adding Context Length Specialization (CCL) #466
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Signed-off-by: vjanfaza <vjanfaza@apex-scl01-giga-linux.qualcomm.com>
quic-vjanfaza
requested review from
quic-rishinr,
ochougul,
quic-hemagnih and
quic-amitraj
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Signed-off-by: vjanfaza <vjanfaza@apex-scl01-giga-linux.qualcomm.com>
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how much time does the tests take?
We can choose to only test one model per KV type i.e. for chunked, hybrid, sliding window etc.
chunked -> global + local -> llama4
hybrid -> sliding window + global -> gemma3
sliding window -> mistral
For the above categories, we need to handle CCL in a different way.
Probably for local or sliding window layers, the complete CCL won't apply when it goes beyond sliding window length
And full CCL applies for global layers.
This support needs to be added.
| @@ -102,6 +102,7 @@ def main( | |||
| full_batch_size: Optional[int] = None, | |||
| prompt_len: int = 32, | |||
| ctx_len: int = 128, | |||
| comp_ctx_lengths: Optional[List[int]] = None, | |||
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Add this in the docstring as well of the function.
| @@ -1489,6 +1491,8 @@ def from_pretrained( | |||
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| kv_offload = kwargs.pop("kv_offload", None) | |||
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I think, comp_ctx_length should be handled as a explicit parameter in from_pretrained rather than handling inside the kwargs. After this there will be no need of pooping that var from kwargs and we can add proper docstring as well here.
| self.comp_ctx_lengths = kwargs.pop("comp_ctx_lengths", None) | ||
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Why, I don't think there is any need of this.
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we can use kwargs.get instead of pop. We are planning to use this kwargs for creating the model hash
| for i in range(1, len(self.comp_ctx_lengths)): | ||
| decode_spec = self.build_decode_specialization( | ||
| prefill_seq_len=prefill_seq_len, | ||
| ctx_len=ctx_len, |
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There is no need of if else condition, please handle this for loop inside the build_decode_specialization.
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| from QEfficient.transformers.modeling_attn_mask_utils import _create_causal_mask | |||
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| @dataclass | |||
| class QEffBaseModelOutputWithPast(BaseModelOutputWithPast): | |||
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As these data class is common across all the modelling file, better to keep it in modeling_utils.py.
Context-Length-Specialization technique optimizes the throughput of large language models (LLMs) on Qualcomm devices when handling very large context lengths. The current Ahead Of Time (AOT) compilation on Qualcomm devices doesn't predict the number of tokens needed, leading to significant throughput drops during the prefilling and the decoding phases. This happens because the system performs attention calculations based on large context length. To address this issue, we introduce Compute Context Length (CCL), an additional ONNX variable that allows for dynamic context-length specialization. By generating tokens using smaller, more manageable context lengths (CCL), we optimize memory reads and attention calculations, thereby improving throughput.