Upgrade tensorrt_llm to v0.18.2
Signed-off-by: makaveli10 <vineet.suryan@collabora.com>
This commit is contained in:
makaveli10
@@ -23,7 +23,8 @@ from tensorrt_llm._utils import (str_dtype_to_torch, str_dtype_to_trt,
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from tensorrt_llm.bindings import GptJsonConfig, KVCacheType
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from tensorrt_llm.runtime import PYTHON_BINDINGS, ModelConfig, SamplingConfig
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from tensorrt_llm.runtime.session import Session, TensorInfo
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if PYTHON_BINDINGS:
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from tensorrt_llm.runtime import ModelRunnerCpp
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SAMPLE_RATE = 16000
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N_FFT = 400
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@@ -255,8 +256,17 @@ class WhisperDecoding:
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class WhisperTRTLLM(object):
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def __init__(self, engine_dir, assets_dir=None, device=None, is_multilingual=False,
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language="en", task="transcribe"):
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def __init__(self,
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engine_dir,
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assets_dir=None,
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device=None,
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is_multilingual=False,
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language="en",
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task="transcribe",
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use_py_session=False,
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num_beams=1,
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debug_mode=False,
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max_output_len=96):
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world_size = 1
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runtime_rank = tensorrt_llm.mpi_rank()
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runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank)
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@@ -268,13 +278,6 @@ class WhisperTRTLLM(object):
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self.num_languages = encoder_config['num_languages']
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is_multilingual = (decoder_config['vocab_size'] >= 51865)
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self.encoder = WhisperEncoding(engine_dir)
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self.decoder = WhisperDecoding(engine_dir,
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runtime_mapping,
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debug_mode=False)
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self.n_mels = self.encoder.n_mels
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# self.tokenizer = get_tokenizer(num_languages=self.encoder.num_languages,
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# tokenizer_dir=assets_dir)
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self.device = device
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self.tokenizer = get_tokenizer(
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is_multilingual,
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@@ -282,7 +285,28 @@ class WhisperTRTLLM(object):
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language=language,
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task=task,
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)
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self.filters = mel_filters(self.device, self.encoder.n_mels, assets_dir)
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if use_py_session:
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self.encoder = WhisperEncoding(engine_dir)
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self.decoder = WhisperDecoding(engine_dir,
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runtime_mapping,
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debug_mode=False)
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else:
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json_config = GptJsonConfig.parse_file(engine_dir / 'decoder' /
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'config.json')
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assert json_config.model_config.supports_inflight_batching
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runner_kwargs = dict(engine_dir=engine_dir,
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is_enc_dec=True,
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max_batch_size=1,
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max_input_len=3000,
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max_output_len=max_output_len,
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max_beam_width=num_beams,
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debug_mode=debug_mode,
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kv_cache_free_gpu_memory_fraction=0.9,
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cross_kv_cache_fraction=0.5)
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self.model_runner_cpp = ModelRunnerCpp.from_dir(**runner_kwargs)
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self.filters = mel_filters(self.device, self.n_mels, assets_dir)
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self.use_py_session = use_py_session
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def log_mel_spectrogram(
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self,
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@@ -355,16 +379,38 @@ class WhisperTRTLLM(object):
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prompt_id = torch.tensor(prompt_id)
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batch_size = mel.shape[0]
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decoder_input_ids = prompt_id.repeat(batch_size, 1)
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encoder_output, encoder_output_lengths = self.encoder.get_audio_features(mel, mel_input_lengths)
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encoder_max_input_length = torch.max(encoder_output_lengths).item()
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output_ids = self.decoder.generate(decoder_input_ids,
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encoder_output,
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encoder_max_input_length,
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encoder_output_lengths,
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self.tokenizer.eot,
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max_new_tokens=max_new_tokens,
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num_beams=num_beams)
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if self.use_py_session:
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encoder_output, encoder_output_lengths = self.encoder.get_audio_features(mel, mel_input_lengths)
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encoder_max_input_length = torch.max(encoder_output_lengths).item()
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output_ids = self.decoder.generate(decoder_input_ids,
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encoder_output,
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encoder_max_input_length,
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encoder_output_lengths,
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self.tokenizer.eot,
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max_new_tokens=max_new_tokens,
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num_beams=num_beams)
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else:
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with torch.no_grad():
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if isinstance(mel, list):
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mel = [
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m.transpose(1, 2).type(
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str_dtype_to_torch("float16")).squeeze(0)
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for m in mel
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]
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else:
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mel = mel.transpose(1, 2)
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outputs = self.model_runner_cpp.generate(
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batch_input_ids=decoder_input_ids,
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encoder_input_features=mel,
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encoder_output_lengths=mel_input_lengths // 2,
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max_new_tokens=max_new_tokens,
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end_id=self.tokenizer.eot,
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pad_id=self.tokenizer.eot,
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num_beams=num_beams,
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output_sequence_lengths=True,
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return_dict=True)
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torch.cuda.synchronize()
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output_ids = outputs['output_ids'].cpu().numpy().tolist()
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texts = []
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for i in range(len(output_ids)):
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text = self.tokenizer.decode(output_ids[i][0]).strip()
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@@ -379,7 +425,8 @@ class WhisperTRTLLM(object):
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batch_size=1,
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num_beams=1,
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padding_strategy="max",
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):
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max_new_tokens=96,
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):
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mel = mel.type(str_dtype_to_torch(dtype))
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mel = mel.unsqueeze(0)
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# repeat the mel spectrogram to match the batch size
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@@ -393,7 +440,13 @@ class WhisperTRTLLM(object):
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dtype=torch.int32,
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device=mel.device)
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predictions = self.process_batch(mel, features_input_lengths, text_prefix, num_beams)
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predictions = self.process_batch(
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mel,
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features_input_lengths,
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text_prefix,
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num_beams,
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max_new_tokens=max_new_tokens
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)
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prediction = predictions[0]
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# remove all special tokens in the prediction
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