mirror of
https://github.com/kvcache-ai/ktransformers.git
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[ADD] support multi-gpu qlen>1 q5_k
This commit is contained in:
chenxl
parent
f293803156
commit
f5f79f5c0e
63 changed files with 3271 additions and 1285 deletions
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@ -5,8 +5,8 @@ Description :
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Author : Azure-Tang, Boxin Zhang, chenht2022
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Date : 2024-07-25 11:25:24
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Version : 0.1.0
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LastEditors : Azure
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LastEditTime : 2024-07-26 09:27:41
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LastEditors : kkk1nak0
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LastEditTime : 2024-08-11 12:14:39
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Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
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'''
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@ -19,7 +19,9 @@ import torch
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import sys, os
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from ktransformers.operators.base_operator import BaseInjectedModule
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sys.path.append(os.path.dirname(__file__) + "/../ktransformers_ext/build")
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sys.path.append(os.path.join(os.path.dirname(__file__), "..", "ktransformers_ext", "build"))
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sys.path.append(os.path.join(os.path.dirname(__file__), "..", "ktransformers_ext", "build", "Release"))
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sys.path.append(os.path.join(os.path.dirname(__file__), "..", "ktransformers_ext", "build", "Debug"))
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import cpuinfer_ext
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from cpuinfer_ext.moe import MOEConfig, MOE
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import ctypes
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@ -78,6 +80,25 @@ class MLPExpertsBase(ABC):
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gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate_exps.weight"]["ggml_type"]
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up_type = self.gguf_loader.tensor_info[key + ".ffn_up_exps.weight"]["ggml_type"]
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down_type = self.gguf_loader.tensor_info[key + ".ffn_down_exps.weight"]["ggml_type"]
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elif key + ".ffn_down.0.weight" in self.gguf_loader.tensor_info:
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# for supporting Mixtral-8x7B-Instuct
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gate = []
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up = []
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down = []
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for i in range(8):
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gatei, upi, downi = f".ffn_gate.{i}.weight", f".ffn_up.{i}.weight", f".ffn_down.{i}.weight"
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targets = [gatei, upi, downi]
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tensors = self.load_multi(key, targets, device=device)
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gate_it, up_it, down_it = tensors[gatei], tensors[upi], tensors[downi]
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gate.append(gate_it)
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up.append(up_it)
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down.append(down_it)
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gate = torch.stack(gate)
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up = torch.stack(up)
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down = torch.stack(down)
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gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate.0.weight"]["ggml_type"]
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up_type = self.gguf_loader.tensor_info[key + ".ffn_up.0.weight"]["ggml_type"]
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down_type = self.gguf_loader.tensor_info[key + ".ffn_down.0.weight"]["ggml_type"]
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else:
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raise ValueError(f"Experts {key} not found in gguf_loader")
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res = {key:{"gate": gate, "up": up, "down": down, "gate_type": gate_type, "up_type": up_type, "down_type": down_type}}
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@ -94,7 +115,8 @@ class MLPCPUExperts(MLPExpertsBase):
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expert_ids_cpu:Tensor = None
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weights_cpu:Tensor = None
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output_cpu:Tensor = None
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output_gpu:Tensor = None
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output_gpu_map:dict = {} # Manage output tensor buffer on different gpu
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#stream_map:dict = {} # Manage cuda stream on different gpu
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CPU_INFER = cpuinfer_ext.CPUInfer(Config().cpu_infer)
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def __init__(
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self,
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@ -113,81 +135,83 @@ class MLPCPUExperts(MLPExpertsBase):
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self.out_device = out_device
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def load(self, w: dict | nn.Parameter | tuple | None = None, device:str|None = None, warmup:bool = False):
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if device:
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assert device.lower() == "cpu", "MLPCPUExperts can only be loaded on CPU, Parameter \"device\" can be cpu or None."
