[DeepSeek-V4] Implement model integration, decoders, and configuration stack

Author: parambole

src/maxtext/common/common_types.py

@@ -113,6 +113,7 @@ class DecoderBlockType(enum.Enum):
   SIMPLE_MLP = "simple_mlp"
   LLAMA4 = "llama4"
   OLMO3 = "olmo3"
+  DEEPSEEK4 = "deepseek4"
 
 
 class AttentionType(enum.Enum):

src/maxtext/configs/models/deepseek4-284b.yml

@@ -0,0 +1,64 @@
+# Copyright 2023–2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Model config for DeepSeek-V4-Flash 284B (https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash)
+
+base_emb_dim: 4096
+base_num_query_heads: 64
+base_num_kv_heads: 1
+base_num_decoder_layers: 43
+base_mlp_dim: 2048
+base_moe_mlp_dim: 2048
+vocab_size: 129280
+head_dim: 512
+
+# --- Standard Defaults ---
+enable_dropout: false
+logits_via_embedding: false
+normalization_layer_epsilon: 1.0e-6
+
+# --- V4 Specific Architectural Keys ---
+decoder_block: "deepseek4"
+mhc_expansion_rate: 4
+first_num_hash_layers: 3
+indexer_head_dim: 128
+indexer_n_heads: 64
+indexer_topk: 512
+
+# Note: Layers (0, 1, 2) are prefix layers as `first_num_hash_layers=3`.
+# The 44th layer (MTP module with compress_ratio=0) has been explicitly dropped for now.
+# This leaves exactly 43 layers: 3 prefix [0,0,4] + 40 scanned.
+# `compress_ratio=0` uses sliding window attention. In this case, layer (0, 1).
+compress_ratios: [0, 0, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4]
+
+# --- MoE configuration ---
+mlp_activations: ["silu", "linear"]
+num_experts: 256
+num_experts_per_tok: 6
+mlp_activations_limit: 10
+shared_experts: 1
+routed_score_func: "sqrtsoftplus"
+
+# --- Attention configuration ---
+attention_type: 'compressed'
+q_lora_rank: 1024
+o_groups: 8
+o_lora_rank: 1024
+sliding_window_size: 128
+
+# --- RoPE ---
+rope_type: "default"
+rope_max_timescale: 10000                  # Main RoPE theta
+compressed_rope_max_timescale: 160000      # Compressed RoPE theta
+max_position_embeddings: 1048576

src/maxtext/configs/types.py

@@ -227,7 +227,7 @@ class ProfilerType(str, Enum):
     "deepseek3-test",
     "deepseek3-tiny",
     "deepseek3.2-671b",
-    "deepseek4",
+    "deepseek4-284b",
     "deepseek-custom",
     "kimi-k2-1t",
     "gemma-7b",
@@ -553,7 +553,7 @@ class Attention(BaseModel):
       "autoselected",
       description="The attention algorithm to use (dot_product, flash, etc).",
   )
-  attention_type: Literal["global", "local_sliding", "chunk", "mla", "full"] = Field(
+  attention_type: Literal["global", "local_sliding", "chunk", "mla", "full", "compressed"] = Field(
       "global", description="The variant of attention to use."
   )
   share_kv_projections: bool = Field(
@@ -2936,6 +2936,8 @@ def calculate_global_batch_sizes(per_device_batch_size, expansion_factor, num_de
         raise ValueError("`local_checkpoint_period` must be > 0 for emergency checkpointing.")
     if self.moba and self.attention not in ("dot_product"):
       raise ValueError("MoBA is only supported with dot_product attention.")
+    if self.decoder_block == DecoderBlockType.DEEPSEEK4 and self.attention != "dot_product":
+      raise ValueError("DeepSeek4 decoder block currently only supports dot_product attention.")
     if self.use_indexer:
       if self.q_lora_rank == 0:
         raise NotImplementedError("Sparse indexer has not implemented for q_lora_rank = 0.")

src/maxtext/layers/attention_compressed.py

@@ -680,17 +680,20 @@ def __init__(
       rngs: Optional[nnx.Rngs] = None,
       **kwargs,
   ):
-    """Initializes the CompressedAttention layer.
+    """Inherits all standard Attention hyperparameters and selectively instantiates
+    an underlying HCA or CSA compressor based on the provided `compress_ratio`.
 
