a3c45d3b36
project_7 docker build succeeds on competition platform. Diff was: 5 ENV lines + .dockerignore whitelist. ENV lines may override base image paths or trigger patch_ops.sh failures. .dockerignore whitelist may exclude files the build needs. Now Dockerfile is byte-identical to project_7.
452 lines
18 KiB
C++
452 lines
18 KiB
C++
// ix_full_bridge_v2.cpp — Complete bridge to ALL ixformer::infer C++ functions
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//
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// Base image has ixformer::infer namespace with 14 functions.
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// Previous ix_full_bridge.cpp only bridged 4 (silu_and_mul, rms_norm,
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// fused_add_rms_norm, linear). This file bridges ALL 14.
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//
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// The base image's _ixformer_torch.cpython-310.so and libixformer.so
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// export these symbols in the ixformer::infer namespace (confirmed by nm -D).
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//
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// Compile:
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// torch.utils.cpp_extension.load(
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// name="ix_full_bridge_v2",
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// sources=["ix_full_bridge_v2.cpp"],
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// extra_ldflags=[<all ixformer .so files>, "-Wl,-rpath,..."],
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// extra_cflags=["-O2", "-std=c++17"],
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// )
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//
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// Upstream reference: xllm_latest/core/kernels/ilu/ixformer.h
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#include <torch/extension.h>
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#include <optional>
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#include <string>
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#include <tuple>
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#include <vector>
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// ============================================================================
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// Forward declarations — ixformer::infer namespace from base image .so
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// Signatures EXACTLY match upstream_ref/xllm_latest/core/kernels/ilu/ixformer.h
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// ============================================================================
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namespace ixformer { namespace infer {
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// --- Attention ---
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torch::Tensor ixinfer_flash_attn_unpad_with_block_tables(
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torch::Tensor& query,
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torch::Tensor& key_cache,
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torch::Tensor& value_cache,
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torch::Tensor& out,
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torch::Tensor& block_tables,
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torch::Tensor& cu_seq_q,
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torch::Tensor& cu_seq_k,
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int64_t max_seq_q,
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int64_t max_seq_k,
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bool is_causal,
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int64_t window_left,
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int64_t window_right,
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double scale,
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double softcap,
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bool sqrt_alibi,
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const std::optional<torch::Tensor>& alibi_slopes,
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const std::optional<torch::Tensor>& sinks,
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std::optional<torch::Tensor>& lse);
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torch::Tensor xllm_paged_attention(
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torch::Tensor& out,
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torch::Tensor& query,
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torch::Tensor& key_cache,
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torch::Tensor& value_cache,
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int64_t num_kv_heads,
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double scale,
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torch::Tensor& block_tables,
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torch::Tensor& context_lens,
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int64_t block_size,
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int64_t max_context_len,
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const std::optional<torch::Tensor>& alibi_slopes,
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bool causal,
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int32_t window_left,
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int32_t window_right,
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double softcap,
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bool enable_cuda_graph,
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bool use_sqrt_alibi,
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const std::optional<torch::Tensor>& sinks);
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// --- Activation ---
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void silu_and_mul(torch::Tensor& input, torch::Tensor& output);
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// --- Linear ---
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torch::Tensor ixformer_linear(torch::Tensor& input,
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torch::Tensor& weight,
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int64_t act_type,
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const std::optional<torch::Tensor>& bias,
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const std::optional<torch::Tensor>& out,
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const std::optional<bool> persistent);
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torch::Tensor ixformer_linear_ex(torch::Tensor& input,
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torch::Tensor& weight,
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const c10::optional<torch::Tensor>& bias,
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const c10::optional<torch::Tensor>& out);
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// --- Cache ---
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void xllm_reshape_and_cache(torch::Tensor& key,
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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_token_stride,
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int64_t value_token_stride);
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// --- RoPE ---
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void xllm_rotary_embedding(torch::Tensor& positions,
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torch::Tensor& query,
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torch::Tensor& key,
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int64_t head_size,
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torch::Tensor& cos_sin_cache,
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bool is_neox);
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// --- Norm ---
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void residual_rms_norm(torch::Tensor& input,
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torch::Tensor& residual,
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torch::Tensor& weight,
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torch::Tensor& output,
