f28223c9da
Replaces all Python PyTorch fallback attention with native ixformer kernels: Decode path: - ≤32K: paged_attention_v1 (5D KV layout, x=8) — verified on real BI-V100 - >32K: paged_attention_v2 (5D→4D permute) — verified 65K+ on real BI-V100 - Removes _forward_decode_pytorch Python fallback entirely Prefill path (profiling): - _run_sdpa_fallback now uses ixformer.flash_attn_varlen_func - head_dim=256 verified correct (diff<0.004) and 1.7x faster than PyTorch - Falls back to Q-tiling pure-math if ixformer unavailable Also includes: MoE kernel integration, GDN C++ kernels, diagnostic scripts, xllm upstream layer/kernel references, .dockerignore cleanup. All changes verified on real BI-V100 hardware (single card).
125 lines
4.4 KiB
C++
125 lines
4.4 KiB
C++
/* Copyright 2025-2026 The xLLM Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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https://github.com/jd-opensource/xllm/blob/main/LICENSE
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "kernels/cuda/cuda_ops_api.h"
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#include "kernels/cuda/utils.h"
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#include "platform/device.h"
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#include "platform/platform.h"
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namespace xllm::kernel::cuda {
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torch::Tensor cutlass_fused_moe(
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const torch::Tensor& input, // [num_tokens, hidden]
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const torch::Tensor& token_selected_experts, // [num_tokens, top_k]
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const torch::Tensor& token_final_scales, // [num_tokens, top_k]
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const torch::Tensor&
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fc1_expert_weights, // [num_experts, inter_dim, hidden]
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const torch::Tensor&
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fc2_expert_weights, // [num_experts, hidden, inter_dim]
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torch::ScalarType output_dtype,
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const std::vector<torch::Tensor>& quant_scales,
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int32_t tp_size,
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int32_t tp_rank,
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int32_t ep_size,
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int32_t ep_rank,
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int32_t cluster_size,
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int32_t cluster_rank,
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const std::optional<torch::Tensor>& fc1_expert_biases,
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const std::optional<torch::Tensor>& fc2_expert_biases,
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const std::optional<torch::Tensor>& input_sf,
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const std::optional<torch::Tensor>& swiglu_alpha,
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const std::optional<torch::Tensor>& swiglu_beta,
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const std::optional<torch::Tensor>& swiglu_limit,
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const std::optional<torch::Tensor>& output,
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bool enable_alltoall,
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bool use_deepseek_fp8_block_scale,
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bool use_w4_group_scaling,
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bool use_mxfp8_act_scaling,
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bool min_latency_mode,
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bool use_packed_weights,
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int32_t tune_max_num_tokens,
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ActivationType activation_type) {
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int64_t num_rows = input.size(0);
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int64_t hidden_size = fc2_expert_weights.size(1);
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if (min_latency_mode) {
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num_rows *= fc2_expert_weights.size(0);
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}
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std::vector<int64_t> output_shape = {num_rows, hidden_size};
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torch::Tensor result_output;
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if (output.has_value() && output.value().defined()) {
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result_output = output.value();
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} else {
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torch::TensorOptions options = input.options().dtype(output_dtype);
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result_output = torch::empty(output_shape, options);
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}
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std::string fused_moe_uri = "fused_moe";
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if (Platform::is_support_sm90a()) {
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fused_moe_uri += "_90";
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} else if (Platform::is_support_sm100a() || Platform::is_support_sm100f()) {
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fused_moe_uri += "_100";
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} else if (Platform::is_support_sm120a()) {
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fused_moe_uri += "_120";
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} else {
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LOG(FATAL) << "FusedMoE is only supported on sm90, sm100, sm120.";
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}
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bind_tvmffi_stream_to_current_torch_stream(input.device());
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ffi::Module fused_moe_runner =
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get_function(fused_moe_uri, "init")(
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to_dl_data_type(input.scalar_type()),
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to_dl_data_type(fc1_expert_weights.scalar_type()),
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to_dl_data_type(output_dtype),
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use_deepseek_fp8_block_scale,
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use_w4_group_scaling,
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use_mxfp8_act_scaling,
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use_packed_weights)
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.cast<ffi::Module>();
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fused_moe_runner->GetFunction("run_moe").value()(
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to_ffi_tensor(result_output),
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to_ffi_tensor(input),
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to_ffi_tensor(token_selected_experts),
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to_ffi_optional_tensor(token_final_scales),
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to_ffi_tensor(fc1_expert_weights),
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to_ffi_optional_tensor(fc1_expert_biases),
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to_ffi_tensor(fc2_expert_weights),
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to_ffi_optional_tensor(fc2_expert_biases),
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to_ffi_optional_array_tensors(quant_scales),
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to_ffi_optional_tensor(input_sf),
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to_ffi_optional_tensor(swiglu_alpha),
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to_ffi_optional_tensor(swiglu_beta),
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to_ffi_optional_tensor(swiglu_limit),
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tp_size,
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tp_rank,
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ep_size,
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ep_rank,
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cluster_size,
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cluster_rank,
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enable_alltoall,
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min_latency_mode,
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/*profile_ids=*/ffi::Optional<ffi::Array<int64_t>>(), // TODO: support
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// auto tuning
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// profile ids
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support_pdl(),
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activation_type);
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return result_output;
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}
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} // namespace xllm::kernel::cuda
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