File size: 13,176 Bytes
8c38801 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 | diff --git a/src/reap/data.py b/src/reap/data.py
index c41066d..2831c31 100644
--- a/src/reap/data.py
+++ b/src/reap/data.py
@@ -27,7 +27,10 @@ import logging
import torch
from datasets import Dataset, DatasetDict, load_dataset
from transformers import AutoTokenizer, BatchEncoding
-from vllm import TokensPrompt
+try:
+ from vllm import TokensPrompt
+except ImportError: # apex: only needed with return_vllm_tokens_prompt
+ TokensPrompt = None
logger = logging.getLogger(__name__)
@@ -214,6 +217,8 @@ def _load_raw_dataset(dataset_name, split, subset=None):
return load_dataset(
"json", data_files={"train": file_url}, split="train", streaming=False
)
+ elif dataset_name.endswith((".json", ".jsonl")): # apex: a local dataset file
+ return load_dataset("json", data_files=dataset_name, split="train")
else:
load_kwargs = {}
if subset is not None:
@@ -241,6 +246,8 @@ def load_category_batches(
# load dataset processor
proc_cls = DATASET_REGISTRY.get(dataset_name)
+ if proc_cls is None and dataset_name.endswith((".json", ".jsonl")):
+ proc_cls = ApexCalibrationDataset
if proc_cls is None:
raise ValueError(
f"No DatasetProcessor registered for '{dataset_name}'. "
@@ -962,6 +969,24 @@ class SWESmithTrajectoriesDataset(ChatDatasetProcessor):
}
+class ApexCalibrationDataset(BaseDatasetProcessor):
+ """apex-moe's calibration prompts with their datasets' answers (one JSON line per prompt):
+ "messages" (user prompt + the dataset's answer) go through the chat template as in
+ ChatDatasetProcessor; rows with "text" only (FineWeb-Edu passages) are tokenized raw as in
+ LMDatasetProcessor."""
+
+ category_field: str = "source"
+
+ @staticmethod
+ def _map_fn(sample: dict[str, any]) -> dict[str, any]:
+ return sample
+
+ def _encode_sample(self, sample: dict) -> torch.Tensor:
+ if sample.get(self.messages_field):
+ return ChatDatasetProcessor._encode_sample(self, sample)
+ return LMDatasetProcessor._encode_sample(self, sample)
+
+
DATASET_REGISTRY: dict[str, BaseDatasetProcessor] = {
"m-a-p/CodeFeedback-Filtered-Instruction": CodeFeedbackChatDataset,
"allenai/tulu-3-sft-mixture": TuluSFTMixtureChatDataset,
diff --git a/src/reap/main.py b/src/reap/main.py
index acc0a2a..8977325 100644
--- a/src/reap/main.py
+++ b/src/reap/main.py
@@ -52,7 +52,6 @@ from reap.model_util import (
patched_model_map,
get_super_expert_indices,
)
-from reap.eval import run_evaluate
from reap.cluster_plots import plot_cluster_analysis
from reap.metrics import get_distance_fn
@@ -118,8 +117,13 @@ def create_results_directory(model_name: str, dataset_name: str) -> pathlib.Path
def _setup_observer(model, obs_args):
"""Create and return an MoETransformerObserver for the given model."""
