chore: import upstream snapshot with attribution

python/mlc_llm/model/gemma3/gemma3_loader.py

@@ -0,0 +1,71 @@
+"""
+This file specifies how MLC's Gemma3 parameter maps from other formats, for example HuggingFace
+PyTorch, HuggingFace safetensors.
+"""
+
+import functools
+
+from mlc_llm.loader import ExternMapping
+from mlc_llm.loader.standard_loader import make_standard_hf_loader
+from mlc_llm.quantization import Quantization
+
+from .gemma3_model import Gemma3Config, Gemma3ForCausalLM
+
+
+def huggingface(model_config: Gemma3Config, quantization: Quantization) -> ExternMapping:
+    """Create HF weight mapping for Gemma3."""
+    model = Gemma3ForCausalLM(model_config)
+    if quantization is not None:
+        model.to(quantization.model_dtype)
+    _, _named_params, _ = model.export_tvm(
+        spec=model.get_default_spec(),
+        allow_extern=True,
+    )
+    named_parameters = dict(_named_params)
+    mlc_prefix = "language_model."
+    if model_config.is_text_model:
+        hf_prefix = ""
+    else:
+        hf_prefix = "language_model."
+
+    def name_transform(name: str) -> str:
+        if name.startswith(mlc_prefix):
+            name = name[len(mlc_prefix) :]
+        return f"{hf_prefix}{name}"
+
+    def num_layers(config: object) -> int:
+        return config.text_config.num_hidden_layers
+
+    base_loader = make_standard_hf_loader(
+        model_cls=Gemma3ForCausalLM,
+        include_qkv=False,
+        include_gate_up=True,
+        gate_up_target_name="gate_up_proj",
+        num_layers_getter=num_layers,
+        layer_prefix=f"{mlc_prefix}model.layers",
+        name_transform=name_transform,
+    )
+    mapping = base_loader(model_config, quantization)
+
+    def add_one(name: str) -> None:
+        mlc_param = named_parameters[mlc_prefix + name]
+        mapping.add_mapping(
+            mlc_prefix + name,
+            [name_transform(mlc_prefix + name)],
+            functools.partial(
+                lambda x, dtype: (x + 1).astype(dtype),
+                dtype=mlc_param.dtype,
+            ),
+        )
+
+    for i in range(model_config.text_config.num_hidden_layers):
+        add_one(f"model.layers.{i}.input_layernorm.weight")
+        add_one(f"model.layers.{i}.post_attention_layernorm.weight")
+        add_one(f"model.layers.{i}.pre_feedforward_layernorm.weight")
+        add_one(f"model.layers.{i}.post_feedforward_layernorm.weight")
+        add_one(f"model.layers.{i}.self_attn.k_norm.weight")
+        add_one(f"model.layers.{i}.self_attn.q_norm.weight")
+
+    add_one("model.norm.weight")
+
+    return mapping

python/mlc_llm/model/gemma3/gemma3_model.py

@@ -0,0 +1,667 @@
+"""Implementation for Gemma3 architecture."""
+
+import dataclasses
+from typing import Any, Dict, Optional  # noqa: UP035
+
+from tvm import tirx
+from tvm.relax.frontend import nn
+from tvm.relax.frontend.nn import Tensor, op
+
+from mlc_llm import op as op_ext
+from mlc_llm.model.gemma.gemma_model import GemmaEmbedding
+from mlc_llm.model.model_utils import index_last_token
+from mlc_llm.nn import PagedKVCache, RopeMode
+from mlc_llm.support import logging
+from mlc_llm.support import tensor_parallel as tp
+from mlc_llm.support.config import ConfigBase
+from mlc_llm.support.style import bold
+
+logger = logging.getLogger(__name__)
+
+
+@dataclasses.dataclass
+class Gemma3TextConfig(ConfigBase):
+    """Configuration of the text model inside Gemma3"""
+
+    # NOTE More fields have defaults due to Huggingface Gemma3 configs missing fields
+    # The defaults for these fields can be found in the transformers library
+    hidden_size: int
+    intermediate_size: int
+    num_hidden_layers: int
+    attention_bias: bool = False
+    num_attention_heads: int = 8
+    num_key_value_heads: int = 4
+    head_dim: int = 256
+    rms_norm_eps: float = 1e-6
+    hidden_activation: Optional[str] = "gelu_pytorch_tanh"
+    position_embedding_base: int = 1_000_000
+    rope_scaling: int = 0
+    context_window_size: int = 131_072
+    prefill_chunk_size: int = 0
+
+    query_pre_attn_scalar: int = 256
+    sliding_window_size: int = None
+    sliding_window_pattern = 6
+    kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)  # noqa: UP006
+
+    def __post_init__(self):
+        if self.hidden_activation is None:
+            self.hidden_activation = self.kwargs.get("hidden_act", None)
+        if self.sliding_window_size is None:
+            self.sliding_window_size = self.kwargs.get("sliding_window", None)
+        if self.hidden_activation not in ("gelu", "gelu_pytorch_tanh"):
+            raise ValueError("Only GeLU is supported as the activation for gemma.")
