Refine checkpoint converter

paddlenlp/trainer/auto_trainer.py

@@ -722,6 +722,8 @@
                         )
 
             if self.args.to_static:
+                if self.model_wrapped._mode is None:
+                    self.model_wrapped.train()
                 model_state_dict = {
                     key: value
                     for key, value in self.model_wrapped.state_dict("param").items()
@@ -757,7 +759,7 @@
 
             if self.args.auto_parallel_resume_form_hybrid_parallel:
                 CheckpointConverter(
-                    resume_from_checkpoint, state_dict, parameter_to_structured_name
+                    resume_from_checkpoint, state_dict, parameter_to_structured_name, self.args
                 ).load_from_hybrid_parallel_checkpoint()
             else:
                 ckpt_path = os.path.join(resume_from_checkpoint, DIST_CKPT_PATH)

paddlenlp/trainer/utils/ckpt_converter.py

@@ -33,15 +33,22 @@
 MODEL_WEIGHT_SUFFIX = ".pdparams"
 OPTIMIZER_WEIGHT_SUFFIX = ".pdopt"
 SCHEDULER_NAME = "scheduler.pdparams"
+SCALAR_NAME = "scalar.pdparams"
 MODEL_META_FILE_NAME = "model_meta.json"
 OPTIMIZER_STATE_NAME_SUFFIX = [".moment1", ".moment2", ".beta1_pow_acc", ".beta2_pow_acc", ".master_weight"]
 MODEL_STATE_FILE_MIN_SIZE = 512
 
 
 class CheckpointConverter:
-    def __init__(self, hybrid_parallel_ckpt_path, state_dict, parameter_to_structured_name, patch_dict=None):
+    def __init__(
+        self, hybrid_parallel_ckpt_path, state_dict, parameter_to_structured_name, trainging_args=None, patch_dict=None
+    ):
         self.use_dist = True if paddle.distributed.get_world_size() > 1 else False
         self.path = hybrid_parallel_ckpt_path
+
+        if trainging_args.ignore_load_lr_and_optim:
+            state_dict.pop("optimizer")
+
         self.auto_parallel_state_dict = self.flatten_state_dict(state_dict)
         self.parameter_to_structured_name = self.gather_global_object(parameter_to_structured_name)
         model_state_global_shape = {}
@@ -74,9 +81,9 @@
             for k, v in self.auto_parallel_state_dict.items():
                 if k in self.patch_dict:
                     del_keys.append(k)
-
             for k in del_keys:
                 self.auto_parallel_state_dict[self.patch_dict[k]] = self.auto_parallel_state_dict[k]
+            for k in del_keys:
                 self.auto_parallel_state_dict.pop(k)
 
         flags = [
@@ -896,25 +903,26 @@
         return renamed_state_dict
 
     def rename_using_optimizer_state_order(self, model_state_keys, optimizer_state_dict):
-
         name_mapping = {}
         suffix_bucket = {}
         assert len(optimizer_state_dict) % len(model_state_keys) == 0
         for suffix in OPTIMIZER_STATE_NAME_SUFFIX:
             suffix_bucket[suffix] = []
-        for satte_name, satte_value in optimizer_state_dict.items():
-            if "moment1" in satte_name:
-                suffix_bucket[".moment1"].append(satte_name)
-            elif "moment2" in satte_name:
-                suffix_bucket[".moment2"].append(satte_name)
-            elif "beta1_pow_acc" in satte_name:
-                suffix_bucket[".beta1_pow_acc"].append(satte_name)
-            elif "beta2_pow_acc" in satte_name:
-                suffix_bucket[".beta2_pow_acc"].append(satte_name)
+        for opt_name, opt_value in optimizer_state_dict.items():
+            if "moment1" in opt_name:
+                suffix_bucket[".moment1"].append(opt_name)
+            elif "moment2" in opt_name:
+                suffix_bucket[".moment2"].append(opt_name)
+            elif "beta1_pow_acc" in opt_name:
+                suffix_bucket[".beta1_pow_acc"].append(opt_name)
+            elif "beta2_pow_acc" in opt_name:
+                suffix_bucket[".beta2_pow_acc"].append(opt_name)
             else:
-                suffix_bucket[".master_weight"].append(satte_name)
+                suffix_bucket[".master_weight"].append(opt_name)
 
