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Commit 54e6d065 authored by muyangli's avatar muyangli
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[major] support NVFP4; upgrade to 0.1

parent c7f41661
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Showing with 402 additions and 112 deletions
nunchaku/csrc/ops.h
View file @ 54e6d065
......@@ -28,7 +28,10 @@ namespace nunchaku::ops {
std::optional<torch::Tensor> out_linearattn,// linear [B, (M), N / 3]
bool act_unsigned,
std::vector<float> lora_scales,
bool fuse_silu
bool fuse_silu,
bool fp4,
float alpha,
std::optional<torch::Tensor> wcscales
) {
spdlog::trace("running gemm_w4a4: ");
......@@ -63,7 +66,10 @@ namespace nunchaku::ops {
getTensor(out_linearattn),
act_unsigned,
lora_scales,
fuse_silu
fuse_silu,
fp4,
alpha,
getTensor(wcscales)
);
Tensor::synchronizeDevice();
}
......
nunchaku/csrc/pybind.cpp
View file @ 54e6d065
......@@ -11,6 +11,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
py::class_<QuantizedFluxModel>(m, "QuantizedFluxModel")
.def(py::init<>())
.def("init", &QuantizedFluxModel::init,
py::arg("use_fp4"),
py::arg("bf16"),
py::arg("deviceId")
)
......@@ -33,6 +34,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
.def("init", &QuantizedSanaModel::init,
py::arg("config"),
py::arg("pag_layers"),
py::arg("use_fp4"),
py::arg("bf16"),
py::arg("deviceId")
)
......
nunchaku/csrc/sana.h
View file @ 54e6d065
......@@ -8,7 +8,7 @@
class QuantizedSanaModel : public ModuleWrapper<SanaModel> {
public:
void init(pybind11::dict config, std::vector<int> pag_layers, bool bf16, int8_t deviceId) {
void init(pybind11::dict config, std::vector<int> pag_layers, bool use_fp4, bool bf16, int8_t deviceId) {
spdlog::info("Initializing QuantizedSanaModel");
SanaConfig cfg{
.num_layers = config["num_layers"].cast<int>(),
......@@ -17,6 +17,7 @@ public:
.num_cross_attention_heads = config["num_cross_attention_heads"].cast<int>(),
.expand_ratio = config["mlp_ratio"].cast<double>(),
.pag_layers = pag_layers,
.use_fp4 = use_fp4,
};
net = std::make_unique<SanaModel>(cfg, bf16 ? Tensor::BF16 : Tensor::FP16, Device::cuda((int)deviceId));
}
......
nunchaku/models/transformer_flux.py
View file @ 54e6d065
......@@ -108,13 +108,12 @@ class EmbedND(nn.Module):
return emb.unsqueeze(1)
def load_quantized_module(path: str, device: str | torch.device = "cuda") -> QuantizedFluxModel:
def load_quantized_module(path: str, device: str | torch.device = "cuda", use_fp4: bool = False) -> QuantizedFluxModel:
device = torch.device(device)
assert device.type == "cuda"
m = QuantizedFluxModel()
cutils.disable_memory_auto_release()
m.init(True, 0 if device.index is None else device.index)
m.init(use_fp4, True, 0 if device.index is None else device.index)
m.load(path)
return m
......@@ -153,8 +152,10 @@ class NunchakuFluxTransformer2dModel(FluxTransformer2DModel, NunchakuModelLoader
@utils.validate_hf_hub_args
def from_pretrained(cls, pretrained_model_name_or_path: str | os.PathLike, **kwargs):
device = kwargs.get("device", "cuda")
precision = kwargs.get("precision", "int4")
assert precision in ["int4", "fp4"]
transformer, transformer_block_path = cls._build_model(pretrained_model_name_or_path, **kwargs)
m = load_quantized_module(transformer_block_path, device=device)
m = load_quantized_module(transformer_block_path, device=device, use_fp4=precision == "fp4")
transformer.inject_quantized_module(m, device)
return transformer
......
nunchaku/test.py
View file @ 54e6d065
......@@ -4,7 +4,13 @@ from diffusers import FluxPipeline
from .models.transformer_flux import NunchakuFluxTransformer2dModel
if __name__ == "__main__":
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-schnell")
capability = torch.cuda.get_device_capability(0)
sm = f"{capability[0]}{capability[1]}"
precision = "fp4" if sm == "120" else "int4"
transformer = NunchakuFluxTransformer2dModel.from_pretrained(
f"mit-han-lab/svdq-{precision}-flux.1-schnell", precision=precision
)
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-schnell", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
......
setup.py
View file @ 54e6d065
import os
import re
import subprocess
import sys
import setuptools
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
import torch
from packaging import version as packaging_version
from torch.utils.cpp_extension import BuildExtension, CUDA_HOME, CUDAExtension
class CustomBuildExtension(BuildExtension):
......@@ -19,6 +24,40 @@ class CustomBuildExtension(BuildExtension):
super().build_extensions()
def get_sm_targets() -> list[str]:
nvcc_path = os.path.join(CUDA_HOME, "bin/nvcc") if CUDA_HOME else "nvcc"
try:
nvcc_output = subprocess.check_output([nvcc_path, "--version"]).decode()
match = re.search(r"release (\d+\.\d+), V(\d+\.\d+\.\d+)", nvcc_output)
if match:
nvcc_version = match.group(2)
else:
raise Exception("nvcc version not found")
print(f"Found nvcc version: {nvcc_version}")
except:
raise Exception("nvcc not found")
support_sm120 = packaging_version.parse(nvcc_version) >= packaging_version.parse("12.8")
install_mode = os.getenv("NUNCHAKU_INSTALL_MODE", "FAST")
if install_mode == "FAST":
ret = []
for i in range(torch.cuda.device_count()):
capability = torch.cuda.get_device_capability(i)
sm = f"{capability[0]}{capability[1]}"
if sm == "120" and support_sm120:
sm = "120a"
assert sm in ["80", "86", "89", "120a"], f"Unsupported SM {sm}"
if sm not in ret:
ret.append(sm)
else:
assert install_mode == "ALL"
ret = ["80", "86", "89"]
if support_sm120:
ret.append("120a")
return ret
if __name__ == "__main__":
fp = open("nunchaku/__version__.py", "r").read()
version = eval(fp.strip().split()[-1])
......@@ -55,12 +94,6 @@ if __name__ == "__main__":
NVCC_FLAGS = [
"-DENABLE_BF16=1",
"-DBUILD_NUNCHAKU=1",
"-gencode",
"arch=compute_86,code=sm_86",
"-gencode",
"arch=compute_89,code=sm_89",
# "-gencode",
# "arch=compute_89,code=sm_120a",
"-g",
"-std=c++20",
"-UNDEBUG",
......@@ -75,13 +108,21 @@ if __name__ == "__main__":
"-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
"-U__CUDA_NO_BFLOAT162_OPERATORS__",
"-U__CUDA_NO_BFLOAT162_CONVERSIONS__",
"--threads=2",
"--threads=3",
"--expt-relaxed-constexpr",
"--expt-extended-lambda",
"--generate-line-info",
"--ptxas-options=--allow-expensive-optimizations=true",
]
# https://github.com/NVIDIA/cutlass/pull/1479#issuecomment-2052300487
sm_targets = get_sm_targets()
print(f"Detected SM targets: {sm_targets}", file=sys.stderr)
assert len(sm_targets) > 0, "No SM targets found"
for target in sm_targets:
NVCC_FLAGS += ["-gencode", f"arch=compute_{target},code=sm_{target}"]
NVCC_MSVC_FLAGS = ["-Xcompiler", "/Zc:__cplusplus"]
nunchaku_extension = CUDAExtension(
......
