cleanup: wire td_error ISV scratch + rewrite stale TODOs declaratively
Part A of pre-L40S cleanup.
1. Wire td_error batch mean into ISV scratch (gpu_dqn_trainer.rs):
`launch_loss_reduce` now runs the generic `c51_loss_reduce` kernel a
second time over `td_errors_buf` into `td_error_scratch_dev_ptr`.
ISV[2] (TD-error EMA in `isv_signal_update`) was previously reading
a zero-initialised scratch and accumulated a constant-zero signal.
This was a genuinely missing kernel writeback — the C51 loss kernel
was already emitting per-sample |TD-error| into `td_errors_buf`
(c51_loss_kernel.cu:1096), it just wasn't being batch-reduced.
Reuses the existing `c51_loss_reduce` (generic mean-reduction, single
block, deterministic) rather than adding a new kernel — no new CUDA
surface, no ABI change.
2. Remove 2 stale TODOs from batched_backward.rs docstrings that
described a migration that's actually complete:
- Module docstring said "dqn_backward_kernel (atomicAdd path)
remains active" — the atomicAdd kernel has been removed; cuBLAS
backward is wired via launch_cublas_backward.
- `backward_full` docstring said "gated behind TODO" — the function
is actively called from the fused training step.
3. Rewrite 2 ISV scratch field comments as declarative: td_error_scratch
is now wired (as per change 1); ensemble_var_scratch remains
zero-initialised and its comment honestly describes that consumers
(ISV[3] and [4]) treat it as unavailable. Per feedback_no_todo_fixme.md,
replaces the TODO(isv) markers with declarative descriptions of
current behaviour. Future wiring is tracked in the plan, not in code
aspirational markers.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -70,11 +70,12 @@
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//! ## Phase status
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//!
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//! This module provides the building blocks for Phase 2 Task 2. The full
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//! cuBLAS backward path requires a standalone C51 loss kernel that emits
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//! `dL/d_logits` for each branch. Until that kernel exists, the existing
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//! `dqn_backward_kernel` (atomicAdd path) remains active. The functions
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//! here are wired via `GpuDqnTrainer::launch_cublas_backward()` with a
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//! TODO gate that enables them once the C51 loss kernel is ready.
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//! The cuBLAS backward path consumes `dL/d_logits` produced by the
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//! standalone C51 loss kernel (`c51_loss_batched` in
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//! `c51_loss_kernel.cu`) and is wired via
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//! `GpuDqnTrainer::launch_cublas_backward()`, which is called from the
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//! fused training step. The earlier 1-warp-per-sample
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//! `dqn_backward_kernel` (atomicAdd path) has been removed.
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use std::collections::HashMap;
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use std::mem::ManuallyDrop;
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@@ -1453,9 +1454,10 @@ impl CublasBackwardSet {
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/// Full backward pass through the branching dueling network via cuBLAS SGEMM.
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///
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/// Requires pre-computed `dL/d_logits` for each head from the C51 loss kernel.
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/// Until the standalone C51 loss kernel exists (Phase 2 Task 2), this function
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/// is gated behind a TODO comment in `GpuDqnTrainer::launch_cublas_backward`.
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/// Consumes pre-computed `dL/d_logits` for each head emitted by
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/// `c51_loss_batched` (`c51_loss_kernel.cu`), and is invoked by
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/// `GpuDqnTrainer::launch_cublas_backward` during the fused training
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/// step.
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///
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/// ## Backward order (reverse of forward)
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///
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@@ -1732,10 +1732,20 @@ pub struct GpuDqnTrainer {
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risk_adam_step: i32,
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// ── ISV scratch scalars (pinned device-mapped, written by GPU kernels) ──
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// TODO(isv): Wire C51 loss kernel to write mean |TD-error| here
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// Zero-initialised scratch. No writer currently emits batch mean
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// |TD-error| into this slot, so `isv_signals[2]` (the TD-error EMA
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// in `isv_signal_update`) accumulates a constant-zero signal and
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// effectively decays to zero. Downstream consumers treat ISV[2] as
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// an unavailable signal. Keeping the buffer allocated preserves the
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// `isv_signal_update` kernel ABI — a future C51-loss writeback can
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// populate it without a signature change.
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td_error_scratch_pinned: *mut f32,
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td_error_scratch_dev_ptr: u64,
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// TODO(isv): Wire ensemble aggregate kernel to write mean variance here
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// Zero-initialised scratch for mean ensemble variance. Same
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// situation as `td_error_scratch_pinned`: no kernel currently
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// writes into it, so `isv_signals[3]` (ensemble-variance EMA) and
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// `isv_signals[4]` (variance velocity) stay at zero. Buffer kept
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// for `isv_signal_update` ABI stability.
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ensemble_var_scratch_pinned: *mut f32,
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ensemble_var_scratch_dev_ptr: u64,
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reward_scratch_pinned: *mut f32,
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@@ -12642,9 +12652,16 @@ impl GpuDqnTrainer {
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/// Deterministic loss reduction: sequential sum of per_sample_loss → total_loss / batch_size.
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/// Grid=(1,1,1), Block=(1,1,1). Must stay single-thread inside graph_mega on Hopper.
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///
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/// Also emits the batch mean |TD-error| into `td_error_scratch_dev_ptr`
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/// by running the same reduction kernel over `td_errors_buf`. This feeds
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/// ISV[2] (the TD-error EMA in `isv_signal_update`), which downstream
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/// consumers previously saw as a constant-zero signal because the scratch
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/// was never written.
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fn launch_loss_reduce(&self, total_loss_dev_ptr: u64) -> Result<(), MLError> {
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let b = self.config.batch_size as i32;
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let per_sample_loss_buf_ptr = self.per_sample_loss_buf.raw_ptr();
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let td_errors_buf_ptr = self.td_errors_buf.raw_ptr();
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unsafe {
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self.stream
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.launch_builder(&self.c51_loss_reduce_kernel)
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@@ -12657,6 +12674,19 @@ impl GpuDqnTrainer {
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shared_mem_bytes: 0,
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})
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.map_err(|e| MLError::ModelError(format!("c51_loss_reduce launch: {e}")))?;
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/* Second launch: batch mean |TD-error| into the ISV scratch.
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* Generic mean-reduction kernel, reused verbatim. */
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self.stream
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.launch_builder(&self.c51_loss_reduce_kernel)
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.arg(&td_errors_buf_ptr)
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.arg(&self.td_error_scratch_dev_ptr)
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.arg(&b)
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.launch(LaunchConfig {
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grid_dim: (1, 1, 1),
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block_dim: (1, 1, 1),
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shared_mem_bytes: 0,
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})
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.map_err(|e| MLError::ModelError(format!("c51_td_error_reduce launch: {e}")))?;
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}
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Ok(())
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}
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