[Kernel] Add ModelOpt FP4 Checkpoint Support (#12520)

Signed-off-by: Pavani Majety <pmajety@nvidia.com>

csrc/ops.h

@@ -160,14 +160,16 @@ torch::Tensor ggml_moe_a8(torch::Tensor X, torch::Tensor W,
 int64_t ggml_moe_get_block_size(int64_t type);
 
 #ifndef USE_ROCM
+
+bool cutlass_scaled_mm_supports_fp4(int64_t cuda_device_capability);
+bool cutlass_scaled_mm_supports_fp8(int64_t cuda_device_capability);
+bool cutlass_scaled_mm_supports_block_fp8(int64_t cuda_device_capability);
+
 void cutlass_scaled_fp4_mm(torch::Tensor& D, torch::Tensor const& A,
                            torch::Tensor const& B, torch::Tensor const& A_sf,
                            torch::Tensor const& B_sf,
                            torch::Tensor const& alpha);
 
-bool cutlass_scaled_mm_supports_fp8(int64_t cuda_device_capability);
-bool cutlass_scaled_mm_supports_block_fp8(int64_t cuda_device_capability);
-
 void cutlass_scaled_mm(torch::Tensor& out, torch::Tensor const& a,
                        torch::Tensor const& b, torch::Tensor const& a_scales,
                        torch::Tensor const& b_scales,

csrc/quantization/fp4/nvfp4_scaled_mm_entry.cu

@@ -36,3 +36,9 @@ void cutlass_scaled_fp4_mm(torch::Tensor& D, torch::Tensor const& A,
                               "be compiled using CUDA 12.8 and target "
                               "compute capability 100 or above.");
 }
+
+bool cutlass_scaled_mm_supports_fp4(int64_t cuda_device_capability) {
+  int runtimeVersion;
+  cudaRuntimeGetVersion(&runtimeVersion);
+  return cuda_device_capability >= 100 && runtimeVersion >= 12080;
+}

csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu

@@ -201,10 +201,11 @@ void runGemm(at::Tensor& D, at::Tensor const& A, at::Tensor const& B,
 #endif  // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
 
 #define CHECK_TYPE(x, st, m) \
-  TORCH_CHECK(x.scalar_type() == st, "Inconsistency of Tensor type:", m)
-#define CHECK_TH_CUDA(x, m) TORCH_CHECK(x.is_cuda(), m, "must be a CUDA tensor")
+  TORCH_CHECK(x.scalar_type() == st, ": Inconsistency of Tensor type:", m)
+#define CHECK_TH_CUDA(x, m) \
+  TORCH_CHECK(x.is_cuda(), m, ": must be a CUDA tensor")
 #define CHECK_CONTIGUOUS(x, m) \
-  TORCH_CHECK(x.is_contiguous(), m, "must be contiguous")
+  TORCH_CHECK(x.is_contiguous(), m, ": must be contiguous")
 #define CHECK_INPUT(x, st, m) \
   CHECK_TH_CUDA(x, m);        \
   CHECK_CONTIGUOUS(x, m);     \

csrc/torch_bindings.cpp

@@ -434,6 +434,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "                 Tensor! output_scale, Tensor input_scale) -> ()");
   ops.impl("scaled_fp4_quant", torch::kCUDA, &scaled_fp4_quant);
 
+  // Check if cutlass_scaled_mm_fp4 is supported for CUDA devices
+  // of the given capability
+  ops.def("cutlass_scaled_mm_supports_fp4(int cuda_device_capability) -> bool");
+  ops.impl("cutlass_scaled_mm_supports_fp4", &cutlass_scaled_mm_supports_fp4);
 #endif
 
   // Quantized GEMM for GPTQ.

