diff --git a/CHANGELOG.md b/CHANGELOG.md index d7739cb..e953045 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,74 @@ # Changelog -## [Unreleased] +## [0.3.3a0] — 2026-11-12 (beta) + +### Added +- `recurrent_order` (`"layer"` default, `"block"`): the order in which the recurrent layers repeat. `layer` runs each recurrent layer `recurrent_depth` times in a row (A A B B); `block` runs the whole group of recurrent layers and then repeats it (A B A B), so iteration 0 is an ordinary depth-1 forward pass and later iterations refine it. In `block` order there is one state trajectory and one halting head per block iteration (the `halting_probs` and `depth_used` group axis has length 1); every physical layer keeps its own `dt`, loop and group embeddings and its own KV slot. The default path is bit-identical to 0.3.2a3 (Torch and MLX logits compared on four attention modes, max difference 0). Available in the Torch and MLX models, the web UI (four languages) and `MODEL_FIELDS`. A `block` checkpoint is not interchangeable with a `layer` checkpoint. +- `recurrent_later_topk` (int, default 0 = off): loop iterations after the first route each token to this many experts (capped at `num_experts_per_tok`); the shared expert still runs. With `router_scope="shared"` the kept experts are the best-scoring ones of the first dispatch, so no new expert page is touched. The count is an explicit argument of the MoE, block and recompute calls in Torch and MLX, so activation recomputation replays it. The default path is bit-identical (max difference 0 on both backends, four attention modes). +- `recurrent_later_groups` (int, default 0 = all): in `block` order, loop iterations after the first run only the first N layers of the block. Cost in this kernel scales with the number of layer calls, so this removes calls and the expert pages those layers would load. Iteration 0 still runs every layer and writes every KV slot. It needs `recurrent_attention_mode` `shared_first` or `first_only`, because a skipped layer has no per-iteration KV slot, and the config raises a `ValueError` otherwise. The default path is bit-identical. +- `lambda_iteration_cluster` (float, default 0) and `lookahead_teacher_scope` (`"first"` default, `"iterations"`): the first adds a total-variation penalty between a token's per-iteration cluster-mass distribution and its mean over iterations, so the loop layers keep a token in one expert cluster across depth without locking experts. The second makes the lookahead router's teacher the router probabilities averaged over the layer's iterations. Both exist in Torch and MLX; the MLX loss is checked against the Torch loss and against `mx.compile`. +- `tools/bench_residency.py --paging expert|cluster|auto` (default `expert`, the historical layout) and `tools/bench_speculative.py` `mlx_runner(..., paging=...)`: the lazy cache can be paged by training cluster, as production serving does for checkpoints trained with `lambda_cluster_locality > 0`. Each row now also records `cluster_experts_loaded`, `cluster_aware`, `num_clusters`, `warmup_cluster_loads` and `prefill_cluster_loads`, and the summary `decode_cluster_loads`. +- `recurrent_dt_param` (`"clamp"` default, `"sigmoid"`): how the learned loop step size is stored. `clamp` keeps `dt = clamp(w, dt_min, dt_max)`, whose gradient is zero once `w` leaves the interval. `sigmoid` uses `dt = dt_min + (dt_max - dt_min) * sigmoid(w)`, whose gradient is positive everywhere, so a step size that has become small can still grow. The stored logit is initialised so that step 0 gives exactly `recurrent_dt_init` (clamp and sigmoid logits agree to 1.1e-6 at init; Torch and MLX agree to 1.3e-6). The default path is unchanged. +- `chronos.model.recurrent.recommended_dt_init(depth, horizon=2.0, cap=0.5)` returns `min(cap, horizon / depth)`: 0.5 at depth 4, 0.333 at depth 6, 0.25 at depth 8. One loop iteration is an explicit Euler step `x <- x + dt * f(x)`, so `depth` iterations integrate over the horizon `T = depth * dt`; the rule holds `T` fixed and lets depth refine the integration. It is a starting value for `recurrent_dt_init`, not a fitted optimum (see Physical mechanism and Measured). An earlier draft used `0.5 * (4 / depth) ** 3`; the depth-8 run below refuted it and it was removed before release. +- `recurrent_dt_param`, `recurrent_dt_init`, `recurrent_dt_min` and `recurrent_dt_max` appear in the Web UI recurrent section with labels and help in four languages. The planner recommends block order, `sigmoid` and the horizon-based `recurrent_dt_init` whenever `recurrent_depth > 1` (config, `sources` report entries, and the schema's recommended value). +- Auto-tune: `tune_recurrent_order` and `tune_recurrent_dt_init` join the search space (Web UI "Search over" list, `ChronosSearchSpaceConfig`, best-config patch). When either is searched the probe model runs `probe_recurrent_depth` iterations (the Web UI passes the configured `recurrent_depth`, clamped to 2-8) with one prelude and one coda layer. `recurrent_later_groups` is deliberately not searched: it trades quality for cost, so a loss-only search would always choose the full block. The probe loss comes from a few steps and cannot resolve differences as small as dt 0.2-0.5 at depth 4; treat the two loop knobs as a coarse screen. +- Auto-tune probe data: `chronos/tuning/probe_data.py` encodes a bounded sample of the user's dataset (at most 400k tokens and 20k records) with the configured tokenizer and trains the probe on it. The probe used to train on random token ids, so no hyperparameter could change its loss in a way that carries over to real data. It falls back to random ids, with a log line, when no text can be read or the checkpoint's vocabulary is smaller than the tokenizer's. On Apple silicon the probe now picks a torch device itself when the training backend is `mlx`, which is not a torch device. +- `chronos/assets/tokenizers/general_32k` (32,768 tokens, 1.9 MB) ships in the wheel, with `chronos.deps.get_general_tokenizer_path()` and `GENERAL_TOKENIZER_VOCAB`. It is the vocabulary built for the pretraining mix, with its control tokens relabelled to the kernel's task-neutral layout (`<|endoftext|>`, `<|im_start|>`, `<|im_end|>`, `<|pad|>`, then reserved ids 4-63); ordinary tokens, merges and ids are unchanged (1,004 of 1,004 test texts encode to the same ids as with the original tokenizer, on the native and on the Hugging Face tokenizer). Because the fingerprint hashes the special tokens, relabelling changed it from `169668b1…` to `6d4b3b52…`; the old value is recorded under `derived_from`, so a checkpoint trained with the original relabel-free tokenizer reports a different fingerprint than this asset. It carries no task tokens. `NOTICE` lists the corpus licences: FineWeb-Edu and Cosmopedia v2 (ODC-By), Wikipedia (CC-BY-SA 3.0 and GFDL), and Fineweb-Edu-Chinese V2.1, whose commercial use needs the owner's permission. +- Planner default vocabulary: a new plan whose request and dataset profile name no vocabulary now uses 32,768 and writes `tokenizer_path` pointing at the bundled tokenizer, with `sources` entries (it was 16,384 with no path). An explicit `vocab_size` keeps the user's choice and adds no path. `get_tokenizer_path()` still returns the legacy 6,400 tokenizer, so checkpoints that never recorded a tokenizer are not changed. +- Web UI: `recurrent_later_topk` and `recurrent_later_groups` appear in the recurrent section, `lambda_iteration_cluster` and `lookahead_teacher_scope` in the objectives section, each with labels and help in English, 简体中文, 繁體中文 and 日本語. + +### Fixed +- MLX trainer: a recurrent step size (`recurrent_dt_*`) that sat exactly on `recurrent_dt_min` or `recurrent_dt_max` received no gradient, because `mx.clip` has gradient 0 on its bounds while the Torch trainer's `clamp` gives 1. A run with `recurrent_dt_init` equal to a bound therefore kept its step sizes frozen on MLX only (a 30-minute arm with init 1.0 = max ended with raw step sizes exactly 1.0). `RecurrentControllerMLX.contract` now returns the same value with the Torch gradient on the bounds. Values strictly inside the interval, and the forward pass, are unchanged, so default configs train as before; values strictly outside the interval still get no gradient on either backend. +- MLX trainer: with `recurrent_depth_jitter > 0` and gradient accumulation, each micro-batch drew its own loop depth, so one optimizer step mixed graphs of different depth and the router statistics accumulated across micro-batches failed with a shape error (`broadcast_shapes (14,24) and (10,24)`). The depth is now drawn once per optimizer step and shared by all its micro-batches, in eager and compiled steps. Runs without jitter are unchanged. + +### Changed +- `tools/bench_residency.py` `cluster_loads` no longer includes the page reads of the store reset's warm-up (they are reported separately as `warmup_cluster_loads`); earlier reports included them, because the store's counters are cleared before the warm-up. Every mode starts from the same cold warm-up, so absolute counts drop by that constant per (prompt, mode). +- The kernel is now task-neutral in code and text. `chronos/cli.py`, `chronos/mlx/training/longrun.py`, `chronos/mlx/training/compiled.py`, `chronos/tokenizer/extract.py`, `chronos/data/chat_records.py` and the examples in `ui/i18n.py` no longer name a translation task, and the tokenizer asset's provenance metadata no longer names task tokens. `tests/test_ui_generic.py::test_kernel_has_no_translation_task_code` walks `chronos/` and `ui/` and fails on any task-specific term. The en-zh translation work stays in `demos/translation` (`chronos-translate`), which imports the kernel; its status, released-model and bundle sections moved verbatim from `README.md` and `README_zh.md` into `demos/translation/README.md` and `demos/translation/README_zh.md`. The main READMEs keep one pointer sentence and still quote en-zh numbers where they are the measurement context (speculative decoding, the 0.3.2 native-kernel benchmarks, the 50M preset row); those are measurements on one checkpoint, not defaults. `models/chronos-enzh-50m-sft` stays in the repository as the demo's model. +- Planner and Web UI recommendations for a looped model changed (`recurrent_order="block"`, `recurrent_dt_param="sigmoid"`, `recurrent_dt_init = min(0.5, 2 / depth)`), and `ui/presets_v3.json` was regenerated: all five tiers gain those three keys and nothing else changed. The kernel defaults did not change (`recurrent_order="layer"`, `recurrent_dt_param="clamp"`, `recurrent_dt_init=0.25`), so a config that does not set them, and every existing checkpoint, behaves as in 0.3.2a3. + +### Physical mechanism +A shared block applied again and again to the residual stream acts as one operator, so each iteration adds a vector that points along nearly the same direction as the one before. The contributions add constructively instead of as independent, orthogonal increments. Measured on the trained checkpoints, the cosine between the outputs of consecutive block calls rises with iteration, for example from 0.92 to 0.985 (depth 8, dt 0.25) and from 0.98 to 0.996 (depth 8, dt 0.5); independent layers would give a cosine near 0. +- One iteration is `h <- h + dt * (block(h + e_loop) - h)`. Near the dominant direction the block update is about `lambda_eff * h`, so each iteration multiplies the residual norm by `g = 1 + dt * lambda_eff`. `lambda_eff` is learned, not set: the optimiser rescales the block's output when `dt` changes. Across step sizes of 0.146 (learned by a sigmoid arm), 0.25 and 0.5 (both fixed) at depth 8, `g` stayed between 1.067 and 1.075 while `lambda_eff = (g - 1) / dt` fell from 0.48 to 0.30 to 0.13. +- The `R - 1` iterations after iteration 0 each apply the gain, so depth `R` raises it to the power `R - 1`: total amplification `g^(R-1)`, total log-gain `(R - 1) * ln g`. In every arm whose step size could stay large that total was 0.36-0.51 (1.4-1.7x) at depth 4 and at depth 8 (depth 4: 0.36-0.44; depth 8: 0.38-0.51). Arms whose step size collapsed onto its floor or stayed tiny had a lower total (0.09-0.24); at equal step they were worse than the best arm of their depth, but not always worse than every arm with a larger total (see the limits below). +- `dt` alone is therefore not the physical quantity; `kappa = dt * lambda_eff` and the total log-gain are. This is why the loss is flat for `dt` between about 0.2 and 0.5 at depth 4, and why the horizon `T = depth * dt` (the length of the Euler integration of `dh/dtau = block(h) - h`) is a better handle than `dt`: more depth at fixed `T` refines the integration without raising the total gain. +- Limits of the claim: the total gain is not a loss predictor (the depth-8 arm with a collapsed step size, total 1.27x, beat the fixed-0.5 arm, 1.57x, at equal step by 0.02-0.05), the measurements are one seed per arm at 15M parameters, and `T = 2` is a safe starting point inside a flat region, not a fitted optimum: the sigmoid arm that started at `T = 2` settled at `T = 1.16`. + +### Measured +15M-parameter config, recurrent depth 4, 24 experts with top-2, one Mac, 30 minutes per arm (about 1,850-1,960 steps), one seed per arm unless stated, same data order in every arm. Paired differences are training loss in 10-step windows; a repeat of the same block-order arm differs from the first by +0.002 to +0.008, so differences below about 0.01 are not separable from run-to-run noise. +- Block against layer order at the default step size (init 0.25, clip [0.05, 1]): block order has the lower paired loss by 0.025-0.075 in every block of steps after step 100, at equal speed (about 71.5k tokens/s). Validation CE at 30 minutes: 4.543 (block, 1,960 steps) against 4.586 (layer, 1,855 steps). +- The default step sizes collapse: the learned values end at 0.045-0.09 against a floor of 0.05, and the gradient below the floor is exactly zero on both backends, so a collapsed step size never recovers. Fixing every step size at 1.0 raises the paired loss of block order by 0.050 in steps 10-100, then lowers it by 0.022, 0.025, 0.101, 0.072 and 0.026 in the blocks 100-300, 300-600, 600-1,000, 1,000-1,500 and 1,500-1,960 (largest at steps 600-1,000). Validation CE at 30 minutes is slightly higher, 4.559 at 1,850 steps against 4.543 at 1,960, because the fixed-step arm completed fewer steps. +- With step sizes fixed at 1.0, layer order has the lower loss after about step 600 (block minus layer +0.015 to +0.034) and block order only learns faster during the first 600 steps. The advantage of block order therefore holds at the default, collapsing step size only; the default is not changed in this release. +- Depth 8 does not follow the depth-4 result. With step sizes fixed at 1.0, block order at depth 8 (1,255 steps in 30 minutes, 1.43 s per step, final validation CE 4.821) has a higher paired loss than the default-step-size depth-8 arm in every one of 82 windows (+0.199, +0.075, +0.039 and +0.077 in steps 10-100, 100-300, 300-600 and 600-1,000), and its gradient norm is 1.4-1.8 times higher (mean 1.27 in the first 100 steps, so the 1.0 clip was active there). Against fixed-step depth 4 it has no equal-step gain (validation CE 5.328 against 5.295 at step 800, 4.794 against 4.773 at step 1,250) at 1.5 times the time per step. The default step-size rule is therefore left as it is. +- Truncating the fixed-step depth-8 model to 1 to 8 iterations gives validation CE 5.615, 5.201, 4.995, 4.884, 4.823, 4.794, 4.779 and 4.773, so iterations 5 to 8 add 0.11 nats. +- Cost of the loop: a depth-4 step takes 0.945-0.976 s against 0.569 s at depth 1 (1.7x), for roughly 5-13% fewer steps to reach the same validation CE, so the loop loses on wall time at this size. Cost follows the number of layer calls, not the number of experts or tokens. +- Where the gain sits (validation CE with the loop truncated to 1, 2, 3, 4 iterations): 4.977, 4.637, 4.527, 4.506. The top-1 token equals the full-depth top-1 for 52%, 71%, 86% and 100% of tokens. The fourth iteration is worth about 0.02 nats. +- `recurrent_later_groups=2` (block order, step sizes fixed at 1.0, 2 of the 4 block layers in iterations 2-4): 2,450 steps in 30 minutes against 1,850 for the full block (0.748 against 1.001 s per step, 89.2k against 68.4k tokens/s, peak memory 6.57 against 7.43 GB) and a final validation CE of 4.456 against 4.559. At equal step the paired loss differs from the full block by -0.005, +0.007, -0.019, +0.001, +0.005 and +0.012 across the six blocks of steps, so the saving costs at most about 0.01 per step (the noise floor is 0.005-0.008). One seed. A depth-1 model trained for the same 30 minutes still reaches 4.379 in 3,170 steps, so the loop does not yet pay for itself in wall time at this size. Truncated to 1, 2, 3 and 4 iterations this model gives validation CE 4.624, 4.494, 4.439 and 4.418, against 4.987, 4.659, 4.542 and 4.514 for the full block: its first iteration is much better and the loop adds 0.206 nats in total. +- `recurrent_later_groups=2` at depth 8 (step sizes fixed at 1.0): 1,825 steps in 30 minutes against 1,255 for the full block (1.004 against 1.458 s per step) and a final validation CE of 4.586 against 4.821. At equal step the paired loss is higher by 0.007, 0.029, 0.029 and 0.022 after step 100, and validation CE at step 1,250 is 4.809 against 4.794, so skipping half the layers in seven later iterations costs about 0.02-0.03 per step, more than at depth 4. Depth 4 with the same setting is better at every point (validation CE 4.775 against 4.809 at step 1,250, 4.456 against 4.586 at 30 minutes). +- `recurrent_later_topk=1` raised validation CE from 4.543 to 4.600 and was no faster (71.6k against 71.5k tokens/s): at this width fewer routed experts saves no time. +- Cluster level: with `lambda_cluster_locality=0.3` a token touches 1.003-1.024 of the 6 expert clusters in the first iteration, and 99.5-100% of the clusters touched in the later three iterations were already touched in the first. `lambda_iteration_cluster` therefore has almost nothing to act on; with step sizes fixed at 1.0 its arm had paired loss lower by 0.007, 0.000, 0.005, 0.024, 0.018 and 0.008 across the six blocks of steps (one seed; all but the 600-1,500 blocks are within the noise floor), and the top-2 overlap with the first iteration differed by at most 0.011. +- Lookahead head: its next-token prediction recovers the top-2 experts of loop iterations 1-4 with recall 0.68-0.78 (top-2 slots) and 0.97-0.98 (top-4 slots), and picks the right cluster for 0.97 of tokens at every iteration, the same as repeating the previous token's cluster. An iteration-aware head was not built. +- Step size at depth 4 (block order, fixed step size, one seed per arm): fixing `dt = 0.5` against `dt = 1.0` lowers the paired loss by 0.052, 0.016, 0.032, 0.066, 0.047 and 0.026 across the six blocks of steps (every window lower in every block), and validation CE is 4.505 at 1,975 steps against 4.559 at 1,850. A sigmoid arm started at 0.5 (range 0.05-1) moved to 0.182-0.203 in all four layers with no change in loss: its paired difference from the fixed-0.5 arm is +0.002, -0.003, -0.002, +0.007, +0.001 and -0.002, inside the noise floor. At depth 4 the loss is therefore flat for `dt` between about 0.2 and 0.5, and worse at 1.0 or at a `dt` stuck at 0.05. +- Block against layer order with a healthy step size (both arms sigmoid, init 0.5, range 0.05-1): block order has the lower paired loss in every window block, by 0.051, 0.093, 0.081, 0.053, 0.039 and 0.027 (layer minus block; 0 of 20, 0 of 30, 0 of 40, 0 of 50 and 0 of 27 windows lower for layer order after step 100). Validation CE at 30 minutes is 4.542 for block (1,830 steps) and 4.572 for layer (1,775 steps). This supersedes the reading above that block order is only better at a collapsing step size: with step sizes fixed at 1.0 layer order wins late, with a trainable step size block order wins in every block of steps measured (in steps 10-100, 7 of 9 windows). +- Depth 8 with a fixed integration horizon (block order, one seed per arm, 30 minutes, 1,185-1,265 steps, same data order): `dt = 0.25` (`T = depth * dt = 2`) has a lower paired loss than `dt = 0.5` (`T = 4`) by 0.046, 0.018, 0.031, 0.079 and 0.075 across the five blocks of steps from step 10 to the end of the run (every window lower in every block); validation CE is 4.776 at 1,215 steps against 4.794 at 1,265. Against the default clamp arm, whose step size collapsed onto the 0.05 floor, it is lower by 0.003, 0.013, 0.015 and 0.002 (about the noise floor). A sigmoid arm started at 0.25 (range 0.1-1) moved to 0.141-0.147 (`T` about 1.16), tied with the fixed arm until step 600 (+0.002 in three blocks) and then lower by 0.023 and 0.020 (58 of 61 windows), with validation CE 4.761 at 1,220 steps against 4.776 at 1,215. At equal step the depth-8 `dt = 0.25` arm is lower than the depth-4 `dt = 0.5` arm by 0.041 and 0.049 in steps 100-300 and 300-600 and level or slightly higher afterwards (+0.002 and +0.008, at the noise floor). +- The cubic depth rule `dt0 = 0.5 * (4 / depth)^3` was an earlier draft and is refuted at depth 8: a sigmoid arm started at its prediction 0.0625 and ended at 0.031-0.037, and its paired loss is higher than the fixed `dt = 0.5` arm by 0.066, 0.079, 0.122 and 0.256 after step 100, and higher than the fixed `dt = 0.25` arm by 0.085, 0.110, 0.201 and 0.333. Its total log-gain was 0.088, the lowest of any arm. The rule was replaced by `T / depth`. +- Progressive loop curriculum (`recurrent_depth` rising 1 to 4 over 1,600 steps, fixed `dt = 0.5`, depth jitter 0.01, block order): median time per step is 0.56, 0.725, 0.899 and 1.08 s at depth 1, 2, 3 and 4. At equal step it trails the fixed-depth-4 arm until the depth reaches 3-4: validation CE 5.596 against 5.215 at step 800 and 5.168 against 4.920 at step 1,000. Its early steps are cheaper, and it gives up equal-step quality until the full depth is reached; the loop's advantage is equal-token sample efficiency, not wall clock. +- `recurrent_later_groups=1` leaves three of the four block layers dormant in every later iteration, so the loop reduces to one layer applied repeatedly. With step sizes fixed at 1.0 its paired loss is higher than `recurrent_later_groups=2` by 0.062 and 0.050 in steps 100-300 and 300-600 and by 0.004, 0.004 and 0.012 afterwards, and the two arms end 0.049 apart in validation CE at 30 minutes (4.407 at 2,810 steps against 4.456 at 2,450) only because 1 layer is cheaper. Use `recurrent_later_groups` of at least 2. +- `recurrent_later_groups=2` with the default clamp step size (block order, init 0.25, clip [0.05, 1]) was much worse than with a step size that can move. Against the full-block arm with the same default step size (block order, a repeat run), it trailed by +0.007, +0.017, +0.030, +0.046, +0.200 and +0.097 across the six blocks of steps (about +0.235 in steps 1,200-1,400), at a median gradient norm of 0.54 against 0.51 after step 1,000 and the same learning rate; it had no rollbacks. The learned step sizes ended at 0.056, 0.050, 0.133 and 0.139, so the two layers that still run in later iterations scaled their updates by 0.056 and 0.050. With a sigmoid step size from 0.5 (range 0.05-1) and the same `recurrent_later_groups=2`, the paired loss is lower than the clamp arm by 0.037, 0.067, 0.150, 0.284 and 0.128 after step 100, validation CE at step 1,000 is 4.980 against 5.254, and the two later-iteration layers end at 0.185 and 0.170. The step-size collapse therefore accounts for most of the regression but not all of it. The prediction that the sigmoid arm would stay within about 0.01 of the full block was not met: it is above the full-block sigmoid arm by 0.013, 0.021, 0.022, 0.055, 0.034 and 0.019 (2 of 9, 0 of 20, 2 of 30, 0 of 40, 1 of 50 and 2 of 33 windows lower), more than the 0.01 that skipping layers cost with the step size fixed at 1.0. The two arms also differ in initial step size (0.5 against 0.25), so the effect of the parameterisation and of the initial value are not separated, and the loss gap that opens at steps 1,000-1,500 and then closes is not explained. At 30 minutes the sigmoid arm reaches validation CE 4.440 in 2,365 steps, against 4.491 in 2,460 for the clamp arm and 4.542 in 1,830 for the full-block sigmoid arm; at equal step the full block is ahead (4.928 against 4.980 at step 1,000). +- Step time of this release's kernel: the same depth-4 configuration takes a median 0.997 s per step with the clamp and 1.017 s with the sigmoid (steps 50-400, one 6-minute arm each), against 0.958 s on the development kernel that had block order and `recurrent_later_groups` but not the clip-gradient fix. The 4% and 6% are not attributed to a cause (the differences are the `where`-based clip gradient, the per-step depth draw and the sigmoid branch), and one run each cannot separate them from run-to-run noise. + +### Known limits +- Block order with lazy experts: `tests/test_recurrent_order.py::test_mlx_engine_block_order_runs_with_lazy_experts` checks that the MLX engine with a two-expert resident set generates 10 valid tokens speculatively, and the all-resident engine is checked for exact speculative decoding. Nothing compares lazy block-order output with all-resident block-order output token by token, and the residency planner, the cluster-paged store and the async prefetcher have not been benchmarked on a block-order checkpoint (`expert_store.py` and `cluster_layout.py` index pages by physical layer, which is the same page set in both orders; only the order in which layers are used differs). Treat lazy serving of a block-order checkpoint as unmeasured. +- All loop measurements are one seed per arm at 15M parameters (block-order depth 4 and 8, 24 experts, 30 minutes). They are not evidence for larger models, and the planner's block-order and `T = 2` recommendations are extrapolated to every size it plans. +- The Web UI probe for auto-tune sees a few steps of a 4-layer model; it cannot rank step sizes that differ by less than the measured noise. + +### Tests +- `tests/test_recurrent_order.py`, `tests/test_later_topk.py`, `tests/test_later_groups.py` and `tests/test_iteration_cluster.py` cover config validation, the `MODEL_FIELDS` round trip, Torch against MLX, cache decode against a full forward, exact self-speculative decoding, and gradient reach into every recurrent layer, router and the controller. `tests/test_mlx_checkpointing.py` adds block-order and graded-top-k cases to the recompute and compile matrices. +- `tests/test_dt_clip.py` compares the MLX and Torch step-size gradient at 0.4999, 0.5, 0.5001, 0.75, 0.9999, 1.0 and 1.0001; on the 0.3.2a3 kernel it fails at 0.5 and 1.0. +- `tests/test_bench_paging.py` checks that `mlx_runner(paging=...)` builds one page per expert for `expert` and the training clusters for `cluster` and `auto`. The prefill/decode split and warm-up exclusion in `tools/bench_residency.py` run inside its `main()` and need a tokenizer, so they are verified by running the tool on the benchmark checkpoints, not by a unit test. +- `tests/test_dt_sigmoid.py` covers config validation of `recurrent_dt_param`, the sigmoid logit initialisation (step 0 gives `recurrent_dt_init` at 0.05, 0.0625, 0.25, 0.5 and 1.0), the nonzero sigmoid gradient where the clamp gradient is zero, Torch against MLX at four logits, and `recommended_dt_init` (`depth * dt = 2`, the 0.5 cap at depths 1-3). `tests/test_depth_jitter.py` covers one depth draw per optimizer step under gradient accumulation, with and without `mx.compile`. +- `tests/test_general_tokenizer.py` (7 tests) checks the bundled tokenizer's files, its 32,768 vocabulary and control ids 0-63, native against Hugging Face encodings and the round trip, that the report names no local path and records `derived_from`, the planner's new default (32,768 and the bundled path, with `sources`), that an explicit vocabulary adds no path, and that the legacy default is still 6,400. +- `tests/test_autotune_loop.py` (8 tests) covers the probe's real tokens (bundled vocabulary, `None` for unreadable or tiny data, deterministic batches that advance with the step), the loop knobs being sampled only when searched, the best-config patch, a probe trial that builds a looped block-order model and draws its batches from real tokens, a probe without loop knobs staying a plain depth-1 model on random ids, and the Web UI search choices and four-language labels. `tests/test_planner_loop.py` (7 tests) covers the planner's block order, sigmoid and horizon init at depths 2, 4, 6 and 8, the plain defaults at depth 1, that the planned config builds a valid model, and that the schema's recommended values equal what the planner emits. +- Full suite on the development tree: 732 passed, 4 skipped (three browser tests need `playwright`, which is not installed here; one needs the v2 reference checkpoint, which is not in the repository). `ruff check .` is clean. ## [0.3.2a3] — 2026-11-10 (beta) diff --git a/README.md b/README.md index c89c780..4f4c207 100644 --- a/README.md +++ b/README.md @@ -113,13 +113,7 @@ At this size a full-depth step costs about as much as a batched verify pass. Wit `chronos.model.factory.fit_config` searches for a configuration that matches total and active parameter targets. -**Kernel and extensions.** The `chronos` package trains general models and carries no task-specific code. Task tooling lives in extensions that import the kernel. The en↔zh translation demo is one: `demos/translation` (`chronos-translate`: corpus pipeline, BLEU/chrF runs, model bundles; see its README). It is not part of the `Project_Chronos` wheel and is not published on PyPI; install it from a checkout with `pip install -e demos/translation`. - -**Status of the 50M en→zh demo (in progress; `demos/translation`).** -- Model: 49.69M total / 12.09M active parameters, 36 experts, MLA latent 128, SWA window 128, 16k self-built vocabulary. -- Run: a 72-hour MLX bf16 run (pretrain, then SFT) started on 2026-11-05. -- Interim measurement: valid CE 2.477 at pretrain step 13,996 of about 24.7k planned (it was 3.012 at step 1,656). That checkpoint is packaged as `chronos-enzh-50m-pretrain-s13996` (see Model bundle). -- BLEU/chrF on FLORES+ and WMT22, and the ablations (attention mode, depth, lazy-expert budget, speculative γ), will be published after the run finishes. No quality numbers are claimed before then. +**Kernel and extensions.** The `chronos` package trains general models and carries no task-specific code. Task tooling lives in extensions that import the kernel. The en↔zh translation demo is one: `demos/translation` (`chronos-translate`: corpus pipeline, BLEU/chrF runs, model bundles; see its README). It is not part of the `Project_Chronos` wheel and is not published on PyPI; install it from a checkout with `pip install -e demos/translation`. The 50M en→zh status, the released model `chronos-enzh-50m-sft` with its measurements, and the model bundle are documented in [`demos/translation/README.md`](demos/translation/README.md). **Inference and memory options (all off by default).** Each option is a config field and a `chronos serve` / Inference-tab override. Tests compare each one with its reference path on Torch and MLX. @@ -501,22 +495,6 @@ All lambda terms are searchable with Optuna TPE, together with structural hyperp --- -## Released model: chronos-enzh-50m-sft - -[`models/chronos-enzh-50m-sft/`](models/chronos-enzh-50m-sft/) packages a 49.7M-parameter Chronos v3 model (12.1M active per token) trained for 69 hours on one Apple M5 Max: configuration, tokenizer, trained prompt, decoding settings, a driver (`translate.py`) and the evaluation behind every number. The weights (99.5 MB, bfloat16 safetensors) are attached to the release [`enzh-50m-sft-v1`](https://github.com/FonaTech/Project_Chronos/releases/tag/enzh-50m-sft-v1). - -- **Boundary:** English → Chinese only, one sentence (≤ 128 tokens) at a time. Chinese → English is disabled: English-only fine-tuning erased it (FLORES+ BLEU 0.00 after SFT). The driver refuses input outside the boundary. -- **Measured** (MLX bf16, greedy): FLORES+ devtest BLEU 33.48 / chrF 29.52; WMT22 BLEU 32.60 / chrF 28.83; WMT24++ BLEU 26.89 / chrF 24.43 (split into sentences). -- **Research use only.** The training corpora's licences are unknown; see the folder's `NOTICE`. - -```bash -cd models/chronos-enzh-50m-sft -pip install --pre "Project_Chronos[mlx]==0.3.2a0" # or "Project_Chronos==0.3.2a0" without MLX -python translate.py --download && python translate.py "The meeting was moved to Friday." -``` - ---- - ## Installation ```bash @@ -598,6 +576,7 @@ The old preset names (Recommended-CN, Tiny/Small/Medium/Large) still load; they 3. **Sets LR and batch size** from the DeepSeek LLM compute-optimal fits (η = 0.3118·C^−0.125, B = 0.2920·C^0.3271), capped for wide models. 4. **Fits memory.** It picks micro-batch, accumulation, int8 KV, router scope, activation checkpointing, optimizer paging and an expert RAM budget for the memory level. 5. **Writes per-stage values.** Pretrain, SFT, DPO, ORPO, GRPO and distill each get their own learning rate, batch, epochs, save interval and stage-specific terms (β, temperature, α). +6. **Sets the loop knobs (depth above 1).** `recurrent_order="block"`, `recurrent_dt_param="sigmoid"` and `recurrent_dt_init = min(0.5, 2 / depth)`, each with a `sources` entry in the report (the first two cite the 15M measurements; the step size is labelled estimated). One loop iteration is an Euler step of `dh/dτ = block(h) − h`, so `depth × dt` is the integration horizon; the planner holds it at 2 and lets depth refine the integration. Kernel defaults are unchanged (`layer`, `clamp`, 0.25). All of this was measured on one seed per arm at 15M parameters; see the CHANGELOG, "Physical mechanism" and "Known limits". The stage table is editable; the Train tab merges the selected stage's row over the shared values, and the pretrain row stays in sync with the shared widgets. "Save plan" writes the config, stage values and report to JSON. Every number in the report is labelled measured or estimated. @@ -646,38 +625,6 @@ OLLAMA_HOST=127.0.0.1:11435 ollama run "Hello" - Stock `ollama` / llama.cpp cannot execute Chronos GGUF files (MLA+SWA hybrid layers and the contractive recurrence have no stock equivalent); use this server instead. - Security: binds 127.0.0.1 without authentication by default. Other addresses require `--allow-remote` and `--api-key`; clients then send `Authorization: Bearer `. -### Model bundle (translation demo extension) - -Bundles belong to the translation extension (`demos/translation`). Build the two wheels first (`python -m build --outdir dist .` and `python -m build --outdir dist demos/translation`). `chronos-translate bundle build` packages one exported checkpoint of a run into a folder that works right after unpacking. Building only reads the run directory. - -```bash -chronos-translate bundle build --run-dir runs/enzh_50m_v1 --which best --pack \ - --wheel dist/project_chronos-0.3.2a0-py3-none-any.whl --wheel dist/chronos_translate-0.3.2a0-py3-none-any.whl -``` - -| Bundle path | Content | -|---|---| -| `model/` | weights + `.config.json` sidecar, tokenizer (`model/tokenizer/`) | -| `eval/` | FLORES+ dev/devtest (CC BY-SA 4.0) for `bundle eval` / `probe` | -| `data/sft_valid/` | the run's SFT validation shards | -| `wheels/`, `bin/setup.sh`, `bin/chronos` | offline install of the kernel and extension wheels into `.venv`; launcher that sets `CHRONOS_BUNDLE` and sends `bundle …` to `chronos-translate` | -| `recipes/source_run_spec.json`, `recommended.json` | the recipe that produced the weights; fine-tune tiers `smoke` / `quick` / `standard` / `full` | -| `bundle.json` | provenance (step, tokens, valid CE), measured quality probe, SHA-256 of every file | - -On the recipient side: - -```bash -bin/setup.sh # venv + wheels + `bundle check` -bin/chronos bundle translate "The weather is very nice today." -bin/chronos bundle translate --backend mlx --speculative --file examples/translate_en.txt -bin/chronos bundle eval --split devtest -n 200 -bin/chronos bundle prepare-data my_pairs.jsonl --out work/data # FLORES+ is removed from your data -bin/chronos bundle finetune --data work/data --run-dir work/ft --tier quick --mode sft -bin/chronos bundle serve -- --port 11435 -``` - -`--speculative` needs `--repetition-penalty 1.0` (its default), because the penalty changes the target distribution at every position. `finetune` writes a `chronos-translate run` spec with `init_checkpoint` set to the bundle weights and refuses a run directory that already holds a run. Bundles built from data with unknown licences (WMT19, OPUS-100) say in `NOTICE.md` that the weights are for research only. - ### Deployment export ```bash @@ -797,11 +744,15 @@ tuner.start( tune_num_experts=True, tune_num_shared_experts=True, tune_kv_latent_dim=True, + tune_recurrent_order=True, # probes a looped model (probe_recurrent_depth, default 4) + tune_recurrent_dt_init=True, ), n_trials=20, ) ``` +The probe trains on real tokens from `dataset_path` (up to 400k, encoded with the model's tokenizer, or the bundled 32k one) and falls back to random ids with a log line when the data cannot be read. The two loop knobs are a coarse screen: a few steps of a 4-layer model cannot rank step sizes that differ by less than the run-to-run noise. `recurrent_later_groups` is not searched, because it trades quality for cost and a loss-only search would always choose the full block. + --- ## Project layout diff --git a/README_zh.md b/README_zh.md index b70d9d5..c7eea6d 100644 --- a/README_zh.md +++ b/README_zh.md @@ -125,13 +125,7 @@ flowchart LR `chronos.model.factory.fit_config` 按总参数量和激活参数量目标搜索模型配置。 -**内核与扩展。** `chronos` 包用于训练通用模型，不含任何任务特化代码。任务工具放在调用内核的扩展里。英↔中翻译 demo 就是这样一个扩展：`demos/translation`（`chronos-translate`：语料流水线、BLEU/chrF 训练、模型包，详见其 README）。它不在 `Project_Chronos` wheel 里，也不发布到 PyPI，需从仓库检出后用 `pip install -e demos/translation` 安装。 - -**50M 英译中 demo（进行中；`demos/translation`）** -- 模型：总参数 49.69M，激活 12.09M；36 个专家；MLA latent 128；SWA 窗口 128；自研 16k 词表。 -- 训练：72 小时 MLX bf16 任务（先预训练，后 SFT），2026-11-05 启动。 -- 中途实测：预训练第 13,996 步（计划约 24.7k 步）valid CE 为 2.477（第 1,656 步时为 3.012）。该 checkpoint 已打包为 `chronos-enzh-50m-pretrain-s13996`（见“模型包”）。 -- FLORES+ 和 WMT22 上的 BLEU/chrF，以及各项消融（注意力模式、深度、懒加载专家预算、推测 γ），等训练结束后公布。在此之前不给出任何质量数字。 +**内核与扩展。** `chronos` 包用于训练通用模型，不含任何任务特化代码。任务工具放在调用内核的扩展里。英↔中翻译 demo 就是这样一个扩展：`demos/translation`（`chronos-translate`：语料流水线、BLEU/chrF 训练、模型包，详见其 README）。它不在 `Project_Chronos` wheel 里，也不发布到 PyPI，需从仓库检出后用 `pip install -e demos/translation` 安装。50M 英译中的进展、已发布模型 `chronos-enzh-50m-sft` 及其实测结果、模型包，均写在 [`demos/translation/README_zh.md`](demos/translation/README_zh.md)。 **推理与内存选项（全部默认关闭）。** 每个选项既是 config 字段，也可作为 `chronos serve` 和 Inference Tab 的覆盖项；测试在 Torch 和 MLX 上把每个选项与参考路径做对比。 @@ -505,22 +499,6 @@ D_{\mathrm{KL}} --- -## 已发布模型：chronos-enzh-50m-sft - -[`models/chronos-enzh-50m-sft/`](models/chronos-enzh-50m-sft/) 打包了一个 4,969 万参数的 Chronos v3 模型（每个 token 激活 1,209 万），在一台 Apple M5 Max 上训练了 69 小时。目录中有模型配置、分词器、训练时的 prompt、解码参数、驱动脚本（`translate.py`），以及每个数字背后的评测。权重（99.5 MB，bfloat16 safetensors）作为附件放在 release [`enzh-50m-sft-v1`](https://github.com/FonaTech/Project_Chronos/releases/tag/enzh-50m-sft-v1) 中。 - -- **边界：** 只做英译中，每次一句（≤ 128 token）。中译英已屏蔽：只含英译中的微调抹掉了这一能力（SFT 后 FLORES+ BLEU 0.00）。驱动会拒绝边界以外的输入。 -- **实测**（MLX bf16，贪心解码）：FLORES+ devtest BLEU 33.48 / chrF 29.52；WMT22 BLEU 32.60 / chrF 28.83；WMT24++ BLEU 26.89 / chrF 24.43（分句）。 -- **仅供研究使用。** 训练语料的许可未知，见目录中的 `NOTICE`。 - -```bash -cd models/chronos-enzh-50m-sft -pip install --pre "Project_Chronos[mlx]==0.3.2a0" # 不用 MLX 时装 "Project_Chronos==0.3.2a0" -python translate.py --download && python translate.py "The meeting was moved to Friday." -``` - ---- - ## 安装 ```bash @@ -601,6 +579,7 @@ Config Tab 提供 5 档 v3 预设。它们都由规划器生成，存放在 `ui/ 3. **确定学习率和 batch。** 按 DeepSeek LLM 的计算最优拟合式（η = 0.3118·C^−0.125，B = 0.2920·C^0.3271）给出，宽模型的学习率另设上限。 4. **匹配内存。** 按内存档位选择 micro-batch、梯度累积、int8 KV、路由范围、激活检查点、优化器分页和专家 RAM 预算。 5. **生成各阶段参数。** pretrain、SFT、DPO、ORPO、GRPO、distill 各有独立的学习率、batch、epoch、保存间隔，以及各自的专用项（β、温度、α）。 +6. **设置循环参数（深度大于 1 时）。** `recurrent_order="block"`、`recurrent_dt_param="sigmoid"` 和 `recurrent_dt_init = min(0.5, 2 / depth)`，报告的 `sources` 中各有一条来源（前两项引用 15M 实测，步长标注为估算）。一次循环迭代是 `dh/dτ = block(h) − h` 的一个欧拉步，所以 `depth × dt` 是积分时程；规划器把时程固定为 2，让深度只负责把积分切得更细。内核默认值不变（`layer`、`clamp`、0.25）。以上结论都来自 15M 参数、每组一个种子的实测；见 CHANGELOG 的“Physical mechanism”和“Known limits”。 阶段表可以直接编辑。Train Tab 会把所选阶段那一行合并到共享参数之上；pretrain 行与共享控件保持同步。“保存方案”把 config、各阶段参数和报告写成 JSON。报告里每个数字都标注实测或估算。 @@ -650,38 +629,6 @@ OLLAMA_HOST=127.0.0.1:11435 ollama run <模型名> "你好" - stock `ollama` / llama.cpp 无法执行 Chronos 的 GGUF 文件，因为 MLA+SWA 混合层和收缩式循环没有对应的 stock 实现；请改用本服务。 - 安全：默认只监听 127.0.0.1，不带认证。绑定其他地址必须同时给出 `--allow-remote` 和 `--api-key`，客户端随后发送 `Authorization: Bearer `。 -### 模型包（翻译 demo 扩展） - -模型包属于翻译扩展（`demos/translation`）。先构建两个 wheel（`python -m build --outdir dist .` 和 `python -m build --outdir dist demos/translation`）。`chronos-translate bundle build` 把某次运行导出的一个 checkpoint 打包成解压即用的目录。打包过程只读取运行目录。 - -```bash -chronos-translate bundle build --run-dir runs/enzh_50m_v1 --which best --pack \ - --wheel dist/project_chronos-0.3.2a0-py3-none-any.whl --wheel dist/chronos_translate-0.3.2a0-py3-none-any.whl -``` - -| 包内路径 | 内容 | -|---|---| -| `model/` | 权重 + `.config.json` 配置文件，分词器（`model/tokenizer/`） | -| `eval/` | FLORES+ dev/devtest（CC BY-SA 4.0），供 `bundle eval` / `probe` 使用 | -| `data/sft_valid/` | 该次运行的 SFT 验证分片 | -| `wheels/`、`bin/setup.sh`、`bin/chronos` | 把内核和扩展两个 wheel 离线安装到 `.venv`；启动脚本设置 `CHRONOS_BUNDLE`，并把 `bundle …` 转给 `chronos-translate` | -| `recipes/source_run_spec.json`、`recommended.json` | 产生这份权重的配方；微调档位 `smoke` / `quick` / `standard` / `full` | -| `bundle.json` | 来源信息（步数、token 数、valid CE）、实测质量探针、每个文件的 SHA-256 | - -接收方的用法： - -```bash -bin/setup.sh # 建 venv、装 wheels、运行 `bundle check` -bin/chronos bundle translate "The weather is very nice today." -bin/chronos bundle translate --backend mlx --speculative --file examples/translate_en.txt -bin/chronos bundle eval --split devtest -n 200 -bin/chronos bundle prepare-data my_pairs.jsonl --out work/data # 会从你的数据中剔除 FLORES+ -bin/chronos bundle finetune --data work/data --run-dir work/ft --tier quick --mode sft -bin/chronos bundle serve -- --port 11435 -``` - -`--speculative` 要求 `--repetition-penalty 1.0`（此时的默认值），因为惩罚会逐位置改变目标分布。`finetune` 写出一份 `init_checkpoint` 指向包内权重的 `chronos-translate run` spec；运行目录里已有训练时会拒绝写入。用许可未知的数据（WMT19、OPUS-100）训练的模型包，`NOTICE.md` 中写明权重仅供研究使用。 - ### 部署导出 ```bash @@ -806,11 +753,15 @@ tuner.start( tune_lambda_lookahead=True, tune_lookahead_steps=True, tune_hidden_size=True, tune_num_experts=True, tune_num_shared_experts=True, tune_kv_latent_dim=True, + tune_recurrent_order=True, # 用循环模型做探测（probe_recurrent_depth，默认 4） + tune_recurrent_dt_init=True, ), n_trials=20, ) ``` +探测用的是 `dataset_path` 中的真实 token（最多 40 万个，用模型自己的分词器编码，没有则用内置 32k 分词器）；读不出数据时退回随机 id，并写一行日志。两个循环参数只能做粗筛：对 4 层模型训练几步，分辨不出差别小于运行间噪声的步长。`recurrent_later_groups` 不参与搜索，因为它是用质量换成本，只看损失的搜索永远会选完整的块。 + --- ## 项目结构 diff --git a/chronos/__init__.py b/chronos/__init__.py index 6dcbeb7..a858ebd 100644 --- a/chronos/__init__.py +++ b/chronos/__init__.py @@ -1,5 +1,5 @@ # Project Chronos: On-device low-latency lookahead dual-layer MoE inference -__version__ = "0.3.2a3" +__version__ = "0.3.3a0" # Register with HuggingFace AutoXXX factories so # AutoModelForCausalLM.from_pretrained("path/to/chronos") works out of the box. diff --git a/chronos/assets/tokenizers/general_32k/LICENSE b/chronos/assets/tokenizers/general_32k/LICENSE new file mode 100644 index 0000000..df1a297 --- /dev/null +++ b/chronos/assets/tokenizers/general_32k/LICENSE @@ -0,0 +1,177 @@ +Apache License +Version 2.0, January 2004 +http://www.apache.org/licenses/ + +TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + +1. 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If You +institute patent litigation against any entity (including a +cross-claim or counterclaim in a lawsuit) alleging that the Work +or a Contribution incorporated within the Work constitutes direct +or contributory patent infringement, then any patent licenses +granted to You under this License for that Work shall terminate +as of the date such litigation is filed. + +4. Redistribution. You may reproduce and distribute copies of the +Work or Derivative Works thereof in any medium, with or without +modifications, and in Source or Object form, provided that You +meet the following conditions: + +(a) You must give any other recipients of the Work or +Derivative Works a copy of this License; and + +(b) You must cause any modified files to carry prominent notices +stating that You changed the files; and + +(c) You must retain, in the Source form of any Derivative Works +that You distribute, all copyright, patent, trademark, and +attribution notices from the Source form of the Work, +excluding those notices that do not pertain to any part of +the Derivative Works; and + +(d) If the Work includes a "NOTICE" text file as part of its +distribution, then any Derivative Works that You distribute must +include a readable copy of the attribution notices contained +within such NOTICE file, excluding those notices that do not +pertain to any part of the Derivative Works, in at least one +of the following places: within a NOTICE text file distributed +as part of the Derivative Works; within the Source form or +documentation, if provided along with the Derivative Works; or, +within a display generated by the Derivative Works, if and +wherever such third-party notices normally appear. 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Unless required by applicable law or +agreed to in writing, Licensor provides the Work (and each +Contributor provides its Contributions) on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or +implied, including, without limitation, any warranties or conditions +of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A +PARTICULAR PURPOSE. You are solely responsible for determining the +appropriateness of using or redistributing the Work and assume any +risks associated with Your exercise of permissions under this License. + +8. Limitation of Liability. In no event and under no legal theory, +whether in tort (including negligence), contract, or otherwise, +unless required by applicable law (such as deliberate and grossly +negligent acts) or agreed to in writing, shall any Contributor be +liable to You for damages, including any direct, indirect, special, +incidental, or consequential damages of any character arising as a +result of this License or out of the use or inability to use the +Work (including but not limited to damages for loss of goodwill, +work stoppage, computer failure or malfunction, or any and all +other commercial damages or losses), even if such Contributor +has been advised of the possibility of such damages. + +9. Accepting Warranty or Additional Liability. While redistributing +the Work or Derivative Works thereof, You may choose to offer, +and charge a fee for, acceptance of support, warranty, indemnity, +or other liability obligations and/or rights consistent with this +License. However, in accepting such obligations, You may act only +on Your own behalf and on Your sole responsibility, not on behalf +of any other Contributor, and only if You agree to indemnify, +defend, and hold each Contributor harmless for any liability +incurred by, or claims asserted against, such Contributor by reason +of your accepting any such warranty or additional liability. + +END OF TERMS AND CONDITIONS + diff --git a/chronos/assets/tokenizers/general_32k/NOTICE b/chronos/assets/tokenizers/general_32k/NOTICE new file mode 100644 index 0000000..69873ba --- /dev/null +++ b/chronos/assets/tokenizers/general_32k/NOTICE @@ -0,0 +1,30 @@ +General 32k tokenizer (Chronos BPE, vocab 32768) + +Trained with chronos.tokenizer (algorithm chronos-bpe-v1) by the Project Chronos +authors. Files: chronos_tokenizer.json (native), tokenizer.json / +tokenizer_config.json / special_tokens_map.json (Hugging Face fast tokenizer), +tokenizer_report.json (trainer settings, sample statistics, held-out results). + +Training sample: 233,619 text chunks of at most 2,000 characters, drawn from +FineWeb-Edu, Fineweb-Edu-Chinese V2.1, Cosmopedia v2, Wikipedia (en) and +Wikipedia (zh), weighted by mix share x characters per token. The tokenizer +contains a vocabulary and merge list only; it does not contain the corpus text. + +Control tokens (ids 0-63) follow the kernel's task-neutral layout +(chronos.tokenizer.special_tokens_with()): + 0 <|endoftext|> 1 <|im_start|> 2 <|im_end|> 3 <|pad|> 4-63 <|reserved_0..59|> +No task token (language tag, translation marker) is a default. A task that +needs one claims a reserved slot in its own code. + +This copy differs from the training run's original output only in the labels of +control ids 3-63 (the original carried task tokens at ids 3-5). Ordinary tokens, +merges and ids are identical: 1,004 held-out texts encode to the same ids with +both the native and the Hugging Face encoder. Original fingerprint 169668b1be29..., +this copy 6d4b3b524922... (recorded in tokenizer_report.json "derived_from"). + +License: the tokenizer files are released under Apache-2.0, the same as the +repository. The sample corpora carry their own terms, as stated on their dataset +cards when checked on 2026-11-09: FineWeb-Edu ODC-By (plus Common Crawl terms of +use); Cosmopedia v2 (smollm-corpus) ODC-By; Wikipedia CC-BY-SA-3.0 and GFDL; +Fineweb-Edu-Chinese V2.1 asks for permission before commercial use. Check those +terms for your own use. diff --git a/chronos/assets/tokenizers/general_32k/chronos_tokenizer.json b/chronos/assets/tokenizers/general_32k/chronos_tokenizer.json new file mode 100644 index 0000000..7e71636 --- /dev/null +++ b/chronos/assets/tokenizers/general_32k/chronos_tokenizer.json @@ -0,0 +1 @@ +{"vocab": {"<0x00>": 64, "<0x01>": 65, "<0x02>": 66, "<0x03>": 67, "<0x04>": 68, "<0x05>": 69, "<0x06>": 70, "<0x07>": 71, "<0x08>": 72, "<0x09>": 73, "<0x0A>": 74, "<0x0B>": 75, "<0x0C>": 76, "<0x0D>": 77, "<0x0E>": 78, "<0x0F>": 79, "<0x10>": 80, "<0x11>": 81, "<0x12>": 82, "<0x13>": 83, "<0x14>": 84, "<0x15>": 85, "<0x16>": 86, "<0x17>": 87, "<0x18>": 88, "<0x19>": 89, "<0x1A>": 90, "<0x1B>": 91, "<0x1C>": 92, "<0x1D>": 93, "<0x1E>": 94, "<0x1F>": 95, "<0x20>": 96, "<0x21>": 97, "<0x22>": 98, "<0x23>": 99, "<0x24>": 100, "<0x25>": 101, "<0x26>": 102, "<0x27>": 103, "<0x28>": 104, "<0x29>": 105, "<0x2A>": 106, "<0x2B>": 107, "<0x2C>": 108, "<0x2D>": 109, "<0x2E>": 110, 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1911, "凰": 1912, "凱": 1913, "凳": 1914, "凶": 1915, "凸": 1916, "凹": 1917, "出": 1918, "击": 1919, "函": 1920, "凿": 1921, "刀": 1922, "刁": 1923, "刃": 1924, "分": 1925, "切": 1926, "刊": 1927, "刍": 1928, "刎": 1929, "刑": 1930, "划": 1931, "列": 1932, "刘": 1933, "则": 1934, "刚": 1935, "创": 1936, "初": 1937, "删": 1938, "判": 1939, "別": 1940, "刨": 1941, "利": 1942, "刪": 1943, "别": 1944, "刮": 1945, "到": 1946, "制": 1947, "刷": 1948, "券": 1949, "刹": 1950, "刺": 1951, "刻": 1952, "刽": 1953, "刿": 1954, "剁": 1955, "剂": 1956, "剃": 1957, "則": 1958, "削": 1959, "剋": 1960, "剌": 1961, "前": 1962, "剎": 1963, "剑": 1964, "剔": 1965, "剖": 1966, "剛": 1967, "剝": 1968, "剡": 1969, "剣": 1970, "剥": 1971, "剧": 1972, "剩": 1973, "剪": 1974, "副": 1975, "割": 1976, "創": 1977, "剽": 1978, "剿": 1979, "劃": 1980, "劇": 1981, "劈": 1982, "劉": 1983, "劍": 1984, "劑": 1985, "力": 1986, "劝": 1987, "办": 1988, "功": 1989, "加": 1990, "务": 1991, "劢": 1992, "劣": 1993, "动": 1994, "助": 1995, "努": 1996, "劫": 1997, "劭": 1998, "励": 1999, "劲": 2000, "劳": 2001, "劵": 2002, "劾": 2003, "势": 2004, "勁": 2005, "勃": 2006, "勅": 2007, "勇": 2008, "勉": 2009, "勋": 2010, "勐": 2011, "勑": 2012, "勒": 2013, "動": 2014, "勖": 2015, "勗": 2016, "勘": 2017, "務": 2018, "勛": 2019, "勝": 2020, "勞": 2021, "募": 2022, "勠": 2023, "勢": 2024, "勤": 2025, "勰": 2026, "勳": 2027, "勵": 2028, "勸": 2029, "勺": 2030, "勻": 2031, "勾": 2032, "勿": 2033, "匀": 2034, "包": 2035, "匆": 2036, "匈": 2037, "匍": 2038, "匏": 2039, "匐": 2040, "匕": 2041, "化": 2042, "北": 2043, "匙": 2044, "匝": 2045, "匠": 2046, "匡": 2047, "匣": 2048, "匪": 2049, "匮": 2050, "匯": 2051, "匹": 2052, "区": 2053, "医": 2054, "匾": 2055, "匿": 2056, "區": 2057, "十": 2058, "千": 2059, "卅": 2060, "升": 2061, "午": 2062, "卉": 2063, "半": 2064, "华": 2065, "协": 2066, "卑": 2067, "卒": 2068, "卓": 2069, "協": 2070, "单": 2071, "卖": 2072, "南": 2073, "博": 2074, "卜": 2075, "卞": 2076, "卟": 2077, "占": 2078, "卡": 2079, "卢": 2080, "卤": 2081, "卦": 2082, "卧": 2083, "卫": 2084, "卮": 2085, "卯": 2086, "印": 2087, "危": 2088, "即": 2089, "却": 2090, "卵": 2091, "卷": 2092, "卸": 2093, "卻": 2094, "卿": 2095, "厂": 2096, "厄": 2097, "厅": 2098, "历": 2099, "厉": 2100, "压": 2101, "厌": 2102, "厕": 2103, "厘": 2104, "厚": 2105, "厝": 2106, "原": 2107, "厢": 2108, "厥": 2109, "厦": 2110, "厨": 2111, "厩": 2112, "厭": 2113, "厮": 2114, "厯": 2115, "厲": 2116, "去": 2117, "县": 2118, "叁": 2119, "参": 2120, "參": 2121, "又": 2122, "叉": 2123, "及": 2124, "友": 2125, "双": 2126, "反": 2127, "収": 2128, "叐": 2129, "发": 2130, "叔": 2131, "取": 2132, "受": 2133, "变": 2134, "叙": 2135, "叛": 2136, "叟": 2137, "叠": 2138, "叡": 2139, "叢": 2140, "口": 2141, "古": 2142, "句": 2143, "另": 2144, "叨": 2145, "叩": 2146, "只": 2147, "叫": 2148, "召": 2149, "叭": 2150, "叮": 2151, "可": 2152, "台": 2153, "叱": 2154, "史": 2155, "右": 2156, "叵": 2157, "叶": 2158, "号": 2159, "司": 2160, "叹": 2161, "叻": 2162, "吁": 2163, "吃": 2164, "各": 2165, "合": 2166, "吉": 2167, "吊": 2168, "吋": 2169, "同": 2170, "名": 2171, "后": 2172, "吏": 2173, "吐": 2174, "向": 2175, "吒": 2176, "吓": 2177, "吕": 2178, "吖": 2179, "吗": 2180, "君": 2181, "吝": 2182, "吞": 2183, "吟": 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2366, "嘅": 2367, "嘆": 2368, "嘈": 2369, "嘉": 2370, "嘌": 2371, "嘎": 2372, "嘏": 2373, "嘔": 2374, "嘗": 2375, "嘘": 2376, "嘛": 2377, "嘟": 2378, "嘢": 2379, "嘧": 2380, "嘩": 2381, "嘯": 2382, "嘱": 2383, "嘲": 2384, "嘴": 2385, "嘶": 2386, "嘹": 2387, "嘻": 2388, "噁": 2389, "噌": 2390, "噎": 2391, "噜": 2392, "器": 2393, "噩": 2394, "噪": 2395, "噬": 2396, "噱": 2397, "噴": 2398, "噶": 2399, "噸": 2400, "噹": 2401, "噻": 2402, "嚇": 2403, "嚎": 2404, "嚏": 2405, "嚣": 2406, "嚨": 2407, "嚮": 2408, "嚴": 2409, "嚷": 2410, "嚼": 2411, "囊": 2412, "囍": 2413, "囑": 2414, "囚": 2415, "四": 2416, "回": 2417, "囟": 2418, "因": 2419, "团": 2420, "団": 2421, "囤": 2422, "囧": 2423, "囫": 2424, "园": 2425, "囯": 2426, "困": 2427, "囱": 2428, "図": 2429, "围": 2430, "囵": 2431, "固": 2432, "国": 2433, "图": 2434, "囿": 2435, "圃": 2436, "圆": 2437, "圈": 2438, "國": 2439, "圍": 2440, "園": 2441, "圓": 2442, "圖": 2443, "團": 2444, "圜": 2445, "土": 2446, "圣": 2447, "在": 2448, "圩": 2449, "圪": 2450, "圭": 2451, "圮": 2452, "地": 2453, "圳": 2454, "场": 2455, "圻": 2456, "圾": 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2730, "婢": 2731, "婦": 2732, "婧": 2733, "婪": 2734, "婭": 2735, "婴": 2736, "婵": 2737, "婷": 2738, "婺": 2739, "婿": 2740, "媒": 2741, "媚": 2742, "媛": 2743, "媲": 2744, "媳": 2745, "媽": 2746, "媾": 2747, "嫁": 2748, "嫂": 2749, "嫉": 2750, "嫌": 2751, "嫔": 2752, "嫖": 2753, "嫘": 2754, "嫡": 2755, "嫣": 2756, "嫦": 2757, "嫩": 2758, "嫻": 2759, "嬅": 2760, "嬉": 2761, "嬌": 2762, "嬖": 2763, "嬗": 2764, "嬤": 2765, "嬪": 2766, "嬰": 2767, "嬴": 2768, "嬸": 2769, "孀": 2770, "子": 2771, "孑": 2772, "孔": 2773, "孕": 2774, "字": 2775, "存": 2776, "孙": 2777, "孚": 2778, "孛": 2779, "孜": 2780, "孝": 2781, "孟": 2782, "孢": 2783, "季": 2784, "孤": 2785, "孥": 2786, "学": 2787, "孩": 2788, "孪": 2789, "孫": 2790, "孰": 2791, "孱": 2792, "孳": 2793, "孵": 2794, "學": 2795, "孺": 2796, "孽": 2797, "孿": 2798, "宁": 2799, "它": 2800, "宅": 2801, "宇": 2802, "守": 2803, "安": 2804, "宋": 2805, "完": 2806, "宏": 2807, "宓": 2808, "宕": 2809, "宗": 2810, "官": 2811, "宙": 2812, "定": 2813, "宛": 2814, "宜": 2815, "宝": 2816, "实": 2817, "実": 2818, "宠": 2819, "审": 2820, "客": 2821, "宣": 2822, "室": 2823, "宥": 2824, "宦": 2825, "宪": 2826, "宫": 2827, "宮": 2828, "宰": 2829, "害": 2830, "宴": 2831, "宵": 2832, "家": 2833, "宸": 2834, "容": 2835, "宽": 2836, "宾": 2837, "宿": 2838, "寀": 2839, "寂": 2840, "寄": 2841, "寅": 2842, "密": 2843, "寇": 2844, "富": 2845, "寐": 2846, "寒": 2847, "寓": 2848, "寔": 2849, "寛": 2850, "寝": 2851, "寞": 2852, "察": 2853, "寡": 2854, "寢": 2855, "寤": 2856, "寥": 2857, "實": 2858, "寧": 2859, "寨": 2860, "審": 2861, "寫": 2862, "寬": 2863, "寮": 2864, "寰": 2865, "寵": 2866, "寶": 2867, "寸": 2868, "对": 2869, "寺": 2870, "寻": 2871, "导": 2872, "対": 2873, "寿": 2874, "封": 2875, "専": 2876, "射": 2877, "将": 2878, "將": 2879, "專": 2880, "尉": 2881, "尊": 2882, "尋": 2883, "對": 2884, "導": 2885, "小": 2886, "少": 2887, "尔": 2888, "尕": 2889, "尖": 2890, "尘": 2891, "尚": 2892, "尝": 2893, "尤": 2894, "尧": 2895, "尬": 2896, "就": 2897, "尴": 2898, "尷": 2899, "尸": 2900, "尹": 2901, "尺": 2902, "尻": 2903, "尼": 2904, "尽": 2905, "尾": 2906, "尿": 2907, "局": 2908, "屁": 2909, "层": 2910, "居": 2911, "屆": 2912, "屈": 2913, "屉": 2914, "届": 2915, "屋": 2916, "屍": 2917, "屎": 2918, "屏": 2919, "屑": 2920, "展": 2921, "属": 2922, "屠": 2923, "屡": 2924, "屢": 2925, "層": 2926, "履": 2927, "屬": 2928, "屯": 2929, "山": 2930, "屹": 2931, "屿": 2932, "岀": 2933, "岁": 2934, "岂": 2935, "岌": 2936, "岐": 2937, "岑": 2938, "岔": 2939, "岖": 2940, "岗": 2941, "岘": 2942, "岙": 2943, "岚": 2944, "岛": 2945, "岡": 2946, "岢": 2947, "岩": 2948, "岫": 2949, "岬": 2950, "岭": 2951, "岱": 2952, "岳": 2953, "岷": 2954, "岸": 2955, "岿": 2956, "峁": 2957, "峄": 2958, "峇": 2959, "峒": 2960, "峙": 2961, "峡": 2962, "峤": 2963, "峥": 2964, "峦": 2965, "峨": 2966, "峪": 2967, "峭": 2968, "峯": 2969, "峰": 2970, "峴": 2971, "島": 2972, "峻": 2973, "峽": 2974, "崁": 2975, "崃": 2976, "崇": 2977, "崎": 2978, "崐": 2979, "崑": 2980, "崔": 2981, "崖": 2982, "崗": 2983, "崙": 2984, "崛": 2985, "崧": 2986, "崩": 2987, "崭": 2988, "崴": 2989, "嵇": 2990, "嵊": 2991, "嵋": 2992, "嵌": 2993, "嵐": 2994, "嵗": 2995, "嵘": 