diff --git a/docs/supported_models/generative_models.md b/docs/supported_models/generative_models.md index 486839ae9..5c9f47cb9 100644 --- a/docs/supported_models/generative_models.md +++ b/docs/supported_models/generative_models.md @@ -28,6 +28,7 @@ python3 -m sglang.launch_server \ | **Command-R** (Cohere) | `CohereForAI/c4ai-command-r-v01` | Cohere’s open conversational LLM (Command series) optimized for long context, retrieval-augmented generation, and tool use. | | **DBRX** (Databricks) | `databricks/dbrx-instruct` | Databricks’ 132B-parameter MoE model (36B active) trained on 12T tokens; competes with GPT-3.5 quality as a fully open foundation model. | | **Grok** (xAI) | `xai-org/grok-1` | xAI’s grok-1 model known for vast size(314B parameters) and high quality; integrated in SGLang for high-performance inference. | +| **Arctic** (Snowflake) | `Snowflake/snowflake-arctic-instruct` | Snowflake’s dense-MoE model (17B active, 480B total) with top-2 routing, built for enterprise-grade reasoning, code, and instruction tasks. | | **ChatGLM** (GLM-130B family) | `THUDM/chatglm2-6b` | Zhipu AI’s bilingual chat model (6B) excelling at Chinese-English dialogue; fine-tuned for conversational quality and alignment. | | **InternLM 2** (7B, 20B) | `internlm/internlm2-7b` | Next-gen InternLM (7B and 20B) from SenseTime, offering strong reasoning and ultra-long context support (up to 200K tokens). | | **ExaONE 3** (Korean-English) | `LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct` | LG AI Research’s Korean-English model (7.8B) trained on 8T tokens; provides high-quality bilingual understanding and generation. | diff --git a/python/sglang/srt/configs/__init__.py b/python/sglang/srt/configs/__init__.py index 1e8370ba7..7e07fe3a6 100644 --- a/python/sglang/srt/configs/__init__.py +++ b/python/sglang/srt/configs/__init__.py @@ -1,3 +1,4 @@ +from sglang.srt.configs.arctic import ArcticConfig from sglang.srt.configs.chatglm import ChatGLMConfig from sglang.srt.configs.dbrx import DbrxConfig from sglang.srt.configs.deepseekvl2 import DeepseekVL2Config @@ -5,6 +6,7 @@ from sglang.srt.configs.exaone import ExaoneConfig from sglang.srt.configs.janus_pro import MultiModalityConfig __all__ = [ + "ArcticConfig", "ExaoneConfig", "ChatGLMConfig", "DbrxConfig", diff --git a/python/sglang/srt/configs/arctic.py b/python/sglang/srt/configs/arctic.py new file mode 100644 index 000000000..ff3887373 --- /dev/null +++ b/python/sglang/srt/configs/arctic.py @@ -0,0 +1,127 @@ +# SPDX-License-Identifier: Apache-2.0 + +"""Arctic model configuration""" + +from typing import Any, Dict, Optional + +from transformers.configuration_utils import PretrainedConfig +from transformers.utils import logging + +logger = logging.get_logger(__name__) + +ARCTIC_PRETRAINED_CONFIG_ARCHIVE_MAP = { + "arctic": "https://huggingface.co/Snowflake/snowflake-arctic-instruct/tree/main/config.json", +} + + +class ArcticConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`ArcticModel`]. It is used to instantiate an + Arctic model according to the specified arguments, defining the model architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + vocab_size (`int`, *optional*, defaults to 32000): + Vocabulary size of the Arctic model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`ArcticModel`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 14336): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer encoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer encoder. + num_key_value_heads (`int`, *optional*, defaults to 8): + This is the number of key_value heads that should be used to implement Grouped Query Attention. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 4096): + The maximum sequence length that this model might ever be used with. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + rms_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). + pad_token_id (`int`, *optional*): + The id of the padding token. + bos_token_id (`int`, *optional*, defaults to 1): + The id of the "beginning-of-sequence" token. + eos_token_id (`int`, *optional*, defaults to 2): + The id of the "end-of-sequence" token. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether the model's input and output word embeddings should be tied. + rope_theta (`float`, *optional*, defaults to 1000000.0): + The base period of the RoPE embeddings. + sliding_window (`int`, *optional*): + Sliding window attention window size. If not specified, will default to `4096`. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + num_experts_per_tok (`int`, *optional*, defaults to 2): + The number of experts to root per-token, can be also interpreted as the `top-p` routing parameter + num_local_experts (`int`, *optional*, defaults to 8): + Number of experts per Sparse MLP layer. + moe_layer_frequency (`int`, *optional*, defaults to 2): + Frequency of MoE layers in the model. + """ + + model_type = "arctic" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=32000, + hidden_size=4096, + intermediate_size=14336, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=8, + hidden_act="silu", + max_position_embeddings=4096, + initializer_range=0.02, + rms_norm_eps=1e-5, + use_cache=True, + pad_token_id=None, + bos_token_id=1, + eos_token_id=2, + tie_word_embeddings=False, + rope_theta=1e6, + sliding_window=None, + attention_dropout=0.0, + num_experts_per_tok=1, + num_local_experts=8, + moe_layer_frequency=2, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + 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.sliding_window = sliding_window + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.attention_dropout = attention_dropout + self.num_experts_per_tok = num_experts_per_tok + self.num_local_experts = num_local_experts + self.moe_layer_frequency = moe_layer_frequency + + # For backward compatibility + self._attn_implementation = kwargs.pop("_attn_implementation", "eager") + self.use_residual = kwargs.pop("use_residual", True) + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) diff --git a/python/sglang/srt/hf_transformers_utils.py b/python/sglang/srt/hf_transformers_utils.py index 0a189a7bf..9c6ecb3e3 100644 --- a/python/sglang/srt/hf_transformers_utils.py +++ b/python/sglang/srt/hf_transformers_utils.py @@ -31,6 +31,7 @@ from transformers import ( from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES from sglang.srt.configs import ( + ArcticConfig, ChatGLMConfig, DbrxConfig, DeepseekVL2Config, @@ -41,6 +42,7 @@ from sglang.srt.connector import create_remote_connector from sglang.srt.utils import is_remote_url _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = { + ArcticConfig.model_type: ArcticConfig, ChatGLMConfig.model_type: ChatGLMConfig, DbrxConfig.model_type: DbrxConfig, ExaoneConfig.model_type: ExaoneConfig, diff --git a/python/sglang/srt/models/arctic.py b/python/sglang/srt/models/arctic.py new file mode 100644 index 000000000..3049b8a5c --- /dev/null +++ b/python/sglang/srt/models/arctic.py @@ -0,0 +1,634 @@ +# Copyright 2023-2025 SGLang Team +# 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. +# ============================================================================== +# Copyright 2024 Cohere and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# 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. +# ============================================================================== + +# Adapted from +# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/arctic.py + +"""Inference-only Snowflake Arctic model.""" + +import logging +from typing import Iterable, List, Optional, Set, Tuple, Union + +import torch +from torch import nn + +from sglang.srt.configs.arctic import ArcticConfig +from sglang.srt.distributed import ( + get_pp_group, + get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size, + tensor_model_parallel_all_reduce, +) +from sglang.srt.layers.activation import SiluAndMul +from sglang.srt.layers.fused_moe import fused_experts, fused_topk +from sglang.srt.layers.layernorm import RMSNorm +from sglang.srt.layers.linear import ( + MergedColumnParallelLinear, + QKVParallelLinear, + ReplicatedLinear, + RowParallelLinear, +) +from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput +from sglang.srt.layers.quantization import QuantizationConfig +from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.layers.rotary_embedding import get_rope +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) +from sglang.srt.model_executor.forward_batch_info import ForwardBatch +from sglang.srt.model_executor.utils import set_weight_attrs +from sglang.srt.model_loader.weight_utils import default_weight_loader +from sglang.srt.platforms import current_platform + +from .interfaces import SupportsPP, SupportsQuant +from .utils import ( + extract_layer_index, + is_pp_missing_parameter, + make_empty_intermediate_tensors_factory, + make_layers, + maybe_prefix, +) + +logger = logging.getLogger(__name__) + + +class ArcticMLP(nn.Module): + def __init__( + self, + config: ArcticConfig, + expert_id: int = -1, + is_residual_mlp: bool = False, + quant_config: Optional[QuantizationConfig] = None, + reduce_results: bool = True, + prefix: str = "", + ): + super().__init__() + self.hidden_size = config.hidden_size + self.expert_id = expert_id + + self.ffn_dim = ( + config.intermediate_size if not is_residual_mlp else self.hidden_size + ) + + self.w13 = MergedColumnParallelLinear( + self.hidden_size, [self.ffn_dim] * 2, bias=False, quant_config=quant_config + ) + self.w2 = RowParallelLinear( + self.ffn_dim, + self.hidden_size, + bias=False, + reduce_results=reduce_results, + quant_config=quant_config, + ) + if config.hidden_act != "silu": + raise ValueError( + f"Unsupported activation: {config.hidden_act}. " + "Only silu is supported for now." + ) + self.act_fn = SiluAndMul() + + def forward(self, hidden_states): + gate_up, _ = self.w13(hidden_states) + hidden_states = self.act_fn(gate_up) + hidden_states, _ = self.w2(hidden_states) + return hidden_states + + +class ArcticMoE(nn.Module): + """ + Model-parallel implementation of Arctic MoE Layer. + """ + + def __init__( + self, + config: ArcticConfig, + tp_size: Optional[int] = None, + params_dtype: Optional[torch.dtype] = None, + quant_config: Optional[QuantizationConfig] = None, + reduce_results: bool = True, + prefix: str = "", + ): + super().__init__() + + layer_id = extract_layer_index(prefix) + self.tp_size = tp_size or get_tensor_model_parallel_world_size() + self.hidden_size = config.hidden_size + self.num_experts = config.num_local_experts + self.layer_id = layer_id + self.top_k = config.num_experts_per_tok + self.intermediate_size = config.intermediate_size // self.tp_size + + self.is_moe_layer = (layer_id + 1) % config.moe_layer_frequency == 0 + self.is_quant = quant_config is not None + self.reduce_results = reduce_results + # Some other parameters + if params_dtype is None: + params_dtype = torch.get_default_dtype() + self.params_dtype = params_dtype + + if not self.is_moe_layer: + self.mlp = ArcticMLP( + config, + quant_config=quant_config, + reduce_results=reduce_results, + prefix=f"{prefix}.mlp", + ) + else: + self.gate = ReplicatedLinear( + self.hidden_size, + self.num_experts, + bias=False, + params_dtype=self.params_dtype, + quant_config=quant_config, + prefix=f"{prefix}.gate", + ) + if self.is_quant: + raise NotImplementedError("Quantization is not supported yet.") + else: + self.ws = nn.Parameter( + torch.empty( + self.num_experts, + 2 * self.intermediate_size, + self.hidden_size, + device=current_platform.device_type, + dtype=self.params_dtype, + ) + ) + self.w2s = nn.Parameter( + torch.empty( + self.num_experts, + self.hidden_size, + self.intermediate_size, + device=current_platform.device_type, + dtype=self.params_dtype, + ) + ) + set_weight_attrs( + self.ws, + { + "weight_loader": self.weight_loader, + }, + ) + set_weight_attrs( + self.w2s, + { + "weight_loader": self.weight_loader, + }, + ) + + def weight_loader( + self, + param: nn.Parameter, + loaded_weight: torch.Tensor, + weight_name: str, + expert_id: int, + ): + tp_rank = get_tensor_model_parallel_rank() + param_data = param.data + shard_size = self.intermediate_size + shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size) + if weight_name.endswith("w1.weight"): + param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :] + if weight_name.endswith("w3.weight"): + param_data[expert_id, shard_size : 2 * shard_size, :] = loaded_weight[ + shard, : + ] + if weight_name.endswith("w2.weight"): + param_data[expert_id, :, :] = loaded_weight[:, shard] + + def local_moe_fused(self, hidden_states: torch.Tensor) -> torch.Tensor: + num_tokens, hidden_size = hidden_states.shape + hidden_states = hidden_states.view(-1, self.hidden_size) + # router_logits: (num_tokens, n_experts) + router_logits, _ = self.gate(hidden_states) + do_normalize = self.top_k > 1 + topk_weights, topk_ids = fused_topk( + hidden_states, router_logits, self.top_k, renormalize=do_normalize + ) + # topk_ids: (num_tokens, k) + if self.is_quant: + raise NotImplementedError("Quantization is not supported yet.") + # if 2 * num_tokens <= self.num_experts: + # # If much fewer tokens than experts, use selective dequantize. + # ws_dequantized = self.ws.ds_selective_dequantize(topk_ids.flatten()) + # w2s_dequantized = self.w2s.ds_selective_dequantize(topk_ids.flatten()) + # # We gathered the experts to the tokens so update the mapping. + # topk_ids = torch.arange( + # 0, + # topk_ids.numel(), + # device=topk_ids.device, + # ).reshape(topk_ids.shape) + # else: + # ws_dequantized = self.ws.ds_dequantize() + # w2s_dequantized = self.w2s.ds_dequantize() + + final_hidden_states = fused_experts( + hidden_states, + self.ws, + self.w2s, + topk_weights, + topk_ids, + inplace=True, + ) + if self.reduce_results and self.tp_size > 1: + final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states) + return final_hidden_states.view(num_tokens, hidden_size) + + def forward(self, hidden_states: torch.Tensor): + if self.is_moe_layer: + final_hidden_states = self.local_moe_fused(hidden_states) + else: + final_hidden_states = self.mlp(hidden_states) + return final_hidden_states + + +class ArcticAttention(nn.Module): + def __init__( + self, + config: ArcticConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + layer_idx = extract_layer_index(prefix) + + tp_size = get_tensor_model_parallel_world_size() + self.total_num_heads = config.num_attention_heads + assert self.total_num_heads % tp_size == 0 + self.num_heads = self.total_num_heads // tp_size + self.total_num_kv_heads = config.num_key_value_heads + if self.total_num_kv_heads >= tp_size: + assert self.total_num_kv_heads % tp_size == 0 + else: + assert tp_size % self.total_num_kv_heads == 0 + self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) + self.head_dim = self.hidden_size // self.total_num_heads + self.q_size = self.num_heads * self.head_dim + self.kv_size = self.num_kv_heads * self.head_dim + + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.scaling = self.head_dim**-0.5 + + self.qkv_proj = QKVParallelLinear( + self.hidden_size, + self.head_dim, + self.total_num_heads, + self.total_num_kv_heads, + bias=False, + quant_config=quant_config, + ) + self.o_proj = RowParallelLinear( + self.total_num_heads * self.head_dim, + self.hidden_size, + bias=False, + reduce_results=True, + quant_config=quant_config, + ) + + self.rotary_emb = get_rope( + self.head_dim, + rotary_dim=self.head_dim, + max_position=self.max_position_embeddings, + base=int(self.rope_theta), + is_neox_style=True, + ) + + self.attn = RadixAttention( + self.num_heads, + self.head_dim, + self.scaling, + num_kv_heads=self.num_kv_heads, + layer_id=layer_idx, + prefix=f"{prefix}.attn", + ) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + forward_batch: ForwardBatch, + ) -> torch.Tensor: + qkv, _ = self.qkv_proj(hidden_states) + q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) + q, k = self.rotary_emb(positions, q, k) + attn_output = self.attn(q, k, v, forward_batch) + output, _ = self.o_proj(attn_output) + return output + + +class ArcticDecoderLayer(nn.Module): + def __init__( + self, + config: ArcticConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + layer_idx = extract_layer_index(prefix) + is_moe_layer = (layer_idx + 1) % config.moe_layer_frequency == 0 + self.use_residual = config.use_residual and is_moe_layer + self.self_attn = ArcticAttention( + config, quant_config=quant_config, prefix=f"{prefix}.self_attn" + ) + self.block_sparse_moe = ArcticMoE( + config, + quant_config=quant_config, + reduce_results=(not self.use_residual), + prefix=f"{prefix}.block_sparse_moe", + ) + + self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = RMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + + if self.use_residual: + self.residual_layernorm = RMSNorm( + config.hidden_size, eps=config.rms_norm_eps + ) + self.residual_mlp = ArcticMLP( + config, + is_residual_mlp=True, + reduce_results=False, + prefix=f"{prefix}.residual_mlp", + ) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + forward_batch: ForwardBatch, + ) -> torch.Tensor: + residual_input = hidden_states + hidden_states = self.input_layernorm(hidden_states) + hidden_states = self.self_attn( + positions=positions, + hidden_states=hidden_states, + forward_batch=forward_batch, + ) + hidden_states = residual_input + hidden_states + + residual_attn = hidden_states + if self.use_residual: + hidden_states = self.residual_layernorm(hidden_states) + hidden_states = self.residual_mlp(hidden_states) + residual_mlp = hidden_states + hidden_states = self.post_attention_layernorm(residual_input) + hidden_states = self.block_sparse_moe(hidden_states) + hidden_states = residual_mlp + hidden_states + hidden_states = tensor_model_parallel_all_reduce(hidden_states) + hidden_states = residual_attn + hidden_states + else: + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.block_sparse_moe(hidden_states) + hidden_states = residual_attn + hidden_states + return hidden_states + + +class ArcticModel(nn.Module): + def __init__( + self, + *, + config: ArcticConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ): + super().__init__() + + self.vocab_size = config.vocab_size + self.embed_tokens = VocabParallelEmbedding( + self.vocab_size, config.hidden_size, org_num_embeddings=self.vocab_size + ) + self.start_layer, self.end_layer, self.layers = make_layers( + config.num_hidden_layers, + lambda prefix: ArcticDecoderLayer(config, quant_config, prefix=prefix), + prefix=f"{prefix}.layers", + ) + self._attn_implementation = config._attn_implementation + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( + ["hidden_states"], config.hidden_size + ) + + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.embed_tokens(input_ids) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + input_embeds: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + if input_embeds is not None: + hidden_states = input_embeds + else: + hidden_states = self.get_input_embeddings(input_ids) + + for layer in self.layers[self.start_layer : self.end_layer]: + hidden_states = layer(positions, hidden_states, forward_batch) + + hidden_states = self.norm(hidden_states) + return hidden_states + + +class ArcticForCausalLM(nn.Module): + packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]} + + def __init__( + self, + *, + config: ArcticConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ): + super().