[Misc] Reduce supported Punica dtypes

CMakeLists.txt

@@ -211,23 +211,11 @@ define_gpu_extension_target(
 
 set(VLLM_PUNICA_EXT_SRC
   "csrc/punica/bgmv/bgmv_bf16_bf16_bf16.cu"
-  "csrc/punica/bgmv/bgmv_bf16_bf16_fp16.cu"
-  "csrc/punica/bgmv/bgmv_bf16_fp16_bf16.cu"
-  "csrc/punica/bgmv/bgmv_bf16_fp16_fp16.cu"
   "csrc/punica/bgmv/bgmv_bf16_fp32_bf16.cu"
-  "csrc/punica/bgmv/bgmv_bf16_fp32_fp16.cu"
-  "csrc/punica/bgmv/bgmv_fp16_bf16_bf16.cu"
-  "csrc/punica/bgmv/bgmv_fp16_bf16_fp16.cu"
-  "csrc/punica/bgmv/bgmv_fp16_fp16_bf16.cu"
   "csrc/punica/bgmv/bgmv_fp16_fp16_fp16.cu"
-  "csrc/punica/bgmv/bgmv_fp16_fp32_bf16.cu"
   "csrc/punica/bgmv/bgmv_fp16_fp32_fp16.cu"
   "csrc/punica/bgmv/bgmv_fp32_bf16_bf16.cu"
-  "csrc/punica/bgmv/bgmv_fp32_bf16_fp16.cu"
-  "csrc/punica/bgmv/bgmv_fp32_fp16_bf16.cu"
   "csrc/punica/bgmv/bgmv_fp32_fp16_fp16.cu"
-  "csrc/punica/bgmv/bgmv_fp32_fp32_bf16.cu"
-  "csrc/punica/bgmv/bgmv_fp32_fp32_fp16.cu"
   "csrc/punica/punica_ops.cc")
 
 #

csrc/punica/bgmv/generator.py

@@ -18,6 +18,26 @@
             if weight_dtype == "fp32":
                 # FP32 weights are not supported.
                 continue
+            if output_dtype == "fp32":
+                # LoRA A matrix.
+                if input_dtype != weight_dtype:
+                    # NOTE(woosuk): While Punica supports the case where the
+                    # input and weight dtypes are different, we only generate
+                    # the kernels the same dtypes to reduce the binary size.
+                    continue
+            elif input_dtype == "fp32":
+                # LoRA B matrix.
+                if output_dtype != weight_dtype:
+                    # NOTE(woosuk): While Punica supports the case where the
+                    # output and weight dtypes are different, we only generate
+                    # the kernels the same dtypes to reduce the binary size.
+                    continue
+            elif not (input_dtype == output_dtype == weight_dtype):
+                # NOTE(woosuk): While Punica supports mixed data types for
+                # input, output, and weight, we only generate the kernels with
+                # the same data types to reduce the binary size.
+                continue
+
             kernel_definition = TEMPLATE.format(
                 input_dtype=DTYPE_MAP[input_dtype],
                 output_dtype=DTYPE_MAP[output_dtype],

csrc/punica/punica_ops.cc

@@ -50,6 +50,23 @@ inline bool launch_bgmv_kernel(out_T *Y, const in_T *X, const W_T *W,
                                int64_t y_offset, int64_t full_y_size,
                                int64_t batch_size, int64_t num_layers,
                                int64_t layer_idx, float scale) {
+  // NOTE(woosuk): While Punica supports various combinations of input/output
+  // data types, we limit the supported data types to reduce the binary size.
+  constexpr bool is_input_float = std::is_same<in_T, float>::value;
+  constexpr bool is_output_float = std::is_same<out_T, float>::value;
+  if (is_input_float) {
+    if (!std::is_same<out_T, W_T>::value) {
+      return false;
+    }
+  } else if (is_output_float) {
+    if (!std::is_same<in_T, W_T>::value) {
+      return false;
+    }
+  } else if (!(std::is_same<in_T, W_T>::value &&
+               std::is_same<out_T, W_T>::value)) {
+    return false;
+  }
+
   switch (pack_u32(in_features, out_features)) {
 #define CASE_ONESIDE(_in_T, _out_T, _W_T, feat_in, feat_out)                   \
   case pack_u32(feat_in, feat_out):                                            \

tests/lora/test_layers.py

@@ -413,7 +413,9 @@ def test_lm_head_logits_processor(dist_init, num_loras, device,
 
     def _pretest():
         linear = ParallelLMHead(vocab_size + lora_config.lora_extra_vocab_size,
-                                1024, vocab_size)
+                                1024,
+                                vocab_size,
+                                params_dtype=torch.float16)
         linear.weight.data = torch.rand_like(linear.weight.data)
         linear.weight.data[:, vocab_size:] = 0
         logits_processor = LogitsProcessor(
@@ -445,7 +447,7 @@ def _pretest():
             num_inputs=8 * num_loras,  # * 3,
             input_size=(1, 1024),
             input_range=(0, 1),
-            input_type=torch.float32,
+            input_type=torch.float16,
         )
         lora_mapping = LoRAMapping(index_mapping, prompt_mapping)
 
