#!/usr/bin/env python # coding=utf-8 ''' Description : Author : Azure-Tang, Boxin Zhang, chenht2022 Date : 2024-07-25 11:25:24 Version : 0.1.0 LastEditors : Azure LastEditTime : 2024-07-26 09:27:41 Copyright (c) 2024 by KVCache.AI, All Rights Reserved. ''' from typing import Any, Union import numpy as np import numpy.typing as npt from torch import Tensor, nn import torch.nn.functional as F import torch import sys, os from ktransformers.operators.base_operator import BaseInjectedModule sys.path.append(os.path.dirname(__file__) + "/../ktransformers_ext/build") import cpuinfer_ext from cpuinfer_ext.moe import MOEConfig, MOE import ctypes from ktransformers.util.custom_gguf import GGUFLoader from ktransformers.util.utils import InferenceState from ktransformers.server.config.config import Config from transformers.activations import ACT2FN from transformers.configuration_utils import PretrainedConfig from abc import ABC, abstractmethod from ktransformers.operators.linear import QuantizedLinearMarlin, QuantizedLinearTorch, KTransformerLinear import time # class Base(BaseInjectedModule, ABC): class MLPExpertsBase(ABC): def __init__(self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, orig_module: nn.Module, device: str = "cuda", **kwargs): # super().__init__(key, gguf_loader, config, orig_module, device, **kwargs) self.key = key self.gguf_loader = gguf_loader self.config = config self.device = device @abstractmethod def forward(self, input_tensor, expert_ids, weights): pass @abstractmethod def load(self, w: dict | nn.Parameter | tuple | None = None, device: str = "cpu", warmup: bool = False): pass @abstractmethod def unload(): pass def load_weights(self, override_key: str | None = None, device: str = "cpu"): res = {} if override_key is not None: keys = override_key else: keys = [self.key] gate = None up = None down = None gate_type = None up_type = None down_type = None for key in keys: if key + ".ffn_gate_exps.weight" in self.gguf_loader.tensor_info: targets = [".ffn_gate_exps.weight", ".ffn_up_exps.weight", ".ffn_down_exps.weight" ] tensors = self.load_multi(key, targets, device=device) gate = tensors[".ffn_gate_exps.weight"] up = tensors[".ffn_up_exps.weight"] down = tensors[".ffn_down_exps.weight"] gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate_exps.weight"]["ggml_type"] up_type = self.gguf_loader.tensor_info[key + ".ffn_up_exps.weight"]["ggml_type"] down_type = self.gguf_loader.tensor_info[key + ".ffn_down_exps.weight"]["ggml_type"] else: raise ValueError(f"Experts {key} not found in gguf_loader") res = {key:{"gate": gate, "up": up, "down": down, "gate_type": gate_type, "up_type": up_type, "down_type": down_type}} return res def load_multi(self, key: str, keys: list[str], device: str = "cpu"): tensors = {} for k in keys: tensors[k] = self.gguf_loader.load_gguf_tensor(key + k, device=device) return tensors class MLPCPUExperts(MLPExpertsBase): input_tensor_cpu:Tensor = None expert_ids_cpu:Tensor = None weights_cpu:Tensor = None output_cpu:Tensor = None output_gpu:Tensor = None CPU_INFER = cpuinfer_ext.CPUInfer(Config().cpu_infer) def __init__( self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, n_routed_experts: int, orig_module: nn.Module = None, device: str = "cpu", out_device: str = "cuda", # this device mean which device the output should on **kwargs ): super().