Fix #390: Add missing fwd_prepare_T function #564
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- Implement fwd_prepare_T in wy_fast.py to resolve import error - Handle tensor format conversion between head-first and seq-first - Add comprehensive tests for parallel_delta_rule and fwd_prepare_T - Document tensor format expectations This fixes the ImportError when importing fwd_prepare_T from fla.ops.delta_rule.wy_fast and properly handles the format mismatch between head-first [B, H, T, K] and seq-first [B, T, H, K] tensors.
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Note Other AI code review bot(s) detectedCodeRabbit has detected other AI code review bot(s) in this pull request and will avoid duplicating their findings in the review comments. This may lead to a less comprehensive review. WalkthroughAdds a new public function fwd_prepare_T in fla/ops/delta_rule/wy_fast.py to compute the A matrix via chunk_scaled_dot_kkt_fwd and solve_tril, and introduces tests validating parallel delta-rule behavior and the new function’s shape/availability. Changes
Sequence Diagram(s)sequenceDiagram
participant Caller
participant wy_fast
participant KKT as chunk_scaled_dot_kkt_fwd
participant Tri as solve_tril
Caller->>wy_fast: fwd_prepare_T(k, beta, chunk_size, cu_seqlens)
wy_fast->>KKT: compute A0 = chunk_scaled_dot_kkt_fwd(k, beta, cu_seqlens, chunk_size, fp32)
KKT-->>wy_fast: A0
wy_fast->>Tri: A = solve_tril(A0, cu_seqlens, dtype=k.dtype)
Tri-->>wy_fast: A
wy_fast-->>Caller: A
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Summary of Changes
Hello @liqiongyu, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request addresses an import error by implementing the fwd_prepare_T function, which was previously missing. The new function is responsible for preparing a transformation matrix for delta rule computations, including necessary tensor format conversions. The changes also introduce new tests to validate the function's correctness and include detailed docstrings for better code understanding.
Highlights
- Implementation of Missing Function: The previously missing
fwd_prepare_Tfunction has been implemented infla/ops/delta_rule/wy_fast.py. This function is crucial for computing the transformation matrix A in the parallel delta rule algorithm. - Tensor Format Conversion Logic: The
fwd_prepare_Tfunction now correctly handles tensor format conversions. It takes head-first[B, H, T, K]tensors, converts them to seq-first[B, T, H, K]for internal computations usingchunk_scaled_dot_kkt_fwdandsolve_tril, and then converts the result back to head-first format before returning. - New Test Coverage: A new test file,
tests/ops/test_parallel_delta.py, has been added, includingtest_fwd_prepare_T. This test verifies the function's importability, error-free execution, and ensures the output tensor has the correct shape[B, H, T, chunk_size]. - Improved Documentation: Comprehensive docstrings have been added to the
fwd_prepare_Tfunction, clearly explaining its purpose, the mathematical operation it performs, its arguments (including tensor shapes), and the format of its return value.
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Code Review
This pull request introduces the missing fwd_prepare_T function to resolve an ImportError. The implementation correctly wraps existing operations and handles tensor layout conversions. However, there is a critical issue where torch.transpose is used without a subsequent .contiguous() call. This creates non-contiguous tensor views, which are then passed to Triton kernels that expect C-contiguous tensors. This mismatch in memory layout will cause the kernels to read incorrect data, leading to silent numerical errors. I've added comments with suggestions to fix this by ensuring all transposed tensors are made contiguous before use in Triton kernels.
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Actionable comments posted: 4
🧹 Nitpick comments (4)
fla/ops/delta_rule/wy_fast.py (1)
299-300: Trailing whitespace.Line 299 has trailing whitespace after the comma in the parameter list. Please strip it to keep CI linting green.
- beta: torch.Tensor, + beta: torch.Tensor,tests/ops/test_parallel_delta.py (3)
11-21: Comment block contradicts some public docs; fine for tests, but consider tightening phrasing.The note states “actual implementation expects [B, H, T, K],” while parallel_delta_rule’s public API generally documents [B, T, H, K]. Since tests deliberately use head-first to match current kernels, it’s okay, but consider rephrasing to: “These tests intentionally use head-first [B, H, T, K] to match current kernel conventions.” This avoids suggesting the public API is head-first.
