[MoE] DeepSeek-V3/R1

README.md

@@ -16,6 +16,7 @@
 
 Big updates have landed in LLM Compressor! Check out these exciting new features:
 
+* **Large Model Support with Sequential Onloading** As of llm-compressor>=0.6.0, you can now quantize very large language models on a single GPU. Models are broken into disjoint layers which are then onloaded to the GPU one layer at a time. For more information on sequential onloading, see [Big Modeling with Sequential Onloading](examples/big_models_with_sequential_onloading/README.md) as well as the [DeepSeek-R1 Example](examples/quantizing_moe/deepseek_r1_example.py).
 * **Preliminary FP4 Quantization Support:** Quantize weights and activations to FP4 and seamlessly run the compressed model in vLLM. Model weights and activations are quantized following the NVFP4 [configuration](https://github.com/neuralmagic/compressed-tensors/blob/f5dbfc336b9c9c361b9fe7ae085d5cb0673e56eb/src/compressed_tensors/quantization/quant_scheme.py#L104). See examples of [weight-only quantization](examples/quantization_w4a16_fp4/llama3_example.py) and [fp4 activation support](examples/quantization_w4a4_fp4/llama3_example.py). Support is currently preliminary and additional support will be added for MoEs.
 * **Axolotl Sparse Finetuning Integration:** Seamlessly finetune sparse LLMs with our Axolotl integration. Learn how to create [fast sparse open-source models with Axolotl and LLM Compressor](https://developers.redhat.com/articles/2025/06/17/axolotl-meets-llm-compressor-fast-sparse-open). See also the [Axolotl integration docs](https://docs.axolotl.ai/docs/custom_integrations.html#llmcompressor).
 * **AutoAWQ Integration:** Perform low-bit weight-only quantization efficiently using AutoAWQ, now part of LLM Compressor. *Note: This integration should be considered experimental for now. Enhanced support, including for MoE models and improved handling of larger models via layer sequential pipelining, is planned for upcoming releases.* [See the details](https://github.com/vllm-project/llm-compressor/pull/1177).

examples/quantizing_moe/deepseek_r1_example.py

@@ -0,0 +1,86 @@
+from datasets import load_dataset
+from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor.modeling import prepare_for_calibration
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+
+# Select model and load it.
+
+# This script takes about 48 hours on 1xA100 to complete.
+# Future improvements will reduce this runtime (#1561, #1558).
+
+# For DeepSeek-R1, we require a full precision model in order to properly calibrate
+# `DeepSeek-R1-0528-BF16` is a DeepSeek-V3 FP8 model which has been converted to BF16
+
+model_id = "unsloth/DeepSeek-R1-0528-BF16"
+config = AutoConfig.from_pretrained(model_id)
+del config.quantization_config  # fp8 qconfig no longer appplies to bf16 model
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype="auto", config=config
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = prepare_for_calibration(model)
+
+# 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)
+
+# Configure the quantization algorithm to run.
+# since the MoE gate layers are sensitive to quantization, we add them to the ignore
+# list so they remain at full precision
+recipe = GPTQModifier(
+    targets="Linear", scheme="W4A16", ignore=["lm_head", "re:.*mlp.gate$"]
+)
+
+# Apply algorithms.
+# due to the large size of DeepSeekV3, we specify sequential targets such that
+# only one MLP is loaded into GPU memory at a time
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    sequential_targets=["DeepseekV3Attention", "DeepseekV3MLP"],
+)
+
+# 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

@@ -0,0 +1,3 @@
+# flake8: noqa
+
+from .prepare import *

src/llmcompressor/modeling/deepseek_v3.py

@@ -0,0 +1,52 @@
+import torch
+from transformers.models.deepseek_v3.modeling_deepseek_v3 import DeepseekV3MoE
+
+
+class DeepseekV3MoECalibrate(torch.nn.Module):
+    """
+    Patched DeepseekV3MoE which sends all tokens to all experts for calibration
+    """
+
+    def __init__(self, config, experts, gate, shared_experts):
+        super().__init__()
+        self.config = config
+        self.experts = experts
+        self.gate = gate
+        self.shared_experts = shared_experts
+
+    def forward(self, hidden_states):
+        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
+        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 in range(len(self.experts)):
+            expert = self.experts[expert_idx]
+            mask = expert_mask[expert_idx]
+            token_indices, weight_indices = torch.where(mask)
+
+            expert_weights = topk_weights[token_indices, weight_indices]
+            expert_input = hidden_states[token_indices]
+            expert_output = expert(expert_input)
+            weighted_output = expert_output * expert_weights.unsqueeze(-1)
+
+            if token_indices.numel() > 0:
+                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 replace(module: DeepseekV3MoE) -> DeepseekV3MoECalibrate:
+    return DeepseekV3MoECalibrate(
+        module.config, module.experts, module.gate, module.shared_experts
+    )

src/llmcompressor/modeling/prepare.py

@@ -0,0 +1,20 @@
+from compressed_tensors.utils import replace_module
+from transformers import PreTrainedModel
+
+from llmcompressor.modeling.deepseek_v3 import replace as replace_DeepseekV3MoE
+
+__all__ = ["prepare_for_calibration"]
+
+replacements = {
+    "DeepseekV3MoE": replace_DeepseekV3MoE,
+}
+
+
+def prepare_for_calibration(model: PreTrainedModel) -> PreTrainedModel:
+    for name, module in model.named_modules():
+        cls_name = module.__class__.__name__
+        if cls_name in replacements:
+            new_module = replacements[cls_name](module)
+            replace_module(model, name, new_module)
+
+    return model