__init__(key, gguf_loader, config, orig_module, device, **kwargs) assert device.lower() == "cpu", "MLPCPUExperts can only be loaded on CPU" self.n_routed_experts = n_routed_experts self.out_device = out_device def load(self, w: dict | nn.Parameter | tuple | None = None, device:str|None = None, warmup:bool = False): if device: assert device.lower() == "cpu", "MLPCPUExperts can only be loaded on CPU, Parameter \"device\" can be cpu or None." if w is None: w = self.load_weights()[self.key] self.gate = w["gate"] self.up = w["up"] self.down = w["down"] self.gate_type = w["gate_type"] self.up_type = w["up_type"] self.down_type = w["down_type"] gate_ptr = ctypes.addressof( ctypes.cast(self.gate.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents ) up_ptr = ctypes.addressof( ctypes.cast(self.up.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents ) down_ptr = ctypes.addressof( ctypes.cast(self.down.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents ) # print(self.gate_qtype, self.up_qtype, self.down_qtype) n_routed_experts = self.n_routed_experts # n_routed_experts = len(self.orig_module) moe_config = MOEConfig( n_routed_experts, self.config.num_experts_per_tok, self.config.hidden_size, self.config.moe_intermediate_size, 64, 10, 1024, gate_ptr, up_ptr, down_ptr, self.gate_type, self.up_type, self.down_type, 30, # TODO: get from model.dtype ) # print(n_routed_experts, hidden_size, moe_intermediate_size) num_experts_per_tok = self.config.num_experts_per_tok self.moe = MOE(moe_config) self.cpu_infer = MLPCPUExperts.CPU_INFER if warmup: self.cpu_infer.submit(self.moe.warm_up) self.cpu_infer.sync() if MLPCPUExperts.output_gpu == None: MLPCPUExperts.input_tensor_cpu = torch.empty((self.config.hidden_size), device="cpu", pin_memory=True) MLPCPUExperts.expert_ids_cpu = torch.empty((num_experts_per_tok), device="cpu", dtype=torch.long, pin_memory=True) MLPCPUExperts.weights_cpu = torch.empty((num_experts_per_tok), device="cpu", dtype=torch.float32, pin_memory=True) MLPCPUExperts.output_cpu = torch.empty((self.config.hidden_size), device="cpu", pin_memory=True) MLPCPUExperts.output_gpu = torch.empty((self.config.hidden_size), device=self.out_device) def submit_for_one_decode(self, input_tensor, expert_ids, weights): MLPCPUExperts.input_tensor_cpu.copy_(input_tensor, non_blocking=True) MLPCPUExperts.expert_ids_cpu.copy_(expert_ids, non_blocking=True) MLPCPUExperts.weights_cpu.copy_(weights, non_blocking=True) self.cpu_infer.submit_with_cuda_stream(torch.cuda.current_stream().cuda_stream, self.moe.forward, 1, expert_ids.size(0), MLPCPUExperts.expert_ids_cpu.data_ptr(), MLPCPUExperts.weights_cpu.data_ptr(), MLPCPUExperts.input_tensor_cpu.data_ptr(), MLPCPUExperts.output_cpu.data_ptr()) def sync_for_one_decode(self): self.cpu_infer.sync_with_cuda_stream(torch.cuda.current_stream().cuda_stream) MLPCPUExperts.output_gpu.copy_(MLPCPUExperts.output_cpu, non_blocking=True) #print("capturing experts finish") return MLPCPUExperts.output_gpu def forward(self, input_tensor, expert_ids, weights): # generate, capture and run cuda graph if input_tensor.size(0)==1: #print("capturing experts") MLPCPUExperts.input_tensor_cpu.copy_(input_tensor, non_blocking=True) MLPCPUExperts.expert_ids_cpu.copy_(expert_ids, non_blocking=True) MLPCPUExperts.weights_cpu.copy_(weights, non_blocking=True) self.cpu_infer.submit_with_cuda_stream(torch.cuda.current_stream().cuda_stream, self.moe.forward, 1, expert_ids.size(1), MLPCPUExperts.expert_ids_cpu.data_ptr(), MLPCPUExperts.weights_cpu.data_ptr(), MLPCPUExperts.input_tensor_cpu.data_ptr(), MLPCPUExperts.output_cpu.data_ptr()) self.cpu_infer.sync_with_cuda_stream(torch.cuda.current_stream().cuda_stream) MLPCPUExperts.output_gpu.copy_(MLPCPUExperts.output_cpu, non_blocking=True) #print("capturing experts finish") return MLPCPUExperts.output_gpu else: input_tensor = input_tensor.contiguous().cpu() expert_ids = expert_ids.contiguous().cpu() weights = weights.contiguous().to(torch.float32).cpu() output = torch.empty_like(input_tensor).contiguous() self.cpu_infer.submit(self.moe.forward, expert_ids.size(0), expert_ids.size(1), expert_ids.data_ptr(), weights.data_ptr(), input_tensor.data_ptr(), output.data_ptr()) self.cpu_infer.sync() return output.to(device=object.