[WiP] Whisper Implementation #147
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| self.scaling = self.head_dim**-0.5 | ||
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| self.k_proj = ColumnParallelLinear(self.d_model, |
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Can we use QKVColumnLinear for this?
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Or no because of the bias?
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Maybe we should extend QKVParallelLinear for this case?
Could also do this in a follow up PR
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@robertgshaw2-neuralmagic we could use the QKVParallelLinear in two scenarios:
- encoder attention
- decoder self-attention
we cannot use it for decoder cross attention, as queries are coming from a different source then keys and values.
I decided not to introduce the new module, since our current implementation (that is very close to T5) is still failing and we are not sure yet why. I'll start refactoring once we fix the current issues, as adding more moving parts may obfuscate the path to the solution.
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@robertgshaw2-neuralmagic but nothing prevents us from adding QKVParallelLinear support for the T5 model (once again, only for encoder attention and decoder self-attention)!
| stride=2, | ||
| padding=1) | ||
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| self.embed_positions = nn.Embedding(self.max_source_positions, |
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could this be VocabParallelEmbedding?
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yes, this part is definitely parallelizable, my omission, I did introduce the VocabParallelEmbedding here before 🥴
| super().__init__() | ||
| self.d_model = config.d_model | ||
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| self.embed_tokens = nn.Embedding(config.vocab_size, self.d_model) |
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Can these be VocabParallelEmbedding?
| # TODO: For now we are not implementing the sampling method | ||
| return hidden_states | ||
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| def load_weights( |
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Can you match the style of llama in this function? They have been very consistent with this logic across functions
https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/llama.py#L363
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Looking good, seems like we could adhere to their convention
| if kv_caches[0][0] is None: | ||
| hidden_states = None | ||
| else: | ||
| hidden_states = self.decoder(input_ids=decoder_input_ids, |
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the decoder_input_ids is something wrong? when we go into the else branch, there is no decoder_input_ids variable.
| return hidden_states | ||
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| class WhisperDecoderBlock(nn.Module): |
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why not just keep the same name with the WhisperDecoderLayer as in transformers library?
| self.prefix = prefix | ||
| self.multi_modal_data = multi_modal_data |
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| self.multi_modal_data = multi_modal_data |
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