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+ poolside-banner +

+ +

+ Get an API key · + Release blog post · + Technical report +

+ +
+ +# Laguna M.1-FP8 + +Laguna M.1-FP8 is a 225B total parameter Mixture-of-Experts model with 23B activated parameters per token designed for agentic coding and long-horizon work. This is the FP8-quantized variant of [Laguna M.1](https://huggingface.co/poolside/Laguna-M.1). + +> [!NOTE] +> This is the FP8 variant. The [BF16](https://huggingface.co/poolside/Laguna-M.1) and [NVFP4](https://huggingface.co/poolside/Laguna-M.1-NVFP4) variants are also available on Hugging Face. + +## Highlights + +* **Large sparse MoE for agentic coding**: Laguna M.1 is a 70-layer MoE transformer with 225B total parameters and 23B activated parameters per token +* **High-capacity expert routing**: After 3 dense SwiGLU layers, Laguna M.1 uses 67 sparse MoE layers with 256 experts, top-k=16 routing and auxiliary-loss-free load balancing +* **Global attention architecture**: Laguna M.1 uses global attention across all layers with 64 Q-heads, 8 KV-heads and softplus attention output gating +* **Native reasoning support**: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request +* **Apache 2.0 license**: Use and modify freely for commercial and non-commercial purposes + +--- + +## Model overview + +- Training: pre-training, post-training and reinforcement learning stages +- Number of parameters: 225B total with 23B activated per token +- Optimizer: Muon +- Layers: 70 layers with global attention +- Experts: 256 experts with 1 shared expert; top-k=16 routing +- Dense layers: first 3 layers are dense SwiGLU; remaining 67 layers are sparse MoE +- Attention: 64 Q-heads, 8 KV-heads, head dimension 128, with softplus attention output gating +- Positional encoding: RoPE with YaRN +- Modality: text-to-text +- Context window: 262,144 tokens +- Reasoning support: interleaved thinking with preserved thinking +- Quantization: FP8 (weights), detected automatically from `quantization_config` + +## Benchmark results + +

+ benchmarks +

+ +| Model | Parameters | SWE-bench Verified | SWE-bench Multilingual | SWE-bench Pro (Public Dataset) | Terminal-Bench 2.0 | +|---------------------------|----------------------|--------------------|------------------------|--------------------------------|--------------------| +| **Laguna M.1 (BF16)** | 225B-A23B | 74.6% | 63.1% | 49.2% | 45.8% | +| Devstral 2 | 123B dense | 72.2% | 61.3% | - | 32.6% | +| GLM-4.7 | 355B-A32B | 73.8% | 66.7% | - | 41.0% | +| DeepSeek-V4 Flash | 284B-A13B | 79.0% | 73.3% | 52.6% | 56.9% | +| Qwen3.5-397B-A17B | 397B-A17B | 76.2% | 69.3% | 50.9% | 52.5% | +| Claude Sonnet 4.6 | - | 79.6% | - | - | 59.1% | + +*Scores shown are for the BF16 reference model; see the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1) for full benchmarking methodology. We used the highest publicly-referenced scores for all comparison models across each benchmark.* + +## Usage + +Laguna M.1 has upstream support in vLLM, SGLang, and TRT-LLM thanks to the support of the team at NVIDIA. + +> [!NOTE] +> For complete usage instructions, see the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1). + +### Deployment + +#### vLLM + +The full vLLM recipe is on the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1). Quantization is detected automatically from `quantization_config` in this checkpoint, so the same command works with `poolside/Laguna-M.1-FP8` substituted for the model ID. No extra flags required. + +```shell +pip install 'vllm>=0.21.0' + +vllm serve \ + --model poolside/Laguna-M.1-FP8 \ + --tool-call-parser poolside_v1 \ + --reasoning-parser poolside_v1 \ + --enable-auto-tool-choice \ + --served-model-name laguna \ + --default-chat-template-kwargs '{"enable_thinking": true}' +``` + +#### SGLang + +Laguna M.1 is supported in SGLang via [sgl-project/sglang#28400](https://github.com/sgl-project/sglang/pull/28400). Quantization is detected automatically from `quantization_config`, so no extra flags are required. A full serving recipe will be added to the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1). + +#### TRT-LLM + +Laguna is supported in TensorRT-LLM thanks to the team at NVIDIA ([NVIDIA/TensorRT-LLM#13559](https://github.com/NVIDIA/TensorRT-LLM/pull/13559), with partial-RoPE fusion in [#15110](https://github.com/NVIDIA/TensorRT-LLM/pull/15110)). The full recipe is on the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1). Quantization is detected automatically from `quantization_config` in this checkpoint, so no extra flags are required. + +## Controlling reasoning + +Laguna M.1 has native reasoning support and is designed to work best with *preserved thinking*, where `reasoning` content from prior assistant messages is preserved in the message history. This model will generally reason before calling tools and between tool calls. See the main [Laguna M.1 model card](https://huggingface.co/poolside/Laguna-M.1#controlling-reasoning) for streaming, tool-call, and preserved-thinking examples. + +### Disabling reasoning + +You can disable thinking by setting `enable_thinking` to `False` in a request or by not providing `--default-chat-template-kwargs {"enable_thinking": True}` or equivalent when starting the server. + +## License + +This model is licensed under the [Apache 2.0 License](https://huggingface.co/poolside/Laguna-M.1-FP8/blob/main/LICENSE.md). + +## Intended and Responsible Use + +Laguna M.1 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. Laguna M.1 