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Refactor flashinfer/__init__.py so that applications could selectively pack submodules without modifying __init__.py #2027

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Refactor flashinfer/__init__.py so that applications could selectively pack submodules without modifying __init__.py #2027

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2a231f0
Refactor imports with try-except for modules
bangshengtang Nov 3, 2025
c41d23e
Replace try-except with contextlib.suppress for imports
bangshengtang Nov 3, 2025
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368 changes: 239 additions & 129 deletions flashinfer/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,134 +19,244 @@
from .version import __version__ as __version__
from .version import __git_version__ as __git_version__

import contextlib

# JIT compilation support
from . import jit as jit
from .activation import gelu_and_mul as gelu_and_mul
from .activation import gelu_tanh_and_mul as gelu_tanh_and_mul
from .activation import silu_and_mul as silu_and_mul
from .activation import (
silu_and_mul_scaled_nvfp4_experts_quantize as silu_and_mul_scaled_nvfp4_experts_quantize,
)
from .attention import BatchAttention as BatchAttention
from .attention import (
BatchAttentionWithAttentionSinkWrapper as BatchAttentionWithAttentionSinkWrapper,
)
from .autotuner import autotune as autotune
from .cascade import (
BatchDecodeWithSharedPrefixPagedKVCacheWrapper as BatchDecodeWithSharedPrefixPagedKVCacheWrapper,
)
from .cascade import (
BatchPrefillWithSharedPrefixPagedKVCacheWrapper as BatchPrefillWithSharedPrefixPagedKVCacheWrapper,
)
from .cascade import (
MultiLevelCascadeAttentionWrapper as MultiLevelCascadeAttentionWrapper,
)
from .cascade import merge_state as merge_state
from .cascade import merge_state_in_place as merge_state_in_place
from .cascade import merge_states as merge_states
from .decode import (
BatchDecodeMlaWithPagedKVCacheWrapper as BatchDecodeMlaWithPagedKVCacheWrapper,
)
from .decode import (
BatchDecodeWithPagedKVCacheWrapper as BatchDecodeWithPagedKVCacheWrapper,
)
from .decode import (
CUDAGraphBatchDecodeWithPagedKVCacheWrapper as CUDAGraphBatchDecodeWithPagedKVCacheWrapper,
)
from .decode import (
fast_decode_plan as fast_decode_plan,
)
from .decode import cudnn_batch_decode_with_kv_cache as cudnn_batch_decode_with_kv_cache
from .decode import single_decode_with_kv_cache as single_decode_with_kv_cache
from .fp4_quantization import (
SfLayout,
block_scale_interleave,
nvfp4_block_scale_interleave,
e2m1_and_ufp8sf_scale_to_float,
fp4_quantize,
mxfp4_dequantize_host,
mxfp4_dequantize,
mxfp4_quantize,
nvfp4_quantize,
nvfp4_batched_quantize,
shuffle_matrix_a,
shuffle_matrix_sf_a,
scaled_fp4_grouped_quantize,
)
from .fp8_quantization import mxfp8_dequantize_host, mxfp8_quantize
from .fused_moe import (
RoutingMethodType,
GatedActType,
cutlass_fused_moe,
reorder_rows_for_gated_act_gemm,
trtllm_fp4_block_scale_moe,
trtllm_fp4_block_scale_routed_moe,
trtllm_fp8_block_scale_moe,
trtllm_fp8_per_tensor_scale_moe,
)
from .gemm import SegmentGEMMWrapper as SegmentGEMMWrapper
from .gemm import bmm_fp8 as bmm_fp8
from .gemm import mm_fp4 as mm_fp4
from .gemm import mm_fp8 as mm_fp8