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torch::Tensor& residual_output,
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const std::optional<torch::Tensor>& fused_bias,
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double alpha,
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double eps,
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bool is_post);
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void rms_norm(torch::Tensor& input,
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torch::Tensor& weight,
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torch::Tensor& output,
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const std::optional<torch::Tensor>& fused_bias,
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double eps);
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// --- MoE ---
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void topk_softmax(torch::Tensor& topk_weights,
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torch::Tensor& topk_indices,
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torch::Tensor& token_expert_indices,
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torch::Tensor& gating_output,
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bool renormalize);
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void moe_compute_token_index_api(
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torch::Tensor& topk_ids,
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torch::Tensor& src_dst,
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torch::Tensor& dst_src,
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torch::Tensor& expert_sizes_gpu,
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const c10::optional<torch::Tensor>& expert_mask,
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const c10::optional<torch::Tensor>& expert_sizes_cpu,
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const c10::optional<torch::Tensor>& expand_tokens_gpu,
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int64_t start_expert_id,
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int64_t end_expert_id,
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int64_t num_experts);
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void moe_expand_input(torch::Tensor outputs,
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torch::Tensor inputs,
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torch::Tensor dst_to_src,
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const c10::optional<torch::Tensor>& src_to_dst,
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int64_t dst_tokens,
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int64_t expand_factor);
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void moe_w16a16_group_gemm(torch::Tensor output,
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torch::Tensor inputs,
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torch::Tensor weights,
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torch::Tensor tokens_per_experts,
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const c10::optional<torch::Tensor>& dst_to_src,
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const c10::optional<torch::Tensor>& bias,
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std::string format,
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int64_t persistent,
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int64_t output_n);
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void moe_output_reduce_sum(torch::Tensor outputs,
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torch::Tensor inputs,
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const c10::optional<torch::Tensor>& mul_weight,
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const c10::optional<torch::Tensor>& mask,
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const c10::optional<torch::Tensor>& extra_residual,
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double scaling_factor);
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}} // namespace ixformer::infer
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// ============================================================================
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// Python wrappers — thin wrappers that match ix_bridge.py's expected API
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// ============================================================================
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// --- silu_and_mul ---
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torch::Tensor ix_silu_and_mul(torch::Tensor input) {
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int64_t half_dim = input.size(-1) / 2;
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auto output = input.new_empty({input.size(0), half_dim});
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ixformer::infer::silu_and_mul(input, output);
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return output;
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}
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// --- rms_norm ---
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void ix_rms_norm(torch::Tensor output, torch::Tensor input,
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torch::Tensor weight, double eps) {
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ixformer::infer::rms_norm(input, weight, output,
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/*fused_bias=*/std::nullopt, eps);
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}
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// --- fused_add_rms_norm ---
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// residual_rms_norm does: output = rms_norm(input + alpha*residual, weight, eps)
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// residual_output = input + alpha*residual
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void ix_fused_add_rms_norm(torch::Tensor input, torch::Tensor residual,
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torch::Tensor weight, torch::Tensor output,
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torch::Tensor residual_output, double eps) {
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ixformer::infer::residual_rms_norm(input, residual, weight,
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output, residual_output,
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/*fused_bias=*/std::nullopt,
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/*alpha=*/1.0, eps,
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/*is_post=*/false);
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}
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// --- linear ---
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torch::Tensor ix_linear(torch::Tensor input, torch::Tensor weight,
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const c10::optional<torch::Tensor>& bias) {
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auto input_2d = input.view({-1, input.size(-1)});
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int64_t m = input_2d.size(0);
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if (m <= 1 && !bias.has_value()) {
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return ixformer::infer::ixformer_linear_ex(
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input, weight, bias, /*out=*/c10::optional<torch::Tensor>());
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}
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return ixformer::infer::ixformer_linear(
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input, weight, /*act_type=*/0, bias,
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/*out=*/std::nullopt, /*persistent=*/std::nullopt);
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}
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// --- rotary_embedding ---
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void ix_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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ixformer::infer::xllm_rotary_embedding(
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positions, query, key, head_size, cos_sin_cache, is_neox);
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}
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// --- reshape_and_cache ---
