chore: import upstream snapshot with attribution

python/mlc_llm/model/eagle/eagle_loader.py

@@ -0,0 +1,105 @@
+"""
+This file specifies how MLC's EAGLE parameter maps from other formats, for example HuggingFace
+PyTorch, HuggingFace safetensors.
+"""
+
+import functools
+
+import numpy as np
+
+from mlc_llm.loader import ExternMapping
+from mlc_llm.loader.standard_loader import make_standard_hf_loader
+from mlc_llm.quantization import Quantization, make_awq_quant
+
+from .eagle_model import EagleConfig, EagleForCausalLM
+
+awq_quant = make_awq_quant(EagleForCausalLM)
+
+
+huggingface = make_standard_hf_loader(
+    model_cls=EagleForCausalLM,
+    layer_prefix="layers",
+    add_unused=["rotary_emb.inv_freq"],
+)
+
+
+def awq(model_config: EagleConfig, quantization: Quantization) -> ExternMapping:
+    """Returns a parameter mapping that maps from the names of MLC LLM parameters to
+    the names of AWQ parameters.
+    Parameters
+    ----------
+    model_config : EagleConfig
+        The configuration of the Eagle model.
+
+    quantization : Quantization
+        The quantization configuration.
+
+    Returns
+    -------
+    param_map : ExternMapping
+        The parameter mapping from MLC to AWQ.
+    """
+    model, _ = awq_quant(model_config, quantization)
+    _, _named_params, _ = model.export_tvm(
+        spec=model.get_default_spec(),
+        allow_extern=True,
+    )
+    named_parameters = dict(_named_params)
+
+    mapping = ExternMapping()
+
+    for i in range(model_config.num_hidden_layers):
+        # Add QKV in self attention
+        attn = f"layers.{i}.self_attn"
+        for quantize_suffix in ["qweight", "qzeros", "scales"]:
+            mlc_name = f"{attn}.qkv_proj.{quantize_suffix}"
+            assert mlc_name in named_parameters
+            mlc_param = named_parameters[mlc_name]
+            mapping.add_mapping(
+                mlc_name,
+                [
+                    f"{attn}.q_proj.{quantize_suffix}",
+                    f"{attn}.k_proj.{quantize_suffix}",
+                    f"{attn}.v_proj.{quantize_suffix}",
+                ],
+                functools.partial(
+                    lambda q, k, v, dtype: np.concatenate(
+                        [q, k, v],
+                        axis=1,  # AWQ GEMM would transpose the weight
+                    ).astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+
+        # Concat gate and up in MLP
+        mlp = f"layers.{i}.mlp"
+        for quantize_suffix in ["qweight", "qzeros", "scales"]:
+            mlc_name = f"{mlp}.gate_up_proj.{quantize_suffix}"
+            assert mlc_name in named_parameters
+            mlc_param = named_parameters[mlc_name]
+            mapping.add_mapping(
+                mlc_name,
+                [
+                    f"{mlp}.gate_proj.{quantize_suffix}",
+                    f"{mlp}.up_proj.{quantize_suffix}",
+                ],
+                functools.partial(
+                    lambda gate, up, dtype: np.concatenate(
+                        [gate, up],
+                        axis=1,  # AWQ GEMM would transpose the weight
+                    ).astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+
+        # inv_freq is not used in the model
+        mapping.add_unused(f"{attn}.rotary_emb.inv_freq")
+
+    for mlc_name, mlc_param in named_parameters.items():
+        if mlc_name not in mapping.param_map:
+            mapping.add_mapping(
+                mlc_name,
+                [mlc_name],
+                functools.partial(lambda x, dtype: x.astype(dtype), dtype=mlc_param.dtype),
+            )
+    return mapping

