mirror of
https://github.com/kvcache-ai/ktransformers.git
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337 lines
14 KiB
Python
337 lines
14 KiB
Python
import os, sys
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import time
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from typing import Optional
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sys.path.insert(0, os.path.dirname(__file__) + "/../build")
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from kt_kernel import kt_kernel_ext
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from kt_kernel_ext.kvcache import ggml_type
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import torch
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from torch import nn
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from torch.nn import init
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from torch_attention import apply_rotary_pos_emb, DeepseekV2RMSNorm, KDeepSeekV3Cache, DeepseekV3YarnRotaryEmbedding
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seed = 42 # 你可以选择任何整数作为种子
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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qlen = 1024
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kvlen = 0
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page_table = range(20)
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bsz_tensors = torch.tensor([1])
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page_size = 256
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pages_count = 200
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tp_count = 4
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hidden_size = 7168
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q_lora_rank = 1536
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kv_lora_rank = 512
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num_heads = 128
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nope_size = 128
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rope_size = 64
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rope_theta = 10000
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max_qlen = 1024
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max_kvlen = 4096
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max_position_embeddings = 163840
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rope_scaling = {
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"beta_fast": 32,
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"beta_slow": 1,
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"factor": 40,
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"mscale": 1.0,
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"mscale_all_dim": 1.0,
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"original_max_position_embeddings": 4096,
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"type": "yarn",
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}
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CPUInfer = kt_kernel_ext.CPUInfer(64)
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validation_iter = 100
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q_a_proj = nn.Linear(hidden_size, q_lora_rank, bias=False, dtype=torch.float16)
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q_b_proj = nn.Linear(q_lora_rank, num_heads * (nope_size + rope_size), bias=False, dtype=torch.float16)
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kv_a_proj_with_mqa = nn.Linear(hidden_size, kv_lora_rank + rope_size, bias=False, dtype=torch.float16)
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kv_b_proj = nn.Linear(kv_lora_rank, num_heads * (nope_size + nope_size), bias=False, dtype=torch.float16)
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o_proj = nn.Linear(num_heads * nope_size, hidden_size, bias=False, dtype=torch.float16)
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init.normal_(q_a_proj.weight, mean=0.0, std=0.02)
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init.normal_(q_b_proj.weight, mean=0.0, std=0.02)
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init.normal_(kv_a_proj_with_mqa.weight, mean=0.0, std=0.02)
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init.normal_(kv_b_proj.weight, mean=0.0, std=0.02)
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init.normal_(o_proj.weight, mean=0.0, std=0.02)
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q_a_proj_weight = q_a_proj.weight.to(torch.float16).to("cpu").contiguous()
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q_b_proj_weight = q_b_proj.weight.to(torch.float16).to("cpu").contiguous()
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kv_a_proj_with_mqa_weight = kv_a_proj_with_mqa.weight.to("cpu").to(torch.float16).contiguous()
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kv_b_proj_weight = kv_b_proj.weight.to(torch.float16).to("cpu").contiguous()
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o_proj_weight = o_proj.weight.to(torch.float16).to("cpu").contiguous()
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config = kt_kernel_ext.mla.MLAConfig(
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hidden_size,
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q_lora_rank,
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kv_lora_rank,
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num_heads,
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nope_size,
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rope_size,
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)
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config.max_qlen = max_qlen
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config.max_kvlen = max_kvlen
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config.max_position_embeddings = max_position_embeddings
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config.rope_scaling_factor = rope_scaling["factor"]
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config.rope_theta = rope_theta
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config.rope_scaling_beta_fast = rope_scaling["beta_fast"]
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config.rope_scaling_beta_slow = rope_scaling["beta_slow"]
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config.rope_scaling_mscale = rope_scaling["mscale"]
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config.rope_scaling_mscale_all_dim = rope_scaling["mscale_all_dim"]
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config.rope_scaling_original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
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config.q_a_proj = q_a_proj_weight.data_ptr()
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config.q_b_proj = q_b_proj_weight.data_ptr()