2996, "嵩": 2997, "嵬": 2998, "嵯": 2999, "嵴": 3000, "嶂": 3001, "嶄": 3002, "嶋": 3003, "嶟": 3004, "嶷": 3005, "嶺": 3006, "嶼": 3007, "嶽": 3008, "巂": 3009, "巅": 3010, "巍": 3011, "巒": 3012, "巔": 3013, "巖": 3014, "川": 3015, "州": 3016, "巡": 3017, "巢": 3018, "工": 3019, "左": 3020, "巧": 3021, "巨": 3022, "巩": 3023, "巫": 3024, "差": 3025, "巯": 3026, "己": 3027, "已": 3028, "巳": 3029, "巴": 3030, "巷": 3031, "巻": 3032, "巽": 3033, "巾": 3034, "币": 3035, "市": 3036, "布": 3037, "帅": 3038, "帆": 3039, "师": 3040, "希": 3041, "帐": 3042, "帑": 3043, "帕": 3044, "帖": 3045, "帘": 3046, "帙": 3047, "帚": 3048, "帛": 3049, "帜": 3050, "帝": 3051, "帥": 3052, "带": 3053, "帧": 3054, "師": 3055, "席": 3056, "帮": 3057, "帯": 3058, "帰": 3059, "帳": 3060, "帶": 3061, "帷": 3062, "常": 3063, "帼": 3064, "帽": 3065, "幀": 3066, "幂": 3067, "幄": 3068, "幅": 3069, "幌": 3070, "幔": 3071, "幕": 3072, "幞": 3073, "幟": 3074, "幡": 3075, "幢": 3076, "幣": 3077, "幫": 3078, "干": 3079, "平": 3080, "年": 3081, "幵": 3082, "并": 3083, "幸": 3084, "幹": 3085, "幺": 3086, "幻": 3087, "幼": 3088, "幽": 3089, "幾": 3090, "广": 3091, "庁": 3092, "広": 3093, "庄": 3094, "庆": 3095, "庇": 3096, "床": 3097, "庋": 3098, "序": 3099, "庐": 3100, "庑": 3101, "库": 3102, "应": 3103, "底": 3104, "庖": 3105, "店": 3106, "庙": 3107, "庚": 3108, "府": 3109, "庞": 3110, "废": 3111, "庠": 3112, "度": 3113, "座": 3114, "庫": 3115, "庭": 3116, "庵": 3117, "庶": 3118, "康": 3119, "庸": 3120, "庾": 3121, "廁": 3122, "廂": 3123, "廈": 3124, "廉": 3125, "廊": 3126, "廍": 3127, "廒": 3128, "廓": 3129, "廕": 3130, "廖": 3131, "廚": 3132, "廟": 3133, "廠": 3134, "廢": 3135, "廣": 3136, "廩": 3137, "廪": 3138, "廬": 3139, "廳": 3140, "延": 3141, "廷": 3142, "建": 3143, "廿": 3144, "开": 3145, "弁": 3146, "异": 3147, "弃": 3148, "弄": 3149, "弇": 3150, "弈": 3151, "弊": 3152, "弋": 3153, "式": 3154, "弑": 3155, "弒": 3156, "弓": 3157, "弔": 3158, "引": 3159, "弗": 3160, "弘": 3161, "弛": 3162, "弟": 3163, "张": 3164, "弢": 3165, "弥": 3166, "弦": 3167, "弧": 3168, "弩": 3169, "弭": 3170, "弯": 3171, "弱": 3172, "張": 3173, "強": 3174, "弹": 3175, "强": 3176, "弼": 3177, "弾": 3178, "彈": 3179, "彌": 3180, "彎": 3181, "归": 3182, "当": 3183, "录": 3184, "彖": 3185, "彗": 3186, "彙": 3187, "彝": 3188, "形": 3189, "彤": 3190, "彥": 3191, "彦": 3192, "彧": 3193, "彩": 3194, "彪": 3195, "彬": 3196, "彭": 3197, "彰": 3198, "影": 3199, "彷": 3200, "役": 3201, "彻": 3202, "彼": 3203, "往": 3204, "征": 3205, "径": 3206, "待": 3207, "徇": 3208, "很": 3209, "徉": 3210, "徊": 3211, "律": 3212, "後": 3213, "徐": 3214, "徑": 3215, "徒": 3216, "徕": 3217, "得": 3218, "徘": 3219, "徙": 3220, "徜": 3221, "從": 3222, "御": 3223, "徨": 3224, "復": 3225, "循": 3226, "徭": 3227, "微": 3228, "徳": 3229, "徴": 3230, "徵": 3231, "德": 3232, "徹": 3233, "徽": 3234, "心": 3235, "必": 3236, "忆": 3237, "忌": 3238, "忍": 3239, "忏": 3240, "忒": 3241, "志": 3242, "忘": 3243, "忙": 3244, "忠": 3245, "忡": 3246, "忤": 3247, "忧": 3248, "快": 3249, "忱": 3250, "念": 3251, "忻": 3252, "忽": 3253, "忾": 3254, "忿": 3255, "怀": 3256, "态": 3257, "怂": 3258, "怅": 3259, "怎": 3260, "怒": 3261, "怕": 3262, "怖": 3263, "怙": 3264, "怛": 3265, "怜": 3266, "思": 3267, "怠": 3268, "怡": 3269, "急": 3270, "怦": 3271, "性": 3272, "怨": 3273, "怪": 3274, "怯": 3275, "怵": 3276, "总": 3277, "恂": 3278, "恃": 3279, "恆": 3280, "恋": 3281, "恍": 3282, "恐": 3283, "恒": 3284, "恕": 3285, "恙": 3286, "恚": 3287, "恢": 3288, "恣": 3289, "恤": 3290, "恥": 3291, "恨": 3292, "恩": 3293, "恪": 3294, "恫": 3295, "恬": 3296, "恭": 3297, "息": 3298, "恰": 3299, "恳": 3300, "恵": 3301, "恶": 3302, "恸": 3303, "恺": 3304, "恻": 3305, "恼": 3306, "恽": 3307, "恿": 3308, "悄": 3309, "悅": 3310, "悉": 3311, "悌": 3312, "悍": 3313, "悔": 3314, "悖": 3315, "悚": 3316, "悝": 3317, "悟": 3318, "悠": 3319, "患": 3320, "悦": 3321, "您": 3322, "悪": 3323, "悬": 3324, "悭": 3325, "悯": 3326, "悱": 3327, "悲": 3328, "悴": 3329, "悶": 3330, "悸": 3331, "悼": 3332, "情": 3333, "惆": 3334, "惇": 3335, "惊": 3336, "惋": 3337, "惑": 3338, "惕": 3339, "惘": 3340, "惚": 3341, "惜": 3342, "惟": 3343, "惠": 3344, "惡": 3345, "惣": 3346, "惦": 3347, "惧": 3348, "惨": 3349, "惩": 3350, "惫": 3351, "惬": 3352, "惭": 3353, "惮": 3354, "惯": 3355, "惰": 3356, "惱": 3357, "想": 3358, "惶": 3359, "惹": 3360, "惺": 3361, "愁": 3362, "愈": 3363, "愉": 3364, "愍": 3365, "意": 3366, "愕": 3367, "愚": 3368, "愛": 3369, "感": 3370, "愤": 3371, "愧": 3372, "愫": 3373, "愷": 3374, "愿": 3375, "慈": 3376, "態": 3377, "慌": 3378, "慎": 3379, "慑": 3380, "慕": 3381, "慘": 3382, "慟": 3383, "慢": 3384, "慣": 3385, "慧": 3386, "慨": 3387, "慮": 3388, "慰": 3389, "慶": 3390, "慷": 3391, "慾": 3392, "憂": 3393, "憋": 3394, "憎": 3395, "憐": 3396, "憑": 3397, "憔": 3398, "憤": 3399, "憧": 3400, "憨": 3401, "憩": 3402, "憫": 3403, "憬": 3404, "憲": 3405, "憶": 3406, "憾": 3407, "懂": 3408, "懇": 3409, "懈": 3410, "應": 3411, "懋": 3412, "懑": 3413, "懒": 3414, "懦": 3415, "懲": 3416, "懵": 3417, "懶": 3418, "懷": 3419, "懸": 3420, "懺": 3421, "懼": 3422, "懿": 3423, "戀": 3424, "戈": 3425, "戊": 3426, "戌": 3427, "戍": 3428, "戎": 3429, "戏": 3430, "成": 3431, "我": 3432, "戒": 3433, "戕": 3434, "或": 3435, "戗": 3436, "战": 3437, "戚": 3438, "戛": 3439, "戟": 3440, "戡": 3441, "戢": 3442, "戦": 3443, "截": 3444, "戮": 3445, "戰": 3446, "戲": 3447, "戳": 3448, "戴": 3449, "戶": 3450, "户": 3451, "戸": 3452, "戾": 3453, "房": 3454, "所": 3455, "扁": 3456, "扇": 3457, "扈": 3458, "扉": 3459, "手": 3460, "才": 3461, "扎": 3462, "扑": 3463, "扒": 3464, "打": 3465, "扔": 3466, "托": 3467, "扛": 3468, "扣": 3469, "扦": 3470, "执": 3471, "扩": 3472, "扪": 3473, "扫": 3474, "扬": 3475, "扭": 3476, "扮": 3477, "扯": 3478, "扰": 3479, "扳": 3480, "扶": 3481, "批": 3482, "扼": 3483, "找": 3484, "承": 3485, "技": 3486, "抄": 3487, "抉": 3488, "把": 3489, "抑": 3490, "抒": 3491, "抓": 3492, "投": 3493, "抖": 3494, "抗": 3495, "折": 3496, "抚": 3497, "抛": 3498, "抟": 3499, "抠": 3500, "抢": 3501, "护": 3502, "报": 3503, "抨": 3504, "披": 3505, "抬": 3506, "抱": 3507, "抵": 3508, "抹": 3509, "押": 3510, "抽": 3511, "拂": 3512, "担": 3513, "拆": 3514, "拇": 3515, "拈": 3516, "拉": 3517, "拋": 3518, "拌": 3519, "拍": 3520, "拎": 3521, "拐": 3522, "拒": 3523, "拓": 3524, "拔": 3525, "拖": 3526, "拗": 3527, "拘": 3528, "拙": 3529, "拚": 3530, "招": 3531, "拜": 3532, "拟": 3533, "拢": 3534, "拣": 3535, "拥": 3536, "拦": 3537, "拧": 3538, "拨": 3539, "择": 3540, "括": 3541, "拭": 3542, "拮": 3543, "拯": 3544, "拱": 3545, "拳": 3546, "拴": 3547, "拷": 3548, "拼": 3549, "拽": 3550, "拾": 3551, "拿": 3552, "持": 3553, "挂": 3554, "指": 3555, "挈": 3556, "按": 3557, "挑": 3558, "挖": 3559, "挚": 3560, "挛": 3561, "挝": 3562, "挞": 3563, "挟": 3564, "挠": 3565, "挡": 3566, "挣": 3567, "挤": 3568, "挥": 3569, "挨": 3570, "挪": 3571, "挫": 3572, "振": 3573, "挹": 3574, "挺": 3575, "挽": 3576, "挾": 3577, "捂": 3578, "捅": 3579, "捆": 3580, "捉": 3581, "捌": 3582, "捍": 3583, "捎": 3584, "捏": 3585, "捐": 3586, "捕": 3587, "捞": 3588, "损": 3589, "捡": 3590, "换": 3591, "捣": 3592, "捧": 3593, "捨": 3594, "捩": 3595, "捭": 3596, "据": 3597, "捲": 3598, "捶": 3599, "捷": 3600, "捺": 3601, "捻": 3602, "掀": 3603, "掂": 3604, "掃": 3605, "授": 3606, "掉": 3607, "掌": 3608, "掏": 3609, "掐": 3610, "排": 3611, "掖": 3612, "掘": 3613, "掙": 3614, "掛": 3615, "掟": 3616, "掠": 3617, "採": 3618, "探": 3619, "掣": 3620, "接": 3621, "控": 3622, "推": 3623, "掩": 3624, "措": 3625, "掬": 3626, "掰": 3627, "掲": 3628, "掳": 3629, "掷": 3630, "掸": 3631, "掺": 3632, "掾": 3633, "揀": 3634, "揄": 3635, "揆": 3636, "揉": 3637, "描": 3638, "提": 3639, "插": 3640, "揖": 3641, "揚": 3642, "換": 3643, "握": 3644, "揣": 3645, "揪": 3646, "揭": 3647, "揮": 3648, "援": 3649, "揽": 3650, "搀": 3651, "搁": 3652, "搂": 3653, "搅": 3654, "損": 3655, "搏": 3656, "搐": 3657, "搓": 3658, "搔": 3659, "搖": 3660, "搗": 3661, "搜": 3662, "搞": 3663, "搪": 3664, "搬": 3665, "搭": 3666, "搶": 3667, "携": 3668, "摄": 3669, "摆": 3670, "摇": 3671, "摈": 3672, "摊": 3673, "摒": 3674, "摔": 3675, "摘": 3676, "摧": 3677, "摩": 3678, "摭": 3679, "摯": 3680, "摸": 3681, "摹": 3682, "摺": 3683, "摻": 3684, "撂": 3685, "撃": 3686, "撇": 3687, "撈": 3688, "撐": 3689, "撑": 3690, "撒": 3691, "撓": 3692, "撕": 3693, "撞": 3694, "撤": 3695, "撥": 3696, "撩": 3697, "撫": 3698, "撬": 3699, "播": 3700, "撮": 3701, "撰": 3702, "撲": 3703, "撷": 3704, "撸": 3705, "撻": 3706, "撼": 3707, "撾": 3708, "撿": 3709, "擁": 3710, "擂": 3711, "擄": 3712, "擅": 3713, "擇": 3714, "擊": 3715, "擋": 3716, "操": 3717, "擎": 3718, "擒": 3719, "擔": 3720, "擘": 3721, "據": 3722, "擠": 3723, "擢": 3724, "擦": 3725, "擬": 3726, "擱": 3727, "擲": 3728, "擴": 3729, "擷": 3730, "擺": 3731, "擾": 3732, "攀": 3733, "攏": 3734, "攒": 3735, "攔": 3736, "攘": 3737, "攜": 3738, "攝": 3739, "攤": 3740, "攪": 3741, "攫": 3742, "攬": 3743, "支": 3744, "收": 3745, "攸": 3746, "改": 3747, "攻": 3748, "放": 3749, "政": 3750, "敁": 3751, "故": 3752, "效": 3753, "敌": 3754, "敍": 3755, "敏": 3756, "救": 3757, "敕": 3758, "敖": 3759, "敗": 3760, "敘": 3761, "教": 3762, "敛": 3763, "敝": 3764, "敞": 3765, "敢": 3766, "散": 3767, "敦": 3768, "敬": 3769, "数": 3770, "敲": 3771, "整": 3772, "敵": 3773, "敷": 3774, "數": 3775, "斂": 3776, "斃": 3777, "文": 3778, "斋": 3779, "斌": 3780, "斎": 3781, "斐": 3782, "斑": 3783, "斓": 3784, "斗": 3785, "料": 3786, "斛": 3787, "斜": 3788, "斟": 3789, "斡": 3790, "斤": 3791, "斥": 3792, "斧": 3793, "斩": 3794, "斫": 3795, "斬": 3796, "断": 3797, "斯": 3798, "新": 3799, "斷": 3800, "方": 3801, "於": 3802, "施": 3803, "旁": 3804, "旃": 3805, "旅": 3806, "旋": 3807, "旌": 3808, "族": 3809, "旒": 3810, "旗": 3811, "无": 3812, "既": 3813, "日": 3814, "旦": 3815, "旧": 3816, "旨": 3817, "早": 3818, "旬": 3819, "旭": 3820, "旱": 3821, "时": 3822, "旷": 3823, "旸": 3824, "旺": 3825, "旻": 3826, "旼": 3827, "昀": 3828, "昂": 3829, "昃": 3830, "昆": 3831, "昇": 3832, "昉": 3833, "昊": 3834, "昌": 3835, "明": 3836, "昏": 3837, "易": 3838, "昔": 3839, "昕": 3840, "昙": 3841, "星": 3842, "映": 3843, "春": 3844, "昧": 3845, "昨": 3846, "昭": 3847, "是": 3848, "昱": 3849, "昴": 3850, "昵": 3851, "昶": 3852, "昺": 3853, "昼": 3854, "显": 3855, "晁": 3856, "時": 3857, "晃": 3858, "晉": 3859, "晋": 3860, "晏": 3861, "晒": 3862, "晓": 3863, "晔": 3864, "晕": 3865, "晖": 3866, "晗": 3867, "晚": 3868, "晝": 3869, "晞": 3870, "晟": 3871, "晤": 3872, "晦": 3873, "晨": 3874, "晩": 3875, "普": 3876, "景": 3877, "晰": 3878, "晴": 3879, "晶": 3880, "晷": 3881, "智": 3882, "晾": 3883, "暂": 3884, "暄": 3885, "暇": 3886, "暈": 3887, "暉": 3888, "暎": 3889, "暐": 3890, "暑": 3891, "暖": 3892, "暗": 3893, "暘": 3894, "暝": 3895, "暠": 3896, "暢": 3897, "暧": 3898, "暨": 3899, "暫": 3900, "暮": 3901, "暱": 3902, "暴": 3903, "暹": 3904, "曄": 3905, "曆": 3906, "曇": 3907, "曉": 3908, "曖": 3909, "曙": 3910, "曜": 3911, "曝": 3912, "曠": 3913, "曦": 3914, "曩": 3915, "曬": 3916, "曰": 3917, "曲": 3918, "曳": 3919, "更": 3920, "書": 3921, "曹": 3922, "曼": 3923, "曾": 3924, "替": 3925, "最": 3926, "會": 3927, "月": 3928, "有": 3929, "朊": 3930, "朋": 3931, "服": 3932, "朏": 3933, "朐": 3934, "朓": 3935, "朔": 3936, "朕": 3937, "朗": 3938, "望": 3939, "朝": 3940, "期": 3941, "朦": 3942, "朧": 3943, "木": 3944, "未": 3945, "末": 3946, "本": 3947, "札": 3948, "术": 3949, "朱": 3950, "朴": 3951, "朵": 3952, "机": 3953, "朽": 3954, "杀": 3955, "杂": 3956, "权": 3957, "杆": 3958, "杈": 3959, "杉": 3960, "李": 3961, "杏": 3962, "材": 3963, "村": 3964, "杓": 3965, "杖": 3966, "杜": 3967, "杞": 3968, "束": 3969, "杠": 3970, "条": 3971, "来": 3972, "杨": 3973, "杭": 3974, "杯": 3975, "杰": 3976, "東": 3977, "杲": 3978, "杳": 3979, "杵": 3980, "杷": 3981, "松": 3982, "板": 3983, "极": 3984, "构": 3985, "枇": 3986, "枉": 3987, "枋": 3988, "析": 3989, "枕": 3990, "林": 3991, "枚": 3992, "果": 3993, "枝": 3994, "枞": 3995, "枢": 3996, "枣": 3997, "枨": 3998, "枪": 3999, "枫": 4000, "枭": 4001, "枯": 4002, "枳": 4003, "架": 4004, "枷": 4005, "枸": 4006, "柄": 4007, "柏": 4008, "某": 4009, "柑": 4010, "柒": 4011, "染": 4012, "柔": 4013, "柘": 4014, "柚": 4015, "柜": 4016, "柞": 4017, "柠": 4018, "柢": 4019, "查": 4020, "柩": 4021, "柬": 4022, "柯": 4023, "柱": 4024, "柲": 4025, "柳": 4026, "柴": 4027, "柵": 4028, "査": 4029, "柿": 4030, "栀": 4031, "栃": 4032, "栄": 4033, "栅": 4034, "标": 4035, "栈": 4036, "栉": 4037, "栋": 4038, "栎": 4039, "栏": 4040, "树": 4041, "栓": 4042, "栖": 4043, "栗": 4044, "校": 4045, "栢": 4046, "栩": 4047, "株": 4048, "栱": 4049, "样": 4050, "核": 4051, "根": 4052, "栻": 4053, "格": 4054, "栽": 4055, "栾": 4056, "桀": 4057, "桁": 4058, "桂": 4059, "桃": 4060, "桅": 4061, "框": 4062, "案": 4063, "桉": 4064, "桌": 4065, "桎": 4066, "桐": 4067, "桑": 4068, "桓": 4069, "桔": 4070, "桜": 4071, "桡": 4072, "桢": 4073, "档": 4074, "桥": 4075, "桦": 4076, "桧": 4077, "桨": 4078, "桩": 4079, "桫": 4080, "桶": 4081, "桿": 4082, "梁": 4083, "梅": 4084, "梆": 4085, "梏": 4086, "梓": 4087, "梗": 4088, "條": 4089, "梟": 4090, "梢": 4091, "梦": 4092, "梧": 4093, "梨": 4094, "梭": 4095, "梯": 4096, "械": 4097, "梳": 4098, "梵": 4099, "梶": 4100, "检": 4101, "棂": 4102, "棄": 4103, "棉": 4104, "棋": 4105, "棍": 4106, "棒": 4107, "棕": 4108, "棗": 4109, "棘": 4110, "棚": 4111, "棟": 4112, "棠": 4113, "棣": 4114, "棧": 4115, "棨": 4116, "森": 4117, "棱": 4118, "棲": 4119, "棵": 4120, "棹": 4121, "棺": 4122, "棻": 4123, "椁": 4124, "椅": 4125, "植": 4126, "椎": 4127, "椒": 4128, "椤": 4129, "椭": 4130, "椰": 4131, "椹": 4132, "椽": 4133, "椿": 4134, "楂": 4135, "楊": 4136, "楓": 4137, "楔": 4138, "楚": 4139, "楝": 4140, "楞": 4141, "楠": 4142, "楣": 4143, "楦": 4144, "楨": 4145, "楫": 4146, "業": 4147, "極": 4148, "楷": 4149, "楸": 4150, "楹": 4151, "楼": 4152, "楽": 4153, "概": 4154, "榄": 4155, "榆": 4156, "榈": 4157, "榉": 4158, "榊": 4159, "榎": 4160, "榔": 4161, "榕": 4162, "榘": 4163, "榛": 4164, "榜": 4165, "榦": 4166, "榧": 4167, "榨": 4168, "榫": 4169, "榭": 4170, "榮": 4171, "榴": 4172, "榷": 4173, "榻": 4174, "槁": 4175, "槃": 4176, "構": 4177, "槌": 4178, "槍": 4179, "槎": 4180, "槐": 4181, "槓": 4182, "様": 4183, "槛": 4184, "槟": 4185, "槭": 4186, "槳": 4187, "槻": 4188, "槽": 4189, "槿": 4190, "樁": 4191, "樂": 4192, "樊": 4193, "樋": 4194, "樑": 4195, "樓": 4196, "樗": 4197, "標": 4198, "樞": 4199, "樟": 4200, "模": 4201, "樣": 4202, "権": 4203, "横": 4204, "樱": 4205, "樵": 4206, "樸": 4207, "樹": 4208, "樺": 4209, "樽": 4210, "樾": 4211, "橄": 4212, "橇": 4213, "橋": 4214, "橘": 4215, "橙": 4216, "機": 4217, "橡": 4218, "橢": 4219, "橥": 4220, "橦": 4221, "橫": 4222, "橱": 4223, "橼": 4224, "檀": 4225, "檄": 4226, "檎": 4227, "檐": 4228, "檔": 4229, "檗": 4230, "檜": 4231, "檢": 4232, "檩": 4233, "檬": 4234, "檯": 4235, "檳": 4236, "檸": 4237, "檻": 4238, "櫂": 4239, "櫃": 4240, "櫚": 4241, "櫛": 4242, "櫸": 4243, "櫻": 4244, "欄": 4245, "權": 4246, "欒": 4247, "欖": 4248, "欠": 4249, "次": 4250, "欢": 4251, "欣": 4252, "欤": 4253, "欧": 4254, "欲": 4255, "欺": 4256, "欽": 4257, "款": 4258, "歆": 4259, "歇": 4260, "歉": 4261, "歌": 4262, "歎": 4263, "歐": 4264, "歙": 4265, "歡": 4266, "止": 4267, "正": 4268, "此": 4269, "步": 4270, "武": 4271, "歧": 4272, "歩": 4273, "歪": 4274, "歲": 4275, "歳": 4276, "歴": 4277, "歷": 4278, "歸": 4279, "歹": 4280, "死": 4281, "歼": 4282, "歿": 4283, "殁": 4284, "殃": 4285, "殆": 4286, "殇": 4287, "殉": 4288, "殊": 4289, "残": 4290, "殓": 4291, "殖": 4292, "殘": 4293, "殚": 4294, "殡": 4295, "殤": 4296, "殭": 4297, "殯": 4298, "殲": 4299, "殴": 4300, "段": 4301, "殷": 4302, "殺": 4303, "殼": 4304, "殿": 4305, "毀": 4306, "毁": 4307, "毂": 4308, "毅": 4309, "毆": 4310, "毋": 4311, "毌": 4312, "母": 4313, "毎": 4314, "每": 4315, "毒": 4316, "毓": 4317, "比": 4318, "毕": 4319, "毖": 4320, "毗": 4321, "毘": 4322, "毙": 4323, "毛": 4324, "毡": 4325, "毫": 4326, "毯": 4327, "毽": 4328, "氏": 4329, "氐": 4330, "民": 4331, "氓": 4332, "气": 4333, "氖": 4334, "気": 4335, "氘": 4336, "氙": 4337, "氚": 4338, "氛": 4339, "氟": 4340, "氡": 4341, "氢": 4342, "氣": 4343, "氦": 4344, "氧": 4345, "氨": 4346, "氩": 4347, "氪": 4348, "氫": 4349, "氮": 4350, "氯": 4351, "氰": 4352, "水": 4353, "永": 4354, "氹": 4355, "氾": 4356, "汀": 4357, "汁": 4358, "求": 4359, "汇": 4360, "汉": 4361, "汊": 4362, "汎": 4363, "汐": 4364, "汕": 4365, "汗": 4366, "汙": 4367, "汛": 4368, "汜": 4369, "汝": 4370, "汞": 4371, "江": 4372, "池": 4373, "污": 4374, "汤": 4375, "汨": 4376, "汪": 4377, "汰": 4378, "汲": 4379, "汴": 4380, "汶": 4381, "汹": 4382, "決": 4383, "汽": 4384, "汾": 4385, "沁": 4386, "沂": 4387, "沃": 4388, "沅": 4389, "沆": 4390, "沈": 4391, "沉": 4392, "沌": 4393, "沐": 4394, "沒": 4395, "沓": 4396, "沔": 4397, "沖": 4398, "沙": 4399, "沛": 4400, "沟": 4401, "没": 4402, "沢": 4403, "沣": 4404, "沤": 4405, "沥": 4406, "沦": 4407, "沧": 4408, "沪": 4409, "沫": 4410, "沭": 4411, "沮": 4412, "沱": 4413, "河": 4414, "沸": 4415, "油": 4416, "治": 4417, "沼": 4418, "沽": 4419, "沾": 4420, "沿": 4421, "況": 4422, "泃": 4423, "泄": 4424, "泉": 4425, "泊": 4426, "泌": 4427, "泓": 4428, "法": 4429, "泗": 4430, "泛": 4431, "泞": 4432, "泠": 4433, "泡": 4434, "波": 4435, "泣": 4436, "泥": 4437, "注": 4438, "泪": 4439, "泮": 4440, "泯": 4441, "泰": 4442, "泱": 4443, "泳": 4444, "泵": 4445, "泸": 4446, "泺": 4447, "泻": 4448, "泼": 4449, "泽": 4450, "泾": 4451, "洁": 4452, "洄": 4453, "洋": 4454, "洎": 4455, "洒": 4456, "洗": 4457, "洙": 4458, "洛": 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4550, "深": 4551, "淳": 4552, "淵": 4553, "混": 4554, "淸": 4555, "淹": 4556, "淺": 4557, "添": 4558, "淼": 4559, "清": 4560, "済": 4561, "渊": 4562, "渍": 4563, "渎": 4564, "渐": 4565, "渔": 4566, "渗": 4567, "渚": 4568, "減": 4569, "渝": 4570, "渠": 4571, "渡": 4572, "渣": 4573, "渤": 4574, "渥": 4575, "渦": 4576, "温": 4577, "測": 4578, "渭": 4579, "港": 4580, "渲": 4581, "渴": 4582, "游": 4583, "渺": 4584, "渾": 4585, "湃": 4586, "湄": 4587, "湊": 4588, "湍": 4589, "湎": 4590, "湖": 4591, "湘": 4592, "湛": 4593, "湟": 4594, "湧": 4595, "湮": 4596, "湯": 4597, "湳": 4598, "湾": 4599, "湿": 4600, "満": 4601, "溃": 4602, "溅": 4603, "溉": 4604, "溏": 4605, "源": 4606, "準": 4607, "溜": 4608, "溝": 4609, "溟": 4610, "溢": 4611, "溥": 4612, "溧": 4613, "溪": 4614, "溫": 4615, "溯": 4616, "溱": 4617, "溴": 4618, "溶": 4619, "溺": 4620, "滁": 4621, "滂": 4622, "滄": 4623, "滅": 4624, "滇": 4625, "滋": 4626, "滌": 4627, "滑": 4628, "滓": 4629, "滔": 4630, "滕": 4631, "滗": 4632, "滘": 4633, "滙": 4634, "滚": 4635, "滞": 4636, "满": 4637, "滤": 4638, "滥": 4639, "滦": 4640, "滨": 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5551, "穰": 5552, "穴": 5553, "究": 5554, "穷": 5555, "穹": 5556, "空": 5557, "穿": 5558, "突": 5559, "窃": 5560, "窄": 5561, "窈": 5562, "窋": 5563, "窍": 5564, "窑": 5565, "窒": 5566, "窕": 5567, "窖": 5568, "窗": 5569, "窘": 5570, "窜": 5571, "窝": 5572, "窟": 5573, "窠": 5574, "窥": 5575, "窦": 5576, "窩": 5577, "窪": 5578, "窮": 5579, "窯": 5580, "窺": 5581, "窿": 5582, "竄": 5583, "竇": 5584, "竊": 5585, "立": 5586, "竑": 5587, "竖": 5588, "站": 5589, "竜": 5590, "竞": 5591, "竟": 5592, "章": 5593, "竣": 5594, "童": 5595, "竭": 5596, "端": 5597, "競": 5598, "竹": 5599, "竺": 5600, "竽": 5601, "竿": 5602, "笃": 5603, "笄": 5604, "笆": 5605, "笈": 5606, "笋": 5607, "笏": 5608, "笑": 5609, "笔": 5610, "笙": 5611, "笛": 5612, "笞": 5613, "笠": 5614, "笥": 5615, "符": 5616, "笨": 5617, "第": 5618, "笳": 5619, "笹": 5620, "笺": 5621, "笼": 5622, "筆": 5623, "等": 5624, "筋": 5625, "筌": 5626, "筍": 5627, "筏": 5628, "筐": 5629, "筑": 5630, "筒": 5631, "答": 5632, "策": 5633, "筚": 5634, "筛": 5635, "筝": 5636, "筠": 5637, "筦": 5638, "筮": 5639, "筱": 5640, "筵": 5641, "筷": 5642, "筹": 5643, "签": 5644, "简": 5645, "箍": 5646, "箐": 5647, "箔": 5648, "箕": 5649, "算": 5650, "箝": 5651, "管": 5652, "箫": 5653, "箭": 5654, "箱": 5655, "箴": 5656, "箸": 5657, "節": 5658, "篁": 5659, "範": 5660, "篆": 5661, "篇": 5662, "築": 5663, "篑": 5664, "篓": 5665, "篠": 5666, "篡": 5667, "篤": 5668, "篩": 5669, "篪": 5670, "篮": 5671, "篱": 5672, "篷": 5673, "簇": 5674, "簋": 5675, "簠": 5676, "簡": 5677, "簧": 5678, "簪": 5679, "簷": 5680, "簸": 5681, "簽": 5682, "簾": 5683, "簿": 5684, "籀": 5685, "籁": 5686, "籃": 5687, "籌": 5688, "籍": 5689, "籠": 5690, "籤": 5691, "籬": 5692, "籲": 5693, "米": 5694, "籴": 5695, "类": 5696, "籼": 5697, "籽": 5698, "粉": 5699, "粑": 5700, "粒": 5701, "粕": 5702, "粗": 5703, "粘": 5704, "粟": 5705, "粤": 5706, "粥": 5707, "粪": 5708, "粮": 5709, "粱": 5710, "粲": 5711, "粳": 5712, "粵": 5713, "粹": 5714, "粽": 5715, "精": 5716, "粿": 5717, "糅": 5718, "糊": 5719, "糕": 5720, "糖": 5721, "糙": 5722, "糜": 5723, "糞": 5724, "糟": 5725, "糠": 5726, "糧": 5727, "糯": 5728, "糰": 5729, "糸": 5730, "系": 5731, "糾": 5732, "紀": 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5824, "繁": 5825, "繆": 5826, "繇": 5827, "織": 5828, "繕": 5829, "繞": 5830, "繡": 5831, "繩": 5832, "繪": 5833, "繫": 5834, "繭": 5835, "繳": 5836, "繹": 5837, "繼": 5838, "纂": 5839, "續": 5840, "纏": 5841, "纓": 5842, "纖": 5843, "纜": 5844, "纠": 5845, "纡": 5846, "红": 5847, "纣": 5848, "纤": 5849, "纥": 5850, "约": 5851, "级": 5852, "纨": 5853, "纪": 5854, "纫": 5855, "纬": 5856, "纭": 5857, "纯": 5858, "纰": 5859, "纱": 5860, "纲": 5861, "纳": 5862, "纵": 5863, "纶": 5864, "纷": 5865, "纸": 5866, "纹": 5867, "纺": 5868, "纽": 5869, "纾": 5870, "线": 5871, "绀": 5872, "绂": 5873, "练": 5874, "组": 5875, "绅": 5876, "细": 5877, "织": 5878, "终": 5879, "绊": 5880, "绌": 5881, "绍": 5882, "绎": 5883, "经": 5884, "绑": 5885, "绒": 5886, "结": 5887, "绕": 5888, "绘": 5889, "给": 5890, "绚": 5891, "绛": 5892, "络": 5893, "绝": 5894, "绞": 5895, "统": 5896, "绢": 5897, "绣": 5898, "绥": 5899, "绦": 5900, "继": 5901, "绩": 5902, "绪": 5903, "绫": 5904, "续": 5905, "绮": 5906, "绯": 5907, "绰": 5908, "绳": 5909, "维": 5910, "绵": 5911, "绶": 5912, "绷": 5913, "绸": 5914, "综": 5915, "绽": 5916, "绾": 5917, "绿": 5918, "缀": 5919, "缁": 5920, "缄": 5921, "缅": 5922, "缆": 5923, "缈": 5924, "缉": 5925, "缎": 5926, "缐": 5927, "缓": 5928, "缔": 5929, "缕": 5930, "编": 5931, "缗": 5932, "缘": 5933, "缙": 5934, "缚": 5935, "缛": 5936, "缜": 5937, "缝": 5938, "缟": 5939, "缠": 5940, "缢": 5941, "缣": 5942, "缤": 5943, "缥": 5944, "缨": 5945, "缩": 5946, "缪": 5947, "缫": 5948, "缬": 5949, "缭": 5950, "缮": 5951, "缰": 5952, "缴": 5953, "缵": 5954, "缶": 5955, "缸": 5956, "缺": 5957, "缽": 5958, "罂": 5959, "罃": 5960, "罄": 5961, "罅": 5962, "罐": 5963, "网": 5964, "罔": 5965, "罕": 5966, "罗": 5967, "罘": 5968, "罚": 5969, "罡": 5970, "罢": 5971, "罩": 5972, "罪": 5973, "置": 5974, "罰": 5975, "署": 5976, "罵": 5977, "罷": 5978, "罹": 5979, "羁": 5980, "羅": 5981, "羈": 5982, "羊": 5983, "羌": 5984, "美": 5985, "羔": 5986, "羚": 5987, "羞": 5988, "羟": 5989, "羡": 5990, "羣": 5991, "群": 5992, "羥": 5993, "羧": 5994, "羨": 5995, "義": 5996, "羯": 5997, "羰": 5998, "羲": 5999, "羸": 6000, "羹": 6001, "羽": 6002, "羿": 6003, "翀": 6004, "翁": 6005, "翅": 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6279, "芙": 6280, "芜": 6281, "芝": 6282, "芡": 6283, "芥": 6284, "芦": 6285, "芩": 6286, "芪": 6287, "芬": 6288, "芭": 6289, "芮": 6290, "芯": 6291, "花": 6292, "芳": 6293, "芷": 6294, "芸": 6295, "芹": 6296, "芽": 6297, "芾": 6298, "苄": 6299, "苇": 6300, "苋": 6301, "苌": 6302, "苍": 6303, "苏": 6304, "苑": 6305, "苓": 6306, "苔": 6307, "苕": 6308, "苗": 6309, "苛": 6310, "苜": 6311, "苞": 6312, "苟": 6313, "苡": 6314, "苣": 6315, "若": 6316, "苦": 6317, "苧": 6318, "苫": 6319, "苯": 6320, "英": 6321, "苴": 6322, "苷": 6323, "苹": 6324, "苻": 6325, "茁": 6326, "茂": 6327, "范": 6328, "茄": 6329, "茅": 6330, "茉": 6331, "茌": 6332, "茎": 6333, "茏": 6334, "茗": 6335, "茜": 6336, "茧": 6337, "茨": 6338, "茫": 6339, "茬": 6340, "茭": 6341, "茯": 6342, "茱": 6343, "茲": 6344, "茴": 6345, "茵": 6346, "茶": 6347, "茸": 6348, "茹": 6349, "荀": 6350, "荃": 6351, "荆": 6352, "草": 6353, "荊": 6354, "荐": 6355, "荒": 6356, "荔": 6357, "荚": 6358, "荞": 6359, "荟": 6360, "荠": 6361, "荡": 6362, "荣": 6363, "荤": 6364, "荥": 6365, "荧": 6366, "荨": 6367, "荩": 6368, "荪": 6369, "荫": 6370, "药": 6371, "荳": 6372, "荷": 6373, "荻": 6374, "荼": 6375, "莅": 6376, "莆": 6377, "莉": 6378, "莊": 6379, "莎": 6380, "莒": 6381, "莓": 6382, "莖": 6383, "莘": 6384, "莞": 6385, "莠": 6386, "莢": 6387, "莨": 6388, "莪": 6389, "莫": 6390, "莱": 6391, "莲": 6392, "莴": 6393, "获": 6394, "莹": 6395, "莺": 6396, "莽": 6397, "菀": 6398, "菁": 6399, "菅": 6400, "菇": 6401, "菊": 6402, "菌": 6403, "菏": 6404, "菓": 6405, "菖": 6406, "菜": 6407, "菟": 6408, "菠": 6409, "菩": 6410, "菫": 6411, "華": 6412, "菰": 6413, "菱": 6414, "菲": 6415, "菴": 6416, "菸": 6417, "菽": 6418, "萃": 6419, "萄": 6420, "萇": 6421, "萊": 6422, "萌": 6423, "萍": 6424, "萎": 6425, "萘": 6426, "萜": 6427, "萝": 6428, "萤": 6429, "营": 6430, "萦": 6431, "萧": 6432, "萨": 6433, "萩": 6434, "萬": 6435, "萱": 6436, "萸": 6437, "萼": 6438, "落": 6439, "葆": 6440, "葉": 6441, "著": 6442, "葛": 6443, "葡": 6444, "董": 6445, "葦": 6446, "葩": 6447, "葫": 6448, "葬": 6449, "葱": 6450, "葵": 6451, "葺": 6452, "蒂": 6453, "蒋": 6454, "蒌": 6455, "蒐": 6456, "蒙": 6457, "蒜": 6458, "蒡": 6459, "蒨": 6460, "蒯": 6461, "蒲": 6462, "蒴": 6463, "蒸": 6464, "蒺": 6465, "蒼": 6466, "蒽": 6467, "蒿": 6468, "蓁": 6469, "蓄": 6470, "蓉": 6471, "蓋": 6472, "蓓": 6473, "蓖": 6474, "蓝": 6475, "蓟": 6476, "蓦": 6477, "蓬": 6478, "蓮": 6479, "蓼": 6480, "蓿": 6481, "蔑": 6482, "蔓": 6483, "蔔": 6484, "蔗": 6485, "蔚": 6486, "蔡": 6487, "蔣": 6488, "蔥": 6489, "蔫": 6490, "蔬": 6491, "蔭": 6492, "蔵": 6493, "蔷": 6494, "蔺": 6495, "蔻": 6496, "蔼": 6497, "蔽": 6498, "蕃": 6499, "蕈": 6500, "蕉": 6501, "蕊": 6502, "蕙": 6503, "蕨": 6504, "蕩": 6505, "蕪": 6506, "蕭": 6507, "蕲": 6508, "蕴": 6509, "蕾": 6510, "薄": 6511, "薅": 6512, "薇": 6513, "薈": 6514, "薊": 6515, "薏": 6516, "薑": 6517, "薔": 6518, "薙": 6519, "薛": 6520, "薦": 6521, "薨": 6522, "薩": 6523, "薪": 6524, "薫": 6525, "薮": 6526, "薯": 6527, "薰": 6528, "藁": 6529, "藉": 6530, "藍": 6531, "藏": 6532, "藐": 6533, "藓": 6534, "藔": 6535, "藕": 6536, "藜": 6537, "藝": 6538, "藤": 6539, "藥": 6540, "藩": 6541, "藪": 6542, "藻": 6543, "藿": 6544, "蘄": 6545, "蘅": 6546, "蘆": 6547, "蘇": 6548, "蘊": 6549, "蘋": 6550, "蘑": 6551, "蘖": 6552, "蘧": 6553, "蘭": 6554, "蘸": 6555, "蘿": 6556, "虎": 6557, "虏": 6558, "虐": 6559, "虑": 6560, "虔": 6561, "處": 6562, "虚": 6563, "虛": 6564, "虜": 6565, "虞": 6566, "號": 6567, "虢": 6568, "虧": 6569, "虫": 6570, "虬": 6571, "虱": 6572, "虹": 6573, "虻": 6574, "虽": 6575, "虾": 6576, "蚀": 6577, "蚁": 6578, "蚂": 6579, "蚊": 6580, "蚋": 6581, "蚌": 6582, "蚓": 6583, "蚕": 6584, "蚜": 6585, "蚣": 6586, "蚤": 6587, "蚩": 6588, "蚪": 6589, "蚯": 6590, "蚱": 6591, "蚴": 6592, "蚵": 6593, "蚶": 6594, "蛀": 6595, "蛇": 6596, "蛉": 6597, "蛊": 6598, "蛋": 6599, "蛎": 6600, "蛔": 6601, "蛙": 6602, "蛛": 6603, "蛟": 6604, "蛤": 6605, "蛭": 6606, "蛮": 6607, "蛰": 6608, "蛳": 6609, "蛴": 6610, "蛹": 6611, "蛺": 6612, "蛻": 6613, "蛾": 6614, "蜀": 6615, "蜂": 6616, "蜃": 6617, "蜇": 6618, "蜈": 6619, "蜊": 6620, "蜍": 6621, "蜒": 6622, "蜓": 6623, "蜕": 6624, "蜗": 6625, "蜘": 6626, "蜚": 6627, "蜜": 6628, "蜡": 6629, "蜢": 6630, "蜥": 6631, "蜱": 6632, "蜴": 6633, "蜻": 6634, "蜿": 6635, "蝇": 6636, "蝉": 6637, "蝌": 6638, "蝎": 6639, "蝕": 6640, "蝗": 6641, "蝙": 6642, "蝠": 6643, "蝦": 6644, "蝮": 6645, "蝰": 6646, "蝴": 6647, "蝶": 6648, "蝸": 6649, "蝼": 6650, "蝽": 6651, "螂": 6652, "螃": 6653, "螅": 6654, "螈": 6655, "融": 6656, "螟": 6657, "螢": 6658, "螨": 6659, "螫": 6660, "螬": 6661, "螭": 6662, "螯": 6663, "螳": 6664, "螺": 6665, "螽": 6666, "蟀": 6667, "蟋": 6668, "蟑": 6669, "蟒": 6670, "蟓": 6671, "蟠": 6672, "蟬": 6673, "蟲": 6674, "蟹": 6675, "蟻": 6676, "蟾": 6677, "蠅": 6678, "蠊": 6679, "蠍": 6680, "蠕": 6681, "蠟": 6682, "蠡": 6683, "蠢": 6684, "蠣": 6685, "蠱": 6686, "蠶": 6687, "蠹": 6688, "蠻": 6689, "血": 6690, "衄": 6691, "衅": 6692, "衆": 6693, "行": 6694, "衍": 6695, "術": 6696, "衔": 6697, "街": 6698, "衙": 6699, "衛": 6700, "衝": 6701, "衞": 6702, "衡": 6703, "衢": 6704, "衣": 6705, "补": 6706, "表": 6707, "衫": 6708, "衬": 6709, "衮": 6710, "衰": 6711, "衷": 6712, "衽": 6713, "袁": 6714, "袂": 6715, "袄": 6716, "袈": 6717, "袋": 6718, "袍": 6719, "袒": 6720, "袖": 6721, "袜": 6722, "袞": 6723, "袢": 6724, "袤": 6725, "被": 6726, "袭": 6727, "袱": 6728, "裁": 6729, "裂": 6730, "装": 6731, "裏": 6732, "裒": 6733, "裔": 6734, "裕": 6735, "裘": 6736, "裙": 6737, "補": 6738, "裝": 6739, "裟": 6740, "裡": 6741, "裤": 6742, "裨": 6743, "裱": 6744, "裳": 6745, "裴": 6746, "裸": 6747, "裹": 6748, "製": 6749, "褂": 6750, "複": 6751, "褐": 6752, "褒": 6753, "褚": 6754, "褥": 6755, "褪": 6756, "褫": 6757, "褲": 6758, "褶": 6759, "襄": 6760, "襞": 6761, "襟": 6762, "襦": 6763, "襪": 6764, "襯": 6765, "襲": 6766, "襻": 6767, "西": 6768, "要": 6769, "覃": 6770, "覆": 6771, "覇": 6772, "見": 6773, "規": 6774, "覓": 6775, "視": 6776, "覧": 6777, "親": 6778, "覲": 6779, "観": 6780, "覺": 6781, "覽": 6782, "觀": 6783, "见": 6784, "观": 6785, "觃": 6786, "规": 6787, "觅": 6788, "视": 6789, "览": 6790, "觉": 6791, "觊": 6792, "觎": 6793, "觐": 6794, "觑": 6795, "角": 6796, "觚": 6797, "觞": 6798, "解": 6799, "触": 6800, "觸": 6801, "言": 6802, "訂": 6803, "計": 6804, "訊": 6805, "討": 6806, "訓": 6807, "訕": 6808, "訖": 6809, "託": 6810, "記": 6811, "訝": 6812, "訟": 6813, "訢": 6814, "訣": 6815, "訥": 6816, "訪": 6817, "設": 6818, "許": 6819, "訳": 6820, "訴": 6821, "訶": 6822, "診": 6823, "註": 6824, "証": 6825, "詐": 6826, "詔": 6827, "評": 6828, "詛": 6829, "詞": 6830, "詠": 6831, "詢": 6832, "詣": 6833, "試": 6834, "詩": 6835, "詭": 6836, "詮": 6837, "話": 6838, "該": 6839, "詳": 6840, "詹": 6841, "誅": 6842, "誇": 6843, "誉": 6844, "誊": 6845, "誌": 6846, "認": 6847, "誓": 6848, "誕": 6849, "誘": 6850, "語": 6851, "誠": 6852, "誡": 6853, "誣": 6854, "誤": 6855, "誥": 6856, "誦": 6857, "說": 6858, "説": 6859, "読": 6860, "誰": 6861, "課": 6862, "誼": 6863, "調": 6864, "談": 6865, "請": 6866, "諏": 6867, "諒": 6868, "論": 6869, "諜": 6870, "諡": 6871, "諦": 6872, "諧": 6873, "諫": 6874, "諭": 6875, "諮": 6876, "諱": 6877, "諷": 6878, "諸": 6879, "諺": 6880, "諾": 6881, "謀": 6882, "謁": 6883, "謂": 6884, "謄": 6885, "謇": 6886, "謊": 6887, "謎": 6888, "謙": 6889, "謚": 6890, "講": 6891, "謝": 6892, "謠": 6893, "謨": 6894, "謫": 6895, "謬": 6896, "謹": 6897, "證": 6898, "譏": 6899, "識": 6900, "譚": 6901, "譜": 6902, "警": 6903, "譬": 6904, "譯": 6905, "議": 6906, "譴": 6907, "護": 6908, "譽": 6909, "讀": 6910, "變": 6911, "讓": 6912, "讚": 6913, "计": 6914, "订": 6915, "讣": 6916, "认": 6917, "讥": 6918, "讦": 6919, "讧": 6920, "讨": 6921, "让": 6922, "讫": 6923, "训": 6924, "议": 6925, "讯": 6926, "记": 6927, "讲": 6928, "讳": 6929, "讴": 6930, "讶": 6931, "讷": 6932, "许": 6933, "讹": 6934, "论": 6935, "讼": 6936, "讽": 6937, "设": 6938, "访": 6939, "诀": 6940, "证": 6941, "诂": 6942, "诃": 6943, "评": 6944, "诅": 6945, "识": 6946, "诈": 6947, "诉": 6948, "诊": 6949, "诋": 6950, "词": 6951, "诏": 6952, "译": 6953, "诒": 6954, "试": 6955, "诗": 6956, "诘": 6957, "诙": 6958, "诚": 6959, "诛": 6960, "话": 6961, "诞": 6962, "诟": 6963, "诠": 6964, "诡": 6965, "询": 6966, "诣": 6967, "诤": 6968, "该": 6969, "详": 6970, "诩": 6971, "诫": 6972, "诬": 6973, "语": 6974, "误": 6975, "诰": 6976, "诱": 6977, "诲": 6978, "说": 6979, "诵": 6980, "请": 6981, "诸": 6982, "诺": 6983, "读": 6984, "诽": 6985, "课": 6986, "诿": 6987, "谀": 6988, "谁": 6989, "调": 6990, "谄": 6991, "谅": 6992, "谆": 6993, "谈": 6994, "谊": 6995, "谋": 6996, "谌": 6997, "谍": 6998, "谎": 6999, "谏": 7000, "谐": 7001, "谑": 7002, "谒": 7003, "谓": 7004, "谔": 7005, "谕": 7006, "谗": 7007, "谘": 7008, "谙": 7009, "谚": 7010, "谛": 7011, "谜": 7012, "谟": 7013, "谢": 7014, "谣": 7015, "谤": 7016, "谥": 7017, "谦": 7018, "谧": 7019, "谨": 7020, "谩": 7021, "谪": 7022, "谬": 7023, "谭": 7024, "谯": 7025, "谱": 7026, "谲": 7027, "谴": 7028, "谵": 7029, "谶": 7030, "谷": 7031, "谿": 7032, "豁": 7033, "豆": 7034, "豇": 7035, "豈": 7036, "豊": 7037, "豌": 7038, "豎": 7039, "豐": 7040, "豔": 7041, "豕": 7042, "豚": 7043, "象": 7044, "豢": 7045, "豪": 7046, "豫": 7047, "豬": 7048, "豳": 7049, "豹": 7050, "貂": 7051, "貉": 7052, "貊": 7053, "貌": 7054, "貓": 7055, "貝": 7056, "貞": 7057, "負": 7058, "財": 7059, "貢": 7060, "貧": 7061, "貨": 7062, "販": 7063, "貪": 7064, "貫": 7065, "責": 7066, "貯": 7067, "貳": 7068, "貴": 7069, "貶": 7070, "買": 7071, "貸": 7072, "費": 7073, "貼": 7074, "貽": 7075, "貿": 7076, "賀": 7077, "賁": 7078, "賂": 7079, "賃": 7080, "賄": 7081, "資": 7082, "賈": 7083, "賊": 7084, "賑": 7085, "賓": 7086, "賜": 7087, "賞": 7088, "賠": 7089, "賡": 7090, "賢": 7091, "賣": 7092, "賤": 7093, "賦": 7094, "質": 7095, "賬": 7096, "賭": 7097, "賴": 7098, "賸": 7099, "賺": 7100, "購": 7101, "賽": 7102, "贈": 7103, "贊": 7104, "贏": 7105, "贓": 7106, "贖": 7107, "贛": 7108, "贝": 7109, "贞": 7110, "负": 7111, "贡": 7112, "财": 7113, "责": 7114, "贤": 7115, "败": 7116, "账": 7117, "货": 7118, "质": 7119, "贩": 7120, "贪": 7121, "贫": 7122, "贬": 7123, "购": 7124, "贮": 7125, "贯": 7126, "贰": 7127, "贱": 7128, "贲": 7129, "贴": 7130, "贵": 7131, "贷": 7132, "贸": 7133, "费": 7134, "贺": 7135, "贻": 7136, "贼": 7137, "贽": 7138, "贾": 7139, "贿": 7140, "赀": 7141, "赁": 7142, "赂": 7143, "赃": 7144, "资": 7145, "赅": 7146, "赈": 7147, "赉": 7148, "赊": 7149, "赋": 7150, "赌": 7151, "赎": 7152, "赏": 7153, "赐": 7154, "赓": 7155, "赔": 7156, "赖": 7157, "赘": 7158, "赙": 7159, "赚": 7160, "赛": 7161, "赜": 7162, "赝": 7163, "赞": 7164, "赟": 7165, "赠": 7166, "赡": 7167, "赢": 7168, "赣": 7169, "赤": 7170, "赦": 7171, "赫": 7172, "赭": 7173, "走": 7174, "赳": 7175, "赴": 7176, "赵": 7177, "赶": 7178, "起": 7179, "趁": 7180, "超": 7181, "越": 7182, "趋": 7183, "趕": 7184, "趙": 7185, "趟": 7186, "趣": 7187, "趨": 7188, "足": 7189, "趴": 7190, "趸": 7191, "趺": 7192, "趾": 7193, "跃": 7194, "跆": 7195, "跋": 7196, "跌": 7197, "跑": 7198, "跖": 7199, "跗": 7200, "跚": 7201, "跛": 7202, "距": 7203, "跟": 7204, "跡": 7205, "跤": 7206, "跨": 7207, "跪": 7208, "路": 7209, "跳": 7210, "践": 7211, "跷": 7212, "跸": 7213, "跻": 7214, "踊": 7215, "踌": 7216, "踏": 7217, "踐": 7218, "踔": 7219, "踝": 7220, "踞": 7221, "踢": 7222, "踩": 7223, "踪": 7224, "踵": 7225, "蹂": 7226, "蹄": 7227, "蹇": 7228, "蹈": 7229, "蹊": 7230, "蹒": 7231, "蹟": 7232, "蹤": 7233, "蹦": 7234, "蹬": 7235, "蹭": 7236, "蹲": 7237, "蹴": 7238, "蹶": 7239, "躁": 7240, "躇": 7241, "躍": 7242, "躏": 7243, "身": 7244, "躬": 7245, "躯": 7246, "躲": 7247, "躺": 7248, "軀": 7249, "車": 7250, "軋": 7251, "軌": 7252, "軍": 7253, "軒": 7254, "軟": 7255, "軫": 7256, "軸": 7257, "軼": 7258, "軾": 7259, "較": 7260, "輅": 7261, "載": 7262, "輒": 7263, "輓": 7264, "輔": 7265, "輕": 7266, "輛": 7267, "輝": 7268, "輟": 7269, "輩": 7270, "輪": 7271, "輯": 7272, "輸": 7273, "輻": 7274, "輾": 7275, "輿": 7276, "轄": 7277, "轉": 7278, "轍": 7279, "轎": 7280, "轟": 7281, "车": 7282, "轧": 7283, "轨": 7284, "轩": 7285, "轫": 7286, "转": 7287, "轭": 7288, "轮": 7289, "软": 7290, "轰": 7291, "轲": 7292, "轴": 7293, "轶": 7294, "轻": 7295, "轼": 7296, "载": 7297, "轿": 7298, "较": 7299, "辄": 7300, "辅": 7301, "辆": 7302, "辇": 7303, "辈": 7304, "辉": 7305, "辊": 7306, "辋": 7307, "辍": 7308, "辎": 7309, "辏": 7310, "辐": 7311, "辑": 7312, "输": 7313, "辕": 7314, "辖": 7315, "辗": 7316, "辙": 7317, "辛": 7318, "辜": 7319, "辞": 7320, "辟": 7321, "辣": 7322, "辦": 7323, "辨": 7324, "辩": 7325, "辫": 7326, "辭": 7327, "辯": 7328, "辰": 7329, "辱": 7330, "農": 7331, "边": 7332, "辺": 7333, "辻": 7334, "込": 7335, "辽": 7336, "达": 7337, "迁": 7338, "迂": 7339, "迄": 7340, "迅": 7341, "过": 7342, "迈": 7343, "迎": 7344, "运": 7345, "近": 7346, "迓": 7347, "返": 7348, "迕": 7349, "还": 7350, "这": 7351, "进": 7352, "远": 7353, "违": 7354, "连": 7355, "迟": 7356, "迢": 7357, "迤": 7358, "迥": 7359, "迦": 7360, "迨": 7361, "迩": 7362, "迪": 7363, "迫": 7364, "迭": 7365, "述": 7366, "迳": 7367, "迴": 7368, "迷": 7369, "迸": 7370, "迹": 7371, "迺": 7372, "追": 7373, "退": 7374, "送": 7375, "适": 7376, "逃": 7377, "逅": 7378, "逆": 7379, "选": 7380, "逊": 7381, "逋": 7382, "逍": 7383, "透": 7384, "逐": 7385, "逑": 7386, "递": 7387, "途": 7388, "逕": 7389, "逖": 7390, "逗": 7391, "這": 7392, "通": 7393, "逛": 7394, "逝": 7395, "逞": 7396, "速": 7397, "造": 7398, "逢": 7399, "連": 7400, "逮": 7401, "週": 7402, "進": 7403, "逵": 7404, "逸": 7405, "逻": 7406, "逼": 7407, "逾": 7408, "遁": 7409, "遂": 7410, "遇": 7411, "遊": 7412, "運": 7413, "遍": 7414, "過": 7415, "遏": 7416, "遐": 7417, "遑": 7418, "遒": 7419, "道": 7420, "達": 7421, "違": 7422, "遗": 7423, "遘": 7424, "遙": 7425, "遜": 7426, "遞": 7427, "遠": 7428, "遣": 7429, "遥": 7430, "遨": 7431, "適": 7432, "遭": 7433, "遮": 7434, "遲": 7435, "遴": 7436, "遵": 7437, "遷": 7438, "選": 7439, "遺": 7440, "遼": 7441, "遽": 7442, "避": 7443, "邀": 7444, "邁": 7445, "邂": 7446, "邃": 7447, "還": 7448, "邇": 7449, "邈": 7450, "邊": 7451, "邏": 7452, "邑": 7453, "邓": 7454, "邕": 7455, "邙": 7456, "邛": 7457, "邝": 7458, "邠": 7459, "邡": 7460, "邢": 7461, "那": 7462, "邦": 7463, "邨": 7464, "邪": 7465, "邬": 7466, "邮": 7467, "邯": 7468, "邰": 7469, "邱": 7470, "邳": 7471, "邵": 7472, "邸": 7473, "邹": 7474, "邺": 7475, "邻": 7476, "郁": 7477, "郊": 7478, "郎": 7479, "郐": 7480, "郑": 7481, "郓": 7482, "郗": 7483, "郝": 7484, "郡": 7485, "郢": 7486, "郤": 7487, "郦": 7488, "郧": 7489, "部": 7490, "郫": 7491, "郭": 7492, "郯": 7493, "郴": 7494, "郵": 7495, "郷": 7496, "郸": 7497, "都": 7498, "郾": 7499, "鄂": 7500, "鄉": 7501, "鄒": 7502, "鄔": 7503, "鄙": 7504, "鄜": 7505, "鄞": 7506, "鄠": 7507, "鄢": 7508, "鄧": 7509, "鄭": 7510, "鄰": 7511, "鄱": 7512, "鄲": 7513, "鄴": 7514, "鄺": 7515, "酆": 7516, "酈": 7517, "酉": 7518, "酋": 7519, "酌": 7520, "配": 7521, "酐": 7522, "酒": 7523, "酗": 7524, "酚": 7525, "酝": 7526, "酞": 7527, "酢": 7528, "酣": 7529, "酥": 7530, "酪": 7531, "酬": 7532, "酮": 7533, "酯": 7534, "酰": 7535, "酱": 7536, "酵": 7537, "酶": 7538, "酷": 7539, "酸": 7540, "酿": 7541, "醃": 7542, "醇": 7543, "醉": 7544, "醋": 7545, "醌": 7546, "醍": 7547, "醐": 7548, "醒": 7549, "醚": 7550, "醛": 7551, "醜": 7552, "醣": 7553, "醪": 7554, "醫": 7555, "醬": 7556, "醮": 7557, "醯": 7558, "醴": 7559, "釀": 7560, "釁": 7561, "釆": 7562, "采": 7563, "釉": 7564, "释": 7565, "釋": 7566, "里": 7567, "重": 7568, "野": 7569, "量": 7570, "釐": 7571, "金": 7572, "釗": 7573, "釘": 7574, "釜": 7575, "針": 7576, "釣": 7577, "釧": 7578, "鈉": 7579, "鈍": 7580, "鈎": 7581, "鈔": 7582, "鈕": 7583, "鈞": 7584, "鈣": 7585, "鈦": 7586, "鈴": 7587, "鈺": 7588, "鈾": 7589, "鉀": 7590, "鉄": 7591, "鉅": 7592, "鉉": 7593, "鉗": 7594, "鉛": 7595, "鉢": 7596, "鉤": 7597, "鉬": 7598, "鉴": 7599, "鉻": 7600, "銀": 7601, "銃": 7602, "銅": 7603, "銓": 7604, "銖": 7605, "銘": 7606, "銜": 7607, "銩": 7608, "銮": 7609, "銳": 7610, "銷": 7611, "鋁": 7612, "鋅": 7613, "鋇": 7614, "鋒": 7615, "鋤": 7616, "鋪": 7617, "鋯": 7618, "鋰": 7619, "鋸": 7620, "鋼": 7621, "錄": 7622, "錇": 7623, "錐": 7624, "錘": 7625, "錚": 7626, "錢": 7627, "錦": 7628, "錨": 7629, "錫": 7630, "錯": 7631, "録": 7632, "錶": 7633, "鍊": 7634, "鍋": 7635, "鍍": 7636, "鍛": 7637, "鍵": 7638, "鍾": 7639, "鎂": 7640, "鎊": 7641, "鎌": 7642, "鎏": 7643, "鎔": 7644, "鎖": 7645, "鎗": 7646, "鎚": 7647, "鎧": 7648, "鎬": 7649, "鎮": 7650, "鎳": 7651, "鏈": 7652, "鏖": 7653, "鏗": 7654, "鏘": 7655, "鏞": 7656, "鏡": 7657, "鏢": 7658, "鏤": 7659, "鏽": 7660, "鐘": 7661, "鐮": 7662, "鐳": 7663, "鐵": 7664, "鐸": 7665, "鑄": 7666, "鑑": 7667, "鑒": 7668, "鑛": 7669, "鑣": 7670, "鑫": 7671, "鑰": 7672, "鑲": 7673, "鑼": 7674, "鑽": 7675, "鑾": 7676, "鑿": 7677, "钆": 7678, "钇": 7679, "针": 7680, "钉": 7681, "钊": 7682, "钌": 7683, "钍": 7684, "钎": 7685, "钐": 7686, "钒": 7687, "钓": 7688, "钕": 7689, "钗": 7690, "钘": 7691, "钙": 7692, "钚": 7693, "钛": 7694, "钜": 7695, "钝": 7696, "钞": 7697, "钟": 7698, "钠": 7699, "钡": 7700, "钢": 7701, "钣": 7702, "钤": 7703, "钥": 7704, "钦": 7705, "钧": 7706, "钨": 7707, "钩": 7708, "钪": 7709, "钮": 7710, "钯": 7711, "钰": 7712, "钱": 7713, "钳": 7714, "钴": 7715, "钵": 7716, "钺": 7717, "钻": 7718, "钼": 7719, "钽": 7720, "钾": 7721, "钿": 7722, "铀": 7723, "铁": 7724, "铂": 7725, "铃": 7726, "铅": 7727, "铆": 7728, "铈": 7729, "铉": 7730, "铊": 7731, "铋": 7732, "铌": 7733, "铍": 7734, "铎": 7735, "铐": 7736, "铑": 7737, "铒": 7738, "铛": 7739, "铜": 7740, "铝": 7741, "铟": 7742, "铠": 7743, "铡": 7744, "铢": 7745, "铣": 7746, "铤": 7747, "铨": 7748, "铪": 7749, "铬": 7750, "铭": 7751, "铮": 7752, "铯": 7753, "铰": 7754, "铱": 7755, "铲": 7756, "铳": 7757, "铵": 7758, "银": 7759, "铷": 7760, "铸": 7761, "铺": 7762, "铼": 7763, "链": 7764, "铿": 7765, "销": 7766, "锁": 7767, "锂": 7768, "锄": 7769, "锅": 7770, "锆": 7771, "锈": 7772, "锉": 7773, "锋": 7774, "锌": 7775, "锏": 7776, "锐": 7777, "锑": 7778, "锕": 7779, "锗": 7780, "错": 7781, "锚": 7782, "锛": 7783, "锝": 7784, "锟": 7785, "锡": 7786, "锢": 7787, "锣": 7788, "锤": 7789, "锥": 7790, "锦": 7791, "锭": 7792, "键": 7793, "锯": 7794, "锰": 7795, "锲": 7796, "锴": 7797, "锵": 7798, "锶": 7799, "锷": 7800, "锺": 7801, "锻": 7802, "镀": 7803, "镁": 7804, "镂": 7805, "镇": 7806, "镉": 7807, "镊": 7808, "镌": 7809, "镍": 7810, "镐": 7811, "镑": 7812, "镒": 7813, "镓": 7814, "镕": 7815, "镖": 7816, "镗": 7817, "镛": 7818, "镜": 7819, "镝": 7820, "镞": 7821, "镣": 7822, "镦": 7823, "镧": 7824, "镭": 7825, "镯": 7826, "镰": 7827, "镳": 7828, "镶": 7829, "長": 7830, "长": 7831, "門": 7832, "閃": 7833, "閉": 7834, "開": 7835, "閏": 7836, "閑": 7837, "閒": 7838, "間": 7839, "閔": 7840, "閘": 7841, "関": 7842, "閣": 7843, "閤": 7844, "閥": 7845, "閨": 7846, "閩": 7847, "閱": 7848, "閲": 7849, "閻": 7850, "闆": 7851, "闇": 7852, "闈": 7853, "闊": 7854, "闋": 7855, "闐": 7856, "闕": 7857, "闖": 7858, "闘": 7859, "關": 7860, "闡": 7861, "闢": 7862, "门": 7863, "闩": 7864, "闪": 7865, "闫": 7866, "闭": 7867, "问": 7868, "闯": 7869, "闰": 7870, "闱": 7871, "闲": 7872, "闳": 7873, "间": 7874, "闵": 7875, "闷": 7876, "闸": 7877, "闹": 7878, "闺": 7879, "闻": 7880, "闼": 7881, "闽": 7882, "闾": 7883, "阀": 7884, "阁": 7885, "阂": 7886, "阅": 7887, "阆": 7888, "阇": 7889, "阈": 7890, "阉": 7891, "阎": 7892, "阐": 7893, "阑": 7894, "阔": 7895, "阕": 7896, "阖": 7897, "阗": 7898, "阙": 7899, "阚": 7900, "阜": 7901, "队": 7902, "阡": 7903, "阪": 7904, "阮": 7905, "阯": 7906, "阱": 7907, "防": 7908, "阳": 7909, "阴": 7910, "阵": 7911, "阶": 7912, "阻": 7913, "阿": 7914, "陀": 7915, "陂": 7916, "附": 7917, "际": 7918, "陆": 7919, "陇": 7920, "陈": 7921, "陉": 7922, "陋": 7923, "陌": 7924, "降": 7925, "限": 7926, "陕": 7927, "陛": 7928, "陜": 7929, "陝": 7930, "陞": 7931, "陟": 7932, "陡": 7933, "院": 7934, "陣": 7935, "除": 7936, "陨": 7937, "险": 7938, "陪": 7939, "陰": 7940, "陲": 7941, "陳": 7942, "陵": 7943, "陶": 7944, "陷": 7945, "陸": 7946, "険": 7947, "陽": 7948, "隅": 7949, "隆": 7950, "隈": 7951, "隊": 7952, "隋": 7953, "隍": 7954, "階": 7955, "随": 7956, "隐": 7957, "隔": 7958, "隕": 7959, "隗": 7960, "隘": 7961, "隙": 7962, "際": 7963, "障": 7964, "隧": 7965, "隨": 7966, "險": 7967, "隰": 7968, "隱": 7969, "隴": 7970, "隶": 7971, "隷": 7972, "隸": 7973, "隹": 7974, "隻": 7975, "隼": 7976, "隽": 7977, "难": 7978, "雀": 7979, "雁": 7980, "雄": 7981, "雅": 7982, "集": 7983, "雇": 7984, "雉": 7985, "雋": 7986, "雌": 7987, "雍": 7988, "雎": 7989, "雏": 7990, "雑": 7991, "雒": 7992, "雕": 7993, "雖": 7994, "雙": 7995, "雛": 7996, "雜": 7997, "雞": 7998, "雠": 7999, "離": 8000, "難": 8001, "雨": 8002, "雪": 8003, "雯": 8004, "雲": 8005, "雳": 8006, "零": 8007, "雷": 8008, "雹": 8009, "電": 8010, "雾": 8011, "需": 8012, "霁": 8013, "霄": 8014, "霆": 8015, "震": 8016, "霈": 8017, "霉": 8018, "霍": 8019, "霎": 8020, "霏": 8021, "霑": 8022, "霓": 8023, "霖": 8024, "霜": 8025, "霞": 8026, "霧": 8027, "霨": 8028, "霰": 8029, "露": 8030, "霸": 8031, "霹": 8032, "霾": 8033, "靂": 8034, "靈": 8035, "青": 8036, "靓": 8037, "靖": 8038, "静": 8039, "靚": 8040, "靛": 8041, "靜": 8042, "非": 8043, "靠": 8044, "靡": 8045, "面": 8046, "革": 8047, "靳": 8048, "靴": 8049, "靶": 8050, "靺": 8051, "靼": 8052, "鞅": 8053, "鞋": 8054, "鞍": 8055, "鞏": 8056, "鞑": 8057, "鞘": 8058, "鞠": 8059, "鞨": 8060, "鞫": 8061, "鞭": 8062, "韃": 8063, "韋": 8064, "韌": 8065, "韓": 8066, "韜": 8067, "韞": 8068, "韦": 8069, "韧": 8070, "韩": 8071, "韫": 8072, "韬": 8073, "韭": 8074, "音": 8075, "韵": 8076, "韶": 8077, "韻": 8078, "響": 8079, "頁": 8080, "頂": 8081, "頃": 8082, "項": 8083, "順": 8084, "須": 8085, "頌": 8086, "預": 8087, "頑": 8088, "頒": 8089, "頓": 8090, "頗": 8091, "領": 8092, "頜": 8093, "頡": 8094, "頤": 8095, "頫": 8096, "頭": 8097, "頰": 8098, "頷": 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14905, "▁stim": 14906, "整理": 14907, "▁dat": 14908, "命令": 14909, "olved": 14910, "小的": 14911, "▁narr": 14912, "▁wear": 14913, "匹配": 14914, "水平的": 14915, "生活的": 14916, "▁rail": 14917, "RI": 14918, "可能是": 14919, "估计": 14920, "eters": 14921, "athy": 14922, "论述": 14923, "▁Mod": 14924, "慢性": 14925, "▁probably": 14926, "建构": 14927, "▁bacteria": 14928, "▁presence": 14929, "消耗": 14930, "增值": 14931, "▁We": 14932, "sect": 14933, "中全会": 14934, "▁user": 14935, "reating": 14936, "稳定的": 14937, "附近": 14938, "十八大": 14939, "效力": 14940, "▁workers": 14941, "外部連結": 14942, "全球化": 14943, "也可": 14944, "people": 14945, "ightly": 14946, "方便": 14947, "unt": 14948, "风格": 14949, "▁sugar": 14950, "▁bul": 14951, "AA": 14952, "位於": 14953, "增加了": 14954, "van": 14955, "新发展": 14956, "\t\t": 14957, "保存": 14958, "博物": 14959, "一条": 14960, "变动": 14961, "▁commer": 14962, "▁▁▁▁▁": 14963, "ilit": 14964, "bing": 14965, "onic": 14966, "▁accur": 14967, "bec": 14968, "都会": 14969, "达成": 14970, "GB": 14971, "中华民族伟大复兴": 14972, "足够": 14973, "▁cold": 14974, "方位": 14975, "概率": 14976, "mat": 14977, "的权利": 14978, "将会": 14979, "人士": 14980, "▁organization": 14981, "▁max": 14982, "za": 14983, "gra": 14984, "Today": 14985, "委会": 14986, "糖尿": 14987, "====": 14988, "相互作用": 14989, "▁moment": 14990, "图书": 14991, "▁.▁": 14992, "协作": 14993, "▁middle": 14994, "尝试": 14995, "▁Chinese": 14996, "▁names": 14997, "▁goes": 14998, "long": 14999, "目的是": 15000, "▁laun": 15001, "irection": 15002, "rupt": 15003, "lam": 15004, "▁gene": 15005, "最近": 15006, "▁Education": 15007, "不利": 15008, "▁figure": 15009, "的使用": 15010, "▁Black": 15011, "出生": 15012, "引导学生": 15013, "▁immune": 15014, "▁store": 15015, "协会": 15016, "必要的": 15017, "批判": 15018, "标准化": 15019, "寻找": 15020, "▁resistance": 15021, "▁date": 15022, "▁eight": 15023, "后果": 15024, "▁party": 15025, "Sec": 15026, "的有效": 15027, "有人": 15028, "▁filled": 15029, "男子": 15030, "edia": 15031, "▁capital": 15032, "已成为": 15033, "ees": 15034, "rief": 15035, "▁i": 15036, "联合国": 15037, "$▁": 15038, "存在着": 15039, "▁Br": 15040, "▁Mex": 15041, "产物": 15042, "estic": 15043, "ibly": 15044, "信仰": 15045, "▁swe": 15046, "配套": 15047, "▁plays": 15048, "住房": 15049, "antic": 15050, "ams": 15051, "othes": 15052, "精确": 15053, "▁buildings": 15054, "gery": 15055, "▁map": 15056, "车辆": 15057, "聚焦": 15058, "▁rout": 15059, "Similar": 15060, "顺序": 15061, "▁concerns": 15062, "Reg": 15063, "总量": 15064, "研究院": 15065, "▁genetic": 15066, "写作": 15067, "于是": 15068, "生态文明": 15069, "▁alone": 15070, "\"(": 15071, "▁emphas": 15072, "rael": 15073, "繁荣": 15074, "▁contract": 15075, "知识和": 15076, "失去": 15077, "itz": 15078, "概述": 15079, "ademic": 15080, "Gu": 15081, "overs": 15082, "▁assist": 15083, "▁moving": 15084, "▁regions": 15085, "▁identity": 15086, "▁birds": 15087, "▁Ne": 15088, "发送": 15089, "人民的": 15090, "难题": 15091, "▁pow": 15092, "EL": 15093, "iology": 15094, "课堂教学": 15095, "▁Museum": 15096, "ounced": 15097, "▁Pre": 15098, "此时": 15099, "被称为": 15100, "相反": 15101, "营销": 15102, "享有": 15103, "▁Go": 15104, "onds": 15105, "▁illustr": 15106, "▁observed": 15107, "Go": 15108, "Com": 15109, "糖尿病": 15110, "平面": 15111, "gor": 15112, "eech": 15113, "▁anything": 15114, "omy": 15115, "仪器": 15116, "aughter": 15117, "国内外": 15118, "万人": 15119, "▁Mount": 15120, "▁antib": 15121, "▁Cour": 15122, "▁miles": 15123, "旨在": 15124, "》▁": 15125, "动作": 15126, "▁demand": 15127, "管理和": 15128, "▁discover": 15129, "＂,": 15130, "▁expected": 15131, "Both": 15132, "大脑": 15133, "依托": 15134, "以上的": 15135, "TO": 15136, "指南": 15137, "▁decisions": 15138, "▁bodies": 15139, "云计算": 15140, "已有": 15141, "orial": 15142, "潜力": 15143, "做法": 15144, "批评": 15145, "berg": 15146, "赋予": 15147, "刑法": 15148, "▁existing": 15149, "▁promote": 15150, "病变": 15151, "allowing": 15152, "▁web": 15153, "立场": 15154, "your": 15155, "▁principles": 15156, "▁Council": 15157, "有时": 15158, "的多": 15159, "his": 15160, "存款": 15161, "GF": 15162, "▁office": 15163, "小康": 15164, "幸福": 15165, "▁sounds": 15166, "▁changed": 15167, "▁signs": 15168, "美术": 15169, "食物": 15170, "▁helped": 15171, "民生": 15172, "高等教育": 15173, "Before": 15174, "▁planet": 15175, "数据中心": 15176, "患者的": 15177, "▁concepts": 15178, "struct": 15179, "▁eventually": 15180, "▁reasons": 15181, "▁equal": 15182, "▁legal": 15183, "前沿": 15184, "证书": 15185, "▁board": 15186, "科学技术": 15187, "▁guide": 15188, "▁glass": 15189, "的认识": 15190, "中小企业": 15191, "prov": 15192, "官方": 15193, "远程": 15194, "地震": 15195, "视角": 15196, "地區": 15197, "thes": 15198, "▁Republic": 15199, "▁machine": 15200, "▁introduced": 15201, "ka": 15202, "打破": 15203, "▁virt": 15204, "间接": 15205, "一批": 15206, "理由": 15207, "MA": 15208, "计量": 15209, "▁trade": 15210, "astern": 15211, "意思": 15212, "测定": 15213, "rehens": 15214, "ographic": 15215, "失败": 15216, "▁limit": 15217, "初期": 15218, "以便": 15219, "主观": 15220, "颗粒": 15221, "▁Des": 15222, "▁comfort": 15223, "血液": 15224, "引起的": 15225, "▁tour": 15226, "癌症": 15227, "Every": 15228, "取决": 15229, "irgin": 15230, "多样性": 15231, "semb": 15232, "▁Pal": 15233, "III": 15234, "▁records": 15235, "▁Western": 15236, "也要": 15237, "侵犯": 15238, "立足": 15239, "▁gives": 15240, "History": 15241, "腐败": 15242, "▁citiz": 15243, "驾驶": 15244, "exas": 15245, "iance": 15246, "遵守": 15247, "inding": 15248, "中学": 15249, "》《": 15250, "▁mouth": 15251, "与其他": 15252, "产量": 15253, "仅仅": 15254, "价值的": 15255, "排名": 15256, "停止": 15257, "▁Port": 15258, "▁laws": 15259, "专项": 15260, "Read": 15261, "与此": 15262, "▁solutions": 15263, "能否": 15264, "也在": 15265, "inger": 15266, "大于": 15267, "▁colors": 15268, "nam": 15269, "▁eating": 15270, "▁felt": 15271, "器官": 15272, "四川": 15273, "有一个": 15274, "的市场": 15275, "▁turned": 15276, "ygen": 15277, "▁title": 15278, "▁fuel": 15279, "回归": 15280, "通用": 15281, "▁recently": 15282, "本科": 15283, "主义的": 15284, "▁util": 15285, "更是": 15286, "def": 15287, "El": 15288, "可能性": 15289, "实用": 15290, "iant": 15291, "▁experim": 15292, "▁connected": 15293, "作物": 15294, "大国": 15295, "▁corn": 15296, "提供的": 15297, "force": 15298, "▁journey": 15299, "▁pregn": 15300, "医药": 15301, "▁CD": 15302, "▁attract": 15303, "发生了": 15304, "优质": 15305, "法人": 15306, "公益": 15307, "IF": 15308, "asts": 15309, "查询": 15310, "▁dynam": 15311, "▁lived": 15312, "ded": 15313, "取决于": 15314, "前景": 15315, "▁distance": 15316, "特定的": 15317, "rich": 15318, "參考資料": 15319, "enty": 15320, "夏季": 15321, "mod": 15322, "强大": 15323, "变更": 15324, "▁nearly": 15325, "▁radio": 15326, "所有权": 15327, "斯坦": 15328, "uilding": 15329, "阐述": 15330, "不可能": 15331, "ogether": 15332, "lease": 15333, "的环境": 15334, "系数": 15335, "aud": 15336, "▁hundred": 15337, "选举": 15338, "ichael": 15339, "▁UK": 15340, "Em": 15341, "▁risks": 15342, "▁ap": 15343, "▁Her": 15344, "两者": 15345, "▁woman": 15346, "地下": 15347, "unk": 15348, "无效": 15349, "▁beg": 15350, "▁effort": 15351, "练习": 15352, "▁degree": 15353, "indu": 15354, "▁Street": 15355, "过程的": 15356, "地面": 15357, "▁happens": 15358, "▁leave": 15359, "...▁": 15360, "最早": 15361, "▁essay": 15362, "▁Phil": 15363, "▁tim": 15364, "西北": 15365, "取代": 15366, "▁diabetes": 15367, "超越": 15368, "对称": 15369, "联盟": 15370, "年前": 15371, "▁bran": 15372, "▁effectively": 15373, "复制": 15374, "依照": 15375, "▁passed": 15376, "以此": 15377, "石油": 15378, "▁landsc": 15379, "cription": 15380, "电网": 15381, "▁core": 15382, "▁opportunity": 15383, "▁gets": 15384, "灾害": 15385, "▁connection": 15386, "鲜明": 15387, "丰富的": 15388, "▁rise": 15389, "▁providing": 15390, "熟悉": 15391, "▁Cor": 15392, "现金": 15393, "借款": 15394, "了中国": 15395, "爆发": 15396, "进行的": 15397, "▁easier": 15398, "▁told": 15399, "▁watch": 15400, ");▁": 15401, "▁ur": 15402, "their": 15403, "國際": 15404, "女子": 15405, "▁alternative": 15406, "▁recorded": 15407, "正义": 15408, "音樂": 15409, "▁Law": 15410, "局面": 15411, "▁pet": 15412, "▁hair": 15413, "▁cop": 15414, "▁crop": 15415, "返回": 15416, "不够": 15417, "货物": 15418, "▁Japanese": 15419, "▁atmosp": 15420, "asion": 15421, "分享": 15422, "▁winter": 15423, "急性": 15424, "ctors": 15425, "▁root": 15426, "帝国": 15427, "共有": 15428, "国防": 15429, "培养学生": 15430, "参考资料": 15431, "体现在": 15432, "至关": 15433, "消息": 15434, "cript": 15435, "▁standards": 15436, "可用": 15437, "衔接": 15438, "west": 15439, "亚洲": 15440, "抗日": 15441, "zing": 15442, "xiety": 15443, "▁Div": 15444, "辩证": 15445, "▁illness": 15446, "▁operations": 15447, "的发生": 15448, "的中国": 15449, "一带一路": 15450, "基因组": 15451, "积极性": 15452, "iable": 15453, "▁Olymp": 15454, "開始": 15455, "历史上": 15456, "Rep": 15457, "esus": 15458, "].": 15459, "意识形态": 15460, "▁divid": 15461, "▁appoint": 15462, "▁Dem": 15463, "代表大会": 15464, "▁plans": 15465, "icularly": 15466, "text": 15467, "▁artists": 15468, "rese": 15469, "税务": 15470, "债权人": 15471, "▁capacity": 15472, "ognitive": 15473, "EE": 15474, "部队": 15475, "thlet": 15476, "运算": 15477, "▁fully": 15478, "第七": 15479, "主管": 15480, "器件": 15481, "▁effic": 15482, "主持": 15483, "pected": 15484, "aker": 15485, "▁Inter": 15486, "▁France": 15487, "▁Sou": 15488, "nel": 15489, "orage": 15490, "Furthermore": 15491, "武器": 15492, "▁believed": 15493, "无人": 15494, "集群": 15495, "icians": 15496, "ache": 15497, "一方": 15498, "交往": 15499, 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"合法权益": 17244, "研究和": 17245, "▁origin": 17246, "▁anxiety": 17247, "致力": 17248, "年夏季": 17249, "担当": 17250, "ootball": 17251, "▁moist": 17252, "▁conflict": 17253, "▁tal": 17254, "标的": 17255, "Brit": 17256, "带有": 17257, "爆炸": 17258, "▁circum": 17259, "▁industrial": 17260, "▁referred": 17261, "的战略": 17262, "地图": 17263, "权限": 17264, "▁regularly": 17265, "演化": 17266, "电话": 17267, "unct": 17268, "▁Australian": 17269, "▁Mexico": 17270, "Par": 17271, "▁closer": 17272, "▁recommended": 17273, "说的": 17274, "对外开放": 17275, "Ag": 17276, "▁prepared": 17277, "iana": 17278, "stream": 17279, "▁diagnosis": 17280, "▁topic": 17281, "设定": 17282, "负载": 17283, "mas": 17284, "eorg": 17285, "▁continues": 17286, "uses": 17287, "在内的": 17288, "搭建": 17289, "inet": 17290, "或是": 17291, "▁pret": 17292, "待遇": 17293, "的政策": 17294, "▁discussion": 17295, "更大的": 17296, "mic": 17297, "▁flowers": 17298, "债务人": 17299, "▁frequency": 17300, "媒介": 17301, "▁liquid": 17302, "▁operation": 17303, "体制机制": 17304, "肯定": 17305, "增大": 17306, "冠軍": 17307, "▁Town": 17308, "既是": 17309, "的分析": 17310, "▁burn": 17311, "ibilities": 17312, "奠定了": 17313, "情节": 17314, "▁Water": 17315, "▁inform": 17316, "▁hun": 17317, "搜索": 17318, "istant": 17319, "黄河": 17320, "塑造": 17321, "▁radiation": 17322, "表演": 17323, "Ge": 17324, "uan": 17325, "对中国": 17326, "▁generations": 17327, "▁muscles": 17328, "▁Project": 17329, "名为": 17330, "▁manufacture": 17331, "▁vast": 17332, "▁Mag": 17333, "▁guidelines": 17334, "模糊": 17335, "▁follows": 17336, "公约": 17337, "公有": 17338, "动能": 17339, "▁describe": 17340, "▁cars": 17341, "▁Do": 17342, "▁opin": 17343, "▁dro": 17344, "▁Direct": 17345, "▁yellow": 17346, "协助": 17347, "市民": 17348, "▁bank": 17349, "相信": 17350, "Fl": 17351, "bur": 17352, "个别": 17353, "这一点": 17354, "',▁": 17355, "工作中": 17356, "其中的": 17357, "▁AD": 17358, "高端": 17359, "▁Gold": 17360, "arp": 17361, "任意": 17362, "gment": 17363, "▁director": 17364, "曾经": 17365, "信息安全": 17366, "告诉": 17367, "▁Ben": 17368, "▁vegetables": 17369, "asy": 17370, "又称": 17371, "ferent": 17372, "坚持以": 17373, "urance": 17374, "aval": 17375, "的前": 17376, "活跃": 17377, "▁raised": 17378, "▁teams": 17379, "协定": 17380, "ls": 17381, "主教": 17382, "▁experts": 17383, "人生": 17384, "▁storm": 17385, "▁Asia": 17386, "▁indicate": 17387, "标签": 17388, "▁settle": 17389, "诈骗": 17390, "聚集": 17391, "RC": 17392, "VD": 17393, "民国": 17394, "历史的": 17395, "▁barri": 17396, "▁professionals": 17397, "之後": 17398, "期刊": 17399, "獲得": 17400, "▁random": 17401, "ora": 17402, "确实": 17403, "的患者": 17404, "▁clim": 17405, "▁choices": 17406, "▁cognitive": 17407, "▁option": 17408, "▁particles": 17409, "考虑到": 17410, "▁beliefs": 17411, "▁pric": 17412, "ulture": 17413, "氛围": 17414, "就是要": 17415, "科学发展": 17416, "东南": 17417, "isation": 17418, "iration": 17419, "▁nutrition": 17420, "主流": 17421, "利益的": 17422, "▁worry": 17423, "▁accurate": 17424, "erse": 17425, "▁aircraft": 17426, "▁collected": 17427, "▁lies": 17428, "▁periods": 17429, "充分利用": 17430, "Ant": 17431, "用地": 17432, "党的十八大以来": 17433, "遊戲": 17434, "▁treatments": 17435, "cking": 17436, "▁Egypt": 17437, "▁nuclear": 17438, "▁Miss": 17439, "▁sides": 17440, "减排": 17441, "\b\u0006": 17442, "机器学习": 17443, "oseph": 17444, "▁Lab": 17445, "作为一种": 17446, "PE": 17447, "伙伴": 17448, "aks": 17449, "▁sustainable": 17450, "▁HIV": 17451, "压缩": 17452, "不仅仅": 17453, "人身": 17454, "▁reaction": 17455, "研究成果": 17456, "负责人": 17457, "类的": 17458, ":▁“": 17459, "经济发展的": 17460, "icing": 17461, "今后": 17462, "▁roots": 17463, "五个": 17464, "▁impacts": 17465, "什么是": 17466, "▁familiar": 17467, "标识": 17468, "▁Franc": 17469, "▁artist": 17470, "dec": 17471, "during": 17472, "▁dangerous": 17473, "有关的": 17474, "还需要": 17475, "它们的": 17476, "▁Journal": 17477, "金融危机": 17478, "▁responses": 17479, "North": 17480, "▁copy": 17481, "善于": 17482, "河北": 17483, "▁cris": 17484, "lig": 17485, "▁It": 17486, "cher": 17487, "▁Zeal": 17488, "结算": 17489, "引擎": 17490, "▁execut": 17491, "▁Lear": 17492, "▁disp": 17493, "模型的": 17494, "▁Rad": 17495, "pping": 17496, "扩散": 17497, "▁keeping": 17498, "▁faster": 17499, "病例": 17500, "产业结构": 17501, "ita": 17502, "isp": 17503, "Japan": 17504, "overty": 17505, "eless": 17506, "ups": 17507, "订立": 17508, "▁univers": 17509, "理论与": 17510, "ulating": 17511, "dr": 17512, "高职": 17513, "都可以": 17514, "▁centuries": 17515, "▁institutions": 17516, "▁plot": 17517, "▁Catholic": 17518, "▁infections": 17519, "开启": 17520, "▁tumor": 17521, "延长": 17522, "▁suggested": 17523, "▁pip": 17524, "friend": 17525, "ican": 17526, "obe": 17527, "定量": 17528, "工业互联网": 17529, "▁Sing": 17530, "几种": 17531, "被动": 17532, "▁increasingly": 17533, "▁announced": 17534, "算力": 17535, "正如": 17536, "▁noted": 17537, "▁violence": 17538, "▁meat": 17539, "提前": 17540, "所述": 17541, "rence": 17542, "教学的": 17543, "了对": 17544, "▁statement": 17545, "第八": 17546, "…▁": 17547, "▁amazing": 17548, "规章": 17549, "▁efficiency": 17550, "ingly": 17551, "调度": 17552, "▁Academy": 17553, "阿尔": 17554, "▁diversity": 17555, "初级": 17556, "faces": 17557, "zym": 17558, "▁incred": 17559, "first": 17560, "instead": 17561, "变化的": 17562, "就要": 17563, "▁Prov": 17564, "红军": 17565, "ependent": 17566, ")▁-▁": 17567, "日报": 17568, "ette": 17569, "的一项": 17570, "各方": 17571, "很少": 17572, "irds": 17573, "oom": 17574, "LE": 17575, "原料": 17576, "▁prem": 17577, "推理": 17578, "認為": 17579, "▁hearing": 17580, "expected": 17581, "天文": 17582, "夫妻": 17583, "▁Civil": 17584, "▁regional": 17585, "▁taught": 17586, "Christ": 17587, "▁stem": 17588, "时空": 17589, "机理": 17590, "▁vs": 17591, "的性质": 17592, "▁Mem": 17593, "▁neighbor": 17594, "他們": 17595, "建模": 17596, "表述": 17597, "交付": 17598, "同比": 17599, "arliament": 17600, "▁founded": 17601, "作了": 17602, "朋友": 17603, "需求的": 17604, "▁pollution": 17605, "▁creates": 17606, "二者": 17607, "孔子": 17608, "的重要组成部分": 17609, "部长": 17610, "▁operating": 17611, "▁begins": 17612, "▁Records": 17613, "Add": 17614, "▁stars": 17615, "报表": 17616, "▁Emp": 17617, "▁legisl": 17618, "界面": 17619, "▁ecosystem": 17620, "▁Zealand": 17621, "▁aid": 17622, "天气": 17623, "研究者": 17624, "▁mort": 17625, "fit": 17626, "西藏": 17627, "关心": 17628, "起点": 17629, "FC": 17630, "临时": 17631, "▁measured": 17632, "oly": 17633, "▁Britain": 17634, "▁News": 17635, "▁Out": 17636, "家长": 17637, "不动": 17638, "中文": 17639, "enses": 17640, "\",\"": 17641, "已知": 17642, "第三方": 17643, "多元化": 17644, "ses": 17645, "主要的": 17646, "oves": 17647, "疼痛": 17648, "删除": 17649, "家的": 17650, "态势": 17651, "▁mm": 17652, "火箭": 17653, "▁syndrome": 17654, "加剧": 17655, "既要": 17656, "gr": 17657, "读者": 17658, "▁phase": 17659, "受益": 17660, "企业和": 17661, "▁phenomen": 17662, "来实现": 17663, "▁networks": 17664, "智力": 17665, "监察": 17666, "开设": 17667, "▁hour": 17668, "opes": 17669, "周边": 17670, "▁stone": 17671, "HO": 17672, "▁Spec": 17673, "▁Rel": 17674, "▁disapp": 17675, "最小": 17676, "故意": 17677, "发生在": 17678, "晶体": 17679, "▁poly": 17680, "▁fort": 17681, "数值": 17682, "▁valid": 17683, "orph": 17684, "▁motion": 17685, "Who": 17686, "▁significance": 17687, "对抗": 17688, "▁adop": 17689, "▁component": 17690, "创造了": 17691, "网上": 17692, "icious": 17693, "▁revealed": 17694, "▁compos": 17695, "诚信": 17696, "▁row": 17697, "村民": 17698, "既有": 17699, "跨境": 17700, "▁priv": 17701, "长度": 17702, "▁References": 17703, "不久": 17704, "kins": 17705, "不但": 17706, "限度": 17707, "代替": 17708, "线路": 17709, "快速发展": 17710, "▁joint": 17711, "han": 17712, "inder": 17713, "ptions": 17714, "▁compl": 17715, "落后": 17716, "PI": 