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if w is None: w = self.load_weights()[self.key]
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self.gate = w["gate"]
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self.up = w["up"]
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self.down = w["down"]
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self.gate_type = w["gate_type"]
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self.up_type = w["up_type"]
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self.down_type = w["down_type"]
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gate_ptr = ctypes.addressof(
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ctypes.cast(self.gate.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents
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)
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up_ptr = ctypes.addressof(
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ctypes.cast(self.up.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents
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)
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down_ptr = ctypes.addressof(
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ctypes.cast(self.down.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents
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)
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# print(self.gate_qtype, self.up_qtype, self.down_qtype)
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n_routed_experts = self.n_routed_experts
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# n_routed_experts = len(self.orig_module)
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moe_config = MOEConfig(
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n_routed_experts,
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self.config.num_experts_per_tok,
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self.config.hidden_size,
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self.config.moe_intermediate_size,
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64,
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10,
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1024,
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gate_ptr,
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up_ptr,
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down_ptr,
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self.gate_type,
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self.up_type,
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self.down_type,
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30, # TODO: get from model.dtype
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)
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# print(n_routed_experts, hidden_size, moe_intermediate_size)
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num_experts_per_tok = self.config.num_experts_per_tok
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self.moe = MOE(moe_config)
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self.cpu_infer = MLPCPUExperts.CPU_INFER
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if warmup:
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self.cpu_infer.submit(self.moe.warm_up())
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self.cpu_infer.sync()
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if MLPCPUExperts.output_gpu == None:
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MLPCPUExperts.input_tensor_cpu = torch.empty((self.config.hidden_size), device="cpu", pin_memory=True)
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MLPCPUExperts.expert_ids_cpu = torch.empty((num_experts_per_tok), device="cpu", dtype=torch.long, pin_memory=True)
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MLPCPUExperts.weights_cpu = torch.empty((num_experts_per_tok), device="cpu", dtype=torch.float32, pin_memory=True)
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MLPCPUExperts.output_cpu = torch.empty((self.config.hidden_size), device="cpu", pin_memory=True)
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MLPCPUExperts.output_gpu = torch.empty((self.config.hidden_size), device=self.out_device)
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with torch.device(self.out_device):
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if device:
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assert device.lower() == "cpu", "MLPCPUExperts can only be loaded on CPU, Parameter \"device\" can be cpu or None."
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if w is None: w = self.load_weights()[self.key]
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self.gate = w["gate"]
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self.up = w["up"]
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self.down = w["down"]
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self.gate_type = w["gate_type"]
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self.up_type = w["up_type"]
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self.down_type = w["down_type"]
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gate_ptr = ctypes.addressof(
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ctypes.cast(self.gate.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents
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)
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up_ptr = ctypes.addressof(
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ctypes.cast(self.up.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents
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)
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down_ptr = ctypes.addressof(
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ctypes.cast(self.down.ctypes.data, ctypes.POINTER(ctypes.c_uint64)).contents
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)
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# print(self.gate_qtype, self.up_qtype, self.down_qtype)
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n_routed_experts = self.n_routed_experts
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# n_routed_experts = len(self.orig_module)
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moe_config = MOEConfig(
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n_routed_experts,
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self.config.num_experts_per_tok,
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self.config.hidden_size,
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self.config.moe_intermediate_size,
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64,
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10,
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1024,
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gate_ptr,
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up_ptr,
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down_ptr,
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self.gate_type,
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self.up_type,
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self.down_type,
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30, # TODO: get from model.dtype
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)
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# print(n_routed_experts, hidden_size, moe_intermediate_size)
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num_experts_per_tok = self.config.num_experts_per_tok
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self.moe = MOE(moe_config)
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self.cpu_infer = MLPCPUExperts.CPU_INFER
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if warmup:
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self.cpu_infer.submit(self.moe.warm_up())
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self.cpu_infer.sync()
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if self.out_device not in MLPCPUExperts.output_gpu_map:
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MLPCPUExperts.output_gpu_map[self.out_device] = torch.zeros((self.config.hidden_size), device=self.out_device)
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if MLPCPUExperts.input_tensor_cpu == None:
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MLPCPUExperts.input_tensor_cpu = torch.zeros((self.config.hidden_size), device="cpu", pin_memory=True)
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MLPCPUExperts.expert_ids_cpu = torch.zeros((num_experts_per_tok), device="cpu", dtype=torch.long, pin_memory=True)
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MLPCPUExperts.weights_cpu = torch.zeros((num_experts_per_tok), device="cpu", dtype=torch.float32, pin_memory=True)
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MLPCPUExperts.output_cpu = torch.zeros((self.config.hidden_size), device="cpu", pin_memory=True, dtype=torch.bfloat16)
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def submit_for_one_decode(self, input_tensor, expert_ids, weights):
