Support DP MLA (#1970)

python/sglang/srt/models/deepseek_v2.py

@@ -22,7 +22,9 @@ import torch
 from torch import nn
 from transformers import PretrainedConfig
 from vllm.distributed import (
+    get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
+    get_tp_group,
     tensor_model_parallel_all_reduce,
 )
 from vllm.model_executor.layers.fused_moe import FusedMoE
@@ -338,6 +340,7 @@ class DeepseekV2AttentionMLA(nn.Module):
         cache_config=None,
         quant_config: Optional[QuantizationConfig] = None,
         layer_id=None,
+        use_dp=False,
     ) -> None:
         super().__init__()
         self.layer_id = layer_id
@@ -351,29 +354,80 @@ class DeepseekV2AttentionMLA(nn.Module):
         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.num_local_heads = num_heads if use_dp else 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,
+        if use_dp:
+            # For data parallel attention
+            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,
+                )
+                self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
+                self.q_b_proj = ReplicatedLinear(
+                    q_lora_rank,
+                    self.num_heads * self.qk_head_dim,
+                    bias=False,
+                    quant_config=quant_config,
+                )
+            else:
+                self.q_proj = ReplicatedLinear(
+                    self.hidden_size,
+                    self.num_heads * self.qk_head_dim,
+                    bias=False,
+                    quant_config=quant_config,
+                )
+            self.kv_b_proj = ReplicatedLinear(
+                self.kv_lora_rank,
+                self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
                 bias=False,
                 quant_config=quant_config,
             )
-            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,
+            # O projection.
+            self.o_proj = ReplicatedLinear(
+                self.num_heads * self.v_head_dim,
+                self.hidden_size,
                 bias=False,
                 quant_config=quant_config,
             )
         else:
-            self.q_proj = ColumnParallelLinear(
+            # For tensor parallel attention
+            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,
+                )
+                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,
+                )
+            else:
+                self.q_proj = ColumnParallelLinear(
+                    self.hidden_size,
+                    self.num_heads * self.qk_head_dim,
+                    bias=False,
+                    quant_config=quant_config,
+                )
+            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,
+            )
+            # O projection.
+            self.o_proj = RowParallelLinear(
+                self.num_heads * self.v_head_dim,
                 self.hidden_size,
-                self.num_heads * self.qk_head_dim,
                 bias=False,
                 quant_config=quant_config,
             )
@@ -385,19 +439,6 @@ class DeepseekV2AttentionMLA(nn.Module):
             quant_config=quant_config,
         )
         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,
-        )
-        # O projection.
-        self.o_proj = RowParallelLinear(
-            self.num_heads * self.v_head_dim,
-            self.hidden_size,
-            bias=False,
-            quant_config=quant_config,
-        )
         rope_scaling["rope_type"] = "deepseek_yarn"
         self.rotary_emb = get_rope(
             qk_rope_head_dim,
@@ -491,6 +532,36 @@ class DeepseekV2AttentionMLA(nn.Module):
         return output
 
 
+def all_gather(
+    input_tensor: torch.Tensor, forward_batch: ForwardBatch, rank, world_size, group
+):
+    if world_size == 1:
+        return input_tensor
+
+    all_lens = forward_batch.global_num_tokens
+    max_len = max(forward_batch.global_num_tokens)
+
+    padded_tensor = torch.nn.functional.pad(
+        input_tensor, (0, 0, 0, max_len - input_tensor.shape[0])
+    )
+
+    torch.distributed.all_gather_into_tensor(
+        forward_batch.gathered_buffer, padded_tensor, group=group
+    )
+
+    gathered_tensors = torch.concat(
+        [
+            forward_batch.gathered_buffer[i * max_len : i * max_len + all_lens[i]]
+            for i in range(world_size)
+        ]
+    )
+
+    start_index = 0 if rank == 0 else sum(all_lens[:rank])
+    end_index = start_index + all_lens[rank]
+
+    return gathered_tensors, start_index, end_index
+
+
 class DeepseekV2DecoderLayer(nn.Module):
 