src/FluxModel.cpp
View file @ 54e6d065
......@@ -259,19 +259,19 @@ void Attention::setForceFP16(Module *module, bool value) {
});
}
FluxSingleTransformerBlock::FluxSingleTransformerBlock(int dim, int num_attention_heads, int attention_head_dim, int mlp_ratio, Tensor::ScalarType dtype, Device device) :
FluxSingleTransformerBlock::FluxSingleTransformerBlock(int dim, int num_attention_heads, int attention_head_dim, int mlp_ratio, bool use_fp4, Tensor::ScalarType dtype, Device device) :
dim(dim),
dim_head(attention_head_dim / num_attention_heads),
num_heads(num_attention_heads),
mlp_hidden_dim(dim * mlp_ratio),
norm(dim, dtype, device),
mlp_fc1(dim, mlp_hidden_dim, true, dtype, device),
mlp_fc2(mlp_hidden_dim, dim, true, dtype, device),
qkv_proj(dim, dim * 3, true, dtype, device),
mlp_fc1(dim, mlp_hidden_dim, true, use_fp4, dtype, device),
mlp_fc2(mlp_hidden_dim, dim, true, use_fp4, dtype, device),
qkv_proj(dim, dim * 3, true, use_fp4, dtype, device),
norm_q(dim_head, 1e-6, false, dtype, device),
norm_k(dim_head, 1e-6, false, dtype, device),
attn(num_attention_heads, attention_head_dim / num_attention_heads, device),
out_proj(dim, dim, true, dtype, device)
out_proj(dim, dim, true, use_fp4, dtype, device)
{
registerChildren
(norm, "norm")
......@@ -327,28 +327,28 @@ Tensor FluxSingleTransformerBlock::forward(Tensor hidden_states, Tensor temb, Te
return hidden_states;
}
JointTransformerBlock::JointTransformerBlock(int dim, int num_attention_heads, int attention_head_dim, bool context_pre_only, Tensor::ScalarType dtype, Device device) :
JointTransformerBlock::JointTransformerBlock(int dim, int num_attention_heads, int attention_head_dim, bool context_pre_only, bool use_fp4, Tensor::ScalarType dtype, Device device) :
dim(dim),
dim_head(attention_head_dim / num_attention_heads),
num_heads(num_attention_heads),
context_pre_only(context_pre_only),
norm1(dim, false, dtype, device),
norm1_context(dim, context_pre_only, dtype, device),
qkv_proj(dim, dim * 3, true, dtype, device),
qkv_proj_context(dim, dim * 3, true, dtype, device),
qkv_proj(dim, dim * 3, true, use_fp4, dtype, device),
qkv_proj_context(dim, dim * 3, true, use_fp4, dtype, device),
norm_q(dim_head, 1e-6, false, dtype, device),
norm_k(dim_head, 1e-6, false, dtype, device),
norm_added_q(dim_head, 1e-6, false, dtype, device),
norm_added_k(dim_head, 1e-6, false, dtype, device),
attn(num_attention_heads, attention_head_dim / num_attention_heads, device),
out_proj(dim, dim, true, dtype, device),
out_proj_context(dim, dim, true, dtype, device),
out_proj(dim, dim, true, use_fp4, dtype, device),
out_proj_context(dim, dim, true, use_fp4, dtype, device),
norm2(dim, 1e-6, false, dtype, device),
norm2_context(dim, 1e-6, false, dtype, device),
mlp_fc1(dim, dim * 4, true, dtype, device),
mlp_fc2(dim * 4, dim, true, dtype, device),
mlp_context_fc1(dim, dim * 4, true, dtype, device),
mlp_context_fc2(dim * 4, dim, true, dtype, device)
mlp_fc1(dim, dim * 4, true, use_fp4, dtype, device),
mlp_fc2(dim * 4, dim, true, use_fp4, dtype, device),
mlp_context_fc1(dim, dim * 4, true, use_fp4, dtype, device),
mlp_context_fc2(dim * 4, dim, true, use_fp4, dtype, device)
{
registerChildren
(norm1, "norm1")
......@@ -607,13 +607,13 @@ std::tuple<Tensor, Tensor> JointTransformerBlock::forward(Tensor hidden_states,
return { hidden_states, encoder_hidden_states };
}
FluxModel::FluxModel(Tensor::ScalarType dtype, Device device) {
FluxModel::FluxModel(bool use_fp4, Tensor::ScalarType dtype, Device device) {
for (int i = 0; i < 19; i++) {
transformer_blocks.push_back(std::make_unique<JointTransformerBlock>(3072, 24, 3072, false, dtype, device));
transformer_blocks.push_back(std::make_unique<JointTransformerBlock>(3072, 24, 3072, false, use_fp4, dtype, device));
registerChildren(*transformer_blocks.back(), format("transformer_blocks.{}", i));
}
for (int i = 0; i < 38; i++) {
single_transformer_blocks.push_back(std::make_unique<FluxSingleTransformerBlock>(3072, 24, 3072, 4, dtype, Device::cuda()));
single_transformer_blocks.push_back(std::make_unique<FluxSingleTransformerBlock>(3072, 24, 3072, 4, use_fp4, dtype, Device::cuda()));
registerChildren(*single_transformer_blocks.back(), format("single_transformer_blocks.{}", i));
}
}
......