+        if self.attention_bias:
+            raise ValueError('Only "False" attention_bias is supported for gemma')
+        if self.position_embedding_base == 1000000 and "rope_theta" in self.kwargs:
+            self.position_embedding_base = self.kwargs.pop("rope_theta")
+        if self.context_window_size == 0:
+            for name in ["max_position_embeddings", "max_sequence_length"]:
+                if name in self.kwargs:
+                    self.context_window_size = self.kwargs.pop(name)
+                    logger.info(
+                        "%s not found in config.json. Falling back to %s (%d)",
+                        bold("context_window_size"),
+                        bold(name),
+                        self.context_window_size,
+                    )
+                    break
+            else:
+                raise ValueError(
+                    "Unable to determine the maximum sequence length, because none of "
+                    "`context_window_size`, `max_position_embeddings` or `max_sequence_length` is "
+                    "provided in `config.json`."
+                )
+        assert self.num_attention_heads % self.num_key_value_heads == 0
+        if self.prefill_chunk_size == 0:
+            logger.info(
+                "%s defaults to %d",
+                bold("prefill_chunk_size"),
+                min(self.context_window_size, 8192),
+            )
+            self.prefill_chunk_size = min(self.context_window_size, 8192)
+        elif self.prefill_chunk_size > self.context_window_size:
+            logger.info(
+                "Overriding %s from %d to %d",
+                bold("prefill_chunk_size"),
+                self.prefill_chunk_size,
+                min(self.context_window_size, 8192),
+            )
+            self.prefill_chunk_size = min(self.context_window_size, 8192)
+        # NOTE: override the context window size with the Gemma2 sliding window size,
+        # as the sliding window attention every other layer is yet to be supported.
+        self.context_window_size = max(self.sliding_window_size, 8192)
+
+
+@dataclasses.dataclass
+class Gemma3Config(ConfigBase):
+    """Configuration of the Gemma3 model"""
+
+    text_config: Gemma3TextConfig = None
+    vocab_size: int = 262_208
+    tensor_parallel_shards: int = 1
+    max_batch_size: int = 1
+    context_window_size: int = -1
+    sliding_window_size: int = -1
+    prefill_chunk_size: int = -1
+    is_text_model: bool = False
+    kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)  # noqa: UP006
+
+    def __post_init__(self):
+        if self.text_config is None:
+            self.is_text_model = True
+            self.text_config = Gemma3TextConfig.from_dict(self.kwargs)
+
+        text_config_dict: Dict[str, Any]  # noqa: UP006
+        if isinstance(self.text_config, Gemma3TextConfig):
+            text_config_dict = dataclasses.asdict(self.text_config)
+        else:
+            text_config_dict = dict(self.text_config)
+
+        for k, v in text_config_dict.pop("kwargs", {}).items():
+            text_config_dict[k] = v
+
+        self.text_config = Gemma3TextConfig.from_dict(text_config_dict)
+
+        for k in ["context_window_size", "prefill_chunk_size", "sliding_window_size"]:
+            if getattr(self, k) <= 0:
+                if hasattr(self.text_config, k):
+                    setattr(self, k, getattr(self.text_config, k))
+
+
+class Gemma3MLP(nn.Module):
+    def __init__(self, config: Gemma3Config):
+        super().__init__()
+        if config.text_config.intermediate_size % config.tensor_parallel_shards != 0:
+            raise ValueError(
+                f"Cannot split MLP intermediate size {config.text_config.intermediate_size} "
+                f"evenly to {config.tensor_parallel_shards} GPUs."