-    Inherits all standard Attention hyperparameters and selectively instantiates
-    an underlying HCA or CSA compressor based on the provided `layer_type`.
+    Highlights of DeepSeek-V4 attention integration:
+    - Shared-KV: The layer supports decoupling Q and KV heads for heavy compression.
+    - MQA: Multi-Query Attention used alongside heavy KV compression.
+    - 3 Different Attention Modes: Sliding Window (prefix), HCA (128x), and CSA (4x).
+    - Dual RoPE Theta: Uses 10000 for standard uncompressed tokens and 160000 for compressed.
 
     Args:
       (See maxtext.layers.attentions.Attention for standard attention arguments)
       q_lora_rank: The rank for the LoRA projection in the compressed query.
-      compress_ratio: The compression ratio for the compressor.
+      compress_ratio: The compression ratio (0, 4, or 128) for the compressor.
     """
-    """Initializes the Compressed Attention module."""
     self.q_lora_rank = q_lora_rank
     self.compress_ratio = compress_ratio
 
@@ -809,20 +812,22 @@ def _init_projections(self, inputs_q_shape: Tuple, inputs_kv_shape: Tuple) -> No
         rngs=self.rngs,
     )
 
-    # DeepSeek-V4 uses a separate RoPE theta (160000) for compressed tokens.
-    # We must instantiate a dedicated rotary embedding for the compressors
-    self.compress_rotary_embedding = DeepSeekV4RotaryEmbedding(
+    # Override the base rotary embedding with the correct theta for this layer.
+    # CSA / HCA layers use compressed_rope_max_timescale (160000).
+    # Sliding window prefix layers use rope_max_timescale (10000).
+    rope_theta = self.config.compressed_rope_max_timescale if self.compress_ratio > 0 else self.config.rope_max_timescale
+    self.rotary_embedding = DeepSeekV4RotaryEmbedding(
         head_dim=self.config.head_dim,
-        partial_rotary_factor=1.0,
-        rope_theta=self.config.compressed_rope_max_timescale,
-        dtype=self.dtype,
+        partial_rotary_factor=self.config.qk_rope_head_dim / self.config.head_dim,
+        rope_theta=rope_theta,
+        fprop_dtype=self.dtype,
     )
 
     if self.compress_ratio > 4:
       self.hca_compressor = DeepseekV4HCACompressor(
           config=self.config,
           compress_ratio=self.compress_ratio,
-          rotary_embedding=self.compress_rotary_embedding,
+          rotary_embedding=self.rotary_embedding,
           kernel_init=self.kernel_init,
           quant=self.quant,
           model_mode=self.model_mode,
@@ -832,7 +837,7 @@ def _init_projections(self, inputs_q_shape: Tuple, inputs_kv_shape: Tuple) -> No
       self.csa_compressor = DeepseekV4CSACompressor(
           config=self.config,
           compress_ratio=self.compress_ratio,
-          rotary_embedding=self.compress_rotary_embedding,
+          rotary_embedding=self.rotary_embedding,
           kernel_init=self.kernel_init,
           quant=self.quant,
           model_mode=self.model_mode,
@@ -1047,7 +1052,7 @@ def __call__(
     # -> [batch, q_length, emb_dim]
     final_out = self.o_b_proj(grouped_flat)
 
