[main][quantization] Support deepseek w4a8 per-channel quantization (#3011)

### What this PR does / why we need it?
1.Support deepseek w4a8 per-channel quantization
2.The eager mode supports converting weights to the NZ format
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
#### How to get weights using Modelslim

##### Installation steps

git clone https://gitcode.com/Ascend/msit.git
cd msit/msmodelslim
bash install.sh

##### Generate w4a8 per-channel weights

cd /example/DeepSeek
Command reference: msmodelslim/example/DeepSeek/README.md

- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9

---------

Signed-off-by: Wang Kunpeng <1289706727@qq.com>

docs/source/user_guide/feature_guide/quantization.md

@@ -108,18 +108,19 @@ Please convert DeepSeek series models using `br_release_MindStudio_8.1.RC2_TR5_2
 
 ### 3. When converting deepseek series models with modelslim, what should you pay attention?
 
-When using the weight generated by modelslim with the `--dynamic` parameter, if torchair graph mode is enabled, please modify the configuration file in the CANN package to prevent incorrect inference results.
+When the mla portion of the weights used `W8A8_DYNAMIC` quantization, if torchair graph mode is enabled, please modify the configuration file in the CANN package to prevent incorrect inference results.
 
 The operation steps are as follows:
 
 1. Search in the CANN package directory used, for example:
 find /usr/local/Ascend/ -name fusion_config.json
 
-2. Add `"AddRmsNormDynamicQuantFusionPass":"off",` to the fusion_config.json you find, the location is as follows:
+2. Add `"AddRmsNormDynamicQuantFusionPass":"off",` and `"MultiAddRmsNormDynamicQuantFusionPass":"off",` to the fusion_config.json you find, the location is as follows:
 
 ```bash
 {
     "Switch":{
         "GraphFusion":{
             "AddRmsNormDynamicQuantFusionPass":"off",
+            "MultiAddRmsNormDynamicQuantFusionPass":"off",
 ```

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -35,6 +35,11 @@ QWEN_DENSE_MODELS = [
     "vllm-ascend/Qwen3-8B-W8A8", "vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8"
 ]
 
+DEEPSEEK_W4A8_MODELS = [
+    "vllm-ascend/DeepSeek-V3-W4A8-Pruing",
+    "vllm-ascend/DeepSeek-V3.1-W4A8-puring"
+]
+
 
 def test_models_distributed_QwQ():
     example_prompts = [
@@ -109,14 +114,15 @@ def test_models_distributed_Qwen3_W4A8DYNAMIC():
         vllm_model.generate_greedy(example_prompts, max_tokens)
 
 
+@pytest.mark.parametrize("model", DEEPSEEK_W4A8_MODELS)
 @patch.dict(os.environ, {"VLLM_ASCEND_MLA_PA": "1"})
-def test_models_distributed_DeepSeek_W4A8DYNAMIC():
+def test_models_distributed_DeepSeek_W4A8DYNAMIC(model):
     prompts = [
         "Hello, my name is",
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download("vllm-ascend/DeepSeek-V3-W4A8-Pruing"),
+            snapshot_download(model),
             dtype="auto",
             tensor_parallel_size=2,
             quantization="ascend",

tests/ut/quantization/test_w4a8_dynamic.py

@@ -1,4 +1,3 @@
-import copy
 from unittest.mock import Mock, patch
 
 import torch
@@ -95,19 +94,19 @@ class TestAscendW4A8DynamicFusedMoEMethod(TestBase):
         # old quant version weight
         param_dict = self.quant_method.get_dynamic_quant_param(
             self.experts, self.input_size, self.output_size, torch.bfloat16)
-        self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.bfloat16)
+        self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.float32)
         self.assertEqual(param_dict["w13_weight_scale"].shape,
                          (self.experts, 2 * self.input_size, 1))
         self.assertEqual(param_dict["w13_weight_scale_second"].dtype,
-                         torch.bfloat16)
+                         torch.float32)
         self.assertEqual(param_dict["w13_weight_scale_second"].shape,
                          (self.experts, 2 * self.input_size,
                           self.output_size // self.group_size))
-        self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.bfloat16)
+        self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.float32)
         self.assertEqual(param_dict["w2_weight_scale"].shape,
                          (self.experts, self.output_size, 1))
         self.assertEqual(param_dict["w2_weight_scale_second"].dtype,
-                         torch.bfloat16)
+                         torch.float32)
         self.assertEqual(param_dict["w2_weight_scale_second"].shape,
                          (self.experts, self.output_size,
                           self.input_size // self.group_size))
@@ -119,40 +118,87 @@ class TestAscendW4A8DynamicFusedMoEMethod(TestBase):
         self.assertEqual(
             param_dict["w2_scale_bias"].shape,
             (self.experts, self.output_size, 16 // self.quant_method.tp_size))
+        # per-channel weight
+        self.quant_method.is_per_channel_weight = True
+        param_dict = self.quant_method.get_dynamic_quant_param(
+            self.experts, self.input_size, self.output_size, torch.bfloat16)
+        pergroup_param = [
+            "w13_weight_scale_second", "w13_weight_offset_second",
+            "w2_weight_scale_second", "w2_weight_offset_second"
+        ]
+        is_contains = any(key in param_dict for key in pergroup_param)
+        self.assertFalse(is_contains)
 