+            )
+        self.intermediate_size = (
+            config.text_config.intermediate_size // config.tensor_parallel_shards
+        )
+        self.gate_up_proj = nn.Linear(
+            in_features=config.text_config.hidden_size,
+            out_features=2 * self.intermediate_size,
+            bias=False,
+        )
+        self.down_proj = nn.Linear(
+            self.intermediate_size, config.text_config.hidden_size, bias=False
+        )
+
+    def forward(self, x: Tensor):
+        concat_x1_x2 = self.gate_up_proj(x)
+        x1, x2 = op.split(concat_x1_x2, 2, axis=-1)
+        return self.down_proj(op.gelu(x1, approximate="tanh") * x2)
+
+
+class Gemma3Attention(nn.Module):
+    def __init__(self, config: Gemma3Config):
+        self.head_dim = config.text_config.head_dim
+        self.num_q_heads = config.text_config.num_attention_heads // config.tensor_parallel_shards
+        self.num_kv_heads = config.text_config.num_key_value_heads
+        assert self.num_kv_heads % config.tensor_parallel_shards == 0, (
+            f"num_kv_heads({self.num_kv_heads}) must be divisible by tensor_parallel_shards"
+        )
+        assert self.num_kv_heads >= config.tensor_parallel_shards, (
+            f"Too large tensor_parallel_shards, must be smaller than {self.num_kv_heads}"
+        )
+        self.num_kv_heads = self.num_kv_heads // config.tensor_parallel_shards
+        self.q_proj = nn.Linear(
+            in_features=config.text_config.hidden_size,
+            out_features=self.num_q_heads * self.head_dim,
+            bias=config.text_config.attention_bias,
+        )
+        self.k_proj = nn.Linear(
+            in_features=config.text_config.hidden_size,
+            out_features=self.num_kv_heads * self.head_dim,
+            bias=config.text_config.attention_bias,
+        )
+        self.v_proj = nn.Linear(
+            in_features=config.text_config.hidden_size,
+            out_features=self.num_kv_heads * self.head_dim,
+            bias=config.text_config.attention_bias,
+        )
+        self.o_proj = nn.Linear(
+            in_features=self.num_q_heads * self.head_dim,
+            out_features=config.text_config.hidden_size,
+            bias=config.text_config.attention_bias,
+        )
+        self.q_norm = nn.RMSNorm(
+            config.text_config.head_dim, -1, config.text_config.rms_norm_eps, bias=False
+        )
+        self.k_norm = nn.RMSNorm(
+            config.text_config.head_dim, -1, config.text_config.rms_norm_eps, bias=False
+        )
+        # self.scaling_factor = (self.head_dim / config.text_config.query_pre_attn_scalar) ** 0.5
+        self.scaling = config.text_config.query_pre_attn_scalar**-0.5
+
+    def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
+        d, h_q = self.head_dim, self.num_q_heads
+        b, s, _ = hidden_states.shape
+        # QKV Projection
+        q_proj = op.reshape(self.q_proj(hidden_states), (b, s, -1, d))
+        k_proj = op.reshape(self.k_proj(hidden_states), (b, s, -1, d))
+        v_proj = op.reshape(self.v_proj(hidden_states), (b, s, -1, d))
+
+        q_norm = self.q_norm(q_proj)
+        k_norm = self.k_norm(k_proj)
+
+        qkv = op.concat([q_norm, k_norm, v_proj], dim=2)
+
+        # Attention
+        output = op.reshape(
+            paged_kv_cache.attention_with_fused_qkv(
+                layer_id, qkv, self.num_q_heads, sm_scale=self.scaling
+            ),
+            (b, s, h_q * d),
+        )
+        return self.o_proj(output)
+
+
+class Gemma3DecoderLayer(nn.Module):
+    def __init__(self, config: Gemma3Config):
+        rms_norm_eps = config.text_config.rms_norm_eps
+        self.self_attn = Gemma3Attention(config)
+        self.mlp = Gemma3MLP(config)
+        # Gemma RMSNorm adds 1 to the weights. It is already fused in the loader
+        self.input_layernorm = nn.RMSNorm(
+            config.text_config.hidden_size, -1, rms_norm_eps, bias=False
+        )
+        self.post_attention_layernorm = nn.RMSNorm(
+            config.text_config.hidden_size, -1, rms_norm_eps, bias=False
+        )
+        self.pre_feedforward_layernorm = nn.RMSNorm(
+            config.text_config.hidden_size, -1, rms_norm_eps, bias=False
+        )
+        self.post_feedforward_layernorm = nn.RMSNorm(
+            config.text_config.hidden_size, -1, rms_norm_eps, bias=False
+        )
+
+        def _set_tp():
+            def _set(layer, hint):
+                layer.weight.attrs["shard_strategy"] = hint
+
+            i = self.mlp.intermediate_size
+            _set(self.self_attn.q_proj, tp.ShardSingleDim("_shard_q", dim=0))
+            _set(self.self_attn.k_proj, tp.ShardSingleDim("_shard_k", dim=0))
