Just use accelerate on tpu

modeling/lazy_loader.py

@@ -56,6 +56,7 @@ import collections
 import _codecs
 import os
 from typing import Any, Callable, Dict, Optional, Tuple, Type
+import accelerate
 
 from torch.nn import Module
 from torch.storage import UntypedStorage
@@ -64,6 +65,7 @@ from torch.storage import UntypedStorage
 # support it yet.
 try:
     import safetensors
+
     HAS_SAFETENSORS = True
 except ModuleNotFoundError:
     HAS_SAFETENSORS = False
@@ -71,17 +73,6 @@ except ModuleNotFoundError:
 import utils
 from logger import logger
 
-# Accelerate is used to load with empty modules. TPU version doesn't come
-# packaged with it so we use an in-house solution in that case
-try:
-    import accelerate
-    HAS_ACCELERATE = True
-except ModuleNotFoundError:
-    HAS_ACCELERATE = False
-
-_EXTRA_STATE_KEY_SUFFIX = "_extra_state"
-
-
 # Storage of zipfile handles for each shard
 torch_checkpoint_file_handles = {}
 
@@ -331,116 +322,6 @@ def _rebuild_tensor(lazy_storage: LazyTensor, storage_offset, shape, stride):
     )
     return lazy_storage
 
-# Modified version of https://github.com/pytorch/pytorch/blob/v1.11.0-rc4/torch/nn/modules/module.py#L1346-L1438
-def _load_from_state_dict(
-    self,
-    state_dict,
-    prefix,
-    local_metadata,
-    strict,
-    missing_keys,
-    unexpected_keys,
-    error_msgs,
-):
-    for hook in self._load_state_dict_pre_hooks.values():
-        hook(
-            state_dict,
-            prefix,
-            local_metadata,
-            strict,
-            missing_keys,
-            unexpected_keys,
-            error_msgs,
-        )
-
-    persistent_buffers = {
-        k: v
-        for k, v in self._buffers.items()
-        if k not in self._non_persistent_buffers_set
-    }
-    local_name_params = itertools.chain(
-        self._parameters.items(), persistent_buffers.items()
-    )
-    local_state = {k: v for k, v in local_name_params if v is not None}
-
-    for name, param in local_state.items():
-        key = prefix + name
-        if key in state_dict:
-            input_param = state_dict[key]
-            if not torch.overrides.is_tensor_like(input_param):
-                error_msgs.append(
-                    'While copying the parameter named "{}", '
-                    "expected torch.Tensor or Tensor-like object from checkpoint but "
-                    "received {}".format(key, type(input_param))
-                )
-                continue
-
-            # This is used to avoid copying uninitialized parameters into
-            # non-lazy modules, since they dont have the hook to do the checks
-            # in such case, it will error when accessing the .shape attribute.
-            is_param_lazy = torch.nn.parameter.is_lazy(param)
-            # Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+
-            if (
-                not is_param_lazy
-                and len(param.shape) == 0
-                and len(input_param.shape) == 1
-            ):
-                input_param = input_param[0]
-
-            if not is_param_lazy and input_param.shape != param.shape:
-                # local shape should match the one in checkpoint
-                error_msgs.append(
-                    "size mismatch for {}: copying a param with shape {} from checkpoint, "
-                    "the shape in current model is {}.".format(
-                        key, input_param.shape, param.shape
-                    )
-                )
-                continue
-            try:
-                with torch.no_grad():
-                    # param.copy_(input_param)
-                    new_param = torch.nn.Parameter(
-                        input_param, requires_grad=param.requires_grad
-                    )  # This line is new
-                    if name in self._parameters:  # This line is new
-                        self._parameters[name] = new_param  # This line is new
-                    if name in persistent_buffers:  # This line is new
-                        self._buffers[name] = new_param  # This line is new
-            except Exception as ex:
-                error_msgs.append(
-                    'While copying the parameter named "{}", '
-                    "whose dimensions in the model are {} and "
-                    "whose dimensions in the checkpoint are {}, "
-                    "an exception occurred : {}.".format(
-                        key, param.size(), input_param.size(), ex.args
-                    )
-                )
-        elif strict:
-            missing_keys.append(key)
-
-    extra_state_key = prefix + _EXTRA_STATE_KEY_SUFFIX
-    if (
-        hasattr(Module, "set_extra_state")
-        and getattr(self.__class__, "set_extra_state", Module.set_extra_state)
-        is not Module.set_extra_state
-    ):  # if getattr(self.__class__, "set_extra_state", Module.set_extra_state) is not Module.set_extra_state:
-        if extra_state_key in state_dict:
-            self.set_extra_state(state_dict[extra_state_key])
-        elif strict:
-            missing_keys.append(extra_state_key)
-    elif strict and (extra_state_key in state_dict):
-        unexpected_keys.append(extra_state_key)
-
-    if strict:
-        for key in state_dict.keys():
-            if key.startswith(prefix) and key != extra_state_key:
-                input_name = key[len(prefix) :]
-                input_name = input_name.split(".", 1)[
-                    0
-                ]  # get the name of param/buffer/child
-                if input_name not in self._modules and input_name not in local_state:
-                    unexpected_keys.append(key)
-
 
 def safetensors_load_tensor_independently(
     checkpoint_file: str, tensor_key: str, device: Any
@@ -572,29 +453,8 @@ def use_lazy_load(
             patch_safetensors(callback)
 
         if dematerialized_modules:
-            if HAS_ACCELERATE:
             init_empty_weights = accelerate.init_empty_weights()
             init_empty_weights.__enter__()
-            else:
-                # TPU doesn't use accelerate package
-                old_linear_init = torch.nn.Linear.__init__
-                old_embedding_init = torch.nn.Embedding.__init__
-                old_layernorm_init = torch.nn.LayerNorm.__init__
-
-                def linear_init(self, *args, device=None, **kwargs):
-                    return old_linear_init(self, *args, device="meta", **kwargs)
-
-                def embedding_init(self, *args, device=None, **kwargs):
-                    return old_embedding_init(self, *args, device="meta", **kwargs)
-
-                def layernorm_init(self, *args, device=None, **kwargs):
-                    return old_layernorm_init(self, *args, device="meta", **kwargs)
-
-                torch.nn.Linear.__init__ = linear_init
-                torch.nn.Embedding.__init__ = embedding_init
-                torch.nn.LayerNorm.__init__ = layernorm_init
-                old_load_from_state_dict = torch.nn.Module._load_from_state_dict
-                torch.nn.Module._load_from_state_dict = _load_from_state_dict
 
         with use_custom_unpickler(_LazyUnpickler):
             yield True
@@ -609,13 +469,7 @@ def use_lazy_load(
         )
 
         if dematerialized_modules:
-            if HAS_ACCELERATE:
             init_empty_weights.__exit__(None, None, None)
-            else:
-                torch.nn.Linear.__init__ = old_linear_init
-                torch.nn.Embedding.__init__ = old_embedding_init
-                torch.nn.LayerNorm.__init__ = old_layernorm_init
-                torch.nn.Module._load_from_state_dict = old_load_from_state_dict
 
 
 def post_load_cleanup() -> None:

requirements_mtj.txt

@@ -34,3 +34,4 @@ ijson
 ftfy
 pydub
 sentencepiece
+accelerate==0.18.0