1.解决qwen lora模型推理结果异常问题

2.修复medusa推理时内存访问越界问题
3.benchmark_moe添加deepseek_v2模型

benchmarks/kernels/benchmark_moe.py

@@ -284,6 +284,11 @@ def main(args: argparse.Namespace):
         topk = config.num_experts_per_tok
         intermediate_size = config.intermediate_size
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
+    elif config.architectures[0] == "DeepseekV2ForCausalLM":
+        E = config.n_routed_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     else:
         # Default: Mixtral.
         E = config.num_local_experts
@@ -306,7 +311,7 @@ def main(args: argparse.Namespace):
 
     ray.init(address=None,
                  ignore_reinit_error=True,
-                 num_gpus=args.tp_size)
+                 num_gpus=1)
     num_gpus = int(ray.available_resources()["GPU"])
     workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
 

csrc/attention/attention_with_mask_kernels.cu

@@ -298,7 +298,7 @@ __device__ void paged_attention_with_mask_kernel(
       qk += (alibi_slope != 0) ? alibi_slope * (token_idx - seq_len + 1) : 0;
 
       // used for tree-style attention
-      if (attn_masks != nullptr) {
+      if (attn_masks != nullptr && token_idx < seq_len) {
         const int* attn_masks_ptr = attn_masks + seq_idx * attn_masks_stride;
         if (attn_masks_ptr[token_idx] == 0) {
           qk = -FLT_MAX;

csrc/attention/attention_with_mask_kernels_opt.cu

@@ -329,7 +329,7 @@ __device__ void paged_attention_with_mask_kernel_opt(
       qk += (alibi_slope != 0) ? alibi_slope * (token_idx - seq_len + 1) : 0;
 
       // used for tree-style attention
-      if (attn_masks != nullptr) {
+      if (attn_masks != nullptr && token_idx < seq_len) {
         const int* attn_masks_ptr = attn_masks + seq_idx * attn_masks_stride;
         if (attn_masks_ptr[token_idx] == 0) {
           qk = -FLT_MAX;

csrc/attention/attention_with_mask_kernels_opt_tc.cu

@@ -294,7 +294,7 @@ __device__ void paged_attention_with_mask_kernel_TC(
         }
 
         // used for tree-style attention
-        if (attn_masks != nullptr) {
+        if (attn_masks != nullptr && token_idx < seq_len) {
           const int* attn_masks_ptr = attn_masks + seq_idx * attn_masks_stride;
           if (attn_masks_ptr[token_idx] == 0) {
             qk_vec[i] = -FLT_MAX;

examples/medusa/medusa_benchmark_throughput.py

@@ -42,9 +42,6 @@ def sample_requests(
     # Only keep the first two turns of each conversation.
     dataset = [data["prompt"] for data in dataset]
 
-    # Shuffle the dataset.
-    random.shuffle(dataset)
-
     # Filter out sequences that are too long or too short
     filtered_dataset: List[Tuple[str, int, int]] = []
     for i in range(len(dataset)):

vllm/model_executor/models/qwen2.py

@@ -363,18 +363,9 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA):
         if config.tie_word_embeddings:
             self.lm_head = self.model.embed_tokens
         else:
-            # self.lm_head = ParallelLMHead(config.vocab_size,
-            #                               config.hidden_size,
-            #                               quant_config=quant_config)
-            self.unpadded_vocab_size = config.vocab_size
-            if lora_config:
-                self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
-            self.lm_head = ParallelLMHead(
-                self.unpadded_vocab_size,
+            self.lm_head = ParallelLMHead(config.vocab_size,
                                           config.hidden_size,
-                org_num_embeddings=config.vocab_size,
-                quant_config=quant_config,
-            )
+                                          quant_config=quant_config)
 
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()