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void ix_reshape_and_cache(torch::Tensor key, torch::Tensor value,
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torch::Tensor key_cache, torch::Tensor value_cache,
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torch::Tensor slot_mapping) {
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// token stride = product of dims after dim 0 for key/value
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// key shape: [num_tokens, num_heads, head_dim]
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int64_t key_token_stride = 1;
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for (int i = 1; i < key.dim(); i++) key_token_stride *= key.size(i);
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int64_t value_token_stride = 1;
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for (int i = 1; i < value.dim(); i++) value_token_stride *= value.size(i);
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ixformer::infer::xllm_reshape_and_cache(
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key, value, key_cache, value_cache, slot_mapping,
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key_token_stride, value_token_stride);
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}
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// --- paged_attention (decode) ---
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torch::Tensor ix_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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const c10::optional<torch::Tensor>& alibi_slopes) {
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return ixformer::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_slopes,
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/*causal=*/true, /*window_left=*/-1, /*window_right=*/-1,
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/*softcap=*/0.0, /*enable_cuda_graph=*/false,
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/*use_sqrt_alibi=*/false, /*sinks=*/std::nullopt);
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}
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// --- flash_attn_prefill ---
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torch::Tensor ix_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_query_len, int64_t max_seq_len,
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double scale, bool is_causal,
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int64_t window_left, int64_t window_right) {
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std::optional<torch::Tensor> lse = std::nullopt;
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return ixformer::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_query_len, max_seq_len,
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is_causal, window_left, window_right, scale,
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/*softcap=*/0.0, /*sqrt_alibi=*/false,
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/*alibi_slopes=*/std::nullopt, /*sinks=*/std::nullopt, lse);
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}
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// --- MoE: topk_softmax ---
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// Returns (topk_weights, topk_ids, token_expert_indices)
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std::tuple<torch::Tensor, torch::Tensor, torch::Tensor>
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ix_topk_softmax(torch::Tensor gating_output, int64_t topk, bool renormalize) {
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int64_t num_tokens = gating_output.size(0);
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auto topk_weights = torch::empty({num_tokens, topk},
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torch::dtype(torch::kFloat32).device(gating_output.device()));
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auto topk_ids = torch::empty({num_tokens, topk},
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torch::dtype(torch::kInt32).device(gating_output.device()));
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auto token_expert_indices = torch::empty({num_tokens, topk},
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torch::dtype(torch::kInt32).device(gating_output.device()));
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auto gating_f32 = gating_output.to(torch::kFloat32);
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ixformer::infer::topk_softmax(
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topk_weights, topk_ids, token_expert_indices, gating_f32, renormalize);
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return std::make_tuple(topk_weights, topk_ids, token_expert_indices);
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}
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// --- MoE: moe_gen_idx ---
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// Equivalent to xllm::kernel::ilu::moe_gen_idx
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std::vector<torch::Tensor>
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ix_moe_gen_idx(torch::Tensor expert_id, int64_t expert_num) {
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auto src_dst = expert_id.new_empty({expert_id.numel()});
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auto dst_src = torch::empty_like(src_dst);
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auto expert_sizes_gpu = expert_id.new_empty({expert_num});
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ixformer::infer::moe_compute_token_index_api(
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expert_id, src_dst, dst_src, expert_sizes_gpu,
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/*expert_mask=*/c10::nullopt,
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/*expert_sizes_cpu=*/c10::nullopt,
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/*expand_tokens_gpu=*/c10::nullopt,
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/*start_expert_id=*/0,
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/*end_expert_id=*/expert_num,
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/*num_experts=*/expert_num);
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auto expert_sizes_cumsum = expert_sizes_gpu.cumsum(-1);
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return {src_dst, dst_src, expert_sizes_gpu, expert_sizes_cumsum};
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}
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// --- MoE: moe_expand_input ---
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torch::Tensor ix_moe_expand_input(torch::Tensor input,
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torch::Tensor gather_index,
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torch::Tensor combine_idx,
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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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ixformer::infer::moe_expand_input(
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output, input, combine_idx, gather_index, dst_tokens, topk);
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return output;
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}
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// --- MoE: group_gemm ---
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torch::Tensor ix_group_gemm(torch::Tensor inputs, torch::Tensor weights,
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torch::Tensor tokens_per_experts,
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int64_t output_n) {
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int64_t total_tokens = inputs.size(0);
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auto output = inputs.new_empty({total_tokens, output_n});
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ixformer::infer::moe_w16a16_group_gemm(
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output, inputs, weights, tokens_per_experts,
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/*dst_to_src=*/c10::nullopt,
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/*bias=*/c10::nullopt,
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/*format=*/"default",
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/*persistent=*/0,
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output_n);
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return output;
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}