-    return final_out
+    return final_out, None
 
 
 def compressed_attention(

src/maxtext/layers/attentions.py

@@ -850,6 +850,7 @@ def init_rotary_embedding(self):
           shard_mode=self.config.shard_mode,
           rngs=self.rngs,
       )
+
     elif self.is_qwen3_hybrid:
       rotary_embedding = PartialRotaryEmbedding(
           min_timescale=self.config.rope_min_timescale,

src/maxtext/layers/decoders.py

@@ -41,6 +41,7 @@
 from maxtext.layers.quantizations import AqtQuantization as Quant
 from maxtext.models import (
     deepseek,
+    deepseek4,
     deepseek_batchsplit,
     deepseek_batchsplit_fp8,
     gemma,
@@ -467,6 +468,10 @@ def get_decoder_layers(self):
             deepseek.DeepSeekDenseLayerToLinen,
             deepseek.DeepSeekMoELayerToLinen,
         ]
+      case DecoderBlockType.DEEPSEEK4:
+        return (
+            [deepseek4.DeepSeek4ScannableBlockToLinen] if self.config.scan_layers else [deepseek4.DeepSeek4LayerToLinen]
+        )
       case DecoderBlockType.GEMMA:
         return [gemma.GemmaDecoderLayerToLinen]
       case DecoderBlockType.GEMMA2:
@@ -632,6 +637,7 @@ def get_norm_layer(self, num_features: int):
         DecoderBlockType.MISTRAL,
         DecoderBlockType.MIXTRAL,
         DecoderBlockType.DEEPSEEK,
+        DecoderBlockType.DEEPSEEK4,
         DecoderBlockType.GEMMA,
         DecoderBlockType.GEMMA2,
         DecoderBlockType.GEMMA3,
@@ -1061,6 +1067,17 @@ def __call__(
               previous_chunk,
               slot,
           )
+        elif cfg.decoder_block == DecoderBlockType.DEEPSEEK4:
+          y = self._apply_deepseek4_scanned_blocks(
+              y,
+              decoder_segment_ids,
+              decoder_positions,
+              deterministic,
+              model_mode,
+              previous_chunk,
+              slot,
+              decoder_input_tokens,
+          )
         else:
           RemattedBlockLayer = RemattedBlockLayers[0]
           scan_length = int(cfg.num_decoder_layers / cfg.inhomogeneous_layer_cycle_interval)
@@ -1195,8 +1212,10 @@ def __call__(
                   "is_nope_layer": llama4.determine_is_nope_layer(lyr, self.config.nope_layer_interval),
                   "is_moe_layer": llama4.determine_is_moe_layer(lyr, self.config.interleave_moe_layer_step),
               }
-            if cfg.decoder_block in (DecoderBlockType.QWEN3_NEXT, DecoderBlockType.QWEN3_5):
+            if cfg.decoder_block in (DecoderBlockType.QWEN3_NEXT, DecoderBlockType.QWEN3_5, DecoderBlockType.DEEPSEEK4):
               layer_kwargs = {"layer_idx": lyr}
+            if cfg.decoder_block == DecoderBlockType.DEEPSEEK4:
+              layer_call_kwargs["decoder_input_tokens"] = decoder_input_tokens
             kv_cache = None
             if kv_caches is not None:
               # For all decoder blocks (including QWEN3_NEXT/QWEN3_5 with vLLM flat-list
@@ -1423,6 +1442,102 @@ def _apply_gemma4_scanned_blocks(
 
