mirror of
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>
464 lines
23 KiB
Python
464 lines
23 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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import time
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from os import path
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from pathlib import Path
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from typing import Optional, Union
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import numpy as np
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import torch
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from tensorrt_llm import logger
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from tensorrt_llm._utils import (numpy_to_dtype, str_dtype_to_np,
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str_dtype_to_torch, torch_to_numpy)
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from tensorrt_llm.functional import (LayerNormPositionType, LayerNormType,
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MLPType)
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from tensorrt_llm.mapping import Mapping
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from tensorrt_llm.models import ( # TODO: probably need to change model name to distinguish from other models
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DecoderModel, EncoderModel)
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layernorm_type_map = {i.name: i.value for i in LayerNormType}
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layernorm_position_map = {i.name: i.value for i in LayerNormPositionType}
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mlp_type_map = {i.name: i.value for i in MLPType}
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def parse_t5_config(config, component, args):
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if component == 'encoder':
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args.n_layer = config.getint(component, 'num_layers')
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args.n_head = config.getint(component, 'num_heads')
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args.head_size = config.getint(component, 'd_kv')
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args.hidden_size = config.getint(component, 'd_model')
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args.ffn_hidden_size = config.getint(component, 'd_ff')
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args.vocab_size = config.getint(component, 'vocab_size')
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args.n_positions = config.getint(component, 'n_positions', fallback=512)
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args.has_position_embedding = config.getboolean(
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component, 'has_position_embedding',
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fallback=False) # TODO: hardcoded here
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args.has_token_type_embedding = config.getboolean(
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component, 'has_token_type_embedding', fallback=False)
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args.has_embedding_layernorm = config.getboolean(
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component, 'has_embedding_layernorm', fallback=False)
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args.has_embedding_scale = config.getboolean(component,
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'has_embedding_scale',
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fallback=False)
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args.q_scaling = config.getfloat(component, 'q_scaling', fallback=1.0)
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args.has_attention_qkvo_bias = config.getboolean(
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component, 'has_attention_qkvo_bias',
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fallback=False) # TODO: hardcoded here
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args.has_mlp_bias = config.getboolean(component,
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'has_mlp_bias',
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fallback=False)
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args.has_model_final_layernorm = config.getboolean(
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component, 'has_model_final_layernorm', fallback=True)
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args.layernorm_eps = config.getfloat(component, 'layer_norm_epsilon')
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args.layernorm_position = layernorm_position_map[config.get(
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component, 'layernorm_position',
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fallback='pre_layernorm')] # TODO: hardcoded here
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args.layernorm_type = layernorm_type_map[config.get(
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component, 'layernorm_type',
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fallback='RmsNorm')] # TODO: hardcoded here
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args.hidden_act = config.get(component, 'dense_act_fn')
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args.gated_act = config.getboolean(component, 'is_gated_act')
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args.mlp_type = mlp_type_map['GatedMLP' if args.gated_act else 'MLP']
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args.relative_attention = config.getboolean(component,
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'relative_attention',
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fallback=True)
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args.num_buckets = config.getint(component,
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'relative_attention_num_buckets')
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args.max_distance = config.getint(component,
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'relative_attention_max_distance')
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args.ckpt_weight_dtype = config.get(component, 'weight_data_type')
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elif component == 'decoder':
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args.n_layer = config.getint(component, 'num_decoder_layers')
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args.n_head = config.getint(component, 'num_heads')
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args.head_size = config.getint(component, 'd_kv')
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args.hidden_size = config.getint(component, 'd_model')
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args.ffn_hidden_size = config.getint(component, 'd_ff')
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args.vocab_size = config.getint(component, 'vocab_size')
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args.n_positions = config.getint(component, 'n_positions', fallback=512)
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args.has_position_embedding = config.getboolean(
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component, 'has_position_embedding',
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fallback=False) # TODO: hardcoded here
