update torch MLA kernel

ktransformers/operators/balance_serve_attention.py

@@ -288,3 +288,170 @@ class KQwen3MoeAttention(BaseInjectedModule, Qwen3MoeAttention):
         attn_output = self.o_proj(attn_output.view(q_len, self.num_heads * self.head_dim), bsz_tensors)
 
         return attn_output
+
+
+class deepseek_torch_attn(BaseInjectedModule, DeepseekV2Attention):
+    def __init__(self,
+                    key: str,
+                    gguf_loader : GGUFLoader,
+                    config: PretrainedConfig,
+                    orig_module: nn.Module,
+                    prefill_device: str = "cuda",
+                    generate_device: str = "cuda",
+                    chunck_size: int = 1000,
+                    **kwargs):
+            BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, prefill_device, **kwargs)
+            self.orig_module.__init__(orig_module.config,
+                orig_module.layer_idx)
+            self.chunck_size = chunck_size # TODO, generate chunck_size automatically.
+
+
+    def get_absorbed(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        if not (hasattr(self, 'q_absorb') and hasattr(self, 'out_absorb')):
+            kv_b_proj = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
+            q_absorb = kv_b_proj[:, :self.qk_nope_head_dim, :].reshape(-1, self.kv_lora_rank)
+            out_absorb = kv_b_proj[:, self.qk_nope_head_dim:, :].reshape(-1, self.kv_lora_rank)
+            self.q_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.qk_nope_head_dim, 
+                                    bias=False, dtype=q_absorb.dtype, device=q_absorb.device)
+            self.q_absorb.weight.data = q_absorb
+            self.out_absorb = nn.Linear(self.kv_lora_rank, self.num_heads * self.v_head_dim, 
+                                        bias=False, dtype=out_absorb.dtype, device=out_absorb.device)
+            self.out_absorb.weight.data = out_absorb
+            #del self.orig_module.kv_b_proj
+        q_absorb = self.q_absorb.weight.view(self.num_heads, self.qk_nope_head_dim, self.kv_lora_rank)
+        out_absorb = self.out_absorb.weight.view(self.num_heads, self.v_head_dim, self.kv_lora_rank)
+        return q_absorb, out_absorb
+    
+
+
+    def forward(self,
+                hidden_states: torch.Tensor,
+                kv_cache: KDeepSeekV3Cache,
+                position_ids: torch.Tensor,
+                wrapper: None,
+                num_tokens_tensors: torch.Tensor,
+                page_idx: torch.Tensor,
+                page_offset: torch.Tensor,
+                attention_masks: Optional[list[torch.Tensor]] = None,
+                q_indptr: Optional[torch.Tensor] = None,
+                kv_indices: Optional[torch.Tensor] = None,
+                kv_indptr: Optional[torch.Tensor] = None,
+                bsz_tensors: Optional[torch.Tensor] = None,
+                last_page_len: Optional[torch.Tensor] = None,
+                ):
+        # range bsz_tensors
+        final_attention_output = torch.tensor([], device=hidden_states.device)
+        for i in range(bsz_tensors[0]):
+            batch_num_tokens_tensors = q_indptr[i+1] - q_indptr[i]
+            batch_last_page_len = last_page_len[i]
+            # kv_total_len is kv_len, batch_compressed_kv is compressed_kv, batch_k_pe is k_pe
+            batch_page_idx = page_idx[q_indptr[i]:q_indptr[i+1]]
+            batch_page_offset = page_offset[q_indptr[i]:q_indptr[i+1]]
+            # kv_page_nums is the number of pages for the current batch
+            kv_page_nums = kv_indptr[i+1] - kv_indptr[i]
+            # kv_total_len is the total length of the kv cache for the current batch (kv_len for algorithm)
