xpu devices support llama-7b basic mode inference (turn on BlockAtten…

llm/docs/inference.md

@@ -83,7 +83,10 @@ PaddleNLP 针对于Transformer 系列编写了高性能自定义算子，提升
 
 ```shell
 git clone https://github.com/PaddlePaddle/PaddleNLP
+#GPU设备安装自定义算子
 cd ./paddlenlp/csrc && python setup_cuda.py install
+#XPU设备安装自定义算子
+cd ./paddlenlp/csrc/xpu/src && sh cmake_build.sh 
 ```
 
 ### 2.3 关闭BlockAttention的高性能推理
@@ -163,6 +166,9 @@ python predictor.py  --model_name_or_path ./inference --inference_model --quant_
 # 动态图模型推理命令参考
 python predictor.py --model_name_or_path meta-llama/Llama-2-7b-chat --inference_model --dtype float16 --block_attn
 
+# XPU设备动态图模型推理命令参考
+python predictor.py --model_name_or_path meta-llama/Llama-2-7b-chat --inference_model --dtype float16 --block_attn --device xpu
+
 # Weight Only Int8 动态图推理参考
 python predictor.py --model_name_or_path meta-llama/Llama-2-7b-chat --inference_model --dtype float16 --quant_type weight_only_int8 --block_attn
 
@@ -179,6 +185,9 @@ python predictor.py --model_name_or_path meta-llama/Llama-2-7b-chat --inference_
 # 动转静命令参考
 python export_model.py --model_name_or_path meta-llama/Llama-2-7b-chat --inference_model --output_path ./inference --dtype float16 --block_attn
 
+# XPU设备动转静命令参考
+python export_model.py --model_name_or_path meta-llama/Llama-2-7b-chat --inference_model --output_path ./inference --dtype float16 --block_attn --device xpu
+
 # Weight Only Int8 动转静命令参考
 python export_model.py --model_name_or_path meta-llama/Llama-2-7b-chat --inference_model --output_path ./inference --dtype float16 --quant_type weight_only_int8 --block_attn
 
@@ -194,6 +203,9 @@ python export_model.py  --model_name_or_path meta-llama/Llama-2-7b-chat --infere
 # 静态图推理命令参考
 python predictor.py  --model_name_or_path ./inference --inference_model --dtype "float16" --mode "static" --block_attn
 
+# XPU设备静态图推理命令参考
+python predictor.py  --model_name_or_path ./inference --inference_model --dtype "float16" --mode "static" --block_attn --device xpu
+
 # Weight Only Int8 静态图推理命令参考
 python predictor.py  --model_name_or_path ./inference --inference_model --dtype "float16" --mode "static" --quant_type weight_only_int8 --block_attn
 

llm/predictor.py

@@ -650,6 +650,11 @@ def _create_predictor(self, predictor_args: PredictorArgument):
         if predictor_args.device in paddle.device.get_all_custom_device_type():
             device_id = int(os.environ.get("FLAGS_selected_{}s".format(predictor_args.device), 0))
             config.enable_custom_device(predictor_args.device, device_id)
+        elif predictor_args.device == "xpu":
+            raise ValueError(
+                "you should export xpu static model with --block_attn flag and use predictor with --block_attn too"
+                "https://github.com/PaddlePaddle/PaddleNLP/blob/develop/llm/docs/inference.md"
+            )
         else:
             device_id = int(os.environ.get("FLAGS_selected_gpus", 0))
             config.enable_use_gpu(100, device_id)
@@ -920,7 +925,9 @@ def _preprocess(self, source):
             source = [self.tokenizer.apply_chat_template(sentence, tokenize=False) for sentence in source]
 