+    def build_layer(self,
+                    is_new_quant_version=True,
+                    is_per_channel_weight=False):
+        layer = torch.nn.Module()
+        if is_new_quant_version:
+            layer.w13_weight = torch.nn.Parameter(torch.zeros(
+                (self.experts, self.input_size, self.output_size),
+                dtype=torch.int8),
+                                                  requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(torch.zeros(
+                (self.experts, self.output_size // 2, self.input_size),
+                dtype=torch.int8),
+                                                 requires_grad=False)
+            w13_scale_bias = torch.zeros(
+                (self.experts, 2 * self.input_size, 1), dtype=torch.float32)
+            layer.w13_scale_bias = torch.nn.Parameter(w13_scale_bias,
+                                                      requires_grad=False)
+            w2_scale_bias = torch.zeros((self.experts, self.output_size,
+                                         16 // self.quant_method.tp_size),
+                                        dtype=torch.float32)
+            layer.w2_scale_bias = torch.nn.Parameter(w2_scale_bias,
+                                                     requires_grad=False)
+        else:
+            layer.w13_weight = torch.nn.Parameter(torch.zeros(
+                (self.experts, 2 * self.input_size, self.output_size),
+                dtype=torch.int8),
+                                                  requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(torch.zeros(
+                (self.experts, self.output_size, self.input_size),
+                dtype=torch.int8),
+                                                 requires_grad=False)
+        layer.w13_weight_scale = torch.nn.Parameter(torch.ones(
+            (self.experts, 2 * self.input_size, 1), dtype=torch.float32),
+                                                    requires_grad=False)
+        layer.w2_weight_scale = torch.nn.Parameter(torch.ones(
+            (self.experts, self.output_size, 1), dtype=torch.float32),
+                                                   requires_grad=False)
+        if not is_per_channel_weight:
+            layer.w13_weight_scale_second = torch.nn.Parameter(
+                torch.ones((self.experts, 2 * self.input_size,
+                            self.output_size // self.group_size),
+                           dtype=torch.float32),
+                requires_grad=False)
+            layer.w13_weight_offset_second = torch.nn.Parameter(
+                torch.empty_like(layer.w13_weight_scale_second.data),
+                requires_grad=False)
+            layer.w2_weight_scale_second = torch.nn.Parameter(
+                torch.ones((self.experts, self.output_size,
+                            self.input_size // self.group_size),
+                           dtype=torch.float32),
+                requires_grad=False)
+            layer.w2_weight_offset_second = torch.nn.Parameter(
+                torch.empty_like(layer.w2_weight_scale_second.data),
+                requires_grad=False)
+        return layer
+
+    @patch('torch_npu.npu_format_cast')
     @patch('torch_npu.npu_quantize')
     @patch('torch.Tensor.npu')
-    def test_process_weights_after_loading(self, mock_npu, mock_npu_quantize):
-        # old quant version weight
-        layer = torch.nn.Module()
-        layer.w13_weight = torch.nn.Parameter(torch.zeros(
-            (self.experts, 2 * self.input_size, self.output_size),
-            dtype=torch.int8),
-                                              requires_grad=False)
-        layer.w2_weight = torch.nn.Parameter(torch.zeros(
-            (self.experts, self.output_size, self.input_size),