39-66: Small hygiene: drop clones unless mutation is expected.parallel_delta_rule shouldn’t mutate inputs; cloning q/k/v/beta is likely unnecessary and costs memory. Consider passing them directly unless you’ve observed in-place modifications.
1-104: Strip trailing whitespace to keep pre-commit hooks green.CI reported trailing whitespace fixes; a few lines here contain trailing spaces (e.g., lines with comments and some function signatures). Please run the repo’s pre-commit hooks or strip trailing spaces in this file.
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fla/ops/delta_rule/wy_fast.py(1 hunks)tests/ops/test_parallel_delta.py(1 hunks)
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🧬 Code Graph Analysis (2)
tests/ops/test_parallel_delta.py (3)
fla/ops/delta_rule/parallel.py (2)
parallel_delta_rule(302-349)naive_delta_rule_parallel(352-403)fla/ops/delta_rule/wy_fast.py (1)
fwd_prepare_T(297-337)fla/utils.py (1)
assert_close(77-89)
fla/ops/delta_rule/wy_fast.py (2)
fla/ops/common/chunk_scaled_dot_kkt.py (1)
chunk_scaled_dot_kkt_fwd(218-305)fla/ops/utils/solve_tril.py (1)
solve_tril(334-381)
🪛 Ruff (0.12.2)
tests/ops/test_parallel_delta.py
5-5: torch.nn.functional imported but unused
Remove unused import: torch.nn.functional
(F401)
7-7: fla.ops.delta_rule.parallel.naive_delta_rule_parallel imported but unused
Remove unused import: fla.ops.delta_rule.parallel.naive_delta_rule_parallel
(F401)
9-9: fla.utils.assert_close imported but unused
Remove unused import: fla.utils.assert_close
(F401)
🪛 Flake8 (7.2.0)
tests/ops/test_parallel_delta.py
[error] 5-5: 'torch.nn.functional as F' imported but unused
(F401)
[error] 7-7: 'fla.ops.delta_rule.parallel.naive_delta_rule_parallel' imported but unused
(F401)
[error] 9-9: 'fla.utils.assert_close' imported but unused
(F401)
🪛 GitHub Actions: lint
tests/ops/test_parallel_delta.py
[error] 1-1: Trailing whitespace detected and removed by pre-commit hook 'trailing-whitespace'.
[error] 1-1: End-of-file newline added by pre-commit hook 'end-of-file-fixer'.
[error] 1-1: isort: Import order issues fixed by pre-commit hook.
[error] 5-5: Flake8: F401 'torch.nn.functional as F' imported but unused.
[error] 7-7: Flake8: F401 'fla.ops.delta_rule.parallel.naive_delta_rule_parallel' imported but unused.
[error] 9-9: Flake8: F401 'fla.utils.assert_close' imported but unused.
fla/ops/delta_rule/wy_fast.py
[error] 1-1: Trailing whitespace detected and removed by pre-commit hook 'trailing-whitespace'.
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🔇 Additional comments (1)
fla/ops/delta_rule/wy_fast.py (1)
297-337: Great: fwd_prepare_T restores the missing symbol and correctly bridges head-first to seq-first.The implementation matches internal utilities (chunk_scaled_dot_kkt_fwd → solve_tril) and returns the expected head-first shape [B, H, T, BT]. This should resolve the ImportError from issue #390 and aligns with existing patterns in this module.
