AFD: update DeepSeek support#2
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@Oliver-ss could you spend some time reviewing the code? the follow-up features will be updated with independent PRs |
…ect#26445) Signed-off-by: Nick Hill <nhill@redhat.com>
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There seems to be some inconsistency in understanding. According to the content of the paper, the number of attention instances has no correlation with the TP parallel scale, and your configuration here should be 1A1F. |
| group_name="afd", | ||
| timeout=timedelta(minutes=2), | ||
| ) | ||
| ffn_ranks = [i for i in range(ffn_size, ffn_size + attn_size)] |
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there should be range(attn_size, ffn_size + attn_size)
| hidden_states, residual = self.post_attention_layernorm( | ||
| hidden_states, residual) | ||
| # ---------ascend ffn need data | ||
| if forward_ctx.moe_comm_method_name is not None: |
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Hi, where is this moe_comm_method_name field defined for ForwardContext?
Purpose
This PR corresponds to the RFC vllm-project#22799 and a follow-up PR of vllm-project#25162.
This PR is in collaboration with @chopper0126 @CZRZ
Later, we are going to support the following features:
Test Plan
At this stage, we used 4 GTX3090 GPUs to test the feasibility of our implementation. Both attention and FFN sides shard across 2 GPUs.
Test Result
By sending a request to the model, we got:

Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.