-            dtype=torch.int8),
-                                             requires_grad=False)
-        layer.w13_weight_scale = torch.nn.Parameter(torch.ones(
-            (self.experts, 2 * self.input_size, 1), dtype=torch.bfloat16),
-                                                    requires_grad=False)
-        layer.w13_weight_scale_second = torch.nn.Parameter(torch.ones(
-            (self.experts, 2 * self.input_size,
-             self.output_size // self.group_size),
-            dtype=torch.bfloat16),
-                                                           requires_grad=False)
-        layer.w2_weight_scale = torch.nn.Parameter(torch.ones(
-            (self.experts, self.output_size, 1), dtype=torch.bfloat16),
-                                                   requires_grad=False)
-        layer.w2_weight_scale_second = torch.nn.Parameter(torch.ones(
-            (self.experts, self.output_size,
-             self.input_size // self.group_size),
-            dtype=torch.bfloat16),
-                                                          requires_grad=False)
-        new_layer = copy.deepcopy(layer)
-
+    def test_process_weights_after_loading(self, mock_npu, mock_npu_quantize,
+                                           mock_npu_format_cast):
         mock_npu.return_value = torch.Tensor()
         mock_npu_quantize.return_value = torch.Tensor()
+
+        def func_by_args(weight, num_format):
+            return weight
+
+        mock_npu_format_cast.side_effect = func_by_args
+        # old quant version weight
+        layer = self.build_layer(is_new_quant_version=False)
         self.quant_method.process_weights_after_loading(layer)
         self.assertTrue(hasattr(layer, "w13_scale_bias"))
         self.assertEqual(layer.w13_scale_bias.data.shape,
@@ -164,23 +210,17 @@ class TestAscendW4A8DynamicFusedMoEMethod(TestBase):
         self.assertEqual(layer.w2_scale_bias.data.dtype, torch.float32)
         # new quant version weight
         self.quant_method.new_quant_version = True
-        new_layer.w13_weight.data = torch.zeros(
-            (self.experts, self.input_size, self.output_size),
-            dtype=torch.int8)
-        new_layer.w2_weight.data = torch.zeros(
-            (self.experts, self.output_size // 2, self.input_size),
-            dtype=torch.int8)
-        w13_scale_bias = torch.zeros((self.experts, 2 * self.input_size, 1),
-                                     dtype=torch.float32)
-        new_layer.w13_scale_bias = torch.nn.Parameter(w13_scale_bias,
-                                                      requires_grad=False)
-        w2_scale_bias = torch.zeros(
-            (self.experts, self.output_size, 16 // self.quant_method.tp_size),
-            dtype=torch.float32)
-        new_layer.w2_scale_bias = torch.nn.Parameter(w2_scale_bias,
-                                                     requires_grad=False)
+        new_layer = self.build_layer(is_new_quant_version=True)
         self.quant_method.process_weights_after_loading(new_layer)
         self.assertEqual(new_layer.w13_scale_bias.data.shape,
                          (self.experts, 2 * self.input_size))
         self.assertEqual(new_layer.w2_scale_bias.data.shape,
                          (self.experts, self.output_size))
+        self.assertFalse(hasattr(new_layer, "w13_weight_scale_second"))
+        # per-channel weight
+        self.quant_method.is_per_channel_weight = True
+        per_channel_layer = self.build_layer(is_new_quant_version=True,
+                                             is_per_channel_weight=True)
+        self.quant_method.process_weights_after_loading(per_channel_layer)
+        self.assertEqual(new_layer.w13_scale_bias.data.shape,
+                         (self.experts, 2 * self.input_size))

