[Chore] Remove unused PolyNorm layer (#27110)

Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>

benchmarks/kernels/benchmark_polynorm.py

@@ -1,155 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import itertools
-
-import torch
-
-from vllm import _custom_ops as vllm_ops
-from vllm.triton_utils import triton
-
-
-def polynorm_naive(
-    x: torch.Tensor,
-    weight: torch.Tensor,
-    bias: torch.Tensor,
-    eps: float = 1e-6,
-):
-    orig_shape = x.shape
-    x = x.view(-1, x.shape[-1])
-
-    def norm(x, eps: float):
-        return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + eps)
-
-    x = x.float()
-    return (
-        (
-            weight[0] * norm(x**3, eps)
-            + weight[1] * norm(x**2, eps)
-            + weight[2] * norm(x, eps)
-            + bias
-        )
-        .to(weight.dtype)
-        .view(orig_shape)
-    )
-
-
-def polynorm_vllm(
-    x: torch.Tensor,
-    weight: torch.Tensor,
-    bias: torch.Tensor,
-    eps: float = 1e-6,
-):
-    orig_shape = x.shape
-    x = x.view(-1, x.shape[-1])
-
-    out = torch.empty_like(x)
-    vllm_ops.poly_norm(out, x, weight, bias, eps)
-    output = out
-
-    output = output.view(orig_shape)
-    return output
-
-
-def calculate_diff(batch_size, seq_len, hidden_dim):
-    dtype = torch.bfloat16
-    x = torch.randn(batch_size, seq_len, hidden_dim, dtype=dtype, device="cuda")
-    weight = torch.ones(3, dtype=dtype, device="cuda")
-    bias = torch.ones(1, dtype=dtype, device="cuda")
-
-    output_naive = polynorm_naive(x, weight, bias)
-    output_vllm = polynorm_vllm(x, weight, bias)
-
-    if torch.allclose(output_naive, output_vllm, atol=1e-2, rtol=1e-2):
-        print("✅ All implementations match")
-    else:
-        print("❌ Implementations differ")
-
-
-batch_size_range = [2**i for i in range(0, 7, 2)]
-seq_length_range = [2**i for i in range(6, 11, 1)]
-dim_range = [2048, 4096]
-configs = list(itertools.product(dim_range, batch_size_range, seq_length_range))
-
-
-def get_benchmark():
-    @triton.testing.perf_report(
-        triton.testing.Benchmark(
-            x_names=["dim", "batch_size", "seq_len"],
-            x_vals=[list(_) for _ in configs],
-            line_arg="provider",
-            line_vals=["naive", "vllm"],
-            line_names=["Naive", "vLLM"],
-            styles=[("blue", "-"), ("red", "-")],
-            ylabel="us",
-            plot_name="polynorm-perf",
-            args={},
-        )
-    )
-    def benchmark(dim, batch_size, seq_len, provider):
-        dtype = torch.bfloat16
-        hidden_dim = dim * 4
-
-        x = torch.randn(batch_size, seq_len, hidden_dim, dtype=dtype, device="cuda")
-        weight = torch.ones(3, dtype=dtype, device="cuda")
-        bias = torch.ones(1, dtype=dtype, device="cuda")
-
-        quantiles = [0.5, 0.2, 0.8]
-
-        if provider == "naive":
-            ms, min_ms, max_ms = triton.testing.do_bench(
-                lambda: polynorm_naive(x, weight, bias),
-                quantiles=quantiles,
-            )
-        else:
-            ms, min_ms, max_ms = triton.testing.do_bench(
-                lambda: polynorm_vllm(x, weight, bias),
-                quantiles=quantiles,
-            )
-
-        return 1000 * ms, 1000 * max_ms, 1000 * min_ms
-
-    return benchmark
-
-
-if __name__ == "__main__":
-    import argparse
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=4,
-        help="Batch size",
-    )
-    parser.add_argument(
-        "--seq-len",
-        type=int,
-        default=128,
-        help="Sequence length",
-    )
-    parser.add_argument(
-        "--hidden-dim",
-        type=int,
-        default=8192,
-        help="Intermediate size of MLP",
-    )
-    parser.add_argument(
-        "--save-path",
-        type=str,
-        default="./configs/polnorm/",
-        help="Path to save polnorm benchmark results",
-    )
-
-    args = parser.parse_args()
-
-    # Run correctness test
-    calculate_diff(
-        batch_size=args.batch_size,
-        seq_len=args.seq_len,
-        hidden_dim=args.hidden_dim,
-    )
-
-    benchmark = get_benchmark()
-    # Run performance benchmark
-    benchmark.run(print_data=True, save_path=args.save_path)