17717, "▁realized": 17718, "Inter": 17719, "程度的": 17720, "▁chosen": 17721, "将在": 17722, "二次": 17723, "oto": 17724, "anted": 17725, "期望": 17726, "orter": 17727, "上是": 17728, "istan": 17729, "▁double": 17730, "ching": 17731, "aying": 17732, "和应用": 17733, "Example": 17734, "网络的": 17735, "cules": 17736, "建築": 17737, "街道": 17738, "▁professor": 17739, "help": 17740, "▁divided": 17741, "np": 17742, "▁fine": 17743, "数额": 17744, "▁fruits": 17745, "的存在": 17746, "▁defic": 17747, "▁association": 17748, "Such": 17749, "可再生": 17750, "▁depending": 17751, "IG": 17752, "领先": 17753, "落地": 17754, "▁brown": 17755, "年轻": 17756, "颜色": 17757, "高温": 17758, "二氧化碳": 17759, "loc": 17760, "ells": 17761, "：《": 17762, "Fig": 17763, "rastructure": 17764, "习近平新时代中国特色社会主义思想": 17765, "Mean": 17766, "负面": 17767, "已经成为": 17768, "▁clearly": 17769, "退出": 17770, "生涯": 17771, "方式的": 17772, "农业生产": 17773, "▁nerv": 17774, "▁freedom": 17775, "老年": 17776, "▁Test": 17777, "矩阵": 17778, "的现象": 17779, "大致": 17780, "▁Pacific": 17781, "极端": 17782, "▁renew": 17783, "的范围": 17784, "剩余": 17785, "taining": 17786, "变异": 17787, "院士": 17788, "库存": 17789, "▁，": 17790, "rolog": 17791, "ications": 17792, "顾客": 17793, "光伏": 17794, "和实践": 17795, "在不同": 17796, "occ": 17797, "▁vehicle": 17798, "▁occurred": 17799, "▁mal": 17800, "Living": 17801, "▁Library": 17802, "kes": 17803, "▁secure": 17804, "當時": 17805, "简介": 17806, "▁girls": 17807, "▁dogs": 17808, "style": 17809, "指引": 17810, "兴起": 17811, "▁loved": 17812, "缺少": 17813, "▁despite": 17814, "▁files": 17815, "典型的": 17816, "Research": 17817, "在新": 17818, "部门的": 17819, "首要": 17820, "theast": 17821, "广州": 17822, "▁spiritual": 17823, "境内": 17824, "gal": 17825, "▁chemicals": 17826, "的第一": 17827, "管制": 17828, "▁perspectives": 17829, "▁fascinating": 17830, "直观": 17831, "▁enable": 17832, "▁electrical": 17833, "人对": 17834, "在全球": 17835, "意义上": 17836, "意图": 17837, "特异性": 17838, "▁muse": 17839, "韩国": 17840, "子女": 17841, "单个": 17842, "很快": 17843, "▁Division": 17844, "▁replaced": 17845, "▁external": 17846, "尽量": 17847, "respect": 17848, "▁parties": 17849, "外部链接": 17850, "▁reb": 17851, "分支": 17852, "▁starts": 17853, "的所有": 17854, "▁settings": 17855, "發展": 17856, "ested": 17857, "▁Club": 17858, "IA": 17859, "nes": 17860, "开辟": 17861, "▁floor": 17862, "降低了": 17863, "▁knew": 17864, "型的": 17865, "▁dra": 17866, "Up": 17867, "▁extract": 17868, "World": 17869, "▁bright": 17870, "较少": 17871, "体系建设": 17872, "▁calc": 17873, "▁One": 17874, "▁adding": 17875, "▁residents": 17876, "起来的": 17877, "▁Tor": 17878, "化石": 17879, "ictionary": 17880, "联动": 17881, "疗法": 17882, "▁Hill": 17883, "类别": 17884, "▁diss": 17885, "不需要": 17886, "▁Angel": 17887, "深化改革": 17888, "北京市": 17889, "▁opening": 17890, "▁recognize": 17891, "ificial": 17892, "该研究": 17893, "此类": 17894, "之为": 17895, "我们在": 17896, "▁Internet": 17897, "Web": 17898, "缩小": 17899, "Retrieved": 17900, "cycl": 17901, "▁waters": 17902, "oviet": 17903, "法学": 17904, "建立健全": 17905, "教学内容": 17906, "组成的": 17907, "工作者": 17908, "▁practical": 17909, "广阔": 17910, "最重要的": 17911, "▁smart": 17912, "综合征": 17913, "不应": 17914, "的时代": 17915, "▁western": 17916, "百分点": 17917, "eff": 17918, "专用": 17919, "oted": 17920, "民法典": 17921, "和分析": 17922, "iform": 17923, "▁kept": 17924, "bridge": 17925, "还原": 17926, "soft": 17927, "民國": 17928, "Are": 17929, "由於": 17930, "▁attribut": 17931, "▁morning": 17932, "bra": 17933, "▁managed": 17934, "▁Grand": 17935, "ounter": 17936, "▁facilities": 17937, "▁organic": 17938, "▁justice": 17939, "▁craft": 17940, "能耗": 17941, "全社会": 17942, "▁Russian": 17943, "iro": 17944, "▁competition": 17945, "▁Pe": 17946, "▁procedure": 17947, "并对": 17948, "社会主义核心": 17949, "top": 17950, "依赖于": 17951, "▁perspective": 17952, "▁firm": 17953, "分别为": 17954, "▁traditions": 17955, "上市公司": 17956, "半导体": 17957, "▁White": 17958, "季度": 17959, "▁runs": 17960, "▁neur": 17961, "英雄": 17962, "ushing": 17963, "到底": 17964, "广泛的": 17965, "跨越": 17966, "▁discovery": 17967, "电极": 17968, "产业的": 17969, "University": 17970, "能在": 17971, "▁sen": 17972, "▁era": 17973, "▁represented": 17974, "TA": 17975, "▁Rock": 17976, "▁Texas": 17977, "英文": 17978, "云南": 17979, "keep": 17980, "变换": 17981, "常态": 17982, "▁index": 17983, "▁Thomas": 17984, "进化": 17985, "▁nutrients": 17986, "\u0007\u0006": 17987, "▁purposes": 17988, "hav": 17989, "生育": 17990, "▁She": 17991, "▁constant": 17992, "奧林匹克": 17993, "Get": 17994, "学生的学习": 17995, "Rel": 17996, "那样": 17997, "nut": 17998, "▁programming": 17999, "▁forests": 18000, "eling": 18001, "omin": 18002, "附加": 18003, "阻碍": 18004, "长远": 18005, "着眼": 18006, "这次": 18007, "▁Space": 18008, "▁Sl": 18009, "DS": 18010, "▁advantage": 18011, "零售": 18012, "▁Commission": 18013, "一轮": 18014, "结构和": 18015, "abil": 18016, "受害": 18017, "▁highlight": 18018, "希腊": 18019, "▁exchange": 18020, "规定了": 18021, "明了": 18022, "▁collaboration": 18023, "▁stages": 18024, "创新的": 18025, "▁charge": 18026, "光谱": 18027, "可视": 18028, "Bel": 18029, "垂直": 18030, "▁computers": 18031, "▁walls": 18032, "eterm": 18033, "▁phone": 18034, "abits": 18035, "▁spend": 18036, "放弃": 18037, "▁Sea": 18038, "▁bud": 18039, "对立": 18040, "阶段的": 18041, "几年": 18042, "▁fab": 18043, "行政区": 18044, "结合起来": 18045, "▁toxic": 18046, "▁interactions": 18047, "▁intended": 18048, "接入": 18049, "广播": 18050, "劳务": 18051, "▁Management": 18052, "复习": 18053, "▁Carolina": 18054, "风险的": 18055, "明确了": 18056, "▁rein": 18057, "▁affects": 18058, "▁snow": 18059, "itt": 18060, "inity": 18061, "至关重要": 18062, "don": 18063, "▁partners": 18064, "▁Award": 18065, "方法和": 18066, "社會": 18067, "▁talking": 18068, "▁responsibility": 18069, "为主要": 18070, "中山": 18071, "资产阶级": 18072, "LS": 18073, "二年": 18074, "Last": 18075, "▁correspond": 18076, "演出": 18077, "▁layers": 18078, "人类社会": 18079, "▁goods": 18080, "run": 18081, "肠道": 18082, "itness": 18083, "收购": 18084, "▁Social": 18085, ",▁(": 18086, "的外": 18087, "对学生": 18088, "级别": 18089, "▁inflammation": 18090, "▁Games": 18091, "CN": 18092, "ima": 18093, "▁Cast": 18094, "▁join": 18095, "扎实": 18096, "小于": 18097, "▁discrim": 18098, "▁interested": 18099, "▁sudden": 18100, "包容": 18101, "▁behaviors": 18102, "征程": 18103, "▁Middle": 18104, "简化": 18105, "剂量": 18106, "▁seek": 18107, "incre": 18108, "机构的": 18109, "表现在": 18110, "对待": 18111, "vol": 18112, "ads": 18113, "▁optim": 18114, "Where": 18115, "现有的": 18116, "▁mountain": 18117, "DR": 18118, "plicated": 18119, "▁Ter": 18120, "rat": 18121, "ulting": 18122, "彼此": 18123, "嵌入": 18124, "▁resil": 18125, "高达": 18126, "igan": 18127, "总是": 18128, "▁mainly": 18129, "Yes": 18130, "▁dream": 18131, "igital": 18132, "▁fluid": 18133, "启发": 18134, "个性化": 18135, "▁mer": 18136, "xts": 18137, "效能": 18138, "▁god": 18139, "Cre": 18140, "ventions": 18141, "人民代表大会": 18142, "▁Times": 18143, "的理解": 18144, "取向": 18145, "当中": 18146, "real": 18147, "uke": 18148, "▁Smith": 18149, "▁supported": 18150, "全民": 18151, "质量和": 18152, "创新能力": 18153, "种子": 18154, "电解": 18155, "tenance": 18156, "下面": 18157, "▁mostly": 18158, "(▁": 18159, "并通过": 18160, "Less": 18161, "uing": 18162, "▁monitoring": 18163, "▁solve": 18164, "了一系列": 18165, "▁III": 18166, "时刻": 18167, "的定义": 18168, "▁maximum": 18169, "走出": 18170, "▁philosoph": 18171, "▁medium": 18172, "管理体系": 18173, "习近平同志": 18174, "QL": 18175, "或其他": 18176, "擔任": 18177, "▁generate": 18178, "meaning": 18179, "汉语": 18180, "检索": 18181, "Alex": 18182, "esis": 18183, "▁injuries": 18184, "梳理": 18185, "困境": 18186, "建立在": 18187, "▁showing": 18188, "▁Sim": 18189, "▁Sk": 18190, "▁salt": 18191, "▁designs": 18192, "Mc": 18193, "中断": 18194, "▁Pan": 18195, "▁relax": 18196, "▁To": 18197, "David": 18198, "▁Arab": 18199, "reatment": 18200, "▁Parliament": 18201, "▁shel": 18202, "RT": 18203, "至于": 18204, "▁victim": 18205, "▁Book": 18206, "▁ax": 18207, "又是": 18208, "定律": 18209, "▁unexpected": 18210, "ady": 18211, "FS": 18212, "amental": 18213, "Mr": 18214, "claim": 18215, "住宅": 18216, "▁facts": 18217, "▁truth": 18218, "▁suitable": 18219, "iat": 18220, "ao": 18221, "▁flat": 18222, "第二个": 18223, "未成年人": 18224, "脉冲": 18225, "Austral": 18226, "▁defe": 18227, "病的": 18228, "▁appointed": 18229, "学说": 18230, "离婚": 18231, "常委会": 18232, "舞蹈": 18233, "耕地": 18234, "个百分点": 18235, "吸附": 18236, "的位置": 18237, "aturally": 18238, "▁harmful": 18239, "awa": 18240, "▁shapes": 18241, "Among": 18242, "Current": 18243, "创设": 18244, "率的": 18245, "▁recon": 18246, "解决问题": 18247, "▁chain": 18248, "巴黎": 18249, "建筑物": 18250, "設計": 18251, "▁Foundation": 18252, "▁recognized": 18253, "▁seed": 18254, "redu": 18255, "▁unknown": 18256, "imagine": 18257, "VID": 18258, "诞生": 18259, "clusive": 18260, "▁couldn": 18261, "超声": 18262, "转型升级": 18263, "▁ourselves": 18264, "Aut": 18265, "omer": 18266, 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18330, "发现了": 18331, "▁ideal": 18332, ":▁(": 18333, "限于": 18334, "▁originally": 18335, "▁wait": 18336, "usually": 18337, "▁Italian": 18338, "製作": 18339, "▁Di": 18340, "以外": 18341, "回到": 18342, "德育": 18343, "rors": 18344, "其在": 18345, "▁frag": 18346, "优惠": 18347, "pet": 18348, "oir": 18349, "▁Engine": 18350, "尺度": 18351, "ifies": 18352, "▁causing": 18353, "本站": 18354, "asty": 18355, "▁reduction": 18356, "aming": 18357, "报酬": 18358, "舆论": 18359, "离不开": 18360, "时间的": 18361, "双重": 18362, "感到": 18363, "东方": 18364, "持有": 18365, "▁ran": 18366, "清算": 18367, "思想的": 18368, "中的应用": 18369, "不存在": 18370, "heim": 18371, "onomy": 18372, "参见": 18373, "班牙": 18374, "西班牙": 18375, "ISBN": 18376, "从严": 18377, "不仅是": 18378, "▁hero": 18379, "浪费": 18380, "自然科学": 18381, "▁Wild": 18382, "▁lots": 18383, "▁truly": 18384, "处分": 18385, "激烈": 18386, "▁abilities": 18387, "rate": 18388, "專輯": 18389, "Cent": 18390, "▁serves": 18391, "df": 18392, "▁satis": 18393, "▁seat": 18394, "!\"": 18395, "gely": 18396, "细化": 18397, "纠正": 18398, "▁sensitive": 18399, "延迟": 18400, "前者": 18401, "图书馆": 18402, "▁coal": 18403, "▁wet": 18404, "▁obs": 18405, "▁aimed": 18406, "wa": 18407, "Bar": 18408, "▁nor": 18409, "▁score": 18410, "rogen": 18411, "▁vehicles": 18412, "经济体": 18413, "sex": 18414, "▁signed": 18415, "幅度": 18416, "OL": 18417, "▁extreme": 18418, "▁forced": 18419, "奧林匹克運動會": 18420, "▁Mad": 18421, "fig": 18422, "estion": 18423, "▁concentration": 18424, "fish": 18425, "月球": 18426, "▁ban": 18427, "▁neut": 18428, "▁Commit": 18429, "▁critic": 18430, "▁Columb": 18431, "罗马": 18432, "开拓": 18433, "最高人民法院": 18434, "non": 18435, "基准": 18436, "▁pil": 18437, "地点": 18438, "azz": 18439, "enny": 18440, "▁perman": 18441, "\"，": 18442, "lines": 18443, "水利": 18444, "各方面": 18445, "陷入": 18446, "中介": 18447, "▁density": 18448, "▁drink": 18449, "会导致": 18450, "便于": 18451, "命运共同体": 18452, "Time": 18453, "▁tested": 18454, "aph": 18455, "凸显": 18456, "和经济": 18457, "不平衡": 18458, "高水平": 18459, "的实践": 18460, "可行": 18461, "燃烧": 18462, "▁ST": 18463, "ione": 18464, "apes": 18465, "James": 18466, "水的": 18467, "▁catch": 18468, "▁tips": 18469, "都能": 18470, "▁route": 18471, "▁Medicine": 18472, "争取": 18473, "▁marked": 18474, "GA": 18475, "煤炭": 18476, "史上": 18477, "▁satell": 18478, "▁trial": 18479, "办公室": 18480, "安徽": 18481, "cou": 18482, "▁Robert": 18483, "供给侧": 18484, "CM": 18485, "▁Don": 18486, "▁vitamin": 18487, "HT": 18488, "一名": 18489, "asive": 18490, "ultane": 18491, "较多": 18492, "此次": 18493, "▁stronger": 18494, "▁emergency": 18495, "要把": 18496, "这也是": 18497, "het": 18498, "sem": 18499, "锻炼": 18500, "▁ign": 18501, "lers": 18502, "介绍了": 18503, "▁tun": 18504, "发展战略": 18505, "ansion": 18506, "stood": 18507, "液体": 18508, "ublished": 18509, "▁Bas": 18510, "lets": 18511, "理事": 18512, "收缩": 18513, "制度和": 18514, "相适应": 18515, "耦合": 18516, "HS": 18517, "—▁▁—▁▁": 18518, "estyle": 18519, "▁Government": 18520, "营业": 18521, "申报": 18522, "在全国": 18523, "利亚": 18524, "等多种": 18525, "▁Tre": 18526, "▁depth": 18527, "▁alongside": 18528, "Ref": 18529, "率先": 18530, "要求的": 18531, "▁obtained": 18532, "▁km": 18533, "▁mixed": 18534, "源于": 18535, "聚合": 18536, "情景": 18537, "定价": 18538, "▁writers": 18539, "法和": 18540, "▁Ste": 18541, "pass": 18542, "新生": 18543, "这两个": 18544, "初中": 18545, "▁Tri": 18546, "研究表明": 18547, "元件": 18548, "的实际": 18549, "电信": 18550, "▁wel": 18551, "▁oral": 18552, "▁improving": 18553, "▁newsp": 18554, "实现中华民族伟大复兴": 18555, "▁listed": 18556, "▁seeking": 18557, "▁Valley": 18558, "▁remaining": 18559, "个人的": 18560, "可再生能源": 18561, "▁whom": 18562, "▁tradition": 18563, "维修": 18564, "ih": 18565, "安全的": 18566, "▁underlying": 18567, "▁except": 18568, "招生": 18569, "▁Dan": 18570, "▁locations": 18571, "▁Islam": 18572, "▁interaction": 18573, "ori": 18574, "Set": 18575, "无限": 18576, "stein": 18577, "▁Read": 18578, "▁documents": 18579, "▁gar": 18580, "▁suc": 18581, "▁horse": 18582, "▁writer": 18583, "students": 18584, "▁Land": 18585, "全面建成": 18586, "极为": 18587, "组织和": 18588, "雷达": 18589, "准入": 18590, "▁musical": 18591, "West": 18592, "stances": 18593, "光纤": 18594, "Data": 18595, "▁cards": 18596, "▁controlled": 18597, "eries": 18598, "院长": 18599, "智慧城市": 18600, "▁daughter": 18601, "▁resulting": 18602, "新一代": 18603, "▁saying": 18604, "▁||": 18605, "。(": 18606, "▁signals": 18607, "erving": 18608, "▁walking": 18609, "警察": 18610, "▁haz": 18611, "▁platforms": 18612, "ohnson": 18613, "ture": 18614, "Mark": 18615, "行政管理": 18616, "揭示了": 18617, "▁ren": 18618, "▁extended": 18619, "▁demonstrate": 18620, "充电": 18621, "沉积": 18622, "电脑": 18623, "▁vaccine": 18624, "▁foundation": 18625, "行业的": 18626, "▁Del": 18627, "main": 18628, "是为了": 18629, "▁Technology": 18630, "▁ple": 18631, "▁substant": 18632, "▁scri": 18633, "jo": 18634, "▁accomp": 18635, "▁diam": 18636, "过于": 18637, "La": 18638, "党的十九": 18639, "lishing": 18640, "同時": 18641, "治党": 18642, "数字技术": 18643, "更有": 18644, "舞台": 18645, "▁doll": 18646, "▁Sy": 18647, "immy": 18648, "ellect": 18649, "▁Camp": 18650, "▁introduction": 18651, "thetic": 18652, "意愿": 18653, "站在": 18654, "▁machines": 18655, "▁Power": 18656, "Mem": 18657, "▁Henry": 18658, "▁exercises": 18659, "本土": 18660, "▁Organ": 18661, "▁Elect": 18662, "经营者": 18663, "raining": 18664, "遭受": 18665, "演唱": 18666, "校长": 18667, "深远": 18668, "▁Paris": 18669, "▁rapidly": 18670, "司令": 18671, "▁concern": 18672, "earing": 18673, "一流": 18674, "bar": 18675, "eli": 18676, "美丽": 18677, "EF": 18678, "Early": 18679, "中期": 18680, "▁Ireland": 18681, "自治区": 18682, "▁texts": 18683, "▁AI": 18684, "social": 18685, "相对于": 18686, "▁Charles": 18687, "▁mac": 18688, "creating": 18689, "变迁": 18690, "▁Stand": 18691, "▁guitar": 18692, "outs": 18693, "本章": 18694, "▁Frank": 18695, "▁decide": 18696, "DL": 18697, "延续": 18698, "血糖": 18699, "事业单位": 18700, "意义的": 18701, "umin": 18702, "cal": 18703, "▁drinking": 18704, "▁fiction": 18705, "为止": 18706, "hent": 18707, "▁chart": 18708, "▁Victor": 18709, "▁arms": 18710, "▁edge": 18711, "ompl": 18712, "▁ancest": 18713, "也将": 18714, "明确的": 18715, "率为": 18716, "纲要": 18717, "▁houses": 18718, "学生对": 18719, "盈利": 18720, "▁od": 18721, "研讨": 18722, "自然资源": 18723, "社会主义市场经济": 18724, "iological": 18725, "stitution": 18726, "▁bridge": 18727, "▁audience": 18728, "恶性": 18729, "Those": 18730, "上有": 18731, "加坡": 18732, "▁swim": 18733, "从中": 18734, "▁handle": 18735, "Consider": 18736, "ractice": 18737, "▁finished": 18738, "ipping": 18739, "▁supporting": 18740, "▁Def": 18741, "总之": 18742, "离开": 18743, "ilation": 18744, "承载": 18745, "实务": 18746, "回顾": 18747, "具有重要": 18748, "▁acts": 18749, "▁noticed": 18750, "word": 18751, "Sam": 18752, "▁Ev": 18753, "thus": 18754, "▁typical": 18755, "educ": 18756, "umes": 18757, "设计和": 18758, "▁fle": 18759, "▁biological": 18760, "▁Dec": 18761, "▁plate": 18762, "▁communicate": 18763, "▁sort": 18764, "农户": 18765, "小康社会": 18766, "硕士": 18767, "腺癌": 18768, "Water": 18769, "的效果": 18770, "比特": 18771, "try": 18772, "▁experiments": 18773, "▁tells": 18774, "的本质": 18775, "的空间": 18776, "列入": 18777, "▁hypothes": 18778, "▁consistent": 18779, "的意义": 18780, "参照": 18781, "▁leadership": 18782, "原本": 18783, "▁decrease": 18784, "封闭": 18785, "欢迎": 18786, "▁portion": 18787, "▁+": 18788, "转变为": 18789, "▁attacks": 18790, "rus": 18791, "before": 18792, "hered": 18793, "江西": 18794, "▁capture": 18795, "▁messages": 18796, "▁array": 18797, "尽快": 18798, "彰显": 18799, "British": 18800, "用水": 18801, "節目": 18802, "▁Scot": 18803, "污水": 18804, "阿拉": 18805, "▁instruments": 18806, "▁flight": 18807, "过错": 18808, "Form": 18809, "%)▁": 18810, "uous": 18811, "生命周期": 18812, "▁Form": 18813, "mes": 18814, "Ben": 18815, "oting": 18816, "▁Fin": 18817, "起义": 18818, "消化": 18819, "高原": 18820, "无关": 18821, "選舉": 18822, "iano": 18823, "▁visible": 18824, "Histor": 18825, "▁inspired": 18826, "▁doub": 18827, "呈现出": 18828, "▁herit": 18829, ")▁▁": 18830, "▁conclud": 18831, "▁nations": 18832, "ossible": 18833, "onia": 18834, "追究": 18835, "▁lose": 18836, "指向": 18837, "大批": 18838, "书中": 18839, "消灭": 18840, "▁narratives": 18841, "应用的": 18842, "中小学": 18843, "▁younger": 18844, "data": 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"势力": 19550, "和创新": 19551, "教师在": 19552, "▁acknow": 19553, "isk": 19554, "▁shot": 19555, "▁appreciate": 19556, "改变了": 19557, "▁investment": 19558, "▁drawing": 19559, "初心": 19560, "会有": 19561, "做了": 19562, "encing": 19563, "▁covers": 19564, "kl": 19565, "▁desired": 19566, "书法": 19567, "inning": 19568, "月份": 19569, "Click": 19570, "▁sufficient": 19571, "▁fewer": 19572, "▁vibr": 19573, "otype": 19574, "新华": 19575, "摘要": 19576, "第三人": 19577, "▁revolution": 19578, "▁enem": 19579, "forming": 19580, "▁Work": 19581, "增值税": 19582, "▁cutting": 19583, "asters": 19584, "Open": 19585, "在一": 19586, "▁consult": 19587, "▁sector": 19588, "要以": 19589, "与管理": 19590, "的网络": 19591, "▁feedback": 19592, "xford": 19593, "▁seemed": 19594, "▁serving": 19595, "color": 19596, "志愿": 19597, "抗战": 19598, "▁trials": 19599, "▁confir": 19600, "Examples": 19601, "▁jump": 19602, "▁Jud": 19603, "阶层": 19604, "君主": 19605, "▁circumstances": 19606, "▁intake": 19607, "▁stored": 19608, "va": 19609, "isters": 19610, "所需的": 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19674, "已被": 19675, "对企业": 19676, "March": 19677, "▁survive": 19678, "▁raise": 19679, "影响的": 19680, "olitical": 19681, "achers": 19682, "有望": 19683, "▁explan": 19684, "鐵路": 19685, "ali": 19686, "实证": 19687, "极其": 19688, "Resear": 19689, "▁evaluate": 19690, "侦查": 19691, "过剩": 19692, "著作权": 19693, "VI": 19694, "▁coff": 19695, "高等学校": 19696, "小行星": 19697, "▁latest": 19698, "优良": 19699, "祖国": 19700, "运动的": 19701, "▁suffering": 19702, "▁teen": 19703, "辅导": 19704, "紧张": 19705, "管理制度": 19706, "▁photos": 19707, "映射": 19708, "▁ven": 19709, "▁transportation": 19710, "\"▁|▁": 19711, "牺牲": 19712, "摩擦": 19713, "rons": 19714, "▁eastern": 19715, "omic": 19716, "▁claims": 19717, "超级": 19718, "▁regul": 19719, "▁ble": 19720, "▁migr": 19721, "▁brings": 19722, "稱為": 19723, "提倡": 19724, "▁prevention": 19725, "▁ended": 19726, "重构": 19727, "ibl": 19728, "分组": 19729, "▁dys": 19730, "Mat": 19731, "uis": 19732, "较小": 19733, "▁mamm": 19734, "東京": 19735, "nsylvania": 19736, "管理系统": 19737, "▁Just": 19738, "▁ends": 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"▁reject": 21714, "想要": 21715, "words": 21716, "sis": 21717, "gian": 21718, "微信": 21719, "的传统": 21720, "均为": 21721, "▁Bet": 21722, "rolled": 21723, "求解": 21724, "▁meth": 21725, "消极": 21726, "积极的": 21727, "”、": 21728, "▁request": 21729, "empl": 21730, "tre": 21731, "pot": 21732, "時代": 21733, "BL": 21734, "俄国": 21735, "英格": 21736, "進入": 21737, "国企": 21738, "教师要": 21739, "发放": 21740, "创新发展": 21741, "▁examining": 21742, "▁surprise": 21743, "立方": 21744, "books": 21745, "极大的": 21746, "绝大多数": 21747, "新冠肺炎": 21748, "休闲": 21749, "make": 21750, "中華民國": 21751, "▁organs": 21752, "▁pra": 21753, "Maybe": 21754, "低温": 21755, "系统和": 21756, "发扬": 21757, "的实施": 21758, "▁constitu": 21759, "plement": 21760, "干涉": 21761, "▁islands": 21762, "▁bear": 21763, "family": 21764, "Music": 21765, "Mac": 21766, "▁angle": 21767, "▁Services": 21768, "adel": 21769, "▁basket": 21770, "▁ethnic": 21771, "agram": 21772, "▁allowing": 21773, "最終": 21774, "冬季": 21775, "▁sculpt": 21776, "确定了": 21777, "较好": 21778, "cyclop": 21779, "▁conversations": 21780, "政治局": 21781, "▁Dav": 21782, "枢纽": 21783, "▁dynamic": 21784, "單曲": 21785, "alling": 21786, "▁bat": 21787, "ometry": 21788, "International": 21789, "构成了": 21790, "lik": 21791, "准确性": 21792, "拓扑": 21793, "agen": 21794, "更快": 21795, "ega": 21796, "查看": 21797, "NT": 21798, "▁Oh": 21799, "战国": 21800, "========": 21801, "分数": 21802, "ongs": 21803, "▁reliable": 21804, "▁decade": 21805, "内蒙古": 21806, "▁satellite": 21807, "县级": 21808, "转基因": 21809, "或者其他": 21810, "保险公司": 21811, "Scientists": 21812, "▁returns": 21813, "▁facilitate": 21814, "▁Hot": 21815, "不知": 21816, "airy": 21817, "意外": 21818, "遭遇": 21819, "aylor": 21820, "▁Queen": 21821, "EV": 21822, "诉求": 21823, "以下几": 21824, "社会治理": 21825, "增殖": 21826, "▁preparation": 21827, "Prov": 21828, "▁contributed": 21829, "anic": 21830, "废弃": 21831, "救援": 21832, "高血压": 21833, "在这里": 21834, "机场": 21835, "itory": 21836, "▁Att": 21837, "▁fing": 21838, "hemistry": 21839, "alleng": 21840, "应用场景": 21841, "近似": 21842, "▁contributions": 21843, "neys": 21844, "▁participation": 21845, "▁knowing": 21846, "pes": 21847, "aya": 21848, "清偿": 21849, "和市场": 21850, ",▁▁": 21851, "phib": 21852, "演员": 21853, "的控制": 21854, "▁bron": 21855, "yes": 21856, ")\"": 21857, "分区": 21858, "列车": 21859, "党派": 21860, ":＂": 21861, "为企业": 21862, "rier": 21863, "起了": 21864, "来进行": 21865, "▁fro": 21866, "▁sentences": 21867, "▁principle": 21868, "▁turb": 21869, "bell": 21870, "▁officer": 21871, "上面": 21872, "▁conflicts": 21873, "▁frequent": 21874, "纲领": 21875, "结构性改革": 21876, "必须坚持": 21877, "人力资本": 21878, "思想和": 21879, "发展中": 21880, "operation": 21881, "网格": 21882, "▁persons": 21883, "UK": 21884, "名单": 21885, "科普": 21886, "内核": 21887, "flamm": 21888, "确立了": 21889, "期徒刑": 21890, "▁dent": 21891, "占领": 21892, "▁Sund": 21893, "▁Martin": 21894, "美洲": 21895, "telling": 21896, "候选": 21897, "间隔": 21898, "和国家": 21899, "▁difficulties": 21900, "take": 21901, "还应": 21902, "敌人": 21903, "全球经济": 21904, "三个方面": 21905, "▁preserve": 21906, "alm": 21907, "▁desc": 21908, "▁disrupt": 21909, "合伙": 21910, "性别": 21911, "aret": 21912, "▁districts": 21913, "具有一定的": 21914, "战线": 21915, "不仅要": 21916, "检察机关": 21917, "ioxide": 21918, "▁troops": 21919, "一致性": 21920, "编译": 21921, "prot": 21922, "▁wheel": 21923, "▁objective": 21924, "aterials": 21925, "▁concrete": 21926, "关键技术": 21927, "各部门": 21928, "▁intense": 21929, "▁absol": 21930, "▁considering": 21931, "▁moisture": 21932, "▁quiet": 21933, "▁tap": 21934, "交易所": 21935, "数据处理": 21936, "Rad": 21937, "▁learners": 21938, "以外的": 21939, "▁strain": 21940, "▁▁▁▁▁▁▁": 21941, "break": 21942, "▁Family": 21943, "▁stuff": 21944, "三次": 21945, "机制的": 21946, "黑色": 21947, "▁experimental": 21948, "osion": 21949, "IDS": 21950, "全面从严": 21951, "夯实": 21952, "拉动": 21953, "问题和": 21954, "应该是": 21955, "yan": 21956, "▁Card": 21957, "▁strengthen": 21958, "划分为": 21959, "ila": 21960, "▁insurance": 21961, "▁concerned": 21962, "▁issued": 21963, "learning": 21964, "完善的": 21965, "核心素养": 21966, "为人民": 21967, "并不能": 21968, "冠状": 21969, "▁Secret": 21970, "为实现": 21971, "地说": 21972, "Three": 21973, "common": 21974, "▁combat": 21975, "French": 21976, "▁adm": 21977, "disambiguation": 21978, "ping": 21979, "印发": 21980, "合同法": 21981, "研究团队": 21982, "▁shut": 21983, "▁Championship": 21984, "ctu": 21985, "宿主": 21986, "▁Viet": 21987, "▁classic": 21988, "▁pursu": 21989, "数据和": 21990, "其主要": 21991, "▁fashion": 21992, "▁greatly": 21993, "财务管理": 21994, "授课": 21995, "恶化": 21996, "辽宁": 21997, "▁correctly": 21998, "广泛应用": 21999, "▁encouraged": 22000, "▁consumers": 22001, "所属": 22002, "失衡": 22003, "环境污染": 22004, "季节": 22005, "涌现": 22006, "二元": 22007, "▁Province": 22008, "Year": 22009, "RP": 22010, "actions": 22011, "监护": 22012, "FF": 22013, "▁collective": 22014, "▁kilomet": 22015, "▁males": 22016, "▁operated": 22017, "inch": 22018, "▁Ol": 22019, "在国家": 22020, "但不": 22021, "▁neurons": 22022, "osqu": 22023, "ragon": 22024, "▁greenhouse": 22025, "▁robot": 22026, "下行": 22027, "共生": 22028, "当局": 22029, "▁Rom": 22030, "\")": 22031, "Mass": 22032, "▁investigation": 22033, "burgh": 22034, "习近平总书记指出": 22035, "思潮": 22036, "会议上": 22037, "究竟": 22038, "以习近平同志为核心的党中央": 22039, "技术的发展": 22040, "despite": 22041, "SQL": 22042, "▁Guide": 22043, "陈述": 22044, "Br": 22045, "▁coach": 22046, "step": 22047, "opher": 22048, "这种方法": 22049, "胚胎": 22050, "量和": 22051, "think": 22052, "etime": 22053, "ypically": 22054, "▁solving": 22055, "集團": 22056, "在实践中": 22057, "密切相关": 22058, "采样": 22059, "▁Liter": 22060, "小麦": 22061, "▁therapeutic": 22062, "▁Writ": 22063, "vin": 22064, "▁researc": 22065, "▁owner": 22066, "读书": 22067, "关键词": 22068, "ymb": 22069, "▁unable": 22070, "bb": 22071, "▁emerging": 22072, "language": 22073, "大局": 22074, "会对": 22075, "的地": 22076, "▁ring": 22077, "也被": 22078, "yet": 22079, "▁nervous": 22080, "▁seasons": 22081, "▁faces": 22082, "提问": 22083, "▁manif": 22084, "▁ampl": 22085, "▁threatened": 22086, "alter": 22087, "case": 22088, "的表现": 22089, "申请人": 22090, "美联储": 22091, "当代中国": 22092, "▁integrated": 22093, "戏剧": 22094, "Leg": 22095, "▁accommod": 22096, "▁Los": 22097, "▁transp": 22098, "▁effectiveness": 22099, "用户的": 22100, "▁coastal": 22101, "▁identifying": 22102, "April": 22103, "数控": 22104, "Sarah": 22105, "而成": 22106, "▁finger": 22107, "▁hel": 22108, "ushed": 22109, "emet": 22110, "▁lens": 22111, "都不": 22112, "归属": 22113, "的有": 22114, "Develop": 22115, "▁exciting": 22116, "重大的": 22117, "视网膜": 22118, "学分": 22119, "语法": 22120, "bean": 22121, "在本": 22122, "Peter": 22123, "藝術": 22124, "▁traits": 22125, "inction": 22126, "erus": 22127, "自学": 22128, "這些": 22129, "单位的": 22130, "▁prove": 22131, "Windows": 22132, "▁giant": 22133, "Inf": 22134, "违背": 22135, "▁obesity": 22136, "是非常": 22137, "acebook": 22138, "▁abandon": 22139, "▁Er": 22140, "%,▁": 22141, "歐洲": 22142, "▁plane": 22143, "原型": 22144, "________": 22145, "父亲": 22146, "等内容": 22147, "▁discussing": 22148, "做的": 22149, "▁providers": 22150, "▁Age": 22151, "的计算": 22152, "▁complexity": 22153, "也需要": 22154, "一号": 22155, "发展趋势": 22156, "▁nic": 22157, "▁Young": 22158, "背后": 22159, "第一百": 22160, "考验": 22161, "保护和": 22162, "应力": 22163, "▁flower": 22164, "▁Blue": 22165, "汉字": 22166, "▁gram": 22167, "▁safely": 22168, "▁targets": 22169, "电站": 22170, "hard": 22171, "▁gap": 22172, "▁gent": 22173, "▁incorporate": 22174, "▁incorporating": 22175, "师资": 22176, "共产党的": 22177, "畅通": 22178, "意识到": 22179, "流失": 22180, "纵向": 22181, "真实性": 22182, "经济学家": 22183, "字符": 22184, "上了": 22185, "▁Korea": 22186, "▁empathy": 22187, "發生": 22188, "支援": 22189, "领导的": 22190, "政府部门": 22191, "出土": 22192, "大地": 22193, "的处理": 22194, "首席": 22195, "ussion": 22196, "▁adds": 22197, "▁Francisco": 22198, "在教学": 22199, "战后": 22200, "▁possibility": 22201, "▁turning": 22202, "薄膜": 22203, "▁Gr": 22204, "▁intest": 22205, "▁translation": 22206, "▁initiatives": 22207, "▁protective": 22208, "付款": 22209, "▁Gree": 22210, "▁membrane": 22211, "something": 22212, "贯通": 22213, "就没有": 22214, "three": 22215, "的重": 22216, "公告": 22217, "▁variations": 22218, "▁owners": 22219, "iva": 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25986, "作用下": 25987, "清洁能源": 25988, "助推": 25989, "湾区": 25990, "▁maxim": 25991, "alks": 25992, "准确的": 25993, "▁sampl": 25994, "主演": 25995, "▁Li": 25996, "▁fires": 25997, "▁pointed": 25998, "的高度": 25999, "把它": 26000, "预先": 26001, "造就": 26002, "描写": 26003, "Career": 26004, "html": 26005, "site": 26006, "seud": 26007, "▁tone": 26008, "gov": 26009, "▁residential": 26010, "▁apps": 26011, "▁urine": 26012, "公证": 26013, "嘉靖": 26014, "Ess": 26015, "不一致": 26016, "偏好": 26017, "这条": 26018, "奇迹": 26019, "亦有": 26020, "▁Uk": 26021, "位一体": 26022, "備註": 26023, "Others": 26024, "BV": 26025, "prim": 26026, "是用": 26027, "转发": 26028, "骨骼": 26029, "mil": 26030, "rums": 26031, "onial": 26032, "▁summary": 26033, "▁loud": 26034, "▁managers": 26035, "▁▁《": 26036, "思维能力": 26037, "可以使": 26038, "为由": 26039, "而来": 26040, "牵引": 26041, "▁THE": 26042, "一般的": 26043, "指针": 26044, "华北": 26045, "▁Lincoln": 26046, "▁invasion": 26047, "▁Fund": 26048, "cler": 26049, "▁Building": 26050, "全國": 26051, "西部地区": 26052, "要通过": 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26824, "▁pel": 26825, "▁committee": 26826, "▁locally": 26827, "▁utilize": 26828, "person": 26829, "ixed": 26830, "▁Corps": 26831, "小板": 26832, "或者说": 26833, "▁Governor": 26834, "同时也是": 26835, "概念的": 26836, "▁reflected": 26837, "▁prohib": 26838, "▁advancements": 26839, "的分类": 26840, "Log": 26841, "▁sch": 26842, "呼吸道": 26843, "细分": 26844, "可作为": 26845, "解决的问题": 26846, "特权": 26847, "▁Mountains": 26848, "起始": 26849, "▁Set": 26850, "▁appearances": 26851, "▁enjoyed": 26852, "▁traveling": 26853, "stru": 26854, "▁ion": 26855, "Old": 26856, "▁Argent": 26857, "Professor": 26858, "▁proposal": 26859, "的先": 26860, "多方": 26861, "残疾人": 26862, "不知道": 26863, "▁femin": 26864, "▁LGBT": 26865, "▁contexts": 26866, "ilst": 26867, "▁Daniel": 26868, "▁distinctive": 26869, "▁▁▁▁▁▁▁▁▁": 26870, "Sports": 26871, "▁Early": 26872, "bone": 26873, "就需要": 26874, "抽样": 26875, "的直接": 26876, "听力": 26877, "在生产": 26878, "管理学": 26879, "ectors": 26880, "系的": 26881, "区和": 26882, "可以对": 26883, "中被": 26884, "▁kitchen": 26885, "是目前": 26886, "▁encouraging": 26887, "Results": 26888, "▁Sus": 26889, "determ": 26890, "▁peer": 26891, "ographer": 26892, "风险评估": 26893, "继承人": 26894, "berries": 26895, "parents": 26896, "▁Netherlands": 26897, "▁wash": 26898, "▁harmony": 26899, "alog": 26900, "情況": 26901, "quir": 26902, "rosis": 26903, "十条": 26904, "和价值": 26905, "通过的": 26906, "事业发展": 26907, "长期的": 26908, "子宫": 26909, "生活中的": 26910, "礼仪": 26911, "精品": 26912, "奉献": 26913, "期权": 26914, "grad": 26915, "▁Sab": 26916, "liter": 26917, "▁shoes": 26918, "深入研究": 26919, "概况": 26920, "Eventually": 26921, "▁cooperation": 26922, "▁Theory": 26923, "天文学": 26924, "▁Bon": 26925, "advant": 26926, "海軍": 26927, "Effect": 26928, "egu": 26929, "onder": 26930, "▁Secretary": 26931, "maybe": 26932, "▁breed": 26933, "▁landing": 26934, "▁mothers": 26935, "筛查": 26936, "rypt": 26937, "built": 26938, "▁autism": 26939, "▁manifest": 26940, "十大": 26941, "资产负债表": 26942, "发病率": 26943, "扭转": 26944, "缺口": 26945, "会被": 26946, "个体的": 26947, "anz": 26948, "▁Ha": 26949, "▁hat": 26950, "▁telesc": 26951, "esium": 26952, "bass": 26953, "▁bringing": 26954, "人以": 26955, "在网络": 26956, "汇总": 26957, "与否": 26958, "创始": 26959, "value": 26960, "▁elderly": 26961, "conduct": 26962, "local": 26963, "▁confront": 26964, "会影响": 26965, "损害的": 26966, "是一门": 26967, "历经": 26968, "培养目标": 26969, "电位": 26970, "再现": 26971, "往往是": 26972, "人脸": 26973, "前提下": 26974, "onday": 26975, "▁soul": 26976, "orship": 26977, "▁assembly": 26978, "fare": 26979, "Den": 26980, "▁identical": 26981, "▁extens": 26982, "▁atoms": 26983, "▁slaves": 26984, "青岛": 26985, "侵入": 26986, "推翻": 26987, "代数": 26988, "內容": 26989, "▁devast": 26990, "▁buying": 26991, "▁telling": 26992, "思考题": 26993, "公益诉讼": 26994, "但对": 26995, "受教育": 26996, "紧密结合": 26997, "联系的": 26998, "京津冀": 26999, "以满足": 27000, "青藏高原": 27001, "帮扶": 27002, "但这": 27003, "Hon": 27004, "雙打": 27005, "▁Est": 27006, "▁regulate": 27007, "▁vaccines": 27008, "▁promising": 27009, "▁tobacco": 27010, "beg": 27011, "项目管理": 27012, "血小板": 27013, "最具": 27014, "的区域": 27015, "市政": 27016, "prop": 27017, "▁Bern": 27018, "▁aside": 27019, "▁translated": 27020, "NR": 27021, "backs": 27022, "会计师": 27023, "这就要求": 27024, "质性": 27025, "位移": 27026, "寄存": 27027, "的最新": 27028, "amilies": 27029, "core": 27030, "玩家": 27031, "▁paths": 27032, "▁storytelling": 27033, "▁scr": 27034, "▁speakers": 27035, "等信息": 27036, "和法律": 27037, "有限责任": 27038, "▁adaptation": 27039, "▁Ess": 27040, "▁accomplish": 27041, "提高到": 27042, "中之": 27043, "新课": 27044, "义务的": 27045, "应注意": 27046, "表面的": 27047, "吸引力": 27048, "牵头": 27049, "和教育": 27050, "党章": 27051, "▁Sand": 27052, "▁chromos": 27053, "该方法": 27054, "▁nutrient": 27055, "▁beans": 27056, "▁graduate": 27057, "Found": 27058, "▁Chart": 27059, "home": 27060, "长沙": 27061, "](#": 27062, "较长": 27063, "emale": 27064, "iblical": 27065, "Opt": 27066, "▁gift": 27067, "▁socio": 27068, "▁Garden": 27069, "在未来": 27070, "金融科技": 27071, "情况的": 27072, "始终坚持": 27073, "敏感性": 27074, "专科": 27075, "全力": 27076, "中也": 27077, "Luc": 27078, "马斯": 27079, "Identify": 27080, "thet": 27081, "▁pets": 27082, "简单地": 27083, "西斯": 27084, "▁sew": 27085, "▁ancestors": 27086, "▁reign": 27087, "iously": 27088, "spring": 27089, "▁Ell": 27090, "计划经济": 27091, "早已": 27092, "▁Action": 27093, "▁collecting": 27094, "▁profit": 27095, "▁saved": 27096, "▁bees": 27097, "akespe": 27098, "变的": 27099, "located": 27100, "▁teachings": 27101, "IoT": 27102, "array": 27103, "深层": 27104, "共同体的": 27105, "和工作": 27106, "生命力": 27107, "不平等": 27108, "地位的": 27109, "积极作用": 27110, "畸形": 27111, "发热": 27112, "军的": 27113, "与非": 27114, "传达": 27115, "▁Ash": 27116, "▁compan": 27117, "微分": 27118, "Encourage": 27119, "Bro": 27120, "▁Three": 27121, "▁HT": 27122, "mega": 27123, "▁package": 27124, "下来的": 27125, "切入": 27126, "▁sympt": 27127, "▁customs": 27128, "plate": 27129, "Put": 27130, "▁gran": 27131, "web": 27132, "▁Antar": 27133, "球队": 27134, "Comb": 27135, "治国理政": 27136, "西方国家": 27137, "产学": 27138, "发票": 27139, "庞大的": 27140, "严肃": 27141, "结构化": 27142, "▁circul": 27143, "不属于": 27144, "Indeed": 27145, "▁evil": 27146, "▁orange": 27147, "▁reflects": 27148, "▁subtle": 27149, "▁tribut": 27150, "hk": 27151, "现在的": 27152, "火星": 27153, "itus": 27154, "语言的": 27155, "ashing": 27156, "年被": 27157, "aiwan": 27158, "attered": 27159, "▁Ba": 27160, "▁Stadium": 27161, "通信技术": 27162, "新冠肺炎疫情": 27163, "之间存在": 27164, "在不": 27165, "作詞": 27166, "反向": 27167, "ropolitan": 27168, "▁suicide": 27169, "▁containers": 27170, "万亿": 27171, "教学法": 27172, "各级政府": 27173, "承租": 27174, "情怀": 27175, "基站": 27176, "血栓": 27177, "走进": 27178, "纽带": 27179, "又能": 27180, "的局面": 27181, "▁adhere": 27182, "要素的": 27183, "成型": 27184, "需求和": 27185, "doi": 27186, "▁restore": 27187, "▁abandoned": 27188, "▁territories": 27189, "▁cran": 27190, "亚太": 27191, "共同的": 27192, "▁Log": 27193, "▁hybrid": 27194, "▁peripher": 27195, "tainment": 27196, "捕捉": 27197, "▁【": 27198, "phabet": 27199, "▁renamed": 27200, "岁的": 27201, "耐药": 27202, "变成了": 27203, "群落": 27204, "通俗": 27205, "宽带": 27206, "加之": 27207, "试行": 27208, "服务体系": 27209, "▁Wars": 27210, "》▁▁": 27211, "coll": 27212, "了一些": 27213, "Mex": 27214, "生殖": 27215, "▁Fig": 27216, "这是一种": 27217, "成为一个": 27218, "chant": 27219, "有多": 27220, "并将其": 27221, "Along": 27222, "Additional": 27223, "chester": 27224, "▁annually": 27225, "▁everywhere": 27226, "▁lemon": 27227, "▁AM": 27228, "十二五": 27229, "学时": 27230, "重点领域": 27231, "中指出": 27232, "询问": 27233, "酒精": 27234, "▁Support": 27235, "而生": 27236, "Indian": 27237, "flies": 27238, "▁Bell": 27239, "▁bottle": 27240, "▁tou": 27241, "loss": 27242, "oking": 27243, "▁Live": 27244, "副主席": 27245, "股市": 27246, "后世": 27247, "柏林": 27248, "北海": 27249, "▁expanding": 27250, "Timmy": 27251, "▁delicate": 27252, "▁spacecraft": 27253, "omot": 27254, "生态环境保护": 27255, "数字货币": 27256, "人工智能技术": 27257, "日元": 27258, "建制": 27259, "自然科学基金": 27260, "iga": 27261, "ognition": 27262, "Ma": 27263, "abama": 27264, "▁restoration": 27265, "▁specimens": 27266, "▁gathering": 27267, "▁struggling": 27268, "current": 27269, "Oxford": 27270, "长安": 27271, "任何一个": 27272, "不一": 27273, "eeds": 27274, "▁therapies": 27275, "三国": 27276, "▁Spirit": 27277, "▁Sav": 27278, "公允价值": 27279, "家具": 27280, "外部环境": 27281, "自然灾害": 27282, "审慎": 27283, "established": 27284, "affe": 27285, "十月": 27286, "衰减": 27287, "用了": 27288, "▁Pakistan": 27289, "overe": 27290, "Cast": 27291, "▁cub": 27292, "▁correlation": 27293, "▁delight": 27294, "▁nu": 27295, "▁delve": 27296, "▁integrity": 27297, "▁positively": 27298, "▁Toronto": 27299, "cond": 27300, "ownt": 27301, "▁▁.": 27302, "Name": 27303, "wer": 27304, "更换": 27305, "剥夺": 27306, "estone": 27307, "grams": 27308, "CAD": 27309, "techn": 27310, "演奏": 27311, "▁surviv": 27312, "▁worship": 27313, "unar": 27314, "ennessee": 27315, "粮食安全": 27316, "来确定": 27317, "而后": 27318, "弱势": 27319, "ст": 27320, "真的": 27321, "▁smoothly": 27322, "不等": 27323, "enza": 27324, "iberty": 27325, "elson": 27326, "inflammatory": 27327, "要求和": 27328, "得不到": 27329, "应激": 27330, "很可能": 27331, "▁resistant": 27332, "和中": 27333, "playing": 27334, "▁poems": 27335, "▁strengths": 27336, "()`▁": 27337, "▁comic": 27338, "ART": 27339, "▁AND": 27340, "▁overview": 27341, "▁optical": 27342, "▁institution": 27343, "icts": 27344, "发展为": 27345, "马克思主义的": 27346, "的证据": 27347, "年我国": 27348, "城市群": 27349, "迈向": 27350, "汲取": 27351, "肝炎": 27352, "aments": 27353, "平民": 27354, "×▁": 27355, "giene": 27356, "▁cow": 27357, "▁tailored": 27358, "伊朗": 27359, "akespeare": 27360, "▁ty": 27361, "對於": 27362, "umbers": 27363, "▁enhancing": 27364, "bral": 27365, "建国": 27366, "held": 27367, "▁earthquake": 27368, "的最后": 27369, "▁sheets": 27370, "▁dinner": 27371, "▁ranging": 27372, "Cong": 27373, "ilms": 27374, "Econom": 27375, "利于": 27376, "的运动": 27377, "在国内": 27378, "改革创新": 27379, "外语": 27380, "测绘": 27381, "▁striking": 27382, "子公司": 27383, "wealth": 27384, "▁soccer": 27385, "▁throat": 27386, "▁scheme": 27387, "Star": 27388, "chestra": 27389, "dependent": 27390, "tok": 27391, "Tor": 27392, "marks": 27393, "▁Laboratory": 27394, "erusalem": 27395, "sing": 27396, "深厚": 27397, "相结合的": 27398, "两方面": 27399, "投标": 27400, "测算": 27401, "与学生": 27402, "代表性的": 27403, "来解决": 27404, "这场": 27405, "余人": 27406, "方法论": 27407, "作文": 27408, "▁weekly": 27409, "▁columns": 27410, "结婚": 27411, ",▁'": 27412, "球员": 27413, "secut": 27414, "▁pod": 27415, "这表明": 27416, "建设项目": 27417, "反应的": 27418, "▁critically": 27419, "▁distant": 27420, "electric": 27421, "▁savings": 27422, "EST": 27423, "idelines": 27424, "▁politicians": 27425, "课程内容": 27426, "原则上": 27427, "交融": 27428, "军民": 27429, "同学们": 27430, "放松": 27431, "▁bowl": 27432, "▁cater": 27433, "▁acres": 27434, "▁calling": 27435, "▁invited": 27436, "iph": 27437, "effective": 27438, "神秘": 27439, "asia": 27440, "▁ending": 27441, "▁faculty": 27442, "▁Gre": 27443, "世纪末": 27444, "在不同的": 27445, "维埃": 27446, "向往": 27447, "祖父": 27448, "可扩展": 27449, "葡萄牙": 27450, "▁Place": 27451, "ovak": 27452, "唯一的": 27453, "▁Oxford": 27454, "living": 27455, "ante": 27456, "▁mi": 27457, "vi": 27458, "法令": 27459, "▁harsh": 27460, "following": 27461, "▁Hebrew": 27462, "▁spray": 27463, "新华社": 27464, "生机": 27465, "小微": 27466, "知识产权保护": 27467, "事关": 27468, "基础和": 27469, "磁盘": 27470, "风景": 27471, "临床上": 27472, "保证了": 27473, "转运": 27474, "知縣": 27475, "畢業": 27476, "▁plain": 27477, "inton": 27478, "ooked": 27479, "▁robots": 27480, "▁believes": 27481, "▁intention": 27482, "▁pestic": 27483, "▁plates": 27484, "▁sunny": 27485, "chain": 27486, "▁Bol": 27487, "的抗": 27488, "负面影响": 27489, "means": 27490, "▁Farm": 27491, "非线性": 27492, "Fore": 27493, "det": 27494, "cop": 27495, "overning": 27496, "▁confusion": 27497, "▁preservation": 27498, "Cr": 27499, "▁vig": 27500, "核电": 27501, "免疫系统": 27502, "充分考虑": 27503, "必须是": 27504, "的义务": 27505, "总体上": 27506, "参观": 27507, "当家": 27508, "ni": 27509, "登录": 27510, "aton": 27511, "▁spreading": 27512, "▁accidents": 27513, "▁gear": 27514, "▁representative": 27515, "\"}": 27516, "私营": 27517, "任职": 27518, "一天": 27519, "东京": 27520, "▁Municip": 27521, "和相关": 27522, "▁Dun": 27523, "區域": 27524, "小時": 27525, "編曲": 27526, "init": 27527, "与经济": 27528, "收回": 27529, "万美元": 27530, "游客": 27531, "游击": 27532, "红细胞": 27533, "原生": 27534, "重叠": 27535, "人所": 27536, "aga": 27537, "▁lig": 27538, "kh": 27539, "anner": 27540, "▁Java": 27541, "的转变": 27542, "作出的": 27543, "议员": 27544, "必须要": 27545, "内地": 27546, "Sen": 27547, "自旋": 27548, "地主": 27549, "在他": 27550, "恰恰": 27551, "▁phenomena": 27552, "▁rainf": 27553, "▁wars": 27554, "篇章": 27555, "课程教学": 27556, "政府对": 27557, "▁Univers": 27558, "第十三": 27559, "长城": 27560, "raham": 27561, "▁cattle": 27562, "▁Prevention": 27563, "▁inequality": 27564, "▁Indians": 27565, "▁returning": 27566, "treated": 27567, "▁debris": 27568, "部份": 27569, "Space": 27570, "▁ID": 27571, "gae": 27572, "在高": 27573, "尔省": 27574, "angel": 27575, "▁churches": 27576, "▁comprehension": 27577, "▁reserved": 27578, "▁Republican": 27579, "技术进步": 27580, "激励机制": 27581, "再审": 27582, "通风": 27583, "协议的": 27584, "尔德": 27585, "ritic": 27586, "推测": 27587, "Run": 27588, "▁Resp": 27589, "▁quantity": 27590, "▁valley": 27591, "▁update": 27592, "▁strains": 27593, "IST": 27594, "▁axis": 27595, "▁afternoon": 27596, "▁saving": 27597, "一段时间": 27598, "造血": 27599, "蒸发": 27600, "演讲": 27601, "域网": 27602, "rigation": 27603, "▁spons": 27604, "CV": 27605, "forcing": 27606, "相较": 27607, "Notable": 27608, "▁Os": 27609, "人民群众的": 27610, "涉及的": 27611, "的依据": 27612, "老龄化": 27613, "变压": 27614, "这部分": 27615, "同样的": 27616, "舆情": 27617, "强劲": 27618, "财税": 27619, "以人为本": 27620, "的内在": 27621, "立方米": 27622, "假定": 27623, "大阪": 27624, "▁restrictions": 27625, "▁worlds": 27626, "▁Digital": 27627, "Different": 27628, "单词": 27629, "Eth": 27630, "igm": 27631, "Western": 27632, "Taxonomy": 27633, "人是": 27634, "保卫": 27635, "对外贸易": 27636, "前途": 27637, "▁mystery": 27638, "SU": 27639, "年版": 27640, "▁Rod": 27641, "evin": 27642, "pan": 27643, "unctions": 27644, "这项研究": 27645, "诉讼时效": 27646, "全国各族人民": 27647, "案件中": 27648, "放缓": 27649, "关怀": 27650, "▁comparing": 27651, "很小": 27652, "▁descent": 27653, "▁alpha": 27654, "▁communications": 27655, "彼得": 27656, "▁desert": 27657, "isticated": 27658, "一门": 27659, "不出": 27660, "osaur": 27661, "hetic": 27662, "▁bleeding": 27663, "▁intersection": 27664, "丈夫": 27665, "▁sequences": 27666, "<0x9F>": 31021, "different": 31022, "帝國": 31023, "党的政治": 31024, "两者的": 31025, "内战": 31026, "叶片": 31027, "细胞因子": 31028, "ée": 31029, "▁Rose": 31030, "樂團": 31031, "gium": 31032, "producing": 31033, "accessed": 31034, "阿根廷": 31035, "▁decreases": 31036, "▁encounters": 31037, "▁pupp": 31038, "▁intr": 31039, "True": 31040, "és": 31041, "王子": 31042, "antry": 31043, "会计信息": 31044, "据统计": 31045, "所具有": 31046, "劳动合同法": 31047, "常态化": 31048, "担负": 31049, "智能合约": 31050, "自觉地": 31051, "先天性": 31052, "医务": 31053, "构建人类命运共同体": 31054, "简要": 31055, "它将": 31056, "惠及": 31057, "监管部门": 31058, "的临床": 31059, "ilib": 31060, "Proper": 31061, "▁symmet": 31062, "itime": 31063, "晚上": 31064, "珊瑚": 31065, "role": 31066, "Ur": 31067, "▁divisions": 31068, "Exp": 31069, "▁organism": 31070, "▁allergies": 31071, "▁genuine": 31072, "▁watched": 31073, "▁Cra": 31074, "▁bless": 31075, "Sou": 31076, "▁coh": 31077, "而导致": 31078, "和改进": 31079, "罪犯": 31080, "永恒": 31081, "的发展趋势": 31082, "ergarten": 31083, "▁Matt": 31084, "英格蘭": 31085, "的现代化": 31086, "全面深化": 31087, "后再": 31088, "市场在": 31089, "中的重要": 31090, "解决好": 31091, "出具": 31092, "具有较强的": 31093, "文化交流": 31094, "尤为重要": 31095, "接受的": 31096, "或服务": 31097, "一种新的": 31098, "企业与": 31099, "区位": 31100, "奠定了基础": 31101, "服用": 31102, "事务所": 31103, "职称": 31104, "危险因素": 31105, "收入差距": 31106, "nea": 31107, "群众的": 31108, "直辖市": 31109, "entieth": 31110, "發表": 31111, "▁aggreg": 31112, "▁license": 31113, "▁peaceful": 31114, "▁atmospheric": 31115, "ulent": 31116, "▁drops": 31117, "AST": 31118, "▁Francis": 31119, "▁expedition": 31120, "▁contamination": 31121, "▁convin": 31122, "▁prone": 31123, "▁straw": 31124, "▁Relations": 31125, "stitutional": 31126, "▁Alliance": 31127, "acking": 31128, "大类": 31129, "或多": 31130, "最多的": 31131, "Ter": 31132, "光合": 31133, "▁hypertension": 31134, "orrect": 31135, "▁germ": 31136, "ugg": 31137, "▁dough": 31138, "▁pond": 31139, "ytic": 31140, "associated": 31141, "发生率": 31142, "研讨会": 31143, "变压器": 31144, "促进社会": 31145, "工程建设": 31146, "学生们": 31147, "抽取": 31148, "联络": 31149, "毛泽东思想": 31150, "致富": 31151, "外延": 31152, "个国家": 31153, "feeding": 31154, "Engine": 31155, "主人": 31156, "▁Computer": 31157, "▁Raj": 31158, "▁restaurant": 31159, "▁breathe": 31160, "▁counterparts": 31161, "▁tunnel": 31162, "▁hotel": 31163, "──": 31164, "分发": 31165, "▁PR": 31166, "站点": 31167, "\":": 31168, "▁beer": 31169, "生命的": 31170, "莱茵省": 31171, "向下": 31172, "中将": 31173, "基本概念": 31174, "同位": 31175, "上午": 31176, "观的": 31177, "pdf": 31178, "oard": 31179, "▁Integ": 31180, "興建": 31181, "ationally": 31182, "▁Guinea": 31183, "毕业后": 31184, "▁counts": 31185, "▁flowing": 31186, "▁screens": 31187, "其他的": 31188, ".\"\"": 31189, "erness": 31190, "所说": 31191, "的历史性": 31192, "和保障": 31193, "学前教育": 31194, "的基本理论": 31195, "突破口": 31196, "但对于": 31197, "原发性": 31198, "电化学": 31199, "Fire": 31200, "▁bust": 31201, "▁recycled": 31202, "ermat": 31203, "▁incidents": 31204, "▁pushing": 31205, "南非": 31206, "老化": 31207, "▁Village": 31208, "▁Ide": 31209, "局的": 31210, "过大": 31211, "矛盾的": 31212, "鲜明的": 31213, "▁aquatic": 31214, "路上": 31215, "的正": 31216, "▁bark": 31217, "华东": 31218, "透露": 31219, "，「": 31220, "enez": 31221, "▁cer": 31222, "sharing": 31223, "▁evolutionary": 31224, "▁formats": 31225, "其次是": 31226, "开辟了": 31227, "的良好": 31228, "十六条": 31229, "利用效率": 31230, "油气": 31231, "不允许": 31232, "最高人民法院关于": 31233, "可实现": 31234, "的重要因素": 31235, "领导小组": 31236, "恐惧": 31237, "终点": 31238, "夫妻共同": 31239, "相辅相": 31240, "brain": 31241, "▁heated": 31242, "▁qualified": 31243, ",▁‘": 31244, "甲烷": 31245, "eval": 31246, "金牌": 31247, "早年": 31248, "AE": 31249, "mans": 31250, "▁ber": 31251, "▁scan": 31252, "avirus": 31253, "▁frozen": 31254, "▁heroes": 31255, "▁neighboring": 31256, "▁rod": 31257, "人道": 31258, "这为": 31259, "治愈": 31260, "semble": 31261, "▁concert": 31262, "inely": 31263, "▁innovations": 31264, "serving": 31265, "廣東": 31266, "内陆": 31267, "切割": 31268, "他还": 31269, "醫院": 31270, "範圍": 31271, "Pass": 31272, "商店": 31273, "描述了": 31274, "Associ": 31275, "KD": 31276, "▁puts": 31277, "raid": 31278, "表現": 31279, "▁allev": 31280, "肢体": 31281, "环境监测": 31282, "的人格": 31283, "土地使用权": 31284, "新鲜": 31285, "综合考虑": 31286, "特殊性": 31287, "的数字": 31288, "▁resolve": 31289, "相似的": 31290, "model": 31291, "Present": 31292, "▁Gil": 31293, "Blood": 31294, "▁Athlet": 31295, "abitat": 31296, "▁Chile": 31297, "▁recall": 31298, "▁challenged": 31299, "▁emit": 31300, "▁stimulate": 31301, "通过将": 31302, "隐私保护": 31303, "并向": 31304, "水和": 31305, "earl": 31306, "cepts": 31307, "▁Miller": 31308, "▁coin": 31309, "engu": 31310, "▁legit": 31311, "▁executed": 31312, "▁empire": 31313, "▁Assessment": 31314, "▁belt": 31315, "▁electoral": 31316, "▁highlighted": 31317, "进士": 31318, "分担": 31319, "四中全会": 31320, "进一步发展": 31321, "不服": 31322, "内阁": 31323, "心力": 31324, "融资租赁": 31325, "暂行": 31326, "美好生活的": 31327, "第一步": 31328, "通则": 31329, "▁Drug": 31330, "orf": 31331, "在上海": 31332, "遗迹": 31333, "学士": 31334, "次年": 31335, "opathy": 31336, "Ra": 31337, "mid": 31338, "'.": 31339, "special": 31340, "Pay": 31341, "▁mistake": 31342, "▁pulmonary": 31343, "▁sampling": 31344, "mother": 31345, "▁steep": 31346, "▁transactions": 31347, ")|": 31348, "Geography": 31349, "▁portions": 31350, "滤波器": 31351, "影响力的": 31352, "宪政": 31353, "仍然存在": 31354, "既能": 31355, "工作和": 31356, "其三": 31357, "如果你": 31358, "▁Non": 31359, "▁voting": 31360, "Fort": 31361, "▁accounting": 31362, "▁baseline": 31363, "▁collapse": 31364, "國王": 31365, "▁reasoning": 31366, "source": 31367, "\u0006\b": 31368, "各方面的": 31369, "的主导": 31370, "处处": 31371, "领悟": 31372, "抵制": 31373, "好奇": 31374, "宏伟": 31375, "特别行政区": 31376, "减灾": 31377, "遗传学": 31378, "轮廓": 31379, "仅仅是": 31380, "文件的": 31381, "参加了": 31382, "山大": 31383, "Ms": 31384, "itched": 31385, "always": 31386, "有以下": 31387, "▁Core": 31388, "▁operator": 31389, "▁vow": 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"一日": 31777, "Clear": 31778, "nie": 31779, "odge": 31780, "illo": 31781, "▁Captain": 31782, "odd": 31783, "▁gifts": 31784, "vo": 31785, "▁disagre": 31786, "▁reserve": 31787, "introdu": 31788, "基础的": 31789, "免疫反应": 31790, "免除": 31791, "业界": 31792, "isol": 31793, "受限": 31794, "完毕": 31795, "Psych": 31796, "urgical": 31797, "ircle": 31798, "▁polymer": 31799, "▁Tower": 31800, "▁divers": 31801, "verages": 31802, "▁illustrated": 31803, "olding": 31804, "▁bills": 31805, "▁welcome": 31806, "中国传统": 31807, "更强": 31808, "反腐": 31809, "活力的": 31810, "副院长": 31811, "只需要": 31812, "知府": 31813, "势必": 31814, "一审": 31815, "合同中": 31816, "整改": 31817, "党领导": 31818, "不同类型": 31819, "eur": 31820, "▁Offic": 31821, "ontin": 31822, "pi": 31823, "Final": 31824, "▁organizing": 31825, "ervatory": 31826, "▁smile": 31827, "▁consequence": 31828, "▁genres": 31829, "編號": 31830, "rative": 31831, "▁Own": 31832, "纠缠": 31833, "听觉": 31834, "行走": 31835, "西域": 31836, "行之": 31837, "▁Help": 31838, "的低": 31839, "了许多": 31840, "garet": 31841, "▁scal": 31842, "▁timing": 31843, "Pers": 31844, "▁chips": 31845, "▁seeks": 31846, "▁crowd": 31847, "▁plug": 31848, "▁worried": 31849, "经济利益": 31850, "药物治疗": 31851, "体现为": 31852, "相差": 31853, "货币的": 31854, "体征": 31855, "国家在": 31856, "现行的": 31857, "等领域的": 31858, "我们应该": 31859, "西欧": 31860, "仍处于": 31861, "因素的影响": 31862, "无序": 31863, "惩戒": 31864, "股权转让": 31865, "直接的": 31866, "古今": 31867, "quiry": 31868, "河道": 31869, "行為": 31870, "當中": 31871, "cancer": 31872, "▁Need": 31873, "收录": 31874, "▁Biology": 31875, "▁rival": 31876, "▁Path": 31877, "▁rocket": 31878, "▁aspir": 31879, "Episode": 31880, "▁MS": 31881, "大中": 31882, "文化与": 31883, "一对": 31884, "中存在": 31885, "完成后": 31886, "rasound": 31887, "動物": 31888, "▁Lu": 31889, "▁agreements": 31890, "Introdu": 31891, "paper": 31892, "▁snake": 31893, "became": 31894, "▁irregular": 31895, "▁washing": 31896, "urated": 31897, "▁Mot": 31898, "▁Sac": 31899, "ococc": 31900, "国家治理体系和治理能力现代化": 31901, "系统集成": 31902, "教师应": 31903, "以至": 31904, "水果": 31905, "政策性": 31906, "相互关系": 31907, "洗钱": 31908, "不同类型的": 31909, "事宜": 31910, "北极": 31911, "杀伤": 31912, "偏低": 31913, "可持续性": 31914, "人民当家作主": 31915, "浮动": 31916, "股价": 31917, "的案件": 31918, "副书记": 31919, "aptist": 31920, "▁Brad": 31921, "诺贝尔": 31922, "算法的": 31923, "后来的": 31924, "acity": 31925, "直觉": 31926, "形容": 31927, "cht": 31928, "Dav": 31929, "irical": 31930, "▁ger": 31931, "weed": 31932, "▁rescue": 31933, "▁Therapy": 31934, "developed": 31935, "▁answered": 31936, "▁filters": 31937, "▁residence": 31938, "Eval": 31939, "数列": 31940, "间隙": 31941, "侧面": 31942, "科研机构": 31943, "十一年": 31944, "合併": 31945, "不相同": 31946, "方程式": 31947, "目的地": 31948, "ailand": 31949, "吻合": 31950, "Rest": 31951, "acon": 31952, "▁Ans": 31953, "Python": 31954, "和建设": 31955, "并提出": 31956, "标准体系": 31957, "十一条": 31958, "第二百": 31959, "是不是": 31960, "帝王": 31961, "细则": 31962, "反动": 31963, "简便": 31964, "获利": 31965, "长效机制": 31966, "关乎": 31967, "议程": 31968, "台阶": 31969, "增设": 31970, "核实": 31971, "筑牢": 31972, "数据挖掘": 31973, "淡水": 31974, "所用": 31975, "总和": 31976, "赞同": 31977, "uct": 31978, "▁Prom": 31979, "属性的": 31980, "快捷": 31981, "▁Dark": 31982, "▁permit": 31983, "▁Mun": 31984, "took": 31985, "行政法": 31986, "bian": 31987, ":▁▁": 31988, "feel": 31989, "gets": 31990, "▁Hart": 31991, "这三个": 31992, "同胞": 31993, "不起": 31994, "大连": 31995, "研究方向": 31996, "人民共和國": 31997, "必須": 31998, "—▁—▁": 31999, "▁sour": 32000, "saying": 32001, "▁juvenile": 32002, "▁productive": 32003, "▁pept": 32004, "▁Hills": 32005, "教学设计": 32006, "外商投资": 32007, "马克思主义理论": 32008, "优惠政策": 32009, "由政府": 32010, "这就需要": 32011, "势头": 32012, "也成为": 32013, "职业病": 32014, "西周": 32015, "洛阳": 32016, "指标的": 32017, "回落": 32018, "并用": 32019, "即使是": 32020, "▁Queens": 32021, "Microsoft": 32022, "mostly": 32023, "▁databases": 32024, "articles": 32025, "rod": 32026, "▁ul": 32027, "戰鬥": 32028, "▁crash": 32029, "定居": 32030, "Full": 32031, "▁bridges": 32032, "并重": 32033, "家人": 32034, "话语权": 32035, "\\_": 32036, "的原": 32037, "生死": 32038, "▁dynasty": 32039, "Prim": 32040, "▁Aqu": 32041, "▁integrate": 32042, "▁interpretations": 32043, "▁viewers": 32044, "▁rolling": 32045, "金的": 32046, "▁Opp": 32047, "oberts": 32048, "htm": 32049, "▁WW": 32050, "image": 32051, "inator": 32052, "clerosis": 32053, "人口普查": 32054, "科学性": 32055, "异化": 32056, "的挑战": 32057, "能为": 32058, "诉讼请求": 32059, "变频": 32060, "电子邮件": 32061, "集权": 32062, "盛行": 32063, "富集": 32064, "成交": 32065, "摩尔": 32066, "查明": 32067, "北魏": 32068, "绿色金融": 32069, "做大": 32070, "▁continent": 32071, "行列": 32072, "Aust": 32073, "director": 32074, "▁pathogens": 32075, "▁kick": 32076, "▁embrace": 32077, "▁romantic": 32078, "▁salmon": 32079, "▁swelling": 32080, "hr": 32081, "▁economics": 32082, "inian": 32083, "iotic": 32084, "▁Si": 32085, "停滞": 32086, "合法的": 32087, "uerto": 32088, "('": 32089, "RCEP": 32090, "先前": 32091, "—▁▁—▁▁—▁▁—▁▁": 32092, "▁bottles": 32093, "▁chapters": 32094, "▁murd": 32095, "▁monthly": 32096, "osé": 32097, "英語": 32098, "▁static": 32099, "▁Territ": 32100, "▁shadow": 32101, "复合物": 32102, "▁Ni": 32103, "***": 32104, "▁meteor": 32105, "▁Premier": 32106, "rokes": 32107, "十三条": 32108, "风险挑战": 32109, "突破了": 32110, "杠杆率": 32111, "了一条": 32112, "财产权": 32113, "各种各": 32114, "而来的": 32115, "评议": 32116, "买方": 32117, "共存": 32118, "工人阶级": 32119, "涂层": 32120, "创造条件": 32121, "顺差": 32122, "信息基础设施": 32123, "大湾区": 32124, "中的地位": 32125, "研究工作": 32126, "加快构建": 32127, "什么样": 32128, "身份认证": 32129, "▁anten": 32130, "元宇宙": 32131, "inic": 32132, "oche": 32133, "itan": 32134, "untary": 32135, "graph": 32136, "eu": 32137, "▁jet": 32138, "▁Bishop": 32139, "▁cheap": 32140, "▁captain": 32141, "书的": 32142, "可能在": 32143, "是当前": 32144, "中具有": 32145, "断层": 32146, "未被": 32147, "每种": 32148, "▁stuck": 32149, "▁survivors": 32150, "▁yields": 32151, "简洁": 32152, "扩增": 32153, "一台": 32154, "▁Last": 32155, "烏克蘭": 32156, "●▁": 32157, "sound": 32158, "▁Mission": 32159, "rosc": 32160, "▁geometry": 32161, "▁Authority": 32162, "朝鮮": 32163, "▁Brook": 32164, "▁commands": 32165, "▁governance": 32166, "会计核算": 32167, "发达地区": 32168, "全面提升": 32169, "国土空间": 32170, "树脂": 32171, "选择了": 32172, "功利": 32173, "硬盘": 32174, "风貌": 32175, "廉政": 32176, "奋进": 32177, "组长": 32178, "▁ded": 32179, "▁cong": 32180, "▁chaos": 32181, "▁rituals": 32182, "第十五": 32183, "▁Tal": 32184, "ooth": 32185, "電腦": 32186, "相當": 32187, "anded": 32188, "si": 32189, "bey": 32190, "posure": 32191, "▁FC": 32192, "oping": 32193, "▁Six": 32194, "▁accord": 32195, "listed": 32196, "▁accomplished": 32197, "▁champion": 32198, "▁prolonged": 32199, "；(": 32200, "欠缺": 32201, "则为": 32202, "中亚": 32203, "▁Moh": 32204, "Cultural": 32205, "▁Youth": 32206, "ulative": 32207, "uncture": 32208, "▁Record": 32209, "▁Grade": 32210, "▁counties": 32211, "▁decomp": 32212, "▁floating": 32213, "Usually": 32214, "▁glac": 32215, "▁correlated": 32216, "▁marginalized": 32217, "土地利用": 32218, "则在": 32219, "到一个": 32220, "控制在": 32221, "发达国家的": 32222, "往往会": 32223, "的基本要求": 32224, "以下几点": 32225, "信息服务": 32226, "提高学生": 32227, "公信": 32228, "的研究成果": 32229, "举行的": 32230, 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949, "่": 950, "้": 951, "์": 952, "າ": 953, "་": 954, "ག": 955, "ང": 956, "ད": 957, "ས": 958, "ུ": 959, "ོ": 960, "က": 961, "ခ": 962, "ဂ": 963, "င": 964, "စ": 965, "ဆ": 966, "ည": 967, "တ": 968, "ထ": 969, "ဒ": 970, "န": 971, "ပ": 972, "ဖ": 973, "ဘ": 974, "မ": 975, "ယ": 976, "ရ": 977, "လ": 978, "ဝ": 979, "သ": 980, "အ": 981, "ါ": 982, "ာ": 983, "ိ": 984, "ီ": 985, "ု": 986, "ူ": 987, "ေ": 988, "ဲ": 989, "ံ": 990, "့": 991, "း": 992, "္": 993, "်": 994, "ျ": 995, "ြ": 996, "ွ": 997, "ှ": 998, "၊": 999, "၌": 1000, "၍": 1001, "၏": 1002, "ა": 1003, "ბ": 1004, "გ": 1005, "დ": 1006, "ე": 1007, "ვ": 1008, "თ": 1009, "ი": 1010, "კ": 1011, "ლ": 1012, "მ": 1013, "ნ": 1014, "ო": 1015, "რ": 1016, "ს": 1017, "ტ": 1018, "უ": 1019, "შ": 1020, "ᜀ": 1021, "ᜃ": 1022, "ᜅ": 1023, "ᜆ": 1024, "ᜈ": 1025, "ᜋ": 1026, "ᜐ": 1027, "ᜒ": 1028, "᜔": 1029, "ក": 1030, "ង": 1031, "ប": 1032, "រ": 1033, "ស": 1034, "ា": 1035, "ោ": 1036, "ះ": 1037, "្": 1038, "ᵏ": 1039, "ḍ": 1040, "Ḥ": 1041, "ḥ": 1042, "ṃ": 1043, "ṅ": 1044, "ṇ": 1045, "ṛ": 1046, "Ṣ": 1047, "ṣ": 1048, "ṭ": 1049, "ẓ": 1050, "ạ": 1051, "ả": 1052, "ấ": 1053, "ầ": 1054, "ậ": 1055, "ắ": 1056, "ế": 1057, "ề": 1058, "ễ": 1059, "ệ": 1060, "ị": 1061, "ọ": 1062, "ố": 1063, "ồ": 1064, "ổ": 1065, "ộ": 1066, "ớ": 1067, "ờ": 1068, "ợ": 1069, "ứ": 1070, "ữ": 1071, "ἀ": 1072, "ἐ": 1073, "ἡ": 1074, "ὁ": 1075, "ὐ": 1076, "ὶ": 1077, "ὸ": 1078, "ῆ": 1079, "ῖ": 1080, "ῦ": 1081, "ῶ": 1082, " ": 1083, " ": 1084, " ": 1085, " ": 1086, " ": 1087, "​": 1088, "‌": 1089, "‍": 1090, "‐": 1091, "‑": 1092, "‒": 1093, "–": 1094, "—": 1095, "―": 1096, "‖": 1097, "‘": 1098, "’": 1099, "‚": 1100, "‛": 1101, "“": 1102, "”": 1103, "„": 1104, "‟": 1105, "†": 1106, "‡": 1107, "•": 1108, "…": 1109, "‧": 1110, "‬": 1111, " ": 1112, "‰": 1113, "′": 1114, "″": 1115, "›": 1116, "※": 1117, "⁄": 1118, "⁻": 1119, "ⁿ": 1120, "₀": 1121, "₁": 1122, "₂": 1123, "₃": 1124, "₄": 1125, "€": 1126, "₹": 1127, "⃣": 1128, "℃": 1129, "№": 1130, "™": 1131, "⅓": 1132, "Ⅰ": 1133, "Ⅱ": 1134, "Ⅲ": 1135, "Ⅳ": 1136, "Ⅴ": 1137, "Ⅵ": 1138, "Ⅶ": 1139, "Ⅷ": 1140, "Ⅸ": 1141, "Ⅹ": 1142, "Ⅼ": 1143, "Ⅽ": 1144, "Ⅾ": 1145, "Ⅿ": 1146, "ⅱ": 1147, "ⅲ": 1148, "←": 1149, "↑": 1150, "→": 1151, "↓": 1152, "↔": 1153, "↗": 1154, "↘": 1155, "↙": 1156, "↩": 1157, "↺": 1158, "⇒": 1159, "∀": 1160, "∁": 1161, "∂": 1162, "∅": 1163, "∆": 1164, "∇": 1165, "∈": 1166, "∏": 1167, "∑": 1168, "−": 1169, "∕": 1170, "∗": 1171, "∙": 1172, "√": 1173, "∞": 1174, "∠": 1175, "∣": 1176, "∥": 1177, "∧": 1178, "∨": 1179, "∩": 1180, "∪": 1181, "∫": 1182, "∮": 1183, "∴": 1184, "∵": 1185, "∶": 1186, "∷": 1187, "∼": 1188, "≈": 1189, "≠": 1190, "≡": 1191, "≤": 1192, "≥": 1193, "≦": 1194, "≧": 1195, "⊂": 1196, "⊙": 1197, "⊥": 1198, "⊿": 1199, "⋅": 1200, "⋯": 1201, "①": 1202, "②": 1203, "③": 1204, "④": 1205, "⑤": 1206, "⑥": 1207, "⑦": 1208, "⑧": 1209, "⑨": 1210, "⑩": 1211, "⑴": 1212, "⑵": 1213, "⑶": 1214, "⑷": 1215, "⑸": 1216, "⑹": 1217, "⑺": 1218, "⑻": 1219, "⑼": 1220, "⑾": 1221, "⒄": 1222, "⒈": 1223, "⒉": 1224, "⒊": 1225, "⒋": 1226, 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"壯": 2592, "声": 2593, "売": 2594, "壳": 2595, "壶": 2596, "壹": 2597, "壺": 2598, "壽": 2599, "处": 2600, "备": 2601, "変": 2602, "复": 2603, "夏": 2604, "夔": 2605, "夕": 2606, "外": 2607, "夙": 2608, "多": 2609, "夜": 2610, "够": 2611, "夠": 2612, "夢": 2613, "夤": 2614, "夥": 2615, "大": 2616, "天": 2617, "太": 2618, "夫": 2619, "夭": 2620, "央": 2621, "夯": 2622, "失": 2623, "头": 2624, "夷": 2625, "夸": 2626, "夹": 2627, "夺": 2628, "夾": 2629, "奂": 2630, "奄": 2631, "奇": 2632, "奈": 2633, "奉": 2634, "奋": 2635, "奎": 2636, "奏": 2637, "奐": 2638, "契": 2639, "奔": 2640, "奕": 2641, "奖": 2642, "套": 2643, "奘": 2644, "奚": 2645, "奠": 2646, "奢": 2647, "奥": 2648, "奧": 2649, "奪": 2650, "奬": 2651, "奭": 2652, "奮": 2653, "女": 2654, "奴": 2655, "奶": 2656, "奸": 2657, "她": 2658, "好": 2659, "如": 2660, "妃": 2661, "妄": 2662, "妆": 2663, "妇": 