mirror of
https://github.com/kvcache-ai/ktransformers.git
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238 lines
No EOL
12 KiB
Python
238 lines
No EOL
12 KiB
Python
"""
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Date: 2024-11-07 07:02:20
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LastEditors: djw
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LastEditTime: 2024-12-10 08:48:32
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"""
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import torch
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from torch import nn
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import queue
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import signal
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import queue
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from typing import AsyncIterable
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from fastapi import FastAPI, Request
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from fastapi.responses import StreamingResponse
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from contextlib import asynccontextmanager
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from pydantic import BaseModel, Field
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import asyncio
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import multiprocessing
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import time
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import torch.multiprocessing as mp
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import random
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import torch.distributed as dist
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import zmq
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import tempfile
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from ktransformers.server.balance_serve.inference.forward_batch import ForwardBatchInput, ForwardBatchOutput
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from ktransformers.server.config.config import Config
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from ktransformers.models.custom_modeling_deepseek_v3 import KDeepseekV3ForCausalLM
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from ktransformers.models.custom_modeling_deepseek_v2 import KDeepseekV2ForCausalLM
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from ktransformers.models.custom_modeling_qwen2_moe import KQwen2MoeForCausalLM
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from ktransformers.models.custom_modeling_qwen3_moe import KQwen3MoeForCausalLM
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from ktransformers.server.balance_serve.inference.query_manager import QueryManager
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from ktransformers.server.balance_serve.settings import sched_ext
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def pad_num_tokens(num_tokens):
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return (num_tokens + 63) // 64 * 64
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def deduplicate_and_sort(lst):
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return sorted(set(lst))
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def generate_cuda_graphs(chunk_size: int) -> list:
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# 如果输入不符合要求，assert掉
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assert chunk_size <= 1024 or chunk_size % 1024 == 0, "chunk_size must <= 1024 or a multiple of 1024"
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base_list = [1, 2, 3, Config().max_batch_size, 64, 256, 512, chunk_size]
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if chunk_size <= 1024:
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return base_list
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multiples = [i for i in range(1024, chunk_size + 1, 1024)]
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return deduplicate_and_sort(base_list + multiples)
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class ModelRunner:
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"""A CudaGraphRunner runs the forward pass of a model with CUDA graph and torch.compile."""
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model: KDeepseekV3ForCausalLM | KQwen2MoeForCausalLM | KQwen3MoeForCausalLM
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input: ForwardBatchInput | list[ForwardBatchInput]
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output: ForwardBatchOutput
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def __init__(self, model = None, device = None, use_cuda_graph = False, max_decode_batch_size = 1, max_chunk_size = 4096, num_mini_batches: int = 1, page_size = 256, block_num = 8):
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self.stream = torch.cuda.Stream(device=device)
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# 先注释掉
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self.model = model # Compile and move model to the specified device
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self.device = device
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self.input = None
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self.features_buf = None
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self.output = None
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self.graph_memory_pool = None
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self.cuda_graphs = generate_cuda_graphs(Config().chunk_size)
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self.use_cuda_graph = use_cuda_graph
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self.model_time = 0
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self.page_size = page_size
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self.block_num = block_num
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# GPU timing for model execution
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self.start_model_event = torch.cuda.Event(enable_timing=True)
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self.end_model_event = torch.cuda.Event(enable_timing=True)
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self.graphs = [torch.cuda.CUDAGraph() for _ in range(len(self.cuda_graphs))]
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self.page_idx_buf = [torch.zeros([self.cuda_graphs[i]], dtype=torch.int32, device = self.device) for i in range(len(self.cuda_graphs))]
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self.page_offset_buf = [torch.zeros([self.cuda_graphs[i]], dtype=torch.int32, device = self.device) for i in range(len(self.cuda_graphs))]
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self.num_mini_batches = num_mini_batches
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self.max_chunk_size = max_chunk_size
