quantize.py

import fnmatch
from typing import Union

import torch

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 LayerQuantConfig, QuantConfig
from ..parameter import Parameter
from .layers import (FP8Linear, FP8RowLinear, Fp8RowwiseAttention,
                     Fp8RowwiseGatedMLP, Fp8RowwiseMLP, Fp8RowwiseRmsNorm,
                     Int8SmoothQuantLinear, Int8SmoothQuantRowLinear,
                     QServeAttention, QServeGatedMLP, QServeMLP, QServeRmsNorm,
                     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, model_config=None):
    assert quant_config.quant_mode.is_weight_only()

    try:
        model_cfg = model.config
    except AttributeError:
        model_cfg = model_config

    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"
        init_params["tp_rank"] = model_cfg.mapping.tp_rank

    model = quantize_layers(
        model,
        quant_config,
        quant_map,
        preprocess_init_params,
    )
    return model


def weight_only_groupwise_quantize(model,
                                   quant_config: QuantConfig,
                                   model_config=None):
    assert quant_config.quant_mode.is_weight_only()

    try:
        model_cfg = model.config
    except AttributeError:
        model_cfg = model_config

    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
        init_params[
            "use_int8_weight"] = quant_config.quant_algo == QuantAlgo.W8A16_GPTQ
        init_params["tp_rank"] = model_cfg.mapping.tp_rank

    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_cls_map = {
        RmsNorm: Fp8RowwiseRmsNorm,
        GatedMLP: Fp8RowwiseGatedMLP,
        MLP: Fp8RowwiseMLP,
        Attention: Fp8RowwiseAttention,
    }

    if quant_config.exclude_modules is None:
        exclude_modules = ['*ln_f', '*ln_embed']
    else:
        exclude_modules = quant_config.exclude_modules

    def extract_layer_idx(name):
        ss = name.split('.')
        for s in ss:
            if s.isdigit():
                return int(s)
        return None

    # Meta's LLaMA 3.1 recipe:
    # (1) Skip quantization for the first and last Transformer layers
    # (2) Skip quantization for the Attention layers
    if quant_config.use_meta_recipe:
        exclude_modules.extend(['*input_layernorm', '*attention'])

    for name, layer, parent in model.named_modules_with_parent():
        module_name = name.rsplit('.', 1)[-1]

        if quant_config.use_meta_recipe:
            local_layer_idx = extract_layer_idx(name)
            mapping = model.config.mapping
            layers_range = mapping.pp_layers(model.config.num_hidden_layers)
            if mapping.is_first_pp_rank() and local_layer_idx == 0:
                continue
            if mapping.is_last_pp_rank(
            ) and local_layer_idx == len(layers_range) - 1:
                continue

        quant_cls = None
        for cls in quant_cls_map:
            if isinstance(layer, cls):
                quant_cls = quant_cls_map[cls]
                break
        if quant_cls is None:
            continue

        is_excluded = False
        for exclude_module in exclude_modules:
            if fnmatch.fnmatchcase(name, exclude_module):
                is_excluded = True
                break
        if is_excluded:
            continue

        init_params = get_init_params(layer, quant_cls)
        init_params["quant_mode"] = quant_config.quant_mode
        if isinstance(layer, Attention):
            init_params[
                "num_attention_heads"] = layer.num_attention_heads * layer.tp_size
        quant_layer = quant_cls(**init_params, clamp_val=quant_config.clamp_val)
        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


# TODO: These functions should be moved to ModelOpt.
def qserve_quantize_weight_per_group(linear_weight: torch.HalfTensor,
                                     s1_scales: torch.FloatTensor,
                                     s2_scales: torch.FloatTensor,
                                     s2_szeros: torch.FloatTensor,
                                     group_size: int) -> torch.CharTensor:
    out_features = linear_weight.shape[0]
    in_features = linear_weight.shape[1]

    # Step 1: Quantize the weights to int8
    linear_weight = linear_weight.div(
        s1_scales.reshape(out_features, 1).to(linear_weight.device))
    linear_weight = linear_weight.round()
    # assert linear_weight.min() >= -119 and linear_weight.max() <= 119, "Stage 1: Quantized weight out of range" # 119 is the "magic" number
    assert (linear_weight.min() >= -128 and linear_weight.max()
            <= 127), "Stage 1: Quantized weight out of range"

