mirror of https://github.com/hpcaitech/ColossalAI
90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
import os
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import pytest
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import torch
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from packaging import version
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import colossalai
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from colossalai.inference.tensor_parallel.engine import TPInferEngine
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from colossalai.logging import disable_existing_loggers
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from colossalai.shardformer import ShardConfig
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from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
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from tests.kit.model_zoo import model_zoo
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os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "true"
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TPSIZE = 2
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BATCH_SIZE = 8
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MAX_INPUT_LEN = 12
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MAX_OUTPUT_LEN = 100
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CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.5")
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def init_to_get_rotary(self, base=10000):
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self.config.head_dim_ = self.config.hidden_size // self.config.num_attention_heads
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if not hasattr(self.config, "rope_scaling"):
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rope_scaling_factor = 1.0
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else:
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rope_scaling_factor = self.config.rope_scaling.factor if self.config.rope_scaling is not None else 1.0
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if hasattr(self.config, "max_sequence_length"):
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max_seq_len = self.config.max_sequence_length
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elif hasattr(self.config, "max_position_embeddings"):
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max_seq_len = self.config.max_position_embeddings * rope_scaling_factor
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else:
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max_seq_len = 2048 * rope_scaling_factor
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base = float(base)
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inv_freq = 1.0 / (
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base ** (torch.arange(0, self.config.head_dim_, 2, device="cpu", dtype=torch.float32) / self.config.head_dim_)
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)
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t = torch.arange(max_seq_len + 1024 * 64, device="cpu", dtype=torch.float32) / rope_scaling_factor
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freqs = torch.outer(t, inv_freq)
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self._cos_cached = torch.cos(freqs).to(torch.float16).cuda()
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self._sin_cached = torch.sin(freqs).to(torch.float16).cuda()
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return
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@parameterize(
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"test_config",
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[
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{
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"tp_size": TPSIZE,
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}
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],
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)
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def run_llama_test(test_config):
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sub_model_zoo = model_zoo.get_sub_registry("transformers_llama_for_casual_lm")
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for name, (model_fn, data_gen_fn, _, _, _) in sub_model_zoo.items():
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orig_model = model_fn()
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init_to_get_rotary(orig_model.model, base=10000)
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orig_model = orig_model.half()
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data = data_gen_fn()
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shard_config = ShardConfig(
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enable_tensor_parallelism=True if test_config["tp_size"] > 1 else False, inference_only=True
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)
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infer_engine = TPInferEngine(orig_model, shard_config, BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
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generate_kwargs = dict(do_sample=False)
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outputs = infer_engine.generate(data, **generate_kwargs)
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assert outputs is not None
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def check_llama(rank, world_size, port):
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disable_existing_loggers()
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colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
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run_llama_test()
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@pytest.mark.skipif(not CUDA_SUPPORT, reason="kv-cache manager engine requires cuda version to be higher than 11.5")
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@pytest.mark.dist
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@rerun_if_address_is_in_use()
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@clear_cache_before_run()
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def test_llama():
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spawn(check_llama, TPSIZE)
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if __name__ == "__main__":
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test_llama()
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