wehub-resource-sync
252 lines
9.7 KiB
Python
252 lines
9.7 KiB
Python
"""
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Implementation for EAGLE architecture.
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"""
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import dataclasses
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from typing import Optional
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from tvm import tirx
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from tvm.relax.frontend import nn
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from tvm.relax.frontend.nn import Tensor, op
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from mlc_llm import op as op_ext
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from mlc_llm.model.llama.llama_model import LlamaAttention, LlamaConfig, LlamaFFN
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from mlc_llm.nn import PagedKVCache, RopeMode
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from mlc_llm.support import logging
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from mlc_llm.support import tensor_parallel as tp
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logger = logging.getLogger(__name__)
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@dataclasses.dataclass
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class EagleConfig(LlamaConfig):
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"""Configuration of the Eagle model."""
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bias: bool = True # Whether to use bias in the fc layers
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class EagleDecoderLayer(nn.Module):
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def __init__(self, config: EagleConfig, index: int):
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rms_norm_eps = config.rms_norm_eps
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self.self_attn = LlamaAttention(config)
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self.mlp = LlamaFFN(config)
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self.index = index
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if self.index != 0:
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self.input_layernorm = nn.RMSNorm(config.hidden_size, -1, rms_norm_eps, bias=False)
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self.post_attention_layernorm = nn.RMSNorm(config.hidden_size, -1, rms_norm_eps, bias=False)
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def _set_tp():
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def _set(layer, hint):
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layer.weight.attrs["shard_strategy"] = hint
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hd = config.head_dim
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q = self.self_attn.num_q_heads * hd
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k = self.self_attn.num_kv_heads * hd
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v = self.self_attn.num_kv_heads * hd
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i = self.mlp.intermediate_size
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_set(
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self.self_attn.qkv_proj,
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tp.ShardSingleDim("_shard_qkv", segs=[q, k, v], dim=0),
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)
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_set(self.self_attn.o_proj, tp.ShardSingleDim("_shard_o", dim=1))
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_set(
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self.mlp.gate_up_proj,
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tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=0),
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)
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_set(self.mlp.down_proj, tp.ShardSingleDim("_shard_mlp_down", dim=1))
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self.tensor_parallel_shards = config.tensor_parallel_shards
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_set_tp()
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def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
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if self.index != 0:
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hidden_states = self.input_layernorm(hidden_states)
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out = self.self_attn(hidden_states, paged_kv_cache, layer_id)
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hidden_states = self._apply_residual(out, residual=hidden_states)
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out = self.mlp(self.post_attention_layernorm(hidden_states))
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hidden_states = self._apply_residual(out, residual=hidden_states)
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return hidden_states
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def _apply_residual(self, out, residual):
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if self.tensor_parallel_shards > 1:
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return op.ccl_allreduce(out, "sum") + residual
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return out + residual
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class EagleForCausalLM(nn.Module):
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def __init__(self, config: EagleConfig):
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# Put the model definition here to align with EAGLE's original structure
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assert config.hidden_size % config.num_attention_heads == 0
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self.embed_tokens = nn.Embedding("vocab_size", config.hidden_size)
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self.layers = nn.ModuleList(
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[EagleDecoderLayer(config, i) for i in range(config.num_hidden_layers)]
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)
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self.fc = nn.Linear(
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in_features=2 * config.hidden_size,
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out_features=config.hidden_size,
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bias=config.bias,
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)
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self.num_hidden_layers = config.num_hidden_layers
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self.num_attention_heads = config.num_attention_heads
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self.num_key_value_heads = config.num_key_value_heads
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self.head_dim = config.head_dim
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self.hidden_size = config.hidden_size
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self.vocab_size = config.vocab_size
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self.rope_theta = config.position_embedding_base
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self.tensor_parallel_shards = config.tensor_parallel_shards
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self.dtype = "float32"
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def fuse_embed_hidden_states(self, input_embed: Tensor, hidden_states: Tensor):
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hidden_states = op.concat([input_embed, hidden_states], dim=-1)
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hidden_states = self.fc(hidden_states)
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return hidden_states
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def forward_to_last_hidden_states(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache):
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for layer_id, layer in enumerate(self.layers):
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hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
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return hidden_states
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def forward(self, input_embed: Tensor, hidden_states: Tensor, paged_kv_cache: PagedKVCache):
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hidden_states = self.fuse_embed_hidden_states(input_embed, hidden_states)