+            _set(self.self_attn.v_proj, tp.ShardSingleDim("_shard_v", dim=0))
+            _set(self.self_attn.q_norm, tp.ShardSingleDim("_shard_q_norm", dim=0))
+            _set(self.self_attn.k_norm, tp.ShardSingleDim("_shard_k_norm", dim=0))
+            _set(self.self_attn.o_proj, tp.ShardSingleDim("_shard_o", dim=1))
+            _set(
+                self.mlp.gate_up_proj,
+                tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=0),
+            )
+            _set(self.mlp.down_proj, tp.ShardSingleDim("_shard_mlp_down", dim=1))
+
+        self.tensor_parallel_shards = config.tensor_parallel_shards
+        _set_tp()
+
+    def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
+        out = self.self_attn(self.input_layernorm(hidden_states), paged_kv_cache, layer_id)
+        out = self._apply_post_matmul_norm(out, norm=self.post_attention_layernorm)
+        hidden_states = out + hidden_states
+
+        out = self.pre_feedforward_layernorm(hidden_states)
+        out = self.mlp(out)
+        out = self._apply_post_matmul_norm(out, norm=self.post_feedforward_layernorm)
+        hidden_states = out + hidden_states
+
+        return hidden_states
+
+    def _apply_post_matmul_norm(self, out: Tensor, norm: nn.Tensor):
+        if self.tensor_parallel_shards > 1:
+            return norm(op.ccl_allreduce(out, "sum"))
+        return norm(out)
+
+
+class Gemma3TextModel(nn.Module):
+    def __init__(self, config: Gemma3Config):
+        self.hidden_size = config.text_config.hidden_size
+        assert config.text_config.hidden_size % config.text_config.num_attention_heads == 0
+        self.embed_tokens = GemmaEmbedding("vocab_size", config.text_config.hidden_size)
+        self.layers = nn.ModuleList(
+            [Gemma3DecoderLayer(config) for _ in range(config.text_config.num_hidden_layers)]
+        )
+        self.norm = nn.RMSNorm(
+            config.text_config.hidden_size,
+            -1,
+            config.text_config.rms_norm_eps,
+            bias=False,
+        )
+
+    def forward(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        hidden_states = input_embed
+        hidden_states = hidden_states * (self.hidden_size**0.5)
+        for layer_id, layer in enumerate(self.layers):
+            hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class Gemma3LanguageModel(nn.Module):
+    def __init__(self, config: Gemma3Config):
+        self.model = Gemma3TextModel(config)
+        self.config = config
+        self.num_hidden_layers = config.text_config.num_hidden_layers
+        self.num_attention_heads = config.text_config.num_attention_heads
+        self.num_key_value_heads = config.text_config.num_key_value_heads
+        self.head_dim = config.text_config.head_dim
+        self.hidden_size = config.text_config.hidden_size
+        self.vocab_size = config.vocab_size
+        self.rope_theta = config.text_config.position_embedding_base
+        self.rope_scaling = config.text_config.rope_scaling
+        self.tensor_parallel_shards = config.tensor_parallel_shards
+        self.dtype = "float32"
+
+    def to(self, dtype: Optional[str] = None):
+        super().to(dtype=dtype)
+        if dtype is not None:
+            self.dtype = dtype
+
+    def get_logits(self, hidden_states: Tensor):
+        logits = self.model.embed_tokens.lm_head_forward(hidden_states)
+        if logits.dtype != "float32":
+            logits = logits.astype("float32")
+        return logits
+
+    def batch_forward(
+        self,
+        input_embeds: Tensor,
+        paged_kv_cache: PagedKVCache,
+        logit_positions: Optional[Tensor] = None,
+    ):
+        op_ext.configure()
+
+        hidden_states = self.model(input_embeds, paged_kv_cache)
+        if logit_positions is not None:
+            hidden_states = op.take(hidden_states, logit_positions, axis=1)
+        logits = self.get_logits(hidden_states)
+        return logits
+
+    def embed(self, input_ids: Tensor):
+        if self.tensor_parallel_shards > 1:
+            input_ids = op.ccl_broadcast_from_worker0(input_ids)
+        return self.model.embed_tokens(input_ids)
+
+    def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        op_ext.configure()
+
+        hidden_states = self.model(input_embed, paged_kv_cache)
+        hidden_states = index_last_token(hidden_states)
+        logits = self.get_logits(hidden_states)
+        return logits, paged_kv_cache
+
+    def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        op_ext.configure()
+
+        hidden_states = self.model(input_embed, paged_kv_cache)
+        logits = self.get_logits(hidden_states)
