Feat (graph/equalize): improvements for llm equalization

src/brevitas/graph/base.py

@@ -296,6 +296,15 @@ class FnToModule(CallableToModule):
  def match_node(self, node: Node) -> bool:
  return node.op == 'call_function' and node.target is self.old_callable
 
+ def move_node_args_to_kwargs(self, node: Node):
+ super().move_node_args_to_kwargs(node)
+ # Moving to stateful modules, we remove the 'training' argument if it is passed to the
+ # functional version of the layer since it is not needed anymore
+ kwargs = dict(node.kwargs)
+ if 'training' in kwargs:
+ del kwargs['training']
+ node.kwargs = immutable_dict(kwargs)
+
 
 class MethodToModule(CallableToModule):
 

src/brevitas/graph/equalize.py

@@ -64,6 +64,8 @@
 
 _select_op = (operator.getitem, operator.__getitem__)
 
+_reshaping_op = ('view', 'reshape', 'flatten', 'contiguous', torch.reshape, torch.flatten)
+
 _scale_varying_activations = (
  torch.nn.Sigmoid, torch.nn.Tanh, torch.nn.ReLU6, torch.nn.GELU, torch.nn.SiLU)
 
@@ -73,6 +75,8 @@
 
 _batch_norm = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)
 
+_ignore_ops = (getattr, 'size')
+
 
 # Required for being hashable
 @dataclass(eq=True, frozen=True)
@@ -279,7 +283,8 @@ def _combine_weights_bias(
  weight = weight.data.reshape(weight.shape[0], -1)
  bias = bias.reshape(-1, 1)
 
- weight = torch.where(torch.abs(weight) < EPSILON, torch.tensor(EPSILON).type_as(weight), weight)
+ weight = torch.where(
+ torch.abs(weight) <= EPSILON, torch.tensor(EPSILON).type_as(weight), weight)
  factor = torch.abs(bias) / torch.abs(weight)
 
  # From https://github.com/Xilinx/Vitis-AI/blob/master/src/vai_quantizer/vai_q_pytorch/nndct_shared/optimization/commander.py#L450
@@ -398,7 +403,6 @@ def _no_equalize():
  scale_fn = _select_scale_computation_fn(scale_computation_type)
  sink_weights = [transpose(m, axis) for m, axis in sink_axes.items()]
  sinks_range = scale_fn(torch.cat([w.reshape(w.size(0), -1) for w in sink_weights], 1))
- sinks_range = torch.clamp(sinks_range, EPSILON)
 
  # Determine the srcs_range based on where we are performing activation equalization or
  # weight equalization
@@ -431,10 +435,18 @@ def _no_equalize():
  "Detected source and sink with non compatible shapes, equalization is skipped")
  return _no_equalize()
 
+ # Instead of clipping very low values, which would cause their reciprocal to be very large
+ # thus hindering quantization, we set both sources and sinks to one,
+ # which is the no-op equivalent for equalization.
+ channelwise_no_equalize = (sinks_range <= EPSILON) | (srcs_range <= EPSILON)
+ sinks_range = torch.where(
+ channelwise_no_equalize, torch.tensor(1., dtype=dtype, device=device), sinks_range)
+ srcs_range = torch.where(
+ channelwise_no_equalize, torch.tensor(1., dtype=dtype, device=device), srcs_range)
+
  srcs_range = torch.pow(srcs_range, alpha)
  sinks_range = torch.pow(sinks_range, 1 - alpha)
  scaling_factors = srcs_range / sinks_range
- scaling_factors = torch.clamp(scaling_factors, EPSILON)
  inverse_scaling_factors = torch.reciprocal(scaling_factors)
 
  if list_of_act_val is not None and list_of_insert_mul_node_fn is not None:
@@ -455,8 +467,8 @@ def _no_equalize():
  torch.reshape(inverse_scaling_factors, src_broadcast_size)),
  attr='weight')
  for module, axis in sink_axes.items():
- src_broadcast_size = [1] * module.weight.ndim
- src_broadcast_size[axis] = module.weight.size(axis)
+ sink_broadcast_size = [1] * module.weight.ndim
+ sink_broadcast_size[axis] = module.weight.size(axis)
  if isinstance(module, _batch_norm):
  # We re-compute the bias as function of running_mean and running_var to adjust the
  # additive factor for equalization.
@@ -466,7 +478,7 @@ def _no_equalize():
  module, module.bias.clone() + additive_factor * (scaling_factors - 1), attr='bias')
  _update_weights(
  module,
- module.weight.clone() * torch.reshape(scaling_factors, src_broadcast_size),
+ module.weight.clone() * torch.reshape(scaling_factors, sink_broadcast_size),
  attr='weight')
 