         for suffix, old_names in suffix_bucket.items():
+            if len(old_names) == 0:
+                continue
             assert len(old_names) == len(model_state_keys)
             for i in range(len(old_names)):
                 name_mapping[old_names[i]] = model_state_keys[i] + suffix
@@ -1011,6 +1019,9 @@
                 cur_rank_optimizer_state_file_names.append(file_name)
         if SCHEDULER_NAME in cur_rank_model_state_file_names:
             cur_rank_model_state_file_names.remove(SCHEDULER_NAME)
+        if SCALAR_NAME in cur_rank_model_state_file_names:
+            cur_rank_model_state_file_names.remove(SCALAR_NAME)
+
         return cur_rank_model_state_file_names, cur_rank_optimizer_state_file_names
 
     def get_distribution_rank_from_file_name(self, file_name):

scripts/distribute/ci_case_auto.sh

@@ -54,6 +54,7 @@ function llama_case_list_auto() {
     llama_static_auto_recompute_bs16_fp16_DP2-MP2-PP2-VPP2-Sharding2_stage2
 
     llama_align_dygraph_dy2st_auto_bs2_bf16_DP2-MP1-PP1
+    llama_convert_hybrid_ckpt_to_auto_parallel_bs2_fp32_DP2-MP1-PP1
 }
 
 function llm_gpt_case_list_auto() {
@@ -1062,6 +1063,165 @@ function llama_align_dygraph_dy2st_auto_bs2_bf16_DP2-MP1-PP1() {
     echo "=========== $FUNCNAME run  end ==========="
 }
 
