Feat (core/stats): HQO for scale/zero point (#937)

noxfile.py

@@ -120,7 +120,7 @@ def tests_brevitas_cpu(session, pytorch, jit_status):
 @nox.parametrize("pytorch", PYTORCH_VERSIONS, ids=PYTORCH_IDS)
 @nox.parametrize("jit_status", JIT_STATUSES, ids=JIT_IDS)
 def tests_brevitas_examples_cpu(session, pytorch, jit_status):
-    session.env['PYTORCH_JIT'] = '{}'.format(int(jit_status == 'jit_enabled'))
+    session.env['BREVITAS_JIT'] = '{}'.format(int(jit_status == 'jit_enabled'))
     install_pytorch(pytorch, session)
     install_torchvision(pytorch, session)  # For CV eval scripts
     session.install('--upgrade', '.[test, tts, stt, vision]')

src/brevitas/core/stats/stats_op.py

@@ -10,6 +10,8 @@
 
 import brevitas
 from brevitas import config
+from brevitas.core.function_wrapper.misc import Identity
+from brevitas.core.function_wrapper.ops_ste import ScalarClampMinSte
 from brevitas.core.utils import StatelessBuffer
 from brevitas.function.ops import max_int
 from brevitas.quant_tensor import _unpack_quant_tensor
@@ -544,3 +546,223 @@ def forward(self, x):
                 x = self.input_view_shape_impl(x)
                 self.internal_candidate = self.mse_init_op(x).detach()
             return self.internal_candidate
+
+
+class HalfQuadraticOptimizerScale(torch.nn.Module):
+    # References:
+    # https://mobiusml.github.io/hqq_blog/
+    # https://github.com/mobiusml/hqq?tab=readme-ov-file
+
+    def __init__(
+            self,
+            proxy_module,
+            hqo_init_op_scale,
+            keepdim: bool,
+            inner_stats_input_view_shape_impl: torch.nn.Module,
+            scaling_min_val: Optional[float] = None,
+            stats_reduce_dim: Optional[int] = None,
+            int_scaling_impl=None,
+            bit_width_impl=None,
+            hqo_beta_scale: float = 1e5,
+            hqo_kappa_scale: float = 1.01,
+            hqo_lp_norm_scale: float = .7,
+            hqo_iters_scale: int = 1000):
+        super(HalfQuadraticOptimizerScale, self).__init__()
+        self.hqo_init_op = hqo_init_op_scale
+        self.input_view_shape_impl = inner_stats_input_view_shape_impl
+        self.proxy_forward = proxy_module.forward
+        self.set_local_loss_mode = lambda enabled: _set_local_loss_mode(proxy_module, enabled)
+        self.internal_candidate = None
+        self.hqo_iters = hqo_iters_scale
+        self.stats_reduce_dim = stats_reduce_dim
+        self.local_loss_mode: bool = False
+
+        self.beta = hqo_beta_scale
+        self.kappa = hqo_kappa_scale
+        self.lp_norm = hqo_lp_norm_scale
+
+        self.int_scaling_impl = int_scaling_impl
+        self.msb_clamp_bit_width_impl = bit_width_impl
+        if scaling_min_val is not None and scaling_min_val != 0:
+            self.clamp_min_ste = ScalarClampMinSte(scaling_min_val)
+        else:
+            self.clamp_min_ste = Identity()
+        self.keepdim = keepdim
+
+    def parameter_search(self, xl, x):
+        best_loss = torch.tensor(float('inf'), device=x.device, dtype=x.dtype)
+        candidate = xl
+        best_candidate = candidate
+        beta = self.beta
+        with torch.no_grad():
+            for i in range(0, self.hqo_iters):
+                self.internal_candidate = candidate
+                self.set_local_loss_mode(True)
+                quant_tensor = self.proxy_forward(x).detach()
+                self.set_local_loss_mode(False)
+                loss = torch.abs(quant_tensor.value - x).mean()
+
+                best_candidate = torch.where(loss < best_loss, candidate, best_candidate)
+                if loss >= best_loss:
+                    break
+                best_loss = torch.min(loss, best_loss)
+                W_e = shrink_lp_op(x - quant_tensor.value, beta, self.lp_norm)
+                zero_point = quant_tensor.zero_point
+                num = self.input_view_shape_impl(x - W_e).detach()
+                den = self.input_view_shape_impl(
+                    torch.round(quant_tensor.value / quant_tensor.scale) - zero_point).detach()
+                mask = (num != 0.) & (den != 0.)
+                if self.stats_reduce_dim is None:
+                    candidate = masked_median(num / den, mask)
+                else:
+                    candidate = masked_median(
+                        num / den, mask, dim=self.stats_reduce_dim, keepdim=self.keepdim)
+                candidate = candidate.type_as(self.internal_candidate)
+                candidate = self.clamp_min_ste(candidate)
+                bit_width = self.msb_clamp_bit_width_impl()
+                int_threshold = self.int_scaling_impl(bit_width)
+                candidate = candidate * int_threshold
+                candidate[torch.isnan(candidate)] = self.internal_candidate[torch.isnan(candidate)]
+                candidate[torch.isinf(candidate)] = self.internal_candidate[torch.isinf(candidate)]
+                beta *= self.kappa
+        return best_candidate
+
+    def optimize(self, x):
+        x_view = self.input_view_shape_impl(x)
+
+        init = self.hqo_init_op(x_view).detach()
+        best_candidate = self.parameter_search(init, x_view)
+
+        # Save for evaluation by other modules (e.g. zp) invoking local loss mode
+        self.internal_candidate = best_candidate.detach()
+        torch.cuda.empty_cache()
+        return best_candidate
+
+    def forward(self, x):
