Support MiniMax M2.1 (FP8 checkpoint) (#817)

## What does this PR do?

**Type of change:** ? new feature

**Overview:** ?

Support loading the MiniMax M2.1 (FP8) checkpoint for PTQ.

## Usage
scripts/huggingface_example.sh --model <minimax checkpoint> --quant
nvfp4 --trust_remote_code


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<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

* **New Features**
  * Added MiniMax M2.1 model quantization support with nvfp4 format.
* Extended FP8 quantization capabilities with configurable dtype
parameter for enhanced precision control.

* **Improvements**
  * Enhanced detection of quantized linear module variants.
  * Improved weight unpacking for FP8-based linear modules.

* **Documentation**
  * Updated supported models table to include MiniMax M2.1.

<sub>✏️ Tip: You can customize this high-level summary in your review
settings.</sub>

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Signed-off-by: Chenjie Luo <108829653+cjluo-nv@users.noreply.github.com>

Author: cjluo-nv

examples/deepseek/ptq.py

@@ -56,6 +56,7 @@
 from modelopt.torch.export.model_config import KV_CACHE_FP8
 from modelopt.torch.export.quant_utils import get_quant_config
 from modelopt.torch.quantization.nn import TensorQuantizer
+from modelopt.torch.quantization.triton import weight_dequant
 from modelopt.torch.quantization.utils import (
     is_quantized_column_parallel_linear,
     is_quantized_parallel_linear,
@@ -77,7 +78,6 @@
     )
 
 import model as deekseep_model  # noqa: E402
-from ds_kernel import weight_dequant  # noqa: E402
 from kernel import act_quant, fp8_gemm  # noqa: E402
 
 

examples/llm_ptq/README.md

@@ -111,6 +111,7 @@ Please reference our [framework scripts](#framework-scripts) and our [docs](http
 | DeepSeek V3, R1, V3.1, V3.2<sup>7</sup> | - | - | - | - | ✅ |
 | GLM-4.7<sup>8</sup> | ✅ | - | - | - | ✅ |
 | Kimi K2 | - | - | - | - | ✅ |
+| MiniMax M2.1 | - | - | - | - | ✅ |
 | T5 | ✅ | ✅ | ✅ | ✅ | - |
 | Whisper | ✅ | ❌ | ❌ | ❌ | - |
 

examples/llm_ptq/example_utils.py

@@ -243,7 +243,7 @@ def build_quant_cfg(
         quant_cfg["quant_cfg"]["*image*"] = {"enable": False}
         quant_cfg["quant_cfg"]["*vision*"] = {"enable": False}
 
-    if model_type in ["qwen3moe", "qwen3next"] and qformat == "nvfp4":
+    if model_type in ["qwen3moe", "qwen3next", "minimax"] and qformat == "nvfp4":
         # Disable the attention projection layers to retain accuracy
         quant_cfg["quant_cfg"]["model*.*attn*in_proj*"] = {"enable": False}
         quant_cfg["quant_cfg"]["model*.*attn*q_proj*"] = {"enable": False}

modelopt/torch/export/layer_utils.py

@@ -346,7 +346,14 @@ def is_moe(module: nn.Module) -> bool:
 def is_quantlinear(module: nn.Module) -> bool:
     """Returns whether the module is a quantized linear layer."""
     name = type(module).__name__
-    return ("QuantLinear" in name or "QuantCompressedLinear" in name) and "lora" not in name.lower()
+    return (
+        any(
+            keyword in name
+            for keyword in ["QuantLinear", "QuantCompressedLinear", "QuantFP8Linear"]
+        )
+        and "lora" not in name.lower()
+        and "ds_kernel" not in name.lower()
+    )
 
 
 def dup_kv_weight(v: torch.Tensor, head_size: int, num_head: int, tp_size: int) -> torch.Tensor:

modelopt/torch/export/model_utils.py

@@ -55,6 +55,7 @@
     "Deepseek": "deepseek",
     "Whisper": "whisper",
     "gptoss": "gptoss",
+    "MiniMax": "minimax",
 }
 
 __doc__ = f"""Utility functions for model type detection and classification.

modelopt/torch/export/unified_export_hf.py

@@ -589,7 +589,9 @@ def _process_quantized_modules(
         if is_modelopt_qlora and (hasattr(sub_module, "base_layer")):
             continue
 
-        if hasattr(sub_module, "weight_packed"):
+        if hasattr(sub_module, "weight_packed") or (
+            "QuantFP8Linear" in type(sub_module).__name__ and sub_module.weight.element_size() <= 1
+        ):
             sub_module.unpack_weight()
         if get_quantization_format(sub_module) != QUANTIZATION_NONE:
             if is_quantlinear(sub_module):

modelopt/torch/quantization/plugins/huggingface.py

@@ -22,6 +22,8 @@
 from typing import TYPE_CHECKING
 
 import torch
+import transformers
+from packaging import version
 from torch import Tensor
 from torch.nn.functional import linear
 