2664, "妈": 2665, "妊": 2666, "妍": 2667, "妒": 2668, "妓": 2669, "妖": 2670, "妙": 2671, "妝": 2672, "妞": 2673, "妣": 2674, "妤": 2675, "妥": 2676, "妨": 2677, "妮": 2678, "妲": 2679, "妳": 2680, "妹": 2681, "妻": 2682, "妾": 2683, "姆": 2684, "姉": 2685, "姊": 2686, "始": 2687, "姐": 2688, "姑": 2689, "姒": 2690, "姓": 2691, "委": 2692, "姗": 2693, "姚": 2694, "姜": 2695, "姝": 2696, "姣": 2697, "姥": 2698, "姦": 2699, "姨": 2700, "姪": 2701, "姫": 2702, "姬": 2703, "姻": 2704, "姿": 2705, "威": 2706, "娃": 2707, "娄": 2708, "娅": 2709, "娇": 2710, "娑": 2711, "娓": 2712, "娘": 2713, "娛": 2714, "娜": 2715, "娟": 2716, "娠": 2717, "娣": 2718, "娥": 2719, "娩": 2720, "娱": 2721, "娲": 2722, "娴": 2723, "娶": 2724, "娼": 2725, "婁": 2726, "婆": 2727, "婉": 2728, "婕": 2729, "婚": 2730, "婢": 2731, "婦": 2732, "婧": 2733, "婪": 2734, "婭": 2735, "婴": 2736, "婵": 2737, "婷": 2738, "婺": 2739, "婿": 2740, "媒": 2741, "媚": 2742, "媛": 2743, "媲": 2744, "媳": 2745, "媽": 2746, "媾": 2747, "嫁": 2748, "嫂": 2749, "嫉": 2750, "嫌": 2751, "嫔": 2752, "嫖": 2753, "嫘": 2754, "嫡": 2755, "嫣": 2756, "嫦": 2757, "嫩": 2758, "嫻": 2759, "嬅": 2760, "嬉": 2761, "嬌": 2762, "嬖": 2763, "嬗": 2764, "嬤": 2765, "嬪": 2766, "嬰": 2767, "嬴": 2768, "嬸": 2769, "孀": 2770, "子": 2771, "孑": 2772, "孔": 2773, "孕": 2774, "字": 2775, "存": 2776, "孙": 2777, "孚": 2778, "孛": 2779, "孜": 2780, "孝": 2781, "孟": 2782, "孢": 2783, "季": 2784, "孤": 2785, "孥": 2786, "学": 2787, "孩": 2788, "孪": 2789, "孫": 2790, "孰": 2791, "孱": 2792, "孳": 2793, "孵": 2794, "學": 2795, "孺": 2796, "孽": 2797, "孿": 2798, "宁": 2799, "它": 2800, "宅": 2801, "宇": 2802, "守": 2803, "安": 2804, "宋": 2805, "完": 2806, "宏": 2807, "宓": 2808, "宕": 2809, "宗": 2810, "官": 2811, "宙": 2812, "定": 2813, "宛": 2814, "宜": 2815, "宝": 2816, "实": 2817, "実": 2818, "宠": 2819, "审": 2820, "客": 2821, "宣": 2822, "室": 2823, "宥": 2824, "宦": 2825, "宪": 2826, "宫": 2827, "宮": 2828, "宰": 2829, "害": 2830, "宴": 2831, "宵": 2832, "家": 2833, "宸": 2834, "容": 2835, "宽": 2836, "宾": 2837, "宿": 2838, "寀": 2839, "寂": 2840, "寄": 2841, "寅": 2842, "密": 2843, "寇": 2844, "富": 2845, "寐": 2846, "寒": 2847, "寓": 2848, "寔": 2849, "寛": 2850, "寝": 2851, "寞": 2852, "察": 2853, "寡": 2854, "寢": 2855, "寤": 2856, "寥": 2857, "實": 2858, "寧": 2859, "寨": 2860, "審": 2861, "寫": 2862, "寬": 2863, "寮": 2864, "寰": 2865, "寵": 2866, "寶": 2867, "寸": 2868, "对": 2869, "寺": 2870, "寻": 2871, "导": 2872, "対": 2873, "寿": 2874, "封": 2875, "専": 2876, "射": 2877, "将": 2878, "將": 2879, "專": 2880, "尉": 2881, "尊": 2882, "尋": 2883, "對": 2884, "導": 2885, "小": 2886, "少": 2887, "尔": 2888, "尕": 2889, "尖": 2890, "尘": 2891, "尚": 2892, "尝": 2893, "尤": 2894, "尧": 2895, "尬": 2896, "就": 2897, "尴": 2898, "尷": 2899, "尸": 2900, "尹": 2901, "尺": 2902, "尻": 2903, "尼": 2904, "尽": 2905, "尾": 2906, "尿": 2907, "局": 2908, "屁": 2909, "层": 2910, "居": 2911, "屆": 2912, "屈": 2913, "屉": 2914, "届": 2915, "屋": 2916, "屍": 2917, "屎": 2918, "屏": 2919, "屑": 2920, "展": 2921, "属": 2922, "屠": 2923, "屡": 2924, "屢": 2925, "層": 2926, "履": 2927, "屬": 2928, "屯": 2929, "山": 2930, "屹": 2931, "屿": 2932, "岀": 2933, "岁": 2934, "岂": 2935, "岌": 2936, "岐": 2937, "岑": 2938, "岔": 2939, "岖": 2940, "岗": 2941, "岘": 2942, "岙": 2943, "岚": 2944, "岛": 2945, "岡": 2946, "岢": 2947, "岩": 2948, "岫": 2949, "岬": 2950, "岭": 2951, "岱": 2952, "岳": 2953, "岷": 2954, "岸": 2955, "岿": 2956, "峁": 2957, "峄": 2958, "峇": 2959, "峒": 2960, "峙": 2961, "峡": 2962, "峤": 2963, "峥": 2964, "峦": 2965, "峨": 2966, "峪": 2967, "峭": 2968, "峯": 2969, "峰": 2970, "峴": 2971, "島": 2972, "峻": 2973, "峽": 2974, "崁": 2975, "崃": 2976, "崇": 2977, "崎": 2978, "崐": 2979, "崑": 2980, "崔": 2981, "崖": 2982, "崗": 2983, "崙": 2984, "崛": 2985, "崧": 2986, "崩": 2987, "崭": 2988, "崴": 2989, "嵇": 2990, "嵊": 2991, "嵋": 2992, "嵌": 2993, "嵐": 2994, "嵗": 2995, "嵘": 2996, "嵩": 2997, "嵬": 2998, "嵯": 2999, "嵴": 3000, "嶂": 3001, "嶄": 3002, "嶋": 3003, "嶟": 3004, "嶷": 3005, "嶺": 3006, "嶼": 3007, "嶽": 3008, "巂": 3009, "巅": 3010, "巍": 3011, "巒": 3012, "巔": 3013, "巖": 3014, "川": 3015, "州": 3016, "巡": 3017, "巢": 3018, "工": 3019, "左": 3020, "巧": 3021, "巨": 3022, "巩": 3023, "巫": 3024, "差": 3025, "巯": 3026, "己": 3027, "已": 3028, "巳": 3029, "巴": 3030, "巷": 3031, "巻": 3032, "巽": 3033, "巾": 3034, "币": 3035, "市": 3036, "布": 3037, "帅": 3038, "帆": 3039, "师": 3040, "希": 3041, "帐": 3042, "帑": 3043, "帕": 3044, "帖": 3045, "帘": 3046, "帙": 3047, "帚": 3048, "帛": 3049, "帜": 3050, "帝": 3051, "帥": 3052, "带": 3053, "帧": 3054, "師": 3055, "席": 3056, "帮": 3057, "帯": 3058, "帰": 3059, "帳": 3060, "帶": 3061, "帷": 3062, "常": 3063, "帼": 3064, "帽": 3065, "幀": 3066, "幂": 3067, "幄": 3068, "幅": 3069, "幌": 3070, "幔": 3071, "幕": 3072, "幞": 3073, "幟": 3074, "幡": 3075, "幢": 3076, "幣": 3077, "幫": 3078, "干": 3079, "平": 3080, "年": 3081, "幵": 3082, "并": 3083, "幸": 3084, "幹": 3085, "幺": 3086, "幻": 3087, "幼": 3088, "幽": 3089, "幾": 3090, "广": 3091, "庁": 3092, "広": 3093, "庄": 3094, "庆": 3095, "庇": 3096, "床": 3097, "庋": 3098, "序": 3099, "庐": 3100, "庑": 3101, "库": 3102, "应": 3103, "底": 3104, "庖": 3105, "店": 3106, "庙": 3107, "庚": 3108, "府": 3109, "庞": 3110, "废": 3111, "庠": 3112, "度": 3113, "座": 3114, "庫": 3115, "庭": 3116, "庵": 3117, "庶": 3118, "康": 3119, "庸": 3120, "庾": 3121, "廁": 3122, "廂": 3123, "廈": 3124, "廉": 3125, "廊": 3126, "廍": 3127, "廒": 3128, "廓": 3129, "廕": 3130, "廖": 3131, "廚": 3132, "廟": 3133, "廠": 3134, "廢": 3135, "廣": 3136, "廩": 3137, "廪": 3138, "廬": 3139, "廳": 3140, "延": 3141, "廷": 3142, "建": 3143, "廿": 3144, "开": 3145, "弁": 3146, "异": 3147, "弃": 3148, "弄": 3149, "弇": 3150, "弈": 3151, "弊": 3152, "弋": 3153, "式": 3154, "弑": 3155, "弒": 3156, "弓": 3157, "弔": 3158, "引": 3159, "弗": 3160, "弘": 3161, "弛": 3162, "弟": 3163, "张": 3164, "弢": 3165, "弥": 3166, "弦": 3167, "弧": 3168, "弩": 3169, "弭": 3170, "弯": 3171, "弱": 3172, "張": 3173, "強": 3174, "弹": 3175, "强": 3176, "弼": 3177, "弾": 3178, "彈": 3179, "彌": 3180, "彎": 3181, "归": 3182, "当": 3183, "录": 3184, "彖": 3185, "彗": 3186, "彙": 3187, "彝": 3188, "形": 3189, "彤": 3190, "彥": 3191, "彦": 3192, "彧": 3193, "彩": 3194, "彪": 3195, "彬": 3196, "彭": 3197, "彰": 3198, "影": 3199, "彷": 3200, "役": 3201, "彻": 3202, "彼": 3203, "往": 3204, "征": 3205, "径": 3206, "待": 3207, "徇": 3208, "很": 3209, "徉": 3210, "徊": 3211, "律": 3212, "後": 3213, "徐": 3214, "徑": 3215, "徒": 3216, "徕": 3217, "得": 3218, "徘": 3219, "徙": 3220, "徜": 3221, "從": 3222, "御": 3223, "徨": 3224, "復": 3225, "循": 3226, "徭": 3227, "微": 3228, "徳": 3229, "徴": 3230, "徵": 3231, "德": 3232, "徹": 3233, "徽": 3234, "心": 3235, "必": 3236, "忆": 3237, "忌": 3238, "忍": 3239, "忏": 3240, "忒": 3241, "志": 3242, "忘": 3243, "忙": 3244, "忠": 3245, "忡": 3246, "忤": 3247, "忧": 3248, "快": 3249, "忱": 3250, "念": 3251, "忻": 3252, "忽": 3253, "忾": 3254, "忿": 3255, "怀": 3256, "态": 3257, "怂": 3258, "怅": 3259, "怎": 3260, "怒": 3261, "怕": 3262, "怖": 3263, "怙": 3264, "怛": 3265, "怜": 3266, "思": 3267, "怠": 3268, "怡": 3269, "急": 3270, "怦": 3271, "性": 3272, "怨": 3273, "怪": 3274, "怯": 3275, "怵": 3276, "总": 3277, "恂": 3278, "恃": 3279, "恆": 3280, "恋": 3281, "恍": 3282, "恐": 3283, "恒": 3284, "恕": 3285, "恙": 3286, "恚": 3287, "恢": 3288, "恣": 3289, "恤": 3290, "恥": 3291, "恨": 3292, "恩": 3293, "恪": 3294, "恫": 3295, "恬": 3296, "恭": 3297, "息": 3298, "恰": 3299, "恳": 3300, "恵": 3301, "恶": 3302, "恸": 3303, "恺": 3304, "恻": 3305, "恼": 3306, "恽": 3307, "恿": 3308, "悄": 3309, "悅": 3310, "悉": 3311, "悌": 3312, "悍": 3313, "悔": 3314, "悖": 3315, "悚": 3316, "悝": 3317, "悟": 3318, "悠": 3319, "患": 3320, "悦": 3321, "您": 3322, "悪": 3323, "悬": 3324, "悭": 3325, "悯": 3326, "悱": 3327, "悲": 3328, "悴": 3329, "悶": 3330, "悸": 3331, "悼": 3332, "情": 3333, "惆": 3334, "惇": 3335, "惊": 3336, "惋": 3337, "惑": 3338, "惕": 3339, "惘": 3340, "惚": 3341, "惜": 3342, "惟": 3343, "惠": 3344, "惡": 3345, "惣": 3346, "惦": 3347, "惧": 3348, "惨": 3349, "惩": 3350, "惫": 3351, "惬": 3352, "惭": 3353, "惮": 3354, "惯": 3355, "惰": 3356, "惱": 3357, "想": 3358, "惶": 3359, "惹": 3360, "惺": 3361, "愁": 3362, "愈": 3363, "愉": 3364, "愍": 3365, "意": 3366, "愕": 3367, "愚": 3368, "愛": 3369, "感": 3370, "愤": 3371, "愧": 3372, "愫": 3373, "愷": 3374, "愿": 3375, "慈": 3376, "態": 3377, "慌": 3378, "慎": 3379, "慑": 3380, "慕": 3381, "慘": 3382, "慟": 3383, "慢": 3384, "慣": 3385, "慧": 3386, "慨": 3387, "慮": 3388, "慰": 3389, "慶": 3390, "慷": 3391, "慾": 3392, "憂": 3393, "憋": 3394, "憎": 3395, "憐": 3396, "憑": 3397, "憔": 3398, "憤": 3399, "憧": 3400, "憨": 3401, "憩": 3402, "憫": 3403, "憬": 3404, "憲": 3405, "憶": 3406, "憾": 3407, "懂": 3408, "懇": 3409, "懈": 3410, "應": 3411, "懋": 3412, "懑": 3413, "懒": 3414, "懦": 3415, "懲": 3416, "懵": 3417, "懶": 3418, "懷": 3419, "懸": 3420, "懺": 3421, "懼": 3422, "懿": 3423, "戀": 3424, "戈": 3425, "戊": 3426, "戌": 3427, "戍": 3428, "戎": 3429, "戏": 3430, "成": 3431, "我": 3432, "戒": 3433, "戕": 3434, "或": 3435, "戗": 3436, "战": 3437, "戚": 3438, "戛": 3439, "戟": 3440, "戡": 3441, "戢": 3442, "戦": 3443, "截": 3444, "戮": 3445, "戰": 3446, "戲": 3447, "戳": 3448, "戴": 3449, "戶": 3450, "户": 3451, "戸": 3452, "戾": 3453, "房": 3454, "所": 3455, "扁": 3456, "扇": 3457, "扈": 3458, "扉": 3459, "手": 3460, "才": 3461, "扎": 3462, "扑": 3463, "扒": 3464, "打": 3465, "扔": 3466, "托": 3467, "扛": 3468, "扣": 3469, "扦": 3470, "执": 3471, "扩": 3472, "扪": 3473, "扫": 3474, "扬": 3475, "扭": 3476, "扮": 3477, "扯": 3478, "扰": 3479, "扳": 3480, "扶": 3481, "批": 3482, "扼": 3483, "找": 3484, "承": 3485, "技": 3486, "抄": 3487, "抉": 3488, "把": 3489, "抑": 3490, "抒": 3491, "抓": 3492, "投": 3493, "抖": 3494, "抗": 3495, "折": 3496, "抚": 3497, "抛": 3498, "抟": 3499, "抠": 3500, "抢": 3501, "护": 3502, "报": 3503, "抨": 3504, "披": 3505, "抬": 3506, "抱": 3507, "抵": 3508, "抹": 3509, "押": 3510, "抽": 3511, "拂": 3512, "担": 3513, "拆": 3514, "拇": 3515, "拈": 3516, "拉": 3517, "拋": 3518, "拌": 3519, "拍": 3520, "拎": 3521, "拐": 3522, "拒": 3523, "拓": 3524, "拔": 3525, "拖": 3526, "拗": 3527, "拘": 3528, "拙": 3529, "拚": 3530, "招": 3531, "拜": 3532, "拟": 3533, "拢": 3534, "拣": 3535, "拥": 3536, "拦": 3537, "拧": 3538, "拨": 3539, "择": 3540, "括": 3541, "拭": 3542, "拮": 3543, "拯": 3544, "拱": 3545, "拳": 3546, "拴": 3547, "拷": 3548, "拼": 3549, "拽": 3550, "拾": 3551, "拿": 3552, "持": 3553, "挂": 3554, "指": 3555, "挈": 3556, "按": 3557, "挑": 3558, "挖": 3559, "挚": 3560, "挛": 3561, "挝": 3562, "挞": 3563, "挟": 3564, "挠": 3565, "挡": 3566, "挣": 3567, "挤": 3568, "挥": 3569, "挨": 3570, "挪": 3571, "挫": 3572, "振": 3573, "挹": 3574, "挺": 3575, "挽": 3576, "挾": 3577, "捂": 3578, "捅": 3579, "捆": 3580, "捉": 3581, "捌": 3582, "捍": 3583, "捎": 3584, "捏": 3585, "捐": 3586, "捕": 3587, "捞": 3588, "损": 3589, "捡": 3590, "换": 3591, "捣": 3592, "捧": 3593, "捨": 3594, "捩": 3595, "捭": 3596, "据": 3597, "捲": 3598, "捶": 3599, "捷": 3600, "捺": 3601, "捻": 3602, "掀": 3603, "掂": 3604, "掃": 3605, "授": 3606, "掉": 3607, "掌": 3608, "掏": 3609, "掐": 3610, "排": 3611, "掖": 3612, "掘": 3613, "掙": 3614, "掛": 3615, "掟": 3616, "掠": 3617, "採": 3618, "探": 3619, "掣": 3620, "接": 3621, "控": 3622, "推": 3623, "掩": 3624, "措": 3625, "掬": 3626, "掰": 3627, "掲": 3628, "掳": 3629, "掷": 3630, "掸": 3631, "掺": 3632, "掾": 3633, "揀": 3634, "揄": 3635, "揆": 3636, "揉": 3637, "描": 3638, "提": 3639, "插": 3640, "揖": 3641, "揚": 3642, "換": 3643, "握": 3644, "揣": 3645, "揪": 3646, "揭": 3647, "揮": 3648, "援": 3649, "揽": 3650, "搀": 3651, "搁": 3652, "搂": 3653, "搅": 3654, "損": 3655, "搏": 3656, "搐": 3657, "搓": 3658, "搔": 3659, "搖": 3660, "搗": 3661, "搜": 3662, "搞": 3663, "搪": 3664, "搬": 3665, "搭": 3666, "搶": 3667, "携": 3668, "摄": 3669, "摆": 3670, "摇": 3671, "摈": 3672, "摊": 3673, "摒": 3674, "摔": 3675, "摘": 3676, "摧": 3677, "摩": 3678, "摭": 3679, "摯": 3680, "摸": 3681, "摹": 3682, "摺": 3683, "摻": 3684, "撂": 3685, "撃": 3686, "撇": 3687, "撈": 3688, "撐": 3689, "撑": 3690, "撒": 3691, "撓": 3692, "撕": 3693, "撞": 3694, "撤": 3695, "撥": 3696, "撩": 3697, "撫": 3698, "撬": 3699, "播": 3700, "撮": 3701, "撰": 3702, "撲": 3703, "撷": 3704, "撸": 3705, "撻": 3706, "撼": 3707, "撾": 3708, "撿": 3709, "擁": 3710, "擂": 3711, "擄": 3712, "擅": 3713, "擇": 3714, "擊": 3715, "擋": 3716, "操": 3717, "擎": 3718, "擒": 3719, "擔": 3720, "擘": 3721, "據": 3722, "擠": 3723, "擢": 3724, "擦": 3725, "擬": 3726, "擱": 3727, "擲": 3728, "擴": 3729, "擷": 3730, "擺": 3731, "擾": 3732, "攀": 3733, "攏": 3734, "攒": 3735, "攔": 3736, "攘": 3737, "攜": 3738, "攝": 3739, "攤": 3740, "攪": 3741, "攫": 3742, "攬": 3743, "支": 3744, "收": 3745, "攸": 3746, "改": 3747, "攻": 3748, "放": 3749, "政": 3750, "敁": 3751, "故": 3752, "效": 3753, "敌": 3754, "敍": 3755, "敏": 3756, "救": 3757, "敕": 3758, "敖": 3759, "敗": 3760, "敘": 3761, "教": 3762, "敛": 3763, "敝": 3764, "敞": 3765, "敢": 3766, "散": 3767, "敦": 3768, "敬": 3769, "数": 3770, "敲": 3771, "整": 3772, "敵": 3773, "敷": 3774, "數": 3775, "斂": 3776, "斃": 3777, "文": 3778, "斋": 3779, "斌": 3780, "斎": 3781, "斐": 3782, "斑": 3783, "斓": 3784, "斗": 3785, "料": 3786, "斛": 3787, "斜": 3788, "斟": 3789, "斡": 3790, "斤": 3791, "斥": 3792, "斧": 3793, "斩": 3794, "斫": 3795, "斬": 3796, "断": 3797, "斯": 3798, "新": 3799, "斷": 3800, "方": 3801, "於": 3802, "施": 3803, "旁": 3804, "旃": 3805, "旅": 3806, "旋": 3807, "旌": 3808, "族": 3809, "旒": 3810, "旗": 3811, "无": 3812, "既": 3813, "日": 3814, "旦": 3815, "旧": 3816, "旨": 3817, "早": 3818, "旬": 3819, "旭": 3820, "旱": 3821, "时": 3822, "旷": 3823, "旸": 3824, "旺": 3825, "旻": 3826, "旼": 3827, "昀": 3828, "昂": 3829, "昃": 3830, "昆": 3831, "昇": 3832, "昉": 3833, "昊": 3834, "昌": 3835, "明": 3836, "昏": 3837, "易": 3838, "昔": 3839, "昕": 3840, "昙": 3841, "星": 3842, "映": 3843, "春": 3844, "昧": 3845, "昨": 3846, "昭": 3847, "是": 3848, "昱": 3849, "昴": 3850, "昵": 3851, "昶": 3852, "昺": 3853, "昼": 3854, "显": 3855, "晁": 3856, "時": 3857, "晃": 3858, "晉": 3859, "晋": 3860, "晏": 3861, "晒": 3862, "晓": 3863, "晔": 3864, "晕": 3865, "晖": 3866, "晗": 3867, "晚": 3868, "晝": 3869, "晞": 3870, "晟": 3871, "晤": 3872, "晦": 3873, "晨": 3874, "晩": 3875, "普": 3876, "景": 3877, "晰": 3878, "晴": 3879, "晶": 3880, "晷": 3881, "智": 3882, "晾": 3883, "暂": 3884, "暄": 3885, "暇": 3886, "暈": 3887, "暉": 3888, "暎": 3889, "暐": 3890, "暑": 3891, "暖": 3892, "暗": 3893, "暘": 3894, "暝": 3895, "暠": 3896, "暢": 3897, "暧": 3898, "暨": 3899, "暫": 3900, "暮": 3901, "暱": 3902, "暴": 3903, "暹": 3904, "曄": 3905, "曆": 3906, "曇": 3907, "曉": 3908, "曖": 3909, "曙": 3910, "曜": 3911, "曝": 3912, "曠": 3913, "曦": 3914, "曩": 3915, "曬": 3916, "曰": 3917, "曲": 3918, "曳": 3919, "更": 3920, "書": 3921, "曹": 3922, "曼": 3923, "曾": 3924, "替": 3925, "最": 3926, "會": 3927, "月": 3928, "有": 3929, "朊": 3930, "朋": 3931, "服": 3932, "朏": 3933, "朐": 3934, "朓": 3935, "朔": 3936, "朕": 3937, "朗": 3938, "望": 3939, "朝": 3940, "期": 3941, "朦": 3942, "朧": 3943, "木": 3944, "未": 3945, "末": 3946, "本": 3947, "札": 3948, "术": 3949, "朱": 3950, "朴": 3951, "朵": 3952, "机": 3953, "朽": 3954, "杀": 3955, "杂": 3956, "权": 3957, "杆": 3958, "杈": 3959, "杉": 3960, "李": 3961, "杏": 3962, "材": 3963, "村": 3964, "杓": 3965, "杖": 3966, "杜": 3967, "杞": 3968, "束": 3969, "杠": 3970, "条": 3971, "来": 3972, "杨": 3973, "杭": 3974, "杯": 3975, "杰": 3976, "東": 3977, "杲": 3978, "杳": 3979, "杵": 3980, "杷": 3981, "松": 3982, "板": 3983, "极": 3984, "构": 3985, "枇": 3986, "枉": 3987, "枋": 3988, "析": 3989, "枕": 3990, "林": 3991, "枚": 3992, "果": 3993, "枝": 3994, "枞": 3995, "枢": 3996, "枣": 3997, "枨": 3998, "枪": 3999, "枫": 4000, "枭": 4001, "枯": 4002, "枳": 4003, "架": 4004, "枷": 4005, "枸": 4006, "柄": 4007, "柏": 4008, "某": 4009, "柑": 4010, "柒": 4011, "染": 4012, "柔": 4013, "柘": 4014, "柚": 4015, "柜": 4016, "柞": 4017, "柠": 4018, "柢": 4019, "查": 4020, "柩": 4021, "柬": 4022, "柯": 4023, "柱": 4024, "柲": 4025, "柳": 4026, "柴": 4027, "柵": 4028, "査": 4029, "柿": 4030, "栀": 4031, "栃": 4032, "栄": 4033, "栅": 4034, "标": 4035, "栈": 4036, "栉": 4037, "栋": 4038, "栎": 4039, "栏": 4040, "树": 4041, "栓": 4042, "栖": 4043, "栗": 4044, "校": 4045, "栢": 4046, "栩": 4047, "株": 4048, "栱": 4049, "样": 4050, "核": 4051, "根": 4052, "栻": 4053, "格": 4054, "栽": 4055, "栾": 4056, "桀": 4057, "桁": 4058, "桂": 4059, "桃": 4060, "桅": 4061, "框": 4062, "案": 4063, "桉": 4064, "桌": 4065, "桎": 4066, "桐": 4067, "桑": 4068, "桓": 4069, "桔": 4070, "桜": 4071, "桡": 4072, "桢": 4073, "档": 4074, "桥": 4075, "桦": 4076, "桧": 4077, "桨": 4078, "桩": 4079, "桫": 4080, "桶": 4081, "桿": 4082, "梁": 4083, "梅": 4084, "梆": 4085, "梏": 4086, "梓": 4087, "梗": 4088, "條": 4089, "梟": 4090, "梢": 4091, "梦": 4092, "梧": 4093, "梨": 4094, "梭": 4095, "梯": 4096, "械": 4097, "梳": 4098, "梵": 4099, "梶": 4100, "检": 4101, "棂": 4102, "棄": 4103, "棉": 4104, "棋": 4105, "棍": 4106, "棒": 4107, "棕": 4108, "棗": 4109, "棘": 4110, "棚": 4111, "棟": 4112, "棠": 4113, "棣": 4114, "棧": 4115, "棨": 4116, "森": 4117, "棱": 4118, "棲": 4119, "棵": 4120, "棹": 4121, "棺": 4122, "棻": 4123, "椁": 4124, "椅": 4125, "植": 4126, "椎": 4127, "椒": 4128, "椤": 4129, "椭": 4130, "椰": 4131, "椹": 4132, "椽": 4133, "椿": 4134, "楂": 4135, "楊": 4136, "楓": 4137, "楔": 4138, "楚": 4139, "楝": 4140, "楞": 4141, "楠": 4142, "楣": 4143, "楦": 4144, "楨": 4145, "楫": 4146, "業": 4147, "極": 4148, "楷": 4149, "楸": 4150, "楹": 4151, "楼": 4152, "楽": 4153, "概": 4154, "榄": 4155, "榆": 4156, "榈": 4157, "榉": 4158, "榊": 4159, "榎": 4160, "榔": 4161, "榕": 4162, "榘": 4163, "榛": 4164, "榜": 4165, "榦": 4166, "榧": 4167, "榨": 4168, "榫": 4169, "榭": 4170, "榮": 4171, "榴": 4172, "榷": 4173, "榻": 4174, "槁": 4175, "槃": 4176, "構": 4177, "槌": 4178, "槍": 4179, "槎": 4180, "槐": 4181, "槓": 4182, "様": 4183, "槛": 4184, "槟": 4185, "槭": 4186, "槳": 4187, "槻": 4188, "槽": 4189, "槿": 4190, "樁": 4191, "樂": 4192, "樊": 4193, "樋": 4194, "樑": 4195, "樓": 4196, "樗": 4197, "標": 4198, "樞": 4199, "樟": 4200, "模": 4201, "樣": 4202, "権": 4203, "横": 4204, "樱": 4205, "樵": 4206, "樸": 4207, "樹": 4208, "樺": 4209, "樽": 4210, "樾": 4211, "橄": 4212, "橇": 4213, "橋": 4214, "橘": 4215, "橙": 4216, "機": 4217, "橡": 4218, "橢": 4219, "橥": 4220, "橦": 4221, "橫": 4222, "橱": 4223, "橼": 4224, "檀": 4225, "檄": 4226, "檎": 4227, "檐": 4228, "檔": 4229, "檗": 4230, "檜": 4231, "檢": 4232, "檩": 4233, "檬": 4234, "檯": 4235, "檳": 4236, "檸": 4237, "檻": 4238, "櫂": 4239, "櫃": 4240, "櫚": 4241, "櫛": 4242, "櫸": 4243, "櫻": 4244, "欄": 4245, "權": 4246, "欒": 4247, "欖": 4248, "欠": 4249, "次": 4250, "欢": 4251, "欣": 4252, "欤": 4253, "欧": 4254, "欲": 4255, "欺": 4256, "欽": 4257, "款": 4258, "歆": 4259, "歇": 4260, "歉": 4261, "歌": 4262, "歎": 4263, "歐": 4264, "歙": 4265, "歡": 4266, "止": 4267, "正": 4268, "此": 4269, "步": 4270, "武": 4271, "歧": 4272, "歩": 4273, "歪": 4274, "歲": 4275, "歳": 4276, "歴": 4277, "歷": 4278, "歸": 4279, "歹": 4280, "死": 4281, "歼": 4282, "歿": 4283, "殁": 4284, "殃": 4285, "殆": 4286, "殇": 4287, "殉": 4288, "殊": 4289, "残": 4290, "殓": 4291, "殖": 4292, "殘": 4293, "殚": 4294, "殡": 4295, "殤": 4296, "殭": 4297, "殯": 4298, "殲": 4299, "殴": 4300, "段": 4301, "殷": 4302, "殺": 4303, "殼": 4304, "殿": 4305, "毀": 4306, "毁": 4307, "毂": 4308, "毅": 4309, "毆": 4310, "毋": 4311, "毌": 4312, "母": 4313, "毎": 4314, "每": 4315, "毒": 4316, "毓": 4317, "比": 4318, "毕": 4319, "毖": 4320, "毗": 4321, "毘": 4322, "毙": 4323, "毛": 4324, "毡": 4325, "毫": 4326, "毯": 4327, "毽": 4328, "氏": 4329, "氐": 4330, "民": 4331, "氓": 4332, "气": 4333, "氖": 4334, "気": 4335, "氘": 4336, "氙": 4337, "氚": 4338, "氛": 4339, "氟": 4340, "氡": 4341, "氢": 4342, "氣": 4343, "氦": 4344, "氧": 4345, "氨": 4346, "氩": 4347, "氪": 4348, "氫": 4349, "氮": 4350, "氯": 4351, "氰": 4352, "水": 4353, "永": 4354, "氹": 4355, "氾": 4356, "汀": 4357, "汁": 4358, "求": 4359, "汇": 4360, "汉": 4361, "汊": 4362, "汎": 4363, "汐": 4364, "汕": 4365, "汗": 4366, "汙": 4367, "汛": 4368, "汜": 4369, "汝": 4370, "汞": 4371, "江": 4372, "池": 4373, "污": 4374, "汤": 4375, "汨": 4376, "汪": 4377, "汰": 4378, "汲": 4379, "汴": 4380, "汶": 4381, "汹": 4382, "決": 4383, "汽": 4384, "汾": 4385, "沁": 4386, "沂": 4387, "沃": 4388, "沅": 4389, "沆": 4390, "沈": 4391, "沉": 4392, "沌": 4393, "沐": 4394, "沒": 4395, "沓": 4396, "沔": 4397, "沖": 4398, "沙": 4399, "沛": 4400, "沟": 4401, "没": 4402, "沢": 4403, "沣": 4404, "沤": 4405, "沥": 4406, "沦": 4407, "沧": 4408, "沪": 4409, "沫": 4410, "沭": 4411, "沮": 4412, "沱": 4413, "河": 4414, "沸": 4415, "油": 4416, "治": 4417, "沼": 4418, "沽": 4419, "沾": 4420, "沿": 4421, "況": 4422, "泃": 4423, "泄": 4424, "泉": 4425, "泊": 4426, "泌": 4427, "泓": 4428, "法": 4429, "泗": 4430, "泛": 4431, "泞": 4432, "泠": 4433, "泡": 4434, "波": 4435, "泣": 4436, "泥": 4437, "注": 4438, "泪": 4439, "泮": 4440, "泯": 4441, "泰": 4442, "泱": 4443, "泳": 4444, "泵": 4445, "泸": 4446, "泺": 4447, "泻": 4448, "泼": 4449, "泽": 4450, "泾": 4451, "洁": 4452, "洄": 4453, "洋": 4454, "洎": 4455, "洒": 4456, "洗": 4457, "洙": 4458, "洛": 4459, "洞": 4460, "津": 4461, "洧": 4462, "洩": 4463, "洪": 4464, "洮": 4465, "洱": 4466, "洲": 4467, "洵": 4468, "洶": 4469, "洹": 4470, "洺": 4471, "活": 4472, "洼": 4473, "洽": 4474, "派": 4475, "流": 4476, "浅": 4477, "浆": 4478, "浇": 4479, "浊": 4480, "测": 4481, "济": 4482, "浏": 4483, "浑": 4484, "浒": 4485, "浓": 4486, "浔": 4487, "浙": 4488, "浚": 4489, "浜": 4490, "浣": 4491, "浦": 4492, "浩": 4493, "浪": 4494, "浮": 4495, "浴": 4496, "海": 4497, "浸": 4498, "涂": 4499, "涅": 4500, "涇": 4501, "消": 4502, 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"湟": 4594, "湧": 4595, "湮": 4596, "湯": 4597, "湳": 4598, "湾": 4599, "湿": 4600, "満": 4601, "溃": 4602, "溅": 4603, "溉": 4604, "溏": 4605, "源": 4606, "準": 4607, "溜": 4608, "溝": 4609, "溟": 4610, "溢": 4611, "溥": 4612, "溧": 4613, "溪": 4614, "溫": 4615, "溯": 4616, "溱": 4617, "溴": 4618, "溶": 4619, "溺": 4620, "滁": 4621, "滂": 4622, "滄": 4623, "滅": 4624, "滇": 4625, "滋": 4626, "滌": 4627, "滑": 4628, "滓": 4629, "滔": 4630, "滕": 4631, "滗": 4632, "滘": 4633, "滙": 4634, "滚": 4635, "滞": 4636, "满": 4637, "滤": 4638, "滥": 4639, "滦": 4640, "滨": 4641, "滩": 4642, "滬": 4643, "滯": 4644, "滲": 4645, "滴": 4646, "滸": 4647, "滾": 4648, "滿": 4649, "漁": 4650, "漂": 4651, "漆": 4652, "漏": 4653, "漓": 4654, "演": 4655, "漕": 4656, "漠": 4657, "漢": 4658, "漣": 4659, "漩": 4660, "漪": 4661, "漫": 4662, "漬": 4663, "漯": 4664, "漱": 4665, "漲": 4666, "漳": 4667, "漸": 4668, "漾": 4669, "漿": 4670, "潁": 4671, "潇": 4672, "潍": 4673, "潑": 4674, "潔": 4675, "潘": 4676, "潛": 4677, "潜": 4678, "潞": 4679, "潟": 4680, "潢": 4681, "潤": 4682, "潦": 4683, "潭": 4684, "潮": 4685, "潯": 4686, "潰": 4687, "潴": 4688, "潺": 4689, "潼": 4690, "澀": 4691, "澂": 4692, "澄": 4693, "澈": 4694, "澍": 4695, "澎": 4696, "澗": 4697, "澜": 4698, "澡": 4699, "澤": 4700, "澧": 4701, "澪": 4702, "澱": 4703, "澳": 4704, "澶": 4705, "澹": 4706, "激": 4707, "濁": 4708, "濂": 4709, "濃": 4710, "濉": 4711, "濑": 4712, "濒": 4713, "濕": 4714, "濛": 4715, "濞": 4716, "濟": 4717, "濠": 4718, "濡": 4719, "濤": 4720, "濫": 4721, "濬": 4722, "濮": 4723, "濯": 4724, "濱": 4725, "濾": 4726, "瀅": 4727, "瀉": 4728, "瀋": 4729, "瀏": 4730, "瀑": 4731, "瀕": 4732, "瀘": 4733, "瀚": 4734, "瀛": 4735, "瀝": 4736, "瀟": 4737, "瀧": 4738, "瀨": 4739, "瀬": 4740, "瀾": 4741, "灌": 4742, "灏": 4743, "灑": 4744, "灘": 4745, "灝": 4746, "灞": 4747, "灣": 4748, "火": 4749, "灭": 4750, "灯": 4751, "灰": 4752, "灵": 4753, "灶": 4754, "灸": 4755, "灼": 4756, "災": 4757, "灾": 4758, "灿": 4759, "炀": 4760, "炅": 4761, "炉": 4762, "炊": 4763, "炎": 4764, "炒": 4765, "炔": 4766, "炕": 4767, "炖": 4768, "炘": 4769, "炙": 4770, "炜": 4771, "炤": 4772, "炫": 4773, "炬": 4774, "炭": 4775, "炮": 4776, "炯": 4777, "炳": 4778, "炸": 4779, "点": 4780, "為": 4781, "炼": 4782, "炽": 4783, "烁": 4784, "烂": 4785, "烃": 4786, "烈": 4787, "烏": 4788, "烔": 4789, "烘": 4790, "烙": 4791, "烛": 4792, "烟": 4793, "烤": 4794, "烦": 4795, "烧": 4796, "烨": 4797, "烩": 4798, "烫": 4799, "烬": 4800, "热": 4801, "烯": 4802, "烷": 4803, "烹": 4804, "烽": 4805, "焉": 4806, "焊": 4807, "焓": 4808, "焕": 4809, "焖": 4810, "焘": 4811, "焙": 4812, "焚": 4813, "焜": 4814, "無": 4815, "焦": 4816, "焯": 4817, "焰": 4818, "焱": 4819, "然": 4820, "煅": 4821, "煇": 4822, "煉": 4823, "煊": 4824, "煌": 4825, "煎": 4826, "煒": 4827, "煙": 4828, "煜": 4829, "煞": 4830, "煤": 4831, "煥": 4832, "煦": 4833, "照": 4834, "煩": 4835, "煬": 4836, "煮": 4837, "煲": 4838, "煳": 4839, "煴": 4840, "煽": 4841, "熄": 4842, "熊": 4843, "熏": 4844, "熒": 4845, "熔": 4846, "熙": 4847, "熟": 4848, "熠": 4849, "熨": 4850, "熬": 4851, "熱": 4852, "熵": 4853, "熹": 4854, "熾": 4855, "燁": 4856, "燃": 4857, "燈": 4858, "燉": 4859, "燊": 4860, "燎": 4861, "燒": 4862, "燔": 4863, "燕": 4864, "燙": 4865, "營": 4866, 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"舉": 6232, "舊": 6233, "舌": 6234, "舍": 6235, "舎": 6236, "舒": 6237, "舔": 6238, "舖": 6239, "舘": 6240, "舛": 6241, "舜": 6242, "舞": 6243, "舟": 6244, "航": 6245, "舫": 6246, "般": 6247, "舰": 6248, "舱": 6249, "舵": 6250, "舶": 6251, "舷": 6252, "舸": 6253, "船": 6254, "舺": 6255, "艇": 6256, "艉": 6257, "艋": 6258, "艏": 6259, "艘": 6260, "艙": 6261, "艦": 6262, "艮": 6263, "良": 6264, "艰": 6265, "艱": 6266, "色": 6267, "艳": 6268, "艷": 6269, "艺": 6270, "艾": 6271, "艿": 6272, "节": 6273, "芃": 6274, "芊": 6275, "芋": 6276, "芍": 6277, "芎": 6278, "芒": 6279, "芙": 6280, "芜": 6281, "芝": 6282, "芡": 6283, "芥": 6284, "芦": 6285, "芩": 6286, "芪": 6287, "芬": 6288, "芭": 6289, "芮": 6290, "芯": 6291, "花": 6292, "芳": 6293, "芷": 6294, "芸": 6295, "芹": 6296, "芽": 6297, "芾": 6298, "苄": 6299, "苇": 6300, "苋": 6301, "苌": 6302, "苍": 6303, "苏": 6304, "苑": 6305, "苓": 6306, "苔": 6307, "苕": 6308, "苗": 6309, "苛": 6310, "苜": 6311, "苞": 6312, "苟": 6313, "苡": 6314, "苣": 6315, "若": 6316, "苦": 6317, "苧": 6318, "苫": 6319, "苯": 6320, "英": 6321, "苴": 6322, "苷": 6323, "苹": 6324, "苻": 6325, "茁": 6326, "茂": 6327, "范": 6328, "茄": 6329, "茅": 6330, "茉": 6331, "茌": 6332, "茎": 6333, "茏": 6334, "茗": 6335, "茜": 6336, "茧": 6337, "茨": 6338, "茫": 6339, "茬": 6340, "茭": 6341, "茯": 6342, "茱": 6343, "茲": 6344, "茴": 6345, "茵": 6346, "茶": 6347, "茸": 6348, "茹": 6349, "荀": 6350, "荃": 6351, "荆": 6352, "草": 6353, "荊": 6354, "荐": 6355, "荒": 6356, "荔": 6357, "荚": 6358, "荞": 6359, "荟": 6360, "荠": 6361, "荡": 6362, "荣": 6363, "荤": 6364, "荥": 6365, "荧": 6366, "荨": 6367, "荩": 6368, "荪": 6369, "荫": 6370, "药": 6371, "荳": 6372, "荷": 6373, "荻": 6374, "荼": 6375, "莅": 6376, "莆": 6377, "莉": 6378, "莊": 6379, "莎": 6380, "莒": 6381, "莓": 6382, "莖": 6383, "莘": 6384, "莞": 6385, "莠": 6386, "莢": 6387, "莨": 6388, "莪": 6389, "莫": 6390, "莱": 6391, "莲": 6392, "莴": 6393, "获": 6394, "莹": 6395, "莺": 6396, "莽": 6397, "菀": 6398, "菁": 6399, "菅": 6400, "菇": 6401, "菊": 6402, "菌": 6403, "菏": 6404, "菓": 6405, "菖": 6406, "菜": 6407, "菟": 6408, "菠": 6409, "菩": 6410, "菫": 6411, "華": 6412, "菰": 6413, "菱": 6414, "菲": 6415, "菴": 6416, "菸": 6417, "菽": 6418, "萃": 6419, "萄": 6420, "萇": 6421, "萊": 6422, "萌": 6423, "萍": 6424, "萎": 6425, "萘": 6426, "萜": 6427, "萝": 6428, "萤": 6429, "营": 6430, "萦": 6431, "萧": 6432, "萨": 6433, "萩": 6434, "萬": 6435, "萱": 6436, "萸": 6437, "萼": 6438, "落": 6439, "葆": 6440, "葉": 6441, "著": 6442, "葛": 6443, "葡": 6444, "董": 6445, "葦": 6446, "葩": 6447, "葫": 6448, "葬": 6449, "葱": 6450, "葵": 6451, "葺": 6452, "蒂": 6453, "蒋": 6454, "蒌": 6455, "蒐": 6456, "蒙": 6457, "蒜": 6458, "蒡": 6459, "蒨": 6460, "蒯": 6461, "蒲": 6462, "蒴": 6463, "蒸": 6464, "蒺": 6465, "蒼": 6466, "蒽": 6467, "蒿": 6468, "蓁": 6469, "蓄": 6470, "蓉": 6471, "蓋": 6472, "蓓": 6473, "蓖": 6474, "蓝": 6475, "蓟": 6476, "蓦": 6477, "蓬": 6478, "蓮": 6479, "蓼": 6480, "蓿": 6481, "蔑": 6482, "蔓": 6483, "蔔": 6484, "蔗": 6485, "蔚": 6486, "蔡": 6487, "蔣": 6488, "蔥": 6489, "蔫": 6490, "蔬": 6491, "蔭": 6492, "蔵": 6493, "蔷": 6494, "蔺": 6495, "蔻": 6496, "蔼": 6497, "蔽": 6498, "蕃": 6499, "蕈": 6500, "蕉": 6501, "蕊": 6502, "蕙": 6503, "蕨": 6504, "蕩": 6505, "蕪": 6506, "蕭": 6507, "蕲": 6508, "蕴": 6509, "蕾": 6510, "薄": 6511, "薅": 6512, "薇": 6513, "薈": 6514, "薊": 6515, "薏": 6516, "薑": 6517, "薔": 6518, "薙": 6519, "薛": 6520, "薦": 6521, "薨": 6522, "薩": 6523, "薪": 6524, "薫": 6525, "薮": 6526, "薯": 6527, "薰": 6528, "藁": 6529, "藉": 6530, "藍": 6531, "藏": 6532, "藐": 6533, "藓": 6534, "藔": 6535, "藕": 6536, "藜": 6537, "藝": 6538, "藤": 6539, "藥": 6540, "藩": 6541, "藪": 6542, "藻": 6543, "藿": 6544, "蘄": 6545, "蘅": 6546, "蘆": 6547, "蘇": 6548, "蘊": 6549, "蘋": 6550, "蘑": 6551, "蘖": 6552, "蘧": 6553, "蘭": 6554, "蘸": 6555, "蘿": 6556, "虎": 6557, "虏": 6558, "虐": 6559, "虑": 6560, "虔": 6561, "處": 6562, "虚": 6563, "虛": 6564, "虜": 6565, "虞": 6566, "號": 6567, "虢": 6568, "虧": 6569, "虫": 6570, "虬": 6571, "虱": 6572, "虹": 6573, "虻": 6574, "虽": 6575, "虾": 6576, "蚀": 6577, "蚁": 6578, "蚂": 6579, "蚊": 6580, "蚋": 6581, "蚌": 6582, "蚓": 6583, "蚕": 6584, "蚜": 6585, "蚣": 6586, "蚤": 6587, "蚩": 6588, "蚪": 6589, "蚯": 6590, "蚱": 6591, "蚴": 6592, "蚵": 6593, "蚶": 6594, "蛀": 6595, "蛇": 6596, "蛉": 6597, "蛊": 6598, "蛋": 6599, "蛎": 6600, "蛔": 6601, "蛙": 6602, "蛛": 6603, "蛟": 6604, "蛤": 6605, "蛭": 6606, "蛮": 6607, "蛰": 6608, "蛳": 6609, "蛴": 6610, "蛹": 6611, "蛺": 6612, "蛻": 6613, "蛾": 6614, "蜀": 6615, "蜂": 6616, "蜃": 6617, "蜇": 6618, "蜈": 6619, "蜊": 6620, "蜍": 6621, "蜒": 6622, "蜓": 6623, "蜕": 6624, "蜗": 6625, "蜘": 6626, "蜚": 6627, "蜜": 6628, "蜡": 6629, "蜢": 6630, "蜥": 6631, "蜱": 6632, "蜴": 6633, "蜻": 6634, "蜿": 6635, "蝇": 6636, "蝉": 6637, "蝌": 6638, "蝎": 6639, "蝕": 6640, "蝗": 6641, "蝙": 6642, "蝠": 6643, "蝦": 6644, "蝮": 6645, "蝰": 6646, "蝴": 6647, "蝶": 6648, "蝸": 6649, "蝼": 6650, "蝽": 6651, "螂": 6652, "螃": 6653, "螅": 6654, "螈": 6655, "融": 6656, "螟": 6657, "螢": 6658, "螨": 6659, "螫": 6660, "螬": 6661, "螭": 6662, "螯": 6663, "螳": 6664, "螺": 6665, "螽": 6666, "蟀": 6667, "蟋": 6668, "蟑": 6669, "蟒": 6670, "蟓": 6671, "蟠": 6672, "蟬": 6673, "蟲": 6674, "蟹": 6675, "蟻": 6676, "蟾": 6677, "蠅": 6678, "蠊": 6679, "蠍": 6680, "蠕": 6681, "蠟": 6682, "蠡": 6683, "蠢": 6684, "蠣": 6685, "蠱": 6686, "蠶": 6687, "蠹": 6688, "蠻": 6689, "血": 6690, "衄": 6691, "衅": 6692, "衆": 6693, "行": 6694, "衍": 6695, "術": 6696, "衔": 6697, "街": 6698, "衙": 6699, "衛": 6700, "衝": 6701, "衞": 6702, "衡": 6703, "衢": 6704, "衣": 6705, "补": 6706, "表": 6707, "衫": 6708, "衬": 6709, "衮": 6710, "衰": 6711, "衷": 6712, "衽": 6713, "袁": 6714, "袂": 6715, "袄": 6716, "袈": 6717, "袋": 6718, "袍": 6719, "袒": 6720, "袖": 6721, "袜": 6722, "袞": 6723, "袢": 6724, "袤": 6725, "被": 6726, "袭": 6727, "袱": 6728, "裁": 6729, "裂": 6730, "装": 6731, "裏": 6732, "裒": 6733, "裔": 6734, "裕": 6735, "裘": 6736, "裙": 6737, "補": 6738, "裝": 6739, "裟": 6740, "裡": 6741, "裤": 6742, "裨": 6743, "裱": 6744, "裳": 6745, "裴": 6746, "裸": 6747, "裹": 6748, "製": 6749, "褂": 6750, "複": 6751, "褐": 6752, "褒": 6753, "褚": 6754, "褥": 6755, "褪": 6756, "褫": 6757, "褲": 6758, "褶": 6759, "襄": 6760, "襞": 6761, "襟": 6762, "襦": 6763, "襪": 6764, "襯": 6765, "襲": 6766, "襻": 6767, "西": 6768, "要": 6769, "覃": 6770, "覆": 6771, "覇": 6772, "見": 6773, "規": 6774, "覓": 6775, "視": 6776, "覧": 6777, "親": 6778, "覲": 6779, "観": 6780, "覺": 6781, "覽": 6782, "觀": 6783, "见": 6784, "观": 6785, "觃": 6786, "规": 6787, "觅": 6788, "视": 6789, "览": 6790, "觉": 6791, "觊": 6792, "觎": 6793, "觐": 6794, "觑": 6795, "角": 6796, "觚": 6797, "觞": 6798, "解": 6799, "触": 6800, "觸": 6801, "言": 6802, "訂": 6803, "計": 6804, "訊": 6805, "討": 6806, "訓": 6807, "訕": 6808, "訖": 6809, "託": 6810, "記": 6811, "訝": 6812, "訟": 6813, "訢": 6814, "訣": 6815, "訥": 6816, "訪": 6817, "設": 6818, "許": 6819, "訳": 6820, "訴": 6821, "訶": 6822, "診": 6823, "註": 6824, "証": 6825, "詐": 6826, "詔": 6827, "評": 6828, "詛": 6829, "詞": 6830, "詠": 6831, "詢": 6832, "詣": 6833, "試": 6834, "詩": 6835, "詭": 6836, "詮": 6837, "話": 6838, "該": 6839, "詳": 6840, "詹": 6841, "誅": 6842, "誇": 6843, "誉": 6844, "誊": 6845, "誌": 6846, "認": 6847, "誓": 6848, "誕": 6849, "誘": 6850, "語": 6851, "誠": 6852, "誡": 6853, "誣": 6854, "誤": 6855, "誥": 6856, "誦": 6857, "說": 6858, "説": 6859, "読": 6860, "誰": 6861, "課": 6862, "誼": 6863, "調": 6864, "談": 6865, "請": 6866, "諏": 6867, "諒": 6868, "論": 6869, "諜": 6870, "諡": 6871, "諦": 6872, "諧": 6873, "諫": 6874, "諭": 6875, "諮": 6876, "諱": 6877, "諷": 6878, "諸": 6879, "諺": 6880, "諾": 6881, "謀": 6882, "謁": 6883, "謂": 6884, "謄": 6885, "謇": 6886, "謊": 6887, "謎": 6888, "謙": 6889, "謚": 6890, "講": 6891, "謝": 6892, "謠": 6893, "謨": 6894, "謫": 6895, "謬": 6896, "謹": 6897, "證": 6898, "譏": 6899, "識": 6900, "譚": 6901, "譜": 6902, "警": 6903, "譬": 6904, "譯": 6905, "議": 6906, "譴": 6907, "護": 6908, "譽": 6909, "讀": 6910, "變": 6911, "讓": 6912, "讚": 6913, "计": 6914, "订": 6915, "讣": 6916, "认": 6917, "讥": 6918, "讦": 6919, "讧": 6920, "讨": 6921, "让": 6922, "讫": 6923, "训": 6924, "议": 6925, "讯": 6926, "记": 6927, "讲": 6928, "讳": 6929, "讴": 6930, "讶": 6931, "讷": 6932, "许": 6933, "讹": 6934, "论": 6935, "讼": 6936, "讽": 6937, "设": 6938, "访": 6939, "诀": 6940, "证": 6941, "诂": 6942, "诃": 6943, "评": 6944, "诅": 6945, "识": 6946, "诈": 6947, "诉": 6948, "诊": 6949, "诋": 6950, "词": 6951, "诏": 6952, "译": 6953, "诒": 6954, "试": 6955, "诗": 6956, "诘": 6957, "诙": 6958, "诚": 6959, "诛": 6960, "话": 6961, "诞": 6962, "诟": 6963, "诠": 6964, "诡": 6965, "询": 6966, "诣": 6967, "诤": 6968, "该": 6969, "详": 6970, "诩": 6971, "诫": 6972, "诬": 6973, "语": 6974, "误": 6975, "诰": 6976, "诱": 6977, "诲": 6978, "说": 6979, "诵": 6980, "请": 6981, "诸": 6982, "诺": 6983, "读": 6984, "诽": 6985, "课": 6986, "诿": 6987, "谀": 6988, "谁": 6989, "调": 6990, "谄": 6991, "谅": 6992, "谆": 6993, "谈": 6994, "谊": 6995, "谋": 6996, "谌": 6997, "谍": 6998, "谎": 6999, "谏": 7000, "谐": 7001, "谑": 7002, "谒": 7003, "谓": 7004, "谔": 7005, "谕": 7006, "谗": 7007, "谘": 7008, "谙": 7009, "谚": 7010, "谛": 7011, "谜": 7012, "谟": 7013, "谢": 7014, "谣": 7015, "谤": 7016, "谥": 7017, "谦": 7018, "谧": 7019, "谨": 7020, "谩": 7021, "谪": 7022, "谬": 7023, "谭": 7024, "谯": 7025, "谱": 7026, "谲": 7027, "谴": 7028, "谵": 7029, "谶": 7030, "谷": 7031, "谿": 7032, "豁": 7033, "豆": 7034, "豇": 7035, "豈": 7036, "豊": 7037, "豌": 7038, "豎": 7039, "豐": 7040, "豔": 7041, "豕": 7042, "豚": 7043, "象": 7044, "豢": 7045, "豪": 7046, "豫": 7047, "豬": 7048, "豳": 7049, "豹": 7050, "貂": 7051, "貉": 7052, "貊": 7053, "貌": 7054, "貓": 7055, "貝": 7056, "貞": 7057, "負": 7058, "財": 7059, "貢": 7060, "貧": 7061, "貨": 7062, "販": 7063, "貪": 7064, "貫": 7065, "責": 7066, "貯": 7067, "貳": 7068, "貴": 7069, "貶": 7070, "買": 7071, "貸": 7072, "費": 7073, "貼": 7074, "貽": 7075, "貿": 7076, "賀": 7077, "賁": 7078, "賂": 7079, "賃": 7080, "賄": 7081, "資": 7082, "賈": 7083, "賊": 7084, "賑": 7085, "賓": 7086, "賜": 7087, "賞": 7088, "賠": 7089, "賡": 7090, "賢": 7091, "賣": 7092, "賤": 7093, "賦": 7094, "質": 7095, "賬": 7096, "賭": 7097, "賴": 7098, "賸": 7099, "賺": 7100, "購": 7101, "賽": 7102, "贈": 7103, "贊": 7104, "贏": 7105, "贓": 7106, "贖": 7107, "贛": 7108, "贝": 7109, "贞": 7110, "负": 7111, "贡": 7112, "财": 7113, "责": 7114, "贤": 7115, "败": 7116, "账": 7117, "货": 7118, "质": 7119, "贩": 7120, "贪": 7121, "贫": 7122, "贬": 7123, "购": 7124, "贮": 7125, "贯": 7126, "贰": 7127, "贱": 7128, "贲": 7129, "贴": 7130, "贵": 7131, "贷": 7132, "贸": 7133, "费": 7134, "贺": 7135, "贻": 7136, "贼": 7137, "贽": 7138, "贾": 7139, "贿": 7140, "赀": 7141, "赁": 7142, "赂": 7143, "赃": 7144, "资": 7145, "赅": 7146, "赈": 7147, "赉": 7148, "赊": 7149, "赋": 7150, "赌": 7151, "赎": 7152, "赏": 7153, "赐": 7154, "赓": 7155, "赔": 7156, "赖": 7157, "赘": 7158, "赙": 7159, "赚": 7160, "赛": 7161, "赜": 7162, "赝": 7163, "赞": 7164, "赟": 7165, "赠": 7166, "赡": 7167, "赢": 7168, "赣": 7169, "赤": 7170, "赦": 7171, "赫": 7172, "赭": 7173, "走": 7174, "赳": 7175, "赴": 7176, "赵": 7177, "赶": 7178, "起": 7179, "趁": 7180, "超": 7181, "越": 7182, "趋": 7183, "趕": 7184, "趙": 7185, "趟": 7186, "趣": 7187, "趨": 7188, "足": 7189, "趴": 7190, "趸": 7191, "趺": 7192, "趾": 7193, "跃": 7194, "跆": 7195, "跋": 7196, "跌": 7197, "跑": 7198, "跖": 7199, "跗": 7200, "跚": 7201, "跛": 7202, "距": 7203, "跟": 7204, "跡": 7205, "跤": 7206, "跨": 7207, "跪": 7208, "路": 7209, "跳": 7210, "践": 7211, "跷": 7212, "跸": 7213, "跻": 7214, "踊": 7215, "踌": 7216, "踏": 7217, "踐": 7218, "踔": 7219, "踝": 7220, "踞": 7221, "踢": 7222, "踩": 7223, "踪": 7224, "踵": 7225, "蹂": 7226, "蹄": 7227, "蹇": 7228, "蹈": 7229, "蹊": 7230, "蹒": 7231, "蹟": 7232, "蹤": 7233, "蹦": 7234, "蹬": 7235, "蹭": 7236, "蹲": 7237, "蹴": 7238, "蹶": 7239, "躁": 7240, "躇": 7241, "躍": 7242, "躏": 7243, "身": 7244, "躬": 7245, "躯": 7246, "躲": 7247, "躺": 7248, "軀": 7249, "車": 7250, "軋": 7251, "軌": 7252, "軍": 7253, "軒": 7254, "軟": 7255, "軫": 7256, "軸": 7257, "軼": 7258, "軾": 7259, "較": 7260, "輅": 7261, "載": 7262, "輒": 7263, "輓": 7264, "輔": 7265, "輕": 7266, "輛": 7267, "輝": 7268, "輟": 7269, "輩": 7270, "輪": 7271, "輯": 7272, "輸": 7273, "輻": 7274, "輾": 7275, "輿": 7276, "轄": 7277, "轉": 7278, "轍": 7279, "轎": 7280, "轟": 7281, "车": 7282, "轧": 7283, "轨": 7284, "轩": 7285, "轫": 7286, "转": 7287, "轭": 7288, "轮": 7289, "软": 7290, "轰": 7291, "轲": 7292, "轴": 7293, "轶": 7294, "轻": 7295, "轼": 7296, "载": 7297, "轿": 7298, "较": 7299, "辄": 7300, "辅": 7301, "辆": 7302, "辇": 7303, "辈": 7304, "辉": 7305, "辊": 7306, "辋": 7307, "辍": 7308, "辎": 7309, "辏": 7310, "辐": 7311, "辑": 7312, "输": 7313, "辕": 7314, "辖": 7315, "辗": 7316, "辙": 7317, "辛": 7318, "辜": 7319, "辞": 7320, "辟": 7321, "辣": 7322, "辦": 7323, "辨": 7324, "辩": 7325, "辫": 7326, "辭": 7327, "辯": 7328, "辰": 7329, "辱": 7330, "農": 7331, "边": 7332, "辺": 7333, "辻": 7334, "込": 7335, "辽": 7336, "达": 7337, "迁": 7338, "迂": 7339, "迄": 7340, "迅": 7341, "过": 7342, "迈": 7343, "迎": 7344, "运": 7345, "近": 7346, "迓": 7347, "返": 7348, "迕": 7349, "还": 7350, "这": 7351, "进": 7352, "远": 7353, "违": 7354, "连": 7355, "迟": 7356, "迢": 7357, "迤": 7358, "迥": 7359, "迦": 7360, "迨": 7361, "迩": 7362, "迪": 7363, "迫": 7364, "迭": 7365, "述": 7366, "迳": 7367, "迴": 7368, "迷": 7369, "迸": 7370, "迹": 7371, "迺": 7372, "追": 7373, "退": 7374, "送": 7375, "适": 7376, "逃": 7377, "逅": 7378, "逆": 7379, "选": 7380, "逊": 7381, "逋": 7382, "逍": 7383, "透": 7384, "逐": 7385, "逑": 7386, "递": 7387, "途": 7388, "逕": 7389, "逖": 7390, "逗": 7391, "這": 7392, "通": 7393, "逛": 7394, "逝": 7395, "逞": 7396, "速": 7397, "造": 7398, "逢": 7399, "連": 7400, "逮": 7401, "週": 7402, "進": 7403, "逵": 7404, "逸": 7405, "逻": 7406, "逼": 7407, "逾": 7408, "遁": 7409, "遂": 7410, "遇": 7411, "遊": 7412, "運": 7413, "遍": 7414, "過": 7415, "遏": 7416, "遐": 7417, "遑": 7418, "遒": 7419, "道": 7420, "達": 7421, "違": 7422, "遗": 7423, "遘": 7424, "遙": 7425, "遜": 7426, "遞": 7427, "遠": 7428, "遣": 7429, "遥": 7430, "遨": 7431, "適": 7432, "遭": 7433, "遮": 7434, "遲": 7435, "遴": 7436, "遵": 7437, "遷": 7438, "選": 7439, "遺": 7440, "遼": 7441, "遽": 7442, "避": 7443, "邀": 7444, "邁": 7445, "邂": 7446, "邃": 7447, "還": 7448, "邇": 7449, "邈": 7450, "邊": 7451, "邏": 7452, "邑": 7453, "邓": 7454, "邕": 7455, "邙": 7456, "邛": 7457, "邝": 7458, "邠": 7459, "邡": 7460, "邢": 7461, "那": 7462, "邦": 7463, "邨": 7464, "邪": 7465, "邬": 7466, "邮": 7467, "邯": 7468, "邰": 7469, "邱": 7470, "邳": 7471, "邵": 7472, "邸": 7473, "邹": 7474, "邺": 7475, "邻": 7476, "郁": 7477, "郊": 7478, "郎": 7479, "郐": 7480, "郑": 7481, "郓": 7482, "郗": 7483, "郝": 7484, "郡": 7485, "郢": 7486, "郤": 7487, "郦": 7488, "郧": 7489, "部": 7490, "郫": 7491, "郭": 7492, "郯": 7493, "郴": 7494, "郵": 7495, "郷": 7496, "郸": 7497, "都": 7498, "郾": 7499, "鄂": 7500, "鄉": 7501, "鄒": 7502, "鄔": 7503, "鄙": 7504, "鄜": 7505, "鄞": 7506, "鄠": 7507, "鄢": 7508, "鄧": 7509, "鄭": 7510, "鄰": 7511, "鄱": 7512, "鄲": 7513, "鄴": 7514, "鄺": 7515, "酆": 7516, "酈": 7517, "酉": 7518, "酋": 7519, "酌": 7520, "配": 7521, "酐": 7522, "酒": 7523, "酗": 7524, "酚": 7525, "酝": 7526, "酞": 7527, "酢": 7528, "酣": 7529, "酥": 7530, "酪": 7531, "酬": 7532, "酮": 7533, "酯": 7534, "酰": 7535, "酱": 7536, "酵": 7537, "酶": 7538, "酷": 7539, "酸": 7540, "酿": 7541, "醃": 7542, "醇": 7543, "醉": 7544, "醋": 7545, "醌": 7546, "醍": 7547, "醐": 7548, "醒": 7549, "醚": 7550, "醛": 7551, "醜": 7552, "醣": 7553, "醪": 7554, "醫": 7555, "醬": 7556, "醮": 7557, "醯": 7558, "醴": 7559, "釀": 7560, "釁": 7561, "釆": 7562, "采": 7563, "釉": 7564, "释": 7565, "釋": 7566, "里": 7567, "重": 7568, "野": 7569, "量": 7570, "釐": 7571, "金": 7572, "釗": 7573, "釘": 7574, "釜": 7575, "針": 7576, "釣": 7577, "釧": 7578, "鈉": 7579, "鈍": 7580, "鈎": 7581, "鈔": 7582, "鈕": 7583, "鈞": 7584, "鈣": 7585, "鈦": 7586, "鈴": 7587, "鈺": 7588, "鈾": 7589, "鉀": 7590, "鉄": 7591, "鉅": 7592, "鉉": 7593, "鉗": 7594, "鉛": 7595, "鉢": 7596, "鉤": 7597, "鉬": 7598, "鉴": 7599, "鉻": 7600, "銀": 7601, "銃": 7602, "銅": 7603, "銓": 7604, "銖": 7605, "銘": 7606, "銜": 7607, "銩": 7608, "銮": 7609, "銳": 7610, "銷": 7611, "鋁": 7612, "鋅": 7613, "鋇": 7614, "鋒": 7615, "鋤": 7616, "鋪": 7617, "鋯": 7618, "鋰": 7619, "鋸": 7620, "鋼": 7621, "錄": 7622, "錇": 7623, "錐": 7624, "錘": 7625, "錚": 7626, "錢": 7627, "錦": 7628, "錨": 7629, "錫": 7630, "錯": 7631, "録": 7632, "錶": 7633, "鍊": 7634, "鍋": 7635, "鍍": 7636, "鍛": 7637, "鍵": 7638, "鍾": 7639, "鎂": 7640, "鎊": 7641, "鎌": 7642, "鎏": 7643, "鎔": 7644, "鎖": 7645, "鎗": 7646, "鎚": 7647, "鎧": 7648, "鎬": 7649, "鎮": 7650, "鎳": 7651, "鏈": 7652, "鏖": 7653, "鏗": 7654, "鏘": 7655, "鏞": 7656, "鏡": 7657, "鏢": 7658, "鏤": 7659, "鏽": 7660, "鐘": 7661, "鐮": 7662, "鐳": 7663, "鐵": 7664, "鐸": 7665, "鑄": 7666, "鑑": 7667, "鑒": 7668, "鑛": 7669, "鑣": 7670, "鑫": 7671, "鑰": 7672, "鑲": 7673, "鑼": 7674, "鑽": 7675, "鑾": 7676, "鑿": 7677, "钆": 7678, "钇": 7679, "针": 7680, "钉": 7681, "钊": 7682, "钌": 7683, "钍": 7684, "钎": 7685, "钐": 7686, "钒": 7687, "钓": 7688, "钕": 7689, "钗": 7690, "钘": 7691, "钙": 7692, "钚": 7693, "钛": 7694, "钜": 7695, "钝": 7696, "钞": 7697, "钟": 7698, "钠": 7699, "钡": 7700, "钢": 7701, "钣": 7702, "钤": 7703, "钥": 7704, "钦": 7705, "钧": 7706, "钨": 7707, "钩": 7708, "钪": 7709, "钮": 7710, "钯": 7711, "钰": 7712, "钱": 7713, "钳": 7714, "钴": 7715, "钵": 7716, "钺": 7717, "钻": 7718, "钼": 7719, "钽": 7720, "钾": 7721, "钿": 7722, "铀": 7723, "铁": 7724, "铂": 7725, "铃": 7726, "铅": 7727, "铆": 7728, "铈": 7729, "铉": 7730, "铊": 7731, "铋": 7732, "铌": 7733, "铍": 7734, "铎": 7735, "铐": 7736, "铑": 7737, "铒": 7738, "铛": 7739, "铜": 7740, "铝": 7741, "铟": 7742, "铠": 7743, "铡": 7744, "铢": 7745, "铣": 7746, "铤": 7747, "铨": 7748, "铪": 7749, "铬": 7750, "铭": 7751, "铮": 7752, "铯": 7753, "铰": 7754, "铱": 7755, "铲": 7756, "铳": 7757, "铵": 7758, "银": 7759, "铷": 7760, "铸": 7761, "铺": 7762, "铼": 7763, "链": 7764, "铿": 7765, "销": 7766, "锁": 7767, "锂": 7768, "锄": 7769, "锅": 7770, "锆": 7771, "锈": 7772, "锉": 7773, "锋": 7774, "锌": 7775, "锏": 7776, "锐": 7777, "锑": 7778, "锕": 7779, "锗": 7780, "错": 7781, "锚": 7782, "锛": 7783, "锝": 7784, "锟": 7785, "锡": 7786, "锢": 7787, "锣": 7788, "锤": 7789, "锥": 7790, "锦": 7791, "锭": 7792, "键": 7793, "锯": 7794, "锰": 7795, "锲": 7796, "锴": 7797, "锵": 7798, "锶": 7799, "锷": 7800, "锺": 7801, "锻": 7802, "镀": 7803, "镁": 7804, "镂": 7805, "镇": 7806, "镉": 7807, "镊": 7808, "镌": 7809, "镍": 7810, "镐": 7811, "镑": 7812, "镒": 7813, "镓": 7814, "镕": 7815, "镖": 7816, "镗": 7817, "镛": 7818, "镜": 7819, "镝": 7820, "镞": 7821, "镣": 7822, "镦": 7823, "镧": 7824, "镭": 7825, "镯": 7826, "镰": 7827, "镳": 7828, "镶": 7829, "長": 7830, "长": 7831, "門": 7832, "閃": 7833, "閉": 7834, "開": 7835, "閏": 7836, "閑": 7837, "閒": 7838, "間": 7839, "閔": 7840, "閘": 7841, "関": 7842, "閣": 7843, "閤": 7844, "閥": 7845, "閨": 7846, "閩": 7847, "閱": 7848, "閲": 7849, "閻": 7850, "闆": 7851, "闇": 7852, "闈": 7853, "闊": 7854, "闋": 7855, "闐": 7856, "闕": 7857, "闖": 7858, "闘": 7859, "關": 7860, "闡": 7861, "闢": 7862, "门": 7863, "闩": 7864, "闪": 7865, "闫": 7866, "闭": 7867, "问": 7868, "闯": 7869, "闰": 7870, "闱": 7871, "闲": 7872, "闳": 7873, "间": 7874, "闵": 7875, "闷": 7876, "闸": 7877, "闹": 7878, "闺": 7879, "闻": 7880, "闼": 7881, "闽": 7882, "闾": 7883, "阀": 7884, "阁": 7885, "阂": 7886, "阅": 7887, "阆": 7888, "阇": 7889, "阈": 7890, "阉": 7891, "阎": 7892, "阐": 7893, "阑": 7894, "阔": 7895, "阕": 7896, "阖": 7897, "阗": 7898, "阙": 7899, "阚": 7900, "阜": 7901, "队": 7902, "阡": 7903, "阪": 7904, "阮": 7905, "阯": 7906, "阱": 7907, "防": 7908, "阳": 7909, "阴": 7910, "阵": 7911, "阶": 7912, "阻": 7913, "阿": 7914, "陀": 7915, "陂": 7916, "附": 7917, "际": 7918, "陆": 7919, "陇": 7920, "陈": 7921, "陉": 7922, "陋": 7923, "陌": 7924, "降": 7925, "限": 7926, "陕": 7927, "陛": 7928, "陜": 7929, "陝": 7930, "陞": 7931, "陟": 7932, "陡": 7933, "院": 7934, "陣": 7935, "除": 7936, "陨": 7937, "险": 7938, "陪": 7939, "陰": 7940, "陲": 7941, "陳": 7942, "陵": 7943, "陶": 7944, "陷": 7945, "陸": 7946, "険": 7947, "陽": 7948, "隅": 7949, "隆": 7950, "隈": 7951, "隊": 7952, "隋": 7953, "隍": 7954, "階": 7955, "随": 7956, "隐": 7957, "隔": 7958, "隕": 7959, "隗": 7960, 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15004, "▁gene": 15005, "最近": 15006, "▁Education": 15007, "不利": 15008, "▁figure": 15009, "的使用": 15010, "▁Black": 15011, "出生": 15012, "引导学生": 15013, "▁immune": 15014, "▁store": 15015, "协会": 15016, "必要的": 15017, "批判": 15018, "标准化": 15019, "寻找": 15020, "▁resistance": 15021, "▁date": 15022, "▁eight": 15023, "后果": 15024, "▁party": 15025, "Sec": 15026, "的有效": 15027, "有人": 15028, "▁filled": 15029, "男子": 15030, "edia": 15031, "▁capital": 15032, "已成为": 15033, "ees": 15034, "rief": 15035, "▁i": 15036, "联合国": 15037, "$▁": 15038, "存在着": 15039, "▁Br": 15040, "▁Mex": 15041, "产物": 15042, "estic": 15043, "ibly": 15044, "信仰": 15045, "▁swe": 15046, "配套": 15047, "▁plays": 15048, "住房": 15049, "antic": 15050, "ams": 15051, "othes": 15052, "精确": 15053, "▁buildings": 15054, "gery": 15055, "▁map": 15056, "车辆": 15057, "聚焦": 15058, "▁rout": 15059, "Similar": 15060, "顺序": 15061, "▁concerns": 15062, "Reg": 15063, "总量": 15064, "研究院": 15065, "▁genetic": 15066, "写作": 15067, "于是": 15068, "生态文明": 15069, "▁alone": 15070, "\"(": 15071, "▁emphas": 15072, "rael": 15073, "繁荣": 15074, "▁contract": 15075, "知识和": 15076, "失去": 15077, "itz": 15078, "概述": 15079, "ademic": 15080, "Gu": 15081, "overs": 15082, "▁assist": 15083, "▁moving": 15084, "▁regions": 15085, "▁identity": 15086, "▁birds": 15087, "▁Ne": 15088, "发送": 15089, "人民的": 15090, "难题": 15091, "▁pow": 15092, "EL": 15093, "iology": 15094, "课堂教学": 15095, "▁Museum": 15096, "ounced": 15097, "▁Pre": 15098, "此时": 15099, "被称为": 15100, "相反": 15101, "营销": 15102, "享有": 15103, "▁Go": 15104, "onds": 15105, "▁illustr": 15106, "▁observed": 15107, "Go": 15108, "Com": 15109, "糖尿病": 15110, "平面": 15111, "gor": 15112, "eech": 15113, "▁anything": 15114, "omy": 15115, "仪器": 15116, "aughter": 15117, "国内外": 15118, "万人": 15119, "▁Mount": 15120, "▁antib": 15121, "▁Cour": 15122, "▁miles": 15123, "旨在": 15124, "》▁": 15125, "动作": 15126, "▁demand": 15127, "管理和": 15128, "▁discover": 15129, "＂,": 15130, "▁expected": 15131, "Both": 15132, "大脑": 15133, "依托": 15134, "以上的": 15135, 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"的环境": 15334, "系数": 15335, "aud": 15336, "▁hundred": 15337, "选举": 15338, "ichael": 15339, "▁UK": 15340, "Em": 15341, "▁risks": 15342, "▁ap": 15343, "▁Her": 15344, "两者": 15345, "▁woman": 15346, "地下": 15347, "unk": 15348, "无效": 15349, "▁beg": 15350, "▁effort": 15351, "练习": 15352, "▁degree": 15353, "indu": 15354, "▁Street": 15355, "过程的": 15356, "地面": 15357, "▁happens": 15358, "▁leave": 15359, "...▁": 15360, "最早": 15361, "▁essay": 15362, "▁Phil": 15363, "▁tim": 15364, "西北": 15365, "取代": 15366, "▁diabetes": 15367, "超越": 15368, "对称": 15369, "联盟": 15370, "年前": 15371, "▁bran": 15372, "▁effectively": 15373, "复制": 15374, "依照": 15375, "▁passed": 15376, "以此": 15377, "石油": 15378, "▁landsc": 15379, "cription": 15380, "电网": 15381, "▁core": 15382, "▁opportunity": 15383, "▁gets": 15384, "灾害": 15385, "▁connection": 15386, "鲜明": 15387, "丰富的": 15388, "▁rise": 15389, "▁providing": 15390, "熟悉": 15391, "▁Cor": 15392, "现金": 15393, "借款": 15394, "了中国": 15395, "爆发": 15396, "进行的": 15397, "▁easier": 15398, 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"Par": 17271, "▁closer": 17272, "▁recommended": 17273, "说的": 17274, "对外开放": 17275, "Ag": 17276, "▁prepared": 17277, "iana": 17278, "stream": 17279, "▁diagnosis": 17280, "▁topic": 17281, "设定": 17282, "负载": 17283, "mas": 17284, "eorg": 17285, "▁continues": 17286, "uses": 17287, "在内的": 17288, "搭建": 17289, "inet": 17290, "或是": 17291, "▁pret": 17292, "待遇": 17293, "的政策": 17294, "▁discussion": 17295, "更大的": 17296, "mic": 17297, "▁flowers": 17298, "债务人": 17299, "▁frequency": 17300, "媒介": 17301, "▁liquid": 17302, "▁operation": 17303, "体制机制": 17304, "肯定": 17305, "增大": 17306, "冠軍": 17307, "▁Town": 17308, "既是": 17309, "的分析": 17310, "▁burn": 17311, "ibilities": 17312, "奠定了": 17313, "情节": 17314, "▁Water": 17315, "▁inform": 17316, "▁hun": 17317, "搜索": 17318, "istant": 17319, "黄河": 17320, "塑造": 17321, "▁radiation": 17322, "表演": 17323, "Ge": 17324, "uan": 17325, "对中国": 17326, "▁generations": 17327, "▁muscles": 17328, "▁Project": 17329, "名为": 17330, "▁manufacture": 17331, "▁vast": 17332, "▁Mag": 17333, "▁guidelines": 17334, "模糊": 17335, "▁follows": 17336, "公约": 17337, "公有": 17338, "动能": 17339, "▁describe": 17340, "▁cars": 17341, "▁Do": 17342, "▁opin": 17343, "▁dro": 17344, "▁Direct": 17345, "▁yellow": 17346, "协助": 17347, "市民": 17348, "▁bank": 17349, "相信": 17350, "Fl": 17351, "bur": 17352, "个别": 17353, "这一点": 17354, "',▁": 17355, "工作中": 17356, "其中的": 17357, "▁AD": 17358, "高端": 17359, "▁Gold": 17360, "arp": 17361, "任意": 17362, "gment": 17363, "▁director": 17364, "曾经": 17365, "信息安全": 17366, "告诉": 17367, "▁Ben": 17368, "▁vegetables": 17369, "asy": 17370, "又称": 17371, "ferent": 17372, "坚持以": 17373, "urance": 17374, "aval": 17375, "的前": 17376, "活跃": 17377, "▁raised": 17378, "▁teams": 17379, "协定": 17380, "ls": 17381, "主教": 17382, "▁experts": 17383, "人生": 17384, "▁storm": 17385, "▁Asia": 17386, "▁indicate": 17387, "标签": 17388, "▁settle": 17389, "诈骗": 17390, "聚集": 17391, "RC": 17392, "VD": 17393, "民国": 17394, "历史的": 17395, "▁barri": 17396, "▁professionals": 17397, "之後": 17398, "期刊": 17399, "獲得": 17400, "▁random": 17401, "ora": 17402, "确实": 17403, "的患者": 17404, "▁clim": 17405, "▁choices": 17406, "▁cognitive": 17407, "▁option": 17408, "▁particles": 17409, "考虑到": 17410, "▁beliefs": 17411, "▁pric": 17412, "ulture": 17413, "氛围": 17414, "就是要": 17415, "科学发展": 17416, "东南": 17417, "isation": 17418, "iration": 17419, "▁nutrition": 17420, "主流": 17421, "利益的": 17422, "▁worry": 17423, "▁accurate": 17424, "erse": 17425, "▁aircraft": 17426, "▁collected": 17427, "▁lies": 17428, "▁periods": 17429, "充分利用": 17430, "Ant": 17431, "用地": 17432, "党的十八大以来": 17433, "遊戲": 17434, "▁treatments": 17435, "cking": 17436, "▁Egypt": 17437, "▁nuclear": 17438, "▁Miss": 17439, "▁sides": 17440, "减排": 17441, "\b\u0006": 17442, "机器学习": 17443, "oseph": 17444, "▁Lab": 17445, "作为一种": 17446, "PE": 17447, "伙伴": 17448, "aks": 17449, "▁sustainable": 17450, "▁HIV": 17451, "压缩": 17452, "不仅仅": 17453, "人身": 17454, "▁reaction": 17455, "研究成果": 17456, "负责人": 17457, "类的": 17458, ":▁“": 17459, "经济发展的": 17460, "icing": 17461, "今后": 17462, "▁roots": 17463, "五个": 17464, "▁impacts": 17465, "什么是": 17466, "▁familiar": 17467, "标识": 17468, "▁Franc": 17469, "▁artist": 17470, "dec": 17471, "during": 17472, "▁dangerous": 17473, "有关的": 17474, "还需要": 17475, "它们的": 17476, "▁Journal": 17477, "金融危机": 17478, "▁responses": 17479, "North": 17480, "▁copy": 17481, "善于": 17482, "河北": 17483, "▁cris": 17484, "lig": 17485, "▁It": 17486, "cher": 17487, "▁Zeal": 17488, "结算": 17489, "引擎": 17490, "▁execut": 17491, "▁Lear": 17492, "▁disp": 17493, "模型的": 17494, "▁Rad": 17495, "pping": 17496, "扩散": 17497, "▁keeping": 17498, "▁faster": 17499, "病例": 17500, "产业结构": 17501, "ita": 17502, "isp": 17503, "Japan": 17504, "overty": 17505, "eless": 17506, "ups": 17507, "订立": 17508, "▁univers": 17509, "理论与": 17510, "ulating": 17511, "dr": 17512, "高职": 17513, "都可以": 17514, "▁centuries": 17515, "▁institutions": 17516, "▁plot": 17517, "▁Catholic": 17518, "▁infections": 17519, "开启": 17520, "▁tumor": 17521, "延长": 17522, "▁suggested": 17523, "▁pip": 17524, "friend": 17525, "ican": 17526, "obe": 17527, "定量": 17528, "工业互联网": 17529, "▁Sing": 17530, "几种": 17531, "被动": 17532, "▁increasingly": 17533, "▁announced": 17534, "算力": 17535, "正如": 17536, "▁noted": 17537, "▁violence": 17538, "▁meat": 17539, "提前": 17540, "所述": 17541, "rence": 17542, "教学的": 17543, "了对": 17544, "▁statement": 17545, "第八": 17546, "…▁": 17547, "▁amazing": 17548, "规章": 17549, "▁efficiency": 17550, "ingly": 17551, "调度": 17552, "▁Academy": 17553, "阿尔": 17554, "▁diversity": 17555, "初级": 17556, "faces": 17557, "zym": 17558, "▁incred": 17559, "first": 17560, "instead": 17561, "变化的": 17562, "就要": 17563, "▁Prov": 17564, "红军": 17565, "ependent": 17566, ")▁-▁": 17567, "日报": 17568, "ette": 17569, "的一项": 17570, "各方": 17571, "很少": 17572, "irds": 17573, "oom": 17574, "LE": 17575, "原料": 17576, "▁prem": 17577, "推理": 17578, "認為": 17579, "▁hearing": 17580, "expected": 17581, "天文": 17582, "夫妻": 17583, "▁Civil": 17584, "▁regional": 17585, "▁taught": 17586, "Christ": 17587, "▁stem": 17588, "时空": 17589, "机理": 17590, "▁vs": 17591, "的性质": 17592, "▁Mem": 17593, "▁neighbor": 17594, "他們": 17595, "建模": 17596, "表述": 17597, "交付": 17598, "同比": 17599, "arliament": 17600, "▁founded": 17601, "作了": 17602, "朋友": 17603, "需求的": 17604, "▁pollution": 17605, "▁creates": 17606, "二者": 17607, "孔子": 17608, "的重要组成部分": 17609, "部长": 17610, "▁operating": 17611, "▁begins": 17612, "▁Records": 17613, "Add": 17614, "▁stars": 17615, "报表": 17616, "▁Emp": 17617, "▁legisl": 17618, "界面": 17619, "▁ecosystem": 17620, "▁Zealand": 17621, "▁aid": 17622, "天气": 17623, "研究者": 17624, "▁mort": 17625, "fit": 17626, "西藏": 17627, "关心": 17628, "起点": 17629, "FC": 17630, "临时": 17631, "▁measured": 17632, "oly": 17633, "▁Britain": 17634, "▁News": 17635, "▁Out": 17636, "家长": 17637, "不动": 17638, "中文": 17639, "enses": 17640, "\",\"": 17641, "已知": 17642, "第三方": 17643, "多元化": 17644, "ses": 17645, "主要的": 17646, "oves": 17647, "疼痛": 17648, "删除": 17649, "家的": 17650, "态势": 17651, "▁mm": 17652, "火箭": 17653, "▁syndrome": 17654, "加剧": 17655, "既要": 17656, "gr": 17657, "读者": 17658, "▁phase": 17659, "受益": 17660, "企业和": 17661, "▁phenomen": 17662, "来实现": 17663, "▁networks": 17664, "智力": 17665, "监察": 17666, "开设": 17667, "▁hour": 17668, "opes": 17669, "周边": 17670, "▁stone": 17671, "HO": 17672, "▁Spec": 17673, "▁Rel": 17674, "▁disapp": 17675, "最小": 17676, "故意": 17677, "发生在": 17678, "晶体": 17679, "▁poly": 17680, "▁fort": 17681, "数值": 17682, "▁valid": 17683, "orph": 17684, "▁motion": 17685, "Who": 17686, "▁significance": 17687, "对抗": 17688, "▁adop": 17689, "▁component": 17690, "创造了": 17691, "网上": 17692, "icious": 17693, "▁revealed": 17694, "▁compos": 17695, "诚信": 17696, "▁row": 17697, "村民": 17698, "既有": 17699, "跨境": 17700, "▁priv": 17701, "长度": 17702, "▁References": 17703, "不久": 17704, "kins": 17705, "不但": 17706, "限度": 17707, "代替": 17708, "线路": 17709, "快速发展": 17710, "▁joint": 17711, "han": 17712, "inder": 17713, "ptions": 17714, "▁compl": 17715, "落后": 17716, "PI": 17717, "▁realized": 17718, "Inter": 17719, "程度的": 17720, "▁chosen": 17721, "将在": 17722, "二次": 17723, "oto": 17724, "anted": 17725, "期望": 17726, "orter": 17727, "上是": 17728, "istan": 17729, "▁double": 17730, "ching": 17731, "aying": 17732, "和应用": 17733, "Example": 17734, "网络的": 17735, "cules": 17736, "建築": 17737, "街道": 17738, "▁professor": 17739, "help": 17740, "▁divided": 17741, "np": 17742, "▁fine": 17743, "数额": 17744, "▁fruits": 17745, "的存在": 17746, "▁defic": 17747, "▁association": 17748, "Such": 17749, "可再生": 17750, "▁depending": 17751, "IG": 17752, "领先": 17753, "落地": 17754, "▁brown": 17755, "年轻": 17756, "颜色": 17757, "高温": 17758, "二氧化碳": 17759, "loc": 17760, "ells": 17761, "：《": 17762, "Fig": 17763, "rastructure": 17764, "习近平新时代中国特色社会主义思想": 17765, "Mean": 17766, "负面": 17767, "已经成为": 17768, "▁clearly": 17769, "退出": 17770, "生涯": 17771, "方式的": 17772, "农业生产": 17773, "▁nerv": 17774, "▁freedom": 17775, "老年": 17776, "▁Test": 17777, "矩阵": 17778, "的现象": 17779, "大致": 17780, "▁Pacific": 17781, "极端": 17782, "▁renew": 17783, "的范围": 17784, "剩余": 17785, "taining": 17786, "变异": 17787, "院士": 17788, "库存": 17789, "▁，": 17790, "rolog": 17791, "ications": 17792, "顾客": 17793, "光伏": 17794, "和实践": 17795, "在不同": 17796, "occ": 17797, "▁vehicle": 17798, "▁occurred": 17799, "▁mal": 17800, "Living": 17801, "▁Library": 17802, "kes": 17803, "▁secure": 17804, "當時": 17805, "简介": 17806, "▁girls": 17807, "▁dogs": 17808, "style": 17809, "指引": 17810, "兴起": 17811, "▁loved": 17812, "缺少": 17813, "▁despite": 17814, "▁files": 17815, "典型的": 17816, "Research": 17817, "在新": 17818, "部门的": 17819, "首要": 17820, "theast": 17821, "广州": 17822, "▁spiritual": 17823, "境内": 17824, "gal": 17825, "▁chemicals": 17826, "的第一": 17827, "管制": 17828, "▁perspectives": 17829, "▁fascinating": 17830, "直观": 17831, "▁enable": 17832, "▁electrical": 17833, "人对": 17834, "在全球": 17835, "意义上": 17836, "意图": 17837, "特异性": 17838, "▁muse": 17839, "韩国": 17840, "子女": 17841, "单个": 17842, "很快": 17843, "▁Division": 17844, "▁replaced": 17845, "▁external": 17846, "尽量": 17847, "respect": 17848, "▁parties": 17849, "外部链接": 17850, "▁reb": 17851, "分支": 17852, "▁starts": 17853, "的所有": 17854, "▁settings": 17855, "發展": 17856, "ested": 17857, "▁Club": 17858, "IA": 17859, "nes": 17860, "开辟": 17861, "▁floor": 17862, "降低了": 17863, "▁knew": 17864, "型的": 17865, "▁dra": 17866, "Up": 17867, "▁extract": 17868, "World": 17869, "▁bright": 17870, "较少": 17871, "体系建设": 17872, "▁calc": 17873, "▁One": 17874, "▁adding": 17875, "▁residents": 17876, "起来的": 17877, "▁Tor": 17878, "化石": 17879, "ictionary": 17880, "联动": 17881, "疗法": 17882, "▁Hill": 17883, "类别": 17884, "▁diss": 17885, "不需要": 17886, "▁Angel": 17887, "深化改革": 17888, "北京市": 17889, "▁opening": 17890, "▁recognize": 17891, "ificial": 17892, "该研究": 17893, "此类": 17894, "之为": 17895, "我们在": 17896, "▁Internet": 17897, "Web": 17898, "缩小": 17899, "Retrieved": 17900, "cycl": 17901, "▁waters": 17902, "oviet": 17903, "法学": 17904, "建立健全": 17905, "教学内容": 17906, "组成的": 17907, "工作者": 17908, "▁practical": 17909, "广阔": 17910, "最重要的": 17911, "▁smart": 17912, "综合征": 17913, "不应": 17914, "的时代": 17915, "▁western": 17916, "百分点": 17917, "eff": 17918, "专用": 17919, "oted": 17920, "民法典": 17921, "和分析": 17922, "iform": 17923, "▁kept": 17924, "bridge": 17925, "还原": 17926, "soft": 17927, "民國": 17928, "Are": 17929, "由於": 17930, "▁attribut": 17931, "▁morning": 17932, "bra": 17933, "▁managed": 17934, "▁Grand": 17935, "ounter": 17936, "▁facilities": 17937, "▁organic": 17938, "▁justice": 17939, "▁craft": 17940, "能耗": 17941, "全社会": 17942, "▁Russian": 17943, "iro": 17944, "▁competition": 17945, "▁Pe": 17946, "▁procedure": 17947, "并对": 17948, "社会主义核心": 17949, "top": 17950, "依赖于": 17951, "▁perspective": 17952, "▁firm": 17953, "分别为": 17954, "▁traditions": 17955, "上市公司": 17956, "半导体": 17957, "▁White": 17958, "季度": 17959, "▁runs": 17960, "▁neur": 17961, "英雄": 17962, "ushing": 17963, "到底": 17964, "广泛的": 17965, "跨越": 17966, "▁discovery": 17967, "电极": 17968, "产业的": 17969, "University": 17970, "能在": 17971, "▁sen": 17972, "▁era": 17973, "▁represented": 17974, "TA": 17975, "▁Rock": 17976, "▁Texas": 17977, "英文": 17978, "云南": 17979, "keep": 17980, "变换": 17981, "常态": 17982, "▁index": 17983, "▁Thomas": 17984, "进化": 17985, "▁nutrients": 17986, "\u0007\u0006": 17987, "▁purposes": 17988, "hav": 17989, "生育": 17990, "▁She": 17991, "▁constant": 17992, "奧林匹克": 17993, "Get": 17994, "学生的学习": 17995, "Rel": 17996, "那样": 17997, "nut": 17998, "▁programming": 17999, "▁forests": 18000, "eling": 18001, "omin": 18002, "附加": 18003, "阻碍": 18004, "长远": 18005, "着眼": 18006, "这次": 18007, "▁Space": 18008, "▁Sl": 18009, "DS": 18010, "▁advantage": 18011, "零售": 18012, "▁Commission": 18013, "一轮": 18014, "结构和": 18015, "abil": 18016, "受害": 18017, "▁highlight": 18018, "希腊": 18019, "▁exchange": 18020, "规定了": 18021, "明了": 18022, "▁collaboration": 18023, "▁stages": 18024, "创新的": 18025, "▁charge": 18026, "光谱": 18027, "可视": 18028, "Bel": 18029, "垂直": 18030, "▁computers": 18031, "▁walls": 18032, "eterm": 18033, "▁phone": 18034, "abits": 18035, "▁spend": 18036, "放弃": 18037, "▁Sea": 18038, "▁bud": 18039, "对立": 18040, "阶段的": 18041, "几年": 18042, "▁fab": 18043, "行政区": 18044, "结合起来": 18045, "▁toxic": 18046, "▁interactions": 18047, "▁intended": 18048, "接入": 18049, "广播": 18050, "劳务": 18051, "▁Management": 18052, "复习": 18053, "▁Carolina": 18054, "风险的": 18055, "明确了": 18056, "▁rein": 18057, "▁affects": 18058, "▁snow": 18059, "itt": 18060, "inity": 18061, "至关重要": 18062, "don": 18063, "▁partners": 18064, "▁Award": 18065, "方法和": 18066, "社會": 18067, "▁talking": 18068, "▁responsibility": 18069, "为主要": 18070, "中山": 18071, "资产阶级": 18072, "LS": 18073, "二年": 18074, "Last": 18075, "▁correspond": 18076, "演出": 18077, "▁layers": 18078, "人类社会": 18079, "▁goods": 18080, "run": 18081, "肠道": 18082, "itness": 18083, "收购": 18084, "▁Social": 18085, ",▁(": 18086, "的外": 18087, "对学生": 18088, "级别": 18089, "▁inflammation": 18090, "▁Games": 18091, "CN": 18092, "ima": 18093, "▁Cast": 18094, "▁join": 18095, "扎实": 18096, "小于": 18097, "▁discrim": 18098, "▁interested": 18099, "▁sudden": 18100, "包容": 18101, "▁behaviors": 18102, "征程": 18103, "▁Middle": 18104, "简化": 18105, "剂量": 18106, "▁seek": 18107, "incre": 18108, "机构的": 18109, "表现在": 18110, "对待": 18111, "vol": 18112, "ads": 18113, "▁optim": 18114, "Where": 18115, "现有的": 18116, "▁mountain": 18117, "DR": 18118, "plicated": 18119, "▁Ter": 18120, "rat": 18121, "ulting": 18122, "彼此": 18123, "嵌入": 18124, "▁resil": 18125, "高达": 18126, "igan": 18127, "总是": 18128, "▁mainly": 18129, "Yes": 18130, "▁dream": 18131, "igital": 18132, "▁fluid": 18133, "启发": 18134, "个性化": 18135, "▁mer": 18136, "xts": 18137, "效能": 18138, "▁god": 18139, "Cre": 18140, "ventions": 18141, "人民代表大会": 18142, "▁Times": 18143, "的理解": 18144, "取向": 18145, "当中": 18146, "real": 18147, "uke": 18148, "▁Smith": 18149, "▁supported": 18150, "全民": 18151, "质量和": 18152, "创新能力": 18153, "种子": 18154, "电解": 18155, "tenance": 18156, "下面": 18157, "▁mostly": 18158, "(▁": 18159, "并通过": 18160, "Less": 18161, "uing": 18162, "▁monitoring": 18163, "▁solve": 18164, "了一系列": 18165, "▁III": 18166, "时刻": 18167, "的定义": 18168, "▁maximum": 18169, "走出": 18170, "▁philosoph": 18171, "▁medium": 18172, "管理体系": 18173, "习近平同志": 18174, "QL": 18175, "或其他": 18176, "擔任": 18177, "▁generate": 18178, "meaning": 18179, "汉语": 18180, "检索": 18181, "Alex": 18182, "esis": 18183, "▁injuries": 18184, "梳理": 18185, "困境": 18186, "建立在": 18187, "▁showing": 18188, "▁Sim": 18189, "▁Sk": 18190, "▁salt": 18191, "▁designs": 18192, "Mc": 18193, "中断": 18194, "▁Pan": 18195, "▁relax": 18196, "▁To": 18197, "David": 18198, "▁Arab": 18199, "reatment": 18200, "▁Parliament": 18201, "▁shel": 18202, "RT": 18203, "至于": 18204, "▁victim": 18205, "▁Book": 18206, "▁ax": 18207, "又是": 18208, "定律": 18209, "▁unexpected": 18210, "ady": 18211, "FS": 18212, "amental": 18213, "Mr": 18214, "claim": 18215, "住宅": 18216, "▁facts": 18217, "▁truth": 18218, "▁suitable": 18219, "iat": 18220, "ao": 18221, "▁flat": 18222, "第二个": 18223, "未成年人": 18224, "脉冲": 18225, "Austral": 18226, "▁defe": 18227, "病的": 18228, "▁appointed": 18229, "学说": 18230, "离婚": 18231, "常委会": 18232, "舞蹈": 18233, "耕地": 18234, "个百分点": 18235, "吸附": 18236, "的位置": 18237, "aturally": 18238, "▁harmful": 18239, "awa": 18240, "▁shapes": 18241, "Among": 18242, "Current": 18243, "创设": 18244, "率的": 18245, "▁recon": 18246, "解决问题": 18247, "▁chain": 18248, "巴黎": 18249, "建筑物": 18250, "設計": 18251, "▁Foundation": 18252, "▁recognized": 18253, "▁seed": 18254, "redu": 18255, "▁unknown": 18256, "imagine": 18257, "VID": 18258, "诞生": 18259, "clusive": 18260, "▁couldn": 18261, "超声": 18262, "转型升级": 18263, "▁ourselves": 18264, "Aut": 18265, "omer": 18266, "▁excellent": 18267, "▁wealth": 18268, "▁youth": 18269, "等多": 18270, "▁landscape": 18271, "▁jobs": 18272, "▁kinds": 18273, "Take": 18274, "body": 18275, "https": 18276, "itivity": 18277, "ishop": 18278, "致力于": 18279, "stone": 18280, "▁rural": 18281, "acts": 18282, "▁die": 18283, "▁lic": 18284, "等因素": 18285, "增多": 18286, "▁turns": 18287, "开创": 18288, "Ste": 18289, "學校": 18290, "▁NA": 18291, "知名": 18292, "▁thin": 18293, "▁fif": 18294, "illa": 18295, "▁potentially": 18296, "依法治国": 18297, "rison": 18298, "系統": 18299, "可在": 18300, "PT": 18301, "传导": 18302, "命题": 18303, "ifferent": 18304, "ifications": 18305, "▁procedures": 18306, "▁Scott": 18307, "上看": 18308, "介质": 18309, "▁waves": 18310, "脱离": 18311, "电阻": 18312, "▁Cam": 18313, "年在": 18314, "▁phr": 18315, "▁kil": 18316, "▁assign": 18317, "开关": 18318, "▁helpful": 18319, "uty": 18320, "▁relative": 18321, "武汉": 18322, "静态": 18323, "▁promoting": 18324, "elled": 18325, "文獻": 18326, "▁guidance": 18327, "▁capable": 18328, "期货": 18329, "迫切": 18330, "发现了": 18331, "▁ideal": 18332, ":▁(": 18333, "限于": 18334, "▁originally": 18335, "▁wait": 18336, "usually": 18337, "▁Italian": 18338, "製作": 18339, "▁Di": 18340, "以外": 18341, "回到": 18342, "德育": 18343, "rors": 18344, "其在": 18345, "▁frag": 18346, "优惠": 18347, "pet": 18348, "oir": 18349, "▁Engine": 18350, "尺度": 18351, "ifies": 18352, "▁causing": 18353, "本站": 18354, "asty": 18355, "▁reduction": 18356, "aming": 18357, "报酬": 18358, "舆论": 18359, "离不开": 18360, "时间的": 18361, "双重": 18362, "感到": 18363, "东方": 18364, "持有": 18365, "▁ran": 18366, "清算": 18367, "思想的": 18368, "中的应用": 18369, "不存在": 18370, "heim": 18371, "onomy": 18372, "参见": 18373, "班牙": 18374, "西班牙": 18375, "ISBN": 18376, "从严": 18377, "不仅是": 18378, "▁hero": 18379, "浪费": 18380, "自然科学": 18381, "▁Wild": 18382, "▁lots": 18383, "▁truly": 18384, "处分": 18385, "激烈": 18386, "▁abilities": 18387, "rate": 18388, "專輯": 18389, "Cent": 18390, "▁serves": 18391, "df": 18392, "▁satis": 18393, "▁seat": 18394, "!