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// --- MoE: moe_combine_result ---
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torch::Tensor ix_moe_combine_result(torch::Tensor input, torch::Tensor weight) {
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// input: [T*topk, H], weight: [T, topk]
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auto input_3d = input.view({-1, weight.size(1), input.size(1)});
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auto output = input.new_empty({input_3d.size(0), input_3d.size(2)});
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ixformer::infer::moe_output_reduce_sum(
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output, input_3d, weight,
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/*mask=*/c10::nullopt,
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/*extra_residual=*/c10::nullopt,
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/*scaling_factor=*/1.0);
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return output;
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}
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// --- MoE: fused_moe_forward (7-step pipeline) ---
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// This is the full fused MoE forward: topk → gen_idx → expand → gemm(w13) →
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// silu_mul → gemm(w2) → combine
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torch::Tensor ix_fused_moe_forward(
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torch::Tensor hidden_states,
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torch::Tensor router_logits,
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torch::Tensor w13, // [num_experts, 2*intermediate, hidden]
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torch::Tensor w2, // [num_experts, hidden, intermediate]
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int64_t topk,
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int64_t num_experts,
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bool renormalize) {
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// Step 1: topk_softmax
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auto [topk_weights, topk_ids, token_expert_indices] =
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ix_topk_softmax(router_logits, topk, renormalize);
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if (renormalize) {
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auto sum = topk_weights.sum(-1, /*keepdim=*/true);
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topk_weights = topk_weights / sum;
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}
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// Step 2: moe_gen_idx
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auto idx_results = ix_moe_gen_idx(topk_ids.view({-1}), num_experts);
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auto& src_dst = idx_results[0];
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auto& dst_src = idx_results[1];
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auto& expert_sizes_gpu = idx_results[2];
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// Step 3: moe_expand_input
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auto expanded = ix_moe_expand_input(hidden_states, src_dst, dst_src, topk);
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// Step 4: group_gemm (w13: gate_up projection)
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int64_t intermediate_2x = w13.size(1);
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auto gate_up = ix_group_gemm(expanded, w13.view({-1, w13.size(2)}),
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expert_sizes_gpu, intermediate_2x);
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// Step 5: silu_and_mul
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auto activated = ix_silu_and_mul(gate_up);
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// Step 6: group_gemm (w2: down projection)
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int64_t hidden_size = w2.size(1);
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auto down = ix_group_gemm(activated, w2.view({-1, w2.size(2)}),
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expert_sizes_gpu, hidden_size);
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// Step 7: moe_combine_result
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auto output = ix_moe_combine_result(down, topk_weights);
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return output;
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}
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// ============================================================================
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// Module registration — ALL 14 functions + fused pipeline
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// ============================================================================
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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// Activation
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m.def("silu_and_mul", &ix_silu_and_mul,
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"Fused SiLU+mul activation via ixformer::infer");
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// Norm
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m.def("rms_norm", &ix_rms_norm,
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"RMSNorm via ixformer::infer");
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m.def("fused_add_rms_norm", &ix_fused_add_rms_norm,
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"Residual + RMSNorm via ixformer::infer");
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// Linear
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m.def("linear", &ix_linear,
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"GEMM via ixformer::infer (linear/linear_ex)");
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// RoPE
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m.def("rotary_embedding", &ix_rotary_embedding,
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"Rotary position embedding via ixformer::infer");
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// Cache
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m.def("reshape_and_cache", &ix_reshape_and_cache,
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"KV cache reshape+store via ixformer::infer");
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// Attention
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m.def("paged_attention", &ix_paged_attention,
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"Paged attention decode via ixformer::infer");
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m.def("flash_attn_prefill", &ix_flash_attn_prefill,
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"Flash attention prefill via ixformer::infer");
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// MoE (individual steps)
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m.def("topk_softmax", &ix_topk_softmax,
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"MoE topk+softmax routing via ixformer::infer");
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m.def("moe_gen_idx", &ix_moe_gen_idx,
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"MoE compute token index via ixformer::infer");
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m.def("moe_expand_input", &ix_moe_expand_input,
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"MoE expand input for expert dispatch via ixformer::infer");
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m.def("group_gemm", &ix_group_gemm,
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"MoE grouped GEMM via ixformer::infer");
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m.def("moe_combine_result", &ix_moe_combine_result,
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"MoE output reduce sum via ixformer::infer");
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// MoE (fused 7-step pipeline)
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m.def("fused_moe_forward", &ix_fused_moe_forward,
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"Complete fused MoE forward (7-step pipeline) via ixformer::infer");
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}
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