__init__() + self.config = config + self.supports_torch_tp = True + self.model = ArcticModel( + config=config, + quant_config=quant_config, + prefix=maybe_prefix(prefix, "model"), + ) + self.vocab_size = config.vocab_size + self.lm_head = ParallelLMHead( + self.vocab_size, + config.hidden_size, + quant_config=quant_config, + ) + if self.config.tie_word_embeddings: + self.lm_head.weight = self.model.embed_tokens.weight + self.num_experts = config.num_local_experts + self.num_experts_per_tok = config.num_experts_per_tok + self.unpadded_vocab_size = config.vocab_size + self.logits_processor = LogitsProcessor(self.config) + self.make_empty_intermediate_tensors = ( + self.model.make_empty_intermediate_tensors + ) + + def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: + return self.model.get_input_embeddings(input_ids) + + @torch.no_grad() + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + input_embeds: Optional[torch.Tensor] = None, + ) -> LogitsProcessorOutput: + hidden_states = self.model(input_ids, positions, forward_batch, input_embeds) + return self.logits_processor( + input_ids, hidden_states, self.lm_head, forward_batch + ) + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]: + stacked_params_mapping = [ + # (param_name, shard_name, shard_id) + ("qkv_proj", "q_proj", "q"), + ("qkv_proj", "k_proj", "k"), + ("qkv_proj", "v_proj", "v"), + ] + + mlp_params_mapping: List[Tuple[str, str, int]] = [] + expert_params_mapping: List[Tuple[str, str, int]] = [] + num_layers = self.config.num_hidden_layers + + for layer in range(num_layers): + mlp_params_mapping.append( + ( + f"layers.{layer}.residual_mlp.w13.weight", + f"layers.{layer}.residual_mlp.w1.weight", + 0, + ) + ) + mlp_params_mapping.append( + ( + f"layers.{layer}.residual_mlp.w13.weight", + f"layers.{layer}.residual_mlp.w3.weight", + 1, + ) + ) + if (layer + 1) % self.config.moe_layer_frequency != 0: + # MLP layers + mlp_params_mapping.append( + ( + f"layers.{layer}.block_sparse_moe.mlp.w13.weight", + f"layers.{layer}.block_sparse_moe.mlp.w1.weight", + 0, + ) + ) + mlp_params_mapping.append( + ( + f"layers.{layer}.block_sparse_moe.mlp.w13.weight", + f"layers.{layer}.block_sparse_moe.mlp.w3.weight", + 1, + ) + ) + else: + # MoE layers + for expert_id in range(self.config.num_local_experts): + expert_params_mapping.append( + ("ws", f"experts.{expert_id}.w1.weight", expert_id) + ) + expert_params_mapping.append( + ("w2s", f"experts.{expert_id}.w2.weight", expert_id) + ) + expert_params_mapping.append( + ("ws", f"experts.{expert_id}.w3.weight", expert_id) + ) + + params_dict = dict(self.named_parameters()) + loaded_params: Set[str] = set() + + logger.info( + "It will take ~10 minutes loading from the 16-bit weights. " + "Alternatively, use the prequantized 8-bit weights of arctic " + "and set load-format to `sharded_state` will accelerate loading." + ) + for name, loaded_weight in weights: + for param_name, weight_name, shard_id in stacked_params_mapping: + if weight_name not in name: + continue + name = name.replace(weight_name, param_name) + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + if is_pp_missing_parameter(name, self): + continue + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader(param, loaded_weight, shard_id) + break + else: + for param_name, weight_name, shard_id in mlp_params_mapping: + if weight_name not in name: + continue + name = name.replace(weight_name, param_name) + if is_pp_missing_parameter(name, self): + continue + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader(param, loaded_weight, shard_id) + break + else: + for param_name, weight_name, shard_id in expert_params_mapping: + if weight_name not in name: + continue + name = name.replace(weight_name, param_name) + if is_pp_missing_parameter(name, self): + continue + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader( + param, loaded_weight, weight_name, expert_id=shard_id + ) + break + else: + if name.endswith(".bias") and name not in params_dict: + continue + if is_pp_missing_parameter(name, self): + continue + param = params_dict[name] + + weight_loader = getattr( + param, "weight_loader", default_weight_loader + ) + weight_loader(param, loaded_weight) + loaded_params.add(name) + return loaded_params