tests/ut/torchair/quantization/test_torchair_w4a8_dynamic.py

@@ -1,4 +1,3 @@
-import copy
 from unittest.mock import Mock, patch
 
 import torch
@@ -85,19 +84,19 @@ class TestAscendW4A8DynamicFusedMoEMethod(TestBase):
         # old quant version weight
         param_dict = self.quant_method.get_dynamic_quant_param(
             self.experts, self.input_size, self.output_size, torch.bfloat16)
-        self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.bfloat16)
+        self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.float32)
         self.assertEqual(param_dict["w13_weight_scale"].shape,
                          (self.experts, 2 * self.input_size, 1))
         self.assertEqual(param_dict["w13_weight_scale_second"].dtype,
-                         torch.bfloat16)
+                         torch.float32)
         self.assertEqual(param_dict["w13_weight_scale_second"].shape,
                          (self.experts, 2 * self.input_size,
                           self.output_size // self.group_size))
-        self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.bfloat16)
+        self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.float32)
         self.assertEqual(param_dict["w2_weight_scale"].shape,
                          (self.experts, self.output_size, 1))
         self.assertEqual(param_dict["w2_weight_scale_second"].dtype,
-                         torch.bfloat16)
+                         torch.float32)
         self.assertEqual(param_dict["w2_weight_scale_second"].shape,
                          (self.experts, self.output_size,
                           self.input_size // self.group_size))
@@ -109,40 +108,80 @@ class TestAscendW4A8DynamicFusedMoEMethod(TestBase):
         self.assertEqual(
             param_dict["w2_scale_bias"].shape,
             (self.experts, self.output_size, 16 // self.quant_method.tp_size))
+        # per-channel weight
+        self.quant_method.is_per_channel_weight = True
+        param_dict = self.quant_method.get_dynamic_quant_param(
+            self.experts, self.input_size, self.output_size, torch.bfloat16)
+        pergroup_param = [
+            "w13_weight_scale_second", "w13_weight_offset_second",
+            "w2_weight_scale_second", "w2_weight_offset_second"
+        ]
+        is_contains = any(key in param_dict for key in pergroup_param)
+        self.assertFalse(is_contains)
+
+    def build_layer(self,
+                    is_new_quant_version=True,
+                    is_per_channel_weight=False):
+        layer = torch.nn.Module()
+        if is_new_quant_version:
+            layer.w13_weight = torch.nn.Parameter(torch.zeros(
+                (self.experts, self.input_size, self.output_size),
+                dtype=torch.int8),
+                                                  requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(torch.zeros(
+                (self.experts, self.output_size // 2, self.input_size),
+                dtype=torch.int8),
+                                                 requires_grad=False)
+            w13_scale_bias = torch.zeros(
+                (self.experts, 2 * self.input_size, 1), dtype=torch.float32)
+            layer.w13_scale_bias = torch.nn.Parameter(w13_scale_bias,
+                                                      requires_grad=False)
+            w2_scale_bias = torch.zeros((self.experts, self.output_size,
+                                         16 // self.quant_method.tp_size),
+                                        dtype=torch.float32)
+            layer.w2_scale_bias = torch.nn.Parameter(w2_scale_bias,
+                                                     requires_grad=False)
+        else:
+            layer.w13_weight = torch.nn.Parameter(torch.zeros(
+                (self.experts, 2 * self.input_size, self.output_size),
+                dtype=torch.int8),
+                                                  requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(torch.zeros(
+                (self.experts, self.output_size, self.input_size),
+                dtype=torch.int8),
+                                                 requires_grad=False)
+        layer.w13_weight_scale = torch.nn.Parameter(torch.ones(
+            (self.experts, 2 * self.input_size, 1), dtype=torch.float32),
+                                                    requires_grad=False)
+        layer.w2_weight_scale = torch.nn.Parameter(torch.ones(
+            (self.experts, self.output_size, 1), dtype=torch.float32),
+                                                   requires_grad=False)
+        if not is_per_channel_weight:
+            layer.w13_weight_scale_second = torch.nn.Parameter(
+                torch.ones((self.experts, 2 * self.input_size,
+                            self.output_size // self.group_size),
+                           dtype=torch.float32),
+                requires_grad=False)
+            layer.w13_weight_offset_second = torch.nn.Parameter(
+                torch.empty_like(layer.w13_weight_scale_second.data),
+                requires_grad=False)
+            layer.w2_weight_scale_second = torch.nn.Parameter(
+                torch.ones((self.experts, self.output_size,
+                            self.input_size // self.group_size),
+                           dtype=torch.float32),
+                requires_grad=False)
+            layer.w2_weight_offset_second = torch.nn.Parameter(
+                torch.empty_like(layer.w2_weight_scale_second.data),
+                requires_grad=False)
+        return layer
 
     @patch('torch_npu.npu_quantize')
     @patch('torch.Tensor.npu')
     def test_process_weights_after_loading(self, mock_npu, mock_npu_quantize):
-        # old quant version weight
-        layer = torch.nn.Module()
-        layer.w13_weight = torch.nn.Parameter(torch.zeros(
-            (self.experts, 2 * self.input_size, self.output_size),
-            dtype=torch.int8),
-                                              requires_grad=False)
-        layer.w2_weight = torch.nn.Parameter(torch.zeros(
-            (self.experts, self.output_size, self.input_size),
-            dtype=torch.int8),
-                                             requires_grad=False)
-        layer.w13_weight_scale = torch.nn.Parameter(torch.ones(
-            (self.experts, 2 * self.input_size, 1), dtype=torch.bfloat16),
-                                                    requires_grad=False)
-        layer.w13_weight_scale_second = torch.nn.Parameter(torch.ones(
-            (self.experts, 2 * self.input_size,
-             self.output_size // self.group_size),
-            dtype=torch.bfloat16),
-                                                           requires_grad=False)
-        layer.w2_weight_scale = torch.nn.Parameter(torch.ones(
-            (self.experts, self.output_size, 1), dtype=torch.bfloat16),
-                                                   requires_grad=False)
-        layer.w2_weight_scale_second = torch.nn.Parameter(torch.ones(
-            (self.experts, self.output_size,
-             self.input_size // self.group_size),
-            dtype=torch.bfloat16),
-                                                          requires_grad=False)
-        new_layer = copy.deepcopy(layer)
-
         mock_npu.return_value = torch.Tensor()
         mock_npu_quantize.return_value = torch.Tensor()
+        # old quant version weight
+        layer = self.build_layer(is_new_quant_version=False)
         self.quant_method.process_weights_after_loading(layer)
         self.assertTrue(hasattr(layer, "w13_scale_bias"))
         self.assertEqual(layer.w13_scale_bias.data.shape,
@@ -154,23 +193,17 @@ class TestAscendW4A8DynamicFusedMoEMethod(TestBase):
         self.assertEqual(layer.w2_scale_bias.data.dtype, torch.float32)
         # new quant version weight
         self.quant_method.new_quant_version = True
-        new_layer.w13_weight.data = torch.zeros(
-            (self.experts, self.input_size, self.output_size),
-            dtype=torch.int8)
-        new_layer.w2_weight.data = torch.zeros(
-            (self.experts, self.output_size // 2, self.input_size),
-            dtype=torch.int8)
-        w13_scale_bias = torch.zeros((self.experts, 2 * self.input_size, 1),
-                                     dtype=torch.float32)
-        new_layer.w13_scale_bias = torch.nn.Parameter(w13_scale_bias,
-                                                      requires_grad=False)
-        w2_scale_bias = torch.zeros(
-            (self.experts, self.output_size, 16 // self.quant_method.tp_size),
-            dtype=torch.float32)
-        new_layer.w2_scale_bias = torch.nn.Parameter(w2_scale_bias,
-                                                     requires_grad=False)
+        new_layer = self.build_layer(is_new_quant_version=True)
         self.quant_method.process_weights_after_loading(new_layer)
         self.assertEqual(new_layer.w13_scale_bias.data.shape,
                          (self.experts, 2 * self.input_size))
         self.assertEqual(new_layer.w2_scale_bias.data.shape,
                          (self.experts, self.output_size))
+        self.assertFalse(hasattr(new_layer, "w13_weight_scale_second"))
+        # per-channel weight
+        self.quant_method.is_per_channel_weight = True
+        per_channel_layer = self.build_layer(is_new_quant_version=True,
+                                             is_per_channel_weight=True)
+        self.quant_method.process_weights_after_loading(per_channel_layer)
+        self.assertEqual(new_layer.w13_scale_bias.data.shape,
+                         (self.experts, 2 * self.input_size))

vllm_ascend/quantization/w4a8_dynamic.py

@@ -27,6 +27,7 @@ from vllm.forward_context import get_forward_context
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.distributed.parallel_state import get_mc2_group
 from vllm_ascend.ops.moe.experts_selector import select_experts
+from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ
 
 
 class AscendW4A8DynamicLinearMethod:
@@ -132,6 +133,8 @@ class AscendW4A8DynamicFusedMoEMethod:
         vllm_config = get_current_vllm_config()
         self.group_size = vllm_config.quant_config.quant_description.get(
             "group_size", 256)
+        # NOTE: the weights are quantized from bf16 to int4 through a per-channel quantization process
+        self.is_per_channel_weight = self.group_size == 0
         quant_version = vllm_config.quant_config.quant_description.get(
             "version", "0")
         # NOTE: new quantize weights: 2 int4 pack into int8
@@ -182,44 +185,44 @@ class AscendW4A8DynamicFusedMoEMethod:
             num_experts,
             2 * intermediate_size_per_partition,
             1,
-            dtype=params_dtype)
+            dtype=torch.float32)
 
         param_dict["w13_weight_offset"] = torch.empty(
             num_experts,
             2 * intermediate_size_per_partition,
             1,
-            dtype=params_dtype)
-
-        param_dict["w13_weight_scale_second"] = torch.empty(
-            num_experts,
-            2 * intermediate_size_per_partition,
-            hidden_sizes // self.group_size,
-            dtype=params_dtype)
-
-        param_dict["w13_weight_offset_second"] = torch.empty(
-            num_experts,
-            2 * intermediate_size_per_partition,
-            hidden_sizes // self.group_size,
-            dtype=params_dtype)
+            dtype=torch.float32)
 