         for i, text in enumerate(source):
-            add_special_tokens = self.tokenizer.chat_template is None or isinstance(self.tokenizer, (ChatGLMv2Tokenizer, ChatGLMTokenizer)) 
+            add_special_tokens = self.tokenizer.chat_template is None or isinstance(
+                self.tokenizer, (ChatGLMv2Tokenizer, ChatGLMTokenizer)
+            )
             add_special_tokens = add_special_tokens if not self.benchmark else False
             tokens = self.tokenizer(
                 text,
@@ -1076,6 +1083,15 @@ def _create_predictor(self, predictor_args: PredictorArgument):
         if predictor_args.device in paddle.device.get_all_custom_device_type():
             device_id = int(os.environ.get("FLAGS_selected_{}s".format(predictor_args.device), 0))
             config.enable_custom_device(predictor_args.device, device_id)
+        elif predictor_args.device == "xpu":
+            config.enable_xpu()
+            device_id = int(os.environ.get("FLAGS_selected_xpus", 0))
+            config.set_xpu_device_id(device_id)
+            xpu_config = paddle.inference.XpuConfig()
+            xpu_config.device_id = device_id
+            xpu_config.l3_size = 63 * 1024 * 1024
+            xpu_config.l3_autotune_size = 63 * 1024 * 1024
+            config.set_xpu_config(xpu_config)
         else:
             device_id = int(os.environ.get("FLAGS_selected_gpus", 0))
             config.enable_use_gpu(100, device_id)
@@ -1331,6 +1347,11 @@ def create_predictor(
                         tensor_parallel_rank=tensor_parallel_rank,
                     )
                 else:
+                    if predictor_args.device == "xpu":
+                        raise ValueError(
+                            "you should run xpu dynamic model with --block_attn flag"
+                            "https://github.com/PaddlePaddle/PaddleNLP/blob/develop/llm/docs/inference.md"
+                        )
                     from paddlenlp.experimental.transformers import (
                         LlamaForCausalLMInferenceModel as LlamaInferenceModel,
                     )
@@ -1588,7 +1609,9 @@ def predict():
 
 def benchmark(predictor, predictor_args, model_args):
     # Just construct a simple benchmark input. We pad input to the src_length.
-    benchmark_texts = [predictor.tokenizer.pad_token * predictor_args.src_length for _ in range(predictor_args.batch_size)]
+    benchmark_texts = [
+        predictor.tokenizer.pad_token * predictor_args.src_length for _ in range(predictor_args.batch_size)
+    ]
 
     batch_benchmark_texts = batchfy_text(benchmark_texts, predictor_args.batch_size)
     print("***********Start Benchmark**********")

paddlenlp/experimental/transformers/bloom/modeling.py

@@ -19,7 +19,6 @@
 from paddle import Tensor, nn
 from paddle.distributed import fleet
 from paddle.nn.quant import weight_quantize
-from paddlenlp_ops import get_padding_offset, get_padding_offset_v2
 
 from paddlenlp.experimental.transformers.fused_transformer_layers import (
     FusedBlockMultiTransformer,
@@ -219,6 +218,8 @@
     def remove_padding(self, input_ids, seq_lens_this_time):
         cum_offsets_now = paddle.cumsum(paddle.max(seq_lens_this_time) - seq_lens_this_time)
         token_num = paddle.sum(seq_lens_this_time)
+        from paddlenlp_ops import get_padding_offset
+
         ids_remove_padding, cum_offsets, padding_offset = get_padding_offset(
             input_ids, cum_offsets_now, token_num, seq_lens_this_time
         )
@@ -592,6 +593,8 @@
     def remove_padding(self, input_ids, seq_lens_this_time):
         cum_offsets_now = paddle.cumsum(self.max_seq_len - seq_lens_this_time)
         token_num = paddle.sum(seq_lens_this_time)
+        from paddlenlp_ops import get_padding_offset_v2
+
         ids_remove_padding, cum_offsets, padding_offset, cu_seqlens_q, cu_seqlens_k = get_padding_offset_v2(
             input_ids, cum_offsets_now, token_num, seq_lens_this_time
         )

paddlenlp/experimental/transformers/chatglm/modeling.py

@@ -18,7 +18,6 @@
 from paddle import nn
 from paddle.distributed import fleet
 from paddle.nn.quant import weight_quantize
-from paddlenlp_ops import get_padding_offset
 
 from paddlenlp.experimental.transformers.fused_transformer_layers import (
     FusedMultiTransformerConfig,
@@ -273,6 +272,8 @@
     def remove_padding(self, input_ids, seq_lens_this_time):
         cum_offsets_now = paddle.cumsum(paddle.max(seq_lens_this_time) - seq_lens_this_time)
         token_num = paddle.sum(seq_lens_this_time)
+        from paddlenlp_ops import get_padding_offset
+
         ids_remove_padding, cum_offsets, padding_offset = get_padding_offset(
             input_ids, cum_offsets_now, token_num, seq_lens_this_time
         )