- Remove unused imports in test file - Fix trailing whitespace issues - Add missing end-of-file newlines - Improve code formatting
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Regarding the test failures: I noticed that CI tests are failing, but this appears to be an issue with the CI script itself, not with our code changes. The error message shows: /home/ubuntu/actions-runner-1/_work/_temp/c86c198c-cf94-47a7-ab3a-d24820baa14d.sh: line 539: unexpected EOF while looking for matching ' This is a shell script syntax error that occurs when parsing the PR information. It might be caused by certain special characters or formatting in the PR description that's causing the CI script parsing to fail. Please note that:
I believe these test failures are unrelated to our actual code changes and might require a fix to the CI configuration. |
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@liqiongyu Thank you! I will fix CI first. |
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Should CC @yzhangcs Please have a look. |
- Add @input_guard decorator to fwd_prepare_T function for input validation - Add .contiguous() calls after all transpose operations - Ensures all tensors have contiguous memory layout before passing to Triton kernels - Fixes potential numerical errors from non-contiguous tensor access
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Thank you for your feedback @zhiyuan1i! I have made the suggested changes:
These changes should fix the potential issues with non-contiguous tensor access and ensure numerical correctness. |
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@liqiongyu Thank you! Adding input guards before the inner functions might be unnecessary. However, whatever, the overhead isn't high. |
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Actionable comments posted: 0
🔭 Outside diff range comments (2)
fla/ops/delta_rule/parallel.py (2)
266-266: Fix incorrect grid calculation for parallel kernelThe grid calculation is missing the
Hdimension, which will cause the kernel to incorrectly index into the tensors. The grid should be(triton.cdiv(T, BT), B, H)to match the 3D kernel launch pattern used elsewhere in the file.Apply this diff to fix the grid calculation:
- grid = (triton.cdiv(T, BT), B * H) + grid = (triton.cdiv(T, BT), B, H)
358-409: Update naive implementation to match new tensor formatThe
naive_delta_rule_parallelfunction still expects head-first format[B, H, T, K]as indicated by line 359:b, h, l, d_k = q.shape. This is inconsistent with the main implementation which now uses seq-first format[B, T, H, K].The naive implementation should be updated to match the new tensor format for consistency:
def naive_delta_rule_parallel(q, k, v, beta, BM=128, BN=32): - b, h, l, d_k = q.shape + b, l, h, d_k = q.shape q = q * (d_k ** -0.5) v = v * beta[..., None] k_beta = k * beta[..., None] # compute (I - tri(diag(beta) KK^T))^{-1} - q, k, v, k_beta = map(lambda x: rearrange(x, 'b h (n c) d -> b h n c d', c=BN), [q, k, v, k_beta]) + q, k, v, k_beta = map(lambda x: rearrange(x, 'b (n c) h d -> b h n c d', c=BN), [q, k, v, k_beta])Note: You'll need to update all the rearrange patterns and tensor operations accordingly throughout this function.
🧹 Nitpick comments (1)
fla/ops/delta_rule/parallel.py (1)
342-346: Consider removing the DeprecationWarning in favor of a direct errorSince the
head_firstformat is deprecated and raises a DeprecationWarning, consider raising aValueErrordirectly instead. DeprecationWarning is typically suppressed by default and may not be visible to users.Apply this diff to make the deprecation more visible:
if head_first: - raise DeprecationWarning( + raise ValueError( "head_first is deprecated and will be removed in a future version. " - "Please use head_first=False for now instead." + "Please convert your tensors to seq-first format [B, T, H, K] and use head_first=False." )
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fla/ops/delta_rule/parallel.py (5)
fla/ops/generalized_delta_rule/iplr/chunk.py (1)
grid(219-223)fla/ops/gla/chunk.py (3)
grid(828-828)grid(896-896)grid(979-979)fla/ops/rwkv6/chunk.py (3)
grid(83-83)grid(855-855)grid(949-949)fla/ops/generalized_delta_rule/dplr/fused_recurrent.py (1)
grid(129-129)fla/ops/delta_rule/wy_fast.py (1)
fwd_prepare_T(297-332)
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🔇 Additional comments (4)
fla/ops/delta_rule/parallel.py (4)
24-43: LGTM! Kernel signature properly updated for H dimensionThe kernel signature correctly includes the new
Hparameter and maintains consistency with the grid launch pattern.
44-82: LGTM! Pointer arithmetic correctly accounts for H dimensionThe memory pointer calculations have been properly updated to incorporate the head dimension
Hin the offset calculations. The patterni_b * T * H * ... + i_h * ...is consistently applied across all tensor pointers.
331-340: Fix documentation inconsistency with actual tensor layoutThe docstring mentions that when
output_attentions=True, the attention scores have shape[B, T, H, T], which correctly reflects the seq-first format now used in the implementation. This is good.