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config.kv_a_proj_with_mqa = kv_a_proj_with_mqa_weight.data_ptr()
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config.kv_b_proj = kv_b_proj_weight.data_ptr()
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config.o_proj = o_proj_weight.data_ptr()
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config.q_a_proj_type = ggml_type.FP16
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config.q_b_proj_type = ggml_type.FP16
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config.kv_a_proj_with_mqa_type = ggml_type.FP16
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config.kv_b_proj_type = ggml_type.FP16
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config.w_o_type = ggml_type.FP16
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config.pool = CPUInfer.backend_
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mla = kt_kernel_ext.mla.MLA(config)
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mla.load_weights()
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mla.set_local_pages(pages_count)
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input = torch.randn((qlen, hidden_size), dtype=torch.float16).to("cpu").contiguous()
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output = torch.zeros((qlen, hidden_size), dtype=torch.float16).to("cpu").contiguous()
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mla.forward([qlen], [page_table], [kvlen], input.data_ptr(), output.data_ptr())
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print("CPU MLA Output: ", output)
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softmax_scale = (nope_size + rope_size) ** -0.5
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# 1代表的是压缩的kv的头数
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k_caches = torch.randn(1, pages_count, page_size, 1, kv_lora_rank + rope_size).to(torch.float16)
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kv_cache = KDeepSeekV3Cache(page_size=page_size, kv_lora_rank=kv_lora_rank, k_caches=k_caches)
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q_a_layernorm = DeepseekV2RMSNorm(q_lora_rank)
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x = torch.randn(q_lora_rank, dtype=torch.float16) * 100
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print(x)
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print(q_a_layernorm(x))
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kv_a_layernorm = DeepseekV2RMSNorm(kv_lora_rank)
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q_absorb = torch.randn(num_heads, nope_size, kv_lora_rank, dtype=torch.float16)
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out_absorb = torch.randn(num_heads, nope_size, kv_lora_rank, dtype=torch.float16)
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rotary_emb = DeepseekV3YarnRotaryEmbedding(
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rope_size,
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max_position_embeddings=max_position_embeddings,
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scaling_factor=rope_scaling["factor"],
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base=rope_theta,
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beta_fast=rope_scaling["beta_fast"],
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beta_slow=rope_scaling["beta_slow"],
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mscale=rope_scaling["mscale"],
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mscale_all_dim=rope_scaling["mscale_all_dim"],
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original_max_position_embeddings=rope_scaling["original_max_position_embeddings"],
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)
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# 构造一个qlen 长度的输入 hidden_states, 对应的历史 kv_indptr 是[0:bsz]
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# kv_indices 是[0:bsz]，page_idx=[0:bsz], page_offset=[kvlen:qlen+kvlen]
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# last_page_len = [qlen+kvlen,...] layer_idx = 1
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# position_ids = [kvlen:qlen+kvlen]
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hidden_states = torch.randn(qlen, hidden_size, dtype=torch.float16)
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q_indptr = torch.tensor([0, qlen]).to(torch.int32)
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kv_indptr = torch.tensor([0, (qlen + kvlen + page_size - 1) // page_size]).to(torch.int32)
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kv_indices = torch.tensor(range(pages_count)).to(torch.int32)
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page_idx = torch.tensor([i // page_size for i in range(kvlen, kvlen + qlen)]).to(torch.int32)
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page_offset = torch.tensor([i % page_size for i in range(kvlen, kvlen + qlen)]).to(torch.int32)
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last_page_len = torch.tensor([(qlen + kvlen) % page_size], device=hidden_states.device)
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position_ids = torch.tensor(range(kvlen, kvlen + qlen)).to(torch.int32)
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# 按照行创建 mask [qlen,kvlen+qlen]
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attention_masks = torch.zeros((qlen, kvlen + qlen), dtype=torch.float16)
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for i in range(qlen):
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attention_masks[i, i + kvlen + 1 : i + kvlen + qlen] = -65504.0
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def torch_attn(
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hidden_states: torch.Tensor,
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kv_cache: KDeepSeekV3Cache,
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position_ids: torch.Tensor,
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page_idx: torch.Tensor,
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page_offset: torch.Tensor,
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attention_masks: Optional[list[torch.Tensor]] = None,
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q_indptr: Optional[torch.Tensor] = None,
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kv_indices: Optional[torch.Tensor] = None,
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kv_indptr: Optional[torch.Tensor] = None,
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bsz_tensors: Optional[torch.Tensor] = None,
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last_page_len: Optional[torch.Tensor] = None,
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layer_idx: Optional[int] = None,
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):
