Fix AWQ Dequant and Weight Loading of deepseek v2 (#6842)

python/sglang/srt/models/deepseek_v2.py

@@ -2137,8 +2137,14 @@ class DeepseekV2ForCausalLM(nn.Module):
                         ):
                             q_a_proj_weight = cached_a_proj[q_a_proj_name]
                             kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
+                            cat_dim = 0
+                            if (
+                                self.quant_config.get_name() == "awq"
+                                or self.quant_config.get_name() == "moe_wna16"
+                            ):
+                                cat_dim = 1
                             fused_weight = torch.cat(
-                                [q_a_proj_weight, kv_a_proj_weight], dim=0
+                                [q_a_proj_weight, kv_a_proj_weight], dim=cat_dim
                             )
                             param_name = (
                                 name.replace("q_a_proj", "fused_qkv_a_proj_with_mqa")

sgl-kernel/csrc/gemm/awq_kernel.cu

@@ -130,10 +130,12 @@ __global__ void __launch_bounds__(256) dequantize_weights(
     int* __restrict__ qzeros,
     OutputT* __restrict__ output,
     int group_size,
-    int qweight_cols) {
+    int qweight_cols,
+    int qweight_rows) {
 #if CUDA_VERSION >= 12000
   int col = blockIdx.x * blockDim.x + threadIdx.x;
   int row = blockIdx.y * blockDim.y + threadIdx.y;
+  if (col >= qweight_cols || row >= qweight_rows) return;
 
   int group_idx = row / group_size;
   int scale_offset = 8 * col + group_idx * qweight_cols * 8;
@@ -188,8 +190,8 @@ torch::Tensor awq_dequantize(torch::Tensor qweight, torch::Tensor scales, torch:
 
   int x_num_threads = 16;
   int y_num_threads = 16;
-  int x_blocks = qweight_cols / x_num_threads;
+  int x_blocks = (qweight_cols + x_num_threads - 1) / x_num_threads;
-  int y_blocks = qweight_rows / y_num_threads;
+  int y_blocks = (qweight_rows + y_num_threads - 1) / y_num_threads;
 
   const at::cuda::OptionalCUDAGuard device_guard(device_of(qweight));
 
@@ -206,13 +208,13 @@ torch::Tensor awq_dequantize(torch::Tensor qweight, torch::Tensor scales, torch:
   if (scales.scalar_type() == at::ScalarType::Half) {
     auto _scales = reinterpret_cast<half*>(scales.data_ptr<at::Half>());
     auto _output = reinterpret_cast<half*>(output.data_ptr<at::Half>());
-    dequantize_weights<half>
+    dequantize_weights<half><<<num_blocks, threads_per_block, 0, stream>>>(
-        <<<num_blocks, threads_per_block, 0, stream>>>(_qweight, _scales, _zeros, _output, group_size, qweight_cols);
+        _qweight, _scales, _zeros, _output, group_size, qweight_cols, qweight_rows);
   } else {
     auto _scales = reinterpret_cast<__nv_bfloat16*>(scales.data_ptr<at::BFloat16>());
     auto _output = reinterpret_cast<__nv_bfloat16*>(output.data_ptr<at::BFloat16>());
-    dequantize_weights<__nv_bfloat16>
+    dequantize_weights<__nv_bfloat16><<<num_blocks, threads_per_block, 0, stream>>>(
-        <<<num_blocks, threads_per_block, 0, stream>>>(_qweight, _scales, _zeros, _output, group_size, qweight_cols);
+        _qweight, _scales, _zeros, _output, group_size, qweight_cols, qweight_rows);
   }
 
   return output;

sgl-kernel/tests/test_awq_dequant.py

@@ -67,8 +67,8 @@ def sglang_awq_dequantize(
     "qweight_row,qweight_col,is_bf16_act",
     list(
         itertools.product(
-            [3584, 18944, 128, 256, 512, 1024],
+            [3584, 18944, 128, 256, 512, 1024, 1536],
-            [448, 576, 4736, 16, 32, 64, 128],
+            [448, 576, 4736, 16, 32, 64, 128, 72],
             [True, False],
         )
     ),
@@ -77,7 +77,6 @@ def test_awq_dequant_compare_implementations(
     qweight_row: int, qweight_col: int, is_bf16_act: bool
 ):
     device = torch.device("cuda")
-
     qweight = torch.randint(
         0,
         torch.iinfo(torch.int32).max,