support TEQ quantization method (#1093)

neural_compressor/adaptor/pytorch.py

@@ -4522,6 +4522,9 @@ def quantize(self, tune_cfg, model, dataloader, calib_func=None):
         if 'GPTQ' in all_algo:
             q_model._model = self.gptq_quantize(q_model._model, tune_cfg, dataloader)
 
+        if 'TEQ' in all_algo:
+            q_model._model = self.teq_quantize(q_model._model, tune_cfg, dataloader, calib_func)
+
         if 'AWQ' in all_algo: # includes RTN in AWQ
             q_model._model = self.awq_quantize(q_model._model, tune_cfg, dataloader, calib_func)
         elif 'RTN' in all_algo:
@@ -4582,6 +4585,43 @@ def gptq_quantize(self, model, tune_cfg, dataloader):
         )
         return model
 
+    def teq_quantize(self, model, tune_cfg, dataloader, calib_func):
+        logger.debug("quantizing with the TEQ algorithm")
+        from .torch_utils.weight_only import teq_quantize
+        # get example inputs if not provided.
+        if self.example_inputs is None:
+            if dataloader is None:
+                assert False, "Please provide dataloader or example_inputs for TEQ algorithm."
+            try:
+                for idx, (input, label) in enumerate(dataloader):
+                    self.example_inputs = input
+                    break
+            except:
+                for idx, input in enumerate(dataloader):
+                    self.example_inputs = input
+                    break
+
+        if 'teq_args' in self.recipes:
+            wbits = self.recipes.get('wbits', 4)
+            group_size = self.recipes.get('group_size', 128)
+            sym = self.recipes.get('scheme', False)
+            folding = self.recipes.get('folding', True)
+
+        weight_config = {
+            'wbits': wbits,
+            'group_size': group_size,
+            'sym': sym,
+            'folding': folding
+        }
+        quantizer = teq_quantize(
+            model,
+            weight_config,
+            dataloader,
+            example_inputs=self.example_inputs,
+            calib_func=calib_func
+        )
+        return quantizer.model
+
     def awq_quantize(self, model, tune_cfg, dataloader, calib_func):
         logger.debug("quantizing with the AWQ algorithm")
         from .torch_utils.weight_only import awq_quantize

neural_compressor/adaptor/torch_utils/model_wrapper.py

@@ -21,6 +21,8 @@
 import math
 import torch
 from torch.nn import functional as F
+from torch.autograd import Function
+from .weight_only import quant_weight
 from packaging.version import Version
 
 
@@ -355,3 +357,95 @@ def extra_repr(self) -> str:
         return 'in_features={}, out_features={}, bits={}, group_size={}, bias={}'.format(
             self.in_features, self.out_features, self.bits, self.groupsize, self.bias is not None
         )
+
+
+class FakeAffineTensorQuantFunction(Function):
+    """Fake version of affine quantization
+    """
+
+    @staticmethod
+    def forward(ctx, inputs, num_bits=4, group_size=1024):
+        """
+
+        As it will be only applied on activation with per tensor granularity, broadcast is not needed.
+
+        Args:
+            ctx: Pytorch convention.
+            inputs: A Tensor of type float32.
+            min_range: A float.
+            max_range: A float.
+            num_bits: An integer
+
+        Returns:
+            outputs: A Tensor of type output_dtype
+        """
+        return quant_weight(inputs, num_bits, group_size)
+
+    @staticmethod
+    def backward(ctx, grad_outputs):
+        """
+        Args:
+            ctx: Pytorch convention.
+            grad_output: A tensor of gradient of outputs
+
+        Returns:
+            grad_inputs: A tensor of gradient
+        """
+        return grad_outputs, None, None
+
+
+class TEQLinearFakeQuant(torch.nn.Module):
+    """
+    wrapper quantization linear
+    """
+
+    def __init__(self, orig_layer, alpha=None, num_bits=4, group_size=-1):
+        """
+        A forward hook to linear module
+        :param orig_layer: the original module
+        :param alpha: trainable alpha/scale
+        :param num_bits: quantization level
+        :param group_size: for fine-grained quantization
+        """
+        super(TEQLinearFakeQuant, self).__init__()
+        self.orig_layer = orig_layer
+        self.alpha = alpha
+
+        self.num_bits = num_bits
+        self.group_size = group_size
+
+    def forward(self, x):
+        alpha = torch.clip(self.alpha, 1e-5)
+        shape_len = len(x.shape) - 1
+        shape = (1,) * shape_len + (-1,)
+        x = x / alpha.view(shape)
+        weight = self.orig_layer.weight
+        weight = weight * alpha.unsqueeze(dim=0)
+        weight_q = FakeAffineTensorQuantFunction().apply(weight, self.num_bits, self.group_size)
+        return F.linear(x, weight_q, self.orig_layer.bias)
+
+
+class TEQMulLinear(torch.nn.Module):
+    """
+    Trainable Equivalent Transformation (TEQ): linear wrapper to apply scale to input
+    """
+
+    def __init__(self, module, input_scale):
+        """
+        A forward hook to save input max of a module
+        :param module: the linear module
+        :param input_scale: scale for input
+        """
+
+        super().__init__()
+        self.register_buffer('input_scale', input_scale)
+        self.add_module('sq_linear', module)
+
+    @property
+    def weight(self):
+        return self.sq_linear.weight
+
+    def forward(self, X):
+        X = torch.mul(X, self.input_scale)
+        X = self.sq_linear(X)
+        return X