from .gemm import tgv_gemm_sm100 as tgv_gemm_sm100
from .mla import BatchMLAPagedAttentionWrapper as BatchMLAPagedAttentionWrapper
from .norm import fused_add_rmsnorm as fused_add_rmsnorm
from .norm import layernorm as layernorm
from .norm import gemma_fused_add_rmsnorm as gemma_fused_add_rmsnorm
from .norm import gemma_rmsnorm as gemma_rmsnorm
from .norm import rmsnorm as rmsnorm
from .page import append_paged_kv_cache as append_paged_kv_cache
from .page import append_paged_mla_kv_cache as append_paged_mla_kv_cache
from .page import get_batch_indices_positions as get_batch_indices_positions
from .page import get_seq_lens as get_seq_lens
from .pod import PODWithPagedKVCacheWrapper as PODWithPagedKVCacheWrapper
from .prefill import (
BatchPrefillWithPagedKVCacheWrapper as BatchPrefillWithPagedKVCacheWrapper,
)
from .prefill import (
BatchPrefillWithRaggedKVCacheWrapper as BatchPrefillWithRaggedKVCacheWrapper,
)
from .prefill import single_prefill_with_kv_cache as single_prefill_with_kv_cache
from .prefill import (
single_prefill_with_kv_cache_return_lse as single_prefill_with_kv_cache_return_lse,
)
from .quantization import packbits as packbits
from .quantization import segment_packbits as segment_packbits
from .rope import apply_llama31_rope as apply_llama31_rope
from .rope import apply_llama31_rope_inplace as apply_llama31_rope_inplace
from .rope import apply_llama31_rope_pos_ids as apply_llama31_rope_pos_ids
from .rope import (
apply_llama31_rope_pos_ids_inplace as apply_llama31_rope_pos_ids_inplace,
)
from .rope import apply_rope as apply_rope
from .rope import apply_rope_inplace as apply_rope_inplace
from .rope import apply_rope_pos_ids as apply_rope_pos_ids
from .rope import apply_rope_pos_ids_inplace as apply_rope_pos_ids_inplace
from .rope import apply_rope_with_cos_sin_cache as apply_rope_with_cos_sin_cache
from .rope import (
apply_rope_with_cos_sin_cache_inplace as apply_rope_with_cos_sin_cache_inplace,
)
from .sampling import chain_speculative_sampling as chain_speculative_sampling
from .sampling import min_p_sampling_from_probs as min_p_sampling_from_probs
from .sampling import sampling_from_logits as sampling_from_logits
from .sampling import sampling_from_probs as sampling_from_probs
from .sampling import softmax as softmax
from .sampling import top_k_mask_logits as top_k_mask_logits
from .sampling import top_k_renorm_probs as top_k_renorm_probs
from .sampling import top_k_sampling_from_probs as top_k_sampling_from_probs
from .sampling import (
top_k_top_p_sampling_from_logits as top_k_top_p_sampling_from_logits,
)
from .sampling import top_k_top_p_sampling_from_probs as top_k_top_p_sampling_from_probs
from .sampling import top_p_renorm_probs as top_p_renorm_probs
from .sampling import top_p_sampling_from_probs as top_p_sampling_from_probs
from .sparse import BlockSparseAttentionWrapper as BlockSparseAttentionWrapper
from .sparse import (
VariableBlockSparseAttentionWrapper as VariableBlockSparseAttentionWrapper,
)
from .trtllm_low_latency_gemm import (
prepare_low_latency_gemm_weights as prepare_low_latency_gemm_weights,
)
from .utils import next_positive_power_of_2 as next_positive_power_of_2
from .xqa import xqa as xqa
from .xqa import xqa_mla as xqa_mla