src/FluxModel.h
View file @ 54e6d065
......@@ -77,7 +77,7 @@ public:
static constexpr bool USE_4BIT = true;
using GEMM = std::conditional_t<USE_4BIT, GEMM_W4A4, GEMM_W8A8>;
FluxSingleTransformerBlock(int dim, int num_attention_heads, int attention_head_dim, int mlp_ratio, Tensor::ScalarType dtype, Device device);
FluxSingleTransformerBlock(int dim, int num_attention_heads, int attention_head_dim, int mlp_ratio, bool use_fp4, Tensor::ScalarType dtype, Device device);
Tensor forward(Tensor hidden_states, Tensor temb, Tensor rotary_emb);
public:
......@@ -101,7 +101,7 @@ public:
static constexpr bool USE_4BIT = true;
using GEMM = std::conditional_t<USE_4BIT, GEMM_W4A4, GEMM_W8A8>;
JointTransformerBlock(int dim, int num_attention_heads, int attention_head_dim, bool context_pre_only, Tensor::ScalarType dtype, Device device);
JointTransformerBlock(int dim, int num_attention_heads, int attention_head_dim, bool context_pre_only, bool use_fp4, Tensor::ScalarType dtype, Device device);
std::tuple<Tensor, Tensor> forward(Tensor hidden_states, Tensor encoder_hidden_states, Tensor temb, Tensor rotary_emb, Tensor rotary_emb_context, float sparsityRatio);
public:
......@@ -128,7 +128,7 @@ private:
class FluxModel : public Module {
public:
FluxModel(Tensor::ScalarType dtype, Device device);
FluxModel(bool use_fp4, Tensor::ScalarType dtype, Device device);
Tensor forward(Tensor hidden_states, Tensor encoder_hidden_states, Tensor temb, Tensor rotary_emb_img, Tensor rotary_emb_context, Tensor rotary_emb_single);
public:
......
src/Linear.cpp
View file @ 54e6d065
......@@ -96,23 +96,33 @@ Tensor GEMV_AWQ::forward(Tensor x) {
#define NO_LORA_FUSION 0
GEMM_W4A4::GEMM_W4A4(int in_features, int out_features, bool bias, Tensor::ScalarType dtype, Device device) :
GEMM_W4A4::GEMM_W4A4(int in_features, int out_features, bool bias, bool use_fp4, Tensor::ScalarType dtype, Device device) :
in_features(in_features), out_features(out_features),
in_features_pad(ceilDiv(in_features, 128) * 128), out_features_pad(ceilDiv(out_features, 128) * 128),
use_fp4(use_fp4),
lora_rank(0), dtype(dtype)
{
this->qweight = Tensor::allocate({out_features_pad, in_features_pad / 2}, Tensor::INT8, device, true);
this->wscales = Tensor::allocate({in_features_pad / 64, out_features_pad}, dtype, device, true);
if (use_fp4) {
this->wscales = Tensor::allocate({in_features_pad / 16, out_features_pad}, Tensor::FP8_E4M3, device, true);
} else {
this->wscales = Tensor::allocate({in_features_pad / 64, out_features_pad}, dtype, device, true);
}
this->bias = bias ? Tensor::allocate({out_features_pad}, dtype, device, true) : Tensor{};
this->lora_down = Tensor::allocate({in_features_pad, lora_rank}, dtype, device, true);
this->lora_up = Tensor::allocate({out_features_pad, lora_rank}, dtype, device, true);
// TODO: smooth factor in FC1+FC2 fusion
// TODO: smooth factor in non-Lora fusion
this->smooth = Tensor::allocate({in_features_pad}, dtype, device, true);
// FIXME: reset wtscale and wcscales to default values when reloading the weights
this->wtscale = Tensor::allocate({1}, Tensor::FP32, Device::cpu(), true);
*this->wtscale.data_ptr<float>() = 1.0f;
this->wcscales = Tensor::allocate({0}, dtype, device, true);
registerParams
(qweight, "qweight")
(wscales, "wscales")
......@@ -120,6 +130,8 @@ GEMM_W4A4::GEMM_W4A4(int in_features, int out_features, bool bias, Tensor::Scala
(lora_down, "lora_down", ParamFlags::Optional)
(lora_up, "lora_up", ParamFlags::Optional)
(smooth, "smooth")
(wtscale, "wtscale", ParamFlags::Optional)
(wcscales, "wcscales", ParamFlags::Optional)
;
#if NO_LORA_FUSION
......@@ -137,6 +149,21 @@ void GEMM_W4A4::loadParam(std::string key, Tensor &dst, Tensor src) {
} else {
dst.copy_(src);
}
} else if (key == "wcscales") {
assert(src.ndims() == 1);
assert(src.shape[0] == out_features_pad);
dst = src.copy(this->qweight.device());
} else if (key == "wtscale") {
assert(src.numel() == 1);
if (src.dtype() == Tensor::BF16) {
*dst.data_ptr<float>() = float(*src.data_ptr<__nv_bfloat16>());
} else if (src.dtype() == Tensor::FP16) {
*dst.data_ptr<float>() = float(*src.data_ptr<half>());
} else if (src.dtype() == Tensor::FP32) {
dst.copy_(src);
} else {
assert(false);
}
} else {
Module::loadParam(key, dst, src);
}
......@@ -167,7 +194,10 @@ void GEMM_W4A4::forward(Tensor x, Tensor out, Tensor pool, Tensor norm_q, Tensor
debug("gemm.nolora.out", out);
#endif
kernels::gemm_w4a4(qact.act, qweight, out, {}, qact.ascales, wscales, {}, pool, qact.lora_act, this->lora_up, {}, {}, norm_q, norm_k, rotary_emb, this->bias, {}, {}, {}, qact.is_unsigned, this->lora_scales, false);
kernels::gemm_w4a4(
qact.act, qweight, out, {}, qact.ascales, wscales, {}, pool, qact.lora_act, this->lora_up, {}, {}, norm_q, norm_k, rotary_emb, this->bias, {}, {}, {}, qact.is_unsigned, this->lora_scales, false,
use_fp4, *this->wtscale.data_ptr<float>(), wcscales.numel() > 0 ? wcscales: Tensor{}
);
debug("gemm.out", out);
#else
......@@ -215,9 +245,13 @@ std::variant<Tensor, GEMM_W4A4::QuantizedActivation> GEMM_W4A4::forward_quant(Qu
out = Tensor::allocate(shape, dtype, qweight.device());
} else {
qout.act = Tensor::allocate({M, out_features_pad / 2}, Tensor::INT8, qweight.device());
qout.ascales = Tensor::allocate({out_features_pad / 64, M}, dtype, qweight.device());
if (use_fp4) {
qout.ascales = Tensor::allocate({out_features_pad / 16, M}, Tensor::FP8_E4M3, qweight.device());
} else {
qout.ascales = Tensor::allocate({out_features_pad / 64, M}, dtype, qweight.device());
}
qout.lora_act = Tensor::allocate({M, lora_rank}, Tensor::FP32, qweight.device());
qout.is_unsigned = true;
