New kernels 0222 (#12)

* New kernels 0222

* Test build

* Add to setup

* Fix cc

* Separate pybindings

* Fix casting

* Fix name of pybindings

* Fix pybindings

* Build on tag

* Bump to 0.0.6

* Upgrade to 2.2.1

.github/workflows/build.yaml

@@ -91,7 +91,7 @@ jobs:
           # Install torch
           $cudaVersion = $env:CUDA_VERSION.Replace('.', '')
           $cudaVersionPytorch = $cudaVersion.Substring(0, $cudaVersion.Length - 1)
-          if ([int]$cudaVersionPytorch -gt 118) { $pytorchVersion = "torch==2.2.0" } else {$pytorchVersion = "torch==2.0.1"}
+          $pytorchVersion = "torch==2.2.1"
           python -m pip install --upgrade --no-cache-dir $pytorchVersion+cu$cudaVersionPytorch --index-url https://download.pytorch.org/whl/cu$cudaVersionPytorch
           python -m pip install build setuptools wheel ninja
 

awq_ext/pybind_awq_v2.cpp

@@ -0,0 +1,10 @@
+#include <pybind11/pybind11.h>
+#include <torch/extension.h>
+#include "quantization_new/gemm/gemm_cuda.h"
+#include "quantization_new/gemv/gemv_cuda.h"
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
+{
+    m.def("gemm_forward_cuda_prefill", &gemm_forward_cuda_prefill, "New quantized GEMM kernel.");
+    m.def("gemv_forward_cuda_decode", &gemv_forward_cuda_decode, "New quantized GEMM kernel.");
+}

awq_ext/quantization_new/dequantize.cuh

@@ -0,0 +1,77 @@
+/*
+Modified from NVIDIA FasterTransformer: https://github.com/NVIDIA/FasterTransformer/blob/main/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h
+
+@article{lin2023awq,
+  title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
+  author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
+  journal={arXiv},
+  year={2023}
+}
+*/
+#include <cuda_fp16.h>
+#pragma once
+
+__inline__ __device__ void dequantize_s4_to_fp16x2(half2 const &source, uint4 *result)
+{
+    // uint4 result;
+
+    uint32_t *h = reinterpret_cast<uint32_t *>(result);
+    uint32_t const i4s = reinterpret_cast<uint32_t const &>(source);
+
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint32_t immLut = (0xf0 & 0xcc) | 0xaa;
+    static constexpr uint32_t BOTTOM_MASK = 0x000f000f;
+    static constexpr uint32_t TOP_MASK = 0x00f000f0;
+    static constexpr uint32_t I4s_TO_F16s_MAGIC_NUM = 0x64006400;
+
+    // Note that the entire sequence only requires 1 shift instruction. This is thanks to the register packing
+    // format and the fact that we force our integers to be unsigned, and account for this in the fp16 subtractions.
+    // In addition, I exploit the fact that sub and fma have the same throughput in order to convert elt_23 and
+    // elt_67 to fp16 without having to shift them to the bottom bits before hand.
+
+    // Shift right by 8 to now consider elt_45 and elt_67. Issue first to hide RAW dependency if we issue
+    // immediately before required.
+    const uint32_t top_i4s = i4s >> 8;
+    // Extract elt_01 - (i4s & 0x000f000f) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                 : "=r"(h[0])
+                 : "r"(i4s), "n"(BOTTOM_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+    // Extract elt_23 (i4s & 0x00f000f0) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                 : "=r"(h[1])
+                 : "r"(i4s), "n"(TOP_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+    // Extract elt_45 (top_i4s & 0x000f000f) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                 : "=r"(h[2])
+                 : "r"(top_i4s), "n"(BOTTOM_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+    // Extract elt_67 (top_i4s & 0x00f000f0) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                 : "=r"(h[3])
+                 : "r"(top_i4s), "n"(TOP_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+
+    // I use inline PTX below because I am not sure if the compiler will emit float2half instructions if I use the
+    // half2 ctor. In this case, I chose performance reliability over code readability.
+
+    // This is the half2 {1032, 1032} represented as an integer.
+    // static constexpr uint32_t FP16_TOP_MAGIC_NUM = 0x64086408;
+    // Haotian: subtract {1024, 1024} instead, we do not need to map to [-8, 7]
+    static constexpr uint32_t FP16_TOP_MAGIC_NUM = 0x64006400;
+    // This is the half2 {1 / 16, 1 / 16} represented as an integer.
+    static constexpr uint32_t ONE_SIXTEENTH = 0x2c002c00;
+    // This is the half2 {-72, -72} represented as an integer.
+    // static constexpr uint32_t NEG_72 = 0xd480d480;
+    // Haotian: Let's use {-64, -64}.
+    static constexpr uint32_t NEG_64 = 0xd400d400;
+
+    // Finally, we construct the output numbers.
+    // Convert elt_01
+    asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(h[0]) : "r"(h[0]), "r"(FP16_TOP_MAGIC_NUM));
+    // Convert elt_23
+    asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(h[1]) : "r"(h[1]), "r"(ONE_SIXTEENTH), "r"(NEG_64));
+    // Convert elt_45
+    asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(h[2]) : "r"(h[2]), "r"(FP16_TOP_MAGIC_NUM));
+    // Convert elt_67
+    asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(h[3]) : "r"(h[3]), "r"(ONE_SIXTEENTH), "r"(NEG_64));
+
+    // return result;
+}