feat(intel): add diffusers/transformers support (#1746)

* feat(intel): add diffusers support

* try to consume upstream container image

* Debug

* Manually install deps

* Map transformers/hf cache dir to modelpath if not specified

* fix(compel): update initialization, pass by all gRPC options

* fix: add dependencies, implement transformers for xpu

* base it from the oneapi image

* Add pillow

* set threads if specified when launching the API

* Skip conda install if intel

* defaults to non-intel

* ci: add to pipelines

* prepare compel only if enabled

* Skip conda install if intel

* fix cleanup

* Disable compel by default

* Install torch 2.1.0 with Intel

* Skip conda on some setups

* Detect python

* Quiet output

* Do not override system python with conda

* Prefer python3

* Fixups

* exllama2: do not install without conda (overrides pytorch version)

* exllama/exllama2: do not install if not using cuda

* Add missing dataset dependency

* Small fixups, symlink to python, add requirements

* Add neural_speed to the deps

* correctly handle model offloading

* fix: device_map == xpu

* go back at calling python, fixed at dockerfile level

* Exllama2 restricted to only nvidia gpus

* Tokenizer to xpu

backend/python/transformers/run.sh

@@ -3,10 +3,16 @@
 ##
 ## A bash script wrapper that runs the transformers server with conda
 
-export PATH=$PATH:/opt/conda/bin
 
-# Activate conda environment
-source activate transformers
+if [ -d "/opt/intel" ]; then
+    # Assumes we are using the Intel oneAPI container image
+    # https://github.com/intel/intel-extension-for-pytorch/issues/538
+    export XPU=1
+else
+    export PATH=$PATH:/opt/conda/bin
+    # Activate conda environment
+    source activate transformers
+fi
 
 # get the directory where the bash script is located
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"

backend/python/transformers/transformers_server.py

@@ -16,7 +16,15 @@ import backend_pb2_grpc
 import grpc
 import torch
 import torch.cuda
-from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed
+
+XPU=os.environ.get("XPU", "0") == "1"
+if XPU:
+    import intel_extension_for_pytorch as ipex
+    from intel_extension_for_transformers.transformers.modeling import AutoModelForCausalLM
+    from transformers import AutoTokenizer, AutoModel, set_seed
+else:
+    from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed
+
 
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
 
@@ -69,12 +77,25 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         model_name = request.Model
         try:
             if request.Type == "AutoModelForCausalLM":
-                self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode)
+                if XPU:
+                    self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode,
+                                              device_map="xpu", load_in_4bit=True)
+                else:
+                    self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode)
             else:
                 self.model = AutoModel.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode)
 
             self.tokenizer = AutoTokenizer.from_pretrained(model_name)
             self.CUDA = False
+            self.XPU = False
+
+            if XPU:
+                self.XPU = True
+                try:
+                    print("Optimizing model", model_name, "to XPU.", file=sys.stderr)
+                    self.model = ipex.optimize_transformers(self.model, inplace=True, dtype=torch.float16, device="xpu")
+                except Exception as err:
+                    print("Not using XPU:", err, file=sys.stderr)
 
             if request.CUDA or torch.cuda.is_available():
                 try:
@@ -139,6 +160,8 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         inputs = self.tokenizer(request.Prompt, return_tensors="pt").input_ids
         if self.CUDA:
             inputs = inputs.to("cuda")
+        if XPU:
+            inputs = inputs.to("xpu")
 
         outputs = self.model.generate(inputs,max_new_tokens=max_tokens, temperature=request.Temperature, top_p=request.TopP)