     def __init__(
@@ -505,6 +576,14 @@ class DeepseekV2DecoderLayer(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)
+        self.enable_dp_attention = (
+            not global_server_args_dict["disable_mla"]
+            and global_server_args_dict["enable_dp_attention"]
+        )
+        if self.enable_dp_attention:
+            self.tp_rank = get_tensor_model_parallel_rank()
+            self.tp_size = get_tensor_model_parallel_world_size()
+            self.tp_group = get_tp_group().device_group
         if not global_server_args_dict["disable_mla"]:
             self.self_attn = DeepseekV2AttentionMLA(
                 config=config,
@@ -523,6 +602,7 @@ class DeepseekV2DecoderLayer(nn.Module):
                 cache_config=cache_config,
                 quant_config=quant_config,
                 layer_id=layer_id,
+                use_dp=self.enable_dp_attention,
             )
         else:
             self.self_attn = DeepseekV2Attention(
@@ -569,20 +649,32 @@ class DeepseekV2DecoderLayer(nn.Module):
         residual: Optional[torch.Tensor],
     ) -> torch.Tensor:
         # Self Attention
-        if residual is None:
-            residual = hidden_states
-            hidden_states = self.input_layernorm(hidden_states)
-        else:
-            hidden_states, residual = self.input_layernorm(hidden_states, residual)
-        hidden_states = self.self_attn(
-            positions=positions,
-            hidden_states=hidden_states,
-            forward_batch=forward_batch,
-        )
+        if not forward_batch.forward_mode.is_idle():
+            if residual is None:
+                residual = hidden_states
+                hidden_states = self.input_layernorm(hidden_states)
+            else:
+                hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+            hidden_states = self.self_attn(
+                positions=positions,
+                hidden_states=hidden_states,
+                forward_batch=forward_batch,
+            )
+            hidden_states, residual = self.post_attention_layernorm(
+                hidden_states, residual
+            )
 
         # Fully Connected
-        hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
-        hidden_states = self.mlp(hidden_states)
+        if self.enable_dp_attention:
+            hidden_states, start_idx, end_idx = all_gather(
+                hidden_states, forward_batch, self.tp_rank, self.tp_size, self.tp_group
+            )
+            hidden_states = self.mlp(hidden_states)
+            hidden_states = hidden_states[start_idx:end_idx]
+        else:
+            hidden_states = self.mlp(hidden_states)
+
         return hidden_states, residual
 
 
@@ -603,6 +695,7 @@ class DeepseekV2Model(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
+            enable_tp=not global_server_args_dict["enable_dp_attention"],
         )
         self.layers = nn.ModuleList(
             [
@@ -630,7 +723,8 @@ class DeepseekV2Model(nn.Module):
             hidden_states, residual = layer(
                 positions, hidden_states, forward_batch, residual
             )
-        hidden_states, _ = self.norm(hidden_states, residual)
+        if not forward_batch.forward_mode.is_idle():
+            hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
 
@@ -646,10 +740,18 @@ class DeepseekV2ForCausalLM(nn.Module):
         self.config = config
         self.quant_config = quant_config
         self.model = DeepseekV2Model(config, cache_config, quant_config)
-        self.lm_head = ParallelLMHead(
-            config.vocab_size, config.hidden_size, quant_config=quant_config
-        )
-        self.logits_processor = LogitsProcessor(config)
+        if global_server_args_dict["enable_dp_attention"]:
+            self.lm_head = ReplicatedLinear(
+                config.hidden_size,
+                config.vocab_size,
+                bias=False,
+            )
+            self.logits_processor = LogitsProcessor(config, skip_all_gather=True)
+        else:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size, config.hidden_size, quant_config=quant_config
+            )
+            self.logits_processor = LogitsProcessor(config)
 
     @torch.no_grad()
     def forward(
@@ -659,9 +761,10 @@ class DeepseekV2ForCausalLM(nn.Module):
         forward_batch: ForwardBatch,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, forward_batch)
-        return self.logits_processor(
-            input_ids, hidden_states, self.lm_head.weight, forward_batch
-        )
+        if not forward_batch.forward_mode.is_idle():
+            return self.logits_processor(
+                input_ids, hidden_states, self.lm_head.weight, forward_batch
+            )
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [