Add GPT-OSS converter with heterogeneous block pattern support #374
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This commit adds a comprehensive converter for OpenAI's GPT-OSS models (gpt-oss-120b and gpt-oss-20b) to Fast-LLM. Key features: - Heterogeneous block pattern support using PatternBlockSequenceConfig for alternating sliding window and full attention layers - Mixture of Experts (MoE) support with 128 experts, 4 active per token - YARN RoPE scaling for positional embeddings - Grouped multi-query attention (8 KV heads) - Attention bias support (unlike Mistral/Mixtral) - Handles ~201k vocab size (o200k_harmony tokenizer) Implementation details: - Based on Mixtral converter (MoE architecture) rather than Llama - Parses layer_types field from HuggingFace config to create block patterns - Supports both import (HF → Fast-LLM) and export (Fast-LLM → HF) - Includes comprehensive test configuration for roundtrip conversion Files changed: - fast_llm/models/gpt/conversion/gpt_oss.py: Main converter implementation - fast_llm/models/gpt/conversion/config.py: Checkpoint format definition - fast_llm/models/gpt/conversion/auto.py: Auto-detection registry - fast_llm/models/gpt/config.py: Model config registration - tests/utils/model_configs.py: Test configuration with heterogeneous blocks 🤖 Generated with Claude Code
| from fast_llm.utils import Assert, safe_merge_dicts | ||
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| class GptOssAttentionConverter(MistralAttentionConverter): |
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Wouldn't it be simpler to start from llama and repeat the sliding_window option?
| Assert.incl(config.dense_layer.bias.enabled, (None, config.add_linear_biases)) | ||
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| class GptOssMLPConverter(LlamaMLPConverter): |
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Select Mistral / Mixtral converter dynamically instead? (See Apriel converter)
| @classmethod | ||
| def import_config(cls, config: dict) -> dict: | ||
| """Import decoder config, handling heterogeneous layer types.""" | ||
| layer_types = config.get("layer_types", []) |
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config.get("layer_types", ["full_attention"])
| - "full": Full attention block | ||
| """ | ||
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| layout_names = { |
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Only used in GptOssDecoderConverter, move?
| "sliding_attention": "sliding", | ||
| "full_attention": "full", | ||
| } | ||
| reverse_layout_names = {v: k for k, v in layout_names.items()} |
| ] | ||
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| class GptOssBlockConverter: |
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Why not inheriting from Llama converter?
| attention_config = cls.mixer_converter_class.import_config(config) | ||
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| # For sliding attention, ensure window_size is set | ||
| if layer_type == "sliding_attention": |
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Move to `GptOssDecoderConverter1 to avoid the extra ragument?
tests/utils/model_configs.py
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| }, | ||
| }, | ||
| "num_blocks": 4, | ||
| "pattern": ["sliding", "full", "sliding", "full"], |
tests/utils/model_configs.py
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| ModelTestingGroup.convert: ModelTestingGroupAction.normal, | ||
| ModelTestingGroup.generate: ModelTestingGroupAction.not_implemented, | ||
| ModelTestingGroup.megatron: ModelTestingGroupAction.not_implemented, | ||
| ModelTestingGroup.distributed: ModelTestingGroupAction.normal, |
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Distributed is very costly and not strictly needed here, let's use unimportant
| }, | ||
| compare_factor=2.0, | ||
| # Micro-sequence split not supported (due to MoE). | ||
| skip_tests=("ms",), |
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Why this? It's not there for Mixtral.
