[0.11.0]Chery pick pta upgrade change (#3940)

This PR cherry-pick two commit from main to upgrade torch-npu to 2.7.1
official release

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

tests/ut/ops/test_layernorm.py

@@ -7,7 +7,6 @@ from vllm.model_executor.layers.layernorm import RMSNorm
 
 from tests.ut.base import PytestBase
 from vllm_ascend.quantization.w8a8 import AscendW8A8LinearMethod
-from vllm_ascend.utils import version_check
 
 
 def mock_rms_norm(x, weight, eps):
@@ -18,15 +17,6 @@ def mock_add_rms_norm(x, residual, weight, eps):
     return 2 * x, None, 2 * residual
 
 
-def mock_add_rms_norm_quant(x, residual, weight, quant_scale, quant_offset,
-                            epsilon):
-    x_out = 2 * x
-    residual_out = 2 * residual
-    x_out_quant = x_out.to(torch.int8)
-    residual_out_quant = residual_out.to(torch.int8)
-    return x_out_quant, None, residual_out_quant
-
-
 def mock_add_rms_norm_quant_with_bias(x, residual, weight, quant_scale,
                                       quant_offset, beta, epsilon):
     x_out = 2 * x
@@ -43,10 +33,8 @@ class TestAscendRMSNorm(PytestBase):
         mocker.patch("torch_npu.npu_rms_norm", side_effect=mock_rms_norm)
         mocker.patch("torch_npu.npu_add_rms_norm",
                      side_effect=mock_add_rms_norm)
-        torch_npu_check = version_check()
-        arnq_side_effect = mock_add_rms_norm_quant_with_bias if torch_npu_check else mock_add_rms_norm_quant
         mocker.patch("torch_npu.npu_add_rms_norm_quant",
-                     side_effect=arnq_side_effect)
+                     side_effect=mock_add_rms_norm_quant_with_bias)
         mocker.patch("torch.ops.vllm.maybe_wait_prefetch_done",
                      side_effect=lambda x: None)
 
@@ -82,8 +70,7 @@ class TestAscendRMSNorm(PytestBase):
 
         mock_model_instance = mocker.MagicMock()
         mock_forward_context.model_instance = mock_model_instance
-        torch_npu_check = version_check()
-        num_hidden_layers = 3 if torch_npu_check else 2
+        num_hidden_layers = 3
         mock_model_instance.model.layers = [
             mocker.MagicMock() for _ in range(num_hidden_layers)
         ]
@@ -136,37 +123,34 @@ class TestAscendRMSNorm(PytestBase):
         assert mock_forward_context.fusion_linear == "gate_up_dense"
         assert mock_forward_context.layer_idx == 1
 
-        if torch_npu_check:
-            mock_forward_context.fusion_linear = "gate_moe"
+        mock_forward_context.fusion_linear = "gate_moe"
+        x_out, residual_out = layer.forward_oot(x, residual)
+
+        assert mock_get_forward_context.call_count == 5
+        fusion_linear_expected = "qkv_moe"
+        assert mock_forward_context.fusion_linear == fusion_linear_expected
+        assert mock_forward_context.layer_idx == 2
+
         x_out, residual_out = layer.forward_oot(x, residual)
 
         assert mock_get_forward_context.call_count == 6
-        fusion_linear_expected = "qkv_moe" if torch_npu_check else "qkv_dense"
+        fusion_linear_expected = "gate_moe"
         assert mock_forward_context.fusion_linear == fusion_linear_expected
         assert mock_forward_context.layer_idx == 2
 
+        # last layer returned directly
         x_out, residual_out = layer.forward_oot(x, residual)
 
         assert mock_get_forward_context.call_count == 7
-        fusion_linear_expected = "gate_moe" if torch_npu_check else "qkv_dense"
-        assert mock_forward_context.fusion_linear == fusion_linear_expected
-        assert mock_forward_context.layer_idx == 2
+        assert mock_forward_context.fusion_linear == "qkv_moe"
+        assert mock_forward_context.layer_idx == 3
 
-        if not torch_npu_check:
-            return
-        # last layer returned directly
         x_out, residual_out = layer.forward_oot(x, residual)
 
         assert mock_get_forward_context.call_count == 8
         assert mock_forward_context.fusion_linear == "qkv_moe"
         assert mock_forward_context.layer_idx == 3
 
-        x_out, residual_out = layer.forward_oot(x, residual)
-
-        assert mock_get_forward_context.call_count == 9
-        assert mock_forward_context.fusion_linear == "qkv_moe"
-        assert mock_forward_context.layer_idx == 3
-
 
 if __name__ == '__main__':
     unittest.main()

tests/ut/torchair/quantization/test_torchair_w8a8_dynamic.py

@@ -23,9 +23,9 @@ class TestAscendW8A8FusedMoEMethod(TestBase):
     @patch("torch_npu.npu_swiglu")
     @patch("torch_npu.npu_dynamic_quant")
     @patch("torch_npu.npu_moe_finalize_routing")
-    @patch("torch_npu.npu_moe_init_routing")
+    @patch("torch_npu.npu_moe_init_routing_quant")
     def test_torchair_fused_experts_with_all2all(
-            self, mock_moe_init_routing, mock_moe_finalize_routing,
+            self, mock_npu_moe_init_routing_quant, mock_moe_finalize_routing,
             mock_dynamic_quant, mock_swiglu, mock_grouped_matmul,
             mock_moe_re_routing, mock_all_to_all_single):
 
@@ -38,11 +38,10 @@ class TestAscendW8A8FusedMoEMethod(TestBase):
         placeholder_ones = torch.ones(self.num_tokens, dtype=torch.int32)
         mock_all_to_all_single.side_effect = lambda output, input, *args, **kwargs: output.copy_(
             input)
-        mock_moe_init_routing.return_value = (
-            placeholder_int8,
-            placeholder_ones,
-            placeholder_ones,
-        )
+        mock_npu_moe_init_routing_quant.return_value = (
+            placeholder_int8, placeholder_ones, placeholder_ones,
+            torch.bincount(placeholder_ones, minlength=len(expert_map)),
+            torch.randn(self.num_tokens))
         mock_moe_re_routing.return_value = (placeholder_int8, self.placeholder,
                                             torch.randint(0,
                                                           100,