45d0a343b5* feat: Implement Q-GaLore optimizer and custom embedding learning rate in the Unsloth trainer. * feat: Implement QGaLoreAdamW8bit optimizer with 8-bit states, GaLore low-rank gradient projection, and optional INT8 weight quantization, along with supporting projector and tests. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * feat: Introduce Q-GaLore AdamW optimizer with low-rank quantized gradient projection and integrate into the trainer, along with dedicated tests. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * feat: Implement Q-GaLore AdamW optimizer with gradient projection and quantization, including trainer integration and corresponding tests. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix 3 bugs in Q-GaLore optimizer and add weight_quant forward hooks 1. Fix use-after-delete crash: move `del p._saved_data` after the weight decay block so decoupled weight decay can reference the current weights correctly (p.data). 2. Fix substring matching in make_q_galore_param_groups: split parameter names on "." and check exact component matches to prevent false positives (e.g. "not_q_proj" matching "q_proj"). 3. Implement forward pre-hooks for weight_quant: after the optimizer quantizes weights to INT8, replace p.data with a 1-element placeholder to free float memory. A register_forward_pre_hook dequantizes back to float before each forward pass. The trainer calls install_weight_quant_hooks() when weight_quant is enabled. 4. Update test_weight_decay_uses_saved_data to match the fixed code path (decoupled decay uses p.data, expected value 2.7). Add test_weight_quant_hook_restores_float to verify the INT8-to-float hook round-trip. All 24/24 Q-GaLore tests pass. Benchmarked on Llama-3.2-1B-Instruct FFT: Q-GaLore saves 32% VRAM (10.63 -> 7.24 GB) with better loss convergence (1.3 vs 2.0 at step 100). No regressions in 31-notebook sweep across Llama, Qwen, Mistral, Phi, Gemma, vision, and GRPO. * Default weight_quant to False in QGaloreConfig Benchmarks show weight_quant=True adds ~1 GB on Llama-3.2-1B due to INT8 copy/scale overhead exceeding savings from the placeholder trick. Users can still opt in explicitly. The optimizer logic is unchanged. * Optimize Q-GaLore projector and optimizer step performance Projector (q_galore_projector.py): - Use torch.svd_lowrank with oversampling p=10 (Halko et al. 2009) instead of full SVD for large matrices. Falls back to full SVD when min(m,n) <= 2*rank. SVD steps are 6-8x faster on Llama-3.2-1B (22s -> 3s for first step). - Cache the dequantized ortho matrix between project() and project_back() to avoid redundant dequantization when quant=True. - Replace F.cosine_similarity with torch.dot for 1-D unit vectors in the adaptive schedule. Remove unused torch.nn.functional import. - Use collections.deque(maxlen=queue_size) instead of list with manual pop(0). Optimizer (q_galore_adamw.py): - Remove redundant .clone() on dequantized weights (line 151) and on float data before re-quantization (line 211). _dequantize already returns a fresh tensor and _quantize/_quantize_stochastic only reads its input. - Consolidate per-group torch.cuda.synchronize() into a single call after all param groups complete. - Use torch.empty instead of torch.zeros for the scalar placeholder tensor that is never read. Verified: 24/24 unit tests pass. Llama-3.2-1B 61-step training produces losses within 0.24% relative diff (correlation >0.9999) of the original. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com>
21 lines
784 B
Python
21 lines
784 B
Python
# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .q_galore_projector import GaLoreProjector
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from .q_galore_adamw import QGaLoreAdamW8bit
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__all__ = [
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"GaLoreProjector",
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"QGaLoreAdamW8bit",
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]
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