Accelerate LLaMA model loading (#234)

vllm/model_executor/models/aquila.py

@@ -34,7 +34,8 @@ from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+from vllm.model_executor.weight_utils import (
+    hf_model_weights_iterator, load_padded_tensor_parallel_vocab,
     load_tensor_parallel_weights)
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
@@ -280,8 +281,7 @@ class AquilaForCausalLM(nn.Module):
         return next_tokens
 
     _column_parallel_weights = [
-        "embed_tokens.weight", "lm_head.weight", "qkv_proj.weight",
-        "gate_proj.weight", "up_proj.weight"
+        "qkv_proj.weight", "gate_proj.weight", "up_proj.weight"
     ]
     _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
 
@@ -309,16 +309,6 @@ class AquilaForCausalLM(nn.Module):
             if "rotary_emb.inv_freq" in name:
                 continue
 
-            if "embed_tokens" in name or "lm_head" in name:
-                param = state_dict[name]
-                # Consider padding in the vocab size.
-                padded_vocab_size = (param.shape[0] * tp_size)
-                num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows,
-                                         loaded_weight.shape[1])
-                extra_rows = extra_rows.to(loaded_weight)
-                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
-
             is_attention_weight = False
             for weight_name, shard_size, offset in attention_weight_specs:
                 if weight_name not in name:
@@ -356,6 +346,11 @@ class AquilaForCausalLM(nn.Module):
                 continue
 
             param = state_dict[name]
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
             load_tensor_parallel_weights(param, loaded_weight, name,
                                          self._column_parallel_weights,
                                          self._row_parallel_weights,

vllm/model_executor/models/baichuan.py

@@ -32,9 +32,11 @@ from vllm.sequence import SequenceOutputs
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
-from vllm.model_executor.layers.attention import PagedAttentionWithRoPE, PagedAttentionWithALiBi
+from vllm.model_executor.layers.attention import (PagedAttentionWithRoPE,
+                                                  PagedAttentionWithALiBi)
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+from vllm.model_executor.weight_utils import (
+    hf_model_weights_iterator, load_padded_tensor_parallel_vocab,
     load_tensor_parallel_weights)
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
@@ -295,10 +297,7 @@ class BaiChuanBaseForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
-    _column_parallel_weights = [
-        "embed_tokens.weight",
-        "lm_head.weight",
-    ]
+    _column_parallel_weights = []
     _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
 
     def load_weights(self,
@@ -314,16 +313,6 @@ class BaiChuanBaseForCausalLM(nn.Module):
             if "rotary_emb.inv_freq" in name:
                 continue
 
-            if "embed_tokens" in name or "lm_head" in name:
-                # Consider padding in the vocab size.
-                param = state_dict[name]
-                padded_vocab_size = param.shape[0] * tp_world_size
-                num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows,
-                                         loaded_weight.shape[1])
-                extra_rows = extra_rows.to(loaded_weight)
-                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
-
             if "W_pack" in name:
                 total_num_heads = self.config.num_attention_heads
                 hidden_size = self.config.hidden_size
@@ -355,6 +344,12 @@ class BaiChuanBaseForCausalLM(nn.Module):
                 continue
 
             param = state_dict[name]
+
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tp_rank)
+                continue
+
             load_tensor_parallel_weights(
                 param,
                 loaded_weight,

vllm/model_executor/models/gpt2.py

@@ -31,7 +31,8 @@ from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.attention import PagedAttention
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+from vllm.model_executor.weight_utils import (
+    hf_model_weights_iterator, load_padded_tensor_parallel_vocab,
     load_tensor_parallel_weights)
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
@@ -224,7 +225,7 @@ class GPT2LMHeadModel(nn.Module):
                                    input_metadata)
         return next_tokens
 
-    _column_parallel_weights = ["wte.weight", "c_fc.weight", "c_fc.bias"]
+    _column_parallel_weights = ["c_fc.weight", "c_fc.bias"]
     _row_parallel_weights = ["c_proj.weight"]
 
     def load_weights(self,
@@ -261,14 +262,9 @@ class GPT2LMHeadModel(nn.Module):
             param = state_dict[name]
 
             if name == "transformer.wte.weight":
-                # Consider padding in the vocab size.
-                padded_vocab_size = (param.shape[0] *
-                                     tensor_model_parallel_world_size)
-                num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows,
-                                         loaded_weight.shape[1])
-                extra_rows = extra_rows.to(loaded_weight)
-                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
 
             # For the fused QKV linear layer, manually shard the weights.
             if "c_attn" in name:

vllm/model_executor/models/gpt_bigcode.py

@@ -32,7 +32,8 @@ from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.attention import PagedAttention
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+from vllm.model_executor.weight_utils import (
+    hf_model_weights_iterator, load_padded_tensor_parallel_vocab,
     load_tensor_parallel_weights)
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
@@ -252,7 +253,7 @@ class GPTBigCodeForCausalLM(nn.Module):
                                    input_metadata)
         return next_tokens
 
