init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

vllm_ascend/quantization/methods/w8a8_pdmix.py

@@ -0,0 +1,101 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# 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.
+#
+"""W8A8 Prefill-Decode Mix quantization methods.
+
+This module provides quantization methods that use different strategies
+for prefill and decode phases:
+- Prefill: Uses dynamic W8A8 quantization
+- Decode (KV consumer): Uses static W8A8 quantization
+"""
+
+from typing import Any
+
+import torch
+from vllm.config import get_current_vllm_config
+
+from .base import AscendLinearScheme
+from .registry import register_scheme
+from .w8a8_dynamic import AscendW8A8DynamicFusedMoEMethod, AscendW8A8DynamicLinearMethod
+from .w8a8_static import AscendW8A8LinearMethod
+
+
+@register_scheme("W8A8_MIX", "linear")
+class AscendW8A8PDMixLinearMethod(AscendLinearScheme):
+    """Linear method for W8A8 prefill-decode mix quantization.
+
+    This scheme uses composition to delegate to the appropriate quantization
+    method based on the execution phase:
+    - Static W8A8 for KV consumer (decode phase)
+    - Dynamic W8A8 for prefill phase
+
+    The static method is used for weight/parameter specifications since
+    it requires more parameters (input_scale, deq_scale, etc.) that are
+    needed for static quantization during decode.
+    """
+
+    def __init__(self):
+        self._static_method = AscendW8A8LinearMethod()
+        self._dynamic_method = AscendW8A8DynamicLinearMethod()
+
+        kv_transfer_config = get_current_vllm_config().kv_transfer_config
+        self._is_kv_consumer = kv_transfer_config is not None and kv_transfer_config.is_kv_consumer
+
+    def get_weight(self, input_size: int, output_size: int, params_dtype: torch.dtype) -> dict[str, Any]:
+        return self._static_method.get_weight(input_size, output_size, params_dtype)
+
+    def get_pertensor_param(self, params_dtype: torch.dtype, **kwargs: Any) -> dict[str, Any]:
+        return self._static_method.get_pertensor_param(params_dtype)
+
+    def get_perchannel_param(
+        self,
+        output_size: int,
+        params_dtype: torch.dtype,
+    ) -> dict[str, Any]:
+        return self._static_method.get_perchannel_param(output_size, params_dtype)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+        tp_rank: int | None = 0,
+    ) -> torch.Tensor:
+        if layer.is_kv_consumer:
+            return self._static_method.apply(layer, x, bias, tp_rank)
+        else:
+            return self._dynamic_method.apply(layer, x, bias, tp_rank)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        self._static_method.process_weights_after_loading(layer)
+        layer.weight_scale_fp32 = layer.weight_scale.data.to(torch.float32)
+        layer.is_kv_consumer = self._is_kv_consumer
+
+
+@register_scheme("W8A8_MIX", "moe")
+class AscendW8A8PDMixFusedMoeMethod(AscendW8A8DynamicFusedMoEMethod):
+    def get_dynamic_quant_param(
+        self, num_experts: int, intermediate_size_per_partition: int, hidden_sizes: int, params_dtype: torch.dtype
+    ) -> dict[str, Any]:
+        param_dict = super().get_dynamic_quant_param(
+            num_experts, intermediate_size_per_partition, hidden_sizes, params_dtype
+        )
+        param_dict["w2_deq_scale"] = torch.empty(num_experts, hidden_sizes, dtype=torch.float32)
+        param_dict["w13_deq_scale"] = torch.empty(num_experts, 2 * intermediate_size_per_partition, dtype=torch.float32)
+        param_dict["w2_input_offset"] = torch.empty(num_experts, 1, dtype=torch.int8)
+        param_dict["w13_input_offset"] = torch.empty(num_experts, 1, dtype=torch.int8)
+
+        return param_dict