Enable CC_METHOD for dsv3 by default && fix test script && fix tgi st…

…ream api (#732)

Co-authored-by: shihaobai <[email protected]>

lightllm/models/deepseek2/layer_infer/transformer_layer_infer.py

@@ -58,7 +58,7 @@ def __init__(self, layer_num, tp_rank, world_size, network_config, mode=[]):
             if mscale_all_dim:
                 mscale = get_deepseek_mscale(scaling_factor, mscale_all_dim)
                 self.softmax_scale = self.softmax_scale * mscale * mscale
-        self.enable_cc_method = os.getenv("ENABLE_CC_METHOD", "False").upper() in ["ON", "TRUE", "1"]
+        self.enable_cc_method = not os.getenv("DISABLE_CC_METHOD", "False").upper() in ["ON", "TRUE", "1"]
         super().__init__(layer_num, tp_rank, world_size, network_config, mode)
         self.enable_dp = os.getenv("ENABLE_DP", "0").upper() in ["ON", "TRUE", "1"]
         if self.enable_dp:

lightllm/models/deepseek2/layer_weights/transformer_layer_weight.py

@@ -66,7 +66,7 @@ def fuse_vb_o(self, layer_weight):
 class Deepseek2TransformerLayerWeight(TransformerLayerWeight):
     def __init__(self, layer_num, tp_rank, world_size, data_type, network_config, mode=[], quant_cfg=None):
         self.enable_dp = os.getenv("ENABLE_DP", "0").upper() in ["ON", "TRUE", "1"]
-        self.enable_cc_method = os.getenv("ENABLE_CC_METHOD", "False").upper() in ["ON", "TRUE", "1"]
+        self.enable_cc_method = not os.getenv("DISABLE_CC_METHOD", "False").upper() in ["ON", "TRUE", "1"]
         super().__init__(layer_num, tp_rank, world_size, data_type, network_config, mode, quant_cfg)
         return
 

lightllm/server/api_tgi.py

@@ -9,7 +9,7 @@
 import json
 
 
-def format_tgi_params(params, num_beam: int):
+def format_tgi_params(params, num_beam: int = 1):
     """
     tgi params format -> lightllm server params format
     pub(crate) struct GenerateParameters {

test/model/model_infer.py

@@ -38,16 +38,20 @@ def test_model_inference(world_size, model_class, batch_size, input_len, output_
 
 def tppart_model_infer(model_class, model_kvargs, batch_size, input_len, output_len, ans_queue):
     import torch
-    from lightllm.distributed import set_custom_reduce
+    from lightllm.distributed import custom_comm_ops
+    from lightllm.utils.device_utils import set_current_device_id
+
     import torch.distributed as dist
 
     rank_id = model_kvargs["tp_rank"]
     world_size = model_kvargs["world_size"]
 
     torch.cuda.set_device(rank_id)
-
+    set_current_device_id(rank_id)
     dist.init_process_group("nccl", init_method="tcp://127.0.0.1:28765", rank=rank_id, world_size=world_size)
-    set_custom_reduce()
+
+    custom_comm_ops.set_custom_reduce()
+    custom_comm_ops.set_custom_gather()
     dist.barrier()
 
     torch.cuda.empty_cache()
@@ -59,7 +63,9 @@ def tppart_model_infer(model_class, model_kvargs, batch_size, input_len, output_
     test_data = test_data.reshape(-1)
     test_data = torch.from_numpy(test_data).cuda()
 