is subject to the [Apache 2.0 License](https://huggingface.co/poolside/Laguna-M.1-FP8/blob/main/LICENSE.md), and should be used consistently with Poolside's [Acceptable Use Policy](https://poolside.ai/legal/acceptable-use-policy). We advise against circumventing Laguna M.1 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case. + +Please report security vulnerabilities or safety concerns to [security@poolside.ai](mailto:security@poolside.ai). diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..4baa3fded6d2b5bee442b9f968ecbab6d879d029 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,132 @@ +{#- Iteration on laguna_glm_thinking_v5/chat_template.jinja -#} +{#- Adds a default system message (used when no system message is provided in `messages`). -#} +{{- "〈|EOS|〉" -}} +{%- set enable_thinking = enable_thinking | default(false) -%} +{%- set render_assistant_messages_raw = render_assistant_messages_raw | default(false) -%} +{%- set add_generation_prompt = add_generation_prompt | default(false) -%} + +{#- ───── header (system message) ───── -#} +{%- set system_message = "You are a helpful, conversationally-fluent assistant made by Poolside. You are here to be helpful to users through natural language conversations." -%} +{%- if messages and messages[0].role == "system" -%} + {%- set system_message = messages[0].content -%} +{%- endif -%} + +{%- if (system_message and system_message.strip()) or tools -%} + {{- "\n" -}} + + {%- if system_message and system_message.strip() -%} + {{- "\n" -}} + {{- system_message.rstrip() -}} + {%- endif -%} + + {%- if tools -%} + {{- "\n\n### Tools\n\n" -}} + {%- set ns = namespace(tool_string="You may call functions to assist with the user query.\n" + ~ "All available function signatures are listed below:\n" + ~ "\n") -%} + {%- for tool in tools -%} + {%- set ns.tool_string = ns.tool_string ~ (tool | tojson) ~ "\n" -%} + {%- endfor -%} + {%- if enable_thinking -%} + {%- set tool_string = ns.tool_string + "\n\n" ~ + "Wrap your thinking in '', '' tags, followed by a function call. For each function call, return an unescaped XML-like object with function name and arguments within '' and '' tags, like here:\n" ~ + " your thoughts here \n" ~ + "function-name\nargument-key\nvalue-of-argument-key\n" ~ + "" -%} + {%- else -%} + {%- set tool_string = ns.tool_string + "\n\n" ~ + "For each function call, return an unescaped XML-like object " ~ + "with function name and arguments within '' and '' tags, like here:\n" ~ + "function-name\nargument-key\nvalue-of-argument-key\n" ~ + "" -%} + {%- endif -%} + {{- tool_string -}} + {%- endif -%} + + {{- "\n\n" -}} +{%- endif -%} + +{#- ───── main loop ───── -#} +{%- for message in messages -%} + {%- set content = message.content if message.content is string else "" -%} + {%- if message.role == "user" -%} + {{- "\n" + content + "\n\n" -}} + {%- elif message.role == "assistant" -%} + {%- generation -%} + {{- "\n" -}} + {%- if render_assistant_messages_raw -%} + {#- Raw mode: prepend the generation prompt token, then dump content verbatim. -#} + {#- The generation prompt is when enable_thinking, otherwise. -#} + {#- Only prepend if content doesn't already start with it. -#} + {%- if enable_thinking -%} + {%- if not content.startswith('') -%} + {{- '' -}} + {%- endif -%} + {%- else -%} + {%- if not content.startswith('') -%} + {{- '' -}} + {%- endif -%} + {%- endif -%} + {{- content -}} + {#- Append closing tag if content doesn't already end with it. -#} + {%- if not content.endswith('\n') and not content.endswith('') -%} + {{- '\n' -}} + {%- endif -%} + {{- "\n" -}} + {%- else -%} + {#- Extract reasoning content from message.reasoning (vLLM field name) or message.reasoning_content, or from tags -#} + {%- set reasoning_content = '' %} + {%- if message.reasoning is string %} + {%- set reasoning_content = message.reasoning %} + {%- elif message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- endif %} + {#- Always strip tags from content if present to avoid duplication -#} + {%- if '' in content %} + {%- if not reasoning_content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- endif %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} + {#- Display reasoning content for all messages -#} + {%- if reasoning_content -%} + {{- '\n' + reasoning_content.strip() + '\n\n' -}} + {%- else -%} + {{- '\n' -}} + {%- endif -%} + {#- Display main content -#} + {%- if content.strip() -%} + {{- content.strip() ~ "\n" -}} + {%- endif -%} + {%- if message.tool_calls -%} + {%- for tool_call in message.tool_calls -%} + {%- set function_data = tool_call.function -%} + {{- '' + function_data.name }} + {% set _args = function_data.arguments %} + {%- for k, v in _args.items() -%} + {{- "" ~ k ~ "\n" -}} + {{- ""}}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{{ "\n" -}} + {%- endfor -%} + {{- "\n" -}} + {%- endfor -%} + {%- endif -%} + {{- "\n" -}} + {%- endif -%} + {%- endgeneration -%} + {%- elif message.role == "tool" -%} + {{- "\n" + content + "\n\n" -}} + {%- elif message.role == "system" and loop.index0 != 0 -%} + {#- Render additional system messages (skip the first one which is handled separately in the header) -#} + {{- "\n" + content + "\n\n" -}} + {%- endif -%} +{%- endfor -%} +{#- ───── generation prompt ───── -#} +{%- if add_generation_prompt -%} + {{- "\n" -}} + {#- ───── Include reasoning mode directive ───── -#} + {%- if not enable_thinking %} + {{- '' -}} + {%- else %} + {{- '' -}} + {%- endif %} +{%- endif -%} diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..1a850f8b6dbe2a99272f65da1165734d209e747c --- /dev/null +++ b/config.json @@ -0,0 +1,526 @@ +{ + "architectures": [ + "LagunaForCausalLM" + ], + "auto_map": { + "AutoConfig": "configuration_laguna.LagunaConfig", + "AutoModelForCausalLM": "modeling_laguna.LagunaForCausalLM" + }, + "model_type": "laguna", + "vocab_size": 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0000000000000000000000000000000000000000..4f06f65a2703e14c0efca657b3d7394295c32a6b --- /dev/null +++ b/configuration_laguna.py @@ -0,0 +1,187 @@ +# ruff: noqa +# Copyright 2025 Poolside and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from transformers.configuration_utils import PreTrainedConfig +from transformers.modeling_rope_utils import RopeParameters + + +class LagunaConfig(PreTrainedConfig): + r""" + Configuration class for Laguna model. + + Laguna is Poolside's MoE architecture with: + - Attention output gating (softplus gate) + - Sigmoid routing instead of softmax + - No QKV bias + - Explicit head_dim parameter + + Args: + head_dim (`int`, *optional*, defaults to 128): + Dimension of attention heads. Laguna uses explicit head_dim rather than + computing it from hidden_size // num_attention_heads. + qkv_bias (`bool`, *optional*, defaults to `False`): + Whether to add bias to QKV projections. Laguna uses no QKV bias. + attention_bias (`bool`, *optional*, defaults to `False`): + Whether to add bias to attention output projection. Laguna uses no attention bias. + gating (`bool`, *optional*, defaults to `True`): + Whether to use softplus output gating on attention. When True, a g_proj linear + layer is added and attn_output = attn_output * softplus(g_proj(x)). + sliding_window (`int`, *optional*): + Sliding window attention size. Used by layers whose type in ``layer_types`` + is ``"sliding_attention"``. When ``None``, all layers use full attention. + layer_types (`list[str]`, *optional*): + Per-layer attention type. Each element should be ``"sliding_attention"`` or + ``"global_attention"``. Length must equal ``num_hidden_layers``. When ``None``, + all layers default to global attention. + swa_attention_sink_enabled (`bool`, *optional*, defaults to `False`): + Whether to enable learnable attention sinks on sliding-window attention layers. + When enabled, a per-head bias parameter is added that allows the model to attend + to position 0 even when it falls outside the sliding window. + swa_rope_parameters (`RopeParameters`, *optional*): + Separate RoPE configuration for sliding-window attention layers. When ``None``, + SWA layers use the same RoPE as global attention layers. + vocab_size (`int`, *optional*, defaults to 100352): + Vocabulary size of the Laguna model. + hidden_size (`int`, *optional*, defaults to 2048): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 8192): + Dimension of the MLP representations for dense layers. + num_hidden_layers (`int`, *optional*, defaults to 48): + Number of hidden layers in the Transformer. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads. + num_key_value_heads (`int`, *optional*, defaults to 8): + Number of key-value heads for GQA. + max_position_embeddings (`int`, *optional*, defaults to 4096): + Maximum sequence length. + rms_norm_eps (`float`, *optional*, defaults to 1e-6): + Epsilon for RMSNorm layers. + num_experts (`int`, *optional*, defaults to 256): + Number of routed experts. + num_experts_per_tok (`int`, *optional*, defaults to 16): + Number of experts selected per token (top-k). + moe_intermediate_size (`int`, *optional*, defaults to 1024): + Intermediate size of routed experts. + shared_expert_intermediate_size (`int`, *optional*, defaults to 1024): + Intermediate size of the shared expert. + norm_topk_prob (`bool`, *optional*, defaults to `True`): + Whether to normalize top-k routing probabilities. + decoder_sparse_step (`int`, *optional*, defaults to 1): + Frequency of MoE layers (1 = every layer is MoE after mlp_only_layers). + mlp_only_layers (`list[int]`, *optional*, defaults to `[0]`): + Layer indices that use dense MLP instead of MoE. + router_aux_loss_coef (`float`, *optional*, defaults to 0.001): + Auxiliary loss coefficient for load balancing. + rope_parameters (`RopeParameters`, *optional*): + RoPE configuration. Defaults to rope_theta=500000.0. + """ + + model_type = "laguna" + keys_to_ignore_at_inference = ["past_key_values"] + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.g_proj": "colwise", # Laguna-specific gating projection + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + def __init__( + self, + vocab_size: int = 100352, + hidden_size: int = 2048, + intermediate_size: int = 8192, + num_hidden_layers: int = 48, + num_attention_heads: int = 32, + num_key_value_heads: int = 8, + head_dim: int = 128, + qkv_bias: bool = False, + attention_bias: bool = False, + gating: bool = True, + hidden_act: str = "silu", + max_position_embeddings: int = 4096, + initializer_range: float = 0.02, + rms_norm_eps: float = 1e-6, + use_cache: bool = True, + tie_word_embeddings: bool = False, + rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, + attention_dropout: float = 0.0, + sliding_window: int | None = None, + layer_types: list[str] | None = None, + swa_attention_sink_enabled: bool = False, + swa_rope_parameters: RopeParameters | None = None, + num_experts: int = 256, + num_experts_per_tok: int = 16, + moe_intermediate_size: int = 1024, + shared_expert_intermediate_size: int = 1024, + norm_topk_prob: bool = True, + decoder_sparse_step: int = 1, + mlp_only_layers: list[int] | None = None, + router_aux_loss_coef: float = 0.001, + output_router_logits: bool = False, + **kwargs, + ): + # Default mlp_only_layers: first layer is dense (moe_first_k_dense_replace=1) + if mlp_only_layers is None: + mlp_only_layers = [0] + + # Default rope_parameters with Laguna's theta + if rope_parameters is None: + rope_parameters = {"rope_type": "default", "rope_theta": 500000.0} + + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads + self.head_dim = head_dim + self.qkv_bias = qkv_bias + self.attention_bias = attention_bias + self.gating = gating + self.hidden_act = hidden_act + self.max_position_embeddings = max_position_embeddings + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_parameters = rope_parameters + self.attention_dropout = attention_dropout + # Sliding window attention arguments + self.sliding_window = sliding_window + self.layer_types = layer_types + self.swa_attention_sink_enabled = swa_attention_sink_enabled + self.swa_rope_parameters = swa_rope_parameters + # MoE arguments + self.num_experts = num_experts + self.num_experts_per_tok = num_experts_per_tok + self.moe_intermediate_size = moe_intermediate_size + self.shared_expert_intermediate_size = shared_expert_intermediate_size + self.norm_topk_prob = norm_topk_prob + self.decoder_sparse_step = decoder_sparse_step + self.mlp_only_layers = mlp_only_layers + self.router_aux_loss_coef = router_aux_loss_coef + self.output_router_logits = output_router_logits + + super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) + + +__all__ = ["LagunaConfig"] diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000000000000000000000000000000000000..fb6714ed167afe5d1d6b229eb7ef5305bc9a7c26 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,13 @@ +{ + "bos_token_id": 2, + "do_sample": true, + "eos_token_id": [ + 2, + 24 + ], + "max_new_tokens": 4096, + "pad_token_id": 9, + "temperature": 1.0, + "top_p": 1.0, + "min_p": 0.0 +} \ No newline at end of file diff --git a/image/LOAD.md b/image/LOAD.md new file mode 100644 index 0000000000000000000000000000000000000000..7c872e5ba539c51fc7ff8fa8ed49505876239702 --- /dev/null +++ b/image/LOAD.md @@ -0,0 +1,53 @@ +# Image + +`laguna-vllm-v0.19.0-overlay.tar.gz` is `vllm/vllm-openai:v0.19.0` plus the +Laguna model file (`vllm.model_executor.models.laguna`), the `poolside_v1` +tool parser (`vllm.tool_parsers.poolside_v1_tool_parser`), the +`poolside_v1` reasoning parser +(`vllm.reasoning.poolside_v1_reasoning_parser`), and `LagunaConfig` +(`vllm.transformers_utils.configs.laguna`). No CUDA kernels are rebuilt — +the base image's SM90/SM90A binaries are used unchanged. + +## Load + +```bash +gunzip -c laguna-vllm-v0.19.0-overlay.tar.gz | docker load +``` + +The loaded image is tagged `vllm-laguna:v0.19.0-overlay-port`. + +## Integrity check + +```bash +sha256sum laguna-vllm-v0.19.0-overlay.tar.gz +# expected: 4266d7fc0fda731e774beeb932493cc4f2de1a9c6030babd32eae30f7dc60b3a +``` + +## What's inside + +- Base: `vllm/vllm-openai:v0.19.0` (PyTorch, CUDA, Triton, FlashAttention, + compressed-tensors, etc.) +- Added Python modules: + - `vllm/model_executor/models/laguna.py` — `LagunaForCausalLM` + - `vllm/tool_parsers/poolside_v1_tool_parser.py` — `PoolsideV1ToolParser` + - `vllm/reasoning/poolside_v1_reasoning_parser.py` — `PoolsideV1ReasoningParser` + - `vllm/transformers_utils/configs/laguna.py` — `LagunaConfig` +- Registry patches: `LagunaForCausalLM` added to the model registry; + `"poolside_v1"` added to the tool-parser and reasoning-parser lazy-register + dicts; `"laguna"` added to the config registry (so checkpoints don't need + a remote `configuration_laguna.py` and can be served without + `--trust-remote-code`). +- `transformers_utils/config.py::patch_rope_parameters` is patched to leave + a nested `rope_parameters` dict (e.g. `{full_attention: {...}, + sliding_attention: {...}}`) intact instead of overwriting it with the + flat `rope_scaling`. Without this, interleaved-attention configs raise + `KeyError 'full_attention'` at the per-layer attention site on + transformers <5. +- Bundled convenience scripts: + - `/usr/local/bin/serve-laguna.sh` — Laguna-M serve wrapper (TP=4) + - `/usr/local/bin/run_bench_multiple.py` — multi-config benchmark runner + +## Entrypoint + +Default entrypoint is `vllm serve`, same as the base image. The first +positional argument is the model path (mount a checkpoint dir into `/model`). diff --git a/image/laguna-vllm-v0.19.0-overlay.tar.gz b/image/laguna-vllm-v0.19.0-overlay.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..4749e9e2415c2311533f374d475cc43882e710a7 --- /dev/null +++ b/image/laguna-vllm-v0.19.0-overlay.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e37ec8966626070ab4cd87296b13bd80854442ba6a89a896fa136c67503057cd +size 9547511305 diff --git a/model-00001-of-00045.safetensors b/model-00001-of-00045.safetensors new file mode 100644 index 0000000000000000000000000000000000000000..44d29c861478e6ec244bd8b9fa32ff81e3186d60 --- /dev/null +++ b/model-00001-of-00045.