         param_dict["w2_weight_scale"] = torch.empty(num_experts,
                                                     hidden_sizes,
                                                     1,
-                                                    dtype=params_dtype)
+                                                    dtype=torch.float32)
         param_dict["w2_weight_offset"] = torch.empty(num_experts,
                                                      hidden_sizes,
                                                      1,
-                                                     dtype=params_dtype)
-        param_dict["w2_weight_scale_second"] = torch.empty(
-            num_experts,
-            hidden_sizes,
-            intermediate_size_per_partition // self.group_size,
-            dtype=params_dtype)
-        param_dict["w2_weight_offset_second"] = torch.empty(
-            num_experts,
-            hidden_sizes,
-            intermediate_size_per_partition // self.group_size,
-            dtype=params_dtype)
+                                                     dtype=torch.float32)
+        if not self.is_per_channel_weight:
+            param_dict["w13_weight_scale_second"] = torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_sizes // self.group_size,
+                dtype=torch.float32)
+            param_dict["w13_weight_offset_second"] = torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_sizes // self.group_size,
+                dtype=torch.float32)
+
+            param_dict["w2_weight_scale_second"] = torch.empty(
+                num_experts,
+                hidden_sizes,
+                intermediate_size_per_partition // self.group_size,
+                dtype=torch.float32)
+            param_dict["w2_weight_offset_second"] = torch.empty(
+                num_experts,
+                hidden_sizes,
+                intermediate_size_per_partition // self.group_size,
+                dtype=torch.float32)
 
         if self.new_quant_version:
             param_dict["w13_scale_bias"] = torch.empty(
@@ -288,8 +291,8 @@ class AscendW4A8DynamicFusedMoEMethod:
             hidden_states=x,
             w1=layer.w13_weight,
             w2=layer.w2_weight,
-            w1_scale=layer.w13_weight_scale_second,
-            w2_scale=layer.w2_weight_scale_second,
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
             w1_scale_bias=layer.w13_scale_bias,
             w2_scale_bias=layer.w2_scale_bias,
             topk_weights=topk_weights,
@@ -305,6 +308,14 @@ class AscendW4A8DynamicFusedMoEMethod:
             dynamic_eplb=self.dynamic_eplb)
 
     def process_scale(self, weight: torch.Tensor, scale, per_group_scale):
+        scale = scale.transpose(1, 2).contiguous()
+        if self.is_per_channel_weight:
+            scale_np = scale.cpu().numpy()
+            scale_np.dtype = np.uint32
+            scale_uint64_tensor = torch.from_numpy(scale_np.astype(
+                np.int64)).npu()
+            return scale_uint64_tensor, None
+        per_group_scale = per_group_scale.transpose(1, 2).contiguous()
         group_num, k, n = weight.shape
         # the weight of the new version is reduced by half by pack n, so it needs to be restored
         if self.new_quant_version:
@@ -347,13 +358,10 @@ class AscendW4A8DynamicFusedMoEMethod:
 
     def pack_to_int32(self, weight: torch.Tensor):
         if self.new_quant_version:
-            group_num, k, n = weight.shape
-            assert n % 4 == 0, "the last dim of weight needs to be divided by 4"
-            packed_n = n // 4
             # pack 4 int8(int4*2) to int32, because in pytorch, we need to use int32 to represent int4
-            packed_weight = torch.from_numpy(
-                np.frombuffer(weight.cpu().numpy().tobytes(), dtype=np.int32))
-            return packed_weight.reshape(group_num, k, packed_n).npu()
+            assert weight.shape[
+                -1] % 4 == 0, "the last dim of weight needs to be divided by 4"
+            return weight.view(torch.int32).contiguous()
         else:
             return torch_npu.npu_quantize(weight.to(torch.float32),
                                           torch.tensor([1.]).npu(), None,
@@ -365,23 +373,29 @@ class AscendW4A8DynamicFusedMoEMethod:
                 1, 2).contiguous()
             layer.w2_weight.data = layer.w2_weight.data.transpose(
                 1, 2).contiguous()
-        layer.w13_weight_scale.data = layer.w13_weight_scale.data.transpose(
-            1, 2).contiguous()
-        layer.w2_weight_scale.data = layer.w2_weight_scale.data.transpose(
-            1, 2).contiguous()
-        layer.w13_weight_scale_second.data = layer.w13_weight_scale_second.data.transpose(
-            1, 2).contiguous()
-        layer.w2_weight_scale_second.data = layer.w2_weight_scale_second.data.transpose(
-            1, 2).contiguous()
 
