init

model_executor/layers/quantization/deepspeedfp.py

@@ -0,0 +1,195 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Any, Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
+from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig)
+from vllm.model_executor.utils import set_weight_attrs
+
+
+class DeepSpeedFPConfig(QuantizationConfig):
+    """Config for DeepSpeed FP quantizer. It supports fp6 and fp8.
+    
+    Args: 
+        weight_bits: the target quantization bits, 6 or 8.
+        group_size: group size for quantizaiton, default to 128.
+    """
+
+    def __init__(
+        self,
+        weight_bits: int = 8,
+        group_size: int = 512,
+    ) -> None:
+        super().__init__()
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.valid_types = [torch.bfloat16, torch.float16]
+
+        if self.weight_bits not in (6, 8):
+            raise ValueError(
+                "Currently, only 6-bit or 8-bit weight quantization are "
+                f"supported for DeepSpeed FP quantizaiton, but got "
+                f"{self.weight_bits} bits.")
+
+    def __repr__(self) -> str:
+        return (f"DeepSpeedFPConfig(weight_bits={self.weight_bits}), "
+                f"group_size={self.group_size}")
+
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "deepspeedfp"
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "DeepSpeedFPConfig":
+        weight_bits = cls.get_from_keys(config, ["bits"])
+        group_size = cls.get_from_keys(config, ["group_size"])
+        return cls(weight_bits=weight_bits, group_size=group_size)
+
+    def get_linear_method(self) -> "DeepSpeedFPLinearMethod":
+        return DeepSpeedFPLinearMethod(self)
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    # Need to figure it out
+    def get_min_capability(cls) -> int:
+        return 60
+
+    @staticmethod
+    def get_config_filenames() -> list[str]:
+        return [
+            "quant_config.json",
+            "quantize_config.json",
+        ]
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["DeepSpeedFPLinearMethod"]:
+        if isinstance(layer, LinearBase):
+            return DeepSpeedFPLinearMethod(self)
+        return None
+
+
+class DeepSpeedFPLinearMethod(LinearMethodBase):
+    """Linear method for DeepSpeedFP quantizer.
+
+    Args:
+        quant_config: the DeepSpeedFP quantization config.
+    """
+
+    def __init__(self, quant_config: DeepSpeedFPConfig):
+        self.quant_config = quant_config
+        self.weight = None
+
+    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,
+                       weight_loader=None,
+                       **extra_weight_attrs):
+        del output_size
+        del input_size
+        output_size_per_partition = sum(output_partition_sizes)
+        weight = DeepSpeedFPParameter(
+            torch.Size((output_size_per_partition, input_size_per_partition)),
+            params_dtype=params_dtype,
+            quant_config=self.quant_config,
+        )
+        set_weight_attrs(weight, {
+            "input_dim": 1,
+            "output_dim": 0,
+        })
+        layer.register_parameter("weight", weight)
+
+        def quant_weight_loader(param, loaded_weight, *args, **kwargs):
+            # Calls the original weight loader (if any), quantizes the result,
+            # and then loads the quantized parameter.
+            if weight_loader is not None:
+                orig_param_data = param.data
+                param.data = param.ds_dequantize()
+                weight_loader(param, loaded_weight, *args, **kwargs)
+                param.data, loaded_weight = orig_param_data, param.data
+            param.ds_quantize_(loaded_weight.cuda())
+
+        extra_weight_attrs["weight_loader"] = quant_weight_loader
+        set_weight_attrs(weight, extra_weight_attrs)
+
+    def apply(self,
+              layer: torch.nn.Module,
+              x: torch.Tensor,
+              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+        weight = layer.weight
+        y = weight.ds_dequantize()
+        return F.linear(x, y, bias)
+
+
+class DeepSpeedFPParameter(nn.Parameter):
+    """
+    DeepSpeedFP quantized parameter class that implements fp8/fp6
+    quantization deepspeed. Weights are stored in quantized form on
+    GPUs, and can be dequantized on-the-fly when needed by the model.
+    """
+
+    def __new__(cls, orig_shape: torch.Size, params_dtype: torch.dtype,
+                quant_config: DeepSpeedFPConfig):
+        try:
+            import deepspeed
+            if deepspeed.__version__ < "0.14.2":
+                raise ImportError("deepspeed version is wrong. Please "
+                                  "install deepspeed>=0.14.2.")
+            from deepspeed.ops.fp_quantizer import FP_Quantize
+        except ImportError as err:
+            raise ImportError("Please install deepspeed>=0.14.2 via "
+                              "`pip install deepspeed>=0.14.2` to use "
+                              "deepspeedfp quantizer.") from err
+        data = torch.empty((
+            orig_shape.numel() // quant_config.group_size,
+            quant_config.group_size * quant_config.weight_bits // 8 + 4,
+        ),
+                           dtype=torch.int8)
+        self = torch.Tensor._make_subclass(cls, data, data.requires_grad)
+        self.orig_shape = orig_shape
+        self.quant_config = quant_config
+        self.fp_quantizer = FP_Quantize(group_size=quant_config.group_size)
+        self.fp_quantizer.orig_shape = orig_shape
+        self.fp_quantizer.orig_dtype = params_dtype
+        return self
+
+    def ds_quantize_(self, tensor: torch.Tensor):
+        assert tensor.device.type == "cuda" and tensor.dtype != torch.int8
+        return self.data.copy_(
+            self.fp_quantizer.quantize(
+                tensor.data,
+                q_bits=self.quant_config.weight_bits,
+            ))
+
+    def ds_dequantize(self, fp_out=None) -> torch.Tensor:
+        """
+        Return a tensor containing the dequantized weights of this parameter.
+        """
+        assert self.data.device.type == "cuda" and self.data.dtype == torch.int8
+        return self.fp_quantizer.dequantize(
+            self.data, fp_out=fp_out, q_bits=self.quant_config.weight_bits)
+
+    def ds_selective_dequantize(self, indices, fp_out=None) -> torch.Tensor:
+        """
+        Return a tensor where only the weights at `indices` are dequantized
+        (to save HBM -> SRAM bandwidth).
+        """
+        assert self.data.device.type == "cuda" and self.data.dtype == torch.int8
+        return self.fp_quantizer.selective_dequantize(
+            self.data,
+            indices,
+            fp_out=fp_out,
+            q_bits=self.quant_config.weight_bits)