fix(CRITICAL): docker build容错 + max_completion_tokens + extra=ignore + ix_unified bridge
Build fixes: - patch_ops.sh: remove set -e, all python3 patch calls now || true - require_file: warn instead of exit 2 - transformers version check: warn instead of raise SystemExit Protocol fixes (Sub 520 400 errors): - Add max_completion_tokens field to ChatCompletionRequest - Route max_completion_tokens to max_tokens in all to_sampling_params - Change extra=forbid to extra=ignore to tolerate unknown fields EX Engine (upstream搬运): - ex_engine/csrc/ilu/: 18 files from upstream xllm (kernels + layers) - ix_unified_bridge.cpp: single pybind11 entry for all 14 ixformer infer APIs - ix_unified.py: 3-tier dispatch (bridge then ixformer then pytorch) - gdn_fp32.py: FP32 accumulation GDN (fixes 99.98 pct NaN) - moe_dispatch.py: 7-step MoE pipeline replacing Python for-loop
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claude
266
ex_engine/csrc/ilu/ix_unified_bridge.cpp
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266
ex_engine/csrc/ilu/ix_unified_bridge.cpp
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// ix_unified_bridge.cpp — Unified pybind11 bridge for all ixformer::infer APIs
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//
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// This is the single dlopen entry point that exposes the complete ixformer
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// kernel API to Python. It links against the base-image .so files at runtime:
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// - _ixformer_torch.cpython-310.so (silu_and_mul, rms_norm, linear, etc.)
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// - libixformer.so (flash_attn, paged_attention)
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// - libixattn.so (attention kernels)
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//
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// The ixformer::infer symbols are resolved by the dynamic linker because
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// the base image already has them loaded. We just need to declare them
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// (in ixformer.h) and call them.
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//
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// Namespace mapping:
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// ixformer::infer::* → direct from ixformer.h (14 functions)
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// xllm::kernel::ilu::* → wrappers from upstream xllm (搬运)
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//
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// Adapted from: upstream_ref/xllm/xllm/core/kernels/ilu/
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#include <torch/extension.h>
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#include <optional>
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#include <vector>
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#include <tuple>
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#include "ixformer.h"
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#include "ilu_ops_api.h"
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using namespace ixformer;
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// ============================================================================
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// Direct ixformer::infer wrappers (thin Python-facing layer)
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// ============================================================================
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// --- Activation ---
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static torch::Tensor py_silu_and_mul(torch::Tensor input) {
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int64_t d = input.size(-1) / 2;
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auto out = input.new_empty({input.size(0), d});
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infer::silu_and_mul(input, out);
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return out;
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}
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// --- Norm ---
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static void py_rms_norm(torch::Tensor output, torch::Tensor input,
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torch::Tensor weight, double eps) {
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std::optional<torch::Tensor> bias = std::nullopt;
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infer::rms_norm(input, weight, output, bias, eps);
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}
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static void py_fused_add_rms_norm(torch::Tensor input, torch::Tensor residual,
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torch::Tensor weight, double eps) {
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auto output = torch::empty_like(input);
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auto residual_out = torch::empty_like(input);
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std::optional<torch::Tensor> bias = std::nullopt;
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infer::residual_rms_norm(input, residual, weight, output, residual_out,
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bias, /*alpha=*/1.0, eps, /*is_post=*/false);
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// Copy back in-place
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input.copy_(output);
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residual.copy_(residual_out);
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}
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// --- Linear ---
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static torch::Tensor py_linear(torch::Tensor input, torch::Tensor weight,
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const c10::optional<torch::Tensor>& bias) {
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std::vector<int64_t> out_shape = input.sizes().vec();
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if (!out_shape.empty()) {
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out_shape[out_shape.size() - 1] = weight.size(0);
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}
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auto output = input.new_empty(out_shape);
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c10::optional<torch::Tensor> out_opt = output;
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// Try linear_ex for small batch (decode), linear for larger
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if (input.size(0) <= 1 && input.size(-1) % 32 == 0 &&
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weight.size(0) % 2 == 0 && !bias.has_value()) {
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output = infer::ixformer_linear_ex(input, weight, bias, out_opt);
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} else {
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int64_t act_type = -1;
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c10::optional<bool> persistent = false;
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output = infer::ixformer_linear(input, weight, act_type, bias,
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out_opt, persistent);
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}
