Support cross attention kv cache

[larryliu0820]

To avoid excessive computation we want to support kv cache for cross attention in Whisper.

Fundamentally we only run k_proj and v_proj once on the encoder output hidden state, at the first token generation, then we should keep the key_states and value_states and reuse them in all the subsequent token generation.

For whisper-large-v3-turbo, where we have 4 layers of decoder:

WhisperDecoder(
  (embed_tokens): Embedding(51866, 1280, padding_idx=50257)
  (embed_positions): WhisperPositionalEmbedding(448, 1280)
  (layers): ModuleList(
    (0-3): 4 x WhisperDecoderLayer(
      (self_attn): WhisperAttention(
        (k_proj): Linear(in_features=1280, out_features=1280, bias=False)
        (v_proj): Linear(in_features=1280, out_features=1280, bias=True)
        (q_proj): Linear(in_features=1280, out_features=1280, bias=True)
        (out_proj): Linear(in_features=1280, out_features=1280, bias=True)
      )
      (activation_fn): GELUActivation()
      (self_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
      (encoder_attn): WhisperAttention(
        (k_proj): Linear(in_features=1280, out_features=1280, bias=False)
        (v_proj): Linear(in_features=1280, out_features=1280, bias=True)
        (q_proj): Linear(in_features=1280, out_features=1280, bias=True)
        (out_proj): Linear(in_features=1280, out_features=1280, bias=True)
      )
      (encoder_attn_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
      (fc1): Linear(in_features=1280, out_features=5120, bias=True)
      (fc2): Linear(in_features=5120, out_features=1280, bias=True)
      (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
    )
  )
  (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)
)

Without KV cache in encoder_attn, we are doing 2 1280x1280 MM for each layer, so in total 8 1280x1280 MM for each token generated. This largely impacts token/sec perf number.

This PR replaces encoder_attn with a WhisperCrossAttention class, where we replaces if condition with torch.cond. The logic becomes:

• If KV cache values are all zero:
  • Compute KV projections
• Otherwise:
  • Clone from KV cache. Note here we can't directly return KV cache, due to the non-aliasing requirement.
• After torch.cond:
  • Write back the values from either branch back to KV cache

Notice that we still have 1 extra read and 1 extra write, but it should be much faster than MM.

[jackzhxng]

Oh also run make style for formatting

[larryliu0820]

This works, gives correct output, but eventually we still need to copy data from GPU to CPU, just for the predicate. There's no way we can workaround it.

For whisper-large-v3-turbo, there are 4 decoder layers, so we see 4 cudaAsyncMemcpy blocks in each token generation:

This is too expensive to be a good solution.