251-251: fwd_prepare_T is compatible with seq-first k ([B, T, H, K]) — no change requiredI inspected the implementations and call site: fwd_prepare_T in fla/ops/delta_rule/wy_fast.py accepts k of shape [B, T, H, K], calls chunk_scaled_dot_kkt_fwd (which returns [B, T, H, BT]) and then solve_tril (which returns the inverse with the same [B, T, H, BT] layout). parallel.py calls A = fwd_prepare_T(k, beta, BS) and uses A as [B, T, H, BT], so the formats match and the call is correct.
Files checked:
- fla/ops/delta_rule/wy_fast.py — def fwd_prepare_T(...)
- fla/ops/common/chunk_scaled_dot_kkt.py — def chunk_scaled_dot_kkt_fwd(...)
- fla/ops/utils/solve_tril.py — def solve_tril(...)
- fla/ops/delta_rule/parallel.py — callsite A = fwd_prepare_T(k, beta, BS) (line ~251)
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Actionable comments posted: 2
🧹 Nitpick comments (3)
fla/ops/delta_rule/parallel.py (3)
140-143: Fix flake8 E501 (line too long) and improve readabilityCI reports E501 at this location. Break the long tl.make_block_ptr calls and reuse precomputed bases to keep lines within limits and enhance readability.
Apply this diff:
- p_A = tl.make_block_ptr(A + (i_b * T * H * T) + (i_h * T), (T, T), (H*T, 1), (i_t * BT, i_t * BT), (BT, BT), (1, 0)) - p_A_local = tl.make_block_ptr(A_local + (i_b * T * H * BT) + (i_h * BT), (T, BT), (H*BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)) + base_A = A + i_b * T * H * T + i_h * T + p_A = tl.make_block_ptr( + base_A, + (T, T), + (H * T, 1), + (i_t * BT, i_t * BT), + (BT, BT), + (1, 0), + ) + base_A_local = A_local + i_b * T * H * BT + i_h * BT + p_A_local = tl.make_block_ptr( + base_A_local, + (T, BT), + (H * BT, 1), + (i_t * BT, 0), + (BT, BT), + (1, 0), + )
170-180: Kernel pointer math for [B, T, H, ...] layout is sound; minor dtype nit on store
- The 3D grid and head-aware pointer math for q/k/v/beta/attn all look correct.
- Minor nit: Line 235 casts b_o using p_o’s dtype instead of p_o_new’s dtype. They should be identical in practice, but use the matching pointer for correctness.
Apply this diff:
- tl.store(p_o_new, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1)) + tl.store(p_o_new, b_o.to(p_o_new.dtype.element_ty), boundary_check=(0, 1))Also applies to: 186-190, 212-216, 231-235
331-341: Docstrings reflect new layout; deprecation handling for head_firstThe docstrings are correct for [B, T, H, ...]. One note: raising DeprecationWarning as an exception will hard-break callers still using head_first=True. Prefer warnings.warn(..., DeprecationWarning) if you want to warn without breaking, or raise a ValueError if you intend to block this usage.
Apply this diff:
- if head_first: - raise DeprecationWarning( + if head_first: + warnings.warn( "head_first is deprecated and will be removed in a future version. " "Please use head_first=False for now instead." - ) + , DeprecationWarning)
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grid(219-223)fla/ops/gla/chunk.py (3)
grid(828-828)grid(896-896)grid(979-979)fla/ops/rwkv6/chunk.py (3)
grid(83-83)grid(855-855)grid(949-949)fla/ops/delta_rule/wy_fast.py (1)
fwd_prepare_T(297-332)
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🔇 Additional comments (3)
fla/ops/delta_rule/parallel.py (3)
35-35: Head-dimension threading and pointer math look correctIntroducing H as a kernel arg and switching to 3D grid with i_b, i_h indexing is consistent. The base pointers and strides for q/k/v/T correctly reflect a contiguous [B, T, H, ...] layout: (T, K/V) with strides (HK/V, 1), and for A: (T, BT) with strides (HBT, 1). Good job.
Also applies to: 44-53
62-63: Beta and A_local indexing consistent with [B, T, H, ...]
- Beta vector block pointer stride of (H,) relative to T is correct for [B, T, H].
- Conditional storage into A_local (when OUTPUT_ATTENTIONS) is properly keyed to the 3D grid and head offset.
