- Move dead dsv4/ modules to dsv4/_archive/ (52 files)
- model/{dsv4,mtp,layer,layer_schedule}
- layers/{embedding,attention,ffn,norm} (kept linear,mhc,router,moe,shared_expert,grouped_linear - live)
- cache/*, kernels/cache/*, kernels/indexer/{csa_indexer,score_topk,compute_valid_lens}
- kernels/router/{nvfp4_fused_router,dense_router_decode_kernel,dense_router_prefill}
- ops/{topk,topk_select,rope,router}, loader/{hf_checkpoint,layout_convert}
- reference/{attention,compressor,csa_attention,moe_pipeline}
- kernels/compressor/{compress_tail,csa_hca}
- Restore dsv4/ops/{router,custom_ops}.py (needed by live layers)
- Fix dsv4/kernels/{indexer,compressor,attention}/__init__.py (removed broken imports)
- Remove preload_all() from loader.py (dead, referenced nonexistent .cu file)
- Fix loader.py docstring (fused_amax_quantize_nvfp4 → quantize_nvfp4_from_buffer)
- Move broken tests to tests/e2e_archive/
- test_fused_router, production_values_test, e2e/{one_layer,model_construction,csa_hca}
- vLLM has 0 imports of dsv4 (Step 0 confirmed)
163 lines
5.6 KiB
Python
163 lines
5.6 KiB
Python
"""In-graph flush orchestration.
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Called when tail_len crosses the compression threshold. The actual
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compression math is in the csa_hca_compressor kernel; this module
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handles the quantize-scatter-write step and the state rotation.
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The maybe_flush_* functions always run when their attention type
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matches — no host-side `if tail_full` check. The kernels gate
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internally via `valid_mask` computed from `tail_len`. This keeps
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the call sequence identical across forward passes for cudagraph.
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"""
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from __future__ import annotations
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from typing import Optional
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import os
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import torch
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from torch.utils.cpp_extension import load
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from dsv4.cache.schema import LayerCacheSchema, AttentionType
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_flush_mod = None
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def _get_flush_module():
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global _flush_mod
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if _flush_mod is not None:
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return _flush_mod
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kernel_dir = os.path.join(os.path.dirname(__file__), "..", "kernels", "cuda")
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_flush_mod = load(
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name="flush_write",
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sources=[os.path.join(kernel_dir, "flush_write.cu")],
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extra_cuda_cflags=["-O3", "--generate-code=arch=compute_100a,code=[sm_100a]"],
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verbose=False,
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)
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return _flush_mod
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def maybe_flush_csa(
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handle,
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schema: LayerCacheSchema,
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m: int,
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) -> None:
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"""For CSA: emit compressed entries for requests whose tail is full.
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Steps:
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1. Determine which requests have tail_len >= m (valid_mask).
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2. Run the CSA compressor on tail buffers.
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3. Scatter compressed entry + indexer key into paged pool.
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4. Rotate a-stream -> b-stream, clear a-stream.
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"""
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from dsv4.kernels.compressor import csa_compress_tail
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state = handle.state
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paged = handle.paged
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mod = _get_flush_module()
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# Step 1: valid_mask — which requests have a full tail buffer.
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# tail_len is [max_requests], request_slots is [B].
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tail_lens = state.tail_len[handle.request_slots] # [B]
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valid_mask = tail_lens >= m # [B] bool
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# If no requests need flushing, short-circuit.
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if not valid_mask.any().item():
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return
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# Step 2: compress the tail.
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# The compressor kernel takes the tail buffers and produces
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# one compressed entry per request (for those where valid_mask=True).
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entry, indexer_key = csa_compress_tail(
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tail_ka=state.tail_ka,
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tail_za=state.tail_za,
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tail_kb=state.tail_kb,
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tail_zb=state.tail_zb,
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tail_len=state.tail_len,
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request_slots=handle.request_slots,
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m=m,
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)
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# entry: [B, head_dim] BF16
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# indexer_key: [B, indexer_head_dim] BF16
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# Step 3: scatter into the paged pool.
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# The flush position for each request = the position of the last
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# token in the tail (positions before this forward minus 1 would
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# be the wrong reference; we need the tail's last position).
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# For the block table lookup, we use the compressed entry index
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# derived from positions.
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# Use the positions of the requests' current tokens to figure
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# out which entry slot to write into.
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flush_positions = handle.positions # [tokens] -> need per-request
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# For now, derive entry index from the per-request state:
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# compressed_entry_idx = sum of all flushes so far for this request.
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# This is (positions_of_last_appended_token) // m
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# Simplification: use request_slots to look up per-request position.
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# The handle's positions are per-token, not per-request.
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# We need one position per request = position of the last appended token.
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# For a single-token decode, that's just positions[-1] per request.
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# For a general case, take the max position per request.
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# This is computed by the append kernel (stored in tail_len and the
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# actual positions in the tail). For now, use handle.positions
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# and scatter by request.
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# The kernel resolves slot_in_block from positions internally.
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mod.flush_write_csa(
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entry, indexer_key, valid_mask, handle.request_slots,
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handle.positions[:handle.request_slots.shape[0]], # one pos per request
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handle.block_table,
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paged.entries_fp8, paged.entries_rope, paged.inv_scale,
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paged.indexer_keys_fp4, paged.indexer_scale,
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schema.entries_per_block, m, schema.rope_dim,
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schema.entry_head_dim, schema.indexer_head_dim,
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)
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# Step 4: rotate state — current a-stream becomes next b-stream.
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mod.csa_rotate_state(
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valid_mask, handle.request_slots,
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state.tail_ka, state.tail_za, state.tail_kb, state.tail_zb,
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state.tail_len, m, schema.entry_head_dim,
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)
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def maybe_flush_hca(
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handle,
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schema: LayerCacheSchema,
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m_prime: int,
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) -> None:
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"""For HCA: emit one entry per request whose tail_len >= m'."""
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from dsv4.kernels.compressor import hca_compress_tail
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state = handle.state
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paged = handle.paged
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mod = _get_flush_module()
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tail_lens = state.tail_len[handle.request_slots]
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valid_mask = tail_lens >= m_prime
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if not valid_mask.any().item():
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return
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entry = hca_compress_tail(
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tail_ka=state.tail_ka,
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tail_za=state.tail_za,
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tail_len=state.tail_len,
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request_slots=handle.request_slots,
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m=m_prime,
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)
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# entry: [B, head_dim] BF16
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mod.flush_write_hca(
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entry, valid_mask, handle.request_slots,
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handle.positions[:handle.request_slots.shape[0]],
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handle.block_table,
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paged.entries_fp8, paged.entries_rope, paged.inv_scale,
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schema.entries_per_block, m_prime, schema.rope_dim,
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schema.entry_head_dim,
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)
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# Reset tail — no b-stream rotation for HCA.
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mod.hca_reset_state(
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valid_mask, handle.request_slots,
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state.tail_ka, state.tail_za, state.tail_len,
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m_prime, schema.entry_head_dim,
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)
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