qout.is_unsigned = !use_fp4;
qout.actShape = qact.actShape;
next_lora = nextGEMM->lora_down;
......@@ -241,7 +275,10 @@ std::variant<Tensor, GEMM_W4A4::QuantizedActivation> GEMM_W4A4::forward_quant(Qu
}
#endif
kernels::gemm_w4a4(qact.act, qweight, out, qout.act, qact.ascales, wscales, qout.ascales, {}, qact.lora_act, this->lora_up, next_lora, qout.lora_act, {}, {}, {}, this->bias, next_smooth, {}, {}, qact.is_unsigned, this->lora_scales, fuse == FuseOptions::SILU);
kernels::gemm_w4a4(
qact.act, qweight, out, qout.act, qact.ascales, wscales, qout.ascales, {}, qact.lora_act, this->lora_up, next_lora, qout.lora_act, {}, {}, {}, this->bias, next_smooth, {}, {}, qact.is_unsigned, this->lora_scales, fuse == FuseOptions::SILU,
use_fp4, *this->wtscale.data_ptr<float>(), wcscales.numel() > 0 ? wcscales: Tensor{}
);
if (fuse == FuseOptions::EMPTY || fuse == FuseOptions::SILU) {
debug("gemm.out", out);
......@@ -327,7 +364,11 @@ GEMM_W4A4::QuantizedActivation GEMM_W4A4::quantize(Tensor x, bool fuse_glu) {
QuantizedActivation qact;
qact.act = Tensor::allocate({M, in_features_pad / 2}, Tensor::INT8, qweight.device());
qact.ascales = Tensor::allocate({in_features_pad / 64, M}, dtype, qweight.device());
if (use_fp4) {
qact.ascales = Tensor::allocate({in_features_pad / 16, M}, Tensor::FP8_E4M3, qweight.device());
} else {
qact.ascales = Tensor::allocate({in_features_pad / 64, M}, dtype, qweight.device());
}
qact.lora_act = Tensor::allocate({M, lora_rank}, Tensor::FP32, qweight.device());
qact.is_unsigned = false;
qact.actShape = x.shape.dataExtent;
......@@ -336,7 +377,7 @@ GEMM_W4A4::QuantizedActivation GEMM_W4A4::quantize(Tensor x, bool fuse_glu) {
debug("quantize.x", x);
debug("quantize.smooth", this->smooth);
kernels::quantize_w4a4_act_fuse_lora(x, qact.act, qact.ascales, this->lora_down, qact.lora_act, this->smooth, fuse_glu);
kernels::quantize_w4a4_act_fuse_lora(x, qact.act, qact.ascales, this->lora_down, qact.lora_act, this->smooth, fuse_glu, use_fp4);
debug("quantize.qact", qact.act);
debug("quantize.ascales", qact.ascales);
......
src/Linear.h
View file @ 54e6d065
......@@ -64,7 +64,7 @@ public:
};
public:
GEMM_W4A4(int in_features, int out_features, bool bias, Tensor::ScalarType dtype, Device device);
GEMM_W4A4(int in_features, int out_features, bool bias, bool use_fp4, Tensor::ScalarType dtype, Device device);
Tensor forward(Tensor x);
Tensor forward_silu(Tensor x);
std::variant<Tensor, QuantizedActivation> forward(Tensor x, FuseOptions fuse, GEMM_W4A4 *nextGEMM = nullptr);
......@@ -80,6 +80,7 @@ public:
const int out_features;
const int in_features_pad;
const int out_features_pad;
const bool use_fp4;
int lora_rank;
std::vector<float> lora_scales; // every 16 ranks share a scale
......@@ -99,6 +100,9 @@ public:
Tensor smooth;
Tensor wtscale;
Tensor wcscales;
cublasHandle_t handle;
};
......
src/SanaModel.cpp
View file @ 54e6d065
......@@ -8,11 +8,11 @@
using spdlog::fmt_lib::format;
using namespace nunchaku;
SanaLinearAttention::SanaLinearAttention(int dim, bool bias, bool pag, Tensor::ScalarType dtype, Device device) :
SanaLinearAttention::SanaLinearAttention(int dim, bool bias, bool pag, bool use_fp4, Tensor::ScalarType dtype, Device device) :
dim(dim),
dim_pad(ceilDiv(dim, 128) * 128),
qkv_proj(dim, dim_pad * 3, bias, dtype, device),
out_proj(dim_pad, dim, bias, dtype, device),
qkv_proj(dim, dim_pad * 3, bias, use_fp4, dtype, device),
out_proj(dim_pad, dim, bias, use_fp4, dtype, device),
pag_to_v(std::nullopt)
{
registerChildren
......@@ -21,7 +21,7 @@ SanaLinearAttention::SanaLinearAttention(int dim, bool bias, bool pag, Tensor::S
;
if (pag) {
pag_to_v.emplace(dim, dim_pad, bias, dtype, device);
pag_to_v.emplace(dim, dim_pad, bias, use_fp4, dtype, device);
registerChildren(pag_to_v.value(), "pag_to_v");
}
}
......@@ -63,7 +63,11 @@ Tensor SanaLinearAttention::forward(Tensor x, Tensor out) {
qkv_proj.wscales,
{}, {}, qact.lora_act, qkv_proj.lora_up, {}, {}, {}, {}, {}, qkv_proj.bias, {},
vk, q,
qact.is_unsigned, qkv_proj.lora_scales, false);
qact.is_unsigned, qkv_proj.lora_scales, false,
qkv_proj.use_fp4,
*qkv_proj.wtscale.data_ptr<float>(),
qkv_proj.wcscales.numel() > 0 ? qkv_proj.wcscales : Tensor{}
);
debug("vk", vk);
debug("q", q);
......@@ -121,11 +125,11 @@ Tensor SanaLinearAttention::forward_pag(Tensor x, bool cfg) {
return out;
}
MultiHeadCrossAttention::MultiHeadCrossAttention(int num_heads, int head_dim, Tensor::ScalarType dtype, Device device) :
MultiHeadCrossAttention::MultiHeadCrossAttention(int num_heads, int head_dim, bool use_fp4, Tensor::ScalarType dtype, Device device) :
num_heads(num_heads), head_dim(head_dim),
q_linear(num_heads * head_dim, num_heads * head_dim, true, dtype, device),
q_linear(num_heads * head_dim, num_heads * head_dim, true, use_fp4, dtype, device),
kv_linear(num_heads * head_dim, num_heads * head_dim * 2, true, dtype, device),
out_proj(num_heads * head_dim, num_heads * head_dim, true, dtype, device)
out_proj(num_heads * head_dim, num_heads * head_dim, true, use_fp4, dtype, device)
{
registerChildren
(q_linear, "q_linear")
......@@ -173,11 +177,11 @@ Tensor MultiHeadCrossAttention::forward(Tensor x, Tensor cond, Tensor cu_seqlens
return out_proj.forward(attn_output);
}
SanaGLUMBConv::SanaGLUMBConv(int in_features, int hidden_features, Tensor::ScalarType dtype, Device device) :
SanaGLUMBConv::SanaGLUMBConv(int in_features, int hidden_features, bool use_fp4, Tensor::ScalarType dtype, Device device) :
in_features(in_features), hidden_features(hidden_features),
inverted_conv(in_features, hidden_features * 2, true, dtype, device),
inverted_conv(in_features, hidden_features * 2, true, use_fp4, dtype, device),