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args.has_token_type_embedding = config.getboolean(
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component, 'has_token_type_embedding', fallback=False)
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args.has_embedding_layernorm = config.getboolean(
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component, 'has_embedding_layernorm', fallback=False)
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args.has_embedding_scale = config.getboolean(component,
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'has_embedding_scale',
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fallback=False)
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args.q_scaling = config.getfloat(component, 'q_scaling', fallback=1.0)
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args.has_attention_qkvo_bias = config.getboolean(
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component, 'has_attention_qkvo_bias', fallback=False)
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args.has_mlp_bias = config.getboolean(component,
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'has_mlp_bias',
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fallback=False)
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args.has_model_final_layernorm = config.getboolean(
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component, 'has_model_final_layernorm', fallback=True)
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args.layernorm_eps = config.getfloat(component, 'layer_norm_epsilon')
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args.layernorm_position = layernorm_position_map[config.get(
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component, 'layernorm_position',
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fallback='pre_layernorm')] # TODO: hardcoded here
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args.layernorm_type = layernorm_type_map[config.get(component,
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'layernorm_type',
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fallback='RmsNorm')]
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args.hidden_act = config.get(component, 'dense_act_fn')
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args.gated_act = config.getboolean(component, 'is_gated_act')
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args.mlp_type = mlp_type_map['GatedMLP' if args.gated_act else 'MLP']
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args.has_lm_head_bias = config.getboolean(
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component, # TODO: T5 with bias
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'has_lm_head_bias',
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fallback=False)
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args.relative_attention = config.getboolean(component,
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'relative_attention',
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fallback=True)
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args.num_buckets = config.getint(component,
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'relative_attention_num_buckets')
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args.max_distance = config.getint(component,
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'relative_attention_max_distance')
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args.logits_dtype = config.get(component,
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'logits_dtype',
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fallback='float32')
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args.rescale_before_lm_head = config.getboolean(
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component, 'tie_word_embeddings'
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) # default is True (for T5), but False for Flan-T5
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args.encoder_hidden_size = config.getint('encoder', 'd_model')
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args.encoder_num_heads = config.getint('encoder', 'num_heads')
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args.encoder_head_size = config.getint('encoder', 'd_kv')
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args.ckpt_weight_dtype = config.get(component, 'weight_data_type')
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else:
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assert False, 'Unsupported component!'
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return args
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def fuse_qkv(q, k, v):
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qkv_weight = np.concatenate((q, k, v))
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return qkv_weight
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def load_from_hf_t5(tllm_model, pytorch_ckpt_path, component, dtype="float32"):
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torch_dtype = str_dtype_to_torch(dtype)
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pytorch_ckpt = torch.load(path.join(pytorch_ckpt_path, 'pytorch_model.bin'))
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pytorch_model = {
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key: torch_to_numpy(value.to(torch_dtype))
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for key, value in pytorch_ckpt.items()
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}
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if component == "encoder":
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tllm_model.embedding.vocab_embedding.weight.value = np.ascontiguousarray(
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pytorch_model['shared.weight'])
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if tllm_model.embedding.position_embedding:
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tllm_model.embedding.position_embedding.weight.value = pytorch_model[
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'encoder.embed_positions.weight']
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if tllm_model.embedding.token_type_embedding:
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tllm_model.embedding.token_type_embedding.weight.value = pytorch_model[
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'encoder.embed_token_type.weight']
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# all layers use 1st layer's attn table
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# transpose from [num_buckets, num_heads] -> [num_heads, num_buckets]
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# ascontiguousarray is very important! otherwise TRT always receives the original layout
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relative_attention_table = np.ascontiguousarray(pytorch_model[
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f'encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight']
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.T)
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for i in range(tllm_model.num_layers):
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layer = tllm_model.encoder_layers[i]
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layer_prefix = f'encoder.block.{i}.'
