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fix: OpenVINO winograd always disabled (#2252) 

Winograd convolutions were always disabled giving error when inference device was CPU.
This commit implement logic to disable Winograd convolutions only if CPU or NPU are declared.
This commit is contained in:
fakezeta 2024-05-07 08:38:58 +02:00 committed by GitHub
parent fe055d4b36
commit fea9522982
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1 changed files with 18 additions and 6 deletions
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24
backend/python/transformers/transformers_server.py
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@ -150,11 +150,17 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
devices = Core().available_devices
if "GPU" in " ".join(devices):
device_map="AUTO:GPU"
# While working on a fine tuned model, inference may give an inaccuracy and performance drop on GPU if winograd convolutions are selected.
# https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html
if "CPU" or "NPU" in device_map:
if "-CPU" or "-NPU" not in device_map:
ovconfig={"PERFORMANCE_HINT": "CUMULATIVE_THROUGHPUT"}
else:
ovconfig={"PERFORMANCE_HINT": "CUMULATIVE_THROUGHPUT","GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
self.model = OVModelForCausalLM.from_pretrained(model_name,
compile=True,
trust_remote_code=request.TrustRemoteCode,
ov_config={"PERFORMANCE_HINT": "CUMULATIVE_THROUGHPUT","GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"},
ov_config=ovconfig,
device=device_map)
self.OV = True
elif request.Type == "OVModelForFeatureExtraction":
@ -168,11 +174,17 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
devices = Core().available_devices
if "GPU" in " ".join(devices):
device_map="AUTO:GPU"
# While working on a fine tuned model, inference may give an inaccuracy and performance drop on GPU if winograd convolutions are selected.
# https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html
if "CPU" or "NPU" in device_map:
if "-CPU" or "-NPU" not in device_map:
ovconfig={"PERFORMANCE_HINT": "CUMULATIVE_THROUGHPUT"}
else:
ovconfig={"PERFORMANCE_HINT": "CUMULATIVE_THROUGHPUT","GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
self.model = OVModelForFeatureExtraction.from_pretrained(model_name,
compile=True,
trust_remote_code=request.TrustRemoteCode,
ov_config={"PERFORMANCE_HINT": "CUMULATIVE_THROUGHPUT", "GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"},
ov_config=ovconfig,
export=True,
device=device_map)
self.OV = True
@ -234,8 +246,8 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
# Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr)
print("Embeddings:", sentence_embeddings, file=sys.stderr)
# print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr)
# print("Embeddings:", sentence_embeddings, file=sys.stderr)
return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings[0])
async def _predict(self, request, context, streaming=False):