chatglm2 support block_attn and fix some bugs

llm/predict/predictor.py

@@ -134,16 +134,16 @@ class PredictorArgument:
  },
  )
 
- @property
- def total_max_length(self):
- if self.device == "npu":
- return self.src_length + self.max_length
- else:
- return 8192 # Maximum sequence length.
+ total_max_length: int = field(
+ default=2048, metadata={"help": "Super parameter. Maximum sequence length(encoder+decoder)."}
+ )
 
  def __post_init__(self):
  if self.append_attn:
  self.block_attn = True
+ assert (
+ self.src_length + self.max_length <= self.total_max_length
+ ), "src_length + max_length should smaller than total_max_length."
 
 
 @dataclass
@@ -1352,13 +1352,19 @@ def create_predictor(
  predictor_args.model_name_or_path, config=config, dtype=predictor_args.dtype
  )
  model.eval()
-
  elif "chatglmv2forcausallm" in config.architectures[0].lower():
- from paddlenlp.experimental.transformers import (
- ChatGLMv2ForCausalLMInferenceModel as Model,
- )
-
- model = Model.from_pretrained(
+ predictor_args.total_max_length = config.seq_length
+ if predictor_args.block_attn:
+ config.block_size = predictor_args.block_size
+ config.max_seq_len = predictor_args.total_max_length
+ from paddlenlp.experimental.transformers import (
+ ChatGLMv2ForCausalLMBlockInferenceModel as ChatGLMv2InferenceModel,
+ )
+ else:
+ from paddlenlp.experimental.transformers import (
+ ChatGLMv2ForCausalLMInferenceModel as ChatGLMv2InferenceModel,
+ )
+ model = ChatGLMv2InferenceModel.from_pretrained(
  predictor_args.model_name_or_path, config=config, dtype=predictor_args.dtype
  )
  model.eval()
@@ -1522,19 +1528,19 @@ def create_predictor(
  config, predictor_args.batch_size, predictor_args.total_max_length
  )
  elif "chatglmv2forcausallm" in config.architectures[0].lower():
- from paddlenlp.experimental.transformers import (
-  ChatGLMv2ForCausalLMInferenceModel,
- )
-
- cache_kvs_shape = ChatGLMv2ForCausalLMInferenceModel.get_cache_kvs_shape(
- config, predictor_args.batch_size, predictor_args.total_max_length
- )
- elif "chatglmv2forcausallm" in config.architectures[0].lower():
- from paddlenlp.experimental.transformers import (
- ChatGLMv2ForCausalLMInferenceModel,
- )
+ predictor_args.total_max_length = config.seq_length
+ if predictor_args.block_attn:
+  config.block_size = predictor_args.block_size
+ config.max_seq_len = predictor_args.total_max_length
+  from paddlenlp.experimental.transformers import (
+  ChatGLMv2ForCausalLMBlockInferenceModel as ChatGLMv2InferenceModel,
+  )
+  else:
+  from paddlenlp.experimental.transformers import (
+  ChatGLMv2ForCausalLMInferenceModel as ChatGLMv2InferenceModel,
+  )
 
- cache_kvs_shape = ChatGLMv2ForCausalLMInferenceModel.get_cache_kvs_shape(
+ cache_kvs_shape = ChatGLMv2InferenceModel.get_cache_kvs_shape(
  config, predictor_args.batch_size, predictor_args.total_max_length
  )
  elif "chatglmforcausallm" in config.architectures[0].lower():

paddlenlp/experimental/transformers/chatglm_v2/modeling.py

@@ -21,13 +21,17 @@
 from paddle.nn.quant import weight_quantize
 
 from paddlenlp.experimental.transformers.fused_transformer_layers import (
+ FusedBlockMultiTransformer,
+ FusedBlockMultiTransformerWeightOnly,
  FusedMultiTransformerBase,
  FusedMultiTransformerConfig,
  FusedMultiTransformerWeightOnly,
 )
 from paddlenlp.experimental.transformers.generation_utils import (
+ GenerationBlockInferenceModel,
  GenerationInferenceModel,
 )
+from paddlenlp.experimental.transformers.utils import infererence_model_from_pretrained
 from paddlenlp.transformers import ChatGLMv2Config, ChatGLMv2PretrainedModel
 from paddlenlp.transformers.chatglm_v2.modeling import (
  Embedding,
@@ -39,9 +43,7 @@
  register_base_model,
 )
 
-__all__ = [
- "ChatGLMv2ForCausalLMInferenceModel",
-]
+__all__ = ["ChatGLMv2ForCausalLMInferenceModel", "ChatGLMv2ForCausalLMBlockInferenceModel"]
 
 
 @register_base_model
@@ -176,17 +178,20 @@
  kv_num_heads=config.multi_query_group_num,
  )
 