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global out_absorb
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global q_absorb
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# range bsz_tensors
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final_attention_output = torch.tensor([], device=hidden_states.device)
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for i in range(bsz_tensors[0]):
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batch_num_tokens_tensors = q_indptr[i + 1] - q_indptr[i]
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batch_last_page_len = last_page_len[i]
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# kv_total_len is kv_len, batch_compressed_kv is compressed_kv, batch_k_pe is k_pe
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batch_page_idx = page_idx[q_indptr[i] : q_indptr[i + 1]]
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batch_page_offset = page_offset[q_indptr[i] : q_indptr[i + 1]]
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# kv_page_nums is the number of pages for the current batch
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kv_page_nums = kv_indptr[i + 1] - kv_indptr[i]
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# kv_total_len is the total length of the kv cache for the current batch (kv_len for algorithm)
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kv_total_len = kv_page_nums * page_size
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if batch_last_page_len is not None:
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kv_total_len = kv_total_len - (page_size - batch_last_page_len)
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# print(f"kv_total_len's shape {kv_total_len.shape}")
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# kv_index is the index of the kv cache pages for the current batch
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kv_index = kv_indices[kv_indptr[i] : kv_indptr[i + 1]]
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# we can index [kv_index, page_offset_indices] to get the kv cache for the current batch
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# from q_indptr[i] to q_indptr[i+1] is the range of the current batch
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batch_hidden_states = hidden_states[q_indptr[i] : q_indptr[i + 1]]
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batch_position_ids = position_ids[q_indptr[i] : q_indptr[i + 1]]
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qlen, _ = batch_hidden_states.size()
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# print("qlen -> ", qlen)
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q_lora = q_a_proj(batch_hidden_states)
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print("q_a_proj", q_a_proj.weight)
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print("q_lora", q_lora)
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q = q_b_proj(q_a_layernorm(q_lora))
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print("q_b_proj", q_b_proj.weight)
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# for v3, bsz, qlen, num_heads(128), qk_head_dim(192=128(nope)+64(rope))
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q = q.view(qlen, num_heads, nope_size + rope_size)
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# q_nope is [qlen, num_heads(128), qk_nope_head_dim(128)]
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# q_pe is [qlen, num_heads(128), qk_rope_head_dim(64)]
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q_nope, q_pe = torch.split(q, [nope_size, rope_size], dim=-1)
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print("q_nope", q_nope)
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print("q_pe", q_pe)
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# compressed_kv is [qlen, kv_lora_rank(512) + rope(64)]
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compressed_kv = kv_a_proj_with_mqa(batch_hidden_states)
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# compressed_kv is [qlen, kv_lora_rank(512)], k_pe is [qlen, rope(64)]
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compressed_kv, k_pe = torch.split(compressed_kv, [kv_lora_rank, rope_size], dim=-1)
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compressed_kv = compressed_kv.contiguous()
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compressed_kv = kv_a_layernorm(compressed_kv)
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# k_pe is [qlen, 1, qk_rope_head_dim(64)]
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print("compressed_kv ", compressed_kv)
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print("k_pe ", k_pe)
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k_pe = k_pe.view(qlen, 1, rope_size)
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# compressed_kv is [qlen, 1, kv_lora_rank(512)]
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compressed_kv = compressed_kv.view(qlen, 1, kv_lora_rank)
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cos, sin = rotary_emb(q_pe, batch_position_ids)
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# print(f"q_pe shape{q_pe.shape}, k_pe shape {k_pe.shape}")
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q_pe, k_pe = apply_rotary_pos_emb(q_pe.unsqueeze(0), k_pe.unsqueeze(0), cos, sin, unsqueeze_dim=1)
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q_pe = q_pe.squeeze(0)
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# q_pe is [num_heads(128), qlen, qk_rope_head_dim(64)]
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q_pe.transpose_(0, 1)
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if kv_cache is not None:
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cache_kwargs = {
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"sin": sin,
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"cos": cos,
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"page_idx": batch_page_idx,
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"page_offset": batch_page_offset,
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} # Specific to RoPE models
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compressed_kv_with_k_pe = kv_cache.update(
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compressed_kv.unsqueeze(0), k_pe, layer_idx, batch_page_idx, batch_page_offset, cache_kwargs
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)
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compressed_kv = compressed_kv_with_k_pe[:, :, :, :kv_lora_rank].view(-1, page_size, kv_lora_rank)
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k_pe = compressed_kv_with_k_pe[:, :, :, kv_lora_rank:].view(-1, page_size, rope_size)