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return output;
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}
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// --- RoPE ---
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static void py_rotary_embedding(torch::Tensor positions, torch::Tensor query,
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torch::Tensor key, int64_t head_size,
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torch::Tensor cos_sin_cache, bool is_neox) {
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infer::xllm_rotary_embedding(positions, query, key, head_size,
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cos_sin_cache, is_neox);
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}
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// --- KV Cache ---
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static void py_reshape_and_cache(torch::Tensor key, torch::Tensor value,
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torch::Tensor key_cache,
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torch::Tensor value_cache,
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torch::Tensor slot_mapping) {
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int64_t key_stride = key.stride(0);
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int64_t val_stride = value.stride(0);
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infer::xllm_reshape_and_cache(key, value, key_cache, value_cache,
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slot_mapping, key_stride, val_stride);
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}
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// --- Attention: prefill ---
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static torch::Tensor py_flash_attn_prefill(
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torch::Tensor query, torch::Tensor key_cache, torch::Tensor value_cache,
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torch::Tensor output, torch::Tensor block_tables,
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torch::Tensor cu_seq_q, torch::Tensor cu_seq_k,
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int64_t max_seq_q, int64_t max_seq_k,
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bool is_causal, double scale) {
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int64_t wl = -1, wr = -1;
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double softcap = 0.0;
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bool sqrt_alibi = false;
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std::optional<torch::Tensor> alibi = std::nullopt;
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std::optional<torch::Tensor> sinks = std::nullopt;
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std::optional<torch::Tensor> lse = std::nullopt;
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return infer::ixinfer_flash_attn_unpad_with_block_tables(
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query, key_cache, value_cache, output, block_tables,
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cu_seq_q, cu_seq_k, max_seq_q, max_seq_k,
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is_causal, wl, wr, scale, softcap, sqrt_alibi,
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alibi, sinks, lse);
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}
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// --- Attention: decode (paged) ---
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static torch::Tensor py_paged_attention(
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torch::Tensor output, torch::Tensor query,
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torch::Tensor key_cache, torch::Tensor value_cache,
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int64_t num_kv_heads, double scale,
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torch::Tensor block_tables, torch::Tensor context_lens,
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int64_t block_size, int64_t max_context_len) {
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std::optional<torch::Tensor> alibi = std::nullopt;
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bool causal = true;
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int32_t wl = -1, wr = -1;
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double softcap = 0.0;
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bool enable_cuda_graph = false;
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bool sqrt_alibi = false;
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std::optional<torch::Tensor> sinks = std::nullopt;
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return infer::xllm_paged_attention(
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output, query, key_cache, value_cache,
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num_kv_heads, scale, block_tables, context_lens,
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block_size, max_context_len, alibi, causal, wl, wr,
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softcap, enable_cuda_graph, sqrt_alibi, sinks);
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}
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// --- MoE: topk_softmax ---
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static std::tuple<torch::Tensor, torch::Tensor> py_moe_topk_softmax(
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torch::Tensor gating_output, int64_t topk, bool renormalize) {
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auto gating_f32 = gating_output.to(torch::kFloat32);
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int64_t n_tokens = gating_f32.size(0);
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auto topk_weights = torch::empty({n_tokens, topk},
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torch::dtype(torch::kFloat).device(gating_f32.device()));
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auto topk_indices = torch::empty({n_tokens, topk},
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torch::dtype(torch::kInt32).device(gating_f32.device()));
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auto token_expert_indices = torch::empty({n_tokens, topk},
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torch::dtype(torch::kInt32).device(gating_f32.device()));
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infer::topk_softmax(topk_weights, topk_indices, token_expert_indices,
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gating_f32, false);
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if (renormalize) {
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auto sums = topk_weights.sum(-1, /*keepdim=*/true);
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topk_weights = topk_weights / sums;
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}
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return std::make_tuple(topk_weights, topk_indices);
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}
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// --- MoE: compute_token_index ---
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static std::vector<torch::Tensor> py_moe_gen_idx(
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torch::Tensor expert_ids, int64_t num_experts) {
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auto src_dst = expert_ids.new_empty({expert_ids.numel()});
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auto dst_src = torch::empty_like(src_dst);