- if self.use_weight_only:
- self.transformer_block = FusedMultiTransformerWeightOnly(transformer_config)
- else:
- self.transformer_block = FusedMultiTransformerBase(transformer_config)
+ self.set_transformer_block(transformer_config)
 
  self.post_layer_norm = config.post_layer_norm
  if self.post_layer_norm:
  LayerNormFunc = RMSNorm if config.rmsnorm else nn.LayerNorm
  # Final layer norm before output.
  self.final_layernorm = LayerNormFunc(config.hidden_size, epsilon=config.layernorm_epsilon, config=config)
 
+ def set_transformer_block(self, transformer_config):
+ if self.use_weight_only:
+ self.transformer_block = FusedMultiTransformerWeightOnly(transformer_config)
+ else:
+ self.transformer_block = FusedMultiTransformerBase(transformer_config)
+
  def get_input_embeddings(self):
  return self.embedding.word_embeddings
 
@@ -341,7 +346,7 @@
 
  if self.use_weight_only:
  linear_quanted_weight_tensor, linear_weight_scale_tensor = weight_quantize(
- out_proj_weight, algo=self.quant_algo
+ paddle.to_tensor(out_proj_weight), algo=self.quant_algo
  )
  self.transformer_block.linear_weights[i].set_value(linear_quanted_weight_tensor)
  self.transformer_block.linear_weights_scale[i].set_value(linear_weight_scale_tensor)
@@ -352,7 +357,7 @@
 
  if self.use_weight_only:
  ffn1_quanted_weight_tensor, ffn1_weight_scale_tensor = weight_quantize(
- ffn1_weight, algo=self.quant_algo
+ paddle.to_tensor(ffn1_weight), algo=self.quant_algo
  )
  self.transformer_block.ffn1_weights[i].set_value(ffn1_quanted_weight_tensor)
  self.transformer_block.ffn1_weights_scale[i].set_value(ffn1_weight_scale_tensor)
@@ -361,20 +366,87 @@
 
  if self.use_weight_only:
  ffn2_quanted_weight_tensor, ffn2_weight_scale_tensor = weight_quantize(
- ffn2_weight, algo=self.quant_algo
+ paddle.to_tensor(ffn2_weight), algo=self.quant_algo
  )
  self.transformer_block.ffn2_weights[i].set_value(ffn2_quanted_weight_tensor)
  self.transformer_block.ffn2_weights_scale[i].set_value(ffn2_weight_scale_tensor)
  else:
  self.transformer_block.ffn2_weights[i].set_value(ffn2_weight)
 
 
+@register_base_model
+class ChatGLMv2BlockInferenceModel(ChatGLMv2InferenceModel):
+ def __init__(self, config: ChatGLMv2Config):
+ super().__init__(config)
+ self.max_seq_len = config.max_sequence_length
+ self.block_size = config.block_size
+
+ def set_transformer_block(self, transformer_config):
+ if self.use_weight_only:
+ self.transformer_block = FusedBlockMultiTransformerWeightOnly(transformer_config)
+ else:
+ self.transformer_block = FusedBlockMultiTransformer(transformer_config)
+
+ 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
+ )
+ return ids_remove_padding, padding_offset, cum_offsets, cu_seqlens_q, cu_seqlens_k
+
+ def forward(
+ self,
+ input_ids=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ caches=None,
+ pre_caches=None,
+ output_attentions=False,
+ output_hidden_states=None,
+ return_dict=False,
+ **kwargs,
+ ):
+ seq_lens_this_time = kwargs.get("seq_lens_this_time", None)
+ rope_emb = kwargs.get("rope_emb", None)
+ ids_remove_padding, padding_offset, cum_offsets, cu_seqlens_q, cu_seqlens_k = self.remove_padding(
+ input_ids, seq_lens_this_time
+ )
+ kwargs["cu_seqlens_q"] = cu_seqlens_q
+ kwargs["cu_seqlens_k"] = cu_seqlens_k
+ kwargs["padding_offsets"] = padding_offset
+ kwargs["max_input_length"] = self.max_seq_len
+
+ inputs_embeds = self.embedding.word_embeddings(ids_remove_padding)
+
+ with dy2st_nocheck_guard_context():
+ hidden_states, _ = self.transformer_block(
+ input_ids=input_ids,
+ src=inputs_embeds,
+ cum_offsets=cum_offsets,
+ attn_mask=attention_mask,
+ caches=caches,
+ pre_caches=None,
+ rotary_embs=rope_emb,
+ **kwargs,
+ )
+ hidden_states = self.final_layernorm(hidden_states)
+
+ return tuple(v for v in [hidden_states, None, None, None] if v is not None)
+
+
 class ChatGLMv2ForCausalLMInferenceModel(GenerationInferenceModel, ChatGLMv2PretrainedModel):
  def __init__(self, config: ChatGLMv2Config):
  super().__init__(config)
  self.max_sequence_length = config.max_sequence_length
  self.chatglm_v2 = ChatGLMv2InferenceModel(config)
 