+            kv_total_len = kv_page_nums * kv_cache.page_size
+            if batch_last_page_len is not None:
+                kv_total_len = kv_total_len - (kv_cache.page_size - batch_last_page_len)
+            # print(f"kv_total_len's shape {kv_total_len.shape}")
+            # kv_index is the index of the kv cache pages for the current batch
+            kv_index = kv_indices[kv_indptr[i]:kv_indptr[i+1]]
+            # we can index [kv_index, page_offset_indices] to get the kv cache for the current batch
+            # from q_indptr[i] to q_indptr[i+1] is the range of the current batch
+            batch_hidden_states = hidden_states[q_indptr[i]:q_indptr[i+1]]
+            batch_position_ids = position_ids[q_indptr[i]:q_indptr[i+1]]
+            q_len, _ = batch_hidden_states.size()
+            # print("q_len -> ", q_len)
+
+            if self.q_lora_rank is None:
+                q = self.q_proj(batch_hidden_states, batch_num_tokens_tensors)
+            else:
+                q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(batch_hidden_states, batch_num_tokens_tensors), batch_num_tokens_tensors), batch_num_tokens_tensors)
+            # for v3, bsz, q_len, num_heads(128), qk_head_dim(192=128(nope)+64(rope))
+            q = q.view(q_len, self.num_heads, self.q_head_dim)
+            # q_nope is [q_len, num_heads(128), qk_nope_head_dim(128)]
+            # q_pe is [q_len, num_heads(128), qk_rope_head_dim(64)]
+            q_nope, q_pe = torch.split(
+                q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
+            )
+            # compressed_kv is [q_len, kv_lora_rank(512) + rope(64)]
+            compressed_kv = self.kv_a_proj_with_mqa(batch_hidden_states, batch_num_tokens_tensors)
+            # compressed_kv is [q_len, kv_lora_rank(512)], k_pe is [q_len, rope(64)]
+            compressed_kv, k_pe = torch.split(
+                compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+            )
+            compressed_kv = compressed_kv.contiguous()
+            compressed_kv = self.kv_a_layernorm(compressed_kv, batch_num_tokens_tensors)
+            # k_pe is [q_len, 1, qk_rope_head_dim(64)]
+            k_pe = k_pe.view(q_len, 1, self.qk_rope_head_dim)
+            # compressed_kv is [q_len, 1, kv_lora_rank(512)]
+            compressed_kv = compressed_kv.view(q_len, 1, self.kv_lora_rank)
+            
+            cos, sin = self.rotary_emb(q_pe, batch_position_ids.unsqueeze(0))
+            # print(f"q_pe shape{q_pe.shape}, k_pe shape {k_pe.shape}")
+            q_pe, k_pe = apply_rotary_pos_emb(q_pe.unsqueeze(0), k_pe.unsqueeze(0), cos, sin, unsqueeze_dim=2)
+            q_pe = q_pe.squeeze(0)
+            # q_pe is [num_heads(128), q_len, qk_rope_head_dim(64)]
+            q_pe.transpose_(0, 1)            
+            if kv_cache is not None:
+                cache_kwargs = {"sin": sin, "cos": cos, "page_idx": batch_page_idx, "page_offset": batch_page_offset}  # Specific to RoPE models
+                compressed_kv_with_k_pe = kv_cache.update(compressed_kv.unsqueeze(0), k_pe, self.layer_idx, batch_page_idx, batch_page_offset, cache_kwargs)
+                compressed_kv = compressed_kv_with_k_pe [:, :, :, :self.kv_lora_rank].view(-1, kv_cache.page_size, self.kv_lora_rank)
+                k_pe = compressed_kv_with_k_pe [:, :, :, self.kv_lora_rank:].view(-1, kv_cache.page_size, self.qk_rope_head_dim)
+            # q_absorb is [num_heads(128), qk_nope_head_dim(128), kv_lora_rank(512)]
+            # out_absorb is [num_heads(128), kv_lora_rank(512), v_head_dim(128)] v_head_dim is also the nope dim