-    _column_parallel_weights = ["wte.weight", "c_fc.weight", "c_fc.bias"]
+    _column_parallel_weights = ["c_fc.weight", "c_fc.bias"]
     _row_parallel_weights = ["c_proj.weight"]
 
     def load_weights(self,
@@ -328,14 +329,9 @@ class GPTBigCodeForCausalLM(nn.Module):
             param = state_dict[name]
 
             if name == "transformer.wte.weight":
-                # Consider padding in the vocab size.
-                padded_vocab_size = param.shape[
-                    0] * tensor_model_parallel_world_size
-                num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows,
-                                         loaded_weight.shape[1])
-                extra_rows = extra_rows.to(loaded_weight)
-                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
 
             load_tensor_parallel_weights(param, loaded_weight, name,
                                          self._column_parallel_weights,

vllm/model_executor/models/internlm.py

@@ -14,7 +14,8 @@ from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.parallel_utils.tensor_parallel import (
     ColumnParallelLinear, RowParallelLinear, VocabParallelEmbedding)
-from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+from vllm.model_executor.weight_utils import (
+    hf_model_weights_iterator, load_padded_tensor_parallel_vocab,
     load_tensor_parallel_weights)
 from vllm.sequence import SequenceOutputs
 
@@ -225,8 +226,7 @@ class InternLMForCausalLM(nn.Module):
         return next_tokens
 
     _column_parallel_weights = [
-        "embed_tokens.weight", "lm_head.weight", "qkv_proj.weight",
-        "gate_proj.weight", "up_proj.weight"
+        "qkv_proj.weight", "gate_proj.weight", "up_proj.weight"
     ]
     _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
 
@@ -234,8 +234,6 @@ class InternLMForCausalLM(nn.Module):
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      use_np_cache: bool = False):
-        tensor_model_parallel_world_size = (
-            get_tensor_model_parallel_world_size())
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
@@ -246,14 +244,9 @@ class InternLMForCausalLM(nn.Module):
 
             if "embed_tokens" in name or "lm_head" in name:
                 param = state_dict[name]
-                # Consider padding in the vocab size.
-                padded_vocab_size = (param.shape[0] *
-                                     tensor_model_parallel_world_size)
-                num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows,
-                                         loaded_weight.shape[1])
-                extra_rows = extra_rows.to(loaded_weight)
-                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
 
             is_attention_weight = False
             for stride_id, att_weight_name in enumerate(

vllm/model_executor/models/llama.py

@@ -36,8 +36,9 @@ from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
-                                              load_tensor_parallel_weights)
+from vllm.model_executor.weight_utils import (
+    load_tensor_parallel_weights, load_padded_tensor_parallel_vocab,
+    hf_model_weights_iterator)
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.parallel_utils.tensor_parallel import (
@@ -263,15 +264,15 @@ class LlamaForCausalLM(nn.Module):
         return next_tokens
 
     _column_parallel_weights = [
-        "embed_tokens.weight", "lm_head.weight", "qkv_proj.weight",
-        "gate_proj.weight", "up_proj.weight"
+        "qkv_proj.weight", "gate_proj.weight", "up_proj.weight"
     ]
     _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
 
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     use_np_cache: bool = False):
+                     use_np_cache: bool = False,
+                     use_safetensor: bool = True):
         tp_size = get_tensor_model_parallel_world_size()
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         q_proj_shard_size = (self.config.hidden_size // tp_size)
@@ -288,20 +289,10 @@ class LlamaForCausalLM(nn.Module):
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, use_np_cache, use_safetensor):
             if "rotary_emb.inv_freq" in name:
                 continue
 
-            if "embed_tokens" in name or "lm_head" in name:
-                param = state_dict[name]
-                # Consider padding in the vocab size.
-                padded_vocab_size = (param.shape[0] * tp_size)
-                num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows,
-                                         loaded_weight.shape[1])
-                extra_rows = extra_rows.to(loaded_weight)
-                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
-
             is_attention_weight = False
             for weight_name, shard_size, offset in attention_weight_specs:
                 if weight_name not in name:
@@ -339,6 +330,12 @@ class LlamaForCausalLM(nn.Module):
                 continue
 
             param = state_dict[name]
+
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
             load_tensor_parallel_weights(param, loaded_weight, name,
                                          self._column_parallel_weights,
                                          self._row_parallel_weights,

vllm/model_executor/models/qwen.py

@@ -20,6 +20,7 @@ from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.weight_utils import (
     hf_model_weights_iterator,
+    load_padded_tensor_parallel_vocab,
     load_tensor_parallel_weights,
 )
 from vllm.model_executor.parallel_utils.parallel_state import (
@@ -241,7 +242,7 @@ class QWenLMHeadModel(nn.Module):
                                    input_metadata)
         return next_tokens
 