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# q_absorb is [num_heads(128), qk_nope_head_dim(128), kv_lora_rank(512)]
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# out_absorb is [num_heads(128), kv_lora_rank(512), v_head_dim(128)] v_head_dim is also the nope dim
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# q_absorb, out_absorb = get_absorbed()
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# q_nope is [num_heads(128), qlen, qk_nope_head_dim(128)]
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q_nope = q_nope.transpose(0, 1) # qlen is 1, no GPU overhead, same below
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# q_nope is [num_heads(128), qlen, kv_lora_rank(512)]
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q_nope = torch.matmul(q_nope, q_absorb) # batched MM
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# # q_nope is [qlen, num_heads(128), kv_lora_rank(512)]
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# q_nope = q_nope.transpose(0, 1)
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# we need to index out the compressed_kv and k_pe for the current batch
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batch_compressed_kv = None
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batch_k_pe = None
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for page_index in kv_index:
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if kv_total_len > page_size:
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tmp_compressed_kv = compressed_kv[page_index, 0:page_size, :]
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tmp_k_pe = k_pe[page_index, 0:page_size, :]
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if batch_compressed_kv is None or batch_k_pe is None:
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batch_compressed_kv = tmp_compressed_kv
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batch_k_pe = tmp_k_pe
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else:
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batch_compressed_kv = torch.cat((batch_compressed_kv, tmp_compressed_kv), dim=0)
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batch_k_pe = torch.cat((batch_k_pe, tmp_k_pe), dim=0)
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kv_total_len -= page_size
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else:
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tmp_compressed_kv = compressed_kv[page_index, 0:kv_total_len, :]
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tmp_k_pe = k_pe[page_index, 0:kv_total_len, :]
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if batch_compressed_kv is None or batch_k_pe is None:
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batch_compressed_kv = tmp_compressed_kv
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batch_k_pe = tmp_k_pe
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else:
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batch_compressed_kv = torch.cat((batch_compressed_kv, tmp_compressed_kv), dim=0)
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batch_k_pe = torch.cat((batch_k_pe, tmp_k_pe), dim=0)
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break
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# batch_compressed_kv is [kv_total_len(k_len), kv_lora_rank(512)]
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# batch_k_pe is [kv_total_len(k_len), qk_rope_head_dim(64)]
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pe_weights = torch.matmul(q_pe, batch_k_pe.mT)
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print("pe_weights", pe_weights)
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attention_weights = (pe_weights + torch.matmul(q_nope, batch_compressed_kv.mT)) * softmax_scale
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# attention_weights is [num_heads(128), qlen, k_len]
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# attention_weights = attention_weights.transpose(0,1).unsqueeze(0).squeeze(-1).expand(qlen,-1,-1).transpose(0,1)
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# attention_masks[i] is [qlen, k_len]
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attention_weights = attention_weights + attention_masks[i]
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# attention_weights shape is [num_heads(128), qlen, k_len]
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attention_weights = nn.functional.softmax(attention_weights, dim=-1, dtype=torch.float16).to(q_pe.dtype)
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attn_output = torch.matmul(attention_weights, batch_compressed_kv) # [num_heads(128),qlen, lora_rank(512)]
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# out_absorb shape is [num_heads(128), kv_lora_rank(512), v_head_dim(128)]
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out_absorb = out_absorb.transpose(1, 2)
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# q for qlen, n for num_heads, h for v_head_dim, v for kv_lora_rank
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attn_output = torch.matmul(attn_output, out_absorb) # [num_heads(128), qlen, v_head_dim(128)]
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attn_output = attn_output.transpose(0, 1) # [qlen, num_heads(128), v_head_dim(128)]
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attn_output = attn_output.reshape(qlen, num_heads * nope_size)
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attn_output = o_proj(attn_output)
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final_attention_output = torch.cat((final_attention_output, attn_output), dim=0)
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return final_attention_output
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torch_output = torch_attn(
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input,
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kv_cache,
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position_ids,
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page_idx,
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page_offset,
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attention_masks=attention_masks,
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q_indptr=q_indptr,
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kv_indices=kv_indices,
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kv_indptr=kv_indptr,
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bsz_tensors=bsz_tensors,
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last_page_len=last_page_len,
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layer_idx=0,
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)
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print("Torch Output: ", torch_output)
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