# ============================================================================
# Activation functions
# ============================================================================
with contextlib.suppress(ImportError):
from .activation import gelu_and_mul as gelu_and_mul
from .activation import gelu_tanh_and_mul as gelu_tanh_and_mul
from .activation import silu_and_mul as silu_and_mul
from .activation import (
silu_and_mul_scaled_nvfp4_experts_quantize as silu_and_mul_scaled_nvfp4_experts_quantize,
)
Comment on lines +30 to +36
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@coderabbitai coderabbitai bot Nov 3, 2025

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⚠️ Potential issue | 🟠 Major

Don't silently swallow real ImportErrors

Blanket contextlib.suppress(ImportError) means any ImportError raised while importing .activation (including missing transitive deps or a bad refactor) now disappears, import flashinfer succeeds, and callers only discover the breakage later via an unhelpful AttributeError. Please differentiate “module truly not packaged” from “module broken”: e.g., probe importlib.util.find_spec and only skip when the spec is absent, or cache the caught exception and surface it via __getattr__ so users still see the real failure instead of a silent drop in exports.

🤖 Prompt for AI Agents 
In flashinfer/__init__.py around lines 30 to 36, the blanket
contextlib.suppress(ImportError) hides real import errors from .activation;
change the logic to first check
importlib.util.find_spec("flashinfer.activation") and only skip imports if the
spec is None (module not packaged), otherwise attempt the import and on
ImportError cache the caught exception in a module-level variable (e.g.,
_activation_import_error) and implement __getattr__ to raise that cached
exception when consumers try to access the missing attributes; this preserves
silent skipping for genuinely absent optional modules but surfaces real
ImportErrors to callers.


# ============================================================================
# Attention modules
# ============================================================================
with contextlib.suppress(ImportError):
from .attention import BatchAttention as BatchAttention
from .attention import (
BatchAttentionWithAttentionSinkWrapper as BatchAttentionWithAttentionSinkWrapper,
)

# ============================================================================
# Autotuner
# ============================================================================
with contextlib.suppress(ImportError):
from .autotuner import autotune as autotune

# ============================================================================
# Cascade attention
# ============================================================================
with contextlib.suppress(ImportError):
from .cascade import (
BatchDecodeWithSharedPrefixPagedKVCacheWrapper as BatchDecodeWithSharedPrefixPagedKVCacheWrapper,
)
from .cascade import (
BatchPrefillWithSharedPrefixPagedKVCacheWrapper as BatchPrefillWithSharedPrefixPagedKVCacheWrapper,
)
from .cascade import (
MultiLevelCascadeAttentionWrapper as MultiLevelCascadeAttentionWrapper,
)
from .cascade import merge_state as merge_state
from .cascade import merge_state_in_place as merge_state_in_place
from .cascade import merge_states as merge_states

# ============================================================================
# Decode operations
# ============================================================================
with contextlib.suppress(ImportError):
from .decode import (
BatchDecodeMlaWithPagedKVCacheWrapper as BatchDecodeMlaWithPagedKVCacheWrapper,
)
from .decode import (
BatchDecodeWithPagedKVCacheWrapper as BatchDecodeWithPagedKVCacheWrapper,
)
from .decode import (
CUDAGraphBatchDecodeWithPagedKVCacheWrapper as CUDAGraphBatchDecodeWithPagedKVCacheWrapper,
)
from .decode import (
cudnn_batch_decode_with_kv_cache as cudnn_batch_decode_with_kv_cache,
)
from .decode import fast_decode_plan as fast_decode_plan
from .decode import single_decode_with_kv_cache as single_decode_with_kv_cache

# ============================================================================
# FP4 quantization
# ============================================================================
with contextlib.suppress(ImportError):
from .fp4_quantization import (
SfLayout,
block_scale_interleave,
nvfp4_block_scale_interleave,
e2m1_and_ufp8sf_scale_to_float,
fp4_quantize,
mxfp4_dequantize_host,
mxfp4_dequantize,
mxfp4_quantize,
nvfp4_quantize,
nvfp4_batched_quantize,
shuffle_matrix_a,
shuffle_matrix_sf_a,
scaled_fp4_grouped_quantize,
)

# ============================================================================
# FP8 quantization
# ============================================================================
with contextlib.suppress(ImportError):
from .fp8_quantization import mxfp8_dequantize_host, mxfp8_quantize

# ============================================================================
# Fused mixture-of-experts (MoE)
# ============================================================================
with contextlib.suppress(ImportError):
from .fused_moe import (
RoutingMethodType,
GatedActType,
cutlass_fused_moe,
reorder_rows_for_gated_act_gemm,
trtllm_fp4_block_scale_moe,
trtllm_fp4_block_scale_routed_moe,
trtllm_fp8_block_scale_moe,
trtllm_fp8_per_tensor_scale_moe,
)

# ============================================================================
# GEMM operations
# ============================================================================
with contextlib.suppress(ImportError):
from .gemm import SegmentGEMMWrapper as SegmentGEMMWrapper
from .gemm import bmm_fp8 as bmm_fp8
from .gemm import mm_fp4 as mm_fp4
from .gemm import mm_fp8 as mm_fp8
from .gemm import tgv_gemm_sm100 as tgv_gemm_sm100