depth_conv(hidden_features * 2, true, dtype, device),
point_conv(hidden_features, in_features, false, dtype, device)
point_conv(hidden_features, in_features, false, use_fp4, dtype, device)
{
registerChildren
(inverted_conv, "inverted_conv")
......@@ -200,11 +204,11 @@ Tensor SanaGLUMBConv::forward(Tensor x, int H, int W) {
return point_conv.forward_quant(qact);
}
SanaLinearTransformerBlock::SanaLinearTransformerBlock(int hidden_size, int intermediate_size, int num_cross_attention_heads, bool pag, Tensor::ScalarType dtype, Device device) :
SanaLinearTransformerBlock::SanaLinearTransformerBlock(int hidden_size, int intermediate_size, int num_cross_attention_heads, bool pag, bool use_fp4, Tensor::ScalarType dtype, Device device) :
hidden_size(hidden_size), num_cross_attention_heads(num_cross_attention_heads),
attn(hidden_size, false, pag, dtype, device),
cross_attn(num_cross_attention_heads, hidden_size / num_cross_attention_heads, dtype, device),
ff(hidden_size, intermediate_size, dtype, device),
attn(hidden_size, false, pag, use_fp4, dtype, device),
cross_attn(num_cross_attention_heads, hidden_size / num_cross_attention_heads, use_fp4, dtype, device),
ff(hidden_size, intermediate_size, use_fp4, dtype, device),
norm1(hidden_size, 1e-6, false, dtype, device),
norm2(hidden_size, 1e-6, false, dtype, device)
{
......@@ -313,6 +317,7 @@ SanaModel::SanaModel(SanaConfig config, Tensor::ScalarType dtype, Device device)
ceilDiv(int(round(config.expand_ratio * inner_dim)), 64) * 64,
config.num_cross_attention_heads,
std::find(config.pag_layers.begin(), config.pag_layers.end(), i) != config.pag_layers.end(),
config.use_fp4,
dtype, device
));
registerChildren(*transformer_blocks.back(), format("transformer_blocks.{}", i));
......
src/SanaModel.h
View file @ 54e6d065
......@@ -7,7 +7,7 @@
class SanaLinearAttention : public Module {
public:
SanaLinearAttention(int dim, bool bias, bool pag, Tensor::ScalarType dtype, Device device);
SanaLinearAttention(int dim, bool bias, bool pag, bool use_fp4, Tensor::ScalarType dtype, Device device);
Tensor forward(Tensor x, Tensor out = {});
Tensor forward_pag(Tensor x, bool cfg);
......@@ -25,7 +25,7 @@ private:
class MultiHeadCrossAttention : public Module {
public:
MultiHeadCrossAttention(int num_heads, int head_dim, Tensor::ScalarType dtype, Device device);
MultiHeadCrossAttention(int num_heads, int head_dim, bool use_fp4, Tensor::ScalarType dtype, Device device);
Tensor forward(Tensor x, Tensor cond, Tensor cu_seqlens_img, Tensor cu_seqlens_txt);
......@@ -41,7 +41,7 @@ private:
class SanaGLUMBConv : public Module {
public:
SanaGLUMBConv(int in_features, int hidden_features, Tensor::ScalarType dtype, Device device);
SanaGLUMBConv(int in_features, int hidden_features, bool use_fp4, Tensor::ScalarType dtype, Device device);
Tensor forward(Tensor x, int H, int W);
......@@ -57,7 +57,7 @@ private:
class SanaLinearTransformerBlock : public Module {
public:
SanaLinearTransformerBlock(int hidden_size, int intermediate_size, int num_cross_attention_heads, bool pag, Tensor::ScalarType dtype, Device device);
SanaLinearTransformerBlock(int hidden_size, int intermediate_size, int num_cross_attention_heads, bool pag, bool use_fp4, Tensor::ScalarType dtype, Device device);
Tensor forward(Tensor hidden_states, Tensor encoder_hidden_states, Tensor timestep, Tensor cu_seqlens_img, Tensor cu_seqlens_txt, int H, int W, bool pag, bool cfg);
......@@ -83,6 +83,7 @@ struct SanaConfig {
int num_cross_attention_heads;
double expand_ratio;
std::vector<int> pag_layers;
bool use_fp4;
};
class SanaModel : public Module {
......
src/Serialization.cpp
View file @ 54e6d065
......@@ -117,6 +117,8 @@ void SafeTensors::parseHeader() {
{ "I8", Tensor::INT8 },
{ "I32", Tensor::INT32 },
{ "I64", Tensor::INT64 },
{ "F8_E4M3", Tensor::FP8_E4M3 },
{ "F8_E5M2", Tensor::FP8_E5M2 },
};
auto check = [](bool cond, std::source_location location = std::source_location::current()) {
......
src/Tensor.h
View file @ 54e6d065
......@@ -218,7 +218,8 @@ public:
enum ScalarType {
INVALID_SCALAR_TYPE,
INT8, INT32, INT64,
FP16, FP32, BF16
FP16, FP32, BF16,
FP8_E4M3, FP8_E5M2,
};
struct TensorOptions {
......@@ -545,6 +546,8 @@ inline const std::map<Tensor::ScalarType, size_t> Tensor::scalarSize = {
{FP16, 2},
{FP32, 4},
{BF16, 2},
{FP8_E4M3, 1},
{FP8_E5M2, 1},
};
struct TensorsProvider {
......
src/common.h
View file @ 54e6d065
......@@ -9,6 +9,7 @@
#include <memory>
#include <source_location>
#include <vector>
#include <list>
#include <stack>
#include <map>
#include <unordered_map>
......@@ -79,6 +80,15 @@ constexpr T ceilDiv(T a, T b) {
return (a + b - 1) / b;
}
template<typename T>
constexpr int log2Up(T value) {
if (value <= 0)
return 0;
if (value == 1)
return 0;
return log2Up((value + 1) / 2) + 1;
}
struct CUBLASWrapper {
cublasHandle_t handle = nullptr;
......
src/interop/torch.cpp
View file @ 54e6d065
......@@ -28,6 +28,8 @@ Tensor from_torch(at::Tensor input) {
{ at::ScalarType::Float, Tensor::FP32 },
{ at::ScalarType::Half, Tensor::FP16 },
{ at::ScalarType::BFloat16, Tensor::BF16 },
{ at::ScalarType::Float8_e4m3fn, Tensor::FP8_E4M3 },
{ at::ScalarType::Float8_e5m2, Tensor::FP8_E5M2 },
};
result.scalarType = mapType.at(input.scalar_type());
......@@ -53,6 +55,8 @@ at::Tensor to_torch(Tensor input) {
{ Tensor::FP32, at::ScalarType::Float },
{ Tensor::FP16, at::ScalarType::Half },
{ Tensor::BF16, at::ScalarType::BFloat16 },
{ Tensor::FP8_E4M3, at::ScalarType::Float8_e4m3fn },
{ Tensor::FP8_E5M2, at::ScalarType::Float8_e5m2 },
};
c10::TensorOptions opts(mapType.at(input.scalar_type()));
......
