Merge pull request #29 from risingsunomi/pr139-dev-oct24

Pr139 dev oct24

exo/inference/torch/model/hf.py

@@ -9,6 +9,7 @@
 from exo.inference.shard import Shard
 from exo.helpers import DEBUG
 from exo.inference.torch.utils import extract_layers
+from exo.inference.torch.model.hf_safe_tensor_shard import HFSafeTensorShard
 
 from transformers import (
   AutoModelForCausalLM,
@@ -52,124 +53,86 @@ def __init__(
 
     # class vars
     self.shard = shard
-    self.hidden_states = None
-    self.input_ids = None
-    self.inputs_embeds = None
-    self.attention_mask = None
-    self.position_embeddings = None
-    self.past_key_values = None
-    self.cache_position = None
-    self.position_ids = None
-    self.causal_mask = None
     self.local_model_path = local_model_path
-    self.is_sharded_model = False
-
+    self.weight_map = weight_map
+    self.device = device
+    self.dtype = dtype
+    self.device_map = device_map
+    self.offload_buffers = offload_buffers
+    self.model_safetensors_path = self.local_model_path/"model.safetensors.index.json"
+    self.safetensor_sharder = HFSafeTensorShard(
+      self.local_model_path,
+      self.shard
+    )
     # setup logit processors
     self.logits_processor = LogitsProcessorList([
       TopKLogitsWarper(top_k),
       TemperatureLogitsWarper(temp),
       TopPLogitsWarper(top_p)
     ])
 
-    self.device = device
-    self.dtype = dtype
-    self.device_map = device_map
-
-    self.offload_buffers = offload_buffers
-
-    self.model_safetensors_path = self.local_model_path/"model.safetensors.index.json"
-
-    # setup pytorch and transformer llm
+    # setup sharded llm
     try:
-      if weight_map:
-        print("loading shard model")
-        self.llm_model = self.load_sharded_model(
-          shard,
-          weight_map,
-          offload_buffers=self.offload_buffers
-        )
-
-        self.is_sharded_model = True
-
-        # clear out edited safetensor json
-        # this is needed because shard downloader just
-        # appends and not redownloads the file
-        os.remove(self.model_safetensors_path)
-
-        self.llm_model = AutoModelForCausalLM.from_config(self.llm_model_config).to(self.device)
-        self.model = self.llm_model.model.to(self.device)
-      else:
-        print("loading full model")
-        self.llm_model = AutoModelForCausalLM.from_pretrained(
-          pretrained_model_name_or_path=self.local_model_path,
-          torch_dtype=self.dtype,
-          device_map=self.device_map,
-          offload_buffers=offload_buffers
-        ).to(self.device)
-
+      self.llm_model = self.load_sharded_model()
       self.model = self.llm_model.model.to(self.device)
+
+      # restore originals for next run, if one
+      self.safetensor_sharder.restore_backups()
     except Exception as err:
-      print(f"error loading and splitting model: {err}")
+      print(f"error loading and sharding model: {err}")
       raise
 
-  def load_sharded_model(
-    self,
-    shard: Shard,
-    weight_map: dict,
-    offload_buffers: bool
-  ) -> AutoModelForCausalLM:
+    # forward variables
+    self.hidden_states = None
+    self.input_ids = None
+    self.inputs_embeds = None
+    self.attention_mask = None
+    self.position_embeddings = None
+    self.past_key_values = None
+    self.cache_position = None
+    self.position_ids = None
+    self.causal_mask = None
+
+  def load_sharded_model(self) -> AutoModelForCausalLM:
     """
     Loads sharded version of model where only needed
     weights are loaded for necessary layers
 
-    Args:
-
     Returns:
+      llm_model (AutoModelForCausalLM) - sharded llm model with only needed layers loaded
     """
     if DEBUG >= 4:
       print("load_sharded_model called")
-      print(f"shard: {shard}")
 
-    # break out layers per shard range
-    layer_weight_map = extract_layers(
-      weight_map,
-      shard
-    )
-
-    # rewrite model.safetensors.index.json for only needed layers
-    try:
-      mst_json = {}
-      with open(self.model_safetensors_path, "r") as mst_file:
-        mst_json = json.load(mst_file)
-        mst_json["weight_map"] = layer_weight_map
-
-      if DEBUG >= 4:
-        print(f"rewritten safetensor index \n{json.dumps(mst_json, indent=4)}")
-
-      os.remove(self.model_safetensors_path)
-
-      with open(self.model_safetensors_path, "w") as mst_file:
-        json.dump(mst_json, mst_file, indent=4)
-    except Exception as err:
-      print(f"err: {err}")
-      raise
+    # modify safetensor
+    self.safetensor_sharder.modify_safetensor()
+    self.safetensor_sharder.create_safetensor_index()
+    self.safetensor_sharder.shard_safetensor_index(self.weight_map)
 
     # load model
     try:
-      shard_num_hidden_layers = shard.end_layer - shard.start_layer
+      shard_num_hidden_layers = (self.shard.end_layer - self.shard.start_layer) + 1
       if DEBUG >= 4:
         print(f"config with {shard_num_hidden_layers} layers")
 
       llm_model = AutoModelForCausalLM.from_pretrained(
         pretrained_model_name_or_path=self.local_model_path,
         device_map=self.device_map,
         torch_dtype=self.dtype,
-        offload_buffers=offload_buffers,
+        offload_buffers=self.offload_buffers,
         local_files_only=True,
-        num_hidden_layers=shard_num_hidden_layers
+        num_hidden_layers=shard_num_hidden_layers,
+        use_safetensors=True,
+        low_cpu_mem_usage=True
       )
 
-      return llm_model.to(self.device)
+      # restore backup for next run
+      self.safetensor_sharder.restore_backups()
+
+      if self.device_map == "auto":
+        return llm_model
+      else:
+        return llm_model.to(self.device)
 
     except Exception as err:
       print(f"err: {err}")
@@ -265,14 +228,11 @@ def forward(
       self.cache_position = model_inputs["cache_position"]
       self.past_key_values = model_inputs["past_key_values"]
 
-    if DEBUG >= 4:
-      print(f"model_inputs: {model_inputs}")
+      if DEBUG >= 4:
+        print(f"model_inputs: {model_inputs}")
 
     # run through decoder layers
-    if self.is_sharded_model:
-      layer_amt = range(self.shard.end_layer - self.shard.start_layer)
-    else:
-      layer_amt = range(self.shard.start_layer, self.shard.end_layer)
+    layer_amt = range(self.shard.end_layer - self.shard.start_layer)
 
     if DEBUG >= 4:
       print(f"hidden_states: {self.hidden_states}")
@@ -317,7 +277,7 @@ def forward(
     # shard is last layer says true at the start and not detecting last layer correctly
     if self.shard.is_last_layer():
       self.hidden_states = self.model.norm(self.hidden_states)
-      if use_legacy_cache and self.next_decoder_cache is not None:
+      if use_legacy_cache:
         self.past_key_values = self.next_decoder_cache.to_legacy_cache()
       else:
         self.past_key_values = self.next_decoder_cache