Support incremental state for E2E ASR experiments #173
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change weight-c-init from Optional[float] = None to float = 1.0
jinpoon
reviewed
Mar 23, 2021
| normalize_after: bool, optional | ||
| if True, apply post layer normalization; otherwise apply pre layer normalization | ||
| (default=True). | ||
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Would you mind adding the doc string of the right_window also? Just wanted to make sure the legal range of this parameter.
experiments/e2e_asr/extension.py
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| state["saved_query"] = query_not_ready | ||
| state["saved_key"] = k | ||
| state["saved_value"] = v | ||
| incremental_state["attn_state"] = state |
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When there are no ready queries, would it be more efficient to just return the empty tensor here?
experiments/e2e_asr/extension.py
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| input: Tensor, | ||
| mask_pad: Optional[Tensor] = None, | ||
| attn_mask: Optional[Tensor] = None, | ||
| incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, |
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Can you change data structure of incremental_state to Optional[Dict[str, Optional[Tensor]]? Seems the SRUCell does use the incremental_state and I should write some glue codes inheriting from the fair_seq's incremental class, and assign & maintain a layer specific hash as the key to this dictionary, in stead of attn_state
experiments/e2e_asr/extension.py
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| c0: Optional[Tensor] = None, | ||
| mask_pad: Optional[Tensor] = None, | ||
| attn_mask: Optional[Tensor] = None, | ||
| incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None, |
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