+        if not self.local_loss_mode:
+            with torch.no_grad():
+                return self.optimize(x)
+        else:
+            # This is invoked for the zero-point whenever scale is being optimized first
+            if self.internal_candidate is None:
+                x = self.input_view_shape_impl(x)
+                self.internal_candidate = self.hqo_init_op(x).detach()
+            return self.internal_candidate
+
+
+class HalfQuadraticOptimizerZeroPoint(torch.nn.Module):
+    # References:
+    # https://mobiusml.github.io/hqq_blog/
+    # https://github.com/mobiusml/hqq?tab=readme-ov-file
+
+    def __init__(
+            self,
+            proxy_module,
+            keepdim: bool,
+            hqo_init_op_zp: torch.nn.Module,
+            inner_stats_input_view_shape_impl: torch.nn.Module,
+            stats_reduce_dim: Optional[int] = None,
+            hqo_beta_zp: float = 1e0,
+            hqo_kappa_zp: float = 1.01,
+            hqo_lp_norm_zp: float = .5,
+            hqo_iters_zp: int = 1000):
+        super(HalfQuadraticOptimizerZeroPoint, self).__init__()
+        self.hqo_init_op_zp = hqo_init_op_zp
+        self.input_view_shape_impl = inner_stats_input_view_shape_impl
+        self.proxy_forward = proxy_module.forward
+        self.set_local_loss_mode = lambda enabled: _set_local_loss_mode(proxy_module, enabled)
+        self.internal_candidate = None
+        self.stats_reduce_dim = stats_reduce_dim
+        self.local_loss_mode: bool = False
+        self.beta = hqo_beta_zp
+        self.kappa = hqo_kappa_zp
+        self.lp_norm = hqo_lp_norm_zp
+        self.hqo_iters = hqo_iters_zp
+        self.keepdim = keepdim
+
+    def parameter_search(self, xl, x):
+        best_loss = torch.tensor(float('inf'), device=x.device, dtype=x.dtype)
+        candidate = xl
+        best_candidate = candidate
+        with torch.no_grad():
+            for i in range(0, self.hqo_iters):
+                self.internal_candidate = candidate
+                self.set_local_loss_mode(True)
+                quant_tensor = self.proxy_forward(x).detach()
+                self.set_local_loss_mode(False)
+                qt_value = self.input_view_shape_impl(quant_tensor.value)
+                qt_scale = self.input_view_shape_impl(quant_tensor.scale)
+                qt_int = self.input_view_shape_impl(quant_tensor.int())
+                loss = torch.abs(qt_value - x).mean()
+                best_candidate = torch.where(loss < best_loss, candidate, best_candidate)
+                if loss >= best_loss:
+                    break
+                best_loss = torch.min(loss, best_loss)
+                W_e = shrink_lp_op(x - qt_value, self.beta, self.lp_norm)
+
+                val = self.input_view_shape_impl((x - W_e) - qt_int * qt_scale)
+
+                if self.stats_reduce_dim is None:
+                    candidate = torch.mean(val)
+                else:
+                    candidate = torch.mean(val, dim=self.stats_reduce_dim, keepdim=self.keepdim)
+                self.beta *= self.kappa
+        return best_candidate
+
+    def optimize(self, x):
+        x_view = self.input_view_shape_impl(x)
+
+        init = self.hqo_init_op_zp(x_view).detach()
+
+        best_candidate = self.parameter_search(init, x)
+
+        # Save for evaluation by other modules (e.g. zp) invoking local loss mode
+        self.internal_candidate = best_candidate.detach()
+        torch.cuda.empty_cache()
+        return best_candidate
+
+    def forward(self, x):
+        if not self.local_loss_mode:
+            with torch.no_grad():
+                return self.optimize(x)
+        else:
+            # This is invoked for the zero-point whenever scale is being optimized first
+            if self.internal_candidate is None:
+                x = self.input_view_shape_impl(x)
+                self.internal_candidate = self.hqo_init_op_zp(x).detach()
+            return self.internal_candidate
+
+
+def masked_median(x, mask, dim=None, keepdim=False):
+    """Compute the median of tensor x along dim, ignoring values where mask is False.
+    x and mask need to be broadcastable.
+
+    Args:
+        x (Tensor): Tensor to compute median of.
+        mask (BoolTensor): Same shape as x with True where x is valid and False
+            where x should be masked. Mask should not be all False in any column of
+            dimension dim to avoid NaNs from zero division.
+        dim (int, optional): Dimension to take median of. Defaults to 0.
+
+    Returns:
+        Tensor: Same shape as x, except dimension dim reduced.
+    """
+    # uncomment this assert for safety but might impact performance
+    # assert (
+    #     mask.sum(dim=dim).ne(0).all()
+    # ), "mask should not be all False in any column, causes zero division"
+    x_nan = x.float().masked_fill(~mask, float("nan"))
+    if dim is None:
+        x_median = x_nan.nanmedian()
+    else:
+        x_median, _ = x_nan.nanmedian(dim=dim, keepdim=keepdim)
+    return x_median
+
+
+# Shrinking operator
+def shrink_lp_op(x: Tensor, beta: float, lp_norm: float) -> Tensor:
+    if lp_norm == 1:
+        return torch.sign(x) * torch.nn.functional.relu(torch.abs(x) - 1.0 / beta)
+    else:
+        return torch.sign(x) * torch.nn.functional.relu(
+            torch.abs(x) - (1.0 / beta) * torch.pow(torch.abs(x), lp_norm - 1))

