[ArgBinder] Enhance shape variable handling and assertions#1467
[ArgBinder] Enhance shape variable handling and assertions#1467LeiWang1999 merged 6 commits intotile-ai:mainfrom
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- Implemented special handling for comparing if_then_else expressions to simplify conditions involving NULL checks. - Added methods to set shared shape variables and finalize deferred bindings, generating cascading if_then_else expressions and runtime assertions for non-NULL buffers. - Updated the binding logic to defer shape variable bindings for shared variables, ensuring proper handling across multiple nullable buffers.
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WalkthroughReplaces single-buffer DLTensor binding with a batch-style BindDLTensors that processes multiple nullable buffers in two passes: collect shared symbolic shape sources, then emit per-buffer NULL guards, guarded shape/stride/device/dtype bindings, and deferred/cascaded runtime checks for shared shapes and pointer consistency. Changes
Sequence Diagram(s)sequenceDiagram
participant MPA as make_packed_api
participant AB as ArgBinder
participant Buf as Buffers (nullable handles)
participant Runtime as Runtime (packed calls / assertions)
MPA->>AB: BindDLTensors(buffer_defs, device_type, device_id, func_name, used_set)
AB->>AB: First pass — collect symbolic shape vars & source buffers
loop For each buffer
Buf->>AB: expose handle (may be NULL)
AB->>AB: create is_null guard for handle
AB->>AB: if not NULL -> guarded loads: shape, strides, device, dtype, data ptr
AB->>AB: record shape sources or defer binding if shared
end
AB->>AB: Second pass — materialize bindings
alt Shared shape vars present
AB->>AB: build cascaded binding expr from first non-NULL source
AB->>Runtime: emit packed-call/assert: "≥1 non-NULL source" if all NULL
AB->>Runtime: emit packed-call/assert: shape equality checks across buffers
end
AB->>Runtime: emit other runtime checks (dtype/device/byte-offset) guarded by is_null
AB->>MPA: return finalized bindings & guarded checks
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes
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Actionable comments posted: 0
🧹 Nitpick comments (1)
src/transform/arg_binder.cc (1)
161-232: Verify the cascading expression build order matches the intended semantics.The cascading expression is built by iterating in reverse order, but the fallback for the last binding (first in reverse) is
temp_vardirectly rather than a sentinel value like0. This means if all buffers are NULL except the last one processed, the shape will still be extracted correctly.However, I want to confirm: the comment on line 172 says the fallback should be
0, but the implementation uses the lasttemp_varas the base. This appears intentional (to avoid an invalid zero shape value), but the comment is slightly misleading.Also, the pairwise equality constraints (lines 212-230) generate O(n²) assertions for n buffers. For typical use cases with 2-3 buffers this is fine, but worth noting for scalability.
🔎 Consider clarifying the comment to match the implementation:
// Step 1: Build cascading if_then_else expression // Traverse bindings in reverse order to build: - // if_then_else(!is_null_0, temp_0, if_then_else(!is_null_1, temp_1, 0)) + // if_then_else(!is_null_0, temp_0, if_then_else(!is_null_1, temp_1, temp_last)) + // The last binding's temp_var is used as the final fallback (not 0). PrimExpr cascading_expr;
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src/transform/arg_binder.cc(3 hunks)src/transform/arg_binder.h(1 hunks)src/transform/make_packed_api.cc(4 hunks)
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🔇 Additional comments (7)
src/transform/make_packed_api.cc (2)
384-413: LGTM! Proper handling of shared shape variables across multiple carriers.The logic correctly distinguishes between:
- Single carrier → must be non-NULL to provide the shape (immediate binding)
- Multiple carriers → shape can come from any non-NULL buffer (deferred binding via cascading if_then_else)
This enables nullable buffer support where shape variables can be extracted from whichever buffer is non-NULL at runtime.