\"": 18395, "gely": 18396, "细化": 18397, "纠正": 18398, "▁sensitive": 18399, "延迟": 18400, "前者": 18401, "图书馆": 18402, "▁coal": 18403, "▁wet": 18404, "▁obs": 18405, "▁aimed": 18406, "wa": 18407, "Bar": 18408, "▁nor": 18409, "▁score": 18410, "rogen": 18411, "▁vehicles": 18412, "经济体": 18413, "sex": 18414, "▁signed": 18415, "幅度": 18416, "OL": 18417, "▁extreme": 18418, "▁forced": 18419, "奧林匹克運動會": 18420, "▁Mad": 18421, "fig": 18422, "estion": 18423, "▁concentration": 18424, "fish": 18425, "月球": 18426, "▁ban": 18427, "▁neut": 18428, "▁Commit": 18429, "▁critic": 18430, "▁Columb": 18431, "罗马": 18432, "开拓": 18433, "最高人民法院": 18434, "non": 18435, "基准": 18436, "▁pil": 18437, "地点": 18438, "azz": 18439, "enny": 18440, "▁perman": 18441, "\"，": 18442, "lines": 18443, "水利": 18444, "各方面": 18445, "陷入": 18446, "中介": 18447, "▁density": 18448, "▁drink": 18449, "会导致": 18450, "便于": 18451, "命运共同体": 18452, "Time": 18453, "▁tested": 18454, "aph": 18455, "凸显": 18456, "和经济": 18457, "不平衡": 18458, "高水平": 18459, "的实践": 18460, "可行": 18461, "燃烧": 18462, "▁ST": 18463, "ione": 18464, "apes": 18465, "James": 18466, "水的": 18467, "▁catch": 18468, "▁tips": 18469, "都能": 18470, "▁route": 18471, "▁Medicine": 18472, "争取": 18473, "▁marked": 18474, "GA": 18475, "煤炭": 18476, "史上": 18477, "▁satell": 18478, "▁trial": 18479, "办公室": 18480, "安徽": 18481, "cou": 18482, "▁Robert": 18483, "供给侧": 18484, "CM": 18485, "▁Don": 18486, "▁vitamin": 18487, "HT": 18488, "一名": 18489, "asive": 18490, "ultane": 18491, "较多": 18492, "此次": 18493, "▁stronger": 18494, "▁emergency": 18495, "要把": 18496, "这也是": 18497, "het": 18498, "sem": 18499, "锻炼": 18500, "▁ign": 18501, "lers": 18502, "介绍了": 18503, "▁tun": 18504, "发展战略": 18505, "ansion": 18506, "stood": 18507, "液体": 18508, "ublished": 18509, "▁Bas": 18510, "lets": 18511, "理事": 18512, "收缩": 18513, "制度和": 18514, "相适应": 18515, "耦合": 18516, "HS": 18517, "—▁▁—▁▁": 18518, "estyle": 18519, "▁Government": 18520, "营业": 18521, "申报": 18522, "在全国": 18523, "利亚": 18524, "等多种": 18525, "▁Tre": 18526, "▁depth": 18527, "▁alongside": 18528, "Ref": 18529, "率先": 18530, "要求的": 18531, "▁obtained": 18532, "▁km": 18533, "▁mixed": 18534, "源于": 18535, "聚合": 18536, "情景": 18537, "定价": 18538, "▁writers": 18539, "法和": 18540, "▁Ste": 18541, "pass": 18542, "新生": 18543, "这两个": 18544, "初中": 18545, "▁Tri": 18546, "研究表明": 18547, "元件": 18548, "的实际": 18549, "电信": 18550, "▁wel": 18551, "▁oral": 18552, "▁improving": 18553, "▁newsp": 18554, "实现中华民族伟大复兴": 18555, "▁listed": 18556, "▁seeking": 18557, "▁Valley": 18558, "▁remaining": 18559, "个人的": 18560, "可再生能源": 18561, "▁whom": 18562, "▁tradition": 18563, "维修": 18564, "ih": 18565, "安全的": 18566, "▁underlying": 18567, "▁except": 18568, "招生": 18569, "▁Dan": 18570, "▁locations": 18571, "▁Islam": 18572, "▁interaction": 18573, "ori": 18574, "Set": 18575, "无限": 18576, "stein": 18577, "▁Read": 18578, "▁documents": 18579, "▁gar": 18580, "▁suc": 18581, "▁horse": 18582, "▁writer": 18583, "students": 18584, "▁Land": 18585, "全面建成": 18586, "极为": 18587, "组织和": 18588, "雷达": 18589, "准入": 18590, "▁musical": 18591, "West": 18592, "stances": 18593, "光纤": 18594, "Data": 18595, "▁cards": 18596, "▁controlled": 18597, "eries": 18598, "院长": 18599, "智慧城市": 18600, "▁daughter": 18601, "▁resulting": 18602, "新一代": 18603, "▁saying": 18604, "▁||": 18605, "。(": 18606, "▁signals": 18607, "erving": 18608, "▁walking": 18609, "警察": 18610, "▁haz": 18611, "▁platforms": 18612, "ohnson": 18613, "ture": 18614, "Mark": 18615, "行政管理": 18616, "揭示了": 18617, "▁ren": 18618, "▁extended": 18619, "▁demonstrate": 18620, "充电": 18621, "沉积": 18622, "电脑": 18623, "▁vaccine": 18624, "▁foundation": 18625, "行业的": 18626, "▁Del": 18627, "main": 18628, "是为了": 18629, "▁Technology": 18630, "▁ple": 18631, "▁substant": 18632, "▁scri": 18633, "jo": 18634, "▁accomp": 18635, "▁diam": 18636, "过于": 18637, "La": 18638, "党的十九": 18639, "lishing": 18640, "同時": 18641, "治党": 18642, "数字技术": 18643, "更有": 18644, "舞台": 18645, "▁doll": 18646, "▁Sy": 18647, "immy": 18648, "ellect": 18649, "▁Camp": 18650, "▁introduction": 18651, "thetic": 18652, "意愿": 18653, "站在": 18654, "▁machines": 18655, "▁Power": 18656, "Mem": 18657, "▁Henry": 18658, "▁exercises": 18659, "本土": 18660, "▁Organ": 18661, "▁Elect": 18662, "经营者": 18663, "raining": 18664, "遭受": 18665, "演唱": 18666, "校长": 18667, "深远": 18668, "▁Paris": 18669, "▁rapidly": 18670, "司令": 18671, "▁concern": 18672, "earing": 18673, "一流": 18674, "bar": 18675, "eli": 18676, "美丽": 18677, "EF": 18678, "Early": 18679, "中期": 18680, "▁Ireland": 18681, "自治区": 18682, "▁texts": 18683, "▁AI": 18684, "social": 18685, "相对于": 18686, "▁Charles": 18687, "▁mac": 18688, "creating": 18689, "变迁": 18690, "▁Stand": 18691, "▁guitar": 18692, "outs": 18693, "本章": 18694, "▁Frank": 18695, "▁decide": 18696, "DL": 18697, "延续": 18698, "血糖": 18699, "事业单位": 18700, "意义的": 18701, "umin": 18702, "cal": 18703, "▁drinking": 18704, "▁fiction": 18705, "为止": 18706, "hent": 18707, "▁chart": 18708, "▁Victor": 18709, "▁arms": 18710, "▁edge": 18711, "ompl": 18712, "▁ancest": 18713, "也将": 18714, "明确的": 18715, "率为": 18716, "纲要": 18717, "▁houses": 18718, "学生对": 18719, "盈利": 18720, "▁od": 18721, "研讨": 18722, "自然资源": 18723, "社会主义市场经济": 18724, "iological": 18725, "stitution": 18726, "▁bridge": 18727, "▁audience": 18728, "恶性": 18729, "Those": 18730, "上有": 18731, "加坡": 18732, "▁swim": 18733, "从中": 18734, "▁handle": 18735, "Consider": 18736, "ractice": 18737, "▁finished": 18738, "ipping": 18739, "▁supporting": 18740, "▁Def": 18741, "总之": 18742, "离开": 18743, "ilation": 18744, "承载": 18745, "实务": 18746, "回顾": 18747, "具有重要": 18748, "▁acts": 18749, "▁noticed": 18750, "word": 18751, "Sam": 18752, "▁Ev": 18753, "thus": 18754, "▁typical": 18755, "educ": 18756, "umes": 18757, "设计和": 18758, "▁fle": 18759, "▁biological": 18760, "▁Dec": 18761, "▁plate": 18762, "▁communicate": 18763, "▁sort": 18764, "农户": 18765, "小康社会": 18766, "硕士": 18767, "腺癌": 18768, "Water": 18769, "的效果": 18770, "比特": 18771, "try": 18772, "▁experiments": 18773, "▁tells": 18774, "的本质": 18775, "的空间": 18776, "列入": 18777, "▁hypothes": 18778, "▁consistent": 18779, "的意义": 18780, "参照": 18781, "▁leadership": 18782, "原本": 18783, "▁decrease": 18784, "封闭": 18785, "欢迎": 18786, "▁portion": 18787, "▁+": 18788, "转变为": 18789, "▁attacks": 18790, "rus": 18791, "before": 18792, "hered": 18793, "江西": 18794, "▁capture": 18795, "▁messages": 18796, "▁array": 18797, "尽快": 18798, "彰显": 18799, "British": 18800, "用水": 18801, "節目": 18802, "▁Scot": 18803, "污水": 18804, "阿拉": 18805, "▁instruments": 18806, "▁flight": 18807, "过错": 18808, "Form": 18809, "%)▁": 18810, "uous": 18811, "生命周期": 18812, "▁Form": 18813, "mes": 18814, "Ben": 18815, "oting": 18816, "▁Fin": 18817, "起义": 18818, "消化": 18819, "高原": 18820, "无关": 18821, "選舉": 18822, "iano": 18823, "▁visible": 18824, "Histor": 18825, "▁inspired": 18826, "▁doub": 18827, "呈现出": 18828, "▁herit": 18829, ")▁▁": 18830, "▁conclud": 18831, "▁nations": 18832, "ossible": 18833, "onia": 18834, "追究": 18835, "▁lose": 18836, "指向": 18837, "大批": 18838, "书中": 18839, "消灭": 18840, "▁narratives": 18841, "应用的": 18842, "中小学": 18843, "▁younger": 18844, "data": 18845, "▁cert": 18846, "解决了": 18847, "▁Lord": 18848, "组建": 18849, "化工": 18850, "比賽": 18851, "▁foss": 18852, "agues": 18853, "权和": 18854, "每天": 18855, "增长的": 18856, "解放军": 18857, "▁wants": 18858, "四大": 18859, "血清": 18860, "污染物": 18861, "一位": 18862, "▁cm": 18863, "南方": 18864, "操作系统": 18865, "▁secondary": 18866, "sug": 18867, "ctic": 18868, "orrow": 18869, "▁credit": 18870, "▁amb": 18871, "night": 18872, "Paul": 18873, "rust": 18874, 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18940, "遭到": 18941, "▁molecules": 18942, "▁dim": 18943, "频繁": 18944, "中国科学院": 18945, "ylvan": 18946, "工作人员": 18947, "终身": 18948, "生产经营": 18949, "BI": 18950, "ighter": 18951, "本课程": 18952, "▁allerg": 18953, "缺点": 18954, "onomic": 18955, "former": 18956, "▁wis": 18957, "澳大利": 18958, "神经网络": 18959, "入侵": 18960, "▁mere": 18961, "▁slowly": 18962, "▁Sub": 18963, "缩短": 18964, "▁Rob": 18965, "▁derived": 18966, "gent": 18967, "▁functional": 18968, "▁Northern": 18969, "学习和": 18970, "reme": 18971, "▁scene": 18972, "▁hidden": 18973, "否定": 18974, "▁Minister": 18975, "占据": 18976, "筛选": 18977, "性疾病": 18978, "▁stret": 18979, "sen": 18980, "互通": 18981, "》：": 18982, "又有": 18983, "机的": 18984, "▁religion": 18985, "TR": 18986, "▁accompl": 18987, "但由于": 18988, "时间内": 18989, "▁painting": 18990, "▁albums": 18991, "gs": 18992, "小结": 18993, "▁technical": 18994, "常用的": 18995, "PU": 18996, "▁intervention": 18997, "独特的": 18998, "注射": 18999, "tery": 19000, "aturing": 19001, "前后": 19002, "Second": 19003, "▁membran": 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19324, "▁boundaries": 19325, "▁regulations": 19326, "▁electronic": 19327, "▁Control": 19328, "▁positions": 19329, "▁distingu": 19330, "最多": 19331, "习题": 19332, "保健": 19333, "California": 19334, "▁skill": 19335, "uter": 19336, "恶意": 19337, "前期": 19338, "对手": 19339, "▁cup": 19340, "所未": 19341, "薄弱": 19342, "每次": 19343, "奥林匹克": 19344, "aser": 19345, "抵抗": 19346, "病原": 19347, "▁expressed": 19348, "▁authority": 19349, "▁infrastructure": 19350, "▁administration": 19351, "▁everyday": 19352, "基本上": 19353, "能够在": 19354, "获得的": 19355, "thesis": 19356, "show": 19357, "农药": 19358, "▁agreed": 19359, "Following": 19360, "▁Human": 19361, "irus": 19362, "Life": 19363, "▁ships": 19364, "荧光": 19365, "高考": 19366, "新加坡": 19367, "oston": 19368, "▁heritage": 19369, "表征": 19370, "法律规定": 19371, "草案": 19372, "但其": 19373, "奖励": 19374, "严峻": 19375, "湖北": 19376, "coming": 19377, "▁matters": 19378, "▁succeed": 19379, "老年人": 19380, "较强的": 19381, "▁prison": 19382, "▁detailed": 19383, "弥补": 19384, "▁inner": 19385, "▁proport": 19386, "Sch": 19387, "经历了": 19388, "会上": 19389, "磁场": 19390, "黄金": 19391, "adium": 19392, "▁facing": 19393, "近期": 19394, "▁Po": 19395, "▁massive": 19396, "▁Line": 19397, "本国": 19398, "党史": 19399, "两国": 19400, "▁Fe": 19401, "▁featured": 19402, "遥感": 19403, "uracy": 19404, "atre": 19405, "国土": 19406, "储存": 19407, "▁customers": 19408, "those": 19409, "effect": 19410, "出售": 19411, "▁Vol": 19412, "▁division": 19413, "年起": 19414, "契约": 19415, "中国式": 19416, "▁couple": 19417, "iral": 19418, "▁pig": 19419, "DF": 19420, "▁pit": 19421, "台北": 19422, "流感": 19423, "资产的": 19424, "▁sequence": 19425, "▁views": 19426, "在实际": 19427, "race": 19428, "▁Food": 19429, "特殊的": 19430, "ocyt": 19431, "▁tro": 19432, "高压": 19433, "国际化": 19434, "ivalent": 19435, "▁entirely": 19436, "广义": 19437, "▁astron": 19438, "consider": 19439, "完成了": 19440, "▁instruction": 19441, "▁habits": 19442, "fficial": 19443, "永远": 19444, "Dem": 19445, "残疾": 19446, "omething": 19447, "建立起": 19448, "双向": 19449, "Ident": 19450, "偏差": 19451, "的条件": 19452, "作出了": 19453, "▁variables": 19454, "▁manip": 19455, "▁struggle": 19456, "hy": 19457, "专业的": 19458, "made": 19459, "exp": 19460, "▁soldiers": 19461, "Only": 19462, "随时": 19463, "元年": 19464, "giving": 19465, "ideos": 19466, "打开": 19467, "美学": 19468, "▁naturally": 19469, "▁administr": 19470, "▁Chicago": 19471, "称之为": 19472, "▁indigenous": 19473, "草原": 19474, "▁department": 19475, "▁sell": 19476, "着重": 19477, "学生学习": 19478, "▁fly": 19479, "科院": 19480, "hus": 19481, "ocation": 19482, "Inc": 19483, "anes": 19484, "▁weap": 19485, "▁controls": 19486, "▁molecular": 19487, "ipal": 19488, "▁bones": 19489, "▁conventional": 19490, "▁kit": 19491, "lements": 19492, "▁narrow": 19493, "智能制造": 19494, "▁Bank": 19495, "Though": 19496, "▁attend": 19497, "osite": 19498, "工会": 19499, "战役": 19500, "关闭": 19501, "▁millions": 19502, "▁prices": 19503, "很好": 19504, "▁fell": 19505, "▁street": 19506, "语音": 19507, "基督": 19508, "是实现": 19509, "带宽": 19510, "本案": 19511, "突发": 19512, "要坚持": 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"▁revolution": 19578, "▁enem": 19579, "forming": 19580, "▁Work": 19581, "增值税": 19582, "▁cutting": 19583, "asters": 19584, "Open": 19585, "在一": 19586, "▁consult": 19587, "▁sector": 19588, "要以": 19589, "与管理": 19590, "的网络": 19591, "▁feedback": 19592, "xford": 19593, "▁seemed": 19594, "▁serving": 19595, "color": 19596, "志愿": 19597, "抗战": 19598, "▁trials": 19599, "▁confir": 19600, "Examples": 19601, "▁jump": 19602, "▁Jud": 19603, "阶层": 19604, "君主": 19605, "▁circumstances": 19606, "▁intake": 19607, "▁stored": 19608, "va": 19609, "isters": 19610, "所需的": 19611, "▁sweet": 19612, "Rev": 19613, "▁Budd": 19614, "▁gluc": 19615, "fin": 19616, "▁representation": 19617, "rument": 19618, "▁simpl": 19619, "參考文獻": 19620, "▁rub": 19621, "法律的": 19622, "改为": 19623, "▁conversation": 19624, "产阶级": 19625, "▁mobile": 19626, "责任的": 19627, "热情": 19628, "结核": 19629, "和文化": 19630, "itudes": 19631, "▁domin": 19632, "分辨率": 19633, "本人": 19634, "action": 19635, "▁Bill": 19636, "▁string": 19637, "▁Centre": 19638, "▁simultane": 19639, "▁schem": 19640, "瓶颈": 19641, "▁kill": 19642, "毫米": 19643, "▁successfully": 19644, "还能": 19645, "▁somew": 19646, "tw": 19647, "全身": 19648, "发挥了": 19649, "▁instrument": 19650, "▁stands": 19651, "我的": 19652, "olesterol": 19653, "▁centre": 19654, "▁fold": 19655, "▁bond": 19656, "有益": 19657, "里的": 19658, "▁monitor": 19659, "inds": 19660, "▁meals": 19661, "进程中": 19662, "受理": 19663, "George": 19664, "共产主义": 19665, "FL": 19666, "壮大": 19667, "变为": 19668, "▁paid": 19669, "Meanwhile": 19670, "▁alter": 19671, "格尔": 19672, "各族": 19673, "甲基": 19674, "已被": 19675, "对企业": 19676, "March": 19677, "▁survive": 19678, "▁raise": 19679, "影响的": 19680, "olitical": 19681, "achers": 19682, "有望": 19683, "▁explan": 19684, "鐵路": 19685, "ali": 19686, "实证": 19687, "极其": 19688, "Resear": 19689, "▁evaluate": 19690, "侦查": 19691, "过剩": 19692, "著作权": 19693, "VI": 19694, "▁coff": 19695, "高等学校": 19696, "小行星": 19697, "▁latest": 19698, "优良": 19699, "祖国": 19700, "运动的": 19701, "▁suffering": 19702, "▁teen": 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21678, "▁volcan": 21679, "▁certainly": 21680, "▁Disease": 21681, "SE": 21682, "▁mixture": 21683, "好处": 21684, "和政治": 21685, "ela": 21686, "mem": 21687, "▁regist": 21688, "熟练": 21689, "电机": 21690, "不断提升": 21691, "动手": 21692, "全新的": 21693, "▁sensitivity": 21694, "日的": 21695, "ocking": 21696, "法案": 21697, "▁、": 21698, "影响了": 21699, "灵活性": 21700, "同期": 21701, "全世界": 21702, "▁push": 21703, "▁videos": 21704, "▁Stat": 21705, "議員": 21706, "所致": 21707, "导师": 21708, "身心": 21709, "提升了": 21710, "Mil": 21711, "▁So": 21712, "yal": 21713, "▁reject": 21714, "想要": 21715, "words": 21716, "sis": 21717, "gian": 21718, "微信": 21719, "的传统": 21720, "均为": 21721, "▁Bet": 21722, "rolled": 21723, "求解": 21724, "▁meth": 21725, "消极": 21726, "积极的": 21727, "”、": 21728, "▁request": 21729, "empl": 21730, "tre": 21731, "pot": 21732, "時代": 21733, "BL": 21734, "俄国": 21735, "英格": 21736, "進入": 21737, "国企": 21738, "教师要": 21739, "发放": 21740, "创新发展": 21741, "▁examining": 21742, "▁surprise": 21743, "立方": 21744, "books": 21745, 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"▁conflicts": 21873, "▁frequent": 21874, "纲领": 21875, "结构性改革": 21876, "必须坚持": 21877, "人力资本": 21878, "思想和": 21879, "发展中": 21880, "operation": 21881, "网格": 21882, "▁persons": 21883, "UK": 21884, "名单": 21885, "科普": 21886, "内核": 21887, "flamm": 21888, "确立了": 21889, "期徒刑": 21890, "▁dent": 21891, "占领": 21892, "▁Sund": 21893, "▁Martin": 21894, "美洲": 21895, "telling": 21896, "候选": 21897, "间隔": 21898, "和国家": 21899, "▁difficulties": 21900, "take": 21901, "还应": 21902, "敌人": 21903, "全球经济": 21904, "三个方面": 21905, "▁preserve": 21906, "alm": 21907, "▁desc": 21908, "▁disrupt": 21909, "合伙": 21910, "性别": 21911, "aret": 21912, "▁districts": 21913, "具有一定的": 21914, "战线": 21915, "不仅要": 21916, "检察机关": 21917, "ioxide": 21918, "▁troops": 21919, "一致性": 21920, "编译": 21921, "prot": 21922, "▁wheel": 21923, "▁objective": 21924, "aterials": 21925, "▁concrete": 21926, "关键技术": 21927, "各部门": 21928, "▁intense": 21929, "▁absol": 21930, "▁considering": 21931, "▁moisture": 21932, "▁quiet": 21933, "▁tap": 21934, "交易所": 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"▁correctly": 21998, "广泛应用": 21999, "▁encouraged": 22000, "▁consumers": 22001, "所属": 22002, "失衡": 22003, "环境污染": 22004, "季节": 22005, "涌现": 22006, "二元": 22007, "▁Province": 22008, "Year": 22009, "RP": 22010, "actions": 22011, "监护": 22012, "FF": 22013, "▁collective": 22014, "▁kilomet": 22015, "▁males": 22016, "▁operated": 22017, "inch": 22018, "▁Ol": 22019, "在国家": 22020, "但不": 22021, "▁neurons": 22022, "osqu": 22023, "ragon": 22024, "▁greenhouse": 22025, "▁robot": 22026, "下行": 22027, "共生": 22028, "当局": 22029, "▁Rom": 22030, "\")": 22031, "Mass": 22032, "▁investigation": 22033, "burgh": 22034, "习近平总书记指出": 22035, "思潮": 22036, "会议上": 22037, "究竟": 22038, "以习近平同志为核心的党中央": 22039, "技术的发展": 22040, "despite": 22041, "SQL": 22042, "▁Guide": 22043, "陈述": 22044, "Br": 22045, "▁coach": 22046, "step": 22047, "opher": 22048, "这种方法": 22049, "胚胎": 22050, "量和": 22051, "think": 22052, "etime": 22053, "ypically": 22054, "▁solving": 22055, "集團": 22056, "在实践中": 22057, "密切相关": 22058, "采样": 22059, "▁Liter": 22060, "小麦": 22061, "▁therapeutic": 22062, "▁Writ": 22063, "vin": 22064, "▁researc": 22065, "▁owner": 22066, "读书": 22067, "关键词": 22068, "ymb": 22069, "▁unable": 22070, "bb": 22071, "▁emerging": 22072, "language": 22073, "大局": 22074, "会对": 22075, "的地": 22076, "▁ring": 22077, "也被": 22078, "yet": 22079, "▁nervous": 22080, "▁seasons": 22081, "▁faces": 22082, "提问": 22083, "▁manif": 22084, "▁ampl": 22085, "▁threatened": 22086, "alter": 22087, "case": 22088, "的表现": 22089, "申请人": 22090, "美联储": 22091, "当代中国": 22092, "▁integrated": 22093, "戏剧": 22094, "Leg": 22095, "▁accommod": 22096, "▁Los": 22097, "▁transp": 22098, "▁effectiveness": 22099, "用户的": 22100, "▁coastal": 22101, "▁identifying": 22102, "April": 22103, "数控": 22104, "Sarah": 22105, "而成": 22106, "▁finger": 22107, "▁hel": 22108, "ushed": 22109, "emet": 22110, "▁lens": 22111, "都不": 22112, "归属": 22113, "的有": 22114, "Develop": 22115, "▁exciting": 22116, "重大的": 22117, "视网膜": 22118, "学分": 22119, "语法": 22120, "bean": 22121, "在本": 22122, "Peter": 22123, "藝術": 22124, "▁traits": 22125, "inction": 22126, "erus": 22127, "自学": 22128, "這些": 22129, "单位的": 22130, "▁prove": 22131, "Windows": 22132, "▁giant": 22133, "Inf": 22134, "违背": 22135, "▁obesity": 22136, "是非常": 22137, "acebook": 22138, "▁abandon": 22139, "▁Er": 22140, "%,▁": 22141, "歐洲": 22142, "▁plane": 22143, "原型": 22144, "________": 22145, "父亲": 22146, "等内容": 22147, "▁discussing": 22148, "做的": 22149, "▁providers": 22150, "▁Age": 22151, "的计算": 22152, "▁complexity": 22153, "也需要": 22154, "一号": 22155, "发展趋势": 22156, "▁nic": 22157, "▁Young": 22158, "背后": 22159, "第一百": 22160, "考验": 22161, "保护和": 22162, "应力": 22163, "▁flower": 22164, "▁Blue": 22165, "汉字": 22166, "▁gram": 22167, "▁safely": 22168, "▁targets": 22169, "电站": 22170, "hard": 22171, "▁gap": 22172, "▁gent": 22173, "▁incorporate": 22174, "▁incorporating": 22175, "师资": 22176, "共产党的": 22177, "畅通": 22178, "意识到": 22179, "流失": 22180, "纵向": 22181, "真实性": 22182, "经济学家": 22183, "字符": 22184, "上了": 22185, "▁Korea": 22186, 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"生命力": 27107, "不平等": 27108, "地位的": 27109, "积极作用": 27110, "畸形": 27111, "发热": 27112, "军的": 27113, "与非": 27114, "传达": 27115, "▁Ash": 27116, "▁compan": 27117, "微分": 27118, "Encourage": 27119, "Bro": 27120, "▁Three": 27121, "▁HT": 27122, "mega": 27123, "▁package": 27124, "下来的": 27125, "切入": 27126, "▁sympt": 27127, "▁customs": 27128, "plate": 27129, "Put": 27130, "▁gran": 27131, "web": 27132, "▁Antar": 27133, "球队": 27134, "Comb": 27135, "治国理政": 27136, "西方国家": 27137, "产学": 27138, "发票": 27139, "庞大的": 27140, "严肃": 27141, "结构化": 27142, "▁circul": 27143, "不属于": 27144, "Indeed": 27145, "▁evil": 27146, "▁orange": 27147, "▁reflects": 27148, "▁subtle": 27149, "▁tribut": 27150, "hk": 27151, "现在的": 27152, "火星": 27153, "itus": 27154, "语言的": 27155, "ashing": 27156, "年被": 27157, "aiwan": 27158, "attered": 27159, "▁Ba": 27160, "▁Stadium": 27161, "通信技术": 27162, "新冠肺炎疫情": 27163, "之间存在": 27164, "在不": 27165, "作詞": 27166, "反向": 27167, "ropolitan": 27168, "▁suicide": 27169, "▁containers": 27170, "万亿": 27171, "教学法": 27172, "各级政府": 27173, "承租": 27174, "情怀": 27175, "基站": 27176, "血栓": 27177, "走进": 27178, "纽带": 27179, "又能": 27180, "的局面": 27181, "▁adhere": 27182, "要素的": 27183, "成型": 27184, "需求和": 27185, "doi": 27186, "▁restore": 27187, "▁abandoned": 27188, "▁territories": 27189, "▁cran": 27190, "亚太": 27191, "共同的": 27192, "▁Log": 27193, "▁hybrid": 27194, "▁peripher": 27195, "tainment": 27196, "捕捉": 27197, "▁【": 27198, "phabet": 27199, "▁renamed": 27200, "岁的": 27201, "耐药": 27202, "变成了": 27203, "群落": 27204, "通俗": 27205, "宽带": 27206, "加之": 27207, "试行": 27208, "服务体系": 27209, "▁Wars": 27210, "》▁▁": 27211, "coll": 27212, "了一些": 27213, "Mex": 27214, "生殖": 27215, "▁Fig": 27216, "这是一种": 27217, "成为一个": 27218, "chant": 27219, "有多": 27220, "并将其": 27221, "Along": 27222, "Additional": 27223, "chester": 27224, "▁annually": 27225, "▁everywhere": 27226, "▁lemon": 27227, "▁AM": 27228, "十二五": 27229, "学时": 27230, "重点领域": 27231, "中指出": 27232, "询问": 27233, "酒精": 27234, "▁Support": 27235, "而生": 27236, "Indian": 27237, "flies": 27238, "▁Bell": 27239, "▁bottle": 27240, "▁tou": 27241, "loss": 27242, "oking": 27243, "▁Live": 27244, "副主席": 27245, "股市": 27246, "后世": 27247, "柏林": 27248, "北海": 27249, "▁expanding": 27250, "Timmy": 27251, "▁delicate": 27252, "▁spacecraft": 27253, "omot": 27254, "生态环境保护": 27255, "数字货币": 27256, "人工智能技术": 27257, "日元": 27258, "建制": 27259, "自然科学基金": 27260, "iga": 27261, "ognition": 27262, "Ma": 27263, "abama": 27264, "▁restoration": 27265, "▁specimens": 27266, "▁gathering": 27267, "▁struggling": 27268, "current": 27269, "Oxford": 27270, "长安": 27271, "任何一个": 27272, "不一": 27273, "eeds": 27274, "▁therapies": 27275, "三国": 27276, "▁Spirit": 27277, "▁Sav": 27278, "公允价值": 27279, "家具": 27280, "外部环境": 27281, "自然灾害": 27282, "审慎": 27283, "established": 27284, "affe": 27285, "十月": 27286, "衰减": 27287, "用了": 27288, "▁Pakistan": 27289, "overe": 27290, "Cast": 27291, "▁cub": 27292, "▁correlation": 27293, "▁delight": 27294, "▁nu": 27295, "▁delve": 27296, "▁integrity": 27297, "▁positively": 27298, "▁Toronto": 27299, "cond": 27300, "ownt": 27301, "▁▁.": 27302, "Name": 27303, "wer": 27304, "更换": 27305, "剥夺": 27306, "estone": 27307, "grams": 27308, "CAD": 27309, "techn": 27310, "演奏": 27311, "▁surviv": 27312, "▁worship": 27313, "unar": 27314, "ennessee": 27315, "粮食安全": 27316, "来确定": 27317, "而后": 27318, "弱势": 27319, "ст": 27320, "真的": 27321, "▁smoothly": 27322, "不等": 27323, "enza": 27324, "iberty": 27325, "elson": 27326, "inflammatory": 27327, "要求和": 27328, "得不到": 27329, "应激": 27330, "很可能": 27331, "▁resistant": 27332, "和中": 27333, "playing": 27334, "▁poems": 27335, "▁strengths": 27336, "()`▁": 27337, "▁comic": 27338, "ART": 27339, "▁AND": 27340, "▁overview": 27341, "▁optical": 27342, "▁institution": 27343, "icts": 27344, "发展为": 27345, "马克思主义的": 27346, "的证据": 27347, "年我国": 27348, "城市群": 27349, "迈向": 27350, "汲取": 27351, "肝炎": 27352, "aments": 27353, "平民": 27354, "×▁": 27355, "giene": 27356, "▁cow": 27357, "▁tailored": 27358, "伊朗": 27359, "akespeare": 27360, "▁ty": 27361, "對於": 27362, "umbers": 27363, "▁enhancing": 27364, "bral": 27365, "建国": 27366, "held": 27367, "▁earthquake": 27368, "的最后": 27369, "▁sheets": 27370, "▁dinner": 27371, "▁ranging": 27372, "Cong": 27373, "ilms": 27374, "Econom": 27375, "利于": 27376, "的运动": 27377, "在国内": 27378, "改革创新": 27379, "外语": 27380, "测绘": 27381, "▁striking": 27382, "子公司": 27383, "wealth": 27384, "▁soccer": 27385, "▁throat": 27386, "▁scheme": 27387, "Star": 27388, "chestra": 27389, "dependent": 27390, "tok": 27391, "Tor": 27392, "marks": 27393, "▁Laboratory": 27394, "erusalem": 27395, "sing": 27396, "深厚": 27397, "相结合的": 27398, "两方面": 27399, "投标": 27400, "测算": 27401, "与学生": 27402, "代表性的": 27403, "来解决": 27404, "这场": 27405, "余人": 27406, "方法论": 27407, "作文": 27408, "▁weekly": 27409, "▁columns": 27410, "结婚": 27411, ",▁'": 27412, "球员": 27413, "secut": 27414, "▁pod": 27415, "这表明": 27416, "建设项目": 27417, "反应的": 27418, "▁critically": 27419, "▁distant": 27420, "electric": 27421, "▁savings": 27422, "EST": 27423, "idelines": 27424, "▁politicians": 27425, "课程内容": 27426, "原则上": 27427, "交融": 27428, "军民": 27429, "同学们": 27430, "放松": 27431, "▁bowl": 27432, "▁cater": 27433, "▁acres": 27434, "▁calling": 27435, "▁invited": 27436, "iph": 27437, "effective": 27438, "神秘": 27439, "asia": 27440, "▁ending": 27441, "▁faculty": 27442, "▁Gre": 27443, "世纪末": 27444, "在不同的": 27445, "维埃": 27446, "向往": 27447, "祖父": 27448, "可扩展": 27449, "葡萄牙": 27450, "▁Place": 27451, "ovak": 27452, "唯一的": 27453, "▁Oxford": 27454, "living": 27455, "ante": 27456, "▁mi": 27457, "vi": 27458, "法令": 27459, "▁harsh": 27460, "following": 27461, "▁Hebrew": 27462, "▁spray": 27463, "新华社": 27464, "生机": 27465, "小微": 27466, "知识产权保护": 27467, "事关": 27468, "基础和": 27469, "磁盘": 27470, "风景": 27471, "临床上": 27472, "保证了": 27473, "转运": 27474, "知縣": 27475, "畢業": 27476, "▁plain": 27477, "inton": 27478, "ooked": 27479, "▁robots": 27480, "▁believes": 27481, "▁intention": 27482, "▁pestic": 27483, "▁plates": 27484, "▁sunny": 27485, "chain": 27486, "▁Bol": 27487, "的抗": 27488, "负面影响": 27489, "means": 27490, "▁Farm": 27491, "非线性": 27492, "Fore": 27493, "det": 27494, "cop": 27495, "overning": 27496, "▁confusion": 27497, "▁preservation": 27498, "Cr": 27499, "▁vig": 27500, "核电": 27501, "免疫系统": 27502, "充分考虑": 27503, "必须是": 27504, "的义务": 27505, "总体上": 27506, "参观": 27507, "当家": 27508, "ni": 27509, "登录": 27510, "aton": 27511, "▁spreading": 27512, "▁accidents": 27513, "▁gear": 27514, "▁representative": 27515, "\"}": 27516, "私营": 27517, "任职": 27518, "一天": 27519, "东京": 27520, "▁Municip": 27521, "和相关": 27522, "▁Dun": 27523, "區域": 27524, "小時": 27525, "編曲": 27526, "init": 27527, "与经济": 27528, "收回": 27529, "万美元": 27530, "游客": 27531, "游击": 27532, "红细胞": 27533, "原生": 27534, "重叠": 27535, "人所": 27536, "aga": 27537, "▁lig": 27538, "kh": 27539, "anner": 27540, "▁Java": 27541, "的转变": 27542, "作出的": 27543, "议员": 27544, "必须要": 27545, "内地": 27546, "Sen": 27547, "自旋": 27548, "地主": 27549, "在他": 27550, "恰恰": 27551, "▁phenomena": 27552, "▁rainf": 27553, "▁wars": 27554, "篇章": 27555, "课程教学": 27556, "政府对": 27557, "▁Univers": 27558, "第十三": 27559, "长城": 27560, "raham": 27561, "▁cattle": 27562, "▁Prevention": 27563, "▁inequality": 27564, "▁Indians": 27565, "▁returning": 27566, "treated": 27567, "▁debris": 27568, "部份": 27569, "Space": 27570, "▁ID": 27571, "gae": 27572, "在高": 27573, "尔省": 27574, "angel": 27575, "▁churches": 27576, "▁comprehension": 27577, "▁reserved": 27578, "▁Republican": 27579, "技术进步": 27580, "激励机制": 27581, "再审": 27582, "通风": 27583, "协议的": 27584, "尔德": 27585, "ritic": 27586, "推测": 27587, "Run": 27588, "▁Resp": 27589, "▁quantity": 27590, "▁valley": 27591, "▁update": 27592, "▁strains": 27593, "IST": 27594, "▁axis": 27595, "▁afternoon": 27596, "▁saving": 27597, "一段时间": 27598, "造血": 27599, "蒸发": 27600, "演讲": 27601, "域网": 27602, "rigation": 27603, "▁spons": 27604, "CV": 27605, "forcing": 27606, "相较": 27607, "Notable": 27608, "▁Os": 27609, "人民群众的": 27610, "涉及的": 27611, "的依据": 27612, "老龄化": 27613, "变压": 27614, "这部分": 27615, "同样的": 27616, "舆情": 27617, "强劲": 27618, "财税": 27619, "以人为本": 27620, "的内在": 27621, "立方米": 27622, "假定": 27623, "大阪": 27624, "▁restrictions": 27625, "▁worlds": 27626, "▁Digital": 27627, "Different": 27628, "单词": 27629, "Eth": 27630, "igm": 27631, "Western": 27632, "Taxonomy": 27633, "人是": 27634, "保卫": 27635, "对外贸易": 27636, "前途": 27637, "▁mystery": 27638, "SU": 27639, "年版": 27640, "▁Rod": 27641, "evin": 27642, "pan": 27643, "unctions": 27644, "这项研究": 27645, "诉讼时效": 27646, "全国各族人民": 27647, "案件中": 27648, "放缓": 27649, "关怀": 27650, "▁comparing": 27651, "很小": 27652, "▁descent": 27653, "▁alpha": 27654, "▁communications": 27655, "彼得": 27656, "▁desert": 27657, "isticated": 27658, "一门": 27659, "不出": 27660, "osaur": 27661, "hetic": 27662, "▁bleeding": 27663, "▁intersection": 27664, "丈夫": 27665, "▁sequences": 27666, "<0x9F>": 31021, "different": 31022, "帝國": 31023, "党的政治": 31024, "两者的": 31025, "内战": 31026, "叶片": 31027, "细胞因子": 31028, "ée": 31029, "▁Rose": 31030, "樂團": 31031, "gium": 31032, "producing": 31033, "accessed": 31034, "阿根廷": 31035, "▁decreases": 31036, "▁encounters": 31037, "▁pupp": 31038, "▁intr": 31039, "True": 31040, "és": 31041, "王子": 31042, "antry": 31043, "会计信息": 31044, "据统计": 31045, "所具有": 31046, "劳动合同法": 31047, "常态化": 31048, "担负": 31049, "智能合约": 31050, "自觉地": 31051, "先天性": 31052, "医务": 31053, "构建人类命运共同体": 31054, "简要": 31055, "它将": 31056, "惠及": 31057, "监管部门": 31058, "的临床": 31059, "ilib": 31060, "Proper": 31061, "▁symmet": 31062, "itime": 31063, "晚上": 31064, "珊瑚": 31065, "role": 31066, "Ur": 31067, "▁divisions": 31068, "Exp": 31069, "▁organism": 31070, "▁allergies": 31071, "▁genuine": 31072, "▁watched": 31073, "▁Cra": 31074, "▁bless": 31075, "Sou": 31076, "▁coh": 31077, "而导致": 31078, "和改进": 31079, "罪犯": 31080, "永恒": 31081, "的发展趋势": 31082, "ergarten": 31083, "▁Matt": 31084, "英格蘭": 31085, "的现代化": 31086, "全面深化": 31087, "后再": 31088, "市场在": 31089, "中的重要": 31090, "解决好": 31091, "出具": 31092, "具有较强的": 31093, "文化交流": 31094, "尤为重要": 31095, "接受的": 31096, "或服务": 31097, "一种新的": 31098, "企业与": 31099, "区位": 31100, "奠定了基础": 31101, "服用": 31102, "事务所": 31103, "职称": 31104, "危险因素": 31105, "收入差距": 31106, "nea": 31107, "群众的": 31108, "直辖市": 31109, "entieth": 31110, "發表": 31111, "▁aggreg": 31112, "▁license": 31113, "▁peaceful": 31114, "▁atmospheric": 31115, "ulent": 31116, "▁drops": 31117, "AST": 31118, "▁Francis": 31119, "▁expedition": 31120, "▁contamination": 31121, "▁convin": 31122, "▁prone": 31123, "▁straw": 31124, "▁Relations": 31125, "stitutional": 31126, "▁Alliance": 31127, "acking": 31128, "大类": 31129, "或多": 31130, "最多的": 31131, "Ter": 31132, "光合": 31133, "▁hypertension": 31134, "orrect": 31135, "▁germ": 31136, "ugg": 31137, "▁dough": 31138, "▁pond": 31139, "ytic": 31140, "associated": 31141, "发生率": 31142, "研讨会": 31143, "变压器": 31144, "促进社会": 31145, "工程建设": 31146, "学生们": 31147, "抽取": 31148, "联络": 31149, "毛泽东思想": 31150, "致富": 31151, "外延": 31152, "个国家": 31153, "feeding": 31154, "Engine": 31155, "主人": 31156, "▁Computer": 31157, "▁Raj": 31158, "▁restaurant": 31159, "▁breathe": 31160, "▁counterparts": 31161, "▁tunnel": 31162, "▁hotel": 31163, "──": 31164, "分发": 31165, "▁PR": 31166, "站点": 31167, "\":": 31168, "▁beer": 31169, "生命的": 31170, "莱茵省": 31171, "向下": 31172, "中将": 31173, "基本概念": 31174, "同位": 31175, "上午": 31176, "观的": 31177, "pdf": 31178, "oard": 31179, "▁Integ": 31180, "興建": 31181, "ationally": 31182, "▁Guinea": 31183, "毕业后": 31184, "▁counts": 31185, "▁flowing": 31186, "▁screens": 31187, "其他的": 31188, ".\"\"": 31189, "erness": 31190, "所说": 31191, "的历史性": 31192, "和保障": 31193, "学前教育": 31194, "的基本理论": 31195, "突破口": 31196, "但对于": 31197, "原发性": 31198, "电化学": 31199, "Fire": 31200, "▁bust": 31201, "▁recycled": 31202, "ermat": 31203, "▁incidents": 31204, "▁pushing": 31205, "南非": 31206, "老化": 31207, "▁Village": 31208, "▁Ide": 31209, "局的": 31210, "过大": 31211, "矛盾的": 31212, "鲜明的": 31213, "▁aquatic": 31214, "路上": 31215, "的正": 31216, "▁bark": 31217, "华东": 31218, "透露": 31219, "，「": 31220, "enez": 31221, "▁cer": 31222, "sharing": 31223, "▁evolutionary": 31224, "▁formats": 31225, "其次是": 31226, "开辟了": 31227, "的良好": 31228, "十六条": 31229, "利用效率": 31230, "油气": 31231, "不允许": 31232, "最高人民法院关于": 31233, "可实现": 31234, "的重要因素": 31235, "领导小组": 31236, "恐惧": 31237, "终点": 31238, "夫妻共同": 31239, "相辅相": 31240, "brain": 31241, "▁heated": 31242, "▁qualified": 31243, ",▁‘": 31244, "甲烷": 31245, "eval": 31246, "金牌": 31247, "早年": 31248, "AE": 31249, "mans": 31250, "▁ber": 31251, "▁scan": 31252, "avirus": 31253, "▁frozen": 31254, "▁heroes": 31255, "▁neighboring": 31256, "▁rod": 31257, "人道": 31258, "这为": 31259, "治愈": 31260, "semble": 31261, "▁concert": 31262, "inely": 31263, "▁innovations": 31264, "serving": 31265, "廣東": 31266, "内陆": 31267, "切割": 31268, "他还": 31269, "醫院": 31270, "範圍": 31271, "Pass": 31272, "商店": 31273, "描述了": 31274, "Associ": 31275, "KD": 31276, "▁puts": 31277, "raid": 31278, "表現": 31279, "▁allev": 31280, "肢体": 31281, "环境监测": 31282, "的人格": 31283, "土地使用权": 31284, "新鲜": 31285, "综合考虑": 31286, "特殊性": 31287, "的数字": 31288, "▁resolve": 31289, "相似的": 31290, "model": 31291, "Present": 31292, "▁Gil": 31293, "Blood": 31294, "▁Athlet": 31295, "abitat": 31296, "▁Chile": 31297, "▁recall": 31298, "▁challenged": 31299, "▁emit": 31300, "▁stimulate": 31301, "通过将": 31302, "隐私保护": 31303, "并向": 31304, "水和": 31305, "earl": 31306, "cepts": 31307, "▁Miller": 31308, "▁coin": 31309, "engu": 31310, "▁legit": 31311, "▁executed": 31312, "▁empire": 31313, "▁Assessment": 31314, "▁belt": 31315, "▁electoral": 31316, "▁highlighted": 31317, "进士": 31318, "分担": 31319, "四中全会": 31320, "进一步发展": 31321, "不服": 31322, "内阁": 31323, "心力": 31324, "融资租赁": 31325, "暂行": 31326, "美好生活的": 31327, "第一步": 31328, "通则": 31329, "▁Drug": 31330, "orf": 31331, "在上海": 31332, "遗迹": 31333, "学士": 31334, "次年": 31335, "opathy": 31336, "Ra": 31337, "mid": 31338, "'.": 31339, "special": 31340, "Pay": 31341, "▁mistake": 31342, "▁pulmonary": 31343, "▁sampling": 31344, "mother": 31345, "▁steep": 31346, "▁transactions": 31347, ")|": 31348, "Geography": 31349, "▁portions": 31350, "滤波器": 31351, "影响力的": 31352, "宪政": 31353, "仍然存在": 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"▁hesit": 31418, "anol": 31419, "oots": 31420, "损失的": 31421, "网络中": 31422, "循环经济": 31423, "马克思列宁": 31424, "纤维素": 31425, "学校教育": 31426, "等诸多": 31427, "风俗": 31428, "肾小": 31429, "并举": 31430, "计算的": 31431, "欺骗": 31432, "▁Dal": 31433, "联合会": 31434, "▁trigg": 31435, "▁occupation": 31436, "▁artifacts": 31437, "▁brave": 31438, "▁declined": 31439, "▁safegu": 31440, "Jesus": 31441, "北大": 31442, "technology": 31443, "在过去": 31444, "升华": 31445, "权衡": 31446, "珍贵": 31447, "引人": 31448, "先导": 31449, "izoph": 31450, "製造": 31451, "▁resident": 31452, "OW": 31453, "▁perf": 31454, "▁attitude": 31455, "▁contest": 31456, "▁partnerships": 31457, "维亚": 31458, "▁Quebec": 31459, "▁departments": 31460, "ancies": 31461, "shape": 31462, "▁differentiation": 31463, "▁Baseball": 31464, "的认知": 31465, "企业对": 31466, "十五条": 31467, "能够实现": 31468, "％，": 31469, "积极推动": 31470, "四个方面": 31471, "水准": 31472, "之分": 31473, "纵深": 31474, "要约": 31475, "这种方式": 31476, "的探索": 31477, "道光": 31478, "一般情况下": 31479, "▁Deb": 31480, "暗示": 31481, 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31677, "棒球": 31678, "Help": 31679, "▁PDF": 31680, "▁hearts": 31681, "clean": 31682, "▁extraction": 31683, "▁с": 31684, "▁physicians": 31685, "HER": 31686, "而这些": 31687, "九龍": 31688, "复旦": 31689, "两点": 31690, "▁LGBTQ": 31691, "▁dismiss": 31692, "▁feas": 31693, "▁walked": 31694, "人有": 31695, "其为": 31696, "▁soy": 31697, "ificate": 31698, "▁icon": 31699, "rost": 31700, "▁Lie": 31701, "▁bull": 31702, "▁storms": 31703, "letal": 31704, "icated": 31705, "block": 31706, "▁strip": 31707, "结构中": 31708, "孙中山": 31709, "地方的": 31710, "屋顶": 31711, "新阶段": 31712, "的主观": 31713, "集中于": 31714, "之于": 31715, "如果我们": 31716, "留置": 31717, "社保": 31718, "周知": 31719, "的业务": 31720, "vocals": 31721, "ombia": 31722, "▁mindfulness": 31723, "▁reinforce": 31724, "▁upset": 31725, "▁pine": 31726, "▁pride": 31727, "▁span": 31728, "和资源": 31729, "率领": 31730, "平安": 31731, "controlled": 31732, "Obs": 31733, "▁Les": 31734, "irs": 31735, "德尔": 31736, "▁enabled": 31737, "▁ethics": 31738, "arians": 31739, "dale": 31740, "guitar": 31741, "MHz": 31742, "急救": 31743, "进行分类": 31744, "Video": 31745, "erald": 31746, "?▁(": 31747, "▁Stephen": 31748, "▁MRI": 31749, "Online": 31750, "grim": 31751, "OF": 31752, "Illinois": 31753, "▁complement": 31754, "▁tab": 31755, "▁deleg": 31756, "▁Luther": 31757, "Mur": 31758, "acting": 31759, "embered": 31760, "Firstly": 31761, "强调的是": 31762, "政策措施": 31763, "成员的": 31764, "要将": 31765, "共同发展": 31766, "具体而言": 31767, "回升": 31768, "攻击者": 31769, "区域性": 31770, "落脚": 31771, "而没有": 31772, "论语": 31773, "沿革": 31774, "众所": 31775, "▁enhances": 31776, "一日": 31777, "Clear": 31778, "nie": 31779, "odge": 31780, "illo": 31781, "▁Captain": 31782, "odd": 31783, "▁gifts": 31784, "vo": 31785, "▁disagre": 31786, "▁reserve": 31787, "introdu": 31788, "基础的": 31789, "免疫反应": 31790, "免除": 31791, "业界": 31792, "isol": 31793, "受限": 31794, "完毕": 31795, "Psych": 31796, "urgical": 31797, "ircle": 31798, "▁polymer": 31799, "▁Tower": 31800, "▁divers": 31801, "verages": 31802, "▁illustrated": 31803, "olding": 31804, "▁bills": 31805, "▁welcome": 31806, "中国传统": 31807, "更强": 31808, "反腐": 31809, "活力的": 31810, "副院长": 31811, "只需要": 31812, "知府": 31813, "势必": 31814, "一审": 31815, "合同中": 31816, "整改": 31817, "党领导": 31818, "不同类型": 31819, "eur": 31820, "▁Offic": 31821, "ontin": 31822, "pi": 31823, "Final": 31824, "▁organizing": 31825, "ervatory": 31826, "▁smile": 31827, "▁consequence": 31828, "▁genres": 31829, "編號": 31830, "rative": 31831, "▁Own": 31832, "纠缠": 31833, "听觉": 31834, "行走": 31835, "西域": 31836, "行之": 31837, "▁Help": 31838, "的低": 31839, "了许多": 31840, "garet": 31841, "▁scal": 31842, "▁timing": 31843, "Pers": 31844, "▁chips": 31845, "▁seeks": 31846, "▁crowd": 31847, "▁plug": 31848, "▁worried": 31849, "经济利益": 31850, "药物治疗": 31851, "体现为": 31852, "相差": 31853, "货币的": 31854, "体征": 31855, "国家在": 31856, "现行的": 31857, "等领域的": 31858, "我们应该": 31859, "西欧": 31860, "仍处于": 31861, "因素的影响": 31862, "无序": 31863, "惩戒": 31864, "股权转让": 31865, "直接的": 31866, "古今": 31867, "quiry": 31868, "河道": 31869, "行為": 31870, "當中": 31871, "cancer": 31872, "▁Need": 31873, "收录": 31874, "▁Biology": 31875, "▁rival": 31876, "▁Path": 31877, "▁rocket": 31878, "▁aspir": 31879, "Episode": 31880, "▁MS": 31881, "大中": 31882, "文化与": 31883, "一对": 31884, "中存在": 31885, "完成后": 31886, "rasound": 31887, "動物": 31888, "▁Lu": 31889, "▁agreements": 31890, "Introdu": 31891, "paper": 31892, "▁snake": 31893, "became": 31894, "▁irregular": 31895, "▁washing": 31896, "urated": 31897, "▁Mot": 31898, "▁Sac": 31899, "ococc": 31900, "国家治理体系和治理能力现代化": 31901, "系统集成": 31902, "教师应": 31903, "以至": 31904, "水果": 31905, "政策性": 31906, "相互关系": 31907, "洗钱": 31908, "不同类型的": 31909, "事宜": 31910, "北极": 31911, "杀伤": 31912, "偏低": 31913, "可持续性": 31914, "人民当家作主": 31915, "浮动": 31916, "股价": 31917, "的案件": 31918, "副书记": 31919, "aptist": 31920, "▁Brad": 31921, "诺贝尔": 31922, "算法的": 31923, "后来的": 31924, "acity": 31925, "直觉": 31926, "形容": 31927, "cht": 31928, "Dav": 31929, "irical": 31930, "▁ger": 31931, "weed": 31932, "▁rescue": 31933, "▁Therapy": 31934, "developed": 31935, "▁answered": 31936, "▁filters": 31937, "▁residence": 31938, "Eval": 31939, "数列": 31940, "间隙": 31941, "侧面": 31942, "科研机构": 31943, "十一年": 31944, "合併": 31945, "不相同": 31946, "方程式": 31947, "目的地": 31948, "ailand": 31949, "吻合": 31950, "Rest": 31951, "acon": 31952, "▁Ans": 31953, "Python": 31954, "和建设": 31955, "并提出": 31956, "标准体系": 31957, "十一条": 31958, "第二百": 31959, "是不是": 31960, "帝王": 31961, "细则": 31962, "反动": 31963, "简便": 31964, "获利": 31965, "长效机制": 31966, "关乎": 31967, "议程": 31968, "台阶": 31969, "增设": 31970, "核实": 31971, "筑牢": 31972, "数据挖掘": 31973, "淡水": 31974, "所用": 31975, "总和": 31976, "赞同": 31977, "uct": 31978, "▁Prom": 31979, "属性的": 31980, "快捷": 31981, "▁Dark": 31982, "▁permit": 31983, "▁Mun": 31984, "took": 31985, "行政法": 31986, "bian": 31987, ":▁▁": 31988, "feel": 31989, "gets": 31990, "▁Hart": 31991, "这三个": 31992, "同胞": 31993, "不起": 31994, "大连": 31995, "研究方向": 31996, "人民共和國": 31997, "必須": 31998, "—▁—▁": 31999, "▁sour": 32000, "saying": 32001, "▁juvenile": 32002, "▁productive": 32003, "▁pept": 32004, "▁Hills": 32005, "教学设计": 32006, "外商投资": 32007, "马克思主义理论": 32008, "优惠政策": 32009, "由政府": 32010, "这就需要": 32011, "势头": 32012, "也成为": 32013, "职业病": 32014, "西周": 32015, "洛阳": 32016, "指标的": 32017, "回落": 32018, "并用": 32019, "即使是": 32020, "▁Queens": 32021, "Microsoft": 32022, "mostly": 32023, "▁databases": 32024, "articles": 32025, "rod": 32026, "▁ul": 32027, "戰鬥": 32028, "▁crash": 32029, "定居": 32030, "Full": 32031, "▁bridges": 32032, "并重": 32033, "家人": 32034, "话语权": 32035, "\\_": 32036, "的原": 32037, "生死": 32038, "▁dynasty": 32039, "Prim": 32040, "▁Aqu": 32041, "▁integrate": 32042, "▁interpretations": 32043, "▁viewers": 32044, "▁rolling": 32045, "金的": 32046, "▁Opp": 32047, "oberts": 32048, "htm": 32049, "▁WW": 32050, "image": 32051, "inator": 32052, "clerosis": 32053, "人口普查": 32054, "科学性": 32055, "异化": 32056, "的挑战": 32057, "能为": 32058, "诉讼请求": 32059, "变频": 32060, "电子邮件": 32061, "集权": 32062, "盛行": 32063, "富集": 32064, "成交": 32065, "摩尔": 32066, "查明": 32067, "北魏": 32068, "绿色金融": 32069, "做大": 32070, "▁continent": 32071, "行列": 32072, "Aust": 32073, "director": 32074, "▁pathogens": 32075, "▁kick": 32076, "▁embrace": 32077, "▁romantic": 32078, "▁salmon": 32079, "▁swelling": 32080, "hr": 32081, "▁economics": 32082, "inian": 32083, "iotic": 32084, "▁Si": 32085, "停滞": 32086, "合法的": 32087, "uerto": 32088, "('": 32089, "RCEP": 32090, "先前": 32091, "—▁▁—▁▁—▁▁—▁▁": 32092, "▁bottles": 32093, "▁chapters": 32094, "▁murd": 32095, "▁monthly": 32096, "osé": 32097, "英語": 32098, "▁static": 32099, "▁Territ": 32100, "▁shadow": 32101, "复合物": 32102, "▁Ni": 32103, "***": 32104, "▁meteor": 32105, "▁Premier": 32106, "rokes": 32107, "十三条": 32108, "风险挑战": 32109, "突破了": 32110, "杠杆率": 32111, "了一条": 32112, "财产权": 32113, "各种各": 32114, "而来的": 32115, "评议": 32116, "买方": 32117, "共存": 32118, "工人阶级": 32119, "涂层": 32120, "创造条件": 32121, "顺差": 32122, "信息基础设施": 32123, "大湾区": 32124, "中的地位": 32125, "研究工作": 32126, "加快构建": 32127, "什么样": 32128, "身份认证": 32129, "▁anten": 32130, "元宇宙": 32131, "inic": 32132, "oche": 32133, "itan": 32134, "untary": 32135, "graph": 32136, "eu": 32137, "▁jet": 32138, "▁Bishop": 32139, "▁cheap": 32140, "▁captain": 32141, "书的": 32142, "可能在": 32143, "是当前": 32144, "中具有": 32145, "断层": 32146, "未被": 32147, "每种": 32148, "▁stuck": 32149, "▁survivors": 32150, "▁yields": 32151, "简洁": 32152, "扩增": 32153, "一台": 32154, "▁Last": 32155, "烏克蘭": 32156, "●▁": 32157, "sound": 32158, "▁Mission": 32159, "rosc": 32160, "▁geometry": 32161, "▁Authority": 32162, "朝鮮": 32163, "▁Brook": 32164, "▁commands": 32165, "▁governance": 32166, "会计核算": 32167, "发达地区": 32168, "全面提升": 32169, "国土空间": 32170, "树脂": 32171, "选择了": 32172, "功利": 32173, "硬盘": 32174, "风貌": 32175, "廉政": 32176, "奋进": 32177, "组长": 32178, "▁ded": 32179, "▁cong": 32180, "▁chaos": 32181, "▁rituals": 32182, "第十五": 32183, "▁Tal": 32184, "ooth": 32185, "電腦": 32186, "相當": 32187, "anded": 32188, "si": 32189, "bey": 32190, "posure": 32191, "▁FC": 32192, "oping": 32193, "▁Six": 32194, "▁accord": 32195, "listed": 32196, "▁accomplished": 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["▁ch", "ampion"], ["▁prolong", "ed"], ["；", "("], ["欠", "缺"], ["则", "为"], ["中", "亚"], ["▁M", "oh"], ["C", "ultural"], ["▁Y", "outh"], ["ul", "ative"], ["unct", "ure"], ["▁Rec", "ord"], ["▁Gra", "de"], ["▁count", "ies"], ["▁dec", "omp"], ["▁flo", "ating"], ["Us", "ually"], ["▁gl", "ac"], ["▁cor", "related"], ["▁marginal", "ized"], ["土地", "利用"], ["则", "在"], ["到", "一个"], ["控制", "在"], ["发达", "国家的"], ["往往", "会"], ["的基本", "要求"], ["以下几", "点"], ["信息", "服务"], ["提高", "学生"], ["公", "信"], ["的研究", "成果"], ["举", "行的"], ["一般", "为"], ["op", "ath"], ["▁C", "ro"], ["im", "s"], ["▁sh", "ield"], ["▁K", "han"], ["inte", "gr"], ["▁w", "ed"], ["▁un", "c"], ["▁Mary", "land"], ["▁e", "co"], ["bor", "iginal"], ["▁she", "ep"], ["▁support", "ive"], ["▁K", "al"], ["▁ar", "c"], ["布", "拉"], ["ar", "on"], ["弟", "子"], ["甲", "骨"], ["公众", "号"], ["反", "过来"], ["敏", "捷"], ["来", "完成"], ["疱", "疹"], ["к", "о"], ["在", "其他"], ["▁fin", "ance"], ["W", "orking"], ["▁I", "ce"], ["举", "报"], ["開", "放"], ["vers", "ion"], ["▁pol", "ym"], ["▁de", "er"], ["▁c", "oding"], ["▁pr", "iest"], ["App", "lying"], ["▁con", "secut"], ["▁dang", "ers"], ["ache", "lor"], ["▁j", "aw"], ["教育", "工作"], ["协同", "创新"], ["资料", "的"], ["和", "学习"], ["哲学", "的"], ["座谈", "会"], ["开启", "了"], ["突发", "事件"], ["主要", "集中在"], ["共产", "国际"], ["生产", "关系"], ["的人", "生"], ["通", "气"], ["是", "我们党"], ["并", "由"], ["开发", "了"], ["限", "额"], ["这些", "都"], ["最", "重要"], ["▁Writ", "ing"], ["▁T", "okyo"], ["▁Im", "perial"], ["M", "F"], ["H", "ung"], ["尔", "兰"], ["▁op", "ens"], ["▁che", "cking"], ["▁T", "oday"], ["用", "法"], ["cer", "y"], ["light", "ing"], ["▁distingu", "ished"], ["▁navig", "ation"], ["un", "es"], ["學", "習"], ["▁newsp", "apers"], ["Supp", "ose"], ["▁med", "ian"], ["▁loc", "ate"], ["印", "尼"], ["id", "el"], ["性", "等"], ["学院", "教授"], ["卵", "巢"], ["多样", "的"], ["占", "总"], ["pl", "ane"], ["音", "频"], ["▁N", "otes"], ["▁antib", "ody"], ["▁le", "ak"], ["▁su", "p"], ["▁sk", "ull"], ["▁cl", "uster"], ["泰", "国"], ["▁collabor", "ate"], ["▁g", "uests"], ["▁K", 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").