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# attention table for all layers
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layer.attention.rel_attn_table.value = relative_attention_table
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layer.attention.qkv.weight.value = fuse_qkv(
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pytorch_model[f'{layer_prefix}layer.0.SelfAttention.q.weight'],
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pytorch_model[f'{layer_prefix}layer.0.SelfAttention.k.weight'],
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pytorch_model[f'{layer_prefix}layer.0.SelfAttention.v.weight'])
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layer.attention.dense.weight.value = pytorch_model[
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f'{layer_prefix}layer.0.SelfAttention.o.weight']
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if tllm_model.has_attention_qkvo_bias:
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layer.attention.qkv.bias.value = fuse_qkv(
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pytorch_model[
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f'{layer_prefix}layer.0.SelfAttention.q.bias'],
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pytorch_model[
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f'{layer_prefix}layer.0.SelfAttention.k.bias'],
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pytorch_model[f'{layer_prefix}layer.0.SelfAttention.v.bias']
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)
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layer.attention.dense.bias.value = pytorch_model[
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f'{layer_prefix}layer.0.SelfAttention.o.bias']
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layer.attention_layernorm.weight.value = pytorch_model[
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f'{layer_prefix}layer.0.layer_norm.weight']
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if tllm_model.layernorm_type != LayerNormType.RmsNorm:
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layer.attention_layernorm.bias.value = pytorch_model[
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f'{layer_prefix}layer.0.layer_norm.bias']
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layer.mlp.fc.weight.value = pytorch_model[
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f'{layer_prefix}layer.1.DenseReluDense.wi.weight']
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layer.mlp.proj.weight.value = pytorch_model[
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f'{layer_prefix}layer.1.DenseReluDense.wo.weight']
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if tllm_model.has_mlp_bias:
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layer.mlp.fc.bias.value = pytorch_model[
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f'{layer_prefix}layer.1.DenseReluDense.wi.bias']
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layer.mlp.proj.bias.value = pytorch_model[
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f'{layer_prefix}layer.1.DenseReluDense.wo.bias']
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layer.mlp_layernorm.weight.value = pytorch_model[
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f'{layer_prefix}layer.1.layer_norm.weight']
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if tllm_model.layernorm_type != LayerNormType.RmsNorm:
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layer.mlp_layernorm.bias.value = pytorch_model[
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f'{layer_prefix}layer.1.layer_norm.bias']
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if tllm_model.final_layernorm:
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tllm_model.final_layernorm.weight.value = pytorch_model[
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'encoder.final_layer_norm.weight']
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if tllm_model.layernorm_type != LayerNormType.RmsNorm:
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tllm_model.final_layernorm.bias.value = pytorch_model[
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'encoder.final_layer_norm.bias']
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if component == "decoder":
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tllm_model.embedding.vocab_embedding.weight.value = pytorch_model[
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'shared.weight']
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if tllm_model.embedding.position_embedding:
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tllm_model.embedding.position_embedding.weight.value = pytorch_model[
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'decoder.embed_positions.weight']
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if tllm_model.embedding.token_type_embedding:
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tllm_model.embedding.token_type_embedding.weight.value = pytorch_model[
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'decoder.embed_token_type.weight']
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# all layers use 1st layer's attn table
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# transpose from [num_buckets, num_heads] --> [num_heads, num_buckets]
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# ascontiguousarray is very important! otherwise TRT always receives the original layout
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relative_attention_table = np.ascontiguousarray(pytorch_model[
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f'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight']
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.T)
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for i in range(tllm_model.num_layers):
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layer = tllm_model.decoder_layers[i]
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layer_prefix = f'decoder.block.{i}.'
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# attention table for all layers
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layer.self_attention.rel_attn_table.value = relative_attention_table
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# self attn
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layer.self_attention.qkv.weight.value = fuse_qkv(
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pytorch_model[f'{layer_prefix}layer.0.SelfAttention.q.weight'],
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pytorch_model[f'{layer_prefix}layer.0.SelfAttention.k.weight'],
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pytorch_model[f'{layer_prefix}layer.0.SelfAttention.v.weight'])
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layer.self_attention.dense.weight.value = pytorch_model[