524-537: LGTM! Correct ordering of SetSharedShapeVars before buffer binding and finalization after.The sequence is correct:
SetSharedShapeVarsbeforeBindDLTensorcalls so the binder knows which shape vars to deferFinalizeDeferredBindingsafter all buffers are bound to generate cascading expressions and assertionssrc/transform/arg_binder.cc (3)
31-91: Well-designed pattern matching for simplifying if_then_else comparisons.The logic correctly transforms:
if_then_else(guard_a, val_a, fallback) == if_then_else(guard_b, val_b, fallback)into:
!guard_a || !guard_b || (val_a == val_b)This avoids spurious assertion failures when comparing guarded shape values where one or both buffers may be NULL. The Cast unwrapping is also handled correctly.
156-159: LGTM! Simple setter for shared shape variables.
658-689: LGTM! Correct deferred binding logic for shared shape variables.The implementation properly:
- Checks if the shape dimension is a shared variable via
shared_shape_vars_.count- Creates a temporary variable to hold the buffer-specific shape value
- Records the deferred binding with both
temp_varandis_null_var- Falls back to immediate
BindNullablefor non-shared variablesThe naming convention (
arg_name + "_shape_dim_" + k + "_temp") provides good traceability for debugging.src/transform/arg_binder.h (2)
161-176: LGTM! Clear and well-documented public API.The documentation properly describes:
- Purpose of
SetSharedShapeVarsfor marking variables needing special handling- What
FinalizeDeferredBindingsgenerates (cascading if_then_else + runtime assertion)Minor note: The documentation could also mention the pairwise equality constraints that are generated, but this is optional.
179-190: LGTM! Well-structured private state for deferred binding tracking.The
DeferredShapeBindingstruct cleanly captures the essential information needed for finalization, and the map structure allows efficient lookup by shape variable node.
…sed code - Removed deprecated methods for setting shared shape variables and finalizing deferred bindings, streamlining the argument binding process. - Simplified the logic for handling shape values in the `BindDLTensor` function, ensuring immediate binding for normal shape variables. - Enhanced clarity by eliminating unnecessary comments and code related to cascading if_then_else expressions for shared variables.
…ndling - Replaced the single `BindDLTensor` method with `BindDLTensors` to support multiple buffers, improving flexibility in handling DLTensor bindings. - Introduced a two-pass approach for shape variable handling, allowing for better management of symbolic dimensions and null checks. - Updated the logic to assert non-null conditions at runtime and utilize cascaded if_then_else expressions for shape retrieval, enhancing robustness. - Removed deprecated code and streamlined the binding process for clarity and maintainability.
…in test output - Updated string formatting for better readability in the `test_nullable_shared_shape` function. - Ensured consistent use of double quotes for string literals. - Added a missing newline at the end of the file for proper formatting.
…ensors - Streamlined the calculation of allocation size by replacing a lambda function with a direct loop, enhancing readability and maintainability. - Improved clarity in the null check message for data pointers, ensuring better understanding of the binding process.
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Actionable comments posted: 1
🧹 Nitpick comments (1)
src/transform/make_packed_api.cc (1)
516-523: Batch binding integration looks correct.The switch from per-buffer
BindDLTensorcalls to a singleBindDLTensorsbatch call properly integrates the new nullable buffer handling. All required parameters are passed correctly.Minor: Extra blank line at 523 could be removed for consistency.
🔎 Apply this diff to remove the extra blank line:
for (const auto &[var, buffer] : buffer_def) { // Prefer buffer data var name in diagnostics to avoid exposing low-level // handle vars arg_buffer_declarations.push_back(DeclBuffer(buffer, nop)); } - // reset global symbol to attach prefix
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📒 Files selected for processing (5)
src/transform/arg_binder.cc(1 hunks)src/transform/arg_binder.h(2 hunks)src/transform/make_packed_api.cc(3 hunks)testing/python/transform/test_nullable_buffer_params.py(1 hunks)tilelang/engine/phase.py(1 hunks)
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🧠 Learnings (1)
📚 Learning: 2025-12-18T04:50:00.512Z
Learnt from: silentCoder-dev
Repo: tile-ai/tilelang PR: 1464
File: testing/python/language/test_tilelang_language_rand.py:14-14
Timestamp: 2025-12-18T04:50:00.512Z
Learning: In `testing/python/language/test_tilelang_language_rand.py`, the TileLang kernel uses `blk_M = M` (single block) and calls `rng_rand()` four times per element to align results with the Triton implementation, which uses `blk_M = 128` (multiple blocks) and calls the RNG once per element. These differences compensate for internal RNG behavior differences between TileLang and Triton.
