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max_input_len,
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max_new_tokens,
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use_cache,
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max_beam_width,
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max_num_tokens: int = None,
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prompt_embedding_table_size: int = 0,
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gather_all_token_logits: bool = False,
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lora_target_modules: List[str] = None):
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'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
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ranges of the dimensions of when using TRT dynamic shapes.
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@return: a list contains values which can be fed into the self.forward()
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'''
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# Prepare inputs
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head_size = self.hidden_size // self.num_heads
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remove_input_padding = default_net().plugin_config.remove_input_padding
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use_gpt_attention_plugin = default_net(
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).plugin_config.gpt_attention_plugin
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use_gemm_plugin = default_net().plugin_config.gemm_plugin
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paged_kv_cache = default_net().plugin_config.paged_kv_cache
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tokens_per_block = default_net().plugin_config.tokens_per_block
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use_custom_all_reduce = default_net(
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).plugin_config.use_custom_all_reduce
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use_lora_plugin = default_net().plugin_config.lora_plugin
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model_inputs = self.prepare_basic_inputs(
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max_batch_size,
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max_beam_width,
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max_input_len,
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max_new_tokens,
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self.num_kv_heads,
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head_size,
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self.num_layers,
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self.kv_dtype,
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remove_input_padding=remove_input_padding,
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use_gpt_attention_plugin=use_gpt_attention_plugin,
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use_gemm_plugin=use_gemm_plugin,
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use_custom_all_reduce=use_custom_all_reduce,
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paged_kv_cache=paged_kv_cache,
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tokens_per_block=tokens_per_block,
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dtype=self.dtype,
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num_heads=self.num_heads,
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mapping=self.mapping,
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max_num_tokens=max_num_tokens,
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prompt_embedding_table_size=prompt_embedding_table_size,
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gather_all_token_logits=gather_all_token_logits,
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use_lora_plugin=use_lora_plugin,
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lora_target_modules=lora_target_modules)
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return (
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model_inputs['input_ids'],
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model_inputs['position_ids'],
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True,
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model_inputs['last_token_ids'],
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model_inputs['attention_mask'],
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KeyValueCacheParams(
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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'],
|
|
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['hidden_states_input'],
|
|
model_inputs['all_reduce_workspace'],
|
|
model_inputs['prompt_embedding_table'],
|
|
model_inputs['tasks'],
|
|
model_inputs['prompt_vocab_size'],
|
|
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']),
|
|
)
|