# ============================================================================
# Multi-latent attention (MLA)
# ============================================================================
with contextlib.suppress(ImportError):
from .mla import BatchMLAPagedAttentionWrapper as BatchMLAPagedAttentionWrapper

# ============================================================================
# Normalization operations
# ============================================================================
with contextlib.suppress(ImportError):
from .norm import fused_add_rmsnorm as fused_add_rmsnorm
from .norm import layernorm as layernorm
from .norm import gemma_fused_add_rmsnorm as gemma_fused_add_rmsnorm
from .norm import gemma_rmsnorm as gemma_rmsnorm
from .norm import rmsnorm as rmsnorm

# ============================================================================
# Paged KV cache operations
# ============================================================================
with contextlib.suppress(ImportError):
from .page import append_paged_kv_cache as append_paged_kv_cache
from .page import append_paged_mla_kv_cache as append_paged_mla_kv_cache
from .page import get_batch_indices_positions as get_batch_indices_positions
from .page import get_seq_lens as get_seq_lens

# ============================================================================
# POD (Persistent Output Decoding)
# ============================================================================
with contextlib.suppress(ImportError):
from .pod import PODWithPagedKVCacheWrapper as PODWithPagedKVCacheWrapper


# ============================================================================
# Prefill operations
# ============================================================================
with contextlib.suppress(ImportError):
from .prefill import (
BatchPrefillWithPagedKVCacheWrapper as BatchPrefillWithPagedKVCacheWrapper,
)
from .prefill import (
BatchPrefillWithRaggedKVCacheWrapper as BatchPrefillWithRaggedKVCacheWrapper,
)
from .prefill import single_prefill_with_kv_cache as single_prefill_with_kv_cache
from .prefill import (
single_prefill_with_kv_cache_return_lse as single_prefill_with_kv_cache_return_lse,
)

# ============================================================================
# Quantization utilities
# ============================================================================
with contextlib.suppress(ImportError):
from .quantization import packbits as packbits
from .quantization import segment_packbits as segment_packbits

# ============================================================================
# RoPE (Rotary Position Embedding)
# ============================================================================
with contextlib.suppress(ImportError):
from .rope import apply_llama31_rope as apply_llama31_rope
from .rope import apply_llama31_rope_inplace as apply_llama31_rope_inplace
from .rope import apply_llama31_rope_pos_ids as apply_llama31_rope_pos_ids
from .rope import (
apply_llama31_rope_pos_ids_inplace as apply_llama31_rope_pos_ids_inplace,
)
from .rope import apply_rope as apply_rope
from .rope import apply_rope_inplace as apply_rope_inplace
from .rope import apply_rope_pos_ids as apply_rope_pos_ids
from .rope import apply_rope_pos_ids_inplace as apply_rope_pos_ids_inplace
from .rope import apply_rope_with_cos_sin_cache as apply_rope_with_cos_sin_cache
from .rope import (
apply_rope_with_cos_sin_cache_inplace as apply_rope_with_cos_sin_cache_inplace,
)

# ============================================================================
# Sampling operations
# ============================================================================
with contextlib.suppress(ImportError):
from .sampling import chain_speculative_sampling as chain_speculative_sampling
from .sampling import min_p_sampling_from_probs as min_p_sampling_from_probs
from .sampling import sampling_from_logits as sampling_from_logits
from .sampling import sampling_from_probs as sampling_from_probs
from .sampling import softmax as softmax
from .sampling import top_k_mask_logits as top_k_mask_logits
from .sampling import top_k_renorm_probs as top_k_renorm_probs
from .sampling import top_k_sampling_from_probs as top_k_sampling_from_probs
from .sampling import (
top_k_top_p_sampling_from_logits as top_k_top_p_sampling_from_logits,
)
from .sampling import (
top_k_top_p_sampling_from_probs as top_k_top_p_sampling_from_probs,
)
from .sampling import top_p_renorm_probs as top_p_renorm_probs
from .sampling import top_p_sampling_from_probs as top_p_sampling_from_probs