-    b_req_idx = model_part.req_manager.alloc(batch_size).int()
+    b_req_idx = torch.tensor(
+        [model_part.req_manager.alloc() for _ in range(batch_size)], dtype=torch.int32, device="cuda"
+    )
     b_start_loc = torch.zeros(batch_size, dtype=torch.int32, device="cuda")
     b_seq_len = torch.zeros(batch_size, dtype=torch.int32, device="cuda")
     b_ready_cache_len = torch.zeros(batch_size, dtype=torch.int32, device="cuda")
@@ -68,7 +74,8 @@ def tppart_model_infer(model_class, model_kvargs, batch_size, input_len, output_
         b_seq_len[i] = input_len
 
     total_token_num = input_len * batch_size
-    mem_indexes = model_part.req_manager.mem_manager.alloc(test_data.shape[0])
+    mem_indexes = model_part.req_manager.mem_manager.alloc(test_data.shape[0]).cuda()
+
     logics = model_part.forward(
         batch_size,
         total_token_num,
@@ -89,7 +96,7 @@ def tppart_model_infer(model_class, model_kvargs, batch_size, input_len, output_
         b_start_loc = b_start_loc + torch.arange(0, batch_size, dtype=torch.int32, device="cuda")
         total_token_num += batch_size
         b_seq_len += 1
-        mem_indexes = model_part.req_manager.mem_manager.alloc(predict_ids.shape[0])
+        mem_indexes = model_part.req_manager.mem_manager.alloc(predict_ids.shape[0]).cuda()
         logics = model_part.forward(
             batch_size,
             total_token_num,
@@ -108,10 +115,6 @@ def tppart_model_infer(model_class, model_kvargs, batch_size, input_len, output_
     model_part.mem_manager.free_all()
     model_part.req_manager.free_all()
 
-    if rank_id == 0:
-        print("can use mem size:", model_part.mem_manager.can_use_mem_size)
-        print("can use req size:", model_part.req_manager.can_use_req_size)
-
     b_req_idx = None
     b_start_loc = None
     b_seq_len = None
@@ -124,15 +127,17 @@ def tppart_model_infer(model_class, model_kvargs, batch_size, input_len, output_
 
     prefill_start_time = time.time()
 
-    b_req_idx = model_part.req_manager.alloc(batch_size).int()
+    b_req_idx = torch.tensor(
+        [model_part.req_manager.alloc() for _ in range(batch_size)], dtype=torch.int32, device="cuda"
+    )
     b_start_loc = torch.zeros(batch_size, dtype=torch.int32, device="cuda")
     b_seq_len = torch.zeros(batch_size, dtype=torch.int32, device="cuda")
     for i in range(batch_size):
         b_start_loc[i] = i * input_len
         b_seq_len[i] = input_len
 
     total_token_num = batch_size * input_len
-    mem_indexes = model_part.req_manager.mem_manager.alloc(test_data.shape[0])
+    mem_indexes = model_part.req_manager.mem_manager.alloc(test_data.shape[0]).cuda()
     logics = model_part.forward(
         batch_size,
         total_token_num,
@@ -159,7 +164,7 @@ def tppart_model_infer(model_class, model_kvargs, batch_size, input_len, output_
         b_start_loc = b_start_loc + torch.arange(0, batch_size, dtype=torch.int32, device="cuda")
         total_token_num += batch_size
         b_seq_len += 1
-        mem_indexes = model_part.req_manager.mem_manager.alloc(predict_ids.shape[0])
+        mem_indexes = model_part.req_manager.mem_manager.alloc(predict_ids.shape[0]).cuda()
         logics = model_part.forward(
             batch_size,
             total_token_num,

test/model/test_model.py

@@ -25,6 +25,7 @@
 from lightllm.utils.config_utils import get_dtype
 from lightllm.utils.config_utils import get_config_json
 
+
 def get_model(weight_dir):
     model_cfg = get_config_json(weight_dir)
     model_type = model_cfg["model_type"]
@@ -68,7 +69,7 @@ def get_model(weight_dir):
 
 class TestModelInfer(unittest.TestCase):
     def test_model_infer(self):
-        model_dir = "/nvme/ci_performance/models/DeepSeek-V2-Lite-Chat/"
+        model_dir = "/nvme/models/llama3/Meta-Llama-3-8B/"
         model_class = get_model(model_dir)
         data_type = get_dtype(model_dir)
         mode = "triton_gqa_flashdecoding"