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hidden_states = self.forward_to_last_hidden_states(hidden_states, paged_kv_cache)
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return hidden_states
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def to(self, dtype: Optional[str] = None):
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super().to(dtype=dtype)
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if dtype is not None:
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self.dtype = dtype
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def batch_forward(
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self,
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hidden_states: Tensor,
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paged_kv_cache: PagedKVCache,
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logit_positions: Optional[Tensor] = None,
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):
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op_ext.configure()
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hidden_states = self.forward_to_last_hidden_states(hidden_states, paged_kv_cache)
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if logit_positions is not None:
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hidden_states = op.take(hidden_states, logit_positions, axis=1)
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return hidden_states
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def embed(self, input_ids: Tensor):
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if self.tensor_parallel_shards > 1:
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input_ids = op.ccl_broadcast_from_worker0(input_ids)
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return self.embed_tokens(input_ids)
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def prefill_to_last_hidden_states(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache):
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op_ext.configure()
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hidden_states = self.forward_to_last_hidden_states(hidden_states, paged_kv_cache)
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return hidden_states, paged_kv_cache
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def decode_to_last_hidden_states(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache):
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op_ext.configure()
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hidden_states = self.forward_to_last_hidden_states(hidden_states, paged_kv_cache)
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return hidden_states, paged_kv_cache
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def batch_prefill_to_last_hidden_states(
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self,
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hidden_states: Tensor,
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paged_kv_cache: PagedKVCache,
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):
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hidden_states = self.batch_forward(hidden_states, paged_kv_cache)
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return hidden_states, paged_kv_cache
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def batch_decode_to_last_hidden_states(
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self, hidden_states: Tensor, paged_kv_cache: PagedKVCache
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):
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hidden_states = self.batch_forward(hidden_states, paged_kv_cache)
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return hidden_states, paged_kv_cache
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def create_paged_kv_cache(
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self,
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max_batch_size: tirx.Var,
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max_total_seq_len: tirx.Var,
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prefill_chunk_size: tirx.Var,
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page_size: tirx.Var,
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support_sliding_window: tirx.Var,
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) -> PagedKVCache:
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return PagedKVCache.create_generic(
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attn_kind="mha",
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max_batch_size=max_batch_size,
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max_total_seq_len=max_total_seq_len,
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prefill_chunk_size=prefill_chunk_size,
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page_size=page_size,
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support_sliding_window=support_sliding_window,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads // self.tensor_parallel_shards,
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num_key_value_heads=self.num_key_value_heads // self.tensor_parallel_shards,
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qk_head_dim=self.head_dim,
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v_head_dim=self.head_dim,
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rope_mode=RopeMode.NORMAL,
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rope_scale=1,
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rope_theta=self.rope_theta,
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dtype=self.dtype,
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)
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def get_default_spec(self):
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mod_spec = {
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"embed": {
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"input_ids": nn.spec.Tensor(["seq_len"], "int32"),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"fuse_embed_hidden_states": {
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"input_embed": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype),
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"hidden_states": nn.spec.Tensor(["seq_len", self.hidden_size], self.dtype),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"prefill_to_last_hidden_states": {
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"hidden_states": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"decode_to_last_hidden_states": {
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"hidden_states": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"batch_prefill_to_last_hidden_states": {
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"hidden_states": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"batch_decode_to_last_hidden_states": {
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"hidden_states": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
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"paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
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"$": {
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"param_mode": "packed",
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"effect_mode": "none",
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},
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},
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"create_paged_kv_cache": {
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"max_batch_size": int,
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"max_total_seq_len": int,
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"prefill_chunk_size": int,
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"page_size": int,
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"support_sliding_window": int,
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"$": {
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"param_mode": "none",
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"effect_mode": "none",
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},
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},
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}
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return nn.spec.ModuleSpec.from_raw(mod_spec, self)
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