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