  return scaling_factors
@@ -547,9 +559,7 @@ def _is_scale_invariant_function(node: Node) -> bool:
 
 
 def _is_reshaping_op(node: Node) -> bool:
- return (
- node.op == 'call_function' and node.target in [torch.flatten, torch.reshape] or
- node.op == 'call_method' and node.target in ['view', 'reshape', 'flatten'])
+ return node.target in _reshaping_op
 
 
 def find_srcs(graph_model: GraphModule, starting_node: Node,
@@ -575,6 +585,8 @@ def find_srcs(graph_model: GraphModule, starting_node: Node,
  node.op == 'call_function' and node.target in _residual_fns):
  find_srcs(graph_model, node, state)
  find_sinks(graph_model, node, state)
+ elif node.target in _ignore_ops:
+ continue
  else:
  # If we meet an unrecognized op, we add None to invalidate the region
  state.srcs.add(_UNSUPPORTED_OP)
@@ -606,6 +618,8 @@ def find_sinks(graph_model: GraphModule, starting_node: Node,
  node.op == 'call_function' and node.target in _residual_fns):
  find_sinks(graph_model, node, state)
  find_srcs(graph_model, node, state)
+ elif node.target in _ignore_ops:
+ continue
  else:
  # If we meet an unrecognized op, we add None to invalidate the region
  state.sinks.add(_UNSUPPORTED_OP)
@@ -699,8 +713,6 @@ def find_module(self, model, regions: List):
  """
  Iterate through the model looking at immediate children of every module to look for supported modules.
  This allows us to stop the search when we meet a top-level module that is supported.
- Specifically, it allows to map nn.MultiheadAttetion to its quantized counterpart and not its
- Linear submodules.
  """
  if isinstance(model,
  _supported_layers) and not isinstance(model, _batch_norm + (nn.LayerNorm,)):
@@ -713,7 +725,7 @@ def setup(self):
  for region in self.regions:
  batch_dim = 0
  if hasattr(region, 'batch_first'):
- batch_dim = 0 if region.batch_first == True else 1
+ batch_dim = 0 if region.batch_first else 1
 
  hook_fn = partial(
  self.forward_stats_hook, name=region, batch_dim=batch_dim, use_inp=True)
@@ -761,16 +773,14 @@ def forward_stats_hook(self, module, *args, name, batch_dim=0, use_inp=True, **k
  # Extra check for batch_dim
  if hasattr(x, 'names') and 'N' in x.names:
  batch_dim = x.names.index('N')
- x = x.transpose(0, batch_dim)
 
  self.batch_dim_act_map[name] = batch_dim
 
+ input_scales = self.scale_fn(x, dim=batch_dim)
  if name not in self.float_act_map:
- self.float_act_map[name] = self.scale_fn(x, dim=batch_dim)
+ self.float_act_map[name] = input_scales
  else:
- batch_data = torch.cat([self.float_act_map[name].unsqueeze(batch_dim), x],
- dim=batch_dim)
- self.float_act_map[name] = self.scale_fn(batch_data, dim=batch_dim)
+ self.float_act_map[name] = torch.max(self.float_act_map[name], input_scales)
 
  def insert_mul_node(self, scale, shape, axis, region, batch_dim=0):
  broadcastable_shape = [1] * len(shape)
@@ -832,7 +842,7 @@ def setup(self):
  for name in region.srcs + region.sinks:
  module = name_to_module[name]
  if hasattr(module, 'batch_first'):
- batch_dim = 0 if module.batch_first == True else 1
+ batch_dim = 0 if module.batch_first else 1
  for name in region_to_search:
  act_module = name_to_module[name]
  use_inp = True if region_to_search == region.sinks else False
@@ -907,16 +917,14 @@ def forward_stats_hook(self, module, *args, name, batch_dim=0, use_inp=True, **k
  # Extra check for batch_dim
  if hasattr(x, 'names') and 'N' in x.names:
  batch_dim = x.names.index('N')
- x = x.transpose(0, batch_dim)
 
