[NVFP4] Compressed Tensors Hacks

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -23,10 +23,10 @@
     CompressedTensorsMoEMethod)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS, CompressedTensors24,
-    CompressedTensorsScheme, CompressedTensorsW4A16Fp4,
-    CompressedTensorsW4A16Sparse24, CompressedTensorsW8A8Fp8,
-    CompressedTensorsW8A8Int8, CompressedTensorsW8A16Fp8,
-    CompressedTensorsWNA16)
+    CompressedTensorsScheme, CompressedTensorsW4A4Fp4,
+    CompressedTensorsW4A16Fp4, CompressedTensorsW4A16Sparse24,
+    CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8,
+    CompressedTensorsW8A16Fp8, CompressedTensorsWNA16)
 from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
     find_matched_target, is_activation_quantization_format,
     should_ignore_layer)
@@ -183,8 +183,8 @@
                             "weights"].type == QuantizationType.FLOAT
                     else:
                         target_scheme_map[target][
-                            "input_activations"] = QuantizationArgs.model_validate(  # noqa: E501
-                                quant_config.get("input_activations"))
+                            "input_activations"] = quant_config.get(
+                                "input_activations")
         return target_scheme_map
 
     @classmethod
@@ -232,6 +232,27 @@
         return (is_weight_only and is_group_quant and is_float_type
                 and is_4_bits and is_group_size_16 and is_symmetric)
 
+    def _is_fp4a4_nvfp4(self, weight_quant: BaseModel, input_quant: BaseModel):
+
+        print(input_quant)
+        is_weight_act_quant = weight_quant is not None and input_quant is not None
+        is_group_quant = (input_quant.get("strategy") == "tensor_group"
+                          and weight_quant.strategy
+                          == QuantizationStrategy.GROUP.value)
+        is_symmetric = weight_quant.symmetric and input_quant.get("symmetric")
+
+        is_group_size_16 = weight_quant.group_size == 16 and input_quant.get(
+            "group_size") == 16
+        is_float_type = weight_quant.type == QuantizationType.FLOAT and input_quant.get(
+            "type") == QuantizationType.FLOAT.value
+        is_4_bits = weight_quant.num_bits == 4 and input_quant.get(
+            "num_bits") == 4
+
+        print(is_weight_act_quant, is_group_quant, is_symmetric,
+              is_group_size_16, is_float_type, is_4_bits)
+        return (is_weight_act_quant and is_group_quant and is_float_type
+                and is_4_bits and is_group_size_16 and is_symmetric)
+
     def _is_static_tensor_w8a8(self, weight_quant: BaseModel,
                                input_quant: BaseModel) -> bool:
         is_8_bits = weight_quant.num_bits == input_quant.num_bits == 8
@@ -334,6 +355,10 @@
         if self._is_fp4a16_nvfp4(weight_quant, input_quant):
             return CompressedTensorsW4A16Fp4()
 
+        if self._is_fp4a4_nvfp4(weight_quant, input_quant):
+            print("DONE")
+            return CompressedTensorsW4A4Fp4()
+
         if self._is_wNa16_group_channel(weight_quant, input_quant):
             if (self.quant_format == CompressionFormat.marlin_24.value
                     and weight_quant.num_bits in W4A16SPARSE24_SUPPORTED_BITS):

vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from .compressed_tensors_scheme import CompressedTensorsScheme
+from .compressed_tensors_w4a4_nvfp4 import CompressedTensorsW4A4Fp4
 from .compressed_tensors_w4a16_24 import (W4A16SPARSE24_SUPPORTED_BITS,
                                           CompressedTensorsW4A16Sparse24)
 from .compressed_tensors_w4a16_nvfp4 import CompressedTensorsW4A16Fp4
@@ -17,5 +18,6 @@
     "CompressedTensorsW8A16Fp8", "CompressedTensorsW4A16Sparse24",
     "CompressedTensorsW8A8Int8", "CompressedTensorsW8A8Fp8",
     "WNA16_SUPPORTED_BITS", "W4A16SPARSE24_SUPPORTED_BITS",
-    "CompressedTensors24", "CompressedTensorsW4A16Fp4"
+    "CompressedTensors24", "CompressedTensorsW4A16Fp4",
+    "CompressedTensorsW4A4Fp4"
 ]