Applied to files:
testing/python/transform/test_nullable_buffer_params.py
🧬 Code graph analysis (2)
testing/python/transform/test_nullable_buffer_params.py (3)
tilelang/language/symbolics.py (1)
dynamic(10-21)tilelang/language/v2/dtypes.py (2)
int32(202-202)float32(257-257)tilelang/language/kernel.py (1)
threads(214-218)
src/transform/arg_binder.h (1)
src/transform/arg_binder.cc (2)
BindDLTensors(314-925)BindDLTensors(314-318)
🪛 Ruff (0.14.8)
testing/python/transform/test_nullable_buffer_params.py
16-16: Unused function argument: a
(ARG001)
17-17: Unused function argument: b
(ARG001)
18-18: Unused function argument: c
(ARG001)
59-59: Consider moving this statement to an else block
(TRY300)
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🔇 Additional comments (7)
testing/python/transform/test_nullable_buffer_params.py (1)
7-69: LGTM!The test comprehensively exercises nullable buffer parameter handling by testing various combinations of NULL and non-NULL buffers. The logic correctly validates that at least one non-NULL buffer is required for shared shape variables.
Note: The static analysis warnings about unused function arguments (a, b, c) are false positives—these parameters are intentionally unused as the test focuses on NULL pointer handling rather than computation.
src/transform/make_packed_api.cc (1)
396-404: Clear documentation of the new nullable binding approach.The comment effectively explains how the new
BindDLTensorsmethod handles nullable carriers through runtime assertions and cascaded if_then_else expressions, eliminating the need to force one carrier to be non-NULL.src/transform/arg_binder.h (1)
99-112: Well-designed API evolution from single-buffer to batch binding.The updated
BindDLTensorssignature properly supports the new nullable buffer handling model by accepting a vector of buffer definitions and a set of used buffers, enabling efficient batch processing with shared shape variable coordination.src/transform/arg_binder.cc (4)
314-347: Well-structured first pass for shape variable collection.The first pass correctly collects all symbolic shape variables across buffers and tracks their sources (buffer name, dimension index, handle pointer). This enables efficient coordination of shared shape variables in the second pass.
349-409: Proper NULL guard and shape buffer initialization.The implementation correctly:
- Creates per-buffer is_null guards with explicit NULL checks for unused buffers
- Pre-creates all shape buffers with guarded TVMArrayGet calls to avoid dereferencing NULL handles
- Emits non-NULL assertions for used buffers
594-698: Sophisticated cascaded binding for shared symbolic shape variables.The cascaded if_then_else construction for shared shape variables is well-designed:
- Asserts at least one carrier buffer is non-NULL (lines 603-627)
- Builds cascaded expression in reverse order to prioritize first non-NULL buffer (lines 635-673)
- Properly handles used (non-nullable) buffers by always using their shape values (lines 654-666)
- Falls back to nullable binding for single-source or first-time variables (lines 681-686)
The reverse iteration pattern ensures correct precedence when multiple buffers share the same shape variable.
411-924: Comprehensive and robust batch binding implementation.The main binding loop properly handles all DLTensor fields with:
- Guarded field access using if_then_else to prevent NULL dereference throughout
- Structured error messages via tvm_call_packed with kernel/buffer context
- Proper dtype compatibility checks including FP8 variants and bool handling
- Stride binding with auto-broadcast and compact array support
- Byte offset validation for both constant and symbolic offsets
- Device type/ID consistency checks
- Data pointer NULL validation with appropriate guards for size-0 arrays
The implementation correctly coordinates nullable buffers with shared shape variables while maintaining safe memory access patterns.
Removed debug print statements after MakePackedAPI transformation.
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Summary by CodeRabbit
New Features
Bug Fixes
Refactor
Tests
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