Deprecate disable-mla (#5481)

python/sglang/srt/models/minicpm3.py

@@ -93,158 +93,6 @@ def input_to_float8(x, dtype=torch.float8_e4m3fn):
     return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()
 
 
-class MiniCPM3Attention(nn.Module):
-
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        hidden_size: int,
-        num_heads: int,
-        qk_nope_head_dim: int,
-        qk_rope_head_dim: int,
-        v_head_dim: int,
-        q_lora_rank: int,
-        kv_lora_rank: int,
-        rope_theta: float = 10000,
-        rope_scaling: Optional[Dict[str, Any]] = None,
-        max_position_embeddings: int = 8192,
-        quant_config: Optional[QuantizationConfig] = None,
-        layer_id=None,
-        prefix: str = "",
-    ) -> None:
-        super().__init__()
-        self.layer_id = layer_id
-        self.hidden_size = hidden_size
-        self.qk_nope_head_dim = qk_nope_head_dim
-        self.qk_rope_head_dim = qk_rope_head_dim
-        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
-        self.v_head_dim = v_head_dim
-        self.q_lora_rank = q_lora_rank
-        self.kv_lora_rank = kv_lora_rank
-        self.num_heads = num_heads
-        tp_size = get_tensor_model_parallel_world_size()
-        assert num_heads % tp_size == 0
-        self.num_local_heads = num_heads // tp_size
-        self.scaling = self.qk_head_dim**-0.5
-        self.rope_theta = rope_theta
-        self.max_position_embeddings = max_position_embeddings
-
-        if self.q_lora_rank is not None:
-            self.q_a_proj = ReplicatedLinear(
-                self.hidden_size,
-                self.q_lora_rank,
-                bias=False,
-                quant_config=quant_config,
-                prefix=add_prefix("q_a_proj", prefix),
-            )
-            self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
-            self.q_b_proj = ColumnParallelLinear(
-                q_lora_rank,
-                self.num_heads * self.qk_head_dim,
-                bias=False,
-                quant_config=quant_config,
-                prefix=add_prefix("q_b_proj", prefix),
-            )
-        else:
-            self.q_proj = ColumnParallelLinear(
-                self.hidden_size,
-                self.num_heads * self.qk_head_dim,
-                bias=False,
-                quant_config=quant_config,
-                prefix=add_prefix("q_proj", prefix),
-            )
-
-        self.kv_a_proj_with_mqa = ReplicatedLinear(
-            self.hidden_size,
-            self.kv_lora_rank + self.qk_rope_head_dim,
-            bias=False,
-            quant_config=quant_config,
-            prefix=add_prefix("kv_a_proj_with_mqa", prefix),
-        )
-        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
-        self.kv_b_proj = ColumnParallelLinear(
-            self.kv_lora_rank,
-            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
-            bias=False,
-            quant_config=quant_config,
-            prefix=add_prefix("kv_b_proj", prefix),
-        )
-        # O projection.
-        self.o_proj = RowParallelLinear(
-            self.num_heads * self.v_head_dim,
-            self.hidden_size,
-            bias=False,
-            quant_config=quant_config,
-            prefix=add_prefix("o_proj", prefix),
-        )
-        self.rotary_emb = get_rope(
-            qk_rope_head_dim,
-            rotary_dim=qk_rope_head_dim,
-            max_position=max_position_embeddings,
-            base=rope_theta,
-            rope_scaling=rope_scaling,
-        )
-
-        # TODO support head_size 96
-        self.attn = RadixAttention(
-            self.num_local_heads,
-            128,
-            self.scaling,
-            num_kv_heads=self.num_local_heads,
-            layer_id=layer_id,
-            quant_config=quant_config,
-            prefix=add_prefix("attn", prefix),
-        )
-
-    def forward(
-        self,
-        positions: torch.Tensor,
-        hidden_states: torch.Tensor,
-        forward_batch: ForwardBatch,
-    ) -> torch.Tensor:
-        if self.q_lora_rank is not None:
-            q = self.q_a_proj(hidden_states)[0]
-            q = self.q_a_layernorm(q)
-            q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
-        else:
-            q = self.q_proj(hidden_states)[0].view(
-                -1, self.num_local_heads, self.qk_head_dim
-            )
-        _, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
-        latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
-        kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
-        latent_cache = latent_cache.unsqueeze(1)
-        kv_a = self.kv_a_layernorm(kv_a.contiguous())
-        kv = self.kv_b_proj(kv_a)[0]
-        kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
-        k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
-        k_pe = latent_cache[:, :, self.kv_lora_rank :]
-        original_shapes = [q_pe.shape, k_pe.shape]
-        q_pe, k_pe = self.rotary_emb(
-            positions, q_pe.reshape(q_pe.shape[0], -1), k_pe.reshape(k_pe.shape[0], -1)
-        )
-        q_pe, k_pe = q_pe.view(original_shapes[0]), k_pe.view(original_shapes[1])
-        q[..., self.qk_nope_head_dim :] = q_pe
-        k = torch.empty_like(q)
-        k[..., : self.qk_nope_head_dim] = k_nope
-        k[..., self.qk_nope_head_dim :] = k_pe
-        q = torch.nn.functional.pad(q, [0, 128 - self.qk_head_dim], value=0).view(
-            -1, self.num_local_heads * 128
-        )
-        k = torch.nn.functional.pad(k, [0, 128 - self.qk_head_dim], value=0).view(
-            -1, self.num_local_heads * 128
-        )
-        v = torch.nn.functional.pad(v, [0, 128 - self.v_head_dim], value=0).view(
-            -1, self.num_local_heads * 128
-        )
-        attn_output = self.attn(q, k, v, forward_batch)
-        attn_output = attn_output.view(-1, self.num_local_heads, 128)[
-            ..., : self.v_head_dim
-        ].reshape(-1, self.num_local_heads * self.v_head_dim)
-        output, _ = self.o_proj(attn_output)
-        return output
-
-
 class MiniCPM3AttentionMLA(nn.Module):
 