    # Step 2: Quantize the weights to int4
    linear_weight = linear_weight.reshape(out_features,
                                          in_features // group_size, group_size)
    s2_szeros = s2_szeros.reshape(out_features, in_features // group_size,
                                  1).to(torch.float16).to(linear_weight.device)
    s2_scales = s2_scales.reshape(out_features, in_features // group_size,
                                  1).to(torch.float16).to(linear_weight.device)
    linear_weight = linear_weight.add(s2_szeros).div(s2_scales).round()
    assert (linear_weight.min() >= 0 and linear_weight.max()
            <= 15), "Stage 2: Quantized weight out of range"

    qweight = linear_weight.reshape(out_features, in_features).to(torch.int8)
    return qweight


def qserve_quantize_weight_per_channel(
        linear_weight: torch.HalfTensor, s1_scales: torch.FloatTensor,
        s1_szeros: torch.FloatTensor) -> torch.CharTensor:
    out_features = linear_weight.shape[0]
    in_features = linear_weight.shape[1]

    # Step 1: Quantize the weights to int4
    s1_scales = s1_scales.reshape(out_features, 1).to(linear_weight.device)
    s1_szeros = s1_szeros.reshape(out_features, 1).to(linear_weight.device)

    qweight = linear_weight.add(s1_szeros).div(s1_scales).round()
    assert (qweight.min() >= 0
            and qweight.max() <= 15), "Quantized weight out of range"

    return qweight.reshape(out_features, in_features).to(torch.int8)


# Pack the quantized weights, scales and zeros and apply the reordering required by QServe kernels.
# Return: processed [qweight, s1_scales, s2_scales, s2_zeros]
def qserve_pack_reorder_per_group(qweight: torch.CharTensor,
                                  s1_scales: torch.FloatTensor,
                                  s2_scales: torch.FloatTensor,
                                  s2_szeros: torch.FloatTensor, group_size):
    out_features = qweight.shape[0]
    in_features = qweight.shape[1]

    outputs = []

    s1_scales = s1_scales.reshape(out_features).to(torch.float16)
    s2_szeros = s2_szeros.reshape(out_features,
                                  in_features // group_size).to(torch.int8)
    s2_scales = s2_scales.reshape(out_features,
                                  in_features // group_size).to(torch.int8)

    # Step 3: Pack the quantized weights to real quantized weights
    # ---- Repack the weight ---- #
    assert qweight.dtype == torch.int8
    # pack to M // 32, K // 32, (8, 4), ([2], 2, 2, 4)
    W_unpack_reorder = (qweight.reshape(
        out_features // 32,
        2,
        2,
        8,
        in_features // 32,
        2,
        4,
        4,
    ).permute(0, 4, 3, 6, 1, 5, 2, 7).contiguous())
    W_unpack_reorder = (W_unpack_reorder.permute(0, 1, 2, 3, 5, 6, 7,
                                                 4).contiguous().to(torch.int8))
    # B_fp16_reorder = B_fp16_reorder[:, :, :, :, :, :, [3, 2, 1, 0]].contiguous()
    # [16, 0, 17, 1, ...]
    W_unpack_repacked = (W_unpack_reorder[..., 1] << 4) + W_unpack_reorder[...,
                                                                           0]
    W_unpack_repacked = W_unpack_repacked.reshape(out_features // 32,
                                                  in_features // 32, 32, 16)
    W_unpack_repacked = W_unpack_repacked.reshape(out_features,
                                                  in_features // 2)

    outputs.append(W_unpack_repacked)

    # for the last dimension, organize as 0, 8, 16, 24, 1, 9, 17, 25, ... following the requirement of tensor core gemm
    # ---- Pack the scales ---- #
    outputs.append(s1_scales.reshape(out_features))

    s2_scales = (s2_scales.reshape(out_features, in_features //
                                   group_size).transpose(0, 1).contiguous())
    s2_scales = s2_scales.reshape(in_features // group_size, out_features // 32,
                                  32)
    s2_scales = (s2_scales.reshape(in_features // group_size,
                                   out_features // 32, 4,
                                   8).transpose(-2, -1).contiguous())
    s2_scales = s2_scales.reshape(in_features // group_size,
                                  out_features).contiguous()
    outputs.append(s2_scales)

    # ---- Pack the zeros ---- #
    s2_szeros = (s2_szeros.reshape(out_features, in_features //
                                   group_size).transpose(0, 1).contiguous())
    s2_szeros = s2_szeros.reshape(in_features // group_size, out_features // 32,
                                  32)
    s2_szeros = (s2_szeros.reshape(in_features // group_size,
                                   out_features // 32, 4,
                                   8).transpose(-2, -1).contiguous())
    s2_szeros = (s2_szeros.reshape(in_features // group_size,
                                   out_features).contiguous())