paddlenlp/experimental/transformers/chatglm_v2/modeling.py

@@ -19,7 +19,6 @@
 import paddle.distributed.fleet as fleet
 import paddle.nn as nn
 from paddle.nn.quant import weight_quantize
-from paddlenlp_ops import get_padding_offset
 
 from paddlenlp.experimental.transformers.fused_transformer_layers import (
     FusedMultiTransformerBase,
@@ -202,6 +201,8 @@
     def remove_padding(self, input_ids, seq_lens_this_time):
         cum_offsets_now = paddle.cumsum(paddle.max(seq_lens_this_time) - seq_lens_this_time)
         token_num = paddle.sum(seq_lens_this_time)
+        from paddlenlp_ops import get_padding_offset
+
         ids_remove_padding, cum_offsets, padding_offset = get_padding_offset(
             input_ids, cum_offsets_now, token_num, seq_lens_this_time
         )

paddlenlp/experimental/transformers/fused_transformer_layers.py

@@ -15,7 +15,7 @@
 
 import paddle
 import paddle.distributed as dist
-from paddle.framework import LayerHelper, in_dynamic_mode
+from paddle.framework import LayerHelper, core, in_dynamic_mode
 from paddle.incubate.nn.functional import (
     fused_layer_norm,
     fused_rms_norm,
@@ -29,23 +29,24 @@
 from paddlenlp.utils.import_utils import is_paddlenlp_ops_available
 from paddlenlp.utils.log import logger
 
-if is_paddlenlp_ops_available():
+if not is_paddlenlp_ops_available():
+    logger.warning(
+        "The paddlenlp_ops package is not installed. you can read the docs and install it by hand, "
+        "you can refer to: https://github.com/PaddlePaddle/PaddleNLP/blob/develop/csrc/README.md"
+    )
+
+from paddlenlp_ops import rebuild_padding_v2
+
+if core.is_compiled_with_cuda():
     from paddlenlp_ops import (
         dequant_int8,
         encode_rotary_qk,
         qkv_transpose_split,
         quant_int8,
         rebuild_padding,
-        rebuild_padding_v2,
         transpose_remove_padding,
         write_cache_kv,
     )
-else:
-    logger.warning(
-        "The paddlenlp_ops package is not installed. you can read the docs and install it by hand, "
-        "you can refer to: https://github.com/PaddlePaddle/PaddleNLP/blob/develop/csrc/README.md"
-    )
-
 
 __all__ = [
     "FusedMultiTransformerConfig",
@@ -1348,6 +1349,9 @@
 class FusedBlockMultiTransformer(FusedMultiTransformerBase):
     def __init__(self, config: FusedMultiTransformerConfig):
         super().__init__(config)
+        if not core.is_compiled_with_cuda():
+            self.cache_k_per_batch_maxs = paddle.full(shape=[10, 6], fill_value=0, dtype="float32")
+            self.cache_v_per_batch_maxs = paddle.full(shape=[10, 6], fill_value=0, dtype="float32")
 