-        layer.w13_weight_scale_second.data, w13_bias = self.process_scale(
+        w13_weight_scale_second = layer.w13_weight_scale_second.data if hasattr(
+            layer, "w13_weight_scale_second") else None
+        w2_weight_scale_second = layer.w2_weight_scale_second.data if hasattr(
+            layer, "w2_weight_scale_second") else None
+        layer.w13_weight_scale.data, w13_bias = self.process_scale(
             layer.w13_weight, layer.w13_weight_scale.data,
-            layer.w13_weight_scale_second.data)
-        layer.w2_weight_scale_second.data, w2_bias = self.process_scale(
+            w13_weight_scale_second)
+        layer.w2_weight_scale.data, w2_bias = self.process_scale(
             layer.w2_weight, layer.w2_weight_scale.data,
-            layer.w2_weight_scale_second.data)
+            w2_weight_scale_second)
+        if hasattr(layer, "w13_weight_scale_second"):
+            # scale_second is no longer used, release this part of the memory
+            del layer.w13_weight_scale_second
+            del layer.w2_weight_scale_second
+            del layer.w13_weight_offset_second
+            del layer.w2_weight_offset_second
 
         self.update_bias(layer, w13_bias, w2_bias)
 
+        layer.w13_weight.data = torch_npu.npu_format_cast(
+            layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ)
+        layer.w2_weight.data = torch_npu.npu_format_cast(
+            layer.w2_weight.data, ACL_FORMAT_FRACTAL_NZ)
         layer.w13_weight.data = self.pack_to_int32(layer.w13_weight.data)
         layer.w2_weight.data = self.pack_to_int32(layer.w2_weight.data)

vllm_ascend/torchair/quantization/torchair_w4a8_dynamic.py

@@ -139,6 +139,8 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
         vllm_config = get_current_vllm_config()
         self.group_size = vllm_config.quant_config.quant_description.get(
             "group_size", 256)
+        # NOTE: the weights are quantized from bf16 to int4 through a per-channel quantization process
+        self.is_per_channel_weight = self.group_size == 0
         quant_version = vllm_config.quant_config.quant_description.get(
             "version", "0")
         # NOTE: new quantize weights: 2 int4 pack into int8
@@ -188,44 +190,45 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
             num_experts,
             2 * intermediate_size_per_partition,
             1,
-            dtype=params_dtype)
+            dtype=torch.float32)
 
         param_dict["w13_weight_offset"] = torch.empty(
             num_experts,
             2 * intermediate_size_per_partition,
             1,
-            dtype=params_dtype)
-
-        param_dict["w13_weight_scale_second"] = torch.empty(
-            num_experts,
-            2 * intermediate_size_per_partition,
-            hidden_sizes // self.group_size,
-            dtype=params_dtype)
-
-        param_dict["w13_weight_offset_second"] = torch.empty(
-            num_experts,
-            2 * intermediate_size_per_partition,
-            hidden_sizes // self.group_size,
-            dtype=params_dtype)
+            dtype=torch.float32)
 
         param_dict["w2_weight_scale"] = torch.empty(num_experts,
                                                     hidden_sizes,
                                                     1,
-                                                    dtype=params_dtype)
+                                                    dtype=torch.float32)
         param_dict["w2_weight_offset"] = torch.empty(num_experts,
                                                      hidden_sizes,
                                                      1,
-                                                     dtype=params_dtype)
-        param_dict["w2_weight_scale_second"] = torch.empty(
-            num_experts,
-            hidden_sizes,
-            intermediate_size_per_partition // self.group_size,
-            dtype=params_dtype)
-        param_dict["w2_weight_offset_second"] = torch.empty(
-            num_experts,
-            hidden_sizes,
-            intermediate_size_per_partition // self.group_size,
-            dtype=params_dtype)
+                                                     dtype=torch.float32)
+
+        if not self.is_per_channel_weight:
+            param_dict["w13_weight_scale_second"] = torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_sizes // self.group_size,
+                dtype=torch.float32)
+            param_dict["w13_weight_offset_second"] = torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_sizes // self.group_size,
+                dtype=torch.float32)
+
+            param_dict["w2_weight_scale_second"] = torch.empty(
+                num_experts,
+                hidden_sizes,
+                intermediate_size_per_partition // self.group_size,
+                dtype=torch.float32)
+            param_dict["w2_weight_offset_second"] = torch.empty(
+                num_experts,
+                hidden_sizes,
+                intermediate_size_per_partition // self.group_size,
+                dtype=torch.float32)
 
