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import fnmatch
from .._utils import get_init_params
from ..layers import (MLP, Attention, ColumnLinear, Embedding, GatedMLP,
LayerNorm, RmsNorm, RowLinear)
from ..layers.moe import MixtureOfExperts
from ..models.modeling_utils import QuantConfig
from ..parameter import Parameter
from .layers import (FP8Linear, FP8RowLinear, Fp8RowwiseGatedMLP, Fp8RowwiseMLP,
Fp8RowwiseRmsNorm, Int8SmoothQuantLinear,
Int8SmoothQuantRowLinear, SmoothQuantAttention,
SmoothQuantGatedMLP, SmoothQuantLayerNorm, SmoothQuantMLP,
SmoothQuantRmsNorm, WeightOnlyGroupwiseQuantColumnLinear,
WeightOnlyGroupwiseQuantRowLinear,
WeightOnlyQuantColumnLinear, WeightOnlyQuantEmbedding,
WeightOnlyQuantRowLinear)
from .mode import W8A8_SQ_PLUGIN_LIST, QuantAlgo, QuantMode
def quantize_layers(
model,
quant_config: QuantConfig,
quant_map,
preprocess_init_params=None,
):
exclude_modules = quant_config.exclude_modules or [
'*lm_head',
'*router',
'*vocab_embedding',
'*position_embedding',
'*block_embedding',
'*shared_expert_gate',
]
for name, module, parent in model.named_modules_with_parent():
module_name = name.rsplit('.', 1)[-1]
is_excluded = False
for exclude_module in exclude_modules:
if fnmatch.fnmatchcase(name, exclude_module):
is_excluded = True
# MOE module will be quantize when initialization.
# We need to re-initialize a FP version of MOE module.
if isinstance(module, MixtureOfExperts):
init_params = get_init_params(module, MixtureOfExperts)
init_params["quant_mode"] = QuantMode(0)
original_layer = MixtureOfExperts(**init_params)
if parent is not None:
setattr(parent, module_name, original_layer)
else:
model = original_layer
break
if not is_excluded:
quant_cls = None
for cls in quant_map:
if isinstance(module, cls):
quant_cls = quant_map[cls]
break
if quant_cls is None:
continue
init_params = get_init_params(module, quant_cls)
if "bias" in init_params:
init_params["bias"] = init_params["bias"] is not None
if isinstance(module, ColumnLinear):
init_params[
"out_features"] = module.out_features * module.tp_size
elif isinstance(module, RowLinear):
init_params["in_features"] = module.in_features * module.tp_size
if preprocess_init_params is not None:
preprocess_init_params(init_params, name, module)
quant_layer = quant_cls(**init_params)
if parent is not None:
setattr(parent, module_name, quant_layer)
else:
model = quant_layer
setattr(model, 'quant_mode', quant_config.quant_mode)
return model
def weight_only_quantize(model, quant_config: QuantConfig):
assert quant_config.quant_mode.is_weight_only()
quant_map = {
ColumnLinear: WeightOnlyQuantColumnLinear,
RowLinear: WeightOnlyQuantRowLinear,
Embedding: WeightOnlyQuantEmbedding,
}
def preprocess_init_params(init_params, name, module):
init_params["quant_mode"] = quant_config.quant_mode
if isinstance(module, ColumnLinear):
module_name = name.rsplit('.', 1)[-1]
init_params["transb"] = module_name == "lm_head"
model = quantize_layers(
model,
quant_config,
quant_map,
preprocess_init_params,
)
return model
def weight_only_groupwise_quantize(model, quant_config: QuantConfig):
assert quant_config.quant_mode.is_weight_only()
quant_map = {
ColumnLinear: WeightOnlyGroupwiseQuantColumnLinear,
RowLinear: WeightOnlyGroupwiseQuantRowLinear,
}
def preprocess_init_params(init_params, name, module):
init_params["group_size"] = quant_config.group_size
init_params["pre_quant_scale"] = quant_config.pre_quant_scale
init_params["zero"] = quant_config.has_zero_point
init_params[
"use_w4a8_awq"] = quant_config.quant_algo == QuantAlgo.W4A8_AWQ
