fix npu sft ckpt load bug and no FA bug

llm/finetune_generation.py

@@ -137,6 +137,43 @@ def main():
         weight_double_quant_block_size=model_args.weight_double_quant_block_size,
     )
 
+    model_config = AutoConfig.from_pretrained(
+            model_args.model_name_or_path,
+            tensor_parallel_output=training_args.tensor_parallel_output,
+            tensor_parallel_degree=training_args.tensor_parallel_degree,
+            tensor_parallel_rank=training_args.tensor_parallel_rank,
+            dtype=dtype,
+            from_aistudio=model_args.from_aistudio,
+            quantization_config=quantization_config,
+        )
+    if hasattr(model_config, "use_flash_attention"):
+        model_config.use_flash_attention = model_args.use_flash_attention
+
+    model_config.use_fused_rms_norm = model_args.use_fused_rms_norm
+    model_config.fuse_attention_qkv = model_args.fuse_attention_qkv
+    model_config.fuse_attention_ffn = model_args.fuse_attention_ffn
+    model_config.recompute_granularity = model_args.recompute_granularity
+    model_config.virtual_pp_degree = model_args.virtual_pp_degree
+    model_config.sequence_parallel = model_args.sequence_parallel
+    model_config.fuse_sequence_parallel_allreduce = model_args.fuse_sequence_parallel_allreduce
+    model_config.use_fused_rope = model_args.use_fused_rope
+
+    model_config.no_recompute_layers = model_args.no_recompute_layers
+    model_config.pp_recompute_interval = model_args.pp_recompute_interval
+    model_config.recompute_use_reentrant = model_args.recompute_use_reentrant
+    model_config.use_recompute = training_args.recompute
+
+    model_config.tensor_parallel_degree = training_args.tensor_parallel_degree
+    model_config.tensor_parallel_rank = training_args.tensor_parallel_rank
+
+    # Config for model using dropout, such as GPT.
+    model_config.hidden_dropout_prob = model_args.hidden_dropout_prob
+    model_config.attention_probs_dropout_prob = model_args.attention_probs_dropout_prob
+
+    model_config.sep_parallel_degree = training_args.sep_parallel_degree
+    model_config.tensor_parallel_output = True
+    model_config.seq_length = data_args.max_length
+
     if training_args.pipeline_parallel_degree > 1:
         if data_args.eval_with_do_generation and training_args.do_eval:
             raise ValueError("Plese set eval_with_do_generation to false in pipeline parallel mode.")
@@ -145,63 +182,13 @@ def main():
         if not training_args.autotuner_benchmark:
             model = AutoModelForCausalLMPipe.from_pretrained(
                 model_args.model_name_or_path,
-                tensor_parallel_output=training_args.tensor_parallel_output,
-                tensor_parallel_degree=training_args.tensor_parallel_degree,
-                tensor_parallel_rank=training_args.tensor_parallel_rank,
-                use_flash_attention=model_args.use_flash_attention,
-                dtype=dtype,
+                config=model_config,
                 from_aistudio=model_args.from_aistudio,
-                quantization_config=quantization_config,
             )
         else:
             # NOTE(gongenlei): new add autotuner_benchmark
-            model_config = AutoConfig.from_pretrained(
-                model_args.model_name_or_path,
-                tensor_parallel_output=training_args.tensor_parallel_output,
-                tensor_parallel_degree=training_args.tensor_parallel_degree,
-                tensor_parallel_rank=training_args.tensor_parallel_rank,
-                dtype=dtype,
-                from_aistudio=model_args.from_aistudio,
-                quantization_config=quantization_config,
-            )
             model = AutoModelForCausalLMPipe.from_config(model_config, dtype=dtype)
     else:
-        model_config = AutoConfig.from_pretrained(
-            model_args.model_name_or_path,
-            tensor_parallel_output=training_args.tensor_parallel_output,
-            tensor_parallel_degree=training_args.tensor_parallel_degree,
-            tensor_parallel_rank=training_args.tensor_parallel_rank,
-            dtype=dtype,
-            from_aistudio=model_args.from_aistudio,
-            quantization_config=quantization_config,
-        )
-        if hasattr(model_config, "use_flash_attention"):
-            model_config.use_flash_attention = model_args.use_flash_attention
-
-        model_config.use_fused_rms_norm = model_args.use_fused_rms_norm
-        model_config.fuse_attention_qkv = model_args.fuse_attention_qkv
-        model_config.fuse_attention_ffn = model_args.fuse_attention_ffn
-        model_config.recompute_granularity = model_args.recompute_granularity
-        model_config.virtual_pp_degree = model_args.virtual_pp_degree
-        model_config.sequence_parallel = model_args.sequence_parallel
-        model_config.fuse_sequence_parallel_allreduce = model_args.fuse_sequence_parallel_allreduce
-        model_config.use_fused_rope = model_args.use_fused_rope
-
-        model_config.no_recompute_layers = model_args.no_recompute_layers
-        model_config.pp_recompute_interval = model_args.pp_recompute_interval
-        model_config.recompute_use_reentrant = model_args.recompute_use_reentrant
-        model_config.use_recompute = training_args.recompute
-
-        model_config.tensor_parallel_degree = training_args.tensor_parallel_degree
-        model_config.tensor_parallel_rank = training_args.tensor_parallel_rank
-
-        # Config for model using dropout, such as GPT.
-        model_config.hidden_dropout_prob = model_args.hidden_dropout_prob
-        model_config.attention_probs_dropout_prob = model_args.attention_probs_dropout_prob
-
-        model_config.sep_parallel_degree = training_args.sep_parallel_degree
-        model_config.tensor_parallel_output = True
-        model_config.seq_length = data_args.max_length
         if not training_args.autotuner_benchmark:
             model = AutoModelForCausalLM.from_pretrained(
                 model_args.model_name_or_path,