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f'{layer_prefix}layer.0.SelfAttention.o.weight']
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if tllm_model.has_attention_qkvo_bias:
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layer.self_attention.qkv.bias.value = fuse_qkv(
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pytorch_model[
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f'{layer_prefix}layer.0.SelfAttention.q.bias'],
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pytorch_model[
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f'{layer_prefix}layer.0.SelfAttention.k.bias'],
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pytorch_model[f'{layer_prefix}layer.0.SelfAttention.v.bias']
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)
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layer.self_attention.dense.bias.value = pytorch_model[
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f'{layer_prefix}layer.0.SelfAttention.o.bias']
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layer.self_attention_layernorm.weight.value = pytorch_model[
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f'{layer_prefix}layer.0.layer_norm.weight']
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if tllm_model.layernorm_type != LayerNormType.RmsNorm:
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layer.self_attention_layernorm.bias.value = pytorch_model[
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f'{layer_prefix}layer.0.layer_norm.bias']
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# cross attn
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layer.cross_attention.qkv.weight.value = fuse_qkv(
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pytorch_model[
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f'{layer_prefix}layer.1.EncDecAttention.q.weight'],
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pytorch_model[
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f'{layer_prefix}layer.1.EncDecAttention.k.weight'],
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pytorch_model[f'{layer_prefix}layer.1.EncDecAttention.v.weight']
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)
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layer.cross_attention.dense.weight.value = pytorch_model[
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f'{layer_prefix}layer.1.EncDecAttention.o.weight']
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if tllm_model.has_attention_qkvo_bias:
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layer.cross_attention.qkv.bias.value = fuse_qkv(
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pytorch_model[
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f'{layer_prefix}layer.1.EncDecAttention.q.bias'],
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pytorch_model[
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f'{layer_prefix}layer.1.EncDecAttention.k.bias'],
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pytorch_model[
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f'{layer_prefix}layer.1.EncDecAttention.v.bias'])
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layer.cross_attention.dense.bias.value = pytorch_model[
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f'{layer_prefix}layer.1.EncDecAttention.o.bias']
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layer.cross_attention_layernorm.weight.value = pytorch_model[
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f'{layer_prefix}layer.1.layer_norm.weight']
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if tllm_model.layernorm_type != LayerNormType.RmsNorm:
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layer.cross_attention_layernorm.bias.value = pytorch_model[
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f'{layer_prefix}layer.1.layer_norm.bias']
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layer.mlp.fc.weight.value = pytorch_model[
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f'{layer_prefix}layer.2.DenseReluDense.wi.weight']
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layer.mlp.proj.weight.value = pytorch_model[
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f'{layer_prefix}layer.2.DenseReluDense.wo.weight']
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if tllm_model.has_mlp_bias:
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layer.mlp.fc.bias.value = pytorch_model[
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f'{layer_prefix}layer.2.DenseReluDense.wi.bias']
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layer.mlp.proj.bias.value = pytorch_model[
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f'{layer_prefix}layer.2.DenseReluDense.wo.bias']
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layer.mlp_layernorm.weight.value = pytorch_model[
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f'{layer_prefix}layer.2.layer_norm.weight']
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if tllm_model.layernorm_type != LayerNormType.RmsNorm:
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layer.mlp_layernorm.bias.value = pytorch_model[
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f'{layer_prefix}layer.2.layer_norm.bias']
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if tllm_model.final_layernorm:
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tllm_model.final_layernorm.weight.value = pytorch_model[
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'decoder.final_layer_norm.weight']
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if tllm_model.layernorm_type != LayerNormType.RmsNorm:
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tllm_model.final_layernorm.bias.value = pytorch_model[
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'decoder.final_layer_norm.bias']
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tllm_model.lm_head.weight.value = pytorch_model['lm_head.weight']
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# TODO: only support t5, biases are not loaded
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def load_from_binary_t5(tllm_model: Union[EncoderModel, DecoderModel],
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dir_path,
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args,
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mapping: Optional[Mapping] = None,
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dtype='float32',
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use_parallel_embedding=False,
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sharding_dim=0,
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share_embedding_table=False,
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scaling_factors=None):
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logger.info('Loading weights from binary...')