+function llama_convert_hybrid_ckpt_to_auto_parallel_bs2_fp32_DP2-MP1-PP1() {
+    echo "=========== $FUNCNAME run begin ==========="
+    export PYTHONPATH=$root_path/:$PYTHONPATH
+    export FLAGS_call_stack_level=3
+    export NVIDIA_TF32_OVERRIDE=0
+    export FLAGS_enable_pir_api=1
+    export FLAGS_max_inplace_grad_add=3
+
+    echo "---- run hybrid and save ckpt ----"
+    dy_task_name="llama_hybrid_ckpt_bs2_fp32_DP2-MP1-PP1"
+    dy_case_out_dir="dy_output/$dy_task_name"
+    dy_case_log_dir="dy_output/$dy_task_name""_log"
+    rm -rf $dy_case_out_dir
+    rm -rf $dy_case_log_dir
+
+    python -u -m paddle.distributed.launch \
+        --gpus "0,1" \
+        --log_dir $dy_case_log_dir \
+        ../../run_pretrain.py \
+        --model_name_or_path "facebook/llama-7b" \
+        --tokenizer_name_or_path "facebook/llama-7b" \
+        --input_dir "./data" \
+        --output_dir $dy_case_out_dir \
+        --split 949,50,1 \
+        --weight_decay 0.01 \
+        --warmup_ratio 0.01 \
+        --warmup_steps 30 \
+        --max_grad_norm 0.0 \
+        --learning_rate 3e-05 \
+        --min_learning_rate 3e-06 \
+        --max_steps 5 \
+        --logging_steps 1 \
+        --eval_steps 1000 \
+        --save_steps 3 \
+        --continue_training 0 \
+        --do_train true \
+        --do_eval false \
+        --do_predict false \
+        --disable_tqdm true \
+        --skip_profile_timer true \
+        --save_total_limit 2 \
+        --device gpu \
+        --disable_tqdm true \
+        --dataloader_num_workers 1 \
+        --distributed_dataloader 0 \
+        --per_device_train_batch_size 1 \
+        --gradient_accumulation_steps 1 \
+        --per_device_eval_batch_size 2 \
+        --recompute false \
+        --recompute_use_reentrant true \
+        --recompute_granularity full \
+        --pp_recompute_interval 0 \
+        --bf16 0 \
+        --fp16_opt_level "O2"  \
+        --amp_custom_black_list "reduce_sum" "c_softmax_with_cross_entropy" \
+        --amp_custom_white_list "lookup_table" "lookup_table_v2" \
+        --amp_master_grad false \
+        --enable_linear_fused_grad_add false \
+        --fuse_attention_ffn true \
+        --fuse_attention_qkv false \
+        --fuse_sequence_parallel_allreduce false \
+        --use_flash_attention 0 \
+        --use_fused_rope false \
+        --use_fused_rms_norm 0 \
+        --max_seq_length 4096 \
+        --sep_parallel_degree 1 \
+        --sequence_parallel false \
+        --pipeline_parallel_degree 1 \
+        --sharding_parallel_degree 1 \
+        --tensor_parallel_degree 1 \
+        --virtual_pp_degree 1 \
+        --sharding "" \
+        --to_static 0 \
+        --num_hidden_layers 2 \
+        >>${log_path}/$FUNCNAME 2>&1
+    dy_loss=`cat $dy_case_log_dir/workerlog.0 | grep 'global_step: 4' | awk -F 'loss: ' '{print $2}' | awk -F ',' '{print $1}'`
+    dy_ips=-1
+    dy_mem=-1
+    echo "hybrid result: loss=$dy_loss ips=$dy_ips mem=$dy_mem"
+
+    echo "---- run auto parallel resueme from hybrid ckpt ----"
+    auto_task_name="llama_auto_parallel_bs2_fp32_DP2-MP1-PP1"
+    auto_case_out_dir="auto_output/$auto_task_name"
+    auto_case_log_dir="auto_output/$auto_task_name""_log"
+    rm -rf $auto_case_out_dir
+    rm -rf $auto_case_log_dir
+
+    python -u -m paddle.distributed.launch \
+        --gpus "0,1" \
+        --log_dir $auto_case_log_dir \
+        run_pretrain_auto.py \
+        --model_name_or_path "facebook/llama-7b" \
+        --tokenizer_name_or_path "facebook/llama-7b" \
+        --input_dir "./data" \
+        --output_dir $auto_case_out_dir \
+        --split 949,50,1 \
+        --weight_decay 0.01 \
+        --warmup_ratio 0.01 \
+        --warmup_steps 30 \
+        --max_grad_norm 0.0 \
+        --learning_rate 3e-05 \
+        --min_learning_rate 3e-06 \
+        --max_steps 4 \
+        --logging_steps 1 \
+        --eval_steps 1000 \
+        --save_steps 1000 \
+        --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 1 \
+        --per_device_eval_batch_size 2 \
+        --recompute false \
+        --recompute_use_reentrant true \
+        --recompute_granularity full \
+        --pp_recompute_interval 0 \
+        --bf16 0 \
+        --fp16_opt_level "O2"  \
+        --amp_custom_black_list "reduce_sum" "c_softmax_with_cross_entropy" \
+        --amp_custom_white_list "lookup_table" "lookup_table_v2" \
+        --amp_master_grad false \
+        --fuse_attention_ffn true \
+        --fuse_attention_qkv false \
+        --fuse_sequence_parallel_allreduce false \
+        --use_flash_attention 0 \
+        --use_fused_rope false \
+        --use_fused_rms_norm 0 \
+        --max_seq_length 4096 \
+        --sep_parallel_degree 1 \
+        --sequence_parallel false \
+        --pipeline_parallel_degree 1 \
+        --sharding_parallel_degree 1 \
+        --tensor_parallel_degree 1 \
+        --virtual_pp_degree 1 \
+        --pipeline_schedule_mode "VPP" \
+        --sharding "" \
+        --to_static 1 \
+        --num_hidden_layers 2 \
+        --resume_from_checkpoint "dy_output/llama_hybrid_ckpt_bs2_fp32_DP2-MP1-PP1/checkpoint-3" \
+        --auto_parallel_resume_form_hybrid_parallel 1 \
+        >>${log_path}/$FUNCNAME 2>&1
+    auto_loss=`cat $auto_case_log_dir/workerlog.0 | grep 'global_step: 4' | awk -F 'loss: ' '{print $2}' | awk -F ',' '{print $1}'`
+    auto_ips=-1
+    auto_mem=-1
+    echo "auto result: loss=$auto_loss ips=$auto_ips mem=$auto_mem"
+
+    check_result $FUNCNAME ${dy_loss} ${auto_loss} ${dy_ips} ${auto_ips} ${dy_mem} ${auto_mem}
+    echo "=========== $FUNCNAME run  end ==========="
+}
+
 function llm_gpt_dygraph_auto_bs8_fp32_DP2() {
     echo "=========== $FUNCNAME run begin ==========="
     export PYTHONPATH=$root_path/:$PYTHONPATH