tests/models/decoder_only/language/test_nvfp4.py

@@ -0,0 +1,82 @@
+# SPDX-License-Identifier: Apache-2.0
+# flake8: noqa
+"""Tests Model Optimizer nvfp4 models against ground truth generation
+Note: these tests will only pass on B200
+"""
+import os
+from typing import List
+
+import pytest
+from transformers import AutoTokenizer
+
+from tests.quantization.utils import is_quant_method_supported
+from vllm import LLM, SamplingParams
+
+os.environ["TOKENIZERS_PARALLELISM"] = "true"
+
+MAX_MODEL_LEN = 1024
+
+MODELS = ["nvidia/Llama-3.3-70B-Instruct-FP4"]
+
+EXPECTED_STRS_MAP = {
+    "nvidia/Llama-3.3-70B-Instruct-FP4": [
+        'vLLM (Vectorized Large Language Model) is indeed a high-throughput and memory-efficient inference',
+        'Here are the major milestones in the development of artificial intelligence (AI) from 1950 to ',
+        'Artificial intelligence (AI) and human intelligence (HI) are two distinct forms of intelligence that process',
+        'A neural network is a type of machine learning model inspired by the structure and function of the human brain',
+        'In the heart of a cutting-edge robotics lab, a team of engineers had been working tirelessly to push',
+        'The COVID-19 pandemic has had a profound impact on global economic structures and future business models, leading',
+        'The Mona Lisa, painted by Leonardo da Vinci in the early 16th century, is one of',
+        'Here are the translations:\n\n* Japanese: (Sasuga no tori ga miwa o ts'
+    ]
+}
+
+
+# This test compares against golden strings for exact match since
+# there is no baseline implementation to compare against
+# and is unstable w.r.t specifics of the fp4 implementation or
+# the hardware being run on.
+# Disabled to prevent it from breaking the build
+@pytest.mark.skip(
+    reason=
+    "Prevent unstable test based on golden strings from breaking the build "
+    " and test input model being too large and hanging the system.")
+@pytest.mark.quant_model
+@pytest.mark.skipif(not is_quant_method_supported("nvfp4"),
+                    reason="nvfp4 is not supported on this GPU type.")
+@pytest.mark.parametrize("model_name", MODELS)
+def test_models(example_prompts, model_name) -> None:
+    model = LLM(
+        model=model_name,
+        max_model_len=MAX_MODEL_LEN,
+        trust_remote_code=True,
+        enforce_eager=True,
+        quantization="nvfp4",
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    formatted_prompts = [
+        tokenizer.apply_chat_template([{
+            "role": "user",
+            "content": prompt
+        }],
+                                      tokenize=False,
+                                      add_generation_prompt=True)
+        for prompt in example_prompts
+    ]
+    params = SamplingParams(max_tokens=20, temperature=0)
+    generations: List[str] = []
+    # Note: these need to be run 1 at a time due to numerical precision,
+    # since the expected strs were generated this way.
+    for prompt in formatted_prompts:
+        outputs = model.generate(prompt, params)
+        generations.append(outputs[0].outputs[0].text)
+    del model
+
+    print(model_name, generations)
+    expected_strs = EXPECTED_STRS_MAP[model_name]
+    for i in range(len(example_prompts)):
+        generated_str = generations[i]
+        expected_str = expected_strs[i]
+        assert expected_str == generated_str, (
+            f"Test{i}:\nExpected: {expected_str!r}\nvLLM: {generated_str!r}")

vllm/_custom_ops.py

@@ -467,6 +467,10 @@ if hasattr(torch.ops._C, "ggml_dequantize"):
 
 
 # cutlass
+def cutlass_scaled_mm_supports_fp4(cuda_device_capability: int) -> bool:
+    return torch.ops._C.cutlass_scaled_mm_supports_fp4(cuda_device_capability)
+
+
 def cutlass_scaled_fp4_mm(a: torch.Tensor, b: torch.Tensor,
                           block_scale_a: torch.Tensor,
                           block_scale_b: torch.Tensor, alpha: torch.Tensor,

vllm/config.py

@@ -613,7 +613,7 @@ class ModelConfig:
         optimized_quantization_methods = [
             "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
             "awq_marlin", "fbgemm_fp8", "compressed_tensors",
-            "compressed-tensors", "experts_int8", "quark"
+            "compressed-tensors", "experts_int8", "quark", "nvfp4"
         ]
         if self.quantization is not None:
             self.quantization = self.quantization.lower()

vllm/model_executor/layers/linear.py

@@ -30,12 +30,23 @@ from vllm.model_executor.utils import set_weight_attrs
 logger = init_logger(__name__)
 