csrc/layernorm_kernels.cu

@@ -148,211 +148,6 @@ fused_add_rms_norm_kernel(
   }
 }
 
-/* Function specialization in the case of FP16/BF16 tensors.
-   Additional optimizations we can make in this case are
-   packed and vectorized operations, which help with the
-   memory latency bottleneck.
-
-   _f16VecPN struct extends _f16Vec to add operations specifically required for
-   polynomial normalization (poly norm).
-   The original _f16Vec does not include the sum-of-powers computation or
-   in-place polynomial normalization logic. */
-template <typename scalar_t, int width>
-struct alignas(16) _f16VecPN : _f16Vec<scalar_t, width> {
-  using Base = _f16Vec<scalar_t, width>;
-  using Converter = typename Base::Converter;
-  using T1 = typename Base::T1;
-  using T2 = typename Base::T2;
-  using Base::data;
-
-  __device__ auto sum_pows() const {
-    float s2 = 0.0f, s4 = 0.0f, s6 = 0.0f;
-
-#pragma unroll
-    for (int i = 0; i < width; i += 2) {
-      float2 z = Converter::convert(T2{data[i], data[i + 1]});
-      float x2 = z.x * z.x;
-      float x4 = x2 * x2;
-      float x6 = x4 * x2;
-
-      float y2 = z.y * z.y;
-      float y4 = y2 * y2;
-      float y6 = y4 * y2;
-
-      s2 += x2 + y2;
-      s4 += x4 + y4;
-      s6 += x6 + y6;
-    }
-    return std::make_tuple(s2, s4, s6);
-  }
-
-  __device__ void poly_norm_inplace(const float w2_inv_std,
-                                    const float w1_inv_std2,
-                                    const float w0_inv_std3, const float bias) {
-#pragma unroll
-    for (int i = 0; i < width; i += 2) {
-      float2 z = Converter::convert(T2{data[i], data[i + 1]});
-
-      float x2 = z.x * z.x;
-      float x3 = x2 * z.x;
-      z.x = w2_inv_std * z.x + w1_inv_std2 * x2 + w0_inv_std3 * x3 + bias;
-
-      float y2 = z.y * z.y;
-      float y3 = y2 * z.y;
-      z.y = w2_inv_std * z.y + w1_inv_std2 * y2 + w0_inv_std3 * y3 + bias;
-
-      auto out = Converter::convert(z);
-      data[i] = out.x;
-      data[i + 1] = out.y;
-    }
-  }
-};
-
-template <typename scalar_t, int width>
-__global__ std::enable_if_t<(width > 0) && _typeConvert<scalar_t>::exists>
-poly_norm_kernel(scalar_t* __restrict__ out,           // [..., hidden_size]
-                 const scalar_t* __restrict__ input,   // [..., hidden_size]
-                 const scalar_t* __restrict__ weight,  // [3]
-                 const scalar_t* __restrict__ bias,    // [1]
-                 const float epsilon, const int hidden_size) {
-  // Sanity checks on our vector struct and type-punned pointer arithmetic
-  static_assert(std::is_pod_v<_f16VecPN<scalar_t, width>>);
-  static_assert(sizeof(_f16VecPN<scalar_t, width>) == sizeof(scalar_t) * width);
-
-  /* These and the argument pointers are all declared `restrict` as they are
-     not aliased in practice. Argument pointers should not be dereferenced
-     in this kernel as that would be undefined behavior */
-  auto* __restrict__ input_v =
-      reinterpret_cast<const _f16VecPN<scalar_t, width>*>(input);
-  const int vec_hidden_size = hidden_size / width;
-  float variance = 0.0f;
-  float variance2 = 0.0f;
-  float variance3 = 0.0f;
-
-  for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
-    int id = blockIdx.x * vec_hidden_size + idx;
-    _f16VecPN<scalar_t, width> temp = input_v[id];
-    auto [x2, x4, x6] = temp.sum_pows();
-
-    variance += x2;
-    variance2 += x4;
-    variance3 += x6;
-  }
-
-  float3 thread_variances = make_float3(variance, variance2, variance3);
-
-  struct SumOp {
-    __device__ float3 operator()(const float3& a, const float3& b) const {
-      return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);
-    }
-  };
-
-  using BlockReduce = cub::BlockReduce<float3, 1024>;
-  __shared__ typename BlockReduce::TempStorage reduceStore;
-  float3 block_variances =
-      BlockReduce(reduceStore).Reduce(thread_variances, SumOp{}, blockDim.x);
-
-  variance = block_variances.x;
-  variance2 = block_variances.y;
-  variance3 = block_variances.z;
-
-  __shared__ float s_w2_inv_std;
-  __shared__ float s_w1_inv_std2;
-  __shared__ float s_w0_inv_std3;
-  __shared__ float s_bias;
-
-  if (threadIdx.x == 0) {
-    float w0 = (float)weight[0];
-    float w1 = (float)weight[1];
-    float w2 = (float)weight[2];
-    s_bias = (float)bias[0];
-
-    s_w2_inv_std = w2 * rsqrtf(variance / hidden_size + epsilon);
-    s_w1_inv_std2 = w1 * rsqrtf(variance2 / hidden_size + epsilon);
-    s_w0_inv_std3 = w0 * rsqrtf(variance3 / hidden_size + epsilon);
-  }
-  __syncthreads();
-
-  auto* __restrict__ out_v = reinterpret_cast<_f16VecPN<scalar_t, width>*>(out);
-
-  for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
-    int id = blockIdx.x * vec_hidden_size + idx;
-    _f16VecPN<scalar_t, width> temp = input_v[id];
-    temp.poly_norm_inplace(s_w2_inv_std, s_w1_inv_std2, s_w0_inv_std3, s_bias);
-    out_v[id] = temp;
-  }
-}
-
-/* Generic poly_norm_kernel
-   The width field is not used here but necessary for other specializations.
- */
-template <typename scalar_t, int width>
-__global__ std::enable_if_t<(width == 0) || !_typeConvert<scalar_t>::exists>
-poly_norm_kernel(scalar_t* __restrict__ out,           // [..., hidden_size]
-                 const scalar_t* __restrict__ input,   // [..., hidden_size]
-                 const scalar_t* __restrict__ weight,  // [3]
-                 const scalar_t* __restrict__ bias,    // [1]
-                 const float epsilon, const int hidden_size) {
-  float variance = 0.0f;
-  float variance2 = 0.0f;
-  float variance3 = 0.0f;
-
-  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    float x = (float)input[blockIdx.x * hidden_size + idx];
-    float x2 = x * x;
-    float x4 = x2 * x2;
-    float x6 = x4 * x2;
-
-    variance += x2;
-    variance2 += x4;
-    variance3 += x6;
-  }
-
-  float3 thread_variances = make_float3(variance, variance2, variance3);
-
-  struct SumOp {
-    __device__ float3 operator()(const float3& a, const float3& b) const {
-      return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);
-    }
-  };
-
-  using BlockReduce = cub::BlockReduce<float3, 1024>;
-  __shared__ typename BlockReduce::TempStorage reduceStore;
-  float3 block_variances =
-      BlockReduce(reduceStore).Reduce(thread_variances, SumOp{}, blockDim.x);
-
-  variance = block_variances.x;
-  variance2 = block_variances.y;
-  variance3 = block_variances.z;
-
-  __shared__ float s_w2_inv_std;
-  __shared__ float s_w1_inv_std2;
-  __shared__ float s_w0_inv_std3;
-  __shared__ float s_bias;
-
-  if (threadIdx.x == 0) {
-    float w0 = (float)weight[0];
-    float w1 = (float)weight[1];
-    float w2 = (float)weight[2];
-    s_bias = (float)bias[0];
-
-    s_w2_inv_std = w2 * rsqrtf(variance / hidden_size + epsilon);
-    s_w1_inv_std2 = w1 * rsqrtf(variance2 / hidden_size + epsilon);
-    s_w0_inv_std3 = w0 * rsqrtf(variance3 / hidden_size + epsilon);
-  }
-  __syncthreads();
-
-  for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    float x = (float)input[blockIdx.x * hidden_size + idx];
-    float x2 = x * x;
-    float x3 = x2 * x;
-
-    out[blockIdx.x * hidden_size + idx] =
-        (scalar_t)(x * s_w2_inv_std + x2 * s_w1_inv_std2 + x3 * s_w0_inv_std3 +
-                   s_bias);
-  }
-}
-
 }  // namespace vllm
 
 void rms_norm(torch::Tensor& out,     // [..., hidden_size]
@@ -444,50 +239,3 @@ void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
     LAUNCH_FUSED_ADD_RMS_NORM(0);
   }
 }
-
-#define LAUNCH_FUSED_POLY_NORM(width)                                         \
-  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "poly_norm_kernel", [&] { \
-    vllm::poly_norm_kernel<scalar_t, width><<<grid, block, 0, stream>>>(      \
-        out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),                 \
-        weight.data_ptr<scalar_t>(), bias.data_ptr<scalar_t>(), epsilon,      \
-        hidden_size);                                                         \
-  });
-
-void poly_norm(torch::Tensor& out,     // [..., hidden_size]
-               torch::Tensor& input,   // [..., hidden_size]
-               torch::Tensor& weight,  // [3]
-               torch::Tensor& bias,    // [1]
-               double epsilon) {
-  TORCH_CHECK(out.is_contiguous());
-  TORCH_CHECK(input.is_contiguous());
-  TORCH_CHECK(out.data_ptr() != input.data_ptr());
-
-  int hidden_size = input.size(-1);
-  int num_tokens = input.numel() / hidden_size;
-
-  dim3 grid(num_tokens);
-  /* This kernel is memory-latency bound in many scenarios.
-     When num_tokens is large, a smaller block size allows
-     for increased block occupancy on CUs and better latency
-     hiding on global mem ops. */
-  const int max_block_size = (num_tokens < 256) ? 1024 : 256;
-  dim3 block(std::min(hidden_size, max_block_size));
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  /*If the tensor types are FP16/BF16, try to use the optimized kernel
-    with packed + vectorized ops.
-    Max optimization is achieved with a width-8 vector of FP16/BF16s
-    since we can load at most 128 bits at once in a global memory op.
-    However, this requires each tensor's data to be aligned to 16
-    bytes.
-   */
-  auto inp_ptr = reinterpret_cast<std::uintptr_t>(input.data_ptr());
-  auto out_ptr = reinterpret_cast<std::uintptr_t>(out.data_ptr());
-  bool ptrs_are_aligned = inp_ptr % 16 == 0 && out_ptr % 16 == 0;
-  bool batch_invariant_launch = vllm::vllm_is_batch_invariant();
-  if (ptrs_are_aligned && hidden_size % 8 == 0 && !batch_invariant_launch) {
-    LAUNCH_FUSED_POLY_NORM(8);
-  } else {
-    LAUNCH_FUSED_POLY_NORM(0);
-  }
-}

csrc/ops.h

@@ -92,9 +92,6 @@ void rms_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
 void fused_add_rms_norm(torch::Tensor& input, torch::Tensor& residual,
                         torch::Tensor& weight, double epsilon);
 
-void poly_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
-               torch::Tensor& bias, double epsilon);
-
 void apply_repetition_penalties_(torch::Tensor& logits,
                                  const torch::Tensor& prompt_mask,
                                  const torch::Tensor& output_mask,

csrc/torch_bindings.cpp

@@ -175,12 +175,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "float epsilon) -> ()");
   ops.impl("fused_add_rms_norm", torch::kCUDA, &fused_add_rms_norm);
 
-  // Polynomial Normalization.
-  ops.def(
-      "poly_norm(Tensor! out, Tensor input, Tensor weight, Tensor bias, float "
-      "epsilon) -> ()");
-  ops.impl("poly_norm", torch::kCUDA, &poly_norm);
-
   // Apply repetition penalties to logits in-place
   ops.def(
       "apply_repetition_penalties_(Tensor! logits, Tensor prompt_mask, "

tests/kernels/core/test_layernorm.py

@@ -6,7 +6,7 @@ import torch
 
 from tests.kernels.quant_utils import FP8_DTYPE
 from tests.kernels.utils import opcheck
-from vllm.model_executor.layers.layernorm import PolyNorm, RMSNorm
+from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.platforms import current_platform
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]
@@ -70,38 +70,6 @@ def test_rms_norm(
         )
 
 
-@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
-@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
-@pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("seed", SEEDS)
-@pytest.mark.parametrize("device", CUDA_DEVICES)
-@torch.inference_mode()
-def test_poly_norm(
-    num_tokens: int,
-    hidden_size: int,
-    dtype: torch.dtype,
-    seed: int,
-    device: str,
-) -> None:
-    current_platform.seed_everything(seed)
-    torch.set_default_device(device)
-    layer = PolyNorm().to(dtype=dtype)
-    layer.weight.data.normal_(mean=1.0, std=0.1)
-    layer.bias.data.normal_(mean=1.0, std=0.1)
-    scale = 1 / (2 * hidden_size)
-    x = torch.randn(num_tokens, hidden_size, dtype=dtype)
-    x *= scale
-
-    ref_out = layer.forward_native(x)
-    out = layer(x)
-    torch.testing.assert_close(out, ref_out, atol=1e-2, rtol=1e-2)
-
-    opcheck(
-        torch.ops._C.poly_norm,
-        (out, x, layer.weight.data, layer.bias.data, layer.variance_epsilon),
-    )
-
-
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
 @pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
 @pytest.mark.parametrize("add_residual", ADD_RESIDUAL)

vllm/_custom_ops.py

@@ -339,18 +339,6 @@ def fused_add_rms_norm(
     torch.ops._C.fused_add_rms_norm(input, residual, weight, epsilon)
 
 
-def poly_norm(
-    out: torch.Tensor,
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    bias: torch.Tensor,
-    epsilon: float,
-) -> None:
-    # TODO: Remove this contiguous call when the kernel is updated to support non-contiguous input
-    input_contiguous = input.contiguous()
-    torch.ops._C.poly_norm(out, input_contiguous, weight, bias, epsilon)
-
-
 def apply_repetition_penalties_torch(
     logits: torch.Tensor,
     prompt_mask: torch.Tensor,

vllm/model_executor/layers/layernorm.py

@@ -58,22 +58,6 @@ def fused_add_rms_norm(
     return x, residual
 
 
-def poly_norm(
-    x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, variance_epsilon: float
-) -> torch.Tensor:
-    from vllm import _custom_ops as ops
-
-    out = torch.empty_like(x)
-    ops.poly_norm(
-        out,
-        x,
-        weight,
-        bias,
-        variance_epsilon,
-    )
-    return out
-
-
 def rocm_aiter_rms_norm_impl(
     x: torch.Tensor, weight: torch.Tensor, variance_epsilon: float
 ) -> torch.Tensor:
@@ -385,53 +369,6 @@ class GemmaRMSNorm(CustomOp):
         return self.forward_native(x, residual)
 
 
-@CustomOp.register("poly_norm")
-class PolyNorm(CustomOp):
-    """Polynomial normalization.
-
-    Computes x -> w_0 * RMSNorm(x^3) + w_1 * RMSNorm(x^2) + w_2 * RMSNorm(x) + b
-    where w_n is the learned weight and b is the bias.
-    Refer to https://arxiv.org/html/2411.03884v1
-    """
-
-    def __init__(
-        self,
-        eps: float = 1e-6,
-    ) -> None:
-        super().__init__()
-        self.weight = torch.nn.Parameter(torch.ones(3) / 3)
-        self.bias = torch.nn.Parameter(torch.zeros(1))
-        self.variance_epsilon = eps
-
-    def _norm(self, x):
-        return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + self.variance_epsilon)
-
-    def forward_native(
-        self,
-        x: torch.Tensor,
-    ) -> torch.Tensor:
-        """PyTorch-native implementation equivalent to forward().
-
-        Refer to https://github.com/BryceZhuo/PolyCom?tab=readme-ov-file/README.md
-        """
-
-        orig_dtype = x.dtype
-        x_float = x.to(torch.float32)
-        output = (
-            self.weight[0] * self._norm(x_float**3)
-            + self.weight[1] * self._norm(x_float**2)
-            + self.weight[2] * self._norm(x_float)
-            + self.bias
-        )
-        return output.to(orig_dtype)
-
-    def forward_cuda(
-        self,
-        x: torch.Tensor,
-    ) -> torch.Tensor:
-        return poly_norm(x, self.weight, self.bias, self.variance_epsilon)
-
-
 class LayerNorm(nn.Module):
     """
     Layer Normalization.