+            q_absorb, out_absorb = self.get_absorbed()
+            # q_nope is [num_heads(128), q_len, qk_nope_head_dim(128)]
+            q_nope = q_nope.transpose(0, 1) # q_len is 1, no GPU overhead, same below
+            # q_nope is [num_heads(128), q_len, kv_lora_rank(512)]
+            q_nope = torch.matmul(q_nope, q_absorb) # batched MM
+
+            # # q_nope is [q_len, num_heads(128), kv_lora_rank(512)]
+            # q_nope = q_nope.transpose(0, 1)
+
+            # we need to index out the compressed_kv and k_pe for the current batch
+            batch_compressed_kv = None
+            batch_k_pe = None
+            for page_index in kv_index:
+                if kv_total_len > kv_cache.page_size:
+                    tmp_compressed_kv = compressed_kv[page_index, 0:kv_cache.page_size, :]
+                    tmp_k_pe = k_pe[page_index, 0:kv_cache.page_size, :]
+                    if batch_compressed_kv is None or batch_k_pe is None:
+                        batch_compressed_kv = tmp_compressed_kv
+                        batch_k_pe = tmp_k_pe
+                    else: 
+                        batch_compressed_kv = torch.cat((batch_compressed_kv, tmp_compressed_kv), dim=0)
+                        batch_k_pe = torch.cat((batch_k_pe, tmp_k_pe), dim=0)
+                    kv_total_len -= kv_cache.page_size
+                else:
+                    tmp_compressed_kv = compressed_kv[page_index, 0:kv_total_len, :]
+                    tmp_k_pe = k_pe[page_index, 0:kv_total_len, :]
+                    if batch_compressed_kv is None or batch_k_pe is None:
+                        batch_compressed_kv = tmp_compressed_kv
+                        batch_k_pe = tmp_k_pe
+                    else: 
+                        batch_compressed_kv = torch.cat((batch_compressed_kv, tmp_compressed_kv), dim=0)
+                        batch_k_pe = torch.cat((batch_k_pe, tmp_k_pe), dim=0)
+                    break
+            # batch_compressed_kv is [kv_total_len(k_len), kv_lora_rank(512)]
+            # batch_k_pe is [kv_total_len(k_len), qk_rope_head_dim(64)]
+            attention_weights = (torch.matmul(q_pe,batch_k_pe.mT) + torch.matmul(q_nope, batch_compressed_kv.mT)) * self.softmax_scale
+            # attention_weights is [num_heads(128), q_len, k_len]
+            
+            # attention_weights = attention_weights.transpose(0,1).unsqueeze(0).squeeze(-1).expand(q_len,-1,-1).transpose(0,1)
+            
+            # attention_masks[i] is [q_len, k_len]
+            
+            attention_weights = (attention_weights + attention_masks[i])
+            # attention_weights shape is [num_heads(128), q_len, k_len]
+            attention_weights = nn.functional.softmax(attention_weights,dim=-1,dtype=torch.float32).to(q_pe.dtype)
+            attn_output = torch.matmul(attention_weights, batch_compressed_kv) # [num_heads(128),q_len, lora_rank(512)]
+            # out_absorb shape is [num_heads(128), kv_lora_rank(512), v_head_dim(128)]
+            out_absorb = out_absorb.transpose(1,2)
+            # q for q_len, n for num_heads, h for v_head_dim, v for kv_lora_rank
+            attn_output = torch.matmul(attn_output, out_absorb) # [num_heads(128), q_len, v_head_dim(128)]
+            attn_output = attn_output.transpose(0, 1) # [q_len, num_heads(128), v_head_dim(128)]
+            attn_output = attn_output.reshape(q_len, self.num_heads * self.v_head_dim)
+            attn_output = self.o_proj(attn_output, batch_num_tokens_tensors)
+            final_attention_output = torch.cat((final_attention_output, attn_output), dim=0)
+        return final_attention_output
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