[Slim-LM] Introduce HFLoad for loading Pytorch and SafeTensor weights (…

…mlc-ai#1113)

python/mlc_chat/compiler/parameter/__init__.py

@@ -2,5 +2,5 @@
 A subpackage of the compiler that represents mapping between external parameters, quantized
 parameters and parameters in MLC-defined models.
 """
-from .hf_torch_loader import HFTorchLoader
+from .hf_loader import HFLoader
 from .mapping import ExternMapping, QuantizeMapping

python/mlc_chat/compiler/parameter/hf_torch_loader.py → python/mlc_chat/compiler/parameter/hf_loader.py

@@ -1,96 +1,39 @@
 """A weight loader for HuggingFace's PyTorch format"""
-import dataclasses
+
 import gc
 import json
 import logging
-import time
 from collections import OrderedDict, defaultdict
-from contextlib import contextmanager
 from pathlib import Path
-from typing import Dict, Iterator, List, Set, Tuple
+from typing import Dict, Iterator, List, Tuple
 
 import numpy as np
 from tqdm import tqdm
 from tvm.runtime import NDArray
 from tvm.runtime.ndarray import array as as_ndarray
 
 from .mapping import ExternMapping
+from .stats import Stats
+from .utils import check_parameter_usage, load_safetensor_shard, load_torch_shard
 
 logger = logging.getLogger(__name__)
 
 
-@dataclasses.dataclass
-class Stats:
-    """Statistics of the loading process of HuggingFace PyTorch loader.
-
-    Attributes
-    ----------
-    load_time_sec : float
-        Time used in loading the parameters.
-
-    map_time_sec : float
-        Time used in applying the mapping function, i.e. `ExternMapping.map_func`.
-
-    quant_time_sec : float
-        Time used in quantizing the parameters, i.e. `QuantizeMapping.quant_func`.
-
-    current_memory_gb : float
-        The current RAM usage in GB.
-
-    total_memory_gb : float
-        The total size data loaded from disk in GB.
-
-    max_memory_gb : float
-        The maximum RAM usage in GB.
-    """
-
-    load_time_sec: float = 0.0
-    map_time_sec: float = 0.0
-    quant_time_sec: float = 0.0
-
-    current_memory_gb: float = 0.0
-    total_memory_gb: float = 0.0
-    max_memory_gb: float = 0.0
-
-    def timer(self, attr):
-        """A context manager to time the scope and add the time to the attribute."""
-
-        @contextmanager
-        def timed_scope():
-            start_time = time.time()
-            yield
-            elapsed_time = time.time() - start_time
-            setattr(self, attr, getattr(self, attr) + elapsed_time)
-
-        return timed_scope()
-
-    def mem_add(self, nbytes: int):
-        """Add the memory usage by the given number of bytes."""
-        mem_gb = float(nbytes) / float(1024**3)
-        self.current_memory_gb += mem_gb
-        self.total_memory_gb += mem_gb
-        self.max_memory_gb = max(self.max_memory_gb, self.current_memory_gb)
-
-    def mem_rm(self, nbytes: int):
-        """Remove the memory usage by the given number of bytes."""
-        mem_gb = float(nbytes) / float(1024**3)
-        self.current_memory_gb -= mem_gb
-
-
-class HFTorchLoader:  # pylint: disable=too-few-public-methods
-    """A loader loading HuggingFace's PyTorch format and converts them to MLC's parameters.
+class HFLoader:  # pylint: disable=too-few-public-methods
+    """A loader loading HuggingFace's PyTorch/SafeTensor format and converts them
+    to MLC's parameters.
 
     Attributes
     ----------
     stats : Stats
         Statistics of the loading process.
 
     extern_param_map : ExternMapping
-        The parameter mapping from MLC to HuggingFace PyTorch.
+        The parameter mapping from MLC to HuggingFace PyTorch/SafeTensor.
 
     torch_to_path : Dict[str, Path]
-        A mapping from PyTorch parameter name to the path of the file containing it, or the path
-        meaning all parameters are stored in a single file.
+        A mapping from PyTorch/SafeTensor parameter name to the path of the file containing it,
+        or the path meaning all parameters are stored in a single file.
 
     cached_files : Dict[Path, Dict[str, np.ndarray]]
         A cache of the loaded files. The key is the path of the file, and the value is a mapping
@@ -113,20 +56,23 @@ def __init__(
         ----------
         path : pathlib.Path
             Path to either a JSON indexing file, or a PyTorch bin file.
-            1) For JSON indexing file, it is usually `pytorch_model.bin.index.json` in the repo,
-            which contains a `weight_map` that maps each PyTorch parameter to the file containing
-            the weight. 2) For PyTorch bin file, it is usually `pytorch_model.bin` in the repo,
+            1) For JSON indexing file, it is usually `pytorch_model.bin.index.json`
+            or `model.safetensors.index.json` in the repo, which contains a `weight_map` that
+            maps each PyTorch parameter to the file containing the weight.
+            2) For PyTorch bin file, it is usually `pytorch_model.bin` in the repo,
+            which contains all the parameters.
+            3) For safetensor file, it is usually `model.safetensors` in the repo,
             which contains all the parameters.
 