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MLPCPUExperts.input_tensor_cpu.copy_(input_tensor, non_blocking=True)
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MLPCPUExperts.expert_ids_cpu.copy_(expert_ids, non_blocking=True)
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MLPCPUExperts.weights_cpu.copy_(weights, non_blocking=True)
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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()))
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self.cpu_infer.submit_with_cuda_stream(torch.cuda.current_stream(self.out_device).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()))
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def sync_for_one_decode(self):
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self.cpu_infer.sync_with_cuda_stream(torch.cuda.current_stream().cuda_stream)
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MLPCPUExperts.output_gpu.copy_(MLPCPUExperts.output_cpu, non_blocking=True)
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#print("capturing experts finish")
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return MLPCPUExperts.output_gpu
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self.cpu_infer.sync_with_cuda_stream(torch.cuda.current_stream(self.out_device).cuda_stream)
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MLPCPUExperts.output_gpu_map[self.out_device].copy_(MLPCPUExperts.output_cpu, non_blocking=True)
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return MLPCPUExperts.output_gpu_map[self.out_device]
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def forward(self, input_tensor, expert_ids, weights):
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# generate, capture and run cuda graph
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# print(expert_ids)
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if input_tensor.size(0)==1:
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# TODO: this branch is unreachable, but the shape of input_tensor([1,hidden_size]) and input_tensor_cpu([hidden_size]) is not compatible
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#print("capturing experts")
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MLPCPUExperts.input_tensor_cpu.copy_(input_tensor, non_blocking=True)
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MLPCPUExperts.expert_ids_cpu.copy_(expert_ids, non_blocking=True)
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MLPCPUExperts.weights_cpu.copy_(weights, non_blocking=True)
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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()))
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self.cpu_infer.sync_with_cuda_stream(torch.cuda.current_stream().cuda_stream)
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MLPCPUExperts.output_gpu.copy_(MLPCPUExperts.output_cpu, non_blocking=True)
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#print("capturing experts finish")
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return MLPCPUExperts.output_gpu
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MLPCPUExperts.output_gpu_map[self.out_device].copy_(MLPCPUExperts.output_cpu, non_blocking=True)
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return MLPCPUExperts.output_gpu_map[self.out_device]
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else:
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input_tensor = input_tensor.contiguous().cpu()
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expert_ids = expert_ids.contiguous().cpu()
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@ -195,7 +219,7 @@ class MLPCPUExperts(MLPExpertsBase):
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output = torch.empty_like(input_tensor).contiguous()
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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()))
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self.cpu_infer.sync()
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return output.to(device=object.__getattribute__(self, "device"))
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return output.to(device=object.__getattribute__(self, "out_device"))
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def unload(self):
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return
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@ -222,6 +246,24 @@ class MLPCPUExperts(MLPExpertsBase):
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gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate_exps.weight"]["ggml_type"]
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up_type = self.gguf_loader.tensor_info[key + ".ffn_up_exps.weight"]["ggml_type"]
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down_type = self.gguf_loader.tensor_info[key + ".ffn_down_exps.weight"]["ggml_type"]
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elif key + ".ffn_down.0.weight" in self.gguf_loader.tensor_info:
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# for supporting Mixtral-8x7B-Instuct
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gate = []
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up = []
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down = []
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for i in range(8):
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gate_it = self.gguf_loader.get_mmap_tensor(f"{key}.ffn_gate.{i}.weight")
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up_it = self.gguf_loader.get_mmap_tensor(f"{key}.ffn_up.{i}.weight")
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down_it = self.gguf_loader.get_mmap_tensor(f"{key}.ffn_down.{i}.weight")
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gate.append(gate_it)
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up.append(up_it)
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down.append(down_it)
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gate = np.stack(gate)
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up = np.stack(up)
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down = np.stack(down)
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gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate.0.weight"]["ggml_type"]
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up_type = self.gguf_loader.tensor_info[key + ".ffn_up.0.weight"]["ggml_type"]
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down_type = self.gguf_loader.tensor_info[key + ".ffn_down.0.weight"]["ggml_type"]
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else:
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raise ValueError(f"Experts {key} not found in gguf_loader")
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res = {key:{"gate": gate, "up": up, "down": down, "gate_type": gate_type, "up_type": up_type, "down_type": down_type}}
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@ -299,7 +341,7 @@ class MLPExpertsMarlin(MLPExpertsBase):
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gate_type = self.gguf_loader.tensor_info[key + ".ffn_gate_exps.weight"]["ggml_type"]
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up_type = self.gguf_loader.tensor_info[key + ".ffn_up_exps.weight"]["ggml_type"]
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down_type = self.gguf_loader.tensor_info[key + ".ffn_down_exps.weight"]["ggml_type"]
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# tensors = self.load_multi(key, [".ffn_gate_exps.weight", ".ffn_up_exps.weight", ".ffn_down_exps.weight"])
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# tensors = self.load_multi(key, [".ffn_gate_exps.weight", ".ffn_up_exps.weight", ".ffn_down_exps.weight"])
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res = {key:{"gate": gate, "up": up, "down": down, "gate_type": gate_type, "up_type": up_type, "down_type": down_type}}
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return res
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@ -359,6 +401,11 @@ class MLPExpertsTorch(MLPExpertsBase):
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self.down = None
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def forward(self, hidden_states_cpu: torch.Tensor, selected_experts_cpu: torch.Tensor, routing_weights_cpu: torch.Tensor) -> torch.Tensor:
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org_device = hidden_states_cpu.device
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hidden_states_cpu = hidden_states_cpu.to(self.device)
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selected_experts_cpu = selected_experts_cpu.to(self.device)
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routing_weights_cpu = routing_weights_cpu.to(self.device)
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batch_sequence_length, hidden_dim = hidden_states_cpu.size()
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@ -388,27 +435,29 @@ class MLPExpertsTorch(MLPExpertsBase):
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# the `top_x` tensor here.
