Mixtral scaling: Reduce perplexity from 4.294 to 4.269

awq/models/base.py

@@ -35,6 +35,7 @@
 
 from awq.models._config import AwqConfig
 from awq.modules.act import ScaledActivation
+from awq.modules.moe import ScaledMixtralSparseMoeBlock
 from awq.modules.linear import WQLinear_GEMM, WQLinear_GEMV
 from awq.modules.exllama import WQLinear_Exllama
 from awq.modules.exllamav2 import WQLinear_ExllamaV2
@@ -96,11 +97,13 @@ def quantize(
  split="train",
  text_column="text",
  duo_scaling=True,
- modules_to_not_convert=None,
  export_compatible=False,
  ):
  self.quant_config: AwqConfig = AwqConfig.from_dict(quant_config)
 
+ if hasattr(self, "modules_to_not_convert"):
+ self.quant_config.modules_to_not_convert = self.modules_to_not_convert
+
  self.quantizer = AwqQuantizer(
  self,
  self.model,
@@ -112,7 +115,7 @@ def quantize(
  split,
  text_column,
  duo_scaling,
- modules_to_not_convert=modules_to_not_convert,
+ modules_to_not_convert=self.quant_config.modules_to_not_convert,
  export_compatible=export_compatible,
  )
  self.quantizer.quantize()
@@ -402,6 +405,9 @@ def _load_quantized_modules(
  # Replace activation functions
  self._scale_activations(self, layer)
 
+ # Replace mixture of experts
+ self._scale_moe(self, layer)
+
  # Replace nn.Linear with WQLinear
  for name, module in named_linears.items():
  if use_exllama:
@@ -436,5 +442,19 @@ def _scale_activations(self, layer):
  )
 
  # scale activation
- scaled_act = ScaledActivation(scale_dict["scale_layer"], scale_like)
- set_op_by_name(layer, scale_dict["scale_name"], scaled_act)
+ scaled_act = ScaledActivation(scale_dict['scale_layer'], scale_like)
+ set_op_by_name(layer, scale_dict['scale_name'], scaled_act)
+
+ def _scale_moe(self, layer):
+ if hasattr(self, "get_moe_for_scaling") and hasattr(layer.block_sparse_moe, "scales"):
+ scale_dict: dict = self.get_moe_for_scaling(layer)
+
+ if not isinstance(scale_dict['scale_layer'], ScaledMixtralSparseMoeBlock):
+ param = next(layer.parameters())
+
+ # get activation scale
+ scale_like = torch.ones(scale_dict['scale_shape'], dtype=param.dtype, device=param.device)
+
+ # scale moe
+ scaled_act = ScaledMixtralSparseMoeBlock(scale_dict['scale_layer'], scale_like)
+ set_op_by_name(layer, scale_dict['scale_name'], scaled_act)

awq/models/mixtral.py

@@ -6,13 +6,26 @@
 from awq.modules.fused.model import MixtralModel
 from transformers.models.mixtral.modeling_mixtral import (
  MixtralDecoderLayer as OldMixtralDecoderLayer,
- MixtralForCausalLM as OldMixtralForCausalLM
+ MixtralForCausalLM as OldMixtralForCausalLM,
+ MixtralBLockSparseTop2MLP as OldMixtralBLockSparseTop2MLP,
 )
 from awq.modules.fused.norm import FasterTransformerRMSNorm
 
+def _transformers_version_check():
+ import transformers
+ tv = transformers.__version__.split('.')
+ if len(tv) == 4:
+ major, minor, patch, dev = tv
+ else:
+ major, minor, patch = tv
+
+ if int(major) == 4 and int(minor) < 37:
+ raise Exception("Mixtral requires a minimum of 4.37.0.dev0: pip install git+https://github.com/huggingface/transformers.git")
+
 class MixtralAWQForCausalLM(BaseAWQForCausalLM):
  layer_type = "MixtralDecoderLayer"
  max_new_tokens_key = "max_position_embeddings"
+ modules_to_not_convert = ["gate"]
 
  @staticmethod
  def fuse_layers(model: OldMixtralForCausalLM):
@@ -21,6 +34,7 @@ def fuse_layers(model: OldMixtralForCausalLM):
 
  @staticmethod
  def get_model_layers(model: OldMixtralForCausalLM):
+ _transformers_version_check()
  return model.model.layers
 