src/kernels/awq/gemv_awq.cu
View file @ 54e6d065
......@@ -140,8 +140,10 @@ __global__ void gemv_kernel(
for (int i = 0; i < Num; ++i)
psum[i] = static_cast<accum_t>(0.f);
extern __shared__ uint8_t shmem[];
float(*out_smem)[Num * kInterleave] = reinterpret_cast<float(*)[Num * kInterleave]>(shmem);
// extern __shared__ uint8_t shmem[];
// float(*out_smem)[Num * kInterleave] = reinterpret_cast<float(*)[Num * kInterleave]>(shmem);
__shared__ float out_smem[BlockSize / WARP_SIZE * 2][Num * kInterleave];
const int blk_row_offset = blockIdx.x * NPerBlock * kInterleave;
const int thd_row_offset = (threadIdx.x / kThreadsNumPerTile) % kInterleave;
......
src/kernels/zgemm/gemm_base.cuh
View file @ 54e6d065
......@@ -319,10 +319,10 @@ public:
int warpId = threadIdx.x / WARP_SIZE;
#pragma unroll
for (int i = 0; i < WARP_M_TILES; i++) {
if (pred) {
//if (pred) {
// out[i] = load(&act[((warpId * WARP_M_TILES + i) * K / WARP_K + k) * WARP_SIZE + laneId]);
out[i] = load(&act[((k * NUM_WARPS + warpId) * WARP_M_TILES + i) * WARP_SIZE + laneId]);
}
out[i] = load_pred(&act[((k * NUM_WARPS + warpId) * WARP_M_TILES + i) * WARP_SIZE + laneId], pred);
//}
}
}
......@@ -336,12 +336,12 @@ public:
// int offset = K / WARP_K * WARP_SIZE;
#pragma unroll
for (int i = 0; i < WARP_N_TILES; i++) {
if (pred) {
//if (pred) {
// out[i] = load(&wgt[(i * K / WARP_K + k) * WARP_SIZE + laneId]);
// out[i] = load(&wgt[(i + k * WARP_N_TILES) * WARP_SIZE + laneId]);
out[i] = load(&ptr[i * WARP_SIZE]);
out[i] = load_pred(&ptr[i * WARP_SIZE], pred);
// ptr += offset;
}
//}
}
}
......@@ -352,11 +352,11 @@ public:
int warpId = threadIdx.x / WARP_SIZE;
#pragma unroll
for (int i = 0; i < ASCALES_NUM_PACKS; i++) {
if (pred && laneId < ASCALES_VALID_LANES) {
// if (pred && laneId < ASCALES_VALID_LANES) {
// out[i] = ascales[(group * M / WARP_M + warpId) * ASCALES_VALID_LANES * ASCALES_NUM_PACKS + i * ASCALES_VALID_LANES + laneId];
out[i] = ascales[(group * NUM_WARPS + warpId) * ASCALES_NUM_PACKS * ASCALES_VALID_LANES + i * ASCALES_VALID_LANES + laneId];
out[i] = load_pred(&ascales[(group * NUM_WARPS + warpId) * ASCALES_NUM_PACKS * ASCALES_VALID_LANES + i * ASCALES_VALID_LANES + laneId], pred && laneId < ASCALES_VALID_LANES);
}
// }
}
}
......@@ -373,13 +373,13 @@ public:
#pragma unroll
for (int i = 0; i < WSCALES_NUM_PACKS; i++) {
if (pred && laneId < WSCALES_VALID_LANES) {
// if (pred && laneId < WSCALES_VALID_LANES) {
// out[i] = wscales[group * N / WARP_N * WSCALES_VALID_LANES * WSCALES_NUM_PACKS + i * WSCALES_VALID_LANES + laneId];
// out[i] = load(&wscales[group * N / WARP_N * WSCALES_VALID_LANES * WSCALES_NUM_PACKS + i * WSCALES_VALID_LANES + laneId]);
out[i] = load(&wscales[(group * WSCALES_NUM_PACKS + i) * WSCALES_VALID_LANES + laneId]);
out[i] = load_pred(&wscales[(group * WSCALES_NUM_PACKS + i) * WSCALES_VALID_LANES + laneId], pred && laneId < WSCALES_VALID_LANES);
// out[i] = load(&ptr[i * WSCALES_VALID_LANES]);
}
// }
}
}
......@@ -400,7 +400,7 @@ public:
return __shfl_sync(~0, block[packIdx].data[elementIdx], srcLane);
}
template<typename F>
template<bool FAST_I2F = false, typename F>
__device__ __forceinline__
static void apply_scales(F &&getpsum, ascale_warp ascale, wscale_warp wscale, fpsum_warp &fpsum) {
const int laneId = threadIdx.x % WARP_SIZE;
......@@ -429,12 +429,31 @@ public:
// printf("before ws2 = %f %f fsum.data[%d] = %f %f\n", (float)ws2.x, (float)ws2.y, target, (float)fsum.data[target].x, (float)fsum.data[target].y);
// }
fsum.data[0] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[0]), __int2float_rn(psum.data[1]))), __hmul2(asx[i], ws1), fsum.data[0]);
fsum.data[1] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[2]), __int2float_rn(psum.data[3]))), __hmul2(asy[i], ws1), fsum.data[1]);
fsum.data[2] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[4]), __int2float_rn(psum.data[5]))), __hmul2(asx[i], ws2), fsum.data[2]);
fsum.data[3] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[6]), __int2float_rn(psum.data[7]))), __hmul2(asy[i], ws2), fsum.data[3]);
auto scale_fma_normal = [&]() ALWAYSINLINE {
fsum.data[0] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[0]), __int2float_rn(psum.data[1]))), __hmul2(asx[i], ws1), fsum.data[0]);
fsum.data[1] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[2]), __int2float_rn(psum.data[3]))), __hmul2(asy[i], ws1), fsum.data[1]);
fsum.data[2] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[4]), __int2float_rn(psum.data[5]))), __hmul2(asx[i], ws2), fsum.data[2]);
fsum.data[3] = __hfma2(float22half2<half2_t>(make_float2(__int2float_rn(psum.data[6]), __int2float_rn(psum.data[7]))), __hmul2(asy[i], ws2), fsum.data[3]);
};
// should be faster on sm_80
auto scale_fma_fast = [&]() ALWAYSINLINE {
