Add EXAONE 4.0 model support for DeepSpeed inference v2 @

deepspeed/inference/v2/engine_factory.py

@@ -24,6 +24,7 @@
     QwenPolicy,
     Qwen2Policy,
     Qwen2MoePolicy,
+    ExaonePolicy,
 )
 from .model_implementations.inference_policy_base import POLICIES, InferenceV2Policy
 from .model_implementations.flat_model_helpers import make_metadata_filename, ModelMetadata
@@ -129,6 +130,12 @@ def build_hf_engine(path: str,
             policy = Qwen2Policy(model_config, checkpoint_engine=checkpoint_engine)
         elif model_config.model_type == "qwen2_moe":
             policy = Qwen2MoePolicy(model_config, checkpoint_engine=checkpoint_engine)
+        elif model_config.model_type == "exaone4":
+            # Ensure we're using the correct version of transformers for EXAONE 4.0
+            import transformers
+            assert version.parse(transformers.__version__) >= version.parse("4.54.0"), \
+                f"EXAONE 4.0 requires transformers >= 4.54.0, you have version {transformers.__version__}"
+            policy = ExaonePolicy(model_config, checkpoint_engine=checkpoint_engine)
         else:
             raise ValueError(f"Unsupported model type {model_config.model_type}")
 

deepspeed/inference/v2/model_implementations/__init__.py

@@ -19,3 +19,4 @@
 from .qwen import *
 from .qwen_v2 import *
 from .qwen_v2_moe import *
+from .exaone import *

deepspeed/inference/v2/model_implementations/exaone/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .container import ExaoneTransformerContainer, ExaoneNonTransformerContainer
+from .model import ExaoneInferenceModel
+from .policy import ExaonePolicy

deepspeed/inference/v2/model_implementations/exaone/container.py

@@ -0,0 +1,92 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# Create a container object to save model-specific tensors for EXAONE 4.0
+
+from ..common_parameters import *
+from ..layer_container_base import LayerContainer
+"""
+HF EXAONE 4.0 model structure:
+
+Exaone4ForCausalLM(
+  (model): Exaone4Model(
+    (embed_tokens): Embedding(102400, 5120)
+    (layers): ModuleList(
+      (0-63): 64 x Exaone4DecoderLayer(
+        (self_attn): Exaone4Attention(
+          (q_proj): Linear(in_features=5120, out_features=5120, bias=False)
+          (k_proj): Linear(in_features=5120, out_features=1024, bias=False)
+          (v_proj): Linear(in_features=5120, out_features=1024, bias=False)
+          (o_proj): Linear(in_features=5120, out_features=5120, bias=False)
+          (rotary_emb): Exaone4RotaryEmbedding()
+        )
+        (mlp): Exaone4MLP(
+          (gate_proj): Linear(in_features=5120, out_features=27392, bias=False)
+          (up_proj): Linear(in_features=5120, out_features=27392, bias=False)
+          (down_proj): Linear(in_features=27392, out_features=5120, bias=False)
+          (act_fn): SiLUActivation()
+        )
+        (input_layernorm): Exaone4RMSNorm()
+        (post_attention_layernorm): Exaone4RMSNorm()
+      )
+    )
+    (norm): Exaone4RMSNorm()
+  )
+  (lm_head): Linear(in_features=5120, out_features=102400, bias=False)
+)
+
+Key EXAONE 4.0 features:
+- Hybrid attention: sliding_attention (local) vs full_attention (global) layers
+- Grouped Query Attention: 40 query heads, 8 key-value heads
+- QK-Reorder-Norm: RMSNorm applied after Q/K projections
+- SiLU activation in MLP
+"""
+
+
+class ExaoneTransformerContainer(LayerContainer):
+    """
+    Transformer layer container for the EXAONE 4.0 model.
+    Handles both sliding_attention and full_attention layer types.
+    """
+    qkv_w: UnfusedQKVParameter
+    attn_out_w: AttentionOutputParameter
+    mlp_1_w: GatedMLPParameter
+    mlp_2_w: MLP2Parameter
+    attn_norm_gamma: NormParameter
+    mlp_norm_gamma: NormParameter
+
+    PARAM_MAPPING = {
+        # Attention parameters - Q, K, V projections
+        "self_attn.q_proj.weight": "qkv_w.q_params",
+        "self_attn.k_proj.weight": "qkv_w.k_params",
+        "self_attn.v_proj.weight": "qkv_w.v_params",
+        "self_attn.o_proj.weight": "attn_out_w.params",
+
+        # MLP parameters - gate, up, down projections
+        "mlp.gate_proj.weight": "mlp_1_w.gate_params",
+        "mlp.up_proj.weight": "mlp_1_w.up_params",
+        "mlp.down_proj.weight": "mlp_2_w.params",
+
+        # Normalization parameters
+        "input_layernorm.weight": "attn_norm_gamma.params",
+        "post_attention_layernorm.weight": "mlp_norm_gamma.params",
+    }
+
+
+class ExaoneNonTransformerContainer(LayerContainer):
+    """
+    Non-Transformer layer container for the EXAONE 4.0 model.
+    Contains embedding, final normalization, and output projection parameters.
+    """
+    word_emb: EmbeddingParameter
+    word_unembed: UnembedParameter
+    final_norm: NormParameter
+
+    PARAM_MAPPING = {
+        # Embedding and output parameters
+        "model.embed_tokens.weight": "word_emb.params",
+        "model.norm.weight": "final_norm.params",
+        "lm_head.weight": "word_unembed.params",
+    }