src/brevitas/core/zero_point.py

@@ -281,7 +281,7 @@ def state_dict(self, destination=None, prefix='', keep_vars=False):
         output_dict = super(ParameterFromStatsFromParameterZeroPoint, self).state_dict(
             destination=destination, prefix=prefix, keep_vars=keep_vars)
         # Avoid saving the init value
-        if not self.init_done:
+        if not self.init_done and not config._FULL_STATE_DICT:
             del output_dict[prefix + 'value']
         return output_dict
 

src/brevitas/quant/base.py

@@ -37,6 +37,8 @@
 from brevitas.core.stats import MSE
 from brevitas.core.stats import NegativeMinOrZero
 from brevitas.core.stats import NegativePercentileOrZero
+from brevitas.core.stats.stats_op import HalfQuadraticOptimizerScale
+from brevitas.core.stats.stats_op import HalfQuadraticOptimizerZeroPoint
 from brevitas.core.utils import SingleArgStatelessBuffer
 from brevitas.core.zero_point import ParameterFromRuntimeZeroPoint
 from brevitas.core.zero_point import ParameterFromStatsFromParameterZeroPoint
@@ -458,7 +460,7 @@ class MSEAsymmetricScaleSubInjector(MSESubInjectorBase):
     stats_impl = MSE
     stats_reduce_dim = (this << 1).stats_reduce_dim
     device = (this << 1).device
-    type = (this << 1).type
+    dtype = (this << 1).dtype
 
 
 class MSEZeroPointSubInjector(MSESubInjectorBase):
@@ -470,7 +472,7 @@ class MSEZeroPointSubInjector(MSESubInjectorBase):
     stats_impl = MSE
     stats_reduce_dim = (this << 1).stats_reduce_dim
     device = (this << 1).device
-    type = (this << 1).type
+    dtype = (this << 1).dtype
 
 
 class MSEAsymmetricScale(ExtendedInjector):
@@ -520,3 +522,40 @@ class MSEWeightZeroPoint(MSEZeroPoint):
 
 class MSEActZeroPoint(MSEZeroPoint):
     zero_point_impl = ParameterFromRuntimeZeroPoint
+
+
+class HQOZeroPoint(ExtendedInjector):
+
+    hqo_init_op_zp = NegativeMinOrZero
+    inner_stats_input_view_shape_impl = this.zero_point_stats_input_view_shape_impl
+    stats_impl_zp = HalfQuadraticOptimizerZeroPoint
+
+    @value
+    def zero_point_stats_impl():
+        return this.stats_impl_zp
+
+
+class HQOScale(ExtendedInjector):
+    scaling_impl_type = ScalingImplType.PARAMETER_FROM_STATS
+    inner_stats_input_view_shape_impl = this.scaling_stats_input_view_shape_impl
+    stats_impl_scale = HalfQuadraticOptimizerScale
+
+    @value
+    def scaling_stats_impl():
+        return this.stats_impl_scale
+
+
+class HQOAsymmetricScale(HQOScale):
+    hqo_init_op_scale = AbsMinMax
+
+
+class HQOSymmetricScale(HQOScale):
+    hqo_init_op_scale = AbsMax
+
+
+class HQOActZeroPoint(HQOZeroPoint):
+    zero_point_impl = ParameterFromRuntimeZeroPoint
+
+
+class HQOWeightZeroPoint(HQOZeroPoint):
+    zero_point_impl = ParameterFromStatsFromParameterZeroPoint

src/brevitas/quant/scaled_int.py

@@ -3,6 +3,7 @@
 
 from brevitas.core.function_wrapper import TensorClamp
 from brevitas.quant.base import *
+from brevitas.quant.base import HQOSymmetricScale
 from brevitas.quant.solver.act import ActQuantSolver
 from brevitas.quant.solver.bias import BiasQuantSolver
 from brevitas.quant.solver.trunc import TruncQuantSolver
@@ -443,3 +444,27 @@ class Int8AccumulatorAwareZeroCenterWeightQuant(AccumulatorAwareZeroCenterWeight
         >>> conv.quant_weight()
     """
     bit_width = 8
+
+
+class Int8WeightPerTensorFloatHQO(HQOSymmetricScale, Int8WeightPerTensorFloat):
+    """
+    8-bit narrow per-tensor signed int weight quantizer with per-tensor floating-point scale factor computed
+    from HQO local loss.
+
+    Examples:
+        >>> from brevitas.nn import QuantLinear
+        >>> fc = QuantLinear(10, 5, bias=False, weight_quant=Int8WeightPerTensorFloatHQO)
+    """
+    pass
+
+
+class Int8WeightPerChannelFloatHQO(HQOSymmetricScale, Int8WeightPerChannelFloat):
+    """
+    8-bit narrow per-tensor signed int weight quantizer with per-tensor floating-point scale factor computed
+    from HQO local loss.
+
+    Examples:
+        >>> from brevitas.nn import QuantLinear
+        >>> fc = QuantLinear(10, 5, bias=False, weight_quant=Int8WeightPerChannelFloatHQO)
+    """
+    pass