fsum.data[0] = __hfma2(float22half2<half2_t>(make_float2(int2float_fast(psum.data[0]), int2float_fast(psum.data[1]))), __hmul2(asx[i], ws1), fsum.data[0]);
fsum.data[1] = __hfma2(float22half2<half2_t>(make_float2(int2float_fast(psum.data[2]), int2float_fast(psum.data[3]))), __hmul2(asy[i], ws1), fsum.data[1]);
fsum.data[2] = __hfma2(float22half2<half2_t>(make_float2(int2float_fast(psum.data[4]), int2float_fast(psum.data[5]))), __hmul2(asx[i], ws2), fsum.data[2]);
fsum.data[3] = __hfma2(float22half2<half2_t>(make_float2(int2float_fast(psum.data[6]), int2float_fast(psum.data[7]))), __hmul2(asy[i], ws2), fsum.data[3]);
};
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ <= 800
if constexpr (FAST_I2F) {
scale_fma_fast();
} else {
scale_fma_normal();
}
#else
scale_fma_normal();
#endif
// if (threadIdx.x == 3 && j == 1 && i == 0) {
// printf("before ws2 = %f %f fsum.data[%d] = %f %f\n", (float)ws2.x, (float)ws2.y, target, (float)fsum.data[target].x, (float)fsum.data[target].y);
// }
......@@ -575,9 +594,9 @@ public:
(plugins(i * INSN_M + row, pack), ...);
bool pred = i * INSN_M + row < maxRows && laneId * PACK_SIZE < maxCols;
if (pred) {
store(reinterpret_cast<pack_t *>(&output[(i * INSN_M + row) * stride + laneId * PACK_SIZE]), pack);
}
// if (pred) {
store_pred(reinterpret_cast<pack_t *>(&output[(i * INSN_M + row) * stride + laneId * PACK_SIZE]), pack, pred);
// }
}
__syncwarp();
......@@ -602,9 +621,9 @@ public:
(plugins(i * INSN_M + 8 + row, pack), ...);
bool pred = i * INSN_M + 8 + row < maxRows && laneId * PACK_SIZE < maxCols;
if (pred) {
store(reinterpret_cast<pack_t *>(&output[(i * INSN_M + 8 + row) * stride + laneId * PACK_SIZE]), pack);
}
// if (pred) {
store_pred(reinterpret_cast<pack_t *>(&output[(i * INSN_M + 8 + row) * stride + laneId * PACK_SIZE]), pack, pred);
// }
}
__syncwarp();
}
......@@ -680,33 +699,61 @@ public:
}
};
template<bool USE_BIAS = true, bool USE_SCALE = false>
struct EpilogueBias {
struct Arguments {
const packed_wscale_t *bias; // [N / BLOCK_N, WSCALES_NUM_PACKS, WSCALES_VALID_LANES] of packed_wscale_t
const packed_wscale_t *scale;
};
__device__ __forceinline__
void apply_bias(fpsum_warp &fpsum, int M, int N, int K, const packed_wscale_t *bias) {
void apply_bias(fpsum_warp &fpsum, int M, int N, int K, const packed_wscale_t *bias, const packed_wscale_t *scale) {
const int laneId = threadIdx.x % WARP_SIZE;
// if (laneId == 0) {
// printf("block.x=%d block.y=%d warpId=%d bias=%p\n", blockIdx.x, blockIdx.y, threadIdx.x / WARP_SIZE, bias);
// }
wscale_warp b;
load_wscale(bias, 0, N, b, true);
wscale_warp b, s;
if constexpr (USE_BIAS) {
load_wscale(bias, 0, N, b, true);
}
if constexpr (USE_SCALE) {
load_wscale(scale, 0, N, s, true);
}
for (int j = 0; j < WARP_N_TILES; j++) {
half2_t b1 = broadcast_wscale(b, j * 4, laneId);
half2_t b2 = broadcast_wscale(b, j * 4 + 2, laneId);
half2_t b1, b2;
half2_t s1, s2;
if constexpr (USE_BIAS) {
b1 = broadcast_wscale(b, j * 4, laneId);
b2 = broadcast_wscale(b, j * 4 + 2, laneId);
}
if constexpr (USE_SCALE) {
s1 = broadcast_wscale(s, j * 4, laneId);
s2 = broadcast_wscale(s, j * 4 + 2, laneId);
}
for (int i = 0; i < WARP_M_TILES; i++) {
auto &fsum = fpsum[i * WARP_N_TILES + j];
fsum.data[0] = __hadd2(fsum.data[0], b1);
fsum.data[1] = __hadd2(fsum.data[1], b1);
fsum.data[2] = __hadd2(fsum.data[2], b2);
fsum.data[3] = __hadd2(fsum.data[3], b2);
if constexpr (USE_SCALE && USE_BIAS) {
fsum.data[0] = __hfma2(fsum.data[0], s1, b1);
fsum.data[1] = __hfma2(fsum.data[1], s1, b1);
fsum.data[2] = __hfma2(fsum.data[2], s2, b2);
fsum.data[3] = __hfma2(fsum.data[3], s2, b2);
} else if constexpr (USE_SCALE) {
fsum.data[0] = __hmul2(fsum.data[0], s1);
fsum.data[1] = __hmul2(fsum.data[1], s1);
fsum.data[2] = __hmul2(fsum.data[2], s2);
fsum.data[3] = __hmul2(fsum.data[3], s2);
} else if constexpr (USE_BIAS) {
fsum.data[0] = __hadd2(fsum.data[0], b1);
fsum.data[1] = __hadd2(fsum.data[1], b1);
fsum.data[2] = __hadd2(fsum.data[2], b2);
fsum.data[3] = __hadd2(fsum.data[3], b2);
}
}
}
}
......@@ -714,10 +761,13 @@ public:
__device__ __forceinline__
void operator()(const BlockInfo binfo, fpsum_warp &fpsum, int M, int N, int K, Arguments args) {
const int bn = binfo.bn;
apply_bias(
fpsum, M, N, K,
args.bias + bn * WSCALES_NUM_PACKS * WSCALES_VALID_LANES
);
if constexpr (USE_BIAS || USE_SCALE) {
apply_bias(
fpsum, M, N, K,
args.bias + bn * WSCALES_NUM_PACKS * WSCALES_VALID_LANES,
args.scale + bn * WSCALES_NUM_PACKS * WSCALES_VALID_LANES
);
}
}
};
......@@ -797,7 +847,8 @@ public:
using typename Base::unpack_fpsum; \
using typename Base::EpilogueDefault; \
using typename Base::EpilogueNop; \
using typename Base::EpilogueBias;
template<bool USE_BIAS, bool USE_SCALE> \
using EpilogueBias = typename Base::EpilogueBias<USE_BIAS, USE_SCALE>;
template<typename kernel, typename ...T>
......