try:
+ norm_topk_prob = getattr(model.config, "norm_topk_prob", None)
+ if norm_topk_prob is None and model.__class__.__name__ == "Qwen3_5MoeForCausalLM":
+ # apex: Qwen3.5/3.6's router always renormalizes its top-k weights (transformers
+ # 5 Qwen3_5MoeTopKRouter) and its config has no norm_topk_prob
+ norm_topk_prob = True
renormalize_router_weights = (
- getattr(model.config, "norm_topk_prob", False)
+ bool(norm_topk_prob)
and obs_args.renormalize_router_weights
)
if renormalize_router_weights:
@@ -547,10 +551,11 @@ def smoke_test(model: nn.Module, tokenizer: AutoTokenizer):
return_tensors="pt",
add_generation_prompt=True,
tokenize=True,
+ return_dict=True, # apex: transformers 5 returns a BatchEncoding either way
# enable_thinking=False,
).to(model.device)
outputs = model.generate(
- inputs,
+ **inputs,
max_new_tokens=50,
do_sample=True,
)
@@ -829,6 +834,7 @@ def main():
torch.cuda.empty_cache()
gc.collect()
model_args.model_name = merged_model_dir
+ from reap.eval import run_evaluate # apex: lazy, pulls in vllm / lm_eval / evalplus
run_evaluate(model_args, merged_model_dir / "eval", eval_args, reap_args.seed)
diff --git a/src/reap/model_util.py b/src/reap/model_util.py
index 16bb5d2..d6a0140 100644
--- a/src/reap/model_util.py
+++ b/src/reap/model_util.py
@@ -104,6 +104,30 @@ MODEL_ATTRS = {
"num_experts": "num_experts",
"num_experts_per_tok": "num_experts_per_tok",
},
+ # apex: transformers 5 Qwen3.5/3.6 and Qwen4Exp (Qwen3.8): 3D experts, router `gate`
+ "Qwen3_5MoeForCausalLM": {
+ "moe_block": "mlp",
+ "gate_proj": "gate_up_proj",
+ "up_proj": "gate_up_proj",
+ "down_proj": "down_proj",
+ "experts": "experts",
+ "fused": True,
+ "router": "gate",
+ "num_experts": "num_experts",
+ "num_experts_per_tok": "num_experts_per_tok",
+ },
+ # apex: transformers 5 Qwen3.5/3.6 and Qwen4Exp (Qwen3.8): 3D experts, router `gate`
+ "Qwen4ExpForCausalLM": {
+ "moe_block": "mlp",
+ "gate_proj": "gate_up_proj",
+ "up_proj": "gate_up_proj",
+ "down_proj": "down_proj",
+ "experts": "experts",
+ "fused": True,
+ "router": "gate",
+ "num_experts": "num_experts",
+ "num_experts_per_tok": "num_experts_per_tok",
+ },
"Glm4MoeForCausalLM": {
"moe_block": "mlp",
"gate_proj": "gate_proj",
diff --git a/src/reap/observer.py b/src/reap/observer.py
index 2668200..859021d 100644
--- a/src/reap/observer.py
+++ b/src/reap/observer.py
@@ -220,6 +220,10 @@ class MoETransformerObserverConfig(BaseTransformerObserverHookConfig):
num_experts_attr_name: str = "num_experts"
top_k_attr_name: str = "top_k"
fused_experts: bool = False
+ # apex: transformers 5 MoE blocks (Qwen3.5/3.6, Qwen4Exp): the block returns only its hidden
+ # states, the router is `gate` (logits, top-k weights, top-k indices) and the experts are one
+ # module with 3D `gate_up_proj` [E, 2I, H] and `down_proj` [E, H, I]
+ fused_3d_experts: bool = False
distance_measure: str = "angular"
renormalize_router_weights: bool = False
record_pruning_metrics_only: bool = False
@@ -328,7 +332,7 @@ class MoETransformerObserver(BaseTransformerObserver):
@torch.no_grad()
def _hook_fn(module, args, output):
- if not len(output) >= 2:
+ if not self.hook_config.fused_3d_experts and not len(output) >= 2:
raise ValueError(
f"Expected output of module {module.__class__.__name__} at layer "
f"{layer_number} to be a tuple of at least length 2, got {len(output)}."
@@ -350,7 +354,33 @@ class MoETransformerObserver(BaseTransformerObserver):
activations = torch.zeros((num_experts, *flat_input.shape), device=device)
- if self.hook_config.fused_experts:
+ if self.hook_config.fused_3d_experts:
+ # as the loop below: router logits, top-k by logit, every expert's unweighted
+ # output on every token
+ router_logits = torch.nn.functional.linear(flat_input, module.gate.weight)
+ _, selected_experts = torch.topk(router_logits, top_k, dim=-1)
+ experts = module.experts
+ fp8 = hasattr(experts, "gate_up_proj_scale_inv") # transformers FP8Experts
+ for idx in range(num_experts):
+ if fp8: # the module's own block-FP8 GEMMs, as its forward computes them
+ out = experts.linear(
+ flat_input, experts.gate_up_proj[idx], experts.gate_up_proj_scale_inv[idx]
+ )
+ out = experts.linear(
+ experts._apply_gate(out),