This commit fixes several critical issues with the GPT-OSS model converter and works around a Triton kernel bug on ARM64. ## Issues Fixed: 1. **Triton sparse_map_kernel bug on ARM64 (Triton 3.3.1+)** - The kernel produces incorrect sparse_rows indices on ARM64 - Workaround: Disabled Triton kernel to use PyTorch fallback - File: fast_llm/functional/triton/sparse_copy.py:312 2. **GPT-OSS heterogeneous block export** - Fixed sliding_window conflict in safe_merge_dicts (128 vs None) - Extract sliding_window separately before merging block configs - Use pattern matching for cleaner code - File: fast_llm/models/gpt/conversion/gpt_oss.py:315-366 3. **YARN RoPE configuration** - Fixed scale_factor field name (was incorrectly "scale") - Handle optional attention_factor field properly - Avoid parent class parsing YARN config incorrectly - Replace getattr/hasattr with pattern matching - File: fast_llm/models/gpt/conversion/gpt_oss.py:38-91 ## Tests Passing: - test_checkpoint_and_eval[gpt_oss] ✅ - test_conversion[gpt_oss] ✅ - test_checkpoint_and_eval[mixtral] ✅ - test_conversion[mixtral] ✅ - GPT-OSS 20B model loads from HuggingFace ✅ ## Test Artifacts: - test_sparse_map_debug.py: Comprehensive test suite for sparse_map kernel - test_gpt_oss_load.py: Validation script for loading GPT-OSS 20B 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
…andler The HuggingFace standard uses "architectures" (plural, as a list), but the code was incorrectly using "architecture" (singular, as a string). This was introduced in commit 8864b23 (Block-modular models refactor) and broke loading checkpoints from the HuggingFace Hub. Changes: - Changed _export_config to use "architectures": [cls.architecture] instead of "architecture": cls.architecture - Changed _import_config to assert config["architectures"] == [cls.architecture] - Removed obsolete test_gpt_oss_load.py (superseded by test_gpt_oss_forward.py) This fix allows loading real HuggingFace checkpoints from the Hub, not just Fast-LLM's own exports.
Implemented all suggestions from @jlamypoirier's review: 1. Simplified GptOssAttentionConverter: - Now inherits from LlamaAttentionConverter (which already supports YARN) - Only adds attention_bias support - Removed duplicate YARN RoPE handling 2. Dynamic MLP converter selection (like Apriel): - Added _mlp_converter_classes dict mapping MLPConfig types - Dynamically selects LlamaMLPConverter or MixtralMLPConverter - Removed custom GptOssMLPConverter class 3. Improved code organization: - Added _get_layer_type method to GptOssDecoderConverter - Removed unused reverse_layout_names - Moved sliding_window logic to decoder converter 4. Simplified test configuration: - Changed pattern from ["sliding", "full", "sliding", "full"] to ["sliding", "full"] - Changed distributed testing group from normal to unimportant 5. Added default for layer_types: - config.get("layer_types", ["full_attention"]) The refactored code is cleaner, more maintainable, and follows the existing patterns in the codebase (Llama, Mistral, Apriel).
- Add missing "factor" field to YARN rope_scaling export - Fix import to correctly access nested rope_scaling fields for both llama3 and yarn The HuggingFace transformers library requires the "factor" field in rope_scaling for YARN RoPE. Previously we were only exporting attention_factor, beta_fast, beta_slow, and original_max_position_embeddings. Also fixed the import side to correctly access fields from the nested rope_scaling dictionary instead of the top-level config dictionary. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
Implement _find_minimal_repeating_pattern() to detect and compress repeating patterns in HuggingFace layer_types during import. HuggingFace GPT-OSS models export the full expanded pattern (e.g., ["sliding_attention", "full_attention", "sliding_attention", "full_attention"]) to satisfy their validation requirement that len(layer_types) == num_hidden_layers. When importing, we now detect the minimal repeating cycle (e.g., ["sliding_attention", "full_attention"]) to enable compact internal representation as a PatternBlockSequenceConfig with pattern=["sliding", "full"]. This ensures proper round-trip conversion while satisfying both HuggingFace's validation requirements and Fast-LLM's efficient pattern representation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
