"""Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/linear.py""" from __future__ import annotations import itertools import logging from typing import TYPE_CHECKING, Dict, List, Optional, Tuple import torch from torch import nn from torch.nn.parameter import Parameter, UninitializedParameter from sglang.srt.distributed import ( divide, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, get_tp_group, split_tensor_along_last_dim, tensor_model_parallel_all_gather, tensor_model_parallel_all_reduce, ) from sglang.srt.distributed.device_communicators.pynccl_allocator import ( use_symmetric_memory, ) from sglang.srt.layers.dp_attention import ( get_attention_tp_group, is_allocation_symmetric, ) from sglang.srt.layers.parameter import ( BasevLLMParameter, BlockQuantScaleParameter, PackedColumnParameter, PackedvLLMParameter, PerTensorScaleParameter, RowvLLMParameter, _ColumnvLLMParameter, ) from sglang.srt.layers.utils import pad_or_narrow_weight from sglang.srt.utils import get_bool_env_var, is_cpu, is_hip, is_npu, set_weight_attrs if TYPE_CHECKING: from sglang.srt.layers.quantization.base_config import ( QuantizationConfig, QuantizeMethodBase, ) _is_hip = is_hip() _disable_hip_linear_quant = _is_hip and get_bool_env_var( "SGLANG_ROCM_DISABLE_LINEARQUANT" ) logger = logging.getLogger(__name__) WEIGHT_LOADER_V2_SUPPORTED = [ "CompressedTensorsLinearMethod", "AWQMarlinLinearMethod", "AWQLinearMethod", "AWQLinearAscendMethod", "GPTQMarlinLinearMethod", "Fp8LinearMethod", "BlockInt8LinearMethod", "MarlinLinearMethod", "QQQLinearMethod", "GPTQMarlin24LinearMethod", "TPUInt8LinearMethod", "GPTQLinearMethod", "FBGEMMFp8LinearMethod", "GPTQLinearAscendMethod", "ModelOptFp8LinearMethod", "ModelOptFp4LinearMethod", "IPEXAWQLinearMethod", "PetitNvFp4LinearMethod", "QuarkInt4Fp8LinearMethod", ] _is_cpu = is_cpu() _is_npu = is_npu() def adjust_marlin_shard(param, shard_size, shard_offset): marlin_tile_size = getattr(param, "marlin_tile_size", None) if marlin_tile_size is None: return shard_size, shard_offset return shard_size * marlin_tile_size, shard_offset * marlin_tile_size def adjust_bitsandbytes_4bit_shard( param: Parameter, shard_offsets: Dict[str, Tuple[int, int]], loaded_shard_id: str ) -> Tuple[int, int]: """Adjust the quantization offsets and sizes for BitsAndBytes sharding.""" total, _ = shard_offsets["total"] orig_offset, orig_size = shard_offsets[loaded_shard_id] quantized_total = param.data.shape[0] quantized_offset = orig_offset * quantized_total // total quantized_size = orig_size * quantized_total // total return quantized_size, quantized_offset def adjust_scalar_to_fused_array(param, loaded_weight, shard_id): """For fused modules (QKV and MLP) we have an array of length N that holds 1 scale for each "logical" matrix. So the param is an array of length N. The loaded_weight corresponds to one of the shards on disk. Here, we slice the param based on the shard_id for loading. """ qkv_idxs = {"q": 0, "k": 1, "v": 2} if isinstance(shard_id, str): shard_id = qkv_idxs[shard_id] elif not isinstance(shard_id, int): raise ValueError(f"Unknown Shard Id {shard_id}") # AutoFP8 scales do not have a shape # compressed-tensors scales do have a shape if len(loaded_weight.shape) != 0: assert loaded_weight.shape[0] == 1 loaded_weight = loaded_weight[0] return param[shard_id], loaded_weight def adjust_shard_offsets(shard_offsets, loaded_weight, dim): actual_weight_size = loaded_weight.size(dim) target_weight_size = shard_offsets[-1][-1] + shard_offsets[-1][-2] if actual_weight_size != target_weight_size: new_shard_offsets = [] new_offset = 0 for shard_id, shard_offset, shard_size in shard_offsets: actual_shard_size = actual_weight_size * shard_size // target_weight_size new_shard_offsets.append((shard_id, new_offset, actual_shard_size)) new_offset += actual_shard_size return new_shard_offsets return shard_offsets class LinearBase(torch.nn.Module): """Base linear layer. Args: input_size: input dimension of the linear layer. output_size: output dimension of the linear layer. bias: If true, add bias. skip_bias_add: If true, skip adding bias but instead return it. params_dtype: Data type for the parameters. quant_config: Quantization configure. """ def __init__( self, input_size: int, output_size: int, skip_bias_add: bool = False, params_dtype: Optional[torch.dtype] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() # Keep input parameters self.input_size = input_size self.output_size = output_size self.skip_bias_add = skip_bias_add if params_dtype is None: params_dtype = torch.get_default_dtype() self.params_dtype = params_dtype self.quant_config = quant_config if quant_config is None: from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod self.quant_method: Optional[QuantizeMethodBase] = UnquantizedLinearMethod() else: self.quant_method = quant_config.get_quant_method(self, prefix=prefix) def forward(self, x: torch.Tensor) -> torch.Tensor: raise NotImplementedError class ReplicatedLinear(LinearBase): """Replicated linear layer. Args: input_size: input dimension of the linear layer. output_size: output dimension of the linear layer. bias: If true, add bias. skip_bias_add: If true, skip adding bias but instead return it. params_dtype: Data type for the parameters. quant_config: Quantization configure. prefix: The name of the layer in the state dict, including all parents (e.g. model.layers.0.qkv_proj) """ def __init__( self, input_size: int, output_size: int, bias: bool = True, skip_bias_add: bool = False, params_dtype: Optional[torch.dtype] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__( input_size, output_size, skip_bias_add, params_dtype, quant_config, prefix=prefix, ) # All the linear layer supports quant method. assert self.quant_method is not None self.quant_method.create_weights( self, self.input_size, [self.output_size], self.input_size, self.output_size, self.params_dtype, weight_loader=self.weight_loader, ) if bias: self.bias = Parameter( torch.empty(self.output_size, dtype=self.params_dtype) ) set_weight_attrs( self.bias, { "output_dim": 0, "weight_loader": self.weight_loader, }, ) else: self.register_parameter("bias", None) def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor): # If the weight on disk does not have a shape, give it one # (such scales for AutoFp8). if len(loaded_weight.shape) == 0: loaded_weight = loaded_weight.reshape(1) # The per-tensor quant-scale must be 1 dimension if _is_npu: if param.size() != loaded_weight.size() and param.size(0) == 1: if torch.allclose(loaded_weight, loaded_weight[0]): loaded_weight = loaded_weight[:1] else: raise ValueError(f"{loaded_weight} are not all equal") if param.dtype == torch.int8 or loaded_weight.dtype == torch.int8: assert ( param.dtype == loaded_weight.dtype ), "init para dtype and loaded weight dtype should be the same" assert param.size() == loaded_weight.size() param.data.copy_(loaded_weight) def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: bias = self.bias if not self.skip_bias_add else None assert self.quant_method is not None output = self.quant_method.apply(self, x, bias) output_bias = self.bias if self.skip_bias_add else None return output, output_bias def extra_repr(self) -> str: s = f"in_features={self.input_size}" s += f", output_features={self.output_size}" s += f", bias={self.bias is not None}" return s class ColumnParallelLinear(LinearBase): """Linear layer with column parallelism. The linear layer is defined as Y = XA + b. A is parallelized along its second dimension as A = [A_1, ..., A_p]. Args: input_size: first dimension of matrix A. output_size: second dimension of matrix A. bias: If true, add bias. gather_output: If true, call all-gather on output and make Y available to all GPUs, otherwise, every GPU will have its output which is Y_i = XA_i skip_bias_add: This was added to enable performance optimizations where bias can be fused with other element-wise operations. we skip adding bias but instead return it. params_dtype: Data type for the parameters. quant_config: Quantization configure. output_sizes: list of output sizes packed into one output, like for QKV the list would be size 3. prefix: The name of the layer in the state dict, including all parents (e.g. model.layers.0.qkv_proj) """ def __init__( self, input_size: int, output_size: int, bias: bool = True, gather_output: bool = False, skip_bias_add: bool = False, params_dtype: Optional[torch.dtype] = None, quant_config: Optional[QuantizationConfig] = None, output_sizes: Optional[List[int]] = None, prefix: str = "", tp_rank: Optional[int] = None, tp_size: Optional[int] = None, use_presharded_weights: bool = False, skip_block_quant_check: bool = False, ): super().__init__( input_size, output_size, skip_bias_add, params_dtype, quant_config, prefix ) self.gather_output = gather_output self.use_presharded_weights = use_presharded_weights # Divide the weight matrix along the last dimension. if tp_rank is None: tp_rank = get_tensor_model_parallel_rank() if tp_size is None: tp_size = get_tensor_model_parallel_world_size() self.tp_rank, self.tp_size = tp_rank, tp_size assert self.quant_method is not None self.output_size_per_partition = divide(self.output_size, tp_size) self.output_partition_sizes = [self.output_size_per_partition] # If QKV or MergedColumn, use output size of each partition. if hasattr(self, "output_sizes"): self.output_partition_sizes = [ divide(output_size, tp_size) for output_size in self.output_sizes ] if output_sizes is None: output_sizes = [output_size] self.quant_method.create_weights( layer=self, input_size_per_partition=self.input_size, output_partition_sizes=self.output_partition_sizes, input_size=self.input_size, output_size=self.output_size, params_dtype=self.params_dtype, skip_block_quant_check=skip_block_quant_check, weight_loader=( self.weight_loader_v2 if self.quant_method.__class__.__name__ in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader ), ) if bias: self.bias = Parameter( torch.zeros(self.output_size_per_partition, dtype=params_dtype) ) set_weight_attrs( self.bias, { "output_dim": 0, "weight_loader": self.weight_loader, }, ) else: self.register_parameter("bias", None) def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor): output_dim = getattr(param, "output_dim", None) param_data = param.data # Special case for GGUF is_gguf_weight = getattr(param, "is_gguf_weight", False) is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False) if is_gguf_weight_type: param.weight_type = loaded_weight.item() # Materialize GGUF UninitializedParameter if is_gguf_weight and isinstance(param, UninitializedParameter): param.materialize(loaded_weight.shape, dtype=loaded_weight.dtype) # bitsandbytes loads the weights of the specific portion # no need to narrow here use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False) if output_dim is not None and not use_bitsandbytes_4bit: shard_size = param_data.shape[output_dim] start_idx = self.tp_rank * shard_size if _is_cpu: from sglang.srt.model_loader.weight_utils import ( narrow_padded_param_and_loaded_weight, ) param_data, loaded_weight = narrow_padded_param_and_loaded_weight( param_data, loaded_weight, 0, # param_data_start start_idx, output_dim, shard_size, not self.use_presharded_weights, ) else: if not self.use_presharded_weights: loaded_weight = loaded_weight.narrow( output_dim, start_idx, shard_size ) # Special case for loading scales off disk, which often do not # have a shape (such as in the case of AutoFP8). if len(loaded_weight.shape) == 0: loaded_weight = loaded_weight.reshape(1) assert param_data.shape == loaded_weight.shape param_data.copy_(loaded_weight) def weight_loader_v2(self, param: Parameter, loaded_weight: torch.Tensor): # Special case for loading scales off disk, which often do not # have a shape (such as in the case of AutoFP8). if len(loaded_weight.shape) == 0: assert loaded_weight.numel() == 1 loaded_weight = loaded_weight.reshape(1) if isinstance(param, _ColumnvLLMParameter): param.load_column_parallel_weight( loaded_weight, tp_rank=self.tp_rank, use_presharded_weights=self.use_presharded_weights, ) else: # FIXME: This branch is needed to load deepseek v3 awq. # However, we should fix this and avoid the branching here. # After QuantizedRL reload, params might still need tp_rank try: param.load_column_parallel_weight( loaded_weight, tp_rank=self.tp_rank, use_presharded_weights=self.use_presharded_weights, ) except TypeError: # Fallback for parameters that don't accept additional args param.load_column_parallel_weight(loaded_weight) def forward(self, input_): bias = self.bias if not self.skip_bias_add else None # Matrix multiply. assert self.quant_method is not None output_parallel = self.quant_method.apply(self, input_, bias) if self.gather_output: # All-gather across the partitions. output = tensor_model_parallel_all_gather(output_parallel) else: output = output_parallel output_bias = self.bias if self.skip_bias_add else None return output, output_bias def extra_repr(self) -> str: s = f"in_features={self.input_size}" s += f", output_features={self.output_size_per_partition}" s += f", bias={self.bias is not None}" s += f", tp_size={self.tp_size}" s += f", gather_output={self.gather_output}" return s class MergedColumnParallelLinear(ColumnParallelLinear): """Packed linear layers with column parallelism. Similar to ColumnParallelLinear, but the weight matrix is concatenated along the output dimension. When the weight matrix is loaded, the different partitions are sharded separately. Args: input_size: input dimension of the linear layer. output_sizes: list of output dimensions of the linear layer. bias: If true, add bias. gather_output: If true, call all-gather on output and make the output available to all GPUs, otherwise, every GPU will have its own output. skip_bias_add: This was added to enable performance optimizations where bias can be fused with other element-wise operations. we skip adding bias but instead return it. params_dtype: Data type for the parameters. quant_config: Quantization configure. prefix: The name of the layer in the state dict, including all parents (e.g. model.layers.0.qkv_proj) """ def __init__( self, input_size: int, output_sizes: List[int], bias: bool = True, gather_output: bool = False, skip_bias_add: bool = False, params_dtype: Optional[torch.dtype] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", tp_rank: Optional[int] = None, tp_size: Optional[int] = None, use_presharded_weights: bool = False, ): self.output_sizes = output_sizes if tp_rank is None: tp_rank = get_tensor_model_parallel_rank() if tp_size is None: tp_size = get_tensor_model_parallel_world_size() self.tp_rank, self.tp_size = tp_rank, tp_size assert all(output_size % tp_size == 0 for output_size in output_sizes) self.use_presharded_weights = use_presharded_weights super().__init__( input_size=input_size, output_size=sum(output_sizes), bias=bias, gather_output=gather_output, skip_bias_add=skip_bias_add, params_dtype=params_dtype, quant_config=quant_config, prefix=prefix, tp_rank=tp_rank, tp_size=tp_size, use_presharded_weights=use_presharded_weights, ) self.prefix = prefix def weight_loader( self, param: Parameter, loaded_weight: torch.Tensor, loaded_shard_id: Optional[int] = None, ): # Special case for GGUF # initialize GGUF param after we know the quantize type is_gguf_weight = getattr(param, "is_gguf_weight", False) is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False) if is_gguf_weight_type: param.data[loaded_shard_id].copy_(loaded_weight) param.shard_weight_type[loaded_shard_id] = loaded_weight.item() return if is_gguf_weight: output_dim = getattr(param, "output_dim", None) shard_size = loaded_weight.size(output_dim) // self.tp_size start_idx = self.tp_rank * shard_size loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size) param.shard_id.append(loaded_shard_id) param.shard_id_map[loaded_shard_id] = len(param.data_container) param.data_container.append(loaded_weight) return param_data = param.data output_dim = getattr(param, "output_dim", None) # Special case for AQLM codebooks. is_metadata = getattr(param, "is_metadata", False) # Special case for per-tensor scale to load scalar into fused array. needs_scalar_to_array = getattr(param, "needs_scalar_to_array", False) if loaded_shard_id is None: # Loaded weight is already fused on disk (qkv/mlp). if output_dim is None: if needs_scalar_to_array: param_data, loaded_weight = adjust_scalar_to_fused_array( param_data, loaded_weight, 0 ) assert param_data.shape == loaded_weight.shape param_data.copy_(loaded_weight) return current_shard_offset = 0 shard_offsets: List[Tuple[int, int, int]] = [] for i, output_size in enumerate(self.output_sizes): shard_offsets.append((i, current_shard_offset, output_size)) current_shard_offset += output_size packed_dim = getattr(param, "packed_dim", None) use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False) if _is_cpu: shard_offsets = adjust_shard_offsets( shard_offsets, loaded_weight, output_dim ) for shard_id, shard_offset, shard_size in shard_offsets: # Special case for Quantization. # If quantized, we need to adjust the offset and size to account # for the packing. if packed_dim == output_dim: shard_size = shard_size // param.pack_factor shard_offset = shard_offset // param.pack_factor # Special case for Marlin. shard_size, shard_offset = adjust_marlin_shard( param, shard_size, shard_offset ) if use_bitsandbytes_4bit: index = list(itertools.accumulate([0] + self.output_sizes)) orig_offsets = { str(i): (index[i], size) for i, size in enumerate(self.output_sizes) } orig_offsets["total"] = (self.output_size, 0) shard_size, shard_offset = adjust_bitsandbytes_4bit_shard( param, orig_offsets, str(shard_id) ) loaded_weight_shard = loaded_weight.narrow( output_dim, shard_offset, shard_size ) self.weight_loader(param, loaded_weight_shard, shard_id) return assert loaded_shard_id < len(self.output_sizes) if output_dim is not None: shard_offset = sum(self.output_sizes[:loaded_shard_id]) // self.tp_size shard_size = self.output_sizes[loaded_shard_id] // self.tp_size # Special case for quantization. # If quantized, we need to adjust the offset and size to account # for the packing. packed_dim = getattr(param, "packed_dim", None) if packed_dim == output_dim: shard_size = shard_size // param.pack_factor shard_offset = shard_offset // param.pack_factor # Special case for Marlin. shard_size, shard_offset = adjust_marlin_shard( param, shard_size, shard_offset ) use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False) if use_bitsandbytes_4bit: shard_size = loaded_weight.shape[output_dim] shard_offset = loaded_weight.shape[output_dim] * loaded_shard_id param_data = param_data.narrow(output_dim, shard_offset, shard_size) start_idx = self.tp_rank * shard_size if _is_cpu: from sglang.srt.model_loader.weight_utils import ( narrow_padded_param_and_loaded_weight, ) param_data, loaded_weight = narrow_padded_param_and_loaded_weight( param_data, loaded_weight, 0, # param_data_start start_idx, output_dim, shard_size, not use_bitsandbytes_4bit and not self.use_presharded_weights, ) else: # bitsandbytes loads the weights of the specific portion # no need to narrow here if not use_bitsandbytes_4bit and not self.use_presharded_weights: # Padding for special case like qwen2_5_VL's mlp which is not 8-aligned end_idx = start_idx + shard_size if end_idx > loaded_weight.shape[output_dim]: loaded_weight = pad_or_narrow_weight( loaded_weight, output_dim, start_idx, shard_size ) else: loaded_weight = loaded_weight.narrow( output_dim, start_idx, shard_size ) # Special case for AQLM codebooks. elif is_metadata: # metadata indicates fixed size concatenated along dim 0 shard_size = loaded_weight.shape[0] shard_offset = loaded_shard_id * shard_size param_data = param_data.narrow(0, shard_offset, shard_size) # Special case for per-tensor scales in fused case. elif needs_scalar_to_array: param_data, loaded_weight = adjust_scalar_to_fused_array( param_data, loaded_weight, loaded_shard_id ) else: ignore_warning = getattr(param, "ignore_warning", False) if not ignore_warning: logger.warning( "Loading a weight without `output_dim` attribute in " "MergedColumnParallelLinear, assume the weight is " "the same for all partitions." ) assert param_data.shape == loaded_weight.shape param_data.copy_(loaded_weight) def _load_fused_module_from_checkpoint( self, param: BasevLLMParameter, loaded_weight: torch.Tensor ): """ Handle special case for models where MLP layers are already fused on disk. In this case, we have no shard id. This function determmines the shard id by splitting these layers and then calls the weight loader using the shard id. An example of a model with these fused layers: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct """ current_shard_offset = 0 shard_offsets: List[Tuple[int, int, int]] = [] for i, output_size in enumerate(self.output_sizes): shard_offsets.append((i, current_shard_offset, output_size)) current_shard_offset += output_size for shard_id, shard_offset, shard_size in shard_offsets: # Special case for Quantization. # If quantized, we need to adjust the offset and size to account # for the packing. if ( isinstance(param, (PackedColumnParameter, PackedvLLMParameter)) and param.packed_dim == param.output_dim ): shard_size, shard_offset = param.adjust_shard_indexes_for_packing( shard_size=shard_size, shard_offset=shard_offset ) loaded_weight_shard = loaded_weight.narrow( param.output_dim, shard_offset, shard_size ) self.weight_loader_v2(param, loaded_weight_shard, shard_id) def _load_merged_block_scale( self, param: BasevLLMParameter, loaded_weight: torch.Tensor ): """ Handle block-wise scale loading for MergedColumnParallelLinear. Similar to QKVParallelLinear._load_qkv_block_scale, but for merged column layers. """ weight_block_size = self.quant_method.quant_config.weight_block_size block_n, _ = weight_block_size[0], weight_block_size[1] block_n = 1 if getattr(param, "format_ue8m0", False) else block_n # Calculate block sizes for each shard shard_block_sizes = [] shard_block_offsets = [] current_block_offset = 0 for output_size in self.output_sizes: shard_block_size = (output_size + block_n - 1) // block_n shard_block_sizes.append(shard_block_size) shard_block_offsets.append(current_block_offset) current_block_offset += shard_block_size # Load each shard for shard_id, (shard_block_offset, shard_block_size) in enumerate( zip(shard_block_offsets, shard_block_sizes) ): # Extract the shard from loaded_weight loaded_weight_shard = loaded_weight.narrow( param.output_dim, shard_block_offset, shard_block_size ) # Calculate per-rank offset and size (considering TP) rank_shard_offset = shard_block_offset // self.tp_size rank_shard_size = shard_block_size // self.tp_size # Load into the parameter param.load_merged_column_weight( loaded_weight=loaded_weight_shard, shard_id=shard_id, shard_offset=rank_shard_offset, shard_size=rank_shard_size, tp_rank=self.tp_rank, tp_size=self.tp_size, use_presharded_weights=self.use_presharded_weights, ) def weight_loader_v2( self, param: BasevLLMParameter, loaded_weight: torch.Tensor, loaded_shard_id: Optional[int] = None, ): if loaded_shard_id is None: if isinstance(param, PerTensorScaleParameter): param.load_merged_column_weight( loaded_weight=loaded_weight, shard_id=0, tp_rank=self.tp_rank, tp_size=self.tp_size, ) return elif isinstance(param, BlockQuantScaleParameter): self._load_merged_block_scale(param, loaded_weight) return elif type(param) in (RowvLLMParameter, BasevLLMParameter): param.load_merged_column_weight( loaded_weight=loaded_weight, tp_rank=self.tp_rank, tp_size=self.tp_size, ) return # TODO: @dsikka - move to parameter.py self._load_fused_module_from_checkpoint(param, loaded_weight) return assert loaded_shard_id < len(self.output_sizes) if isinstance(param, BlockQuantScaleParameter): weight_block_size = self.quant_method.quant_config.weight_block_size raw_block_n, _ = weight_block_size[0], weight_block_size[1] block_n = 1 if getattr(param, "format_ue8m0", False) else raw_block_n shard_offset = ( (sum(self.output_sizes[:loaded_shard_id]) + block_n - 1) // block_n ) // self.tp_size shard_size = ( (self.output_sizes[loaded_shard_id] + block_n - 1) // block_n // self.tp_size ) else: shard_offset = sum(self.output_sizes[:loaded_shard_id]) // self.tp_size shard_size = self.output_sizes[loaded_shard_id] // self.tp_size param.load_merged_column_weight( loaded_weight=loaded_weight, shard_id=loaded_shard_id, shard_offset=shard_offset, shard_size=shard_size, use_presharded_weights=self.use_presharded_weights, tp_rank=self.tp_rank, tp_size=self.tp_size, ) class QKVParallelLinear(ColumnParallelLinear): """Linear layers for the attention's QKV transformation. Linear layers for the linear transformation of the query, key, and value vectors in the attention layer. The weight matrix is concatenated along the output dimension. The layer is parallelized along the head dimension. When the number of key/value heads is smaller than the number of query heads (e.g., multi-query/grouped-query attention), the key/value head may be replicated while the query heads are partitioned. Args: hidden_size: input hidden state size of the transformer. head_size: size of each attention head. total_num_heads: total number of attention query heads. total_num_kv_heads: total number of attention key/value heads. If None, assume total_num_kv_heads = total_num_heads. bias: If true, add bias. skip_bias_add: This was added to enable performance optimizations where bias can be fused with other element-wise operations. we skip adding bias but instead return it. params_dtype: Data type for the parameters. quant_config: Quantization configure. prefix: The name of the layer in the state dict, including all parents (e.g. model.layers.0.qkv_proj) """ def __init__( self, hidden_size: int, head_size: int, total_num_heads: int, total_num_kv_heads: Optional[int] = None, bias: bool = True, skip_bias_add: bool = False, params_dtype: Optional[torch.dtype] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", tp_rank: Optional[int] = None, tp_size: Optional[int] = None, load_presharded_attn: bool = False, v_head_size: Optional[int] = None, skip_block_quant_check: bool = False, ): self.hidden_size = hidden_size self.head_size = head_size self.v_head_size = v_head_size if v_head_size is not None else head_size self.total_num_heads = total_num_heads if total_num_kv_heads is None: total_num_kv_heads = total_num_heads self.total_num_kv_heads = total_num_kv_heads # Divide the weight matrix along the last dimension. if tp_rank is None: tp_rank = get_tensor_model_parallel_rank() if tp_size is None: tp_size = get_tensor_model_parallel_world_size() self.tp_rank, self.tp_size = tp_rank, tp_size self.num_heads = divide(self.total_num_heads, tp_size) if tp_size >= self.total_num_kv_heads: self.num_kv_heads = 1 self.num_kv_head_replicas = divide(tp_size, self.total_num_kv_heads) else: self.num_kv_heads = divide(self.total_num_kv_heads, tp_size) self.num_kv_head_replicas = 1 self.q_proj_shard_size = self.num_heads * self.head_size self.kv_proj_shard_size = self.num_kv_heads * self.head_size self.v_proj_shard_size = self.num_kv_heads * self.v_head_size input_size = self.hidden_size output_size = ( self.num_heads * self.head_size + self.num_kv_heads * self.head_size + self.num_kv_heads * self.v_head_size ) * tp_size self.output_sizes = [ self.num_heads * self.head_size * tp_size, # q_proj self.num_kv_heads * self.head_size * tp_size, # k_proj self.num_kv_heads * self.v_head_size * tp_size, # v_proj ] self.use_presharded_weights = load_presharded_attn quant_config = None if _disable_hip_linear_quant else quant_config super().__init__( input_size=input_size, output_size=output_size, bias=bias, gather_output=False, skip_bias_add=skip_bias_add, params_dtype=params_dtype, quant_config=quant_config, prefix=prefix, tp_rank=tp_rank, tp_size=tp_size, use_presharded_weights=self.use_presharded_weights, skip_block_quant_check=skip_block_quant_check, ) def _get_shard_offset_mapping(self, loaded_shard_id: str): shard_offset_mapping = { "q": 0, "k": self.num_heads * self.head_size, "v": (self.num_heads + self.num_kv_heads) * self.head_size, "total": (self.num_heads + self.num_kv_heads) * self.head_size + self.num_kv_heads * self.v_head_size, } return shard_offset_mapping.get(loaded_shard_id) def _get_shard_size_mapping(self, loaded_shard_id: str): shard_size_mapping = { "q": self.num_heads * self.head_size, "k": self.num_kv_heads * self.head_size, "v": self.num_kv_heads * self.v_head_size, } return shard_size_mapping.get(loaded_shard_id) def _load_fused_module_from_checkpoint( self, param: BasevLLMParameter, loaded_weight: torch.Tensor ): """ Handle special case for models where QKV layers are already fused on disk. In this case, we have no shard id. This function determmines the shard id by splitting these layers and then calls the weight loader using the shard id. An example of a model with these fused layers: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct """ shard_offsets = [ # (shard_id, shard_offset, shard_size) ("q", 0, self.total_num_heads * self.head_size), ( "k", self.total_num_heads * self.head_size, self.total_num_kv_heads * self.head_size, ), ( "v", (self.total_num_heads + self.total_num_kv_heads) * self.head_size, self.total_num_kv_heads * self.v_head_size, ), ] for shard_id, shard_offset, shard_size in shard_offsets: # Special case for Quantization. # If quantized, we need to adjust the offset and size to account # for the packing. if ( isinstance(param, (PackedColumnParameter, PackedvLLMParameter)) and param.packed_dim == param.output_dim ): shard_size, shard_offset = param.adjust_shard_indexes_for_packing( shard_size=shard_size, shard_offset=shard_offset ) if not self.use_presharded_weights: loaded_weight_shard = loaded_weight.narrow( param.output_dim, shard_offset, shard_size ) self.weight_loader_v2(param, loaded_weight_shard, shard_id) def _load_qkv_block_scale( self, param: BasevLLMParameter, loaded_weight: torch.Tensor ): block_n, _ = self.quant_method.quant_config.weight_block_size q_size = self.total_num_heads * self.head_size // block_n k_size = self.total_num_kv_heads * self.head_size // block_n v_size = self.total_num_kv_heads * self.head_size // block_n shard_offsets = [ # (shard_id, shard_offset, shard_size) ("q", 0, q_size), ("k", q_size, k_size), ("v", q_size + k_size, v_size), ] for shard_id, shard_offset, shard_size in shard_offsets: loaded_weight_shard = loaded_weight.narrow( param.output_dim, shard_offset, shard_size ) rank_shard_offset = self._get_shard_offset_mapping(shard_id) // block_n rank_shard_size = self._get_shard_size_mapping(shard_id) // block_n param.load_qkv_weight( loaded_weight=loaded_weight_shard, num_heads=self.num_kv_head_replicas, shard_id=shard_id, shard_offset=rank_shard_offset, shard_size=rank_shard_size, tp_rank=self.tp_rank, use_presharded_weights=self.use_presharded_weights, ) def weight_loader_v2( self, param: BasevLLMParameter, loaded_weight: torch.Tensor, loaded_shard_id: Optional[str] = None, ): if loaded_shard_id is None: # special case for certain models if isinstance(param, PerTensorScaleParameter): param.load_qkv_weight(loaded_weight=loaded_weight, shard_id=0) return elif type(param) in (RowvLLMParameter, BasevLLMParameter): param.load_qkv_weight(loaded_weight=loaded_weight) return elif isinstance(param, BlockQuantScaleParameter): self._load_qkv_block_scale(param, loaded_weight) return # TODO: @dsikka - move to parameter.py self._load_fused_module_from_checkpoint(param, loaded_weight) return assert loaded_shard_id in ["q", "k", "v"] shard_offset = self._get_shard_offset_mapping(loaded_shard_id) shard_size = self._get_shard_size_mapping(loaded_shard_id) if isinstance(param, BlockQuantScaleParameter): weight_block_size = self.quant_method.quant_config.weight_block_size raw_block_n, _ = weight_block_size[0], weight_block_size[1] block_n = 1 if getattr(param, "format_ue8m0", False) else raw_block_n shard_offset = (shard_offset + block_n - 1) // block_n shard_size = (shard_size + block_n - 1) // block_n param.load_qkv_weight( loaded_weight=loaded_weight, num_heads=self.num_kv_head_replicas, shard_id=loaded_shard_id, shard_offset=shard_offset, shard_size=shard_size, tp_rank=self.tp_rank, use_presharded_weights=self.use_presharded_weights, ) def weight_loader( self, param: Parameter, loaded_weight: torch.Tensor, loaded_shard_id: Optional[str] = None, ): # Special case for GGUF # initialize GGUF param after we know the quantize type is_gguf_weight = getattr(param, "is_gguf_weight", False) is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False) if is_gguf_weight_type and loaded_shard_id is not None: idx_map = {"q": 0, "k": 1, "v": 2} param.data[idx_map[loaded_shard_id]].copy_(loaded_weight) param.shard_weight_type[loaded_shard_id] = loaded_weight.item() return if is_gguf_weight: output_dim = getattr(param, "output_dim", None) shard_size = loaded_weight.size(output_dim) // self.tp_size start_idx = self.tp_rank * shard_size loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size) param.shard_id.append(loaded_shard_id) param.shard_id_map[loaded_shard_id] = len(param.data_container) param.data_container.append(loaded_weight) return param_data = param.data output_dim = getattr(param, "output_dim", None) # Special case for AQLM codebooks. is_metadata = getattr(param, "is_metadata", False) # Special case for per-tensor scales in fused case. needs_scalar_to_array = getattr(param, "needs_scalar_to_array", False) if loaded_shard_id is None: # Loaded weight is already fused on disk (qkv/mlp). if output_dim is None: if needs_scalar_to_array: param_data, loaded_weight = adjust_scalar_to_fused_array( param_data, loaded_weight, 0 ) assert param_data.shape == loaded_weight.shape param_data.copy_(loaded_weight) return shard_offsets = [ # (shard_id, shard_offset, shard_size) ("q", 0, self.total_num_heads * self.head_size), ( "k", self.total_num_heads * self.head_size, self.total_num_kv_heads * self.head_size, ), ( "v", (self.total_num_heads + self.total_num_kv_heads) * self.head_size, self.total_num_kv_heads * self.v_head_size, ), ] use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False) packed_dim = getattr(param, "packed_dim", None) if _is_cpu: shard_offsets = adjust_shard_offsets( shard_offsets, loaded_weight, output_dim ) for shard_id, shard_offset, shard_size in shard_offsets: # Special case for Quantized Weights. # If quantized, we need to adjust the offset and size to account # for the packing. if packed_dim == output_dim: shard_size = shard_size // param.pack_factor shard_offset = shard_offset // param.pack_factor # Special case for Marlin. shard_size, shard_offset = adjust_marlin_shard( param, shard_size, shard_offset ) if use_bitsandbytes_4bit: orig_qkv_offsets = { "q": (0, self.total_num_heads * self.head_size), "k": ( self.total_num_heads * self.head_size, self.total_num_kv_heads * self.head_size, ), "v": ( (self.total_num_heads + self.total_num_kv_heads) * self.head_size, self.total_num_kv_heads * self.v_head_size, ), "total": ( (self.total_num_heads + self.total_num_kv_heads) * self.head_size + self.total_num_kv_heads * self.v_head_size, 0, ), } shard_size, shard_offset = adjust_bitsandbytes_4bit_shard( param, orig_qkv_offsets, shard_id ) if not self.use_presharded_weights: loaded_weight_shard = loaded_weight.narrow( output_dim, shard_offset, shard_size ) self.weight_loader(param, loaded_weight_shard, shard_id) return assert loaded_shard_id in ["q", "k", "v"] # If output dim is defined, use the default loading process. if output_dim is not None: if loaded_shard_id == "q": shard_offset = 0 shard_size = self.num_heads * self.head_size elif loaded_shard_id == "k": shard_offset = self.num_heads * self.head_size shard_size = self.num_kv_heads * self.head_size elif loaded_shard_id == "v": shard_offset = (self.num_heads + self.num_kv_heads) * self.head_size shard_size = self.num_kv_heads * self.v_head_size # Special case for Quantized Weights. # If quantized, we need to adjust the offset and size to account # for the packing. packed_dim = getattr(param, "packed_dim", None) if packed_dim == output_dim: shard_size = shard_size // param.pack_factor shard_offset = shard_offset // param.pack_factor # Special case for Marlin. shard_size, shard_offset = adjust_marlin_shard( param, shard_size, shard_offset ) use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False) if use_bitsandbytes_4bit: orig_qkv_offsets = { "q": (0, self.num_heads * self.head_size), "k": ( self.num_heads * self.head_size, self.num_kv_heads * self.head_size, ), "v": ( (self.num_heads + self.num_kv_heads) * self.head_size, self.num_kv_heads * self.v_head_size, ), "total": ( (self.num_heads + self.num_kv_heads) * self.head_size + self.num_kv_heads * self.v_head_size, 0, ), } shard_size, shard_offset = adjust_bitsandbytes_4bit_shard( param, orig_qkv_offsets, loaded_shard_id ) param_data = param_data.narrow(output_dim, shard_offset, shard_size) if loaded_shard_id == "q": shard_id = self.tp_rank else: shard_id = self.tp_rank // self.num_kv_head_replicas start_idx = shard_id * shard_size if _is_cpu: from sglang.srt.model_loader.weight_utils import ( narrow_padded_param_and_loaded_weight, ) param_data, loaded_weight = narrow_padded_param_and_loaded_weight( param_data, loaded_weight, 0, # param_data_start start_idx, output_dim, shard_size, not use_bitsandbytes_4bit and not self.use_presharded_weights, ) else: # bitsandbytes loads the weights of the specific portion # no need to narrow here if not use_bitsandbytes_4bit and not self.use_presharded_weights: loaded_weight = loaded_weight.narrow( output_dim, start_idx, shard_size ) # Special case for for AQLM codebooks. elif is_metadata: # metadata indicates fixed size concatenated along dim 0 shard_size = loaded_weight.shape[0] shard_index = ["q", "k", "v"].index(loaded_shard_id) param_data = param_data.narrow(0, shard_index * shard_size, shard_size) # Special case for per-tensor scales in fused case. elif needs_scalar_to_array: param_data, loaded_weight = adjust_scalar_to_fused_array( param_data, loaded_weight, loaded_shard_id ) else: ignore_warning = getattr(param, "ignore_warning", False) if not ignore_warning: logger.warning( "Loading a weight without `output_dim` attribute in " "QKVParallelLinear, assume the weight is the same " "for all partitions." ) assert ( param_data.shape == loaded_weight.shape ), f"{param_data.shape=} {loaded_weight.shape=}" param_data.copy_(loaded_weight) class RowParallelLinear(LinearBase): """Linear layer with row parallelism. The linear layer is defined as Y = XA + b. A is parallelized along its first dimension and X along its second dimension as: - - | A_1 | | . | A = | . | X = [X_1, ..., X_p] | . | | A_p | - - Arguments: input_size: first dimension of matrix A. output_size: second dimension of matrix A. bias: If true, add bias. Note that bias is not parallelized. input_is_parallel: If true, we assume that the input is already split across the GPUs and we do not split again. skip_bias_add: This was added to enable performance optimization where bias can be fused with other element-wise operations. We skip adding bias but instead return it. params_dtype: Data type for the parameters. quant_config: Quantization configure. """ def __init__( self, input_size: int, output_size: int, bias: bool = True, input_is_parallel: bool = True, skip_bias_add: bool = False, params_dtype: Optional[torch.dtype] = None, reduce_results: bool = True, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", tp_rank: Optional[int] = None, tp_size: Optional[int] = None, use_presharded_weights: bool = False, use_dp_attention_reduce: bool = False, ): quant_config = None if _disable_hip_linear_quant else quant_config super().__init__( input_size, output_size, skip_bias_add, params_dtype, quant_config, prefix ) self.input_is_parallel = input_is_parallel self.reduce_results = reduce_results self.use_dp_attention_reduce = use_dp_attention_reduce # Divide the weight matrix along the last dimension. if tp_rank is None: tp_rank = get_tensor_model_parallel_rank() if tp_size is None: tp_size = get_tensor_model_parallel_world_size() self.tp_rank, self.tp_size = tp_rank, tp_size self.input_size_per_partition = divide(input_size, self.tp_size) assert self.quant_method is not None self.use_presharded_weights = use_presharded_weights self.quant_method.create_weights( layer=self, input_size_per_partition=self.input_size_per_partition, output_partition_sizes=[self.output_size], input_size=self.input_size, output_size=self.output_size, params_dtype=self.params_dtype, weight_loader=( self.weight_loader_v2 if self.quant_method.__class__.__name__ in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader ), ) if bias: self.bias = Parameter(torch.zeros(self.output_size, dtype=params_dtype)) set_weight_attrs( self.bias, { "output_dim": 0, "weight_loader": self.weight_loader, }, ) else: self.register_parameter("bias", None) def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor): input_dim = getattr(param, "input_dim", None) use_bitsandbytes_4bit = getattr(param, "use_bitsandbytes_4bit", False) # Special case for GGUF is_gguf_weight = getattr(param, "is_gguf_weight", False) is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False) if is_gguf_weight_type: param.weight_type = loaded_weight.item() # Materialize GGUF UninitializedParameter if is_gguf_weight and isinstance(param, UninitializedParameter): weight_shape = list(loaded_weight.shape) if input_dim: weight_shape[input_dim] = weight_shape[input_dim] // self.tp_size param.materialize(tuple(weight_shape), dtype=loaded_weight.dtype) param_data = param.data # bitsandbytes loads the weights of the specific portion # no need to narrow here if ( input_dim is not None and not use_bitsandbytes_4bit and not self.use_presharded_weights ): shard_size = param_data.shape[input_dim] start_idx = self.tp_rank * shard_size if _is_cpu: from sglang.srt.model_loader.weight_utils import ( narrow_padded_param_and_loaded_weight, ) param_data, loaded_weight = narrow_padded_param_and_loaded_weight( param_data, loaded_weight, 0, # param_data_start start_idx, input_dim, shard_size, ) else: # Padding for special case like qwen2_5_VL's mlp which is not 8-aligned end_idx = start_idx + shard_size if end_idx > loaded_weight.shape[input_dim]: loaded_weight = pad_or_narrow_weight( loaded_weight, input_dim, start_idx, shard_size ) else: loaded_weight = loaded_weight.narrow( input_dim, start_idx, shard_size ) # Special case for loading scales off disk, which often do not # have a shape (such as in the case of AutoFP8). if len(loaded_weight.shape) == 0: loaded_weight = loaded_weight.reshape(1) assert ( param_data.shape == loaded_weight.shape ), f"{param_data.shape=} {loaded_weight.shape=}" param_data.copy_(loaded_weight) def weight_loader_v2(self, param: BasevLLMParameter, loaded_weight: torch.Tensor): # Special case for loading scales off disk, which often do not # have a shape (such as in the case of AutoFP8). if len(loaded_weight.shape) == 0: assert loaded_weight.numel() == 1 loaded_weight = loaded_weight.reshape(1) if isinstance(param, RowvLLMParameter): # This `BasevLLMParameter` is defined in sglang/srt/layers/parameter.py, # It supports additional parameters like tp_rank and use_presharded_weights. param.load_row_parallel_weight( loaded_weight, tp_rank=self.tp_rank, use_presharded_weights=self.use_presharded_weights, ) else: # `params` is defined in `vllm/model_executor/parameter.py`, # It does not support additional parameters. # However, after QuantizedRL reload, params might still need tp_rank try: param.load_row_parallel_weight( loaded_weight, tp_rank=self.tp_rank, use_presharded_weights=self.use_presharded_weights, ) except TypeError: # Fallback for parameters that don't accept additional args param.load_row_parallel_weight(loaded_weight) def forward(self, input_, skip_all_reduce=False): if self.input_is_parallel: input_parallel = input_ else: splitted_input = split_tensor_along_last_dim( input_, num_partitions=self.tp_size ) input_parallel = splitted_input[self.tp_rank].contiguous() # Matrix multiply. assert self.quant_method is not None # Only fuse bias add into GEMM for rank 0 (this ensures that # bias will not get added more than once in TP>1 case) bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias with use_symmetric_memory( get_tp_group(), disabled=not is_allocation_symmetric() ): output_parallel = self.quant_method.apply(self, input_parallel, bias=bias_) if self.reduce_results and self.tp_size > 1 and not skip_all_reduce: if self.use_dp_attention_reduce: output = get_attention_tp_group().all_reduce(output_parallel) else: output = tensor_model_parallel_all_reduce(output_parallel) else: output = output_parallel output_bias = self.bias if self.skip_bias_add else None return output, output_bias def extra_repr(self) -> str: s = f"input_features={self.input_size_per_partition}" s += f", output_features={self.output_size}" s += f", bias={self.bias is not None}" s += f", tp_size={self.tp_size}" s += f", reduce_results={self.reduce_results}" return s class MergedColumnParallelRepeatedLinear(LinearBase): """Merged column parallel linear and repeated linear layer. TODO: quantization is not supported yet. Args: input_size: input dimension of the linear layer. column_output_sizes: output dimension of the column linear layers. repeated_output_sizes: output dimension of the repeated linear layers. skip_bias_add: If true, skip adding bias but instead return it. params_dtype: Data type for the parameters. quant_config: Quantization configure. """ def __init__( self, input_size: int, column_output_sizes: List[int], repeated_output_sizes: List[int], skip_bias_add: bool = False, params_dtype: Optional[torch.dtype] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): output_size = sum(column_output_sizes) + sum(repeated_output_sizes) super().__init__( input_size=input_size, output_size=output_size, skip_bias_add=skip_bias_add, params_dtype=params_dtype, quant_config=quant_config, prefix=prefix, ) self.num_column_parallel = len(column_output_sizes) self.tp_rank = get_tensor_model_parallel_rank() self.tp_size = get_tensor_model_parallel_world_size() self.output_partition_sizes = [ divide(x, self.tp_size) for x in column_output_sizes ] + repeated_output_sizes self.quant_method.create_weights( layer=self, input_size_per_partition=self.input_size, output_partition_sizes=self.output_partition_sizes, input_size=self.input_size, output_size=self.output_size, params_dtype=self.params_dtype, skip_block_quant_check=True, weight_loader=self.weight_loader, ) self.prefix = prefix def forward(self, input_: torch.Tensor) -> torch.Tensor: return self.quant_method.apply(self, input_) def weight_loader( self, param: Parameter, loaded_weight: torch.Tensor, loaded_shard_id: int ) -> torch.Tensor: output_dim = param.output_dim shard_offset = sum(self.output_partition_sizes[:loaded_shard_id]) shard_size = self.output_partition_sizes[loaded_shard_id] param_data = param.data.narrow(output_dim, shard_offset, shard_size) if loaded_shard_id < self.num_column_parallel: start_idx = self.tp_rank * shard_size loaded_weight = loaded_weight.narrow(output_dim, start_idx, shard_size) param_data.copy_(loaded_weight) class ColumnParallelBatchedLinear(nn.Module): """Column parallel batched linear layer. TODO: quantization is not supported yet. Args: batch: batch dimension of the linear layer. input_size: input dimension of the linear layer. output_size: output dimension of the linear layer. dtype: Data type for the parameters. """ def __init__( self, batch: int, input_size: int, output_size: int, dtype: torch.dtype ): super().__init__() self.tp_rank = get_tensor_model_parallel_rank() self.tp_size = get_tensor_model_parallel_world_size() self.weight = nn.Parameter( torch.empty(batch, output_size // self.tp_size, input_size, dtype=dtype), requires_grad=False, ) setattr(self.weight, "weight_loader", self.weight_loader) def forward(self, input: torch.Tensor) -> torch.Tensor: return torch.bmm(input, self.weight.transpose(-1, -2)) def weight_loader( self, param: Parameter, loaded_weight: torch.Tensor, loaded_shard_id: int ) -> torch.Tensor: shard_size = self.weight.shape[-2] start_idx = self.tp_rank * shard_size loaded_weight = loaded_weight.narrow(0, start_idx, shard_size) param.data[loaded_shard_id].copy_(loaded_weight)