__getattribute__(self, "device")) def unload(self): return def load_weights(self, override_key: str | None = None, device: str = "cpu"): res = {} if override_key is not None: keys = override_key else: keys = [self.key] gate = None up = None down = None gate_type = None up_type = None down_type = None for key in keys: if key + ".ffn_gate_exps.weight" in self.gguf_loader.tensor_info: gate = self.gguf_loader.get_mmap_tensor(key + ".ffn_gate_exps.weight") up = self.gguf_loader.get_mmap_tensor(key + ".ffn_up_exps.weight") down = self.gguf_loader.get_mmap_tensor(key + ".ffn_down_exps.weight") gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate_exps.weight"]["ggml_type"] up_type = self.gguf_loader.tensor_info[key + ".ffn_up_exps.weight"]["ggml_type"] down_type = self.gguf_loader.tensor_info[key + ".ffn_down_exps.weight"]["ggml_type"] else: raise ValueError(f"Experts {key} not found in gguf_loader") res = {key:{"gate": gate, "up": up, "down": down, "gate_type": gate_type, "up_type": up_type, "down_type": down_type}} return res class MLPExpertsMarlin(MLPExpertsBase): expert_num: int loaded_experts_idx: list[int] def __init__( self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, n_routed_experts: int, orig_module: nn.Module = None, device: str = "cuda", **kwargs ): super().__init__(key, gguf_loader, config, orig_module, device, **kwargs) self.expert_num = n_routed_experts self.loaded_experts_idx = [] self.act_fn = ACT2FN[config.hidden_act] assert device.lower() != "cpu", "Marlin experts can only be loaded on GPU" self.device = device # create empty marlin experts according to the number of experts per token # up self.up_projs = [QuantizedLinearMarlin(key+ "." + "ffn_up_exps", gguf_loader, config, device=device) for i in range(self.expert_num)] # gate self.gate_projs = [QuantizedLinearMarlin(key+ "." + "ffn_gate_exps", gguf_loader, config, device=device) for i in range(self.expert_num)] # down self.down_projs = [QuantizedLinearMarlin(key+ "." + "ffn_down_exps", gguf_loader, config, device=device) for i in range(self.expert_num)] def load(self, w: dict | nn.Parameter | tuple | None = None, device: str | None = None, warmup: bool = False): if device is None: device = self.device assert device.lower() != "cpu", "Marlin experts can only be loaded on GPU" if w is None: w = self.load_weights()[self.key] if isinstance(w, dict): self.gate = nn.Parameter(torch.from_numpy(w["gate"])) self.up = nn.Parameter(torch.from_numpy(w["up"])) self.down = nn.Parameter(torch.from_numpy(w["down"])) for i in range(self.expert_num): self.up_projs[i].load(self.up[i,...], device=device) self.gate_projs[i].load(self.gate[i,...], device=device) self.down_projs[i].load(self.down[i,...], device=device) self.loaded_experts_idx.append(i) return def unload(self): for i in self.loaded_experts_idx: self.up_projs[i].unload() self.gate_projs[i].unload() self.down_projs[i].unload() self.loaded_experts_idx = [] def load_weights(self, override_key: str | None = None): res = {} if override_key is not None: keys = override_key else: keys = [self.key] gate = None up = None down = None gate_type = None up_type = None down_type = None for key in keys: if key + ".ffn_gate_exps.weight" in self.gguf_loader.tensor_info: gate = self.gguf_loader.load_gguf_tensor(key + ".ffn_gate_exps.weight") up = self.gguf_loader.load_gguf_tensor(key + ".ffn_up_exps.weight") down = self.gguf_loader.load_gguf_tensor(key + ".ffn_down_exps.weight") gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate_exps.weight"]["ggml_type"] up_type = self.gguf_loader.tensor_info[key + ".ffn_up_exps.weight"]["ggml_type"] down_type = self.gguf_loader.tensor_info[key + ".ffn_down_exps.weight"]["ggml_type"] # tensors = self.load_multi(key, [".ffn_gate_exps.weight", ".ffn_up_exps.weight", ".ffn_down_exps.weight"]) res = {key:{"gate": gate, "up": up, "down": down, "gate_type": gate_type, "up_type": up_type, "down_type": down_type}} return res def forward(self, input_tensor:torch.Tensor, expert_ids, weights): # forward device = input_tensor.device input_tensor = input_tensor.to("cuda") outs = torch.zeros_like(input_tensor) for expert_idx in range(expert_ids.size(0)): down_proj = self.down_projs[expert_idx] gate_proj = self.gate_projs[expert_idx] up_proj = self.up_projs[expert_idx] outs += down_proj(self.act_fn(gate_proj(input_tensor)) * up_proj(input_tensor)) * weights[expert_idx] outs = outs.to(device) return outs class MLPExpertsTorch(MLPExpertsBase): expert_num: int loaded_experts_idx: list[int] gate: torch.Tensor up: torch.Tensor down: torch.Tensor def __init__( self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, n_routed_experts: int, orig_module: nn.Module = None, device: str = "cpu", **kwargs ): super().__init__(key, gguf_loader, config, orig_module, device, **kwargs) self.expert_num = n_routed_experts # self.loaded_experts_idx = [] self.act_fn = ACT2FN[config.hidden_act] self.device = device self.gate = None self.up = None self.donw = None self.dtype = torch.get_default_dtype() def load(self, w: dict | nn.Parameter | tuple | None = None, device: str | None = None, warmup: bool = False): if device is None: device = self.device if w is None: w = self.load_weights(device=device)[self.key] if isinstance(w, dict): self.gate = w["gate"].to(device=device, dtype=self.dtype) self.up = w["up"].to(device=device, dtype=self.dtype) self.down = w["down"].to(device=device, dtype=self.dtype) def unload(self): if self.gate is not None: self.gate = None self.up = None self.down = None def forward(self, hidden_states_cpu: torch.Tensor, selected_experts_cpu: torch.Tensor, routing_weights_cpu: torch.Tensor) -> torch.Tensor: batch_sequence_length, hidden_dim = hidden_states_cpu.size() final_hidden_states = torch.zeros( (batch_sequence_length, hidden_dim), dtype=self.gate.dtype, device=hidden_states_cpu.device ) org_dtype = hidden_states_cpu.dtype hidden_states_cpu = hidden_states_cpu.to(self.gate.dtype) routing_weights_cpu = routing_weights_cpu.to(self.gate.dtype) # One hot encode the selected experts to create an expert mask # this will be used to easily index which expert is going to be sollicitated expert_mask = torch.nn.functional.one_hot(selected_experts_cpu, num_classes=self.expert_num).permute(2, 1, 0) # Loop over all available experts in the model and perform the computation on each expert for expert_idx in range(self.expert_num): idx, top_x = torch.where(expert_mask[expert_idx]) # Index the correct hidden states and compute the expert hidden state for # the current expert. We need to make sure to multiply the output hidden # states by `routing_weights` on the corresponding tokens (top-1 and top-2) current_state = hidden_states_cpu[None, top_x].reshape(-1, hidden_dim) G = current_state @ self.gate[expert_idx,...].T A = self.act_fn(G) U = current_state @ self.up[expert_idx,...].T H = A * U # Element-wise multiplication current_hidden_states = H @ self.down[expert_idx,...].T * routing_weights_cpu[top_x, idx, None] # However `index_add_` only support torch tensors for indexing so we'll use # the `top_x` tensor here. final_hidden_states.index_add_(0, top_x, current_hidden_states) return final_hidden_states.to(org_dtype) EXPERTS_MAP = { "MLPCPUExperts": MLPCPUExperts, "MLPExpertsTorch": MLPExpertsTorch, "MLPExpertsMarlin": MLPExpertsMarlin, } class KTransformersMLPExpert(BaseInjectedModule, MLPExpertsBase): def __init__(self, key: str, gguf_loader: GGUFLoader, config: PretrainedConfig, orig_module: nn.Module, device: str = "cuda", prefill_device:str = "cuda", prefill_mlp_type: str | None = "MLPExpertsTorch", generate_device: str = "cpu", generate_mlp_type: str | None = "MLPCPUExperts", **kwargs): BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs) MLPExpertsBase.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs) if generate_mlp_type is not None: self.generate_experts = EXPERTS_MAP[generate_mlp_type](key, gguf_loader, config, len(orig_module), device=generate_device, **kwargs) else: self.generate_experts = None if prefill_mlp_type is not None: self.prefill_experts = EXPERTS_MAP[prefill_mlp_type](key, gguf_loader, config, len(orig_module), device=prefill_device, **kwargs) else: self.prefill_experts = None self.gpu_mlp_type = prefill_mlp_type self.cpu_mlp_type = generate_mlp_type self.mode = InferenceState.UNLOAD def load(self, w: dict = None, mode: InferenceState = None, warmup: bool = True): # TODO support w as input if not mode: mode = InferenceState.GENERATE if mode == InferenceState.GENERATE: self.prefill_experts.unload() self.generate_experts.load(w, warmup=warmup) self.device = self.generate_experts.device self.mode = mode elif mode == InferenceState.PREFILL: self.generate_experts.unload() self.prefill_experts.load(w, warmup=warmup) self.device = self.prefill_experts.device self.mode = mode elif mode == InferenceState.UNLOAD: self.unload() self.mode = mode self.device = self.generate_experts.device else: raise ValueError("mode must be either InferenceState.GENERATE, InferenceState.PREFILL or InferenceState.UNLOAD") def unload(self): if self.generate_experts is not None: self.generate_experts.unload() if self.prefill_experts is not None: self.prefill_experts.unload() self.device = self.generate_experts.device def forward(self, input_tensor, expert_ids, weights): if self.mode == InferenceState.GENERATE: assert self.generate_experts is not None, "generate_experts is None" return self.generate_experts.forward(input_tensor, expert_ids, weights) elif self.mode == InferenceState.PREFILL: assert self.prefill_experts is not None, "prefill_experts is None" return self.prefill_experts.forward(input_tensor, expert_ids, weights) else: raise ValueError("load or set_inference_mode before forward") def set_inference_mode(self, mode: InferenceState): if mode == InferenceState.GENERATE: self.load(mode=InferenceState.GENERATE, warmup=False) elif mode == InferenceState.PREFILL: self.load(mode=InferenceState.PREFILL, warmup=False) elif mode == InferenceState.UNLOAD: self.unload() else: raise ValueError("mode must be either InferenceState.GENERATE, InferenceState.PREFILL or InferenceState.UNLOAD") from ktransformers.models.modeling_deepseek import DeepseekV2MoE from ktransformers.models.