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feat(transformers): merge musicgen functionalities to a single backend (#4620)
* feat(transformers): merge musicgen functionalities to a single backend So we optimize space Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * specify type in tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Some adaptations for the MusicgenForConditionalGeneration type Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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Ettore Di Giacinto
GitHub
parent
212c8e1a6d
commit
8027fdf1c7
21 changed files with 187 additions and 414 deletions
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@ -22,6 +22,8 @@ import torch.cuda
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XPU=os.environ.get("XPU", "0") == "1"
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from transformers import AutoTokenizer, AutoModel, set_seed, TextIteratorStreamer, StoppingCriteriaList, StopStringCriteria
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from transformers import AutoProcessor, MusicgenForConditionalGeneration
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from scipy.io import wavfile
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_ONE_DAY_IN_SECONDS = 60 * 60 * 24
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@ -191,6 +193,9 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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export=True,
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device=device_map)
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self.OV = True
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elif request.Type == "MusicgenForConditionalGeneration":
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self.processor = AutoProcessor.from_pretrained(model_name)
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self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
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else:
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print("Automodel", file=sys.stderr)
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self.model = AutoModel.from_pretrained(model_name,
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@ -201,19 +206,22 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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torch_dtype=compute)
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if request.ContextSize > 0:
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self.max_tokens = request.ContextSize
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else:
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elif request.Type != "MusicgenForConditionalGeneration":
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self.max_tokens = self.model.config.max_position_embeddings
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else:
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self.max_tokens = 512
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self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
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self.XPU = False
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if request.Type != "MusicgenForConditionalGeneration":
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self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
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self.XPU = False
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if XPU and self.OV == False:
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self.XPU = True
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try:
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print("Optimizing model", model_name, "to XPU.", file=sys.stderr)
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self.model = ipex.optimize_transformers(self.model, inplace=True, dtype=torch.float16, device="xpu")
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except Exception as err:
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print("Not using XPU:", err, file=sys.stderr)
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if XPU and self.OV == False:
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self.XPU = True
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try:
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print("Optimizing model", model_name, "to XPU.", file=sys.stderr)
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self.model = ipex.optimize_transformers(self.model, inplace=True, dtype=torch.float16, device="xpu")
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except Exception as err:
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print("Not using XPU:", err, file=sys.stderr)
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except Exception as err:
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print("Error:", err, file=sys.stderr)
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@ -380,6 +388,93 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
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finally:
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await iterations.aclose()
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def SoundGeneration(self, request, context):
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model_name = request.model
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try:
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if self.processor is None:
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if model_name == "":
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return backend_pb2.Result(success=False, message="request.model is required")
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self.processor = AutoProcessor.from_pretrained(model_name)
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if self.model is None:
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if model_name == "":
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return backend_pb2.Result(success=False, message="request.model is required")
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self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
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inputs = None
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if request.text == "":
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inputs = self.model.get_unconditional_inputs(num_samples=1)
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elif request.HasField('src'):
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# TODO SECURITY CODE GOES HERE LOL
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# WHO KNOWS IF THIS WORKS???
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sample_rate, wsamples = wavfile.read('path_to_your_file.wav')
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if request.HasField('src_divisor'):
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wsamples = wsamples[: len(wsamples) // request.src_divisor]
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inputs = self.processor(
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audio=wsamples,
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sampling_rate=sample_rate,
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text=[request.text],
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padding=True,
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return_tensors="pt",
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)
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else:
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inputs = self.processor(
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text=[request.text],
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padding=True,
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return_tensors="pt",
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)
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tokens = 256
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if request.HasField('duration'):
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tokens = int(request.duration * 51.2) # 256 tokens = 5 seconds, therefore 51.2 tokens is one second
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guidance = 3.0
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if request.HasField('temperature'):
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guidance = request.temperature
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dosample = True
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if request.HasField('sample'):
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dosample = request.sample
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audio_values = self.model.generate(**inputs, do_sample=dosample, guidance_scale=guidance, max_new_tokens=tokens)
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print("[transformers-musicgen] SoundGeneration generated!", file=sys.stderr)
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sampling_rate = self.model.config.audio_encoder.sampling_rate
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wavfile.write(request.dst, rate=sampling_rate, data=audio_values[0, 0].numpy())
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print("[transformers-musicgen] SoundGeneration saved to", request.dst, file=sys.stderr)
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print("[transformers-musicgen] SoundGeneration for", file=sys.stderr)
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print("[transformers-musicgen] SoundGeneration requested tokens", tokens, file=sys.stderr)
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print(request, file=sys.stderr)
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except Exception as err:
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return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
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return backend_pb2.Result(success=True)
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# The TTS endpoint is older, and provides fewer features, but exists for compatibility reasons
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def TTS(self, request, context):
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model_name = request.model
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try:
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if self.processor is None:
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if model_name == "":
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return backend_pb2.Result(success=False, message="request.model is required")
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self.processor = AutoProcessor.from_pretrained(model_name)
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if self.model is None:
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if model_name == "":
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return backend_pb2.Result(success=False, message="request.model is required")
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self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
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inputs = self.processor(
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text=[request.text],
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padding=True,
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return_tensors="pt",
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)
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tokens = 512 # No good place to set the "length" in TTS, so use 10s as a sane default
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audio_values = self.model.generate(**inputs, max_new_tokens=tokens)
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print("[transformers-musicgen] TTS generated!", file=sys.stderr)
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sampling_rate = self.model.config.audio_encoder.sampling_rate
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wavfile.write(request.dst, rate=sampling_rate, data=audio_values[0, 0].numpy())
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print("[transformers-musicgen] TTS saved to", request.dst, file=sys.stderr)
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print("[transformers-musicgen] TTS for", file=sys.stderr)
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print(request, file=sys.stderr)
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except Exception as err:
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return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
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return backend_pb2.Result(success=True)
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async def serve(address):
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# Start asyncio gRPC server
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server = grpc.aio.server(migration_thread_pool=futures.ThreadPoolExecutor(max_workers=MAX_WORKERS))
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