OptimizedLinear implementation

deepspeed/linear/__init__.py

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+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from .optimized_linear import OptimizedLinear
+from .config import LoRAConfig, QuantizationConfig

deepspeed/linear/config.py

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+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from dataclasses import dataclass
+
+
+@dataclass
+class LoRAConfig:
+    lora_r: int = 64
+    lora_alpha: float = 16
+    base_weight_sharding: int = 1
+
+
+@dataclass
+class QuantizationConfig:
+    q_bits: int = 8
+    rounding: str = "nearest"
+    mantissa_bits: int = 3
+    group_size: int = 512

deepspeed/linear/optimized_linear.py

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+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import torch
+import math
+import torch.nn as nn
+import torch.nn.functional as F
+from deepspeed.accelerator import get_accelerator
+import deepspeed.comm as dist
+
+from .config import LoRAConfig, QuantizationConfig
+from .quantization import QuantizedParameter, QuantizedLinear
+
+
+class OptimizedLinear(nn.Module):
+    """
+    Optimized version of nn.Linear that adds features such as:
+      * LoRA w. base weight sharding
+      * FP [6,8,12] quantization
+
+    Arguments:
+        input_dim: Required: size of each input sample
+        output_dim: Required: size of each output sample
+        bias: Optional: If set to False, the layer will not learn an additive bias. Default: False
+        lora_config: Optional: LoRAConfig defining lora features and base-weight-sharding degree
+        quantization_config: Optional: QuantizationConfig defining quantization features
+        dtype: Optional: parameter dtype, only supports bfloat16 currently
+
+    Returns:
+        Returns a new nn.Module depending on the input config. Either native
+        torch.nn.Linear, QuantizedLinear, or the full-featured DSOptimizedLinear.
+    """
+
+    def __new__(self,
+                input_dim: int,
+                output_dim: int,
+                bias: bool = False,
+                lora_config: LoRAConfig = None,
+                quantization_config: QuantizationConfig = None,
+                dtype=torch.bfloat16):
+
+        if lora_config is None and quantization_config is None:
+            # Everything disabled, fall back to normal nn.Linear
+            self = nn.Linear(input_dim, output_dim, bias=bias)
+        elif lora_config:
+            # lora enabled w. optional quant
+            self = DSOptimizedLinear(input_dim=input_dim,
+                                     output_dim=output_dim,
+                                     bias=bias,
+                                     lora_config=lora_config,
+                                     quantization_config=quantization_config,
+                                     dtype=dtype)
+        elif quantization_config:
+            # only quantization enabled, no lora
+            self = QuantizedLinear(input_dim=input_dim,
+                                   output_dim=output_dim,
+                                   bias=bias,
+                                   quantization_config=quantization_config,
+                                   dtype=dtype)
+        return self
+
+
+class DSOptimizedLinear(nn.Module):
+
+    def __init__(self,
+                 input_dim: int,
+                 output_dim: int,
+                 bias: bool = False,
+                 lora_config: LoRAConfig = None,
+                 quantization_config: QuantizationConfig = None,
+                 device=None,
+                 dtype=torch.bfloat16):
+        super().__init__()
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.bias = bias
+        self.lora_config = lora_config
+        self.quantization_config = quantization_config
+        device = get_accelerator().current_device() if device is None else device
+        assert self.lora_config is not None, "DSOptimizedLinear requires a LoRA config"
+
+        self.zero_shards = self.lora_config.base_weight_sharding
+        self.sharded_weight_size = int(float(self.input_dim) // self.zero_shards)
+        w = torch.nn.Parameter(torch.empty((self.output_dim, self.sharded_weight_size), dtype=dtype))
+        torch.nn.init.xavier_uniform_(w)
+        if self.quantization_config is not None:
+            self.base_weight = QuantizedParameter(w)
+        else:
+            self.base_weight = w
+
+        self.base_weight.requires_grad = False
+
+        # Use RS lora for now.
+        self.lora_scaling_factor = self.lora_config.lora_alpha / math.sqrt(self.lora_config.lora_r)
+        # Keeping lora weights in bf16 precision for ease of training.
+        self.lora_weight_1 = nn.Linear(self.input_dim,
+                                       self.lora_config.lora_r,
+                                       bias=self.bias,
+                                       device=device,
+                                       dtype=dtype)
+        self.lora_weight_2 = nn.Linear(self.lora_config.lora_r,
+                                       self.output_dim,
+                                       bias=self.bias,
+                                       device=device,
+                                       dtype=dtype)
+        self.lora_weight_1.weight.requires_grad = True
+        self.lora_weight_2.weight.requires_grad = True
+
+    def full_weight(self):
+        # This assumes weights are evenly sharded across gpus. which might not be correct.
+        # in that case, we should flatten before all_gather.
+        local_weight = self.base_weight.dequantized() if isinstance(self.base_weight,
+                                                                    QuantizedParameter) else self.base_weight
+        tensor_list = [
+            torch.zeros_like(local_weight, device=local_weight.device, dtype=local_weight.dtype)
+            for _ in range(self.zero_shards)
+        ]
+        dist.all_gather(tensor_list, local_weight)
+        weight = nn.Parameter(torch.cat([tensor for tensor in tensor_list], dim=1))
+        return weight
+
+    def linear_without_F_linear(self, input, weight):
+        output = torch.mm(input.reshape(-1, input.shape[-1]), weight)
+        output = output.view(*input.shape[:-1], weight.shape[1])
+        return output
+
+    def forward(self, input_tensor):
+        # Gather the sharded base weight
+        if self.zero_shards > 1:
+            with torch.no_grad():
+                base_weight = self.full_weight()
+        elif self.quantization_config:
+            base_weight = self.base_weight.dequantized()
+        else:
+            base_weight = self.base_weight
+
+        base_weight_output = F.linear(input_tensor, base_weight)
+        lora_output = self.lora_weight_2(self.lora_weight_1(input_tensor))
+        return base_weight_output + self.lora_scaling_factor * lora_output