paddlenlp/transformers/llama/modeling.py

@@ -1381,9 +1381,9 @@ def _prepare_decoder_attention_mask(attention_mask, input_shape, past_key_values
                         input_shape, past_key_values_length=past_key_values_length
                     )
                     if get_env_device() == "npu":
-                        expanded_attn_mask = expanded_attn_mask.astype("bool")
-                        combined_attention_mask = combined_attention_mask.astype("bool")
-                    expanded_attn_mask = expanded_attn_mask & combined_attention_mask
+                        expanded_attn_mask = expanded_attn_mask.astype("bool") & combined_attention_mask.astype("bool")
+                    else:
+                        expanded_attn_mask = expanded_attn_mask & combined_attention_mask
             # [bsz, seq_len, seq_len] -> [bsz, 1, seq_len, seq_len]
             elif len(attention_mask.shape) == 3:
                 expanded_attn_mask = attention_mask.unsqueeze(1).astype("bool")
@@ -1394,9 +1394,9 @@ def _prepare_decoder_attention_mask(attention_mask, input_shape, past_key_values
             expanded_attn_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length)
         # Convert bool attention_mask to float attention mask, which will be added to attention_scores later
         if get_env_device() == "npu":
-            x = paddle.to_tensor(0.0, dtype="float16")
-            y = paddle.to_tensor(paddle.finfo(dtype).min, dtype="float16")
-            expanded_attn_mask = expanded_attn_mask.astype("float16")
+            x = paddle.to_tensor(0.0, dtype="float32")
+            y = paddle.to_tensor(paddle.finfo(dtype).min, dtype="float32")
+            expanded_attn_mask = expanded_attn_mask.astype("float32")
             expanded_attn_mask = paddle.where(expanded_attn_mask, x, y).astype(dtype)
         elif get_env_device() == "xpu":
             x = paddle.to_tensor(0.0, dtype=dtype)

paddlenlp/transformers/llama/modeling_pp.py

@@ -18,6 +18,7 @@
 from paddle.distributed.fleet.meta_parallel import LayerDesc, PipelineLayer
 from paddle.distributed.fleet.utils import recompute
 
+from paddlenlp.utils.tools import get_env_device
 from paddlenlp.transformers.model_utils import PipelinePretrainedModel
 
 from .modeling import (
@@ -153,6 +154,11 @@ def forward(self, args):
                 attention_mask, (batch_size, seq_length), 0, input_embeds.dtype
             )
             attention_mask.stop_gradient = True
+            if get_env_device() == "npu":
+                attention_mask = attention_mask.astype("bool")
+        elif get_env_device() == "npu":
+            attention_mask = paddle.tril(paddle.ones((seq_length, seq_length), dtype="bool"))
+            attention_mask.stop_gradient = True
 
         if self.config.alibi and attention_mask is None:
             attention_mask = LlamaModel._prepare_decoder_attention_mask(