@@ -494,7 +496,7 @@ def _pretest():
             num_inputs=8 * num_loras * 3,
             input_size=(1, 1024),
             input_range=(0, 1),
-            input_type=torch.float32,
+            input_type=torch.float16,
         )
         lora_mapping = LoRAMapping(index_mapping, prompt_mapping)
 
@@ -533,11 +535,17 @@ def test_linear_parallel(dist_init, num_loras, orientation, device) -> None:
 
     def create_random_linear_parallel_layer():
         if orientation == "row":
-            linear = RowParallelLinear(4096, 4096, bias=False)
+            linear = RowParallelLinear(4096,
+                                       4096,
+                                       bias=False,
+                                       params_dtype=torch.float16)
             linear.weight.data = torch.rand_like(linear.weight.data)
             lora_linear = RowParallelLinearWithLoRA(linear)
         else:
-            linear = ColumnParallelLinear(4096, 4096, bias=False)
+            linear = ColumnParallelLinear(4096,
+                                          4096,
+                                          bias=False,
+                                          params_dtype=torch.float16)
             linear.weight.data = torch.rand_like(linear.weight.data)
             lora_linear = ColumnParallelLinearWithLoRA(linear)
         lora_linear.create_lora_weights(max_loras, lora_config)
@@ -561,7 +569,7 @@ def create_random_linear_parallel_layer():
             num_inputs=32 * num_loras,
             input_size=(1, 4096),
             input_range=(0, 1),
-            input_type=torch.float32,
+            input_type=torch.float16,
         )
         lora_mapping = LoRAMapping(index_mapping, prompt_mapping)
 
@@ -600,7 +608,7 @@ def create_random_linear_parallel_layer():
             num_inputs=32 * num_loras,
             input_size=(1, 4096),
             input_range=(0, 1),
-            input_type=torch.float32,
+            input_type=torch.float16,
         )
         lora_mapping = LoRAMapping(index_mapping, prompt_mapping)
 
@@ -633,15 +641,24 @@ def test_column_parallel_packed(dist_init, num_loras, repeats, device) -> None:
     def create_column_parallel_packed_layer():
         if repeats == 2:
             linear = MergedColumnParallelLinear(4096, [4096] * repeats,
-                                                bias=False)
+                                                bias=False,
+                                                params_dtype=torch.float16)
             linear.weight.data = torch.rand_like(linear.weight.data)
             lora_linear = MergedColumnParallelLinearWithLoRA(linear)
         elif repeats == 3:
-            linear = QKVParallelLinear(4096, 64, 32, bias=False)
+            linear = QKVParallelLinear(4096,
+                                       64,
+                                       32,
+                                       bias=False,
+                                       params_dtype=torch.float16)
             linear.weight.data = torch.rand_like(linear.weight.data)
             lora_linear = MergedQKVParallelLinearWithLora(linear)
         else:
-            linear = QKVParallelLinear(4096, 64, 32, bias=False)
+            linear = QKVParallelLinear(4096,
+                                       64,
+                                       32,
+                                       bias=False,
+                                       params_dtype=torch.float16)
             linear.weight.data = torch.rand_like(linear.weight.data)
             lora_linear = QKVParallelLinearWithLora(linear)
 
@@ -676,7 +693,7 @@ class FakeConfig:
             num_inputs=32 * num_loras,
             input_size=(1, 4096),
             input_range=(0, 1),
-            input_type=torch.float32,
+            input_type=torch.float16,
         )
         lora_mapping = LoRAMapping(index_mapping, prompt_mapping)
 
@@ -716,7 +733,7 @@ class FakeConfig:
             num_inputs=32 * num_loras,
             input_size=(1, 4096),
             input_range=(0, 1),
-            input_type=torch.float32,
+            input_type=torch.float16,
         )
         lora_mapping = LoRAMapping(index_mapping, prompt_mapping)