Added GLM Modeling

examples/quantizing_moe/glm4_7_example.py

@@ -0,0 +1,82 @@
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modeling.glm4_moe import CalibrationGlm4MoeMoE  # noqa: F401
+from llmcompressor.modifiers.awq import AWQModifier
+
+# Load the model
+model_id = "zai-org/GLM-4.7"
+model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+# MoE calibration is now handled automatically by the pipeline.
+# The `CalibrationGlm4MoeMoE` modules (from `llmcompressor.modeling.glm4_moe`)
+# will be applied during calibration to enable proper expert calibration.
+# These replace the original `Glm4MoeMoE` class from
+# `transformers.models.glm4_moe.modeling_glm4_moe`.
+
+# Select calibration dataset.
+DATASET_ID = "HuggingFaceH4/ultrachat_200k"
+DATASET_SPLIT = "train_sft"
+
+# Select number of samples. 512 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 2048
+
+# Load dataset and preprocess.
+ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]")
+ds = ds.shuffle(seed=42)
+
+
+def preprocess(example):
+    return {
+        "text": tokenizer.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+        )
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        add_special_tokens=False,
+    )
+
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+
+moe_ignores = [
+    # Layers 0-2: Dense layer - ignore entire layers
+    "model.layers.0.*",
+    "model.layers.1.*",
+    "model.layers.2.*",
+    # Ignore the output head
+    "lm_head",
+]
+
+# Configure the quantization algorithm to run.
+#   * quantize the weights to 4 bit with GPTQ with a group size 128
+recipe = AWQModifier(targets="Linear", scheme="W4A16", ignore=moe_ignores)
+
+# Apply algorithms.
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+)
+
+# Save to disk compressed.
+SAVE_DIR = model_id.rstrip("/").split("/")[-1] + "-W4A16-G128"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)

src/llmcompressor/modeling/__init__.py

@@ -11,6 +11,7 @@
 
 # trigger registration
 from .deepseek_v3 import CalibrationDeepseekV3MoE  # noqa: F401
+from .glm4_moe import CalibrationGlm4MoeMoE  # noqa: F401
 from .llama4 import SequentialLlama4TextMoe  # noqa: F401
 from .qwen3_moe import CalibrationQwen3MoeSparseMoeBlock  # noqa: F401
 from .qwen3_vl_moe import CalibrateQwen3VLMoeTextSparseMoeBlock  # noqa: F401

src/llmcompressor/modeling/glm4_moe.py

@@ -0,0 +1,92 @@
+import torch
+from transformers.models.glm4_moe.configuration_glm4_moe import Glm4MoeConfig
+from transformers.models.glm4_moe.modeling_glm4_moe import (
+    Glm4MoeMoE as OriginalGlm4MoeMoE,
+)
+
+from llmcompressor.modeling.moe_context import MoECalibrationModule
+
+
+@MoECalibrationModule.register("Glm4MoeMoE")
+class CalibrationGlm4MoeMoE(MoECalibrationModule):
+    """
+    Calibration version of Glm4MoeMoE that sends all tokens to all experts.
+    During calibration, when calibrate_all_experts=True, all tokens are sent to
+    all experts to ensure proper quantization statistics are collected for every
+    expert, not just those activated by the calibration data routing.
+    """
+
+    is_permanent = False
+
+    def __init__(
+        self,
+        original: OriginalGlm4MoeMoE,
+        config: Glm4MoeConfig,
+        calibrate_all_experts: bool = True,
+    ):
+        super().__init__()
+        self.config = config
+        self.experts = original.experts
+        self.gate = original.gate
+        self.shared_experts = original.shared_experts
+        self.calibrate_all_experts = calibrate_all_experts
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass with optional calibration mode.
+        When calibrate_all_experts=True:
+            - All tokens are sent to all experts for calibration
+            - Routing weights are still used for final output combination
+            - This ensures all experts see calibration data
+        When calibrate_all_experts=False:
+            - Normal MoE routing behavior (only routed tokens go to each expert)
+        """
+        residuals = hidden_states
+        orig_shape = hidden_states.shape
+        topk_indices, topk_weights = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+
+        # Begin MoE - inline the moe() method logic with calibration support
+        final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype)
+        expert_mask = torch.nn.functional.one_hot(
+            topk_indices, num_classes=len(self.experts)
+        )
+        expert_mask = expert_mask.permute(2, 0, 1)
+
+        for expert_idx, expert in enumerate(self.experts):
+            mask = expert_mask[expert_idx]
+            token_indices, weight_indices = torch.where(mask)
+            has_tokens = token_indices.numel() > 0
+
+            if self.calibrate_all_experts:
+                # When calibrating, run all tokens through the expert to gather stats.
+                # The output is still calculated using only the routed tokens.
+                expert_output_full = expert(hidden_states)
+                if not has_tokens:
+                    # No tokens routed to this expert, but stats were gathered.
+                    continue
+                expert_output = expert_output_full[token_indices]
+            else:
+                # Standard MoE behavior: only process tokens routed to this expert.
+                if not has_tokens:
+                    continue
+                expert_output = expert(hidden_states[token_indices])
+
+            # Common logic for combining expert outputs
+            expert_weights = topk_weights[token_indices, weight_indices]
+            weighted_output = expert_output * expert_weights.unsqueeze(-1)
+            final_hidden_states.index_add_(0, token_indices, weighted_output)
+        # End MoE
+
+        hidden_states = final_hidden_states.type(hidden_states.dtype).view(*orig_shape)
+        hidden_states = hidden_states + self.shared_experts(residuals)
+        return hidden_states
+
+    def restore(self, original: torch.nn.Module) -> torch.nn.Module:
+        """
+        Restore the original module structure.
+
+        Since is_permanent=False, this method is called when exiting
+        the calibration context to restore the original MoE module.
+        """
+        return original