  @staticmethod
@@ -29,6 +43,14 @@ def get_act_for_scaling(module):
  is_scalable=False
  )
 
+ @staticmethod
+ def get_moe_for_scaling(module: OldMixtralDecoderLayer):
+ return dict(
+ scale_name="block_sparse_moe",
+ scale_layer=module.block_sparse_moe,
+ scale_shape=(module.block_sparse_moe.num_experts, module.block_sparse_moe.hidden_dim),
+ )
+
  @staticmethod
  def move_embed(model: OldMixtralForCausalLM, device: str):
  model.model.embed_tokens = model.model.embed_tokens.to(device)
@@ -53,19 +75,18 @@ def get_layers_for_scaling(module: OldMixtralDecoderLayer, input_feat, module_kw
  layers=[module.self_attn.o_proj],
  inp=input_feat['self_attn.o_proj'],
  ))
-
- # linear in
+
+ # NOTE: Scaled in awq.quantize.scale.scale_moe_experts, awq.modules.moe.ScaledMixtralSparseMoeBlock
+ # Experts: Not a linear layer, special handling is introduced in awq.quantize.quantizer
  layers.append(dict(
- prev_op=module.post_attention_layernorm,
- layers=[
- w for expert in module.block_sparse_moe.experts
- for w in [expert.w1, expert.w3]
- ],
+ prev_op=module.block_sparse_moe,
+ layers=module.block_sparse_moe.experts,
  inp=input_feat['block_sparse_moe'],
  module2inspect=module.block_sparse_moe,
  ))
 
- # linear out
+ # scaling w2 
+ expert: OldMixtralBLockSparseTop2MLP
  for i, expert in enumerate(module.block_sparse_moe.experts):
  layers.append(dict(
  prev_op=expert.w3,

awq/modules/moe.py

@@ -0,0 +1,75 @@
+import torch
+from transformers.models.mixtral.modeling_mixtral import MixtralSparseMoeBlock
+
+class ScaledMixtralSparseMoeBlock(torch.nn.Module):
+ """
+ This is a modified sparse MoE that scales experts individually.
+
+ Modified version of:
+ transformers.models.mixtral.modeling_mixtral.MixtralSparseMoeBlock
+ """
+
+ def __init__(self, prev_op: MixtralSparseMoeBlock, scales: torch.Tensor):
+ super().__init__()
+ self.hidden_dim = prev_op.hidden_dim
+ self.ffn_dim = prev_op.ffn_dim
+ self.num_experts = prev_op.num_experts
+ self.top_k = prev_op.top_k
+
+ # gating
+ self.gate = prev_op.gate
+
+ # experts
+ self.experts = prev_op.experts
+
+ # [expert_num, hidden_dim]
+ self.scales = torch.nn.Parameter(scales.data)
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ """ """
+ batch_size, sequence_length, hidden_dim = hidden_states.shape
+ hidden_states = hidden_states.view(-1, hidden_dim)
+ # router_logits: (batch * sequence_length, n_experts)
+ router_logits = self.gate(hidden_states)
+
+ routing_weights = torch.nn.functional.softmax(router_logits, dim=1, dtype=torch.float)
+ routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+ routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+ # we cast back to the input dtype
+ routing_weights = routing_weights.to(hidden_states.dtype)
+
+ final_hidden_states = torch.zeros(
+ (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+ )
+
+ # One hot encode the selected experts to create an expert mask
+ # this will be used to easily index which expert is going to be sollicitated
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+ # Loop over all available experts in the model and perform the computation on each expert
+ for expert_idx in range(self.num_experts):
+ expert_layer = self.experts[expert_idx]
+ idx, top_x = torch.where(expert_mask[expert_idx])
+
+ if top_x.shape[0] == 0:
+ continue
+
+ # in torch it is faster to index using lists than torch tensors
+ top_x_list = top_x.tolist()
+ idx_list = idx.tolist()
+
+ # Index the correct hidden states and compute the expert hidden state for
+ # the current expert. We need to make sure to multiply the output hidden
+ # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+
+ ### NOTE: We scale weights here, modified from original MoE.
+ current_state = hidden_states[None, top_x_list].reshape(
+ -1, hidden_dim) / self.scales[expert_idx] # <-- scales
+
+ current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
+
+ # However `index_add_` only support torch tensors for indexing so we'll use
+ # the `top_x` tensor here.
+ final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+ final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+ return final_hidden_states, router_logits