python/mlc_llm/model/eagle/eagle_model.py

@@ -0,0 +1,251 @@
+"""
+Implementation for EAGLE architecture.
+"""
+
+import dataclasses
+from typing import Optional
+
+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.llama.llama_model import LlamaAttention, LlamaConfig, LlamaFFN
+from mlc_llm.nn import PagedKVCache, RopeMode
+from mlc_llm.support import logging
+from mlc_llm.support import tensor_parallel as tp
+
+logger = logging.getLogger(__name__)
+
+
+@dataclasses.dataclass
+class EagleConfig(LlamaConfig):
+    """Configuration of the Eagle model."""
+
+    bias: bool = True  # Whether to use bias in the fc layers
+
+
+class EagleDecoderLayer(nn.Module):
+    def __init__(self, config: EagleConfig, index: int):
+        rms_norm_eps = config.rms_norm_eps
+        self.self_attn = LlamaAttention(config)
+        self.mlp = LlamaFFN(config)
+        self.index = index
+        if self.index != 0:
+            self.input_layernorm = nn.RMSNorm(config.hidden_size, -1, rms_norm_eps, bias=False)
+        self.post_attention_layernorm = nn.RMSNorm(config.hidden_size, -1, rms_norm_eps, bias=False)
+
+        def _set_tp():
+            def _set(layer, hint):
+                layer.weight.attrs["shard_strategy"] = hint
+
+            hd = config.head_dim
+            q = self.self_attn.num_q_heads * hd
+            k = self.self_attn.num_kv_heads * hd
+            v = self.self_attn.num_kv_heads * hd
+            i = self.mlp.intermediate_size
+            _set(
+                self.self_attn.qkv_proj,
+                tp.ShardSingleDim("_shard_qkv", segs=[q, k, v], 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):
+        if self.index != 0:
+            hidden_states = self.input_layernorm(hidden_states)
+        out = self.self_attn(hidden_states, paged_kv_cache, layer_id)
+        hidden_states = self._apply_residual(out, residual=hidden_states)
+        out = self.mlp(self.post_attention_layernorm(hidden_states))
+        hidden_states = self._apply_residual(out, residual=hidden_states)
+        return hidden_states
+
+    def _apply_residual(self, out, residual):
+        if self.tensor_parallel_shards > 1:
+            return op.ccl_allreduce(out, "sum") + residual
+        return out + residual
+
+
+class EagleForCausalLM(nn.Module):
+    def __init__(self, config: EagleConfig):
+        # Put the model definition here to align with EAGLE's original structure
+        assert config.hidden_size % config.num_attention_heads == 0
+        self.embed_tokens = nn.Embedding("vocab_size", config.hidden_size)
+        self.layers = nn.ModuleList(
+            [EagleDecoderLayer(config, i) for i in range(config.num_hidden_layers)]
+        )
+        self.fc = nn.Linear(
+            in_features=2 * config.hidden_size,
+            out_features=config.hidden_size,
+            bias=config.bias,
+        )
+
+        self.num_hidden_layers = config.num_hidden_layers
+        self.num_attention_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.hidden_size = config.hidden_size
+        self.vocab_size = config.vocab_size
+        self.rope_theta = config.position_embedding_base
+        self.tensor_parallel_shards = config.tensor_parallel_shards
+        self.dtype = "float32"
+
+    def fuse_embed_hidden_states(self, input_embed: Tensor, hidden_states: Tensor):
+        hidden_states = op.concat([input_embed, hidden_states], dim=-1)
+        hidden_states = self.fc(hidden_states)
+        return hidden_states
+
+    def forward_to_last_hidden_states(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache):
+        for layer_id, layer in enumerate(self.layers):
+            hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
+        return hidden_states
+
+    def forward(self, input_embed: Tensor, hidden_states: Tensor, paged_kv_cache: PagedKVCache):
+        hidden_states = self.fuse_embed_hidden_states(input_embed, hidden_states)
+        hidden_states = self.forward_to_last_hidden_states(hidden_states, paged_kv_cache)
+        return hidden_states
+
+    def to(self, dtype: Optional[str] = None):
+        super().to(dtype=dtype)
+        if dtype is not None:
+            self.dtype = dtype
+
+    def batch_forward(
+        self,
+        hidden_states: Tensor,
+        paged_kv_cache: PagedKVCache,
+        logit_positions: Optional[Tensor] = None,
+    ):
+        op_ext.configure()
+
+        hidden_states = self.forward_to_last_hidden_states(hidden_states, paged_kv_cache)
+        if logit_positions is not None:
+            hidden_states = op.take(hidden_states, logit_positions, axis=1)
+        return hidden_states
+
+    def embed(self, input_ids: Tensor):
+        if self.tensor_parallel_shards > 1:
+            input_ids = op.ccl_broadcast_from_worker0(input_ids)
+        return self.embed_tokens(input_ids)
+
+    def prefill_to_last_hidden_states(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache):
+        op_ext.configure()
+
+        hidden_states = self.forward_to_last_hidden_states(hidden_states, paged_kv_cache)
+        return hidden_states, paged_kv_cache
+
+    def decode_to_last_hidden_states(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache):
+        op_ext.configure()
+
+        hidden_states = self.forward_to_last_hidden_states(hidden_states, paged_kv_cache)
+        return hidden_states, paged_kv_cache
+
+    def batch_prefill_to_last_hidden_states(
+        self,
+        hidden_states: Tensor,
+        paged_kv_cache: PagedKVCache,
+    ):
+        hidden_states = self.batch_forward(hidden_states, paged_kv_cache)
+        return hidden_states, paged_kv_cache
+
+    def batch_decode_to_last_hidden_states(
+        self, hidden_states: Tensor, paged_kv_cache: PagedKVCache
+    ):
+        hidden_states = self.batch_forward(hidden_states, paged_kv_cache)
+        return hidden_states, 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:
+        return PagedKVCache.create_generic(
+            attn_kind="mha",
+            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"),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "fuse_embed_hidden_states": {
+                "input_embed": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype),
+                "hidden_states": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "prefill_to_last_hidden_states": {
+                "hidden_states": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "decode_to_last_hidden_states": {
+                "hidden_states": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_prefill_to_last_hidden_states": {
+                "hidden_states": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_decode_to_last_hidden_states": {
+                "hidden_states": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "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)