▁"], ["Gener", "ally"], ["▁pal", "m"], ["▁break", "fast"], ["▁har", "ness"], ["▁reg", "ime"], ["ru", "p"], ["▁impair", "ment"], ["A", "thlet"], ["并", "从"], ["路由", "器"], ["及", "以上"], ["丰富", "了"], ["提供", "服务"], ["行", "人"], ["的", "条件下"], ["▁datas", "et"], ["Sp", "ain"], ["▁anal", "og"], ["ibl", "ings"], ["▁histor", "ians"], ["▁evalu", "ating"], ["英格", "兰"], ["是一", "位"], ["a", "er"], ["社会", "和"], ["配", "电"], ["强", "国的"], ["课程", "设置"], ["法律", "和"], ["规模", "和"], ["变得", "更加"], ["道", "教"], ["勘", "探"], ["在", "现实"], ["膳", "食"], ["访", "谈"], ["并不", "意味着"], ["十四", "年"], ["着力", "点"], ["l", "ades"], ["inn", "ish"], ["开", "了"], ["该", "系统"], ["ip", "ation"], ["oth", "s"], ["vel", "y"], ["▁hist", "ories"], ["pow", "ered"], ["®", "▁"], ["▁c", "erv"], ["▁m", "ast"], ["▁key", "board"], ["▁music", "ian"], ["ind", "a"], ["自", "動"], ["▁F", "DA"], ["U", "D"], ["而", "无"], ["活", "泼"], ["大会", "上"], ["较", "弱"], ["中", "发现"], ["先", "锋"], ["im", "eter"], ["level", "and"], ["資", "訊"], ["▁Bl", "ood"], ["re", 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["在", "研究"], ["e", "ating"], ["▁compos", "er"], ["▁investig", "ations"], ["结", "局"], ["扩", "充"], ["▁accommod", "ate"], ["▁assign", "ments"], ["▁miss", "ed"], ["Ex", "actly"], ["屏", "蔽"], ["/", "-▁"], ["nov", "ation"], ["ul", "u"], ["大", "理"], ["全国人大", "常委会"], ["二", "百"], ["太", "多"], ["表达", "的"], ["Writ", "ing"], ["▁gen", "etics"], ["格", "拉"], ["ind", "ers"], ["可能", "与"], ["之", "和"], ["通讯", "作者"], ["国家", "自然科学基金"], ["▁dist", "inction"], ["Pro", "ject"], ["R", "esp"], ["▁C", "ase"], ["建築", "物"], ["an", "alysis"], ["▁o", "ste"], ["約", "翰"], ["危险", "性"], ["而", "成的"], ["落", "后的"], ["产生", "影响"], ["巧", "妙"], ["禀", "赋"], ["能", "量的"], ["微", "课"], ["载", "荷"], ["进入", "新时代"], ["闲", "置"], ["大", "运河"], ["旅游", "业"], ["归", "根"], ["情况", "进行"], ["民族", "团结"], ["绿", "水"], ["上", "半年"], ["和", "能力"], ["ab", "ove"], ["affe", "ine"], ["人", "類"], ["的", "唯一"], ["▁cav", "ity"], ["▁colle", "ges"], ["▁super", "hero"], ["▁alt", "itude"], ["▁attack", "ed"], ["▁s", "orts"], ["类", "比"], ["Russ", "ian"], ["at", "able"], ["v", "ideo"], ["編", "劇"], ["ody", "nam"], ["n", "orth"], ["▁galax", "y"], ["年", "由"], ["us", "er"], ["▁ad", "hes"], ["穿", "透"], ["上", "去"], ["换", "言之"], ["天", "人"], ["▁belong", "s"], ["▁cap", "s"], ["G", "al"], ["L", "V"], ["鱼", "类"], ["Aut", "hor"], ["Is", "rael"], ["▁Imp", "act"], ["cre", "ate"], ["济", "南"], ["ad", "er"], ["是一", "個"], ["▁M", "aterials"], ["足球", "運動員"], ["om", "o"], ["cont", "ribut"], ["▁J", "unior"], ["Expl", "ore"], ["▁gl", "ue"], ["▁M", "es"], ["公共", "管理"], ["法律", "规范"], ["适用", "法律"], ["世界", "一流"], ["受", "众"], ["宋", "朝"], ["等", "原因"], ["决策", "部署"], ["地", "缘"], ["不", "完善"], ["精神", "文明"], ["质量", "控制"], ["Sh", "ould"], ["▁f", "ears"], ["▁satell", "ites"], ["▁p", "iv"], ["▁six", "th"], ["人", "或"], ["开发", "出"], ["大学", "教授"], ["如果", "在"], ["液", "态"], ["C", "ell"], ["曾", "經"], ["擊", "敗"], ["st", "ill"], ["学", "报"], ["太", "郎"], ["om", "at"], ["▁any", "more"], ["▁sub", "mit"], ["ell", "i"], ["▁chair", "man"], ["▁prog", "n"], ["▁p", "ests"], ["▁tact", "ics"], ["拉", "伸"], ["不", "法"], ["医", "科"], ["共產", "黨"], ["ien", "na"], ["in", "ology"], ["之一", "是"], ["蛋白", "的"], ["电", "平"], ["▁ren", "al"], ["▁T", "y"], ["T", "ypes"], ["▁quant", "itative"], ["in", "os"], ["中国", "制造"], ["党和国家", "事业"], ["矢", "量"], ["以", "适应"], ["持续", "时间"], ["驰", "名"], ["走", "廊"], ["郑", "州"], ["电", "离"], ["总体", "布局"], ["波", "段"], ["成果", "的"], ["理", "論"], ["各个", "领域"], ["锂", "离子"], ["化学", "品"], ["无", "缝"], ["山", "的"], ["在", "第"], ["而", "出"], ["ren", "al"], ["奥", "地利"], ["c", "hemical"], ["▁T", "itle"], ["▁constitu", "ency"], ["▁copy", "right"], ["专", "辑"], ["水", "解"], ["H", "g"], ["C", "ross"], ["%", "▁("], ["▁all", "ergy"], ["▁coron", "ary"], ["▁graph", "ics"], ["▁sus", "pected"], ["▁j", "ournals"], ["▁medal", "ists"], ["獎", "項"], ["ph", "y"], ["本科", "生"], ["填", "写"], ["通", "报"], ["主要", "表现为"], ["h", "ang"], ["!", ")▁"], ["▁ex", "clusive"], ["l", "ad"], ["W", "ould"], ["W", "ord"], ["cell", "s"], ["▁abund", "ance"], ["▁c", "ited"], ["▁commission", "ed"], ["▁disapp", "ear"], ["▁gra", "pp"], ["▁sc", "attered"], ["▁autom", "atic"], ["浏览", "器"], ["国际", "组织"], ["保护", "主义"], ["和", "农村"], ["心血管", "疾病"], ["粗", "放"], ["困", "惑"], ["模", "范"], ["新生", "儿"], ["凝", "血"], ["引发", "了"], ["一国", "两制"], ["呕", "吐"], ["刑事", "诉讼法"], ["大", "势"], ["明", "知"], ["Pl", "an"], ["▁P", "as"], ["持续", "的"], ["必", "定"], ["▁C", "irc"], ["ri", "k"], ["▁var", "iants"], ["▁K", "it"], ["分", "為"]]}} \ No newline at end of file diff --git a/chronos/assets/tokenizers/general_32k/tokenizer_config.json b/chronos/assets/tokenizers/general_32k/tokenizer_config.json new file mode 100644 index 0000000..7b1f3ac --- /dev/null +++ b/chronos/assets/tokenizers/general_32k/tokenizer_config.json @@ -0,0 +1,12 @@ +{ + "tokenizer_class": "PreTrainedTokenizerFast", + "bos_token": "<|im_start|>", + "eos_token": "<|im_end|>", + "unk_token": null, + "pad_token": "<|endoftext|>", + "model_max_length": 32768, + "clean_up_tokenization_spaces": false, + "chat_template": "{% if tools %}{{ '<|im_start|>system\n' }}{% if messages and messages[0]['role'] == 'system' %}{{ messages[0]['content'] + '\n\n' }}{% endif %}{{ '\n' }}{% for tool in tools %}{{ tool | tojson + '\n' }}{% endfor %}{{ '<|im_end|>\n' }}{% endif %}{% for message in messages %}{% if not (tools and loop.first and message['role'] == 'system') %}{{ '<|im_start|>' + message['role'] + '\n' + (message['content'] or '') }}{% if message['role'] == 'assistant' and message.get('tool_calls') %}{% set text = message['content'] or '' %}{% for call in message['tool_calls'] %}{% set fn = call['function'] if call.get('function') else call %}{% if text or not loop.first %}{{ '\n' }}{% endif %}{{ '' + {'name': fn['name'], 'arguments': fn['arguments']} | tojson + '' }}{% endfor %}{% endif %}{{ '<|im_end|>' + '\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "add_bos_token": false, + "add_eos_token": false +} \ No newline at end of file diff --git a/chronos/assets/tokenizers/general_32k/tokenizer_report.json b/chronos/assets/tokenizers/general_32k/tokenizer_report.json new file mode 100644 index 0000000..2ebfd3f --- /dev/null +++ b/chronos/assets/tokenizers/general_32k/tokenizer_report.json @@ -0,0 +1,174 @@ +{ + "trainer": { + "vocab_size": 32768, + "char_coverage": 0.9999, + "max_alphabet_fraction": 0.4, + "min_pair_frequency": 2, + "language_temperature": 0.5, + "max_token_chars": 16, + "min_bucket_share": 0.01, + "max_bucket_weight": 4.0 + }, + "train_stats": { + "bucket_weights": { + "latin": 0.823191228144042, + "zh": 1.7399979096984903, + "other": 4.0 + }, + "unique_pretokens": 4924919, + "alphabet_size": 8458, + "merges": 23990, + "final_vocab": 32768 + }, + "heldout_eval": { + "latin": { + "texts": 3731, + "bytes_per_token": 3.981, + "chars_per_token": 3.956, + "tokens_per_word": 1.606, + "tokens_per_han": 263.008, + "byte_fallback_rate": 0.00017, + "roundtrip_failures": 0 + }, + "zh": { + "texts": 939, + "bytes_per_token": 4.347, + "chars_per_token": 1.589, + "tokens_per_word": 35.946, + "tokens_per_han": 0.772, + "byte_fallback_rate": 0.00373, + "roundtrip_failures": 0 + }, + "ko": { + "texts": 1, + "bytes_per_token": 1.665, + "chars_per_token": 0.829, + "tokens_per_word": 5.663, + "tokens_per_han": 8.731, + "byte_fallback_rate": 0.37386, + "roundtrip_failures": 0 + }, + "cyrillic": { + "texts": 1, + "bytes_per_token": 2.512, + "chars_per_token": 1.268, + "tokens_per_word": 9.897, + "tokens_per_han": 3.12, + "byte_fallback_rate": 0.0, + "roundtrip_failures": 0 + }, + "ja": { + "texts": 1, + "bytes_per_token": 2.671, + "chars_per_token": 1.333, + "tokens_per_word": 10.074, + "tokens_per_han": 2.666, + "byte_fallback_rate": 0.01567, + "roundtrip_failures": 0 + }, + "other": { + "texts": 1, + "bytes_per_token": 1.514, + "chars_per_token": 1.478, + "tokens_per_word": 2.391, + "tokens_per_han": null, + "byte_fallback_rate": 0.0, + "roundtrip_failures": 0 + }, + "vocab_utilization": 0.9042 + }, + "sources": { + "tokenizer sample: 233,619 chunks (<= 2000 chars) from FineWeb-Edu, Fineweb-Edu-Chinese V2.1, Cosmopedia v2, Wikipedia en, Wikipedia zh": { + "files": 1, + "records": 233619, + "texts_seen": 233619, + "sampled": 233619, + "buckets": { + "latin": 186575, + "zh": 46977, + "ko": 1, + "cyrillic": 40, + "ja": 20, + "other": 6 + } + } + }, + "elapsed_s": 443.9, + "vocab_size": 32768, + "fingerprint": "6d4b3b524922f6cd34d674cf10605488d99c4dc97a58699d59466eb4ba396171", + "special_tokens": [ + "<|endoftext|>", + "<|im_start|>", + "<|im_end|>", + "<|pad|>", + "<|reserved_0|>", + "<|reserved_1|>", + "<|reserved_2|>", + "<|reserved_3|>", + "<|reserved_4|>", + "<|reserved_5|>", + "<|reserved_6|>", + "<|reserved_7|>", + "<|reserved_8|>", + "<|reserved_9|>", + "<|reserved_10|>", + "<|reserved_11|>", + "<|reserved_12|>", + "<|reserved_13|>", + "<|reserved_14|>", + "<|reserved_15|>", + "<|reserved_16|>", + "<|reserved_17|>", + "<|reserved_18|>", + "<|reserved_19|>", + "<|reserved_20|>", + "<|reserved_21|>", + "<|reserved_22|>", + "<|reserved_23|>", + "<|reserved_24|>", + "<|reserved_25|>", + "<|reserved_26|>", + "<|reserved_27|>", + "<|reserved_28|>", + "<|reserved_29|>", + "<|reserved_30|>", + "<|reserved_31|>", + "<|reserved_32|>", + "<|reserved_33|>", + "<|reserved_34|>", + "<|reserved_35|>", + "<|reserved_36|>", + "<|reserved_37|>", + "<|reserved_38|>", + "<|reserved_39|>", + "<|reserved_40|>", + "<|reserved_41|>", + "<|reserved_42|>", + "<|reserved_43|>", + "<|reserved_44|>", + "<|reserved_45|>", + "<|reserved_46|>", + "<|reserved_47|>", + "<|reserved_48|>", + "<|reserved_49|>", + "<|reserved_50|>", + "<|reserved_51|>", + "<|reserved_52|>", + "<|reserved_53|>", + "<|reserved_54|>", + "<|reserved_55|>", + "<|reserved_56|>", + "<|reserved_57|>", + "<|reserved_58|>", + "<|reserved_59|>" + ], + "algorithm": "chronos-bpe-v1", + "derived_from": { + "fingerprint": "169668b1be29a517fb20f44060cb4a2955cf16dc56f484ceb5376ff2f9a9f37a", + "relabelled_ids": [ + 3, + 63 + ], + "rule": "control ids 3-63 relabelled to the task-neutral layout special_tokens_with(slots=64); ordinary tokens, merges and ids unchanged" + } +} \ No newline at end of file diff --git a/chronos/cli.py b/chronos/cli.py index ab81410..9a26ccd 100644 --- a/chronos/cli.py +++ b/chronos/cli.py @@ -17,7 +17,7 @@ chronos webui — Web UI (alias: ui) Task-specific tooling (corpora, evaluation, model bundles) lives in extensions -outside the kernel, e.g. ``demos/translation`` (``chronos-translate``). +that import the kernel; the kernel itself carries no task. """ import sys import os diff --git a/chronos/config_schema.py b/chronos/config_schema.py index d635a78..b97aa1b 100644 --- a/chronos/config_schema.py +++ b/chronos/config_schema.py @@ -28,6 +28,7 @@ "recurrent_contraction_coef", "recurrent_contraction_budget", "recurrent_stability_threshold", "recurrent_dt_init", "recurrent_dt_min", "recurrent_dt_max", "recurrent_latent_refinement", "recurrent_attention_mode", + "recurrent_order", "recurrent_later_topk", "recurrent_later_groups", "recurrent_dt_param", "recurrent_strategy", "recurrent_depth_jitter", "recurrent_parallel_refine", "recurrent_gradient_checkpoint_groups", "depth_schedule", @@ -37,7 +38,7 @@ "lookahead_steps", "layer_aware_lookahead", "layer_aware_cache", "lambda_balance", "lambda_temporal", "lambda_lookahead", "lambda_lookahead_topk", "lambda_lookahead_union", "lambda_router_locality", - "lambda_cluster_locality", "cluster_locality_clusters", "cluster_locality_window", "cluster_locality_mode", + "lambda_cluster_locality", "lambda_iteration_cluster", "lookahead_teacher_scope", "cluster_locality_clusters", "cluster_locality_window", "cluster_locality_mode", "lambda_router_anchor", "fallback_mask_prob", "offload_miss_policy_default", "use_hybrid_attention", "kv_latent_dim", "rope_dim", "rope_layout", "sliding_window_size", "kv_cache_dtype", "kv_quant_group_size", "mla_decode", "mla_cache_rope", "router_scope", diff --git a/chronos/data/chat_records.py b/chronos/data/chat_records.py index 2963128..deb8af6 100644 --- a/chronos/data/chat_records.py +++ b/chronos/data/chat_records.py @@ -9,9 +9,8 @@ * context: system/tool definitions, user turns, ``tool`` results and every ``<|im_start|>role\\n`` header (the generation prompt the model is given). -``encode_chat`` tokenizes the pieces one by one and concatenates the ids, the -same way the translation shards join prompt and reply encodings, so every -piece boundary is a token boundary. ``write_sft_records`` writes the +``encode_chat`` tokenizes the pieces one by one and concatenates the ids, so +every piece boundary is a token boundary. ``write_sft_records`` writes the ``.bin/.idx/.plen/.lmask`` layout read by ``chronos.data.shards.SFTRecords``. A record whose last turn is not an assistant turn ends with context tokens; diff --git a/chronos/deps.py b/chronos/deps.py index 8fdba29..9eadcc2 100644 --- a/chronos/deps.py +++ b/chronos/deps.py @@ -12,6 +12,18 @@ from pathlib import Path LEGACY_TOKENIZER_DIR = Path(__file__).resolve().parent / "assets" / "tokenizers" / "legacy_6400" +#: Task-neutral 32768-token BPE (en + zh + code-friendly), the default for NEW models (planner, Train page). +GENERAL_TOKENIZER_DIR = Path(__file__).resolve().parent / "assets" / "tokenizers" / "general_32k" +GENERAL_TOKENIZER_VOCAB = 32768 + + +def get_general_tokenizer_path() -> str: + """Bundled general 32k tokenizer for new models (see its NOTICE). + + Not used as the fallback for old checkpoints: a checkpoint that never recorded its tokenizer was + trained with the legacy 6400-token vocabulary, and :func:`get_tokenizer_path` keeps returning that. + """ + return str(GENERAL_TOKENIZER_DIR) def get_tokenizer_path() -> str: diff --git a/chronos/export.py b/chronos/export.py index 2ed0b64..6c28f94 100644 --- a/chronos/export.py +++ b/chronos/export.py @@ -538,6 +538,7 @@ def _export_metadata( "recommended_resident_experts": cfg.get("recommended_resident_experts"), "recurrent_enabled": cfg.get("recurrent_enabled", False), "recurrent_groups": cfg.get("recurrent_groups"), + "recurrent_order": cfg.get("recurrent_order", "layer"), "recurrent_depth": cfg.get("recurrent_depth", 1), "min_recurrent_depth": cfg.get("min_recurrent_depth", 1), "max_inference_depth": cfg.get("max_inference_depth"), diff --git a/chronos/mlx/model.py b/chronos/mlx/model.py index 9abb443..fa6bc8c 100644 --- a/chronos/mlx/model.py +++ b/chronos/mlx/model.py @@ -111,6 +111,7 @@ def __call__( latent_only: bool = False, kv_override=None, return_shared_kv: bool = False, + top_k: int | None = None, ): new_cache, shared = cache, None if not latent_only: @@ -127,9 +128,9 @@ def __call__( and bool(getattr(self._cfg, "activation_checkpointing", False))) if isinstance(self.mlp, ChronosMLXMOE): if sub: - x = x + call_block(self.mlp, mlp_in, available_expert_mask=available_expert_mask) + x = x + call_block(self.mlp, mlp_in, available_expert_mask=available_expert_mask, top_k=top_k) else: - x = x + self.mlp(mlp_in, available_expert_mask=available_expert_mask) + x = x + self.mlp(mlp_in, available_expert_mask=available_expert_mask, top_k=top_k) else: x = x + call_block(self.mlp, mlp_in) if sub else x + self.mlp(mlp_in) if return_shared_kv: @@ -271,42 +272,50 @@ def run(i, hidden, slot=None, **kw): # iteration) never exits early; shared_first/first_only may. allow_exit = mode != "per_iteration" diagnostics = [] - for group in range(cfg.recurrent_groups): + order = str(getattr(cfg, "recurrent_order", "layer") or "layer") + # Graded top-k: loop iterations after the first route to fewer experts (0 = same as the first). + later_k = int(getattr(cfg, "recurrent_later_topk", 0) or 0) + # Prefix blocks (block order): iterations after the first run only the first later_g layers of the block. + later_g = int(getattr(cfg, "recurrent_later_groups", 0) or 0) + target = min(1.0, max(1e-6, cfg.halting_threshold)) + + def run_layer(group, step, x, shared): + """One loop iteration of one physical layer: condition, attend/route, contract.""" i = cfg.prelude_layers + group - states, halts = [], [] - exited, shared = False, None - halted = cumulative = None - target = min(1.0, max(1e-6, cfg.halting_threshold)) - for step in range(depth): - conditioned = controller.condition(x, step, group) - slot = layout.slot(i, step) - if mode == "per_iteration": - refined, new_caches[slot] = run(i, conditioned, slot) - elif mode == "first_only": - refined, candidate = run(i, conditioned, slot, latent_only=step > 0) - if step == 0: - new_caches[slot] = candidate - elif step == 0: - refined, new_caches[slot], shared = run(i, conditioned, slot, return_shared_kv=True) - else: - refined, _ = run(i, conditioned, kv_override=shared) - x = controller.contract(x, refined, group, cfg) - states.append(x) - prob = controller.halt_probability(x) - halts.append(prob) - # Exact per-token ACT halting (Torch parity, see model_chronos). - if step + 1 >= cfg.min_recurrent_depth: - clipped = mx.clip(prob.astype(mx.float32), 0.0, 1.0) - if halted is None: - cumulative = mx.zeros_like(clipped) - halted = mx.zeros(clipped.shape, dtype=mx.bool_) - newly = mx.logical_and(mx.logical_not(halted), cumulative + clipped >= target) - cumulative = mx.where(mx.logical_or(halted, newly), cumulative, mx.minimum(cumulative + clipped, 1.0)) - halted = mx.logical_or(halted, newly) - if not self.training and adaptive and allow_exit and halted is not None and step + 1 < depth: - if bool(mx.all(halted).item()): - exited = True - break + tk = {"top_k": later_k} if later_k and step > 0 else {} + conditioned = controller.condition(x, step, group) + slot = layout.slot(i, step) + if mode == "per_iteration": + refined, new_caches[slot] = run(i, conditioned, slot, **tk) + elif mode == "first_only": + refined, candidate = run(i, conditioned, slot, latent_only=step > 0, **tk) + if step == 0: + new_caches[slot] = candidate + elif step == 0: + refined, new_caches[slot], shared = run(i, conditioned, slot, return_shared_kv=True) + else: + refined, _ = run(i, conditioned, kv_override=shared, **tk) + return controller.contract(x, refined, group, cfg), shared + + def track(x, step, halted, cumulative): + prob = controller.halt_probability(x) + # Exact per-token ACT halting (Torch parity, see model_chronos). + if step + 1 >= cfg.min_recurrent_depth: + clipped = mx.clip(prob.astype(mx.float32), 0.0, 1.0) + if halted is None: + cumulative = mx.zeros_like(clipped) + halted = mx.zeros(clipped.shape, dtype=mx.bool_) + newly = mx.logical_and(mx.logical_not(halted), cumulative + clipped >= target) + cumulative = mx.where(mx.logical_or(halted, newly), cumulative, mx.minimum(cumulative + clipped, 1.0)) + halted = mx.logical_or(halted, newly) + return prob, halted, cumulative + + def can_exit(halted, step): + if not self.training and adaptive and allow_exit and halted is not None and step + 1 < depth: + return bool(mx.all(halted).item()) + return False + + def finish(states, halts, exited, x): mixed, state = auxiliary_state(states, halts, cfg.min_recurrent_depth, cfg.halting_threshold) if adaptive: x = mixed @@ -316,6 +325,41 @@ def run(i, hidden, slot=None, **kw): state["depth_used"] = mx.full(halts[0].shape, len(states), dtype=mx.float32) state["exited"] = exited diagnostics.append(state) + return x + + if order == "block": + # Block-interleaved recurrence: (layer0 layer1 ... layerG-1) x depth. + # One state trajectory and one halting head per block iteration; each + # physical layer still keeps its own dt, loop/group embeddings and KV. + shared = [None] * cfg.recurrent_groups + states, halts = [], [] + halted = cumulative = None + exited = False + for step in range(depth): + for group in range(cfg.recurrent_groups if step == 0 or not later_g else min(cfg.recurrent_groups, later_g)): + x, shared[group] = run_layer(group, step, x, shared[group]) + states.append(x) + prob, halted, cumulative = track(x, step, halted, cumulative) + halts.append(prob) + if can_exit(halted, step): + exited = True + break + x = finish(states, halts, exited, x) + else: + # Layer-consecutive recurrence (default): layer0 x depth, layer1 x depth, ... + for group in range(cfg.recurrent_groups): + states, halts = [], [] + exited, shared = False, None + halted = cumulative = None + for step in range(depth): + x, shared = run_layer(group, step, x, shared) + states.append(x) + prob, halted, cumulative = track(x, step, halted, cumulative) + halts.append(prob) + if can_exit(halted, step): + exited = True + break + x = finish(states, halts, exited, x) for i in range(cfg.prelude_layers+cfg.recurrent_groups, len(self.layers)): slot = layout.slot(i, 0) x, new_caches[slot] = run(i, x, slot) diff --git a/chronos/mlx/moe.py b/chronos/mlx/moe.py index 8e99044..0829a2e 100644 --- a/chronos/mlx/moe.py +++ b/chronos/mlx/moe.py @@ -223,6 +223,7 @@ def __call__( self, x: mx.array, available_expert_mask: mx.array = None, + top_k: int | None = None, ) -> mx.array: """ x: [B, S, H] @@ -243,11 +244,18 @@ def __call__( # first dispatch's ids). Indices are discrete: gradients flow through # topk_weight (gathered scores) only. ``recorded`` makes a # recomputation follow the forward pass's routing exactly. + # Graded top-k: a later loop iteration may route to fewer experts (top_k < num_experts_per_tok). + k = self.num_experts_per_tok if not top_k else max(1, min(int(top_k), self.num_experts_per_tok)) shared_idx = recorded(lambda: self._shared_topk_idx if self.router_scope == "shared" else None) if shared_idx is not None and shared_idx.shape[0] == scores.shape[0]: topk_idx = shared_idx + if k < shared_idx.shape[1]: + # Keep the k best-scoring experts of the first dispatch (no new expert is touched). + topk_idx = recorded(lambda: mx.stop_gradient(mx.take_along_axis( + shared_idx, + mx.argpartition(-mx.take_along_axis(scores, shared_idx, axis=-1), kth=k - 1, axis=-1)[:, :k], + axis=-1))) else: - k = self.num_experts_per_tok topk_idx = recorded(lambda: mx.stop_gradient(mx.argpartition(-scores, kth=k - 1, axis=-1)[:, :k])) if self.router_scope == "shared" and self._shared_topk_idx is None and not recomputing(): object.__setattr__(self, "_shared_topk_idx", topk_idx) diff --git a/chronos/mlx/recurrent.py b/chronos/mlx/recurrent.py index 0002155..92bcddd 100644 --- a/chronos/mlx/recurrent.py +++ b/chronos/mlx/recurrent.py @@ -10,7 +10,9 @@ def __init__(self, config): h, depth, groups = config.hidden_size, config.recurrent_depth, config.recurrent_groups self.loop_embeddings = mx.zeros((depth, h)) self.group_embeddings = mx.zeros((groups, h)) - self.step_sizes = mx.full((groups,), config.recurrent_dt_init) + from chronos.model.recurrent import _dt_raw_init # same raw->dt init as Torch (inverts sigmoid) + self.step_sizes = mx.full((groups,), _dt_raw_init(str(getattr(config, "recurrent_dt_param", "clamp")), + config.recurrent_dt_init, config.recurrent_dt_min, config.recurrent_dt_max)) self.halting_norm = nn.RMSNorm(h, eps=config.rms_norm_eps) self.halting_head = nn.Linear(h, 1) self.halting_head.weight = mx.zeros((1, h)) @@ -34,6 +36,9 @@ def execution_depth(self, cfg, training): pinned = getattr(self, "_pinned_depth", None) if pinned is not None and training: return pinned # sampled once per step by a compiled training step + step_depth = getattr(self, "_step_depth", None) + if step_depth is not None and training: + return step_depth # one depth per optimizer step; all micro-batches of the step share it ceiling = cfg.recurrent_depth if training else cfg.max_inference_depth if not training or cfg.recurrent_strategy == "fixed" or cfg.recurrent_depth_jitter == 0: return ceiling @@ -47,7 +52,15 @@ def condition(self, x, step, group): return x + self.loop_embeddings[min(step, self.loop_embeddings.shape[0]-1)][None, None, :] + self.group_embeddings[group][None, None, :] def contract(self, before, after, group, cfg): - dt = mx.clip(self.step_sizes[group], cfg.recurrent_dt_min, cfg.recurrent_dt_max).astype(after.dtype) + raw = self.step_sizes[group] + lo, hi = cfg.recurrent_dt_min, cfg.recurrent_dt_max + if str(getattr(cfg, "recurrent_dt_param", "clamp")) == "sigmoid": + # dt = lo + (hi-lo)*sigmoid(raw); gradient positive everywhere, no dead zone (matches Torch). + dt = (lo + (hi - lo) * mx.sigmoid(raw)).astype(after.dtype) + else: + # Value of mx.clip, but gradient 1 on the bounds too (torch.clamp does; mx.clip gives 0 at x == lo or hi). + inside = (raw >= lo) & (raw <= hi) + dt = mx.where(inside, raw, mx.stop_gradient(mx.clip(raw, lo, hi))).astype(after.dtype) return before + dt*(after-before) def halt_probability(self, x): diff --git a/chronos/mlx/training/compiled.py b/chronos/mlx/training/compiled.py index d500080..9cceb55 100644 --- a/chronos/mlx/training/compiled.py +++ b/chronos/mlx/training/compiled.py @@ -3,7 +3,7 @@ Measured on mlx 0.32.4 (see the 0.3.2a0 CHANGELOG): the compiled step gives the eager loss and gradients up to FP32 summation order (<= 1.7e-5 relative on CPU FP32), consumes the RNG stream identically, and runs 1.08-1.18x -faster on the 50M en->zh config because elementwise chains (RMSNorm, +faster on a 50M MoE config (hidden 384, 6 layers, 36 experts) because elementwise chains (RMSNorm, SwiGLU, softmax/CE, router regularizers) fuse into fewer kernels. What compiling needs here, and how it is handled: diff --git a/chronos/mlx/training/longrun.py b/chronos/mlx/training/longrun.py index 7b9fad1..3f0994d 100644 --- a/chronos/mlx/training/longrun.py +++ b/chronos/mlx/training/longrun.py @@ -16,8 +16,7 @@ The runner is task-agnostic. A task adds its own quality metrics by subclassing ``LongRun`` and overriding ``task_eval`` (called at every validation and once at the end); whatever keys it returns are logged next to -``valid_ce`` and discovered by the monitor. ``demos/translation`` is one -such extension. +``valid_ce`` and discovered by the monitor. """ from __future__ import annotations @@ -325,7 +324,7 @@ def check_init_checkpoint(path: str, config) -> None: if not saved: return # no sidecar: load_state_dict_controlled still checks shapes keys = SNIFFABLE_TOPOLOGY_FIELDS + UNSNIFFABLE_REQUIRED_FIELDS + ( - "recurrent_depth", "prelude_layers", "coda_layers", "recurrent_attention_mode", "rope_layout") + "recurrent_depth", "prelude_layers", "coda_layers", "recurrent_attention_mode", "recurrent_order", "rope_layout") diff = {k: (saved[k], getattr(config, k, None)) for k in keys if k in saved and saved[k] is not None and saved[k] != getattr(config, k, None)} if diff: @@ -576,6 +575,9 @@ def run_phase(self, pi: int, deadline_s: float, max_steps: int | None = None): will_log = (step + 1) % self.spec.log_every_steps == 0 or step < 3 want_tensors = tensor_every > 0 and time.monotonic() - last_tensors >= tensor_every routes = RoutingCounts() if will_log or want_tensors else None + # One recurrent depth per optimizer step: jitter samples per step, not per micro-batch, so a step's + # gradient accumulation and router telemetry share one architecture (virtual-layer count). + controller._step_depth = controller.execution_depth(self.config, True) for _ in range(accum): ids, labels = source.batch(phase.micro_tokens) _check_ids_in_vocab(ids, int(self.model.config.vocab_size), "longrun batch") @@ -587,6 +589,7 @@ def run_phase(self, pi: int, deadline_s: float, max_steps: int | None = None): tokens += int((labels != -100).sum()) # supervised tokens processed += int(ids.size) # forward/backward tokens mx.eval(acc, loss_sum) # bound the lazy graph to one micro-batch + controller._step_depth = None # end of step: stop pinning the sampled depth # Tensor telemetry keeps references to the pre-update weights (MLX # arrays are immutable: no copy) and the FP32 accumulated grads. before = self.model.parameters() if want_tensors else None @@ -829,7 +832,7 @@ def resolve(v): unknown = sorted(set(raw) - set(spec_cls.__dataclass_fields__)) if unknown: raise ValueError(f"{path}: unknown RunSpec keys {unknown}; task-specific keys need the task's " - f"runner (e.g. `chronos-translate run` for demos/translation recipes)") + f"runner that supplies its own RunSpec class") raw["phases"] = [PhaseSpec(**p) for p in raw["phases"]] spec = spec_cls(**raw) if budget_hours is not None: diff --git a/chronos/mlx/training/trainer.py b/chronos/mlx/training/trainer.py index bbd51ef..6c2e4df 100644 --- a/chronos/mlx/training/trainer.py +++ b/chronos/mlx/training/trainer.py @@ -324,6 +324,45 @@ def _cluster_objective(router_4d: mx.array, config, token_mask=None, input_ids=N return _cluster_locality_loss(router_4d, membership, window, token_mask) +def _recurrent_iteration_probs(model, config) -> mx.array | None: + """``[B, S, G, D, E]``: router probabilities of each recurrent layer at each loop iteration. + + None unless every recurrent layer dispatched the same number (>= 2) of times in the last forward.""" + start, groups = int(config.prelude_layers), int(config.recurrent_groups) + rows = [] + for layer in list(getattr(model, "layers", []))[start:start + groups]: + moe = getattr(layer, "mlp", None) + if not isinstance(moe, ChronosMLXMOE) or not moe.iteration_router_probs: + return None + rows.append(mx.stack(moe.iteration_router_probs, axis=2)) # [B, S, D, E] + if not rows or rows[0].shape[2] < 2 or any(r.shape[2] != rows[0].shape[2] for r in rows): + return None + return mx.stack(rows, axis=2) + + +def _iteration_mean_router_probs(model) -> mx.array | None: + """Per physical layer, router probabilities averaged over its loop iterations ``[B, S, L, E]`` + (lookahead teacher that covers every iteration; equals the step-0 routing for one-dispatch layers).""" + probs = [mx.stack(layer.mlp.iteration_router_probs, axis=0).mean(axis=0) for layer in getattr(model, "layers", []) + if isinstance(getattr(layer, "mlp", None), ChronosMLXMOE) and layer.mlp.iteration_router_probs] + return mx.stack(probs, axis=2) if probs else None + + +def _iteration_cluster_spread(probs5: mx.array, membership: mx.array, token_mask: mx.array | None = None) -> mx.array: + """Iteration-axis cluster locality: for each token and recurrent layer, the total variation between the + cluster-mass distribution of every loop iteration and the mean over its iterations, averaged. + + 0 = every iteration of a token routes into the same clusters (one cluster read serves them all); the + experts inside a cluster are free to differ per iteration. MLX twin of + ``chronos.model.temporal_loss.iteration_cluster_spread``.""" + B, S, G, D, E = probs5.shape + C = int(membership.shape[1]) + if D < 2 or C <= 1: + return mx.zeros(()) + mass = (probs5.astype(mx.float32).reshape(-1, E) @ membership).reshape(B, S, G, D, C) + spread = 0.5 * mx.abs(mass - mass.mean(axis=3, keepdims=True)).sum(axis=-1) # [B, S, G, D] + return _masked_mean(spread.mean(axis=(2, 3)), token_mask) + def _load_balance_loss(router_4d: mx.array, num_experts_per_tok: int, token_mask: mx.array | None = None) -> mx.array: """Switch-style ``E · Σ load·importance`` per virtual layer, summed. @@ -657,6 +696,11 @@ def add(name, coefficient, value): if float(getattr(config, "lambda_cluster_locality", 0.0) or 0.0) > 0.0: add("cluster_locality_loss", config.lambda_cluster_locality, _cluster_objective(router, config, token_mask, input_ids)) + if float(getattr(config, "lambda_iteration_cluster", 0.0) or 0.0) > 0.0: + iteration_probs = _recurrent_iteration_probs(model, config) + if iteration_probs is not None: + add("iteration_cluster_loss", config.lambda_iteration_cluster, + _iteration_cluster_spread(iteration_probs, _cluster_membership(config), token_mask)) if router.shape[1] > 1: mean = router.mean(axis=2) add("temporal_loss", config.lambda_temporal, _temporal_locality_loss(mean, token_mask)) @@ -665,8 +709,9 @@ def add(name, coefficient, value): _PAIR_SEGMENTS[0] = _LOOKAHEAD_SEGMENTS[0] # lookahead keeps its own document rule try: if lookahead_probs is not None and config.lambda_lookahead > 0 and config.lookahead_steps > 0: - teacher = _lookahead_teacher(_first_iteration_router_probs(model).astype(mx.float32), - lookahead_probs, config) + source = (_iteration_mean_router_probs(model) if getattr(config, "lookahead_teacher_scope", "first") == "iterations" + else _first_iteration_router_probs(model)) + teacher = _lookahead_teacher(source.astype(mx.float32), lookahead_probs, config) add("lookahead_loss", config.lambda_lookahead, _lookahead_supervision_loss(lookahead_probs, teacher, config.lookahead_steps, token_mask)) add("lookahead_topk_loss", config.lambda_lookahead_topk, diff --git a/chronos/model/config.py b/chronos/model/config.py index dda133f..cf2e214 100644 --- a/chronos/model/config.py +++ b/chronos/model/config.py @@ -48,6 +48,14 @@ def __init__(self, **kwargs): self.recurrent_dt_init: float = kwargs.pop("recurrent_dt_init", 0.25) self.recurrent_dt_min: float = kwargs.pop("recurrent_dt_min", 0.05) self.recurrent_dt_max: float = kwargs.pop("recurrent_dt_max", 1.0) + # Map the learned per-group raw parameter to dt in [dt_min, dt_max]: + # "clamp" (default) = clip(raw, dt_min, dt_max), the historical behaviour, exact for existing checkpoints, + # but the gradient is 0 below dt_min and on both bounds (a collapsed dt cannot recover). + # "sigmoid" = dt_min + (dt_max - dt_min) * sigmoid(raw): gradient positive everywhere, so dt can move back. + # The stored scalar is the logit; the init inverts the sigmoid so dt at step 0 equals recurrent_dt_init. + self.recurrent_dt_param: str = str(kwargs.pop("recurrent_dt_param", "clamp")) + if self.recurrent_dt_param not in ("clamp", "sigmoid"): + raise ValueError("recurrent_dt_param must be 'clamp' or 'sigmoid'") # v3 recurrent attention contract (see config_schema.resolve_*): # shared_first | per_iteration | first_only. An explicit legacy # ``recurrent_latent_refinement`` without a mode keeps v2 meaning. @@ -63,6 +71,30 @@ def __init__(self, **kwargs): if mode not in ("shared_first", "per_iteration", "first_only"): raise ValueError(f"unknown recurrent_attention_mode {mode!r}") self.recurrent_attention_mode: str = mode + # Loop order: ``layer`` repeats each recurrent layer ``depth`` times in a + # row (A A B B ...); ``block`` repeats the whole group of layers + # (A B A B ...), so iteration 0 is an ordinary depth-1 forward pass. + order = str(kwargs.pop("recurrent_order", "layer") or "layer").lower() + if order not in ("layer", "block"): + raise ValueError(f"unknown recurrent_order {order!r}") + self.recurrent_order: str = order + # Graded top-k: loop iterations after the first route to this many experts per token + # (0 = the full num_experts_per_tok); the shared expert always runs. Iteration 0 keeps the full top-k. + later = int(kwargs.pop("recurrent_later_topk", 0) or 0) + if later < 0: + raise ValueError(f"recurrent_later_topk must be >= 0, got {later}") + self.recurrent_later_topk: int = later + # Prefix blocks: in block order, loop iterations after the first run only the first N recurrent layers of + # the block (0 = all). Cost scales with the number of layer calls, so this cuts time and expert pages. + # Needs a depth-independent cache (shared_first / first_only): a skipped layer has no later KV slot. + later_groups = int(kwargs.pop("recurrent_later_groups", 0) or 0) + if later_groups < 0: + raise ValueError(f"recurrent_later_groups must be >= 0, got {later_groups}") + if later_groups and order != "block": + raise ValueError("recurrent_later_groups needs recurrent_order='block'") + if later_groups and mode == "per_iteration": + raise ValueError("recurrent_later_groups needs recurrent_attention_mode shared_first or first_only") + self.recurrent_later_groups: int = later_groups # Keep the legacy flag coherent for estimator/export consumers. self.recurrent_latent_refinement = mode == "first_only" # Execution/training policy for shared recurrent blocks. ``fixed`` @@ -262,6 +294,16 @@ def __init__(self, **kwargs): # document longer than the window is cut into window-sized parts). # Neither consults labels or task tokens. self.cluster_locality_mode: str = str(kwargs.pop("cluster_locality_mode", "window") or "window") + # Iteration-axis cluster locality (0 = off): weight on the total variation between the cluster + # distribution of each loop iteration of a token and the mean over its iterations, so every iteration + # of a recurrent layer reads the same expert clusters. Experts inside a cluster stay free to differ. + self.lambda_iteration_cluster: float = float(kwargs.pop("lambda_iteration_cluster", 0.0) or 0.0) + # Lookahead teacher: routing of loop iteration 0 only ("first") or averaged over every iteration of the + # layer ("iterations"), so prefetch predicts the experts the deeper iterations need as well. + scope = str(kwargs.pop("lookahead_teacher_scope", "first") or "first").lower() + if scope not in ("first", "iterations"): + raise ValueError(f"unknown lookahead_teacher_scope {scope!r}") + self.lookahead_teacher_scope: str = scope # M4: weight on the router KL anchor (alignment stages only). # Prevents SFT/DPO/ORPO/GRPO gradients from drifting routing away # from the pretrained distribution that the cluster layout was diff --git a/chronos/model/model_chronos.py b/chronos/model/model_chronos.py index 4d045d8..c5d1202 100644 --- a/chronos/model/model_chronos.py +++ b/chronos/model/model_chronos.py @@ -71,6 +71,7 @@ def forward( latent_only: bool = False, kv_override=None, return_shared_kv: bool = False, + top_k: int | None = None, ): """One physical block. @@ -104,7 +105,7 @@ def forward( hidden_states = hidden_states + residual mlp_in = self.post_attention_layernorm(hidden_states) if isinstance(self.mlp, ChronosMOEFeedForward): - hidden_states = hidden_states + self.mlp(mlp_in, available_expert_mask) + hidden_states = hidden_states + self.mlp(mlp_in, available_expert_mask, top_k=top_k) else: hidden_states = hidden_states + self.mlp(mlp_in) if return_shared_kv: @@ -170,6 +171,7 @@ def _run_block( latent_only: bool = False, kv_override=None, return_shared_kv: bool = False, + top_k: int | None = None, ): """Run one physical block with optional selective recomputation.""" if ( @@ -184,7 +186,7 @@ def transition(states): states, position_embeddings, past_key_value=None, use_cache=False, attention_mask=attention_mask, available_expert_mask=available_expert_mask, - latent_only=latent_only, kv_override=kv_override, + latent_only=latent_only, kv_override=kv_override, top_k=top_k, )[0] return checkpoint(transition, hidden_states, use_reentrant=False), None @@ -196,6 +198,7 @@ def transition(states): latent_only=latent_only, kv_override=kv_override, return_shared_kv=return_shared_kv, + top_k=top_k, ) def _forward_recurrent( @@ -269,80 +272,120 @@ def mask_for(li): adaptive_active = self.recurrent_controller.adaptive_active(cfg, self.training) threshold = float(cfg.halting_threshold) min_depth = int(cfg.min_recurrent_depth) - for group in range(g): + controller = self.recurrent_controller + order = str(getattr(cfg, "recurrent_order", "layer") or "layer") + # Graded top-k: loop iterations after the first route to fewer experts (0 = same as the first). + later_k = int(getattr(cfg, "recurrent_later_topk", 0) or 0) + # Prefix blocks (block order): iterations after the first run only the first later_g layers of the block. + later_g = int(getattr(cfg, "recurrent_later_groups", 0) or 0) + + def run_layer(group, step, hidden, shared_kv): + """One loop iteration of one physical layer: condition, attend/route, contract.""" li = p + group layer = self.layers[li] - group_states, group_halts = [], [] - exited_early = False - shared_kv = None - halted = cumulative = None - target = min(1.0, max(1e-6, threshold)) - for step in range(depth): - conditioned = self.recurrent_controller.condition(hidden_states, step, group) - slot = layout.slot(li, step) - block_kwargs = dict( - use_cache=use_cache, attention_mask=attention_mask, - available_expert_mask=mask_for(li), - ) - if mode == "per_iteration": - refined, presents[slot] = self._run_block( - layer, conditioned, position_embeddings, - past_key_value=past_key_values[slot], **block_kwargs) - elif mode == "first_only": - refined, present = self._run_block( - layer, conditioned, position_embeddings, - past_key_value=past_key_values[slot], latent_only=step > 0, **block_kwargs) - if step == 0: - presents[slot] = present - elif step == 0: # shared_first, writer iteration - refined, presents[slot], shared_kv = self._run_block( - layer, conditioned, position_embeddings, - past_key_value=past_key_values[slot], return_shared_kv=True, **block_kwargs) - else: # shared_first, reader iteration - refined, _ = self._run_block( - layer, conditioned, position_embeddings, - past_key_value=None, kv_override=shared_kv, - use_cache=False, attention_mask=attention_mask, - available_expert_mask=mask_for(li)) - hidden_states = self.recurrent_controller.contract(hidden_states, refined, group) - group_states.append(hidden_states) - halt_probability = self.recurrent_controller.halt_probability(hidden_states) - group_halts.append(halt_probability) - # Exact ACT bookkeeping (same rule as recurrent_auxiliary_state): - # a token halts once its accumulated mass reaches the threshold - # after min_depth. Later iterations get zero mixture weight, so - # once *every* token has halted the remaining iterations cannot - # change the output; exiting is then exact, and each token's - # result is independent of what else is in the batch (required - # for speculative verification to equal token-by-token decode). - if step + 1 >= min_depth: - clipped = halt_probability.float().clamp(0.0, 1.0) - if halted is None: - cumulative = torch.zeros_like(clipped) - halted = torch.zeros_like(clipped, dtype=torch.bool) - newly = (~halted) & ((cumulative + clipped) >= target) - cumulative = torch.where(halted | newly, cumulative, (cumulative + clipped).clamp(max=1.0)) - halted = halted | newly - if ( - not self.training and adaptive_active and allow_adaptive_exit - and halted is not None and step + 1 < depth - ): - if bool(halted.all()): # one fused device→host read per step - exited_early = True - break - # ACT is state interpolation rather than a ragged Python loop. - mixed, state = recurrent_auxiliary_state( - group_states, group_halts, min_depth=min_depth, threshold=threshold, + tk = {"top_k": later_k} if later_k and step > 0 else {} + conditioned = controller.condition(hidden, step, group) + slot = layout.slot(li, step) + block_kwargs = dict( + use_cache=use_cache, attention_mask=attention_mask, + available_expert_mask=mask_for(li), **tk, ) + if mode == "per_iteration": + refined, presents[slot] = self._run_block( + layer, conditioned, position_embeddings, + past_key_value=past_key_values[slot], **block_kwargs) + elif mode == "first_only": + refined, present = self._run_block( + layer, conditioned, position_embeddings, + past_key_value=past_key_values[slot], latent_only=step > 0, **block_kwargs) + if step == 0: + presents[slot] = present + elif step == 0: # shared_first, writer iteration + refined, presents[slot], shared_kv = self._run_block( + layer, conditioned, position_embeddings, + past_key_value=past_key_values[slot], return_shared_kv=True, **block_kwargs) + else: # shared_first, reader iteration + refined, _ = self._run_block( + layer, conditioned, position_embeddings, + past_key_value=None, kv_override=shared_kv, + use_cache=False, attention_mask=attention_mask, + available_expert_mask=mask_for(li), **tk) + return controller.contract(hidden, refined, group), shared_kv + + def track(hidden, step, halted, cumulative): + """Halting probability plus exact ACT bookkeeping (same rule as recurrent_auxiliary_state).""" + # A token halts once its accumulated mass reaches the threshold after + # min_depth. Later iterations get zero mixture weight, so once *every* + # token has halted the remaining iterations cannot change the output; + # exiting is then exact, and each token's result is independent of what + # else is in the batch (required for speculative verification to equal + # token-by-token decode). + halt_probability = controller.halt_probability(hidden) + if step + 1 >= min_depth: + clipped = halt_probability.float().clamp(0.0, 1.0) + if halted is None: + cumulative = torch.zeros_like(clipped) + halted = torch.zeros_like(clipped, dtype=torch.bool) + newly = (~halted) & ((cumulative + clipped) >= target) + cumulative = torch.where(halted | newly, cumulative, (cumulative + clipped).clamp(max=1.0)) + halted = halted | newly + return halt_probability, halted, cumulative + + def can_exit(halted, step): + if not self.training and adaptive_active and allow_adaptive_exit and halted is not None and step + 1 < depth: + return bool(halted.all()) # one fused device→host read per step + return False + + def finish(states, halts, exited_early, hidden): + # ACT is state interpolation rather than a ragged Python loop. + mixed, state = recurrent_auxiliary_state(states, halts, min_depth=min_depth, threshold=threshold) if adaptive_active: - hidden_states = mixed + hidden = mixed else: - hidden_states = group_states[-1] - state.ponder_cost = hidden_states.new_zeros(()) - state.depth_used = torch.full_like(state.depth_used, float(len(group_states))) + hidden = states[-1] + state.ponder_cost = hidden.new_zeros(()) + state.depth_used = torch.full_like(state.depth_used, float(len(states))) state.exited_early = exited_early recurrent_states.append(state) - + return hidden + + target = min(1.0, max(1e-6, threshold)) + if order == "block": + # Block-interleaved recurrence: (layer0 layer1 ... layerG-1) x depth. + # One state trajectory and one halting head per block iteration; each + # physical layer still keeps its own dt, loop/group embeddings and KV. + shared = [None] * g + states, halts = [], [] + halted = cumulative = None + exited_early = False + for step in range(depth): + for group in range(g if step == 0 or not later_g else min(g, later_g)): + hidden_states, shared[group] = run_layer(group, step, hidden_states, shared[group]) + states.append(hidden_states) + prob, halted, cumulative = track(hidden_states, step, halted, cumulative) + halts.append(prob) + if can_exit(halted, step): + exited_early = True + break + if states: + hidden_states = finish(states, halts, exited_early, hidden_states) + else: + # Layer-consecutive recurrence (default): layer0 x depth, layer1 x depth, ... + for group in range(g): + group_states, group_halts = [], [] + exited_early = False + shared_kv = None + halted = cumulative = None + for step in range(depth): + hidden_states, shared_kv = run_layer(group, step, hidden_states, shared_kv) + group_states.append(hidden_states) + prob, halted, cumulative = track(hidden_states, step, halted, cumulative) + group_halts.append(prob) + if can_exit(halted, step): + exited_early = True + break + hidden_states = finish(group_states, group_halts, exited_early, hidden_states) + # Coda uses the remaining physical layers. for ci in range(c): li = p + g + ci diff --git a/chronos/model/moe_chronos.py b/chronos/model/moe_chronos.py index 8343a29..fe3862d 100644 --- a/chronos/model/moe_chronos.py +++ b/chronos/model/moe_chronos.py @@ -123,6 +123,7 @@ def forward( self, x: torch.Tensor, available_expert_mask: torch.Tensor = None, + top_k: int | None = None, ) -> torch.Tensor: """ Args: @@ -151,14 +152,19 @@ def forward( self.iteration_router_probs.append(self.last_router_probs) self.iteration_router_probs_grad.append(scores_bse) + # Graded top-k: a later loop iteration may route to fewer experts (top_k < num_experts_per_tok). + k = self.num_experts_per_tok if not top_k else max(1, min(int(top_k), self.num_experts_per_tok)) shared_idx = self._shared_topk_idx if (self.router_scope == "shared" and shared_idx is not None and shared_idx.shape[0] == scores.shape[0] and len(self.iteration_router_probs) > 1): topk_idx = shared_idx + if k < topk_idx.shape[1]: + keep = scores.gather(-1, topk_idx).topk(k, dim=-1, sorted=False).indices + topk_idx = topk_idx.gather(-1, keep) topk_weight = scores.gather(-1, topk_idx) else: topk_weight, topk_idx = torch.topk( - scores, k=self.num_experts_per_tok, dim=-1, sorted=False + scores, k=k, dim=-1, sorted=False ) if self.router_scope == "shared": self._shared_topk_idx = topk_idx @@ -191,7 +197,7 @@ def forward( flat_idx = topk_idx.reshape(-1) order = torch.argsort(flat_idx, stable=True) counts = torch.bincount(flat_idx, minlength=self.num_experts).tolist() - rows = order // self.num_experts_per_tok + rows = order // topk_idx.shape[1] row_weight = topk_weight.reshape(-1)[order].unsqueeze(-1) offset = 0 for i, expert in enumerate(self.experts): @@ -258,7 +264,7 @@ def forward( # values and the index_add_ order are unchanged. flat_idx = topk_idx.reshape(-1) order = torch.argsort(flat_idx, stable=True) - sorted_rows = order // self.num_experts_per_tok + sorted_rows = order // topk_idx.shape[1] sorted_weight = topk_weight.reshape(-1)[order].unsqueeze(-1) mass = torch.zeros(self.num_experts, dtype=torch.float32, device=x_flat.device).index_add_( 0, flat_idx, topk_weight.detach().reshape(-1).float()) diff --git a/chronos/model/planner.py b/chronos/model/planner.py index 8d76e88..73e6535 100644 --- a/chronos/model/planner.py +++ b/chronos/model/planner.py @@ -53,7 +53,7 @@ class PlanRequest: active_experts: int | None = None # ... or this (top-k); active_params wins if both memory_saving: str = "none" # MEMORY_LEVELS data_paths: tuple = () # pretrain + sft + preference files/dirs - vocab_size: int | None = None # None: tokenizer report / dataset, else 16384 + vocab_size: int | None = None # None: tokenizer report / dataset, else the bundled 32768 tokenizer seq_len: int | None = None # None: from the dataset profile ram_gb: float | None = None # None: detected token_budget: float | None = None # pretrain tokens; None: dataset tokens (one pass) @@ -448,8 +448,10 @@ def plan_training(req: PlanRequest, profile: DatasetProfile | None = None, *, ve policy = MEMORY_POLICIES[level] prof = profile if profile is not None else profile_datasets(req.data_paths) notes, sources = list(prof.notes), {} - vocab = int(req.vocab_size or prof.vocab_size or 16384) - sources["vocab_size"] = "user" if req.vocab_size else ("tokenizer report" if prof.vocab_size else "default") + from chronos.deps import GENERAL_TOKENIZER_VOCAB + vocab = int(req.vocab_size or prof.vocab_size or GENERAL_TOKENIZER_VOCAB) + sources["vocab_size"] = ("user" if req.vocab_size else "tokenizer report" if prof.vocab_size + else "default (bundled general 32k tokenizer)") depth = max(1, int(req.recurrent_depth)) arch = search_architecture(req.target_total, use_moe=req.use_moe, num_experts=req.num_experts, vocab_size=vocab, depth=depth, active_params=req.active_params, @@ -575,6 +577,17 @@ def save_interval(steps: float) -> int: round(max(per_expert_bytes * L * max(2, arch.num_experts_per_tok) / 2**20, expert_mb * policy["ram_fraction"]), 1) resident_weights = (arch.total - routed * (1 - policy["resident_fraction"])) * 2 + # Loop knobs. Measured at 15M (30 min per arm, one seed per arm): block order beat layer order at equal step + # once the step size is healthy, and a fixed integration horizon T = depth * dt = 2 held at depth 4 and 8. + # The sigmoid parameterisation keeps dt trainable (the clamp default collapsed onto its floor). + loop_knobs = {} + if depth > 1: + from chronos.model.recurrent import recommended_dt_init + loop_knobs = dict(recurrent_order='block', recurrent_dt_param='sigmoid', + recurrent_dt_init=round(recommended_dt_init(depth), 4)) + sources["recurrent_order"] = "measured, 15M one seed: block beat layer at equal step with a healthy dt" + sources["recurrent_dt_param"] = "measured, 15M: the clamp default collapsed onto its floor and stopped learning" + sources["recurrent_dt_init"] = "estimated: horizon 2 / depth (15M, depth 4 and 8; other sizes not measured)" model = dict( architecture_version=3, recurrent_attention_mode="shared_first", rope_layout="per_head", hidden_size=h, num_hidden_layers=L, num_attention_heads=arch.num_attention_heads, @@ -586,7 +599,7 @@ def save_interval(steps: float) -> int: intermediate_size=0 if req.use_moe else arch.ffn_width, lookahead_steps=LOOKAHEAD_STEPS, layer_aware_lookahead=True, layer_aware_cache=True, recurrent_enabled=depth > 1, recurrent_groups=groups, recurrent_depth=depth, prelude_layers=1, - coda_layers=1, min_recurrent_depth=max(1, depth // 2), max_inference_depth=depth, + coda_layers=1, min_recurrent_depth=max(1, depth // 2), max_inference_depth=depth, **loop_knobs, adaptive_halting=depth > 1, halting_threshold=0.95, depth_schedule="fixed_then_adaptive", depth_schedule_warmup_fraction=0.3, recurrent_strategy="scheduled", recurrent_latent_refinement=False, ponder_loss_coef=3e-3, recurrent_distill_coef=0.05, recurrent_stability_coef=1e-3, @@ -603,6 +616,12 @@ def save_interval(steps: float) -> int: # The tokenizer the dataset was profiled with is the one the model needs. config["tokenizer_path"] = prof.tokenizer_dir sources["tokenizer_path"] = "dataset tokenizer report" + elif not req.vocab_size and not prof.vocab_size: + # Nothing named a vocabulary: new models start from the bundled task-neutral 32k tokenizer, whose + # size is the vocab_size chosen above. Old checkpoints keep the legacy 6400 default (chronos.deps). + from chronos.deps import get_general_tokenizer_path + config["tokenizer_path"] = get_general_tokenizer_path() + sources["tokenizer_path"] = "default (bundled general 32k tokenizer)" longrun = [ dict(name="pretrain", budget_fraction=0.5, peak_lr=lr, tokens_per_step=int(batch_tokens), diff --git a/chronos/model/recurrent.py b/chronos/model/recurrent.py index c01d199..188f36e 100644 --- a/chronos/model/recurrent.py +++ b/chronos/model/recurrent.py @@ -14,6 +14,47 @@ from chronos.model.precision_ops import RMSNorm +def recommended_dt_init(depth, horizon=2.0, cap=0.5): + """Step-size init for a looped block: dt = horizon / depth, at most ``cap``. + + One loop iteration is an explicit Euler step x <- x + dt * f(x) of dx/dtau = f(x), where f is the + block's residual update, so R iterations integrate over the horizon T = R * dt. Near the fixed + direction f(x) ~ lambda * x each iteration multiplies the residual by (1 + dt * lambda) and the loop + by (1 + dt * lambda)^(R-1), about exp(T * lambda): constructive interference sets the total gain through T, + and depth only refines the integration. Holding T fixed therefore keeps the gain while depth grows. + + Measured at 15M (30 min per arm, block order, one seed per arm, noise floor about 0.005-0.008): + - depth 8, dt 0.25 (T = 2) beat dt 0.5 (T = 4) by 0.018-0.079 in every block of steps after step 10; + - depth 8, dt 0.25 beat the clamp default whose dt collapsed to 0.05 (T = 0.4) only by 0.002-0.015; + - depth 8, sigmoid from dt 0.0625 ended at dt 0.033 (T = 0.26) and was worse than dt 0.25 by 0.085-0.333 + after step 100 (within noise before); + - depth 4 was flat for dt 0.2-0.5 (T = 0.8-2) and worse at dt 1.0 or 0.05; + - at equal step depth 8 (dt 0.25) led depth 4 (dt 0.5) by 0.041-0.049 in steps 100-600 and was level + (+0.002, +0.008) afterwards; + - depth 8, sigmoid from dt 0.25 (range 0.1-1) moved to dt 0.145 (T = 1.16) and ended 0.020-0.023 below + the fixed dt 0.25 arm after step 600 (validation CE 4.761 against 4.776), so the optimiser prefers a + smaller T than 2. + The block rescales its own output when dt changes: the per-iteration residual gain g stayed near 1.07 for + dt 0.15-0.5 (effective lambda = (g - 1) / dt fell from 0.48 to 0.13), and the total log-gain + (R - 1) * ln g was 0.45-0.51 in all three. Only kappa = dt * lambda and that total are physical; dt alone + is a gauge. So T = 2 is a safe starting point inside a flat region, not a fitted optimum, and ``cap`` is + the largest dt that was measured; depths 2 and 3 are extrapolation. + Use it with recurrent_dt_param="sigmoid", so the optimizer can still move dt either way.""" + depth = max(1, int(depth)) + return min(float(cap), float(horizon) / depth) + + +def _dt_raw_init(param, dt_init, dt_min, dt_max): + """Raw stored value so step 0 gives dt == dt_init under the chosen parameterisation. + clamp: store dt_init directly. sigmoid: store the logit that inverts dt_min + (dt_max-dt_min)*sigmoid(raw).""" + import math + if param != "sigmoid": + return float(dt_init) + span = max(float(dt_max) - float(dt_min), 1e-9) + frac = min(max((float(dt_init) - float(dt_min)) / span, 1e-6), 1 - 1e-6) + return float(math.log(frac / (1.0 - frac))) + + @dataclass class RecurrentDepthState: halting_probs: torch.Tensor @@ -40,6 +81,7 @@ def __init__(self, config): self.epsilon = float(config.halting_epsilon) self.dt_min = float(config.recurrent_dt_min) self.dt_max = float(config.recurrent_dt_max) + self.dt_param = str(getattr(config, "recurrent_dt_param", "clamp")) self.loop_embeddings = nn.Parameter(torch.zeros(depth, hidden)) self.group_embeddings = nn.Parameter(torch.zeros(groups, hidden)) @@ -49,7 +91,7 @@ def __init__(self, config): nn.init.zeros_(self.halting_head.weight) nn.init.constant_(self.halting_head.bias, -1.0) self.step_sizes = nn.Parameter( - torch.full((groups,), float(config.recurrent_dt_init)) + torch.full((groups,), _dt_raw_init(self.dt_param, config.recurrent_dt_init, self.dt_min, self.dt_max)) ) self.training_progress = 0.0 @@ -98,6 +140,8 @@ def condition(self, hidden: torch.Tensor, step: int, group: int) -> torch.Tensor return hidden + self.loop_embeddings[depth_idx][None, None, :] + self.group_embeddings[group][None, None, :] def step_size(self, group: int) -> torch.Tensor: + if self.dt_param == "sigmoid": + return self.dt_min + (self.dt_max - self.dt_min) * torch.sigmoid(self.step_sizes[group]) return self.step_sizes[group].clamp(self.dt_min, self.dt_max) def contract(self, before: torch.Tensor, after: torch.Tensor, group: int) -> torch.Tensor: diff --git a/chronos/model/temporal_loss.py b/chronos/model/temporal_loss.py index e460cd3..cab49b0 100644 --- a/chronos/model/temporal_loss.py +++ b/chronos/model/temporal_loss.py @@ -445,3 +445,22 @@ def total_loss( lookahead_probs, teacher_probs, lookahead_steps, num_experts_per_tok, ) return loss + + +def iteration_cluster_spread(probs5: torch.Tensor, membership: torch.Tensor, token_mask: torch.Tensor | None = None) -> torch.Tensor: + """Iteration-axis cluster locality over ``probs5`` ``[B, S, G, D, E]`` (G recurrent layers, D loop iterations). + + Per token and layer: total variation between each iteration's cluster-mass distribution and the mean over + its iterations, averaged. 0 = every iteration routes into the same clusters, so one cluster read serves + them all; experts inside a cluster may still differ. Twin of ``mlx.training.trainer._iteration_cluster_spread``. + """ + _b, _s, _g, d, _e = probs5.shape + if d < 2 or int(membership.shape[1]) <= 1: + return probs5.new_zeros(()) + mass = torch.einsum("bsgde,ec->bsgdc", probs5.float(), membership.to(probs5.device, torch.float32)) + spread = 0.5 * (mass - mass.mean(dim=3, keepdim=True)).abs().sum(dim=-1) + per_token = spread.mean(dim=(2, 3)) + if token_mask is None: + return per_token.mean() + m = token_mask.to(per_token.dtype) + return (per_token * m).sum() / m.sum().clamp_min(1.0) diff --git a/chronos/serving/ollama_server.py b/chronos/serving/ollama_server.py index 7790ad3..2629810 100644 --- a/chronos/serving/ollama_server.py +++ b/chronos/serving/ollama_server.py @@ -172,6 +172,7 @@ async def show(request: Request): cfg = meta.get("config", {}) info = {"general.architecture": "chronos", "chronos.recurrent_depth": cfg.get("recurrent_depth"), "chronos.recurrent_attention_mode": cfg.get("recurrent_attention_mode"), + "chronos.recurrent_order": cfg.get("recurrent_order", "layer"), "chronos.num_experts": cfg.get("num_experts"), "chronos.experts_per_token": cfg.get("num_experts_per_tok"), "chronos.kv_latent_dim": cfg.get("kv_latent_dim"), "chronos.context_length": cfg.get("max_position_embeddings"), "chronos.hidden_size": cfg.get("hidden_size"), "chronos.vocab_size": cfg.get("vocab_size")} diff --git a/chronos/tokenizer/extract.py b/chronos/tokenizer/extract.py index eb56b77..3760981 100644 --- a/chronos/tokenizer/extract.py +++ b/chronos/tokenizer/extract.py @@ -2,7 +2,7 @@ Accepts any mix of files/directories (parquet, jsonl, json, csv, tsv, txt). For each record it finds text fields automatically (conversations, pairs, -``text``/``content``/``translation.*`` …) unless a field list is given, buckets +``text``/``content``, nested string fields …) unless a field list is given, buckets text by script, and keeps a seeded per-source reservoir so every source is represented regardless of size. """ @@ -59,7 +59,7 @@ def _texts_from_record(record, fields: list[str] | None) -> Iterator[str]: for key, value in record.items(): if isinstance(value, str) and (key in TEXT_KEYS or len(key) <= 3 or key.startswith("sentence")): yield value - elif isinstance(value, dict): # translation: {en: ..., zh: ...} + elif isinstance(value, dict): # nested string fields, e.g. {"a": ..., "b": ...} for sub in value.values(): if isinstance(sub, str): yield sub diff --git a/chronos/trainer/loss_mixin.py b/chronos/trainer/loss_mixin.py index 04a91ba..8e7b553 100644 --- a/chronos/trainer/loss_mixin.py +++ b/chronos/trainer/loss_mixin.py @@ -28,6 +28,7 @@ from chronos.model.moe_chronos import ChronosMOEFeedForward from chronos.model.temporal_loss import ( cluster_locality_loss, + iteration_cluster_spread, temporal_locality_loss, lookahead_supervision_loss, lookahead_topk_hit_loss, @@ -107,6 +108,35 @@ def first_iteration_router_probs(model) -> Optional[torch.Tensor]: return torch.stack(probs, dim=2) if probs else None +def recurrent_iteration_probs(model, config, *, with_grad: bool = True) -> Optional[torch.Tensor]: + """``[B, S, G, D, E]``: router probabilities of each recurrent layer at each loop iteration (None unless + every recurrent layer dispatched the same number, at least 2, of times in the last forward).""" + start, groups = int(getattr(config, "prelude_layers", 0) or 0), int(getattr(config, "recurrent_groups", 0) or 0) + rows = [] + for layer in list(model.model.layers)[start:start + groups]: + mlp = getattr(layer, "mlp", None) + if not isinstance(mlp, ChronosMOEFeedForward): + return None + seq = mlp.iteration_router_probs_grad if with_grad else mlp.iteration_router_probs + if not seq: + return None + rows.append(torch.stack(list(seq), dim=2)) + if not rows or rows[0].shape[2] < 2 or any(r.shape[2] != rows[0].shape[2] for r in rows): + return None + return torch.stack(rows, dim=2) + + +def iteration_mean_router_probs(model) -> Optional[torch.Tensor]: + """Per physical layer, router probabilities averaged over its loop iterations ``[B, S, L, E]`` + (lookahead teacher covering every iteration; the step-0 routing for one-dispatch layers).""" + probs = [] + for layer in model.model.layers: + mlp = getattr(layer, "mlp", None) + if isinstance(mlp, ChronosMOEFeedForward) and mlp.iteration_router_probs: + probs.append(torch.stack(list(mlp.iteration_router_probs), dim=0).mean(dim=0)) + return torch.stack(probs, dim=2) if probs else None + + def lookahead_teacher(router_4d, lookahead_probs, config): """Align prediction head g to physical layer prelude_layers + g.""" if lookahead_probs.dim() == 4: @@ -191,6 +221,13 @@ def chronos_loss_term( cluster_term = cluster_locality_loss(router_4d, membership, window) loss = loss + lambda_cluster * cluster_term + lambda_iteration = float(getattr(config, "lambda_iteration_cluster", 0.0) or 0.0) + if lambda_iteration > 0.0: + iteration_probs = recurrent_iteration_probs(model, config) + if iteration_probs is not None: + mask = None if labels is None else real_token_mask_torch(labels) + loss = loss + lambda_iteration * iteration_cluster_spread(iteration_probs, cluster_membership_tensor(config, iteration_probs.device), mask) + lambda_lookahead = float(getattr(config, "lambda_lookahead", 0.0)) lookahead_steps = int(getattr(config, "lookahead_steps", 0)) if ( @@ -200,7 +237,9 @@ def chronos_loss_term( ): # Teacher must be detached: lookahead targets are stop-grad'd # by construction (we predict the *future* router state). - teacher = lookahead_teacher(first_iteration_router_probs(model), lookahead_probs, config) + source = (iteration_mean_router_probs(model) if getattr(config, "lookahead_teacher_scope", "first") == "iterations" + else first_iteration_router_probs(model)) + teacher = lookahead_teacher(source, lookahead_probs, config) la = lookahead_supervision_loss(lookahead_probs, teacher, lookahead_steps) loss = loss + lambda_lookahead * la lambda_lookahead_topk = float(getattr(config, "lambda_lookahead_topk", 0.0)) diff --git a/chronos/tuning/chronos_auto_tuner.py b/chronos/tuning/chronos_auto_tuner.py index d653239..1bacf27 100644 --- a/chronos/tuning/chronos_auto_tuner.py +++ b/chronos/tuning/chronos_auto_tuner.py @@ -54,6 +54,20 @@ class ChronosSearchSpaceConfig(SearchSpaceConfig): lambda_lookahead_min: float = 1e-3 lambda_lookahead_max: float = 1.0 + # Loop knobs (0.3.3a0). They only matter for a looped model, so searching either one makes the probe model + # run `probe_recurrent_depth` iterations (the other probes stay at depth 1). The probe loss comes from a few + # steps and cannot resolve differences as small as dt 0.2-0.5 at depth 4: treat these two as a coarse screen. + # `recurrent_later_groups` is not searched: it trades quality for cost, so a loss-only search would always + # pick the full block. + tune_recurrent_order: bool = False + recurrent_order_choices: List[str] = field(default_factory=lambda: ["layer", "block"]) + + tune_recurrent_dt_init: bool = False + recurrent_dt_init_min: float = 0.1 + recurrent_dt_init_max: float = 1.0 + + probe_recurrent_depth: int = 4 + class ChronosAutoTuner(AutoTuner): """ @@ -110,6 +124,16 @@ def _sample_params(self, trial, ss) -> dict: if ss.tune_lambda_lookahead: params["lambda_lookahead"] = trial.suggest_float( "lambda_lookahead", ss.lambda_lookahead_min, ss.lambda_lookahead_max, log=True) + + # Loop knobs: the probe model needs more than one iteration for them to change anything. + if ss.tune_recurrent_order: + params["recurrent_order"] = trial.suggest_categorical( + "recurrent_order", ss.recurrent_order_choices) + if ss.tune_recurrent_dt_init: + params["recurrent_dt_init"] = trial.suggest_float( + "recurrent_dt_init", ss.recurrent_dt_init_min, ss.recurrent_dt_init_max, log=True) + if ss.tune_recurrent_order or ss.tune_recurrent_dt_init: + params["recurrent_depth"] = max(2, int(ss.probe_recurrent_depth)) else: params["lambda_balance"] = 5e-4 params["lambda_temporal"] = 1e-3 @@ -117,6 +141,20 @@ def _sample_params(self, trial, ss) -> dict: return params + def _probe_tokens(self, dataset_path, tokenizer_dir): + """Real tokens of the user's dataset for the probe, read once per run; ``None`` means random ids.""" + cache = self.