awq/quantize/quantizer.py

@@ -4,9 +4,10 @@
 import functools
 import torch.nn as nn
 from tqdm import tqdm
-from typing import Dict, List
 from collections import defaultdict
+from typing import Dict, List, Union
 from awq.utils.utils import clear_memory
+from transformers import PreTrainedModel
 from awq.utils.calib_data import get_calib_dataset
 from awq.quantize.scale import apply_scale, apply_clip
 from awq.modules.linear import WQLinear_GEMM, WQLinear_GEMV
@@ -17,14 +18,15 @@
  set_op_by_name,
  exclude_layers_to_not_quantize
 )
+from transformers.models.mixtral.modeling_mixtral import MixtralBLockSparseTop2MLP
 
 
 class AwqQuantizer:
  def __init__(self, awq_model, model, tokenizer, w_bit, group_size, version, 
  calib_data, split, text_column, duo_scaling, modules_to_not_convert=None,
  export_compatible=False) -> None:
  self.awq_model = awq_model
- self.model = model
+ self.model: PreTrainedModel = model
  self.tokenizer = tokenizer
  self.w_bit = w_bit
  self.group_size = group_size
@@ -162,7 +164,8 @@ def _apply_quant(self, module, named_linears: Dict[str, nn.Linear]):
  clear_memory()
 
  @torch.no_grad()
- def _search_best_scale(self, module, prev_op, layers: List[nn.Linear], inp: torch.Tensor, module2inspect=None, kwargs={}):
+ def _search_best_scale(self, module, prev_op, layers: Union[List[nn.Linear], List[MixtralBLockSparseTop2MLP]],
+ inp: torch.Tensor, module2inspect=None, kwargs={}):
  if module2inspect is None:
  assert len(layers) == 1
  module2inspect = layers[0]
@@ -174,13 +177,23 @@ def _search_best_scale(self, module, prev_op, layers: List[nn.Linear], inp: torc
  inp = inp.to(next(module2inspect.parameters()).device)
 
  # [STEP 1]: Compute maximum of weight
- weight = torch.cat([_m.weight for _m in layers], dim=0)
- org_shape = weight.shape
- weight = weight.view(-1, self.group_size)
- w_scale = weight.abs() / weight.abs().amax(dim=1, keepdim=True)
- w_scale = w_scale.view(org_shape)
- w_max = w_scale.mean(0)
- clear_memory(weight)
+ def _get_w_max(layer_weights):
+ weight = torch.cat([_m.weight for _m in layer_weights], dim=0)
+ org_shape = weight.shape
+ weight = weight.view(-1, self.group_size)
+ w_scale = weight.abs() / weight.abs().amax(dim=1, keepdim=True)
+ w_scale = w_scale.view(org_shape)
+ w_max = w_scale.mean(0)
+ clear_memory(weight)
+
+ return w_max
+
+ if type(layers[0]) == nn.Linear:
+ w_max = _get_w_max(layers)
+ else:
+ # FIXME: Specific to Mixtral
+ weights = [[expert.w1, expert.w3] for expert in layers]
+ w_max = [_get_w_max(weight) for weight in weights]
 
  # [STEP 2]: Compute maximum of x
  x_max = inp.abs().view(-1, inp.shape[-1]).mean(0)
@@ -194,12 +207,23 @@ def _search_best_scale(self, module, prev_op, layers: List[nn.Linear], inp: torc
  fp16_output = fp16_output[0]
 
  # [STEP 4]: Compute loss
- best_scales = self._compute_best_scale(
- inp, w_max, x_max, module2inspect,
- layers, fp16_output, module_kwargs
- )
+ if type(layers[0]) == nn.Linear:
+ best_scales = self._compute_best_scale(
+ inp, w_max, x_max, module2inspect,
+ layers, fp16_output, module_kwargs
+ )
+ else:
+ best_scales = [
+ self._compute_best_scale(
+ inp, w_max[i], x_max, module2inspect,
+ experts, fp16_output, module_kwargs
+ ) for i, experts in enumerate(weights)
+ ]
+
+ prev_op_name = get_op_name(module, prev_op)
+ layer_names = tuple([get_op_name(module, m) for m in layers])
 
