Merge branch 'vllm-project:main' into main

Author: haichuan1221

.github/workflows/remove_label_not_ready_comment.yml

@@ -0,0 +1,23 @@
+name: Remove ready Label on notready Comment
+
+on:
+  issue_comment:
+    types: [created]
+
+jobs:
+  add-ready-label:
+    runs-on: ubuntu-latest
+    if: github.event.issue.pull_request && contains(github.event.comment.body, '/notready')
+    steps:
+        -   name: Remove ready label
+            uses: actions/github-script@v5
+            with:
+                script: |
+                    github.rest.issues.removeLabel({
+                        owner: context.repo.owner,
+                        repo: context.repo.repo,
+                        issue_number: context.issue.number,
+                        name: 'ready'
+                    })
+            env:
+                GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

Dockerfile.tpu

@@ -12,6 +12,9 @@ RUN pip install "numpy<2"
 RUN pip install torch_xla[tpu] -f https://storage.googleapis.com/libtpu-releases/index.html
 RUN pip install torch_xla[pallas] -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
 
+# Fix FastAPI dependence
+RUN pip install "starlette<0.38.0"
+
 # Build vLLM.
 COPY . /workspace/vllm
 ENV VLLM_TARGET_DEVICE="tpu"

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -13,7 +13,7 @@
 from vllm import _custom_ops as ops
 from vllm.utils import FlexibleArgumentParser
 
-DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())[1:]
+DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]
 DEFAULT_TP_SIZES = [1]
 

csrc/quantization/awq/gemm_kernels.cu

@@ -17,14 +17,6 @@ Shang and Dang, Xingyu and Han, Song}, journal={arXiv}, year={2023}
 namespace vllm {
 namespace awq {
 
-// Pack two half values.
-static inline __device__ __host__ unsigned __pack_half2(const half x,
-                                                        const half y) {
-  unsigned v0 = *((unsigned short*)&x);
-  unsigned v1 = *((unsigned short*)&y);
-  return (v1 << 16) | v0;
-}
-
 template <int N>
 __global__ void __launch_bounds__(64)
     gemm_forward_4bit_cuda_m16nXk32(int G, int split_k_iters,
@@ -42,11 +34,7 @@ __global__ void __launch_bounds__(64)
   __shared__ half A_shared[16 * (32 + 8)];
   __shared__ half B_shared[32 * (N + 8)];
 
-  __shared__ half scaling_factors_shared[N];
-  __shared__ half zeros_shared[N];
-
   int j_factors1 = ((OC + N - 1) / N);
-  int blockIdx_x = 0;
   int blockIdx_y = blockIdx.x % ((M + 16 - 1) / 16 * j_factors1);
   int blockIdx_z = blockIdx.x / ((M + 16 - 1) / 16 * j_factors1);
 
@@ -60,7 +48,6 @@ __global__ void __launch_bounds__(64)
 
   static constexpr int row_stride_warp = 32 * 8 / 32;
   static constexpr int row_stride = 2 * 32 * 8 / N;
-  bool ld_zero_flag = (threadIdx.y * 32 + threadIdx.x) * 8 < N;
   // TODO: Haotian: blockIdx_y / j_factors1 in A loading to support bsz > 16
   bool ld_A_flag =
       (blockIdx_y / j_factors1 * 16 + threadIdx.y * row_stride_warp +
@@ -145,11 +132,7 @@ __global__ void __launch_bounds__(64)
       uint32_t B_loaded =
           *(uint32_t*)(B_ptr_local + ax0_ax1_fused_0 * row_stride * (OC / 8));
       uint4 B_loaded_fp16 = dequantize_s4_to_fp16x2(B_loaded);
-      // uint4 B_loaded_zero = *(uint4*)(zeros_shared + (threadIdx.x % (cta_N /
-      // 8)) * 8);
 
-      // uint4 B_loaded_scale = *(uint4*)(scaling_factors_shared + (threadIdx.x
-      // % (cta_N / 8)) * 8);
       // - zero and * scale
       // TODO (Haotian): can save 4 assembly instructions if sormulate as deq =
       // q * scale - zero * scale.
@@ -367,17 +350,11 @@ __global__ void __launch_bounds__(64)
 __global__ void __launch_bounds__(64)
     dequantize_weights(int* __restrict__ B, half* __restrict__ scaling_factors,
                        int* __restrict__ zeros, half* __restrict__ C, int G) {
-  int j_factors1 = 4;
-  int row_stride2 = 4;
-  int split_k_iters = 1;
   static constexpr uint32_t ZERO = 0x0;
   half B_shared[32 * (128 + 8)];
 
   half* B_shared_ptr2 = B_shared;
 
-  half B_shared_warp[32];
-  int OC = 512;
-
   int N = blockDim.x * gridDim.x;  // 2
   int col = (blockIdx.x * blockDim.x + threadIdx.x);
   int row = blockIdx.y * blockDim.y + threadIdx.y;