 WEIGHT_LOADER_V2_SUPPORTED = [
-    "CompressedTensorsLinearMethod", "AWQMarlinLinearMethod",
-    "AWQLinearMethod", "GPTQMarlinLinearMethod", "Fp8LinearMethod",
-    "MarlinLinearMethod", "QQQLinearMethod", "GPTQMarlin24LinearMethod",
-    "TPUInt8LinearMethod", "GPTQLinearMethod", "FBGEMMFp8LinearMethod",
-    "ModelOptFp8LinearMethod", "IPEXAWQLinearMethod", "IPEXGPTQLinearMethod",
-    "HQQMarlinMethod", "QuarkLinearMethod"
+    "CompressedTensorsLinearMethod",
+    "AWQMarlinLinearMethod",
+    "AWQLinearMethod",
+    "GPTQMarlinLinearMethod",
+    "Fp8LinearMethod",
+    "MarlinLinearMethod",
+    "QQQLinearMethod",
+    "GPTQMarlin24LinearMethod",
+    "TPUInt8LinearMethod",
+    "GPTQLinearMethod",
+    "FBGEMMFp8LinearMethod",
+    "ModelOptFp8LinearMethod",
+    "IPEXAWQLinearMethod",
+    "IPEXGPTQLinearMethod",
+    "HQQMarlinMethod",
+    "QuarkLinearMethod",
+    "ModelOptNvFp4LinearMethod",
 ]
 
 

vllm/model_executor/layers/quantization/__init__.py

@@ -14,6 +14,7 @@ QUANTIZATION_METHODS: List[str] = [
     "ptpc_fp8",
     "fbgemm_fp8",
     "modelopt",
+    "nvfp4",
     # The order of gptq methods is important for config.py iteration over
     # override_quantization_method(..)
     "marlin",
@@ -97,7 +98,7 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
     from .hqq_marlin import HQQMarlinConfig
     from .ipex_quant import IPEXConfig
     from .marlin import MarlinConfig
-    from .modelopt import ModelOptFp8Config
+    from .modelopt import ModelOptFp8Config, ModelOptNvFp4Config
     from .moe_wna16 import MoeWNA16Config
     from .neuron_quant import NeuronQuantConfig
     from .ptpc_fp8 import PTPCFp8Config
@@ -112,6 +113,7 @@ def get_quantization_config(quantization: str) -> Type[QuantizationConfig]:
         "fp8": Fp8Config,
         "fbgemm_fp8": FBGEMMFp8Config,
         "modelopt": ModelOptFp8Config,
+        "nvfp4": ModelOptNvFp4Config,
         # The order of gptq methods is important for config.py iteration over
         # override_quantization_method(..)
         "marlin": MarlinConfig,

vllm/model_executor/layers/quantization/modelopt.py

@@ -1,24 +1,31 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 import torch
 from torch.nn import Module
 from torch.nn.parameter import Parameter
 
+from vllm._custom_ops import (cutlass_scaled_fp4_mm,
+                              cutlass_scaled_mm_supports_fp4, scaled_fp4_quant)
 from vllm.logger import init_logger
-from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               UnquantizedLinearMethod)
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    is_layer_skipped)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     Fp8LinearOp, requantize_with_max_scale)
 from vllm.model_executor.parameter import (ModelWeightParameter,
                                            PerTensorScaleParameter)
+from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
 
-ACTIVATION_SCHEMES = ["static"]
+QUANT_ALGOS = ["FP8", "NVFP4"]
+KV_CACHE_QUANT_ALGOS = ["FP8"]
 
 
 class ModelOptFp8Config(QuantizationConfig):
@@ -54,12 +61,13 @@ class ModelOptFp8Config(QuantizationConfig):
     def from_config(cls, config: Dict[str, Any]) -> "ModelOptFp8Config":
         quant_config = cls.get_from_keys(config, ["quantization"])
         quant_method = quant_config["quant_algo"]
-        is_checkpoint_fp8_serialized = ("FP8" in quant_method)
-        if not is_checkpoint_fp8_serialized:
-            raise ValueError("ModelOpt currently only supports static FP8 "
-                             "quantization in vLLM. Please check the "
+        if quant_method not in QUANT_ALGOS:
+            raise ValueError(f"ModelOpt currently only supports: {QUANT_ALGOS}"
+                             " quantizations in vLLM. Please check the "
                              "`hf_quant_config.json` file for your model's "
                              "quant configuration.")
+        is_checkpoint_fp8_serialized = ("FP8" in quant_method)
+
         return cls(is_checkpoint_fp8_serialized)
 
     def get_quant_method(self, layer: torch.nn.Module,
@@ -72,15 +80,6 @@ class ModelOptFp8Config(QuantizationConfig):
         return None
 