Also applies to: 69-80
252-253: Attn shape and 3D grid updates align with new [B, T, H, ...] layout
- attn shape changed to [B, T, H, T] consistently.
- Kernel grids expanded to (ceildiv(T, ...), B, H). Looks correct and consistent with other kernels in the repo.
Also applies to: 266-267, 279-280, 291-297
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Just get noticed from @yzhangcs , this function was deprecated |
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Actionable comments posted: 3
♻️ Duplicate comments (1)
tests/ops/test_parallel_delta.py (1)
83-101: Parametrize chunk_size to cover all supported BT values for solve_tril.Broaden coverage by running test_fwd_prepare_T for chunk_size in {16, 32, 64}. This guards against regressions in supported block sizes.
Apply:
-@pytest.mark.skipif( - device_platform == 'intel', - reason='Intel Triton Failure' -) -def test_fwd_prepare_T(): +@pytest.mark.skipif(device_platform == 'intel', reason='Intel Triton Failure') +@pytest.mark.parametrize('chunk_size', [16, 32, 64]) +def test_fwd_prepare_T(chunk_size: int): @@ - chunk_size = 32
🧹 Nitpick comments (2)
tests/ops/test_parallel_delta.py (2)
16-21: Remove outdated comment block that contradicts current test behavior.The test now compares against naive_delta_rule_parallel, but this block states the comparison is skipped. Remove or update to avoid confusion.
Apply:
-# NOTE ON TEST IMPLEMENTATION: -# We currently skip comparing parallel_delta_rule against naive_delta_rule_parallel -# because the naive implementation produces NaN values. This will be addressed in a -# future update. For now, we only verify that parallel_delta_rule runs without errors -# and produces outputs with the expected shapes.
57-66: Call parallel_delta_rule with seq-first tensors and convert back to head-first.This matches the function’s documented contract, eliminates warnings, and reduces the chance of format-related regressions.
Apply:
- # Test forward pass - o_parallel, attn_parallel = parallel_delta_rule( - q=q.clone(), - k=k.clone(), - v=v.clone(), - beta=beta.clone(), - scale=scale, - output_attentions=output_attentions - ) + # Test forward pass (convert to seq-first [B, T, H, K] to match API) + q_sf = q.transpose(1, 2).contiguous() + k_sf = k.transpose(1, 2).contiguous() + v_sf = v.transpose(1, 2).contiguous() + beta_sf = beta.transpose(1, 2).contiguous() + o_parallel, attn_parallel = parallel_delta_rule( + q=q_sf, k=k_sf, v=v_sf, beta=beta_sf, scale=scale, output_attentions=output_attentions + ) + # Convert back to head-first for assertions and comparisons + o_parallel_hf = o_parallel.transpose(1, 2).contiguous()
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🔇 Additional comments (1)
tests/ops/test_parallel_delta.py (1)
91-103: LGTM: fwd_prepare_T smoke test and shape validation are appropriate.The test ensures importability, a successful run, and validates the output shape [B, H, T, chunk_size], which matches the function’s documented head-first contract.
| # IMPORTANT NOTE ON TENSOR FORMATS: | ||
| # While the documentation for some functions states inputs should be in [B, T, H, K] format, | ||
| # the actual implementation expects [B, H, T, K] format (head-first). | ||
| # All tests in this file use the head-first format to match the actual implementation. | ||
|
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Clarify tensor-format note; align tests with the API to avoid silent mismatches.
parallel_delta_rule’s public API expects [B, T, H, K] (seq-first) per its docstring, and it emits a warning when passed head-first tensors while head_first=False. Feeding head-first tensors here risks masking format bugs behind warnings.
Recommend converting inputs to seq-first before calling, then converting outputs back to head-first for assertions. See suggested changes on Lines 57-66, 67-69, and 79-80.
Run this to list all call sites using parallel_delta_rule and check for any reliance on head-first shapes:
🏁 Script executed:
#!/bin/bash
rg -n -A 2 $'parallel_delta_rule\\('Length of output: 579
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#!/bin/bash
set -euo pipefail
echo "==== tests/ops/test_parallel_delta.py (full file) ===="
sed -n '1,240p' tests/ops/test_parallel_delta.py || true
echo
echo "==== fla/ops/delta_rule/parallel.py (around function) ===="
sed -n '1,420p' fla/ops/delta_rule/parallel.py || trueLength of output: 18238
Fix tensor-format mismatch: parallel_delta_rule docstring/behavior disagree with implementation (head-first).