         extern_param_map : ExternMapping
-            Maps an MLC parameter to a list of PyTorch parameters.
+            Maps an MLC parameter to a list of PyTorch/SafeTensor parameters.
         """
         assert path.is_file()
         self.stats = Stats()
         self.extern_param_map = extern_param_map
         self.cached_files = {}
         self.torch_to_path = {}
-        if path.suffix == ".bin":
+        if path.suffix in (".bin", ".safetensors"):
             self._load_file(path)
             for name in self.cached_files[path].keys():
                 self.torch_to_path[name] = path
@@ -137,7 +83,7 @@ def __init__(
                 self.torch_to_path[torch_name] = path.parent / path_str
         else:
             raise FileNotFoundError(f"Unknown file suffix: {path}")
-        _check_parameter_usage(extern_param_map, set(self.torch_to_path.keys()))
+        check_parameter_usage(extern_param_map, set(self.torch_to_path.keys()))
 
     def load(self) -> Iterator[Tuple[str, NDArray]]:
         """Load the parameters and yield the MLC parameter and its value."""
@@ -148,21 +94,8 @@ def load(self) -> Iterator[Tuple[str, NDArray]]:
         cached_files = list(self.cached_files.keys())
         for path in cached_files:
             self._unload_file(path)
-
-        logger.info(
-            "Time used: "
-            "PyTorch loading: %.3f sec; "
-            "Pre-quantization mapping: %.3f sec; "
-            "Quantization: %.3f sec",
-            self.stats.load_time_sec,
-            self.stats.map_time_sec,
-            self.stats.quant_time_sec,
-        )
-        logger.info(
-            "Memory usage: Total size loaded from disk: %.3f GB; Peak memory usage: %.3f GB",
-            self.stats.total_memory_gb,
-            self.stats.max_memory_gb,
-        )
+        self.stats.log_time_info("HF")
+        self.stats.log_mem_usage()
 
     def _load_mlc_param(self, mlc_name: str) -> np.ndarray:
         torch_names = self.extern_param_map.param_map[mlc_name]
@@ -190,53 +123,24 @@ def _load_mlc_param(self, mlc_name: str) -> np.ndarray:
         return param
 
     def _load_file(self, path: Path) -> None:
-        logger.info("Loading PyTorch parameters from: %s", path)
+        logger.info("Loading HF parameters from: %s", path)
+        load_func = load_safetensor_shard if path.suffix == ".safetensors" else load_torch_shard
         with self.stats.timer("load_time_sec"):
             result = {}
-            for name, param in _load_torch_shard(path):
+            for name, param in load_func(path):
                 result[name] = param
                 self.stats.mem_add(param.nbytes)
             self.cached_files[path] = result
 
     def _unload_file(self, path: Path) -> None:
-        logger.info("Unloading PyTorch weight file: %s", path)
+        logger.info("Unloading HF weight file: %s", path)
         with self.stats.timer("load_time_sec"):
             for _, param in self.cached_files[path].items():
                 self.stats.mem_rm(param.nbytes)
             del self.cached_files[path]
             gc.collect()
 
 
-def _check_parameter_usage(param_map: ExternMapping, torch_weights: Set[str]):
-    used_torch_names = set(sum(param_map.param_map.values(), ()))
-    # Check 1. All PyTorch parameters in the weight files are used unless explicitly specified
-    unused_torch_names = torch_weights - used_torch_names - param_map.unused_params
-    if unused_torch_names:
-        logger.warning(
-            "Unused torch parameters: %s",
-            ", ".join(sorted(unused_torch_names)),
-        )
-    # Check 2. All PyTorch parameters required are stored in the weight files
-    nonexistent_torch_names = used_torch_names - torch_weights
-    if nonexistent_torch_names:
-        raise ValueError(
-            "The following torch parameters do not exist in the weight files:\n  "
-            + "\n  ".join(sorted(nonexistent_torch_names)),
-        )
-
-
-def _load_torch_shard(path: Path):
-    import torch  # pylint: disable=import-outside-toplevel
-
-    for name, param in torch.load(path, map_location=torch.device("cpu")).items():
-        param = param.detach().cpu()
-        dtype = str(param.dtype)
-        if dtype == "torch.bfloat16":
-            param = param.float()
-        param = param.numpy()
-        yield name, param
-
-
 def _loading_order(param_map: ExternMapping, torch_to_path: Dict[str, Path]) -> List[str]:
     # Step 1. Build a map from path to torch parameters
     path_to_torch: Dict[Path, List[str]] = defaultdict(list)
@@ -257,4 +161,4 @@ def _loading_order(param_map: ExternMapping, torch_to_path: Dict[str, Path]) ->
     return list(order.keys())
 
