2024-06-15 09:54:33 +00:00
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# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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2024-06-07 17:35:58 +00:00
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import os
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from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
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from llamafactory.hparams import get_infer_args
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from llamafactory.model import load_model, load_tokenizer
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2024-06-10 13:24:15 +00:00
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TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
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INFER_ARGS = {
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"model_name_or_path": TINY_LLAMA,
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"template": "llama3",
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}
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2024-06-07 17:35:58 +00:00
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def test_attention():
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attention_available = ["off"]
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if is_torch_sdpa_available():
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attention_available.append("sdpa")
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if is_flash_attn_2_available():
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attention_available.append("fa2")
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llama_attention_classes = {
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"off": "LlamaAttention",
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"sdpa": "LlamaSdpaAttention",
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"fa2": "LlamaFlashAttention2",
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}
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for requested_attention in attention_available:
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2024-06-10 13:24:15 +00:00
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model_args, _, finetuning_args, _ = get_infer_args({"flash_attn": requested_attention, **INFER_ARGS})
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2024-06-07 21:20:54 +00:00
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tokenizer_module = load_tokenizer(model_args)
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model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args)
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2024-06-07 17:35:58 +00:00
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for module in model.modules():
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if "Attention" in module.__class__.__name__:
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2024-06-07 17:57:36 +00:00
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assert module.__class__.__name__ == llama_attention_classes[requested_attention]
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