Remove accelerate from langchain

apps/language_models/langchain/gen.py

@@ -109,51 +109,48 @@ def get_config(
         return_model=False,
         raise_exception=False,
     ):
-        from accelerate import init_empty_weights
+        from transformers import AutoConfig
 
-        with init_empty_weights():
-            from transformers import AutoConfig
-
-            try:
-                config = AutoConfig.from_pretrained(
-                    base_model,
-                    use_auth_token=use_auth_token,
-                    trust_remote_code=trust_remote_code,
-                    offload_folder=offload_folder,
-                )
-            except OSError as e:
-                if raise_exception:
-                    raise
-                if "not a local folder and is not a valid model identifier listed on" in str(
-                    e
-                ) or "404 Client Error" in str(
-                    e
-                ):
-                    # e.g. llama, gpjt, etc.
-                    # e.g. HF TGI but not model on HF or private etc.
-                    # HF TGI server only should really require prompt_type, not HF model state
-                    return None, None
-                else:
-                    raise
-            if triton_attn and "mpt-" in base_model.lower():
-                config.attn_config["attn_impl"] = "triton"
-            if long_sequence:
-                if "mpt-7b-storywriter" in base_model.lower():
-                    config.update({"max_seq_len": 83968})
-                if "mosaicml/mpt-7b-chat" in base_model.lower():
-                    config.update({"max_seq_len": 4096})
-                if "mpt-30b" in base_model.lower():
-                    config.update({"max_seq_len": 2 * 8192})
-            if return_model and issubclass(
-                config.__class__, tuple(AutoModel._model_mapping.keys())
+        try:
+            config = AutoConfig.from_pretrained(
+                base_model,
+                use_auth_token=use_auth_token,
+                trust_remote_code=trust_remote_code,
+                offload_folder=offload_folder,
+            )
+        except OSError as e:
+            if raise_exception:
+                raise
+            if "not a local folder and is not a valid model identifier listed on" in str(
+                e
+            ) or "404 Client Error" in str(
+                e
             ):
-                model = AutoModel.from_config(
-                    config,
-                    trust_remote_code=trust_remote_code,
-                )
+                # e.g. llama, gpjt, etc.
+                # e.g. HF TGI but not model on HF or private etc.
+                # HF TGI server only should really require prompt_type, not HF model state
+                return None, None
             else:
-                # can't infer
-                model = None
+                raise
+        if triton_attn and "mpt-" in base_model.lower():
+            config.attn_config["attn_impl"] = "triton"
+        if long_sequence:
+            if "mpt-7b-storywriter" in base_model.lower():
+                config.update({"max_seq_len": 83968})
+            if "mosaicml/mpt-7b-chat" in base_model.lower():
+                config.update({"max_seq_len": 4096})
+            if "mpt-30b" in base_model.lower():
+                config.update({"max_seq_len": 2 * 8192})
+        if return_model and issubclass(
+            config.__class__, tuple(AutoModel._model_mapping.keys())
+        ):
+            model = AutoModel.from_config(
+                config,
+                trust_remote_code=trust_remote_code,
+            )
+        else:
+            # can't infer
+            model = None
         if "falcon" in base_model.lower():
             config.use_cache = False
 
@@ -177,22 +174,6 @@ def get_non_lora_model(
         """
 
         device_map = None
-        if model is not None:
-            # NOTE: Can specify max_memory={0: max_mem, 1: max_mem}, to shard model
-            # NOTE: Some models require avoiding sharding some layers,
-            # then would pass no_split_module_classes and give list of those layers.
-            from accelerate import infer_auto_device_map
-
-            device_map = infer_auto_device_map(
-                model,
-                dtype=torch.float16 if load_half else torch.float32,
-            )
-            if hasattr(model, "model"):
-                device_map_model = infer_auto_device_map(
-                    model.model,
-                    dtype=torch.float16 if load_half else torch.float32,
-                )
-                device_map.update(device_map_model)
 
         n_gpus = torch.cuda.device_count() if torch.cuda.is_available else 0
 

apps/language_models/langchain/gpt_langchain.py

@@ -372,7 +372,7 @@ def get_embedding(
         from langchain.embeddings import HuggingFaceEmbeddings
 
         torch_dtype, context_class = get_dtype()
-        model_kwargs = dict(device=args.device)
+        model_kwargs = dict(device="cpu")
         if "instructor" in hf_embedding_model:
             encode_kwargs = {"normalize_embeddings": True}
             embedding = HuggingFaceInstructEmbeddings(

apps/language_models/langchain/langchain_requirements.txt

@@ -16,7 +16,7 @@ pandas==2.0.2
 matplotlib==3.7.1
 loralib==0.1.1
 bitsandbytes==0.39.0
-accelerate==0.20.3
+# accelerate==0.20.3
 peft==0.4.0
 # 4.31.0+ breaks load_in_8bit=True (https://github.com/huggingface/transformers/issues/25026)
 transformers==4.30.2

apps/stable_diffusion/web/index.py

@@ -115,8 +115,8 @@ def resource_path(relative_path):
         txt2img_sendto_inpaint,
         txt2img_sendto_outpaint,
         txt2img_sendto_upscaler,
-        # h2ogpt_upload,
-        # h2ogpt_web,
+        h2ogpt_upload,
+        h2ogpt_web,
         img2img_web,
         img2img_custom_model,
         img2img_hf_model_id,
@@ -248,10 +248,10 @@ def register_outputgallery_button(button, selectedid, inputs, outputs):
                         upscaler_status,
                     ]
                 )
-            # with gr.TabItem(label="DocuChat Upload", id=11):
-            #     h2ogpt_upload.render()
-            # with gr.TabItem(label="DocuChat(Experimental)", id=12):
-            #     h2ogpt_web.render()
+            with gr.TabItem(label="DocuChat Upload", id=11):
+                h2ogpt_upload.render()
+            with gr.TabItem(label="DocuChat(Experimental)", id=12):
+                h2ogpt_web.render()
 
         # send to buttons
         register_button_click(

apps/stable_diffusion/web/ui/__init__.py

@@ -79,6 +79,10 @@
     llm_chat_api,
 )
 from apps.stable_diffusion.web.ui.generate_config import model_config_web
+from apps.stable_diffusion.web.ui.h2ogpt import (
+    h2ogpt_upload,
+    h2ogpt_web,
+)
 from apps.stable_diffusion.web.ui.minigpt4_ui import minigpt4_web
 from apps.stable_diffusion.web.ui.outputgallery_ui import (
     outputgallery_web,

apps/stable_diffusion/web/ui/h2ogpt.py

@@ -274,9 +274,7 @@ def chat(curr_system_message, history, device, precision):
 
     upload_path = None
     database = None
-    database_directory = os.path.abspath(
-        "apps/language_models/langchain/db_path/"
-    )
+    database_directory = "db_dir_UserData"
 
     def read_path():
         global upload_path