Short: The public docstring claims seq-first ([B, T, H, ...]) but the kernels and Forward (ParallelDeltaRuleFunction) clearly operate on head-first tensors ([B, H, T, ...]). Tests pass head-first tensors and currently only get a warning — this can mask real format bugs. We should make the API, implementation, and tests consistent.
Files to change
-
fla/ops/delta_rule/parallel.py
- Issue: docstring for parallel_delta_rule documents q/k/v/beta as
[B, T, H, ...]but code (B, H, T, K = k.shape and all kernels) use head-first[B, H, T, ...]. - Issue: wrapper currently raises a DeprecationWarning if head_first=True and only emits a warning (no conversion) when a head-first tensor is passed with head_first=False.
- Recommended fix (pick one):
- Preferred (safe, backwards-compatible): Auto-detect format and convert:
- Auto-detect input format (e.g. head-first if q.shape[1] < q.shape[2], otherwise seq-first).
- If seq-first, rearrange inputs to head-first before calling ParallelDeltaRuleFunction.apply and rearrange outputs back to seq-first before returning.
- Keep head_first as an optional override (emit a DeprecationWarning but do not raise), but rely on auto-detection by default.
- Alternative: If you want to keep the current internal layout, update the docstring to document head-first
[B, H, T, ...]and remove/adjust the misleading warning.
- Preferred (safe, backwards-compatible): Auto-detect format and convert:
- Minimal example (replace current head_first/warning block with auto-detect + conversions):
- Before:
- if head_first:
raise DeprecationWarning(...) - if not head_first and q.shape[1] < q.shape[2]:
warnings.warn(...) - o, attn = ParallelDeltaRuleFunction.apply(...)
- if head_first:
- After (sketch):
- is_head_first = head_first or (q.shape[1] < q.shape[2])
- if head_first:
warnings.warn("head_first is deprecated", DeprecationWarning) - if not is_head_first:
q_h = rearrange(q, 'b t h k -> b h t k'); ... (k_h, v_h, beta_h) - else:
q_h, k_h, v_h, beta_h = q, k, v, beta - o_h, attn = ParallelDeltaRuleFunction.apply(q_h, k_h, v_h, beta_h, scale, output_attentions)
- o = rearrange(o_h, 'b h t v -> b t h v') if not is_head_first else o_h
- return o, attn
- Before:
- Also update the docstring to clearly state the accepted public input/output formats and the internal conversion behavior (or document that the function requires head-first if you choose the docstring-update alternative).
- Issue: docstring for parallel_delta_rule documents q/k/v/beta as
-
tests/ops/test_parallel_delta.py
- Issue: tests currently construct head-first tensors and call parallel_delta_rule without conversion; that causes the wrapper warning.
- If you implement the wrapper auto-conversion above, tests can remain as-is (no warning). If you instead change the API to require seq-first, update the test call-site to convert head-first -> seq-first before calling and convert outputs back for assertions. Example (if converting at test-level):
- q_seq = rearrange(q, 'b h t k -> b t h k'); k_seq = rearrange(k, 'b h t k -> b t h k'); v_seq = rearrange(v, 'b h t v -> b t h v'); beta_seq = rearrange(beta, 'b h t -> b t h')
- o_seq, attn = parallel_delta_rule(q_seq, k_seq, v_seq, beta_seq, ...)
- o = rearrange(o_seq, 'b t h v -> b h t v')
Why fix: leaving the mismatch and only warning risks silent bugs and confusion for users; tests should either exercise the public API shape or the public API should be documented/implemented to accept what callers expect.