-    _column_parallel_weights = ["wte.weight", "lm_head.weight"]
+    _column_parallel_weights = []
     _row_parallel_weights = ["c_proj.weight"]
 
     def load_weights(
@@ -259,16 +260,6 @@ class QWenLMHeadModel(nn.Module):
             if "rotary_emb.inv_freq" in name:
                 continue
 
-            if "wte" in name or "lm_head" in name:
-                # Consider padding in the vocab size.
-                param = state_dict[name]
-                padded_vocab_size = param.shape[0] * tp_world_size
-                num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows,
-                                         loaded_weight.shape[1])
-                extra_rows = extra_rows.to(loaded_weight)
-                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
-
             if "c_attn" in name:
                 total_num_heads = self.config.num_attention_heads
                 hidden_size = self.config.hidden_size
@@ -306,6 +297,12 @@ class QWenLMHeadModel(nn.Module):
                 continue
 
             param = state_dict[name]
+
+            if "wte" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tp_rank)
+                continue
+
             load_tensor_parallel_weights(
                 param,
                 loaded_weight,

vllm/model_executor/weight_utils.py

@@ -3,13 +3,19 @@ import filelock
 import glob
 import json
 import os
-from typing import Iterator, List, Optional, Tuple
+from collections import defaultdict
+from typing import Iterator, List, Optional, Tuple, Any
 
 from huggingface_hub import snapshot_download
+from safetensors.torch import load_file, save_file, safe_open
 import numpy as np
 import torch
 from tqdm.auto import tqdm
 
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
 
 class Disabledtqdm(tqdm):
 
@@ -17,43 +23,118 @@ class Disabledtqdm(tqdm):
         super().__init__(*args, **kwargs, disable=True)
 
 
-def hf_model_weights_iterator(
-    model_name_or_path: str,
-    cache_dir: Optional[str] = None,
-    use_np_cache: bool = False,
-) -> Iterator[Tuple[str, torch.Tensor]]:
-    # Prepare file lock directory to prevent multiple processes from
-    # downloading the same model weights at the same time.
+def get_lock(model_name_or_path: str, cache_dir: Optional[str] = None):
     lock_dir = cache_dir if cache_dir is not None else "/tmp"
     lock_file_name = model_name_or_path.replace("/", "-") + ".lock"
     lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name))
-
+    return lock
+
+
+def _shared_pointers(tensors):
+    ptrs = defaultdict(list)
+    for k, v in tensors.items():
+        ptrs[v.data_ptr()].append(k)
+    failing = []
+    for _, names in ptrs.items():
+        if len(names) > 1:
+            failing.append(names)
+    return failing
+
+
+def convert_bin_to_safetensor_file(
+    pt_filename: str,
+    sf_filename: str,
+):
+    loaded = torch.load(pt_filename, map_location="cpu")
+    if "state_dict" in loaded:
+        loaded = loaded["state_dict"]
+    shared = _shared_pointers(loaded)
+    for shared_weights in shared:
+        for name in shared_weights[1:]:
+            loaded.pop(name)
+
+    # For tensors to be contiguous
+    loaded = {k: v.contiguous() for k, v in loaded.items()}
+
+    dirname = os.path.dirname(sf_filename)
+    os.makedirs(dirname, exist_ok=True)
+    save_file(loaded, sf_filename, metadata={"format": "pt"})
+
+    # check file size
+    sf_size = os.stat(sf_filename).st_size
+    pt_size = os.stat(pt_filename).st_size
+    if (sf_size - pt_size) / pt_size > 0.01:
+        raise RuntimeError(f"""The file size different is more than 1%:
+         - {sf_filename}: {sf_size}
+         - {pt_filename}: {pt_size}
+         """)
+
+    # check if the tensors are the same
+    reloaded = load_file(sf_filename)
+    for k in loaded:
+        pt_tensor = loaded[k]
+        sf_tensor = reloaded[k]
+        if not torch.equal(pt_tensor, sf_tensor):
+            raise RuntimeError(f"The output tensors do not match for key {k}")
+
+
+def prepare_hf_model_weights(
+    model_name_or_path: str,
+    cache_dir: Optional[str] = None,
+    use_safetensor: bool = False,
+):
     # Download model weights from huggingface.
     is_local = os.path.isdir(model_name_or_path)
+    allow_patterns = "*.safetensors" if use_safetensor else "*.bin"
     if not is_local:
-        with lock:
+        # Use file lock to prevent multiple processes from
+        # downloading the same model weights at the same time.
+        with get_lock(model_name_or_path, cache_dir):
             hf_folder = snapshot_download(model_name_or_path,
-                                          allow_patterns="*.bin",
+                                          allow_patterns=allow_patterns,
                                           cache_dir=cache_dir,
                                           tqdm_class=Disabledtqdm)
     else:
         hf_folder = model_name_or_path
-
-    hf_bin_files = [
-        x for x in glob.glob(os.path.join(hf_folder, "*.bin"))
-        if not x.endswith("training_args.bin")
+    hf_weights_files = glob.glob(os.path.join(hf_folder, allow_patterns))
+    if not use_safetensor:
+        hf_weights_files = [
+            x for x in hf_weights_files if not x.endswith("training_args.bin")
         ]
 