deepspeed/inference/v2/model_implementations/exaone/model.py

@@ -0,0 +1,271 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from typing import Iterable, Optional, Tuple
+
+import torch
+
+import deepspeed.comm as dist
+
+from ...allocator import empty_from
+from ...inference_utils import ActivationType, DtypeEnum
+from .. import *
+from ...modules.configs import *
+from ...modules.interfaces import *
+from ...ragged import RaggedBatchWrapper
+
+from .container import ExaoneNonTransformerContainer, ExaoneTransformerContainer
+
+
+class ExaoneInferenceModel(DSTransformerModelBase):
+    """
+    Inference model implementation for ragged batching for EXAONE 4.0 models.
+
+    Key features:
+    - Hybrid attention: sliding_attention (local) vs full_attention (global) layers
+    - QK-Reorder-Norm: RMSNorm applied after Q/K projections
+    - Conditional RoPE: Skip RoPE for full_attention layers
+    - Grouped Query Attention: 40 query heads, 8 key-value heads
+    """
+
+    _non_transformer: Optional[ExaoneNonTransformerContainer]
+    """
+    Embed + unembed container. Specializing the type annotation.
+    """
+
+    _transformer: Optional[Iterable[ExaoneTransformerContainer]]
+    """
+    Per-layer transformer container. Specializing the type annotation.
+    """
+
+    # EXAONE 4.0 specific attributes
+    _layer_types: Optional[list] = None
+    """
+    Layer types for hybrid attention: 'sliding_attention' or 'full_attention'
+    """
+
+    def __init__(self, config, engine_config, base_mp_group):
+        super().__init__(config, engine_config, base_mp_group)
+
+        # Store layer types for hybrid attention handling
+        if hasattr(self._config, 'layer_types'):
+            self._layer_types = self._config.layer_types
+        else:
+            # Fallback: infer from sliding_window_pattern (LLLG = 3 local, 1 global)
+            pattern = getattr(self._config, 'sliding_window_pattern', 'LLLG')
+            layer_types = []
+            for i in range(self.num_layers):
+                if pattern[i % len(pattern)] == 'G':
+                    layer_types.append('full_attention')
+                else:
+                    layer_types.append('sliding_attention')
+            self._layer_types = layer_types
+
+    """
+    Properties inherited from `DSInferenceModelBase`
+    """
+
+    @property
+    def max_sequence_length(self) -> int:
+        return self._config.max_position_embeddings
+
+    """
+    Properties inherited from `DSTransformerModelBase`
+    """
+
+    @property
+    def num_layers(self) -> int:
+        return self._config.num_hidden_layers
+
+    @property
+    def model_dim(self) -> int:
+        return self._config.hidden_size
+
+    @property
+    def vocab_size(self) -> int:
+        return self._config.vocab_size
+
+    @property
+    def head_size(self) -> int:
+        return getattr(self._config, 'head_dim', self.model_dim // self.n_heads)
+
+    @property
+    def n_heads(self) -> int:
+        return self._config.num_attention_heads
+
+    @property
+    def intermediate_dim(self) -> int:
+        return self._config.intermediate_size
+
+    @property
+    def n_heads_kv(self) -> int:
+        return self._config.num_key_value_heads
+
+    @property
+    def activation_dtype(self) -> DtypeEnum:
+        if self._config.torch_dtype == torch.float16:
+            return DtypeEnum.fp16
+        elif self._config.torch_dtype == torch.bfloat16:
+            return DtypeEnum.bf16
