Use streaming transfers

1) Added the the write_state_dict and read_state_dict implementations into checkpointing.py

2) Replaced existing torch.save/torch.load with those

3)Added unit tests for write_state_dict/read_state_dict for all the different possible types of torch tensors

4) Added checksum to read/write_state_dict that uses zlib.crc32

Author: Krishn1412

torchft/checkpointing.py

@@ -18,16 +18,99 @@
 import urllib.request
 from http.server import BaseHTTPRequestHandler
 from typing import Callable, Generic, TypeVar
+from dataclasses import dataclass
+import pickle
+from io import BufferedIOBase
+from typing import Tuple
+import struct
 
 import torch
-
+from torch.utils._pytree import tree_flatten, tree_unflatten
+from hashlib import sha256
 from torchft.http import _IPv6HTTPServer
 
 logger: logging.Logger = logging.getLogger(__name__)
 
 T = TypeVar("T")
 
 
+@dataclass
+class TensorMetadata:
+    nbytes: int
+    dtype: torch.dtype
+    storage_offset: int
+    size: Tuple[int, ...]
+    stride: Tuple[int, ...]
+
+
+def write_state_dict(state_dict: object, f: BufferedIOBase) -> None:
+    """
+    Write the state_dict to the file-like object.
+    """
+    values, spec = tree_flatten(state_dict)
+
+    storages = []
+    non_tensor_values = []
+    for value in values:
+        if isinstance(value, torch.Tensor):
+            storage = value.untyped_storage()
+            storages.append(storage)
+            non_tensor_values.append(
+                TensorMetadata(
+                    nbytes=storage.nbytes(),
+                    dtype=value.dtype,
+                    storage_offset=value.storage_offset(),
+                    size=value.size(),
+                    stride=value.stride(),
+                )
+            )
+        else:
+            non_tensor_values.append(value)
+
+    meta_buf = pickle.dumps((non_tensor_values, spec))
+    checksum = sha256(meta_buf).hexdigest()
+    total_length = len(meta_buf) + len(checksum)
+
+    f.write(struct.pack("<q", total_length))
+    f.write(meta_buf)
+    f.write(checksum.encode("utf-8"))
+
+
+    for storage in storages:
+        storage._write_file(f, False, False, 1)
+
+
+def read_state_dict(f: BufferedIOBase) -> object:
+    """
+    Read the state_dict from the file-like object.
+    """
+
+    total_length = struct.unpack("<q", f.read(8))[0]
+    meta_buf = f.read(total_length - 64)
+    checksum = f.read(64).decode("utf-8")
+
+    # Verify checksum
+    actual_checksum = sha256(meta_buf).hexdigest()
+    if checksum != actual_checksum:
+        raise ValueError("Checksum mismatch! Data may be corrupted.")
+    non_tensor_values, spec = pickle.loads(meta_buf)
+    values = []
+    for value in non_tensor_values:
+        if isinstance(value, TensorMetadata):
+            data = f.read(value.nbytes)
+
+            tensor = torch.as_strided(
+                torch.frombuffer(data, dtype=value.dtype),
+                size=value.size,
+                stride=value.stride,
+                storage_offset=value.storage_offset,
+            )
+            values.append(tensor)
+        else:
+            values.append(value)
+
+    return tree_unflatten(values, spec)
+
 class CheckpointServer(Generic[T]):
     """
     This is an HTTP server that can be used to transfer checkpoints
@@ -69,7 +152,7 @@ def do_GET(self):
 
                     sd = state_dict()
 
-                    torch.save(sd, self.wfile)
+                    write_state_dict(sd, self.wfile)
 
             def err(self, msg: str) -> None:
                 logger.error(msg)
@@ -100,7 +183,8 @@ def load_from_address(cls, address: str) -> T:
             data = f.read()
 
         reader = io.BytesIO(data)
-        return torch.load(reader, weights_only=True)
+        state_dict = read_state_dict(reader)
+        return state_dict
 
     def address(self) -> str:
         """

torchft/checkpointing_test.py

@@ -4,11 +4,14 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import unittest
 import urllib.error
 from unittest import TestCase
 from unittest.mock import MagicMock
-
-from torchft.checkpointing import CheckpointServer
+from io import BytesIO
+import torch
+from typing import Tuple
+from checkpointing import CheckpointServer, TensorMetadata, write_state_dict, read_state_dict
 
 
 class TestCheckpointing(TestCase):
@@ -33,3 +36,67 @@ def test_checkpoint_server(self) -> None:
             CheckpointServer.load_from_address(addr)
 
         server.shutdown()
+
+    def setUp(self):
+        self.file = BytesIO()
+
+    def test_scalar_tensor(self):
+        tensor = torch.tensor(42, dtype=torch.int32)
+        state_dict = {'scalar': tensor}
+        write_state_dict(state_dict, self.file)
+        self.file.seek(0)
+
+        result = read_state_dict(self.file)
+        self.assertTrue(torch.equal(result['scalar'], tensor))
+
+    def test_strided_tensor(self):
+        base_tensor = torch.arange(16, dtype=torch.float32).reshape(4, 4)
+        strided_tensor = base_tensor[::2, ::2]
+        state_dict = {'strided': strided_tensor}
+        write_state_dict(state_dict, self.file)
+        self.file.seek(0)
+
+        result = read_state_dict(self.file)
+        self.assertTrue(torch.equal(result['strided'], strided_tensor))
+
+    def test_tensor_with_offset(self):
+        base_tensor = torch.arange(10, dtype=torch.float64)
+        offset_tensor = base_tensor[2:]
+        state_dict = {'offset': offset_tensor}
+        write_state_dict(state_dict, self.file)
+        self.file.seek(0)
+
+        result = read_state_dict(self.file)
+        self.assertTrue(torch.equal(result['offset'], offset_tensor))
+
+    def test_nested_tensors(self):
+        tensor1 = torch.tensor([1, 2, 3], dtype=torch.int32)
+        tensor2 = torch.tensor([[1.5, 2.5], [3.5, 4.5]], dtype=torch.float64)
+        state_dict = {'nested': {'tensor1': tensor1, 'tensor2': tensor2}}
+        write_state_dict(state_dict, self.file)
+        self.file.seek(0)
+
+        result = read_state_dict(self.file)
+        self.assertTrue(torch.equal(result['nested']['tensor1'], tensor1))
+        self.assertTrue(torch.equal(result['nested']['tensor2'], tensor2))
+
+    def test_various_data_types(self):
+        tensor_float32 = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32)
+        tensor_int16 = torch.tensor([1, 2, 3], dtype=torch.int16)
+        tensor_bool = torch.tensor([True, False, True], dtype=torch.bool)
+        state_dict = {
+            'float32': tensor_float32,
+            'int16': tensor_int16,
+            'bool': tensor_bool,
+        }
+        write_state_dict(state_dict, self.file)
+        self.file.seek(0)
+
+        result = read_state_dict(self.file)
+        self.assertTrue(torch.equal(result['float32'], tensor_float32))
+        self.assertTrue(torch.equal(result['int16'], tensor_int16))
+        self.assertTrue(torch.equal(result['bool'], tensor_bool))
+
+
+if __name__ == '__main__':
+    unittest.main()