+    if len(hf_weights_files) == 0 and use_safetensor:
+        logger.warning("No *.safetensors files found, "
+                       "fall back to *.bin files")
+        return prepare_hf_model_weights(model_name_or_path,
+                                        cache_dir=cache_dir,
+                                        use_safetensor=False)
+    return hf_folder, hf_weights_files, use_safetensor
+
+
+def hf_model_weights_iterator(
+    model_name_or_path: str,
+    cache_dir: Optional[str] = None,
+    use_np_cache: bool = False,
+    use_safetensor: bool = False,
+) -> Iterator[Tuple[str, torch.Tensor]]:
+    hf_folder, hf_weights_files, use_safetensor = prepare_hf_model_weights(
+        model_name_or_path, cache_dir=cache_dir, use_safetensor=use_safetensor)
+
     if use_np_cache:
+        # Currently np_cache only support *.bin checkpoints
+        assert use_safetensor is False
+
         # Convert the model weights from torch tensors to numpy arrays for
         # faster loading.
         np_folder = os.path.join(hf_folder, "np")
         os.makedirs(np_folder, exist_ok=True)
         weight_names_file = os.path.join(np_folder, "weight_names.json")
-        with lock:
+        # Use file lock to prevent multiple processes from
+        # dumping the same model weights to numpy at the same time.
+        with get_lock(model_name_or_path, cache_dir):
             if not os.path.exists(weight_names_file):
                 weight_names = []
-                for bin_file in hf_bin_files:
+                for bin_file in hf_weights_files:
                     state = torch.load(bin_file, map_location="cpu")
                     for name, param in state.items():
                         param_path = os.path.join(np_folder, name)
@@ -71,8 +152,14 @@ def hf_model_weights_iterator(
             with open(param_path, "rb") as f:
                 param = np.load(f)
             yield name, torch.from_numpy(param)
+    elif use_safetensor:
+        for st_file in hf_weights_files:
+            with safe_open(st_file, framework="pt") as f:
+                for name in f.keys():
+                    param = f.get_slice(name)
+                    yield name, param
     else:
-        for bin_file in hf_bin_files:
+        for bin_file in hf_weights_files:
             state = torch.load(bin_file, map_location="cpu")
             for name, param in state.items():
                 yield name, param
@@ -80,9 +167,26 @@ def hf_model_weights_iterator(
             torch.cuda.empty_cache()
 
 
+def load_padded_tensor_parallel_vocab(
+    param: torch.Tensor,
+    loaded_weight: Any,  # `torch.Tensor` or `PySafeSlice`
+    tensor_model_parallel_rank: int,
+) -> None:
+    shard_size = param.shape[0]
+    start_idx = tensor_model_parallel_rank * shard_size
+    end_idx = (tensor_model_parallel_rank + 1) * shard_size
+    loaded_weight = loaded_weight[start_idx:end_idx]
+
+    # convert PySafeSlice object to torch.Tensor
+    if not isinstance(loaded_weight, torch.Tensor):
+        loaded_weight = loaded_weight[:]
+
+    param[:loaded_weight.shape[0]].copy_(loaded_weight)
+
+
 def load_tensor_parallel_weights(
     param: torch.Tensor,
-    loaded_weight: torch.Tensor,
+    loaded_weight: Any,  # `torch.Tensor` or `PySafeSlice`
     param_name: str,
     column_parallel_weight_names: List[str],
     row_parallel_weight_names: List[str],
@@ -102,6 +206,11 @@ def load_tensor_parallel_weights(
             end_idx = (tensor_model_parallel_rank + 1) * shard_size
             loaded_weight = loaded_weight[:, start_idx:end_idx]
             break
+
+    # convert PySafeSlice object to torch.Tensor
+    if not isinstance(loaded_weight, torch.Tensor):
+        loaded_weight = loaded_weight[:]
+
     assert param.shape == loaded_weight.shape, (
         f"{param_name} shape mismatch between model and checkpoint: "
         f"{param.shape} != {loaded_weight.shape}")