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add map_location argument to torch.load calls #195

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add map_location argument to torch.load calls #195

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -15,7 +15,7 @@ This is the official codebase for **scGPT: Towards Building a Foundation Model f

**[2023.12.31]** New tutorials about zero-shot applications are now available! Please see find them in the [tutorials/zero-shot](tutorials/zero-shot) directory. We also provide a new continual pretrained model checkpoint for cell embedding related tasks. Please see the [notebook](tutorials/zero-shot/Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb) for more details.

**[2023.11.07]** As requested by many, now we have made flash-attention an optional dependency. The pretrained weights can be loaded on pytorch CPU, GPU, and flash-attn backends using the same [load_pretrained](https://github.com/bowang-lab/scGPT/blob/f6097112fe5175cd4e221890ed2e2b1815f54010/scgpt/utils/util.py#L304) function, `load_pretrained(target_model, torch.load("path_to_ckpt.pt"))`. An example usage is also [here](https://github.com/bowang-lab/scGPT/blob/f6097112fe5175cd4e221890ed2e2b1815f54010/scgpt/tasks/cell_emb.py#L258).
**[2023.11.07]** As requested by many, now we have made flash-attention an optional dependency. The pretrained weights can be loaded on pytorch CPU, GPU, and flash-attn backends using the same [load_pretrained](https://github.com/bowang-lab/scGPT/blob/f6097112fe5175cd4e221890ed2e2b1815f54010/scgpt/utils/util.py#L304) function, `load_pretrained(target_model, torch.load("path_to_ckpt.pt", map_location=device))`. An example usage is also [here](https://github.com/bowang-lab/scGPT/blob/f6097112fe5175cd4e221890ed2e2b1815f54010/scgpt/tasks/cell_emb.py#L258).

**[2023.09.05]** We have release a new feature for reference mapping samples to a custom reference dataset or to all the millions of cells collected from CellXGene! With the help of the [faiss](https://github.com/facebookresearch/faiss) library, we achieved a great time and memory efficiency. The index of over 33 millions cells only takes less than 1GB of memory and the similarity search takes less than **1 second for 10,000 query cells on GPU**. Please see the [Reference mapping tutorial](https://github.com/bowang-lab/scGPT/blob/main/tutorials/Tutorial_Reference_Mapping.ipynb) for more details.

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4 changes: 2 additions & 2 deletions examples/finetune_integration.py
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Expand Up @@ -405,12 +405,12 @@ def prepare_dataloader(
)
if config.load_model is not None:
try:
model.load_state_dict(torch.load(model_file))
model.load_state_dict(torch.load(model_file, map_location=device))
logger.info(f"Loading all model params from {model_file}")
except:
# only load params that are in the model and match the size
model_dict = model.state_dict()
pretrained_dict = torch.load(model_file)
pretrained_dict = torch.load(model_file, map_location=device)
pretrained_dict = {
k: v
for k, v in pretrained_dict.items()
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4 changes: 2 additions & 2 deletions tutorials/Tutorial_Annotation.ipynb
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Expand Up @@ -1271,12 +1271,12 @@
")\n",
"if config.load_model is not None:\n",
" try:\n",
" model.load_state_dict(torch.load(model_file))\n",
" model.load_state_dict(torch.load(model_file, map_location=device))\n",
" logger.info(f\"Loading all model params from {model_file}\")\n",
" except:\n",
" # only load params that are in the model and match the size\n",
" model_dict = model.state_dict()\n",
" pretrained_dict = torch.load(model_file)\n",
" pretrained_dict = torch.load(model_file, map_location=device)\n",
" pretrained_dict = {\n",
" k: v\n",
" for k, v in pretrained_dict.items()\n",
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4 changes: 2 additions & 2 deletions tutorials/Tutorial_Attention_GRN.ipynb
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Expand Up @@ -598,12 +598,12 @@
")\n",
"\n",
"try:\n",
" model.load_state_dict(torch.load(model_file))\n",
" model.load_state_dict(torch.load(model_file, map_location=device))\n",
" print(f\"Loading all model params from {model_file}\")\n",
"except:\n",
" # only load params that are in the model and match the size\n",
" model_dict = model.state_dict()\n",
" pretrained_dict = torch.load(model_file)\n",
" pretrained_dict = torch.load(model_file, map_location=device)\n",
" pretrained_dict = {\n",
" k: v\n",
" for k, v in pretrained_dict.items()\n",
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4 changes: 2 additions & 2 deletions tutorials/Tutorial_GRN.ipynb
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Expand Up @@ -521,12 +521,12 @@
")\n",
"\n",
"try:\n",
" model.load_state_dict(torch.load(model_file))\n",
" model.load_state_dict(torch.load(model_file, map_location=device))\n",
" print(f\"Loading all model params from {model_file}\")\n",
"except:\n",
" # only load params that are in the model and match the size\n",
" model_dict = model.state_dict()\n",
" pretrained_dict = torch.load(model_file)\n",
" pretrained_dict = torch.load(model_file, map_location=device)\n",
" pretrained_dict = {\n",
" k: v\n",
" for k, v in pretrained_dict.items()\n",
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2 changes: 1 addition & 1 deletion tutorials/Tutorial_Integration.ipynb
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Expand Up @@ -689,7 +689,7 @@
" pre_norm=config.pre_norm,\n",
")\n",
"if config.load_model is not None:\n",
" load_pretrained(model, torch.load(model_file), verbose=False)\n",
" load_pretrained(model, torch.load(model_file, map_location=device), verbose=False)\n",
"\n",
"model.to(device)\n",
"wandb.watch(model)"
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2 changes: 1 addition & 1 deletion tutorials/Tutorial_Multiomics.ipynb
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Expand Up @@ -869,7 +869,7 @@
],
"source": [
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"model_dict = torch.load(model_file)\n",
"model_dict = torch.load(model_file, map_location=device)\n",
"ntokens = len(vocab) # size of vocabulary\n",
"model = MultiOmicTransformerModel(\n",
" ntokens,\n",
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6 changes: 3 additions & 3 deletions tutorials/Tutorial_Perturbation.ipynb
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Expand Up @@ -659,7 +659,7 @@
"if load_param_prefixs is not None and load_model is not None:\n",
" # only load params that start with the prefix\n",
" model_dict = model.state_dict()\n",
" pretrained_dict = torch.load(model_file)\n",
" pretrained_dict = torch.load(model_file, map_location=device)\n",
" pretrained_dict = {\n",
" k: v\n",
" for k, v in pretrained_dict.items()\n",
Expand All @@ -671,12 +671,12 @@
" model.load_state_dict(model_dict)\n",
"elif load_model is not None:\n",
" try:\n",
" model.load_state_dict(torch.load(model_file))\n",
" model.load_state_dict(torch.load(model_file, map_location=device))\n",
" logger.info(f\"Loading all model params from {model_file}\")\n",
" except:\n",
" # only load params that are in the model and match the size\n",
" model_dict = model.state_dict()\n",
" pretrained_dict = torch.load(model_file)\n",
" pretrained_dict = torch.load(model_file, map_location=device)\n",
" pretrained_dict = {\n",
" k: v\n",
" for k, v in pretrained_dict.items()\n",
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