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Caution Review failedThe pull request is closed. WalkthroughThis pull request refactors several functions across multiple modules by removing the Changes
Sequence Diagram(s)sequenceDiagram
participant Test as Test Suite
participant FRwkv7 as fused_recurrent_rwkv7
participant RWkv7 as rwkv7_fn
Test->>FRwkv7: Initiate forward pass
FRwkv7->>RWkv7: Call without input_precision
RWkv7-->>FRwkv7: Return computed tensor
FRwkv7-->>Test: Pass along output
sequenceDiagram
participant Caller
participant Func as chunk_dplr_delta_rule
Caller->>Func: Invoke function with tensor q (float32)
Func->>Func: Check tensor data type
alt Tensor is float32
Func->>Caller: Emit warning ("ChunkDeltaRuleFunction does not support float32...")
else
Func->>Caller: Proceed with computation
end
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* [Attn] Remove `head_first` & rename `offsets` to `cu_seqlens` * Delete 256 headdim tests * [DeltaNet] Remove `head_first` & rename `offsets` to `cu_seqlens` * Rename `offsets` to `cu_seqlens` across GLA/GSA/RWKV6 * Fix NSA tests * Fix TTT checks * Rename `offsets` to `cu_seqlens` in DPLR and IPLR implementations, updating related logic and heuristics for variable-length sequences.
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tests/ops/test_rwkv7.py:18
- Consider adding tests for torch.float16 if needed to cover alternative precision paths, or ensure that bfloat16 is the exclusively supported type.
@pytest.mark.parametrize("dtype", [torch.bfloat16])
fla/ops/rwkv7/fused_recurrent.py:22
- Ensure that removing the input_precision parameter does not break downstream calls expecting this parameter; update documentation if necessary.
input_precision: Optional[torch.dtype] = torch.bfloat16,
fla/ops/rwkv7/fused_addcmul.py:55
- Confirm that replacing the explicit conversion to tl.float32 with conversion to DTYPE maintains numerical stability and performance; verify that the DTYPE parameter is set appropriately at all call sites.
b_hiddn = tl.load(hidden_ptr + (xindex), xmask, other=0.).to(DTYPE)
fla/ops/generalized_delta_rule/dplr/chunk.py:333
- [nitpick] The warning message for float32 support could be enhanced by providing explicit guidance on alternative behavior or raising an error for unsupported dtypes.
if q.dtype == torch.float32:
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Summary by CodeRabbit
Refactor
Tests