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b290b33be8f24274158893c4581c92652ddab16ca649163964fb4015e90d2944 +size 5118025988 diff --git a/model-00002-of-00045.safetensors b/model-00002-of-00045.safetensors new file 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All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Optional +from collections.abc import Callable + +import torch +import torch.nn.functional as F +from torch import nn +from transformers import initialization as init +from transformers.utils import auto_docstring, can_return_tuple, is_grouped_mm_available +from transformers.generation import GenerationMixin +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache +from transformers.integrations import ( + use_kernelized_func, + use_kernel_func_from_hub, + use_kernel_forward_from_hub, +) +from transformers.masking_utils import create_causal_mask +from transformers.utils.generic import OutputRecorder, TransformersKwargs, maybe_autocast, check_model_inputs +from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from transformers.modeling_layers import GradientCheckpointingLayer +from transformers.modeling_outputs import MoeModelOutputWithPast, MoeCausalLMOutputWithPast +from transformers.processing_utils import Unpack +from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from transformers.modeling_flash_attention_utils import FlashAttentionKwargs + +from .configuration_laguna import LagunaConfig + + +@use_kernel_forward_from_hub("RMSNorm") +class LagunaRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + LagunaRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class LagunaRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: LagunaConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: LagunaConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + + Returns + ------- + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class LagunaMLP(nn.Module): + def __init__(self, config, intermediate_size=None): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class LagunaTopKRouter(nn.Module): + """Laguna MoE router using sigmoid scoring (not softmax).""" + + def __init__(self, config): + super().__init__() + self.top_k = config.num_experts_per_tok + self.num_experts = config.num_experts + self.norm_topk_prob = config.norm_topk_prob + self.hidden_dim = config.hidden_size + self.weight = nn.Parameter(torch.zeros(self.num_experts, self.hidden_dim)) + + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + hidden_states = hidden_states.reshape(-1, self.hidden_dim) + router_logits = F.linear(hidden_states, self.weight) + # Laguna-specific: sigmoid routing in float32 for precision + routing_weights = torch.sigmoid(router_logits.float()) + routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1) + if self.norm_topk_prob: + routing_weights = routing_weights / routing_weights.sum(dim=-1, keepdim=True) + routing_weights = routing_weights.to(hidden_states.dtype) + return router_logits, routing_weights, selected_experts + + +class LagunaSparseMoeBlock(nn.Module): + """Laguna MoE block using sigmoid router, per-expert MLPs, and a shared expert.""" + + def __init__(self, config): + super().__init__() + self.num_experts = config.num_experts + self.top_k = config.num_experts_per_tok + self.gate = LagunaTopKRouter(config) + self.experts = nn.ModuleList( + [LagunaMLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(self.num_experts)] + ) + self.shared_expert = LagunaMLP(config, intermediate_size=config.shared_expert_intermediate_size) + self.shared_expert_gate = ( + nn.Linear(config.hidden_size, 1, bias=False) if getattr(config, "moe_shared_gate", False) else None + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + batch_size, sequence_length, hidden_dim = hidden_states.shape + hidden_states = hidden_states.view(-1, hidden_dim) + + shared_expert_output = self.shared_expert(hidden_states) + if self.shared_expert_gate is not None: + shared_expert_output = shared_expert_output * torch.sigmoid(self.shared_expert_gate(hidden_states)) + + # Routed experts + _, routing_weights, selected_experts = self.gate(hidden_states) + final_hidden_states = torch.zeros_like(hidden_states) + + expert_mask = F.one_hot(selected_experts, num_classes=self.num_experts) + expert_mask = expert_mask.permute(2, 1, 0) + + for expert_idx in range(self.num_experts): + top_k_pos, token_idx = torch.where(expert_mask[expert_idx]) + if token_idx.shape[0] == 0: + continue + current_state = hidden_states[token_idx] + current_hidden_states = self.experts[expert_idx](current_state) + current_hidden_states = current_hidden_states * routing_weights[token_idx, top_k_pos, None] + final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype)) + + final_hidden_states = final_hidden_states + shared_expert_output + final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim) + return final_hidden_states + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + + Returns + ------- + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +# Laguna attention is identical to Qwen2MoE attention except: +# - No QKV bias +# - Explicit head_dim from config +# - Output gating: attn_output = attn_output * softplus(g_proj(hidden_states)) +# - No sliding window (full attention only) +@use_kernelized_func(apply_rotary_pos_emb) +class LagunaAttention(nn.Module): + def __init__(self, config: LagunaConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = config.head_dim + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + # Laguna: no QKV bias, explicit head_dim + self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * config.head_dim, bias=False) + self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * config.head_dim, bias=False) + self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * config.head_dim, bias=False) + self.o_proj = nn.Linear(config.num_attention_heads * config.head_dim, config.hidden_size, bias=False) + # Laguna-specific: gating projection + self.g_proj = nn.Linear(config.hidden_size, config.num_attention_heads * config.head_dim, bias=False) + # QK normalization (RMSNorm applied per-head after reshape, before RoPE) + self.q_norm = LagunaRMSNorm(config.head_dim, eps=config.rms_norm_eps) + self.k_norm = LagunaRMSNorm(config.head_dim, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + cache_position: torch.LongTensor | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + # QK normalization (applied per-head before RoPE) + query_states = self.q_norm(query_states) + key_states = self.k_norm(key_states) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs) + + attention_interface: Callable = eager_attention_forward + if self.config._attn_implementation != "eager": + attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + + # Laguna-specific: apply gating BEFORE o_proj + # gate values are computed from original hidden_states, applied in attention dimension + gate = F.softplus(self.g_proj(hidden_states).float()).to(attn_output.dtype) + attn_output = attn_output * gate + + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights + + +class LagunaDecoderLayer(GradientCheckpointingLayer): + """Laguna decoder layer with gated attention and sigmoid-routed MoE.""" + + def __init__(self, config: LagunaConfig, layer_idx: int): + super().__init__() + self.self_attn = LagunaAttention(config, layer_idx) + # Use MoE or dense MLP based on layer configuration + if (layer_idx not in config.mlp_only_layers) and ( + config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0 + ): + self.mlp = LagunaSparseMoeBlock(config) + else: + self.mlp = LagunaMLP(config, intermediate_size=config.intermediate_size) + self.input_layernorm = LagunaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = LagunaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.hidden_size = config.hidden_size + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + cache_position: torch.LongTensor | None = None, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class LagunaPreTrainedModel(PreTrainedModel): + config: LagunaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["LagunaDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _can_compile_fullgraph = ( + is_grouped_mm_available() + ) # https://huggingface.co/docs/transformers/experts_interface#torchcompile + _supports_attention_backend = True + _can_record_outputs = { + "router_logits": OutputRecorder(LagunaTopKRouter, index=0), + "hidden_states": LagunaDecoderLayer, + "attentions": LagunaAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + std = self.config.initializer_range + if isinstance(module, LagunaTopKRouter): + init.normal_(module.weight, mean=0.0, std=std) + + +class LagunaModel(LagunaPreTrainedModel): + def __init__(self, config: LagunaConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [LagunaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = LagunaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = LagunaRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @check_model_inputs + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + cache_position: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ): + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if cache_position is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange( + past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device + ) + + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + # Laguna uses full attention only (no sliding window) + causal_mask = create_causal_mask( + config=self.config, + input_embeds=inputs_embeds, + attention_mask=attention_mask, + cache_position=cache_position, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return MoeModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +def load_balancing_loss_func( + gate_logits: torch.Tensor | tuple[torch.Tensor] | None, + num_experts: int | None = None, + top_k=2, + attention_mask: torch.Tensor | None = None, +) -> torch.Tensor | int: + r""" + Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. + + See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss + function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between + experts is too unbalanced. + + Args: + gate_logits: + Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of + shape [batch_size X sequence_length, num_experts]. + num_experts: + Number of experts + top_k: + The number of experts to route per-token, can be also interpreted as the `top-k` routing + parameter. + attention_mask (`torch.Tensor`, *optional*): + The attention_mask used in forward function + shape [batch_size X sequence_length] if not None. + + Returns + ------- + The auxiliary loss. + """ + if gate_logits is None or not isinstance(gate_logits, tuple): + return 0 + + if isinstance(gate_logits, tuple): + compute_device = gate_logits[0].device + concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0) + + routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) + + _, selected_experts = torch.topk(routing_weights, top_k, dim=-1) + + expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) + + if attention_mask is None: + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.mean(expert_mask.float(), dim=0) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.mean(routing_weights, dim=0) + else: + batch_size, sequence_length = attention_mask.shape + num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length) + + # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask + expert_attention_mask = ( + attention_mask[None, :, :, None, None] + .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) + .reshape(-1, top_k, num_experts) + .to(compute_device) + ) + + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( + expert_attention_mask, dim=0 + ) + + # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert + router_per_expert_attention_mask = ( + attention_mask[None, :, :, None] + .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) + .reshape(-1, num_experts) + .to(compute_device) + ) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum( + router_per_expert_attention_mask, dim=0 + ) + + overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) + return overall_loss * num_experts + + +@auto_docstring +class LagunaForCausalLM(LagunaPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_rep"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = LagunaModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.router_aux_loss_coef = config.router_aux_loss_coef + self.num_experts = config.num_experts + self.num_experts_per_tok = config.num_experts_per_tok + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_router_logits: bool | None = None, + cache_position: torch.LongTensor | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> MoeCausalLMOutputWithPast: + r""" + Labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + """ + # TODO (Joe) add example here after we got rid of the stale mistral example + + output_router_logits = ( + output_router_logits if output_router_logits is not None else self.config.output_router_logits + ) + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs: MoeModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_router_logits=output_router_logits, + cache_position=cache_position, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size, **kwargs) + + aux_loss = None + if output_router_logits: + aux_loss = load_balancing_loss_func( + outputs.router_logits, + self.num_experts, + self.num_experts_per_tok, + attention_mask, + ) + if labels is not None and isinstance(aux_loss, torch.Tensor): + loss += self.router_aux_loss_coef * aux_loss.to(loss.device) + + return MoeCausalLMOutputWithPast( + loss=loss, + aux_loss=aux_loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + router_logits=outputs.router_logits, + ) + + +__all__ = ["LagunaForCausalLM", "LagunaModel", "LagunaPreTrainedModel"] diff --git a/serve.sh b/serve.sh new file mode 100644 index 0000000000000000000000000000000000000000..7aea2ac4a9956eaa3947013e4e4dd3f9a158a103 --- /dev/null +++ b/serve.sh @@ -0,0 +1,26 @@ +#!/usr/bin/env bash +# Minimal serve command for Laguna-M.1 on 4× H200. +# Expects the bundled image to be loaded (`docker load < image/...tar.gz`) +# and this script run from the downloaded repo root. +set -euo pipefail + +MODEL_DIR="${MODEL_DIR:-$(cd "$(dirname "$0")" && pwd)}" +PORT="${PORT:-8000}" +GPUS="${GPUS:-0,1,2,3}" + +docker run --rm --gpus "\"device=${GPUS}\"" --network host \ + -v "${MODEL_DIR}":/model \ + laguna-vllm:v0.19.0-overlay \ + /model \ + --tensor-parallel-size 4 \ + --trust-remote-code \ + --dtype bfloat16 \ + --kv-cache-dtype fp8 \ + --max-model-len 4096 \ + --gpu-memory-utilization 0.85 \ + --served-model-name laguna \ + --reasoning-parser poolside_v1 \ + --tool-call-parser poolside_v1 \ + --enable-auto-tool-choice \ + --default-chat-template-kwargs '{"enable_thinking": true}' \ + --port "${PORT}" diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..60bc5592a5aa7f65befc3b621650446679284266 --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,9 @@ +{ + "bos_token": "〈|EOS|〉", + "cls_token": "〈|CLS|〉", + "eos_token": "〈|EOS|〉", + "mask_token": "〈|MASK|〉", + "pad_token": "〈|PAD|〉", + "sep_token": "〈|SEP|〉", + "unk_token": "〈|UNK|〉" +} \ No newline at end of file diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000000000000000000000000000000000000..d70204e945d30af663b9a2482f8d77306f566350 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,501195 @@ +{ + "version": "1.0", + "truncation": null, + "padding": null, + "added_tokens": [ + { + "id": 0, + "content": "〈|UNK|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 1, + "content": "〈|CODE_START|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 2, + "content": "〈|EOS|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 3, + "content": "〈|CODE_END|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 4, + "content": "〈|META_START|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 5, + "content": "〈|META_END|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 6, + "content": "〈|FIM_MIDDLE|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 7, + "content": "〈|FIM_SUFFIX|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 8, + "content": "〈|SEP|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 9, + "content": "〈|PAD|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 10, + "content": "〈|CLS|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 11, + "content": "〈|FIM_START|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 12, + "content": "〈|MASK|〉", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 13, + "content": "|◊|", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 14, + "content": "〈|", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 15, + "content": "|〉", + "single_word": false, + "lstrip": false, + "rstrip": 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"Limitations": 56256, + "())).": 56257, + "Ġspecimens": 56258, + "/feature": 56259, + "_tcp": 56260, + "-environment": 56261, + "ĠâŁ": 56262, + "URLS": 56263, + "ĠFriendly": 56264, + "ĠVBScript": 56265, + "Ġnotifier": 56266, + ";#": 56267, + "/policy": 56268, + "Ġfinancing": 56269, + "åŁºç¡Ģ": 56270, + "-Web": 56271, + "quin": 56272, + "Ġrake": 56273, + "Xmx": 56274, + "ÂĪ": 56275, + ".Cache": 56276, + "Ġflee": 56277, + "aginator": 56278, + "ĉgame": 56279, + ".ToArray": 56280, + "=,": 56281, + "Ġ\"\").": 56282, + "èĬ±": 56283, + "ĉevent": 56284, + "Ġmarble": 56285, + "InOut": 56286, + "ZA": 56287, + "RuntimeException": 56288, + "ĠMomentum": 56289, + "_suite": 56290, + "Ġprisoners": 56291, + "Ġhrs": 56292, + ".readline": 56293, + "Ġdll": 56294, + "Ġhabitats": 56295, + "ãĥ§ãĥ³": 56296, + "urement": 56297, + "Ġкл": 56298, + "Plex": 56299, + "ÑĦоÑĢм": 56300, + "Ġpstmt": 56301, + "à¸Ī": 56302, + "Ġindispensable": 56303, + "ĠiPads": 56304, + "gary": 56305, + "rather": 56306, + "Quota": 56307, + "Ġtransitioned": 56308, + ",color": 56309, + "ĠDecomp": 56310, + "Euler": 56311, + "ĠgetDefault": 56312, + "Ġíķľ": 56313, + "Nano": 56314, + "Regions": 56315, + ".removeEventListener": 56316, + "DCA": 56317, + "cto": 56318, + "!\\!": 56319, + "Ġhelium": 56320, + "ĠChecklist": 56321, + "Going": 56322, + "Ġenvision": 56323, + "Validating": 56324, + "-The": 56325, + "ĠDataTypes": 56326, + "ByID": 56327, + "/rviz": 56328, + "ĠAdditions": 56329, + "Ġohms": 56330, + ">>>>>": 56414, + ".Router": 56415, + "_PROFILE": 56416, + "-Sol": 56417, + "ĠPreparedStatement": 56418, + "Ġohm": 56419, + "ĠAdj": 56420, + "_else": 56421, + "Ġsaver": 56422, + "ĠLeaving": 56423, + "\\hbox": 56424, + "FDA": 56425, + ".shtml": 56426, + "Ġjournaling": 56427, + "ĠcPanel": 56428, + "ĠApplet": 56429, + "attoo": 56430, + "Ġacquires": 56431, + "ĉcreate": 56432, + "alu": 56433, + "vor": 56434, + "ĠLinksys": 56435, + "Others": 56436, + "MEA": 56437, + "Displays": 56438, + "$id": 56439, + "ĠNewer": 56440, + "Ġscikit": 56441, + "Ġnursing": 56442, + "appiness": 56443, + "-arg": 56444, + "abh": 56445, + "Ġinsist": 56446, + "inging": 56447, + "YPTO": 56448, + "Ġrods": 56449, + "ĠAO": 56450, + "-namespace": 56451, + "spective": 56452, + "Fatal": 56453, + "Ġbic": 56454, + "Ġcoaching": 56455, + "{-\\": 56456, + "ĠStadium": 56457, + "ĠDBI": 56458, + "Ġjealous": 56459, + "ĠKaren": 56460, + "ĠReject": 56461, + "-Module": 56462, + "Ġwm": 56463, + "ophe": 56464, + "OpenSSL": 56465, + "_OCC": 56466, + "wang": 56467, + "_hresult": 56468, + "/QtCore": 56469, + "Ġholistic": 56470, + "Ġazure": 56471, + "Ġnz": 56472, + "ĠZK": 56473, + "'>\"": 56474, + "VNC": 56475, + "Ġstarred": 56476, + "abilit": 56477, + "Microservices": 56478, + "Ġrnd": 56479, + "acio": 56480, + "iqueness": 56481, + "ĠInterop": 56482, + "모": 56483, + "Warn": 56484, + "puesta": 56485, + "ĠGX": 56486, + ".TagHelper": 56487, + ".Deserialize": 56488, + "Yn": 56489, + "/spec": 56490, + "ampton": 56491, + "/filter": 56492, + "ĉĉĉĠĠĠĠĠĠĠ": 56493, + "valuation": 56494, + "firefox": 56495, + "ĠConvenience": 56496, + "Ġwherein": 56497, + ".SQLException": 56498, + "]=\\": 56499, + "dae": 56500, + "iratory": 56501, + "_BOUNDS": 56502, + "_QUEUE": 56503, + "ynb": 56504, + "AYOUT": 56505, + ".dm": 56506, + "ĠPid": 56507, + "()'": 56508, + "SSID": 56509, + "ĠHelpers": 56510, + ":Type": 56511, + "ZG": 56512, + "Ġhibernate": 56513, + "ĠRé": 56514, + "@cs": 56515, + "Ġfacade": 56516, + "isle": 56517, + "ottie": 56518, + "_prepare": 56519, + "Ġ******************************************************************************": 56520, + "ĠVocabulary": 56521, + "Ġ':'": 56522, + "Brains": 56523, + ".cost": 56524, + "eis": 56525, + "Ġcof": 56526, + "ĠLiberty": 56527, + "ĠæĽ´": 56528, + "Ġprofessionally": 56529, + "Javascript": 56530, + ".sock": 56531, + "TimeStamp": 56532, + "ĠMTA": 56533, + ".Symbol": 56534, + "bedo": 56535, + "Ġstranger": 56536, + "ĠKap": 56537, + "autiful": 56538, + "Xy": 56539, + "_CENTER": 56540, + "Ġ$\\$": 56541, + "xcc": 56542, + "ĠWeyl": 56543, + "Ġshowcased": 56544, + "åĨħéĥ¨": 56545, + "ĠEmpirical": 56546, + "ussi": 56547, + "ĠGraphing": 56548, + "Synchronous": 56549, + "Optimal": 56550, + "_degree": 56551, + "\"text": 56552, + ".setForeground": 56553, + "ĠMeyer": 56554, + "Ġsubmenu": 56555, + "ACF": 56556, + "-interactive": 56557, + "Mathematical": 56558, + "Ġgaug": 56559, + ".HttpServletResponse": 56560, + "TexCoord": 56561, + "PROGRAM": 56562, + "Bubble": 56563, + "ardi": 56564, + "][:": 56565, + "Ġstain": 56566, + "plib": 56567, + "Ġ\\@": 56568, + "ĠUploading": 56569, + "UH": 56570, + "qua": 56571, + "Ġ...;": 56572, + "âĺ": 56573, + "ModelProperty": 56574, + ".balance": 56575, + "ennium": 56576, + "ĠYe": 56577, + "ück": 56578, + "ĠISPs": 56579, + ".delay": 56580, + 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"ĠReporter": 56672, + "ĠPill": 56673, + "Ġunhealthy": 56674, + "æīĢæľīçļĦ": 56675, + "ĠSymposium": 56676, + "\"https": 56677, + ".Debugger": 56678, + "ĠFoster": 56679, + "}*": 56680, + "ĉIf": 56681, + "esgue": 56682, + "Ġhemisphere": 56683, + "Ġdod": 56684, + "ĠParsers": 56685, + "&.": 56686, + "nullptr": 56687, + "ferenced": 56688, + "ĠJython": 56689, + "ĠMilk": 56690, + "PreferredGap": 56691, + "beg": 56692, + "WARN": 56693, + "istle": 56694, + ".big": 56695, + "/No": 56696, + "ĠGLOBAL": 56697, + "sms": 56698, + "perts": 56699, + "ï¼ļhttps": 56700, + "Ġzer": 56701, + "ĠByteBuffer": 56702, + "(intervals": 56703, + "ëĭ¨": 56704, + "Ġmocked": 56705, + "Ġpine": 56706, + "ABCDEF": 56707, + "æŀĦ建": 56708, + "Ġ.$": 56709, + "Verified": 56710, + "-opacity": 56711, + "Vz": 56712, + "è§Ĵèī²": 56713, + "ĠBearer": 56714, + "Ġeducate": 56715, + "ocoder": 56716, + "atsapp": 56717, + "ụ": 56718, + "@xxxxxxxxxx": 56719, + "sess": 56720, + "stub": 56721, + "/micro": 56722, + "/bg": 56723, + "apunov": 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