# ============================================================================
# Sparse attention
# ============================================================================
with contextlib.suppress(ImportError):
from .sparse import BlockSparseAttentionWrapper as BlockSparseAttentionWrapper
from .sparse import (
VariableBlockSparseAttentionWrapper as VariableBlockSparseAttentionWrapper,
)

# ============================================================================
# TRT-LLM low-latency GEMM
# ============================================================================
with contextlib.suppress(ImportError):
from .trtllm_low_latency_gemm import (
prepare_low_latency_gemm_weights as prepare_low_latency_gemm_weights,
)

# ============================================================================
# Utilities
# ============================================================================
with contextlib.suppress(ImportError):
from .utils import next_positive_power_of_2 as next_positive_power_of_2

# ============================================================================
# XQA (Cross-Query Attention)
# ============================================================================
with contextlib.suppress(ImportError):
from .xqa import xqa as xqa
from .xqa import xqa_mla as xqa_mla
Comment on lines 19 to +262
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@gemini-code-assist gemini-code-assist bot Nov 3, 2025

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medium

The as <name> part of the imports is redundant when the imported name is the same as the alias. Removing these redundant aliases improves code readability and conciseness.

from .version import __version__
from .version import __git_version__

import contextlib

# JIT compilation support
from . import jit

# ============================================================================
# Activation functions
# ============================================================================
with contextlib.suppress(ImportError):
    from .activation import gelu_and_mul
    from .activation import gelu_tanh_and_mul
    from .activation import silu_and_mul
    from .activation import (
        silu_and_mul_scaled_nvfp4_experts_quantize,
    )

# ============================================================================
# Attention modules
# ============================================================================
with contextlib.suppress(ImportError):
    from .attention import BatchAttention
    from .attention import (
        BatchAttentionWithAttentionSinkWrapper,
    )

# ============================================================================
# Autotuner
# ============================================================================
with contextlib.suppress(ImportError):
    from .autotuner import autotune

# ============================================================================
# Cascade attention
# ============================================================================
with contextlib.suppress(ImportError):
    from .cascade import (
        BatchDecodeWithSharedPrefixPagedKVCacheWrapper,
    )
    from .cascade import (
        BatchPrefillWithSharedPrefixPagedKVCacheWrapper,
    )
    from .cascade import (
        MultiLevelCascadeAttentionWrapper,
    )
    from .cascade import merge_state
    from .cascade import merge_state_in_place
    from .cascade import merge_states

# ============================================================================
# Decode operations
# ============================================================================
with contextlib.suppress(ImportError):
    from .decode import (
        BatchDecodeMlaWithPagedKVCacheWrapper,
    )
    from .decode import (
        BatchDecodeWithPagedKVCacheWrapper,
    )
    from .decode import (
        CUDAGraphBatchDecodeWithPagedKVCacheWrapper,
    )
    from .decode import (
        cudnn_batch_decode_with_kv_cache,
    )
    from .decode import fast_decode_plan
    from .decode import single_decode_with_kv_cache

# ============================================================================
# FP4 quantization
# ============================================================================
with contextlib.suppress(ImportError):
    from .fp4_quantization import (
        SfLayout,
        block_scale_interleave,
        nvfp4_block_scale_interleave,
        e2m1_and_ufp8sf_scale_to_float,
        fp4_quantize,
        mxfp4_dequantize_host,
        mxfp4_dequantize,
        mxfp4_quantize,
        nvfp4_quantize,
        nvfp4_batched_quantize,
        shuffle_matrix_a,
        shuffle_matrix_sf_a,
        scaled_fp4_grouped_quantize,
    )

# ============================================================================
# FP8 quantization
# ============================================================================
with contextlib.suppress(ImportError):
    from .fp8_quantization import mxfp8_dequantize_host, mxfp8_quantize

# ============================================================================
# Fused mixture-of-experts (MoE)
# ============================================================================
with contextlib.suppress(ImportError):
    from .fused_moe import (
        RoutingMethodType,
        GatedActType,
        cutlass_fused_moe,
        reorder_rows_for_gated_act_gemm,
        trtllm_fp4_block_scale_moe,
        trtllm_fp4_block_scale_routed_moe,
        trtllm_fp8_block_scale_moe,
        trtllm_fp8_per_tensor_scale_moe,
    )