+ experts.down_proj[idx],
+ experts.down_proj_scale_inv[idx],
+ )
+ else:
+ gate, up = torch.nn.functional.linear(
+ flat_input, experts.gate_up_proj[idx]
+ ).chunk(2, dim=-1)
+ out = torch.nn.functional.linear(
+ experts.act_fn(gate) * up, experts.down_proj[idx]
+ )
+ activations[idx] = out.to(device)
+
+ elif self.hook_config.fused_experts:
_, router_scores = output # (num_experts, total_tokens)
router_logits = module.router(flat_input) # (total_tokens, num_experts)
_, selected_experts = torch.topk(router_logits, top_k, dim=-1)
@@ -484,6 +514,22 @@ class Qwen3MoEObserverHookConfig(MoETransformerObserverConfig):
module_class_name_to_hook_regex: Optional[str] = "Qwen3MoeSparseMoeBlock"
+@dataclass
+class Qwen35MoEObserverHookConfig(MoETransformerObserverConfig):
+ module_class_name_to_hook_regex: Optional[str] = "Qwen3_5MoeSparseMoeBlock"
+ num_experts_attr_name: str = "experts.num_experts"
+ top_k_attr_name: str = "gate.top_k"
+ fused_3d_experts: bool = True
+
+
+@dataclass
+class Qwen4ExpMoEObserverHookConfig(MoETransformerObserverConfig):
+ module_class_name_to_hook_regex: Optional[str] = "Qwen4ExpTextSparseMoeBlock"
+ num_experts_attr_name: str = "experts.num_experts"
+ top_k_attr_name: str = "gate.top_k"
+ fused_3d_experts: bool = True
+
+
@dataclass
class Llama4MoEObserverHookConfig(MoETransformerObserverConfig):
module_class_name_to_hook_regex: Optional[str] = "Llama4TextMoe"
@@ -533,4 +579,6 @@ OBSERVER_CONFIG_REGISTRY = {
"Ernie4_5_MoEForCausalLM": Ernie4_5MoEObserverHookConfig,
"Ernie4_5_MoeForCausalLM": Ernie4_5MoEObserverHookConfig,
"Glm4MoeForCausalLM": Glm44MoEObserverHookConfig,
+ "Qwen3_5MoeForCausalLM": Qwen35MoEObserverHookConfig,
+ "Qwen4ExpForCausalLM": Qwen4ExpMoEObserverHookConfig,
}
diff --git a/src/reap/prune.py b/src/reap/prune.py
index 5767c4e..12d9106 100644
--- a/src/reap/prune.py
+++ b/src/reap/prune.py
@@ -33,7 +33,6 @@ from reap.cluster import (
dynamic_frequency_penalized_clustering,
)
from reap.model_util import get_moe, assert_merge, MODEL_ATTRS, patched_model_map, get_super_expert_indices
-from reap.eval import run_evaluate
import shutil
logger = logging.getLogger(__name__)
@@ -139,10 +138,23 @@ def prune(
]
moe.experts.down_proj.data = moe.experts.down_proj[retained_expert_indicies]
moe.num_experts = len(retained_expert_indicies)
- moe.router.weight.data = moe.router.weight.data[retained_expert_indicies]
- moe.router.out_features = len(retained_expert_indicies)
- if hasattr(moe.router, "num_experts"): # transformers >= 4.54+
- moe.router.num_experts = len(retained_expert_indicies)
+ # apex: the router attribute from MODEL_ATTRS (Llama-4: router; Qwen3.5/4Exp: gate)
+ router = getattr(moe, model_attrs["router"])
+ router.weight.data = router.weight.data[retained_expert_indicies]
+ router.out_features = len(retained_expert_indicies)
+ if hasattr(router, "num_experts"): # transformers >= 4.54+
+ router.num_experts = len(retained_expert_indicies)
+ if hasattr(moe.experts, "num_experts"): # apex: transformers 5 3D experts
+ moe.experts.num_experts = len(retained_expert_indicies)
+ for name in ( # apex: transformers FP8Experts keep per-expert block scales
+ "gate_up_proj_scale_inv",
+ "down_proj_scale_inv",
+ "gate_up_proj_activation_scale",
+ "down_proj_activation_scale",
+ ):
+ param = getattr(moe.experts, name, None)
+ if param is not None:
+ param.data = param.data[retained_expert_indicies]
# patch config and dump
logger.info("Saving pruned model...")
@@ -310,15 +322,15 @@ def main():
logger.info("Pruning completed.")
- dump_args_to_yaml(
+ dump_args_to_yaml( # apex: upstream passed these positionally to a **kwargs signature
pruned_model_dir,
- reap_args,
- ds_args,
- obs_args,
- model_args,
- eval_args,
- prune_args,
- cluster_args,
+ reap_args=reap_args,
+ ds_args=ds_args,
+ obs_args=obs_args,
+ model_args=model_args,
+ eval_args=eval_args,
+ prune_args=prune_args,
+ cluster_args=cluster_args,
)
# eval
@@ -330,6 +342,8 @@ def main():
torch.cuda.empty_cache()
gc.collect()
model_args.model_name = pruned_model_dir
+ from reap.eval import run_evaluate # apex: lazy, pulls in vllm / lm_eval / evalplus
+
run_evaluate(model_args, pruned_model_dir / "eval", eval_args, reap_args.seed)
|