[Model] LoRA gptbigcode implementation (#3949)

csrc/punica/bgmv/bgmv_config.h

@@ -28,6 +28,7 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, narrow, 2752) \
     f(in_T, out_T, W_T, narrow, 2816) \
     f(in_T, out_T, W_T, narrow, 3072) \
+    f(in_T, out_T, W_T, narrow, 3328) \
     f(in_T, out_T, W_T, narrow, 3456) \
     f(in_T, out_T, W_T, narrow, 3584) \
     f(in_T, out_T, W_T, narrow, 4096) \
@@ -36,6 +37,7 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, narrow, 5504) \
     f(in_T, out_T, W_T, narrow, 5632) \
     f(in_T, out_T, W_T, narrow, 6144) \
+    f(in_T, out_T, W_T, narrow, 6400) \
     f(in_T, out_T, W_T, narrow, 6848) \
     f(in_T, out_T, W_T, narrow, 6912) \
     f(in_T, out_T, W_T, narrow, 7168) \
@@ -97,6 +99,7 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, 2752, narrow) \
     f(in_T, out_T, W_T, 2816, narrow) \
     f(in_T, out_T, W_T, 3072, narrow) \
+    f(in_T, out_T, W_T, 3328, narrow) \
     f(in_T, out_T, W_T, 3456, narrow) \
     f(in_T, out_T, W_T, 3584, narrow) \
     f(in_T, out_T, W_T, 4096, narrow) \
@@ -105,6 +108,7 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, 5504, narrow) \
     f(in_T, out_T, W_T, 5632, narrow) \
     f(in_T, out_T, W_T, 6144, narrow) \
+    f(in_T, out_T, W_T, 6400, narrow) \
     f(in_T, out_T, W_T, 6848, narrow) \
     f(in_T, out_T, W_T, 6912, narrow) \
     f(in_T, out_T, W_T, 7168, narrow) \

tests/lora/test_punica.py

@@ -58,6 +58,7 @@ H1 = H2 = [
     2560,
     2752,
     3072,
+    3328,
     3456,
     3584,
     4096,
@@ -66,6 +67,7 @@ H1 = H2 = [
     5504,
     5632,
     6144,
+    6400,
     6848,
     6912,
     7168,

vllm/lora/models.py

@@ -310,7 +310,9 @@ class LoRAModel:
             if part_name not in expected_lora_modules:
                 unexpected_modules.append(module)
         # loaded lora's target modules must be a subset of expected_lora_modules
+
         if unexpected_modules:
+            print(unexpected_modules, "modules")
             raise ValueError(
                 f"While loading {lora_dir}, expected"
                 f" target modules in {expected_lora_modules}"

vllm/model_executor/models/gpt_bigcode.py

@@ -25,7 +25,7 @@ from torch import nn
 from transformers import GPTBigCodeConfig
 
 from vllm.attention import Attention, AttentionMetadata
-from vllm.config import CacheConfig
+from vllm.config import CacheConfig, LoRAConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
@@ -191,14 +191,19 @@ class GPTBigCodeModel(nn.Module):
         config: GPTBigCodeConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
     ):
         super().__init__()
         self.config = config
         assert not config.add_cross_attention
 
         self.embed_dim = config.hidden_size
-
+        lora_vocab = (lora_config.lora_extra_vocab_size *
-        self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.wte = VocabParallelEmbedding(self.vocab_size,
+                                          self.embed_dim,
+                                          org_num_embeddings=config.vocab_size)
         self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
         self.h = nn.ModuleList([
             GPTBigCodeBlock(config, cache_config, quant_config)
@@ -226,19 +231,35 @@ class GPTBigCodeModel(nn.Module):
 
 
 class GPTBigCodeForCausalLM(nn.Module):
+    packed_modules_mapping = {"c_attn": ["c_attn"]}
+
+    supported_lora_modules = ["c_fc", "c_proj", "wte", "lm_head", "c_attn"]
+
+    embedding_modules = {
+        "wte": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+
+    embedding_padding_modules = []
 
     def __init__(
         self,
         config: GPTBigCodeConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        lora_config: Optional[LoRAConfig] = None,
     ):
         super().__init__()
         self.config = config
         self.quant_config = quant_config
-        self.transformer = GPTBigCodeModel(config, cache_config, quant_config)
+        self.transformer = GPTBigCodeModel(config, cache_config, quant_config,
+                                           lora_config)
         self.lm_head_weight = self.transformer.wte.weight
-        self.logits_processor = LogitsProcessor(config.vocab_size)
+        self.unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
+                                                config.vocab_size)
         self.sampler = Sampler()
 
     def forward(