# ============================================================================
# GEMM operations
# ============================================================================
with contextlib.suppress(ImportError):
    from .gemm import SegmentGEMMWrapper
    from .gemm import bmm_fp8
    from .gemm import mm_fp4
    from .gemm import mm_fp8
    from .gemm import tgv_gemm_sm100

# ============================================================================
# Multi-latent attention (MLA)
# ============================================================================
with contextlib.suppress(ImportError):
    from .mla import BatchMLAPagedAttentionWrapper

# ============================================================================
# Normalization operations
# ============================================================================
with contextlib.suppress(ImportError):
    from .norm import fused_add_rmsnorm
    from .norm import layernorm
    from .norm import gemma_fused_add_rmsnorm
    from .norm import gemma_rmsnorm
    from .norm import rmsnorm

# ============================================================================
# Paged KV cache operations
# ============================================================================
with contextlib.suppress(ImportError):
    from .page import append_paged_kv_cache
    from .page import append_paged_mla_kv_cache
    from .page import get_batch_indices_positions
    from .page import get_seq_lens

# ============================================================================
# POD (Persistent Output Decoding)
# ============================================================================
with contextlib.suppress(ImportError):
    from .pod import PODWithPagedKVCacheWrapper


# ============================================================================
# Prefill operations
# ============================================================================
with contextlib.suppress(ImportError):
    from .prefill import (
        BatchPrefillWithPagedKVCacheWrapper,
    )
    from .prefill import (
        BatchPrefillWithRaggedKVCacheWrapper,
    )
    from .prefill import single_prefill_with_kv_cache
    from .prefill import (
        single_prefill_with_kv_cache_return_lse,
    )

# ============================================================================
# Quantization utilities
# ============================================================================
with contextlib.suppress(ImportError):
    from .quantization import packbits
    from .quantization import segment_packbits

# ============================================================================
# RoPE (Rotary Position Embedding)
# ============================================================================
with contextlib.suppress(ImportError):
    from .rope import apply_llama31_rope
    from .rope import apply_llama31_rope_inplace
    from .rope import apply_llama31_rope_pos_ids
    from .rope import (
        apply_llama31_rope_pos_ids_inplace,
    )
    from .rope import apply_rope
    from .rope import apply_rope_inplace
    from .rope import apply_rope_pos_ids
    from .rope import apply_rope_pos_ids_inplace
    from .rope import apply_rope_with_cos_sin_cache
    from .rope import (
        apply_rope_with_cos_sin_cache_inplace,
    )

# ============================================================================
# Sampling operations
# ============================================================================
with contextlib.suppress(ImportError):
    from .sampling import chain_speculative_sampling
    from .sampling import min_p_sampling_from_probs
    from .sampling import sampling_from_logits
    from .sampling import sampling_from_probs
    from .sampling import softmax
    from .sampling import top_k_mask_logits
    from .sampling import top_k_renorm_probs
    from .sampling import top_k_sampling_from_probs
    from .sampling import (
        top_k_top_p_sampling_from_logits,
    )
    from .sampling import (
        top_k_top_p_sampling_from_probs,
    )
    from .sampling import top_p_renorm_probs
    from .sampling import top_p_sampling_from_probs

# ============================================================================
# Sparse attention
# ============================================================================
with contextlib.suppress(ImportError):
    from .sparse import BlockSparseAttentionWrapper
    from .sparse import (
        VariableBlockSparseAttentionWrapper,
    )

# ============================================================================
# TRT-LLM low-latency GEMM
# ============================================================================
with contextlib.suppress(ImportError):
    from .trtllm_low_latency_gemm import (
        prepare_low_latency_gemm_weights,
    )

# ============================================================================
# Utilities
# ============================================================================
with contextlib.suppress(ImportError):
    from .utils import next_positive_power_of_2

# ============================================================================
# XQA (Cross-Query Attention)
# ============================================================================
with contextlib.suppress(ImportError):
    from .xqa import xqa
    from .xqa import xqa_mla