         if self.new_quant_version:
             param_dict["w13_scale_bias"] = torch.empty(
@@ -318,8 +321,8 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
                 hidden_states=x,
                 w1=layer.w13_weight,
                 w2=layer.w2_weight,
-                w1_scale=layer.w13_weight_scale_second,
-                w2_scale=layer.w2_weight_scale_second,
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
                 w1_scale_bias=layer.w13_scale_bias,
                 w2_scale_bias=layer.w2_scale_bias,
                 topk_weights=topk_weights,
@@ -343,8 +346,8 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
                 hidden_states=x,
                 w1=layer.w13_weight,
                 w2=layer.w2_weight,
-                w1_scale=layer.w13_weight_scale_second,
-                w2_scale=layer.w2_weight_scale_second,
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
                 w1_scale_bias=layer.w13_scale_bias,
                 w2_scale_bias=layer.w2_scale_bias,
                 topk_weights=topk_weights,
@@ -357,6 +360,14 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
             )
 
     def process_scale(self, weight: torch.Tensor, scale, per_group_scale):
+        scale = scale.transpose(1, 2).contiguous()
+        if self.is_per_channel_weight:
+            scale_np = scale.cpu().numpy()
+            scale_np.dtype = np.uint32
+            scale_uint64_tensor = torch.from_numpy(scale_np.astype(
+                np.int64)).npu()
+            return scale_uint64_tensor, None
+        per_group_scale = per_group_scale.transpose(1, 2).contiguous()
         group_num, k, n = weight.shape
         # the weight of the new version is reduced by half by pack n, so it needs to be restored
         if self.new_quant_version:
@@ -399,13 +410,10 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
 
     def pack_to_int32(self, weight: torch.Tensor):
         if self.new_quant_version:
-            group_num, k, n = weight.shape
-            assert n % 4 == 0, "the last dim of weight needs to be divided by 4"
-            packed_n = n // 4
             # pack 4 int8(int4*2) to int32, because in pytorch, we need to use int32 to represent int4
-            packed_weight = torch.from_numpy(
-                np.frombuffer(weight.cpu().numpy().tobytes(), dtype=np.int32))
-            return packed_weight.reshape(group_num, k, packed_n).npu()
+            assert weight.shape[
+                -1] % 4 == 0, "the last dim of weight needs to be divided by 4"
+            return weight.view(torch.int32).contiguous()
         else:
             return torch_npu.npu_quantize(weight.to(torch.float32),
                                           torch.tensor([1.]).npu(), None,
@@ -417,21 +425,22 @@ class TorchairAscendW4A8DynamicFusedMoEMethod:
                 1, 2).contiguous()
             layer.w2_weight.data = layer.w2_weight.data.transpose(
                 1, 2).contiguous()
-        layer.w13_weight_scale.data = layer.w13_weight_scale.data.transpose(
-            1, 2).contiguous()
-        layer.w2_weight_scale.data = layer.w2_weight_scale.data.transpose(
-            1, 2).contiguous()
-        layer.w13_weight_scale_second.data = layer.w13_weight_scale_second.data.transpose(
-            1, 2).contiguous()
-        layer.w2_weight_scale_second.data = layer.w2_weight_scale_second.data.transpose(
-            1, 2).contiguous()
-
-        layer.w13_weight_scale_second.data, w13_bias = self.process_scale(
+        w13_weight_scale_second = layer.w13_weight_scale_second.data if hasattr(
+            layer, "w13_weight_scale_second") else None
+        w2_weight_scale_second = layer.w2_weight_scale_second.data if hasattr(
+            layer, "w2_weight_scale_second") else None
+        layer.w13_weight_scale.data, w13_bias = self.process_scale(
             layer.w13_weight, layer.w13_weight_scale.data,
-            layer.w13_weight_scale_second.data)
-        layer.w2_weight_scale_second.data, w2_bias = self.process_scale(
+            w13_weight_scale_second)
+        layer.w2_weight_scale.data, w2_bias = self.process_scale(
             layer.w2_weight, layer.w2_weight_scale.data,
-            layer.w2_weight_scale_second.data)
+            w2_weight_scale_second)
+        if hasattr(layer, "w13_weight_scale_second"):
+            # scale_second is no longer used, release this part of the memory
+            del layer.w13_weight_scale_second
+            del layer.w2_weight_scale_second
+            del layer.w13_weight_offset_second
+            del layer.w2_weight_offset_second
 
         self.update_bias(layer, w13_bias, w2_bias)