[Compressor][NVFP4] Support FP4 Compression

src/compressed_tensors/compressors/quantized_compressors/__init__.py

@@ -15,4 +15,5 @@
 
 from .base import *
 from .naive_quantized import *
+from .nvfp4_quantized import *
 from .pack_quantized import *

src/compressed_tensors/compressors/quantized_compressors/nvfp4_quantized.py

@@ -0,0 +1,190 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# 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.
+
+
+from typing import Dict, Optional, Tuple
+
+import numpy
+import torch
+from compressed_tensors.compressors.base import BaseCompressor
+from compressed_tensors.compressors.quantized_compressors.base import (
+    BaseQuantizationCompressor,
+)
+from compressed_tensors.config import CompressionFormat
+from compressed_tensors.quantization import QuantizationArgs
+from compressed_tensors.quantization.lifecycle.forward import dequantize, quantize
+from torch import Tensor
+
+
+__all__ = ["pack_fp4_to_uint8", "unpack_fp4_from_uint8"]
+
+FLOAT_TO_E2M1 = [
+    0.0,
+    0.5,
+    1.0,
+    1.5,
+    2.0,
+    3.0,
+    4.0,
+    6.0,
+]
+
+
+@BaseCompressor.register(name=CompressionFormat.nvfp4_pack_quantized.value)
+class NVFP4PackedCompressor(BaseQuantizationCompressor):
+    """
+    Implements compression of FP4 values. Weights of each quantized layer
+    are packed into uint8. Only supports symmetric weight compression for now.
+    """
+
+    @property
+    def compression_param_names(self) -> Tuple[str]:
+        """
+        Returns a tuple of compression parameter names introduced by
+        the compressor during compression
+        """
+        return (
+            "weight_packed",
+            "weight_scale",
+            "weight_zero_point",
+            "weight_global_scale",
+        )
+
+    def compress_weight(
+        self,
+        weight: Tensor,
+        scale: Tensor,
+        global_scale: Tensor,
+        quantization_args: QuantizationArgs,
+        device: Optional[torch.device] = None,
+        zero_point: Optional[torch.Tensor] = None,
+        g_idx: Optional[torch.Tensor] = None,
+    ) -> Dict[str, torch.Tensor]:
+
+        quantized_weight = quantize(
+            x=weight,
+            scale=scale,
+            global_scale=global_scale,
+            zero_point=zero_point,
+            args=quantization_args,
+        )
+        compressed_dict = {}
+        weight_packed = pack_fp4_to_uint8(quantized_weight)
+        if device is not None:
+            weight_packed = weight_packed.to(device)
+        compressed_dict["weight_packed"] = weight_packed
+        return compressed_dict
+
+    def decompress_weight(
+        self,
+        compressed_data: Dict[str, Tensor],
+        quantization_args: Optional[QuantizationArgs] = None,
+    ) -> torch.Tensor:
+
+        weight = compressed_data["weight_packed"]
+        scale = compressed_data["weight_scale"]
+        global_scale = compressed_data["weight_global_scale"]
+        m, n = weight.shape
+        # TODO: use a user provided dequant dtype
+        unpacked = unpack_fp4_from_uint8(weight, m, n * 2)
+        decompressed_weight = dequantize(
+            x_q=unpacked, scale=scale, global_scale=global_scale, dtype=unpacked.dtype
+        )
+
+        return decompressed_weight
+
+
+def pack_fp4_to_uint8(x: torch.Tensor) -> torch.Tensor:
+    """
+    Packs a tensor with values in the fp4 range into uint8.
+    As there are 16 valid fp4 values, two fp4 values can be
+    packed into one uint8. Each fp4 value is mapped to its
+    particular index (e.g. 0.5 is mapped to index 1, 6.0 is mapped
+    to index 7) which is then represented using 4 bits. Consecutive
+    pairs of 4 bits are then packed into an uint8.
+
+    :param x: tensor to pack
+    returns: a packed tensor in uint8
+    """
+
+    m, n = x.shape
+    device = x.device
+
+    # Create lookup table for FP4 values to indices
+    # Map the absolute values to 0-7 indices
+    kE2M1 = torch.tensor(FLOAT_TO_E2M1, device=device, dtype=x.dtype)
+
+    # Find closest valid FP4 value index for each element
+    abs_x = torch.abs(x)
+    abs_indices = torch.zeros_like(abs_x, dtype=torch.long)
+    for i, val in enumerate(kE2M1):
+        abs_indices = torch.where(torch.isclose(abs_x, val), i, abs_indices)
+
+    # Apply sign bit (bit 3) to get final 4-bit representation
+    indices = abs_indices + (torch.signbit(x) << 3).to(torch.long)
+
+    # Reshape to prepare for packing pairs of values
+    indices = indices.reshape(-1)
+
+    # Handle odd length by padding if necessary
+    if indices.numel() % 2 != 0:
+        indices = torch.cat([indices, torch.zeros(1, dtype=torch.long, device=device)])
+
+    # Reshape to pair consecutive elements
+    indices = indices.reshape(-1, 2)
+
+    # Pack pairs of 4-bit values into 8-bit values
+    packed = (indices[:, 0] | (indices[:, 1] << 4)).to(torch.uint8)
+
+    return packed.reshape(m, n // 2)
+
+
+kE2M1ToFloat = torch.tensor(
+    [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0], dtype=torch.float32
+)
+
+# reference: : https://github.com/vllm-project/vllm/pull/16362
+def unpack_fp4_from_uint8(
+    a: torch.Tensor, m: int, n: int, dtype: Optional[torch.dtype] = torch.bfloat16
+) -> torch.Tensor:
+    """
+    Unpacks uint8 values into fp4. Each uint8 consists of two fp4 values
+    (i.e. first four bits correspond to one fp4 value, last four corresond to a consecutive
+    fp4 value). The bits represent an index, which are mapped to an fp4 value.
+
+    :param a: tensor to unpack
+    :param m: original dim 0 size of the unpacked tensor
+    :param n: original dim 1 size of the unpacked tensor
+    :param dtype: dense dtype to cast the unpacked tensor to
+    """
+    assert a.dtype == torch.uint8
+
+    # Vectorized nibble processing
+    a_flat = a.flatten()
+    high = (a_flat & 0xF0) >> 4  # Upper nibbles
+    low = a_flat & 0x0F  # Lower nibbles
+
+    # Combine nibbles for batch processing
+    combined = torch.stack((low, high), dim=1).flatten()
+
+    # Vectorized sign and magnitude extraction
+    signs = (combined & 0x08).to(torch.bool)  # Sign bits
+    abs_vals = (combined & 0x07).to(torch.long)  # Magnitude indices
+
+    # Device-aware lookup and sign application
+    kE2M1 = kE2M1ToFloat.to(device=a.device)
+    values = kE2M1[abs_vals] * torch.where(signs, -1.0, 1.0)
+
+    # Reshape to final form
+    return values.reshape(m, n).to(dtype=dtype)