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self.bsz_tensor_buf = torch.empty((1, ),dtype=torch.int32, device=device)
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self.num_tokens_tensor_buf = torch.empty((1, ),dtype=torch.int32, device=device)
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def model_attn_plan(self, batch, cuda_graph_idx=0):
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if isinstance(self.model, KDeepseekV3ForCausalLM):
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self.model.flash_infer_attn_plan(batch, self.bsz_tensor_buf, self.num_tokens_tensor_buf,
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num_heads=self.model.config.num_attention_heads, head_dim_ckv=self.model.config.kv_lora_rank,
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head_dim_kpe=self.model.config.qk_rope_head_dim, page_size=self.model.cache.page_size, causal=True,
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sm_scale=self.model.model.layers[0].self_attn.softmax_scale, q_data_type=torch.bfloat16, kv_data_type=torch.bfloat16)
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elif isinstance(self.model, KQwen2MoeForCausalLM) or isinstance(self.model, KQwen3MoeForCausalLM):
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self.model.flash_infer_attn_plan(batch, self.bsz_tensor_buf, self.num_tokens_tensor_buf,
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num_q_heads=self.model.config.num_attention_heads, num_kv_heads=self.model.config.num_key_value_heads,
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head_dim=self.model.config.head_dim if hasattr(self.model.config, 'head_dim') else self.model.config.hidden_size // self.model.config.num_attention_heads,
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page_size=self.model.cache.page_size, causal=True,
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q_data_type=torch.bfloat16, kv_data_type=torch.bfloat16, cuda_graph_idx=cuda_graph_idx)
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else:
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assert False, "model type not supported"
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def warmup(self):
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def capture_graphs(cuda_graph_idx):
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with torch.cuda.graph(self.graphs[cuda_graph_idx], pool=self.graph_memory_pool, stream=self.stream):
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self.outputs_buf[cuda_graph_idx] = self.model(self.input[cuda_graph_idx], self.features_buf[cuda_graph_idx], self.bsz_tensor_buf, self.num_tokens_tensor_buf, self.page_idx_buf[cuda_graph_idx], self.page_offset_buf[cuda_graph_idx], cuda_graph_idx=cuda_graph_idx)
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self.graph_memory_pool = self.graphs[cuda_graph_idx].pool()
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self.input = []
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self.features_buf = []
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self.outputs_buf = []
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self.bsz_tensor_buf = torch.tensor([0], dtype=torch.int32, device=self.device)
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self.num_tokens_tensor_buf = torch.tensor([0], dtype=torch.int32, device=self.device)
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for i in range(len(self.cuda_graphs)):
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prefill_query_length = (self.cuda_graphs[i] - Config().max_decode_batch_size) // Config().max_prefill_batch_size if self.cuda_graphs[i] > Config().max_decode_batch_size else 0 #@TODO only supprot 2 prefill batch
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self.input.append(ForwardBatchInput.gen_max_forward_batch(device=self.device, num_mini_batches = self.num_mini_batches, prefill_query_length=prefill_query_length, prefill_active_length=prefill_query_length, page_size=self.page_size, cuda_lens=self.cuda_graphs[i]))
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self.features_buf.append(self.model.batch_embeddings(self.input[i]))
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batch_size = self.input[i].minibatch.q_indptr.size(0)-1
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num_tokens = self.features_buf[i][0].size(0)
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print("capturing cuda graph", batch_size, num_tokens)
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if isinstance(self.model, KQwen2MoeForCausalLM) or isinstance(self.model, KQwen3MoeForCausalLM):
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self.model.init_wrapper(self.use_cuda_graph, self.device, num_tokens ,batch_size, self.block_num, i) # TODO: 1024 is a magic number(max_batch_tokens)
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self.bsz_tensor_buf[0] = batch_size
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self.num_tokens_tensor_buf[0] = num_tokens
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self.model_attn_plan(self.input[i], i)
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page_idx, page_offset = self.model.cache.get_page_table(self.input[i].minibatch.position_ids, self.input[i].minibatch.q_indptr, self.input[i].minibatch.kv_indptr, self.input[i].minibatch.kv_indices, self.num_tokens_tensor_buf)
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self.page_idx_buf[i][:num_tokens].copy_(page_idx[:num_tokens])
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self.page_offset_buf[i][:num_tokens].copy_(page_offset[:num_tokens])
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self.page_idx_buf[i][num_tokens:].fill_(self.model.cache.max_cache_len // self.model.cache.page_size -1)
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self.outputs_buf.append(None)
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torch.cuda.synchronize()
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for warm_up_iters in range(11):
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with torch.cuda.stream(self.stream):
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self.outputs_buf[i] = self.model(self.input[i], self.features_buf[i], self.bsz_tensor_buf, self.num_tokens_tensor_buf, self.page_idx_buf[i], self.page_offset_buf[i], cuda_graph_idx=i)
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torch.cuda.synchronize()
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self.outputs_buf[i].num_batchs = batch_size
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capture_graphs(i)
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with torch.cuda.stream(self.stream):
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self.graphs[i].replay()
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self.sync(calc_time=False)
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print(f"cuda_graph: {i+1}/{len(self.cuda_graphs)}, warmup finished.")