+        return logits, paged_kv_cache
+
+    def batch_prefill(
+        self,
+        input_embeds: Tensor,
+        logit_positions: Tensor,
+        paged_kv_cache: PagedKVCache,
+    ):
+        if self.tensor_parallel_shards > 1:
+            logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
+        logits = self.batch_forward(input_embeds, paged_kv_cache, logit_positions)
+        return logits, paged_kv_cache
+
+    def batch_decode(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
+        logits = self.batch_forward(input_embeds, paged_kv_cache)
+        return logits, paged_kv_cache
+
+    def batch_verify(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
+        logits = self.batch_forward(input_embeds, paged_kv_cache)
+        return logits, paged_kv_cache
+
+    def create_paged_kv_cache(
+        self,
+        max_batch_size: tirx.Var,
+        max_total_seq_len: tirx.Var,
+        prefill_chunk_size: tirx.Var,
+        page_size: tirx.Var,
+        support_sliding_window: tirx.Var,
+    ) -> PagedKVCache:
+        # if "factor" in self.rope_scaling:
+        #     rope_scaling = self.rope_scaling["factor"]
+        # else:
+        #     rope_scaling = 1
+        return PagedKVCache.create_generic(
+            attn_kind=[
+                (
+                    "mha_sliding"
+                    if ((i + 1) % self.config.text_config.sliding_window_pattern)
+                    else "mha"
+                )
+                for i in range(self.num_hidden_layers)
+            ],
+            max_batch_size=max_batch_size,
+            max_total_seq_len=max_total_seq_len,
+            prefill_chunk_size=prefill_chunk_size,
+            page_size=page_size,
+            support_sliding_window=support_sliding_window,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads // self.tensor_parallel_shards,
+            num_key_value_heads=self.num_key_value_heads // self.tensor_parallel_shards,
+            qk_head_dim=self.head_dim,
+            v_head_dim=self.head_dim,
+            rope_mode=RopeMode.NORMAL,
+            rope_scale=1,
+            rope_theta=self.rope_theta,
+            dtype=self.dtype,
+        )
+
+    def get_default_spec(self):
+        mod_spec = {
+            "embed": {
+                "input_ids": nn.spec.Tensor(["seq_len"], "int32"),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "prefill": {
+                "input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "decode": {
+                "input_embed": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_prefill": {
+                "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_decode": {
+                "input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_verify": {
+                "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "create_paged_kv_cache": {
+                "max_batch_size": int,
+                "max_total_seq_len": int,
+                "prefill_chunk_size": int,
+                "page_size": int,
+                "support_sliding_window": int,
+                "$": {
+                    "param_mode": "none",
+                    "effect_mode": "none",
+                },
+            },
+        }
+        return nn.spec.ModuleSpec.from_raw(mod_spec, self)
+
+
+class Gemma3ForCausalLM(nn.Module):
+    def __init__(self, config: Gemma3Config):
+        super().__init__()
+        self.config = config
+        self.language_model = Gemma3LanguageModel(config)
+        self.vocab_size = config.vocab_size
+        self.dtype = "float32"
+        self.tensor_parallel_shards = config.tensor_parallel_shards
+
+    def to(self, dtype: Optional[str] = None):
+        super().to(dtype=dtype)
+        self.language_model.to(dtype=dtype)
+        if dtype is not None:
+            self.dtype = dtype
+
+    def get_logits(self, hidden_states: Tensor):
+        logits = self.language_model.model.embed_tokens.lm_head_forward(hidden_states)
+        if logits.dtype != "float32":
+            logits = logits.astype("float32")
+        return logits
+
+    def batch_forward(
+        self,
+        input_embeds: Tensor,
+        paged_kv_cache: PagedKVCache,
+        logit_positions: Optional[Tensor] = None,
+    ):
+        op_ext.configure()
+
+        hidden_states = self.language_model.model(input_embeds, paged_kv_cache)
+        if logit_positions is not None:
+            hidden_states = op.take(hidden_states, logit_positions, axis=1)
+        logits = self.get_logits(hidden_states)
+        return logits
+
+    def embed(self, input_ids: Tensor):
+        if self.tensor_parallel_shards > 1:
+            input_ids = op.ccl_broadcast_from_worker0(input_ids)