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final_hidden_states.index_add_(0, top_x, current_hidden_states)
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return final_hidden_states.to(org_dtype)
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return final_hidden_states.to(org_dtype, device=org_device)
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EXPERTS_MAP = {
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"MLPCPUExperts": MLPCPUExperts,
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"MLPExpertsTorch": MLPExpertsTorch,
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"MLPExpertsMarlin": MLPExpertsMarlin,
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}
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class KTransformersMLPExpert(BaseInjectedModule, MLPExpertsBase):
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def __init__(self,
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key: str,
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gguf_loader: GGUFLoader,
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config: PretrainedConfig,
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orig_module: nn.Module,
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device: str = "cuda",
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# device: str = "cuda",
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prefill_device:str = "cuda",
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prefill_mlp_type: str | None = "MLPExpertsTorch",
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generate_device: str = "cpu",
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generate_mlp_type: str | None = "MLPCPUExperts",
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**kwargs):
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BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
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MLPExpertsBase.__init__(self, key, gguf_loader, config, orig_module, device, **kwargs)
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BaseInjectedModule.__init__(self, key, gguf_loader, config, orig_module, generate_device, **kwargs)
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MLPExpertsBase.__init__(self, key, gguf_loader, config, orig_module, generate_device, **kwargs)
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if generate_mlp_type is not None:
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self.generate_experts = EXPERTS_MAP[generate_mlp_type](key, gguf_loader, config, len(orig_module), device=generate_device, **kwargs)
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else:
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@ -471,6 +520,7 @@ class KTransformersMLPExpert(BaseInjectedModule, MLPExpertsBase):
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from ktransformers.models.modeling_deepseek import DeepseekV2MoE
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from ktransformers.models.modeling_qwen2_moe import Qwen2MoeSparseMoeBlock
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from ktransformers.models.modeling_mixtral import MixtralSparseMoeBlock
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class Qwen2MoeSparseMoeBlockInjected(BaseInjectedModule, Qwen2MoeSparseMoeBlock):
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@ -578,7 +628,6 @@ class Qwen2MoeSparseMoeBlockInjected(BaseInjectedModule, Qwen2MoeSparseMoeBlock)
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return final_hidden_states
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class DeepseekV2MoEInjected(BaseInjectedModule, DeepseekV2MoE):
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def forward(self, hidden_states):
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identity = hidden_states
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@ -587,7 +636,7 @@ class DeepseekV2MoEInjected(BaseInjectedModule, DeepseekV2MoE):
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topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
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hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
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if sequence_length == 1:
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if sequence_length == 1 and hasattr(self.experts.generate_experts, "submit_for_one_decode"):
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self.experts.generate_experts.submit_for_one_decode(hidden_states[0], topk_idx[0], topk_weight[0])
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if self.config.n_shared_experts is not None:
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y_ = self.shared_experts(identity).squeeze(0)
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|
|
@ -677,3 +726,102 @@ class DeepseekV2MoEInjected(BaseInjectedModule, DeepseekV2MoE):
|
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.type(new_x.dtype)
|
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)
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return final_out
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class MisrtalSparseMoEBlockInjected(BaseInjectedModule, MixtralSparseMoeBlock):
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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""" """
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orig_shape = hidden_states.shape
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batch_size, sequence_length, hidden_dim = hidden_states.shape
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if self.training and self.jitter_noise > 0:
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hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
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hidden_states = hidden_states.view(-1, hidden_dim)
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# router_logits: (batch * sequence_length, n_experts)
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router_logits = self.gate(hidden_states)
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routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
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routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
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routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
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# 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"):
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||||
self.experts.generate_experts.submit_for_one_decode(hidden_states[0], selected_experts[0], routing_weights[0])
|
||||
y = self.experts.generate_experts.sync_for_one_decode().unsqueeze(0)
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||||
y.resize_(*orig_shape)
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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()
|
||||
|
||||
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.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
|
||||
Loading…
Add table
Add a link
Reference in a new issue