src/compressed_tensors/config/base.py

@@ -32,6 +32,7 @@ class CompressionFormat(Enum):
     naive_quantized = "naive-quantized"
     pack_quantized = "pack-quantized"
     marlin_24 = "marlin-24"
+    nvfp4_pack_quantized = "nvfp4-pack-quantized"
 
 
 @unique

tests/test_compressors/quantized_compressors/test_nvfp4_quant.py

@@ -0,0 +1,43 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# 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.
+
+import torch
+from compressed_tensors.compressors.quantized_compressors.nvfp4_quantized import (
+    pack_fp4_to_uint8,
+    unpack_fp4_from_uint8,
+)
+
+
+def test_pack_unpack():
+    x = torch.Tensor(
+        [
+            [-0.5000, -6.0000, -0.5000, -1.5000, -1.0000, 6.0000, 0.0000, -0.0000],
+            [-1.0000, -6.0000, -0.5000, -0.0000, 0.5000, 0.5000, -0.0000, 0.0000],
+            [-3.0000, -6.0000, -0.5000, -2.0000, -0.5000, -1.5000, -0.0000, -0.0000],
+            [1.5000, 6.0000, -0.0000, -0.5000, 1.0000, 1.0000, -0.0000, 0.0000],
+        ]
+    )
+
+    dense_dtype = torch.bfloat16
+    x = x.to(dense_dtype)
+    m, n = x.shape
+    packed = pack_fp4_to_uint8(x)
+    assert packed.dtype == torch.uint8
+    unpacked = unpack_fp4_from_uint8(packed, m, n, dtype=dense_dtype)
+    assert unpacked.dtype == dense_dtype
+
+    assert torch.equal(unpacked, x)  # misleading as -0 and 0 are considered equal
+    sign_bitx = torch.signbit(x)
+    sign_bitout = torch.signbit(unpacked)
+    assert torch.equal(sign_bitout, sign_bitx)