@@ -38,7 +40,6 @@
     kitchen = None
 
 import torch.nn as nn
-import transformers
 from transformers.models.t5.modeling_t5 import T5Attention
 
 from modelopt.torch.opt.dynamic import DynamicModule
@@ -48,6 +49,13 @@
 from ..conversion import register
 from ..nn import QuantInputBase, QuantModule, QuantModuleRegistry, TensorQuantizer
 from ..nn.modules.quant_linear import _QuantLinear
+from ..triton import IS_AVAILABLE as IS_TRITON_AVAILABLE
+
+if IS_TRITON_AVAILABLE:
+    from ..triton import weight_dequant
+else:
+    weight_dequant = None
+
 from ..utils import replace_function
 from .attention import register_attention_for_kv_quant
 from .custom import CUSTOM_MODEL_PLUGINS, _ParallelLinear, _QuantFunctionalMixin
@@ -57,6 +65,8 @@
 
 __all__ = ["register_hf_attentions_on_the_fly"]
 
+TRANSFORMERS_VERSION_GE_5_0 = version.parse(transformers.__version__) >= version.parse("5.0.0")
+
 
 class _QuantAttention(QuantModule):
     """Attention class for KV Cache quantization compatible with new_attention_interface in transformers >= 4.48.0."""
@@ -447,10 +457,24 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
             # If any of the experts are in calibration mode, we will forward all tokens to all experts
             # This is used only for calibration, we need to re-calculate the actual outputs again using
             # the original top_k
-            original_top_k = self.top_k
-            self.top_k = self.num_experts
-            super().forward(hidden_states)
-            self.top_k = original_top_k
+            if TRANSFORMERS_VERSION_GE_5_0:
+                assert hasattr(self, "gate")
+                # Path for transformers >= 5.0
+                original_top_k = self.gate.topk
+                self.gate.topk = self.gate.num_experts
+                super().forward(hidden_states)
+                self.gate.topk = original_top_k
+            else:
+                # Path for transformers < 5.0
+                original_top_k = self.top_k
+                if hasattr(self, "num_experts"):
+                    self.top_k = self.num_experts
+                elif hasattr(self, "experts"):
+                    self.top_k = self.experts.num_experts
+                else:
+                    raise ValueError(f"Could not find num_experts in module {self}")
+                super().forward(hidden_states)
+                self.top_k = original_top_k
         return super().forward(hidden_states)
 
 
@@ -693,6 +717,53 @@ def unpack_weight(self):
             del self.weight_scale
 
 
+class _QuantFP8Linear(QuantModule):
+    def _setup(self):
+        self.input_quantizer = TensorQuantizer()
+        self.weight_quantizer = TensorQuantizer()
+        assert self.weight_scale_inv.ndim == 2, "Weight scale inverse must be 2D"
+        assert self.weight.ndim == 2, "Weight must be 2D"
+        self.block_size = max(
+            self.weight.shape[0] // self.weight_scale_inv.shape[0],
+            self.weight.shape[1] // self.weight_scale_inv.shape[1],
+        )
+        assert self.block_size == 128, "Block size must be 128"
+
+    def _get_weight_and_scale_inv(self):
+        if isinstance(self.weight, torch.distributed.tensor.DTensor):
+            weight = self.weight._local_tensor.contiguous()
+            scale_inv = self.weight_scale_inv._local_tensor.contiguous()
+        else:
+            weight = self.weight.contiguous()
+            scale_inv = self.weight_scale_inv.contiguous()
+        return weight, scale_inv
+
+    def forward(self, input: Tensor) -> Tensor:
+        assert weight_dequant is not None, "Triton is not available"
+        if self.weight.element_size() == 1:
+            with torch.cuda.device(self.weight.device):
+                weight, scale_inv = self._get_weight_and_scale_inv()
+                weight = weight_dequant(weight, scale_inv, self.block_size, dtype=input.dtype)
+        else:
+            weight = self.weight
+        return linear(
+            self.input_quantizer(input),
+            self.weight_quantizer(weight),
+            self.bias,
+        )
+
+    def unpack_weight(self):
+        assert weight_dequant is not None, "Triton is not available"
+        with torch.cuda.device(self.weight.device):
+            weight, scale_inv = self._get_weight_and_scale_inv()
+            self.weight = nn.Parameter(
+                weight_dequant(weight, scale_inv, self.block_size, dtype=torch.get_default_dtype()),
+                requires_grad=False,
+            )
+        if hasattr(self, "weight_scale_inv"):
+            del self.weight_scale_inv
+
+
 try:
     from transformers.models.llama4.modeling_llama4 import Llama4TextExperts, Llama4TextMoe
 
