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72 lines
2.4 KiB
72 lines
2.4 KiB
import pytest
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import torch
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from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM
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from colossalai.inference.modeling.models.glide_llama import GlideLlamaConfig, GlideLlamaForCausalLM
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from colossalai.inference.spec.drafter import Drafter
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from colossalai.utils import get_current_device
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NUM_LAYERS = 1
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MAX_LEN = 100
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SPEC_NUM = 5
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@pytest.mark.parametrize("spec_num", [SPEC_NUM])
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def test_drafter(spec_num: int):
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torch.manual_seed(123)
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device = get_current_device()
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
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toy_config = LlamaConfig(num_hidden_layers=NUM_LAYERS)
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toy_config.pad_token_id = tokenizer.eos_token_id
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drafter_model = LlamaForCausalLM(toy_config)
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drafter_model = drafter_model.eval().cuda()
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drafter = Drafter(drafter_model, tokenizer, device=device)
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input_ids = torch.randint(low=5, high=1000, size=(1, 6)).to(device)
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out = drafter.speculate(input_ids, spec_num)
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past_kv_length = input_ids.size(1) + spec_num - 1
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assert out.speculated_length == spec_num
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assert out.next_tokens.shape == (spec_num,)
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assert out.logits.shape == (spec_num, len(tokenizer))
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assert out.past_key_values[0][0].size(2) == past_kv_length
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reject_num = max(0, spec_num - 1)
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trimmed_past_key_values = drafter.trim_kv_cache(out.past_key_values, reject_num)
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assert trimmed_past_key_values[0][0].size(2) == past_kv_length - reject_num
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def test_spec_dec():
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spec_num = SPEC_NUM
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device = get_current_device()
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tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
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tokenizer.pad_token = tokenizer.eos_token
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# Dummy config for Glide Model
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glide_config = GlideLlamaConfig(
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intermediate_size=8192,
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large_hidden_size=4096,
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large_num_attention_heads=32,
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num_hidden_layers=NUM_LAYERS,
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)
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drafter_model = GlideLlamaForCausalLM(glide_config)
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assert hasattr(drafter_model, "model")
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assert hasattr(drafter_model.model, "layers")
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for _, layer in enumerate(drafter_model.model.layers):
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assert hasattr(layer, "cross_attn")
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# Init the Drafter by providing the sharded drafter model
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drafter = Drafter(drafter_model, tokenizer, device=device, dtype=torch.float16)
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input_ids = torch.randint(low=5, high=1000, size=(1, 6)).to(device)
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out = drafter.speculate(input_ids, spec_num, past_key_values=None)
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if __name__ == "__main__":
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test_drafter(spec_num=SPEC_NUM)
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test_spec_dec()
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