     def compute_attn(
         self,
@@ -1375,43 +1379,80 @@
             v_quant_scales = self.cache_v_scales
             k_dequant_scales = self.cache_k_out_scales
             v_dequant_scales = self.cache_v_out_scales
-
-        fmha_out = paddle.incubate.nn.functional.block_multihead_attention(
-            qkv_out,
-            caches[2 * i],
-            caches[2 * i + 1],
-            kwargs.get("seq_lens_encoder", None),
-            kwargs.get("seq_lens_decoder", None),
-            kwargs.get("seq_lens_this_time", None),
-            kwargs.get("padding_offsets", None),
-            kwargs.get("cum_offsets", None),
-            kwargs.get("cu_seqlens_q", None),
-            kwargs.get("cu_seqlens_k", None),
-            kwargs.get("block_tables", None),
-            pre_caches[2 * i] if pre_caches is not None else None,  # pre_key_cache
-            pre_caches[2 * i + 1] if pre_caches is not None else None,  # pre_value_cache
-            k_quant_scales[i] if k_quant_scales is not None else None,
-            v_quant_scales[i] if v_quant_scales is not None else None,
-            k_dequant_scales[i] if k_dequant_scales is not None else None,
-            v_dequant_scales[i] if v_dequant_scales is not None else None,
-            None,  # qkv_out_scales
-            None,  # qkv_bias
-            None,  # out_shifts
-            None,  # out_smooths
-            kwargs.get("max_enc_len_this_time", None),
-            kwargs.get("max_dec_len_this_time", None),
-            rotary_embs,
-            attn_mask,
-            kwargs.get("tgt_mask", None),
-            kwargs.get("max_input_length", -1),
-            kwargs.get("block_size", 64),
-            self.use_neox_rotary_style,
-            self.config.use_dynamic_cachekv_quant,
-            quant_round_type=self.config.quant_round_type,
-            quant_max_bound=self.config.quant_max_bound,
-            quant_min_bound=self.config.quant_min_bound,
-        )[0]
-
+        if not core.is_compiled_with_cuda():
+            fmha_out = paddle.incubate.nn.functional.block_multihead_attention_xpu(
+                qkv_out,
+                caches[2 * i],
+                caches[2 * i + 1],
+                kwargs.get("seq_lens_encoder", None),
+                kwargs.get("seq_lens_decoder", None),
+                kwargs.get("seq_lens_this_time", None),
+                kwargs.get("padding_offsets", None),
+                kwargs.get("cum_offsets", None),
+                kwargs.get("cu_seqlens_q", None),
+                kwargs.get("cu_seqlens_k", None),
+                kwargs.get("block_tables", None),
+                self.cache_k_per_batch_maxs,
+                self.cache_v_per_batch_maxs,
+                pre_caches[2 * i] if pre_caches is not None else None,  # pre_key_cache
+                pre_caches[2 * i + 1] if pre_caches is not None else None,  # pre_value_cache
+                k_quant_scales[i] if k_quant_scales is not None else None,
+                v_quant_scales[i] if v_quant_scales is not None else None,
+                k_dequant_scales[i] if k_dequant_scales is not None else None,
+                v_dequant_scales[i] if v_dequant_scales is not None else None,
+                None,  # qkv_out_scales
+                None,  # qkv_bias
+                None,  # out_shifts
+                None,  # out_smooths
+                kwargs.get("max_enc_len_this_time", None),
+                kwargs.get("max_dec_len_this_time", None),
+                rotary_embs,
+                attn_mask,
+                kwargs.get("tgt_mask", None),
+                kwargs.get("max_input_length", -1),
+                kwargs.get("block_size", 64),
+                self.use_neox_rotary_style,
+                self.config.use_dynamic_cachekv_quant,
+                quant_round_type=self.config.quant_round_type,
+                quant_max_bound=self.config.quant_max_bound,
+                quant_min_bound=self.config.quant_min_bound,
+            )[0]
+        else:
+            fmha_out = paddle.incubate.nn.functional.block_multihead_attention(
+                qkv_out,
+                caches[2 * i],
+                caches[2 * i + 1],
+                kwargs.get("seq_lens_encoder", None),
+                kwargs.get("seq_lens_decoder", None),
+                kwargs.get("seq_lens_this_time", None),
+                kwargs.get("padding_offsets", None),
+                kwargs.get("cum_offsets", None),
+                kwargs.get("cu_seqlens_q", None),
+                kwargs.get("cu_seqlens_k", None),
+                kwargs.get("block_tables", None),
+                pre_caches[2 * i] if pre_caches is not None else None,  # pre_key_cache
+                pre_caches[2 * i + 1] if pre_caches is not None else None,  # pre_value_cache
+                k_quant_scales[i] if k_quant_scales is not None else None,
+                v_quant_scales[i] if v_quant_scales is not None else None,
+                k_dequant_scales[i] if k_dequant_scales is not None else None,
+                v_dequant_scales[i] if v_dequant_scales is not None else None,
+                None,  # qkv_out_scales
+                None,  # qkv_bias
+                None,  # out_shifts
+                None,  # out_smooths
+                kwargs.get("max_enc_len_this_time", None),
+                kwargs.get("max_dec_len_this_time", None),
+                rotary_embs,
+                attn_mask,
+                kwargs.get("tgt_mask", None),
+                kwargs.get("max_input_length", -1),
+                kwargs.get("block_size", 64),
+                self.use_neox_rotary_style,
+                self.config.use_dynamic_cachekv_quant,
+                quant_round_type=self.config.quant_round_type,
+                quant_max_bound=self.config.quant_max_bound,
+                quant_min_bound=self.config.quant_min_bound,
+            )[0]
         out_linear_out = self.compute_out_linear(fmha_out, i)
 
         return out_linear_out