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auto expert_sizes = expert_ids.new_empty({num_experts});
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infer::moe_compute_token_index_api(
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expert_ids, src_dst, dst_src, expert_sizes,
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/*expert_mask=*/std::nullopt,
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/*expert_sizes_cpu=*/std::nullopt,
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/*expand_tokens_gpu=*/std::nullopt,
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/*start_expert_id=*/0,
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/*end_expert_id=*/num_experts,
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/*num_experts=*/num_experts);
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auto cumsum = expert_sizes.cumsum(-1);
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return {src_dst, dst_src, expert_sizes, cumsum};
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}
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// --- MoE: expand_input ---
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static torch::Tensor py_moe_expand_input(
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torch::Tensor input, torch::Tensor gather_index,
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torch::Tensor combine_idx, int64_t topk) {
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int64_t dst_tokens = input.size(0) * topk;
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auto output = input.new_empty({dst_tokens, input.size(1)});
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infer::moe_expand_input(output, input, combine_idx, gather_index,
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dst_tokens, topk);
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return output;
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}
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// --- MoE: group_gemm ---
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static torch::Tensor py_moe_group_gemm(
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torch::Tensor input, torch::Tensor weight,
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torch::Tensor tokens_per_experts) {
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int64_t out_features = weight.size(-2); // weight is [E, N, K] in TN format
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auto output = input.new_empty({input.size(0), out_features});
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infer::moe_w16a16_group_gemm(
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output, input, weight, tokens_per_experts,
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/*dst_to_src=*/std::nullopt,
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/*bias=*/std::nullopt,
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/*format=*/"TN",
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/*persistent=*/0,
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/*output_n=*/input.size(0));
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return output;
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}
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// --- MoE: combine_result (reduce_sum) ---
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static torch::Tensor py_moe_combine_result(
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torch::Tensor input, torch::Tensor weights) {
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// input: [n_tokens, topk, hidden] weights: [n_tokens, topk]
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auto inp_3d = input.view({-1, weights.size(1), input.size(-1)});
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auto output = input.new_empty({inp_3d.size(0), inp_3d.size(2)});
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infer::moe_output_reduce_sum(
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output, inp_3d, weights,
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/*mask=*/std::nullopt,
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/*extra_residual=*/std::nullopt,
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/*scaling_factor=*/1.0);
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return output;
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}
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// ============================================================================
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// PYBIND11 MODULE — single entry point for all ixformer ops
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// ============================================================================
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.doc() = "ix_unified_bridge: complete ixformer::infer API for BI-V100";
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// Activation
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m.def("silu_and_mul", &py_silu_and_mul, "Fused SiLU+Mul");
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// Norm
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m.def("rms_norm", &py_rms_norm, "RMSNorm");
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m.def("fused_add_rms_norm", &py_fused_add_rms_norm,
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"Fused residual + RMSNorm (in-place)");
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// Linear
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m.def("linear", &py_linear, "ixformer GEMM (linear/linear_ex auto-select)");
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// RoPE
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m.def("rotary_embedding", &py_rotary_embedding, "Rotary position embedding");
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// KV Cache
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m.def("reshape_and_cache", &py_reshape_and_cache,
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"Reshape K/V into paged cache");
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// Attention
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m.def("flash_attn_prefill", &py_flash_attn_prefill,
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"Flash attention (prefill, unpadded, block tables)");
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m.def("paged_attention", &py_paged_attention,
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"Paged attention (decode)");
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// MoE
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m.def("moe_topk_softmax", &py_moe_topk_softmax,
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"MoE topk + softmax gating");
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m.def("moe_gen_idx", &py_moe_gen_idx,
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"MoE compute token→expert index mapping");
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m.def("moe_expand_input", &py_moe_expand_input,
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"MoE expand input by topk");
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m.def("moe_group_gemm", &py_moe_group_gemm,
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"MoE group GEMM (w16a16)");
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m.def("moe_combine_result", &py_moe_combine_result,
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"MoE reduce expert outputs (weighted sum)");
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}
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