     return y
 
+  def _apply_deepseek4_scanned_blocks(
+      self,
+      y,
+      decoder_segment_ids,
+      decoder_positions,
+      deterministic,
+      model_mode,
+      previous_chunk,
+      slot,
+      decoder_input_tokens,
+  ):
+    """Applies DeepSeek V4 scanned decoder blocks.
+
+    DeepSeek V4 has some number of prefix layers (defined by `first_num_hash_layers`)
+    that use static Hash Routing. The remaining layers alternate `compress_ratio=128` (HCA)
+    and `compress_ratio=4` (CSA) and are evaluated in a single `nn.scan` block.
+
+    For DeepSeek4-Flash (43 hidden layers total):
+    - 3 Prefix layers (Indices 0, 1, 2)
+    - 40 Scanned layers: 20 perfectly repeating chunks of [128, 4]
+    """
+
+    cfg = self.config
+    mesh = self.mesh
+
+    broadcast_args = (
+        decoder_segment_ids,
+        decoder_positions,
+        deterministic,
+        model_mode,
+        slot,
+        previous_chunk,
+    )
+
+    layer_call_kwargs = {
+        "previous_chunk": previous_chunk,
+        "slot": slot,
+        "decoder_input_tokens": decoder_input_tokens,
+    }
+
+    # 1. Prefix Unrolling
+    # Prefix layers are unrolled (unscanned) for two architectural reasons:
+    #   1. Heterogeneous Attention: JAX nn.scan requires identical computation graphs, but the first few layers
+    #      use different attention configurations (e.g., DeepSeek-V4 uses compress_ratios [0, 0, 4] for layers 0, 1, 2).
+    #   2. Static Hash Routing: The first `first_num_hash_layers` (which is 3 for DeepSeek-V4) use deterministic
+    #      token-to-expert Hash Routing instead of learned top-k routing.
+    # Therefore, these prefix layers are instantiated individually before we scan the remaining uniform blocks.
+    num_hash_layers = cfg.first_num_hash_layers
+    for layer_idx in range(num_hash_layers):
+      prefix_layer = deepseek4.DeepSeek4LayerToLinen(
+          config=cfg,
+          mesh=mesh,
+          name=f"layers_{layer_idx}",
+          quant=self.quant,
+          model_mode=self.model_mode,
+          layer_idx=layer_idx,
+      )
+      y, _ = prefix_layer(
+          y,
+          decoder_segment_ids,
+          decoder_positions,
+          deterministic,
+          model_mode,
+          **layer_call_kwargs,
+      )
+
+    # 2. Chunked Scanning
+    # The remaining layers perfectly alternate HCA (128) and CSA (4).
+    num_remaining_layers = cfg.num_decoder_layers - num_hash_layers
+    num_full_blocks = num_remaining_layers // 2
+
+    if num_full_blocks > 0:
+      ScannableBlockToLinen = deepseek4.DeepSeek4ScannableBlockToLinen
+      policy = self.get_remat_policy()
+      RemattedDeepSeek4Block = self.set_remat_policy([ScannableBlockToLinen], policy)[0]
+
+      y, _ = nn.scan(
+          RemattedDeepSeek4Block,
+          variable_axes={
+              "params": cfg.param_scan_axis,
+              "cache": 0,
+              "intermediates": 0,
+              "aqt": 0,
+              "_overwrite_with_gradient": 0,
+          },
+          split_rngs={"params": True, "dropout": cfg.enable_dropout},
+          in_axes=(nn.broadcast,) * len(broadcast_args),
+          length=num_full_blocks,
+          metadata_params={
+              nn.PARTITION_NAME: "layers",
+              "abstract_init": False,
+          },
+      )(config=cfg, mesh=mesh, quant=self.quant, model_mode=model_mode, name="scanned_blocks",)(y, *broadcast_args)
+
+    return y
+
   def _apply_gemma4_small_layers(
       self,
       y,

src/maxtext/layers/embeddings.py

@@ -1803,7 +1803,7 @@ def qwen3_omni_mrope_embedding_as_linen(
   )
 
 
-class DeepSeekV4RotaryEmbedding(nnx.Module):
+class DeepSeekV4RotaryEmbedding(RotaryEmbedding):
   """DeepSeek-V4 partial rotary embedding with interleaved frequencies.
 
   DeepSeek-V4 uses an interleaved positional encoding where consecutive channels
@@ -1822,12 +1822,23 @@ def __init__(
       head_dim: int,
       partial_rotary_factor: float = 64.0 / 512.0,
       rope_theta: float = 10000.0,
-      dtype: Any = jnp.float32,
+      fprop_dtype: Any = jnp.float32,
+      min_timescale: int = 10000,
+      max_timescale: int = 10000,
+      mesh: Any = None,
+      **kwargs,
   ):
+    super().__init__(
+        min_timescale=min_timescale,
+        max_timescale=max_timescale,
+        mesh=mesh,
+        fprop_dtype=fprop_dtype,
+        **kwargs,
+    )
     self.head_dim = head_dim
     self.partial_rotary_factor = partial_rotary_factor
     self.rope_theta = rope_theta
-    self.dtype = dtype
+    self.fprop_dtype = fprop_dtype
 
     # Compute the partial rotary dimension (rope_head_dim)
     self.dim = int(head_dim * partial_rotary_factor)