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock class Qwen2MoeSparseMoeBlockInjected(BaseInjectedModule, Qwen2MoeSparseMoeBlock): def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: """ """ orig_shape = hidden_states.shape batch_size, sequence_length, hidden_dim = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_dim) # router_logits: (batch * sequence_length, n_experts) router_logits = self.gate(hidden_states) routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float) routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1) if self.norm_topk_prob: routing_weights /= routing_weights.sum(dim=-1, keepdim=True) # we cast back to the input dtype routing_weights = routing_weights.to(hidden_states.dtype) if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode"): self.experts.generate_experts.submit_for_one_decode(hidden_states[0], selected_experts[0], routing_weights[0]) shared_expert_output = self.shared_expert(hidden_states) shared_expert_output = F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output y = self.experts.generate_experts.sync_for_one_decode().unsqueeze(0) y += shared_expert_output y.resize_(*orig_shape) return y, router_logits hidden_states_expert = hidden_states.to(self.experts.device) if isinstance(self.experts, MLPExpertsBase) else hidden_states_expert.cpu() selected_experts_expert = selected_experts.to(self.experts.device) if isinstance(self.experts, MLPExpertsBase) else selected_experts_expert.cpu() routing_weights_expert = routing_weights.to(self.experts.device) if isinstance(self.experts, MLPExpertsBase) else routing_weights_expert.cpu() shared_expert_output = self.shared_expert(hidden_states) shared_expert_output = ( F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output ) if isinstance(self.experts, MLPExpertsBase): y = ( self.moe_on_cpuinfer( hidden_states_expert, selected_experts_expert, routing_weights_expert ) .view(*orig_shape) .to(device=hidden_states.device) ) elif hidden_states_expert.size(0) > 10: y = self.moe_infer( hidden_states_expert, selected_experts_expert, routing_weights_expert, orig_shape ).to(device=hidden_states.device) else: y = self.moe_infer_simple( hidden_states_expert, selected_experts_expert, routing_weights_expert ).to(device=hidden_states.device) y += shared_expert_output y.resize_(*orig_shape) return y, router_logits @torch.no_grad() def moe_on_cpuinfer(self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor) -> torch.Tensor: outs = torch.empty_like(x) outs = self.experts(x, topk_ids, topk_weight) return outs @torch.no_grad() # TODO may bugs here def moe_infer_simple(self, hidden_states_cpu: torch.Tensor, selected_experts_cpu: torch.Tensor, routing_weights_cpu: torch.Tensor) -> torch.Tensor: ''' hidden_states_cpu: [num_tokens, hidden_size] topk_ids, topk_weight: [num_tokens, num_selected_experts] ''' outs = torch.zeros_like(hidden_states_cpu) for token_idx in range(selected_experts_cpu.size(0)): for expert_idx in range(selected_experts_cpu.size(1)): expert = self.experts[selected_experts_cpu[token_idx, expert_idx]] outs[token_idx] += expert.forward(hidden_states_cpu[token_idx]) * routing_weights_cpu[token_idx, expert_idx] return outs @torch.no_grad() # TODO may bugs here def moe_infer(self, hidden_states_cpu: torch.Tensor, selected_experts_cpu: torch.Tensor, routing_weights_cpu: torch.Tensor, orig_shape: tuple) -> torch.Tensor: batch_size, sequence_length, hidden_dim = orig_shape final_hidden_states = torch.zeros( (batch_size * sequence_length, hidden_dim), dtype=hidden_states_cpu.dtype, device=hidden_states_cpu.device ) # One hot encode the selected experts to create an expert mask # this will be used to easily index which expert is going to be sollicitated expert_mask = torch.nn.functional.one_hot(selected_experts_cpu, num_classes=self.num_experts).permute(2, 1, 0) # Loop over all available experts in the model and perform the computation on each expert for expert_idx in range(self.num_experts): expert_layer = self.experts[expert_idx] idx, top_x = torch.where(expert_mask[expert_idx]) # Index the correct hidden states and compute the expert hidden state for # the current expert. We need to make sure to multiply the output hidden # states by `routing_weights` on the corresponding tokens (top-1 and top-2) current_state = hidden_states_cpu[None, top_x].reshape(-1, hidden_dim) current_hidden_states = expert_layer.forward(current_state) * routing_weights_cpu[top_x, idx, None] # However `index_add_` only support torch tensors for indexing so we'll use # the `top_x` tensor here. final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states_cpu.dtype)) return final_hidden_states class DeepseekV2MoEInjected(BaseInjectedModule, DeepseekV2MoE): def forward(self, hidden_states): identity = hidden_states orig_shape = hidden_states.shape sequence_length = orig_shape[1] topk_idx, topk_weight, aux_loss = self.gate(hidden_states) hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) if sequence_length == 1: self.experts.generate_experts.submit_for_one_decode(hidden_states[0], topk_idx[0], topk_weight[0]) if self.config.n_shared_experts is not None: y_ = self.shared_experts(identity).squeeze(0) y = self.experts.generate_experts.sync_for_one_decode().unsqueeze(0) y += y_ y.resize_(*orig_shape) return y if self.config.n_shared_experts is not None: y_ = self.shared_experts(identity).squeeze(0) if isinstance(self.experts, MLPExpertsBase): y = self.moe_on_cpuinfer(hidden_states, topk_idx, topk_weight).view(*orig_shape).to(device=hidden_states.device) elif hidden_states.size(0) > 10: # TODO may bugs here y = ( self.moe_infer(hidden_states, topk_idx, topk_weight) .view(*orig_shape) .to(device=hidden_states.device) ) else: # TODO may bugs here y = ( self.moe_infer_simple(hidden_states, topk_idx, topk_weight) .view(*orig_shape) .to(device=hidden_states.device) ) if self.config.n_shared_experts is not None: y += y_ return y @torch.no_grad() def moe_on_cpuinfer(self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor) -> torch.Tensor: outs = torch.empty_like(x) outs = self.experts(x, topk_ids, topk_weight) return outs @torch.no_grad() # TODO may bugs here def moe_infer_simple( self, x: torch.Tensor, topk_ids: torch.Tensor, topk_weight: torch.Tensor ) -> torch.Tensor: """ x: [num_tokens, hidden_size] topk_ids, topk_weight: [num_tokens, num_selected_experts] """ outs = torch.zeros_like(x) for token_idx in range(topk_ids.size(0)): for expert_idx in range(topk_ids.size(1)): expert = self.experts[topk_ids[token_idx, expert_idx]] outs[token_idx] += ( expert.forward(x[token_idx]) * topk_weight[token_idx, expert_idx] ) return outs @torch.no_grad() # TODO may bugs here def moe_infer(self, x, topk_ids, topk_weight): cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts))) cnts.scatter_(1, topk_ids, 1) tokens_per_expert = cnts.sum(dim=0) idxs = topk_ids.view(-1).argsort() sorted_tokens = x[idxs // topk_ids.shape[1]] tokens_per_expert = tokens_per_expert.cpu().numpy() outputs = [] start_idx = 0 for i, num_tokens in enumerate(tokens_per_expert): end_idx = start_idx + num_tokens if num_tokens == 0: continue expert = self.experts[i + self.ep_rank * self.experts_per_rank] tokens_for_this_expert = sorted_tokens[start_idx:end_idx] expert_out = expert.forward(tokens_for_this_expert) outputs.append(expert_out) start_idx = end_idx outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0) new_x = torch.empty_like(outs) new_x[idxs] = outs final_out = ( new_x.view(*topk_ids.shape, -1) .type(topk_weight.dtype) .mul_(topk_weight.unsqueeze(dim=-1)) .sum(dim=1) .type(new_x.dtype) ) return final_out