 
-class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
-    """
-    Supports loading kv-cache scaling factors from FP8 checkpoints.
-    """
-
-    def __init__(self, quant_config: ModelOptFp8Config):
-        super().__init__(quant_config)
-
-
 class ModelOptFp8LinearMethod(LinearMethodBase):
     """Linear method for Model Optimizer static quantization.
     Supports loading FP8 checkpoints with static weight scale and
@@ -162,3 +161,250 @@ class ModelOptFp8LinearMethod(LinearMethodBase):
                                      weight_scale=layer.weight_scale,
                                      input_scale=layer.input_scale,
                                      bias=bias)
+
+
+class ModelOptNvFp4Config(QuantizationConfig):
+    """Config class for ModelOpt FP4."""
+
+    def __init__(
+        self,
+        is_checkpoint_nvfp4_serialized: bool,
+        kv_cache_quant_algo: str,
+        exclude_modules: List[str],
+        group_size: int = 16,
+    ) -> None:
+        self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
+        if is_checkpoint_nvfp4_serialized:
+            logger.warning(
+                "Detected ModelOpt NVFP4 checkpoint. Please note that"
+                " the format is experimental and could change in future.")
+
+            self.group_size = group_size
+            self.kv_cache_quant_algo = kv_cache_quant_algo
+            self.exclude_modules = exclude_modules
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "modelopt_nvfp4"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.bfloat16, torch.half, torch.float8_e4m3fn]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 100
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return ["hf_quant_config.json"]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "ModelOptNvFp4Config":
+        quant_config = cls.get_from_keys(config, ["quantization"])
+        quant_method = quant_config["quant_algo"]
+        if quant_method not in QUANT_ALGOS:
+            raise ValueError(f"ModelOpt currently only supports: {QUANT_ALGOS}"
+                             " quantizations in vLLM. Please check the "
+                             "`hf_quant_config.json` file for your model's "
+                             "quant configuration.")
+        is_checkpoint_nvfp4_serialized = ("NVFP4" in quant_method)
+        kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
+        group_size = quant_config["group_size"]
+        exclude_modules = quant_config["exclude_modules"]
+        if not (group_size and kv_cache_quant_algo and exclude_modules):
+            raise ValueError("NVFP4 quantization requires group size and "
+                             "kv_cache_quant_algo specified in "
+                             "hf_quant_config.json")
+        return cls(is_checkpoint_nvfp4_serialized, kv_cache_quant_algo,
+                   exclude_modules, group_size)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+        if isinstance(layer, LinearBase):
+            if is_layer_skipped(prefix, self.exclude_modules):
+                return UnquantizedLinearMethod()
+            return ModelOptNvFp4LinearMethod(self)
+        elif isinstance(layer, Attention):
+            return ModelOptFp8KVCacheMethod(self)
+        return None
+
+
+def cutlass_fp4_supported() -> bool:
+    if not current_platform.is_cuda():
+        return False
+    capability_tuple = current_platform.get_device_capability()
+    capability = -1 if capability_tuple is None else capability_tuple.to_int()
+    return cutlass_scaled_mm_supports_fp4(capability)
+
+
+class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from FP8 checkpoints.
+    """
+
+    def __init__(self, quant_config: Union[ModelOptFp8Config,
+                                           ModelOptNvFp4Config]):
+        super().__init__(quant_config)
+
+
+class ModelOptNvFp4LinearMethod(LinearMethodBase):
+    """Linear method for Model Optimizer NVFP4.
+    Supports loading NVFP4 checkpoints with the following structure:
+    
+    input_scale: torch.float32, scalar ,
+    weight: NVFP4(represented as byte) Shape: [1, X, y/2]
+    weight_scale: FP8-E4M3, Shape: [X, Y], aka per block scale,
+    weight_scale_2: torch.float32, scalar,
+    Args: quant_config: The ModelOpt quantization config.
+    """
+
+    def __init__(self, quant_config: ModelOptNvFp4Config):
+        self.quant_config = quant_config
+        self.cutlass_nvfp4_supported = cutlass_fp4_supported()
+        if not self.cutlass_nvfp4_supported:
+            raise ValueError("Current platform does not support NVFP4"
+                             " quantization. Please use Blackwell and above.")
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del input_size, output_size
+        if not self.quant_config.is_checkpoint_nvfp4_serialized:
+            raise ValueError("NVFP4 quantization was selected, "
+                             " dynamic quantization is not supported.")
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+