    # (q - s2_zeros) * s2_scales = q * s2_scales - s2_zeros * s2_scales,
    # We convert the s2_zeros -> -s2_zeros * s2_scales
    s2_szeros = (-s2_szeros).int()  # It has been pre-scaled in DeepCompressor
    s2_szeros = s2_szeros.to(torch.int8)

    outputs.append(s2_szeros)

    return outputs


def qserve_pack_reorder_per_channel(qweight: torch.CharTensor,
                                    s1_scales: torch.FloatTensor,
                                    s1_szeros: torch.FloatTensor):
    out_features = qweight.shape[0]
    in_features = qweight.shape[1]

    outputs = []

    # ---- Repack the weight ---- #
    assert qweight.dtype == torch.int8
    # pack to M // 32, K // 32, (8, 4), ([2], 2, 2, 4)
    W_unpack_reorder = (qweight.reshape(
        out_features // 32,
        2,
        2,
        8,
        in_features // 32,
        2,
        4,
        4,
    ).permute(0, 4, 3, 6, 1, 5, 2, 7).contiguous())
    W_unpack_reorder = (W_unpack_reorder.permute(0, 1, 2, 3, 5, 6, 7,
                                                 4).contiguous())
    # B_fp16_reorder = B_fp16_reorder[:, :, :, :, :, :, [3, 2, 1, 0]].contiguous()
    # [16, 0, 17, 1, ...]
    W_unpack_repacked = (W_unpack_reorder[..., 1] << 4) + W_unpack_reorder[...,
                                                                           0]
    W_unpack_repacked = W_unpack_repacked.reshape(out_features // 32,
                                                  in_features // 32, 32, 16)
    W_unpack_repacked = W_unpack_repacked.reshape(out_features, in_features //
                                                  2).contiguous()

    outputs.append(W_unpack_repacked)

    # ---- Pack the scales and zeros ---- #
    s1_scales = s1_scales.reshape(out_features).contiguous()
    outputs.append(s1_scales.half())

    s1_szeros = s1_szeros.reshape(out_features).contiguous().half()
    outputs.append(s1_szeros)

    return outputs


# TODO: Duplicates smooth_quantize and quantize_layers
def qserve_quantize(model, quant_config: QuantConfig):
    quant_mode = quant_config.quant_mode
    assert quant_config.quant_mode.is_qserve_w4a8()

    quant_map = {
        RmsNorm: QServeRmsNorm,
        LayerNorm: QServeRmsNorm,
        GatedMLP: QServeGatedMLP,
        MLP: QServeMLP,
        Attention: QServeAttention,
    }

    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


# Now consider the kv cache is enabled for all layers
def kv_cache_quantize(model):
    for name, module in model.named_modules():
        if isinstance(module,
                      (Attention, SmoothQuantAttention, Fp8RowwiseAttention)):
            module.kv_cache_scaling_factor = Parameter(shape=(1, ),
                                                       dtype='float32')
    return model


def quantize(model, quant_config: Union[QuantConfig, LayerQuantConfig]):
    quant_mode = quant_config.layer_quant_mode

    for name, module, parent in model.named_modules_with_parent():
        if quant_config.quant_algo == QuantAlgo.MIXED_PRECISION:
            if name in quant_mode.keys():
                layer_quant_mode = quant_mode[name]
            else:
                continue
        else:
            layer_quant_mode = quant_mode
        if layer_quant_mode == QuantMode(0):
            continue

        layer_quant_cfg = quant_config.get_quant_cfg(name)

        if layer_quant_mode.has_fp8_qdq():
            module = fp8_quantize(module, layer_quant_cfg)
        elif layer_quant_mode.has_fp8_rowwise():
            module = fp8_rowwise_quantize(module, layer_quant_cfg)
        elif layer_quant_mode.is_qserve_w4a8():
            model = qserve_quantize(model, quant_config)
        elif layer_quant_mode.has_act_and_weight_quant():
            module = smooth_quantize(module, layer_quant_cfg)
        elif layer_quant_mode.is_weight_only():
            if layer_quant_mode.has_per_group_scaling():
                module = weight_only_groupwise_quantize(module, layer_quant_cfg,
                                                        model.config)
            else:
                module = weight_only_quantize(module, layer_quant_cfg,
                                              model.config)

        if parent is not None:  # for per layer
            module_name = name.rsplit('.', 1)[-1]
            setattr(parent, module_name, module)
        else:  # for all layer
            model = module
            break

    if quant_config.quant_mode.has_kv_cache_quant():
        model = kv_cache_quantize(model)

    setattr(model, 'quant_mode', quant_config.quant_mode)
    return model