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https://github.com/NVIDIA/TensorRT-LLM.git
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110 lines
4.3 KiB
Python
110 lines
4.3 KiB
Python
import tempfile
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from transformers import AutoTokenizer
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from utils.llm_data import llm_models_root
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from utils.util import force_ampere, skip_no_modelopt, skip_pre_ada
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import tensorrt_llm
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from tensorrt_llm.builder import BuildConfig, build
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from tensorrt_llm.executor import GenerationExecutor
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from tensorrt_llm.models import LLaMAForCausalLM
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from tensorrt_llm.models.modeling_utils import QuantConfig
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from tensorrt_llm.quantization import QuantAlgo
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from tensorrt_llm.sampling_params import SamplingParams
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tensorrt_llm.logger.set_level('info')
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batch_input_text = [
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"Born in north-east France, Soyer trained as a",
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"What is large language model?"
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]
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@force_ampere
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@skip_no_modelopt
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def test_int4_awq_quantization():
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max_batch_size, max_isl, max_osl = 8, 256, 256
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hf_model_dir = str(llm_models_root() / "llama-models/llama-7b-hf")
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cnn_dailymail_path = str(llm_models_root() / "datasets/cnn_dailymail")
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checkpoint_dir = tempfile.TemporaryDirectory("llama-checkpoint").name
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quant_config = QuantConfig(QuantAlgo.W4A16_AWQ)
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LLaMAForCausalLM.quantize(hf_model_dir,
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checkpoint_dir,
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quant_config=quant_config,
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calib_dataset=cnn_dailymail_path,
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calib_batches=32,
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calib_batch_size=32)
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llama = LLaMAForCausalLM.from_checkpoint(checkpoint_dir)
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engine = build(
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llama,
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BuildConfig(
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max_batch_size=max_batch_size,
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max_input_len=max_isl,
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max_seq_len=max_osl + max_isl,
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max_num_tokens=max_batch_size * max_isl,
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))
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engine_dir = "llama-awq-quantized"
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engine_temp = tempfile.TemporaryDirectory(engine_dir)
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engine_dir = engine_temp.name
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engine.save(engine_dir)
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tokenizer = AutoTokenizer.from_pretrained(hf_model_dir)
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with GenerationExecutor.create(engine_dir) as executor:
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batch_input_ids = [tokenizer.encode(inp) for inp in batch_input_text]
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outputs = executor.generate(
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batch_input_ids, sampling_params=SamplingParams(max_tokens=10))
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for idx, output in enumerate(outputs):
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print(f"Input: {batch_input_text[idx]}")
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output_text = tokenizer.decode(output.outputs[0].token_ids)
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print(f'Output: {output_text}')
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# TODO: TRTLLM-185, check the score when the test infra is ready, hard coded value is not stable, cause flaky tests in L0
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@skip_pre_ada
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@skip_no_modelopt
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def test_fp8_quantization():
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max_batch_size, max_isl, max_osl = 8, 256, 256
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hf_model_dir = str(llm_models_root() / "llama-models/llama-7b-hf")
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cnn_dailymail_path = str(llm_models_root() / "datasets/cnn_dailymail")
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checkpoint_dir = tempfile.TemporaryDirectory("llama-checkpoint").name
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quant_config = QuantConfig(QuantAlgo.FP8)
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LLaMAForCausalLM.quantize(hf_model_dir,
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checkpoint_dir,
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quant_config=quant_config,
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calib_dataset=cnn_dailymail_path,
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calib_batches=32)
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llama = LLaMAForCausalLM.from_checkpoint(checkpoint_dir)
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engine = build(
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llama,
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BuildConfig(max_batch_size=max_batch_size,
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max_input_len=max_isl,
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max_seq_len=max_osl + max_isl,
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max_num_tokens=max_batch_size * max_isl,
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strongly_typed=True))
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engine_dir = "llama-fp8-quantized"
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engine_temp = tempfile.TemporaryDirectory(engine_dir)
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engine_dir = engine_temp.name
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engine.save(engine_dir)
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tokenizer = AutoTokenizer.from_pretrained(hf_model_dir)
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with GenerationExecutor.create(engine_dir) as executor:
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batch_input_ids = [tokenizer.encode(inp) for inp in batch_input_text]
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outputs = executor.generate(
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batch_input_ids, sampling_params=SamplingParams(max_tokens=10))
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for idx, output in enumerate(outputs):
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print(f"Input: {batch_input_text[idx]}")
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output_text = tokenizer.decode(output.outputs[0].token_ids)
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print(f'Output: {output_text}')
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# TODO: TRTLLM-185, check the score when the test infra is ready, hard coded value is not stable, cause flaky tests in L0
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if __name__ == "__main__":
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test_int4_awq_quantization()
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test_fp8_quantization()
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