 
-__all__ = ["HFTorchLoader"]
+__all__ = ["HFLoader"]

python/mlc_chat/compiler/parameter/stats.py

@@ -0,0 +1,86 @@
+"""Statistics of the loading process of parameter loaders"""
+import dataclasses
+import logging
+import time
+from contextlib import contextmanager
+
+logger = logging.getLogger(__name__)
+
+
+@dataclasses.dataclass
+class Stats:
+    """Statistics of the loading process of parameter loaders.
+
+    Attributes
+    ----------
+    load_time_sec : float
+        Time used in loading the parameters.
+
+    map_time_sec : float
+        Time used in applying the mapping function, i.e. `ExternMapping.map_func`.
+
+    quant_time_sec : float
+        Time used in quantizing the parameters, i.e. `QuantizeMapping.quant_func`.
+
+    current_memory_gb : float
+        The current RAM usage in GB.
+
+    total_memory_gb : float
+        The total size data loaded from disk in GB.
+
+    max_memory_gb : float
+        The maximum RAM usage in GB.
+    """
+
+    load_time_sec: float = 0.0
+    map_time_sec: float = 0.0
+    quant_time_sec: float = 0.0
+
+    current_memory_gb: float = 0.0
+    total_memory_gb: float = 0.0
+    max_memory_gb: float = 0.0
+
+    def timer(self, attr):
+        """A context manager to time the scope and add the time to the attribute."""
+
+        @contextmanager
+        def timed_scope():
+            start_time = time.time()
+            yield
+            elapsed_time = time.time() - start_time
+            setattr(self, attr, getattr(self, attr) + elapsed_time)
+
+        return timed_scope()
+
+    def mem_add(self, nbytes: int):
+        """Add the memory usage by the given number of bytes."""
+        mem_gb = float(nbytes) / float(1024**3)
+        self.current_memory_gb += mem_gb
+        self.total_memory_gb += mem_gb
+        self.max_memory_gb = max(self.max_memory_gb, self.current_memory_gb)
+
+    def mem_rm(self, nbytes: int):
+        """Remove the memory usage by the given number of bytes."""
+        mem_gb = float(nbytes) / float(1024**3)
+        self.current_memory_gb -= mem_gb
+
+    def log_time_info(self, weight_format: str):
+        """Log the time used in loading, pre-quantization and quantization."""
+        logger.info(
+            "Time used: "
+            "%s loading: %.3f sec; "
+            "Pre-quantization mapping: %.3f sec; "
+            "Quantization: %.3f sec",
+            weight_format,
+            self.load_time_sec,
+            self.map_time_sec,
+            self.quant_time_sec,
+        )
+
+    def log_mem_usage(self):
+        """Log the Memory usage information."""
+        logger.info(
+            "Memory usage: Total size loaded from disk: %.3f GB; Peak memory usage: %.3f GB",
+            self.total_memory_gb,
+            self.max_memory_gb,
+        )

python/mlc_chat/compiler/parameter/utils.py

@@ -0,0 +1,52 @@
+"""Common utilities for loading parameters"""
+import logging
+from pathlib import Path
+from typing import Iterator, Set, Tuple
+
+import numpy as np
+
+from .mapping import ExternMapping
+
+logger = logging.getLogger(__name__)
+
+
+def check_parameter_usage(param_map: ExternMapping, extern_weights: Set[str]):
+    """Check that all external parameters have been used and are stored in the weights file."""
+    used_extern_names = set(sum(param_map.param_map.values(), []))
+    # Check 1. All extern parameters in the weight files are used unless explicitly specified
+    unused_extern_names = extern_weights - used_extern_names - param_map.unused_params
+    if unused_extern_names:
+        logger.warning(
+            "Unused extern parameters: %s",
+            ", ".join(sorted(unused_extern_names)),
+        )
+    # Check 2. All extern parameters required are stored in the weight files
+    nonexistent_extern_names = used_extern_names - extern_weights
+    if nonexistent_extern_names:
+        raise ValueError(
+            "The following extern parameters do not exist in the weight files:\n  "
+            + "\n  ".join(sorted(nonexistent_extern_names)),
+        )
+
+
+def load_torch_shard(path: Path) -> Iterator[Tuple[str, np.ndarray]]:
+    """Load and yield PyTorch format parameters."""
+    import torch  # pylint: disable=import-outside-toplevel
+
+    for name, param in torch.load(path, map_location=torch.device("cpu")).items():
+        param = param.detach().cpu()
+        dtype = str(param.dtype)
+        if dtype == "torch.bfloat16":
+            param = param.float()
+        param = param.numpy()
+        yield name, param
+
+
+def load_safetensor_shard(path: Path) -> Iterator[Tuple[str, np.ndarray]]:
+    """Load and yield SafeTensor format parameters."""
+    import safetensors  # pylint: disable=import-outside-toplevel,import-error
+
+    with safetensors.safe_open(path, framework="numpy", device="cpu") as in_file:
+        for name in in_file.keys():
+            param = in_file.get_tensor(name)
+            yield name, param