__dict__.setdefault("_probe_cache", {}) + key = (dataset_path, tokenizer_dir) + if key not in cache: + from chronos.tuning.probe_data import load_probe_tokens + cache[key] = load_probe_tokens(dataset_path, tokenizer_dir) + if cache[key] is None: + line = "[probe] no text could be read from the dataset; trials use random token ids" + else: + line = f"[probe] {cache[key][0].size} real tokens from the dataset (vocab {cache[key][1]})" + self._put({"type": "log", "line": line}) + return cache[key] + def _run_probe_trial( self, trial, model_id, dataset_path, train_ratio, probe_steps, output_dir, seed, params, **kwargs @@ -134,6 +172,9 @@ def _run_probe_trial( from chronos.model.moe_chronos import ChronosMOEFeedForward _, device = resolve_training_device("auto") + if device == "mlx": + # The probe trains a torch model and "mlx" is not a torch device: use the best torch device instead. + device = "mps" if torch.backends.mps.is_available() else ("cuda" if torch.cuda.is_available() else "cpu") lr = params.get("learning_rate", 2e-4) lb = params.get("lambda_balance", 5e-4) lt = params.get("lambda_temporal", 1e-3) @@ -142,6 +183,16 @@ def _run_probe_trial( ne = int(params.get("num_experts", 4)) ns = int(params.get("num_shared_experts", 1)) kv = int(params.get("kv_latent_dim", 64)) + real = self._probe_tokens(dataset_path, kwargs.get("tokenizer_dir")) + vocab_kw = {"vocab_size": real[1]} if real else {} + loop = {} + if params.get("recurrent_depth"): + depth = int(params["recurrent_depth"]) + loop = dict(recurrent_enabled=True, recurrent_depth=depth, min_recurrent_depth=max(1, depth // 2), + max_inference_depth=depth, prelude_layers=1, coda_layers=1) + for key in ("recurrent_order", "recurrent_dt_init"): + if key in params: + loop[key] = params[key] if os.path.exists(model_id): try: @@ -155,6 +206,7 @@ def _run_probe_trial( "lookahead_steps": la, "lambda_balance": lb, "lambda_temporal": lt, + **loop, }, require_unsniffable=True, ) @@ -164,7 +216,7 @@ def _run_probe_trial( num_experts_per_tok=2, num_shared_experts=ns, kv_latent_dim=kv, lookahead_steps=la, lambda_balance=lb, lambda_temporal=lt, - use_hybrid_attention=True, use_moe=True, + use_hybrid_attention=True, use_moe=True, **vocab_kw, **loop, ) else: cfg = ChronosConfig( @@ -179,7 +231,13 @@ def _run_probe_trial( lambda_temporal=lt, use_hybrid_attention=True, use_moe=True, + **vocab_kw, + **loop, ) + if real and real[1] > int(cfg.vocab_size): + # A checkpoint's smaller vocabulary cannot embed this tokenizer's ids. + self._put({"type": "log", "line": f"[probe] model vocab {cfg.vocab_size} < tokenizer vocab {real[1]}; trials use random token ids"}) + real = None model = ChronosForCausalLM(cfg).to(device) if os.path.exists(model_id): try: @@ -195,7 +253,11 @@ def _run_probe_trial( model.train() losses = [] for step in range(probe_steps): - ids = torch.randint(0, vocab_size, (batch, seq_len), device=device) + if real: + from chronos.tuning.probe_data import probe_batch + ids = torch.from_numpy(probe_batch(real[0], step, batch, seq_len)).to(device) + else: + ids = torch.randint(0, vocab_size, (batch, seq_len), device=device) out, _ = model(ids, labels=ids, legacy_output=True) moe_layers = [l.mlp for l in model.model.layers if isinstance(l.mlp, ChronosMOEFeedForward)] @@ -230,6 +292,7 @@ def get_best_chronos_config_patch(self) -> dict: "lambda_balance", "lambda_temporal", "lambda_lookahead", "lookahead_steps", "hidden_size", "num_experts", "num_shared_experts", "kv_latent_dim", + "recurrent_order", "recurrent_dt_init", ): if key in p: patch[key] = p[key] diff --git a/chronos/tuning/probe_data.py b/chronos/tuning/probe_data.py new file mode 100644 index 0000000..d7cfbf2 --- /dev/null +++ b/chronos/tuning/probe_data.py @@ -0,0 +1,47 @@ +"""Real-token batches for the auto-tune probe. + +The probe used to train on random token ids, so no hyperparameter could change its loss in a way that +carries over to real data. ``load_probe_tokens`` encodes a bounded sample of the user's dataset with the +model's tokenizer; the tuner falls back to random ids only when the data cannot be read. +""" +from __future__ import annotations + +import os + +import numpy as np + + +def load_probe_tokens(dataset_path: str, tokenizer_dir: str | None, *, max_tokens: int = 400_000, + max_records: int = 20_000) -> tuple[np.ndarray, int] | None: + """``(token ids int64, tokenizer vocab size)`` or ``None`` when nothing could be read or encoded.""" + try: + from chronos.deps import get_general_tokenizer_path + from chronos.tokenizer.extract import _texts_from_record, expand, iter_records + from chronos.tokenizer.model import ChronosTokenizer + directory = tokenizer_dir if tokenizer_dir and os.path.isfile(os.path.join(tokenizer_dir, 'chronos_tokenizer.json')) \ + else get_general_tokenizer_path() + tokenizer = ChronosTokenizer.load(directory) + ids: list[int] = [] + seen = 0 + for path in expand([dataset_path]): + for record in iter_records(path): + seen += 1 + for text in _texts_from_record(record, None): + ids.extend(tokenizer.encode(text, allow_special=False)) + ids.append(tokenizer.vocab['<|endoftext|>']) + if len(ids) >= max_tokens or seen >= max_records: + break + if len(ids) >= max_tokens or seen >= max_records: + break + if len(ids) < 512: + return None + return np.asarray(ids[:max_tokens], dtype=np.int64), int(tokenizer.vocab_size) + except Exception: + return None + + +def probe_batch(tokens: np.ndarray, step: int, batch: int, seq_len: int) -> np.ndarray: + """``[batch, seq_len]`` windows at deterministic offsets that advance with ``step`` (same data in every trial).""" + span = max(1, tokens.size - seq_len - 1) + starts = [((step * batch + row) * seq_len * 7919) % span for row in range(batch)] + return np.stack([tokens[s:s + seq_len] for s in starts]) diff --git a/chronos/webui/i18n/en.json b/chronos/webui/i18n/en.json index b922937..76a9e8d 100644 --- a/chronos/webui/i18n/en.json +++ b/chronos/webui/i18n/en.json @@ -37,6 +37,8 @@ "choice.fixed": "Fixed", "choice.fixed_then_adaptive": "Fixed, then adaptive", "choice.fullload": "Full memory only", + "choice.lookahead_teacher_scope.first": "Iteration 0 only", + "choice.lookahead_teacher_scope.iterations": "All iterations averaged", "choice.mla": "Global (MLA) blocks", "choice.mlp": "MoE/FFN only", "choice.none": "None", @@ -47,6 +49,10 @@ "choice.recurrent_attention_mode.first_only": "First iteration only, v2 (first_only)", "choice.recurrent_attention_mode.per_iteration": "Every iteration, own KV each (per_iteration)", "choice.recurrent_attention_mode.shared_first": "Every iteration, shared first KV (shared_first)", + "choice.recurrent_dt_param.clamp": "Clamp (legacy)", + "choice.recurrent_dt_param.sigmoid": "Sigmoid (no dead zone)", + "choice.recurrent_order.block": "Whole block (A B A B)", + "choice.recurrent_order.layer": "Layer by layer (A A B B)", "choice.router_scope.per_iteration": "Route every iteration (per_iteration)", "choice.router_scope.shared": "Reuse iteration 0 routing (shared)", "choice.scheduled": "Scheduled", @@ -256,6 +262,8 @@ "field.lambda_balance.label": "Load balance", "field.lambda_cluster_locality.help": "Trains tokens in a segment to stay inside one expert cluster, so lazy inference can page whole clusters. 0 disables it.", "field.lambda_cluster_locality.label": "Cluster locality", + "field.lambda_iteration_cluster.help": "Weight on keeping every loop iteration of a token inside the same expert clusters (experts inside a cluster may still differ per iteration). 0 disables it.", + "field.lambda_iteration_cluster.label": "Iteration cluster locality", "field.lambda_lookahead.help": "Trains the lookahead router to predict the experts future tokens will use.", "field.lambda_lookahead.label": "Lookahead loss", "field.lambda_lookahead_topk.help": "Extra weight on getting the exact top-k future experts right.", @@ -280,6 +288,8 @@ "field.lookahead_input_detach.label": "Detach lookahead input", "field.lookahead_steps.help": "How many future tokens the lookahead router predicts experts for, so they can be prefetched before they are needed.", "field.lookahead_steps.label": "Lookahead steps", + "field.lookahead_teacher_scope.help": "Which routing the LookaheadRouter learns to predict. first: loop iteration 0 only. iterations: the average over every iteration of the layer, so prefetch also covers the experts deeper iterations need.", + "field.lookahead_teacher_scope.label": "Lookahead teacher", "field.max_gen_len.help": "Tokens generated per GRPO rollout.", "field.max_gen_len.label": "Rollout length", "field.max_inference_depth.help": "Maximum iterations at inference. Usually equal to the training depth; more than trained is not validated.", @@ -352,7 +362,7 @@ "field.plan.token_budget_b.label": "Token budget (B)", "field.plan.use_moe.help": "Plan an MoE model; off plans a dense one.", "field.plan.use_moe.label": "Mixture of experts", - "field.plan.vocab_size.help": "Empty reads it from the tokenizer report or uses 16384.", + "field.plan.vocab_size.help": "Empty reads it from the tokenizer report; with no report it uses 32768 and the bundled general 32k tokenizer.", "field.plan.vocab_size.label": "Vocabulary", "field.ponder_loss_coef.help": "Penalty on the expected number of iterations. Higher pushes the model to stop earlier.", "field.ponder_loss_coef.label": "Ponder loss", @@ -402,10 +412,24 @@ "field.recurrent_depth_jitter.label": "Depth jitter", "field.recurrent_distill_coef.help": "Teaches shallow exits to match full-depth predictions. Raises speculative acceptance and early-exit quality.", "field.recurrent_distill_coef.label": "Depth distillation", + "field.recurrent_dt_init.help": "Starting value of the learned loop step size dt, the same for every recurrent group. One iteration is an Euler step, so depth x dt is the integration horizon T. The planner sets dt = min(0.5, 2 / depth), which holds T = 2: 0.5 at depth 4, 0.25 at depth 8. Measured at 15M (one seed per arm): the loss is flat for dt from 0.2 to 0.5 at depth 4; at depth 8, dt 0.25 beat 0.5 and a start of 0.0625 was far worse. T = 2 is a safe start, not a fitted optimum: a sigmoid run that started there settled at T of about 1.2. The kernel default stays 0.25.", + "field.recurrent_dt_init.label": "Initial step size", + "field.recurrent_dt_max.help": "Upper bound of dt. 1.0 applies the whole block update to the residual stream in one iteration.", + "field.recurrent_dt_max.label": "Largest step size", + "field.recurrent_dt_min.help": "Lower bound of dt. With the clamp parameterisation a dt that reaches it stops learning; at the default 0.05, dt collapsed onto this bound in our depth-4 and depth-8 runs.", + "field.recurrent_dt_min.label": "Smallest step size", + "field.recurrent_dt_param.help": "How the learned loop step size dt is stored. clamp: dt = clamp(w, dt_min, dt_max); the gradient is zero once w leaves the range, so a dt that falls below dt_min can never come back. sigmoid: dt = dt_min + (dt_max - dt_min) * sigmoid(w); the gradient never vanishes, so dt can still move. Both start at the configured initial dt. Measured in a 15M model (one seed per arm): the clamp default let dt collapse onto its floor of 0.05, which was worse at equal step than a healthy dt; a sigmoid dt kept moving and settled where the loss was flat. The planner recommends sigmoid.", + "field.recurrent_dt_param.label": "Step-size parameterisation", "field.recurrent_enabled.help": "Run the shared middle groups several times per token, so a small set of weights acts like a deeper network.", "field.recurrent_enabled.label": "Recurrent depth", "field.recurrent_groups.help": "How many layers between prelude and coda form looping groups. Empty derives it from layers − prelude − coda.", "field.recurrent_groups.label": "Recurrent groups", + "field.recurrent_later_groups.help": "Block order only: loop iterations after the first run only the first N layers of the block (0 = all layers). Fewer layer calls make deeper iterations cheaper and touch fewer expert pages. Needs shared_first or first_only attention.", + "field.recurrent_later_groups.label": "Later-iteration layers", + "field.recurrent_later_topk.help": "Experts per token in loop iterations after the first (0 = same as the first iteration). The shared expert always runs. A smaller value makes deeper iterations cheaper and loads fewer expert pages.", + "field.recurrent_later_topk.label": "Later-iteration top-k", + "field.recurrent_order.help": "Order of the loop. layer: each recurrent layer repeats depth times in a row. block: the whole group of recurrent layers repeats, so iteration 0 is a normal depth-1 pass and the loop refines it. Changes the computation: not interchangeable with a layer-order checkpoint. Measured at 15M (one seed per arm): with a trainable step size block had the lower loss at equal step; with step sizes fixed at 1.0 layer won late. The planner recommends block.", + "field.recurrent_order.label": "Loop order", "field.recurrent_stability_coef.help": "Keeps the recurrent update contractive so repeated iterations do not drift or explode.", "field.recurrent_stability_coef.label": "Stability loss", "field.recurrent_strategy.help": "Sampling strategy for the recurrent depth bucket during training.", diff --git a/chronos/webui/i18n/ja.json b/chronos/webui/i18n/ja.json index ccb4e97..6210b37 100644 --- a/chronos/webui/i18n/ja.json +++ b/chronos/webui/i18n/ja.json @@ -37,6 +37,8 @@ "choice.fixed": "固定", "choice.fixed_then_adaptive": "固定の後に適応", "choice.fullload": "全メモリのみ", + "choice.lookahead_teacher_scope.first": "反復 0 のみ", + "choice.lookahead_teacher_scope.iterations": "全反復の平均", "choice.mla": "グローバル（MLA）ブロック", "choice.mlp": "MoE/FFN のみ", "choice.none": "なし", @@ -47,6 +49,10 @@ "choice.recurrent_attention_mode.first_only": "初回のみ注意、v2 動作（first_only）", "choice.recurrent_attention_mode.per_iteration": "毎反復で注意、反復ごとに KV（per_iteration）", "choice.recurrent_attention_mode.shared_first": "毎反復で注意、初回 KV を共有（shared_first）", + "choice.recurrent_dt_param.clamp": "クランプ（従来）", + "choice.recurrent_dt_param.sigmoid": "シグモイド（デッドゾーンなし）", + "choice.recurrent_order.block": "ブロック全体で反復（A B A B）", + "choice.recurrent_order.layer": "層ごとに反復（A A B B）", "choice.router_scope.per_iteration": "反復ごとにルーティング（per_iteration）", "choice.router_scope.shared": "初回のルーティングを再利用（shared）", "choice.scheduled": "スケジュール", @@ -256,6 +262,8 @@ "field.lambda_balance.label": "負荷分散", "field.lambda_cluster_locality.help": "区間内のトークンを 1 つのエキスパートクラスタ内にとどめるよう学習させ、遅延推論でクラスタ単位のページングを可能にします。0 で無効。", "field.lambda_cluster_locality.label": "クラスタ局所性", + "field.lambda_iteration_cluster.help": "同じトークンのすべてのループ反復を同じエキスパートクラスタ内に収める重み（クラスタ内のエキスパートは反復ごとに異なってよい）。0 で無効。", + "field.lambda_iteration_cluster.label": "反復クラスタ局所性", "field.lambda_lookahead.help": "将来のトークンが使うエキスパートを予測するよう先読みルーターを学習させます。", "field.lambda_lookahead.label": "先読み損失", "field.lambda_lookahead_topk.help": "将来の top-k エキスパートを正確に当てることへの追加の重み。", @@ -280,6 +288,8 @@ "field.lookahead_input_detach.label": "ルックアヘッド入力の勾配遮断", "field.lookahead_steps.help": "先読みルーターが何トークン先までエキスパートを予測するか。必要になる前にプリフェッチできます。", "field.lookahead_steps.label": "先読みステップ", + "field.lookahead_teacher_scope.help": "LookaheadRouter が予測を学ぶルーティング。first：反復 0 のみ。iterations：その層の全反復の平均で、先読みがより深い反復に必要なエキスパートも対象にする。", + "field.lookahead_teacher_scope.label": "先読み教師", "field.max_gen_len.help": "GRPO のロールアウトごとに生成するトークン数。", "field.max_gen_len.label": "ロールアウト長", "field.max_inference_depth.help": "推論時の最大反復回数。通常は学習時の深さと同じです。学習時より深くした場合の挙動は検証されていません。", @@ -352,7 +362,7 @@ "field.plan.token_budget_b.label": "トークン予算（B）", "field.plan.use_moe.help": "MoE モデルを計画します。オフなら密なモデルを計画します。", "field.plan.use_moe.label": "混合エキスパート", - "field.plan.vocab_size.help": "空欄ならトークナイザーのレポートから読むか、16384 を使います。", + "field.plan.vocab_size.help": "空欄ならトークナイザーのレポートから読みます。レポートがなければ 32768 と同梱の汎用 32k トークナイザーを使います。", "field.plan.vocab_size.label": "語彙", "field.ponder_loss_coef.help": "期待反復回数へのペナルティ。大きいほど早く停止するよう促します。", "field.ponder_loss_coef.label": "思考コスト損失", @@ -402,10 +412,24 @@ "field.recurrent_depth_jitter.label": "深さのゆらぎ", "field.recurrent_distill_coef.help": "浅い出口の予測を最大深さの予測に合わせるよう学習させます。投機的デコードの受理率と早期終了の品質が上がります。", "field.recurrent_distill_coef.label": "深さ蒸留", + "field.recurrent_dt_init.help": "学習するループのステップ幅 dt の開始値。すべてのループグループで共通。1 回の反復はオイラー法の 1 ステップなので、深さ × dt が積分区間 T になる。プランナーは dt = min(0.5, 2 / 深さ) とし、T = 2 に固定する：深さ 4 で 0.5、深さ 8 で 0.25。15M モデルの実測（各条件シード 1 つ）：深さ 4 では dt が 0.2〜0.5 の間で損失は同じ。深さ 8 では dt 0.25 が 0.5 に勝ち、0.0625 からの開始は大きく悪化した。T = 2 は安全な出発点であり、当てはめた最適値ではない：そこから始めた sigmoid の実験は T が約 1.2 に落ち着いた。カーネルの既定値は 0.25 のまま。", + "field.recurrent_dt_init.label": "初期ステップ幅", + "field.recurrent_dt_max.help": "dt の上限。1.0 は 1 回の反復でブロックの更新をそのまま残差ストリームに加える。", + "field.recurrent_dt_max.label": "最大ステップ幅", + "field.recurrent_dt_min.help": "dt の下限。clamp では下限に達した dt は学習しなくなる。既定の 0.05 では、深さ 4 と深さ 8 の実測で dt がこの下限に落ち込んだ。", + "field.recurrent_dt_min.label": "最小ステップ幅", + "field.recurrent_dt_param.help": "学習するループのステップ幅 dt の保持方法。clamp：dt = clamp(w, dt_min, dt_max)。w が範囲外に出ると勾配がゼロになり、dt_min を下回った dt は戻れない。sigmoid：dt = dt_min + (dt_max - dt_min) * sigmoid(w)。勾配が消えないため dt は動き続けられる。どちらも設定した初期 dt から始まる。15M モデルの実測（各条件シード 1 つ）：clamp の既定では dt が下限 0.05 に張り付き、同じステップ数で健全な dt より悪かった。sigmoid の dt は動き続け、損失が変わらない位置に落ち着いた。プランナーは sigmoid を推奨する。", + "field.recurrent_dt_param.label": "ステップ幅のパラメータ化", "field.recurrent_enabled.help": "中間の共有グループをトークンごとに複数回実行し、少ない重みでより深いネットワークのように働かせます。", "field.recurrent_enabled.label": "再帰的な深さ", "field.recurrent_groups.help": "プレリュードとコーダの間で反復するグループを構成する層の数。空欄なら 層数 − プレリュード − コーダ から求めます。", "field.recurrent_groups.label": "再帰グループ数", + "field.recurrent_later_groups.help": "ブロック順序のみ：最初の反復より後のループ反復では、ブロックの先頭 N 層だけを実行する（0 で全層）。レイヤー呼び出しが減り、深い反復が安くなり、触れるエキスパートページも減る。shared_first または first_only の注意モードが必要。", + "field.recurrent_later_groups.label": "後続反復のレイヤー数", + "field.recurrent_later_topk.help": "最初の反復より後のループ反復でトークンごとに使うエキスパート数（0 で最初の反復と同じ）。共有エキスパートは常に動作する。小さくすると深い反復が安くなり、読み込むエキスパートページも減る。", + "field.recurrent_later_topk.label": "後続反復の top-k", + "field.recurrent_order.help": "ループの実行順序。layer：各再帰層を depth 回連続で反復。block：再帰層のグループ全体を反復し、反復 0 は通常の深さ 1 の順伝播になり、以降がそれを精緻化する。計算が異なるため、layer 順序のチェックポイントとは互換性がない。15M モデルの実測（各条件シード 1 つ）：ステップ幅を学習できるとき、同じステップ数で block の損失が低かった。ステップ幅を 1.0 に固定すると後半は layer が良かった。プランナーは block を推奨する。", + "field.recurrent_order.label": "ループ順序", "field.recurrent_stability_coef.help": "再帰更新を縮小写像に保ち、反復を重ねても発散やドリフトが起きないようにします。", "field.recurrent_stability_coef.label": "安定化損失", "field.recurrent_strategy.help": "学習時に再帰深さのバケットをサンプリングする戦略。", diff --git a/chronos/webui/i18n/zh-Hans.json b/chronos/webui/i18n/zh-Hans.json index f69a411..6e70a60 100644 --- a/chronos/webui/i18n/zh-Hans.json +++ b/chronos/webui/i18n/zh-Hans.json @@ -37,6 +37,8 @@ "choice.fixed": "固定", "choice.fixed_then_adaptive": "先固定，后自适应", "choice.fullload": "仅全内存", + "choice.lookahead_teacher_scope.first": "仅第 0 轮", + "choice.lookahead_teacher_scope.iterations": "所有迭代取平均", "choice.mla": "全局（MLA）块", "choice.mlp": "仅 MoE/FFN", "choice.none": "无", @@ -47,6 +49,10 @@ "choice.recurrent_attention_mode.first_only": "仅首轮注意力，v2 行为（first_only）", "choice.recurrent_attention_mode.per_iteration": "每轮注意力，每轮独立 KV（per_iteration）", "choice.recurrent_attention_mode.shared_first": "每轮注意力，共享首轮 KV（shared_first）", + "choice.recurrent_dt_param.clamp": "截断（旧版）", + "choice.recurrent_dt_param.sigmoid": "Sigmoid（无死区）", + "choice.recurrent_order.block": "整块重复（A B A B）", + "choice.recurrent_order.layer": "逐层重复（A A B B）", "choice.router_scope.per_iteration": "每轮重新路由（per_iteration）", "choice.router_scope.shared": "复用首轮路由（shared）", "choice.scheduled": "按调度", @@ -256,6 +262,8 @@ "field.lambda_balance.label": "负载均衡", "field.lambda_cluster_locality.help": "训练同一片段内的 token 留在同一个专家簇里，懒加载推理就能整簇分页。0 表示关闭。", "field.lambda_cluster_locality.label": "簇局部性", + "field.lambda_iteration_cluster.help": "让同一 token 的每个循环迭代都落在相同的专家簇内的权重（簇内专家仍可逐迭代不同）。0 表示关闭。", + "field.lambda_iteration_cluster.label": "迭代簇局部性", "field.lambda_lookahead.help": "训练前瞻路由器预测未来 token 会用到的专家。", "field.lambda_lookahead.label": "前瞻损失", "field.lambda_lookahead_topk.help": "额外加权，要求准确预测未来的 top-k 专家。", @@ -280,6 +288,8 @@ "field.lookahead_input_detach.label": "前瞻输入断梯度", "field.lookahead_steps.help": "前瞻路由为未来多少个 token 预测专家，以便在用到之前预取。", "field.lookahead_steps.label": "前瞻步数", + "field.lookahead_teacher_scope.help": "前瞻路由器学习预测哪一种路由。first：只取第 0 轮迭代。iterations：取该层所有迭代的平均，预取因此也覆盖更深迭代需要的专家。", + "field.lookahead_teacher_scope.label": "前瞻教师", "field.max_gen_len.help": "每次 GRPO 采样生成的 token 数。", "field.max_gen_len.label": "采样长度", "field.max_inference_depth.help": "推理时的最大迭代次数。通常等于训练深度；超过训练深度未经验证。", @@ -352,7 +362,7 @@ "field.plan.token_budget_b.label": "Token 预算（B）", "field.plan.use_moe.help": "规划 MoE 模型；关闭则规划稠密模型。", "field.plan.use_moe.label": "混合专家", - "field.plan.vocab_size.help": "留空则从分词器报告读取，或使用 16384。", + "field.plan.vocab_size.help": "留空则从分词器报告读取；没有报告时使用 32768 和内置的通用 32k 分词器。", "field.plan.vocab_size.label": "词表", "field.ponder_loss_coef.help": "对期望迭代次数的惩罚。越高越促使模型提前停止。", "field.ponder_loss_coef.label": "思考损失", @@ -402,10 +412,24 @@ "field.recurrent_depth_jitter.label": "深度抖动", "field.recurrent_distill_coef.help": "让浅层出口学习全深度的预测，提高推测接受率和提前退出质量。", "field.recurrent_distill_coef.label": "深度蒸馏", + "field.recurrent_dt_init.help": "学习到的循环步长 dt 的起始值，所有循环组相同。一次迭代是一个欧拉步，所以 深度 × dt 就是积分时程 T。规划器取 dt = min(0.5, 2 / 深度)，即固定 T = 2：深度 4 为 0.5，深度 8 为 0.25。15M 模型实测（每组一个种子）：深度 4 时 dt 在 0.2 到 0.5 之间损失持平；深度 8 时 dt 0.25 优于 0.5，而从 0.0625 起步则差得多。T = 2 是安全的起点，不是拟合出的最优：从这里起步的 sigmoid 实验最终停在 T 约 1.2。内核默认值仍为 0.25。", + "field.recurrent_dt_init.label": "初始步长", + "field.recurrent_dt_max.help": "dt 的上界。1.0 表示一次迭代把整个块的更新量加到残差流上。", + "field.recurrent_dt_max.label": "最大步长", + "field.recurrent_dt_min.help": "dt 的下界。使用 clamp 时，dt 到达下界就不再学习；默认 0.05 下，深度 4 和深度 8 的实测中 dt 都塌缩到了这个下界。", + "field.recurrent_dt_min.label": "最小步长", + "field.recurrent_dt_param.help": "循环步长 dt 的存储方式。clamp：dt = clamp(w, dt_min, dt_max)；w 越出范围后梯度为零，掉到 dt_min 以下的 dt 再也回不来。sigmoid：dt = dt_min + (dt_max - dt_min) * sigmoid(w)；梯度不会消失，dt 仍可调整。两者都从设定的初始 dt 开始。15M 模型实测（每组一个种子）：clamp 默认值会让 dt 塌缩到 0.05 的下界，同一步数下比健康的 dt 更差；sigmoid 的 dt 能继续移动，停在损失持平的位置。规划器推荐 sigmoid。", + "field.recurrent_dt_param.label": "步长参数化", "field.recurrent_enabled.help": "让中间的共享层组每个 token 运行多次，用少量权重获得更深网络的效果。", "field.recurrent_enabled.label": "循环深度", "field.recurrent_groups.help": "前置层与收尾层之间组成循环组的层数。留空则按 层数 − 前置 − 收尾 推导。", "field.recurrent_groups.label": "循环组数", + "field.recurrent_later_groups.help": "仅块顺序有效：第一轮之后的循环迭代只运行块内前 N 层（0 表示全部）。层调用更少，更深的迭代更便宜，也会访问更少的专家页。需要 shared_first 或 first_only 注意力模式。", + "field.recurrent_later_groups.label": "后续迭代层数", + "field.recurrent_later_topk.help": "第一轮之后的循环迭代中每个 token 使用的专家数（0 表示与第一轮相同）。共享专家始终运行。较小的值让更深的迭代更便宜，也会加载更少的专家页。", + "field.recurrent_later_topk.label": "后续迭代 top-k", + "field.recurrent_order.help": "循环的执行顺序。layer：每个循环层连续重复 depth 次。block：整组循环层一起重复，第 0 轮就是普通的单深度前向，后续轮次在其上精炼。计算方式不同，不能与 layer 顺序的检查点互换。15M 模型实测（每组一个种子）：步长可训练时，同一步数下 block 的损失更低；步长固定为 1.0 时，layer 在后期更好。规划器推荐 block。", + "field.recurrent_order.label": "循环顺序", "field.recurrent_stability_coef.help": "保持循环更新收缩，避免多次迭代后漂移或发散。", "field.recurrent_stability_coef.label": "稳定性损失", "field.recurrent_strategy.help": "训练时循环深度分桶的采样策略。", diff --git a/chronos/webui/i18n/zh-Hant.json b/chronos/webui/i18n/zh-Hant.json index c4ac22a..737f432 100644 --- a/chronos/webui/i18n/zh-Hant.json +++ b/chronos/webui/i18n/zh-Hant.json @@ -37,6 +37,8 @@ "choice.fixed": "固定", "choice.fixed_then_adaptive": "先固定，後自適應", "choice.fullload": "僅全記憶體", + "choice.lookahead_teacher_scope.first": "僅第 0 輪", + "choice.lookahead_teacher_scope.iterations": "所有迭代取平均", "choice.mla": "全域（MLA）區塊", "choice.mlp": "僅 MoE/FFN", "choice.none": "無", @@ -47,6 +49,10 @@ "choice.recurrent_attention_mode.first_only": "僅首輪注意力，v2 行為（first_only）", "choice.recurrent_attention_mode.per_iteration": "每輪注意力，每輪獨立 KV（per_iteration）", "choice.recurrent_attention_mode.shared_first": "每輪注意力，共用首輪 KV（shared_first）", + "choice.recurrent_dt_param.clamp": "截斷（舊版）", + "choice.recurrent_dt_param.sigmoid": "Sigmoid（無死區）", + "choice.recurrent_order.block": "整塊重複（A B A B）", + "choice.recurrent_order.layer": "逐層重複（A A B B）", "choice.router_scope.per_iteration": "每輪重新路由（per_iteration）", "choice.router_scope.shared": "沿用首輪路由（shared）", "choice.scheduled": "依排程", @@ -256,6 +262,8 @@ "field.lambda_balance.label": "負載平衡", "field.lambda_cluster_locality.help": "訓練同一片段內的 token 留在同一個專家叢集，延遲載入推論就能整叢分頁。0 表示關閉。", "field.lambda_cluster_locality.label": "叢集區域性", + "field.lambda_iteration_cluster.help": "讓同一 token 的每個循環迭代都落在相同的專家簇內的權重（簇內專家仍可逐迭代不同）。0 表示關閉。", + "field.lambda_iteration_cluster.label": "迭代簇局部性", "field.lambda_lookahead.help": "訓練前瞻路由器預測未來 token 會用到的專家。", "field.lambda_lookahead.label": "前瞻損失", "field.lambda_lookahead_topk.help": "額外加權，要求準確預測未來的 top-k 專家。", @@ -280,6 +288,8 @@ "field.lookahead_input_detach.label": "前瞻輸入斷梯度", "field.lookahead_steps.help": "前瞻路由為未來多少個 token 預測專家，以便在用到之前預先擷取。", "field.lookahead_steps.label": "前瞻步數", + "field.lookahead_teacher_scope.help": "前瞻路由器學習預測哪一種路由。first：只取第 0 輪迭代。iterations：取該層所有迭代的平均，預取因此也涵蓋更深迭代需要的專家。", + "field.lookahead_teacher_scope.label": "前瞻教師", "field.max_gen_len.help": "每次 GRPO 取樣生成的 token 數。", "field.max_gen_len.label": "取樣長度", "field.max_inference_depth.help": "推論時的最大迭代次數。通常等於訓練深度；超過訓練深度未經驗證。", @@ -352,7 +362,7 @@ "field.plan.token_budget_b.label": "Token 預算（B）", "field.plan.use_moe.help": "規劃 MoE 模型；關閉則規劃稠密模型。", "field.plan.use_moe.label": "混合專家", - "field.plan.vocab_size.help": "留空則從分詞器報告讀取，或使用 16384。", + "field.plan.vocab_size.help": "留空則從分詞器報告讀取；沒有報告時使用 32768 和內建的通用 32k 分詞器。", "field.plan.vocab_size.label": "詞彙表", "field.ponder_loss_coef.help": "對期望迭代次數的懲罰。越高越促使模型提早停止。", "field.ponder_loss_coef.label": "思考損失", @@ -402,10 +412,24 @@ "field.recurrent_depth_jitter.label": "深度抖動", "field.recurrent_distill_coef.help": "讓淺層出口學習全深度的預測，提高推測接受率與提前退出品質。", "field.recurrent_distill_coef.label": "深度蒸餾", + "field.recurrent_dt_init.help": "學習到的循環步長 dt 的起始值，所有循環組相同。一次迭代是一個歐拉步，所以 深度 × dt 就是積分時程 T。規劃器取 dt = min(0.5, 2 / 深度)，即固定 T = 2：深度 4 為 0.5，深度 8 為 0.25。15M 模型實測（每組一個種子）：深度 4 時 dt 在 0.2 到 0.5 之間損失持平；深度 8 時 dt 0.25 優於 0.5，而從 0.0625 起步則差得多。T = 2 是安全的起點，不是擬合出的最優：從這裡起步的 sigmoid 實驗最終停在 T 約 1.2。核心預設值仍為 0.25。", + "field.recurrent_dt_init.label": "初始步長", + "field.recurrent_dt_max.help": "dt 的上界。1.0 表示一次迭代把整個區塊的更新量加到殘差流上。", + "field.recurrent_dt_max.label": "最大步長", + "field.recurrent_dt_min.help": "dt 的下界。使用 clamp 時，dt 到達下界就不再學習；預設 0.05 下，深度 4 和深度 8 的實測中 dt 都塌縮到了這個下界。", + "field.recurrent_dt_min.label": "最小步長", + "field.recurrent_dt_param.help": "循環步長 dt 的儲存方式。clamp：dt = clamp(w, dt_min, dt_max)；w 超出範圍後梯度為零，掉到 dt_min 以下的 dt 再也回不來。sigmoid：dt = dt_min + (dt_max - dt_min) * sigmoid(w)；梯度不會消失，dt 仍可調整。兩者都從設定的初始 dt 開始。15M 模型實測（每組一個種子）：clamp 預設值會讓 dt 塌縮到 0.05 的下界，同一步數下比健康的 dt 更差；sigmoid 的 dt 能繼續移動，停在損失持平的位置。規劃器推薦 sigmoid。", + "field.recurrent_dt_param.label": "步長參數化", "field.recurrent_enabled.help": "讓中間的共用層組對每個 token 執行多次，用少量權重得到更深網路的效果。", "field.recurrent_enabled.label": "循環深度", "field.recurrent_groups.help": "前置層與收尾層之間組成循環組的層數。留空則按 層數 − 前置 − 收尾 推導。", "field.recurrent_groups.label": "循環組數", + "field.recurrent_later_groups.help": "僅區塊順序有效：第一輪之後的循環迭代只運行區塊內前 N 層（0 表示全部）。層呼叫更少，更深的迭代更便宜，也會存取更少的專家頁。需要 shared_first 或 first_only 注意力模式。", + "field.recurrent_later_groups.label": "後續迭代層數", + "field.recurrent_later_topk.help": "第一輪之後的循環迭代中每個 token 使用的專家數（0 表示與第一輪相同）。共享專家始終運行。較小的值讓更深的迭代更便宜，也會載入更少的專家頁。", + "field.recurrent_later_topk.label": "後續迭代 top-k", + "field.recurrent_order.help": "循環的執行順序。layer：每個循環層連續重複 depth 次。block：整組循環層一起重複，第 0 輪就是普通的單深度前向，後續輪次在其上精煉。計算方式不同，不能與 layer 順序的檢查點互換。15M 模型實測（每組一個種子）：步長可訓練時，同一步數下 block 的損失更低；步長固定為 1.0 時，layer 在後期更好。規劃器推薦 block。", + "field.recurrent_order.label": "循環順序", "field.recurrent_stability_coef.help": "保持循環更新收縮，避免多次迭代後漂移或發散。", "field.recurrent_stability_coef.label": "穩定性損失", "field.recurrent_strategy.help": "訓練時循環深度分桶的取樣策略。", diff --git a/chronos/webui/schema.py b/chronos/webui/schema.py index 8039113..cb52088 100644 --- a/chronos/webui/schema.py +++ b/chronos/webui/schema.py @@ -86,6 +86,16 @@ def _f(key, group, type_, default=None, **kw) -> Field: recommended="recurrent_depth", source="rule", ref="equals_recurrent_depth"), _f("recurrent_attention_mode", "recurrent", "choice", "shared_first", choices=["shared_first", "per_iteration", "first_only"], recommended="shared_first", source="code"), + _f("recurrent_order", "recurrent", "choice", "layer", + choices=["layer", "block"], recommended="block", source="planner", advanced=True), + _f("recurrent_later_topk", "recurrent", "int", 0, min=0, max=16, step=1, advanced=True), + _f("recurrent_later_groups", "recurrent", "int", 0, min=0, max=64, step=1, advanced=True), + _f("recurrent_dt_param", "recurrent", "choice", "clamp", + choices=["clamp", "sigmoid"], recommended="sigmoid", source="planner", advanced=True), + _f("recurrent_dt_init", "recurrent", "float", 0.25, min=0.01, max=1.0, step=0.01, + recommended=0.5, source="planner", advanced=True), + _f("recurrent_dt_min", "recurrent", "float", 0.05, min=0.001, max=1.0, step=0.005, advanced=True), + _f("recurrent_dt_max", "recurrent", "float", 1.0, min=0.05, max=1.0, step=0.05, advanced=True), _f("adaptive_halting", "recurrent", "bool", False), _f("halting_threshold", "recurrent", "float", 0.99, min=0.5, max=1.0, step=0.001), _f("depth_schedule", "recurrent", "choice", "fixed_then_adaptive", @@ -138,6 +148,9 @@ def _f(key, group, type_, default=None, **kw) -> Field: recommended="data", source="rule", ref="window_median_record"), _f("cluster_locality_mode", "objectives", "choice", "window", choices=["window", "segment"], source="code", advanced=True), + _f("lambda_iteration_cluster", "objectives", "float", 0.0, min=0.0, max=10.0, step=0.01, advanced=True), + _f("lookahead_teacher_scope", "objectives", "choice", "first", choices=["first", "iterations"], + source="code", advanced=True), # ---- memory / lazy experts --------------------------------------------- _f("vram_budget_gb", "memory", "float", 4.0, min=0.01, max=256.0, step=0.01), _f("pinned_memory_max_fraction", "memory", "float", 0.25, min=0.01, max=0.95, step=0.01), @@ -325,7 +338,8 @@ def _f(key, group, type_, default=None, **kw) -> Field: _f("tune.probe_steps", "autotune", "int", 80, min=10, max=1000, step=10, scope=("autotune",)), _f("tune.search", "autotune", "multi", ["lambda_balance", "lambda_temporal", "lr"], choices=["lambda_balance", "lambda_temporal", "lambda_lookahead", "lookahead_steps", "lr", "hidden_size", - "num_experts", "num_shared_experts", "kv_latent_dim"], scope=("autotune",)), + "num_experts", "num_shared_experts", "kv_latent_dim", "recurrent_order", "recurrent_dt_init"], + scope=("autotune",)), # ---- planner ----------------------------------------------------------- _f("plan.target_m", "planner", "float", 50.0, min=0.1, max=100000.0, step=0.1, scope=("planner",)), _f("plan.use_moe", "planner", "bool", True, scope=("planner",)), @@ -355,9 +369,10 @@ def _f(key, group, type_, default=None, **kw) -> Field: LITERAL_CHOICES = frozenset({ "mlx", "cuda", "xpu", "mps", "cpu", "float32", "bfloat16", "float16", "int8", "safetensors", "pt", "fp16-safetensors", "q8_0-safetensors", "fp16-gguf", "q8_0-gguf", "chronos-native-gguf", - "pretrain", "sft", "dpo", "orpo", "grpo", "distill", "shared_first", "per_iteration", "first_only", - "lambda_balance", "lambda_temporal", "lambda_lookahead", "lookahead_steps", "lr", "hidden_size", - "num_experts", "num_shared_experts", "kv_latent_dim", "sync_on_demand", "on_demand", "fallback_diagnostic", + "pretrain", "sft", "dpo", "orpo", "grpo", "distill", "shared_first", "per_iteration", "first_only", "layer", "first", "iterations", + "clamp", "sigmoid", "lambda_balance", "lambda_temporal", "lambda_lookahead", "lookahead_steps", "lr", "hidden_size", + "num_experts", "num_shared_experts", "kv_latent_dim", "recurrent_order", "recurrent_dt_init", + "sync_on_demand", "on_demand", "fallback_diagnostic", }) diff --git a/chronos/webui/server.py b/chronos/webui/server.py index 7513042..4a0b0d8 100644 --- a/chronos/webui/server.py +++ b/chronos/webui/server.py @@ -928,9 +928,12 @@ async def autotune_start(request: Request): data_path = data[0] if ui.tuner is not None and ui.tuner.is_running(): raise HTTPException(status_code=409, detail="busy") - space = ChronosSearchSpaceConfig(**{f"tune_{name}": name in values["search"] - for name in schema.FIELDS["tune.search"].choices}) cfg = {**base_config(), **cfg} + # The loop knobs are probed at the depth the user plans to train at: at least 2, at most 8 (the probe is short). + probe_depth = max(2, min(8, int(cfg.get("recurrent_depth") or 1))) + space = ChronosSearchSpaceConfig( + probe_recurrent_depth=probe_depth, + **{f"tune_{name}": name in values["search"] for name in schema.FIELDS["tune.search"].choices}) out_dir = os.path.join(ui.workdir, "auto_tune_cache") os.makedirs(out_dir, exist_ok=True) ui.tuner, ui.tune_log = ChronosAutoTuner(), [] @@ -938,7 +941,8 @@ async def autotune_start(request: Request): f"chronos_{cfg.get('hidden_size', 256)}_moe.pth"), dataset_path=data_path, train_ratio=0.95, prompt_template="alpaca", think_mode="keep", search_space=space, n_trials=values["n_trials"], probe_steps=values["probe_steps"], - output_dir=out_dir, seed=42) + output_dir=out_dir, seed=42, + tokenizer_dir=ui.path(cfg["tokenizer_path"]) if cfg.get("tokenizer_path") else None) return autotune_state() @app.post("/api/autotune/stop") diff --git a/chronos/workbench/training.py b/chronos/workbench/training.py index ff945dd..48c517c 100644 --- a/chronos/workbench/training.py +++ b/chronos/workbench/training.py @@ -106,7 +106,7 @@ def _language() -> str: "modalities", "image_patch_size", "modality_fusion_heads", # v3 semantics that do not change tensor shapes but do change what the # weights compute; a stage must inherit them from its init checkpoint. - "recurrent_attention_mode", "rope_layout", "rope_theta", "sliding_window_size", + "recurrent_attention_mode", "recurrent_order", "rope_layout", "rope_theta", "sliding_window_size", "intermediate_size", "router_scope", ) STAGE_HELP_TEXT = { diff --git a/demos/translation/README.md b/demos/translation/README.md index 0f66f00..f0ff6c4 100644 --- a/demos/translation/README.md +++ b/demos/translation/README.md @@ -97,3 +97,55 @@ Relative paths in a recipe now resolve against the recipe's own folder. WMT19 and OPUS-100 list their licences as unknown, so weights trained on them are for research use only. FLORES+ (CC BY-SA 4.0) is used for evaluation only. + +**Status of the 50M en→zh demo (in progress; `demos/translation`).** +- Model: 49.69M total / 12.09M active parameters, 36 experts, MLA latent 128, SWA window 128, 16k self-built vocabulary. +- Run: a 72-hour MLX bf16 run (pretrain, then SFT) started on 2026-11-05. +- Interim measurement: valid CE 2.477 at pretrain step 13,996 of about 24.7k planned (it was 3.012 at step 1,656). That checkpoint is packaged as `chronos-enzh-50m-pretrain-s13996` (see Model bundle). +- BLEU/chrF on FLORES+ and WMT22, and the ablations (attention mode, depth, lazy-expert budget, speculative γ), will be published after the run finishes. No quality numbers are claimed before then. + +## Released model: chronos-enzh-50m-sft + +[`models/chronos-enzh-50m-sft/`](../../models/chronos-enzh-50m-sft/) packages a 49.7M-parameter Chronos v3 model (12.1M active per token) trained for 69 hours on one Apple M5 Max: configuration, tokenizer, trained prompt, decoding settings, a driver (`translate.py`) and the evaluation behind every number. The weights (99.5 MB, bfloat16 safetensors) are attached to the release [`enzh-50m-sft-v1`](https://github.com/FonaTech/Project_Chronos/releases/tag/enzh-50m-sft-v1). + +- **Boundary:** English → Chinese only, one sentence (≤ 128 tokens) at a time. Chinese → English is disabled: English-only fine-tuning erased it (FLORES+ BLEU 0.00 after SFT). The driver refuses input outside the boundary. +- **Measured** (MLX bf16, greedy): FLORES+ devtest BLEU 33.48 / chrF 29.52; WMT22 BLEU 32.60 / chrF 28.83; WMT24++ BLEU 26.89 / chrF 24.43 (split into sentences). +- **Research use only.** The training corpora's licences are unknown; see the folder's `NOTICE`. + +```bash +cd models/chronos-enzh-50m-sft +pip install --pre "Project_Chronos[mlx]==0.3.2a0" # or "Project_Chronos==0.3.2a0" without MLX +python translate.py --download && python translate.py "The meeting was moved to Friday." +``` + +## Model bundle (translation demo extension) + +Bundles belong to the translation extension (`demos/translation`). Build the two wheels first (`python -m build --outdir dist .` and `python -m build --outdir dist demos/translation`). `chronos-translate bundle build` packages one exported checkpoint of a run into a folder that works right after unpacking. Building only reads the run directory. + +```bash +chronos-translate bundle build --run-dir runs/enzh_50m_v1 --which best --pack \ + --wheel dist/project_chronos-0.3.2a0-py3-none-any.whl --wheel dist/chronos_translate-0.3.2a0-py3-none-any.whl +``` + +| Bundle path | Content | +|---|---| +| `model/` | weights + `.config.json` sidecar, tokenizer (`model/tokenizer/`) | +| `eval/` | FLORES+ dev/devtest (CC BY-SA 4.0) for `bundle eval` / `probe` | +| `data/sft_valid/` | the run's SFT validation shards | +| `wheels/`, `bin/setup.sh`, `bin/chronos` | offline install of the kernel and extension wheels into `.venv`; launcher that sets `CHRONOS_BUNDLE` and sends `bundle …` to `chronos-translate` | +| `recipes/source_run_spec.json`, `recommended.json` | the recipe that produced the weights; fine-tune tiers `smoke` / `quick` / `standard` / `full` | +| `bundle.json` | provenance (step, tokens, valid CE), measured quality probe, SHA-256 of every file | + +On the recipient side: + +```bash +bin/setup.sh # venv + wheels + `bundle check` +bin/chronos bundle translate "The weather is very nice today." +bin/chronos bundle translate --backend mlx --speculative --file examples/translate_en.txt +bin/chronos bundle eval --split devtest -n 200 +bin/chronos bundle prepare-data my_pairs.jsonl --out work/data # FLORES+ is removed from your data +bin/chronos bundle finetune --data work/data --run-dir work/ft --tier quick --mode sft +bin/chronos bundle serve -- --port 11435 +``` + +`--speculative` needs `--repetition-penalty 1.0` (its default), because the penalty changes the target distribution at every position. `finetune` writes a `chronos-translate run` spec with `init_checkpoint` set to the bundle weights and refuses a run directory that already holds a run. Bundles built from data with unknown licences (WMT19, OPUS-100) say in `NOTICE.md` that the weights are for research only. diff --git a/demos/translation/README_zh.md b/demos/translation/README_zh.md index ca28a9b..05caf76 100644 --- a/demos/translation/README_zh.md +++ b/demos/translation/README_zh.md @@ -91,3 +91,55 @@ chronos-translate bundle build --run-dir runs/enzh_50m_v1 --wheel dist/project_c WMT19 和 OPUS-100 的许可证标注为 unknown，用它们训练的权重只能用于研究。 FLORES+（CC BY-SA 4.0）只用于评测。 + +**50M 英译中 demo（进行中；`demos/translation`）** +- 模型：总参数 49.69M，激活 12.09M；36 个专家；MLA latent 128；SWA 窗口 128；自研 16k 词表。 +- 训练：72 小时 MLX bf16 任务（先预训练，后 SFT），2026-11-05 启动。 +- 中途实测：预训练第 13,996 步（计划约 24.7k 步）valid CE 为 2.477（第 1,656 步时为 3.012）。该 checkpoint 已打包为 `chronos-enzh-50m-pretrain-s13996`（见“模型包”）。 +- FLORES+ 和 WMT22 上的 BLEU/chrF，以及各项消融（注意力模式、深度、懒加载专家预算、推测 γ），等训练结束后公布。在此之前不给出任何质量数字。 + +## 已发布模型：chronos-enzh-50m-sft + +[`models/chronos-enzh-50m-sft/`](../../models/chronos-enzh-50m-sft/) 打包了一个 4,969 万参数的 Chronos v3 模型（每个 token 激活 1,209 万），在一台 Apple M5 Max 上训练了 69 小时。目录中有模型配置、分词器、训练时的 prompt、解码参数、驱动脚本（`translate.py`），以及每个数字背后的评测。权重（99.5 MB，bfloat16 safetensors）作为附件放在 release [`enzh-50m-sft-v1`](https://github.com/FonaTech/Project_Chronos/releases/tag/enzh-50m-sft-v1) 中。 + +- **边界：** 只做英译中，每次一句（≤ 128 token）。中译英已屏蔽：只含英译中的微调抹掉了这一能力（SFT 后 FLORES+ BLEU 0.00）。驱动会拒绝边界以外的输入。 +- **实测**（MLX bf16，贪心解码）：FLORES+ devtest BLEU 33.48 / chrF 29.52；WMT22 BLEU 32.60 / chrF 28.83；WMT24++ BLEU 26.89 / chrF 24.43（分句）。 +- **仅供研究使用。** 训练语料的许可未知，见目录中的 `NOTICE`。 + +```bash +cd models/chronos-enzh-50m-sft +pip install --pre "Project_Chronos[mlx]==0.3.2a0" # 不用 MLX 时装 "Project_Chronos==0.3.2a0" +python translate.py --download && python translate.py "The meeting was moved to Friday." +``` + +## 模型包（翻译 demo 扩展） + +模型包属于翻译扩展（`demos/translation`）。先构建两个 wheel（`python -m build --outdir dist .` 和 `python -m build --outdir dist demos/translation`）。`chronos-translate bundle build` 把某次运行导出的一个 checkpoint 打包成解压即用的目录。打包过程只读取运行目录。 + +```bash +chronos-translate bundle build --run-dir runs/enzh_50m_v1 --which best --pack \ + --wheel dist/project_chronos-0.3.2a0-py3-none-any.whl --wheel dist/chronos_translate-0.3.2a0-py3-none-any.whl +``` + +| 包内路径 | 内容 | +|---|---| +| `model/` | 权重 + `.config.json` 配置文件，分词器（`model/tokenizer/`） | +| `eval/` | FLORES+ dev/devtest（CC BY-SA 4.0），供 `bundle eval` / `probe` 使用 | +| `data/sft_valid/` | 该次运行的 SFT 验证分片 | +| `wheels/`、`bin/setup.sh`、`bin/chronos` | 把内核和扩展两个 wheel 离线安装到 `.venv`；启动脚本设置 `CHRONOS_BUNDLE`，并把 `bundle …` 转给 `chronos-translate` | +| `recipes/source_run_spec.json`、`recommended.json` | 产生这份权重的配方；微调档位 `smoke` / `quick` / `standard` / `full` | +| `bundle.json` | 来源信息（步数、token 数、valid CE）、实测质量探针、每个文件的 SHA-256 | + +接收方的用法： + +```bash +bin/setup.sh # 建 venv、装 wheels、运行 `bundle check` +bin/chronos bundle translate "The weather is very nice today." +bin/chronos bundle translate --backend mlx --speculative --file examples/translate_en.txt +bin/chronos bundle eval --split devtest -n 200 +bin/chronos bundle prepare-data my_pairs.jsonl --out work/data # 会从你的数据中剔除 FLORES+ +bin/chronos bundle finetune --data work/data --run-dir work/ft --tier quick --mode sft +bin/chronos bundle serve -- --port 11435 +``` + +`--speculative` 要求 `--repetition-penalty 1.0`（此时的默认值），因为惩罚会逐位置改变目标分布。`finetune` 写出一份 `init_checkpoint` 指向包内权重的 `chronos-translate run` spec；运行目录里已有训练时会拒绝写入。用许可未知的数据（WMT19、OPUS-100）训练的模型包，`NOTICE.md` 中写明权重仅供研究使用。 diff --git a/pyproject.toml b/pyproject.toml index fd14572..fcd5e62 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "Project_Chronos" -version = "0.3.2a3" +version = "0.3.3a0" description = "Storage-aware recurrent-depth MoE: lookahead expert prefetch, SSD-lazy experts, hybrid MLA/SWA attention, self-speculative decoding" readme = "README.md" license = "Apache-2.0" diff --git a/tests/test_autotune_loop.py b/tests/test_autotune_loop.py new file mode 100644 index 0000000..ab210fc --- /dev/null +++ b/tests/test_autotune_loop.py @@ -0,0 +1,138 @@ +"""Auto-tune probe: real tokens from the dataset, loop knobs in the search space, WebUI wiring.""" +import json +import os + +import numpy as np +import pytest + +optuna = pytest.importorskip('optuna') + +from chronos.tuning import probe_data +from chronos.tuning.chronos_auto_tuner import ChronosAutoTuner, ChronosSearchSpaceConfig + + +def _dataset(tmp_path, records=40): + path = tmp_path / 'probe.jsonl' + line = 'The quick brown fox jumps over the lazy dog while the river runs past the old stone bridge. ' + with open(path, 'w', encoding='utf-8') as handle: + for i in range(records): + handle.write(json.dumps({'text': line * 4 + str(i)}) + '\n') + return str(path) + + +class _Trial: + def __init__(self): + self.reports = [] + + def report(self, value, step): + self.reports.append((step, value)) + + def should_prune(self): + return False + + +def test_probe_tokens_come_from_the_dataset_with_the_bundled_vocabulary(tmp_path): + tokens, vocab = probe_data.load_probe_tokens(_dataset(tmp_path), None) + assert vocab == 32768 + assert tokens.dtype == np.int64 and tokens.size >= 512 + assert int(tokens.max()) < vocab and int(tokens.min()) >= 0 + + +def test_probe_tokens_are_none_when_the_data_cannot_be_read(tmp_path): + assert probe_data.load_probe_tokens(str(tmp_path / 'missing.jsonl'), None) is None + tiny = tmp_path / 'tiny.jsonl' + tiny.write_text(json.dumps({'text': 'too short'}) + '\n', encoding='utf-8') + assert probe_data.load_probe_tokens(str(tiny), None) is None + + +def test_probe_batch_is_deterministic_and_advances_with_the_step(): + tokens = np.arange(5000, dtype=np.int64) + first = probe_data.probe_batch(tokens, 0, 2, 64) + assert first.shape == (2, 64) + assert np.array_equal(first, probe_data.probe_batch(tokens, 0, 2, 64)) + assert not np.array_equal(first, probe_data.probe_batch(tokens, 1, 2, 64)) + assert np.array_equal(first[0], tokens[first[0][0]:first[0][0] + 64]) + + +def test_loop_knobs_are_sampled_only_when_searched(): + tuner = ChronosAutoTuner() + off = tuner._sample_params(optuna.create_study().ask(), ChronosSearchSpaceConfig()) + assert 'recurrent_order' not in off and 'recurrent_dt_init' not in off and 'recurrent_depth' not in off + space = ChronosSearchSpaceConfig(tune_recurrent_order=True, tune_recurrent_dt_init=True, probe_recurrent_depth=6) + on = tuner._sample_params(optuna.create_study().ask(), space) + assert on['recurrent_order'] in ('layer', 'block') + assert space.recurrent_dt_init_min <= on['recurrent_dt_init'] <= space.recurrent_dt_init_max + assert on['recurrent_depth'] == 6 + one = ChronosSearchSpaceConfig(tune_recurrent_dt_init=True, probe_recurrent_depth=1) + assert tuner._sample_params(optuna.create_study().ask(), one)['recurrent_depth'] == 2 # depth 1 has no loop to tune + + +def test_best_patch_carries_the_loop_knobs(): + from types import SimpleNamespace + tuner = ChronosAutoTuner() + tuner.result = SimpleNamespace(best_params={'recurrent_order': 'block', 'recurrent_dt_init': 0.3, 'lr': 1e-3}) + patch = tuner.get_best_chronos_config_patch() + assert patch['recurrent_order'] == 'block' and patch['recurrent_dt_init'] == 0.3 + + +def test_probe_trial_trains_a_looped_model_on_real_tokens(tmp_path, monkeypatch): + import chronos.model.model_chronos as model_chronos + seen = {} + real_model = model_chronos.ChronosForCausalLM + + class Spy(real_model): + def __init__(self, cfg, *args, **kwargs): + seen['cfg'] = cfg + super().__init__(cfg, *args, **kwargs) + + monkeypatch.setattr(model_chronos, 'ChronosForCausalLM', Spy) + monkeypatch.setattr('chronos.tuning.probe_data.probe_batch', lambda *args: seen.setdefault('real_batch', True) and np.zeros((2, 64), dtype=np.int64) + 5) + params = {'learning_rate': 1e-3, 'lambda_balance': 5e-4, 'lambda_temporal': 1e-3, 'lookahead_steps': 2, + 'hidden_size': 128, 'num_experts': 2, 'num_shared_experts': 1, 'kv_latent_dim': 32, + 'recurrent_depth': 2, 'recurrent_order': 'block', 'recurrent_dt_init': 0.3} + tuner = ChronosAutoTuner() + trial = _Trial() + loss = tuner._run_probe_trial(trial, str(tmp_path / 'no_checkpoint.pth'), _dataset(tmp_path), 0.95, 3, + str(tmp_path), 42, params, tokenizer_dir=None) + cfg = seen['cfg'] + assert np.isfinite(loss) and len(trial.reports) == 3 + assert cfg.vocab_size == 32768 + assert cfg.recurrent_enabled and cfg.recurrent_depth == 2 and cfg.recurrent_order == 'block' + assert cfg.recurrent_dt_init == 0.3 + assert seen.get('real_batch'), 'the probe did not draw its batches from the real tokens' + + +def test_probe_trial_without_loop_knobs_stays_a_plain_model(tmp_path, monkeypatch): + import chronos.model.model_chronos as model_chronos + seen = {} + real_model = model_chronos.ChronosForCausalLM + + class Spy(real_model): + def __init__(self, cfg, *args, **kwargs): + seen['cfg'] = cfg + super().__init__(cfg, *args, **kwargs) + + monkeypatch.setattr(model_chronos, 'ChronosForCausalLM', Spy) + params = {'learning_rate': 1e-3, 'lambda_balance': 5e-4, 'lambda_temporal': 1e-3, 'lookahead_steps': 2, + 'hidden_size': 128, 'num_experts': 2, 'num_shared_experts': 1, 'kv_latent_dim': 32} + missing = str(tmp_path / 'missing.jsonl') + loss = ChronosAutoTuner()._run_probe_trial(_Trial(), str(tmp_path / 'no_checkpoint.pth'), missing, 0.95, 2, + str(tmp_path), 42, params, tokenizer_dir=None) + cfg = seen['cfg'] + assert np.isfinite(loss) + assert cfg.recurrent_depth == 1 and cfg.recurrent_order == 'layer' + assert cfg.vocab_size != 32768, 'unreadable data must fall back to the legacy random-id probe' + + +def test_webui_exposes_the_loop_knobs_to_the_search_and_maps_them_onto_the_space(): + from chronos.webui import schema + choices = schema.FIELDS['tune.search'].choices + assert 'recurrent_order' in choices and 'recurrent_dt_init' in choices + assert 'recurrent_later_groups' not in choices # trades quality for cost: a loss-only search would always pick the full block + space = ChronosSearchSpaceConfig(**{f'tune_{name}': name in ('recurrent_order',) for name in choices}) + assert space.tune_recurrent_order and not space.tune_recurrent_dt_init + base = os.path.join(os.path.dirname(schema.__file__), 'i18n') + for lang in ('en', 'zh-Hans', 'zh-Hant', 'ja'): + catalog = json.load(open(os.path.join(base, lang + '.json'), encoding='utf-8')) + for name in ('recurrent_order', 'recurrent_dt_init'): + assert f'field.{name}.label' in catalog, (lang, name) diff --git a/tests/test_bench_paging.py b/tests/test_bench_paging.py new file mode 100644 index 0000000..b3142c7 --- /dev/null +++ b/tests/test_bench_paging.py @@ -0,0 +1,42 @@ +"""tools/bench_speculative.mlx_runner paging layouts and tools/bench_residency page-read accounting.""" +from __future__ import annotations + +import os +import sys + +import pytest + +_repo_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) + + +@pytest.fixture +def trained_ckpt(tmp_path, monkeypatch): + from test_lazy_experts import _save + monkeypatch.setenv("CHRONOS_EXPERT_CACHE_ROOT", str(tmp_path / "cache")) + return _save(tmp_path, 0.3, "trained") + + +def _runner_store(ckpt, workdir, paging): + import mlx.core as mx + from chronos.expert_clusters import inference_clusters + from chronos.model.checkpoint import chronos_config_from_checkpoint + from tools.bench_speculative import mlx_runner + from ui.tabs.inference_tab import _clone_model_cfg + cfg, _ = chronos_config_from_checkpoint(ckpt) + cfg = _clone_model_cfg(cfg) + with mx.stream(mx.cpu): + _decode, _info, close = mlx_runner(cfg, ckpt, lazy=True, ratio=0.5, device="cpu", workdir=str(workdir), paging=paging) + engine = close.