@@ -796,6 +867,14 @@ def unpack_weight(self):
 except ImportError:
     pass
 
+try:
+    from transformers.integrations.finegrained_fp8 import FP8Linear
+
+    if FP8Linear not in QuantModuleRegistry:
+        QuantModuleRegistry.register({FP8Linear: "hf.FP8Linear"})(_QuantFP8Linear)
+except ImportError:
+    pass
+
 
 class _QuantGptOssExperts(_QuantFunctionalMixin):
     """Quantized wrapper for `transformers.GptOssExperts`.
@@ -910,6 +989,17 @@ def register_falcon_linears_on_the_fly(model):
             QuantModuleRegistry.register({linear_type: linear_type.__name__})(_QuantLinear)
 
 
+def register_minimax_m2_moe_on_the_fly(model):
+    """Register MiniMax M2 MoE modules as a QUANT_MODULE.
+
+    MiniMax M2 MoE modules are defined in the model card, so we need to register them on the fly.
+    """
+    if type(model).__name__ in ["MiniMaxM2ForCausalLM"]:
+        moe_type = type(model.model.layers[0].block_sparse_moe)
+        if QuantModuleRegistry.get(moe_type) is None:
+            QuantModuleRegistry.register({moe_type: moe_type.__name__})(_QuantSparseMoe)
+
+
 def _is_supported_hf_model(model):
     """Check if the model a valid model for transformers quantization specific support."""
     supported_models = [transformers.PreTrainedModel]
@@ -975,6 +1065,7 @@ def _is_param_grad_enabled_for_auto_quantize(pname, model):
     [
         register_falcon_linears_on_the_fly,
         register_dbrx_moe_on_the_fly,
+        register_minimax_m2_moe_on_the_fly,
         register_hf_attentions_on_the_fly,
         convert_hf_parallel_linears_on_the_fly,
     ]

modelopt/torch/quantization/triton/__init__.py

@@ -32,6 +32,7 @@
     ):
         # fp4_kernel works on any CUDA GPU with triton
         from .fp4_kernel import *
+        from .fp8_kernel import *
 
         # fp4_kernel_hopper requires compute >= 8.9 (uses tl.float8e4nv)
         if torch.cuda.get_device_capability() >= (8, 9):

examples/deepseek/ds_kernel.py …/torch/quantization/triton/fp8_kernel.pyexamples/deepseek/ds_kernel.py renamed to modelopt/torch/quantization/triton/fp8_kernel.py examples/deepseek/ds_kernel.py renamed to modelopt/torch/quantization/triton/fp8_kernel.py

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -35,32 +35,18 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
+"""FP8 Triton Kernel Implementations."""
 
 import torch
 import triton
 import triton.language as tl
 
-"""Reference: https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/kernel.py"""
-
 
 @triton.jit
 def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr):
-    """
-    Dequantizes weights using the provided scaling factors and stores the result.
+    """Dequantizes weights using the provided scaling factors and stores the result.
+
+    Reference: https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/kernel.py
 
     Args:
         x_ptr (tl.pointer): Pointer to the quantized weights.
@@ -86,14 +72,21 @@ def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr):
     tl.store(y_ptr + offs, y, mask=mask)
 
 
-def weight_dequant(x: torch.Tensor, s: torch.Tensor, block_size: int = 128) -> torch.Tensor:
-    """
-    Dequantizes the given weight tensor using the provided scale tensor.
+def weight_dequant(
+    x: torch.Tensor,
+    s: torch.Tensor,
+    block_size: int = 128,
+    dtype: torch.dtype = torch.get_default_dtype(),
+) -> torch.Tensor:
+    """Dequantizes the given weight tensor using the provided scale tensor.
+
+    Reference: https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/kernel.py
 
     Args:
         x (torch.Tensor): The quantized weight tensor of shape (M, N).
         s (torch.Tensor): The scale tensor of shape (M//block_size, N//block_size).
         block_size (int, optional): The block size to use for dequantization. Defaults to 128.
+        dtype (torch.dtype, optional): The dtype of the output tensor. Defaults to torch.get_default_dtype().
 
     Returns:
         torch.Tensor: The dequantized weight tensor of the same shape as `x`.
@@ -104,7 +97,7 @@ def weight_dequant(x: torch.Tensor, s: torch.Tensor, block_size: int = 128) -> t
     assert x.is_contiguous() and s.is_contiguous(), "Input tensors must be contiguous"
     assert x.dim() == 2 and s.dim() == 2, "Input tensors must have 2 dimensions"
     M, N = x.size()
-    y = torch.empty_like(x, dtype=torch.get_default_dtype())
+    y = torch.empty_like(x, dtype=dtype)
     grid = lambda meta: (triton.cdiv(M, meta["BLOCK_SIZE"]), triton.cdiv(N, meta["BLOCK_SIZE"]))
     weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size)
     return y