+        else:
+            raise NotImplementedError("Only fp16 and bf16 are supported")
+
+    @property
+    def mlp_activation_fn(self) -> ActivationType:
+        activation = self._config.hidden_act.lower()
+        # EXAONE 4.0 uses gated SiLU activation like LLaMA
+        if activation == "silu":
+            return ActivationType.SiGLU
+        elif activation == "gelu":
+            return ActivationType.GEGLU
+        elif activation == "relu":
+            return ActivationType.ReGLU
+        else:
+            raise NotImplementedError(f"Activation {activation} not supported")
+
+    @property
+    def norm_type(self) -> NormTypeEnum:
+        return NormTypeEnum.RMSNorm
+
+    @property
+    def positional_embedding_type(self) -> PositionalEmbeddingType:
+        return PositionalEmbeddingType.rotate_half
+
+    @property
+    def positional_embedding_config(self) -> Optional[RotateHalfConfig]:
+        return RotateHalfConfig(theta_base=self._config.rope_theta)
+
+    """
+    Helper methods for EXAONE 4.0 specific features
+    """
+
+    def is_global_attention_layer(self, layer_idx: int) -> bool:
+        """Check if layer uses global (full) attention vs local (sliding) attention"""
+        if self._layer_types and layer_idx < len(self._layer_types):
+            return self._layer_types[layer_idx] == 'full_attention'
+        return False
+
+    def should_apply_rope(self, layer_idx: int) -> bool:
+        """EXAONE 4.0 skips RoPE for global attention layers"""
+        return not self.is_global_attention_layer(layer_idx)
+
+    """
+    Forward implementations
+    """
+
+    def _forward_embed(self, ragged_batch: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Performs the embedding lookup prior to running the transformer of the model.
+
+        Arguments:
+            ragged_batch (RaggedBatchWrapper): The batch to embed.
+
+        Returns:
+            torch.Tensor: The embedded batch.
+        """
+        embed = self.embed(ragged_batch, self._non_transformer.word_emb)
+
+        if embed.shape[-1] != self.model_dim:
+            raise ValueError(f"Embedding output shape {embed.shape} does not match model_dim {self.model_dim}")
+
+        return embed
+
+    def _forward_transformer_layer(self, layer_idx: int, residual: torch.Tensor, hidden_states: torch.Tensor,
+                                   ragged_batch_info: RaggedBatchWrapper) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Executes one transformer layer with EXAONE 4.0 specific features:
+        - Hybrid attention (sliding vs full)
+        - QK-Reorder-Norm (RMSNorm after Q/K projections)
+        - Conditional RoPE (skip for global layers)
+
+        Arguments:
+            layer_idx (int): The index of the layer to execute.
+            residual (torch.Tensor): The residual tensor from the previous layer.
+            hidden_states (torch.Tensor): The hidden states from the previous layer.
+            ragged_batch_info (RaggedBatchWrapper): The batch metadata.
+        """
+        cur_params = self._transformer[layer_idx]
+        kv_cache = self.state_manager.get_cache(layer_idx)
+
+        # QKV projection
+        hidden_states = self.qkv(hidden_states, cur_params.qkv_w, b=None)
+
+        # EXAONE 4.0 attention with hybrid pattern and conditional RoPE
+        # NOTE: The attention module should handle QK-Reorder-Norm internally
+        # and respect the RoPE configuration based on layer type