+ @classmethod
+ def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
+ return infererence_model_from_pretrained(cls, pretrained_model_name_or_path, args, kwargs)
+
  @classmethod
  def get_cache_kvs_shape(cls, config: ChatGLMv2Config, max_batch_size: int = None, max_length: int = None):
  """get cache_kvs tensor for opt model
@@ -487,3 +559,119 @@
  @paddle.no_grad()
  def set_state_dict(self, state_dict):
  self.chatglm_v2.set_state_dict(state_dict)
+
+
+class ChatGLMv2ForCausalLMBlockInferenceModel(GenerationBlockInferenceModel, ChatGLMv2PretrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.chatglm_v2 = ChatGLMv2BlockInferenceModel(config)
+ self.max_sequence_length = config.max_sequence_length
+
+ @classmethod
+ def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
+ return infererence_model_from_pretrained(cls, pretrained_model_name_or_path, args, kwargs)
+
+ @classmethod
+ def get_cache_kvs_shape(cls, config: ChatGLMv2Config, max_batch_size: int = None, max_length: int = None):
+ """get cache_kvs tensor for chatglmv2 model
+
+ Args:
+ max_batch_size (int): the max batch size
+ max_length (int | None, optional): the max_length of cache_kvs. Defaults to None.
+
+ Returns:
+ list[paddle.Tensor]: the list tensor shape for cache
+ """
+ max_block_per_seq = (config.max_seq_len + config.block_size - 1) // config.block_size
+ if max_batch_size == -1:
+ max_block_nums = None
+ else:
+ max_block_nums = max_batch_size * max_block_per_seq
+
+ cache_kvs = []
+ for _ in range(config.num_hidden_layers):
+ cache_kv_shape = [
+ max_block_nums,
+ config.multi_query_group_num,
+ config.block_size,
+ config.hidden_size // config.num_attention_heads,
+ ]
+ cache_kvs.append(cache_kv_shape)
+ cache_kvs.append(cache_kv_shape)
+ return cache_kvs
+
+ def prepare_inputs_for_generation(self, **kwargs):
+ # only last token for inputs_ids if cache is defined in kwargs
+ input_ids = kwargs["input_ids"]
+ src_mask = kwargs.get("src_mask", None)
+ block_tables = kwargs.get("block_tables", None)
+
+ pre_caches = kwargs.get("pre_caches", None)
+ caches = kwargs.get("caches", None)
+
+ rope_emb = kwargs["rope_emb"]
+ seq_lens_this_time = kwargs["seq_lens_this_time"]
+ seq_lens_encoder = kwargs["seq_lens_encoder"]
+ seq_lens_decoder = kwargs["seq_lens_decoder"]
+ k_quant_scales = kwargs.get("k_quant_scales", None)
+ v_quant_scales = kwargs.get("v_quant_scales", None)
+ k_dequant_scales = kwargs.get("k_dequant_scales", None)
+ v_dequant_scales = kwargs.get("v_dequant_scales", None)
+ model_inputs = {
+ "input_ids": input_ids,
+ "src_mask": src_mask,
+ "rope_emb": rope_emb,
+ "pre_caches": pre_caches,
+ "caches": caches,
+ "seq_lens_this_time": seq_lens_this_time,
+ "seq_lens_encoder": seq_lens_encoder,
+ "seq_lens_decoder": seq_lens_decoder,
+ "block_tables": block_tables,
+ "k_quant_scales": k_quant_scales,
+ "v_quant_scales": v_quant_scales,
+ "k_dequant_scales": k_dequant_scales,
+ "v_dequant_scales": v_dequant_scales,
+ }
+ return model_inputs
+
+ def forward(
+ self,
+ input_ids,
+ src_mask=None,
+ pre_caches=None,
+ caches=None,
+ seq_lens_this_time=None,
+ seq_lens_encoder=None,
+ seq_lens_decoder=None,
+ rope_emb=None,
+ block_tables=None,
+ k_quant_scales=None,
+ v_quant_scales=None,
+ k_dequant_scales=None,
+ v_dequant_scales=None,
+ ):
+ outputs = self.chatglm_v2(
+ input_ids,
+ src_mask=src_mask,
+ caches=caches,
+ rope_emb=rope_emb,
+ block_tables=block_tables,
+ pre_caches=pre_caches,
+ seq_lens_this_time=seq_lens_this_time,
+ seq_lens_encoder=seq_lens_encoder,
+ seq_lens_decoder=seq_lens_decoder,
+ k_quant_scales=k_quant_scales,
+ v_quant_scales=v_quant_scales,
+ k_dequant_scales=k_dequant_scales,
+ v_dequant_scales=v_dequant_scales,
+ )
+
+ hidden_states = outputs[0]
+ lm_logits = self.chatglm_v2.output_layer(hidden_states)
+ output = (lm_logits,) + outputs[1:]
+
+ return output
+
+ @paddle.no_grad()
+ def set_state_dict(self, state_dict):
+ self.chatglm_v2.set_state_dict(state_dict)