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def run(self, batch: sched_ext.BatchQueryTodo = None, query_manager: QueryManager = None):
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with torch.cuda.stream(self.stream):
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batch_size = len(batch.prefill_mini_batches) # TODO: calc this
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num_tokens = 0
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for i in range(len(batch.decode_mini_batches)):
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batch_size += len(batch.decode_mini_batches[i])
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num_tokens += len(batch.decode_mini_batches[i])
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print(f'decode_batch_i: {len(batch.decode_mini_batches[i])},')
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for i in range(len(batch.prefill_mini_batches)):
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num_tokens += batch.prefill_mini_batches[i][2]
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print(f'prefill_batch_i: {batch.prefill_mini_batches[i][2]},')
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# cuda graph idx equal to min idx i in self.cuda_graphs, that self.cuda_graphs[i] > num_tokens
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cuda_graph_idx = next((i for i, token in enumerate(self.cuda_graphs) if token >= num_tokens), len(self.cuda_graphs))
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if not self.use_cuda_graph:
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cuda_graph_idx = 0
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# if cuda_graph_idx == len(self.cuda_graphs):
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# assert False, "num_tokens is too large"
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if self.use_cuda_graph:
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self.input[cuda_graph_idx].fill(batch, query_manager, self.page_size)
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else:
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self.input = [ForwardBatchInput(batch=batch, query_manager=query_manager, device=self.device)]
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if self.use_cuda_graph:
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self.features = self.model.batch_embeddings(self.input[cuda_graph_idx], device=self.device)
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else:
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self.features = self.model.batch_embeddings(self.input[cuda_graph_idx], device=self.device)
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self.bsz_tensor_buf.copy_(batch_size)
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self.num_tokens_tensor_buf.copy_(torch.tensor([num_tokens], dtype=torch.int32, device=self.device))
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if self.use_cuda_graph:
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self.features_buf[cuda_graph_idx][0].copy_(self.features[0], non_blocking=True)
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self.model_attn_plan(self.input[cuda_graph_idx], cuda_graph_idx)
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self.start_model_event.record(self.stream)
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page_idx, page_offset = self.model.cache.get_page_table(self.input[cuda_graph_idx].minibatch.position_ids, self.input[cuda_graph_idx].minibatch.q_indptr, self.input[cuda_graph_idx].minibatch.kv_indptr, self.input[cuda_graph_idx].minibatch.kv_indices, self.num_tokens_tensor_buf)
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if self.use_cuda_graph:
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self.page_idx_buf[cuda_graph_idx][:num_tokens].copy_(page_idx[:num_tokens])
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self.page_offset_buf[cuda_graph_idx][:num_tokens].copy_(page_offset[:num_tokens])
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self.page_idx_buf[cuda_graph_idx][num_tokens:].fill_(self.model.cache.max_cache_len // self.model.cache.page_size -1)
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self.replay(cuda_graph_idx)
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self.output = ForwardBatchOutput()
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self.output.top_ps.append(self.input[cuda_graph_idx].minibatch.top_ps)
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self.output.temperatures.append(self.input[cuda_graph_idx].minibatch.temperatures)
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self.output.logits.append(self.outputs_buf[cuda_graph_idx].logits[0][self.input[cuda_graph_idx].minibatch.logits_start].clone())
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else:
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self.output = self.model(self.input[cuda_graph_idx], self.features, self.bsz_tensor_buf, self.num_tokens_tensor_buf, page_idx, page_offset)
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self.output.logits[0] = self.output.logits[0][self.input[cuda_graph_idx].minibatch.logits_start]
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self.output.top_ps.append(self.input[cuda_graph_idx].minibatch.top_ps)
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self.output.temperatures.append(self.input[cuda_graph_idx].minibatch.temperatures)
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self.end_model_event.record(self.stream)
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def replay(self, cuda_graph_idx=-1):
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with torch.cuda.stream(self.stream):
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if cuda_graph_idx != -1:
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self.graphs[cuda_graph_idx].replay()
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else:
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self.graphs.replay()
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def sync(self, calc_time = True):
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self.stream.synchronize()
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if calc_time:
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self.model_time = self.start_model_event.elapsed_time(self.end_model_event) # In ms |