tests/llmcompressor/modeling/test_calib_glm4_moe.py

@@ -0,0 +1,91 @@
+import contextlib
+from functools import partial
+
+import pytest
+import torch
+from transformers import AutoModelForCausalLM
+
+from llmcompressor.modeling.glm4_moe import CalibrationGlm4MoeMoE
+from llmcompressor.modeling.moe_context import moe_calibration_context
+from llmcompressor.utils.dev import skip_weights_download
+from llmcompressor.utils.helpers import calibration_forward_context
+from tests.testing_utils import requires_cadence, requires_gpu
+
+Glm4MoeConfig = pytest.importorskip(
+    "transformers.models.glm4_moe.configuration_glm4_moe",
+    reason="Glm4MoeConfig not available in this version of transformers",
+).Glm4MoeConfig
+OriginalGlm4MoeMoE = pytest.importorskip(
+    "transformers.models.glm4_moe.modeling_glm4_moe",
+    reason="Glm4MoeMoE not available in this version of transformers",
+).Glm4MoeMoE
+
+
+@requires_cadence("weekly")
+@pytest.mark.parametrize("model_stub", ["zai-org/GLM-4.7"])
+def test_calib_replace_glm4moe_all_experts(model_stub):
+    with skip_weights_download():
+        model = AutoModelForCausalLM.from_pretrained(model_stub, trust_remote_code=True)
+
+    with contextlib.ExitStack() as stack:
+        stack.enter_context(calibration_forward_context(model))
+        stack.enter_context(moe_calibration_context(model, calibrate_all_experts=True))
+
+        # Find a GLM4 MoE layer
+        moe_layer = None
+        for _, module in model.named_modules():
+            if isinstance(module, CalibrationGlm4MoeMoE):
+                moe_layer = module
+                break
+
+        assert moe_layer is not None
+
+        num_experts = len(moe_layer.experts)
+        expert_triggered = [False for _ in range(num_experts)]
+
+        # Define the hook function
+        def hook_fn(i, module, input, output):
+            expert_triggered[i] = True
+
+        # Attach hooks using functools.partial to bind each index
+        for i, expert in enumerate(moe_layer.experts):
+            expert.register_forward_hook(partial(hook_fn, i))
+
+        # Create dummy input tensor that simulates hidden_states
+        hidden_dim = model.config.hidden_size
+        batch, seq_len = 4, 32
+        sample = torch.randn(batch, seq_len, hidden_dim, dtype=torch.float32)
+
+        # Forward through the MoE layer directly
+        with torch.no_grad():
+            _ = moe_layer(sample)
+
+        # Assert all experts are used
+        assert all(
+            expert_triggered
+        ), f"Not all experts were triggered: {expert_triggered}"
+
+
+@requires_gpu
+def test_calib_glm4moe_module():
+    config = Glm4MoeConfig()
+    with torch.device("cuda"):
+        original = OriginalGlm4MoeMoE(config).eval()
+
+    # Create dummy input tensor that simulates hidden_states
+    hidden_dim = config.hidden_size
+    batch, seq_len = 4, 32
+    sample = torch.randn(batch, seq_len, hidden_dim, device="cuda")
+
+    with calibration_forward_context(original):
+        true_output = original(sample)
+
+    module = CalibrationGlm4MoeMoE(original, config, calibrate_all_experts=True)
+    with calibration_forward_context(module):
+        output = module(sample)
+        assert torch.allclose(true_output, output, atol=1e-6)
+
+    module = CalibrationGlm4MoeMoE(original, config, calibrate_all_experts=False)
+    with calibration_forward_context(module):
+        output = module(sample)
+        assert torch.allclose(true_output, output, atol=1e-6)