change xmx condition (#9896)

python/llm/src/bigdl/llm/transformers/low_bit_linear.py

@@ -470,7 +470,7 @@ class LowBitLinear(nn.Linear):
             try:
                 import intel_extension_for_pytorch
                 import linear_q4_0
-                from bigdl.llm.utils.xmx_checker import use_xmx
+                from bigdl.llm.transformers.models.utils import use_xmx
             except ModuleNotFoundError:
                 invalidInputError(False,
                                   "Please `pip install bigdl_core_xe` first.")

python/llm/src/bigdl/llm/transformers/models/utils.py

@@ -21,6 +21,11 @@ from bigdl.llm.ggml.quantize import ggml_tensor_qtype
 from bigdl.llm.transformers.utils import get_ipex_version, get_xpu_device_type
 
 
+SYM_INT4 = ggml_tensor_qtype["sym_int4"]
+SYM_INT8 = ggml_tensor_qtype["sym_int8"]
+FP8 = ggml_tensor_qtype["fp8"]
+
+
 def init_kv_cache(batch_size, num_heads, head_dim, current_length, max_length, dtype, device):
     key_cache_storage = torch.empty(batch_size, num_heads,
                                     max_length, head_dim,
@@ -263,3 +268,15 @@ def mlp_fusion_check(x, qtype, training):
     if training or x.requires_grad:
         return False
     return True
+
+
+def use_xmx(x: torch.Tensor, qtype: int):
+    device = get_xpu_device_type(x)
+    return (
+        device in ["arc", "flex", "pvc"]
+        and qtype in [SYM_INT4, SYM_INT8, FP8]
+        and (
+            (device != "pvc" and x.dtype == torch.float32 and 1 < x.size(0) <= 64)
+            or 1 < x.size(0) <= 8
+        )
+    )

python/llm/src/bigdl/llm/utils/xmx_checker.py

@@ -1,52 +0,0 @@
-#
-# Copyright 2016 The BigDL Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-import torch
-import intel_extension_for_pytorch as ipex
-from bigdl.llm.ggml.quantize import ggml_tensor_qtype
-
-
-SYM_INT4 = ggml_tensor_qtype["sym_int4"]
-SYM_INT8 = ggml_tensor_qtype["sym_int8"]
-NF4 = ggml_tensor_qtype["nf4"]
-NF3 = ggml_tensor_qtype["nf3"]
-FP8 = ggml_tensor_qtype["fp8"]
-FP4 = ggml_tensor_qtype["fp4"]
-MOFQ4 = ggml_tensor_qtype["mixed_fp4"]
-MOFQ8 = ggml_tensor_qtype["mixed_fp8"]
-
-
-class XMXChecker:
-    def __init__(self):
-        self.support_xmx = self.check_xmx()
-        self.supported_qtype = [SYM_INT4, SYM_INT8, FP8]
-
-    @staticmethod
-    def check_xmx():
-        name = torch.xpu.get_device_name(0)
-        # todo: not sure how to check xmx or how to get device name for now
-        return "Arc(TM)" in name or "GPU Max" in name or "GPU Flex" in name
-
-    def check(self, input_tensor: torch.Tensor, qtype: int):
-        return self.support_xmx and 1 < input_tensor.shape[0] <= 8 and \
-            qtype in self.supported_qtype
-
-
-xmx_checker = XMXChecker()
-
-
-def use_xmx(input_tensor: torch.Tensor, qtype: int):
-    return xmx_checker.check(input_tensor, qtype)