     def __init__(
@@ -434,44 +282,25 @@ class MiniCPM3DecoderLayer(nn.Module):
         rope_theta = getattr(config, "rope_theta", 10000)
         rope_scaling = getattr(config, "rope_scaling", None)
         max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
-        if not global_server_args_dict["disable_mla"]:
-            self.self_attn = MiniCPM3AttentionMLA(
-                config=config,
-                hidden_size=self.hidden_size,
-                num_heads=config.num_attention_heads,
-                qk_nope_head_dim=config.qk_nope_head_dim,
-                qk_rope_head_dim=config.qk_rope_head_dim,
-                v_head_dim=self.hidden_size // config.num_attention_heads,
-                q_lora_rank=(
-                    config.q_lora_rank if hasattr(config, "q_lora_rank") else None
-                ),
-                kv_lora_rank=config.kv_lora_rank,
-                rope_theta=rope_theta,
-                rope_scaling=rope_scaling,
-                max_position_embeddings=max_position_embeddings,
-                quant_config=quant_config,
-                layer_id=layer_id,
-                prefix=add_prefix("self_attn", prefix),
-            )
-        else:
-            self.self_attn = MiniCPM3Attention(
-                config=config,
-                hidden_size=self.hidden_size,
-                num_heads=config.num_attention_heads,
-                qk_nope_head_dim=config.qk_nope_head_dim,
-                qk_rope_head_dim=config.qk_rope_head_dim,
-                v_head_dim=self.hidden_size // config.num_attention_heads,
-                q_lora_rank=(
-                    config.q_lora_rank if hasattr(config, "q_lora_rank") else None
-                ),
-                kv_lora_rank=config.kv_lora_rank,
-                rope_theta=rope_theta,
-                rope_scaling=rope_scaling,
-                max_position_embeddings=max_position_embeddings,
-                quant_config=quant_config,
-                layer_id=layer_id,
-                prefix=add_prefix("self_attn", prefix),
-            )
+        self.self_attn = MiniCPM3AttentionMLA(
+            config=config,
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            qk_nope_head_dim=config.qk_nope_head_dim,
+            qk_rope_head_dim=config.qk_rope_head_dim,
+            v_head_dim=self.hidden_size // config.num_attention_heads,
+            q_lora_rank=(
+                config.q_lora_rank if hasattr(config, "q_lora_rank") else None
+            ),
+            kv_lora_rank=config.kv_lora_rank,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            layer_id=layer_id,
+            prefix=add_prefix("self_attn", prefix),
+        )
+
         self.mlp = MiniCPM3MLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
@@ -674,17 +503,16 @@ class MiniCPM3ForCausalLM(nn.Module):
                     )
                     weight_loader(param, loaded_weight)
 
-        if not global_server_args_dict["disable_mla"]:
-            for layer_id in range(self.config.num_hidden_layers):
-                self_attn = self.model.layers[layer_id].self_attn
-                w_kc, w_vc = self_attn.kv_b_proj.weight.unflatten(
-                    0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
-                ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
-                self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
-                self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
-                if hasattr(self_attn.kv_b_proj, "weight_scale"):
-                    self_attn.w_scale = self_attn.kv_b_proj.weight_scale
-                del self_attn.kv_b_proj
+        for layer_id in range(self.config.num_hidden_layers):
+            self_attn = self.model.layers[layer_id].self_attn
+            w_kc, w_vc = self_attn.kv_b_proj.weight.unflatten(
+                0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
+            ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
+            self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
+            self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
+            if hasattr(self_attn.kv_b_proj, "weight_scale"):
+                self_attn.w_scale = self_attn.kv_b_proj.weight_scale
+            del self_attn.kv_b_proj
 
 
 EntryClass = MiniCPM3ForCausalLM