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add deepseekv3

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Chranos
2026-02-11 13:12:46 +08:00
parent dfb4cff2fc
commit 9acbda437e
1 changed files with 42 additions and 27 deletions
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69
vllm-v0.6.2/vllm_mlu/vllm_mlu/attention/backends/mlu_attn.py
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@@ -580,34 +580,49 @@ def unified_flash_attention_v2(
value_cache,
updated_slot_mapping.flatten())
else:
# FIXME: After TMO-1496 is completed, remove this code.
if key.stride() != value.stride():
key = key.contiguous()
value = value.contiguous()
if kv_cache_dtype == 'int8':
mlu_ops.quant_to_linear_cache(key,
value,
key_cache,
value_cache,
key_cache_scale,
value_cache_scale,
attn_metadata.cu_seq_lens,
attn_metadata.max_seq_len,
True, # packed
None, # context_seq_offset
attn_metadata.batch_ids,
attn_metadata.slot_mapping_unpaged)
# unpaged (linear cache) path
if use_mla:
# MLA: 镜像 paged 路径的处理方式
value_to_cache = None
if attn_metadata.prefill_metadata:
# MLA prefill cache 已在 forward_prefill 中写入，跳过
pass
else:
if kv_cache_dtype == 'int8':
mlu_ops.quant_to_paged_cache(
key, value_to_cache,
key_cache, value_cache,
key_cache_scale, value_cache_scale,
updated_slot_mapping.flatten())
else:
mlu_ops.reshape_paged_cache(
key, value_to_cache,
key_cache, value_cache,
updated_slot_mapping.flatten())
else:
mlu_ops.reshape_linear_cache(key,
value,
key_cache,
value_cache,
attn_metadata.cu_seq_lens,
attn_metadata.max_seq_len,
True, # packed
None, # context_seq_offset
attn_metadata.batch_ids,
attn_metadata.slot_mapping_unpaged)
# FIXME: After TMO-1496 is completed, remove this code.
if key.stride() != value.stride():
key = key.contiguous()
value = value.contiguous()
if kv_cache_dtype == 'int8':
mlu_ops.quant_to_linear_cache(
key, value,
key_cache, value_cache,
key_cache_scale, value_cache_scale,
attn_metadata.cu_seq_lens,
attn_metadata.max_seq_len,
True, None,
attn_metadata.batch_ids,
attn_metadata.slot_mapping_unpaged)
else:
mlu_ops.reshape_linear_cache(
key, value,
key_cache, value_cache,
attn_metadata.cu_seq_lens,
attn_metadata.max_seq_len,
True, None,
attn_metadata.batch_ids,
attn_metadata.slot_mapping_unpaged)
if use_mla and attn_metadata.prefill_metadata:
output = torch.empty(query.shape[0], query.shape[1], v_head_size, dtype=query.dtype, device="mlu")
else: