# This code is adapted from huggingface transformers: https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/llama/modeling_llama.py import itertools from typing import List, Optional, Tuple, Union import torch import torch.nn.functional as F from torch import nn from torch.distributed import ProcessGroup from transformers.models.llama.modeling_llama import ( LlamaAttention, LlamaConfig, LlamaDecoderLayer, LlamaForCausalLM, LlamaMLP, LlamaModel, LlamaRMSNorm, ) from colossalai.inference.config import InputMetaData, ModelShardInferenceConfig from colossalai.inference.flash_decoding_utils import FDIntermTensors from colossalai.inference.modeling.backends.attention_backend import AttentionMetaData, get_attention_backend from colossalai.inference.modeling.backends.pre_attention_backend import get_pre_attention_backend from colossalai.inference.utils import can_use_flash_attn2 from colossalai.kernel.kernel_loader import InferenceOpsLoader from colossalai.kernel.triton import get_xine_cache, rms_layernorm from colossalai.logging import get_dist_logger from colossalai.shardformer.layer.parallel_module import ParallelModule from colossalai.tensor.d_tensor import distribute_tensor, is_distributed_tensor inference_ops = InferenceOpsLoader().load() logger = get_dist_logger(__name__) def llama_causal_lm_forward( self: LlamaForCausalLM, input_tokens_ids: torch.Tensor, output_tensor: torch.Tensor, inputmetadata: InputMetaData, k_caches: List[torch.Tensor] = None, v_caches: List[torch.Tensor] = None, ) -> torch.Tensor: """This function will replace the forward function of LlamaForCausalLM. Args: batch (BatchInfo): It stores the necessary input information for this inference. k_caches (List[torch.Tensor]): It holds the GPU memory for the key cache. v_caches (List[torch.Tensor]): It holds the GPU memory for the value cache. high_precision(Optional[bool]): Whether to use float32 for underlying calculations of float16 data to achieve higher precision, defaults to False. """ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) hidden_states = llama_model_forward( self.model, input_tokens_ids=input_tokens_ids, output_tensor=output_tensor, inputmetadata=inputmetadata, k_caches=k_caches, v_caches=v_caches, use_cuda_kernel=inputmetadata.use_cuda_kernel, # Note currently the cuda kernel of layernorm, rotary_embedding_and_cache_copy couldn't pass the unitest but triton kernel could high_precision=inputmetadata.high_precision, ) logits = self.lm_head(hidden_states) return logits def llama_model_forward( self: LlamaModel, input_tokens_ids: torch.Tensor, output_tensor: torch.Tensor, inputmetadata: InputMetaData, k_caches: List[torch.Tensor] = None, v_caches: List[torch.Tensor] = None, use_cuda_kernel: Optional[bool] = True, high_precision: bool = False, ) -> torch.Tensor: """This function will replace the forward function of LlamaModel. Args: batch (BatchInfo, optional): It stores the necessary input information for this inference.. Defaults to None. k_caches (List[torch.Tensor], optional): It holds the GPU memory for the key cache. Defaults to None. v_caches (List[torch.Tensor], optional): It holds the GPU memory for the value cache. Defaults to None. high_precision(Optional[bool]): Whether to use float32 for underlying calculations of float16 data to achieve higher precision, defaults to False. """ block_tables = inputmetadata.block_tables sequence_lengths = inputmetadata.sequence_lengths kv_seq_len = inputmetadata.kv_seq_len # NOTE (yuanheng-zhao): fow now, only triton kernels support verification process # during speculative-decoding (`q_len > 1`) # We will expicitly disable `use_cuda_kernel` here when speculative-decoding is enabled if inputmetadata.use_spec_dec and use_cuda_kernel: use_cuda_kernel = False logger.warning("CUDA kernel is disabled for speculative-decoding.") hidden_states = self.embed_tokens(input_tokens_ids) cu_seqlens = None # NOTE (yuanheng-zhao): we do not use cuda kernels for speculative-decoding for now if inputmetadata.use_spec_dec: # For speculative-decoding Prefill and Verifying Stage if inputmetadata.is_prompts: # output tensor shape is the same as normal Prefill Stage rotary_indexes = [torch.arange(0, length) for length in sequence_lengths] else: # the number of tokens to be verified in parallel plus the correct token in the last step n_tokens = inputmetadata.num_tokens_to_verify + 1 assert n_tokens == hidden_states.size(0) rotary_indexes = [(length - n_tokens + i).view(-1) for i in range(n_tokens) for length in sequence_lengths] rotary_indexes = torch.cat(rotary_indexes, dim=-1) cos_sin = (self._cos_cached[rotary_indexes], self._sin_cached[rotary_indexes]) elif use_cuda_kernel: if can_use_flash_attn2(inputmetadata.dtype): cu_seqlens = F.pad(torch.cumsum(sequence_lengths, dim=0, dtype=torch.int32), (1, 0)) hidden_dim = self._cos_cached.size(-1) total_length = hidden_states.size(0) cos = torch.empty((total_length, hidden_dim), dtype=self._cos_cached.dtype, device=self._cos_cached.device) sin = torch.empty((total_length, hidden_dim), dtype=self._sin_cached.dtype, device=self._sin_cached.device) inference_ops.get_cos_and_sin( self._cos_cached, self._sin_cached, cos, sin, sequence_lengths, kv_seq_len, inputmetadata.is_prompts ) cos_sin = (cos, sin) else: cos_sin = get_xine_cache(sequence_lengths, self._cos_cached, self._sin_cached, inputmetadata.is_prompts) sm_scale = 1.0 / (inputmetadata.head_dim**0.5) norm_output = torch.empty_like(hidden_states) tokens_to_verify = inputmetadata.num_tokens_to_verify if inputmetadata.use_spec_dec else None residual = None for layer_id, decoder_layer in enumerate(self.layers): hidden_states, residual = decoder_layer( hidden_states, residual=residual, block_tables=block_tables, k_cache=k_caches[layer_id], v_cache=v_caches[layer_id], is_prompts=inputmetadata.is_prompts, is_verifier=inputmetadata.use_spec_dec, tokens_to_verify=tokens_to_verify, sequence_lengths=sequence_lengths, cos_sin=cos_sin, fd_inter_tensor=inputmetadata.fd_inter_tensor, kv_seq_len=kv_seq_len, output_tensor=output_tensor, norm_output=norm_output, sm_scale=sm_scale, use_cuda_kernel=use_cuda_kernel, cu_seqlens=cu_seqlens, high_precision=high_precision, ) if inputmetadata.is_prompts: seq_len_cumsum = sequence_lengths.cumsum(dim=0) hidden_states = hidden_states[seq_len_cumsum - 1].contiguous() residual = residual[seq_len_cumsum - 1].contiguous() norm_output = torch.empty_like(hidden_states) hidden_states, _ = self.norm(hidden_states, norm_output, residual, use_cuda_kernel) return hidden_states def llama_decoder_layer_forward( self: LlamaDecoderLayer, hidden_states: torch.Tensor, residual: torch.Tensor, block_tables: torch.Tensor, k_cache: torch.Tensor, v_cache: torch.Tensor, sequence_lengths: torch.Tensor, cos_sin: Tuple[torch.Tensor], fd_inter_tensor: FDIntermTensors, is_prompts: bool = True, is_verifier: bool = False, tokens_to_verify: int = None, kv_seq_len: int = 0, output_tensor: torch.Tensor = None, norm_output: torch.Tensor = None, sm_scale: int = None, use_cuda_kernel: bool = True, cu_seqlens: torch.Tensor = None, high_precision: bool = False, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: """This function will replace the forward function of LlamaDecoderLayer. Args: hidden_states (torch.Tensor): input to the layer of shape [token_num, embed_dim]. residual (torch.Tensor): shape [token_num, embed_dim], used to be added to hidden_states in out_proj. block_tables (torch.Tensor): A 2D tensor of shape [batch_size, max_blocks_per_sequence], storing mapping of token_position_id -> block_id. k_cache (torch.Tensor): It holds the GPU memory for the key cache. v_cache (torch.Tensor): It holds the GPU memory for the key cache. sequence_lengths (torch.Tensor): Holding the sequence length of each sequence. cos_sin (Tuple[torch.Tensor]): Holding cos and sin. fd_inter_tensor (FDIntermTensors): Holding tensors used for storing intermediate values in flash-decoding. is_prompts (bool, optional): Whether the current inference process is in the context input phase. Defaults to True. kv_seq_len (int, optional): The max sequence length of input sequences. Defaults to 0. output_tensor (torch.Tensor, optional): The mid tensor holds the output of attention. Defaults to None. norm_output (torch.Tensor, optional): The mid tensor holds the output of layernorm. Defaults to None. sm_scale (int, optional): Used for flash attention. Defaults to None. use_cuda_kernel: (bool, optional): Whether to use cuda kernel. Defaults to True. cu_seqlens(torch.Tensor, optional): Holding the cumulative sum of sequence length. high_precision(Optional[bool]): Whether to use float32 for underlying calculations of float16 data to achieve higher precision, defaults to False. """ hidden_states, residual = self.input_layernorm(hidden_states, norm_output, residual, use_cuda_kernel) # Self Attention hidden_states = self.self_attn( hidden_states=hidden_states, block_tables=block_tables, k_cache=k_cache, v_cache=v_cache, is_prompts=is_prompts, is_verifier=is_verifier, tokens_to_verify=tokens_to_verify, sequence_lengths=sequence_lengths, cos_sin=cos_sin, fd_inter_tensor=fd_inter_tensor, kv_seq_len=kv_seq_len, output_tensor=output_tensor, sm_scale=sm_scale, cu_seqlens=cu_seqlens, high_precision=high_precision, ) # Fully Connected hidden_states, residual = self.post_attention_layernorm(hidden_states, norm_output, residual, use_cuda_kernel) hidden_states = self.mlp(hidden_states) return hidden_states, residual def llama_rmsnorm_forward( self: LlamaRMSNorm, hidden_states: torch.Tensor, norm_output: torch.Tensor, residual: torch.Tensor = None, use_cuda_kernel: bool = True, ): if use_cuda_kernel: if residual is not None: inference_ops.fused_add_rms_layernorm(hidden_states, residual, self.weight.data, self.variance_epsilon) return hidden_states, residual if norm_output is None: norm_output = torch.empty_like(hidden_states) inference_ops.rms_layernorm(norm_output, hidden_states, self.weight.data, self.variance_epsilon) return norm_output, hidden_states else: return rms_layernorm(hidden_states, self.weight.data, self.variance_epsilon, norm_output, residual) class NopadLlamaMLP(LlamaMLP, ParallelModule): def __init__( self, config: LlamaConfig, mlp_gproj_w: torch.Tensor = None, mlp_uproj_w: torch.Tensor = None, mlp_dproj: ParallelModule = None, process_group: ProcessGroup = None, ): """Replacement of LlamaMLP layer. Args: config (LlamaConfig): Holding the Llama model config. mlp_gproj_w (torch.Tensor, optional): The transposed gate_proj weight. Defaults to None. mlp_uproj_w (torch.Tensor, optional): The transposed up_proj weight. Defaults to None. mlp_dproj (Linear1D_Row, optional): The Linear1D_Row mlp_dproj weight. Defaults to None. """ ParallelModule.__init__(self) self.config = config assert is_distributed_tensor( mlp_gproj_w ), "mlp_gproj_w must be dtensor so we could get the layout of the weight" self.helper_layout = ( mlp_gproj_w.dist_layout ) # NOTE this is a hack for the right load/shard of gate_up_weight(used in _load_from_state_dict) self.gate_up_weight = nn.Parameter( torch.stack([mlp_gproj_w.transpose(0, 1), mlp_uproj_w.transpose(0, 1)], dim=0) ) self.gate_up_dict = { "gate_proj.weight": None, "up_proj.weight": None, } # used and delattr in load/shard of gate/up weight self.down_proj = mlp_dproj self.process_group = process_group @staticmethod def from_native_module( module: LlamaMLP, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs ) -> ParallelModule: """Used for initialize the weight of NopadLlamaMLP by origin LlamaMLP. Args: module (LlamaMLP): The origin LlamaMLP layer. """ config = module.config mlp_gproj_w = module.gate_proj.weight assert is_distributed_tensor( module.gate_proj.weight ), "gate_proj.weight must be dtensor so we could get the layout of the weight" mlp_uproj_w = module.up_proj.weight mlp_dproj = module.down_proj mlp_layer = NopadLlamaMLP( config=config, mlp_gproj_w=mlp_gproj_w, mlp_uproj_w=mlp_uproj_w, mlp_dproj=mlp_dproj, process_group=process_group, ) return mlp_layer def _load_from_state_dict( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ): # NOTE This is a hack to ensure we could load the right weight from LlamaMLP checkpoint due to the use of torch.stack(gate_weight, up_weight) if hasattr(self, "gate_up_dict"): for hook in self._load_state_dict_pre_hooks.values(): hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set} local_name_params = itertools.chain(self._parameters.items(), persistent_buffers.items()) local_state = {k: v for k, v in local_name_params if v is not None} device_mesh = self.helper_layout.device_mesh sharding_spec = self.helper_layout.sharding_spec for weight_name in self.gate_up_dict: prefix_weight_name = prefix + weight_name if prefix_weight_name in state_dict.keys(): w = distribute_tensor(state_dict[prefix_weight_name], device_mesh, sharding_spec) self.gate_up_dict[weight_name] = w.T if None not in self.gate_up_dict.values(): # we've got all the weights of gate/up gate_up_w = torch.stack(list(self.gate_up_dict.values()), dim=0) input_param = nn.Parameter( gate_up_w ) # NOTE gate_up_weight doesn't have to be a distensor, Like input_param = sharded_tensor_to_param(input_param) key = "gate_up_weight" param = local_state.get(key, None) try: with torch.no_grad(): param.copy_(input_param) except Exception as ex: error_msgs.append( 'While copying the parameter named "{}", ' "whose dimensions in the model are {} and " "whose dimensions in the checkpoint are {}, " "an exception occurred : {}.".format(key, param.size(), input_param.size(), ex.args) ) del self.gate_up_dict strict = False # to avoid unexpected_keys super()._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: """ Args: hidden_states (torch.Tensor): input to the layer of shape [token_num, embed_dim]. """ hidden_states = hidden_states.expand(2, -1, -1) gate_up_proj_out = torch.bmm(hidden_states, self.gate_up_weight) act_out = inference_ops.silu_and_mul(gate_up_proj_out) return self.down_proj(act_out) def extra_repr(self) -> str: return f"gate_up_proj MergedLinear1D_Col: in_features={self.gate_up_weight.shape[1]}x2, out_features={self.gate_up_weight.shape[2]}, bias=False" class NopadLlamaAttention(LlamaAttention, ParallelModule): def __init__( self, config: LlamaConfig, layer_idx: Optional[int] = None, attn_qproj_w: torch.Tensor = None, attn_kproj_w: torch.Tensor = None, attn_vproj_w: torch.Tensor = None, attn_oproj: ParallelModule = None, process_group: ProcessGroup = None, model_shard_infer_config: ModelShardInferenceConfig = None, num_heads: int = None, hidden_size: int = None, num_key_value_heads: int = None, ): """This layer will replace the LlamaAttention. Args: config (LlamaConfig): Holding the Llama model config. layer_idx (Optional[int], optional): The decode layer id of this attention layer. Defaults to None. attn_qproj_w (torch.Tensor, optional): The transposed q_proj weight. Defaults to None. attn_kproj_w (torch.Tensor, optional): The transposed k_proj weight. Defaults to None. attn_vproj_w (torch.Tensor, optional): The transposed v_proj weight. Defaults to None. attn_oproj (Linear1D_Row, optional): The Linear1D_Row o_proj weight. Defaults to None. """ ParallelModule.__init__(self) self.config = config self.layer_idx = layer_idx self.o_proj = attn_oproj self.process_group = process_group self.attention_dropout = config.attention_dropout self.hidden_size = hidden_size self.num_heads = num_heads self.head_dim = self.hidden_size // self.num_heads self.num_key_value_heads = num_key_value_heads self.num_key_value_groups = self.num_heads // self.num_key_value_heads self.max_position_embeddings = config.max_position_embeddings self.rope_theta = config.rope_theta self.is_causal = True self.attention_backend = get_attention_backend(model_shard_infer_config) self.pre_attention_backend = get_pre_attention_backend(model_shard_infer_config) if self.num_heads == self.num_key_value_heads: qkv_weight_list = [attn_qproj_w.transpose(0, 1), attn_kproj_w.transpose(0, 1), attn_vproj_w.transpose(0, 1)] self.qkv_weight = nn.Parameter(torch.stack(qkv_weight_list, dim=0)) self.helper_layout = ( attn_qproj_w.dist_layout ) # NOTE this is a hack for the right load/shard of qkv_weight(used in _load_from_state_dict) self.qkv_dict = { "q_proj.weight": None, "k_proj.weight": None, "v_proj.weight": None, } # used and delattr in load/shard of qkv weight else: self.helper_layout = ( attn_qproj_w.dist_layout ) # NOTE this is a hack for the right load/shard of qkv_weight(used in _load_from_state_dict) self.q_proj_weight = nn.Parameter(attn_qproj_w.transpose(0, 1).contiguous()) self.k_proj_weight = nn.Parameter(attn_kproj_w.transpose(0, 1).contiguous()) self.v_proj_weight = nn.Parameter(attn_vproj_w.transpose(0, 1).contiguous()) @staticmethod def from_native_module( module: LlamaAttention, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs ) -> ParallelModule: """Used for initialize the weight of NopadLlamaAttention by origin LlamaAttention. Args: module (LlamaAttention): The origin LlamaAttention layer. """ config = module.config layer_idx = module.layer_idx attn_qproj_w = module.q_proj.weight attn_kproj_w = module.k_proj.weight attn_vproj_w = module.v_proj.weight assert is_distributed_tensor(attn_qproj_w), "attn_qproj_w must be dist tensor" attn_oproj = module.o_proj model_shard_infer_config = kwargs.get("model_shard_infer_config", None) attn_layer = NopadLlamaAttention( config=config, layer_idx=layer_idx, attn_qproj_w=attn_qproj_w, attn_kproj_w=attn_kproj_w, attn_vproj_w=attn_vproj_w, attn_oproj=attn_oproj, process_group=process_group, model_shard_infer_config=model_shard_infer_config, num_heads=module.num_heads, hidden_size=module.hidden_size, num_key_value_heads=module.num_key_value_heads, ) return attn_layer # Replace transformers.models.llama.modeling_llama.LlamaAttention.forward def forward( self, hidden_states: torch.Tensor, block_tables: torch.Tensor, k_cache: torch.Tensor, v_cache: torch.Tensor, sequence_lengths: torch.Tensor, cos_sin: Tuple[torch.Tensor], fd_inter_tensor: FDIntermTensors, is_prompts: bool = True, is_verifier: bool = False, tokens_to_verify: int = None, kv_seq_len: int = 0, output_tensor: torch.Tensor = None, sm_scale: int = None, use_cuda_kernel: bool = True, cu_seqlens: torch.Tensor = None, high_precision: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """ Args: hidden_states (torch.Tensor): input to the layer of shape [token_num, embed_dim]. block_tables (torch.Tensor): A 2D tensor of shape [batch_size, max_blocks_per_sequence], storing mapping of token_position_id -> block_id. k_cache (torch.Tensor): It holds the GPU memory for the key cache. v_cache (torch.Tensor): It holds the GPU memory for the key cache. sequence_lengths (torch.Tensor, optional): Holding the sequence length of each sequence. cos_sin (Tuple[torch.Tensor], optional): Holding cos and sin. fd_inter_tensor (FDIntermTensors, optional): Holding tensors used for storing intermediate values in flash-decoding. is_prompts (bool, optional): Whether the current inference process is in the context input phase. Defaults to True. kv_seq_len (int, optional): The max sequence length of input sequences. Defaults to 0. output_tensor (torch.Tensor, optional): The mid tensor holds the output of attention. Defaults to None. sm_scale (int, optional): Used for flash attention. Defaults to None. use_cuda_kernel: (bool, optional): Whether to use cuda kernel. Defaults to True. cu_seqlens(torch.Tensor, optional): Holding the cumulative sum of sequence length. high_precision(Optional[bool]): Whether to use float32 for underlying calculations of float16 data to achieve higher precision, defaults to False. """ token_nums = hidden_states.size(0) if self.num_heads != self.num_key_value_heads: query_states = torch.mm(hidden_states, self.q_proj_weight).view(-1, self.num_heads, self.head_dim) key_states = torch.mm(hidden_states, self.k_proj_weight).view(-1, self.num_key_value_heads, self.head_dim) value_states = torch.mm(hidden_states, self.v_proj_weight).view(-1, self.num_key_value_heads, self.head_dim) else: # fused qkv hidden_states = hidden_states.expand(3, -1, -1) query_states, key_states, value_states = ( torch.bmm(hidden_states, self.qkv_weight).view(3, token_nums, self.num_heads, self.head_dim).unbind(0) ) block_size = k_cache.size(-2) attn_metadata = AttentionMetaData( query_states=query_states, key_states=key_states, value_states=value_states, k_cache=k_cache, v_cache=v_cache, block_tables=block_tables, block_size=block_size, kv_seq_len=kv_seq_len, sequence_lengths=sequence_lengths, sm_scale=sm_scale, alibi_slopes=None, cu_seqlens=cu_seqlens, output_tensor=output_tensor, use_spec_dec=is_verifier, use_alibi_attn=False, ) if