+        if (input_size_per_partition % 16 != 0):
+            raise ValueError("Unsupported model when in features size is "
+                             "not multiple of 16")
+        # The nvfp4 weight is still represented as
+        weight_dtype = (torch.float8_e4m3fn
+                        if self.quant_config.is_checkpoint_nvfp4_serialized
+                        else params_dtype)
+        # Weight
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                # 2 fp4 items are packed in the input dimension
+                layer.output_size_per_partition,
+                layer.input_size_per_partition // 2,
+                dtype=torch.uint8),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        # Input Weight Scale
+        input_scale = PerTensorScaleParameter(data=torch.empty(
+            len(output_partition_sizes), dtype=torch.float32),
+                                              weight_loader=weight_loader)
+        layer.register_parameter("input_scale", input_scale)
+
+        # Global Weight Scale
+        weight_scale_2 = PerTensorScaleParameter(data=torch.empty(
+            len(output_partition_sizes), dtype=torch.float32),
+                                                 weight_loader=weight_loader)
+        layer.register_parameter("weight_scale_2", weight_scale_2)
+
+        # Per Block Weight Scale
+        weight_scale = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition // self.quant_config.group_size,
+            dtype=weight_dtype,
+        ),
+                                            input_dim=1,
+                                            output_dim=0,
+                                            weight_loader=weight_loader)
+
+        layer.register_parameter("weight_scale", weight_scale)
+
+    def swizzle_blockscale(self, scale: torch.tensor):
+        assert (scale.dtype == torch.float8_e4m3fn)
+        # Pad and blockwise interleave weight_scale
+        scale_ndim = scale.ndim
+        if scale.ndim == 2:
+            scale = scale.unsqueeze(0)
+        assert scale.ndim == 3
+        B, M, K = scale.shape
+        round_up_multiple = lambda x, m: (x + m - 1) // m * m
+        M_padded = round_up_multiple(M, 128)
+        K_padded = round_up_multiple(K, 4)
+        padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
+        padded_scale[:B, :M, :K] = scale
+        batches, rows, cols = padded_scale.shape
+        assert rows % 128 == 0
+        assert cols % 4 == 0
+        padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
+                                            cols // 4, 4)
+        swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
+        swizzled_scale = swizzled_scale.contiguous().cuda()
+        return (swizzled_scale.reshape(M, K)
+                if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+
+        # global scales:
+        input_scale_2 = layer.input_scale.max().to(torch.float32)
+        layer.input_scale = Parameter(input_scale_2, requires_grad=False)
+
+        weight_scale_2 = layer.weight_scale_2.max().to(torch.float32)
+        layer.weight_scale_2 = Parameter(weight_scale_2, requires_grad=False)
+
+        layer.alpha = Parameter(layer.input_scale * layer.weight_scale_2,
+                                requires_grad=False)
+
+        # Swizzle the weight blockscale.
+        # contracting dimension is input dimension
+        # block_size = 16;
+        assert (layer.weight_scale.shape[1] % 16 == 0), (
+            "Expected weight_scale.dim(1) to be divisible by 16")
+        assert (layer.weight_scale.dtype == torch.float8_e4m3fn), (
+            "Weight Block scale must be represented as FP8-E4M3")
+        swizzled_weight_scale = self.swizzle_blockscale(layer.weight_scale)
+
+        layer.weight_scale_swizzled = Parameter(swizzled_weight_scale,
+                                                requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        output_dtype = x.dtype
+
+        # for input only the contracting dimension has a constraint.
+        x_m, _ = x.shape
+        w_n, _ = layer.weight.shape
+        output_shape = [x_m, w_n]
+
+        # quantize BF16 or FP16 to (FP4 and interleaved block scale)
+        s_quant = 1 / layer.input_scale
+        x_fp4, x_blockscale = scaled_fp4_quant(x, s_quant)
+
+        # validate dtypes of quantized input, input block scale,
+        # weight and weight_blockscale
+        assert (x_fp4.dtype == torch.uint8)
+        assert (layer.weight.dtype == torch.uint8)
+        assert (x_blockscale.dtype == torch.float8_e4m3fn)
+        assert (layer.weight_scale_swizzled.dtype == torch.float8_e4m3fn)
+        assert (layer.alpha.dtype == torch.float32)
+
+        out = cutlass_scaled_fp4_mm(x_fp4, layer.weight, x_blockscale,
+                                    layer.weight_scale_swizzled, layer.alpha,
+                                    output_dtype)
+        if bias is not None:
+            out = out + bias
+        return out.view(*output_shape)