+        if self.is_global_attention_layer(layer_idx):
+            # Global attention: full attention, no RoPE
+            hidden_states = self.attn(hidden_states, kv_cache, ragged_batch_info, apply_rotary_pos_emb=False)
+        else:
+            # Local attention: sliding window, with RoPE
+            hidden_states = self.attn(hidden_states,
+                                      kv_cache,
+                                      ragged_batch_info,
+                                      apply_rotary_pos_emb=True,
+                                      sliding_window=getattr(self._config, 'sliding_window', 4096))
+
+        # Attention output projection
+        hidden_states = self.attn_out(hidden_states, cur_params.attn_out_w, b=None)
+
+        if self.tp_size > 1:
+            dist.all_reduce(hidden_states, group=self._base_mp_group)
+
+        # Post-attention normalization
+        residual, hidden_states = self.norm(residual, hidden_states, cur_params.mlp_norm_gamma, beta=None)
+
+        # MLP forward pass (gated SiLU)
+        hidden_states = self.mlp_1(hidden_states, cur_params.mlp_1_w, b=None)
+        hidden_states = self.mlp_2(hidden_states, cur_params.mlp_2_w, b=None)
+
+        if self.tp_size > 1:
+            dist.all_reduce(hidden_states, group=self._base_mp_group)
+
+        # Prepare for next layer normalization
+        if layer_idx != self.num_layers - 1:
+            next_params = self._transformer[layer_idx + 1]
+            residual, hidden_states = self.norm(residual, hidden_states, next_params.attn_norm_gamma, beta=None)
+        else:
+            # On last layer, just perform the residual add
+            residual.add_(hidden_states)
+
+        return residual, hidden_states
+
+    def _forward_unembed(self, hidden_states: torch.Tensor, ragged_batch_info: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Performs unembedding of the hidden states to logits. This will only sample the final
+        token of each sequence.
+        """
+        logits = self.unembed(hidden_states,
+                              self._non_transformer.word_unembed,
+                              ragged_batch_info,
+                              gamma=self._non_transformer.final_norm)
+
+        if self.tp_size > 1:
+            comm_buffer = empty_from(self._comm_logits, (self.tp_size, logits.shape[0], logits.shape[1]))
+            full_logits = empty_from(self._return_logits, (logits.shape[0], self.vocab_size))
+
+            dist.all_gather_into_tensor(comm_buffer, logits, group=self._base_mp_group)
+
+            full_logits.copy_(comm_buffer.permute(1, 0, 2).reshape(logits.shape[0], self.vocab_size))
+
+            return full_logits
+        else:
+            return logits
+
+    def forward(self, wrapped_batch: RaggedBatchWrapper) -> torch.Tensor:
+        """
+        Forward pass for EXAONE 4.0 model with hybrid attention support.
+        """
+        residual = self._forward_embed(wrapped_batch)
+
+        # Initial normalization
+        residual, hidden_states = self.norm(residual, None, self._transformer[0].attn_norm_gamma, beta=None)
+
+        # Forward through all transformer layers
+        for layer_idx in range(self.num_layers):
+            residual, hidden_states = self._forward_transformer_layer(layer_idx, residual, hidden_states,
+                                                                      wrapped_batch)
+
+        return self._forward_unembed(residual, wrapped_batch)