refactor: replace all TORCH_CHECK with CHECK. (#453)

Author: XuZhang99

xllm/core/framework/eplb/eplb_policy.cpp

@@ -76,7 +76,7 @@ std::pair<torch::Tensor, std::vector<bool>> EplbPolicy::rebalance_experts(
 torch::Tensor EplbPolicy::compute_balanced_pack(
     const torch::Tensor& expert_loads) {
   // Parameter Validation
-  TORCH_CHECK(expert_loads.dim() == 1, "expert_loads must be 1D tensor");
+  CHECK_EQ(expert_loads.dim(), 1) << "expert_loads must be 1D tensor";
   const int64_t num_experts = expert_loads.size(0);
 
   // Generate Redundant Experts
@@ -139,7 +139,7 @@ std::pair<torch::Tensor, torch::Tensor> EplbPolicy::update_origin_weights(
     torch::Tensor expert_loads,
     int32_t redundancy_experts) {
   //  Parameter Validation
-  TORCH_CHECK(expert_loads.dim() == 1, "expert_loads must be 1D tensor");
+  CHECK_EQ(expert_loads.dim(), 1) << "expert_loads must be 1D tensor";
   const int64_t num_experts = expert_loads.size(0);
 
   //  Initialize Data Structures

xllm/core/framework/parallel_state/npu_process_group.cpp

@@ -69,7 +69,7 @@ HcclDataType to_hccl_data_type(const torch::Tensor& input) {
     case at::kBFloat16:
       return HCCL_DATA_TYPE_BFP16;
     default:
-      TORCH_CHECK(false, "Unconvertible HCCL type ", type);
+      LOG(FATAL) << "Unconvertible HCCL type: " << type;
   }
 }
 

xllm/core/layers/common/tests/tests_utils.cpp

@@ -249,10 +249,10 @@ torch::Tensor seeded_tensor(const std::string& key,
         out_cpu = map_mod_span(int64_t{});
         break;
       default:
-        TORCH_CHECK(false, "Unsupported integer dtype: ", dtype);
+        LOG(FATAL) << "Unsupported integer dtype: " << dtype;
     }
   } else {
-    TORCH_CHECK(false, "Unsupported dtype for seeded_tensor");
+    LOG(FATAL) << "Unsupported dtype for seeded_tensor";
   }
 
   // Shape & device

xllm/core/layers/mlu/deepseek_v2_attention.cpp

@@ -43,8 +43,8 @@ DeepseekV2AttentionImpl::DeepseekV2AttentionImpl(
   int64_t max_position_embeddings = args.max_position_embeddings();
 
   qk_head_dim_ = qk_nope_head_dim_ + qk_rope_head_dim_;
-  TORCH_CHECK(num_heads % tp_size == 0,
-              "num_heads must be divisible by tensor parallel size");
+  CHECK_EQ(num_heads % tp_size, 0)
+      << "num_heads must be divisible by tensor parallel size";
   num_local_heads_ = num_heads / tp_size;
   float scaling = std::pow(qk_head_dim_, -0.5f);
 

xllm/core/layers/npu/npu_deepseek_v2_decoder_layer_impl.h

@@ -80,11 +80,8 @@ class ExpertBuffer {
     } else {
       auto validate_shape = [](const torch::Tensor& t,
                                const std::vector<int64_t>& expected) {
-        TORCH_CHECK(t.sizes() == expected,
-                    "Shape mismatch. Expected ",
-                    expected,
-                    " got ",
-                    t.sizes());
+        CHECK_EQ(t.sizes(), expected)
+            << "Shape mismatch. Expected " << expected << " got " << t.sizes();
       };
 
       validate_shape(gateup_weight, gateup_weight_shape);

xllm/models/dit/dit.h

@@ -592,7 +592,7 @@ inline torch::Tensor get_timestep_embedding(const torch::Tensor& timesteps,
                                             float downscale_freq_shift = 1.0f,
                                             float scale = 1.0f,
                                             int64_t max_period = 10000) {
-  TORCH_CHECK(timesteps.dim() == 1, "Timesteps should be a 1d-array");
+  CHECK_EQ(timesteps.dim(), 1) << "Timesteps should be a 1d-array";
   int64_t half_dim = embedding_dim / 2;
   // -ln(max_period) * [0, 1, ..., half_dim-1] / (half_dim -
   // downscale_freq_shift

xllm/models/dit/pipeline_flux_base.h

@@ -85,7 +85,7 @@ torch::Tensor get_1d_rotary_pos_embed(
     float ntk_factor = 1.0,
     bool repeat_interleave_real = true,
     torch::Dtype freqs_dtype = torch::kFloat32) {
-  TORCH_CHECK(dim % 2 == 0, "Dimension must be even");
+  CHECK_EQ(dim % 2, 0) << "Dimension must be even";
 
   torch::Tensor pos_tensor = pos;
   if (pos.dim() == 0) {

xllm/models/vlm/minicpmv.h

@@ -306,7 +306,7 @@ torch::Tensor get_1d_sincos_pos_embed_from_grid(int embed_dim,
                                                 std::pair<int, int> version = {
                                                     2,
                                                     0}) {
-  TORCH_CHECK(embed_dim % 2 == 0, "embed_dim must be even");
+  CHECK_EQ(embed_dim % 2, 0) << "embed_dim must be even";
 
   // compute omega
   auto omega = torch::arange(embed_dim / 2, torch::kFloat32);
@@ -332,7 +332,7 @@ torch::Tensor get_2d_sincos_pos_embed_from_grid(int embed_dim,
                                                 std::pair<int, int> version = {
                                                     2,
                                                     0}) {
-  TORCH_CHECK(embed_dim % 2 == 0, "embed_dim must be even");
+  CHECK_EQ(embed_dim % 2, 0) << "embed_dim must be even";
 
   auto emb_h =
       get_1d_sincos_pos_embed_from_grid(embed_dim / 2, grid[0], version);
@@ -382,7 +382,7 @@ class Resampler2_5Impl : public BaseResamplerImpl {
   }
 
   torch::Tensor forward(torch::Tensor x, torch::Tensor tgt_sizes) {
-    TORCH_CHECK(x.size(0) == tgt_sizes.size(0), "Batch size mismatch!");
+    CHECK_EQ(x.size(0), tgt_sizes.size(0)) << "Batch size mismatch!";
 
     int64_t batch_size = x.size(0);
     auto device = x.device();