[BUG] Fix H100 crash/compat with Marlin

auto_gptq/modeling/_base.py

@@ -833,13 +833,9 @@ def from_quantized(
             # format marlin requires marlin kernel
             use_marlin = True
 
-        marlin_compatible, marlin_optimized = _validate_marlin_device_support()
-        if use_marlin and (not MARLIN_AVAILABLE or not marlin_compatible):
-            raise TypeError("use_marlin is true but Marlin is not availble due to cuda/device support.")
-        elif use_marlin and not marlin_optimized:
-            logger.info(
-                "use_marlin is true and your gpu device is supported but not optimized for Marlin."
-            )
+        marlin_compatible = _validate_marlin_device_support()
+        if use_marlin and not MARLIN_AVAILABLE:
+            raise TypeError("use_marlin is true but Marlin is not available due to cuda/device support.")
 
         if not use_marlin and MARLIN_AVAILABLE:
             unsupported_reason = _validate_marlin_compatibility(quantize_config)

auto_gptq/utils/marlin_utils.py

@@ -87,28 +87,15 @@ def prepare_model_for_marlin_load(
 
 
 # Validate marlin support
-def _validate_marlin_device_support() -> Tuple[bool, bool]:
+def _validate_marlin_device_support() -> bool:
     """
-        Validates if the current device is compatible and optimized for Marlin.
+        Validates if the current device is compatible for Marlin.
         ref: https://github.com/IST-DASLab/marlin?tab=readme-ov-file#requirements
 
         Returns:
-            Tuple[bool, bool]: The first indicates if CUDA device is compatible for Marlin,
-                               the second indicates if CUDA device is optimized for Marlin.
+            bool: indicates if CUDA device is compatible for Marlin
         """
-    supported = False
-    optimized = False
-
-    # >=hopper is compatible but not optimized
-    if torch.cuda.get_device_capability()[0] >= 9:
-        supported = True
-        optimized = False
-    # ampere and ada are supported and optimized
-    elif torch.cuda.get_device_capability()[0] >= 8:
-        supported = True
-        optimized = True
-
-    return supported, optimized
+    return torch.cuda.get_device_capability()[0] >= 8
 
 
 # Adapted from https://github.com/rib-2/marlin/tree/conversion

autogptq_extension/marlin/marlin_cuda_kernel.cu

@@ -68,20 +68,14 @@ __device__ inline void cp_async4_pred(void* smem_ptr, const void* glob_ptr, bool
 #endif
 }
 
-// Asynchronous global->shared copy with a chache hint indicating that the values may be evicted immediately; used for
-// quantized weights B, which are only accessed precisely once and should thus not pollute the L2 cache which we need
-// for inputs A and outputs C.
-__device__ inline void cp_async4_stream(void* smem_ptr, const void* glob_ptr) {
+// Asynchronous global->shared copy
+__device__ inline void cp_async4(void *smem_ptr, const void *glob_ptr) {
 #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
   const int BYTES = 16;
   uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
-  asm volatile(
-    "{\n"
-    "   .reg .b64 p;\n"
-    "   createpolicy.fractional.L2::evict_first.b64 p, 1.0;"
-    "   cp.async.cg.shared.global.L2::cache_hint [%0], [%1], %2, p;\n"
-    "}\n" :: "r"(smem), "l"(glob_ptr), "n"(BYTES)
-  );
+  asm volatile("{\n"
+               "   cp.async.cg.shared.global [%0], [%1], %2;\n"
+               "}\n" :: "r"(smem), "l"(glob_ptr), "n"(BYTES));
 #else
   assert(0);
 #endif
@@ -431,14 +425,14 @@ __global__ void Marlin(
       int4* sh_b_stage = sh_b + b_sh_stage * pipe;
       #pragma unroll
       for (int i = 0; i < b_sh_wr_iters; i++) {
-        cp_async4_stream(&sh_b_stage[b_sh_wr_delta * i + b_sh_wr], B_ptr[i]);
+        cp_async4(&sh_b_stage[b_sh_wr_delta * i + b_sh_wr], B_ptr[i]);
         B_ptr[i] += b_gl_rd_delta_o;
       }
       // Only fetch scales if this tile starts a new group
       if (group_blocks != -1 && pipe % (group_blocks / thread_k_blocks) == 0) {
         int4* sh_s_stage = sh_s + s_sh_stage * pipe;
         if (s_sh_wr_pred)
-          cp_async4_stream(&sh_s_stage[s_sh_wr], &s[s_gl_rd]);
+          cp_async4(&sh_s_stage[s_sh_wr], &s[s_gl_rd]);
         s_gl_rd += s_gl_rd_delta;
       }
     }
@@ -692,7 +686,7 @@ __global__ void Marlin(
       // For per-column scales, we only fetch them here in the final step before write-out
       if (group_blocks == -1 && last) {
         if (s_sh_wr_pred)
-          cp_async4_stream(&sh_s[s_sh_wr], &s[s_gl_rd]);
+          cp_async4(&sh_s[s_sh_wr], &s[s_gl_rd]);
         cp_async_fence();
       }
       thread_block_reduce();