- return (get_op_name(module, prev_op), tuple([get_op_name(module, m) for m in layers]), best_scales)
+ return (prev_op_name, layer_names, best_scales)
 
  def _compute_best_scale(self, x, w_max, x_max, module2inspect, linears2scale: List[nn.Linear],
  fp16_output, kwargs={}):

awq/quantize/scale.py

@@ -2,13 +2,19 @@
 import torch.nn as nn
 from typing import Tuple, List
 from awq.modules.act import ScaledActivation
+from awq.modules.moe import ScaledMixtralSparseMoeBlock
 from awq.utils.module import get_op_by_name, set_op_by_name
 from transformers.models.bloom.modeling_bloom import BloomGelu
 from transformers.models.llama.modeling_llama import LlamaRMSNorm
 from transformers.activations import NewGELUActivation, PytorchGELUTanh, GELUActivation
+from transformers.models.mixtral.modeling_mixtral import (
+ MixtralSparseMoeBlock,
+ MixtralBLockSparseTop2MLP,
+)
 
 allowed_norms = [nn.LayerNorm, LlamaRMSNorm]
 allowed_act_fns = [nn.GELU, BloomGelu, NewGELUActivation, PytorchGELUTanh, GELUActivation]
+allowed_moe = [MixtralSparseMoeBlock]
 
 @torch.no_grad()
 def apply_clip(module, clip_list: Tuple[str, torch.Tensor]):
@@ -31,7 +37,12 @@ def apply_scale(module, scales_list, input_feat_dict=None):
  prev_op.cuda()
  for layer in layers:
  layer.cuda()
- scales.cuda()
+
+ if type(scales) == list:
+ for scale in scales:
+ scale.cuda()
+ else:
+ scales.cuda()
 
  if isinstance(prev_op, nn.Linear) and type(layers) == list and isinstance(layers[0], nn.Linear):
  scale_fc_fcs(prev_op, layers, scales)
@@ -48,6 +59,15 @@ def apply_scale(module, scales_list, input_feat_dict=None):
  new_module = ScaledActivation(prev_op, scales)
  set_op_by_name(module, prev_op_name, new_module)
  scale_gelu_fc(prev_op, layers[0], scales)
+
+ elif any(isinstance(prev_op,t) for t in allowed_moe):
+ # scales: [best_scale_expert_0, best_scale_expert_1, ...] -> [expert_index, scales]
+ scales = torch.stack(scales).cuda()
+
+ # apply scales
+ new_module = ScaledMixtralSparseMoeBlock(prev_op, scales)
+ set_op_by_name(module, prev_op_name, new_module)
+ scale_moe_experts(prev_op, layers, scales)
 
  else:
  raise NotImplementedError(
@@ -64,7 +84,12 @@ def apply_scale(module, scales_list, input_feat_dict=None):
  prev_op.cpu()
  for layer in layers:
  layer.cpu()
- scales.cpu()
+
+ if type(scales) == list:
+ for scale in scales:
+ scale.cpu()
+ else:
+ scales.cpu()
 
 @torch.no_grad()
 def scale_ln_fcs(ln: nn.Linear, fcs: List[nn.Linear], scales: torch.Tensor):
@@ -133,3 +158,18 @@ def scale_gelu_fc(gelu: allowed_act_fns, fc: nn.Linear, scales: torch.Tensor):
 
  for p in fc.parameters():
  assert torch.isnan(p).sum() == 0
+
+@torch.no_grad()
+def scale_moe_experts(moe: MixtralSparseMoeBlock, experts: List[MixtralBLockSparseTop2MLP], scales: torch.Tensor):
+ assert any(isinstance(moe, allowed_module) for allowed_module in allowed_moe)
+ assert all(isinstance(m, MixtralBLockSparseTop2MLP) for m in experts)
+
+ # One scale for each expert, applied to w1 and w3 only
+ # Not applied to w2 because it does not take hidden_states as input
+ for i, expert in enumerate(experts):
+ expert.w1.weight.mul_(scales[i].view(1, -1))
+ expert.w3.weight.mul_(scales[i].view(1, -1))
+
+ for expert in experts:
+ for p in expert.parameters():
+ assert torch.isnan(p).sum() == 0