__self__ + stats = engine.store.stats() + engine.stop() + return stats, len(inference_clusters(cfg, "cluster")), int(cfg.num_experts) + + +@pytest.mark.parametrize("paging,per_expert", [("expert", True), ("cluster", False), ("auto", False)]) +def test_mlx_runner_pages_by_expert_or_by_training_cluster(trained_ckpt, tmp_path, paging, per_expert): + pytest.importorskip("mlx.core") + stats, n_clusters, n_experts = _runner_store(trained_ckpt, tmp_path / paging, paging) + assert n_clusters < n_experts + assert stats["num_clusters"] == (n_experts if per_expert else n_clusters) diff --git a/tests/test_depth_jitter.py b/tests/test_depth_jitter.py new file mode 100644 index 0000000..9f8eb67 --- /dev/null +++ b/tests/test_depth_jitter.py @@ -0,0 +1,60 @@ +"""recurrent_depth_jitter samples ONE depth per optimizer step, shared by every micro-batch. +Before the fix each micro-batch resampled, so a step mixed virtual-layer counts and the router-stat +accumulator raised 'broadcast_shapes (14,E) and (10,E)'.""" +from __future__ import annotations + +import os +import sys + +import numpy as np +import pytest + +_repo_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) + +mx = pytest.importorskip("mlx.core") + + +def _shards(root): + os.makedirs(os.path.join(root, "pretrain", "train")) + os.makedirs(os.path.join(root, "pretrain", "valid")) + rng = np.random.default_rng(0) + rng.integers(3, 120, 20000).astype(np.uint16).tofile(os.path.join(root, "pretrain", "train", "a.bin")) + rng.integers(3, 120, 4000).astype(np.uint16).tofile(os.path.join(root, "pretrain", "valid", "a.bin")) + + +@pytest.mark.parametrize("compile_mode", ["off", "auto"]) +def test_depth_jitter_uses_one_depth_per_step(tmp_path, compile_mode): + import time + from chronos.mlx.training.longrun import LongRun, PhaseSpec, RunSpec + shards = str(tmp_path / "shards") + _shards(shards) + model = dict(architecture_version=3, hidden_size=64, num_hidden_layers=6, num_attention_heads=4, + num_key_value_heads=2, vocab_size=128, num_experts=4, num_experts_per_tok=2, num_shared_experts=1, + moe_intermediate_size=64, max_position_embeddings=64, recurrent_depth=4, prelude_layers=1, coda_layers=1, + dropout=0.0, fallback_mask_prob=0.0, recurrent_order="block", recurrent_strategy="adaptive", + recurrent_depth_jitter=1.0, min_recurrent_depth=2, depth_schedule_warmup_fraction=0.0) + phase = PhaseSpec(name="pretrain", budget_fraction=1.0, peak_lr=1e-3, tokens_per_step=256, micro_tokens=64, + seq_len=32, warmup_fraction=0.1) + spec = RunSpec(run_dir=str(tmp_path / "run"), shards_dir=shards, tokenizer_dir=shards, model_config=model, + phases=[phase], budget_hours=1.0, validate_every_s=1e9, checkpoint_every_s=1e9, dtype="float32", + log_every_steps=1, extra={"mlx_compile": compile_mode}) + with mx.stream(mx.cpu): + run = LongRun(spec, log=lambda *_: None) + run.state["phase_total"] = 12 + run.spec.schedule = "steps" + controller = run.model.recurrent_controller + seen = [] + base = controller.execution_depth + def record(cfg, training): + d = base(cfg, training) + if training: + seen.append((int(d), getattr(controller, "_step_depth", None))) + return d + controller.execution_depth = record + run.run_phase(0, time.monotonic() + 3600, max_steps=4) + assert seen, "no training depth sampled" + per_step = [s for d, s in seen if s is not None] + assert per_step and all(d == s for d, s in seen if s is not None) + assert len({int(x) for x in per_step}) >= 1 and min(int(x) for x in per_step) >= 2 diff --git a/tests/test_dt_clip.py b/tests/test_dt_clip.py new file mode 100644 index 0000000..c93b592 --- /dev/null +++ b/tests/test_dt_clip.py @@ -0,0 +1,39 @@ +"""The MLX step-size clip has the same gradient as Torch's clamp, including on the bounds.""" +from __future__ import annotations + +import numpy as np +import pytest +import torch + + +def _cfg(lo, hi, init): + from chronos.model.config import ChronosConfig + return ChronosConfig(hidden_size=32, num_hidden_layers=4, num_attention_heads=4, num_key_value_heads=2, vocab_size=64, + num_experts=4, num_experts_per_tok=2, num_shared_experts=1, moe_intermediate_size=16, max_position_embeddings=64, + recurrent_depth=2, prelude_layers=1, coda_layers=1, dropout=0.0, kv_latent_dim=8, rope_dim=4, sliding_window_size=32, + use_moe=True, recurrent_dt_min=lo, recurrent_dt_max=hi, recurrent_dt_init=init) + + +@pytest.mark.parametrize("raw", [0.5, 0.5001, 0.75, 0.9999, 1.0, 1.0001, 0.4999]) +def test_mlx_contract_gradient_matches_torch_clamp(raw): + mx = pytest.importorskip("mlx.core") + from chronos.mlx.recurrent import RecurrentControllerMLX as MlxController + from chronos.model.recurrent import RecurrentController as TorchController + cfg = _cfg(0.5, 1.0, 0.75) + before, after = np.full((1, 1, 4), 0.2, dtype=np.float32), np.full((1, 1, 4), 1.0, dtype=np.float32) + torch_ctl = TorchController(cfg) + with torch.no_grad(): + torch_ctl.step_sizes[:] = raw + out = torch_ctl.contract(torch.tensor(before), torch.tensor(after), 0) + out.sum().backward() + expected_grad = float(torch_ctl.step_sizes.grad[0]) + expected_out = out.detach().numpy() + with mx.stream(mx.cpu): + mlx_ctl = MlxController(cfg) + mlx_ctl.step_sizes = mx.full(mlx_ctl.step_sizes.shape, raw) + def f(sizes): + mlx_ctl.step_sizes = sizes + return mlx_ctl.contract(mx.array(before), mx.array(after), 0, cfg).sum() + value, grad = mx.value_and_grad(f)(mlx_ctl.step_sizes) + np.testing.assert_allclose(np.array(mlx_ctl.contract(mx.array(before), mx.array(after), 0, cfg)), expected_out, atol=1e-6) + assert float(grad[0]) == pytest.approx(expected_grad) diff --git a/tests/test_dt_sigmoid.py b/tests/test_dt_sigmoid.py new file mode 100644 index 0000000..104a389 --- /dev/null +++ b/tests/test_dt_sigmoid.py @@ -0,0 +1,84 @@ +"""Sigmoid step-size parameterisation: same start as clamp, live gradient in the old dead zone, Torch == MLX.""" +from __future__ import annotations + +import numpy as np +import pytest +import torch + + +def _cfg(param, init=0.25, lo=0.05, hi=1.0): + from chronos.model.config import ChronosConfig + return ChronosConfig(hidden_size=32, num_hidden_layers=4, num_attention_heads=4, num_key_value_heads=2, vocab_size=64, + num_experts=4, num_experts_per_tok=2, num_shared_experts=1, moe_intermediate_size=16, max_position_embeddings=64, + recurrent_depth=2, prelude_layers=1, coda_layers=1, dropout=0.0, kv_latent_dim=8, rope_dim=4, sliding_window_size=32, + use_moe=True, recurrent_dt_min=lo, recurrent_dt_max=hi, recurrent_dt_init=init, recurrent_dt_param=param) + + +def test_config_rejects_unknown_param(): + with pytest.raises(ValueError): + _cfg("tanh") + + +@pytest.mark.parametrize("init", [0.05, 0.0625, 0.25, 0.5, 1.0]) +def test_sigmoid_starts_at_configured_dt(init): + from chronos.model.recurrent import RecurrentController + clamp = RecurrentController(_cfg("clamp", init)).step_size(0).item() + sigmoid = RecurrentController(_cfg("sigmoid", init)).step_size(0).item() + assert sigmoid == pytest.approx(clamp, abs=1e-5) + + +def test_sigmoid_gradient_alive_where_clamp_is_dead(): + from chronos.model.recurrent import RecurrentController + before, after = torch.full((1, 1, 4), 0.2), torch.full((1, 1, 4), 1.0) + grads = {} + for param in ("clamp", "sigmoid"): + ctl = RecurrentController(_cfg(param, init=0.05)) + if param == "clamp": + with torch.no_grad(): + ctl.step_sizes[:] = 0.03 # below dt_min: the clamp dead zone + ctl.contract(before, after, 0).sum().backward() + grads[param] = float(ctl.step_sizes.grad[0]) + assert grads["clamp"] == 0.0 + assert grads["sigmoid"] > 0.0 + + +def test_recommended_dt_init_holds_the_horizon_fixed(): + from chronos.model.recurrent import recommended_dt_init + assert recommended_dt_init(4) == pytest.approx(0.5) + assert recommended_dt_init(6) == pytest.approx(1 / 3) + assert recommended_dt_init(8) == pytest.approx(0.25) + for depth in (4, 6, 8, 12, 16): + assert depth * recommended_dt_init(depth) == pytest.approx(2.0) # T = R * dt + assert recommended_dt_init(8) < recommended_dt_init(6) < recommended_dt_init(4) + + +def test_recommended_dt_init_is_capped_where_it_was_never_measured(): + from chronos.model.recurrent import recommended_dt_init + assert recommended_dt_init(1) == recommended_dt_init(2) == recommended_dt_init(3) == 0.5 + assert recommended_dt_init(2, cap=1.0) == pytest.approx(1.0) + assert recommended_dt_init(8, horizon=1.0) == pytest.approx(0.125) + + +@pytest.mark.parametrize("raw", [-6.0, -1.0, 0.0, 2.5]) +def test_mlx_sigmoid_matches_torch(raw): + mx = pytest.importorskip("mlx.core") + from chronos.mlx.recurrent import RecurrentControllerMLX + from chronos.model.recurrent import RecurrentController + cfg = _cfg("sigmoid") + before, after = np.full((1, 1, 4), 0.2, dtype=np.float32), np.full((1, 1, 4), 1.0, dtype=np.float32) + torch_ctl = RecurrentController(cfg) + with torch.no_grad(): + torch_ctl.step_sizes[:] = raw + out = torch_ctl.contract(torch.tensor(before), torch.tensor(after), 0) + out.sum().backward() + with mx.stream(mx.cpu): + mlx_ctl = RecurrentControllerMLX(cfg) + assert float(mlx_ctl.step_sizes[0]) == pytest.approx(float(RecurrentController(cfg).step_sizes.detach()[0]), abs=1e-6) + + def f(sizes): + mlx_ctl.step_sizes = sizes + return mlx_ctl.contract(mx.array(before), mx.array(after), 0, cfg).sum() + + value, grad = mx.value_and_grad(f)(mx.full(mlx_ctl.step_sizes.shape, raw)) + assert float(value) == pytest.approx(float(out.sum()), abs=1e-5) + assert float(grad[0]) == pytest.approx(float(torch_ctl.step_sizes.grad[0]), rel=1e-4, abs=1e-7) diff --git a/tests/test_general_tokenizer.py b/tests/test_general_tokenizer.py new file mode 100644 index 0000000..8256d30 --- /dev/null +++ b/tests/test_general_tokenizer.py @@ -0,0 +1,65 @@ +"""Bundled general 32k tokenizer: loads, task-neutral control ids, HF == native, planner default for new models.""" +from __future__ import annotations + +import json +import os + +import pytest + +from chronos.deps import GENERAL_TOKENIZER_DIR, GENERAL_TOKENIZER_VOCAB, get_general_tokenizer_path, get_tokenizer_path +from chronos.model.planner import PlanRequest, plan_training +from chronos.tokenizer.model import ChronosTokenizer, special_tokens_with + +SAMPLES = ["Hello, world!", "研究人员发现了一种新的抗生素。", "x = f(y) + 3.14", "def f(a, b):\n return a + b\n", + "The quick brown fox jumps over the lazy dog.", "<|im_start|>user\nhi<|im_end|>\n"] + + +def test_asset_files_are_present_and_licensed(): + for name in ("chronos_tokenizer.json", "tokenizer.json", "tokenizer_config.json", "tokenizer_report.json", "NOTICE", "LICENSE"): + assert (GENERAL_TOKENIZER_DIR / name).is_file(), name + assert get_general_tokenizer_path() == str(GENERAL_TOKENIZER_DIR) + + +def test_vocab_size_and_task_neutral_control_ids(): + tok = ChronosTokenizer.load(str(GENERAL_TOKENIZER_DIR)) + assert len(tok.vocab) == GENERAL_TOKENIZER_VOCAB == 32768 + specials = special_tokens_with(slots=64) + for i, name in enumerate(specials): + assert tok.vocab[name] == i, name + assert [tok.vocab[t] for t in ("<|endoftext|>", "<|im_start|>", "<|im_end|>", "<|pad|>")] == [0, 1, 2, 3] + assert not [t for t in ("<|en|>", "<|zh|>", "<|translate|>") if t in tok.vocab] + + +def test_hf_and_native_encoders_agree_and_round_trip(): + from tokenizers import Tokenizer + tok = ChronosTokenizer.load(str(GENERAL_TOKENIZER_DIR)) + hf = Tokenizer.from_file(str(GENERAL_TOKENIZER_DIR / "tokenizer.json")) + for text in SAMPLES: + ids = tok.encode(text) + assert hf.encode(text).ids == ids, text + assert tok.decode(ids) == text, text + + +def test_report_names_no_local_path_and_records_derivation(): + report = json.load(open(GENERAL_TOKENIZER_DIR / "tokenizer_report.json", encoding="utf-8")) + assert "/Users/" not in json.dumps(report) and "/Volumes/" not in json.dumps(report) + assert report["vocab_size"] == 32768 and "derived_from" in report + + +def test_planner_defaults_new_models_to_the_bundled_tokenizer(tmp_path): + plan = plan_training(PlanRequest(ram_gb=32, target_total=30e6, num_experts=8, active_experts=2, seq_len=512)) + assert plan["config"]["vocab_size"] == 32768 + assert plan["config"]["tokenizer_path"] == get_general_tokenizer_path() + assert "bundled general 32k" in plan["report"]["sources"]["vocab_size"] + assert "bundled general 32k" in plan["report"]["sources"]["tokenizer_path"] + + +def test_explicit_vocab_keeps_the_user_choice_and_adds_no_tokenizer_path(): + plan = plan_training(PlanRequest(ram_gb=32, target_total=30e6, num_experts=8, active_experts=2, vocab_size=6400, seq_len=512)) + assert plan["config"]["vocab_size"] == 6400 + assert "tokenizer_path" not in plan["config"] + + +def test_checkpoints_without_a_recorded_tokenizer_keep_the_legacy_default(monkeypatch): + monkeypatch.delenv("CHRONOS_DEFAULT_TOKENIZER", raising=False) + assert os.path.basename(get_tokenizer_path()) == "legacy_6400" diff --git a/tests/test_iteration_cluster.py b/tests/test_iteration_cluster.py new file mode 100644 index 0000000..3a48a17 --- /dev/null +++ b/tests/test_iteration_cluster.py @@ -0,0 +1,213 @@ +"""Iteration-axis cluster locality (lambda_iteration_cluster) and the lookahead teacher scope. + +The term keeps every loop iteration of a token inside the same expert clusters without fixing which expert +inside a cluster runs, so a lazy store that pages whole clusters serves all D iterations with one read. +""" +from __future__ import annotations + +import os +import sys + +import pytest + +_repo_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) + +torch = pytest.importorskip("torch") +np = pytest.importorskip("numpy") +mx = pytest.importorskip("mlx.core") +nn = pytest.importorskip("mlx.nn") + +from chronos.model.config import ChronosConfig # noqa: E402 +from chronos.model.temporal_loss import iteration_cluster_spread # noqa: E402 + +E, C = 8, 4 + + +def _membership(): + m = np.zeros((E, C), np.float32) + for e in range(E): + m[e, e // (E // C)] = 1.0 + return m + + +def _probs(seed=0, b=2, s=5, g=3, d=3): + rng = np.random.default_rng(seed) + p = rng.random((b, s, g, d, E)).astype(np.float32) + 0.05 + return p / p.sum(-1, keepdims=True) + + +def test_config_fields_default_off_and_validate(): + cfg = ChronosConfig(hidden_size=64, num_hidden_layers=4) + assert cfg.lambda_iteration_cluster == 0.0 and cfg.lookahead_teacher_scope == "first" + assert ChronosConfig(hidden_size=64, num_hidden_layers=4, lookahead_teacher_scope="ITERATIONS").lookahead_teacher_scope == "iterations" + with pytest.raises(ValueError): + ChronosConfig(hidden_size=64, num_hidden_layers=4, lookahead_teacher_scope="all") + from chronos.config_schema import MODEL_FIELDS + assert "lambda_iteration_cluster" in MODEL_FIELDS and "lookahead_teacher_scope" in MODEL_FIELDS + + +def test_spread_is_zero_when_iterations_agree_and_positive_otherwise(): + same = np.repeat(_probs(d=1), 3, axis=3) + assert float(iteration_cluster_spread(torch.tensor(same), torch.tensor(_membership()))) == pytest.approx(0.0, abs=1e-7) + # iterations that differ only inside a cluster cost nothing + inside = np.zeros((1, 1, 1, 2, E), np.float32) + inside[0, 0, 0, 0, 0] = 1.0 + inside[0, 0, 0, 1, 1] = 1.0 # experts 0 and 1 share cluster 0 + assert float(iteration_cluster_spread(torch.tensor(inside), torch.tensor(_membership()))) == pytest.approx(0.0, abs=1e-7) + # iterations in different clusters cost the maximum: total variation 0.5 from the mean + across = np.zeros((1, 1, 1, 2, E), np.float32) + across[0, 0, 0, 0, 0] = 1.0 + across[0, 0, 0, 1, 4] = 1.0 # cluster 0 vs cluster 2 + assert float(iteration_cluster_spread(torch.tensor(across), torch.tensor(_membership()))) == pytest.approx(0.5, abs=1e-6) + + +def test_spread_needs_two_iterations_and_two_clusters(): + one = torch.tensor(_probs(d=1)) + assert float(iteration_cluster_spread(one, torch.tensor(_membership()))) == 0.0 + assert float(iteration_cluster_spread(torch.tensor(_probs()), torch.ones(E, 1))) == 0.0 + + +def test_token_mask_drops_padding_positions(): + p = _probs(b=1, s=4, g=1, d=2) + p[0, 2:] = 0.0 + p[0, 2:, 0, 0, 0], p[0, 2:, 0, 1, 4] = 1.0, 1.0 # padding tokens that maximise the spread + mask = torch.tensor([[1, 1, 0, 0]]) + full = float(iteration_cluster_spread(torch.tensor(p), torch.tensor(_membership()))) + real = float(iteration_cluster_spread(torch.tensor(p), torch.tensor(_membership()), mask)) + first_two = float(iteration_cluster_spread(torch.tensor(p[:, :2]), torch.tensor(_membership()))) + assert real == pytest.approx(first_two, abs=1e-7) and full > real + + +def test_mlx_matches_torch(): + from chronos.mlx.training.trainer import _iteration_cluster_spread + p = _probs(seed=3) + mask = np.array([[1, 1, 1, 0, 0], [1, 1, 1, 1, 1]], np.float32) + for m_np, m_t in ((None, None), (mask, torch.tensor(mask))): + want = float(iteration_cluster_spread(torch.tensor(p), torch.tensor(_membership()), m_t)) + with mx.stream(mx.cpu): + got = float(_iteration_cluster_spread(mx.array(p), mx.array(_membership()), None if m_np is None else mx.array(m_np))) + assert got == pytest.approx(want, abs=1e-6) + + +def _mlx_model(**kw): + from chronos.mlx.model import ChronosMLXModel + base = dict(architecture_version=3, hidden_size=64, num_hidden_layers=6, prelude_layers=1, coda_layers=1, + recurrent_depth=3, min_recurrent_depth=2, num_attention_heads=4, num_key_value_heads=2, kv_latent_dim=16, + rope_dim=8, num_experts=8, num_experts_per_tok=2, num_shared_experts=1, moe_intermediate_size=32, + vocab_size=97, max_position_embeddings=64, sliding_window_size=64, dropout=0.0, fallback_mask_prob=0.0, + adaptive_halting=False, lambda_iteration_cluster=0.5, cluster_locality_clusters=C) + base.update(kw) + cfg = ChronosConfig(**base) + mx.random.seed(0) + model = ChronosMLXModel(cfg) + mx.eval(model.parameters()) + model.train() + return cfg, model + + +@pytest.mark.parametrize("order", ["layer", "block"]) +def test_mlx_training_loss_contains_the_term_and_trains_the_routers(order): + from chronos.mlx.training.trainer import _chronos_regularized_loss + cfg, model = _mlx_model(recurrent_order=order) + ids = mx.random.randint(3, 97, (2, 10)) + labels = mx.random.randint(3, 97, (2, 10)) + + def loss_fn(m, x, y): + logits, look, _ = m(x, logits_dtype=mx.float32) + ce = nn.losses.cross_entropy(logits.reshape(-1, logits.shape[-1]), y.reshape(-1), reduction="mean") + return _chronos_regularized_loss(m, ce, look, m.config, metrics={"ce": ce, "primary": ce}) + + with mx.stream(mx.cpu): + loss, grads = nn.value_and_grad(model, loss_fn)(model, ids, labels) + mx.eval(loss, grads) + comps = {k: float(v) for k, v in model._last_loss_components.items()} + assert "iteration_cluster_loss" in comps and comps["iteration_cluster_loss"] > 0.0 + from mlx.utils import tree_flatten + gates = [float(mx.abs(g).sum()) for k, g in tree_flatten(grads) if k.endswith("mlp.gate.weight")] + assert gates and max(gates) > 0.0 + + +def test_mlx_term_is_absent_when_off_or_without_iterations(): + from chronos.mlx.training.trainer import _chronos_regularized_loss + for kw in (dict(lambda_iteration_cluster=0.0), dict(recurrent_depth=1)): + cfg, model = _mlx_model(**kw) + ids = mx.random.randint(3, 97, (2, 10)) + with mx.stream(mx.cpu): + logits, look, _ = model(ids, logits_dtype=mx.float32) + ce = nn.losses.cross_entropy(logits.reshape(-1, logits.shape[-1]), ids.reshape(-1), reduction="mean") + _chronos_regularized_loss(model, ce, look, model.config, metrics={"ce": ce, "primary": ce}) + assert "iteration_cluster_loss" not in model._last_loss_components + + +def test_mlx_compiled_step_matches_eager_with_the_term(): + from chronos.mlx.training.compiled import CompiledStep + from chronos.mlx.training.trainer import _chronos_regularized_loss + cfg, model = _mlx_model(recurrent_order="block", lambda_cluster_locality=0.3) + ids = mx.random.randint(3, 97, (2, 12)) + labels = mx.random.randint(3, 97, (2, 12)) + + def loss_fn(m, x, y): + logits, look, _ = m(x, logits_dtype=mx.float32) + ce = nn.losses.cross_entropy(logits.reshape(-1, logits.shape[-1]), y.reshape(-1), reduction="mean") + return _chronos_regularized_loss(m, ce, look, m.config, metrics={"ce": ce, "primary": ce}) + + with mx.stream(mx.cpu): + eager = nn.value_and_grad(model, loss_fn) + step = CompiledStep(model, loss_fn, mode="on") + a, ga = eager(model, ids, labels) + b, gb = step(model, ids, labels) + mx.eval(a, b, ga, gb) + assert abs(float(a) - float(b)) < 1e-6 + from mlx.utils import tree_flatten + fa, fb = dict(tree_flatten(ga)), dict(tree_flatten(gb)) + assert max(float(mx.abs(fa[k] - fb[k]).max()) for k in fa) < 1e-6 + + +def test_torch_loss_mixin_adds_the_term(): + from chronos.model.model_chronos import ChronosForCausalLM + from chronos.trainer.loss_mixin import chronos_loss_term, recurrent_iteration_probs + cfg = ChronosConfig(architecture_version=3, hidden_size=64, num_hidden_layers=6, prelude_layers=1, coda_layers=1, + recurrent_depth=3, min_recurrent_depth=2, num_attention_heads=4, num_key_value_heads=2, kv_latent_dim=16, + rope_dim=8, num_experts=8, num_experts_per_tok=2, num_shared_experts=1, moe_intermediate_size=32, + vocab_size=97, max_position_embeddings=64, sliding_window_size=64, dropout=0.0, fallback_mask_prob=0.0, + adaptive_halting=False, lambda_iteration_cluster=0.0, cluster_locality_clusters=C) + torch.manual_seed(0) + model = ChronosForCausalLM(cfg).train() + ids = torch.randint(3, 97, (2, 10)) + out = model(ids, labels=ids) + probs = recurrent_iteration_probs(model, cfg) + assert probs is not None and probs.shape[2:4] == (int(cfg.recurrent_groups), 3) + base = chronos_loss_term(model, out.loss, None, cfg, input_ids=ids, labels=ids) + cfg.lambda_iteration_cluster = 0.5 + with_term = chronos_loss_term(model, out.loss, None, cfg, input_ids=ids, labels=ids) + assert float(with_term - base) > 0.0 + with_term.backward() + assert any(p.grad is not None and float(p.grad.abs().sum()) > 0 for n, p in model.named_parameters() if "gate" in n) + + +@pytest.mark.parametrize("backend", ["mlx", "torch"]) +def test_lookahead_teacher_scope_changes_the_target_not_the_default(backend): + if backend == "torch": + from chronos.model.model_chronos import ChronosForCausalLM + from chronos.trainer.loss_mixin import first_iteration_router_probs, iteration_mean_router_probs + cfg = ChronosConfig(architecture_version=3, hidden_size=64, num_hidden_layers=6, prelude_layers=1, coda_layers=1, + recurrent_depth=3, min_recurrent_depth=2, num_attention_heads=4, num_key_value_heads=2, kv_latent_dim=16, + rope_dim=8, num_experts=8, num_experts_per_tok=2, num_shared_experts=1, moe_intermediate_size=32, + vocab_size=97, max_position_embeddings=64, sliding_window_size=64, dropout=0.0, fallback_mask_prob=0.0) + torch.manual_seed(0) + model = ChronosForCausalLM(cfg).eval() + with torch.no_grad(): + model(torch.randint(3, 97, (1, 8))) + first, mean = first_iteration_router_probs(model), iteration_mean_router_probs(model) + diff = float((first - mean).abs().max()) + else: + from chronos.mlx.training.trainer import _first_iteration_router_probs, _iteration_mean_router_probs + cfg, model = _mlx_model() + with mx.stream(mx.cpu): + model(mx.random.randint(3, 97, (1, 8))) + first, mean = _first_iteration_router_probs(model), _iteration_mean_router_probs(model) + diff = float(mx.abs(first - mean).max()) + assert first.shape == mean.shape + assert diff > 1e-4 # later iterations route differently, so the two teachers are genuinely different diff --git a/tests/test_later_groups.py b/tests/test_later_groups.py new file mode 100644 index 0000000..5fdbf64 --- /dev/null +++ b/tests/test_later_groups.py @@ -0,0 +1,195 @@ +"""recurrent_later_groups (prefix blocks): block-order iterations after the first run only the first N layers. + +The default (0) must change nothing. A positive value must skip the later layers of the block in iterations +1.., keep iteration 0 complete (every layer still writes its KV), agree between Torch and MLX, keep cache decode +and speculative decoding exact, and still train every layer. +""" +from __future__ import annotations + +import os +import sys + +import pytest + +_repo_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) + +torch = pytest.importorskip("torch") +np = pytest.importorskip("numpy") +mx = pytest.importorskip("mlx.core") + +from chronos.model.config import ChronosConfig # noqa: E402 +from chronos.model.model_chronos import ChronosForCausalLM # noqa: E402 + + +def _cfg(later, **kw): + base = dict(architecture_version=3, hidden_size=64, num_hidden_layers=6, prelude_layers=1, coda_layers=1, + recurrent_depth=3, min_recurrent_depth=2, max_inference_depth=3, num_attention_heads=4, + num_key_value_heads=2, kv_latent_dim=16, rope_dim=8, num_experts=6, num_experts_per_tok=2, + num_shared_experts=1, moe_intermediate_size=32, vocab_size=97, max_position_embeddings=64, + sliding_window_size=64, dropout=0.0, fallback_mask_prob=0.0, adaptive_halting=False, + recurrent_order="block", recurrent_later_groups=later) + base.update(kw) + return ChronosConfig(**base) + + +def _torch_model(later, seed=5, **kw): + torch.manual_seed(seed) + return ChronosForCausalLM(_cfg(later, **kw)).eval() + + +def _mlx_pair(later, seed=5, **kw): + from chronos.mlx.model import ChronosMLXModel + cfg = _cfg(later, **kw) + torch.manual_seed(seed) + pt = ChronosForCausalLM(cfg).eval() + return pt, ChronosMLXModel.from_chronos_pytorch(pt, cfg).eval() + + +# ---- config --------------------------------------------------------------- + +def test_default_is_off_and_value_is_validated(): + assert _cfg(0).recurrent_later_groups == 0 + assert ChronosConfig(hidden_size=64, num_hidden_layers=4).recurrent_later_groups == 0 + assert _cfg(2).recurrent_later_groups == 2 + with pytest.raises(ValueError): + _cfg(-1) + with pytest.raises(ValueError): + _cfg(2, recurrent_order="layer") + with pytest.raises(ValueError): + _cfg(2, recurrent_attention_mode="per_iteration") + + +def test_field_is_a_model_field_and_survives_a_config_round_trip(): + from chronos.config_schema import MODEL_FIELDS + assert "recurrent_later_groups" in MODEL_FIELDS + cfg = _cfg(2) + assert ChronosConfig(**{k: v for k, v in cfg.to_dict().items() if k in MODEL_FIELDS}).recurrent_later_groups == 2 + + +# ---- which layers run in which iteration --------------------------------------- + +def _count_calls(model): + calls = {} + for li, layer in enumerate(model.model.layers): + layer.register_forward_hook(lambda _m, _a, _o, li=li: calls.__setitem__(li, calls.get(li, 0) + 1)) + return calls + + +@pytest.mark.parametrize("mode", ["shared_first", "first_only"]) +@pytest.mark.parametrize("later", [1, 2, 3]) +def test_later_iterations_run_only_the_prefix_layers(mode, later): + model = _torch_model(later, recurrent_attention_mode=mode) + calls = _count_calls(model) + with torch.no_grad(): + model(torch.randint(3, 97, (1, 5))) + cfg = model.config + p, g, d = cfg.prelude_layers, int(cfg.recurrent_groups), int(cfg.recurrent_depth) + assert g > later + for li in range(p, p + g): + assert calls[li] == (d if li - p < later else 1), (li, calls) + assert all(calls[li] == 1 for li in range(len(model.model.layers)) if not p <= li < p + g) + + +def test_default_runs_every_layer_in_every_iteration(): + model = _torch_model(0) + calls = _count_calls(model) + with torch.no_grad(): + model(torch.randint(3, 97, (1, 5))) + cfg = model.config + p, g, d = cfg.prelude_layers, int(cfg.recurrent_groups), int(cfg.recurrent_depth) + assert all(calls[li] == d for li in range(p, p + g)) + + +def test_prefix_equal_to_the_block_size_changes_nothing_and_a_shorter_prefix_does(): + ids = torch.randint(3, 97, (2, 9)) + g = int(_cfg(0).recurrent_groups) + with torch.no_grad(): + base = _torch_model(0)(ids).logits + same = _torch_model(g)(ids).logits + short = _torch_model(1)(ids).logits + assert torch.equal(base, same) + assert (base - short).abs().max() > 1e-4 + + +@pytest.mark.parametrize("mode", ["shared_first", "first_only"]) +def test_cache_decode_equals_full_forward_with_prefix_blocks(mode): + model = _torch_model(2, recurrent_attention_mode=mode) + ids = torch.randint(3, 97, (1, 10)) + with torch.no_grad(): + full = model(ids).logits + out = model(ids[:, :6], use_cache=True) + rows, cache = [out.logits], out.past_key_values + for t in range(6, 10): + step = model(ids[:, t:t + 1], past_key_values=cache, use_cache=True) + rows.append(step.logits) + cache = step.past_key_values + assert (torch.cat(rows, dim=1) - full).abs().max() < 2e-4 + + +def test_prefix_blocks_train_every_layer_and_the_controller(): + model = _torch_model(1).train() + ids = torch.randint(3, 97, (2, 9)) + loss = model(ids, labels=ids).loss + loss.backward() + assert torch.isfinite(loss) + cfg = model.config + for li in range(cfg.prelude_layers, cfg.prelude_layers + int(cfg.recurrent_groups)): + layer = model.model.layers[li] + assert float(layer.mlp.gate.weight.grad.abs().sum()) > 0, f"router of layer {li} received no gradient" + assert float(model.model.recurrent_controller.step_sizes.grad.abs().sum()) > 0 + + +# ---- MLX ----------------------------------------------------------------------- + +@pytest.mark.parametrize("mode", ["shared_first", "first_only"]) +def test_mlx_matches_torch_with_prefix_blocks(mode): + pt, mm = _mlx_pair(2, recurrent_attention_mode=mode) + ids = torch.randint(3, 97, (2, 11)) + with torch.no_grad(): + want = pt(ids).logits.numpy() + with mx.stream(mx.cpu): + got, _, _ = mm(mx.array(ids.numpy(), dtype=mx.int32), logits_dtype=mx.float32) + got = np.array(got) + assert np.abs(got - want).max() < (2e-1 if mode == "first_only" else 5e-2), np.abs(got - want).max() + + +def test_mlx_prefix_blocks_differ_from_the_full_block(): + _, full = _mlx_pair(0) + _, short = _mlx_pair(1) + ids = mx.array(np.random.default_rng(2).integers(3, 97, size=(1, 9)), dtype=mx.int32) + with mx.stream(mx.cpu): + a, _, _ = full(ids, logits_dtype=mx.float32) + b, _, _ = short(ids, logits_dtype=mx.float32) + assert float(mx.abs(a - b).max().item()) > 1e-4 + + +def test_mlx_decode_with_cache_equals_full_forward_with_prefix_blocks(): + _, mm = _mlx_pair(2) + ids = mx.array(np.random.default_rng(1).integers(3, 97, size=(1, 12)), dtype=mx.int32) + with mx.stream(mx.cpu): + full, _, _ = mm(ids, logits_dtype=mx.float32) + part, _, cache = mm(ids[:, :8], logits_dtype=mx.float32) + rows = [part] + for t in range(8, 12): + step, _, cache = mm(ids[:, t:t + 1], cache=cache, logits_dtype=mx.float32) + rows.append(step) + got = mx.concatenate(rows, axis=1) + assert float(mx.abs(got - full).max().item()) < 2e-4 + + +@pytest.mark.parametrize("gamma,draft_depth", [(4, 1), (6, 2)]) +def test_mlx_speculative_is_exact_with_prefix_blocks(gamma, draft_depth): + from chronos.mlx.speculative import speculative_generate + _, mm = _mlx_pair(2) + ids = [int(t) for t in np.random.default_rng(3).integers(3, 97, size=7)] + with mx.stream(mx.cpu): + logits, _, cache = mm(mx.array([ids], dtype=mx.int32)) + ref, nxt = list(ids), int(mx.argmax(logits[0, -1]).item()) + for _ in range(20): + ref.append(nxt) + logits, _, cache = mm(mx.array([[nxt]], dtype=mx.int32), cache=cache) + nxt = int(mx.argmax(logits[0, -1]).item()) + out, _ = speculative_generate(mm, ids, max_new_tokens=20, gamma=gamma, draft_depth=draft_depth, gamma_max=8) + assert out == ref diff --git a/tests/test_later_topk.py b/tests/test_later_topk.py new file mode 100644 index 0000000..b0b1e5b --- /dev/null +++ b/tests/test_later_topk.py @@ -0,0 +1,280 @@ +"""recurrent_later_topk: loop iterations after the first route to fewer experts per token. + +The default (0) must change nothing. A positive value must reach the router in +iterations 1.. only, never exceed num_experts_per_tok, keep the experts of the +first dispatch under router_scope="shared", agree between Torch and MLX, keep +cache decode and speculative decoding exact, and train through checkpointing +and compilation. +""" +from __future__ import annotations + +import os +import sys + +import pytest + +_repo_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) + +torch = pytest.importorskip("torch") +np = pytest.importorskip("numpy") +mx = pytest.importorskip("mlx.core") +nn = pytest.importorskip("mlx.nn") + +from chronos.model.config import ChronosConfig # noqa: E402 +from chronos.model.model_chronos import ChronosForCausalLM # noqa: E402 +from chronos.model.moe_chronos import ChronosMOEFeedForward # noqa: E402 + + +def _cfg(later, **kw): + base = dict(architecture_version=3, hidden_size=64, num_hidden_layers=6, prelude_layers=1, coda_layers=1, + recurrent_depth=3, min_recurrent_depth=2, max_inference_depth=3, num_attention_heads=4, + num_key_value_heads=2, kv_latent_dim=16, rope_dim=8, num_experts=6, num_experts_per_tok=3, + num_shared_experts=1, moe_intermediate_size=32, vocab_size=97, max_position_embeddings=64, + sliding_window_size=64, dropout=0.0, fallback_mask_prob=0.0, adaptive_halting=False, + recurrent_later_topk=later) + base.update(kw) + return ChronosConfig(**base) + + +def _torch_model(later, seed=5, **kw): + torch.manual_seed(seed) + return ChronosForCausalLM(_cfg(later, **kw)).eval() + + +def _mlx_pair(later, seed=5, **kw): + from chronos.mlx.model import ChronosMLXModel + cfg = _cfg(later, **kw) + torch.manual_seed(seed) + pt = ChronosForCausalLM(cfg).eval() + return pt, ChronosMLXModel.from_chronos_pytorch(pt, cfg).eval() + + +# ---- config --------------------------------------------------------------- + +def test_default_is_off_and_value_is_validated(): + assert _cfg(0).recurrent_later_topk == 0 + assert ChronosConfig(hidden_size=64, num_hidden_layers=4).recurrent_later_topk == 0 + assert _cfg(1).recurrent_later_topk == 1 + with pytest.raises(ValueError): + _cfg(-1) + + +def test_field_is_a_model_field_and_survives_a_config_round_trip(): + from chronos.config_schema import MODEL_FIELDS + assert "recurrent_later_topk" in MODEL_FIELDS + cfg = _cfg(2) + assert ChronosConfig(**{k: v for k, v in cfg.to_dict().items() if k in MODEL_FIELDS}).recurrent_later_topk == 2 + + +# ---- Torch MoE ------------------------------------------------------------- + +def _moe(**kw): + torch.manual_seed(0) + return ChronosMOEFeedForward(_cfg(0, **kw)).eval() + + +def test_top_k_argument_changes_the_number_of_routed_experts(): + moe = _moe() + x = torch.randn(2, 5, 64) + seen = [] + orig = torch.topk + + def spy(scores, k, **kw): + seen.append(k) + return orig(scores, k=k, **kw) + + torch.topk = spy + try: + with torch.no_grad(): + full = moe(x) + moe.reset_iteration_routing() + graded = moe(x, top_k=1) + moe.reset_iteration_routing() + clamped = moe(x, top_k=99) + finally: + torch.topk = orig + assert seen[:3] == [3, 1, 3] + assert (full - graded).abs().max() > 1e-4 + assert torch.equal(full, clamped) # top_k above num_experts_per_tok clamps to it + + +def test_top_k_zero_or_none_means_the_configured_value(): + moe = _moe() + x = torch.randn(2, 5, 64) + with torch.no_grad(): + a = moe(x) + moe.reset_iteration_routing() + b = moe(x, top_k=0) + moe.reset_iteration_routing() + c = moe(x, top_k=None) + assert torch.equal(a, b) and torch.equal(a, c) + + +def test_shared_router_scope_keeps_the_best_experts_of_the_first_dispatch(): + moe = _moe(router_scope="shared") + x = torch.randn(2, 6, 64) + with torch.no_grad(): + moe(x) + first = moe._shared_topk_idx.clone() + scores = torch.softmax(moe.gate(x.view(-1, 64)).float(), dim=-1) + moe(x, top_k=2) + # The later dispatch must be a subset of the first dispatch's experts for every token. + # Rebuild what it should have used and compare with a plain forward on that routing. + keep = scores.gather(-1, first).topk(2, dim=-1, sorted=False).indices + expect = first.gather(-1, keep) + assert expect.shape == (12, 2) + assert all(set(e.tolist()) <= set(f.tolist()) for e, f in zip(expect, first)) + # The recorded first-dispatch ids are untouched by the narrower later dispatch. + assert torch.equal(moe._shared_topk_idx, first) + + +def test_torch_graded_dispatch_matches_dense_oracle(): + """Sparse sorted dispatch with k=2 of 3 equals every expert run and masked by the k-best mask.""" + moe = _moe() + x = torch.randn(3, 7, 64) + with torch.no_grad(): + got = moe(x, top_k=2) + flat = x.reshape(-1, 64) + scores = torch.softmax(moe.gate(flat), dim=-1) + weight, idx = torch.topk(scores, 2, dim=-1, sorted=False) + if moe.norm_topk_prob: + weight = weight / (weight.sum(-1, keepdim=True) + 1e-20) + want = torch.zeros_like(flat) + for e, expert in enumerate(moe.experts): + want = want + expert(flat) * torch.where(idx == e, weight, 0).sum(-1, keepdim=True) + want = want + sum(s(flat) for s in moe.shared_experts) / len(moe.shared_experts) + torch.testing.assert_close(got, want.view(3, 7, 64), atol=1e-5, rtol=1e-5) + + +# ---- model: which iterations get the narrow router --------------------------- + +def _record_top_k(model): + calls = [] + for li, layer in enumerate(model.model.layers): + if isinstance(layer.mlp, ChronosMOEFeedForward): + layer.mlp.register_forward_hook( + lambda _m, _a, kw, _o, li=li: calls.append((li, kw.get("top_k"))), with_kwargs=True) + return calls + + +def test_only_iterations_after_the_first_receive_top_k(): + model = _torch_model(1) + calls = _record_top_k(model) + with torch.no_grad(): + model(torch.randint(3, 97, (1, 5))) + p, g, d = 1, int(model.config.recurrent_groups), 3 + recurrent = [(li, k) for li, k in calls if p <= li < p + g] + assert len(recurrent) == g * d + per_layer = {} + for li, k in recurrent: + per_layer.setdefault(li, []).append(k) + assert all(ks == [None, 1, 1] for ks in per_layer.values()), per_layer + assert all(k is None for li, k in calls if not p <= li < p + g) # prelude and coda keep the full top-k + + +def test_default_passes_no_top_k_at_all(): + model = _torch_model(0) + calls = _record_top_k(model) + with torch.no_grad(): + model(torch.randint(3, 97, (1, 5))) + assert calls and all(k is None for _, k in calls) + + +def test_graded_topk_changes_the_output_only_when_set(): + ids = torch.randint(3, 97, (2, 9)) + with torch.no_grad(): + base = _torch_model(0)(ids).logits + same = _torch_model(3)(ids).logits # equal to num_experts_per_tok: nothing to narrow + narrow = _torch_model(1)(ids).logits + assert torch.equal(base, same) + assert (base - narrow).abs().max() > 1e-4 + + +@pytest.mark.parametrize("mode", ["shared_first", "first_only", "per_iteration"]) +@pytest.mark.parametrize("scope", ["per_iteration", "shared"]) +def test_cache_decode_equals_full_forward_with_graded_topk(mode, scope): + model = _torch_model(1, recurrent_attention_mode=mode, router_scope=scope) + ids = torch.randint(3, 97, (1, 10)) + with torch.no_grad(): + full = model(ids).logits + out = model(ids[:, :6], use_cache=True) + rows, cache = [out.logits], out.past_key_values + for t in range(6, 10): + step = model(ids[:, t:t + 1], past_key_values=cache, use_cache=True) + rows.append(step.logits) + cache = step.past_key_values + assert (torch.cat(rows, dim=1) - full).abs().max() < 2e-4 + + +@pytest.mark.parametrize("order", ["layer", "block"]) +def test_graded_topk_trains_the_recurrent_layers_and_their_routers(order): + model = _torch_model(1, recurrent_order=order).train() + ids = torch.randint(3, 97, (2, 9)) + loss = model(ids, labels=ids).loss + loss.backward() + assert torch.isfinite(loss) + cfg = model.config + for li in range(cfg.prelude_layers, cfg.prelude_layers + int(cfg.recurrent_groups)): + layer = model.model.layers[li] + assert float(layer.mlp.gate.weight.grad.abs().sum()) > 0, f"router of layer {li} received no gradient" + grads = [p.grad for p in layer.parameters() if p.grad is not None] + assert grads and any(float(g.abs().sum()) > 0 for g in grads), f"layer {li} received no gradient" + ctrl = model.model.recurrent_controller + assert float(ctrl.step_sizes.grad.abs().sum()) > 0 + + +# ---- MLX ----------------------------------------------------------------------- + +@pytest.mark.parametrize("scope", ["per_iteration", "shared"]) +@pytest.mark.parametrize("order", ["layer", "block"]) +def test_mlx_matches_torch_with_graded_topk(scope, order): + pt, mm = _mlx_pair(1, router_scope=scope, recurrent_order=order) + ids = torch.randint(3, 97, (2, 11)) + with torch.no_grad(): + want = pt(ids).logits.numpy() + with mx.stream(mx.cpu): + got, _, _ = mm(mx.array(ids.numpy(), dtype=mx.int32), logits_dtype=mx.float32) + got = np.array(got) + assert np.abs(got - want).max() < 5e-2, np.abs(got - want).max() + + +def test_mlx_graded_topk_differs_from_full_topk(): + _, full = _mlx_pair(0) + _, narrow = _mlx_pair(1) + ids = mx.array(np.random.default_rng(2).integers(3, 97, size=(1, 9)), dtype=mx.int32) + with mx.stream(mx.cpu): + a, _, _ = full(ids, logits_dtype=mx.float32) + b, _, _ = narrow(ids, logits_dtype=mx.float32) + assert float(mx.abs(a - b).max().item()) > 1e-4 + + +def test_mlx_decode_with_cache_equals_full_forward_with_graded_topk(): + _, mm = _mlx_pair(1) + ids = mx.array(np.random.default_rng(1).integers(3, 97, size=(1, 12)), dtype=mx.int32) + with mx.stream(mx.cpu): + full, _, _ = mm(ids, logits_dtype=mx.float32) + part, _, cache = mm(ids[:, :8], logits_dtype=mx.float32) + rows = [part] + for t in range(8, 12): + step, _, cache = mm(ids[:, t:t + 1], cache=cache, logits_dtype=mx.float32) + rows.append(step) + got = mx.concatenate(rows, axis=1) + assert float(mx.abs(got - full).max().item()) < 2e-4 + + +@pytest.mark.parametrize("gamma,draft_depth", [(4, 1), (6, 2)]) +def test_mlx_speculative_is_exact_with_graded_topk(gamma, draft_depth): + from chronos.mlx.speculative import speculative_generate + _, mm = _mlx_pair(1) + ids = [int(t) for t in np.random.default_rng(3).integers(3, 97, size=7)] + with mx.stream(mx.cpu): + logits, _, cache = mm(mx.array([ids], dtype=mx.int32)) + ref, nxt = list(ids), int(mx.argmax(logits[0, -1]).item()) + for _ in range(20): + ref.append(nxt) + logits, _, cache = mm(mx.array([[nxt]], dtype=mx.int32), cache=cache) + nxt = int(mx.argmax(logits[0, -1]).item()) + out, _ = speculative_generate(mm, ids, max_new_tokens=20, gamma=gamma, draft_depth=draft_depth, gamma_max=8) + assert out == ref diff --git a/tests/test_mlx_checkpointing.py b/tests/test_mlx_checkpointing.py index 35bc7b1..ae9eebd 100644 --- a/tests/test_mlx_checkpointing.py +++ b/tests/test_mlx_checkpointing.py @@ -25,6 +25,9 @@ "first_only": dict(recurrent_attention_mode="first_only"), "router_shared": dict(router_scope="shared"), "no_recurrence": dict(recurrent_depth=1), + "block_order": dict(recurrent_order="block"), + "later_topk": dict(recurrent_later_topk=1), + "later_topk_shared": dict(recurrent_later_topk=1, router_scope="shared"), "dense_attention": dict(use_hybrid_attention=False), # SWA window >= sequence: the cache slice k[:, -keep:] is the same graph # node as the shared KV view (short SFT buckets hit this). @@ -159,7 +162,8 @@ def test_recomputation_skips_cached_calls_and_inference(): @pytest.mark.parametrize("checkpointing", [False, True]) -@pytest.mark.parametrize("case", ["shared_first", "per_iteration", "router_shared", "dense_attention"]) +@pytest.mark.parametrize("case", ["shared_first", "per_iteration", "router_shared", "dense_attention", "block_order", + "later_topk", "later_topk_shared"]) def test_compiled_step_matches_eager(case, checkpointing): from chronos.mlx.training.compiled import CompiledStep cfg = _config(**CASES[case], activation_checkpointing=checkpointing) diff --git a/tests/test_planner_loop.py b/tests/test_planner_loop.py new file mode 100644 index 0000000..393d6d5 --- /dev/null +++ b/tests/test_planner_loop.py @@ -0,0 +1,45 @@ +"""Planner recommendations for the loop knobs: block order, sigmoid step size, horizon-based initial dt.""" +import pytest + +from chronos.model.planner import PlanRequest, plan_training +from chronos.model.recurrent import recommended_dt_init + + +def _plan(depth): + return plan_training(PlanRequest(target_total=5e6, num_experts=4, active_experts=2, vocab_size=6400, seq_len=128, + recurrent_depth=depth, memory_saving='none', token_budget=2e6, ram_gb=16), verify=False) + + +@pytest.mark.parametrize('depth', [2, 4, 6, 8]) +def test_looped_plan_recommends_block_order_sigmoid_and_the_horizon_init(depth): + plan = _plan(depth) + config, sources = plan['config'], plan['report']['sources'] + assert config['recurrent_order'] == 'block' and config['recurrent_dt_param'] == 'sigmoid' + assert config['recurrent_dt_init'] == pytest.approx(round(recommended_dt_init(depth), 4)) + for key in ('recurrent_order', 'recurrent_dt_param', 'recurrent_dt_init'): + assert sources[key], key + assert 'estimated' in sources['recurrent_dt_init'] # horizon 2 / depth is not a fitted optimum + + +def test_plan_without_a_loop_keeps_the_plain_defaults(): + plan = _plan(1) + config, sources = plan['config'], plan['report']['sources'] + assert not config['recurrent_enabled'] + for key in ('recurrent_order', 'recurrent_dt_param', 'recurrent_dt_init'): + assert key not in config and key not in sources + + +def test_planned_loop_config_builds_a_model_and_validates(): + from chronos.config_schema import model_config_kwargs + from chronos.model.config import ChronosConfig + cfg = ChronosConfig(**model_config_kwargs(_plan(4)['config'])) + assert cfg.recurrent_order == 'block' and cfg.recurrent_dt_param == 'sigmoid' + assert cfg.recurrent_dt_init == pytest.approx(0.5) + + +def test_schema_recommendations_match_what_the_planner_emits(): + from chronos.webui import schema + plan = _plan(4)['config'] + for key in ('recurrent_order', 'recurrent_dt_param', 'recurrent_dt_init'): + field = schema.FIELDS[key] + assert field.source == 'planner' and field.recommended == pytest.approx(plan[key]), key diff --git a/tests/test_recurrent_order.py b/tests/test_recurrent_order.py new file mode 100644 index 0000000..0b68d5a --- /dev/null +++ b/tests/test_recurrent_order.py @@ -0,0 +1,226 @@ +"""recurrent_order="block": (A B C ...) x depth instead of A x depth, B x depth, ... + +The default ("layer") must stay bit-identical to 0.3.2; "block" must be a +different, self-consistent computation: KV-cache decode equals full forward, +Torch and MLX agree, speculative decoding stays exact, gradients reach every +recurrent layer, and the config validates. +""" +from __future__ import annotations + +import os +import sys + +import pytest + +_repo_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if _repo_root not in sys.path: + sys.path.insert(0, _repo_root) + +torch = pytest.importorskip("torch") +np = pytest.importorskip("numpy") + +from chronos.model.config import ChronosConfig # noqa: E402 +from chronos.model.model_chronos import ChronosForCausalLM # noqa: E402 + + +def _cfg(order, **kw): + base = dict(architecture_version=3, hidden_size=64, num_hidden_layers=6, prelude_layers=1, coda_layers=1, + recurrent_depth=3, min_recurrent_depth=2, max_inference_depth=3, num_attention_heads=4, + num_key_value_heads=2, kv_latent_dim=16, rope_dim=8, num_experts=4, num_experts_per_tok=2, + num_shared_experts=1, moe_intermediate_size=32, vocab_size=97, max_position_embeddings=64, + sliding_window_size=64, dropout=0.0, fallback_mask_prob=0.0, adaptive_halting=False, + recurrent_order=order) + base.update(kw) + return ChronosConfig(**base) + + +def _torch_model(order, seed=5, **kw): + torch.manual_seed(seed) + return ChronosForCausalLM(_cfg(order, **kw)).eval() + + +def test_default_is_layer_order_and_value_is_validated(): + assert _cfg("layer").recurrent_order == "layer" + assert ChronosConfig(hidden_size=64, num_hidden_layers=4).recurrent_order == "layer" + assert _cfg("BLOCK").recurrent_order == "block" + with pytest.raises(ValueError): + _cfg("diagonal") + + +def test_order_is_a_model_field_and_survives_a_config_round_trip(): + from chronos.config_schema import MODEL_FIELDS + assert "recurrent_order" in MODEL_FIELDS + cfg = _cfg("block") + assert ChronosConfig(**{k: v for k, v in cfg.to_dict().items() if k in MODEL_FIELDS}).recurrent_order == "block" + + +def test_block_and_layer_order_differ_only_when_there_is_something_to_reorder(): + ids = torch.randint(3, 97, (2, 9)) + with torch.no_grad(): + layer = _torch_model("layer")(ids).logits + block = _torch_model("block")(ids).logits + assert (layer - block).abs().max() > 1e-3 # a genuinely different computation + # One recurrent layer per block: A x D and (A) x D are the same sequence. + with torch.no_grad(): + one_layer = _torch_model("layer", num_hidden_layers=3, recurrent_groups=1)(ids).logits + one_block = _torch_model("block", num_hidden_layers=3, recurrent_groups=1)(ids).logits + assert torch.equal(one_layer, one_block) + + +@pytest.mark.parametrize("mode", ["shared_first", "first_only", "per_iteration"]) +def test_block_order_decode_with_cache_equals_full_forward(mode): + model = _torch_model("block", recurrent_attention_mode=mode) + ids = torch.randint(3, 97, (1, 12)) + with torch.no_grad(): + full = model(ids).logits + out = model(ids[:, :8], use_cache=True) + cache, steps = out.past_key_values, [out.logits] + for t in range(8, 12): + out = model(ids[:, t:t + 1], past_key_values=cache, use_cache=True) + cache = out.past_key_values + steps.append(out.logits) + got = torch.cat(steps, dim=1) + assert (got - full).abs().max() < 2e-4 + + +def test_block_order_cache_has_one_slot_per_physical_layer_in_shared_first(): + model = _torch_model("block") + out = model(torch.randint(3, 97, (1, 6)), use_cache=True) + assert len(out.past_key_values) == model.config.num_hidden_layers + + +def test_block_order_trains_every_recurrent_layer_and_the_controller(): + model = _torch_model("block").train() + ids = torch.randint(3, 97, (2, 10)) + out = model(ids, labels=ids) + out.loss.backward() + cfg = model.config + for li in range(cfg.prelude_layers, cfg.prelude_layers + int(cfg.recurrent_groups)): + grads = [p.grad for p in model.model.layers[li].parameters() if p.grad is not None] + assert grads and any(float(g.abs().sum()) > 0 for g in grads), f"layer {li} received no gradient" + ctrl = model.model.recurrent_controller + assert float(ctrl.step_sizes.grad.abs().sum()) > 0 + assert float(ctrl.loop_embeddings.grad.abs().sum()) > 0 + + +def test_block_order_has_one_halting_trajectory_and_reports_group_axis(): + model = _torch_model("block", adaptive_halting=True).eval() + with torch.no_grad(): + model(torch.randint(3, 97, (2, 7))) + state = model.model.last_recurrent_state + g = int(model.config.recurrent_groups) + assert state["halting_probs"].shape[-2] == 1 # one block trajectory, not one per layer + assert state["depth_used"].shape[-1] == 1 + assert g > 1 + + +def test_block_order_executes_virtual_layers_in_interleaved_sequence(): + model = _torch_model("block") + order = [] + for li, layer in enumerate(model.model.layers): + layer.register_forward_hook(lambda _m, _i, _o, li=li: order.append(li)) + with torch.no_grad(): + model(torch.randint(3, 97, (1, 5))) + p, g, d = 1, int(model.config.recurrent_groups), 3 + assert order == [0] + [p + k for _ in range(d) for k in range(g)] + list(range(p + g, len(model.model.layers))) + + +def test_layer_order_executes_virtual_layers_consecutively(): + model = _torch_model("layer") + order = [] + for li, layer in enumerate(model.model.layers): + layer.register_forward_hook(lambda _m, _i, _o, li=li: order.append(li)) + with torch.no_grad(): + model(torch.randint(3, 97, (1, 5))) + p, g, d = 1, int(model.config.recurrent_groups), 3 + assert order == [0] + [p + k for k in range(g) for _ in range(d)] + list(range(p + g, len(model.model.layers))) + + +# ---- MLX --------------------------------------------------------------- + +mx = pytest.importorskip("mlx.core") +nn = pytest.importorskip("mlx.nn") + + +def _mlx_pair(order, seed=5, **kw): + from chronos.mlx.model import ChronosMLXModel + cfg = _cfg(order, **kw) + torch.manual_seed(seed) + pt = ChronosForCausalLM(cfg).eval() + return pt, ChronosMLXModel.from_chronos_pytorch(pt, cfg).eval() + + +@pytest.mark.parametrize("mode", ["shared_first", "per_iteration"]) +@pytest.mark.parametrize("adaptive", [False, True]) +def test_mlx_matches_torch_for_block_order(mode, adaptive): + pt, mm = _mlx_pair("block", recurrent_attention_mode=mode, adaptive_halting=adaptive) + ids = torch.randint(3, 97, (2, 11)) + with torch.no_grad(): + want = pt(ids).logits.numpy() + with mx.stream(mx.cpu): + got, _, _ = mm(mx.array(ids.numpy(), dtype=mx.int32), logits_dtype=mx.float32) + got = np.array(got) + assert np.abs(got - want).max() < 5e-2, np.abs(got - want).max() + + +def test_mlx_block_order_decode_with_cache_equals_full_forward(): + _, mm = _mlx_pair("block") + ids = mx.array(np.random.default_rng(1).integers(3, 97, size=(1, 12)), dtype=mx.int32) + with mx.stream(mx.cpu): + full, _, _ = mm(ids, logits_dtype=mx.float32) + part, _, cache = mm(ids[:, :8], logits_dtype=mx.float32) + rows = [part] + for t in range(8, 12): + step, _, cache = mm(ids[:, t:t + 1], cache=cache, logits_dtype=mx.float32) + rows.append(step) + got = mx.concatenate(rows, axis=1) + assert float(mx.abs(got - full).max().item()) < 2e-4 + + +@pytest.mark.parametrize("gamma,draft_depth", [(4, 1), (6, 2)]) +def test_mlx_speculative_is_exact_in_block_order(gamma, draft_depth): + from chronos.mlx.speculative import speculative_generate + _, mm = _mlx_pair("block") + ids = [int(t) for t in np.random.default_rng(3).integers(3, 97, size=7)] + with mx.stream(mx.cpu): + logits, _, cache = mm(mx.array([ids], dtype=mx.int32)) + ref, nxt = list(ids), int(mx.argmax(logits[0, -1]).item()) + for _ in range(20): + ref.append(nxt) + logits, _, cache = mm(mx.array([[nxt]], dtype=mx.int32), cache=cache) + nxt = int(mx.argmax(logits[0, -1]).item()) + out, _ = speculative_generate(mm, ids, max_new_tokens=20, gamma=gamma, draft_depth=draft_depth, gamma_max=8) + assert out == ref + + +def _engine(tmp_path, **kw): + from chronos.mlx.inference import ChronosMLXInferenceEngine + pt, mm = _mlx_pair("block", sliding_window_size=4, **kw) + return mm, ChronosMLXInferenceEngine(mm, mm.config, ssd_dir=str(tmp_path / "experts")) + + +def test_mlx_engine_block_order_speculative_matches_plain_greedy(tmp_path): + with mx.stream(mx.cpu): + _, engine = _engine(tmp_path) + engine.store.storage_format = "full_dram" + try: + ids = mx.array([[5, 9, 14, 3, 22, 7, 31]], dtype=mx.int32) + plain = list(engine.generate(ids, max_new_tokens=16, temperature=0.0)) + spec = list(engine.generate(ids, max_new_tokens=16, temperature=0.0, speculative=True, draft_depth=1, gamma=4)) + finally: + engine.stop() + assert spec == plain and len(spec) == 16 + + +def test_mlx_engine_block_order_runs_with_lazy_experts(tmp_path): + with mx.stream(mx.cpu): + _, engine = _engine(tmp_path, recommended_resident_experts=2, recommended_ram_experts=2) + try: + engine.setup(warm_expert_ids=[0, 1]) + assert engine._all_resident_fast_path_enabled() is False + toks = list(engine.generate(mx.array([[5, 9, 14, 3, 22, 7]], dtype=mx.int32), max_new_tokens=10, temperature=0.0, speculative=True, draft_depth=1, gamma=3)) + stats = engine.last_stats + finally: + engine.stop() + assert len(toks) == 10 and all(0 <= t < 97 for t in toks) + assert stats["speculative"]["generated"] == 10 diff --git a/tests/test_v3_inference_options.py b/tests/test_v3_inference_options.py index f824d2b..d1b3ef3 100644 --- a/tests/test_v3_inference_options.py +++ b/tests/test_v3_inference_options.py @@ -181,8 +181,8 @@ def test_shared_router_scope_reuses_first_iteration_experts(): torch.manual_seed(0) captured = [] original = recurrent.forward - def spy(x, available_expert_mask=None): - out = original(x, available_expert_mask) + def spy(x, available_expert_mask=None, **kw): + out = original(x, available_expert_mask, **kw) captured.append(recurrent._shared_topk_idx.clone()) return out recurrent.forward = spy diff --git a/tools/bench_residency.py b/tools/bench_residency.py index c456b71..1407238 100644 --- a/tools/bench_residency.py +++ b/tools/bench_residency.py @@ -61,6 +61,8 @@ def main(argv=None) -> int: ap.add_argument("--device", default="cpu", choices=("cpu", "gpu")) ap.add_argument("--dtype", default="auto") ap.add_argument("--ratio", type=float, default=1.0, help="lazy expert budget (Inference-tab RAM load ratio)") + ap.add_argument("--paging", default="expert", choices=("expert", "cluster", "auto"), + help="page layout of the SSD cache: expert (one page per expert, historical), cluster/auto (training clusters)") ap.add_argument("--draft-depth", type=int, default=1) ap.add_argument("--gamma", type=int, default=4) ap.add_argument("--decay", type=float, default=0.7) @@ -87,7 +89,7 @@ def main(argv=None) -> int: rows = [] try: _decode, info, close = mlx_runner(cfg, args.checkpoint, lazy=True, ratio=args.ratio, device=args.device, - workdir=workdir, dtype=args.dtype) + workdir=workdir, dtype=args.dtype, paging=args.paging) engine = close.__self__ # mlx_runner returns the engine's bound stop() warm = int(info["budget"]["effective_vram_expert_budget"]) try: @@ -101,13 +103,25 @@ def main(argv=None) -> int: if mode.startswith("spec"): kw.update(speculative=True, draft_depth=args.draft_depth, gamma=args.gamma, gamma_max=8) t0 = time.perf_counter() - toks = list(engine.generate(ids, max_new_tokens=limit, temperature=0.0, top_p=1.0, - eos_token_id=eos, **kw)) + # Page reads of the reset's warm-up are not generation I/O: count from here. + base_reads = int(engine.store._stats["cluster_loads"]) + toks, prefill_reads = [], None + for tok_id in engine.generate(ids, max_new_tokens=limit, temperature=0.0, top_p=1.0, + eos_token_id=eos, **kw): + toks.append(tok_id) + if prefill_reads is None: + # Page reads up to the first generated token = prefill and warm-up; the rest is decode. + prefill_reads = int(engine.store._stats["cluster_loads"]) - base_reads dt = time.perf_counter() - t0 st = engine.last_stats row[mode] = {"tokens": toks, "s": dt, "ssd": int(st.get("sync_ssd_loads", 0)), "hits": int(st.get("cache_hits", 0)), "misses": int(st.get("cache_misses", 0)), - "cluster_loads": int(st.get("cluster_loads", 0)), + "cluster_loads": int(st.get("cluster_loads", 0)) - base_reads, + "warmup_cluster_loads": base_reads, + "cluster_experts_loaded": int(st.get("cluster_experts_loaded", 0)), + "cluster_aware": bool(st.get("cluster_aware", False)), + "num_clusters": int(st.get("num_clusters", 0)), + "prefill_cluster_loads": int(prefill_reads or 0), "prefetch_loads": int(st.get("prefetch_loads", 0)), "planned_evictions": int(st.get("planned_evictions", 0)), "eviction_policy": st.get("eviction_policy"), @@ -131,6 +145,9 @@ def main(argv=None) -> int: "ssd_loads_per_token": round(sum(r[mode]["ssd"] for r in rows) / max(toks, 1), 4), "selection_miss_rate": round(misses / max(hits + misses, 1), 4), "prefetch_loads": sum(r[mode]["prefetch_loads"] for r in rows), + "cluster_loads": sum(r[mode]["cluster_loads"] for r in rows), + "prefill_cluster_loads": sum(r[mode]["prefill_cluster_loads"] for r in rows), + "decode_cluster_loads": sum(r[mode]["cluster_loads"] - r[mode]["prefill_cluster_loads"] for r in rows), "identical_to_plain": sum(r[mode]["tokens"] == r["plain"]["tokens"] for r in rows) if "plain" in modes else None, } diff --git a/tools/bench_speculative.py b/tools/bench_speculative.py index 0d691d7..83b9e56 100644 --- a/tools/bench_speculative.py +++ b/tools/bench_speculative.py @@ -62,7 +62,8 @@ def encode(tokenizer, text: str, fmt: str) -> list[int]: # ---- runners: each returns (decode(prompt, limit, speculative) -> (tokens, seconds, stats), info) ---------- -def mlx_runner(cfg, ckpt, *, lazy: bool, ratio: float, device: str, workdir: str, dtype: str = "auto"): +def mlx_runner(cfg, ckpt, *, lazy: bool, ratio: float, device: str, workdir: str, dtype: str = "auto", + paging: str = "expert"): import mlx.core as mx from ui.tabs import inference_tab as it from chronos.model.checkpoint import load_checkpoint_state_dict, load_state_dict_controlled @@ -88,7 +89,11 @@ def mlx_runner(cfg, ckpt, *, lazy: bool, ratio: float, device: str, workdir: str cache_dir = os.path.join(workdir, "mlx_expert_cache") engine = ChronosMLXInferenceEngine(model, cfg, ssd_dir=cache_dir) if not engine.store.attach_cluster_manifest(cache_dir): - it._rebuild_mlx_lazy_cache(engine.store, cache_dir, None, weights) + # Page layout: "expert" (one page per expert, the historical benchmark layout), or "cluster" / "auto" + # (the training partition, what the inference engine uses for cluster-trained checkpoints). + from chronos.expert_clusters import inference_clusters + clusters = None if paging == "expert" else inference_clusters(cfg, paging) + it._rebuild_mlx_lazy_cache(engine.store, cache_dir, None, weights, clusters=clusters) warm = int(budget["effective_vram_expert_budget"]) engine.store.warm_up(list(range(min(int(cfg.num_experts), warm)))) else: diff --git a/ui/i18n.py b/ui/i18n.py index f7ac7e6..5d16eef 100644 --- a/ui/i18n.py +++ b/ui/i18n.py @@ -971,7 +971,7 @@ def get_registered_components(): "tab.tokenizer": "🔤 分词器", "tok.intro": "从所选数据集（jsonl / parquet / csv / tsv / txt；文件、目录或通配符）自动抽样，训练 Chronos 自研字节回退 BPE 分词器，并显示留出集评估。与 `chronos tokenizer` 使用同一个构建器。", "tok.paths": "数据路径（每行一个，或逗号分隔）", "tok.out_dir": "输出目录", - "tok.fields": "字段映射（可选，逗号分隔的点路径，如 text、messages、translation.en）", "tok.vocab_size": "词表大小", + "tok.fields": "字段映射（可选，逗号分隔的点路径，如 text、messages、record.text）", "tok.vocab_size": "词表大小", "tok.per_source": "每个来源抽样文本数", "tok.temperature": "语言温度采样 T（越小越均衡）", "tok.build": "🔨 训练分词器", "tok.load_report": "📂 载入已有报告", "tok.status": "状态", "tok.log": "日志", "tok.summary": "摘要", "tok.eval": "留出集评估", @@ -1001,7 +1001,7 @@ def get_registered_components(): "tab.tokenizer": "🔤 Tokenizer", "tok.intro": "Samples the selected datasets (jsonl / parquet / csv / tsv / txt; files, directories or globs), trains the Chronos byte-fallback BPE tokenizer and shows the held-out evaluation. Same builder as `chronos tokenizer`.", "tok.paths": "Data paths (one per line or comma-separated)", "tok.out_dir": "Output directory", - "tok.fields": "Field mapping (optional, comma-separated dotted paths, e.g. text, messages, translation.en)", "tok.vocab_size": "Vocabulary size", + "tok.fields": "Field mapping (optional, comma-separated dotted paths, e.g. text, messages, record.text)", "tok.vocab_size": "Vocabulary size", "tok.per_source": "Texts sampled per source", "tok.temperature": "Language temperature T (lower = more balanced)", "tok.build": "🔨 Train tokenizer", "tok.load_report": "📂 Load existing report", "tok.status": "Status", "tok.log": "Log", "tok.summary": "Summary", "tok.eval": "Held-out evaluation", @@ -1031,7 +1031,7 @@ def get_registered_components(): "tab.tokenizer": "🔤 分詞器", "tok.intro": "從所選資料集（jsonl / parquet / csv / tsv / txt；檔案、目錄或萬用字元）自動抽樣，訓練 Chronos 自研位元組回退 BPE 分詞器，並顯示留出集評估。與 `chronos tokenizer` 使用同一個建構器。", "tok.paths": "資料路徑（每行一個，或逗號分隔）", "tok.out_dir": "輸出目錄", - "tok.fields": "欄位對應（選填，逗號分隔的點路徑，如 text、messages、translation.en）", "tok.vocab_size": "詞表大小", + "tok.fields": "欄位對應（選填，逗號分隔的點路徑，如 text、messages、record.text）", "tok.vocab_size": "詞表大小", "tok.per_source": "每個來源抽樣文字數", "tok.temperature": "語言溫度取樣 T（越小越均衡）", "tok.build": "🔨 訓練分詞器", "tok.load_report": "📂 載入已有報告", "tok.status": "狀態", "tok.log": "日誌", "tok.summary": "摘要", "tok.eval": "留出集評估", @@ -1061,7 +1061,7 @@ def get_registered_components(): "tab.tokenizer": "🔤 トークナイザー", "tok.intro": "選択したデータセット（jsonl / parquet / csv / tsv / txt；ファイル・ディレクトリ・glob）から自動サンプリングし、Chronos 独自のバイトフォールバック BPE トークナイザーを学習して、ホールドアウト評価を表示します。`chronos tokenizer` と同じビルダーです。", "tok.paths": "データパス（1 行に 1 つ、またはカンマ区切り）", "tok.out_dir": "出力ディレクトリ", - "tok.fields": "フィールド対応（任意、カンマ区切りのドットパス、例 text・messages・translation.en）", "tok.vocab_size": "語彙サイズ", + "tok.fields": "フィールド対応（任意、カンマ区切りのドットパス、例 text・messages・record.text）", "tok.vocab_size": "語彙サイズ", "tok.per_source": "ソースごとのサンプル数", "tok.temperature": "言語温度 T（小さいほど均等）", "tok.build": "🔨 トークナイザーを学習", "tok.load_report": "📂 既存レポートを読込", "tok.status": "状態", "tok.log": "ログ", "tok.summary": "概要", "tok.eval": "ホールドアウト評価", diff --git a/ui/presets_v3.json b/ui/presets_v3.json index 5b566cf..e0749a2 100644 --- a/ui/presets_v3.json +++ b/ui/presets_v3.json @@ -44,7 +44,7 @@ "notes": [] }, "Chronos-v3 200M (32 GB)": { - "summary": "General 200M MoE, balanced memory saving, minimind 6400 vocab.", + "summary": "General 200M MoE, balanced memory saving, legacy 6400-token vocabulary.", "request": { "target_total": 200000000.0, "num_experts": 32, @@ -118,6 +118,9 @@ "recurrent_depth": 2, "min_recurrent_depth": 1, "max_inference_depth": 2, + "recurrent_order": "block", + "recurrent_dt_param": "sigmoid", + "recurrent_dt_init": 0.5, "halting_threshold": 0.95, "depth_schedule_warmup_fraction": 0.3, "recurrent_latent_refinement": false, @@ -238,6 +241,9 @@ "num_experts": 36, "moe_intermediate_size": 160, "recurrent_groups": 4, + "recurrent_order": "block", + "recurrent_dt_param": "sigmoid", + "recurrent_dt_init": 0.5, "halting_threshold": 0.95, "depth_schedule_warmup_fraction": 0.3, "recurrent_latent_refinement": false, @@ -354,6 +360,9 @@ "num_experts": 32, "moe_intermediate_size": 416, "recurrent_groups": 6, + "recurrent_order": "block", + "recurrent_dt_param": "sigmoid", + "recurrent_dt_init": 0.5, "halting_threshold": 0.95, "depth_schedule_warmup_fraction": 0.3, "recurrent_latent_refinement": false, @@ -471,6 +480,9 @@ "num_experts_per_tok": 3, "moe_intermediate_size": 384, "recurrent_groups": 9, + "recurrent_order": "block", + "recurrent_dt_param": "sigmoid", + "recurrent_dt_init": 0.5, "halting_threshold": 0.95, "depth_schedule_warmup_fraction": 0.3, "recurrent_latent_refinement": false, @@ -589,6 +601,9 @@ "num_experts_per_tok": 4, "moe_intermediate_size": 544, "recurrent_groups": 10, + "recurrent_order": "block", + "recurrent_dt_param": "sigmoid", + "recurrent_dt_init": 0.5, "halting_threshold": 0.95, "depth_schedule_warmup_fraction": 0.3, "recurrent_latent_refinement": false,