src/kernels/zgemm/gemm_utils.cuh
View file @ 54e6d065
......@@ -43,6 +43,41 @@ static T load(const T *addr) {
return *addr;
}
template<typename T>
__device__ __forceinline__
static T load_pred(const T *addr, bool pred) {
if constexpr (sizeof(T) == 4) {
uint32_t data;
asm volatile (
"{ .reg .pred loadpred; setp.ne.b32 loadpred, %2, 0;"
"@loadpred ld.global.nc.b32 %0, [%1];"
"}" : "=r"(data) : "l"(addr), "r"((int)pred));
return *reinterpret_cast<T *>(&data);
}
if constexpr (sizeof(T) == 8) {
uint2 data;
asm volatile (
"{ .reg .pred loadpred; setp.ne.b32 loadpred, %3, 0;"
"@loadpred ld.global.nc.v2.b32 {%0, %1}, [%2];"
"}" : "=r"(data.x), "=r"(data.y) : "l"(addr), "r"((int)pred));
return *reinterpret_cast<T *>(&data);
}
if constexpr (sizeof(T) == 16) {
uint4 data;
asm volatile (
"{ .reg .pred loadpred; setp.ne.b32 loadpred, %5, 0;"
"@loadpred ld.global.nc.v4.b32 {%0, %1, %2, %3}, [%4];"
"}" : "=r"(data.x), "=r"(data.y), "=r"(data.z), "=r"(data.w) : "l"(addr), "r"((int)pred));
return *reinterpret_cast<T *>(&data);
}
T result;
if (pred) {
result = *addr;
}
return result;
}
template<bool shmem = false, typename T>
__device__ __forceinline__
static void store(T *addr, T val) {
......@@ -76,6 +111,39 @@ static void store(T *addr, T val) {
*addr = val;
}
template<typename T>
__device__ __forceinline__
static void store_pred(T *addr, T val, bool pred) {
if constexpr (sizeof(T) == 4) {
uint32_t data = *reinterpret_cast<uint32_t *>(&val);
asm volatile (
"{ .reg .pred storepred; setp.ne.b32 storepred, %0, 0;"
"@storepred st.global.cg.b32 [%1], %2;"
"}" :: "r"((int)pred), "l"(addr), "r"(data));
return;
}
if constexpr (sizeof(T) == 8) {
uint2 data = *reinterpret_cast<uint2 *>(&val);
asm volatile (
"{ .reg .pred storepred; setp.ne.b32 storepred, %0, 0;"
"@storepred st.global.cg.v2.b32 [%1], {%2, %3};"
"}" :: "r"((int)pred), "l"(addr), "r"(data.x), "r"(data.y));
return;
}
if constexpr (sizeof(T) == 16) {
uint4 data = *reinterpret_cast<uint4 *>(&val);
asm volatile (
"{ .reg .pred storepred; setp.ne.b32 storepred, %0, 0;"
"@storepred st.global.cg.v4.b32 [%1], {%2, %3, %4, %5};"
"}" :: "r"((int)pred), "l"(addr), "r"(data.x), "r"(data.y), "r"(data.z), "r"(data.w));
return;
}
if (pred) {
*addr = val;
}
}
__device__ __forceinline__
static float2 half22float2(half2 val) {
return __half22float2(val);
......@@ -159,6 +227,21 @@ uint32_t quantize_float2<8, false>(float2 value) {
return result;
}
__device__ __forceinline__
uint32_t quantize_float2_fp4(float2 value) {
uint32_t result;
asm volatile ("{ .reg .b8 tmp; cvt.rn.satfinite.e2m1x2.f32 tmp, %1, %2; cvt.u32.u8 %0, tmp; }" : "=r"(result) : "f"(value.y), "f"(value.x));
return result;
}
__device__ __forceinline__
uint32_t quantize_float4_fp8(float4 value) {
uint16_t lo, hi;
asm volatile ("cvt.rn.satfinite.e4m3x2.f32 %0, %1, %2;" : "=h"(lo) : "f"(value.y), "f"(value.x));
asm volatile ("cvt.rn.satfinite.e4m3x2.f32 %0, %1, %2;" : "=h"(hi) : "f"(value.w), "f"(value.z));
return uint32_t(lo) | (uint32_t(hi) << 16);
}
__device__ __forceinline__
static float cuda_tanhf(float x) {
float result;
......@@ -271,4 +354,14 @@ static void unrolled_loop(F &&lambda) {
call(std::make_integer_sequence<int, cnt>());
}
// int2float is slow on sm_80 and before
// val in [-4194304, 4194303]
__device__ __forceinline__
static float int2float_fast(int val) {
float fval;
// fval = (val & 0x7FFFFF) ^ 0x4B400000
asm volatile ("lop3.b32 %0, %1, %2, %3, %4;" : "=f"(fval) : "r"(val), "n"(0x7FFFFF), "n"(0x4B400000), "n"((0xF0 & 0xCC) ^ 0xAA));
return fval - 12582912.0f;
}
}; // namespace nunchaku::kernels
\ No newline at end of file
src/kernels/zgemm/gemm_w4a4.cu
View file @ 54e6d065
......@@ -36,9 +36,23 @@ void gemm_w4a4(
Tensor out_linearattn,// linear [B, (M), N / 3]
bool act_unsigned,
std::vector<float> lora_scales, // [R / 16]
bool fuse_silu
bool fuse_silu,
bool fp4,
float alpha,
Tensor wcscales
) {
invoke_launch(ascales.dtype(), [&]<typename Config>() {
Tensor::ScalarType dtype = Tensor::INVALID_SCALAR_TYPE;
if (!fp4) {
dtype = ascales.dtype();
} else {
for (auto tensor : {out, bias, lora_up, lora_down, poolout, wcscales}) {
if (tensor.valid()) {
assert(dtype == Tensor::INVALID_SCALAR_TYPE || dtype == tensor.dtype());
dtype = tensor.dtype();
}
}
}
invoke_launch(dtype, [&]<typename Config>() {
GEMM_W4A4_Launch<Config>::gemm_w4a4(
act,
wgt,
......@@ -61,7 +75,10 @@ void gemm_w4a4(
out_linearattn,
act_unsigned,
lora_scales,
fuse_silu
fuse_silu,
fp4,
alpha,
wcscales
);
});
}
......@@ -72,10 +89,10 @@ void linearattn_vk_mul_q(Tensor q, Tensor vk) {
});
}
void quantize_w4a4_act_fuse_lora(Tensor input, Tensor output, Tensor oscales, Tensor lora_down, Tensor lora_act_out, Tensor smooth, bool fuse_glu) {
void quantize_w4a4_act_fuse_lora(Tensor input, Tensor output, Tensor oscales, Tensor lora_down, Tensor lora_act_out, Tensor smooth, bool fuse_glu, bool fp4) {
invoke_launch(input.dtype(), [&]<typename Config>() {
GEMM_W4A4_Launch<Config>::quantize_w4a4_act_fuse_lora(
input, output, oscales, lora_down, lora_act_out, smooth, fuse_glu
input, output, oscales, lora_down, lora_act_out, smooth, fuse_glu, fp4
);
});
}
......
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