+        return self.language_model.model.embed_tokens(input_ids)
+
+    def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        op_ext.configure()
+
+        hidden_states = self.language_model.model(input_embed, paged_kv_cache)
+        hidden_states = index_last_token(hidden_states)
+        logits = self.get_logits(hidden_states)
+        return logits, paged_kv_cache
+
+    def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        op_ext.configure()
+
+        hidden_states = self.language_model.model(input_embed, paged_kv_cache)
+        logits = self.get_logits(hidden_states)
+        return logits, paged_kv_cache
+
+    def batch_prefill(
+        self,
+        input_embeds: Tensor,
+        logit_positions: Tensor,
+        paged_kv_cache: PagedKVCache,
+    ):
+        if self.tensor_parallel_shards > 1:
+            logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
+        logits = self.batch_forward(input_embeds, paged_kv_cache, logit_positions)
+        return logits, paged_kv_cache
+
+    def batch_decode(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
+        logits = self.batch_forward(input_embeds, paged_kv_cache)
+        return logits, paged_kv_cache
+
+    def batch_verify(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
+        logits = self.batch_forward(input_embeds, paged_kv_cache)
+        return logits, paged_kv_cache
+
+    def create_paged_kv_cache(
+        self,
+        max_batch_size: tirx.Var,
+        max_total_seq_len: tirx.Var,
+        prefill_chunk_size: tirx.Var,
+        page_size: tirx.Var,
+        support_sliding_window: tirx.Var,
+    ) -> PagedKVCache:
+        # if "factor" in self.language_model.rope_scaling:
+        #     rope_scaling = self.language_model.rope_scaling["factor"]
+        # else:
+        #     rope_scaling = 1
+        return PagedKVCache.create_generic(
+            attn_kind=[
+                (
+                    "mha_sliding"
+                    if ((i + 1) % self.config.text_config.sliding_window_pattern)
+                    else "mha"
+                )
+                for i in range(self.language_model.num_hidden_layers)
+            ],
+            max_batch_size=max_batch_size,
+            max_total_seq_len=max_total_seq_len,
+            prefill_chunk_size=prefill_chunk_size,
+            page_size=page_size,
+            support_sliding_window=support_sliding_window,
+            num_hidden_layers=self.language_model.num_hidden_layers,
+            num_attention_heads=self.language_model.num_attention_heads
+            // self.tensor_parallel_shards,
+            num_key_value_heads=self.language_model.num_key_value_heads
+            // self.tensor_parallel_shards,
+            qk_head_dim=self.language_model.head_dim,
+            v_head_dim=self.language_model.head_dim,
+            rope_mode=RopeMode.NORMAL,
+            rope_scale=1,
+            rope_theta=self.language_model.rope_theta,
+            dtype=self.dtype,
+        )
+
+    def get_default_spec(self):
+        mod_spec = {
+            "embed": {
+                "input_ids": nn.spec.Tensor(["seq_len"], "int32"),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "prefill": {
+                "input_embed": nn.spec.Tensor(
+                    [1, "seq_len", self.language_model.hidden_size], self.dtype
+                ),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "decode": {
+                "input_embed": nn.spec.Tensor([1, 1, self.language_model.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_prefill": {
+                "input_embeds": nn.spec.Tensor(
+                    [1, "seq_len", self.language_model.hidden_size], self.dtype
+                ),
+                "logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_decode": {
+                "input_embeds": nn.spec.Tensor(
+                    ["batch_size", 1, self.language_model.hidden_size], self.dtype
+                ),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_verify": {
+                "input_embeds": nn.spec.Tensor(
+                    [1, "seq_len", self.language_model.hidden_size], self.dtype
+                ),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "create_paged_kv_cache": {
+                "max_batch_size": int,
+                "max_total_seq_len": int,
+                "prefill_chunk_size": int,
+                "page_size": int,
+                "support_sliding_window": int,
+                "$": {
+                    "param_mode": "none",
+                    "effect_mode": "none",
+                },
+            },
+        }
+        return nn.spec.ModuleSpec.from_raw(mod_spec, self)