model = quantize_layers(
model,
quant_config,
quant_map,
preprocess_init_params,
)
return model
def smooth_quantize_ootb(
model,
quant_config: QuantConfig,
):
quant_map = {
ColumnLinear: Int8SmoothQuantLinear,
RowLinear: Int8SmoothQuantRowLinear,
}
model = quantize_layers(
model,
quant_config,
quant_map,
)
return model
def smooth_quantize_plugin(model, quant_mode):
quant_map = {
RmsNorm: SmoothQuantRmsNorm,
LayerNorm: SmoothQuantLayerNorm,
GatedMLP: SmoothQuantGatedMLP,
MLP: SmoothQuantMLP,
Attention: SmoothQuantAttention,
}
for name, layer, parent in model.named_modules_with_parent():
layer_name = name.rsplit('.', 1)[-1]
if layer_name in ['ln_f', 'ln_embed']:
continue
quant_cls = None
for cls in quant_map:
if isinstance(layer, cls):
quant_cls = quant_map[cls]
break
if quant_cls is None:
continue
init_params = get_init_params(layer, quant_cls)
init_params["quant_mode"] = quant_mode
if isinstance(layer, Attention):
init_params[
"num_attention_heads"] = layer.num_attention_heads * layer.tp_size
quant_layer = quant_cls(**init_params)
if parent is not None:
setattr(parent, layer_name, quant_layer)
else:
model = quant_layer
setattr(model, 'quant_mode', quant_mode)
return model
def smooth_quantize(model, quant_config: QuantConfig):
assert quant_config.quant_mode.has_act_and_weight_quant()
if quant_config.quant_algo in W8A8_SQ_PLUGIN_LIST:
return smooth_quantize_plugin(model, quant_config.quant_mode)
else:
return smooth_quantize_ootb(model, quant_config)
def fp8_quantize(model, quant_config: QuantConfig):
assert quant_config.quant_mode.has_fp8_qdq()
quant_map = {
ColumnLinear: FP8Linear,
RowLinear: FP8RowLinear,
}
model = quantize_layers(
model,
quant_config,
quant_map,
)
return model
def fp8_rowwise_quantize(model, quant_config: QuantConfig):
assert quant_config.quant_mode.has_fp8_rowwise()
quant_map = {
RmsNorm: Fp8RowwiseRmsNorm,
GatedMLP: Fp8RowwiseGatedMLP,
MLP: Fp8RowwiseMLP,
}
for name, layer, parent in model.named_modules_with_parent():
layer_name = name.rsplit('.', 1)[-1]
if layer_name in ['ln_f', 'ln_embed'] or "input_layernorm" in name:
continue
quant_cls = None
for cls in quant_map:
if isinstance(layer, cls):
quant_cls = quant_map[cls]
break
if quant_cls is None:
continue
init_params = get_init_params(layer, quant_cls)
init_params["quant_mode"] = quant_config.quant_mode
quant_layer = quant_cls(**init_params, clamp_val=quant_config.clamp_val)
if parent is not None:
setattr(parent, layer_name, quant_layer)
else:
model = quant_layer
setattr(model, 'quant_mode', quant_config.quant_mode)
return model
def kv_cache_quantize(model, quant_config: QuantConfig):
assert quant_config.quant_mode.has_kv_cache_quant()
for name, module in model.named_modules():
if isinstance(module, (Attention, SmoothQuantAttention)):
module.kv_cache_scaling_factor = Parameter(shape=(1, ),
dtype='float32')
def quantize(model, quant_config: QuantConfig):
quant_mode = quant_config.quant_mode
if quant_mode.has_fp8_qdq():
model = fp8_quantize(model, quant_config)
elif quant_mode.has_fp8_rowwise():
model = fp8_rowwise_quantize(model, quant_config)
elif quant_mode.has_act_and_weight_quant():
model = smooth_quantize(model, quant_config)
elif quant_mode.is_weight_only():
if quant_mode.has_per_group_scaling():
model = weight_only_groupwise_quantize(model, quant_config)
else:
model = weight_only_quantize(model, quant_config)
if quant_mode.has_kv_cache_quant():
model = kv_cache_quantize(model, quant_config)
return model
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