Deactivating v2 for sharded.

It fails with illegal access on cuda when using sharding.
Took a long while to try and fix it:

All tensors are correct (same as v1).
Scratch size doesn't help
Error only occurs for sequence lengths > 50 (so during warmup most of
the time)

Couldn't figure out why this 50 particular number, nor change anything
to fix the behavior.

server/text_generation_server/utils/gptq/exllamav2.py

@@ -51,7 +51,7 @@ def ext_make_q_matrix(w: dict, temp_dq, key: str = None):
             w["scales"] = w["scales"].half()
 
         # GPTQ with g_idx (act_order)
-        if "g_idx" in w and not (w["g_idx"] == 0).all().item():
+        if w.get("g_idx", None) is not None and not (w["g_idx"] == 0).all().item():
             w["q_perm"] = torch.empty((w["qweight"].shape[0] * 8,), dtype = torch.short, device = w["qweight"].device)
             w["q_invperm"] = torch.empty_like(w["q_perm"])
             # make_q4 segfaults if g_idx is not on cpu in the act-order case. In the non act-order case, None needs to be passed for g_idx.
@@ -113,12 +113,24 @@ class QuantLinear(nn.Module):
         self.maxq = 2 ** self.bits - 1
         self.infeatures = qweight.shape[0] // self.bits * 32
         self.outfeatures = qweight.shape[1]
+        self.padding = - self.outfeatures % 32
+        self.outfeatures = self.outfeatures + self.padding
+
         self.device = qweight.device
         self.qweight = qweight
         self.qzeros = qzeros
         self.scales = scales
         self.g_idx = g_idx
         self.bias = bias if bias is not None else None
+        self.group_size = groupsize
+
+        infeatures = self.infeatures
+        outfeatures = self.outfeatures
+        assert qweight.shape == (infeatures // 32 * self.bits, outfeatures)
+        assert infeatures % self.group_size == 0
+        assert qzeros.shape == (infeatures // self.group_size, outfeatures // 32 * self.bits)
+        assert scales.shape == (infeatures // self.group_size, outfeatures)
+        assert g_idx.shape == (infeatures, ), f"{g_idx.shape}, {infeatures}"
 
         global FIXED_BYTES, LAYERS
         FIXED_BYTES = max(FIXED_BYTES, self.scratch_space_fixed())

server/text_generation_server/utils/layers.py

@@ -35,6 +35,10 @@ except Exception:
 HAS_EXLLAMA = False
 CAN_EXLLAMA = major >= 8
 V2 = os.getenv("EXLLAMA_VERSION", "2") == "2"
+if V2 and int(os.getenv("WORLD_SIZE", "1")) > 1:
+    logger.warning("Disabling exllama v2 and using v1 instead because there are issues when sharding")
+    V2 = False
+
 if os.getenv("DISABLE_EXLLAMA") == "True":
     HAS_EXLLAMA = False
 elif CAN_EXLLAMA:

server/text_generation_server/utils/weights.py

@@ -281,20 +281,10 @@ class Weights:
                     logger.info(f"Using exllama kernels v{HAS_EXLLAMA}")
 
             if use_exllama:
-                if groupsize >= 0:
-                    # Exllama reorders the weights in advance and the activations on the fly, thus
-                    # the scales and zero-points do not need to be reordered.
                 qzeros = self.get_sharded(f"{prefix}.qzeros", dim=0)
                 scales = self.get_sharded(f"{prefix}.scales", dim=0)
-                else:
+                g_idx = self.get_sharded(f"{prefix}.g_idx", dim= 0)
-                    qzeros = self.get_tensor(f"{prefix}.qzeros")
+                g_idx = g_idx - g_idx[0]
-                    scales = self.get_tensor(f"{prefix}.scales")
-
-                # For tp > 1, at this point we know we do not use act-order
-                if self.process_group.size() == 1:
-                    g_idx = self.get_tensor(f"{prefix}.g_idx")
-                else:
-                    g_idx = None
             else:
                 # The triton kernel reorders the scales/zero points instead of the weight/activation.
                 # Thus, each rank needs the full qzeros/scales.