deepspeed/linear/quantization.py

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+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import copy
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from typing import Optional
+
+from deepspeed.accelerator import get_accelerator
+from deepspeed.ops.fp_quantizer import FP_Quantize
+from .config import QuantizationConfig
+
+
+class QuantizedParameter(nn.Parameter):
+    """
+    Quantized parameter class that implements weight quantization. Weights
+    are stored in quantized form on GPUs, and can be dequantized on-the-fly when
+    needed by the model. The weights are actually quantized during any `.to(device)`.
+    """
+
+    def __new__(
+        cls,
+        data: Optional[torch.Tensor] = None,
+        requires_grad: bool = False,  # quantized weights should be frozen by default
+        quantization: QuantizationConfig = None,
+        quantizer=None,
+    ):
+        if data is None:
+            data = torch.empty(0)
+        if quantization is None:
+            quantization = QuantizationConfig()
+        self = torch.Tensor._make_subclass(cls, data, requires_grad)
+        if quantizer is not None:
+            self.quantizer = quantizer
+        else:
+            self.quantizer = FP_Quantize(group_size=quantization.group_size)
+        self._ensure_quantized(self)
+        return self
+
+    def _ensure_quantized(self, tensor: torch.Tensor):
+        # If the tensor is on a cuda device and is not quantized, then quantize it in-place.
+        if tensor.device.type == "cuda" and tensor.dtype != torch.int8:
+            with get_accelerator().stream(get_accelerator().current_stream(tensor.device)):
+                tensor.data = self.quantizer.quantize(tensor.data)
+            assert tensor.dtype == torch.int8
+
+    def dequantized(self) -> torch.Tensor:
+        """
+        Return a tensor containing the dequantized weights of this parameter.
+        """
+        if self.data.device.type == "cuda" and self.data.dtype == torch.int8:
+            with get_accelerator().stream(get_accelerator().current_stream(self.data.device)):
+                return self.quantizer.dequantize(self.data)
+        return self.data
+
+    def __getstate__(self):
+        state = self.__dict__
+        state["data"] = self.data
+        state["requires_grad"] = self.requires_grad
+        return state
+
+    def __setstate__(self, state):
+        self.quantizer = state["quantizer"]
+        self.data = state["data"]
+        self.requires_grad = state["requires_grad"]
+
+    def __deepcopy__(self, memo):
+        new_instance = type(self).__new__(type(self))
+        state = self.__getstate__()
+        new_instance.__setstate__(state)
+        new_instance.quantizer = copy.deepcopy(state["quantizer"])
+        new_instance.data = copy.deepcopy(state["data"])
+        return new_instance
+
+    def __copy__(self):
+        new_instance = type(self).__new__(type(self))
+        state = self.__getstate__()
+        new_instance.__setstate__(state)
+        return new_instance
+
+    def cuda(self, device=None, non_blocking=False):
+        return self.to(device="cuda" if device is None else device, non_blocking=non_blocking)
+
+    def to(self, *args, **kwargs):
+        """
+        Move the parameter to the given device. Then, if the device is a cuda device,
+        quantize it.
+        """
+        tensor = super().to(*args, **kwargs)
+        self._ensure_quantized(tensor)
+        return tensor
+
+
+class QuantizedLinear(nn.Linear):
+    """
+    Linear layer that implements weight quantization. Parameters
+    are stored via `QuantizedParameter` and are dequantized on-the-fly during any
+    forward pass.
+    """
+
+    def __init__(self,
+                 input_dim: int,
+                 output_dim: int,
+                 bias: bool = False,
+                 quantization_config: QuantizationConfig = None,
+                 dtype=torch.bfloat16):
+        super().__init__(input_dim, output_dim, bias=bias, dtype=dtype)
+        self.weight = QuantizedParameter(self.weight.data, quantization=quantization_config)
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return F.linear(input, self.weight.dequantized(), self.bias)