Tag:
🤖 Prompt for AI Agents
tests/ops/test_parallel_delta.py lines 11-15: the comment/docstring and wrapper
for parallel_delta_rule disagree on tensor layout (tests use head-first [B, H,
T, ...] while the public docstring claims seq-first [B, T, H, ...]); update
fla/ops/delta_rule/parallel.py to auto-detect and convert layouts: detect
head-first by comparing q.shape[1] and q.shape[2], if head_first param is True
emit a DeprecationWarning (do not raise), otherwise derive is_head_first =
head_first or (q.shape[1] < q.shape[2]); if inputs are seq-first, rearrange
q/k/v/beta to head-first before calling ParallelDeltaRuleFunction.apply and
rearrange outputs back to seq-first before returning; update the function
docstring to state it accepts either layout and auto-converts (or documents
deprecation of head_first), and leave tests/ops/test_parallel_delta.py unchanged
since tests supply head-first tensors and will now call the wrapper without
warnings.
| # Output should have the same shape as input v | ||
| assert o_parallel.shape == v.shape, f"Expected shape {v.shape}, got {o_parallel.shape}" | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Update shape assertion to use converted head-first output.
After calling with seq-first inputs, o is [B, T, H, K]. Convert to head-first before comparing with v.
Apply:
- # Output should have the same shape as input v
- assert o_parallel.shape == v.shape, f"Expected shape {v.shape}, got {o_parallel.shape}"
+ # Output should have the same shape as input v (head-first)
+ assert o_parallel_hf.shape == v.shape, f"Expected shape {v.shape}, got {o_parallel_hf.shape}"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # Output should have the same shape as input v | |
| assert o_parallel.shape == v.shape, f"Expected shape {v.shape}, got {o_parallel.shape}" | |
| # Output should have the same shape as input v (head-first) | |
| assert o_parallel_hf.shape == v.shape, f"Expected shape {v.shape}, got {o_parallel_hf.shape}" |
🤖 Prompt for AI Agents
In tests/ops/test_parallel_delta.py around lines 67-69, the shape assertion
compares o_parallel in seq-first layout [B, T, H, K] to v which is head-first;
convert o_parallel to head-first before asserting. Rearrange o_parallel from [B,
T, H, K] to [B, H, T, K] (e.g., using .transpose(0,2,1,3) or
einops.rearrange(o_parallel, "b t h k -> b h t k")) and then assert the shape
equals v.shape.
| o_naive, attn_naive = naive_delta_rule_parallel(q.clone(), k.clone(), v.clone(), beta.clone()) | ||
|
|
||
| assert_close(' o', o_parallel, o_naive, 0.01) | ||
| assert_close('attn', attn_naive, attn_parallel, 0.01) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Compare the correct tensor (head-first) against the naive implementation.
Ensure the numerical comparison is performed on head-first tensors.
Apply:
- assert_close(' o', o_parallel, o_naive, 0.01)
+ assert_close(' o', o_parallel_hf, o_naive, 0.01)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| o_naive, attn_naive = naive_delta_rule_parallel(q.clone(), k.clone(), v.clone(), beta.clone()) | |
| assert_close(' o', o_parallel, o_naive, 0.01) | |
| assert_close('attn', attn_naive, attn_parallel, 0.01) | |
| o_naive, attn_naive = naive_delta_rule_parallel(q.clone(), k.clone(), v.clone(), beta.clone()) | |
| assert_close(' o', o_parallel_hf, o_naive, 0.01) | |
| assert_close('attn', attn_naive, attn_parallel, 0.01) |
🤖 Prompt for AI Agents
In tests/ops/test_parallel_delta.py around lines 77 to 80, the assertions
compare tensors in the wrong layout/order; convert both the naive and parallel
outputs to head-first layout (the same head-first dimension ordering used
elsewhere in tests) before asserting and ensure assert_close is called with the
expected (naive/head-first) tensor first and the actual (parallel/head-first)
tensor second; update both the 'o' and 'attn' comparisons to permute/transpose
to head-first and swap the argument order if necessary so the numerical
comparison is performed head-first and expected-before-actual.
Summary
fwd_prepare_Tfunction infla/ops/delta_rule/wy_fast.pythat was being imported from other filesProblem
Issue #390 reported an import error where
fwd_prepare_Twas being imported but didn't exist in the module.Solution
Implemented the
fwd_prepare_Tfunction which:[B, H, T, K]as input[B, T, H, K]for internal processingTest plan
test_fwd_prepare_T()that verifies:All tests are passing.
Fixes #390
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