is_prompts: # prefilling stage self.pre_attention_backend.prefill( attn_metadata, cos=cos_sin[0], sin=cos_sin[1], high_precision=high_precision, ) attn_output = self.attention_backend.prefill( attn_metadata, token_nums=token_nums, ) else: # decoding stage q_len = tokens_to_verify + 1 if is_verifier else 1 self.pre_attention_backend.decode( attn_metadata, cos=cos_sin[0], sin=cos_sin[1], q_len=q_len, ) attn_output = self.attention_backend.decode( attn_metadata, fd_inter_tensor=fd_inter_tensor, num_key_value_groups=self.num_key_value_groups, q_len=q_len, ) attn_output = attn_output.view(-1, self.hidden_size) attn_output = self.o_proj(attn_output) return attn_output def _load_from_state_dict( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ): for hook in self._load_state_dict_pre_hooks.values(): hook(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set} local_name_params = itertools.chain(self._parameters.items(), persistent_buffers.items()) local_state = {k: v for k, v in local_name_params if v is not None} device_mesh = self.helper_layout.device_mesh sharding_spec = self.helper_layout.sharding_spec if self.num_heads == self.num_key_value_heads and hasattr(self, "qkv_dict"): # NOTE This is a hack to ensure we could load the right weight from LlamaAttention checkpoint due to the use of torch.stack(q_weight, k_weight, v_weight) key = "qkv_weight" # NOTE(@lry89757) We will load the sharded checkpoint file according to the weight map from *.index.json # Here we need the weight of q,k,v to stack the weights of q,k,v into one qkv weight. # Unfortunately, it is highly like that all weights of q,k,v are not in the same sharded checkpoint file(like meta-llama/llama3-70B) # so here we will stack them when we really collect all the three weights. for weight_name in self.qkv_dict: prefix_weight_name = prefix + weight_name if prefix_weight_name in state_dict.keys(): w = distribute_tensor(state_dict[prefix_weight_name], device_mesh, sharding_spec) self.qkv_dict[weight_name] = w.T if None not in self.qkv_dict.values(): # we've got all the weights of q, k, v qkv_w = torch.stack(list(self.qkv_dict.values()), dim=0) input_param = nn.Parameter( qkv_w ) # NOTE qkv_weight doesn't have to be a distensor, Like input_param = sharded_tensor_to_param(input_param) param = local_state[key] try: with torch.no_grad(): param.copy_(input_param) except Exception as ex: error_msgs.append( 'While copying the parameter named "{}", ' "whose dimensions in the model are {} and " "whose dimensions in the checkpoint are {}, " "an exception occurred : {}.".format(key, param.size(), input_param.size(), ex.args) ) del self.qkv_dict else: def _load(origin_weight_name="q_proj.weight", local_weight_name="q_proj_weight"): if prefix + origin_weight_name in state_dict.keys(): attn_qproj_w = state_dict[prefix + origin_weight_name] w = distribute_tensor(attn_qproj_w, device_mesh, sharding_spec) input_param = nn.Parameter(w.T) param = local_state[local_weight_name] try: with torch.no_grad(): param.copy_(input_param) except Exception as ex: key = local_weight_name error_msgs.append( 'While copying the parameter named "{}", ' "whose dimensions in the model are {} and " "whose dimensions in the checkpoint are {}, " "an exception occurred : {}.".format(key, param.size(), input_param.size(), ex.args) ) if prefix + "q_proj.weight" in state_dict.keys(): _load(origin_weight_name="q_proj.weight", local_weight_name="q_proj_weight") if prefix + "k_proj.weight" in state_dict.keys(): _load(origin_weight_name="k_proj.weight", local_weight_name="k_proj_weight") if prefix + "v_proj.weight" in state_dict.keys(): _load(origin_weight_name="v_proj.weight", local_weight_name="v_proj_weight") strict = False # to avoid unexpected_keys super()._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs ) def extra_repr(self) -> str: return f"qkv_weight_proj MergedLinear1D_Col: in_features={self.qkv_weight.shape[1]}x3, out_features={self.qkv_weight.shape[2]}, bias=False"