[Kernel] Add ModelOpt FP4 Checkpoint Support

csrc/ops.h

@@ -152,14 +152,16 @@ torch::Tensor ggml_mul_mat_a8(torch::Tensor W, torch::Tensor X, int64_t type,
                               int64_t row);
 
 #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

@@ -424,6 +424,10 @@ 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

@@ -450,6 +450,10 @@ def _ggml_mul_mat_a8_fake(
 
 
 # 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 @@ def _verify_quantization(self) -> None:
         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

@@ -29,12 +29,23 @@
 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 @@
     "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,