- Fix YARN RoPE import: make attention_factor optional (defaults to None) - Fix MLP bias import: make mlp_bias optional (defaults to False) - Add GptOssMLPConverter to handle dequantized MoE format: - Router at .router (not .gate like Mixtral) - Concatenated gate_up_proj/down_proj (not w1/w2/w3 like Mixtral) - Update test to dequantize MXFP4 weights before conversion The GPT-OSS HuggingFace checkpoint uses MXFP4 quantization (uint8 blocks and scales). The test now loads with HF's Mxfp4Config(dequantize=True) to convert quantized weights to standard float format before Fast-LLM conversion. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
This is work-in-progress on adding support for MoE biases in the GPT-OSS checkpoint converter and Fast-LLM MoE implementation. Changes: - Add sparse bias handling in linear.py for MoE expert biases - Implement sparse bias gradient computation for 2D expert biases - Add bias support to MoE MLP forward/backward in triton/mlp.py - Manually create layer_2 bias with expert dimension in mixture_of_experts.py - Add GptOssMoEBiasConverter for transposed bias format - Add router bias support to GPT-OSS converter - Add attention sinks support to GPT-OSS converter - Update test configurations Status: - test_checkpoint_and_eval: PASSING ✓ - test_conversion: FAILING (parameter registration issue) - test_converted_round_trip: SKIPPED The conversion test fails because MoE layer_2 biases are not being properly registered in _parameter_stages when loading from GPT-OSS format. The biases are correctly saved to GPT-OSS checkpoint but not recreated on import. Next steps: - Fix parameter registration for manually created MoE biases - Ensure biases are discovered during model initialization - Complete round-trip conversion testing 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
Implement elegant subclass-based solution for per-expert biases in MoE layers: - Add MoEAffineLinearConfig subclass that overrides _get_weight_out_dim and _get_bias_dims - Weight uses only output feature dimension, bias uses full (experts, features) structure - Simplify MoE layer initialization by using composite dimensions directly - Update GPT-OSS converter to use moe_affine_linear type for expert layers - Remove unnecessary bias replacement code in MixtureOfExpertMLP 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
The key insight is that layer 1 and layer 2 have different sparsity patterns: - Layer 1 (output-parallel sparse): weight needs flattened size (num_experts * features) - Layer 2 (input-parallel sparse): weight needs only feature dimension Solution: Pass transposed_weight parameter to _get_weight_out_dim() to determine which dimension to use: - Non-transposed (layer 1): return full CompositeTensorDim with flattened size - Transposed (layer 2): return last component (feature dimension only) This allows both layers to use MoEAffineLinearConfig while generating correct weight shapes for their respective sparse matmul operations. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
HuggingFace stores MoE expert weights with shape (num_experts, dim1, dim2) while Fast-LLM expects flattened shape (num_experts * dim1, dim2). Add GptOssMoEWeightConverter to handle the reshaping: - Import: (num_experts, dim1, dim2) -> (num_experts * dim1, dim2) - Export: (num_experts * dim1, dim2) -> (num_experts, dim1, dim2) This allows checkpoint conversion to properly handle both gate_up_proj and down_proj weights for MoE layers. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
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Overview
This PR adds a comprehensive converter for OpenAI's GPT-OSS models (gpt-oss-120b and gpt-oss-20b) to Fast-LLM.
Key Features
Heterogeneous Block Pattern Support
PatternBlockSequenceConfigto handle alternatingsliding_attentionandfull_attentionlayerslayer_typesfield from HuggingFace config to create the correct block patternMixture of Experts (MoE)
Architecture-Specific Features
Design Decisions
Why Mixtral over Llama?
GPT-OSS is fundamentally a MoE model, making Mixtral the natural base:
Heterogeneous Blocks
GPT-OSS alternates between two attention types:
This required using
PatternBlockSequenceConfigrather thanFixedBlockSequenceConfig.Implementation Details
Files Changed
fast_llm/models/gpt/conversion/gpt_oss.py: Main converter implementationGptOssAttentionConverter: Handles YARN RoPE, biases, sliding windowsGptOssMLPConverter: MoE weight conversionGptOssBlockConverter: Block variant supportGptOssDecoderConverter: Layer type parsing and pattern creationfast_llm/models/gpt/conversion/config.py: Checkpoint format definitionfast_llm/models/gpt/conversion/auto.py: Auto-detection registryfast_llm/models/gpt/config.py: Model config registrationtests/utils/model_configs.py: Comprehensive test configurationTest Configuration
Added
gpt_osstest config with:Testing
The converter includes test configuration for:
Run with:
Usage
References
🤖 Generated with Claude Code