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a4_nvfp4.py

@@ -0,0 +1,137 @@
+# SPDX-License-Identifier: Apache-2.0
+from typing import Callable, Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import (  # noqa: E501
+    dequantize_to_dtype, ref_nvfp4_quant)
+from vllm.model_executor.parameter import (GroupQuantScaleParameter,
+                                           ModelWeightParameter,
+                                           PerTensorScaleParameter)
+
+__all__ = ["CompressedTensorsW4A4Fp4"]
+
+
+class CompressedTensorsW4A4Fp4(CompressedTensorsScheme):
+
+    def __init__(self):
+        self.group_size = 16
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # dont restrict as emulations
+        return 80
+
+    def create_weights(self, layer: torch.nn.Module,
+                       output_partition_sizes: list[int],
+                       input_size_per_partition: int,
+                       params_dtype: torch.dtype, weight_loader: Callable,
+                       **kwargs):
+        output_size_per_partition = sum(output_partition_sizes)
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+
+        # Weight
+        weight = ModelWeightParameter(data=torch.empty(
+            sum(output_partition_sizes),
+            input_size_per_partition // 2,
+            dtype=torch.uint8),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight_packed", weight)
+
+        # Global Weight Scale
+        weight_global_scale = PerTensorScaleParameter(
+            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+            weight_loader=weight_loader)
+        layer.register_parameter("weight_global_scale", weight_global_scale)
+
+        # Per Group Weight Scale
+        weight_scale = GroupQuantScaleParameter(data=torch.empty(
+            sum(output_partition_sizes),
+            input_size_per_partition // self.group_size,
+            dtype=torch.float8_e4m3fn,
+        ),
+                                                input_dim=1,
+                                                output_dim=0,
+                                                weight_loader=weight_loader)
+
+        layer.register_parameter("weight_scale", weight_scale)
+
+        input_global_scale = PerTensorScaleParameter(
+            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+            weight_loader=weight_loader)
+        layer.register_parameter("input_global_scale", input_global_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) -> None:
+
+        global_input_scale = layer.input_global_scale.max().to(torch.float32)
+        layer.input_global_scale = Parameter(global_input_scale,
+                                             requires_grad=False)
+
+        layer.weight_global_scale = Parameter(
+            layer.weight_global_scale.max().to(torch.float32),
+            requires_grad=False)
+
+        swizzled_weight_scale = self.swizzle_blockscale(layer.weight_scale)
+        layer.weight_scale_swizzled = Parameter(swizzled_weight_scale,
+                                                requires_grad=False)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        x_m, x_k = x.shape
+        output_dtype = x.dtype
+
+        # quantize input to (FP4 and interleaved block scale)
+        x_global_scale = layer.input_global_scale
+        x_fp4, x_blockscale = ref_nvfp4_quant(x, x_global_scale,
+                                              self.group_size)
+
+        # dequantize input
+        x_fp4 = x_fp4.reshape(x_m, x_k // self.group_size, self.group_size)
+        x_blockscale = x_blockscale.unsqueeze(-1) / x_global_scale
+        x_dq = (x_fp4 * x_blockscale).reshape(x_m, x_k).to(output_dtype)
+        del x_fp4, x_blockscale
+
+        # dequantize weight
+        w_fp4 = layer.weight_packed.data.view(torch.uint8)
+        w_blockscale = layer.weight_scale_swizzled.data
+        w_global_scale = layer.weight_global_scale
+        w_dq = dequantize_to_dtype(w_fp4, w_blockscale, w_global_scale,
+                                   output_dtype, x.device, self.group_size)
+
+        # matmul
+        out = torch.matmul(x_dq, w_dq.t())
+        del w_dq, x_dq
+        return out

vllm/model_executor/layers/quantization/compressed_tensors/utils.py

@@ -13,7 +13,7 @@ def is_activation_quantization_format(format: str) -> bool:
     _ACTIVATION_QUANTIZATION_FORMATS = [
         CompressionFormat.naive_quantized.value,
         CompressionFormat.int_quantized.value,
-        CompressionFormat.float_quantized.value,
+        CompressionFormat.float_quantized.value, "nvfp4-pack-quantized"
     ]
     return format in _ACTIVATION_QUANTIZATION_FORMATS