fit for llama3 for auto_parallel

llm/llama/auto_parallel/run_llama3.sh

@@ -0,0 +1,101 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# just for debug
+
+set -x
+unset CUDA_VISIBLE_DEVICES
+
+task_name="llama3_dp2pp4sd2"
+rm -rf output/$task_name/
+rm -rf "output/$task_name""_log"
+
+export SOT_LOG_LEVEL=4
+export PYTHONPATH=../../../:$PYTHONPATH
+
+#ulimit -c unlimited
+#export GLOG_v=10
+
+# export FLAGS_call_stack_level=3
+# export FLAGS_use_cuda_managed_memory=true
+
+# export FLAGS_embedding_deterministic=1        
+# export FLAGS_cudnn_deterministic=1
+# export NVIDIA_TF32_OVERRIDE=0
+
+python -u  -m paddle.distributed.launch \
+    --gpus "0,1,2,3,4,5,6,7" \
+    --log_dir  "output/$task_name""_log" \
+    ./run_pretrain_auto.py \
+    --model_name_or_path "meta-llama/Meta-Llama-3-8B-Instruct" \
+    --tokenizer_name_or_path "meta-llama/Meta-Llama-3-8B-Instruct" \
+    --input_dir "./data" \
+    --output_dir "./output" \
+    --split 949,50,1 \
+    --weight_decay 0.01 \
+    --warmup_ratio 0.01 \
+    --max_grad_norm 1.0 \
+    --learning_rate 3e-05 \
+    --min_learning_rate 3e-06 \
+    --max_steps 30 \
+    --logging_steps 10 \
+    --eval_steps 1000 \
+    --save_steps 50000 \
+    --continue_training 0 \
+    --do_train true \
+    --do_eval false \
+    --do_predict false \
+    --disable_tqdm true \
+    --skip_profile_timer true \
+    --save_total_limit 2 \
+    --device gpu \
+    --disable_tqdm true \
+    --dataloader_num_workers 1 \
+    --distributed_dataloader 0 \
+    --enable_auto_parallel 1 \
+    --per_device_train_batch_size 1 \
+    --gradient_accumulation_steps 4 \
+    --per_device_eval_batch_size 1 \
+    --recompute false \
+    --recompute_use_reentrant true \
+    --recompute_granularity full \
+    --pp_recompute_interval 0 \
+    --bf16 true \
+    --fp16_opt_level "O2"  \
+    --amp_master_grad true \
+    --fuse_attention_ffn false \
+    --fuse_attention_qkv false \
+    --fused_linear_param_grad_add 1 \
+    --fuse_sequence_parallel_allreduce false \
+    --use_flash_attention true \
+    --use_fused_rope true \
+    --use_fused_rms_norm true \
+    --max_seq_length 4096 \
+    --sep_parallel_degree 1 \
+    --sequence_parallel false \
+    --pipeline_parallel_degree 2 \
+    --sharding_parallel_degree 2 \
+    --tensor_parallel_degree 1 \
+    --virtual_pp_degree 4 \
+    --pipeline_schedule_mode "VPP" \
+    --sharding "stage2" \
+    --pipeline_parallel_config "enable_send_recv_overlap" \
+    --data_parallel_config "enable_allreduce_avg_in_gradinent_scale gradient_sync_after_accumulate" \
+    --sharding_parallel_config "enable_stage2_overlap" \
+    --tensor_parallel_config "enable_mp_async_allreduce" \
+    --to_static 1 \
+    --amp_custom_black_list "reduce_sum" "c_softmax_with_cross_entropy" \
+    --amp_custom_white_list "lookup_table" "lookup_table_v2" \
+    --skip_memory_metrics 0 
+

paddlenlp/transformers/llama/modeling_auto.py

@@ -367,24 +367,28 @@ def _init_rope(self):
             self.rotary_emb = LlamaRotaryEmbedding(
                 self.head_dim,
                 max_position_embeddings=self.max_position_embeddings,
+                base=self.config.rope_theta,
             )
         elif self.config.rope_scaling_type == "linear":
             self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
                 self.head_dim,
                 max_position_embeddings=self.max_position_embeddings,
                 scaling_factor=self.config.rope_scaling_factor,
+                base=self.config.rope_theta,
             )
         elif self.config.rope_scaling_type == "ntk":
             self.rotary_emb = LlamaNTKScalingRotaryEmbedding(
                 self.head_dim,
                 max_position_embeddings=self.max_position_embeddings,
                 scaling_factor=self.config.rope_scaling_factor,
+                base=self.config.rope_theta,
             )
         elif self.config.rope_scaling_type == "dynamic_ntk":
             self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
                 self.head_dim,
                 max_position_embeddings=self.max_position_embeddings,
                 scaling_factor=self.config.rope_scaling_factor,
+                base=self.config.rope_theta,
             )
         else:
             raise ValueError(f"Unknown RoPE scaling type {self.config.rope_scaling_type}")