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tik = time.time()
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if mapping is None:
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mapping = Mapping()
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ckpt_np_dtype = str_dtype_to_np(args.ckpt_weight_dtype)
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def fromfile(name, split=True, shape=None) -> Optional[np.ndarray]:
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p = path.join(
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dir_path,
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f'{name}.{str(mapping.tp_rank)}.bin' if split else f'{name}.bin')
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if Path(p).exists():
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# load from original dtype and cast to inference dtype
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t = np.fromfile(p, dtype=ckpt_np_dtype)
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t = numpy_to_dtype(t, dtype)
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if shape is not None:
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t = t.reshape(shape)
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t = np.ascontiguousarray(t)
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return t
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return None
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component = 'encoder' if isinstance(tllm_model, EncoderModel) else 'decoder'
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if mapping.is_first_pp_rank():
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wte = fromfile('shared.weight',
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shape=[args.vocab_size, -1],
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split=False)
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tllm_model.embedding.vocab_embedding.weight.value = wte
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# T5 special: all layers use 1st layer's attn table
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relative_attention_table = fromfile(
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f'{component}.block.0.layer.0.SelfAttention.relative_attention_bias.weight',
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shape=[args.n_head // mapping.tp_size, args.num_buckets])
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# TP is by loading different split weights. PP is by loading different layer weights
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# TODO: fix llama's wrong def of .num_layers field in PP. enc_dec is the correct way
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layers_range = list(
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range(mapping.pp_rank * tllm_model.num_layers,
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(mapping.pp_rank + 1) * tllm_model.num_layers, 1))
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for layer_idx in layers_range:
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pp_offset_layer_idx = layer_idx - mapping.pp_rank * tllm_model.num_layers
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layer = getattr(tllm_model, f'{component}_layers')[pp_offset_layer_idx]
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layer_prefix = f'{component}.block.{layer_idx}'
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|
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self_attention_layer = getattr(
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layer, 'attention' if component == 'encoder' else 'self_attention')
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|
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# attention table for all layers
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self_attention_layer.rel_attn_table.value = relative_attention_table
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# self attention
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attention_hidden_size = args.n_head * args.head_size # head size * num_heads not necessarily equals hidden_dim, such as Flan-T5
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self_attention_layer.qkv.weight.value = fromfile(
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f'{layer_prefix}.layer.0.SelfAttention.qkv.weight',
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shape=[
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3 * attention_hidden_size // mapping.tp_size, args.hidden_size
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|
])
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self_attention_layer.dense.weight.value = fromfile(
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|
f'{layer_prefix}.layer.0.SelfAttention.o.weight',
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|
shape=[args.hidden_size, attention_hidden_size // mapping.tp_size])
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self_attention_layernorm = getattr(
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|
layer, 'self_attention_layernorm'
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|
if component == 'decoder' else 'attention_layernorm')
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self_attention_layernorm.weight.value = fromfile(
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|
f'{layer_prefix}.layer.0.layer_norm.weight', split=False)
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|
|
|
# cross attention
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|
if component == 'decoder':
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|
attention_hidden_size = args.n_head * args.head_size
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|
layer.cross_attention.qkv.weight.value = fromfile(
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|
f'{layer_prefix}.layer.1.EncDecAttention.qkv.weight',
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|
shape=[
|
|
3 * attention_hidden_size // mapping.tp_size,
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|
args.hidden_size
|
|
])
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|
layer.cross_attention.dense.weight.value = fromfile(
|
|
f'{layer_prefix}.layer.1.EncDecAttention.o.weight',
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|
shape=[
|
|
args.hidden_size, attention_hidden_size // mapping.tp_size
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|
])
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|
layer.cross_attention_layernorm.weight.value = fromfile(
|
|
f'{layer_prefix}.layer.1.layer_norm.weight', split=False)
|
|
|
|
# MLP
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|
hf_component_idx = 1 if component == 'encoder' else 2
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|
layer.mlp.fc.weight.value = fromfile(
|
|
f'{layer_prefix}.layer.{hf_component_idx}.DenseReluDense.wi.weight',
|
|
shape=[args.ffn_hidden_size // mapping.tp_size, args.hidden_size])
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|
if args.gated_act:
|
|
layer.mlp.gate.weight.value = fromfile(
|
|
f'{layer_prefix}.layer.{hf_component_idx}.DenseReluDense.wi2.weight',
|
|
shape=[
|
|
args.ffn_hidden_size // mapping.tp_size, args.hidden_size
|
|
])
|
|
layer.mlp.proj.weight.value = fromfile(
|
|
f'{layer_prefix}.layer.{hf_component_idx}.DenseReluDense.wo.weight',
|
|
shape=[args.hidden_size, args.ffn_hidden_size // mapping.tp_size])
|
|
layer.mlp_layernorm.weight.value = fromfile(
|
|
f'{layer_prefix}.layer.{hf_component_idx}.layer_norm.weight',
|
|
split=False)
|
|
|
|
if mapping.is_last_pp_rank():
|
|
if tllm_model.has_model_final_layernorm:
|
|
tllm_model.final_layernorm.weight.value = fromfile(
|
|
f'{component}.final_layer_norm.weight', split=False)
|
|
|
|
if component == 'decoder':
|
|
tllm_model.lm_head.weight.value = fromfile(
|
|
'lm_head.weight',
|
|
shape=[args.vocab_size // mapping.tp_size, args.hidden_size])
|
|
|
|
tok = time.time()
|
|
t = time.strftime('%H:%M:%S', time.gmtime(tok - tik))
|
|
logger.info(f'Weights loaded. Total time: {t}')
|