  self.batch_dim_act_map[name] = batch_dim
 
+ input_scales = self.scale_fn(x, dim=batch_dim)
  if name not in self.float_act_map:
- self.float_act_map[name] = self.scale_fn(x, dim=batch_dim)
+ self.float_act_map[name] = input_scales
  else:
- batch_data = torch.cat([self.float_act_map[name].unsqueeze(batch_dim), x],
- dim=batch_dim)
- self.float_act_map[name] = self.scale_fn(batch_data, dim=batch_dim)
+ self.float_act_map[name] = torch.max(self.float_act_map[name], input_scales)
 
  def insert_mul_node(self, scale, shape, axis, act_node, batch_dim=0):
  broadcastable_shape = [1] * len(shape)

src/brevitas/graph/standardize.py

@@ -107,7 +107,8 @@ class TorchFunctionalToModule(GraphTransform):
  nn.AvgPool1d), (F.avg_pool2d, nn.AvgPool2d),
  (F.avg_pool3d, nn.AvgPool3d), (F.adaptive_avg_pool1d, nn.AdaptiveAvgPool1d),
  (F.adaptive_avg_pool2d,
- nn.AdaptiveAvgPool2d), (F.adaptive_avg_pool3d, nn.AdaptiveAvgPool3d))
+ nn.AdaptiveAvgPool2d), (F.adaptive_avg_pool3d,
+ nn.AdaptiveAvgPool3d), (F.dropout, nn.Dropout))
 
  def __init__(self, fn_to_module_map=FN_TO_MODULE_MAP):
  super().__init__()

src/brevitas_examples/llm/llm_quant/equalize.py

@@ -10,6 +10,7 @@
 from brevitas.fx.brevitas_tracer import value_trace
 from brevitas.graph.equalize import activation_equalization_mode
 from brevitas.graph.equalize import EqualizeGraph
+from brevitas.graph.standardize import TorchFunctionalToModule
 from brevitas_examples.llm.llm_quant.run_utils import apply_layer_ptq_fn
 from brevitas_examples.llm.llm_quant.run_utils import cast_to_float32
 
@@ -26,6 +27,12 @@ def activation_equalization_iter(curr_layer, inps, outs, cached_values, alpha):
  return outs
 
 
+def trace_and_standardize(model, ref_kwargs):
+ graph_model = value_trace(model, value_args=ref_kwargs)
+ graph_model = TorchFunctionalToModule().apply(graph_model)
+ return graph_model
+
+
 @torch.no_grad()
 def apply_act_equalization(
  model,
@@ -49,7 +56,7 @@ def apply_act_equalization(
  # We can't do fp16 tracing on CPU as many kernels are not implemented
  # So we have to cast to fp32 first, trace, apply equalization, and then cast back
  with cast_to_float32(model, dtype):
- graph_model = value_trace(model, value_args=ref_kwargs)
+ graph_model = trace_and_standardize(model, ref_kwargs=ref_kwargs)
  # TODO this is currently running on CPU. We need Accelerate or a TorchDispatchMode
  # or an FX interpreter to run it on GPU
  warnings.warn(
@@ -70,5 +77,5 @@ def apply_weight_equalization(model, dtype, ref_kwargs, scale_computation_type='
  # We can't do fp16 tracing on CPU as many kernels are not implemented
  # So we have to cast to fp32 first, trace, apply equalization, and then cast back
  with cast_to_float32(model, dtype):
- graph_model = value_trace(model, value_args=ref_kwargs)
+ graph_model = trace_and_standardize(model, ref_kwargs=ref_kwargs)
  EqualizeGraph(scale_computation_type=scale_computation_type).apply(graph_model)

src/brevitas_examples/llm/main.py

@@ -303,9 +303,14 @@ def main():
  quantize_embedding=args.quantize_embedding,
  seqlen=args.seqlen)
  # Tie back first/last layer weights in case they got untied
- model.tie_weights()
  print("Model quantization applied.")
 
+ # If any equalization has taken places, the embedding layer and the fully connected one are
+ # not tied anymore, and they need to be treated as standalone, separate layers.
+ # In all other cases we can tie them back so to preserve memory.
+ if args.act_equalization is None and not args.weight_equalization:
+ model.tie_weights()
+
  if args.act_calibration:
  print("Apply act calibration...")
  apply_calibration(model, calibration_loader, args.nsamples)